--- a
+++ b/5_TheFinalRefactoring.ipynb
@@ -0,0 +1,15935 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ETL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import icu_data_defs\n",
+    "import mimic\n",
+    "from constants import column_names,variable_type,clinical_source"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data_dict = icu_data_defs.data_dictionary('config/data_definitions.xlsx')\n",
+    "mimic_etlM = mimic.MimicETLManager('data/mimic_data_test.h5','config/mimic_item_map.csv',data_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2017-07-07 00:04:45) BEGIN ETL for 18 components: [u'heart rate', u'blood pressure systolic', u'blood pressure diastolic', u'blood pressure mean', u'respiratory rate', u'temperature body', u'oxygen saturation pulse oximetry', u'weight body', u'output urine', u'glasgow coma scale motor', u'glasgow coma scale eye opening', u'glasgow coma scale verbal', u'normal saline', u'lactated ringers', u'norepinephrine', u'vasopressin', u'hemoglobin', u'lactate']\n",
+      "(2017-07-07 00:04:45)>> HEART RATE: 1/18\n",
+      "(2017-07-07 00:04:45)>>>> Extract...\n",
+      "(2017-07-07 00:04:45)>>>>>> Extracting 5 items from chartevents\n",
+      "(2017-07-07 00:05:50)<<<<<< --- (65.0s)\n",
+      "(2017-07-07 00:05:50)>>>>>> Combine DF\n",
+      "(2017-07-07 00:05:50)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:05:50)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:06:10)<<<<<< --- (20.0s)\n",
+      "(2017-07-07 00:06:10)<<<< --- (85.0s)\n",
+      "(2017-07-07 00:06:10)>>>> Transform...\n",
+      "(2017-07-07 00:07:57)<<<< --- (107.0s)\n",
+      "(2017-07-07 00:07:57)>>>> Clean...\n",
+      "(2017-07-07 00:08:48)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:08:48)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:08:48)>>>>>> Drop OOB data | (7922986, 6)\n",
+      "(2017-07-07 00:08:52)>>>>>>>> heart rate, beats/min, 7923117\n",
+      "(2017-07-07 00:10:06)<<<<<<<< --- (74.0s)\n",
+      "(2017-07-07 00:10:06)>>>>>>>> heart rate, no_units, 31\n",
+      "(2017-07-07 00:10:06)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:10:06)<<<<<< --- (78.0s)\n",
+      "(2017-07-07 00:10:06)<<<< --- (129.0s)\n",
+      "(2017-07-07 00:10:06)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:10:06)>>>>>> Save EXTRACTED DF: (7952939, 5)\n",
+      "(2017-07-07 00:10:32)<<<<<< --- (26.0s)\n",
+      "(2017-07-07 00:10:32)>>>>>> Save TRANSFORMED DF: (7923711, 6)\n",
+      "(2017-07-07 00:10:48)<<<<<< --- (16.0s)\n",
+      "(2017-07-07 00:10:48)>>>>>> Save FINAL DF: (7922986, 6)\n",
+      "(2017-07-07 00:11:23)<<<<<< --- (35.0s)\n",
+      "(2017-07-07 00:11:23)<<<< --- (77.0s)\n",
+      "(2017-07-07 00:11:26)<< --- (401.0s)\n",
+      "(2017-07-07 00:11:26)>> BLOOD PRESSURE SYSTOLIC: 2/18\n",
+      "(2017-07-07 00:11:26)>>>> Extract...\n",
+      "(2017-07-07 00:11:26)>>>>>> Extracting 14 items from chartevents\n",
+      "(2017-07-07 00:12:54)<<<<<< --- (88.0s)\n",
+      "(2017-07-07 00:12:54)>>>>>> Combine DF\n",
+      "(2017-07-07 00:12:55)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 00:12:55)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:12:58)<<<<<< --- (3.0s)\n",
+      "(2017-07-07 00:12:58)<<<< --- (92.0s)\n",
+      "(2017-07-07 00:12:58)>>>> Transform...\n",
+      "(2017-07-07 00:14:38)<<<< --- (100.0s)\n",
+      "(2017-07-07 00:14:38)>>>> Clean...\n",
+      "(2017-07-07 00:20:17)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:20:22)<<<<<< --- (5.0s)\n",
+      "(2017-07-07 00:20:22)>>>>>> Drop OOB data | (5974186, 40)\n",
+      "(2017-07-07 00:20:27)>>>>>>>> blood pressure systolic, mmHg, 6177439\n",
+      "(2017-07-07 00:23:13)<<<<<<<< --- (166.0s)\n",
+      "(2017-07-07 00:23:13)>>>>>>>> blood pressure systolic, no_units, 155328836\n",
+      "(2017-07-07 00:23:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:23:13)>>>>>>>> blood pressure systolic, cc/min, 153573\n",
+      "(2017-07-07 00:23:19)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-07 00:23:19)<<<<<< --- (177.0s)\n",
+      "(2017-07-07 00:23:19)<<<< --- (521.0s)\n",
+      "(2017-07-07 00:23:19)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:23:19)>>>>>> Save EXTRACTED DF: (6374824, 5)\n",
+      "(2017-07-07 00:23:30)<<<<<< --- (11.0s)\n",
+      "(2017-07-07 00:23:30)>>>>>> Save TRANSFORMED DF: (5974719, 15)\n",
+      "(2017-07-07 00:23:50)<<<<<< --- (20.0s)\n",
+      "(2017-07-07 00:23:50)>>>>>> Save FINAL DF: (5974186, 40)\n",
+      "(2017-07-07 00:24:23)<<<<<< --- (33.0s)\n",
+      "(2017-07-07 00:24:23)<<<< --- (64.0s)\n",
+      "(2017-07-07 00:24:30)<< --- (784.0s)\n",
+      "(2017-07-07 00:24:30)>> BLOOD PRESSURE DIASTOLIC: 3/18\n",
+      "(2017-07-07 00:24:30)>>>> Extract...\n",
+      "(2017-07-07 00:24:30)>>>>>> Extracting 16 items from chartevents\n",
+      "(2017-07-07 00:26:22)<<<<<< --- (112.0s)\n",
+      "(2017-07-07 00:26:22)>>>>>> Combine DF\n",
+      "(2017-07-07 00:26:22)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:26:22)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:26:28)<<<<<< --- (6.0s)\n",
+      "(2017-07-07 00:26:28)<<<< --- (118.0s)\n",
+      "(2017-07-07 00:26:28)>>>> Transform...\n",
+      "(2017-07-07 00:28:10)<<<< --- (102.0s)\n",
+      "(2017-07-07 00:28:10)>>>> Clean...\n",
+      "(2017-07-07 00:34:29)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:34:34)<<<<<< --- (5.0s)\n",
+      "(2017-07-07 00:34:34)>>>>>> Drop OOB data | (5976313, 42)\n",
+      "(2017-07-07 00:34:39)>>>>>>>> blood pressure diastolic, mmHg, 6194656\n",
+      "(2017-07-07 00:38:15)<<<<<<<< --- (216.0s)\n",
+      "(2017-07-07 00:38:15)>>>>>>>> blood pressure diastolic, no_units, 155384138\n",
+      "(2017-07-07 00:38:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:38:15)>>>>>>>> blood pressure diastolic, cc/min, 151640\n",
+      "(2017-07-07 00:38:21)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-07 00:38:21)<<<<<< --- (227.0s)\n",
+      "(2017-07-07 00:38:21)<<<< --- (611.0s)\n",
+      "(2017-07-07 00:38:21)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:38:21)>>>>>> Save EXTRACTED DF: (6371282, 5)\n",
+      "(2017-07-07 00:38:33)<<<<<< --- (12.0s)\n",
+      "(2017-07-07 00:38:33)>>>>>> Save TRANSFORMED DF: (5976845, 17)\n",
+      "(2017-07-07 00:38:54)<<<<<< --- (21.0s)\n",
+      "(2017-07-07 00:38:54)>>>>>> Save FINAL DF: (5976313, 42)\n",
+      "(2017-07-07 00:39:58)<<<<<< --- (64.0s)\n",
+      "(2017-07-07 00:39:58)<<<< --- (97.0s)\n",
+      "(2017-07-07 00:40:05)<< --- (935.0s)\n",
+      "(2017-07-07 00:40:05)>> BLOOD PRESSURE MEAN: 4/18\n",
+      "(2017-07-07 00:40:05)>>>> Extract...\n",
+      "(2017-07-07 00:40:05)>>>>>> Extracting 3 items from chartevents\n",
+      "(2017-07-07 00:41:30)<<<<<< --- (85.0s)\n",
+      "(2017-07-07 00:41:30)>>>>>> Combine DF\n",
+      "(2017-07-07 00:41:30)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:41:30)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:41:31)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 00:41:31)<<<< --- (86.0s)\n",
+      "(2017-07-07 00:41:31)>>>> Transform...\n",
+      "(2017-07-07 00:42:18)<<<< --- (47.0s)\n",
+      "(2017-07-07 00:42:18)>>>> Clean...\n",
+      "(2017-07-07 00:42:34)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:42:34)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:42:34)>>>>>> Drop OOB data | (2415995, 3)\n",
+      "(2017-07-07 00:42:35)>>>>>>>> blood pressure mean, mmHg, 2536236\n",
+      "(2017-07-07 00:43:11)<<<<<<<< --- (36.0s)\n",
+      "(2017-07-07 00:43:11)<<<<<< --- (37.0s)\n",
+      "(2017-07-07 00:43:11)<<<< --- (53.0s)\n",
+      "(2017-07-07 00:43:11)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:43:11)>>>>>> Save EXTRACTED DF: (2536271, 5)\n",
+      "(2017-07-07 00:43:15)<<<<<< --- (4.0s)\n",
+      "(2017-07-07 00:43:15)>>>>>> Save TRANSFORMED DF: (2416029, 3)\n",
+      "(2017-07-07 00:43:18)<<<<<< --- (3.0s)\n",
+      "(2017-07-07 00:43:18)>>>>>> Save FINAL DF: (2415995, 3)\n",
+      "(2017-07-07 00:43:25)<<<<<< --- (7.0s)\n",
+      "(2017-07-07 00:43:25)<<<< --- (14.0s)\n",
+      "(2017-07-07 00:43:26)<< --- (201.0s)\n",
+      "(2017-07-07 00:43:26)>> RESPIRATORY RATE: 5/18\n",
+      "(2017-07-07 00:43:26)>>>> Extract...\n",
+      "(2017-07-07 00:43:26)>>>>>> Extracting 4 items from chartevents\n",
+      "(2017-07-07 00:44:57)<<<<<< --- (91.0s)\n",
+      "(2017-07-07 00:44:57)>>>>>> Combine DF\n",
+      "(2017-07-07 00:44:57)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:44:57)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:45:13)<<<<<< --- (16.0s)\n",
+      "(2017-07-07 00:45:13)<<<< --- (107.0s)\n",
+      "(2017-07-07 00:45:13)>>>> Transform...\n",
+      "(2017-07-07 00:46:59)<<<< --- (106.0s)\n",
+      "(2017-07-07 00:46:59)>>>> Clean...\n",
+      "(2017-07-07 00:49:50)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:49:55)<<<<<< --- (5.0s)\n",
+      "(2017-07-07 00:49:55)>>>>>> Drop OOB data | (7780015, 6)\n",
+      "(2017-07-07 00:49:59)>>>>>>>> respiratory rate, insp/min, 6108262\n",
+      "(2017-07-07 00:50:53)<<<<<<<< --- (54.0s)\n",
+      "(2017-07-07 00:50:53)>>>>>>>> respiratory rate, no_units, 15560032\n",
+      "(2017-07-07 00:50:53)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:50:53)>>>>>>>> respiratory rate, Breath, 1671901\n",
+      "(2017-07-07 00:50:54)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-07 00:50:54)<<<<<< --- (59.0s)\n",
+      "(2017-07-07 00:50:54)<<<< --- (235.0s)\n",
+      "(2017-07-07 00:50:54)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:50:54)>>>>>> Save EXTRACTED DF: (7810019, 5)\n",
+      "(2017-07-07 00:51:11)<<<<<< --- (17.0s)\n",
+      "(2017-07-07 00:51:11)>>>>>> Save TRANSFORMED DF: (7780717, 5)\n",
+      "(2017-07-07 00:51:26)<<<<<< --- (15.0s)\n",
+      "(2017-07-07 00:51:26)>>>>>> Save FINAL DF: (7780015, 6)\n",
+      "(2017-07-07 00:52:17)<<<<<< --- (51.0s)\n",
+      "(2017-07-07 00:52:17)<<<< --- (83.0s)\n",
+      "(2017-07-07 00:52:20)<< --- (534.0s)\n",
+      "(2017-07-07 00:52:20)>> TEMPERATURE BODY: 6/18\n",
+      "(2017-07-07 00:52:20)>>>> Extract...\n",
+      "(2017-07-07 00:52:20)>>>>>> Extracting 4 items from chartevents\n",
+      "(2017-07-07 00:53:00)<<<<<< --- (40.0s)\n",
+      "(2017-07-07 00:53:00)>>>>>> Combine DF\n",
+      "(2017-07-07 00:53:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:53:00)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:53:05)<<<<<< --- (5.0s)\n",
+      "(2017-07-07 00:53:05)<<<< --- (45.0s)\n",
+      "(2017-07-07 00:53:05)>>>> Transform...\n",
+      "(2017-07-07 00:53:33)<<<< --- (28.0s)\n",
+      "(2017-07-07 00:53:33)>>>> Clean...\n",
+      "(2017-07-07 00:53:44)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:53:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:53:44)>>>>>> Drop OOB data | (1731794, 4)\n",
+      "(2017-07-07 00:53:45)>>>>>>>> temperature body, degF, 1734754\n",
+      "(2017-07-07 00:54:15)<<<<<<<< --- (30.0s)\n",
+      "(2017-07-07 00:54:15)<<<<<< --- (31.0s)\n",
+      "(2017-07-07 00:54:15)<<<< --- (42.0s)\n",
+      "(2017-07-07 00:54:15)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:54:15)>>>>>> Save EXTRACTED DF: (1751447, 5)\n",
+      "(2017-07-07 00:54:20)<<<<<< --- (5.0s)\n",
+      "(2017-07-07 00:54:20)>>>>>> Save TRANSFORMED DF: (1731875, 4)\n",
+      "(2017-07-07 00:54:24)<<<<<< --- (4.0s)\n",
+      "(2017-07-07 00:54:24)>>>>>> Save FINAL DF: (1731794, 4)\n",
+      "(2017-07-07 00:54:30)<<<<<< --- (6.0s)\n",
+      "(2017-07-07 00:54:30)<<<< --- (15.0s)\n",
+      "(2017-07-07 00:54:31)<< --- (131.0s)\n",
+      "(2017-07-07 00:54:31)>> OXYGEN SATURATION PULSE OXIMETRY: 7/18\n",
+      "(2017-07-07 00:54:31)>>>> Extract...\n",
+      "(2017-07-07 00:54:31)>>>>>> Extracting 2 items from chartevents\n",
+      "(2017-07-07 00:55:18)<<<<<< --- (47.0s)\n",
+      "(2017-07-07 00:55:18)>>>>>> Combine DF\n",
+      "(2017-07-07 00:55:19)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 00:55:19)>>>>>> Clean UOM\n",
+      "(2017-07-07 00:55:36)<<<<<< --- (17.0s)\n",
+      "(2017-07-07 00:55:36)<<<< --- (65.0s)\n",
+      "(2017-07-07 00:55:36)>>>> Transform...\n",
+      "(2017-07-07 00:57:05)<<<< --- (89.0s)\n",
+      "(2017-07-07 00:57:05)>>>> Clean...\n",
+      "(2017-07-07 00:57:37)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 00:57:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 00:57:37)>>>>>> Drop OOB data | (6073019, 2)\n",
+      "(2017-07-07 00:57:40)>>>>>>>> oxygen saturation pulse oximetry, percent, 6073172\n",
+      "(2017-07-07 00:58:29)<<<<<<<< --- (49.0s)\n",
+      "(2017-07-07 00:58:29)<<<<<< --- (52.0s)\n",
+      "(2017-07-07 00:58:29)<<<< --- (84.0s)\n",
+      "(2017-07-07 00:58:29)>>>> Save DataFrames...\n",
+      "(2017-07-07 00:58:29)>>>>>> Save EXTRACTED DF: (6099827, 5)\n",
+      "(2017-07-07 00:58:46)<<<<<< --- (17.0s)\n",
+      "(2017-07-07 00:58:46)>>>>>> Save TRANSFORMED DF: (6073540, 2)\n",
+      "(2017-07-07 00:58:56)<<<<<< --- (10.0s)\n",
+      "(2017-07-07 00:58:56)>>>>>> Save FINAL DF: (6073019, 2)\n",
+      "(2017-07-07 00:59:26)<<<<<< --- (30.0s)\n",
+      "(2017-07-07 00:59:26)<<<< --- (57.0s)\n",
+      "(2017-07-07 00:59:28)<< --- (297.0s)\n",
+      "(2017-07-07 00:59:28)>> WEIGHT BODY: 8/18\n",
+      "(2017-07-07 00:59:28)>>>> Extract...\n",
+      "(2017-07-07 00:59:28)>>>>>> Extracting 3 items from chartevents\n",
+      "(2017-07-07 01:00:01)<<<<<< --- (33.0s)\n",
+      "(2017-07-07 01:00:01)>>>>>> Combine DF\n",
+      "(2017-07-07 01:00:01)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:00:01)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:00:01)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:00:01)<<<< --- (33.0s)\n",
+      "(2017-07-07 01:00:01)>>>> Transform...\n",
+      "(2017-07-07 01:00:03)<<<< --- (2.0s)\n",
+      "(2017-07-07 01:00:03)>>>> Clean...\n",
+      "(2017-07-07 01:00:04)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:00:04)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:00:04)>>>>>> Drop OOB data | (94457, 3)\n",
+      "(2017-07-07 01:00:04)>>>>>>>> weight body, kg, 94457\n",
+      "(2017-07-07 01:00:06)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:00:06)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:00:06)<<<< --- (3.0s)\n",
+      "(2017-07-07 01:00:06)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:00:06)>>>>>> Save EXTRACTED DF: (95425, 5)\n",
+      "(2017-07-07 01:00:06)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:00:06)>>>>>> Save TRANSFORMED DF: (94484, 3)\n",
+      "(2017-07-07 01:00:07)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:00:07)>>>>>> Save FINAL DF: (94457, 3)\n",
+      "(2017-07-07 01:00:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:00:07)<<<< --- (1.0s)\n",
+      "(2017-07-07 01:00:07)<< --- (39.0s)\n",
+      "(2017-07-07 01:00:07)>> OUTPUT URINE: 9/18\n",
+      "(2017-07-07 01:00:07)>>>> Extract...\n",
+      "(2017-07-07 01:00:08)>>>>>> Extracting 2 items from chartevents\n",
+      "(2017-07-07 01:00:34)<<<<<< --- (26.0s)\n",
+      "(2017-07-07 01:00:34)>>>>>> Extracting 29 items from outputevents\n",
+      "(2017-07-07 01:01:07)<<<<<< --- (33.0s)\n",
+      "(2017-07-07 01:01:07)>>>>>> Combine DF\n",
+      "(2017-07-07 01:01:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:01:07)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:01:18)<<<<<< --- (11.0s)\n",
+      "(2017-07-07 01:01:18)<<<< --- (71.0s)\n",
+      "(2017-07-07 01:01:18)>>>> Transform...\n",
+      "(2017-07-07 01:02:46)<<<< --- (88.0s)\n",
+      "(2017-07-07 01:02:46)>>>> Clean...\n",
+      "(2017-07-07 01:12:53)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:13:01)<<<<<< --- (8.0s)\n",
+      "(2017-07-07 01:13:01)>>>>>> Drop OOB data | (3624068, 61)\n",
+      "(2017-07-07 01:13:07)>>>>>>>> output urine, mL, 3218015\n",
+      "(2017-07-07 01:17:34)<<<<<<<< --- (267.0s)\n",
+      "(2017-07-07 01:17:34)>>>>>>>> output urine, no_units, 36244648\n",
+      "(2017-07-07 01:17:39)<<<<<<<< --- (5.0s)\n",
+      "(2017-07-07 01:17:39)<<<<<< --- (278.0s)\n",
+      "(2017-07-07 01:17:40)<<<< --- (894.0s)\n",
+      "(2017-07-07 01:17:40)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:17:40)>>>>>> Save EXTRACTED DF: (3644639, 5)\n",
+      "(2017-07-07 01:17:42)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:17:42)>>>>>> Save TRANSFORMED DF: (3625860, 54)\n",
+      "(2017-07-07 01:17:59)<<<<<< --- (17.0s)\n",
+      "(2017-07-07 01:17:59)>>>>>> Save FINAL DF: (3624068, 61)\n",
+      "(2017-07-07 01:18:46)<<<<<< --- (47.0s)\n",
+      "(2017-07-07 01:18:46)<<<< --- (66.0s)\n",
+      "(2017-07-07 01:18:54)<< --- (1127.0s)\n",
+      "(2017-07-07 01:18:54)>> GLASGOW COMA SCALE MOTOR: 10/18\n",
+      "(2017-07-07 01:18:54)>>>> Extract...\n",
+      "(2017-07-07 01:18:54)>>>>>> Extracting 1 items from chartevents\n",
+      "(2017-07-07 01:19:40)<<<<<< --- (46.0s)\n",
+      "(2017-07-07 01:19:40)>>>>>> Combine DF\n",
+      "(2017-07-07 01:19:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:19:40)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:19:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:19:40)<<<< --- (46.0s)\n",
+      "(2017-07-07 01:19:40)>>>> Transform...\n",
+      "(2017-07-07 01:19:55)<<<< --- (15.0s)\n",
+      "(2017-07-07 01:19:55)>>>> Clean...\n",
+      "(2017-07-07 01:20:21)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:20:21)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:20:21)>>>>>> Drop OOB data | (949198, 1)\n",
+      "(2017-07-07 01:20:21)>>>>>>>> glasgow coma scale motor, no_units, 949198\n",
+      "(2017-07-07 01:20:28)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:20:28)<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:20:28)<<<< --- (33.0s)\n",
+      "(2017-07-07 01:20:28)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:20:28)>>>>>> Save EXTRACTED DF: (952565, 5)\n",
+      "(2017-07-07 01:20:29)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:20:29)>>>>>> Save TRANSFORMED DF: (949241, 1)\n",
+      "(2017-07-07 01:20:31)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:20:31)>>>>>> Save FINAL DF: (949198, 1)\n",
+      "(2017-07-07 01:20:40)<<<<<< --- (9.0s)\n",
+      "(2017-07-07 01:20:40)<<<< --- (12.0s)\n",
+      "(2017-07-07 01:20:40)<< --- (106.0s)\n",
+      "(2017-07-07 01:20:40)>> GLASGOW COMA SCALE EYE OPENING: 11/18\n",
+      "(2017-07-07 01:20:40)>>>> Extract...\n",
+      "(2017-07-07 01:20:40)>>>>>> Extracting 1 items from chartevents\n",
+      "(2017-07-07 01:21:20)<<<<<< --- (40.0s)\n",
+      "(2017-07-07 01:21:20)>>>>>> Combine DF\n",
+      "(2017-07-07 01:21:20)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:21:20)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:21:20)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:21:20)<<<< --- (40.0s)\n",
+      "(2017-07-07 01:21:20)>>>> Transform...\n",
+      "(2017-07-07 01:21:36)<<<< --- (16.0s)\n",
+      "(2017-07-07 01:21:36)>>>> Clean...\n",
+      "(2017-07-07 01:22:01)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:22:01)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:22:01)>>>>>> Drop OOB data | (953595, 1)\n",
+      "(2017-07-07 01:22:01)>>>>>>>> glasgow coma scale eye opening, no_units, 953595\n",
+      "(2017-07-07 01:22:08)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:22:08)<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:22:08)<<<< --- (32.0s)\n",
+      "(2017-07-07 01:22:08)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:22:08)>>>>>> Save EXTRACTED DF: (956672, 5)\n",
+      "(2017-07-07 01:22:09)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:22:09)>>>>>> Save TRANSFORMED DF: (953638, 1)\n",
+      "(2017-07-07 01:22:11)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:22:11)>>>>>> Save FINAL DF: (953595, 1)\n",
+      "(2017-07-07 01:22:17)<<<<<< --- (6.0s)\n",
+      "(2017-07-07 01:22:17)<<<< --- (9.0s)\n",
+      "(2017-07-07 01:22:18)<< --- (98.0s)\n",
+      "(2017-07-07 01:22:18)>> GLASGOW COMA SCALE VERBAL: 12/18\n",
+      "(2017-07-07 01:22:18)>>>> Extract...\n",
+      "(2017-07-07 01:22:18)>>>>>> Extracting 1 items from chartevents\n",
+      "(2017-07-07 01:22:41)<<<<<< --- (23.0s)\n",
+      "(2017-07-07 01:22:41)>>>>>> Combine DF\n",
+      "(2017-07-07 01:22:41)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:22:41)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:22:42)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:22:42)<<<< --- (24.0s)\n",
+      "(2017-07-07 01:22:42)>>>> Transform...\n",
+      "(2017-07-07 01:22:57)<<<< --- (15.0s)\n",
+      "(2017-07-07 01:22:57)>>>> Clean...\n",
+      "(2017-07-07 01:23:23)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:23:23)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:23:23)>>>>>> Drop OOB data | (950913, 1)\n",
+      "(2017-07-07 01:23:23)>>>>>>>> glasgow coma scale verbal, no_units, 950913\n",
+      "(2017-07-07 01:23:30)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:23:30)<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:23:30)<<<< --- (33.0s)\n",
+      "(2017-07-07 01:23:30)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:23:30)>>>>>> Save EXTRACTED DF: (954700, 5)\n",
+      "(2017-07-07 01:23:31)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:23:31)>>>>>> Save TRANSFORMED DF: (950956, 1)\n",
+      "(2017-07-07 01:23:33)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:23:33)>>>>>> Save FINAL DF: (950913, 1)\n",
+      "(2017-07-07 01:23:39)<<<<<< --- (6.0s)\n",
+      "(2017-07-07 01:23:39)<<<< --- (9.0s)\n",
+      "(2017-07-07 01:23:39)<< --- (81.0s)\n",
+      "(2017-07-07 01:23:39)>> NORMAL SALINE: 13/18\n",
+      "(2017-07-07 01:23:39)>>>> Extract...\n",
+      "(2017-07-07 01:23:39)>>>>>> Extracting 2 items from chartevents\n",
+      "(2017-07-07 01:23:39)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:23:39)>>>>>> Extracting 4 items from inputevents_cv\n",
+      "(2017-07-07 01:23:40)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:23:40)>>>>>> Extracting 2 items from inputevents_mv\n",
+      "(2017-07-07 01:23:59)<<<<<< --- (19.0s)\n",
+      "(2017-07-07 01:23:59)>>>>>> Combine DF\n",
+      "(2017-07-07 01:23:59)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:23:59)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:24:01)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:24:01)<<<< --- (22.0s)\n",
+      "(2017-07-07 01:24:01)>>>> Transform...\n",
+      "(2017-07-07 01:27:29)<<<< --- (208.0s)\n",
+      "(2017-07-07 01:27:29)>>>> Clean...\n",
+      "(2017-07-07 01:37:56)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:37:56)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:37:56)>>>>>> Drop OOB data | (504938, 17)\n",
+      "(2017-07-07 01:37:56)>>>>>>>> normal saline, mL, 49234\n",
+      "(2017-07-07 01:38:23)<<<<<<<< --- (27.0s)\n",
+      "(2017-07-07 01:38:23)>>>>>>>> normal saline, mL/hr, 456566\n",
+      "(2017-07-07 01:38:39)<<<<<<<< --- (16.0s)\n",
+      "(2017-07-07 01:38:39)>>>>>>>> normal saline, no_units, 1010035\n",
+      "(2017-07-07 01:38:39)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:38:39)<<<<<< --- (43.0s)\n",
+      "(2017-07-07 01:38:39)<<<< --- (670.0s)\n",
+      "(2017-07-07 01:38:39)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:38:39)>>>>>> Save EXTRACTED DF: (817373, 5)\n",
+      "(2017-07-07 01:38:39)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:38:39)>>>>>> Save TRANSFORMED DF: (770238, 17)\n",
+      "(2017-07-07 01:38:41)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:38:41)>>>>>> Save FINAL DF: (504938, 16)\n",
+      "(2017-07-07 01:38:44)<<<<<< --- (3.0s)\n",
+      "(2017-07-07 01:38:44)<<<< --- (5.0s)\n",
+      "(2017-07-07 01:38:45)<< --- (906.0s)\n",
+      "(2017-07-07 01:38:45)>> LACTATED RINGERS: 14/18\n",
+      "(2017-07-07 01:38:45)>>>> Extract...\n",
+      "(2017-07-07 01:38:45)>>>>>> Extracting 2 items from chartevents\n",
+      "(2017-07-07 01:38:45)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:38:45)>>>>>> Extracting 7 items from inputevents_cv\n",
+      "(2017-07-07 01:38:49)<<<<<< --- (4.0s)\n",
+      "(2017-07-07 01:38:49)>>>>>> Extracting 2 items from inputevents_mv\n",
+      "(2017-07-07 01:38:50)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:38:50)>>>>>> Combine DF\n",
+      "(2017-07-07 01:38:50)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:38:50)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:38:52)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:38:52)<<<< --- (7.0s)\n",
+      "(2017-07-07 01:38:52)>>>> Transform...\n",
+      "(2017-07-07 01:39:09)<<<< --- (17.0s)\n",
+      "(2017-07-07 01:39:09)>>>> Clean...\n",
+      "(2017-07-07 01:39:49)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:39:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:39:49)>>>>>> Drop OOB data | (254195, 20)\n",
+      "(2017-07-07 01:39:49)>>>>>>>> lactated ringers, mL, 248586\n",
+      "(2017-07-07 01:40:07)<<<<<<<< --- (18.0s)\n",
+      "(2017-07-07 01:40:07)>>>>>>>> lactated ringers, mL/hr, 2161\n",
+      "(2017-07-07 01:40:10)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-07 01:40:10)>>>>>>>> lactated ringers, no_units, 4578\n",
+      "(2017-07-07 01:40:10)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:40:10)<<<<<< --- (21.0s)\n",
+      "(2017-07-07 01:40:10)<<<< --- (61.0s)\n",
+      "(2017-07-07 01:40:10)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:40:10)>>>>>> Save EXTRACTED DF: (504306, 5)\n",
+      "(2017-07-07 01:40:11)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:40:11)>>>>>> Save TRANSFORMED DF: (268525, 20)\n",
+      "(2017-07-07 01:40:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:40:11)>>>>>> Save FINAL DF: (254195, 20)\n",
+      "(2017-07-07 01:40:13)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:40:13)<<<< --- (3.0s)\n",
+      "(2017-07-07 01:40:13)<< --- (88.0s)\n",
+      "(2017-07-07 01:40:13)>> NOREPINEPHRINE: 15/18\n",
+      "(2017-07-07 01:40:13)>>>> Extract...\n",
+      "(2017-07-07 01:40:13)>>>>>> Extracting 2 items from inputevents_cv\n",
+      "(2017-07-07 01:40:31)<<<<<< --- (18.0s)\n",
+      "(2017-07-07 01:40:31)>>>>>> Extracting 1 items from inputevents_mv\n",
+      "(2017-07-07 01:40:33)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:40:33)>>>>>> Combine DF\n",
+      "(2017-07-07 01:40:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:40:33)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:40:40)<<<<<< --- (7.0s)\n",
+      "(2017-07-07 01:40:40)<<<< --- (27.0s)\n",
+      "(2017-07-07 01:40:40)>>>> Transform...\n",
+      "(2017-07-07 01:41:36)<<<< --- (56.0s)\n",
+      "(2017-07-07 01:41:36)>>>> Clean...\n",
+      "(2017-07-07 01:42:15)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:42:15)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:42:15)>>>>>> Drop OOB data | (389986, 5)\n",
+      "(2017-07-07 01:42:15)>>>>>>>> norepinephrine, mcg, 268547\n",
+      "(2017-07-07 01:42:24)<<<<<<<< --- (9.0s)\n",
+      "(2017-07-07 01:42:24)>>>>>>>> norepinephrine, mcg/kg/min, 286781\n",
+      "(2017-07-07 01:42:26)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:42:26)>>>>>>>> norepinephrine, mcg/min, 14226\n",
+      "(2017-07-07 01:42:28)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:42:28)<<<<<< --- (13.0s)\n",
+      "(2017-07-07 01:42:28)<<<< --- (52.0s)\n",
+      "(2017-07-07 01:42:28)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:42:28)>>>>>> Save EXTRACTED DF: (1136938, 5)\n",
+      "(2017-07-07 01:42:29)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:42:29)>>>>>> Save TRANSFORMED DF: (452091, 5)\n",
+      "(2017-07-07 01:42:29)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:42:29)>>>>>> Save FINAL DF: (389986, 5)\n",
+      "(2017-07-07 01:42:31)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:42:31)<<<< --- (3.0s)\n",
+      "(2017-07-07 01:42:31)<< --- (138.0s)\n",
+      "(2017-07-07 01:42:31)>> VASOPRESSIN: 16/18\n",
+      "(2017-07-07 01:42:31)>>>> Extract...\n",
+      "(2017-07-07 01:42:31)>>>>>> Extracting 10 items from chartevents\n",
+      "(2017-07-07 01:42:32)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:42:32)>>>>>> Extracting 4 items from inputevents_cv\n",
+      "(2017-07-07 01:42:38)<<<<<< --- (6.0s)\n",
+      "(2017-07-07 01:42:38)>>>>>> Extracting 1 items from inputevents_mv\n",
+      "(2017-07-07 01:42:39)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:42:39)>>>>>> Combine DF\n",
+      "(2017-07-07 01:42:39)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:42:39)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:42:41)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:42:41)<<<< --- (10.0s)\n",
+      "(2017-07-07 01:42:41)>>>> Transform...\n",
+      "(2017-07-07 01:42:46)<<<< --- (5.0s)\n",
+      "(2017-07-07 01:42:46)>>>> Clean...\n",
+      "(2017-07-07 01:43:20)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:43:20)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:43:20)>>>>>> Drop OOB data | (110447, 38)\n",
+      "(2017-07-07 01:43:20)>>>>>>>> vasopressin, units, 57425\n",
+      "(2017-07-07 01:43:26)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-07 01:43:26)>>>>>>>> vasopressin, units/min, 111787\n",
+      "(2017-07-07 01:43:35)<<<<<<<< --- (9.0s)\n",
+      "(2017-07-07 01:43:35)>>>>>>>> vasopressin, no_units, 1104477\n",
+      "(2017-07-07 01:43:35)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:43:35)>>>>>>>> vasopressin, ml, 273\n",
+      "(2017-07-07 01:43:35)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:43:35)<<<<<< --- (15.0s)\n",
+      "(2017-07-07 01:43:35)<<<< --- (49.0s)\n",
+      "(2017-07-07 01:43:35)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:43:35)>>>>>> Save EXTRACTED DF: (339184, 5)\n",
+      "(2017-07-07 01:43:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:43:35)>>>>>> Save TRANSFORMED DF: (113161, 28)\n",
+      "(2017-07-07 01:43:36)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:43:36)>>>>>> Save FINAL DF: (110447, 38)\n",
+      "(2017-07-07 01:43:36)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:43:36)<<<< --- (1.0s)\n",
+      "(2017-07-07 01:43:36)<< --- (65.0s)\n",
+      "(2017-07-07 01:43:36)>> HEMOGLOBIN: 17/18\n",
+      "(2017-07-07 01:43:36)>>>> Extract...\n",
+      "(2017-07-07 01:43:36)>>>>>> Extracting 3 items from chartevents\n",
+      "(2017-07-07 01:44:38)<<<<<< --- (62.0s)\n",
+      "(2017-07-07 01:44:38)>>>>>> Extracting 2 items from labevents\n",
+      "(2017-07-07 01:45:03)<<<<<< --- (25.0s)\n",
+      "(2017-07-07 01:45:03)>>>>>> Combine DF\n",
+      "(2017-07-07 01:45:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:45:03)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:45:06)<<<<<< --- (3.0s)\n",
+      "(2017-07-07 01:45:06)<<<< --- (90.0s)\n",
+      "(2017-07-07 01:45:06)>>>> Transform...\n",
+      "(2017-07-07 01:45:27)<<<< --- (21.0s)\n",
+      "(2017-07-07 01:45:27)>>>> Clean...\n",
+      "(2017-07-07 01:46:45)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:46:46)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:46:46)>>>>>> Drop OOB data | (671135, 44)\n",
+      "(2017-07-07 01:46:46)>>>>>>>> hemoglobin, g/dL, 984886\n",
+      "(2017-07-07 01:47:32)<<<<<<<< --- (46.0s)\n",
+      "(2017-07-07 01:47:32)>>>>>>>> hemoglobin, no_units, 22147474\n",
+      "(2017-07-07 01:47:32)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:47:32)<<<<<< --- (46.0s)\n",
+      "(2017-07-07 01:47:32)<<<< --- (125.0s)\n",
+      "(2017-07-07 01:47:32)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:47:32)>>>>>> Save EXTRACTED DF: (1167921, 5)\n",
+      "(2017-07-07 01:47:34)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:47:34)>>>>>> Save TRANSFORMED DF: (671162, 11)\n",
+      "(2017-07-07 01:47:36)<<<<<< --- (2.0s)\n",
+      "(2017-07-07 01:47:36)>>>>>> Save FINAL DF: (671135, 44)\n",
+      "(2017-07-07 01:47:41)<<<<<< --- (5.0s)\n",
+      "(2017-07-07 01:47:41)<<<< --- (9.0s)\n",
+      "(2017-07-07 01:47:42)<< --- (246.0s)\n",
+      "(2017-07-07 01:47:42)>> LACTATE: 18/18\n",
+      "(2017-07-07 01:47:42)>>>> Extract...\n",
+      "(2017-07-07 01:47:42)>>>>>> Extracting 3 items from chartevents\n",
+      "(2017-07-07 01:48:34)<<<<<< --- (52.0s)\n",
+      "(2017-07-07 01:48:34)>>>>>> Extracting 1 items from labevents\n",
+      "(2017-07-07 01:48:37)<<<<<< --- (3.0s)\n",
+      "(2017-07-07 01:48:37)>>>>>> Combine DF\n",
+      "(2017-07-07 01:48:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:48:37)>>>>>> Clean UOM\n",
+      "(2017-07-07 01:48:38)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:48:38)<<<< --- (56.0s)\n",
+      "(2017-07-07 01:48:38)>>>> Transform...\n",
+      "(2017-07-07 01:48:44)<<<< --- (6.0s)\n",
+      "(2017-07-07 01:48:44)>>>> Clean...\n",
+      "(2017-07-07 01:49:00)>>>>>> Nominal to OneHot\n",
+      "(2017-07-07 01:49:01)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:49:01)>>>>>> Drop OOB data | (177451, 63)\n",
+      "(2017-07-07 01:49:01)>>>>>>>> lactate, mmol/L, 382219\n",
+      "(2017-07-07 01:49:07)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-07 01:49:07)>>>>>>>> lactate, no_units, 9937272\n",
+      "(2017-07-07 01:49:07)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:49:07)<<<<<< --- (6.0s)\n",
+      "(2017-07-07 01:49:07)<<<< --- (23.0s)\n",
+      "(2017-07-07 01:49:07)>>>> Save DataFrames...\n",
+      "(2017-07-07 01:49:07)>>>>>> Save EXTRACTED DF: (393608, 5)\n",
+      "(2017-07-07 01:49:08)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:49:08)>>>>>> Save TRANSFORMED DF: (177479, 7)\n",
+      "(2017-07-07 01:49:08)<<<<<< --- (0.0s)\n",
+      "(2017-07-07 01:49:08)>>>>>> Save FINAL DF: (177451, 63)\n",
+      "(2017-07-07 01:49:09)<<<<<< --- (1.0s)\n",
+      "(2017-07-07 01:49:09)<<<< --- (2.0s)\n",
+      "(2017-07-07 01:49:09)<< --- (87.0s)\n",
+      "(2017-07-07 01:49:09) --- (6264.0s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "etl_info=mimic_etlM.etl(panel_id=12, overwrite=True, save_steps=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>stat</th>\n",
+       "      <th>component</th>\n",
+       "      <th>EXTRACTED_id_count</th>\n",
+       "      <th>EXTRACTED_data_count</th>\n",
+       "      <th>TRANSFORMED_id_count</th>\n",
+       "      <th>TRANSFORMED_data_count</th>\n",
+       "      <th>CLEANED_id_count</th>\n",
+       "      <th>CLEANED_data_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>56716</td>\n",
+       "      <td>7952939</td>\n",
+       "      <td>56545</td>\n",
+       "      <td>7923873</td>\n",
+       "      <td>56545</td>\n",
+       "      <td>7923135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>56680</td>\n",
+       "      <td>6374824</td>\n",
+       "      <td>56506</td>\n",
+       "      <td>6331588</td>\n",
+       "      <td>56506</td>\n",
+       "      <td>6331002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>56677</td>\n",
+       "      <td>6371282</td>\n",
+       "      <td>56507</td>\n",
+       "      <td>6346872</td>\n",
+       "      <td>56507</td>\n",
+       "      <td>6346070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>blood pressure mean</td>\n",
+       "      <td>21921</td>\n",
+       "      <td>2536271</td>\n",
+       "      <td>21921</td>\n",
+       "      <td>2536271</td>\n",
+       "      <td>21921</td>\n",
+       "      <td>2534398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>56673</td>\n",
+       "      <td>7810019</td>\n",
+       "      <td>56501</td>\n",
+       "      <td>7781312</td>\n",
+       "      <td>56501</td>\n",
+       "      <td>7780557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>temperature body</td>\n",
+       "      <td>48916</td>\n",
+       "      <td>1751447</td>\n",
+       "      <td>48760</td>\n",
+       "      <td>1734835</td>\n",
+       "      <td>48760</td>\n",
+       "      <td>1734221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>49011</td>\n",
+       "      <td>6099827</td>\n",
+       "      <td>48848</td>\n",
+       "      <td>6073693</td>\n",
+       "      <td>48848</td>\n",
+       "      <td>6073120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>weight body</td>\n",
+       "      <td>31866</td>\n",
+       "      <td>95425</td>\n",
+       "      <td>31708</td>\n",
+       "      <td>94484</td>\n",
+       "      <td>31708</td>\n",
+       "      <td>94446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>output urine</td>\n",
+       "      <td>52344</td>\n",
+       "      <td>3633605</td>\n",
+       "      <td>52252</td>\n",
+       "      <td>3628884</td>\n",
+       "      <td>52252</td>\n",
+       "      <td>3627083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>glasgow coma scale motor</td>\n",
+       "      <td>27184</td>\n",
+       "      <td>952565</td>\n",
+       "      <td>27183</td>\n",
+       "      <td>949241</td>\n",
+       "      <td>27183</td>\n",
+       "      <td>949198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>glasgow coma scale eye opening</td>\n",
+       "      <td>27190</td>\n",
+       "      <td>956672</td>\n",
+       "      <td>27189</td>\n",
+       "      <td>953638</td>\n",
+       "      <td>27189</td>\n",
+       "      <td>953595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>glasgow coma scale verbal</td>\n",
+       "      <td>27188</td>\n",
+       "      <td>954700</td>\n",
+       "      <td>27186</td>\n",
+       "      <td>950956</td>\n",
+       "      <td>27186</td>\n",
+       "      <td>950913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>normal saline</td>\n",
+       "      <td>19770</td>\n",
+       "      <td>771272</td>\n",
+       "      <td>19767</td>\n",
+       "      <td>771272</td>\n",
+       "      <td>19767</td>\n",
+       "      <td>505941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>lactated ringers</td>\n",
+       "      <td>16593</td>\n",
+       "      <td>269864</td>\n",
+       "      <td>16579</td>\n",
+       "      <td>269655</td>\n",
+       "      <td>16579</td>\n",
+       "      <td>255324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>7358</td>\n",
+       "      <td>632673</td>\n",
+       "      <td>7341</td>\n",
+       "      <td>631779</td>\n",
+       "      <td>7341</td>\n",
+       "      <td>569450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>vasopressin</td>\n",
+       "      <td>2349</td>\n",
+       "      <td>172851</td>\n",
+       "      <td>2342</td>\n",
+       "      <td>172253</td>\n",
+       "      <td>2342</td>\n",
+       "      <td>169229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>57036</td>\n",
+       "      <td>1167921</td>\n",
+       "      <td>57030</td>\n",
+       "      <td>985037</td>\n",
+       "      <td>57030</td>\n",
+       "      <td>985006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>lactate</td>\n",
+       "      <td>34319</td>\n",
+       "      <td>393608</td>\n",
+       "      <td>34287</td>\n",
+       "      <td>382411</td>\n",
+       "      <td>34287</td>\n",
+       "      <td>382347</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "stat                         component  EXTRACTED_id_count  \\\n",
+       "0                           heart rate               56716   \n",
+       "1              blood pressure systolic               56680   \n",
+       "2             blood pressure diastolic               56677   \n",
+       "3                  blood pressure mean               21921   \n",
+       "4                     respiratory rate               56673   \n",
+       "5                     temperature body               48916   \n",
+       "6     oxygen saturation pulse oximetry               49011   \n",
+       "7                          weight body               31866   \n",
+       "8                         output urine               52344   \n",
+       "9             glasgow coma scale motor               27184   \n",
+       "10      glasgow coma scale eye opening               27190   \n",
+       "11           glasgow coma scale verbal               27188   \n",
+       "12                       normal saline               19770   \n",
+       "13                    lactated ringers               16593   \n",
+       "14                      norepinephrine                7358   \n",
+       "15                         vasopressin                2349   \n",
+       "16                          hemoglobin               57036   \n",
+       "17                             lactate               34319   \n",
+       "\n",
+       "stat  EXTRACTED_data_count  TRANSFORMED_id_count  TRANSFORMED_data_count  \\\n",
+       "0                  7952939                 56545                 7923873   \n",
+       "1                  6374824                 56506                 6331588   \n",
+       "2                  6371282                 56507                 6346872   \n",
+       "3                  2536271                 21921                 2536271   \n",
+       "4                  7810019                 56501                 7781312   \n",
+       "5                  1751447                 48760                 1734835   \n",
+       "6                  6099827                 48848                 6073693   \n",
+       "7                    95425                 31708                   94484   \n",
+       "8                  3633605                 52252                 3628884   \n",
+       "9                   952565                 27183                  949241   \n",
+       "10                  956672                 27189                  953638   \n",
+       "11                  954700                 27186                  950956   \n",
+       "12                  771272                 19767                  771272   \n",
+       "13                  269864                 16579                  269655   \n",
+       "14                  632673                  7341                  631779   \n",
+       "15                  172851                  2342                  172253   \n",
+       "16                 1167921                 57030                  985037   \n",
+       "17                  393608                 34287                  382411   \n",
+       "\n",
+       "stat  CLEANED_id_count  CLEANED_data_count  \n",
+       "0                56545             7923135  \n",
+       "1                56506             6331002  \n",
+       "2                56507             6346070  \n",
+       "3                21921             2534398  \n",
+       "4                56501             7780557  \n",
+       "5                48760             1734221  \n",
+       "6                48848             6073120  \n",
+       "7                31708               94446  \n",
+       "8                52252             3627083  \n",
+       "9                27183              949198  \n",
+       "10               27189              953595  \n",
+       "11               27186              950913  \n",
+       "12               19767              505941  \n",
+       "13               16579              255324  \n",
+       "14                7341              569450  \n",
+       "15                2342              169229  \n",
+       "16               57030              985006  \n",
+       "17               34287              382347  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "etl_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "etl_info['data_loss'] = (etl_info['EXTRACTED_data_count'] - etl_info['CLEANED_data_count'])\n",
+    "etl_info['id_loss'] = (etl_info['EXTRACTED_id_count'] - etl_info['CLEANED_id_count'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "etl_info['%data_loss'] = (etl_info['data_loss']/etl_info['EXTRACTED_data_count']).sort_values().apply(lambda x: str(np.round(x*100,3))+'%')\n",
+    "etl_info['%id_loss'] = (etl_info['id_loss']/etl_info['EXTRACTED_id_count']).sort_values().apply(lambda x: str(np.round(x*100,3))+'%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>stat</th>\n",
+       "      <th>component</th>\n",
+       "      <th>EXTRACTED_id_count</th>\n",
+       "      <th>EXTRACTED_data_count</th>\n",
+       "      <th>TRANSFORMED_id_count</th>\n",
+       "      <th>TRANSFORMED_data_count</th>\n",
+       "      <th>CLEANED_id_count</th>\n",
+       "      <th>CLEANED_data_count</th>\n",
+       "      <th>data_loss</th>\n",
+       "      <th>id_loss</th>\n",
+       "      <th>%data_loss</th>\n",
+       "      <th>%id_loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>blood pressure mean</td>\n",
+       "      <td>21921</td>\n",
+       "      <td>2536271</td>\n",
+       "      <td>21921</td>\n",
+       "      <td>2536271</td>\n",
+       "      <td>21921</td>\n",
+       "      <td>2534398</td>\n",
+       "      <td>1873</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.074%</td>\n",
+       "      <td>0.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>glasgow coma scale eye opening</td>\n",
+       "      <td>27190</td>\n",
+       "      <td>956672</td>\n",
+       "      <td>27189</td>\n",
+       "      <td>953638</td>\n",
+       "      <td>27189</td>\n",
+       "      <td>953595</td>\n",
+       "      <td>3077</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.322%</td>\n",
+       "      <td>0.004%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>glasgow coma scale motor</td>\n",
+       "      <td>27184</td>\n",
+       "      <td>952565</td>\n",
+       "      <td>27183</td>\n",
+       "      <td>949241</td>\n",
+       "      <td>27183</td>\n",
+       "      <td>949198</td>\n",
+       "      <td>3367</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.353%</td>\n",
+       "      <td>0.004%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>glasgow coma scale verbal</td>\n",
+       "      <td>27188</td>\n",
+       "      <td>954700</td>\n",
+       "      <td>27186</td>\n",
+       "      <td>950956</td>\n",
+       "      <td>27186</td>\n",
+       "      <td>950913</td>\n",
+       "      <td>3787</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.397%</td>\n",
+       "      <td>0.007%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>57036</td>\n",
+       "      <td>1167921</td>\n",
+       "      <td>57030</td>\n",
+       "      <td>985037</td>\n",
+       "      <td>57030</td>\n",
+       "      <td>985006</td>\n",
+       "      <td>182915</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15.662%</td>\n",
+       "      <td>0.011%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>normal saline</td>\n",
+       "      <td>19770</td>\n",
+       "      <td>771272</td>\n",
+       "      <td>19767</td>\n",
+       "      <td>771272</td>\n",
+       "      <td>19767</td>\n",
+       "      <td>505941</td>\n",
+       "      <td>265331</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34.402%</td>\n",
+       "      <td>0.015%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>lactated ringers</td>\n",
+       "      <td>16593</td>\n",
+       "      <td>269864</td>\n",
+       "      <td>16579</td>\n",
+       "      <td>269655</td>\n",
+       "      <td>16579</td>\n",
+       "      <td>255324</td>\n",
+       "      <td>14540</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5.388%</td>\n",
+       "      <td>0.084%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>lactate</td>\n",
+       "      <td>34319</td>\n",
+       "      <td>393608</td>\n",
+       "      <td>34287</td>\n",
+       "      <td>382411</td>\n",
+       "      <td>34287</td>\n",
+       "      <td>382347</td>\n",
+       "      <td>11261</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2.861%</td>\n",
+       "      <td>0.093%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>output urine</td>\n",
+       "      <td>52344</td>\n",
+       "      <td>3633605</td>\n",
+       "      <td>52252</td>\n",
+       "      <td>3628884</td>\n",
+       "      <td>52252</td>\n",
+       "      <td>3627083</td>\n",
+       "      <td>6522</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0.179%</td>\n",
+       "      <td>0.176%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>7358</td>\n",
+       "      <td>632673</td>\n",
+       "      <td>7341</td>\n",
+       "      <td>631779</td>\n",
+       "      <td>7341</td>\n",
+       "      <td>569450</td>\n",
+       "      <td>63223</td>\n",
+       "      <td>17</td>\n",
+       "      <td>9.993%</td>\n",
+       "      <td>0.231%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>vasopressin</td>\n",
+       "      <td>2349</td>\n",
+       "      <td>172851</td>\n",
+       "      <td>2342</td>\n",
+       "      <td>172253</td>\n",
+       "      <td>2342</td>\n",
+       "      <td>169229</td>\n",
+       "      <td>3622</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.095%</td>\n",
+       "      <td>0.298%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>56677</td>\n",
+       "      <td>6371282</td>\n",
+       "      <td>56507</td>\n",
+       "      <td>6346872</td>\n",
+       "      <td>56507</td>\n",
+       "      <td>6346070</td>\n",
+       "      <td>25212</td>\n",
+       "      <td>170</td>\n",
+       "      <td>0.396%</td>\n",
+       "      <td>0.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>56716</td>\n",
+       "      <td>7952939</td>\n",
+       "      <td>56545</td>\n",
+       "      <td>7923873</td>\n",
+       "      <td>56545</td>\n",
+       "      <td>7923135</td>\n",
+       "      <td>29804</td>\n",
+       "      <td>171</td>\n",
+       "      <td>0.375%</td>\n",
+       "      <td>0.302%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>56673</td>\n",
+       "      <td>7810019</td>\n",
+       "      <td>56501</td>\n",
+       "      <td>7781312</td>\n",
+       "      <td>56501</td>\n",
+       "      <td>7780557</td>\n",
+       "      <td>29462</td>\n",
+       "      <td>172</td>\n",
+       "      <td>0.377%</td>\n",
+       "      <td>0.303%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>56680</td>\n",
+       "      <td>6374824</td>\n",
+       "      <td>56506</td>\n",
+       "      <td>6331588</td>\n",
+       "      <td>56506</td>\n",
+       "      <td>6331002</td>\n",
+       "      <td>43822</td>\n",
+       "      <td>174</td>\n",
+       "      <td>0.687%</td>\n",
+       "      <td>0.307%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>temperature body</td>\n",
+       "      <td>48916</td>\n",
+       "      <td>1751447</td>\n",
+       "      <td>48760</td>\n",
+       "      <td>1734835</td>\n",
+       "      <td>48760</td>\n",
+       "      <td>1734221</td>\n",
+       "      <td>17226</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0.984%</td>\n",
+       "      <td>0.319%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>49011</td>\n",
+       "      <td>6099827</td>\n",
+       "      <td>48848</td>\n",
+       "      <td>6073693</td>\n",
+       "      <td>48848</td>\n",
+       "      <td>6073120</td>\n",
+       "      <td>26707</td>\n",
+       "      <td>163</td>\n",
+       "      <td>0.438%</td>\n",
+       "      <td>0.333%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>weight body</td>\n",
+       "      <td>31866</td>\n",
+       "      <td>95425</td>\n",
+       "      <td>31708</td>\n",
+       "      <td>94484</td>\n",
+       "      <td>31708</td>\n",
+       "      <td>94446</td>\n",
+       "      <td>979</td>\n",
+       "      <td>158</td>\n",
+       "      <td>1.026%</td>\n",
+       "      <td>0.496%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "stat                         component  EXTRACTED_id_count  \\\n",
+       "3                  blood pressure mean               21921   \n",
+       "10      glasgow coma scale eye opening               27190   \n",
+       "9             glasgow coma scale motor               27184   \n",
+       "11           glasgow coma scale verbal               27188   \n",
+       "16                          hemoglobin               57036   \n",
+       "12                       normal saline               19770   \n",
+       "13                    lactated ringers               16593   \n",
+       "17                             lactate               34319   \n",
+       "8                         output urine               52344   \n",
+       "14                      norepinephrine                7358   \n",
+       "15                         vasopressin                2349   \n",
+       "2             blood pressure diastolic               56677   \n",
+       "0                           heart rate               56716   \n",
+       "4                     respiratory rate               56673   \n",
+       "1              blood pressure systolic               56680   \n",
+       "5                     temperature body               48916   \n",
+       "6     oxygen saturation pulse oximetry               49011   \n",
+       "7                          weight body               31866   \n",
+       "\n",
+       "stat  EXTRACTED_data_count  TRANSFORMED_id_count  TRANSFORMED_data_count  \\\n",
+       "3                  2536271                 21921                 2536271   \n",
+       "10                  956672                 27189                  953638   \n",
+       "9                   952565                 27183                  949241   \n",
+       "11                  954700                 27186                  950956   \n",
+       "16                 1167921                 57030                  985037   \n",
+       "12                  771272                 19767                  771272   \n",
+       "13                  269864                 16579                  269655   \n",
+       "17                  393608                 34287                  382411   \n",
+       "8                  3633605                 52252                 3628884   \n",
+       "14                  632673                  7341                  631779   \n",
+       "15                  172851                  2342                  172253   \n",
+       "2                  6371282                 56507                 6346872   \n",
+       "0                  7952939                 56545                 7923873   \n",
+       "4                  7810019                 56501                 7781312   \n",
+       "1                  6374824                 56506                 6331588   \n",
+       "5                  1751447                 48760                 1734835   \n",
+       "6                  6099827                 48848                 6073693   \n",
+       "7                    95425                 31708                   94484   \n",
+       "\n",
+       "stat  CLEANED_id_count  CLEANED_data_count  data_loss  id_loss %data_loss  \\\n",
+       "3                21921             2534398       1873        0     0.074%   \n",
+       "10               27189              953595       3077        1     0.322%   \n",
+       "9                27183              949198       3367        1     0.353%   \n",
+       "11               27186              950913       3787        2     0.397%   \n",
+       "16               57030              985006     182915        6    15.662%   \n",
+       "12               19767              505941     265331        3    34.402%   \n",
+       "13               16579              255324      14540       14     5.388%   \n",
+       "17               34287              382347      11261       32     2.861%   \n",
+       "8                52252             3627083       6522       92     0.179%   \n",
+       "14                7341              569450      63223       17     9.993%   \n",
+       "15                2342              169229       3622        7     2.095%   \n",
+       "2                56507             6346070      25212      170     0.396%   \n",
+       "0                56545             7923135      29804      171     0.375%   \n",
+       "4                56501             7780557      29462      172     0.377%   \n",
+       "1                56506             6331002      43822      174     0.687%   \n",
+       "5                48760             1734221      17226      156     0.984%   \n",
+       "6                48848             6073120      26707      163     0.438%   \n",
+       "7                31708               94446        979      158     1.026%   \n",
+       "\n",
+       "stat %id_loss  \n",
+       "3        0.0%  \n",
+       "10     0.004%  \n",
+       "9      0.004%  \n",
+       "11     0.007%  \n",
+       "16     0.011%  \n",
+       "12     0.015%  \n",
+       "13     0.084%  \n",
+       "17     0.093%  \n",
+       "8      0.176%  \n",
+       "14     0.231%  \n",
+       "15     0.298%  \n",
+       "2        0.3%  \n",
+       "0      0.302%  \n",
+       "4      0.303%  \n",
+       "1      0.307%  \n",
+       "5      0.319%  \n",
+       "6      0.333%  \n",
+       "7      0.496%  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "etl_info.sort_values('%id_loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "component\n",
+       "heart rate                          1.000000\n",
+       "respiratory rate                    0.982005\n",
+       "blood pressure diastolic            0.800954\n",
+       "blood pressure systolic             0.799053\n",
+       "oxygen saturation pulse oximetry    0.766505\n",
+       "output urine                        0.457784\n",
+       "blood pressure mean                 0.319873\n",
+       "temperature body                    0.218881\n",
+       "hemoglobin                          0.124320\n",
+       "glasgow coma scale eye opening      0.120356\n",
+       "glasgow coma scale verbal           0.120017\n",
+       "glasgow coma scale motor            0.119801\n",
+       "norepinephrine                      0.071872\n",
+       "normal saline                       0.063856\n",
+       "lactate                             0.048257\n",
+       "lactated ringers                    0.032225\n",
+       "vasopressin                         0.021359\n",
+       "weight body                         0.011920\n",
+       "Name: CLEANED_data_count, dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_count = etl_info.set_index('component')['CLEANED_data_count']\n",
+    "data_count.sort_values(ascending=False)/data_count.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "etl_info.component = etl_info.component.astype(str)\n",
+    "etl_info.to_hdf('data/mimic_data_test.h5','etl_info',format='t', data_columns=[column_names.COMPONENT])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test/VALIDATE/Train split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "import mimic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train (80%): 47180 > [139698, 127590, 178959, 139276, 196600] ...\n",
+      "Validate (10%): 5898 > [112338, 107467, 158733, 144544, 115417] ...\n",
+      "Test (10%): 5898 > [167957, 164747, 124147, 184424, 136508] ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "random_state=42\n",
+    "\n",
+    "all_ids = mimic.get_all_hadm_ids()\n",
+    "\n",
+    "#these test IDs will never be touched again. They are sacred\n",
+    "train_ids,test_ids = train_test_split(all_ids,test_size=0.1,random_state=random_state)\n",
+    "train_ids,validate_ids = train_test_split(train_ids,test_size=(1.0/9.0),random_state=random_state)\n",
+    "\n",
+    "print 'Train (80%):', len(train_ids),'>',train_ids[:5],'...'\n",
+    "print 'Validate (10%):', len(validate_ids),'>',validate_ids[:5],'...'\n",
+    "print 'Test (10%):', len(test_ids),'>',test_ids[:5],'...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Models and Stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#my stuff\n",
+    "import icu_data_defs\n",
+    "import transformers\n",
+    "import utils\n",
+    "import features\n",
+    "from constants import column_names,variable_type,clinical_source\n",
+    "import units\n",
+    "\n",
+    "#other stuff\n",
+    "from sklearn.linear_model import LinearRegression,ElasticNet\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "#make pretty pictures\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Pre-processors for each df\n",
+    "\"\"\"\n",
+    "hdf5_fname = 'data/mimic_extract.h5'\n",
+    "data_dict = icu_data_defs.data_dictionary('config/data_definitions.xlsx')\n",
+    "mimic_etlM = mimic.MimicETLManager(hdf5_fname,'config/mimic_item_map.csv',data_dict)\n",
+    "\n",
+    "factory = features.DataSetFactory(features=None,\n",
+    "                                  resample_freq=None,\n",
+    "                                  data_dict=data_dict,\n",
+    "                                  ETL_manager=mimic_etlM,\n",
+    "                                  hdf5_fname_target=None\n",
+    "                                 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ETL_manager': <mimic.MimicETLManager at 0xd766908>,\n",
+       " 'data_dict': <icu_data_defs.data_dictionary at 0xd7668d0>,\n",
+       " 'features': None,\n",
+       " 'force_preprocessing': True,\n",
+       " 'hdf5_fname_target': None,\n",
+       " 'pre_processors': do_nothing(),\n",
+       " 'resample_freq': None,\n",
+       " 'save_ETL_steps': False}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factory.get_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Some features\n",
+    "\"\"\"\n",
+    "\n",
+    "#LACTATE (for labels)\n",
+    "component = data_dict.components.LACTATE\n",
+    "label = features.Feature('LABEL',{column_names.COMPONENT:component},'mean')\n",
+    "lac_mean = features.SimpleFeature('mean',component,\n",
+    "                                           pre_processor=transformers.GroubyAndFFill(level=column_names.ID),\n",
+    "                                           fillna_method=transformers.fill_mean())\n",
+    "lac_most_recent = features.SimpleFeature('last',component,\n",
+    "                                           fillna_method=Pipeline([\n",
+    "                                                        ('ffill',transformers.GroubyAndFFill(level=column_names.ID)),\n",
+    "                                                        ('fill_mean',transformers.fill_mean())\n",
+    "                                                    ]))\n",
+    "lac_count = features.SimpleFeature('count',component,\n",
+    "                                           fillna_method=transformers.fill_zero())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lactate -> Next Lactate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression without CrossValidation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "factory.resample_freq='2H'\n",
+    "factory.hdf5_fname_target = 'data/qn_combine_all.h5'\n",
+    "factory.pre_processors = Pipeline([\n",
+    "                                ('drop_small_columns',transformers.remove_small_columns(threshold=50)),\n",
+    "                                ('drop_low_id_count',transformers.record_threshold(threshold=20)),\n",
+    "                                ('quantitative only',transformers.filter_var_type([variable_type.QUANTITATIVE])),\n",
+    "                                ('combine_like_columns',transformers.combine_like_cols())\n",
+    "                            ])\n",
+    "\n",
+    "factory.features = [lac_mean,lac_most_recent,lac_count,label]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ETL_manager': <mimic.MimicETLManager at 0xd766908>,\n",
+       " 'data_dict': <icu_data_defs.data_dictionary at 0xd7668d0>,\n",
+       " 'features': [<features.SimpleFeature at 0xe248550>,\n",
+       "  <features.SimpleFeature at 0xe248668>,\n",
+       "  <features.SimpleFeature at 0xe2486a0>,\n",
+       "  <features.Feature at 0xe2484e0>],\n",
+       " 'force_preprocessing': True,\n",
+       " 'hdf5_fname_target': 'data/qn_combine_all.h5',\n",
+       " 'pre_processors': Pipeline(steps=[('drop_small_columns', remove_small_columns(threshold=50)), ('drop_low_id_count', record_threshold(threshold=20)), ('quantitative only', filter_var_type(var_types=['qn'])), ('combine_like_columns', combine_like_cols())]),\n",
+       " 'pre_processors__combine_like_columns': combine_like_cols(),\n",
+       " 'pre_processors__drop_low_id_count': record_threshold(threshold=20),\n",
+       " 'pre_processors__drop_low_id_count__threshold': 20,\n",
+       " 'pre_processors__drop_small_columns': remove_small_columns(threshold=50),\n",
+       " 'pre_processors__drop_small_columns__threshold': 50,\n",
+       " 'pre_processors__quantitative only': filter_var_type(var_types=['qn']),\n",
+       " 'pre_processors__quantitative only__var_types': ['qn'],\n",
+       " 'pre_processors__steps': [('drop_small_columns',\n",
+       "   remove_small_columns(threshold=50)),\n",
+       "  ('drop_low_id_count', record_threshold(threshold=20)),\n",
+       "  ('quantitative only', filter_var_type(var_types=['qn'])),\n",
+       "  ('combine_like_columns', combine_like_cols())],\n",
+       " 'resample_freq': '2H',\n",
+       " 'save_ETL_steps': False}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factory.get_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2017-07-12 19:56:04) FEATURIZE... #F=4, #ids=47180, fit->True\n",
+      "(2017-07-12 19:56:04)>> PRE-PROCESSING & JOIN: #C=1, [u'lactate']\n",
+      "(2017-07-12 19:56:05)>>>> lactate - 1/1\n",
+      "(2017-07-12 19:56:05)>>>>>> READ DF...\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (2.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> PREPROCESS...\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> *fit* Filter columns (remove_small_columns) (142289, 63)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> *transform* Filter columns (remove_small_columns) (142289, 63)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> *fit* Filter columns (record_threshold) (142289, 4)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> *transform* Filter columns (record_threshold) (142289, 4)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> *fit* Filter columns (filter_var_type) (142289, 4)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> *transform* Filter columns (filter_var_type) (142289, 4)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> FIT Combine like columns (142289, 4)\n",
+      "(2017-07-12 19:56:07)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+      "(2017-07-12 19:56:07)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:07)>>>>>> TRANSFORM Combine like columns (142289, 4)\n",
+      "(2017-07-12 19:56:07)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+      "(2017-07-12 19:56:12)<<<<<<<< --- (5.0s)\n",
+      "(2017-07-12 19:56:12)<<<<<< --- (5.0s)\n",
+      "(2017-07-12 19:56:12)>>>>>> SAVE DF... (142197, 1) -> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:12)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:12)<<<< --- (7.0s)\n",
+      "(2017-07-12 19:56:12)>>>> Smart join: n=47180, ['simple_processing/390097392/390097392/lactate']\n",
+      "(2017-07-12 19:56:12)>>>>>> JOINING dataframes\n",
+      "(2017-07-12 19:56:12)>>>>>>>> Slice & Join: 100001 --> 110571, n=5000\n",
+      "(2017-07-12 19:56:12)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:13)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:13)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:13)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:13)>>>>>>>> Slice & Join: 110573 --> 121159, n=5000\n",
+      "(2017-07-12 19:56:13)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:13)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:13)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:13)>>>>>>>> Slice & Join: 121164 --> 131717, n=5000\n",
+      "(2017-07-12 19:56:13)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:14)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:14)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:14)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:14)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)>>>>>>>> Slice & Join: 131719 --> 142369, n=5000\n",
+      "(2017-07-12 19:56:14)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:14)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:14)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)>>>>>>>> Slice & Join: 142372 --> 152958, n=5000\n",
+      "(2017-07-12 19:56:14)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:14)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:14)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:14)>>>>>>>> Slice & Join: 152960 --> 163720, n=5000\n",
+      "(2017-07-12 19:56:14)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:15)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:15)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:15)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:15)>>>>>>>> Slice & Join: 163721 --> 174138, n=5000\n",
+      "(2017-07-12 19:56:15)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:15)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:15)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:15)>>>>>>>> Slice & Join: 174141 --> 184726, n=5000\n",
+      "(2017-07-12 19:56:15)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:16)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:16)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:16)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:16)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)>>>>>>>> Slice & Join: 184727 --> 195306, n=5000\n",
+      "(2017-07-12 19:56:16)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:16)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:16)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)>>>>>>>> Slice & Join: 195309 --> 199999, n=2180\n",
+      "(2017-07-12 19:56:16)>>>>>>>>>> simple_processing/390097392/390097392/lactate\n",
+      "(2017-07-12 19:56:16)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)>>>>>>>> Append slice\n",
+      "(2017-07-12 19:56:16)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:16)<<<<<< --- (4.0s)\n",
+      "(2017-07-12 19:56:16)<<<< --- (4.0s)\n",
+      "(2017-07-12 19:56:16)>>>> Read JOINED DF\n",
+      "(2017-07-12 19:56:17)<<<< --- (1.0s)\n",
+      "(2017-07-12 19:56:17)<< --- (13.0s)\n",
+      "(2017-07-12 19:56:17)>> APPLY FEATURES, #F=4 to df=(142197, 1)\n",
+      "(2017-07-12 19:56:17)>>>> Featurizing: MEAN, {'component': 'lactate'}\n",
+      "(2017-07-12 19:56:17)>>>>>> *fit* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:56:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:17)>>>>>> *transform* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:56:33)<<<<<< --- (16.0s)\n",
+      "(2017-07-12 19:56:33)>>>>>> *transform* RESAMPLE (142197, 1), rule=2H, func=mean\n",
+      "(2017-07-12 19:56:33)>>>>>>>> Resampling\n",
+      "(2017-07-12 19:56:33)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:56:33)>>>>>>>> Aggregating\n",
+      "(2017-07-12 19:57:45)<<<<<<<< --- (72.0s)\n",
+      "(2017-07-12 19:57:45)<<<<<< --- (72.0s)\n",
+      "(2017-07-12 19:57:45)>>>>>> Join\n",
+      "(2017-07-12 19:57:45)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:57:45)<<<< --- (88.0s)\n",
+      "(2017-07-12 19:57:45)>>>> Featurizing: LAST, {'component': 'lactate'}\n",
+      "(2017-07-12 19:57:45)>>>>>> *fit* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:57:45)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:57:45)>>>>>> *transform* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:57:45)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:57:45)>>>>>> *transform* RESAMPLE (142197, 1), rule=2H, func=last\n",
+      "(2017-07-12 19:57:45)>>>>>>>> Resampling\n",
+      "(2017-07-12 19:57:45)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:57:45)>>>>>>>> Aggregating\n",
+      "(2017-07-12 19:58:32)<<<<<<<< --- (47.0s)\n",
+      "(2017-07-12 19:58:32)<<<<<< --- (47.0s)\n",
+      "(2017-07-12 19:58:49)>>>>>> Join\n",
+      "(2017-07-12 19:58:56)<<<<<< --- (7.0s)\n",
+      "(2017-07-12 19:58:56)<<<< --- (71.0s)\n",
+      "(2017-07-12 19:58:56)>>>> Featurizing: COUNT, {'component': 'lactate'}\n",
+      "(2017-07-12 19:58:56)>>>>>> *fit* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:58:56)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:58:56)>>>>>> *transform* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:58:56)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:58:56)>>>>>> *transform* RESAMPLE (142197, 1), rule=2H, func=count\n",
+      "(2017-07-12 19:58:56)>>>>>>>> Resampling\n",
+      "(2017-07-12 19:58:56)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:58:56)>>>>>>>> Aggregating\n",
+      "(2017-07-12 19:59:41)<<<<<<<< --- (45.0s)\n",
+      "(2017-07-12 19:59:41)<<<<<< --- (45.0s)\n",
+      "(2017-07-12 19:59:41)>>>>>> Join\n",
+      "(2017-07-12 19:59:44)<<<<<< --- (3.0s)\n",
+      "(2017-07-12 19:59:44)<<<< --- (48.0s)\n",
+      "(2017-07-12 19:59:44)>>>> Featurizing: LABEL, {'component': 'lactate'}\n",
+      "(2017-07-12 19:59:44)>>>>>> *fit* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:59:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:59:44)>>>>>> *transform* Filter columns (DataSpecFilter) (142197, 1)\n",
+      "(2017-07-12 19:59:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:59:44)>>>>>> *transform* RESAMPLE (142197, 1), rule=2H, func=mean\n",
+      "(2017-07-12 19:59:44)>>>>>>>> Resampling\n",
+      "(2017-07-12 19:59:44)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 19:59:44)>>>>>>>> Aggregating\n",
+      "(2017-07-12 20:00:56)<<<<<<<< --- (72.0s)\n",
+      "(2017-07-12 20:00:56)<<<<<< --- (72.0s)\n",
+      "(2017-07-12 20:00:56)>>>>>> Join\n",
+      "(2017-07-12 20:00:59)<<<<<< --- (3.0s)\n",
+      "(2017-07-12 20:00:59)<<<< --- (75.0s)\n",
+      "(2017-07-12 20:00:59)<< --- (282.0s)\n",
+      "(2017-07-12 20:00:59) --- (295.0s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_lactate_train = factory.fit_transform(train_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2017-07-12 20:00:59) FEATURIZE... #F=4, #ids=5898, fit->False\n",
+      "(2017-07-12 20:00:59)>> PRE-PROCESSING & JOIN: #C=1, [u'lactate']\n",
+      "(2017-07-12 20:00:59)>>>> lactate - 1/1\n",
+      "(2017-07-12 20:00:59)>>>>>> READ DF...\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (1.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>> PREPROCESS...\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>> *transform* Filter columns (remove_small_columns) (18340, 63)\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>> *transform* Filter columns (record_threshold) (18340, 4)\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>> *transform* Filter columns (filter_var_type) (18340, 4)\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>> TRANSFORM Combine like columns (18340, 4)\n",
+      "(2017-07-12 20:01:00)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+      "(2017-07-12 20:01:00)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>> SAVE DF... (18336, 1) -> simple_processing/390097392/-1238157839/lactate\n",
+      "(2017-07-12 20:01:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)<<<< --- (1.0s)\n",
+      "(2017-07-12 20:01:00)>>>> Smart join: n=5898, ['simple_processing/390097392/-1238157839/lactate']\n",
+      "(2017-07-12 20:01:00)>>>>>> JOINING dataframes\n",
+      "(2017-07-12 20:01:00)>>>>>>>> Slice & Join: 100019 --> 185046, n=5000\n",
+      "(2017-07-12 20:01:00)>>>>>>>>>> simple_processing/390097392/-1238157839/lactate\n",
+      "(2017-07-12 20:01:00)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:00)>>>>>>>> Append slice\n",
+      "(2017-07-12 20:01:01)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-12 20:01:01)>>>>>>>> Slice & Join: 185050 --> 199993, n=898\n",
+      "(2017-07-12 20:01:01)>>>>>>>>>> simple_processing/390097392/-1238157839/lactate\n",
+      "(2017-07-12 20:01:01)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:01)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:01)>>>>>>>> Append slice\n",
+      "(2017-07-12 20:01:01)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:01)<<<<<< --- (1.0s)\n",
+      "(2017-07-12 20:01:01)<<<< --- (1.0s)\n",
+      "(2017-07-12 20:01:01)>>>> Read JOINED DF\n",
+      "(2017-07-12 20:01:01)<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:01)<< --- (2.0s)\n",
+      "(2017-07-12 20:01:01)>> APPLY FEATURES, #F=4 to df=(18336, 1)\n",
+      "(2017-07-12 20:01:01)>>>> Featurizing: MEAN, {'component': 'lactate'}\n",
+      "(2017-07-12 20:01:01)>>>>>> *transform* Filter columns (DataSpecFilter) (18336, 1)\n",
+      "(2017-07-12 20:01:03)<<<<<< --- (2.0s)\n",
+      "(2017-07-12 20:01:03)>>>>>> *transform* RESAMPLE (18336, 1), rule=2H, func=mean\n",
+      "(2017-07-12 20:01:03)>>>>>>>> Resampling\n",
+      "(2017-07-12 20:01:03)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:03)>>>>>>>> Aggregating\n",
+      "(2017-07-12 20:01:12)<<<<<<<< --- (9.0s)\n",
+      "(2017-07-12 20:01:12)<<<<<< --- (9.0s)\n",
+      "(2017-07-12 20:01:12)>>>>>> Join\n",
+      "(2017-07-12 20:01:12)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:12)<<<< --- (11.0s)\n",
+      "(2017-07-12 20:01:12)>>>> Featurizing: LAST, {'component': 'lactate'}\n",
+      "(2017-07-12 20:01:12)>>>>>> *transform* Filter columns (DataSpecFilter) (18336, 1)\n",
+      "(2017-07-12 20:01:12)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:12)>>>>>> *transform* RESAMPLE (18336, 1), rule=2H, func=last\n",
+      "(2017-07-12 20:01:12)>>>>>>>> Resampling\n",
+      "(2017-07-12 20:01:12)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:12)>>>>>>>> Aggregating\n",
+      "(2017-07-12 20:01:17)<<<<<<<< --- (5.0s)\n",
+      "(2017-07-12 20:01:17)<<<<<< --- (5.0s)\n",
+      "(2017-07-12 20:01:19)>>>>>> Join\n",
+      "(2017-07-12 20:01:21)<<<<<< --- (2.0s)\n",
+      "(2017-07-12 20:01:21)<<<< --- (9.0s)\n",
+      "(2017-07-12 20:01:21)>>>> Featurizing: COUNT, {'component': 'lactate'}\n",
+      "(2017-07-12 20:01:21)>>>>>> *transform* Filter columns (DataSpecFilter) (18336, 1)\n",
+      "(2017-07-12 20:01:21)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:21)>>>>>> *transform* RESAMPLE (18336, 1), rule=2H, func=count\n",
+      "(2017-07-12 20:01:21)>>>>>>>> Resampling\n",
+      "(2017-07-12 20:01:21)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:21)>>>>>>>> Aggregating\n",
+      "(2017-07-12 20:01:27)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-12 20:01:27)<<<<<< --- (6.0s)\n",
+      "(2017-07-12 20:01:27)>>>>>> Join\n",
+      "(2017-07-12 20:01:27)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:27)<<<< --- (6.0s)\n",
+      "(2017-07-12 20:01:27)>>>> Featurizing: LABEL, {'component': 'lactate'}\n",
+      "(2017-07-12 20:01:27)>>>>>> *transform* Filter columns (DataSpecFilter) (18336, 1)\n",
+      "(2017-07-12 20:01:27)<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:27)>>>>>> *transform* RESAMPLE (18336, 1), rule=2H, func=mean\n",
+      "(2017-07-12 20:01:27)>>>>>>>> Resampling\n",
+      "(2017-07-12 20:01:27)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-12 20:01:27)>>>>>>>> Aggregating\n",
+      "(2017-07-12 20:01:36)<<<<<<<< --- (9.0s)\n",
+      "(2017-07-12 20:01:36)<<<<<< --- (9.0s)\n",
+      "(2017-07-12 20:01:36)>>>>>> Join\n",
+      "(2017-07-12 20:01:37)<<<<<< --- (1.0s)\n",
+      "(2017-07-12 20:01:37)<<<< --- (10.0s)\n",
+      "(2017-07-12 20:01:37)<< --- (36.0s)\n",
+      "(2017-07-12 20:01:37) --- (38.0s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_lactate_validate = factory.transform(validate_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_train = df_lactate_train.loc[:,['LABEL']].shift(-1).dropna().iloc[:,0]\n",
+    "y_validate = df_lactate_validate.loc[:,['LABEL']].shift(-1).dropna().iloc[:,0]\n",
+    "\n",
+    "X_train = df_lactate_train.drop('LABEL',axis=1).loc[y_train.index]\n",
+    "X_validate = df_lactate_validate.drop('LABEL',axis=1).loc[y_validate.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(131099, 3) (131099L,)\n",
+      "(16937, 3) (16937L,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print X_train.shape,y_train.shape\n",
+    "print X_validate.shape,y_validate.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x315d3940>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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P0n5jTF2SbNv+iqTvGGO+dBOPfVPSj0r6b1du3yPpdtu2Py3pDUm/aIzhEwoA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\nR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jTZ+vj9O7W5w1ZvZ8uqL9AAAAAAAAAX0lRyWpOpnOzw6mczHzw+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zgb3UtFzkrvY06dTKH9PXGsZvnBjB9yrE1SDWN0+c54DTi2EaGFBf3K8xpzhZ0M7Gsqz6RLGI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\ncG3D9KGIrC9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zY//o/1T42PNtHbOw9LI++E/fxcrmDjms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I+4UWpFrEJOiVhI/f20zAAAAAC8tNTM5t1L3OdI\n+huXa0EFbMfR/gOnNTyW1saehHbuGKAPLepGvlDQ6MRMTfozvzs8pcf3Hdb0/GJVgYB09/UbdM/7\nNygc8v7ybgCo1e/sdCaj5HS24pZFbx1J6olnDiuTL0qSopGg7r19i95/RY/nwW7RshQOltTf3apI\nxPvQG8srv/8n0gV1J6KcswIAADSYpXo2f778b8MwIpKM+ce/aZpmsQa1YRX2HzitPa+dlCQdHJ6S\nJN153XovSwIk1a4/c94q6TsvHddLb48sbOvpiOuBXdu1qbelqsf22tyl41llps+kva4FwPJq8Tt7\ncmpa6bxd0dibt0r61vNH9Yo5trBtc1+LHtq9XV1t1W+FtJRyy4yu9iYlmr1v4YGVK7//I+GgrKIt\niXNWAACARrJkz2ZJMgzjA5Ie01z7jKCkPsMw7jNN86VqF4eVGx5LL3kb8EKt+jMfPZPSo08PKZnK\nS5ICknbW0WJV1VIOWxJNYfX3d2v4nX2Z5b8KgNeq+TvbcRyNjCdlK6pwBTN9T4zO6OE9Q5pI5SRJ\nwYB0zwc26u7rN3je897KZ+fHPVpm+BHnrAAAAI1t2bBZ0lckfbYcLhuGcauk/yzp5moWhtXZ2JNY\nmB1Vvo1LQz22ULFtW8Onx5W3w1Xtz2wVbX3/lRN67sBpOfPbOltjemDXoLYONPbiUIV8Ri3xiPr6\nOhUMNm6gDtSrSsbeav3OLhQKOnlmQqFo05p/D5RsR0+/dlJ7Xx2WPT+wdrfF9dA9g9rU2+pKnWtV\ntAqKhGz1r2ujZYaPNeo5K+1BAAAA5qwkbG5ZPIvZNM0XDcPw9tpJvMfOHQOSdM4fvbg01FsLlVw+\nr7HkrPoG1ikYLFXtOMNjs3r06SGNTmYXtt18Va8+eutlikVCVTuu16x8VvFoUBv7ugiZAQ9VMvZW\n43f2bDqjdD6vcKx5zftIpnJ6eO8hHR+ZXdh2o9Gjj9++xdNx1bZt2cW8OtualGhe+/NDfSi/3xeH\nso2A9iAAAABzVhI2Jw3D+JRpmk9KkmEYn9ZcSw3UkWAgwAntJaqeLketRX/mkm1r76sn9fRrJxdm\n3bUlovrMXdt0xaaOqh3Xa1Yhp3gkoJ7eDoVCjRumA35Rydjr9u/s5NS0snlHPX2dUnr1vwMcx9Gr\nB8f0jeePqmDNhWTNsbDuu2ub3re1y7U618IqZNUSD6tzXbendcA95fd/T0+rxsZmvC7HNfV0PgYA\nAOCllYTNX5D0NcMw/kpzrVAPSfqZqlYFYMXq5XLU8eSkclagqv2ZzyQzenTvIZ2aONua+IbL1+kT\nt29RU2wlw5n/WFZesZCj/u5WLhsH6kg9jL22bWtkfFJ2IKrQGmceZ3KW/unZI3rzSHJh2+Ub23X/\n3YNqS0TdKnXVilZB0ZCt9T18wAZ/qIcxAQAAoB4sm86YpvmupFsMw0hICpqm2ThTEIAG4HULlXLY\n4QRjVevPbNuOnjtwWt975YRK89OZE/GwPn2n97PuqsWy8ooEHfV1tiga9S7wAXBhXo+9+UJBoxMz\nisSatNYo9tDwtB59+pBSGUuSFA4F9JO3bNat7+v3rNesbdtySnl1tyXU1ETXNvhHo7YHAQAAWK1l\nw2bDMO6U9CVJnfO3JUmmad5T1coArIiXLVTK/ZkjsSZVK5YYn87q0aeHzukh+r4tXfrUnVvV0tR4\nM32LRUshFdXT0aJ4LOZ1OQAuwsuxd2Y2ramZ3JpbFllFW//88nHtf+PMwrb+rmZ99p7t6uvyriey\nlc+qLRFRexstM+A/jdoeBAAAYLVWct35X0v6XUnHqlsKAD9JzcwqlS5UrT+z7Th66a0Rfeel47JK\ncz1E49GQPrlzq67b3q1Ag63wXioWFXAsdbU2q7m53etyANSpcsuitY69Z5IZPbznkM4kz7YjumPH\ngH7ipk0Kh7xZdLRo5RVyItrQ18nCpwAAAIDPrSRsPmma5t9UvRIAvjGenFSuGKxaf+ap2bwe2zek\noZOphW1XbGrXfXcNqt3DHqLVUCqVpFJBbS1xtba0eV0OgDpl27bOjE1KobW1LLIdRy+8eUbf/cFx\nFUtz7YjaElE9uGtQgxu8+YCrVCopYBe0rr1F/b1dzAYFAAAAGsBKwuavGIbxNUl7JBXLGwmggYuz\nHUf7D5w+p5enV/0v3XRO2BF2f/aZ4zj6oTmmb71wTHmrJEmKRoL6+K2X6cYrextqNrNt2ypZebW3\nxNTWyiXjgJsabQzO5fMan5xVOLq22cypdEGPPj2kQyenF7Zds61Ln75jm5rj3iyuauUzakvEaJkB\n+EB5TF3cj9rPYyoAAKiulfyF8X/M///ORdscSYTNwEXsP3Bae147KUkLK5N71dvTLZWGHctJZQr6\np2cO68fHz67kvnWgVfffPaiutsZZJMpxHBULWbUlompfR8gCVEMjjcGp1KymMwVF1jj2vnkkqSee\nOaxsfm6+QCwS0r07t+iGy9d58gGeZeUVD0u9fV20zAB8ojymRsJBWcW51mZ+HVMBAED1rSRsHjBN\n86qqVwI0kOGx9JK3/SY1M6vp9NrDjuUcGBrXk88dXQhDwqGAPnLzZt12TX/DzJyZC5lzSjSF1d/f\neD2ngXrSKGPw2MSk8qWgImtoWZS3Svrm80f1Q3NsYdvmvhY9tHu7Jx/glYpFBWSx+CngQ40ypgIA\ngNpYSdj8rGEYn5D0HdM0i8s+GoA29iQWZtOVb/tVJWHHcjI5S08+d0RvHE4ubNvYk9ADu7ert6M6\nwbYXCvmMEvGw+lj8CqgJv4/BpVJJZ8anFFhjy6LjIzN6eO8hJVN5SVIwIN3zgY26+/oNCgVr+0FX\n+WqO9pY4LYMAn/L7mAoAAGprJWHzvZJ+QZIMwyhvc0zTDFWrKMDvdu4YkKRz+oX6TalU0sj4VNX6\nM79zbFJPPHNYs1lLkhQKBvTBD2zUndetr3kYUi1WIad4JKANvZ0KhRgygVrx8xiczeY0PjWrSKx5\n1V9bsm39yw+HtffVYdlzawCquz2uh3Zv16beFpcrXZ5l5dUUEVdzAD5XHkMX92wGAAC4mGXDZtM0\nOZsAVikYCPi6l10ul9f4VHX6M+cKRX3z+WN69eDZS7v7u5r14O5BDXQ3xkwZy8orFpL6u1sViUS8\nLge45Ph1DE6lZpXKWmsKmidSOf3lN9/WkVOphW03Xdmrj912mWKR2n7YVSoWFZSlvs5WRaPRmh4b\ngPvKY2pPT6vGxma8LgcAANS5ZcNmwzB6JP1vkjoXbzdN8/+pVlEAvFMOO6oRNB8antZj+4Y0nS5I\nkgIB6e7rN+ie929QOOT/9hJFK69w0FFfZwsBC4AVcxxHYxOTsuywwpHV9TN2HEevHhzTN54/qoI1\nt3BXcyysz9y9TVdv6apGuUvWUizk1NEaV2tLW02PDQAAAKA+rKSNxrclvSHpWJVrAeCxsYlJ5YvB\nVYcdyylYJT310nG99PbIwrZ17XE9uHtQm3pbXT2WF4pWQaFASetY+ArAKhWLRY2MTysYiSsUXl2r\niUzO0hPPHNFbR8/2vb9iU7s+c/eg2ppr+4GXVcgpEQupv7+LlhkAAADAJWwlYbNM0/z5ahcCwDvn\nLEYVcXeG8bEzM3rk6bMLVUnSzmv69eGbNyka9ncf41KxqIAsdbcl1NTk/gKKABpbJptVcjqzpitJ\n3h2e0qNPD2kmM9f3PhwK6P57LteOLZ01DXuLlqVwsETbIAAAAACSVhY2/5NhGL8gaY+kYnmjaZrH\nq1YVgJrJ5fIam5xZU4/QpVjFkp568ZieO3Ba8+tUqbM1pvvv3qZt69tdPVatlUolyS6ovSWulgSX\nigNYvelUSjM5e9VBs1W09c8/OK79b55Z2DbQ3ayHdm/XVdt7lEym3S71ghzHUcnKqbOtSYlmf4/p\nAAAAANyzkrC5XdKvSxpftM2RtK0qFQGomUoWo1rKyfG0nnj8DZ0aPxt63HRlrz5262WKRf07m9m2\nbdnFvNoSMbW1dntdDgAfchxHo+OTKjphhcOra3VxeiKth/cc0shkVpIUkHTHjgF9+KZNNe17b+Wz\nSjSF1dlFywwAAAAA51pJ2Hy/pF7TNLPVLgZAbTiOo/HklOv9mUu2radfO6W9r56U7czNZ25rjugz\ndw/qik0drh2n1soz+FqbI2pfR8gMYG0sy9LIREqhSFyhVYS0tuPo+TfO6Ls/OK6SPTe2tieiemD3\noAZreKVI0SooErLVv66NlhkAAAAALmglYfNhSZ2SCJuBBlDuzxwMxxWOuDcjbSSZ0SNPD50zm/n6\n7et0784taoqtqD183SmHzIl4WB3M4ANQgXQmo+R0VpHY6tpmTKcLeuzpIR06Ob2w7dptXfr0ndtq\nNraWr+qYa5nh7pUwAAAAABrLSv5KcSS9bRjGm5IK5Y2mad5TtaoAVEU2m9P41KyrbTNs29Fzb5zW\n914+sTDjLhEP66c/epUu60m4dpxaK+QzaolH1NHbqWCwdpenA2g8yalpZfL2qoPmNw5P6J+ePaxs\nviRJikVC+uTOLbr+8nU1+/DLKmTVEg+rk6s6AAAAAKzASsLm3696FQCqrhr9mSemc3rk6UM6PjK7\nsO3qLZ369J3btHlDR80WqnJTPpdVyMlrQ2+nQiH/9pcG4D3HcTQynpStqMKraDuRL5T0jeeP6tWD\nYwvbLutr1YO7B9XVFq9Gqe9RtAqKhmyt7+lgLAQAAACwYhcNmw3D+G1JGUmvmab5L24f2DCMWyT9\ngWmauw3DuF7SNyUdnL/7L0zTfMTtYwLLsR1H+w+c1vBYWht7Etq5Y0BBn7dOKPdnLpRCrvVnth1H\nL709ou+8dFxW0ZYkxaMh3btzi67fXrsZd26yrLxiIWlT/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4gm9ObpYV44eQXH89BUFR94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bwEfvP8GH3nMIx54rbodhgEXIQHcHn3jkdnp7rizILT0+WGh3NoOWYYvIztCa\ne+fPtXPdzMaK8y6kecnVekipFmAAdTPiwvAs//JzP2BkMt0MtTXvGobB5fE0G78jZ1OphXRk7QXz\nbnFomrcvTWPOW0ZrGumGsQ/ccYhH33+YsOHT3dW5YNO+zrzLY/em7x3ffuUy5WpAw0+L34stN/c+\ne2Z4yWqXvV5cvlaBXURkL2gVlhsNvx2FEUYJpu1i23OF5dYHwThJmCk32p3Jc3nK9fbKxrXoyNoL\nN+drdiv3dGawtjEvJUkSwjBs5iMnzVzkuY7kjJUh70TYOQfXyWPb21cAFxER2QvW8k76gud5zxaL\nxYeA2zzP+yrw54rF4nc3eWz7Wmu58tBomR9dmCSqJAuWcgPkMzZ93VmCMGZ4orrMUea88OYo5VqA\na1v4YUSv7S4oAj9271E+fPcRqrUG/+pzr3Du6tzxOrI2f/ZP3Mb77jzM5GT6mCgMMQg4UMiTz3cv\nGff8HNPnXruqZdgisiPN7/gt5B06sjZH+jpWnHfTJbMm5ZpPEqcZzlGccP7KLK5jETfXgxfyDi+8\nOUqlnn6wBdpd0pVawMhkDcc2+NGFSUYmqwtiMyDNZj7YbXHfbV2cOHKAiYl0nKtFWwyNlanVQ3IZ\ne9nM5tVe+3KX96L9WGAXkb1tccdyGKWFZatVWDbAdMB10hiokfFKWkieF3sxPl1vr45Zi86cw/GD\nBfq7F27Stx1dynEcE4chUTMf2bIWxlq0ispuRwbX7Vw21qL/QCdJpNxkERGRjbKWM4J/AfwiQLFY\nLHqe93Hgt4H7NnNg+9H8jquj/Xli4EcXJhmbXrhMzaB5ImUYHB8ocMvRLn7r62eXHK9cDfDDiDhO\nmHVMTMNsL+X2g3hBvMXQWJnPffNNvnPmKnV/7mQzn7V5z6kDnDzUlY4xikhin57OHB35riXPudyy\na32QF5Gd6uhABy+fHaNSD0iShGMDBX70ziQjU9Vlc0TjBIIgYvHK4TiBejN7f7rcoNYImHWt9rxb\nyDskcYLrWJSrPpOzDeIkxphXZLZNA8OErGOQdRJ6u/Ic7D+w4IPxStEW65ln9+MGgPuxwC4ie0cU\nRVRrNYIgSjuWFxeWTTCMhFrDZ2y8zHizQ3msWVCeuY4u5XzGbncoj0+nOcwZNy3I3ny4i5/+8ZOb\n9CrnhGFI3Iy1aBWQF2+2Z7kW2UyHYi1ERER2kLUUm7PFYvGHrQvFYvFNz/OcTRzTvjW/4+rls2PU\n/ZCaH5EsqngkQBSlRYtL4xVuOda9zNHS/NG6HzW76myCcC7z2XVM/HqcFkCShB+eG+Xq1NwJqGFA\nb2eWrGtxtL9AHMeEfo1C1qCrc2N2ahYR2XZJQt0P8YOIBHhneHbVzaoMWFJoXiyKE2qNCMMw2vNu\nIe/gOiYjUzUq9YA4SRYUml3b5MdO9nJ5bArLNHEzOW4+0nvjr28V+3EDwP1YYBeR3SmKIirVGn4Q\ntqMwEgzsZsayH0ftzfnGZybnupVn6svuN7Ac0zA40JVJi8rd2QURGB3ZuY97L745yvdeH8G2DMIo\n4dCB/A29tsXdyOaiWIvWn4q1EBER2Z3W8s79pud5/5S0mxngTwNL22jlhs3vsPLDiCCMMWDZzNCE\n9EQN4PvNzQUXC8KYhHQjwTBK6My7JCQYpDsl+wGYJEyU6sxW5wrRuYzNwQM5jvR1cLA3x103F+hw\nE246OsDYWGnjXrCIyDa7PF5NN/yxTMIoXhKbsV4J6XJl2zKJkwTbhMtjJRpBQpywpPsqYydk7JCP\nPXgLVyZqW1L83Y8bAO7HAruI7HytjmU/CJtRGOkZu2W7lKoJY9MNxmZqzYJyGn0xXV57l3IuY6dx\nF93NLOVmUflAVwZrmViJxd7nDQAwXfXpybvty4slSUIURUu6kZf+aZJx0yLycrEWIiIisrutpdj8\nK8D/Cfw3IACeAv7iZg5qP5kfnVFtLuM2DAPXbmZ/hq0NqyCOkwUddzU/YmmQxUKtYrVhzOWRtjJE\ngzDgwtV6u5htGgY9BZdsxubeW/q5+1QnPzw/weeevIhhGDx+n8/dpw5oMyUR2fVac+/l8XI7Zzkt\nAC/NaZ4vIZ1XW39fTRglxElMkgRUagF+GM97dPMYSUwUNDDzHdx+8hAfuvsIz54ZZmiszG/+0Rvk\nMjZ3nu7nrpt7l8y92uzu+u3HAruI7CxxHFOr1Wn4AUEUE4Qx9SBmppowMVtfsDnf+HSt+d5xbaYB\nvV2tTfmy7Q7l/u4chdyNLUo1gHtuOUBPd5aJiVnioA7mCtnIjovjdGBZykAWERHZr65ZbC4Wi1PA\nX2ld9jzPAG4GZjZxXPvG/OgMgOMDBfJZp53Z/GKza/kD3gBfeuYdZisBzXoICXCsv4PTx7p5/cLU\nkmOHUdwuNHflbY72d/DOlVkmSnXCMCJO5ooSP3ZzL5ZhMDZdp7/D4IHbe3hzqMoTr05QqqadE+Mz\nZyk/eJM+qIvIrteae5MkIevadHWY9BRcegsZ3hkuMTZTwzSMZT/kD/SkG7POVPxVIzcg/ZKwESft\nLxLnC4M6AG4mRxibxPPGVa4GlKo+nXmXCyMlSqX6krlXm92JiOxsYRg2O5YjwihmsuQzOl1nqhwx\nPttobtJXY6bsX/MLzJasazU7k5sF5e65LmXbuv4u4YWRFslcFrJpYBjpyh/TNLBth6OHuulwHWUj\n73CtL6MnKj59Ha6+jBYRkS13zWKz53l/Dfi/gPnBhheA05s0pn1l8eZE+azDn3r8Fp49M8zwWIUH\n7jjIQ3cd5tkzw9imiWHQ7rrLZ9LM5ltXyGxuFUESYLoc8IO3xqj7EVE8V/SwLIP33TrA0f483z0z\nhGEkTNdyFC/VuDxexQ+j9vEaQaTNlERkT2jNZYZhUMg73Hq0m+ODBS6NVfjJB06QJAl//P0hRiYX\nbhSYz1jksg639OUpDs0wVapfs+Dcep6WOAqIoxDLdjFMi6S5ueCLb45ytL8A0J57W38uN/dqszsR\nkZ0jjiPiKEhP1A0DA4P//q23mCz5jM3UGZ+u0wiiax+I1t4paZZyq1O5v5mrXMitvdibJEl7kz2T\n9LzfMEgLyEYaq9eKtHCclY/bKl7+8SsjKl7uAq0vox3bbOd368toERHZSmuJ0fibwN2kBee/C3wE\n+IlNHNO+stxmRc+cGeYPnr1ApR4QRQlfe+FdejuzGEa6BDhqVpsrtTTX7YUVMpvn88OYRpAWmduF\nZtOgvyvLu8OTjE9Ok8lmMM10yVtrWbZrWzT89MQ441jaTElE9oTFc2+tEfLlefNuLmvhB3E6X87L\n1UiAUsVnKE64/UQPr70zwWwlWPY5lnQzJzFh0MAwbWw3t/C+i8bVmntd22pff63XoPlZRGRr1Ot1\nRqcqXJmocnWyxuhUHQMDy84smPe//crwqsdpdSm3Nufrb3Ys93Vl19SlHIYhSRySJDGWmXYgmwbt\nSAvbMskUsjiOc0PZyCpe7i76MlpERLbbWorNo8Vi8R3P884A7y0Wi/+f53l/dbMHtl8s3qzog+89\nxD/4Ty8wWaq36xvDEzVGJmvAwoxQP4wJopiRyeo1n2e5Jdw5J4G4getkyOWzVOpzmwS28j8T4IU3\nRgB4/L4T3H3qwPpfrIjIDjF/7j060MHXn7+4YN4NKstnZNYaUfO/gGo9xFqls2v+nBsFDZIkXlJk\nhnRed0yD+24fbI9raKxMrR4uyGxe7TVoszsRkY3X8AMuXp3h8ni5XVQena4zMdugESx8nzBWKOYa\npF3K/T05BrqzzYJyWlReqUs5SRIC3ychhnmFZGvRRntO3sXdgnzkobEy5WpAGMfYpsnQWHlTn09u\njL6MFhGR7baWYnPF87xHgTPAJz3P+z6w9FOvrMvizYqefvUKU6XGkg2qVlqmnSSsuplVy8Il3CFx\nFGDlO8jm0pOP++84CMwVlpPmQR+++wgPN8c3MNDJ2FhpTa9LRGQnmz/3Pv3qFabKS+fd1dT8mHpQ\nv+Zj4jgiDv12ZAaky6PnyzgW+ayNwfIb2K0092qzOxGRG5ckCbPVgEsjMwyNzjI80Swqz9SZLvvX\n9d6w2F//zF30dWVx7LlCdBzHRGFAHIdEQbQoIzntSrYsC7eQu+GO5I1SrjSYmK0vuCw7V+vL5/mZ\nzSIiIltpLcXmvw78CvBrzT+LwD/cxDHtW3GS8PwbI9d9UjtT8Ze9fnE3cxLHRGEDw7TIZHPkc1lu\nPdbN8YFCOxe6XAuo1EK+8NR53ro0wy/91B1rzmRr5bnN77Lbz3luG/HziOOEp1+9op+pyCZo/Rv9\n5kuXiNcSvLzIanN1kiTpfGuYS7uZkzQ30zIN4jghn7EhgS8/e4EX3hzlA7cPYpJ2LFfrARU/Iggi\n7r/jIB9aNAdo3t34n0GcJHzj+Xd54/zEvv2ZiuxFQRgzOlXlyniZiyOzjEzWGJmuMTbdoO6vMUsZ\n6OnMpBnKzY35+nuyDHTn+Cf/9WWSJCaJI5I4JiFhoNPEMgMsWgVlE9uycN0Ctm3vmo32Xr84vepl\n2VlaX0arUUhERLbLNYvNxWLxh8DfaF789OYOZ39qfVD+3utXOXd5tp2FtlbRCkWS1glskiREQSPd\nUbpZ9DANAz+IFxSav/XSJSZm6/hBhGEYnDk/wbNnhtfcOdfKcwPaS7f2c9fdRvw8vvX9i/qZimyS\nVj7+TKVBGN1A69oi7W5mJ7t8IcGgWXAwqIUhs1WfuLlxa7kW8O7VElk3fXueLjfaef0jkzUMFs4B\n65ln9lqBeqPfe549M8zTrw0ThLHmXZFdJkkSStWAq5NpUfnSWImrE1VGpupMLrNycCWuYzLQ3Swk\nt2MvcvQWXEwi4jjCNMC2rTTWwoDQr2EYJoZpYtoOhmFy9ODeiJ+rzovaW+6yiIiIyHwrFps9z3uH\nhRHBCxSLxVObMqJ9qPVBeWSyShDGK//Q12BxN3M7MsPJYpkGCWnRoqsjQ0fO5tJYpf38lXpIzY8w\nAMsA17bWvKFEqyt7craOa1vtY+9nG7E5x4Wrszd8DBFZ3gtvjFCq+it+YXe9kiQhDn0Mc5lu5nlM\nwyCKY5Ik3bA1SWhu6pTO3UEYY5pR+5hggAF+GC2YA9Y77+61LwY3eiMkbawksvOFUczoVG2uqDxa\nYmSqxuh0nVpjbV3KAD0Ft7lBX5qhfKDT5UDBpjNnYdtpTrJjzctLtiwybm7ZruTV5v3driPrLFhJ\n2ZF1tnE0IiIistOt1tn8ka0axH42v1gQRtfX0byc+d3MYVDHspz2yW+cpFmhtmVQyKcniccGOtof\npAt5h7of4ocxnXmXQt5Z84YSz54ZZmSyRsOPaDSXIu73zSg2YnOOk4e6ePXs2A0dQ0SWipOEqVKD\nME5uKI+zJYljosjHdrIr3scg7ZbryDm4tkmlHuLaaY5zpR60ozwc28SxTRp+tODLR9e2FswB6513\n91oxdaM3Qjo20ME7877o07wrsn1KVZ+rk1WGxytcGa9webzM6FSNidnGivuZLObaJv3Njfn6ujL0\ndTn0dboM9GTJZax2XrJtWTiOTcZ1N33Dvd3mZx66if/2zbeJ4gTLNPiZh27a7iGJiIjIDrZasfkf\nAFXg+8Vi8be2aDz7zrNnhrk6UaVaD9fdXbdcN3MSRxQ6OgijZMFxDeD4YIGbD3dzbKCDD773EL/1\nlTfbnXH93VmODRTIZ5328uq1uDRWaRew/TDi4IHcvt+MovX65y9Vv16P33eCUql+Q8cQkaWePTNM\nvRG2N0NdryRJiKMA07SuWWg+daSLmw93cWygg7cuzXDm/AQAhZzN7Sd6mCqnGy594PZB3h6a5sz5\nCXKuRQL0FDL85P0nFswB651399ou9Rsx1y4+XmdndkFms4hsruGJCsPjFS6NlRgerzAynW7SV72O\nLuXuDpf+nmyzoOwy2J1hsCdDb2cG20qji1zHxXUdbHst29ZIi21Z9BQyhHGM3cydFhEREVnJamda\n7wI14OoWjWVfGhor0wiidW1O1bI4m9myHUwnw8EDeUanahA0u+MMA9c2OXm4i1/46K0APPWDy7zx\n7hRBGBOEMbef6Fl1U8CVsj5bxYu08OHwwB0HV80A3WuZoctpbc5xQ8cwb/wYIrJQnCR87/WrlGrB\nDXU1J0lMHIZYjrum+1fqAVcmKvzwnQlmKz5xAn4Q0dvpksva3Ha8pz0XDo9X6etOV6XYtknGNrnc\njD260Xl3aLTMsf4Oclm7ndu/m23EXLv4eD/xwE3cc2pvZK2K7BTlWsDlsRJx1Mz7NYzmOazB3/sP\nz6/pGE6zS7lVUB7oznCwN8vB3iz5jINtm7iOg+u6mKa5eS9mnxkaLVP3Q4IoxrFMhkbL2z2kDdF6\nX5yo+PR1uHvy84iIiMh2WLHYXCwW/4+tHMh+VauH1Brhdec0r9TNbLtznXUTM3WO9uUZnqxSD2IM\noKNZXGh54c1RyrWgfXmq3Fj2JCtOEr7x/Lt884V3GZms0ZGzF2R9Xm9n2V7LDBWR3ePZM8NcHCnf\n0KaAURhgmuaaC80JMDpVY3K20c7mt8x0rr00ViGIEt66NAOkc+H87uPRZp7/yGSN4tBU+z43Mu8C\nPHbvUc27IrKhGn7A8HiJK+MVrk6mG/ONTtcZm57rUjata3cVd3W47aJyf6fDYG+Wg705DnS5ZByb\njOuQyWSW34RVNty7V2epNDcF9IOYdxftKbJbtd4XHdtsb9Cu90UREZEbpzVk2yyXsbEtEz9ce17z\n/ELz4m7m+WqNkKtTNSDNqwO4/UTvurrYnj0zzNOvDTMyWW1ngxbyTjvr83o7y/ZaZqiI7B6XxiqY\npoHBKrvgrqAVm2HZaysyz7e4JpKkB2xfLlcDvvXSJQA++N5DADz/xgijUzFRFFOq+u3xg+ZdEdla\nURThBwFBEFCqBYxM1hiZqjMylRaTx2bqTJb8da3W+8g9RzjQ5TDQ5XCwN0ch5+DYlorKO8TEbH3V\ny7uV3hdFREQ2h4rN2+z4YGFJAWIlrSJz64Q7jiKSOFzQzTyfYRj4YUzWtRjoSu+TzzoLOpfvv+Mg\nI5M1/DDCtS3uv+Ng+7b5UReXx8skSYJrWzT8CD+MgLVvILjYXssMFZHd49hAB65tkRBc+87ztJZ+\nr6fQbBppJ3OSJJhGWmh2bRPLNMi6NuVq0C4mf+vlS5wdmiafTfOYCzmHmXJ6mx9GmndFZMPFcUwQ\nBDT8gDiOieIEP4yYmGkwOp12J0/M+ozPNhifqVOphWs+dlfeob8nx0BPjv7uLF9+5hxJFDL/675P\nf/gEuVxW0Rc7VMaxVr28W+l9UUREZHOo2LzNPvjeQ3zxqfM0An/F+yRxBIa5oJs5Dn0M08Ra1M08\nn2mkxQzXnjshXHwS9aG7DmOw/DLs+Uuuy9UAyzLam1EdPJDjgTsOrjvrs/W4obEytXrI0GiZp1+9\noqw0Edl0D911mDiO+e0/PstaGvDamwBazpq66wzSLub5x85lbDKORca1ml8cQm8hy313DJIAf/jd\nC5jNYnS5FnDm/AQHurKUqwF+GGE2IzfuOtWneVdErksURQRBgB+E7UJynKQbSEdRTKUeNbuSQyZL\nPuPTdcZmakzM1Ne8ebVtGfR3p8XkgZ4c/T1ZDhRcegsW+YyFY6cb9Dm2xR88cw5rUaNER0d+M166\nbJCbD3cyMlkjIX2Pu/lw53YPaUN88L2HODs0zfBUlWP9He1VRSIiInJj1lVs9jzvFeBF4A+LxeL/\n2Ngh7S/PvXaVMFo+QiOOIwwMDHOuWJwkMaFfx3Zz1yx69Pfk+Oj7jnB+uMzF0TIZx+TiSGlBcWG1\nZdjzl5IV8g69nVkGurMbsqFf63mffvVKu6D91uW5vFLZPPthc0aR1ZiGgWmaOLZJI1g9wiiJY+I4\nvK5uZssyiBblQYdRTNa16Sm47dzSSiNs/9szMIjjtNBsmgYdza5mSAijGKf5xeGtx7rX/e9V8+72\n0bwrmyFJEsIwpOH7hGGYFpHjZMGf6UxkkGBRrkeMz6SRF+MzNcama4xN1xfs3XEtnTmH/p5mQbk7\nx0Dz74WsBXHamJAWlk2yGZdcNrvkfHUtmc2ys0yV/HYfetK8vBc899pVLo1XcGyTS+MVnnvtqt4P\nRURENsB6z/Y+XiwWhz3PUxvCDWplh86XxBFxHGNa9oIT9CgKSOIIJ7O2H/tUqcGoMJ1PAAAgAElE\nQVRzr48SRAnVesCVcZ9LYxV+8PYESZLw8D1HV3384qVlD997lHtOHbiOV3dtykrbetqcUSSda1zH\nWlBsXpyHH0cBpmldd2zGchsPNoIYP6xTawTkMg4duXR+b815rVUjfhhRyDm4zSXKfhjTmXfJZdK3\n68vj1et/sYto3t16mnflRpQrVSanZ9JO5GYhOY4TogQsw8K0bUwznUPqQdjuTB6brjM+nRaVJ2br\na94U1TIN+podygPd2WYERno541iEgZ9uSm2bOJaRFpazGbKZLmUr71HvXi2tenm30vuhiIjI5lix\n2Ox53i8CVeC1YrFYnH9bsVgcbv55459697mj/fn2RipJEhMFPpbtYtnOgvuFfg3TsrGc5fOZl+OH\nMe+OlOnKu1TqIXGc4AcRcZzwwpuj1yw2t5ZctzqxHr/vBBMT5et8hatTVtrW04m1CBzpy7U3O4Vl\nNl4NG9jXMd+uRZJA3Y+a/9n0NVeKQFqATAvODo/ec6RdiK7WA65OVdtFoo2YIzXvbj3Nu3IjZkp1\n/Lh5XmgCJMxWG80u5bkO5fGZGqXq2ruUC60u5e5cO/pisCdHTyHTjvUJQx/aheUY1zHIdRVwHOfa\nTyB7xuJVmCutytxt9H4oIiKyOVbrbP4loAZ8D/hHWzOcfcgwyDgWM7MlDMvGchbuuD0Xm5HFMK5v\n0xSDNLM53cyv9XRr7zhZHLGxuAN7IywuaK83i1TWTifWIvD25VmCMF668WocksTxhheaW+Ik7RoM\nwpjjA4UFc95yEQtxknDmnSneOD+xYXOk5t2tp3lXbsQLr4/yznCZseka482i8vV0KR/oyrY7k1ub\n9A305NorJoAFhWUjbmCbJq5rke/uxLYVe7HfZVyLYN6mkBl3b2wQ2Hr/m6j49HW4ej8UERHZICue\nPRaLxUdXus3zvF8oFov/bXOGtH8kScJb745hJD6ZbH7JJixRGJDE4ZpjMxZLMz5N7jx5gMlSg3OX\nZ4jiGDBIEnbExlCrZUbL5lChSfa7JEl4/d1J4iXdzH66guQ6YzPWo5BzyGed9vw7fx6Mk4SnX73S\n/jf6ycdu29AII827W0/zrtyI3/ij4jXv05G1m3EXC6MvejuzWIvj2pKEIGgQNPxmx3JaWFbHsqyk\ntzNDpRa2Nwjs7Vx5g/LdpPV+ODDQydjY3ogGERER2QnW26rw7wEVm2/AzGyJ2UqDsp8wUV7anRIG\ndQzDxHZzKx7DNiG8xio20zS59XgPAKNTNSr1gCCMGZ2qtfMjVXTYX1Rokv1sutLgH/3G80xXwnmF\n5rVvvLoW8+s68TLNh2k39crdrYvzfTs7sxuely9bS/OubATTMOjrzizZnK+/O0c+u/wpfRzH+I06\npgF2M1/ZdW06erqxrL3RnSqb78RgJyOTNQwjjVc5Mdi53UMSERGRHWy9xWbt/rFOs6UylUaNq9MB\nX3rmIsWL0wtuT5KE0K9hORlMc/kPAQZppEUhZzNdWTmbz7FNCnmHy/M2oPLDNLM5jdZwlBspIvvG\na+fG+PUv/pBg3vLzOApI4njdK0gWMwywLZM4SZasVmlJANexVuxuXTwvX7g6q2KzyD72l3/uTrKO\nQ29XBstcOVItjmPCoIFppOeAtmXgZm3yvT0qLMsN+fMfvx2A4akqh3vz7csiIiIiy1lvsXltQXHS\nVipXmC3XwXK5MDTL73z9TWqNaMF94jgiChqrdtdZpoFppNnL9WD1tmbXTj9YzN+AyrUtGn605DYR\nkb0qCCN+/Xdf4uvPX1pwfRjUMU0Ly9m45cCWaZBxLQo5h9mKT60RLuluNgyD3s7MihFGi/N9Tx7q\n2rDxicjuc9ctfUzOLGwuSJKEMGhgkGBbRhqdpsKybBLbNPmVn75TcRMiIiKyJisWmz3P+/sr3GQA\nmx9ouUdUqlVmSjUSw6Ee2/yPp87xw/OT7dttyyCM0qzQJFm5u84A8lmbIIxxbJOgmZ9hmQZxnCxb\n/b/3tv4lG1ANjZapNUJyWXvJbSIie83Zi2P8xlfeYnS63r7uRjZeXU3GMTl1tJsH7zhIAjzxymXK\n1YBKPSCOY6I4LRDlXIv7bx9c8TiL830fv+8EExPlDRuniOwurYxl4mhexrKiMERERERkZ1qts3m1\nqIx/vNED2WvqjQZTM2UibGwnxxsXJvn9p9+hXEs7UyzT4PabeqnWfS5cGgcsbCe75DimAQO9WW45\n0kM2Y1NvhGQzFvVGxGSpwehUFTCYmK0veeyf+ehtCy4rL1JE9ouG7/MHz5zn6y8Ot+Msujpc7jjR\nybOvXrqh2Ayj+T+tSKN8xubYQIH77zzIh5qbrsZJgkFaMD460EESx3y/OAbA/Xek91vJ4nxf01Ry\nlch+1tudx0wMbd63w2UsmL9oMaPvAURERGSfWq3Y/I+KxeKycRme5214UJfneQbw/wJ3A3XgV4vF\n4vmNfp7N1vB9pmbKhLGB7eQIGiFfeuZtXj473r7PscEC3vEeXr8wSehX6e4qcLi/QBDGNIKYE4MF\nbjnWzZXxanvX+uWWW8dJwrNnhrk0VuEbLw5t5csUEdmRkiThwuVx/uu33uH88Fw38PtvH2SgYPDc\nj0ZX3Xi1JetaJEkaaXHToQJvX56lUgvxw4hjAwUeuGOQy6vM0cttCPfIvcc25kWKyL7Skc9RrYTb\nPQy5hs88dgu/8823SZI0v/8zj92y3UMSERER2RarFZtfAt4H4Hne/1MsFv/avNt+p3XbBvokkCkW\niz/ued4DwL9sXrcrBEHA5EwJPwTHzWJb8Nalab74nfPMVHwg7VJ+5N6jfPrx2/jtP3yNOKiRyxfI\nGwbHBjr5hY/eel3POb+YoWKziOx3pXKF7/7wKl9+7jK1RlqYyTgWP/PQTTxy7yBffOICsWFjGAHJ\nCjsPZF2Lzz5yM4+873i7gPz0q1c4d6VEIe8ADg/eeVArRUREZIHRqQYnDna24+5GpxrbPSQRERGR\nbbHWGI2HVrlto3wI+BpAsVh83vO8D2zCc2y4KIqYmJqlHia4bhbHhUYQ8bXnL/L86yPt+/V3Z/ns\no7dwfLBAEgWcGMhydarQvv1GN+o7faSLc1dmF1wWEdkPfN/nytg0f/C9YV55a6J9/U2HOvn0h08y\n2G1x09FBTh6e5sz5aUwjJGpWmw0DDnRmKeQdHrv36LJF5MUZysq6FxFZv716zrp4c1dtwi0iIiL7\n1WrF5vl9X4uLyyv0hN2QLmBm3uXQ8zyzWCzGm/BcNyyOYyanZ6g1YpxMDre5ZeKFq7N8/slzTM6m\n3QwG8OPvPcSfuO9E2unQqDJ44jA/+cFbKXQMb1jx4m//2Xv557/zCiOTNQ4eyPG3/sy9N/gKRUR2\ntiRJmJia5uylMl98+iKTpXTeNQ2Dj37gGD9+Zx/dHTbdXV0YhsFDdx0mSRKef3OU6VKDnoJLbyFD\nPutwfHDlDVOXi8QQEZH1aZ2zjk7XGezJ7plz1tZ7yETFp6/D1ReTIiIism8ZyQpriT3Pe7lYLL5v\n8d+Xu7wRPM/7F8BzxWLx883LF4vF4olVHrIZBe9rSosbM5SqAY6bxWgusw7CiC8/dZ5vvnCxPbD+\n7ix//qfu5NYTvSRJQhzUOXKwF9tercYvIrJuW7GT3LbMvYuVyhXGpir88feH+ep33yVuvpcN9ub4\n5U+8h6MHHAb7Osnnlm68KiKygfbNvCsiskNo3l2DX/uH/46zpc1ploiCBr/6eIFP/ezHN+X4IrIj\nXdfcu1rVs8/zvF9sHrD199YTHFjn4FbzLPDTwOc9z3sQeO1aDxgbK23CMJaXJAmzpRKzFR/bzaVF\n5koVgEtjZX7viXOMTdfa97//jkE+9uBNZByLsdEZXCui/0APU1M1BgY6t3TsG2W3jht279h367hh\n9459t44b0rFvhe38+QRBwMR0idGZkC88dYGh0blNAO+7fZCPPXAcx/TJ2nkq5YBKOQB2/+91N459\nt44bdu/Yd+u4YfeOfT/Mu5tlt/7Or2Wvvi7Yu69Nr2t30by7utbvvdEINvV5ZmdrG/oz2s3/f92t\nY9+t44bdO/bdOm64/rl3tWLzt4FHl/k7wBPXN6w1+X3gJzzPe7Z5+Zc24TnWZbZUZqbcwHIyOJl8\n+/oojnni5cs8+cpl4uZ3n10dLp96+BS3He8BIAgadOdsurp6t2PoIiJ7Qisyo9qIOfNOiT/87gX8\nME1ZymdtPv3wKW491knGihno69vm0YqIiIiIiIjsTysWm4vF4pYWe4vFYgL8pa18zmsplSvMlutg\nuTiZ3ILbrk5W+fwTb3Nlotq+7p5b+vmZh06Sy6Q/1qBRpb+nQE7LuEVE1i39wq9OI7b50tMXef3C\nVPu224738OlHTpG1Y7qyFl1d3ds4UhEREREREZH9bdXwYM/z/iQwShpp8W+Ah4CXgL9TLBZHN394\n26NarTFdqpIYDpa7sMgcxwnPnBnmGy8OETXbmTuyNp/88Cl+7OY0XaSVz3x4oEf5zCIi6+T7PhPT\nJWIc3hn1+cKTb1CqpUsCbcvgYw/exIN3HiT06wz0FshmMts8YhEREREREZH9bcVKqOd5/zfwQcAF\nRoBx4H8HHgP+I/CJrRjgVqrV60zPVogSG9vJLbl9fKbG5588x8WRuYzQHzt5gJ/98M0Ucg4AURji\nmCGHDh5obx4oIiJr14rMqPkJmBm+9sJFnvvh1fbth/vy/PxjtzDQnYWoztGDvZimuY0jFhERERER\nERFYvbP5J4C7gDwwBPQXi8UQ+LLneW9sxeC2iu/7TM6UCWMD28kt+aHEScL3fjTC15+/SBClGaFZ\n1+ITH7qZu0/3tYvKc/nMm7F/oojI3lcqV5gp17GcLOOlKr/77dcYmUo3XzWAD999mI9+4DjEIRkz\npK9f+cwiIiIiIiIiO8VqxeagWCxGQMnzvAvNQnNLfZPHtSWCIGBqpkQjMnCcLLa19D5TpQZf+M45\nzl+ZbV932/Fufu7h03R3uHPHUj6ziMi6tb70ixIb08ny7GtX+foLF9txRd0dLp999DSnjnQT+DV6\nClk6Cx3bPGoRERERERERmW+1YnO8wt8Bkk0Yy5aJoojJ6VnqQYLjZnGWWX2dJAkvFcf4o+fepRFE\nALiOyU998CQf8Aba3cxxHJNEDY4M9mJZy1SrRURkRfMjMxw3S7nc4PPfOcu5y3Nf8N11uo+f/dDN\nZF2LsFHlUH83juNs46hFREREREREZDmrFZt/zPO8882/H533dwM4vLnD2hxxHDM5PUOtEeNkcjju\n8vebrfp86anzvHlxun3dzYc7+fQjpznQNde5HIYBGStm4KCWcYuIXK/5kRmOa/Da+Qm+9PR5ao30\nC76MY/GzH7qZe27tJ44izLjOoUN9ysMXERERERER2aFWKzbftsptu2onpiRJmJqZpVILsd0sTmbl\nQsWZc+P8j2cuUGukqSG2ZfCT95/gg+85hDmvwBH6dbo6XLo6uzd9/CIie4nv+0xMl4hxsN0cdT/k\nD7/7Li+fHWvf56ZDnfz8o6fp7cwSBg0KWYuebn2xJyIiIiIiIrKTrVhsLhaL7y6+zvO8I8CvNv87\nsYnj2hBJkjBbKjFb8bHdHE5m5WXXlXrAl595h9fOT7avOz5Y4DMfOc1AT27BfYNGjYEDBbKZzKaN\nXURkr0mShPHJaeohOE4OC3j3aonffeJtpkoNAEzD4KMfOMbDdx/BNA1Cv0ZvV46OfH57By8iIiIi\nIiIi17RaZ3Ob53l/EvifgY8DzwB/eTMHtRFmS2Vmyg0sJ4OTWb1I8ca7U/z+U+cp1wIALNPg8fcf\n48N3H8Ey57qZ4zjGiBscPdiLae6q5m4RkW2Vzsn19Is/xyCKY7798mWefOUySXMXgP7uLD//2C0c\nGyiQJAmRX+NQfze2vaa3KhERERERERHZZit+gvc8b5C0g/kvAgHwu8D7i8XiY1s0tnUplSvMlutg\nuTiZ3Kr3XW7p9uG+PJ/5yGkO93UsuG8Y+OQc6OvXMm4RkbWqNxpMzpRJcNpf/E3M1Pnct9/i0lil\nfb/77xjk4w/ehOtYhEFAxo7pP3hA+cwiIiIiIiIiu8hq7WJDwJeATxWLxVcAPM/7M1syqnWoVmtM\nl6okhoPlrl5kBnj70gxf+M45Zio+AIYBj9xzlMfedxTbWti1HPp1ugsZOgsdyx1KREQWieOYiamZ\ndmQGpDEaLxbH+KPvXsAPYwA6sjafeuQ0d9zUC0AYNOjKOXR1KQ9fREREREREZLdZrdj8a8BfAL7g\ned7ngP++JSO6TvVGg6mZMhE2tnPtIrMfRHz1+Ys8//pI+7r+7iyfffQ0xwc7l9xf+cwiItdncWQG\npLn4v//UeV6/MNW+n3e8h089corOvAtA0Kgy0NtJNqv5VkRERERERGQ3Wm2DwF8Hft3zvPcCvwT8\nMdDred7fAn6jWCxOrvTYrRCGIVfHJgljA9vJrSl8+t2rJX7vybeZnG20r3voPYf4E/efwLEXdjMr\nn1lE5Pq0IjMw3AVZ+WeHpvnCk+coNXPxbcvg4w/exAN3HsQwDJIkIQ7rHBnsxbKs7Rq+iIiIiIiI\niNyga9Zoi8Xia8Df9DzvfwV+mrTw/PeBrk0e26oq1RqJmcG2rp3nGYQx33xxiGfODNPch4rezgyf\nfuQ0p44sfRlh4JN3DQ4on1lE5JqWi8yAdO792gsXee6HV9vXHenL8/OP3cpgb3q/dj7zoPKZRURE\nRERERHa7tTQEA1AsFkPSDOcveZ73i5s3pI11eazM7z15jtGpWvu6+25PN6LKuEs76AK/Rk8hq3xm\nEZE1WC4yA2B4osLnvv12e+41gA/ffYSPfuBYOxdf+cwiIiIiIiIie8uai82L/DrwnzdyIBstimOe\nfOUKT7x8mThJ+5m78g6feuQ0tx3vWfYxQaPGYF8nGdfdyqGKiOw6vu8zMV0ixlkQmREnCc+eGeaP\nvz9EFKdzb3eHy2cfPc2pI3NFZeUzi4iIiIiIiOw96y027+i1zlcnq3z+yXNcGa+0r7vnln5+5qGT\n5DJLX7LymUVE1iZJEsYnp6gFaWTG/PUh0+UGn3/yHOevzLavu+t0Hz/7oZvbc6/ymUVERERERET2\nrvUWm5Nr32XrxXHCM2eG+caLcx11+azNJz98ivfcfGDZx4SBT86BPuUzi4hc04WhEYLEXRCZAXDm\n3ARfevo8dT8CIOtafOJDN3PPLf3t+yifWURERERERGRvW7HY7Hne31/hJgPYcTkTEzN1fu/Jt7k4\nUm5fd+fJXj754VMUcs6yjwn9Ot2FjPKZRUTWKDEsDCNuX677IX/w7AVeeWu8fd3Jw5189iO30Ns5\nF5GhfGYRERERERGRvW+1zubV2s7+8UYPZL3iJOH510f42vMXCcK0AJJ1LT7x0M3cfUvfit1zQaPG\nwIEC2YzyQkVE1uPdqyV+94m3mSo1ADANg5+47xgfvusIpjk39waNKv09BXK57HYNVURERERERES2\nwGrF5ieAKvBGsVisrHK/bTNdbvDFp85z7vJcPuhtx7v5uYdP092xfPN1HMcQKZ9ZRGS9ojjm2y9d\n5skfXKa5/yr93Vn+1GO3cHSg0L6f8plFRERERPaWJI65OjzCuXNvbdgxp6YKTE7OrVI/efKUPj+I\n7GKrFZt/i7TY/CzwP23NcNbuO69c4T9/9S0aQZoP6jomP/XgTXzg9sEVu5nDMCBjxQwcVD6ziMh6\njE7V+K2vnOXS2Nx3kA/ceZCPPXgC1547IVQ+s4iIiIjI3lOducrvPz/N19/83iYdf5R//bc/wenT\nt27K8UVk861WbL6zWCzWVnuw53nZYrFY3+Axrcl/+PKb7b+fPNzJZx45zYGulZdoKy9UROTG/bP/\n+ip+M7KoI+fw6UdOcfuJ3gX30XwrIiIiIrJ35bsHKfQe3e5hiMgOtVqx+b94nvc14L8Xi8XS/Bs8\nz+sEfhH4KPBzmzi+VdmWwU/ef4IPvucQ5iqdc0GjykBvJ9ms8plFRG5Eq9DsnejhUw+fojO/MLJI\n862IiIiIiIjI/rVasfmzwF8Cvu953jRwCQiBk0Af8K+b99kWn3z4JCcPdjHYm1/xPsoLFRHZWI5t\n8rEHT/DAHQcXxGO08vA134qIiIiIiIjsXysWm4vFYgz8W+Dfep53N3ArEAPnisXiq1s0vhV95tFT\nvHtl5X0LlRcqIrLx/u4v3kvGdhZcpzx8EREREREREYHVO5vbmsXlbS8wr5XyQkVENseBrgyVaty+\nHPh1uvOu5lsRERERERERWVuxeTdRXqiIyNYI/RoDPQXNtyIiIiIiIiIC7KFis/KZRUS2Riuf+fBA\nj+ZbEREREREREWnbE8XmKAxxrUj5zCIimywMfVwzor9f+cwiIiIiIiIistCuLzbP5TN3bfdQRET2\nvEP93TTqyXYPQ0RERERERER2IHO7B3AjgkaNvq4cXV2F7R6KiMi+0NWp+VZERERERERElrdrO5vz\nuSxHDyqfWURERERERERERGQn2LWdzY7jqNAsIiIiIiIiIiIiskPsmM5mz/MuAWebF58rFot/bzvH\nIyIiIiIiIiIiIiJrtyOKzZ7nnQZeKhaLP7vdYxERERERERERERGR67cjis3A+4Fjnud9G6gCf7NY\nLJ69xmNEREREREREREREZIcwkiTZ0if0PO+Xgb8BJIDR/POvAIPFYvELnuc9BPyrYrF4/zUOtbUD\nFxHZ+YwteA7NvSIiczTviohsLc27a/Br//DfcbZ0ZFOOPTv6DqbjUug9uinHL09d5t//bx/ltttu\n25Tji8i6XNfcu+WdzcVi8TeA35h/ned5OSBs3v6s53mH13KssbHSxg9wCwwMdO7Kse/WccPuHftu\nHTfs3rHv1nFDOvatsBt/Prv997obx75bxw27d+y7ddywe8eueXf9duvv/Fr26uuCvfva9Lp2F827\nq2v93huNYLuHckMmJ8u75newW/+t7dZxw+4d+24dN1z/3Gtu0jiu1z8A/hcAz/PuBoa2dzgiIiIi\nIiIiIiIicj12SmbzPwH+i+d5PwUEwF/Y3uGIiIiIiIiIiIiIyPXYEcXmYrE4Dfz0do9DRERERERE\nRERERNZnp8RoiIiIiIiIiIiIiMgupmKziIiIiIiIiIiIiNwwFZtFRERERERERERE5Iap2CwiIiIi\nIiIiIiIiN0zFZhERERERERERkf+/vfuOl6Os/jj+uQlFQleKEJAAwkEwhiIgRWJCUJpIwIKAdBAE\nQX/0Jk3BQrXQQlOKFBFBEUInISoKhBKQgwqiKAYQCCUBSXJ/f5xncudutt7dzd65+b5fr7xyd3d2\n5tnZnTNnzjzzjIg0TcVmEREREREREREREWmais0iIiIiIiIiIiIi0jQVm0VERERERERERESkaSo2\ni4iIiIiIiIiIiEjTVGwWERERERERERERkaap2CwiIiIiIiIiIiIiTVOxWURERERERERERESapmKz\niIiIiIiIiIiIiDRNxWYRERERERERERERaZqKzSIiIiIiIiIiIiLSNBWbRURERERERERERKRpKjaL\niIiIiIiIiIiISNNUbBYRERERERERERGRpqnYLCIiIiIiIiIiIiJNW6DTDRAREREREREREemePZt/\n/OP5ts1/2LDVGDx4cNvmLyIqNouIiIiIiIiISD8w482XOeu6Vxiy5Istn/f0aS9x3pE7sPrqa7R8\n3iLSQ8VmERERERERERHpF4YsuRyLLT20080QkT7SmM0iIiIiIiIiIiIi0jQVm0VERERERERERESk\naSo2i4iIiIiIiIiIiEjTVGwWERERERERERERkaap2CwiIiIiIiIiIiIiTVOxWURERERERERERESa\npmKziIiIiIiIiIiIiDRNxWYRERERERERERERadoCnW6AiIiIiIiIiIhIO3XPns0//vF8S+f52muL\n8eqrb815PGzYagwePLilyxApGhWbRURERERERERkQJvx5sucdd0rDFnyxbbMf/q0lzjvyB1YffU1\n2jJ/kaJQsVlERERERERERAa8IUsux2JLD+10M0QGtI4Vm81sLPB5d98tPd4YOA94D7jT3U/tVNtE\nRERERERERETq1Y5hOkppmA4pgo4Um83sXODTwKO5py8Exrr7383sVjMb4e6PdaJ9IiIiIiIiIiIi\n9Wr3MB1vv/4fjthlPT70oVWanlfpWNOgQra0Tqd6Nk8CbgK+CmBmiwMLufvf0+vjgTGAis0iIiIi\nIiIiItLvtXOYjunTpnLWdY+1pZjdykJ2ObNmzQK6GDx4UNPzKi2Ut3Le5bRq/uUK/JmBVuhva7HZ\nzPYBvgl0A13p/73d/QYzG5mbdAngjdzjN4FV29k2EREREREREZGBZtDsGQye9lR75v3Wv5k+6ANt\nmTfAjDdfJcpHxZr3vJr/Iou3Z92/89ZrfHvcnbxvsfe3Zf7Tpj7Lwosu1Zb5t3Pe82L+77z1Khef\ntt+AurFkV3d3d0cWnIrNX3X3XVPP5j+4+zrptUOBBdz97I40TkREREREREREREQa0p4+5g1y9zeB\nd81sVTPrAj4DTOxws0RERERERERERESkTp0as7mcA4FriAL4He7+pw63R0RERERERERERETq1LFh\nNERERERERERERERk4OgXw2iIiIiIiIiIiIiISLGp2CwiIiIiIiIiIiIiTVOxWURERERERERERESa\npmKziIiIiIiIiIiIiDRtgU43oBlm9gLwTHr4e3c/vpPtqcbMuoDzgRHAO8B+7v5sZ1tVPzN7GJiW\nHj7n7vt2sj21mNnGwHfdfZSZrQ5cAcwGprj7wR1tXA0lbV8X+A09v/ML3P2GzrVubma2AHAZMAxY\nCPgO8BQFWOcV2v5P+vk6BzCzQcA4wIj1fCDwLm1e70WKu1Ds2Fu0uAvFjb1Fi7tQ3NiruNvU8scC\nn3f33dq1jHmhyHG5Hvl40um2tEK5bdbdf93RRrVIuW3a3Z/qbKtaw8yWA0f4NgcAAB+KSURBVB4C\nxrj7M7WmL4p5mRsVKectelwtWs5b1HwXipfzFjXfheLmvK3KdwtbbE4b9cPu/rlOt6VOOwILu/um\naQM/Oz3X75nZwgDuPrrTbamHmR0JfAV4Kz11NnCcu080swvM7HPufnPnWlhZmbZvAJzl7ud0rlU1\n7Q684u57mNlSwGPAoxRjnefbvjTR7lPo/+sc4LNAt7tvbmYjgdOBLtq43gsYd6GgsbdocReKG3sL\nGnehuLFXcbcPzOxc4NPE+iq6QsblepSJJwNBuW12QBSbKb9NF/63mAocFwLTO92WVpqXuVEBc97C\nxtWi5bxFzXehsDlvUfNdKG7O25J8t8jDaGwArGRm95jZb8xszU43qIbNgdsB3P1B4OOdbU5DRgCL\nmtl4M7sr7cD6s78CY3OPN3D3ienv24Ax875JdZur7cB2Zna/mV1iZot2qF3VXA+cmP4eDMwE1i/I\nOs+3fRDwHrHOt+/n65wU3A9ID1cBXqP9671ocReKG3uLFnehuLG3iHEXiht7FXf7ZhJwUJvmPa8V\nNS7XozSeDATlttkBoWSbHkZs0wPBmcAFwL873ZAWm5e5UdFy3iLH1aLlvEXNd6GYOW9R810oaM7b\nqny3EMVmM9vHzJ4ws8ez/4EXgdPTGbAzgKs628qalqDn0hCAmal7ehFMB37g7p8hDnSu7s9td/eb\niCCU6cr9/Saw5LxtUf3KtP1B4Eh3Hwk8C5zciXZV4+7T3f1tM1scuAE4noKs8zJtPwH4I3BEf17n\nGXefbWZXAD8ErqGF632AxF0obuwtVNyF4sbeIsZdKG7sVdytrlzsNbMN+tsllk0qalyuqUw8KbwK\nsWbAyG3T5wFXd7g5TTOzvYCX3P1OesengaAtudEAyXmLHFcLlfMWNd+FYua8Rc13odg5byvy3X67\nEee5+2XuPtzdP5b9T4xBdUt6fRKwQkcbWdsbwOK5x4PcfXanGtOgZ0jJl7v/Bfgv/X995+XX8+LA\n651qSB/8yt0np79vAtbtZGMqMbOVgXuAn7r7tRRonZdpeyHWecbd9wLWBC4BFsm91NR6HyBxF4ob\ne4sed6FAcaBEYWJAUWOv4m7Vec8Ve9394Wbm2Q8VNS7Pt0q22es63Z5Wy2/TZrZIjcn7u72Brczs\nXiKW/sxi/OaBoC250QDJeYscV4ue8xYi96qgEPlXUfNdKHbO22y+W4hicwUnAd8AMLMRxEDb/dkk\nYFsAM/sE8ERnm9OQfYCzAMxsReLH9WJHW9SYR8xsi/T3NsDEahP3M+PNLLsMakug3x1wmtnywHjg\nKHf/aXp6chHWeYW29/t1DmBmu5vZMenhO8As4KE0rhK0Z70XLe5CcWNv0eMuFDf2FiUGFDL2Ku4K\nxY3LjRgwPUorbLMDQoVtuigFurLcfaS7j/K4QeWjwB7u/lKn29Ui8zI3KlrOW+S4WvSct6j5LhQg\n/ypqvgvFzXlble8W9gaBwHeBq8xsO2Lsk70625yabiLOMk9Kj/fuZGMadClwuZlNJBKwfQp0phTg\nCGCcmS0I/Bn4RYfb04iDgB+Z2f+A/9Azdk5/ciywFHCimX0L6AYOI9rd39d5ubZ/Ezi3n69zgF8S\n2+X9RCw/FHia6JXTrvVetLgLxY29RY+7UNzYW4S4C8WNvYq7UtS43IjuTjeghcpts9u4+7udbVZL\nlG7Thw2Qz5UZSL9DmLe5UdFy3iLH1aLnvEXNd6EYOW9R810obs7bkny3q7t7oO2DRERERERERERE\nRGReK/IwGiIiIiIiIiIiIiLST6jYLCIiIiIiIiIiIiJNU7FZRERERERERERERJqmYrOIiIiIiIiI\niIiINE3FZhERERERERERERFpmorNIiIiIiIiIiIiItK0BTrdAOnNzAYBNwC7A08BewKrApcDX3b3\n63LTfgM4GxgGdAHPAE+mvwG6gXHufkGafjDwT+AGdz8sN5+TgN2A4e7+bnpuJHCyu48ys+eAPd19\nQoU2Xw48B9yfvacPn/sedx9dY5qTgTvdfVKVadre1lbI2uHup1Z4fU9grz6uy9nAMHf/R4XX7wUu\nd/efNTrvPrTlXuCk7Psws0nA8em5USXTngSs4u77VJjXSOAKd1+11vKIbSD7/c4mtpGdgL+4+63N\nfzIZqCrE4FFAd5Xt9RDgLGBld38p9/yngNOBIcBg4LfAscDawJVEjF4FeAt4FXgHOIbav/PsN30K\ncG+j27KZbQjs7O7HVJlmCeCn7j62yjT1bJNNtbVV2hUXa8VqM1uFiPVtP7mflnVf9n2Y2WbA54Bl\nKbPu691fVvndz1lemvZeIvbuBYwGbgR2d/fprfh8Unzl4qu7TzAzA75PxAqAJ4DD3P2/6X1rAD8g\nYuc7gANHuvvf0+vPASPz23c+/0jb//7ufmnp68DmwBfS0yOAR9PfNwBrUjtX29PdR5vZ7L5s53Xm\nttsDH3b3c6tMU09e2VRbW6GdMbNWHK+VZ7ZSWtacvMHMxgHnAFNKP1sj+9Iq+7A5y8u+32x9AFOA\nL7n70S35cFJYrcpx03aa1R0GAQsScewb6fXLiTzgv2k2XUTOuwHwrXLLq3f7JfLJ+6ptL1U+/2XE\nfuGfVabZH3gjX3MpM03b29oKrTi2rjLvpnLIVspy0Oz7MLPbgKOBm0s/Wyv2Qbmc93568uDniO3p\n/cBQd/9JCz5a4alnc/9zEHC7u88Apqd/EEXiz5dMOxZ4Lff4X+6+vruvl/6tnxWak22APwJfNLP3\nlcxrZaIokted/s/aUkn+9e4q01XzqTqmGUkUbKrJr7NyWtHWVqhnnb7dxnnP84N/M/sw8BdivZdb\n961eJ6W/358AJ5jZgg3MQ+Y/lWJwNXsBvwL2y54ws4WAq4Fd3H1dYD1gLeBr7j4li9HAzcCJ6fEm\n1Pc7b3YbXhtYrsY07ycKL7Xa0e62tkq74mKtdTCvP38+tm5DnOCopJH9ZT3Ly97ztrt3A+OIYp5I\nJh9fZwDTzWwF4B7gIncf4e4jiALGLwHMbPn0+rXuvqa7f4yIt5PM7ANpvvXkc98xs6GlT7r76Vne\nTBRA1k//zmDe5L/15LYbAEvUmGZetLUV2hkz+2X+m6zt7k9ROf9t5b60V/7r7o8AK5nZOnW+Xwau\nluS4SVZ3WBf4GLCJmW2de/3EXDzN6hLVYs+8iGGj6OmUV8mmwMI1pilSvG1XvaEVOWTLmdmiRI1z\nGn2Lt422uzTe/grYycyWaWAeA5Z6Nvc/Xwc2TH9PAv4MrANMADY3s0XcfYaZfQh4k9iQ6rU3kbx3\nAbsAV+Reuwj4kpnd6O6/K3nfA6kdlTwEPJ9/Ip0p+zawCLA0cJS735jafTlR6Hgb2J+08zKz37v7\nJukM6u5Eb8DZwJeAjYCPA5eY2ViiZ8sFREFkOvB1d3+MnnXWSFsPI3p/bUccmP8R+CSwTJrveDNb\nDrgU+BDwHnAc8AjwmLsPTfN5Afimu99gZkentg8BhgJrpPdekg5g5mpHiSeB36f57pGWNw14EFjM\n3fdJZ9CuBD6TlrOHu08GbnH3V6rM+w/AE+nM3napfUOB81IbRwOvEIWKFYgE41lgeGr3fUTisRQw\n1t3dzD4BnEvsnF8Bvuruz5YsdxvgtirtegyYWuV1ByamdfIF4P+A9xG/sf3c/YEK75uzPsxsIrAr\n8NMqy5H5W7kYvF2lic1sOBGH9ifia3bSbghRHFgcwN1nplizWMksSpPeOb/zKm5x91eiM+CcdnyH\n2HaXJrbBndz9JTPblbiaYDbwJ+Ao4FRgUTM7FvgxEduGAisCE9x9TyIerJj2CTunOHRYau/DwMF9\naauZLQLcAVxDxNubiF5X6wH/Ab7g7q+nnnynpeU9CxxI7BeWc/djzGwroufsUu4+28yeJA4iHqS5\nuLgwUST9eFruwrl2HEfsbz4CPE7Ekjmxupz0Hfw6ffZ7gcnAGCJ2HZr+rQ2c4+7npV4oHyIK/csC\nJxLf68bE/maXNK/jiCuSZqb1eVSZxW8GnEz0tCinrv2lxVVRFxC5yPLE975ThfdMoWd9jAd+aGan\nuftbVZYj8498fM1yy6OB8e6ePzHyPeDZ9Ns7kOgxd232ortfbWY7pNe+Q+3iAURMuxTYutaEOXXn\nalkbzGzFtJwliRzqWnc/NsWWnxA9qf9H5MgL0zu3XYaS3JnofXgg0G1mzwO/SPNZhyhSfy/1wOtL\nWzcFLgO2Bb5C73z1Unc/3cy6iPxuS2I/cqW7/8DMHifitZvZ1cDr7n6wmW1M9Fz8Ps3HzA2Ai4mD\n+fHArrkrKaYRRfihwCnu/lNSHK+yDh4Dpqaea/Xkts8B1wHbE7n/8cDhwIeBw939F2WOD4539/H5\nhaY84fH8ui/92NS/L/0o8ENgUeJY6ix3/3GF9+TXxzXAkekzyvyrVTluqSHAQsBLuefqict5dW2/\nJe0r3R7OdvcfmdnSxHa5FlEzOJyoJawI/NbMPknkYr2OJYmYvAMwysxeTMu8CFiJiH/Huvs9fWzr\nzsAJabln0juGneruV6QceRyRA85K010DvAis5u5vm9kDRI/dH5jZl4AtiNrF1sR3tRqxTz2kXDtK\n5I+tP5OWN4PosbuBx5Uw91KmNkL9OeRIInZ2pbbdmD77jmm6bd395bS+f52W8yJwPpEjDyV6Ik9M\nVzldnD7nW8Ch7v5wyXJHEyeoK2lkH1S2nlXhrfl62S+BQ4gcfL6mns39iJmNIJK1NwHc/QB3z868\nzCQSrW3T4y8SCVDeUDN7JP2bnP5fJ817WSK43QxcT5zZzHsV+BpweUqI5yhpx1zc/TJ3v7vk6YOB\nfd3940Tw/lZ6/nxiGI/hxKUlx3sa0iMVmhcngvzI1HvlZqIn4JVE0NrX3Z8kioVHpvl/NVsXDba1\ny8z2InqIb5vO8gIs6O6bEjugb6fnfgTcnXrcfIEomA8igujaFpWUBYgeKhCF1V+nv4cT6/4TwLFm\ntkSFdZZv55PunvXC+T4ReDcFrGTSl919Y2JHeFx675crzTe9fqq7e3q4IfBpYkd1FnBr+oxdRLEG\n4mz1Ke6+Zpp+lbR+rgUOSD2Ff058T+ultsw5KMz5NFEUqdSu8elgodLr09z9oHTgcwCwXVre94gE\nutL78utjIvH7EplLjRhcyd7Adamg+V7Wq8PdXyeS8kfM7FEzO5e4rGpKtZllv/Ma0/Taxs1sdWBN\nd9/E3dcC/gbslooeZwNjUswdTMSRE4kD1zOIg4zJ7r4Zccn4pma2HpHg/TsVmtcm4vgmHr2xXybi\nb6NtXZhIwK73nqtuRgBnpvZNS+1eFrgQ2MGjx8zviKL4b4iCB+n/t4H1zWwYccljdpDTTFw8FBjk\n7msT+4DNcpNtQsS5tYjLIz+TxeoG1kF32rddRRwgjSXib74H8EeJWPsVohB0RnpufTMbbmbbEMWP\n9dK/NYhi1BypN+ir7j6zSrvq3V9uCrybfiNrEAeV21Z4z1PZ+nD32USBpSPDVUn/UiW+rkecJJrD\n3We7+3XuPovYFv5YZpYT6Cma1NJN5AofMLN9621zvblabhkAXwauSXnSCOBrZvZ+osizaIofWxFx\n+Of0zm3nyp3d/c9EPLww5UgnAA+5+4ZEznmCmQ1rtK3p+7gE2N57Ogfk89VjLIZTOghYyd0/Spz0\n+ryZbUvveDycKKJD7/y32Zj5M+DotD7epXcvtZXc/ZNETndWel8+vy0333yeWTW3zb3thfTZJxMn\nRrYiYvOx6fXS44PL0j4sr2pniwb3pfsAp6V93GjKF/+60nvy62MC8Nlqy5CBrZU5bpLVHR4lrsCe\nSu8C7KklNYkfVVtQg9tvZl96bw9ZbPk2MXTi2sAewLfd/XvAv4nt8XXKHEumGHoLEXvvJJ2kTPH2\nc8DFZrZoo21NHSROALbyNDwUvWPYmem5U4BXUk68ZXq8DnA3MDL12h1G+XrDJkRO+TFgBzNbp4Fj\n64WJ2soX3X0j4AMlk+ZrI1mO10jNZSOi48NHiX3K1LROnyA6QEJ0ZrjF3T+SHu/o7lukdfCN9NxV\nwLkp3v4fcKPNfcVyrXjbyD6oUj2r3Hvy62MCqjcAKjb3N2sAL1R4rZsoEmfjyu1InJXPnzXMLmfJ\nX67yZHptV+Aed59GBNHhaaczh7vfQiT0Z7Tgs3wlLeME4mxi1ptvJBEocPfbPPXSSp+PtAPcDfiy\nmZ1OJEb5noBdKdBuSBTGJxNn/Iaks5iN+ChRjDjP3d/JPX97+n8KceYMYgd2aWrjc8QZzY2AW4nE\nfDTR8+OTKTlf3t2fTu+9191nufvLxNhVSzbQxk2AB9z95XTQdUXJ61nviXxbGzHJ3d/2GAOum54z\ngc8TZ/AAXnT3rEfGC8QOLz/NmkRR4xEAd/8FsHo6cQCAxbAti7h7ftiXPvG4BGsnYGszO4XopVHa\nW7SS54ntTKScajF4Lma2ABGvspMr15Mr+nkUc1cgYuriRG+KQ1vW2p7l/A04wsz2N7MziULBYvTE\njxfTdHumON+Ve++1wF2p1/WPiDhSuj2NInpy/SHF3B2Y+8RXPU4jkuBxueem5uJLFsc2Ah70njH1\nLgZGu/szwBJmthRR2PgxMQTTNkQszjQTF0eSvs+0vPxYdFOydUn0XuhLzM0S4OeBP7j7uyn+5vcL\nd6Y49zxR8PcU/18gYu5o4Ofu/j+Pgu5l9BR9MlvTsy9rirtPBC4ws68RB14fRjFXGlcpvs6meg+4\nbspfiblQyTxKdeWfT9vKXsRwGivVamxfuftZwD/N7HBie1mQ6HU3khhaCXef6u7DvedkUPb5K+XO\neWOAA1MsnkCc/Gl0eIQuIhbd6e5/zT1fLl8dRco9PTplXE3EoFuBMWb2EaKn2KxUZN2GKERDEzHT\n4hLk5d39rvTUuJJJ7khtmkJPvtqIWrlt5vbc8/en31F+mnLHBxuXLGvL3PybvaT+CGARMzuGKPos\nWs+bsgJjOvEh86eW5rj0HkZjGeKquh/mXs+G0chqEl9vrvllHU757WEL4io3PIauy3cc6GrgWHIM\nUTSfTMTMwcDqDbZxWaI378+89xV25WLYKHriyX+JWs9IeuoNWxB1lHXS9/NJeo7df+fu01OcfpbG\nctThxIm1rGduabzN10b6Em+nuPu/U9teoXy9Ib+c50unSfWfD7v7zQDu/iCxnyo9HhmRi+3Nqmef\nXM7zRK4831OxuX+ZTfRgruQ+YKPUW/nlLHGo095Ej7VniZ5GsyjpCZUcSvSa3rzMa414gCgIP0Tv\nSxzfy0+UklTouaRvJeLShiWJS6yvYO6DkMHAjNwObD2ix12jhcw3gJ2BM9NlK5ms8NydW3bptjKI\nOPi5jQj+nyJ67M0iCvv5S+jeKXlvI5cVzShZ9nslr5drayP+l3+Qkuiq0zD3b3RQmWV30XsMwk8R\nv9+mpZ3Nn4gzu/cTiU29n/09yh+UikDtGFxqeyJJuinF1j2Bbc1sRTPb2MwOcvdXPXro7UvEm/1b\n3WgzW59IWruIG79kJyLfI7dtmNkyVjKGmJl9nbh6YiqxLf2Z8jH3+izmEsXgQ/rQ1GuIhDl/s5B8\nfMziWGlMyeItRCI6lviufkMk4VvTU9zIz7MvcXFGyXvyv4dybW1UPp5W+q1VmqbS/qiLuYtxtYYt\nqpvFcAVXE5csXkZcIaKYK42qFF8foqSHspl1mdmNqXj5ING7vtQm9PR4fo0Y/iBvOXrf14TUAeM8\nokdvW8bRNLOziF7MzxG9616hJx7np1u9TI+sSrlz3mDixptz8l8aP7HUTeSqO6fL5DOV4nFeFm9+\nD6xLFFKzGyV9nugBlxW0momZpblzpfy3r2rltuWmKzdNpeMDAFLHi9nVegA26Aaiw9GTpCt3GjAT\nxeP5Wcty3NIJ04mz6+l9Ndi8UGl7KI23lq6MzR7Xeyw5iOjskMXbTak+fEY5s4he0UeZ2Qdzz5eL\nYZXiyXiiEP0pogj7KNGr+wl3z2JUM/F2ntYbqPA79N5X49VTbyiNt2tRfWiPRtWzTy5HuW+iYnP/\n8jd67sQ9l1QIvIPo5VVumIKyG0AqRKxE3EV2NY+7cm4P7JqCbX4ZrxGXDJzYlw+Qlrc0cTbnW+5+\nOzEcQ1Z4vJ90uUS6pOSi9PxMi7H5NiQuezmP2Alsk3vvTGABd38D+IuZ7Zabz/19aOrz7v4bIkk+\nrca0d9MztvRqxM7md6k375rEJezPEAXVE+hd+GjGH4hLp4da3EF4l1pvaEKlAForsDrwfoux9TCz\nLxLr9vXcNKWFj77sqDJrArPc/XTiu8v/RmpZFfhrzalkflUtBpf7ze4NHJfi6mruvjKRmOxPDE10\nkpl9LDf9OsSluK02kuiRdjHwNDFkzWAihm5kMaYkwDlEr+SZ9CRnY4gbc11LfMZ103vz09wHjDWz\nZVOyfiE9l7Q14lHiMuTdc+ul3Hp9ENjYYox/iEsd701//5Y4oJjoMU7/2kT8fbQP7SnnjtS+LjNb\nmeg1Mi80En/vIa7+eV/q2bI3ufHp0r5iaK7gU23+9diSuIz2Z8R4jFugmCuNqxRfLyYKGPnLs78F\nLJt62J4PbGYx/jww514Wm9KTQ95NDC+QvT6S6OGWHXTmf//fJy4R3qRMW5rZTjJjgB+4+y+JcXyH\nEtvLBKIzBykm30f0zp4JLFAjd87H43uIYe+wuLni42k5jehy9/uIoSAuyRdh8tPklrenmQ0ysyFE\nT8d70jHJg0QnlfuIGH081W9KWjePcd6fthi/n7TcSvryvbXiu4byxwf58UDHAHflHje73C2J38iv\nSTdXr/D99WJmi8GcIb5k/tSqHHe/Cu8ZTdzTo9o863mtEWMovz1MoKfesBZwW+rNnMXSaseSpfH2\n4DSftYl4O6TBNr7q7vcS+7JK46vn4+2+aXnLEIX0e1OP6BnEFd8PpDafSM8QGs1yYLHcVe+tjrdN\nS50s/2pmOwJY3C9qeaK3daZl9YYa++S8cstQ7puo2NyPpAPnD+SHHyjjemIMuFvS43zPjBVs7jGb\nzyXORF6eO/OFu98PPEMEk169O9LlCTeULtjMNjCzW0ufL/M5XiMuAXnKzB4mLq0ZknoPf50Y720y\nMUZl1svvFqIQMR4YbHGzp98RPUNWTdPcDlyYgstuwH5m9hhxpumLfWlrchRReF+Xyj1dDgNGW9wQ\n5ZfE+D3Z+KATiRu4QOwkFqdyL9655p++qw+WmzhdQnMA0RvwQXr3XqvaK8fMPmtmpZfBVNPd4N9Z\nG/9H7NB/ktbP1+j5PrLpN/DeA/hvbmZvmNmb6f/zS9p+azpJUs5jwKNm5kRS8yYxFmDZ9pUYRYwD\nLjKXGjH42JLf7B7E7+mykunOJhLFvxKX5V1qZm5mfybGJi3tEVzxN2tmp5jZAZVez733WmBdi3Hz\n7iK2kVXT5cvfAO5I2+Z0Yrz5PwKfsBiq6BzgZDN7iEiCJxExdypxKfjd6XK0U4n49gSRWH23L21N\n+4djiEv0Bpf7/Cm2HgD8ysyeIIqb2ZU49wEfpCfGPkLveNtsXLwU+BexDi8hxiGs+HlK5j0uVxip\n6z11vDZX/HX3W4l9wkPE9/EcvQ9gNmbum59cUPL7ndP7qI795ThiH/kwcXOy39OzX67Y/lT0Xo/e\nhRaZT1WKr+4+lThAPMLMHjOzKcRlyjum118lTvqMNbOnzexp4qTZ5t4z9uVpxBA7U1KsOwX4XO5q\nre7c8mZR+SZp1eJxxVytxBnAVWb2J+Ky24eI7eV8YHrKW+8ADkm9XW8nTuAZEXPK5c4TiPHsDyZu\nOLRIio13AUek4RsaaWsWS64krlg4pMxnzx5fRE9MfBj4lcdwTBBxaNHU2eJ+ojd5peJHX2LmnsDh\naX3ke79Xams23xXScUY1VXPbGs/nlTs+mJp779b0Ln50pxicxeNevSTr2JeeDExK++yt6H2cVK3d\nI2ldRxgpoBbmuPsRNaSs7jA55birER0KMqeUqUusVWF5c+5tUef2mzmZ3tvD34nt4SRgzZQXX0nc\nYBpiG/gtMWZzpWPJu4DjzGwnom7xiRS3fw7slr9KocG2fhdYO8W8SjHsVOI7epzIbU9L3xtZu919\nOpGPr0Dlk3vl4m3FY+vUo3gXYkzqPxEnSSvNqzTeNlJzKdu2Ms9XmuYrwGFp/fyQuJnrzNz0Y4A7\nc9OvnIu3b5S2s9o+KB2vlO6TF0n75FptVb0h6erubstVZNJHZnYIcQOhn3S6LeWY2UXu/tVOt6Me\nRWmrxSWXJ6VeHLWm3ZO4eeI+dUy7IHCOx91oC8Vi/Ng7cmNHtWKeCxFngzdz99LLg0SA/hWD0wmw\nTbznZnr9VlHa2mhctLgL90nuPqGOaXckbqTXkuEr5qV27C8tht/YzN2PrjmxzBf6U3xtVCO5WqcV\npa2NxEwzWwW4L12dWc+8x7l7y4etard27UvN7BfEb+LJmhPLgFWUGFyk7bcobW3k2Nri6qCT3H10\nnfMuRM2lVLvydjObSBTCX6k58QCnns39z4XEDTfe1+mGlLIYT/mqTrejHkVqK3FX8XYcEHyE+D0V\n0cutLDQnhxB3I1ahWarpTzF4BWKc4yIoSlsbjYuNnJFfgAL24m3H/tLiMtZ9qD1Elcxf+lN8bVS7\ncrV2KEpbG42ZdcVji+E+itqLt+X7UjP7OPB3FZqFAsTgIm2/RWor7Tm2LlrNpVTL83Yz2xm4QYXm\noJ7NIiIiIiIiIiIiItI09WwWERERERERERERkaap2CwiIiIiIiIiIiIiTVOxWURERERERERERESa\npmKziIiIiIiIiIiIiDRNxWYRERERERERERERadr/A2PETDK0TR95AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1ef99c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_viz = X_train.join(y_train).sample(1000)\n",
+    "sns.pairplot(df_viz, size=5,kind=\"reg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "lin_reg = LinearRegression()\n",
+    "\n",
+    "pipeline = Pipeline([\n",
+    "        ('scaler',scaler),\n",
+    "        ('lin_reg',lin_reg)\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pipeline.fit(X_train,y_train)\n",
+    "y_pred = pipeline.predict(X_validate)\n",
+    "rmse = np.sqrt(mean_squared_error(y_validate,y_pred))\n",
+    "r_2 = pipeline.score(X_validate,y_validate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.58625725051\n",
+      "R^2: 0.542388741971\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.309044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.201063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>2.043892</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                               coef\n",
+       "feature component status variable_type units  description          \n",
+       "MEAN    lactate   known  qn            mmol/L all         -0.309044\n",
+       "COUNT   lactate   known  qn            mmol/L all          0.201063\n",
+       "LAST    lactate   known  qn            mmol/L all          2.043892"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print 'RMSE:',rmse\n",
+    "print 'R^2:',r_2\n",
+    "pd.Series(lin_reg.coef_,index=X_train.columns,name='coef').sort_values().to_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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NCiLhNUe61G4wQR7OtZjMdxvDxeX28vGBBrZVn+ZMkygymFNo5eYrC5hXnIHJZCIudmJX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\nEXVBEIQUQkRdEAQhhfh/1MQU7CPQtdAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3ad726a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WbJblCBknmxMnSjh9eirr1n3a1f3I3BDaN4y1uvoYTucMbLaUPp+m2t2d7PvM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\ndwlRmuASQh4qQmw8TqCVhISNuN1uTT4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eeiDgd/bOO6JTUm2ti0cfLeT06QQmTWpk06ZF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\nYEgjjKgbDAZDGmFE3WAwGNIII+oGg8GQRhhRNxgMhjTCiLrBYDCkEUb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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3caa4f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7yn7gWtyDnfY8j/F3D6MWZQd4I+MLoU35lyxZyp496ROClixZknH+sXiCrt4h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\nQghhuf8PRmIZ0uTptyYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x28ee93c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = X_validate.copy()\n",
+    "data['pred'] = y_pred\n",
+    "for col_name in X_validate.columns:\n",
+    "    pipeline.fit(X_train.loc[:,[col_name]],y_train)\n",
+    "    print col_name\n",
+    "    val_col = data.loc[:,[col_name]].sort_values(col_name)\n",
+    "    sns.plt.scatter(X_validate[col_name],y_validate)\n",
+    "    sns.plt.plot(val_col,pipeline.predict(val_col))\n",
+    "    sns.plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With Cross Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score,ShuffleSplit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R2: 0.550172721419, 0.0467741587239\n",
+      "RMSE: 1.6467916238, 0.0985459060145\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R2: 0.556988588385, 0.0212943169516\n",
+      "RMSE: 1.62975088077, 0.0276377946618\n"
+     ]
+    }
+   ],
+   "source": [
+    "scores_r2 = cross_val_score(pipeline,X_train,y_train, scoring='r2',cv=10)\n",
+    "scores_nmse = cross_val_score(pipeline,X_train,y_train, scoring='neg_mean_squared_error',cv=10)\n",
+    "\n",
+    "print 'Cross Validation, K-Fold'\n",
+    "print 'R^2: {}, {}'.format(scores_r2.mean(),scores_r2.std())\n",
+    "print 'RMSE: {}, {}'.format(np.sqrt(-1.0*scores_nmse).mean(),np.sqrt(-1.0*scores_nmse).std())\n",
+    "\n",
+    "cv_shuffle = ShuffleSplit(n_splits=10,test_size=0.1)\n",
+    "\n",
+    "scores_r2 = cross_val_score(pipeline,X_train,y_train, scoring='r2',cv=cv_shuffle)\n",
+    "scores_nmse = cross_val_score(pipeline,X_train,y_train, scoring='neg_mean_squared_error', cv=cv_shuffle)\n",
+    "\n",
+    "print '\\nCross Validation, ShuffleSplit'\n",
+    "print 'R^2: {}, {}'.format(scores_r2.mean(),scores_r2.std())\n",
+    "print 'RMSE: {}, {}'.format(np.sqrt(-1.0*scores_nmse).mean(),np.sqrt(-1.0*scores_nmse).std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Data => Lactate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature creation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data_dict' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-232-8bd284bffaf5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m weight = {\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mcolumn_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOMPONENT\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mdata_dict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomponents\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWEIGHT_BODY\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m }\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'data_dict' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "reload(transformers)\n",
+    "reload(features)\n",
+    "m_ureg = units.MedicalUreg()\n",
+    "is_summable = lambda x: m_ureg.is_volume(str(x)) or m_ureg.is_mass(str(x))\n",
+    "\n",
+    "\"\"\"\n",
+    "Data Specs\n",
+    "\"\"\"\n",
+    "qn_not_sum = {\n",
+    "    column_names.VAR_TYPE : variable_type.QUANTITATIVE,\n",
+    "    column_names.UNITS: lambda units: not is_summable(units)\n",
+    "}\n",
+    "\n",
+    "weight = {\n",
+    "    column_names.COMPONENT : data_dict.components.WEIGHT_BODY\n",
+    "}\n",
+    "\n",
+    "intervention_summable = {\n",
+    "    column_names.CLINICAL_SOURCE : clinical_source.INTERVENTION,\n",
+    "    column_names.UNITS: is_summable\n",
+    "}\n",
+    "\n",
+    "uop_summable = {\n",
+    "    column_names.COMPONENT : data_dict.components.OUTPUT_URINE,\n",
+    "    column_names.UNITS: is_summable\n",
+    "}\n",
+    "\n",
+    "not_nominal = {\n",
+    "    column_names.VAR_TYPE : [variable_type.QUANTITATIVE, variable_type.ORDINAL]\n",
+    "}\n",
+    "\n",
+    "is_nominal = {\n",
+    "    column_names.VAR_TYPE : variable_type.NOMINAL\n",
+    "}\n",
+    "\n",
+    "\"\"\"\n",
+    "Features\n",
+    "\"\"\"\n",
+    "\n",
+    "f_qn_mean = features.Feature('MEAN','mean',\n",
+    "                               data_specs=[qn_not_sum,weight],\n",
+    "                               pre_processor=transformers.GroubyAndFFill(level=column_names.ID),\n",
+    "                               fillna_method=transformers.fill_mean()\n",
+    "                            )\n",
+    "\n",
+    "f_qn_most_recent = features.Feature('LAST','last',\n",
+    "                                       data_specs=[qn_not_sum,weight],\n",
+    "                                       fillna_method=Pipeline([\n",
+    "                                                    ('ffill',transformers.GroubyAndFFill(level=column_names.ID)),\n",
+    "                                                    ('fill_mean',transformers.fill_mean())\n",
+    "                                                ])\n",
+    "                                   )\n",
+    "\n",
+    "f_qn_std = features.Feature('STD','std',fillna_method=transformers.fill_zero())\n",
+    "\n",
+    "\n",
+    "f_sum = features.Feature('SUM','sum',\n",
+    "                             data_specs=[intervention_summable,uop_summable],\n",
+    "                             fillna_method=transformers.fill_zero()\n",
+    "                        )\n",
+    "f_count = features.Feature('COUNT','count',\n",
+    "                           data_specs=[not_nominal],\n",
+    "                           fillna_method=transformers.fill_zero())\n",
+    "\n",
+    "f_count_nom = features.Feature('COUNT','sum',\n",
+    "                             data_specs=[is_nominal],\n",
+    "                             fillna_method=transformers.fill_zero()\n",
+    "                        )\n",
+    "\n",
+    "label = features.Feature('LABEL','mean',{\n",
+    "                                column_names.COMPONENT : data_dict.components.LACTATE,\n",
+    "                                column_names.VAR_TYPE : variable_type.QUANTITATIVE\n",
+    "                            })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ETL_manager': <mimic.MimicETLManager at 0xd8958d0>,\n",
+       " 'data_dict': <icu_data_defs.data_dictionary at 0xd895898>,\n",
+       " 'features': None,\n",
+       " 'force_preprocessing': True,\n",
+       " 'hdf5_fname_target': None,\n",
+       " 'pre_processors': do_nothing(),\n",
+       " 'resample_freq': None,\n",
+       " 'save_ETL_steps': False}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factory.get_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ETL_manager': <mimic.MimicETLManager at 0xd8958d0>,\n",
+       " 'data_dict': <icu_data_defs.data_dictionary at 0xd895898>,\n",
+       " 'features': [<features.Feature at 0xeae9f60>,\n",
+       "  <features.Feature at 0xeb06198>,\n",
+       "  <features.Feature at 0xeb06208>,\n",
+       "  <features.Feature at 0xeb06278>,\n",
+       "  <features.Feature at 0xeb062e8>,\n",
+       "  <features.Feature at 0xeb062b0>],\n",
+       " 'force_preprocessing': True,\n",
+       " 'hdf5_fname_target': 'data/combine_like.h5',\n",
+       " 'pre_processors': Pipeline(steps=[('drop_small_columns', remove_small_columns(threshold=50)), ('drop_low_id_count', record_threshold(threshold=20)), ('combine_like_columns', combine_like_cols())]),\n",
+       " 'pre_processors__combine_like_columns': combine_like_cols(),\n",
+       " 'pre_processors__drop_low_id_count': record_threshold(threshold=20),\n",
+       " 'pre_processors__drop_low_id_count__threshold': 20,\n",
+       " 'pre_processors__drop_small_columns': remove_small_columns(threshold=50),\n",
+       " 'pre_processors__drop_small_columns__threshold': 50,\n",
+       " 'pre_processors__steps': [('drop_small_columns',\n",
+       "   remove_small_columns(threshold=50)),\n",
+       "  ('drop_low_id_count', record_threshold(threshold=20)),\n",
+       "  ('combine_like_columns', combine_like_cols())],\n",
+       " 'resample_freq': '2H',\n",
+       " 'save_ETL_steps': False}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factory.hdf5_fname_target = 'data/combine_like.h5'\n",
+    "factory.resample_freq='2H'\n",
+    "factory.pre_processors = Pipeline([\n",
+    "                                ('drop_small_columns',transformers.remove_small_columns(threshold=50)),\n",
+    "                                ('drop_low_id_count',transformers.record_threshold(threshold=20)),\n",
+    "                                ('combine_like_columns',transformers.combine_like_cols())\n",
+    "                            ])\n",
+    "factory.features = [f_qn_mean,f_qn_most_recent,f_qn_std,f_sum,f_count,label]\n",
+    "factory.get_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2017-07-13 16:58:10) FEATURIZE... #F=6, #ids=47180, fit->True\n",
+      "(2017-07-13 16:58:10)>> PRE-PROCESSING & JOIN: #C=18, [u'glasgow coma scale eye opening', u'glasgow coma scale motor', u'blood pressure systolic', u'oxygen saturation pulse oximetry', u'lactate', u'hemoglobin', u'blood pressure mean', u'vasopressin', u'glasgow coma scale verbal', u'weight body', u'normal saline', u'norepinephrine', u'temperature body', u'blood pressure diastolic', u'heart rate', u'output urine', u'lactated ringers', u'respiratory rate']\n",
+      "(2017-07-13 16:58:10)>>>> glasgow coma scale eye opening - 1/18\n",
+      "(2017-07-13 16:58:10)>>>>>> READ DF...\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> *fit* Filter columns (remove_small_columns) (764133, 1)\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> *transform* Filter columns (remove_small_columns) (764133, 1)\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> *fit* Filter columns (record_threshold) (764133, 1)\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> *transform* Filter columns (record_threshold) (764133, 1)\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> FIT Combine like columns (764133, 1)\n",
+      "(2017-07-13 16:58:13)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 16:58:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:13)>>>>>> TRANSFORM Combine like columns (764133, 1)\n",
+      "(2017-07-13 16:58:13)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 16:58:20)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 16:58:20)<<<<<< --- (7.0s)\n",
+      "(2017-07-13 16:58:20)>>>>>> SAVE DF... (764133, 1) -> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 16:58:32)<<<<<< --- (12.0s)\n",
+      "(2017-07-13 16:58:32)<<<< --- (22.0s)\n",
+      "(2017-07-13 16:58:32)>>>> glasgow coma scale motor - 2/18\n",
+      "(2017-07-13 16:58:32)>>>>>> READ DF...\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> *fit* Filter columns (remove_small_columns) (760640, 1)\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> *transform* Filter columns (remove_small_columns) (760640, 1)\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> *fit* Filter columns (record_threshold) (760640, 1)\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> *transform* Filter columns (record_threshold) (760640, 1)\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> FIT Combine like columns (760640, 1)\n",
+      "(2017-07-13 16:58:35)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 16:58:35)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:58:35)>>>>>> TRANSFORM Combine like columns (760640, 1)\n",
+      "(2017-07-13 16:58:35)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 16:58:42)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 16:58:42)<<<<<< --- (7.0s)\n",
+      "(2017-07-13 16:58:42)>>>>>> SAVE DF... (760640, 1) -> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 16:58:46)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 16:58:46)<<<< --- (14.0s)\n",
+      "(2017-07-13 16:58:46)>>>> blood pressure systolic - 3/18\n",
+      "(2017-07-13 16:58:46)>>>>>> READ DF...\n",
+      "(2017-07-13 16:59:01)<<<<<< --- (15.0s)\n",
+      "(2017-07-13 16:59:01)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 16:59:01)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:59:01)>>>>>> *fit* Filter columns (remove_small_columns) (4784649, 40)\n",
+      "(2017-07-13 16:59:03)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 16:59:03)>>>>>> *transform* Filter columns (remove_small_columns) (4784649, 40)\n",
+      "(2017-07-13 16:59:04)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 16:59:04)>>>>>> *fit* Filter columns (record_threshold) (4784649, 13)\n",
+      "(2017-07-13 16:59:04)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:59:04)>>>>>> *transform* Filter columns (record_threshold) (4784649, 13)\n",
+      "(2017-07-13 16:59:04)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:59:04)>>>>>> FIT Combine like columns (4784649, 13)\n",
+      "(2017-07-13 16:59:04)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 16:59:05)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 16:59:05)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
+      "(2017-07-13 16:59:05)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 16:59:05)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 16:59:05)>>>>>> TRANSFORM Combine like columns (4784649, 13)\n",
+      "(2017-07-13 16:59:05)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
+      "(2017-07-13 17:00:00)<<<<<<<< --- (55.0s)\n",
+      "(2017-07-13 17:00:00)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:01:07)<<<<<<<< --- (67.0s)\n",
+      "(2017-07-13 17:01:07)<<<<<< --- (122.0s)\n",
+      "(2017-07-13 17:01:10)>>>>>> SAVE DF... (4784625, 2) -> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:01:23)<<<<<< --- (13.0s)\n",
+      "(2017-07-13 17:01:23)<<<< --- (157.0s)\n",
+      "(2017-07-13 17:01:23)>>>> oxygen saturation pulse oximetry - 4/18\n",
+      "(2017-07-13 17:01:23)>>>>>> READ DF...\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> *fit* Filter columns (remove_small_columns) (4846290, 2)\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> *transform* Filter columns (remove_small_columns) (4846290, 2)\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> *fit* Filter columns (record_threshold) (4846290, 2)\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> *transform* Filter columns (record_threshold) (4846290, 2)\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> FIT Combine like columns (4846290, 2)\n",
+      "(2017-07-13 17:01:28)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+      "(2017-07-13 17:01:28)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:01:28)>>>>>> TRANSFORM Combine like columns (4846290, 2)\n",
+      "(2017-07-13 17:01:28)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+      "(2017-07-13 17:02:49)<<<<<<<< --- (81.0s)\n",
+      "(2017-07-13 17:02:49)<<<<<< --- (81.0s)\n",
+      "(2017-07-13 17:02:51)>>>>>> SAVE DF... (4846252, 1) -> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:03:07)<<<<<< --- (16.0s)\n",
+      "(2017-07-13 17:03:07)<<<< --- (104.0s)\n",
+      "(2017-07-13 17:03:07)>>>> lactate - 5/18\n",
+      "(2017-07-13 17:03:07)>>>>>> READ DF...\n",
+      "(2017-07-13 17:03:09)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:03:09)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:03:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:09)>>>>>> *fit* Filter columns (remove_small_columns) (142289, 63)\n",
+      "(2017-07-13 17:03:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:09)>>>>>> *transform* Filter columns (remove_small_columns) (142289, 63)\n",
+      "(2017-07-13 17:03:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:09)>>>>>> *fit* Filter columns (record_threshold) (142289, 4)\n",
+      "(2017-07-13 17:03:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:09)>>>>>> *transform* Filter columns (record_threshold) (142289, 4)\n",
+      "(2017-07-13 17:03:10)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:03:10)>>>>>> FIT Combine like columns (142289, 4)\n",
+      "(2017-07-13 17:03:10)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+      "(2017-07-13 17:03:10)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:10)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:10)>>>>>> TRANSFORM Combine like columns (142289, 4)\n",
+      "(2017-07-13 17:03:10)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+      "(2017-07-13 17:03:13)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:03:13)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:03:13)>>>>>> SAVE DF... (142197, 1) -> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:03:14)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:03:14)<<<< --- (7.0s)\n",
+      "(2017-07-13 17:03:14)>>>> hemoglobin - 6/18\n",
+      "(2017-07-13 17:03:14)>>>>>> READ DF...\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> *fit* Filter columns (remove_small_columns) (538726, 44)\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> *transform* Filter columns (remove_small_columns) (538726, 44)\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> *fit* Filter columns (record_threshold) (538726, 8)\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> *transform* Filter columns (record_threshold) (538726, 8)\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> FIT Combine like columns (538726, 8)\n",
+      "(2017-07-13 17:03:17)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
+      "(2017-07-13 17:03:17)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:17)>>>>>> TRANSFORM Combine like columns (538726, 8)\n",
+      "(2017-07-13 17:03:17)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
+      "(2017-07-13 17:03:32)<<<<<<<< --- (15.0s)\n",
+      "(2017-07-13 17:03:32)<<<<<< --- (15.0s)\n",
+      "(2017-07-13 17:03:32)>>>>>> SAVE DF... (538658, 1) -> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:03:34)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:03:34)<<<< --- (20.0s)\n",
+      "(2017-07-13 17:03:34)>>>> blood pressure mean - 7/18\n",
+      "(2017-07-13 17:03:34)>>>>>> READ DF...\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> *fit* Filter columns (remove_small_columns) (1927597, 3)\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> *transform* Filter columns (remove_small_columns) (1927597, 3)\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> *fit* Filter columns (record_threshold) (1927597, 3)\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> *transform* Filter columns (record_threshold) (1927597, 3)\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> FIT Combine like columns (1927597, 3)\n",
+      "(2017-07-13 17:03:37)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:03:37)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:03:37)>>>>>> TRANSFORM Combine like columns (1927597, 3)\n",
+      "(2017-07-13 17:03:37)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:04:16)<<<<<<<< --- (39.0s)\n",
+      "(2017-07-13 17:04:16)<<<<<< --- (39.0s)\n",
+      "(2017-07-13 17:04:17)>>>>>> SAVE DF... (1926877, 1) -> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:04:23)<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:04:23)<<<< --- (49.0s)\n",
+      "(2017-07-13 17:04:23)>>>> vasopressin - 8/18\n",
+      "(2017-07-13 17:04:23)>>>>>> READ DF...\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> *fit* Filter columns (remove_small_columns) (88280, 38)\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> *transform* Filter columns (remove_small_columns) (88280, 38)\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> *fit* Filter columns (record_threshold) (88280, 6)\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> *transform* Filter columns (record_threshold) (88280, 6)\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> FIT Combine like columns (88280, 5)\n",
+      "(2017-07-13 17:04:26)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
+      "(2017-07-13 17:04:26)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
+      "(2017-07-13 17:04:26)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:26)>>>>>> TRANSFORM Combine like columns (88280, 5)\n",
+      "(2017-07-13 17:04:26)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
+      "(2017-07-13 17:04:29)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:29)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
+      "(2017-07-13 17:04:29)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:29)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:29)>>>>>> SAVE DF... (87871, 2) -> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:04:29)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:29)<<<< --- (6.0s)\n",
+      "(2017-07-13 17:04:29)>>>> glasgow coma scale verbal - 9/18\n",
+      "(2017-07-13 17:04:29)>>>>>> READ DF...\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> *fit* Filter columns (remove_small_columns) (762059, 1)\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> *transform* Filter columns (remove_small_columns) (762059, 1)\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> *fit* Filter columns (record_threshold) (762059, 1)\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> *transform* Filter columns (record_threshold) (762059, 1)\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> FIT Combine like columns (762059, 1)\n",
+      "(2017-07-13 17:04:32)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 17:04:32)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:32)>>>>>> TRANSFORM Combine like columns (762059, 1)\n",
+      "(2017-07-13 17:04:32)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 17:04:38)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:04:38)<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:04:38)>>>>>> SAVE DF... (762059, 1) -> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:04:41)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:41)<<<< --- (12.0s)\n",
+      "(2017-07-13 17:04:41)>>>> weight body - 10/18\n",
+      "(2017-07-13 17:04:41)>>>>>> READ DF...\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> *fit* Filter columns (remove_small_columns) (75471, 3)\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> *transform* Filter columns (remove_small_columns) (75471, 3)\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> *fit* Filter columns (record_threshold) (75471, 3)\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> *transform* Filter columns (record_threshold) (75471, 3)\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> FIT Combine like columns (75471, 3)\n",
+      "(2017-07-13 17:04:44)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
+      "(2017-07-13 17:04:44)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:44)>>>>>> TRANSFORM Combine like columns (75471, 3)\n",
+      "(2017-07-13 17:04:44)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
+      "(2017-07-13 17:04:46)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:04:46)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:04:46)>>>>>> SAVE DF... (75462, 1) -> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:04:46)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:46)<<<< --- (5.0s)\n",
+      "(2017-07-13 17:04:46)>>>> normal saline - 11/18\n",
+      "(2017-07-13 17:04:46)>>>>>> READ DF...\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> *fit* Filter columns (remove_small_columns) (401527, 16)\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> *transform* Filter columns (remove_small_columns) (401527, 16)\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> *fit* Filter columns (record_threshold) (401527, 7)\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> *transform* Filter columns (record_threshold) (401527, 7)\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> FIT Combine like columns (401527, 6)\n",
+      "(2017-07-13 17:04:49)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:04:49)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
+      "(2017-07-13 17:04:49)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>>>> ('normal saline', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:04:49)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:04:49)>>>>>> TRANSFORM Combine like columns (401527, 6)\n",
+      "(2017-07-13 17:04:49)>>>>>>>> ('normal saline', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:04:52)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:04:52)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:04:59)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:04:59)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
+      "(2017-07-13 17:05:07)<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:05:07)<<<<<< --- (18.0s)\n",
+      "(2017-07-13 17:05:07)>>>>>> SAVE DF... (401378, 3) -> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:05:09)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:05:09)<<<< --- (23.0s)\n",
+      "(2017-07-13 17:05:09)>>>> norepinephrine - 12/18\n",
+      "(2017-07-13 17:05:09)>>>>>> READ DF...\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> *fit* Filter columns (remove_small_columns) (312618, 5)\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> *transform* Filter columns (remove_small_columns) (312618, 5)\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> *fit* Filter columns (record_threshold) (312618, 5)\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> *transform* Filter columns (record_threshold) (312618, 5)\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> FIT Combine like columns (312618, 5)\n",
+      "(2017-07-13 17:05:11)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
+      "(2017-07-13 17:05:11)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
+      "(2017-07-13 17:05:11)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/min')\n",
+      "(2017-07-13 17:05:11)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:11)>>>>>> TRANSFORM Combine like columns (312618, 5)\n",
+      "(2017-07-13 17:05:11)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/min')\n",
+      "(2017-07-13 17:05:13)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:05:13)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
+      "(2017-07-13 17:05:17)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:05:17)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
+      "(2017-07-13 17:05:24)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:05:24)<<<<<< --- (13.0s)\n",
+      "(2017-07-13 17:05:24)>>>>>> SAVE DF... (312569, 3) -> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:05:25)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:05:25)<<<< --- (16.0s)\n",
+      "(2017-07-13 17:05:25)>>>> temperature body - 13/18\n",
+      "(2017-07-13 17:05:25)>>>>>> READ DF...\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> *fit* Filter columns (remove_small_columns) (1383215, 4)\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> *transform* Filter columns (remove_small_columns) (1383215, 4)\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> *fit* Filter columns (record_threshold) (1383215, 4)\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> *transform* Filter columns (record_threshold) (1383215, 4)\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> FIT Combine like columns (1383215, 4)\n",
+      "(2017-07-13 17:05:28)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+      "(2017-07-13 17:05:28)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:05:28)>>>>>> TRANSFORM Combine like columns (1383215, 4)\n",
+      "(2017-07-13 17:05:28)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+      "(2017-07-13 17:05:54)<<<<<<<< --- (26.0s)\n",
+      "(2017-07-13 17:05:54)<<<<<< --- (26.0s)\n",
+      "(2017-07-13 17:05:55)>>>>>> SAVE DF... (1382975, 1) -> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:05:59)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:05:59)<<<< --- (34.0s)\n",
+      "(2017-07-13 17:05:59)>>>> blood pressure diastolic - 14/18\n",
+      "(2017-07-13 17:05:59)>>>>>> READ DF...\n",
+      "(2017-07-13 17:06:09)<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:06:09)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:06:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:06:09)>>>>>> *fit* Filter columns (remove_small_columns) (4786202, 42)\n",
+      "(2017-07-13 17:06:11)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:06:11)>>>>>> *transform* Filter columns (remove_small_columns) (4786202, 42)\n",
+      "(2017-07-13 17:06:11)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:06:11)>>>>>> *fit* Filter columns (record_threshold) (4786202, 14)\n",
+      "(2017-07-13 17:06:12)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:06:12)>>>>>> *transform* Filter columns (record_threshold) (4786202, 14)\n",
+      "(2017-07-13 17:06:12)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:06:12)>>>>>> FIT Combine like columns (4786202, 14)\n",
+      "(2017-07-13 17:06:12)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:06:13)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:06:13)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
+      "(2017-07-13 17:06:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:06:13)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:06:13)>>>>>> TRANSFORM Combine like columns (4786202, 14)\n",
+      "(2017-07-13 17:06:13)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:07:29)<<<<<<<< --- (76.0s)\n",
+      "(2017-07-13 17:07:29)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
+      "(2017-07-13 17:08:14)<<<<<<<< --- (45.0s)\n",
+      "(2017-07-13 17:08:14)<<<<<< --- (121.0s)\n",
+      "(2017-07-13 17:08:16)>>>>>> SAVE DF... (4786025, 2) -> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:08:33)<<<<<< --- (17.0s)\n",
+      "(2017-07-13 17:08:33)<<<< --- (154.0s)\n",
+      "(2017-07-13 17:08:33)>>>> heart rate - 15/18\n",
+      "(2017-07-13 17:08:33)>>>>>> READ DF...\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (14.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> *fit* Filter columns (remove_small_columns) (6342226, 6)\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> *transform* Filter columns (remove_small_columns) (6342226, 6)\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> *fit* Filter columns (record_threshold) (6342226, 3)\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> *transform* Filter columns (record_threshold) (6342226, 3)\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> FIT Combine like columns (6342226, 3)\n",
+      "(2017-07-13 17:08:47)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+      "(2017-07-13 17:08:47)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:08:47)>>>>>> TRANSFORM Combine like columns (6342226, 3)\n",
+      "(2017-07-13 17:08:47)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+      "(2017-07-13 17:10:28)<<<<<<<< --- (101.0s)\n",
+      "(2017-07-13 17:10:28)<<<<<< --- (101.0s)\n",
+      "(2017-07-13 17:10:31)>>>>>> SAVE DF... (6342201, 1) -> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:10:51)<<<<<< --- (20.0s)\n",
+      "(2017-07-13 17:10:51)<<<< --- (138.0s)\n",
+      "(2017-07-13 17:10:51)>>>> output urine - 16/18\n",
+      "(2017-07-13 17:10:51)>>>>>> READ DF...\n",
+      "(2017-07-13 17:11:17)<<<<<< --- (26.0s)\n",
+      "(2017-07-13 17:11:17)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:11:17)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:17)>>>>>> *fit* Filter columns (remove_small_columns) (2911908, 61)\n",
+      "(2017-07-13 17:11:18)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:11:18)>>>>>> *transform* Filter columns (remove_small_columns) (2911908, 61)\n",
+      "(2017-07-13 17:11:18)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:18)>>>>>> *fit* Filter columns (record_threshold) (2911908, 10)\n",
+      "(2017-07-13 17:11:19)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:11:19)>>>>>> *transform* Filter columns (record_threshold) (2911908, 10)\n",
+      "(2017-07-13 17:11:19)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:19)>>>>>> FIT Combine like columns (2911908, 10)\n",
+      "(2017-07-13 17:11:19)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:11:19)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:19)>>>>>>>> ('output urine', 'unknown', 'nom', 'no_units')\n",
+      "(2017-07-13 17:11:19)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:19)>>>>>>>> ('output urine', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:11:19)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:19)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:11:19)>>>>>> TRANSFORM Combine like columns (2911908, 10)\n",
+      "(2017-07-13 17:11:19)>>>>>>>> ('output urine', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:11:51)<<<<<<<< --- (32.0s)\n",
+      "(2017-07-13 17:11:51)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:12:31)<<<<<<<< --- (40.0s)\n",
+      "(2017-07-13 17:12:31)<<<<<< --- (72.0s)\n",
+      "(2017-07-13 17:12:33)>>>>>> SAVE DF... (2911908, 5) -> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:12:49)<<<<<< --- (16.0s)\n",
+      "(2017-07-13 17:12:49)<<<< --- (118.0s)\n",
+      "(2017-07-13 17:12:49)>>>> lactated ringers - 17/18\n",
+      "(2017-07-13 17:12:49)>>>>>> READ DF...\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> *fit* Filter columns (remove_small_columns) (205961, 20)\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> *transform* Filter columns (remove_small_columns) (205961, 20)\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> *fit* Filter columns (record_threshold) (205961, 7)\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> *transform* Filter columns (record_threshold) (205961, 7)\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> FIT Combine like columns (205961, 7)\n",
+      "(2017-07-13 17:12:52)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:12:52)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL/hr')\n",
+      "(2017-07-13 17:12:52)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>>>> ('lactated ringers', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:12:52)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:12:52)>>>>>> TRANSFORM Combine like columns (205961, 7)\n",
+      "(2017-07-13 17:12:52)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL/hr')\n",
+      "(2017-07-13 17:12:55)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:12:55)>>>>>>>> ('lactated ringers', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:12:56)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:12:56)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:13:00)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:13:00)<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:13:00)>>>>>> SAVE DF... (205946, 3) -> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:13:02)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:13:02)<<<< --- (13.0s)\n",
+      "(2017-07-13 17:13:02)>>>> respiratory rate - 18/18\n",
+      "(2017-07-13 17:13:02)>>>>>> READ DF...\n",
+      "(2017-07-13 17:13:13)<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:13:13)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:13:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:13)>>>>>> *fit* Filter columns (remove_small_columns) (6225819, 6)\n",
+      "(2017-07-13 17:13:14)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:13:14)>>>>>> *transform* Filter columns (remove_small_columns) (6225819, 6)\n",
+      "(2017-07-13 17:13:14)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:14)>>>>>> *fit* Filter columns (record_threshold) (6225819, 5)\n",
+      "(2017-07-13 17:13:14)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:14)>>>>>> *transform* Filter columns (record_threshold) (6225819, 5)\n",
+      "(2017-07-13 17:13:14)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:14)>>>>>> FIT Combine like columns (6225819, 5)\n",
+      "(2017-07-13 17:13:15)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+      "(2017-07-13 17:13:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:15)>>>>>>>> ('respiratory rate', 'unknown', 'nom', 'no_units')\n",
+      "(2017-07-13 17:13:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:15)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
+      "(2017-07-13 17:13:15)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:13:15)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:13:15)>>>>>> TRANSFORM Combine like columns (6225819, 5)\n",
+      "(2017-07-13 17:13:15)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+      "(2017-07-13 17:14:47)<<<<<<<< --- (92.0s)\n",
+      "(2017-07-13 17:14:47)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
+      "(2017-07-13 17:15:03)<<<<<<<< --- (16.0s)\n",
+      "(2017-07-13 17:15:03)<<<<<< --- (108.0s)\n",
+      "(2017-07-13 17:15:08)>>>>>> SAVE DF... (6225819, 4) -> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:15:42)<<<<<< --- (34.0s)\n",
+      "(2017-07-13 17:15:42)<<<< --- (160.0s)\n",
+      "(2017-07-13 17:15:42)>>>> Smart join: n=47180, ['390097392/390097392/glasgow coma scale eye opening', '390097392/390097392/glasgow coma scale motor', '390097392/390097392/blood pressure systolic', '390097392/390097392/oxygen saturation pulse oximetry', '390097392/390097392/lactate', '390097392/390097392/hemoglobin', '390097392/390097392/blood pressure mean', '390097392/390097392/vasopressin', '390097392/390097392/glasgow coma scale verbal', '390097392/390097392/weight body', '390097392/390097392/normal saline', '390097392/390097392/norepinephrine', '390097392/390097392/temperature body', '390097392/390097392/blood pressure diastolic', '390097392/390097392/heart rate', '390097392/390097392/output urine', '390097392/390097392/lactated ringers', '390097392/390097392/respiratory rate']\n",
+      "(2017-07-13 17:15:42)>>>>>> JOINING dataframes\n",
+      "(2017-07-13 17:15:42)>>>>>>>> Slice & Join: 100001 --> 110571, n=5000\n",
+      "(2017-07-13 17:15:42)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:15:43)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:15:43)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:15:44)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:15:44)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:15:51)<<<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:15:51)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:15:59)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:15:59)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:16:08)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:16:08)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:16:12)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:16:12)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:16:18)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:16:18)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:16:21)<<<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:16:21)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:16:26)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:16:26)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:16:30)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:16:30)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:16:35)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:16:35)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:16:40)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:16:40)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:16:46)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:16:46)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:16:57)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:16:57)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:17:06)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:17:06)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:17:14)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:17:14)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:17:19)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:17:19)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:17:29)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:17:29)<<<<<<<< --- (107.0s)\n",
+      "(2017-07-13 17:17:29)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:17:34)<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:17:34)>>>>>>>> Slice & Join: 110573 --> 121159, n=5000\n",
+      "(2017-07-13 17:17:34)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:17:34)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:17:34)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:17:36)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:17:36)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:17:41)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:17:41)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:17:49)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:17:49)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:17:53)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:17:53)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:17:58)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:17:58)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:18:04)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:18:04)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:18:08)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:18:08)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:18:13)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:18:13)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:18:17)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:18:17)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:18:22)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:18:22)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:18:27)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:18:27)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:18:33)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:18:33)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:18:42)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:18:42)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:18:52)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:18:52)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:19:00)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:19:00)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:19:05)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:19:05)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:19:16)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:19:16)<<<<<<<< --- (102.0s)\n",
+      "(2017-07-13 17:19:16)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:19:20)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:19:20)>>>>>>>> Slice & Join: 121164 --> 131717, n=5000\n",
+      "(2017-07-13 17:19:20)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:19:21)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:19:21)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:19:23)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:19:23)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:19:27)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:19:27)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:19:35)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:19:35)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:19:39)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:19:39)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:19:44)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:19:44)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:19:50)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:19:50)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:19:54)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:19:54)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:20:00)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:20:00)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:20:04)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:20:04)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:20:09)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:20:09)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:20:14)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:20:14)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:20:20)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:20:20)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:20:29)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:20:29)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:20:38)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:20:38)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:20:46)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:20:46)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:20:52)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:20:52)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:21:03)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:21:03)<<<<<<<< --- (103.0s)\n",
+      "(2017-07-13 17:21:03)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:21:07)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:21:07)>>>>>>>> Slice & Join: 131719 --> 142369, n=5000\n",
+      "(2017-07-13 17:21:07)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:21:08)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:21:08)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:21:09)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:21:09)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:21:14)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:14)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:21:22)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:21:22)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:21:27)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:27)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:21:32)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:32)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:21:39)<<<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:21:39)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:21:44)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:44)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:21:49)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:49)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:21:54)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:54)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:21:59)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:21:59)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:22:04)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:22:04)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:22:11)<<<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:22:11)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:22:20)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:22:20)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:22:30)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:22:30)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:22:38)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:22:38)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:22:44)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:22:44)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:22:56)<<<<<<<<<< --- (12.0s)\n",
+      "(2017-07-13 17:22:56)<<<<<<<< --- (109.0s)\n",
+      "(2017-07-13 17:22:56)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:22:59)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:22:59)>>>>>>>> Slice & Join: 142372 --> 152958, n=5000\n",
+      "(2017-07-13 17:22:59)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:23:00)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:23:00)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:23:02)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:23:02)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:23:07)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:23:07)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:23:14)<<<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:23:14)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:23:18)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:23:18)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:23:23)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:23:23)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:23:29)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:23:29)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:23:33)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:23:33)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:23:38)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:23:38)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:23:42)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:23:42)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:23:47)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:23:47)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:23:52)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:23:52)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:23:58)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:23:58)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:24:06)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:24:06)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:24:16)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:24:16)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:24:24)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:24:24)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:24:30)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:24:30)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:24:41)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:24:41)<<<<<<<< --- (102.0s)\n",
+      "(2017-07-13 17:24:41)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:24:45)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:24:45)>>>>>>>> Slice & Join: 152960 --> 163720, n=5000\n",
+      "(2017-07-13 17:24:45)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:24:46)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:24:46)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:24:48)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:24:48)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:24:52)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:24:52)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:25:00)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:25:00)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:25:04)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:25:04)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:25:08)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:25:08)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:25:14)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:25:14)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:25:18)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:25:18)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:25:23)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:25:23)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:25:28)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:25:28)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:25:32)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:25:32)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:25:37)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:25:37)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:25:43)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:25:43)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:25:52)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:25:52)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:26:01)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:26:01)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:26:09)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:26:09)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:26:15)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:26:15)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:26:26)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:26:26)<<<<<<<< --- (101.0s)\n",
+      "(2017-07-13 17:26:26)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:26:30)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:26:30)>>>>>>>> Slice & Join: 163721 --> 174138, n=5000\n",
+      "(2017-07-13 17:26:30)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:26:31)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:26:31)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:26:32)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:26:32)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:26:37)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:26:37)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:26:45)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:26:45)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:26:49)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:26:49)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:26:53)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:26:53)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:26:59)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:26:59)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:27:03)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:27:03)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:27:08)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:27:08)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:27:12)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:27:12)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:27:17)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:27:17)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:27:22)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:27:22)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:27:28)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:27:28)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:27:36)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:27:36)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:27:45)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:27:45)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:27:53)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:27:53)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:27:59)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:27:59)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:28:09)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:28:09)<<<<<<<< --- (99.0s)\n",
+      "(2017-07-13 17:28:09)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:28:14)<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:28:14)>>>>>>>> Slice & Join: 174141 --> 184726, n=5000\n",
+      "(2017-07-13 17:28:14)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:28:14)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:28:14)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:28:16)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:28:16)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:28:20)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:28:20)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:28:28)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:28:28)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:28:32)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:28:32)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:28:36)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:28:36)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:28:42)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:28:42)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:28:46)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:28:46)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:28:51)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:28:51)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:28:56)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:28:56)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:29:00)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:29:00)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:29:05)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:29:05)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:29:11)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:29:11)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:29:20)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:29:20)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:29:29)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:29:29)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:29:37)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:29:37)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:29:43)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:29:43)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:29:54)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:29:54)<<<<<<<< --- (100.0s)\n",
+      "(2017-07-13 17:29:54)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:29:57)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:29:57)>>>>>>>> Slice & Join: 184727 --> 195306, n=5000\n",
+      "(2017-07-13 17:29:57)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:29:57)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:29:57)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:29:59)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:29:59)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:30:04)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:04)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:30:12)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:30:12)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:30:16)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:30:16)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:30:21)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:21)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:30:27)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:30:27)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:30:32)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:32)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:30:37)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:37)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:30:42)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:42)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:30:47)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:47)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:30:52)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:30:52)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:30:58)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:30:58)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:31:07)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:31:07)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:31:17)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:31:17)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:31:26)<<<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:31:26)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:31:31)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:31:31)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:31:42)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:31:42)<<<<<<<< --- (105.0s)\n",
+      "(2017-07-13 17:31:42)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:31:46)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:31:46)>>>>>>>> Slice & Join: 195309 --> 199999, n=2180\n",
+      "(2017-07-13 17:31:46)>>>>>>>>>> 390097392/390097392/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:31:46)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:31:46)>>>>>>>>>> 390097392/390097392/glasgow coma scale motor\n",
+      "(2017-07-13 17:31:47)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:31:47)>>>>>>>>>> 390097392/390097392/blood pressure systolic\n",
+      "(2017-07-13 17:31:50)<<<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:31:50)>>>>>>>>>> 390097392/390097392/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:31:53)<<<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:31:53)>>>>>>>>>> 390097392/390097392/lactate\n",
+      "(2017-07-13 17:31:55)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:31:55)>>>>>>>>>> 390097392/390097392/hemoglobin\n",
+      "(2017-07-13 17:31:57)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:31:57)>>>>>>>>>> 390097392/390097392/blood pressure mean\n",
+      "(2017-07-13 17:32:00)<<<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:32:00)>>>>>>>>>> 390097392/390097392/vasopressin\n",
+      "(2017-07-13 17:32:02)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:32:02)>>>>>>>>>> 390097392/390097392/glasgow coma scale verbal\n",
+      "(2017-07-13 17:32:04)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:32:04)>>>>>>>>>> 390097392/390097392/weight body\n",
+      "(2017-07-13 17:32:06)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:32:06)>>>>>>>>>> 390097392/390097392/normal saline\n",
+      "(2017-07-13 17:32:08)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:32:08)>>>>>>>>>> 390097392/390097392/norepinephrine\n",
+      "(2017-07-13 17:32:11)<<<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:32:11)>>>>>>>>>> 390097392/390097392/temperature body\n",
+      "(2017-07-13 17:32:13)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:32:13)>>>>>>>>>> 390097392/390097392/blood pressure diastolic\n",
+      "(2017-07-13 17:32:18)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:32:18)>>>>>>>>>> 390097392/390097392/heart rate\n",
+      "(2017-07-13 17:32:23)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:32:23)>>>>>>>>>> 390097392/390097392/output urine\n",
+      "(2017-07-13 17:32:27)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:32:27)>>>>>>>>>> 390097392/390097392/lactated ringers\n",
+      "(2017-07-13 17:32:30)<<<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:32:30)>>>>>>>>>> 390097392/390097392/respiratory rate\n",
+      "(2017-07-13 17:32:35)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:32:35)<<<<<<<< --- (49.0s)\n",
+      "(2017-07-13 17:32:35)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:32:37)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:32:37)<<<<<< --- (1015.0s)\n",
+      "(2017-07-13 17:32:37)<<<< --- (1015.0s)\n",
+      "(2017-07-13 17:32:37)>>>> Read JOINED DF\n",
+      "(2017-07-13 17:32:48)<<<< --- (11.0s)\n",
+      "(2017-07-13 17:32:48)<< --- (2078.0s)\n",
+      "(2017-07-13 17:32:48)>> APPLY FEATURES, #F=6 to df=(8057130, 34)\n",
+      "(2017-07-13 17:32:48)>>>> Featurizing: MEAN, [{'units': <function <lambda> at 0x000000000DA4D4A8>, 'variable_type': 'qn'}, {'component': 'weight body'}]\n",
+      "(2017-07-13 17:32:48)>>>>>> *fit* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:32:49)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:32:49)>>>>>> *transform* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:33:25)<<<<<< --- (36.0s)\n",
+      "(2017-07-13 17:33:25)>>>>>> *transform* RESAMPLE (8057130, 22), rule=2H, func=mean\n",
+      "(2017-07-13 17:33:25)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:33:29)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:33:29)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:36:12)<<<<<<<< --- (163.0s)\n",
+      "(2017-07-13 17:36:12)<<<<<< --- (167.0s)\n",
+      "(2017-07-13 17:36:14)>>>>>> Join\n",
+      "(2017-07-13 17:36:14)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:36:14)<<<< --- (206.0s)\n",
+      "(2017-07-13 17:36:14)>>>> Featurizing: LAST, [{'units': <function <lambda> at 0x000000000DA4D4A8>, 'variable_type': 'qn'}, {'component': 'weight body'}]\n",
+      "(2017-07-13 17:36:14)>>>>>> *fit* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:36:14)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:36:14)>>>>>> *transform* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:36:15)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:36:15)>>>>>> *transform* RESAMPLE (8057130, 22), rule=2H, func=last\n",
+      "(2017-07-13 17:36:15)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:36:19)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:36:19)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:38:20)<<<<<<<< --- (121.0s)\n",
+      "(2017-07-13 17:38:20)<<<<<< --- (125.0s)\n",
+      "(2017-07-13 17:38:53)>>>>>> Join\n",
+      "(2017-07-13 17:39:09)<<<<<< --- (16.0s)\n",
+      "(2017-07-13 17:39:09)<<<< --- (175.0s)\n",
+      "(2017-07-13 17:39:09)>>>> Featurizing: STD, []\n",
+      "(2017-07-13 17:39:09)>>>>>> *fit* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:39:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:39:09)>>>>>> *transform* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:39:10)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:39:10)>>>>>> *transform* RESAMPLE (8057130, 34), rule=2H, func=std\n",
+      "(2017-07-13 17:39:10)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:39:19)<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:39:19)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:42:32)<<<<<<<< --- (193.0s)\n",
+      "(2017-07-13 17:42:32)<<<<<< --- (202.0s)\n",
+      "(2017-07-13 17:42:34)>>>>>> Join\n",
+      "(2017-07-13 17:42:46)<<<<<< --- (12.0s)\n",
+      "(2017-07-13 17:42:46)<<<< --- (217.0s)\n",
+      "(2017-07-13 17:42:46)>>>> Featurizing: SUM, [{'units': <function <lambda> at 0x000000000DA4DA58>, 'clinical_source': 'intervention'}, {'units': <function <lambda> at 0x000000000DA4DA58>, 'component': 'output urine'}]\n",
+      "(2017-07-13 17:42:46)>>>>>> *fit* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:42:46)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:42:46)>>>>>> *transform* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:42:47)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:42:47)>>>>>> *transform* RESAMPLE (8057130, 4), rule=2H, func=sum\n",
+      "(2017-07-13 17:42:47)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:42:49)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:42:49)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:45:31)<<<<<<<< --- (162.0s)\n",
+      "(2017-07-13 17:45:31)<<<<<< --- (164.0s)\n",
+      "(2017-07-13 17:45:32)>>>>>> Join\n",
+      "(2017-07-13 17:45:41)<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:45:41)<<<< --- (175.0s)\n",
+      "(2017-07-13 17:45:41)>>>> Featurizing: COUNT, []\n",
+      "(2017-07-13 17:45:41)>>>>>> *fit* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:45:41)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:45:41)>>>>>> *transform* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:45:43)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:45:43)>>>>>> *transform* RESAMPLE (8057130, 34), rule=2H, func=count\n",
+      "(2017-07-13 17:45:43)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:45:49)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:45:49)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:47:55)<<<<<<<< --- (126.0s)\n",
+      "(2017-07-13 17:47:55)<<<<<< --- (132.0s)\n",
+      "(2017-07-13 17:47:56)>>>>>> Join\n",
+      "(2017-07-13 17:48:09)<<<<<< --- (13.0s)\n",
+      "(2017-07-13 17:48:09)<<<< --- (148.0s)\n",
+      "(2017-07-13 17:48:09)>>>> Featurizing: LABEL, {'variable_type': 'qn', 'component': 'lactate'}\n",
+      "(2017-07-13 17:48:09)>>>>>> *fit* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:48:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:48:09)>>>>>> *transform* Filter columns (DataSpecFilter) (8057130, 34)\n",
+      "(2017-07-13 17:48:09)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:48:09)>>>>>> *transform* RESAMPLE (8057130, 1), rule=2H, func=mean\n",
+      "(2017-07-13 17:48:10)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:48:11)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:48:11)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:50:49)<<<<<<<< --- (158.0s)\n",
+      "(2017-07-13 17:50:49)<<<<<< --- (160.0s)\n",
+      "(2017-07-13 17:50:49)>>>>>> Join\n",
+      "(2017-07-13 17:50:59)<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:50:59)<<<< --- (170.0s)\n",
+      "(2017-07-13 17:50:59)<< --- (1091.0s)\n",
+      "(2017-07-13 17:50:59) --- (3169.0s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_train = factory.fit_transform(train_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2017-07-13 17:50:59) FEATURIZE... #F=6, #ids=5898, fit->False\n",
+      "(2017-07-13 17:50:59)>> PRE-PROCESSING & JOIN: #C=18, [u'glasgow coma scale eye opening', u'glasgow coma scale motor', u'blood pressure systolic', u'oxygen saturation pulse oximetry', u'lactate', u'hemoglobin', u'blood pressure mean', u'vasopressin', u'glasgow coma scale verbal', u'weight body', u'normal saline', u'norepinephrine', u'temperature body', u'blood pressure diastolic', u'heart rate', u'output urine', u'lactated ringers', u'respiratory rate']\n",
+      "(2017-07-13 17:51:01)>>>> glasgow coma scale eye opening - 1/18\n",
+      "(2017-07-13 17:51:01)>>>>>> READ DF...\n",
+      "(2017-07-13 17:51:03)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:51:03)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:51:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:03)>>>>>> *transform* Filter columns (remove_small_columns) (95854, 1)\n",
+      "(2017-07-13 17:51:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:03)>>>>>> *transform* Filter columns (record_threshold) (95854, 1)\n",
+      "(2017-07-13 17:51:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:03)>>>>>> TRANSFORM Combine like columns (95854, 1)\n",
+      "(2017-07-13 17:51:03)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 17:51:05)<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:51:05)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:51:05)>>>>>> SAVE DF... (95854, 1) -> 390097392/-1238157839/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:51:06)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:51:06)<<<< --- (5.0s)\n",
+      "(2017-07-13 17:51:06)>>>> glasgow coma scale motor - 2/18\n",
+      "(2017-07-13 17:51:06)>>>>>> READ DF...\n",
+      "(2017-07-13 17:51:07)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:51:07)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:51:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:07)>>>>>> *transform* Filter columns (remove_small_columns) (95358, 1)\n",
+      "(2017-07-13 17:51:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:07)>>>>>> *transform* Filter columns (record_threshold) (95358, 1)\n",
+      "(2017-07-13 17:51:07)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:07)>>>>>> TRANSFORM Combine like columns (95358, 1)\n",
+      "(2017-07-13 17:51:07)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 17:51:08)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:51:08)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:51:08)>>>>>> SAVE DF... (95358, 1) -> 390097392/-1238157839/glasgow coma scale motor\n",
+      "(2017-07-13 17:51:08)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:08)<<<< --- (2.0s)\n",
+      "(2017-07-13 17:51:08)>>>> blood pressure systolic - 3/18\n",
+      "(2017-07-13 17:51:08)>>>>>> READ DF...\n",
+      "(2017-07-13 17:51:28)<<<<<< --- (20.0s)\n",
+      "(2017-07-13 17:51:28)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:51:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:28)>>>>>> *transform* Filter columns (remove_small_columns) (624981, 40)\n",
+      "(2017-07-13 17:51:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:28)>>>>>> *transform* Filter columns (record_threshold) (624981, 13)\n",
+      "(2017-07-13 17:51:28)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:51:28)>>>>>> TRANSFORM Combine like columns (624981, 13)\n",
+      "(2017-07-13 17:51:28)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
+      "(2017-07-13 17:51:37)<<<<<<<< --- (9.0s)\n",
+      "(2017-07-13 17:51:37)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:51:47)<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:51:47)<<<<<< --- (19.0s)\n",
+      "(2017-07-13 17:51:48)>>>>>> SAVE DF... (624976, 2) -> 390097392/-1238157839/blood pressure systolic\n",
+      "(2017-07-13 17:51:51)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:51:51)<<<< --- (43.0s)\n",
+      "(2017-07-13 17:51:51)>>>> oxygen saturation pulse oximetry - 4/18\n",
+      "(2017-07-13 17:51:51)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:03)<<<<<< --- (12.0s)\n",
+      "(2017-07-13 17:52:03)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:03)>>>>>> *transform* Filter columns (remove_small_columns) (645714, 2)\n",
+      "(2017-07-13 17:52:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:03)>>>>>> *transform* Filter columns (record_threshold) (645714, 2)\n",
+      "(2017-07-13 17:52:03)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:03)>>>>>> TRANSFORM Combine like columns (645714, 2)\n",
+      "(2017-07-13 17:52:03)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+      "(2017-07-13 17:52:17)<<<<<<<< --- (14.0s)\n",
+      "(2017-07-13 17:52:17)<<<<<< --- (14.0s)\n",
+      "(2017-07-13 17:52:18)>>>>>> SAVE DF... (645708, 1) -> 390097392/-1238157839/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:52:20)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:52:20)<<<< --- (29.0s)\n",
+      "(2017-07-13 17:52:20)>>>> lactate - 5/18\n",
+      "(2017-07-13 17:52:20)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:21)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:21)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:21)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:21)>>>>>> *transform* Filter columns (remove_small_columns) (18340, 63)\n",
+      "(2017-07-13 17:52:21)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:21)>>>>>> *transform* Filter columns (record_threshold) (18340, 4)\n",
+      "(2017-07-13 17:52:21)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:21)>>>>>> TRANSFORM Combine like columns (18340, 4)\n",
+      "(2017-07-13 17:52:21)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+      "(2017-07-13 17:52:22)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:22)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:22)>>>>>> SAVE DF... (18336, 1) -> 390097392/-1238157839/lactate\n",
+      "(2017-07-13 17:52:22)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:22)<<<< --- (2.0s)\n",
+      "(2017-07-13 17:52:22)>>>> hemoglobin - 6/18\n",
+      "(2017-07-13 17:52:22)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:25)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:52:25)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:25)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:25)>>>>>> *transform* Filter columns (remove_small_columns) (67101, 44)\n",
+      "(2017-07-13 17:52:25)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:25)>>>>>> *transform* Filter columns (record_threshold) (67101, 8)\n",
+      "(2017-07-13 17:52:25)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:25)>>>>>> TRANSFORM Combine like columns (67101, 8)\n",
+      "(2017-07-13 17:52:25)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
+      "(2017-07-13 17:52:26)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:26)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:26)>>>>>> SAVE DF... (67092, 1) -> 390097392/-1238157839/hemoglobin\n",
+      "(2017-07-13 17:52:27)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:27)<<<< --- (5.0s)\n",
+      "(2017-07-13 17:52:27)>>>> blood pressure mean - 7/18\n",
+      "(2017-07-13 17:52:27)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:30)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:52:30)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:30)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:30)>>>>>> *transform* Filter columns (remove_small_columns) (265602, 3)\n",
+      "(2017-07-13 17:52:30)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:30)>>>>>> *transform* Filter columns (record_threshold) (265602, 3)\n",
+      "(2017-07-13 17:52:30)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:30)>>>>>> TRANSFORM Combine like columns (265602, 3)\n",
+      "(2017-07-13 17:52:30)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:52:36)<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:52:36)<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:52:36)>>>>>> SAVE DF... (265512, 1) -> 390097392/-1238157839/blood pressure mean\n",
+      "(2017-07-13 17:52:37)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:37)<<<< --- (10.0s)\n",
+      "(2017-07-13 17:52:37)>>>> vasopressin - 8/18\n",
+      "(2017-07-13 17:52:37)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:38)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:38)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:38)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:38)>>>>>> *transform* Filter columns (remove_small_columns) (12008, 38)\n",
+      "(2017-07-13 17:52:38)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:38)>>>>>> *transform* Filter columns (record_threshold) (12008, 6)\n",
+      "(2017-07-13 17:52:38)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:38)>>>>>> TRANSFORM Combine like columns (12008, 5)\n",
+      "(2017-07-13 17:52:38)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
+      "(2017-07-13 17:52:39)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:39)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
+      "(2017-07-13 17:52:39)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:39)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:39)>>>>>> SAVE DF... (12008, 2) -> 390097392/-1238157839/vasopressin\n",
+      "(2017-07-13 17:52:39)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:39)<<<< --- (2.0s)\n",
+      "(2017-07-13 17:52:39)>>>> glasgow coma scale verbal - 9/18\n",
+      "(2017-07-13 17:52:39)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:40)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:40)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:40)>>>>>> *transform* Filter columns (remove_small_columns) (95582, 1)\n",
+      "(2017-07-13 17:52:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:40)>>>>>> *transform* Filter columns (record_threshold) (95582, 1)\n",
+      "(2017-07-13 17:52:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:40)>>>>>> TRANSFORM Combine like columns (95582, 1)\n",
+      "(2017-07-13 17:52:40)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
+      "(2017-07-13 17:52:41)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:41)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:41)>>>>>> SAVE DF... (95582, 1) -> 390097392/-1238157839/glasgow coma scale verbal\n",
+      "(2017-07-13 17:52:42)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:42)<<<< --- (3.0s)\n",
+      "(2017-07-13 17:52:42)>>>> weight body - 10/18\n",
+      "(2017-07-13 17:52:42)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:43)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:43)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:43)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:43)>>>>>> *transform* Filter columns (remove_small_columns) (9544, 3)\n",
+      "(2017-07-13 17:52:43)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:43)>>>>>> *transform* Filter columns (record_threshold) (9544, 3)\n",
+      "(2017-07-13 17:52:43)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:43)>>>>>> TRANSFORM Combine like columns (9544, 3)\n",
+      "(2017-07-13 17:52:43)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
+      "(2017-07-13 17:52:43)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:43)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:43)>>>>>> SAVE DF... (9543, 1) -> 390097392/-1238157839/weight body\n",
+      "(2017-07-13 17:52:43)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:43)<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:43)>>>> normal saline - 11/18\n",
+      "(2017-07-13 17:52:43)>>>>>> READ DF...\n",
+      "(2017-07-13 17:52:53)<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:52:53)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:52:53)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:53)>>>>>> *transform* Filter columns (remove_small_columns) (52436, 16)\n",
+      "(2017-07-13 17:52:53)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:53)>>>>>> *transform* Filter columns (record_threshold) (52436, 7)\n",
+      "(2017-07-13 17:52:53)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:53)>>>>>> TRANSFORM Combine like columns (52436, 6)\n",
+      "(2017-07-13 17:52:53)>>>>>>>> ('normal saline', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:52:54)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:54)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:52:55)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:55)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
+      "(2017-07-13 17:52:56)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:52:56)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:52:56)>>>>>> SAVE DF... (52433, 3) -> 390097392/-1238157839/normal saline\n",
+      "(2017-07-13 17:52:56)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:52:56)<<<< --- (13.0s)\n",
+      "(2017-07-13 17:52:56)>>>> norepinephrine - 12/18\n",
+      "(2017-07-13 17:52:56)>>>>>> READ DF...\n",
+      "(2017-07-13 17:53:00)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:53:00)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:53:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:00)>>>>>> *transform* Filter columns (remove_small_columns) (42506, 5)\n",
+      "(2017-07-13 17:53:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:00)>>>>>> *transform* Filter columns (record_threshold) (42506, 5)\n",
+      "(2017-07-13 17:53:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:00)>>>>>> TRANSFORM Combine like columns (42506, 5)\n",
+      "(2017-07-13 17:53:00)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/min')\n",
+      "(2017-07-13 17:53:00)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:00)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
+      "(2017-07-13 17:53:01)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:53:01)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
+      "(2017-07-13 17:53:01)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:01)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:53:01)>>>>>> SAVE DF... (42500, 3) -> 390097392/-1238157839/norepinephrine\n",
+      "(2017-07-13 17:53:02)<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:53:02)<<<< --- (6.0s)\n",
+      "(2017-07-13 17:53:02)>>>> temperature body - 13/18\n",
+      "(2017-07-13 17:53:02)>>>>>> READ DF...\n",
+      "(2017-07-13 17:53:10)<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:53:10)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:53:10)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:10)>>>>>> *transform* Filter columns (remove_small_columns) (179045, 4)\n",
+      "(2017-07-13 17:53:10)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:10)>>>>>> *transform* Filter columns (record_threshold) (179045, 4)\n",
+      "(2017-07-13 17:53:10)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:53:10)>>>>>> TRANSFORM Combine like columns (179045, 4)\n",
+      "(2017-07-13 17:53:10)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+      "(2017-07-13 17:53:14)<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:53:14)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:53:14)>>>>>> SAVE DF... (179027, 1) -> 390097392/-1238157839/temperature body\n",
+      "(2017-07-13 17:53:16)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:53:16)<<<< --- (14.0s)\n",
+      "(2017-07-13 17:53:16)>>>> blood pressure diastolic - 14/18\n",
+      "(2017-07-13 17:53:16)>>>>>> READ DF...\n",
+      "(2017-07-13 17:54:00)<<<<<< --- (44.0s)\n",
+      "(2017-07-13 17:54:00)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:54:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:54:00)>>>>>> *transform* Filter columns (remove_small_columns) (625110, 42)\n",
+      "(2017-07-13 17:54:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:54:00)>>>>>> *transform* Filter columns (record_threshold) (625110, 14)\n",
+      "(2017-07-13 17:54:00)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:54:00)>>>>>> TRANSFORM Combine like columns (625110, 14)\n",
+      "(2017-07-13 17:54:00)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+      "(2017-07-13 17:54:13)<<<<<<<< --- (13.0s)\n",
+      "(2017-07-13 17:54:13)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
+      "(2017-07-13 17:54:20)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:54:20)<<<<<< --- (20.0s)\n",
+      "(2017-07-13 17:54:21)>>>>>> SAVE DF... (625092, 2) -> 390097392/-1238157839/blood pressure diastolic\n",
+      "(2017-07-13 17:54:23)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:54:23)<<<< --- (67.0s)\n",
+      "(2017-07-13 17:54:23)>>>> heart rate - 15/18\n",
+      "(2017-07-13 17:54:23)>>>>>> READ DF...\n",
+      "(2017-07-13 17:54:33)<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:54:33)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:54:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:54:33)>>>>>> *transform* Filter columns (remove_small_columns) (830998, 6)\n",
+      "(2017-07-13 17:54:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:54:33)>>>>>> *transform* Filter columns (record_threshold) (830998, 3)\n",
+      "(2017-07-13 17:54:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:54:33)>>>>>> TRANSFORM Combine like columns (830998, 3)\n",
+      "(2017-07-13 17:54:33)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+      "(2017-07-13 17:54:51)<<<<<<<< --- (18.0s)\n",
+      "(2017-07-13 17:54:51)<<<<<< --- (18.0s)\n",
+      "(2017-07-13 17:54:51)>>>>>> SAVE DF... (830994, 1) -> 390097392/-1238157839/heart rate\n",
+      "(2017-07-13 17:54:55)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:54:55)<<<< --- (32.0s)\n",
+      "(2017-07-13 17:54:55)>>>> output urine - 16/18\n",
+      "(2017-07-13 17:54:55)>>>>>> READ DF...\n",
+      "(2017-07-13 17:55:33)<<<<<< --- (38.0s)\n",
+      "(2017-07-13 17:55:33)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:55:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:33)>>>>>> *transform* Filter columns (remove_small_columns) (356212, 61)\n",
+      "(2017-07-13 17:55:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:33)>>>>>> *transform* Filter columns (record_threshold) (356212, 10)\n",
+      "(2017-07-13 17:55:33)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:33)>>>>>> TRANSFORM Combine like columns (356212, 10)\n",
+      "(2017-07-13 17:55:33)>>>>>>>> ('output urine', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:55:40)<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:55:40)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:55:45)<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:55:45)<<<<<< --- (12.0s)\n",
+      "(2017-07-13 17:55:46)>>>>>> SAVE DF... (356212, 5) -> 390097392/-1238157839/output urine\n",
+      "(2017-07-13 17:55:48)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:55:48)<<<< --- (53.0s)\n",
+      "(2017-07-13 17:55:48)>>>> lactated ringers - 17/18\n",
+      "(2017-07-13 17:55:48)>>>>>> READ DF...\n",
+      "(2017-07-13 17:55:51)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:55:51)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:55:51)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:51)>>>>>> *transform* Filter columns (remove_small_columns) (25320, 20)\n",
+      "(2017-07-13 17:55:51)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:51)>>>>>> *transform* Filter columns (record_threshold) (25320, 7)\n",
+      "(2017-07-13 17:55:51)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:51)>>>>>> TRANSFORM Combine like columns (25320, 7)\n",
+      "(2017-07-13 17:55:51)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL/hr')\n",
+      "(2017-07-13 17:55:52)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:55:52)>>>>>>>> ('lactated ringers', 'unknown', 'qn', 'no_units')\n",
+      "(2017-07-13 17:55:52)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:52)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
+      "(2017-07-13 17:55:53)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:55:53)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:55:53)>>>>>> SAVE DF... (25316, 3) -> 390097392/-1238157839/lactated ringers\n",
+      "(2017-07-13 17:55:53)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:55:53)<<<< --- (5.0s)\n",
+      "(2017-07-13 17:55:53)>>>> respiratory rate - 18/18\n",
+      "(2017-07-13 17:55:53)>>>>>> READ DF...\n",
+      "(2017-07-13 17:56:15)<<<<<< --- (22.0s)\n",
+      "(2017-07-13 17:56:15)>>>>>> PREPROCESS...\n",
+      "(2017-07-13 17:56:15)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:56:15)>>>>>> *transform* Filter columns (remove_small_columns) (817124, 6)\n",
+      "(2017-07-13 17:56:15)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:56:15)>>>>>> *transform* Filter columns (record_threshold) (817124, 5)\n",
+      "(2017-07-13 17:56:15)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:56:15)>>>>>> TRANSFORM Combine like columns (817124, 5)\n",
+      "(2017-07-13 17:56:15)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+      "(2017-07-13 17:56:31)<<<<<<<< --- (16.0s)\n",
+      "(2017-07-13 17:56:31)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
+      "(2017-07-13 17:56:34)<<<<<<<< --- (3.0s)\n",
+      "(2017-07-13 17:56:34)<<<<<< --- (19.0s)\n",
+      "(2017-07-13 17:56:34)>>>>>> SAVE DF... (817124, 4) -> 390097392/-1238157839/respiratory rate\n",
+      "(2017-07-13 17:56:40)<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:56:40)<<<< --- (47.0s)\n",
+      "(2017-07-13 17:56:40)>>>> Smart join: n=5898, ['390097392/-1238157839/glasgow coma scale eye opening', '390097392/-1238157839/glasgow coma scale motor', '390097392/-1238157839/blood pressure systolic', '390097392/-1238157839/oxygen saturation pulse oximetry', '390097392/-1238157839/lactate', '390097392/-1238157839/hemoglobin', '390097392/-1238157839/blood pressure mean', '390097392/-1238157839/vasopressin', '390097392/-1238157839/glasgow coma scale verbal', '390097392/-1238157839/weight body', '390097392/-1238157839/normal saline', '390097392/-1238157839/norepinephrine', '390097392/-1238157839/temperature body', '390097392/-1238157839/blood pressure diastolic', '390097392/-1238157839/heart rate', '390097392/-1238157839/output urine', '390097392/-1238157839/lactated ringers', '390097392/-1238157839/respiratory rate']\n",
+      "(2017-07-13 17:56:41)>>>>>> JOINING dataframes\n",
+      "(2017-07-13 17:56:41)>>>>>>>> Slice & Join: 100019 --> 185046, n=5000\n",
+      "(2017-07-13 17:56:41)>>>>>>>>>> 390097392/-1238157839/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:56:41)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:56:41)>>>>>>>>>> 390097392/-1238157839/glasgow coma scale motor\n",
+      "(2017-07-13 17:56:42)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:56:42)>>>>>>>>>> 390097392/-1238157839/blood pressure systolic\n",
+      "(2017-07-13 17:56:50)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:56:50)>>>>>>>>>> 390097392/-1238157839/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:57:03)<<<<<<<<<< --- (13.0s)\n",
+      "(2017-07-13 17:57:03)>>>>>>>>>> 390097392/-1238157839/lactate\n",
+      "(2017-07-13 17:57:07)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:57:07)>>>>>>>>>> 390097392/-1238157839/hemoglobin\n",
+      "(2017-07-13 17:57:12)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:57:12)>>>>>>>>>> 390097392/-1238157839/blood pressure mean\n",
+      "(2017-07-13 17:57:20)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:57:20)>>>>>>>>>> 390097392/-1238157839/vasopressin\n",
+      "(2017-07-13 17:57:24)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:57:24)>>>>>>>>>> 390097392/-1238157839/glasgow coma scale verbal\n",
+      "(2017-07-13 17:57:31)<<<<<<<<<< --- (7.0s)\n",
+      "(2017-07-13 17:57:31)>>>>>>>>>> 390097392/-1238157839/weight body\n",
+      "(2017-07-13 17:57:35)<<<<<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:57:35)>>>>>>>>>> 390097392/-1238157839/normal saline\n",
+      "(2017-07-13 17:57:41)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:57:41)>>>>>>>>>> 390097392/-1238157839/norepinephrine\n",
+      "(2017-07-13 17:57:47)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:57:47)>>>>>>>>>> 390097392/-1238157839/temperature body\n",
+      "(2017-07-13 17:57:53)<<<<<<<<<< --- (6.0s)\n",
+      "(2017-07-13 17:57:53)>>>>>>>>>> 390097392/-1238157839/blood pressure diastolic\n",
+      "(2017-07-13 17:58:01)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:58:01)>>>>>>>>>> 390097392/-1238157839/heart rate\n",
+      "(2017-07-13 17:58:11)<<<<<<<<<< --- (10.0s)\n",
+      "(2017-07-13 17:58:11)>>>>>>>>>> 390097392/-1238157839/output urine\n",
+      "(2017-07-13 17:58:19)<<<<<<<<<< --- (8.0s)\n",
+      "(2017-07-13 17:58:19)>>>>>>>>>> 390097392/-1238157839/lactated ringers\n",
+      "(2017-07-13 17:58:24)<<<<<<<<<< --- (5.0s)\n",
+      "(2017-07-13 17:58:24)>>>>>>>>>> 390097392/-1238157839/respiratory rate\n",
+      "(2017-07-13 17:58:35)<<<<<<<<<< --- (11.0s)\n",
+      "(2017-07-13 17:58:35)<<<<<<<< --- (114.0s)\n",
+      "(2017-07-13 17:58:35)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:58:49)<<<<<<<< --- (14.0s)\n",
+      "(2017-07-13 17:58:49)>>>>>>>> Slice & Join: 185050 --> 199993, n=898\n",
+      "(2017-07-13 17:58:49)>>>>>>>>>> 390097392/-1238157839/glasgow coma scale eye opening\n",
+      "(2017-07-13 17:58:49)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:58:49)>>>>>>>>>> 390097392/-1238157839/glasgow coma scale motor\n",
+      "(2017-07-13 17:58:49)<<<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:58:49)>>>>>>>>>> 390097392/-1238157839/blood pressure systolic\n",
+      "(2017-07-13 17:58:51)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:58:51)>>>>>>>>>> 390097392/-1238157839/oxygen saturation pulse oximetry\n",
+      "(2017-07-13 17:58:52)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:52)>>>>>>>>>> 390097392/-1238157839/lactate\n",
+      "(2017-07-13 17:58:53)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:53)>>>>>>>>>> 390097392/-1238157839/hemoglobin\n",
+      "(2017-07-13 17:58:54)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:54)>>>>>>>>>> 390097392/-1238157839/blood pressure mean\n",
+      "(2017-07-13 17:58:55)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:55)>>>>>>>>>> 390097392/-1238157839/vasopressin\n",
+      "(2017-07-13 17:58:56)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:56)>>>>>>>>>> 390097392/-1238157839/glasgow coma scale verbal\n",
+      "(2017-07-13 17:58:57)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:57)>>>>>>>>>> 390097392/-1238157839/weight body\n",
+      "(2017-07-13 17:58:58)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:58:58)>>>>>>>>>> 390097392/-1238157839/normal saline\n",
+      "(2017-07-13 17:59:00)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:59:00)>>>>>>>>>> 390097392/-1238157839/norepinephrine\n",
+      "(2017-07-13 17:59:01)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:59:01)>>>>>>>>>> 390097392/-1238157839/temperature body\n",
+      "(2017-07-13 17:59:02)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:59:02)>>>>>>>>>> 390097392/-1238157839/blood pressure diastolic\n",
+      "(2017-07-13 17:59:04)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:59:04)>>>>>>>>>> 390097392/-1238157839/heart rate\n",
+      "(2017-07-13 17:59:05)<<<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:59:05)>>>>>>>>>> 390097392/-1238157839/output urine\n",
+      "(2017-07-13 17:59:07)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:59:07)>>>>>>>>>> 390097392/-1238157839/lactated ringers\n",
+      "(2017-07-13 17:59:09)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:59:09)>>>>>>>>>> 390097392/-1238157839/respiratory rate\n",
+      "(2017-07-13 17:59:11)<<<<<<<<<< --- (2.0s)\n",
+      "(2017-07-13 17:59:11)<<<<<<<< --- (22.0s)\n",
+      "(2017-07-13 17:59:11)>>>>>>>> Append slice\n",
+      "(2017-07-13 17:59:12)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:59:12)<<<<<< --- (151.0s)\n",
+      "(2017-07-13 17:59:12)<<<< --- (152.0s)\n",
+      "(2017-07-13 17:59:12)>>>> Read JOINED DF\n",
+      "(2017-07-13 17:59:14)<<<< --- (2.0s)\n",
+      "(2017-07-13 17:59:14)<< --- (495.0s)\n",
+      "(2017-07-13 17:59:14)>> APPLY FEATURES, #F=6 to df=(1049530, 34)\n",
+      "(2017-07-13 17:59:14)>>>> Featurizing: MEAN, [{'units': <function <lambda> at 0x000000000DA4D4A8>, 'variable_type': 'qn'}, {'component': 'weight body'}]\n",
+      "(2017-07-13 17:59:14)>>>>>> *transform* Filter columns (DataSpecFilter) (1049530, 34)\n",
+      "(2017-07-13 17:59:18)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 17:59:18)>>>>>> *transform* RESAMPLE (1049530, 22), rule=2H, func=mean\n",
+      "(2017-07-13 17:59:18)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:59:19)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 17:59:19)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:59:40)<<<<<<<< --- (21.0s)\n",
+      "(2017-07-13 17:59:40)<<<<<< --- (22.0s)\n",
+      "(2017-07-13 17:59:40)>>>>>> Join\n",
+      "(2017-07-13 17:59:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:59:40)<<<< --- (26.0s)\n",
+      "(2017-07-13 17:59:40)>>>> Featurizing: LAST, [{'units': <function <lambda> at 0x000000000DA4D4A8>, 'variable_type': 'qn'}, {'component': 'weight body'}]\n",
+      "(2017-07-13 17:59:40)>>>>>> *transform* Filter columns (DataSpecFilter) (1049530, 34)\n",
+      "(2017-07-13 17:59:40)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:59:40)>>>>>> *transform* RESAMPLE (1049530, 22), rule=2H, func=last\n",
+      "(2017-07-13 17:59:40)>>>>>>>> Resampling\n",
+      "(2017-07-13 17:59:40)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 17:59:40)>>>>>>>> Aggregating\n",
+      "(2017-07-13 17:59:56)<<<<<<<< --- (16.0s)\n",
+      "(2017-07-13 17:59:56)<<<<<< --- (16.0s)\n",
+      "(2017-07-13 18:00:00)>>>>>> Join\n",
+      "(2017-07-13 18:00:04)<<<<<< --- (4.0s)\n",
+      "(2017-07-13 18:00:04)<<<< --- (24.0s)\n",
+      "(2017-07-13 18:00:04)>>>> Featurizing: STD, []\n",
+      "(2017-07-13 18:00:04)>>>>>> *transform* Filter columns (DataSpecFilter) (1049530, 34)\n",
+      "(2017-07-13 18:00:04)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 18:00:04)>>>>>> *transform* RESAMPLE (1049530, 34), rule=2H, func=std\n",
+      "(2017-07-13 18:00:04)>>>>>>>> Resampling\n",
+      "(2017-07-13 18:00:05)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 18:00:05)>>>>>>>> Aggregating\n",
+      "(2017-07-13 18:00:28)<<<<<<<< --- (23.0s)\n",
+      "(2017-07-13 18:00:28)<<<<<< --- (24.0s)\n",
+      "(2017-07-13 18:00:28)>>>>>> Join\n",
+      "(2017-07-13 18:00:30)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 18:00:30)<<<< --- (26.0s)\n",
+      "(2017-07-13 18:00:30)>>>> Featurizing: SUM, [{'units': <function <lambda> at 0x000000000DA4DA58>, 'clinical_source': 'intervention'}, {'units': <function <lambda> at 0x000000000DA4DA58>, 'component': 'output urine'}]\n",
+      "(2017-07-13 18:00:30)>>>>>> *transform* Filter columns (DataSpecFilter) (1049530, 34)\n",
+      "(2017-07-13 18:00:30)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 18:00:30)>>>>>> *transform* RESAMPLE (1049530, 4), rule=2H, func=sum\n",
+      "(2017-07-13 18:00:30)>>>>>>>> Resampling\n",
+      "(2017-07-13 18:00:31)<<<<<<<< --- (1.0s)\n",
+      "(2017-07-13 18:00:31)>>>>>>>> Aggregating\n",
+      "(2017-07-13 18:00:51)<<<<<<<< --- (20.0s)\n",
+      "(2017-07-13 18:00:51)<<<<<< --- (21.0s)\n",
+      "(2017-07-13 18:00:52)>>>>>> Join\n",
+      "(2017-07-13 18:00:55)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 18:00:55)<<<< --- (25.0s)\n",
+      "(2017-07-13 18:00:55)>>>> Featurizing: COUNT, []\n",
+      "(2017-07-13 18:00:55)>>>>>> *transform* Filter columns (DataSpecFilter) (1049530, 34)\n",
+      "(2017-07-13 18:00:55)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 18:00:55)>>>>>> *transform* RESAMPLE (1049530, 34), rule=2H, func=count\n",
+      "(2017-07-13 18:00:55)>>>>>>>> Resampling\n",
+      "(2017-07-13 18:00:55)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 18:00:55)>>>>>>>> Aggregating\n",
+      "(2017-07-13 18:01:10)<<<<<<<< --- (15.0s)\n",
+      "(2017-07-13 18:01:10)<<<<<< --- (15.0s)\n",
+      "(2017-07-13 18:01:10)>>>>>> Join\n",
+      "(2017-07-13 18:01:13)<<<<<< --- (3.0s)\n",
+      "(2017-07-13 18:01:13)<<<< --- (18.0s)\n",
+      "(2017-07-13 18:01:13)>>>> Featurizing: LABEL, {'variable_type': 'qn', 'component': 'lactate'}\n",
+      "(2017-07-13 18:01:13)>>>>>> *transform* Filter columns (DataSpecFilter) (1049530, 34)\n",
+      "(2017-07-13 18:01:13)<<<<<< --- (0.0s)\n",
+      "(2017-07-13 18:01:13)>>>>>> *transform* RESAMPLE (1049530, 1), rule=2H, func=mean\n",
+      "(2017-07-13 18:01:13)>>>>>>>> Resampling\n",
+      "(2017-07-13 18:01:13)<<<<<<<< --- (0.0s)\n",
+      "(2017-07-13 18:01:13)>>>>>>>> Aggregating\n",
+      "(2017-07-13 18:01:34)<<<<<<<< --- (21.0s)\n",
+      "(2017-07-13 18:01:34)<<<<<< --- (21.0s)\n",
+      "(2017-07-13 18:01:34)>>>>>> Join\n",
+      "(2017-07-13 18:01:36)<<<<<< --- (2.0s)\n",
+      "(2017-07-13 18:01:36)<<<< --- (23.0s)\n",
+      "(2017-07-13 18:01:36)<< --- (142.0s)\n",
+      "(2017-07-13 18:01:36) --- (637.0s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_validate = factory.transform(validate_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "utils.deconstruct_and_write(df_train,factory.hdf5_fname_target,'train')\n",
+    "utils.deconstruct_and_write(df_validate,factory.hdf5_fname_target,'validate')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5143470, 117)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data & Set up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression,ElasticNet\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.model_selection import cross_val_score,ShuffleSplit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_crossval(pipeline,X,y):\n",
+    "    scores_r2 = cross_val_score(pipeline,X,y, scoring='r2',cv=10)\n",
+    "    scores_nmse = cross_val_score(pipeline,X,y, scoring='neg_mean_squared_error',cv=10)\n",
+    "\n",
+    "    print 'Cross Validation, K-Fold'\n",
+    "    print 'R^2: {}, {}'.format(scores_r2.mean(),scores_r2.std())\n",
+    "    print 'RMSE: {}, {}'.format(np.sqrt(-1.0*scores_nmse).mean(),np.sqrt(-1.0*scores_nmse).std())\n",
+    "\n",
+    "    cv_shuffle = ShuffleSplit(n_splits=10,test_size=0.1)\n",
+    "\n",
+    "    scores_r2 = cross_val_score(pipeline,X,y, scoring='r2',cv=cv_shuffle)\n",
+    "    scores_nmse = cross_val_score(pipeline,X,y, scoring='neg_mean_squared_error', cv=cv_shuffle)\n",
+    "\n",
+    "    print '\\nCross Validation, ShuffleSplit'\n",
+    "    print 'R^2: {}, {}'.format(scores_r2.mean(),scores_r2.std())\n",
+    "    print 'RMSE: {}, {}'.format(np.sqrt(-1.0*scores_nmse).mean(),np.sqrt(-1.0*scores_nmse).std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import utils"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From this point forward, we will be using our \"simple data\", resampled for 2hr intervals per the above featurizing strategies for each component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "hdf5_fname = 'data/combine_like.h5'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# this can take a little while (10-15 minutes), just FYI\n",
+    "df_train_simple = utils.read_and_reconstruct(hdf5_fname,'train')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "MultiIndex: 5143470 entries, (100001, 2117-09-11 10:00:00) to (199999, 2136-04-10 12:00:00)\n",
+      "Columns: 117 entries, (MEAN, blood pressure systolic, known, qn, mmHg, all) to (LABEL, lactate, known, qn, mmol/L, all)\n",
+      "dtypes: float64(83), int64(34)\n",
+      "memory usage: 4.5+ GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_train_simple.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">MEAN</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"9\" halign=\"left\">COUNT</th>\n",
+       "      <th>LABEL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>component</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">blood pressure systolic</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">vasopressin</th>\n",
+       "      <th>weight body</th>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">output urine</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">lactated ringers</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">respiratory rate</th>\n",
+       "      <th>lactate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th>known</th>\n",
+       "      <th>unknown</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">known</th>\n",
+       "      <th>unknown</th>\n",
+       "      <th>known</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+       "      <th>known</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>...</th>\n",
+       "      <th>nom</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">nom</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>units</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>cc/min</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>units</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>kg</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>...</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+       "      <th>Breath</th>\n",
+       "      <th>mmol/L</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>...</th>\n",
+       "      <th>3686_Voiding qs</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>618_&gt;60/min retracts</th>\n",
+       "      <th>618_&gt;60/minute</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">100001</th>\n",
+       "      <th>2117-09-11 10:00:00</th>\n",
+       "      <td>121.931614</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>78.475979</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 12:00:00</th>\n",
+       "      <td>121.931614</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.749218</td>\n",
+       "      <td>11.097371</td>\n",
+       "      <td>78.475979</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 14:00:00</th>\n",
+       "      <td>192.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>191.623236</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 16:00:00</th>\n",
+       "      <td>130.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>82.750000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>304.495290</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 18:00:00</th>\n",
+       "      <td>157.200000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>99.250000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>89.996990</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 117 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "feature                                       MEAN            \\\n",
+       "component                  blood pressure systolic             \n",
+       "status                                       known   unknown   \n",
+       "variable_type                                   qn        qn   \n",
+       "units                                         mmHg    cc/min   \n",
+       "description                                    all       all   \n",
+       "id     datetime                                                \n",
+       "100001 2117-09-11 10:00:00              121.931614  69.78821   \n",
+       "       2117-09-11 12:00:00              121.931614  69.78821   \n",
+       "       2117-09-11 14:00:00              192.000000  69.78821   \n",
+       "       2117-09-11 16:00:00              130.500000  69.78821   \n",
+       "       2117-09-11 18:00:00              157.200000  69.78821   \n",
+       "\n",
+       "feature                                                                \\\n",
+       "component                  oxygen saturation pulse oximetry   lactate   \n",
+       "status                                                known     known   \n",
+       "variable_type                                            qn        qn   \n",
+       "units                                               percent    mmol/L   \n",
+       "description                                             all       all   \n",
+       "id     datetime                                                         \n",
+       "100001 2117-09-11 10:00:00                        97.022222  1.900000   \n",
+       "       2117-09-11 12:00:00                        97.022222  1.749218   \n",
+       "       2117-09-11 14:00:00                        97.022222  1.900000   \n",
+       "       2117-09-11 16:00:00                        97.022222  1.900000   \n",
+       "       2117-09-11 18:00:00                        99.250000  1.900000   \n",
+       "\n",
+       "feature                                                                \\\n",
+       "component                  hemoglobin blood pressure mean vasopressin   \n",
+       "status                          known               known       known   \n",
+       "variable_type                      qn                  qn          qn   \n",
+       "units                            g/dL                mmHg       units   \n",
+       "description                       all                 all         all   \n",
+       "id     datetime                                                         \n",
+       "100001 2117-09-11 10:00:00  13.000000           78.475979    1.045024   \n",
+       "       2117-09-11 12:00:00  11.097371           78.475979    1.045024   \n",
+       "       2117-09-11 14:00:00  13.000000          122.000000    1.045024   \n",
+       "       2117-09-11 16:00:00  13.000000           82.750000    1.045024   \n",
+       "       2117-09-11 18:00:00  13.000000           99.000000    1.045024   \n",
+       "\n",
+       "feature                                                          ...    \\\n",
+       "component                            weight body normal saline   ...     \n",
+       "status                                     known         known   ...     \n",
+       "variable_type                                 qn            qn   ...     \n",
+       "units                      units/min          kg         mL/hr   ...     \n",
+       "description                      all         all           all   ...     \n",
+       "id     datetime                                                  ...     \n",
+       "100001 2117-09-11 10:00:00  0.401782   82.797527     98.220699   ...     \n",
+       "       2117-09-11 12:00:00  0.401782   82.797527     98.220699   ...     \n",
+       "       2117-09-11 14:00:00  0.401782   82.797527    191.623236   ...     \n",
+       "       2117-09-11 16:00:00  0.401782   82.797527    304.495290   ...     \n",
+       "       2117-09-11 18:00:00  0.401782   82.797527     89.996990   ...     \n",
+       "\n",
+       "feature                              COUNT                                  \\\n",
+       "component                     output urine          lactated ringers         \n",
+       "status                             unknown                     known         \n",
+       "variable_type                          nom       qn               qn         \n",
+       "units                             no_units no_units               mL mL/hr   \n",
+       "description                3686_Voiding qs      all              all   all   \n",
+       "id     datetime                                                              \n",
+       "100001 2117-09-11 10:00:00               0        0                0     0   \n",
+       "       2117-09-11 12:00:00               0        0                0     0   \n",
+       "       2117-09-11 14:00:00               1        0                0     0   \n",
+       "       2117-09-11 16:00:00               0        0                0     0   \n",
+       "       2117-09-11 18:00:00               0        0                0     0   \n",
+       "\n",
+       "feature                                                                    \\\n",
+       "component                           respiratory rate                        \n",
+       "status                      unknown            known              unknown   \n",
+       "variable_type                    qn               qn                  nom   \n",
+       "units                      no_units         insp/min             no_units   \n",
+       "description                     all              all 618_>60/min retracts   \n",
+       "id     datetime                                                             \n",
+       "100001 2117-09-11 10:00:00        0                0                    0   \n",
+       "       2117-09-11 12:00:00        0                0                    0   \n",
+       "       2117-09-11 14:00:00        0                3                    3   \n",
+       "       2117-09-11 16:00:00        0                2                    2   \n",
+       "       2117-09-11 18:00:00        0                2                    2   \n",
+       "\n",
+       "feature                                            LABEL  \n",
+       "component                                        lactate  \n",
+       "status                                             known  \n",
+       "variable_type                                 qn      qn  \n",
+       "units                                     Breath  mmol/L  \n",
+       "description                618_>60/minute    all     all  \n",
+       "id     datetime                                           \n",
+       "100001 2117-09-11 10:00:00              0      0     1.9  \n",
+       "       2117-09-11 12:00:00              0      0     NaN  \n",
+       "       2117-09-11 14:00:00              3      0     NaN  \n",
+       "       2117-09-11 16:00:00              2      0     NaN  \n",
+       "       2117-09-11 18:00:00              2      0     NaN  \n",
+       "\n",
+       "[5 rows x 117 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train_simple.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df_validate = utils.read_and_reconstruct(hdf5_fname,'validate')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "MultiIndex: 642187 entries, (100024, 2170-09-19 12:00:00) to (199993, 2161-11-17 08:00:00)\n",
+      "Columns: 117 entries, (MEAN, blood pressure systolic, known, qn, mmHg, all) to (LABEL, lactate, known, qn, mmol/L, all)\n",
+      "dtypes: float64(83), int64(34)\n",
+      "memory usage: 579.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_validate.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict NEXT LACTATE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Labels from lactate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th>LABEL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>component</th>\n",
+       "      <th>lactate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>status</th>\n",
+       "      <th>known</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>qn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>units</th>\n",
+       "      <th>mmol/L</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>131100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.533278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.486946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>32.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "feature                LABEL\n",
+       "component            lactate\n",
+       "status                 known\n",
+       "variable_type             qn\n",
+       "units                 mmol/L\n",
+       "description              all\n",
+       "count          131100.000000\n",
+       "mean                2.533278\n",
+       "std                 2.486946\n",
+       "min                 0.000000\n",
+       "25%                 1.200000\n",
+       "50%                 1.800000\n",
+       "75%                 2.800000\n",
+       "max                32.000000"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train_simple.loc[:,['LABEL']].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_train = df_train_simple.loc[:,['LABEL']].shift(-1).dropna().iloc[:,0]\n",
+    "X_train = df_train_simple.drop('LABEL',axis=1).loc[y_train.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_validate = df_validate.loc[:,['LABEL']].shift(-1).dropna().iloc[:,0]\n",
+    "X_validate = df_validate.drop('LABEL',axis=1).loc[y_validate.index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make a bunch more features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import boxcox\n",
+    "from sklearn.preprocessing import PolynomialFeatures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#use boxcox transformation to create all MEAN,LAST,SUM features as log scale\n",
+    "def shift_boxcox(x,lambda_1=None,lambda_2=0,alpha=None):\n",
+    "    return boxcox(x+lambda_2,lmbda=lambda_1,alpha=alpha)\n",
+    "\n",
+    "def smart_shift_boxcox(x):\n",
+    "    shift = x[x > 0].min()/2.0\n",
+    "    return shift_boxcox(x,lambda_2=shift)[0]\n",
+    "\n",
+    "def smart_shift_log(x):\n",
+    "    shift = x[x > 0].min()/2.0\n",
+    "    return np.log(x+shift)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "X_train_log = X_train.sort_index(axis=1).loc[:,['MEAN','SUM','LAST']].apply(smart_shift_log).dropna(axis=1,how='any')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0L"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(X_train_log == -np.inf).sum().sort_values().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0L"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(X_train_log.apply(pd.isnull)).sum().sort_values().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0L"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(X_train_log > 5000000).sum().sort_values().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(131099L, 231L)"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poly = PolynomialFeatures(2)\n",
+    "X_train_poly = poly.fit_transform(X_train.iloc[:,:20])\n",
+    "X_train_poly.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Selection: Univariate Statistical Tests "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading:\n",
+    "- sklearn documentation on feature selection: http://scikit-learn.org/stable/modules/feature_selection.html\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.feature_selection import f_regression,mutual_info_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F statistic\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "feature   component                         status  variable_type  units       description\n",
+       "LAST      lactate                           known   qn             mmol/L      all            206061.335045\n",
+       "MEAN      lactate                           known   qn             mmol/L      all            183167.794101\n",
+       "LAST_log  lactate                           known   qn             mmol/L      all            102721.867103\n",
+       "MEAN_log  lactate                           known   qn             mmol/L      all             96494.756565\n",
+       "MEAN      norepinephrine                    known   qn             mcg/kg/min  all              9948.507561\n",
+       "SUM       norepinephrine                    known   qn             mcg         all              9266.424960\n",
+       "LAST      norepinephrine                    known   qn             mcg/kg/min  all              9005.636136\n",
+       "COUNT     lactate                           known   qn             mmol/L      all              8548.222436\n",
+       "          vasopressin                       known   qn             units/min   all              8162.052107\n",
+       "SUM_log   norepinephrine                    known   qn             mcg         all              8028.261138\n",
+       "COUNT     norepinephrine                    known   qn             mcg/kg/min  all              6289.049805\n",
+       "                                                                   mcg         all              5911.903941\n",
+       "          vasopressin                       known   qn             units       all              5152.160164\n",
+       "STD       lactate                           known   qn             mmol/L      all              4847.445701\n",
+       "MEAN      oxygen saturation pulse oximetry  known   qn             percent     all              4455.021070\n",
+       "LAST      oxygen saturation pulse oximetry  known   qn             percent     all              3923.622565\n",
+       "COUNT     hemoglobin                        known   qn             g/dL        all              3497.815448\n",
+       "MEAN      blood pressure systolic           known   qn             mmHg        all              3439.847207\n",
+       "SUM_log   output urine                      known   qn             mL          all              3179.817944\n",
+       "LAST_log  norepinephrine                    known   qn             mcg/kg/min  all              3109.145887\n",
+       "MEAN_log  norepinephrine                    known   qn             mcg/kg/min  all              2797.569260\n",
+       "LAST      blood pressure systolic           known   qn             mmHg        all              2670.081114\n",
+       "MEAN_log  blood pressure systolic           known   qn             mmHg        all              2312.787316\n",
+       "LAST      heart rate                        known   qn             beats/min   all              1836.461577\n",
+       "MEAN      heart rate                        known   qn             beats/min   all              1831.113142\n",
+       "STD       hemoglobin                        known   qn             g/dL        all              1765.684329\n",
+       "          norepinephrine                    known   qn             mcg         all              1740.057674\n",
+       "MEAN      respiratory rate                  known   qn             insp/min    all              1738.736028\n",
+       "MEAN_log  oxygen saturation pulse oximetry  known   qn             percent     all              1621.688709\n",
+       "SUM       output urine                      known   qn             mL          all              1562.173569\n",
+       "                                                                                                  ...      \n",
+       "LAST_log  heart rate                        known   qn             beats/min   all               523.087323\n",
+       "LAST      vasopressin                       known   qn             units/min   all               519.918575\n",
+       "MEAN_log  respiratory rate                  known   qn             insp/min    all               511.666329\n",
+       "MEAN      vasopressin                       known   qn             units/min   all               494.699660\n",
+       "STD       output urine                      known   qn             mL          all               478.999002\n",
+       "MEAN_log  normal saline                     known   qn             mL/hr       all               455.966794\n",
+       "LAST      blood pressure diastolic          known   qn             mmHg        all               454.601976\n",
+       "MEAN_log  blood pressure diastolic          known   qn             mmHg        all               423.592778\n",
+       "SUM       lactated ringers                  known   qn             mL          all               419.265085\n",
+       "LAST_log  normal saline                     known   qn             mL/hr       all               388.928763\n",
+       "STD       vasopressin                       known   qn             units       all               361.906652\n",
+       "MEAN_log  temperature body                  known   qn             degF        all               306.077160\n",
+       "COUNT     blood pressure systolic           known   qn             mmHg        all               286.010252\n",
+       "          blood pressure diastolic          known   qn             mmHg        all               284.466268\n",
+       "LAST_log  respiratory rate                  known   qn             insp/min    all               267.792047\n",
+       "MEAN_log  vasopressin                       known   qn             units       all               256.259132\n",
+       "LAST_log  blood pressure diastolic          known   qn             mmHg        all               248.627169\n",
+       "SUM       normal saline                     known   qn             mL          all               241.810438\n",
+       "COUNT     norepinephrine                    known   qn             mcg/min     all               205.627244\n",
+       "MEAN      blood pressure mean               known   qn             mmHg        all               190.257895\n",
+       "LAST_log  temperature body                  known   qn             degF        all               181.728452\n",
+       "MEAN_log  blood pressure mean               known   qn             mmHg        all               181.058389\n",
+       "COUNT     heart rate                        known   qn             beats/min   all               148.466296\n",
+       "          normal saline                     known   qn             mL          all               145.905508\n",
+       "SUM_log   normal saline                     known   qn             mL          all               145.548289\n",
+       "STD       temperature body                  known   qn             degF        all               130.819046\n",
+       "          lactated ringers                  known   qn             mL          all               121.699986\n",
+       "LAST      norepinephrine                    known   qn             mcg/min     all               120.702131\n",
+       "MEAN      norepinephrine                    known   qn             mcg/min     all               109.419899\n",
+       "LAST      blood pressure mean               known   qn             mmHg        all               104.173134\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First, try f_regression\n",
+    "test_F = SelectKBest(score_func=f_regression, k=4)\n",
+    "test_F.fit(X=X_all, y=y_train)\n",
+    "# summarize scores\n",
+    "np.set_printoptions(precision=3)\n",
+    "scores_f_stat = pd.Series(test_F.scores_, index=X_all.columns)\n",
+    "print 'F statistic'\n",
+    "scores_f_stat[scores_f_stat > 100].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#First, try mutual_info_regression\n",
+    "column_subset = scores_f_stat[scores_f_stat > 100].index\n",
+    "test_MI = SelectKBest(score_func=mutual_info_regression, k=4)\n",
+    "test_MI.fit(X=X_train.loc[:,column_subset], y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mutual Information\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "feature  component                         status   variable_type  units       description        \n",
+       "LAST     lactate                           known    qn             mmol/L      all                    0.415996\n",
+       "MEAN     lactate                           known    qn             mmol/L      all                    0.394249\n",
+       "COUNT    lactate                           known    qn             mmol/L      all                    0.039893\n",
+       "LAST     vasopressin                       known    qn             units/min   all                    0.037253\n",
+       "MEAN     vasopressin                       known    qn             units/min   all                    0.036767\n",
+       "LAST     temperature body                  known    qn             degF        all                    0.036017\n",
+       "SUM      output urine                      known    qn             mL          all                    0.035154\n",
+       "LAST     norepinephrine                    known    qn             mcg/kg/min  all                    0.034334\n",
+       "MEAN     blood pressure systolic           known    qn             mmHg        all                    0.034279\n",
+       "SUM      norepinephrine                    known    qn             mcg         all                    0.033200\n",
+       "COUNT    hemoglobin                        known    qn             g/dL        all                    0.031458\n",
+       "LAST     blood pressure systolic           known    qn             mmHg        all                    0.029687\n",
+       "COUNT    blood pressure diastolic          known    qn             mmHg        all                    0.029663\n",
+       "MEAN     norepinephrine                    known    qn             mcg/kg/min  all                    0.029261\n",
+       "         heart rate                        known    qn             beats/min   all                    0.029089\n",
+       "         oxygen saturation pulse oximetry  known    qn             percent     all                    0.026755\n",
+       "COUNT    blood pressure systolic           known    qn             mmHg        all                    0.026295\n",
+       "MEAN     temperature body                  known    qn             degF        all                    0.026260\n",
+       "COUNT    heart rate                        known    qn             beats/min   all                    0.024714\n",
+       "MEAN     respiratory rate                  known    qn             insp/min    all                    0.023390\n",
+       "LAST     heart rate                        known    qn             beats/min   all                    0.023328\n",
+       "         respiratory rate                  known    qn             insp/min    all                    0.022331\n",
+       "MEAN     blood pressure diastolic          known    qn             mmHg        all                    0.021609\n",
+       "COUNT    vasopressin                       known    qn             units/min   all                    0.021371\n",
+       "STD      lactate                           known    qn             mmol/L      all                    0.020652\n",
+       "LAST     oxygen saturation pulse oximetry  known    qn             percent     all                    0.020243\n",
+       "         vasopressin                       known    qn             units       all                    0.019617\n",
+       "COUNT    norepinephrine                    known    qn             mcg         all                    0.019080\n",
+       "                                                                   mcg/kg/min  all                    0.016895\n",
+       "LAST     blood pressure diastolic          known    qn             mmHg        all                    0.016568\n",
+       "COUNT    output urine                      unknown  nom            no_units    3686(ml)_Voiding qs    0.015722\n",
+       "STD      norepinephrine                    known    qn             mcg         all                    0.015428\n",
+       "COUNT    temperature body                  known    qn             degF        all                    0.014821\n",
+       "STD      hemoglobin                        known    qn             g/dL        all                    0.014320\n",
+       "         oxygen saturation pulse oximetry  known    qn             percent     all                    0.013721\n",
+       "COUNT    output urine                      known    qn             mL          all                    0.012492\n",
+       "MEAN     vasopressin                       known    qn             units       all                    0.012208\n",
+       "COUNT    vasopressin                       known    qn             units       all                    0.011931\n",
+       "         output urine                      unknown  nom            no_units    3686(ml)_No Void       0.011376\n",
+       "                                                                               3686_Voiding qs        0.010830\n",
+       "STD      norepinephrine                    known    qn             mcg/kg/min  all                    0.010356\n",
+       "         temperature body                  known    qn             degF        all                    0.010256\n",
+       "         output urine                      known    qn             mL          all                    0.010235\n",
+       "LAST     blood pressure mean               known    qn             mmHg        all                    0.008840\n",
+       "MEAN     blood pressure mean               known    qn             mmHg        all                    0.008441\n",
+       "STD      lactated ringers                  known    qn             mL          all                    0.004985\n",
+       "SUM      lactated ringers                  known    qn             mL          all                    0.004659\n",
+       "COUNT    normal saline                     known    qn             mL/hr       all                    0.004292\n",
+       "STD      vasopressin                       known    qn             units       all                    0.001572\n",
+       "MEAN     norepinephrine                    known    qn             mcg/min     all                    0.001520\n",
+       "COUNT    norepinephrine                    known    qn             mcg/min     all                    0.000230\n",
+       "LAST     norepinephrine                    known    qn             mcg/min     all                    0.000172\n",
+       "SUM      normal saline                     known    qn             mL          all                    0.000000\n",
+       "COUNT    normal saline                     known    qn             mL          all                    0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_MI = pd.Series(test_MI.scores_, index=column_subset)\n",
+    "print 'Mutual Information'\n",
+    "scores_MI.sort_values(ascending=False, inplace=True)\n",
+    "scores_MI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#keep only features that are not completely independent\n",
+    "ft_to_keep = scores_MI[scores_MI > 0].index.tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "lin_reg = LinearRegression()\n",
+    "\n",
+    "pipeline = Pipeline([\n",
+    "        ('scaler',scaler),\n",
+    "        ('lin_reg',lin_reg)\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('LAST', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('LAST', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('SUM', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('SUM', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('COUNT', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('COUNT', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('LAST', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('LAST', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('MEAN', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('STD', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('LAST', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('LAST', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('LAST', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT',\n",
+       "  'output urine',\n",
+       "  'unknown',\n",
+       "  'nom',\n",
+       "  'no_units',\n",
+       "  '3686(ml)_Voiding qs'),\n",
+       " ('STD', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('STD', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('STD', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('COUNT', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686(ml)_No Void'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686_Voiding qs'),\n",
+       " ('STD', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('STD', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('STD', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'blood pressure mean', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'blood pressure mean', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('STD', 'lactated ringers', 'known', 'qn', 'mL', 'all'),\n",
+       " ('SUM', 'lactated ringers', 'known', 'qn', 'mL', 'all'),\n",
+       " ('COUNT', 'normal saline', 'known', 'qn', 'mL/hr', 'all'),\n",
+       " ('STD', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.622420215047, 0.0442661206573\n",
+      "RMSE: 1.50723996602, 0.0840855344198\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.622202179615, 0.0128197301283\n",
+      "RMSE: 1.49287683117, 0.0309458282731\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(ft_to_keep)\n",
+    "X = X_train.loc[:,ft_to_keep]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('LAST', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('LAST', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('SUM', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('SUM', 'norepinephrine', 'known', 'qn', 'mcg', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.609481500224, 0.0458419477475\n",
+      "RMSE: 1.53282675876, 0.0852943847586\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.617300485834, 0.0174067375634\n",
+      "RMSE: 1.5116581112, 0.0302020472368\n"
+     ]
+    }
+   ],
+   "source": [
+    "#what about only the top 10 features\n",
+    "ft_subset = ft_to_keep[:10]\n",
+    "display(ft_subset)\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.603299205901, 0.0437181370702\n",
+      "RMSE: 1.5458364002, 0.0916080291197\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.607198386216, 0.0154803771698\n",
+      "RMSE: 1.54443026751, 0.0334231132899\n"
+     ]
+    }
+   ],
+   "source": [
+    "#what about only the top 3 features (just lactate)\n",
+    "ft_subset = ft_to_keep[:3]\n",
+    "display(ft_subset)\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.49961351463\n",
+      "R^2: 0.591014273736\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.080479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.213287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>1.715051</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                               coef\n",
+       "feature component status variable_type units  description          \n",
+       "COUNT   lactate   known  qn            mmol/L all          0.080479\n",
+       "MEAN    lactate   known  qn            mmol/L all          0.213287\n",
+       "LAST    lactate   known  qn            mmol/L all          1.715051"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.fit(X,y)\n",
+    "y_pred = pipeline.predict(X_validate.loc[:,ft_subset])\n",
+    "rmse = np.sqrt(mean_squared_error(y_validate,y_pred))\n",
+    "r_2 = pipeline.score(X_validate.loc[:,ft_subset],y_validate)\n",
+    "print 'RMSE:',rmse\n",
+    "print 'R^2:',r_2\n",
+    "pd.Series(lin_reg.coef_,index=X.columns,name='coef').sort_values().to_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('LAST', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('SUM', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('SUM', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('COUNT', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('COUNT', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('LAST', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('LAST', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('MEAN', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('STD', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('LAST', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('LAST', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('LAST', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT',\n",
+       "  'output urine',\n",
+       "  'unknown',\n",
+       "  'nom',\n",
+       "  'no_units',\n",
+       "  '3686(ml)_Voiding qs'),\n",
+       " ('STD', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('STD', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('STD', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('COUNT', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686(ml)_No Void'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686_Voiding qs'),\n",
+       " ('STD', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('STD', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('STD', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'blood pressure mean', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'blood pressure mean', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('STD', 'lactated ringers', 'known', 'qn', 'mL', 'all'),\n",
+       " ('SUM', 'lactated ringers', 'known', 'qn', 'mL', 'all'),\n",
+       " ('COUNT', 'normal saline', 'known', 'qn', 'mL/hr', 'all'),\n",
+       " ('STD', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.189424749758, 0.0465986925613\n",
+      "RMSE: 2.21729405564, 0.169855331145\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.216514087631, 0.0127902734363\n",
+      "RMSE: 2.20940132041, 0.0415296149012\n"
+     ]
+    }
+   ],
+   "source": [
+    "#what about everything except the top 3 features (no lactate)\n",
+    "ft_subset = ft_to_keep[3:]\n",
+    "display(ft_subset)\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.17298373678\n",
+      "R^2: 0.141258578157\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipeline.fit(X,y)\n",
+    "y_pred = pipeline.predict(X_validate.loc[:,ft_subset])\n",
+    "rmse = np.sqrt(mean_squared_error(y_validate,y_pred))\n",
+    "r_2 = pipeline.score(X_validate.loc[:,ft_subset],y_validate)\n",
+    "print 'RMSE:',rmse\n",
+    "print 'R^2:',r_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">COUNT</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">output urine</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">unknown</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nom</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">no_units</th>\n",
+       "      <th>3686(ml)_Voiding qs</th>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3686(ml)_No Void</th>\n",
+       "      <td>-inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3686_Voiding qs</th>\n",
+       "      <td>-inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>1.385742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-1.330078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.316650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.271240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.262451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.234985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.214600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.210205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.207031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">MEAN</th>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.190430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.188232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.171265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.151367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.138062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.134277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.132446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.127930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.117065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.111328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.106934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.104797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.104126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>lactated ringers</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.088989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.083679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.078735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.066528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.058716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.052826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.046234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.038727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.036499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.034180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">STD</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.033203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.027985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.027451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.027069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>lactated ringers</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.018402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.018051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.016144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.014946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.013573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.013390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.011131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">STD</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.009987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.009438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.002140</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                   coef\n",
+       "feature component                        status  variable_type units      description                  \n",
+       "COUNT   output urine                     unknown nom           no_units   3686(ml)_Voiding qs       inf\n",
+       "                                                                          3686(ml)_No Void         -inf\n",
+       "                                                                          3686_Voiding qs          -inf\n",
+       "        blood pressure systolic          known   qn            mmHg       all                  1.385742\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                 -1.330078\n",
+       "STD     lactate                          known   qn            mmol/L     all                  0.316650\n",
+       "COUNT   hemoglobin                       known   qn            g/dL       all                  0.271240\n",
+       "MEAN    vasopressin                      known   qn            units/min  all                 -0.262451\n",
+       "COUNT   vasopressin                      known   qn            units/min  all                  0.234985\n",
+       "MEAN    oxygen saturation pulse oximetry known   qn            percent    all                 -0.214600\n",
+       "SUM     norepinephrine                   known   qn            mcg        all                  0.210205\n",
+       "LAST    heart rate                       known   qn            beats/min  all                  0.207031\n",
+       "MEAN    blood pressure systolic          known   qn            mmHg       all                 -0.190430\n",
+       "        norepinephrine                   known   qn            mcg/kg/min all                  0.188232\n",
+       "LAST    vasopressin                      known   qn            units/min  all                  0.171265\n",
+       "SUM     output urine                     known   qn            mL         all                 -0.151367\n",
+       "MEAN    temperature body                 known   qn            degF       all                 -0.138062\n",
+       "COUNT   temperature body                 known   qn            degF       all                  0.134277\n",
+       "MEAN    respiratory rate                 known   qn            insp/min   all                  0.132446\n",
+       "COUNT   normal saline                    known   qn            mL/hr      all                  0.127930\n",
+       "LAST    norepinephrine                   known   qn            mcg/min    all                  0.117065\n",
+       "COUNT   vasopressin                      known   qn            units      all                  0.111328\n",
+       "        norepinephrine                   known   qn            mcg        all                  0.106934\n",
+       "MEAN    norepinephrine                   known   qn            mcg/min    all                 -0.104797\n",
+       "LAST    norepinephrine                   known   qn            mcg/kg/min all                  0.104126\n",
+       "SUM     lactated ringers                 known   qn            mL         all                  0.088989\n",
+       "STD     norepinephrine                   known   qn            mcg/kg/min all                 -0.083679\n",
+       "LAST    vasopressin                      known   qn            units      all                  0.078735\n",
+       "COUNT   norepinephrine                   known   qn            mcg/kg/min all                  0.066528\n",
+       "LAST    oxygen saturation pulse oximetry known   qn            percent    all                 -0.058716\n",
+       "COUNT   heart rate                       known   qn            beats/min  all                 -0.052826\n",
+       "LAST    respiratory rate                 known   qn            insp/min   all                  0.046234\n",
+       "COUNT   output urine                     known   qn            mL         all                  0.038727\n",
+       "STD     norepinephrine                   known   qn            mcg        all                 -0.036499\n",
+       "COUNT   norepinephrine                   known   qn            mcg/min    all                  0.034180\n",
+       "STD     output urine                     known   qn            mL         all                  0.033203\n",
+       "        hemoglobin                       known   qn            g/dL       all                  0.027985\n",
+       "LAST    temperature body                 known   qn            degF       all                 -0.027451\n",
+       "MEAN    blood pressure diastolic         known   qn            mmHg       all                  0.027069\n",
+       "STD     lactated ringers                 known   qn            mL         all                 -0.018402\n",
+       "MEAN    vasopressin                      known   qn            units      all                 -0.018051\n",
+       "LAST    blood pressure mean              known   qn            mmHg       all                  0.016144\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                 -0.014946\n",
+       "MEAN    heart rate                       known   qn            beats/min  all                 -0.013573\n",
+       "STD     vasopressin                      known   qn            units      all                 -0.013390\n",
+       "LAST    blood pressure systolic          known   qn            mmHg       all                 -0.011131\n",
+       "STD     temperature body                 known   qn            degF       all                  0.009987\n",
+       "        oxygen saturation pulse oximetry known   qn            percent    all                  0.009438\n",
+       "MEAN    blood pressure mean              known   qn            mmHg       all                 -0.002140"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coef = pd.Series(lin_reg.coef_,index=X.columns,name='coef')\n",
+    "np.set_printoptions(precision=2)\n",
+    "coef.loc[coef.abs().sort_values(ascending=False).index].astype(np.float16).to_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('LAST', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('SUM', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('SUM', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.103753971611, 0.0755201671306\n",
+      "RMSE: 2.32920660434, 0.172847896719\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.127022629955, 0.0144089421445\n",
+      "RMSE: 2.32325614946, 0.0195046834546\n"
+     ]
+    }
+   ],
+   "source": [
+    "#what about everything except the top 10 non-lactate features (no lactate)\n",
+    "ft_subset = ft_to_keep[3:13]\n",
+    "display(ft_subset)\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ridge Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "ridge_reg = Ridge(alpha=0.5)\n",
+    "\n",
+    "pipeline_ridge = Pipeline([\n",
+    "        ('scaler',scaler),\n",
+    "        ('ridge_reg',ridge_reg)\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.603299205308, 0.043718179257\n",
+      "RMSE: 1.5458363785, 0.0916077642775\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.614953366884, 0.0104873401688\n",
+      "RMSE: 1.54301052763, 0.0335509061572\n"
+     ]
+    }
+   ],
+   "source": [
+    "#what about only the top 3 features (just lactate)\n",
+    "ft_subset = ft_to_keep[:3]\n",
+    "display(ft_subset)\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline_ridge,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('LAST', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('SUM', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('SUM', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('MEAN', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('COUNT', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('COUNT', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('LAST', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('LAST', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('MEAN', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units/min', 'all'),\n",
+       " ('STD', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('LAST', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('LAST', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('LAST', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT',\n",
+       "  'output urine',\n",
+       "  'unknown',\n",
+       "  'nom',\n",
+       "  'no_units',\n",
+       "  '3686(ml)_Voiding qs'),\n",
+       " ('STD', 'norepinephrine', 'known', 'qn', 'mcg', 'all'),\n",
+       " ('COUNT', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('STD', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('STD', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('COUNT', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('MEAN', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686(ml)_No Void'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686_Voiding qs'),\n",
+       " ('STD', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('STD', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('STD', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('LAST', 'blood pressure mean', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('MEAN', 'blood pressure mean', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('STD', 'lactated ringers', 'known', 'qn', 'mL', 'all'),\n",
+       " ('SUM', 'lactated ringers', 'known', 'qn', 'mL', 'all'),\n",
+       " ('COUNT', 'normal saline', 'known', 'qn', 'mL/hr', 'all'),\n",
+       " ('STD', 'vasopressin', 'known', 'qn', 'units', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all'),\n",
+       " ('COUNT', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/min', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.189441318892, 0.0465687690099\n",
+      "RMSE: 2.21727618395, 0.169888960662\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.215310922663, 0.0161840844225\n",
+      "RMSE: 2.1902704788, 0.0213265878904\n"
+     ]
+    }
+   ],
+   "source": [
+    "#what about everything except the top 3 features (no lactate)\n",
+    "ft_subset = ft_to_keep[3:]\n",
+    "display(ft_subset)\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline_ridge,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.17294211726\n",
+      "R^2: 0.141291473077\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipeline_ridge.fit(X,y)\n",
+    "y_pred = pipeline_ridge.predict(X_validate.loc[:,ft_subset])\n",
+    "rmse = np.sqrt(mean_squared_error(y_validate,y_pred))\n",
+    "r_2 = pipeline_ridge.score(X_validate.loc[:,ft_subset],y_validate)\n",
+    "print 'RMSE:',rmse\n",
+    "print 'R^2:',r_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>coef</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>1.362305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-1.306641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.316406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.271973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.262207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.234985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.214600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.210205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.205566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">MEAN</th>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.189453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.188110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.171143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.152832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.137939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.134155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.132202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.128052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.116821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.111328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.106567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.104614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.104187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>lactated ringers</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.088989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.083740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.078674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">COUNT</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.066467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">output urine</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">unknown</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nom</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">no_units</th>\n",
+       "      <th>3686(ml)_Voiding qs</th>\n",
+       "      <td>-0.066162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3686(ml)_No Void</th>\n",
+       "      <td>-0.066162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3686_Voiding qs</th>\n",
+       "      <td>-0.066162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.058807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.052551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.046387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.038910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.036407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.034271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">STD</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.033234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.028030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.027710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.025726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>lactated ringers</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.018448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.018036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.016174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.013504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.013390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.011971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.011902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">STD</th>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.009979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>0.009499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>-0.002190</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                   coef\n",
+       "feature component                        status  variable_type units      description                  \n",
+       "COUNT   blood pressure systolic          known   qn            mmHg       all                  1.362305\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                 -1.306641\n",
+       "STD     lactate                          known   qn            mmol/L     all                  0.316406\n",
+       "COUNT   hemoglobin                       known   qn            g/dL       all                  0.271973\n",
+       "MEAN    vasopressin                      known   qn            units/min  all                 -0.262207\n",
+       "COUNT   vasopressin                      known   qn            units/min  all                  0.234985\n",
+       "MEAN    oxygen saturation pulse oximetry known   qn            percent    all                 -0.214600\n",
+       "SUM     norepinephrine                   known   qn            mcg        all                  0.210205\n",
+       "LAST    heart rate                       known   qn            beats/min  all                  0.205566\n",
+       "MEAN    blood pressure systolic          known   qn            mmHg       all                 -0.189453\n",
+       "        norepinephrine                   known   qn            mcg/kg/min all                  0.188110\n",
+       "LAST    vasopressin                      known   qn            units/min  all                  0.171143\n",
+       "SUM     output urine                     known   qn            mL         all                 -0.152832\n",
+       "MEAN    temperature body                 known   qn            degF       all                 -0.137939\n",
+       "COUNT   temperature body                 known   qn            degF       all                  0.134155\n",
+       "MEAN    respiratory rate                 known   qn            insp/min   all                  0.132202\n",
+       "COUNT   normal saline                    known   qn            mL/hr      all                  0.128052\n",
+       "LAST    norepinephrine                   known   qn            mcg/min    all                  0.116821\n",
+       "COUNT   vasopressin                      known   qn            units      all                  0.111328\n",
+       "        norepinephrine                   known   qn            mcg        all                  0.106567\n",
+       "MEAN    norepinephrine                   known   qn            mcg/min    all                 -0.104614\n",
+       "LAST    norepinephrine                   known   qn            mcg/kg/min all                  0.104187\n",
+       "SUM     lactated ringers                 known   qn            mL         all                  0.088989\n",
+       "STD     norepinephrine                   known   qn            mcg/kg/min all                 -0.083740\n",
+       "LAST    vasopressin                      known   qn            units      all                  0.078674\n",
+       "COUNT   norepinephrine                   known   qn            mcg/kg/min all                  0.066467\n",
+       "        output urine                     unknown nom           no_units   3686(ml)_Voiding qs -0.066162\n",
+       "                                                                          3686(ml)_No Void    -0.066162\n",
+       "                                                                          3686_Voiding qs     -0.066162\n",
+       "LAST    oxygen saturation pulse oximetry known   qn            percent    all                 -0.058807\n",
+       "COUNT   heart rate                       known   qn            beats/min  all                 -0.052551\n",
+       "LAST    respiratory rate                 known   qn            insp/min   all                  0.046387\n",
+       "COUNT   output urine                     known   qn            mL         all                  0.038910\n",
+       "STD     norepinephrine                   known   qn            mcg        all                 -0.036407\n",
+       "COUNT   norepinephrine                   known   qn            mcg/min    all                  0.034271\n",
+       "STD     output urine                     known   qn            mL         all                  0.033234\n",
+       "        hemoglobin                       known   qn            g/dL       all                  0.028030\n",
+       "LAST    temperature body                 known   qn            degF       all                 -0.027710\n",
+       "MEAN    blood pressure diastolic         known   qn            mmHg       all                  0.025726\n",
+       "STD     lactated ringers                 known   qn            mL         all                 -0.018448\n",
+       "MEAN    vasopressin                      known   qn            units      all                 -0.018036\n",
+       "LAST    blood pressure mean              known   qn            mmHg       all                  0.016174\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                 -0.013504\n",
+       "STD     vasopressin                      known   qn            units      all                 -0.013390\n",
+       "LAST    blood pressure systolic          known   qn            mmHg       all                 -0.011971\n",
+       "MEAN    heart rate                       known   qn            beats/min  all                 -0.011902\n",
+       "STD     temperature body                 known   qn            degF       all                  0.009979\n",
+       "        oxygen saturation pulse oximetry known   qn            percent    all                  0.009499\n",
+       "MEAN    blood pressure mean              known   qn            mmHg       all                 -0.002190"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coef = pd.Series(ridge_reg.coef_,index=X.columns,name='coef')\n",
+    "np.set_printoptions(precision=2)\n",
+    "coef.loc[coef.abs().sort_values(ascending=False).index].astype(np.float16).to_frame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LASSO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Elastic net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict DELTA Lactate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "id_grouped = df_train_simple.loc[:,['LABEL']].groupby(level='id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lac_filled = id_grouped.ffill()\n",
+    "lac_next = id_grouped.shift(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lac_all = lac_filled\n",
+    "lac_all.columns = ['last']\n",
+    "lac_all['next'] = lac_next"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "lac_all = lac_all.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lac_all['delta'] = lac_all['next'] - lac_all['last']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id      datetime           \n",
+       "100007  2145-04-02 14:00:00   -1.200000\n",
+       "100010  2109-12-10 12:00:00    0.300000\n",
+       "100012  2177-03-14 10:00:00    0.200000\n",
+       "        2177-03-15 08:00:00   -0.400000\n",
+       "        2177-03-15 14:00:00    0.500000\n",
+       "        2177-03-15 20:00:00   -0.800000\n",
+       "100018  2176-08-30 08:00:00   -0.400000\n",
+       "        2176-08-30 10:00:00    0.050000\n",
+       "        2176-08-30 12:00:00    0.150000\n",
+       "100020  2142-12-03 00:00:00   -0.100000\n",
+       "100028  2142-12-24 14:00:00   -1.700000\n",
+       "        2142-12-25 02:00:00   -0.400000\n",
+       "100034  2164-04-23 10:00:00    1.300000\n",
+       "100035  2115-02-22 08:00:00   -0.600000\n",
+       "        2115-02-22 12:00:00    2.600000\n",
+       "        2115-02-22 14:00:00    1.900000\n",
+       "        2115-02-22 16:00:00    1.300000\n",
+       "        2115-02-22 18:00:00   -0.100000\n",
+       "        2115-02-22 20:00:00   -0.400000\n",
+       "        2115-02-22 22:00:00    0.100000\n",
+       "        2115-02-23 04:00:00    0.800000\n",
+       "        2115-02-23 10:00:00   -1.100000\n",
+       "        2115-02-23 12:00:00   -0.400000\n",
+       "        2115-02-23 20:00:00   -2.300000\n",
+       "        2115-02-24 04:00:00   -1.900000\n",
+       "        2115-02-24 10:00:00   -0.600000\n",
+       "        2115-02-25 04:00:00   -0.400000\n",
+       "        2115-02-25 08:00:00   -0.100000\n",
+       "        2115-03-18 10:00:00    1.100000\n",
+       "100036  2187-07-17 12:00:00    2.500000\n",
+       "                                 ...   \n",
+       "199972  2186-08-30 16:00:00   -2.550000\n",
+       "        2186-08-30 20:00:00   -0.700000\n",
+       "        2186-08-31 00:00:00    0.400000\n",
+       "        2186-08-31 04:00:00    0.000000\n",
+       "        2186-09-01 18:00:00   -0.600000\n",
+       "199976  2182-02-04 14:00:00    0.500000\n",
+       "        2182-02-05 12:00:00   -0.200000\n",
+       "        2182-02-08 04:00:00    0.200000\n",
+       "        2182-02-10 02:00:00    0.000000\n",
+       "        2182-02-10 22:00:00   -0.100000\n",
+       "        2182-02-14 04:00:00   -0.300000\n",
+       "        2182-02-14 10:00:00   -0.300000\n",
+       "        2182-02-16 02:00:00    0.000000\n",
+       "        2182-02-19 02:00:00    0.000000\n",
+       "        2182-02-20 02:00:00   -0.100000\n",
+       "        2182-02-21 04:00:00    0.200000\n",
+       "199979  2182-02-06 14:00:00    2.800000\n",
+       "199981  2110-09-24 20:00:00   -0.100000\n",
+       "        2110-09-25 06:00:00    0.000000\n",
+       "199988  2169-02-07 00:00:00    0.600000\n",
+       "        2169-02-07 10:00:00   -0.600000\n",
+       "        2169-02-07 16:00:00    0.000000\n",
+       "        2169-02-07 22:00:00    0.100000\n",
+       "        2169-02-10 04:00:00    0.300000\n",
+       "199994  2188-07-08 02:00:00   -0.300000\n",
+       "        2188-07-08 04:00:00   -0.100000\n",
+       "        2188-07-08 06:00:00    0.100000\n",
+       "199998  2119-02-20 12:00:00    1.066667\n",
+       "        2119-02-20 20:00:00   -0.866667\n",
+       "199999  2136-04-06 14:00:00   -0.100000\n",
+       "Name: delta, dtype: float64"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lac_all['delta']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_train = lac_all['delta']\n",
+    "X_train = df_train_simple.drop('LABEL',axis=1).loc[y_train.index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " (103614, 116) (103614L,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print X_train.shape,y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x70b3d160>"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12f82a160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_train.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import SelectKBest\n",
+    "from sklearn.feature_selection import f_regression,mutual_info_regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F statistic\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>5121.703950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>4405.805231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">COUNT</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>824.431294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">unknown</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">nom</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">no_units</th>\n",
+       "      <th>3686(ml)_No Void</th>\n",
+       "      <td>824.249418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3686(ml)_Voiding qs</th>\n",
+       "      <td>824.249418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3686_Voiding qs</th>\n",
+       "      <td>824.249418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>824.212850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>587.076624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>583.837514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>572.123998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>560.276342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>542.806918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>496.861685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>436.301842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">MEAN</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>391.429354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>316.353842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>204.089980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>197.558528</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
+       "      <th>glasgow coma scale verbal</th>\n",
+       "      <th>known</th>\n",
+       "      <th>ord</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>190.630230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>glasgow coma scale eye opening</th>\n",
+       "      <th>known</th>\n",
+       "      <th>ord</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>190.582698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>glasgow coma scale motor</th>\n",
+       "      <th>known</th>\n",
+       "      <th>ord</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>189.051236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>186.148321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">MEAN</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>181.673583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>179.492879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SUM</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>170.609542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>169.692445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>output urine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>161.613709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>148.085099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>135.673055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>134.616420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temperature body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>degF</th>\n",
+       "      <th>all</th>\n",
+       "      <td>132.745661</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">STD</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>all</th>\n",
+       "      <td>131.650040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>98.570392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>89.839651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>77.443307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lactated ringers</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>all</th>\n",
+       "      <td>72.431532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>57.945149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>all</th>\n",
+       "      <td>40.503057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>all</th>\n",
+       "      <td>39.969332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">STD</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">norepinephrine</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">known</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">qn</th>\n",
+       "      <th>mcg/kg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>33.573852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>30.374406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
+       "      <th>blood pressure diastolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>30.187089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>blood pressure systolic</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>29.545881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>27.391705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">respiratory rate</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">unknown</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">nom</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">no_units</th>\n",
+       "      <th>618_&gt;60/min retracts</th>\n",
+       "      <td>25.034871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>618_&gt;60/minute</th>\n",
+       "      <td>25.034871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>24.948311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>24.667634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STD</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>24.286963</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">COUNT</th>\n",
+       "      <th>weight body</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>kg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>24.281988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>heart rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>beats/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>23.736168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>22.047297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>all</th>\n",
+       "      <td>20.516949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>20.338711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>20.004498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">STD</th>\n",
+       "      <th>respiratory rate</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th>all</th>\n",
+       "      <td>19.785598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>glasgow coma scale motor</th>\n",
+       "      <th>known</th>\n",
+       "      <th>ord</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>19.484828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COUNT</th>\n",
+       "      <th>norepinephrine</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mcg</th>\n",
+       "      <th>all</th>\n",
+       "      <td>19.255977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LAST</th>\n",
+       "      <th>vasopressin</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>units</th>\n",
+       "      <th>all</th>\n",
+       "      <td>15.246262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MEAN</th>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>known</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>all</th>\n",
+       "      <td>14.671838</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                          0\n",
+       "feature component                        status  variable_type units      description                      \n",
+       "LAST    lactate                          known   qn            mmol/L     all                   5121.703950\n",
+       "MEAN    lactate                          known   qn            mmol/L     all                   4405.805231\n",
+       "COUNT   output urine                     known   qn            mL         all                    824.431294\n",
+       "                                         unknown nom           no_units   3686(ml)_No Void       824.249418\n",
+       "                                                                          3686(ml)_Voiding qs    824.249418\n",
+       "                                                                          3686_Voiding qs        824.249418\n",
+       "        hemoglobin                       known   qn            g/dL       all                    824.212850\n",
+       "LAST    hemoglobin                       known   qn            g/dL       all                    587.076624\n",
+       "MEAN    respiratory rate                 known   qn            insp/min   all                    583.837514\n",
+       "SUM     output urine                     known   qn            mL         all                    572.123998\n",
+       "LAST    respiratory rate                 known   qn            insp/min   all                    560.276342\n",
+       "        oxygen saturation pulse oximetry known   qn            percent    all                    542.806918\n",
+       "MEAN    oxygen saturation pulse oximetry known   qn            percent    all                    496.861685\n",
+       "STD     hemoglobin                       known   qn            g/dL       all                    436.301842\n",
+       "MEAN    hemoglobin                       known   qn            g/dL       all                    391.429354\n",
+       "        heart rate                       known   qn            beats/min  all                    316.353842\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                    204.089980\n",
+       "LAST    norepinephrine                   known   qn            mcg/kg/min all                    197.558528\n",
+       "COUNT   glasgow coma scale verbal        known   ord           no_units   all                    190.630230\n",
+       "        glasgow coma scale eye opening   known   ord           no_units   all                    190.582698\n",
+       "        glasgow coma scale motor         known   ord           no_units   all                    189.051236\n",
+       "LAST    heart rate                       known   qn            beats/min  all                    186.148321\n",
+       "MEAN    norepinephrine                   known   qn            mcg/kg/min all                    181.673583\n",
+       "        blood pressure systolic          known   qn            mmHg       all                    179.492879\n",
+       "SUM     norepinephrine                   known   qn            mcg        all                    170.609542\n",
+       "COUNT   lactate                          known   qn            mmol/L     all                    169.692445\n",
+       "STD     output urine                     known   qn            mL         all                    161.613709\n",
+       "LAST    blood pressure systolic          known   qn            mmHg       all                    148.085099\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                    135.673055\n",
+       "COUNT   vasopressin                      known   qn            units/min  all                    134.616420\n",
+       "        temperature body                 known   qn            degF       all                    132.745661\n",
+       "STD     lactate                          known   qn            mmol/L     all                    131.650040\n",
+       "        blood pressure diastolic         known   qn            mmHg       all                     98.570392\n",
+       "        blood pressure systolic          known   qn            mmHg       all                     89.839651\n",
+       "COUNT   norepinephrine                   known   qn            mcg/kg/min all                     77.443307\n",
+       "        lactated ringers                 known   qn            mL         all                     72.431532\n",
+       "        oxygen saturation pulse oximetry known   qn            percent    all                     57.945149\n",
+       "STD     oxygen saturation pulse oximetry known   qn            percent    all                     40.503057\n",
+       "LAST    normal saline                    known   qn            mL/hr      all                     39.969332\n",
+       "STD     norepinephrine                   known   qn            mcg/kg/min all                     33.573852\n",
+       "                                                               mcg        all                     30.374406\n",
+       "COUNT   blood pressure diastolic         known   qn            mmHg       all                     30.187089\n",
+       "        blood pressure systolic          known   qn            mmHg       all                     29.545881\n",
+       "LAST    norepinephrine                   known   qn            mcg/min    all                     27.391705\n",
+       "COUNT   respiratory rate                 unknown nom           no_units   618_>60/min retracts    25.034871\n",
+       "                                                                          618_>60/minute          25.034871\n",
+       "                                         known   qn            insp/min   all                     24.948311\n",
+       "MEAN    norepinephrine                   known   qn            mcg/min    all                     24.667634\n",
+       "STD     blood pressure mean              known   qn            mmHg       all                     24.286963\n",
+       "COUNT   weight body                      known   qn            kg         all                     24.281988\n",
+       "        heart rate                       known   qn            beats/min  all                     23.736168\n",
+       "        norepinephrine                   known   qn            mcg/min    all                     22.047297\n",
+       "        normal saline                    known   qn            mL/hr      all                     20.516949\n",
+       "        vasopressin                      known   qn            units      all                     20.338711\n",
+       "MEAN    blood pressure mean              known   qn            mmHg       all                     20.004498\n",
+       "STD     respiratory rate                 known   qn            insp/min   all                     19.785598\n",
+       "        glasgow coma scale motor         known   ord           no_units   all                     19.484828\n",
+       "COUNT   norepinephrine                   known   qn            mcg        all                     19.255977\n",
+       "LAST    vasopressin                      known   qn            units      all                     15.246262\n",
+       "MEAN    normal saline                    known   qn            mL/hr      all                     14.671838"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First, try f_regression\n",
+    "test_F = SelectKBest(score_func=f_regression, k=4)\n",
+    "test_F.fit(X=X_train, y=y_train)\n",
+    "# summarize scores\n",
+    "np.set_printoptions(precision=3)\n",
+    "scores_f_stat = pd.Series(test_F.scores_, index=X_train.columns)\n",
+    "print 'F statistic'\n",
+    "scores_f_stat.sort_values(ascending=False).to_frame().head(60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "feature  component                         status   variable_type  units       description        \n",
+       "LAST     lactate                           known    qn             mmol/L      all                    5121.703950\n",
+       "MEAN     lactate                           known    qn             mmol/L      all                    4405.805231\n",
+       "COUNT    output urine                      known    qn             mL          all                     824.431294\n",
+       "                                           unknown  nom            no_units    3686_Voiding qs         824.249418\n",
+       "                                                                               3686(ml)_No Void        824.249418\n",
+       "                                                                               3686(ml)_Voiding qs     824.249418\n",
+       "         hemoglobin                        known    qn             g/dL        all                     824.212850\n",
+       "LAST     hemoglobin                        known    qn             g/dL        all                     587.076624\n",
+       "MEAN     respiratory rate                  known    qn             insp/min    all                     583.837514\n",
+       "SUM      output urine                      known    qn             mL          all                     572.123998\n",
+       "LAST     respiratory rate                  known    qn             insp/min    all                     560.276342\n",
+       "         oxygen saturation pulse oximetry  known    qn             percent     all                     542.806918\n",
+       "MEAN     oxygen saturation pulse oximetry  known    qn             percent     all                     496.861685\n",
+       "STD      hemoglobin                        known    qn             g/dL        all                     436.301842\n",
+       "MEAN     hemoglobin                        known    qn             g/dL        all                     391.429354\n",
+       "         heart rate                        known    qn             beats/min   all                     316.353842\n",
+       "         blood pressure diastolic          known    qn             mmHg        all                     204.089980\n",
+       "LAST     norepinephrine                    known    qn             mcg/kg/min  all                     197.558528\n",
+       "COUNT    glasgow coma scale verbal         known    ord            no_units    all                     190.630230\n",
+       "         glasgow coma scale eye opening    known    ord            no_units    all                     190.582698\n",
+       "         glasgow coma scale motor          known    ord            no_units    all                     189.051236\n",
+       "LAST     heart rate                        known    qn             beats/min   all                     186.148321\n",
+       "MEAN     norepinephrine                    known    qn             mcg/kg/min  all                     181.673583\n",
+       "         blood pressure systolic           known    qn             mmHg        all                     179.492879\n",
+       "SUM      norepinephrine                    known    qn             mcg         all                     170.609542\n",
+       "COUNT    lactate                           known    qn             mmol/L      all                     169.692445\n",
+       "STD      output urine                      known    qn             mL          all                     161.613709\n",
+       "LAST     blood pressure systolic           known    qn             mmHg        all                     148.085099\n",
+       "         blood pressure diastolic          known    qn             mmHg        all                     135.673055\n",
+       "COUNT    vasopressin                       known    qn             units/min   all                     134.616420\n",
+       "         temperature body                  known    qn             degF        all                     132.745661\n",
+       "STD      lactate                           known    qn             mmol/L      all                     131.650040\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_f_stat[scores_f_stat > 100].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SelectKBest(k=4,\n",
+       "      score_func=<function mutual_info_regression at 0x000000000D259978>)"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Then, try mutual_info_regression\n",
+    "column_subset = scores_f_stat[scores_f_stat > 100].index\n",
+    "test_MI = SelectKBest(score_func=mutual_info_regression, k=4)\n",
+    "test_MI.fit(X=X_train.loc[:,column_subset], y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mutual Information\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "feature  component                         status   variable_type  units       description        \n",
+       "LAST     lactate                           known    qn             mmol/L      all                    0.269033\n",
+       "MEAN     lactate                           known    qn             mmol/L      all                    0.252243\n",
+       "COUNT    lactate                           known    qn             mmol/L      all                    0.048210\n",
+       "STD      lactate                           known    qn             mmol/L      all                    0.044504\n",
+       "MEAN     blood pressure systolic           known    qn             mmHg        all                    0.040341\n",
+       "LAST     blood pressure systolic           known    qn             mmHg        all                    0.038664\n",
+       "COUNT    hemoglobin                        known    qn             g/dL        all                    0.037712\n",
+       "LAST     heart rate                        known    qn             beats/min   all                    0.036297\n",
+       "         oxygen saturation pulse oximetry  known    qn             percent     all                    0.035913\n",
+       "MEAN     respiratory rate                  known    qn             insp/min    all                    0.035371\n",
+       "         heart rate                        known    qn             beats/min   all                    0.035358\n",
+       "         oxygen saturation pulse oximetry  known    qn             percent     all                    0.034470\n",
+       "LAST     respiratory rate                  known    qn             insp/min    all                    0.034249\n",
+       "SUM      output urine                      known    qn             mL          all                    0.034072\n",
+       "MEAN     blood pressure diastolic          known    qn             mmHg        all                    0.032687\n",
+       "COUNT    output urine                      unknown  nom            no_units    3686_Voiding qs        0.027410\n",
+       "                                                                               3686(ml)_Voiding qs    0.026453\n",
+       "LAST     blood pressure diastolic          known    qn             mmHg        all                    0.025733\n",
+       "STD      hemoglobin                        known    qn             g/dL        all                    0.023400\n",
+       "COUNT    output urine                      unknown  nom            no_units    3686(ml)_No Void       0.022447\n",
+       "                                           known    qn             mL          all                    0.021493\n",
+       "STD      output urine                      known    qn             mL          all                    0.014348\n",
+       "MEAN     hemoglobin                        known    qn             g/dL        all                    0.013675\n",
+       "LAST     hemoglobin                        known    qn             g/dL        all                    0.010910\n",
+       "         norepinephrine                    known    qn             mcg/kg/min  all                    0.009456\n",
+       "COUNT    temperature body                  known    qn             degF        all                    0.009249\n",
+       "MEAN     norepinephrine                    known    qn             mcg/kg/min  all                    0.007947\n",
+       "COUNT    glasgow coma scale motor          known    ord            no_units    all                    0.005813\n",
+       "         glasgow coma scale verbal         known    ord            no_units    all                    0.005309\n",
+       "         glasgow coma scale eye opening    known    ord            no_units    all                    0.003716\n",
+       "         vasopressin                       known    qn             units/min   all                    0.001552\n",
+       "SUM      norepinephrine                    known    qn             mcg         all                    0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_MI = pd.Series(test_MI.scores_, index=column_subset)\n",
+    "print 'Mutual Information'\n",
+    "scores_MI.sort_values(ascending=False, inplace=True)\n",
+    "scores_MI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#keep only features that are not completely independent\n",
+    "ft_to_keep = scores_MI[scores_MI > 0].index.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "lin_reg = LinearRegression()\n",
+    "\n",
+    "pipeline = Pipeline([\n",
+    "        ('scaler',scaler),\n",
+    "        ('lin_reg',lin_reg)\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x70b53ba8>"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAECCAYAAAASDQdFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfbc5a8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_train.iloc[0].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id      datetime           \n",
+       "100007  2145-04-02 14:00:00   -1.2\n",
+       "100010  2109-12-10 12:00:00    0.3\n",
+       "100012  2177-03-14 10:00:00    0.2\n",
+       "        2177-03-15 08:00:00   -0.4\n",
+       "        2177-03-15 14:00:00    0.5\n",
+       "Name: delta, dtype: float64"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(103614, 31)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.loc[:,ft_to_keep].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('LAST', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('COUNT', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('STD', 'lactate', 'known', 'qn', 'mmol/L', 'all'),\n",
+       " ('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('LAST', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('LAST', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('MEAN', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('MEAN', 'heart rate', 'known', 'qn', 'beats/min', 'all'),\n",
+       " ('MEAN', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all'),\n",
+       " ('LAST', 'respiratory rate', 'known', 'qn', 'insp/min', 'all'),\n",
+       " ('SUM', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('MEAN', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686_Voiding qs'),\n",
+       " ('COUNT',\n",
+       "  'output urine',\n",
+       "  'unknown',\n",
+       "  'nom',\n",
+       "  'no_units',\n",
+       "  '3686(ml)_Voiding qs'),\n",
+       " ('LAST', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all'),\n",
+       " ('STD', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686(ml)_No Void'),\n",
+       " ('COUNT', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('STD', 'output urine', 'known', 'qn', 'mL', 'all'),\n",
+       " ('MEAN', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'hemoglobin', 'known', 'qn', 'g/dL', 'all'),\n",
+       " ('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('COUNT', 'temperature body', 'known', 'qn', 'degF', 'all'),\n",
+       " ('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all'),\n",
+       " ('COUNT', 'glasgow coma scale motor', 'known', 'ord', 'no_units', 'all'),\n",
+       " ('COUNT', 'glasgow coma scale verbal', 'known', 'ord', 'no_units', 'all'),\n",
+       " ('COUNT',\n",
+       "  'glasgow coma scale eye opening',\n",
+       "  'known',\n",
+       "  'ord',\n",
+       "  'no_units',\n",
+       "  'all'),\n",
+       " ('COUNT', 'vasopressin', 'known', 'qn', 'units/min', 'all')]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.114040689882, 0.0133743001894\n",
+      "RMSE: 1.26323854479, 0.0694617794055\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.113702774615, 0.00551556543904\n",
+      "RMSE: 1.27302288592, 0.0296756264639\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(ft_to_keep)\n",
+    "X = X_train.loc[:,ft_to_keep]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>lactate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>lactate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>lactate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>lactate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmol/L</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  feature component status variable_type   units description\n",
+       "0    LAST   lactate  known            qn  mmol/L         all\n",
+       "1    MEAN   lactate  known            qn  mmol/L         all\n",
+       "2   COUNT   lactate  known            qn  mmol/L         all\n",
+       "3     STD   lactate  known            qn  mmol/L         all"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.0586587824567, 0.0116600441805\n",
+      "RMSE: 1.30226321647, 0.0739792056815\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.0544639418642, 0.00680518065802\n",
+      "RMSE: 1.30526405426, 0.043709856272\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(pd.DataFrame(ft_to_keep[:4], columns = X_train.columns.names))\n",
+    "X = X_train.loc[:,ft_to_keep[:4]]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale eye opening</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale motor</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale verbal</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SUM</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_No Void</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>temperature body</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>degF</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>vasopressin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>units/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   feature                         component   status variable_type  \\\n",
+       "13    LAST          blood pressure diastolic    known            qn   \n",
+       "10    MEAN          blood pressure diastolic    known            qn   \n",
+       "0     MEAN           blood pressure systolic    known            qn   \n",
+       "1     LAST           blood pressure systolic    known            qn   \n",
+       "25   COUNT    glasgow coma scale eye opening    known           ord   \n",
+       "23   COUNT          glasgow coma scale motor    known           ord   \n",
+       "24   COUNT         glasgow coma scale verbal    known           ord   \n",
+       "3     LAST                        heart rate    known            qn   \n",
+       "6     MEAN                        heart rate    known            qn   \n",
+       "19    LAST                        hemoglobin    known            qn   \n",
+       "18    MEAN                        hemoglobin    known            qn   \n",
+       "2    COUNT                        hemoglobin    known            qn   \n",
+       "14     STD                        hemoglobin    known            qn   \n",
+       "20    LAST                    norepinephrine    known            qn   \n",
+       "22    MEAN                    norepinephrine    known            qn   \n",
+       "11   COUNT                      output urine  unknown           nom   \n",
+       "9      SUM                      output urine    known            qn   \n",
+       "15   COUNT                      output urine  unknown           nom   \n",
+       "16   COUNT                      output urine    known            qn   \n",
+       "17     STD                      output urine    known            qn   \n",
+       "12   COUNT                      output urine  unknown           nom   \n",
+       "7     MEAN  oxygen saturation pulse oximetry    known            qn   \n",
+       "4     LAST  oxygen saturation pulse oximetry    known            qn   \n",
+       "8     LAST                  respiratory rate    known            qn   \n",
+       "5     MEAN                  respiratory rate    known            qn   \n",
+       "21   COUNT                  temperature body    known            qn   \n",
+       "26   COUNT                       vasopressin    known            qn   \n",
+       "\n",
+       "         units          description  \n",
+       "13        mmHg                  all  \n",
+       "10        mmHg                  all  \n",
+       "0         mmHg                  all  \n",
+       "1         mmHg                  all  \n",
+       "25    no_units                  all  \n",
+       "23    no_units                  all  \n",
+       "24    no_units                  all  \n",
+       "3    beats/min                  all  \n",
+       "6    beats/min                  all  \n",
+       "19        g/dL                  all  \n",
+       "18        g/dL                  all  \n",
+       "2         g/dL                  all  \n",
+       "14        g/dL                  all  \n",
+       "20  mcg/kg/min                  all  \n",
+       "22  mcg/kg/min                  all  \n",
+       "11    no_units      3686_Voiding qs  \n",
+       "9           mL                  all  \n",
+       "15    no_units     3686(ml)_No Void  \n",
+       "16          mL                  all  \n",
+       "17          mL                  all  \n",
+       "12    no_units  3686(ml)_Voiding qs  \n",
+       "7      percent                  all  \n",
+       "4      percent                  all  \n",
+       "8     insp/min                  all  \n",
+       "5     insp/min                  all  \n",
+       "21        degF                  all  \n",
+       "26   units/min                  all  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.0319361339321, 0.00456736679582\n",
+      "RMSE: 1.32049694072, 0.071864874518\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.0324873008507, 0.00390809431423\n",
+      "RMSE: 1.30736495491, 0.0368334916341\n"
+     ]
+    }
+   ],
+   "source": [
+    "ft_subset = ft_to_keep[4:]\n",
+    "display(pd.DataFrame(ft_subset, columns = X_train.columns.names).sort_values('component'))\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean        117.052692\n",
+       "std          19.336874\n",
+       "min           0.000000\n",
+       "25%         104.000000\n",
+       "50%         119.333333\n",
+       "75%         124.000000\n",
+       "max         264.600000\n",
+       "Name: (MEAN, blood pressure systolic, known, qn, mmHg, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x210d07588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'blood pressure systolic', 'known', 'qn', 'mmHg', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean        117.167958\n",
+       "std          21.516603\n",
+       "min           0.000000\n",
+       "25%         103.000000\n",
+       "50%         119.000000\n",
+       "75%         126.000000\n",
+       "max         279.000000\n",
+       "Name: (LAST, blood pressure systolic, known, qn, mmHg, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1286a9240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'hemoglobin', 'known', 'qn', 'g/dL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.369014\n",
+       "std           0.708152\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           1.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, hemoglobin, known, qn, g/dL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2532054e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'heart rate', 'known', 'qn', 'beats/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         91.683936\n",
+       "std          17.608580\n",
+       "min           0.000000\n",
+       "25%          80.000000\n",
+       "50%          94.000000\n",
+       "75%         100.000000\n",
+       "max         256.000000\n",
+       "Name: (LAST, heart rate, known, qn, beats/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x27b0445c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         96.840750\n",
+       "std           4.976904\n",
+       "min           0.000000\n",
+       "25%          96.000000\n",
+       "50%          97.000000\n",
+       "75%          99.000000\n",
+       "max         100.000000\n",
+       "Name: (LAST, oxygen saturation pulse oximetry, known, qn, percent, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x236862cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'respiratory rate', 'known', 'qn', 'insp/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         20.614695\n",
+       "std           5.648175\n",
+       "min           0.000000\n",
+       "25%          17.300000\n",
+       "50%          20.112851\n",
+       "75%          23.400000\n",
+       "max          93.000000\n",
+       "Name: (MEAN, respiratory rate, known, qn, insp/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bbd72b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'heart rate', 'known', 'qn', 'beats/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         93.182155\n",
+       "std          17.292502\n",
+       "min           0.000000\n",
+       "25%          80.714286\n",
+       "50%          95.500000\n",
+       "75%         102.321742\n",
+       "max         220.000000\n",
+       "Name: (MEAN, heart rate, known, qn, beats/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x8f59d0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'oxygen saturation pulse oximetry', 'known', 'qn', 'percent', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         96.891940\n",
+       "std           4.421747\n",
+       "min           0.000000\n",
+       "25%          96.333333\n",
+       "50%          97.022222\n",
+       "75%          99.000000\n",
+       "max         100.000000\n",
+       "Name: (MEAN, oxygen saturation pulse oximetry, known, qn, percent, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x300afd320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'respiratory rate', 'known', 'qn', 'insp/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         20.582440\n",
+       "std           6.228727\n",
+       "min           0.000000\n",
+       "25%          17.000000\n",
+       "50%          20.015902\n",
+       "75%          24.000000\n",
+       "max         115.000000\n",
+       "Name: (LAST, respiratory rate, known, qn, insp/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x26247d278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('SUM', 'output urine', 'known', 'qn', 'mL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean        105.191149\n",
+       "std         182.456413\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%          40.000000\n",
+       "75%         140.000000\n",
+       "max        6675.000000\n",
+       "Name: (SUM, output urine, known, qn, mL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf54c6ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         59.016396\n",
+       "std          11.665412\n",
+       "min           0.000000\n",
+       "25%          52.250000\n",
+       "50%          58.820530\n",
+       "75%          64.000000\n",
+       "max         183.500000\n",
+       "Name: (MEAN, blood pressure diastolic, known, qn, mmHg, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x688fea58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686_Voiding qs')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          1.076003\n",
+       "std           0.913084\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           1.000000\n",
+       "75%           2.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, output urine, unknown, nom, no_units, 3686_Voiding qs), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf54c6f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686(ml)_Voiding qs')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          1.076003\n",
+       "std           0.913084\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           1.000000\n",
+       "75%           2.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, output urine, unknown, nom, no_units, 3686(ml)_Voiding qs), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x220d69cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'blood pressure diastolic', 'known', 'qn', 'mmHg', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         59.238875\n",
+       "std          13.020916\n",
+       "min           0.000000\n",
+       "25%          51.000000\n",
+       "50%          60.172308\n",
+       "75%          65.000000\n",
+       "max         298.000000\n",
+       "Name: (LAST, blood pressure diastolic, known, qn, mmHg, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x32638cac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('STD', 'hemoglobin', 'known', 'qn', 'g/dL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.048572\n",
+       "std           0.269877\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           0.000000\n",
+       "max          10.818734\n",
+       "Name: (STD, hemoglobin, known, qn, g/dL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x129376128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'output urine', 'unknown', 'nom', 'no_units', '3686(ml)_No Void')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          1.076003\n",
+       "std           0.913084\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           1.000000\n",
+       "75%           2.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, output urine, unknown, nom, no_units, 3686(ml)_No Void), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1bc765da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'output urine', 'known', 'qn', 'mL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          1.075608\n",
+       "std           0.913059\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           1.000000\n",
+       "75%           2.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, output urine, known, qn, mL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbde2b748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('STD', 'output urine', 'known', 'qn', 'mL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         13.968748\n",
+       "std          42.411059\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%          10.606602\n",
+       "max        1767.766953\n",
+       "Name: (STD, output urine, known, qn, mL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbde18c50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'hemoglobin', 'known', 'qn', 'g/dL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         10.393699\n",
+       "std           1.768262\n",
+       "min           0.000000\n",
+       "25%           9.200000\n",
+       "50%          10.300000\n",
+       "75%          11.200000\n",
+       "max          97.000000\n",
+       "Name: (MEAN, hemoglobin, known, qn, g/dL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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cztBQVc7Lt7IW7axFi7WYvZkWqW+kugS0PTP/tG5+PSI2ZOYrwL3AEeAYsCsiBoAbgJuB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\nCElSkQEhSSoyICRJRQaEJKnIgJAkFf1/qTiBcAAPJdgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x234ddcf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'hemoglobin', 'known', 'qn', 'g/dL', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean         10.351729\n",
+       "std           1.860388\n",
+       "min           0.000000\n",
+       "25%           9.100000\n",
+       "50%          10.200000\n",
+       "75%          11.400000\n",
+       "max          97.000000\n",
+       "Name: (LAST, hemoglobin, known, qn, g/dL, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1980048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('LAST', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.052286\n",
+       "std           0.101931\n",
+       "min           0.000000\n",
+       "25%           0.037942\n",
+       "50%           0.037942\n",
+       "75%           0.037942\n",
+       "max          10.000000\n",
+       "Name: (LAST, norepinephrine, known, qn, mcg/kg/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf46402e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'temperature body', 'known', 'qn', 'degF', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.753769\n",
+       "std           1.319150\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           1.000000\n",
+       "max         120.000000\n",
+       "Name: (COUNT, temperature body, known, qn, degF, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12b61c160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('MEAN', 'norepinephrine', 'known', 'qn', 'mcg/kg/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.057853\n",
+       "std           0.092927\n",
+       "min           0.000000\n",
+       "25%           0.045118\n",
+       "50%           0.045118\n",
+       "75%           0.045118\n",
+       "max           5.777778\n",
+       "Name: (MEAN, norepinephrine, known, qn, mcg/kg/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x242b00860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'glasgow coma scale motor', 'known', 'ord', 'no_units', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.267145\n",
+       "std           0.481816\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           0.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, glasgow coma scale motor, known, ord, no_units, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x122745c88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'glasgow coma scale verbal', 'known', 'ord', 'no_units', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.268699\n",
+       "std           0.483543\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           1.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, glasgow coma scale verbal, known, ord, no_units, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x242b0c828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'glasgow coma scale eye opening', 'known', 'ord', 'no_units', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.269037\n",
+       "std           0.483664\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           1.000000\n",
+       "max           8.000000\n",
+       "Name: (COUNT, glasgow coma scale eye opening, known, ord, no_units, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x23627b438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n",
+      "('COUNT', 'vasopressin', 'known', 'qn', 'units/min', 'all')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "count    103614.000000\n",
+       "mean          0.144903\n",
+       "std           0.758535\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%           0.000000\n",
+       "max          24.000000\n",
+       "Name: (COUNT, vasopressin, known, qn, units/min, all), dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ADWCkY3wfMDBuvNkxNjpuGQN1+pMkTa3aF7gj4gTgfmBTZn6R1rWK5zWAvbR2/gvGje+p\nxhvjapsT1O6t258kaerUvcB9PK3TRGsy81+r4UcjYllmPgicTytItgHrI6IfmA+cDGwHHgJWAo9U\nf4czsxkRByJiEbATOBe4vu6KdcvAwHwGBxvlwi6ZTr30mnPR5ly0ORf11AoL4IPAq4F1EXEdMAa8\nD/h0dQH7ceDOzByLiJuBrbROU63NzIMRMQRsiohh4ABwWbXcK4E7aB3xbMnMbXVXrFtGRvaza1ez\n120ArTfBdOml15yLNueizblom2xo1gqLzHw/8P4JHjpzgtqNwMZxY/uBiyeofZjWJ6ckSdOIX8qT\nJBUZFpKkIsNCklRkWEiSigwLSVKRYSFJKjIsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lS\nkWEhSSoyLCRJRYaFJKnIsJAkFRkWkqQiw0KSVGRYSJKKDAtJUpFhIUkqMiwkSUWGhSSpyLCQJBUZ\nFpKkIsNCklRkWEiSigwLSVLRnF43MF5E9AGfARYDzwJ/nJk/6G1XkjSzTbuwAH4PeEVmvjUilgA3\nVWPTztiRI/zkyf/m+9/f0bMeTjzxTcyePbtnry9pZpiOYXEGcC9AZn4zIn6rx/28oGdGf8qXtjb5\n6ne+0ZvXH/kZf33tOzjppN/oyetLmjmmY1gsAEY67h+OiFmZeaRXDb2YVw4s5LjXvKEnrz125Ag/\n+tEP/+/+nj3H8dRT+7rag0c20swwHcNiFGh03H/RoJjVfIIjB171y+9qAmMjT/JM/8KevDbAU08m\nH73lu8w77rU9ef1n9z3Fh1et4I1v/PWevP6L6UVwTlfORdvLcS66dWZhOobF14C3A3dGxGnAd16s\nePMdN/Z1pStJmsGmY1jcBayIiK9V99/Ty2YkSdA3NjbW6x4kSdOcX8qTJBUZFpKkIsNCklRkWEiS\niqbjp6GOir8h9Ysi4lu0v8z4X5n5R73sp9uqn4b5WGa+LSJOAm4FjgDbM3NNT5vrsnFz8Wbgq8AT\n1cNDmfnl3nXXHRExB/gccCLQD6wHvssM3C5eYC5+zCS3i5dsWPAS+g2pX7aIeAVAZp7V6156ISKu\nBd4FPP9tq5uAtZk5HBFDEXFhZv5j7zrsngnm4i3AJzPzU73rqifeCfw8M98dEa8G/gP4d2bmdtE5\nF6+hNQ8fYZLbxUv5NNQv/IYUMG1/Q6oLFgOviojNEfHPVXjOJN8DLuq4/5bMHK5u3wOc0/2Weub/\nzQVwQUQ8EBGfjYje/NxB930JWFfdng0cBk6ZodtF51zMAg7R2i7ePpnt4qUcFhP+hlSvmumxZ4BP\nZOa5wGrgCzNpLjLzLlo7g+d1fqu/CQx0t6PemWAuvglcm5nLgR8A1/eir27LzGcy8+mIaABfBj7E\nDN0uJpiLDwMPA9dMZrt4Ke9QJvUbUi9zTwBfAMjMHcBu4Fd72lFvdW4HDWBvrxqZBr6SmY9Wt+8C\n3tzLZropIk4A7gc2ZeYXmcHbxQRzMent4qUcFl8DVgIczW9Ivcy9F/gkQES8ntYb4Sc97ai3vh0R\ny6rb5wPDL1b8Mre542f+zwa+1ctmuiUijgc2A3+WmZuq4Udn4nbxAnMx6e3ipXyB29+QatsIfD4i\nhmn97+m9M/goC+Aa4JaImAs8DtzZ4356aTXw6Yg4CPwPcEWP++mWDwKvBtZFxHXAGPA+WnMx07aL\niebiamDDZLYLfxtKklT0Uj4NJUnqEsNCklRkWEiSigwLSVKRYSFJKjIsJElFhoUkqciwkCQV/S81\nM9Z8RLzKZAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116985a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in X:\n",
+    "    print '@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'\n",
+    "    print col\n",
+    "    display(X[col].describe())\n",
+    "    X[col].hist()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "next_lac = df_train_simple.loc[:,['LABEL']].shift(-1).dropna().iloc[:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x12fea83c8>"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TfOnp45Stqf4YG7ojPHz/Xvq65k9+LMQqF+nrihGJhBdfWURE1jwlM0RERERkSVzXZXg0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Ygq62jDuhizy21W31/dZvW+Pj3/xPbarBZCjO7ONnV1UiQUAAAAexdhBgCswfM8LS5FFUvZCoXb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\nkm2ZyjimXMfSR941selFySffMazAa2p2oa6zp4a1U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JLN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\nF+2GEYfZfeHxgbYF83b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Fvv3enblPWJfH5KgLkWUZvb099Pb2ALWJLkdHGR0dY2xikjFX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\nXiLBkOcLspoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12fea8400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(lac_all.sample(1000),size=5,kind=\"reg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale eye opening</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale motor</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale verbal</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SUM</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_No Void</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>temperature body</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>degF</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>vasopressin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>units/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   feature                         component   status variable_type  \\\n",
+       "13    LAST          blood pressure diastolic    known            qn   \n",
+       "10    MEAN          blood pressure diastolic    known            qn   \n",
+       "0     MEAN           blood pressure systolic    known            qn   \n",
+       "1     LAST           blood pressure systolic    known            qn   \n",
+       "25   COUNT    glasgow coma scale eye opening    known           ord   \n",
+       "23   COUNT          glasgow coma scale motor    known           ord   \n",
+       "24   COUNT         glasgow coma scale verbal    known           ord   \n",
+       "3     LAST                        heart rate    known            qn   \n",
+       "6     MEAN                        heart rate    known            qn   \n",
+       "19    LAST                        hemoglobin    known            qn   \n",
+       "18    MEAN                        hemoglobin    known            qn   \n",
+       "2    COUNT                        hemoglobin    known            qn   \n",
+       "14     STD                        hemoglobin    known            qn   \n",
+       "20    LAST                    norepinephrine    known            qn   \n",
+       "22    MEAN                    norepinephrine    known            qn   \n",
+       "11   COUNT                      output urine  unknown           nom   \n",
+       "9      SUM                      output urine    known            qn   \n",
+       "15   COUNT                      output urine  unknown           nom   \n",
+       "16   COUNT                      output urine    known            qn   \n",
+       "17     STD                      output urine    known            qn   \n",
+       "12   COUNT                      output urine  unknown           nom   \n",
+       "7     MEAN  oxygen saturation pulse oximetry    known            qn   \n",
+       "4     LAST  oxygen saturation pulse oximetry    known            qn   \n",
+       "8     LAST                  respiratory rate    known            qn   \n",
+       "5     MEAN                  respiratory rate    known            qn   \n",
+       "21   COUNT                  temperature body    known            qn   \n",
+       "26   COUNT                       vasopressin    known            qn   \n",
+       "\n",
+       "         units          description  \n",
+       "13        mmHg                  all  \n",
+       "10        mmHg                  all  \n",
+       "0         mmHg                  all  \n",
+       "1         mmHg                  all  \n",
+       "25    no_units                  all  \n",
+       "23    no_units                  all  \n",
+       "24    no_units                  all  \n",
+       "3    beats/min                  all  \n",
+       "6    beats/min                  all  \n",
+       "19        g/dL                  all  \n",
+       "18        g/dL                  all  \n",
+       "2         g/dL                  all  \n",
+       "14        g/dL                  all  \n",
+       "20  mcg/kg/min                  all  \n",
+       "22  mcg/kg/min                  all  \n",
+       "11    no_units      3686_Voiding qs  \n",
+       "9           mL                  all  \n",
+       "15    no_units     3686(ml)_No Void  \n",
+       "16          mL                  all  \n",
+       "17          mL                  all  \n",
+       "12    no_units  3686(ml)_Voiding qs  \n",
+       "7      percent                  all  \n",
+       "4      percent                  all  \n",
+       "8     insp/min                  all  \n",
+       "5     insp/min                  all  \n",
+       "21        degF                  all  \n",
+       "26   units/min                  all  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('lin_reg', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ft_subset = ft_to_keep[4:]\n",
+    "display(pd.DataFrame(ft_subset, columns = X_train.columns.names).sort_values('component'))\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = y_train\n",
+    "\n",
+    "pipeline.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>(LAST, hemoglobin, known, qn, g/dL, all)</th>\n",
+       "      <td>-0.158447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, output urine, known, qn, mL, all)</th>\n",
+       "      <td>-0.077087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LAST, oxygen saturation pulse oximetry, known, qn, percent, all)</th>\n",
+       "      <td>-0.057892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(SUM, output urine, known, qn, mL, all)</th>\n",
+       "      <td>-0.049957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LAST, heart rate, known, qn, beats/min, all)</th>\n",
+       "      <td>-0.047211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, blood pressure diastolic, known, qn, mmHg, all)</th>\n",
+       "      <td>-0.045105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, norepinephrine, known, qn, mcg/kg/min, all)</th>\n",
+       "      <td>-0.037781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LAST, blood pressure systolic, known, qn, mmHg, all)</th>\n",
+       "      <td>-0.025742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, temperature body, known, qn, degF, all)</th>\n",
+       "      <td>-0.022003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, glasgow coma scale verbal, known, ord, no_units, all)</th>\n",
+       "      <td>-0.021484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, oxygen saturation pulse oximetry, known, qn, percent, all)</th>\n",
+       "      <td>-0.011589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, glasgow coma scale eye opening, known, ord, no_units, all)</th>\n",
+       "      <td>-0.006264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, blood pressure systolic, known, qn, mmHg, all)</th>\n",
+       "      <td>0.003986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, output urine, unknown, nom, no_units, 3686_Voiding qs)</th>\n",
+       "      <td>0.006649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, output urine, unknown, nom, no_units, 3686(ml)_No Void)</th>\n",
+       "      <td>0.006649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, output urine, unknown, nom, no_units, 3686(ml)_Voiding qs)</th>\n",
+       "      <td>0.006649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, glasgow coma scale motor, known, ord, no_units, all)</th>\n",
+       "      <td>0.009048</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LAST, blood pressure diastolic, known, qn, mmHg, all)</th>\n",
+       "      <td>0.015083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(STD, output urine, known, qn, mL, all)</th>\n",
+       "      <td>0.021332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(STD, hemoglobin, known, qn, g/dL, all)</th>\n",
+       "      <td>0.024475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, vasopressin, known, qn, units/min, all)</th>\n",
+       "      <td>0.025925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, respiratory rate, known, qn, insp/min, all)</th>\n",
+       "      <td>0.040039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LAST, respiratory rate, known, qn, insp/min, all)</th>\n",
+       "      <td>0.046997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, hemoglobin, known, qn, g/dL, all)</th>\n",
+       "      <td>0.071655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(LAST, norepinephrine, known, qn, mcg/kg/min, all)</th>\n",
+       "      <td>0.072510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(MEAN, heart rate, known, qn, beats/min, all)</th>\n",
+       "      <td>0.075928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(COUNT, hemoglobin, known, qn, g/dL, all)</th>\n",
+       "      <td>0.079651</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                           0\n",
+       "(LAST, hemoglobin, known, qn, g/dL, all)           -0.158447\n",
+       "(COUNT, output urine, known, qn, mL, all)          -0.077087\n",
+       "(LAST, oxygen saturation pulse oximetry, known,... -0.057892\n",
+       "(SUM, output urine, known, qn, mL, all)            -0.049957\n",
+       "(LAST, heart rate, known, qn, beats/min, all)      -0.047211\n",
+       "(MEAN, blood pressure diastolic, known, qn, mmH... -0.045105\n",
+       "(MEAN, norepinephrine, known, qn, mcg/kg/min, all) -0.037781\n",
+       "(LAST, blood pressure systolic, known, qn, mmHg... -0.025742\n",
+       "(COUNT, temperature body, known, qn, degF, all)    -0.022003\n",
+       "(COUNT, glasgow coma scale verbal, known, ord, ... -0.021484\n",
+       "(MEAN, oxygen saturation pulse oximetry, known,... -0.011589\n",
+       "(COUNT, glasgow coma scale eye opening, known, ... -0.006264\n",
+       "(MEAN, blood pressure systolic, known, qn, mmHg...  0.003986\n",
+       "(COUNT, output urine, unknown, nom, no_units, 3...  0.006649\n",
+       "(COUNT, output urine, unknown, nom, no_units, 3...  0.006649\n",
+       "(COUNT, output urine, unknown, nom, no_units, 3...  0.006649\n",
+       "(COUNT, glasgow coma scale motor, known, ord, n...  0.009048\n",
+       "(LAST, blood pressure diastolic, known, qn, mmH...  0.015083\n",
+       "(STD, output urine, known, qn, mL, all)             0.021332\n",
+       "(STD, hemoglobin, known, qn, g/dL, all)             0.024475\n",
+       "(COUNT, vasopressin, known, qn, units/min, all)     0.025925\n",
+       "(MEAN, respiratory rate, known, qn, insp/min, all)  0.040039\n",
+       "(LAST, respiratory rate, known, qn, insp/min, all)  0.046997\n",
+       "(MEAN, hemoglobin, known, qn, g/dL, all)            0.071655\n",
+       "(LAST, norepinephrine, known, qn, mcg/kg/min, all)  0.072510\n",
+       "(MEAN, heart rate, known, qn, beats/min, all)       0.075928\n",
+       "(COUNT, hemoglobin, known, qn, g/dL, all)           0.079651"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(lin_reg.coef_,index=ft_subset).sort_values().to_frame().astype(np.float16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale eye opening</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale motor</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale verbal</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SUM</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_No Void</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>temperature body</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>degF</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>vasopressin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>units/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   feature                         component   status variable_type  \\\n",
+       "13    LAST          blood pressure diastolic    known            qn   \n",
+       "10    MEAN          blood pressure diastolic    known            qn   \n",
+       "0     MEAN           blood pressure systolic    known            qn   \n",
+       "1     LAST           blood pressure systolic    known            qn   \n",
+       "25   COUNT    glasgow coma scale eye opening    known           ord   \n",
+       "23   COUNT          glasgow coma scale motor    known           ord   \n",
+       "24   COUNT         glasgow coma scale verbal    known           ord   \n",
+       "3     LAST                        heart rate    known            qn   \n",
+       "6     MEAN                        heart rate    known            qn   \n",
+       "19    LAST                        hemoglobin    known            qn   \n",
+       "18    MEAN                        hemoglobin    known            qn   \n",
+       "2    COUNT                        hemoglobin    known            qn   \n",
+       "14     STD                        hemoglobin    known            qn   \n",
+       "20    LAST                    norepinephrine    known            qn   \n",
+       "22    MEAN                    norepinephrine    known            qn   \n",
+       "11   COUNT                      output urine  unknown           nom   \n",
+       "9      SUM                      output urine    known            qn   \n",
+       "15   COUNT                      output urine  unknown           nom   \n",
+       "16   COUNT                      output urine    known            qn   \n",
+       "17     STD                      output urine    known            qn   \n",
+       "12   COUNT                      output urine  unknown           nom   \n",
+       "7     MEAN  oxygen saturation pulse oximetry    known            qn   \n",
+       "4     LAST  oxygen saturation pulse oximetry    known            qn   \n",
+       "8     LAST                  respiratory rate    known            qn   \n",
+       "5     MEAN                  respiratory rate    known            qn   \n",
+       "21   COUNT                  temperature body    known            qn   \n",
+       "26   COUNT                       vasopressin    known            qn   \n",
+       "\n",
+       "         units          description  \n",
+       "13        mmHg                  all  \n",
+       "10        mmHg                  all  \n",
+       "0         mmHg                  all  \n",
+       "1         mmHg                  all  \n",
+       "25    no_units                  all  \n",
+       "23    no_units                  all  \n",
+       "24    no_units                  all  \n",
+       "3    beats/min                  all  \n",
+       "6    beats/min                  all  \n",
+       "19        g/dL                  all  \n",
+       "18        g/dL                  all  \n",
+       "2         g/dL                  all  \n",
+       "14        g/dL                  all  \n",
+       "20  mcg/kg/min                  all  \n",
+       "22  mcg/kg/min                  all  \n",
+       "11    no_units      3686_Voiding qs  \n",
+       "9           mL                  all  \n",
+       "15    no_units     3686(ml)_No Void  \n",
+       "16          mL                  all  \n",
+       "17          mL                  all  \n",
+       "12    no_units  3686(ml)_Voiding qs  \n",
+       "7      percent                  all  \n",
+       "4      percent                  all  \n",
+       "8     insp/min                  all  \n",
+       "5     insp/min                  all  \n",
+       "21        degF                  all  \n",
+       "26   units/min                  all  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross Validation, K-Fold\n",
+      "R^2: 0.0352837618466, 0.00476129329963\n",
+      "RMSE: 1.08841685152, 0.0695178043988\n",
+      "\n",
+      "Cross Validation, ShuffleSplit\n",
+      "R^2: 0.0355914023088, 0.00252844609666\n",
+      "RMSE: 1.09085945366, 0.029331024701\n"
+     ]
+    }
+   ],
+   "source": [
+    "#lets try the magnitudal of change only\n",
+    "ft_subset = ft_to_keep[4:]\n",
+    "display(pd.DataFrame(ft_subset, columns = X_train.columns.names).sort_values('component'))\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = np.abs(y_train)\n",
+    "\n",
+    "run_crossval(pipeline,X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#what about DIRECTION only\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "log_reg = LogisticRegression()\n",
+    "\n",
+    "log_reg_pipeline = Pipeline([\n",
+    "        ('scaler',StandardScaler()),\n",
+    "        ('log_reg',log_reg)\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th>component</th>\n",
+       "      <th>status</th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>units</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure diastolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>blood pressure systolic</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mmHg</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale eye opening</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale motor</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>glasgow coma scale verbal</td>\n",
+       "      <td>known</td>\n",
+       "      <td>ord</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>heart rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>beats/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>hemoglobin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>g/dL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>norepinephrine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mcg/kg/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SUM</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_No Void</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>STD</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>output urine</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>nom</td>\n",
+       "      <td>no_units</td>\n",
+       "      <td>3686(ml)_Voiding qs</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>oxygen saturation pulse oximetry</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>percent</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LAST</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MEAN</td>\n",
+       "      <td>respiratory rate</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>insp/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>temperature body</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>degF</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>COUNT</td>\n",
+       "      <td>vasopressin</td>\n",
+       "      <td>known</td>\n",
+       "      <td>qn</td>\n",
+       "      <td>units/min</td>\n",
+       "      <td>all</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   feature                         component   status variable_type  \\\n",
+       "13    LAST          blood pressure diastolic    known            qn   \n",
+       "10    MEAN          blood pressure diastolic    known            qn   \n",
+       "0     MEAN           blood pressure systolic    known            qn   \n",
+       "1     LAST           blood pressure systolic    known            qn   \n",
+       "25   COUNT    glasgow coma scale eye opening    known           ord   \n",
+       "23   COUNT          glasgow coma scale motor    known           ord   \n",
+       "24   COUNT         glasgow coma scale verbal    known           ord   \n",
+       "3     LAST                        heart rate    known            qn   \n",
+       "6     MEAN                        heart rate    known            qn   \n",
+       "19    LAST                        hemoglobin    known            qn   \n",
+       "18    MEAN                        hemoglobin    known            qn   \n",
+       "2    COUNT                        hemoglobin    known            qn   \n",
+       "14     STD                        hemoglobin    known            qn   \n",
+       "20    LAST                    norepinephrine    known            qn   \n",
+       "22    MEAN                    norepinephrine    known            qn   \n",
+       "11   COUNT                      output urine  unknown           nom   \n",
+       "9      SUM                      output urine    known            qn   \n",
+       "15   COUNT                      output urine  unknown           nom   \n",
+       "16   COUNT                      output urine    known            qn   \n",
+       "17     STD                      output urine    known            qn   \n",
+       "12   COUNT                      output urine  unknown           nom   \n",
+       "7     MEAN  oxygen saturation pulse oximetry    known            qn   \n",
+       "4     LAST  oxygen saturation pulse oximetry    known            qn   \n",
+       "8     LAST                  respiratory rate    known            qn   \n",
+       "5     MEAN                  respiratory rate    known            qn   \n",
+       "21   COUNT                  temperature body    known            qn   \n",
+       "26   COUNT                       vasopressin    known            qn   \n",
+       "\n",
+       "         units          description  \n",
+       "13        mmHg                  all  \n",
+       "10        mmHg                  all  \n",
+       "0         mmHg                  all  \n",
+       "1         mmHg                  all  \n",
+       "25    no_units                  all  \n",
+       "23    no_units                  all  \n",
+       "24    no_units                  all  \n",
+       "3    beats/min                  all  \n",
+       "6    beats/min                  all  \n",
+       "19        g/dL                  all  \n",
+       "18        g/dL                  all  \n",
+       "2         g/dL                  all  \n",
+       "14        g/dL                  all  \n",
+       "20  mcg/kg/min                  all  \n",
+       "22  mcg/kg/min                  all  \n",
+       "11    no_units      3686_Voiding qs  \n",
+       "9           mL                  all  \n",
+       "15    no_units     3686(ml)_No Void  \n",
+       "16          mL                  all  \n",
+       "17          mL                  all  \n",
+       "12    no_units  3686(ml)_Voiding qs  \n",
+       "7      percent                  all  \n",
+       "4      percent                  all  \n",
+       "8     insp/min                  all  \n",
+       "5     insp/min                  all  \n",
+       "21        degF                  all  \n",
+       "26   units/min                  all  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train accuracy: 0.550456502017\n"
+     ]
+    }
+   ],
+   "source": [
+    "ft_subset = ft_to_keep[4:]\n",
+    "display(pd.DataFrame(ft_subset, columns = X_train.columns.names).sort_values('component'))\n",
+    "X = X_train.loc[:,ft_subset]\n",
+    "y = (y_train / np.abs(y_train)).fillna(0)\n",
+    "\n",
+    "log_reg_pipeline.fit(X,y)\n",
+    "print 'Train accuracy:',log_reg_pipeline.score(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+       "      <th>known</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">nom</th>\n",
+       "      <th>qn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>units</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+       "      <th>mL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>3686_Voiding qs</th>\n",
+       "      <th>3686(ml)_Voiding qs</th>\n",
+       "      <th>3686(ml)_No Void</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>100007</th>\n",
+       "      <th>2145-04-02 14:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100010</th>\n",
+       "      <th>2109-12-10 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">100012</th>\n",
+       "      <th>2177-03-14 10:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2177-03-15 08:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2177-03-15 14:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2177-03-15 20:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">100018</th>\n",
+       "      <th>2176-08-30 08:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2176-08-30 10:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2176-08-30 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100020</th>\n",
+       "      <th>2142-12-03 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">100028</th>\n",
+       "      <th>2142-12-24 14:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2142-12-25 02:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100034</th>\n",
+       "      <th>2164-04-23 10:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"16\" valign=\"top\">100035</th>\n",
+       "      <th>2115-02-22 08:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-22 12:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-22 14:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-22 16:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-22 18:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-22 20:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-22 22:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-23 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-23 10:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-23 12:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-23 20:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-24 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-24 10:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-25 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-02-25 08:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2115-03-18 10:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100036</th>\n",
+       "      <th>2187-07-17 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">199972</th>\n",
+       "      <th>2186-08-30 16:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2186-08-30 20:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2186-08-31 00:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2186-08-31 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2186-09-01 18:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"11\" valign=\"top\">199976</th>\n",
+       "      <th>2182-02-04 14:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-05 12:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-08 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-10 02:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-10 22:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-14 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-14 10:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-16 02:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-19 02:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-20 02:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182-02-21 04:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199979</th>\n",
+       "      <th>2182-02-06 14:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">199981</th>\n",
+       "      <th>2110-09-24 20:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2110-09-25 06:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">199988</th>\n",
+       "      <th>2169-02-07 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2169-02-07 10:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2169-02-07 16:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2169-02-07 22:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2169-02-10 04:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">199994</th>\n",
+       "      <th>2188-07-08 02:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2188-07-08 04:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2188-07-08 06:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">199998</th>\n",
+       "      <th>2119-02-20 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2119-02-20 20:00:00</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199999</th>\n",
+       "      <th>2136-04-06 14:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>103614 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "status                             unknown                      \\\n",
+       "variable_type                          nom                       \n",
+       "units                             no_units                       \n",
+       "description                3686_Voiding qs 3686(ml)_Voiding qs   \n",
+       "id     datetime                                                  \n",
+       "100007 2145-04-02 14:00:00               0                   0   \n",
+       "100010 2109-12-10 12:00:00               0                   0   \n",
+       "100012 2177-03-14 10:00:00               0                   0   \n",
+       "       2177-03-15 08:00:00               2                   2   \n",
+       "       2177-03-15 14:00:00               2                   2   \n",
+       "       2177-03-15 20:00:00               2                   2   \n",
+       "100018 2176-08-30 08:00:00               0                   0   \n",
+       "       2176-08-30 10:00:00               0                   0   \n",
+       "       2176-08-30 12:00:00               0                   0   \n",
+       "100020 2142-12-03 00:00:00               0                   0   \n",
+       "100028 2142-12-24 14:00:00               2                   2   \n",
+       "       2142-12-25 02:00:00               2                   2   \n",
+       "100034 2164-04-23 10:00:00               0                   0   \n",
+       "100035 2115-02-22 08:00:00               0                   0   \n",
+       "       2115-02-22 12:00:00               2                   2   \n",
+       "       2115-02-22 14:00:00               2                   2   \n",
+       "       2115-02-22 16:00:00               2                   2   \n",
+       "       2115-02-22 18:00:00               2                   2   \n",
+       "       2115-02-22 20:00:00               2                   2   \n",
+       "       2115-02-22 22:00:00               2                   2   \n",
+       "       2115-02-23 04:00:00               2                   2   \n",
+       "       2115-02-23 10:00:00               2                   2   \n",
+       "       2115-02-23 12:00:00               2                   2   \n",
+       "       2115-02-23 20:00:00               1                   1   \n",
+       "       2115-02-24 04:00:00               2                   2   \n",
+       "       2115-02-24 10:00:00               2                   2   \n",
+       "       2115-02-25 04:00:00               2                   2   \n",
+       "       2115-02-25 08:00:00               2                   2   \n",
+       "       2115-03-18 10:00:00               0                   0   \n",
+       "100036 2187-07-17 12:00:00               0                   0   \n",
+       "...                                    ...                 ...   \n",
+       "199972 2186-08-30 16:00:00               1                   1   \n",
+       "       2186-08-30 20:00:00               2                   2   \n",
+       "       2186-08-31 00:00:00               2                   2   \n",
+       "       2186-08-31 04:00:00               2                   2   \n",
+       "       2186-09-01 18:00:00               2                   2   \n",
+       "199976 2182-02-04 14:00:00               2                   2   \n",
+       "       2182-02-05 12:00:00               2                   2   \n",
+       "       2182-02-08 04:00:00               2                   2   \n",
+       "       2182-02-10 02:00:00               1                   1   \n",
+       "       2182-02-10 22:00:00               1                   1   \n",
+       "       2182-02-14 04:00:00               2                   2   \n",
+       "       2182-02-14 10:00:00               1                   1   \n",
+       "       2182-02-16 02:00:00               2                   2   \n",
+       "       2182-02-19 02:00:00               2                   2   \n",
+       "       2182-02-20 02:00:00               1                   1   \n",
+       "       2182-02-21 04:00:00               2                   2   \n",
+       "199979 2182-02-06 14:00:00               0                   0   \n",
+       "199981 2110-09-24 20:00:00               1                   1   \n",
+       "       2110-09-25 06:00:00               1                   1   \n",
+       "199988 2169-02-07 00:00:00               0                   0   \n",
+       "       2169-02-07 10:00:00               2                   2   \n",
+       "       2169-02-07 16:00:00               2                   2   \n",
+       "       2169-02-07 22:00:00               2                   2   \n",
+       "       2169-02-10 04:00:00               0                   0   \n",
+       "199994 2188-07-08 02:00:00               0                   0   \n",
+       "       2188-07-08 04:00:00               0                   0   \n",
+       "       2188-07-08 06:00:00               0                   0   \n",
+       "199998 2119-02-20 12:00:00               0                   0   \n",
+       "       2119-02-20 20:00:00               3                   3   \n",
+       "199999 2136-04-06 14:00:00               0                   0   \n",
+       "\n",
+       "status                                      known  \n",
+       "variable_type                                  qn  \n",
+       "units                                          mL  \n",
+       "description                3686(ml)_No Void   all  \n",
+       "id     datetime                                    \n",
+       "100007 2145-04-02 14:00:00                0     0  \n",
+       "100010 2109-12-10 12:00:00                0     0  \n",
+       "100012 2177-03-14 10:00:00                0     0  \n",
+       "       2177-03-15 08:00:00                2     2  \n",
+       "       2177-03-15 14:00:00                2     2  \n",
+       "       2177-03-15 20:00:00                2     2  \n",
+       "100018 2176-08-30 08:00:00                0     0  \n",
+       "       2176-08-30 10:00:00                0     0  \n",
+       "       2176-08-30 12:00:00                0     0  \n",
+       "100020 2142-12-03 00:00:00                0     0  \n",
+       "100028 2142-12-24 14:00:00                2     2  \n",
+       "       2142-12-25 02:00:00                2     2  \n",
+       "100034 2164-04-23 10:00:00                0     0  \n",
+       "100035 2115-02-22 08:00:00                0     0  \n",
+       "       2115-02-22 12:00:00                2     2  \n",
+       "       2115-02-22 14:00:00                2     2  \n",
+       "       2115-02-22 16:00:00                2     2  \n",
+       "       2115-02-22 18:00:00                2     2  \n",
+       "       2115-02-22 20:00:00                2     2  \n",
+       "       2115-02-22 22:00:00                2     2  \n",
+       "       2115-02-23 04:00:00                2     2  \n",
+       "       2115-02-23 10:00:00                2     2  \n",
+       "       2115-02-23 12:00:00                2     2  \n",
+       "       2115-02-23 20:00:00                1     1  \n",
+       "       2115-02-24 04:00:00                2     2  \n",
+       "       2115-02-24 10:00:00                2     2  \n",
+       "       2115-02-25 04:00:00                2     2  \n",
+       "       2115-02-25 08:00:00                2     2  \n",
+       "       2115-03-18 10:00:00                0     0  \n",
+       "100036 2187-07-17 12:00:00                0     0  \n",
+       "...                                     ...   ...  \n",
+       "199972 2186-08-30 16:00:00                1     1  \n",
+       "       2186-08-30 20:00:00                2     2  \n",
+       "       2186-08-31 00:00:00                2     2  \n",
+       "       2186-08-31 04:00:00                2     2  \n",
+       "       2186-09-01 18:00:00                2     2  \n",
+       "199976 2182-02-04 14:00:00                2     2  \n",
+       "       2182-02-05 12:00:00                2     2  \n",
+       "       2182-02-08 04:00:00                2     2  \n",
+       "       2182-02-10 02:00:00                1     1  \n",
+       "       2182-02-10 22:00:00                1     1  \n",
+       "       2182-02-14 04:00:00                2     2  \n",
+       "       2182-02-14 10:00:00                1     1  \n",
+       "       2182-02-16 02:00:00                2     2  \n",
+       "       2182-02-19 02:00:00                2     2  \n",
+       "       2182-02-20 02:00:00                1     1  \n",
+       "       2182-02-21 04:00:00                2     2  \n",
+       "199979 2182-02-06 14:00:00                0     0  \n",
+       "199981 2110-09-24 20:00:00                1     1  \n",
+       "       2110-09-25 06:00:00                1     1  \n",
+       "199988 2169-02-07 00:00:00                0     0  \n",
+       "       2169-02-07 10:00:00                2     2  \n",
+       "       2169-02-07 16:00:00                2     2  \n",
+       "       2169-02-07 22:00:00                2     2  \n",
+       "       2169-02-10 04:00:00                0     0  \n",
+       "199994 2188-07-08 02:00:00                0     0  \n",
+       "       2188-07-08 04:00:00                0     0  \n",
+       "       2188-07-08 06:00:00                0     0  \n",
+       "199998 2119-02-20 12:00:00                0     0  \n",
+       "       2119-02-20 20:00:00                3     3  \n",
+       "199999 2136-04-06 14:00:00                0     0  \n",
+       "\n",
+       "[103614 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " X_train.loc[:,ft_subset].loc[:,'COUNT'].loc[:,'output urine']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th>known</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">nom</th>\n",
+       "      <th>qn</th>\n",
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+       "    <tr>\n",
+       "      <th></th>\n",
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+       "      <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+       "      <th>no_units</th>\n",
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+       "      <th></th>\n",
+       "      <th>description</th>\n",
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+       "      <th>3686(ml)_No Void</th>\n",
+       "      <th>3686(ml)_Voiding qs</th>\n",
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+       "      <th>all</th>\n",
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+       "      <th rowspan=\"30\" valign=\"top\">100001</th>\n",
+       "      <th>2117-09-11 10:00:00</th>\n",
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+       "    <tr>\n",
+       "      <th>2117-09-11 14:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 16:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 18:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>2117-09-11 20:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 22:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 00:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 02:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 04:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 06:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>2117-09-12 08:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 10:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 12:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 14:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>2117-09-12 16:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>2117-09-12 18:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 20:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 22:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 00:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 02:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 04:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 06:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 08:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 10:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 14:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 16:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 18:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 20:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"30\" valign=\"top\">199999</th>\n",
+       "      <th>2136-04-08 02:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 04:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 06:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 08:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 10:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 12:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 14:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 16:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 18:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 20:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 22:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 00:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 02:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 04:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 06:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 08:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 10:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 14:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 16:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 18:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 20:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 22:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 02:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 04:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 06:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 08:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 10:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 12:00:00</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5143470 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "status                     known          unknown                      \\\n",
+       "variable_type                 qn              nom                       \n",
+       "units                         mL         no_units                       \n",
+       "description                  all 3686(ml)_No Void 3686(ml)_Voiding qs   \n",
+       "id     datetime                                                         \n",
+       "100001 2117-09-11 10:00:00     0                0                   0   \n",
+       "       2117-09-11 12:00:00     0                0                   0   \n",
+       "       2117-09-11 14:00:00     1                1                   1   \n",
+       "       2117-09-11 16:00:00     0                0                   0   \n",
+       "       2117-09-11 18:00:00     0                0                   0   \n",
+       "       2117-09-11 20:00:00     1                1                   1   \n",
+       "       2117-09-11 22:00:00     0                0                   0   \n",
+       "       2117-09-12 00:00:00     1                1                   1   \n",
+       "       2117-09-12 02:00:00     0                0                   0   \n",
+       "       2117-09-12 04:00:00     1                1                   1   \n",
+       "       2117-09-12 06:00:00     1                1                   1   \n",
+       "       2117-09-12 08:00:00     2                2                   2   \n",
+       "       2117-09-12 10:00:00     1                1                   1   \n",
+       "       2117-09-12 12:00:00     1                1                   1   \n",
+       "       2117-09-12 14:00:00     1                1                   1   \n",
+       "       2117-09-12 16:00:00     1                1                   1   \n",
+       "       2117-09-12 18:00:00     0                0                   0   \n",
+       "       2117-09-12 20:00:00     0                0                   0   \n",
+       "       2117-09-12 22:00:00     0                0                   0   \n",
+       "       2117-09-13 00:00:00     1                1                   1   \n",
+       "       2117-09-13 02:00:00     0                0                   0   \n",
+       "       2117-09-13 04:00:00     1                1                   1   \n",
+       "       2117-09-13 06:00:00     0                0                   0   \n",
+       "       2117-09-13 08:00:00     0                0                   0   \n",
+       "       2117-09-13 10:00:00     2                2                   2   \n",
+       "       2117-09-13 12:00:00     0                0                   0   \n",
+       "       2117-09-13 14:00:00     0                0                   0   \n",
+       "       2117-09-13 16:00:00     0                0                   0   \n",
+       "       2117-09-13 18:00:00     1                1                   1   \n",
+       "       2117-09-13 20:00:00     0                0                   0   \n",
+       "...                          ...              ...                 ...   \n",
+       "199999 2136-04-08 02:00:00     1                1                   1   \n",
+       "       2136-04-08 04:00:00     0                0                   0   \n",
+       "       2136-04-08 06:00:00     1                1                   1   \n",
+       "       2136-04-08 08:00:00     0                0                   0   \n",
+       "       2136-04-08 10:00:00     0                0                   0   \n",
+       "       2136-04-08 12:00:00     1                1                   1   \n",
+       "       2136-04-08 14:00:00     1                1                   1   \n",
+       "       2136-04-08 16:00:00     0                0                   0   \n",
+       "       2136-04-08 18:00:00     1                1                   1   \n",
+       "       2136-04-08 20:00:00     0                0                   0   \n",
+       "       2136-04-08 22:00:00     1                1                   1   \n",
+       "       2136-04-09 00:00:00     1                1                   1   \n",
+       "       2136-04-09 02:00:00     1                1                   1   \n",
+       "       2136-04-09 04:00:00     0                0                   0   \n",
+       "       2136-04-09 06:00:00     1                1                   1   \n",
+       "       2136-04-09 08:00:00     1                1                   1   \n",
+       "       2136-04-09 10:00:00     1                1                   1   \n",
+       "       2136-04-09 12:00:00     0                0                   0   \n",
+       "       2136-04-09 14:00:00     0                0                   0   \n",
+       "       2136-04-09 16:00:00     0                0                   0   \n",
+       "       2136-04-09 18:00:00     1                1                   1   \n",
+       "       2136-04-09 20:00:00     0                0                   0   \n",
+       "       2136-04-09 22:00:00     1                1                   1   \n",
+       "       2136-04-10 00:00:00     0                0                   0   \n",
+       "       2136-04-10 02:00:00     0                0                   0   \n",
+       "       2136-04-10 04:00:00     1                1                   1   \n",
+       "       2136-04-10 06:00:00     1                1                   1   \n",
+       "       2136-04-10 08:00:00     0                0                   0   \n",
+       "       2136-04-10 10:00:00     1                1                   1   \n",
+       "       2136-04-10 12:00:00     1                1                   1   \n",
+       "\n",
+       "status                                               \n",
+       "variable_type                                    qn  \n",
+       "units                                      no_units  \n",
+       "description                3686_Voiding qs      all  \n",
+       "id     datetime                                      \n",
+       "100001 2117-09-11 10:00:00               0        0  \n",
+       "       2117-09-11 12:00:00               0        0  \n",
+       "       2117-09-11 14:00:00               1        0  \n",
+       "       2117-09-11 16:00:00               0        0  \n",
+       "       2117-09-11 18:00:00               0        0  \n",
+       "       2117-09-11 20:00:00               1        0  \n",
+       "       2117-09-11 22:00:00               0        0  \n",
+       "       2117-09-12 00:00:00               1        0  \n",
+       "       2117-09-12 02:00:00               0        0  \n",
+       "       2117-09-12 04:00:00               1        0  \n",
+       "       2117-09-12 06:00:00               1        0  \n",
+       "       2117-09-12 08:00:00               2        0  \n",
+       "       2117-09-12 10:00:00               1        0  \n",
+       "       2117-09-12 12:00:00               1        0  \n",
+       "       2117-09-12 14:00:00               1        0  \n",
+       "       2117-09-12 16:00:00               1        0  \n",
+       "       2117-09-12 18:00:00               0        0  \n",
+       "       2117-09-12 20:00:00               0        0  \n",
+       "       2117-09-12 22:00:00               0        0  \n",
+       "       2117-09-13 00:00:00               1        0  \n",
+       "       2117-09-13 02:00:00               0        0  \n",
+       "       2117-09-13 04:00:00               1        0  \n",
+       "       2117-09-13 06:00:00               0        0  \n",
+       "       2117-09-13 08:00:00               0        0  \n",
+       "       2117-09-13 10:00:00               2        0  \n",
+       "       2117-09-13 12:00:00               0        0  \n",
+       "       2117-09-13 14:00:00               0        0  \n",
+       "       2117-09-13 16:00:00               0        0  \n",
+       "       2117-09-13 18:00:00               1        0  \n",
+       "       2117-09-13 20:00:00               0        0  \n",
+       "...                                    ...      ...  \n",
+       "199999 2136-04-08 02:00:00               1        0  \n",
+       "       2136-04-08 04:00:00               0        0  \n",
+       "       2136-04-08 06:00:00               1        0  \n",
+       "       2136-04-08 08:00:00               0        0  \n",
+       "       2136-04-08 10:00:00               0        0  \n",
+       "       2136-04-08 12:00:00               1        0  \n",
+       "       2136-04-08 14:00:00               1        0  \n",
+       "       2136-04-08 16:00:00               0        0  \n",
+       "       2136-04-08 18:00:00               1        0  \n",
+       "       2136-04-08 20:00:00               0        0  \n",
+       "       2136-04-08 22:00:00               1        0  \n",
+       "       2136-04-09 00:00:00               1        0  \n",
+       "       2136-04-09 02:00:00               1        0  \n",
+       "       2136-04-09 04:00:00               0        0  \n",
+       "       2136-04-09 06:00:00               1        0  \n",
+       "       2136-04-09 08:00:00               1        0  \n",
+       "       2136-04-09 10:00:00               1        0  \n",
+       "       2136-04-09 12:00:00               0        0  \n",
+       "       2136-04-09 14:00:00               0        0  \n",
+       "       2136-04-09 16:00:00               0        0  \n",
+       "       2136-04-09 18:00:00               1        0  \n",
+       "       2136-04-09 20:00:00               0        0  \n",
+       "       2136-04-09 22:00:00               1        0  \n",
+       "       2136-04-10 00:00:00               0        0  \n",
+       "       2136-04-10 02:00:00               0        0  \n",
+       "       2136-04-10 04:00:00               1        0  \n",
+       "       2136-04-10 06:00:00               1        0  \n",
+       "       2136-04-10 08:00:00               0        0  \n",
+       "       2136-04-10 10:00:00               1        0  \n",
+       "       2136-04-10 12:00:00               1        0  \n",
+       "\n",
+       "[5143470 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train_simple.loc[:,'COUNT'].loc[:,'output urine']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df_uop = utils.read_and_reconstruct('data/mimic_extract.h5','output urine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>405108 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "component                      output urine                                 \n",
+       "status                              unknown                                 \n",
+       "variable_type                           nom                                 \n",
+       "units                              no_units                                 \n",
+       "description                3686(ml)_No Void 3686(ml)_Voiding qs 3686_No Void\n",
+       "id     datetime                                                             \n",
+       "100023 2130-05-28 09:30:00                1                   0            0\n",
+       "100025 2191-07-13 13:00:00                0                   1            0\n",
+       "       2191-07-13 16:00:00                0                   1            0\n",
+       "       2191-07-13 21:30:00                0                   1            0\n",
+       "       2191-07-14 00:30:00                0                   1            0\n",
+       "       2191-07-14 03:00:00                0                   1            0\n",
+       "       2191-07-14 06:00:00                0                   1            0\n",
+       "       2191-07-14 09:00:00                0                   1            0\n",
+       "       2191-07-14 12:00:00                0                   1            0\n",
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+       "       2191-07-14 20:00:00                0                   1            0\n",
+       "       2191-07-15 00:00:00                0                   1            0\n",
+       "100029 2185-04-17 16:00:00                0                   1            0\n",
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+       "       2185-04-18 08:00:00                0                   1            0\n",
+       "       2185-04-18 12:00:00                0                   1            0\n",
+       "       2185-04-18 16:00:00                0                   1            0\n",
+       "       2185-04-18 20:00:00                0                   1            0\n",
+       "       2185-04-19 00:00:00                0                   1            0\n",
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+       "       2185-04-19 08:00:00                0                   1            0\n",
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+       "       2185-04-19 16:45:00                0                   1            0\n",
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+       "       2185-04-20 00:00:00                0                   1            0\n",
+       "       2185-04-20 04:00:00                0                   1            0\n",
+       "       2185-04-20 08:00:00                0                   1            0\n",
+       "...                                     ...                 ...          ...\n",
+       "199918 2111-05-23 16:30:00                0                   1            0\n",
+       "       2111-05-23 20:30:00                0                   1            0\n",
+       "       2111-05-24 00:30:00                0                   1            0\n",
+       "       2111-05-24 04:30:00                0                   1            0\n",
+       "       2111-05-24 07:30:00                0                   1            0\n",
+       "       2111-05-24 11:30:00                0                   1            0\n",
+       "       2111-05-24 16:00:00                0                   1            0\n",
+       "       2111-05-24 20:30:00                0                   1            0\n",
+       "       2111-05-25 00:30:00                0                   1            0\n",
+       "       2111-05-25 04:30:00                0                   1            0\n",
+       "       2111-05-25 08:30:00                0                   1            0\n",
+       "       2111-05-25 12:30:00                0                   1            0\n",
+       "       2111-05-25 16:30:00                0                   1            0\n",
+       "       2111-05-25 20:30:00                0                   1            0\n",
+       "       2111-05-26 00:30:00                0                   1            0\n",
+       "       2111-05-26 04:30:00                0                   1            0\n",
+       "       2111-05-26 08:30:00                0                   1            0\n",
+       "       2111-05-26 12:30:00                0                   1            0\n",
+       "       2111-05-26 16:30:00                0                   1            0\n",
+       "       2111-05-26 20:30:00                0                   1            0\n",
+       "       2111-05-27 00:30:00                0                   1            0\n",
+       "       2111-05-27 04:30:00                0                   1            0\n",
+       "       2111-05-27 08:30:00                0                   1            0\n",
+       "       2111-05-27 12:30:00                0                   1            0\n",
+       "       2111-05-27 17:00:00                0                   1            0\n",
+       "       2111-05-27 20:30:00                0                   1            0\n",
+       "       2111-05-27 23:30:00                0                   1            0\n",
+       "       2111-05-28 03:30:00                0                   1            0\n",
+       "       2111-05-28 08:00:00                0                   1            0\n",
+       "       2111-05-28 11:30:00                0                   1            0\n",
+       "\n",
+       "[405108 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_uop_nom = df_uop.loc[:,idx[:,:,'nom',:,:]]\n",
+    "\n",
+    "df_uop_nom.loc[df_uop_nom.sum(axis=1) > 0].iloc[:,:3].sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df_head = df_uop.iloc[:1000,:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df_head.iloc[0:5,0] = 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
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+       "      <th>component</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">output urine</th>\n",
+       "    </tr>\n",
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+       "      <th></th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>units</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>226559</th>\n",
+       "      <th>226560</th>\n",
+       "      <th>40055(ml)</th>\n",
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+       "      <th>40405(ml)</th>\n",
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+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th>datetime</th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2117-09-13 16:09:00</th>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2117-09-15 00:07:00</th>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"12\" valign=\"top\">100003</th>\n",
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+       "      <td>150.0</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <th>2150-04-17 20:00:00</th>\n",
+       "      <td>200.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150-04-17 21:00:00</th>\n",
+       "      <td>80.0</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150-04-17 22:00:00</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2150-04-17 23:00:00</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2150-04-18 08:00:00</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150-04-18 09:00:00</th>\n",
+       "      <td>160.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"30\" valign=\"top\">100031</th>\n",
+       "      <th>2140-11-14 04:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 05:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 06:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 07:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 08:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2140-11-14 09:00:00</th>\n",
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+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2140-11-14 10:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2140-11-14 11:00:00</th>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <th>2140-11-14 12:00:00</th>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <th>2140-11-14 16:00:00</th>\n",
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+       "      <th>2140-11-14 17:00:00</th>\n",
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+       "      <th>2140-11-14 19:00:00</th>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>2140-11-14 20:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 21:00:00</th>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "    </tr>\n",
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+       "      <th>2140-11-15 01:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 02:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 04:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 06:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 07:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 08:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 09:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 10:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 11:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 12:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 13:00:00</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "component                  output urine                                      \n",
+       "status                            known                                      \n",
+       "variable_type                        qn                                      \n",
+       "units                                mL                                      \n",
+       "description                      226559  226560 40055(ml) 40069(ml) 40405(ml)\n",
+       "id     datetime                                                              \n",
+       "100001 2117-09-11 13:49:00         25.0   300.0       NaN       NaN       NaN\n",
+       "       2117-09-11 18:34:00         25.0   400.0       NaN       NaN       NaN\n",
+       "       2117-09-11 22:00:00         25.0   200.0       NaN       NaN       NaN\n",
+       "       2117-09-12 02:00:00         25.0   500.0       NaN       NaN       NaN\n",
+       "       2117-09-12 04:00:00         25.0   650.0       NaN       NaN       NaN\n",
+       "       2117-09-12 06:00:00          NaN   450.0       NaN       NaN       NaN\n",
+       "       2117-09-12 07:00:00          NaN   600.0       NaN       NaN       NaN\n",
+       "       2117-09-12 09:40:00          NaN   400.0       NaN       NaN       NaN\n",
+       "       2117-09-12 11:11:00          NaN   400.0       NaN       NaN       NaN\n",
+       "       2117-09-12 13:12:00          NaN   400.0       NaN       NaN       NaN\n",
+       "       2117-09-12 14:48:00          NaN   400.0       NaN       NaN       NaN\n",
+       "       2117-09-12 22:00:00          NaN   500.0       NaN       NaN       NaN\n",
+       "       2117-09-13 02:00:00          NaN   200.0       NaN       NaN       NaN\n",
+       "       2117-09-13 08:00:00          NaN   500.0       NaN       NaN       NaN\n",
+       "       2117-09-13 09:00:00          NaN   500.0       NaN       NaN       NaN\n",
+       "       2117-09-13 16:09:00          NaN   800.0       NaN       NaN       NaN\n",
+       "       2117-09-14 04:08:00          NaN   400.0       NaN       NaN       NaN\n",
+       "       2117-09-15 00:07:00          NaN  1200.0       NaN       NaN       NaN\n",
+       "100003 2150-04-17 19:00:00        150.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-17 20:00:00        200.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-17 21:00:00         80.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-17 22:00:00         50.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-17 23:00:00         60.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 00:00:00        100.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 01:00:00        200.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 02:00:00        140.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 04:00:00        180.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 06:00:00        200.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 08:00:00        360.0     NaN       NaN       NaN       NaN\n",
+       "       2150-04-18 09:00:00        160.0     NaN       NaN       NaN       NaN\n",
+       "...                                 ...     ...       ...       ...       ...\n",
+       "100031 2140-11-14 04:00:00          NaN     NaN      30.0       NaN       NaN\n",
+       "       2140-11-14 05:00:00          NaN     NaN      40.0       NaN       NaN\n",
+       "       2140-11-14 06:00:00          NaN     NaN      40.0       NaN       NaN\n",
+       "       2140-11-14 07:00:00          NaN     NaN      33.0       NaN       NaN\n",
+       "       2140-11-14 08:00:00          NaN     NaN      55.0       NaN       NaN\n",
+       "       2140-11-14 09:00:00          NaN     NaN     400.0       NaN       NaN\n",
+       "       2140-11-14 10:00:00          NaN     NaN     300.0       NaN       NaN\n",
+       "       2140-11-14 11:00:00          NaN     NaN     160.0       NaN       NaN\n",
+       "       2140-11-14 12:00:00          NaN     NaN      80.0       NaN       NaN\n",
+       "       2140-11-14 13:00:00          NaN     NaN      35.0       NaN       NaN\n",
+       "       2140-11-14 14:00:00          NaN     NaN      35.0       NaN       NaN\n",
+       "       2140-11-14 15:00:00          NaN     NaN      65.0       NaN       NaN\n",
+       "       2140-11-14 16:00:00          NaN     NaN      35.0       NaN       NaN\n",
+       "       2140-11-14 17:00:00          NaN     NaN     320.0       NaN       NaN\n",
+       "       2140-11-14 18:00:00          NaN     NaN     450.0       NaN       NaN\n",
+       "       2140-11-14 19:00:00          NaN     NaN     320.0       NaN       NaN\n",
+       "       2140-11-14 20:00:00          NaN     NaN      90.0       NaN       NaN\n",
+       "       2140-11-14 21:00:00          NaN     NaN      45.0       NaN       NaN\n",
+       "       2140-11-14 23:00:00          NaN     NaN      60.0       NaN       NaN\n",
+       "       2140-11-15 01:00:00          NaN     NaN      50.0       NaN       NaN\n",
+       "       2140-11-15 02:00:00          NaN     NaN      22.0       NaN       NaN\n",
+       "       2140-11-15 04:00:00          NaN     NaN      40.0       NaN       NaN\n",
+       "       2140-11-15 06:00:00          NaN     NaN      80.0       NaN       NaN\n",
+       "       2140-11-15 07:00:00          NaN     NaN      30.0       NaN       NaN\n",
+       "       2140-11-15 08:00:00          NaN     NaN      80.0       NaN       NaN\n",
+       "       2140-11-15 09:00:00          NaN     NaN      35.0       NaN       NaN\n",
+       "       2140-11-15 10:00:00          NaN     NaN      40.0       NaN       NaN\n",
+       "       2140-11-15 11:00:00          NaN     NaN      45.0       NaN       NaN\n",
+       "       2140-11-15 12:00:00          NaN     NaN      50.0       NaN       NaN\n",
+       "       2140-11-15 13:00:00          NaN     NaN      35.0       NaN       NaN\n",
+       "\n",
+       "[1000 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "resampled = df_head.groupby(level='id').resample(rule='12H',level='datetime',label='right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>component</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">output urine</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">known</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">qn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>units</th>\n",
+       "      <th colspan=\"5\" halign=\"left\">mL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>226559</th>\n",
+       "      <th>226560</th>\n",
+       "      <th>40055(ml)</th>\n",
+       "      <th>40069(ml)</th>\n",
+       "      <th>40405(ml)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"8\" valign=\"top\">100001</th>\n",
+       "      <th>2117-09-12 00:00:00</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 12:00:00</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-14 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-14 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-15 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-15 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">100003</th>\n",
+       "      <th>2150-04-18 00:00:00</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150-04-18 12:00:00</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150-04-19 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150-04-19 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"10\" valign=\"top\">100006</th>\n",
+       "      <th>2108-04-07 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-07 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-08 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-08 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-09 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-09 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-10 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-10 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-11 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2108-04-11 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"8\" valign=\"top\">100007</th>\n",
+       "      <th>2145-04-01 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-01 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-02 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-02 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-03 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-03 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-04 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145-04-04 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"15\" valign=\"top\">100029</th>\n",
+       "      <th>2185-04-25 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-26 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-26 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-27 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-27 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-28 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-28 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-29 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-29 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-30 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-04-30 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-05-01 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-05-01 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-05-02 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2185-05-02 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">100030</th>\n",
+       "      <th>2199-12-05 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199-12-05 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199-12-06 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199-12-06 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199-12-07 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199-12-07 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"9\" valign=\"top\">100031</th>\n",
+       "      <th>2140-11-12 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-12 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-13 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-13 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-14 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-15 12:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2140-11-16 00:00:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>158 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "component                  output urine                                     \n",
+       "status                            known                                     \n",
+       "variable_type                        qn                                     \n",
+       "units                                mL                                     \n",
+       "description                      226559 226560 40055(ml) 40069(ml) 40405(ml)\n",
+       "id     datetime                                                             \n",
+       "100001 2117-09-12 00:00:00            3      3         0         0         0\n",
+       "       2117-09-12 12:00:00            2      6         0         0         0\n",
+       "       2117-09-13 00:00:00            0      3         0         0         0\n",
+       "       2117-09-13 12:00:00            0      3         0         0         0\n",
+       "       2117-09-14 00:00:00            0      1         0         0         0\n",
+       "       2117-09-14 12:00:00            0      1         0         0         0\n",
+       "       2117-09-15 00:00:00            0      0         0         0         0\n",
+       "       2117-09-15 12:00:00            0      1         0         0         0\n",
+       "100003 2150-04-18 00:00:00            5      0         0         0         0\n",
+       "       2150-04-18 12:00:00            8      1         0         0         0\n",
+       "       2150-04-19 00:00:00            0      4         0         0         0\n",
+       "       2150-04-19 12:00:00            0      4         0         0         0\n",
+       "100006 2108-04-07 00:00:00            0      0         0         2         0\n",
+       "       2108-04-07 12:00:00            0      0         0         3         0\n",
+       "       2108-04-08 00:00:00            0      0         0         5         0\n",
+       "       2108-04-08 12:00:00            0      0         0         4         0\n",
+       "       2108-04-09 00:00:00            0      0         4         8         0\n",
+       "       2108-04-09 12:00:00            0      0         9         0         0\n",
+       "       2108-04-10 00:00:00            0      0         3         0         0\n",
+       "       2108-04-10 12:00:00            0      0         2         1         0\n",
+       "       2108-04-11 00:00:00            0      0         0         4         0\n",
+       "       2108-04-11 12:00:00            0      0         0         3         0\n",
+       "100007 2145-04-01 00:00:00            0      0        11         0         0\n",
+       "       2145-04-01 12:00:00            0      0        12         0         0\n",
+       "       2145-04-02 00:00:00            0      0         6         0         0\n",
+       "       2145-04-02 12:00:00            0      0         0         0         0\n",
+       "       2145-04-03 00:00:00            0      0         7         0         0\n",
+       "       2145-04-03 12:00:00            0      0         6         0         0\n",
+       "       2145-04-04 00:00:00            0      0         0         1         0\n",
+       "       2145-04-04 12:00:00            0      0         0         2         0\n",
+       "...                                 ...    ...       ...       ...       ...\n",
+       "100029 2185-04-25 12:00:00            0      0         0         0         0\n",
+       "       2185-04-26 00:00:00            0      0         0         0         0\n",
+       "       2185-04-26 12:00:00            0      0         0         0         0\n",
+       "       2185-04-27 00:00:00            0      0         0         0         0\n",
+       "       2185-04-27 12:00:00            0      0         0         0         0\n",
+       "       2185-04-28 00:00:00            0      0         0         0         0\n",
+       "       2185-04-28 12:00:00            0      0         0         0         0\n",
+       "       2185-04-29 00:00:00            0      0         0         0         0\n",
+       "       2185-04-29 12:00:00            0      0         0         0         0\n",
+       "       2185-04-30 00:00:00            0      0         0         0         0\n",
+       "       2185-04-30 12:00:00            0      0         0         0         0\n",
+       "       2185-05-01 00:00:00            0      0         0         0         0\n",
+       "       2185-05-01 12:00:00            0      0         0         0         0\n",
+       "       2185-05-02 00:00:00            0      0         0         0         0\n",
+       "       2185-05-02 12:00:00            0      0         0         0         0\n",
+       "100030 2199-12-05 00:00:00            0      0         0         2         0\n",
+       "       2199-12-05 12:00:00            0      0         0         1         0\n",
+       "       2199-12-06 00:00:00            0      0         0         3         0\n",
+       "       2199-12-06 12:00:00            0      0         0         1         0\n",
+       "       2199-12-07 00:00:00            0      0         0         1         0\n",
+       "       2199-12-07 12:00:00            0      0         0         1         0\n",
+       "100031 2140-11-12 00:00:00            0      0         5         0         0\n",
+       "       2140-11-12 12:00:00            0      0        13         0         0\n",
+       "       2140-11-13 00:00:00            0      0        13         0         0\n",
+       "       2140-11-13 12:00:00            0      0        13         0         0\n",
+       "       2140-11-14 00:00:00            0      0        12         0         0\n",
+       "       2140-11-14 12:00:00            0      0        12         0         0\n",
+       "       2140-11-15 00:00:00            0      0        11         0         0\n",
+       "       2140-11-15 12:00:00            0      0         9         0         0\n",
+       "       2140-11-16 00:00:00            0      0         2         0         0\n",
+       "\n",
+       "[158 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resampled.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>feature</th>\n",
+       "      <th colspan=\"10\" halign=\"left\">MEAN</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"9\" halign=\"left\">COUNT</th>\n",
+       "      <th>LABEL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>component</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">blood pressure systolic</th>\n",
+       "      <th>oxygen saturation pulse oximetry</th>\n",
+       "      <th>lactate</th>\n",
+       "      <th>hemoglobin</th>\n",
+       "      <th>blood pressure mean</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">vasopressin</th>\n",
+       "      <th>weight body</th>\n",
+       "      <th>normal saline</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">output urine</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">lactated ringers</th>\n",
+       "      <th colspan=\"4\" halign=\"left\">respiratory rate</th>\n",
+       "      <th>lactate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th>known</th>\n",
+       "      <th>unknown</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>known</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">known</th>\n",
+       "      <th>unknown</th>\n",
+       "      <th>known</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+       "      <th>known</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>variable_type</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>...</th>\n",
+       "      <th>nom</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">nom</th>\n",
+       "      <th>qn</th>\n",
+       "      <th>qn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>units</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>cc/min</th>\n",
+       "      <th>percent</th>\n",
+       "      <th>mmol/L</th>\n",
+       "      <th>g/dL</th>\n",
+       "      <th>mmHg</th>\n",
+       "      <th>units</th>\n",
+       "      <th>units/min</th>\n",
+       "      <th>kg</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>...</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>mL</th>\n",
+       "      <th>mL/hr</th>\n",
+       "      <th>no_units</th>\n",
+       "      <th>insp/min</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+       "      <th>Breath</th>\n",
+       "      <th>mmol/L</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>...</th>\n",
+       "      <th>3686_Voiding qs</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "      <th>618_&gt;60/min retracts</th>\n",
+       "      <th>618_&gt;60/minute</th>\n",
+       "      <th>all</th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"30\" valign=\"top\">100001</th>\n",
+       "      <th>2117-09-11 10:00:00</th>\n",
+       "      <td>121.931614</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>78.475979</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 12:00:00</th>\n",
+       "      <td>121.931614</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.749218</td>\n",
+       "      <td>11.097371</td>\n",
+       "      <td>78.475979</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 14:00:00</th>\n",
+       "      <td>192.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>191.623236</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 16:00:00</th>\n",
+       "      <td>130.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.022222</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>82.750000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>304.495290</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 18:00:00</th>\n",
+       "      <td>157.200000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>99.250000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>89.996990</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 20:00:00</th>\n",
+       "      <td>174.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>98.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>108.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>4.992476</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-11 22:00:00</th>\n",
+       "      <td>187.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>119.250000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>4.598437</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 00:00:00</th>\n",
+       "      <td>181.250000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.250000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>111.250000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>5.012718</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 02:00:00</th>\n",
+       "      <td>191.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.250000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>121.750000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>112.499998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 04:00:00</th>\n",
+       "      <td>192.333333</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.333333</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>121.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>99.999998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 06:00:00</th>\n",
+       "      <td>193.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>98.800000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.180000</td>\n",
+       "      <td>121.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>99.999998</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 08:00:00</th>\n",
+       "      <td>191.400000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>122.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>42.677410</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 10:00:00</th>\n",
+       "      <td>150.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.500000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>98.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.397169</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 12:00:00</th>\n",
+       "      <td>177.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.500000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>112.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.846205</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 14:00:00</th>\n",
+       "      <td>199.750000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>98.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>122.750000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.995883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 16:00:00</th>\n",
+       "      <td>207.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.250000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>129.750000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.995883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 18:00:00</th>\n",
+       "      <td>144.666667</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>86.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.995883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 20:00:00</th>\n",
+       "      <td>152.750000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.500000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>96.750000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>2.497427</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-12 22:00:00</th>\n",
+       "      <td>154.666667</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>1.997942</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 00:00:00</th>\n",
+       "      <td>152.666667</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.333333</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>91.666667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>1.997942</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 02:00:00</th>\n",
+       "      <td>170.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.500000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.225000</td>\n",
+       "      <td>99.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>2.497427</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 04:00:00</th>\n",
+       "      <td>182.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.333333</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>113.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>2.332260</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 06:00:00</th>\n",
+       "      <td>177.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>116.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.000896</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 08:00:00</th>\n",
+       "      <td>176.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>123.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.000896</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 10:00:00</th>\n",
+       "      <td>170.166667</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.666667</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>119.666667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.000896</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 12:00:00</th>\n",
+       "      <td>184.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>119.166667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.844802</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 14:00:00</th>\n",
+       "      <td>197.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>125.600000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>4.013458</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 16:00:00</th>\n",
+       "      <td>197.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.250000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>129.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.254352</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 18:00:00</th>\n",
+       "      <td>170.333333</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>114.666667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.001483</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2117-09-13 20:00:00</th>\n",
+       "      <td>167.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>97.000000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>11.200000</td>\n",
+       "      <td>110.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>82.797527</td>\n",
+       "      <td>3.001483</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"30\" valign=\"top\">199999</th>\n",
+       "      <th>2136-04-08 02:00:00</th>\n",
+       "      <td>144.333333</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.333333</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.300000</td>\n",
+       "      <td>72.333333</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 04:00:00</th>\n",
+       "      <td>140.333333</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.666667</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.700000</td>\n",
+       "      <td>72.666667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 06:00:00</th>\n",
+       "      <td>147.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>75.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 08:00:00</th>\n",
+       "      <td>106.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>107.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 10:00:00</th>\n",
+       "      <td>148.666667</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.333333</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>79.666667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 12:00:00</th>\n",
+       "      <td>139.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>87.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 14:00:00</th>\n",
+       "      <td>138.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>86.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 16:00:00</th>\n",
+       "      <td>141.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>75.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 18:00:00</th>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 20:00:00</th>\n",
+       "      <td>133.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>81.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-08 22:00:00</th>\n",
+       "      <td>135.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>75.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 00:00:00</th>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 02:00:00</th>\n",
+       "      <td>148.333333</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.333333</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>11.900000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 04:00:00</th>\n",
+       "      <td>146.400000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>90.600000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.380000</td>\n",
+       "      <td>74.200000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 06:00:00</th>\n",
+       "      <td>142.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>95.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>73.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 08:00:00</th>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>72.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 10:00:00</th>\n",
+       "      <td>123.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>79.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 12:00:00</th>\n",
+       "      <td>119.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 14:00:00</th>\n",
+       "      <td>137.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>71.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 16:00:00</th>\n",
+       "      <td>128.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>70.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 18:00:00</th>\n",
+       "      <td>141.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>74.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 20:00:00</th>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>93.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>75.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-09 22:00:00</th>\n",
+       "      <td>137.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>72.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 00:00:00</th>\n",
+       "      <td>120.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>64.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 02:00:00</th>\n",
+       "      <td>127.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.333333</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 04:00:00</th>\n",
+       "      <td>128.666667</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.333333</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.566667</td>\n",
+       "      <td>65.666667</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 06:00:00</th>\n",
+       "      <td>127.500000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>95.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>57.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 08:00:00</th>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>92.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 10:00:00</th>\n",
+       "      <td>138.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>94.000000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2136-04-10 12:00:00</th>\n",
+       "      <td>136.000000</td>\n",
+       "      <td>69.78821</td>\n",
+       "      <td>96.500000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>108.500000</td>\n",
+       "      <td>1.045024</td>\n",
+       "      <td>0.401782</td>\n",
+       "      <td>71.400000</td>\n",
+       "      <td>98.220699</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5143470 rows × 117 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "feature                                       MEAN            \\\n",
+       "component                  blood pressure systolic             \n",
+       "status                                       known   unknown   \n",
+       "variable_type                                   qn        qn   \n",
+       "units                                         mmHg    cc/min   \n",
+       "description                                    all       all   \n",
+       "id     datetime                                                \n",
+       "100001 2117-09-11 10:00:00              121.931614  69.78821   \n",
+       "       2117-09-11 12:00:00              121.931614  69.78821   \n",
+       "       2117-09-11 14:00:00              192.000000  69.78821   \n",
+       "       2117-09-11 16:00:00              130.500000  69.78821   \n",
+       "       2117-09-11 18:00:00              157.200000  69.78821   \n",
+       "       2117-09-11 20:00:00              174.500000  69.78821   \n",
+       "       2117-09-11 22:00:00              187.500000  69.78821   \n",
+       "       2117-09-12 00:00:00              181.250000  69.78821   \n",
+       "       2117-09-12 02:00:00              191.500000  69.78821   \n",
+       "       2117-09-12 04:00:00              192.333333  69.78821   \n",
+       "       2117-09-12 06:00:00              193.000000  69.78821   \n",
+       "       2117-09-12 08:00:00              191.400000  69.78821   \n",
+       "       2117-09-12 10:00:00              150.500000  69.78821   \n",
+       "       2117-09-12 12:00:00              177.500000  69.78821   \n",
+       "       2117-09-12 14:00:00              199.750000  69.78821   \n",
+       "       2117-09-12 16:00:00              207.500000  69.78821   \n",
+       "       2117-09-12 18:00:00              144.666667  69.78821   \n",
+       "       2117-09-12 20:00:00              152.750000  69.78821   \n",
+       "       2117-09-12 22:00:00              154.666667  69.78821   \n",
+       "       2117-09-13 00:00:00              152.666667  69.78821   \n",
+       "       2117-09-13 02:00:00              170.000000  69.78821   \n",
+       "       2117-09-13 04:00:00              182.000000  69.78821   \n",
+       "       2117-09-13 06:00:00              177.500000  69.78821   \n",
+       "       2117-09-13 08:00:00              176.000000  69.78821   \n",
+       "       2117-09-13 10:00:00              170.166667  69.78821   \n",
+       "       2117-09-13 12:00:00              184.500000  69.78821   \n",
+       "       2117-09-13 14:00:00              197.000000  69.78821   \n",
+       "       2117-09-13 16:00:00              197.500000  69.78821   \n",
+       "       2117-09-13 18:00:00              170.333333  69.78821   \n",
+       "       2117-09-13 20:00:00              167.000000  69.78821   \n",
+       "...                                            ...       ...   \n",
+       "199999 2136-04-08 02:00:00              144.333333  69.78821   \n",
+       "       2136-04-08 04:00:00              140.333333  69.78821   \n",
+       "       2136-04-08 06:00:00              147.000000  69.78821   \n",
+       "       2136-04-08 08:00:00              106.500000  69.78821   \n",
+       "       2136-04-08 10:00:00              148.666667  69.78821   \n",
+       "       2136-04-08 12:00:00              139.500000  69.78821   \n",
+       "       2136-04-08 14:00:00              138.500000  69.78821   \n",
+       "       2136-04-08 16:00:00              141.000000  69.78821   \n",
+       "       2136-04-08 18:00:00              143.000000  69.78821   \n",
+       "       2136-04-08 20:00:00              133.500000  69.78821   \n",
+       "       2136-04-08 22:00:00              135.500000  69.78821   \n",
+       "       2136-04-09 00:00:00              145.000000  69.78821   \n",
+       "       2136-04-09 02:00:00              148.333333  69.78821   \n",
+       "       2136-04-09 04:00:00              146.400000  69.78821   \n",
+       "       2136-04-09 06:00:00              142.500000  69.78821   \n",
+       "       2136-04-09 08:00:00              143.000000  69.78821   \n",
+       "       2136-04-09 10:00:00              123.500000  69.78821   \n",
+       "       2136-04-09 12:00:00              119.000000  69.78821   \n",
+       "       2136-04-09 14:00:00              137.500000  69.78821   \n",
+       "       2136-04-09 16:00:00              128.500000  69.78821   \n",
+       "       2136-04-09 18:00:00              141.500000  69.78821   \n",
+       "       2136-04-09 20:00:00              140.000000  69.78821   \n",
+       "       2136-04-09 22:00:00              137.000000  69.78821   \n",
+       "       2136-04-10 00:00:00              120.500000  69.78821   \n",
+       "       2136-04-10 02:00:00              127.000000  69.78821   \n",
+       "       2136-04-10 04:00:00              128.666667  69.78821   \n",
+       "       2136-04-10 06:00:00              127.500000  69.78821   \n",
+       "       2136-04-10 08:00:00              143.000000  69.78821   \n",
+       "       2136-04-10 10:00:00              138.000000  69.78821   \n",
+       "       2136-04-10 12:00:00              136.000000  69.78821   \n",
+       "\n",
+       "feature                                                                \\\n",
+       "component                  oxygen saturation pulse oximetry   lactate   \n",
+       "status                                                known     known   \n",
+       "variable_type                                            qn        qn   \n",
+       "units                                               percent    mmol/L   \n",
+       "description                                             all       all   \n",
+       "id     datetime                                                         \n",
+       "100001 2117-09-11 10:00:00                        97.022222  1.900000   \n",
+       "       2117-09-11 12:00:00                        97.022222  1.749218   \n",
+       "       2117-09-11 14:00:00                        97.022222  1.900000   \n",
+       "       2117-09-11 16:00:00                        97.022222  1.900000   \n",
+       "       2117-09-11 18:00:00                        99.250000  1.900000   \n",
+       "       2117-09-11 20:00:00                        98.000000  1.900000   \n",
+       "       2117-09-11 22:00:00                        97.000000  1.900000   \n",
+       "       2117-09-12 00:00:00                        97.250000  1.900000   \n",
+       "       2117-09-12 02:00:00                        96.250000  1.900000   \n",
+       "       2117-09-12 04:00:00                        97.333333  1.900000   \n",
+       "       2117-09-12 06:00:00                        98.800000  1.900000   \n",
+       "       2117-09-12 08:00:00                        99.000000  1.900000   \n",
+       "       2117-09-12 10:00:00                        97.500000  1.900000   \n",
+       "       2117-09-12 12:00:00                        97.500000  1.900000   \n",
+       "       2117-09-12 14:00:00                        98.000000  1.900000   \n",
+       "       2117-09-12 16:00:00                        97.250000  1.900000   \n",
+       "       2117-09-12 18:00:00                        97.000000  1.900000   \n",
+       "       2117-09-12 20:00:00                        96.500000  1.900000   \n",
+       "       2117-09-12 22:00:00                        97.000000  1.900000   \n",
+       "       2117-09-13 00:00:00                        96.333333  1.900000   \n",
+       "       2117-09-13 02:00:00                        96.500000  1.900000   \n",
+       "       2117-09-13 04:00:00                        97.333333  1.900000   \n",
+       "       2117-09-13 06:00:00                        97.000000  1.900000   \n",
+       "       2117-09-13 08:00:00                        99.000000  1.900000   \n",
+       "       2117-09-13 10:00:00                        97.666667  1.900000   \n",
+       "       2117-09-13 12:00:00                        97.000000  1.900000   \n",
+       "       2117-09-13 14:00:00                        96.000000  1.900000   \n",
+       "       2117-09-13 16:00:00                        96.250000  1.900000   \n",
+       "       2117-09-13 18:00:00                        97.000000  1.900000   \n",
+       "       2117-09-13 20:00:00                        97.000000  1.900000   \n",
+       "...                                                     ...       ...   \n",
+       "199999 2136-04-08 02:00:00                        96.333333  1.800000   \n",
+       "       2136-04-08 04:00:00                        94.666667  1.800000   \n",
+       "       2136-04-08 06:00:00                        94.500000  1.800000   \n",
+       "       2136-04-08 08:00:00                        92.500000  1.800000   \n",
+       "       2136-04-08 10:00:00                        92.333333  1.800000   \n",
+       "       2136-04-08 12:00:00                        92.500000  1.800000   \n",
+       "       2136-04-08 14:00:00                        94.500000  1.800000   \n",
+       "       2136-04-08 16:00:00                        93.500000  1.800000   \n",
+       "       2136-04-08 18:00:00                        92.000000  1.800000   \n",
+       "       2136-04-08 20:00:00                        94.500000  1.800000   \n",
+       "       2136-04-08 22:00:00                        92.500000  1.800000   \n",
+       "       2136-04-09 00:00:00                        93.500000  1.800000   \n",
+       "       2136-04-09 02:00:00                        94.333333  1.800000   \n",
+       "       2136-04-09 04:00:00                        90.600000  1.800000   \n",
+       "       2136-04-09 06:00:00                        95.500000  1.800000   \n",
+       "       2136-04-09 08:00:00                        93.500000  1.800000   \n",
+       "       2136-04-09 10:00:00                        94.500000  1.800000   \n",
+       "       2136-04-09 12:00:00                        94.000000  1.800000   \n",
+       "       2136-04-09 14:00:00                        93.500000  1.800000   \n",
+       "       2136-04-09 16:00:00                        93.000000  1.800000   \n",
+       "       2136-04-09 18:00:00                        94.000000  1.800000   \n",
+       "       2136-04-09 20:00:00                        93.000000  1.800000   \n",
+       "       2136-04-09 22:00:00                        94.000000  1.800000   \n",
+       "       2136-04-10 00:00:00                        96.500000  1.800000   \n",
+       "       2136-04-10 02:00:00                        94.333333  1.800000   \n",
+       "       2136-04-10 04:00:00                        92.333333  1.800000   \n",
+       "       2136-04-10 06:00:00                        95.500000  1.800000   \n",
+       "       2136-04-10 08:00:00                        92.000000  1.800000   \n",
+       "       2136-04-10 10:00:00                        94.000000  1.800000   \n",
+       "       2136-04-10 12:00:00                        96.500000  1.800000   \n",
+       "\n",
+       "feature                                                                \\\n",
+       "component                  hemoglobin blood pressure mean vasopressin   \n",
+       "status                          known               known       known   \n",
+       "variable_type                      qn                  qn          qn   \n",
+       "units                            g/dL                mmHg       units   \n",
+       "description                       all                 all         all   \n",
+       "id     datetime                                                         \n",
+       "100001 2117-09-11 10:00:00  13.000000           78.475979    1.045024   \n",
+       "       2117-09-11 12:00:00  11.097371           78.475979    1.045024   \n",
+       "       2117-09-11 14:00:00  13.000000          122.000000    1.045024   \n",
+       "       2117-09-11 16:00:00  13.000000           82.750000    1.045024   \n",
+       "       2117-09-11 18:00:00  13.000000           99.000000    1.045024   \n",
+       "       2117-09-11 20:00:00  12.500000          108.000000    1.045024   \n",
+       "       2117-09-11 22:00:00  11.000000          119.250000    1.045024   \n",
+       "       2117-09-12 00:00:00  11.000000          111.250000    1.045024   \n",
+       "       2117-09-12 02:00:00  11.000000          121.750000    1.045024   \n",
+       "       2117-09-12 04:00:00  11.000000          121.000000    1.045024   \n",
+       "       2117-09-12 06:00:00  11.180000          121.000000    1.045024   \n",
+       "       2117-09-12 08:00:00  11.300000          122.000000    1.045024   \n",
+       "       2117-09-12 10:00:00  11.300000           98.000000    1.045024   \n",
+       "       2117-09-12 12:00:00  11.300000          112.000000    1.045024   \n",
+       "       2117-09-12 14:00:00  11.300000          122.750000    1.045024   \n",
+       "       2117-09-12 16:00:00  11.300000          129.750000    1.045024   \n",
+       "       2117-09-12 18:00:00  11.300000           86.000000    1.045024   \n",
+       "       2117-09-12 20:00:00  11.300000           96.750000    1.045024   \n",
+       "       2117-09-12 22:00:00  11.300000           94.000000    1.045024   \n",
+       "       2117-09-13 00:00:00  11.300000           91.666667    1.045024   \n",
+       "       2117-09-13 02:00:00  11.225000           99.500000    1.045024   \n",
+       "       2117-09-13 04:00:00  11.200000          113.000000    1.045024   \n",
+       "       2117-09-13 06:00:00  11.200000          116.000000    1.045024   \n",
+       "       2117-09-13 08:00:00  11.200000          123.000000    1.045024   \n",
+       "       2117-09-13 10:00:00  11.200000          119.666667    1.045024   \n",
+       "       2117-09-13 12:00:00  11.200000          119.166667    1.045024   \n",
+       "       2117-09-13 14:00:00  11.200000          125.600000    1.045024   \n",
+       "       2117-09-13 16:00:00  11.200000          129.000000    1.045024   \n",
+       "       2117-09-13 18:00:00  11.200000          114.666667    1.045024   \n",
+       "       2117-09-13 20:00:00  11.200000          110.500000    1.045024   \n",
+       "...                               ...                 ...         ...   \n",
+       "199999 2136-04-08 02:00:00  11.300000           72.333333    1.045024   \n",
+       "       2136-04-08 04:00:00  11.700000           72.666667    1.045024   \n",
+       "       2136-04-08 06:00:00  11.900000           75.500000    1.045024   \n",
+       "       2136-04-08 08:00:00  11.900000          107.500000    1.045024   \n",
+       "       2136-04-08 10:00:00  11.900000           79.666667    1.045024   \n",
+       "       2136-04-08 12:00:00  11.900000           87.500000    1.045024   \n",
+       "       2136-04-08 14:00:00  11.900000           86.500000    1.045024   \n",
+       "       2136-04-08 16:00:00  11.900000           75.500000    1.045024   \n",
+       "       2136-04-08 18:00:00  11.900000           80.000000    1.045024   \n",
+       "       2136-04-08 20:00:00  11.900000           81.000000    1.045024   \n",
+       "       2136-04-08 22:00:00  11.900000           75.500000    1.045024   \n",
+       "       2136-04-09 00:00:00  11.900000           75.000000    1.045024   \n",
+       "       2136-04-09 02:00:00  11.900000           74.000000    1.045024   \n",
+       "       2136-04-09 04:00:00  12.380000           74.200000    1.045024   \n",
+       "       2136-04-09 06:00:00  12.500000           73.500000    1.045024   \n",
+       "       2136-04-09 08:00:00  12.500000           72.500000    1.045024   \n",
+       "       2136-04-09 10:00:00  12.500000           79.500000    1.045024   \n",
+       "       2136-04-09 12:00:00  12.500000           75.000000    1.045024   \n",
+       "       2136-04-09 14:00:00  12.500000           71.500000    1.045024   \n",
+       "       2136-04-09 16:00:00  12.500000           70.500000    1.045024   \n",
+       "       2136-04-09 18:00:00  12.500000           74.500000    1.045024   \n",
+       "       2136-04-09 20:00:00  12.500000           75.500000    1.045024   \n",
+       "       2136-04-09 22:00:00  12.500000           72.500000    1.045024   \n",
+       "       2136-04-10 00:00:00  12.500000           64.000000    1.045024   \n",
+       "       2136-04-10 02:00:00  12.500000           72.000000    1.045024   \n",
+       "       2136-04-10 04:00:00  12.566667           65.666667    1.045024   \n",
+       "       2136-04-10 06:00:00  12.600000           57.500000    1.045024   \n",
+       "       2136-04-10 08:00:00  12.600000           71.000000    1.045024   \n",
+       "       2136-04-10 10:00:00  12.600000           77.000000    1.045024   \n",
+       "       2136-04-10 12:00:00  12.600000          108.500000    1.045024   \n",
+       "\n",
+       "feature                                                          ...    \\\n",
+       "component                            weight body normal saline   ...     \n",
+       "status                                     known         known   ...     \n",
+       "variable_type                                 qn            qn   ...     \n",
+       "units                      units/min          kg         mL/hr   ...     \n",
+       "description                      all         all           all   ...     \n",
+       "id     datetime                                                  ...     \n",
+       "100001 2117-09-11 10:00:00  0.401782   82.797527     98.220699   ...     \n",
+       "       2117-09-11 12:00:00  0.401782   82.797527     98.220699   ...     \n",
+       "       2117-09-11 14:00:00  0.401782   82.797527    191.623236   ...     \n",
+       "       2117-09-11 16:00:00  0.401782   82.797527    304.495290   ...     \n",
+       "       2117-09-11 18:00:00  0.401782   82.797527     89.996990   ...     \n",
+       "       2117-09-11 20:00:00  0.401782   82.797527      4.992476   ...     \n",
+       "       2117-09-11 22:00:00  0.401782   82.797527      4.598437   ...     \n",
+       "       2117-09-12 00:00:00  0.401782   82.797527      5.012718   ...     \n",
+       "       2117-09-12 02:00:00  0.401782   82.797527    112.499998   ...     \n",
+       "       2117-09-12 04:00:00  0.401782   82.797527     99.999998   ...     \n",
+       "       2117-09-12 06:00:00  0.401782   82.797527     99.999998   ...     \n",
+       "       2117-09-12 08:00:00  0.401782   82.797527     42.677410   ...     \n",
+       "       2117-09-12 10:00:00  0.401782   82.797527      3.397169   ...     \n",
+       "       2117-09-12 12:00:00  0.401782   82.797527      3.846205   ...     \n",
+       "       2117-09-12 14:00:00  0.401782   82.797527      3.995883   ...     \n",
+       "       2117-09-12 16:00:00  0.401782   82.797527      3.995883   ...     \n",
+       "       2117-09-12 18:00:00  0.401782   82.797527      3.995883   ...     \n",
+       "       2117-09-12 20:00:00  0.401782   82.797527      2.497427   ...     \n",
+       "       2117-09-12 22:00:00  0.401782   82.797527      1.997942   ...     \n",
+       "       2117-09-13 00:00:00  0.401782   82.797527      1.997942   ...     \n",
+       "       2117-09-13 02:00:00  0.401782   82.797527      2.497427   ...     \n",
+       "       2117-09-13 04:00:00  0.401782   82.797527      2.332260   ...     \n",
+       "       2117-09-13 06:00:00  0.401782   82.797527      3.000896   ...     \n",
+       "       2117-09-13 08:00:00  0.401782   82.797527      3.000896   ...     \n",
+       "       2117-09-13 10:00:00  0.401782   82.797527      3.000896   ...     \n",
+       "       2117-09-13 12:00:00  0.401782   82.797527      3.844802   ...     \n",
+       "       2117-09-13 14:00:00  0.401782   82.797527      4.013458   ...     \n",
+       "       2117-09-13 16:00:00  0.401782   82.797527      3.254352   ...     \n",
+       "       2117-09-13 18:00:00  0.401782   82.797527      3.001483   ...     \n",
+       "       2117-09-13 20:00:00  0.401782   82.797527      3.001483   ...     \n",
+       "...                              ...         ...           ...   ...     \n",
+       "199999 2136-04-08 02:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 04:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 06:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 08:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 10:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 12:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 14:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 16:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 18:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 20:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-08 22:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-09 00:00:00  0.401782   71.500000     98.220699   ...     \n",
+       "       2136-04-09 02:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 04:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 06:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 08:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 10:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 12:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 14:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 16:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 18:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 20:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-09 22:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 00:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 02:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 04:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 06:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 08:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 10:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "       2136-04-10 12:00:00  0.401782   71.400000     98.220699   ...     \n",
+       "\n",
+       "feature                              COUNT                                  \\\n",
+       "component                     output urine          lactated ringers         \n",
+       "status                             unknown                     known         \n",
+       "variable_type                          nom       qn               qn         \n",
+       "units                             no_units no_units               mL mL/hr   \n",
+       "description                3686_Voiding qs      all              all   all   \n",
+       "id     datetime                                                              \n",
+       "100001 2117-09-11 10:00:00               0        0                0     0   \n",
+       "       2117-09-11 12:00:00               0        0                0     0   \n",
+       "       2117-09-11 14:00:00               1        0                0     0   \n",
+       "       2117-09-11 16:00:00               0        0                0     0   \n",
+       "       2117-09-11 18:00:00               0        0                0     0   \n",
+       "       2117-09-11 20:00:00               1        0                0     0   \n",
+       "       2117-09-11 22:00:00               0        0                0     0   \n",
+       "       2117-09-12 00:00:00               1        0                0     0   \n",
+       "       2117-09-12 02:00:00               0        0                0     0   \n",
+       "       2117-09-12 04:00:00               1        0                0     0   \n",
+       "       2117-09-12 06:00:00               1        0                1     0   \n",
+       "       2117-09-12 08:00:00               2        0                1     0   \n",
+       "       2117-09-12 10:00:00               1        0                1     0   \n",
+       "       2117-09-12 12:00:00               1        0                0     0   \n",
+       "       2117-09-12 14:00:00               1        0                0     0   \n",
+       "       2117-09-12 16:00:00               1        0                1     1   \n",
+       "       2117-09-12 18:00:00               0        0                0     1   \n",
+       "       2117-09-12 20:00:00               0        0                0     0   \n",
+       "       2117-09-12 22:00:00               0        0                0     0   \n",
+       "       2117-09-13 00:00:00               1        0                0     0   \n",
+       "       2117-09-13 02:00:00               0        0                0     0   \n",
+       "       2117-09-13 04:00:00               1        0                0     0   \n",
+       "       2117-09-13 06:00:00               0        0                0     0   \n",
+       "       2117-09-13 08:00:00               0        0                0     0   \n",
+       "       2117-09-13 10:00:00               2        0                0     0   \n",
+       "       2117-09-13 12:00:00               0        0                1     0   \n",
+       "       2117-09-13 14:00:00               0        0                1     0   \n",
+       "       2117-09-13 16:00:00               0        0                0     0   \n",
+       "       2117-09-13 18:00:00               1        0                0     0   \n",
+       "       2117-09-13 20:00:00               0        0                0     0   \n",
+       "...                                    ...      ...              ...   ...   \n",
+       "199999 2136-04-08 02:00:00               1        0                0     0   \n",
+       "       2136-04-08 04:00:00               0        0                0     0   \n",
+       "       2136-04-08 06:00:00               1        0                0     0   \n",
+       "       2136-04-08 08:00:00               0        0                0     0   \n",
+       "       2136-04-08 10:00:00               0        0                0     0   \n",
+       "       2136-04-08 12:00:00               1        0                0     0   \n",
+       "       2136-04-08 14:00:00               1        0                0     0   \n",
+       "       2136-04-08 16:00:00               0        0                0     0   \n",
+       "       2136-04-08 18:00:00               1        0                0     0   \n",
+       "       2136-04-08 20:00:00               0        0                0     0   \n",
+       "       2136-04-08 22:00:00               1        0                0     0   \n",
+       "       2136-04-09 00:00:00               1        0                0     0   \n",
+       "       2136-04-09 02:00:00               1        0                0     0   \n",
+       "       2136-04-09 04:00:00               0        0                0     0   \n",
+       "       2136-04-09 06:00:00               1        0                0     0   \n",
+       "       2136-04-09 08:00:00               1        0                0     0   \n",
+       "       2136-04-09 10:00:00               1        0                0     0   \n",
+       "       2136-04-09 12:00:00               0        0                0     0   \n",
+       "       2136-04-09 14:00:00               0        0                0     0   \n",
+       "       2136-04-09 16:00:00               0        0                0     0   \n",
+       "       2136-04-09 18:00:00               1        0                0     0   \n",
+       "       2136-04-09 20:00:00               0        0                0     0   \n",
+       "       2136-04-09 22:00:00               1        0                0     0   \n",
+       "       2136-04-10 00:00:00               0        0                0     0   \n",
+       "       2136-04-10 02:00:00               0        0                0     0   \n",
+       "       2136-04-10 04:00:00               1        0                0     0   \n",
+       "       2136-04-10 06:00:00               1        0                0     0   \n",
+       "       2136-04-10 08:00:00               0        0                0     0   \n",
+       "       2136-04-10 10:00:00               1        0                0     0   \n",
+       "       2136-04-10 12:00:00               1        0                0     0   \n",
+       "\n",
+       "feature                                                                    \\\n",
+       "component                           respiratory rate                        \n",
+       "status                      unknown            known              unknown   \n",
+       "variable_type                    qn               qn                  nom   \n",
+       "units                      no_units         insp/min             no_units   \n",
+       "description                     all              all 618_>60/min retracts   \n",
+       "id     datetime                                                             \n",
+       "100001 2117-09-11 10:00:00        0                0                    0   \n",
+       "       2117-09-11 12:00:00        0                0                    0   \n",
+       "       2117-09-11 14:00:00        0                3                    3   \n",
+       "       2117-09-11 16:00:00        0                2                    2   \n",
+       "       2117-09-11 18:00:00        0                2                    2   \n",
+       "       2117-09-11 20:00:00        0                1                    1   \n",
+       "       2117-09-11 22:00:00        0                0                    0   \n",
+       "       2117-09-12 00:00:00        0                0                    0   \n",
+       "       2117-09-12 02:00:00        0                0                    0   \n",
+       "       2117-09-12 04:00:00        0                0                    0   \n",
+       "       2117-09-12 06:00:00        0                0                    0   \n",
+       "       2117-09-12 08:00:00        0                0                    0   \n",
+       "       2117-09-12 10:00:00        0                0                    0   \n",
+       "       2117-09-12 12:00:00        0                0                    0   \n",
+       "       2117-09-12 14:00:00        0                0                    0   \n",
+       "       2117-09-12 16:00:00        0                0                    0   \n",
+       "       2117-09-12 18:00:00        0                0                    0   \n",
+       "       2117-09-12 20:00:00        0                0                    0   \n",
+       "       2117-09-12 22:00:00        0                0                    0   \n",
+       "       2117-09-13 00:00:00        0                0                    0   \n",
+       "       2117-09-13 02:00:00        0                0                    0   \n",
+       "       2117-09-13 04:00:00        0                0                    0   \n",
+       "       2117-09-13 06:00:00        0                0                    0   \n",
+       "       2117-09-13 08:00:00        0                0                    0   \n",
+       "       2117-09-13 10:00:00        0                2                    2   \n",
+       "       2117-09-13 12:00:00        0                0                    0   \n",
+       "       2117-09-13 14:00:00        0                2                    2   \n",
+       "       2117-09-13 16:00:00        0                1                    1   \n",
+       "       2117-09-13 18:00:00        0                1                    1   \n",
+       "       2117-09-13 20:00:00        0                0                    0   \n",
+       "...                             ...              ...                  ...   \n",
+       "199999 2136-04-08 02:00:00        0                2                    2   \n",
+       "       2136-04-08 04:00:00        0                2                    2   \n",
+       "       2136-04-08 06:00:00        0                2                    2   \n",
+       "       2136-04-08 08:00:00        0                2                    2   \n",
+       "       2136-04-08 10:00:00        0                2                    2   \n",
+       "       2136-04-08 12:00:00        0                2                    2   \n",
+       "       2136-04-08 14:00:00        0                2                    2   \n",
+       "       2136-04-08 16:00:00        0                2                    2   \n",
+       "       2136-04-08 18:00:00        0                2                    2   \n",
+       "       2136-04-08 20:00:00        0                2                    2   \n",
+       "       2136-04-08 22:00:00        0                2                    2   \n",
+       "       2136-04-09 00:00:00        0                2                    2   \n",
+       "       2136-04-09 02:00:00        0                2                    2   \n",
+       "       2136-04-09 04:00:00        0                4                    4   \n",
+       "       2136-04-09 06:00:00        0                2                    2   \n",
+       "       2136-04-09 08:00:00        0                2                    2   \n",
+       "       2136-04-09 10:00:00        0                2                    2   \n",
+       "       2136-04-09 12:00:00        0                2                    2   \n",
+       "       2136-04-09 14:00:00        0                2                    2   \n",
+       "       2136-04-09 16:00:00        0                2                    2   \n",
+       "       2136-04-09 18:00:00        0                2                    2   \n",
+       "       2136-04-09 20:00:00        0                2                    2   \n",
+       "       2136-04-09 22:00:00        0                2                    2   \n",
+       "       2136-04-10 00:00:00        0                2                    2   \n",
+       "       2136-04-10 02:00:00        0                2                    2   \n",
+       "       2136-04-10 04:00:00        0                2                    2   \n",
+       "       2136-04-10 06:00:00        0                2                    2   \n",
+       "       2136-04-10 08:00:00        0                2                    2   \n",
+       "       2136-04-10 10:00:00        0                2                    2   \n",
+       "       2136-04-10 12:00:00        0                2                    2   \n",
+       "\n",
+       "feature                                            LABEL  \n",
+       "component                                        lactate  \n",
+       "status                                             known  \n",
+       "variable_type                                 qn      qn  \n",
+       "units                                     Breath  mmol/L  \n",
+       "description                618_>60/minute    all     all  \n",
+       "id     datetime                                           \n",
+       "100001 2117-09-11 10:00:00              0      0     1.9  \n",
+       "       2117-09-11 12:00:00              0      0     NaN  \n",
+       "       2117-09-11 14:00:00              3      0     NaN  \n",
+       "       2117-09-11 16:00:00              2      0     NaN  \n",
+       "       2117-09-11 18:00:00              2      0     NaN  \n",
+       "       2117-09-11 20:00:00              1      0     NaN  \n",
+       "       2117-09-11 22:00:00              0      0     NaN  \n",
+       "       2117-09-12 00:00:00              0      0     NaN  \n",
+       "       2117-09-12 02:00:00              0      0     NaN  \n",
+       "       2117-09-12 04:00:00              0      0     NaN  \n",
+       "       2117-09-12 06:00:00              0      0     NaN  \n",
+       "       2117-09-12 08:00:00              0      0     NaN  \n",
+       "       2117-09-12 10:00:00              0      0     NaN  \n",
+       "       2117-09-12 12:00:00              0      0     NaN  \n",
+       "       2117-09-12 14:00:00              0      0     NaN  \n",
+       "       2117-09-12 16:00:00              0      0     NaN  \n",
+       "       2117-09-12 18:00:00              0      0     NaN  \n",
+       "       2117-09-12 20:00:00              0      0     NaN  \n",
+       "       2117-09-12 22:00:00              0      0     NaN  \n",
+       "       2117-09-13 00:00:00              0      0     NaN  \n",
+       "       2117-09-13 02:00:00              0      0     NaN  \n",
+       "       2117-09-13 04:00:00              0      0     NaN  \n",
+       "       2117-09-13 06:00:00              0      0     NaN  \n",
+       "       2117-09-13 08:00:00              0      0     NaN  \n",
+       "       2117-09-13 10:00:00              2      0     NaN  \n",
+       "       2117-09-13 12:00:00              0      0     NaN  \n",
+       "       2117-09-13 14:00:00              2      0     NaN  \n",
+       "       2117-09-13 16:00:00              1      0     NaN  \n",
+       "       2117-09-13 18:00:00              1      0     NaN  \n",
+       "       2117-09-13 20:00:00              0      0     NaN  \n",
+       "...                                   ...    ...     ...  \n",
+       "199999 2136-04-08 02:00:00              2      0     NaN  \n",
+       "       2136-04-08 04:00:00              2      0     NaN  \n",
+       "       2136-04-08 06:00:00              2      0     NaN  \n",
+       "       2136-04-08 08:00:00              2      0     NaN  \n",
+       "       2136-04-08 10:00:00              2      0     NaN  \n",
+       "       2136-04-08 12:00:00              2      0     NaN  \n",
+       "       2136-04-08 14:00:00              2      0     NaN  \n",
+       "       2136-04-08 16:00:00              2      0     NaN  \n",
+       "       2136-04-08 18:00:00              2      0     NaN  \n",
+       "       2136-04-08 20:00:00              2      0     NaN  \n",
+       "       2136-04-08 22:00:00              2      0     NaN  \n",
+       "       2136-04-09 00:00:00              2      0     NaN  \n",
+       "       2136-04-09 02:00:00              2      0     NaN  \n",
+       "       2136-04-09 04:00:00              4      0     NaN  \n",
+       "       2136-04-09 06:00:00              2      0     NaN  \n",
+       "       2136-04-09 08:00:00              2      0     NaN  \n",
+       "       2136-04-09 10:00:00              2      0     NaN  \n",
+       "       2136-04-09 12:00:00              2      0     NaN  \n",
+       "       2136-04-09 14:00:00              2      0     NaN  \n",
+       "       2136-04-09 16:00:00              2      0     NaN  \n",
+       "       2136-04-09 18:00:00              2      0     NaN  \n",
+       "       2136-04-09 20:00:00              2      0     NaN  \n",
+       "       2136-04-09 22:00:00              2      0     NaN  \n",
+       "       2136-04-10 00:00:00              2      0     NaN  \n",
+       "       2136-04-10 02:00:00              2      0     NaN  \n",
+       "       2136-04-10 04:00:00              2      0     NaN  \n",
+       "       2136-04-10 06:00:00              2      0     NaN  \n",
+       "       2136-04-10 08:00:00              2      0     NaN  \n",
+       "       2136-04-10 10:00:00              2      0     NaN  \n",
+       "       2136-04-10 12:00:00              2      0     NaN  \n",
+       "\n",
+       "[5143470 rows x 117 columns]"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train_simple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [Root]",
+   "language": "python",
+   "name": "Python [Root]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}