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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "from IPython.display import display, HTML\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Set up"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Our first step is to instantiate some of the objects we will need to clean and featurize the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import units\n",
+ "import icu_data_defs\n",
+ "import mimic\n",
+ "import logger\n",
+ "import pandas as pd\n",
+ "import utils"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#random state used for random things\n",
+ "random_state=42\n",
+ "\n",
+ "#Create a unit registry\n",
+ "ureg = units.MedicalUreg()\n",
+ "\n",
+ "#We will need a data dictionary for some of the cleaning steps\n",
+ "data_dict = icu_data_defs.data_dictionary('config/data_definitions.xlsx')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we will define the features we wish to use in multivariate regression"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The **labels**, in this case, are going to be a random lactate randomly sampled from the hospital admissions. There is a small gotcha in certain admissions where the VERY FIRST observation timewise is a lactate measurement. Clinically, this is somewhat bizarre. Practically, it means that these \"first lactates\" are ignored and not considered in sampling. If an admission has only one lactate, and that is the first lactate, then that admission is excluded."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.base import BaseEstimator,TransformerMixin\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "import features\n",
+ "import transformers\n",
+ "\n",
+ "from constants import ALL,column_names,variable_type,SEG_ID\n",
+ "\n",
+ "#will ensure that there is at least 1 lactate before the sampled lactate\n",
+ "class keep_one_before_sample(features.preserve_datetime_sample):\n",
+ " \n",
+ " def transform(self, df, **transform_params):\n",
+ " df = df.groupby(level=column_names.ID).apply(lambda grp: grp.iloc[1:]).reset_index(0,drop=True)\n",
+ " return super(keep_one_before_sample,self).transform(df)\n",
+ " \n",
+ "\n",
+ "hdf5_fname = 'data/mimic_simple_all'\n",
+ "path = 'cleaned'\n",
+ "data_needs = [(data_dict.components.LACTATE,ALL)]\n",
+ "\n",
+ "\n",
+ "label_cleaners = Pipeline ([\n",
+ " ('quantitative only',transformers.filter_var_type([variable_type.QUANTITATIVE])),\n",
+ " ('combine_like_columns',transformers.combine_like_cols()),\n",
+ " ('max_col',transformers.max_col_only()),\n",
+ " ('two_or_more_lactate',transformers.more_than_n_component(1,data_dict.components.LACTATE)),\n",
+ " ]) \n",
+ "\n",
+ "sampler = keep_one_before_sample(random_state)\n",
+ "sample_lactate = features.simple_featurizer(\n",
+ " sampler,\n",
+ " transformers.do_nothing(),\n",
+ " [{column_names.COMPONENT : data_dict.components.LACTATE}],\n",
+ " sampler.name + '_' + data_dict.components.LACTATE\n",
+ ")\n",
+ "\n",
+ "label_pipeline = Pipeline ([\n",
+ " ('data_loaader',features.data_loader(hdf5_fname,path,data_needs)),\n",
+ " ('cleaners',label_cleaners),\n",
+ " ('sampler',sample_lactate)\n",
+ " ])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-26 16:50:43) Make DF for 1 components...\n",
+ "lactate\n",
+ "(2017-06-26 16:50:43)>> LACTATE: 1/1\n",
+ "(2017-06-26 16:50:43)>>>> Opening...\n",
+ "(2017-06-26 16:50:45)<<<< DONE (2.0s)\n",
+ "(2017-06-26 16:50:45)>>>> Filter & sort - (177451, 63)\n",
+ "(2017-06-26 16:50:45)<<<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45)>>>> Convert to dask - (2960, 63)\n",
+ "(2017-06-26 16:50:45)<<<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45)>>>> Join\n",
+ "(2017-06-26 16:50:45)<<<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45)<< DONE (2.0s)\n",
+ "(2017-06-26 16:50:45)>> Dask DF back to pandas\n",
+ "(2017-06-26 16:50:45)<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45) DONE (2.0s)\n",
+ "(2017-06-26 16:50:45) FIT Combine like columns (2960, 63)\n",
+ "(2017-06-26 16:50:45)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-26 16:50:45)<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45)>> ('lactate', 'unknown', 'nom', 'no_units')\n",
+ "(2017-06-26 16:50:45)<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-26 16:50:45)<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45) DONE (0.0s)\n",
+ "(2017-06-26 16:50:45) TRANSFORM Combine like columns (2960, 63)\n",
+ "(2017-06-26 16:50:45)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-26 16:50:45)<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-26 16:50:45)<< DONE (0.0s)\n",
+ "(2017-06-26 16:50:45) DONE (0.0s)\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>lactate_known_qn_mmol/L_all_SAMPLE</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>datetime</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>100009</th>\n",
+ " <th>2162-05-17 17:14:00</th>\n",
+ " <td>1.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>100095</th>\n",
+ " <th>2108-10-01 06:53:00</th>\n",
+ " <td>1.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>100262</th>\n",
+ " <th>2142-04-29 18:46:00</th>\n",
+ " <td>3.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>100765</th>\n",
+ " <th>2126-03-12 03:57:00</th>\n",
+ " <td>1.3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101148</th>\n",
+ " <th>2197-12-17 02:50:00</th>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101274</th>\n",
+ " <th>2170-03-28 18:12:00</th>\n",
+ " <td>1.3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101757</th>\n",
+ " <th>2133-01-03 19:28:00</th>\n",
+ " <td>1.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102095</th>\n",
+ " <th>2114-11-27 22:28:00</th>\n",
+ " <td>2.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102365</th>\n",
+ " <th>2137-10-27 01:28:00</th>\n",
+ " <td>0.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102633</th>\n",
+ " <th>2177-03-24 08:56:00</th>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103002</th>\n",
+ " <th>2143-02-16 15:13:00</th>\n",
+ " <td>1.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103074</th>\n",
+ " <th>2125-10-21 20:17:00</th>\n",
+ " <td>0.7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103194</th>\n",
+ " <th>2154-08-23 22:21:00</th>\n",
+ " <td>1.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103631</th>\n",
+ " <th>2158-06-29 06:14:00</th>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104958</th>\n",
+ " <th>2200-03-11 03:11:00</th>\n",
+ " <td>4.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105399</th>\n",
+ " <th>2113-07-28 05:23:00</th>\n",
+ " <td>1.3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105452</th>\n",
+ " <th>2155-05-11 07:41:00</th>\n",
+ " <td>4.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105501</th>\n",
+ " <th>2172-07-08 08:44:00</th>\n",
+ " <td>15.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105588</th>\n",
+ " <th>2148-05-07 14:59:00</th>\n",
+ " <td>3.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105694</th>\n",
+ " <th>2157-05-06 01:13:00</th>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105764</th>\n",
+ " <th>2134-03-01 12:23:00</th>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106296</th>\n",
+ " <th>2170-11-05 21:16:00</th>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106767</th>\n",
+ " <th>2148-03-16 11:54:00</th>\n",
+ " <td>2.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106909</th>\n",
+ " <th>2176-02-07 21:02:00</th>\n",
+ " <td>1.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107064</th>\n",
+ " <th>2175-05-30 12:52:00</th>\n",
+ " <td>3.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107660</th>\n",
+ " <th>2188-04-11 04:13:00</th>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107880</th>\n",
+ " <th>2106-06-19 04:22:00</th>\n",
+ " <td>1.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107882</th>\n",
+ " <th>2127-04-07 13:08:00</th>\n",
+ " <td>6.3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108015</th>\n",
+ " <th>2126-01-02 19:28:00</th>\n",
+ " <td>2.7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108329</th>\n",
+ " <th>2174-01-10 01:10:00</th>\n",
+ " <td>11.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
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+ " <th>190539</th>\n",
+ " <th>2186-11-22 03:10:00</th>\n",
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+ " <th>191151</th>\n",
+ " <th>2139-03-20 09:52:00</th>\n",
+ " <td>1.0</td>\n",
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+ " <th>191517</th>\n",
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+ " <td>6.0</td>\n",
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+ " <th>191817</th>\n",
+ " <th>2165-05-08 17:17:00</th>\n",
+ " <td>1.4</td>\n",
+ " </tr>\n",
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+ " <th>192097</th>\n",
+ " <th>2190-03-01 18:38:00</th>\n",
+ " <td>1.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>192123</th>\n",
+ " <th>2142-04-25 01:15:00</th>\n",
+ " <td>1.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>192224</th>\n",
+ " <th>2164-06-13 12:47:00</th>\n",
+ " <td>2.6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194013</th>\n",
+ " <th>2169-07-05 13:24:00</th>\n",
+ " <td>2.1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194111</th>\n",
+ " <th>2198-08-03 04:38:00</th>\n",
+ " <td>1.3</td>\n",
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+ " <tr>\n",
+ " <th>194340</th>\n",
+ " <th>2129-09-07 08:18:00</th>\n",
+ " <td>4.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195160</th>\n",
+ " <th>2171-03-04 15:33:00</th>\n",
+ " <td>1.0</td>\n",
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+ " <tr>\n",
+ " <th>195290</th>\n",
+ " <th>2138-02-14 03:33:00</th>\n",
+ " <td>1.5</td>\n",
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+ " <tr>\n",
+ " <th>195392</th>\n",
+ " <th>2149-11-29 20:40:00</th>\n",
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+ " <th>195962</th>\n",
+ " <th>2180-12-11 17:48:00</th>\n",
+ " <td>1.2</td>\n",
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+ " <th>196159</th>\n",
+ " <th>2163-01-08 00:29:00</th>\n",
+ " <td>4.7</td>\n",
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+ " <tr>\n",
+ " <th>196271</th>\n",
+ " <th>2141-12-20 09:13:00</th>\n",
+ " <td>5.0</td>\n",
+ " </tr>\n",
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+ " <th>197430</th>\n",
+ " <th>2130-06-01 15:00:00</th>\n",
+ " <td>3.2</td>\n",
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+ " <th>197569</th>\n",
+ " <th>2133-03-02 03:03:00</th>\n",
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+ " <th>198608</th>\n",
+ " <th>2176-05-28 20:29:00</th>\n",
+ " <td>2.5</td>\n",
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+ " <th>199004</th>\n",
+ " <th>2189-06-12 23:06:00</th>\n",
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+ " <tr>\n",
+ " <th>199280</th>\n",
+ " <th>2121-01-04 00:46:00</th>\n",
+ " <td>1.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199286</th>\n",
+ " <th>2159-07-27 15:21:00</th>\n",
+ " <td>1.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199488</th>\n",
+ " <th>2107-09-08 18:11:00</th>\n",
+ " <td>1.7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199634</th>\n",
+ " <th>2174-05-18 01:01:00</th>\n",
+ " <td>0.6</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>328 rows × 1 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " lactate_known_qn_mmol/L_all_SAMPLE\n",
+ "id datetime \n",
+ "100009 2162-05-17 17:14:00 1.5\n",
+ "100095 2108-10-01 06:53:00 1.1\n",
+ "100262 2142-04-29 18:46:00 3.8\n",
+ "100765 2126-03-12 03:57:00 1.3\n",
+ "101148 2197-12-17 02:50:00 2.0\n",
+ "101274 2170-03-28 18:12:00 1.3\n",
+ "101757 2133-01-03 19:28:00 1.1\n",
+ "102095 2114-11-27 22:28:00 2.5\n",
+ "102365 2137-10-27 01:28:00 0.6\n",
+ "102633 2177-03-24 08:56:00 3.0\n",
+ "103002 2143-02-16 15:13:00 1.5\n",
+ "103074 2125-10-21 20:17:00 0.7\n",
+ "103194 2154-08-23 22:21:00 1.8\n",
+ "103631 2158-06-29 06:14:00 1.0\n",
+ "104958 2200-03-11 03:11:00 4.6\n",
+ "105399 2113-07-28 05:23:00 1.3\n",
+ "105452 2155-05-11 07:41:00 4.8\n",
+ "105501 2172-07-08 08:44:00 15.6\n",
+ "105588 2148-05-07 14:59:00 3.4\n",
+ "105694 2157-05-06 01:13:00 0.8\n",
+ "105764 2134-03-01 12:23:00 1.0\n",
+ "106296 2170-11-05 21:16:00 0.9\n",
+ "106767 2148-03-16 11:54:00 2.4\n",
+ "106909 2176-02-07 21:02:00 1.2\n",
+ "107064 2175-05-30 12:52:00 3.4\n",
+ "107660 2188-04-11 04:13:00 0.8\n",
+ "107880 2106-06-19 04:22:00 1.6\n",
+ "107882 2127-04-07 13:08:00 6.3\n",
+ "108015 2126-01-02 19:28:00 2.7\n",
+ "108329 2174-01-10 01:10:00 11.9\n",
+ "... ...\n",
+ "190539 2186-11-22 03:10:00 1.5\n",
+ "190691 2144-05-13 15:19:00 2.5\n",
+ "190707 2119-12-25 01:11:00 2.8\n",
+ "190712 2137-07-19 14:39:00 4.8\n",
+ "191096 2131-11-18 10:17:00 1.6\n",
+ "191151 2139-03-20 09:52:00 1.0\n",
+ "191517 2157-03-07 20:28:00 2.6\n",
+ "191682 2201-04-08 05:50:00 6.0\n",
+ "191817 2165-05-08 17:17:00 1.4\n",
+ "192097 2190-03-01 18:38:00 1.1\n",
+ "192123 2142-04-25 01:15:00 1.1\n",
+ "192224 2164-06-13 12:47:00 2.6\n",
+ "194013 2169-07-05 13:24:00 2.1\n",
+ "194111 2198-08-03 04:38:00 1.3\n",
+ "194340 2129-09-07 08:18:00 4.4\n",
+ "195160 2171-03-04 15:33:00 1.0\n",
+ "195290 2138-02-14 03:33:00 1.5\n",
+ "195392 2149-11-29 20:40:00 5.1\n",
+ "195962 2180-12-11 17:48:00 1.2\n",
+ "196159 2163-01-08 00:29:00 4.7\n",
+ "196271 2141-12-20 09:13:00 5.0\n",
+ "197430 2130-06-01 15:00:00 3.2\n",
+ "197569 2133-03-02 03:03:00 1.8\n",
+ "198161 2144-05-08 03:07:00 2.3\n",
+ "198608 2176-05-28 20:29:00 2.5\n",
+ "199004 2189-06-12 23:06:00 1.3\n",
+ "199280 2121-01-04 00:46:00 1.5\n",
+ "199286 2159-07-27 15:21:00 1.2\n",
+ "199488 2107-09-08 18:11:00 1.7\n",
+ "199634 2174-05-18 01:01:00 0.6\n",
+ "\n",
+ "[328 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#bogus ids and make labels:\n",
+ "train_ids = mimic.get_all_hadm_ids()[:1000]\n",
+ "test_ids = mimic.get_all_hadm_ids()[1000:1100]\n",
+ "labels = label_pipeline.fit_transform(X=train_ids)\n",
+ "labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets start with just some basic **lactate** features"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we need to find some way to identify all of the discrete data needs for every feature in our list, and then create a featurizing pipeline for each of those things.\n",
+ "\n",
+ "Since we cannot hold our entire data set in memory, we need to do this using the following steps:\n",
+ "\n",
+ "1. **Data Loader** - will open the relevent transformed/cleaned dataframes for a given feature group with shared (currently IDENTICAL) data needs. This will both load AND filter data to relevent ids...there is no need to \"fit\" an ID filter because this will change from run to run or fold-to-fold\n",
+ "2. **Cleaning pipeline** - For each loaded data set, we will fit a series of data-dependent cleaning steps such that, for each train-test combination, each cleaning step can be FIT to the train data and then applied to the test data\n",
+ "3. **Segmenter** - This will take the cleaned data set and, using whatever behavior is encoded within the segmenter, it will assign a \"segment\" to each datetime within a give timeseries. There may be multiple segments per timeseries, and there may be some datetimes that ARE NOT part of a segment (encoded by segment index = -1 'NO SEGMENT')\n",
+ "4. **Featurizer** - this is either a featurizer object, or a pdFeatureUnion bundle that will join a bundle of featurizer results. Importantly, the _data loader_ should load all data needed by the featurizer.\n",
+ "\n",
+ "The resulting pipline will then be unioned with other pipelines into a pdFeatureUnion pipeline. The final steps to the overall pipeline will be:\n",
+ "1. **Feature processing pipeline** - In most cases this will just be dropping all-NaN features, and do a standardized scaling. It could potentially include feature selection/creation.\n",
+ "2. ** A Model ** - so that we can finish the entire pipeline and run cross validation!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Data Loading and segmenting\n",
+ "\n",
+ "initially will start with a segmenter that segments by n hours before a series of \"end datetimes\" which, in this case, will be the datetime of the sampled lactate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Cleaning pipeline\n",
+ "Now, for this series of machine learning models, we are going to use a simplified/cleaned version of our dataset. Specifically, we are going to combine all columns with the same component and unit into a single column (that is the \"combine line colummns\" cleaning step below)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from features import load_and_segment,data_loader\n",
+ "from segmenting import n_hrs_before\n",
+ "\n",
+ "hdf5_fname = 'data/mimic_simple_all'\n",
+ "path = 'cleaned'\n",
+ "component = data_dict.components.HEART_RATE\n",
+ "data_needs = [(component,ALL)]\n",
+ "segmenter = n_hrs_before(n_hrs=ALL)\n",
+ "\n",
+ "loader = features.load_and_segment(hdf5_fname,path,data_needs,segmenter)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-26 18:07:21) Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-26 18:07:21)>> HEART RATE: 1/1\n",
+ "(2017-06-26 18:07:21)>>>> Opening...\n",
+ "(2017-06-26 18:07:21)<<<< DONE (0.0s)\n",
+ "(2017-06-26 18:07:21)>>>> Filter & sort - (7922986, 6)\n",
+ "(2017-06-26 18:07:22)<<<< DONE (1.0s)\n",
+ "(2017-06-26 18:07:22)>>>> Convert to dask - (70798, 6)\n",
+ "(2017-06-26 18:07:22)<<<< DONE (0.0s)\n",
+ "(2017-06-26 18:07:22)>>>> Join\n",
+ "(2017-06-26 18:07:22)<<<< DONE (0.0s)\n",
+ "(2017-06-26 18:07:22)<< DONE (1.0s)\n",
+ "(2017-06-26 18:07:22)>> Dask DF back to pandas\n",
+ "(2017-06-26 18:07:22)<< DONE (0.0s)\n",
+ "(2017-06-26 18:07:22) DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
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+ " <th colspan=\"6\" halign=\"left\">heart rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">beats/min</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <th>description</th>\n",
+ " <th>211(BPM)</th>\n",
+ " <th>211(bpm)</th>\n",
+ " <th>220045(bpm)</th>\n",
+ " <th>1332</th>\n",
+ " <th>1341</th>\n",
+ " <th>1725</th>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">100009</th>\n",
+ " <th>2162-05-17 18:23:00</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 18:25:00</th>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 18:30:00</th>\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",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 18:45:00</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 19:00:00</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate \n",
+ "status known unknown \n",
+ "variable_type qn qn \n",
+ "units beats/min no_units \n",
+ "description 211(BPM) 211(bpm) 220045(bpm) 1332 1341 1725\n",
+ "id datetime \n",
+ "100009 2162-05-17 18:23:00 NaN NaN 80.0 NaN NaN NaN\n",
+ " 2162-05-17 18:25:00 NaN NaN 80.0 NaN NaN NaN\n",
+ " 2162-05-17 18:30:00 NaN NaN 80.0 NaN NaN NaN\n",
+ " 2162-05-17 18:45:00 NaN NaN 80.0 NaN NaN NaN\n",
+ " 2162-05-17 19:00:00 NaN NaN 80.0 NaN NaN NaN"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y = labels\n",
+ "ids = y.index.get_level_values(column_names.ID).unique().tolist()\n",
+ "df_loaded = data_loader(hdf5_fname,path,data_needs).fit_transform(ids)\n",
+ "df_loaded.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'df_loaded' 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-1-d96ee5ae9400>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msegmenting\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0msegmenter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msegmenting\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperiodic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_hrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m48\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdf_segmented\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msegmenter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf_loaded\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m: name 'df_loaded' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "import segmenting\n",
+ "import constants\n",
+ "reload(segmenting)\n",
+ "segmenter = segmenting.periodic(n_hrs=48)\n",
+ "df_segmented = segmenter.fit_transform(X=df_loaded, y=y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 10:31:32) Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-22 10:31:32)>> HEART RATE: 1/1\n",
+ "(2017-06-22 10:31:32)>>>> Opening...\n",
+ "(2017-06-22 10:31:33)<<<< DONE (1.0s)\n",
+ "(2017-06-22 10:31:33)>>>> Join (70798, 6) to None\n",
+ "(2017-06-22 10:31:33)<<<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:33)<< DONE (1.0s)\n",
+ "(2017-06-22 10:31:33) DONE (1.0s)\n",
+ "(2017-06-22 10:31:33) Segment df (70798, 6)\n",
+ "(2017-06-22 10:31:33)>> Get Segments\n",
+ "(2017-06-22 10:31:33)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:33)>> Apply Segments\n",
+ "(2017-06-22 10:31:34)<< DONE (1.0s)\n",
+ "(2017-06-22 10:31:34) DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <th>component</th>\n",
+ " <th colspan=\"6\" halign=\"left\">heart rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
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+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"5\" valign=\"top\">100009</th>\n",
+ " <th>2162-05-17 18:23:00</th>\n",
+ " <th>-1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <th>-1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 18:30:00</th>\n",
+ " <th>-1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 18:45:00</th>\n",
+ " <th>-1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2162-05-17 19:00:00</th>\n",
+ " <th>-1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>80.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate \\\n",
+ "status known unknown \n",
+ "variable_type qn qn \n",
+ "units beats/min no_units \n",
+ "description 211(BPM) 211(bpm) 220045(bpm) 1332 \n",
+ "id datetime seg_id \n",
+ "100009 2162-05-17 18:23:00 -1 NaN NaN 80.0 NaN \n",
+ " 2162-05-17 18:25:00 -1 NaN NaN 80.0 NaN \n",
+ " 2162-05-17 18:30:00 -1 NaN NaN 80.0 NaN \n",
+ " 2162-05-17 18:45:00 -1 NaN NaN 80.0 NaN \n",
+ " 2162-05-17 19:00:00 -1 NaN NaN 80.0 NaN \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 1341 1725 \n",
+ "id datetime seg_id \n",
+ "100009 2162-05-17 18:23:00 -1 NaN NaN \n",
+ " 2162-05-17 18:25:00 -1 NaN NaN \n",
+ " 2162-05-17 18:30:00 -1 NaN NaN \n",
+ " 2162-05-17 18:45:00 -1 NaN NaN \n",
+ " 2162-05-17 19:00:00 -1 NaN NaN "
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_loaded = loader.fit_transform(labels)\n",
+ "df_loaded.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "318\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "233"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print df_loaded.index.get_level_values('id').unique().size\n",
+ "df_loaded[df_loaded.index.get_level_values('seg_id') != -1].index.get_level_values('id').unique().size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 10:31:36) Drop Small columns, threshold = 50 | (70798, 6)\n",
+ "(2017-06-22 10:31:36) DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 10:31:36) FIT Combine like columns (70798, 2)\n",
+ "(2017-06-22 10:31:36)>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 10:31:36)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:36) DONE (0.0s)\n",
+ "(2017-06-22 10:31:36) TRANSFORM Combine like columns (70798, 2)\n",
+ "(2017-06-22 10:31:36)>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 10:31:38)<< DONE (2.0s)\n",
+ "(2017-06-22 10:31:38) DONE (2.0s)\n"
+ ]
+ },
+ {
+ "data": {
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+ " <th>-1</th>\n",
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+ " <th>-1</th>\n",
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+ " <th>-1</th>\n",
+ " <td>96.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>70798 rows × 1 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units beats/min\n",
+ "description all\n",
+ "id datetime seg_id \n",
+ "100009 2162-05-17 18:23:00 -1 80.0\n",
+ " 2162-05-17 18:25:00 -1 80.0\n",
+ " 2162-05-17 18:30:00 -1 80.0\n",
+ " 2162-05-17 18:45:00 -1 80.0\n",
+ " 2162-05-17 19:00:00 -1 80.0\n",
+ " 2162-05-17 19:15:00 -1 80.0\n",
+ " 2162-05-17 19:30:00 -1 80.0\n",
+ " 2162-05-17 19:45:00 -1 88.0\n",
+ " 2162-05-17 20:00:00 -1 92.0\n",
+ " 2162-05-17 21:00:00 -1 80.0\n",
+ " 2162-05-17 22:00:00 -1 80.0\n",
+ " 2162-05-17 23:00:00 -1 80.0\n",
+ " 2162-05-18 00:00:00 -1 80.0\n",
+ " 2162-05-18 01:00:00 -1 79.0\n",
+ " 2162-05-18 02:00:00 -1 79.0\n",
+ " 2162-05-18 03:00:00 -1 80.0\n",
+ " 2162-05-18 04:00:00 -1 80.0\n",
+ " 2162-05-18 05:00:00 -1 72.0\n",
+ " 2162-05-18 06:00:00 -1 87.0\n",
+ " 2162-05-18 07:00:00 -1 70.0\n",
+ " 2162-05-18 08:00:00 -1 70.0\n",
+ " 2162-05-18 09:00:00 -1 70.0\n",
+ " 2162-05-18 10:00:00 -1 58.0\n",
+ " 2162-05-18 11:00:00 -1 55.0\n",
+ " 2162-05-18 11:30:00 -1 56.0\n",
+ " 2162-05-18 11:45:00 -1 52.0\n",
+ " 2162-05-18 12:00:00 -1 54.0\n",
+ " 2162-05-18 12:15:00 -1 49.0\n",
+ " 2162-05-18 12:30:00 -1 59.0\n",
+ " 2162-05-18 12:45:00 -1 59.0\n",
+ "... ...\n",
+ "199488 2107-09-22 12:00:00 -1 88.0\n",
+ " 2107-09-22 13:00:00 -1 77.0\n",
+ " 2107-09-22 14:00:00 -1 89.0\n",
+ " 2107-09-22 15:00:00 -1 89.0\n",
+ " 2107-09-22 16:00:00 -1 99.0\n",
+ " 2107-09-22 17:00:00 -1 91.0\n",
+ " 2107-09-22 18:00:00 -1 99.0\n",
+ " 2107-09-22 19:30:00 -1 88.0\n",
+ " 2107-09-22 20:30:00 -1 86.0\n",
+ " 2107-09-22 21:00:00 -1 89.0\n",
+ " 2107-09-22 22:00:00 -1 87.0\n",
+ " 2107-09-22 23:00:00 -1 90.0\n",
+ " 2107-09-23 00:00:00 -1 91.0\n",
+ " 2107-09-23 01:00:00 -1 98.0\n",
+ " 2107-09-23 02:00:00 -1 99.0\n",
+ " 2107-09-23 03:00:00 -1 93.0\n",
+ " 2107-09-23 04:00:00 -1 87.0\n",
+ " 2107-09-23 05:00:00 -1 92.0\n",
+ " 2107-09-23 06:00:00 -1 97.0\n",
+ " 2107-09-23 07:00:00 -1 89.0\n",
+ " 2107-09-23 08:00:00 -1 108.0\n",
+ " 2107-09-23 09:00:00 -1 105.0\n",
+ " 2107-09-23 10:00:00 -1 84.0\n",
+ " 2107-09-23 11:00:00 -1 81.0\n",
+ " 2107-09-23 12:00:00 -1 77.0\n",
+ " 2107-09-23 13:00:00 -1 91.0\n",
+ " 2107-09-23 14:00:00 -1 96.0\n",
+ " 2107-09-23 15:00:00 -1 96.0\n",
+ " 2107-09-23 16:00:00 -1 108.0\n",
+ " 2107-09-23 17:00:00 -1 96.0\n",
+ "\n",
+ "[70798 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from transformers import remove_small_columns,record_threshold,combine_like_cols\n",
+ "def cleaner_pipeline():\n",
+ " return Pipeline([\n",
+ " ('drop_small_columns',remove_small_columns(threshold=50)),\n",
+ " ('drop_low_id_count',record_threshold(threshold=5)), \n",
+ " ('combine_like_columns',combine_like_cols())\n",
+ " ])\n",
+ "\n",
+ "cleaners = cleaner_pipeline()\n",
+ "\n",
+ "df_cleaned = cleaners.fit_transform(X=df_loaded,y=labels)\n",
+ "df_cleaned"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 10:31:40) FIT features...\n",
+ "(2017-06-22 10:31:40)>> MEAN_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (1.0s)\n",
+ "(2017-06-22 10:31:41)>> STD_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41)>> LAST_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41)>> COUNT_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41) DONE (1.0s)\n",
+ "(2017-06-22 10:31:41) Start Feature Union on DF (70798, 1)\n",
+ "(2017-06-22 10:31:41)>> MEAN_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41)>> STD_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41)>> LAST_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41)>> COUNT_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:31:41)<< DONE (0.0s)\n",
+ "(2017-06-22 10:31:41) DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
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+ " <tr>\n",
+ " <th>103194</th>\n",
+ " <th>0</th>\n",
+ " <td>102.444444</td>\n",
+ " <td>4.390647</td>\n",
+ " <td>102.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103631</th>\n",
+ " <th>0</th>\n",
+ " <td>90.226415</td>\n",
+ " <td>9.348576</td>\n",
+ " <td>96.0</td>\n",
+ " <td>106.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104958</th>\n",
+ " <th>0</th>\n",
+ " <td>92.000000</td>\n",
+ " <td>1.414214</td>\n",
+ " <td>91.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105399</th>\n",
+ " <th>0</th>\n",
+ " <td>90.704918</td>\n",
+ " <td>6.960231</td>\n",
+ " <td>89.0</td>\n",
+ " <td>61.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105501</th>\n",
+ " <th>0</th>\n",
+ " <td>101.937500</td>\n",
+ " <td>8.225722</td>\n",
+ " <td>101.0</td>\n",
+ " <td>16.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105694</th>\n",
+ " <th>0</th>\n",
+ " <td>80.225989</td>\n",
+ " <td>12.666832</td>\n",
+ " <td>66.0</td>\n",
+ " <td>177.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105764</th>\n",
+ " <th>0</th>\n",
+ " <td>75.000000</td>\n",
+ " <td>5.291503</td>\n",
+ " <td>76.0</td>\n",
+ " <td>4.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106296</th>\n",
+ " <th>0</th>\n",
+ " <td>133.222222</td>\n",
+ " <td>7.408704</td>\n",
+ " <td>138.0</td>\n",
+ " <td>18.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106909</th>\n",
+ " <th>0</th>\n",
+ " <td>81.838843</td>\n",
+ " <td>17.787870</td>\n",
+ " <td>63.0</td>\n",
+ " <td>968.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107660</th>\n",
+ " <th>0</th>\n",
+ " <td>88.080000</td>\n",
+ " <td>13.910188</td>\n",
+ " <td>91.0</td>\n",
+ " <td>25.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107880</th>\n",
+ " <th>0</th>\n",
+ " <td>89.000000</td>\n",
+ " <td>11.481210</td>\n",
+ " <td>83.0</td>\n",
+ " <td>45.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107882</th>\n",
+ " <th>0</th>\n",
+ " <td>90.964286</td>\n",
+ " <td>20.411475</td>\n",
+ " <td>72.0</td>\n",
+ " <td>28.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108015</th>\n",
+ " <th>0</th>\n",
+ " <td>100.500000</td>\n",
+ " <td>6.663332</td>\n",
+ " <td>86.0</td>\n",
+ " <td>16.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108329</th>\n",
+ " <th>0</th>\n",
+ " <td>126.333333</td>\n",
+ " <td>9.646601</td>\n",
+ " <td>104.0</td>\n",
+ " <td>42.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108462</th>\n",
+ " <th>0</th>\n",
+ " <td>89.600000</td>\n",
+ " <td>3.209361</td>\n",
+ " <td>87.0</td>\n",
+ " <td>5.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108923</th>\n",
+ " <th>0</th>\n",
+ " <td>86.000000</td>\n",
+ " <td>2.828427</td>\n",
+ " <td>88.0</td>\n",
+ " <td>2.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109185</th>\n",
+ " <th>0</th>\n",
+ " <td>91.157143</td>\n",
+ " <td>13.611228</td>\n",
+ " <td>101.0</td>\n",
+ " <td>280.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109235</th>\n",
+ " <th>0</th>\n",
+ " <td>98.416667</td>\n",
+ " <td>10.312157</td>\n",
+ " <td>100.0</td>\n",
+ " <td>24.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109451</th>\n",
+ " <th>0</th>\n",
+ " <td>67.550000</td>\n",
+ " <td>7.250953</td>\n",
+ " <td>62.0</td>\n",
+ " <td>20.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109718</th>\n",
+ " <th>0</th>\n",
+ " <td>85.200000</td>\n",
+ " <td>9.420343</td>\n",
+ " <td>96.0</td>\n",
+ " <td>15.0</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>185910</th>\n",
+ " <th>0</th>\n",
+ " <td>106.234043</td>\n",
+ " <td>8.270463</td>\n",
+ " <td>112.0</td>\n",
+ " <td>47.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>186516</th>\n",
+ " <th>0</th>\n",
+ " <td>78.446154</td>\n",
+ " <td>13.608397</td>\n",
+ " <td>83.0</td>\n",
+ " <td>65.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>188038</th>\n",
+ " <th>0</th>\n",
+ " <td>69.416667</td>\n",
+ " <td>16.822395</td>\n",
+ " <td>60.0</td>\n",
+ " <td>84.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>188256</th>\n",
+ " <th>0</th>\n",
+ " <td>105.617857</td>\n",
+ " <td>12.959284</td>\n",
+ " <td>87.0</td>\n",
+ " <td>280.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>188576</th>\n",
+ " <th>0</th>\n",
+ " <td>83.402609</td>\n",
+ " <td>14.957631</td>\n",
+ " <td>128.0</td>\n",
+ " <td>1150.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>188869</th>\n",
+ " <th>0</th>\n",
+ " <td>67.038462</td>\n",
+ " <td>13.075646</td>\n",
+ " <td>78.0</td>\n",
+ " <td>78.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>189081</th>\n",
+ " <th>0</th>\n",
+ " <td>74.555556</td>\n",
+ " <td>3.045944</td>\n",
+ " <td>80.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>189243</th>\n",
+ " <th>0</th>\n",
+ " <td>100.285714</td>\n",
+ " <td>3.860669</td>\n",
+ " <td>94.0</td>\n",
+ " <td>7.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>189332</th>\n",
+ " <th>0</th>\n",
+ " <td>87.578947</td>\n",
+ " <td>5.398722</td>\n",
+ " <td>99.0</td>\n",
+ " <td>19.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190159</th>\n",
+ " <th>0</th>\n",
+ " <td>109.878049</td>\n",
+ " <td>13.082804</td>\n",
+ " <td>87.0</td>\n",
+ " <td>41.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190462</th>\n",
+ " <th>0</th>\n",
+ " <td>107.333333</td>\n",
+ " <td>12.785408</td>\n",
+ " <td>117.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190539</th>\n",
+ " <th>0</th>\n",
+ " <td>74.400000</td>\n",
+ " <td>6.651846</td>\n",
+ " <td>74.0</td>\n",
+ " <td>35.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190707</th>\n",
+ " <th>0</th>\n",
+ " <td>82.315789</td>\n",
+ " <td>29.238583</td>\n",
+ " <td>102.0</td>\n",
+ " <td>19.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>191096</th>\n",
+ " <th>0</th>\n",
+ " <td>71.850000</td>\n",
+ " <td>6.588841</td>\n",
+ " <td>83.0</td>\n",
+ " <td>40.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>191517</th>\n",
+ " <th>0</th>\n",
+ " <td>83.214286</td>\n",
+ " <td>7.062126</td>\n",
+ " <td>76.0</td>\n",
+ " <td>14.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>191817</th>\n",
+ " <th>0</th>\n",
+ " <td>86.900000</td>\n",
+ " <td>7.812209</td>\n",
+ " <td>98.0</td>\n",
+ " <td>50.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>192123</th>\n",
+ " <th>0</th>\n",
+ " <td>125.060606</td>\n",
+ " <td>11.365901</td>\n",
+ " <td>112.0</td>\n",
+ " <td>33.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194013</th>\n",
+ " <th>0</th>\n",
+ " <td>67.654639</td>\n",
+ " <td>5.309031</td>\n",
+ " <td>68.0</td>\n",
+ " <td>194.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194111</th>\n",
+ " <th>0</th>\n",
+ " <td>41.090909</td>\n",
+ " <td>1.758098</td>\n",
+ " <td>40.0</td>\n",
+ " <td>11.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194340</th>\n",
+ " <th>0</th>\n",
+ " <td>100.596774</td>\n",
+ " <td>8.501610</td>\n",
+ " <td>105.0</td>\n",
+ " <td>62.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195160</th>\n",
+ " <th>0</th>\n",
+ " <td>78.971429</td>\n",
+ " <td>6.228425</td>\n",
+ " <td>80.0</td>\n",
+ " <td>35.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195290</th>\n",
+ " <th>0</th>\n",
+ " <td>65.333333</td>\n",
+ " <td>13.613719</td>\n",
+ " <td>70.0</td>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195962</th>\n",
+ " <th>0</th>\n",
+ " <td>105.659420</td>\n",
+ " <td>21.987221</td>\n",
+ " <td>96.0</td>\n",
+ " <td>138.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>197569</th>\n",
+ " <th>0</th>\n",
+ " <td>94.491228</td>\n",
+ " <td>8.398815</td>\n",
+ " <td>93.0</td>\n",
+ " <td>57.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>198161</th>\n",
+ " <th>0</th>\n",
+ " <td>91.477889</td>\n",
+ " <td>19.453641</td>\n",
+ " <td>66.0</td>\n",
+ " <td>701.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>198608</th>\n",
+ " <th>0</th>\n",
+ " <td>92.989899</td>\n",
+ " <td>13.632594</td>\n",
+ " <td>85.0</td>\n",
+ " <td>198.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199004</th>\n",
+ " <th>0</th>\n",
+ " <td>74.000000</td>\n",
+ " <td>9.396389</td>\n",
+ " <td>74.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199280</th>\n",
+ " <th>0</th>\n",
+ " <td>67.274510</td>\n",
+ " <td>6.798598</td>\n",
+ " <td>59.0</td>\n",
+ " <td>102.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199286</th>\n",
+ " <th>0</th>\n",
+ " <td>107.637681</td>\n",
+ " <td>10.213732</td>\n",
+ " <td>118.0</td>\n",
+ " <td>138.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199488</th>\n",
+ " <th>0</th>\n",
+ " <td>113.222222</td>\n",
+ " <td>7.612125</td>\n",
+ " <td>119.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>233 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " heart rate_known_qn_beats/min_all_MEAN \\\n",
+ "id seg_id \n",
+ "100095 0 86.918367 \n",
+ "100262 0 138.809524 \n",
+ "100765 0 103.750000 \n",
+ "101274 0 94.785714 \n",
+ "101757 0 100.700000 \n",
+ "102095 0 133.000000 \n",
+ "102365 0 85.209205 \n",
+ "102633 0 108.300000 \n",
+ "103002 0 71.666667 \n",
+ "103074 0 86.118519 \n",
+ "103194 0 102.444444 \n",
+ "103631 0 90.226415 \n",
+ "104958 0 92.000000 \n",
+ "105399 0 90.704918 \n",
+ "105501 0 101.937500 \n",
+ "105694 0 80.225989 \n",
+ "105764 0 75.000000 \n",
+ "106296 0 133.222222 \n",
+ "106909 0 81.838843 \n",
+ "107660 0 88.080000 \n",
+ "107880 0 89.000000 \n",
+ "107882 0 90.964286 \n",
+ "108015 0 100.500000 \n",
+ "108329 0 126.333333 \n",
+ "108462 0 89.600000 \n",
+ "108923 0 86.000000 \n",
+ "109185 0 91.157143 \n",
+ "109235 0 98.416667 \n",
+ "109451 0 67.550000 \n",
+ "109718 0 85.200000 \n",
+ "... ... \n",
+ "185910 0 106.234043 \n",
+ "186516 0 78.446154 \n",
+ "188038 0 69.416667 \n",
+ "188256 0 105.617857 \n",
+ "188576 0 83.402609 \n",
+ "188869 0 67.038462 \n",
+ "189081 0 74.555556 \n",
+ "189243 0 100.285714 \n",
+ "189332 0 87.578947 \n",
+ "190159 0 109.878049 \n",
+ "190462 0 107.333333 \n",
+ "190539 0 74.400000 \n",
+ "190707 0 82.315789 \n",
+ "191096 0 71.850000 \n",
+ "191517 0 83.214286 \n",
+ "191817 0 86.900000 \n",
+ "192123 0 125.060606 \n",
+ "194013 0 67.654639 \n",
+ "194111 0 41.090909 \n",
+ "194340 0 100.596774 \n",
+ "195160 0 78.971429 \n",
+ "195290 0 65.333333 \n",
+ "195962 0 105.659420 \n",
+ "197569 0 94.491228 \n",
+ "198161 0 91.477889 \n",
+ "198608 0 92.989899 \n",
+ "199004 0 74.000000 \n",
+ "199280 0 67.274510 \n",
+ "199286 0 107.637681 \n",
+ "199488 0 113.222222 \n",
+ "\n",
+ " heart rate_known_qn_beats/min_all_STD \\\n",
+ "id seg_id \n",
+ "100095 0 13.800249 \n",
+ "100262 0 10.766339 \n",
+ "100765 0 3.201562 \n",
+ "101274 0 5.294098 \n",
+ "101757 0 8.932463 \n",
+ "102095 0 9.396389 \n",
+ "102365 0 8.493087 \n",
+ "102633 0 8.300602 \n",
+ "103002 0 1.154701 \n",
+ "103074 0 7.869807 \n",
+ "103194 0 4.390647 \n",
+ "103631 0 9.348576 \n",
+ "104958 0 1.414214 \n",
+ "105399 0 6.960231 \n",
+ "105501 0 8.225722 \n",
+ "105694 0 12.666832 \n",
+ "105764 0 5.291503 \n",
+ "106296 0 7.408704 \n",
+ "106909 0 17.787870 \n",
+ "107660 0 13.910188 \n",
+ "107880 0 11.481210 \n",
+ "107882 0 20.411475 \n",
+ "108015 0 6.663332 \n",
+ "108329 0 9.646601 \n",
+ "108462 0 3.209361 \n",
+ "108923 0 2.828427 \n",
+ "109185 0 13.611228 \n",
+ "109235 0 10.312157 \n",
+ "109451 0 7.250953 \n",
+ "109718 0 9.420343 \n",
+ "... ... \n",
+ "185910 0 8.270463 \n",
+ "186516 0 13.608397 \n",
+ "188038 0 16.822395 \n",
+ "188256 0 12.959284 \n",
+ "188576 0 14.957631 \n",
+ "188869 0 13.075646 \n",
+ "189081 0 3.045944 \n",
+ "189243 0 3.860669 \n",
+ "189332 0 5.398722 \n",
+ "190159 0 13.082804 \n",
+ "190462 0 12.785408 \n",
+ "190539 0 6.651846 \n",
+ "190707 0 29.238583 \n",
+ "191096 0 6.588841 \n",
+ "191517 0 7.062126 \n",
+ "191817 0 7.812209 \n",
+ "192123 0 11.365901 \n",
+ "194013 0 5.309031 \n",
+ "194111 0 1.758098 \n",
+ "194340 0 8.501610 \n",
+ "195160 0 6.228425 \n",
+ "195290 0 13.613719 \n",
+ "195962 0 21.987221 \n",
+ "197569 0 8.398815 \n",
+ "198161 0 19.453641 \n",
+ "198608 0 13.632594 \n",
+ "199004 0 9.396389 \n",
+ "199280 0 6.798598 \n",
+ "199286 0 10.213732 \n",
+ "199488 0 7.612125 \n",
+ "\n",
+ " heart rate_known_qn_beats/min_all_LAST \\\n",
+ "id seg_id \n",
+ "100095 0 94.0 \n",
+ "100262 0 139.0 \n",
+ "100765 0 106.0 \n",
+ "101274 0 97.0 \n",
+ "101757 0 97.0 \n",
+ "102095 0 133.0 \n",
+ "102365 0 82.0 \n",
+ "102633 0 97.0 \n",
+ "103002 0 71.0 \n",
+ "103074 0 90.0 \n",
+ "103194 0 102.0 \n",
+ "103631 0 96.0 \n",
+ "104958 0 91.0 \n",
+ "105399 0 89.0 \n",
+ "105501 0 101.0 \n",
+ "105694 0 66.0 \n",
+ "105764 0 76.0 \n",
+ "106296 0 138.0 \n",
+ "106909 0 63.0 \n",
+ "107660 0 91.0 \n",
+ "107880 0 83.0 \n",
+ "107882 0 72.0 \n",
+ "108015 0 86.0 \n",
+ "108329 0 104.0 \n",
+ "108462 0 87.0 \n",
+ "108923 0 88.0 \n",
+ "109185 0 101.0 \n",
+ "109235 0 100.0 \n",
+ "109451 0 62.0 \n",
+ "109718 0 96.0 \n",
+ "... ... \n",
+ "185910 0 112.0 \n",
+ "186516 0 83.0 \n",
+ "188038 0 60.0 \n",
+ "188256 0 87.0 \n",
+ "188576 0 128.0 \n",
+ "188869 0 78.0 \n",
+ "189081 0 80.0 \n",
+ "189243 0 94.0 \n",
+ "189332 0 99.0 \n",
+ "190159 0 87.0 \n",
+ "190462 0 117.0 \n",
+ "190539 0 74.0 \n",
+ "190707 0 102.0 \n",
+ "191096 0 83.0 \n",
+ "191517 0 76.0 \n",
+ "191817 0 98.0 \n",
+ "192123 0 112.0 \n",
+ "194013 0 68.0 \n",
+ "194111 0 40.0 \n",
+ "194340 0 105.0 \n",
+ "195160 0 80.0 \n",
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+ "[233 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#mean, std, and last of all quantitative values\n",
+ "aggregator = features.segment_mean()\n",
+ "mean_component = features.simple_featurizer(\n",
+ " aggregator, #aggregator\n",
+ " transformers.fill_mean(), #how to fill empty features\n",
+ " [{\n",
+ " column_names.COMPONENT : component,\n",
+ " column_names.VAR_TYPE : variable_type.QUANTITATIVE\n",
+ " }], #what do we apply this to?\n",
+ " aggregator.name + '_' + component\n",
+ " )\n",
+ "\n",
+ "aggregator = features.segment_std()\n",
+ "std_component = features.simple_featurizer(\n",
+ " aggregator, #aggregator\n",
+ " transformers.fill_mean(), #how to fill empty features\n",
+ " [{\n",
+ " column_names.COMPONENT : component,\n",
+ " column_names.VAR_TYPE : variable_type.QUANTITATIVE\n",
+ " }], #what do we apply this to?\n",
+ " aggregator.name + '_' + component\n",
+ " )\n",
+ "\n",
+ "aggregator = features.segment_last()\n",
+ "last_component = features.simple_featurizer(\n",
+ " aggregator, #aggregator\n",
+ " transformers.fill_mean(), #how to fill empty features\n",
+ " [{\n",
+ " column_names.COMPONENT : component,\n",
+ " column_names.VAR_TYPE : variable_type.QUANTITATIVE\n",
+ " }], #what do we apply this to?\n",
+ " aggregator.name + '_' + component\n",
+ " )\n",
+ "\n",
+ "#count of everything, including nominal\n",
+ "aggregator = features.segment_count()\n",
+ "count_component = features.simple_featurizer(\n",
+ " aggregator, #aggregator\n",
+ " transformers.fill_mean(), #how to fill empty features\n",
+ " [{column_names.COMPONENT : component}], #what do we apply this to?\n",
+ " aggregator.name + '_' + component\n",
+ " )\n",
+ "\n",
+ "featurizer = features.pdFeatureUnion([\n",
+ " (mean_component.name,mean_component),\n",
+ " (std_component.name,std_component),\n",
+ " (last_component.name,last_component),\n",
+ " (count_component.name,count_component)\n",
+ "])\n",
+ "\n",
+ "featurizer.fit_transform(X=df_cleaned,y=labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Put it all together"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 10:32:03) Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-22 10:32:03)>> HEART RATE: 1/1\n",
+ "(2017-06-22 10:32:03)>>>> Opening...\n",
+ "(2017-06-22 10:32:04)<<<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:04)>>>> Join (70798, 6) to None\n",
+ "(2017-06-22 10:32:04)<<<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:04)<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:04) DONE (1.0s)\n",
+ "(2017-06-22 10:32:04) Segment df (70798, 6)\n",
+ "(2017-06-22 10:32:04)>> Get Segments\n",
+ "(2017-06-22 10:32:04)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:04)>> Apply Segments\n",
+ "(2017-06-22 10:32:04)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:04) DONE (0.0s)\n",
+ "(2017-06-22 10:32:04) Drop Small columns, threshold = 50 | (70798, 6)\n",
+ "(2017-06-22 10:32:05) DONE (1.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 10:32:05) FIT Combine like columns (70798, 2)\n",
+ "(2017-06-22 10:32:05)>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 10:32:05)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:05) DONE (0.0s)\n",
+ "(2017-06-22 10:32:05) TRANSFORM Combine like columns (70798, 2)\n",
+ "(2017-06-22 10:32:05)>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 10:32:06)<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:06) DONE (1.0s)\n",
+ "(2017-06-22 10:32:06) FIT features...\n",
+ "(2017-06-22 10:32:06)>> MEAN_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:06)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:06)>> STD_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:06)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:06)>> LAST_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:07)<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:07)>> COUNT_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:07)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:07) DONE (1.0s)\n",
+ "(2017-06-22 10:32:07) Start Feature Union on DF (70798, 1)\n",
+ "(2017-06-22 10:32:07)>> MEAN_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:07)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:07)>> STD_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:07)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:07)>> LAST_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:07)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:07)>> COUNT_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 10:32:07)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:07) DONE (0.0s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "feature_pipeline = Pipeline ([\n",
+ " ('data_loader',loader),\n",
+ " ('cleaners',cleaner_pipeline()),\n",
+ " ('featurizer',featurizer)\n",
+ " ])\n",
+ "\n",
+ "df_features = feature_pipeline.fit_transform(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "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>heart rate_known_qn_beats/min_all_MEAN</th>\n",
+ " <th>heart rate_known_qn_beats/min_all_STD</th>\n",
+ " <th>heart rate_known_qn_beats/min_all_LAST</th>\n",
+ " <th>heart rate_known_qn_beats/min_all_COUNT</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>seg_id</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>100095</th>\n",
+ " <th>0</th>\n",
+ " <td>86.918367</td>\n",
+ " <td>13.800249</td>\n",
+ " <td>94.0</td>\n",
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+ " <th>0</th>\n",
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+ " <td>10.766339</td>\n",
+ " <td>139.0</td>\n",
+ " <td>42.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>100765</th>\n",
+ " <th>0</th>\n",
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+ " <td>3.201562</td>\n",
+ " <td>106.0</td>\n",
+ " <td>4.0</td>\n",
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+ " <th>0</th>\n",
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+ " <td>5.294098</td>\n",
+ " <td>97.0</td>\n",
+ " <td>14.0</td>\n",
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+ " <th>101757</th>\n",
+ " <th>0</th>\n",
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+ " <td>10.0</td>\n",
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+ " <th>0</th>\n",
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+ " <td>9.396389</td>\n",
+ " <td>133.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
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+ " <td>8.493087</td>\n",
+ " <td>82.0</td>\n",
+ " <td>239.0</td>\n",
+ " </tr>\n",
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+ " <th>102633</th>\n",
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+ " <td>1.154701</td>\n",
+ " <td>71.0</td>\n",
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+ " </tr>\n",
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+ " <th>0</th>\n",
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+ " <th>105501</th>\n",
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+ " <th>186516</th>\n",
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+ " <td>14.957631</td>\n",
+ " <td>128.0</td>\n",
+ " <td>1150.0</td>\n",
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+ " <tr>\n",
+ " <th>188869</th>\n",
+ " <th>0</th>\n",
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+ " <td>78.0</td>\n",
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+ " <tr>\n",
+ " <th>189081</th>\n",
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+ " <td>9.0</td>\n",
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+ " <tr>\n",
+ " <th>189243</th>\n",
+ " <th>0</th>\n",
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+ " <td>7.0</td>\n",
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+ " <tr>\n",
+ " <th>189332</th>\n",
+ " <th>0</th>\n",
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+ " <td>5.398722</td>\n",
+ " <td>99.0</td>\n",
+ " <td>19.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190159</th>\n",
+ " <th>0</th>\n",
+ " <td>109.878049</td>\n",
+ " <td>13.082804</td>\n",
+ " <td>87.0</td>\n",
+ " <td>41.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190462</th>\n",
+ " <th>0</th>\n",
+ " <td>107.333333</td>\n",
+ " <td>12.785408</td>\n",
+ " <td>117.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190539</th>\n",
+ " <th>0</th>\n",
+ " <td>74.400000</td>\n",
+ " <td>6.651846</td>\n",
+ " <td>74.0</td>\n",
+ " <td>35.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>190707</th>\n",
+ " <th>0</th>\n",
+ " <td>82.315789</td>\n",
+ " <td>29.238583</td>\n",
+ " <td>102.0</td>\n",
+ " <td>19.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>191096</th>\n",
+ " <th>0</th>\n",
+ " <td>71.850000</td>\n",
+ " <td>6.588841</td>\n",
+ " <td>83.0</td>\n",
+ " <td>40.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>191517</th>\n",
+ " <th>0</th>\n",
+ " <td>83.214286</td>\n",
+ " <td>7.062126</td>\n",
+ " <td>76.0</td>\n",
+ " <td>14.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>191817</th>\n",
+ " <th>0</th>\n",
+ " <td>86.900000</td>\n",
+ " <td>7.812209</td>\n",
+ " <td>98.0</td>\n",
+ " <td>50.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>192123</th>\n",
+ " <th>0</th>\n",
+ " <td>125.060606</td>\n",
+ " <td>11.365901</td>\n",
+ " <td>112.0</td>\n",
+ " <td>33.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194013</th>\n",
+ " <th>0</th>\n",
+ " <td>67.654639</td>\n",
+ " <td>5.309031</td>\n",
+ " <td>68.0</td>\n",
+ " <td>194.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194111</th>\n",
+ " <th>0</th>\n",
+ " <td>41.090909</td>\n",
+ " <td>1.758098</td>\n",
+ " <td>40.0</td>\n",
+ " <td>11.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>194340</th>\n",
+ " <th>0</th>\n",
+ " <td>100.596774</td>\n",
+ " <td>8.501610</td>\n",
+ " <td>105.0</td>\n",
+ " <td>62.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195160</th>\n",
+ " <th>0</th>\n",
+ " <td>78.971429</td>\n",
+ " <td>6.228425</td>\n",
+ " <td>80.0</td>\n",
+ " <td>35.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195290</th>\n",
+ " <th>0</th>\n",
+ " <td>65.333333</td>\n",
+ " <td>13.613719</td>\n",
+ " <td>70.0</td>\n",
+ " <td>3.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>195962</th>\n",
+ " <th>0</th>\n",
+ " <td>105.659420</td>\n",
+ " <td>21.987221</td>\n",
+ " <td>96.0</td>\n",
+ " <td>138.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>197569</th>\n",
+ " <th>0</th>\n",
+ " <td>94.491228</td>\n",
+ " <td>8.398815</td>\n",
+ " <td>93.0</td>\n",
+ " <td>57.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>198161</th>\n",
+ " <th>0</th>\n",
+ " <td>91.477889</td>\n",
+ " <td>19.453641</td>\n",
+ " <td>66.0</td>\n",
+ " <td>701.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>198608</th>\n",
+ " <th>0</th>\n",
+ " <td>92.989899</td>\n",
+ " <td>13.632594</td>\n",
+ " <td>85.0</td>\n",
+ " <td>198.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199004</th>\n",
+ " <th>0</th>\n",
+ " <td>74.000000</td>\n",
+ " <td>9.396389</td>\n",
+ " <td>74.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199280</th>\n",
+ " <th>0</th>\n",
+ " <td>67.274510</td>\n",
+ " <td>6.798598</td>\n",
+ " <td>59.0</td>\n",
+ " <td>102.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199286</th>\n",
+ " <th>0</th>\n",
+ " <td>107.637681</td>\n",
+ " <td>10.213732</td>\n",
+ " <td>118.0</td>\n",
+ " <td>138.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>199488</th>\n",
+ " <th>0</th>\n",
+ " <td>113.222222</td>\n",
+ " <td>7.612125</td>\n",
+ " <td>119.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>233 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " heart rate_known_qn_beats/min_all_MEAN \\\n",
+ "id seg_id \n",
+ "100095 0 86.918367 \n",
+ "100262 0 138.809524 \n",
+ "100765 0 103.750000 \n",
+ "101274 0 94.785714 \n",
+ "101757 0 100.700000 \n",
+ "102095 0 133.000000 \n",
+ "102365 0 85.209205 \n",
+ "102633 0 108.300000 \n",
+ "103002 0 71.666667 \n",
+ "103074 0 86.118519 \n",
+ "103194 0 102.444444 \n",
+ "103631 0 90.226415 \n",
+ "104958 0 92.000000 \n",
+ "105399 0 90.704918 \n",
+ "105501 0 101.937500 \n",
+ "105694 0 80.225989 \n",
+ "105764 0 75.000000 \n",
+ "106296 0 133.222222 \n",
+ "106909 0 81.838843 \n",
+ "107660 0 88.080000 \n",
+ "107880 0 89.000000 \n",
+ "107882 0 90.964286 \n",
+ "108015 0 100.500000 \n",
+ "108329 0 126.333333 \n",
+ "108462 0 89.600000 \n",
+ "108923 0 86.000000 \n",
+ "109185 0 91.157143 \n",
+ "109235 0 98.416667 \n",
+ "109451 0 67.550000 \n",
+ "109718 0 85.200000 \n",
+ "... ... \n",
+ "185910 0 106.234043 \n",
+ "186516 0 78.446154 \n",
+ "188038 0 69.416667 \n",
+ "188256 0 105.617857 \n",
+ "188576 0 83.402609 \n",
+ "188869 0 67.038462 \n",
+ "189081 0 74.555556 \n",
+ "189243 0 100.285714 \n",
+ "189332 0 87.578947 \n",
+ "190159 0 109.878049 \n",
+ "190462 0 107.333333 \n",
+ "190539 0 74.400000 \n",
+ "190707 0 82.315789 \n",
+ "191096 0 71.850000 \n",
+ "191517 0 83.214286 \n",
+ "191817 0 86.900000 \n",
+ "192123 0 125.060606 \n",
+ "194013 0 67.654639 \n",
+ "194111 0 41.090909 \n",
+ "194340 0 100.596774 \n",
+ "195160 0 78.971429 \n",
+ "195290 0 65.333333 \n",
+ "195962 0 105.659420 \n",
+ "197569 0 94.491228 \n",
+ "198161 0 91.477889 \n",
+ "198608 0 92.989899 \n",
+ "199004 0 74.000000 \n",
+ "199280 0 67.274510 \n",
+ "199286 0 107.637681 \n",
+ "199488 0 113.222222 \n",
+ "\n",
+ " heart rate_known_qn_beats/min_all_STD \\\n",
+ "id seg_id \n",
+ "100095 0 13.800249 \n",
+ "100262 0 10.766339 \n",
+ "100765 0 3.201562 \n",
+ "101274 0 5.294098 \n",
+ "101757 0 8.932463 \n",
+ "102095 0 9.396389 \n",
+ "102365 0 8.493087 \n",
+ "102633 0 8.300602 \n",
+ "103002 0 1.154701 \n",
+ "103074 0 7.869807 \n",
+ "103194 0 4.390647 \n",
+ "103631 0 9.348576 \n",
+ "104958 0 1.414214 \n",
+ "105399 0 6.960231 \n",
+ "105501 0 8.225722 \n",
+ "105694 0 12.666832 \n",
+ "105764 0 5.291503 \n",
+ "106296 0 7.408704 \n",
+ "106909 0 17.787870 \n",
+ "107660 0 13.910188 \n",
+ "107880 0 11.481210 \n",
+ "107882 0 20.411475 \n",
+ "108015 0 6.663332 \n",
+ "108329 0 9.646601 \n",
+ "108462 0 3.209361 \n",
+ "108923 0 2.828427 \n",
+ "109185 0 13.611228 \n",
+ "109235 0 10.312157 \n",
+ "109451 0 7.250953 \n",
+ "109718 0 9.420343 \n",
+ "... ... \n",
+ "185910 0 8.270463 \n",
+ "186516 0 13.608397 \n",
+ "188038 0 16.822395 \n",
+ "188256 0 12.959284 \n",
+ "188576 0 14.957631 \n",
+ "188869 0 13.075646 \n",
+ "189081 0 3.045944 \n",
+ "189243 0 3.860669 \n",
+ "189332 0 5.398722 \n",
+ "190159 0 13.082804 \n",
+ "190462 0 12.785408 \n",
+ "190539 0 6.651846 \n",
+ "190707 0 29.238583 \n",
+ "191096 0 6.588841 \n",
+ "191517 0 7.062126 \n",
+ "191817 0 7.812209 \n",
+ "192123 0 11.365901 \n",
+ "194013 0 5.309031 \n",
+ "194111 0 1.758098 \n",
+ "194340 0 8.501610 \n",
+ "195160 0 6.228425 \n",
+ "195290 0 13.613719 \n",
+ "195962 0 21.987221 \n",
+ "197569 0 8.398815 \n",
+ "198161 0 19.453641 \n",
+ "198608 0 13.632594 \n",
+ "199004 0 9.396389 \n",
+ "199280 0 6.798598 \n",
+ "199286 0 10.213732 \n",
+ "199488 0 7.612125 \n",
+ "\n",
+ " heart rate_known_qn_beats/min_all_LAST \\\n",
+ "id seg_id \n",
+ "100095 0 94.0 \n",
+ "100262 0 139.0 \n",
+ "100765 0 106.0 \n",
+ "101274 0 97.0 \n",
+ "101757 0 97.0 \n",
+ "102095 0 133.0 \n",
+ "102365 0 82.0 \n",
+ "102633 0 97.0 \n",
+ "103002 0 71.0 \n",
+ "103074 0 90.0 \n",
+ "103194 0 102.0 \n",
+ "103631 0 96.0 \n",
+ "104958 0 91.0 \n",
+ "105399 0 89.0 \n",
+ "105501 0 101.0 \n",
+ "105694 0 66.0 \n",
+ "105764 0 76.0 \n",
+ "106296 0 138.0 \n",
+ "106909 0 63.0 \n",
+ "107660 0 91.0 \n",
+ "107880 0 83.0 \n",
+ "107882 0 72.0 \n",
+ "108015 0 86.0 \n",
+ "108329 0 104.0 \n",
+ "108462 0 87.0 \n",
+ "108923 0 88.0 \n",
+ "109185 0 101.0 \n",
+ "109235 0 100.0 \n",
+ "109451 0 62.0 \n",
+ "109718 0 96.0 \n",
+ "... ... \n",
+ "185910 0 112.0 \n",
+ "186516 0 83.0 \n",
+ "188038 0 60.0 \n",
+ "188256 0 87.0 \n",
+ "188576 0 128.0 \n",
+ "188869 0 78.0 \n",
+ "189081 0 80.0 \n",
+ "189243 0 94.0 \n",
+ "189332 0 99.0 \n",
+ "190159 0 87.0 \n",
+ "190462 0 117.0 \n",
+ "190539 0 74.0 \n",
+ "190707 0 102.0 \n",
+ "191096 0 83.0 \n",
+ "191517 0 76.0 \n",
+ "191817 0 98.0 \n",
+ "192123 0 112.0 \n",
+ "194013 0 68.0 \n",
+ "194111 0 40.0 \n",
+ "194340 0 105.0 \n",
+ "195160 0 80.0 \n",
+ "195290 0 70.0 \n",
+ "195962 0 96.0 \n",
+ "197569 0 93.0 \n",
+ "198161 0 66.0 \n",
+ "198608 0 85.0 \n",
+ "199004 0 74.0 \n",
+ "199280 0 59.0 \n",
+ "199286 0 118.0 \n",
+ "199488 0 119.0 \n",
+ "\n",
+ " heart rate_known_qn_beats/min_all_COUNT \n",
+ "id seg_id \n",
+ "100095 0 98.0 \n",
+ "100262 0 42.0 \n",
+ "100765 0 4.0 \n",
+ "101274 0 14.0 \n",
+ "101757 0 10.0 \n",
+ "102095 0 1.0 \n",
+ "102365 0 239.0 \n",
+ "102633 0 10.0 \n",
+ "103002 0 3.0 \n",
+ "103074 0 270.0 \n",
+ "103194 0 9.0 \n",
+ "103631 0 106.0 \n",
+ "104958 0 2.0 \n",
+ "105399 0 61.0 \n",
+ "105501 0 16.0 \n",
+ "105694 0 177.0 \n",
+ "105764 0 4.0 \n",
+ "106296 0 18.0 \n",
+ "106909 0 968.0 \n",
+ "107660 0 25.0 \n",
+ "107880 0 45.0 \n",
+ "107882 0 28.0 \n",
+ "108015 0 16.0 \n",
+ "108329 0 42.0 \n",
+ "108462 0 5.0 \n",
+ "108923 0 2.0 \n",
+ "109185 0 280.0 \n",
+ "109235 0 24.0 \n",
+ "109451 0 20.0 \n",
+ "109718 0 15.0 \n",
+ "... ... \n",
+ "185910 0 47.0 \n",
+ "186516 0 65.0 \n",
+ "188038 0 84.0 \n",
+ "188256 0 280.0 \n",
+ "188576 0 1150.0 \n",
+ "188869 0 78.0 \n",
+ "189081 0 9.0 \n",
+ "189243 0 7.0 \n",
+ "189332 0 19.0 \n",
+ "190159 0 41.0 \n",
+ "190462 0 6.0 \n",
+ "190539 0 35.0 \n",
+ "190707 0 19.0 \n",
+ "191096 0 40.0 \n",
+ "191517 0 14.0 \n",
+ "191817 0 50.0 \n",
+ "192123 0 33.0 \n",
+ "194013 0 194.0 \n",
+ "194111 0 11.0 \n",
+ "194340 0 62.0 \n",
+ "195160 0 35.0 \n",
+ "195290 0 3.0 \n",
+ "195962 0 138.0 \n",
+ "197569 0 57.0 \n",
+ "198161 0 701.0 \n",
+ "198608 0 198.0 \n",
+ "199004 0 1.0 \n",
+ "199280 0 102.0 \n",
+ "199286 0 138.0 \n",
+ "199488 0 9.0 \n",
+ "\n",
+ "[233 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "component status variable_type units description\n",
+ "heart rate known qn beats/min all 9.396389\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "feature_pipeline.get_params()['featurizer'].get_params()['STD_heart rate'].get_params()['feature_fillna'].means"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 10:32:30) Make DF for 1 components...\n",
+ "lactate\n",
+ "(2017-06-22 10:32:30)>> LACTATE: 1/1\n",
+ "(2017-06-22 10:32:30)>>>> Opening...\n",
+ "(2017-06-22 10:32:31)<<<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:31)>>>> Join (187, 63) to None\n",
+ "(2017-06-22 10:32:31)<<<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:31)<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:31) DONE (1.0s)\n",
+ "51\n",
+ "51\n",
+ "51\n",
+ "(2017-06-22 10:32:31) TRANSFORM Combine like columns (187, 63)\n",
+ "(2017-06-22 10:32:31)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-22 10:32:31)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:31)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 10:32:31)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:31) DONE (0.0s)\n",
+ "30\n",
+ "30\n",
+ "30\n",
+ "30\n",
+ "(2017-06-22 10:32:31) Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-22 10:32:31)>> HEART RATE: 1/1\n",
+ "(2017-06-22 10:32:31)>>>> Opening...\n",
+ "(2017-06-22 10:32:32)<<<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:32)>>>> Join (7191, 6) to None\n",
+ "(2017-06-22 10:32:32)<<<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32)<< DONE (1.0s)\n",
+ "(2017-06-22 10:32:32) DONE (1.0s)\n",
+ "(2017-06-22 10:32:32) Segment df (7191, 6)\n",
+ "(2017-06-22 10:32:32)>> Get Segments\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32)>> Apply Segments\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32) DONE (0.0s)\n",
+ "(2017-06-22 10:32:32) Drop Small columns, threshold = 50 | (7191, 6)\n",
+ "(2017-06-22 10:32:32) DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 10:32:32) TRANSFORM Combine like columns (7191, 2)\n",
+ "(2017-06-22 10:32:32)>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32) DONE (0.0s)\n",
+ "(2017-06-22 10:32:32) Start Feature Union on DF (7191, 1)\n",
+ "(2017-06-22 10:32:32)>> MEAN_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32)>> STD_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32)>> LAST_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32)>> COUNT_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 10:32:32)<< DONE (0.0s)\n",
+ "(2017-06-22 10:32:32) DONE (0.0s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_labels = label_pipeline.transform(X=test_ids)\n",
+ "test_features = feature_pipeline.transform(test_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(233, 4) (328, 1)\n",
+ "(25, 4) (30, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print df_features.shape, labels.shape\n",
+ "print test_features.shape, test_labels.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we will use this framework to define a more efficient helper method"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.pipeline import FeatureUnion\n",
+ "def build_features(component,var_type,is_intervention):\n",
+ " \n",
+ " count_aggregator = features.segment_count()\n",
+ " tag = count_aggregator.name + '_' + component\n",
+ " count_featurizer = features.simple_featurizer(\n",
+ " count_aggregator, #aggregator\n",
+ " transformers.fill_zero(), #how to fill empty features\n",
+ " [{column_names.COMPONENT : component}], #what do we apply this to?\n",
+ " tag\n",
+ " )\n",
+ " \n",
+ " feature_list = [count_featurizer]\n",
+ " \n",
+ " #If this is nominal data, we are done.\n",
+ " if var_type == variable_type.NOMINAL:\n",
+ " return feature_list\n",
+ " \n",
+ " #Fill interventions with 0, observations with mean\n",
+ " if is_intervention: \n",
+ " fill_method=transformers.fill_zero\n",
+ " else: \n",
+ " fill_method=transformers.fill_mean\n",
+ " \n",
+ " #we will use mean, std, and last to describe each segment\n",
+ " aggregators = [\n",
+ " features.segment_mean,\n",
+ " features.segment_std,\n",
+ " features.segment_last\n",
+ " ]\n",
+ " \n",
+ " #this will only be applied to this component and variable type\n",
+ " slice_dict = {\n",
+ " column_names.COMPONENT : component,\n",
+ " column_names.VAR_TYPE : variable_type\n",
+ " }\n",
+ " \n",
+ " #create featurizers\n",
+ " for aggregator in aggregators:\n",
+ " agg_instace = aggregator()\n",
+ " tag = agg_instace.name + '_' + component\n",
+ " featurizer = features.simple_featurizer(\n",
+ " agg_instace, #aggregator\n",
+ " fill_method(), #how to fill empty features\n",
+ " [slice_dict], #what do we apply this to?\n",
+ " tag\n",
+ " )\n",
+ " feature_list.append(featurizer)\n",
+ "\n",
+ " return feature_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'COUNT_lactate': simple_featurizer(aggregator=None, fillna=None, name='COUNT_lactate',\n",
+ " slice_dict_list=None),\n",
+ " 'COUNT_lactate__aggregator': None,\n",
+ " 'COUNT_lactate__feature_aggregator': segment_count(),\n",
+ " 'COUNT_lactate__feature_fillna': fill_zero(),\n",
+ " 'COUNT_lactate__fillna': None,\n",
+ " 'COUNT_lactate__flatten_columns': flatten_index(axis=1, suffix='COUNT'),\n",
+ " 'COUNT_lactate__flatten_columns__axis': 1,\n",
+ " 'COUNT_lactate__flatten_columns__suffix': 'COUNT',\n",
+ " 'COUNT_lactate__name': 'COUNT_lactate',\n",
+ " 'COUNT_lactate__preprocessor': multislice_filter(slice_dict_list=[{'component': 'lactate'}]),\n",
+ " 'COUNT_lactate__preprocessor__slice_dict_list': [{'component': 'lactate'}],\n",
+ " 'COUNT_lactate__slice_dict_list': None,\n",
+ " 'LAST_lactate': simple_featurizer(aggregator=None, fillna=None, name='LAST_lactate',\n",
+ " slice_dict_list=None),\n",
+ " 'LAST_lactate__aggregator': None,\n",
+ " 'LAST_lactate__feature_aggregator': segment_last(),\n",
+ " 'LAST_lactate__feature_fillna': fill_mean(),\n",
+ " 'LAST_lactate__fillna': None,\n",
+ " 'LAST_lactate__flatten_columns': flatten_index(axis=1, suffix='LAST'),\n",
+ " 'LAST_lactate__flatten_columns__axis': 1,\n",
+ " 'LAST_lactate__flatten_columns__suffix': 'LAST',\n",
+ " 'LAST_lactate__name': 'LAST_lactate',\n",
+ " 'LAST_lactate__preprocessor': multislice_filter(slice_dict_list=[{'variable_type': <class 'constants.variable_type'>, 'component': 'lactate'}]),\n",
+ " 'LAST_lactate__preprocessor__slice_dict_list': [{'component': 'lactate',\n",
+ " 'variable_type': constants.variable_type}],\n",
+ " 'LAST_lactate__slice_dict_list': None,\n",
+ " 'MEAN_lactate': simple_featurizer(aggregator=None, fillna=None, name='MEAN_lactate',\n",
+ " slice_dict_list=None),\n",
+ " 'MEAN_lactate__aggregator': None,\n",
+ " 'MEAN_lactate__feature_aggregator': segment_mean(),\n",
+ " 'MEAN_lactate__feature_fillna': fill_mean(),\n",
+ " 'MEAN_lactate__fillna': None,\n",
+ " 'MEAN_lactate__flatten_columns': flatten_index(axis=1, suffix='MEAN'),\n",
+ " 'MEAN_lactate__flatten_columns__axis': 1,\n",
+ " 'MEAN_lactate__flatten_columns__suffix': 'MEAN',\n",
+ " 'MEAN_lactate__name': 'MEAN_lactate',\n",
+ " 'MEAN_lactate__preprocessor': multislice_filter(slice_dict_list=[{'variable_type': <class 'constants.variable_type'>, 'component': 'lactate'}]),\n",
+ " 'MEAN_lactate__preprocessor__slice_dict_list': [{'component': 'lactate',\n",
+ " 'variable_type': constants.variable_type}],\n",
+ " 'MEAN_lactate__slice_dict_list': None,\n",
+ " 'STD_lactate': simple_featurizer(aggregator=None, fillna=None, name='STD_lactate',\n",
+ " slice_dict_list=None),\n",
+ " 'STD_lactate__aggregator': None,\n",
+ " 'STD_lactate__feature_aggregator': segment_std(),\n",
+ " 'STD_lactate__feature_fillna': fill_mean(),\n",
+ " 'STD_lactate__fillna': None,\n",
+ " 'STD_lactate__flatten_columns': flatten_index(axis=1, suffix='STD'),\n",
+ " 'STD_lactate__flatten_columns__axis': 1,\n",
+ " 'STD_lactate__flatten_columns__suffix': 'STD',\n",
+ " 'STD_lactate__name': 'STD_lactate',\n",
+ " 'STD_lactate__preprocessor': multislice_filter(slice_dict_list=[{'variable_type': <class 'constants.variable_type'>, 'component': 'lactate'}]),\n",
+ " 'STD_lactate__preprocessor__slice_dict_list': [{'component': 'lactate',\n",
+ " 'variable_type': constants.variable_type}],\n",
+ " 'STD_lactate__slice_dict_list': None,\n",
+ " 'n_jobs': 1,\n",
+ " 'transformer_list': [('COUNT_lactate',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='COUNT_lactate',\n",
+ " slice_dict_list=None)),\n",
+ " ('MEAN_lactate',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='MEAN_lactate',\n",
+ " slice_dict_list=None)),\n",
+ " ('STD_lactate',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='STD_lactate',\n",
+ " slice_dict_list=None)),\n",
+ " ('LAST_lactate',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='LAST_lactate',\n",
+ " slice_dict_list=None))],\n",
+ " 'transformer_weights': None}"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "component = data_dict.components.HEART_RATE\n",
+ "featurizers = build_features(component,\n",
+ " var_type=variable_type.QUANTITATIVE,\n",
+ " is_intervention=False)\n",
+ "lactate_feature_union = FeatureUnion([(f.name,f) for f in featurizers])\n",
+ "lactate_feature_union.get_params()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, this should look slightly different for an intervention, lets say normal saline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'COUNT_normal saline': simple_featurizer(aggregator=None, fillna=None, name='COUNT_normal saline',\n",
+ " slice_dict_list=None),\n",
+ " 'COUNT_normal saline__aggregator': None,\n",
+ " 'COUNT_normal saline__feature_aggregator': segment_count(),\n",
+ " 'COUNT_normal saline__feature_fillna': fill_zero(),\n",
+ " 'COUNT_normal saline__fillna': None,\n",
+ " 'COUNT_normal saline__flatten_columns': flatten_index(axis=1, suffix='COUNT'),\n",
+ " 'COUNT_normal saline__flatten_columns__axis': 1,\n",
+ " 'COUNT_normal saline__flatten_columns__suffix': 'COUNT',\n",
+ " 'COUNT_normal saline__name': 'COUNT_normal saline',\n",
+ " 'COUNT_normal saline__preprocessor': multislice_filter(slice_dict_list=[{'component': 'normal saline'}]),\n",
+ " 'COUNT_normal saline__preprocessor__slice_dict_list': [{'component': 'normal saline'}],\n",
+ " 'COUNT_normal saline__slice_dict_list': None,\n",
+ " 'LAST_normal saline': simple_featurizer(aggregator=None, fillna=None, name='LAST_normal saline',\n",
+ " slice_dict_list=None),\n",
+ " 'LAST_normal saline__aggregator': None,\n",
+ " 'LAST_normal saline__feature_aggregator': segment_last(),\n",
+ " 'LAST_normal saline__feature_fillna': fill_zero(),\n",
+ " 'LAST_normal saline__fillna': None,\n",
+ " 'LAST_normal saline__flatten_columns': flatten_index(axis=1, suffix='LAST'),\n",
+ " 'LAST_normal saline__flatten_columns__axis': 1,\n",
+ " 'LAST_normal saline__flatten_columns__suffix': 'LAST',\n",
+ " 'LAST_normal saline__name': 'LAST_normal saline',\n",
+ " 'LAST_normal saline__preprocessor': multislice_filter(slice_dict_list=[{'variable_type': <class 'constants.variable_type'>, 'component': 'normal saline'}]),\n",
+ " 'LAST_normal saline__preprocessor__slice_dict_list': [{'component': 'normal saline',\n",
+ " 'variable_type': constants.variable_type}],\n",
+ " 'LAST_normal saline__slice_dict_list': None,\n",
+ " 'MEAN_normal saline': simple_featurizer(aggregator=None, fillna=None, name='MEAN_normal saline',\n",
+ " slice_dict_list=None),\n",
+ " 'MEAN_normal saline__aggregator': None,\n",
+ " 'MEAN_normal saline__feature_aggregator': segment_mean(),\n",
+ " 'MEAN_normal saline__feature_fillna': fill_zero(),\n",
+ " 'MEAN_normal saline__fillna': None,\n",
+ " 'MEAN_normal saline__flatten_columns': flatten_index(axis=1, suffix='MEAN'),\n",
+ " 'MEAN_normal saline__flatten_columns__axis': 1,\n",
+ " 'MEAN_normal saline__flatten_columns__suffix': 'MEAN',\n",
+ " 'MEAN_normal saline__name': 'MEAN_normal saline',\n",
+ " 'MEAN_normal saline__preprocessor': multislice_filter(slice_dict_list=[{'variable_type': <class 'constants.variable_type'>, 'component': 'normal saline'}]),\n",
+ " 'MEAN_normal saline__preprocessor__slice_dict_list': [{'component': 'normal saline',\n",
+ " 'variable_type': constants.variable_type}],\n",
+ " 'MEAN_normal saline__slice_dict_list': None,\n",
+ " 'STD_normal saline': simple_featurizer(aggregator=None, fillna=None, name='STD_normal saline',\n",
+ " slice_dict_list=None),\n",
+ " 'STD_normal saline__aggregator': None,\n",
+ " 'STD_normal saline__feature_aggregator': segment_std(),\n",
+ " 'STD_normal saline__feature_fillna': fill_zero(),\n",
+ " 'STD_normal saline__fillna': None,\n",
+ " 'STD_normal saline__flatten_columns': flatten_index(axis=1, suffix='STD'),\n",
+ " 'STD_normal saline__flatten_columns__axis': 1,\n",
+ " 'STD_normal saline__flatten_columns__suffix': 'STD',\n",
+ " 'STD_normal saline__name': 'STD_normal saline',\n",
+ " 'STD_normal saline__preprocessor': multislice_filter(slice_dict_list=[{'variable_type': <class 'constants.variable_type'>, 'component': 'normal saline'}]),\n",
+ " 'STD_normal saline__preprocessor__slice_dict_list': [{'component': 'normal saline',\n",
+ " 'variable_type': constants.variable_type}],\n",
+ " 'STD_normal saline__slice_dict_list': None,\n",
+ " 'n_jobs': 1,\n",
+ " 'transformer_list': [('COUNT_normal saline',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='COUNT_normal saline',\n",
+ " slice_dict_list=None)),\n",
+ " ('MEAN_normal saline',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='MEAN_normal saline',\n",
+ " slice_dict_list=None)),\n",
+ " ('STD_normal saline',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='STD_normal saline',\n",
+ " slice_dict_list=None)),\n",
+ " ('LAST_normal saline',\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='LAST_normal saline',\n",
+ " slice_dict_list=None))],\n",
+ " 'transformer_weights': None}"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "component = data_dict.components.NORMAL_SALINE\n",
+ "featurizers = build_features(component,\n",
+ " var_type=variable_type.QUANTITATIVE,\n",
+ " is_intervention=True)\n",
+ "ns_feature_union = FeatureUnion([(f.name,f) for f in featurizers])\n",
+ "ns_feature_union.get_params()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sure enough, for interventions we see that na values will be filled with 0 instead of the population mean!\n",
+ "\n",
+ "Finally, lets put this all together for our data dictionary and a list of data we want to use. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "from constants import clinical_source\n",
+ "def get_all_featurizers(components,data_dict):\n",
+ " feature_list = []\n",
+ " for component in components:\n",
+ " clinical_src = data_dict.get_clinical_source(component)\n",
+ " is_intervention = clinical_src == clinical_source.INTERVENTION\n",
+ " variable_type = data_dict.get_variable_type(component)\n",
+ " feature_list += build_features(component,\n",
+ " variable_type,\n",
+ " is_intervention=is_intervention)\n",
+ " \n",
+ " return feature_list\n",
+ "\n",
+ "# vital signs\n",
+ "components = [data_dict.components.LACTATE,data_dict.components.RESPIRATORY_RATE]\n",
+ "featurizer_list = get_all_featurizers(components,data_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, lets put this list of featurizers into a completed pipeline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{(('lactate',\n",
+ " 'all'),): [simple_featurizer(aggregator=None, fillna=None, name='COUNT_lactate',\n",
+ " slice_dict_list=None),\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='MEAN_lactate',\n",
+ " slice_dict_list=None),\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='STD_lactate',\n",
+ " slice_dict_list=None),\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='LAST_lactate',\n",
+ " slice_dict_list=None)],\n",
+ " (('respiratory rate',\n",
+ " 'all'),): [simple_featurizer(aggregator=None, fillna=None, name='COUNT_respiratory rate',\n",
+ " slice_dict_list=None),\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='MEAN_respiratory rate',\n",
+ " slice_dict_list=None),\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='STD_respiratory rate',\n",
+ " slice_dict_list=None),\n",
+ " simple_featurizer(aggregator=None, fillna=None, name='LAST_respiratory rate',\n",
+ " slice_dict_list=None)]}"
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#organize by data needs\n",
+ "data_needs_dict = {}\n",
+ "for featurizer in featurizer_list:\n",
+ " data_needs = tuple(featurizer.get_data_needs())\n",
+ " data_needs_dict[data_needs] = data_needs_dict.get(data_needs,[]) + [featurizer]\n",
+ " \n",
+ "data_needs_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "segmenter = n_hrs_before(n_hrs=ALL)\n",
+ "\n",
+ "feature_tuples = []\n",
+ "\n",
+ "for data_needs,featurizers in data_needs_dict.iteritems():\n",
+ " loader = features.load_and_segment(hdf5_fname,path,data_needs,segmenter)\n",
+ " featurizer_union = FeatureUnion([(f.name,f) for f in featurizers])\n",
+ " \n",
+ " feature_pipeline = Pipeline ([\n",
+ " ('LOADER',loader),\n",
+ " ('cleaners',cleaner_pipeline()),\n",
+ " ('feature_union',featurizer_union)\n",
+ " ])\n",
+ " feature_tuples.append((str(data_needs),feature_pipeline))\n",
+ "\n",
+ "all_features = FeatureUnion(feature_tuples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 01:59:08) Make DF for 1 components...\n",
+ "lactate\n",
+ "(2017-06-22 01:59:08)>> LACTATE: 1/1\n",
+ "(2017-06-22 01:59:08)>>>> Opening...\n",
+ "(2017-06-22 01:59:09)<<<< DONE (1.0s)\n",
+ "(2017-06-22 01:59:09)>>>> Join (2960, 63) to None\n",
+ "(2017-06-22 01:59:09)<<<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:09)<< DONE (1.0s)\n",
+ "(2017-06-22 01:59:09) DONE (1.0s)\n",
+ "482\n",
+ "482\n",
+ "482\n",
+ "(2017-06-22 01:59:09) FIT Combine like columns (2960, 63)\n",
+ "(2017-06-22 01:59:09)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 01:59:09)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:09)>> ('lactate', 'unknown', 'nom', 'no_units')\n",
+ "(2017-06-22 01:59:09)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:09)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-22 01:59:09)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:09) DONE (0.0s)\n",
+ "(2017-06-22 01:59:09) TRANSFORM Combine like columns (2960, 63)\n",
+ "(2017-06-22 01:59:09)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-22 01:59:09)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:09)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 01:59:09)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:09) DONE (0.0s)\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_lbl = label_pipeline.fit_transform(X=train_ids)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 01:59:13) Make DF for 1 components...\n",
+ "respiratory rate\n",
+ "(2017-06-22 01:59:13)>> RESPIRATORY RATE: 1/1\n",
+ "(2017-06-22 01:59:13)>>>> Opening...\n",
+ "(2017-06-22 01:59:14)<<<< DONE (1.0s)\n",
+ "(2017-06-22 01:59:14)>>>> Join (69806, 6) to None\n",
+ "(2017-06-22 01:59:14)<<<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:14)<< DONE (1.0s)\n",
+ "(2017-06-22 01:59:14) DONE (1.0s)\n",
+ "(2017-06-22 01:59:14) Segment df (69806, 6)\n",
+ "(2017-06-22 01:59:14)>> Get Segments\n",
+ "(2017-06-22 01:59:14)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:14)>> Apply Segments\n",
+ "(2017-06-22 01:59:15)<< DONE (1.0s)\n",
+ "(2017-06-22 01:59:15) DONE (1.0s)\n",
+ "(2017-06-22 01:59:15) Drop Small columns, threshold = 50 | (69806, 6)\n",
+ "(2017-06-22 01:59:15) DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 01:59:15) FIT Combine like columns (69806, 2)\n",
+ "(2017-06-22 01:59:15)>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+ "(2017-06-22 01:59:15)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:15) DONE (0.0s)\n",
+ "(2017-06-22 01:59:15) TRANSFORM Combine like columns (69806, 2)\n",
+ "(2017-06-22 01:59:15)>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+ "(2017-06-22 01:59:17)<< DONE (2.0s)\n",
+ "(2017-06-22 01:59:17) DONE (2.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 01:59:17) Make DF for 1 components...\n",
+ "lactate\n",
+ "(2017-06-22 01:59:17)>> LACTATE: 1/1\n",
+ "(2017-06-22 01:59:17)>>>> Opening...\n",
+ "(2017-06-22 01:59:17)<<<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17)>>>> Join (2806, 63) to None\n",
+ "(2017-06-22 01:59:17)<<<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) Segment df (2806, 63)\n",
+ "(2017-06-22 01:59:17)>> Get Segments\n",
+ "(2017-06-22 01:59:17)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17)>> Apply Segments\n",
+ "(2017-06-22 01:59:17)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) Drop Small columns, threshold = 50 | (2806, 63)\n",
+ "(2017-06-22 01:59:17) DONE (0.0s)\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "(2017-06-22 01:59:17) FIT Combine like columns (2806, 4)\n",
+ "(2017-06-22 01:59:17)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 01:59:17)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) TRANSFORM Combine like columns (2806, 4)\n",
+ "(2017-06-22 01:59:17)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 01:59:17)<< DONE (0.0s)\n",
+ "(2017-06-22 01:59:17) DONE (0.0s)\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m<ipython-input-58-ab9dced7f884>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_ft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_features\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_lbl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;32mC:\\Users\\genkinjz\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\sklearn\\pipeline.pyc\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m 745\u001b[0m \u001b[0mXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msparse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtocsr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 746\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 747\u001b[1;33m \u001b[0mXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 748\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mXs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 749\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mC:\\Users\\genkinjz\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\numpy\\core\\shape_base.pyc\u001b[0m in \u001b[0;36mhstack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m 278\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 279\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 280\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
+ ]
+ }
+ ],
+ "source": [
+ "train_ft = all_features.fit_transform(train_lbl)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-22 00:04:15) Make DF for 1 components...\n",
+ "lactate\n",
+ "(2017-06-22 00:04:15)>> LACTATE: 1/1\n",
+ "(2017-06-22 00:04:15)>>>> Opening...\n",
+ "(2017-06-22 00:04:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:15)>>>> Join (2960, 63) to None\n",
+ "(2017-06-22 00:04:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:15) DONE (0.0s)\n",
+ "482\n",
+ "482\n",
+ "482\n",
+ "(2017-06-22 00:04:15) FIT Combine like columns (2960, 63)\n",
+ "(2017-06-22 00:04:15)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 00:04:16)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:16)>> ('lactate', 'unknown', 'nom', 'no_units')\n",
+ "(2017-06-22 00:04:16)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:16)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-22 00:04:16)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:16) DONE (1.0s)\n",
+ "(2017-06-22 00:04:16) TRANSFORM Combine like columns (2960, 63)\n",
+ "(2017-06-22 00:04:16)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-22 00:04:16)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:16)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 00:04:16)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:16) DONE (0.0s)\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "328\n",
+ "(2017-06-22 00:04:16) FIT features...\n",
+ "(2017-06-22 00:04:16)>> ((u'heart rate', 'all'),)\n",
+ "(2017-06-22 00:04:16)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:16)>> Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-22 00:04:16)>>>> HEART RATE: 1/1\n",
+ "(2017-06-22 00:04:16)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:22)<<<<<< DONE (6.0s)\n",
+ "(2017-06-22 00:04:22)>>>>>> Join (70798, 6) to None\n",
+ "(2017-06-22 00:04:22)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:22)<<<< DONE (6.0s)\n",
+ "(2017-06-22 00:04:22)<< DONE (6.0s)\n",
+ "(2017-06-22 00:04:22)>> Segment df (70798, 6)\n",
+ "(2017-06-22 00:04:22)>>>> Get Segments\n",
+ "(2017-06-22 00:04:22)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:22)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:23)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:23)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:23)>> Drop Small columns, threshold = 50 | (70798, 6)\n",
+ "(2017-06-22 00:04:23)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:04:23)>> FIT Combine like columns (70798, 2)\n",
+ "(2017-06-22 00:04:23)>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 00:04:23)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:23)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:23)>> TRANSFORM Combine like columns (70798, 2)\n",
+ "(2017-06-22 00:04:23)>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 00:04:25)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:04:25)<< DONE (2.0s)\n",
+ "(2017-06-22 00:04:25)>> FIT features...\n",
+ "(2017-06-22 00:04:25)>>>> COUNT_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:25)>>>> MEAN_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:25)>>>> STD_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:25)>>>> LAST_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:25)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:25)>> ((u'oxygen saturation pulse oximetry', 'all'),)\n",
+ "(2017-06-22 00:04:25)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:25)>> Make DF for 1 components...\n",
+ "oxygen saturation pulse oximetry\n",
+ "(2017-06-22 00:04:25)>>>> OXYGEN SATURATION PULSE OXIMETRY: 1/1\n",
+ "(2017-06-22 00:04:25)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:28)<<<<<< DONE (3.0s)\n",
+ "(2017-06-22 00:04:28)>>>>>> Join (70742, 2) to None\n",
+ "(2017-06-22 00:04:28)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:28)<<<< DONE (3.0s)\n",
+ "(2017-06-22 00:04:28)<< DONE (3.0s)\n",
+ "(2017-06-22 00:04:28)>> Segment df (70742, 2)\n",
+ "(2017-06-22 00:04:28)>>>> Get Segments\n",
+ "(2017-06-22 00:04:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:28)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:29)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:29)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:29)>> Drop Small columns, threshold = 50 | (70742, 2)\n",
+ "(2017-06-22 00:04:29)<< DONE (0.0s)\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "(2017-06-22 00:04:29)>> FIT Combine like columns (70742, 2)\n",
+ "(2017-06-22 00:04:29)>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+ "(2017-06-22 00:04:29)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:29)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:29)>> TRANSFORM Combine like columns (70742, 2)\n",
+ "(2017-06-22 00:04:29)>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+ "(2017-06-22 00:04:30)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:30)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:30)>> FIT features...\n",
+ "(2017-06-22 00:04:30)>>>> COUNT_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:04:31)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:31)>>>> MEAN_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:04:31)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:31)>>>> STD_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:04:31)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:31)>>>> LAST_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:04:31)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:31)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:31)>> ((u'temperature body', 'all'),)\n",
+ "(2017-06-22 00:04:31)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:31)>> Make DF for 1 components...\n",
+ "temperature body\n",
+ "(2017-06-22 00:04:31)>>>> TEMPERATURE BODY: 1/1\n",
+ "(2017-06-22 00:04:31)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:32)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:32)>>>>>> Join (24795, 4) to None\n",
+ "(2017-06-22 00:04:32)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:32)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:32)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:32)>> Segment df (24795, 4)\n",
+ "(2017-06-22 00:04:32)>>>> Get Segments\n",
+ "(2017-06-22 00:04:32)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:32)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:32)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:32)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:32)>> Drop Small columns, threshold = 50 | (24795, 4)\n",
+ "(2017-06-22 00:04:32)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:04:32)>> FIT Combine like columns (24795, 4)\n",
+ "(2017-06-22 00:04:32)>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+ "(2017-06-22 00:04:32)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:32)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:32)>> TRANSFORM Combine like columns (24795, 4)\n",
+ "(2017-06-22 00:04:32)>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+ "(2017-06-22 00:04:33)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:33)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:33)>> FIT features...\n",
+ "(2017-06-22 00:04:33)>>>> COUNT_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:33)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:33)>>>> MEAN_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:33)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:33)>>>> STD_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:33)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:33)>>>> LAST_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:33)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:33)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:33)>> ((u'blood pressure mean', 'all'),)\n",
+ "(2017-06-22 00:04:33)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:33)>> Make DF for 1 components...\n",
+ "blood pressure mean\n",
+ "(2017-06-22 00:04:33)>>>> BLOOD PRESSURE MEAN: 1/1\n",
+ "(2017-06-22 00:04:33)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:34)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:34)>>>>>> Join (5008, 3) to None\n",
+ "(2017-06-22 00:04:34)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:34)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:34)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:34)>> Segment df (5008, 3)\n",
+ "(2017-06-22 00:04:34)>>>> Get Segments\n",
+ "(2017-06-22 00:04:34)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:34)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:34)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:34)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:34)>> Drop Small columns, threshold = 50 | (5008, 3)\n",
+ "(2017-06-22 00:04:34)<< DONE (0.0s)\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "(2017-06-22 00:04:34)>> FIT Combine like columns (5008, 3)\n",
+ "(2017-06-22 00:04:34)>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:04:34)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:34)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:34)>> TRANSFORM Combine like columns (5008, 3)\n",
+ "(2017-06-22 00:04:34)>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:04:35)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:35)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:35)>> FIT features...\n",
+ "(2017-06-22 00:04:35)>>>> COUNT_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:04:35)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:35)>>>> MEAN_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:04:35)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:35)>>>> STD_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:04:35)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:35)>>>> LAST_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:04:35)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:35)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:35)>> ((u'respiratory rate', 'all'),)\n",
+ "(2017-06-22 00:04:35)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:35)>> Make DF for 1 components...\n",
+ "respiratory rate\n",
+ "(2017-06-22 00:04:35)>>>> RESPIRATORY RATE: 1/1\n",
+ "(2017-06-22 00:04:35)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:40)<<<<<< DONE (5.0s)\n",
+ "(2017-06-22 00:04:40)>>>>>> Join (69806, 6) to None\n",
+ "(2017-06-22 00:04:40)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:40)<<<< DONE (5.0s)\n",
+ "(2017-06-22 00:04:40)<< DONE (5.0s)\n",
+ "(2017-06-22 00:04:40)>> Segment df (69806, 6)\n",
+ "(2017-06-22 00:04:40)>>>> Get Segments\n",
+ "(2017-06-22 00:04:40)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:40)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:41)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:41)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:41)>> Drop Small columns, threshold = 50 | (69806, 6)\n",
+ "(2017-06-22 00:04:41)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:04:41)>> FIT Combine like columns (69806, 2)\n",
+ "(2017-06-22 00:04:41)>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+ "(2017-06-22 00:04:41)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:41)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:41)>> TRANSFORM Combine like columns (69806, 2)\n",
+ "(2017-06-22 00:04:41)>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+ "(2017-06-22 00:04:43)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:04:43)<< DONE (2.0s)\n",
+ "(2017-06-22 00:04:43)>> FIT features...\n",
+ "(2017-06-22 00:04:43)>>>> COUNT_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:43)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>>>> MEAN_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:43)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>>>> STD_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:43)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>>>> LAST_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:43)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>> ((u'weight body', 'all'),)\n",
+ "(2017-06-22 00:04:43)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>> Make DF for 1 components...\n",
+ "weight body\n",
+ "(2017-06-22 00:04:43)>>>> WEIGHT BODY: 1/1\n",
+ "(2017-06-22 00:04:43)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:43)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>>>>>> Join (897, 3) to None\n",
+ "(2017-06-22 00:04:43)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>> Segment df (897, 3)\n",
+ "(2017-06-22 00:04:43)>>>> Get Segments\n",
+ "(2017-06-22 00:04:43)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:43)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:44)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:44)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:44)>> Drop Small columns, threshold = 50 | (897, 3)\n",
+ "(2017-06-22 00:04:44)<< DONE (0.0s)\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "(2017-06-22 00:04:44)>> FIT Combine like columns (897, 2)\n",
+ "(2017-06-22 00:04:44)>>>> ('weight body', 'known', 'qn', 'kg')\n",
+ "(2017-06-22 00:04:44)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>> TRANSFORM Combine like columns (897, 2)\n",
+ "(2017-06-22 00:04:44)>>>> ('weight body', 'known', 'qn', 'kg')\n",
+ "(2017-06-22 00:04:44)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>> FIT features...\n",
+ "(2017-06-22 00:04:44)>>>> COUNT_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:04:44)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>>>> MEAN_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:04:44)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>>>> STD_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:04:44)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>>>> LAST_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:04:44)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>> ((u'blood pressure diastolic', 'all'),)\n",
+ "(2017-06-22 00:04:44)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:44)>> Make DF for 1 components...\n",
+ "blood pressure diastolic\n",
+ "(2017-06-22 00:04:44)>>>> BLOOD PRESSURE DIASTOLIC: 1/1\n",
+ "(2017-06-22 00:04:44)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:54)<<<<<< DONE (10.0s)\n",
+ "(2017-06-22 00:04:54)>>>>>> Join (70597, 15) to None\n",
+ "(2017-06-22 00:04:54)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:54)<<<< DONE (10.0s)\n",
+ "(2017-06-22 00:04:54)<< DONE (10.0s)\n",
+ "(2017-06-22 00:04:54)>> Segment df (70597, 15)\n",
+ "(2017-06-22 00:04:54)>>>> Get Segments\n",
+ "(2017-06-22 00:04:54)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:54)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:55)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:55)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:55)>> Drop Small columns, threshold = 50 | (70597, 15)\n",
+ "(2017-06-22 00:04:55)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:04:55)>> FIT Combine like columns (70597, 6)\n",
+ "(2017-06-22 00:04:55)>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:04:55)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:55)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:55)>> TRANSFORM Combine like columns (70597, 6)\n",
+ "(2017-06-22 00:04:55)>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:04:57)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:04:57)<< DONE (2.0s)\n",
+ "(2017-06-22 00:04:57)>> FIT features...\n",
+ "(2017-06-22 00:04:57)>>>> COUNT_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:57)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:57)>>>> MEAN_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:57)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:57)>>>> STD_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:57)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:57)>>>> LAST_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:04:57)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:57)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:57)>> ((u'blood pressure systolic', 'all'),)\n",
+ "(2017-06-22 00:04:57)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:57)>> Make DF for 1 components...\n",
+ "blood pressure systolic\n",
+ "(2017-06-22 00:04:57)>>>> BLOOD PRESSURE SYSTOLIC: 1/1\n",
+ "(2017-06-22 00:04:57)>>>>>> Opening...\n",
+ "(2017-06-22 00:04:58)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:58)>>>>>> Join (70612, 40) to None\n",
+ "(2017-06-22 00:04:58)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:58)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:58)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:58)>> Segment df (70612, 40)\n",
+ "(2017-06-22 00:04:58)>>>> Get Segments\n",
+ "(2017-06-22 00:04:58)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:58)>>>> Apply Segments\n",
+ "(2017-06-22 00:04:59)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:59)<< DONE (1.0s)\n",
+ "(2017-06-22 00:04:59)>> Drop Small columns, threshold = 50 | (70612, 40)\n",
+ "(2017-06-22 00:04:59)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:04:59)>> FIT Combine like columns (70612, 6)\n",
+ "(2017-06-22 00:04:59)>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:04:59)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:59)<< DONE (0.0s)\n",
+ "(2017-06-22 00:04:59)>> TRANSFORM Combine like columns (70612, 6)\n",
+ "(2017-06-22 00:04:59)>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:01)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:01)<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:01)>> FIT features...\n",
+ "(2017-06-22 00:05:01)>>>> COUNT_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:01)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:01)>>>> MEAN_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:01)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:01)>>>> STD_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:01)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:01)>>>> LAST_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:01)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:01)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:01) DONE (45.0s)\n",
+ "(2017-06-22 00:05:01) Start Feature Union on DF (328, 1)\n",
+ "(2017-06-22 00:05:01)>> ((u'heart rate', 'all'),)\n",
+ "(2017-06-22 00:05:01)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:01)>> Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-22 00:05:01)>>>> HEART RATE: 1/1\n",
+ "(2017-06-22 00:05:01)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:02)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:02)>>>>>> Join (70798, 6) to None\n",
+ "(2017-06-22 00:05:02)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:02)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:02)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:02)>> Segment df (70798, 6)\n",
+ "(2017-06-22 00:05:02)>>>> Get Segments\n",
+ "(2017-06-22 00:05:02)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:02)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:03)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:03)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:03)>> Drop Small columns, threshold = 50 | (70798, 6)\n",
+ "(2017-06-22 00:05:03)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:05:03)>> TRANSFORM Combine like columns (70798, 2)\n",
+ "(2017-06-22 00:05:03)>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 00:05:05)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:05)<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:05)>> Start Feature Union on DF (70798, 1)\n",
+ "(2017-06-22 00:05:05)>>>> COUNT_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:05)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:05)>>>> MEAN_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:05)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:05)>>>> STD_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:05)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:05)>>>> LAST_heart rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:05)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:05)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:05)>> ((u'oxygen saturation pulse oximetry', 'all'),)\n",
+ "(2017-06-22 00:05:05)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:05)>> Make DF for 1 components...\n",
+ "oxygen saturation pulse oximetry\n",
+ "(2017-06-22 00:05:05)>>>> OXYGEN SATURATION PULSE OXIMETRY: 1/1\n",
+ "(2017-06-22 00:05:05)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:06)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:06)>>>>>> Join (70742, 2) to None\n",
+ "(2017-06-22 00:05:06)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:06)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:06)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:06)>> Segment df (70742, 2)\n",
+ "(2017-06-22 00:05:06)>>>> Get Segments\n",
+ "(2017-06-22 00:05:06)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:06)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:06)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:06)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:06)>> Drop Small columns, threshold = 50 | (70742, 2)\n",
+ "(2017-06-22 00:05:06)<< DONE (0.0s)\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "(2017-06-22 00:05:06)>> TRANSFORM Combine like columns (70742, 2)\n",
+ "(2017-06-22 00:05:06)>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+ "(2017-06-22 00:05:08)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:08)<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:08)>> Start Feature Union on DF (70742, 1)\n",
+ "(2017-06-22 00:05:08)>>>> COUNT_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:05:08)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:08)>>>> MEAN_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:05:08)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:08)>>>> STD_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:05:08)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:08)>>>> LAST_oxygen saturation pulse oximetry\n",
+ "317\n",
+ "317\n",
+ "317\n",
+ "232\n",
+ "(2017-06-22 00:05:09)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:09)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:09)>> ((u'temperature body', 'all'),)\n",
+ "(2017-06-22 00:05:09)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)>> Make DF for 1 components...\n",
+ "temperature body\n",
+ "(2017-06-22 00:05:09)>>>> TEMPERATURE BODY: 1/1\n",
+ "(2017-06-22 00:05:09)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:09)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)>>>>>> Join (24795, 4) to None\n",
+ "(2017-06-22 00:05:09)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)>> Segment df (24795, 4)\n",
+ "(2017-06-22 00:05:09)>>>> Get Segments\n",
+ "(2017-06-22 00:05:09)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:09)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:09)>> Drop Small columns, threshold = 50 | (24795, 4)\n",
+ "(2017-06-22 00:05:09)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:05:09)>> TRANSFORM Combine like columns (24795, 4)\n",
+ "(2017-06-22 00:05:09)>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+ "(2017-06-22 00:05:10)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:10)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:10)>> Start Feature Union on DF (24795, 1)\n",
+ "(2017-06-22 00:05:10)>>>> COUNT_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> MEAN_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> STD_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> LAST_temperature body\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> ((u'blood pressure mean', 'all'),)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> Make DF for 1 components...\n",
+ "blood pressure mean\n",
+ "(2017-06-22 00:05:10)>>>> BLOOD PRESSURE MEAN: 1/1\n",
+ "(2017-06-22 00:05:10)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:10)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>>>> Join (5008, 3) to None\n",
+ "(2017-06-22 00:05:10)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> Segment df (5008, 3)\n",
+ "(2017-06-22 00:05:10)>>>> Get Segments\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> Drop Small columns, threshold = 50 | (5008, 3)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "(2017-06-22 00:05:10)>> TRANSFORM Combine like columns (5008, 3)\n",
+ "(2017-06-22 00:05:10)>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> Start Feature Union on DF (5008, 1)\n",
+ "(2017-06-22 00:05:10)>>>> COUNT_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> MEAN_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> STD_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>>>> LAST_blood pressure mean\n",
+ "33\n",
+ "33\n",
+ "33\n",
+ "21\n",
+ "(2017-06-22 00:05:10)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> ((u'respiratory rate', 'all'),)\n",
+ "(2017-06-22 00:05:10)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:10)>> Make DF for 1 components...\n",
+ "respiratory rate\n",
+ "(2017-06-22 00:05:10)>>>> RESPIRATORY RATE: 1/1\n",
+ "(2017-06-22 00:05:10)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:11)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:11)>>>>>> Join (69806, 6) to None\n",
+ "(2017-06-22 00:05:11)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:11)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:11)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:11)>> Segment df (69806, 6)\n",
+ "(2017-06-22 00:05:11)>>>> Get Segments\n",
+ "(2017-06-22 00:05:11)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:11)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:13)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:13)<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:13)>> Drop Small columns, threshold = 50 | (69806, 6)\n",
+ "(2017-06-22 00:05:13)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:05:13)>> TRANSFORM Combine like columns (69806, 2)\n",
+ "(2017-06-22 00:05:13)>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+ "(2017-06-22 00:05:15)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:15)<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:15)>> Start Feature Union on DF (69806, 1)\n",
+ "(2017-06-22 00:05:15)>>>> COUNT_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> MEAN_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> STD_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> LAST_respiratory rate\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> ((u'weight body', 'all'),)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> Make DF for 1 components...\n",
+ "weight body\n",
+ "(2017-06-22 00:05:15)>>>> WEIGHT BODY: 1/1\n",
+ "(2017-06-22 00:05:15)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:15)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>>>> Join (897, 3) to None\n",
+ "(2017-06-22 00:05:15)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> Segment df (897, 3)\n",
+ "(2017-06-22 00:05:15)>>>> Get Segments\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> Drop Small columns, threshold = 50 | (897, 3)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "(2017-06-22 00:05:15)>> TRANSFORM Combine like columns (897, 2)\n",
+ "(2017-06-22 00:05:15)>>>> ('weight body', 'known', 'qn', 'kg')\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> Start Feature Union on DF (897, 1)\n",
+ "(2017-06-22 00:05:15)>>>> COUNT_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> MEAN_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> STD_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>>>> LAST_weight body\n",
+ "239\n",
+ "239\n",
+ "239\n",
+ "152\n",
+ "(2017-06-22 00:05:15)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> ((u'blood pressure diastolic', 'all'),)\n",
+ "(2017-06-22 00:05:15)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:15)>> Make DF for 1 components...\n",
+ "blood pressure diastolic\n",
+ "(2017-06-22 00:05:15)>>>> BLOOD PRESSURE DIASTOLIC: 1/1\n",
+ "(2017-06-22 00:05:15)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:16)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:16)>>>>>> Join (70597, 15) to None\n",
+ "(2017-06-22 00:05:16)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:16)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:16)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:16)>> Segment df (70597, 15)\n",
+ "(2017-06-22 00:05:16)>>>> Get Segments\n",
+ "(2017-06-22 00:05:16)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:16)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:17)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:17)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:17)>> Drop Small columns, threshold = 50 | (70597, 15)\n",
+ "(2017-06-22 00:05:17)<< DONE (0.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:05:17)>> TRANSFORM Combine like columns (70597, 6)\n",
+ "(2017-06-22 00:05:17)>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:19)<<<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:19)<< DONE (2.0s)\n",
+ "(2017-06-22 00:05:19)>> Start Feature Union on DF (70597, 1)\n",
+ "(2017-06-22 00:05:19)>>>> COUNT_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:19)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:19)>>>> MEAN_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:19)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:19)>>>> STD_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:19)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:19)>>>> LAST_blood pressure diastolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:19)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:19)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:19)>> ((u'blood pressure systolic', 'all'),)\n",
+ "(2017-06-22 00:05:19)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:19)>> Make DF for 1 components...\n",
+ "blood pressure systolic\n",
+ "(2017-06-22 00:05:19)>>>> BLOOD PRESSURE SYSTOLIC: 1/1\n",
+ "(2017-06-22 00:05:19)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:20)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:20)>>>>>> Join (70612, 40) to None\n",
+ "(2017-06-22 00:05:20)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:20)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:20)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:20)>> Segment df (70612, 40)\n",
+ "(2017-06-22 00:05:20)>>>> Get Segments\n",
+ "(2017-06-22 00:05:20)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:20)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:21)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:21)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:21)>> Drop Small columns, threshold = 50 | (70612, 40)\n",
+ "(2017-06-22 00:05:22)<< DONE (1.0s)\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "(2017-06-22 00:05:22)>> TRANSFORM Combine like columns (70612, 6)\n",
+ "(2017-06-22 00:05:22)>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:23)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:23)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:23)>> Start Feature Union on DF (70612, 1)\n",
+ "(2017-06-22 00:05:23)>>>> COUNT_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:23)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:23)>>>> MEAN_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:23)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:23)>>>> STD_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:23)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:23)>>>> LAST_blood pressure systolic\n",
+ "318\n",
+ "318\n",
+ "318\n",
+ "233\n",
+ "(2017-06-22 00:05:24)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:24)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:24) DONE (23.0s)\n",
+ "(2017-06-22 00:05:24) Make DF for 1 components...\n",
+ "lactate\n",
+ "(2017-06-22 00:05:24)>> LACTATE: 1/1\n",
+ "(2017-06-22 00:05:24)>>>> Opening...\n",
+ "(2017-06-22 00:05:24)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)>>>> Join (187, 63) to None\n",
+ "(2017-06-22 00:05:24)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24) DONE (0.0s)\n",
+ "51\n",
+ "51\n",
+ "51\n",
+ "(2017-06-22 00:05:24) TRANSFORM Combine like columns (187, 63)\n",
+ "(2017-06-22 00:05:24)>> ('lactate', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-22 00:05:24)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-22 00:05:24)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24) DONE (0.0s)\n",
+ "30\n",
+ "30\n",
+ "30\n",
+ "30\n",
+ "(2017-06-22 00:05:24) Start Feature Union on DF (30, 1)\n",
+ "(2017-06-22 00:05:24)>> ((u'heart rate', 'all'),)\n",
+ "(2017-06-22 00:05:24)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)>> Make DF for 1 components...\n",
+ "heart rate\n",
+ "(2017-06-22 00:05:24)>>>> HEART RATE: 1/1\n",
+ "(2017-06-22 00:05:24)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:24)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)>>>>>> Join (7191, 6) to None\n",
+ "(2017-06-22 00:05:24)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)>> Segment df (7191, 6)\n",
+ "(2017-06-22 00:05:24)>>>> Get Segments\n",
+ "(2017-06-22 00:05:24)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:24)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:25)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:25)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:25)>> Drop Small columns, threshold = 50 | (7191, 6)\n",
+ "(2017-06-22 00:05:25)<< DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 00:05:25)>> TRANSFORM Combine like columns (7191, 2)\n",
+ "(2017-06-22 00:05:25)>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
+ "(2017-06-22 00:05:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)>> Start Feature Union on DF (7191, 1)\n",
+ "(2017-06-22 00:05:25)>>>> COUNT_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)>>>> MEAN_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)>>>> STD_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)>>>> LAST_heart rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:25)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)>> ((u'oxygen saturation pulse oximetry', 'all'),)\n",
+ "(2017-06-22 00:05:25)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:25)>> Make DF for 1 components...\n",
+ "oxygen saturation pulse oximetry\n",
+ "(2017-06-22 00:05:25)>>>> OXYGEN SATURATION PULSE OXIMETRY: 1/1\n",
+ "(2017-06-22 00:05:25)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:26)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:26)>>>>>> Join (7292, 2) to None\n",
+ "(2017-06-22 00:05:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:26)>> Segment df (7292, 2)\n",
+ "(2017-06-22 00:05:26)>>>> Get Segments\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Drop Small columns, threshold = 50 | (7292, 2)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 00:05:26)>> TRANSFORM Combine like columns (7292, 2)\n",
+ "(2017-06-22 00:05:26)>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Start Feature Union on DF (7292, 1)\n",
+ "(2017-06-22 00:05:26)>>>> COUNT_oxygen saturation pulse oximetry\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> MEAN_oxygen saturation pulse oximetry\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> STD_oxygen saturation pulse oximetry\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> LAST_oxygen saturation pulse oximetry\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> ((u'temperature body', 'all'),)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Make DF for 1 components...\n",
+ "temperature body\n",
+ "(2017-06-22 00:05:26)>>>> TEMPERATURE BODY: 1/1\n",
+ "(2017-06-22 00:05:26)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>>>> Join (1938, 4) to None\n",
+ "(2017-06-22 00:05:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Segment df (1938, 4)\n",
+ "(2017-06-22 00:05:26)>>>> Get Segments\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Drop Small columns, threshold = 50 | (1938, 4)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 00:05:26)>> TRANSFORM Combine like columns (1938, 4)\n",
+ "(2017-06-22 00:05:26)>>>> ('temperature body', 'known', 'qn', 'degF')\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Start Feature Union on DF (1938, 1)\n",
+ "(2017-06-22 00:05:26)>>>> COUNT_temperature body\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> MEAN_temperature body\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> STD_temperature body\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> LAST_temperature body\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "24\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> ((u'blood pressure mean', 'all'),)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Make DF for 1 components...\n",
+ "blood pressure mean\n",
+ "(2017-06-22 00:05:26)>>>> BLOOD PRESSURE MEAN: 1/1\n",
+ "(2017-06-22 00:05:26)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>>>> Join (1331, 3) to None\n",
+ "(2017-06-22 00:05:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>> Segment df (1331, 3)\n",
+ "(2017-06-22 00:05:26)>>>> Get Segments\n",
+ "(2017-06-22 00:05:26)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:26)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:27)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:27)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:27)>> Drop Small columns, threshold = 50 | (1331, 3)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "5\n",
+ "5\n",
+ "5\n",
+ "(2017-06-22 00:05:27)>> TRANSFORM Combine like columns (1331, 3)\n",
+ "(2017-06-22 00:05:27)>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>> Start Feature Union on DF (1331, 1)\n",
+ "(2017-06-22 00:05:27)>>>> COUNT_blood pressure mean\n",
+ "5\n",
+ "5\n",
+ "5\n",
+ "4\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>>>> MEAN_blood pressure mean\n",
+ "5\n",
+ "5\n",
+ "5\n",
+ "4\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>>>> STD_blood pressure mean\n",
+ "5\n",
+ "5\n",
+ "5\n",
+ "4\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>>>> LAST_blood pressure mean\n",
+ "5\n",
+ "5\n",
+ "5\n",
+ "4\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>> ((u'respiratory rate', 'all'),)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>> Make DF for 1 components...\n",
+ "respiratory rate\n",
+ "(2017-06-22 00:05:27)>>>> RESPIRATORY RATE: 1/1\n",
+ "(2017-06-22 00:05:27)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:27)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>>>>>> Join (6295, 6) to None\n",
+ "(2017-06-22 00:05:27)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>> Segment df (6295, 6)\n",
+ "(2017-06-22 00:05:27)>>>> Get Segments\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:27)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:27)>> Drop Small columns, threshold = 50 | (6295, 6)\n",
+ "(2017-06-22 00:05:27)<< DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 00:05:27)>> TRANSFORM Combine like columns (6295, 2)\n",
+ "(2017-06-22 00:05:27)>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
+ "(2017-06-22 00:05:28)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:28)>> Start Feature Union on DF (6295, 1)\n",
+ "(2017-06-22 00:05:28)>>>> COUNT_respiratory rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> MEAN_respiratory rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> STD_respiratory rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> LAST_respiratory rate\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> ((u'weight body', 'all'),)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> Make DF for 1 components...\n",
+ "weight body\n",
+ "(2017-06-22 00:05:28)>>>> WEIGHT BODY: 1/1\n",
+ "(2017-06-22 00:05:28)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:28)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>>>> Join (78, 3) to None\n",
+ "(2017-06-22 00:05:28)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> Segment df (78, 3)\n",
+ "(2017-06-22 00:05:28)>>>> Get Segments\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> Drop Small columns, threshold = 50 | (78, 3)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "17\n",
+ "17\n",
+ "17\n",
+ "(2017-06-22 00:05:28)>> TRANSFORM Combine like columns (78, 2)\n",
+ "(2017-06-22 00:05:28)>>>> ('weight body', 'known', 'qn', 'kg')\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> Start Feature Union on DF (78, 1)\n",
+ "(2017-06-22 00:05:28)>>>> COUNT_weight body\n",
+ "17\n",
+ "17\n",
+ "17\n",
+ "10\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> MEAN_weight body\n",
+ "17\n",
+ "17\n",
+ "17\n",
+ "10\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> STD_weight body\n",
+ "17\n",
+ "17\n",
+ "17\n",
+ "10\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> LAST_weight body\n",
+ "17\n",
+ "17\n",
+ "17\n",
+ "10\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> ((u'blood pressure diastolic', 'all'),)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> Make DF for 1 components...\n",
+ "blood pressure diastolic\n",
+ "(2017-06-22 00:05:28)>>>> BLOOD PRESSURE DIASTOLIC: 1/1\n",
+ "(2017-06-22 00:05:28)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:28)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>>>> Join (7055, 15) to None\n",
+ "(2017-06-22 00:05:28)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>> Segment df (7055, 15)\n",
+ "(2017-06-22 00:05:28)>>>> Get Segments\n",
+ "(2017-06-22 00:05:28)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:28)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:29)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:29)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:29)>> Drop Small columns, threshold = 50 | (7055, 15)\n",
+ "(2017-06-22 00:05:29)<< DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 00:05:29)>> TRANSFORM Combine like columns (7055, 6)\n",
+ "(2017-06-22 00:05:29)>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:29)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)>> Start Feature Union on DF (7055, 1)\n",
+ "(2017-06-22 00:05:29)>>>> COUNT_blood pressure diastolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:29)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)>>>> MEAN_blood pressure diastolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:29)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)>>>> STD_blood pressure diastolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:29)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)>>>> LAST_blood pressure diastolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:29)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)>> ((u'blood pressure systolic', 'all'),)\n",
+ "(2017-06-22 00:05:29)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:29)>> Make DF for 1 components...\n",
+ "blood pressure systolic\n",
+ "(2017-06-22 00:05:29)>>>> BLOOD PRESSURE SYSTOLIC: 1/1\n",
+ "(2017-06-22 00:05:29)>>>>>> Opening...\n",
+ "(2017-06-22 00:05:30)<<<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:30)>>>>>> Join (7057, 40) to None\n",
+ "(2017-06-22 00:05:30)<<<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)<<<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:30)<< DONE (1.0s)\n",
+ "(2017-06-22 00:05:30)>> Segment df (7057, 40)\n",
+ "(2017-06-22 00:05:30)>>>> Get Segments\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)>>>> Apply Segments\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)>> Drop Small columns, threshold = 50 | (7057, 40)\n",
+ "(2017-06-22 00:05:30)<< DONE (0.0s)\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "(2017-06-22 00:05:30)>> TRANSFORM Combine like columns (7057, 6)\n",
+ "(2017-06-22 00:05:30)>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)>> Start Feature Union on DF (7057, 1)\n",
+ "(2017-06-22 00:05:30)>>>> COUNT_blood pressure systolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)>>>> MEAN_blood pressure systolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)>>>> STD_blood pressure systolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)>>>> LAST_blood pressure systolic\n",
+ "29\n",
+ "29\n",
+ "29\n",
+ "25\n",
+ "(2017-06-22 00:05:30)<<<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30)<< DONE (0.0s)\n",
+ "(2017-06-22 00:05:30) DONE (6.0s)\n",
+ "(233, 32) (328, 1)\n",
+ "(25, 32) (30, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_lbl = label_pipeline.transform(X=test_ids)\n",
+ "test_ft = all_features.transform(test_lbl)\n",
+ "\n",
+ "print train_ft.shape, train_lbl.shape\n",
+ "print test_ft.shape, test_lbl.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Test-train split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Small train: 11794 > [101710, 163819, 191145, 186834, 190681] ...\n",
+ "Small test: 5898 > [172993, 130996, 105594, 100458, 102476] ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "`from sklearn.model_selection import train_test_split\n",
+ "import mimic\n",
+ "\n",
+ "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",
+ "\n",
+ "print 'Train:', len(train_ids),'>', train_ids[:5],'...'\n",
+ "print 'Test:', len(test_ids),'>',test_ids[:5],'...'\n",
+ "\n",
+ "# Small training set (20% train, 10% test)\n",
+ "\n",
+ "small_ids,ignore = train_test_split(train_ids,test_size=(2.0/3.0),random_state=random_state)\n",
+ "\n",
+ "train_ids_small,test_ids_small = train_test_split(small_ids,test_size=(1.0/3.0),random_state=random_state)\n",
+ "\n",
+ "print 'Small train:', len(train_ids_small),'>',train_ids_small[:5],'...'\n",
+ "print 'Small test:', len(test_ids_small),'>',test_ids_small[:5],'...'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets get our labels now!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.pipeline import Pipeline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "train = train_ids_small\n",
+ "test = test_ids_small\n",
+ "\n",
+ "label extractor = lactate_label_extractor(data_dict.components.LACTATE,random_state=random_state)\n",
+ "label_pipeline = Pipeline([\n",
+ " ('clean',cleaner_pipeline())\n",
+ " ('label_extractor',extractor)\n",
+ " ])\n",
+ "\n",
+ "lbl_train = filter_and_transform(df_data,label_pipeline,ids=train,fit_pipeline=True)\n",
+ "lbl_test = filter_and_transform(df_data,label_pipeline,ids=test,fit_pipeline=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we need to make our **FEATURES**. The first step in this process is to define how we are going to **\"segment\"** the data. What that means is that we need to identify which chunks of each timeseries to use, and then we will create each entry/rows/instance by aggregating the timeseries data in each chunk. \n",
+ "\n",
+ "In this case, we will use once of our pre-made segmenters, the n-hrs before segmenter. This will get fit to the labels, and then determine a block of time n-hours before the label as the segment. Specifically we are going to use all time before"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import segmenting\n",
+ "from constants import ALL\n",
+ "\n",
+ "segmenter = segmenting.n_hrs_before(df_context,n_hrs=ALL)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#4. Which features do we want, and to which columns should those features be applied?\n",
+ "var_type_filter = transformers.filter_var_type([constants.variable_type.ORDINAL,constants.variable_type.QUANTITATIVE])\n",
+ "feature_tuples = [\n",
+ " ('MEAN',\n",
+ " features.segment_mean(),\n",
+ " var_type_filter,\n",
+ " transformers.fill_mean()),\n",
+ " ('STD',\n",
+ " features.segment_std(),\n",
+ " var_type_filter,\n",
+ " transformers.fill_mean()),\n",
+ " ('COUNT',\n",
+ " features.segment_count(),\n",
+ " constants.ALL,\n",
+ " transformers.fill_zero()),\n",
+ " ('LAST',\n",
+ " features.segment_last(),\n",
+ " var_type_filter,\n",
+ " transformers.fill_mean()),\n",
+ " ('SUM',\n",
+ " features.segment_sum(),\n",
+ " transformers.summable_only(ureg,[data_dict.components.WEIGHT_BODY]),\n",
+ " transformers.fill_mean())\n",
+ "]\n",
+ "\n",
+ "\n",
+ "valid_ids = labels.index.get_level_values(constants.column_names.ID).unique().tolist()\n",
+ "end_dt = labels.reset_index(constants.column_names.DATETIME,drop=False).iloc[:,0]\n",
+ "\n",
+ "feature_pipeline = Pipeline([\n",
+ " ('filter_ids',transformers.filter_ids()),\n",
+ " ('segmenter',), #we are doing multivariate regression, so need to segment in this way for now\n",
+ " ('featurizer',features.pdFeatureUnion(feature_tuples)),\n",
+ " ('feature_cleaner',features.feature_cleaner())\n",
+ " ])\n",
+ "\n",
+ "featurizer = features.pdFeatureUnion(feature_tuples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import utils\n",
+ "df_all = utils.open_df('data/mimic_data','cleaned/all')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "logger.log('CLEANING',new_level=True)\n",
+ "df_cleaned = cleaners.fit_transform(df_all)\n",
+ "logger.end_log_level()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import utils\n",
+ "import icu_data_defs\n",
+ "import mimic\n",
+ "import transformers\n",
+ "import units\n",
+ "import logger\n",
+ "import constants\n",
+ "import segmenting\n",
+ "import features\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.base import TransformerMixin,BaseEstimator\n",
+ "from sklearn.preprocessing import StandardScaler"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Featurize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:18:55) Make DF for 11794 admission - [u'glasgow coma scale eye opening', u'glasgow coma scale motor', u'blood pressure systolic', u'oxygen saturation pulse oximetry', u'glasgow coma scale verbal', u'hemoglobin', u'lactate', u'blood pressure mean', u'vasopressin', 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",
+ "(2017-06-07 21:18:55)>> GLASGOW COMA SCALE EYE OPENING\n",
+ "(2017-06-07 21:18:55)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>glasgow coma scale eye opening</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>ord</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>184</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>190539.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>3.277445</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.074103</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>3.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale eye opening\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description 184\n",
+ "count 190539.000000\n",
+ "mean 3.277445\n",
+ "std 1.074103\n",
+ "min 1.000000\n",
+ "25% 3.000000\n",
+ "50% 4.000000\n",
+ "75% 4.000000\n",
+ "max 4.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:18:55)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:55)>>>> Clean (190539, 1)\n",
+ "(2017-06-07 21:18:55)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:55)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:18:55)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:55)>>>> Drop Small columns, threshold = 5 | (190539, 1)\n",
+ "(2017-06-07 21:18:55)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:55)>>>> Drop OOB data | (190539, 1)\n",
+ "(2017-06-07 21:18:55)>>>>>> glasgow coma scale eye opening, no_units, 190539\n",
+ "(2017-06-07 21:18:57)<<<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:18:57)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:18:57)>>>> Drop Small columns, threshold = 50 | (190539, 1)\n",
+ "(2017-06-07 21:18:57)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:57)>>>> Combine like columns (190539, 1)\n",
+ "(2017-06-07 21:18:57)>>>>>> (u'glasgow coma scale eye opening', 'known', u'ord', 'no_units')\n",
+ "(2017-06-07 21:18:57)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
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+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>190539.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>3.277445</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.074103</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
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+ " <th>25%</th>\n",
+ " <td>3.000000</td>\n",
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+ " <td>4.000000</td>\n",
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+ " <th>max</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale eye opening\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description all\n",
+ "count 190539.000000\n",
+ "mean 3.277445\n",
+ "std 1.074103\n",
+ "min 1.000000\n",
+ "25% 3.000000\n",
+ "50% 4.000000\n",
+ "75% 4.000000\n",
+ "max 4.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((190539, 1), (190539, 1), 0L, 0, '0.0% records')\n",
+ "(2017-06-07 21:18:57)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:57)>>>> Join (190539, 1) to None\n",
+ "(2017-06-07 21:18:57)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:57)<< DONE (2.0s)\n",
+ "(2017-06-07 21:18:57)>> GLASGOW COMA SCALE MOTOR\n",
+ "(2017-06-07 21:18:57)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <th>description</th>\n",
+ " <th>454</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>189663.000000</td>\n",
+ " </tr>\n",
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+ " <th>mean</th>\n",
+ " <td>5.218493</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.468247</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
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+ " <th>25%</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>6.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
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+ " </tr>\n",
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+ " <th>max</th>\n",
+ " <td>6.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale motor\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description 454\n",
+ "count 189663.000000\n",
+ "mean 5.218493\n",
+ "std 1.468247\n",
+ "min 1.000000\n",
+ "25% 5.000000\n",
+ "50% 6.000000\n",
+ "75% 6.000000\n",
+ "max 6.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:18:57)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:57)>>>> Clean (189663, 1)\n",
+ "(2017-06-07 21:18:57)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:57)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:18:57)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:18:57)>>>> Drop Small columns, threshold = 5 | (189663, 1)\n",
+ "(2017-06-07 21:18:58)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:18:58)>>>> Drop OOB data | (189663, 1)\n",
+ "(2017-06-07 21:18:58)>>>>>> glasgow coma scale motor, no_units, 189663\n",
+ "(2017-06-07 21:19:00)<<<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:19:00)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:19:00)>>>> Drop Small columns, threshold = 50 | (189663, 1)\n",
+ "(2017-06-07 21:19:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:19:00)>>>> Combine like columns (189663, 1)\n",
+ "(2017-06-07 21:19:00)>>>>>> (u'glasgow coma scale motor', 'known', u'ord', 'no_units')\n",
+ "(2017-06-07 21:19:00)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " <th>status</th>\n",
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+ ],
+ "text/plain": [
+ "component glasgow coma scale motor\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description all\n",
+ "count 189663.000000\n",
+ "mean 5.218493\n",
+ "std 1.468247\n",
+ "min 1.000000\n",
+ "25% 5.000000\n",
+ "50% 6.000000\n",
+ "75% 6.000000\n",
+ "max 6.000000"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((189663, 1), (189663, 1), 0L, 0, '0.0% records')\n",
+ "(2017-06-07 21:19:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:19:00)>>>> Join (189663, 1) to (190539, 1)\n",
+ "(2017-06-07 21:19:02)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:19:02)<< DONE (5.0s)\n",
+ "(2017-06-07 21:19:02)>> BLOOD PRESSURE SYSTOLIC\n",
+ "(2017-06-07 21:19:02)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"14\" halign=\"left\">blood pressure systolic</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"9\" halign=\"left\">known</th>\n",
+ " <th colspan=\"5\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
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+ " <th>units</th>\n",
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+ " <th colspan=\"5\" halign=\"left\">cc/min</th>\n",
+ " </tr>\n",
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+ " <th>count</th>\n",
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+ " <td>115.723810</td>\n",
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+ " <td>69.786441</td>\n",
+ " <td>73.389474</td>\n",
+ " <td>70.557196</td>\n",
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+ " <td>23.432058</td>\n",
+ " <td>48.30003</td>\n",
+ " <td>277.732478</td>\n",
+ " <td>36.817110</td>\n",
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+ " <td>24.923535</td>\n",
+ " <td>12.060643</td>\n",
+ " <td>11.149791</td>\n",
+ " <td>10.591928</td>\n",
+ " <td>10.264764</td>\n",
+ " <td>10.922431</td>\n",
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+ " <td>44.000000</td>\n",
+ " <td>40.000000</td>\n",
+ " <td>47.000000</td>\n",
+ " <td>33.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
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+ " <td>104.000000</td>\n",
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+ " <td>104.000000</td>\n",
+ " <td>102.00000</td>\n",
+ " <td>103.000000</td>\n",
+ " <td>100.750000</td>\n",
+ " <td>94.000000</td>\n",
+ " <td>98.000000</td>\n",
+ " <td>58.000000</td>\n",
+ " <td>63.000000</td>\n",
+ " <td>63.000000</td>\n",
+ " <td>67.000000</td>\n",
+ " <td>64.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
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+ " <td>118.000000</td>\n",
+ " <td>118.000000</td>\n",
+ " <td>117.00000</td>\n",
+ " <td>117.000000</td>\n",
+ " <td>129.500000</td>\n",
+ " <td>108.000000</td>\n",
+ " <td>115.000000</td>\n",
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+ " <td>71.000000</td>\n",
+ " <td>71.000000</td>\n",
+ " <td>73.000000</td>\n",
+ " <td>71.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>139.000000</td>\n",
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+ " <td>135.000000</td>\n",
+ " <td>134.00000</td>\n",
+ " <td>133.000000</td>\n",
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+ " <td>124.000000</td>\n",
+ " <td>130.000000</td>\n",
+ " <td>75.000000</td>\n",
+ " <td>78.000000</td>\n",
+ " <td>76.000000</td>\n",
+ " <td>80.000000</td>\n",
+ " <td>78.000000</td>\n",
+ " </tr>\n",
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+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>295.000000</td>\n",
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+ " <td>113.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure systolic \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmHg \n",
+ "description 6 51 442 \n",
+ "count 0.0 420257.000000 419.000000 \n",
+ "mean NaN 122.266249 117.883055 \n",
+ "std NaN 26.043698 26.740167 \n",
+ "min NaN 0.000000 60.000000 \n",
+ "25% NaN 104.000000 99.000000 \n",
+ "50% NaN 120.000000 118.000000 \n",
+ "75% NaN 139.000000 134.000000 \n",
+ "max NaN 295.000000 250.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 455 220050 220179 224167 \n",
+ "count 309232.000000 225246.00000 256585.000000 156.000000 \n",
+ "mean 120.410820 119.27193 120.142943 129.628205 \n",
+ "std 23.432058 48.30003 277.732478 36.817110 \n",
+ "min 0.000000 -2.00000 0.000000 0.000000 \n",
+ "25% 104.000000 102.00000 103.000000 100.750000 \n",
+ "50% 118.000000 117.00000 117.000000 129.500000 \n",
+ "75% 135.000000 134.00000 133.000000 150.000000 \n",
+ "max 296.000000 14868.00000 119119.020000 240.000000 \n",
+ "\n",
+ "component \\\n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units cc/min \n",
+ "description 225309 227243 3313 3315 3317 \n",
+ "count 18089.000000 105.000000 28686.000000 341.000000 295.000000 \n",
+ "mean 110.893952 115.723810 66.384695 70.645161 69.786441 \n",
+ "std 24.545781 24.923535 12.060643 11.149791 10.591928 \n",
+ "min 0.000000 48.000000 0.000000 44.000000 40.000000 \n",
+ "25% 94.000000 98.000000 58.000000 63.000000 63.000000 \n",
+ "50% 108.000000 115.000000 67.000000 71.000000 71.000000 \n",
+ "75% 124.000000 130.000000 75.000000 78.000000 76.000000 \n",
+ "max 287.000000 190.000000 449.000000 103.000000 98.000000 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 3321 3323 \n",
+ "count 285.000000 271.000000 \n",
+ "mean 73.389474 70.557196 \n",
+ "std 10.264764 10.922431 \n",
+ "min 47.000000 33.000000 \n",
+ "25% 67.000000 64.000000 \n",
+ "50% 73.000000 71.000000 \n",
+ "75% 80.000000 78.000000 \n",
+ "max 107.000000 113.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:19:06)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:19:06)>>>> Clean (1186786, 15)\n",
+ "(2017-06-07 21:19:06)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:19:06)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:19:14)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:19:14)>>>> Drop Small columns, threshold = 5 | (1186786, 40)\n",
+ "(2017-06-07 21:19:15)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:19:15)>>>> Drop OOB data | (1186786, 13)\n",
+ "(2017-06-07 21:19:16)>>>>>> blood pressure systolic, mmHg, 1230089\n",
+ "(2017-06-07 21:20:14)<<<<<< DONE (58.0s)\n",
+ "(2017-06-07 21:20:14)>>>>>> blood pressure systolic, cc/min, 29878\n",
+ "(2017-06-07 21:20:14)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:20:14)<<<< DONE (59.0s)\n",
+ "(2017-06-07 21:20:14)>>>> Drop Small columns, threshold = 50 | (1186786, 13)\n",
+ "(2017-06-07 21:20:14)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:20:14)>>>> Combine like columns (1186786, 13)\n",
+ "(2017-06-07 21:20:14)>>>>>> (u'blood pressure systolic', 'known', u'qn', u'mmHg')\n",
+ "(2017-06-07 21:20:15)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:20:15)>>>>>> (u'blood pressure systolic', 'unknown', u'qn', u'cc/min')\n",
+ "(2017-06-07 21:20:32)<<<<<< DONE (17.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">blood pressure systolic</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " <th>unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>qn</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mmHg</th>\n",
+ " <th>cc/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1.157847e+06</td>\n",
+ " <td>28930.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>1.207507e+02</td>\n",
+ " <td>66.432747</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.406445e+01</td>\n",
+ " <td>12.064723</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.040000e+02</td>\n",
+ " <td>58.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.180000e+02</td>\n",
+ " <td>67.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>1.360000e+02</td>\n",
+ " <td>75.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>4.110000e+02</td>\n",
+ " <td>449.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure systolic \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units mmHg cc/min\n",
+ "description all all\n",
+ "count 1.157847e+06 28930.000000\n",
+ "mean 1.207507e+02 66.432747\n",
+ "std 2.406445e+01 12.064723\n",
+ "min 0.000000e+00 0.000000\n",
+ "25% 1.040000e+02 58.000000\n",
+ "50% 1.180000e+02 67.000000\n",
+ "75% 1.360000e+02 75.000000\n",
+ "max 4.110000e+02 449.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((1186786, 14), (1186777, 2), 73190L, 0, '0.0% records')\n",
+ "(2017-06-07 21:20:32)<<<< DONE (18.0s)\n",
+ "(2017-06-07 21:20:32)>>>> Join (1186777, 2) to (190760, 2)\n",
+ "(2017-06-07 21:20:39)<<<< DONE (7.0s)\n",
+ "(2017-06-07 21:20:39)<< DONE (97.0s)\n",
+ "(2017-06-07 21:20:39)>> OXYGEN SATURATION PULSE OXIMETRY\n",
+ "(2017-06-07 21:20:39)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">oxygen saturation pulse oximetry</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"2\" halign=\"left\">percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>220277(%)</th>\n",
+ " <th>646(%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>553064.000000</td>\n",
+ " <td>680431.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>96.458469</td>\n",
+ " <td>97.228694</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>29.151745</td>\n",
+ " <td>3.761861</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>95.000000</td>\n",
+ " <td>96.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>97.000000</td>\n",
+ " <td>98.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>9891.000000</td>\n",
+ " <td>109.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component oxygen saturation pulse oximetry \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units percent \n",
+ "description 220277(%) 646(%)\n",
+ "count 553064.000000 680431.000000\n",
+ "mean 96.458469 97.228694\n",
+ "std 29.151745 3.761861\n",
+ "min 0.000000 0.000000\n",
+ "25% 95.000000 96.000000\n",
+ "50% 97.000000 98.000000\n",
+ "75% 99.000000 100.000000\n",
+ "max 9891.000000 109.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:20:41)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:20:41)>>>> Clean (1233449, 2)\n",
+ "(2017-06-07 21:20:41)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:20:41)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:20:41)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:20:41)>>>> Drop Small columns, threshold = 5 | (1233449, 2)\n",
+ "(2017-06-07 21:20:41)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:20:41)>>>> Drop OOB data | (1233449, 2)\n",
+ "(2017-06-07 21:20:41)>>>>>> oxygen saturation pulse oximetry, percent, 1233495\n",
+ "(2017-06-07 21:21:01)<<<<<< DONE (20.0s)\n",
+ "(2017-06-07 21:21:01)<<<< DONE (20.0s)\n",
+ "(2017-06-07 21:21:01)>>>> Drop Small columns, threshold = 50 | (1233449, 2)\n",
+ "(2017-06-07 21:21:01)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:01)>>>> Combine like columns (1233449, 2)\n",
+ "(2017-06-07 21:21:01)>>>>>> (u'oxygen saturation pulse oximetry', 'known', u'qn', u'percent')\n",
+ "(2017-06-07 21:21:08)<<<<<< DONE (7.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>oxygen saturation pulse oximetry</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>percent</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>1.233435e+06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>9.684161e+01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>3.834325e+00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000e+00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>9.500000e+01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>9.800000e+01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>9.900000e+01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.000000e+02</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component oxygen saturation pulse oximetry\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units percent\n",
+ "description all\n",
+ "count 1.233435e+06\n",
+ "mean 9.684161e+01\n",
+ "std 3.834325e+00\n",
+ "min 0.000000e+00\n",
+ "25% 9.500000e+01\n",
+ "50% 9.800000e+01\n",
+ "75% 9.900000e+01\n",
+ "max 1.000000e+02"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((1233449, 2), (1233435, 1), 60L, 0, '0.0% records')\n",
+ "(2017-06-07 21:21:08)<<<< DONE (7.0s)\n",
+ "(2017-06-07 21:21:08)>>>> Join (1233435, 1) to (1193505, 4)\n",
+ "(2017-06-07 21:21:21)<<<< DONE (13.0s)\n",
+ "(2017-06-07 21:21:21)<< DONE (42.0s)\n",
+ "(2017-06-07 21:21:21)>> GLASGOW COMA SCALE VERBAL\n",
+ "(2017-06-07 21:21:21)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>glasgow coma scale verbal</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>ord</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>723</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>189945.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.893353</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.911507</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale verbal\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description 723\n",
+ "count 189945.000000\n",
+ "mean 2.893353\n",
+ "std 1.911507\n",
+ "min 1.000000\n",
+ "25% 1.000000\n",
+ "50% 2.000000\n",
+ "75% 5.000000\n",
+ "max 5.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:21:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:21)>>>> Clean (189945, 1)\n",
+ "(2017-06-07 21:21:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:21)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:21:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:21)>>>> Drop Small columns, threshold = 5 | (189945, 1)\n",
+ "(2017-06-07 21:21:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:21)>>>> Drop OOB data | (189945, 1)\n",
+ "(2017-06-07 21:21:21)>>>>>> glasgow coma scale verbal, no_units, 189945\n",
+ "(2017-06-07 21:21:24)<<<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:21:24)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:21:24)>>>> Drop Small columns, threshold = 50 | (189945, 1)\n",
+ "(2017-06-07 21:21:24)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:24)>>>> Combine like columns (189945, 1)\n",
+ "(2017-06-07 21:21:24)>>>>>> (u'glasgow coma scale verbal', 'known', u'ord', 'no_units')\n",
+ "(2017-06-07 21:21:24)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>glasgow coma scale verbal</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>ord</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>no_units</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>189945.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.893353</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.911507</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale verbal\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description all\n",
+ "count 189945.000000\n",
+ "mean 2.893353\n",
+ "std 1.911507\n",
+ "min 1.000000\n",
+ "25% 1.000000\n",
+ "50% 2.000000\n",
+ "75% 5.000000\n",
+ "max 5.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((189945, 1), (189945, 1), 0L, 0, '0.0% records')\n",
+ "(2017-06-07 21:21:24)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:24)>>>> Join (189945, 1) to (1388960, 5)\n",
+ "(2017-06-07 21:21:32)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:21:32)<< DONE (11.0s)\n",
+ "(2017-06-07 21:21:32)>> HEMOGLOBIN\n",
+ "(2017-06-07 21:21:32)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"11\" halign=\"left\">hemoglobin</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"9\" halign=\"left\">known</th>\n",
+ " <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"9\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"9\" halign=\"left\">g/dL</th>\n",
+ " <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1165</th>\n",
+ " <th>220228(g/dl)</th>\n",
+ " <th>50811</th>\n",
+ " <th>50811(g/dl)</th>\n",
+ " <th>50811(gm/dl)</th>\n",
+ " <th>51222</th>\n",
+ " <th>51222(g/dl)</th>\n",
+ " <th>51222(gm/dl)</th>\n",
+ " <th>814(gm/dl)</th>\n",
+ " <th>50811</th>\n",
+ " <th>51222</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>0.0</td>\n",
+ " <td>27394.000000</td>\n",
+ " <td>10595.000000</td>\n",
+ " <td>3097.000000</td>\n",
+ " <td>4169.000000</td>\n",
+ " <td>73675.000000</td>\n",
+ " <td>17720.000000</td>\n",
+ " <td>23327.000000</td>\n",
+ " <td>37635.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>10.055235</td>\n",
+ " <td>10.249825</td>\n",
+ " <td>10.433904</td>\n",
+ " <td>10.443536</td>\n",
+ " <td>10.315149</td>\n",
+ " <td>10.515125</td>\n",
+ " <td>10.588340</td>\n",
+ " <td>10.268957</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>1.800061</td>\n",
+ " <td>2.175106</td>\n",
+ " <td>2.919614</td>\n",
+ " <td>2.030175</td>\n",
+ " <td>1.932909</td>\n",
+ " <td>1.959018</td>\n",
+ " <td>1.956675</td>\n",
+ " <td>1.615244</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2.200000</td>\n",
+ " <td>3.400000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>8.800000</td>\n",
+ " <td>8.700000</td>\n",
+ " <td>9.100000</td>\n",
+ " <td>9.100000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>9.300000</td>\n",
+ " <td>9.300000</td>\n",
+ " <td>9.200000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>9.900000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>10.300000</td>\n",
+ " <td>10.300000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>10.200000</td>\n",
+ " <td>10.300000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>11.100000</td>\n",
+ " <td>11.600000</td>\n",
+ " <td>11.600000</td>\n",
+ " <td>11.700000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.200000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>23.000000</td>\n",
+ " <td>19.400000</td>\n",
+ " <td>130.000000</td>\n",
+ " <td>21.000000</td>\n",
+ " <td>23.300000</td>\n",
+ " <td>24.400000</td>\n",
+ " <td>22.500000</td>\n",
+ " <td>18.900000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component hemoglobin \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units g/dL \n",
+ "description 1165 220228(g/dl) 50811 50811(g/dl) \n",
+ "count 0.0 27394.000000 10595.000000 3097.000000 \n",
+ "mean NaN 10.055235 10.249825 10.433904 \n",
+ "std NaN 1.800061 2.175106 2.919614 \n",
+ "min NaN 0.000000 0.000000 0.000000 \n",
+ "25% NaN 8.800000 8.700000 9.100000 \n",
+ "50% NaN 9.900000 10.100000 10.300000 \n",
+ "75% NaN 11.100000 11.600000 11.600000 \n",
+ "max NaN 23.000000 19.400000 130.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 50811(gm/dl) 51222 51222(g/dl) 51222(gm/dl) \n",
+ "count 4169.000000 73675.000000 17720.000000 23327.000000 \n",
+ "mean 10.443536 10.315149 10.515125 10.588340 \n",
+ "std 2.030175 1.932909 1.959018 1.956675 \n",
+ "min 0.000000 0.000000 2.200000 3.400000 \n",
+ "25% 9.100000 9.000000 9.300000 9.300000 \n",
+ "50% 10.300000 10.100000 10.200000 10.300000 \n",
+ "75% 11.700000 11.400000 11.400000 11.400000 \n",
+ "max 21.000000 23.300000 24.400000 22.500000 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 814(gm/dl) 50811 51222 \n",
+ "count 37635.000000 0.0 0.0 \n",
+ "mean 10.268957 NaN NaN \n",
+ "std 1.615244 NaN NaN \n",
+ "min 0.000000 NaN NaN \n",
+ "25% 9.200000 NaN NaN \n",
+ "50% 10.100000 NaN NaN \n",
+ "75% 11.200000 NaN NaN \n",
+ "max 18.900000 NaN NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:21:33)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:33)>>>> Clean (134961, 14)\n",
+ "(2017-06-07 21:21:33)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:33)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:21:34)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:34)>>>> Drop Small columns, threshold = 5 | (134961, 21)\n",
+ "(2017-06-07 21:21:34)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:34)>>>> Drop OOB data | (134961, 10)\n",
+ "(2017-06-07 21:21:34)>>>>>> hemoglobin, g/dL, 197612\n",
+ "(2017-06-07 21:21:41)<<<<<< DONE (7.0s)\n",
+ "(2017-06-07 21:21:41)>>>>>> hemoglobin, no_units, 269922\n",
+ "(2017-06-07 21:21:41)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:41)<<<< DONE (7.0s)\n",
+ "(2017-06-07 21:21:41)>>>> Drop Small columns, threshold = 50 | (134961, 10)\n",
+ "(2017-06-07 21:21:41)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:41)>>>> Combine like columns (134961, 8)\n",
+ "(2017-06-07 21:21:41)>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
+ "(2017-06-07 21:21:42)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>hemoglobin</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>g/dL</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>134941.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>10.395801</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.964194</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>9.100000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>10.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>11.400000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>24.400000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component hemoglobin\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units g/dL\n",
+ "description all\n",
+ "count 134941.000000\n",
+ "mean 10.395801\n",
+ "std 1.964194\n",
+ "min 0.000000\n",
+ "25% 9.100000\n",
+ "50% 10.200000\n",
+ "75% 11.400000\n",
+ "max 24.400000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((134961, 13), (134941, 1), 62677L, 0, '0.0% records')\n",
+ "(2017-06-07 21:21:42)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:42)>>>> Join (134941, 1) to (1388967, 6)\n",
+ "(2017-06-07 21:21:51)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:21:51)<< DONE (19.0s)\n",
+ "(2017-06-07 21:21:51)>> LACTATE\n",
+ "(2017-06-07 21:21:51)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"7\" halign=\"left\">lactate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"4\" halign=\"left\">known</th>\n",
+ " <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"4\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"4\" halign=\"left\">mmol/L</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1531</th>\n",
+ " <th>225668</th>\n",
+ " <th>50813</th>\n",
+ " <th>818</th>\n",
+ " <th>225668</th>\n",
+ " <th>50813</th>\n",
+ " <th>818</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>12763.000000</td>\n",
+ " <td>14445.000000</td>\n",
+ " <td>36017.000000</td>\n",
+ " <td>14604.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.740604</td>\n",
+ " <td>2.554469</td>\n",
+ " <td>2.648551</td>\n",
+ " <td>2.816597</td>\n",
+ " <td>75.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.807148</td>\n",
+ " <td>2.945601</td>\n",
+ " <td>2.548654</td>\n",
+ " <td>2.937797</td>\n",
+ " <td>105.500332</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.200000</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>0.100000</td>\n",
+ " <td>0.200000</td>\n",
+ " <td>0.800000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.300000</td>\n",
+ " <td>38.100000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.800000</td>\n",
+ " <td>1.800000</td>\n",
+ " <td>1.800000</td>\n",
+ " <td>1.800000</td>\n",
+ " <td>75.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>3.000000</td>\n",
+ " <td>2.900000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>3.100000</td>\n",
+ " <td>112.700000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>31.000000</td>\n",
+ " <td>170.000000</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>31.000000</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactate \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmol/L \n",
+ "description 1531 225668 50813 818 \n",
+ "count 12763.000000 14445.000000 36017.000000 14604.000000 \n",
+ "mean 2.740604 2.554469 2.648551 2.816597 \n",
+ "std 2.807148 2.945601 2.548654 2.937797 \n",
+ "min 0.200000 0.200000 0.100000 0.200000 \n",
+ "25% 1.200000 1.200000 1.200000 1.300000 \n",
+ "50% 1.800000 1.800000 1.800000 1.800000 \n",
+ "75% 3.000000 2.900000 3.000000 3.100000 \n",
+ "max 31.000000 170.000000 32.000000 31.000000 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 225668 50813 818 \n",
+ "count 2.000000 0.0 0.0 \n",
+ "mean 75.400000 NaN NaN \n",
+ "std 105.500332 NaN NaN \n",
+ "min 0.800000 NaN NaN \n",
+ "25% 38.100000 NaN NaN \n",
+ "50% 75.400000 NaN NaN \n",
+ "75% 112.700000 NaN NaN \n",
+ "max 150.000000 NaN NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:21:52)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:52)>>>> Clean (36176, 10)\n",
+ "(2017-06-07 21:21:52)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:52)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:21:52)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:52)>>>> Drop Small columns, threshold = 5 | (36176, 31)\n",
+ "(2017-06-07 21:21:52)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:52)>>>> Drop OOB data | (36176, 5)\n",
+ "(2017-06-07 21:21:52)>>>>>> lactate, mmol/L, 77829\n",
+ "(2017-06-07 21:21:53)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:53)>>>>>> lactate, no_units, 36176\n",
+ "(2017-06-07 21:21:53)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:53)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:53)>>>> Drop Small columns, threshold = 50 | (36176, 5)\n",
+ "(2017-06-07 21:21:53)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:21:53)>>>> Combine like columns (36176, 4)\n",
+ "(2017-06-07 21:21:53)>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-07 21:21:54)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\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>36151.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.647771</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.550192</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.100000</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>3.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>32.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactate\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units mmol/L\n",
+ "description all\n",
+ "count 36151.000000\n",
+ "mean 2.647771\n",
+ "std 2.550192\n",
+ "min 0.100000\n",
+ "25% 1.200000\n",
+ "50% 1.800000\n",
+ "75% 3.000000\n",
+ "max 32.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((36176, 9), (36151, 1), 41712L, 2, '0.0293% records')\n",
+ "(2017-06-07 21:21:54)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:21:54)>>>> Join (36151, 1) to (1515017, 7)\n",
+ "(2017-06-07 21:22:02)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:22:02)<< DONE (11.0s)\n",
+ "(2017-06-07 21:22:02)>> BLOOD PRESSURE MEAN\n",
+ "(2017-06-07 21:22:02)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">blood pressure mean</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">mmHg</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>220052</th>\n",
+ " <th>220181</th>\n",
+ " <th>225312</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>226772.000000</td>\n",
+ " <td>256986.000000</td>\n",
+ " <td>18276.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>79.916207</td>\n",
+ " <td>76.241892</td>\n",
+ " <td>78.484187</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>32.658920</td>\n",
+ " <td>27.999114</td>\n",
+ " <td>81.573943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-40.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-37.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>68.000000</td>\n",
+ " <td>65.000000</td>\n",
+ " <td>66.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>77.000000</td>\n",
+ " <td>75.000000</td>\n",
+ " <td>74.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>88.000000</td>\n",
+ " <td>85.000000</td>\n",
+ " <td>86.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>9381.000000</td>\n",
+ " <td>8284.000000</td>\n",
+ " <td>8684.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure mean \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmHg \n",
+ "description 220052 220181 225312\n",
+ "count 226772.000000 256986.000000 18276.000000\n",
+ "mean 79.916207 76.241892 78.484187\n",
+ "std 32.658920 27.999114 81.573943\n",
+ "min -40.000000 0.000000 -37.000000\n",
+ "25% 68.000000 65.000000 66.000000\n",
+ "50% 77.000000 75.000000 74.000000\n",
+ "75% 88.000000 85.000000 86.000000\n",
+ "max 9381.000000 8284.000000 8684.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:22:03)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:22:03)>>>> Clean (475862, 3)\n",
+ "(2017-06-07 21:22:03)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:03)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:22:03)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:03)>>>> Drop Small columns, threshold = 5 | (475862, 3)\n",
+ "(2017-06-07 21:22:03)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:03)>>>> Drop OOB data | (475862, 3)\n",
+ "(2017-06-07 21:22:03)>>>>>> blood pressure mean, mmHg, 502034\n",
+ "(2017-06-07 21:22:14)<<<<<< DONE (11.0s)\n",
+ "(2017-06-07 21:22:14)<<<< DONE (11.0s)\n",
+ "(2017-06-07 21:22:14)>>>> Drop Small columns, threshold = 50 | (475862, 3)\n",
+ "(2017-06-07 21:22:14)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:14)>>>> Combine like columns (475862, 3)\n",
+ "(2017-06-07 21:22:14)>>>>>> (u'blood pressure mean', 'known', u'qn', u'mmHg')\n",
+ "(2017-06-07 21:22:17)<<<<<< DONE (3.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>blood pressure mean</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>mmHg</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>475685.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>77.764329</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>17.291234</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>67.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>76.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>87.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>361.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure mean\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units mmHg\n",
+ "description all\n",
+ "count 475685.000000\n",
+ "mean 77.764329\n",
+ "std 17.291234\n",
+ "min 0.000000\n",
+ "25% 67.000000\n",
+ "50% 76.000000\n",
+ "75% 87.000000\n",
+ "max 361.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((475862, 3), (475685, 1), 26349L, 0, '0.0% records')\n",
+ "(2017-06-07 21:22:17)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:22:17)>>>> Join (475685, 1) to (1542075, 8)\n",
+ "(2017-06-07 21:22:29)<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:22:29)<< DONE (27.0s)\n",
+ "(2017-06-07 21:22:29)>> VASOPRESSIN\n",
+ "(2017-06-07 21:22:29)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"21\" halign=\"left\">vasopressin</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"15\" halign=\"left\">known</th>\n",
+ " <th colspan=\"6\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"15\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"6\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"8\" halign=\"left\">units</th>\n",
+ " <th colspan=\"7\" halign=\"left\">units/min</th>\n",
+ " <th colspan=\"4\" halign=\"left\">ml</th>\n",
+ " <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1136(U)</th>\n",
+ " <th>1222(U)</th>\n",
+ " <th>1327(U)</th>\n",
+ " <th>2248(U)</th>\n",
+ " <th>2445(U)</th>\n",
+ " <th>30051(U)</th>\n",
+ " <th>6255(U)</th>\n",
+ " <th>7341(U)</th>\n",
+ " <th>1136(units/hour)</th>\n",
+ " <th>1222(units/hour)</th>\n",
+ " <th>...</th>\n",
+ " <th>30051(Umin)(U/min)</th>\n",
+ " <th>30051(units/hour)</th>\n",
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+ " <th>42273</th>\n",
+ " <th>42802</th>\n",
+ " <th>46570</th>\n",
+ " <th>30051</th>\n",
+ " <th>42802</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>12442.000000</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>20747.000000</td>\n",
+ " <td>776.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>33.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>1.026667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.507845</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.033556</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>0.928640</td>\n",
+ " <td>0.055511</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>4.281212</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.708957</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>7.126552</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.028483</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>1.186254</td>\n",
+ " <td>0.077455</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.691116</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.040000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.040000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.200000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.025333</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>0.026250</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.040000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>1.520000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>2.400000</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.020000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>3.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>722.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>0.800000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.020000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>8 rows × 28 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component vasopressin \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units units \n",
+ "description 1136(U) 1222(U) 1327(U) 2248(U) 2445(U) 30051(U) \n",
+ "count 3.000000 0.0 0.0 0.0 0.0 12442.000000 \n",
+ "mean 1.026667 NaN NaN NaN NaN 2.507845 \n",
+ "std 1.708957 NaN NaN NaN NaN 7.126552 \n",
+ "min 0.040000 NaN NaN NaN NaN 0.000000 \n",
+ "25% 0.040000 NaN NaN NaN NaN 2.200000 \n",
+ "50% 0.040000 NaN NaN NaN NaN 2.400000 \n",
+ "75% 1.520000 NaN NaN NaN NaN 2.400000 \n",
+ "max 3.000000 NaN NaN NaN NaN 722.400000 \n",
+ "\n",
+ "component ... \\\n",
+ "status ... \n",
+ "variable_type ... \n",
+ "units units/min ... \n",
+ "description 6255(U) 7341(U) 1136(units/hour) 1222(units/hour) ... \n",
+ "count 0.0 0.0 3.000000 0.0 ... \n",
+ "mean NaN NaN 0.033556 NaN ... \n",
+ "std NaN NaN 0.028483 NaN ... \n",
+ "min NaN NaN 0.000667 NaN ... \n",
+ "25% NaN NaN 0.025333 NaN ... \n",
+ "50% NaN NaN 0.050000 NaN ... \n",
+ "75% NaN NaN 0.050000 NaN ... \n",
+ "max NaN NaN 0.050000 NaN ... \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 30051(Umin)(U/min) 30051(units/hour) 6255(units/hour) \n",
+ "count 20747.000000 776.000000 0.0 \n",
+ "mean 0.928640 0.055511 NaN \n",
+ "std 1.186254 0.077455 NaN \n",
+ "min 0.000000 0.000000 NaN \n",
+ "25% 0.040000 0.026250 NaN \n",
+ "50% 0.040000 0.040000 NaN \n",
+ "75% 2.400000 0.050000 NaN \n",
+ "max 8.000000 0.800000 NaN \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units ml no_units \n",
+ "description 7341(units/hour) 30051 42273 42802 46570 30051 42802 \n",
+ "count 0.0 33.000000 0.0 0.0 1.0 1.0 0.0 \n",
+ "mean NaN 4.281212 NaN NaN 6.0 0.0 NaN \n",
+ "std NaN 1.691116 NaN NaN NaN NaN NaN \n",
+ "min NaN 0.000000 NaN NaN 6.0 0.0 NaN \n",
+ "25% NaN 3.000000 NaN NaN 6.0 0.0 NaN \n",
+ "50% NaN 3.000000 NaN NaN 6.0 0.0 NaN \n",
+ "75% NaN 6.020000 NaN NaN 6.0 0.0 NaN \n",
+ "max NaN 6.020000 NaN NaN 6.0 0.0 NaN \n",
+ "\n",
+ "[8 rows x 28 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:22:29)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:29)>>>> Clean (23280, 31)\n",
+ "(2017-06-07 21:22:29)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:29)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:22:29)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:29)>>>> Drop Small columns, threshold = 5 | (23280, 28)\n",
+ "(2017-06-07 21:22:29)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:29)>>>> Drop OOB data | (23280, 6)\n",
+ "(2017-06-07 21:22:30)>>>>>> vasopressin, units, 12442\n",
+ "(2017-06-07 21:22:30)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:30)>>>>>> vasopressin, units/min, 23422\n",
+ "(2017-06-07 21:22:30)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:30)>>>>>> vasopressin, ml, 33\n",
+ "(2017-06-07 21:22:30)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:30)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:22:30)>>>> Drop Small columns, threshold = 50 | (23280, 6)\n",
+ "(2017-06-07 21:22:30)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:30)>>>> Combine like columns (23280, 5)\n",
+ "(2017-06-07 21:22:30)>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
+ "(2017-06-07 21:22:30)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:30)>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
+ "(2017-06-07 21:22:31)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">vasopressin</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>units</th>\n",
+ " <th>units/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>12441.000000</td>\n",
+ " <td>22685.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.449981</td>\n",
+ " <td>0.849527</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>3.021413</td>\n",
+ " <td>1.139410</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>2.200000</td>\n",
+ " <td>0.040000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.400000</td>\n",
+ " <td>0.040000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.400000</td>\n",
+ " <td>2.400000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>144.000000</td>\n",
+ " <td>4.800000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component vasopressin \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units units units/min\n",
+ "description all all\n",
+ "count 12441.000000 22685.000000\n",
+ "mean 2.449981 0.849527\n",
+ "std 3.021413 1.139410\n",
+ "min 0.000000 0.000000\n",
+ "25% 2.200000 0.040000\n",
+ "50% 2.400000 0.040000\n",
+ "75% 2.400000 2.400000\n",
+ "max 144.000000 4.800000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((23280, 28), (23258, 2), 781L, 0, '0.0% records')\n",
+ "(2017-06-07 21:22:31)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:22:31)>>>> Join (23258, 2) to (1545464, 9)\n",
+ "(2017-06-07 21:22:39)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:22:39)<< DONE (10.0s)\n",
+ "(2017-06-07 21:22:39)>> WEIGHT BODY\n",
+ "(2017-06-07 21:22:39)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">weight body</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">kg</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>763</th>\n",
+ " <th>224639</th>\n",
+ " <th>3693(gms)(grams)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>9489.000000</td>\n",
+ " <td>10119.000000</td>\n",
+ " <td>178.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>85.255536</td>\n",
+ " <td>86.870566</td>\n",
+ " <td>0.003098</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>24.257167</td>\n",
+ " <td>27.099830</td>\n",
+ " <td>0.000785</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000675</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>69.099998</td>\n",
+ " <td>70.200000</td>\n",
+ " <td>0.002702</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>82.400002</td>\n",
+ " <td>83.400000</td>\n",
+ " <td>0.003260</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>97.099998</td>\n",
+ " <td>100.200000</td>\n",
+ " <td>0.003559</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>300.000000</td>\n",
+ " <td>839.800000</td>\n",
+ " <td>0.004845</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component weight body \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units kg \n",
+ "description 763 224639 3693(gms)(grams)\n",
+ "count 9489.000000 10119.000000 178.000000\n",
+ "mean 85.255536 86.870566 0.003098\n",
+ "std 24.257167 27.099830 0.000785\n",
+ "min 0.000000 0.000000 0.000675\n",
+ "25% 69.099998 70.200000 0.002702\n",
+ "50% 82.400002 83.400000 0.003260\n",
+ "75% 97.099998 100.200000 0.003559\n",
+ "max 300.000000 839.800000 0.004845"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:22:40)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Clean (19786, 3)\n",
+ "(2017-06-07 21:22:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:22:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Drop Small columns, threshold = 5 | (19786, 3)\n",
+ "(2017-06-07 21:22:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Drop OOB data | (19786, 3)\n",
+ "(2017-06-07 21:22:40)>>>>>> weight body, kg, 19786\n",
+ "(2017-06-07 21:22:40)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Drop Small columns, threshold = 50 | (19786, 3)\n",
+ "(2017-06-07 21:22:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Combine like columns (19786, 3)\n",
+ "(2017-06-07 21:22:40)>>>>>> (u'weight body', 'known', u'qn', u'kg')\n",
+ "(2017-06-07 21:22:40)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
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+ " <th>all</th>\n",
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+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>19784.000000</td>\n",
+ " </tr>\n",
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+ " <th>mean</th>\n",
+ " <td>85.239931</td>\n",
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+ " <th>std</th>\n",
+ " <td>25.873351</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
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+ " <th>25%</th>\n",
+ " <td>69.400002</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>82.600000</td>\n",
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+ " <th>75%</th>\n",
+ " <td>98.400000</td>\n",
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+ " <th>max</th>\n",
+ " <td>300.000000</td>\n",
+ " </tr>\n",
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+ "</div>"
+ ],
+ "text/plain": [
+ "component weight body\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units kg\n",
+ "description all\n",
+ "count 19784.000000\n",
+ "mean 85.239931\n",
+ "std 25.873351\n",
+ "min 0.000000\n",
+ "25% 69.400002\n",
+ "50% 82.600000\n",
+ "75% 98.400000\n",
+ "max 300.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((19786, 3), (19784, 1), 2L, 0, '0.0% records')\n",
+ "(2017-06-07 21:22:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:40)>>>> Join (19784, 1) to (1546902, 11)\n",
+ "(2017-06-07 21:22:49)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:22:49)<< DONE (10.0s)\n",
+ "(2017-06-07 21:22:49)>> NORMAL SALINE\n",
+ "(2017-06-07 21:22:49)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
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+ " <thead>\n",
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+ " </tr>\n",
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+ " <th>status</th>\n",
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+ " <th colspan=\"8\" halign=\"left\">mL</th>\n",
+ " <th colspan=\"5\" halign=\"left\">mL/hr</th>\n",
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+ " <th>225158(L)</th>\n",
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+ " <th>30190(ml)</th>\n",
+ " <th>41913(ml)</th>\n",
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+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>9.0</td>\n",
+ " <td>7827.000000</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>462.549904</td>\n",
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+ " <td>4.952516</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>77.198774</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>384.448372</td>\n",
+ " <td>NaN</td>\n",
+ " <td>271.403678</td>\n",
+ " <td>9.266936</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>287.993454</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
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+ " <td>0.127507</td>\n",
+ " <td>0.100000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.004152</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>200.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>10.000000</td>\n",
+ " <td>0.800000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.000659</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>35.798919</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>15.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>124.999998</td>\n",
+ " <td>2.050000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>63.500001</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
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+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>10000.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1500.000000</td>\n",
+ " <td>80.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>51947.999400</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component normal saline \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mL \n",
+ "description 30190 225158 225158(L) 225158(ml) 30190(ml) \n",
+ "count 9.0 7827.000000 0.0 588.000000 775.000000 \n",
+ "mean 0.0 462.549904 NaN 147.955984 4.952516 \n",
+ "std 0.0 384.448372 NaN 271.403678 9.266936 \n",
+ "min 0.0 0.000000 NaN 0.127507 0.100000 \n",
+ "25% 0.0 200.000000 NaN 10.000000 0.800000 \n",
+ "50% 0.0 500.000000 NaN 35.798919 1.000000 \n",
+ "75% 0.0 500.000000 NaN 124.999998 2.050000 \n",
+ "max 0.0 10000.000000 NaN 1500.000000 80.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units mL/hr \n",
+ "description 41913(ml) 44053(ml) 44440(ml) 225158(mL/hour) 225158(mL/min) \n",
+ "count 0.0 0.0 0.0 94729.000000 0.0 \n",
+ "mean NaN NaN NaN 77.198774 NaN \n",
+ "std NaN NaN NaN 287.993454 NaN \n",
+ "min NaN NaN NaN 0.004152 NaN \n",
+ "25% NaN NaN NaN 6.000659 NaN \n",
+ "50% NaN NaN NaN 15.000000 NaN \n",
+ "75% NaN NaN NaN 63.500001 NaN \n",
+ "max NaN NaN NaN 51947.999400 NaN \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 30190(mL/hour) 41913(mL/hour) 44440(mL/hour) 30190 44053 \n",
+ "count 60.0 0.0 0.0 34.0 0.0 \n",
+ "mean 0.0 NaN NaN 0.0 NaN \n",
+ "std 0.0 NaN NaN 0.0 NaN \n",
+ "min 0.0 NaN NaN 0.0 NaN \n",
+ "25% 0.0 NaN NaN 0.0 NaN \n",
+ "50% 0.0 NaN NaN 0.0 NaN \n",
+ "75% 0.0 NaN NaN 0.0 NaN \n",
+ "max 0.0 NaN NaN 0.0 NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:22:50)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:22:50)>>>> Clean (103822, 17)\n",
+ "(2017-06-07 21:22:50)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:50)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:22:50)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:50)>>>> Drop Small columns, threshold = 5 | (103822, 16)\n",
+ "(2017-06-07 21:22:51)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:22:51)>>>> Drop OOB data | (103822, 8)\n",
+ "(2017-06-07 21:22:51)>>>>>> normal saline, mL, 9199\n",
+ "(2017-06-07 21:22:53)<<<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:22:53)>>>>>> normal saline, mL/hr, 94789\n",
+ "(2017-06-07 21:22:55)<<<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:22:55)>>>>>> normal saline, no_units, 103856\n",
+ "(2017-06-07 21:22:55)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:55)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:22:55)>>>> Drop Small columns, threshold = 50 | (103822, 8)\n",
+ "(2017-06-07 21:22:55)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:55)>>>> Combine like columns (103822, 5)\n",
+ "(2017-06-07 21:22:55)>>>>>> (u'normal saline', 'known', u'qn', u'mL')\n",
+ "(2017-06-07 21:22:55)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:22:55)>>>>>> (u'normal saline', 'known', u'qn', u'mL/hr')\n",
+ "(2017-06-07 21:22:57)<<<<<< DONE (2.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
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+ " <tr>\n",
+ " <th>status</th>\n",
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+ " <tr>\n",
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+ " <th>mL</th>\n",
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+ " <th>description</th>\n",
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+ " </thead>\n",
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+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>9190.000000</td>\n",
+ " <td>94782.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>403.831819</td>\n",
+ " <td>75.628628</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>388.761192</td>\n",
+ " <td>194.704919</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
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+ " <th>25%</th>\n",
+ " <td>100.000000</td>\n",
+ " <td>6.000001</td>\n",
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+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>250.000000</td>\n",
+ " <td>14.995838</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>500.000000</td>\n",
+ " <td>63.342520</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>10000.000000</td>\n",
+ " <td>8571.429000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component normal saline \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mL mL/hr\n",
+ "description all all\n",
+ "count 9190.000000 94782.000000\n",
+ "mean 403.831819 75.628628\n",
+ "std 388.761192 194.704919\n",
+ "min 0.000000 0.000000\n",
+ "25% 100.000000 6.000001\n",
+ "50% 250.000000 14.995838\n",
+ "75% 500.000000 63.342520\n",
+ "max 10000.000000 8571.429000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((103822, 16), (103772, 2), 50L, 2, '0.0508% records')\n",
+ "(2017-06-07 21:22:57)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:22:57)>>>> Join (103772, 2) to (1550148, 12)\n",
+ "(2017-06-07 21:23:06)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:23:06)<< DONE (17.0s)\n",
+ "(2017-06-07 21:23:06)>> NOREPINEPHRINE\n",
+ "(2017-06-07 21:23:06)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"5\" halign=\"left\">norepinephrine</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"5\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"5\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">mcg</th>\n",
+ " <th>mcg/kg/min</th>\n",
+ " <th>mcg/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>221906(mg)</th>\n",
+ " <th>30047(mg)</th>\n",
+ " <th>30120(mg)</th>\n",
+ " <th>30120(mcgkgmin)</th>\n",
+ " <th>30047(mcgmin)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>16187.000000</td>\n",
+ " <td>1632.000000</td>\n",
+ " <td>37019.000000</td>\n",
+ " <td>58405.000000</td>\n",
+ " <td>3035.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>159.361275</td>\n",
+ " <td>913.854309</td>\n",
+ " <td>649.333353</td>\n",
+ " <td>0.147238</td>\n",
+ " <td>13.276772</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2837.146697</td>\n",
+ " <td>2065.996665</td>\n",
+ " <td>1142.376948</td>\n",
+ " <td>0.397275</td>\n",
+ " <td>25.624617</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.001595</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>40.006897</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>147.360001</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>2.666667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>83.569031</td>\n",
+ " <td>360.000000</td>\n",
+ " <td>377.459839</td>\n",
+ " <td>0.083117</td>\n",
+ " <td>6.133333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>181.773259</td>\n",
+ " <td>900.000000</td>\n",
+ " <td>794.580017</td>\n",
+ " <td>0.189630</td>\n",
+ " <td>13.333333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>359550.584270</td>\n",
+ " <td>32000.000000</td>\n",
+ " <td>96768.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>250.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component norepinephrine \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mcg mcg/kg/min \n",
+ "description 221906(mg) 30047(mg) 30120(mg) 30120(mcgkgmin) \n",
+ "count 16187.000000 1632.000000 37019.000000 58405.000000 \n",
+ "mean 159.361275 913.854309 649.333353 0.147238 \n",
+ "std 2837.146697 2065.996665 1142.376948 0.397275 \n",
+ "min 0.001595 0.000000 0.000000 0.000000 \n",
+ "25% 40.006897 150.000000 147.360001 0.040000 \n",
+ "50% 83.569031 360.000000 377.459839 0.083117 \n",
+ "75% 181.773259 900.000000 794.580017 0.189630 \n",
+ "max 359550.584270 32000.000000 96768.000000 50.000000 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units mcg/min \n",
+ "description 30047(mcgmin) \n",
+ "count 3035.000000 \n",
+ "mean 13.276772 \n",
+ "std 25.624617 \n",
+ "min 0.000000 \n",
+ "25% 2.666667 \n",
+ "50% 6.133333 \n",
+ "75% 13.333333 \n",
+ "max 250.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:23:06)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:06)>>>> Clean (79901, 5)\n",
+ "(2017-06-07 21:23:06)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:06)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:23:06)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:06)>>>> Drop Small columns, threshold = 5 | (79901, 5)\n",
+ "(2017-06-07 21:23:06)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:06)>>>> Drop OOB data | (79901, 5)\n",
+ "(2017-06-07 21:23:06)>>>>>> norepinephrine, mcg, 54838\n",
+ "(2017-06-07 21:23:09)<<<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:23:09)>>>>>> norepinephrine, mcg/kg/min, 58405\n",
+ "(2017-06-07 21:23:09)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:09)>>>>>> norepinephrine, mcg/min, 3035\n",
+ "(2017-06-07 21:23:09)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:09)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:23:09)>>>> Drop Small columns, threshold = 50 | (79901, 5)\n",
+ "(2017-06-07 21:23:09)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:09)>>>> Combine like columns (79901, 5)\n",
+ "(2017-06-07 21:23:09)>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
+ "(2017-06-07 21:23:09)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:09)>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
+ "(2017-06-07 21:23:11)<<<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:23:11)>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/min')\n",
+ "(2017-06-07 21:23:11)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">norepinephrine</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mcg</th>\n",
+ " <th>mcg/kg/min</th>\n",
+ " <th>mcg/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>54829.000000</td>\n",
+ " <td>58390.000000</td>\n",
+ " <td>2974.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>505.964727</td>\n",
+ " <td>0.142429</td>\n",
+ " <td>10.307035</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1042.709780</td>\n",
+ " <td>0.208669</td>\n",
+ " <td>13.728729</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>80.048355</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>2.666667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>234.375017</td>\n",
+ " <td>0.082963</td>\n",
+ " <td>6.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>576.468018</td>\n",
+ " <td>0.189461</td>\n",
+ " <td>12.466667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>96768.000000</td>\n",
+ " <td>10.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component norepinephrine \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mcg mcg/kg/min mcg/min\n",
+ "description all all all\n",
+ "count 54829.000000 58390.000000 2974.000000\n",
+ "mean 505.964727 0.142429 10.307035\n",
+ "std 1042.709780 0.208669 13.728729\n",
+ "min 0.000000 0.000000 0.000000\n",
+ "25% 80.048355 0.040000 2.666667\n",
+ "50% 234.375017 0.082963 6.000000\n",
+ "75% 576.468018 0.189461 12.466667\n",
+ "max 96768.000000 10.000000 100.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((79901, 5), (79849, 3), 85L, 0, '0.0% records')\n",
+ "(2017-06-07 21:23:11)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:23:11)>>>> Join (79849, 3) to (1637026, 14)\n",
+ "(2017-06-07 21:23:21)<<<< DONE (10.0s)\n",
+ "(2017-06-07 21:23:21)<< DONE (15.0s)\n",
+ "(2017-06-07 21:23:21)>> TEMPERATURE BODY\n",
+ "(2017-06-07 21:23:21)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"4\" halign=\"left\">temperature body</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"4\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"4\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"4\" halign=\"left\">degF</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>223761(?F)</th>\n",
+ " <th>223762(?C)(degC)</th>\n",
+ " <th>676(Deg. C)(degC)</th>\n",
+ " <th>678(Deg. F)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>103803.000000</td>\n",
+ " <td>14719.00000</td>\n",
+ " <td>77753.000000</td>\n",
+ " <td>152788.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>98.381161</td>\n",
+ " <td>99.25841</td>\n",
+ " <td>98.816350</td>\n",
+ " <td>98.573288</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.901359</td>\n",
+ " <td>9.66066</td>\n",
+ " <td>2.448264</td>\n",
+ " <td>2.589280</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>32.00000</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>97.500000</td>\n",
+ " <td>97.52000</td>\n",
+ " <td>98.060002</td>\n",
+ " <td>97.599998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>98.400000</td>\n",
+ " <td>98.60000</td>\n",
+ " <td>98.960002</td>\n",
+ " <td>98.599998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.300000</td>\n",
+ " <td>99.68000</td>\n",
+ " <td>99.860002</td>\n",
+ " <td>99.599998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>421.000000</td>\n",
+ " <td>217.40000</td>\n",
+ " <td>106.160002</td>\n",
+ " <td>106.900002</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component temperature body \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units degF \n",
+ "description 223761(?F) 223762(?C)(degC) 676(Deg. C)(degC) \n",
+ "count 103803.000000 14719.00000 77753.000000 \n",
+ "mean 98.381161 99.25841 98.816350 \n",
+ "std 2.901359 9.66066 2.448264 \n",
+ "min 0.000000 32.00000 32.000000 \n",
+ "25% 97.500000 97.52000 98.060002 \n",
+ "50% 98.400000 98.60000 98.960002 \n",
+ "75% 99.300000 99.68000 99.860002 \n",
+ "max 421.000000 217.40000 106.160002 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 678(Deg. F) \n",
+ "count 152788.000000 \n",
+ "mean 98.573288 \n",
+ "std 2.589280 \n",
+ "min 0.000000 \n",
+ "25% 97.599998 \n",
+ "50% 98.599998 \n",
+ "75% 99.599998 \n",
+ "max 106.900002 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:23:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:21)>>>> Clean (348445, 4)\n",
+ "(2017-06-07 21:23:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:21)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:23:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:21)>>>> Drop Small columns, threshold = 5 | (348445, 4)\n",
+ "(2017-06-07 21:23:21)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:21)>>>> Drop OOB data | (348445, 4)\n",
+ "(2017-06-07 21:23:21)>>>>>> temperature body, degF, 349063\n",
+ "(2017-06-07 21:23:31)<<<<<< DONE (10.0s)\n",
+ "(2017-06-07 21:23:31)<<<< DONE (10.0s)\n",
+ "(2017-06-07 21:23:31)>>>> Drop Small columns, threshold = 50 | (348445, 4)\n",
+ "(2017-06-07 21:23:31)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:31)>>>> Combine like columns (348445, 4)\n",
+ "(2017-06-07 21:23:31)>>>>>> (u'temperature body', 'known', u'qn', u'degF')\n",
+ "(2017-06-07 21:23:33)<<<<<< DONE (2.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>temperature body</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",
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+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>degF</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>348397.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>98.576780</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.448804</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>97.699997</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>98.600000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.599998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>107.060000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component temperature body\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units degF\n",
+ "description all\n",
+ "count 348397.000000\n",
+ "mean 98.576780\n",
+ "std 2.448804\n",
+ "min 0.000000\n",
+ "25% 97.699997\n",
+ "50% 98.600000\n",
+ "75% 99.599998\n",
+ "max 107.060000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((348445, 4), (348397, 1), 666L, 0, '0.0% records')\n",
+ "(2017-06-07 21:23:33)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:23:33)>>>> Join (348397, 1) to (1638590, 17)\n",
+ "(2017-06-07 21:23:45)<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:23:45)<< DONE (24.0s)\n",
+ "(2017-06-07 21:23:45)>> BLOOD PRESSURE DIASTOLIC\n",
+ "(2017-06-07 21:23:45)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"15\" halign=\"left\">blood pressure diastolic</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"11\" halign=\"left\">known</th>\n",
+ " <th colspan=\"4\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"11\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"4\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"11\" halign=\"left\">mmHg</th>\n",
+ " <th colspan=\"4\" halign=\"left\">cc/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>8364</th>\n",
+ " <th>8368</th>\n",
+ " <th>8440</th>\n",
+ " <th>8441</th>\n",
+ " <th>8555</th>\n",
+ " <th>220051</th>\n",
+ " <th>220180</th>\n",
+ " <th>224643</th>\n",
+ " <th>225310</th>\n",
+ " <th>227242</th>\n",
+ " <th>8503(cmH20)(cmH2O)</th>\n",
+ " <th>8502</th>\n",
+ " <th>8504</th>\n",
+ " <th>8506</th>\n",
+ " <th>8507</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>0.0</td>\n",
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+ " <td>256448.000000</td>\n",
+ " <td>135.000000</td>\n",
+ " <td>18076.000000</td>\n",
+ " <td>94.000000</td>\n",
+ " <td>341.000000</td>\n",
+ " <td>28689.000000</td>\n",
+ " <td>295.000000</td>\n",
+ " <td>285.000000</td>\n",
+ " <td>271.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>59.795535</td>\n",
+ " <td>57.503916</td>\n",
+ " <td>58.283753</td>\n",
+ " <td>60.088173</td>\n",
+ " <td>61.041212</td>\n",
+ " <td>65.366250</td>\n",
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+ " <td>38.871186</td>\n",
+ " <td>42.926316</td>\n",
+ " <td>38.361624</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>14.164921</td>\n",
+ " <td>16.900056</td>\n",
+ " <td>15.770894</td>\n",
+ " <td>13.216323</td>\n",
+ " <td>301.010444</td>\n",
+ " <td>420.484995</td>\n",
+ " <td>14.652733</td>\n",
+ " <td>35.784021</td>\n",
+ " <td>16.762069</td>\n",
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+ " <td>10.445309</td>\n",
+ " <td>8.420720</td>\n",
+ " <td>22.165308</td>\n",
+ " <td>9.062450</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>17.000000</td>\n",
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+ " <td>4.000000</td>\n",
+ " <td>11.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>51.000000</td>\n",
+ " <td>46.000000</td>\n",
+ " <td>47.000000</td>\n",
+ " <td>52.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>52.000000</td>\n",
+ " <td>58.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>25.744570</td>\n",
+ " <td>31.000000</td>\n",
+ " <td>33.000000</td>\n",
+ " <td>36.000000</td>\n",
+ " <td>32.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>58.000000</td>\n",
+ " <td>60.000000</td>\n",
+ " <td>57.000000</td>\n",
+ " <td>59.000000</td>\n",
+ " <td>58.000000</td>\n",
+ " <td>61.000000</td>\n",
+ " <td>66.000000</td>\n",
+ " <td>57.000000</td>\n",
+ " <td>62.000000</td>\n",
+ " <td>29.422365</td>\n",
+ " <td>37.000000</td>\n",
+ " <td>39.000000</td>\n",
+ " <td>41.000000</td>\n",
+ " <td>38.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>68.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>68.000000</td>\n",
+ " <td>67.000000</td>\n",
+ " <td>67.000000</td>\n",
+ " <td>72.000000</td>\n",
+ " <td>76.000000</td>\n",
+ " <td>66.000000</td>\n",
+ " <td>72.000000</td>\n",
+ " <td>34.571279</td>\n",
+ " <td>43.000000</td>\n",
+ " <td>45.000000</td>\n",
+ " <td>48.000000</td>\n",
+ " <td>44.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>298.000000</td>\n",
+ " <td>110.000000</td>\n",
+ " <td>201.000000</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>90100.970000</td>\n",
+ " <td>114108.980000</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>4341.000000</td>\n",
+ " <td>106.000000</td>\n",
+ " <td>61.051408</td>\n",
+ " <td>742.000000</td>\n",
+ " <td>69.000000</td>\n",
+ " <td>378.000000</td>\n",
+ " <td>76.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure diastolic \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmHg \n",
+ "description 8364 8368 8440 \n",
+ "count 0.0 420151.000000 383.000000 \n",
+ "mean NaN 59.795535 57.503916 \n",
+ "std NaN 14.164921 16.900056 \n",
+ "min NaN 0.000000 17.000000 \n",
+ "25% NaN 51.000000 46.000000 \n",
+ "50% NaN 58.000000 60.000000 \n",
+ "75% NaN 68.000000 70.000000 \n",
+ "max NaN 298.000000 110.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 8441 8555 220051 220180 \n",
+ "count 308872.000000 4253.000000 225199.000000 256448.000000 \n",
+ "mean 58.283753 60.088173 61.041212 65.366250 \n",
+ "std 15.770894 13.216323 301.010444 420.484995 \n",
+ "min 0.000000 0.000000 -12.000000 0.000000 \n",
+ "25% 47.000000 52.000000 50.000000 52.000000 \n",
+ "50% 57.000000 59.000000 58.000000 61.000000 \n",
+ "75% 68.000000 67.000000 67.000000 72.000000 \n",
+ "max 201.000000 150.000000 90100.970000 114108.980000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 224643 225310 227242 8503(cmH20)(cmH2O) \n",
+ "count 135.000000 18076.000000 94.000000 341.000000 \n",
+ "mean 67.296296 59.304437 62.180851 30.050071 \n",
+ "std 14.652733 35.784021 16.762069 6.998469 \n",
+ "min 34.000000 1.000000 11.000000 13.240064 \n",
+ "25% 58.000000 50.000000 50.000000 25.744570 \n",
+ "50% 66.000000 57.000000 62.000000 29.422365 \n",
+ "75% 76.000000 66.000000 72.000000 34.571279 \n",
+ "max 100.000000 4341.000000 106.000000 61.051408 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units cc/min \n",
+ "description 8502 8504 8506 8507 \n",
+ "count 28689.000000 295.000000 285.000000 271.000000 \n",
+ "mean 37.555939 38.871186 42.926316 38.361624 \n",
+ "std 10.445309 8.420720 22.165308 9.062450 \n",
+ "min 0.000000 12.000000 4.000000 11.000000 \n",
+ "25% 31.000000 33.000000 36.000000 32.000000 \n",
+ "50% 37.000000 39.000000 41.000000 38.000000 \n",
+ "75% 43.000000 45.000000 48.000000 44.000000 \n",
+ "max 742.000000 69.000000 378.000000 76.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:23:48)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:23:48)>>>> Clean (1187390, 15)\n",
+ "(2017-06-07 21:23:48)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:48)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:23:48)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:23:48)>>>> Drop Small columns, threshold = 5 | (1187390, 15)\n",
+ "(2017-06-07 21:23:49)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:23:49)>>>> Drop OOB data | (1187390, 14)\n",
+ "(2017-06-07 21:23:50)>>>>>> blood pressure diastolic, mmHg, 1233952\n",
+ "(2017-06-07 21:25:00)<<<<<< DONE (70.0s)\n",
+ "(2017-06-07 21:25:00)>>>>>> blood pressure diastolic, cc/min, 29540\n",
+ "(2017-06-07 21:25:00)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:25:00)<<<< DONE (71.0s)\n",
+ "(2017-06-07 21:25:00)>>>> Drop Small columns, threshold = 50 | (1187390, 14)\n",
+ "(2017-06-07 21:25:01)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:25:01)>>>> Combine like columns (1187390, 14)\n",
+ "(2017-06-07 21:25:01)>>>>>> (u'blood pressure diastolic', 'known', u'qn', u'mmHg')\n",
+ "(2017-06-07 21:25:01)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:25:01)>>>>>> (u'blood pressure diastolic', 'unknown', u'qn', u'cc/min')\n",
+ "(2017-06-07 21:25:18)<<<<<< DONE (17.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " <td>28914.000000</td>\n",
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+ " </tr>\n",
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+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure diastolic \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units mmHg cc/min\n",
+ "description all all\n",
+ "count 1.158759e+06 28914.000000\n",
+ "mean 6.018887e+01 37.571292\n",
+ "std 1.480046e+01 10.429457\n",
+ "min 0.000000e+00 0.000000\n",
+ "25% 5.000000e+01 31.000000\n",
+ "50% 5.900000e+01 37.000000\n",
+ "75% 6.900000e+01 43.000000\n",
+ "max 4.450000e+02 742.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((1187390, 15), (1187351, 2), 75819L, 0, '0.0% records')\n",
+ "(2017-06-07 21:25:18)<<<< DONE (17.0s)\n",
+ "(2017-06-07 21:25:18)>>>> Join (1187351, 2) to (1644835, 18)\n",
+ "(2017-06-07 21:25:34)<<<< DONE (16.0s)\n",
+ "(2017-06-07 21:25:34)<< DONE (109.0s)\n",
+ "(2017-06-07 21:25:34)>> HEART RATE\n",
+ "(2017-06-07 21:25:34)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"6\" halign=\"left\">heart rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">beats/min</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>211(BPM)(beat/min)</th>\n",
+ " <th>211(bpm)(beat/min)</th>\n",
+ " <th>220045(bpm)(beat/min)</th>\n",
+ " <th>1332</th>\n",
+ " <th>1341</th>\n",
+ " <th>1725</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>693377.000000</td>\n",
+ " <td>330413.000000</td>\n",
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+ " <td>86.795088</td>\n",
+ " <td>154.349049</td>\n",
+ " <td>87.826761</td>\n",
+ " <td>NaN</td>\n",
+ " <td>104.250000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>18.003278</td>\n",
+ " <td>15.560119</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>100.250000</td>\n",
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+ " <td>104.500000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>100.000000</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units beats/min \n",
+ "description 211(BPM)(beat/min) 211(bpm)(beat/min) 220045(bpm)(beat/min) \n",
+ "count 693377.000000 330413.000000 567348.000000 \n",
+ "mean 86.795088 154.349049 87.826761 \n",
+ "std 18.003278 15.560119 20.641808 \n",
+ "min 0.000000 1.000000 0.000000 \n",
+ "25% 74.000000 144.000000 74.000000 \n",
+ "50% 85.000000 156.000000 87.000000 \n",
+ "75% 98.000000 166.000000 100.000000 \n",
+ "max 303.000000 292.000000 6632.000000 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 1332 1341 1725 \n",
+ "count 0.0 4.000000 0.0 \n",
+ "mean NaN 104.250000 NaN \n",
+ "std NaN 9.878428 NaN \n",
+ "min NaN 92.000000 NaN \n",
+ "25% NaN 100.250000 NaN \n",
+ "50% NaN 104.500000 NaN \n",
+ "75% NaN 108.500000 NaN \n",
+ "max NaN 116.000000 NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:25:36)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:25:36)>>>> Clean (1591085, 6)\n",
+ "(2017-06-07 21:25:36)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:25:36)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:25:36)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:25:36)>>>> Drop Small columns, threshold = 5 | (1591085, 6)\n",
+ "(2017-06-07 21:25:37)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:25:37)>>>> Drop OOB data | (1591085, 3)\n",
+ "(2017-06-07 21:25:37)>>>>>> heart rate, beats/min, 1591138\n",
+ "(2017-06-07 21:26:11)<<<<<< DONE (34.0s)\n",
+ "(2017-06-07 21:26:11)<<<< DONE (34.0s)\n",
+ "(2017-06-07 21:26:11)>>>> Drop Small columns, threshold = 50 | (1591085, 3)\n",
+ "(2017-06-07 21:26:11)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:26:11)>>>> Combine like columns (1591085, 3)\n",
+ "(2017-06-07 21:26:11)>>>>>> (u'heart rate', 'known', u'qn', u'beats/min')\n",
+ "(2017-06-07 21:26:19)<<<<<< DONE (8.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " <tr>\n",
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+ " <th>25%</th>\n",
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+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>1.180000e+02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>3.030000e+02</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units beats/min\n",
+ "description all\n",
+ "count 1.591080e+06\n",
+ "mean 1.011863e+02\n",
+ "std 3.250182e+01\n",
+ "min 0.000000e+00\n",
+ "25% 7.700000e+01\n",
+ "50% 9.200000e+01\n",
+ "75% 1.180000e+02\n",
+ "max 3.030000e+02"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((1591085, 6), (1591080, 1), 62L, 0, '0.0% records')\n",
+ "(2017-06-07 21:26:19)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:26:19)>>>> Join (1591080, 1) to (1644862, 20)\n",
+ "(2017-06-07 21:26:36)<<<< DONE (17.0s)\n",
+ "(2017-06-07 21:26:36)<< DONE (62.0s)\n",
+ "(2017-06-07 21:26:36)>> OUTPUT URINE\n",
+ "(2017-06-07 21:26:36)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"21\" halign=\"left\">output urine</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"10\" halign=\"left\">known</th>\n",
+ " <th>...</th>\n",
+ " <th colspan=\"10\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"10\" halign=\"left\">qn</th>\n",
+ " <th>...</th>\n",
+ " <th colspan=\"10\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"10\" halign=\"left\">mL</th>\n",
+ " <th>...</th>\n",
+ " <th colspan=\"10\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\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",
+ " <th>41857(ml)</th>\n",
+ " <th>42042(ml)</th>\n",
+ " <th>42592(ml)</th>\n",
+ " <th>42666(ml)</th>\n",
+ " <th>42810(ml)</th>\n",
+ " <th>...</th>\n",
+ " <th>44253</th>\n",
+ " <th>44325</th>\n",
+ " <th>44684</th>\n",
+ " <th>44706</th>\n",
+ " <th>44824</th>\n",
+ " <th>45415</th>\n",
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+ " <th>46180</th>\n",
+ " <th>46578</th>\n",
+ " <th>46658</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>236361.000000</td>\n",
+ " <td>12134.000000</td>\n",
+ " <td>385310.000000</td>\n",
+ " <td>12063.000000</td>\n",
+ " <td>322.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
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+ " <td>2.000000</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>117.298465</td>\n",
+ " <td>304.842616</td>\n",
+ " <td>118.369532</td>\n",
+ " <td>284.002901</td>\n",
+ " <td>156.270186</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>696.0</td>\n",
+ " <td>600.0</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>427.681976</td>\n",
+ " <td>204.713335</td>\n",
+ " <td>130.160936</td>\n",
+ " <td>201.201076</td>\n",
+ " <td>250.854114</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>35.355339</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>696.0</td>\n",
+ " <td>600.0</td>\n",
+ " <td>700.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>40.000000</td>\n",
+ " <td>160.000000</td>\n",
+ " <td>40.000000</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>696.0</td>\n",
+ " <td>600.0</td>\n",
+ " <td>712.500000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>80.000000</td>\n",
+ " <td>250.000000</td>\n",
+ " <td>80.000000</td>\n",
+ " <td>250.000000</td>\n",
+ " <td>80.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>696.0</td>\n",
+ " <td>600.0</td>\n",
+ " <td>725.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>150.000000</td>\n",
+ " <td>400.000000</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>400.000000</td>\n",
+ " <td>123.750000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>696.0</td>\n",
+ " <td>600.0</td>\n",
+ " <td>737.500000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>160220.000000</td>\n",
+ " <td>2275.000000</td>\n",
+ " <td>7300.000000</td>\n",
+ " <td>2400.000000</td>\n",
+ " <td>2000.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>696.0</td>\n",
+ " <td>600.0</td>\n",
+ " <td>750.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>8 rows × 51 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) \n",
+ "count 236361.000000 12134.000000 385310.000000 12063.000000 \n",
+ "mean 117.298465 304.842616 118.369532 284.002901 \n",
+ "std 427.681976 204.713335 130.160936 201.201076 \n",
+ "min 0.000000 0.000000 0.000000 0.000000 \n",
+ "25% 40.000000 160.000000 40.000000 150.000000 \n",
+ "50% 80.000000 250.000000 80.000000 250.000000 \n",
+ "75% 150.000000 400.000000 150.000000 400.000000 \n",
+ "max 160220.000000 2275.000000 7300.000000 2400.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 40405(ml) 41857(ml) 42042(ml) 42592(ml) 42666(ml) \n",
+ "count 322.000000 0.0 0.0 1.0 1.0 \n",
+ "mean 156.270186 NaN NaN 696.0 600.0 \n",
+ "std 250.854114 NaN NaN NaN NaN \n",
+ "min 0.000000 NaN NaN 696.0 600.0 \n",
+ "25% 50.000000 NaN NaN 696.0 600.0 \n",
+ "50% 80.000000 NaN NaN 696.0 600.0 \n",
+ "75% 123.750000 NaN NaN 696.0 600.0 \n",
+ "max 2000.000000 NaN NaN 696.0 600.0 \n",
+ "\n",
+ "component ... \\\n",
+ "status ... unknown \n",
+ "variable_type ... qn \n",
+ "units ... no_units \n",
+ "description 42810(ml) ... 44253 44325 44684 44706 44824 45415 45991 \n",
+ "count 2.000000 ... 0.0 1.0 0.0 0.0 1.0 0.0 0.0 \n",
+ "mean 725.000000 ... NaN 0.0 NaN NaN 0.0 NaN NaN \n",
+ "std 35.355339 ... NaN NaN NaN NaN NaN NaN NaN \n",
+ "min 700.000000 ... NaN 0.0 NaN NaN 0.0 NaN NaN \n",
+ "25% 712.500000 ... NaN 0.0 NaN NaN 0.0 NaN NaN \n",
+ "50% 725.000000 ... NaN 0.0 NaN NaN 0.0 NaN NaN \n",
+ "75% 737.500000 ... NaN 0.0 NaN NaN 0.0 NaN NaN \n",
+ "max 750.000000 ... NaN 0.0 NaN NaN 0.0 NaN NaN \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 46180 46578 46658 \n",
+ "count 0.0 0.0 0.0 \n",
+ "mean NaN NaN NaN \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN NaN \n",
+ "25% NaN NaN NaN \n",
+ "50% NaN NaN NaN \n",
+ "75% NaN NaN NaN \n",
+ "max NaN NaN NaN \n",
+ "\n",
+ "[8 rows x 51 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:26:44)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:26:44)>>>> Clean (725171, 54)\n",
+ "(2017-06-07 21:26:44)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:26:44)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:26:51)<<<< DONE (7.0s)\n",
+ "(2017-06-07 21:26:51)>>>> Drop Small columns, threshold = 5 | (725171, 55)\n",
+ "(2017-06-07 21:26:52)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:26:52)>>>> Drop OOB data | (725171, 10)\n",
+ "(2017-06-07 21:26:52)>>>>>> output urine, mL, 646190\n",
+ "(2017-06-07 21:27:16)<<<<<< DONE (24.0s)\n",
+ "(2017-06-07 21:27:16)>>>>>> output urine, no_units, 2176313\n",
+ "(2017-06-07 21:27:16)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:16)<<<< DONE (24.0s)\n",
+ "(2017-06-07 21:27:16)>>>> Drop Small columns, threshold = 50 | (725171, 10)\n",
+ "(2017-06-07 21:27:16)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:16)>>>> Combine like columns (725171, 10)\n",
+ "(2017-06-07 21:27:16)>>>>>> (u'output urine', 'known', u'qn', u'mL')\n",
+ "(2017-06-07 21:27:16)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:16)>>>>>> (u'output urine', 'unknown', 'nom', 'no_units')\n",
+ "(2017-06-07 21:27:27)<<<<<< DONE (11.0s)\n",
+ "(2017-06-07 21:27:27)>>>>>> (u'output urine', 'unknown', u'qn', 'no_units')\n",
+ "(2017-06-07 21:27:35)<<<<<< DONE (8.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " <th colspan=\"4\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">nom</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mL</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>3686(ml)_No Void</th>\n",
+ " <th>3686(ml)_Voiding qs</th>\n",
+ " <th>3686_Voiding qs</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>645840.000000</td>\n",
+ " <td>725171.000000</td>\n",
+ " <td>725171.000000</td>\n",
+ " <td>725171.000000</td>\n",
+ " <td>800.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>124.077543</td>\n",
+ " <td>0.001202</td>\n",
+ " <td>0.107296</td>\n",
+ " <td>0.000154</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>132.986829</td>\n",
+ " <td>0.034656</td>\n",
+ " <td>0.309490</td>\n",
+ " <td>0.012427</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>45.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>80.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>160.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>7300.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component output urine \\\n",
+ "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",
+ "count 645840.000000 725171.000000 725171.000000 \n",
+ "mean 124.077543 0.001202 0.107296 \n",
+ "std 132.986829 0.034656 0.309490 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 45.000000 0.000000 0.000000 \n",
+ "50% 80.000000 0.000000 0.000000 \n",
+ "75% 160.000000 0.000000 0.000000 \n",
+ "max 7300.000000 1.000000 1.000000 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 3686_Voiding qs all \n",
+ "count 725171.000000 800.0 \n",
+ "mean 0.000154 0.0 \n",
+ "std 0.012427 0.0 \n",
+ "min 0.000000 0.0 \n",
+ "25% 0.000000 0.0 \n",
+ "50% 0.000000 0.0 \n",
+ "75% 0.000000 0.0 \n",
+ "max 1.000000 0.0 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((725171, 53), (725171, 5), -2096467L, 0, '0.0% records')\n",
+ "(2017-06-07 21:27:36)<<<< DONE (20.0s)\n",
+ "(2017-06-07 21:27:36)>>>> Join (725171, 5) to (1963446, 21)\n",
+ "(2017-06-07 21:27:50)<<<< DONE (14.0s)\n",
+ "(2017-06-07 21:27:50)<< DONE (74.0s)\n",
+ "(2017-06-07 21:27:50)>> LACTATED RINGERS\n",
+ "(2017-06-07 21:27:50)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"20\" halign=\"left\">lactated ringers</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"14\" halign=\"left\">known</th>\n",
+ " <th colspan=\"6\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"14\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"6\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"12\" halign=\"left\">mL</th>\n",
+ " <th colspan=\"2\" halign=\"left\">mL/hr</th>\n",
+ " <th colspan=\"6\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1634(ml)</th>\n",
+ " <th>225828</th>\n",
+ " <th>225828(ml)</th>\n",
+ " <th>2971(ml)</th>\n",
+ " <th>30021</th>\n",
+ " <th>30021(ml)</th>\n",
+ " <th>44184(ml)</th>\n",
+ " <th>44367(ml)</th>\n",
+ " <th>44521(ml)</th>\n",
+ " <th>44815(ml)</th>\n",
+ " <th>46207(ml)</th>\n",
+ " <th>46781(ml)</th>\n",
+ " <th>225828(mL/hour)</th>\n",
+ " <th>30021(mL/hour)</th>\n",
+ " <th>30021</th>\n",
+ " <th>44184</th>\n",
+ " <th>44367</th>\n",
+ " <th>44521</th>\n",
+ " <th>44815</th>\n",
+ " <th>46207</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>0.0</td>\n",
+ " <td>97.000000</td>\n",
+ " <td>9614.000000</td>\n",
+ " <td>5.0</td>\n",
+ " <td>104.0</td>\n",
+ " <td>41253.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>296.000000</td>\n",
+ " <td>148.0</td>\n",
+ " <td>954.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>696.855670</td>\n",
+ " <td>479.561926</td>\n",
+ " <td>100.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>139.773697</td>\n",
+ " <td>500.0</td>\n",
+ " <td>1000.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>467.222506</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>276.372823</td>\n",
+ " <td>436.213858</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>211.933281</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>468.711347</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>180.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>100.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>500.0</td>\n",
+ " <td>1000.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>5.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>99.999996</td>\n",
+ " <td>100.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>10.000000</td>\n",
+ " <td>500.0</td>\n",
+ " <td>1000.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>99.980496</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>499.999980</td>\n",
+ " <td>100.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>100.000000</td>\n",
+ " <td>500.0</td>\n",
+ " <td>1000.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>246.457485</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>999.999960</td>\n",
+ " <td>100.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>500.0</td>\n",
+ " <td>1000.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>980.400012</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>100.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8000.000000</td>\n",
+ " <td>500.0</td>\n",
+ " <td>1000.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>500.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2999.999996</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactated ringers \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mL \n",
+ "description 1634(ml) 225828 225828(ml) 2971(ml) 30021 \n",
+ "count 0.0 97.000000 9614.000000 5.0 104.0 \n",
+ "mean NaN 696.855670 479.561926 100.0 0.0 \n",
+ "std NaN 276.372823 436.213858 0.0 0.0 \n",
+ "min NaN 180.000000 0.000000 100.0 0.0 \n",
+ "25% NaN 500.000000 99.999996 100.0 0.0 \n",
+ "50% NaN 500.000000 499.999980 100.0 0.0 \n",
+ "75% NaN 1000.000000 999.999960 100.0 0.0 \n",
+ "max NaN 1000.000000 6000.000000 100.0 0.0 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 30021(ml) 44184(ml) 44367(ml) 44521(ml) 44815(ml) 46207(ml) \n",
+ "count 41253.000000 1.0 1.0 0.0 0.0 1.0 \n",
+ "mean 139.773697 500.0 1000.0 NaN NaN 500.0 \n",
+ "std 211.933281 NaN NaN NaN NaN NaN \n",
+ "min 0.000000 500.0 1000.0 NaN NaN 500.0 \n",
+ "25% 10.000000 500.0 1000.0 NaN NaN 500.0 \n",
+ "50% 100.000000 500.0 1000.0 NaN NaN 500.0 \n",
+ "75% 150.000000 500.0 1000.0 NaN NaN 500.0 \n",
+ "max 8000.000000 500.0 1000.0 NaN NaN 500.0 \n",
+ "\n",
+ "component \\\n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units mL/hr no_units \n",
+ "description 46781(ml) 225828(mL/hour) 30021(mL/hour) 30021 44184 44367 \n",
+ "count 0.0 296.000000 148.0 954.0 1.0 1.0 \n",
+ "mean NaN 467.222506 0.0 0.0 0.0 0.0 \n",
+ "std NaN 468.711347 0.0 0.0 NaN NaN \n",
+ "min NaN 5.000000 0.0 0.0 0.0 0.0 \n",
+ "25% NaN 99.980496 0.0 0.0 0.0 0.0 \n",
+ "50% NaN 246.457485 0.0 0.0 0.0 0.0 \n",
+ "75% NaN 980.400012 0.0 0.0 0.0 0.0 \n",
+ "max NaN 2999.999996 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 44521 44815 46207 \n",
+ "count 0.0 0.0 1.0 \n",
+ "mean NaN NaN 0.0 \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN 0.0 \n",
+ "25% NaN NaN 0.0 \n",
+ "50% NaN NaN 0.0 \n",
+ "75% NaN NaN 0.0 \n",
+ "max NaN NaN 0.0 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:27:51)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:27:51)>>>> Clean (52255, 20)\n",
+ "(2017-06-07 21:27:51)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:51)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:27:51)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:51)>>>> Drop Small columns, threshold = 5 | (52255, 20)\n",
+ "(2017-06-07 21:27:52)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:27:52)>>>> Drop OOB data | (52255, 7)\n",
+ "(2017-06-07 21:27:52)>>>>>> lactated ringers, mL, 51068\n",
+ "(2017-06-07 21:27:53)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:27:53)>>>>>> lactated ringers, mL/hr, 444\n",
+ "(2017-06-07 21:27:53)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:53)>>>>>> lactated ringers, no_units, 954\n",
+ "(2017-06-07 21:27:53)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:53)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:27:53)>>>> Drop Small columns, threshold = 50 | (52255, 7)\n",
+ "(2017-06-07 21:27:53)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:53)>>>> Combine like columns (52255, 7)\n",
+ "(2017-06-07 21:27:53)>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
+ "(2017-06-07 21:27:53)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:27:53)>>>>>> ('lactated ringers', 'known', 'qn', 'mL/hr')\n",
+ "(2017-06-07 21:27:54)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:27:54)>>>>>> ('lactated ringers', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-07 21:27:54)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">lactated ringers</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " <th>unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mL</th>\n",
+ " <th>mL/hr</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>51061.000000</td>\n",
+ " <td>444.000000</td>\n",
+ " <td>954.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>204.541875</td>\n",
+ " <td>311.481671</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>300.710507</td>\n",
+ " <td>441.491600</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>15.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>100.000000</td>\n",
+ " <td>99.972558</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>200.000000</td>\n",
+ " <td>499.999980</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>8000.000000</td>\n",
+ " <td>2999.999996</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactated ringers \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units mL mL/hr no_units\n",
+ "description all all all\n",
+ "count 51061.000000 444.000000 954.0\n",
+ "mean 204.541875 311.481671 0.0\n",
+ "std 300.710507 441.491600 0.0\n",
+ "min 0.000000 0.000000 0.0\n",
+ "25% 15.000000 0.000000 0.0\n",
+ "50% 100.000000 99.972558 0.0\n",
+ "75% 200.000000 499.999980 0.0\n",
+ "max 8000.000000 2999.999996 0.0"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((52255, 20), (52249, 3), 18L, 2, '0.0593% records')\n",
+ "(2017-06-07 21:27:54)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:27:54)>>>> Join (52249, 3) to (2005450, 26)\n",
+ "(2017-06-07 21:28:05)<<<< DONE (11.0s)\n",
+ "(2017-06-07 21:28:05)<< DONE (15.0s)\n",
+ "(2017-06-07 21:28:05)>> RESPIRATORY RATE\n",
+ "(2017-06-07 21:28:05)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"4\" halign=\"left\">respiratory rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"2\" halign=\"left\">insp/min</th>\n",
+ " <th>Breath</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>220210</th>\n",
+ " <th>618(BPM)(breath/min)</th>\n",
+ " <th>3603</th>\n",
+ " <th>8113</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>560572.000000</td>\n",
+ " <td>675421.000000</td>\n",
+ " <td>326779.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>19.870985</td>\n",
+ " <td>20.105434</td>\n",
+ " <td>49.832115</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>7.827766</td>\n",
+ " <td>6.396381</td>\n",
+ " <td>13.518997</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>16.000000</td>\n",
+ " <td>16.000000</td>\n",
+ " <td>40.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>19.000000</td>\n",
+ " <td>19.000000</td>\n",
+ " <td>48.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>23.000000</td>\n",
+ " <td>24.000000</td>\n",
+ " <td>59.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>2822.000000</td>\n",
+ " <td>127.000000</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component respiratory rate \n",
+ "status known unknown \n",
+ "variable_type qn qn \n",
+ "units insp/min Breath no_units\n",
+ "description 220210 618(BPM)(breath/min) 3603 8113\n",
+ "count 560572.000000 675421.000000 326779.000000 0.0\n",
+ "mean 19.870985 20.105434 49.832115 NaN\n",
+ "std 7.827766 6.396381 13.518997 NaN\n",
+ "min 0.000000 0.000000 0.000000 NaN\n",
+ "25% 16.000000 16.000000 40.000000 NaN\n",
+ "50% 19.000000 19.000000 48.000000 NaN\n",
+ "75% 23.000000 24.000000 59.000000 NaN\n",
+ "max 2822.000000 127.000000 150.000000 NaN"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:28:08)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:28:08)>>>> Clean (1562723, 5)\n",
+ "(2017-06-07 21:28:08)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:28:08)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:28:17)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:28:17)>>>> Drop Small columns, threshold = 5 | (1562723, 5)\n",
+ "(2017-06-07 21:28:18)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:28:18)>>>> Drop OOB data | (1562723, 4)\n",
+ "(2017-06-07 21:28:19)>>>>>> respiratory rate, insp/min, 1235993\n",
+ "(2017-06-07 21:28:44)<<<<<< DONE (25.0s)\n",
+ "(2017-06-07 21:28:44)>>>>>> respiratory rate, no_units, 1562723\n",
+ "(2017-06-07 21:28:44)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:28:44)>>>>>> respiratory rate, Breath, 326779\n",
+ "(2017-06-07 21:28:44)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:28:44)<<<< DONE (26.0s)\n",
+ "(2017-06-07 21:28:44)>>>> Drop Small columns, threshold = 50 | (1562723, 4)\n",
+ "(2017-06-07 21:28:45)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:28:45)>>>> Combine like columns (1562723, 3)\n",
+ "(2017-06-07 21:28:45)>>>>>> (u'respiratory rate', 'known', u'qn', u'insp/min')\n",
+ "(2017-06-07 21:28:45)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:28:45)>>>>>> (u'respiratory rate', 'unknown', u'qn', u'Breath')\n",
+ "(2017-06-07 21:29:07)<<<<<< DONE (22.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">respiratory rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " <th>unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>qn</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>insp/min</th>\n",
+ " <th>Breath</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1.235933e+06</td>\n",
+ " <td>326779.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>1.999278e+01</td>\n",
+ " <td>49.832115</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>6.175009e+00</td>\n",
+ " <td>13.518997</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.600000e+01</td>\n",
+ " <td>40.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.900000e+01</td>\n",
+ " <td>48.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.400000e+01</td>\n",
+ " <td>59.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.400000e+02</td>\n",
+ " <td>150.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component respiratory rate \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units insp/min Breath\n",
+ "description all all\n",
+ "count 1.235933e+06 326779.000000\n",
+ "mean 1.999278e+01 49.832115\n",
+ "std 6.175009e+00 13.518997\n",
+ "min 0.000000e+00 0.000000\n",
+ "25% 1.600000e+01 40.000000\n",
+ "50% 1.900000e+01 48.000000\n",
+ "75% 2.400000e+01 59.000000\n",
+ "max 1.400000e+02 150.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((1562723, 4), (1562712, 2), 60L, 0, '0.0% records')\n",
+ "(2017-06-07 21:29:07)<<<< DONE (22.0s)\n",
+ "(2017-06-07 21:29:07)>>>> Join (1562712, 2) to (2014264, 29)\n",
+ "(2017-06-07 21:29:25)<<<< DONE (18.0s)\n",
+ "(2017-06-07 21:29:25)<< DONE (80.0s)\n",
+ "(2017-06-07 21:29:25) DONE (630.0s)\n",
+ "(2017-06-07 21:29:25) Extract lactate label\n",
+ "(2017-06-07 21:29:54) DONE (29.0s)\n",
+ "(2017-06-07 21:29:54) Segment df (1421738, 31)\n",
+ "(2017-06-07 21:29:54)>> Get Segments\n",
+ "(2017-06-07 21:29:57)<< DONE (3.0s)\n",
+ "(2017-06-07 21:29:57)>> Apply Segments\n",
+ "(2017-06-07 21:30:09)<< DONE (12.0s)\n",
+ "(2017-06-07 21:30:09) DONE (15.0s)\n",
+ "(2017-06-07 21:30:09) Start Feature Union on DF (1421738, 31)\n",
+ "(2017-06-07 21:30:09)>> MEAN\n",
+ "(2017-06-07 21:30:09)<< DONE (0.0s)\n",
+ "(2017-06-07 21:30:09)>> STD\n",
+ "(2017-06-07 21:30:09)<< DONE (0.0s)\n",
+ "(2017-06-07 21:30:09)>> COUNT\n",
+ "(2017-06-07 21:30:40)<< DONE (31.0s)\n",
+ "(2017-06-07 21:30:40)>> LAST\n",
+ "(2017-06-07 21:32:26)<< DONE (106.0s)\n",
+ "(2017-06-07 21:32:26)>> SUM\n",
+ "(2017-06-07 21:32:26)<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:26)>> HStack\n",
+ "(2017-06-07 21:32:26)<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:26) DONE (137.0s)\n",
+ "(2017-06-07 21:32:26) Make DF for 5898 admission - [u'glasgow coma scale eye opening', u'glasgow coma scale motor', u'blood pressure systolic', u'oxygen saturation pulse oximetry', u'glasgow coma scale verbal', u'hemoglobin', u'lactate', u'blood pressure mean', u'vasopressin', 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",
+ "(2017-06-07 21:32:26)>> GLASGOW COMA SCALE EYE OPENING\n",
+ "(2017-06-07 21:32:26)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>glasgow coma scale eye opening</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>ord</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>184</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>93875.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>3.283430</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.060477</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>3.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>4.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale eye opening\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description 184\n",
+ "count 93875.000000\n",
+ "mean 3.283430\n",
+ "std 1.060477\n",
+ "min 1.000000\n",
+ "25% 3.000000\n",
+ "50% 4.000000\n",
+ "75% 4.000000\n",
+ "max 4.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:32:27)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:32:27)>>>> Clean (93875, 1)\n",
+ "(2017-06-07 21:32:27)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:27)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:32:27)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:27)>>>> Drop Small columns, threshold = 5 | (93875, 1)\n",
+ "(2017-06-07 21:32:27)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:27)>>>> Drop OOB data | (93875, 1)\n",
+ "(2017-06-07 21:32:27)>>>>>> glasgow coma scale eye opening, no_units, 93875\n",
+ "(2017-06-07 21:32:28)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:32:28)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Drop Small columns, threshold = 50 | (93875, 1)\n",
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Combine like columns (93875, 1)\n",
+ "(2017-06-07 21:32:28)>>>>>> (u'glasgow coma scale eye opening', 'known', u'ord', 'no_units')\n",
+ "(2017-06-07 21:32:28)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
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+ " <thead>\n",
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+ " </tr>\n",
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+ " <th>status</th>\n",
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+ " <th>min</th>\n",
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+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale eye opening\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description all\n",
+ "count 93875.000000\n",
+ "mean 3.283430\n",
+ "std 1.060477\n",
+ "min 1.000000\n",
+ "25% 3.000000\n",
+ "50% 4.000000\n",
+ "75% 4.000000\n",
+ "max 4.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((93875, 1), (93875, 1), 0L, 0, '0.0% records')\n",
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Join (93875, 1) to None\n",
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)<< DONE (2.0s)\n",
+ "(2017-06-07 21:32:28)>> GLASGOW COMA SCALE MOTOR\n",
+ "(2017-06-07 21:32:28)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ ],
+ "text/plain": [
+ "component glasgow coma scale motor\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description 454\n",
+ "count 93468.000000\n",
+ "mean 5.274479\n",
+ "std 1.404227\n",
+ "min 1.000000\n",
+ "25% 5.000000\n",
+ "50% 6.000000\n",
+ "75% 6.000000\n",
+ "max 6.000000"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Clean (93468, 1)\n",
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Drop Small columns, threshold = 5 | (93468, 1)\n",
+ "(2017-06-07 21:32:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:28)>>>> Drop OOB data | (93468, 1)\n",
+ "(2017-06-07 21:32:28)>>>>>> glasgow coma scale motor, no_units, 93468\n",
+ "(2017-06-07 21:32:29)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:32:29)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:32:29)>>>> Drop Small columns, threshold = 50 | (93468, 1)\n",
+ "(2017-06-07 21:32:29)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:29)>>>> Combine like columns (93468, 1)\n",
+ "(2017-06-07 21:32:29)>>>>>> (u'glasgow coma scale motor', 'known', u'ord', 'no_units')\n",
+ "(2017-06-07 21:32:29)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ ],
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+ "component glasgow coma scale motor\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description all\n",
+ "count 93468.000000\n",
+ "mean 5.274479\n",
+ "std 1.404227\n",
+ "min 1.000000\n",
+ "25% 5.000000\n",
+ "50% 6.000000\n",
+ "75% 6.000000\n",
+ "max 6.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((93468, 1), (93468, 1), 0L, 0, '0.0% records')\n",
+ "(2017-06-07 21:32:29)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:29)>>>> Join (93468, 1) to (93875, 1)\n",
+ "(2017-06-07 21:32:30)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:32:30)<< DONE (2.0s)\n",
+ "(2017-06-07 21:32:30)>> BLOOD PRESSURE SYSTOLIC\n",
+ "(2017-06-07 21:32:30)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
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+ " <thead>\n",
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+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"9\" halign=\"left\">known</th>\n",
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+ " <td>127.000000</td>\n",
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+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>122.730456</td>\n",
+ " <td>115.371981</td>\n",
+ " <td>119.610662</td>\n",
+ " <td>119.420057</td>\n",
+ " <td>120.033175</td>\n",
+ " <td>122.766355</td>\n",
+ " <td>110.431082</td>\n",
+ " <td>133.592593</td>\n",
+ " <td>66.547118</td>\n",
+ " <td>71.827381</td>\n",
+ " <td>73.533835</td>\n",
+ " <td>76.109375</td>\n",
+ " <td>72.645669</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>26.519557</td>\n",
+ " <td>26.518574</td>\n",
+ " <td>23.618350</td>\n",
+ " <td>292.905918</td>\n",
+ " <td>357.465107</td>\n",
+ " <td>26.127618</td>\n",
+ " <td>79.539756</td>\n",
+ " <td>26.536922</td>\n",
+ " <td>11.704124</td>\n",
+ " <td>15.125027</td>\n",
+ " <td>13.635709</td>\n",
+ " <td>12.622461</td>\n",
+ " <td>12.848030</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>14.000000</td>\n",
+ " <td>80.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>48.000000</td>\n",
+ " <td>45.000000</td>\n",
+ " <td>45.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>105.000000</td>\n",
+ " <td>96.000000</td>\n",
+ " <td>103.000000</td>\n",
+ " <td>102.000000</td>\n",
+ " <td>103.000000</td>\n",
+ " <td>101.500000</td>\n",
+ " <td>94.000000</td>\n",
+ " <td>112.750000</td>\n",
+ " <td>59.000000</td>\n",
+ " <td>64.750000</td>\n",
+ " <td>63.000000</td>\n",
+ " <td>68.000000</td>\n",
+ " <td>65.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>120.000000</td>\n",
+ " <td>116.000000</td>\n",
+ " <td>118.000000</td>\n",
+ " <td>116.000000</td>\n",
+ " <td>117.000000</td>\n",
+ " <td>122.000000</td>\n",
+ " <td>107.000000</td>\n",
+ " <td>134.000000</td>\n",
+ " <td>67.000000</td>\n",
+ " <td>72.500000</td>\n",
+ " <td>73.000000</td>\n",
+ " <td>76.000000</td>\n",
+ " <td>73.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>139.000000</td>\n",
+ " <td>134.000000</td>\n",
+ " <td>135.000000</td>\n",
+ " <td>133.000000</td>\n",
+ " <td>133.000000</td>\n",
+ " <td>142.500000</td>\n",
+ " <td>124.000000</td>\n",
+ " <td>147.750000</td>\n",
+ " <td>75.000000</td>\n",
+ " <td>80.250000</td>\n",
+ " <td>82.000000</td>\n",
+ " <td>84.250000</td>\n",
+ " <td>79.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>298.000000</td>\n",
+ " <td>215.000000</td>\n",
+ " <td>266.000000</td>\n",
+ " <td>95119.040000</td>\n",
+ " <td>127105.990000</td>\n",
+ " <td>196.000000</td>\n",
+ " <td>6918.000000</td>\n",
+ " <td>205.000000</td>\n",
+ " <td>129.000000</td>\n",
+ " <td>119.000000</td>\n",
+ " <td>130.000000</td>\n",
+ " <td>120.000000</td>\n",
+ " <td>120.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure systolic \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmHg \n",
+ "description 6 51 442 \n",
+ "count 0.0 201737.000000 207.000000 \n",
+ "mean NaN 122.730456 115.371981 \n",
+ "std NaN 26.519557 26.518574 \n",
+ "min NaN 0.000000 50.000000 \n",
+ "25% NaN 105.000000 96.000000 \n",
+ "50% NaN 120.000000 116.000000 \n",
+ "75% NaN 139.000000 134.000000 \n",
+ "max NaN 298.000000 215.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 455 220050 220179 224167 \n",
+ "count 156265.000000 105900.000000 126692.000000 107.000000 \n",
+ "mean 119.610662 119.420057 120.033175 122.766355 \n",
+ "std 23.618350 292.905918 357.465107 26.127618 \n",
+ "min 0.000000 0.000000 0.000000 70.000000 \n",
+ "25% 103.000000 102.000000 103.000000 101.500000 \n",
+ "50% 118.000000 116.000000 117.000000 122.000000 \n",
+ "75% 135.000000 133.000000 133.000000 142.500000 \n",
+ "max 266.000000 95119.040000 127105.990000 196.000000 \n",
+ "\n",
+ "component \\\n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units cc/min \n",
+ "description 225309 227243 3313 3315 3317 \n",
+ "count 8024.000000 54.000000 12911.000000 168.000000 133.000000 \n",
+ "mean 110.431082 133.592593 66.547118 71.827381 73.533835 \n",
+ "std 79.539756 26.536922 11.704124 15.125027 13.635709 \n",
+ "min 14.000000 80.000000 0.000000 0.000000 48.000000 \n",
+ "25% 94.000000 112.750000 59.000000 64.750000 63.000000 \n",
+ "50% 107.000000 134.000000 67.000000 72.500000 73.000000 \n",
+ "75% 124.000000 147.750000 75.000000 80.250000 82.000000 \n",
+ "max 6918.000000 205.000000 129.000000 119.000000 130.000000 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 3321 3323 \n",
+ "count 128.000000 127.000000 \n",
+ "mean 76.109375 72.645669 \n",
+ "std 12.622461 12.848030 \n",
+ "min 45.000000 45.000000 \n",
+ "25% 68.000000 65.000000 \n",
+ "50% 76.000000 73.000000 \n",
+ "75% 84.250000 79.500000 \n",
+ "max 120.000000 120.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:32:33)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:32:33)>>>> Clean (578256, 15)\n",
+ "(2017-06-07 21:32:33)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:33)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:32:33)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:33)>>>> Drop Small columns, threshold = 5 | (578256, 14)\n",
+ "(2017-06-07 21:32:33)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:32:33)>>>> Drop OOB data | (578256, 13)\n",
+ "(2017-06-07 21:32:34)>>>>>> blood pressure systolic, mmHg, 598986\n",
+ "(2017-06-07 21:33:03)<<<<<< DONE (29.0s)\n",
+ "(2017-06-07 21:33:03)>>>>>> blood pressure systolic, cc/min, 13467\n",
+ "(2017-06-07 21:33:03)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:03)<<<< DONE (30.0s)\n",
+ "(2017-06-07 21:33:03)>>>> Drop Small columns, threshold = 50 | (578256, 13)\n",
+ "(2017-06-07 21:33:03)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:03)>>>> Combine like columns (578256, 13)\n",
+ "(2017-06-07 21:33:03)>>>>>> (u'blood pressure systolic', 'known', u'qn', u'mmHg')\n",
+ "(2017-06-07 21:33:03)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:03)>>>>>> (u'blood pressure systolic', 'unknown', u'qn', u'cc/min')\n",
+ "(2017-06-07 21:33:12)<<<<<< DONE (9.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">blood pressure systolic</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " <th>unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>qn</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mmHg</th>\n",
+ " <th>cc/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>565232.00000</td>\n",
+ " <td>13022.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>120.54268</td>\n",
+ " <td>66.621935</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>24.36781</td>\n",
+ " <td>11.733246</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.00000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>103.00000</td>\n",
+ " <td>59.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>118.00000</td>\n",
+ " <td>67.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>136.00000</td>\n",
+ " <td>75.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>311.00000</td>\n",
+ " <td>129.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure systolic \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units mmHg cc/min\n",
+ "description all all\n",
+ "count 565232.00000 13022.000000\n",
+ "mean 120.54268 66.621935\n",
+ "std 24.36781 11.733246\n",
+ "min 0.00000 0.000000\n",
+ "25% 103.00000 59.000000\n",
+ "50% 118.00000 67.000000\n",
+ "75% 136.00000 75.000000\n",
+ "max 311.00000 129.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((578256, 14), (578254, 2), 34199L, 0, '0.0% records')\n",
+ "(2017-06-07 21:33:12)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:33:12)>>>> Join (578254, 2) to (94010, 2)\n",
+ "(2017-06-07 21:33:16)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:33:16)<< DONE (46.0s)\n",
+ "(2017-06-07 21:33:16)>> OXYGEN SATURATION PULSE OXIMETRY\n",
+ "(2017-06-07 21:33:16)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">oxygen saturation pulse oximetry</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"2\" halign=\"left\">percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>220277(%)</th>\n",
+ " <th>646(%)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>255273.000000</td>\n",
+ " <td>335406.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>96.966040</td>\n",
+ " <td>97.243172</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>19.772876</td>\n",
+ " <td>3.688021</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>95.000000</td>\n",
+ " <td>96.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>97.000000</td>\n",
+ " <td>98.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>9892.000000</td>\n",
+ " <td>100.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component oxygen saturation pulse oximetry \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units percent \n",
+ "description 220277(%) 646(%)\n",
+ "count 255273.000000 335406.000000\n",
+ "mean 96.966040 97.243172\n",
+ "std 19.772876 3.688021\n",
+ "min 0.000000 0.000000\n",
+ "25% 95.000000 96.000000\n",
+ "50% 97.000000 98.000000\n",
+ "75% 99.000000 100.000000\n",
+ "max 9892.000000 100.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:33:17)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:33:17)>>>> Clean (590655, 2)\n",
+ "(2017-06-07 21:33:17)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:17)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:33:17)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:17)>>>> Drop Small columns, threshold = 5 | (590655, 2)\n",
+ "(2017-06-07 21:33:17)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:17)>>>> Drop OOB data | (590655, 2)\n",
+ "(2017-06-07 21:33:17)>>>>>> oxygen saturation pulse oximetry, percent, 590679\n",
+ "(2017-06-07 21:33:27)<<<<<< DONE (10.0s)\n",
+ "(2017-06-07 21:33:27)<<<< DONE (10.0s)\n",
+ "(2017-06-07 21:33:27)>>>> Drop Small columns, threshold = 50 | (590655, 2)\n",
+ "(2017-06-07 21:33:27)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:27)>>>> Combine like columns (590655, 2)\n",
+ "(2017-06-07 21:33:27)>>>>>> (u'oxygen saturation pulse oximetry', 'known', u'qn', u'percent')\n",
+ "(2017-06-07 21:33:30)<<<<<< DONE (3.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
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+ " <th>units</th>\n",
+ " <th>percent</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>590653.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>97.105262</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>3.597904</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>96.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>98.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>100.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component oxygen saturation pulse oximetry\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units percent\n",
+ "description all\n",
+ "count 590653.000000\n",
+ "mean 97.105262\n",
+ "std 3.597904\n",
+ "min 0.000000\n",
+ "25% 96.000000\n",
+ "50% 98.000000\n",
+ "75% 99.000000\n",
+ "max 100.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((590655, 2), (590653, 1), 26L, 0, '0.0% records')\n",
+ "(2017-06-07 21:33:30)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:33:30)>>>> Join (590653, 1) to (582457, 4)\n",
+ "(2017-06-07 21:33:37)<<<< DONE (7.0s)\n",
+ "(2017-06-07 21:33:37)<< DONE (21.0s)\n",
+ "(2017-06-07 21:33:37)>> GLASGOW COMA SCALE VERBAL\n",
+ "(2017-06-07 21:33:37)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>glasgow coma scale verbal</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>ord</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>723</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>93655.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.897144</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.913756</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale verbal\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description 723\n",
+ "count 93655.000000\n",
+ "mean 2.897144\n",
+ "std 1.913756\n",
+ "min 1.000000\n",
+ "25% 1.000000\n",
+ "50% 2.000000\n",
+ "75% 5.000000\n",
+ "max 5.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:33:37)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:37)>>>> Clean (93655, 1)\n",
+ "(2017-06-07 21:33:37)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:37)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:33:37)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:37)>>>> Drop Small columns, threshold = 5 | (93655, 1)\n",
+ "(2017-06-07 21:33:37)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:37)>>>> Drop OOB data | (93655, 1)\n",
+ "(2017-06-07 21:33:37)>>>>>> glasgow coma scale verbal, no_units, 93655\n",
+ "(2017-06-07 21:33:39)<<<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:33:39)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:33:39)>>>> Drop Small columns, threshold = 50 | (93655, 1)\n",
+ "(2017-06-07 21:33:39)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:39)>>>> Combine like columns (93655, 1)\n",
+ "(2017-06-07 21:33:39)>>>>>> (u'glasgow coma scale verbal', 'known', u'ord', 'no_units')\n",
+ "(2017-06-07 21:33:39)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>glasgow coma scale verbal</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>ord</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>no_units</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>93655.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.897144</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.913756</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component glasgow coma scale verbal\n",
+ "status known\n",
+ "variable_type ord\n",
+ "units no_units\n",
+ "description all\n",
+ "count 93655.000000\n",
+ "mean 2.897144\n",
+ "std 1.913756\n",
+ "min 1.000000\n",
+ "25% 1.000000\n",
+ "50% 2.000000\n",
+ "75% 5.000000\n",
+ "max 5.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((93655, 1), (93655, 1), 0L, 0, '0.0% records')\n",
+ "(2017-06-07 21:33:39)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:39)>>>> Join (93655, 1) to (668033, 5)\n",
+ "(2017-06-07 21:33:43)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:33:43)<< DONE (6.0s)\n",
+ "(2017-06-07 21:33:43)>> HEMOGLOBIN\n",
+ "(2017-06-07 21:33:43)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"11\" halign=\"left\">hemoglobin</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"9\" halign=\"left\">known</th>\n",
+ " <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"9\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"9\" halign=\"left\">g/dL</th>\n",
+ " <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1165</th>\n",
+ " <th>220228(g/dl)</th>\n",
+ " <th>50811</th>\n",
+ " <th>50811(g/dl)</th>\n",
+ " <th>50811(gm/dl)</th>\n",
+ " <th>51222</th>\n",
+ " <th>51222(g/dl)</th>\n",
+ " <th>51222(gm/dl)</th>\n",
+ " <th>814(gm/dl)</th>\n",
+ " <th>50811</th>\n",
+ " <th>51222</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>0.0</td>\n",
+ " <td>13206.000000</td>\n",
+ " <td>4780.000000</td>\n",
+ " <td>1512.000000</td>\n",
+ " <td>2065.000000</td>\n",
+ " <td>36859.000000</td>\n",
+ " <td>8475.000000</td>\n",
+ " <td>12017.000000</td>\n",
+ " <td>18565.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>10.087063</td>\n",
+ " <td>10.359351</td>\n",
+ " <td>10.211111</td>\n",
+ " <td>10.346102</td>\n",
+ " <td>10.346374</td>\n",
+ " <td>10.489015</td>\n",
+ " <td>10.478996</td>\n",
+ " <td>10.292920</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>1.789972</td>\n",
+ " <td>2.136835</td>\n",
+ " <td>1.991397</td>\n",
+ " <td>2.107787</td>\n",
+ " <td>1.935027</td>\n",
+ " <td>1.964976</td>\n",
+ " <td>1.885599</td>\n",
+ " <td>1.596385</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>3.200000</td>\n",
+ " <td>3.700000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>3.200000</td>\n",
+ " <td>2.900000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>8.800000</td>\n",
+ " <td>8.900000</td>\n",
+ " <td>8.900000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>9.200000</td>\n",
+ " <td>9.200000</td>\n",
+ " <td>9.200000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>9.900000</td>\n",
+ " <td>10.200000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>10.200000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>10.200000</td>\n",
+ " <td>10.200000</td>\n",
+ " <td>10.100000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>11.200000</td>\n",
+ " <td>11.700000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.600000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.400000</td>\n",
+ " <td>11.200000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>19.300000</td>\n",
+ " <td>19.800000</td>\n",
+ " <td>20.200000</td>\n",
+ " <td>20.700000</td>\n",
+ " <td>21.900000</td>\n",
+ " <td>23.800000</td>\n",
+ " <td>23.100000</td>\n",
+ " <td>26.299999</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component hemoglobin \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units g/dL \n",
+ "description 1165 220228(g/dl) 50811 50811(g/dl) 50811(gm/dl) \n",
+ "count 0.0 13206.000000 4780.000000 1512.000000 2065.000000 \n",
+ "mean NaN 10.087063 10.359351 10.211111 10.346102 \n",
+ "std NaN 1.789972 2.136835 1.991397 2.107787 \n",
+ "min NaN 0.000000 0.000000 3.200000 3.700000 \n",
+ "25% NaN 8.800000 8.900000 8.900000 9.000000 \n",
+ "50% NaN 9.900000 10.200000 10.100000 10.200000 \n",
+ "75% NaN 11.200000 11.700000 11.400000 11.600000 \n",
+ "max NaN 19.300000 19.800000 20.200000 20.700000 \n",
+ "\n",
+ "component \\\n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 51222 51222(g/dl) 51222(gm/dl) 814(gm/dl) 50811 \n",
+ "count 36859.000000 8475.000000 12017.000000 18565.000000 0.0 \n",
+ "mean 10.346374 10.489015 10.478996 10.292920 NaN \n",
+ "std 1.935027 1.964976 1.885599 1.596385 NaN \n",
+ "min 2.000000 3.200000 2.900000 0.000000 NaN \n",
+ "25% 9.000000 9.200000 9.200000 9.200000 NaN \n",
+ "50% 10.100000 10.200000 10.200000 10.100000 NaN \n",
+ "75% 11.400000 11.400000 11.400000 11.200000 NaN \n",
+ "max 21.900000 23.800000 23.100000 26.299999 NaN \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 51222 \n",
+ "count 0.0 \n",
+ "mean NaN \n",
+ "std NaN \n",
+ "min NaN \n",
+ "25% NaN \n",
+ "50% NaN \n",
+ "75% NaN \n",
+ "max NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:33:44)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:33:44)>>>> Clean (66715, 14)\n",
+ "(2017-06-07 21:33:44)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:44)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:33:44)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:44)>>>> Drop Small columns, threshold = 5 | (66715, 16)\n",
+ "(2017-06-07 21:33:44)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:44)>>>> Drop OOB data | (66715, 8)\n",
+ "(2017-06-07 21:33:44)>>>>>> hemoglobin, g/dL, 97479\n",
+ "(2017-06-07 21:33:48)<<<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:33:48)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:33:48)>>>> Drop Small columns, threshold = 50 | (66715, 8)\n",
+ "(2017-06-07 21:33:48)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:48)>>>> Combine like columns (66715, 8)\n",
+ "(2017-06-07 21:33:48)>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
+ "(2017-06-07 21:33:49)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
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+ " <th>variable_type</th>\n",
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+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>66711.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>10.391383</td>\n",
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+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.948147</td>\n",
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+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>9.100000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>10.100000</td>\n",
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+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>11.400000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>26.299999</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component hemoglobin\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units g/dL\n",
+ "description all\n",
+ "count 66711.000000\n",
+ "mean 10.391383\n",
+ "std 1.948147\n",
+ "min 0.000000\n",
+ "25% 9.100000\n",
+ "50% 10.100000\n",
+ "75% 11.400000\n",
+ "max 26.299999"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((66715, 13), (66711, 1), 30770L, 0, '0.0% records')\n",
+ "(2017-06-07 21:33:49)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:33:49)>>>> Join (66711, 1) to (668038, 6)\n",
+ "(2017-06-07 21:33:53)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:33:53)<< DONE (10.0s)\n",
+ "(2017-06-07 21:33:53)>> LACTATE\n",
+ "(2017-06-07 21:33:53)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"7\" halign=\"left\">lactate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"4\" halign=\"left\">known</th>\n",
+ " <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"4\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"4\" halign=\"left\">mmol/L</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1531</th>\n",
+ " <th>225668</th>\n",
+ " <th>50813</th>\n",
+ " <th>818</th>\n",
+ " <th>225668</th>\n",
+ " <th>50813</th>\n",
+ " <th>818</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>6431.000000</td>\n",
+ " <td>6832.000000</td>\n",
+ " <td>17650.000000</td>\n",
+ " <td>7264.000000</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.807416</td>\n",
+ " <td>2.372753</td>\n",
+ " <td>2.552463</td>\n",
+ " <td>2.810871</td>\n",
+ " <td>4.4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.881607</td>\n",
+ " <td>3.500056</td>\n",
+ " <td>2.390068</td>\n",
+ " <td>2.872278</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>0.050000</td>\n",
+ " <td>0.300000</td>\n",
+ " <td>4.4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.300000</td>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.300000</td>\n",
+ " <td>4.4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.800000</td>\n",
+ " <td>1.700000</td>\n",
+ " <td>1.800000</td>\n",
+ " <td>1.800000</td>\n",
+ " <td>4.4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>3.100000</td>\n",
+ " <td>2.700000</td>\n",
+ " <td>2.900000</td>\n",
+ " <td>3.200000</td>\n",
+ " <td>4.4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>36.000000</td>\n",
+ " <td>170.000000</td>\n",
+ " <td>36.000000</td>\n",
+ " <td>36.000000</td>\n",
+ " <td>4.4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactate \\\n",
+ "status known unknown \n",
+ "variable_type qn qn \n",
+ "units mmol/L no_units \n",
+ "description 1531 225668 50813 818 225668 \n",
+ "count 6431.000000 6832.000000 17650.000000 7264.000000 1.0 \n",
+ "mean 2.807416 2.372753 2.552463 2.810871 4.4 \n",
+ "std 2.881607 3.500056 2.390068 2.872278 NaN \n",
+ "min 0.300000 0.050000 0.050000 0.300000 4.4 \n",
+ "25% 1.300000 1.200000 1.200000 1.300000 4.4 \n",
+ "50% 1.800000 1.700000 1.800000 1.800000 4.4 \n",
+ "75% 3.100000 2.700000 2.900000 3.200000 4.4 \n",
+ "max 36.000000 170.000000 36.000000 36.000000 4.4 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 50813 818 \n",
+ "count 0.0 0.0 \n",
+ "mean NaN NaN \n",
+ "std NaN NaN \n",
+ "min NaN NaN \n",
+ "25% NaN NaN \n",
+ "50% NaN NaN \n",
+ "75% NaN NaN \n",
+ "max NaN NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:33:54)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:33:54)>>>> Clean (17713, 10)\n",
+ "(2017-06-07 21:33:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:54)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:33:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:54)>>>> Drop Small columns, threshold = 5 | (17713, 14)\n",
+ "(2017-06-07 21:33:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:54)>>>> Drop OOB data | (17713, 4)\n",
+ "(2017-06-07 21:33:54)>>>>>> lactate, mmol/L, 38177\n",
+ "(2017-06-07 21:33:54)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:54)>>>> Drop Small columns, threshold = 50 | (17713, 4)\n",
+ "(2017-06-07 21:33:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:33:54)>>>> Combine like columns (17713, 4)\n",
+ "(2017-06-07 21:33:54)>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
+ "(2017-06-07 21:33:55)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\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>17705.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.553035</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.389333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.050000</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>2.900000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>36.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactate\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units mmol/L\n",
+ "description all\n",
+ "count 17705.000000\n",
+ "mean 2.553035\n",
+ "std 2.389333\n",
+ "min 0.050000\n",
+ "25% 1.200000\n",
+ "50% 1.800000\n",
+ "75% 2.900000\n",
+ "max 36.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((17713, 9), (17705, 1), 20478L, 0, '0.0% records')\n",
+ "(2017-06-07 21:33:55)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:33:55)>>>> Join (17705, 1) to (730624, 7)\n",
+ "(2017-06-07 21:33:59)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:33:59)<< DONE (6.0s)\n",
+ "(2017-06-07 21:33:59)>> BLOOD PRESSURE MEAN\n",
+ "(2017-06-07 21:33:59)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">blood pressure mean</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">mmHg</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>220052</th>\n",
+ " <th>220181</th>\n",
+ " <th>225312</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>106321.000000</td>\n",
+ " <td>126912.000000</td>\n",
+ " <td>8087.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>80.828011</td>\n",
+ " <td>76.854332</td>\n",
+ " <td>77.33053</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>33.685722</td>\n",
+ " <td>338.401762</td>\n",
+ " <td>88.05119</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-38.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-30.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>69.000000</td>\n",
+ " <td>65.000000</td>\n",
+ " <td>67.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>78.000000</td>\n",
+ " <td>74.000000</td>\n",
+ " <td>74.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>90.000000</td>\n",
+ " <td>85.000000</td>\n",
+ " <td>84.00000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>8687.000000</td>\n",
+ " <td>120130.030000</td>\n",
+ " <td>7763.00000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure mean \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmHg \n",
+ "description 220052 220181 225312\n",
+ "count 106321.000000 126912.000000 8087.00000\n",
+ "mean 80.828011 76.854332 77.33053\n",
+ "std 33.685722 338.401762 88.05119\n",
+ "min -38.000000 0.000000 -30.00000\n",
+ "25% 69.000000 65.000000 67.00000\n",
+ "50% 78.000000 74.000000 74.00000\n",
+ "75% 90.000000 85.000000 84.00000\n",
+ "max 8687.000000 120130.030000 7763.00000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:00)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:00)>>>> Clean (229225, 3)\n",
+ "(2017-06-07 21:34:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:00)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:00)>>>> Drop Small columns, threshold = 5 | (229225, 3)\n",
+ "(2017-06-07 21:34:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:00)>>>> Drop OOB data | (229225, 3)\n",
+ "(2017-06-07 21:34:00)>>>>>> blood pressure mean, mmHg, 241320\n",
+ "(2017-06-07 21:34:05)<<<<<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:05)<<<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:05)>>>> Drop Small columns, threshold = 50 | (229225, 3)\n",
+ "(2017-06-07 21:34:05)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:05)>>>> Combine like columns (229225, 3)\n",
+ "(2017-06-07 21:34:05)>>>>>> (u'blood pressure mean', 'known', u'qn', u'mmHg')\n",
+ "(2017-06-07 21:34:07)<<<<<< DONE (2.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>blood pressure mean</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>mmHg</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>229141.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>77.960487</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>17.670114</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>67.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>76.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>87.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>361.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure mean\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units mmHg\n",
+ "description all\n",
+ "count 229141.000000\n",
+ "mean 77.960487\n",
+ "std 17.670114\n",
+ "min 0.000000\n",
+ "25% 67.000000\n",
+ "50% 76.000000\n",
+ "75% 87.000000\n",
+ "max 361.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((229225, 3), (229141, 1), 12179L, 0, '0.0% records')\n",
+ "(2017-06-07 21:34:07)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:34:07)>>>> Join (229141, 1) to (744003, 8)\n",
+ "(2017-06-07 21:34:13)<<<< DONE (6.0s)\n",
+ "(2017-06-07 21:34:13)<< DONE (14.0s)\n",
+ "(2017-06-07 21:34:13)>> VASOPRESSIN\n",
+ "(2017-06-07 21:34:13)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"21\" halign=\"left\">vasopressin</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"15\" halign=\"left\">known</th>\n",
+ " <th colspan=\"6\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"15\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"6\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"8\" halign=\"left\">units</th>\n",
+ " <th colspan=\"7\" halign=\"left\">units/min</th>\n",
+ " <th colspan=\"4\" halign=\"left\">ml</th>\n",
+ " <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1136(U)</th>\n",
+ " <th>1222(U)</th>\n",
+ " <th>1327(U)</th>\n",
+ " <th>2248(U)</th>\n",
+ " <th>2445(U)</th>\n",
+ " <th>30051(U)</th>\n",
+ " <th>6255(U)</th>\n",
+ " <th>7341(U)</th>\n",
+ " <th>1136(units/hour)</th>\n",
+ " <th>1222(units/hour)</th>\n",
+ " <th>...</th>\n",
+ " <th>30051(Umin)(U/min)</th>\n",
+ " <th>30051(units/hour)</th>\n",
+ " <th>6255(units/hour)</th>\n",
+ " <th>7341(units/hour)</th>\n",
+ " <th>30051</th>\n",
+ " <th>42273</th>\n",
+ " <th>42802</th>\n",
+ " <th>46570</th>\n",
+ " <th>30051</th>\n",
+ " <th>42802</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>5930.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>...</td>\n",
+ " <td>9941.000000</td>\n",
+ " <td>516.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.484185</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>1.441951</td>\n",
+ " <td>0.061290</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.946331</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>1.184972</td>\n",
+ " <td>0.090906</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.200000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>1.800000</td>\n",
+ " <td>0.040000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>2.400000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>2.400000</td>\n",
+ " <td>0.080000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>60.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>10.000000</td>\n",
+ " <td>0.826667</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>8 rows × 28 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component vasopressin \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units units \n",
+ "description 1136(U) 1222(U) 1327(U) 2248(U) 2445(U) 30051(U) \n",
+ "count 0.0 0.0 0.0 0.0 0.0 5930.000000 \n",
+ "mean NaN NaN NaN NaN NaN 2.484185 \n",
+ "std NaN NaN NaN NaN NaN 2.946331 \n",
+ "min NaN NaN NaN NaN NaN 0.000000 \n",
+ "25% NaN NaN NaN NaN NaN 1.200000 \n",
+ "50% NaN NaN NaN NaN NaN 2.400000 \n",
+ "75% NaN NaN NaN NaN NaN 2.400000 \n",
+ "max NaN NaN NaN NaN NaN 60.000000 \n",
+ "\n",
+ "component ... \\\n",
+ "status ... \n",
+ "variable_type ... \n",
+ "units units/min ... \n",
+ "description 6255(U) 7341(U) 1136(units/hour) 1222(units/hour) ... \n",
+ "count 0.0 0.0 0.0 0.0 ... \n",
+ "mean NaN NaN NaN NaN ... \n",
+ "std NaN NaN NaN NaN ... \n",
+ "min NaN NaN NaN NaN ... \n",
+ "25% NaN NaN NaN NaN ... \n",
+ "50% NaN NaN NaN NaN ... \n",
+ "75% NaN NaN NaN NaN ... \n",
+ "max NaN NaN NaN NaN ... \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 30051(Umin)(U/min) 30051(units/hour) 6255(units/hour) \n",
+ "count 9941.000000 516.000000 0.0 \n",
+ "mean 1.441951 0.061290 NaN \n",
+ "std 1.184972 0.090906 NaN \n",
+ "min 0.000000 0.000000 NaN \n",
+ "25% 0.040000 0.040000 NaN \n",
+ "50% 1.800000 0.040000 NaN \n",
+ "75% 2.400000 0.080000 NaN \n",
+ "max 10.000000 0.826667 NaN \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units ml no_units \n",
+ "description 7341(units/hour) 30051 42273 42802 46570 30051 42802 \n",
+ "count 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "mean NaN NaN NaN NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN NaN NaN NaN \n",
+ "\n",
+ "[8 rows x 28 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:13)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:13)>>>> Clean (11209, 31)\n",
+ "(2017-06-07 21:34:13)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:13)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:13)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:13)>>>> Drop Small columns, threshold = 5 | (11209, 33)\n",
+ "(2017-06-07 21:34:13)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:13)>>>> Drop OOB data | (11209, 5)\n",
+ "(2017-06-07 21:34:13)>>>>>> vasopressin, units, 5930\n",
+ "(2017-06-07 21:34:13)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:13)>>>>>> vasopressin, units/min, 11415\n",
+ "(2017-06-07 21:34:14)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:14)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:14)>>>> Drop Small columns, threshold = 50 | (11209, 5)\n",
+ "(2017-06-07 21:34:14)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:14)>>>> Combine like columns (11209, 5)\n",
+ "(2017-06-07 21:34:14)>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
+ "(2017-06-07 21:34:14)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:14)>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
+ "(2017-06-07 21:34:14)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">vasopressin</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>units</th>\n",
+ " <th>units/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>5930.000000</td>\n",
+ " <td>10948.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.484185</td>\n",
+ " <td>1.316514</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.946331</td>\n",
+ " <td>1.193791</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.200000</td>\n",
+ " <td>0.040000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.400000</td>\n",
+ " <td>1.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.400000</td>\n",
+ " <td>2.400000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>60.000000</td>\n",
+ " <td>5.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component vasopressin \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units units units/min\n",
+ "description all all\n",
+ "count 5930.000000 10948.000000\n",
+ "mean 2.484185 1.316514\n",
+ "std 2.946331 1.193791\n",
+ "min 0.000000 0.000000\n",
+ "25% 1.200000 0.040000\n",
+ "50% 2.400000 1.200000\n",
+ "75% 2.400000 2.400000\n",
+ "max 60.000000 5.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((11209, 30), (11200, 2), 475L, 1, '0.4274% records')\n",
+ "(2017-06-07 21:34:14)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:14)>>>> Join (11200, 2) to (745692, 9)\n",
+ "(2017-06-07 21:34:18)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:34:18)<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:18)>> WEIGHT BODY\n",
+ "(2017-06-07 21:34:18)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">weight body</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">kg</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>763</th>\n",
+ " <th>224639</th>\n",
+ " <th>3693(gms)(grams)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>4596.000000</td>\n",
+ " <td>4105.000000</td>\n",
+ " <td>97.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>84.717737</td>\n",
+ " <td>88.638685</td>\n",
+ " <td>0.003049</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>24.173361</td>\n",
+ " <td>27.543902</td>\n",
+ " <td>0.000888</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000782</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>68.500000</td>\n",
+ " <td>72.400000</td>\n",
+ " <td>0.002600</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>82.099998</td>\n",
+ " <td>84.800000</td>\n",
+ " <td>0.003200</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>97.300003</td>\n",
+ " <td>102.000000</td>\n",
+ " <td>0.003690</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>283.000000</td>\n",
+ " <td>927.000000</td>\n",
+ " <td>0.004595</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component weight body \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units kg \n",
+ "description 763 224639 3693(gms)(grams)\n",
+ "count 4596.000000 4105.000000 97.000000\n",
+ "mean 84.717737 88.638685 0.003049\n",
+ "std 24.173361 27.543902 0.000888\n",
+ "min 0.000000 0.000000 0.000782\n",
+ "25% 68.500000 72.400000 0.002600\n",
+ "50% 82.099998 84.800000 0.003200\n",
+ "75% 97.300003 102.000000 0.003690\n",
+ "max 283.000000 927.000000 0.004595"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:18)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:18)>>>> Clean (8798, 3)\n",
+ "(2017-06-07 21:34:18)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:18)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:18)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:18)>>>> Drop Small columns, threshold = 5 | (8798, 3)\n",
+ "(2017-06-07 21:34:18)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:18)>>>> Drop OOB data | (8798, 3)\n",
+ "(2017-06-07 21:34:18)>>>>>> weight body, kg, 8798\n",
+ "(2017-06-07 21:34:19)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:19)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:19)>>>> Drop Small columns, threshold = 50 | (8798, 3)\n",
+ "(2017-06-07 21:34:19)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:19)>>>> Combine like columns (8798, 3)\n",
+ "(2017-06-07 21:34:19)>>>>>> (u'weight body', 'known', u'qn', u'kg')\n",
+ "(2017-06-07 21:34:19)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>weight body</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>kg</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>8797.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>85.517542</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>25.773247</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>69.700000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>83.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.300003</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>283.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component weight body\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units kg\n",
+ "description all\n",
+ "count 8797.000000\n",
+ "mean 85.517542\n",
+ "std 25.773247\n",
+ "min 0.000000\n",
+ "25% 69.700000\n",
+ "50% 83.000000\n",
+ "75% 99.300003\n",
+ "max 283.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((8798, 3), (8797, 1), 1L, 0, '0.0% records')\n",
+ "(2017-06-07 21:34:19)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:19)>>>> Join (8797, 1) to (746358, 11)\n",
+ "(2017-06-07 21:34:23)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:34:23)<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:23)>> NORMAL SALINE\n",
+ "(2017-06-07 21:34:23)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"15\" halign=\"left\">normal saline</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"13\" halign=\"left\">known</th>\n",
+ " <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"13\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"8\" halign=\"left\">mL</th>\n",
+ " <th colspan=\"5\" halign=\"left\">mL/hr</th>\n",
+ " <th colspan=\"2\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>30190</th>\n",
+ " <th>225158</th>\n",
+ " <th>225158(L)</th>\n",
+ " <th>225158(ml)</th>\n",
+ " <th>30190(ml)</th>\n",
+ " <th>41913(ml)</th>\n",
+ " <th>44053(ml)</th>\n",
+ " <th>44440(ml)</th>\n",
+ " <th>225158(mL/hour)</th>\n",
+ " <th>225158(mL/min)</th>\n",
+ " <th>30190(mL/hour)</th>\n",
+ " <th>41913(mL/hour)</th>\n",
+ " <th>44440(mL/hour)</th>\n",
+ " <th>30190</th>\n",
+ " <th>44053</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3.0</td>\n",
+ " <td>3776.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>986.000000</td>\n",
+ " <td>513.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>43877.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>28.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>12.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.0</td>\n",
+ " <td>476.925662</td>\n",
+ " <td>NaN</td>\n",
+ " <td>122.512144</td>\n",
+ " <td>5.421131</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>78.148208</td>\n",
+ " <td>7028.571429</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.0</td>\n",
+ " <td>373.842501</td>\n",
+ " <td>NaN</td>\n",
+ " <td>251.742402</td>\n",
+ " <td>9.628846</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>197.985623</td>\n",
+ " <td>2697.134435</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.016678</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.020988</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>200.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>8.294235</td>\n",
+ " <td>1.100000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>6.013423</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>20.122664</td>\n",
+ " <td>2.100000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>15.300982</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.0</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>99.999996</td>\n",
+ " <td>4.700000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>62.075080</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>0.0</td>\n",
+ " <td>6000.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>3000.000000</td>\n",
+ " <td>117.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>7500.000000</td>\n",
+ " <td>18000.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component normal saline \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mL \n",
+ "description 30190 225158 225158(L) 225158(ml) 30190(ml) \n",
+ "count 3.0 3776.000000 0.0 986.000000 513.000000 \n",
+ "mean 0.0 476.925662 NaN 122.512144 5.421131 \n",
+ "std 0.0 373.842501 NaN 251.742402 9.628846 \n",
+ "min 0.0 0.000000 NaN 0.016678 0.000000 \n",
+ "25% 0.0 200.000000 NaN 8.294235 1.100000 \n",
+ "50% 0.0 500.000000 NaN 20.122664 2.100000 \n",
+ "75% 0.0 500.000000 NaN 99.999996 4.700000 \n",
+ "max 0.0 6000.000000 NaN 3000.000000 117.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units mL/hr \n",
+ "description 41913(ml) 44053(ml) 44440(ml) 225158(mL/hour) 225158(mL/min) \n",
+ "count 0.0 0.0 0.0 43877.000000 70.000000 \n",
+ "mean NaN NaN NaN 78.148208 7028.571429 \n",
+ "std NaN NaN NaN 197.985623 2697.134435 \n",
+ "min NaN NaN NaN 0.020988 6000.000000 \n",
+ "25% NaN NaN NaN 6.013423 6000.000000 \n",
+ "50% NaN NaN NaN 15.300982 6000.000000 \n",
+ "75% NaN NaN NaN 62.075080 6000.000000 \n",
+ "max NaN NaN NaN 7500.000000 18000.000000 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 30190(mL/hour) 41913(mL/hour) 44440(mL/hour) 30190 44053 \n",
+ "count 28.0 0.0 0.0 12.0 0.0 \n",
+ "mean 0.0 NaN NaN 0.0 NaN \n",
+ "std 0.0 NaN NaN 0.0 NaN \n",
+ "min 0.0 NaN NaN 0.0 NaN \n",
+ "25% 0.0 NaN NaN 0.0 NaN \n",
+ "50% 0.0 NaN NaN 0.0 NaN \n",
+ "75% 0.0 NaN NaN 0.0 NaN \n",
+ "max 0.0 NaN NaN 0.0 NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:24)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:24)>>>> Clean (49143, 17)\n",
+ "(2017-06-07 21:34:24)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:24)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:24)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:24)>>>> Drop Small columns, threshold = 5 | (49143, 15)\n",
+ "(2017-06-07 21:34:24)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:24)>>>> Drop OOB data | (49143, 7)\n",
+ "(2017-06-07 21:34:24)>>>>>> normal saline, mL, 5275\n",
+ "(2017-06-07 21:34:25)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:25)>>>>>> normal saline, mL/hr, 43975\n",
+ "(2017-06-07 21:34:26)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:26)>>>>>> normal saline, no_units, 12\n",
+ "(2017-06-07 21:34:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:26)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:34:26)>>>> Drop Small columns, threshold = 50 | (49143, 7)\n",
+ "(2017-06-07 21:34:26)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:26)>>>> Combine like columns (49143, 5)\n",
+ "(2017-06-07 21:34:26)>>>>>> (u'normal saline', 'known', u'qn', u'mL')\n",
+ "(2017-06-07 21:34:26)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:26)>>>>>> (u'normal saline', 'known', u'qn', u'mL/hr')\n",
+ "(2017-06-07 21:34:27)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">normal saline</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mL</th>\n",
+ " <th>mL/hr</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>5275.000000</td>\n",
+ " <td>43935.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>364.824514</td>\n",
+ " <td>85.965834</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>380.025542</td>\n",
+ " <td>292.200820</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.020988</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>49.999999</td>\n",
+ " <td>6.017431</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>250.000000</td>\n",
+ " <td>15.357691</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>500.000000</td>\n",
+ " <td>62.833339</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>6000.000000</td>\n",
+ " <td>7500.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component normal saline \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mL mL/hr\n",
+ "description all all\n",
+ "count 5275.000000 43935.000000\n",
+ "mean 364.824514 85.965834\n",
+ "std 380.025542 292.200820\n",
+ "min 0.000000 0.020988\n",
+ "25% 49.999999 6.017431\n",
+ "50% 250.000000 15.357691\n",
+ "75% 500.000000 62.833339\n",
+ "max 6000.000000 7500.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((49143, 15), (49092, 2), 55L, 3, '0.1501% records')\n",
+ "(2017-06-07 21:34:27)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:27)>>>> Join (49092, 2) to (747628, 12)\n",
+ "(2017-06-07 21:34:32)<<<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:32)<< DONE (9.0s)\n",
+ "(2017-06-07 21:34:32)>> NOREPINEPHRINE\n",
+ "(2017-06-07 21:34:32)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"5\" halign=\"left\">norepinephrine</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"5\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"5\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">mcg</th>\n",
+ " <th>mcg/kg/min</th>\n",
+ " <th>mcg/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>221906(mg)</th>\n",
+ " <th>30047(mg)</th>\n",
+ " <th>30120(mg)</th>\n",
+ " <th>30120(mcgkgmin)</th>\n",
+ " <th>30047(mcgmin)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>8148.000000</td>\n",
+ " <td>438.000000</td>\n",
+ " <td>19680.000000</td>\n",
+ " <td>29898.000000</td>\n",
+ " <td>903.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>126.284480</td>\n",
+ " <td>685.164003</td>\n",
+ " <td>686.694749</td>\n",
+ " <td>0.136690</td>\n",
+ " <td>9.294083</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>213.561785</td>\n",
+ " <td>1388.862177</td>\n",
+ " <td>962.666821</td>\n",
+ " <td>0.196081</td>\n",
+ " <td>11.256667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.001600</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>31.658734</td>\n",
+ " <td>120.000000</td>\n",
+ " <td>160.000000</td>\n",
+ " <td>0.037647</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>80.024033</td>\n",
+ " <td>320.000000</td>\n",
+ " <td>384.000000</td>\n",
+ " <td>0.080000</td>\n",
+ " <td>6.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>179.931926</td>\n",
+ " <td>655.000000</td>\n",
+ " <td>863.100098</td>\n",
+ " <td>0.190000</td>\n",
+ " <td>12.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>9797.541298</td>\n",
+ " <td>14400.000000</td>\n",
+ " <td>25200.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>60.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component norepinephrine \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mcg mcg/kg/min \n",
+ "description 221906(mg) 30047(mg) 30120(mg) 30120(mcgkgmin) \n",
+ "count 8148.000000 438.000000 19680.000000 29898.000000 \n",
+ "mean 126.284480 685.164003 686.694749 0.136690 \n",
+ "std 213.561785 1388.862177 962.666821 0.196081 \n",
+ "min 0.001600 0.000000 0.000000 0.000000 \n",
+ "25% 31.658734 120.000000 160.000000 0.037647 \n",
+ "50% 80.024033 320.000000 384.000000 0.080000 \n",
+ "75% 179.931926 655.000000 863.100098 0.190000 \n",
+ "max 9797.541298 14400.000000 25200.000000 9.000000 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units mcg/min \n",
+ "description 30047(mcgmin) \n",
+ "count 903.000000 \n",
+ "mean 9.294083 \n",
+ "std 11.256667 \n",
+ "min 0.000000 \n",
+ "25% 2.000000 \n",
+ "50% 6.000000 \n",
+ "75% 12.000000 \n",
+ "max 60.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:32)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:32)>>>> Clean (40268, 5)\n",
+ "(2017-06-07 21:34:32)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:32)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:32)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:32)>>>> Drop Small columns, threshold = 5 | (40268, 5)\n",
+ "(2017-06-07 21:34:32)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:32)>>>> Drop OOB data | (40268, 5)\n",
+ "(2017-06-07 21:34:32)>>>>>> norepinephrine, mcg, 28266\n",
+ "(2017-06-07 21:34:33)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:33)>>>>>> norepinephrine, mcg/kg/min, 29898\n",
+ "(2017-06-07 21:34:33)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:33)>>>>>> norepinephrine, mcg/min, 903\n",
+ "(2017-06-07 21:34:33)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:33)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:33)>>>> Drop Small columns, threshold = 50 | (40268, 5)\n",
+ "(2017-06-07 21:34:33)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:33)>>>> Combine like columns (40268, 5)\n",
+ "(2017-06-07 21:34:33)>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
+ "(2017-06-07 21:34:33)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:33)>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
+ "(2017-06-07 21:34:34)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:34)>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/min')\n",
+ "(2017-06-07 21:34:34)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">norepinephrine</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mcg</th>\n",
+ " <th>mcg/kg/min</th>\n",
+ " <th>mcg/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>28265.000000</td>\n",
+ " <td>29898.000000</td>\n",
+ " <td>903.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>525.119421</td>\n",
+ " <td>0.136690</td>\n",
+ " <td>9.294083</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>867.548814</td>\n",
+ " <td>0.196081</td>\n",
+ " <td>11.256667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>79.995710</td>\n",
+ " <td>0.037647</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>240.363680</td>\n",
+ " <td>0.080000</td>\n",
+ " <td>6.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>612.000061</td>\n",
+ " <td>0.190000</td>\n",
+ " <td>12.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>25200.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>60.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component norepinephrine \n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mcg mcg/kg/min mcg/min\n",
+ "description all all all\n",
+ "count 28265.000000 29898.000000 903.000000\n",
+ "mean 525.119421 0.136690 9.294083\n",
+ "std 867.548814 0.196081 11.256667\n",
+ "min 0.000000 0.000000 0.000000\n",
+ "25% 79.995710 0.037647 2.000000\n",
+ "50% 240.363680 0.080000 6.000000\n",
+ "75% 612.000061 0.190000 12.000000\n",
+ "max 25200.000000 9.000000 60.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((40268, 5), (40268, 3), 1L, 0, '0.0% records')\n",
+ "(2017-06-07 21:34:34)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:34)>>>> Join (40268, 3) to (788924, 14)\n",
+ "(2017-06-07 21:34:40)<<<< DONE (6.0s)\n",
+ "(2017-06-07 21:34:40)<< DONE (8.0s)\n",
+ "(2017-06-07 21:34:40)>> TEMPERATURE BODY\n",
+ "(2017-06-07 21:34:40)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"4\" halign=\"left\">temperature body</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"4\" halign=\"left\">known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"4\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"4\" halign=\"left\">degF</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>223761(?F)</th>\n",
+ " <th>223762(?C)(degC)</th>\n",
+ " <th>676(Deg. C)(degC)</th>\n",
+ " <th>678(Deg. F)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>51128.000000</td>\n",
+ " <td>8556.000000</td>\n",
+ " <td>37088.000000</td>\n",
+ " <td>74823.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>98.388801</td>\n",
+ " <td>99.676010</td>\n",
+ " <td>98.795385</td>\n",
+ " <td>98.520238</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>9.432410</td>\n",
+ " <td>10.395099</td>\n",
+ " <td>2.509944</td>\n",
+ " <td>2.558967</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>97.500000</td>\n",
+ " <td>97.700000</td>\n",
+ " <td>98.060002</td>\n",
+ " <td>97.599998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>98.300000</td>\n",
+ " <td>98.960000</td>\n",
+ " <td>98.960002</td>\n",
+ " <td>98.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.200000</td>\n",
+ " <td>100.040000</td>\n",
+ " <td>99.860002</td>\n",
+ " <td>99.500000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1111.000000</td>\n",
+ " <td>214.880000</td>\n",
+ " <td>106.879998</td>\n",
+ " <td>109.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component temperature body \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units degF \n",
+ "description 223761(?F) 223762(?C)(degC) 676(Deg. C)(degC) \n",
+ "count 51128.000000 8556.000000 37088.000000 \n",
+ "mean 98.388801 99.676010 98.795385 \n",
+ "std 9.432410 10.395099 2.509944 \n",
+ "min 0.000000 32.000000 32.000000 \n",
+ "25% 97.500000 97.700000 98.060002 \n",
+ "50% 98.300000 98.960000 98.960002 \n",
+ "75% 99.200000 100.040000 99.860002 \n",
+ "max 1111.000000 214.880000 106.879998 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 678(Deg. F) \n",
+ "count 74823.000000 \n",
+ "mean 98.520238 \n",
+ "std 2.558967 \n",
+ "min 0.000000 \n",
+ "25% 97.599998 \n",
+ "50% 98.500000 \n",
+ "75% 99.500000 \n",
+ "max 109.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:40)>>>> Clean (171306, 4)\n",
+ "(2017-06-07 21:34:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:40)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:40)>>>> Drop Small columns, threshold = 5 | (171306, 4)\n",
+ "(2017-06-07 21:34:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:40)>>>> Drop OOB data | (171306, 4)\n",
+ "(2017-06-07 21:34:40)>>>>>> temperature body, degF, 171595\n",
+ "(2017-06-07 21:34:45)<<<<<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:45)<<<< DONE (5.0s)\n",
+ "(2017-06-07 21:34:45)>>>> Drop Small columns, threshold = 50 | (171306, 4)\n",
+ "(2017-06-07 21:34:45)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:45)>>>> Combine like columns (171306, 4)\n",
+ "(2017-06-07 21:34:45)>>>>>> (u'temperature body', 'known', u'qn', u'degF')\n",
+ "(2017-06-07 21:34:46)<<<<<< DONE (1.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>temperature body</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",
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+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>171258.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>98.537549</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>2.476494</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>97.600000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>98.599998</td>\n",
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+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>99.500000</td>\n",
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+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>109.000000</td>\n",
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+ ],
+ "text/plain": [
+ "component temperature body\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units degF\n",
+ "description all\n",
+ "count 171258.000000\n",
+ "mean 98.537549\n",
+ "std 2.476494\n",
+ "min 0.000000\n",
+ "25% 97.600000\n",
+ "50% 98.599998\n",
+ "75% 99.500000\n",
+ "max 109.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((171306, 4), (171258, 1), 337L, 0, '0.0% records')\n",
+ "(2017-06-07 21:34:46)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:46)>>>> Join (171258, 1) to (789883, 17)\n",
+ "(2017-06-07 21:34:52)<<<< DONE (6.0s)\n",
+ "(2017-06-07 21:34:52)<< DONE (12.0s)\n",
+ "(2017-06-07 21:34:52)>> BLOOD PRESSURE DIASTOLIC\n",
+ "(2017-06-07 21:34:52)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"11\" halign=\"left\">known</th>\n",
+ " <th colspan=\"4\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"11\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"4\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
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+ " <th>units</th>\n",
+ " <th colspan=\"11\" halign=\"left\">mmHg</th>\n",
+ " <th colspan=\"4\" halign=\"left\">cc/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
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+ " <td>57.914339</td>\n",
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+ " <td>43.437500</td>\n",
+ " <td>40.456693</td>\n",
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+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>14.172999</td>\n",
+ " <td>15.289519</td>\n",
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+ " <td>17.126268</td>\n",
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+ " <td>9.74400</td>\n",
+ " <td>10.232533</td>\n",
+ " <td>11.288958</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>30.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>-2.00000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>35.000000</td>\n",
+ " <td>5.000000</td>\n",
+ " <td>35.000000</td>\n",
+ " <td>16.182301</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>21.00000</td>\n",
+ " <td>22.000000</td>\n",
+ " <td>18.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
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+ " <td>50.000000</td>\n",
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+ " <td>48.000000</td>\n",
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+ " <td>52.000000</td>\n",
+ " <td>53.000000</td>\n",
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+ " <td>57.000000</td>\n",
+ " <td>26.480129</td>\n",
+ " <td>32.000000</td>\n",
+ " <td>34.00000</td>\n",
+ " <td>36.000000</td>\n",
+ " <td>34.000000</td>\n",
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+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
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+ " <td>66.000000</td>\n",
+ " <td>57.000000</td>\n",
+ " <td>65.000000</td>\n",
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+ " <td>37.000000</td>\n",
+ " <td>40.00000</td>\n",
+ " <td>42.500000</td>\n",
+ " <td>40.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>67.000000</td>\n",
+ " <td>72.000000</td>\n",
+ " <td>67.000000</td>\n",
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+ " <td>69.00000</td>\n",
+ " <td>72.000000</td>\n",
+ " <td>77.000000</td>\n",
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+ " <td>47.500000</td>\n",
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+ " <td>87.000000</td>\n",
+ " <td>81.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component blood pressure diastolic \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mmHg \n",
+ "description 8364 8368 8440 \n",
+ "count 0.0 201694.000000 193.000000 \n",
+ "mean NaN 59.708504 62.316062 \n",
+ "std NaN 14.172999 15.289519 \n",
+ "min NaN 0.000000 30.000000 \n",
+ "25% NaN 50.000000 50.000000 \n",
+ "50% NaN 58.000000 60.000000 \n",
+ "75% NaN 67.000000 72.000000 \n",
+ "max NaN 294.000000 100.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 8441 8555 220051 220180 \n",
+ "count 156096.000000 2142.000000 105866.00000 126625.000000 \n",
+ "mean 57.992835 56.979458 64.22657 64.386472 \n",
+ "std 15.497842 13.526784 480.58826 331.364549 \n",
+ "min 0.000000 0.000000 -2.00000 0.000000 \n",
+ "25% 47.000000 48.000000 52.00000 52.000000 \n",
+ "50% 57.000000 56.000000 60.00000 61.000000 \n",
+ "75% 67.000000 64.000000 69.00000 72.000000 \n",
+ "max 226.000000 155.000000 85100.98000 75104.970000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 224643 225310 227242 8503(cmH20)(cmH2O) \n",
+ "count 97.000000 8020.000000 51.000000 166.000000 \n",
+ "mean 66.422680 57.914339 68.352941 31.190366 \n",
+ "std 17.126268 14.396624 16.369268 6.976237 \n",
+ "min 35.000000 5.000000 35.000000 16.182301 \n",
+ "25% 53.000000 49.000000 57.000000 26.480129 \n",
+ "50% 66.000000 57.000000 65.000000 30.157925 \n",
+ "75% 77.000000 66.000000 80.000000 35.858508 \n",
+ "max 110.000000 232.000000 110.000000 57.373613 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units cc/min \n",
+ "description 8502 8504 8506 8507 \n",
+ "count 12913.000000 133.00000 128.000000 127.000000 \n",
+ "mean 37.754124 40.75188 43.437500 40.456693 \n",
+ "std 10.500318 9.74400 10.232533 11.288958 \n",
+ "min 0.000000 21.00000 22.000000 18.000000 \n",
+ "25% 32.000000 34.00000 36.000000 34.000000 \n",
+ "50% 37.000000 40.00000 42.500000 40.000000 \n",
+ "75% 43.000000 46.00000 50.250000 47.500000 \n",
+ "max 647.000000 74.00000 87.000000 81.000000 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:34:54)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:34:54)>>>> Clean (578689, 15)\n",
+ "(2017-06-07 21:34:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:54)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:34:54)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:34:54)>>>> Drop Small columns, threshold = 5 | (578689, 15)\n",
+ "(2017-06-07 21:34:55)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:34:55)>>>> Drop OOB data | (578689, 14)\n",
+ "(2017-06-07 21:34:55)>>>>>> blood pressure diastolic, mmHg, 600950\n",
+ "(2017-06-07 21:35:30)<<<<<< DONE (35.0s)\n",
+ "(2017-06-07 21:35:30)>>>>>> blood pressure diastolic, cc/min, 13301\n",
+ "(2017-06-07 21:35:30)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:35:30)<<<< DONE (35.0s)\n",
+ "(2017-06-07 21:35:30)>>>> Drop Small columns, threshold = 50 | (578689, 14)\n",
+ "(2017-06-07 21:35:30)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:35:30)>>>> Combine like columns (578689, 14)\n",
+ "(2017-06-07 21:35:30)>>>>>> (u'blood pressure diastolic', 'known', u'qn', u'mmHg')\n",
+ "(2017-06-07 21:35:31)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:35:31)>>>>>> (u'blood pressure diastolic', 'unknown', u'qn', u'cc/min')\n",
+ "(2017-06-07 21:35:39)<<<<<< DONE (8.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " <th>mean</th>\n",
+ " <td>60.228537</td>\n",
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+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>14.861842</td>\n",
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+ " <th>75%</th>\n",
+ " <td>69.000000</td>\n",
+ " <td>43.000000</td>\n",
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+ " <tr>\n",
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+ " <td>647.000000</td>\n",
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+ " </tbody>\n",
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+ ],
+ "text/plain": [
+ "component blood pressure diastolic \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units mmHg cc/min\n",
+ "description all all\n",
+ "count 565812.000000 13012.000000\n",
+ "mean 60.228537 37.783354\n",
+ "std 14.861842 10.494939\n",
+ "min 0.000000 0.000000\n",
+ "25% 50.000000 32.000000\n",
+ "50% 59.000000 37.000000\n",
+ "75% 69.000000 43.000000\n",
+ "max 448.000000 647.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((578689, 15), (578670, 2), 35427L, 0, '0.0% records')\n",
+ "(2017-06-07 21:35:39)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:35:39)>>>> Join (578670, 2) to (792967, 18)\n",
+ "(2017-06-07 21:35:48)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:35:48)<< DONE (56.0s)\n",
+ "(2017-06-07 21:35:48)>> HEART RATE\n",
+ "(2017-06-07 21:35:48)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"3\" halign=\"left\">known</th>\n",
+ " <th colspan=\"3\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"3\" halign=\"left\">beats/min</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>211(BPM)(beat/min)</th>\n",
+ " <th>211(bpm)(beat/min)</th>\n",
+ " <th>220045(bpm)(beat/min)</th>\n",
+ " <th>1332</th>\n",
+ " <th>1341</th>\n",
+ " <th>1725</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>342633.000000</td>\n",
+ " <td>153000.000000</td>\n",
+ " <td>264113.000000</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>85.993935</td>\n",
+ " <td>154.783721</td>\n",
+ " <td>90.048595</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>17.964728</td>\n",
+ " <td>15.873190</td>\n",
+ " <td>170.147868</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>73.000000</td>\n",
+ " <td>144.000000</td>\n",
+ " <td>75.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>84.000000</td>\n",
+ " <td>156.000000</td>\n",
+ " <td>87.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>97.000000</td>\n",
+ " <td>166.000000</td>\n",
+ " <td>103.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>300.000000</td>\n",
+ " <td>218.000000</td>\n",
+ " <td>86101.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units beats/min \n",
+ "description 211(BPM)(beat/min) 211(bpm)(beat/min) 220045(bpm)(beat/min) \n",
+ "count 342633.000000 153000.000000 264113.000000 \n",
+ "mean 85.993935 154.783721 90.048595 \n",
+ "std 17.964728 15.873190 170.147868 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 73.000000 144.000000 75.000000 \n",
+ "50% 84.000000 156.000000 87.000000 \n",
+ "75% 97.000000 166.000000 103.000000 \n",
+ "max 300.000000 218.000000 86101.000000 \n",
+ "\n",
+ "component \n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 1332 1341 1725 \n",
+ "count 0.0 0.0 0.0 \n",
+ "mean NaN NaN NaN \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN NaN \n",
+ "25% NaN NaN NaN \n",
+ "50% NaN NaN NaN \n",
+ "75% NaN NaN NaN \n",
+ "max NaN NaN NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:35:49)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:35:49)>>>> Clean (759724, 6)\n",
+ "(2017-06-07 21:35:49)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:35:49)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:35:49)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:35:49)>>>> Drop Small columns, threshold = 5 | (759724, 6)\n",
+ "(2017-06-07 21:35:49)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:35:49)>>>> Drop OOB data | (759724, 3)\n",
+ "(2017-06-07 21:35:50)>>>>>> heart rate, beats/min, 759746\n",
+ "(2017-06-07 21:36:06)<<<<<< DONE (16.0s)\n",
+ "(2017-06-07 21:36:06)<<<< DONE (17.0s)\n",
+ "(2017-06-07 21:36:06)>>>> Drop Small columns, threshold = 50 | (759724, 3)\n",
+ "(2017-06-07 21:36:06)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:06)>>>> Combine like columns (759724, 3)\n",
+ "(2017-06-07 21:36:06)>>>>>> (u'heart rate', 'known', u'qn', u'beats/min')\n",
+ "(2017-06-07 21:36:10)<<<<<< DONE (4.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th>heart rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
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+ " <th>beats/min</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
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+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>759721.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>101.127339</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>32.758477</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>77.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>92.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>120.000000</td>\n",
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+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>300.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component heart rate\n",
+ "status known\n",
+ "variable_type qn\n",
+ "units beats/min\n",
+ "description all\n",
+ "count 759721.000000\n",
+ "mean 101.127339\n",
+ "std 32.758477\n",
+ "min 0.000000\n",
+ "25% 77.000000\n",
+ "50% 92.000000\n",
+ "75% 120.000000\n",
+ "max 300.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((759724, 6), (759721, 1), 25L, 0, '0.0% records')\n",
+ "(2017-06-07 21:36:10)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:36:10)>>>> Join (759721, 1) to (793006, 20)\n",
+ "(2017-06-07 21:36:19)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:36:19)<< DONE (31.0s)\n",
+ "(2017-06-07 21:36:19)>> OUTPUT URINE\n",
+ "(2017-06-07 21:36:19)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
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+ " <td>6175.000000</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " </tr>\n",
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+ " <th>std</th>\n",
+ " <td>121.702750</td>\n",
+ " <td>201.128231</td>\n",
+ " <td>122.603773</td>\n",
+ " <td>198.742273</td>\n",
+ " <td>78.220562</td>\n",
+ " <td>NaN</td>\n",
+ " <td>371.23106</td>\n",
+ " <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",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>0.000000</td>\n",
+ " <td>-1500.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
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+ " <td>100.00000</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",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>40.000000</td>\n",
+ " <td>200.000000</td>\n",
+ " <td>40.000000</td>\n",
+ " <td>150.000000</td>\n",
+ " <td>50.000000</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",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>150.000000</td>\n",
+ " <td>400.000000</td>\n",
+ " <td>140.000000</td>\n",
+ " <td>400.000000</td>\n",
+ " <td>130.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>493.75000</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",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>4000.000000</td>\n",
+ " <td>1800.000000</td>\n",
+ " <td>3105.000000</td>\n",
+ " <td>3350.000000</td>\n",
+ " <td>850.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>625.00000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>8 rows × 51 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) \n",
+ "count 114944.000000 6175.000000 185482.000000 6459.000000 \n",
+ "mean 117.290454 313.692955 113.807256 283.836352 \n",
+ "std 121.702750 201.128231 122.603773 198.742273 \n",
+ "min 0.000000 -1500.000000 0.000000 0.000000 \n",
+ "25% 40.000000 200.000000 40.000000 150.000000 \n",
+ "50% 80.000000 300.000000 80.000000 250.000000 \n",
+ "75% 150.000000 400.000000 140.000000 400.000000 \n",
+ "max 4000.000000 1800.000000 3105.000000 3350.000000 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 40405(ml) 41857(ml) 42042(ml) 42592(ml) 42666(ml) 42810(ml) \n",
+ "count 855.000000 0.0 2.00000 0.0 0.0 0.0 \n",
+ "mean 104.824561 NaN 362.50000 NaN NaN NaN \n",
+ "std 78.220562 NaN 371.23106 NaN NaN NaN \n",
+ "min 0.000000 NaN 100.00000 NaN NaN NaN \n",
+ "25% 50.000000 NaN 231.25000 NaN NaN NaN \n",
+ "50% 90.000000 NaN 362.50000 NaN NaN NaN \n",
+ "75% 130.000000 NaN 493.75000 NaN NaN NaN \n",
+ "max 850.000000 NaN 625.00000 NaN NaN NaN \n",
+ "\n",
+ "component ... \\\n",
+ "status ... unknown \n",
+ "variable_type ... qn \n",
+ "units ... no_units \n",
+ "description ... 44253 44325 44684 44706 44824 45415 45991 46180 46578 \n",
+ "count ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "mean ... 0.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "std ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "min ... 0.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "25% ... 0.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "50% ... 0.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "75% ... 0.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "max ... 0.0 NaN NaN NaN NaN NaN NaN NaN NaN \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 46658 \n",
+ "count 0.0 \n",
+ "mean NaN \n",
+ "std NaN \n",
+ "min NaN \n",
+ "25% NaN \n",
+ "50% NaN \n",
+ "75% NaN \n",
+ "max NaN \n",
+ "\n",
+ "[8 rows x 51 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:36:25)<<<< DONE (6.0s)\n",
+ "(2017-06-07 21:36:25)>>>> Clean (350906, 54)\n",
+ "(2017-06-07 21:36:25)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:25)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:36:28)<<<< DONE (3.0s)\n",
+ "(2017-06-07 21:36:28)>>>> Drop Small columns, threshold = 5 | (350906, 54)\n",
+ "(2017-06-07 21:36:28)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:28)>>>> Drop OOB data | (350906, 10)\n",
+ "(2017-06-07 21:36:28)>>>>>> output urine, mL, 313915\n",
+ "(2017-06-07 21:36:40)<<<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:36:40)>>>>>> output urine, no_units, 1053076\n",
+ "(2017-06-07 21:36:40)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:40)<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:36:40)>>>> Drop Small columns, threshold = 50 | (350906, 10)\n",
+ "(2017-06-07 21:36:40)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:40)>>>> Combine like columns (350906, 10)\n",
+ "(2017-06-07 21:36:40)>>>>>> (u'output urine', 'known', u'qn', u'mL')\n",
+ "(2017-06-07 21:36:40)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:40)>>>>>> (u'output urine', 'unknown', 'nom', 'no_units')\n",
+ "(2017-06-07 21:36:46)<<<<<< DONE (6.0s)\n",
+ "(2017-06-07 21:36:46)>>>>>> (u'output urine', 'unknown', u'qn', 'no_units')\n",
+ "(2017-06-07 21:36:50)<<<<<< DONE (4.0s)\n"
+ ]
+ },
+ {
+ "data": {
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
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+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " <th colspan=\"4\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>qn</th>\n",
+ " <th colspan=\"3\" halign=\"left\">nom</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>mL</th>\n",
+ " <th colspan=\"3\" halign=\"left\">no_units</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>3686(ml)_No Void</th>\n",
+ " <th>3686(ml)_Voiding qs</th>\n",
+ " <th>3686_Voiding qs</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>313743.000000</td>\n",
+ " <td>350906.000000</td>\n",
+ " <td>350906.000000</td>\n",
+ " <td>350906.000000</td>\n",
+ " <td>355.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>122.412638</td>\n",
+ " <td>0.001200</td>\n",
+ " <td>0.103891</td>\n",
+ " <td>0.000180</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>131.133621</td>\n",
+ " <td>0.034617</td>\n",
+ " <td>0.305120</td>\n",
+ " <td>0.013398</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>40.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>80.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>150.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>4000.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component output urine \\\n",
+ "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",
+ "count 313743.000000 350906.000000 350906.000000 \n",
+ "mean 122.412638 0.001200 0.103891 \n",
+ "std 131.133621 0.034617 0.305120 \n",
+ "min 0.000000 0.000000 0.000000 \n",
+ "25% 40.000000 0.000000 0.000000 \n",
+ "50% 80.000000 0.000000 0.000000 \n",
+ "75% 150.000000 0.000000 0.000000 \n",
+ "max 4000.000000 1.000000 1.000000 \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type qn \n",
+ "units no_units \n",
+ "description 3686_Voiding qs all \n",
+ "count 350906.000000 355.0 \n",
+ "mean 0.000180 0.0 \n",
+ "std 0.013398 0.0 \n",
+ "min 0.000000 0.0 \n",
+ "25% 0.000000 0.0 \n",
+ "50% 0.000000 0.0 \n",
+ "75% 0.000000 0.0 \n",
+ "max 1.000000 0.0 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((350906, 53), (350906, 5), -1015657L, 0, '0.0% records')\n",
+ "(2017-06-07 21:36:50)<<<< DONE (10.0s)\n",
+ "(2017-06-07 21:36:50)>>>> Join (350906, 5) to (942361, 21)\n",
+ "(2017-06-07 21:36:58)<<<< DONE (8.0s)\n",
+ "(2017-06-07 21:36:58)<< DONE (39.0s)\n",
+ "(2017-06-07 21:36:58)>> LACTATED RINGERS\n",
+ "(2017-06-07 21:36:58)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
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+ " <thead>\n",
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+ " </tr>\n",
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+ " <th>status</th>\n",
+ " <th colspan=\"14\" halign=\"left\">known</th>\n",
+ " <th colspan=\"6\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
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+ " </tr>\n",
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+ " <th colspan=\"2\" halign=\"left\">mL/hr</th>\n",
+ " <th colspan=\"6\" halign=\"left\">no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>1634(ml)</th>\n",
+ " <th>225828</th>\n",
+ " <th>225828(ml)</th>\n",
+ " <th>2971(ml)</th>\n",
+ " <th>30021</th>\n",
+ " <th>30021(ml)</th>\n",
+ " <th>44184(ml)</th>\n",
+ " <th>44367(ml)</th>\n",
+ " <th>44521(ml)</th>\n",
+ " <th>44815(ml)</th>\n",
+ " <th>46207(ml)</th>\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",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
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+ " <td>NaN</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>100.003500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <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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+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>500.000000</td>\n",
+ " <td>357.782820</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>2000.000000</td>\n",
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+ " <td>9050.00000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>3113.564575</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component lactated ringers \\\n",
+ "status known \n",
+ "variable_type qn \n",
+ "units mL \n",
+ "description 1634(ml) 225828 225828(ml) 2971(ml) 30021 \n",
+ "count 0.0 58.000000 4985.000000 0.0 53.0 \n",
+ "mean NaN 666.379310 457.298098 NaN 0.0 \n",
+ "std NaN 306.277585 558.438082 NaN 0.0 \n",
+ "min NaN 200.000000 0.000000 NaN 0.0 \n",
+ "25% NaN 500.000000 100.003500 NaN 0.0 \n",
+ "50% NaN 500.000000 357.782820 NaN 0.0 \n",
+ "75% NaN 1000.000000 999.550425 NaN 0.0 \n",
+ "max NaN 2000.000000 28500.000000 NaN 0.0 \n",
+ "\n",
+ "component \\\n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 30021(ml) 44184(ml) 44367(ml) 44521(ml) 44815(ml) 46207(ml) \n",
+ "count 17887.00000 0.0 0.0 0.0 0.0 0.0 \n",
+ "mean 152.96111 NaN NaN NaN NaN NaN \n",
+ "std 241.34666 NaN NaN NaN NaN NaN \n",
+ "min 0.00000 NaN NaN NaN NaN NaN \n",
+ "25% 10.00000 NaN NaN NaN NaN NaN \n",
+ "50% 100.00000 NaN NaN NaN NaN NaN \n",
+ "75% 150.00000 NaN NaN NaN NaN NaN \n",
+ "max 9050.00000 NaN NaN NaN NaN NaN \n",
+ "\n",
+ "component \\\n",
+ "status unknown \n",
+ "variable_type qn \n",
+ "units mL/hr no_units \n",
+ "description 46781(ml) 225828(mL/hour) 30021(mL/hour) 30021 44184 44367 \n",
+ "count 0.0 186.000000 71.0 461.0 0.0 0.0 \n",
+ "mean NaN 390.569193 0.0 0.0 NaN NaN \n",
+ "std NaN 456.754619 0.0 0.0 NaN NaN \n",
+ "min NaN 2.502769 0.0 0.0 NaN NaN \n",
+ "25% NaN 80.475401 0.0 0.0 NaN NaN \n",
+ "50% NaN 199.976871 0.0 0.0 NaN NaN \n",
+ "75% NaN 582.863952 0.0 0.0 NaN NaN \n",
+ "max NaN 3113.564575 0.0 0.0 NaN NaN \n",
+ "\n",
+ "component \n",
+ "status \n",
+ "variable_type \n",
+ "units \n",
+ "description 44521 44815 46207 \n",
+ "count 0.0 0.0 0.0 \n",
+ "mean NaN NaN NaN \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN NaN \n",
+ "25% NaN NaN NaN \n",
+ "50% NaN NaN NaN \n",
+ "75% NaN NaN NaN \n",
+ "max NaN NaN NaN "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:36:58)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:58)>>>> Clean (23566, 20)\n",
+ "(2017-06-07 21:36:58)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:58)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:36:58)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:58)>>>> Drop Small columns, threshold = 5 | (23566, 20)\n",
+ "(2017-06-07 21:36:59)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:36:59)>>>> Drop OOB data | (23566, 7)\n",
+ "(2017-06-07 21:36:59)>>>>>> lactated ringers, mL, 22983\n",
+ "(2017-06-07 21:36:59)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:36:59)>>>>>> lactated ringers, mL/hr, 257\n",
+ "(2017-06-07 21:37:00)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:37:00)>>>>>> lactated ringers, no_units, 461\n",
+ "(2017-06-07 21:37:00)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:00)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:37:00)>>>> Drop Small columns, threshold = 50 | (23566, 7)\n",
+ "(2017-06-07 21:37:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:00)>>>> Combine like columns (23566, 7)\n",
+ "(2017-06-07 21:37:00)>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
+ "(2017-06-07 21:37:00)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:00)>>>>>> ('lactated ringers', 'known', 'qn', 'mL/hr')\n",
+ "(2017-06-07 21:37:00)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:00)>>>>>> ('lactated ringers', 'unknown', 'qn', 'no_units')\n",
+ "(2017-06-07 21:37:00)<<<<<< DONE (0.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"3\" halign=\"left\">lactated ringers</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
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+ " <td>257.000000</td>\n",
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+ " <th>mean</th>\n",
+ " <td>219.918896</td>\n",
+ " <td>282.668754</td>\n",
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+ " <td>359.912106</td>\n",
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+ " <td>99.999996</td>\n",
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+ " <th>75%</th>\n",
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+ ],
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+ "component lactated ringers \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units mL mL/hr no_units\n",
+ "description all all all\n",
+ "count 22978.000000 257.000000 461.0\n",
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+ "75% 248.401788 411.476382 0.0\n",
+ "max 28500.000000 3113.564575 0.0"
+ ]
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((23566, 20), (23566, 3), 5L, 0, '0.0% records')\n",
+ "(2017-06-07 21:37:00)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:00)>>>> Join (23566, 3) to (964641, 26)\n",
+ "(2017-06-07 21:37:06)<<<< DONE (6.0s)\n",
+ "(2017-06-07 21:37:06)<< DONE (8.0s)\n",
+ "(2017-06-07 21:37:06)>> RESPIRATORY RATE\n",
+ "(2017-06-07 21:37:06)>>>> Opening...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"4\" halign=\"left\">respiratory rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th colspan=\"2\" halign=\"left\">known</th>\n",
+ " <th colspan=\"2\" halign=\"left\">unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " <th colspan=\"2\" halign=\"left\">qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th colspan=\"2\" halign=\"left\">insp/min</th>\n",
+ " <th>Breath</th>\n",
+ " <th>no_units</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>220210</th>\n",
+ " <th>618(BPM)(breath/min)</th>\n",
+ " <th>3603</th>\n",
+ " <th>8113</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>262083.000000</td>\n",
+ " <td>332289.000000</td>\n",
+ " <td>150731.000000</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>20.097110</td>\n",
+ " <td>20.108883</td>\n",
+ " <td>50.193185</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>9.195059</td>\n",
+ " <td>6.406466</td>\n",
+ " <td>13.773678</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>16.000000</td>\n",
+ " <td>16.000000</td>\n",
+ " <td>40.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>19.000000</td>\n",
+ " <td>20.000000</td>\n",
+ " <td>49.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>23.000000</td>\n",
+ " <td>24.000000</td>\n",
+ " <td>60.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>2615.000000</td>\n",
+ " <td>123.000000</td>\n",
+ " <td>148.000000</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component respiratory rate \n",
+ "status known unknown \n",
+ "variable_type qn qn \n",
+ "units insp/min Breath no_units\n",
+ "description 220210 618(BPM)(breath/min) 3603 8113\n",
+ "count 262083.000000 332289.000000 150731.000000 0.0\n",
+ "mean 20.097110 20.108883 50.193185 NaN\n",
+ "std 9.195059 6.406466 13.773678 NaN\n",
+ "min 0.000000 0.000000 0.000000 NaN\n",
+ "25% 16.000000 16.000000 40.000000 NaN\n",
+ "50% 19.000000 20.000000 49.000000 NaN\n",
+ "75% 23.000000 24.000000 60.000000 NaN\n",
+ "max 2615.000000 123.000000 148.000000 NaN"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2017-06-07 21:37:08)<<<< DONE (2.0s)\n",
+ "(2017-06-07 21:37:08)>>>> Clean (745082, 5)\n",
+ "(2017-06-07 21:37:08)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:08)>>>> Nominal to OneHot\n",
+ "(2017-06-07 21:37:12)<<<< DONE (4.0s)\n",
+ "(2017-06-07 21:37:12)>>>> Drop Small columns, threshold = 5 | (745082, 5)\n",
+ "(2017-06-07 21:37:13)<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:37:13)>>>> Drop OOB data | (745082, 3)\n",
+ "(2017-06-07 21:37:13)>>>>>> respiratory rate, insp/min, 594372\n",
+ "(2017-06-07 21:37:25)<<<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:37:25)>>>>>> respiratory rate, Breath, 150731\n",
+ "(2017-06-07 21:37:25)<<<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:25)<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:37:25)>>>> Drop Small columns, threshold = 50 | (745082, 3)\n",
+ "(2017-06-07 21:37:25)<<<< DONE (0.0s)\n",
+ "(2017-06-07 21:37:25)>>>> Combine like columns (745082, 3)\n",
+ "(2017-06-07 21:37:25)>>>>>> (u'respiratory rate', 'known', u'qn', u'insp/min')\n",
+ "(2017-06-07 21:37:26)<<<<<< DONE (1.0s)\n",
+ "(2017-06-07 21:37:26)>>>>>> (u'respiratory rate', 'unknown', u'qn', u'Breath')\n",
+ "(2017-06-07 21:37:37)<<<<<< DONE (11.0s)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>component</th>\n",
+ " <th colspan=\"2\" halign=\"left\">respiratory rate</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>status</th>\n",
+ " <th>known</th>\n",
+ " <th>unknown</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable_type</th>\n",
+ " <th>qn</th>\n",
+ " <th>qn</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>units</th>\n",
+ " <th>insp/min</th>\n",
+ " <th>Breath</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>description</th>\n",
+ " <th>all</th>\n",
+ " <th>all</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>594344.000000</td>\n",
+ " <td>150731.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>20.093735</td>\n",
+ " <td>50.193185</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>6.210768</td>\n",
+ " <td>13.773678</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>16.000000</td>\n",
+ " <td>40.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>20.000000</td>\n",
+ " <td>49.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>24.000000</td>\n",
+ " <td>60.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>126.000000</td>\n",
+ " <td>148.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "component respiratory rate \n",
+ "status known unknown\n",
+ "variable_type qn qn\n",
+ "units insp/min Breath\n",
+ "description all all\n",
+ "count 594344.000000 150731.000000\n",
+ "mean 20.093735 50.193185\n",
+ "std 6.210768 13.773678\n",
+ "min 0.000000 0.000000\n",
+ "25% 16.000000 40.000000\n",
+ "50% 20.000000 49.000000\n",
+ "75% 24.000000 60.000000\n",
+ "max 126.000000 148.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "((745082, 4), (745075, 2), 28L, 0, '0.0% records')\n",
+ "(2017-06-07 21:37:37)<<<< DONE (12.0s)\n",
+ "(2017-06-07 21:37:37)>>>> Join (745075, 2) to (969222, 29)\n",
+ "(2017-06-07 21:37:46)<<<< DONE (9.0s)\n",
+ "(2017-06-07 21:37:46)<< DONE (40.0s)\n",
+ "(2017-06-07 21:37:46) DONE (320.0s)\n",
+ "(2017-06-07 21:37:46) Extract lactate label\n",
+ "(2017-06-07 21:38:01) DONE (15.0s)\n",
+ "(2017-06-07 21:38:01) Segment df (699465, 31)\n",
+ "(2017-06-07 21:38:01)>> Get Segments\n",
+ "(2017-06-07 21:38:03)<< DONE (2.0s)\n",
+ "(2017-06-07 21:38:03)>> Apply Segments\n",
+ "(2017-06-07 21:38:09)<< DONE (6.0s)\n",
+ "(2017-06-07 21:38:09) DONE (8.0s)\n",
+ "(2017-06-07 21:38:09) Start Feature Union on DF (699465, 31)\n",
+ "(2017-06-07 21:38:09)>> MEAN\n",
+ "(2017-06-07 21:38:09)<< DONE (0.0s)\n",
+ "(2017-06-07 21:38:09)>> STD\n",
+ "(2017-06-07 21:38:09)<< DONE (0.0s)\n",
+ "(2017-06-07 21:38:09)>> COUNT\n",
+ "(2017-06-07 21:38:25)<< DONE (16.0s)\n",
+ "(2017-06-07 21:38:25)>> LAST\n",
+ "(2017-06-07 21:39:20)<< DONE (55.0s)\n",
+ "(2017-06-07 21:39:20)>> SUM\n",
+ "(2017-06-07 21:39:20)<< DONE (0.0s)\n",
+ "(2017-06-07 21:39:20)>> HStack\n",
+ "(2017-06-07 21:39:20)<< DONE (0.0s)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#small data set for now\n",
+ "train_ids = train_ids_small\n",
+ "test_ids = test_ids_small\n",
+ "\n",
+ "train_ft,train_lbl = lin_reg_features_and_labels(train_ids,hdf5_fname,feature_components,label_components,cleaners,feature_tuples)\n",
+ "oh = lin_reg_features_and_labels(test_ids,hdf5_fname,feature_components,label_components,cleaners,feature_tuples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train: (6616, 119) (6616L,)\n",
+ "Test: (3344, 119) (3344L,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print \"Train:\",train_ft.shape, train_lbl.shape\n",
+ "print \"Test:\",test_ft.shape, test_lbl.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "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>0</th>\n",
+ " <th>1</th>\n",
+ " <th>2</th>\n",
+ " <th>3</th>\n",
+ " <th>4</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>glasgow coma scale eye opening_known_ord_no_units_all_MEAN</th>\n",
+ " <td>2.981695</td>\n",
+ " <td>2.981695</td>\n",
+ " <td>2.981695</td>\n",
+ " <td>2.981695</td>\n",
+ " <td>2.981695</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>glasgow coma scale motor_known_ord_no_units_all_MEAN</th>\n",
+ " <td>4.896178</td>\n",
+ " <td>4.896178</td>\n",
+ " <td>4.896178</td>\n",
+ " <td>4.896178</td>\n",
+ " <td>4.896178</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure systolic_known_qn_mmHg_all_MEAN</th>\n",
+ " <td>118.585375</td>\n",
+ " <td>135.551724</td>\n",
+ " <td>121.176471</td>\n",
+ " <td>118.585375</td>\n",
+ " <td>138.829268</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure systolic_unknown_qn_cc/min_all_MEAN</th>\n",
+ " <td>67.213383</td>\n",
+ " <td>67.213383</td>\n",
+ " <td>67.213383</td>\n",
+ " <td>67.213383</td>\n",
+ " <td>67.213383</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>oxygen saturation pulse oximetry_known_qn_percent_all_MEAN</th>\n",
+ " <td>97.086505</td>\n",
+ " <td>94.655172</td>\n",
+ " <td>98.529412</td>\n",
+ " <td>97.086505</td>\n",
+ " <td>94.936170</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>glasgow coma scale verbal_known_ord_no_units_all_MEAN</th>\n",
+ " <td>2.822271</td>\n",
+ " <td>2.822271</td>\n",
+ " <td>2.822271</td>\n",
+ " <td>2.822271</td>\n",
+ " <td>2.822271</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hemoglobin_known_qn_g/dL_all_MEAN</th>\n",
+ " <td>13.000000</td>\n",
+ " <td>11.725000</td>\n",
+ " <td>10.912500</td>\n",
+ " <td>16.000000</td>\n",
+ " <td>8.933333</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactate_known_qn_mmol/L_all_MEAN</th>\n",
+ " <td>2.487958</td>\n",
+ " <td>1.100000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.487958</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure mean_known_qn_mmHg_all_MEAN</th>\n",
+ " <td>78.098120</td>\n",
+ " <td>87.103448</td>\n",
+ " <td>76.117647</td>\n",
+ " <td>78.098120</td>\n",
+ " <td>85.428571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vasopressin_known_qn_units_all_MEAN</th>\n",
+ " <td>2.438013</td>\n",
+ " <td>2.438013</td>\n",
+ " <td>2.438013</td>\n",
+ " <td>2.438013</td>\n",
+ " <td>2.438013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vasopressin_known_qn_units/min_all_MEAN</th>\n",
+ " <td>0.662927</td>\n",
+ " <td>0.662927</td>\n",
+ " <td>0.662927</td>\n",
+ " <td>0.662927</td>\n",
+ " <td>0.662927</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight body_known_qn_kg_all_MEAN</th>\n",
+ " <td>83.453349</td>\n",
+ " <td>83.453349</td>\n",
+ " <td>83.453349</td>\n",
+ " <td>83.453349</td>\n",
+ " <td>83.453349</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normal saline_known_qn_mL_all_MEAN</th>\n",
+ " <td>616.280061</td>\n",
+ " <td>616.280061</td>\n",
+ " <td>20.000000</td>\n",
+ " <td>616.280061</td>\n",
+ " <td>616.280061</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normal saline_known_qn_mL/hr_all_MEAN</th>\n",
+ " <td>88.819017</td>\n",
+ " <td>88.819017</td>\n",
+ " <td>4.521185</td>\n",
+ " <td>88.819017</td>\n",
+ " <td>300.265113</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg_all_MEAN</th>\n",
+ " <td>442.367701</td>\n",
+ " <td>442.367701</td>\n",
+ " <td>442.367701</td>\n",
+ " <td>442.367701</td>\n",
+ " <td>442.367701</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg/kg/min_all_MEAN</th>\n",
+ " <td>0.151286</td>\n",
+ " <td>0.151286</td>\n",
+ " <td>0.151286</td>\n",
+ " <td>0.151286</td>\n",
+ " <td>0.151286</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg/min_all_MEAN</th>\n",
+ " <td>11.641748</td>\n",
+ " <td>11.641748</td>\n",
+ " <td>11.641748</td>\n",
+ " <td>11.641748</td>\n",
+ " <td>11.641748</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>temperature body_known_qn_degF_all_MEAN</th>\n",
+ " <td>98.122104</td>\n",
+ " <td>95.920000</td>\n",
+ " <td>97.604706</td>\n",
+ " <td>98.122104</td>\n",
+ " <td>99.572000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure diastolic_known_qn_mmHg_all_MEAN</th>\n",
+ " <td>61.010500</td>\n",
+ " <td>72.206897</td>\n",
+ " <td>57.823529</td>\n",
+ " <td>61.010500</td>\n",
+ " <td>69.951220</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure diastolic_unknown_qn_cc/min_all_MEAN</th>\n",
+ " <td>41.533994</td>\n",
+ " <td>41.533994</td>\n",
+ " <td>41.533994</td>\n",
+ " <td>41.533994</td>\n",
+ " <td>41.533994</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>heart rate_known_qn_beats/min_all_MEAN</th>\n",
+ " <td>88.502874</td>\n",
+ " <td>62.655172</td>\n",
+ " <td>75.764706</td>\n",
+ " <td>88.502874</td>\n",
+ " <td>83.717391</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>output urine_known_qn_mL_all_MEAN</th>\n",
+ " <td>150.213438</td>\n",
+ " <td>487.500000</td>\n",
+ " <td>94.444444</td>\n",
+ " <td>150.213438</td>\n",
+ " <td>287.857143</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>output urine_unknown_qn_no_units_all_MEAN</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_known_qn_mL_all_MEAN</th>\n",
+ " <td>440.510867</td>\n",
+ " <td>440.510867</td>\n",
+ " <td>800.000000</td>\n",
+ " <td>440.510867</td>\n",
+ " <td>440.510867</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_known_qn_mL/hr_all_MEAN</th>\n",
+ " <td>207.151224</td>\n",
+ " <td>207.151224</td>\n",
+ " <td>207.151224</td>\n",
+ " <td>207.151224</td>\n",
+ " <td>207.151224</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_unknown_qn_no_units_all_MEAN</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>respiratory rate_known_qn_insp/min_all_MEAN</th>\n",
+ " <td>19.336424</td>\n",
+ " <td>15.068966</td>\n",
+ " <td>16.562500</td>\n",
+ " <td>19.336424</td>\n",
+ " <td>19.446809</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>respiratory rate_unknown_qn_Breath_all_MEAN</th>\n",
+ " <td>46.714318</td>\n",
+ " <td>46.714318</td>\n",
+ " <td>46.714318</td>\n",
+ " <td>46.714318</td>\n",
+ " <td>46.714318</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>glasgow coma scale eye opening_known_ord_no_units_all_STD</th>\n",
+ " <td>0.564250</td>\n",
+ " <td>0.564250</td>\n",
+ " <td>0.564250</td>\n",
+ " <td>0.564250</td>\n",
+ " <td>0.564250</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>glasgow coma scale motor_known_ord_no_units_all_STD</th>\n",
+ " <td>0.761486</td>\n",
+ " <td>0.761486</td>\n",
+ " <td>0.761486</td>\n",
+ " <td>0.761486</td>\n",
+ " <td>0.761486</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure systolic_known_qn_mmHg_all_LAST</th>\n",
+ " <td>119.025309</td>\n",
+ " <td>144.000000</td>\n",
+ " <td>107.000000</td>\n",
+ " <td>119.025309</td>\n",
+ " <td>136.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure systolic_unknown_qn_cc/min_all_LAST</th>\n",
+ " <td>70.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " <td>70.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>oxygen saturation pulse oximetry_known_qn_percent_all_LAST</th>\n",
+ " <td>96.865129</td>\n",
+ " <td>95.000000</td>\n",
+ " <td>94.000000</td>\n",
+ " <td>96.865129</td>\n",
+ " <td>97.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>glasgow coma scale verbal_known_ord_no_units_all_LAST</th>\n",
+ " <td>2.827318</td>\n",
+ " <td>2.827318</td>\n",
+ " <td>2.827318</td>\n",
+ " <td>2.827318</td>\n",
+ " <td>2.827318</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>hemoglobin_known_qn_g/dL_all_LAST</th>\n",
+ " <td>13.000000</td>\n",
+ " <td>13.000000</td>\n",
+ " <td>11.100000</td>\n",
+ " <td>15.600000</td>\n",
+ " <td>8.800000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactate_known_qn_mmol/L_all_LAST</th>\n",
+ " <td>2.424450</td>\n",
+ " <td>1.100000</td>\n",
+ " <td>2.600000</td>\n",
+ " <td>2.424450</td>\n",
+ " <td>2.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure mean_known_qn_mmHg_all_LAST</th>\n",
+ " <td>78.002572</td>\n",
+ " <td>90.000000</td>\n",
+ " <td>69.000000</td>\n",
+ " <td>78.002572</td>\n",
+ " <td>81.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vasopressin_known_qn_units_all_LAST</th>\n",
+ " <td>1.944078</td>\n",
+ " <td>1.944078</td>\n",
+ " <td>1.944078</td>\n",
+ " <td>1.944078</td>\n",
+ " <td>1.944078</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>vasopressin_known_qn_units/min_all_LAST</th>\n",
+ " <td>0.580697</td>\n",
+ " <td>0.580697</td>\n",
+ " <td>0.580697</td>\n",
+ " <td>0.580697</td>\n",
+ " <td>0.580697</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>weight body_known_qn_kg_all_LAST</th>\n",
+ " <td>83.437229</td>\n",
+ " <td>83.437229</td>\n",
+ " <td>83.437229</td>\n",
+ " <td>83.437229</td>\n",
+ " <td>83.437229</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normal saline_known_qn_mL_all_LAST</th>\n",
+ " <td>608.646326</td>\n",
+ " <td>608.646326</td>\n",
+ " <td>20.000000</td>\n",
+ " <td>608.646326</td>\n",
+ " <td>608.646326</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normal saline_known_qn_mL/hr_all_LAST</th>\n",
+ " <td>102.570817</td>\n",
+ " <td>102.570817</td>\n",
+ " <td>2.998982</td>\n",
+ " <td>102.570817</td>\n",
+ " <td>1000.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg_all_LAST</th>\n",
+ " <td>404.510889</td>\n",
+ " <td>404.510889</td>\n",
+ " <td>404.510889</td>\n",
+ " <td>404.510889</td>\n",
+ " <td>404.510889</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg/kg/min_all_LAST</th>\n",
+ " <td>0.126223</td>\n",
+ " <td>0.126223</td>\n",
+ " <td>0.126223</td>\n",
+ " <td>0.126223</td>\n",
+ " <td>0.126223</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg/min_all_LAST</th>\n",
+ " <td>8.396190</td>\n",
+ " <td>8.396190</td>\n",
+ " <td>8.396190</td>\n",
+ " <td>8.396190</td>\n",
+ " <td>8.396190</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>temperature body_known_qn_degF_all_LAST</th>\n",
+ " <td>98.237947</td>\n",
+ " <td>96.400000</td>\n",
+ " <td>98.240000</td>\n",
+ " <td>98.237947</td>\n",
+ " <td>99.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure diastolic_known_qn_mmHg_all_LAST</th>\n",
+ " <td>60.584437</td>\n",
+ " <td>71.000000</td>\n",
+ " <td>52.000000</td>\n",
+ " <td>60.584437</td>\n",
+ " <td>67.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>blood pressure diastolic_unknown_qn_cc/min_all_LAST</th>\n",
+ " <td>43.285714</td>\n",
+ " <td>43.285714</td>\n",
+ " <td>43.285714</td>\n",
+ " <td>43.285714</td>\n",
+ " <td>43.285714</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>heart rate_known_qn_beats/min_all_LAST</th>\n",
+ " <td>88.100059</td>\n",
+ " <td>72.000000</td>\n",
+ " <td>85.000000</td>\n",
+ " <td>88.100059</td>\n",
+ " <td>83.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>output urine_known_qn_mL_all_LAST</th>\n",
+ " <td>134.812808</td>\n",
+ " <td>300.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>134.812808</td>\n",
+ " <td>150.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>output urine_unknown_qn_no_units_all_LAST</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_known_qn_mL_all_LAST</th>\n",
+ " <td>421.071475</td>\n",
+ " <td>421.071475</td>\n",
+ " <td>1000.000000</td>\n",
+ " <td>421.071475</td>\n",
+ " <td>421.071475</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_known_qn_mL/hr_all_LAST</th>\n",
+ " <td>269.133890</td>\n",
+ " <td>269.133890</td>\n",
+ " <td>269.133890</td>\n",
+ " <td>269.133890</td>\n",
+ " <td>269.133890</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_unknown_qn_no_units_all_LAST</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>respiratory rate_known_qn_insp/min_all_LAST</th>\n",
+ " <td>19.578249</td>\n",
+ " <td>20.000000</td>\n",
+ " <td>21.000000</td>\n",
+ " <td>19.578249</td>\n",
+ " <td>27.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>respiratory rate_unknown_qn_Breath_all_LAST</th>\n",
+ " <td>45.428571</td>\n",
+ " <td>45.428571</td>\n",
+ " <td>45.428571</td>\n",
+ " <td>45.428571</td>\n",
+ " <td>45.428571</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normal saline_known_qn_mL_all_SUM</th>\n",
+ " <td>2242.123639</td>\n",
+ " <td>2242.123639</td>\n",
+ " <td>60.000000</td>\n",
+ " <td>2242.123639</td>\n",
+ " <td>2242.123639</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>norepinephrine_known_qn_mcg_all_SUM</th>\n",
+ " <td>13543.870106</td>\n",
+ " <td>13543.870106</td>\n",
+ " <td>13543.870106</td>\n",
+ " <td>13543.870106</td>\n",
+ " <td>13543.870106</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>output urine_known_qn_mL_all_SUM</th>\n",
+ " <td>4157.734811</td>\n",
+ " <td>1950.000000</td>\n",
+ " <td>850.000000</td>\n",
+ " <td>4157.734811</td>\n",
+ " <td>4030.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lactated ringers_known_qn_mL_all_SUM</th>\n",
+ " <td>2747.836982</td>\n",
+ " <td>2747.836982</td>\n",
+ " <td>2400.000000</td>\n",
+ " <td>2747.836982</td>\n",
+ " <td>2747.836982</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>119 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 0 \\\n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 2.981695 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 4.896178 \n",
+ "blood pressure systolic_known_qn_mmHg_all_MEAN 118.585375 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_MEAN 67.213383 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 97.086505 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.822271 \n",
+ "hemoglobin_known_qn_g/dL_all_MEAN 13.000000 \n",
+ "lactate_known_qn_mmol/L_all_MEAN 2.487958 \n",
+ "blood pressure mean_known_qn_mmHg_all_MEAN 78.098120 \n",
+ "vasopressin_known_qn_units_all_MEAN 2.438013 \n",
+ "vasopressin_known_qn_units/min_all_MEAN 0.662927 \n",
+ "weight body_known_qn_kg_all_MEAN 83.453349 \n",
+ "normal saline_known_qn_mL_all_MEAN 616.280061 \n",
+ "normal saline_known_qn_mL/hr_all_MEAN 88.819017 \n",
+ "norepinephrine_known_qn_mcg_all_MEAN 442.367701 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_MEAN 0.151286 \n",
+ "norepinephrine_known_qn_mcg/min_all_MEAN 11.641748 \n",
+ "temperature body_known_qn_degF_all_MEAN 98.122104 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_MEAN 61.010500 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 41.533994 \n",
+ "heart rate_known_qn_beats/min_all_MEAN 88.502874 \n",
+ "output urine_known_qn_mL_all_MEAN 150.213438 \n",
+ "output urine_unknown_qn_no_units_all_MEAN 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_MEAN 440.510867 \n",
+ "lactated ringers_known_qn_mL/hr_all_MEAN 207.151224 \n",
+ "lactated ringers_unknown_qn_no_units_all_MEAN 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_MEAN 19.336424 \n",
+ "respiratory rate_unknown_qn_Breath_all_MEAN 46.714318 \n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 0.564250 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 0.761486 \n",
+ "... ... \n",
+ "blood pressure systolic_known_qn_mmHg_all_LAST 119.025309 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_LAST 70.000000 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 96.865129 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.827318 \n",
+ "hemoglobin_known_qn_g/dL_all_LAST 13.000000 \n",
+ "lactate_known_qn_mmol/L_all_LAST 2.424450 \n",
+ "blood pressure mean_known_qn_mmHg_all_LAST 78.002572 \n",
+ "vasopressin_known_qn_units_all_LAST 1.944078 \n",
+ "vasopressin_known_qn_units/min_all_LAST 0.580697 \n",
+ "weight body_known_qn_kg_all_LAST 83.437229 \n",
+ "normal saline_known_qn_mL_all_LAST 608.646326 \n",
+ "normal saline_known_qn_mL/hr_all_LAST 102.570817 \n",
+ "norepinephrine_known_qn_mcg_all_LAST 404.510889 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_LAST 0.126223 \n",
+ "norepinephrine_known_qn_mcg/min_all_LAST 8.396190 \n",
+ "temperature body_known_qn_degF_all_LAST 98.237947 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_LAST 60.584437 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 43.285714 \n",
+ "heart rate_known_qn_beats/min_all_LAST 88.100059 \n",
+ "output urine_known_qn_mL_all_LAST 134.812808 \n",
+ "output urine_unknown_qn_no_units_all_LAST 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_LAST 421.071475 \n",
+ "lactated ringers_known_qn_mL/hr_all_LAST 269.133890 \n",
+ "lactated ringers_unknown_qn_no_units_all_LAST 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_LAST 19.578249 \n",
+ "respiratory rate_unknown_qn_Breath_all_LAST 45.428571 \n",
+ "normal saline_known_qn_mL_all_SUM 2242.123639 \n",
+ "norepinephrine_known_qn_mcg_all_SUM 13543.870106 \n",
+ "output urine_known_qn_mL_all_SUM 4157.734811 \n",
+ "lactated ringers_known_qn_mL_all_SUM 2747.836982 \n",
+ "\n",
+ " 1 \\\n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 2.981695 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 4.896178 \n",
+ "blood pressure systolic_known_qn_mmHg_all_MEAN 135.551724 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_MEAN 67.213383 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 94.655172 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.822271 \n",
+ "hemoglobin_known_qn_g/dL_all_MEAN 11.725000 \n",
+ "lactate_known_qn_mmol/L_all_MEAN 1.100000 \n",
+ "blood pressure mean_known_qn_mmHg_all_MEAN 87.103448 \n",
+ "vasopressin_known_qn_units_all_MEAN 2.438013 \n",
+ "vasopressin_known_qn_units/min_all_MEAN 0.662927 \n",
+ "weight body_known_qn_kg_all_MEAN 83.453349 \n",
+ "normal saline_known_qn_mL_all_MEAN 616.280061 \n",
+ "normal saline_known_qn_mL/hr_all_MEAN 88.819017 \n",
+ "norepinephrine_known_qn_mcg_all_MEAN 442.367701 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_MEAN 0.151286 \n",
+ "norepinephrine_known_qn_mcg/min_all_MEAN 11.641748 \n",
+ "temperature body_known_qn_degF_all_MEAN 95.920000 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_MEAN 72.206897 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 41.533994 \n",
+ "heart rate_known_qn_beats/min_all_MEAN 62.655172 \n",
+ "output urine_known_qn_mL_all_MEAN 487.500000 \n",
+ "output urine_unknown_qn_no_units_all_MEAN 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_MEAN 440.510867 \n",
+ "lactated ringers_known_qn_mL/hr_all_MEAN 207.151224 \n",
+ "lactated ringers_unknown_qn_no_units_all_MEAN 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_MEAN 15.068966 \n",
+ "respiratory rate_unknown_qn_Breath_all_MEAN 46.714318 \n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 0.564250 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 0.761486 \n",
+ "... ... \n",
+ "blood pressure systolic_known_qn_mmHg_all_LAST 144.000000 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_LAST 70.000000 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 95.000000 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.827318 \n",
+ "hemoglobin_known_qn_g/dL_all_LAST 13.000000 \n",
+ "lactate_known_qn_mmol/L_all_LAST 1.100000 \n",
+ "blood pressure mean_known_qn_mmHg_all_LAST 90.000000 \n",
+ "vasopressin_known_qn_units_all_LAST 1.944078 \n",
+ "vasopressin_known_qn_units/min_all_LAST 0.580697 \n",
+ "weight body_known_qn_kg_all_LAST 83.437229 \n",
+ "normal saline_known_qn_mL_all_LAST 608.646326 \n",
+ "normal saline_known_qn_mL/hr_all_LAST 102.570817 \n",
+ "norepinephrine_known_qn_mcg_all_LAST 404.510889 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_LAST 0.126223 \n",
+ "norepinephrine_known_qn_mcg/min_all_LAST 8.396190 \n",
+ "temperature body_known_qn_degF_all_LAST 96.400000 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_LAST 71.000000 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 43.285714 \n",
+ "heart rate_known_qn_beats/min_all_LAST 72.000000 \n",
+ "output urine_known_qn_mL_all_LAST 300.000000 \n",
+ "output urine_unknown_qn_no_units_all_LAST 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_LAST 421.071475 \n",
+ "lactated ringers_known_qn_mL/hr_all_LAST 269.133890 \n",
+ "lactated ringers_unknown_qn_no_units_all_LAST 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_LAST 20.000000 \n",
+ "respiratory rate_unknown_qn_Breath_all_LAST 45.428571 \n",
+ "normal saline_known_qn_mL_all_SUM 2242.123639 \n",
+ "norepinephrine_known_qn_mcg_all_SUM 13543.870106 \n",
+ "output urine_known_qn_mL_all_SUM 1950.000000 \n",
+ "lactated ringers_known_qn_mL_all_SUM 2747.836982 \n",
+ "\n",
+ " 2 \\\n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 2.981695 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 4.896178 \n",
+ "blood pressure systolic_known_qn_mmHg_all_MEAN 121.176471 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_MEAN 67.213383 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 98.529412 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.822271 \n",
+ "hemoglobin_known_qn_g/dL_all_MEAN 10.912500 \n",
+ "lactate_known_qn_mmol/L_all_MEAN 2.000000 \n",
+ "blood pressure mean_known_qn_mmHg_all_MEAN 76.117647 \n",
+ "vasopressin_known_qn_units_all_MEAN 2.438013 \n",
+ "vasopressin_known_qn_units/min_all_MEAN 0.662927 \n",
+ "weight body_known_qn_kg_all_MEAN 83.453349 \n",
+ "normal saline_known_qn_mL_all_MEAN 20.000000 \n",
+ "normal saline_known_qn_mL/hr_all_MEAN 4.521185 \n",
+ "norepinephrine_known_qn_mcg_all_MEAN 442.367701 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_MEAN 0.151286 \n",
+ "norepinephrine_known_qn_mcg/min_all_MEAN 11.641748 \n",
+ "temperature body_known_qn_degF_all_MEAN 97.604706 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_MEAN 57.823529 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 41.533994 \n",
+ "heart rate_known_qn_beats/min_all_MEAN 75.764706 \n",
+ "output urine_known_qn_mL_all_MEAN 94.444444 \n",
+ "output urine_unknown_qn_no_units_all_MEAN 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_MEAN 800.000000 \n",
+ "lactated ringers_known_qn_mL/hr_all_MEAN 207.151224 \n",
+ "lactated ringers_unknown_qn_no_units_all_MEAN 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_MEAN 16.562500 \n",
+ "respiratory rate_unknown_qn_Breath_all_MEAN 46.714318 \n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 0.564250 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 0.761486 \n",
+ "... ... \n",
+ "blood pressure systolic_known_qn_mmHg_all_LAST 107.000000 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_LAST 70.000000 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 94.000000 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.827318 \n",
+ "hemoglobin_known_qn_g/dL_all_LAST 11.100000 \n",
+ "lactate_known_qn_mmol/L_all_LAST 2.600000 \n",
+ "blood pressure mean_known_qn_mmHg_all_LAST 69.000000 \n",
+ "vasopressin_known_qn_units_all_LAST 1.944078 \n",
+ "vasopressin_known_qn_units/min_all_LAST 0.580697 \n",
+ "weight body_known_qn_kg_all_LAST 83.437229 \n",
+ "normal saline_known_qn_mL_all_LAST 20.000000 \n",
+ "normal saline_known_qn_mL/hr_all_LAST 2.998982 \n",
+ "norepinephrine_known_qn_mcg_all_LAST 404.510889 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_LAST 0.126223 \n",
+ "norepinephrine_known_qn_mcg/min_all_LAST 8.396190 \n",
+ "temperature body_known_qn_degF_all_LAST 98.240000 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_LAST 52.000000 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 43.285714 \n",
+ "heart rate_known_qn_beats/min_all_LAST 85.000000 \n",
+ "output urine_known_qn_mL_all_LAST 50.000000 \n",
+ "output urine_unknown_qn_no_units_all_LAST 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_LAST 1000.000000 \n",
+ "lactated ringers_known_qn_mL/hr_all_LAST 269.133890 \n",
+ "lactated ringers_unknown_qn_no_units_all_LAST 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_LAST 21.000000 \n",
+ "respiratory rate_unknown_qn_Breath_all_LAST 45.428571 \n",
+ "normal saline_known_qn_mL_all_SUM 60.000000 \n",
+ "norepinephrine_known_qn_mcg_all_SUM 13543.870106 \n",
+ "output urine_known_qn_mL_all_SUM 850.000000 \n",
+ "lactated ringers_known_qn_mL_all_SUM 2400.000000 \n",
+ "\n",
+ " 3 4 \n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 2.981695 2.981695 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 4.896178 4.896178 \n",
+ "blood pressure systolic_known_qn_mmHg_all_MEAN 118.585375 138.829268 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_MEAN 67.213383 67.213383 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 97.086505 94.936170 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.822271 2.822271 \n",
+ "hemoglobin_known_qn_g/dL_all_MEAN 16.000000 8.933333 \n",
+ "lactate_known_qn_mmol/L_all_MEAN 2.487958 2.000000 \n",
+ "blood pressure mean_known_qn_mmHg_all_MEAN 78.098120 85.428571 \n",
+ "vasopressin_known_qn_units_all_MEAN 2.438013 2.438013 \n",
+ "vasopressin_known_qn_units/min_all_MEAN 0.662927 0.662927 \n",
+ "weight body_known_qn_kg_all_MEAN 83.453349 83.453349 \n",
+ "normal saline_known_qn_mL_all_MEAN 616.280061 616.280061 \n",
+ "normal saline_known_qn_mL/hr_all_MEAN 88.819017 300.265113 \n",
+ "norepinephrine_known_qn_mcg_all_MEAN 442.367701 442.367701 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_MEAN 0.151286 0.151286 \n",
+ "norepinephrine_known_qn_mcg/min_all_MEAN 11.641748 11.641748 \n",
+ "temperature body_known_qn_degF_all_MEAN 98.122104 99.572000 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_MEAN 61.010500 69.951220 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 41.533994 41.533994 \n",
+ "heart rate_known_qn_beats/min_all_MEAN 88.502874 83.717391 \n",
+ "output urine_known_qn_mL_all_MEAN 150.213438 287.857143 \n",
+ "output urine_unknown_qn_no_units_all_MEAN 0.000000 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_MEAN 440.510867 440.510867 \n",
+ "lactated ringers_known_qn_mL/hr_all_MEAN 207.151224 207.151224 \n",
+ "lactated ringers_unknown_qn_no_units_all_MEAN 0.000000 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_MEAN 19.336424 19.446809 \n",
+ "respiratory rate_unknown_qn_Breath_all_MEAN 46.714318 46.714318 \n",
+ "glasgow coma scale eye opening_known_ord_no_uni... 0.564250 0.564250 \n",
+ "glasgow coma scale motor_known_ord_no_units_all... 0.761486 0.761486 \n",
+ "... ... ... \n",
+ "blood pressure systolic_known_qn_mmHg_all_LAST 119.025309 136.000000 \n",
+ "blood pressure systolic_unknown_qn_cc/min_all_LAST 70.000000 70.000000 \n",
+ "oxygen saturation pulse oximetry_known_qn_perce... 96.865129 97.000000 \n",
+ "glasgow coma scale verbal_known_ord_no_units_al... 2.827318 2.827318 \n",
+ "hemoglobin_known_qn_g/dL_all_LAST 15.600000 8.800000 \n",
+ "lactate_known_qn_mmol/L_all_LAST 2.424450 2.000000 \n",
+ "blood pressure mean_known_qn_mmHg_all_LAST 78.002572 81.000000 \n",
+ "vasopressin_known_qn_units_all_LAST 1.944078 1.944078 \n",
+ "vasopressin_known_qn_units/min_all_LAST 0.580697 0.580697 \n",
+ "weight body_known_qn_kg_all_LAST 83.437229 83.437229 \n",
+ "normal saline_known_qn_mL_all_LAST 608.646326 608.646326 \n",
+ "normal saline_known_qn_mL/hr_all_LAST 102.570817 1000.000000 \n",
+ "norepinephrine_known_qn_mcg_all_LAST 404.510889 404.510889 \n",
+ "norepinephrine_known_qn_mcg/kg/min_all_LAST 0.126223 0.126223 \n",
+ "norepinephrine_known_qn_mcg/min_all_LAST 8.396190 8.396190 \n",
+ "temperature body_known_qn_degF_all_LAST 98.237947 99.000000 \n",
+ "blood pressure diastolic_known_qn_mmHg_all_LAST 60.584437 67.000000 \n",
+ "blood pressure diastolic_unknown_qn_cc/min_all_... 43.285714 43.285714 \n",
+ "heart rate_known_qn_beats/min_all_LAST 88.100059 83.000000 \n",
+ "output urine_known_qn_mL_all_LAST 134.812808 150.000000 \n",
+ "output urine_unknown_qn_no_units_all_LAST 0.000000 0.000000 \n",
+ "lactated ringers_known_qn_mL_all_LAST 421.071475 421.071475 \n",
+ "lactated ringers_known_qn_mL/hr_all_LAST 269.133890 269.133890 \n",
+ "lactated ringers_unknown_qn_no_units_all_LAST 0.000000 0.000000 \n",
+ "respiratory rate_known_qn_insp/min_all_LAST 19.578249 27.000000 \n",
+ "respiratory rate_unknown_qn_Breath_all_LAST 45.428571 45.428571 \n",
+ "normal saline_known_qn_mL_all_SUM 2242.123639 2242.123639 \n",
+ "norepinephrine_known_qn_mcg_all_SUM 13543.870106 13543.870106 \n",
+ "output urine_known_qn_mL_all_SUM 4157.734811 4030.000000 \n",
+ "lactated ringers_known_qn_mL_all_SUM 2747.836982 2747.836982 \n",
+ "\n",
+ "[119 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 96,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_ft.head().T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "id datetime \n",
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+ "101263 2195-02-17 05:25:00 2.1\n",
+ " ... \n",
+ "198773 2109-09-03 09:00:00 2.3\n",
+ "198826 2128-07-11 20:29:00 4.8\n",
+ "198851 2148-04-12 17:52:00 1.6\n",
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+ "199714 2128-02-02 15:31:00 1.8\n",
+ "199723 2125-10-17 14:31:00 2.0\n",
+ "199779 2198-08-25 04:05:00 1.9\n",
+ "199813 2181-07-23 13:32:00 0.7\n",
+ "199877 2195-10-27 04:07:00 1.0\n",
+ "199922 2177-07-27 14:05:00 1.9\n",
+ "199925 2140-02-11 20:56:00 1.6\n",
+ "199963 2101-08-25 05:48:00 1.6\n",
+ "199999 2136-04-06 15:29:00 1.8\n",
+ "Name: (known, qn, mmol/L, all), dtype: float64"
+ ]
+ },
+ "execution_count": 167,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "hdf5_fname_features = 'data/feature_sets'\n",
+ "utils.save_df(train_ft,hdf5_fname,'simple_mvr/train_ft')\n",
+ "utils.save_df(train_lbl,hdf5_fname,'simple_mvr/train_lbl')\n",
+ "utils.save_df(test_ft,hdf5_fname,'simple_mvr/test_ft')\n",
+ "utils.save_df(test_lbl,hdf5_fname,'simple_mvr/test_lbl')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Models!!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'No object named simple_mvr/train_ft in the file'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m<ipython-input-6-28af48515f76>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mhdf5_fname_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'data/feature_sets'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtrain_ft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdf5_fname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'simple_mvr/train_ft'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mtrain_lbl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdf5_fname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'simple_mvr/train_ft'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mtest_ft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdf5_fname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'simple_mvr/test_ft'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mtest_lbl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdf5_fname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'simple_mvr/test_lbl'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mC:\\Users\\genkinjz\\icu_ml_project\\v5\\utils.pyc\u001b[0m in \u001b[0;36mopen_df\u001b[1;34m(hdf5_fname, path)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mopen_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdf5_fname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[0mstore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHDFStore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdf5_fname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[0mstore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mC:\\Users\\genkinjz\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\pandas\\io\\pytables.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 461\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 463\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mC:\\Users\\genkinjz\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\pandas\\io\\pytables.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 675\u001b[0m \u001b[0mgroup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mgroup\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 677\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No object named %s in the file'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 678\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'No object named simple_mvr/train_ft in the file'"
+ ]
+ }
+ ],
+ "source": [
+ "hdf5_fname_features = 'data/feature_sets'\n",
+ "\n",
+ "train_ft = utils.open_df(hdf5_fname,'simple_mvr/train_ft')\n",
+ "train_lbl = utils.open_df(hdf5_fname,'simple_mvr/train_ft')\n",
+ "test_ft = utils.open_df(hdf5_fnaame,'simple_mvr/test_ft')\n",
+ "test_lbl = utils.open_df(hdf5_fname,'simple_mvr/test_lbl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 218,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import mean_squared_error\n",
+ "\n",
+ "def try_model(train_ft,train_lbl,test_ft,test_lbl,prep_pipeline,model):\n",
+ " # apply final cleaning to our data\n",
+ " X_train = prep_pipeline.fit_transform(train_ft)\n",
+ " y_train = train_lbl.reset_index(drop=True)\n",
+ " X_test = prep_pipeline.fit_transform(test_ft)\n",
+ " y_test = test_lbl.reset_index(drop=True).values\n",
+ " \n",
+ " model = model.fit(X_train,y_train)\n",
+ " y_pred = model.predict(X_test)\n",
+ " \n",
+ " rmse = pd.np.sqrt(mean_squared_error(y_test,y_pred))\n",
+ "\n",
+ " results = pd.DataFrame(zip(y_test,y_pred), columns=['actual','predicted'])\n",
+ " \n",
+ " display(results.head())\n",
+ " display(results.describe())\n",
+ " print(\"Training set score: %f\" % model.score(X_train, y_train))\n",
+ " print(\"Test set score: %f\" % model.score(X_test, y_test))\n",
+ " \n",
+ " print \"RMSE:\",rmse\n",
+ " \n",
+ " return model,results,rmse\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Linear Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "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>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.9</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2.3</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6.7</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.4</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2.0</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 0.9 -4.735030e+09\n",
+ "1 2.3 -4.735030e+09\n",
+ "2 6.7 -4.735030e+09\n",
+ "3 1.4 -4.735030e+09\n",
+ "4 2.0 -4.735030e+09"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3344.000000</td>\n",
+ " <td>3.344000e+03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.223583</td>\n",
+ " <td>1.749131e+00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.849955</td>\n",
+ " <td>4.585368e+12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>-1.152666e+14</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.200000</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.700000</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.600000</td>\n",
+ " <td>-4.735030e+09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>19.800000</td>\n",
+ " <td>1.460645e+14</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 3344.000000 3.344000e+03\n",
+ "mean 2.223583 1.749131e+00\n",
+ "std 1.849955 4.585368e+12\n",
+ "min 0.300000 -1.152666e+14\n",
+ "25% 1.200000 -4.735030e+09\n",
+ "50% 1.700000 -4.735030e+09\n",
+ "75% 2.600000 -4.735030e+09\n",
+ "max 19.800000 1.460645e+14"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.420901\n",
+ "Test set score: -6143647084954342802849792.000000\n",
+ "RMSE: 4.58468261388e+12\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "lin_reg = LinearRegression()\n",
+ "\n",
+ "model,results,rmse = try_model(train_ft,train_lbl,test_ft,test_lbl,StandardScaler(),lin_reg)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Elastic Net"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'try_model' 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-10-c7a0eae13a62>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mElasticNet\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrmse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtry_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_ft\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_lbl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_ft\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_lbl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mElasticNet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m: name 'try_model' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import ElasticNet\n",
+ "model,results,rmse = try_model(train_ft,train_lbl,test_ft,test_lbl,StandardScaler(),ElasticNet())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n",
+ " max_iter=1000, normalize=False, positive=False, precompute=False,\n",
+ " random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ElasticNet()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "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>lactate_known_qn_mmol/L_all_MEAN</th>\n",
+ " <th>lactate_known_qn_mmol/L_all_LAST</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2.487958</td>\n",
+ " <td>2.42445</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1.100000</td>\n",
+ " <td>1.10000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.60000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2.487958</td>\n",
+ " <td>2.42445</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.00000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " lactate_known_qn_mmol/L_all_MEAN lactate_known_qn_mmol/L_all_LAST\n",
+ "0 2.487958 2.42445\n",
+ "1 1.100000 1.10000\n",
+ "2 2.000000 2.60000\n",
+ "3 2.487958 2.42445\n",
+ "4 2.000000 2.00000"
+ ]
+ },
+ "execution_count": 195,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_ft.loc[:,model.coef_ > 0].head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "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>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.9</td>\n",
+ " <td>2.232344</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2.3</td>\n",
+ " <td>2.258501</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6.7</td>\n",
+ " <td>2.265061</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.4</td>\n",
+ " <td>2.232344</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2.0</td>\n",
+ " <td>2.232344</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 0.9 2.232344\n",
+ "1 2.3 2.258501\n",
+ "2 6.7 2.265061\n",
+ "3 1.4 2.232344\n",
+ "4 2.0 2.232344"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3344.000000</td>\n",
+ " <td>3344.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.223583</td>\n",
+ " <td>2.232323</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.849955</td>\n",
+ " <td>0.134357</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>1.254813</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.200000</td>\n",
+ " <td>2.232344</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.700000</td>\n",
+ " <td>2.232345</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.600000</td>\n",
+ " <td>2.254982</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>19.800000</td>\n",
+ " <td>6.962132</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 3344.000000 3344.000000\n",
+ "mean 2.223583 2.232323\n",
+ "std 1.849955 0.134357\n",
+ "min 0.300000 1.254813\n",
+ "25% 1.200000 2.232344\n",
+ "50% 1.700000 2.232345\n",
+ "75% 2.600000 2.254982\n",
+ "max 19.800000 6.962132"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.004094\n",
+ "Test set score: 0.005918\n",
+ "RMSE: 1.84419716799\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([ 8.01371947e-06, -1.13534758e-05])"
+ ]
+ },
+ "execution_count": 196,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#enet with pca\n",
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "pipeline = Pipeline([\n",
+ "# ('scaler',StandardScaler()),\n",
+ " ('pca',PCA(n_components=0.95))\n",
+ " ])\n",
+ "\n",
+ "model,results,rmse = try_model(train_ft,train_lbl,test_ft,test_lbl,pipeline,ElasticNet())\n",
+ "model.coef_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 203,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# lets try DELTA lactate\n",
+ "train_ft_delta = train_ft.loc[train_ft.loc[:,train_ft.columns.str.contains('lactate_')].iloc[:,2] > 0]\n",
+ "last_lactate = train_ft_delta.loc[:,train_ft.columns.str.contains('lactate_')].iloc[:,3]\n",
+ "train_lbl_delta = train_lbl.reset_index(drop=True).loc[train_ft_delta.index] - last_lactate\n",
+ "\n",
+ "test_ft_delta = test_ft.loc[train_ft.loc[:,test_ft.columns.str.contains('lactate_')].iloc[:,2] > 0]\n",
+ "last_lactate = test_ft_delta.loc[:,test_ft.columns.str.contains('lactate_')].iloc[:,3]\n",
+ "test_lbl_delta = test_lbl.reset_index(drop=True).loc[test_ft_delta.index] - last_lactate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 204,
+ "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>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>-1.500000</td>\n",
+ " <td>-0.266715</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>4.331777</td>\n",
+ " <td>-0.172069</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>-0.152334</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>-0.768223</td>\n",
+ " <td>-0.172069</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>-1.700000</td>\n",
+ " <td>-0.618851</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 -1.500000 -0.266715\n",
+ "1 4.331777 -0.172069\n",
+ "2 0.300000 -0.152334\n",
+ "3 -0.768223 -0.172069\n",
+ "4 -1.700000 -0.618851"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1877.000000</td>\n",
+ " <td>1877.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>-0.148187</td>\n",
+ " <td>-0.171677</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.526032</td>\n",
+ " <td>0.099309</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-12.100000</td>\n",
+ " <td>-1.356982</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>-0.968223</td>\n",
+ " <td>-0.172069</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>-0.300000</td>\n",
+ " <td>-0.172069</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>-0.124506</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>15.600000</td>\n",
+ " <td>-0.036472</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 1877.000000 1877.000000\n",
+ "mean -0.148187 -0.171677\n",
+ "std 1.526032 0.099309\n",
+ "min -12.100000 -1.356982\n",
+ "25% -0.968223 -0.172069\n",
+ "50% -0.300000 -0.172069\n",
+ "75% 0.300000 -0.124506\n",
+ "max 15.600000 -0.036472"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.054252\n",
+ "Test set score: 0.033211\n",
+ "RMSE: 1.5000780178\n"
+ ]
+ }
+ ],
+ "source": [
+ "model,results,rmse = try_model(train_ft_delta,train_lbl_delta,test_ft_delta,test_lbl_delta,StandardScaler(),ElasticNet())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 205,
+ "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>lactate_known_qn_mmol/L_all_MEAN</th>\n",
+ " <th>lactate_known_qn_mmol/L_all_LAST</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>3.800000</td>\n",
+ " <td>3.800000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2.413916</td>\n",
+ " <td>2.368223</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2.250000</td>\n",
+ " <td>1.700000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>2.413916</td>\n",
+ " <td>2.368223</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>9.000000</td>\n",
+ " <td>9.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " lactate_known_qn_mmol/L_all_MEAN lactate_known_qn_mmol/L_all_LAST\n",
+ "1 3.800000 3.800000\n",
+ "2 2.413916 2.368223\n",
+ "4 2.250000 1.700000\n",
+ "6 2.413916 2.368223\n",
+ "8 9.000000 9.000000"
+ ]
+ },
+ "execution_count": 205,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_ft_delta.loc[:,abs(model.coef_) > 0].head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 206,
+ "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>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>-1.500000</td>\n",
+ " <td>-0.190492</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>4.331777</td>\n",
+ " <td>-0.195569</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>-0.172998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>-0.768223</td>\n",
+ " <td>-0.172998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>-1.700000</td>\n",
+ " <td>-0.172998</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 -1.500000 -0.190492\n",
+ "1 4.331777 -0.195569\n",
+ "2 0.300000 -0.172998\n",
+ "3 -0.768223 -0.172998\n",
+ "4 -1.700000 -0.172998"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>1877.000000</td>\n",
+ " <td>1877.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>-0.148187</td>\n",
+ " <td>-0.171677</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.526032</td>\n",
+ " <td>0.105956</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-12.100000</td>\n",
+ " <td>-0.283685</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>-0.968223</td>\n",
+ " <td>-0.194433</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>-0.300000</td>\n",
+ " <td>-0.172998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>-0.172998</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>15.600000</td>\n",
+ " <td>2.654343</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 1877.000000 1877.000000\n",
+ "mean -0.148187 -0.171677\n",
+ "std 1.526032 0.105956\n",
+ "min -12.100000 -0.283685\n",
+ "25% -0.968223 -0.194433\n",
+ "50% -0.300000 -0.172998\n",
+ "75% 0.300000 -0.172998\n",
+ "max 15.600000 2.654343"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.005331\n",
+ "Test set score: -0.002815\n",
+ "RMSE: 1.52777172148\n"
+ ]
+ }
+ ],
+ "source": [
+ "pipeline = Pipeline([\n",
+ "# ('scaler',StandardScaler()),\n",
+ " ('pca',PCA(n_components=0.95))\n",
+ " ])\n",
+ "\n",
+ "model,results,rmse = try_model(train_ft_delta,train_lbl_delta,test_ft_delta,test_lbl_delta,pipeline,ElasticNet())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Random Forest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 207,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.ensemble import RandomForestClassifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 208,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "train_lbl_clf = (train_lbl > 2).astype(int)\n",
+ "test_lbl_clf = (test_lbl > 2).astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 209,
+ "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>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 0 0\n",
+ "1 1 1\n",
+ "2 1 0\n",
+ "3 0 0\n",
+ "4 0 0"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3344.000000</td>\n",
+ " <td>3344.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.365730</td>\n",
+ " <td>0.238038</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.481706</td>\n",
+ " <td>0.425946</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 3344.000000 3344.000000\n",
+ "mean 0.365730 0.238038\n",
+ "std 0.481706 0.425946\n",
+ "min 0.000000 0.000000\n",
+ "25% 0.000000 0.000000\n",
+ "50% 0.000000 0.000000\n",
+ "75% 1.000000 0.000000\n",
+ "max 1.000000 1.000000"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.903416\n",
+ "Test set score: 0.694677\n",
+ "RMSE: 0.552560373631\n"
+ ]
+ }
+ ],
+ "source": [
+ "clf,results,rmse = try_model(train_ft,train_lbl_clf,test_ft,test_lbl_clf,StandardScaler(),RandomForestClassifier(n_jobs=2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 210,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th>predicted</th>\n",
+ " <th>abnormal</th>\n",
+ " <th>normal</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>actual</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>abnormal</th>\n",
+ " <td>499</td>\n",
+ " <td>724</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normal</th>\n",
+ " <td>297</td>\n",
+ " <td>1824</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "predicted abnormal normal\n",
+ "actual \n",
+ "abnormal 499 724\n",
+ "normal 297 1824"
+ ]
+ },
+ "execution_count": 210,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str_results = results.replace(to_replace=[0,1],value=['normal','abnormal'])\n",
+ "pd.crosstab(str_results['actual'],str_results['predicted'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 211,
+ "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>0</th>\n",
+ " <th>1</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>94</th>\n",
+ " <td>lactate_known_qn_mmol/L_all_LAST</td>\n",
+ " <td>0.105640</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>hemoglobin_known_qn_g/dL_all_MEAN</td>\n",
+ " <td>0.096484</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93</th>\n",
+ " <td>hemoglobin_known_qn_g/dL_all_LAST</td>\n",
+ " <td>0.088996</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>hemoglobin_known_qn_g/dL_all_STD</td>\n",
+ " <td>0.081528</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>lactate_known_qn_mmol/L_all_MEAN</td>\n",
+ " <td>0.074967</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>62</th>\n",
+ " <td>hemoglobin_known_qn_g/dL_all_COUNT</td>\n",
+ " <td>0.034051</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>107</th>\n",
+ " <td>heart rate_known_qn_beats/min_all_LAST</td>\n",
+ " <td>0.018052</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>temperature body_known_qn_degF_all_MEAN</td>\n",
+ " <td>0.017661</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>heart rate_known_qn_beats/min_all_MEAN</td>\n",
+ " <td>0.017269</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>blood pressure systolic_known_qn_mmHg_all_MEAN</td>\n",
+ " <td>0.013622</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>85</th>\n",
+ " <td>respiratory rate_known_qn_insp/min_all_COUNT</td>\n",
+ " <td>0.013351</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>63</th>\n",
+ " <td>lactate_known_qn_mmol/L_all_COUNT</td>\n",
+ " <td>0.012803</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105</th>\n",
+ " <td>blood pressure diastolic_known_qn_mmHg_all_LAST</td>\n",
+ " <td>0.012785</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>oxygen saturation pulse oximetry_known_qn_perc...</td>\n",
+ " <td>0.011848</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>blood pressure diastolic_known_qn_mmHg_all_STD</td>\n",
+ " <td>0.011683</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td>output urine_known_qn_mL_all_LAST</td>\n",
+ " <td>0.011472</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td>temperature body_known_qn_degF_all_LAST</td>\n",
+ " <td>0.011305</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>blood pressure diastolic_known_qn_mmHg_all_MEAN</td>\n",
+ " <td>0.011030</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48</th>\n",
+ " <td>heart rate_known_qn_beats/min_all_STD</td>\n",
+ " <td>0.010999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>lactate_known_qn_mmol/L_all_STD</td>\n",
+ " <td>0.010906</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>79</th>\n",
+ " <td>output urine_unknown_nom_no_units_3686(ml)_Voi...</td>\n",
+ " <td>0.010892</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>blood pressure systolic_known_qn_mmHg_all_STD</td>\n",
+ " <td>0.010776</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>117</th>\n",
+ " <td>output urine_known_qn_mL_all_SUM</td>\n",
+ " <td>0.010145</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>oxygen saturation pulse oximetry_known_qn_perc...</td>\n",
+ " <td>0.010020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>respiratory rate_known_qn_insp/min_all_MEAN</td>\n",
+ " <td>0.009950</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>89</th>\n",
+ " <td>blood pressure systolic_known_qn_mmHg_all_LAST</td>\n",
+ " <td>0.009950</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54</th>\n",
+ " <td>respiratory rate_known_qn_insp/min_all_STD</td>\n",
+ " <td>0.009800</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>113</th>\n",
+ " <td>respiratory rate_known_qn_insp/min_all_LAST</td>\n",
+ " <td>0.009220</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>74</th>\n",
+ " <td>blood pressure diastolic_known_qn_mmHg_all_COUNT</td>\n",
+ " <td>0.009123</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>temperature body_known_qn_degF_all_STD</td>\n",
+ " <td>0.008873</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66</th>\n",
+ " <td>vasopressin_known_qn_units/min_all_COUNT</td>\n",
+ " <td>0.000466</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td>vasopressin_known_qn_units_all_STD</td>\n",
+ " <td>0.000379</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>lactated ringers_known_qn_mL/hr_all_MEAN</td>\n",
+ " <td>0.000283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>respiratory rate_unknown_qn_Breath_all_MEAN</td>\n",
+ " <td>0.000219</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103</th>\n",
+ " <td>norepinephrine_known_qn_mcg/min_all_LAST</td>\n",
+ " <td>0.000193</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47</th>\n",
+ " <td>blood pressure diastolic_unknown_qn_cc/min_all...</td>\n",
+ " <td>0.000184</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>83</th>\n",
+ " <td>lactated ringers_known_qn_mL/hr_all_COUNT</td>\n",
+ " <td>0.000131</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>114</th>\n",
+ " <td>respiratory rate_unknown_qn_Breath_all_LAST</td>\n",
+ " <td>0.000120</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106</th>\n",
+ " <td>blood pressure diastolic_unknown_qn_cc/min_all...</td>\n",
+ " <td>0.000085</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td>blood pressure systolic_unknown_qn_cc/min_all_...</td>\n",
+ " <td>0.000076</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>84</th>\n",
+ " <td>lactated ringers_unknown_qn_no_units_all_COUNT</td>\n",
+ " <td>0.000076</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>111</th>\n",
+ " <td>lactated ringers_known_qn_mL/hr_all_LAST</td>\n",
+ " <td>0.000073</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>65</th>\n",
+ " <td>vasopressin_known_qn_units_all_COUNT</td>\n",
+ " <td>0.000071</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>lactated ringers_known_qn_mL/hr_all_STD</td>\n",
+ " <td>0.000054</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>90</th>\n",
+ " <td>blood pressure systolic_unknown_qn_cc/min_all_...</td>\n",
+ " <td>0.000053</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>blood pressure systolic_unknown_qn_cc/min_all_...</td>\n",
+ " <td>0.000026</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>respiratory rate_unknown_qn_Breath_all_STD</td>\n",
+ " <td>0.000014</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>72</th>\n",
+ " <td>norepinephrine_known_qn_mcg/min_all_COUNT</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>53</th>\n",
+ " <td>lactated ringers_unknown_qn_no_units_all_STD</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75</th>\n",
+ " <td>blood pressure diastolic_unknown_qn_cc/min_all...</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>112</th>\n",
+ " <td>lactated ringers_unknown_qn_no_units_all_LAST</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>blood pressure systolic_unknown_qn_cc/min_all_STD</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>lactated ringers_unknown_qn_no_units_all_MEAN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>output urine_unknown_qn_no_units_all_STD</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>norepinephrine_known_qn_mcg/min_all_MEAN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>norepinephrine_known_qn_mcg/min_all_STD</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>blood pressure diastolic_unknown_qn_cc/min_all...</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>output urine_unknown_qn_no_units_all_MEAN</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>86</th>\n",
+ " <td>respiratory rate_unknown_qn_Breath_all_COUNT</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109</th>\n",
+ " <td>output urine_unknown_qn_no_units_all_LAST</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>119 rows × 2 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 0 1\n",
+ "94 lactate_known_qn_mmol/L_all_LAST 0.105640\n",
+ "6 hemoglobin_known_qn_g/dL_all_MEAN 0.096484\n",
+ "93 hemoglobin_known_qn_g/dL_all_LAST 0.088996\n",
+ "34 hemoglobin_known_qn_g/dL_all_STD 0.081528\n",
+ "7 lactate_known_qn_mmol/L_all_MEAN 0.074967\n",
+ "62 hemoglobin_known_qn_g/dL_all_COUNT 0.034051\n",
+ "107 heart rate_known_qn_beats/min_all_LAST 0.018052\n",
+ "17 temperature body_known_qn_degF_all_MEAN 0.017661\n",
+ "20 heart rate_known_qn_beats/min_all_MEAN 0.017269\n",
+ "2 blood pressure systolic_known_qn_mmHg_all_MEAN 0.013622\n",
+ "85 respiratory rate_known_qn_insp/min_all_COUNT 0.013351\n",
+ "63 lactate_known_qn_mmol/L_all_COUNT 0.012803\n",
+ "105 blood pressure diastolic_known_qn_mmHg_all_LAST 0.012785\n",
+ "32 oxygen saturation pulse oximetry_known_qn_perc... 0.011848\n",
+ "46 blood pressure diastolic_known_qn_mmHg_all_STD 0.011683\n",
+ "108 output urine_known_qn_mL_all_LAST 0.011472\n",
+ "104 temperature body_known_qn_degF_all_LAST 0.011305\n",
+ "18 blood pressure diastolic_known_qn_mmHg_all_MEAN 0.011030\n",
+ "48 heart rate_known_qn_beats/min_all_STD 0.010999\n",
+ "35 lactate_known_qn_mmol/L_all_STD 0.010906\n",
+ "79 output urine_unknown_nom_no_units_3686(ml)_Voi... 0.010892\n",
+ "30 blood pressure systolic_known_qn_mmHg_all_STD 0.010776\n",
+ "117 output urine_known_qn_mL_all_SUM 0.010145\n",
+ "4 oxygen saturation pulse oximetry_known_qn_perc... 0.010020\n",
+ "26 respiratory rate_known_qn_insp/min_all_MEAN 0.009950\n",
+ "89 blood pressure systolic_known_qn_mmHg_all_LAST 0.009950\n",
+ "54 respiratory rate_known_qn_insp/min_all_STD 0.009800\n",
+ "113 respiratory rate_known_qn_insp/min_all_LAST 0.009220\n",
+ "74 blood pressure diastolic_known_qn_mmHg_all_COUNT 0.009123\n",
+ "45 temperature body_known_qn_degF_all_STD 0.008873\n",
+ ".. ... ...\n",
+ "66 vasopressin_known_qn_units/min_all_COUNT 0.000466\n",
+ "37 vasopressin_known_qn_units_all_STD 0.000379\n",
+ "24 lactated ringers_known_qn_mL/hr_all_MEAN 0.000283\n",
+ "27 respiratory rate_unknown_qn_Breath_all_MEAN 0.000219\n",
+ "103 norepinephrine_known_qn_mcg/min_all_LAST 0.000193\n",
+ "47 blood pressure diastolic_unknown_qn_cc/min_all... 0.000184\n",
+ "83 lactated ringers_known_qn_mL/hr_all_COUNT 0.000131\n",
+ "114 respiratory rate_unknown_qn_Breath_all_LAST 0.000120\n",
+ "106 blood pressure diastolic_unknown_qn_cc/min_all... 0.000085\n",
+ "59 blood pressure systolic_unknown_qn_cc/min_all_... 0.000076\n",
+ "84 lactated ringers_unknown_qn_no_units_all_COUNT 0.000076\n",
+ "111 lactated ringers_known_qn_mL/hr_all_LAST 0.000073\n",
+ "65 vasopressin_known_qn_units_all_COUNT 0.000071\n",
+ "52 lactated ringers_known_qn_mL/hr_all_STD 0.000054\n",
+ "90 blood pressure systolic_unknown_qn_cc/min_all_... 0.000053\n",
+ "3 blood pressure systolic_unknown_qn_cc/min_all_... 0.000026\n",
+ "55 respiratory rate_unknown_qn_Breath_all_STD 0.000014\n",
+ "72 norepinephrine_known_qn_mcg/min_all_COUNT 0.000000\n",
+ "53 lactated ringers_unknown_qn_no_units_all_STD 0.000000\n",
+ "75 blood pressure diastolic_unknown_qn_cc/min_all... 0.000000\n",
+ "112 lactated ringers_unknown_qn_no_units_all_LAST 0.000000\n",
+ "31 blood pressure systolic_unknown_qn_cc/min_all_STD 0.000000\n",
+ "25 lactated ringers_unknown_qn_no_units_all_MEAN 0.000000\n",
+ "50 output urine_unknown_qn_no_units_all_STD 0.000000\n",
+ "16 norepinephrine_known_qn_mcg/min_all_MEAN 0.000000\n",
+ "44 norepinephrine_known_qn_mcg/min_all_STD 0.000000\n",
+ "19 blood pressure diastolic_unknown_qn_cc/min_all... 0.000000\n",
+ "22 output urine_unknown_qn_no_units_all_MEAN 0.000000\n",
+ "86 respiratory rate_unknown_qn_Breath_all_COUNT 0.000000\n",
+ "109 output urine_unknown_qn_no_units_all_LAST 0.000000\n",
+ "\n",
+ "[119 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 211,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.DataFrame(list(zip(train_ft.columns, clf.feature_importances_))).sort_values(1,ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multilayer Perceptron (MLP)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 221,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
+ " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
+ " hidden_layer_sizes=(50, 50, 50), learning_rate='constant',\n",
+ " learning_rate_init=0.001, max_iter=10, momentum=0.9,\n",
+ " nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
+ " solver='adam', tol=0.0001, validation_fraction=0.1, verbose=10,\n",
+ " warm_start=False)"
+ ]
+ },
+ "execution_count": 221,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.neural_network import MLPRegressor\n",
+ "mlp = MLPRegressor(hidden_layer_sizes=(50,50,50), max_iter=10,\n",
+ " verbose=10, random_state=random_state)\n",
+ "mlp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 222,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Iteration 1, loss = 3.02117880\n",
+ "Iteration 2, loss = 1.67737187\n",
+ "Iteration 3, loss = 1.31006102\n",
+ "Iteration 4, loss = 1.18114299\n",
+ "Iteration 5, loss = 1.10769512\n",
+ "Iteration 6, loss = 1.07136569\n",
+ "Iteration 7, loss = 1.03435899\n",
+ "Iteration 8, loss = 1.01107775\n",
+ "Iteration 9, loss = 0.99628229\n",
+ "Iteration 10, loss = 0.96720048\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.9</td>\n",
+ " <td>1.700520</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2.3</td>\n",
+ " <td>3.023481</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6.7</td>\n",
+ " <td>3.531821</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.4</td>\n",
+ " <td>1.885652</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2.0</td>\n",
+ " <td>2.015354</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 0.9 1.700520\n",
+ "1 2.3 3.023481\n",
+ "2 6.7 3.531821\n",
+ "3 1.4 1.885652\n",
+ "4 2.0 2.015354"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3344.000000</td>\n",
+ " <td>3344.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.223583</td>\n",
+ " <td>2.320773</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.849955</td>\n",
+ " <td>1.183730</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>0.139628</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.703305</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.700000</td>\n",
+ " <td>2.275739</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.600000</td>\n",
+ " <td>2.596062</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>19.800000</td>\n",
+ " <td>17.974408</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 3344.000000 3344.000000\n",
+ "mean 2.223583 2.320773\n",
+ "std 1.849955 1.183730\n",
+ "min 0.300000 0.139628\n",
+ "25% 1.200000 1.703305\n",
+ "50% 1.700000 2.275739\n",
+ "75% 2.600000 2.596062\n",
+ "max 19.800000 17.974408"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.495340\n",
+ "Test set score: 0.348530\n",
+ "RMSE: 1.49294383274\n"
+ ]
+ }
+ ],
+ "source": [
+ "mlp,results,rmse = try_model(train_ft,train_lbl,test_ft,test_lbl,StandardScaler(),mlp)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 223,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Iteration 1, loss = 2.71985045\n",
+ "Iteration 2, loss = 1.42103496\n",
+ "Iteration 3, loss = 1.17207591\n",
+ "Iteration 4, loss = 1.08982518\n",
+ "Iteration 5, loss = 1.03187321\n",
+ "Iteration 6, loss = 0.99369883\n",
+ "Iteration 7, loss = 0.97197471\n",
+ "Iteration 8, loss = 0.94572791\n",
+ "Iteration 9, loss = 0.98458782\n",
+ "Iteration 10, loss = 0.97873262\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.9</td>\n",
+ " <td>1.527006</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2.3</td>\n",
+ " <td>3.131615</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6.7</td>\n",
+ " <td>4.002848</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1.4</td>\n",
+ " <td>1.631142</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2.0</td>\n",
+ " <td>2.001493</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "0 0.9 1.527006\n",
+ "1 2.3 3.131615\n",
+ "2 6.7 4.002848\n",
+ "3 1.4 1.631142\n",
+ "4 2.0 2.001493"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>actual</th>\n",
+ " <th>predicted</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>3344.000000</td>\n",
+ " <td>3344.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>2.223583</td>\n",
+ " <td>2.273984</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>1.849955</td>\n",
+ " <td>1.299995</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.300000</td>\n",
+ " <td>-0.344607</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>1.200000</td>\n",
+ " <td>1.675261</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>1.700000</td>\n",
+ " <td>2.200784</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>2.600000</td>\n",
+ " <td>2.517554</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>19.800000</td>\n",
+ " <td>25.244543</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " actual predicted\n",
+ "count 3344.000000 3344.000000\n",
+ "mean 2.223583 2.273984\n",
+ "std 1.849955 1.299995\n",
+ "min 0.300000 -0.344607\n",
+ "25% 1.200000 1.675261\n",
+ "50% 1.700000 2.200784\n",
+ "75% 2.600000 2.517554\n",
+ "max 19.800000 25.244543"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set score: 0.519520\n",
+ "Test set score: 0.312346\n",
+ "RMSE: 1.53384389398\n"
+ ]
+ }
+ ],
+ "source": [
+ "mlp.set_params(hidden_layer_sizes=(100,100,100))\n",
+ "mlp,results,rmse = try_model(train_ft,train_lbl,test_ft,test_lbl,StandardScaler(),mlp)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def heatmap(df_ts):\n",
+ " sns.set(context=\"paper\", font=\"monospace\")\n",
+ " corrmat = df_ts.corr()\n",
+ " # Set up the matplotlib figure\n",
+ " f, ax = plt.subplots(figsize=(50, 50))\n",
+ " # Draw the heatmap using seaborn\n",
+ " sns.heatmap(corrmat, vmax=1, square=True)\n",
+ " return"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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tGl988QU1atRg5syZhISEMHv2bLKzs/nhhx+4/fbbGT58eLn7e+/e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\nU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PgG0bVrV7p163bF61WVwcanTp1iw4YNlV2NKiMyMpIlS5YwZ84cUlNTf3ZeVlYW3377rYGa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MrGInLVUGXjwKiyceBU2fjnp8rGgVFl48CpsnHgVNk4AKpsHDBVNg4wRpWNA6bKxoFR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\naqt+ogBYPBVGcgAsB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k5G3ZZCQHoEHUECM5x/7uNpIDUL+X/0FtzkdRU/+DL50rU+8dwdx72oCi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T08nKyqJTp0588skn3HLLLUycOJG77rqLzMxMGjduDFQ/4u7/+3//j4ULF3LTTTcxe/Zs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tq1a6u7GDVGeHg4ixYtYvbs2cybN+8352Vk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OnQBYrVb+/e9/U1hYeMWidRERERERERERERERERERERERERGpfDddsXGNGjVYtGgRQUFBZYpf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\ncvL3Nvj7bqZOKoO8DZ0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wmLB+DpnNWGmTEBPC0wNfXZZ58BAKKionD48GGkpqZi0qRJwjs/7ty5E0DB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cF7772H9vZ2hIeHM89zSv/Ow8Pjob3x/v37mWwJ3YfqoZxTpqDyXxUKBe/3bN26FXFxcSTr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\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QEPjDotw+rKmpCYWFhfD19QXAvW3pX//6V5W/z5gxg9fYVq9ejdWrV6O+vh6GhoZYvXo1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UNDU1mecRRHarU2ptLNX4PDw8XPj48fvFxcVBrVZDq9VixIgRGDFiBHx9fSWJTUTdp7i42Pya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\n3XrrraaTgtLevXv15ptvKjY2Vi+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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x52768518>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "combined = train_ft.copy()\n",
+ "combined['next_lactate'] = train_lbl.reset_index(drop=True)\n",
+ "heatmap(combined)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "#helper methods\n",
+ "\n",
+ "reload(logger)\n",
+ "\n",
+ "\n",
+ "\n",
+ "def create_df(hdf5_fname,components,ids=constants.ALL):\n",
+ " #Load up our data dict and simple data components\n",
+ " \n",
+ " df_all = None\n",
+ " logger.log('Make DF ...'.format(len(hadm_ids)),new_level=True)\n",
+ " \n",
+ " id_filter = transformers.filter_ids(ids=ids)\n",
+ " \n",
+ " for component in components:\n",
+ " logger.log(component.upper(),new_level=True)\n",
+ "\n",
+ " logger.log('Opening...')\n",
+ " df = utils.open_df(hdf5_fname,'cleaned/{}'.format(component))\n",
+ " if ids != constants.ALL: \n",
+ " df_filtered = id_filter.transform(df)\n",
+ " else: df_filtered = df\n",
+ " logger.log('Join {} to {}'.format(df_filtered.shape, None if df_all is None else df_all.shape))\n",
+ " if df_all is None: \n",
+ " df_all = df_filtered\n",
+ " else: \n",
+ " df_all = df_all.join(df_filtered,how='outer')\n",
+ " del df,df_filtered\n",
+ " \n",
+ " logger.end_log_level()\n",
+ " logger.end_log()\n",
+ " \n",
+ " df_all.sort_index(axis=1,inplace=True)\n",
+ " df_all.sort_index(inplace=True)\n",
+ " \n",
+ " \n",
+ " return df_all\n",
+ "\n",
+ "\n",
+ "\n",
+ "def clean_and_combine_simple_data(hadm_ids,data_dict):\n",
+ " #Load up our data dict and simple data components\n",
+ " hdf5_fname = 'data/mimic_simple_all'\n",
+ " components = data_dict.get_panel_defintions(12).component.unique().tolist() #12 is \"simple data\"\n",
+ " \n",
+ " filter_ids =transformers.filter_ids(hadm_ids)\n",
+ " #This will be our cleaning pipeline\n",
+ " custom_cleaners = Pipeline([\n",
+ " ('filter_ids',filter_ids),\n",
+ " ('one_hotter',transformers.nominal_to_onehot()),\n",
+ " ('drop_small_columns5',transformers.remove_small_columns(threshold=5)),\n",
+ " ('drop_oob_values',transformers.oob_value_remover(data_dict)),\n",
+ " ('drop_small_columns50',transformers.remove_small_columns(threshold=50)),\n",
+ " ('combine_like_columns',transformers.combine_like_cols())\n",
+ " ])\n",
+ " \n",
+ " df_all = None\n",
+ " logger.log('Make DF for {} admission'.format(len(hadm_ids)),new_level=True)\n",
+ " for component in components:\n",
+ " logger.log(component.upper(),new_level=True)\n",
+ "\n",
+ " logger.log('Opening...')\n",
+ " df = utils.open_df(hdf5_fname,'cleaned/{}'.format(component))\n",
+ "\n",
+ " display(df.describe())\n",
+ "\n",
+ " logger.log('Clean {}'.format(df.shape))\n",
+ " df_cleaned = custom_cleaners.fit_transform(df)\n",
+ "\n",
+ " display(df_cleaned.describe())\n",
+ "\n",
+ " print utils.data_loss(filter_ids.transform(df),df_cleaned)\n",
+ " \n",
+ " logger.log('Join {} to {}'.format(df_cleaned.shape, None if df_all is None else df_all.shape))\n",
+ " if df_all is None: df_all = df_cleaned\n",
+ " else : df_all = df_all.join(df_cleaned,how='outer')\n",
+ " del df,df_cleaned\n",
+ " \n",
+ " logger.end_log_level()\n",
+ " logger.end_log()\n",
+ "\n",
+ " return df_all\n",
+ "\n",
+ "\n",
+ "def features_and_labels(df,data_dict,random_state,n_hrs,feature_tuples):\n",
+ " \n",
+ " labels = create_lactate_labels(df,data_dict,random_state)\n",
+ " valid_ids = labels.index.get_level_values(constants.column_names.ID).unique().tolist()\n",
+ " \n",
+ " end_dt = labels.reset_index(constants.column_names.DATETIME,drop=False).iloc[:,0]\n",
+ " df_context = mimic.get_context_data(valid_ids)\n",
+ " \n",
+ " \n",
+ " pipeline = Pipeline([\n",
+ " ('filter',transformers.filter_ids(valid_ids)),\n",
+ " ('segmenter',segmenting.n_hrs_before(end_dt,df_context,n_hrs)),\n",
+ " ('featurizer',features.featurizer(feature_tuples)),\n",
+ " ('feature_cleaner',features.pdFeatureUnion())\n",
+ " ])\n",
+ " \n",
+ " return pipeline.transform(df),labels\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def clean_and_combine_data(hdf5_fname,components,cleaners,train_ids,test_ids):\n",
+ " filter_ids = transformers.filter_ids(hadm_ids)\n",
+ " \n",
+ " df_all = None\n",
+ " logger.log('Make DF for {} admission - {}'.format(len(hadm_ids),components),new_level=True)\n",
+ " for component in components:\n",
+ " logger.log(component.upper(),new_level=True)\n",
+ "\n",
+ " logger.log('Opening...')\n",
+ " df = utils.open_df(hdf5_fname,'cleaned/{}'.format(component))\n",
+ " df_test = transformers.filter_ids(test_ids).transform(test_ids).sort_index(axis=1).sort_index()\n",
+ " df_train = transformers.filter_ids(train_ids).transform(test_ids).sort_index(axis=1).sort_index()\n",
+ " \n",
+ " display(df.describe())\n",
+ "\n",
+ " logger.log('Clean {}'.format(df.shape))\n",
+ " df_cleaned = cleaners.fit_transform(df_test)\n",
+ "\n",
+ " display(df_cleaned.describe())\n",
+ "\n",
+ " print utils.data_loss(df,df_cleaned)\n",
+ " \n",
+ " logger.log('Join {} to {}'.format(df_cleaned.shape, None if df_all is None else df_all.shape))\n",
+ " if df_all is None: df_all = df_cleaned\n",
+ " else : df_all = df_all.join(df_cleaned,how='outer')\n",
+ " del df,df_cleaned\n",
+ " \n",
+ " logger.end_log_level()\n",
+ " logger.end_log()\n",
+ "\n",
+ " return df_all\n",
+ "\n",
+ "\n",
+ "def lin_reg_feature_pipeline(labels,feature_tuples):\n",
+ " valid_ids = labels.index.get_level_values(constants.column_names.ID).unique().tolist()\n",
+ " df_context = mimic.get_context_data(valid_ids)\n",
+ " end_dt = labels.reset_index(constants.column_names.DATETIME,drop=False).iloc[:,0]\n",
+ " \n",
+ " return Pipeline([\n",
+ " ('filter_ids',transformers.filter_ids(ids=valid_ids)),\n",
+ " ('segmenter',segmenting.n_hrs_before(end_dt,df_context,n_hrs=None)), #we are doing multivariate regression, so need to segment in this way for now\n",
+ " ('featurizer',features.pdFeatureUnion(feature_tuples)),\n",
+ " ('feature_cleaner',features.feature_cleaner())\n",
+ " ])\n",
+ "\n",
+ "def lin_reg_features_and_labels(hadm_ids,hdf5_fname,feature_components,label_components,cleaners,\n",
+ " feature_tuples,n_hrs=constants.ALL):\n",
+ " #get all data, both for features and labels\n",
+ " components = list(set(feature_components + label_components))\n",
+ " df_all = clean_and_combine_data(hdf5_fname,components,cleaners,hadm_ids)\n",
+ "\n",
+ " #extract labels\n",
+ " labels = label_extractor.transform(df_all)\n",
+ "\n",
+ " #feature pipeline\n",
+ " feature_pipeline = lin_reg_feature_pipeline(labels,feature_tuples)\n",
+ " feature_pipeline.set_params(segmenter__n_hrs=n_hrs)\n",
+ " df_features = feature_pipeline.transform(df_all)\n",
+ " return df_features,labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "left = pd.DataFrame([(1,2,3,4),(5,6,7,8),(9,10,11,12)],columns=['A','B','C','D'])\n",
+ "r1 = pd.DataFrame([(3,4),(5,6)],columns=['E','F'],index=[0,1])\n",
+ "r2 = pd.DataFrame([(34,234),(5,6)],columns=['E','F'],index=[2,3])\n",
+ "\n",
+ "partitions = [r1,r2]\n",
+ "left"
+ ]
+ },
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+ "execution_count": 10,
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+ "metadata": {},
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+ "for df_partition in partitions:\n",
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+ "left"
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A</th>\n",
+ " <th>B</th>\n",
+ " <th>C</th>\n",
+ " <th>D</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A B C D\n",
+ "0 1 2 3 4\n",
+ "1 5 6 7 8"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "class lactate_label_extractor(BaseEstimator,TransformerMixin,has_data_needs):\n",
+ "\n",
+ " def __init__(self,lactate_component,random_state):\n",
+ " self.lactate_component = lactate_component\n",
+ " self.random_state = random_state\n",
+ " super(lactate_label_extractor,self).__init__((lactate_component,ALL))\n",
+ " \n",
+ " def fit(self, df, y=None):\n",
+ " return self\n",
+ "\n",
+ " def transform(self, df):\n",
+ " logger.log('Extract lactate label',new_level=True)\n",
+ " lactate = df.groupby(level=constants.column_names.ID).apply(self.sample_lactate).dropna().reset_index(level=0,drop=True)\n",
+ " logger.end_log_level()\n",
+ " return labels\n",
+ "\n",
+ " def sample_lactate(grp):\n",
+ " eligible_lactate = grp.iloc[1:].loc[:,self.lactate_component]\n",
+ " if eligible_lactate.size == 0: return pd.np.nan\n",
+ " \n",
+ " return eligible_lactate.sample(n=1,random_state=self.random_state)"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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
+}