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8_All_Data.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#my stuff\n",
"import icu_data_defs\n",
"import transformers\n",
"import utils\n",
"import features\n",
"from constants import column_names,variable_type,clinical_source\n",
"import units\n",
"import mimic\n",
"import logger\n",
"\n",
"#other stuff\n",
"from sklearn.model_selection import train_test_split,cross_val_score,ShuffleSplit\n",
"from sklearn.linear_model import LinearRegression,ElasticNet\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.base import TransformerMixin,BaseEstimator,clone\n",
"\n",
"#make pretty pictures\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"#Every time...\n",
"random_state=42"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#HELPER FUNCTIONS\n",
"\n",
"def run_crossval(pipeline,X,y):\n",
" scores_r2 = cross_val_score(pipeline,X,y, scoring='r2',cv=10)\n",
" scores_nmse = cross_val_score(pipeline,X,y, scoring='neg_mean_squared_error',cv=10)\n",
"\n",
" print 'Cross Validation, K-Fold'\n",
" print 'R^2: {}, {}, {}'.format(scores_r2.mean(),scores_r2.std(),list(scores_r2))\n",
" print 'RMSE: {}, {}, {}'.format(np.sqrt(-1.0*scores_nmse).mean(),np.sqrt(-1.0*scores_nmse).std(),list(scores_nmse))\n",
"\n",
" cv_shuffle = ShuffleSplit(n_splits=10,test_size=0.1)\n",
"\n",
" scores_r2 = cross_val_score(pipeline,X,y, scoring='r2',cv=cv_shuffle)\n",
" scores_nmse = cross_val_score(pipeline,X,y, scoring='neg_mean_squared_error', cv=cv_shuffle)\n",
"\n",
" print '\\nCross Validation, ShuffleSplit'\n",
" print 'R^2: {}, {}, {}'.format(scores_r2.mean(),scores_r2.std(),list(scores_r2))\n",
" print 'RMSE: {}, {}, {}'.format(np.sqrt(-1.0*scores_nmse).mean(),np.sqrt(-1.0*scores_nmse).std(),list(scores_nmse))\n",
" return\n",
"\n",
"\"\"\"\n",
"Visualize data\n",
"\"\"\"\n",
"#Visualize\n",
"def viz_per_feature(df_features,df_labels): \n",
" plot_cnt = len(df_labels.columns)+1\n",
" \n",
" df_corr = pd.DataFrame(index=df_features.columns,columns=df_labels.columns)\n",
" \n",
" for i,col_name in enumerate(df_features.columns):\n",
" print col_name,'{}/{}'.format(i,df_features.shape[1])\n",
" col = df_features.loc[:,col_name]\n",
" display(col.describe().apply(lambda x: '%.4f' % x).to_frame())\n",
" #determine # of filled values\n",
" mode = col.mode()[0]\n",
" print mode\n",
" mode_count = (col == mode).sum()\n",
" print \"MODE:\",mode\n",
" print mode_count\n",
" print mode_count/float(col.shape[0])\n",
"\n",
"\n",
" # plot histogram of column (all of df_train)\n",
" fig, axarr = plt.subplots(1,plot_cnt,figsize=(5*(plot_cnt), 5))\n",
" ax = plt.subplot(1, plot_cnt, 1)\n",
" std = col.std()\n",
" mean = col.mean()\n",
" col.loc[(col < (mean + 3.0*std)) & (col > (mean - 3.0*std))].hist()\n",
" ax.set_title('{}_{}\\n{}'.format(col_name[0],col_name[1],col_name[2:]))\n",
" ax.set_xlabel(col_name[-2])\n",
" ax.set_ylabel('COUNT')\n",
"\n",
" #plot this column vs. each label\n",
" for i,label_name in enumerate(df_labels.columns):\n",
" y = df_labels.loc[:,label_name].dropna()\n",
" \n",
" x = col.loc[y.index]\n",
" ax = plt.subplot(1, plot_cnt, 2+i)\n",
" sns.regplot(x, y)\n",
" corr = np.corrcoef(x, y)[0][1]\n",
" ax.set_title('{}_{} vs. {} \\n PCC (r) = {}'.format(col_name[0],col_name[1],label_name[0],corr))\n",
" df_corr.loc[col_name,label_name]=corr\n",
" ax.set_xlabel(col_name[-2])\n",
" ax.set_ylabel(label_name)\n",
" \n",
" plt.tight_layout()\n",
" plt.show()\n",
" \n",
" return df_corr\n",
" \n",
"\"\"\"\n",
"Test/train/validate split\n",
"\"\"\"\n",
"\n",
"def test_train_val_split(all_ids=None,test_size=0.1,random_state=42,print_ids=False):\n",
"\n",
" if all_ids is None:\n",
" all_ids = mimic.get_all_hadm_ids()\n",
" \n",
" validate_size = test_size/(1-test_size)\n",
" train_size = (1-test_size)*(1-validate_size)\n",
" #these test IDs will never be touched again. They are sacred\n",
" train_val_ids,test_ids = train_test_split(all_ids,test_size=test_size,random_state=random_state)\n",
" train_ids,validate_ids = train_test_split(train_val_ids,test_size=validate_size,random_state=random_state)\n",
"\n",
" if print_ids:\n",
" print 'Train {}:'.format(int(train_size*100)), len(train_ids),'>',train_ids[:5],'...'\n",
" print 'Validate {}:'.format(int(train_size*100)), len(validate_ids),'>',validate_ids[:5],'...'\n",
" print 'Test {}:'.format(int(test_size*100)), len(test_ids),'>',test_ids[:5],'...'\n",
" return train_ids,validate_ids,test_ids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ETL"
]
},
{
"cell_type": "code",
"execution_count": 62,
"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>component</th>\n",
" <th>units</th>\n",
" <th>variable_type</th>\n",
" <th>clinical_source</th>\n",
" <th>lower</th>\n",
" <th>upper</th>\n",
" <th>list_id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>def_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>heart rate</td>\n",
" <td>beats/min</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>blood pressure systolic</td>\n",
" <td>mmHg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>blood pressure diastolic</td>\n",
" <td>mmHg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>blood pressure mean</td>\n",
" <td>mmHg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>respiratory rate</td>\n",
" <td>insp/min</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>temperature body</td>\n",
" <td>degF</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>150.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>oxygen saturation pulse oximetry</td>\n",
" <td>percent</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>weight body</td>\n",
" <td>kg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>700.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>output urine</td>\n",
" <td>mL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>30000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>output urine</td>\n",
" <td>mL/hr</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>5000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>output urine</td>\n",
" <td>mL/kg/hr</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>glasgow coma scale motor</td>\n",
" <td>no_units</td>\n",
" <td>ord</td>\n",
" <td>observation</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>glasgow coma scale eye opening</td>\n",
" <td>no_units</td>\n",
" <td>ord</td>\n",
" <td>observation</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>glasgow coma scale verbal</td>\n",
" <td>no_units</td>\n",
" <td>ord</td>\n",
" <td>observation</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>normal saline</td>\n",
" <td>mL</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>30000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>normal saline</td>\n",
" <td>mL/hr</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>lactated ringers</td>\n",
" <td>mL</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>30000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>lactated ringers</td>\n",
" <td>mL/hr</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>norepinephrine</td>\n",
" <td>mcg</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>100000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>norepinephrine</td>\n",
" <td>mcg/min</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>norepinephrine</td>\n",
" <td>mcg/kg/min</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>vasopressin</td>\n",
" <td>units</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>300.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>vasopressin</td>\n",
" <td>units/min</td>\n",
" <td>qn</td>\n",
" <td>intervention</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>lactate</td>\n",
" <td>mmol/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>50.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>lactate</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>50.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>hemoglobin</td>\n",
" <td>g/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>white blood cell count</td>\n",
" <td>x10e3/uL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>red blood cell count</td>\n",
" <td>x10e6/uL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>hematocrit</td>\n",
" <td>percent</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>mean corpuscular volume</td>\n",
" <td>fL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>200.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>glucose serum</td>\n",
" <td>mmol/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>glucose serum</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>glucose fingerstick</td>\n",
" <td>mmol/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>glucose fingerstick</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>calcium total serum</td>\n",
" <td>mmol/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>calcium total serum</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>calcium ionized serum</td>\n",
" <td>mmol/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>calcium ionized serum</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>magnesium serum</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>phosphorous serum</td>\n",
" <td>mg/dL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>prothrombin time</td>\n",
" <td>seconds</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>partial thromboplastin time</td>\n",
" <td>seconds</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>international normalized ratio</td>\n",
" <td>no_units</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>partial pressure of oxygen arterial</td>\n",
" <td>mmHg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>partial pressure of carbon dioxide arterial</td>\n",
" <td>mmHg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>oxygen saturation arterial</td>\n",
" <td>percent</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>pH arterial</td>\n",
" <td>no_units</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>pH other</td>\n",
" <td>no_units</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>bicarbonate arterial</td>\n",
" <td>mEq/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>200.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>bicarbonate other</td>\n",
" <td>mEq/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>200.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>alanine aminotransferase serum</td>\n",
" <td>U/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>aspartate aminotransferase serum</td>\n",
" <td>U/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>alkaline phosphatase serum</td>\n",
" <td>IU/L</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>fraction of inspired oxygen</td>\n",
" <td>percent</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>fraction of inspired oxygen</td>\n",
" <td>no_units</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>positive end expiratory pressure</td>\n",
" <td>cmH2O</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>tidal volume</td>\n",
" <td>mL</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>central venous pressure</td>\n",
" <td>mm/Hg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>central venous oxygen saturation</td>\n",
" <td>percent</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>end tidal carbon dioxide</td>\n",
" <td>mmHg</td>\n",
" <td>qn</td>\n",
" <td>observation</td>\n",
" <td>0.0</td>\n",
" <td>1000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>74 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" component units variable_type \\\n",
"def_id \n",
"0 heart rate beats/min qn \n",
"1 blood pressure systolic mmHg qn \n",
"2 blood pressure diastolic mmHg qn \n",
"3 blood pressure mean mmHg qn \n",
"4 respiratory rate insp/min qn \n",
"5 temperature body degF qn \n",
"6 oxygen saturation pulse oximetry percent qn \n",
"7 weight body kg qn \n",
"8 output urine mL qn \n",
"9 output urine mL/hr qn \n",
"10 output urine mL/kg/hr qn \n",
"11 glasgow coma scale motor no_units ord \n",
"12 glasgow coma scale eye opening no_units ord \n",
"13 glasgow coma scale verbal no_units ord \n",
"14 normal saline mL qn \n",
"15 normal saline mL/hr qn \n",
"16 lactated ringers mL qn \n",
"17 lactated ringers mL/hr qn \n",
"18 norepinephrine mcg qn \n",
"19 norepinephrine mcg/min qn \n",
"20 norepinephrine mcg/kg/min qn \n",
"21 vasopressin units qn \n",
"22 vasopressin units/min qn \n",
"23 lactate mmol/L qn \n",
"24 lactate mg/dL qn \n",
"25 hemoglobin g/dL qn \n",
"26 white blood cell count x10e3/uL qn \n",
"27 red blood cell count x10e6/uL qn \n",
"28 hematocrit percent qn \n",
"29 mean corpuscular volume fL qn \n",
"... ... ... ... \n",
"44 glucose serum mmol/L qn \n",
"45 glucose serum mg/dL qn \n",
"46 glucose fingerstick mmol/L qn \n",
"47 glucose fingerstick mg/dL qn \n",
"48 calcium total serum mmol/L qn \n",
"49 calcium total serum mg/dL qn \n",
"50 calcium ionized serum mmol/L qn \n",
"51 calcium ionized serum mg/dL qn \n",
"52 magnesium serum mg/dL qn \n",
"53 phosphorous serum mg/dL qn \n",
"54 prothrombin time seconds qn \n",
"55 partial thromboplastin time seconds qn \n",
"56 international normalized ratio no_units qn \n",
"57 partial pressure of oxygen arterial mmHg qn \n",
"58 partial pressure of carbon dioxide arterial mmHg qn \n",
"59 oxygen saturation arterial percent qn \n",
"60 pH arterial no_units qn \n",
"61 pH other no_units qn \n",
"62 bicarbonate arterial mEq/L qn \n",
"63 bicarbonate other mEq/L qn \n",
"64 alanine aminotransferase serum U/L qn \n",
"65 aspartate aminotransferase serum U/L qn \n",
"66 alkaline phosphatase serum IU/L qn \n",
"67 fraction of inspired oxygen percent qn \n",
"68 fraction of inspired oxygen no_units qn \n",
"69 positive end expiratory pressure cmH2O qn \n",
"70 tidal volume mL qn \n",
"71 central venous pressure mm/Hg qn \n",
"72 central venous oxygen saturation percent qn \n",
"73 end tidal carbon dioxide mmHg qn \n",
"\n",
" clinical_source lower upper list_id \n",
"def_id \n",
"0 observation 0.0 500.0 NaN \n",
"1 observation 0.0 500.0 NaN \n",
"2 observation 0.0 500.0 NaN \n",
"3 observation 0.0 500.0 NaN \n",
"4 observation 0.0 150.0 NaN \n",
"5 observation 0.0 150.0 NaN \n",
"6 observation 0.0 100.0 NaN \n",
"7 observation 0.0 700.0 NaN \n",
"8 observation 0.0 30000.0 NaN \n",
"9 observation 0.0 5000.0 NaN \n",
"10 observation 0.0 100.0 NaN \n",
"11 observation NaN NaN 0.0 \n",
"12 observation NaN NaN 2.0 \n",
"13 observation NaN NaN 1.0 \n",
"14 intervention 0.0 30000.0 NaN \n",
"15 intervention 0.0 10000.0 NaN \n",
"16 intervention 0.0 30000.0 NaN \n",
"17 intervention 0.0 10000.0 NaN \n",
"18 intervention 0.0 100000.0 NaN \n",
"19 intervention 0.0 100.0 NaN \n",
"20 intervention 0.0 10.0 NaN \n",
"21 intervention 0.0 300.0 NaN \n",
"22 intervention 0.0 5.0 NaN \n",
"23 observation 0.0 50.0 NaN \n",
"24 observation 0.0 50.0 NaN \n",
"25 observation 0.0 100.0 NaN \n",
"26 observation 0.0 1000.0 NaN \n",
"27 observation 0.0 1000.0 NaN \n",
"28 observation 0.0 100.0 NaN \n",
"29 observation 0.0 200.0 NaN \n",
"... ... ... ... ... \n",
"44 observation 0.0 500.0 NaN \n",
"45 observation 0.0 10000.0 NaN \n",
"46 observation 0.0 500.0 NaN \n",
"47 observation 0.0 10000.0 NaN \n",
"48 observation 0.0 25.0 NaN \n",
"49 observation 0.0 100.0 NaN \n",
"50 observation 0.0 25.0 NaN \n",
"51 observation 0.0 100.0 NaN \n",
"52 observation 0.0 100.0 NaN \n",
"53 observation 0.0 100.0 NaN \n",
"54 observation 0.0 1000.0 NaN \n",
"55 observation 0.0 1000.0 NaN \n",
"56 observation 0.0 100.0 NaN \n",
"57 observation 0.0 1000.0 NaN \n",
"58 observation 0.0 1000.0 NaN \n",
"59 observation 0.0 100.0 NaN \n",
"60 observation 0.0 14.0 NaN \n",
"61 observation 0.0 14.0 NaN \n",
"62 observation 0.0 200.0 NaN \n",
"63 observation 0.0 200.0 NaN \n",
"64 observation 0.0 100000.0 NaN \n",
"65 observation 0.0 100000.0 NaN \n",
"66 observation 0.0 10000.0 NaN \n",
"67 observation 0.0 100.0 NaN \n",
"68 observation 0.0 1.0 NaN \n",
"69 observation 0.0 1000.0 NaN \n",
"70 observation 0.0 10000.0 NaN \n",
"71 observation 0.0 500.0 NaN \n",
"72 observation 0.0 100.0 NaN \n",
"73 observation 0.0 1000.0 NaN \n",
"\n",
"[74 rows x 7 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load Our Data Dict\n",
"data_dict = icu_data_defs.data_dictionary('config/data_definitions.xlsx')\n",
"display(data_dict.get_defs())\n",
"\n",
"#init ETL Manager => mimic_extract data\n",
"etl_fname = 'data/mimic_extract.h5'\n",
"etl_manager = mimic.MimicETLManager(etl_fname,'config/mimic_item_map.csv',data_dict)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"etl_data = etl_manager.etl(components=data_dict.get_components(),save_steps=True) #all components in data dictionary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"etl_data = etl_data.set_index('component')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"etl_data.sort_values('CLEANED_data_count',ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Generation"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'transformers' from 'transformers.pyc'>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(features)\n",
"reload(transformers)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train 80: 47180 > [139698, 127590, 178959, 139276, 196600] ...\n",
"Validate 80: 5898 > [112338, 107467, 158733, 144544, 115417] ...\n",
"Test 10: 5898 > [167957, 164747, 124147, 184424, 136508] ...\n"
]
}
],
"source": [
"random_state=42\n",
"#test/train/val split\n",
"train_ids,validate_ids,test_ids = test_train_val_split(print_ids=True,random_state=random_state);\n",
"\n",
"#create all features\n",
"m_ureg = units.MedicalUreg()\n",
"is_summable = lambda x: m_ureg.is_volume(str(x)) or m_ureg.is_mass(str(x))\n",
"\n",
"\n",
"\"\"\"\n",
"Data Specs\n",
"\"\"\"\n",
"summable = {\n",
" column_names.VAR_TYPE : variable_type.QUANTITATIVE,\n",
" column_names.COMPONENT : lambda comp: comp not in [data_dict.components.WEIGHT_BODY],\n",
" column_names.UNITS: is_summable\n",
"}\n",
"\n",
"ordinal = {\n",
" column_names.VAR_TYPE : variable_type.ORDINAL\n",
"}\n",
"\n",
"quantitative = {\n",
" column_names.VAR_TYPE : variable_type.QUANTITATIVE\n",
"}\n",
"\n",
"nominal = {\n",
" column_names.VAR_TYPE : variable_type.NOMINAL\n",
"}\n",
"\n",
"\"\"\"\n",
"FEATURES\n",
"\"\"\"\n",
"\n",
"F_mean_qn = features.DataSpecsFeaturizer(\n",
" 'mean',\n",
" resample_freq=None,\n",
" data_specs=[quantitative],\n",
" fillna_transformer=Pipeline([\n",
" ('ffill',transformers.GroupbyAndFFill(level=column_names.ID)),\n",
" ('fill_mean',transformers.FillerMean())\n",
" ])\n",
" \n",
")\n",
"\n",
"F_mean_ord = features.DataSpecsFeaturizer(\n",
" 'mean',\n",
" resample_freq=None,\n",
" data_specs=[ordinal],\n",
" fillna_transformer=Pipeline([\n",
" ('ffill',transformers.GroupbyAndFFill(level=column_names.ID)),\n",
" ('fill_mean',transformers.FillerMode())\n",
" ])\n",
" \n",
")\n",
"\n",
"F_last = features.DataSpecsFeaturizer(\n",
" agg_func='last',\n",
" resample_freq=None,\n",
" data_specs=[ordinal,quantitative],\n",
" fillna_transformer=Pipeline([\n",
" ('ffill',transformers.GroupbyAndFFill(level=column_names.ID)),\n",
" ('fill_mean',transformers.FillerMean())\n",
" ])\n",
")\n",
"\n",
"\n",
"F_std = features.DataSpecsFeaturizer(\n",
" 'std',\n",
" resample_freq=None,\n",
" data_specs=[ordinal,quantitative],\n",
" fillna_transformer=transformers.FillerZero()\n",
")\n",
"\n",
"F_sum = features.DataSpecsFeaturizer(\n",
" 'sum',\n",
" resample_freq=None,\n",
" data_specs=[summable],\n",
" fillna_transformer=transformers.FillerZero()\n",
")\n",
"\n",
"F_count = features.DataSpecsFeaturizer(\n",
" 'count',\n",
" resample_freq=None,\n",
" data_specs=[ordinal,quantitative],\n",
" post_processor = transformers.Replacer(0,np.nan),\n",
" fillna_transformer=transformers.FillerZero()\n",
")\n",
"\n",
"F_count_nom = features.DataSpecsFeaturizer(\n",
" 'sum',\n",
" resample_freq=None,\n",
" data_specs=[nominal],\n",
" fillna_transformer=transformers.FillerZero()\n",
")\n",
"\n",
"\"\"\"\n",
"LABELS\n",
"\"\"\"\n",
"qn_lactate_only={\n",
" column_names.COMPONENT : data_dict.components.LACTATE,\n",
" column_names.VAR_TYPE : variable_type.QUANTITATIVE\n",
"}\n",
"L_next_lac = features.DataSpecsFeaturizer(\n",
" agg_func='first',\n",
" resample_freq=None,\n",
" data_specs=qn_lactate_only,\n",
" post_processor=transformers.TimeShifter(column_names.DATETIME,shift='infer',n=-1)\n",
")\n",
"\n",
"L_delta_lac = features.DataSpecsFeaturizer(\n",
" agg_func='last',\n",
" resample_freq=None,\n",
" data_specs=qn_lactate_only,\n",
" post_processor=Pipeline([\n",
" ('group_by_id',transformers.ToGroupby(level=column_names.ID)),\n",
" ('delta',transformers.Delta())\n",
" ])\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Smaller Data Set"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[100014L, 100029L, 100039L, 100046L, 100052L] 9436\n"
]
}
],
"source": [
"reload(logger)\n",
"\n",
"train_subset = pd.Series(train_ids).sample(frac=0.2, random_state=random_state).sort_values().tolist()\n",
"\n",
"print train_subset[:5], len(train_subset)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(features)\n",
"#with more memory/a better processor, might not need these first 2 cleaning steps until post-processing\n",
"combine_like = Pipeline([\n",
" ('drop_small_columns',transformers.remove_small_columns(threshold=1000)),\n",
" ('drop_low_id_count',transformers.record_threshold(threshold=100)),\n",
" ('combine_like_columns',transformers.combine_like_cols())\n",
" ])\n",
"\n",
"drop_low_counts = Pipeline([\n",
" ('row_threshold',transformers.DropNaN(thresh=20)), #this threshold MAY not apply to a larger feature set.\n",
" ('drop_small_columns',transformers.remove_small_columns(threshold=1000)),\n",
" ('drop_low_id_count',transformers.record_threshold(threshold=100)) \n",
" ])\n",
"\n",
"dsf_labels = features.DataSetFactory(\n",
" featurizers=[\n",
" ('NEXT_LACTATE',L_next_lac),\n",
" ('DELTA_LACTATE',L_delta_lac)\n",
" ],\n",
" resample_freq='2H',\n",
" components=[data_dict.components.LACTATE],\n",
" etl_manager = etl_manager,\n",
" pre_processor = combine_like,\n",
" post_processor = transformers.DropNaN(thresh=1) #drop any rows that have NO labels\n",
")\n",
"\n",
"dsf_features = features.DataSetFactory(\n",
" featurizers=[\n",
" ('MEAN_QN',F_mean_qn),\n",
" ('MEAN_ORD',F_mean_ord),\n",
" ('LAST',F_last),\n",
" ('STD',F_std),\n",
" ('SUM',F_sum),\n",
" ('COUNT',F_count),\n",
" ('COUNT_NOMINAL',F_count_nom),\n",
" ],\n",
" resample_freq='2H',\n",
" components=data_dict.get_components(), # simple data\n",
" etl_manager = etl_manager,\n",
" pre_processor = combine_like,\n",
" post_processor = drop_low_counts\n",
"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2017-08-22 11:41:34) Make Feature Set. id_count=9436, #features=2, components=\n",
"(2017-08-22 11:41:34)>> Begin union for 1 transformers\n",
"(2017-08-22 11:41:34)>>>> lactate\n",
"(2017-08-22 11:41:34)>>>>>> Load data from component: LACTATE\n",
"(2017-08-22 11:41:36)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:41:36)>>>>>> *fit* Filter columns (remove_small_columns) (28278, 63)\n",
"(2017-08-22 11:41:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:36)>>>>>> *transform* Filter columns (remove_small_columns) (28278, 63)\n",
"(2017-08-22 11:41:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:36)>>>>>> *fit* Filter columns (record_threshold) (28278, 4)\n",
"(2017-08-22 11:41:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:36)>>>>>> *transform* Filter columns (record_threshold) (28278, 4)\n",
"(2017-08-22 11:41:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:36)>>>>>> FIT Combine like columns (28278, 4)\n",
"(2017-08-22 11:41:36)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 11:41:36)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:36)>>>>>> TRANSFORM Combine like columns (28278, 4)\n",
"(2017-08-22 11:41:36)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 11:41:37)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:41:37)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:41:37)<<<< --- (3.0s)\n",
"(2017-08-22 11:41:37)<< --- (3.0s)\n",
"(2017-08-22 11:41:37)>> Begin union for 2 transformers\n",
"(2017-08-22 11:41:37)>>>> NEXT_LACTATE\n",
"(2017-08-22 11:41:37)>>>>>> *fit* Filter columns (DataSpecFilter) (28278, 1)\n",
"(2017-08-22 11:41:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:37)>>>>>> *transform* Filter columns (DataSpecFilter) (28278, 1)\n",
"(2017-08-22 11:41:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:47)<<<< --- (10.0s)\n",
"(2017-08-22 11:41:47)>>>> DELTA_LACTATE\n",
"(2017-08-22 11:41:47)>>>>>> *fit* Filter columns (DataSpecFilter) (28278, 1)\n",
"(2017-08-22 11:41:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:41:47)>>>>>> *transform* Filter columns (DataSpecFilter) (28278, 1)\n",
"(2017-08-22 11:41:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:02)<<<< --- (15.0s)\n",
"(2017-08-22 11:42:02)<< --- (25.0s)\n",
"(2017-08-22 11:42:02) --- (28.0s)\n"
]
}
],
"source": [
"df_labels = dsf_labels.fit_transform(train_subset)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2017-08-22 11:42:07) Make Feature Set. id_count=9436, #features=7, components=\n",
"(2017-08-22 11:42:07)>> Begin union for 57 transformers\n",
"(2017-08-22 11:42:07)>>>> heart rate\n",
"(2017-08-22 11:42:07)>>>>>> Load data from component: HEART RATE\n",
"(2017-08-22 11:42:20)<<<<<< --- (13.0s)\n",
"(2017-08-22 11:42:20)>>>>>> *fit* Filter columns (remove_small_columns) (1324365, 6)\n",
"(2017-08-22 11:42:20)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:20)>>>>>> *transform* Filter columns (remove_small_columns) (1324365, 6)\n",
"(2017-08-22 11:42:20)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:20)>>>>>> *fit* Filter columns (record_threshold) (1324365, 3)\n",
"(2017-08-22 11:42:20)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:20)>>>>>> *transform* Filter columns (record_threshold) (1324365, 3)\n",
"(2017-08-22 11:42:20)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:20)>>>>>> FIT Combine like columns (1324365, 3)\n",
"(2017-08-22 11:42:20)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
"(2017-08-22 11:42:20)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:20)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:42:20)>>>>>> TRANSFORM Combine like columns (1324365, 3)\n",
"(2017-08-22 11:42:20)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
"(2017-08-22 11:42:47)<<<<<<<< --- (27.0s)\n",
"(2017-08-22 11:42:47)<<<<<< --- (27.0s)\n",
"(2017-08-22 11:42:47)<<<< --- (40.0s)\n",
"(2017-08-22 11:42:47)>>>> blood pressure systolic\n",
"(2017-08-22 11:42:47)>>>>>> Load data from component: BLOOD PRESSURE SYSTOLIC\n",
"(2017-08-22 11:43:09)<<<<<< --- (22.0s)\n",
"(2017-08-22 11:43:09)>>>>>> *fit* Filter columns (remove_small_columns) (986124, 41)\n",
"(2017-08-22 11:43:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)>>>>>> *transform* Filter columns (remove_small_columns) (986124, 41)\n",
"(2017-08-22 11:43:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)>>>>>> *fit* Filter columns (record_threshold) (986124, 7)\n",
"(2017-08-22 11:43:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)>>>>>> *transform* Filter columns (record_threshold) (986124, 7)\n",
"(2017-08-22 11:43:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)>>>>>> FIT Combine like columns (986124, 6)\n",
"(2017-08-22 11:43:09)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:43:09)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 11:43:09)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:43:09)>>>>>> TRANSFORM Combine like columns (986124, 6)\n",
"(2017-08-22 11:43:09)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 11:43:15)<<<<<<<< --- (6.0s)\n",
"(2017-08-22 11:43:15)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:43:37)<<<<<<<< --- (22.0s)\n",
"(2017-08-22 11:43:37)<<<<<< --- (28.0s)\n",
"(2017-08-22 11:43:49)<<<< --- (62.0s)\n",
"(2017-08-22 11:43:49)>>>> blood pressure diastolic\n",
"(2017-08-22 11:43:49)>>>>>> Load data from component: BLOOD PRESSURE DIASTOLIC\n",
"(2017-08-22 11:44:16)<<<<<< --- (27.0s)\n",
"(2017-08-22 11:44:16)>>>>>> *fit* Filter columns (remove_small_columns) (985854, 42)\n",
"(2017-08-22 11:44:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)>>>>>> *transform* Filter columns (remove_small_columns) (985854, 42)\n",
"(2017-08-22 11:44:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)>>>>>> *fit* Filter columns (record_threshold) (985854, 7)\n",
"(2017-08-22 11:44:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)>>>>>> *transform* Filter columns (record_threshold) (985854, 7)\n",
"(2017-08-22 11:44:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)>>>>>> FIT Combine like columns (985854, 6)\n",
"(2017-08-22 11:44:16)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:44:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 11:44:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:44:16)>>>>>> TRANSFORM Combine like columns (985854, 6)\n",
"(2017-08-22 11:44:16)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:44:37)<<<<<<<< --- (21.0s)\n",
"(2017-08-22 11:44:37)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 11:44:39)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:44:39)<<<<<< --- (23.0s)\n",
"(2017-08-22 11:44:51)<<<< --- (62.0s)\n",
"(2017-08-22 11:44:51)>>>> blood pressure mean\n",
"(2017-08-22 11:44:51)>>>>>> Load data from component: BLOOD PRESSURE MEAN\n",
"(2017-08-22 11:45:00)<<<<<< --- (9.0s)\n",
"(2017-08-22 11:45:00)>>>>>> *fit* Filter columns (remove_small_columns) (951550, 7)\n",
"(2017-08-22 11:45:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:00)>>>>>> *transform* Filter columns (remove_small_columns) (951550, 7)\n",
"(2017-08-22 11:45:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:00)>>>>>> *fit* Filter columns (record_threshold) (951550, 6)\n",
"(2017-08-22 11:45:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:00)>>>>>> *transform* Filter columns (record_threshold) (951550, 6)\n",
"(2017-08-22 11:45:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:00)>>>>>> FIT Combine like columns (951550, 5)\n",
"(2017-08-22 11:45:00)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:45:00)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:00)>>>>>> TRANSFORM Combine like columns (951550, 5)\n",
"(2017-08-22 11:45:00)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:45:22)<<<<<<<< --- (22.0s)\n",
"(2017-08-22 11:45:22)<<<<<< --- (22.0s)\n",
"(2017-08-22 11:45:33)<<<< --- (42.0s)\n",
"(2017-08-22 11:45:33)>>>> respiratory rate\n",
"(2017-08-22 11:45:33)>>>>>> Load data from component: RESPIRATORY RATE\n",
"(2017-08-22 11:45:47)<<<<<< --- (14.0s)\n",
"(2017-08-22 11:45:47)>>>>>> *fit* Filter columns (remove_small_columns) (1321985, 8)\n",
"(2017-08-22 11:45:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)>>>>>> *transform* Filter columns (remove_small_columns) (1321985, 8)\n",
"(2017-08-22 11:45:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)>>>>>> *fit* Filter columns (record_threshold) (1321985, 5)\n",
"(2017-08-22 11:45:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)>>>>>> *transform* Filter columns (record_threshold) (1321985, 5)\n",
"(2017-08-22 11:45:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)>>>>>> FIT Combine like columns (1321985, 5)\n",
"(2017-08-22 11:45:47)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
"(2017-08-22 11:45:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
"(2017-08-22 11:45:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:45:47)>>>>>> TRANSFORM Combine like columns (1321985, 5)\n",
"(2017-08-22 11:45:47)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
"(2017-08-22 11:46:13)<<<<<<<< --- (26.0s)\n",
"(2017-08-22 11:46:13)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
"(2017-08-22 11:46:17)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 11:46:17)<<<<<< --- (30.0s)\n",
"(2017-08-22 11:46:31)<<<< --- (58.0s)\n",
"(2017-08-22 11:46:31)>>>> temperature body\n",
"(2017-08-22 11:46:31)>>>>>> Load data from component: TEMPERATURE BODY\n",
"(2017-08-22 11:46:33)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:46:33)>>>>>> *fit* Filter columns (remove_small_columns) (274747, 4)\n",
"(2017-08-22 11:46:33)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:33)>>>>>> *transform* Filter columns (remove_small_columns) (274747, 4)\n",
"(2017-08-22 11:46:33)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:33)>>>>>> *fit* Filter columns (record_threshold) (274747, 4)\n",
"(2017-08-22 11:46:33)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:33)>>>>>> *transform* Filter columns (record_threshold) (274747, 4)\n",
"(2017-08-22 11:46:33)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:33)>>>>>> FIT Combine like columns (274747, 4)\n",
"(2017-08-22 11:46:33)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
"(2017-08-22 11:46:33)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:33)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:33)>>>>>> TRANSFORM Combine like columns (274747, 4)\n",
"(2017-08-22 11:46:33)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
"(2017-08-22 11:46:39)<<<<<<<< --- (6.0s)\n",
"(2017-08-22 11:46:39)<<<<<< --- (6.0s)\n",
"(2017-08-22 11:46:48)<<<< --- (17.0s)\n",
"(2017-08-22 11:46:48)>>>> oxygen saturation pulse oximetry\n",
"(2017-08-22 11:46:48)>>>>>> Load data from component: OXYGEN SATURATION PULSE OXIMETRY\n",
"(2017-08-22 11:46:54)<<<<<< --- (6.0s)\n",
"(2017-08-22 11:46:54)>>>>>> *fit* Filter columns (remove_small_columns) (982829, 2)\n",
"(2017-08-22 11:46:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:54)>>>>>> *transform* Filter columns (remove_small_columns) (982829, 2)\n",
"(2017-08-22 11:46:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:54)>>>>>> *fit* Filter columns (record_threshold) (982829, 2)\n",
"(2017-08-22 11:46:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:54)>>>>>> *transform* Filter columns (record_threshold) (982829, 2)\n",
"(2017-08-22 11:46:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:54)>>>>>> FIT Combine like columns (982829, 2)\n",
"(2017-08-22 11:46:54)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:46:54)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:46:54)>>>>>> TRANSFORM Combine like columns (982829, 2)\n",
"(2017-08-22 11:46:54)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:47:16)<<<<<<<< --- (22.0s)\n",
"(2017-08-22 11:47:16)<<<<<< --- (22.0s)\n",
"(2017-08-22 11:47:28)<<<< --- (40.0s)\n",
"(2017-08-22 11:47:28)>>>> weight body\n",
"(2017-08-22 11:47:28)>>>>>> Load data from component: WEIGHT BODY\n",
"(2017-08-22 11:47:29)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:47:29)>>>>>> *fit* Filter columns (remove_small_columns) (14779, 3)\n",
"(2017-08-22 11:47:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:47:29)>>>>>> *transform* Filter columns (remove_small_columns) (14779, 3)\n",
"(2017-08-22 11:47:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:47:29)>>>>>> *fit* Filter columns (record_threshold) (14779, 2)\n",
"(2017-08-22 11:47:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:47:29)>>>>>> *transform* Filter columns (record_threshold) (14779, 2)\n",
"(2017-08-22 11:47:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:47:29)>>>>>> FIT Combine like columns (14779, 2)\n",
"(2017-08-22 11:47:29)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
"(2017-08-22 11:47:29)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:47:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:47:29)>>>>>> TRANSFORM Combine like columns (14779, 2)\n",
"(2017-08-22 11:47:29)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
"(2017-08-22 11:47:30)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:47:30)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:47:37)<<<< --- (9.0s)\n",
"(2017-08-22 11:47:37)>>>> output urine\n",
"(2017-08-22 11:47:37)>>>>>> Load data from component: OUTPUT URINE\n",
"(2017-08-22 11:48:26)<<<<<< --- (49.0s)\n",
"(2017-08-22 11:48:26)>>>>>> *fit* Filter columns (remove_small_columns) (604836, 92)\n",
"(2017-08-22 11:48:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)>>>>>> *transform* Filter columns (remove_small_columns) (604836, 92)\n",
"(2017-08-22 11:48:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)>>>>>> *fit* Filter columns (record_threshold) (604836, 10)\n",
"(2017-08-22 11:48:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)>>>>>> *transform* Filter columns (record_threshold) (604836, 10)\n",
"(2017-08-22 11:48:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)>>>>>> FIT Combine like columns (604836, 8)\n",
"(2017-08-22 11:48:26)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
"(2017-08-22 11:48:26)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)>>>>>>>> ('output urine', 'unknown', 'nom', 'no_units')\n",
"(2017-08-22 11:48:26)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:26)>>>>>> TRANSFORM Combine like columns (604836, 8)\n",
"(2017-08-22 11:48:26)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
"(2017-08-22 11:48:40)<<<<<<<< --- (14.0s)\n",
"(2017-08-22 11:48:40)<<<<<< --- (14.0s)\n",
"(2017-08-22 11:48:51)<<<< --- (74.0s)\n",
"(2017-08-22 11:48:51)>>>> glasgow coma scale motor\n",
"(2017-08-22 11:48:51)>>>>>> Load data from component: GLASGOW COMA SCALE MOTOR\n",
"(2017-08-22 11:48:53)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:48:53)>>>>>> *fit* Filter columns (remove_small_columns) (153030, 1)\n",
"(2017-08-22 11:48:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:53)>>>>>> *transform* Filter columns (remove_small_columns) (153030, 1)\n",
"(2017-08-22 11:48:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:53)>>>>>> *fit* Filter columns (record_threshold) (153030, 1)\n",
"(2017-08-22 11:48:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:53)>>>>>> *transform* Filter columns (record_threshold) (153030, 1)\n",
"(2017-08-22 11:48:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:53)>>>>>> FIT Combine like columns (153030, 1)\n",
"(2017-08-22 11:48:53)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
"(2017-08-22 11:48:53)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:48:53)>>>>>> TRANSFORM Combine like columns (153030, 1)\n",
"(2017-08-22 11:48:53)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
"(2017-08-22 11:48:54)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:48:54)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:03)<<<< --- (12.0s)\n",
"(2017-08-22 11:49:03)>>>> glasgow coma scale eye opening\n",
"(2017-08-22 11:49:03)>>>>>> Load data from component: GLASGOW COMA SCALE EYE OPENING\n",
"(2017-08-22 11:49:05)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:05)>>>>>> *fit* Filter columns (remove_small_columns) (153765, 1)\n",
"(2017-08-22 11:49:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:05)>>>>>> *transform* Filter columns (remove_small_columns) (153765, 1)\n",
"(2017-08-22 11:49:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:05)>>>>>> *fit* Filter columns (record_threshold) (153765, 1)\n",
"(2017-08-22 11:49:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:05)>>>>>> *transform* Filter columns (record_threshold) (153765, 1)\n",
"(2017-08-22 11:49:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:05)>>>>>> FIT Combine like columns (153765, 1)\n",
"(2017-08-22 11:49:05)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
"(2017-08-22 11:49:05)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:05)>>>>>> TRANSFORM Combine like columns (153765, 1)\n",
"(2017-08-22 11:49:05)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
"(2017-08-22 11:49:07)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:07)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:15)<<<< --- (12.0s)\n",
"(2017-08-22 11:49:15)>>>> glasgow coma scale verbal\n",
"(2017-08-22 11:49:15)>>>>>> Load data from component: GLASGOW COMA SCALE VERBAL\n",
"(2017-08-22 11:49:17)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:17)>>>>>> *fit* Filter columns (remove_small_columns) (153421, 1)\n",
"(2017-08-22 11:49:17)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:17)>>>>>> *transform* Filter columns (remove_small_columns) (153421, 1)\n",
"(2017-08-22 11:49:17)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:17)>>>>>> *fit* Filter columns (record_threshold) (153421, 1)\n",
"(2017-08-22 11:49:17)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:17)>>>>>> *transform* Filter columns (record_threshold) (153421, 1)\n",
"(2017-08-22 11:49:17)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:17)>>>>>> FIT Combine like columns (153421, 1)\n",
"(2017-08-22 11:49:17)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
"(2017-08-22 11:49:17)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:17)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:17)>>>>>> TRANSFORM Combine like columns (153421, 1)\n",
"(2017-08-22 11:49:17)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
"(2017-08-22 11:49:19)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:19)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:28)<<<< --- (13.0s)\n",
"(2017-08-22 11:49:28)>>>> normal saline\n",
"(2017-08-22 11:49:28)>>>>>> Load data from component: NORMAL SALINE\n",
"(2017-08-22 11:49:30)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:30)>>>>>> *fit* Filter columns (remove_small_columns) (77035, 16)\n",
"(2017-08-22 11:49:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>> *transform* Filter columns (remove_small_columns) (77035, 16)\n",
"(2017-08-22 11:49:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>> *fit* Filter columns (record_threshold) (77035, 2)\n",
"(2017-08-22 11:49:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>> *transform* Filter columns (record_threshold) (77035, 2)\n",
"(2017-08-22 11:49:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>> FIT Combine like columns (77035, 2)\n",
"(2017-08-22 11:49:30)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
"(2017-08-22 11:49:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
"(2017-08-22 11:49:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>> TRANSFORM Combine like columns (77035, 2)\n",
"(2017-08-22 11:49:30)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
"(2017-08-22 11:49:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:30)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
"(2017-08-22 11:49:31)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:31)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:40)<<<< --- (12.0s)\n",
"(2017-08-22 11:49:40)>>>> lactated ringers\n",
"(2017-08-22 11:49:40)>>>>>> Load data from component: LACTATED RINGERS\n",
"(2017-08-22 11:49:42)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:42)>>>>>> *fit* Filter columns (remove_small_columns) (40451, 20)\n",
"(2017-08-22 11:49:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:42)>>>>>> *transform* Filter columns (remove_small_columns) (40451, 20)\n",
"(2017-08-22 11:49:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:42)>>>>>> *fit* Filter columns (record_threshold) (40451, 2)\n",
"(2017-08-22 11:49:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:42)>>>>>> *transform* Filter columns (record_threshold) (40451, 2)\n",
"(2017-08-22 11:49:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:42)>>>>>> FIT Combine like columns (40451, 2)\n",
"(2017-08-22 11:49:42)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
"(2017-08-22 11:49:42)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:42)>>>>>> TRANSFORM Combine like columns (40451, 2)\n",
"(2017-08-22 11:49:42)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
"(2017-08-22 11:49:43)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:43)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:51)<<<< --- (11.0s)\n",
"(2017-08-22 11:49:51)>>>> norepinephrine\n",
"(2017-08-22 11:49:51)>>>>>> Load data from component: NOREPINEPHRINE\n",
"(2017-08-22 11:49:52)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:52)>>>>>> *fit* Filter columns (remove_small_columns) (59948, 5)\n",
"(2017-08-22 11:49:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)>>>>>> *transform* Filter columns (remove_small_columns) (59948, 5)\n",
"(2017-08-22 11:49:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)>>>>>> *fit* Filter columns (record_threshold) (59948, 4)\n",
"(2017-08-22 11:49:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)>>>>>> *transform* Filter columns (record_threshold) (59948, 4)\n",
"(2017-08-22 11:49:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)>>>>>> FIT Combine like columns (59948, 3)\n",
"(2017-08-22 11:49:52)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
"(2017-08-22 11:49:52)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
"(2017-08-22 11:49:52)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:49:52)>>>>>> TRANSFORM Combine like columns (59948, 3)\n",
"(2017-08-22 11:49:52)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
"(2017-08-22 11:49:53)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:49:53)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
"(2017-08-22 11:49:55)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:49:55)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:50:03)<<<< --- (12.0s)\n",
"(2017-08-22 11:50:03)>>>> vasopressin\n",
"(2017-08-22 11:50:03)>>>>>> Load data from component: VASOPRESSIN\n",
"(2017-08-22 11:50:05)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:50:05)>>>>>> *fit* Filter columns (remove_small_columns) (19048, 38)\n",
"(2017-08-22 11:50:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>> *transform* Filter columns (remove_small_columns) (19048, 38)\n",
"(2017-08-22 11:50:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>> *fit* Filter columns (record_threshold) (19048, 2)\n",
"(2017-08-22 11:50:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>> *transform* Filter columns (record_threshold) (19048, 2)\n",
"(2017-08-22 11:50:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>> FIT Combine like columns (19048, 2)\n",
"(2017-08-22 11:50:05)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
"(2017-08-22 11:50:05)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
"(2017-08-22 11:50:05)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>> TRANSFORM Combine like columns (19048, 2)\n",
"(2017-08-22 11:50:05)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
"(2017-08-22 11:50:05)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
"(2017-08-22 11:50:05)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:05)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:13)<<<< --- (10.0s)\n",
"(2017-08-22 11:50:13)>>>> lactate\n",
"(2017-08-22 11:50:13)>>>>>> Load data from component: LACTATE\n",
"(2017-08-22 11:50:15)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:50:15)>>>>>> *fit* Filter columns (remove_small_columns) (28278, 63)\n",
"(2017-08-22 11:50:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:15)>>>>>> *transform* Filter columns (remove_small_columns) (28278, 63)\n",
"(2017-08-22 11:50:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:15)>>>>>> *fit* Filter columns (record_threshold) (28278, 4)\n",
"(2017-08-22 11:50:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:15)>>>>>> *transform* Filter columns (record_threshold) (28278, 4)\n",
"(2017-08-22 11:50:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:15)>>>>>> FIT Combine like columns (28278, 4)\n",
"(2017-08-22 11:50:15)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 11:50:15)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:15)>>>>>> TRANSFORM Combine like columns (28278, 4)\n",
"(2017-08-22 11:50:15)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 11:50:16)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:50:16)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:50:24)<<<< --- (11.0s)\n",
"(2017-08-22 11:50:24)>>>> hemoglobin\n",
"(2017-08-22 11:50:24)>>>>>> Load data from component: HEMOGLOBIN\n",
"(2017-08-22 11:50:27)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:50:27)>>>>>> *fit* Filter columns (remove_small_columns) (108697, 44)\n",
"(2017-08-22 11:50:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:27)>>>>>> *transform* Filter columns (remove_small_columns) (108697, 44)\n",
"(2017-08-22 11:50:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:27)>>>>>> *fit* Filter columns (record_threshold) (108697, 8)\n",
"(2017-08-22 11:50:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:27)>>>>>> *transform* Filter columns (record_threshold) (108697, 8)\n",
"(2017-08-22 11:50:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:27)>>>>>> FIT Combine like columns (108697, 8)\n",
"(2017-08-22 11:50:27)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
"(2017-08-22 11:50:27)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:27)>>>>>> TRANSFORM Combine like columns (108697, 8)\n",
"(2017-08-22 11:50:27)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
"(2017-08-22 11:50:30)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:50:30)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:50:40)<<<< --- (16.0s)\n",
"(2017-08-22 11:50:40)>>>> white blood cell count\n",
"(2017-08-22 11:50:40)>>>>>> Load data from component: WHITE BLOOD CELL COUNT\n",
"(2017-08-22 11:50:45)<<<<<< --- (5.0s)\n",
"(2017-08-22 11:50:45)>>>>>> *fit* Filter columns (remove_small_columns) (102160, 95)\n",
"(2017-08-22 11:50:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>> *transform* Filter columns (remove_small_columns) (102160, 95)\n",
"(2017-08-22 11:50:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>> *fit* Filter columns (record_threshold) (102160, 11)\n",
"(2017-08-22 11:50:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>> *transform* Filter columns (record_threshold) (102160, 11)\n",
"(2017-08-22 11:50:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>> FIT Combine like columns (102160, 11)\n",
"(2017-08-22 11:50:45)>>>>>>>> ('white blood cell count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 11:50:45)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>>>> ('white blood cell count', 'unknown', 'nom', 'no_units')\n",
"(2017-08-22 11:50:45)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>>>> ('white blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 11:50:45)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:50:45)>>>>>> TRANSFORM Combine like columns (102160, 11)\n",
"(2017-08-22 11:50:45)>>>>>>>> ('white blood cell count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 11:50:48)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:50:48)>>>>>>>> ('white blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 11:50:49)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:50:49)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:51:00)<<<< --- (20.0s)\n",
"(2017-08-22 11:51:00)>>>> red blood cell count\n",
"(2017-08-22 11:51:00)>>>>>> Load data from component: RED BLOOD CELL COUNT\n",
"(2017-08-22 11:51:04)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:51:04)>>>>>> *fit* Filter columns (remove_small_columns) (101117, 88)\n",
"(2017-08-22 11:51:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>> *transform* Filter columns (remove_small_columns) (101117, 88)\n",
"(2017-08-22 11:51:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>> *fit* Filter columns (record_threshold) (101117, 6)\n",
"(2017-08-22 11:51:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>> *transform* Filter columns (record_threshold) (101117, 6)\n",
"(2017-08-22 11:51:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>> FIT Combine like columns (101117, 6)\n",
"(2017-08-22 11:51:04)>>>>>>>> ('red blood cell count', 'known', 'qn', 'x10e6/uL')\n",
"(2017-08-22 11:51:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>>>> ('red blood cell count', 'unknown', 'nom', 'no_units')\n",
"(2017-08-22 11:51:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'm/uL')\n",
"(2017-08-22 11:51:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 11:51:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:04)>>>>>> TRANSFORM Combine like columns (101117, 6)\n",
"(2017-08-22 11:51:04)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'm/uL')\n",
"(2017-08-22 11:51:07)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:51:07)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 11:51:07)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:07)>>>>>>>> ('red blood cell count', 'known', 'qn', 'x10e6/uL')\n",
"(2017-08-22 11:51:08)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:51:08)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:51:19)<<<< --- (19.0s)\n",
"(2017-08-22 11:51:19)>>>> hematocrit\n",
"(2017-08-22 11:51:19)>>>>>> Load data from component: HEMATOCRIT\n",
"(2017-08-22 11:51:22)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:51:22)>>>>>> *fit* Filter columns (remove_small_columns) (126133, 38)\n",
"(2017-08-22 11:51:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:22)>>>>>> *transform* Filter columns (remove_small_columns) (126133, 38)\n",
"(2017-08-22 11:51:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:22)>>>>>> *fit* Filter columns (record_threshold) (126133, 6)\n",
"(2017-08-22 11:51:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:22)>>>>>> *transform* Filter columns (record_threshold) (126133, 6)\n",
"(2017-08-22 11:51:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:22)>>>>>> FIT Combine like columns (126133, 6)\n",
"(2017-08-22 11:51:22)>>>>>>>> ('hematocrit', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:51:22)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:22)>>>>>> TRANSFORM Combine like columns (126133, 6)\n",
"(2017-08-22 11:51:22)>>>>>>>> ('hematocrit', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:51:25)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:51:25)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:51:36)<<<< --- (17.0s)\n",
"(2017-08-22 11:51:36)>>>> mean corpuscular volume\n",
"(2017-08-22 11:51:36)>>>>>> Load data from component: MEAN CORPUSCULAR VOLUME\n",
"(2017-08-22 11:51:39)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:51:39)>>>>>> *fit* Filter columns (remove_small_columns) (92054, 35)\n",
"(2017-08-22 11:51:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:39)>>>>>> *transform* Filter columns (remove_small_columns) (92054, 35)\n",
"(2017-08-22 11:51:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:39)>>>>>> *fit* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 11:51:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:39)>>>>>> *transform* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 11:51:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:39)>>>>>> FIT Combine like columns (92054, 1)\n",
"(2017-08-22 11:51:39)>>>>>>>> ('mean corpuscular volume', 'known', 'qn', 'fL')\n",
"(2017-08-22 11:51:39)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:39)>>>>>> TRANSFORM Combine like columns (92054, 1)\n",
"(2017-08-22 11:51:39)>>>>>>>> ('mean corpuscular volume', 'known', 'qn', 'fL')\n",
"(2017-08-22 11:51:40)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:51:40)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:51:51)<<<< --- (15.0s)\n",
"(2017-08-22 11:51:51)>>>> mean corpuscular hemoglobin\n",
"(2017-08-22 11:51:51)>>>>>> Load data from component: MEAN CORPUSCULAR HEMOGLOBIN\n",
"(2017-08-22 11:51:53)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:51:53)>>>>>> *fit* Filter columns (remove_small_columns) (92054, 30)\n",
"(2017-08-22 11:51:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:53)>>>>>> *transform* Filter columns (remove_small_columns) (92054, 30)\n",
"(2017-08-22 11:51:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:53)>>>>>> *fit* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 11:51:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:53)>>>>>> *transform* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 11:51:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:53)>>>>>> FIT Combine like columns (92054, 1)\n",
"(2017-08-22 11:51:53)>>>>>>>> ('mean corpuscular hemoglobin', 'known', 'qn', 'pg')\n",
"(2017-08-22 11:51:53)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:51:53)>>>>>> TRANSFORM Combine like columns (92054, 1)\n",
"(2017-08-22 11:51:53)>>>>>>>> ('mean corpuscular hemoglobin', 'known', 'qn', 'pg')\n",
"(2017-08-22 11:51:54)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:51:54)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:52:05)<<<< --- (14.0s)\n",
"(2017-08-22 11:52:05)>>>> mean corpuscular hemoglobin concentration\n",
"(2017-08-22 11:52:05)>>>>>> Load data from component: MEAN CORPUSCULAR HEMOGLOBIN CONCENTRATION\n",
"(2017-08-22 11:52:07)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:52:07)>>>>>> *fit* Filter columns (remove_small_columns) (92073, 29)\n",
"(2017-08-22 11:52:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:07)>>>>>> *transform* Filter columns (remove_small_columns) (92073, 29)\n",
"(2017-08-22 11:52:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:07)>>>>>> *fit* Filter columns (record_threshold) (92073, 1)\n",
"(2017-08-22 11:52:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:07)>>>>>> *transform* Filter columns (record_threshold) (92073, 1)\n",
"(2017-08-22 11:52:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:07)>>>>>> FIT Combine like columns (92073, 1)\n",
"(2017-08-22 11:52:07)>>>>>>>> ('mean corpuscular hemoglobin concentration', 'unknown', 'qn', 'percent')\n",
"(2017-08-22 11:52:07)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:07)>>>>>> TRANSFORM Combine like columns (92073, 1)\n",
"(2017-08-22 11:52:07)>>>>>>>> ('mean corpuscular hemoglobin concentration', 'unknown', 'qn', 'percent')\n",
"(2017-08-22 11:52:09)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:52:09)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:52:19)<<<< --- (14.0s)\n",
"(2017-08-22 11:52:19)>>>> red cell distribution width\n",
"(2017-08-22 11:52:19)>>>>>> Load data from component: RED CELL DISTRIBUTION WIDTH\n",
"(2017-08-22 11:52:21)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:52:21)>>>>>> *fit* Filter columns (remove_small_columns) (91900, 30)\n",
"(2017-08-22 11:52:21)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:21)>>>>>> *transform* Filter columns (remove_small_columns) (91900, 30)\n",
"(2017-08-22 11:52:21)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:21)>>>>>> *fit* Filter columns (record_threshold) (91900, 1)\n",
"(2017-08-22 11:52:21)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:21)>>>>>> *transform* Filter columns (record_threshold) (91900, 1)\n",
"(2017-08-22 11:52:21)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:21)>>>>>> FIT Combine like columns (91900, 1)\n",
"(2017-08-22 11:52:21)>>>>>>>> ('red cell distribution width', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:52:21)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:21)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:21)>>>>>> TRANSFORM Combine like columns (91900, 1)\n",
"(2017-08-22 11:52:21)>>>>>>>> ('red cell distribution width', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:52:22)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:52:22)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:52:33)<<<< --- (14.0s)\n",
"(2017-08-22 11:52:33)>>>> platelet count\n",
"(2017-08-22 11:52:33)>>>>>> Load data from component: PLATELET COUNT\n",
"(2017-08-22 11:52:36)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:52:36)>>>>>> *fit* Filter columns (remove_small_columns) (96686, 52)\n",
"(2017-08-22 11:52:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:36)>>>>>> *transform* Filter columns (remove_small_columns) (96686, 52)\n",
"(2017-08-22 11:52:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:36)>>>>>> *fit* Filter columns (record_threshold) (96686, 4)\n",
"(2017-08-22 11:52:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:36)>>>>>> *transform* Filter columns (record_threshold) (96686, 4)\n",
"(2017-08-22 11:52:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:36)>>>>>> FIT Combine like columns (96686, 4)\n",
"(2017-08-22 11:52:36)>>>>>>>> ('platelet count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 11:52:36)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:36)>>>>>> TRANSFORM Combine like columns (96686, 4)\n",
"(2017-08-22 11:52:36)>>>>>>>> ('platelet count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 11:52:38)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:52:38)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:52:50)<<<< --- (17.0s)\n",
"(2017-08-22 11:52:50)>>>> sodium serum\n",
"(2017-08-22 11:52:50)>>>>>> Load data from component: SODIUM SERUM\n",
"(2017-08-22 11:52:53)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:52:53)>>>>>> *fit* Filter columns (remove_small_columns) (115251, 72)\n",
"(2017-08-22 11:52:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:53)>>>>>> *transform* Filter columns (remove_small_columns) (115251, 72)\n",
"(2017-08-22 11:52:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:53)>>>>>> *fit* Filter columns (record_threshold) (115251, 7)\n",
"(2017-08-22 11:52:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:53)>>>>>> *transform* Filter columns (record_threshold) (115251, 7)\n",
"(2017-08-22 11:52:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:53)>>>>>> FIT Combine like columns (115251, 7)\n",
"(2017-08-22 11:52:53)>>>>>>>> ('sodium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:52:53)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:53)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:52:53)>>>>>> TRANSFORM Combine like columns (115251, 7)\n",
"(2017-08-22 11:52:53)>>>>>>>> ('sodium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:52:56)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:52:56)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:53:08)<<<< --- (18.0s)\n",
"(2017-08-22 11:53:08)>>>> potassium serum\n",
"(2017-08-22 11:53:08)>>>>>> Load data from component: POTASSIUM SERUM\n",
"(2017-08-22 11:53:21)<<<<<< --- (13.0s)\n",
"(2017-08-22 11:53:21)>>>>>> *fit* Filter columns (remove_small_columns) (138935, 487)\n",
"(2017-08-22 11:53:22)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:53:22)>>>>>> *transform* Filter columns (remove_small_columns) (138935, 487)\n",
"(2017-08-22 11:53:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:22)>>>>>> *fit* Filter columns (record_threshold) (138935, 7)\n",
"(2017-08-22 11:53:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:22)>>>>>> *transform* Filter columns (record_threshold) (138935, 7)\n",
"(2017-08-22 11:53:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:22)>>>>>> FIT Combine like columns (138935, 7)\n",
"(2017-08-22 11:53:22)>>>>>>>> ('potassium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:53:22)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:22)>>>>>> TRANSFORM Combine like columns (138935, 7)\n",
"(2017-08-22 11:53:22)>>>>>>>> ('potassium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:53:26)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 11:53:26)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:53:38)<<<< --- (30.0s)\n",
"(2017-08-22 11:53:38)>>>> chloride serum\n",
"(2017-08-22 11:53:38)>>>>>> Load data from component: CHLORIDE SERUM\n",
"(2017-08-22 11:53:42)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:53:42)>>>>>> *fit* Filter columns (remove_small_columns) (109458, 50)\n",
"(2017-08-22 11:53:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:42)>>>>>> *transform* Filter columns (remove_small_columns) (109458, 50)\n",
"(2017-08-22 11:53:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:42)>>>>>> *fit* Filter columns (record_threshold) (109458, 7)\n",
"(2017-08-22 11:53:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:42)>>>>>> *transform* Filter columns (record_threshold) (109458, 7)\n",
"(2017-08-22 11:53:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:42)>>>>>> FIT Combine like columns (109458, 7)\n",
"(2017-08-22 11:53:42)>>>>>>>> ('chloride serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 11:53:42)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:42)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:42)>>>>>> TRANSFORM Combine like columns (109458, 7)\n",
"(2017-08-22 11:53:42)>>>>>>>> ('chloride serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 11:53:46)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 11:53:46)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:53:57)<<<< --- (19.0s)\n",
"(2017-08-22 11:53:57)>>>> carbon dioxide serum\n",
"(2017-08-22 11:53:57)>>>>>> Load data from component: CARBON DIOXIDE SERUM\n",
"(2017-08-22 11:53:58)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:53:58)>>>>>> *fit* Filter columns (remove_small_columns) (34746, 17)\n",
"(2017-08-22 11:53:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:58)>>>>>> *transform* Filter columns (remove_small_columns) (34746, 17)\n",
"(2017-08-22 11:53:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:58)>>>>>> *fit* Filter columns (record_threshold) (34746, 2)\n",
"(2017-08-22 11:53:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:58)>>>>>> *transform* Filter columns (record_threshold) (34746, 2)\n",
"(2017-08-22 11:53:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:58)>>>>>> FIT Combine like columns (34746, 2)\n",
"(2017-08-22 11:53:58)>>>>>>>> ('carbon dioxide serum', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 11:53:58)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:53:58)>>>>>> TRANSFORM Combine like columns (34746, 2)\n",
"(2017-08-22 11:53:58)>>>>>>>> ('carbon dioxide serum', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 11:53:59)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:53:59)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:54:10)<<<< --- (13.0s)\n",
"(2017-08-22 11:54:10)>>>> blood urea nitrogen serum\n",
"(2017-08-22 11:54:10)>>>>>> Load data from component: BLOOD UREA NITROGEN SERUM\n",
"(2017-08-22 11:54:11)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:54:11)>>>>>> *fit* Filter columns (remove_small_columns) (98988, 17)\n",
"(2017-08-22 11:54:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:11)>>>>>> *transform* Filter columns (remove_small_columns) (98988, 17)\n",
"(2017-08-22 11:54:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:11)>>>>>> *fit* Filter columns (record_threshold) (98988, 3)\n",
"(2017-08-22 11:54:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:11)>>>>>> *transform* Filter columns (record_threshold) (98988, 3)\n",
"(2017-08-22 11:54:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:11)>>>>>> FIT Combine like columns (98988, 3)\n",
"(2017-08-22 11:54:11)>>>>>>>> ('blood urea nitrogen serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:54:11)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:11)>>>>>> TRANSFORM Combine like columns (98988, 3)\n",
"(2017-08-22 11:54:11)>>>>>>>> ('blood urea nitrogen serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:54:14)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:54:14)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:54:26)<<<< --- (16.0s)\n",
"(2017-08-22 11:54:26)>>>> creatinine serum\n",
"(2017-08-22 11:54:26)>>>>>> Load data from component: CREATININE SERUM\n",
"(2017-08-22 11:54:29)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:54:29)>>>>>> *fit* Filter columns (remove_small_columns) (99396, 64)\n",
"(2017-08-22 11:54:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:29)>>>>>> *transform* Filter columns (remove_small_columns) (99396, 64)\n",
"(2017-08-22 11:54:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:29)>>>>>> *fit* Filter columns (record_threshold) (99396, 4)\n",
"(2017-08-22 11:54:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:29)>>>>>> *transform* Filter columns (record_threshold) (99396, 4)\n",
"(2017-08-22 11:54:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:29)>>>>>> FIT Combine like columns (99396, 4)\n",
"(2017-08-22 11:54:29)>>>>>>>> ('creatinine serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:54:29)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:54:29)>>>>>> TRANSFORM Combine like columns (99396, 4)\n",
"(2017-08-22 11:54:29)>>>>>>>> ('creatinine serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:54:32)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:54:32)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:54:43)<<<< --- (17.0s)\n",
"(2017-08-22 11:54:43)>>>> glucose serum\n",
"(2017-08-22 11:54:43)>>>>>> Load data from component: GLUCOSE SERUM\n",
"(2017-08-22 11:55:30)<<<<<< --- (47.0s)\n",
"(2017-08-22 11:55:30)>>>>>> *fit* Filter columns (remove_small_columns) (143627, 1807)\n",
"(2017-08-22 11:55:34)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:55:34)>>>>>> *transform* Filter columns (remove_small_columns) (143627, 1807)\n",
"(2017-08-22 11:55:34)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:55:34)>>>>>> *fit* Filter columns (record_threshold) (143627, 6)\n",
"(2017-08-22 11:55:34)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:55:34)>>>>>> *transform* Filter columns (record_threshold) (143627, 6)\n",
"(2017-08-22 11:55:34)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:55:34)>>>>>> FIT Combine like columns (143627, 6)\n",
"(2017-08-22 11:55:34)>>>>>>>> ('glucose serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:55:34)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:55:34)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:55:34)>>>>>> TRANSFORM Combine like columns (143627, 6)\n",
"(2017-08-22 11:55:34)>>>>>>>> ('glucose serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:55:38)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 11:55:38)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:55:50)<<<< --- (67.0s)\n",
"(2017-08-22 11:55:50)>>>> glucose fingerstick\n",
"(2017-08-22 11:55:50)>>>>>> Load data from component: GLUCOSE FINGERSTICK\n",
"(2017-08-22 11:56:14)<<<<<< --- (24.0s)\n",
"(2017-08-22 11:56:14)>>>>>> *fit* Filter columns (remove_small_columns) (109246, 1087)\n",
"(2017-08-22 11:56:16)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:56:16)>>>>>> *transform* Filter columns (remove_small_columns) (109246, 1087)\n",
"(2017-08-22 11:56:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:16)>>>>>> *fit* Filter columns (record_threshold) (109246, 2)\n",
"(2017-08-22 11:56:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:16)>>>>>> *transform* Filter columns (record_threshold) (109246, 2)\n",
"(2017-08-22 11:56:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:16)>>>>>> FIT Combine like columns (109246, 2)\n",
"(2017-08-22 11:56:16)>>>>>>>> ('glucose fingerstick', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 11:56:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:16)>>>>>> TRANSFORM Combine like columns (109246, 2)\n",
"(2017-08-22 11:56:16)>>>>>>>> ('glucose fingerstick', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 11:56:19)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:56:19)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:56:30)<<<< --- (40.0s)\n",
"(2017-08-22 11:56:30)>>>> calcium total serum\n",
"(2017-08-22 11:56:30)>>>>>> Load data from component: CALCIUM TOTAL SERUM\n",
"(2017-08-22 11:56:32)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:56:32)>>>>>> *fit* Filter columns (remove_small_columns) (77351, 14)\n",
"(2017-08-22 11:56:32)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:32)>>>>>> *transform* Filter columns (remove_small_columns) (77351, 14)\n",
"(2017-08-22 11:56:32)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:32)>>>>>> *fit* Filter columns (record_threshold) (77351, 4)\n",
"(2017-08-22 11:56:32)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:32)>>>>>> *transform* Filter columns (record_threshold) (77351, 4)\n",
"(2017-08-22 11:56:32)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:32)>>>>>> FIT Combine like columns (77351, 4)\n",
"(2017-08-22 11:56:32)>>>>>>>> ('calcium total serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:56:32)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:32)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:32)>>>>>> TRANSFORM Combine like columns (77351, 4)\n",
"(2017-08-22 11:56:32)>>>>>>>> ('calcium total serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:56:33)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:56:33)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:56:45)<<<< --- (15.0s)\n",
"(2017-08-22 11:56:45)>>>> calcium ionized serum\n",
"(2017-08-22 11:56:45)>>>>>> Load data from component: CALCIUM IONIZED SERUM\n",
"(2017-08-22 11:56:48)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:56:48)>>>>>> *fit* Filter columns (remove_small_columns) (39782, 224)\n",
"(2017-08-22 11:56:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:48)>>>>>> *transform* Filter columns (remove_small_columns) (39782, 224)\n",
"(2017-08-22 11:56:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:48)>>>>>> *fit* Filter columns (record_threshold) (39782, 3)\n",
"(2017-08-22 11:56:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:48)>>>>>> *transform* Filter columns (record_threshold) (39782, 3)\n",
"(2017-08-22 11:56:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:48)>>>>>> FIT Combine like columns (39782, 3)\n",
"(2017-08-22 11:56:48)>>>>>>>> ('calcium ionized serum', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 11:56:48)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:56:48)>>>>>> TRANSFORM Combine like columns (39782, 3)\n",
"(2017-08-22 11:56:48)>>>>>>>> ('calcium ionized serum', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 11:56:49)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:56:49)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:56:59)<<<< --- (14.0s)\n",
"(2017-08-22 11:56:59)>>>> magnesium serum\n",
"(2017-08-22 11:56:59)>>>>>> Load data from component: MAGNESIUM SERUM\n",
"(2017-08-22 11:57:07)<<<<<< --- (8.0s)\n",
"(2017-08-22 11:57:07)>>>>>> *fit* Filter columns (remove_small_columns) (90097, 406)\n",
"(2017-08-22 11:57:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:07)>>>>>> *transform* Filter columns (remove_small_columns) (90097, 406)\n",
"(2017-08-22 11:57:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:07)>>>>>> *fit* Filter columns (record_threshold) (90097, 4)\n",
"(2017-08-22 11:57:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:07)>>>>>> *transform* Filter columns (record_threshold) (90097, 4)\n",
"(2017-08-22 11:57:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:07)>>>>>> FIT Combine like columns (90097, 4)\n",
"(2017-08-22 11:57:07)>>>>>>>> ('magnesium serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:57:07)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:07)>>>>>> TRANSFORM Combine like columns (90097, 4)\n",
"(2017-08-22 11:57:07)>>>>>>>> ('magnesium serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:57:10)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:57:10)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:57:21)<<<< --- (22.0s)\n",
"(2017-08-22 11:57:21)>>>> phosphorous serum\n",
"(2017-08-22 11:57:21)>>>>>> Load data from component: PHOSPHOROUS SERUM\n",
"(2017-08-22 11:57:22)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:57:22)>>>>>> *fit* Filter columns (remove_small_columns) (44532, 5)\n",
"(2017-08-22 11:57:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)>>>>>> *transform* Filter columns (remove_small_columns) (44532, 5)\n",
"(2017-08-22 11:57:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)>>>>>> *fit* Filter columns (record_threshold) (44532, 3)\n",
"(2017-08-22 11:57:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)>>>>>> *transform* Filter columns (record_threshold) (44532, 3)\n",
"(2017-08-22 11:57:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)>>>>>> FIT Combine like columns (44532, 3)\n",
"(2017-08-22 11:57:22)>>>>>>>> ('phosphorous serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:57:22)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)>>>>>>>> ('phosphorous serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 11:57:22)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:22)>>>>>> TRANSFORM Combine like columns (44532, 3)\n",
"(2017-08-22 11:57:22)>>>>>>>> ('phosphorous serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 11:57:23)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:57:23)>>>>>>>> ('phosphorous serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 11:57:24)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:57:24)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:57:35)<<<< --- (14.0s)\n",
"(2017-08-22 11:57:35)>>>> prothrombin time\n",
"(2017-08-22 11:57:35)>>>>>> Load data from component: PROTHROMBIN TIME\n",
"(2017-08-22 11:57:37)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:57:37)>>>>>> *fit* Filter columns (remove_small_columns) (61551, 64)\n",
"(2017-08-22 11:57:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:37)>>>>>> *transform* Filter columns (remove_small_columns) (61551, 64)\n",
"(2017-08-22 11:57:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:37)>>>>>> *fit* Filter columns (record_threshold) (61551, 5)\n",
"(2017-08-22 11:57:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:37)>>>>>> *transform* Filter columns (record_threshold) (61551, 5)\n",
"(2017-08-22 11:57:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:37)>>>>>> FIT Combine like columns (61551, 5)\n",
"(2017-08-22 11:57:37)>>>>>>>> ('prothrombin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 11:57:37)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:37)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:37)>>>>>> TRANSFORM Combine like columns (61551, 5)\n",
"(2017-08-22 11:57:37)>>>>>>>> ('prothrombin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 11:57:38)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:57:38)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:57:50)<<<< --- (15.0s)\n",
"(2017-08-22 11:57:50)>>>> partial thromboplastin time\n",
"(2017-08-22 11:57:50)>>>>>> Load data from component: PARTIAL THROMBOPLASTIN TIME\n",
"(2017-08-22 11:57:52)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:57:52)>>>>>> *fit* Filter columns (remove_small_columns) (65729, 73)\n",
"(2017-08-22 11:57:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:52)>>>>>> *transform* Filter columns (remove_small_columns) (65729, 73)\n",
"(2017-08-22 11:57:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:52)>>>>>> *fit* Filter columns (record_threshold) (65729, 4)\n",
"(2017-08-22 11:57:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:52)>>>>>> *transform* Filter columns (record_threshold) (65729, 4)\n",
"(2017-08-22 11:57:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:52)>>>>>> FIT Combine like columns (65729, 4)\n",
"(2017-08-22 11:57:52)>>>>>>>> ('partial thromboplastin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 11:57:52)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:57:52)>>>>>> TRANSFORM Combine like columns (65729, 4)\n",
"(2017-08-22 11:57:52)>>>>>>>> ('partial thromboplastin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 11:57:54)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:57:54)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:05)<<<< --- (15.0s)\n",
"(2017-08-22 11:58:05)>>>> international normalized ratio\n",
"(2017-08-22 11:58:05)>>>>>> Load data from component: INTERNATIONAL NORMALIZED RATIO\n",
"(2017-08-22 11:58:07)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:07)>>>>>> *fit* Filter columns (remove_small_columns) (61672, 135)\n",
"(2017-08-22 11:58:08)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:58:08)>>>>>> *transform* Filter columns (remove_small_columns) (61672, 135)\n",
"(2017-08-22 11:58:08)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:08)>>>>>> *fit* Filter columns (record_threshold) (61672, 4)\n",
"(2017-08-22 11:58:08)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:08)>>>>>> *transform* Filter columns (record_threshold) (61672, 4)\n",
"(2017-08-22 11:58:08)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:08)>>>>>> FIT Combine like columns (61672, 4)\n",
"(2017-08-22 11:58:08)>>>>>>>> ('international normalized ratio', 'known', 'qn', 'no_units')\n",
"(2017-08-22 11:58:08)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:08)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:08)>>>>>> TRANSFORM Combine like columns (61672, 4)\n",
"(2017-08-22 11:58:08)>>>>>>>> ('international normalized ratio', 'known', 'qn', 'no_units')\n",
"(2017-08-22 11:58:09)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:58:09)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:58:20)<<<< --- (15.0s)\n",
"(2017-08-22 11:58:20)>>>> partial pressure of oxygen arterial\n",
"(2017-08-22 11:58:20)>>>>>> Load data from component: PARTIAL PRESSURE OF OXYGEN ARTERIAL\n",
"(2017-08-22 11:58:23)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:58:23)>>>>>> *fit* Filter columns (remove_small_columns) (79854, 55)\n",
"(2017-08-22 11:58:23)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:23)>>>>>> *transform* Filter columns (remove_small_columns) (79854, 55)\n",
"(2017-08-22 11:58:23)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:23)>>>>>> *fit* Filter columns (record_threshold) (79854, 6)\n",
"(2017-08-22 11:58:23)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:23)>>>>>> *transform* Filter columns (record_threshold) (79854, 6)\n",
"(2017-08-22 11:58:23)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:23)>>>>>> FIT Combine like columns (79854, 6)\n",
"(2017-08-22 11:58:23)>>>>>>>> ('partial pressure of oxygen arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:58:23)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:23)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:23)>>>>>> TRANSFORM Combine like columns (79854, 6)\n",
"(2017-08-22 11:58:23)>>>>>>>> ('partial pressure of oxygen arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:58:25)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:25)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:36)<<<< --- (16.0s)\n",
"(2017-08-22 11:58:36)>>>> partial pressure of carbon dioxide arterial\n",
"(2017-08-22 11:58:36)>>>>>> Load data from component: PARTIAL PRESSURE OF CARBON DIOXIDE ARTERIAL\n",
"(2017-08-22 11:58:39)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:58:39)>>>>>> *fit* Filter columns (remove_small_columns) (79883, 44)\n",
"(2017-08-22 11:58:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:39)>>>>>> *transform* Filter columns (remove_small_columns) (79883, 44)\n",
"(2017-08-22 11:58:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:39)>>>>>> *fit* Filter columns (record_threshold) (79883, 6)\n",
"(2017-08-22 11:58:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:39)>>>>>> *transform* Filter columns (record_threshold) (79883, 6)\n",
"(2017-08-22 11:58:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:39)>>>>>> FIT Combine like columns (79883, 6)\n",
"(2017-08-22 11:58:39)>>>>>>>> ('partial pressure of carbon dioxide arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:58:39)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:39)>>>>>> TRANSFORM Combine like columns (79883, 6)\n",
"(2017-08-22 11:58:39)>>>>>>>> ('partial pressure of carbon dioxide arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 11:58:41)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:41)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:52)<<<< --- (16.0s)\n",
"(2017-08-22 11:58:52)>>>> oxygen saturation arterial\n",
"(2017-08-22 11:58:52)>>>>>> Load data from component: OXYGEN SATURATION ARTERIAL\n",
"(2017-08-22 11:58:54)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:58:54)>>>>>> *fit* Filter columns (remove_small_columns) (27982, 25)\n",
"(2017-08-22 11:58:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>> *transform* Filter columns (remove_small_columns) (27982, 25)\n",
"(2017-08-22 11:58:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>> *fit* Filter columns (record_threshold) (27982, 3)\n",
"(2017-08-22 11:58:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>> *transform* Filter columns (record_threshold) (27982, 3)\n",
"(2017-08-22 11:58:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>> FIT Combine like columns (27982, 3)\n",
"(2017-08-22 11:58:54)>>>>>>>> ('oxygen saturation arterial', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:58:54)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>>>> ('oxygen saturation arterial', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 11:58:54)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>> TRANSFORM Combine like columns (27982, 3)\n",
"(2017-08-22 11:58:54)>>>>>>>> ('oxygen saturation arterial', 'known', 'qn', 'percent')\n",
"(2017-08-22 11:58:54)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)>>>>>>>> ('oxygen saturation arterial', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 11:58:54)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:58:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:05)<<<< --- (13.0s)\n",
"(2017-08-22 11:59:05)>>>> pH arterial\n",
"(2017-08-22 11:59:05)>>>>>> Load data from component: PH ARTERIAL\n",
"(2017-08-22 11:59:09)<<<<<< --- (4.0s)\n",
"(2017-08-22 11:59:09)>>>>>> *fit* Filter columns (remove_small_columns) (85890, 139)\n",
"(2017-08-22 11:59:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>> *transform* Filter columns (remove_small_columns) (85890, 139)\n",
"(2017-08-22 11:59:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>> *fit* Filter columns (record_threshold) (85890, 10)\n",
"(2017-08-22 11:59:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>> *transform* Filter columns (record_threshold) (85890, 10)\n",
"(2017-08-22 11:59:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>> FIT Combine like columns (85890, 10)\n",
"(2017-08-22 11:59:09)>>>>>>>> ('pH arterial', 'known', 'qn', 'no_units')\n",
"(2017-08-22 11:59:09)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'UNITS')\n",
"(2017-08-22 11:59:09)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'units')\n",
"(2017-08-22 11:59:09)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:09)>>>>>> TRANSFORM Combine like columns (85890, 10)\n",
"(2017-08-22 11:59:09)>>>>>>>> ('pH arterial', 'known', 'qn', 'no_units')\n",
"(2017-08-22 11:59:11)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:59:11)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'UNITS')\n",
"(2017-08-22 11:59:12)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:59:12)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'units')\n",
"(2017-08-22 11:59:14)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:59:14)<<<<<< --- (5.0s)\n",
"(2017-08-22 11:59:26)<<<< --- (21.0s)\n",
"(2017-08-22 11:59:26)>>>> pH other\n",
"(2017-08-22 11:59:26)>>>>>> Load data from component: PH OTHER\n",
"(2017-08-22 11:59:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)>>>>>> *fit* Filter columns (remove_small_columns) (13179, 6)\n",
"(2017-08-22 11:59:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)>>>>>> *transform* Filter columns (remove_small_columns) (13179, 6)\n",
"(2017-08-22 11:59:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)>>>>>> *fit* Filter columns (record_threshold) (13179, 1)\n",
"(2017-08-22 11:59:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)>>>>>> *transform* Filter columns (record_threshold) (13179, 1)\n",
"(2017-08-22 11:59:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)>>>>>> FIT Combine like columns (13179, 1)\n",
"(2017-08-22 11:59:26)>>>>>>>> ('pH other', 'unknown', 'qn', 'units')\n",
"(2017-08-22 11:59:26)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:26)>>>>>> TRANSFORM Combine like columns (13179, 1)\n",
"(2017-08-22 11:59:26)>>>>>>>> ('pH other', 'unknown', 'qn', 'units')\n",
"(2017-08-22 11:59:27)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 11:59:27)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:59:38)<<<< --- (12.0s)\n",
"(2017-08-22 11:59:38)>>>> bicarbonate arterial\n",
"(2017-08-22 11:59:38)>>>>>> Load data from component: BICARBONATE ARTERIAL\n",
"(2017-08-22 11:59:39)<<<<<< --- (1.0s)\n",
"(2017-08-22 11:59:39)>>>>>> *fit* Filter columns (remove_small_columns) (79298, 31)\n",
"(2017-08-22 11:59:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:39)>>>>>> *transform* Filter columns (remove_small_columns) (79298, 31)\n",
"(2017-08-22 11:59:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:39)>>>>>> *fit* Filter columns (record_threshold) (79298, 3)\n",
"(2017-08-22 11:59:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:39)>>>>>> *transform* Filter columns (record_threshold) (79298, 3)\n",
"(2017-08-22 11:59:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:39)>>>>>> FIT Combine like columns (79298, 3)\n",
"(2017-08-22 11:59:39)>>>>>>>> ('bicarbonate arterial', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:59:39)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:39)>>>>>> TRANSFORM Combine like columns (79298, 3)\n",
"(2017-08-22 11:59:39)>>>>>>>> ('bicarbonate arterial', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:59:42)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 11:59:42)<<<<<< --- (3.0s)\n",
"(2017-08-22 11:59:53)<<<< --- (15.0s)\n",
"(2017-08-22 11:59:53)>>>> bicarbonate other\n",
"(2017-08-22 11:59:53)>>>>>> Load data from component: BICARBONATE OTHER\n",
"(2017-08-22 11:59:55)<<<<<< --- (2.0s)\n",
"(2017-08-22 11:59:55)>>>>>> *fit* Filter columns (remove_small_columns) (99565, 39)\n",
"(2017-08-22 11:59:55)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:55)>>>>>> *transform* Filter columns (remove_small_columns) (99565, 39)\n",
"(2017-08-22 11:59:55)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:55)>>>>>> *fit* Filter columns (record_threshold) (99565, 2)\n",
"(2017-08-22 11:59:55)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:55)>>>>>> *transform* Filter columns (record_threshold) (99565, 2)\n",
"(2017-08-22 11:59:55)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:55)>>>>>> FIT Combine like columns (99565, 2)\n",
"(2017-08-22 11:59:55)>>>>>>>> ('bicarbonate other', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:59:55)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:55)<<<<<< --- (0.0s)\n",
"(2017-08-22 11:59:55)>>>>>> TRANSFORM Combine like columns (99565, 2)\n",
"(2017-08-22 11:59:55)>>>>>>>> ('bicarbonate other', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 11:59:57)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 11:59:57)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:00:09)<<<< --- (16.0s)\n",
"(2017-08-22 12:00:09)>>>> alanine aminotransferase serum\n",
"(2017-08-22 12:00:09)>>>>>> Load data from component: ALANINE AMINOTRANSFERASE SERUM\n",
"(2017-08-22 12:00:12)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:00:12)>>>>>> *fit* Filter columns (remove_small_columns) (23968, 17)\n",
"(2017-08-22 12:00:12)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:12)>>>>>> *transform* Filter columns (remove_small_columns) (23968, 17)\n",
"(2017-08-22 12:00:12)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:12)>>>>>> *fit* Filter columns (record_threshold) (23968, 3)\n",
"(2017-08-22 12:00:12)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:12)>>>>>> *transform* Filter columns (record_threshold) (23968, 3)\n",
"(2017-08-22 12:00:12)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:12)>>>>>> FIT Combine like columns (23968, 3)\n",
"(2017-08-22 12:00:12)>>>>>>>> ('alanine aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:00:12)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:12)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:12)>>>>>> TRANSFORM Combine like columns (23968, 3)\n",
"(2017-08-22 12:00:12)>>>>>>>> ('alanine aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:00:14)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:00:14)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:00:25)<<<< --- (16.0s)\n",
"(2017-08-22 12:00:25)>>>> aspartate aminotransferase serum\n",
"(2017-08-22 12:00:25)>>>>>> Load data from component: ASPARTATE AMINOTRANSFERASE SERUM\n",
"(2017-08-22 12:00:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)>>>>>> *fit* Filter columns (remove_small_columns) (23949, 16)\n",
"(2017-08-22 12:00:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)>>>>>> *transform* Filter columns (remove_small_columns) (23949, 16)\n",
"(2017-08-22 12:00:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)>>>>>> *fit* Filter columns (record_threshold) (23949, 3)\n",
"(2017-08-22 12:00:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)>>>>>> *transform* Filter columns (record_threshold) (23949, 3)\n",
"(2017-08-22 12:00:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)>>>>>> FIT Combine like columns (23949, 3)\n",
"(2017-08-22 12:00:25)>>>>>>>> ('aspartate aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:00:25)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:25)>>>>>> TRANSFORM Combine like columns (23949, 3)\n",
"(2017-08-22 12:00:25)>>>>>>>> ('aspartate aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:00:26)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:00:26)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:00:37)<<<< --- (12.0s)\n",
"(2017-08-22 12:00:37)>>>> alkaline phosphatase serum\n",
"(2017-08-22 12:00:37)>>>>>> Load data from component: ALKALINE PHOSPHATASE SERUM\n",
"(2017-08-22 12:00:38)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:00:38)>>>>>> *fit* Filter columns (remove_small_columns) (23694, 12)\n",
"(2017-08-22 12:00:38)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:38)>>>>>> *transform* Filter columns (remove_small_columns) (23694, 12)\n",
"(2017-08-22 12:00:38)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:38)>>>>>> *fit* Filter columns (record_threshold) (23694, 3)\n",
"(2017-08-22 12:00:38)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:38)>>>>>> *transform* Filter columns (record_threshold) (23694, 3)\n",
"(2017-08-22 12:00:38)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:38)>>>>>> FIT Combine like columns (23694, 3)\n",
"(2017-08-22 12:00:38)>>>>>>>> ('alkaline phosphatase serum', 'known', 'qn', 'IU/L')\n",
"(2017-08-22 12:00:38)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:38)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:38)>>>>>> TRANSFORM Combine like columns (23694, 3)\n",
"(2017-08-22 12:00:38)>>>>>>>> ('alkaline phosphatase serum', 'known', 'qn', 'IU/L')\n",
"(2017-08-22 12:00:39)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:00:39)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:00:51)<<<< --- (14.0s)\n",
"(2017-08-22 12:00:51)>>>> fraction of inspired oxygen\n",
"(2017-08-22 12:00:51)>>>>>> Load data from component: FRACTION OF INSPIRED OXYGEN\n",
"(2017-08-22 12:00:58)<<<<<< --- (7.0s)\n",
"(2017-08-22 12:00:58)>>>>>> *fit* Filter columns (remove_small_columns) (358692, 23)\n",
"(2017-08-22 12:00:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)>>>>>> *transform* Filter columns (remove_small_columns) (358692, 23)\n",
"(2017-08-22 12:00:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)>>>>>> *fit* Filter columns (record_threshold) (358692, 5)\n",
"(2017-08-22 12:00:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)>>>>>> *transform* Filter columns (record_threshold) (358692, 5)\n",
"(2017-08-22 12:00:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)>>>>>> FIT Combine like columns (358692, 5)\n",
"(2017-08-22 12:00:58)>>>>>>>> ('fraction of inspired oxygen', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:00:58)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)>>>>>>>> ('fraction of inspired oxygen', 'unknown', 'qn', 'torr')\n",
"(2017-08-22 12:00:58)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:00:58)>>>>>> TRANSFORM Combine like columns (358692, 5)\n",
"(2017-08-22 12:00:58)>>>>>>>> ('fraction of inspired oxygen', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:01:02)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 12:01:02)>>>>>>>> ('fraction of inspired oxygen', 'unknown', 'qn', 'torr')\n",
"(2017-08-22 12:01:09)<<<<<<<< --- (7.0s)\n",
"(2017-08-22 12:01:09)<<<<<< --- (11.0s)\n",
"(2017-08-22 12:01:22)<<<< --- (31.0s)\n",
"(2017-08-22 12:01:22)>>>> positive end expiratory pressure\n",
"(2017-08-22 12:01:22)>>>>>> Load data from component: POSITIVE END EXPIRATORY PRESSURE\n",
"(2017-08-22 12:01:25)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:01:25)>>>>>> *fit* Filter columns (remove_small_columns) (251312, 8)\n",
"(2017-08-22 12:01:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:25)>>>>>> *transform* Filter columns (remove_small_columns) (251312, 8)\n",
"(2017-08-22 12:01:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:25)>>>>>> *fit* Filter columns (record_threshold) (251312, 6)\n",
"(2017-08-22 12:01:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:25)>>>>>> *transform* Filter columns (record_threshold) (251312, 6)\n",
"(2017-08-22 12:01:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:25)>>>>>> FIT Combine like columns (251312, 6)\n",
"(2017-08-22 12:01:25)>>>>>>>> ('positive end expiratory pressure', 'known', 'qn', 'cmH2O')\n",
"(2017-08-22 12:01:25)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:25)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:25)>>>>>> TRANSFORM Combine like columns (251312, 6)\n",
"(2017-08-22 12:01:25)>>>>>>>> ('positive end expiratory pressure', 'known', 'qn', 'cmH2O')\n",
"(2017-08-22 12:01:31)<<<<<<<< --- (6.0s)\n",
"(2017-08-22 12:01:31)<<<<<< --- (6.0s)\n",
"(2017-08-22 12:01:43)<<<< --- (21.0s)\n",
"(2017-08-22 12:01:43)>>>> tidal volume\n",
"(2017-08-22 12:01:43)>>>>>> Load data from component: TIDAL VOLUME\n",
"(2017-08-22 12:01:47)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:01:47)>>>>>> *fit* Filter columns (remove_small_columns) (170650, 18)\n",
"(2017-08-22 12:01:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:47)>>>>>> *transform* Filter columns (remove_small_columns) (170650, 18)\n",
"(2017-08-22 12:01:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:47)>>>>>> *fit* Filter columns (record_threshold) (170650, 7)\n",
"(2017-08-22 12:01:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:47)>>>>>> *transform* Filter columns (record_threshold) (170650, 7)\n",
"(2017-08-22 12:01:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:47)>>>>>> FIT Combine like columns (170650, 7)\n",
"(2017-08-22 12:01:47)>>>>>>>> ('tidal volume', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:01:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:01:47)>>>>>> TRANSFORM Combine like columns (170650, 7)\n",
"(2017-08-22 12:01:47)>>>>>>>> ('tidal volume', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:01:51)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 12:01:51)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:02:04)<<<< --- (21.0s)\n",
"(2017-08-22 12:02:04)>>>> central venous pressure\n",
"(2017-08-22 12:02:04)>>>>>> Load data from component: CENTRAL VENOUS PRESSURE\n",
"(2017-08-22 12:02:06)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:02:06)>>>>>> *fit* Filter columns (remove_small_columns) (265475, 5)\n",
"(2017-08-22 12:02:06)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:06)>>>>>> *transform* Filter columns (remove_small_columns) (265475, 5)\n",
"(2017-08-22 12:02:06)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:06)>>>>>> *fit* Filter columns (record_threshold) (265475, 3)\n",
"(2017-08-22 12:02:06)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:06)>>>>>> *transform* Filter columns (record_threshold) (265475, 3)\n",
"(2017-08-22 12:02:06)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:06)>>>>>> FIT Combine like columns (265475, 2)\n",
"(2017-08-22 12:02:06)>>>>>>>> ('central venous pressure', 'unknown', 'qn', 'mmHg')\n",
"(2017-08-22 12:02:06)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:06)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:06)>>>>>> TRANSFORM Combine like columns (265475, 2)\n",
"(2017-08-22 12:02:06)>>>>>>>> ('central venous pressure', 'unknown', 'qn', 'mmHg')\n",
"(2017-08-22 12:02:13)<<<<<<<< --- (7.0s)\n",
"(2017-08-22 12:02:13)<<<<<< --- (7.0s)\n",
"(2017-08-22 12:02:25)<<<< --- (21.0s)\n",
"(2017-08-22 12:02:25)>>>> central venous oxygen saturation\n",
"(2017-08-22 12:02:25)>>>>>> Load data from component: CENTRAL VENOUS OXYGEN SATURATION\n",
"(2017-08-22 12:02:26)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:02:26)>>>>>> *fit* Filter columns (remove_small_columns) (40456, 33)\n",
"(2017-08-22 12:02:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)>>>>>> *transform* Filter columns (remove_small_columns) (40456, 33)\n",
"(2017-08-22 12:02:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)>>>>>> *fit* Filter columns (record_threshold) (40456, 4)\n",
"(2017-08-22 12:02:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)>>>>>> *transform* Filter columns (record_threshold) (40456, 4)\n",
"(2017-08-22 12:02:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)>>>>>> FIT Combine like columns (40456, 4)\n",
"(2017-08-22 12:02:26)>>>>>>>> ('central venous oxygen saturation', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:02:26)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)>>>>>>>> ('central venous oxygen saturation', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:02:26)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:26)>>>>>> TRANSFORM Combine like columns (40456, 4)\n",
"(2017-08-22 12:02:26)>>>>>>>> ('central venous oxygen saturation', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:02:27)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:02:27)>>>>>>>> ('central venous oxygen saturation', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:02:28)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:02:28)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:02:39)<<<< --- (14.0s)\n",
"(2017-08-22 12:02:39)>>>> end tidal carbon dioxide\n",
"(2017-08-22 12:02:39)>>>>>> Load data from component: END TIDAL CARBON DIOXIDE\n",
"(2017-08-22 12:02:40)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:02:40)>>>>>> *fit* Filter columns (remove_small_columns) (0, 1)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)>>>>>> *transform* Filter columns (remove_small_columns) (0, 1)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)>>>>>> *fit* Filter columns (record_threshold) (0, 0)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)>>>>>> *transform* Filter columns (record_threshold) (0, 0)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)>>>>>> FIT Combine like columns (0, 0)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)>>>>>> TRANSFORM Combine like columns (0, 0)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)<<<< --- (1.0s)\n",
"(2017-08-22 12:02:40)<< --- (1233.0s)\n",
"(2017-08-22 12:02:40)>> Begin union for 7 transformers\n",
"(2017-08-22 12:02:40)>>>> MEAN_QN\n",
"(2017-08-22 12:02:40)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:02:40)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:02:40)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:02:42)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:03:18)<<<< --- (38.0s)\n",
"(2017-08-22 12:03:18)>>>> MEAN_ORD\n",
"(2017-08-22 12:03:18)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:03:18)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:03:18)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:03:18)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:03:57)<<<< --- (39.0s)\n",
"(2017-08-22 12:03:57)>>>> LAST\n",
"(2017-08-22 12:03:57)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:03:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:03:57)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:03:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:04:30)<<<< --- (33.0s)\n",
"(2017-08-22 12:04:30)>>>> STD\n",
"(2017-08-22 12:04:30)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:04:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:04:30)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:04:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:05:13)<<<< --- (43.0s)\n",
"(2017-08-22 12:05:13)>>>> SUM\n",
"(2017-08-22 12:05:13)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:05:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:05:13)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:05:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:05:50)<<<< --- (37.0s)\n",
"(2017-08-22 12:05:50)>>>> COUNT\n",
"(2017-08-22 12:05:50)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:05:50)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:05:50)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:05:50)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:06:26)<<<< --- (36.0s)\n",
"(2017-08-22 12:06:26)>>>> COUNT_NOMINAL\n",
"(2017-08-22 12:06:26)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:06:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:06:26)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:06:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:07:04)<<<< --- (38.0s)\n",
"(2017-08-22 12:07:04)<< --- (264.0s)\n",
"(2017-08-22 12:07:08)>> *fit* Filter columns (remove_small_columns) (475224, 294)\n",
"(2017-08-22 12:07:09)<< --- (1.0s)\n",
"(2017-08-22 12:07:09)>> *transform* Filter columns (remove_small_columns) (475224, 294)\n",
"(2017-08-22 12:07:10)<< --- (1.0s)\n",
"(2017-08-22 12:07:10)>> *fit* Filter columns (record_threshold) (475224, 264)\n",
"(2017-08-22 12:07:12)<< --- (2.0s)\n",
"(2017-08-22 12:07:12)>> *transform* Filter columns (record_threshold) (475224, 264)\n",
"(2017-08-22 12:07:12)<< --- (0.0s)\n",
"(2017-08-22 12:07:57) --- (1550.0s)\n"
]
}
],
"source": [
"df_features = dsf_features.fit_transform(train_subset)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(475224, 264)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_features.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"utils.deconstruct_and_write(df_features,'data/data_sets.h5','all/train_subset/features')\n",
"utils.deconstruct_and_write(df_labels,'data/data_sets.h5','all/train_subset/labels')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2017-08-22 12:30:14) Make Feature Set. id_count=9436, #features=7, components=\n",
"(2017-08-22 12:30:14)>> Begin union for 57 transformers\n",
"(2017-08-22 12:30:14)>>>> heart rate\n",
"(2017-08-22 12:30:14)>>>>>> Load data from component: HEART RATE\n",
"(2017-08-22 12:30:26)<<<<<< --- (12.0s)\n",
"(2017-08-22 12:30:26)>>>>>> *fit* Filter columns (remove_small_columns) (1324365, 6)\n",
"(2017-08-22 12:30:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:30:26)>>>>>> *transform* Filter columns (remove_small_columns) (1324365, 6)\n",
"(2017-08-22 12:30:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:30:26)>>>>>> *fit* Filter columns (record_threshold) (1324365, 3)\n",
"(2017-08-22 12:30:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:30:26)>>>>>> *transform* Filter columns (record_threshold) (1324365, 3)\n",
"(2017-08-22 12:30:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:30:26)>>>>>> FIT Combine like columns (1324365, 3)\n",
"(2017-08-22 12:30:26)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
"(2017-08-22 12:30:26)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:30:26)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:30:26)>>>>>> TRANSFORM Combine like columns (1324365, 3)\n",
"(2017-08-22 12:30:26)>>>>>>>> ('heart rate', 'known', 'qn', 'beats/min')\n",
"(2017-08-22 12:30:53)<<<<<<<< --- (27.0s)\n",
"(2017-08-22 12:30:53)<<<<<< --- (27.0s)\n",
"(2017-08-22 12:30:53)<<<< --- (39.0s)\n",
"(2017-08-22 12:30:53)>>>> blood pressure systolic\n",
"(2017-08-22 12:30:53)>>>>>> Load data from component: BLOOD PRESSURE SYSTOLIC\n",
"(2017-08-22 12:31:14)<<<<<< --- (21.0s)\n",
"(2017-08-22 12:31:14)>>>>>> *fit* Filter columns (remove_small_columns) (986124, 41)\n",
"(2017-08-22 12:31:15)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:31:15)>>>>>> *transform* Filter columns (remove_small_columns) (986124, 41)\n",
"(2017-08-22 12:31:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:31:15)>>>>>> *fit* Filter columns (record_threshold) (986124, 7)\n",
"(2017-08-22 12:31:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:31:15)>>>>>> *transform* Filter columns (record_threshold) (986124, 7)\n",
"(2017-08-22 12:31:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:31:15)>>>>>> FIT Combine like columns (986124, 6)\n",
"(2017-08-22 12:31:15)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:31:15)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:31:15)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 12:31:15)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:31:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:31:15)>>>>>> TRANSFORM Combine like columns (986124, 6)\n",
"(2017-08-22 12:31:15)>>>>>>>> ('blood pressure systolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 12:31:21)<<<<<<<< --- (6.0s)\n",
"(2017-08-22 12:31:21)>>>>>>>> ('blood pressure systolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:31:42)<<<<<<<< --- (21.0s)\n",
"(2017-08-22 12:31:42)<<<<<< --- (27.0s)\n",
"(2017-08-22 12:31:54)<<<< --- (61.0s)\n",
"(2017-08-22 12:31:54)>>>> blood pressure diastolic\n",
"(2017-08-22 12:31:54)>>>>>> Load data from component: BLOOD PRESSURE DIASTOLIC\n",
"(2017-08-22 12:32:15)<<<<<< --- (21.0s)\n",
"(2017-08-22 12:32:15)>>>>>> *fit* Filter columns (remove_small_columns) (985854, 42)\n",
"(2017-08-22 12:32:16)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:32:16)>>>>>> *transform* Filter columns (remove_small_columns) (985854, 42)\n",
"(2017-08-22 12:32:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:32:16)>>>>>> *fit* Filter columns (record_threshold) (985854, 7)\n",
"(2017-08-22 12:32:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:32:16)>>>>>> *transform* Filter columns (record_threshold) (985854, 7)\n",
"(2017-08-22 12:32:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:32:16)>>>>>> FIT Combine like columns (985854, 6)\n",
"(2017-08-22 12:32:16)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:32:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:32:16)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 12:32:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:32:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:32:16)>>>>>> TRANSFORM Combine like columns (985854, 6)\n",
"(2017-08-22 12:32:16)>>>>>>>> ('blood pressure diastolic', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:32:37)<<<<<<<< --- (21.0s)\n",
"(2017-08-22 12:32:37)>>>>>>>> ('blood pressure diastolic', 'unknown', 'qn', 'cc/min')\n",
"(2017-08-22 12:32:39)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:32:39)<<<<<< --- (23.0s)\n",
"(2017-08-22 12:32:51)<<<< --- (57.0s)\n",
"(2017-08-22 12:32:51)>>>> blood pressure mean\n",
"(2017-08-22 12:32:51)>>>>>> Load data from component: BLOOD PRESSURE MEAN\n",
"(2017-08-22 12:33:00)<<<<<< --- (9.0s)\n",
"(2017-08-22 12:33:00)>>>>>> *fit* Filter columns (remove_small_columns) (951550, 7)\n",
"(2017-08-22 12:33:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:00)>>>>>> *transform* Filter columns (remove_small_columns) (951550, 7)\n",
"(2017-08-22 12:33:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:00)>>>>>> *fit* Filter columns (record_threshold) (951550, 6)\n",
"(2017-08-22 12:33:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:00)>>>>>> *transform* Filter columns (record_threshold) (951550, 6)\n",
"(2017-08-22 12:33:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:00)>>>>>> FIT Combine like columns (951550, 5)\n",
"(2017-08-22 12:33:01)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:33:01)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:01)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:33:01)>>>>>> TRANSFORM Combine like columns (951550, 5)\n",
"(2017-08-22 12:33:01)>>>>>>>> ('blood pressure mean', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:33:21)<<<<<<<< --- (20.0s)\n",
"(2017-08-22 12:33:21)<<<<<< --- (20.0s)\n",
"(2017-08-22 12:33:33)<<<< --- (42.0s)\n",
"(2017-08-22 12:33:33)>>>> respiratory rate\n",
"(2017-08-22 12:33:33)>>>>>> Load data from component: RESPIRATORY RATE\n",
"(2017-08-22 12:33:52)<<<<<< --- (19.0s)\n",
"(2017-08-22 12:33:52)>>>>>> *fit* Filter columns (remove_small_columns) (1321985, 8)\n",
"(2017-08-22 12:33:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)>>>>>> *transform* Filter columns (remove_small_columns) (1321985, 8)\n",
"(2017-08-22 12:33:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)>>>>>> *fit* Filter columns (record_threshold) (1321985, 5)\n",
"(2017-08-22 12:33:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)>>>>>> *transform* Filter columns (record_threshold) (1321985, 5)\n",
"(2017-08-22 12:33:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)>>>>>> FIT Combine like columns (1321985, 5)\n",
"(2017-08-22 12:33:52)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
"(2017-08-22 12:33:52)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
"(2017-08-22 12:33:52)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:33:52)>>>>>> TRANSFORM Combine like columns (1321985, 5)\n",
"(2017-08-22 12:33:52)>>>>>>>> ('respiratory rate', 'known', 'qn', 'insp/min')\n",
"(2017-08-22 12:34:17)<<<<<<<< --- (25.0s)\n",
"(2017-08-22 12:34:17)>>>>>>>> ('respiratory rate', 'unknown', 'qn', 'Breath')\n",
"(2017-08-22 12:34:20)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:34:20)<<<<<< --- (28.0s)\n",
"(2017-08-22 12:34:34)<<<< --- (61.0s)\n",
"(2017-08-22 12:34:34)>>>> temperature body\n",
"(2017-08-22 12:34:34)>>>>>> Load data from component: TEMPERATURE BODY\n",
"(2017-08-22 12:34:36)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:34:36)>>>>>> *fit* Filter columns (remove_small_columns) (274747, 4)\n",
"(2017-08-22 12:34:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:36)>>>>>> *transform* Filter columns (remove_small_columns) (274747, 4)\n",
"(2017-08-22 12:34:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:36)>>>>>> *fit* Filter columns (record_threshold) (274747, 4)\n",
"(2017-08-22 12:34:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:36)>>>>>> *transform* Filter columns (record_threshold) (274747, 4)\n",
"(2017-08-22 12:34:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:36)>>>>>> FIT Combine like columns (274747, 4)\n",
"(2017-08-22 12:34:36)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
"(2017-08-22 12:34:36)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:36)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:36)>>>>>> TRANSFORM Combine like columns (274747, 4)\n",
"(2017-08-22 12:34:36)>>>>>>>> ('temperature body', 'known', 'qn', 'degF')\n",
"(2017-08-22 12:34:42)<<<<<<<< --- (6.0s)\n",
"(2017-08-22 12:34:42)<<<<<< --- (6.0s)\n",
"(2017-08-22 12:34:51)<<<< --- (17.0s)\n",
"(2017-08-22 12:34:51)>>>> oxygen saturation pulse oximetry\n",
"(2017-08-22 12:34:51)>>>>>> Load data from component: OXYGEN SATURATION PULSE OXIMETRY\n",
"(2017-08-22 12:34:57)<<<<<< --- (6.0s)\n",
"(2017-08-22 12:34:57)>>>>>> *fit* Filter columns (remove_small_columns) (982829, 2)\n",
"(2017-08-22 12:34:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:57)>>>>>> *transform* Filter columns (remove_small_columns) (982829, 2)\n",
"(2017-08-22 12:34:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:57)>>>>>> *fit* Filter columns (record_threshold) (982829, 2)\n",
"(2017-08-22 12:34:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:57)>>>>>> *transform* Filter columns (record_threshold) (982829, 2)\n",
"(2017-08-22 12:34:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:57)>>>>>> FIT Combine like columns (982829, 2)\n",
"(2017-08-22 12:34:57)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:34:57)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:57)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:34:57)>>>>>> TRANSFORM Combine like columns (982829, 2)\n",
"(2017-08-22 12:34:57)>>>>>>>> ('oxygen saturation pulse oximetry', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:35:18)<<<<<<<< --- (21.0s)\n",
"(2017-08-22 12:35:18)<<<<<< --- (21.0s)\n",
"(2017-08-22 12:35:30)<<<< --- (39.0s)\n",
"(2017-08-22 12:35:30)>>>> weight body\n",
"(2017-08-22 12:35:30)>>>>>> Load data from component: WEIGHT BODY\n",
"(2017-08-22 12:35:31)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:35:31)>>>>>> *fit* Filter columns (remove_small_columns) (14779, 3)\n",
"(2017-08-22 12:35:31)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:35:31)>>>>>> *transform* Filter columns (remove_small_columns) (14779, 3)\n",
"(2017-08-22 12:35:31)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:35:31)>>>>>> *fit* Filter columns (record_threshold) (14779, 2)\n",
"(2017-08-22 12:35:31)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:35:31)>>>>>> *transform* Filter columns (record_threshold) (14779, 2)\n",
"(2017-08-22 12:35:31)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:35:31)>>>>>> FIT Combine like columns (14779, 2)\n",
"(2017-08-22 12:35:31)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
"(2017-08-22 12:35:31)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:35:31)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:35:31)>>>>>> TRANSFORM Combine like columns (14779, 2)\n",
"(2017-08-22 12:35:31)>>>>>>>> ('weight body', 'known', 'qn', 'kg')\n",
"(2017-08-22 12:35:32)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:35:32)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:35:39)<<<< --- (9.0s)\n",
"(2017-08-22 12:35:39)>>>> output urine\n",
"(2017-08-22 12:35:39)>>>>>> Load data from component: OUTPUT URINE\n",
"(2017-08-22 12:36:26)<<<<<< --- (47.0s)\n",
"(2017-08-22 12:36:26)>>>>>> *fit* Filter columns (remove_small_columns) (604836, 92)\n",
"(2017-08-22 12:36:27)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:36:27)>>>>>> *transform* Filter columns (remove_small_columns) (604836, 92)\n",
"(2017-08-22 12:36:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:27)>>>>>> *fit* Filter columns (record_threshold) (604836, 10)\n",
"(2017-08-22 12:36:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:27)>>>>>> *transform* Filter columns (record_threshold) (604836, 10)\n",
"(2017-08-22 12:36:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:27)>>>>>> FIT Combine like columns (604836, 8)\n",
"(2017-08-22 12:36:27)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:36:27)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:27)>>>>>>>> ('output urine', 'unknown', 'nom', 'no_units')\n",
"(2017-08-22 12:36:27)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:27)>>>>>> TRANSFORM Combine like columns (604836, 8)\n",
"(2017-08-22 12:36:27)>>>>>>>> ('output urine', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:36:40)<<<<<<<< --- (13.0s)\n",
"(2017-08-22 12:36:40)<<<<<< --- (13.0s)\n",
"(2017-08-22 12:36:51)<<<< --- (72.0s)\n",
"(2017-08-22 12:36:51)>>>> glasgow coma scale motor\n",
"(2017-08-22 12:36:51)>>>>>> Load data from component: GLASGOW COMA SCALE MOTOR\n",
"(2017-08-22 12:36:52)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:36:52)>>>>>> *fit* Filter columns (remove_small_columns) (153030, 1)\n",
"(2017-08-22 12:36:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:52)>>>>>> *transform* Filter columns (remove_small_columns) (153030, 1)\n",
"(2017-08-22 12:36:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:52)>>>>>> *fit* Filter columns (record_threshold) (153030, 1)\n",
"(2017-08-22 12:36:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:52)>>>>>> *transform* Filter columns (record_threshold) (153030, 1)\n",
"(2017-08-22 12:36:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:52)>>>>>> FIT Combine like columns (153030, 1)\n",
"(2017-08-22 12:36:52)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
"(2017-08-22 12:36:52)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:52)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:36:52)>>>>>> TRANSFORM Combine like columns (153030, 1)\n",
"(2017-08-22 12:36:52)>>>>>>>> ('glasgow coma scale motor', 'known', 'ord', 'no_units')\n",
"(2017-08-22 12:36:54)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:36:54)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:02)<<<< --- (11.0s)\n",
"(2017-08-22 12:37:02)>>>> glasgow coma scale eye opening\n",
"(2017-08-22 12:37:02)>>>>>> Load data from component: GLASGOW COMA SCALE EYE OPENING\n",
"(2017-08-22 12:37:04)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:04)>>>>>> *fit* Filter columns (remove_small_columns) (153765, 1)\n",
"(2017-08-22 12:37:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:04)>>>>>> *transform* Filter columns (remove_small_columns) (153765, 1)\n",
"(2017-08-22 12:37:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:04)>>>>>> *fit* Filter columns (record_threshold) (153765, 1)\n",
"(2017-08-22 12:37:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:04)>>>>>> *transform* Filter columns (record_threshold) (153765, 1)\n",
"(2017-08-22 12:37:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:04)>>>>>> FIT Combine like columns (153765, 1)\n",
"(2017-08-22 12:37:04)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
"(2017-08-22 12:37:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:04)>>>>>> TRANSFORM Combine like columns (153765, 1)\n",
"(2017-08-22 12:37:04)>>>>>>>> ('glasgow coma scale eye opening', 'known', 'ord', 'no_units')\n",
"(2017-08-22 12:37:06)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:06)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:14)<<<< --- (12.0s)\n",
"(2017-08-22 12:37:14)>>>> glasgow coma scale verbal\n",
"(2017-08-22 12:37:14)>>>>>> Load data from component: GLASGOW COMA SCALE VERBAL\n",
"(2017-08-22 12:37:15)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:15)>>>>>> *fit* Filter columns (remove_small_columns) (153421, 1)\n",
"(2017-08-22 12:37:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:15)>>>>>> *transform* Filter columns (remove_small_columns) (153421, 1)\n",
"(2017-08-22 12:37:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:15)>>>>>> *fit* Filter columns (record_threshold) (153421, 1)\n",
"(2017-08-22 12:37:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:15)>>>>>> *transform* Filter columns (record_threshold) (153421, 1)\n",
"(2017-08-22 12:37:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:15)>>>>>> FIT Combine like columns (153421, 1)\n",
"(2017-08-22 12:37:15)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
"(2017-08-22 12:37:15)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:15)>>>>>> TRANSFORM Combine like columns (153421, 1)\n",
"(2017-08-22 12:37:15)>>>>>>>> ('glasgow coma scale verbal', 'known', 'ord', 'no_units')\n",
"(2017-08-22 12:37:17)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:17)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:25)<<<< --- (11.0s)\n",
"(2017-08-22 12:37:25)>>>> normal saline\n",
"(2017-08-22 12:37:25)>>>>>> Load data from component: NORMAL SALINE\n",
"(2017-08-22 12:37:28)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:37:28)>>>>>> *fit* Filter columns (remove_small_columns) (77035, 16)\n",
"(2017-08-22 12:37:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)>>>>>> *transform* Filter columns (remove_small_columns) (77035, 16)\n",
"(2017-08-22 12:37:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)>>>>>> *fit* Filter columns (record_threshold) (77035, 2)\n",
"(2017-08-22 12:37:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)>>>>>> *transform* Filter columns (record_threshold) (77035, 2)\n",
"(2017-08-22 12:37:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)>>>>>> FIT Combine like columns (77035, 2)\n",
"(2017-08-22 12:37:28)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:37:28)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
"(2017-08-22 12:37:28)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:28)>>>>>> TRANSFORM Combine like columns (77035, 2)\n",
"(2017-08-22 12:37:28)>>>>>>>> ('normal saline', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:37:29)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:29)>>>>>>>> ('normal saline', 'known', 'qn', 'mL/hr')\n",
"(2017-08-22 12:37:29)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:29)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:38)<<<< --- (13.0s)\n",
"(2017-08-22 12:37:38)>>>> lactated ringers\n",
"(2017-08-22 12:37:38)>>>>>> Load data from component: LACTATED RINGERS\n",
"(2017-08-22 12:37:39)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:39)>>>>>> *fit* Filter columns (remove_small_columns) (40451, 20)\n",
"(2017-08-22 12:37:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:39)>>>>>> *transform* Filter columns (remove_small_columns) (40451, 20)\n",
"(2017-08-22 12:37:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:39)>>>>>> *fit* Filter columns (record_threshold) (40451, 2)\n",
"(2017-08-22 12:37:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:39)>>>>>> *transform* Filter columns (record_threshold) (40451, 2)\n",
"(2017-08-22 12:37:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:39)>>>>>> FIT Combine like columns (40451, 2)\n",
"(2017-08-22 12:37:39)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:37:39)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:39)>>>>>> TRANSFORM Combine like columns (40451, 2)\n",
"(2017-08-22 12:37:39)>>>>>>>> ('lactated ringers', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:37:40)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:40)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:48)<<<< --- (10.0s)\n",
"(2017-08-22 12:37:48)>>>> norepinephrine\n",
"(2017-08-22 12:37:48)>>>>>> Load data from component: NOREPINEPHRINE\n",
"(2017-08-22 12:37:49)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:49)>>>>>> *fit* Filter columns (remove_small_columns) (59948, 5)\n",
"(2017-08-22 12:37:49)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)>>>>>> *transform* Filter columns (remove_small_columns) (59948, 5)\n",
"(2017-08-22 12:37:49)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)>>>>>> *fit* Filter columns (record_threshold) (59948, 4)\n",
"(2017-08-22 12:37:49)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)>>>>>> *transform* Filter columns (record_threshold) (59948, 4)\n",
"(2017-08-22 12:37:49)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)>>>>>> FIT Combine like columns (59948, 3)\n",
"(2017-08-22 12:37:49)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
"(2017-08-22 12:37:49)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
"(2017-08-22 12:37:49)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:37:49)>>>>>> TRANSFORM Combine like columns (59948, 3)\n",
"(2017-08-22 12:37:49)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg/kg/min')\n",
"(2017-08-22 12:37:50)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:37:50)>>>>>>>> ('norepinephrine', 'known', 'qn', 'mcg')\n",
"(2017-08-22 12:37:52)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:37:52)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:38:00)<<<< --- (12.0s)\n",
"(2017-08-22 12:38:00)>>>> vasopressin\n",
"(2017-08-22 12:38:00)>>>>>> Load data from component: VASOPRESSIN\n",
"(2017-08-22 12:38:01)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:38:01)>>>>>> *fit* Filter columns (remove_small_columns) (19048, 38)\n",
"(2017-08-22 12:38:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>> *transform* Filter columns (remove_small_columns) (19048, 38)\n",
"(2017-08-22 12:38:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>> *fit* Filter columns (record_threshold) (19048, 2)\n",
"(2017-08-22 12:38:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>> *transform* Filter columns (record_threshold) (19048, 2)\n",
"(2017-08-22 12:38:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>> FIT Combine like columns (19048, 2)\n",
"(2017-08-22 12:38:01)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
"(2017-08-22 12:38:01)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
"(2017-08-22 12:38:01)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>> TRANSFORM Combine like columns (19048, 2)\n",
"(2017-08-22 12:38:01)>>>>>>>> ('vasopressin', 'known', 'qn', 'units/min')\n",
"(2017-08-22 12:38:01)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)>>>>>>>> ('vasopressin', 'known', 'qn', 'units')\n",
"(2017-08-22 12:38:01)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:10)<<<< --- (10.0s)\n",
"(2017-08-22 12:38:10)>>>> lactate\n",
"(2017-08-22 12:38:10)>>>>>> Load data from component: LACTATE\n",
"(2017-08-22 12:38:11)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:38:11)>>>>>> *fit* Filter columns (remove_small_columns) (28278, 63)\n",
"(2017-08-22 12:38:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:11)>>>>>> *transform* Filter columns (remove_small_columns) (28278, 63)\n",
"(2017-08-22 12:38:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:11)>>>>>> *fit* Filter columns (record_threshold) (28278, 4)\n",
"(2017-08-22 12:38:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:11)>>>>>> *transform* Filter columns (record_threshold) (28278, 4)\n",
"(2017-08-22 12:38:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:11)>>>>>> FIT Combine like columns (28278, 4)\n",
"(2017-08-22 12:38:11)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 12:38:11)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:11)>>>>>> TRANSFORM Combine like columns (28278, 4)\n",
"(2017-08-22 12:38:11)>>>>>>>> ('lactate', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 12:38:12)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:38:12)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:38:20)<<<< --- (10.0s)\n",
"(2017-08-22 12:38:20)>>>> hemoglobin\n",
"(2017-08-22 12:38:20)>>>>>> Load data from component: HEMOGLOBIN\n",
"(2017-08-22 12:38:30)<<<<<< --- (10.0s)\n",
"(2017-08-22 12:38:30)>>>>>> *fit* Filter columns (remove_small_columns) (108697, 44)\n",
"(2017-08-22 12:38:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:30)>>>>>> *transform* Filter columns (remove_small_columns) (108697, 44)\n",
"(2017-08-22 12:38:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:30)>>>>>> *fit* Filter columns (record_threshold) (108697, 8)\n",
"(2017-08-22 12:38:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:30)>>>>>> *transform* Filter columns (record_threshold) (108697, 8)\n",
"(2017-08-22 12:38:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:30)>>>>>> FIT Combine like columns (108697, 8)\n",
"(2017-08-22 12:38:30)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
"(2017-08-22 12:38:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:30)>>>>>> TRANSFORM Combine like columns (108697, 8)\n",
"(2017-08-22 12:38:30)>>>>>>>> ('hemoglobin', 'known', 'qn', 'g/dL')\n",
"(2017-08-22 12:38:32)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:38:32)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:38:42)<<<< --- (22.0s)\n",
"(2017-08-22 12:38:42)>>>> white blood cell count\n",
"(2017-08-22 12:38:42)>>>>>> Load data from component: WHITE BLOOD CELL COUNT\n",
"(2017-08-22 12:38:47)<<<<<< --- (5.0s)\n",
"(2017-08-22 12:38:47)>>>>>> *fit* Filter columns (remove_small_columns) (102160, 95)\n",
"(2017-08-22 12:38:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>> *transform* Filter columns (remove_small_columns) (102160, 95)\n",
"(2017-08-22 12:38:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>> *fit* Filter columns (record_threshold) (102160, 11)\n",
"(2017-08-22 12:38:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>> *transform* Filter columns (record_threshold) (102160, 11)\n",
"(2017-08-22 12:38:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>> FIT Combine like columns (102160, 11)\n",
"(2017-08-22 12:38:47)>>>>>>>> ('white blood cell count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 12:38:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>>>> ('white blood cell count', 'unknown', 'nom', 'no_units')\n",
"(2017-08-22 12:38:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>>>> ('white blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 12:38:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:38:47)>>>>>> TRANSFORM Combine like columns (102160, 11)\n",
"(2017-08-22 12:38:47)>>>>>>>> ('white blood cell count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 12:38:49)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:38:49)>>>>>>>> ('white blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 12:38:51)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:38:51)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:39:01)<<<< --- (19.0s)\n",
"(2017-08-22 12:39:01)>>>> red blood cell count\n",
"(2017-08-22 12:39:01)>>>>>> Load data from component: RED BLOOD CELL COUNT\n",
"(2017-08-22 12:39:04)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:39:04)>>>>>> *fit* Filter columns (remove_small_columns) (101117, 88)\n",
"(2017-08-22 12:39:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>> *transform* Filter columns (remove_small_columns) (101117, 88)\n",
"(2017-08-22 12:39:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>> *fit* Filter columns (record_threshold) (101117, 6)\n",
"(2017-08-22 12:39:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>> *transform* Filter columns (record_threshold) (101117, 6)\n",
"(2017-08-22 12:39:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>> FIT Combine like columns (101117, 6)\n",
"(2017-08-22 12:39:04)>>>>>>>> ('red blood cell count', 'known', 'qn', 'x10e6/uL')\n",
"(2017-08-22 12:39:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>>>> ('red blood cell count', 'unknown', 'nom', 'no_units')\n",
"(2017-08-22 12:39:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'm/uL')\n",
"(2017-08-22 12:39:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 12:39:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:04)>>>>>> TRANSFORM Combine like columns (101117, 6)\n",
"(2017-08-22 12:39:04)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'm/uL')\n",
"(2017-08-22 12:39:07)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:39:07)>>>>>>>> ('red blood cell count', 'unknown', 'qn', 'number/hpf')\n",
"(2017-08-22 12:39:07)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:07)>>>>>>>> ('red blood cell count', 'known', 'qn', 'x10e6/uL')\n",
"(2017-08-22 12:39:08)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:39:08)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:39:18)<<<< --- (17.0s)\n",
"(2017-08-22 12:39:18)>>>> hematocrit\n",
"(2017-08-22 12:39:18)>>>>>> Load data from component: HEMATOCRIT\n",
"(2017-08-22 12:39:22)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:39:22)>>>>>> *fit* Filter columns (remove_small_columns) (126133, 38)\n",
"(2017-08-22 12:39:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:22)>>>>>> *transform* Filter columns (remove_small_columns) (126133, 38)\n",
"(2017-08-22 12:39:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:22)>>>>>> *fit* Filter columns (record_threshold) (126133, 6)\n",
"(2017-08-22 12:39:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:22)>>>>>> *transform* Filter columns (record_threshold) (126133, 6)\n",
"(2017-08-22 12:39:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:22)>>>>>> FIT Combine like columns (126133, 6)\n",
"(2017-08-22 12:39:22)>>>>>>>> ('hematocrit', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:39:22)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:22)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:22)>>>>>> TRANSFORM Combine like columns (126133, 6)\n",
"(2017-08-22 12:39:22)>>>>>>>> ('hematocrit', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:39:26)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 12:39:26)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:39:36)<<<< --- (18.0s)\n",
"(2017-08-22 12:39:36)>>>> mean corpuscular volume\n",
"(2017-08-22 12:39:36)>>>>>> Load data from component: MEAN CORPUSCULAR VOLUME\n",
"(2017-08-22 12:39:39)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:39:39)>>>>>> *fit* Filter columns (remove_small_columns) (92054, 35)\n",
"(2017-08-22 12:39:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:39)>>>>>> *transform* Filter columns (remove_small_columns) (92054, 35)\n",
"(2017-08-22 12:39:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:39)>>>>>> *fit* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 12:39:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:39)>>>>>> *transform* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 12:39:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:39)>>>>>> FIT Combine like columns (92054, 1)\n",
"(2017-08-22 12:39:39)>>>>>>>> ('mean corpuscular volume', 'known', 'qn', 'fL')\n",
"(2017-08-22 12:39:39)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:39)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:39:39)>>>>>> TRANSFORM Combine like columns (92054, 1)\n",
"(2017-08-22 12:39:39)>>>>>>>> ('mean corpuscular volume', 'known', 'qn', 'fL')\n",
"(2017-08-22 12:39:40)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:39:40)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:39:51)<<<< --- (15.0s)\n",
"(2017-08-22 12:39:51)>>>> mean corpuscular hemoglobin\n",
"(2017-08-22 12:39:51)>>>>>> Load data from component: MEAN CORPUSCULAR HEMOGLOBIN\n",
"(2017-08-22 12:40:01)<<<<<< --- (10.0s)\n",
"(2017-08-22 12:40:01)>>>>>> *fit* Filter columns (remove_small_columns) (92054, 30)\n",
"(2017-08-22 12:40:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:01)>>>>>> *transform* Filter columns (remove_small_columns) (92054, 30)\n",
"(2017-08-22 12:40:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:01)>>>>>> *fit* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 12:40:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:01)>>>>>> *transform* Filter columns (record_threshold) (92054, 1)\n",
"(2017-08-22 12:40:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:01)>>>>>> FIT Combine like columns (92054, 1)\n",
"(2017-08-22 12:40:01)>>>>>>>> ('mean corpuscular hemoglobin', 'known', 'qn', 'pg')\n",
"(2017-08-22 12:40:01)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:01)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:01)>>>>>> TRANSFORM Combine like columns (92054, 1)\n",
"(2017-08-22 12:40:01)>>>>>>>> ('mean corpuscular hemoglobin', 'known', 'qn', 'pg')\n",
"(2017-08-22 12:40:02)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:40:02)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:40:13)<<<< --- (22.0s)\n",
"(2017-08-22 12:40:13)>>>> mean corpuscular hemoglobin concentration\n",
"(2017-08-22 12:40:13)>>>>>> Load data from component: MEAN CORPUSCULAR HEMOGLOBIN CONCENTRATION\n",
"(2017-08-22 12:40:15)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:40:15)>>>>>> *fit* Filter columns (remove_small_columns) (92073, 29)\n",
"(2017-08-22 12:40:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:15)>>>>>> *transform* Filter columns (remove_small_columns) (92073, 29)\n",
"(2017-08-22 12:40:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:15)>>>>>> *fit* Filter columns (record_threshold) (92073, 1)\n",
"(2017-08-22 12:40:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:15)>>>>>> *transform* Filter columns (record_threshold) (92073, 1)\n",
"(2017-08-22 12:40:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:15)>>>>>> FIT Combine like columns (92073, 1)\n",
"(2017-08-22 12:40:15)>>>>>>>> ('mean corpuscular hemoglobin concentration', 'unknown', 'qn', 'percent')\n",
"(2017-08-22 12:40:15)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:15)>>>>>> TRANSFORM Combine like columns (92073, 1)\n",
"(2017-08-22 12:40:15)>>>>>>>> ('mean corpuscular hemoglobin concentration', 'unknown', 'qn', 'percent')\n",
"(2017-08-22 12:40:16)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:40:16)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:40:26)<<<< --- (13.0s)\n",
"(2017-08-22 12:40:26)>>>> red cell distribution width\n",
"(2017-08-22 12:40:26)>>>>>> Load data from component: RED CELL DISTRIBUTION WIDTH\n",
"(2017-08-22 12:40:28)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:40:28)>>>>>> *fit* Filter columns (remove_small_columns) (91900, 30)\n",
"(2017-08-22 12:40:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:28)>>>>>> *transform* Filter columns (remove_small_columns) (91900, 30)\n",
"(2017-08-22 12:40:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:28)>>>>>> *fit* Filter columns (record_threshold) (91900, 1)\n",
"(2017-08-22 12:40:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:28)>>>>>> *transform* Filter columns (record_threshold) (91900, 1)\n",
"(2017-08-22 12:40:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:28)>>>>>> FIT Combine like columns (91900, 1)\n",
"(2017-08-22 12:40:28)>>>>>>>> ('red cell distribution width', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:40:28)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:28)>>>>>> TRANSFORM Combine like columns (91900, 1)\n",
"(2017-08-22 12:40:28)>>>>>>>> ('red cell distribution width', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:40:29)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:40:29)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:40:39)<<<< --- (13.0s)\n",
"(2017-08-22 12:40:39)>>>> platelet count\n",
"(2017-08-22 12:40:39)>>>>>> Load data from component: PLATELET COUNT\n",
"(2017-08-22 12:40:47)<<<<<< --- (8.0s)\n",
"(2017-08-22 12:40:47)>>>>>> *fit* Filter columns (remove_small_columns) (96686, 52)\n",
"(2017-08-22 12:40:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:47)>>>>>> *transform* Filter columns (remove_small_columns) (96686, 52)\n",
"(2017-08-22 12:40:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:47)>>>>>> *fit* Filter columns (record_threshold) (96686, 4)\n",
"(2017-08-22 12:40:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:47)>>>>>> *transform* Filter columns (record_threshold) (96686, 4)\n",
"(2017-08-22 12:40:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:47)>>>>>> FIT Combine like columns (96686, 4)\n",
"(2017-08-22 12:40:47)>>>>>>>> ('platelet count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 12:40:47)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:47)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:40:47)>>>>>> TRANSFORM Combine like columns (96686, 4)\n",
"(2017-08-22 12:40:47)>>>>>>>> ('platelet count', 'known', 'qn', 'x10e3/uL')\n",
"(2017-08-22 12:40:50)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:40:50)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:41:00)<<<< --- (21.0s)\n",
"(2017-08-22 12:41:00)>>>> sodium serum\n",
"(2017-08-22 12:41:00)>>>>>> Load data from component: SODIUM SERUM\n",
"(2017-08-22 12:41:04)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:41:04)>>>>>> *fit* Filter columns (remove_small_columns) (115251, 72)\n",
"(2017-08-22 12:41:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:04)>>>>>> *transform* Filter columns (remove_small_columns) (115251, 72)\n",
"(2017-08-22 12:41:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:04)>>>>>> *fit* Filter columns (record_threshold) (115251, 7)\n",
"(2017-08-22 12:41:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:04)>>>>>> *transform* Filter columns (record_threshold) (115251, 7)\n",
"(2017-08-22 12:41:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:04)>>>>>> FIT Combine like columns (115251, 7)\n",
"(2017-08-22 12:41:04)>>>>>>>> ('sodium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:41:04)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:04)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:04)>>>>>> TRANSFORM Combine like columns (115251, 7)\n",
"(2017-08-22 12:41:04)>>>>>>>> ('sodium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:41:07)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:41:07)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:41:17)<<<< --- (17.0s)\n",
"(2017-08-22 12:41:18)>>>> potassium serum\n",
"(2017-08-22 12:41:18)>>>>>> Load data from component: POTASSIUM SERUM\n",
"(2017-08-22 12:41:40)<<<<<< --- (22.0s)\n",
"(2017-08-22 12:41:40)>>>>>> *fit* Filter columns (remove_small_columns) (138935, 487)\n",
"(2017-08-22 12:41:41)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:41:41)>>>>>> *transform* Filter columns (remove_small_columns) (138935, 487)\n",
"(2017-08-22 12:41:41)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:41)>>>>>> *fit* Filter columns (record_threshold) (138935, 7)\n",
"(2017-08-22 12:41:41)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:41)>>>>>> *transform* Filter columns (record_threshold) (138935, 7)\n",
"(2017-08-22 12:41:41)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:41)>>>>>> FIT Combine like columns (138935, 7)\n",
"(2017-08-22 12:41:41)>>>>>>>> ('potassium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:41:41)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:41)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:41)>>>>>> TRANSFORM Combine like columns (138935, 7)\n",
"(2017-08-22 12:41:41)>>>>>>>> ('potassium serum', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:41:45)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 12:41:45)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:41:55)<<<< --- (37.0s)\n",
"(2017-08-22 12:41:55)>>>> chloride serum\n",
"(2017-08-22 12:41:55)>>>>>> Load data from component: CHLORIDE SERUM\n",
"(2017-08-22 12:41:58)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:41:58)>>>>>> *fit* Filter columns (remove_small_columns) (109458, 50)\n",
"(2017-08-22 12:41:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:58)>>>>>> *transform* Filter columns (remove_small_columns) (109458, 50)\n",
"(2017-08-22 12:41:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:58)>>>>>> *fit* Filter columns (record_threshold) (109458, 7)\n",
"(2017-08-22 12:41:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:58)>>>>>> *transform* Filter columns (record_threshold) (109458, 7)\n",
"(2017-08-22 12:41:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:58)>>>>>> FIT Combine like columns (109458, 7)\n",
"(2017-08-22 12:41:58)>>>>>>>> ('chloride serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 12:41:58)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:41:58)>>>>>> TRANSFORM Combine like columns (109458, 7)\n",
"(2017-08-22 12:41:58)>>>>>>>> ('chloride serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 12:42:01)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:42:01)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:42:12)<<<< --- (17.0s)\n",
"(2017-08-22 12:42:12)>>>> carbon dioxide serum\n",
"(2017-08-22 12:42:12)>>>>>> Load data from component: CARBON DIOXIDE SERUM\n",
"(2017-08-22 12:42:13)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:42:13)>>>>>> *fit* Filter columns (remove_small_columns) (34746, 17)\n",
"(2017-08-22 12:42:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:13)>>>>>> *transform* Filter columns (remove_small_columns) (34746, 17)\n",
"(2017-08-22 12:42:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:13)>>>>>> *fit* Filter columns (record_threshold) (34746, 2)\n",
"(2017-08-22 12:42:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:13)>>>>>> *transform* Filter columns (record_threshold) (34746, 2)\n",
"(2017-08-22 12:42:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:13)>>>>>> FIT Combine like columns (34746, 2)\n",
"(2017-08-22 12:42:13)>>>>>>>> ('carbon dioxide serum', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:42:13)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:13)>>>>>> TRANSFORM Combine like columns (34746, 2)\n",
"(2017-08-22 12:42:13)>>>>>>>> ('carbon dioxide serum', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:42:14)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:42:14)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:42:24)<<<< --- (12.0s)\n",
"(2017-08-22 12:42:24)>>>> blood urea nitrogen serum\n",
"(2017-08-22 12:42:24)>>>>>> Load data from component: BLOOD UREA NITROGEN SERUM\n",
"(2017-08-22 12:42:46)<<<<<< --- (22.0s)\n",
"(2017-08-22 12:42:46)>>>>>> *fit* Filter columns (remove_small_columns) (98988, 17)\n",
"(2017-08-22 12:42:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:46)>>>>>> *transform* Filter columns (remove_small_columns) (98988, 17)\n",
"(2017-08-22 12:42:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:46)>>>>>> *fit* Filter columns (record_threshold) (98988, 3)\n",
"(2017-08-22 12:42:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:46)>>>>>> *transform* Filter columns (record_threshold) (98988, 3)\n",
"(2017-08-22 12:42:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:46)>>>>>> FIT Combine like columns (98988, 3)\n",
"(2017-08-22 12:42:46)>>>>>>>> ('blood urea nitrogen serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:42:46)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:42:46)>>>>>> TRANSFORM Combine like columns (98988, 3)\n",
"(2017-08-22 12:42:46)>>>>>>>> ('blood urea nitrogen serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:42:49)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:42:49)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:43:00)<<<< --- (36.0s)\n",
"(2017-08-22 12:43:00)>>>> creatinine serum\n",
"(2017-08-22 12:43:00)>>>>>> Load data from component: CREATININE SERUM\n",
"(2017-08-22 12:43:02)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:43:02)>>>>>> *fit* Filter columns (remove_small_columns) (99396, 64)\n",
"(2017-08-22 12:43:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:43:02)>>>>>> *transform* Filter columns (remove_small_columns) (99396, 64)\n",
"(2017-08-22 12:43:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:43:02)>>>>>> *fit* Filter columns (record_threshold) (99396, 4)\n",
"(2017-08-22 12:43:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:43:02)>>>>>> *transform* Filter columns (record_threshold) (99396, 4)\n",
"(2017-08-22 12:43:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:43:02)>>>>>> FIT Combine like columns (99396, 4)\n",
"(2017-08-22 12:43:02)>>>>>>>> ('creatinine serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:43:02)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:43:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:43:02)>>>>>> TRANSFORM Combine like columns (99396, 4)\n",
"(2017-08-22 12:43:02)>>>>>>>> ('creatinine serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:43:05)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:43:05)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:43:16)<<<< --- (16.0s)\n",
"(2017-08-22 12:43:16)>>>> glucose serum\n",
"(2017-08-22 12:43:16)>>>>>> Load data from component: GLUCOSE SERUM\n",
"(2017-08-22 12:44:07)<<<<<< --- (51.0s)\n",
"(2017-08-22 12:44:07)>>>>>> *fit* Filter columns (remove_small_columns) (143627, 1807)\n",
"(2017-08-22 12:44:11)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:44:11)>>>>>> *transform* Filter columns (remove_small_columns) (143627, 1807)\n",
"(2017-08-22 12:44:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:11)>>>>>> *fit* Filter columns (record_threshold) (143627, 6)\n",
"(2017-08-22 12:44:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:11)>>>>>> *transform* Filter columns (record_threshold) (143627, 6)\n",
"(2017-08-22 12:44:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:11)>>>>>> FIT Combine like columns (143627, 6)\n",
"(2017-08-22 12:44:11)>>>>>>>> ('glucose serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:44:11)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:11)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:11)>>>>>> TRANSFORM Combine like columns (143627, 6)\n",
"(2017-08-22 12:44:11)>>>>>>>> ('glucose serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:44:15)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 12:44:15)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:44:26)<<<< --- (70.0s)\n",
"(2017-08-22 12:44:26)>>>> glucose fingerstick\n",
"(2017-08-22 12:44:26)>>>>>> Load data from component: GLUCOSE FINGERSTICK\n",
"(2017-08-22 12:44:49)<<<<<< --- (23.0s)\n",
"(2017-08-22 12:44:49)>>>>>> *fit* Filter columns (remove_small_columns) (109246, 1087)\n",
"(2017-08-22 12:44:51)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:44:51)>>>>>> *transform* Filter columns (remove_small_columns) (109246, 1087)\n",
"(2017-08-22 12:44:51)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:51)>>>>>> *fit* Filter columns (record_threshold) (109246, 2)\n",
"(2017-08-22 12:44:51)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:51)>>>>>> *transform* Filter columns (record_threshold) (109246, 2)\n",
"(2017-08-22 12:44:51)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:51)>>>>>> FIT Combine like columns (109246, 2)\n",
"(2017-08-22 12:44:51)>>>>>>>> ('glucose fingerstick', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:44:51)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:51)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:44:51)>>>>>> TRANSFORM Combine like columns (109246, 2)\n",
"(2017-08-22 12:44:51)>>>>>>>> ('glucose fingerstick', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:44:54)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:44:54)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:45:05)<<<< --- (39.0s)\n",
"(2017-08-22 12:45:05)>>>> calcium total serum\n",
"(2017-08-22 12:45:05)>>>>>> Load data from component: CALCIUM TOTAL SERUM\n",
"(2017-08-22 12:45:07)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:45:07)>>>>>> *fit* Filter columns (remove_small_columns) (77351, 14)\n",
"(2017-08-22 12:45:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:07)>>>>>> *transform* Filter columns (remove_small_columns) (77351, 14)\n",
"(2017-08-22 12:45:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:07)>>>>>> *fit* Filter columns (record_threshold) (77351, 4)\n",
"(2017-08-22 12:45:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:07)>>>>>> *transform* Filter columns (record_threshold) (77351, 4)\n",
"(2017-08-22 12:45:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:07)>>>>>> FIT Combine like columns (77351, 4)\n",
"(2017-08-22 12:45:07)>>>>>>>> ('calcium total serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:45:07)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:07)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:07)>>>>>> TRANSFORM Combine like columns (77351, 4)\n",
"(2017-08-22 12:45:07)>>>>>>>> ('calcium total serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:45:09)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:45:09)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:45:20)<<<< --- (15.0s)\n",
"(2017-08-22 12:45:20)>>>> calcium ionized serum\n",
"(2017-08-22 12:45:20)>>>>>> Load data from component: CALCIUM IONIZED SERUM\n",
"(2017-08-22 12:45:23)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:45:23)>>>>>> *fit* Filter columns (remove_small_columns) (39782, 224)\n",
"(2017-08-22 12:45:24)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:45:24)>>>>>> *transform* Filter columns (remove_small_columns) (39782, 224)\n",
"(2017-08-22 12:45:24)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:24)>>>>>> *fit* Filter columns (record_threshold) (39782, 3)\n",
"(2017-08-22 12:45:24)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:24)>>>>>> *transform* Filter columns (record_threshold) (39782, 3)\n",
"(2017-08-22 12:45:24)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:24)>>>>>> FIT Combine like columns (39782, 3)\n",
"(2017-08-22 12:45:24)>>>>>>>> ('calcium ionized serum', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 12:45:24)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:24)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:24)>>>>>> TRANSFORM Combine like columns (39782, 3)\n",
"(2017-08-22 12:45:24)>>>>>>>> ('calcium ionized serum', 'known', 'qn', 'mmol/L')\n",
"(2017-08-22 12:45:25)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:45:25)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:45:36)<<<< --- (16.0s)\n",
"(2017-08-22 12:45:36)>>>> magnesium serum\n",
"(2017-08-22 12:45:36)>>>>>> Load data from component: MAGNESIUM SERUM\n",
"(2017-08-22 12:45:44)<<<<<< --- (8.0s)\n",
"(2017-08-22 12:45:44)>>>>>> *fit* Filter columns (remove_small_columns) (90097, 406)\n",
"(2017-08-22 12:45:44)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:44)>>>>>> *transform* Filter columns (remove_small_columns) (90097, 406)\n",
"(2017-08-22 12:45:44)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:44)>>>>>> *fit* Filter columns (record_threshold) (90097, 4)\n",
"(2017-08-22 12:45:44)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:44)>>>>>> *transform* Filter columns (record_threshold) (90097, 4)\n",
"(2017-08-22 12:45:44)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:44)>>>>>> FIT Combine like columns (90097, 4)\n",
"(2017-08-22 12:45:44)>>>>>>>> ('magnesium serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:45:44)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:44)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:44)>>>>>> TRANSFORM Combine like columns (90097, 4)\n",
"(2017-08-22 12:45:44)>>>>>>>> ('magnesium serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:45:46)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:45:46)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:45:58)<<<< --- (22.0s)\n",
"(2017-08-22 12:45:58)>>>> phosphorous serum\n",
"(2017-08-22 12:45:58)>>>>>> Load data from component: PHOSPHOROUS SERUM\n",
"(2017-08-22 12:45:59)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:45:59)>>>>>> *fit* Filter columns (remove_small_columns) (44532, 5)\n",
"(2017-08-22 12:45:59)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>> *transform* Filter columns (remove_small_columns) (44532, 5)\n",
"(2017-08-22 12:45:59)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>> *fit* Filter columns (record_threshold) (44532, 3)\n",
"(2017-08-22 12:45:59)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>> *transform* Filter columns (record_threshold) (44532, 3)\n",
"(2017-08-22 12:45:59)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>> FIT Combine like columns (44532, 3)\n",
"(2017-08-22 12:45:59)>>>>>>>> ('phosphorous serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:45:59)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>>>> ('phosphorous serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 12:45:59)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>> TRANSFORM Combine like columns (44532, 3)\n",
"(2017-08-22 12:45:59)>>>>>>>> ('phosphorous serum', 'known', 'qn', 'mg/dL')\n",
"(2017-08-22 12:45:59)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:45:59)>>>>>>>> ('phosphorous serum', 'unknown', 'qn', 'mEq/L')\n",
"(2017-08-22 12:46:00)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:00)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:11)<<<< --- (13.0s)\n",
"(2017-08-22 12:46:11)>>>> prothrombin time\n",
"(2017-08-22 12:46:11)>>>>>> Load data from component: PROTHROMBIN TIME\n",
"(2017-08-22 12:46:13)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:46:13)>>>>>> *fit* Filter columns (remove_small_columns) (61551, 64)\n",
"(2017-08-22 12:46:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:13)>>>>>> *transform* Filter columns (remove_small_columns) (61551, 64)\n",
"(2017-08-22 12:46:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:13)>>>>>> *fit* Filter columns (record_threshold) (61551, 5)\n",
"(2017-08-22 12:46:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:13)>>>>>> *transform* Filter columns (record_threshold) (61551, 5)\n",
"(2017-08-22 12:46:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:13)>>>>>> FIT Combine like columns (61551, 5)\n",
"(2017-08-22 12:46:13)>>>>>>>> ('prothrombin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 12:46:13)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:13)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:13)>>>>>> TRANSFORM Combine like columns (61551, 5)\n",
"(2017-08-22 12:46:13)>>>>>>>> ('prothrombin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 12:46:15)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:46:15)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:46:26)<<<< --- (15.0s)\n",
"(2017-08-22 12:46:26)>>>> partial thromboplastin time\n",
"(2017-08-22 12:46:26)>>>>>> Load data from component: PARTIAL THROMBOPLASTIN TIME\n",
"(2017-08-22 12:46:29)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:46:29)>>>>>> *fit* Filter columns (remove_small_columns) (65729, 73)\n",
"(2017-08-22 12:46:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:29)>>>>>> *transform* Filter columns (remove_small_columns) (65729, 73)\n",
"(2017-08-22 12:46:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:29)>>>>>> *fit* Filter columns (record_threshold) (65729, 4)\n",
"(2017-08-22 12:46:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:29)>>>>>> *transform* Filter columns (record_threshold) (65729, 4)\n",
"(2017-08-22 12:46:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:29)>>>>>> FIT Combine like columns (65729, 4)\n",
"(2017-08-22 12:46:29)>>>>>>>> ('partial thromboplastin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 12:46:29)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:29)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:29)>>>>>> TRANSFORM Combine like columns (65729, 4)\n",
"(2017-08-22 12:46:29)>>>>>>>> ('partial thromboplastin time', 'known', 'qn', 'seconds')\n",
"(2017-08-22 12:46:30)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:30)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:42)<<<< --- (16.0s)\n",
"(2017-08-22 12:46:42)>>>> international normalized ratio\n",
"(2017-08-22 12:46:42)>>>>>> Load data from component: INTERNATIONAL NORMALIZED RATIO\n",
"(2017-08-22 12:46:44)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:46:44)>>>>>> *fit* Filter columns (remove_small_columns) (61672, 135)\n",
"(2017-08-22 12:46:45)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:45)>>>>>> *transform* Filter columns (remove_small_columns) (61672, 135)\n",
"(2017-08-22 12:46:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:45)>>>>>> *fit* Filter columns (record_threshold) (61672, 4)\n",
"(2017-08-22 12:46:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:45)>>>>>> *transform* Filter columns (record_threshold) (61672, 4)\n",
"(2017-08-22 12:46:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:45)>>>>>> FIT Combine like columns (61672, 4)\n",
"(2017-08-22 12:46:45)>>>>>>>> ('international normalized ratio', 'known', 'qn', 'no_units')\n",
"(2017-08-22 12:46:45)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:46:45)>>>>>> TRANSFORM Combine like columns (61672, 4)\n",
"(2017-08-22 12:46:45)>>>>>>>> ('international normalized ratio', 'known', 'qn', 'no_units')\n",
"(2017-08-22 12:46:46)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:46)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:46:58)<<<< --- (16.0s)\n",
"(2017-08-22 12:46:58)>>>> partial pressure of oxygen arterial\n",
"(2017-08-22 12:46:58)>>>>>> Load data from component: PARTIAL PRESSURE OF OXYGEN ARTERIAL\n",
"(2017-08-22 12:47:00)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:00)>>>>>> *fit* Filter columns (remove_small_columns) (79854, 55)\n",
"(2017-08-22 12:47:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:00)>>>>>> *transform* Filter columns (remove_small_columns) (79854, 55)\n",
"(2017-08-22 12:47:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:00)>>>>>> *fit* Filter columns (record_threshold) (79854, 6)\n",
"(2017-08-22 12:47:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:00)>>>>>> *transform* Filter columns (record_threshold) (79854, 6)\n",
"(2017-08-22 12:47:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:00)>>>>>> FIT Combine like columns (79854, 6)\n",
"(2017-08-22 12:47:00)>>>>>>>> ('partial pressure of oxygen arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:47:00)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:00)>>>>>> TRANSFORM Combine like columns (79854, 6)\n",
"(2017-08-22 12:47:00)>>>>>>>> ('partial pressure of oxygen arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:47:02)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:02)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:14)<<<< --- (16.0s)\n",
"(2017-08-22 12:47:14)>>>> partial pressure of carbon dioxide arterial\n",
"(2017-08-22 12:47:14)>>>>>> Load data from component: PARTIAL PRESSURE OF CARBON DIOXIDE ARTERIAL\n",
"(2017-08-22 12:47:16)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:16)>>>>>> *fit* Filter columns (remove_small_columns) (79883, 44)\n",
"(2017-08-22 12:47:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:16)>>>>>> *transform* Filter columns (remove_small_columns) (79883, 44)\n",
"(2017-08-22 12:47:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:16)>>>>>> *fit* Filter columns (record_threshold) (79883, 6)\n",
"(2017-08-22 12:47:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:16)>>>>>> *transform* Filter columns (record_threshold) (79883, 6)\n",
"(2017-08-22 12:47:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:16)>>>>>> FIT Combine like columns (79883, 6)\n",
"(2017-08-22 12:47:16)>>>>>>>> ('partial pressure of carbon dioxide arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:47:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:16)>>>>>> TRANSFORM Combine like columns (79883, 6)\n",
"(2017-08-22 12:47:16)>>>>>>>> ('partial pressure of carbon dioxide arterial', 'known', 'qn', 'mmHg')\n",
"(2017-08-22 12:47:18)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:18)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:30)<<<< --- (16.0s)\n",
"(2017-08-22 12:47:30)>>>> oxygen saturation arterial\n",
"(2017-08-22 12:47:30)>>>>>> Load data from component: OXYGEN SATURATION ARTERIAL\n",
"(2017-08-22 12:47:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>> *fit* Filter columns (remove_small_columns) (27982, 25)\n",
"(2017-08-22 12:47:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>> *transform* Filter columns (remove_small_columns) (27982, 25)\n",
"(2017-08-22 12:47:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>> *fit* Filter columns (record_threshold) (27982, 3)\n",
"(2017-08-22 12:47:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>> *transform* Filter columns (record_threshold) (27982, 3)\n",
"(2017-08-22 12:47:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>> FIT Combine like columns (27982, 3)\n",
"(2017-08-22 12:47:30)>>>>>>>> ('oxygen saturation arterial', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:47:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>>>> ('oxygen saturation arterial', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:47:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:30)>>>>>> TRANSFORM Combine like columns (27982, 3)\n",
"(2017-08-22 12:47:30)>>>>>>>> ('oxygen saturation arterial', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:47:31)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:47:31)>>>>>>>> ('oxygen saturation arterial', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:47:31)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:31)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:47:42)<<<< --- (12.0s)\n",
"(2017-08-22 12:47:42)>>>> pH arterial\n",
"(2017-08-22 12:47:42)>>>>>> Load data from component: PH ARTERIAL\n",
"(2017-08-22 12:47:45)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:47:45)>>>>>> *fit* Filter columns (remove_small_columns) (85890, 139)\n",
"(2017-08-22 12:47:46)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:47:46)>>>>>> *transform* Filter columns (remove_small_columns) (85890, 139)\n",
"(2017-08-22 12:47:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)>>>>>> *fit* Filter columns (record_threshold) (85890, 10)\n",
"(2017-08-22 12:47:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)>>>>>> *transform* Filter columns (record_threshold) (85890, 10)\n",
"(2017-08-22 12:47:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)>>>>>> FIT Combine like columns (85890, 10)\n",
"(2017-08-22 12:47:46)>>>>>>>> ('pH arterial', 'known', 'qn', 'no_units')\n",
"(2017-08-22 12:47:46)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'UNITS')\n",
"(2017-08-22 12:47:46)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'units')\n",
"(2017-08-22 12:47:46)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:46)>>>>>> TRANSFORM Combine like columns (85890, 10)\n",
"(2017-08-22 12:47:46)>>>>>>>> ('pH arterial', 'known', 'qn', 'no_units')\n",
"(2017-08-22 12:47:48)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:48)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'UNITS')\n",
"(2017-08-22 12:47:48)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:47:48)>>>>>>>> ('pH arterial', 'unknown', 'qn', 'units')\n",
"(2017-08-22 12:47:50)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:47:50)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:48:02)<<<< --- (20.0s)\n",
"(2017-08-22 12:48:02)>>>> pH other\n",
"(2017-08-22 12:48:02)>>>>>> Load data from component: PH OTHER\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)>>>>>> *fit* Filter columns (remove_small_columns) (13179, 6)\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)>>>>>> *transform* Filter columns (remove_small_columns) (13179, 6)\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)>>>>>> *fit* Filter columns (record_threshold) (13179, 1)\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)>>>>>> *transform* Filter columns (record_threshold) (13179, 1)\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)>>>>>> FIT Combine like columns (13179, 1)\n",
"(2017-08-22 12:48:02)>>>>>>>> ('pH other', 'unknown', 'qn', 'units')\n",
"(2017-08-22 12:48:02)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)>>>>>> TRANSFORM Combine like columns (13179, 1)\n",
"(2017-08-22 12:48:02)>>>>>>>> ('pH other', 'unknown', 'qn', 'units')\n",
"(2017-08-22 12:48:02)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:02)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:13)<<<< --- (11.0s)\n",
"(2017-08-22 12:48:13)>>>> bicarbonate arterial\n",
"(2017-08-22 12:48:13)>>>>>> Load data from component: BICARBONATE ARTERIAL\n",
"(2017-08-22 12:48:15)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:48:15)>>>>>> *fit* Filter columns (remove_small_columns) (79298, 31)\n",
"(2017-08-22 12:48:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:15)>>>>>> *transform* Filter columns (remove_small_columns) (79298, 31)\n",
"(2017-08-22 12:48:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:15)>>>>>> *fit* Filter columns (record_threshold) (79298, 3)\n",
"(2017-08-22 12:48:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:15)>>>>>> *transform* Filter columns (record_threshold) (79298, 3)\n",
"(2017-08-22 12:48:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:15)>>>>>> FIT Combine like columns (79298, 3)\n",
"(2017-08-22 12:48:15)>>>>>>>> ('bicarbonate arterial', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:48:15)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:15)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:15)>>>>>> TRANSFORM Combine like columns (79298, 3)\n",
"(2017-08-22 12:48:15)>>>>>>>> ('bicarbonate arterial', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:48:17)<<<<<<<< --- (2.0s)\n",
"(2017-08-22 12:48:17)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:48:28)<<<< --- (15.0s)\n",
"(2017-08-22 12:48:28)>>>> bicarbonate other\n",
"(2017-08-22 12:48:28)>>>>>> Load data from component: BICARBONATE OTHER\n",
"(2017-08-22 12:48:30)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:48:30)>>>>>> *fit* Filter columns (remove_small_columns) (99565, 39)\n",
"(2017-08-22 12:48:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:30)>>>>>> *transform* Filter columns (remove_small_columns) (99565, 39)\n",
"(2017-08-22 12:48:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:30)>>>>>> *fit* Filter columns (record_threshold) (99565, 2)\n",
"(2017-08-22 12:48:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:30)>>>>>> *transform* Filter columns (record_threshold) (99565, 2)\n",
"(2017-08-22 12:48:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:30)>>>>>> FIT Combine like columns (99565, 2)\n",
"(2017-08-22 12:48:30)>>>>>>>> ('bicarbonate other', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:48:30)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:30)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:30)>>>>>> TRANSFORM Combine like columns (99565, 2)\n",
"(2017-08-22 12:48:30)>>>>>>>> ('bicarbonate other', 'known', 'qn', 'mEq/L')\n",
"(2017-08-22 12:48:33)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:48:33)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:48:44)<<<< --- (16.0s)\n",
"(2017-08-22 12:48:44)>>>> alanine aminotransferase serum\n",
"(2017-08-22 12:48:44)>>>>>> Load data from component: ALANINE AMINOTRANSFERASE SERUM\n",
"(2017-08-22 12:48:45)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:48:45)>>>>>> *fit* Filter columns (remove_small_columns) (23968, 17)\n",
"(2017-08-22 12:48:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:45)>>>>>> *transform* Filter columns (remove_small_columns) (23968, 17)\n",
"(2017-08-22 12:48:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:45)>>>>>> *fit* Filter columns (record_threshold) (23968, 3)\n",
"(2017-08-22 12:48:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:45)>>>>>> *transform* Filter columns (record_threshold) (23968, 3)\n",
"(2017-08-22 12:48:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:45)>>>>>> FIT Combine like columns (23968, 3)\n",
"(2017-08-22 12:48:45)>>>>>>>> ('alanine aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:48:45)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:45)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:45)>>>>>> TRANSFORM Combine like columns (23968, 3)\n",
"(2017-08-22 12:48:45)>>>>>>>> ('alanine aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:48:46)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:48:46)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:48:57)<<<< --- (13.0s)\n",
"(2017-08-22 12:48:57)>>>> aspartate aminotransferase serum\n",
"(2017-08-22 12:48:57)>>>>>> Load data from component: ASPARTATE AMINOTRANSFERASE SERUM\n",
"(2017-08-22 12:48:58)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:48:58)>>>>>> *fit* Filter columns (remove_small_columns) (23949, 16)\n",
"(2017-08-22 12:48:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)>>>>>> *transform* Filter columns (remove_small_columns) (23949, 16)\n",
"(2017-08-22 12:48:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)>>>>>> *fit* Filter columns (record_threshold) (23949, 3)\n",
"(2017-08-22 12:48:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)>>>>>> *transform* Filter columns (record_threshold) (23949, 3)\n",
"(2017-08-22 12:48:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)>>>>>> FIT Combine like columns (23949, 3)\n",
"(2017-08-22 12:48:58)>>>>>>>> ('aspartate aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:48:58)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)>>>>>> TRANSFORM Combine like columns (23949, 3)\n",
"(2017-08-22 12:48:58)>>>>>>>> ('aspartate aminotransferase serum', 'unknown', 'qn', 'IU/L')\n",
"(2017-08-22 12:48:58)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:48:58)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)<<<< --- (13.0s)\n",
"(2017-08-22 12:49:10)>>>> alkaline phosphatase serum\n",
"(2017-08-22 12:49:10)>>>>>> Load data from component: ALKALINE PHOSPHATASE SERUM\n",
"(2017-08-22 12:49:10)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)>>>>>> *fit* Filter columns (remove_small_columns) (23694, 12)\n",
"(2017-08-22 12:49:10)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)>>>>>> *transform* Filter columns (remove_small_columns) (23694, 12)\n",
"(2017-08-22 12:49:10)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)>>>>>> *fit* Filter columns (record_threshold) (23694, 3)\n",
"(2017-08-22 12:49:10)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)>>>>>> *transform* Filter columns (record_threshold) (23694, 3)\n",
"(2017-08-22 12:49:10)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)>>>>>> FIT Combine like columns (23694, 3)\n",
"(2017-08-22 12:49:10)>>>>>>>> ('alkaline phosphatase serum', 'known', 'qn', 'IU/L')\n",
"(2017-08-22 12:49:10)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:10)>>>>>> TRANSFORM Combine like columns (23694, 3)\n",
"(2017-08-22 12:49:10)>>>>>>>> ('alkaline phosphatase serum', 'known', 'qn', 'IU/L')\n",
"(2017-08-22 12:49:11)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:49:11)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:49:22)<<<< --- (12.0s)\n",
"(2017-08-22 12:49:22)>>>> fraction of inspired oxygen\n",
"(2017-08-22 12:49:22)>>>>>> Load data from component: FRACTION OF INSPIRED OXYGEN\n",
"(2017-08-22 12:49:27)<<<<<< --- (5.0s)\n",
"(2017-08-22 12:49:27)>>>>>> *fit* Filter columns (remove_small_columns) (358692, 23)\n",
"(2017-08-22 12:49:28)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:49:28)>>>>>> *transform* Filter columns (remove_small_columns) (358692, 23)\n",
"(2017-08-22 12:49:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:28)>>>>>> *fit* Filter columns (record_threshold) (358692, 5)\n",
"(2017-08-22 12:49:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:28)>>>>>> *transform* Filter columns (record_threshold) (358692, 5)\n",
"(2017-08-22 12:49:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:28)>>>>>> FIT Combine like columns (358692, 5)\n",
"(2017-08-22 12:49:28)>>>>>>>> ('fraction of inspired oxygen', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:49:28)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:28)>>>>>>>> ('fraction of inspired oxygen', 'unknown', 'qn', 'torr')\n",
"(2017-08-22 12:49:28)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:28)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:28)>>>>>> TRANSFORM Combine like columns (358692, 5)\n",
"(2017-08-22 12:49:28)>>>>>>>> ('fraction of inspired oxygen', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:49:31)<<<<<<<< --- (3.0s)\n",
"(2017-08-22 12:49:31)>>>>>>>> ('fraction of inspired oxygen', 'unknown', 'qn', 'torr')\n",
"(2017-08-22 12:49:38)<<<<<<<< --- (7.0s)\n",
"(2017-08-22 12:49:38)<<<<<< --- (10.0s)\n",
"(2017-08-22 12:49:51)<<<< --- (29.0s)\n",
"(2017-08-22 12:49:51)>>>> positive end expiratory pressure\n",
"(2017-08-22 12:49:51)>>>>>> Load data from component: POSITIVE END EXPIRATORY PRESSURE\n",
"(2017-08-22 12:49:54)<<<<<< --- (3.0s)\n",
"(2017-08-22 12:49:54)>>>>>> *fit* Filter columns (remove_small_columns) (251312, 8)\n",
"(2017-08-22 12:49:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:54)>>>>>> *transform* Filter columns (remove_small_columns) (251312, 8)\n",
"(2017-08-22 12:49:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:54)>>>>>> *fit* Filter columns (record_threshold) (251312, 6)\n",
"(2017-08-22 12:49:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:54)>>>>>> *transform* Filter columns (record_threshold) (251312, 6)\n",
"(2017-08-22 12:49:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:54)>>>>>> FIT Combine like columns (251312, 6)\n",
"(2017-08-22 12:49:54)>>>>>>>> ('positive end expiratory pressure', 'known', 'qn', 'cmH2O')\n",
"(2017-08-22 12:49:54)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:49:54)>>>>>> TRANSFORM Combine like columns (251312, 6)\n",
"(2017-08-22 12:49:54)>>>>>>>> ('positive end expiratory pressure', 'known', 'qn', 'cmH2O')\n",
"(2017-08-22 12:50:00)<<<<<<<< --- (6.0s)\n",
"(2017-08-22 12:50:00)<<<<<< --- (6.0s)\n",
"(2017-08-22 12:50:12)<<<< --- (21.0s)\n",
"(2017-08-22 12:50:12)>>>> tidal volume\n",
"(2017-08-22 12:50:12)>>>>>> Load data from component: TIDAL VOLUME\n",
"(2017-08-22 12:50:16)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:50:16)>>>>>> *fit* Filter columns (remove_small_columns) (170650, 18)\n",
"(2017-08-22 12:50:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:16)>>>>>> *transform* Filter columns (remove_small_columns) (170650, 18)\n",
"(2017-08-22 12:50:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:16)>>>>>> *fit* Filter columns (record_threshold) (170650, 7)\n",
"(2017-08-22 12:50:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:16)>>>>>> *transform* Filter columns (record_threshold) (170650, 7)\n",
"(2017-08-22 12:50:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:16)>>>>>> FIT Combine like columns (170650, 7)\n",
"(2017-08-22 12:50:16)>>>>>>>> ('tidal volume', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:50:16)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:16)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:16)>>>>>> TRANSFORM Combine like columns (170650, 7)\n",
"(2017-08-22 12:50:16)>>>>>>>> ('tidal volume', 'known', 'qn', 'mL')\n",
"(2017-08-22 12:50:20)<<<<<<<< --- (4.0s)\n",
"(2017-08-22 12:50:20)<<<<<< --- (4.0s)\n",
"(2017-08-22 12:50:33)<<<< --- (21.0s)\n",
"(2017-08-22 12:50:33)>>>> central venous pressure\n",
"(2017-08-22 12:50:33)>>>>>> Load data from component: CENTRAL VENOUS PRESSURE\n",
"(2017-08-22 12:50:35)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:50:35)>>>>>> *fit* Filter columns (remove_small_columns) (265475, 5)\n",
"(2017-08-22 12:50:35)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:35)>>>>>> *transform* Filter columns (remove_small_columns) (265475, 5)\n",
"(2017-08-22 12:50:35)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:35)>>>>>> *fit* Filter columns (record_threshold) (265475, 3)\n",
"(2017-08-22 12:50:35)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:35)>>>>>> *transform* Filter columns (record_threshold) (265475, 3)\n",
"(2017-08-22 12:50:35)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:35)>>>>>> FIT Combine like columns (265475, 2)\n",
"(2017-08-22 12:50:35)>>>>>>>> ('central venous pressure', 'unknown', 'qn', 'mmHg')\n",
"(2017-08-22 12:50:35)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:35)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:35)>>>>>> TRANSFORM Combine like columns (265475, 2)\n",
"(2017-08-22 12:50:35)>>>>>>>> ('central venous pressure', 'unknown', 'qn', 'mmHg')\n",
"(2017-08-22 12:50:42)<<<<<<<< --- (7.0s)\n",
"(2017-08-22 12:50:42)<<<<<< --- (7.0s)\n",
"(2017-08-22 12:50:54)<<<< --- (21.0s)\n",
"(2017-08-22 12:50:54)>>>> central venous oxygen saturation\n",
"(2017-08-22 12:50:54)>>>>>> Load data from component: CENTRAL VENOUS OXYGEN SATURATION\n",
"(2017-08-22 12:50:56)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:50:56)>>>>>> *fit* Filter columns (remove_small_columns) (40456, 33)\n",
"(2017-08-22 12:50:56)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)>>>>>> *transform* Filter columns (remove_small_columns) (40456, 33)\n",
"(2017-08-22 12:50:56)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)>>>>>> *fit* Filter columns (record_threshold) (40456, 4)\n",
"(2017-08-22 12:50:56)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)>>>>>> *transform* Filter columns (record_threshold) (40456, 4)\n",
"(2017-08-22 12:50:56)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)>>>>>> FIT Combine like columns (40456, 4)\n",
"(2017-08-22 12:50:56)>>>>>>>> ('central venous oxygen saturation', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:50:56)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)>>>>>>>> ('central venous oxygen saturation', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:50:56)<<<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:50:56)>>>>>> TRANSFORM Combine like columns (40456, 4)\n",
"(2017-08-22 12:50:56)>>>>>>>> ('central venous oxygen saturation', 'unknown', 'qn', 'no_units')\n",
"(2017-08-22 12:50:57)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:50:57)>>>>>>>> ('central venous oxygen saturation', 'known', 'qn', 'percent')\n",
"(2017-08-22 12:50:58)<<<<<<<< --- (1.0s)\n",
"(2017-08-22 12:50:58)<<<<<< --- (2.0s)\n",
"(2017-08-22 12:51:08)<<<< --- (14.0s)\n",
"(2017-08-22 12:51:08)>>>> end tidal carbon dioxide\n",
"(2017-08-22 12:51:08)>>>>>> Load data from component: END TIDAL CARBON DIOXIDE\n",
"(2017-08-22 12:51:09)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:51:09)>>>>>> *fit* Filter columns (remove_small_columns) (0, 1)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)>>>>>> *transform* Filter columns (remove_small_columns) (0, 1)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)>>>>>> *fit* Filter columns (record_threshold) (0, 0)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)>>>>>> *transform* Filter columns (record_threshold) (0, 0)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)>>>>>> FIT Combine like columns (0, 0)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)>>>>>> TRANSFORM Combine like columns (0, 0)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)<<<< --- (1.0s)\n",
"(2017-08-22 12:51:09)<< --- (1255.0s)\n",
"(2017-08-22 12:51:09)>> Begin union for 7 transformers\n",
"(2017-08-22 12:51:09)>>>> MEAN_QN\n",
"(2017-08-22 12:51:09)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:51:09)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:09)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:51:10)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:51:48)<<<< --- (39.0s)\n",
"(2017-08-22 12:51:48)>>>> MEAN_ORD\n",
"(2017-08-22 12:51:48)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:51:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:51:48)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:51:48)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:52:27)<<<< --- (39.0s)\n",
"(2017-08-22 12:52:27)>>>> LAST\n",
"(2017-08-22 12:52:27)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:52:27)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:52:27)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:52:28)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:53:00)<<<< --- (33.0s)\n",
"(2017-08-22 12:53:00)>>>> STD\n",
"(2017-08-22 12:53:00)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:53:00)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:53:00)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:53:01)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:53:43)<<<< --- (43.0s)\n",
"(2017-08-22 12:53:43)>>>> SUM\n",
"(2017-08-22 12:53:43)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:53:43)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:53:43)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:53:43)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:54:19)<<<< --- (36.0s)\n",
"(2017-08-22 12:54:19)>>>> COUNT\n",
"(2017-08-22 12:54:19)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:54:19)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:54:19)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:54:20)<<<<<< --- (1.0s)\n",
"(2017-08-22 12:54:54)<<<< --- (35.0s)\n",
"(2017-08-22 12:54:54)>>>> COUNT_NOMINAL\n",
"(2017-08-22 12:54:54)>>>>>> *fit* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:54:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:54:54)>>>>>> *transform* Filter columns (DataSpecFilter) (1821403, 74)\n",
"(2017-08-22 12:54:54)<<<<<< --- (0.0s)\n",
"(2017-08-22 12:55:30)<<<< --- (36.0s)\n",
"(2017-08-22 12:55:30)<< --- (261.0s)\n",
"(2017-08-22 12:55:34)>> *fit* Filter columns (remove_small_columns) (475224, 294)\n",
"(2017-08-22 12:55:35)<< --- (1.0s)\n",
"(2017-08-22 12:55:35)>> *transform* Filter columns (remove_small_columns) (475224, 294)\n",
"(2017-08-22 12:55:35)<< --- (0.0s)\n",
"(2017-08-22 12:55:35)>> *fit* Filter columns (record_threshold) (475224, 264)\n",
"(2017-08-22 12:55:37)<< --- (2.0s)\n",
"(2017-08-22 12:55:37)>> *transform* Filter columns (record_threshold) (475224, 264)\n",
"(2017-08-22 12:55:37)<< --- (0.0s)\n",
"(2017-08-22 12:55:38) --- (1524.0s)\n"
]
}
],
"source": [
"dsf_features.should_fillna = False\n",
"df_features_nofill = dsf_features.fit_transform(train_subset)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"utils.deconstruct_and_write(df_features_nofill,'data/data_sets.h5','all/train_subset/features_nofill')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Models"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_dict = icu_data_defs.data_dictionary('config/data_definitions.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_features = utils.read_and_reconstruct('data/data_sets.h5','all/train_subset/features')\n",
"df_labels = utils.read_and_reconstruct('data/data_sets.h5','all/train_subset/labels')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(475224, 264)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_features.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Investigate na filling"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_features_nofill = utils.read_and_reconstruct('data/data_sets.h5','all/train_subset/features_nofill')"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(475224, 264)"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_features_nofill.shape"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"feature component status variable_type units description \n",
"MEAN_QN heart rate known qn beats/min all 0.120034\n",
" blood pressure systolic known qn mmHg all 0.163052\n",
" unknown qn cc/min all 0.977644\n",
" blood pressure diastolic known qn mmHg all 0.163096\n",
" unknown qn cc/min all 0.977644\n",
" blood pressure mean known qn mmHg all 0.166189\n",
" respiratory rate known qn insp/min all 0.168668\n",
" unknown qn Breath all 0.961260\n",
" temperature body known qn degF all 0.537250\n",
" oxygen saturation pulse oximetry known qn percent all 0.178303\n",
" weight body known qn kg all 0.971346\n",
" output urine known qn mL all 0.297792\n",
" normal saline known qn mL all 0.989607\n",
" mL/hr all 0.914638\n",
" lactated ringers known qn mL all 0.948631\n",
" norepinephrine known qn mcg all 0.952805\n",
" mcg/kg/min all 0.964854\n",
" vasopressin known qn units all 0.988614\n",
" units/min all 0.986383\n",
" lactate known qn mmol/L all 0.947980\n",
" hemoglobin known qn g/dL all 0.786922\n",
" white blood cell count known qn x10e3/uL all 0.806094\n",
" unknown qn number/hpf all 0.979603\n",
" red blood cell count known qn x10e6/uL all 0.993917\n",
" unknown qn m/uL all 0.807661\n",
" number/hpf all 0.995461\n",
" hematocrit known qn percent all 0.760462\n",
" mean corpuscular volume known qn fL all 0.807796\n",
" mean corpuscular hemoglobin known qn pg all 0.807796\n",
" mean corpuscular hemoglobin concentration unknown qn percent all 0.807701\n",
" ... \n",
"COUNT calcium ionized serum known qn mmol/L all 0.923255\n",
" magnesium serum known qn mg/dL all 0.813762\n",
" phosphorous serum known qn mg/dL all 0.955827\n",
" unknown qn mEq/L all 0.945691\n",
" prothrombin time known qn seconds all 0.881224\n",
" partial thromboplastin time known qn seconds all 0.878975\n",
" international normalized ratio known qn no_units all 0.881180\n",
" partial pressure of oxygen arterial known qn mmHg all 0.857505\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.857518\n",
" oxygen saturation arterial known qn percent all 0.955922\n",
" unknown qn no_units all 0.994270\n",
" pH arterial known qn no_units all 0.993414\n",
" unknown qn UNITS all 0.987961\n",
" units all 0.848198\n",
" pH other unknown qn units all 0.983723\n",
" bicarbonate arterial known qn mEq/L all 0.856527\n",
" bicarbonate other known qn mEq/L all 0.791684\n",
" alanine aminotransferase serum unknown qn IU/L all 0.950341\n",
" aspartate aminotransferase serum unknown qn IU/L all 0.950383\n",
" alkaline phosphatase serum known qn IU/L all 0.951032\n",
" fraction of inspired oxygen known qn percent all 0.963022\n",
" unknown qn torr all 0.806641\n",
" positive end expiratory pressure known qn cmH2O all 0.754461\n",
" tidal volume known qn mL all 0.786236\n",
" central venous pressure unknown qn mmHg all 0.755113\n",
" central venous oxygen saturation known qn percent all 0.967051\n",
" unknown qn no_units all 0.988115\n",
"COUNT_NOMINAL output urine unknown nom no_units 3686(ml)_Voiding qs 0.284748\n",
" white blood cell count unknown nom no_units 51516(#/hpf)(number/hpf)_0-2 0.798158\n",
" red blood cell count unknown nom no_units 51493(#/hpf)(number/hpf)_0-2 0.799781\n",
"dtype: float64"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df_features_nofill.isnull().sum() / df_features_nofill.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_count = pd.DataFrame(index=df_features_nofill.columns,columns=range(0,df_features_nofill.shape[1]))\n",
"df_count.columns.name = 'thresh'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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],
"source": [
"for thresh in df_count.columns:\n",
" print thresh\n",
" df_count.loc[:,thresh] = df_features_nofill.dropna(thresh=thresh).count()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_count = df_count.loc[:,df_count.sum() > 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Y-axis** = proportion of values in that column remaining \n",
"**X-axis** = threshold value selected"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2511b7f0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7xSeR0ZjaspveepWWjgze7mUwZUyzRj5j56sPxvmjL49isdUb400us9nrYCZf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\n0wbc/wPv3rCQ5qoRnwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1543a668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_count.apply(lambda row: row/float(row.loc[0]),axis=1).T.plot.line(legend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Models"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_dict = icu_data_defs.data_dictionary('config/data_definitions.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_features = utils.read_and_reconstruct('data/data_sets.h5','all/train_subset/features')\n",
"df_labels = utils.read_and_reconstruct('data/data_sets.h5','all/train_subset/labels')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_next = df_labels.loc[df_features.index,['NEXT_LACTATE']].iloc[:,0].dropna()\n",
"y_delta = df_labels.loc[df_features.index,['DELTA_LACTATE']].iloc[:,0].dropna()\n",
"y_delta_dir = (y_delta / y_delta.abs()).fillna(0)\n",
"X_next = df_features.loc[y_next.index]\n",
"X_delta = df_features.loc[y_delta.index]\n",
"X_next_no_lac = X_next.loc[:,X_next.columns.get_level_values(column_names.COMPONENT) != data_dict.components.LACTATE]\n",
"X_delta_no_lac = X_delta.loc[:,X_next.columns.get_level_values(column_names.COMPONENT) != data_dict.components.LACTATE]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Next Lactate: (20267, 264) (20267L,)\n",
"Delta Lactate: (18563, 264) (18563L,)\n"
]
}
],
"source": [
"print \"Next Lactate:\",X_next.shape,y_next.shape\n",
"print \"Delta Lactate:\",X_delta.shape,y_delta.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#if I need test & train:\n",
"train_ids_next,test_ids_next = train_test_split(\n",
" y_next.index.get_level_values(column_names.ID).unique().tolist(),\n",
" test_size=.1,\n",
" random_state=random_state\n",
")\n",
"train_ids_delta,test_ids_delta = train_test_split(\n",
" y_delta.index.get_level_values(column_names.ID).unique().tolist(),\n",
" test_size=.1,\n",
" random_state=random_state\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scaler = StandardScaler()\n",
"lin_reg = LinearRegression()\n",
"pipeline = Pipeline([\n",
" ('scaler',StandardScaler()),\n",
" ('model',lin_reg)\n",
" ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next Lactate"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: -7.57338573218e+22, 7.72034126296e+22, [0.83056069812417488, 0.80129359315144733, -2.0832494787516588e+23, 0.75829996680217104, -1.0092783866563815e+23, -5.3836686952272639e+22, -5.3931066579882309e+22, -2.2274170795298564e+23, -8.1604984799375898e+22, -3.5971340392807558e+22]\n",
"RMSE: 6.00806531779e+11, 4.86099034196e+11, [-2.2474652874788532, -1.5110524953853575, -1.7299417997624521e+24, -1.9422164549104937, -6.5815897370487276e+23, -2.259069711285713e+23, -2.6144746520224727e+23, -1.8690449682432424e+24, -8.8099259332066197e+23, -3.4711482538591736e+23]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: -2.00690192378e+23, 5.28823217702e+23, [-1.9958280470095908e+21, 0.71662690587680844, -2.0629413124461863e+23, -1.0387167185426824e+22, -2.8018761213919232, -2.2059639824343904e+19, -6.8918820607882732e+19, -1.7766554493241807e+24, -1.1478369514826485e+22, 0.75751463962802201]\n",
"RMSE: 9.37786475925e+11, 1.2381869756e+12, [-6.1276190467676778e+22, -1.6998423302043939, -2.5018588500624915e+24, -5.390903980054854e+23, -5.805373099615796e+24, -40983856833865.656, -3.1437727139909503e+22, -2.1102599762152789, -1.5066728601929967e+25, -1.1973974243060405e+23]\n"
]
}
],
"source": [
"run_crossval(pipeline,X_next,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20267, 192)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_next_known = X_next.loc[:,X_next.columns.get_level_values('status') == 'known']\n",
"X_next_known.shape"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: 0.706178267694, 0.10517158835, [0.82278553108697838, 0.7955658775366854, 0.79447055302984038, 0.74666792910212509, 0.70442956572236692, 0.61968186070247189, 0.62367914062690266, 0.75924413840877958, 0.4581377458849788, 0.73712033484067541]\n",
"RMSE: 1.49481717799, 0.3251547074, [-2.3505961303647331, -1.554608609704007, -1.7067278068337612, -2.0356874185933043, -1.9274398050485642, -1.595873070535069, -1.8243313367462493, -2.0202032920440542, -5.8498464726020387, -2.5367258509925472]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: 0.747006214631, 0.0552031148036, [0.66924706161802083, 0.78301931366490574, 0.7928271874356293, 0.79460876515379864, 0.75605782646517117, 0.80341605072749078, 0.70143991221744484, 0.73965870361261143, 0.63970011845328112, 0.7900872069587257]\n",
"RMSE: 1.33657653909, 0.0863547540306, [-1.5269286027165017, -2.1175283829441973, -2.1489163261259963, -1.636348585481894, -1.8546550420908263, -1.6286063403690096, -1.8891737014542498, -2.0254493775614741, -1.612884982078479, -1.498448543200714]\n"
]
}
],
"source": [
"run_crossval(pipeline,X_next_known,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20267, 188)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_next_no_lac_known = X_next_no_lac.loc[:,X_next_no_lac.columns.get_level_values('status') == 'known']\n",
"X_next_no_lac_known.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: 0.315325269978, 0.185641333452, [0.55106811954649326, 0.50549940871694643, 0.49625078607904138, 0.34269198073971185, 0.35195458791850909, 0.097152082566815734, 0.2176590292493068, 0.080267889412189319, 0.033455498971844078, 0.47725331657976111]\n",
"RMSE: 2.29267898037, 0.402497292011, [-5.954691778069801, -3.7604039259654862, -4.1831611175131309, -5.2818960513217768, -4.2259589521450547, -3.7884879245612493, -3.7926389500135231, -7.7175518192072072, -10.434638871804955, -5.0443803804643617]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: 0.41476705283, 0.103972720818, [0.44420797549955693, 0.41374452930895222, 0.4469529025134894, 0.45159277091966032, 0.43897077859266387, 0.48300611136113847, 0.49234919562913404, 0.37771965346300951, 0.4807616718080493, 0.11836493920293989]\n",
"RMSE: 2.24563968784, 0.325250791357, [-4.8701172849355592, -3.9762039276520991, -10.087717372071143, -4.0232921840108586, -4.7347289939265416, -5.4119162995243011, -4.6854575417785647, -4.8843557114918568, -4.6575958499514867, -4.1554716832612062]\n"
]
}
],
"source": [
"run_crossval(pipeline,X_next_no_lac_known,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('model', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline.fit(X_next_no_lac_known,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-2.990184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>-2.899350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-2.743561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-2.466549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-2.383972</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-2.308263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-2.305865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-1.420166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>-1.309496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-1.232670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>-0.909758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>-0.881174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.782599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.747987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.688909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>-0.668802</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.649186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>-0.605593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>weight body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>kg</th>\n",
" <th>all</th>\n",
" <td>-0.588251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.543099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>-0.473550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>-0.402325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>-0.377658</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>-0.354982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.329025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.319254</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>-0.312370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.300723</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>-0.300447</td>\n",
" </tr>\n",
" <tr>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.296749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>0.298955</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.304819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.307498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.372849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.394498</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.410295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.415763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.434274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>white blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.462473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.478681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.555780</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.569969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>weight body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>kg</th>\n",
" <th>all</th>\n",
" <td>0.589340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.597188</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.614576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.731552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.778000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.917911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.985446</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>1.107469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>1.435332</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">MEAN_QN</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.440393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>1.467640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.969064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.091229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.369418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>2.616448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.904834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>3.046241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>3.233393</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"COUNT partial pressure of oxygen arterial known qn mmHg all -2.990184\n",
"LAST alkaline phosphatase serum known qn IU/L all -2.899350\n",
"MEAN_QN phosphorous serum known qn mg/dL all -2.743561\n",
"LAST bicarbonate other known qn mEq/L all -2.466549\n",
"COUNT blood pressure systolic known qn mmHg all -2.383972\n",
"LAST blood urea nitrogen serum known qn mg/dL all -2.308263\n",
" magnesium serum known qn mg/dL all -2.305865\n",
" creatinine serum known qn mg/dL all -1.420166\n",
" mean corpuscular volume known qn fL all -1.309496\n",
"COUNT international normalized ratio known qn no_units all -1.232670\n",
"LAST platelet count known qn x10e3/uL all -0.909758\n",
"COUNT respiratory rate known qn insp/min all -0.881174\n",
"MEAN_QN hematocrit known qn percent all -0.782599\n",
"MEAN_ORD glasgow coma scale motor known ord no_units all -0.747987\n",
"COUNT red cell distribution width known qn percent all -0.688909\n",
"MEAN_QN prothrombin time known qn seconds all -0.668802\n",
"LAST red cell distribution width known qn percent all -0.649186\n",
" hemoglobin known qn g/dL all -0.605593\n",
"MEAN_QN weight body known qn kg all -0.588251\n",
"LAST fraction of inspired oxygen known qn percent all -0.543099\n",
"SUM mean corpuscular hemoglobin known qn pg all -0.473550\n",
"COUNT positive end expiratory pressure known qn cmH2O all -0.402325\n",
"MEAN_QN mean corpuscular hemoglobin known qn pg all -0.377658\n",
"COUNT output urine known qn mL all -0.354982\n",
" blood urea nitrogen serum known qn mg/dL all -0.329025\n",
"MEAN_QN glucose serum known qn mg/dL all -0.319254\n",
"LAST vasopressin known qn units all -0.312370\n",
" international normalized ratio known qn no_units all -0.300723\n",
"MEAN_QN red blood cell count known qn x10e6/uL all -0.300447\n",
" calcium ionized serum known qn mmol/L all -0.296749\n",
"... ...\n",
"LAST red blood cell count known qn x10e6/uL all 0.298955\n",
"COUNT vasopressin known qn units/min all 0.304819\n",
" creatinine serum known qn mg/dL all 0.307498\n",
"MEAN_QN vasopressin known qn units all 0.372849\n",
"COUNT tidal volume known qn mL all 0.394498\n",
"LAST norepinephrine known qn mcg/kg/min all 0.410295\n",
" calcium total serum known qn mg/dL all 0.415763\n",
"MEAN_QN international normalized ratio known qn no_units all 0.434274\n",
"COUNT white blood cell count known qn x10e3/uL all 0.462473\n",
"LAST glasgow coma scale motor known ord no_units all 0.478681\n",
"MEAN_QN fraction of inspired oxygen known qn percent all 0.555780\n",
"LAST hematocrit known qn percent all 0.569969\n",
" weight body known qn kg all 0.589340\n",
" glucose serum known qn mg/dL all 0.597188\n",
"MEAN_QN platelet count known qn x10e3/uL all 0.614576\n",
" red cell distribution width known qn percent all 0.731552\n",
" hemoglobin known qn g/dL all 0.778000\n",
"LAST prothrombin time known qn seconds all 0.917911\n",
"SUM mean corpuscular volume known qn fL all 0.985446\n",
"COUNT heart rate known qn beats/min all 1.107469\n",
" prothrombin time known qn seconds all 1.435332\n",
"MEAN_QN creatinine serum known qn mg/dL all 1.440393\n",
" mean corpuscular volume known qn fL all 1.467640\n",
" blood urea nitrogen serum known qn mg/dL all 1.969064\n",
" bicarbonate other known qn mEq/L all 2.091229\n",
" magnesium serum known qn mg/dL all 2.369418\n",
"COUNT blood pressure diastolic known qn mmHg all 2.616448\n",
"LAST phosphorous serum known qn mg/dL all 2.904834\n",
"MEAN_QN alkaline phosphatase serum known qn IU/L all 3.046241\n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 3.233393\n",
"\n",
"[188 rows x 1 columns]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(lin_reg.coef_,index=X_next_no_lac_known.columns).sort_values().to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>3.233393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>3.046241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>2.990184</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.904834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>2.899350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.743561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>2.616448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.466549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>2.383972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.369418</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.308263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.305865</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.091229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.969064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>1.467640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.440393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>1.435332</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.420166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>1.309496</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>1.232670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>1.107469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.985446</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.917911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.909758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.881174</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.782599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.778000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.747987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.731552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.688909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.020501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>pH arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.018971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.018913</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.015243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.015061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.014725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.013631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.013621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>0.012921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.012727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>pH arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.012628</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">STD</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.011277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.010651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.010555</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.010032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.009891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.009646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.009352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.009028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.007774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.007741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.007454</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.006935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.005564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.004454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>temperature body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>degF</th>\n",
" <th>all</th>\n",
" <td>0.004265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.003936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.002107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.001407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000779</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 3.233393\n",
"MEAN_QN alkaline phosphatase serum known qn IU/L all 3.046241\n",
"COUNT partial pressure of oxygen arterial known qn mmHg all 2.990184\n",
"LAST phosphorous serum known qn mg/dL all 2.904834\n",
" alkaline phosphatase serum known qn IU/L all 2.899350\n",
"MEAN_QN phosphorous serum known qn mg/dL all 2.743561\n",
"COUNT blood pressure diastolic known qn mmHg all 2.616448\n",
"LAST bicarbonate other known qn mEq/L all 2.466549\n",
"COUNT blood pressure systolic known qn mmHg all 2.383972\n",
"MEAN_QN magnesium serum known qn mg/dL all 2.369418\n",
"LAST blood urea nitrogen serum known qn mg/dL all 2.308263\n",
" magnesium serum known qn mg/dL all 2.305865\n",
"MEAN_QN bicarbonate other known qn mEq/L all 2.091229\n",
" blood urea nitrogen serum known qn mg/dL all 1.969064\n",
" mean corpuscular volume known qn fL all 1.467640\n",
" creatinine serum known qn mg/dL all 1.440393\n",
"COUNT prothrombin time known qn seconds all 1.435332\n",
"LAST creatinine serum known qn mg/dL all 1.420166\n",
" mean corpuscular volume known qn fL all 1.309496\n",
"COUNT international normalized ratio known qn no_units all 1.232670\n",
" heart rate known qn beats/min all 1.107469\n",
"SUM mean corpuscular volume known qn fL all 0.985446\n",
"LAST prothrombin time known qn seconds all 0.917911\n",
" platelet count known qn x10e3/uL all 0.909758\n",
"COUNT respiratory rate known qn insp/min all 0.881174\n",
"MEAN_QN hematocrit known qn percent all 0.782599\n",
" hemoglobin known qn g/dL all 0.778000\n",
"MEAN_ORD glasgow coma scale motor known ord no_units all 0.747987\n",
"MEAN_QN red cell distribution width known qn percent all 0.731552\n",
"COUNT red cell distribution width known qn percent all 0.688909\n",
"... ...\n",
"STD normal saline known qn mL/hr all 0.020501\n",
"COUNT pH arterial known qn no_units all 0.018971\n",
"MEAN_QN calcium total serum known qn mg/dL all 0.018913\n",
"STD heart rate known qn beats/min all 0.015243\n",
" glasgow coma scale verbal known ord no_units all 0.015061\n",
"LAST heart rate known qn beats/min all 0.014725\n",
"MEAN_QN blood pressure diastolic known qn mmHg all 0.013631\n",
"STD norepinephrine known qn mcg all 0.013621\n",
"COUNT red blood cell count known qn x10e6/uL all 0.012921\n",
"STD sodium serum known qn mEq/L all 0.012727\n",
"MEAN_QN pH arterial known qn no_units all 0.012628\n",
"STD blood pressure diastolic known qn mmHg all 0.011277\n",
" oxygen saturation pulse oximetry known qn percent all 0.010651\n",
" tidal volume known qn mL all 0.010555\n",
"LAST blood pressure diastolic known qn mmHg all 0.010032\n",
" positive end expiratory pressure known qn cmH2O all 0.009891\n",
"STD positive end expiratory pressure known qn cmH2O all 0.009646\n",
"LAST blood pressure mean known qn mmHg all 0.009352\n",
"STD glasgow coma scale motor known ord no_units all 0.009028\n",
"MEAN_QN blood pressure systolic known qn mmHg all 0.007774\n",
"LAST glasgow coma scale verbal known ord no_units all 0.007741\n",
"COUNT normal saline known qn mL all 0.007454\n",
"STD oxygen saturation arterial known qn percent all 0.006935\n",
" blood pressure systolic known qn mmHg all 0.005564\n",
"MEAN_QN vasopressin known qn units/min all 0.004454\n",
"COUNT temperature body known qn degF all 0.004265\n",
"STD glasgow coma scale eye opening known ord no_units all 0.003936\n",
"COUNT calcium total serum known qn mg/dL all 0.002107\n",
"STD lactated ringers known qn mL all 0.001407\n",
"SUM normal saline known qn mL all 0.000779\n",
"\n",
"[188 rows x 1 columns]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(lin_reg.coef_,index=X_next_no_lac_known.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delta Lactate"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: -4.42490910877e+24, 1.16004386357e+25, [0.18539578256696165, 0.1863723379372948, 0.12238207300289694, 0.14409663214983981, -2.3023864868164465, -3.8961533625274928e+25, -4.4765901282298819e+23, -0.077933529564172765, -4.8268312407782157e+24, -1.3067208780809574e+22]\n",
"RMSE: 1.18854666019e+12, 2.44499365967e+12, [-2.072608871931823, -1.4279041958765555, -1.6627797158587307, -2.0949396016612369, -6.8958834141305685, -6.5793600586458556e+25, -7.7492753821503284e+23, -1.5431560385821166, -7.3010583793358416e+24, -3.6785088569395867e+22]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: -1.3119401416e+26, 3.92758832628e+26, [0.13461144573249617, 0.1833626872333205, 0.055624843898550491, 0.14138093307937505, 0.12964451119137443, 0.17059825530223083, -2.4717035406518886e+24, -1.309468438055816e+27, 0.070616892678005305, 0.083045292189327102]\n",
"RMSE: 1.11319004084e+12, 1.9525172545e+12, [-1.6811037224772289, -3.9992242786521322e+25, -1.5967991994195876, -1.4304665890250376, -3.692755225147969e+23, -1.5398034652164549, -1.45416933548647, -8.5052230699802493e+24, -1.7515227277449701, -1.6484155823478232e+24]\n"
]
}
],
"source": [
"run_crossval(pipeline,X_delta,y_delta)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(18563, 192)\n"
]
},
{
"data": {
"text/plain": [
"(18563, 188)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_delta_known = X_delta.loc[:,X_delta.columns.get_level_values('status') == 'known']\n",
"print X_delta_known.shape\n",
"X_delta_no_lac_known = X_delta_no_lac.loc[:,X_delta_no_lac.columns.get_level_values('status') == 'known']\n",
"X_delta_no_lac_known.shape"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: -1.21744949569e+22, 3.65234848707e+22, [0.14342938087158696, 0.16023660599298339, 0.068045167029559539, 0.1128291179533143, -2.5628723539897074, 0.15654511577054087, -1.2174494956895469e+23, -0.22493445852058724, 0.017910136950423095, 0.14652548151609091]\n",
"RMSE: 45907364176.9, 1.37722092526e+11, [-2.1793845730827175, -1.473772008692771, -1.7657291911322535, -2.1714710842985081, -7.4398173777131147, -1.4243262161962469, -2.1074860855507532e+23, -1.7536007134852056, -1.4855077930428862, -2.402589281366637]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: -4.93687880753e+22, 1.48106364226e+23, [0.18063962638697928, 0.16524128995482912, 0.089002656065932273, 0.17585074275279167, 0.087906511332444204, 0.13187088613506182, 0.15527629541046306, 0.14101092692432748, -4.9368788075339139e+23, -0.049190804726641613]\n",
"RMSE: 1.34514655889, 0.143050651342, [-1.51544246908883, -2.9520675752633827, -1.7777212331646235, -1.7772774218060614, -1.4182119177944077, -1.4963074132637493, -1.7603056051824688, -1.6277068063552678, -2.0059578346829752, -1.9678292608221692]\n"
]
}
],
"source": [
"run_crossval(pipeline,X_delta_known,y_delta)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('model', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))])"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline.fit(X_delta_known,y_delta)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-2.478411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-2.233776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>-1.917882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-1.082388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-1.054315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.848891</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.823223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.813544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.613202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>-0.596036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.503297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.484389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.446841</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>-0.438810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>-0.438810</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">LAST</th>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.422640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.404924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>-0.396102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.353778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.327491</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.297426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>-0.259664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.209097</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>-0.193676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.181813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>-0.169126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>-0.163655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.162306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>-0.161241</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>-0.150669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.129045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.136068</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.156790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.166975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.180672</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.208150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.212651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.215182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.215550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>0.276528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.317610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.322523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.323046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.332489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.346981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.374325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.456271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.460086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.485766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.511826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.524210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.547433</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.782700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.846699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>1.096981</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>1.275404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>1.336434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>1.959678</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.185893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.452236</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>192 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"LAST blood urea nitrogen serum known qn mg/dL all -2.478411\n",
" bicarbonate other known qn mEq/L all -2.233776\n",
"MEAN_QN alkaline phosphatase serum known qn IU/L all -1.917882\n",
"COUNT blood pressure systolic known qn mmHg all -1.082388\n",
"MEAN_QN red cell distribution width known qn percent all -1.054315\n",
"COUNT partial pressure of oxygen arterial known qn mmHg all -0.848891\n",
"MEAN_QN creatinine serum known qn mg/dL all -0.823223\n",
" phosphorous serum known qn mg/dL all -0.813544\n",
" lactate known qn mmol/L all -0.613202\n",
"LAST platelet count known qn x10e3/uL all -0.596036\n",
"MEAN_QN glucose serum known qn mg/dL all -0.503297\n",
"COUNT international normalized ratio known qn no_units all -0.484389\n",
"MEAN_ORD glasgow coma scale motor known ord no_units all -0.446841\n",
"COUNT mean corpuscular volume known qn fL all -0.438810\n",
" mean corpuscular hemoglobin known qn pg all -0.438810\n",
"LAST magnesium serum known qn mg/dL all -0.422640\n",
" bicarbonate arterial known qn mEq/L all -0.404924\n",
" hemoglobin known qn g/dL all -0.396102\n",
" glasgow coma scale eye opening known ord no_units all -0.353778\n",
"COUNT bicarbonate arterial known qn mEq/L all -0.327491\n",
"MEAN_QN calcium total serum known qn mg/dL all -0.297426\n",
" red blood cell count known qn x10e6/uL all -0.259664\n",
"COUNT blood pressure mean known qn mmHg all -0.209097\n",
"MEAN_QN mean corpuscular volume known qn fL all -0.193676\n",
" hematocrit known qn percent all -0.181813\n",
" prothrombin time known qn seconds all -0.169126\n",
"COUNT respiratory rate known qn insp/min all -0.163655\n",
"LAST calcium ionized serum known qn mmol/L all -0.162306\n",
"MEAN_QN mean corpuscular hemoglobin known qn pg all -0.161241\n",
"LAST partial thromboplastin time known qn seconds all -0.150669\n",
"... ...\n",
" mean corpuscular hemoglobin known qn pg all 0.129045\n",
"COUNT glasgow coma scale eye opening known ord no_units all 0.136068\n",
"MEAN_QN international normalized ratio known qn no_units all 0.156790\n",
" sodium serum known qn mEq/L all 0.166975\n",
"COUNT hemoglobin known qn g/dL all 0.180672\n",
"LAST mean corpuscular volume known qn fL all 0.208150\n",
" hematocrit known qn percent all 0.212651\n",
" prothrombin time known qn seconds all 0.215182\n",
"MEAN_QN partial thromboplastin time known qn seconds all 0.215550\n",
"LAST red blood cell count known qn x10e6/uL all 0.276528\n",
"MEAN_QN hemoglobin known qn g/dL all 0.317610\n",
"COUNT heart rate known qn beats/min all 0.322523\n",
"LAST glasgow coma scale motor known ord no_units all 0.323046\n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 0.332489\n",
"SUM mean corpuscular volume known qn fL all 0.346981\n",
"LAST calcium total serum known qn mg/dL all 0.374325\n",
"MEAN_QN magnesium serum known qn mg/dL all 0.456271\n",
"MEAN_ORD glasgow coma scale eye opening known ord no_units all 0.460086\n",
"COUNT red cell distribution width known qn percent all 0.485766\n",
"LAST glucose serum known qn mg/dL all 0.511826\n",
"COUNT prothrombin time known qn seconds all 0.524210\n",
"MEAN_QN platelet count known qn x10e3/uL all 0.547433\n",
"LAST creatinine serum known qn mg/dL all 0.782700\n",
" phosphorous serum known qn mg/dL all 0.846699\n",
" red cell distribution width known qn percent all 1.096981\n",
"COUNT blood pressure diastolic known qn mmHg all 1.275404\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 1.336434\n",
"LAST alkaline phosphatase serum known qn IU/L all 1.959678\n",
"MEAN_QN bicarbonate other known qn mEq/L all 2.185893\n",
" blood urea nitrogen serum known qn mg/dL all 2.452236\n",
"\n",
"[192 rows x 1 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(lin_reg.coef_,index=X_delta_known.columns).sort_values().to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.478411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.452236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.233776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.185893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>1.959678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>1.917882</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>1.336434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>1.275404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>1.096981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>1.082388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>1.054315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.848891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.846699</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.823223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.813544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.782700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.613202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.596036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.547433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.524210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.511826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.503297</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.485766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.484389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.460086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.456271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.446841</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.438810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.438810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.422640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.009124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.008944</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>pH arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.008527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>0.008382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.008315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.007723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.007719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.007626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.007389</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.007384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.007264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.007163</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.007024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.006737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.006333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.005861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.005429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.004982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.004842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>pH arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.003694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.003116</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.002982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.001594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.001335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.001077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>weight body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>kg</th>\n",
" <th>all</th>\n",
" <td>0.000618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.000037</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>192 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"LAST blood urea nitrogen serum known qn mg/dL all 2.478411\n",
"MEAN_QN blood urea nitrogen serum known qn mg/dL all 2.452236\n",
"LAST bicarbonate other known qn mEq/L all 2.233776\n",
"MEAN_QN bicarbonate other known qn mEq/L all 2.185893\n",
"LAST alkaline phosphatase serum known qn IU/L all 1.959678\n",
"MEAN_QN alkaline phosphatase serum known qn IU/L all 1.917882\n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 1.336434\n",
" blood pressure diastolic known qn mmHg all 1.275404\n",
"LAST red cell distribution width known qn percent all 1.096981\n",
"COUNT blood pressure systolic known qn mmHg all 1.082388\n",
"MEAN_QN red cell distribution width known qn percent all 1.054315\n",
"COUNT partial pressure of oxygen arterial known qn mmHg all 0.848891\n",
"LAST phosphorous serum known qn mg/dL all 0.846699\n",
"MEAN_QN creatinine serum known qn mg/dL all 0.823223\n",
" phosphorous serum known qn mg/dL all 0.813544\n",
"LAST creatinine serum known qn mg/dL all 0.782700\n",
"MEAN_QN lactate known qn mmol/L all 0.613202\n",
"LAST platelet count known qn x10e3/uL all 0.596036\n",
"MEAN_QN platelet count known qn x10e3/uL all 0.547433\n",
"COUNT prothrombin time known qn seconds all 0.524210\n",
"LAST glucose serum known qn mg/dL all 0.511826\n",
"MEAN_QN glucose serum known qn mg/dL all 0.503297\n",
"COUNT red cell distribution width known qn percent all 0.485766\n",
" international normalized ratio known qn no_units all 0.484389\n",
"MEAN_ORD glasgow coma scale eye opening known ord no_units all 0.460086\n",
"MEAN_QN magnesium serum known qn mg/dL all 0.456271\n",
"MEAN_ORD glasgow coma scale motor known ord no_units all 0.446841\n",
"COUNT mean corpuscular volume known qn fL all 0.438810\n",
" mean corpuscular hemoglobin known qn pg all 0.438810\n",
"LAST magnesium serum known qn mg/dL all 0.422640\n",
"... ...\n",
"STD lactated ringers known qn mL all 0.009124\n",
"MEAN_QN output urine known qn mL all 0.008944\n",
"COUNT pH arterial known qn no_units all 0.008527\n",
" red blood cell count known qn x10e6/uL all 0.008382\n",
"MEAN_QN blood pressure systolic known qn mmHg all 0.008315\n",
"STD central venous oxygen saturation known qn percent all 0.007723\n",
"COUNT hematocrit known qn percent all 0.007719\n",
"MEAN_QN normal saline known qn mL/hr all 0.007626\n",
"LAST blood pressure mean known qn mmHg all 0.007389\n",
"STD glasgow coma scale verbal known ord no_units all 0.007384\n",
" positive end expiratory pressure known qn cmH2O all 0.007264\n",
"COUNT phosphorous serum known qn mg/dL all 0.007163\n",
"STD heart rate known qn beats/min all 0.007024\n",
" oxygen saturation pulse oximetry known qn percent all 0.006737\n",
"LAST blood pressure diastolic known qn mmHg all 0.006333\n",
"COUNT calcium ionized serum known qn mmol/L all 0.005861\n",
"STD norepinephrine known qn mcg/kg/min all 0.005429\n",
"SUM output urine known qn mL all 0.004982\n",
"STD potassium serum known qn mEq/L all 0.004842\n",
"MEAN_QN pH arterial known qn no_units all 0.003694\n",
"COUNT lactate known qn mmol/L all 0.003116\n",
"STD calcium ionized serum known qn mmol/L all 0.002982\n",
" vasopressin known qn units/min all 0.001594\n",
"MEAN_QN blood pressure mean known qn mmHg all 0.001335\n",
"STD sodium serum known qn mEq/L all 0.001077\n",
"LAST weight body known qn kg all 0.000618\n",
"COUNT creatinine serum known qn mg/dL all 0.000318\n",
"LAST central venous oxygen saturation known qn percent all 0.000139\n",
"STD blood pressure mean known qn mmHg all 0.000102\n",
"COUNT norepinephrine known qn mcg/kg/min all 0.000037\n",
"\n",
"[192 rows x 1 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(lin_reg.coef_,index=X_delta_known.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: -0.577282500293, 1.77817895647, [0.01138818384379503, 0.083874882556315145, 0.010901990577526943, 0.0073844188094479568, -5.9107901206792626, 0.052368727411629235, -0.037316285488910861, -0.037724549157442278, 0.011090263836369907, 0.035997485365176218]\n",
"RMSE: 1.62746591409, 0.739057902316, [-2.5153388322966963, -1.6077856741366832, -1.873995569679566, -2.4295612897110566, -14.430777003835995, -1.6002469012531524, -1.795663512715062, -1.485593369625188, -1.4958235238529272, -2.7137331680228467]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: -1.04806660876e+21, 3.14419982629e+21, [-0.01586653632881907, 0.029217689965005733, 0.047366718465182589, 0.006783613497672647, -0.03485629413682112, 0.013165856887549565, 0.0611258889942492, -1.0480666087620327e+22, 0.026213355467124111, 0.047118966885235314]\n",
"RMSE: 1.41398315228, 0.11347090348, [-2.2514474254989842, -1.8832944332826853, -2.0208565072547651, -1.6128582657419084, -2.7202068231278242, -2.106207817953818, -2.2733916824385729, -1.9349813004869416, -1.7150162577010641, -1.6039794951061239]\n"
]
}
],
"source": [
"# Delta, no lactate\n",
"run_crossval(pipeline,X_delta_no_lac_known,y_delta)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>2.994218</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>2.980967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.487813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>2.424465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.061441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>2.029759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.466464</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>1.408530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>1.352608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>1.321007</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">COUNT</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.979189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.873394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.845753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.785178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.750015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.726559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.565066</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.541477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.541477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.532244</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.500282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.500052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.459524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.454835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.434971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.432579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.394361</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.372772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.351652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.335277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.010732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.010673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.010192</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.010040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.008892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.008412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.007960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.007466</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>0.007036</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.006918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.006181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.005952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.005873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.005769</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.005625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.004996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.004836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>pH arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.004807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.004789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.004381</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.004155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.003795</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.003296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.002428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.001823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>alkaline phosphatase serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.001104</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000665</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.000221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>pH arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000098</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"LAST alkaline phosphatase serum known qn IU/L all 2.994218\n",
"MEAN_QN alkaline phosphatase serum known qn IU/L all 2.980967\n",
" blood urea nitrogen serum known qn mg/dL all 2.487813\n",
"LAST blood urea nitrogen serum known qn mg/dL all 2.424465\n",
"MEAN_QN bicarbonate other known qn mEq/L all 2.061441\n",
"LAST bicarbonate other known qn mEq/L all 2.029759\n",
"MEAN_QN creatinine serum known qn mg/dL all 1.466464\n",
"LAST creatinine serum known qn mg/dL all 1.408530\n",
" red cell distribution width known qn percent all 1.352608\n",
"MEAN_QN red cell distribution width known qn percent all 1.321007\n",
"COUNT blood pressure diastolic known qn mmHg all 0.979189\n",
" red cell distribution width known qn percent all 0.873394\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.845753\n",
" blood pressure systolic known qn mmHg all 0.785178\n",
"MEAN_QN mean corpuscular volume known qn fL all 0.750015\n",
"LAST mean corpuscular volume known qn fL all 0.726559\n",
"MEAN_QN glucose serum known qn mg/dL all 0.565066\n",
"COUNT mean corpuscular hemoglobin known qn pg all 0.541477\n",
" mean corpuscular volume known qn fL all 0.541477\n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 0.532244\n",
"LAST bicarbonate arterial known qn mEq/L all 0.500282\n",
" glucose serum known qn mg/dL all 0.500052\n",
"MEAN_ORD glasgow coma scale eye opening known ord no_units all 0.459524\n",
"MEAN_QN platelet count known qn x10e3/uL all 0.454835\n",
"LAST platelet count known qn x10e3/uL all 0.434971\n",
"COUNT bicarbonate arterial known qn mEq/L all 0.432579\n",
"MEAN_QN calcium total serum known qn mg/dL all 0.394361\n",
"LAST calcium total serum known qn mg/dL all 0.372772\n",
" glasgow coma scale eye opening known ord no_units all 0.351652\n",
" hemoglobin known qn g/dL all 0.335277\n",
"... ...\n",
"STD lactated ringers known qn mL all 0.010732\n",
" blood pressure mean known qn mmHg all 0.010673\n",
"MEAN_QN oxygen saturation arterial known qn percent all 0.010192\n",
"STD vasopressin known qn units/min all 0.010040\n",
" blood pressure diastolic known qn mmHg all 0.008892\n",
"COUNT phosphorous serum known qn mg/dL all 0.008412\n",
"LAST mean corpuscular hemoglobin known qn pg all 0.007960\n",
"STD positive end expiratory pressure known qn cmH2O all 0.007466\n",
"COUNT red blood cell count known qn x10e6/uL all 0.007036\n",
"STD glasgow coma scale verbal known ord no_units all 0.006918\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.006181\n",
"LAST tidal volume known qn mL all 0.005952\n",
"COUNT potassium serum known qn mEq/L all 0.005873\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all 0.005769\n",
"STD central venous oxygen saturation known qn percent all 0.005625\n",
" oxygen saturation pulse oximetry known qn percent all 0.004996\n",
"MEAN_QN hematocrit known qn percent all 0.004836\n",
"COUNT pH arterial known qn no_units all 0.004807\n",
"MEAN_QN normal saline known qn mL/hr all 0.004789\n",
"STD sodium serum known qn mEq/L all 0.004381\n",
"COUNT lactated ringers known qn mL all 0.004155\n",
" partial thromboplastin time known qn seconds all 0.003795\n",
"MEAN_QN vasopressin known qn units all 0.003296\n",
" tidal volume known qn mL all 0.002428\n",
"LAST blood pressure mean known qn mmHg all 0.001823\n",
"COUNT alkaline phosphatase serum known qn IU/L all 0.001104\n",
"LAST vasopressin known qn units all 0.000665\n",
" blood pressure diastolic known qn mmHg all 0.000396\n",
"MEAN_QN norepinephrine known qn mcg/kg/min all 0.000221\n",
"LAST pH arterial known qn no_units all 0.000098\n",
"\n",
"[188 rows x 1 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline.fit(X_delta_no_lac_known,y_delta)\n",
"coef_abs = pd.Series(lin_reg.coef_,index=X_delta_no_lac_known.columns).abs().sort_values(ascending=False).to_frame()\n",
"coef_abs"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"column_subset = coef_abs.loc[coef_abs[0] > 0.1].index"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: -0.822960319936, 2.53101313462, [0.045326196482469383, 0.074908720774553683, -0.0024347220290164007, 0.013825443055806019, -8.4153517148528394, 0.048413647527979387, -0.028828943578103283, -0.035565365155825202, 0.03696025471804798, 0.033143283700834969]\n",
"RMSE: 1.68749805489, 0.927287188567, [-2.4289898733969926, -1.6235211519554036, -1.8992639860555578, -2.4137960091013442, -19.660681143123963, -1.6069257694069354, -1.7809713591237477, -1.4825023090550129, -1.4566926107801168, -2.721767941384079]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: 0.0211545361393, 0.0258777120295, [0.023770720202128115, 0.01578799949861498, 0.043522312328604706, 0.015834679716753008, 0.061125854614448305, 0.02258718828748596, -0.038036735884840134, 0.028884242643845814, 0.040945675687134586, -0.0028765757016300242]\n",
"RMSE: 1.37440225479, 0.127304754145, [-1.7947471875106613, -2.51824032755178, -1.4827905166110136, -1.5752130138309364, -1.9252657451966466, -1.4495728071765255, -1.9400898977784051, -2.4744430796561101, -2.1125264050479693, -1.7789916035440101]\n"
]
}
],
"source": [
"run_crossval(pipeline,X_delta_no_lac_known.loc[:,column_subset],y_delta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly I am having some issues with this data set.\n",
"R^2 for K-fold and shuffle split CV are not really correlating and, moreover, i am getting significantly negative R^2; yet the RMSE is low? so I don't trust my results\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error,r2_score"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pipeline.fit(X_delta_no_lac_known,y_delta)\n",
"y_delta_pred = pipeline.predict(X_delta_no_lac_known)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.3553208898763829"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sqrt(mean_squared_error(y_delta, y_delta_pred))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.077660384815361971"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2_score(y_delta, y_delta_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Elastic Net"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import ElasticNetCV,ElasticNet\n",
"# more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge); more L1\n",
"elastic_net_cv = ElasticNetCV(l1_ratio=[0,.01,.1, .5, .7,.9, .95, .99, 1])\n",
"pipeline_enet_cv = Pipeline([\n",
" ('scaler',StandardScaler()),\n",
" ('model',elastic_net_cv)\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"enet = ElasticNet(l1_ratio=0.1)\n",
"pipeline_enet = Pipeline([\n",
" ('scaler',StandardScaler()),\n",
" ('model',enet)\n",
" ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Next Lactate"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false,
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.544238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.527066</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.297295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.296842</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.296639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.289677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.150024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.141234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>-0.140334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>-0.133712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>-0.128436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.103035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.098536</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.096566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.085415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.083936</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>mean corpuscular hemoglobin concentration</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.061537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.059125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temperature body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>degF</th>\n",
" <th>all</th>\n",
" <td>-0.055733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>mean corpuscular hemoglobin concentration</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.053502</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.051468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.049250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.048272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>weight body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>kg</th>\n",
" <th>all</th>\n",
" <td>-0.042889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.041544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.040502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.034840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.029689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>-0.029275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>glucose fingerstick</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.028263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.055137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.059883</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.061283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.065514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>alanine aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.067310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>alanine aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.067888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.069094</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.069113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.073382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.076699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.076817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.077606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.081551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.083795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.085192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.087732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.090002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.090459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.092932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.098364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.099250</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.101039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.106825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.108607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.109054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.138692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.141728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.186427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.397359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.424949</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>260 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"LAST chloride serum unknown qn mEq/L all -0.544238\n",
"MEAN_QN chloride serum unknown qn mEq/L all -0.527066\n",
"LAST bicarbonate arterial known qn mEq/L all -0.297295\n",
" bicarbonate other known qn mEq/L all -0.296842\n",
"MEAN_QN bicarbonate arterial known qn mEq/L all -0.296639\n",
" bicarbonate other known qn mEq/L all -0.289677\n",
"LAST blood urea nitrogen serum known qn mg/dL all -0.150024\n",
"MEAN_QN blood urea nitrogen serum known qn mg/dL all -0.141234\n",
"COUNT output urine known qn mL all -0.140334\n",
"LAST platelet count known qn x10e3/uL all -0.133712\n",
"MEAN_QN platelet count known qn x10e3/uL all -0.128436\n",
"LAST creatinine serum known qn mg/dL all -0.103035\n",
"MEAN_ORD glasgow coma scale motor known ord no_units all -0.098536\n",
"MEAN_QN creatinine serum known qn mg/dL all -0.096566\n",
" oxygen saturation pulse oximetry known qn percent all -0.085415\n",
"LAST oxygen saturation pulse oximetry known qn percent all -0.083936\n",
"MEAN_QN mean corpuscular hemoglobin concentration unknown qn percent all -0.061537\n",
" calcium ionized serum known qn mmol/L all -0.059125\n",
" temperature body known qn degF all -0.055733\n",
"LAST mean corpuscular hemoglobin concentration unknown qn percent all -0.053502\n",
"MEAN_QN blood pressure systolic known qn mmHg all -0.051468\n",
" hematocrit known qn percent all -0.049250\n",
"LAST glasgow coma scale motor known ord no_units all -0.048272\n",
"COUNT weight body known qn kg all -0.042889\n",
"LAST blood pressure systolic known qn mmHg all -0.041544\n",
"MEAN_QN blood pressure mean known qn mmHg all -0.040502\n",
"COUNT phosphorous serum known qn mg/dL all -0.034840\n",
"STD partial pressure of carbon dioxide arterial known qn mmHg all -0.029689\n",
"MEAN_QN vasopressin known qn units/min all -0.029275\n",
"COUNT glucose fingerstick unknown qn no_units all -0.028263\n",
"... ...\n",
"MEAN_QN potassium serum known qn mEq/L all 0.055137\n",
"LAST prothrombin time known qn seconds all 0.059883\n",
"COUNT partial pressure of oxygen arterial known qn mmHg all 0.061283\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.065514\n",
"MEAN_QN alanine aminotransferase serum unknown qn IU/L all 0.067310\n",
"LAST alanine aminotransferase serum unknown qn IU/L all 0.067888\n",
"SUM lactated ringers known qn mL all 0.069094\n",
"MEAN_QN glucose serum known qn mg/dL all 0.069113\n",
" international normalized ratio known qn no_units all 0.073382\n",
"STD pH arterial unknown qn units all 0.076699\n",
"MEAN_QN partial thromboplastin time known qn seconds all 0.076817\n",
"COUNT hematocrit known qn percent all 0.077606\n",
"MEAN_QN heart rate known qn beats/min all 0.081551\n",
"LAST partial thromboplastin time known qn seconds all 0.083795\n",
"STD calcium ionized serum known qn mmol/L all 0.085192\n",
"MEAN_QN norepinephrine known qn mcg/kg/min all 0.087732\n",
"LAST glucose serum known qn mg/dL all 0.090002\n",
"MEAN_QN calcium total serum known qn mg/dL all 0.090459\n",
"LAST calcium total serum known qn mg/dL all 0.092932\n",
"MEAN_QN aspartate aminotransferase serum unknown qn IU/L all 0.098364\n",
"LAST aspartate aminotransferase serum unknown qn IU/L all 0.099250\n",
"COUNT calcium ionized serum known qn mmol/L all 0.101039\n",
" normal saline known qn mL/hr all 0.106825\n",
"SUM norepinephrine known qn mcg all 0.108607\n",
"LAST phosphorous serum unknown qn mEq/L all 0.109054\n",
"MEAN_QN phosphorous serum unknown qn mEq/L all 0.138692\n",
"LAST norepinephrine known qn mcg/kg/min all 0.141728\n",
"COUNT vasopressin known qn units/min all 0.186427\n",
"LAST sodium serum known qn mEq/L all 0.397359\n",
"MEAN_QN sodium serum known qn mEq/L all 0.424949\n",
"\n",
"[260 rows x 1 columns]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pipeline_enet_cv.fit(X_next_no_lac,y_next)\n",
"print elastic_net_cv.l1_ratio_ \n",
"pd.Series(elastic_net_cv.coef_,index=X_next_no_lac.columns).sort_values().to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.713177368099\n",
"RMSE: 1.53557050279\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.658925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.648812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.079476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.077081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.070986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.069797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.068503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.066257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.064092</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.063011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.056932</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.056387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.049475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.046387</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.045934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.045474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.045033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.044941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.043131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.042406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.042292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.040862</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.040823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.040432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.039596</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.038780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.037634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alanine aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.036518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>alanine aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.036488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.035735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">LAST</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>torr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"12\" valign=\"top\">STD</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">vasopressin</th>\n",
" <th rowspan=\"2\" valign=\"top\">known</th>\n",
" <th rowspan=\"2\" valign=\"top\">qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">norepinephrine</th>\n",
" <th rowspan=\"2\" valign=\"top\">known</th>\n",
" <th rowspan=\"2\" valign=\"top\">qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temperature body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>degF</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>central venous pressure</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">STD</th>\n",
" <th rowspan=\"2\" valign=\"top\">respiratory rate</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>Breath</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">blood pressure diastolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">blood pressure systolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th rowspan=\"2\" valign=\"top\">central venous oxygen saturation</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT_NOMINAL</th>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>nom</th>\n",
" <th>no_units</th>\n",
" <th>51493(#/hpf)(number/hpf)_0-2</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>264 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"LAST lactate known qn mmol/L all 0.658925\n",
"MEAN_QN lactate known qn mmol/L all 0.648812\n",
"LAST bicarbonate arterial known qn mEq/L all 0.079476\n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 0.077081\n",
"LAST bicarbonate other known qn mEq/L all 0.070986\n",
"MEAN_QN bicarbonate other known qn mEq/L all 0.069797\n",
"LAST chloride serum unknown qn mEq/L all 0.068503\n",
"MEAN_QN chloride serum unknown qn mEq/L all 0.066257\n",
"LAST norepinephrine known qn mcg/kg/min all 0.064092\n",
"COUNT vasopressin known qn units/min all 0.063011\n",
"SUM norepinephrine known qn mcg all 0.056932\n",
"MEAN_QN norepinephrine known qn mcg/kg/min all 0.056387\n",
" phosphorous serum unknown qn mEq/L all 0.049475\n",
"LAST phosphorous serum unknown qn mEq/L all 0.046387\n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 0.045934\n",
" partial pressure of oxygen arterial known qn mmHg all 0.045474\n",
"LAST aspartate aminotransferase serum unknown qn IU/L all 0.045033\n",
"MEAN_QN aspartate aminotransferase serum unknown qn IU/L all 0.044941\n",
"COUNT pH arterial unknown qn units all 0.043131\n",
"MEAN_QN norepinephrine known qn mcg all 0.042406\n",
"LAST norepinephrine known qn mcg all 0.042292\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all 0.040862\n",
"COUNT bicarbonate arterial known qn mEq/L all 0.040823\n",
" output urine known qn mL all 0.040432\n",
"MEAN_QN partial thromboplastin time known qn seconds all 0.039596\n",
"LAST partial thromboplastin time known qn seconds all 0.038780\n",
" oxygen saturation pulse oximetry known qn percent all 0.037634\n",
" alanine aminotransferase serum unknown qn IU/L all 0.036518\n",
"MEAN_QN alanine aminotransferase serum unknown qn IU/L all 0.036488\n",
"LAST prothrombin time known qn seconds all 0.035735\n",
"... ...\n",
"COUNT red cell distribution width known qn percent all 0.000000\n",
"LAST creatinine serum known qn mg/dL all 0.000000\n",
" blood urea nitrogen serum known qn mg/dL all 0.000000\n",
" fraction of inspired oxygen unknown qn torr all 0.000000\n",
" tidal volume known qn mL all 0.000000\n",
"COUNT blood urea nitrogen serum known qn mg/dL all 0.000000\n",
"STD oxygen saturation pulse oximetry known qn percent all 0.000000\n",
" vasopressin known qn units/min all 0.000000\n",
" units all 0.000000\n",
" norepinephrine known qn mcg/kg/min all 0.000000\n",
" mcg all 0.000000\n",
" lactated ringers known qn mL all 0.000000\n",
" normal saline known qn mL/hr all 0.000000\n",
" glasgow coma scale verbal known ord no_units all 0.000000\n",
" glasgow coma scale eye opening known ord no_units all 0.000000\n",
" glasgow coma scale motor known ord no_units all 0.000000\n",
" output urine known qn mL all 0.000000\n",
" temperature body known qn degF all 0.000000\n",
"LAST central venous pressure unknown qn mmHg all 0.000000\n",
"STD respiratory rate unknown qn Breath all 0.000000\n",
" known qn insp/min all 0.000000\n",
" blood pressure mean known qn mmHg all 0.000000\n",
" blood pressure diastolic unknown qn cc/min all 0.000000\n",
" known qn mmHg all 0.000000\n",
" blood pressure systolic unknown qn cc/min all 0.000000\n",
" known qn mmHg all 0.000000\n",
" heart rate known qn beats/min all 0.000000\n",
"LAST central venous oxygen saturation unknown qn no_units all 0.000000\n",
" known qn percent all 0.000000\n",
"COUNT_NOMINAL red blood cell count unknown nom no_units 51493(#/hpf)(number/hpf)_0-2 0.000000\n",
"\n",
"[264 rows x 1 columns]"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#include lactate\n",
"X=X_next\n",
"y=y_next\n",
"\n",
"pipeline_enet.fit(X,y)\n",
"y_pred = pipeline_enet.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))\n",
"pd.Series(enet.coef_,index=X.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.467102597658\n",
"RMSE: 2.093075308\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.173893</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.173196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.171251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.171212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.149283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.148160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.112246</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.110967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.101676</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.101609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.099697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.095657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.094704</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.090167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.089831</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.088578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.077202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alanine aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.075221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>alanine aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>0.075203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.073315</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.073049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.069818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.069529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.069032</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.068040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.067907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.067340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.067289</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.067190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.066957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">LAST</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>number/hpf</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>UNITS</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>carbon dioxide serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"12\" valign=\"top\">STD</th>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temperature body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>degF</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">respiratory rate</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>Breath</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure systolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT_NOMINAL</th>\n",
" <th>white blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>nom</th>\n",
" <th>no_units</th>\n",
" <th>51516(#/hpf)(number/hpf)_0-2</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">LAST</th>\n",
" <th rowspan=\"2\" valign=\"top\">central venous oxygen saturation</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>central venous pressure</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">fraction of inspired oxygen</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>torr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT_NOMINAL</th>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>nom</th>\n",
" <th>no_units</th>\n",
" <th>51493(#/hpf)(number/hpf)_0-2</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>260 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 0.173893\n",
"LAST chloride serum unknown qn mEq/L all 0.173196\n",
" bicarbonate arterial known qn mEq/L all 0.171251\n",
"MEAN_QN chloride serum unknown qn mEq/L all 0.171212\n",
"LAST bicarbonate other known qn mEq/L all 0.149283\n",
"MEAN_QN bicarbonate other known qn mEq/L all 0.148160\n",
"COUNT vasopressin known qn units/min all 0.112246\n",
"LAST norepinephrine known qn mcg/kg/min all 0.110967\n",
" aspartate aminotransferase serum unknown qn IU/L all 0.101676\n",
"MEAN_QN aspartate aminotransferase serum unknown qn IU/L all 0.101609\n",
" norepinephrine known qn mcg/kg/min all 0.099697\n",
"SUM norepinephrine known qn mcg all 0.095657\n",
"MEAN_QN phosphorous serum unknown qn mEq/L all 0.094704\n",
"LAST phosphorous serum unknown qn mEq/L all 0.090167\n",
" sodium serum known qn mEq/L all 0.089831\n",
"MEAN_QN sodium serum known qn mEq/L all 0.088578\n",
" norepinephrine known qn mcg all 0.077202\n",
" alanine aminotransferase serum unknown qn IU/L all 0.075221\n",
"LAST alanine aminotransferase serum unknown qn IU/L all 0.075203\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all 0.073315\n",
"LAST norepinephrine known qn mcg all 0.073049\n",
" partial thromboplastin time known qn seconds all 0.069818\n",
"MEAN_QN partial thromboplastin time known qn seconds all 0.069529\n",
"LAST oxygen saturation pulse oximetry known qn percent all 0.069032\n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 0.068040\n",
" bicarbonate arterial known qn mEq/L all 0.067907\n",
" partial pressure of oxygen arterial known qn mmHg all 0.067340\n",
" pH arterial unknown qn units all 0.067289\n",
"LAST prothrombin time known qn seconds all 0.067190\n",
" platelet count known qn x10e3/uL all 0.066957\n",
"... ...\n",
"COUNT phosphorous serum unknown qn mEq/L all 0.000000\n",
"LAST mean corpuscular hemoglobin known qn pg all 0.000000\n",
" mean corpuscular volume known qn fL all 0.000000\n",
" hematocrit known qn percent all 0.000000\n",
" red blood cell count unknown qn number/hpf all 0.000000\n",
" pH arterial unknown qn UNITS all 0.000000\n",
"COUNT carbon dioxide serum unknown qn no_units all 0.000000\n",
"STD glasgow coma scale verbal known ord no_units all 0.000000\n",
" blood pressure diastolic known qn mmHg all 0.000000\n",
" glasgow coma scale eye opening known ord no_units all 0.000000\n",
" glasgow coma scale motor known ord no_units all 0.000000\n",
" output urine known qn mL all 0.000000\n",
" oxygen saturation pulse oximetry known qn percent all 0.000000\n",
" temperature body known qn degF all 0.000000\n",
" respiratory rate unknown qn Breath all 0.000000\n",
" known qn insp/min all 0.000000\n",
" blood pressure mean known qn mmHg all 0.000000\n",
" blood pressure diastolic unknown qn cc/min all 0.000000\n",
" blood pressure systolic unknown qn cc/min all 0.000000\n",
"COUNT_NOMINAL white blood cell count unknown nom no_units 51516(#/hpf)(number/hpf)_0-2 0.000000\n",
"STD blood pressure systolic known qn mmHg all 0.000000\n",
" heart rate known qn beats/min all 0.000000\n",
"LAST central venous oxygen saturation unknown qn no_units all 0.000000\n",
" known qn percent all 0.000000\n",
" central venous pressure unknown qn mmHg all 0.000000\n",
" tidal volume known qn mL all 0.000000\n",
" fraction of inspired oxygen unknown qn torr all 0.000000\n",
" known qn percent all 0.000000\n",
"COUNT chloride serum unknown qn mEq/L all 0.000000\n",
"COUNT_NOMINAL red blood cell count unknown nom no_units 51493(#/hpf)(number/hpf)_0-2 0.000000\n",
"\n",
"[260 rows x 1 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#no lactate\n",
"X=X_next_no_lac\n",
"y=y_next\n",
"\n",
"pipeline_enet.fit(X,y)\n",
"y_pred = pipeline_enet.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))\n",
"pd.Series(enet.coef_,index=X.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.402829516053\n",
"RMSE: 2.21570643856\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.158038</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.154725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.137105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.135841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.131882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.124949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.119080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.108715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.101843</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>calcium total serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.101497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.101187</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.100211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.096648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.091954</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.087218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.086323</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.083635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.083227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.082822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.082410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.072381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.072249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.070922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>platelet count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.069625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.068308</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.068172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.063856</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.060968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.059365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.058734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>weight body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>kg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">LAST</th>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">normal saline</th>\n",
" <th rowspan=\"2\" valign=\"top\">known</th>\n",
" <th rowspan=\"2\" valign=\"top\">qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>white blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">LAST</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e6/uL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>mean corpuscular volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>fL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>magnesium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 0.158038\n",
"LAST bicarbonate arterial known qn mEq/L all 0.154725\n",
" bicarbonate other known qn mEq/L all 0.137105\n",
"MEAN_QN bicarbonate other known qn mEq/L all 0.135841\n",
"LAST norepinephrine known qn mcg/kg/min all 0.131882\n",
"COUNT vasopressin known qn units/min all 0.124949\n",
"MEAN_QN norepinephrine known qn mcg/kg/min all 0.119080\n",
"SUM norepinephrine known qn mcg all 0.108715\n",
"MEAN_QN calcium total serum known qn mg/dL all 0.101843\n",
"LAST calcium total serum known qn mg/dL all 0.101497\n",
" prothrombin time known qn seconds all 0.101187\n",
"MEAN_QN prothrombin time known qn seconds all 0.100211\n",
" oxygen saturation pulse oximetry known qn percent all 0.096648\n",
" norepinephrine known qn mcg all 0.091954\n",
"LAST norepinephrine known qn mcg all 0.087218\n",
" oxygen saturation pulse oximetry known qn percent all 0.086323\n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 0.083635\n",
" bicarbonate arterial known qn mEq/L all 0.083227\n",
" partial pressure of oxygen arterial known qn mmHg all 0.082822\n",
" output urine known qn mL all 0.082410\n",
"LAST partial thromboplastin time known qn seconds all 0.072381\n",
"MEAN_QN partial thromboplastin time known qn seconds all 0.072249\n",
"LAST platelet count known qn x10e3/uL all 0.070922\n",
"MEAN_QN platelet count known qn x10e3/uL all 0.069625\n",
"LAST glucose serum known qn mg/dL all 0.068308\n",
"MEAN_QN international normalized ratio known qn no_units all 0.068172\n",
" glucose serum known qn mg/dL all 0.063856\n",
"LAST glasgow coma scale motor known ord no_units all 0.060968\n",
" international normalized ratio known qn no_units all 0.059365\n",
"COUNT calcium ionized serum known qn mmol/L all 0.058734\n",
"... ...\n",
"STD vasopressin known qn units all 0.000000\n",
"LAST partial pressure of carbon dioxide arterial known qn mmHg all 0.000000\n",
"STD positive end expiratory pressure known qn cmH2O all 0.000000\n",
"LAST weight body known qn kg all 0.000000\n",
" hematocrit known qn percent all 0.000000\n",
"STD vasopressin known qn units/min all 0.000000\n",
"COUNT creatinine serum known qn mg/dL all 0.000000\n",
"LAST glasgow coma scale verbal known ord no_units all 0.000000\n",
" normal saline known qn mL all 0.000000\n",
" mL/hr all 0.000000\n",
" lactated ringers known qn mL all 0.000000\n",
"COUNT blood urea nitrogen serum known qn mg/dL all 0.000000\n",
" white blood cell count known qn x10e3/uL all 0.000000\n",
"LAST vasopressin known qn units/min all 0.000000\n",
" hemoglobin known qn g/dL all 0.000000\n",
" red blood cell count known qn x10e6/uL all 0.000000\n",
" mean corpuscular volume known qn fL all 0.000000\n",
"COUNT mean corpuscular volume known qn fL all 0.000000\n",
"LAST mean corpuscular hemoglobin known qn pg all 0.000000\n",
"SUM lactated ringers known qn mL all 0.000000\n",
"COUNT sodium serum known qn mEq/L all 0.000000\n",
"SUM normal saline known qn mL all 0.000000\n",
"LAST blood urea nitrogen serum known qn mg/dL all 0.000000\n",
" creatinine serum known qn mg/dL all 0.000000\n",
"STD hematocrit known qn percent all 0.000000\n",
"COUNT red cell distribution width known qn percent all 0.000000\n",
"LAST magnesium serum known qn mg/dL all 0.000000\n",
"STD tidal volume known qn mL all 0.000000\n",
"COUNT mean corpuscular hemoglobin known qn pg all 0.000000\n",
"STD partial pressure of oxygen arterial known qn mmHg all 0.000000\n",
"\n",
"[188 rows x 1 columns]"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#no unknown\n",
"X=X_next_no_lac_known\n",
"y=y_next\n",
"\n",
"pipeline_enet.fit(X,y)\n",
"y_pred = pipeline_enet.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))\n",
"pd.Series(enet.coef_,index=X.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delta lactate"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.01\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>-0.043555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>pH other</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>-0.041159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.039892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>-0.038499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>-0.034408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>torr</th>\n",
" <th>all</th>\n",
" <td>-0.032692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.028817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>glucose fingerstick</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.028618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>-0.025560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.022975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.022065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.021147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.020847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.020768</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">COUNT</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>-0.020576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>white blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>-0.020526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.019161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.019066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>-0.018849</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>weight body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>kg</th>\n",
" <th>all</th>\n",
" <td>-0.018551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>UNITS</th>\n",
" <th>all</th>\n",
" <td>-0.018510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>-0.018459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.017880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>-0.017297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>aspartate aminotransferase serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>IU/L</th>\n",
" <th>all</th>\n",
" <td>-0.017164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>-0.016324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>-0.016314</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">LAST</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>-0.016061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>-0.016006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>-0.015734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.014219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.014348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.014452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.014611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.015258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.015388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>torr</th>\n",
" <th>all</th>\n",
" <td>0.015869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.015969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.016613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.017732</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>m/uL</th>\n",
" <th>all</th>\n",
" <td>0.017734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.017796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.020345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.020417</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.020969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.022215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.022697</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>m/uL</th>\n",
" <th>all</th>\n",
" <td>0.022825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.023115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.024534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>chloride serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.024659</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.024778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.029072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.033413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.033463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.036102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.037873</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.038734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.039147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.065921</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>260 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"LAST hemoglobin known qn g/dL all -0.043555\n",
"COUNT pH other unknown qn units all -0.041159\n",
"MEAN_QN glucose serum known qn mg/dL all -0.039892\n",
"COUNT output urine known qn mL all -0.038499\n",
"STD vasopressin known qn units all -0.034408\n",
"COUNT fraction of inspired oxygen unknown qn torr all -0.032692\n",
"LAST oxygen saturation pulse oximetry known qn percent all -0.028817\n",
"COUNT glucose fingerstick unknown qn no_units all -0.028618\n",
"MEAN_QN hemoglobin known qn g/dL all -0.025560\n",
"STD calcium ionized serum known qn mmol/L all -0.022975\n",
"LAST calcium ionized serum known qn mmol/L all -0.022065\n",
"STD sodium serum known qn mEq/L all -0.021147\n",
"COUNT central venous oxygen saturation known qn percent all -0.020847\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all -0.020768\n",
"COUNT calcium ionized serum known qn mmol/L all -0.020576\n",
" white blood cell count known qn x10e3/uL all -0.020526\n",
" creatinine serum known qn mg/dL all -0.019161\n",
" chloride serum unknown qn mEq/L all -0.019066\n",
"LAST pH arterial unknown qn units all -0.018849\n",
"COUNT weight body known qn kg all -0.018551\n",
" pH arterial unknown qn UNITS all -0.018510\n",
"SUM output urine known qn mL all -0.018459\n",
"COUNT blood urea nitrogen serum known qn mg/dL all -0.017880\n",
"LAST aspartate aminotransferase serum unknown qn IU/L all -0.017297\n",
"MEAN_QN aspartate aminotransferase serum unknown qn IU/L all -0.017164\n",
"COUNT oxygen saturation arterial known qn percent all -0.016324\n",
"STD glasgow coma scale motor known ord no_units all -0.016314\n",
"LAST sodium serum known qn mEq/L all -0.016061\n",
" partial pressure of carbon dioxide arterial known qn mmHg all -0.016006\n",
" creatinine serum known qn mg/dL all -0.015734\n",
"... ...\n",
" norepinephrine known qn mcg/kg/min all 0.014219\n",
" bicarbonate arterial known qn mEq/L all 0.014348\n",
"COUNT glucose serum known qn mg/dL all 0.014452\n",
"MEAN_QN partial thromboplastin time known qn seconds all 0.014611\n",
"COUNT normal saline known qn mL/hr all 0.015258\n",
"MEAN_QN potassium serum known qn mEq/L all 0.015388\n",
"LAST fraction of inspired oxygen unknown qn torr all 0.015869\n",
"MEAN_QN red cell distribution width known qn percent all 0.015969\n",
"LAST red cell distribution width known qn percent all 0.016613\n",
"MEAN_QN partial pressure of oxygen arterial known qn mmHg all 0.017732\n",
"LAST red blood cell count unknown qn m/uL all 0.017734\n",
" bicarbonate other known qn mEq/L all 0.017796\n",
"SUM norepinephrine known qn mcg all 0.020345\n",
"MEAN_QN bicarbonate other known qn mEq/L all 0.020417\n",
"LAST chloride serum unknown qn mEq/L all 0.020969\n",
" blood urea nitrogen serum known qn mg/dL all 0.022215\n",
" normal saline known qn mL/hr all 0.022697\n",
"MEAN_QN red blood cell count unknown qn m/uL all 0.022825\n",
" blood urea nitrogen serum known qn mg/dL all 0.023115\n",
"LAST respiratory rate known qn insp/min all 0.024534\n",
"MEAN_QN chloride serum unknown qn mEq/L all 0.024659\n",
"COUNT vasopressin known qn units/min all 0.024778\n",
" pH arterial unknown qn units all 0.029072\n",
"LAST potassium serum known qn mEq/L all 0.033413\n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 0.033463\n",
"COUNT hematocrit known qn percent all 0.036102\n",
"MEAN_QN respiratory rate known qn insp/min all 0.037873\n",
"COUNT partial pressure of oxygen arterial known qn mmHg all 0.038734\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.039147\n",
" hemoglobin known qn g/dL all 0.065921\n",
"\n",
"[260 rows x 1 columns]"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#run cv\n",
"X = X_delta_no_lac\n",
"y = y_delta\n",
"\n",
"pipeline_enet_cv.fit(X,y)\n",
"print elastic_net_cv.l1_ratio_ \n",
"pd.Series(elastic_net_cv.coef_,index=X.columns).sort_values().to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"enet.l1_ratio = 0.01"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.125167842053\n",
"RMSE: 1.3199548331\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.145104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>lactate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.142307</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.032426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.029333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.029049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.029005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.028873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.026344</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.025613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.024768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.023953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.023537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.021904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.021876</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.021676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.021073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.020737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.020324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.020125</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>pH other</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.019340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glucose fingerstick</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.018509</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.018424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.016785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.016601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.016502</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">MEAN_QN</th>\n",
" <th>red cell distribution width</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.016436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phosphorous serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.016291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.015980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial thromboplastin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.015881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.015414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glucose fingerstick</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"7\" valign=\"top\">LAST</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>number/hpf</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>white blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>number/hpf</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale verbal</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"19\" valign=\"top\">STD</th>\n",
" <th rowspan=\"2\" valign=\"top\">blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>central venous pressure</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">fraction of inspired oxygen</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>torr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale eye opening</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>respiratory rate</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>Breath</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>bicarbonate other</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>264 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"MEAN_QN lactate known qn mmol/L all 0.145104\n",
"LAST lactate known qn mmol/L all 0.142307\n",
"COUNT output urine known qn mL all 0.032426\n",
" hemoglobin known qn g/dL all 0.029333\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.029049\n",
"MEAN_QN respiratory rate known qn insp/min all 0.029005\n",
"COUNT partial pressure of oxygen arterial known qn mmHg all 0.028873\n",
"LAST oxygen saturation pulse oximetry known qn percent all 0.026344\n",
"COUNT pH arterial unknown qn units all 0.025613\n",
" vasopressin known qn units/min all 0.024768\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all 0.023953\n",
"LAST respiratory rate known qn insp/min all 0.023537\n",
"COUNT hematocrit known qn percent all 0.021904\n",
"SUM norepinephrine known qn mcg all 0.021876\n",
"LAST norepinephrine known qn mcg/kg/min all 0.021676\n",
" potassium serum known qn mEq/L all 0.021073\n",
"COUNT bicarbonate arterial known qn mEq/L all 0.020737\n",
"LAST hemoglobin known qn g/dL all 0.020324\n",
"MEAN_QN norepinephrine known qn mcg/kg/min all 0.020125\n",
"COUNT pH other unknown qn units all 0.019340\n",
" glucose fingerstick unknown qn no_units all 0.018509\n",
"LAST norepinephrine known qn mcg all 0.018424\n",
" pH arterial unknown qn units all 0.016785\n",
" red cell distribution width known qn percent all 0.016601\n",
"SUM output urine known qn mL all 0.016502\n",
"MEAN_QN red cell distribution width known qn percent all 0.016436\n",
" phosphorous serum unknown qn mEq/L all 0.016291\n",
" potassium serum known qn mEq/L all 0.015980\n",
" partial thromboplastin time known qn seconds all 0.015881\n",
" norepinephrine known qn mcg all 0.015414\n",
"... ...\n",
"LAST oxygen saturation arterial unknown qn no_units all 0.000000\n",
" glucose fingerstick unknown qn no_units all 0.000000\n",
"STD central venous oxygen saturation known qn percent all 0.000000\n",
"LAST mean corpuscular hemoglobin known qn pg all 0.000000\n",
" red blood cell count unknown qn number/hpf all 0.000000\n",
" white blood cell count unknown qn number/hpf all 0.000000\n",
" vasopressin known qn units all 0.000000\n",
" lactated ringers known qn mL all 0.000000\n",
" normal saline known qn mL all 0.000000\n",
" glasgow coma scale verbal known ord no_units all 0.000000\n",
"STD blood pressure systolic known qn mmHg all 0.000000\n",
" unknown qn cc/min all 0.000000\n",
" blood pressure diastolic unknown qn cc/min all 0.000000\n",
" blood pressure mean known qn mmHg all 0.000000\n",
" central venous pressure unknown qn mmHg all 0.000000\n",
" positive end expiratory pressure known qn cmH2O all 0.000000\n",
" fraction of inspired oxygen unknown qn torr all 0.000000\n",
" known qn percent all 0.000000\n",
" bicarbonate arterial known qn mEq/L all 0.000000\n",
" pH arterial unknown qn units all 0.000000\n",
" oxygen saturation arterial known qn percent all 0.000000\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.000000\n",
" partial pressure of oxygen arterial known qn mmHg all 0.000000\n",
" glucose serum known qn mg/dL all 0.000000\n",
" vasopressin known qn units/min all 0.000000\n",
" lactated ringers known qn mL all 0.000000\n",
" glasgow coma scale eye opening known ord no_units all 0.000000\n",
" output urine known qn mL all 0.000000\n",
" respiratory rate unknown qn Breath all 0.000000\n",
"LAST bicarbonate other known qn mEq/L all 0.000000\n",
"\n",
"[264 rows x 1 columns]"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#include lactate\n",
"X=X_delta\n",
"y=y_delta\n",
"\n",
"pipeline_enet.fit(X,y)\n",
"y_pred = pipeline_enet.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))\n",
"pd.Series(enet.coef_,index=X.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.0518588147173\n",
"RMSE: 1.37414704102\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.031814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.025829</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.024604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.024476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.024446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.024260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.022589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>pH other</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.022551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.022357</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.021632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pH arterial</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.020759</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.020446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.019529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>glucose fingerstick</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.018750</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.016904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.016063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.015896</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>torr</th>\n",
" <th>all</th>\n",
" <td>0.015663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.015525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.015272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.015234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.014643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.014073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.013549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.013523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.013458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.013180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.012900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.012752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.012723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>white blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>number/hpf</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>blood pressure diastolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">LAST</th>\n",
" <th>respiratory rate</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>Breath</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>temperature body</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>degF</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th rowspan=\"2\" valign=\"top\">normal saline</th>\n",
" <th rowspan=\"2\" valign=\"top\">known</th>\n",
" <th rowspan=\"2\" valign=\"top\">qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th rowspan=\"2\" valign=\"top\">vasopressin</th>\n",
" <th rowspan=\"2\" valign=\"top\">known</th>\n",
" <th rowspan=\"2\" valign=\"top\">qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>units/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>white blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>number/hpf</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red blood cell count</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>number/hpf</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>respiratory rate</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>Breath</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>mean corpuscular hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>pg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>carbon dioxide serum</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th rowspan=\"2\" valign=\"top\">blood pressure diastolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure systolic</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>cc/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose fingerstick</th>\n",
" <th>unknown</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>260 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"COUNT hemoglobin known qn g/dL all 0.031814\n",
"MEAN_QN respiratory rate known qn insp/min all 0.025829\n",
"COUNT partial pressure of carbon dioxide arterial known qn mmHg all 0.024604\n",
" partial pressure of oxygen arterial known qn mmHg all 0.024476\n",
" output urine known qn mL all 0.024446\n",
"MEAN_QN glucose serum known qn mg/dL all 0.024260\n",
"LAST hemoglobin known qn g/dL all 0.022589\n",
"COUNT pH other unknown qn units all 0.022551\n",
"LAST potassium serum known qn mEq/L all 0.022357\n",
"COUNT hematocrit known qn percent all 0.021632\n",
" pH arterial unknown qn units all 0.020759\n",
"LAST respiratory rate known qn insp/min all 0.020446\n",
" oxygen saturation pulse oximetry known qn percent all 0.019529\n",
"COUNT glucose fingerstick unknown qn no_units all 0.018750\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all 0.016904\n",
" potassium serum known qn mEq/L all 0.016063\n",
" hemoglobin known qn g/dL all 0.015896\n",
"COUNT fraction of inspired oxygen unknown qn torr all 0.015663\n",
" bicarbonate arterial known qn mEq/L all 0.015525\n",
"STD vasopressin known qn units all 0.015272\n",
"SUM output urine known qn mL all 0.015234\n",
"MEAN_QN partial pressure of oxygen arterial known qn mmHg all 0.014643\n",
"LAST glucose serum known qn mg/dL all 0.014073\n",
"COUNT central venous oxygen saturation known qn percent all 0.013549\n",
"STD sodium serum known qn mEq/L all 0.013523\n",
"SUM norepinephrine known qn mcg all 0.013458\n",
"COUNT creatinine serum known qn mg/dL all 0.013180\n",
"LAST normal saline known qn mL/hr all 0.012900\n",
"COUNT blood urea nitrogen serum known qn mg/dL all 0.012752\n",
"MEAN_QN blood urea nitrogen serum known qn mg/dL all 0.012723\n",
"... ...\n",
"COUNT white blood cell count unknown qn number/hpf all 0.000000\n",
"LAST blood pressure diastolic unknown qn cc/min all 0.000000\n",
"COUNT vasopressin known qn units all 0.000000\n",
"LAST respiratory rate unknown qn Breath all 0.000000\n",
" temperature body known qn degF all 0.000000\n",
" glasgow coma scale motor known ord no_units all 0.000000\n",
"STD oxygen saturation arterial known qn percent all 0.000000\n",
"COUNT normal saline known qn mL/hr all 0.000000\n",
" mL all 0.000000\n",
"LAST vasopressin known qn units all 0.000000\n",
" units/min all 0.000000\n",
"COUNT oxygen saturation pulse oximetry known qn percent all 0.000000\n",
"LAST white blood cell count unknown qn number/hpf all 0.000000\n",
" red blood cell count unknown qn number/hpf all 0.000000\n",
"COUNT respiratory rate unknown qn Breath all 0.000000\n",
"LAST mean corpuscular hemoglobin known qn pg all 0.000000\n",
"COUNT respiratory rate known qn insp/min all 0.000000\n",
" blood pressure mean known qn mmHg all 0.000000\n",
"LAST carbon dioxide serum unknown qn no_units all 0.000000\n",
"COUNT blood pressure diastolic unknown qn cc/min all 0.000000\n",
" known qn mmHg all 0.000000\n",
" blood pressure systolic unknown qn cc/min all 0.000000\n",
"LAST glucose fingerstick unknown qn no_units all 0.000000\n",
"COUNT blood pressure systolic known qn mmHg all 0.000000\n",
"LAST phosphorous serum known qn mg/dL all 0.000000\n",
" prothrombin time known qn seconds all 0.000000\n",
"COUNT heart rate known qn beats/min all 0.000000\n",
"LAST international normalized ratio known qn no_units all 0.000000\n",
"SUM tidal volume known qn mL all 0.000000\n",
"LAST normal saline known qn mL all 0.000000\n",
"\n",
"[260 rows x 1 columns]"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#no lactate\n",
"X=X_delta_no_lac\n",
"y=y_delta\n",
"\n",
"pipeline_enet.fit(X,y)\n",
"y_pred = pipeline_enet.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))\n",
"pd.Series(enet.coef_,index=X.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.0477577004001\n",
"RMSE: 1.37711571979\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th>component</th>\n",
" <th>status</th>\n",
" <th>variable_type</th>\n",
" <th>units</th>\n",
" <th>description</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.032243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.027828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.027696</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.025784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.025330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.025202</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.022978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.022918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>hematocrit</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.022171</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.020049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.019095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.018048</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">MEAN_QN</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.016565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>potassium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.016524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hemoglobin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>g/dL</th>\n",
" <th>all</th>\n",
" <td>0.016294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.016001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.015474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>output urine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.015372</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.015244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sodium serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.015168</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th>creatinine serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.015129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.015016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.014706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg</th>\n",
" <th>all</th>\n",
" <td>0.013190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.013102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.013100</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">LAST</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.013074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood urea nitrogen serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.012832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>calcium ionized serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmol/L</th>\n",
" <th>all</th>\n",
" <td>0.012682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>white blood cell count</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>x10e3/uL</th>\n",
" <th>all</th>\n",
" <td>0.012452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">STD</th>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bicarbonate arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mEq/L</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>vasopressin</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">SUM</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lactated ringers</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>oxygen saturation arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SUM</th>\n",
" <th>tidal volume</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>normal saline</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COUNT</th>\n",
" <th>heart rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>beats/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>partial pressure of oxygen arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>blood pressure systolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blood pressure diastolic</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_QN</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>phosphorous serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">COUNT</th>\n",
" <th>blood pressure mean</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>respiratory rate</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>insp/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>prothrombin time</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>seconds</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>glucose serum</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mg/dL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MEAN_ORD</th>\n",
" <th>glasgow coma scale motor</th>\n",
" <th>known</th>\n",
" <th>ord</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">MEAN_QN</th>\n",
" <th>central venous oxygen saturation</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fraction of inspired oxygen</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">COUNT</th>\n",
" <th>oxygen saturation pulse oximetry</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>percent</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">normal saline</th>\n",
" <th rowspan=\"2\" valign=\"top\">known</th>\n",
" <th rowspan=\"2\" valign=\"top\">qn</th>\n",
" <th>mL</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mL/hr</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>international normalized ratio</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>no_units</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>positive end expiratory pressure</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>cmH2O</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LAST</th>\n",
" <th>partial pressure of carbon dioxide arterial</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mmHg</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STD</th>\n",
" <th>norepinephrine</th>\n",
" <th>known</th>\n",
" <th>qn</th>\n",
" <th>mcg/kg/min</th>\n",
" <th>all</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"feature component status variable_type units description \n",
"COUNT hemoglobin known qn g/dL all 0.032243\n",
" partial pressure of carbon dioxide arterial known qn mmHg all 0.027828\n",
" partial pressure of oxygen arterial known qn mmHg all 0.027696\n",
"MEAN_QN glucose serum known qn mg/dL all 0.025784\n",
" respiratory rate known qn insp/min all 0.025330\n",
"COUNT output urine known qn mL all 0.025202\n",
"LAST potassium serum known qn mEq/L all 0.022978\n",
" hemoglobin known qn g/dL all 0.022918\n",
"COUNT hematocrit known qn percent all 0.022171\n",
"LAST respiratory rate known qn insp/min all 0.020049\n",
" oxygen saturation pulse oximetry known qn percent all 0.019095\n",
"COUNT bicarbonate arterial known qn mEq/L all 0.018048\n",
"MEAN_QN oxygen saturation pulse oximetry known qn percent all 0.016565\n",
" potassium serum known qn mEq/L all 0.016524\n",
" hemoglobin known qn g/dL all 0.016294\n",
" partial pressure of oxygen arterial known qn mmHg all 0.016001\n",
"LAST glucose serum known qn mg/dL all 0.015474\n",
"SUM output urine known qn mL all 0.015372\n",
"STD vasopressin known qn units all 0.015244\n",
" sodium serum known qn mEq/L all 0.015168\n",
"COUNT creatinine serum known qn mg/dL all 0.015129\n",
" central venous oxygen saturation known qn percent all 0.015016\n",
" blood urea nitrogen serum known qn mg/dL all 0.014706\n",
"SUM norepinephrine known qn mcg all 0.013190\n",
"LAST partial pressure of oxygen arterial known qn mmHg all 0.013102\n",
"MEAN_QN blood urea nitrogen serum known qn mg/dL all 0.013100\n",
"LAST normal saline known qn mL/hr all 0.013074\n",
" blood urea nitrogen serum known qn mg/dL all 0.012832\n",
"STD calcium ionized serum known qn mmol/L all 0.012682\n",
"COUNT white blood cell count known qn x10e3/uL all 0.012452\n",
"... ...\n",
"STD fraction of inspired oxygen known qn percent all 0.000000\n",
" bicarbonate arterial known qn mEq/L all 0.000000\n",
"LAST vasopressin known qn units all 0.000000\n",
"SUM normal saline known qn mL all 0.000000\n",
" lactated ringers known qn mL all 0.000000\n",
"STD oxygen saturation arterial known qn percent all 0.000000\n",
"SUM tidal volume known qn mL all 0.000000\n",
"LAST normal saline known qn mL all 0.000000\n",
"STD partial pressure of carbon dioxide arterial known qn mmHg all 0.000000\n",
"LAST glasgow coma scale motor known ord no_units all 0.000000\n",
"COUNT heart rate known qn beats/min all 0.000000\n",
"STD partial pressure of oxygen arterial known qn mmHg all 0.000000\n",
"COUNT blood pressure systolic known qn mmHg all 0.000000\n",
" blood pressure diastolic known qn mmHg all 0.000000\n",
"MEAN_QN international normalized ratio known qn no_units all 0.000000\n",
"LAST phosphorous serum known qn mg/dL all 0.000000\n",
"COUNT blood pressure mean known qn mmHg all 0.000000\n",
" respiratory rate known qn insp/min all 0.000000\n",
"LAST prothrombin time known qn seconds all 0.000000\n",
"STD glucose serum known qn mg/dL all 0.000000\n",
"MEAN_ORD glasgow coma scale motor known ord no_units all 0.000000\n",
"MEAN_QN central venous oxygen saturation known qn percent all 0.000000\n",
" fraction of inspired oxygen known qn percent all 0.000000\n",
"COUNT oxygen saturation pulse oximetry known qn percent all 0.000000\n",
" normal saline known qn mL all 0.000000\n",
" mL/hr all 0.000000\n",
"LAST international normalized ratio known qn no_units all 0.000000\n",
"STD positive end expiratory pressure known qn cmH2O all 0.000000\n",
"LAST partial pressure of carbon dioxide arterial known qn mmHg all 0.000000\n",
"STD norepinephrine known qn mcg/kg/min all 0.000000\n",
"\n",
"[188 rows x 1 columns]"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#no unknown\n",
"X=X_delta_no_lac_known\n",
"y=y_delta\n",
"\n",
"pipeline_enet.fit(X,y)\n",
"y_pred = pipeline_enet.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))\n",
"pd.Series(enet.coef_,index=X.columns).abs().sort_values(ascending=False).to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decision Trees & Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeRegressor,DecisionTreeClassifier,export_graphviz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Classification"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_delta_dir = (y_delta / y_delta.abs()).fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tree_clf = DecisionTreeClassifier(max_depth=5)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree_clf.fit(X_delta_no_lac,y_delta_dir)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"export_graphviz(\n",
" tree_clf,\n",
" out_file='images/allF_lactate_delta_dir_tree.dot',\n",
" feature_names=X_delta_no_lac.columns.tolist(),\n",
" class_names=['decreased','unchanged','increased'],\n",
" rounded=True,\n",
" filled=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"dot -Tpng images/allF_lactate_delta_dir_tree.dot -o images/allF_lactate_delta_dir_tree.png"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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qOY6EO/P7/lBfmxt37s5f/O3l2fzqNyX59n9bfvxpj80Z973P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\nFiSO4tknR3Gh9AQAXR4x/4XuO8XdMtfvlDhjp28jIzeBgqJsduzfQLDv1Dq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Xw/tOhlFVXiyHiIiIiIrvzT/ZCDm+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ixMbFxTFy5Ejq169vjHt6ehIZGUliYiJh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ePuJu0sL7PSxyIiIiIiIlI9/cHTBYiIiIiIiEj1VLeON7PHL2PesvH4d+rD9NjhzB6/jLp1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\nLJmT4bwptYP79U4dWzJ7ipbOmapKFW0eqM1Ob7MzV04fD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0FgAAAAD8D8y7BwD8tZ6eHu3cuVOO4ygej8txHO3cuVOSNHHiRLndbnm9XhUU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IGk9O\n50PeBpjpzL7pzNqEEfdoJwDI5NrZ2fzAm+6g7hMRkquHyf1XYpSqFGPLjaWoYGdj/kjZjC2/aq4E\nAEwdqEPeZ7t+o4xZQzbhtxJ3PKi7BLWBnPlmb1v+GhtWET42/KDuEgBg1pBNlPvnD30WAADKX2di\n1lAD2rhMtPz3LXwzF2Ld5COUNiOOLkFM6r8Cd2tjsHfPPoRHtH3/CQD27duHyZMmQVdHtHHrzKxb\nOBEeCS+P1nFrXx8vWtu5GrOxcbMRamprMXhQ67h1TW0tkpJTcO7XGJwIl9167sbGJmy3sMIKLU2s\n01+Lc+fjsd3CCmv1dKGiQt+H8vXxwnYLK8yZMxujR8luvKKmtpbUEnEiFL1Vqc+nJk2cgIgTodi2\n3QK7HJ2xcMF8TJpIbT/iaGOqA2FIGk+OnLZA3o6Z6cy+6cza2oLtFlYAIPY9o7P5iTddtSlTsGip\nJtbq6mD+PMnXcxexinHjZiYc7GSznvvcec76ZlOT1vXcKirK2GFvB08vH+xydCbzys/nPCcz2LSB\n7HcMHjQI27eZ4kR4JAoLH4ldb2/YbKhNm4kTocGUuE+fcl6QK+1G+j179sABN1c4O7fPODfQ+vws\n77iDSPbZRU8RnXoP+41aX77oYaJFa6s+YThMDp9DHfsjBqp2J8Pr2B+RlleGqN0bEZ16T7oCcNH0\n6QtsgxKwbPo46M6ZhAtZhbANSsCa2ROhrNCVNo6HiRZsgxKg/uNwjBwgu/UQdeyPpJYgGz2odlek\nHP9xWD8E2ejBOvAiXKKSMW/yKPw4rJ/E2pjqQBiSxpPz90HedtqPzuzrzqytLbANSgAAsa/7nc1P\nvOlOHjkAq/aGQ3v2RMyZJPlzzZLql8h6VAmrNXOklUgSfDkbLlHJiNq9ESaHzzHaPaioBQCsm69G\n9h0GqnaHseZMRKfeQ9HTekq9iZquqLA/tmC2tT8CrHUp+Txr4LwQb+LwAUxRRcZj6zJM/8Wv/fbZ\n2bcPk8ePgY7mYpHss+7kIfzMBXg4tc479XERrZ8oiMbmFlg4HYDWornQX7UM539LhYXTAeitWAIV\nJUU+e0cPPwCAyQYdSrhqzx6w374FXkER2GztBP1Vor2shxdJyyQLX8j5+pG3H2Y6s286szZpsXDi\n7LU7aviQDtPQ2fzLm+6UCT9g6QYz6GotxryfJd8DhlVWgZu378Fum+xeEusfcRqOHn74Ncgbm62d\nxIr79HkNAGDy+LFSafDZ64CJC9a03/rZvfswvP8EzJ64WiT7oqpspNyNxlat/WSY6Ur6zbknjFCH\nzxkTsD/WQbX7QDKc/bEO9x+nwdEgCil3o6XSz82nL00IvGCLGT8sw9zJush6eAGBF2yhMWkNFLrS\nj0OarvRA4AVbTBiujgGqsttXmv2xjtRiqx+E7orUccjh/X+ErX4QAuKtEXnVBZNHzcPw/tRN6cXR\nxlQHwpA0npzOibwdMNOZfdPe2i7dCkbkVRc4GkTB54wJo11F7QMAwIKp68hruGr3gdCcZYyUu9Go\nqi+S6LqZ9fACAEBjUuv8l0kjOc88Uu5Gw0rXjy9O4AXOb2JZXqe56WztgzfdkQMnwzlsFTQmaZO+\nkoRnDSV4VJkFnblW0koE0FqXy2ZsIcMUuipDZ541Yq/7IvKqi8C8PrawYeU3GzZrA4TWrazao/J3\nPWCweC8cd7f/uF9RxnmR7LPuFiDi3CV47G59kbO3s/Tjuo3NLbDY6wWthRrQX7EYcVfSYbHXC7pa\ni2ifBTl5cdYWG6+n7j+n2vN72G3bBK+QaBjaukB/hWjPuHiRtEyy8IUcOW2NvH23H53Z151Nm8Ve\nzhykUcMGt0n6na28vOlO+XEslhlYQldzIebN+knidFlllbiZmwc7003SSgQAxF1JB0C936ooKZL3\nWievQIF5EfG3rltNuZ8vnTsLAHAt+x6ZNtEGeO/d+isWw9DWBRZ7vUjbpy/qAACTxzPvNSwq3k7W\nmLRkffuNw7nsxcSRg6E9b5pwYwC3Hj5GVOJNHDBvXX/pableah1Nnz7D+vBJLFefAr2FMxF/7R6s\nD5+EzoIZUFboxme/N4TTVzRaOY8Srvq9Mmw3aOLwqSvYuv849BbOlEiPpGWShS/kdA7kbYCZzuyb\nzqxN1nm3lWbedKeMGYYVtj5YM38a5qr9IHG6xVU1yCwohc365cKNxSTwfCoa2B8Yj8df48zv5b5n\nKCt0I+8Xe0POk7qe1b8GAEwaLdmY5YMyzjzsDUvVMagP550Bg/r0hMnq+YhKvIlHFc8xclBfoel4\nWujjp8172nXf2DHDJ2GRumjr7/JZt3AxNRI2W1rX39kbS782oOX3JniEWGHOdE0s1ViL1Fvx8Aix\nwhIN+nehHot2BgDoLN1KCe+hogrDNXaIjPOBs68RlmqslUiPpGWShS/kyPm7Ij+vmOnMvunM2joT\nnc1PvOmOGzkF2100sVhdB9MmSr6+rqK6GPeLMrFZW3bPqH5NDMSxaGd47YqBs6+RTGw9Qjjjbbz9\ngKUaa+HsawSPECu+PgQ3tS85/bqxw6VbX6ei1APmG9t3H1lnZye4uzqjZw/R9pHNvJWDE5EnceiA\nKxl2+NABWts5s3/GJqNtqKmtw+BBrXOaamrrkJSShrMxETgReVK6QnDR2NQEc2t7rNBchnV6OoiN\nT4C5tT3W6mpDRZl+TtPhQwdgbm2PORrqGD1SdvtT1dTWkVrCQwLQW5W6hmPihB8RHhIAM0tb7N7j\nikXz52LiBOqcJnG0MdWBMCSNJ+fvg7ztMNOZfdOZtQnD3NoeAGR6zeWls/mHN121KZOwWEsbemtW\nY/5cDYlgiepzAAAgAElEQVTTZRWX4HrmLTjYWAo3FhG/wBDs3uOKszER2GS0rUNseYmN56yHNN3a\nOo9dRVkZDrZW8PQ5it17XEkfPH3G2e9qyqSJ/AmJSM8ePeDu6gxnZ6d2nveUgUcpp0Wyz7r3EBHn\nf8PBHeZkmLcjfTvQmDYZhjvcUfvyNQb160OG1758jeTMXJw+6oaI879JVwAuGps/wdLVF1rz1bFW\nayHikq7D0tUXussXQEVJgTaOt6MlLF19oTF9MkYNHURrIwm1L1+TWo577IZqT+p7TyeOHYnjHrvx\ni8thOPmEYP6sqZg4ljo3ThxtTHUgDEnjyZEjKrxtX84/E/k1ipnO7JvOrK0z0dn8xJvulPFjsNzI\nDjrL5mPeTDWJ02WVVyHz7gPYbl0nrUQAwJNnzwEA61YsIvuJKkoKMNJbASefEMQlXcdarYUAgKcv\n6gEAk8ZJti9pXNJ1AICx/koyTEVJAXbGG+B9/DScfELIckmbF0GP7spwtTGGs5Nj+/brM9JRnHtd\nNPucOwiPOYtD+xzJsMPue6TW0djUDHMHZ6xYuhDr1qzE+YTfYO7gDL3VWlBRVuKz3+12CABgspk6\n96N3r55wsDTDIb9gGJjZYN2alXxxRUHSMsnCF3LktDXy9t1+dGZfdzZt5g6cuVGjRgxrk/Q7W3l5\n01WbNAFLdDZBb5Um5mv8LHG6rNIy3Lh1G/YW4j3LZOJ8Auf5m97q1j3TCH3hMWcR4it4/ezT6ucA\ngMkTxgu04+VYaAQaXr6Sebp0+OzfgwnqiwTOX/wXXeCLFy8QFRWJXzbsFzmz2KQQ7DUPokwQ7N2j\nP3r36M9nO3Y450VUlc+LKeHE92EDpV/MwU3Z00IAwJrFRpRPIpwO7UVG0PhJE+eSgmWqhVV+HwCw\nYYUleqjQv6iyh4oqNqzg/IgprSyQShtTHUgTL+P2RUzTVYSDlz4ybl/kOz5NVxHTdKmLdlt+b0LG\n7Ytw8NLHNF1FeIfboaahii9u5fNinLkSSKZBlwdx7NXbWjh46SMs9iBfOsI0ctsRmoSV530jm9TG\nZCtMP7df6PyUX3wL3uF2ZNz84luM2sXNmztPbt/JQhNTndClJ4pOXtqiHRMQbSWnIIWiWZT6JuIz\ntaHiijxM01Xka6M1DVWYpqvIdw0k/Fz5vFhkHbL0DXGeEnnz+oSgvbVx559TkELmR+gDQNEtrD2J\niqj+AEBez2y3HBI7H1nWoSTnl7B0Fb9Txl7zIMQmhYitEeCcB97hdti4YxYCTrV2EgmNgv4E4ecc\nj/yEFr5wuoUKABBwag/8nOOxZLaeROXgJT4lDBo/aUJ7MXXiIqGLWwfRdjztY9rFVhB9ew3CmsXG\n2OO8V7ixlLx48QKRUVHYs1r0iZsnrpXCz3A2lLv9hwzr110B/brzD2hPHMJZ5FNa+54STnwf3a87\nXxxpKHrxFgCwec4YyicRToeBxhgsnTQYYddKZKolv+oNAGD74vHopcS/WBcAeil1w/bFnB8UD6vZ\nUmljqgNx46maRkHVNAp171tgEHQNXokPKPY55Q3YdSYXqqZRMAi6hpzyBsa0L+c9g0HQNaiaRuFy\n3jOxdDV9/h9K/F1ncvH0dSOtrTiaJEGUchB+Y9KlahoFr8QHlHOB2547PlMd0OXBrZEoP51Gpri8\n+QrSI8gvwvJ92/wZoRnFAm0JCp69If3FzdPXjVA1jeK7nhA+Lq19L5JWJp10ZedNg0m/rM4/Ooh8\n04tqKPpE9amgepKFr4XpkKVviOsCkTevTwjaWxt3/tz6Luc9Q9Pn/6FNQ5TrlqDrsayvkaL4jOk8\nkWUdl9a+J/MX5XohSrrK3f4DP8PZOHGtVGyNAOc82XUmF/PcL8MtPo8MJzQK+hOGW3wezlgvxprp\nwwXaLZ0kePMdYcfr3nP646rK1P5In+6c7+UNzAvFJWVgD0UYzhmNPU6Owo1lQFhYGP77+59QH7lZ\n5DiZT05g4/Sj6Mb1Etru3fqhe7d+AmIJp+Y9CwCgPmIz5ZMI50VjJGdTxOYv/P3Vbt8oI2TDa4Rs\neC2xHknLxBvPMrYP5X/iO/H/+9/rEZa9GVdZ3qRd/cdS3Cg/TtqEZW/GgxeJfHl9/qMJD14kIix7\nMyxj++B8/m68aX4qct68cKfFlCcRt/nLW1Ijky0TouZDp50O3uPiaHzy+jbO5+8m41xleaP+I/W6\nx53+gxeJ5PcHLxLx+Y8mkbTQ1TNv/mHZm/Hk9W3GcnL7TRx/08Wn80fu0zOwjO3DV/43zU9hGdsH\nuU/PICx7M87n7+ZL+/MfTWQbZMrzKsubTIuuPkXxhah1K8vzl06rNG2OF6JNFddnSJSWoHqtfvuA\n9D03RD3w1jXh//qPpe3ua6D1mkbkzesTgvbUJuxaJM51k/c6wBQuS5+Kej8RJ91u3yhj4/SjyHxy\nQmyNAKddns/fjUOpC3CpcD8ZTmgU9CcKT17fxqXC/Vg5UbwXTmRVRGLCgCVQH2EgVjxp6PHdAKgP\n3wInx/Z5jhkVFQl7M9EXrsXEBcPDMQRKiipkWB/V/uijyv+sf/wYzrh4eRW130Z8HzFUtuPipU84\n49/6q7dSPolwOtauNMICdS2cPB8kUy2FxX9tELXOCj2/px8X7/m9KozWcTYRYD3mHxcXRxtTHUgS\nr7mlEcnXL8B8tx5Gq3eDm68NntdW8sUtr2IhOjYAo9W7YbR6N5jv1kPy9QtC8xQlfSJNpu+i2N3M\nTSZ13cxNJm2Sr18g7YTpFaaBuxx05RcUX1L/0XHvQRbcfG3ItPwj3PnOOzpb8916uPcgi8+GSKfh\ndS3Md+vBP8Kd0Qd04dLWgyzbMy9EvoQWQsO7D2yyPgTVhaA6f1SaR/qfm+e1lRit3o2vToh6KK9i\niayjLc51Im9enxC0tzZZncfcdeUf4U7WA28bJq4/TlbejPElPTcFlVOaeLK4fvCmq6SoAg/HEMTE\nSTZP7FFpHtx8bbBqywx4B7f2twiNgv4EQdQ7d7+D+3vpk0cC44cdvoiK3M+U+ESax9xFWzQtLutW\nm+D/+78/ERYW1ibpcxMWFob/73//xAYd5hfN8BJ9LgheLqEUn/RV7Y++ErRPborLOP2tDWuMKZ9E\nOC+b9DiTYN+95x/LVVJUQfWDL6h+8EViPZKWiTfesKldMWxqVwDA1fR48vvV9Hg0t/CPH5RVsBBx\nxp+0M7XXxdX0eIoNcazhVS1M7XVx9Lg7mQdvntz/i5OHKIhTNjrN3NzJz4KLlzWp505+Fm2e3HbD\npnbF0ePuKKvg77OIYkfnG7pwWWln4mp6PEztdQXWBVP9SpPu+cvRGDa1K59fql9UYtjUrjh/ORqm\n9rpw8bLmS7u5pRHDpnblO8ad59Hj7mRadHpF8RsR9917NtlmxWmvsj6fJNHO225kdU7K6hpFB3E+\n38hOpmgWtR4Etb3C4jyyfXBDtBXe9kj4uayCJbIOWfqmuaWRcn3j9QlBe2vjzv9GdjKZH6EP4L8u\ncyNtfGkQ5TohyzqUxT2PN10lRRV4uYQi+pxkz4EKi/Pg4mUNzQ3TcehYa7+X0CjoTxiHjjkh8lgC\nVi7Vl6ktLzFxoVg4Rwvr/+qvCaL+VS0AoFfP3pTw3r04z4UrnpVJZCsOBUV3AABTJ82ihBN9xshj\nCSKnteOXA4iKisSLFy8k0iIOjrv34OfBhvi+2wCR7LOehmPd5CPoyjXOrtK1H1S68o8JDOo+AQDQ\n0EgdyyG+91GSbhE8L7UfOdf3n4caUD6JcDpmDdmE8X2XIOtpuEy1VL/LBwDMG2EGxW970dooftsL\n80aYAQBqPvD/HhFHG1MdiBvPLrEv7BL7ouW/b5FZdRx2iX0Rcc8QD+vox2Ue1iUi4p6hQDsizQ+f\n6xFxzxApZT6UcAAofZVBplH6KoOSPmFHl3Z9YympU5hWYVSyb+NCkSOZVkqZD+ob+ecaSZInd1m5\nv4vqZ25tEfcMUcnmH4OWVRugg6gHom5k2U6ev39A+psbdstT2CX25asDwg/1jaUi65Clb7780URp\nl7w+IWhvbbI4n8SB3cKZ07P6Rze+Y0Q+dOVluh7Iso5kcV3gTbfrN8pYN/mIxPeK5+8f4EKRI3wz\nF+K3ktbfKIRGQX+CqH7Pmcs4rCf1ZWddv1GGv/YrbJsp2TNFot1w3+u5v9c1FvPFEUb2syiM77sE\ns4a23Tj2slFOiIpun/7TixcvEBkZCc+D+0WO4x8YjBOhwVBRafXrgP79MKA/f9ufMoUzbs1iUX1N\nfB87dqwEqpl5WMjph2wz2Ur5JMLpMNlqhBVamvAPlO167rt3OePWdjZW6K1KP27dW1UVdjaccev8\nfP5xa3G0MdWBNPHi4i+gy7cKWK2zFnHx/OMhXb5VQJdvqXOnGxubEBd/Aat11qLLtwqwsLZFRSX/\nmHcRqxh+/oFkGnR5EMdqamuxWmctXPfzz68QppHbjtAkrDxv2GxSG5OtMP3cfqHzU2bWLVhY25Jx\nM7NEX88tqe9koYmpTujSE0UnL23RjgmItpKUnELRLEp9E/GZ2tC9+3no8q0CXxutqKxEl28VUMRz\nDST8XMQqFlmHLH1DnKdE3rw+IWhvbdz5JyWnkPkR+gBQdAtrT6Iiqj8AkNczXx/xXwgjyzqU5PwS\nlq6KijJOhAZLfD+8dz8PFta2UJs2E7scnclwQqOgP0H8dukC/u+/n/jCufshBDW1nOdkfXpT5zX2\n6/fX74vH4j8nCw45jhVamjA1YX4RjbRsMzUG0D7j3ACwx8kRRstmYKCqaOugQ3/LQYC1LpQVWp+1\n9uupgn49VfhsJ43gPJMrqX5JCSe+jxnUmy+ONDyq4mxOabRsBuWTCKfDcMl0LJs+DqGJOTLVcr+M\n03e3WK0B1e70+xSodleExWrOSwceVNRKpY2pDiSJ1/TpCxKyi7D+QAy6r3CEQ8hlVNUzr19PyC5C\n9xWOWH8gBgnZRSLly50+U7zuKxzRfYUj6tgfsf5ADDzPZFDCBaUpTEd20VM4hFwm888ueirQnilu\n9xWO8DyTwdfGxclH3HLyhnN/T8srI/NKy2u9xhF1ROcbWbUdcfWyP7Yg+HK2WG2HKAdRNnHTEtTu\n8stryPrkpqr+LbqvcOSrY6JeS6pfiqyjLc5TIm9enxC0tzZp26OkiOoPAOT1zMNEiy8dYciyDkuq\nX5J1Iu41lCldZYWuCLDWRehvkt3T8str4BByGbOt/eES1TrmTWgU9CcMl6hknHc1gu4cwfu91LE/\nAgB689w7+3zP2SC5vIa6BlXUdEUlPOkOlk0fhy1Lp8skPToGqnaH0bIZ2Oss3loaSSCe8x3YbSVy\nnMCoswj1doWKUmsd9O/TG/37SNdvKyx+DAAw2aBD+STCeTEz4LwUkv3uPd8xFSVF/PfFI/z3heA5\nq4KQtEy88b4dMhnfDpmM2oZX0DGxxf4j1P2wsu7kwXqvJ74dMhk6JrbIupPHmySf3bdDJmP/kRCw\nyiooNsQxAIi/kkZ+j7+ShsbmFkZbYTpZZRXwjzhN2uiY2CL+SholLlO6hBYdE1vauILy1zGxhfVe\nTz7bxuYWfDtkMu2x9iiPKHUmq/ZDB1G3yddvUTSy370ny8XkZyI+U33cL2SR7Yubymcv8O2QyXxt\njvADq6xCZB2y9E1jcwulrfP6hKC9tUmaPyD7cx3g1B8A+Lg48MVPvn6L1EP4jy5dpvzFKZ8s617Y\neSxJuipKigj1dkVg1FmxNQKc88d6ryemLdOHo4cfGU5oFPQnDEcPP1yKCoD+qmUSaZMFg/r3hekm\nPezd0/absxPrZw2XuAo3/ovE7FDYrA2AQtfW5389Vfqhpwr/s9WRAzj9wmcN1H3siO+Dest2/WxV\nHac/smymEeWTCKdj6QxDzPhhGS5nh8pUy+PnnH2ltedYoLsi/Thkd0VVaM+xAABU1D7gOy6ONqY6\nEDee5s7u0NzJeS5561EC+f3WowR8+tLElAzJs4YSXLoVTMZzj16PW49a5zESafPmRVBUlY3gBAcy\nblFVtshlERZXWN68fPrShFuPEuAevR6aO7sjOMEB9Wz+PbIBUOx4yywsT95w4vvHFjbpS1H9KKt2\nwFRGzZ3dcf9xmsg6eeMz+aj8RT40d3bHr2nUPmc9uwqaO7vzXUOIen7WUCKyDln6hmgbRN68PiFo\nT21p90/R+orwYdr9U2RY5FUXuBmfx9zJugLTZ3+oAwB0V6L2DXooc8Z4al6V88UR5Rx2Mz6PlCMf\nKfcRwoeOBvx7pxHnnelKzosgRD2XxEGW7UPYdVCSdBW6KsNmbQASJbxXlb/IR3CCA6z8ZiPyqgsZ\nTmgU9CcIoi554a5bQVy9HY4ZPyzDshlbhNpK0h6ZWD7TCH/8tx3H/ZydYbphDQb1Fzw3jyDo5HmE\nejpTnwX17Y3+faV8FlTC8ZHxutWUTyKcl20bOc+K2O/49wlUUVLEl6f38eXpfYn1SFom3nhdR8xA\n1xEzUNvwCrpmO+HuR93PJ+tuAaz3+aDriBnQNduJrLv8c78I4pOuQddsJ7qOmIH4pGtiayPSIPIS\nJw3uvIXFldRW3DI1NrdQ4lvv80FldQ2trTh+FhdxdIjiG6LNCAsnvrPffYB/5FnaNOnsAdm3b1lo\nZYJos8k3ciRKS5DP8wpL0HXEDL7zsrK6Bl1HzACrjDpfkWhDrLJKkXXIytdAa1sj8ub1CUF7a+PO\nP/lGDpkfoQ8ARTddXRHnjLezDW2Z6PKSVrc08VhllaRvJbmm0qWroqSIUE9nBJ08L7ZGgNOerff5\nYPoKAzh5BZLhhEZBf4JICD9Cez/l7gcIgmgHvPbE98LSJ2SY1kINgWkJOy4pg/r3hemGNe32nCsy\nKhr7t+mIHCckPgNBu7dCWaF1X5f+qt+jv6p0L5EvfFINANi6ci7lkwjnxUR7AQCA/YH/2YuyQjc0\n55xCc84pvmOiImmZeOMpaWyBksYWsD80IfB8KpQ0tkDfyR8Xb9yjjX/xxj3oO/kLtCPSrH39DvpO\n/jgYmUAJB4DU3EIyjdTcQkr6hB1d2sVVNaROYVqFcevhY9gdPUWmdTAyAcVV/H0SSfLkLiv3d1H9\nzK1N38kftx7yj/fKqg3QQdQDUTeybCf5pU9Jf3NTVfsKShpb+OqA8ENxVY3IOmTpm6ZPnyntktcn\nBO2tTRbnk6R5SxNPFucwb7rKCt0QtHsrQuIzBMRiJr/0KeyOnsLPW/dhb0hrv4LQKOhPFG49fIy9\nIeexz5T5XhbvbUd7T+C+l8mK2tfvAAC9e1DnWPXtyXlmVFbNPA+Ym0F9esJ45TzsdXYWbiwlxLiX\n5ab9Isc5eyUYLpbB1Pep9uyH3j2le1dAWRXnHNNZspXySYTzorfcFADwvpF/rzHF75Tx8MonPLzC\nP19fVCQtk6B46TkXoLZKAXYea5GeI/o6ifScC7DzWCtSXEltxdGjtkoBaqsUKGUi0mj5vYnW9hW7\nFnYeaxF6lro+KZ91C4eO25J681n069C47dRWKSD07AFUVPOvoRXFjlu/oHBZaZekPHYea3HouC1f\nvJbfm6C2SoFyTFhadGXiRpw2Iyidlt+bKGkdOm6LF/XUZxltcV5xl0NtlQKy81IoGt83svFrYqDQ\n8gnyQ/GTPNK33Lyor4TaKgW+NkbUR0V1scg6ZOkboi6IvHl9QtDe2rjzz85LIfMj9AH81xRupI0v\nDdztI/TsAbLuuf0pyzqsqC4m60SS+wZduorfKcPFMhhnr0i2vq74SR4OHbfFetuZOBbd2kchNAr6\nE8axaGf4u1zAUo21MrOdM11TquOyRHepMf7vj/bbRxZ//oltW0XrTwNAQEgYwoKOQUVZhL0VJnPm\nNLGKqWPsxPdxY0ZLIpuRh4WcdQmmRpspn0Q4HSZGm7FCcxkCgo/LVMvd+5x5uLaW5uitSr8/VW/V\nXrC1NAcA5BU8lEqbrNekx128BG39Tfi3Yk+4HTyEiqqn+LdiT/xbsSdjWkzH6cIbm5ooeVja7URF\nFf0aN247bf1NiLt4idYu81YOLO12kvm5HTzE1/bobLX1NyHzluhrUWSdD5FOTW0dtPU3we3gIUo4\nkz3d96SUNDKvpJTW+adxFy+Rdrz+a9P9DP7Kl9BCaHjDfgu/wBChdSqo7u/nFZD+54Zoq7x1QtQD\nq7hEZB0y3c/grzZP5M3rE4L21iZt++G2EeWaQZznhw8dYIzPlIc05ZQmHqu4hKwLYW1W1HRVlJUR\nFnQMASGS3evv5xXA0m4n1GbNxe49rfOSCY2C/oSxe48rEuPPYp2e8HGgtrLlJTH+LP635R1fOHdf\nRNZs27oF+LM95z05wWTdSgzqJ9r7roJPXUDIgV1QUWrtO/fv3Qv9e/P3OSaP5/S3WOXUebnE9zEj\nhkgqmxZi7HSr/grKJ/eYKi9GeiugNV8dQTGy27MYAO495NwLrLashWpP+mfqqj2/h9UWzm+FAhb/\n/hviaGOqA3HjdRs7B93GzgEAXEi+QX6/kHwDjc38zy5Z5VUIOBlH2un94owLyTcoNsSx2pevofeL\nM9wDIsk8ePPk/l+cPERBnLLRaeYm695D2Ow/SurJusffn+a16zZ2DtwDIvnOB1Ht6HxDFy4r7Uxc\nSL4BvV+cBdYFU/1Kk250/FV0GzuHzy+Vz2vRbewcRMdfhd4vzrDZf5Qv7cbmT+g2dg7fMe483QMi\nybTo9IriNyIu+90Hss2K015lfT5Jop233cjqnJTVNYoO4nxOzsylaBa1HgS1vbyiUrJ9cEO0Fd72\nSPiZVV4lsg5Z+qax+RPl+sbrE4L21sadf3JmLpkfoQ/gvy5zI218aRDlOiHLOpTFPY83XRUlBYQc\n2IXgU5I9f84rKoXN/qOYoW0MJ5/WNdKERkF/grj7V19pptqPlHAVJQV8Ls/GxePi7ynHxMXjXvhc\nzr/mhbs/2xaY6K/Cn//3v+26nmGb4UYMHijaezECT0QjzM8LKspKZFj/fn3Rv59o6yGYeFjEGYMy\n2byB8kmE82JmtAkA8OYt3e8uJfzBrsYfbPr5eKIgaZl4432jOgzfqA5DTV0D1hiYws2L2q/IzLkD\ny10u+EZ1GNYYmCIz5w5j2nGXr2KNgSm+UR2GuMtXxdZGpEHkJU4a3HkLiyuprbhlamxqpsS33OWC\nyqf0dS6On8VFHB2i+IZoM8LCie9v3r7DsdAI2jTp7AHZt29ZaGWCaLNJ6TckSkuQz+8XFOIb1WF8\n52Xl02p8ozoMrFLq722iDbFKy0TWIStfA61tjcib1ycE7a2NO/+k9BtkfoQ+ABTddHVFnDOH3ffQ\nlokuL2l1SxOPVVpG+laSaypduirKSgjz80LgiWixNQKc9my5ywVT52lit1vr+AOhUdCfIC6ficQf\n7GrKfZ+o2zPhgUzRpCIz5w52ux2Cu/OONkmfl8ED+2Ob4UaB6xn+TRcYGRmJ4YPHYsJo0TY9K67I\nQ05BCrbq7qSEL5mtRy+s/0gAQGnlA2j81DroX1rJ2XBi1NAJIuUrKjfuXgYAjB81jfJ54+5lTJsw\nlzaO4nfK2LDCEhb7taCutoSiUxoePua88GbciCkC7UYM/gEAkF2QCu3F1I2GxdHGVAfCYIp35kog\nAk5xGlROQQpyClLw8PFtOJn5C0zPNcAUOQWtE4MS0iORkB6Jc0fvkvWdU5ACBy/qS9aIPOg0JV6L\nQU5BCpZpUOOIqjEs9iCiLra+rIewfVZbBvMN+/jK4BFqSWqh0yWufl6Y9JjoOdLq4c1HFr6TVpOw\ndCX1kazbMUHG7YvYe8wIJnqOfOeRsPoGhLehYQM5LzaJuuhD8Vf5M84GQKWVBZTrXUI652Ebd5gw\nHbL0De956uClDz9n5oGY9tRG5EG0HyK/c0fv4sbdREo97D1mJFX+BOL440UDZ8O2SWNnip2PrPwk\n7TVIkJ4RQ8bDM8waxRV5IvUNWn5vQuHj27j81zVBd6kp/JzjyftvW1LTwHk47WkfQwnPT+DfKE9S\n8otvIeqiD0L3Jwu0474fedrHCKyDtrKlY81iY6y1CUZxcTEmTJBtn4ubyMhIjBnYCz8NF23hdcGz\nN0gvqoGdFnUT3zXTh9Paj+jDWejzsJqNpZMGk+EPqzkLUsYP6iGJbEauFHB+2E0drkr5vFJQDY2x\n9A8elbv9B9sXj4fOkVQsmjCIolMa7lRwNiOfNETwA/hxAziD8elFNdg8h7rJnTjamOpAGEzxfs1+\ngvSiGujOGEGGeSU+gF9S6wZ56UU1SC+qgcOKyXDWnkqJv+tMLmKyWjeJMQvPxMuPoi82soi8hfSi\n1kWZMVnliMkqR5bbGkq7EUeTJEhajvSiGhgEUTcd8Et6BL+kR7i0czlje+SGrg7oCM0ohlt8Hplv\nelEN7lS8hK+ButA8JIXJ7+UNH2j9bhdzm6xPwhagb3+j+3EWBPolPaKkxXrBeQj/sJpNaQNE/TBd\nT6RtiwC9j7n1y/r8I7ic9wxm4ZlwWDGZ7/wXxafC6kkWvhamQ5a+4b0uGARdwxnrxYxptKc2Ig+z\n8Ezyu1l4JpZOGsynUdzrFt21oK2ukeKcqwSy8iPddVNUDcL0jBv4PRxO30bBszci9XmaPv8P7la8\nIn1vNG8szlgvJvsVsoIdaSKS3eY5Y5BeVIPLec8o5buc94w8LgiiDSh3+w8lvJdSN/I40R6Kazjn\nfw+Frvg1+wkcTnOej/kZzsbqacP40hCE4ZwxCHNJaPP+LAAcDzmBmYM34V//r4tI9tVvH6C4PgNL\nf7CjhE8doi21lsKaKwCAoT3VKJ+FNVcwps9sPnuNUVuQU3UKZ/PsoT5iMwZ8Px49vhsgtQ4CScsk\nbrzcql9RXJ+BaUM4G4YW12cgLHszxaa4PgPF9Rl86Z+6a0mGA0BO1SnkVJ3CnuU3MaD7eLF0XGV5\nI630GF+eDY3lWDmR/6UeZ/PsybyZ9MkiH2kQppHO12mlx5BWegw2CxL42l1xfQai72wnv0ff2Y4J\nA6A54R4AACAASURBVJbAfM6vQrXw1jPA7Itl4+35fHE+fzdyqlo3n4i+sx0fP9O/qIsOUfyuPsIA\nte9ZuMryxpZZIej2jTI+/9GES4X7MWHAEqiPMEAvxaEIvKkLjVFbKG3sVSNnYfb4/osY8yR8K44+\nOl8Awuu2vc5fcXXx8uBFIqLvbMey8faYMGCJ2GkJq9e+KqMAcHzP7cfa95xJPM/fFVLqkWhj3GHt\n6Wvea1pY9maB51dHtwNxr2d01wG6cFnpFud+Ik66ANBfZRzO5e1A9dsHGNZL+PODz380oerNPeQ+\n5ZRVY+QWmM/5lbzXy4o3zU8ReFMXxj+fEOs++OT1bfLaLwp1HzjnkOK33yP36Rmcy+MMim+cfhRq\ng1eh2zeiLzZSH2GAA8lh7fIcc+SwcZg8XrRx8UelebiZmwzzLbsp4VqL6BfKDx3Eud6wHhdggXrr\nC+lYjzmb1Y4dOVES2Yyk3uQsrJv0w3TKZ+rNS5g5dR5tHCVFFRits4KhzXLM/XkpRac05BVyFuyO\nHyN4XHzUcE6bvHk7BfqrjCXWxlQHwqCLt+uACW7mto4BxCZGIDYxAldO3Sfr7GZuMsx3U5/P38xN\nJuMJ0iNK+tLCrY/QdeXUfaRlXkZojDdpZ+9mKFQvE/4R7pS0iHwqqx/DbpubyPp444urhy6t0Bhv\nhMZ443RgKqXtM2m2MHKi1Rx/JRo3c5Oxcsk6kfUwaROnHmTZnrlJvn4B9m6GsDBy4juf9nr9Qvqf\nqS6E1fmIIZxnCaEx3hR/lj7hPENgPS6gtPHYxAgA1GuhMB1tea6b79ZD2OGLjGm0pzYiD0nPY966\nIs4JOp7VcMa61SbOIsPcfG3I+iHyeM1uELdoFGTlH1ldP/5/7r47Kopkbf85d3/3froquCqra8aA\nImYF04qYEMWMYkJEkgFR0VVA3FVcFVHXhBEBMSuSVGYIgsAASs5JgiiIi+u6CuLq3r33nN8fZfdM\nz0zPdM80+J3vOWcOdHVVvU+9FbvCW8r8GfQfit2+LsgvyeQ0Nnjf3IisglSE3L2Eh2kirFjohPOH\nQ+n+VwhMm2SJh2kivG9uRIf2UkOI75sbAZB65L2D20bHoJsnceg0GY8f976ioIPSCmL4paNuJ4Tc\nC8JuXxcAwH73M5g9zYohXxW++sdXsLK0w4Xz/ti2bRunMJriwgV/LFu4Fl/9g9v8XV5RJhIkIrjY\nMy+3nTdL9f4pLhDHkzHzyKEmjL/i+DBMNDZT8L/KygnXQy/C/ef1WLHIHoYGw9G9Wy+teVDQNE1s\n4RIkImzeZUs/b95li+mmlgg4Hsbw4+hmpRAuQSJSGvfNiCAkSERYYMG9n+Urg2uc6tKmivMv57xx\nOkB60Ivis8nRE9s37GG4y3M/HeCD0wE+uH4+hi4nXP3xhTbc2cAWvuJpGafw2sS7fJE9isvz8Ms5\nbxz/OQgd2uvifXMjDpzwwHRTSyxfZI/ePfth1XoLrLJygqGBdLxRVUMMK0ydZMEqk9I5H35senP/\neT1dRrmW15aqT3y5q4tX0zopdBtF4X5sCDbvssUmR09MN2WOd7nkg7qyN0CfjHdPB/gw9FVcTozr\nFpRkM8ra9VAynpJ1U8dDSN24/WhPxw8Ajm5WStu2L8GNkkGVH0qe+GYmxAkRjHyg2mj5eLQNzxdc\n2wmh9CRUn6fM36ABQ+G5fyPyijIxahi3cW9mbirdJqxa4oSA42H0eEco1OR8ahG/sniUlUT3qVxA\n5bH8WLhzJz36PdUe8PFLfSd/o9sJtyKC4LmfXN7qs/ssLGcyx94ZOWSOrXu3XrgfG4K7MbeRIBFh\nl9shLJ6zio6fC0YPH4eB/Q0RGBiIffv2qQ+gIYqKipCVnY5dMxSNmyjDsz9yUNIQh5kGTIPNo3sq\nn6vXa0/2uTx/mwejbtI1pOdvSXvcQ5ffWrA65L8kB+r6fDOa8Tf/5X0M1FNcqweANv/UgVl/Z5xJ\nW4IhXaczeGqD6jePAQC9OqqeQ/xOh5xhKG6Iw4S+NhpzY8sDdWALdzNvG0oayFpMSUMc/b+sf3GZ\nL+KeSNeYKH8N759gjiHzWw4AHj+7hpKGOIzpyTScVtIQh4vptow4dkxNQMHLKEb8V7LXMzjIhpPn\noCptyqAsrrgnxxH35DhcJoXS5UdImYB2ejYf5MbQs9BlgELui0hcyV4P80FuCmVQiHLSrQNZm4l7\ncpyRnrp3ZB2r9m0eo61Iq/m8Fizjpo6HkLq5mrOJjh8ALqbbwmn8FdY4WpMbJUOT+sQXvzUTI5j6\nnZhjjMSqc7hb7M2Qr0yOfHsglB6EqqPK/H2nY4jb+T/g2R856NtJ/dryp7+bUP0mHY8+p3WS/ho4\njb9C901CoOp30td807YHcl9EIudFOEoa4rBg6B4Y91qK9v8jPb/wopHUqa//9Q0eP7uG2/nkXPGy\nkUcxqsd8tJFZGzbqZo6Shjh8+ruJ4f7pb3L5RVrNZSwdIT0Xpg6Vr1Pp9lQefHipQ99OY9H9G4MW\nHz8BZN16iKEhxo/jNs5Oz8hElEgMT/cdDPdl1srnyA0GkrYxMysbcy2lZygzs8i69Yjhwq7J3wkj\n69YmJsaMv3fCwjHVTPl5bl1dHWzdvAkzZs3BHItZDJ7aIDmF7FcdPUr1urWRETnPfV8khqMD8zw3\nH25seaAObOGOnTiFHe7k8oAokRhRIjGSU1Jx1u+kyvhs1zogSiQ9F3nBPwAX/AOQm5VO53eUSIwF\ni5lyKRnKOAUEXkKUSIyVy5nfmlw5/rR3Hw74SOs65bektAz79v4EeTit20hzUcaLL395sPHx8nRX\nykdejhC605aTung11ZHQ5ZjC7ZA7WLnaDl6e7gr1SF1+A+rLkOFg8i10wMeXoa+8PPIdnpWVzWjv\nLviT89yybup4CKkb+Xq6YPFS3A1nN4TXmtwoGVT5oeTlZqUjLDyCkQ8rV9tpJZ8CH31UVJB9uRMn\n8D/PLZSetG2DVPEZOtQI6zZuQnpGJqexQWNjE1JSU3Hxc5uwztkRd8Pv0P1vS6KikuTFjavBtBtV\nPnR1meOub/X06Pfq2llZJCYl44CPL+JjxQrv8vLJGnenTp0QEHgJ6zZuAgBcOHsaS5dYKXBQha++\n+gr2dmvg79/y69xFRUVIz8yC3/kf1HsGkFVei5jMMmy3nsZwtzIdodT/gB5k3J5TUQcLE0PaPaei\nDgAwVF+7ywjlEZlaCAAYY9CL8TcytRCmI5SfX9Vp1wYbF0zGfC9/mBsPZvDUBmlF5DzUyAGqz84Y\n9iFG5qMzSrFmFrOe8eHGlgfqoCyc8y+3EJMpNQYXFJ2OoOh0pPptVciz0xES7A4k6wUxmWV0OFV8\nDlyLw5FbUmNpVLjy2lfwslGcI7wcm4mYzDIsNWMfR287E4GgaOllqg6Hb+DXN4285O9YPl2pfFnE\nZJZh+b5ghtuRWwk4cisB9w44M8oZXzlc0smVGyUr1W8r7qYVMXg4HL4BQJpHQpYdPnA9FUqXFy5l\nJ0xSAIfDN7Bj+XSFusAlLnXlblAvch7zyK0ERv4UVJNLYXMq6hjlnypvsm7qeLRkPV2+Lxi3frJj\njaM1uVEyNCmPmoKPPqrqif2PcYZ9ecsRSk/K2hKubag6PkP6dMMWvzBkldfCeLB6eyJNHz7hUUkN\ngmMyEJNZBvvZ43HrJzu6DxUK76K4zbdR5UOnXRuGu17H9vR72TrKNV4ukBRU0+25PAqfkragk87X\nuBybiS1+ZL/DSVcrLPp+uAJfdbCzMMHZ9Udb5XyK4cD+GDeK25mAjLxCiOKT4b6JeS7cer4FSwju\nCBORs/Umo4Yx/oaJHsBsouK3hpPNUvhfu4N1O73hsGIxhg8ZhF7dtTPCLQtN08QWLvBGGETxyVi+\nYDbttvfoGfj4Sfc/i+KTIYpPhqerE/b+4MJwX+zAXJ/28bsIH7+LiL3pr6AfUXwyVrtKz8OtdvWA\n5YwpCA9UPUenjKcy2RRPVelVl8bSimpGGpXJN5tojFkrnOFksxTDDaWXUpZXkXG0xVTl699fIj3y\neSZ0+aEQci8Gq1094OnqBMsZzLnrdTu96XSwpUldfgweQOw3+PhdZKQnr5j0P1n5xYy88L9G5kJk\n3dTxEFI3a7d60fEDwGKHLSrLeWty00R+S9X1iqfPAAATxo5UCE/Jo/hkxYQgXPSAUU4oGerS2Vr6\n1bYes8ULAEMHD8BGj33IyCvk1Dc2vm9GakYOAm+GQxSfDGebpQgPPEn3YULhr+f56j2xIL+E2Efq\n9E1HBN4Mx0YPsoZ49tBPWDLXHLod2nOOy3HlYpyatqhVxid9ug/G4D7c5gvLn2chozQGy6YzDWZP\nGWml1H8PPWJXuqI2B+OGSMtBRS2xK92v+1Cl4TRFSkEkAGBQ7zGMvykFkRgxQPnlL+3a6GCh6UZ4\nnp8PY0NzBk9tUFRNLgMa0HOkSn99upFv6oySaFiMY15IzYcbWx6oA1u4jNIY+F6TjkN9rzlg3BAL\n7LG/xRpXRmkMvIOWK7hllMZw4ng15gBuxh9RCLtixg6stvBqsbBsOHrDmeYOAOLHQRA/DsLpbamM\nsssmu7ahXGPZAHAyxJWWz1WPQpcDCsn5YfC95oAVM3YolEMuPNXpqFdXsg//ZvwRhs6q6sk8f0Vd\nDkPn4sfkAgNZN3U8hNSNfNnwDlqusm60BjeLcWtQ9aIAV2P244eV/mjXRgcfPjUh4P5ujBtiwWhf\nxEffcYqfyrN2bZjrKR3b69HvZfNLk3oYnnwaAfd3AwDcbQKV6uLFa2I/eEjfcZx4awKhyoe27aAq\nPn27DcGpO1tQ/jyLU7/94VMTip8+Qkx6MDJKYzBngj322N+i+8aWRP3nPHO3CWT1U1Alwc34I/BZ\nf49TnHzLoyr84x9fYeYYG5w/10rrfhkZ8N/P7bKRzLxiiBJS4L7BjuFuPZfd5iVXhInJPJ/JyKGM\nv2HiBJhNGKvg32nlYly8EY71ngdgv2wBhhsOFHYuSMM0sYULunUXooQULJs/i3bzPnYBPmekl96I\nElIgSkiBp4s99mxbxwjv+qMvLt6QXnJuu2U3Xjb8xovbiYDr8PA5xZCVkpELv58V97jLgo1nWcVT\nBZ58/GqbJvvteyFKSKGfL94Ix8Ub4ciMuobhhgPVclKmZ02gLQ9luuGD9Z4HaPlUnIDqMix0+eYK\nvlxDoh7AdstueLrYw3L6ZN5xqdP5oAF9AQA+Z4IYeUBdjJ5dUMLIQ6q8yrqp4yGkruXLmpXzDwjz\nP8oaR2tyo2RYOf/AkJcZdQ0R0Q8Z+WC7ZbdCPBVPnwMAJo7Rbv1PFYRKr2w6Zd241D11fIYOGoCN\nXj7IzCuGySj18xGN75uRmpmHoNukj3FauRhh/kfpPrQlUVlDbCFfOblfpT/L6ZMhSkhB4/tmxrxT\n43ty18bFG+F0P2S/bAFECSkIiXrA0E9I1AP6PYX8z/W0U0ddBN2KxEYvci7y7AFPWFnO4DXHBQAO\nyxfglPmyVpnnGqzfE8ZGqu3pU8gqqUZ0Wh52rJ7HcF8ynf++Q3lEJGYBAMYO6c/4G5GYhSmjhyj4\nd1gwFYGRD+HiG4S186Zg6IDe6NW1s9Y8KGiaJrZwLr5BiE4jZzWj0/Lo/2X9/xwQhsOXpeN9yl9Z\nTT1+dFT85gm+n4TotDxYz2TKjE7Lg7XHCUYcjy79jMikLEb8a/eeY3CQDSfPQVXalEFZXIcv38Ph\ny/cQddKdzlMhZQLa6XnnmvkMPQtdBiiEJqRj7d5z2LlmPmZPYu6vEqKcDOpL7tI4fPkeIz35Fc8A\nADllTzFsgHQfRmDkQwBguKnjIaRuHH++QMcPANYeJxByaKuCvy/BjZKhSX0SQjbfcELVJ2X+jPr1\nguvhS8gqqebUZzR9+Ii0/HJcup+M6LQ8OCychpBDW+m2XShU1TVg7hZfXNq7gVGG+YQHgEt7N9Bu\nBRVkHNhJpz2C7yfB9fAlAIDfzrVYPG0cdNq1VRknVS7k/el9o0O/V9amK8Pa+WY4bePRKuOB/n0G\nY9ggjvepPsmEJFMMh6XM83ezJmt39gEAHqSRb5yhg4wZfx+khcN4uOLZuSUWjgiNDsA+v41YbL4W\nBvrD0E1PuP1imqaJLdzVyFM4HkTOikkyxZBkipFTnIpdG1TvlTl7fR8Cbkv3l1Fhq2vLsHHVTxr7\nPXhuC0KjA+hnzyN2+O0Nd9vzVPyeR+wYcZiazMGJ3YrnVsLjLkGSKcbsKdLzWmx8HZe5M/hKMsXY\nul/uXNltXwTc9sWF/WK6fHD1xxfacFcGLjxXzd+EdbvnYImFIwz0pW1ATR1ZX/9+zCxB0synzKjD\n7mMOkGRKz6iERgcgNDoAt06m02kQul5RiE25A88jdnBc5g5TE+b5un1+G2leVPrk41SnB/1e5Hxd\nwG1fhl6o+1KLK7IZ+UTVLVk3dTyE1I18Xmzdv1RpvfwS3CgZVLml5N06mY74RxGMfKDaF/l4tA3P\nF/Llg6pj8hBKT8rqNVvZ5RMvAAzsa4T9Zzah6Ekmp76/+c8m5Ban0u3gktmOOLH7Dt1HC4Xce9zv\npOPqd7H5WkgyxYhNucPQRWzKHfq9KpQ/Jevuuh06ITz2EvafIefrdruchvlkK7T/mvv5un/84yvM\nn76mVezI+vv7w37Nanz1FTc7shmZ2YgSx8DzBzeG+7Ili5X6NxhAxvRZObmYO0e6DyIrJxcAMHyY\nsPOAoRF3AQAmxmMZf0Mj7mLqlMlKw+jq6GCLy3rMtFyI2eYzGDy1gST1EQBg9CjVc8ZGQ8iepqjo\nWDiuZdpU4cONLQ/UQVm4PT8fxAFfqc2yA76/MJ6FwBrHDYgSS/eEXAi4hAsBl5D7OJlRLuS5RIlj\nECWOQWlZObx/3MVwX2i9iiGD4v1AFMnIf7Y4vdy3M+JUhpaUExh8BVHiGKyw1mxvhSw3Slbu42SE\nRd5j8Fhl5wRAmvdClh1Z3A4Nxyo7J3i5b1cou84uW+j8p7jKx6ku7wcPJvu8D/j+wtAndd45MzuX\nUZYuBJDvdVk3dTyE1I18mV9ovQqRIddZ42hNbpQMTcoPwK/NoGwTTBov3Z/ksvUHOn8oGfUv+X1r\nykMo/Sir82xlli+fYUZDsN7VDRmZ2RhnorifRB6NTU1ISX2EgOCriBLHYJ3jWkSGXKf7OqHwn+Y3\nX9wvV1RUETtp14OlZwTyCshZ+86dvkHApStY70rGLOf9jmOp1ULo6nAbk3311VdYa2vTevYOMjJx\nYe9mTv4zC0ogSkzDzvVMu5ZLLacr9T+wL5n7yi4sg+VU6b3X2YXkPM3wwQM0oc2K8Bhyx6/J8CGM\nv+ExiTAbr9w+m26Hdti0Zilm223FrCnjGTy1QUoW+QYfZaT6/lejgfoAAHHiI9hbM9c0+XBjywN1\nYAsnSkyD7XZv+tl2uzcsp05C6Dkfhp8lGzwVwokS05TGHRRyH6LENCybOwNcwVcG1zjVpU0VZ++T\nATh0TmofkuLjscEWe7Y4quR+6NwVHDp3BdHBJ+gyydUfX2jDnQ1s4UurajiF1yZee+t5yC+twL6T\ngQg8vBu6Hdqh8f0HePqeheXUSbC3nod+vXtgtt1WOC5fwGhfnnw+AzZrynhWmZTO+fBj09uG3Yfp\nMsq1vLZUfeLLXV28mtZJodsoCndECbDd7g2PDbYKbSSXfFBX9gb16wuAlA9ZfeWXkHudsgvLGGXt\n4i0yLyDrpo6HkLpx2Lmfjh8AlmzwVNq2fQlulAyq/FDyMiKDEBGbxMgHqo2Wj0fb8HzBtZ0QSk9C\n9XnK/A0d1A8uPx1BZkEJTEaot9fe+P4DUrPzcSkkCqLENDgtX4DQcz702EoIpGSSsVKv77rijigB\nt6PiIUpMwyF3F6ycbw69zt/QfgvKyLdcp446CAq5D5efyJmDM/t2wGr2NOh2aKcRh8pnxAbXlV+k\n94wIKeurr/4BO6s58L9wvtXOMwSeOMjJf0Z2HqJiE+CxlWmTYNmieSwhuCP0HlkbMBkzkvE39J4Y\nUydPVPC/zm4V/IOvY91WdzisXoHhRobo3bO71jwoaJomtnCBV28iKjYBy62kezH3+PyCg8dO089R\nsQmIik3Arm2b4O3JPGPismM3/IOlczI2zpvx8tcGXtyOn72InXsOMmRJHmXgzBHVe1HZeJaWVyjw\n5ONX2zTZbXRDVKzUHpJ/8HX4B19HTpIYw42kdq746FkTaMtDmW74YN1Wd1o+FSegugwLXb65gi/X\n2xH3YeO8Gbu2bcLcWcx+iktc6nQ+2ICMvQ4eO83Ig9zCYgBAVm4BIw+p8irrpo6HkLqWL2uLbBwR\ncS1Awd+X4EbJWGTjyJCXkyRG2D0xIx9snDcrxPPks82aiRzmGzWFUOmVTaesG5e6p47PUMNBWL/N\nExnZeRg3Vr1Nw8am90h5nEn3Mc52qxBxLYDuQ4WGbD9yzf8Up7TmF5UAIPOegVdvYf02Mm49f8wH\nSxZYQlenA8N/ZXUNzBevwjX/U4y6pm286uBkuwInz89g3b/4D2WBIiPuYspY7gWr+jkhrdeJuyFX\nhyXuCAz1RfOf5BKG5j+bEBjqC4clqg8jAoCxVXvWnzwafq9DWGwAHJa4o5MuOZDbSVcPDkvcERYb\ngIbf69jlDJsChyXu2OZjrdIfH4TFksZN3WYFimtKtqKh4ZbixgW5Jam4f6EMWWHNuH+hjNZjVlEy\na5iUbDFSssVwWOKOxKsvkRXWjANuwQCAsDjpQextPmRTX5DPQ2SFNdMyAMDruJ1CvP16GSIrrBnm\n3zMvbebCMasomS5v8n4DQ32Vpseg7zCa/9m9n43dpoTw4p8V1izl8NmPPB9KRuLVlzSfymdFrPrV\nVndCcmLLE015tiTiUkPhddwODkvcsX7Fjwrv1eU3lzLU/msduk2rfVlFh6XiOXDelXaj3nut9+PF\nQyjI1lPZ9EQ8CGYN01rcKJRU5ijIW7mdXDAv785Wnrj2H3z1kZZLLmbq0VVfgJRqhpasX1T/TvX3\nbGj4vQ5xqaGYuro7Ih4Ew2KyNe5fKIOH8wlMHjuH7tsAaXuj6qcJxMk3MXnsHEwcLcxFiMpwM+oM\nJo+dA+Nhqjd2D9IfgS1rDmLy2DnwOm6HuFTFi6Na2q8y9O1hAP1egxAZGckrHF9Ehodh9nDVBvBl\nUfbiLQCgW8evOYfZNnckjkXlo+njvwEATR//jWNR+dg2V/1Hg55jIOtPHi/+aEZwUjm2zR2JLh3I\nAaMuHdpi29yRCE4qx4s/2Mvr5MHdsW3uSNj4PVDpjw+Ck8ghBJ22/1Lpj+IaW1Dbaty4YHD3b/A6\nwAGLTIgR0pTyl3S+VfutxusAB1T7rabzt6TuDzpsSvlLOi/yDi/D6wAH5B1ehsY//81JdmxBLWIL\nahmy/J2nAgCCk6UGxPlw0gTapMPGjxyKp8K9DnBA9C7y7XYvuwYA8DpAajSP8iML+Txgw6MnDQx+\nVJlPKX/JK73q+FCQ1bu83GNR+UrlGvXqROdR+A/EAHBYRrXS+HXa/otuH6pfSS+JoPxvu5JKu1Hv\nj9kqN0DMNQ/Vpb3xz3/T/K+5zlTJXyhEZD6Fs38its0dCc+Fika41OmUSz4JoWs+easNZNsF2fRc\nlTxhDdNa3ChclTxR0HVsQS2jTmjSbsm3BS3ZRqrSGdc2QlNQ7Wb0rnl0/HmHlwEAnP0TtYqbGrdQ\n4xg2vPijGRGZT9Hf9SquSp7Aalx/5B1ehiM2kzBrRG+6zwakOlD1EwqzRvRG+A+zEZZRzRgLhWVU\nI/yH2Zg1gv9Bb3Uw845gtAHbrqRiY0AyPZ7jgoHdOmJgjy4tPp4tLy9H1dMKjOg5W73nz3jZSOqK\n7tfCGakDgD/+rEdK1WVYGLmhQxty0VaHNl1gYeSGlKrL+OPPeoUwPToaYc/cR+jY9jucl6zGj3dH\nw+VmVySUn0PN7zmC8tMGZ1a8Yvwv+wwA3XUH48yKVxjTh1xwfF6yGgDww0wx7f/nBeQQVNAjqXGv\novo4FNXHwcLIDUeXVOLMilewn3gBAJBSeZmTbApPXqUipuQ4LIzc8POCXFqmhZEbYkqO48mrVIUw\nPToa0XI3TyMXh2Q9D1OpCz5yuHJXBXUcKV1TXM6seIUfZpK56rxaRcOZadVXFXgX1ccp1Y885PNZ\nVhcUx6NLKmld1L+Tzk88eZVK1w9Z+X/+W/mlYPLgo3dzoy0oqo/Do2qyqPqo+jqK6uNgPfYQAGBQ\n1+8xrIc5cmvvM2Q8/T0TANBVp79KmZMHMI1x89UFBU3KX2uAD6+c55EIerQOFkZumDfcQ+G9uri4\n5Gvbf+rAwogcaPjtvXQsR8VzI1O6wE69X2nCPIzTWrqWbdNk05NWfZU1TGtwY2uLNGk35dsBde7a\ngmt/ogmocQA1LmDDH3/WI+d5JH4IHYi06qsw7mOFnxfkYrnxYQzrYU73+YBUv6p+qvDx7yaE5+2F\nhZEbb10mPrmAYT3MMair+gt6ZHEwehqjHt3I3I7Lj13w8e8mznF01RmI7p0Htvw8ZsRdzJjMfV28\noppsuvm2C/d18Y12HjgbfAjvm0n/9L65EWeDD2GjnWIbJw+DSW1Zf/J4+aoONyMvYqOdBzp/Q+bj\nO3+jh412HrgZeREvX7GvKY8fY4aNdh5Yv3OJSn98cDOSHCrr0F5XpT+K68M0UatxU4WHaSI8TBNh\no50HcmIbUJH2Ece9ySbpmxHSjUzrd5L10BD/ZFSkfURF2kckhZON9G57bBUj5hm/tigszabjv3Iq\nGgAwfw05OCrvropvRdpHxv/Uc3pOEl2Ok8Ir6PRT5T09J0lleE31pwxUXBSPirSPCPEn6/3RD6UG\nqWU5UzrIiW2gOZdXFSrEPVB/CCrSPsJyhmbGR4TKByEgir8Dtz222Gjnga1OexTeDx44TIHT/bjb\n9Hsued6hvS7dtj2rq6TDUvHs9pVuzqbe73c/w4uHUJCti7LpCbl7iTVMa3GjoGn5YcurFQudswrZ\njAAAIABJREFUlMpJfhQLAOjVXZ8OT/UnsuGbmrld6tHSELL9kAfVv1P9PRtevqqDKP4OxszqhpC7\nlzDPfBmSwivgveMUpk2ypPs2QNr2qfqpwjxzMpcpSY+j3d43NyLw5gm2IKwYYjACHpsOYdokS7jt\nsYUoXrnxqvlrxjHq625fF+zY50CPZbjA3HQ+KiqfoLy8nDdPrigvL0dFxROYm83nHOZJFcnbrnrC\nXlb/sqEO10MvYpOjJzp3+jwO66SHTY6euB56ES8bFMcwhgbD8TC8CN2+7Q5HNytMshwI/TFtcPHa\nCeQVZQrKTwjcjAhCmqgSNTmfkCaqxCZHTyRIRHiUlUT7cXQjxljCgyWoyflE+wWAzbsU66dBP0PU\n5HzCvFnWqMn5RLtTYZWBrwyh0qaMMwA8ykrC6QAfbHL0RGHyK9TkfEJh8itscvTE6QAflFVIxxcU\nd0pWTc4nhAdLAADi+DDe/vhCG+7KIBteXn+nA3wY+uOav3zj3bh2JxIkItyKJP33rchLSJCIsM+d\ntJETjc0w3dQS4oQIhozsAmJ0ql8fA5UyVy1R7Ds10ZuhwXDa7/XzxBjK3RjVY4iWqE+acJcvN9ry\nbEncjw3B5l222OToie0bFMe76vKBS9nr0F4XmxzJpv6a59LxLhWP5/6NtBv13mf3WV48hEKCRIQE\niUghPTcjgljDtBY3Cvkl2Qry5qwghhTl3ZWVJ23D649pw/qTB592Qii0ZP2ixkLU2IgNLxvqcD82\nBMOndMXNiCAssFiGNFEl9nv6YbqpJT3uAaTtjarf/wYE3SDcJxqbfWkqAEiZlW07PPdvhNuP9oyx\nd4KEzJn9cs4bm3fZ0s8Hj3vA/ef1vMbpADDTdD4iIlp4zjUyEt2/GYhv23MzXP9r0+d19rbc19nN\nB7kh7slxfPo85/zp7ybEPTkO80FuakICWyO7sf7k8fZjPdJqLsN8kBva/w+Zt2//P11gPsgNaTWX\n8faj4lo9hYF638N8kBsuptuq9McHaTVkrbvNP1WfRaK4ljTEKX3fEty4oIeuEQ5ZVuDEwga4TCJ7\nn3NeSOfvKl+n0vm4Z1YOTixswJ5ZOXR+V75WXGPq1mEQTixswOiezPWP52/zFGQdSSQHeOXdr2Sv\np8NdTCdt7FZTEU4sbKA5yPvjAiouKi0nFjZgqympw/kv7yv4E0ImwE/PlL9DlhW0nusbVZ8X0Ba5\nLyJxJXs9zAe5YY6h4vlBIcpJm3/q0O3B62bpWjAVz+186WVg1PtlI5kXs6njIRRKGuJQ0hCnkJ5H\nz66xhmktbhQ0rU98UfqKHGDu0q4Pw/3j3020HKfxZD1FWXrZ2gNtIXQdlQXV91F9IRvefqxH7otI\neIgM8OjZNYzpuRh7ZuVg6QhfGHUzp9t9ADRHVT9VoPoOcZkvrmSvp5/vFnvjZt42uu+VxZHE6Yx6\ndTv/B1zN2cTwO6YnMfZZ+uoh7fbp7yY8rDqnkg8bkqr9YdTNHAP12NexufDiAqMusxF2J0K9Ry1x\n9+5dLJg/l7P/4mLSXnfvzn2u08vTHQd8fNHYSHTQ2NiEAz6+8PJUf577q/9px/qTR21dHS74B8DL\n0x3f6pHvhm/19ODl6Y4L/gGorWNf851qNgVenu5YsHipSn98cMGfrMHq6qoeQ1Fco0TKz3O3BDcu\nSJakoKaqHP/96wNqqsppPSYmsZ/njhKJESUSw8vTHX/89iv++9cH3LgaDAC4cFG6Jr1gMVkDTZMk\n4r9/faBlAMDK1XYK8RoNMcR///qAZdbMtVMuHBOTkunyJu/3gI+v0vSMGD6M5h8fS/Llxi3p2VUu\n/P/7l/TyA8qPPB9Kxh+//UrzKShUfZ5bG90JyYktTzTl2ZK4HXIHK1fbwcvTHfv2Kl6Yoy6/uZQh\nXV0duk2rqJTO31HxrNu4iXaj3l84KzUUw4WHUJCtp7LpuRjIvl7dWtwoZGZlK8gbbUyMlcq7s5Un\nrv0HX32IY8gad79+X+48d0vWL6p/p/p7NtTW1eF2yB10+vY7XAy8hJXLrVFTVY6zficx13IO3bcB\n0vZG1U8TXLt+E3Mt58Bi1iyNwnPBiVOnMddyDqaasZ/nHm08nlHH123cBNu1DvS4hysWLpiPJ09a\ndp0bIPNnBr2/w8Ceeuo9Ayh9Tsbx33XmfpnOjuXTceRWApo+kLnZpg+fcORWAnYsV2+ws+Ncd9af\nPF68foeg6HTsWD4deh2JrQa9ju2xY/l0BEWn48Vr9r0mpiP6Y8fy6Vi+L1ilPz4Iik4HAOi0U5zr\nlwXFNSZT+fdYS3BThZjMMsRklmHH8umove2Nd1G+CNy5EgAQJE5X8N/44RPt79ZPdgCAO0l5Cv4o\nSAqq6fwvvuSJd1G+KL7kSZcTSYHiucjBvbviXZQvrEyVX+QjKaim8142zsYPiusBsvIp3rW3vWn5\nxTWqL7xYvi8YAGg576J88eAo2dcSmSpd49REjrp0qkNORR0t694BZwDA965kvVre3eHwDY1kCIlh\n/bor8GIrO2GSAjgcvoEdy6fDy0bRboe6uLiUO512beh2qar+dzosFc8WP+keCer9SVfmRUF80qQN\nZOupbHqCYzJYw7QWNwpClEeufQBffcRlkb61b7dOgqRVE1BtyYOjLnRbUnyJrP9rWz+pPprqs9nw\n4vU7hEkK0HvZHgTHZGCp2SgUX/LEMZdFsDAxpPsnADRHVb//Czh7NwUWJoYwHcG+rvi96wlGe7DF\nLwzOv9yixzlcYdBTDwa9v2vx8yl3IyMx39yMs//icmJvrHvXbwXlUfeyAf7X7sDT1Ql6nUnd0+vc\nCZ6uTvC/dgd1LxXL63BDAxQn3kWPbt9iscMWDJhggf/pMxInLl5BRp7qvWRfAkMM+uOv5/mwnk8u\nXEt6lAkfv4vwdHXCb8Wp+Ot5Pn4rToWnqxN8/C6isKyCDrvYYQsAoOpxDP56no+/nudDEknWIMJE\nDxRkBd4Mp/1WPY6Bp6sTRPHJSHqkfq+nPE9KtiTyCi276jHZ37LalezD/+t5Ph2e8iOfRnk+Pn4X\nlfKRlW820QSWM6YgXC6Nj7NJ/AafL5z4EulRl2ctgZB7MVjt6gFPVyfs/cFF4f1wQwOaV+xNfwDA\nrbvR9Hsu+aHboT08Xcn+osqnz+mwVDwbPfbRbtT7s4eYc2fqeAgFUXwyRPHJCukJvMm+Dtla3DSV\n31J1PSaR7F3o17snwz0rv1iBj7EF2Xsp707VD23SJxS41GNNQfVvVH/HhrqXDQi5F4Nvh36PwJvh\nWL5gNqoex8DvgBcsZ0yh+zJA2o6o+rUGjC2sGXV4o8c+rN3qhcb33G24Deqvj0ED+rX4+CQi/C7G\nGVpy9v+soRQA0FmX+zrkihk7cDP+CD58IvNxHz414Wb8EayYsUNt2Dk/dGT9yeP1uxcQPw7Cihk7\n0LE9mVvr2F4PK2bsgPhxEF6/e8EqZ8QAU6yYsQPeQctV+uMD8WOyJ7ddG9XzdhTXjNIYpe9bghsX\nxKQH4/LuYoiPvsPl3cVYMWMHMkpjUFAlYQ3jHbQcAHDM9QHER9/RYQHA9xqxhyQ+Kp1Po/wAQEGV\nhC4Xd/bXQnz0He7sr6XLz9OX7HtauYZlk60MGaUxyCiNYcTpbkPsT1J5Ky9bXl8344+o1Jc69Os+\njJbts57YcEnKvcM7LdoiOT8MvtccsGLGDqy28OLFE+Cmo3ZtdOg2of61tF+g4jl1ZwvtRr3fvPQk\nLx5CQbZsyKYnJj2YNUxrcVs2fRsySmMQm0HGNLEZV5BRGoONi4+qCak9NK3D/XsMh+O8/Rg3xAK+\n1xyQnK94LiqrjOxP6ta5b0smQRBwaQc1BdX3Un0xG16/e4Hk/DAs3d0bMenBMBu9FJd3F2OT1TGM\nG2JB9zuAtP1Q9dMED3NuY9wQC4wdPJPVT6TkLMYNscCIAaYaydAWE4fOQ2VV66z7DRqgD4N+fdR7\nBlD8hLRx33XtosYnP9S9bMDFG+HwdLGnL7TT6/wNPF3scfFGOMtc0EAUxd9B9656sHL+AQMnL0Cb\n/uNwIuA6MvNUnzX5EjA06IdP1RmwnkvKXdLjbPicCYKniz1e5SfgU3UGXuUnwNPFHj5nglBYJt03\nkvQ4m9ZPZcpdfKrOQGXKXTQ28bPBnJKZxwhP6TfpcTZrGFme8mF9zgQxwvL1q02aRAkpECWkMPR3\n5SS51O3iDelcAB89awJNeKjTDV8MHzyQlh1zjdi9uH2P7Ev5VC1d9/hUncF4/hJQxVUeIVEPYLtl\nNzxd7LFnm6ItLXVxcdG5bof28HSxBwBU1kjtnlPxbPSSXg5LvT97gHkBKZ80aQPZsiabnqDbd1nD\ntBY3CtkFpQryTObaAICCu+2W3YywMUnkzLR+b+73AHwpWDmTvd2S0EC6XlWmkHyQTxdfUP0r1d+y\noe5lA0KiHqDryOkIun0Xy+bPQmXKXfj97A7L6ZMZl8NSHFX9NMGNiGhYTp+MWVMmqPS3bD7ZkxWb\n/Jh2a3zfjBMXryv4tZw+GTHXzuD2vVi06T+O/t2+F4uYa2dgOX2yQhiTuTaMurrRywf22/fymuMC\ngEH9+2LQAP2WX4eLCMfc77lfVlfylOz37tZFcZ5JG9S9eoPAyIfYuWY+9L4h80J63+hg55r5CIx8\niLpXbxTCDBvQG3k3fNFd7xtYe5zAkCXb0GHyGpy6FY2skpa1Q68Jhg3ohfqY83ifchlRJ8l+gZAH\n0j1MybmlOHz5HnaumY/S0GN4n3IZpaHHsHPNfBy+fA/JuYrfNYb6PfA+5TKWTB/PcM8ue6oga+Ja\ncm+UvPvavdLzJ9YeZD/Ew/M/4X3KZZqDvD8uoOKi0vI+5TIenifrNRGJWS0iE+CnZ8pffcx5Ws9F\nVcrvHREKoQnpWLv3HHaumY8fHa0U3gtRTnTatcXONcRmUFWd9LuFisf1sHQfMfXeb+daXjyEQnRa\nHqLT8hTSc+k++7mO1uJGQdP69CUgdH2SBdXuU/0AG+pevUFoQjp6WKzHpfvJsJ45HqWhx3Bi+xrM\nnjSKbuMB0BxV/VSh6cNH7DpzCzvXzFdoB7niZmwaZk8aBfPxinsdJ679kVFfXA9fguPPF9D0QbVd\nMyFh0Oc7GPTt2Sp2Y6cYc7cbW/mM/32qXNDwug6h0QFwXMa8C9VxmTtCowPQ8Fqx/BnoD0PEuXx8\n27k7tu5fijkOgzF6fjtcjTyFoif/+2yN5RSnQBxYjtx7HyAOLKfTllXI3u5lFSYj4LYvHJe5K4QN\nuO3LCMvXL6VvWb/vP/Cb3wyPu6QgS5IpVpqm/r0NkXvvA2ZNXqrAV3LrV+Te+wDJrV9pvhU10jNf\nW/eTMJSs3HsfcPkIuQvlQVo4b398oQ13ZeDC03j4FJiazEH8I+Y53Pwy0u/16TGQc1y596RnWyg/\n8ulQV2bUQZIphiRTzNCJz45gAEBojHD2gJUhNuUOPI/YwXGZOzauUjxfZ6A/jOZ0YT85sxWdLHNf\nKgc9tP9aB47LSJ//vF46f0fFs/+M9OwN9X63C/N8nToeQkE2L2TTEx7Hfr6utbhRKK7MVpC3fAvp\nz+XdPY/YCR5+9Px2rD95sJWPJbMdFfwKBapeXz6SSNdZcWA5a3r4gOq/qf6cDQ2v6xCbcgemy79D\neNwlzJ5iDXFgOXZtOAlTE+Z9qRRHVb8vAVOTObiwX4zo5BBGHkcnh+DCfjFMTeZwimf5lvGMOr7/\nzCbsPuZA3xPPFdMmtI4d2SdPnmDhPG5pA4CiEjLf8N133O1TeblvxwHfX9DY9Nm2QlMTDvj+Ai/3\n7WpCAv+vfWfWnzxq617gQsAleLlvx7d6ZJ7yW70u8HLfjgsBl1Bbx74faOqUyfBy346F1qtU+uOD\nCwGkHdXVUWdbgXCNEivf09QS3FQhMTmFzp+nZQX4T/MbPC0rwDrHteoDc0SUOAZR4hh4uW/Hm5c1\n+E/zG1wPJncoXAgMVsuFKlOJySm034XWqwCA9vef5jdIe0jWE0Ij7iqNk5L95mUNHWdhkeo12paU\nM8RwMP7T/AbLlizmo04aWTm5tKwHIvJdOHoCOQ8s777KruXsNgLA7dBwrLJzgpf7dnj/uEvh/Yhh\nRgqcboZI97JwyXtdHR26Hamoks7vUvGsd5XawKPen/c7zouHUJAt87LpCQhmv+OotbhR0LT88G0z\nouPiAQD6+n3p8FTbLRu+sZGfTceWAlXn0x7G0nX+aVkBAO3rEdWXUn0rG2rrXuB2aDg6d9dHQPBV\nrLC2wtOyApw5cRRz51jQ/QgAmqOq3/8lXL95G3PnWMDCfIbCu9ETpjDagfWubljjuIEej3DBovmW\nrWbvYFD/vjDQ53ZHafGTpwCA777lvu/JY4MtDp27gsb3ZKzd+P4DDp27Ao8N6u3pth1syvqTR92v\nr3Dx1l14bLBl7J3y2GCLi7fuou5X9vu8zMaPhscGWyzZ4KnSHx9cvEX6Zt0Oit9usqC4ihLTWo0b\nF1wKiUJF4h18LJegIvEOPDbYQpSYhqT0XNrPkg1k/0fy7XP4WC6h/QKA7XZvhTiHDNDHx3IJllpO\nx8dy6d5rKqwy8JUhVNqUcQaApPRcuvw2ZEXjY7kEDVnRdDkvlDk3Q3GnZH0slyD5Nln/CI9J5O2P\nL7Thrgyy4eX1d+jcFYb+uOYv33h3rLOBKDENwaFRAIDg0CiIEtNw/KetAEh9sZw6CRGxSQwZj3PJ\n+Jdq69hkOi1foJIfV70NG9yf9hsdTNbDbkfFq9RvS9QnTbjLlxttebYk7ogSYLvdGx4bbLFni+Lc\nl7p84FL2dDu0o/urymfSdR8qHpefjtBu1Psz+5hnqDQpD5pAlJgGUWKaQnouhUSxhmktbhSyC8sU\n5I1bSPYeyrsrK0/ahuczruDTTgiFlqxf1NiNGsuxoe7XV7gjSkA349m4FBKFZXNnoCLxDk7t3Q7L\nqZMYe+oojqp+qkCNfbxPBsB2uzf97OF7Bht2H6bHjrIYt9CeUe9cfjoCh537lfrlght3Y2E5dRLM\nTRXX74WSNX+mKZ5UVLbKuH7wwAEwGNCPk//isicAgO+6dRWUR+2Ll/APvo5d2zbh2y5kDvXbLp2x\na9sm+AdfR+2LlwphhhsZojT9Ibp/1w2LbBzRf9Qk/FNPH8fPXkRGdsvZ5tEUQwYb4O/XNVi2iOxZ\nSEx5hIPHTmPXtk34vboQf7+uwe/Vhdi1bRMOHjuNwhKpXbPElEe0fqrz0vD36xpU56XhHU/7eJJH\nGYzwlH4TUx6xhpHlKR/24LHTjLB8/WqTpqjYBETFJjD0d83/FADgQrB0jywfPWsCTXio0w1fDDcy\npGXHhROZt8LId+Xfr2tof3+/rmE8fwmo4iqP2xH3YeO8Gbu2bYK3p+K6jLq4uOhcV6cDdm0ja2OV\n1VLdUPGs3yY9u0C9P39Mum+ab5q0gWxZk01P4NWbrGFaixuFrNx8BXljzMhanry7jfNmRtjoePIN\nq9+X2zzTl8QiG/INkRodTter6jwyHpFPF19Q/SvV37Kh9sVL3I64jy79hyPw6k0st1qA6rw0nDmy\nH3NnTaf7UkBa91X9uGLkMCMc9t6FubOmw8Z5M25H3Fcf6DPGmM1h1Kn12zxht9ENjU3vabfGpvfY\nuecAdm3bRPeXQsTLBYMG9sfggQNY9y/+Q97h7du3KC4pwkjDiZyFSLKJsZduXXpxDmM0cAwA4Nff\nnjP+Uu5CobCcHGyZNIZpFJh6pt6zYeFMOwBA5INgQXkJgS/Bbeuag3Q+d+vSi+aQ8Jj9koq0XHJQ\n3nrOerT/mizUm3+/BFlhzfBwll42nBXWjKywZvToqo/KZ0VIyRarTNvYYcqNMHPhSP2/cKYd5/TI\n8jf+LDslW3pZA1/+ssgpJh+LNgu20DLaf60DmwXEyERGoeoJUaF0py0ndfFqoyMhEZcaCq/jdrCa\n5Yj1K35U6kddfnMtQ1Rb8/wlMRZX+7IKKdliHHALBgBUPiObVF+9IRejGQ0cy4uHUKDqqXx6tq45\nyBqmtbipkgcwy6gxh7LNBXz00fxnE8JiA2A1y5GxGa+10ZL1i9IB1d+zYd46Q3gdt8MBt2Ac8wyB\n+fdLeI0NtMX5mz8jMNQXG1b8SJcJoVFUkYmUbDEWfa7rqmA8bAps5m/GMc8QeK33g9dxO2QVKd/Q\n3VJ+2TDcYDwkkhT1HjXE27dvUVxahvEG3CfZYgvIQcCendqr8SnFaH1S5+p+b2b8pdyFQlbVbwCA\nmcOZ5Zl6pt6zYbXpIADAVYnqj6EvgS/BbbIh8wBTajkxiO8yaxh02v4LAKDT9l9wmTUMAJBcVq/g\nd7XpILqs9OzUHtYTBnCSHV9EFq2cpg+hZS0y6YfXAQ44YjNJI06aQJt0zBpBJhfuZdcgpfwlmj7+\nG2P7fauQBlWQzwM2eFubMPhR5eVedstMwlHxyutFlVzZvJw8uDsAaXuiDFS9rWogm+CqXzUitqAW\n/s5TAQAldX8AAF6+JYsebO2JtmVRGX8qb1Xx1xYRmU/h7J8IO7PB8Fyo/NtbnU655pO2uuabt5qC\nahfk0+NtbcIaprW4UeBSFzVpt+TbgpZsI1tbZ7J4HeCA1wEO6KvXASV1fyC2oFawfo/KF3VpGbXz\nNpz9E+HvPBXXXGdikUk/XmOelkRR7RsF/rEFtXj2G78JSHXYE0IOCEfvmkfnyesAB/g7T0VsQS0S\nivgdxjDp1wkpyfzGwHwhkUjwdRsddO9oyDlMUT05vNDpa2GNG9W8JgY9hnZnGjSknqn38vi2Q38s\nNz6MQ4tK8MNMMVaa/ILK3x7h6IM5uF94SFCOLQWDrswLeM+seIUzK16hS/s+qH9XgqL6OKRVKW78\nL3lJNvmYGTii7eeL7cf0WYgzK15hufFhXhzyaonh1EkDVtN52+nrHpg0YDXjvSxk5Q76nIai+jjB\n5WgDdRyH9TCn5T55lYqPfzdBv8sYVh0uHrVXY97y+Vzxihi2n2G4kebY9p86mGG4EQBQ3iBR8Cuv\nt3H6yi8rlQcfvXf6ugfWm15FeN5eJJSfQ3jeXqw3vcqo81MHrUNMyXH88ae0PwzP24thPczxbYf+\nKjlPG6xoBI+PLihoUv5aA1x55TyPRNCjdZg8YA3mDVdu+F9dXFzzlWpHXzWRg02/va9GUX0c7Cde\nAADUvyMHd9/+ScY/fTuP0ihN2oJq0+TTs3jUXtYwX7IcaNKeybcD6ty1Bdf+RBNQaabGBWz48e5o\nBD1aB/uJF7De9CrG9Fko+BiCQnzZWRTVx8HMgN9h+prfc1BUH4dJ/VdzDhOetxcA8MNMMa3nMyte\nwX7iBRTVx6H05UNeHPp2NEZykuaG7dWBWhcfO4LbnAoAPEwl6yDdu3Kf+x4+hKz91Dc8Z/yl3IVC\nXhExXmI2cTbDnXqm3rPBej7ZeBxyL0ilvy+B1uSW/IjU39VLNqJDe10AgOWMpahI+wjvHadofxVp\nH1GR9hG9uuujvKoQD9NEnPhxjV9byMY/fowZ7e6wYqtSd76IfkgM4FjPt6frQ/euvei8ot6zQVP9\nKcO0SeSCmZiH4UjPScL75kaMNDJR0Gl6LvmOlNVBh/a6cFhBDt08ylJc/9ZGR0DL5wNXiOLvwG2P\nLVYsdMJWpz1K/Sjj+jBNRL/nmudUm/O0lqyPP6urxMM0EY57kwsjyqvI5X4Nv5Exs3xbqI6HUKDq\nonx6PFx9WMO0FjdV8gD15Ycq6/JpW7vcVcHv++ZG3Iy8iBULndD5Gz2V4RdarBQmYVpCyPZDHlR6\nqf6eDWaLDeC2xxbHva/g/OFQWM5YymtswAem480xbZIl3PbYwmBSWxhMaosxs7gblJHF+DFmsF+x\nBecPh2K/+xm47bFFek4S/f7QafINFuKfTOu5Iu0jjntfwcM0ESTp3L8pDPoPRYcOupBIWm4sJ5FI\noNNBF4MGDOUcJl5C6mz3bsLmV04BMYg97XvmOIx6pt7LQ7/PQOz39EP2gzqEB0vgs/ssMnJSsNjO\nFL+ca93DiOrgtfUQrbfu3XphxSLS/ovjpUY2anI+oSbnE3r30EdZRSESJCLcjGCvnxONp/LmwVcG\nF3BJGxvnx9mkzXRezRxfOK8m44vUTOk30HRTMmYRxYfhURYZs4waZoKanE/Y7+nH2x9faMNdGSj9\nrFhkz1l/XMAn3u7deiHgeBgOHvfAxWsncPC4BwKOhzHquP1KV5wO8MHLBukB2IPHPTDd1BL6fYjh\nR0oX8jIdVipunNdEb3bLpH36RGMzAECCRPUYoiXqkybc1cXbEnVSE9yPDcHmXbZYtcQJ2zcoH++q\nyweuZY9q258+J+PdmueVSJCIcOogGe+WVZDx7q+fx7sjjJjjXU3KgyZITItRmh6vrezrQ63FTZU8\ngFlGZd2FDs8HfNoJodCS9YtKQ7ya/J1kORCbd9ni1MErCDgehnmzrAUfR7Um8ooykSAR0fX6S+Lg\ncTL2Dg+W0Hldk/MJpw5eQYJEhKQ05fPL2Q/qOPtlg8noSSguLmpRA2LJSSnorWPM2X9xA/nW+KYt\n9znyPt+QNZs3f9Yx/lLuQqHmDVmLH9KVaUCLeqbes2FCX3KR0uNn1wTlJQS+BDfTfg5o83n9aqAe\nWQMqaZB+a+a/vE9zo8rDN2170Fyp97Iw0FO+lqRMFgBMG7BBqTuFEwsbcGJhA7q064P6xhKUNMRp\nrCOjbmTNPb/+Hipfp+LT303o22kMTixswNIRvi0iE1Cv58rfyUFRWV20+acOpg3YAACoeN1y3/K5\nLyJxJXs9JumvwRxDd434cy0nVD39rZmsBb9urkZJQxxsx54HANQ3krXgdx/JWnBvufZDHQ+hUPoq\nQWl6Fg5VPq5tTW6q5AHq6xMffPq7CWk1lzFJfw3a/w/TeJ+sfKpeKUsvW3ugLYSuo7Kg8rxYTf55\nx47Blez1sB17Hk7jr2B0z4W8+k1NsX92MZ1+27HnUdIQh9JX0m/Hu8Vk/mqrqYj2x+anGj11AAAg\nAElEQVR3SNdpMOpmjivZ67E1shu2RnaDh8hAI17P/shBSUMcJn6u9/Lgw4sL+nUeh9Ly4hYdP719\n+xZFRUWY/D33dev7IjKP3bsX9zG6iTH5Rn32/DnjL+UuFB4/JuvSc2ZbMNypZ+o9GxwdiLHcgED2\nCxi+FL4EtyO+B+l87t2rF83hThj7eqw4hnwnbHLZAF1d0oYus16K//71AWf9TtL+/vvXB/z3rw/o\n108fBYVFiBKJVaZt6lQzjTlS/zs6rOWcHln+U83ImdEokXQNhy9/WSQmkW/97W5baRm6ujrY7kbm\nyhIeqj7PLZTutOWkLl5tdCQkbofcwcrVdljn7Ih9exUvngHU5zfXMkS1NRUV5HKZispKRInEuHE1\nGABQUEjOc9fXE0NfxnJtoDoeQoGqp/LpOeLLfp67tbipkgcwy6isuzbgo4/GxiZc8A/AOmdHfKv3\n5c5zt2T9onRwX03+6g8YjJWr7XDjajDuht/BMuulvMYG2uKnvftwwMcX+/b+RJcJoZGekYkokRhO\nDsqN6e9wJ8Z40iSJdJ78968PuHE1GFEiMWJi+c2dDRtqBF3dll3nBoAUSTLGDe7J2X90BjGQ31OP\n+0XDYwxIWaj97S3jL+UuFDLKyJjSfOxghjv1TL1nw5pZ5Pze5dj/fZcStia3uCxiiNN57kTotGsD\nALAyHYF3Ub445rJIwb+sPwsTcuYpJpPd0GBkKlm/WjPLhC5HPfU60mmk3svCdHh/lZxTiqqVxrls\n6mhWv66LTGneOu3awHURMXSblF+pEEYWVBojUwshKahG04dPMB7cW0E/mshRl051kM0L0xHSuGQ5\nyLp/aSjjq6zshEkK4HD4Buxnj4eXjblGcXEtd1R7UVX/+vPf3xGTWYbAnWSvWnENmb97+Tv5Lpdv\nx7imSVtQ9VQ+Pfsd5rKGaS1uquQBLVMe+eij6cMnBEWnw372eOh1/HJnat9F+eJdlC/6duuE4ppf\nEZNZJlgbT+mA6rPZMHStDxwO30DgzpW49ZMdrExH8Orf/68hq7wWMZllsLMYp/T97kCynv3gqAud\nf++ifBG4cyViMsvwIIf/OfFxg3siRdJy55Lfvn2LouJifG+i2B+yQRRP+PTqrtm+UDY8zs4HAMye\nPpnhTj1T7+UxsF8f+B3wwovch5BEXsHZQz9Bkp4D04W22Hv0jKActcXUSUxbEEmPyNqt27o10O1A\n2hvdDu3htm4NAOBhqnRe0nIG+Z4NE8Uh6VEmGt83Y9yo4fjreT78DngpyPL12kbnUa/u3eCw0upz\n+Ae8ef71PB9/Pc9Hv949UVhWAVF8MgJvcNtrR8lzWGnFmY+8/M0Oq+DjdxF1LxtoN/f9x2A5YwoG\n9uvTqunhk2dCI+ReDFa7esDZZin2/uCi1I/L2hU0L7OJJN1UnQW45wdV7yqePgMAVD59DlF8Mq76\nkX1chWVkH1p9A7loyXgkc1+0Oh5CISYxVWl6fL22sYZpLW6aym+Jut74vhn+1+7A2WYp9Dp3UssH\nYJZxWXdt0ycUtKnH6kDpUx3vARMssNrVA1f9DiE88CSs51sI3jcKBff9xwAAksgrtO7+ep6Pq36H\nIIpPRuznusQVE8eMQEoLzkW9ffsWJaVFGKo/gXOYjBJiZ1KvI/f5K4PexIbVqz9qGX8pd6FQ+ozY\njTY2ZH6rUs/UezZYjCd9TEz6ZUF5CYEvwc1x3n46n/U69qQ5pBQoN1wOAOKj7yA++g7dOvfF05fF\nyCiN4cy5sIrYvFxs5op2bciccrs2OlhsRs675VcmtUhYNmSVkb0a8753puOcMtIK4qPvsMnqGO2P\n0ofF+DW89aUOsrJHDCBzWBmlyi/Mbikk54fB95oD5kywx2oLxf4RUM+Tq46ouvriNbkMrv51FTJK\nY+BuEwgAePqSXJ73eyNZxzPoxWxDWktfVNmQT4/jvP2sYVqLm17HnthjfwsB93cjPPk0Au7vxh77\nW7zabE2haT0cMcAUi6dswh77W9i89CR8rzmgoEra93341ATx4yDMmWCPju2/3NofV2jTDqoDlY9U\nX8yGNfuHwveaA9xtArHH/hamjLRqlTJA4WrMAdyMP4LVFrvpsiCP8udZyCiNgcV4u1bjJY++3w1B\n+691WmHdT4IJo4dx9i96SMaLgs8F5ZK9ELOnMvefUc/Ue3kM1O8Nv5/dUZcZA0loIM4e8ERKZh5M\nlzjA+9gFQTlqi6kTmPs7kh/nAAC2Oq1izCtsdSIXnD9My1Twa798AeO7b+Ui5nlgdTjkuZkR3v7z\nRY1h4gTWMNQ7ednKwvLxq22aYpLI5WEb11jT+rOeOxOfqjPg97N0jzcfPWsCrjz46IYvZGWbfS5n\nooSWs9euDbhyDYl6ANstu+G0cjH2bFO0acclLq46p9qZiqdkb0BlTS1ECSm4cpKMnQrLyBptfQOx\n2z52hJFGadIWVFmTT88hT/azga1dNpTJA5j1z2yC4n7fxvfNuHgjHE4rFzMuUP3fik/VGfhUnQH9\n3j1QWFYJUUIKgm4Jc8kcPRf1UPX8zMDJC2C7ZTeunNyPMP+jsJ47s1XnoryPXYDPmSDscVtH5y0b\nZk2ZAMvpk2G7ZTfa9B+HNv3HoetI5ZdoA0B+yROFcipKSMHT50xbvx4+xBaTJDSQzpNP1Rm4cnI/\nRAkpiE1WbhtDFSaMHtbi81xFJaWYOJz7GQRxGlkP69W1sxqf/JBRTNo2iwkjGe7UM/VeHgN6dcOJ\n7Wvw9J4fHp7/CX471yI1/wmmrd+HnwOEmZMVCuutZkKnXVsAwJTRQwAA0WnSC3MjEskaj908M1q/\nvbp2ht08M8Z7WVDxcJEFAFtWzFHqTuF9ymW8T7mMvt31UFRVi+i0PATfT+KTTBqzJ436zDsTybml\naPrwEcZG/fE+5TJObF/TIjIB9XqW5JI9JrK60GnXFltWkMsyE7NLNJatDqEJ6Vi79xwcFk7Dj45W\nGvHnWk6oulNZS/YIVdU1IDotD5f2krOORVVkvrn+NbnLYIwh8zJqdTyEQmx6odL0HHRZzhqmtbip\nkgeor09fAkLXJ1lQ+UP1A2wYsmQb1u49h0t7NyDk0FYsmT5e8D6DwsmbYkSn5WG91Uz1npXg54Aw\nHL58Dz86LqbzEgC8ztwCADw8/xOt0/cpl3Fp7wZEp+UhLr1AEP5cMX5o/xYfDxSXFGGUEffzd5Is\nsh+/m56w+6cLysl+hsljmWfnqGfqvTz69BiIXRtOIv7qM1w+kojdLqeRU5yCNTum4uz1fYJy1BZu\naw/Seuum1wuLzcl+/gdp7OfZqHeLzdeqDcvHL3UXm7xfS7MVLZYm4+FmjGeKg+2irYw7PG0XkTNf\nGQXSM1+mJnM+xxuBrMJkNP/ZhGGDTJB77wN2bTjJ2x9faMNdGbjyXDV/EwJu+6LhtdTG1/EgT5ia\nzEGfHgO1TjOfMqMOqTnknMnyuRtoncyavFRr3atDbModeB6xw5LZjti4Svn5OllOxsPJvhdJpvRs\nEVc9UO3R8/pK+q8kUwyfHcEAgIoaMmf82xuyLjfUgDnnoI6HUKDyQj49bmvZz9e1FjdV8gBmnZJ1\nFzo8H7C1l6vmbxIkfmXIvfcBufc+oEc3fVTUFEGSKUZ4nDDn66g0UP05G+Y4DIbnETv47AjGid13\nMGvyUsH7/tZA+dMChbIsyRSj7tenasMeDyLn6y4fSaTzJPfeB/jsCIYkU4y0HH7n6wb0MUKH9i1v\nR1ZXVwdDjbh/o0RFk3T07sV9bdR4DNnT/fx5LeMv5S4UHmeQ9ZI5s5h7mqhn6j0bHOxsAQCBwVcE\n5SUEWpNb0uf7dB3sbOl87t2rJ7Zs2iCYjOg4ct+Ly3pn6Op8tsGwZDH+0/wGZ04cpf2FRtxVyoXS\nB/UeAObOsaDdEpNT0NjUhHEmYxXipNK3bcsmWraujg62bSHtdHyi6n2WLSln6hRTlbLVQVafU6dI\nzy/IcpB1byncDg3HKjsnrHNcC+8fd3HmGiWW7vHhmvdU/abtGVRVI0ocg+vBFwEAhUVkHxRlz8Bk\nLLPdUcdDKFBlXj49hw+yf4O2FjdV8gD15YdPm9HY1IQLAZewznEtvtXrojL8qhXLhEqaVvhP8xv8\np/kN9PX7orCoGFHiGMHaYiq9VN/Khn6GI7DKzgnXgy8iMuQ6li1ZzKsf/r+KPT8fxAHfX7DvR0+6\njALAzl3kOy/tYSydf/9pfoPrwRcRJY5BzOf6yAVDjYZAV7eV9j2NMlLv8TPEiWTdvdd33O+nHzuc\nnA9/Xv8r4y/lLhTSc0m7O9uMuWeeeqbes8Heeh4AIChE0c7ll8aX4ObjvpHO517fdaU5hMdI55E+\nlkvwsVwC/Z7dUVheBVFimkqOZuP5j8H5yuACLmlj45yckQsA2Gq/Arod2gEAdDu0w1Z7Mk+Z+Hmf\nFQBYft5PEx6ThKT0XDS+/wCTEUb4WC7Bqb3befvjC224KwOlH3vreZz1xwV84u31XVeEnvOBh+8Z\nnLx0Gx6+ZxB6zofRJm1asxSHzl1B3a+vaDcP3zOwnDoJA/uSuQJKF/IyXe2sFfhporeNNla0Xyof\nRIlpKvXQEvVJE+7q4m2JOqkJ7ogSYLvdG07LF2DPFuX3MKrLB65lj+pHKmrIfELlszqIEtNw5Rdi\nJ7aw/PO5iAZiJ0O+f9OkPGiC2OR0penxcd/IGqa1uKmSBzDLqKoyqG14PuDTTgiFlqxfVBqosRwb\nDKYuhe12b1z5ZQ9Cz/lgqeV0XuM+TVGbdpdO/5Vf9kCUmIY4iXSt1cOX2DZIvn2O9sfmlyu8Twbg\n0Lkr+GmLA11+WkLWUIN+0O3QvuXH9SkSTDDhfoYzKpZ8m/Tu2V1QHo+zSN8yZ+Y0hjv1TL2Xx8D+\n+jhzZD/qy7KRGh2O88d8IHmUge9nL8Yen18E5agtpk6eyHhOSiX7LLe5OENXpwMAQFenA7a5OAMA\nEpJTFfw6rF5B6753z+6wsV7Mi8Nhby9GeIfVpG8Pvce+lkK9k5etLCwfv9qmKTqe9LcuTna0/pYt\nmoe/X9fgzBHpGT4+etYEXHnw0Q1fyMqmyllUrObnI1oSXLnejrgPG+fNcLZbBW9P5d826uLiqnOq\nnXlSRdbUKqtrEBWbgGv+ZK90YQnZh/niJZmTMB49QqM0aQuqrMmn57C38jO9rclNlTyAWf/k20IA\naGx6D//g63C2W4Vvu7TM/jsh8ffrGvz9ugb6fXujsKQMUbEJCLx6U5C4qbyl+ls29B81CTbOm3HN\n/xQirgVg2aJ5gvfNyjB18kS4bXRCxLUAnD/mAxvnzUhMUT1O3LmH7N9IjQ6ndff36xpc8z+FqNgE\nxCQk0X6PnfFHVGwCXJzs1HLhEy9XTDAZg5QU5WOv/yfvUFpKDMX168V9ojIlm39jP7AvOXhaUpmN\ngX2HoaQyGwDQp7v6QxFZYc2c5XgdtwMA2HtOY31v/v0S1vDduvTCMc8QbPOxxpihpjAept1GFqtZ\njgiLDUDzn0305hhlaP6zCQDgsET5hTUtwY0LencfoMABAMJiA+DhfEJpmLDYAABAJ131B+XP3/wZ\ngaG+av2pio8LR4oT9U6VX3XyZMGHvyyoMFNXK2/wTl7eBZv5qi8BFEJ32nJqSR0JCapdCIsNgPMy\nL6W81aWFaxnS70mMyZZU5mDy2Dkof0o28Zt/vwRex+3oNvBJDdnYTrWNXHkIBbb0yNcnWbQWN3Xy\nVLWl8uDaf/DRR80LYnh29JCWuQyKD1q6fqnr7+9fKENheQa8jtshJiUEFpOtMXzwOAU9AoCxlXrD\nu3z6eyrtN355rFCPhIQo6QYAYBTP/J4xyQoHzrviZtQZtf11S/mVhX6vwQhRMzDXBtR4cnB37oe6\nYwtqecsx6kWMHObWvIZRr07IrSELEgO66aoN+zrAgbMcZ//PCyMHlU9MO/snYpFJP6XvAKBnp/a4\n5joTNn4P8P3g7zB5sHYfOHZmgxGcVI6mj/+GTtt/sfpr+vhvAMC2uSNZ/QjNjQu6dGjLeD4WRfrG\n/q5XlfrfE5KJjebDGH57dmK2If27qs9zAAhOKlfKQR58OGkCbdLhuXAMYgtqsSeEbLydNaI31s00\n4pV36tLPxofiG5xUjiM23A+vcQWVP/J6+f/snXlcTfv+/1+/6zg4SqaUakdKShNRhpSMpcyRHDMZ\nMx9UdAznoOKcDMmYyEEDZWo3kVIyFEWDaDCFE5lKLufe772P3x8fa9fae6+919p77cq5no9HD9Ze\nn+H9eX/G9RneH1nxsk0LhXFHYuQ851ElnKz0kffkDQBgnG0XzDuYKmpP8p+S36l2Rhxly6Ki8isL\n1Z4dTbsP7zHWUuOXJxPbfFJW1/WlG6b0yMrL+s43NnVRkXZLPB2qbCPrW2fi+J+9LZJbFcgbx+Ru\nm4Ts0leYdzAVMTfL4NbHEDZGHSTKHQBoeh6WGx+XcYwszmQ9xIboLBycN4g2ljmT9RDzDqZCrXlT\nmWMcLjDJTLUJMTfLOMXVrWMbpGXI3vSoLEVFRdBp3Y2Tn/znqrkoOewaMY7120UXxve9Oo1l9K/e\nvD3Um7eHQftesDOcigcvr2L3ZTe0bakHO0Ppl9w2FtSbt5f47UJeABILd8j0l1EazuifK1RYbX+g\nX4pMPWeUhsPDZhvtnSLxKhKPMsiTcZSlD/KfJyM2dyMAwEJ3OAZ1m49uWtK/hzuo0y/44SK3uCxU\n/q463VWq+9jcjRhispDmVlxv4vIwwVXvFrrD4Wy2ArG5G+FstgIWuvSDct20BsBCdzhyn57HEJOF\neP6+8Is/J4n0sZGZiy4o+Cj3qoCtXFSbl1EaDleLNVL9yQuLbb5qaxC9Pn6TAwvd4Sh/Sw6W9+o0\nFmHX5uPxm1zotjbDs3fkd93W9IMF9aVrpvTIKucNWQ74bDdVmQ42/YkyyBsX/DomB48qsxF2bT6y\nn8TAppMbDDRtJPQGAF4R8jcqhkx+KfX320/OIrFwB1YNi+esz5uPogAARh36svbDJAdVr7KfxMgc\nt4ij3aobrhemsXbPFWoes6sB+4PclzOFnOMxMbIEAOTduwUTI0vk3SPr4l305a+LF2d+Yh3Pig3k\ngKf7POlzxSs2TIfr0ImM/nW0BNi/7TQWrJmAvtYD0beXI+u4pTF57FxEnD2EDzVVUFdj/s7+UEMu\nXFw006feZJNFxFlyALZdG/nrYDsPbcLeowEqC18ZmMKXlRdcoNKho0VfA6KeI84ewqbVu2WGoYj+\npLF83npczhQiYA8pQ4PtXDFz0mKJckLF1ctJulHVgD0+mD15Ge03ZfNJ1fnAFqp9iDh7CEs9f5Yq\nl7y0ss1zw07kWzrv3i0MtnNF4QMyJ+M6dCJWbJguagvvFZP1caqNZCsHXzClp7NA+tgbqD/Z5MUn\nr/xQZZ1N2sqekMstbXvWHlLn4r+h4Kv9YEJef58WW4zc/BtYsWE6LiRHYdTwSehp0VdCZwBgbCd/\nblRWf6+upoEtvvuQknEBfoFeGGznilHDJ8F16ESldDBisBv8Ar1wNGqPqL1kkoOqvxeSo2SOJcQx\n6twNRUWqu5C4qKgIXbtwO4idks59LMeGpWtJOzt+pnSjJEvXTscoJ+ZDV+3aaqJdW030tLCFx7jZ\nuJadhikLnKGrLYDHuNkqkZkrBp3obYCONinvJ04fwmbfYNHvv+/bhD2h/qzCbNdWsXaVSxxsYJs2\nQFJmSg7LgdK/27bu8MHcqcSQ4U8LNyAlXYitO8iYZYiDK2b/uAT9bRxpfti644oyskvjxOkvfam2\n2NhAhv7YwDXcIQ6uWOzpi607fLDY0xdDHFxp/vrbOGKIgyuEl2Iwd+pyFBUTo8lD67ijdCEep3jZ\nqOuWi97qo6yziUNVsvNdJxWBaodPnD6EFfPXS5VbXlrYlj0jAzLevVN4C0McXFFwnxjQHuXkjqVr\np+Nu4S2YGluKxsGmxmLjXQXLA1eY0iOtXFPUl2zy4mP7vaSs/0e3PzO+M+jVnPbMpZ3gE1XXL3lj\no0xhCW7fvY6la6fjXGIUxjhPQi+rfhJ6ACR1Jg1ZOq8PYuLIHgdb64bf68ukC6otOZcYJTF+nDdt\nOa18O9qRtSdpbmVhZEDG0IWFhejfX/IQHB8UFBSgvxZ7o4iFFdzX2XU0yJrN03e50NUww9N3pD3u\noCZ/jXDn2Aq5biiO3VpA/KS7Mr631mOe827TQhdz+x7DoRvT0bW9HbpqKlf+7AxmIPNROD7/uxrN\nmzLvn//8b3IWaXi3FfUmGxvUmslep8h8FC6SrS7Uc+ajcEy0ou+TZwqT6XdZeqOILwpE8gPl15Bc\nTL1RWJGMcwWbAABm2sPhaDhPqq75ihOQr2cqHh+h9DWKcwWbMMiIP8OmdaHqVOajcIwwWS1VVr7K\nibY66aefvMuFmfZwlL8na77WemNx7NYCUfvxrOrLWrAGfS1Ynhx8wZQeTRntWX3JJi8+NvWJLRUf\niLFSw3aSF8qzTa8q9cJnHZWGvL5wg9NtPHqTjWO3FuD2s1j00hsPg3Y2EuUGAJaflX+xHJu+cLDR\nQloed9ci53JvP4sV9X1M4VD1rK7b5k1bYXLPIOT/mYioO6tgpj0cvfTGw1pvLGfdZpdHAwAM20lf\nx+YiFxu0W5H2UpXjJ2rdunt39vOdcULu57ktLcne3uzsW7CytEB2Nlm3NjaW/23zn78+so7nx2kz\nAQB2DoMY309yZ55r1hcIcC72FMaMn4hBjgMxyFG5M9Pz53niwMFQVFVVQ0ODue2qqiJjqHW+zOe5\n+ZaNDcZd6fmjLyDfZAcOhmJvsPTLQg4cJOciO2jK/9Zfv/EXbPFndxaRKTw2MlIyUe9kuZUXX124\nyF8Xyk/bDh2lvl/t7YuVy2Wf5+ZDd8rKpEod8QnVLhw4GIqN6/2kyi0vLWzLkKkJOc+dlX0LI11d\nkJtL5ukmuU/Ej9NmitrA3DtkvdrKkn7ugY1O+YApPeL1qS71JZu8+GS1peKw7T+46KPoPjn3MtC+\n4ed4VF2/5PX3j0rv4/r1G/hx2kycjIzGjx7u6Nevr4QeAaBJs5ZSQqDDpb+n0p6TfUOiHvHJseMn\nAAD2A6TnN5PMVJ0/GRktc9whDVMT1a5zA0BhQQEGj7Jl7T4xi7s85gakP7ldXA5zg464XUwuSTPS\nld+WvI9jX67nbCNn7oetCmF87+ZgJfUdAOhptkbk+pnw+OUo7C0M4WDF7gwAE7NH9EVYwg1Uf/yM\nVi2Z562rP5I52tUezBde8y2bLMISiAFPzdbybS9wcScevp5ma9rv1HNYwg0EeY3jFMf2yBSpYRrp\nSn6fU271J22QGpbfYSEWj2O+BMZvmhMSs4rgd5isazjbmmLRGHuJPFEkHq66FIfJv6zy15CwTS9V\nt8MSbsB3yjCp/uSFxbbcdRN0AEDaK2dbU9wtew4AcHOwwpxtJ0XtWN5D8jvVvnFNk7IwpUdamaeo\nL9nkxcelPLLtA7jo40H5KwCAnQU/51SVYcvxZFFboQrk9dkFR3xxs+gJ5mw7iVNpuZjo2BN9TDtJ\n6BEAWo9knp+g4NJnN0YiUogB5v5mBlLfM6WPah9OpeXKHGdIw0RfC5eFt7gJygHRPJ8x+3GD8JLs\nS9gUZdoSsvfMYex0xvfuo52lvgMAzXZtodmuLfr0tMScyeORdi0LTpPnQaDbEXMmczMSrSo029Ft\nU/gHk70yHcylfz94bw7C8rlEHxtXeUF46Qq8NwcBAFyHDsTSOVPg2F/6OL1rl060Z4EOmYs/ePwU\ngrcwG8uVJicAbPwtRCQvFw4eP0WLn4084vE79reF69CBiBEmY/nc6cgrKgZAdMAGPtPDJc/4hqoj\nB4+fwvqVC6WmS9pvdWGbHyZGpA/MvlMA16EDkVtA+gv30c6YtsQH2XcKYGlqjDuFZM7D0pS+jitP\nDr5gSo94+a9LfcmmaPyqqOv3vxj2tu8jedkBkzwa6oqNC+tTv4rWY7bI6+9Kryfi+q07mLbEB5Hn\nEuAxZgT69e4hUR4BoFknZjtqFH89UZ1tGaawqTodeS5BZh8rjmnXLkjO4MfgtzSo8Ym+Nvt1yJv3\nuF/C2UXHHABQXH4bXXTMUVxOxnp6msw2TCnif3vPOp7A48RuzcrgYYzvB/ZwY/Sv2VoPG2ZHYlOY\nByyN7GFlpNyltC79ZiP+ehg+fq5Gy+bMc+cfP5N1yMlDV9ebbGzQFcsfzdbk0s3462FY7BbE6O+P\nxC2IuLSdc3yUn4l++lLfh17ww/iB0i+VV8YvE/HXwwAArdVkz5lS7ij9ULDVlyzkxV0fUPUq/noY\npjr5SpWJLx0JtMg+/OKnt9GnuzNKn5P1uoE93BB4fI6oDSl7Ts58UG0LWzn4gik94nWmLvWZl326\nO2Py0NUIveCHyUNXo0939v2OMvBRD+2txmH3qWU4m75X1M6VvyTnWy0M+bdpqSoUbQfZIq8vDvcr\nwL3HNxF4fA7Sck7B0XoiunfuI1FmAcBlleS8kzhc+mIq7XtWXpWoo3W5dIuMb8y7qGavFVv0teth\n3a+wEMPneLB2L0zJUIkc05f5AQAcJki38zd9mR/cR0ofQwGAZrs20GzXBrY9zTHbYyzSrt+C81Qv\nCHS0MNuD/V47VaLZjm7b2j+EtNdaPaSv9/n478Zyzyk0txLfvAbS2zQmxN1T4R06GYvgX6XP6R46\nGSs1bml+ubhVNk1UXOJ6FYeLnhWBrRxcdMMVeXE3JtjKSrUJh07GYv3yeVL98aXzbkadAQC37t6D\n6xB75BaSvt195DBMX+aHW3cLYWnaFXe+/G5pSt8LVF/6Z0qPrDpT32WDKT55c0sPSh8DAOz78HM5\nbX2wKeiAqH1RBfL625KMc7iek4/py/wQdT4Jk0Y7oZ+1hdS5qOaGfeTG97nsJmvZqLRnxR2XqA/S\n0FBXw37/dbhw8QoWrfOH6xB7TBrtBPeRwyR0GB13ET7+u3Fs12Zavx8ddxHTlwJjsmIAACAASURB\nVPlBTa2l6Hcmmam6G3U+SebYQRqmRga4ePQUJz9coOa5TA0kx71MJGTmqkSWWRv3AQAGL/iF8f2E\nIcw2+zTbtIJmm1awMTPEzFGOuJJzDyOXBUKg1Q4zRzmqQmTOaLaRvU/08NnLAACBFv0SRur58NnL\n2PnTDFZhMv3eqqV8Wz2/hsZgW/h5ue7k8bPneCRk5mJdSCQAYIRdT3i5D8dAa0m7hHzFCcjXMxWP\nrvMCqe/XhURiqccIXmQRhyrnh89exrrZ46TKylc56daZ3Ktxq+ghRtj1xJ3ixwCACUP6YtbGfbhd\n9BAWRvq4W/wEAGBhRO+/5cnBF0zpMRIwn+OqL9nkxcemPjUEfNYnacjrB+6dDsLNghLM2rgP0Rdv\nwH1YX/Qx7yqRxwCgbj9DSgh0PmSES/39dMoNbAs/j8v71ytUJig9XTvyq0T5Z4qTqj/RF2/I7JP4\nxqSzDlJi01QWPjUeMNRnv+6VnqX45dmy8N0+EwAwY7X0s3O+22fCyZ55D3tbDU201dCERTdbjHea\nhey8K5jv5wLt9gKMd5qlCpE500mXPl7V1iRnFE4nhGLtQunn2U4nhNLcyvLLxW1oVKBUt+IyyoNL\nmsTvR6RkcPCQfuZrR5gvpo0lZ74WTVmP9Kx47AjzJX5sXTBl9GLYWNL35rB1xxVlZJcGWzltLAfC\nwdYFFzPPYNrYpSh+RM7vO9i4cA5LGlzKDNuw6vveTartOJ0QigU/+il0XypbPRgIyPm6gpJbcLCt\nvS/VyX4ifLfPREHxLRgbWOD+Q7JeZ2zQMPelMqVHVv3+2u5LVdZ/znnms17Wo+lnxfhqL7my98Qv\norhVgbz+PP7wfdy9fwO+22ci4Uo0Rgx0h5VJXwk9AJI6k4YsnauKpIxT2BHmC//VR2ljiKSMU/Dd\nPhMtf1CTObZgkpmq8wlXomX6l4aBQPV2ZE1NuN0DFRfPfU+TpQVZ08y6lQNLC3Nk3coBwM62wv/V\nvGEdz5SZcwEAdoOdGN9PmsC8/1pfoIez0Scw1n0KHB3sMWigPaNbNsz3nIUDoUdQVV0NjVYybCtU\nf7Gt4P1Tvckmiy2Bv4virIuxEX9n+g6EHgEAdNCUbZ+GcicuC/V8IPQIQnb+BgD45WdfxMUnYs3a\n9QCAkS7OWOa1QEJXVPra6Ug/P7Fm7XqsXOrFKJMq45GnD3kw+ZdV/lQBVRcPhB7BhnU+UuXiK+9N\nTMh+7+zbORjp4iyyWzBpwnhMmTlX1O7k3iX7oKj2iK0cfMGUHln1qr5kkxefvPLDpc24f5+cmXAY\nULuPpT7aHGXZ8OtWkZyqQF7f+rDoLq7fzMKUmXMRER2Dye5u6NfHVkJnAPCdmuScmjhc+tbGCpUn\nOdevSNRrpvRR7UJEdIzM8YA4Jt2M62ff08wJrN0LUzM5x2FpQvZd3sorgqWJEW7lkTQZs9jf8ul+\nOut4pv9E7DwOnCTdduL0nzZhoiuzTQFBRy2c3uePCQt9MbCPNRz7Krf+PtdjDA5FnkPVh4/QUGf+\nDqn6QMbyPguZz07xLRsbunamf08JOhI75ociz2H3xtqx46ZdoQjYd4xVmIruweASBxvYpg2QlJmS\nQ9tG+vqcT2AIls2aBABYv2wOhKmZ8AkkNjhcB9lh8YyJEvnH1h1XlJFdGocizwGo1ReFLP2xgWu4\nroPs4LNwOnwCQ+CzcDpcB9H3Hjv2tYbrIDvEJqZh2axJyLtfCgBwGVQ7BqB0IR6neNmo65aL3uqj\nrLOJQ1Wy810nFYFq8w9FnsPPS2YrtjeOZdnr1qUzANKPuQ6yw51CMq6c6DoE03/aJOrf7hYRO6pU\nv8dWDr5gSo+0ck3RePbGyZ+v48O/rHFFCxP6+S0u7QSfqLp+yRvLFaeewo2cAkz/aROi4i5h0sih\n6GttLqEHQFJn0mAzlls+ezItD4c7kLXtqLhLorEbUzhUPazrlg2Unm+eDZOos3zHBQDdDDvXgx2z\nQjh7ebJ2H5ekGnsrU+eR9cYBI6R/90ydtxSTxo1i9N+hfTt0aN8OfXr3xJxpHkjNuIbh46dAX08X\nc6axP6+hSjq0p3/7bg3aAwBob2gpzTnWbNiKFYvm0tzq6+nQ3HQ1lD6Hx4S4eyq8g0dPIGT7Zql+\nDh49ITVuaX65uFU2TVRc4noVh4ueFYGtHFx0wxV5cTcm2MpKtQkHj57ABu8VUv3xpXMTY9KeZ+fc\nwUinIcjJKwAATBo3ClPnLUV2zl1YmpniTn4hAMDSjL7vqL70z5QeWXWmvssGU3wardRl+rtfTL69\nHPrL33ffWNjg/7uofVEF8vrbstxMXM++janzliIy5hw83Magn00vifIBAE015ber/658xFnGCWNc\nsWClL3YfCMMge+YzoExhU3UsMuYcJo0bhagzF7A1aA+uJsSyKrtsw+VC925GSE6VfpbkH+I/vHlD\nJnQ11FX7caTdnnxIpN9KoP2rryPfCAdbSh7n8+LOvrcL3Jw8EREXgpp/Vislk3V3YkiqqExyI+/b\nqkrR/6n3vcxlf2TwKVtDc/biERw+HQg3J0/s3SjEyd+vIymMeyVuKBpS/q9Fd41Fzr0bhbDvTTZv\nxl9RnVEjgGyEmzPBG4dPkw1kiRnRWLeAXDC7bkEwtuxfAgDYFb4Wy2ZsVaks31ANZU/IYNqki3yj\nX6qkMdQv7fYCDB8wAal/vMC4YTORmBGNUfNNEXBwOTJuxdP6Ob54W1WJ/RG/ovhxPmKC76BrZ9Vd\nBPC2qhIxSaGYM8Gb9SZXCsp9xi35BzhU5bYurdXb4c1b1W1SoMaTbVo2U1kcAKDXlhwwT7r7lPav\noRa7S3rZUFj+lhd3Tlb6mOloggMXC1H96V9KydTfmBx8uPvktcS71x8+if5PvR9gIv2ghCpk+4bq\nMRO0RWXoHKRtGIdN7rZIuvsU439LwNTgi6zL6/8yrVp8j5UjeyAojhwgiblZhqDp5BstaPoArDx2\nFQCwIToLm9zZX5LytRC7agScrMimpOjrpSqN639d199oPPyR/gBBcXcw09EEsatGIG3DOBTt+LFe\nZdBrq4Zxtl1QFjwN0xy6IeZmGXquicLq45lIuvuU1n/XJ/MOpgIAxtnSL8agnmNulsn0v3Ik+QYS\nHz9Qz9R7NlDjOLa0VWuON2/ZGwZUhNevX+OH7xrWqDgAPH9fyNmdV4QWvCIkNzNQdNMi7fHJLO4b\n2hqazLLjSCzcAXujGVg6OAZrR1xGwDh2OvoGN3RbmyFk8kusHXEZ43tuRP7zZOy+7Ib96dNYl8v/\nVewMpyE2dyM+fH6Niiqyca5zu54NLNXXw9LBMbDQHQ4AyHqsOkNdANCiaSs4m61AYiG5KDz7SQx+\ntCUHVH60/V3UTsbmbsT4nhtVKss36pfG0J+0/UEXvTqNxW8TSmBnOA3ZT2Lw8zlrRGavQf7zZHz4\nLDnvowhh1+YDAH676CIaI9QdJzCNGz58fo2M0nA4m61Ai6b8HYDMf57Myb1as7Z4+0518x2idfFW\nql0X19Ei6+KXr8bT/u0s4M/Awf3SPF7cDbZzxeSxc3E0ag8+1FQpJZNtT3Kwt/CB5Lr4m3e16wXU\n+77Wso2q8CkbH0SfD8PeowGYPHYuju1OwPnwm7gex+3b5n8ZPvVnYmSJ4sxPOB9+Ez6LA3A5U4jp\nS0dgwZoJrOvG351juxMw2M4VAHAu8aRK41JX08CimT7YezQAAHAhOQqbvcmhsM3eIfALJAf4A/b4\nwGdxgEpl+YZ8isvIhlKzbg271s2FxtD+6mgJ4Dp0Im4nVcB9zCxcSI6C43hjbNi+FJczhbR+jg/a\ntdGE++jZKM78hP3bTsN16ES8eEkuXle0HqmrkfXUy5lC1n64uAWANhqaovGWKnj9+jXatG74TeZF\nxez6mrruDHo1h0Ev5ouT+9s4AgB8Ny9SSrb6JvJMGPaE+mPKhLk4sT8R8RFZuHWx/KuLQ1WYGlvi\n0e3PiI/IwtoVAUhJF2LKAmd4rnCjlQ+27r7BnsnjZmPrDh+8eVuJ0kfkAlQrs94NLJVsvpay3ljk\nPLE/EUMcyHg3Nv6ESuNSV9PAYk9f7An1BwCcS4yCv99eAIC/315R2711hw/Wrvg23v2G4jSG+qWj\nLcAoJ3fkXXmJyeNm41xiFOxcu8LPfwlS0oV485b//aCq4s3bSpw4fQiLPX1FY2E2LPYkRoHF56Ko\nZ+o9V7fySEmvHXtT/sTlpp7rumVDm9ZkDfz1a37moKXx/v07/PC9audc27TQBQAUVCTT/tVU488w\n2vMqlmv1ctyZaQ+HncEMpJUdxOd/K3fex7BdPwBA+XvJcWHNX7V5Sr3v2l72ZZd8yvZ34frj40h+\nsAN2BjPgZXcaqwelYPOIAoXC0tUww86xFVg9KAVjzDegsCIZIZkTcOjGdFq54TPOxo6X3WmYaZO1\n4Oxy1a4FN2/aCsO7rUDyA7IWfPtZLCb1IIZRJ/X4DVF3VgEAzhVswhjzDSqV5Rvy+bOaGBwRtFbd\nWRBFaQx1tE0LXVjrjUWAazH6d56K289isSmpF07d9UZhRTKtD1CW4d1WACB1qC7Uc2EF+/Vecbdq\nzdqjX+ep2Dm2AnP7HoO13li8+/QcAFjXw5q/XiPzUTiGd1shIaOicsnjh6aqHz9R86ht26h2DKUv\nIOvWF4TxtH+Nu/K3bn03j915bnnuRrq6YP48T+zcvQdVVcqNUwbak/2SObmS69avKmu/a6j3gxxl\nr1vzKVtDE3r4CLb4B2L+PE9cSopHTvYN/PnscUOLxZqGlP9r0V1jkfNSUjxGupLz3MdPqPY8t4ZG\nK6zz9cYWf3Ke+2RkNA7sJYZTDuzdg/mLFgMAVnv7Ynugv0pl+YZqKCgg31M9ezbsGndjqF/6AgEm\nuU/E21d/Yu6cWTgZGQ0DIxMsWrIMccJ4Wj/HF68qK7F+4y+4m5ePooI7sLKUHMOv8yUXpov3k9Qz\n9Z5NXAcOhmKdrzc0NBQb+8UJuV/I1759e5WucwPA23fv0Ub9B5XGoafZGgCQcPMe7V8jXf4uVih4\n9Ccv7pxtTTF7RF/sPZeB6o+flZLJzoKch7pT+lziXeX7GtH/qff2FrLnE/mU7RuKY27QEe/jAnE1\neDk2z3FFYlYRRq87CI9fjrIuh9/gxvkt8+BsSwwRRqXmqDSuVi2bY7XHEGyPJAbpTqXlYtcSNwDA\nriVuWBYcAwDwOyzE5jmuKpXlG6rh3pMKAICVoW6DyhGelIXtkSmYPaIvzm+Zh6vBy1Fy/Od6lUFP\nszXcHKzwNGoTZjr3wam0XJjP8sfKkDNIzCqi9VX1yWoPYkRavK+jnqn3fFL5vgZhCTew2mMIWrVk\n3kcni8Qs7oas27X6AW/evlMoPjaI7Oy0rt9LyMTJKyrm7K5Zpx5o1on5O8uxP7EFscjnF+WEayRY\nmhrjryd3kJ0YjUC/lRBeugKnyfMwfs4y1vpTlMMRsfAPPoR5UyciKeIgshOj8SznskrjFGfO5PHw\n3hyEyjdvcb/kIQDApoe5HF/SaQzpUYSkiINwHUrmgk/Exqk0Lg11NfgumQv/4EMAgMhzCdgbQC5W\n3BuwXlSvvDcHIdBvpUpl+V9DFXW94MvFPj3NTeW4/HpoDPVYoKMN99HOeFVwFXMmj0fkuQQY9XPG\nknVbILx0BZVvvh57WsJLVzi5b9e2Nd68Vf35WfUfWqssDgDQbE0uarxZmED7V1eTP7vSD1+wW6eX\n565Pd2e49JuNs+l78fGzcmt9FoZkb1bpszsS797X1M7PUu8tjWRfbsynbKoi8WY4Ii5th0u/2fBf\ncB57Vl7FyY0lDS3WN5TAf8F59OnuDAC4fDtKpXG1bN4Kk4euRsSl7QCAtJxTWDpxFwBg6cRd2H1q\nGQAg9IIfPEcpdpnJN7gxeehqAJBoc6hn6j1ftGxOvpdv3qu9GPhxBbV+YcVrXKqiMbSDmq31MLCH\nG05tfgrnvjORlnMKMzabY0/MSty8l0jrg/jifU0l/kjcgocv8nHI+xa66DB/Q76vqUT89TBMHrpa\nlOdsUEV5bNWiPtb93qJda/5sHytCXhG7MljXXXPDPmhuyHxpjmM/cu5u0bpv+yq+8Y2vkcTjIXAd\nQr4/Tpzhvm+CCxrqavD1mg3/kDAAQNT5JOzdQs5B7d3iK2pHfPx3I8B3qUpl+V+l4MGX+Sqzbg0s\nCTvCIs/CPyQMc38cj8TjIciKO47yrET5HnlEoKMN95HD8PJOCmZPGoOo80noaj8GS34OhDAlA5Vv\n+F9LqnzzDpuCDiDvfgnyL52CpSn7vdua7dpgtsdYfC67iZiDv8F95DCUvyBrsHXr1fRlfgAA95HD\naP6p56jzSazjFKZIv9RMFu3btq6f+y5asbuUWVXkl7KzbVPXnbr9DKjbz2B0O9C6OwBgybYjygn3\nP8bRC2nYFn4ec8YORtwub1w78iseng9WKCwLI318yAjHtSO/YouXBxIyczFyWSDcfXbS8pLPOBs7\ncbu8McKO2M+NSJJ9wbeytGrZAmtmjMa28PMAgOiLNxC8ZhYAIHjNLFHdWBcSiS1ejeOy5m8oT2Oo\nTwKtdpgwpC+eJ+7HrFEDEX3xBrpPWInlv4cjITMXle/4maeftXEfAGDwgl9EfULdfoGpn6h8V41f\nQ2OQX1qO3JOBsDDS5xx3QqbkOaq6rJkxGgBQ/ZF+NwL1TL1nSzsNdbxR4bqeyG6smmrP38mj+BG7\ns3N13VmPbgnr0czjGBtLsn9hc8hi5YT7RqPA2MACOec/InLXDayY7Y/0rHjM93PB8s0TaeWCrbuG\nhouc44fPwo4wX7ytqsSjZ8TGl7lxb4XC+jtyYHM8HGzJ+TphqurvS/Wc5I3QKHK+LuFKNPy8yPk6\nP689ovZmR5gvVsz+Ng/8DcWJTTqC0KhATBjhiQOb4xG56wYu/fG4XmXQ1hTAyX4i0iP/xPjhs5Bw\nJRouc0ywdd8ypGep5r5UvvHdPhMA4GQ/kfY79ZxwJVqp8NOzuM8Tt1FX7Trb69ev0b6d6u3I6gvI\nnqa4hCTav8ZG/Nmnystnt6dJnruRLs6Y7zkLu0L2o6paue8RhwH9AQA5uXcl3r2qrLWZQb13dJC9\np4lP2f6OWFqY4/9q3iDn+hVs2/oL4uITMcx1LMa6T2FdPhpTPF8zF4VnMdKF7IM6HqHafVAarVph\nnfdP2BJI7jWKiI7B/mBi42p/8A4sWEJs96xZux7btv49zmB8zeQXkv1JPXt8HfuTACD0yDFsCfwd\n8z1n4aLwLHKuX8GLRw/qVQZ9gR4mTRiPNy8ewXPmNEREx6CLqRW8lq9CXHwirU/5u/Oq8jU2/LoV\nd/MLce9OFiwtuJ+/iYvntiaqWS/2Dt6irYrPwAk6knuw4lOv0f7t2lnAWxx599ndxSzPnesgO8z1\nGIM94adQ9eGjUjLZ25AzermFkvW27no09X5gH+t6k40vwqIvIGDfMcz1GIOEoztx82wYnmae++ri\nUBWWJkb4dD8dN8+GIcDbC8LUTIyYuRwTFvrSyiJbd99gzyz3kfAJDEHlm3d4UPYEANDbsnGfAfta\nynpjkTPh6E64DiJneU6e52ZPkisa6i3hs3A6AvYdAwBExV1CyC9kP3PIL6vhtZ6ci/AJDEGAt5dK\nZfnG35vGUL8EHbUw0XUIKrITMMt9JKLiLsF40EQs3fg7hKmZvO6p81k4HQCpY3WhnoWp7PepsHVb\n+eYdNu0KRf79MuQlnoClCfczj1zkotBsq1EP4/p3KrdhK4+8QnZ2P+q6a6ppgKaaBoxuB9mTOc4F\nK9nf6fCNb3yj8ZAcewIjnYitohPRsSqNS6OVOtauXIytQWQ9PDLmHPYHkTXw/UH+onZkzYat2LZp\nrUpl+V+loIjMb1hbKmazpr45/EcktgbtwbyZU5AcewK30+LxvOhWvcqgr6eDSeNG4XVZHuZMm4zI\nmHMw7GkHr9V+iEtKwavXqh0/AKTuAEBcUopS4VD+p84j5xoGjBgv6ufr9vXy+n6mcLnQrm1bRrsd\n/xD/oaaGGHr7vil7w2duTp6chQKAdQuCkXErHiWP85FxKx7LZmxVKBwmUq6fBQBcOFCE7Jgaib8L\nB4po7mTx48jFyLgVj7OXjiolk6UJObgaERdC27Tx9EUpnGYb4Pj53Xj6ohQRcSGw7+0CGwvZlwHw\nKRsbKl7TL4Z7+oJM1M2ZwGxImSof8japbNm/BADgM28nbCwGomtnC3zftJlKZKRkYnKrSJlWRn4q\nvtQ/Xkgtq9kxsg0w8qU7PmWqLzkVwcZiIGa5kcuYdoWvlSgHbOBShux6OQEAMm7FI+NWPMy6ko2j\nhp3MAADJV08DAKxM+nKWgy+o+kHJT6GIbv4OcNEHVa71dfgzIqUI9VG/2LaNaj+0gn1vFwT5RiPM\nnxhqW+nvDqfZtQMepnaFSxtT8jgfm/eSyWe/RSEqz4PnLx8BAMy69mJ0s9LfHTZuaqj5J30TGdUH\n1tWhqtyyoUmT7/DXX6ozJE+NJ5s1bcLaz0xHE4XiCpo+AEl3n6Kw/C2S7j7FJndbhcJh4vxtku+5\n2yahMnSOxF/utkk0d7JYMMwcSXef4niGchtcbIw6AAAOXCzE6w+1B57KXlbBdMVJ7E3OR9nLKhy4\nWAgnK33Ym+jUm2yKQOV9WfA0qTquDJ0jcrtyJNlsUPaSfgnus7fsxiVUXHX1pqxMiqBsOgDATNAW\ni4ZbIHfbJMSuGoGku0/huOmMUnKJIy4PJS8lPxPy9MsEpXemeBVtJ8QZZkk25STdfYqku09hbaAJ\nADDVI4sJZ7KIAWJbIy3GMPjIw4bA3kQHy13JJsEN0VkKycsln/jQtar5GvKSTV3ko91qLG0k36w8\ndhUAsH2qHexNdGAmaIvvv2M/RmED2/apVYvv4WSlj+NLhiFh7SgAwNTgizBdcVLkhkmnDaHfpLuy\nDYeY6JC6XFlNLzPlr0mZ1WurJvptavBFaHoeRvWnf9HcUs9c2/jvmvw/fP7rL05+uPLf//4XzZpw\nM95ib8RsNEVRcp5eAAD8OiYHIZNfSvz9OiaH5g4AxvfcCAB48PKq1DA/fcWXsp/M+gkA4GGzDd20\nBkC3tRm+a/K9hDsqLz58Vn4zNxXW23/SL/h69aGM9v5riYcruq3NMMRkIX4dk4Olg2OQ/zwZWxMG\nS7hjktvZbAXnOKm0/jahRGq5D5n8UuSWCp+Kj0keeXGx1Xv+82QkFu6As9kKJBbuQP5zyQ16Rh3I\nHF/xy6vIfkIuktJtbSYhs3ic0mTmoovGAB91rpvWADh1Xw4AiM3dyDov68IlX811iOGx/OfJyH+e\njM7tiCEhHQ2ysfb2E7J+1KU9v/MNXFC2nNc3jbU9qwvb/kQZ2KazRdNWsNAdjgUOf2DVMHLQeX/6\nNPicqW03mOq/KtuC1zVkk3nndrIPcoizP30avCK0JMYb1DPX/P/HP77DX/+qh3nM79mvi08eO1eh\nuDZ7h+ByphD3S/NwOVMIn8UBCoXDRGIqmRdKiy1GceYnib+02GKaO1nM8liCy5lCnLpwVCmZelqQ\nPvFo1B68eVe7Tvy4vAT9RuojLGIXHpeX4GjUHgy2c0XfXo71JpssqDyuK7M0/ALJ+sSm1bvRt5cj\nTIws8X1T+W0J2/DZwEcYikKl48VL+trZ4/IS2nsmFNWfLEyMLDF78jKkxRbj2O4EXM4UYvSMWsPi\nlEy3kyqk1pPiTMXmNRsyH9jSt5cjFsxYAwAI2OMjkW9s4JLnjv1HAAAuZwpxOVMIy+5kfdzYkGyg\nE146BQCwtuzHWQ6+WDTTB0Ct/BSK6KaxQaVNPC3S0kbVxc6CWuO/jV03qmg/xGHb36uraWCwnSv2\nbzuN6IPkEq4Fayag38haY4hM7Q3btmfBmgkwtmuBDzX0Oeynz8j4WktT9jogk3+q7aqbVia31DPX\ncVDLH9Twn//8h5MfLvz3v/9FyxbqnPxMmaDYWE4W8SlkfJUpLMGj258l/jKFJTR3ALB2BRkLXstO\nkxqmeB40Bl5U0NuAR09IuhZ71h5o8d28CACw2TcY/W0cYWpsie+/57d+qiIONmljgipTeVdeSs3/\nR7clv2dMjS0xd+pyZApLcGJ/IlLShXCZLDnvwNZdXd68ZT8uUUR2af6Z9KdofeMabkq6EHtC/bHY\n0xd7Qv2Rki6UCNPWegAA4Fp2Ks4lEkM7psaWovdUXovHKf5cN35F9cYWVZR1VcheH/WeDf1tHOE1\nm+z327rDR2reyYNL2Rs8gIx3U9KFSEkXwsqMjHe7GZHx7oUkYgyvt1V/znLwBVWuKfkpFNHNN7i1\nE3xRH/WLbVutrqaBIQ6uCN0Rg9ij6QAAzxVu6D2s1igOU7vCd/uoKE+fk719Pcx6y3FJx7gLWSt4\n/eYV7fdnL8j8qa62QCG3nivcYNCrOePYu27eUOGKlzdpbtlAzYVSc6Oq4K9/fcY//h/7fRl2Boqt\nG0zq8RsKK5LxvKoQhRXJGGO+QaFwmLj7glzgvsHpNnaOrZD42+B0m+ZOFo6G81BYkYzrT04oJZNB\nOxsAQFrZQdT8VbsuV1lTBr8Ec6SW7kNlTRnSyg7CTHs4umoOqDfZ+IAqC+8+0deYKmvKaO9VSdQd\ncsZkolUgumoOgK6GGb77h3Ltr66GGQYZLcQGp9vwsjuNwopkbE8dotI4ZUHpMcC1WGrZ3jm2QmVx\nd9UcgGHG5BLecwWbJPKaDVzKSXetoQCAwopkFFYkQ78NWQvu2Iq0rTnPyFqwQduGWwse3o2sBVPy\nUyiim68Zqh5oqvFnBJkv6qOOsm3fmjdtBTPt4Zjb9xiWO5Dv30M3psMvofbwOlO9ZlvHtdXJxX7i\nZfDzl/XeurIeujEdy89qi94p4vZ1DRmnaTTvKF8BAF5/JGOrTl/qszS4yMWGpk3IuSxVjp+osJs3\nZ79uPX+eYue5D+zdgzhhPO7m5SNOGI/tgfxevBATS+ZBH5Xex3/++ijxqgnGtgAAIABJREFU96j0\nPs2dLJYvXYw4YTwOHzmqlEz9+pF165279+BVZe38WXFJCTrqdUbQzt0oLinBzt17MNLVBYMc5Z/n\n5ks2Njwtp38LFJeQb/11vsznuanyUTe9Ut0tIpdw7A3ehUGOA2FlaYFm33M/i8hGRkomJreKlGll\n5Kfie/vqT6ll9T9/yTZmypfu+JSpvuRUhEGOA+HrTQwTrvb2lSgHbOBShlxGEMPrccJ4xAnjYWND\n5gPMzcl+rKhosl7dv1/Dneem6gclP4Uiuvk7wEUfVLk27sr+gltVUB/1i23bqKHRCiNdXXAu9hQy\n01MBAGPGT0RHvc4iN0ztCpc25m5ePubOJ/OWhw7sZcwDs+7km+vlK/q+xsdPyFhOX8DO2PbDh2Ss\naGvDPKc3ZvxENGnWElVV9LEf9axI/6Kurtp1bgD4/NdfaPIPCTM/jMweoVh7tWuJGxKzilDw6E8k\nZhVh8xxXhcJh4lwmuUCt4Igv3scFSvwVHPGluZPForH2SMwqwrHkLKVk6mPaCQCw91wGKt/XjuFL\nn79G16m/Ys+ZdJQ+f4295zLgbGsKByv536F8ySYLKo/ryqyK8J9Vvqf9Xvr8Ne09F1Z7DKGFQSEe\nR93wn0ZtklpW3scFsorT3KAjFo9zQMERX5zfMg+JWUUYsGQn7/GIo6p8URV8yOtgZYif3Mm5Ib/D\nQqn5Kg8u5W54b3KuMDGrCIlZRehlTPqK7p20AQAx6eTCqD6mnTnLwRdcyvz/Alz0sSyYnKsy0m2v\nesFkQMkR5DUODlaGMDfoiGZNv+M1DrbtaauWzeFsa4rI9TNx8TeyH9Ljl6PoOvVXkRumdkzZNk0a\nJvrkjP8rsfbj6StiZFtPszVvcVE8riCGEKn6Lg2PX46i9UhvVH+krzVTz4r0X03+0USl55JF83zN\n2H8bzJs6Ub4jjsQKLwIASq8n4q8ndyT+Sq8n0twBQKDfSgBA2jXpY56qD42/P6R0+argqtR0//Xk\njoQfS1NjLJ87HaXXE5EUcRDCS1dg4+wu4a78BX1uveQh+b7xXcJ9j9wiH3I5W/CWdXDsbwtLU2PW\n+0GoNDLJw7Y8DehD7GalZmYh8lwCAKILReAjPVzyjC8c+9vCezGx2eC9OUhCp2zgkh8jhpCLL4WX\nrpBy1oOs7Zh/uQAg+jypl/16y7blo0qo8kzJT6GIbhobfNZ1qsx37dJJhRLXL8rUY7awbZ801NXg\nOnQgYg/vQvpZcvHN+DnLoGdda9eAqb2oj7aDkqdZpx4SfSP1zLVv/+677/D5s+rPz37/Hft1SJd+\nsxWKa+nEXbh5LxEPXxTg5r1EeI7arFA4TGTmkUtpwv0KEP/be4m/cL8CmjtZjHNYhJv3EpF085hS\nMnXvTM4tnk3fi/c1tetyzytL8ePGroi9sgfPK0txNn0v+nR3hpWRQ73JxobK989oz88ric3VyUNX\nM/rZfYrs+1nsFgQrIwd00TFH0+/YjX+psnVq81OpeRj/G/M3tjJ+5YVZN+9kuWPSl7w6Iy/8hsbK\nyAGThhC7C6EX/CTSyQYuOrIxHQ4AuHkvETfvJcJYQMamnbW7AwCu3CHf71T9agioOkDJT6GIblTB\nzXuJiLi0HZOHrkbEpe24eY/bpa910dcm83LvP9D3Pr98S2yOabbRE/3GpR5uCvOAy6rW+PiZvn5D\n1Ye6ZYJqV3Q1ZdsRbix1SZl2kC1s++KWzVuhT3dnbJgdiaAl5Bt/U5gHftxYu4bHlFdc2s+HLwqw\nK5rYul7mHiw3ryrePAYAGOsz22yWBpfyyJbm39fDut/nv/CPJuzX/eb+OJ53Gc4kEJvfJRnn8Lns\npsRfScY5mjsACPAlF6akXZd++czXMBdE6fLlnRSp6f5cdlPk1teL1KuSR3Sbily/eSW+G7+ER4Uv\nS04mv3XLBBe3yqaJCkveZYtc9KwIXOVgoxtp8HmppKrhQ1bHfr3hvXAmAMDHf7dC8ztcdD7iy2W5\nwpQMCFMy0NuK7NEy7/Zl7iuO9FP9e1lxloMv+GoHGiOL1pF9wF0N9GW6ayz1gJI3+FdvOPbrDUvT\nrvj++6a8xsG2v9VQV4PrEHvEHPwN6acPAwDc5q2CwNZZ5Iap7ePSDuYVlWCB7xYAwH7/dXLzqi5u\n81ahuWEfib657An5PtHR7sA6LGFKhtxwqWdFxixNmjTB58/1sA7HobzMGStpL1VZzqZlAwDunQ7C\nh4xwib97p4No7gBgi5cHAOBKzj2pYVZ/VMzeVUNC6bb8Jf3ywNLyCtp7VbJk2xEAwM6fZmCgdXdY\nGOmjmZLtiYWRPpZ6jMC900GI2+WNhMxc9J/1s0rjlAWlx+eJ+6WWtw8Z4SqLe6B1d6yeRuymrwuJ\nlMhrNnApJ879yBpZQmYuEjJz0cu0CwDArAvZU3A65QYAoJ+FYmuafLBmxmgAtfJTKKKbb9RPfWLb\nFrVq2QIj7HoiOmA5Lu9fDwBw99mJLqOXiNww1UFV1cf80qfwCgwDAIR4z4aRQFuqO3efnVC3nyHR\nl1DP8nRgaqALAHj1lm7v4MmfZC5OoNWOk9zfNflHvezL+Z6D3dgJIxQ7fyeLS9fImbj4w/eRc/6j\nxF/84fs0dwCwYjYZB2fnXZEapvgdZI2Bikr6WY4nz8lZD89JzOfZKH0z+a2bH1zcUnFS75hklIci\naRKXNz3yT6n5nnNe8jyGsYEFpo1divjD93FgczzSs+LhsUxyzxtbd3WRdwessrJLg42c1ubEtkV2\nXhoSrkSL/CkSFlM62JQZeVBuueiRD2wsB2LORLIOtSPMl3MZBrjpwb43+b5NzyI6NjcmZ3O6diZz\nN0kZ5HxdD9OGO1/HV/3+BoHSp7j+VKnPzSHkfN3ahbtgYzkQxgb835fKtn6r/dAKDrYu2Ol3CuHb\nyfm65ZsnYui0ziI3TO0g1zaxvknPipf5fvnmibAe3VJiTEE9KzIm+qGF6u3IqqupyXdYh/mesxSK\na3/wDsTFJyIvvwBx8YnYtvUXhcJhIubseQDAw6K7+L+aNxJ/D4vu0tzJYtnihYiLT8Tho38oJVO/\nPsSGzq6Q/XhVWXvmo7i0DDoG3RC0OwTFpWXYFbIfI12cMWigfb3JJot13mT/ytNy+v4M8We21E07\nBVWOpL2T5k487uLSMtr7ulhamGPlUi88LLqLi8KziItPhHW/WrsVlJ83Lx5JLSv/V8NujqG+4hFH\nns4aA4MG2sN3FbHjtGbteoXKDpe8d3Ei+6Di4hMRF58I297kPhcLM7IPKup0LADArm/D7YOi6hUl\nP4Wi9aoxwaXNWLCElAtjo9rzxI1dN5TMITt/w6CB9rC0MEezZvzu72bbt2q0aoWRLs44G30CmZeT\nAABj3adAx6CbyA1Te6Ns29MYyMsvwDwvsmfsYMguWjmqy1j3KfhOrR2qqsVsHnx55jqWUVern31P\nTZqwtxc612OMQvGE/LIawtRM5N0vhTA1EwHeXgqFw8SZpDQAQHHqKXy6ny7xV5x6iuZOFktmukOY\nmomjp+XbFpVFX2tyZmhP+CnaWn3J43Lo243BriNRKHlcjj3hp+A6yA6OfeXfCcaXbGwo/5NuJ6Tk\nMfmu9Fk4XfSb1/rtAIDdG3+CY19rWJoY8X4ORRVxsEkbE1QdqMhOkFrWPt1Pl/BjaWKEZbMmoTj1\nFBKO7oQwNRN9xkruNWPrri5c9oEoIrs0/0z6U7R94BquMDUTAfuOwWfhdATsOwZhaqZEmAO+nM1L\nu5GDqLhLAIh+Kai8Fo9T/Llu/IrqjS2qKOuqkL0+6j0bHPtaY82CqQAAn8AQqXknDy5lb4QjuU9L\nmJoJYWomelsSm0rm3cia9ilhCgCgn7U5GgqqXFPyUyiim29wayf4oj7qF9u2WkO9JVwH2eH0Pn9c\nidoHAJiw0Bf6drX+mdoVtm1MdyMDAJI6rfrwUULWCQt90cLEQfROllsm8u6XYqHfNgDAvs1r0LWz\ndPsWfMQljtoPLephXP8ZTTicZ5g3cwrvMsScJ3PYZbmZ+HflI4m/stxMmjsA2LZpLQAgNeOa1DCr\nqj/wLiffULp8XZYnNd3/rnwkcrt2JVlPKSl7RAvj6bMXnOIUd0+FR4UvS04mv3XLBBe3yqaJCuvV\na9lzBlz0rAhc5WCjG2nIC78xwYesg+z7w2c5+f5fs2Er57IOcNO5yzCyRyouKQVxSSmwsSbnFsxN\nyfxV1JkLAID+ttzuqOETvtqBxsiClcTWZFdDA5nuGks9oOQN2b4Zg+z7w9LMFM34tq3Bsr/VaKWO\nkU5DcOZ4KK4mkDWFcVM9oWtaW1aZ2j627eC4qZ5oqmkg0bdS+SFPVib/1LOiYwtVhPvdd00Y7Xaw\nHy3JwMSANC4Vr7ltwjDsRDbq/PgT+cDsZsDf4aq3VZU4fDoQbk6e0G4vfaCr3V4ANydPHD4dKHez\nlL6OEdYtCMau8LVKyaXdXoB1C4KRcSsem/d6oeRxvij8k79fR07hVbgt6YGMW/GY5baKVZh8ycaG\nsxePivK54nU5dn6Js5c5s7EQ6+5k41x0/H7RZo3kq6dh46aGgIPLJdw/fUGMGNT8sxrHz+1SiYxD\n+o2T6jb+SgQAwM56OOd4ucpfdyMLJc/xc7toZTE7/wps3NRw/PxuXuOuT5lUIScfWBjbYs4EsqGs\n7qZmtnApQwZ6xEjASn9i0Ktjh06039ftmEl7bgio+rEzfC0tPWcvHm0wmRoStvqgyvKyGVvrVT5Z\nqKJ+UTowUaCftjC2hc+8nTj5+3Ve9VTxuhw//tQPxp0tsGDyz2iroclb2EyUPSkEAHTSYT685WxP\n6vmlzBjRbzX/rBa1DVTboUq3XyuW+sQg87O33Ax5mOq1AQA4biJtuYU+t4M+snj94ROC4u5gpqMJ\n9NpK39Cp11YNMx1NEBR3B68/yD4Ia6ilgaDpA7AhWjkj/npt1RA0fQCS7j7F8qNXUVj+VhR+2oZx\nuPagAn3XnSbvXdnVW75kU4TRvckHc0hSPk2HGfdfQNPzMPYm117eMMCEXNy2ITpLVFaeva3BH+kP\nWMXV35j4P5RyD9Wf/gUAOJP1EJqeh7H6eO3iKheZFEGZdKw+nglNz8O49ZAYH9JrqwaDDq0Y3VPp\nVIQ/0h/Q5KPKByU/ADhZEaMAlDzVn/6FQynSD4vLk4fSu3i80ddJ3zbUgt3lLfIw7kiMiE8NJkY+\nBO3VaL/PO5hKe5aGInmoTF7wSe8uHbByJNk4cP4W9wlkLvnEh65VjbLtSn3ApOu6dZGPdqsxtJGq\nrCdlL6tEcYQkKdeOU1D5Qo1juNC7Swdsn2qHtA3jsMm9YS4IpuLNuP+CpvszWQ9p75mg6m709VJa\nGT1/m7Qt1ga1Y3S3PmRjaUo+fRMy9UyVq68dQVtLAMDbf/JzwfKHz6+RWLgD9kYz0PYHXalu2v6g\nC3ujGUgs3IEPn8mBBgtdMiex+7IbHry8ik91Lqp99aEMl4r2AgBm9z/Ai5wNwasPZIP7p39Xi9JT\nl64d+gMA0opDRem//eQsvCK0EJm9RsL9p38zH2LvqU+MaGSW/iHK27f/fI6bj8jmWzOdoUqkpP7j\nYUtk9hp4RWjh0evbAEhZ01RnrqtMchtrDeAcN6WLS0V7ReUaAB68vAqvCC2k3N8n+o0KPzZ3Iy3+\nzFJ2h7u46P3tP59jf/o0jO+5EaMsfTC+50bsT58mUedbNG2F2f0PIOzafOQ/T4az2Qrae0pm8Til\nycxFF/UN1dZQZeTTv6uRVhzKS9gG7XuJ9Jb7VP6BQ3G45Ku2BjFMuz99GgCgbUsB7fewa/Npzw2B\nsuW8Pqjbjja29kwW8voTRaDSTI0LuGDQvhc8bLZh7YjLGN9zIy/yhEx+KfVP/L04L6qKAABareRf\nXFkXm05uAIB7Ly7TfqeeqfLxNWPWjXxTv3jJbV3c2JBspB49gxyk7G7M37r4m3eV2Hs0AJPHzoWO\nlvT5Ex0tASaPnYu9RwPw5p3sdfHOgq7Y7B2CgD0+SsmloyXAZu8QXM4UYp3/QtwvzROFfz78JrJy\nMzDcwxKXM4VYMENyjKZK2WRh25McKP/j9F58qCHfksJLp2Bs1wIbti+VcP+4nBgC+VBThcMROyXe\nKxs+xWA7crnuncIsUXx/nOan7WILJS8AjBhMDHFGnw8T1YcXL8txNvEkAGBgfyeZ/im46k8aG7Yv\nhbFdC5FudLQE0NeTbL8omQ9H7KTVgxu302Bs1wJhEfLX1RpDPihKDzNbLJpJ6k7i5VjO/rnkuWEn\nsjFxwZoJAABd7U6031dsmE57bgj6WhPDAwHBvrT0RJ8PazCZ+IJKm3heiaeNqn8+iwOk+m/suuGj\n/RCHSi/V33Ohh5ktNq3ejfPhNyV0qgyjhk8CACRcrl0nflxegoQv9binhWyjWNL8f6ipwrkvdZeq\n23Xdpt9IpoVBPdd1+7VibtITAPCigh9DU2/eVmJPqD+mTJgLHW2GcZi2AFMmzMWeUH+8eUv6n6H2\npD+ZssAZ17LTaH3koyclOPgHKdO7t6r+Qi62RJwJE+ntRUU5YuNJGerXe6CE20dPausnlRYuSBsz\n8B1HXbikTRyXoeQb6OAfO0X5CwDXstNg0Ks5Dh2vlc3PfwkMejVHbv6XMYu2AJ0FkmMWtu6GOJBy\nRLn7UFOFo1HsxyVcZJfln0l/g+ycGf3yFe6LinJ4rnDD2hUB+GnhBqxdEQDPFW4SdVxdTQO7tx7D\n0rXTkZIuxGJPX9p7Kq/F44w4I9n3Kas3rvBZn1QpO591UlF6WtiK8lZ4KUaOa0m4lD2jL4ZkPFcQ\nP3o6nWi/L107nfbcEFDlestOH7nl+hvy4dJO8I0q6heVBmpsxIWeFrbY7BuM+IgsrF3B37hX1Two\nJRe3dunEzXC/kQHZ1x0bf5KW9/EpZK+ilVlvhdyOcSZj77TMJFp81DPVJgFALytyzibiTBitXafc\nKtrnNib0NIhB5nefuK2zd2xFjFpsTx1CC4cPav56jeQHO2BnMANtWkhfq2/TQhd2BjOQ/GAHav6S\nbXxQU80Qk3r8hnMFm5SSq00LXUzq8RsKK5IRkbsSz6sKReGvHpSC0tfXseWSHQorkjHMeBmrMPmS\njQ966JALP64/Pi4qD+8+PUd2+WkAQHetIfUmS2UNWUP6/O9qXC5VbE321F1vLD+rjcdvyXpqmxa6\naK/GvObOR5xsoPR8uXQfreyWVF7F8rPaSFVh3ADQuW0vDO9G1oLvPOe+FsylnGirkzXeQzfI+Kzd\nDwLa78duLaA9NwRd25PL7M4WbKKl5/rj4w0mU31Dlf0x5hsaWBLZqKKOUnmuSB/WuW0vTLQKxOpB\nKbzqzqCdDQBSxz7X2Qdw7yVZ761bx3rpjae9E3dL1de6bnPr1PvKmjLceXGBFq88/qwm69gd1JjX\nsbnI9TVj3YPMYz8t5zbXaW5OznNb25C55Z49+Fu3flVZiS3+gZg/zxP6AunzpfoCAebP88QW/0C8\nqpS9bm3ctSsO7N2D1d6+Mt3JQ18gwIG9exAnjMfc+YtwNy9fFH5O9g1cSc+AqXkPxAnj4eu9mlWY\nfMnGhtDDR0T5/LS8HKu9yVnpQY7M85cD7cleoz0h+1BVRepyVPQpNGnWEouWSI4Ti0vIt2dVVTV+\n38H925ONjBPdxkt1e/wEORfn4iy5vswWtvJTuqgrz+87dtLKYmraFTRp1hJBO9mdnVZWd6qQSRVy\n8kHfPrZY50vOc5+O4X6em0sZMjUh3+hjxk8EAHTu1In2+4/TZtKeGwKqfqz2XktLT+jhIw0mU0PC\nVh9UWd4e6F+/AspAFfWL0gHV33Ohbx9b7A3ehZzsG7zq6Wl5Oaxt+sLK0gK/bFyPDprM57lNvtSt\n4yciaPkZE0vqvo0NO+NWBQVk3sPYmPmb7UcPcu46MYk+z0Y9U23H146VEZmfelb5npO/7p3IZY4D\nlpCyadlF+jyXIlS+r8H2yBTMHtEXeprSzwXqabbG7BF9sT0yBZXvZZ8bN9Jtj11L3OB3WKiUXHqa\nrbFriRsSs4qwZPdpFDz6UxT+1eDluJr/EL3nb0diVhF+cmd3cSdfssnCzoIY7j0Ydw3VH4lhqJj0\nu2g90hsrQ7j3m+KMHUD2NIcnZYnK0bPK94hKzQEADLfh3ifaW5DvMr/DcbQww5Mkz6hT8QefSaeV\nhfS7ZWg90ht7zsg2zLoy5Axaj/RG9v2nAEg+d9GRtCWgbDwA4GxL5p2puKo/fsbBOOmGQBsDqpTX\nxkQfqz3IPMDZq3mc/XMpd90E5MJ5j1+OAgD0O7Sh/T5n20nac0PApcz/L8BWH6XPyRz05jmu9Sug\nDCiZqj9+RjCLdoENlA6oPpsLNib6CPIah6vByxtMT1TdikrNoeXnuUwyh9LLmB/7DnW594Rcdm6k\nyzyunOhI1rMv3qaf96eeqXbma6eHOWnLy19UyHHJjso3b+EffAjzpk6EQEf65d4CHW3MmzoR/sGH\nUPmG2KpxHUK+y5wmz0PatSxUfajtS0sePsGOA+Qi8j+CG+8+ATfXYQCAHQfCRekCgLRrWWjWqQd2\nHqrdj7lk3RY069QDN3NJHyfQ0YZhZ33GsA+fjBHlUfmLCpyIJRfhOPZnN7ctjZKHTwAAVR9qRPqV\nRt28oNLIJI/zIHZneTXU1fBHcACmLfGB8NIV+C6Zq1Aa6qJMetjkmSro09NSlPYYYbIc15JwyQ8T\nIzLmHz+HzM920tOh/T5tiQ/tuSGgyrP3liBaeg6f5L4Xr7HAd12nynmg30pVit1gsK3HXKD0SfV3\nXOjT0xLBW9YhOzG6UencY8wIAEBS6lXa79Qz1TZ8zRjpkfXDyvfcLqjsrE0uJF0cRNo/Q13+xmvv\nayoRcWk7XPrNhmZrPaluNFvrwaXfbERc2o73NbLXIXU1jbB04i6EXvBTSi7N1npYOnEXbt5LxK7o\nJXj4okAU/p6VV5FfdhVzA3vj5r1ETBryE6sw+ZKNDYk3wkX5XPn+GS7fjgIAWBrJv7D5eSWxffXx\nczVi04IZ3X38XLsWZm81FgAQmxZMy6O7pelwWdUasVf2MIajiN+6cUvDwpDs2blw9aDI7ZU7MXBZ\n1Rp7YmrbHSpuJn3ZmNbaHO7TneznvP8kWyTDhasHZcrBBnlpURaTTjaYPJSslWfcPcvZPxcdCbTI\n/vpNYR4AAK22+rTfA4/PoT03BFQdCL3gR0tP4g1++kdlqHz/DJvCPOA5ajOmOa+D56jN2BTmwbnN\nphB0IHq+fDuKltbMvHMAAGNBL5FbLvXQ0Zqs02bcrZ3f/vi5WlQmqLCotsRz1GaaXKqqS3zCth3k\nApUHVF/MBZNONljsFoQ9K69K6FNZmRYHDUAXHQtMc16H1mrybT4/riA2SPU0jeS4pMOlPH7N9DQj\n6eRvLugd/EPCMPfH8TLngub+OB7+IWGiC11dh5C2znmqF9Ku36LPBT16ip2HTgAAju3irzzxjZsL\nWcPYeegE7aLatOu30NywD3aGnhD9NrAfKT8+/rtp331hkec4xRkWeY7m38d/Ny18WXKK+z15JgEA\n4OzYXyG3yqbJvg+5FHpveLQo/6PjLqK5YR8s+TlQQiY2elYErnKw0Q1VvrNyyfi86kMN9oZHKyUn\nFQ7fqEpWALDtaQ5fL3LJc0x8Cmf/XHTezagz8TOP3KfTSa8j7ffpy/xozw0BX+1AY6PkEVknDvCl\n2ypSZdniC0r2qg81on5HWai8pfpbLtj2NEfwr97IijsuoU9lZbIdORWWJl2xYeV8aLZrw8n/pNFk\nX2SM8JLot5JHT0X1up917TkESm7xvj067iLtfd1wk65cp8VHPVNtwNdOD2Oyd7T8JT+XAVa+q8a2\n8POYM3YwBFrS78AQaLXDnLGDsS38PCrfke95Fzuy7jlyWSCu5NxD9cdaO96l5RXYFUEurT2ycSEv\nctYH4waRefOjF9JE+i1/+QYRScR2uVPf+lvLLS0ndb/64yeRLrmy/PdwqNvPQHYhOTck0GqHLrpa\nKo2TDZSed0XEi8oTAFzJuQd1+xnYHZmgsrgBwMbMEGtmEJuRZ1K574/hUk66dSZrZ+4+ZK9jp46a\ntN9nbdxHe24IHKzJWsfakEhaeo5eSGswmf4OqKI+UflD9QNcsDEzxM6fZuDakV+xxcuDF3k+ZIRL\n/RN/T1H+8g36z/oZFkYC/OzpBs02zPfPuA8jZ8OSb9yl/U49U/WQiW6dSJ2KSMqkleuzaWRerpdp\nw61f84VJF7Ifv6KSH1tjb6sqERoViAkjPKGtyXAXqqYAE0Z4IjSq9i5UBxuyxjjfzwXZeVdod0M+\neV6CY2dI++e/+igvcvJBbPIRkd4qKsux4wg5K2ZjwXyebZjdeKl+hWnk3M+AXk4KuaXi3HFkLc1t\nbDK38zdMcclKk7i8x87spN/hmXcF1qNb4o+ztWe+tu5bBuvRLZH/gPSf2poCCDpK1ie27hxsXQBA\n5K7mn9WIjGN/xpmL7NJgKycAqP3QCv6rj8J3+0ykZ8XDc5K3wmEB9HtUuZQZefQyJ2vakXH7RHEk\nZZyC9eiW2LqPnY0ORbHoZivSy8VM7ucEuOjBQED2ai/fTNZtdLQ60X733T6T9twQ8FW/v0Gg9Cle\nPupDn0+ek/N1Nf+sFvVrykKlgerPuWDRzRZrF+5C5K4bWDG78ZxDZIKSUXyckJRxivaeiREDyfm6\nzNv083XUM9V2fO30tCLfkU/Lua2PW5iRPU3W/QbSwuGDV5WvsSXwd8z3nAV9gfQ9TfoCPcz3nIUt\ngb/jVaVs+1TGRobYH7wDa9auV0oufYEe9gfvQFx8IuZ5LUNefoEo/JzrV5B+9Rq697BFXHwifFet\nkBMav7LJwtGBzG0fPnpMlM9Py5/h8FH5+4lHupB9BjezbgEAqqqrEbJfcp+BwwCyvhGy/yCqqr/Y\nYDgdi+/U2sFr+SqRuwnjxkiV5UQE2fcwYnjtPStey1fhO7V2orjgXtM/AAAgAElEQVT1BXow7CJp\nX4oKM2jXHlpZSL2Sge/U2iFod4jMNNZXPAB7fTZG+tj2xjpvslfv9Bnu60Bc8t7EhNgzHOs+BQDQ\nqZM+7fcpM+fSnhsCql6tWbuec71q7LBtM4pLybzvtq2/SPXf2HVDyV9VXY2gXcx7LLlApVeRPrGP\nbW+E7PwNOdevSOj078jT8mew7jcQVhZm2PTzWnTQZL6jdLI7sR2amHyJ9jv1TLUvXzM9upP2rPxP\nybutZGHejXz79xlL9jBYmfJn76/yzTsE7DuGuR5jIOgofW1H0FELcz3GIGDfMdreF2l07SxAyC+r\n4RMov7+UhaCjFkJ+WQ1haiYW+m1D3v1SUfg3z4YhI+sOLJ2nQJiaiTULprIKky/Z2BAWfUGUz+V/\nvsTJc+TbYuCXfT51KXlMvt2qPnzEzrAIznFVffgo142ycdSFS9rEGe88CACwMyyCvo/qRg5amDhg\n15Eo0W9LN/6OFiYOyLpLbKYIOmrBsJPk+WO27lwHkX3vlLuqDx+x9zj7815cZJfln0l/TgNl35PC\nR7jlf77EhIW+CPD2woZlngjw9sKEhb4SbZKGeksc+30Dpv+0CcLUTPgsnE57T+W1eJxh0RcY5VNU\nb1zhsz6pUnY+66Si2FqZifI2NjGNs38uZa9bl84AgAkLia3BTrodab9P/2kT7bkhoMq1b+BeueX6\nG/Lh0k7wjSrqF5UGaizHBVsrM+ze+BNung1DgLcXL/IAQF9rcodnWPQFWtuVnH4DAL0OTho5lPZO\n3C1Vn5ko//Ml+oydDQsTQ2xY5ilz/97/Z++8w6I417//zTnxRCLFqCgBQYlYEAEVxYJ0hIVFRUGK\nICDN3rAgiqKJBntDiQ3FXjFqpCo2RAV7LDFRY2KORmOSX0CNJjnnPe8fm10YtjCzO7s7wP25rlxx\nZ57yfe6nMk/TNK76Qk8Hif0f//spL+H99PMv+HTlOiTFRsKqreJ1PVZtzZEUG4lPV67DTz9L1iYE\n+kns7TssEqdLL6Cy6qXM/f2Hj7ByveRb1K5N6p+9qW1CBkvmcVeu3yRLFwCcLr2AJqbWWJW1WfbM\nY4DkjomZ6Ytktn/876fI3smtrmfv3MvwPzN9ESN8VTpr+911QHLnnb+Pp1puNU2TW3/JPb7rN+fI\n8n//51+giak1xs+o3lvLxc7qwFUHG9sE+knW4JZfuQ4AqKx6ifWbczTSKQ2Hb7SlFQD69OqB2ckT\nAAC5x7if3cfF5l06SfawDY1KAAC0+3vORvo8KmkS47c+4KsdEBr3Hz4CACxdMJvxXJtliy+k2iur\nXsr6HU2R5q20v+VCn149sH7ZQlw9ky9nT00ID5Z8lzx0tLoeVla9xO6/65K0rtXlv7DkDOO59LfU\n/18vHin8T0rt32zD5Yt3+QjErqPkEN0Xv/4Is1bsD2SzaMOc0Otg1ZWVv97BhirfX859hTv3JYs9\ng33jVboN9o1HbtEW3Ll/Ga69VBvXxyUY564UoPSKZot6gwaOAgAs2jBRaViuvQJg2uJD1mGy1Sa1\n3eVc9TYDmrVqi0GjmYfkxIekqFxo6DsgBIWlB5B9aAmyDy1hvKuZP4um5mDOqlgET1S8GObx0wew\nMq+7w2Kjsbe9O+JDUhRqig9JqbMsKIKtftdeASi9kg/PkeYI9kvArKTVKvW49gpAgHsEL3ErQxua\ntKFTiqblWErQwFhkH1qCNdtnw6f/UE7tF5cyZPi+scxtfEgKDN83lj0P9ktAbtEWxnNNUNc2NdNT\nsx2JD0lR4Us32viETf8BsLfH90+/AQA4dlE+EaAsTmV2UNdOfNUvRbz4VXJ4ubS/V4eO7e3RsT1/\nFydeui6ZaFZUB6WoW9aU5cG9R5KNKIbNTJT6lfZ5izZMxKINExnvavdF2nJbX+lpLdks9+y339G2\nheq6WpP2pkaM37YW7DYpmyZkq3z/Yks8rn4r2VwQ6676gMBYd1vknLmHq9++gJ+j8kMcAWBIb2sU\n3XyMopuPWelUxkg3yUbx5B3nlYbl52gFs+bvsw6TrTap7V5sUT3OZotrF3MkB3bHyuM3sPL4DcY7\nP0crhPazUei2ps6V0ewOmR3q/BFyyx8qjKtmPnPRpA6apCOsf0fknLkH/0/lJ2Zq+vdztELRzcfo\nMHEnYj26YFmUC2edbVsYosdM5sRpcmB3uHap/tgd3KcDim4+ZuhZEOosFxYbParsnhzYvc76xRZj\ng3/J4kkO7A5jg3/Jnsd6dEHOmXuM54rgkod85IUUvurfSLfOWHn8BtIPVGBwL2tO7S6XfOLD1mxR\n1zbK8jI5kPsmDb611aSuushHu6XPNpJNPVHXjpuSPJG06TT6zjmk8P3D55Xo0Eb5+E4Vz377HUD1\nOEYd7CxbwM6yhdr+NSG0nw0ufP0Mw5bLH8igKC9r54GdZQv4OVopLjMeXRjp8rZvCz9HKyRtOo2k\nTacZbmuX5/pM+5aSg1Mqf3+GFu+zu4xj/F7FCzvXRzzHd79ILqxx7RijMgzXjjEofbAd3/1yDfYW\nvmht1AFx/Tdi64XRWHsqWKEfkd1UOLULYqWRDdJ0rI/gtoCWK9J0LTjeX+H7n14+RGujDnBqF4TL\n3+ei8M4qFN5ZxXBT0572Fr649aQY0w91hKtNDMJ7L5ULs3ObARDZTVUYlshuKuwtfOX8qIOu4mFL\nH+swlD7YjuUn5L+TjnBeodDP3KPMhaYiu6no3IbdWL0mqmxhb+EL5/bDFbq99aT6AgVlGrnEVdvu\nxXfWwN7CF/07SDbI9O8Qifs/XUDxnTVyZaerefVlb93MmQexq4qTi77atlAXdetv73bBuPWkmFFG\nhvWYr7EeKS42I1F4ZxUOX5+PHlaDWberALd8NWhiLHMrspsKgybGsueuNpL2teZzTVDX1srKuciO\n3aZHbWpT1I7qsj1TVzfb/kQdKn+XHBIkHReog0VzO1g0t1PbPx/88Kvk0hKDJqrHyrXzoKu5F+wt\nfLH1wmhsvTCa4VbdfkFoOHSVfCf/6ecfYd6G/bySpTlzXrzjR+zyuJOLgcr335S9wc27ksNJIoYm\nqHQbMTQBe49sxs27FfByUX35m79XME6dz8epMs0uJA0dLNkolbZkvNKwvFzEaN2K/bw4W21S231T\n9kalu9qIfYbji+L9yMpZjKwc5uVjNW28asEOTE2Phm+44g2I3/1wH+0t5TeIsQ2/NoN8w3CqLA+h\nSdXzAbMm6OZyNC8XMU6V5cHJzwwRQYlYMGMt+jp5YFzsLIXpGBc7i1HGFPlX136KGBoQhb1HNjNs\nI2VhSvUmM1WavVzEGCIaUWdc+soHdctzbUIHxyErZzEWr5sFkdcwTu0Ylzw3MjSRuR0XOwtGhiay\n5xFBidh7ZDPjuSaoa5ua6anZnoyLnaWxJk21aYqqvKrJt48lc909HZiLrJXZpmZ9UoSyPktZ+tW1\nD5/tR21++lkyLy7t79Whi40Dutjwd2CLW19feLmIkbZkPNKWMDetrFqwQ64e17artN9R5H9c7Cz0\ndfKQi2tqejSmpkerdFtfcbST5O3zFz/C3IxdG2jt1FTh80dX3+LGbck4LDJY9cWWkcGJ2H1oM27c\nroC3mxjW7Tpi7ac7MGl2NCLHiBT6mZCQikF+oaw0skGajkdX36odhouYWbcmJKSif28P2W9pmryG\nKV4b8uj7+7Bup7x+eruJUXIuDw7ubRAZkoiFqfKX22gahzLqSpsy+vf2wISEVKzbkoF1W5iHX3m7\niTEsIFL2OzhwJHYf2oxhsW5y4WSkZXF2N0QUhpJzeQx3s6eyH5dw0c7V/4SEVHi7qXfhNJdws7Yt\nhbebGOFBkvW44UGjUH61FFnblsqVHw+X6oMHvQb4s46Tiz42dmOLNuqTNrTzVSf5aKMAIGJoHNZt\nycCnq2ZB7BPMuq0HuJU9I0MTmdsJCamM8W5kiKTNr/lcE9S1Tc30lJyrHtNNSEjVWJOm2uojXNqJ\n2qhrJ231eYBkLARUj43UwbaTA2w76e6CBU25fU+yecvYqLlKd7Xzy7aTA7zdxArzPjIkkWEDLm49\nXPzg7SbGpNnRmDSbOfauPQ4xN7OUlQdF4arb5woJqw/+nmd/8wwfGLCfD2rVjHmw/ofG7A4NnnJE\n8cV1UlYHPcP3/yeZq+/fPlql2/7to1H2aDu+/79rsDNTPf/Rw2Iwbj8rxp1n3C+Fr0m/9pJDmPbf\nmK40LDszX5gYqE6nOtqktlsdxM+FgrXpaDoAvp2novjrVSj+mjnH5Nt5ap025oPoXhuw48oYLDqp\neI3fi1cPYWrIbg6pt2Uoyh5tx+pz8vU0rPtyrcTJBlV2tjPzRW9L5XPQfJWBfu2jUPz1Khy9vQDd\nLQZzqvtcyknTJsYyt76dp6Lp33O+TZsYw8U6BmWPtjOea4K6tqmZnpp10Lczf3PB2q67XDTUZnXQ\nM/z0SnKIpHUL+bXH2tDA1Q7arKOVbyRapH2hOliY2MHChL+55Q8MLGRprl3HXKxjGHWsaxsv2Jn5\nYseVMdhxZQzDrW/nqehoOkDO7f4b07H/xnSG2+heG+TaAWX59e/KWwBUz2Nz0VWf6d1bMp5/+vRH\nWFmy//v3o1oHNNvZsdvP/c/3mql8/98/XqOiQrKfe3Si6nnr0YkJ2LhpCyoqLiNQrHoP7/CQYHyR\nl4/jeZrt506Il3w/Gj1ugtKwAsUBMDdnP2/NVpvUdv/9o+7DJBVhZWkJaxvmWHdOago8PZTvIwsL\nHY49+w5gUcYSLMpg7rmrmT97duZgxMhY2HZTvG7+m/v30alj3X97stHo6eGOOakpCjXNSU2psywo\ngq3+QHEAjuflo0XrDzE6KQFZmWtU6gkUByAqUvXeaU1tpw1N2tApRdNyLCUhfhQWZSzBjJRUhAQP\n5dR+cSlDJibGMrdzUlNgYmIsez46SdIG1XyuCerapmZ6arYjc1L528/NV77xoUEZUm1s7fHNN5KL\nXvr3U76fW1mcyuygrp34ql+KePpU8v1O2t+rg6ODPRwd+NvPXfz3weGK6qAUqQ0dHewRKA5Q3A8l\nJcjpUpYH125I9qg0b678m57Izw+B4gCMGBmLESNjGe/q6i/rE06dJO3lj79Uoa2p6m+cNWlvxtyr\nZNtO+YWzNWkeqLot+u34Elz9RnIgaFyA6oOW4wL6YmvBJVz95geInFXv0x46wAEF5XdRWPEVK53K\niPGT/H05OTNXaVgiZ1t82JJ9P8BWm9R2vx1XXE+UEezmiINnrmPZvhIs21fCeFeXjdng5tgBM8K9\nFYY/I9y7zrypK8yadlkzUX4Pk6r4Rc62CPNUfeB5hLcTthZcwsDp8muXasanaTwAMNyjBworvmLE\ntTBe+9/h1S072tYb4+eMZftKkJadh6ABDpzaIC7lzrhZU5nbGeHeMG7WVPY8zl/SjtR8rgnq2lpZ\nmZ8Rzt+l9upq4xM2fQDA3h4PnkjO6uhj255znMrsoK6dsmeOQPzSPeg1epnC9w+e/AwbC+UXrKji\nx18kl3pJ+2x16Gb9IbpZs/8uwSfdrD+EyNlWcT/k31cjXcry6+aDJwAAExX1eqBTZ4icbRG/dA/i\nl+5hvJsR7g03R/7mTvRJ7+6SAzmfPv8Jlubs5vfea6f475A/vr+BiuuSb6iJUar3HCZGDcemXQdR\ncf0WxD7u6PhRO+zMXIyRE2fBLyJJoZ/UiYkIHax4vaM6SNPxx/c36nDJDo/+zkidmIiMzM3IyGQe\nwiz2cUfksEDZ76iQQdi06yDcguTng7MWK7500KYfM+2pExPh0Z/73IbUzt08FV/MdP/b79Hxo3YQ\n+7gj7+RZtO42AElRw5G5aI7KNKZOTITYh/3fQH6e1d/K/b1dOadDirbSUzvPasNX+YkfEYyMzM1I\nWbgSwWJf1vUQUF3maueHiZGhzG3qxESYGBnKnif9XR9rPtcEdW1TMz15J88y0sIXfNf7uuC7rn/z\n7XcAgH69+DuLhk/UtS/beqwOT5//BKC6v1MHB9tOcLDV38WWte3q5zkAYh93jJw4CyMnMvcfqds3\nCI1Olk4AgF8qf4Rpc8WXDivCrGV7xu92Zuy+NwRMV/23Zv7y3/D146sSt/3iVIfVLw75F7fi68dX\n0aer6nGLq+NQlN8pQPndQlY6lSHqIzm7ZO3ByUrD6tNVhJYm7MfVbLVJbZe//DfWYdcmZiGzfkb4\nzICjjfw+BikpUdlYsiseiUsUf79+8uIBLExt0KerCOV3CzE8zQoB/eIwIXglHG3cEOEzA3tPLsPe\nk8y/zfp0FcHLKUxpvFz8KopbEe7dg3Hm2kGFYdYsa6rijvCZwShrHj2Ho/xuIZIzq88XSRi0UGm6\n6oJNWvgoBwAg6huDvSeXYcsXaXB1DOJU/7nYqFlTY5nbCJ8ZaNbUWPZcWodrPtcEdW1TMz0162GE\nzwyNNWmqbX/JSvTpKoJfH8n4xq9PNG49PI/9JSuVlnVVfGTeDX26ipTWg4/Mq9sILvVQWr/WHpyM\ntQcnM9zWbGf+/UJyYWnX9n0YbtSpS8r6E2U2VjcP2LaD6vBLpWSOUNoXq8NH5t0Y+aYpV7+WfCtS\nlO9Satvwwb8lZzY3M1C9n6Z2HnApj/WZXo6StX4/Pv+Z9d+gTTv0Ufj87cNyVNy4DQBIHDFMZRiJ\nI4Zh857DqLhxG2JvV3S0tsKONQsRPTkNoijFl+2ljo9DaOBAhe/UQZqOtw/LeQnPo18vpI6PQ8b6\nrchYv5XxTuztisihAQrd5pWUyp5nLeK2x8rSvA06ujL/dkodHwePfsrn9lXpTB0fB3GN7zLqulUn\nTaGBA7H/WJHCuGqWJy52Vgc+dNS2TdhgP+SVlMItpPq80sWpk9TWKPZ2RV5JKdp090biiGHI/ER+\nXkPd8s231trEhQ9BxvqtmJWxFsEB3ty+fXGwuYmRocxt6vg4xrcvaftT87kmqGtrZXUmdbzqvzN1\noU0Tvvn2ewBAfydHxnN1ypaq/kaVe67plfY/9j6K5zHuP3qMjtbqnT3+4/OfAVT3t+rgYNsRDrbq\nrflSxIm/L3ZVVJek1LRhbbv6ufeD2NsV4+ZkYNwc5r7CHWsWMup15NAAlFZcV9i3126zpeFGT05D\n9OQ0htu6+rb6hJPtRwCAZz//Bss2LVn5MXJVfO7vy9LtuHJXsvclfojqi3rjh3gi+8gpXLn7EP4u\nPWBjaYZt88di1PzPEDhZ8Tz3zJjBCPHWfI2QFGk6XpZu5y3Mmrj37IqZMYOxdPsxLN1+jPFuZsxg\n+Luov0eGLVKb9hiheM3Bgx+ewcaSXd8XKRqA7COn4DXmY7l3mTNHaSVONqiys79LD0T4Kb/jgK8y\nEDvIA0u3H8Oc9fsw1NOZdV0CuJUT42YGMrczYwbDuJmB7Hl8kBeyj5xiPNcEdW1TMz0FZdcZaeEL\nbdddLhpqo0yTupq1WZ+e/Sz51iDtB9TB3sYK9jb83IfClZIKyboPRXVHitTevn0d4e/SA6Pmf4ZR\n8z9juJkZMxjuPZl7xmrnl72NFfxdeiiMKz7IS2824JNunWrcp2rKbm1Xz8GK9yNcO/Yat7+W7J0L\nEaneOxciSsChgi24/fVluDkHoJ1FR2TMyEHqsliMTlP8t3RCWAr8XDU/Y1yKNB3Xjqm3n8SslSUC\n4pl7xRLCUtDbQfnalN4O7kgIS8GW/UuwZT9z3JMQlgI35wCN3Z6rqN5vkjZ+Hed0cU0TG71uzgEQ\ne1bv+RrkFYlDBVsQM0N+3FhTM1t3/u6hOFeRz3A3NY79uTdctCuCrU4pLk7VZ3y59mLO2bINy805\nAOcq8uEW/iFC/BMwe+waTmWmLvxch6Pg7AGFYamq35rWKynDfEdhy/4lWLU1FQNdhrJunwBudcfw\nfWOZ24Qw5n2pIf6Sdqrmc01Q1zbK6ndCGH/76/jKt/qAqvJRF+raSdq/DR2reD3V90/uo52Fet9a\npPelSvtzdehkbY9O1vztr9MWYs8IXL1dqnCcoKitrp1fLk5+cHMOQOqyWKQui2W4ZdvX1Qece0n2\nBP344zNYWbJf02Bt3Z7x264ruzVN7xqq/hv4P69+QcXlKwCA0fGxKt2Ojo/Fxi3bUHH5CgIDVK9p\nGh4chOMFRTier9mapoRRknUFYyZOVRpWYIAIH37I/m8vttqktvvPq19Yhw0Anu6umJMyDYuWrMCi\nJezuGZISERqM4/mFcPGqHoss/VT+W09YyDDsPZCrMI6a+ahKy5yUaYx8jB4Rjo1btjHilrIhs/rM\nFlVhBgaIEBWhfO2ULuMB2NuTb9QtO7WJj43GoiUrMHP2PIQMHcKpzeCS9ybGxjK3c1KmwcTYWPZ8\ndMIobNyyjfFcE/ioVzXr7pyUaRpr0lSbprBtM6RnE7j0Zc6FKbNNzfqkCGX9g7L0q2uf3TmbERmb\niK7dFa9L/ubBQ3SyUW9f1Y8/Ss5ykvat6uBg3w0O9g1jLU9NaudX8clTAKCynEndinx9EBggQmRs\nIiJjmXsw5qRMg6e7+vt2hEIvB8k46seffoblh+zOLAAA67bMe0ntOlorccnEoIvy9cwA8ObeOVR8\neRcAkBCueC+ElITwIdi87ygqvrwLsafqO6SD/b2Qf/oC8k6XsdKpjLjQQQCA8fOWKQ1L7OmCD1uz\n39PKVpvUdm/unWMddm06eTK/Vc4aGw2PvtXtxo4V6YietgAOIsVnmd//7gd0bK/8m4fY0wV5p8tg\n1tsfieFDsHa+fN+kaRzKqCttyvDo2xOzxkZj8Wc7sPizHYx3Yk8XjBhcfTZf1FARNu87CvewsXLh\nrP94Bmd3YYE+yDtdxnC3OEXxuj9NtXP1P2tsdJ31io9wl23cBbGnC2JDJPsNY0MCUVpxA8s27pIr\nP75u1XPv/h7MO3hUxclFHxu7sUUb9Ukb2vmqk3y0UYCknV382Q7MWrIew0QenPomLmXPxKiZzO2s\nsdEwMWome574d/9S87kmqGubmump2UfMGqv6PG1daKuPcGknaqOunbTV5wGSsRtQPZZTB4cuNnDo\not4eCUVYfthGlubaNk4MH8Kog75ufSH2dEH0tAWInraA4VZRH147D06USu6SUpWfUrdc46qv9O4p\nWW/647PnsKo1VldGE1PFY/i/XjxCxVXJvtvRsarvuBkdG4lNObtRcfUGAv280bGDNXZtWouopEnw\nHabY7+zkCQgbOoiVRjZI0/HXi0e8hOfp2h+zkyfg05Xr8OlK5rxpoJ83IkOHKXR7vKj6TJcNK7nd\nd2LV1gIdejDHXrOTJ8DTVfFdzHXpnJ08AYF+3hq7VSdNYUMHYV/uUYVx1SxPXOysDnzoqG2b8OAh\nOF5UggH+1dqWLpittsZAP28cLypBqw4OSIqNxPpl8nvs1C3ffGutTfzICHy6ch1mpn+K4MFi1u0O\nwM3mJsZGMrezkyfAxNhI9jzp7/an5nNNUNfWyurM7OQJGmvSVJsmfP3gWwBAf2fmnK46ZUtVf6PK\nPdf0Svufrn29FL6///AROnZg9/2qNj8+k9znLu1v1cHBzhYOduqP3WojbefGJKdiTDJzz5miPqS2\nXUXeHgj080ZU0iREJU2q0z9btBWuMv7BRyAd29vDtVcAyq4WcfLXwsQUrr0kiwFcewWghYkpH3IA\nAJ+fyIFrrwB0bK96YYRU++cncuoM0/B9Y4wKnl6nOzYEDRyFPSsuYs6Y6kskXXsFYM6YTOxZcRE9\n7QZg0Ghb7Dq2llV4fGpTRdDAUVg0NQeARO+iqTkYEzG3Tn8fT97CSGt8SApyM28w8sd3QIhCN3tW\nXAQAXLtTvemND41jIuZi0dQcRhlkmx5FsNU/NmIugv0kC/F++uWpnB7pOwCYMyYTaePW11k3NLWd\nNjRpQyffmLWyxMrUAwCAkxc+5+yfSxmSLh516sac4HHp6ct4r0/GRMxF1vw8WXomx3yqdn1oCLCx\nR9k1yQVaFm3UGyDxiTbrV9nVIlZ9qi5ZtGGizuPMLdoCAHW2fytTDzDahmC/BGTNz1NYn7Tltj5i\nZ9kCfo5WOPHlD5z8tTIygJ+jZHOPn6MVWhlpvrlOys5zX8PP0Qp2li1UupNq33nu6zrDNDb4F6aI\n1f+jqCYj3TrjTPpQrIyuPjTVz9EKK6MH4Ez6UPTvbIYeM/cjq/gWq/D41MaV1CAnbEryRKxH9YaW\nldEDsDp2gFyeSt1K831TkidGunVmHVdWgjvDZsmB3XFpUYhcPnPRpA7qpqPXR61xJn0okgOrF6cn\nB3bHrokDGf5Tg5xk2n/8v9/V0jjSrTM2JUk2t/g5WmFTkidSg5iHPg11/oiRjpXRAzDOV76/YKun\ntl2UxaspAx0kE0sDujAPPvSxt2S8VwXbPOQjL/imbQtD7JooOTDp2BXuH8u45BMfttY2qUFOODzd\nX5aeBaHOvJc5TUgNcsKCUMlCST9HKxye7q9QHx/tlr7aSG3Wk6HOHylM05n0oQCAC1+rf2ntiS9/\nYDVWECqtjAyQleCusD5nJbizysvVsQOwMnoAw//K6AGYG9yb4c7Y4F9yccV6dFFanusrFs3tYG/h\ni9tPT/ASXtnDnbC38IVFc9WHBEnjLXu4U/bMqV0QPhlyDSOcV8DeonohlMhuKiZ55WKQwyxFQQke\np3ZBGOFcvWBZZDcV6YEXMNtfsqD5/k8XZe9i+q1X6LamPQc5zIKrjeRwgd/e/Kg03kEOsxDXf6PM\nlvYWvojrv5F3O+oqHjZYt3LCbP9TENlVXxguspuKMW474dIhSs79IIdZGNZjPgCJbk3LmdQW0vwB\ngBHOKxDpvApGTVspdCu1W1z/jQo11hWXKruXPdyF0gfbMchhFgz+vtjdoIkxBjnMQumD7Sh7uIsR\npkGNy99bGcofWl87Tmn51NQWusSpXRAjDSOcV8C7i/xiX3Vp8b4FxrhJ2rXrjxUfKqIKLvWpm7lk\nbNypDfMCbztzH8Z7fTLIYRYmeeXK0jOsx3xBtOXK2lEhtSIQ2acAACAASURBVGeK4NKfcOX20xOs\n+m+hU/pAcugO13bGoIkxYvqtZ+S/q01MvR5/1KaLjQO8XMQ4c6GAk7+WH5jCy0VyYaKXixgtP+Bv\nXvzA0W3wchGji42DSndS7QeObqszTCNDE4yJmcmLvtDBcTi2vRwLU6ovj/RyEWNhynoc214O5x6u\n8BjWCVv3rmEVHp/alLFsXjZD77jYWSje9yXDxmKf4QrdHNsuOSCz4rryeRk24ddG7DMcqxbskJWj\nhSnrERcxWal7PpmSNA8RQZLNgM9fVM8lT0lMZ2jychFj1YIdmJKYXqd/TexXm+52zji2vRzjYqvb\nmXGxs7Bh6SGEDmYepivVLNUDSGy5KPUzVvVSn/nAB+ZtLLFh6SEAQOGpw5z9s81zAPDo7w8A6NuT\neZCHe38/xnt9MiUxHTvWFsjSM2vCYoVpqY/UzitpHavJ2QuSNV6W5vJz3bX9r1qwQ64+6Qs+24/a\nnLlQwKpP1SVGhiZYlPqZXJqPbS+HWMmh1bXZsPQQIz8jghKxY22BXHk3MjTBsnnZrNzWV2w7OcDb\nTYxT57mN5ZSx9/Ot8HYTw7aT6jIjjXfv59WHbg/yC0VZ3n1kpGXB2636Yu0JCanYvaEQ08YKy+bT\nxqZj9tTFAABvN7FCjYP8QpGRliX7PSEhFacO30L+XsnGpfJrquvntLHpiAyR9NHPfnqq0I2mcSiL\nt6601eV/7ac7ZNoBICMtC0vmbkDLFtXjix72zsjfW4EJCdWLlSckpGLLqlyED43j7G6QXyjWfrpD\nVn4y0rKQGDWFQ8rZa6/Lv1SDt5sYaz/doXH5ZRPuvs+3YvehzZg2Nh1GhpILf4wMTTBtbDp2H9qM\nfZ8zD7mXugEAKwv5vq92nNKypUqfunZjg7bqE9/atVEnNcHczBJbVuUCAPJO5nL2z6VMew2QjGf7\n9WKOdz1dRIz3+mTa2HTs3lAoS8/sqYsF17/UJ7i0E3ygzfp16nwBqzFMQ2L3Ickl2uq0dUvmbmCM\n2bzdxMhIy0LKRPkNjGzdGhmaYNUnWxllKjIkUek4ZJBfKA7nnJO139L2aWFqppzb+oiFiR3szHxx\n9/lJTv4M32sFOzPJd2g7M18YvsffvNmF73bBzswXFiZ1zNX/rf3Cd7tUugOApk2MMbATP9+R+rWP\nwgzPEoR1Xy57Zmfmi7DuyzHDswQ2rfphQZETTj/4TEUo2tGmKQG2KYjutYGRt9G9NiDAlr8DmFXR\ns20Qw66+nadijk8ZZnhKNrw++Jn9HFL7Fk6Y4VkC387Vc+6+nacise8O9GtfPZ/NZ5xskdrZxbp6\nDjqs+3JE9FjJa11SxgcGFkjsKznY4sYT7nPBXMpJ1zaSOd+OrVxqPfdmvNcnAbYpGO9ySJaeId3S\ndVbmhcDd55Ky3qqZ/NoKIaDNOnr3+UlW/Y2u6dk2CFPc8mRthLSODXdkHuTetIkxRjqtY9RHF+sY\njHc5JFeGmzYxRkSPlXK2nOFZgp5tg1hrK3skmcdW1VZx0VWfcXSwR6A4APkF3A7mbm1qikCxZE9S\noDgArU35m7fenL0NgeIAODqo3nsm1b45u+55axMTY6Sm8HNBekL8KFy7fAkbs6oP5ggUB2Bj1jpc\nu3wJ7m6usLbpgpWr2e3n5lObKhLiR2HPzhwAEr17dubg4/nz6vS3Y1s2I61zUlPw1e0bjPwJCx2u\n0M21y5LLJM+dO8+rxo/nz8OenTmMMsg2PYpgq//j+fMwOkmyP/rJk+pvZVI90ncAsDFrHTZvzKqz\nbmhqO21o0oZOvrGytMTRwwcBAIdyue/n5lKGAvwl3+k8PZjf7wJEfoz3+uTj+fNwsihflp5lSzLU\nrg8NATb2yC+UzHF/9JH+93Nrs37lFxSy6lN1yehx3A5/2rwxCxuz1jHq68asdchY+AnrMDZukuzn\nVtX+mZgYY8e2bEbbMDopASeL8htUfepm/SFEzrYovnKPkz/T5oYQOUsOaBI528K0ueYXpUvJKSyH\nyNkW3aw/VOlOqj2nsO7LvI2bNcW0UMWHXXElxs8Z5zOnYM3EYNkzkbMt1kwMxvnMKRhg/xG6jcrA\nus/ZHYjKpzZlbJoWztA7I9wbVzbOqNPGbJkT5YvsmSMYZSJ75gjMiVL/0OjaYWbPHIEYP8WXRUjd\nxvlXH4a9ZmIwMieF1Fk2e3exwvnMKZgRXn2g3oxwb+ybFysXnybxAECwmyMjTWsmBmPCUNUXAOgT\nbetta9oc++bFAgCOnP9StWMFcCl3vr0ke0td7ZmXivj27sJ4r0/mRPni2KIkWXoWxos1qkP1HTb2\nKL4s6bvam+l//22wm6PCdvZ8pmStTdntb9UOu/jKPVb9opDJnBSCNRODGfV1zcRgzI/VztqHrQWS\ncbOqttm4WVNsmhbOaEfi/Pvi2KKkBlX3HGw7QezjjoISftbYZO89DLGPOxxsO7GKN3tv9Xry0MEi\nPLhYiKzF8yD2qf57OnViIor2bsL86ewv/NAX86ePx87MxUiKql5fm7V4HjYuTYdpy+q2qE8PB1wu\nPIDUidVrqFInJuJw9hrER8gf4Dx/+ngsSUsGAIh93DWyR+hgEbIWV/+9kjoxEbdPH8XlQskZeOfK\nr8rilKbjybOf5NIozSOxjzt2Zi7mrMfEqLr+fWTF/sK62vCVnrryTFtYmpvhcLZkL1NuXjFn/1zy\nw99bchmXR3/mGQwizwGM9/pk/vTxKNq7SZaeJWnJ9aLuK4Pvul54WvK9R5M6I0TY1mN1KCgpZdUv\n1SdMjAyxbfUiRt1Pihpeb/pKNnxk3g19uopw+Stu7WJzQ1P06Sr59t6nqwjNDfmbhyy8lIM+XUX4\nyFz1RZBS7YWXcuoMs1lTY4R583NBqahPDNYln8ek4dX7Y/t0FWHS8DVYl3we9h0GIGZhNxw+u05F\nKNrRpoqRojlIGCRZ29inqwgZY45hpGiOSj/u3YMZ6YzwmYHNKVewLlnSRt76tuzvsNMQ0E+y9+GX\nyuq5sJGiOUiJypa9A4BJw9dgcmhmnWWGrV9lcSti+ohNCtNTu6xJ465ZxlOisuXs5d49mOFu0vA1\nGOau/uUKXNKiKabN2yI9bh8AoPTmEc7+2doIAHrbSv6mc7BxVfhc+n99MlI0BxljjsnSkzBoYZ31\nQ9sUlm9H/sWtGClKQ7OmkvNsmjU1xkhRGvIvbkVh+Xa1wp0cmolJw9cw8m7S8DUYJZ4v55ZLHU6P\n28coEwH94uTaGWlfY9ayPcMv33WJT9i2g+pw+atiVv2dLll7kPva4vyLkv1c6owFuJTH+oqDbUeI\nvV1RoOHlv1K27j8KsbcrHGw7sop36/6jsmehgQNxv/QoshalQlzjb9LU8XEo3LUe6cmjedGoTdKT\nR2PHmoVIHFH9N17WolRsyJgD05YfKHQrTeuONQsRF85+7SIAxIUHYccayfhJ7O2KHWsWsrJT7bhV\n+dXELdc0bV0xH1mLqvewpo6Pw62TB+XKExc7qwNXHXXZJjRwIMNd1qJUTElQfWGgKtKnjpal/enz\nF2qHowi+tdbG0twMuZsk63Zz80vqcC0Pl/Lo//dlmu79mGeKijz6M97rk/Tk0SjctV6WnsWpk+pF\nW6eKwjOSMwutrSwYz7VdtjQhNHCgwjpfcVyyL6u0/JraYRecLmPVL+qScXO4XbxZGxMjQ2zImCNn\ns4rjuxAayDwX0bTlB9i6Yr7Cert1xXxGm21iZCjnNnHEsHozBmCLvY0V/F16oPDiDV7C2/bFWfi7\n9IC9jRWreLd9cVb2LMS7L+4eWonMmaPg79JD9nxmzGAcX5OCuQnBioISNHMTgrFt/lhZevxdemDb\n/LE6S0uId19kzhwl+z0zZjCu71mCC9skaxTP32C/1q63XQdc2PYJZsYMZoR3YPEUxA7y0EqcbJHa\nOT6oeu1a5sxRWJ8SB9MPjFX45AfLNi1xYLFkXcXnpys4++dSTkT9JPdtuPVkXkrp19eB8V6fzE0I\nxvE1KbL0LBofXi/rrxDQZn0qvHiDVXstVCYurXuvlRTjZgbYMnc0o57FB3lx6lvWp8Qx+id/lx7I\nnDkKH48J5S5egHSytoebcwBKr3Dbf6eMw8Xb4OYcgE7Wqtf5S+M9XFydn36uw5GffQ9p49fBzTlA\n9jwhLAUbF+ZjXKSw1r4P8xuFjBk5AAA35wBkzMhhpXFc5DxkzMiRpVGVX03cZszIwTC/UXLu6tI2\nNS5DFhdXu0s1hPhX7/lKG78O8yZmMe6ws+/sjH1rLiEhrHova0JYClanHWRoZuvOz3U4I+1p49dh\nZBDzUmO+tCuCrU4phu9Xj1EszJh7f9iGNS5ynkxrzXtUuZSZuliYnI208dXztwlhKfj8sxt11m8+\nMDO1xOo0yf66E2Xc99dxsYNrL8n39t72zP11A/6+J1X6Xp+Mi5yHjQvzZemZGpchuDaxPlG7fEjL\ntrbwcx2usC7tWyNZJ3z1tvr760qvFLLqdxsCLUxMsTA5W2HdXpicXWdbbfi+sZz/EP8EQY4xNMHB\nvhsCA0TIL+K2pqm1aSsEBkjau8AAEVqb8nemzpacnQgMEMHBXvUcr1T7lpydKt0BgImxMVKnT63T\nHRsSRkXj2sWz2JC5SvYsMECEDZmrcO3iWbgN6I+PbB2xcu16FaFoR5syFsydjd05m2V5NidlGu7e\nqPubRFjIMIa/DZmrkDxJ8Xq+7Vs+Y9hEGkftfKytJTBAhN05m7Fg7myGuz7OvXDt4lnMSZnGCPPI\ngd1IGBWtMMzRCdVjnw2Zq7Bp/Zo6y6au4gG42VOIWFm2xZEDuwEAhz4/WodredjmPQAE+EnWOXm4\nMddB+fv6MN7rkwVzZ+NE3hFZepZ++rHCtNRH2LQZBcWScw2trdvX6X93zma5+qQvwkKGKWyrrl2U\nzD2cK1V/3Ul+UTGr/osAxkxk3++ZGBtj+5bPGGVqdMIonMg70mDqnEMXG4g9XVBwhtt5a6YtP4D4\n7zUDYk8XXtaaSNl24DjEni5w6GKj0p1U+7YDx+sM08SoGWaOYX/noSriQgeh/MhWrP+4+nwqsacL\n1n88A+VHtsLVuTs6eQ7Hmm37WYXHpzZVpE9OwOIUSd8v9nRBQc5qpE9OYLgZLvZmpGvW2Gh8Wbgb\n5Uck6wdLK1T/LTpvcjwSw4cAAJ4+/1mhG03jUASbtNXlf8eKdJl2AFj/8Qx8tnAmo2w7O9qh/MhW\nzBpb3a/MGhuNQ59lIC50EGd3w8Xe2LEiXVaX1n88A5NHhXFIOXvtdfmvWZ93rEjnZD91w9164Ats\n3ncU8ybHw8SoGQBJfZg3OR6b9x3F1gNfMMKUugEA67bmdcYpLVuq9KlrNzZoqz7xrV0bdVITLD9s\ng0OfSb75Hy48w9k/lzLt79EPAODepyfjuZ97X8Z7fZI+OQEFOatl6VmcMl7j+tmY4dJO8IE261fB\nmYusxku6ZrjYG2f3fyZro6R1cO185n46E6NmyF6axsiPxPAhrPvw8fOWsdakaVz1BQc7WwT6eSP/\nxClewsveuReBft5wsLNV6U4ab/bOvbJnYUMH4eH1MmxYmYFAv+ozpWYnT0Dx4d1YkKr9/ZWasiB1\nGnZtWouk2Oo1uhtWZmDj6iVo3aqlQrfStO7atBbxI8M5xRc/Mhy7NknOyg3088auTWtZ2al23Kr8\nauKWa5pyslZhw8rqdbazkyfg7qVTcuWJi53VgauOumwTNnQQw92GlRmYOi4R6rIgdZos7U9/fKZ2\nOIrgW2ttrNqa4/NdkrMkc4/lcfbPpTwGDJSssfQYwByb+ft4Mt7rkwWp01B8eLcsPUsXzK4XbZ0q\nCk6eBgBYt2eu1dN22dKEsKGDFNb5q2fyAQDnLtR9XqYy8k+cYtUv6prPd21h5EdSbCTrvtbE2Ag5\nWavU9q/rcJXxzv/+97//1XywZ88eREZG4nLuK04BXb51FuPmi3F651PGIilCfV79XoU///qjzoUR\nXOkdbMg5fwlCaFA5Vg7ftukdLDmMj48wG0K+8WkPVXEIxU6vfq+C50hzZM3Pk1tsS9RfCksPYO7q\nONQaBvKGdDz5Yks8J3+l955i2PICPMwcCWODf2lFW2Oj6s2f+PM//0UrIwNewzVNyOacvwRB8APV\nP+XwbRvThGwA4CVMdbTxGX9DQUjlv+rNn+gwcScOT/eHaxf5xTeEdsktf4gxm89obTwLAJGRkbhf\n9hax/dlddi7l6+fnsfZUMJaH3IdBk8b1jXT83jZYH/Fc3zIIHTN+bxsAoLyvxU8vH2LB8f5wtYlB\neO+lrP2N39uGsx8+oPqrO/i2NZ91sL6WAyHpfvNXFaYf6ohJXrno3GaAvuU0CC5/fxg5F8Zq/Tvm\nN2VvOPm7dPUMoif542rRMxgZmmhFW2Pj5atK/PnXn2j5Ab/z4p1cDDjnL0EIFSrPyuHbNp1cJHMq\nfIQptHzr5GKAiKBELJixVt9SAAjLPi9fVcLJzww71hagr5OHvuU0CKYtiIVRq39i927tbDaKjIzE\n61//H1YvyuHk78LlM4gcI8KXZ583urGctVNTPLr6lrMfAJz91QcactoIeR59fx9ew+wRGZKIhamZ\nrP1ZOzXl7IdQD3XaqMYC37bhs/2jfGPXTgjJTi9fVcLBvQ12byhE/94e+pZD6Alrp6bYvXs3RowY\noZXw33nnHYzslQWntvIXYSvj/ovzWF8WgsXib9C0kc2za4u3f1XhP//vTxi+x9+hogAw5YgZVgfx\nu4mXqF9QGVAO37aZcsQMAHgJk/JNgpDs8PavKszK64TxLofQ0ZTmloXOlCNmWh0/Seet//vHa07+\nTp85Cx+/APz6048wMaExFB9UVlbhjz//QGtTfuet//leM875SxBCg8qxcvi2zT/fkxwSykeYDSHf\n+LSHqjiEYqfKyiq0aP0hThblw9OD9nPrg6iYUXjnH+9qbZ4bkHw/2zw9AsM92F82eu7mQwyeswmP\n9y+AcbOmWtPWmKh6/RZ//PUfmDY35DXc5oEp+O34El7DJBoHVHZ0B9+2bh4ouWSSjzAbQjng0x6q\n4hCKnapev4VVWDqOLUqCm2MHfcsh6uDgmRtIXL5X6/tT/vie2yHpZy5UwC8iCT/dPg8TI37HJkLn\nvXbdOdtLl7zXTjJmF7JGdbn/7ffo5jkESVHDkblojr7lqIXQy48+4ds2fNYFIeZbQ6rrQrJv5ctX\naN1tAIr2boJHf2d9yxEs+44WIGZSqtbHJ/nLf+Pk7+aDc0jdMBgHFz5Gs6Y0D8kHr99W4a///IHm\nhvzOQwZMb845fwOmNwcAzv4I4aJOOWgs8G0bPusP5Zv+EVIevH5bheFpVsgYcwyONm76ltNgWLo7\nEea2TbQ+75ez6mOED/Zj7efMxSsQRY3H8xslje5bUNMOffD2ofqX3BCEkKHyrTv4tnXTDn0AgJcw\nG1s5EFJ6K1++Qpvu3ijctR4e/XrpW06jZN+xIsROnaf171wvS7dz8nf22l0ETl6CJ4UbYNyM3/sZ\nhI6RawxnexENCyoDyuHbNkauMQDAS5j1Md+EpLnq9RtYiMbg+JoUuPfsqm85jY4DJy4i/uMNWh8P\nXDvGbd3/5S/PYnRaAM7t+7HR3afac3AzzvZqiPQcLNmTQrbQDd8/uY+hY7sjxD8Bs8eu0bcc3qF6\npRy+bcNn3aV8k9igrnopJDu9+r0KbuEfYuPCfPR2oP11+mDOilH4wEJ7++siIyPxv//8iZ1bN3Ly\nd/psKQaKg/DL00cwMW5cYzttUVlVhT/++BOtTfk9n+pdw5b4z6tfeA1vdMIorF+9nLcwCWHCd9lp\nSGijXgHgJUyh5ZvQ2gwh2aeyqgotza1xIu8IPN1d9S2HUMHIuNF4591/aX/d0/J5CAv0Ye3nzKVr\n8I+dgmeXC2Bi1Exr2hoTlS9f488//4Rpyw94Ddegixve3DvH2Q8Azv7qAw05bYQ897/7AQ6iSCSG\nD8Ha+dNY+zPo4sbZD6Ee6rRRjQW+bcNn+0f5xq6dEJKdKl++hllvfxTkrIZH3576ltMoiZ3+Mf5p\n3Frr4/odG1YjIngIaz+nSy/Ad1gkfn74JUyMjbSmTYg0MbXGXy8e6VsGQWgFKt+6g29bNzG1BgBe\nwmxs5UBI6a2seolWHRxQfHg3PF3761tOo2Rv7lFEj5miaP3ihH/wFUlve3cE+yXgwrVivoJs9Bi+\nb4wWJvwewHHrmwrMGUMXTRL1GyrHyhGybYSsTUgIzU4XrhUj2C8Bve1poSKhfVy7mCPWowtKbv1b\n31IaDMYG/0IrI343Dl/59iesjKZL5AhCH1D9U46QbSNkbfUJodmx5Na/EevRBa5dzPUthRAYndsM\ngKtNDO4+PaVvKTrl0c9XMcJ5hb5lEIRgKH90EADg2jFG7t34vW0wfm8bPPr5quzZm7+qUHLvMwBA\nx9a6nUij+qs7hGxrIWtThdB03316Cq42MejcRjjjVkI79HXyQERQIs5donlxvjAyNEHLD/idF79x\npwILU9bzGiZB6Asqz8oRsm30pa2TiwE6uRjgxp0K2bOXryqxda/kMB3nHsLYOC20vDt3qRgRQYno\n6+ShbymElunf2wORIYk4U1akbyk65fqtCmSkZelbBkHojcP5ewAAkcGJcu+snZrC2qkprt9i9p2b\nd60GAPTpKYy+syFDbZRyhGwbIWvjG03aCaHZ6UxZESJDEtG/t4e+pRAEg46mA+BiHYO7zxvXPLs2\nadrEGIbv8Xtw5ne/XkVYd2EcWEfoByoDyhGybYSsTZcIzQ53n5+Ci3UMOprS3DKhPp4e7hidlIDC\nosb1rVObmJgYo7Upv/PWl8orsDFrHa9hEoSuoXKsHCHbRsjahITQ7FRYVITRSQnw9KD93AQTN8cO\niPPvixNXv9a3lAaDcbOmMG1uyGuYl+89xpqJwbyGSTQOqOzoDiHbWsjahITQ7HTi6teI8+8LN8cO\n+pZC1GM8+jsjKWo4ik6f17cUnVJ+/UtkLZ6nbxmNlt2HjwMAEqOG61mJelD5UY6QbSNkbQ0Bodm3\n6PR5JEUNh0d/Z31LIdTA0cYNAf3icOXeCX1LaTA0a2qM5ob8zkPe+/4yJg1XfvE60TigcqAcIdtG\nyNoaC0LLgyv3TiCgXxwcbdz0LYXQAR79eiFxxDAUnb2obyk6peL6bWQtStW3DILQClS+dYeQbS1k\nbdpAaOktOnsRiSOGwaNfL31LIQSGe8+uiA/yQvGlm/qWolMu33mIzJmj9C2D0CNUBpQjZNsIWZsy\nhKa5+NJNxAd5wb1nV31LIQREbwd3hPgnoOxq49p/d+vrCqSNF84+CaLxkHdmLwAgRJSgZyX8Q/VK\nOUK2jZC18U3Pwc3Qc3Az3Pq6+nysV79XYeeRtQAAp27KzzQQmp3KrhYhxD8BvR1ofx3BxNPdFaMT\nRqGw+KS+pTQYTIyN0dqU3/OpyiuuYEPmKs7+3jVsiXcNW6K84orsWWVVFVaulZxb7TZAt/f3ELpH\n3bLTGBCybfSlrb60GULLu8LikxidMAqe7nTGMqEeHn17IjF8CIrPXdK3lAaDiVEzmLb8gNcwK27e\nwfqPZ/AaJkHUJ/YclcwHJIQPkXtn0MUNBl3cUHHzjuxZ5cvXWLNtPwDAtXd33YhsxFAbpRwh20bI\n2vhGk3ZCaHYqPncJieFD4NG3p76lEALD07U/kmIjUVhyRt9SdEr5levYsDJD3zIIQitQ+dYdQra1\nkLVpA6Glt7DkDJJiI+HpKoxv0wSTd/kMLHbYNAwabYv+PX1h+L4xn0ELlt7B7A9svZz7SotK2HHz\n3iVEDZ6kbxkEoRFUjpUjZNsIWZuQEJKdXv1ehTmrYvHFxq/0LYVoREwOcESPmfvhbd8Wxgb/0rcc\nnWCakM3a7Yst8VpUwo6KB88xztde3zIIolFC9U85QraNkLXVJ4Rkx6o3fyJp02lcXxqmbymEQPG1\nm4y5R3uiq7kXDJo0jm+k3/5cAe8uY/UtgyD0zvi9bWT/FtlNhUVzOzk3Y9x2YsO5kVh+IkDunb2F\nL7qae2lVY22o/uoOIdtayNpUISTdb/6qwtYLo/HJkGv6lkLoiNHRM+AxrBPc+vrCyNBE33J0QicX\nA9Zuvyl7o0Ul7Lj25UXERUzWtwyC4AUqz8oRsm30pW3D0kMYMzMEoUnyh9F4uYjh1tdX55oUIaS8\ne/mqElPTo3Hm8Df6lkLoiHGjZsJF3BEeLn6NZix35eYFJEZN0bcMgtA51k5NZf+ekJAK204Ocm62\nrMpFwtRgDIuVvwTJ200MDxc/rWokqI1ShZBtI2RtfKNJOyEkO718VYlJs6NRlndf31IIQiE+nSZh\nQZETurbxQtNGMs8+5YgZa7erg55pUQk7Hv1aAU8bYczLEPqByoByhGwbIWvTJUKyw9u/qrDjyhik\n+13VtxSiATBr5nRY23SByM8PJiaNYwz1z/easXb73z9ea1EJOy5cvITkKcLYJ0gQ6kLlWDlCto2Q\ntQkJIdmpsrIKI0bG4tGDe/qWQgiU5FBPdBuVgYFOnWHcrGndHhoAzQNTWLv97fgSLSphR/lX32HC\nUPm5BIKoCyo7ukPIthayNiEhJDtVvX6L+KV7cHtbqr6lEA2AmePjYdNPBD/PATAxYn+eYX3m4pUb\nmJIYrW8ZjY732lVfHJA6MREOtp30qEZ9qPwoR8i2EbK2hoCQ7Fv58hVGTpyFBxcL9S2F0IAw72TE\nLOyGXl0GolnTxjEPGTC9OWu3+ct/06ISdtz9rhzD3CfoWwahZ6gcKEfIthGytsaCkPLg9dsqLNkV\nj+1pt/UthdAhM8fGoKPrEPi592s034IuXL2JKQmR+pZBEFqByrfuELKthaxNGwgpvZUvXyF6chru\nlx7VtxRCoEyLCkTXkGT49nWEcTP256fVZy7e+gaTwv31LYPQI1QGlCNk2whZmzKEpLnq9RuMmv8Z\n7h5aqW8phACJC5mOgPgucHHyazT3qd746hJGBgljh9mnzgAAIABJREFUnwTROOg5uHrPZ0JYCjpZ\nC+M+GT6heqUcIdtGyNr4ZnXaQUxZOBwxMzzl3rk5B8DFSfn5WEKy06vfq5C6LBb52bS/jlBMyrQp\n+MjWESJfH5gYN46x3buGLVm7/c+rX7SohB1ll8qRPGk8Z39HDuxGUGgkXLzk26vAABFEvj58yCME\njLplpzEgZNvoS1t9aTOElHeVVVWIjE3Et1/d1LcUop4zY3QUOnkOh69bX5gYsT8Dqj5j0IX9ftY3\n985pUQk7Ll67jcmj6G5dovFRs67OGhsNhy42cm4OfZaBkLGpcA+TP49S7OkCX7e+WtVIUBulCiHb\nRsja+EaTdkJIdqp8+RrR0xbgm9MH9S2FECgpk8ehQw8XiLw9YGJspG85OuFCxRVMHZeobxkEoRWo\nfOsOIdtayNq0gZDSW1n1ElFJk/Dwepm+pRBK+AefgZm1ssSeFRdxsiyXz2AJHokaLIwFMAShCVSO\nlSNk2whZm5AQkp1OluViz4qLMGtlqW8pRCOibQtDnEkfiqOXH+lbCqGEcb4NbyMEQdQXqP4pR8i2\nEbK2+oSQ7Hj08iOcSR+Kti0ax6FoBHdavG+B2f6ncO3xMX1L0RneXYRx+TFB6Bt7C18AwLAe8zHI\nYZZSN5O8ciGymyp75moTg7j+GxHTbz0Mmuh2MyDVX90hZFsLWZsqhKT72uNjmO1/Ci3et9C3FEJH\nmLexxLHt5Sg4RfPiQiUuYrK+JRAEb1B5Vo6QbaMvbV4uYuxYW4BxsdV/k0QEJWLVgh1YNi8bRoYm\netFVGyHlXcGpXBzbXg7zNjQv3lgwN7NE/t4K5J1oPGO5xKgp+pZAEHrB200MAJg9dTGmjU1X6mb3\nhkJMSKi+bDsyJBFrP92BVZ9sFUzf2ZChNko5QraNkLXxjSbthJDslHciF/l7K2BuRuNeQph8YGCB\nGZ4luP6k8cyz1zc8bYQzL0PoByoDyhGybYSsTZcIyQ7XnxzDDM8SfGBAc8uE5lhZWuLa5Us4eKjx\nfOusbyRPEc4+QYJQFyrHyhGybYSsTUgIyU4HD+Xi2uVLsLKk73eEYtqaNsf5zCn4/PyX+pZCKGHC\nUPaHsxNETajs6A4h21rI2oSEkOz0+fkvcT5zCtqaNte3FKIBYGluhsuFB3DoeLG+peiMKYnR+pbQ\nKBH7uAMAlqQlY/50YVwSpg5UfpQjZNsIWVtDQEj2PXS8GJcLD8DS3EzfUggNMG3eFuuSz6P05uf6\nlkIoYZj7BH1LIAQAlQPlCNk2QtbWWBBSHpTe/Bzrks/DtHlbfUshdIiluRkqju9Cbt5JfUvRGVMS\nIvUtgSC0BpVv3SFkWwtZmzYQUnpz806i4vgu+hZFKMWyTUtc2PYJDp8q17cUnTEp3F/fEgg9Q2VA\nOUK2jZC1KUNImg+fKseFbZ/Ask1LfUshBIiZqSX2rbmE4tLGs/9uZJBw9kkQjQM35wAAwNS4DIyL\nnKdnNdqB6pVyhGwbIWvjGzfnAGxcmI+EsBTZsxD/BGTMyMHC5GwYvq/83gsh2am4NBf71lyCmSnt\nryMUY2XZFtcunsXB3CP6lkIoIXmSeuuTAwNEOJF3BHNSpsmejU4Yhd05m7F9y2cwMdbt/T2E7lG3\n7DQGhGwbfWmrL22GkPLuYO4RXLt4FlaWtEaJ0AzLD9ug/MhW5Bac0rcUQgmTR4XpWwJB6AWxpwsA\nYHHKeKRPTlDqpiBnNWaNrd6DlRg+BDtWpCN7aRpMjJrpRGtjhtoo5QjZNkLWxjeatBNCslNuwSmU\nH9kKyw/b6FsKIVCs2prj6pl8HDqap28pOmPquER9SyAIrUHlW3cI2dZC1qYNhJTeQ0fzcPVMPqza\nmutbCqGEd/73v//9r+aDPXv2IDIyEpdzX+lLE0EQBEEQBKFFCksPYO7qONQaBvKGdDz5Yku8VsIn\nCIIgCIIgGje55Q8xZvMZrY1nASAyMhL3y94itv9nWouDIAiCIAiCUM3l7w8j58JYrX/H/KbsjVbC\nJwiCIAiCaMxMWxALo1b/xO7du7USfmRkJF7/+v+welGOVsInCIIgCIIgiIaKtVNT7N69GyNGjNBK\n+O+88w5G9sqCU9thWgmfIAiCIAhC10w5YqbV8ZN03vq/f7zWSvgEQRAEQRAEe6JiRuGdf7yrtXlu\nQPL9bPP0CAz36K61OAiCIAiCIAh5Dp65gcTle7W+P+WP729oJXyCIAiCIBoe+44WIGZ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CHEW+jEjV0cvxFGek4snT+dSDPrPszd3xqApT1zAPh6VyWd6TldfuFo5nZ2/zKbepU608Sq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\nGNTJAQCQczvmZcomIiIiojpa7O8BALAYOko0Ln8t3w8Ax1IiAACjv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t8U5QZPD8TiEIlo\nzGXNQcRI85Ued8zyMNHfl0ruw3d/ClzWHBRyquZXzbmvP1RjP777U0T9yPvz3Z9Sr74by427v2C4\n906ELBiLrzv+s1ZzthyWorWlJ8YsD0PIgrGw0e8j2m82oDsAoPzpc9G4/PWLvRIREVHtSU5kwNTe\nHTv3V35W5bd5F7TM7JB+OluU0zKzg+fKANHYuOkLEXk0CVVJTmTA2mWm6O+C4mtYti4I46ZX/tZO\n1fyquQmzvGvsZ9m6IFE/8v6WrQuqV9+N5Y/bF2Ex1KDe86/fug0A2LdZvF65Lse9fus2TO3dsW/z\navTu3qXetRAREb3tzl1Ogtf24Ug4EyqMhZ9Yi2kBg5F/QyrKTQsYjOBji0Vj/vtdkHExRulxfULH\niP6+de8S9iX5wn9/5feDquZXza076F5jP/uSfEX9yPvbl+Rbr75fhfwbUoSfWAsr/akqc7EZW2A+\nrzV8QsfA0zEEBn1tXlGFREREpIwkNRNmjh7YdTBWGPMLCsUAS0eknzkvyg2wdMRCv02iMaeZixEZ\nf1zpcW3c54n+Lii+Dp+AHXCaWXktpmp+1dzEuctq7McnYIeoH3l/PgE76tV3Y3l+8xwsjPUaPKvM\n9ZI7AIC9G1cKYxeLrgIAPm7dCqGH4tC0kzaadtJG6KE4lD2pqPe5iIiIXqXErAuwnOmPkLiTwtia\nPUcxaML3yMi7LMoNmvA9vIMOicYmLNuG6FTF37EkZl2A3cJA0d+FN+5gRXAMJizbJuRUza+ac12x\nQyFX1YrgGFE/8v5WBIs/c6pt343lxo8PYDnTH7uXTUGvzh2qza3ZcxQAoNG8mWi8zd80RPvlnmTu\nwTDdb+pcT0beZazZcxQedjWvQSQiInpbSLMTMGmxOaITK9edBUf4Y8xMHeQUZIhyY2bqYEOol2jM\na60zkjMV18tJsxMwa+Uo0d/XSgqx9cByeK11FnKq5lfNLQ6oeb3d1gPLRf3I+9t6QLzerrZ9N7Z9\ncZugObw5Zq0cBb/5YTDVE6+L0x8gW5NX8bt4bZ389Yv1V+f23esAAL/5YQ1QMRERETW2+IQkfGth\nhR3Bu4UxX//10BxogLSMTFFOc6ABFixaIhpzcHZDRHQsqopPSIKVnYPo74LCS1i6YhUcnN2EnKr5\nVXPjXafU2M/SFatE/cj7W7piVb36biz/qXgES3OzGnPyTFm5+PpM/vrF+uuSrSotIxO+/usx02Ny\ntRkiIiIidZKkZWGY8yzsOnREGFu9bS+0rSYi/WyeKKdtNREL/YNEY05zfRAlSVV6XNspXqK/C67c\ngM/GYDjNrbwnq6r5VXMuC1Yq5Kry2Rgs6kfen89G8edvte27sW3cHYFm3fRhO8ULe9cvxSgLY5X5\n9LN5WL1tL6aNV7wvV36x7PPDj1trIDTyGJp100ezbvoIjTyGsidPG6wGIiIiolchPjkVJtYO2Bl2\nQBhbFbAF/QzNkZZ5WpTrZ2iOBUtXicYc3Wcg4vAxpccd6egq+rugqBhL/dbD0X2GkFM1v2rOeers\nGvtZ6rde1I+8v6V+6+vVd2PbsHUXPmzTESMdXbF/5yaMHvmdQubP0hJYmtZ87SjPlJU/EY3LX7/Y\na11rIKLXw3vqLoCI1GeOnx0A4NiOYuTEVCAnpgKhfrIFDalnDivkQv1OCrljO4oBAN4bnBWOW3Q9\nF2n77iEnpgJbl8luMDt27kAAUBhXNv/w8TChpmM7iuFi64nM8wnIKaz+B4U5hRkIifaHi62ncI60\nfffgYuuJkGh/XP+hsM59Kz3P//KqtoZS8Xs5AvcsgoutJ0wG29Z6XteOfTBz/Cro9TeH9wZnpJyK\nbpDjEhEREamL42bZjUsurBmN0mAXlAa7IHGR7MOmo+dLFHKJi74TchfWjAYAuO9MUzhuXkkpbm4e\nh9JgF8TOGwYAMPSRXQ9WHVc2f5/0qlDThTWjMceyL5Lz7yDzyr1qe8m8cg8B8Rcxx7KvcI6bm8dh\njmVfBMRfRNGPv9a5b2XkeVWbKuXP/o2lkdmYY9kXIwd8qTL7ol4dPoGP3QCY9ukA951pOJx9q9Zz\niYiIiBrLduk4AMCKEXkIsn+IIPuHmPdtAgDgwp2jCrl53yYIuRUjZD9GDD09SeG4PzzKwzrb6wiy\nf4gZQ2Q3kFmVOAQAFMaVzc+6uU+oacWIPJj1nI3Cuym4+rD6B8pffXgKSUUbYNZztnCOdbbXYdZz\nNpKKNuDu46I6962MPK9qq41ViUNwMHuu8Ppg9lzsOeOBZ39W/xDUtKs70Ku9Cbq2Haywr/Cu7MFg\nxwpWI/T0JOF17IVlOJA9W+VxiYiI6O03eYHsu8/02Gu4lvUM17KeIXKn7DvmxJOxCrnInRlCLj32\nGgBg9lInheMWXD6P3OQHuJb1DHs3JQIAho/XBgCFcWXzI4/sFmpKj72Gqc4LcTJLgrO56dX2cjY3\nHVvDVmOq80LhHLnJDzDVeSG2hq3GlRsFde5bGXle1abKEF0L7N2UiGMpEeii20zYjqVEYO+mRAzR\ntRCy35nIPquVnq182OuTijKEhAeqPAcRERHR28Z1tuxBXlmS6yjJfY6S3OeIDZM9hCzhRIxCLjZM\nKuSyJLJFyDMWKV53Xiw6j4KMhyjJfY4D22UPpTW3HwAACuPK5ocfDhVqypJcxzRXL6RKJTidk15t\nL6dz0rEl2A/TXL2EcxRkPMQ0Vy9sCfZD8bXK69ba9q2MPK9qU+VJRRl8AxdimqsXvjO1U5mtKvTg\nZhjrW2CQlmGd5hERERGp266zsmu+paa5CLR6gECrB5ilL1vHc/HeMYXcLH2JkFtqmgsA2Hte8caL\nt3+7gNUW1xBo9QAeurK1KmvTZIuQq44rm3/6h/1CTUtNc2HSdTaKHqTgemn135NfLz2FlKsbYNJ1\ntnCO1RbXYNJ1NlKubsDdssrvyWvbtzLyvKqtofRsZwIP3Wjk/hSLWXHthC33p1h46EajZzs+nIWI\niOhNNcJadnPBkhtX8N8/nuK/fzxFllS2HiUqJlYhlyVNE3IlN64AAMaOc1Y4bnbOefz6833894+n\nOJEs+x2gppYOACiMK5u/K2S3UFPJjSvw9vJEvCQBaenVr9tOS8+Ar58/vL08hXP8+vN9eHt5wtfP\nH/kFleu2a9u3MvK8qq2haWrpYNLUacLrSVOnwWmCC8rKlP8G8Zu+fbDW3w+WFuYYO84ZEZGKD/Qh\nIiKid8uY5WEAgEu7vfA43h+P4/1xfJ0HACDuVIFC7vg6DyF3abfsZt8uaw4qHDf32o+4E+GDx/H+\nOOrrDgAYPF32G7uq48rmhyWdE2q6tNsL88cYIym7GNL8m9X2Is2/ibWHUjF/jLFwjjsRPpg/xhhr\nD6XiUsn9OvetjDyvalOl/OlzLA6Jx/wxxrDR76My+6LeX7bHShcLmA3oDpc1BxEjzRftH2Uoe8jy\n8dyronNtPiyt9TmIiIhIkbXLTADAjTNJ+OP2Rfxx+yKkcXsBADGS4wo5adxeIXfjjOx3buOmL1Q4\nbs7FS/j50in8cfsiksN3AgC0zGS/Cas6rmx+SHisUNONM0nwmu4GyYkMpJ/OrraX9NPZ8Nu8C17T\n3YRz/HzpFLymu8Fv8y4UFF+rc9/KyPOqtsZ2IDYeFkMNYGqkuL63NsqeVMDTNwBe091gN7zmhw8S\nERG9y3xCxwAA9iy+hIR1j5Gw7jECpsuuFzLz4xRyAdOPC7k9iy8BAPz3K95H7tqdXEStvIOEdY/h\nN1l2X5FpAbL/26uOK5ufdDZMqGnP4kuwHzof5y4nIf9G9Z+V5N+QIvzEWtgPnS+cI2rlHdgPnY/w\nE2tx696lOvetjDyvaquLOOlWaPcwQ5/O+ipzndr3hut3K6Hdwwz++12QcVH1+gciIiJqXDbu8wAA\n1zOP4PnNc3h+8xyk0SEAgJiEVIWcNDpEyF3PlD3w1mnmYoXjns+/jIcXU/H85jkk7Zc96HeApSMA\nKIwrmx8acUSo6XrmEXh5TIQkNRPpZ85X20v6mfPwCwqFl8dE4RwPL6bCy2Mi/IJCUfC/h9rWpW9l\n5HlV2+vk4OFEWBjrwdRgoMK+AZaOmOrtJ7ye6u2HiXOXoexJwz0bhIiIqLHYLZT9BuhydACeZO7B\nk8w9OLl9CQDgcFqOQu7k9iVC7nJ0AABgwrJtCsc9X3wLd5O240nmHsRv9AQADJrwPQAojCubv/tY\nhlDT5egALBg/HIlZF5CRd7naXjLyLmPNnqNYMH64cI67SduxYPxwrNlzFIU37tS5b2XkeVWbKuVP\nn2FR0CEsGD8ctsY6KrOvSlBkCobpfgMDzR7qLoWIiOiVmbVStu4sIeQK8o4+Rd7Rp9izVrbu7HhW\nrEJuz9o0IZcQIltv57XWWeG4l66fh/TQfeQdfYodK2Xr6sbMlP2fX3Vc2fzYlN1CTQkhV+A62hPS\n7ATkFKh4TmpBBoIj/GXZ/51Deug+XEd7IjjCH9dKKtfb1bZvZeR5VVttdfuyD2ZP9IP+AHN4rXVG\ncqZ4XdwwA9lvr7Jyk4Wxit/Lsfdw7e8TK0kPh/4Ac+j2M631HCIiIlIfKzsHAMCt4nz8p+IR/lPx\nCFknZdcC0YePKOSyTiYLuVvFsnVZDs5uCsfNyc3Do3sl+E/FIxyXyH4HpDnQAAAUxpXNDw7bJ9R0\nqzgf3p5zEZ+QhLSMzGp7ScvIhK//enh7zhXO8eheCbw958LXfz0KCit/t1TbvpWR51VtDcXeTnbf\n1qSUE8JYWXk5AjZuealsVRuDtsPS3AxGBnovWzIRERFRo7CdIrsnw7W0KDy7IsWzK1JkRMi+74xN\nSlPIZURsE3LX0mSfgTnN9VE47vmCYjzIScSzK1Ikhsk+A9O2mggACuPK5u+OjBdqupYWhYVTnCBJ\ny0L62bxqe0k/m4fV2/Zi4RQn4RwPchKxcIoTVm/bi4IrN+rctzLyvKqttvp0/wqrPT1gYaQLp7k+\niJKo/o3alj1RsDDShaGOZrUZbauJ8FiyVnjtsWQtXBasRNkT5Z931rUGIiIioldhpKMrAODmhSz8\nWVqCP0tLcCpR9v1v9NEEhdypxFghd/NCFgDA0X2GwnFz8i7il5sF+LO0BCmxBwAA/QzNAUBhXNn8\nkH3hQk03L2Rh0ZxpiE9ORVrm6Wp7Scs8jVUBW7BozjThHL/cLMCiOdOwKmALCoqK69y3MvK8qq22\n+vbqiTU+i2BpagxH9xmIOKz6mQOqjLEZAQBISk0XxsrKnyAgaOcrq4GIGtd76i6AiNRHr7/sQurE\n6cPIKcxAxe/l6NVlAHJiKrDQvfKHcTkxFciJqUD7th1x/YdCZJ5PQNzxsGqPa2c+GS0+0gAAaPUy\nEMYdR8xUOl7VrPGr0O7vnwMA2v39c1h96wwASD1zuNo5uZekCudo8ZEGHEfIbnR2rqDyQ8Pa9q1u\n+49sROb5BNiZKz7QShWtXgZwHD4DAV6R8J68Gd4bnJFTWPmDz/oel4iIiEhdTPt0AAAcPV+CzCv3\nUP7s3+j/5T9QGuyCtY66Qq402AWlwS74V5uWKPrxVyTn38E+6dXqDgs34x7QaPZXAIBet0+FcQ/T\nXkrHq/KxG4DPPm4BAPjs4xYYp99VqLM6p67cVziHRrO/wsO0FwAgo/hunftuDEHJhUjOvwM34//P\n3r3H1ZTv/wN/nXNmzjC6iUgpohpJuaRcUkpUKkkJyf1Och0Sxu0gYdyScUkj406ulWpESih3xmWQ\nhjBMzNFtysyZ3/f3x5q9t93eu/ZO22a8no9Hj0d7fS7r/V77TNb5fNZnfVRb4OvUwggT3G2wI7QH\nVg3pgjGbTyHzzlM1RUlERESkHBtjYdPzK4+O4sfnZ1D2RxHM6tshOug5BtgvF9eLDnqO6KDnqK/V\nBE9e3cSNJ6nIuv+dwn5dLEeh9qfCWOQXDSUbFXS3miD3eEX+bRdA/3NjAID+58ZwNB8sjlORu8/P\nyJyj9qc66G41AQBw55nk4Udl81aHg1cWAAC+7JEkvq7RQc8xovMm3HiSiltPT8ptl/fiEm48SYVj\n88FVnmNZn5tK90tEREQfh26O3gCA5JMHcf5SOopLCtHG2gF3s8qwcMY6cb27WWW4m1UGEyMz3Ll/\nHSezErHvaKzCfgf3nQBtLV0AQEc7F/HxkUFT5B6vaFZoBIwaCvPfRg1N0M9XWJhz/KTiF+qcv3xa\n5hzaWroYGTQFAHD2gmT+W9m81eXW3Ws4mZUodexkViIePXkgdcy5ozu6OXpj6vwhsHSsDUvH2rDz\nMFR7fERERETvGzdn4f4t8UQ8zl4Q7t/a2jgg71I5FodHievlXSpH3qVymBqb4fbd60jLSMTuQ4rv\nW4f1l9y3drZ3ER8fM3iK3OMVzZmyDEaGf923GpogqI9w35p0QvHGY+cunpY5h7aWLsYMFu5bz+RI\nxuuUzVsdNn+3BmkZiRjWf4JK7a7cyEFaRqL4WhARERF9SKwNhfniq0+O4l7BGZT/UYSm+nZY4/cM\nga0jxfXW+D3DGr9nqF+nCZ4U3sTNZ6k499MOhf06NxuJWn/NVVsYSObDu5mPl3u8Ir9W81G3tjBP\nXre2MTo1FTa8u/pU8QLkey+yZM5R61MddDMfDwC4WyCZJ1c27/fB48IbuPksVerYzWepeFH6k2YC\nIiIiohrh4y2sXz4Qfwin0k+jsLAIHTs44M/XpdgQtVZc78/XpfjzdSmaNTPDtes3kJCYhJit3yrs\nd2LIeOjqCvdCri6S9dnTp06Re7yiFZFLYWoijP+Zmphg1MjhAID98YrnrU+ln5Y5h66uDqZPFcb/\n0k5K5q2VzVvTZoQJL6/Myjgl/g7+fF2KXd9tQ0JiEpJTUuS2c3XpimlTJuHIwf3YtGE9Bg4eJr4+\nRERE9HHydLACABw+cx0Z13JRVFoO+xameJUQiVUhfcT1XiVE4lVCJJoa6uOHvJ+RnHMbcSk5Cvsd\n49MZOnVqAQCcWzcXHw/t4yz3eEWLR/qgsYEeAKCxgR6GejiI41Qk80auzDl06tRCaB9nAED61Xsq\n560OUYcykJxzG2N8OqvUzrl1c0zs44w984ZhbWgARi7fhYxrueLyHnZfwNPBCiOX74KeTxj0fMJg\n2n9+TYdPRET00fHuLoxVxSemIv1sDgqLS9ChrS1eP7yKqCVzxPVeP7yK1w+voplpY1y/fReJJ05j\n6y7Fz6yFDA+CrrbwnheXzg7i41PHDpV7vKLIOdNgYiSsYzAxMsTIgQF/xfm9wjbpZy/InENXWwtT\nxw4FAJw8c17lvN9HC1ZGIyJqCxZ8GSLOU1WrN8Uh8cRphAwPquHoiIiI/n46tPQEAGReO4xr9zNQ\nWl6EFk3skbTyFSYGrBLXS1r5CkkrX8GwXlM8ePoDsm8lI/l8nMJ+e3UZgzq1hLm91ubO4uP+LqFy\nj1c0qtdiGOg1BgAY6DWGZ8eh4jgVuX4/U+YcdWrpwN8lFABw9V66ynmr252HF5B9KxmeHYdVWbe1\nuTP8u07E/BF7MClwLSJ3jMS1+8pvbEdEREQ1y9vNCQAQn5SG9HMXUVhcAoe2rVCem42o/4SJ65Xn\nZqM8Nxtmpsa4fvseEtMyEbvniMJ+JwztJxlf6tRefHzK6GC5xytaFj5JatxpxIDe4jgVOX3uksw5\ndLW1MGV0MADgZJZkXlHZvD90C1dtQkR0LOZPHSs1RjUrQnh3SsaBreLvtjw3G9vXLkZiWiZSTp/T\nVMhERERK6+nYFgBw6FQOTl++haLSMthbN0dxZhzWTB8qrlecGYfizDg0NTLAjfuPcDzrCrYdS1fY\n77iAHtCpUxsA0LWdZD+ByUFeco9XtDRkAEwa1gMAmDSsh2G9XP6K84LCNhmXb8ucQ6dObUwOEp4p\nP3Xxpsp5q8Pa3Uk4nnUF4wJ6qPU8yrpwMxfHs65geC/Fz/0TERH9HTk7CPcI32cdwoXrf+0X+oUD\nLh8txezxknVnl4+W4vLRUhgbmuFu3g1k5CThYKri9XYDfMZL9kO1lfz7OqTPFLnHK5o6fCkMDf7a\nJ9XABP7uw/+KU/F6O9E+oG+eQ+tzHQzpI6y3y74mWW+nbN7qZm/bFYP9JmHN3P2YG7Ie4SuG4cJ1\nybo4RzsPODt4IXzFMLTzrYN2vnXgPKCR0v1v2LkIMXsjMSF4nviaEBER0fvNx0t4fufAoSM4dToT\nhUVF6ODQHv8reYnoNSvF9f5X8hL/K3kJM7OmuH7jByQkJWPrtu0K+w0ZNwa6On+996Crk/j4tMkT\n5R6vaPnSRTA1EZ5bMjVpjJHDhojjVCQ9I1PmHLo6Opg2eSIA4MQpyX2Psnlrmqd7d/h4eSJ42Gh8\nolUPn2jVQz0js7eu+6bsnItISErGqGFV7wtFREREpCnersKe7QeT05F+/jIKi0vh0NoaZXcysG7B\ndHG9sjsZKLuTAbPGRrh+5z4ST2Uhdp/id5tOGBQAXe06AACXju3Ex6eMCJJ7vKKIsAkwadQQAGDS\nqCFG9Ov1V5ynFLY5nX1Z5hy62nUwZYSwBu/UX8+RqZK3url0bIfJw/vjwDcRiF40A0OmL0T6+cty\n6+Zcu4nEU1kY3s9HbvmsyGgAwOm934i/r7I7Gdj+9XwknspCasZ5ue1UiYGIiIjoXfHxcAMAxB9N\nxKnMsygsKkaH9nmKNeoAACAASURBVG3xR0EeolcsFtf7oyAPfxTkwaypKa7fvI2ElDRs/W63wn5D\nRg+Dro42AMDVSfJ+rWkhY+Qer2j5wjkwbWwEADBtbISRg4V7zQNHkxS2ST9zTuYcujramBYyBgCQ\ndvqMynmrm6tTZ0ydMBqHdsRg46oIDBozCacyz1arL083F/h4uGHQmEn41MAMnxqYoX5z23caAxGp\n1yeaDoCINGd80FfIvJiEtXGzAQBO7b0Q5BMCexvZBwo37v4Pth5QbiMjfV0DuceVfXDO1Mhc6rNh\nfeEBxviUGMwas0ZuG1FsroON5JavjZuNQb6TAKiWd0X2AVW/cOxCfEmVdaqSeuYAth6IRGzESYXX\nUxndHQOwZGModidEw96ma431S0RERPQuhfvZIeXaI8zfJ7xYxKO1Kcb2sIZTC9l7v4jDl7Aq4apS\n/dbXri33uE7tfyvVvnlDXanPjfWFe8Vt6XewYpCj3Dai2JqHfie3fP6+HExwtwGgWt4VGYzaWmWd\ngpiRco8fynmAVQlXcXx2L4XXSBm97c0wbfsZbPr+plIxExEREalLL9tZuPEkFQevLAAA2Bi7w/WL\nsfiioeyG8seuL0PyzdVK9atdq77c47U/VW4ctIG29IZa+p8LG9hn3o/DAPvlctuIYvvygIXc8oNX\nFsCthbCBvSp5VxSyu2GVdaKDnqtcZtfED7Fnx+LCw3jYNfGTKc/O2wsAMG/QsdJzd7eaIHWdWxp1\nAwCF/RIREdHHYcqYeTiZlYhl62cBALo5emNY/4noaOciU3fNloXYsG2ZUv3Wqyt/XlVbS1fu8Yqa\nmkjfuxk1FOa/dx/egoUz1sltI4rNzsNQbvmy9bMwImgyANXyrsjSserxv7tZZQrLEk/sx7L1s7B6\n4XZ4dw+UOj51/hDU+VxbfFxbSxdLwr9BWuYxzI0MQTdHb/Ry7w/v7oFKfxdEREREfwfTx89HWkYi\nlq4W7t/cnL0xYmAoOtu7yNT9+puFWB8ToVS/9fTf7r7VrEmF+1ZD4b5154EtWBweJbeNKDbbrvLH\nE5eunoXRg4QXT6qSt0xsdrWqrJN3qVzu8WMp+7A+JgIHt2UovEaKxCcI8/oO7aoeUyUiIiJ633hZ\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p5Rm0tXSVOk9VectTWVlNOJmVKP593My+OJmVKJPTo8fCfXBDAyO1xkJERET0vrGytIWV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A8SkXZ69uwZ5s+bj02bN+OjClboWm8ymlXvAT0p6zikfvdfxuDMrd8RfGc/WreyxfoNazV6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\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yE757TaedUQs8e/ZMafLz8/PRzlJ968V9hwMAAGam9LVe+7aSr/WMDOWfU7bvKz5JSsHN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\nHKFuYoN9W9eK2SX5R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nmVjWL3YCkNhd0a4pmJ96yuR5G8BxvNOVOxw0wb8V52CNqzAVbu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R/BGav0oesuy38o6obFhfKNWFxaiN2rC6DUgJ2qd9WVOJYdDxezxvNNKEr7LmbE5+PHT3X0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\n/LgVNkyDfWMHdsd8k6HYkHYTG9Ju0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GOVXTFH/xDM1JjrI1Fx0twIx1Hr+XWq9jSE46j1\nmDz010aRuSFkWnWGm/b/s3ffcVFcax/Af5hELBGTGzXJNfe9rzFFTCxJ3hhjEiFqNLYg3YggomAE\nQVApi4YmiqCgIkVAEEGJqFhCExtgQQRFARXsuZZrTSIYW4p5/xhnYZnZZWbdZRb2+X4+88cuz5zz\nnGdQdmfOnIlVGFO/nqPg9FkCHIfESlZnQgghDeTzzobwzDv72ArzHBrNOxvrg/TQCiQHPJ13dlbJ\nvLOPrRDg0mjemUM0bEcqmXc2YQECXFLwjUmjeWcO0fCeEoOXu9A5F01YliZuztniWZkKx+8bk2mI\nmpuNaRPUn3enD8yHMjc9T/rqY87PLE0/RLRnwzUB70kjcSx5Pg7FMwvYHKo+z9umpemHSJE5YPRg\nZnGcaE9buFvyLxC1YMoYpMgc4DS2YYHHaE9brPL6Ft1felG9QT2D7i+9iEQfe4X8Rw9+DykyByT6\n2EuSkzIbg50V8nQa+xmyw92wYErLPxiYEEL0Uf9XmXNqH/2T+2DOAa9NgGXfZfLXw9/0hPdnh+A5\nmFmU++Kv/NfPB7w2AZP6xcO4O3P917LvMgz993e8sSN7+2JSv3gMfqPhHK5l32Ww6huJF9tLc97C\nqm8kLPsuk+dv3H0kLPsuw+i350uSjyYMeG0C3AblyOts3H0kJvWLh7kxd8Erotz495iFC2wGcr8r\nmfXrhohv3pS/nm3yBg54fIDdM/sDAA7/VMfbplm/boizehtfvfsyACDimzcxY8g/eWN9hv0LcVZv\nw/7jhpv2I755E8vMeqNb5xfUG5QOWmbWGxHfvCmvyVfvvoyIb97E/BHcB21L2SYhhDwL8y+ZRZXs\nRn/O+ZnViMFY5eMkf+0zxQzHf4hASWooAODg8VreNq1GDMbaIFeM/uwDAMAqHyd4TOR/gOL3zpZY\nG+SKaROGyd9b5eOEWL9p6P6ybi/Ka/RiJ6z5fobCWKdNGIaclX743tlS7XZj/aZhlY+TvM3Rn32A\nVT5OCPlOvQfYu0do7qFm+maUKTNPccLX3PUTxgyzRsi8hvUTZtr7IT+9CtuSmfUTyiv573MZM8wa\nkQFp+HIIc/9FyLxYTLXlXz/BY1ogIgPSMPGbhvtcQubFYqF3PF55WZpz5nGLtyjkP/EbZ6yNyofH\nNO79Iwu94xEyL1Ye++WQsQiZF4s5MxY+Uw4bf0wCAMlqIHX/hBDC+ub/mPuKbYdwH0Jo/vGbiHJo\nuE40Z+xAlIZaoiiAuR5ccvYGb5vmH7+JRBdTjBrA3JMc5fAZXEfyL3Qum/AhEl1M4WjScG9ulMNn\nWDHlc3Tr0kG9QbWg9bNGKIzV0aQPts4dDdkExQXljDq2R9y0oYJipTQn7ZDg2NYyJm0xH8Y8RMRu\nzFDOz6xGfIpVfg33iPg4TsDxjctQksYs5n7whLLvQJ9ibcgsjP6cqd8qv+nw+Jb/+uL3LtZYGzIL\n08wbFt1e5TcdsTJnnf8OxErethcAms13U8RchbpMMx+OnFX++N7FWus5aqP/7i8bYU3ATIU2R3/+\nIdaGzMKagJkK9TB6sRNiZc6c36eStDBYjWj5B4oTQlT7vD/zGWn4R99yfjZ0gCXcLVfKX08c7o0E\n76NY5cnMlTt5kf9v8NABlvCZlIxBxl8DANwtV8J8KP+9cpNHzofPpGSMHtxwHs7dciU8rFbhpRd1\n/7unh9UquFuulI91kPHXcLdcCcfRQWq3GeC4UaF+owc7YZHLj5g8Ur1rpKuy+M+98OncwUg+JtbE\n4d5Y5XkQQweof86vNbEcMwIAYG8xjvMzm3EjEbfIX/5a5uaE6j1bUJazAQBw4EgFb5s240YibWUo\nxg5nFpWPW+QPz+n8i4AHzvkOaStD4TzJQv5e3CJ/rA5bgO6vvKzeoJ6RzbiR2L8lRZ7T2OFfIG1l\nKFYt5N7XkZUYqTBW50kW2Lk+DoFzFOcpdH/lZaREBivEsu2mRAarNdauXV7E6rAFnGNUlrMBNuNa\nfvFzTRJaV0IIeRY0F0dRt84vYJXF2wr5f/Xuy4izehurLN5WOyeai6M7PnnbDADwhTH3ft7B75jD\naVjD+sdmH8/FUocjWDSJude95ir//byD3zGH29dJ+KAXc++o07DlGPMh//28Vp/K4PZ1Eob1c5S/\n5zRsOaaPWAmjjrp/n+mc8RsUxjqsnyNkFttg9alM4swadGpvhO9Gxet8nkS7LC2Y86GTeda3sLG2\nRVxsw/oWMr/5OFlZg/Kn3+32H+R/iI+NtS3S12Vg7Bjme2NcbAI8Z8/hjQ0KCEH6ugy4TG9YVyEu\nNgEJ8UnoLtH6FmJs3bJDYawu02egIG8PggK0vxaGq5u4tShsrG2xv+iQvNZjx4xD+roMrIqOa2ZP\n0tIsRjLfbe2+GcH5mfXXJogJaDiX5evyLU78uAalm5g5SgePVvO2af21CVLD/TDGhLkOFhMwGx4O\n/OeyAtwckBruh+nWDQ9DjgmYjbggT3T/x0vqDaoFWX9tgsL05fL8x5h8gtRwP6xcoHj+1ejFzogL\n8uTUs3RTLKy/NlGr7+7/eAnJi70Vas32n7zYW6F+YmKFmhWysvmgRoTWirVmc648d0J0geV45jPs\nZBvumpo2ZmMQF9Hw91g2eyZOHshH+e5tAID9h8t527QxG4P0uEiM/Yq5NzsuIgSeM6byxgb5eCA9\nLhIu9g3fmeIiQpCwbCG6d3tFvUHpoO7dXsHaVeEKdRn71ZdIj4vE2lXhao3V1Uf4uo/a6F+btqbG\nKeTqYj8RBZvWIshHe88M0iUTTJl1EyZ9/RnnZ1bDP0G0d8OaXT4O41GxfjEOJTMPMT1YeYa3Tavh\nnyAlYAZGD2HWZoj2ngJ321G8sQummSMlYAammZnK34v2noIYH0dJ5tx4LF0nKj4zzENhrNPMTJG9\n3BsLppnzxifvKALQ/PwcTRMzLqPOHRHj48g59oeSg2E1/BNOPBvL1mD0kIGI9p6C4BktM5dITP9W\nwz/B3vj58t+30UMGIiVgBpbP4X6/BqQ7XoS0RaYfK3/m4vBPrODd6JmLDuN9sH5xBZKDVT9zcfgn\nT5+5OJC5t81bwDMXzRo9c9F7SjR8JHrmYueORvBxjOGMOzn4EIZ/YqX1/peq8WzEHRp8NqHY/tla\nscd6yMDR8J4SjRnWig9Wf9moOxawz5xsFBswIwULnuGZk62VxdfM59vJ5l9zfmY9djhiQ7zlr/1m\nOqBq5wYc2c7cr3ig7ARvm9ZjhyMtMhBjv2Q+O8aGeGP2VO4zOAAgcPZ0pEUGwnmimfy92BBvxIf6\nSDZXaEt8mEL+zhPNkJ+6AoGzp3NirccOR3FmvDz/sV9+hrTIQEQH8a8Lren+hXALWKp2LoQQ0pxv\nxjDXIyZacp83aD7eBpFh8fLXc2b54/C+UyjMOwoAKDnCfz3SfLwNEqLXY9Rw5vx2ZFg8XJ29eGP9\n5gYhIXo9pti5yN+LDIvH8vAEdHul5a9HzpXxPxOKj1GXrlgensCpUWHeUZiP519TYN2GRADQ2NjM\nx9sgf+sBef1GDR+LhOj1WBoa08yempG+ZpvCsZ5i54KsDQXwmxukdq5GXboidnmq4HbbIstxT8+v\nW5txfsacX2/4fCyb/R1O7s9F+a6tAID9pUd527QxG4P02KUY+5UpACAuIhieMxx5Y4O83ZEeuxQu\n9g2f/+IigpGwdCG6d/uHOkN6ZjZmY7D/xwx5TmO/MkV67FKsCuM/n52YngkAzeabsHQh4iKC5XUZ\n+5Up4iKCEerP/39Wc7p26SJvkyWb/R3Kd22FjRn/vYpCcxXa/9roJQrH2sXeFgWZKQjy5n6PFjt+\nTeZKCGl7rKyYz5UODtz1fSba2iBhdcMcqAX+MtSePonjx5jrwsX7+T9XTrS1Qcb6dIwfx3wmSFgd\nh7le/M+xDgkOQsb6dHzn0vC5MmF1HJISEtCjR8ufK5rxnbhnTk+0tUHJwf3y/MePG4uM9emIi131\nTLHaoOn+xdSqa9euSEtdq/B78Z2LC/bsKkBIcJBCbI8e3TmxbK5pqWsl+b1oaRYjmbUYJpt9xfmZ\n9WhTxAY2/HvymzEJlTnJOLKF+X5zsFzJPLfRplgXIcMY08EAgNhAT8yeomSe26wpWBchw3Sbhnlu\nsYGeiA/20vn5Td3/8RJSwnwUxjrGdDDWRciQEubDyX/LqmCF2Ok2Y5G3JhwBs6Zw2gCgZP8AACAA\nSURBVNY0t+AVWu+DECIdK3Pm3IDDJO59Q7ZWFli9quG+ofm+c3H6RBkqDjP3De0/wL+Ggq2VBTak\nJmHcGOa60upVyzHHw403Nvh7f2xITcKM6Q1z9FavWo7E2JXo0V337xtKjF2J1auWy8c6bszXWL1q\nORaHKJ5P+M5d+HmArkZGWLcmXqGGM6ZPxe7c7Qj+3r+ZvfmJ7Z8dF2u+71xUHC6GrZUFJ15oDbTV\nPxGGrvcq6tqlM5IjFgi+3ir02qzYdoXo/srLnDbZ683JEYpra4iJFUPT16YJ0VcGf//9999SJ0FU\ns7OzA578hfVpqVKn0qq0a98RAPDk94cSZ8LVrn1HSfOSun8x1DmOGRszMdnBEdr67y0jIwN2dnYo\nSq7XSvttlek05mYFXayb6TQjSfOSun8x1D2OptOMsGHDBkyaxJ1kTwghfNi/tzeW0QOYNe21eXkA\n0GK1fW1ensb70labgObr4rrhBDoYm2LDhg0abZdlZ2eH+788wYpFqVppX9/1+oh5WMmlY4+eqY1n\n2b8l2wSebazNtU+fBwkhUmA/V0Zb3pQ6lVbFI4tZVLil6uaR9arG+9JGm9qiTr3TymbinS86aPVz\n5q/X/sTieWu10n5b9cH4zgCA49n3W6w/TfeljTbF9g+Iq6H/sql4uefzWvv3QAhpPezs7PD3EyBt\n3XqpU2lVXjBsBwD44/GTFutP031po02x/QPiaugwZTIM2kGrf78MDAwwdUg8Pv5f/XhQmba4ZjCL\nIsRNuvVMbTzL/i3Vptj+gWerCwCU/5SFtSUztTa/gxBCdI183llS65gnpCtMnZ/OV2qhupk6G2m8\nL220KbZ/QFwN9xzZjNA107T6d9rOzg5/XD+DNX7chb+I5nUdxdxYX1eg/o3ZXUd5PtP+Ldkm8Gxj\nFdsfXQ8nhOgiAwMDfNsvFgNfoxugn4Xv7tcBAOFfXX+mNp5l/5ZqU2z/wLPVBQBO3NiKH6rdtH7/\nz7XgT7XSPlHUM/AwADxTvXsGHtb48dJWm8CzjVWMWVnn0PH9kXRdnJBWgv37c+9gmtSptFpdPmce\nrNVSNezyuYPG+9JGm2L7B56thtOC4/F8915av669dEEqxo3gXxSIcBmbMvfd1xS1zDoBxqYdNd6X\nNtoU2z8gvobGph3pPCAhesTOzg6PLxzB6unqPTxeX3V3ZhYHvJ3kJHEmXN2dUzSelzbaFNs/IK7e\n3Z1TtPr3TP59qITOYYjRZYgdAOhk3boMkfZ46kL/gPhjMy0oFs93+186n0fIUwYGBpj3bRJMB7bM\ng9/bgnG+zEMgcsLvtlh/mu5LG22K7R8QX8Nxvi+1yOelRxfKtNJ+W9Wh9yAA0Mm6deg9SNK8pO5f\nDHWPY4feg+i8HCE6xM7ODg9P7kKM5dtSp6IXaC6O9vQMPNwin/vSPe5opf22yj6aeQhUS9XNPrqb\nxvvSRpti+wfE1bDkTBbiC2bQfb5qMjAwwLq16zHR9lupU2k1DDs9BwB4/OAviTPhMuz0nKR5Sd2/\nGOoexylTJ6PdcwZaXTftz7vXsTbMRyvtt1WdB4wGANyvzJc4E67OA0ZLmpfU/YuhznHclFeIqbII\n+hygh+zs7PDkYT3WxSyVOpVWxbCnMQDg8bUaiTPhMuxpLGleUvcvhjrHceO2HEyZ5a39dRduXUTy\n9y5a66MtMjJh5k3VF6dInMmzMTJxajVjaE25iqHu75KRiRNdxyKtjoGBAb53ScaIwTSPRygTJ2bd\npuKUllk7ysTJqMX60sX+xVD32Owp3YyFidpbV4u9TvSwdr9W2tcHHfsMBYAWq2HHPkM13pc22hTb\nP/BsNXScF4LnjHrQfGhCJGZgYID4FWmwNJsodSqtRo9e7QEAty79LnEmXD16tZc0L6n7F0Pd4zjT\n0wGGndtp9Xrkkwd1WBcToZX22yrDN94DADy+ekriTLgM33hP0ryk7l8MdY7jxu25mDLLh65HEiIx\nOzs74O8nWJ+2TupUWpV2LxgCAJ788VjiTLjavWAoaV5S9y+GOscx44eNmOwwRevXh/+6ex1rw/20\n1kdb1KnfKADAg+oCiTN5Np36jWo1Y2hNuYqh7u9Sp36j6PowkWOvB/35289Sp9KqPP/iKwDQ6uv2\n/IuvtJoxtLZcAXG/H/ZOM2DwfHutng/8q/4WUpcFaKX9tqqlr/eKoQvXcXWxLnzUOY6ZOXvgOC+E\nzgcSXTGrndQZEKJvSo+UISE+Vm/7J0TXnL5YjnlTovW2f0IIIUSbjv3nLpZZ99P5NgnRluPVZQhb\nEKfzbRJCCCFS+umXY5j4YaTOt0mIrqk+U4bvZ8XofJuEEEJIY0eOlGJ1XILOt0mIJl26cwx2gzT7\n/UQbbRJCCCGEcfpiOeY5aHYulzbaJEQK5TU/IdrTVufbJIQQQnTN5bpjsOy7TOfbJEQXVFy9h4hv\n3tT5NgkhhLQt5afOY5WPk863SYgUKk+XIWSeZtck0EabhBBCSHOOXryFKIfPdL5NQnRN+anzWOU3\nXW/7J4QQfVF7uRzulit1vk1CdFHZ8ZOIW+Svt/0TQgjRfTQXhxDlzt84Cqdhy3W+TUJIyzlSVoq4\nWOnWrZC6f0J0UVlVLWICZutt/4QQItSRikrERYTobf+EtHblpy8g2nuK1GkI0ppyJYSQ1ur0hXJ4\nS/kcRYn7J0QKZZWnEBvirfNtEkIIaf2OHT+CyLB4ve2fEF3EnN8O1tv+CSGEtE6lR44gYbV0z7KW\nun9CWruyqhrEBnpKnYYgrSlXQghpSUfKjmL1qtZx71BrypWQliD1dVyp+ydEHz0vdQKEaFu79h0B\nAE9+fyhxJoxDJYcx10u6m/Gk7l8o9riRtsd0mhEAoCi5XuJMGNXnSmE7yl1v+xeKPW6EEELajtfm\n5QEAbiwbo7U+yn76FTNNeul0m2wdCFGl10cdAACXjj0Std/RyhI4T9bsxWRNt8mOjRBCCOHjkfUq\nACDa8qbW+rj4cxmGvT1T59vUBra+pO35YHxnAMDx7Pta6+PE6VLYm3vofJtCsTUjhBAinRcM2wEA\n/nj8RGt9lBw+BC/PuTrfplBszYh+cM3oAQCIm3RL1H4Xbh/BCGNXjeaijTaFYutACCGESMHU+em8\nsyTtzTurPl8K25GanculjTaFYmtGSGNdRzHXm+sKVojar/T0JbhbfqnRXDTdJjs2QgghRBt8d78O\nAAj/6rqo/X66W46h//5Oo7loo02h2DoQokrPwMMAgGvBn4rar/zyPcwY8k+N5qLpNtmxEUIIaRld\nPncAANw7mKa1Pg5Xn4PHxNE636ZQbM1I22dsytyHX1OkvfUTKqoPY6qtZtck0EabQrE1I4QQon3d\nnVMAALeTnCTOhFF2/hZcR76v820KxdaXtD1dhtgBAO6VbJA4E8bhqrPw+FZ76xDocv/ssSCEEKmN\n830JAJATfldrfdT8dATmQ2fpfJtCsTUjbVOH3oMAAI8ulEmcCaPkWCU8p0v3uUHq/oVijxshhBD1\n0Vwcom/so7sBANI97mitj7P/LcOYDzV7n6422hSKrRkhrYlhp+cAAI8f/CVxJozDh0vgOXuO3vYv\nFHvcSNvUeQAz5+1+Zb7EmTBKT5yCh4Ol3vYvFHvcCCEtx7CnMQDg8bUaiTNhHC6vgOeMqXrbv1Ds\ncSNtk5EJM0+tvrj1zasqrT4Pd9tRUqchSGvKVSj2d4cQQoQwcWLWcSpO0fLaV1I+R1Hi/oVijwVp\n+zr2GQoAeFi7X2t9HK44idlTbXW+TaHYmhFCCAF69GoPALh16XeJM2EcOVoCV2cvve1fKPa4kbbJ\n8I33AACPr56SOBPG4fLj8JzhqLf9C8UeN0II0VftXjAEADz547HEmTAOlRzGXC/p1t+Wun+h2ONG\n2qZO/Zhrlg+qCyTORLzDx09j9hTdn5MGtK5chWJ/dwgh0nv+xVcAAH/+9rPEmYh3qPQI5ni4SZ2G\nIK0lV/b3gbQ9LXG9Vwwpr+PqQv9C0TVn0pbQk6YJaWFzvaRZcFxX+idE10g9IVjq/gkhhBBtmmnS\nq1W0SYi2OE/W/MQRbbRJCCGESGnY2zNbRZuE6Bp7c49W0SYhhBDSmJfn3FbRJiGaNMJY8w9i0Eab\nhBBCCGHYjtT8XC5ttEmIFNwtv2wVbRJCCCG6Zui/v2sVbRKiCzT9MGlttUkIIaRt8Zio+QebaqNN\nQqQw1VbzaxJoo01CCCGkOa4j328VbRKiazy+HaPX/RNCiL4wHzqrVbRJiC7ynG6n1/0TQgjRfTQX\nhxDlxnyo+ft0tdEmIaTleM6eo9f9E6KLPBykfTCs1P0TQohQnjOm6nX/hLR27rat5+HxrSlXQghp\nraR+jqHU/RMihdlTbVtFm4QQQlo/V2cvve6fEF3kOcNRr/snhBDSOs31kvZZ1lL3T0hrN3tK65mT\n1ppyJYSQljTHw03qFARrTbkS0hKkvo4rdf+E6KPnpU6AEG158vtDqVMgz4COX9tTlFwvdQrkGdDx\nI4SQtuPGMlrIvDGqB1Hl0rFHUqegdfowRkIIIeJFW96UOgW9QHVue45n35c6hVaLakcIIdL54/ET\nqVNotah2+iFu0i2pU9ApVA9CCCFSKEqieUvqotqRxuoKVkidgtbpwxgJIYS0vPCvrkudgk6hehBV\nrgV/KnUKWqcPYySEEF1w72Ca1Cm0WlS7tq+miO6/VxfVjhBCtO92kpPUKegFqnPbc69kg9QpkCbo\nmBBCpJYTflfqFFotql3b9OhCmdQpkGdAx48QQtSnD/NU9GGMRLh0jztSp9BqUe1Ia/L4wV9Sp0Ce\nAR2/tul+Zb7UKZBnQMePkJbz+FqN1CmQZ0DHr22qL06ROgXSytHvECFEiOIUWr9J19Axafse1u6X\nOoVWi2pHCCHArUu/S50CeQZ0/Nqmx1dPSZ0CeQZ0/Agh+urJH4+lToE8Azp+bdOD6gKpUyCtHP0O\nESK9P3/7WeoUiA6i34u2h65Ztm50/Ehb0k7qBAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII\nIYQQQgghhBBCCCFEiHZSJ0Ban3btO6Jd+45Sp9Hi9HXcmkL1azmm04xgOs1I6jS0oi2PTduodoQQ\n0uC1eXl4bV6e1Gm0OH0dt6boY/16fdQBvT7qIHUaWtGWx6ZtVDtCCGmeR9ar8Mh6Veo0tKItj03b\nqHYNPhjfGR+M7yx1GlrRlsembVQ7Qoiue8GwHV4wbJvTK9ry2LRNX2vnmtEDrhk9pE5DK9ry2LSN\nakcIIbrB1NkIps5tc35QWx6btulr7bqO8kTXUZ5Sp9Hi9HXcmkL1I4QQYXx3vw7f3a9LnYZWtOWx\naZs+1q5n4GH0DDwsdRotTl/HrSlUP0LIs+ryuQO6fO4gdRotTl/HrSn6Wj9j044wNm2b97W35bFp\nG9WOEKJLujunoLtzitRptDh9HbemUP2U6zLEDl2G2EmdRovT13FrCtWPkNZvnO9LGOf7ktRpaEVb\nHpu2Ue2E69B7EDr0HiR1Gi1OX8etKVQ/Qoi26OucCn0dt6boa/3so7vBPrqb1GloRVsem7ZR7Qgf\nw07PwbDTc1KnoRVteWzaRrVrOZ0HjEbnAaOlTqPF6eu4NYXqR/SRYU9jGPY0ljqNFqev49YUqp/u\nMzJxgpGJk9RptDh9HbemUP0I0R8mTkYwcWqb6yG15bFpG9WueR37DEXHPkOlTqPF6eu4NYXqRwjR\nph692qNHr/ZSp6EVbXls2ka1azmGb7wHwzfekzqNFqev49YUqh8hRGrtXjBEuxcMpU6jxenruDWF\n6qf7OvUbhU79RkmdRovT13FrCtWPEGk8/+IreP7FV6ROo8Xp67g1heqnG/T1up++jltTqH5En+nf\nU6YJIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC\nCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghrdLzUidACCGECFWUXC91\nCoQQQgghpA24dOyR1CkQQgghrVK05U2pUyBEpx3Pvi91CoQQQohofzx+InUKhOiMuEm3pE6BEEII\nIUoUJdG8MUIIIYQQon3hX12XOgVCCCGEEEIIaVZN0UOpUyCEEEIIIYQQQoiW5YTflToFQgghhBBC\nCGlx6R53pE6BENJKPH7wl9QpEEIIIYQQQgghhBDCUZxC62QRQgghhLQFty79LnUKhBBCCCGEEEII\nIYQQQgghhBA1tZM6AUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggRop3U\nCRDdUldXh42bNsPM3Art2neE6ywPnD13rtn9KquqELl8Jdq174h27TvCzNwKGzdt5sTtKyyC6ywP\neVxAUDAqq6rUjmuKjVe1CdG4BnzjUJarmbkV9hUW8cYJqS2b4+UrV2BmboWAoGC1+tL08RDTNyCu\nfkSc+w/rsa9sC/yjbWE6zQhR6V64cvN8s/tduFKNzIJVMJ1mBNNpRvCPtsW+si2cuIqaYkSle8nj\nkreF4sKVarXjmmLjVW1C9m/6+tf62/LxKRubkNqx7d385Sr8o22RvC1U6bj9o21RUVPMm6em6y2m\nbwAK4+TrlxBC2rL6R39i+4n/wiHlKF6blwffrJO4cPt+s/ud+m894osv4bV5eXhtXh4cUo5i+4n/\ncuIOnv8Zvlkn5XHhO8/i1H/r1Y5rio1XtQnRuAZ841CWq0PKURw8/zNvnJDasjleu/sQDilHEb7z\nrFp9afp4iOkbEFe/1ureb3XILtiE6V6W6PVRBywIc8el/zT/3a/mbBWS1q9Ar486oNdHHTDdyxLZ\nBZs4cSXlRVgQ5i6Pi4wPRs1Z7ncNoXFNsfGqNiH7N3398y+35eNTNjYhtWPb+++NK5juZYnIeMXv\ndo3HPd3LEiXlRbx5arreYvoGoDBOvn4JIUQfPfyjHhVXtiOxxB4eWa9i03Ef3PrtQrP7Xas7hX3n\n4uGR9So8sl5FYok9Kq5s58SdvX0Qm477yONyTy3BtbpTasc1xcar2oTs3/T1vcd35ONTNjYhtWPb\n+/XBNSSW2CP31BKl404sscfZ2wd589R0vcX0DUBhnHz9tmW/3a9Hwf7N8FxojQ/Gd8aiuNn4z7Xm\nP2eevVSN9G3R+GB8Z3wwvjM8F1qjYD/33HJZVTEWxc2Wx8WtD8HZS9zzaULjmmLjVW1C9m/6+pe6\n2/LxKRubkNqx7d24fQWeC60Rtz5E6bg9F1qjrIr/HKKm6y2mbwAK4+TrlxBCWlpdXR0yN22EuYUZ\nXjBsBzd3V5w7d7bZ/aqqKrF8RSReMGyHFwzbwdzCDJmbNnLiCov2wc3dVR4XGBSAqqpKteOaYuNV\nbUL2b/r61u1b8vEpG5uQ2rHtXb5yGeYWZggMClA6bnMLMxQW7ePNU9P1FtM3AIVx8vXbVjz8ox5H\n/7MN8cX2cM3ogR/KvXHrXvPfea7+egp7auLgmtEDrhk9EF9sj6P/2caJO3PzAH4o95bHZVctwdVf\nuZ/BhcY1xcar2oTs3/T1vUd35ONTNjYhtWPb++X+VcQX2yO7SvE7T+Nxxxfb48zNA7x5arreYvoG\noDBOvn4JIYRox/2H9dhXvgX+MbYwdTZC1HoR88Z2rYKpsxFMnY3gH2OLfeU885hqixG13ksel7xd\nybwxgXFNsfGqNiH7N339673b8vEpG5uQ2rHt3fzlKvxjbJG8vcm8sUbj9o+xRUWtinljGqy3mL4B\nKIyTr9+2ov7+I2QVVWBiYBK6jvKEV/RmnL96u9n9Tl68hlVZheg6yhNdR3liYmASsooqOHH7T5yD\nV/RmeVzoujycvHhN7bim2HhVmxCNa8A3DmW5TgxMwv4T/OdthdSWzfHqrV8xMTAJoesU57II7UvT\nx0NM34C4+hFCiD559Gc9Km9sR+qJKfDd/Tq21fjizoOLze53/d4p7P/Pavjufh2+u19H6okpqLzB\nvVZ64ZeD2FbjK4/bdSEc1+9xz1cIjWuKjVe1Cdm/6evffr8jH5+ysQmpHdve3UfXkHpiCnZdCFc6\n7tQTU3DhF/7r0pqut5i+ASiMk6/ftuDeo7+wo/oOHDNq0TPwMPxyLuLiz4+a3e/0jftIKPkvegYe\nRs/Aw3DMqMWO6jucuEOX6uCXc1EeF7HvCk7f4M53FhrXFBuvahOicQ34xqEsV8eMWhy6VMcbJ6S2\nbI7X6h7DMaMWEfuuqNWXpo+HmL4BcfUjhBAAqP/tAbbsKYWN73J0+dwBnstScf7KjWb3qz5/GdEb\n89Hlcwd0+dwBNr7LsWVPKSeu+NhpeC5LlcctTMpC9fnLasc1xcar2oRoXAO+cSjL1cZ3OYqPneaN\nE1JbNscrN3+Gje9yLEzKUqsvTR8PMX0D4urXmt27X4e8fZvh6m8FY9OOCI7ywE9Xmp+rWHuhCmsz\nV8LYtCOMTTvC1d8Kefu4c9hKK4oQHOUhj4tODkbtBe49GkLjmmLjVW1C9m/6+udfb8vHp2xsQmrH\ntnf95hW4+lshOlnxnpjG43b1t0JpRRFvnpqut5i+ASiMk69fQgjRpvqHv2Nb+UVMjtmD7s4p8F5f\nggs3lX+HZJ268gvidp1Ed+cUdHdOweSYPdhWzj0/d6D2OrzXl8jjwrZX4NSVX9SOa4qNV7UJ0bgG\nfONQluvkmD04UHudN05Ibdkcr/7yGybH7EHYdsVrQUL70vTxENM3IK5+bR3zmf4wbHwi0WWIHTyX\npuD8ZeW1Y1Wfv4zoH/LQZYgdugyxg41PJLbs4Z4bKz52Cp5LU+RxCxM3K/m+JCyuKTZe1SZE4xrw\njUNZrjY+kSg+xn9uWEht2Ryv3PwZNj6RWJio+NlKaF+aPh5i+gbE1Y8QohvuP6rH/soshKROxDjf\nlxC7bQ6u3Wl+ztyl6yexbX8Mxvm+hHG+LyEkdSL2V2Zx4iov7EfstjnyuPW7FuHS9ZNqxzXFxqva\nhOzf9PXd327Lx6dsbEJqx7Z3++5VhKROxPpdi5SOOyR1Iiov7OfNU9P1FtM3AIVx8vWrz+ru/YZN\nObtg6TIXHXoPgvv3S3DuUvOfXapqzmHFmg3o0HsQOvQeBEuXudiUs4sTV3T4KNy/XyKPC45ajaoa\n7jk6oXFNsfGqNiEa14BvHMpytXSZi6LDR3njhNSWzfHKf2/A0mUugqNWq9WXpo+HmL4BcfUjhBBV\naC4Og+biqD9OQH/m4jz4vR6lZ7chKtsO9tHdsLZwHm7cbf4+38t3TiGvIg720d1gH90NUdl2KD3L\nvf/z9NUDWFs4Tx635XAYLt/hnlMRGtcUG69qE7J/09f1D+/Ix6dsbEJqx7b3872riMq2w5bDYUrH\nHZVth9NX+e+11XS9xfQNQGGcfP0S/VBXX4dNmzNhYWUGw07Pwd1D4DoZ1ZVYsTIKhp2eg2Gn52Bh\nZYZNmzM5cUVFhXD3cJXHBYUEoKqau26D0Lim2HhVm5D9m76+ffuWfHzKxiakdmx7V65choWVGYJC\nFNfJaDxuCyszFBUV8uap6XqL6RuAwjj5+iXqq//tPjbvLIa1RxA6DxiN2aExOPef5u8trD5zEdFp\nWeg8YDQ6DxgNa48gbN7JvWe0uKwSs0Nj5HEhsWmoPsO9Zig0rik2XtUmROMa8I1DWa7WHkEoLuP/\nHRdSWzbHK9dvwdojCCGxaWr1penjIaZvQFz9CGmt6u7dw6YdebBwdIVhT2O4+wXj3MWfmt2v6nQt\nViSshWFPYxj2NIaFoys27eCuzV90qBTufsHyuKCIaFSdrlU7rik2XtUmROMa8I1DWa4Wjq4oOsQ/\nH1hIbdkcr1y7DgtHVwRFRKvVl6aPh5i+AXH1Iy2j/v5DbNl7BLayaBiZOMErKk3gvQ9XsCqzAEYm\nTjAycYKtLBpb9h7hxBVX1MArKk0eF5q8DdXnr6gd1xQbr2oTonEN+MahLFdbWTSKK2p444TUls3x\n6s2fYSuLRmiy4rkBoX1p+niI6RsQVz9CSOty/2E99h7ZAlm0LUycjBCV5oUrN5qf83P+6fP+TJyM\nYOJkBFm0LfYeUfK8vzQveVzytlCcV/Z8RQFxTbHxqjYh+zd9zT5fUdXYhNSObe/mz1chU/Z8xafj\nlql4xqGm6y2mbwAK4+TrVx/V3buPzbl7YTVTho59hsIjKBLnfmr+s01V7XmsXJuJjn2GomOfobCa\nKcPm3L2cuKLSCngERcrjgleuQVUt99+m0Lim2HhVmxCNa8A3DmW5Ws2UoaiUfw0pIbVlc7xy/Sas\nZsoQvHKNWn1p+niI6RsQVz9CCGlO/b06bMveBPvp5ujRqz28F8zChUvNz/88VVOFuKTl6NGrPXr0\nag/76ebYls197t2BkkJ4L5glj1sSGYRTNdx7i4XGNcXGq9qE7N/09Z2fb8nHp2xsQmrHtnf1v1dg\nP90cSyKDlI7bfro5DpTwXxPUdL3F9A1AYZx8/RL1yc8BT3WD4RvvwV0WIvD8+hmsSEiF4RvvwfCN\n92Ax1U3J+dwjcJeFyOOClq5C1ekzasc1xcar2oRoXIPmz6835Gox1Q1Fh/jPOQmpLZvjlWvXYTHV\nDUFLV6nVl6aPh5i+AXH1I4QQTairq8PGzE0wM7dAuxcM4ermjrPnmv8MWVlVhcjlK9DuBUO0e8EQ\nZuYW2JjJ/Wyxr7AIrm7u8riAwCBUVnE/0wiNa4qNV7UJ0bgGfONQlquZuQX2FRbxxgmpLZvj5ctX\nYGZugYDAILX60vTxENM3IK5+pGXU/3Yfm/OLYOUeiE79RsFjYTTO/edqs/tVmohgCwAAIABJREFU\nn7mIleuy0KnfKHTqNwpW7oHYnF/EiSs6cgIeC6PlcSEx63jnZgmNa4qNV7UJ0bgGfONQlquVeyCK\njpzgjRNSWzbHK9dvwco9ECEx69TqS9PHQ0zfgLj6EULUV1dfj8wtWzHBxg7Pv/gK3Dzn4ez55u9J\nqqo+iajoWDz/4it4/sVXMMHGDplbtnLiCosPwM1znjwucOFiVFVz1wsQGtcUG69qE6JxDfjGoSzX\nCTZ2KCzmv5dHSG3ZHC9fuYoJNnYIXLhYrb40fTzE9A2Iqx/RHLpezKDrxeqPE6DrxYQ0ZfD333//\nLXUSRDU7OzvgyV9Yn5aq9b7MzK2QnZvLef/40SMY0L8/AKBde2ax7ye/PwQAZOfmwszcire9jPVp\nmGhj3WzcnoJ8DPvSVFQcHzY3Vdi8lXGd5YHViUkK7y0NXwJvXz/O/gFBwQhdrPiwbQBY4O+HkKBA\nhffE1HaBvx9CFy9RqJ/QvjR9PMSOU0z9tCVjYyYmOzhCW/+9ZWRkwM7ODkXJ9VppXxX/aFuUVOZz\n3k8OOoTe/+oHADCdxkzOZfMrqcyHf7Qtb3sBM1IwbJBVs3FR87LxobGJqDg+bG6qqKpr07Gxr4cM\nGM2pS+OxAeJqZz/OB+k5EQptJG8LRXpOBGd/+3E+mGa+QP5a0/UW0zcARKV74ceiZIX3ZtosQvym\n+QBU11ebTKcZYcOGDZg0aZIk/RNCWh/27+2NZWNE7eeQchS7Tt/ivL93zud475/M//OvzWMmB7Ft\n7zp9Cw4p/ItMrp48EBMG/rPZuC3ffYLP33pFVBwfNjdVmquJb9ZJrDusuOBn4HhjBGfXcPYP33kW\ny/dwT7Z4jXgLvl+/o/CemNp6jXgLy/ecV6if0L40fTzEjlNM/YRw3XACHYxNsWHDBlH7CWVnZ4f7\nvzzBikWpovab7mWJvfu530/yfiiD8TvM95NeH3UAAFw6xizUt3d/LqZ7WfK2F704DeNH2TQbt2H1\nTgz52FRUHB82N1XYvFXtz8awr4cPHcupS+OxAeJqN2u6DDFrwhTaiIwPRsyaMM7+s6bLMHdmw/cr\nTddbTN8AsCDMHRu2KH638/dagsXLme92qurLp9dHHejzICFEEuznymjLmxppL7HEHievcxce9x2x\nDz27MhP/PbJeBQB5nyev70JiiT1ve46DEvDhvyY0GzdraBbe6f65qDg+bG6qqKpV07Gxr99/fSSn\nLo3HBoir3ag+XiioXa7QRu6pJSioXc7Zf1QfL4x9z0/+WtP1FtM3AGw67oODFxUnrk3oH4TtVUEA\nVNdXjLSymXjniw5a/Zz567U/sXjeWlH7eS60RnEZ93tNZnQp3unFnIP7YHxnAMDxbGbx5eKyPHgu\ntOZtb4l3KkYNtW42LmFRHgb1NxEVx4fNTRU2b1X7szHsa5NBYzh1aTw2QFztnG19kZQZrtBG3PoQ\nJGWGc/Z3tvWF6+SGxUE1XW8xfQPAorjZ2JKvONFgjlMYolJkAFTXVxn/ZVPxcs/ntfbvgRDSetjZ\n2eHvJ0DauvWi9jO3MENObjbn/WPlx9G//wAAwAuG7QAAfzx+AgDIyc2GuYUZb3vr0zNgazOx2bhd\nBXvwpekwUXF82NxUYfNWtT8bw74eN3Y8py6NxwaIq52/bAEWh4UqtBEYFIDFYaGc/f1lCxAcFCJ/\nrel6i+kbANzcXZGYqPgQpYjwpfDx9Qagur7KOEyZDIN20OrfLwMDA0wdEo+P/5f/3BGf+GJ7VF8r\n4LzvP7oQb7zMfG53zegBAIibxJwvrr5WgPhi/s/WTp8l4P/+bd5s3OzhWXj31S9ExfFhc1OFzVvV\n/mwM+7pfz1GcujQeGyCudqPfn4P8k1EKbWRXLUH+ySjO/qPfn4Px/Ru+d2i63mL6BoAfyr1x4Jzi\ndx6LD4Kw9XgQANX15VP+UxbWlszU2vwOQgjRNfJ5Z0ni5+/4xyiZ+xTQaO6T89O5VUmN5o3FKJnH\n5JKCYR9bNRsXNTcbH/YxERXHh81NFVV1aTo29jXvvLFGYwPE1c5+rA/ScyMU2kjeHor0XJ65W2N9\nMG1Ck3ljGqy3mL4BIGq9F34sbjJvzHoR4jc/nTcm8vduz5HNCF0zTat/p+3s7PDH9TNY48f/uUWZ\niYFJyC/lPszqULw33n+zJwCg6yhPAEBdwQoAQH7pKUwMTOLsAwApMgdYmn7YbFx2uBuGDnxbVBwf\nNjdV2LyV8YrejJTcQwrvhbqYYUHiDs7+oevysDSDe37ee9JILJiiOAdCTG29J43E0oxdCvUT2pem\nj4fYcYqpnxBdR3nS9XBCiE4yMDDAt/1iMfA1C8H7pJ6Ygprb3P9PPQfvwetdmHMcvrtfBwCEf3Ud\nAFBzexdST0zhbW9Sv3gMeG1Cs3EuH21G7398LiqOD5ubKmzeqvZnY9jXxt1HcurSeGyAuNoNf9MT\ney+uUGhj14Vw7L3I/Rs0/E1PjOztK3+t6XqL6RsAttX4ovSq4oPPxr4TiNyzwQBU15fPiRtb8UO1\nm9bv/7kW/Kmo/RwzarH7zK+c93fP7I++rzHXbdmHMrNt7z7zKxwz+B84FGf1Nsz6dWs2bpNjX3zW\nq6uoOD5CHhjdXE38ci4ivVxxjkPAqH8jpOA/nP0j9l3BymLuAkuzTd6Az7B/KbwnprazTd7AyuKr\nCvUT2pemj4fYcYqpnxCzss6h4/sj6bo4Ia0E+/fn3sG05oMbsfFdjvxDxznvl6SGot9b/wMA6PK5\nAwDI284/dBw2vty5bQCwNsgVViMGNxuXs9IPJh/1FRXHh81NleZq4rksFcnb9ym8t2jWt5gf8wNn\n/4VJWYhYt4PThs8UM3zvrHiNVExtfaaYIWLdDoX6Ce1L08dD7DjF1E+IacHxeL57L61f1166IBXj\nRvCfM1XG1d8KhSXc+zq2JR9Bn97MfR3Gpsw9+zVFzH3thSW5cPXnv68+MiANY4ZZNxu3Niofgz80\nFRXHh81NFTZvVfuzMezrL4eM5dSl8dgAcbWbae+H+PQlCm1EJwcjPp275sBMez94TGu4L0XT9RbT\nNwAER3lg44+K5xZ9Zi5BRDxzDVxVfZUxNu1I5wEJ0SN2dnZ4fOEIVk9Xfg1Ulckxe1BQeZnzflHA\nBLz3r38AALo7pwAAbicxD4ItqLyMyTF7eNtLdDGF+cdvNhu3de5ofNHndVFxfNjcVGHzVsZ7fQlS\nixW/cwdbD0Lg5jLO/mHbKxCVy12kd87YgZBN+FDhPTG1nTN2IKJyTyjUT2hfmj4eYscppn5idHdO\n0erfM/n3oRLNfoa08YlE/kHuooIlaWENn+mH2AGAvO/8gxWw8YnkbW9tyCxYjfi02bicVf4w+eg9\nUXF82NxUaa5mnktTkLxNccHERe52mL9qA2f/hYmbEZG6ndOGj+MEfO+ieJ+ImNr6OE5AROp2hfoJ\n7UvTx0PsOMXUTxOmBcXi+W7/S+fzCHnKwMAA875NgulA/nvVlAlJnYiymp2c91d5HkSv198HAIzz\nfQkAkBN+FwBQVrMTIakTOfsAgM+kZAwdYNls3CKXHzGg91BRcXzY3FRh81a1PxvDvh5k/DWnLo3H\nBoir3cTh3ti4d6lCG+t3LcLGvUs5+08c7o3JI+fLX2u63mL6BoDYbXOQX6r42XXa2FAk5zJz61TV\nV5lxvi+1yOelRxfKtNJ+Y5Yuc5G7l7uwelnOBvQ3ZuZddeg9CADk+eTuPQBLl7m87aWtDIXNuJHN\nxu1cHwfTT/9PVBwfNjdVmquj+/dLkJShuGj8Etls+IWt5OwfHLUaYbHc70IyNycEzvlO4T0xtZW5\nOSEsNkWhfkL70vTxEDtOMfXTpg69B9F5OUJ0iJ2dHR6e3IUYS+VzpfnQXByaiwPo1lycnoGHW+Rz\nX7rHHdH7RmXb4fgl7r2qiyYV43+6MedG7KOZerPtH79UgKhs/nNQbl8nYfA75s3GySy2oe8bX4iK\n48PmpoqqujQdG/v6g16jOHVpPDZAXO3MPp6LHeWRCm1sORyGHeXcc1NmH8+F1acy+WtN11tM3wCw\ntnAe9lWnKrz37ech+OEgs76L2N+7kjNZiC+YQff5qsnAwADr1q7HRNtvW7xvCysz5OblcN4vP1KB\n/v2YtR4MOz0HAHj84C8AQG5eDiys+NdjSF+XARtr22bjCvL2wNT0S1FxfNjcVGHzVrU/G8O+Hjtm\nHKcujccGiKudzG8+wpYsUmgjKCQAYUsWcfaX+c1HUEDDWhWarreYvgHA3cMViWsSFN4LD1sKXxmz\nToaq+mrLlKmT0e45A62um/bn3etYG+ajlfYbs/YIQl7xEc77pZti0e9d5lpi5wGjAQD3n97fmld8\nBNYeQbztpYb7wfprk2bj8pKWwGTQAFFxfNjcVLnPc19uY7NDY7Bms+I8pLC50yGLXMPZPyQ2DeGJ\nP3Da8HX5FgFuinMPxdTW1+VbhCf+oFA/oX1p+niIHaeY+mnLprxCTJVF0OcAPWRnZ4cnD+uxLoZ7\nPl7TLBxdkbu7kPN++e5t6N+3DwDAsKcxAODxNWb999zdhbBwdOVtLz0uEjZmY5qNK9i0FqafDRYV\nx4fNTRU2b2Xc/YKRmL5R4b3wAB/4hkRw9g+KiEbYynhOG7LZMxHk46HwnpjaymbPRNjKeIX6Ce1L\n08dD7DjF1E9bNm7LwZRZ3tpfd+HWRSR/76K1PjTJVhaN/BLuPKhDycHo9xZzXsfIhJnfVF/MXJfI\nLzkBW1k0b3spATNgNfyTZuOyl3vD5ENjUXF82NxUYfNWxisqDck7ihTeW+Rqi/lxmZz9Q5O3ISKN\nu1afj8N4LJhmrvCemNr6OIxHRFq2Qv2E9qXp4yF2nGLqp8uMTJzoOhZpdQwMDPC9SzJGDBY3j0cM\nWbQtSk7wrPUUfAhvPV3rycSJWeupOOXpOlkn8iFT8by/4Z9YNRu33LvR8xUFxvFhc1OFzVvV/mwM\n+3rIwNGcujQeGyCudg7jfZCWHaHQRvK2UKRlc9eqchjf5PmKGq63mL4BICrNCzuaPF/R1XYR4jKZ\nuUGq6qsJe0o3Y2Gi9tbVYq8TPazdL2o/q5ky5BYe4rx/ZHsK+vd5CwDkD7xn284tPASrmTLOPgCQ\nFhkI67HDm43LT10B08Efiorjw+amSnM18QiKRNJGxXspl/i6wS88lrN/8Mo1WBLPvV/Sb6YDAmdP\nV3hPTG39ZjpgSXyaQv2E9qXp4yF2nGLqJ4TjvBA8Z9SD5kMTIjEDAwPEr0iDpRn/PFxtsp9ujoK9\n3PuRC/OO4j1j5n7kHr3aAwBuXfodAFCwNxf20805+wBAQvR6mI+3aTYua0MBvhjypag4PmxuqrB5\nq9qfjWFfjxo+llOXxmMDxNVuzix/RMUsVmhjSWQQomIWc/afM8sffnOD5K81XW8xfQOA94JZWLch\nUeG9IP9wBC1m1qtSVV9tmenpAMPO7bR6PfLJgzqsi+F+9tU0i6luyN1dxHm/fNdW9O/7LgDA8A1m\nPszjq8z6obm7i2Ax1Y23vfTYpY3O5yqPK8hMgelnn4iK48PmpgqbtzLushAkpmcqvBf+vTd8Fy7l\n7B+0dBXCVio+RwIAZLO/Q5C3u8J7Ymorm/0dwlauVqif0L40fTzEjlNM/bRl4/ZcTJnlQ9cjCZGY\nnZ0d8PcTrE9b13zwMzIzt0B2Dvdz0PFj5RjQn/kc1O4FQwDAkz8eAwCyc3JhZs6/XmrG+nRMtLVp\nNm7PrgIM+9JUVBwfNjdV2LyVcXVzx+pExc9ISyPC4e3jy9k/IDAIoYu5z5Ze4C9DSHCQwntiarvA\nX4bQxWEK9RPal6aPh9hxiqmftmT8sBGTHaZo/frwX3evY224X/PBOsDKPRB5RaWc949siZfP5+rU\nbxQA4EE1M5c7r6gUVu6BnH0AYF2EDNajTZuNy1sTDtNPBoqK48PmpgqbtzIeC6OxZlOTuV7zXCBb\nlsjZPyRmHZYkZHDa8JsxCQGzFNcIFlNbvxmTsCQhQ6F+QvvS9PEQO04x9dNlnfqNouvDRI69HvTn\nbz9LnYqCCTZ2yMnjrjFQcbgY/fsxaww8/+IrACDPPSdvJybY8N/7siE1CbZWFs3G7c7dji9NvhAV\nx4fNTZXmau7mOQ8JaxSfwR2xOAQ+/gGc/QMXLsaicO69PPN95yL4e3+F98TUdr7vXCwKj1Son9C+\nNH08xI5TTP10gb3TDBg8316r5wP/qr+F1GUBzQc/I7peTNeLAd26XqyOzJw9cJwXQucDia6Y1fxT\nvoneyM7NRXZuLhb4++HX2zfw5PeHyFjP/AebkLhG6X5m5k8n0h0oxpPfH+LJ7w/x04WzAIBJkx04\ncT9dOCuPKzlQDADYkrVVdBwfNl7Vpsq+wiKsTkzCAn8/ef8/XTiLujruAl37CosQuniJQr1+vX0D\nC/z9ELp4CSqrquSxYmvbt29fPPn9ISbaWIvuS9PHQ0zfYupHxCupzEdJZT7sx/kgN+YqipLrETCD\nuaFgR5HyGwv8n05wjZu/F0XJ9ShKrkfm0tMAgJAEJ05c5tLT8ri4+czCpEVHt4uO48PGq9rU0ftf\n/eQ1iZrH3Kyxp3Sz/Odia9erZx8UJddj2CDm30lFTTHScyIU9s+NuQr7cT5Iz4nAhSvVnPpoqt5i\n+q6oKcaPRcmwH+cjbzdz6Wn89qBOrboSQkhrs+v0Lew6fQteI97C2dCRuLFsDFZPZi7apB3mLtTP\nckg5CgDIdR+CG8vG4MayMTi2gJks+N36E5y4Ywu+lMflug8BAGRXXhcdx4eNV7WpcvD8z1h3+DK8\nRrwl7//Ygi9R//AP3tjle84r1Ots6Eh4jXgLy/ecx6n/NvxdFlvbd197ETeWjcGEgf8U3Zemj4eY\nvsXUrzXbuz8Xe/fnYtZ0GaqKb+LSsUeIXsx8P9mQxf9QdACY7sUsnLw1dT8uHXuES8ce4VDuOQCA\nh78DJ+5Q7jl53NZU5qRb3p4s0XF82HhVmzqM3+kvr8mG1czFjh07GyYXiq3dO28a49KxRxg/iplE\nUlJehJg1YQr7VxXfxKzpMsSsCUPN2YbvV5qut5i+S8qLsGFLEmZNl8nbPZR7DvX36HMlIUS/nby+\nCyev78KoPl4I/+Ycoi1vwnEQs3DdoYvKJ54mltgDAOZ8mYdoy5uItryJ4NHMQ29Sy2Zw4oJHV8jj\n5nyZBwA4cfVH0XF82HhVmzp6dn1PXpNZQ5m/P0evNPwdElu71436INryJj78F/PQ+bO3D6KgdrnC\n/uHfnMOoPl4oqF2Oa3UNE/41XW8xfZ+9fRAHL67DqD5e8naDR1fg4e/68Te0uCwPxWV5cLb1xYGN\n13E8+z6WeKcCADbnK7/G4LmQOQ+etqwQx7Pv43j2feSnMIs1+y115MTlp9TK49KWMQtK7T64VXQc\nHzZe1aaOd3r1k9ckYRHze5ZfvEn+c7G16/0/xjiefR+jhjJjLasqRlJmuML+BzZeh7OtL5Iyw3H2\nUsM5RE3XW0zfZVXF2JK/Bs62vvJ281Nqce8+XUMghEgnJzcbObnZ8JctwJ1bv+KPx0+wPp2Z+J2Q\nlKB0P3MLZmHkg/tL8MfjJ/jj8RNcOP8TAGCy/SRO3IXzP8njDu4vAQBsydoiOo4PG69qU0f//gPk\nNdlVwDx4dOPGhgVkxdaub9+++OPxE9jaMAsBFBbtw+KwUIX979z6Ff6yBVgcFoqqqkpOfTRVbzF9\nFxbtQ2LiavjLFsjbvXD+J9y92/Y+41VfK0D1tQKMfn8OIq3PI27SLTh9xhzLA+dTle4XX8x8tvYe\nmY+4SbcQN+kWQs2Yz+Aph2Zw4kLNKuRx3iOZxaMqLv8oOo4PG69qU8cbL78nr8ns4cx3nfKfGj4T\nia3d613fRdykW/i/fzOLGpy5eQD5J6MU9o+0Po/R789B/skoXP214XuHpustpu8zNw/gwLl1GP3+\nHHm7oWYVePiHdhfAIoQQ0mju01gf5EZfRVFSPQJcns59UrEgrX/M03lMsr0oSqpHUVI9MsOfzmNK\ndOLEZYaflsfFyXjmjQmM48PGq9rU0fuNfvKaRM19Om/sCM+8MYG169WzD4qS6jHs46fzxmqLkZ4b\nobB/bvRV2I/1QXpuk3ljGq63mL4raovxY3Ey7Mf6yNvNDG+b88byS08hv/QUvCeNxJWtS1BXsAIp\nMub6aXJOidL9JgYy13P3rPBEXcEK1BWswKl05iZhp7A0Ttyp9EB53J4VngCAbftPiI7jw8ar2lTZ\nf+IcUnIPwXvSSHn/p9IDUfcbd470/hPnsDRjl0K9rmxdAu9JI7E0YxdOXrwmjxVbW+N/v4a6ghWw\nNP1QdF+aPh5i+hZTP0II0Tc1t3eh5vYuDH/TE8FfnkH4V9cxqR/zkJTSq9ybN1mpJ5hFM9wG5SD8\nq+sI/+o6ZF8w8/syqmdy4mRfHJXHuQ1iHopXdTNbdBwfNl7Vpo5/dukrr4nLR8znzeM3tsl/LrZ2\nr3Z+F+FfXceA15hr4hd+OYi9F1co7B/85RkMf9MTey+uwPV7DedoNF1vMX1f+OUgSq+mYfibnvJ2\nZV8cxaM/29bnzt1nfsXuM79itskbqJUNwrXgTxFnxTyQOu2o8nkV7EOJs53fx7XgT3Et+FOUzWE+\nK7luOceJK5vzoTwu25lZhCH71M+i4/iw8ao2VQ5dqkN6+U3MNnlD3n/ZnA9R94j7EMdDl+qwsviq\nQr1qZYPkD4M+faPhOrvY2r7bvSOuBX8qf/izmL40fTzE9C2mfoQQwso/dBz5h47DZ4oZru1cjXsH\n07A2iHm4XfL2fUr3s/FdDgDYlxCAewfTcO9gGk5nMe9NDYrjxJ3OWi6P25fALBCzrbBMdBwfNl7V\npkrxsdNI3r4PPlPM5P2fzlqOunsPeGMj1u1QqNe1navhM8UMEet2oPp8w30uYmtr3Ksn7h1Mg9WI\nwaL70vTxENO3mPq1doUluSgsycVMez+U5d5ATdFDRAYwv1+ZO5TPVXT1Z877bowrRk3RQ9QUPcS+\nTOb++7khDpy4fZln5XEb45j77wuKtoqO48PGq9rU0ad3P3lN1kYx14dz9jTcEyO2dm/16ouaoocY\nM4yZT1haUYT49CUK+5fl3sBMez/Epy9B7YWG+1I0XW8xfZdWFGHjj0mYae8nb3df5lnc+43mKhJC\nWkZB5WUUVF7GnLEDcSF6Mm4nOSHRxRQAkFpcq3S/yTHMPL182TjcTnLC7SQnHA9n7kt0SSzixB0P\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sEl11Ipc31kZF3hihd2rzxpwUcrecvHDMfwMexlTIG2PZb5LYsryxHgp5Y9+OxDH//1be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BgO17\ncOoUxecRUhlwx83y4RpxAgM1GkBNoTbTRSKkXP7nX2Dr1Vycik9grN3Xf0A/uG3g7nf0vz9ovyPC\nnB8/v2HTcRPUa8hB7HFa5ytuhoaGiIyMREzMUUyaOIXql1R7N9PTMG4Cc/ts8NYrOrr7wKBPDzRW\nVZZ6GQhhk/zvPzDZyg2cWnUQcyyWvlOkCO/vpYPrWgwd2BeN1VSZLhIhjMr/9g0TLBaiUKYmYo4x\n02/i/l5+g+PGIAzR00ZjFUWpl4EQUpZX9DEEH0igfSZIlWI4yhDRO6JwNvkYBvacgFo1qZ9Aqp7v\nP/LhsNkIdRTAyL5aRfFBdo6oWaMG+nTXkWr+hAjq5t2HmDDfHlbW1hQPTQiLGRoaIjIqEsdPxWCc\n4WRah02qvVt3bsJk1jhYWzM7H+ngug5D/6LxdULyv33DhBmLGR1fJ4RUjPf9sl/pgOHDhqJJYzqH\nkFRv+fn5GDNuAn4VFiImhtl9+R3X+WDonz0pzo0QltiwZTcCdxyh+WEiEAUFBfTr1w9Lli1HjRo1\n8WdvWjdEiCTcTM/AmEnTYGXFzHxWUbtt7UYY9NNDY1UVqZeBEDbJ//YdExesBKdmHRoPJGwVW9PF\nxcWF6VIQ4QwaNAjXr1+Hk4srunXtgrZt1ZkuEiGMePUqB8MNRyPr2XOcPn0aDRs2ZKQczZs3R5s2\nbbBqnTW+ff+Kbh0HQEZGhpGyEMKkQk4hQvY7ISrGExERERg+fDjTRSKEVCHc7606lnpuwdeCX+ir\n2ZC+t4RVEu/nwWTrDQwZNgIhoaGM/f8cNGgQrl2/Dk9fV3Rq3xWtmlN/kUhPYWEhPPwcsHmLO7UH\nCSGs1bx5c7RRb4P1IUvx49dX/E+tL7UrCavdzUlE6BVjDB0+BKGhIYy2M69fv45NoW5o37YLWjSl\ndiaRvsspCbB0G48hBoMREsLc7wMhpPLgfb+cXZzQtWs3tFVvy3SRSDUUF38Ko8cYYvBgZr9fsrKy\nGD1mFAJDvXH9yVF0+sMAdWrVY6QspHr6+O01As9NwVeZbCSeO8NYfAchhLBNUdyZuzW+//iKbh0o\n7oxI39sPOVjhNw5vv7zAmUTm4jCB4n6cW/AudNFohjZ/0AZFhD0KORw4h8fAc+cpmg8nhFRKReND\nW7yQ9iIG7VWHoE5NGh8i0vPpx2tsS5uKb7IvcZbB8SFePPIyrwh8/VGIP9UVQd0wwiZnH73H9F0P\nMGToCISEhtE4ASFVBG8c0MpxLb58+44B3bXo95uwSkJyBibYeGOIwVDG57VHjRoFHz8vnEw8jL96\njUBdeQVGykKqr8LCQniFOCAoyoPGAQkhpQwaNAjXb9zA2qgT0GmhgjaNGjBdJEL4KuRw4HbwGryP\npSEiYjuj37Oi/pDDGm5/qEcn6g+RKiUhOR0Tlm/EkCHM9qcIqUxkZWUxevQo+AV64XzaEei2Hwb5\nOtT/J9LF4RQi4rgzdp/ewHj/n9desrRZia9fv+Gv3j3pe0KqpcLCQjis3wz3gK2M/14SQoTDjf++\nAfcDl6HdtC5aq8gxXSRCihRygLVxWfA994Id42TqbbDGbwm+//wKrRb9qN1HpO7D19fYeHQSPv96\nicSztM5XEnhxDxu9NuLQ4YMYOcIQCgo07qTRUbgAABynSURBVEGqp/j4OIwZz/w+G0XrFX1C0KWD\nBtRbNGWkHIQwLSfvHcYsWIXnue9w+kwitQNIGby/ly7uXuiq3RHqrVsyXSRCGJHzOg+GxrPxLPs1\nTp9htt/EG3dzC4hEl3at0KZZI8bKQkh1V1jIgVPIPnhGxtA8FqlyZGVlMWr0KPgFeCEx+Qh6dx4O\neTkazyJVx9sPObDdxPy+WkXxQbaO+JL/DX/16k7zRIRV4i9exbi5KzDYwIDioQlhOd58pLf3Rhw9\nfggGg0aiXj1qv5HqKfF8HKbNGIMhLJmPdPHwQtdOHaHeugUj5SCEaTm5eTA0mcuK8XVCSMV43y8n\nZxd069oVbdvSOYSkenr1KgfDRxoi69kznD7Ngn35b1yHq3fwP3FufzBWFkKqu8JCDhx9wuERspPm\nh4lQePNBi62W4OvXrxg4gNYNESJOcQlnMGqCEQYPHsL8eOCN63DduBldtNpBvWUzRspBCNNy8t5i\n9GwbPM95S+OBhM1ia7q4uLgwXQoiHBkZGYwbNw6ZmZmwtbOHiooKevagYCtSvaSm3cTgYcMhU6MG\n4uLi0KpVK0bLo6Ojg44dO8LdZxUePE2DnvZQyNaqzWiZCJGmr98+Y03YDCRc3YedO3fAyMiI6SIR\nQqog3vfW2X870l98xKD2qqhdqwbTxSLVHIcDRFx6isW7b8LYdDq2RWxHzZo1GSuPjIwMxo8fh8yn\nmXD1sIOSogp0OlJ/kUjel6+fYOVghqMn9mAHtQcJISzHa1f6bXPG8w830bHxYNSqQeM4hF044ODC\no22IvrYIptONERGxjRXtzKeZmXD3tYdifRVoaVI7k0gHh8PB3thQrPKeBRMTY2xj+PeBEFJ5lJxX\nX2Fni4YNVdCjOx1yRKSDw+EgOCQQ5hZmMDY2xrZtzH+/GjRogPETxmHPgUjE3wyHplofNJCnTRuJ\n5D1/lwH/sxPQQK0mEs4wH99BCCFsUyruLCsNep0o7oxIz8NnN7HMZzTkG9REfALz32kZGRmMGz8e\nmZlP4eCzFSr166FbuxbUjyOM+5z/HTM9orAvMQU7du6k+XBCSKXFGx/aeygKZ+9tQxulPqhfh8aH\niOS9/HQLW9MmQ7FJLZw+E894u5PXD3MJ3IH07C8YqKFI8ciEcRwOsP3qK1gdesSKeGRCiPjxvj+r\n1nkj9f5TDO3VGbVlazFdLFLNcTgchB1KwJzVITA2NWXNvPa4ceMQvTMS0fuDodelP1RVGjNaJlJ9\nfPn6CTZrzHAsYS+tiyGElFE0j/U0C6tCD0K5Xh10ba0GmsYibPP5WwHmhp/DwauZrJnXKu4PeSHt\nfiaG9upC/SFS6XE4HIQdjMcctyAYm5hg27YIxvtThFQmDRo0wPjx47BrbxSOnA2Ddpu+UK5P/X8i\nHfnfP2PD7lk4d3M/a/ZJ47WXHNzWIvXWPQwb0Ad1assyXSxCpObTl68wW7IKe46exI4d7OjHEEIE\nxx03m4DMp1lwjkqAsnwtdP5DgcbNCOM+f/+FhQcf4/Ctd6wbJ/MOcUJm7k10bjUEtWrS+iEiHU9z\nM7D+73Go37AWEk4zv36oKuPFPURGbkdQcCD69x+AJo2bMF0sQqSGw+EgJDQIFrPYsc9G8TxvJuzX\n+UBFsT66a7Wj9YqkWrl57zFGzl2JmnXqIi4+gdoBpFzcfbq467vtnNegobISunfuRH8vSbWSdusu\nhhpZoEat2oiLZ379W8l4tZUbgqHcQAHd2rem30tCpOzz12+YsToU+xKusmackRBxa9CgAcaNH4ed\ne6Kw/2Qouv6vL1QUKY6HVH4Ps25iqfdoyNdnx75avHkix9XuSLl9H8P661N8EGEch8NByM7DmLli\nDcVDE1KJ8OYjo6IiEb49GH30+6ORGs1HkuqDw+FgW1QwFi6zYM98JG983WUtGirR+DqpftJu3cWw\nKTNZM75OCKlYyXOcbFfYQaVhQ/TsQecQkuolNS0Ng4cOhUyNGoiLY378sKhdWTLOrRPFuREibZ++\n5MPczgN7jyfSOkciEt580EpHJ6Sk3cTwoUNQpzatGyLkv+BwOAgK2wrz2fPZNR74NBP2azZARVER\n3bXbU7uNVCs37zzAcIulqFFbnsYDCdvF1nRxcXFhuhREeDVr1sTo0aMhLy+P5Ta2OJOYiM6dO6Np\nE5oYJ1Xb+/cfsMLeAXPnL0TPnro4ceIEmrDk/72WlhYMDIZgc7AXDsaHoH5dZWi00KaGMKnSOBwO\nTlzcAafAaXj7+SVOnToJAwMDpotFCKnCtLS0MMTAAD5h0Qg//xjK8rWg9UcD2siNMCL9xUfM23kT\nkVeewt1jPTzWr2fFQoOS/cVVbja4fC0RHdrpoJEqO9rNpGrhcDjYfzQKc5dPRk7eC5yk9iAhpJLg\njeOERvog8V445GspoZlSJ8iAGpaEec/fpyPy2lxcfBwJj/XuWL/eg3XtTNf1triafhbt2uhAVZna\nmURy7j5Og/1GMxw4EQ53D3d4sOT3gRBSeZSeV1+OM4ln0FmnM5o0acp00UgVlpqaAmPTqQgLC4W7\nuzs8PNjz/VJWVoaJqQnOXUxEdLwbvnx/A3XVHpCtKcd00UgVlP/jAw6numFn8jL0+lMXp+LYE99B\nCCFsUyruLIHizojkff76AcEHVmFjlBX0e+ni5Cn2fKdL9uNWePjjXNojaKv/gcYqDZguGqmGOBwO\ndsQlw9htG16+z8fJU6doPpwQUukpKyvD1NQE5y8lYu+F1fhS8BYtlXpAtkYdpotGqqD8nx9x/MFq\nHLxjg1592TU+VBSPvGUHtl1+DiW5GtBqUo/ikQkjMrK/YMGBx4i+9opV8ciEEPHjfX+8/IMQsv8U\nVOrXhbZmSxoHJIxIu/8U5i5B2HrkDNw9PNg3r21ijMSzifD0X4V3H96gq5Y+6tSmeW0iGRwOB4dO\nRMHSaQpev6V1MYQQ/krOY9lv2o4L916hU3NlNFasy3TRCAGHA+y+9ABmwYnI/lyIk6fiWPU9K9Uf\n2ncSKg3qQVuzFfWHSKWUdj8T5s4B2Hr49D/9KRrPI0QU3Lh2Y5w7n4jgvS74+OUNOrTURW1Z6v8T\nyeBwOEi4vhPrdpjgwzf27ZOmpaWFIUMMsNHHD0GRe6Gi1AA6HegAFlK1cTgcRB2IweT5K/AiJw8n\nT1J8HiGVFXfcbAzk5evCIXg/LmZ+hlZjeTSqT4fPEOnjcIC9qa8xa+8jvPoui5On4ln1feGtHwra\n6o1TqVtQr7YSWqrRfkdEcr5+/4DdF12x9cxS9OrDrvVDVZmysjKMjY2RmHgGjqsckJeXB339XpCT\no3EPUrWlpqXAdPo0hIWza5+NkvO8tq4eOHv1JnT+p44mqipMF40Qifrw6QscN23FIjdf6Orq48TJ\nk9QOIP+q5N9LGwdnJF5Mgo5WezRppMZ00QiRqPcfP8F+zUYssHWGbk9dVv29LLXvwlofnEu5B22N\nFmjSUJHpohFS5XE4HOw4cRHTVgXg5dvPtM8EqfJ463jOnk+E33ZnfPicB622ehTHQyqlz18/IHjf\nKmzY/s++WifZMy7Oiw/y2rQZQdEHoKJYHzrtNSg+iDAi7fYDmC5zRfiev+HuTvHQhFQ2ysrKMDbh\nzke6uTvizds89OimD7k61H4jVVv6rVTMsTRB5M4w1s5H2jg4I/FSEnQ60vg6qfq44+teWLDCBT11\n2TW+TgipWKlznJbb4MyZRHTurIOm9HtMqrj3799jhZ095s5fgJ49dXHiBHvGD0vFubl44GxyGjfO\nTY3i3AiRNA6Hg6gjcTCydsOL3Pe0zpH8J9z5oCHY6OWNwOAtUFFWRmedTjQfRIgIUtNuYprZbIRt\njWDteKCt81okJqWgc3sNNFFryHTRCJGoDx8/w2FjEBY6baD1CaSyiK3p4uLiwnQpiOj69OkDQ0ND\nHI2JgYurGzIzn6Jly5Y0iEmqnNevc+EfEIRppma4dfs2fH19sXHjRsjLyzNdtFKaNWsGcwtz5Obl\nYPPWNUi6dQoK8opo3rgtatRgvqFOiLj8/FWA8zeOwjNyIWLObsN0cxPs27cXmpqaTBeNEFINcL+3\nFsh58xbuUSeQcO8NFOVqoY1qPdSsQYPMRPLSX3yA+/EHsD94C800OuLQ4SOYOHEi08Uqg9dfPH4i\nBhs3u+F5diaaNW2JRqrUXyT/3c+fBTh5+ghWrJ6HXQe3wHQ6tQcJIZVPs2bNYGFhjjdvc7D9mDvu\nvI6HfC1FqCmoo4YMjeMQ6Xv2/iaO3VqHval2aNuhGQ4fOcTqduaJuBj4b12Nl6+fommjllBVpnYm\nEZ87j1KxOcoZHsFL0KJ1Uxw+zM7fB0JI5cH7fsXEHIWrmwsyn2aiVcuWaNKkKdNFI1VISsoNODo5\nwNJqEZo2bYJDh9j5/ZKTk4OJiTFatGyB6MMBOH0nDIWFHDSq3xZ1atFBw+S/+/QtD4n3tyDiyjzk\n/bgPP39feHmxL76DEELYhuLOiDS8+5SLQ6dDsDp8Bp7l3oWfny82svQ7zevHHTt1Gmu37EdWzju0\naKSMxioNmC4aqQYKfv5CzMV0LPTZg4jYSzAxs8DefftoPpwQUmWUHB/adSwAFzK3oLCwEKp11VG7\nJo0Pkf/u8488XHoWjj23F+Adh73jQ9x+2AzkvHkHj50JOP3oIxTlakBdRR41KB6ZSEF69hd4JDyD\nw7EnaKaphUOH/2blvAIhRLyaNWsGc3ML5OTmYY3/Fpy6chOKCnXRtkUT1KxRg+nikWog9X4mXEL2\nYZl3JP5o2QaHDh9m5fdHTk4OxsbGaNGiBYLD/RC1PxCcQg5aNddEXfl6TBePVBE/fxYg7vzfcPSc\ni70x4bQuhhAisKJ5rIRzcN9zBllvPqOFigIaK9LYGpG+gl+FiE15CqvIS4g8dw+m5hbYu28/K79n\npfpDfmHUHyKVTuq9TLiE7MEyr+2s7k8RUpnIycnB2ITb/9+2yx9HLgSDU8jBH6ptIVeb+v9EPH7+\nKsDlWzHwO7AIJ5IiYGbB3v4/t71kjpzXr+G2wRcnEi9BqYECNNq0pPYSqVIKfv7EkZNnMNduDcJ3\nH4aJ6XSKzyOkiuCNm8UmXoLn36l49v47mivWQaP6tZkuGqkGfv7i4Pjdt1h29Cmir72GqcVM7N13\ngJXfF95+R3lvc7Bl/zqkZ8VDvrYimijRfkdEfD7m5+FUWhiC4uYg5+s9Vq8fqqpKxj34+fkiIMAf\nHA4HmpqaqFePxj1I1ZKSegNOzg6wsl7M6n02ivadOX4Kq/3C8fRlDlo2bYQmqnT4Lalact++R/Cu\nv2Fm54k7T17A19ePlee5EPYq+nsZexxunpvw9PkLtGz2B5o0UmO6aISIVW7eGwRu3YHpC5bj9oNH\nrD3/CigRr3YyHmtDduHpqzy0aNwQTRoqMl00Qqqcgp+/cPTcDSzcsB3bjibCZLo5zWORaqPkeNaW\nKH/sjwsCp5CD5o01IF+HxrMI+737mIuDCSFYHToDz17/s68WC9t3xfFBuVi90R8nziVx44NaN6f4\nICIVqbfvw8knFNZuPmjavCXFQxNSiZVsvwUE+iIsIgAcTiHattFE3brUfiNVy82MFKzdsAp2TpZo\n1rwp++cjY4/DzdMXT5+9RMtmTWl8nVQ5uXlvEbhtB6YvtMHth49ZPb5OCKkY7/t1NCYGLq5uyMx8\nipatWqJpEzqHkFQtr1/nwj8gENNMpuPW7dus/n6VjnPbgqcv/olzU6M4N0LEreDnT/wdfxHznH2w\ndX8sTKab0fwwEQvefNCrnBy4rlmHE6fioaSkCM226qhZk9YNEVKRlNSbcHRZjcVLbdH0jz/YPx54\n/CTcNgXj6YtstGzaBE3UGjJdNELEKvfNOwRFH4TZcjfcfvSM1ieQyiRWhsPhcJguBfnvOBwOdu3a\nBQ8PD6Snp0OjbVv8NaA/OnToABUVZfqDRCqdX79+4d27d3j8+DGSr17DpctXoKSkhNmzZ2PFihVQ\nUlJiuogVSk1NhYuLK2JijqKuvAK6/q8/NFvqQEWxCerJ12e6eIQI7Uv+J7x5n42Hz9Jx424i8r99\ngaHhKLi4OKNLly5MF48QUk2lpqbC1cUZR4/GQEG+Nv5sq4xOf9RH4wZ1UL9OLaaLR6qI/IJCvM//\ngXuvPuNS5kc8yfkAba0OsFvpiKlTp0JGht2HePL6i+7uHsjISEfrlm2h370/NNU7QKmBCuTk5Jgu\nIqkkPn3+hNd5L3H7/k1cSj6DL18/Y5ThKDhTe5AQUgWkpqbCxZk7jiNXWwGaqn+imaI2Gsg1hlwt\nBaaLR6qogl/f8LXgPbI/3sXjd5eQ8/4JtDp0wkpH+8rVzlzngYxb6WjZrC16dOoH9RbtoVhfBXVq\nUzuTCO77j2/48OktHmXdwfVb55H14hE6aWnDfqVdpfh9IIRUHr/Pq7dtq4EBA/5Cxw4doKysQvPq\nRCj5+fl49+4tbt2+jbNnE/Ho0UNoa2vDzq7yfL/ev3+P9evXIzgoFB8/fYBG455oqdQNqgqtUbe2\nIh0EQQRSyPmFrz8+IO9zJrLe38DDnKtQbKCEufMqT3wHIYSwTam4MzkFdG3fH5ot/ok7k6O4MyK4\nQs4vfPryHi9zn+Du0+vIeJAERUUlzJlbeb7TRf0493VIz7gF9eaN0VdbHe1bNYFy/bqQry3LdBFJ\nFfHx6ze8evMBNx+/xNnUB/iS/x2jDA3h7OJC8+GEkCrt9/GhNio90axeFzSs2xrytRQhQ+NDRAAc\nzi/k//yAN18z8eJLKp68rXzjQ6XikeVk0adNfXRqLI9G9Wujfh36PSDi8a2gEO/yf+J+7ldczsrH\nk9xP6NSxA+wdKkc8MiFE/LjfHxccjYmBQl159O/eAZ01W6JJQyXUr0dz10Q88r//wLuPX3DnyQtc\nSL2HR8+yod1JC3b2KyvN94fXbwkJCcXHjx/QtZM+tNv3QIs/1KFYXxk1alB7jQjuy5ePeP0mG3cf\n3sTlG2fwNf8zrZMmhIisOJ5+DTJu3YF6ExX00VTD//5QglK9OpCXpW8UkYxP3wrw6v1XZDx/h3N3\ns/Hl249/5rVcK833rGx/qCM6t2tF/SHCKsX9qefc/lTWy0rXnyKkMinq/weH4sPHD+jYRg+azbqh\nacM2UJBXorh2IpSv3z/h7cdsPMlOR9qjs9x90kZVrv5/amoqXF1dcfToUdSvVw8DevVAZ612aKqm\nivoKdOgmqXw+ff6Cl69zcfP2A5y5fBWfv3zFqFGj4OxceX4vCSGCKxo3W7sGGbfvoI1affRuWRea\navJQlq8FOdkaTBeRVBGfvv9CzqcfuJWTjwtPPuHLtwKMGlX5xsl4+x3J11FAh+Z90UpVG0p1G0O+\nNq0fIoIr5BTiy/f3eP3hCZ7kpuDei+RKF8dZlfHGPUJDQ/Hhwwf00u+NHj16Ql29LZSVlOmANlLp\n8PbZuH37Fs6eP1vp9tn4fb1i21bN0a9HJ7RXbwUVxfqQq1Ob6SISIpRfhYV4//EzHj/LxvVb93El\n9RaUFJUwe84cageQ/6T476U70jMy0LZNKwzorYsOmhpQVlaEvFwdpotIiFB+/SrEuw8f8DjzGa6l\npuPytRQoKSpWqr+XZdoxLZqib+d2aN/6Dyg3qAe5OrTvAiGi+PTlG7Lz3iP90TMk/r+9u2uKuzzj\nAPxz2zGrssDC1LDTabXQ2gFCQxIwepAXZ8iBjl8jZznqkR71c/R7OHoAByap1SQQoMr6MoK1jgOx\nA0sgmk1nCD0ADbSdCbHJbP7rdX2C+2T3/9xvzzPzcb693XTPBD95e+d4NjZuZuSFk/n9cyfyy2d/\nk46nu/Mzezw8BrbubuXWd+v5+psv8umXM/nws+Ldq7V3nrryzNM589LxjA7+Ln2/6E1nx9OtDo82\ncbt5J42bm6l//kUuXZ3P4pdfZeTIkbzxZjHewQAO5od+5J935rDHT7yc40fH8/yv+9PVpR9J8TSb\nt9NYX8unn9Xz3pWL+eLvizlyZCRvvlm0fuSe+vrL4xl8YSDV7q485b1QCmZrayuNmxtZ+vKrTM99\ntFNf7+7K+fPFqa8D9/ef7zj9dmAgr5w9m8GhwfRUq95xonC2trbSWG9kaXEpV69N56/vv5/u7u6c\nP1+c+uH/nHMbH8lg/3OpdlXyVNmcG/wYG7e+y/I/V/O3T5by7pW53Pru9s4eyp/0h3k09u2Ld3Tk\nlbOnM/qHkdT6Dqez094QJMnt282sNRqpf/xJLl5+L58vLmVk5EjeeKMY/az/qgc+96ucOTmawYHn\nd89t5m0plq2tu2lsbGbpH19n+qNP88H1Dws3bwu7Ljyxvb293eooeLhmZmby9ttv5/Lly6nX61ld\nXU2z2Wx1WPBASqVSqtVqBgYGMjY2lldffTUTExMpF7CRvLy8nLfeeitTU1OZvT6flRvL2dzcaHVY\n8MAqlc70Ha7l2PGjmZiYyOuvv55ardbqsACS7PneTk5mbnYmKys3snHr21aHRZsoH3oyPdVqhoaH\nc/rM2bz22ms5ceJEq8P6Ub7PFy9d2skX19bkixxcZ6UzfbVaRkedB4H29f25cnJyKrMzc1m5sZJb\n36rj8GgcerKcarUnw8NDOXP2dPucMxd2z5l3nDM5uPKhcnp6ejM0PJTTp08V+vcAFIe+Ov+vcrmc\n3t7eDA0N5dSpYn+/ms1mpqam8s477+TKB9eytLSUmxuN3L17t9WhUQClUildndX09/fn5EvjhZ7v\nAHjc7K1Xzs3OZ2VlOZu31Cs5uFKplO6uavr7B/LiyWLPYSZ78rhLF1NfWMjqWiPNO3daHRZtorPS\nkVpfX46OHsvEuXP64cBPzr760Pu79aFN9SEOplQqpauyWx96udj1oX3zyNens3LjG/PIPDQ788jd\nGRo+Uvh5ZODh2rt/Oj87m+WVlWxsbrY6LNpE+dCh9Pb2ZGhoOKdOF3tOcW/ecu3qdBaXFrO+Lm/h\nwVQqnan17e7FnLMXAzw8P8zTX3w39fpC1tbW9bF4ZDo7OlLrO5yjx44Xvq91Lx+azPzsnHyIx0o7\n5VNQJHvz/6tXprO0tJj1m/J/HkylozN9fbWMHjuacwXP//fVj+fmsry87LxEIXVWKqnVajk6Ouq+\nGviJ2Vc3W/goa431NO/8q9Vh0SY6O55J3+FnM3p8rG3qZJOTU5m9PpcbN1bsD/FA7PkWw966x/T0\ndBYXF9NoqHtQPO10z8a9fcVLqdcXsrq2lmZTn5diKZVKqXZ3Z2CgP2PjLzoH8Ejcu6frUuoL9d3/\nS/d0USz3/i8HMjZe/Lxp370L9XpWV9fMq8GP1Fmp3JvH08eCffbt8VzZ3eMxx8Nj4vt7tQb6BzJe\n8Hu19s1Tz81ledk8NQ9PuXwovT279Vzz0ND29p3frk1naXExDXvYFFC5XE5vT28Gh4r/bo36Ou2g\n3errwP15x4l2UCqVUq1Wd75fY8WuHybm3OBh2tlz7Nvdcyz2HgrFsm9ffH5+Z198w94QJO28n1DP\n6tqqcxuFox5IG7nwxPb29narowAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nALiPC6VWRwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHAQpVYHAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAcBClVgcAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAABwED9P8sdWBwEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAHAff/k3+M/u6BIjJ/QAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(\"images/allF_lactate_delta_dir_tree.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regression"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tree_rgr = DecisionTreeRegressor(max_depth=5)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
" max_leaf_nodes=None, min_impurity_split=1e-07,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best')"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree_rgr.fit(X_next_no_lac,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"export_graphviz(\n",
" tree_rgr,\n",
" out_file='images/allF_lactate_next_tree.dot',\n",
" feature_names=X_next_no_lac.columns.tolist(),\n",
" rounded=True,\n",
" filled=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash\n",
"dot -Tpng images/allF_lactate_next_tree.dot -o images/allF_lactate_next_tree.png"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kSURHR/Pmm2+ydOlS1q9fj1ar5ciRI3zxxRdcvXqVmzdv8sMPP/Dtt99y8uRJDh48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\nctixYwcrV64kKSmJmJgYgoODeeGFF+jXrx+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r58BSc3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\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X1EdVtrMq9S6mpkmPRUXnrKrtGhsbITxsLYJXhyqppVjymRS4uE2HRZdu8Jj7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\n8vlM9kf2zhxRuRHOb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uf7rVrX/Nk+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\niIhIzWnd+jl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p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JX76i0d29+w+QfCAVrdqaYMfOH+A1cQJuX7uMmM3fwX3kCKaWdK2Nmo6GSuT3MXAfOQLO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LRqmf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uO8JzY1MWHJoiCd+3nkwF5cunUp0LiMdQ1CiMITt2OnUfbflbyHQhRvkq/QOPkK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5BWAPhdefnu+vt0e8Sr8E5xS9KkaPPtX37+tPc03rlxiGoZAuXRS9fJlmjOynts04tBgX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Yvbl5jjwJ4t4KVn4Whqptx+1mzerbUxEEIIIerwXL9RpfiScxfgHxSK1csW49rFQrwWVuLa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\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XMuQMDUYjeP5cLF+5GstXrqa9s+JYwsvTXeSxSMKBAwcBgLV/Pv+9FAYC/e/TL2ll3VVV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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(\"images/allF_lactate_next_tree.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import GridSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"param_grid = {\n",
" 'max_features' : [None,100, 10],\n",
" 'max_depth' : [None,100,10],\n",
" 'max_leaf_nodes':[None,100,10]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=-1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid={'max_features': [None, 100, 10], 'max_leaf_nodes': [None, 100, 10], 'max_depth': [None, 100, 10]},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#started at 12:23\n",
"rfr = RandomForestRegressor(n_jobs=-1, n_estimators=500)\n",
"rfr_GSCV = GridSearchCV(rfr, param_grid)\n",
"rfr_GSCV.fit(X_next_no_lac,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rfr_best = rfr_GSCV.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Validation, K-Fold\n",
"R^2: 0.545162778881, 0.0560610551177, [0.5353357199991412, 0.64419765201018853, 0.60066694165637935, 0.51164113255753052, 0.5506897060691871, 0.45447956500780218, 0.5164075808759887, 0.55792997698958691, 0.47679462083844104, 0.60348489280891227]\n",
"RMSE: 1.89493670591, 0.307660958064, [-6.0359191693608292, -2.7183859798360133, -3.3285306653695104, -3.941353490953921, -2.9787724877043904, -2.284270327420622, -2.3124564718725207, -3.7272812202764052, -5.6633306460582409, -3.8641033865016774]\n",
"\n",
"Cross Validation, ShuffleSplit\n",
"R^2: 0.738968126661, 0.0322688779377, [0.79096608373049693, 0.74318863610944552, 0.78544549181875334, 0.75310586295581561, 0.72135417013610237, 0.74589170814238726, 0.72316974181240834, 0.68963846413762064, 0.69154521960418125, 0.74537588816066069]\n",
"RMSE: 1.46075071056, 0.0708159770043, [-2.409123632592403, -2.0706215392678837, -2.0806930945793773, -2.0165611329870732, -1.8817301152846566, -1.8627797710600877, -2.2587664771749383, -2.1642473085160336, -2.5663629969184005, -2.0771893416142087]\n"
]
}
],
"source": [
"run_crossval(rfr_best, X_next_no_lac,y_next)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2: 0.965105118898\n",
"RMSE: 0.535604126326\n"
]
}
],
"source": [
"X = X_next_no_lac\n",
"y = y_next\n",
"\n",
"rfr_best.fit(X,y)\n",
"y_pred = rfr_best.predict(X)\n",
"print 'R^2:',r2_score(y, y_pred)\n",
"print 'RMSE:',np.sqrt(mean_squared_error(y, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"feature component status variable_type units description \n",
"COUNT_NOMINAL output urine unknown nom no_units 3686(ml)_Voiding qs 5.042184e-09\n",
"STD glasgow coma scale verbal known ord no_units all 7.146657e-07\n",
"MEAN_QN blood pressure systolic unknown qn cc/min all 3.977447e-06\n",
"COUNT fraction of inspired oxygen known qn percent all 4.020769e-06\n",
"LAST fraction of inspired oxygen known qn percent all 5.500183e-06\n",
"STD respiratory rate unknown qn Breath all 6.207046e-06\n",
"LAST respiratory rate unknown qn Breath all 7.168945e-06\n",
"STD fraction of inspired oxygen known qn percent all 9.517755e-06\n",
" blood pressure systolic unknown qn cc/min all 9.590165e-06\n",
"MEAN_QN blood pressure diastolic unknown qn cc/min all 1.005453e-05\n",
"COUNT blood pressure systolic unknown qn cc/min all 1.134472e-05\n",
" blood pressure diastolic unknown qn cc/min all 1.177821e-05\n",
"MEAN_QN respiratory rate unknown qn Breath all 1.178851e-05\n",
"STD blood pressure diastolic unknown qn cc/min all 1.197285e-05\n",
"LAST blood pressure diastolic unknown qn cc/min all 1.342046e-05\n",
" blood pressure systolic unknown qn cc/min all 1.362234e-05\n",
"COUNT respiratory rate unknown qn Breath all 1.658057e-05\n",
"MEAN_QN fraction of inspired oxygen known qn percent all 2.468269e-05\n",
"STD central venous oxygen saturation unknown qn no_units all 2.958138e-05\n",
"COUNT red blood cell count known qn x10e6/uL all 3.761124e-05\n",
"COUNT_NOMINAL red blood cell count unknown nom no_units 51493(#/hpf)(number/hpf)_0-2 5.196185e-05\n",
"COUNT red blood cell count unknown qn number/hpf all 5.707163e-05\n",
"COUNT_NOMINAL white blood cell count unknown nom no_units 51516(#/hpf)(number/hpf)_0-2 6.975960e-05\n",
"STD vasopressin known qn units all 8.677904e-05\n",
"COUNT phosphorous serum known qn mg/dL all 9.229637e-05\n",
" pH arterial known qn no_units all 9.456117e-05\n",
" weight body known qn kg all 9.524232e-05\n",
" mean corpuscular volume known qn fL all 1.024473e-04\n",
" white blood cell count known qn x10e3/uL all 1.062048e-04\n",
" normal saline known qn mL all 1.104214e-04\n",
" ... \n",
"MEAN_QN prothrombin time known qn seconds all 7.790079e-03\n",
"LAST calcium total serum known qn mg/dL all 8.271342e-03\n",
" alanine aminotransferase serum unknown qn IU/L all 8.306121e-03\n",
"MEAN_QN calcium total serum known qn mg/dL all 8.680029e-03\n",
" alanine aminotransferase serum unknown qn IU/L all 8.768991e-03\n",
"COUNT bicarbonate arterial known qn mEq/L all 8.938727e-03\n",
"SUM output urine known qn mL all 9.238955e-03\n",
"LAST sodium serum known qn mEq/L all 9.610458e-03\n",
"MEAN_QN sodium serum known qn mEq/L all 9.895785e-03\n",
" norepinephrine known qn mcg/kg/min all 1.109099e-02\n",
"SUM norepinephrine known qn mcg all 1.126198e-02\n",
"MEAN_QN norepinephrine known qn mcg all 1.167705e-02\n",
"LAST glucose serum known qn mg/dL all 1.222294e-02\n",
" phosphorous serum unknown qn mEq/L all 1.251923e-02\n",
"MEAN_QN glucose serum known qn mg/dL all 1.304985e-02\n",
"LAST norepinephrine known qn mcg/kg/min all 1.442638e-02\n",
" pH arterial unknown qn units all 1.564968e-02\n",
"MEAN_QN pH arterial unknown qn units all 1.568230e-02\n",
" phosphorous serum unknown qn mEq/L all 1.738769e-02\n",
"LAST norepinephrine known qn mcg all 2.119434e-02\n",
" chloride serum unknown qn mEq/L all 2.441486e-02\n",
" bicarbonate other known qn mEq/L all 2.530007e-02\n",
" international normalized ratio known qn no_units all 2.593338e-02\n",
"MEAN_QN bicarbonate other known qn mEq/L all 2.700419e-02\n",
" chloride serum unknown qn mEq/L all 2.782360e-02\n",
" international normalized ratio known qn no_units all 3.569530e-02\n",
" aspartate aminotransferase serum unknown qn IU/L all 4.247280e-02\n",
"LAST aspartate aminotransferase serum unknown qn IU/L all 4.344836e-02\n",
" bicarbonate arterial known qn mEq/L all 5.312956e-02\n",
"MEAN_QN bicarbonate arterial known qn mEq/L all 5.670753e-02\n",
"dtype: float64"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(rfr_best.feature_importances_,index=X.columns).sort_values()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
}
},
"nbformat": 4,
"nbformat_minor": 0
}