Datasets
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"source": [
"# Import libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import getpass\n",
"import pdvega\n",
"import plotly.graph_objs as go\n",
"\n",
"from plotly.offline import iplot, init_notebook_mode\n",
"import plotly.io as pio\n",
"from plotly.graph_objs import *\n",
"\n",
"# for configuring connection \n",
"from configobj import ConfigObj\n",
"import os\n",
"\n",
"%matplotlib inline\n",
"\n",
"\n",
"import os\n",
"\n",
"\n",
"from sklearn import linear_model\n",
"from sklearn import metrics\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"#configure the notebook for use in offline mode\n",
"init_notebook_mode(connected=True)"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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"text/vnd.plotly.v1+html": [
"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
]
},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"df2= pd.read_csv(\"analysis.csv\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"df2.head()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>hospitalid</th>\n",
" <th>sodium</th>\n",
" <th>electivesurgery</th>\n",
" <th>vent</th>\n",
" <th>dialysis</th>\n",
" <th>gcs</th>\n",
" <th>urine</th>\n",
" <th>wbc</th>\n",
" <th>temperature</th>\n",
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"<p>5 rows × 85 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 hospitalid sodium electivesurgery vent dialysis gcs \\\n",
"0 0 59.0 139.0 -1.0 0.0 0.0 15.0 \n",
"1 1 73.0 134.0 -1.0 0.0 0.0 13.0 \n",
"2 2 73.0 -1.0 1.0 1.0 0.0 15.0 \n",
"3 3 63.0 137.0 -1.0 0.0 0.0 15.0 \n",
"4 4 63.0 135.0 -1.0 0.0 0.0 15.0 \n",
"\n",
" urine wbc temperature ... m11_True m12_True m13_True m14_True \\\n",
"0 -1.0 14.7 36.1 ... 1 0 0 1 \n",
"1 -1.0 14.1 39.3 ... 1 0 0 1 \n",
"2 -1.0 8.0 34.8 ... 0 0 1 0 \n",
"3 -1.0 10.9 36.6 ... 1 0 1 1 \n",
"4 -1.0 5.9 35.0 ... 0 0 1 0 \n",
"\n",
" m15_True m16_True m17_True m18_True m19_True m20_True \n",
"0 1 0 0 0 1 0 \n",
"1 1 0 0 0 1 0 \n",
"2 0 1 0 1 0 0 \n",
"3 1 0 0 1 1 0 \n",
"4 0 0 0 1 0 0 \n",
"\n",
"[5 rows x 85 columns]"
]
},
"metadata": {},
"execution_count": 3
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"del df2['hospitalid']\n",
"\n",
"df2 = df2.drop(df2.columns[[0]], axis=1)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"source": [
"df2.shape"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(95148, 83)"
]
},
"metadata": {},
"execution_count": 5
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"**We moved all the pre-processing including splitting>imputation>Standardization to the CV iterations**"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"source": [
"cols_to_norm=['gcs', 'urine', 'wbc', 'sodium',\n",
" 'temperature', 'respiratoryrate', 'heartrate', 'meanbp', 'creatinine',\n",
" 'ph', 'hematocrit', 'albumin', 'pao2', 'pco2', 'bun', 'glucose',\n",
" 'bilirubin', 'fio2', 'age', 'offset']\n",
"\n",
"X=df2.drop('destcopy', 1)\n",
"y=df2['destcopy']\n",
"df_cols = list(X) #fancy impute removes column names."
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 8,
"source": [
"# Load in our libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"import sklearn\n",
"import xgboost as xgb\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import plotly.offline as py\n",
"py.init_notebook_mode(connected=True)\n",
"import plotly.graph_objs as go\n",
"import plotly.tools as tls\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Going to use these 5 base models for the stacking\n",
"from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
" GradientBoostingClassifier, ExtraTreesClassifier)\n",
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.linear_model import LogisticRegression"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
],
"text/vnd.plotly.v1+html": [
"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
]
},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"source": [
"from sklearn.model_selection import StratifiedKFold"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
"source": [
"\n",
"classes=['Death','Home','Nursing Home','Rehabilitation']\n",
"\n",
"kf_m = StratifiedKFold(n_splits=10)\n",
"\n",
"\n",
"\n",
"# Class to extend the Sklearn classifier\n",
"class SklearnHelper(object):\n",
" def __init__(self, clf, seed=0, params=None):\n",
" params['random_state'] = seed\n",
" self.clf = clf(**params)\n",
"\n",
" def train(self, x_train, y_train):\n",
" self.clf.fit(x_train, y_train)\n",
"\n",
" def predict(self, x):\n",
" return self.clf.predict(x)\n",
"\n",
" def fit(self,x,y):\n",
" return self.clf.fit(x,y)\n",
"\n",
" def feature_importances(self,x,y):\n",
" return(self.clf.fit(x,y).feature_importances_)\n",
" \n",
"\n",
"\n",
"\n",
"#-------------------------------------------------------------\n",
"\n",
"\n",
"\n",
"\n",
"#------------------------------------------\n",
"\n",
"rf_params = {\n",
" 'n_jobs': -1,\n",
" 'n_estimators': 400,\n",
" 'warm_start': True, \n",
" #'max_features': 0.2,\n",
" 'max_depth': 30,\n",
" 'min_samples_leaf': 2,\n",
" 'max_features' : 0.8,\n",
" 'verbose': 0,\n",
" 'criterion':'gini'\n",
"}\n",
"\n",
"\n",
"# Extra Trees Parameters\n",
"et_params = {\n",
" 'n_jobs': -1,\n",
" 'n_estimators':500,\n",
" #'max_features': 0.5,\n",
" 'max_depth': 8,\n",
" 'min_samples_leaf': 2,\n",
" 'verbose': 0\n",
"}\n",
"\n",
"# AdaBoost parameters\n",
"ada_params = {\n",
" 'n_estimators': 500,\n",
" 'learning_rate' : 0.75\n",
"}\n",
"\n",
"# Gradient Boosting parameters\n",
"gb_params = {\n",
" 'n_estimators': 500,\n",
" #'max_features': 0.2,\n",
" 'max_depth': 5,\n",
" 'min_samples_leaf': 2,\n",
" 'verbose': 0\n",
"}\n",
"\n",
"\n",
"\n",
"# Support Vector Classifier parameters \n",
"lr_params = {\n",
" 'penalty' : 'l1',\n",
" 'tol' : 6.75e-05,\n",
" 'C' : 2.5,\n",
" 'max_iter': 66\n",
" }\n",
"\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"**Random Forest**"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 11,
"source": [
"from collections import Counter"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"source": [
"from sklearn.model_selection import KFold\n",
"from sklearn import preprocessing\n",
"from imblearn.over_sampling import SMOTENC\n",
"from sklearn.metrics import f1_score\n",
"from yellowbrick.classifier import ROCAUC\n",
"from sklearn.linear_model import LogisticRegression\n",
"from numpy import loadtxt\n",
"import os\n",
"os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
"from xgboost import XGBClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.datasets import make_classification\n",
"from sklearn.model_selection import StratifiedKFold\n",
"import io \n",
"\n",
"\n",
"\n",
"for fold, (train_index, test_index) in enumerate(kf_m.split(X,y), 1):\n",
" X_train = X.iloc[train_index]\n",
" y_train = y.iloc[train_index] # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
" X_test = X.iloc[test_index]\n",
" y_test = y.iloc[test_index] # See comment on ravel and y_train\n",
" \n",
" \n",
"#------------------------------Standardize Testing Set------------------------------------\n",
" \n",
" std_scale = preprocessing.StandardScaler().fit(X_train[cols_to_norm])\n",
" X_train[cols_to_norm] = std_scale.transform(X_train[cols_to_norm])\n",
" X_test[cols_to_norm] = std_scale.transform(X_test[cols_to_norm])\n",
"#------------------------------------------------------------------------------------------\n",
"\n",
" # Hyperparameters are optimized using hyperopt\n",
"\n",
"\n",
"\n",
"# Class to extend XGboost classifer\n",
" sm = SMOTENC(random_state=50, categorical_features=[1,2,3,22,23,24,25,26,27,28,29,30,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61, 62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81])\n",
" X_train_oversampled, y_train_oversampled = sm.fit_sample(X_train, y_train)\n",
" print(sorted(Counter(y_train_oversampled).items()))\n",
" \n",
"# --------------- Let's Start the fun ------------------------\n",
"\n",
" # Some useful parameters which will come in handy later on\n",
" ntrain = X_train_oversampled.shape[0]\n",
" print(ntrain)\n",
" ntest = X_test.shape[0]\n",
" SEED = 0 # for reproducibility\n",
" # set folds for out-of-fold prediction\n",
" #kf = KFold(ntrain, n_split=5, random_state=SEED)\n",
" \n",
" def get_oof(clf, x_train, y_train, x_test):\n",
" oof_train = np.zeros((ntrain,))\n",
" oof_test = np.zeros((ntest,))\n",
" oof_test_skf = np.empty((10, ntest))\n",
"\n",
"\n",
" for i, (train_index, test_index) in enumerate(kf_m.split(x_train, y_train)):\n",
" x_tr = x_train[train_index]\n",
" y_tr = y_train[train_index]\n",
" x_te = x_train[test_index]\n",
"\n",
" clf.train(x_tr, y_tr)\n",
" \n",
" oof_train[test_index] = clf.predict(x_te)\n",
" oof_test_skf[i, :] = clf.predict(x_test)\n",
"\n",
" oof_test[:] = oof_test_skf.mean(axis=0)\n",
" return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)\n",
" \n",
" # Create 5 objects that represent our 4 models\n",
" #rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
" et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
" #ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\n",
" gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\n",
" #lr = SklearnHelper(clf=LogisticRegression, seed=SEED, params=lr_params)\n",
"\n",
" #------------------------------------------\n",
" # Create our OOF train and test predictions. These base results will be used as new features\n",
" et_oof_train, et_oof_test = get_oof(et, X_train_oversampled, y_train_oversampled, X_test) # Extra Trees\n",
" #rf_oof_train, rf_oof_test = get_oof(rf,X_train_oversampled, y_train_oversampled, X_test) # Random Forest\n",
" #ada_oof_train, ada_oof_test = get_oof(ada, X_train_oversampled, y_train_oversampled, X_test) # AdaBoost \n",
" gb_oof_train, gb_oof_test = get_oof(gb,X_train_oversampled, y_train_oversampled, X_test) # Gradient Boost\n",
" #lr_oof_train, lr_oof_test = get_oof(lr,X_train_oversampled, y_train_oversampled, X_test) # Support Vector Classifier\n",
"\n",
" print(\"Training is complete\")\n",
"\n",
"\n",
"\n",
" #rf_features = rf.feature_importances(X_train_oversampled,y_train_oversampled).tolist()\n",
" et_features = et.feature_importances(X_train_oversampled, y_train_oversampled).tolist()\n",
" #ada_features = ada.feature_importances(X_train_oversampled, y_train_oversampled).tolist()\n",
" gb_features = gb.feature_importances(X_train_oversampled, y_train_oversampled).tolist()\n",
" #lr_features=(map(abs,lr_features)) / (abs(lr_fit.coef_).max())\n",
"\n",
"\n",
"\n",
" cols = df2.drop('destcopy', 1).columns.values\n",
" # Create a dataframe with features\n",
" feature_dataframe = pd.DataFrame( {'features': cols,\n",
" \n",
" 'Extra Trees feature importances': et_features,\n",
" \n",
" 'Gradient Boost feature importances': gb_features,\n",
" #'LR feature importances': lr_features\n",
" })\n",
"\n",
"\n",
"\n",
"\n",
" # Create a dataframe with features\n",
"\n",
"\n",
"\n",
"\n",
" # Scatter plot \n",
" trace = go.Scatter(\n",
" y = feature_dataframe['Extra Trees feature importances'].values,\n",
" x = feature_dataframe['features'].values,\n",
" mode='markers',\n",
" marker=dict(\n",
" sizemode = 'diameter',\n",
" sizeref = 1,\n",
" size = 25,\n",
" # size= feature_dataframe['AdaBoost feature importances'].values,\n",
" #color = np.random.randn(500), #set color equal to a variable\n",
" color = feature_dataframe['Extra Trees feature importances'].values,\n",
" colorscale='Portland',\n",
" showscale=True\n",
" ),\n",
" text = feature_dataframe['features'].values\n",
" )\n",
" data = [trace]\n",
"\n",
" layout= go.Layout(\n",
" autosize= True,\n",
" title= 'Extra Trees Feature Importance',\n",
" hovermode= 'closest',\n",
" # xaxis= dict(\n",
" # title= 'Pop',\n",
" # ticklen= 5,\n",
" # zeroline= False,\n",
" # gridwidth= 2,\n",
" # ),\n",
" yaxis=dict(\n",
" title= 'Feature Importance',\n",
" ticklen= 5,\n",
" gridwidth= 2\n",
" ),\n",
" showlegend= False\n",
" )\n",
" fig = go.Figure(data=data, layout=layout)\n",
" py.iplot(fig,filename='scatter2010')\n",
"\n",
"\n",
"\n",
" # Scatter plot \n",
" trace = go.Scatter(\n",
" y = feature_dataframe['Gradient Boost feature importances'].values,\n",
" x = feature_dataframe['features'].values,\n",
" mode='markers',\n",
" marker=dict(\n",
" sizemode = 'diameter',\n",
" sizeref = 1,\n",
" size = 25,\n",
" # size= feature_dataframe['AdaBoost feature importances'].values,\n",
" #color = np.random.randn(500), #set color equal to a variable\n",
" color = feature_dataframe['Gradient Boost feature importances'].values,\n",
" colorscale='Portland',\n",
" showscale=True\n",
" ),\n",
" text = feature_dataframe['features'].values\n",
" )\n",
" data = [trace]\n",
"\n",
" layout= go.Layout(\n",
" autosize= True,\n",
" title= 'Gradient Boosting Feature Importance',\n",
" hovermode= 'closest',\n",
" # xaxis= dict(\n",
" # title= 'Pop',\n",
" # ticklen= 5,\n",
" # zeroline= False,\n",
" # gridwidth= 2,\n",
" # ),\n",
" yaxis=dict(\n",
" title= 'Feature Importance',\n",
" ticklen= 5,\n",
" gridwidth= 2\n",
" ),\n",
" showlegend= False\n",
" )\n",
" fig = go.Figure(data=data, layout=layout)\n",
" py.iplot(fig,filename='scatter2010')\n",
"\n",
" feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
" feature_dataframe.head(3)\n",
"\n",
" yv = feature_dataframe['mean'].values\n",
" x = feature_dataframe['features'].values\n",
" data = [go.Bar(\n",
" x= x,\n",
" y= yv,\n",
" width = 0.5,\n",
" marker=dict(\n",
" color = feature_dataframe['mean'].values,\n",
" colorscale='Portland',\n",
" showscale=True,\n",
" reversescale = False\n",
" ),\n",
" opacity=0.6\n",
" )]\n",
"\n",
" layout= go.Layout(\n",
" autosize= True,\n",
" title= 'Barplots of Mean Feature Importance',\n",
" hovermode= 'closest',\n",
" # xaxis= dict(\n",
" # title= 'Pop',\n",
" # ticklen= 5,\n",
" # zeroline= False,\n",
" # gridwidth= 2,\n",
" # ),\n",
" yaxis=dict(\n",
" title= 'Feature Importance',\n",
" ticklen= 5,\n",
" gridwidth= 2\n",
" ),\n",
" showlegend= False\n",
" )\n",
" fig = go.Figure(data=data, layout=layout)\n",
" py.iplot(fig, filename='bar-direct-labels')\n",
"\n",
"\n",
"\n",
"\n",
" base_predictions_train = pd.DataFrame( {\n",
" 'ExtraTrees': et_oof_train.ravel(),\n",
" \n",
" 'GradientBoost': gb_oof_train.ravel(),\n",
" #'LR': lr_oof_train.ravel()\n",
" })\n",
" base_predictions_train.head()\n",
"\n",
" data = [\n",
" go.Heatmap(\n",
" z= base_predictions_train.astype(float).corr().values ,\n",
" x=base_predictions_train.columns.values,\n",
" y= base_predictions_train.columns.values,\n",
" colorscale='Viridis',\n",
" showscale=True,\n",
" reversescale = True\n",
" )\n",
" ]\n",
" py.iplot(data, filename='labelled-heatmap')\n",
"\n",
" x_train = np.concatenate(( et_oof_train,gb_oof_train), axis=1)\n",
" x_test = np.concatenate(( et_oof_test, gb_oof_test), axis=1)\n",
" \n",
" gbm = RandomForestClassifier().fit(x_train,y_train_oversampled)\n",
" y_pred = gbm.predict(x_test)\n",
" visualizer = ROCAUC(gbm, classes=classes)\n",
" visualizer.fit(x_train, y_train_oversampled) # Fit the training data to the visualizer\n",
" visualizer.score(x_test, y_test) # Evaluate the model on the test data\n",
" visualizer.poof(\"Ensembel_{}.pdf\".format(fold), clear_figure=True) \n",
" print(f'For fold {fold}:')\n",
" print(f'Accuracy: {gbm.score(x_test, y_test)}')\n",
" f1=f1_score(y_test, y_pred, average='micro')\n",
" print(f'f-score: {f1}')\n",
" print(classification_report_imbalanced(y_test, y_pred))\n",
" K= classification_report_imbalanced(y_test, y_pred)\n",
" df = pd.read_fwf(io.StringIO(K))\n",
" df.loc[\"1\":\"1\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-D.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.loc[\"2\":\"2\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-H.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.loc[\"3\":\"3\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-N.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.loc[\"4\":\"4\",\"pre\":\"sup\"].to_csv(\"RF-Ensemble-R.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" df.iloc[6:7,:].to_csv(\"RF-Ensemble-avg.csv\" , sep=',', encoding='utf-8', doublequote=False, index=False, mode=\"a\", header=False)\n",
" \n",
" "
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[(1, 59596), (2, 59596), (3, 59596), (4, 59596)]\n",
"238384\n",
"Training is complete\n"
]
},
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"text": [
"For fold 1:\n",
"Accuracy: 0.7351828499369483\n",
"f-score: 0.7351828499369484\n"
]
},
{
"output_type": "error",
"ename": "NameError",
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x396 with 0 Axes>"
]
},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"len(y_test)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"list(map(abs,lr_features))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"visualizer\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"fig.write_image(\"images/fig1.png\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"#lr_fit= lr.fit(X_train_oversampled, y_train_oversampled).tolist()\n",
"lr_features = lr_fit.coef_\n",
"len(list(lr_features.flat))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
" \n",
" \n",
" model = AdaBoostClassifier() \n",
" model.fit(X_train_oversampled, y_train_oversampled) \n",
" y_pred = model.predict(X_test.values)\n",
" visualizer = ROCAUC(model, classes=classes)\n",
" visualizer.fit(X_train_oversampled, y_train_oversampled) # Fit the training data to the visualizer\n",
" visualizer.score(X_test.values, y_test) # Evaluate the model on the test data\n",
" visualizer.poof(\"Ada_Indicator_Replace_{}.pdf\".format(fold), clear_figure=True) \n",
" print(f'For fold {fold}:')\n",
" print(f'Accuracy: {model.score(X_test.values, y_test)}')\n",
" f1=f1_score(y_test, y_pred, average='micro')\n",
" print(f'f-score: {f1}')\n",
" print(classification_report_imbalanced(y_test, y_pred))\n",
" \n",
" #\n",
"\n",
" "
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
" feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
" feature_dataframe.head(3)\n",
" \n",
" y = feature_dataframe['mean'].values\n",
" x = feature_dataframe['features'].values\n",
" data = [go.Bar(\n",
" x= x,\n",
" y= y,\n",
" width = 0.5,\n",
" marker=dict(\n",
" color = feature_dataframe['mean'].values,\n",
" colorscale='Portland',\n",
" showscale=True,\n",
" reversescale = False\n",
" ),\n",
" opacity=0.6\n",
" )]\n",
"\n",
" layout= go.Layout(\n",
" autosize= True,\n",
" title= 'Barplots of Mean Feature Importance',\n",
" hovermode= 'closest',\n",
" # xaxis= dict(\n",
" # title= 'Pop',\n",
" # ticklen= 5,\n",
" # zeroline= False,\n",
" # gridwidth= 2,\n",
" # ),\n",
" yaxis=dict(\n",
" title= 'Feature Importance',\n",
" ticklen= 5,\n",
" gridwidth= 2\n",
" ),\n",
" showlegend= False\n",
" )\n",
" fig = go.Figure(data=data, layout=layout)\n",
" py.iplot(fig, filename='bar-direct-labels')\n",
" \n",
" base_predictions_train = pd.DataFrame( {\n",
" 'ExtraTrees': et_oof_train.ravel(),\n",
" 'GradientBoost': gb_oof_train.ravel()\n",
" })\n",
" base_predictions_train.head()\n",
" \n",
" data = [\n",
" go.Heatmap(\n",
" z= base_predictions_train.astype(float).corr().values ,\n",
" x=base_predictions_train.columns.values,\n",
" y= base_predictions_train.columns.values,\n",
" colorscale='Viridis',\n",
" showscale=True,\n",
" reversescale = True\n",
" )\n",
" ]\n",
" py.iplot(data, filename='labelled-heatmap')\n",
" \n",
" #-------------------------------------------------------------------------------------\n",
" x_train = np.concatenate(( et_oof_train, rf_oof_train, ada_oof_train, gb_oof_train, lr_oof_train), axis=1)\n",
" x_test = np.concatenate(( et_oof_test, rf_oof_test, ada_oof_test, gb_oof_test, lr_oof_test), axis=1)\n",
" \n",
" gbm = xgb.XGBClassifier(\n",
" #learning_rate = 0.02,\n",
" n_estimators= 2000,\n",
" max_depth= 4,\n",
" min_child_weight= 2,\n",
" #gamma=1,\n",
" gamma=0.9, \n",
" subsample=0.8,\n",
" colsample_bytree=0.8,\n",
" objective= 'binary:logistic',\n",
" nthread= -1,\n",
" scale_pos_weight=1).fit(x_train, y_train_oversampled)\n",
" predictions = gbm.predict(x_test)\n",
" "
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"len(lr_features)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"cols = df2.drop('destcopy', 1).columns.values"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import plotly.graph_objects as go"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"fig.show()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import numpy as np \n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import StandardScaler\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"x1 = np.random.randn(100)\n",
"x2 = np.random.randn(100)\n",
"x3 = np.random.randn(100)\n",
"\n",
"#Make difference in feature dependance\n",
"y = (3 + x1 + 2*x2 + 5*x3 + 0.2*np.random.randn()) > 0\n",
"\n",
"X = pd.DataFrame({'x1':x1,'x2':x2,'x3':x3})\n",
"\n",
"#Scale your data\n",
"scaler = StandardScaler()\n",
"scaler.fit(X) \n",
"X_scaled = pd.DataFrame(scaler.transform(X),columns = X.columns)\n",
"\n",
"clf = LogisticRegression(random_state = 0)\n",
"clf.fit(X_scaled, y)\n",
"\n",
"feature_importance = abs(clf.coef_[0])\n",
"feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
"sorted_idx = np.argsort(feature_importance)\n",
"pos = np.arange(sorted_idx.shape[0]) + .5\n",
"\n",
"featfig = plt.figure()\n",
"featax = featfig.add_subplot(1, 1, 1)\n",
"featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
"featax.set_yticks(pos)\n",
"featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=8)\n",
"featax.set_xlabel('Relative Feature Importance')\n",
"\n",
"plt.tight_layout() \n",
"plt.show()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"feature_importance"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [],
"outputs": [],
"metadata": {}
}
],
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