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RF_chisquare.py

+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import chi2 
+from sklearn.model_selection import KFold
+from sklearn import preprocessing
+from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, recall_score, precision_score, f1_score, confusion_matrix, accuracy_score
+import matplotlib.pyplot as plt
+import seaborn as sn
+
+## Read data and labels from csv's
+
+label = pd.read_csv('labels.csv')
+data = pd.read_csv('data.csv')
+features = data.values[:,1:]
+
+#Encode the variable 
+
+encode = preprocessing.LabelEncoder()
+encode.fit(label.Class.unique())
+target = encode.transform(label.Class.values)
+
+
+# Performing K-Folds cross validation
+KF = KFold(n_splits=5,shuffle=True)
+for train_index, test_index in KF.split(features):
+
+    # Splitting train & test
+    x_train, x_test = features[train_index], features[test_index]
+    y_train, y_test = target[train_index], target[test_index]
+
+    #Feature Selection with chi2 test
+    ch2 = SelectKBest(chi2, k=20)
+    ch_train = ch2.fit_transform(x_train, y_train)
+    ch_test = ch2.transform(x_test)
+
+
+    # Classification model:RANDOM FOREST
+
+    model = RandomForestClassifier(n_estimators = 10, criterion = 'entropy')
+    model.fit(ch_train, y_train)
+
+    #Predicted values of target
+    y_pred = model.predict(ch_test)
+
+    # Model Scores
+
+    print ("\nAccuracy on Training Set :"+str(model.score(ch_train, y_train)))
+    print ("Checking on Test Set")
+    print ("\nAccuracy on Testing Set :"+str(accuracy_score(y_test,y_pred)))
+  
+
+    print ("\nPrecision Score")
+    print (precision_score(y_test, y_pred,average=None))
+    print ("\nRecall Score")
+    print (recall_score(y_test, y_pred,average=None))
+    print ("\nF1 Score")
+    print (f1_score(y_test, y_pred,average=None))
+
+    #Confusion Matrix
+    conf_mat=confusion_matrix(y_test,y_pred)
+    plt.figure(figsize=(10,7))
+    sn.heatmap(conf_mat, annot=True)
+    plt.xlabel('Predicted')
+    plt.ylabel('Truth')
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