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Chi2_NN.py

+import numpy as np
+import pandas as pd
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import chi2 
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import KFold
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import multilabel_confusion_matrix, precision_recall_fscore_support, recall_score, precision_score, f1_score, confusion_matrix, accuracy_score
+from sklearn import preprocessing
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from keras.utils import to_categorical
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation
+from keras.optimizers import SGD
+
+import seaborn as sn
+import matplotlib.pyplot as plt
+
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.model_selection import cross_val_score
+
+np.random.seed(42)
+
+## Load data and labels ###
+
+label = pd.read_csv('dataset/labels.csv')
+data = pd.read_csv('dataset/data.csv')
+
+#Encode the variable : 
+
+X = data.values[:,1:]
+X = np.asarray(X).astype('float32')
+
+#Encode the variable 
+
+encode = preprocessing.LabelEncoder()
+encode.fit(label.Class.unique())
+y = encode.transform(label.Class.values)
+
+y = np.array(y)
+#encode y to categorical
+encoded = to_categorical(y)
+y = encoded
+
+
+#NN model
+def create_network(n_feats):
+    model = Sequential()
+    model.add(Dense(128, input_dim = n_feats, activation='relu'))
+    #model.add(Dropout(0.5))
+    model.add(Dense(64, activation='relu'))
+    #model.add(Dropout(0.5))
+    model.add(Dense(5, activation='softmax'))
+
+    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+
+    # compile the keras model
+    model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+
+    return model
+
+KF = KFold(n_splits=5,shuffle=True)
+itern =0
+total_train_accuracy = 0
+total_test_accuracy = 0
+total_precision = 0
+total_recall = 0
+total_fscore = 0
+final_conf_mat =0
+
+for train_index, test_index in KF.split(X):
+	# Split train-test
+	x_train, x_test = X[train_index], X[test_index]
+	y_train, y_test = y[train_index], y[test_index]
+
+	#Feature Selection with chi2 test
+	ch2 = SelectKBest(chi2, k=20)
+	x1_train = ch2.fit_transform(x_train, y_train)
+	x1_test = ch2.transform(x_test)
+
+	### Classification model (ADD NN to be fit here)###
+
+	classifier = create_network(x1_train.shape[1])
+	classifier.fit(x1_train, y_train, epochs=100, batch_size=25, validation_data=(x1_test, y_test))
+
+	y_pred = classifier.predict(x1_test)
+
+	#### Scoring #####
+	print ("\nAccuracy on Training Set :")
+	_, accuracy_train = classifier.evaluate(x1_train, y_train)
+	print('Accuracy: %.2f' % (accuracy_train*100))
+	#print("____________")
+	#print (accuracy_score(inverted_y_test,inverted_y_pred))
+	total_train_accuracy += accuracy_train
+
+	print ("\nChecking on Test Set")
+	print ("\nAccuracy on Testing Set :")
+	_, accuracy_test = classifier.evaluate(x1_test, y_test)
+	print('Accuracy: %.2f' % (accuracy_test*100))
+	total_test_accuracy += accuracy_test
+
+
+	##### Decoding the labels ####
+
+	#inverting to_categorical encoding
+	inverted_y_pred = (np.argmax(y_pred, axis=1)).reshape(-1,1)
+	inverted_y_test = (np.argmax(y_test, axis=1)).reshape(-1,1)
+
+	###Scoring ###
+
+	print("Fold no: "+str(itern))
+	itern+=1
+	
+	print ("Checking on Test Set")
+	print ("\nAccuracy on Testing Set :"+str(accuracy_score(inverted_y_test,inverted_y_pred)))
+	#total_test_accuracy += accuracy_score(y_test,y_pred)
+  
+	total_precision+= precision_score(inverted_y_test, inverted_y_pred,average='macro')
+	total_recall+= recall_score(inverted_y_test, inverted_y_pred,average='macro')	
+	total_fscore+= f1_score(inverted_y_test, inverted_y_pred,average='macro')	
+	print ("\nPrecision Score")
+	print (precision_score(inverted_y_test, inverted_y_pred,average='macro'))
+	print ("\nRecall Score")
+	print (recall_score(inverted_y_test, inverted_y_pred,average='macro'))	
+	print ("\nF1 Score")
+	print (f1_score(inverted_y_test, inverted_y_pred,average='macro'))
+
+	#Confusion Matrix
+	conf_mat=confusion_matrix(inverted_y_test, inverted_y_pred)
+	final_conf_mat+=conf_mat
+	print("Confusion matrix :\n")
+	print(conf_mat)
+    
+print("Mean train accuracy : %.2f" % ((total_train_accuracy/5)*100))
+print("Mean test accuracy : %.2f" % ((total_test_accuracy/5)*100))
+print("Mean precision : %.2f" % ((total_precision/5)*100))
+print("Mean recall : %.2f" % ((total_recall/5)*100))
+print("Mean fscore : %.2f" % ((total_fscore/5)*100))
+
+print("Confusion matrix : " )
+print(final_conf_mat)
+#plt.figure(figsize=(5,5))
+sn.heatmap(final_conf_mat, annot=True)
+plt.xlabel('Predicted')
+plt.ylabel('Truth')
+plt.show()
+
+
+
+