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GA_SVM.py

+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 27 20:34:21 2020
+
+@author: ashish
+"""
+from sklearn import preprocessing
+import numpy as np
+import pandas as pd
+import random
+import matplotlib.pyplot
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+import time
+nfeat=50
+X=np.loadtxt('dataset/data.csv', delimiter=',', converters=None, skiprows=1, usecols=range(1,nfeat), unpack=False, ndmin=0, )
+y=np.genfromtxt('dataset/labels.csv',dtype='str',skip_header=1,usecols=[1],delimiter=',')
+df = pd.DataFrame(X,columns=np.arange(1,nfeat))
+label=np.array(y)
+X_train, X_test, y_train, y_test = train_test_split(df, 
+                                                    label, test_size=0.30, 
+                                                    random_state=0)
+
+
+from sklearn.svm import SVC 
+start_time=time.time()
+svm_model_linear = SVC(kernel = 'poly', C = 1,max_iter=1000,tol=0.01).fit(X_train, y_train) 
+svm_predictions = svm_model_linear.predict(X_test)
+print  time.time()-start_time,"Time taken"
+print("Accuracy = "+ str(accuracy_score(y_test,svm_predictions)))
+def initilization_of_population(size,n_feat):
+    population = []
+    for i in range(size):
+        chromosome = np.ones(n_feat,dtype=np.bool)
+        #print chromosome
+        #chromosome[:int(0.3*n_feat)]=False
+        chromosome[:int(0.95*nfeat)]=False
+        #print chromosome,"then"
+        np.random.shuffle(chromosome)
+        population.append(chromosome)
+    
+    return population
+
+def fitness_score(population):
+    scores = []
+    for chromosome in population:
+        if sum(chromosome)==0:
+            chromosome[0]=True
+        start_time=time.time()
+        #print chromosome,sum(chromosome)
+        #logmodel.fit(X_train.iloc[:,chromosome],y_train)
+        #predictions = logmodel.predict(X_test.iloc[:,chromosome])
+        #from sklearn.svm import SVC
+        
+        svm_model_linear = SVC(kernel = 'poly', C = 1,max_iter=1000).fit(X_train.iloc[:,chromosome], y_train) 
+        predictions = svm_model_linear.predict(X_test.iloc[:,chromosome])
+        #print  time.time()-start_time,"Time taken"
+        scores.append(accuracy_score(y_test,predictions))
+    scores, population = np.array(scores), np.array(population) 
+    inds = np.argsort(scores)
+    #print len(population),"bk"
+    return list(scores[inds][::-1]), list(population[inds,:][::-1])
+
+def selection(pop_after_fit,n_parents):
+    population_nextgen = []
+    for i in range(n_parents):
+        #print len(pop_after_fit),"my lord"
+        population_nextgen.append(pop_after_fit[i])
+    return population_nextgen
+
+def crossover(pop_after_sel):
+    population_nextgen=pop_after_sel
+    for i in range(len(pop_after_sel)):
+        child=pop_after_sel[i]
+        child[3:7]=pop_after_sel[(i+1)%len(pop_after_sel)][3:7]
+        population_nextgen.append(child)
+    return population_nextgen
+
+def mutation(pop_after_cross,mutation_rate):
+    population_nextgen = []
+    for i in range(0,len(pop_after_cross)):
+        chromosome = pop_after_cross[i]
+        for j in range(len(chromosome)):
+            if random.random() < mutation_rate:
+                chromosome[j]= not chromosome[j]
+        population_nextgen.append(chromosome)
+    #print(population_nextgen)
+    return population_nextgen
+
+def generations(size,n_feat,n_parents,mutation_rate,n_gen,X_train,
+                                   X_test, y_train, y_test):
+    best_chromo= []
+    best_score= []
+    population_nextgen=initilization_of_population(size,n_feat)
+    for i in range(n_gen):
+        scores, pop_after_fit = fitness_score(population_nextgen)
+        print(scores[:2])
+        pop_after_sel = selection(pop_after_fit,n_parents)
+        pop_after_cross = crossover(pop_after_sel)
+        population_nextgen = mutation(pop_after_cross,mutation_rate)
+        best_chromo.append(pop_after_fit[0])
+        best_score.append(scores[0])
+        print sum(pop_after_fit[0])
+    return best_chromo,best_score
+chromo,score=generations(size=200,n_feat=nfeat-1,n_parents=50,mutation_rate=0.10,n_gen=50,X_train=X_train,X_test=X_test,y_train=y_train,y_test=y_test)
+#logmodel.fit(X_train.iloc[:,chromo[-1]],y_train)
+#predictions = logmodel.predict(X_test.iloc[:,chromo[-1]])
+svm_model_linear = SVC(kernel = 'poly', C = 1).fit(X_train.iloc[:,chromo[-1]],y_train)
+predictions = svm_model_linear.predict(X_test.iloc[:,chromo[-1]])
+print("Accuracy score after genetic algorithm is= "+str(accuracy_score(y_test,predictions)))
+
+### Scoring ####
+from sklearn.metrics import multilabel_confusion_matrix, precision_recall_fscore_support, recall_score, precision_score, f1_score, confusion_matrix, accuracy_score
+
+print("Accuracy score after genetic algorithm is= "+str(accuracy_score(y_test,predictions)))
+
+
+print ("\nAccuracy on Training Set :"+str(accuracy_score( y_train,y_pred_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='macro'))
+print ("\nRecall Score")
+print (recall_score(y_test, y_pred,average='macro'))
+print ("\nF1 Score")
+print (f1_score(y_test, y_pred,average='macro'))
+
+
+#Confusion Matrix
+cm = confusion_matrix(y_test, predictions)   
+print("Confusion matrix : " )
+print(cm)
+#plt.figure(figsize=(5,5))
+sn.heatmap(cm, annot=True)
+plt.xlabel('Predicted')
+plt.ylabel('Truth')
+plt.show()
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