def svm1(X, X_valid, y, Y_valid,train_x,train_y,kaggle_test): #X,y-training set #X_valid -validationset #train_x,whole training set #X_test kaggle m, n = X.shape kf = KFold(m, n_folds=2) gammas = [0.001,0.01,0.1] cs = [1.0,10.0,100.0] accuracy_list = [] for g in gammas: for c in cs: accs = [] counter = 0 for train_index, test_index in kf: X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] clf = svm.SVC(C= c,gamma=g) clf.fit(X_train,y_train) pred = clf.predict(X_test) # print type(pred) corr, acc = accuracy(y_test,pred) accs.append(acc) counter +=1 acc_mean = np.mean(accs) accuracy_list.append((c,g,acc_mean)) sortedaccuracy_list = sorted(accuracy_list, key = lambda x:x[2], reverse = True) print sortedaccuracy_list c_optimal = sortedaccuracy_list[0][0] g_optimal = sortedaccuracy_list[0][1] fname = 'results/svm/svm_accuracy.png' plot_svm_accuracy(accuracy_list,fname) # check validationset print "testing on validationset" clf = svm.SVC(C= c_optimal,gamma=g_optimal) clf.fit(X,y) pred__valid_svm = clf.predict(X_valid) print "results of svm using "+str(c_optimal)+'c'+str(g_optimal)+'gamma' + ":\n%s\n" %(metrics.classification_report(Y_valid,pred__valid_svm)) fname2 = 'results/svm/'+str(c_optimal)+'c'+str(g_optimal)+'gamma'+'cfm_svm.png' cfm_svm(pred__valid_svm,Y_valid,c_optimal,g_optimal,fname2) # classifcation of kaggle testset print "classifcation kaggle" clf = svm.SVC(C= c_optimal,gamma=g_optimal) clf.fit(train_x,train_y) pred_kaggle_svm = clf.predict(kaggle_test) fname3 = 'results/svm/svm_kaggle.csv' write_tokaggle(fname3,pred_kaggle_svm)
def svm2_pca(X, X_valid, y, Y_valid,train_x,train_y,kaggle_test): pcas = [100,500,1000] gammas = [0.001,0.01,0.1] cs = [1.0,10.0,100.0] accuracy_list = [] for pca in pcas: print 'pca',pca accuracy_list_p = [] pca = PCA(n_components = pca,whiten = True) train_x_p = pca.fit_transform(train_x) X_p = pca.transform(X) X_valid_p = pca.transform(X_valid) kaggle_test_p = pca.transform(kaggle_test) m, n = X_p.shape kf = KFold(m, n_folds=2) for g in gammas: for c in cs: accs = [] counter = 0 for train_index, test_index in kf: X_train, X_test = X_p[train_index], X_p[test_index] y_train, y_test = y[train_index], y[test_index] clf = svm.SVC(C= c,gamma=g) clf.fit(X_train,y_train) pred = clf.predict(X_test) # print type(pred) corr, acc = accuracy(y_test,pred) accs.append(acc) counter +=1 acc_mean = np.mean(accs) accuracy_list.append((pca,c,g,acc_mean)) accuracy_list_p.append((c,g,acc_mean)) fname = 'results/svm_pca/svm_pca_accuracy_'+str(pca)+'pca.png' plot_svm_accuracy(accuracy_list_p,fname) sortedaccuracy_list = sorted(accuracy_list, key = lambda x:x[3], reverse = True) print sortedaccuracy_list pca_optimal = sortedaccuracy_list[0][0] c_optimal = sortedaccuracy_list[0][1] g_optimal = sortedaccuracy_list[0][2] pca = PCA(n_components = pca_optimal,whiten = True) train_x_p = pca.fit_transform(train_x) X_p = pca.transform(X) X_valid_p = pca.transform(X_valid) kaggle_test_p = pca.transform(kaggle_test) # check validationset print "testing on validationset" clf = svm.SVC(C= c_optimal,gamma=g_optimal) clf.fit(X_p,y) pred__valid_svm = clf.predict(X_valid_p) print "results of svm using pca "+str(pca_optimal)+'pca components'+str(c_optimal)+'c'+str(g_optimal)+'gamma' + ":\n%s\n" %(metrics.classification_report(Y_valid,pred__valid_svm)) fname2 = 'results/svm_pca/'+str(pca_optimal)+'pca components'+str(c_optimal)+'c'+str(g_optimal)+'gamma'+'cfm_svm_pca.png' cfm_svm(pred__valid_svm,Y_valid,c_optimal,g_optimal,fname2) # classifcation of kaggle testset print "classifcation kaggle" clf = svm.SVC(C= c_optimal,gamma=g_optimal) clf.fit(train_x_p,train_y) pred_kaggle_svm = clf.predict(kaggle_test_p) fname3 = 'results/svm_pca/svm_pca_kaggle.csv' write_tokaggle(fname3,pred_kaggle_svm)
