X = df_merged.drop(["Genre","Song ID","Track ID"], axis = 1) #Split from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y) #Train adaboost_model.fit(X_train,y_train) #Predict y_train_predicted = adaboost_model.predict(X_train) y_test_predicted = adaboost_model.predict(X_test) print "Number of Train Samples: ", (y_train.shape[0]) print "Number of Test Samples: ", (y_test.shape[0]) print "Train Classification Rate: ", (sum(y_train_predicted == y_train)) / float(y_train.shape[0]) print "Test Classification Rate: ", (sum(y_test_predicted == y_test)) / float(y_test.shape[0]) print ml_aux.getUniqueCount(y_train) print ml_aux.getUniqueCount(y_test) print "try func: ", ml_aux.get_error_rate(y_train, y_train_predicted) print ml_aux.plot_confusion_matrix(y_train,y_train_predicted,"Train") plt.show() ml_aux.plot_confusion_matrix(y_test,y_test_predicted,"Test") plt.show()
print y_test_t3_predicted.shape
print y_test_t4_predicted.shape
print y_test_t5_predicted.shape
print y_test_not_predicted.shape
merged_test_predicted = np.hstack([y_test_t1_predicted,
y_test_t2_predicted,
y_test_t3_predicted,
y_test_t4_predicted,
y_test_t5_predicted,
y_test_not_predicted])
#Plot confusion matrix
ml_aux.plot_confusion_matrix(merged_test,merged_test_predicted,"Confusion Matrix")
#Save the plot
plt.savefig("finaltest.png")
