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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+AI&ML Homework - Prof. Caputo, Year 2018
+Homework 2
+@student: Antonio Tavera 
+@id: 243869
+
+Created on Sun Nov 25 16:07:25 2018
+"""
+import os, sys 
+os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt 
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import GridSearchCV
+from sklearn import svm
+import itertools
+
+# Variables 
+X_train = []
+y_train = []
+X_val = []
+y_val = []
+X_test = []
+y_test = []
+C = []
+target_names = []
+gamma = [10**-11, 10**-9, 10**-7, 10**-5, 10**-3, 10**-1, 10]
+
+
+
+###############   Load the dataset  ################
+# The function load the iris dataset from the scikit
+# learn library. It selects only the first two 
+# dimensions and spit the dataset into training, 
+# validation and test set according to the received 
+# proportion 
+# 
+# Returns X_train, X_va, X_test, y_train, y_val, 
+# y_test and the target names
+#
+#####################################################
+def load_dataset(validation_size, test_size):
+	# Load iris dataset 
+	iris = load_iris()
+	# Select the first two dimensions
+	X = iris.data[:, :2]
+	# Load labels and target names
+	y = iris.target
+	target_name = iris.target_names
+	# Split dataset into training and test set
+	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=None)
+	# Split training set into training and validation set
+	X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=validation_size, random_state=None)
+
+	return X_train, X_val, X_test, y_train, y_val, y_test, target_name; 
+
+
+###########  Create and train the model #############
+# Create an SVM model by setting C and gamma if they
+# are received as input variables. Then it trains the
+# model with the received X_train and y_train 
+#
+# Returns the trained model 
+#
+#####################################################
+def train(X_train, y_train, kernel, c, gamma=None):
+	#create the model with the specified kernel and gamma 
+	if gamma == None:
+		model = svm.SVC(kernel=kernel, C=c)
+	else:
+		model = svm.SVC(kernel=kernel, C=c, gamma=gamma)
+	#train the model 
+	model.fit(X_train, y_train)
+	
+	return model 
+	 
+
+################  Test the model  ###################
+# Receives the trained model, the X_test dataset and 
+# its labels. Make a prediction on the model and 
+# compute and print the accuracy. 
+#
+# Returns the accuracy
+# 
+#####################################################
+def test_model(model, X_test, y_test):
+	# Make a prediction on the test set
+	test_predictions = model.predict(X_test)
+	test_predictions = np.round(test_predictions)
+	# Report the accuracy of that prediction 
+	accuracy = accuracy_score(y_test, test_predictions)
+
+	return accuracy 
+
+
+############  Plot Decision Boundaries ################
+# Receives the train data, the model, the title to give 
+# to the figure and the folder where to save the figure
+# It firsts creates a mesh of point to plot in, then it 
+# predicts the decision boundaries for the classifier, 
+# plot the contourf and the data as data points. It 
+# creates the folder where to save the image if not 
+# already exists and procede to save the image inside
+# 
+#####################################################
+def plot_boundaries(X_train, y_train, model, title, folder):
+	# Create a mesh of point to plot in
+	x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
+	y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
+	xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
+	# Start plotting the figure
+	plt.figure()
+	# Predict the decision boundaries for the classifier
+	Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+	Z = Z.reshape(xx.shape)
+	# Plot contours
+	plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu, alpha=0.8)
+	# Plot points
+	plot_colors = "ryb"
+	for i, color in zip(np.unique(y_train), plot_colors):
+		idx = np.where(y_train == i)
+		plt.scatter(X_train[idx, 0], X_train[idx, 1], c=color, 
+			  cmap=plt.cm.RdYlBu, label=target_names[i], s=15, edgecolors='black')
+	# Set labels and titles
+	plt.xlabel('Sepal length')
+	plt.ylabel('Sepal width')
+	plt.title(title)
+	# Create folder to save the created image inside
+	if not os.path.exists(folder):
+		os.makedirs(folder)
+	# Save figure inside the specified folder and close plt
+	plt.legend()
+	plt.savefig(folder + "/" + title + ".jpg")
+	plt.close()
+	
+	return
+
+
+
+
+##################    MAIN     ######################
+####################  Menu  #########################
+# 1.Train a Linear SVM, plot the decision boundaries,
+#	evaluate the method on the validation set, plot 
+#	a graph showing the accuracy varying C, evaluate 
+#	the model on the test set using the best C value.
+#
+# 2.Do the same as the point 1 but using a non linear 
+#	kernel, RBF kernel. Performs a grid search of the 
+#	best parameters for an RBF kernel. Evaluates the 
+#	best parameters on the test set and plot 
+#	boundaries
+#
+# 3.Merge training and validation set, Repeat the grid 
+#	search for gamma and C but this time perform 5-fold 
+#	validation. Evaluate the parameters on the test set.
+#
+# 4.Exit the process
+######################################################	
+while(1):
+	print('\n')
+	print("###### MENU ######")
+	user_choice = str(input(
+				'1. Linear SVM\n' + \
+				'2. RBF Kernel\n' + \
+				'3. K-Fold\n' + \
+				'4. Exit\n' + \
+				'Input: '))
+	print("##################")
+
+
+	if user_choice == '1':
+		# LINEAR SVM 
+		
+		# Load the dataset if not already done 
+		if(len(X_val) == 0):
+			X_train, X_val, X_test, y_train, y_val, y_test, target_names = load_dataset(0.2, 0.3); 
+		
+		# Create array of C value parameters
+		accuracies=[]
+		if(len(C) == 0):
+			C.append(10**-3)
+			for i in range(0, 6): 
+				C.append(C[i]*10)
+		
+		for c in C:
+			# a)Train a linear svm on the training set 
+			model = train(X_train, y_train, "linear", c)
+			# b)Plot the data and the decision boundaries 
+			plot_boundaries(X_train, y_train, model, "Linear SVM with C=" + str(c), "BuondariesLinearSVM")
+			# c)Evaluate method on the validation set 
+			accuracies.append(test_model(model, X_val, y_val))
+		
+		# Show how validation accuracy change when we modify C
+		print('\n'+'How validation accuracy change modifying C:')
+		for c, score in zip(C, accuracies):
+			print("C: %.3f,\t score: %.4f" %(c, score))
+		print('Best score %.4f' %(np.amax(accuracies)))
+		
+		# Plot the data above on a graph
+		plt.axes(xscale='log')
+		plt.plot(C, accuracies, marker='o')
+		plt.show()
+
+		# Use the best value of C and evaluate the model on the test set.
+		# There will be more than one equal C value
+		best_accuracies = np.argwhere(accuracies == np.amax(accuracies))
+		best_accuracy = 0
+		for best in best_accuracies: 
+			# Train a linear svm on the training set with the best c value 
+			model = train(X_train, y_train, "linear", C[best[0]])
+			# Evaluate model on the test set
+			accuracy = test_model(model, X_test, y_test)
+			# Update best accuracy
+			if(accuracy > best_accuracy):
+				best_accuracy = accuracy
+				c = C[best[0]]		
+		print ("Best C: %.4f with accuracy on test set: %.4f"  %(c, best_accuracy))
+			
+	
+	
+	elif user_choice == '2':
+		#RBF KERNEL
+		
+		# Load the dataset if not already done 
+		if(len(X_val) == 0):
+			X_train, X_val, X_test, y_train, y_val, y_test, target_names = load_dataset(0.2, 0.3);
+		
+		# Create array of C value parameters if not already done
+		accuracies=[]
+		if(len(C) == 0):
+			C.append(10**-3)
+			for i in range(0, 6): 
+				C.append(C[i]*10)
+		
+		for c in C:
+			# a)Train a rbf kernel on the training set 
+			model = train(X_train, y_train, "rbf", c, "auto")
+			# b)Plot the data and the decision boundaries 
+			plot_boundaries(X_train, y_train, model, "RBF kernel with C=" + str(c), "BuondariesRBF")
+			# c)Evaluate method on the validation set 
+			accuracies.append(test_model(model, X_val, y_val))
+		
+		# Show how validation accuracy change when we modify C
+		print('\n'+'How validation accuracy change modifying C:')
+		for c, score in zip(C, accuracies):
+			print("C: %.3f,\t score: %.4f" %(c, score))
+		print('Best score %.4f' %(np.amax(accuracies)))
+		
+		# Plot the data above on a graph
+		plt.axes(xscale='log')
+		plt.plot(C, accuracies, marker='o')
+		plt.show()
+
+		# Use the best value of C and evaluate the model on the test set.
+		# There will be more than one equal C value
+		best_accuracies = np.argwhere(accuracies == np.amax(accuracies))
+		best_accuracy = 0
+		for best in best_accuracies: 
+			# Train a svm with rbf kernel on the training set with the best c value 
+			model = train(X_train, y_train, "rbf", C[best[0]], "auto")
+			# Evaluate model on the test set
+			accuracy = test_model(model, X_test, y_test)
+			# Update best accuracy
+			if(accuracy > best_accuracy):
+				best_accuracy = accuracy
+				c = C[best[0]]
+		print("Accuracy on the test set with best C: %.4f is: %.4f"  %(c, best_accuracy))
+		
+		
+		# Perform a Grid Search of the best parameters for an RBF kernel setting c and gamma
+		print("\nPerforming Grid Search of the best parameters")
+		accuracies=[]
+		for c, g in list(itertools.product(C, gamma)) :
+			# Train a rbf kernel on the training set 
+			model = train(X_train, y_train, "rbf", c, g)
+			# Evaluate method on the validation set 
+			accuracies.append(test_model(model, X_val, y_val))
+			#print("C: %.1E, G: %.1E, accuracy: %.2f" %(c, g,test_model(model, X_val, y_val) ))
+			
+		# Save results in a pandas Dataframe 
+		gridSearchTable = pd.DataFrame(np.array(accuracies).reshape(len(C),len(gamma)), np.array(C), np.array(gamma))
+		# Save the table into a .csv file
+		gridSearchTable.to_csv("GridSearchTable.csv")
+		# Search for the best C and Gamma
+		npa = gridSearchTable.values
+		maxCIndex, maxGIndex = np.where(npa == np.amax(npa))
+		
+		for c, g in zip(maxCIndex, maxGIndex):
+			maxC = C[c]
+			maxGamma = gamma[g]
+			print("Max C value: %.4f and max Gamma: %.2E" %(maxC, maxGamma))
+			# Train the model with the best parameters
+			model = train(X_train, y_train, "rbf", maxC, maxGamma)
+			# Evaluate model on the test set 
+			accuracy = test_model(model, X_test, y_test)
+			print("Accuracy on the test set with best parameters is: %.3f" %(accuracy))
+			# Plot the decision boundaries
+			plot_boundaries(X_train, y_train, model, "RBF kernel with best C="+ str(maxC)+ " and gamma=" + str(maxGamma), "GridSearchRBF")
+		
+
+	elif user_choice == '3':
+		# K-FOLD
+		
+		# Load the dataset if not already done 
+		if(len(X_val) == 0):
+			X_train, X_val, X_test, y_train, y_val, y_test, target_names = load_dataset(0.2, 0.3);
+		
+		# Create array of C value parameters if not already done
+		accuracies=[]
+		if(len(C) == 0):
+			C.append(10**-3)
+			for i in range(0, 6): 
+				C.append(C[i]*10)
+		
+		# Merge Training and Validation set 
+		X_train = np.concatenate((X_train, X_val))
+		y_train = np.concatenate((y_train, y_val))
+		X_val = []
+		# Perform a Grid Search for gamma and c with 5-fold validation 
+		parameters = dict(gamma=gamma, C=C)
+		svc = svm.SVC(kernel='rbf')
+		grid = GridSearchCV(svc, param_grid=parameters, cv=5, iid=True)
+		# Fit the model 
+		grid.fit(X_train, y_train)
+		# Print the best params and score
+		print("Best parameters after gridSearch with 5-fold validation are:\n%s" %(grid.best_params_))
+		print("Best score is: %0.2f " %(grid.best_score_))
+		# Evaluate the best parameters on the test set
+		grid.predict(X_test)
+		score = grid.score(X_test, y_test)
+		print("Accuracy on the test set with the best parameters is: %0.2f " %(score))
+		
+		
+	elif user_choice == '4': 
+		# Exit
+		sys.exit()
+	else:
+		print('You made a not correct choice. Try again!')
+