def analyze_knn(data:List[Point]):
N= np.sqrt(len(data)) + 1
numberOfLabels = 2
numberOfNeighbors = 10
numberOfFolds = 10
mumberOfPowers = 3
kernels = KernelType.as_list()
coordinateSystems = CoordinateSystem.as_list()
stats = []
power = 2
dataCopy = copy.deepcopy(data)
trainData, testData = DataAnalyzer.make_cross_validation(dataCopy, numberOfFolds)
for numFolds in range(4, numberOfFolds):
for numNeighbor in range(3, numberOfNeighbors):
#for power in range(2, mumberOfPowers):
for coordinateSystem in coordinateSystems:
for kernel in kernels:
f_scores = []
p_values = []
t_test = ""
for i in range(len(trainData)):
classifier = KnnClassifier()
classifier.train(trainData[i], [0,1],numNeighbor,power,kernel,coordinateSystem)
test_item = testData[i]
predictions = classifier.predict(test_item)
f_score = Metrics.f_score([item.label for item in test_item], predictions)
f_scores.append(f_score)
p_value = Metrics.p_value([item.label for item in test_item], predictions, 2)
p_values.append(p_value[1])
t_test = Metrics.t_test([item.label for item in test_item], predictions)
avFscore = DataAnalyzer.calculateAverage(f_scores)
stat = Stat(numFolds, numNeighbor, power, kernel, coordinateSystem, avFscore, 0, t_test)
#print(stat)
stats.append(stat)
#print(np.array([str(i) for i in stats]).T)
print("KNN ---------------------------------------------------------")
print("")
print("Max f_score general")
print(max(stats, key=lambda x: x.f_score))
print("Max p value general")
print(max(stats, key=lambda x: x.p_value))
print("Max t Wilcoxon general")
print(max(stats, key=lambda x: x.p_value))
print("")
print("KNN ---------------------------------------------------------")
def analyze_svm_one(data:List[Point], kernel = polynomial_kernel, numFolds:int = 4):
trainData, testData = DataAnalyzer.make_cross_validation(data, numFolds)
stats = []
globalReal= []
globalPredict = []
f_scores = []
p_values = []
t_test = ""
for i in range(len(trainData)):
x_train = np.array([[item.x, item.y] for item in trainData[i]])
y_train = np.array([-1 if item.label == 0 else 1 for item in trainData[i]])
x_test = np.array([[item.x, item.y] for item in testData[i]])
y_test = np.array([-1 if item.label == 0 else 1 for item in testData[i]])
classifier = SvmClassifier(kernel=kernel)
classifier.fit(x_train,y_train)
predictions = classifier.predict(x_test)
predictions = list(predictions)
predictions = [0 if item == -1 else 1 for item in predictions]
y_test = [0 if item == -1 else 1 for item in y_test]
f_score = Metrics.f_score(y_test, predictions)
f_scores.append(f_score)
t_test = Metrics.t_test(y_test, predictions)
for item in y_test:
globalReal.append(item)
for item in predictions:
globalPredict.append(item)
avFscore = DataAnalyzer.calculateAverage(f_scores)
stat = StatSvm(numFolds, kernel, avFscore, 0, t_test)
#print(stat)
stats.append(stat)
print("SVM ---------------------------------------------------------")
print("")
print("Max f_score general")
print(max(stats, key=lambda x: x.f_score))
print("Max p value general")
print(max(stats, key=lambda x: x.p_value))
print("Max t Wilcoxon general")
print(max(stats, key=lambda x: x.p_value))
print("")
Metrics.plot_confusion_matrix(globalReal, globalPredict)
print("SVM ---------------------------------------------------------")
return f_scores, globalPredict
def analyze_knn_one(data:List[Point], kNeighbors:int = 6, kernel:KernelType = KernelType.E, numFolds:int = 4, coordinateSystem:CoordinateSystem = CoordinateSystem.Cartesian):
trainData, testData = DataAnalyzer.make_cross_validation(data, numFolds)
stats = []
globalReal= []
globalPredict = []
f_scores = []
p_values = []
t_test = ""
for i in range(len(trainData)):
classifier = KnnClassifier()
classifier.train(trainData[i], [0,1],kNeighbors,2,kernel,coordinateSystem)
test_item = testData[i]
predictions = classifier.predict(test_item)
real_data = [item.label for item in test_item]
f_score = Metrics.f_score(real_data, predictions)
f_scores.append(f_score)
p_value = Metrics.p_value(real_data, predictions, 2)
p_values.append(p_value[1])
t_test = Metrics.t_test(real_data, predictions)
for item in real_data:
globalReal.append(item)
for item in predictions:
globalPredict.append(item)
avFscore = DataAnalyzer.calculateAverage(f_scores)
stat = Stat(numFolds, kNeighbors, 2, kernel, coordinateSystem, avFscore, 0, t_test)
#print(stat)
stats.append(stat)
#print(np.array([str(i) for i in stats]).T)
print("KNN ---------------------------------------------------------")
print("")
print("Max f_score general")
print(max(stats, key=lambda x: x.f_score))
print("Max p value general")
print(max(stats, key=lambda x: x.p_value))
print("Max t Wilcoxon general")
print(max(stats, key=lambda x: x.p_value))
print("")
Metrics.plot_confusion_matrix(globalReal, globalPredict)
print("KNN ---------------------------------------------------------")
return f_scores, globalPredict
def analyze_svm(data:List[Point]):
# x = np.array([[item.x, item.y] for item in data])
# y = np.array([-1 if item.label == 0 else 1 for item in data])
# x_train = x[:100]
# y_train = y[:100]
# x_test = x[100:]
# y_test = y[100:]
# svm = SvmClassifier(kernel=polynomial_kernel)
# svm.fit(x_train,y_train)
# res = svm.predict(x_test)
stats = []
kernels = [polynomial_kernel, linear_kernel, gaussian_kernel]
numberOfFolds = 10
dataCopy = copy.deepcopy(data)
trainData, testData = DataAnalyzer.make_cross_validation(dataCopy, numberOfFolds)
for numFolds in range(4, numberOfFolds):
for kernel in kernels:
f_scores = []
p_values = []
t_test = ""
for i in range(len(trainData)):
x_train = np.array([[item.x, item.y] for item in trainData[i]])
y_train = np.array([-1 if item.label == 0 else 1 for item in trainData[i]])
x_test = np.array([[item.x, item.y] for item in testData[i]])
y_test = np.array([-1 if item.label == 0 else 1 for item in testData[i]])
classifier = SvmClassifier(kernel=kernel)
classifier.fit(x_train,y_train)
predictions = classifier.predict(x_test)
predictions = list(predictions)
predictions = np.array([0 if item == -1 else 1 for item in predictions])
y_test = np.array([0 if item == -1 else 1 for item in y_test])
f_score = Metrics.f_score(y_test, predictions)
f_scores.append(f_score)
p_value = Metrics.p_value(y_test, predictions, 2)
p_values.append(p_value[1])
t_test = Metrics.t_test(y_test, predictions)
avFscore = DataAnalyzer.calculateAverage(f_scores)
avPvalue = DataAnalyzer.calculateAverage(p_values)
stat = StatSvm(numFolds, kernel, avFscore, avPvalue, t_test)
#print(stat)
stats.append(stat)
print("SVM ---------------------------------------------------------")
print("")
print("Max f_score general")
print(max(stats, key=lambda x: x.f_score))
print("Max p value general")
print(max(stats, key=lambda x: x.p_value))
print("Max t Wilcoxon general")
print(max(stats, key=lambda x: x.p_value))
print("")
print("SVM ---------------------------------------------------------")
