def __test_support_vector_machine(self):
if self.__trained_weights is not None and self.__x_test is not None and self.__y_test is not None:
print("Testing SupportVectorMachine")
y_test_predicted = np.array([])
# for i in range(self.x_test.shape[0]):
for r in self.__x_test:
y_predict = np.sign(np.dot(r, self.__trained_weights))
y_test_predicted = np.append(y_test_predicted, y_predict)
accuracy_score = Utils.calculate_accuracy_score(
self.__y_test, y_test_predicted)
print("accuracy of the model: {}".format(accuracy_score))
confusion_mat = Utils.calculate_confusion_matrix(
self.__y_test, y_test_predicted)
print("SVM confusion Matrix")
print(confusion_mat[0])
print(confusion_mat[1])
# write accuracy and confusion matrix to file
try:
report_fl_name = "C:/Users/Brijesh Prajapati/Documents/Projects/Autism_Detection_Hons_Proj/Classifiers/" \
"Classifier_Reports/svm_current_report.json"
classifier_desc = "SVM l_r: " + str(
Learning_rate) + ", r_s: " + str(Reg_strength)
dict_report = {
"desc": classifier_desc,
"accuracy_score": str(accuracy_score),
"tp": str(confusion_mat[0][0]),
"fn": str(confusion_mat[0][1]),
"fp": str(confusion_mat[1][0]),
"tn": str(confusion_mat[1][1]),
"specificity": str(confusion_mat[2][0]),
"sensitivity": str(confusion_mat[2][1])
}
with open(report_fl_name, 'w') as report_fl:
report_fl.write(json.dumps(dict_report))
except Exception as ex:
print("Exception occurred saving SVM report", ex)
# print("SK Learn Metrics")
# print(confusion_matrix(self.y_test, y_test_predicted))
# print(classification_report(self.y_test, y_test_predicted))
else:
print("Train SVM before Testing.")
def __test_adaboost(self):
print("Testing AdaBoost")
y_predictions = np.ones(len(self.__x_test))
for test, i in zip(self.__x_test, range(len(self.__x_test))):
test = np.reshape(test, (-1, len(test)))
y_predictions[i] = self.predict_sample_adaboost(test)
accuracy_score = Utils.calculate_accuracy_score(
self.__y_test, y_predictions)
print("Accuracy: ", accuracy_score)
cf = Utils.calculate_confusion_matrix(self.__y_test, y_predictions)
print("Adaboost Matrix")
print(cf[0])
print(cf[1])
# write accuracy and confusion matrix to file
try:
report_fl_name = "C:/Users/Brijesh Prajapati/Documents/Projects/Autism_Detection_Hons_Proj/Classifiers/" \
"Classifier_Reports/adaboost_current_report.json"
classifier_desc = "Adaboost number of stumps: " + str(
self.__num_classifiers)
dict_report = {
"desc": classifier_desc,
"accuracy_score": str(accuracy_score),
"tp": str(cf[0][0]),
"fn": str(cf[0][1]),
"fp": str(cf[1][0]),
"tn": str(cf[1][1]),
"specificity": str(cf[2][0]),
"sensitivity": str(cf[2][1])
}
with open(report_fl_name, 'w') as report_fl:
report_fl.write(json.dumps(dict_report))
except Exception as ex:
print("Exception occurred saving adaboost report", ex)
def test_main_classifier(self):
self.__load_classifiers()
y_pred = []
for test_data in self.__x_test:
svm_p = self.svm.predict_sample([test_data])
rf_p = self.random_forest.predict([test_data])
mlp_p = self.multi_layer_perceptron.predict(test_data)
adb_p = self.adaboost.predict_sample_adaboost(np.array([test_data]))
predictions = [float(svm_p), rf_p[0], mlp_p[0], adb_p[0]]
y_pred.append(most_common_label(predictions))
accuracy_score = Utils.calculate_accuracy_score(self.__y_test, y_pred)
print("Accuracy of main model: ", accuracy_score)
confusion_mat = Utils.calculate_confusion_matrix(self.__y_test, y_pred)
print("Main Classifier Confusion Matrix")
print(confusion_mat[0])
print(confusion_mat[1])
# write accuracy and confusion matrix to file
try:
report_fl_name = "C:/Users/Brijesh Prajapati/Documents/Projects/Autism_Detection_Hons_Proj/Classifiers/" \
"Classifier_Reports/mainmodel_current_report.json"
classifier_desc = "Main Model"
dict_report = {"desc": classifier_desc, "accuracy_score": str(accuracy_score),
"tp": str(confusion_mat[0][0]), "fn": str(confusion_mat[0][1]),
"fp": str(confusion_mat[1][0]), "tn": str(confusion_mat[1][1]),
"specificity": str(confusion_mat[2][0]), "sensitivity": str(confusion_mat[2][1])}
with open(report_fl_name, 'w') as report_fl:
report_fl.write(json.dumps(dict_report))
except Exception as ex:
print("Exception occurred saving Main Model report", ex)
def __test_MultiLayerPerceptron(self):
print("Testing Multi Layer Perceptron")
mlp_predictions = self.__mlp_classifier.predict(self.__x_test)
accuracy_score = Utils.calculate_accuracy_score(
self.__y_test, mlp_predictions)
print("Accuracy: ", accuracy_score)
cf = Utils.calculate_confusion_matrix(self.__y_test, mlp_predictions)
print("Multi Layer Perceptron Confusion Matrix")
print(cf[0])
print(cf[1])
print(classification_report(self.__y_test, mlp_predictions))
# write accuracy and confusion matrix to file
try:
report_fl_name = "C:/Users/Brijesh Prajapati/Documents/Projects/Autism_Detection_Hons_Proj/Classifiers/" \
"Classifier_Reports/mlp_current_report.json"
classifier_desc = "MLP hidden_layer_size: " + str(self.__hidden_layer_size) + ", max_iteration: " + \
str(self.__max_iter)
dict_report = {
"desc": classifier_desc,
"accuracy_score": str(accuracy_score),
"tp": str(cf[0][0]),
"fn": str(cf[0][1]),
"fp": str(cf[1][0]),
"tn": str(cf[1][1]),
"specificity": str(cf[2][0]),
"sensitivity": str(cf[2][1])
}
with open(report_fl_name, 'w') as report_fl:
report_fl.write(json.dumps(dict_report))
except Exception as ex:
print("Exception occurred saving MLP report", ex)
def __test_random_forest(self):
print("Testing Random Forest Classifier")
y_predictions = self.predict(self.__x_test)
accuracy_score = Utils.calculate_accuracy_score(
self.__y_test, y_predictions)
print("Accuracy: ", accuracy_score)
cf = Utils.calculate_confusion_matrix(self.__y_test, y_predictions)
print("Random Forest Confusion Matrix")
print(cf[0])
print(cf[1])
# write accuracy and confusion matrix to file
try:
report_fl_name = "C:/Users/Brijesh Prajapati/Documents/Projects/Autism_Detection_Hons_Proj/Classifiers/" \
"Classifier_Reports/rf_current_report.json"
classifier_desc = "RF number of trees: " + str(self.__num_tree) + ", max depth: " + \
str(self.__max_depth)
dict_report = {
"desc": classifier_desc,
"accuracy_score": str(accuracy_score),
"tp": str(cf[0][0]),
"fn": str(cf[0][1]),
"fp": str(cf[1][0]),
"tn": str(cf[1][1]),
"specificity": str(cf[2][0]),
"sensitivity": str(cf[2][1])
}
with open(report_fl_name, 'w') as report_fl:
report_fl.write(json.dumps(dict_report))
except Exception as ex:
print("Exception occurred saving random forest report", ex)
# return True
# else:
# return False
return self.value is not None
if __name__ == "__main__":
df = pd.read_csv("D:/TrainingDataset_YEAR_PROJECT/TrainingSet.csv")
# replace labels
df['feature_class'].replace({'ASD': 1.0, 'TD': -1.0}, inplace=True)
X_train, X_test, y_train, y_test = Utils.train_test_split(df, 0.2)
X_train = X_train.values
X_test = X_test.values
y_train = y_train.values
y_test = y_test.values
decision_tree = DecisionTree(max_depth=10)
decision_tree.train_decision_tree(X_train, y_train)
y_pred = decision_tree.predict(X_test)
accuracy_score = Utils.calculate_accuracy_score(y_test, y_pred)
print("Accuracy: ", accuracy_score)
# References:
# https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/
# https://www.youtube.com/channel/UCbXgNpp0jedKWcQiULLbDTA