def train_support_vector_machine(self,
X_train,
X_test,
y_train,
y_test,
save_mdl=False,
save_loc=Weights_File,
max_epochs=17000,
cost_thresh=0.001,
l_r=0.000001,
r_s=10000):
# set global parameters
global Learning_rate
global Reg_strength
Learning_rate = l_r
Reg_strength = r_s
# normalize the data
x_train_cpy = Utils.normalize_numpy_array(X_train,
self.__min_max_scalar)
x_test_cpy = Utils.normalize_numpy_array(X_test, self.__min_max_scalar)
# print(type(x_train_cpy)) # debug
# creating and appending col of 1's to train and test data (this col is the intercept)
intercept_train = np.ones(x_train_cpy.shape[0])
intercept_test = np.ones(x_test_cpy.shape[0])
x_train_cpy = np.hstack(
(x_train_cpy, np.atleast_2d(intercept_train).T))
x_test_cpy = np.hstack((x_test_cpy, np.atleast_2d(intercept_test).T))
# setting attributes
self.__x_train = x_train_cpy
self.__x_test = x_test_cpy
self.__y_train = y_train
self.__y_test = y_test
# using stochastic gradient to find the optimal weights for linear classifier
print("Training Support Vector Machine") # debug
self.__trained_weights = stochastic_gradient_descent(
self.__x_train,
self.__y_train,
max_epochs=max_epochs,
cost_thresh=cost_thresh)
print("Trained Weights: ", self.__trained_weights)
print("Finished Training SupportVectorMachine")
self.__test_support_vector_machine()
if save_mdl:
self.__save_trained_weights(save_loc)
def predict_sample(self, sample_data, return_lbl=False):
try:
if self.__trained_weights is not None:
# min_max_scalar = Utils.calculate_min_max_scalar(pd.read_csv(self.training_data_dir))
# sample_data_copy = sample_data.copy(deep=True)
sample_data_copy = copy.deepcopy(sample_data)
# sample_data_copy = Utils.normalize_dataset(sample_data_copy, self.__min_max_scalar)
sample_data_copy = Utils.normalize_numpy_array(
sample_data_copy, self.__min_max_scalar)
# sample_data_copy.insert(loc=len(sample_data_copy.columns), column='intercept', value=1)
intercept_train = np.ones(sample_data_copy.shape[0])
sample_data_copy = np.hstack(
(sample_data_copy, np.atleast_2d(intercept_train).T))
y_predict = np.sign(
np.dot(sample_data_copy, self.__trained_weights))
if return_lbl is True:
if y_predict > 0:
return 'ASD'
else:
return 'Normal'
return y_predict
else:
print("Train the SVM first OR load the trained weights")
except Exception as e:
print("Error occurred predicting the sample in SVM.")
print('Exception', e)
traceback.print_exc()
def train_MultiLayerPerceptron(self,
X_train,
X_test,
y_train,
y_test,
save_mdl=False,
save_loc=MultilayerPerceptronMdlFl,
hidden_layer_sizes=16,
max_iter=400):
x_train_cpy = Utils.normalize_numpy_array(X_train,
self.__min_max_scalar)
x_test_cpy = Utils.normalize_numpy_array(X_test, self.__min_max_scalar)
self.__x_train = x_train_cpy
self.__x_test = x_test_cpy
self.__y_train = y_train
self.__y_test = y_test
self.__max_iter = max_iter
self.__hidden_layer_size = hidden_layer_sizes
print("Training MultiLayerPerceptron...")
t0 = time.time() # test purposes
self.__mlp_classifier = MLPClassifier(
hidden_layer_sizes=self.__hidden_layer_size,
activation='relu',
max_iter=self.__max_iter,
solver='adam')
self.__mlp_classifier = self.__mlp_classifier.fit(
self.__x_train, self.__y_train)
t1 = time.time() # test purposes
print("Finished Training MultiLayerPerceptron in: ", t1 - t0)
self.__test_MultiLayerPerceptron()
if save_mdl:
self.__save_MultiLayerPerceptron(save_loc)
def predict(self, sample_data, return_lbl=False):
# normalize the sample data
# sample_data = Utils.normalize_dataset(sample_data, self.__min_max_scalar)
sample_data = Utils.normalize_numpy_array(sample_data,
self.__min_max_scalar)
prediction = self.__mlp_classifier.predict([sample_data])
if return_lbl is True:
if prediction > 0:
return 'ASD'
else:
return 'Normal'
else:
return prediction
