def predict(self):
try:
prediction = self.decision_tree.predict(self.predict_data)
XMessage(text='Your model predicts {pred}'.format(pred=prediction), title='Prediction')
except Exception as e:
XError(text='Error making prediction!: {}'.format(e))
def save_model(self, path, filename):
try:
with open(os.path.join(path, filename), 'wb') as stream:
pickle.dump(self.model, stream)
self.dismiss_popup()
except Exception as e:
self.dismiss_popup()
XError(text='Error saving model!: {}'.format(e))
def load_predict_data(self, path, filename):
try:
self.predict_data = pd.read_csv(filename[0]).as_matrix()
self.dismiss_popup()
if self.model:
self.predict_button.disabled = False
except Exception as e:
self.dismiss_popup()
XError(text='Error loading predicting data!: {}'.format(e))
def load_training_data(self, path, filename):
try:
raw_data = pd.read_csv(filename[0]).as_matrix()
self.training_data = split_data(raw_data, train_test=self.train_test)
self.train_button.disabled = False
self.dismiss_popup()
except Exception as e:
self.dismiss_popup()
XError(text='Error loading training data: {}'.format(e))
def load_model(self, path, filename):
try:
with open(os.path.join(path, filename[0]), 'rb') as stream:
self.model = pickle.load(stream)
if self.predict_data:
self.predict_button.disabled = False
self.dismiss_popup()
except Exception as e:
self.dismiss_popup()
XError(text='Error loading model!: {}'.format(e))
def train(self, *args):
lam = self.lam.value
alpha = self.alpha.value
epochs = self.epochs.value
adaptive = self.adaptive.value
dec_amount = self.dec_amount.value
hidden_layer_size = self.hidden_layer_size.value
try:
self.network = neural_network.NeuralNetwork(hidden_layer_size=int(hidden_layer_size),
activation_func='sigmoid')
self.cost, self.costs, self.model = self.network.train(X=self.training_data['X'],
y=self.training_data['y'],
alpha=alpha,
max_epochs=int(epochs),
lam=lam,
adaptive=adaptive,
dec_amount=dec_amount)
self.loading_pop.dismiss()
if self.train_test:
accuracy = self.get_accuracy()
XMessage(text='Training complete! Your Model\'s accuracy is {acc:.2f}%.'.format(acc=accuracy),
title='Your Model Has Been Trained!')
else:
XMessage(text='Training complete!', title='Your Model Has Been Trained!')
if self.predict_data:
self.predict_button.disabled = False
self.training_graph_button.disabled = False
self.draw_network()
except Exception as e:
self.loading_pop.dismiss()
XError(text='Error training model!: {}'.format(e))