def evaluate(self, inp):
inp = bf.scale_data(inp, self.xoffsets, self.xscales, -1, 1)
inp = torch.tensor(inp).float()
temp = self.regressor.forward(inp)
pred = temp.detach().numpy()
return bf.unscale_data(pred[:,np.newaxis], self.yoffset, self.yscale, -1, 1)
def train_on_split_data(self, input_dict, output_dict, epochs = 5000, batch_size = 4096, saving = True):
X_train = bf.scale_data(input_dict["train"], self.xoffsets, self.xscales, -1, 1)
X_val = bf.scale_data(input_dict["val"], self.xoffsets, self.xscales, -1, 1)
X_test = bf.scale_data(input_dict["test"], self.xoffsets, self.xscales, -1, 1)
Y_train = bf.scale_data(output_dict["train"], self.yoffset, self.yscale, -1,1)
Y_val = bf.scale_data(output_dict["val"], self.yoffset, self.yscale, -1,1)
Y_test = bf.scale_data(output_dict["test"], self.yoffset, self.yscale, -1,1)
if saving:
np.save(self.path + self.name+ "x_train.npy", X_train)
np.save(self.path + self.name+"y_train.npy", Y_train)
np.save(self.path + self.name+ "x_test.npy", X_test)
np.save(self.path + self.name+"y_test.npy", Y_test)
np.save(self.path + self.name+"x_val.npy", X_val)
np.save(self.path + self.name+"y_val.npy", Y_val)
self.model = self.build_model()
self.model.compile(loss = lambda y_true, y_pred: self.quantile_regression(y_true, y_pred), optimizer = 'adam')
self.model.summary()
mc = tf.keras.callbacks.ModelCheckpoint(self.path + self.name +'best_model_checkpoint.h5', monitor='val_loss', mode='min', save_best_only=True)
es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience= 500)
start = time.time()
self.model.fit(X_train, Y_train, validation_data = (X_val, Y_val), epochs = epochs, batch_size = batch_size, verbose= 0, callbacks = [mc, es, bf.plot_losses])
stop = time.time()
self.model.save(self.path + self.name+"Model_Save.h5")
self.model.save_weights(self.path + self.name+"Model_Weights.h5")
print('Time to run in minutes was: ', (stop-start)/60)
model_fit = self.model.predict(X_test)
self.model_fit = bf.unscale_data(model_fit, self.yoffset, self.yscale, -1, 1)
self.X_test_fullscale = bf.unscale_data(X_test, self.xoffsets, self.xscales, -1, 1)
self.Y_test_fullscale = bf.unscale_data(Y_test, self.yoffset, self.yscale, -1, 1)
print("Training Complete")
def evaluate(self, inp):
inpsc = bf.scale_data(inp, self.xoffsets, self.xscales, -1, 1)
pred = self.model.predict(inpsc)
return bf.unscale_data(pred, self.yoffset, self.yscale, -1, 1)