def show_result(self):
files = os.listdir(self.output)
for file in files:
if ".pth" in file:
path = os.path.join(self.output, file)
lstm_model = LSTM(self.input_size, self.output_size,
self.nb_neurons)
lstm_model.load_state_dict(torch.load(path))
lstm_model.eval()
print("model : %s loaded" % path)
predictions = []
for (x, _) in self.testing_dataloader:
if x.shape[0] == self.batch_size:
with torch.no_grad():
lstm_model.hidden_cell = (
torch.zeros(1, self.batch_size,
lstm_model.nb_neurons),
torch.zeros(1, self.batch_size,
lstm_model.nb_neurons))
output = lstm_model(x.float())
output = self.data.unnormalizeData(
output).squeeze()
predictions += output.tolist()
plt.plot(predictions, label="prediction")
plt.plot(self.real_data_test, label="target")
plt.title(file)
plt.legend()
plt.show()
def validate():
stock = "MC.PA"
directory = "/Users/baptiste/Desktop/training"
input_size = 4
output_size = 4
nb_neurons = 200
test_split = 0.1
time_window = 5
dataloader = Data(stock)
df = dataloader.getData()
real_data = df.to_numpy()
df_normalized = dataloader.normalizeData(df)
df_normalized = torch.FloatTensor(df_normalized.to_numpy())
test_split = int(test_split * df.shape[0])
real_test_split = real_data[-test_split:-time_window:, 3]
testing_split = df_normalized[-test_split:, :]
files = os.listdir(directory)
for file in files:
if ".pth" in file:
path = os.path.join(directory, file)
lstm_model = LSTM(input_size, output_size, nb_neurons)
lstm_model.load_state_dict(torch.load(path))
print("model : %s loaded" % path)
lstm_model.eval()
predictions = []
for i in range(testing_split.shape[0] - time_window):
x_test = testing_split[i:i + time_window]
with torch.no_grad():
lstm_model.hidden_cell = (torch.zeros(
1, 1, lstm_model.nb_neurons),
torch.zeros(
1, 1, lstm_model.nb_neurons))
predictions.append(
dataloader.unnormalizeData(
lstm_model(x_test).tolist()))
predictions = np.array(predictions)[:, 3, 0]
#plt.figure(15,10)
plt.plot(real_test_split, label="target")
plt.plot(predictions, label="prediction")
plt.title(file)
plt.legend()
plt.show()
