def get_lstm_backtest(serialized_model, dataset):
df = pd.DataFrame.from_dict(dataset)
ts = TimeSeries.from_dataframe(df,
time_col='time_interval',
value_cols=['count'])
scaler = Scaler()
ts = scaler.fit_transform(ts)
model = pickle.loads(serialized_model)
backtest = model.historical_forecasts(series=ts,
start=0.8,
forecast_horizon=1,
stride=1,
retrain=False,
verbose=False)
backtest = scaler.inverse_transform(backtest[1:])
ts = scaler.inverse_transform(ts)
backtest.plot(label='LSTM Model', lw=3, c='orange')
def get_lstm_model(dataset=None, plot=False, verbose=False):
if (dataset is None):
df = pd.read_csv("jeans_day.csv")
else:
df = pd.DataFrame.from_dict(dataset)
ts = TimeSeries.from_dataframe(df,
time_col='time_interval',
value_cols=['count'])
train, val = ts.split_after(0.8)
scaler = Scaler()
train_transformed = scaler.fit_transform(train)
val_transformed = scaler.transform(val)
ts_transformed = scaler.transform(ts)
params = dict()
params['model'] = ["LSTM"]
params['hidden_size'] = [50, 75, 100]
params['n_rnn_layers'] = [1]
params['input_chunk_length'] = [14]
params['output_chunk_length'] = [1]
params['n_epochs'] = [100]
params['dropout'] = [0]
params['batch_size'] = [4, 6]
params['random_state'] = [0, 1]
params['loss_fn'] = [MSELoss()]
lstm = RNNModel.gridsearch(parameters=params,
series=train_transformed,
val_series=val_transformed,
verbose=verbose,
metric=mse)
params = lstm[1]
lstm_model = lstm[0]
lstm_model.fit(train_transformed, verbose=True)
if (plot):
backtest = lstm_model.historical_forecasts(series=ts_transformed,
start=0.8,
forecast_horizon=1,
stride=1,
retrain=False,
verbose=False)
print(val)
print(backtest[1:])
print("R2: {}".format(
r2_score(scaler.inverse_transform(val_transformed),
scaler.inverse_transform(backtest[1:]),
intersect=False)))
print("MAPE: {}".format(
mape(scaler.inverse_transform(val_transformed),
scaler.inverse_transform(backtest[1:]))))
print("MASE: {}".format(
mase(scaler.inverse_transform(val_transformed),
scaler.inverse_transform(backtest[1:]), train)))
print("MAE: {}".format(
mae(scaler.inverse_transform(val_transformed),
scaler.inverse_transform(backtest[1:]))))
scaler.inverse_transform(backtest).plot(label='backtest')
scaler.inverse_transform(ts_transformed).plot(label='actual')
plt.title("H&M Daily, LSTM Model")
plt.xlabel("Date")
plt.ylabel("Count")
plt.legend()
plt.show()
else:
return [lstm_model, params]