fix(timeseries): Sampling method corrected

src/ydata_synthetic/synthesizers/time_series/timegan/model.py

@@ -354,63 +354,3 @@ def build(self, input_shape):
                          hidden_units=self.hidden_dim,
                          output_units=self.hidden_dim)
         return model
-
-
-if __name__ == '__main__':
-    import pandas as pd
-    import numpy as np
-    import matplotlib.pyplot as plt
-    from sklearn.preprocessing import MinMaxScaler
-
-
-    """
-    import quandl
-    quandl_api_key = "ssf2fazqBjcLq-qhWTvo"
-    quandl.ApiConfig.api_key=quandl_api_key
-    quandl.ApiConfig.verify_ssl = False
-
-
-    dataset = []
-    for tick in tickers:
-        dataset.append(quandl.get_table('WIKI/PRICES', ticker=tick))
-
-    data = pd.concat(dataset)
-    """
-    tickers = ['BA', 'CAT', 'DIS', 'GE', 'IBM', 'KO']
-    data = pd.read_csv('wiki_prices.csv')
-    data=data.drop('None', axis=1)
-
-    data = data.set_index(['ticker', 'date']).adj_close.unstack(level=0).loc['2000':, tickers].dropna()
-
-    #Normalize the data
-    scaler = MinMaxScaler()
-    scaled_data = scaler.fit_transform(data).astype(np.float32)
-
-    #Create rolling windows for the data
-    seq_len=24
-    n_seq=6
-
-    dataset = []
-    for i in range(len(data) - seq_len):
-        dataset.append(scaled_data[i:i+seq_len])
-    n_windows=len(dataset)
-
-    noise_dim = 32
-    dim = 128
-    batch_size = 128
-
-    log_step = 100
-    epochs = 500 + 1
-    learning_rate = 5e-4
-    models_dir = './cache'
-
-    gan_args = [batch_size, learning_rate, noise_dim, 24, 2, (0, 1), dim]
-
-    synth = TimeGAN(model_parameters=gan_args, hidden_dim=24, seq_len=seq_len, n_seq=n_seq, gamma=1)
-    synth.train(dataset, train_steps=10)
-
-    synth.sample(1000)
-
-    synth.save('./ts_synth.pkl')
-
-    print('result')