508078a326ad01638f825e560db8d4a369f5fb27,cde/empirical_evaluation/moments_time_series.py,,main,#,12

Before Change

  print(moments_df)

  // dump csv
  dump_dir = os.path.join(DATA_DIR, "moments_time_series")
  if not os.path.exists(dump_dir):
    os.makedirs(dump_dir)
  moments_df.to_csv(os.path.join(dump_dir, "moments_time_series.csv"))

After Change

def main():

  if COMPUTE_MOMENTS:
    // 1) load data
    df = make_overall_eurostoxx_df()


    X, Y, features = target_feature_split(df, "log_ret_1", filter_nan=True, return_features=True)
    X, Y = np.array(X), np.array(Y)
    ndim_x, ndim_y = X.shape[1], 1

    // 2) Fite density model
    mdn = MixtureDensityNetwork("mdn_empirical_no_pca", ndim_x, ndim_y, n_centers=20, n_training_epochs=10,
                                random_seed=22, x_noise_std=0.2, y_noise_std=0.1)
    mdn.fit(X,Y)

    // 3) estimate moments
    n_samples = 10**7
    print("compute mean")
    mean = np.squeeze(mdn.mean_(x_cond=X, n_samples=n_samples))
    print("compute cov")
    cov = np.squeeze(mdn.covariance(x_cond=X, n_samples=n_samples))
    print("compute skewness")
    skew = mdn._skewness_mc(x_cond=X, n_samples=n_samples)
    print("compute kurtosis")
    kurt = mdn._kurtosis_mc(x_cond=X, n_samples=n_samples)

    // 4) save data
    data = np.stack([mean, cov, skew, kurt], axis=-1)
    moments_df = pd.DataFrame(data=data, index=df.dropna().index, columns=["mean", "variance", "skewness", "kurtosis"])
    print(moments_df)

    // dump csv
    if not os.path.exists(dump_dir):
      os.makedirs(dump_dir)
    moments_df.to_csv(dump_file_path)

  else:
    moments_df = pd.read_csv(dump_file_path, index_col=0)

  /Ǜ) plot moment timeseries
  fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(15, 20))

Instances