def get_df():
    (X, y, _) = datasets.make_regression(n_samples=50, n_features=2, bias=1000, n_informative=2, noise=10, coef=True, random_state=42)
    df1 = pd.DataFrame(data=np.concatenate([X, y.reshape((- 1), 1)], axis=1), columns=['feat1', 'feat2', 'target'])
    cats = np.random.randint(low=0, high=10, size=(df1.shape[0], 2))
    df1['cat_1'] = cats[:, 0]
    df1['cat_2'] = cats[:, 1]
    index1 = pd.date_range('2000-01-01', '2000-06-01', periods=df1.shape[0])
    index1 = pd.to_datetime(index1, utc=True)
    df1.index = index1
    return df1
def get_df():
    (X, y, _) = datasets.make_regression(n_samples=50, n_features=2, bias=1000, n_informative=2, noise=10, coef=True, random_state=42)
    df1 = pd.DataFrame(data=np.concatenate([X, y.reshape((- 1), 1)], axis=1), columns=['feat1', 'feat2', 'target'])
    cats = np.random.randint(low=0, high=10, size=(df1.shape[0], 2))
    df1['cat_1'] = cats[:, 0]
    df1['cat_2'] = cats[:, 1]
    index1 = pd.date_range('2000-01-01', '2000-06-01', periods=df1.shape[0])
    index1 = pd.to_datetime(index1, utc=True)
    df1.index = index1
    return df1