def telco_churn(quantile=0.5):
    """Returns dataset in format x, [y1, y2]. This dataset
    is useful for demonstrating multi-output model or for
    experimenting with reduction strategy creation.

    The data is from hyperparameter optimization experiment with
    Kaggle telco churn dataset.

    x: features
    y1: val_loss
    y2: val_f1score

    quantile is for transforming the otherwise continuous y variables into
    labels so that higher value is stronger. If set to 0 then original
    continuous will be returned."""
    import wrangle
    import pandas as pd
    df = pd.read_csv(
        'https://raw.githubusercontent.com/autonomio/examples/master/telco_churn/telco_churn_for_sensitivity.csv'
        )
    df = df.drop(['val_acc', 'loss', 'f1score', 'acc', 'round_epochs'], 1)
    for col in df.iloc[:, 2:].columns:
        df = wrangle.col_to_multilabel(df, col)
    df = wrangle.df_rename_cols(df)
    if quantile > 0:
        y1 = (df.C0 < df.C0.quantile(quantile)).astype(int).values
        y2 = (df.C1 > df.C1.quantile(quantile)).astype(int).values
    else:
        y1 = df.C0.values
        y2 = df.C1.values
    x = df.drop(['C0', 'C1'], 1).values
    return x, [y1, y2]
