88429bfe56ad2103df596ae2c2c982eafdad32a3,featuretools/computational_backends/calculate_feature_matrix.py,,calculate_feature_matrix,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,41

Before Change

            raise TypeError("cutoff_time times must be datetime type: try casting via pd.to_datetime(cutoff_time["time"])")
    assert (cutoff_time[["instance_id", "time"]].duplicated().sum() == 0), \
        "Duplicated rows in cutoff time dataframe."
    pass_columns = [column_name for column_name in cutoff_time.columns[2:]]

    if _check_time_type(cutoff_time["time"].iloc[0]) is None:
        raise ValueError("cutoff_time time values must be datetime or numeric")

After Change

    if "instance_id" in cutoff_time.columns and target_entity.index in cutoff_time.columns:
        raise AttributeError("Cutoff time DataFrame cannot contain both a column named "instance_id" and a column"
                             " with the same name as the target entity index")
    if "time" in cutoff_time.columns and target_entity.time_index in cutoff_time.columns:
        raise AttributeError("Cutoff time DataFrame cannot contain both a column named "time" and a column"
                             " with the same name as the target entity time index")

    // Check that cutoff_time time type matches entityset time type
    if entityset.time_type == NumericTimeIndex:
        if cutoff_time["time"].dtype.name not in PandasTypes._pandas_numerics:
            raise TypeError("cutoff_time times must be numeric: try casting "
                            "via pd.to_numeric(cutoff_time["time"])")

Instances