18f30454593e206a538858287c45bd3ed6c1da37,featuretools/computational_backends/calculate_feature_matrix.py,,calculate_feature_matrix,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,31

Before Change

        map_args = [(id, time) for id, time in zip(instance_ids, cutoff_time)]
        df_args = pd.DataFrame(map_args, columns=["instance_id", "time"])
        to_calc = df_args.values
        cutoff_time = pd.DataFrame(to_calc, columns=["instance_id", "time"])
    else:
        cutoff_time = cutoff_time.copy()

After Change

                    cutoff_time["time"].dtype.name.find("int") == -1 and
                    cutoff_time["time"].dtype.name.find("float") == -1):
                raise TypeError("cutoff_time times must be numeric: try casting via pd.to_numeric(cutoff_time["time"])")
            elif (entityset.time_type == DatetimeTimeIndex and
                  cutoff_time["time"].dtype.name.find("time") == -1):
                raise TypeError("cutoff_time times must be datetime type: try casting via pd.to_datetime(cutoff_time["time"])")
        pass_columns = [column_name for column_name in cutoff_time.columns[2:]]

    if _check_time_type(cutoff_time["time"].iloc[0]) is None:

Instances