from typing import Tuple

import pandas as pd
from pandas import DataFrame
from sklearn.model_selection import train_test_split


def split_stratified_into_train_val_test(
    df_input,
    target_colname="z",
    stratify_colname="y",
    frac_train=0.6,
    frac_val=0.15,
    frac_test=0.25,
    random_state=None,
) -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:
    """
    Splits a Pandas dataframe into three subsets (train, val, and test)
    following fractional ratios provided by the user, where each subset is
    stratified by the values in a specific column (that is, each subset has
    the same relative frequency of the values in the column). It performs this
    splitting by running train_test_split() twice.

    Parameters
    ----------
    df_input : Pandas dataframe
        Input dataframe to be split.
    stratify_colname : str
        The name of the column that will be used for stratification. Usually
        this column would be for the label.
    frac_train : float
    frac_val   : float
    frac_test  : float
        The ratios with which the dataframe will be split into train, val, and
        test data. The values should be expressed as float fractions and should
        sum to 1.0.
    random_state : int, None, or RandomStateInstance
        Value to be passed to train_test_split().

    Returns
    -------
    df_train, df_val, df_test :
        Dataframes containing the three splits.
    """

    if frac_train + frac_val + frac_test != 1.0:
        raise ValueError(
            "fractions %f, %f, %f do not add up to 1.0"
            % (frac_train, frac_val, frac_test)
        )

    if stratify_colname not in df_input.columns:
        raise ValueError("%s is not a column in the dataframe" % (stratify_colname))

    if target_colname not in df_input.columns:
        raise ValueError("%s is not a column in the dataframe" % (target_colname))

    X = df_input  # Contains all columns.
    y = df_input[[target_colname]]  # Dataframe of just the column on which to stratify.
    z = df_input[[stratify_colname]]

    # Split original dataframe into train and temp dataframes.
    df_train, df_temp, y_train, y_temp = train_test_split(
        X, y, stratify=z, test_size=(1.0 - frac_train), random_state=random_state
    )

    if frac_val <= 0:
        assert len(df_input) == len(df_train) + len(df_temp)
        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp

    # Split the temp dataframe into val and test dataframes.
    relative_frac_test = frac_test / (frac_val + frac_test)
    df_val, df_test, y_val, y_test = train_test_split(
        df_temp,
        y_temp,
        stratify=df_temp[[stratify_colname]],
        test_size=relative_frac_test,
        random_state=random_state,
    )

    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)
    return df_train, df_val, df_test, y_train, y_val, y_test