"""
In this module we prepare the dataset for machine learning experiments.
"""

import typing as t
import typing_extensions as te

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import random


class DatasetReader(te.Protocol):
    def __call__(self) -> pd.DataFrame:
        ...


SplitName = te.Literal["train", "test"]


def get_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)

    y = df["y"]
    X = df.drop(columns=["y"])
    X = X.astype({k: str for k in get_categorical_variables_values_mapping().keys()})
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=1
    )
    split_mapping = {"train": (X_train, y_train), "test": (X_test, y_test)}
    return {k: split_mapping[k] for k in splits}


def get_test_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)

    X = df.drop(columns=["y"])
    X = X.sample(n = 442, random_state = 5)
    X = X.astype({k: str for k in get_categorical_variables_values_mapping().keys()})

    split_mapping = {"test": X}
    return {k: split_mapping[k] for k in splits}


def clean_dataset(df: pd.DataFrame) -> pd.DataFrame:
    cleaning_fn = _chain(
        [
            _nothing
        ]
    )
    df = cleaning_fn(df)
    return df


def _chain(functions: t.List[t.Callable[[pd.DataFrame], pd.DataFrame]]):
    def helper(df):
        for fn in functions:
            df = fn(df)
        return df

    return helper


def _nothing(df):
    return df


def get_categorical_column_names() -> t.List[str]:
    return (
        "sex,smoker,region"
    ).split(",")


def get_binary_column_names() -> t.List[str]:
    return "".split(",")


def get_numeric_column_names() -> t.List[str]:
    return (
        "age,bmi,children"
    ).split(",")


def get_column_names() -> t.List[str]:
    return (
        "age,sex,bmi,children,smoker,region"
    ).split(",")


def get_categorical_variables_values_mapping() -> t.Dict[str, t.Sequence[str]]:
    return {
        "sex": ("female", "male"),
        "smoker": ("no", "yes"),
        "region": ("northeast", "northwest", "southeast", "southwest")
    }

640d43371aeb70d6b19dcacdf9db9a1681b02c44
<mask token>


class DatasetReader(te.Protocol):

    def __call__(self) ->pd.DataFrame:
        ...


<mask token>


def get_categorical_variables_values_mapping() ->t.Dict[str, t.Sequence[str]]:
    return {'sex': ('female', 'male'), 'smoker': ('no', 'yes'), 'region': (
        'northeast', 'northwest', 'southeast', 'southwest')}

<mask token>


class DatasetReader(te.Protocol):

    def __call__(self) ->pd.DataFrame:
        ...


<mask token>


def get_binary_column_names() ->t.List[str]:
    return ''.split(',')


def get_numeric_column_names() ->t.List[str]:
    return 'age,bmi,children'.split(',')


def get_column_names() ->t.List[str]:
    return 'age,sex,bmi,children,smoker,region'.split(',')


def get_categorical_variables_values_mapping() ->t.Dict[str, t.Sequence[str]]:
    return {'sex': ('female', 'male'), 'smoker': ('no', 'yes'), 'region': (
        'northeast', 'northwest', 'southeast', 'southwest')}

<mask token>


class DatasetReader(te.Protocol):

    def __call__(self) ->pd.DataFrame:
        ...


<mask token>


def get_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)
    y = df['y']
    X = df.drop(columns=['y'])
    X = X.astype({k: str for k in get_categorical_variables_values_mapping(
        ).keys()})
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
        random_state=1)
    split_mapping = {'train': (X_train, y_train), 'test': (X_test, y_test)}
    return {k: split_mapping[k] for k in splits}


def get_test_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)
    X = df.drop(columns=['y'])
    X = X.sample(n=442, random_state=5)
    X = X.astype({k: str for k in get_categorical_variables_values_mapping(
        ).keys()})
    split_mapping = {'test': X}
    return {k: split_mapping[k] for k in splits}


def clean_dataset(df: pd.DataFrame) ->pd.DataFrame:
    cleaning_fn = _chain([_nothing])
    df = cleaning_fn(df)
    return df


def _chain(functions: t.List[t.Callable[[pd.DataFrame], pd.DataFrame]]):

    def helper(df):
        for fn in functions:
            df = fn(df)
        return df
    return helper


def _nothing(df):
    return df


def get_categorical_column_names() ->t.List[str]:
    return 'sex,smoker,region'.split(',')


def get_binary_column_names() ->t.List[str]:
    return ''.split(',')


def get_numeric_column_names() ->t.List[str]:
    return 'age,bmi,children'.split(',')


def get_column_names() ->t.List[str]:
    return 'age,sex,bmi,children,smoker,region'.split(',')


def get_categorical_variables_values_mapping() ->t.Dict[str, t.Sequence[str]]:
    return {'sex': ('female', 'male'), 'smoker': ('no', 'yes'), 'region': (
        'northeast', 'northwest', 'southeast', 'southwest')}

<mask token>


class DatasetReader(te.Protocol):

    def __call__(self) ->pd.DataFrame:
        ...


SplitName = te.Literal['train', 'test']


def get_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)
    y = df['y']
    X = df.drop(columns=['y'])
    X = X.astype({k: str for k in get_categorical_variables_values_mapping(
        ).keys()})
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
        random_state=1)
    split_mapping = {'train': (X_train, y_train), 'test': (X_test, y_test)}
    return {k: split_mapping[k] for k in splits}


def get_test_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)
    X = df.drop(columns=['y'])
    X = X.sample(n=442, random_state=5)
    X = X.astype({k: str for k in get_categorical_variables_values_mapping(
        ).keys()})
    split_mapping = {'test': X}
    return {k: split_mapping[k] for k in splits}


def clean_dataset(df: pd.DataFrame) ->pd.DataFrame:
    cleaning_fn = _chain([_nothing])
    df = cleaning_fn(df)
    return df


def _chain(functions: t.List[t.Callable[[pd.DataFrame], pd.DataFrame]]):

    def helper(df):
        for fn in functions:
            df = fn(df)
        return df
    return helper


def _nothing(df):
    return df


def get_categorical_column_names() ->t.List[str]:
    return 'sex,smoker,region'.split(',')


def get_binary_column_names() ->t.List[str]:
    return ''.split(',')


def get_numeric_column_names() ->t.List[str]:
    return 'age,bmi,children'.split(',')


def get_column_names() ->t.List[str]:
    return 'age,sex,bmi,children,smoker,region'.split(',')


def get_categorical_variables_values_mapping() ->t.Dict[str, t.Sequence[str]]:
    return {'sex': ('female', 'male'), 'smoker': ('no', 'yes'), 'region': (
        'northeast', 'northwest', 'southeast', 'southwest')}

"""
In this module we prepare the dataset for machine learning experiments.
"""

import typing as t
import typing_extensions as te

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import random


class DatasetReader(te.Protocol):
    def __call__(self) -> pd.DataFrame:
        ...


SplitName = te.Literal["train", "test"]


def get_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)

    y = df["y"]
    X = df.drop(columns=["y"])
    X = X.astype({k: str for k in get_categorical_variables_values_mapping().keys()})
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=1
    )
    split_mapping = {"train": (X_train, y_train), "test": (X_test, y_test)}
    return {k: split_mapping[k] for k in splits}


def get_test_dataset(reader: DatasetReader, splits: t.Iterable[SplitName]):
    df = reader()
    df = clean_dataset(df)

    X = df.drop(columns=["y"])
    X = X.sample(n = 442, random_state = 5)
    X = X.astype({k: str for k in get_categorical_variables_values_mapping().keys()})

    split_mapping = {"test": X}
    return {k: split_mapping[k] for k in splits}


def clean_dataset(df: pd.DataFrame) -> pd.DataFrame:
    cleaning_fn = _chain(
        [
            _nothing
        ]
    )
    df = cleaning_fn(df)
    return df


def _chain(functions: t.List[t.Callable[[pd.DataFrame], pd.DataFrame]]):
    def helper(df):
        for fn in functions:
            df = fn(df)
        return df

    return helper


def _nothing(df):
    return df


def get_categorical_column_names() -> t.List[str]:
    return (
        "sex,smoker,region"
    ).split(",")


def get_binary_column_names() -> t.List[str]:
    return "".split(",")


def get_numeric_column_names() -> t.List[str]:
    return (
        "age,bmi,children"
    ).split(",")


def get_column_names() -> t.List[str]:
    return (
        "age,sex,bmi,children,smoker,region"
    ).split(",")


def get_categorical_variables_values_mapping() -> t.Dict[str, t.Sequence[str]]:
    return {
        "sex": ("female", "male"),
        "smoker": ("no", "yes"),
        "region": ("northeast", "northwest", "southeast", "southwest")
    }
