from abc import ABC, abstractmethod
from pandas import DataFrame, Series
from pandas.api.types import is_numeric_dtype
from sklearn.neighbors import KNeighborsClassifier
from typing import Any, Union

class DataFrameFunction(ABC):
    """A command that may be applied in-place to a dataframe."""

    @abstractmethod
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        """Apply the current function to the given dataframe, in-place.

        The series is described by its label and dataframe."""
        return df


class MVStrategy(DataFrameFunction):
    """A way to handle missing values in a dataframe."""

    @staticmethod
    def list_available(df: DataFrame, label: str, series: Series) -> list['MVStrategy']:
        """Get all the strategies that can be used."""
        choices = [DropStrategy(), ModeStrategy()]
        if is_numeric_dtype(series):
            choices.extend((MeanStrategy(), MedianStrategy(), LinearRegressionStrategy()))
            other_columns = df.select_dtypes(include="number").drop(label, axis=1).columns.to_list()
            if len(other_columns):
                choices.append(KNNStrategy(other_columns))
        return choices


class ScalingStrategy(DataFrameFunction):
    """A way to handle missing values in a dataframe."""

    @staticmethod
    def list_available(df: DataFrame, series: Series) -> list['MVStrategy']:
        """Get all the strategies that can be used."""
        choices = [KeepStrategy()]
        if is_numeric_dtype(series):
            choices.extend((MinMaxStrategy(), ZScoreStrategy()))
            if series.sum() != 0:
                choices.append(UnitLengthStrategy())
        return choices


class DropStrategy(MVStrategy):
    #@typing.override
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        df.dropna(subset=label, inplace=True)
        return df

    def __str__(self) -> str:
        return "Drop"


class PositionStrategy(MVStrategy):
    #@typing.override
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        series.fillna(self.get_value(series), inplace=True)
        return df

    @abstractmethod
    def get_value(self, series: Series) -> Any:
        pass


class MeanStrategy(PositionStrategy):
    #@typing.override
    def get_value(self, series: Series) -> Union[int, float]:
        return series.mean()

    def __str__(self) -> str:
        return "Use mean"


class MedianStrategy(PositionStrategy):
    #@typing.override
    def get_value(self, series: Series) -> Union[int, float]:
        return series.median()

    def __str__(self) -> str:
        return "Use median"


class ModeStrategy(PositionStrategy):
    #@typing.override
    def get_value(self, series: Series) -> Any:
        return series.mode()[0]

    def __str__(self) -> str:
        return "Use mode"


class LinearRegressionStrategy(MVStrategy):
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        series.interpolate(inplace=True)
        return df

    def __str__(self) -> str:
        return "Use linear regression"


class KNNStrategy(MVStrategy):
    def __init__(self, training_features: list[str]):
        self.available_features = training_features
        self.training_features = training_features
        self.n_neighbors = 3

    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        # Remove any training column that have any missing values
        usable_data = df.dropna(subset=self.training_features)
        # Select columns to impute from
        train_data = usable_data.dropna(subset=label)
        # Create train dataframe
        x_train = train_data.drop(label, axis=1)
        y_train = train_data[label]

        reg = KNeighborsClassifier(self.n_neighbors).fit(x_train, y_train)

        # Create test dataframe
        test_data = usable_data[usable_data[label].isnull()]
        if test_data.empty:
            return df
        x_test = test_data.drop(label, axis=1)
        predicted = reg.predict(x_test)

        # Fill with predicated values and patch the original data
        usable_data[label].fillna(Series(predicted), inplace=True)
        df.fillna(usable_data, inplace=True)
        return df

    def count_max(self, df: DataFrame, label: str) -> int:
        usable_data = df.dropna(subset=self.training_features)
        return usable_data[label].count()

    def __str__(self) -> str:
        return "kNN"


class KeepStrategy(ScalingStrategy):
    #@typing.override
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        return df

    def __str__(self) -> str:
        return "No-op"


class MinMaxStrategy(ScalingStrategy):
    #@typing.override
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        minimum = series.min()
        maximum = series.max()
        df[label] = (series - minimum) / (maximum - minimum)
        return df

    def __str__(self) -> str:
        return "Min-max"


class ZScoreStrategy(ScalingStrategy):
    #@typing.override
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        df[label] = (series - series.mean()) / series.std()
        return df

    def __str__(self) -> str:
        return "Z-Score"


class UnitLengthStrategy(ScalingStrategy):
    #@typing.override
    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
        df[label] = series / series.sum()
        return df

    def __str__(self) -> str:
        return "Unit length"