from typing import Dict, Optional, Tuple, Union

from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter

import numpy as np

from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipeline.constants import DENSE, SPARSE, UNSIGNED_DATA, INPUT


class NumericalImputation(AutoSklearnPreprocessingAlgorithm):

    def __init__(
        self,
        strategy: str = 'mean',
        random_state: Optional[Union[int, np.random.RandomState]] = None
    ) -> None:
        self.strategy = strategy
        self.random_state = random_state

    def fit(self, X: PIPELINE_DATA_DTYPE,
            y: Optional[PIPELINE_DATA_DTYPE] = None) -> 'NumericalImputation':
        import sklearn.impute

        self.preprocessor = sklearn.impute.SimpleImputer(
            strategy=self.strategy, copy=False)
        self.preprocessor.fit(X)
        return self

    def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE:
        if self.preprocessor is None:
            raise NotImplementedError()
        return self.preprocessor.transform(X)

    @staticmethod
    def get_properties(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None
                       ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]:
        return {'shortname': 'NumericalImputation',
                'name': 'Numerical Imputation',
                'handles_missing_values': True,
                'handles_nominal_values': True,
                'handles_numerical_features': True,
                'prefers_data_scaled': False,
                'prefers_data_normalized': False,
                'handles_regression': True,
                'handles_classification': True,
                'handles_multiclass': True,
                'handles_multilabel': True,
                'handles_multioutput': True,
                'is_deterministic': True,
                # TODO find out if this is right!
                'handles_sparse': True,
                'handles_dense': True,
                'input': (DENSE, SPARSE, UNSIGNED_DATA),
                'output': (INPUT,),
                'preferred_dtype': None}

    @staticmethod
    def get_hyperparameter_search_space(dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None
                                        ) -> ConfigurationSpace:
        # TODO add replace by zero!
        strategy = CategoricalHyperparameter(
            "strategy", ["mean", "median", "most_frequent"], default_value="mean")
        cs = ConfigurationSpace()
        cs.add_hyperparameter(strategy)
        return cs
