# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import sklearn.impute
import lale.docstrings
import lale.operators
import numpy as np


class SimpleImputerImpl():

    def __init__(self, missing_values=None, strategy='mean', fill_value=None, verbose=0, copy=True):
        self._hyperparams = {
            'missing_values': missing_values,
            'strategy': strategy,
            'fill_value': fill_value,
            'verbose': verbose,
            'copy': copy}
        self._wrapped_model = sklearn.impute.SimpleImputer(**self._hyperparams)

    def fit(self, X, y=None):
        self._wrapped_model.fit(X, y)
        return self

    def transform(self, X):
        return self._wrapped_model.transform(X)

    def transform_schema(self, s_X):
        return s_X

_hyperparams_schema = {
    'description': 'Imputation transformer for completing missing values.',
    'allOf': [{
        'type': 'object',
        'additionalProperties': False,
        'required': ['missing_values', 'strategy', 'fill_value', 'verbose', 'copy'],
        'relevantToOptimizer': ['strategy'],        
        'properties': {
            'missing_values': {
                'anyOf': [{'type': 'number'},
                          {'type': 'string'},
                          {'enum': [np.nan]},
                          {'enum': [None]}],
                'default': np.nan,
                'description': 'The placeholder for the missing values.'},
            'strategy': {
                'anyOf': [
                    {'enum':['constant'], 'forOptimizer': False}, 
                    {'enum': ['mean', 'median', 'most_frequent']}],
                'default': 'mean',
                'description': 'The imputation strategy.'},
            'fill_value': {
                'anyOf': [{'type': 'number'},
                          {'type': 'string'},
                          {'enum': [None]}],
                'default': None,
                'description': 'When strategy == "constant", fill_value is used to replace all occurrences of missing_values'},
            'verbose': {
                'type': 'integer',
                'default': 0,
                'description': 'Controls the verbosity of the imputer.'},
            'copy': {
                'type': 'boolean',
                'default': True,
                'description': 'If True, a copy of X will be created. If False, imputation will'},
        }}]}

_input_fit_schema = {
    'description': 'Fit the imputer on X.',
    'type': 'object',
    'required': ['X'],
    'additionalProperties': False,
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array', 
                'items':{
                    'anyOf':[
                    {'type': 'number'},
                    {'type': 'string'}]}},
            'description': 'Input data, where ``n_samples`` is the number of samples and  ``n_features`` is the number of features.'},
        'y': {}}}

_input_transform_schema = {
    'description': 'Impute all missing values in X.',
    'type': 'object',
    'required': ['X'],
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array', 
                'items':{
                    'anyOf':[
                    {'type': 'number'},
                    {'type': 'string'}]}},
            'description': 'The input data to complete.'},
    },
}
_output_transform_schema = {
    'description': 'The input data to complete.',
    'type': 'array',
    'items': {'type': 'array', 
        'items':{
            'anyOf':[
                {'type': 'number'},
                {'type': 'string'}]}},
}
_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`Simple imputer`_ transformer from scikit-learn for completing missing values.

.. _`Simple imputer`: https://scikit-learn.org/0.20/modules/generated/sklearn.impute.SimpleImputer.html#sklearn-impute-simpleimputer
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.simple_imputer.html',
    'import_from': 'sklearn.impute',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(SimpleImputerImpl, _combined_schemas)

SimpleImputer = lale.operators.make_operator(SimpleImputerImpl, _combined_schemas)