# 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 lale.operators
import lale.docstrings
import sklearn.neighbors

_hyperparams_schema = {
    'description': 'Hyperparameter schema for the KNeighborsClassifier model from scikit-learn.',
    'allOf': [{
        'description':
            'This first sub-object lists all constructor arguments with their '
            'types, one at a time, omitting cross-argument constraints.',
        'type': 'object',
        'additionalProperties': False,
        'required': [
            'n_neighbors', 'weights', 'algorithm', 'leaf_size', 'p', 'metric',
            'metric_params', 'n_jobs'],
        'relevantToOptimizer': [
            'n_neighbors', 'weights', 'algorithm', 'p', 'metric'],
        'properties': {
            'n_neighbors': {
                'description': 'Number of neighbors to use by default for kneighbors queries.',
                'type': 'integer',
                'distribution': 'uniform',
                'minimum': 1,
                'laleMaximum': 'X/maxItems', #number of rows
                'default': 5,
                'maximumForOptimizer': 100},
            'weights': {
                'description': 'Weight function used in prediction.',
                'enum': ['uniform', 'distance'],
                'default': 'uniform'},
            'algorithm': {
                'description': 'Algorithm used to compute the nearest neighbors.',
                'enum': ['ball_tree', 'kd_tree', 'brute', 'auto'],
                'default': 'auto'},
            'leaf_size': {
                'description': 'Leaf size passed to BallTree or KDTree.',
                'type': 'integer',
                'distribution': 'uniform',
                'minimum': 1,
                'default': 30},
            'p': {
                'description': 'Power parameter for the Minkowski metric.',
                'type': 'integer',
                'distribution': 'uniform',
                'minimum': 1,
                'default': 2,
                'maximumForOptimizer': 3},
            'metric': {
                'description': 'The distance metric to use for the tree.',
                'enum': ['euclidean', 'manhattan', 'minkowski'],
                'default': 'minkowski'},
            'metric_params': {
                'description': 'Additional keyword arguments for the metric function.',
                'anyOf': [{
                    'enum': [None]}, {
                    'type': 'object',
                    'propertyNames': {
                        'pattern': '[_a-zA-Z][_a-zA-Z0-9]*'},
                }],
                'default': None},
            'n_jobs': {
                'description': 'Number of parallel jobs to run for the neighbor search.',
                'anyOf': [{
                    'description': '1 unless in joblib.parallel_backend context.',
                    'enum': [None]}, {
                    'description': 'Use all processors.',
                    'enum': [(- 1)]}, {
                    'description': 'Number of CPU cores.',
                    'type': 'integer',
                    'minimum': 1}],
                'default': None}}},
    {   'description': 'The leaf size only matters for tree algorithms.',
        'anyOf': [
        {   'type': 'object',
            'properties': {
                'algorithm': {
                    'enum': ['ball_tree', 'kd_tree']},
            }},
        {   'type': 'object',
            'properties': {
                'leaf_size': {
                    'enum': [30]},
            }}]},
    {   'description': 'The power parameter is specific to the minkowski metric.',
        'anyOf': [
        {   'type': 'object',
            'properties': {
                'metric': {
                    'enum': ['minkowski']},
            }},
        {   'type': 'object',
            'properties': {
                'p': {
                    'enum': [2]},
            }}]}]}

_input_fit_schema = {
    'description': 'Input data schema for training the KNeighborsClassifier model from scikit-learn.',
    'type': 'object',
    'required': ['X', 'y'],
    'additionalProperties': False,
    'properties': {
        'X': {
            'description': 'Features; the outer array is over samples.',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            }},
        'y': {
            'description': 'Target class labels; the array is over samples.',
            'anyOf': [
                {'type': 'array', 'items': {'type': 'number'}},
                {'type': 'array', 'items': {
                    'type': 'array', 'items': {'type': 'number'}}},
                {'type': 'array', 'items': {'type': 'string'}},
                {'type': 'array', 'items': {'type': 'boolean'}}]}}}

_input_predict_schema = {
    'description': 'Input data schema for predictions using the KNeighborsClassifier model from scikit-learn.',
    'type': 'object',
    'required': ['X'],
    'additionalProperties': False,
    'properties': {
        'X': {
            'description': 'Features; the outer array is over samples.',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}}}}}

_output_predict_schema = {
    'description': 'Predicted class label per sample.',
    'anyOf': [
        {'type': 'array', 'items': {'type': 'number'}},
        {'type': 'array', 'items': {
            'type': 'array', 'items': {'type': 'number'}}},
        {'type': 'array', 'items': {'type': 'string'}},
        {'type': 'array', 'items': {'type': 'boolean'}}]}

_input_predict_proba_schema = {
    'description': 'Input data schema for predictions using the KNeighborsClassifier model from scikit-learn.',
    'type': 'object',
    'required': ['X'],
    'additionalProperties': False,
    'properties': {
        'X': {
            'description': 'Features; the outer array is over samples.',
            'type': 'array',
            'items': {
                'type': 'array',
                'items': { 'type': 'number'}}}}}

_output_predict_proba_schema = {
    'description': 'Probability of the sample for each class in the model.',
    'type': 'array',
    'items': {'type': 'array', 'items': {'type': 'number'}}}

_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """`K nearest neighbors classifier`_ from scikit-learn.

.. _`K nearest neighbors classifier`: https://scikit-learn.org/0.20/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn-neighbors-kneighborsclassifier
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.k_neighbors_classifier.html',
    'import_from': 'sklearn.neighbors',
    'type': 'object',
    'tags': {
        'pre': ['~categoricals'],
        'op': ['estimator', 'classifier', 'interpretable'],
        'post': ['probabilities']},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_predict': _input_predict_schema,
        'output_predict': _output_predict_schema,
        'input_predict_proba': _input_predict_proba_schema,
        'output_predict_proba': _output_predict_proba_schema}}
    
class KNeighborsClassifierImpl():
    def __init__(self, **hyperparams):
        self._hyperparams = hyperparams
        self._wrapped_model = sklearn.neighbors.KNeighborsClassifier(**self._hyperparams)

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

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

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

lale.docstrings.set_docstrings(KNeighborsClassifierImpl, _combined_schemas)

KNeighborsClassifier = lale.operators.make_operator(KNeighborsClassifierImpl, _combined_schemas)