# 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.neighbors

import lale.docstrings
import lale.operators

_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/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
""",
    "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,
)