# Copyright (c) 2017, Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can be
# found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause

import unittest
import tempfile
import json

from sklearn.ensemble import GradientBoostingClassifier
from coremltools.converters import sklearn as skl_converter
from coremltools.models.utils import _macos_version
from coremltools.proto import FeatureTypes_pb2, Model_pb2
from coremltools._deps import _HAS_SKLEARN, _HAS_XGBOOST

if _HAS_XGBOOST:
    import xgboost
    from coremltools.converters import xgboost as xgb_converter

@unittest.skipIf(not _HAS_SKLEARN, "Missing sklearn. Skipping tests.")
class GradientBoostingBinaryClassifierScikitTest(unittest.TestCase):
    """
    Unit test class for testing scikit-learn converter.
    """

    @classmethod
    def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        from sklearn.datasets import load_boston

        scikit_data = load_boston()
        scikit_model = GradientBoostingClassifier(random_state=1)
        target = scikit_data["target"] > scikit_data["target"].mean()
        scikit_model.fit(scikit_data["data"], target)

        # Save the data and the model
        self.scikit_data = scikit_data
        self.scikit_model = scikit_model

    def test_conversion(self):
        input_names = self.scikit_data.feature_names
        output_name = "target"
        spec = skl_converter.convert(
            self.scikit_model, input_names, "target"
        ).get_spec()
        self.assertIsNotNone(spec)

        # Test the model class
        self.assertIsNotNone(spec.description)
        self.assertIsNotNone(spec.treeEnsembleClassifier)

        # Test the interface class
        self.assertEqual(spec.description.predictedFeatureName, "target")

        # Test the inputs and outputs
        self.assertEqual(len(spec.description.output), 2)
        self.assertEqual(spec.description.output[0].name, "target")
        self.assertEqual(
            spec.description.output[0].type.WhichOneof("Type"), "int64Type"
        )
        for input_type in spec.description.input:
            self.assertEqual(input_type.type.WhichOneof("Type"), "doubleType")
        self.assertEqual(
            sorted(input_names), sorted(map(lambda x: x.name, spec.description.input))
        )

        # Test the linear regression parameters.
        tr = spec.pipelineClassifier.pipeline.models[
            1
        ].treeEnsembleClassifier.treeEnsemble
        self.assertIsNotNone(tr)
        self.assertEqual(len(tr.nodes), 1416)

    def test_conversion_bad_inputs(self):
        # Error on converting an untrained model
        with self.assertRaises(Exception):
            model = GradientBoostingClassifier()
            spec = skl_converter.convert(model, "data", "out")

        # Check the expected class during covnersion.
        from sklearn.preprocessing import OneHotEncoder

        with self.assertRaises(Exception):
            model = OneHotEncoder()
            spec = skl_converter.convert(model, "data", "out")


@unittest.skipIf(not _HAS_SKLEARN, "Missing sklearn. Skipping tests.")
class GradientBoostingMulticlassClassifierScikitTest(unittest.TestCase):
    """
    Unit test class for testing scikit-learn converter.
    """

    @classmethod
    def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        from sklearn.datasets import load_boston
        import numpy as np

        scikit_data = load_boston()
        scikit_model = GradientBoostingClassifier(random_state=1)
        t = scikit_data.target
        target = np.digitize(t, np.histogram(t)[1]) - 1
        scikit_model.fit(scikit_data.data, target)
        self.target = target

        # Save the data and the model
        self.scikit_data = scikit_data
        self.scikit_model = scikit_model

    def test_conversion(self):
        input_names = self.scikit_data.feature_names
        output_name = "target"
        spec = skl_converter.convert(
            self.scikit_model, input_names, "target"
        ).get_spec()
        self.assertIsNotNone(spec)

        # Test the model class
        self.assertIsNotNone(spec.description)
        self.assertEqual(spec.description.predictedFeatureName, "target")

        # Test the inputs and outputs
        self.assertEqual(len(spec.description.output), 2)
        self.assertEqual(spec.description.output[0].name, "target")
        self.assertEqual(
            spec.description.output[0].type.WhichOneof("Type"), "int64Type"
        )

        for input_type in spec.description.input:
            self.assertEqual(input_type.type.WhichOneof("Type"), "doubleType")
        self.assertEqual(
            sorted(input_names), sorted(map(lambda x: x.name, spec.description.input))
        )

        self.assertEqual(len(spec.pipelineClassifier.pipeline.models), 2)
        tr = spec.pipelineClassifier.pipeline.models[
            -1
        ].treeEnsembleClassifier.treeEnsemble
        self.assertIsNotNone(tr)
        self.assertEqual(len(tr.nodes), 15056)

    def test_conversion_bad_inputs(self):
        # Error on converting an untrained model
        with self.assertRaises(Exception):
            model = GradientBoostingClassifier()
            spec = skl_converter.convert(model, "data", "out")

        # Check the expected class during covnersion.
        from sklearn.preprocessing import OneHotEncoder

        with self.assertRaises(Exception):
            model = OneHotEncoder()
            spec = skl_converter.convert(model, "data", "out")


@unittest.skipIf(_macos_version() >= (10, 16), "rdar://problem/84898245")
@unittest.skipIf(not _HAS_SKLEARN, "Missing sklearn. Skipping tests.")
@unittest.skipIf(not _HAS_XGBOOST, "Skipping, no xgboost")
class GradientBoostingBinaryClassifierXGboostTest(unittest.TestCase):
    """
    Unit test class for testing xgboost converter.
    """

    @classmethod
    def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        from sklearn.datasets import load_boston

        scikit_data = load_boston()
        self.xgb_model = xgboost.XGBClassifier()
        target = scikit_data["target"] > scikit_data["target"].mean()
        self.xgb_model.fit(scikit_data["data"], target)

        # Save the data and the model
        self.scikit_data = scikit_data

    def test_conversion(self):
        input_names = self.scikit_data.feature_names
        output_name = "target"
        spec = xgb_converter.convert(
            self.xgb_model, input_names, output_name, mode="classifier"
        ).get_spec()
        self.assertIsNotNone(spec)

        # Test the model class
        self.assertIsNotNone(spec.description)
        self.assertIsNotNone(spec.treeEnsembleClassifier)

        # Test the interface class
        self.assertEqual(spec.description.predictedFeatureName, output_name)

        # Test the inputs and outputs
        self.assertEqual(len(spec.description.output), 2)
        self.assertEqual(spec.description.output[0].name, output_name)
        self.assertEqual(
            spec.description.output[0].type.WhichOneof("Type"), "int64Type"
        )
        for input_type in spec.description.input:
            self.assertEqual(input_type.type.WhichOneof("Type"), "doubleType")
        self.assertEqual(
            sorted(input_names), sorted(map(lambda x: x.name, spec.description.input))
        )

        # Test the linear regression parameters.
        tr = spec.treeEnsembleClassifier.treeEnsemble
        self.assertIsNotNone(tr)

    def test_conversion_bad_inputs(self):
        # Error on converting an untrained model
        with self.assertRaises(Exception):
            model = xgboost.XGBClassifier()
            spec = xgb_converter.convert(model, "data", "out", mode="classifier")

        # Check the expected class during covnersion.
        with self.assertRaises(Exception):
            model = xgboost.XGBRegressor()
            spec = xgb_converter.convert(model, "data", "out", mode="classifier")


@unittest.skipIf(_macos_version() >= (10, 16), "rdar://problem/84898245")
@unittest.skipIf(not _HAS_SKLEARN, "Missing sklearn. Skipping tests.")
@unittest.skipIf(not _HAS_XGBOOST, "Skipping, no xgboost")
class GradientBoostingMulticlassClassifierXGboostTest(unittest.TestCase):
    """
    Unit test class for testing xgboost converter.
    """

    @classmethod
    def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        from sklearn.datasets import load_boston
        import numpy as np

        scikit_data = load_boston()
        t = scikit_data.target
        target = np.digitize(t, np.histogram(t)[1]) - 1
        dtrain = xgboost.DMatrix(
            scikit_data.data, label=target, feature_names=scikit_data.feature_names
        )
        self.xgb_model = xgboost.train({}, dtrain)
        self.target = target

        # Save the data and the model
        self.scikit_data = scikit_data
        self.n_classes = len(np.unique(self.target))

    def test_conversion(self):

        input_names = self.scikit_data.feature_names
        output_name = "target"
        spec = xgb_converter.convert(
            self.xgb_model,
            input_names,
            output_name,
            mode="classifier",
            n_classes=self.n_classes,
        ).get_spec()
        self.assertIsNotNone(spec)

        # Test the model class
        self.assertIsNotNone(spec.description)
        self.assertEqual(spec.description.predictedFeatureName, output_name)

        # Test the inputs and outputs
        self.assertEqual(len(spec.description.output), 2)
        self.assertEqual(spec.description.output[0].name, output_name)
        self.assertEqual(
            spec.description.output[0].type.WhichOneof("Type"), "int64Type"
        )

        for input_type in spec.description.input:
            self.assertEqual(input_type.type.WhichOneof("Type"), "doubleType")
        self.assertEqual(
            sorted(input_names), sorted(map(lambda x: x.name, spec.description.input))
        )

        # Test the linear regression parameters.
        tr = spec.treeEnsembleClassifier.treeEnsemble
        self.assertIsNotNone(tr)

    def test_conversion_from_file(self):
        import numpy as np

        output_name = "target"
        feature_names = self.scikit_data.feature_names

        xgb_model_json = tempfile.mktemp("xgb_tree_model_classifier.json")
        xgb_json_out = self.xgb_model.get_dump(with_stats=True, dump_format="json")
        with open(xgb_model_json, "w") as f:
            json.dump(xgb_json_out, f)
        spec = xgb_converter.convert(
            xgb_model_json,
            feature_names,
            output_name,
            mode="classifier",
            n_classes=self.n_classes,
        ).get_spec()
        self.assertIsNotNone(spec)

        # Test the model class
        self.assertIsNotNone(spec.description)
        self.assertIsNotNone(spec.treeEnsembleRegressor)

        # Test the interface class
        self.assertEqual(spec.description.predictedFeatureName, output_name)

        # Test the inputs and outputs
        self.assertEqual(len(spec.description.output), 2)
        self.assertEqual(spec.description.output[0].name, output_name)
        self.assertEqual(
            spec.description.output[0].type.WhichOneof("Type"), "int64Type"
        )
        for input_type in spec.description.input:
            self.assertEqual(input_type.type.WhichOneof("Type"), "doubleType")
        self.assertEqual(
            sorted(self.scikit_data.feature_names),
            sorted(map(lambda x: x.name, spec.description.input)),
        )

        # Test the linear regression parameters.
        tr = spec.treeEnsembleClassifier.treeEnsemble
        self.assertIsNotNone(tr)
