
# Copyright (c) 2019-2020, NVIDIA 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.
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# Copyright (c) 2019-2020, NVIDIA 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 cudf
import cupy as cp
import dask_cudf
import pytest
import rmm

import numpy as np
import pandas as pd

from cuml.dask.ensemble import RandomForestClassifier as cuRFC_mg
from cuml.dask.ensemble import RandomForestRegressor as cuRFR_mg
from cuml.dask.common import utils as dask_utils

from dask.array import from_array
from sklearn.datasets import make_regression, make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, r2_score
from dask.distributed import Client


def _prep_training_data(c, X_train, y_train, partitions_per_worker):
    workers = c.has_what().keys()
    n_partitions = partitions_per_worker * len(workers)
    X_cudf = cudf.DataFrame.from_pandas(pd.DataFrame(X_train))
    X_train_df = dask_cudf.from_cudf(X_cudf, npartitions=n_partitions)

    y_cudf = np.array(pd.DataFrame(y_train).values)
    y_cudf = y_cudf[:, 0]
    y_cudf = cudf.Series(y_cudf)
    y_train_df = \
        dask_cudf.from_cudf(y_cudf, npartitions=n_partitions)

    X_train_df, \
        y_train_df = dask_utils.persist_across_workers(c,
                                                       [X_train_df,
                                                        y_train_df],
                                                       workers=workers)
    return X_train_df, y_train_df


@pytest.mark.parametrize('partitions_per_worker', [1, 3])
def test_rf_classification_dask(partitions_per_worker, cluster):

    # Use CUDA_VISIBLE_DEVICES to control the number of workers
    c = Client(cluster)

    try:

        X, y = make_classification(n_samples=10000, n_features=20,
                                   n_clusters_per_class=1, n_informative=10,
                                   random_state=123, n_classes=5)

        X = X.astype(np.float32)
        y = y.astype(np.int32)

        X_train, X_test, y_train, y_test = \
            train_test_split(X, y, test_size=1000)

        cu_rf_params = {
            'n_estimators': 40,
            'max_depth': 16,
            'n_streams': 1,
            'n_bins': 16,
        }

        X_train_df, y_train_df = _prep_training_data(c, X_train, y_train,
                                                     partitions_per_worker)
        cu_rf_mg = cuRFC_mg(**cu_rf_params)
        cu_rf_mg.fit(X_train_df, y_train_df)
        cu_rf_mg_predict = cu_rf_mg.predict(X_test)

        acc_score = accuracy_score(cu_rf_mg_predict, y_test, normalize=True)
        assert acc_score > 0.8

    finally:
        c.close()


@pytest.mark.parametrize('partitions_per_worker', [1, 3])
def test_rf_classification_dask_cudf(partitions_per_worker, cluster):

    # Use CUDA_VISIBLE_DEVICES to control the number of workers
    c = Client(cluster)

    try:

        X, y = make_classification(n_samples=10000, n_features=20,
                                   n_clusters_per_class=1, n_informative=10,
                                   random_state=123, n_classes=5)

        X = X.astype(np.float32)
        y = y.astype(np.int32)

        X_train, X_test, y_train, y_test = \
            train_test_split(X, y, test_size=1000)

        cu_rf_params = {
            'n_estimators': 40,
            'max_depth': 16,
            'n_bins': 16,
        }

        X_train_df, y_train_df = _prep_training_data(c, X_train, y_train,
                                                     partitions_per_worker)

        X_test_cudf = cudf.DataFrame.from_gpu_matrix(rmm.to_device(X_test))
        cu_rf_mg = cuRFC_mg(**cu_rf_params)
        cu_rf_mg.fit(X_train_df, y_train_df)
        cu_rf_mg_predict = cu_rf_mg.predict(X_test_cudf)

        acc_score = accuracy_score(cu_rf_mg_predict, y_test, normalize=True)

        assert acc_score > 0.8

    finally:
        c.close()


@pytest.mark.parametrize('partitions_per_worker', [1, 5])
def test_rf_regression_dask_fil(partitions_per_worker, cluster):

    # Use CUDA_VISIBLE_DEVICES to control the number of workers
    c = Client(cluster)

    try:

        X, y = make_regression(n_samples=10000, n_features=20,
                               n_informative=10, random_state=123)

        X = X.astype(np.float32)
        y = y.astype(np.float32)

        X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                            test_size=1000)

        cu_rf_params = {
            'n_estimators': 50,
            'max_depth': 16,
            'n_bins': 16,
        }

        workers = c.has_what().keys()
        n_partitions = partitions_per_worker * len(workers)

        X_cudf = cudf.DataFrame.from_pandas(pd.DataFrame(X_train))
        X_train_df = \
            dask_cudf.from_cudf(X_cudf, npartitions=n_partitions)

        y_cudf = np.array(pd.DataFrame(y_train).values)
        y_cudf = y_cudf[:, 0]
        y_cudf = cudf.Series(y_cudf)
        y_train_df = \
            dask_cudf.from_cudf(y_cudf, npartitions=n_partitions)
        X_cudf_test = cudf.DataFrame.from_pandas(pd.DataFrame(X_test))
        X_test_df = \
            dask_cudf.from_cudf(X_cudf_test, npartitions=n_partitions)

        X_train_df, y_train_df = dask_utils.persist_across_workers(
            c, [X_train_df, y_train_df], workers=workers)

        cu_rf_mg = cuRFR_mg(**cu_rf_params)
        cu_rf_mg.fit(X_train_df, y_train_df)

        cu_rf_mg_predict = cu_rf_mg.predict(X_test_df).compute()
        cu_rf_mg_predict = cp.asnumpy(cp.array(cu_rf_mg_predict))

        acc_score = r2_score(cu_rf_mg_predict, y_test)

        assert acc_score >= 0.67

    finally:
        c.close()


@pytest.mark.parametrize('partitions_per_worker', [1, 5])
@pytest.mark.parametrize('output_class', [True, False])
def test_rf_classification_dask_fil(partitions_per_worker, cluster,
                                    output_class):

    # Use CUDA_VISIBLE_DEVICES to control the number of workers
    c = Client(cluster)

    try:

        X, y = make_classification(n_samples=10000, n_features=30,
                                   n_clusters_per_class=1, n_informative=20,
                                   random_state=123, n_classes=2)

        X = X.astype(np.float32)
        y = y.astype(np.int32)

        X_train, X_test, y_train, y_test = \
            train_test_split(X, y, test_size=1000)

        cu_rf_params = {
            'n_estimators': 25,
            'max_depth': 13,
            'n_bins': 15,
        }

        X_train_df, y_train_df = _prep_training_data(c, X_train, y_train,
                                                     partitions_per_worker)
        X_test_df, _ = _prep_training_data(c, X_test, y_test,
                                           partitions_per_worker)
        cu_rf_mg = cuRFC_mg(**cu_rf_params)
        cu_rf_mg.fit(X_train_df, y_train_df)
        cu_rf_mg_predict = cu_rf_mg.predict(X_test_df, output_class).compute()
        cu_rf_mg_predict = cp.asnumpy(cp.array(cu_rf_mg_predict))
        if not output_class:
            cu_rf_mg_predict = np.round(cu_rf_mg_predict)

        acc_score = accuracy_score(cu_rf_mg_predict, y_test, normalize=True)

        assert acc_score > 0.8

    finally:
        c.close()


@pytest.mark.parametrize('partitions_per_worker', [1, 5])
@pytest.mark.parametrize('output_class', [True, False])
def test_rf_classification_dask_array(partitions_per_worker, cluster,
                                      output_class):

    # Use CUDA_VISIBLE_DEVICES to control the number of workers
    c = Client(cluster)

    try:

        X, y = make_classification(n_samples=10000, n_features=30,
                                   n_clusters_per_class=1, n_informative=20,
                                   random_state=123, n_classes=2)

        X = X.astype(np.float32)
        y = y.astype(np.int32)

        X_train, X_test, y_train, y_test = \
            train_test_split(X, y, test_size=1000)

        cu_rf_params = {
            'n_estimators': 25,
            'max_depth': 13,
            'n_bins': 15,
        }

        X_train_df, y_train_df = _prep_training_data(c, X_train, y_train,
                                                     partitions_per_worker)
        X_test_dask_array = from_array(X_test)
        cu_rf_mg = cuRFC_mg(**cu_rf_params)
        cu_rf_mg.fit(X_train_df, y_train_df)
        cu_rf_mg_predict = cu_rf_mg.predict(X_test_dask_array,
                                            output_class).compute()
        if not output_class:
            cu_rf_mg_predict = np.round(cu_rf_mg_predict)

        acc_score = accuracy_score(cu_rf_mg_predict, y_test, normalize=True)

        assert acc_score > 0.8

    finally:
        c.close()
