# Copyright (c) 2021, 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 pytest


from cuml.cluster import HDBSCAN
from cuml.cluster import condense_hierarchy
from sklearn.datasets import make_blobs

from cuml.metrics import adjusted_rand_score
from cuml.test.utils import get_pattern

import numpy as np

from cuml.common import logger

import hdbscan
from hdbscan.plots import CondensedTree

from sklearn import datasets

import cupy as cp

test_datasets = {
 "digits": datasets.load_digits(),
 "boston": datasets.load_boston(),
 "diabetes": datasets.load_diabetes(),
 "cancer": datasets.load_breast_cancer(),
}

dataset_names = ['noisy_circles', 'noisy_moons', 'varied']


def assert_cluster_counts(sk_agg, cuml_agg, digits=25):
    sk_unique, sk_counts = np.unique(sk_agg.labels_, return_counts=True)
    sk_counts = np.sort(sk_counts)

    cu_unique, cu_counts = cp.unique(cuml_agg.labels_, return_counts=True)
    cu_counts = cp.sort(cu_counts).get()

    np.testing.assert_almost_equal(sk_counts, cu_counts, decimal=-1 * digits)


def get_children(roots, parents, arr):
    """
    Simple helper function to return the children of the condensed tree
    given an array of parents.
    """
    ret = []
    for root in roots:
        level = np.where(parents == root)
        ret.extend(arr[level])

    return np.array(ret).ravel()


def get_bfs_level(n, roots, parents, children, arr):
    """
    Simple helper function to perform a bfs through n
    levels of a condensed tree.
    """
    level = roots
    for i in range(n-1):
        level = get_children(level, parents, children)

    return get_children(level, parents, arr)


def assert_condensed_trees(sk_agg, min_cluster_size):
    """
    Because of differences in the renumbering and sort ordering,
    the condensed tree arrays from cuml and scikit-learn cannot
    be compared directly. This function performs a BFS through
    the condensed trees, comparing the cluster sizes and lambda
    values at each level of the trees.
    """

    slt = sk_agg.single_linkage_tree_._linkage

    condensed_tree = condense_hierarchy(slt, min_cluster_size)

    cu_parents = condensed_tree._raw_tree["parent"]
    sk_parents = sk_agg.condensed_tree_._raw_tree["parent"]

    cu_children = condensed_tree._raw_tree["child"]
    sk_children = sk_agg.condensed_tree_._raw_tree["child"]

    cu_lambda = condensed_tree._raw_tree["lambda_val"]
    sk_lambda = sk_agg.condensed_tree_._raw_tree["lambda_val"]

    cu_child_size = condensed_tree._raw_tree["child_size"]
    sk_child_size = sk_agg.condensed_tree_._raw_tree["child_size"]

    # Start at the root, perform bfs

    l2_cu = [1000]
    l2_sk = [1000]

    lev = 1
    while len(l2_cu) != 0 or len(l2_sk) != 0:
        l2_cu = get_bfs_level(lev, [1000], cu_parents, cu_children, cu_lambda)
        l2_sk = get_bfs_level(lev, [1000], sk_parents, sk_children, sk_lambda)

        s2_cu = get_bfs_level(lev, [1000], cu_parents, cu_children,
                              cu_child_size)
        s2_sk = get_bfs_level(lev, [1000], sk_parents, sk_children,
                              sk_child_size)

        s2_cu.sort()
        s2_sk.sort()
        l2_cu.sort()
        l2_sk.sort()

        lev += 1

        assert np.allclose(l2_cu, l2_sk, atol=1e-5, rtol=1e-6)
        assert np.allclose(s2_cu, s2_sk, atol=1e-5, rtol=1e-6)
    assert lev > 1


@pytest.mark.parametrize('nrows', [500])
@pytest.mark.parametrize('ncols', [25])
@pytest.mark.parametrize('nclusters', [2, 5])
@pytest.mark.parametrize('min_samples', [25, 60])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('min_cluster_size', [30, 50])
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_blobs(nrows, ncols, nclusters,
                       connectivity,
                       cluster_selection_epsilon,
                       cluster_selection_method,
                       allow_single_cluster,
                       min_cluster_size,
                       max_cluster_size,
                       min_samples):

    X, y = make_blobs(n_samples=int(nrows),
                      n_features=ncols,
                      centers=nclusters,
                      cluster_std=0.7,
                      shuffle=False,
                      random_state=42)

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    cuml_agg.fit(X)
    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    assert_condensed_trees(sk_agg, min_cluster_size)
    assert_cluster_counts(sk_agg, cuml_agg)

    assert(adjusted_rand_score(cuml_agg.labels_, sk_agg.labels_) >= 0.95)
    assert(len(np.unique(sk_agg.labels_)) == len(cp.unique(cuml_agg.labels_)))

    assert np.allclose(np.sort(sk_agg.cluster_persistence_),
           np.sort(cuml_agg.cluster_persistence_), rtol=0.01, atol=0.01)


@pytest.mark.parametrize('dataset', test_datasets.values())
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0, 50.0, 150.0])
@pytest.mark.parametrize('min_samples_cluster_size_bounds', [(150, 150, 0),
                                                             (50, 25, 0)])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_sklearn_datasets(dataset,
                                  connectivity,
                                  cluster_selection_epsilon,
                                  cluster_selection_method,
                                  min_samples_cluster_size_bounds,
                                  allow_single_cluster):

    min_samples, min_cluster_size, max_cluster_size = \
        min_samples_cluster_size_bounds

    X = dataset.data

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       gen_min_span_tree=True,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    cuml_agg.fit(X)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    assert_condensed_trees(sk_agg, min_cluster_size)
    assert_cluster_counts(sk_agg, cuml_agg)

    assert(len(np.unique(sk_agg.labels_)) == len(cp.unique(cuml_agg.labels_)))
    assert(adjusted_rand_score(cuml_agg.labels_, sk_agg.labels_) > 0.85)

    assert np.allclose(np.sort(sk_agg.cluster_persistence_),
           np.sort(cuml_agg.cluster_persistence_), rtol=0.1, atol=0.1)


@pytest.mark.parametrize('dataset', test_datasets.values())
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0, 50.0, 150.0])
@pytest.mark.parametrize('min_samples', [150, 50, 5, 400])
@pytest.mark.parametrize('min_cluster_size', [150, 25, 5, 250])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_sklearn_extract_clusters(dataset,
                                          connectivity,
                                          cluster_selection_epsilon,
                                          cluster_selection_method,
                                          min_samples,
                                          min_cluster_size,
                                          max_cluster_size,
                                          allow_single_cluster):

    X = dataset.data

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       gen_min_span_tree=True,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    cuml_agg._extract_clusters(sk_agg.condensed_tree_)

    assert adjusted_rand_score(cuml_agg.labels_test, sk_agg.labels_) == 1.0
    assert np.allclose(cp.asnumpy(cuml_agg.probabilities_test),
                       sk_agg.probabilities_)


@pytest.mark.parametrize('nrows', [1000])
@pytest.mark.parametrize('dataset', dataset_names)
@pytest.mark.parametrize('min_samples', [15])
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0])
@pytest.mark.parametrize('min_cluster_size', [25])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('cluster_selection_method', ['eom'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_cluster_patterns(dataset, nrows,
                                  connectivity,
                                  cluster_selection_epsilon,
                                  cluster_selection_method,
                                  min_cluster_size,
                                  allow_single_cluster,
                                  max_cluster_size,
                                  min_samples):

    # This also tests duplicate data points
    X, y = get_pattern(dataset, nrows)[0]

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    cuml_agg.fit(X)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    assert_condensed_trees(sk_agg, min_cluster_size)
    assert_cluster_counts(sk_agg, cuml_agg)

    assert(len(np.unique(sk_agg.labels_)) == len(cp.unique(cuml_agg.labels_)))
    assert(adjusted_rand_score(cuml_agg.labels_, sk_agg.labels_) > 0.95)

    assert np.allclose(np.sort(sk_agg.cluster_persistence_),
           np.sort(cuml_agg.cluster_persistence_), rtol=0.1, atol=0.1)


@pytest.mark.parametrize('nrows', [1000])
@pytest.mark.parametrize('dataset', dataset_names)
@pytest.mark.parametrize('min_samples', [5, 50, 400, 800])
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0, 50.0, 150.0])
@pytest.mark.parametrize('min_cluster_size', [10, 25, 100, 350])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_cluster_patterns_extract_clusters(dataset, nrows,
                                                   connectivity,
                                                   cluster_selection_epsilon,
                                                   cluster_selection_method,
                                                   min_cluster_size,
                                                   allow_single_cluster,
                                                   max_cluster_size,
                                                   min_samples):

    # This also tests duplicate data points
    X, y = get_pattern(dataset, nrows)[0]

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    cuml_agg._extract_clusters(sk_agg.condensed_tree_)

    assert adjusted_rand_score(cuml_agg.labels_test, sk_agg.labels_) == 1.0
    assert np.allclose(cp.asnumpy(cuml_agg.probabilities_test),
                       sk_agg.probabilities_)


def test_hdbscan_core_dists_bug_4054():
    """
    This test explicitly verifies that the MRE from
    https://github.com/rapidsai/cuml/issues/4054
    matches the reference impl
    """

    X, y = datasets.make_moons(n_samples=10000, noise=0.12, random_state=0)

    cu_labels_ = HDBSCAN(min_samples=25, min_cluster_size=25).fit_predict(X)
    sk_labels_ = hdbscan.HDBSCAN(min_samples=25,
                                 min_cluster_size=25,
                                 approx_min_span_tree=False).fit_predict(X)

    assert adjusted_rand_score(cu_labels_, sk_labels_) > 0.99


def test_hdbscan_empty_cluster_tree():

    raw_tree = np.recarray(shape=(5,),
                           formats=[np.intp, np.intp, float, np.intp],
                           names=('parent', 'child', 'lambda_val',
                                  'child_size'))

    raw_tree['parent'] = np.asarray([5, 5, 5, 5, 5])
    raw_tree['child'] = [0, 1, 2, 3, 4]
    raw_tree['lambda_val'] = [1.0, 1.0, 1.0, 1.0, 1.0]
    raw_tree['child_size'] = [1, 1, 1, 1, 1]

    condensed_tree = CondensedTree(raw_tree, 0.0, True)

    cuml_agg = HDBSCAN(allow_single_cluster=True,
                       cluster_selection_method="eom")
    cuml_agg._extract_clusters(condensed_tree)

    # We just care that all points are assigned to the root cluster
    assert np.sum(cuml_agg.labels_test.to_output("numpy")) == 0


def test_hdbscan_plots():

    X, y = make_blobs(n_samples=int(100),
                      n_features=100,
                      centers=10,
                      cluster_std=0.7,
                      shuffle=False,
                      random_state=42)

    cuml_agg = HDBSCAN(gen_min_span_tree=True)
    cuml_agg.fit(X)

    assert cuml_agg.condensed_tree_ is not None
    assert cuml_agg.minimum_spanning_tree_ is not None
    assert cuml_agg.single_linkage_tree_ is not None

    cuml_agg = HDBSCAN(gen_min_span_tree=False)
    cuml_agg.fit(X)

    assert cuml_agg.minimum_spanning_tree_ is None

# Copyright (c) 2021, 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 pytest


from cuml.cluster import HDBSCAN
from cuml.cluster import condense_hierarchy
from sklearn.datasets import make_blobs

from cuml.metrics import adjusted_rand_score
from cuml.test.utils import get_pattern

import numpy as np

from cuml.common import logger

import hdbscan
from hdbscan.plots import CondensedTree

from sklearn import datasets

import cupy as cp

test_datasets = {
 "digits": datasets.load_digits(),
 "boston": datasets.load_boston(),
 "diabetes": datasets.load_diabetes(),
 "cancer": datasets.load_breast_cancer(),
}

dataset_names = ['noisy_circles', 'noisy_moons', 'varied']


def assert_cluster_counts(sk_agg, cuml_agg, digits=25):
    sk_unique, sk_counts = np.unique(sk_agg.labels_, return_counts=True)
    sk_counts = np.sort(sk_counts)

    cu_unique, cu_counts = cp.unique(cuml_agg.labels_, return_counts=True)
    cu_counts = cp.sort(cu_counts).get()

    np.testing.assert_almost_equal(sk_counts, cu_counts, decimal=-1 * digits)


def get_children(roots, parents, arr):
    """
    Simple helper function to return the children of the condensed tree
    given an array of parents.
    """
    ret = []
    for root in roots:
        level = np.where(parents == root)
        ret.extend(arr[level])

    return np.array(ret).ravel()


def get_bfs_level(n, roots, parents, children, arr):
    """
    Simple helper function to perform a bfs through n
    levels of a condensed tree.
    """
    level = roots
    for i in range(n-1):
        level = get_children(level, parents, children)

    return get_children(level, parents, arr)


def assert_condensed_trees(sk_agg, min_cluster_size):
    """
    Because of differences in the renumbering and sort ordering,
    the condensed tree arrays from cuml and scikit-learn cannot
    be compared directly. This function performs a BFS through
    the condensed trees, comparing the cluster sizes and lambda
    values at each level of the trees.
    """

    slt = sk_agg.single_linkage_tree_._linkage

    condensed_tree = condense_hierarchy(slt, min_cluster_size)

    cu_parents = condensed_tree._raw_tree["parent"]
    sk_parents = sk_agg.condensed_tree_._raw_tree["parent"]

    cu_children = condensed_tree._raw_tree["child"]
    sk_children = sk_agg.condensed_tree_._raw_tree["child"]

    cu_lambda = condensed_tree._raw_tree["lambda_val"]
    sk_lambda = sk_agg.condensed_tree_._raw_tree["lambda_val"]

    cu_child_size = condensed_tree._raw_tree["child_size"]
    sk_child_size = sk_agg.condensed_tree_._raw_tree["child_size"]

    # Start at the root, perform bfs

    l2_cu = [1000]
    l2_sk = [1000]

    lev = 1
    while len(l2_cu) != 0 or len(l2_sk) != 0:
        l2_cu = get_bfs_level(lev, [1000], cu_parents, cu_children, cu_lambda)
        l2_sk = get_bfs_level(lev, [1000], sk_parents, sk_children, sk_lambda)

        s2_cu = get_bfs_level(lev, [1000], cu_parents, cu_children,
                              cu_child_size)
        s2_sk = get_bfs_level(lev, [1000], sk_parents, sk_children,
                              sk_child_size)

        s2_cu.sort()
        s2_sk.sort()
        l2_cu.sort()
        l2_sk.sort()

        lev += 1

        assert np.allclose(l2_cu, l2_sk, atol=1e-5, rtol=1e-6)
        assert np.allclose(s2_cu, s2_sk, atol=1e-5, rtol=1e-6)
    assert lev > 1


@pytest.mark.parametrize('nrows', [500])
@pytest.mark.parametrize('ncols', [25])
@pytest.mark.parametrize('nclusters', [2, 5])
@pytest.mark.parametrize('min_samples', [25, 60])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('min_cluster_size', [30, 50])
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_blobs(nrows, ncols, nclusters,
                       connectivity,
                       cluster_selection_epsilon,
                       cluster_selection_method,
                       allow_single_cluster,
                       min_cluster_size,
                       max_cluster_size,
                       min_samples):

    X, y = make_blobs(n_samples=int(nrows),
                      n_features=ncols,
                      centers=nclusters,
                      cluster_std=0.7,
                      shuffle=False,
                      random_state=42)

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    cuml_agg.fit(X)
    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    assert_condensed_trees(sk_agg, min_cluster_size)
    assert_cluster_counts(sk_agg, cuml_agg)

    assert(adjusted_rand_score(cuml_agg.labels_, sk_agg.labels_) >= 0.95)
    assert(len(np.unique(sk_agg.labels_)) == len(cp.unique(cuml_agg.labels_)))

    assert np.allclose(np.sort(sk_agg.cluster_persistence_),
           np.sort(cuml_agg.cluster_persistence_), rtol=0.01, atol=0.01)


@pytest.mark.parametrize('dataset', test_datasets.values())
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0, 50.0, 150.0])
@pytest.mark.parametrize('min_samples_cluster_size_bounds', [(150, 150, 0),
                                                             (50, 25, 0)])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_sklearn_datasets(dataset,
                                  connectivity,
                                  cluster_selection_epsilon,
                                  cluster_selection_method,
                                  min_samples_cluster_size_bounds,
                                  allow_single_cluster):

    min_samples, min_cluster_size, max_cluster_size = \
        min_samples_cluster_size_bounds

    X = dataset.data

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       gen_min_span_tree=True,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    cuml_agg.fit(X)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    assert_condensed_trees(sk_agg, min_cluster_size)
    assert_cluster_counts(sk_agg, cuml_agg)

    assert(len(np.unique(sk_agg.labels_)) == len(cp.unique(cuml_agg.labels_)))
    assert(adjusted_rand_score(cuml_agg.labels_, sk_agg.labels_) > 0.85)

    assert np.allclose(np.sort(sk_agg.cluster_persistence_),
           np.sort(cuml_agg.cluster_persistence_), rtol=0.1, atol=0.1)


@pytest.mark.parametrize('dataset', test_datasets.values())
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0, 50.0, 150.0])
@pytest.mark.parametrize('min_samples', [150, 50, 5, 400])
@pytest.mark.parametrize('min_cluster_size', [150, 25, 5, 250])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_sklearn_extract_clusters(dataset,
                                          connectivity,
                                          cluster_selection_epsilon,
                                          cluster_selection_method,
                                          min_samples,
                                          min_cluster_size,
                                          max_cluster_size,
                                          allow_single_cluster):

    X = dataset.data

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       gen_min_span_tree=True,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    cuml_agg._extract_clusters(sk_agg.condensed_tree_)

    assert adjusted_rand_score(cuml_agg.labels_test, sk_agg.labels_) == 1.0
    assert np.allclose(cp.asnumpy(cuml_agg.probabilities_test),
                       sk_agg.probabilities_)


@pytest.mark.parametrize('nrows', [1000])
@pytest.mark.parametrize('dataset', dataset_names)
@pytest.mark.parametrize('min_samples', [15])
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0])
@pytest.mark.parametrize('min_cluster_size', [25])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('cluster_selection_method', ['eom'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_cluster_patterns(dataset, nrows,
                                  connectivity,
                                  cluster_selection_epsilon,
                                  cluster_selection_method,
                                  min_cluster_size,
                                  allow_single_cluster,
                                  max_cluster_size,
                                  min_samples):

    # This also tests duplicate data points
    X, y = get_pattern(dataset, nrows)[0]

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    cuml_agg.fit(X)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    assert_condensed_trees(sk_agg, min_cluster_size)
    assert_cluster_counts(sk_agg, cuml_agg)

    assert(len(np.unique(sk_agg.labels_)) == len(cp.unique(cuml_agg.labels_)))
    assert(adjusted_rand_score(cuml_agg.labels_, sk_agg.labels_) > 0.95)

    assert np.allclose(np.sort(sk_agg.cluster_persistence_),
           np.sort(cuml_agg.cluster_persistence_), rtol=0.1, atol=0.1)


@pytest.mark.parametrize('nrows', [1000])
@pytest.mark.parametrize('dataset', dataset_names)
@pytest.mark.parametrize('min_samples', [5, 50, 400, 800])
@pytest.mark.parametrize('cluster_selection_epsilon', [0.0, 50.0, 150.0])
@pytest.mark.parametrize('min_cluster_size', [10, 25, 100, 350])
@pytest.mark.parametrize('allow_single_cluster', [True, False])
@pytest.mark.parametrize('max_cluster_size', [0])
@pytest.mark.parametrize('cluster_selection_method', ['eom', 'leaf'])
@pytest.mark.parametrize('connectivity', ['knn'])
def test_hdbscan_cluster_patterns_extract_clusters(dataset, nrows,
                                                   connectivity,
                                                   cluster_selection_epsilon,
                                                   cluster_selection_method,
                                                   min_cluster_size,
                                                   allow_single_cluster,
                                                   max_cluster_size,
                                                   min_samples):

    # This also tests duplicate data points
    X, y = get_pattern(dataset, nrows)[0]

    cuml_agg = HDBSCAN(verbose=logger.level_info,
                       allow_single_cluster=allow_single_cluster,
                       min_samples=min_samples,
                       max_cluster_size=max_cluster_size,
                       min_cluster_size=min_cluster_size,
                       cluster_selection_epsilon=cluster_selection_epsilon,
                       cluster_selection_method=cluster_selection_method)

    sk_agg = hdbscan.HDBSCAN(
        allow_single_cluster=allow_single_cluster,
        approx_min_span_tree=False,
        gen_min_span_tree=True,
        min_samples=min_samples,
        min_cluster_size=min_cluster_size,
        cluster_selection_epsilon=cluster_selection_epsilon,
        cluster_selection_method=cluster_selection_method,
        algorithm="generic")

    sk_agg.fit(cp.asnumpy(X))

    cuml_agg._extract_clusters(sk_agg.condensed_tree_)

    assert adjusted_rand_score(cuml_agg.labels_test, sk_agg.labels_) == 1.0
    assert np.allclose(cp.asnumpy(cuml_agg.probabilities_test),
                       sk_agg.probabilities_)


def test_hdbscan_core_dists_bug_4054():
    """
    This test explicitly verifies that the MRE from
    https://github.com/rapidsai/cuml/issues/4054
    matches the reference impl
    """

    X, y = datasets.make_moons(n_samples=10000, noise=0.12, random_state=0)

    cu_labels_ = HDBSCAN(min_samples=25, min_cluster_size=25).fit_predict(X)
    sk_labels_ = hdbscan.HDBSCAN(min_samples=25,
                                 min_cluster_size=25,
                                 approx_min_span_tree=False).fit_predict(X)

    assert adjusted_rand_score(cu_labels_, sk_labels_) > 0.99


def test_hdbscan_empty_cluster_tree():

    raw_tree = np.recarray(shape=(5,),
                           formats=[np.intp, np.intp, float, np.intp],
                           names=('parent', 'child', 'lambda_val',
                                  'child_size'))

    raw_tree['parent'] = np.asarray([5, 5, 5, 5, 5])
    raw_tree['child'] = [0, 1, 2, 3, 4]
    raw_tree['lambda_val'] = [1.0, 1.0, 1.0, 1.0, 1.0]
    raw_tree['child_size'] = [1, 1, 1, 1, 1]

    condensed_tree = CondensedTree(raw_tree, 0.0, True)

    cuml_agg = HDBSCAN(allow_single_cluster=True,
                       cluster_selection_method="eom")
    cuml_agg._extract_clusters(condensed_tree)

    # We just care that all points are assigned to the root cluster
    assert np.sum(cuml_agg.labels_test.to_output("numpy")) == 0


def test_hdbscan_plots():

    X, y = make_blobs(n_samples=int(100),
                      n_features=100,
                      centers=10,
                      cluster_std=0.7,
                      shuffle=False,
                      random_state=42)

    cuml_agg = HDBSCAN(gen_min_span_tree=True)
    cuml_agg.fit(X)

    assert cuml_agg.condensed_tree_ is not None
    assert cuml_agg.minimum_spanning_tree_ is not None
    assert cuml_agg.single_linkage_tree_ is not None

    cuml_agg = HDBSCAN(gen_min_span_tree=False)
    cuml_agg.fit(X)

    assert cuml_agg.minimum_spanning_tree_ is None

# Copyright (c) 2021, 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. # Simple helper function to return the children of the condensed tree     given an array of parents. Simple helper function to perform a bfs through n     levels of a condensed tree. Because of differences in the renumbering and sort ordering,     the condensed tree arrays from cuml and scikit-learn cannot     be compared directly. This function performs a BFS through     the condensed trees, comparing the cluster sizes and lambda     values at each level of the trees. # Start at the root, perform bfs # This also tests duplicate data points # This also tests duplicate data points This test explicitly verifies that the MRE from     https://github.com/rapidsai/cuml/issues/4054     matches the reference impl # We just care that all points are assigned to the root cluster