"""
===================================
Demo of DBSCAN clustering algorithm
===================================

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core
samples in regions of high density and expands clusters from them. This
algorithm is good for data which contains clusters of similar density.

See the :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` example
for a demo of different clustering algorithms on 2D datasets.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Data generation
# ---------------
#
# We use :class:`~sklearn.datasets.make_blobs` to create 3 synthetic clusters.

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(
    n_samples=750, centers=centers, cluster_std=0.4, random_state=0
)

X = StandardScaler().fit_transform(X)

# %%
# We can visualize the resulting data:

import matplotlib.pyplot as plt

plt.scatter(X[:, 0], X[:, 1])
plt.show()

# %%
# Compute DBSCAN
# --------------
#
# One can access the labels assigned by :class:`~sklearn.cluster.DBSCAN` using
# the `labels_` attribute. Noisy samples are given the label :math:`-1`.

import numpy as np

from sklearn import metrics
from sklearn.cluster import DBSCAN

db = DBSCAN(eps=0.3, min_samples=10).fit(X)
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)

print("Estimated number of clusters: %d" % n_clusters_)
print("Estimated number of noise points: %d" % n_noise_)

# %%
# Clustering algorithms are fundamentally unsupervised learning methods.
# However, since :class:`~sklearn.datasets.make_blobs` gives access to the true
# labels of the synthetic clusters, it is possible to use evaluation metrics
# that leverage this "supervised" ground truth information to quantify the
# quality of the resulting clusters. Examples of such metrics are the
# homogeneity, completeness, V-measure, Rand-Index, Adjusted Rand-Index and
# Adjusted Mutual Information (AMI).
#
# If the ground truth labels are not known, evaluation can only be performed
# using the model results itself. In that case, the Silhouette Coefficient comes
# in handy.
#
# For more information, see the
# :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`
# example or the :ref:`clustering_evaluation` module.

print(f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):.3f}")
print(f"Completeness: {metrics.completeness_score(labels_true, labels):.3f}")
print(f"V-measure: {metrics.v_measure_score(labels_true, labels):.3f}")
print(f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):.3f}")
print(
    "Adjusted Mutual Information:"
    f" {metrics.adjusted_mutual_info_score(labels_true, labels):.3f}"
)
print(f"Silhouette Coefficient: {metrics.silhouette_score(X, labels):.3f}")

# %%
# Plot results
# ------------
#
# Core samples (large dots) and non-core samples (small dots) are color-coded
# according to the assigned cluster. Samples tagged as noise are represented in
# black.

unique_labels = set(labels)
core_samples_mask = np.zeros_like(labels, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True

colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 1]

    class_member_mask = labels == k

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(
        xy[:, 0],
        xy[:, 1],
        "o",
        markerfacecolor=tuple(col),
        markeredgecolor="k",
        markersize=14,
    )

    xy = X[class_member_mask & ~core_samples_mask]
    plt.plot(
        xy[:, 0],
        xy[:, 1],
        "o",
        markerfacecolor=tuple(col),
        markeredgecolor="k",
        markersize=6,
    )

plt.title(f"Estimated number of clusters: {n_clusters_}")
plt.show()

"""
===================================
Demo of DBSCAN clustering algorithm
===================================

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core
samples in regions of high density and expands clusters from them. This
algorithm is good for data which contains clusters of similar density.

See the :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` example
for a demo of different clustering algorithms on 2D datasets.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Data generation
# ---------------
#
# We use :class:`~sklearn.datasets.make_blobs` to create 3 synthetic clusters.

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(
    n_samples=750, centers=centers, cluster_std=0.4, random_state=0
)

X = StandardScaler().fit_transform(X)

# %%
# We can visualize the resulting data:

import matplotlib.pyplot as plt

plt.scatter(X[:, 0], X[:, 1])
plt.show()

# %%
# Compute DBSCAN
# --------------
#
# One can access the labels assigned by :class:`~sklearn.cluster.DBSCAN` using
# the `labels_` attribute. Noisy samples are given the label math:`-1`.

import numpy as np

from sklearn import metrics
from sklearn.cluster import DBSCAN

db = DBSCAN(eps=0.3, min_samples=10).fit(X)
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)

print("Estimated number of clusters: %d" % n_clusters_)
print("Estimated number of noise points: %d" % n_noise_)

# %%
# Clustering algorithms are fundamentally unsupervised learning methods.
# However, since :class:`~sklearn.datasets.make_blobs` gives access to the true
# labels of the synthetic clusters, it is possible to use evaluation metrics
# that leverage this "supervised" ground truth information to quantify the
# quality of the resulting clusters. Examples of such metrics are the
# homogeneity, completeness, V-measure, Rand-Index, Adjusted Rand-Index and
# Adjusted Mutual Information (AMI).
#
# If the ground truth labels are not known, evaluation can only be performed
# using the model results itself. In that case, the Silhouette Coefficient comes
# in handy.
#
# For more information, see the
# :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`
# example or the :ref:`clustering_evaluation` module.

print(f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):.3f}")
print(f"Completeness: {metrics.completeness_score(labels_true, labels):.3f}")
print(f"V-measure: {metrics.v_measure_score(labels_true, labels):.3f}")
print(f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):.3f}")
print(
    "Adjusted Mutual Information:"
    f" {metrics.adjusted_mutual_info_score(labels_true, labels):.3f}"
)
print(f"Silhouette Coefficient: {metrics.silhouette_score(X, labels):.3f}")

# %%
# Plot results
# ------------
#
# Core samples (large dots) and non-core samples (small dots) are color-coded
# according to the assigned cluster. Samples tagged as noise are represented in
# black.

unique_labels = set(labels)
core_samples_mask = np.zeros_like(labels, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True

colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 1]

    class_member_mask = labels == k

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(
        xy[:, 0],
        xy[:, 1],
        "o",
        markerfacecolor=tuple(col),
        markeredgecolor="k",
        markersize=14,
    )

    xy = X[class_member_mask & ~core_samples_mask]
    plt.plot(
        xy[:, 0],
        xy[:, 1],
        "o",
        markerfacecolor=tuple(col),
        markeredgecolor="k",
        markersize=6,
    )

plt.title(f"Estimated number of clusters: {n_clusters_}")
plt.show()
