ml4 12 Однородность

Однородность Практика¶

Загружаем данные¶

спользуем уже знакомый нам датасет make_blobs

In [1]:

import matplotlib.pyplot as plt
from sklearn import datasets
import numpy as np
from itertools import cycle, islice

n_samples = 1500
dataset = datasets.make_blobs(n_samples=n_samples, centers=2, center_box=(-7.0, 7.5),
                              cluster_std=[1.4, 1.7],
                              random_state=42)
X_2, _ = datasets.make_blobs(n_samples=n_samples, random_state=170, centers=[[-4, -3]], cluster_std=[1.9])
transformation = [[1.2, -0.8], [-0.4, 1.7]]
X_2 = np.dot(X_2, transformation)
X, y = np.concatenate((dataset[0], X_2)), np.concatenate((dataset[1], np.array([2] * len(X_2))))

import matplotlib.pyplot as plt from sklearn import datasets import numpy as np from itertools import cycle, islice n_samples = 1500 dataset = datasets.make_blobs(n_samples=n_samples, centers=2, center_box=(-7.0, 7.5), cluster_std=[1.4, 1.7], random_state=42) X_2, _ = datasets.make_blobs(n_samples=n_samples, random_state=170, centers=[[-4, -3]], cluster_std=[1.9]) transformation = [[1.2, -0.8], [-0.4, 1.7]] X_2 = np.dot(X_2, transformation) X, y = np.concatenate((dataset[0], X_2)), np.concatenate((dataset[1], np.array([2] * len(X_2))))

In [2]:

# Визуализируем исходные данные
def plot_scatter():
    plt.rcParams['figure.figsize'] = 3, 3
    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
                                         '#f781bf', '#a65628', '#984ea3',
                                         '#999999', '#e41a1c', '#dede00']),
                                  int(max(y_pred) + 1))))
    plt.scatter(X[:, 0], X[:, 1], s=10,ec='k',alpha=0.25,color=colors[y_pred])

# Визуализируем исходные данные def plot_scatter(): plt.rcParams['figure.figsize'] = 3, 3 colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3', '#999999', '#e41a1c', '#dede00']), int(max(y_pred) + 1)))) plt.scatter(X[:, 0], X[:, 1], s=10,ec='k',alpha=0.25,color=colors[y_pred])

Пример Кластеризации¶

Коэффициент силуэта можно посчитать при помощи реализации из библиотеки sklearn

In [3]:

from sklearn.cluster import KMeans
from sklearn.metrics.cluster import homogeneity_score
from sklearn.preprocessing import StandardScaler

# сначала получим предсказанные кластеры при помощи метода кластеризации
kmeans = KMeans(n_clusters=3, random_state=42)
X = StandardScaler().fit_transform(X)
kmeans.fit(X)
y_pred = kmeans.labels_ 

# теперь посчитаем однородность
homogeneity_score(labels_true=y, labels_pred=y_pred)

from sklearn.cluster import KMeans from sklearn.metrics.cluster import homogeneity_score from sklearn.preprocessing import StandardScaler # сначала получим предсказанные кластеры при помощи метода кластеризации kmeans = KMeans(n_clusters=3, random_state=42) X = StandardScaler().fit_transform(X) kmeans.fit(X) y_pred = kmeans.labels_ # теперь посчитаем однородность homogeneity_score(labels_true=y, labels_pred=y_pred)

Out[3]:

0.804474693112785

In [4]:

plot_scatter()

plot_scatter()

Задание¶

Задание 4.12.1¶

Сравните результаты кластеризации алгоритмов k-means, GaussianMixture, AgglomerativeClustering и DBSCAN на исходном датасете при помощи однородности, инициализируйте алгоритмы со следующими параметрами:

• kmeans — n_clusters=3, random_state=42
• GaussianMixture — n_components=3, random_state=42
• AgglomerativeClustering — n_clusters=3
• DBSCAN — eps=0.9, min_samples=35

В качестве ответа укажите максимальное значение однородности, полученное при помощи данных моделей

In [10]:

import warnings
warnings.filterwarnings('ignore')

from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.metrics.cluster import homogeneity_score

# K-Means
kmeans = KMeans(n_clusters=3,
                random_state=42)
kmeans.fit(X)
y_pred = kmeans.labels_
homosc = homogeneity_score(labels_true=y, labels_pred=y_pred)
print('kmeans',round(homosc,4))

# Gaussian Mixture
em_gm = GaussianMixture(n_components=3, 
                        random_state=42)
em_gm.fit(X)       
y_pred = em_gm.predict(X)
homosc = homogeneity_score(labels_true=y, labels_pred=y_pred)
print('gmm',round(homosc,4))

# Agglomerative Cluster
ac = AgglomerativeClustering(n_clusters=3)
ac.fit(X)
y_pred = ac.labels_.astype(np.int)
homosc = homogeneity_score(labels_true=y, labels_pred=y_pred)
print('agglomerative',round(homosc,4))

# DBSCAN
dbscan = DBSCAN(eps=0.9, min_samples=35)
dbscan.fit(X)
y_pred = dbscan.labels_.astype(np.int)
homosc = homogeneity_score(labels_true=y, labels_pred=y_pred)
print('dbscan',round(homosc,4))

import warnings warnings.filterwarnings('ignore') from sklearn.cluster import KMeans from sklearn.mixture import GaussianMixture from sklearn.cluster import AgglomerativeClustering from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler from sklearn.metrics.cluster import homogeneity_score # K-Means kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(X) y_pred = kmeans.labels_ homosc = homogeneity_score(labels_true=y, labels_pred=y_pred) print('kmeans',round(homosc,4)) # Gaussian Mixture em_gm = GaussianMixture(n_components=3, random_state=42) em_gm.fit(X) y_pred = em_gm.predict(X) homosc = homogeneity_score(labels_true=y, labels_pred=y_pred) print('gmm',round(homosc,4)) # Agglomerative Cluster ac = AgglomerativeClustering(n_clusters=3) ac.fit(X) y_pred = ac.labels_.astype(np.int) homosc = homogeneity_score(labels_true=y, labels_pred=y_pred) print('agglomerative',round(homosc,4)) # DBSCAN dbscan = DBSCAN(eps=0.9, min_samples=35) dbscan.fit(X) y_pred = dbscan.labels_.astype(np.int) homosc = homogeneity_score(labels_true=y, labels_pred=y_pred) print('dbscan',round(homosc,4))

kmeans 0.8045
gmm 0.934
agglomerative 0.91
dbscan 0.0004