ml4 13 Полнота

Полнота Практика¶

Загружаем данные¶

спользуем уже знакомый нам датасет make_blobs

In [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))))

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 [3]:

# 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 [4]:

from sklearn.cluster import KMeans
from sklearn.metrics.cluster import completeness_score

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

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

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

Out[4]:

0.7859676398774584

In [5]:

plot_scatter()

plot_scatter()

Задание¶

Задание 4.13.1¶

• Обучите модель GaussianMixture с параметрами n_components=3 и random_state=42 на признаках исходного датасета.
• Посчитайте полноту для получившейся кластеризации.

In [6]:

from sklearn.preprocessing import StandardScaler
from sklearn.mixture import GaussianMixture

model = StandardScaler()
X = model.fit_transform(X)

# Gaussian Mixture
em_gm = GaussianMixture(n_components=3, 
                        random_state=42)
em_gm.fit(X)       
y_pred = em_gm.predict(X)
completeness = completeness_score(labels_true=y,
                                labels_pred=y_pred)
round(completeness,3)

from sklearn.preprocessing import StandardScaler from sklearn.mixture import GaussianMixture model = StandardScaler() X = model.fit_transform(X) # Gaussian Mixture em_gm = GaussianMixture(n_components=3, random_state=42) em_gm.fit(X) y_pred = em_gm.predict(X) completeness = completeness_score(labels_true=y, labels_pred=y_pred) round(completeness,3)

Out[6]:

0.933

Задание 4.13.2¶

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

• K-means – n_clusters=3, random_state=42
• GaussianMixture – n_components=3, random_state=42
• AgglomerativeClustering – n_clusters=3
• DBSCAN – eps=0.9, min_samples=35

In [7]:

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

model = StandardScaler()
X = model.fit_transform(X)

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

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

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

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

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 model = StandardScaler() X = model.fit_transform(X) # K-Means kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(X) y_pred = kmeans.labels_ completeness = completeness_score(labels_true=y, labels_pred=y_pred) print('kmeans',completeness) # Gaussian Mixture em_gm = GaussianMixture(n_components=3, random_state=42) em_gm.fit(X) y_pred = em_gm.predict(X) completeness = completeness_score(labels_true=y, labels_pred=y_pred) print('gmm',completeness) # Agglomerative Cluster ac = AgglomerativeClustering(n_clusters=3) ac.fit(X) y_pred = ac.labels_.astype(np.int) completeness = completeness_score(labels_true=y, labels_pred=y_pred) print('agglomerative',completeness) # DBSCAN dbscan = DBSCAN(eps=0.9, min_samples=35) dbscan.fit(X) y_pred = dbscan.labels_.astype(np.int) completeness = completeness_score(labels_true=y, labels_pred=y_pred) print('dbscan',completeness)

kmeans 0.7828224102025245
gmm 0.9325740421656737
agglomerative 0.9058386997451113
dbscan 0.08342237034907717