Примечание

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Агломерация признаков против отбора признаков по унивариантному методу

В этом примере сравниваются 2 стратегии уменьшения размерности:

Отбор признаков по унивариантному методу с ANOVA
Агломерация признаков с помощью иерархической кластеризации Ward

Оба метода сравниваются в задаче регрессии с использованием BayesianRidge в качестве контролирующего алгоритма.

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

import shutil
import tempfile

import matplotlib.pyplot as plt
import numpy as np
from joblib import Memory
from scipy import linalg, ndimage

from sklearn import feature_selection
from sklearn.cluster import FeatureAgglomeration
from sklearn.feature_extraction.image import grid_to_graph
from sklearn.linear_model import BayesianRidge
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.pipeline import Pipeline

Установка параметров

n_samples = 200
size = 40  # image size
roi_size = 15
snr = 5.0
np.random.seed(0)

Генерация данных

coef = np.zeros((size, size))
coef[0:roi_size, 0:roi_size] = -1.0
coef[-roi_size:, -roi_size:] = 1.0

X = np.random.randn(n_samples, size**2)
for x in X:  # smooth data
    x[:] = ndimage.gaussian_filter(x.reshape(size, size), sigma=1.0).ravel()
X -= X.mean(axis=0)
X /= X.std(axis=0)

y = np.dot(X, coef.ravel())

Добавление шума

noise = np.random.randn(y.shape[0])
noise_coef = (linalg.norm(y, 2) / np.exp(snr / 20.0)) / linalg.norm(noise, 2)
y += noise_coef * noise

Вычисление коэффициентов Bayesian Ridge с помощью GridSearch

cv = KFold(2)  # cross-validation generator for model selection
ridge = BayesianRidge()
cachedir = tempfile.mkdtemp()
mem = Memory(location=cachedir, verbose=1)

Агломерация Ward, за которой следует BayesianRidge

connectivity = grid_to_graph(n_x=size, n_y=size)
ward = FeatureAgglomeration(n_clusters=10, connectivity=connectivity, memory=mem)
clf = Pipeline([("ward", ward), ("ridge", ridge)])
# Select the optimal number of parcels with grid search
clf = GridSearchCV(clf, {"ward__n_clusters": [10, 20, 30]}, n_jobs=1, cv=cv)
clf.fit(X, y)  # set the best parameters
coef_ = clf.best_estimator_.steps[-1][1].coef_
coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_)
coef_agglomeration_ = coef_.reshape(size, size)

________________________________________________________________________________
[Memory] Calling sklearn.cluster._agglomerative.ward_tree...
ward_tree(array([[-0.451933, ..., -0.675318],
       ...,
       [ 0.275706, ..., -1.085711]]), connectivity=<1600x1600 sparse matrix of type '<class 'numpy.int64'>'
        with 7840 stored elements in COOrdinate format>, n_clusters=None, return_distance=False)
________________________________________________________ward_tree - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.cluster._agglomerative.ward_tree...
ward_tree(array([[ 0.905206, ...,  0.161245],
       ...,
       [-0.849835, ..., -1.091621]]), connectivity=<1600x1600 sparse matrix of type '<class 'numpy.int64'>'
        with 7840 stored elements in COOrdinate format>, n_clusters=None, return_distance=False)
________________________________________________________ward_tree - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.cluster._agglomerative.ward_tree...
ward_tree(array([[ 0.905206, ..., -0.675318],
       ...,
       [-0.849835, ..., -1.085711]]), connectivity=<1600x1600 sparse matrix of type '<class 'numpy.int64'>'
        with 7840 stored elements in COOrdinate format>, n_clusters=None, return_distance=False)
________________________________________________________ward_tree - 0.0s, 0.0min

Отбор признаков по унивариантному методу ANOVA, за которым следует BayesianRidge

f_regression = mem.cache(feature_selection.f_regression)  # caching function
anova = feature_selection.SelectPercentile(f_regression)
clf = Pipeline([("anova", anova), ("ridge", ridge)])
# Select the optimal percentage of features with grid search
clf = GridSearchCV(clf, {"anova__percentile": [5, 10, 20]}, cv=cv)
clf.fit(X, y)  # set the best parameters
coef_ = clf.best_estimator_.steps[-1][1].coef_
coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_.reshape(1, -1))
coef_selection_ = coef_.reshape(size, size)

________________________________________________________________________________
[Memory] Calling sklearn.feature_selection._univariate_selection.f_regression...
f_regression(array([[-0.451933, ...,  0.275706],
       ...,
       [-0.675318, ..., -1.085711]]),
array([ 25.267703, ..., -25.026711]))
_____________________________________________________f_regression - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.feature_selection._univariate_selection.f_regression...
f_regression(array([[ 0.905206, ..., -0.849835],
       ...,
       [ 0.161245, ..., -1.091621]]),
array([ -27.447268, ..., -112.638768]))
_____________________________________________________f_regression - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.feature_selection._univariate_selection.f_regression...
f_regression(array([[ 0.905206, ..., -0.849835],
       ...,
       [-0.675318, ..., -1.085711]]),
array([-27.447268, ..., -25.026711]))
_____________________________________________________f_regression - 0.0s, 0.0min

Обратное преобразование для отображения результатов на изображении

plt.close("all")
plt.figure(figsize=(7.3, 2.7))
plt.subplot(1, 3, 1)
plt.imshow(coef, interpolation="nearest", cmap=plt.cm.RdBu_r)
plt.title("True weights")
plt.subplot(1, 3, 2)
plt.imshow(coef_selection_, interpolation="nearest", cmap=plt.cm.RdBu_r)
plt.title("Feature Selection")
plt.subplot(1, 3, 3)
plt.imshow(coef_agglomeration_, interpolation="nearest", cmap=plt.cm.RdBu_r)
plt.title("Feature Agglomeration")
plt.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.16, 0.26)
plt.show()

Истинные веса, Отбор признаков, Агломерация признаков

Попытка удалить временный кеш-каталог, но не беспокойтесь, если это не удастся

shutil.rmtree(cachedir, ignore_errors=True)

Общее время выполнения скрипта: (0 минут 0.513 секунд)

Связанные примеры

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

Демонстрация структурированной иерархической кластеризации Ward на изображении монет

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

HuberRegressor против Ridge на наборе данных с сильными выбросами

Выбор признаков по одному

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

Ортогональное преследование соответствия

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https://scikit-learn.org/1.6/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html