机器学习 Python scikit-learn 中文文档（9）管道流(Pipelining)

管道流(Pipelining)

将所有东西放在一起

与官方文档完美匹配的中文文档，请访问 http://studyai.com

管道流(Pipelining)
用特征脸进行人脸识别
开放式问题: 股票市场结构

将所有东西放在一起
管道流(Pipelining)
我们已经知道一些模型可以做数据转换，一些模型可以用来预测变量。我们可以建立一个组合模型同时完成以上工作:

…/…/images/sphx_glr_plot_digits_pipe_0011.png
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

Define a pipeline to search for the best combination of PCA truncation

and classifier regularization.

logistic = SGDClassifier(loss=‘log’, penalty=‘l2’, early_stopping=True,
max_iter=10000, tol=1e-5, random_state=0)
pca = PCA()
pipe = Pipeline(steps=[(‘pca’, pca), (‘logistic’, logistic)])

digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target

Parameters of pipelines can be set using ‘__’ separated parameter names:

param_grid = {
‘pca__n_components’: [5, 20, 30, 40, 50, 64],
‘logistic__alpha’: np.logspace(-4, 4, 5),
}
search = GridSearchCV(pipe, param_grid, iid=False, cv=5,
return_train_score=False)
search.fit(X_digits, y_digits)
print(“Best parameter (CV score=%0.3f):” % search.best_score_)
print(search.best_params_)

Plot the PCA spectrum

pca.fit(X_digits)

fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
ax0.plot(pca.explained_variance_ratio_, linewidth=2)
ax0.set_ylabel(‘PCA explained variance’)

ax0.axvline(search.best_estimator_.named_steps[‘pca’].n_components,
用特征脸进行人脸识别
该实例用到的数据集来自 LFW_(Labeled Faces in the Wild)。数据已经进行了初步预处理:

http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
“”"

Faces recognition example using eigenfaces and SVMs

The dataset used in this example is a preprocessed excerpt of the
“Labeled Faces in the Wild”, aka LFW_:

http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

… _LFW: http://vis-www.cs.umass.edu/lfw/

Expected results for the top 5 most represented people in the dataset:

================== ============ ======= ========== =======
precision recall f1-score support
================== ============ ======= ========== =======
Ariel Sharon 0.67 0.92 0.77 13
Colin Powell 0.75 0.78 0.76 60
Donald Rumsfeld 0.78 0.67 0.72 27
George W Bush 0.86 0.86 0.86 146
Gerhard Schroeder 0.76 0.76 0.76 25
Hugo Chavez 0.67 0.67 0.67 15
Tony Blair 0.81 0.69 0.75 36

  avg / total       0.80      0.80      0.80       322

================== ============ ======= ========== =======

“”"
from future import print_function

from time import time
import logging
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVC

print(doc)

Display progress logs on stdout

logging.basicConfig(level=logging.INFO, format=’%(asctime)s %(message)s’)

Download the data, if not already on disk and load it as numpy arrays

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

introspect the images arrays to find the shapes (for plotting)

n_samples, h, w = lfw_people.images.shape

for machine learning we use the 2 data directly (as relative pixel

positions info is ignored by this model)

X = lfw_people.data
n_features = X.shape[1]

the label to predict is the id of the person

y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]

print(“Total dataset size:”)
print(“n_samples: %d” % n_samples)
print(“n_features: %d” % n_features)
print(“n_classes: %d” % n_classes)

Split into a training set and a test set using a stratified k fold

split into a training and testing set

X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42)

Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled

dataset): unsupervised feature extraction / dimensionality reduction

n_components = 150

print(“Extracting the top %d eigenfaces from %d faces”
% (n_components, X_train.shape[0]))
t0 = time()
pca = PCA(n_components=n_components, svd_solver=‘randomized’,
whiten=True).fit(X_train)
print(“done in %0.3fs” % (time() - t0))

eigenfaces = pca.components_.reshape((n_components, h, w))

print(“Projecting the input data on the eigenfaces orthonormal basis”)
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print(“done in %0.3fs” % (time() - t0))

Train a SVM classification model

print(“Fitting the classifier to the training set”)
t0 = time()
param_grid = {‘C’: [1e3, 5e3, 1e4, 5e4, 1e5],
‘gamma’: [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel=‘rbf’, class_weight=‘balanced’),
param_grid, cv=5)
clf = clf.fit(X_train_pca, y_train)
print(“done in %0.3fs” % (time() - t0))
print(“Best estimator found by grid search:”)
print(clf.best_estimator_)

Quantitative evaluation of the model quality on the test set

print(“Predicting people’s names on the test set”)
t0 = time()
y_pred = clf.predict(X_test_pca)
print(“done in %0.3fs” % (time() - t0))

print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))

Qualitative evaluation of the predictions using matplotlib

def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
“”“Helper function to plot a gallery of portraits”""
plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
for i in range(n_row * n_col):
plt.subplot(n_row, n_col, i + 1)
plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
plt.title(titles[i], size=12)
plt.xticks(())
plt.yticks(())

plot the result of the prediction on a portion of the test set

def title(y_pred, y_test, target_names, i):
pred_name = target_names[y_pred[i]].rsplit(’ ‘, 1)[-1]
true_name = target_names[y_test[i]].rsplit(’ ', 1)[-1]
return ‘predicted: %s\ntrue: %s’ % (pred_name, true_name)

prediction_titles = [title(y_pred, y_test, target_names, i)
for i in range(y_pred.shape[0])]

plot_gallery(X_test, prediction_titles, h, w)

plot the gallery of the most significative eigenfaces

eigenface_titles = [“eigenface %d” % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)

plt.show()
prediction eigenfaces
Prediction Eigenfaces
数据集中前5名最有代表性样本的预期结果:

               precision    recall  f1-score   support

Gerhard_Schroeder 0.91 0.75 0.82 28
Donald_Rumsfeld 0.84 0.82 0.83 33
Tony_Blair 0.65 0.82 0.73 34
Colin_Powell 0.78 0.88 0.83 58
George_W_Bush 0.93 0.86 0.90 129

  avg / total       0.86      0.84      0.85       282

开放式问题: 股票市场结构
我们可以预测 Google 在特定时间段内的股价变动吗？

Learning a graph structure

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