PythonOK把所有的东西集中在一起
管道流
我们已经看到一些估计器可以进行数据转换，一些估计器可以预测变量。我们还可以创建组合估计器同时完成上述任务：
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 LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
# 定义一个管道来搜索PCA截断和分类器正则化的最佳组合。
pca = PCA()
# 将tolerance设置为较大的值加快示例运行
ogistic = LogisticRegression(max_iter=10000, tol=0.1)
pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])
X_digits, y_digits = datasets.load_digits(return_X_y=True)
# 可以使用“ __”分隔的参数名称来设置管道的参数：
param_grid = {
'pca__n_components': [5, 15, 30, 45, 64],
'logistic__C': np.logspace(-4, 4, 4),
}
search = GridSearchCV(pipe, param_grid, n_jobs=-1)
search.fit(X_digits, y_digits)
print("Best parameter (CV score=%0.3f):" % search.best_score_)
print(search.best_params_)
# 绘制PCA频谱
pca.fit(X_digits)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
ax0.plot(np.arange(1, pca.n_components_ + 1),
pca.explained_variance_ratio_, '+', linewidth=2)
ax0.set_ylabel('PCA explained variance ratio')
ax0.axvline(search.best_estimator_.named_steps['pca'].n_components,
linestyle=':', label='n_components chosen')
ax0.legend(prop=dict(size=12))
使用特征脸进行人脸识别
本示例中使用的数据集是“Labeled Faces in the Wild”（也称为LFW）的预处理摘录
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
"""
===================================================
使用特征脸和支持向量机的人脸识别示例
===================================================
本例中使用的数据集是“Labeled Faces in the Wild”的预处理摘录，也称为LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)
.. _LFW: http://vis-www.cs.umass.edu/lfw/
数据集中最具代表性的前5名人员的预期结果:
================== ============ ======= ========== =======
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 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__)
# 在stdout(标准输出)上显示进度日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
# #############################################################################
# 下载数据（如果尚未存储在磁盘上）并将其作为numpy数组加载
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
# 获取图像维度（用于绘图）
n_samples, h, w = lfw_people.images.shape
# 对于机器学习，我们直接使用2个数据（因为该模型忽略了相对像素位置信息）
X = lfw_people.data
n_features = X.shape[1]
# 要预测的标签是此人的id值
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)
# #############################################################################
# 使用分层采样交叉切分(stratified k fold)将原始数据集分成训练集和测试集
# 分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42)
#############################################################################
# 在人脸数据集（视为未标记数据集）上计算PCA（特征脸）：无监督特征提取/降维
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))
# #############################################################################
# 训练SVM分类模型
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
)
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_)
# #############################################################################
# 测试集上进行模型性能的定性评估
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)))
# #############################################################################
# 使用matplotlib对模型的预测进行定性评估
def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
"""绘制图像的函数"""
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(())
# 绘制部分测试集的预测结果
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)
# 绘制最显著的特征脸
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
plt.show()
预测
特征脸
数据集中最具代表性的前5名人员的预期结果：
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
开放性问题：股票市场结构
我们能预测在给定的时间范围内谷歌股价的变化吗?
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2.7. 新奇点与离群点检测(Novelty and Outlier Detection)
2.1. 高斯混合模型(Gaussian mixture models)
2.4. 双聚类(Biclustering)
2.2. 流形学习(Manifold learning)