【9】例子--1--general--混合多重方法获得feature

Concatenating multiple feature extraction methods 通常有很多方法从数据集中获得features，有时候需要把多重方法混合在一起，才能得到一个更好的结果。 这里讲介绍的是通过FeatureUnion将PCA和univariate selection两种方法获得features混合起来用。

代码：

"""
=================================================
Concatenating multiple feature extraction methods
=================================================

In many real-world examples, there are many ways to extract features from a
dataset. Often it is beneficial to combine several methods to obtain good
performance. This example shows how to use ``FeatureUnion`` to combine
features obtained by PCA and univariate selection.

Combining features using this transformer has the benefit that it allows
cross validation and grid searches over the whole process.

The combination used in this example is not particularly helpful on this
dataset and is only used to illustrate the usage of FeatureUnion.
"""

# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
#
# License: BSD 3 clause

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest

iris = load_iris()

X, y = iris.data, iris.target

# This dataset is way too high-dimensional. Better do PCA:
pca = PCA(n_components=2)

# Maybe some original features where good, too?
selection = SelectKBest(k=1)

# Build estimator from PCA and Univariate selection:

combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])

# Use combined features to transform dataset:
X_features = combined_features.fit(X, y).transform(X)

svm = SVC(kernel="linear")

# Do grid search over k, n_components and C:

pipeline = Pipeline([("features", combined_features), ("svm", svm)])

param_grid = dict(features__pca__n_components=[1, 2, 3],
				  features__univ_select__k=[1, 2],
				  svm__C=[0.1, 1, 10])

grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
grid_search.fit(X, y)
print(grid_search.best_estimator_)

开撸代码：

1.PCA(n_components =2)

n_components 为要保留的主成成分，如果不设置，则所有的成分保留，n_components == min(n_samples, n_features)

2.selection = SelectKBest(k=1)

k为整数或者all,默认的是10，最高的features被选择，all表示不用选择最好的feature

3.FeatureUnion

?

4.SVC

什么鬼？

5.Pipiline

?

PS:

这一章节不理解的概念太多，回头再来撸

参考资料： http://blog.csdn.net/qq_14905099/article/details/49886739 http://scikit-learn.org/stable/auto_examples/feature_stacker.html#sphx-glr-auto-examples-feature-stacker-py