通用範例/範例二: Concatenating multiple feature extraction methods

http://scikit-learn.org/stable/auto_examples/feature_stacker.html

在許多實際應用中,會有很多方法可以從一個數據集中提取特徵。也常常會組合多個方法來獲得良好的特徵。這個例子說明如何使用FeatureUnion 來結合由PCAunivariate selection 時的特徵。

這個範例的主要目的:

  1. 資料集:iris 鳶尾花資料集
  2. 特徵:鳶尾花特徵
  3. 預測目標:是那一種鳶尾花
  4. 機器學習方法:SVM 支持向量機
  5. 探討重點:特徵結合
  6. 關鍵函式: sklearn.pipeline.FeatureUnion

(一)資料匯入及描述

  • 首先先匯入iris 鳶尾花資料集,使用from sklearn.datasets import load_iris將資料存入
  • 準備X (特徵資料) 以及 y (目標資料)
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.grid_search 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

測試資料:
iris為一個dict型別資料。

顯示 說明
('target_names', (3L,)) 共有三種鳶尾花 setosa, versicolor, virginica
('data', (150L, 4L)) 有150筆資料,共四種特徵
('target', (150L,)) 這150筆資料各是那一種鳶尾花
DESCR 資料之描述
feature_names 4個特徵代表的意義

(二)PCA與SelectKBest

  • PCA(n_components = 主要成份數量):Principal Component Analysis(PCA)主成份分析,是一個常用的將資料維度減少的方法。它的原理是找出一個新的座標軸,將資料投影到該軸時,數據的變異量會最大。利用這個方式減少資料維度,又希望能保留住原數據點的特性。

  • SelectKBest(score_func , k ): score_func是選擇特徵值所依據的函式,而K值則是設定要選出多少特徵。

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

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

(三)FeatureUnionc

  • 使用sklearn.pipeline.FeatureUnion合併主成分分析(PCA)和綜合篩選(SelectKBest)。
  • 最後得到選出的特徵
# 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)

(四)找到最佳的結果

  • Scikit-learn的支持向量機分類函式庫利用 SVC() 建立運算物件,之後並可以用運算物件內的方法 .fit() 與 .predict() 來做訓練與預測。

  • 使用GridSearchCV交叉驗證,得到由參數網格計算出的分數網格,並找到分數網格中最佳點。最後顯示這個點所代表的參數

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_)

結果顯示 ``` Fitting 3 folds for each of 18 candidates, totalling 54 fits [CV] featuresuniv_selectk=1, featurespcan_components=1, svmC=0.1 [CV] featuresuniv_selectk=1, featurespcan_components=1, svmC=0.1, score=0.960784 - 0.0s



## (五)完整程式碼
Python source code: feature_stacker.py
http://scikit-learn.org/stable/auto_examples/feature_stacker.html

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

from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.grid_search 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 to 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_)

results matching ""

    No results matching ""