Ex 2: Concatenating multiple feature extraction methods

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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PCA 和univariate 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)
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from sklearn.pipeline import Pipeline, FeatureUnion
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from sklearn.grid_search import GridSearchCV
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from sklearn.svm import SVC
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from sklearn.datasets import load_iris
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from sklearn.decomposition import PCA
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from sklearn.feature_selection import SelectKBest
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​
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iris = load_iris()
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​
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X, y = iris.data, iris.target
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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。
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# This dataset is way to high-dimensional. Better do PCA:
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pca = PCA(n_components=2)
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​
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# Maybe some original features where good, too?
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selection = SelectKBest(k=1)
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(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
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# Build estimator from PCA and Univariate selection:
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​
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combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])
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​
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# Use combined features to transform dataset:
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X_features = combined_features.fit(X, y).transform(X)
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(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
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svm = SVC(kernel="linear")
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​
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# Do grid search over k, n_components and C:
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​
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pipeline = Pipeline([("features", combined_features), ("svm", svm)])
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​
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param_grid = dict(features__pca__n_components=[1, 2, 3],
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                  features__univ_select__k=[1, 2],
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                  svm__C=[0.1, 1, 10])
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​
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grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
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grid_search.fit(X, y)
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print(grid_search.best_estimator_)
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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
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## (五)完整程式碼
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Python source code: feature_stacker.py
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http://scikit-learn.org/stable/auto_examples/feature_stacker.html
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​
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```python
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# Author: Andreas Mueller <[email protected]>
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#
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# License: BSD 3 clause
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​
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from sklearn.pipeline import Pipeline, FeatureUnion
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from sklearn.grid_search import GridSearchCV
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from sklearn.svm import SVC
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from sklearn.datasets import load_iris
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from sklearn.decomposition import PCA
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from sklearn.feature_selection import SelectKBest
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​
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iris = load_iris()
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​
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X, y = iris.data, iris.target
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​
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# This dataset is way to high-dimensional. Better do PCA:
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pca = PCA(n_components=2)
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​
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# Maybe some original features where good, too?
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selection = SelectKBest(k=1)
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​
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# Build estimator from PCA and Univariate selection:
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​
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combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])
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​
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# Use combined features to transform dataset:
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X_features = combined_features.fit(X, y).transform(X)
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​
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svm = SVC(kernel="linear")
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​
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# Do grid search over k, n_components and C:
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​
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pipeline = Pipeline([("features", combined_features), ("svm", svm)])
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​
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param_grid = dict(features__pca__n_components=[1, 2, 3],
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                  features__univ_select__k=[1, 2],
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                  svm__C=[0.1, 1, 10])
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​
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grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
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grid_search.fit(X, y)
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print(grid_search.best_estimator_)
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Ex 1: Plotting Cross-Validated Predictions
Ex 3: Isotonic Regression
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通用範例/範例二: Concatenating multiple feature extraction methods