使用流水线和 GridSearchCV 的多维降维技术

Question

我们都知道使用降维技术定义管道然后定义训练和测试模型的常用方法。然后我们可以应用 GridSearchCv 进行超参数调整。

grid = GridSearchCV(
Pipeline([
    ('reduce_dim', PCA()),
    ('classify', RandomForestClassifier(n_jobs = -1))
    ]),
param_grid=[
    {
        'reduce_dim__n_components': range(0.7,0.9,0.1),
        'classify__n_estimators': range(10,50,5),
        'classify__max_features': ['auto', 0.2],
        'classify__min_samples_leaf': [40,50,60],
        'classify__criterion': ['gini', 'entropy']
    }
],
cv=5, scoring='f1')
grid.fit(X,y)

我能看懂上面的代码

现在我正在浏览 documentation 今天，在那里我发现了一个有点奇怪的部分代码。

pipe = Pipeline([
    # the reduce_dim stage is populated by the param_grid
    ('reduce_dim', 'passthrough'),                        # How does this work??
    ('classify', LinearSVC(dual=False, max_iter=10000))
])

N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
    {
        'reduce_dim': [PCA(iterated_power=7), NMF()],
        'reduce_dim__n_components': N_FEATURES_OPTIONS,   ### No PCA is used..??
        'classify__C': C_OPTIONS
    },
    {
        'reduce_dim': [SelectKBest(chi2)],
        'reduce_dim__k': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
]
reducer_labels = ['PCA', 'NMF', 'KBest(chi2)']

grid = GridSearchCV(pipe, n_jobs=1, param_grid=param_grid)
X, y = load_digits(return_X_y=True)
grid.fit(X, y)

首先在定义管道时，它使用字符串'passthrough'而不是对象。
```
    ('reduce_dim', 'passthrough'),  ```
```
然后在为网格搜索定义不同的降维技术时，它使用了不同的策略。 [PCA(iterated_power=7), NMF()] 这是如何工作的？
```
        'reduce_dim': [PCA(iterated_power=7), NMF()],
        'reduce_dim__n_components': N_FEATURES_OPTIONS,  # here 
```

请有人向我解释代码。

已解决 - 在一行中，顺序是 ['PCA', 'NMF', 'KBest(chi2)']

感谢 - seralouk（见下面的回答）

供参考如果有人找更详细的

Answer 1

据我所知是等价的

在文档中你有这个：

pipe = Pipeline([
    # the reduce_dim stage is populated by the param_grid
    ('reduce_dim', 'passthrough'),
    ('classify', LinearSVC(dual=False, max_iter=10000))
])

N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
    {
        'reduce_dim': [PCA(iterated_power=7), NMF()],
        'reduce_dim__n_components': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
    {
        'reduce_dim': [SelectKBest(chi2)],
        'reduce_dim__k': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
]

最初我们有 ('reduce_dim', 'passthrough'), 然后 'reduce_dim': [PCA(iterated_power=7), NMF()]

PCA的定义在第二行完成。

您可以选择定义：

pipe = Pipeline([
    # the reduce_dim stage is populated by the param_grid
    ('reduce_dim', PCA(iterated_power=7)),
    ('classify', LinearSVC(dual=False, max_iter=10000))
])

N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
    {
        'reduce_dim__n_components': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
    {
        'reduce_dim': [SelectKBest(chi2)],
        'reduce_dim__k': N_FEATURES_OPTIONS,
        'classify__C': C_OPTIONS
    },
]

使用流水线和 GridSearchCV 的多维降维技术

Multiple dimensionality reduction techniques with pipeline and GridSearchCV

machine-learning

scikit-learn

grid-search

hyperparameters

dimensionality-reduction