使用管道和 GridSearchCV 的多维降维技术

Question

我们都知道使用降维技术定义管道的常用方法，然后是用于训练和测试的模型。然后我们可以应用 GridSearchCv 进行超参数调优。

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)

上面的代码我能看懂。

今天我正在浏览documentation，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)

1. 首先在定义管道时，它使用字符串“passthrough”而不是对象。

       ('reduce_dim', 'passthrough'),  ```
   

2. 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()] 这个是如何工作的？

           'reduce_dim': [PCA(iterated_power=7), NMF()],
           'reduce_dim__n_components': N_FEATURES_OPTIONS,  # here 
   

请有人向我解释一下代码。

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

感谢 - seralouk（请参阅下面的答案）

供参考如果有人寻找更多详细信息 123

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'),，然后是'reduce_dim': [PCA(iterated_power=7), NMF()]

PCA 的定义在第二行完成。

---

您可以另外定义：

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
    },
]