所有中间步骤均应为变形器，并进行拟合和转换 [英] All intermediate steps should be transformers and implement fit and transform

问题描述

我正在使用重要的特征选择来实现管道，然后使用相同的特征来训练我的随机森林分类器.以下是我的代码.

I am implementing a pipeline using important features selection and then using the same features to train my random forest classifier. Following is my code.

m = ExtraTreesClassifier(n_estimators = 10)
m.fit(train_cv_x,train_cv_y)
sel = SelectFromModel(m, prefit=True)
X_new = sel.transform(train_cv_x)
clf = RandomForestClassifier(5000)

model = Pipeline([('m', m),('sel', sel),('X_new', X_new),('clf', clf),])
params = {'clf__max_features': ['auto', 'sqrt', 'log2']}

gs = GridSearchCV(model, params)
gs.fit(train_cv_x,train_cv_y)

因此X_new是通过SelectFromModel和sel.transform选择的新功能.然后，我想使用所选的新功能来训练我的RF.

So X_neware the new features selected via SelectFromModel and sel.transform. Then I want to train my RF using the new features selected.

我遇到以下错误:

所有中间步骤均应为变形器，并实现拟合和 转换，ExtraTreesClassifier ...

All intermediate steps should be transformers and implement fit and transform, ExtraTreesClassifier ...

推荐答案

就像追溯说的那样:管道中的每个步骤都需要具有fit()和transform()方法(最后一个除外，后者只需要fit()之所以如此，是因为管道在每一步都将数据的转换链接在一起.

Like the traceback says: each step in your pipeline needs to have a fit() and transform() method (except the last, which just needs fit(). This is because a pipeline chains together transformations of your data at each step.

sel.transform(train_cv_x)不是估算器，也不满足此条件.

sel.transform(train_cv_x) is not an estimator and doesn't meet this criterion.

实际上，根据您要尝试执行的操作，您可以忽略此步骤.在内部，('sel', sel)已经进行了此转换-这就是为什么将其包含在管道中的原因.

In fact, it looks like based on what you're trying to do, you can leave this step out. Internally, ('sel', sel) already does this transformation--that's why it's included in the pipeline.

第二，ExtraTreesClassifier(管道的第一步)也没有transform()方法.您可以在文档字符串类中的此处进行验证.并非为转换数据而建立了监督学习模型.他们是为适应它而做出的，并以此为基础进行预测.

Secondly, ExtraTreesClassifier (the first step in your pipeline), doesn't have a transform() method, either. You can verify that here, in the class docstring. Supervised learning models aren't made for transforming data; they're made for fitting on it and predicting based off that.

什么类型的类可以进行转换?

What type of classes are able to do transformations?

• 扩展您的数据的人.请参见预处理和规范化.
• 可以转换您的数据的人(上述以外的其他方式). 分解和其他无监督的学习方法可以做到这一点.

• Ones that scale your data. See preprocessing and normalization.
• Ones that transform your data (in some other way than the above). Decomposition and other unsupervised learning methods do this.

在两行之间不必过多了解您要在此处进行的操作，这将对您有用:

Without reading between the lines too much about what you're trying to do here, this would work for you:

1. 首先使用train_test_split拆分x和y.由此产生的测试数据集将保留以进行最终测试，并且GridSearchCV交叉验证中的火车数据集将进一步细分为更小的火车和验证集.
2. 构建满足您的回溯尝试告诉您的内容的管道.
3. 将该管道传递到GridSearchCV，.fit()并在X_train/y_train上进行网格搜索，然后在.score()上通过X_test/y_test进行搜索.

1. First split x and y using train_test_split. The test dataset produced by this is held out for final testing, and the train dataset within GridSearchCV's cross-validation will be further broken out into smaller train and validation sets.
2. Build a pipeline that satisfies what your traceback is trying to tell you.
3. Pass that pipeline to GridSearchCV, .fit() that grid search on X_train/y_train, then .score() it on X_test/y_test.

大致上，它看起来像这样:

Roughly, that would look like this:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=444)

sel = SelectFromModel(ExtraTreesClassifier(n_estimators=10, random_state=444), 
                      threshold='mean')
clf = RandomForestClassifier(n_estimators=5000, random_state=444)

model = Pipeline([('sel', sel), ('clf', clf)])
params = {'clf__max_features': ['auto', 'sqrt', 'log2']}

gs = GridSearchCV(model, params)
gs.fit(X_train, y_train)

# How well do your hyperparameter optimizations generalize
# to unseen test data?
gs.score(X_test, y_test)

两个示例供进一步阅读:

Two examples for further reading:

• 管道:链接PCA和逻辑回归
• 用于文本特征提取和评估的示例管道

• Pipelining: chaining a PCA and a logistic regression
• Sample pipeline for text feature extraction and evaluation

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