如何解决Sklearn StackingClassifier：将要素添加为最终估算器的输入
我正在使用管道和堆栈分类器来构建分类管道。在我的设置中，我想将一些额外的原始功能以及上一级模型的预测传递给最终估计器。以图表的方式，如下所示：
我仍然想利用这两个管道（除了添加Feat x / y之外，我已经用来设置所有东西）和StackingClassifier来做到这一点，因为它可以从头到尾训练堆叠模型-干净利落。但是，我看不到将原始功能添加到先前“级别”模型的预测中的选项。有什么好方法吗？
注意：输入到最终估计量的特征与输入到模型1和模型2的特征不同，因此我不是在寻找 pass_through = True 标志。
解决方法
此功能不是快速功能，但我可以想到两种将其组合在一起的方法，同时仍然使用StackingClassifier自动化。每个都有缺点。
将更多功能纳入预测
制作一个虚拟的预测器，通过仅返回输入来获取“预测”，并将其用作基本估计器，以将额外的特征传递给元估计器。使用ColumnTransformer来选择基本估计量的特征或直通特征。
from sklearn.base import ClassifierMixin,TransformerMixin
from sklearn.pipeline import Pipeline
class IdentityPassthrough(ClassifierMixin):
def __init__(self):
pass
def fit(self,X,y):
return self
def predict(self,X):
return X
partial_passthrough = Pipeline([
('pass',ColumnTransformer([('pass','passthrough',['x','y'])])),('ident',IdentityPassthrough()),])
base_features = ColumnTransformer([('pass',['a','b'])])
model = StackingClassifier(estimators=[
('pass',partial_passthrough),('tree',Pipeline([('select',base_features),DecisionTreeClassifier())])),('knn',KNeighborsClassifier())])),])
model.fit(X,y)
使用直通并选择基本特征
对元估计器使用组合，从基础估计器和所需的额外特征中选择要预测的特征。这有点令人担忧，因为您必须知道自己得到正确的列顺序（直到sklearn完成处理功能名称）。即，在下面的代码中，特征0和1是预测的概率（并且，如果堆栈方法不是排他的predict_proba，则由于类预测为负，因此必须为1和3！）和4和5是目标直通变量（在原始帧中分别索引为2和3）。
base_features = ColumnTransformer([('pass',['mean radius','mean texture'])])
model = StackingClassifier(
estimators=[
('tree',DecisionTreeClassifier(random_state=42))])),],final_estimator=Pipeline([
('select',ColumnTransformer([('select',[0,1,4,5])])),('model',LogisticRegression())
]),passthrough=True,)
model.fit(X,y)
模型图：
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<!-- First approach diagram: -->
<div class="sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a4c7712b-4e69-42ca-b31f-ecbe7b6d1d89" type="checkbox" ><label class="sk-toggleable__label" for="a4c7712b-4e69-42ca-b31f-ecbe7b6d1d89">StackingClassifier</label><div class="sk-toggleable__content"><pre>StackingClassifier(estimators=[('pass',Pipeline(steps=[('pass',ColumnTransformer(transformers=[('pass',['mean ' 'perimeter','mean ' 'area'])])),<__main__.IdentityPassthrough object at 0x7f2bfbf1f358>)])),Pipeline(steps=[('select',['mean ' 'radius','mean ' 'texture'])])),KNeighborsClassifier())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>pass</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c41ba408-2542-42cf-be5d-d2bdb1f7ca39" type="checkbox" ><label class="sk-toggleable__label" for="c41ba408-2542-42cf-be5d-d2bdb1f7ca39">pass: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('pass',['mean perimeter','mean area'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d71f311c-d151-402b-80a4-97fcb9464d8f" type="checkbox" ><label class="sk-toggleable__label" for="d71f311c-d151-402b-80a4-97fcb9464d8f">pass</label><div class="sk-toggleable__content"><pre>['mean perimeter','mean area']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3e9e3a12-5622-4e56-9171-dbc690ca50d8" type="checkbox" ><label class="sk-toggleable__label" for="3e9e3a12-5622-4e56-9171-dbc690ca50d8">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b5753df0-b293-4aeb-bbdb-d9adc63b6ac8" type="checkbox" ><label class="sk-toggleable__label" for="b5753df0-b293-4aeb-bbdb-d9adc63b6ac8">IdentityPassthrough</label><div class="sk-toggleable__content"><pre><__main__.IdentityPassthrough object at 0x7f2bfbf1f358></pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>tree</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4952e340-a144-40cd-897b-dcdee029fecb" type="checkbox" ><label class="sk-toggleable__label" for="4952e340-a144-40cd-897b-dcdee029fecb">select: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('pass','mean texture'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7efbe86f-2262-4048-81fd-7c652803cf4f" type="checkbox" ><label class="sk-toggleable__label" for="7efbe86f-2262-4048-81fd-7c652803cf4f">pass</label><div class="sk-toggleable__content"><pre>['mean radius','mean texture']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7ccdbf06-9312-4424-a74e-e1c56b3fbe88" type="checkbox" ><label class="sk-toggleable__label" for="7ccdbf06-9312-4424-a74e-e1c56b3fbe88">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3a2df016-3631-4cc6-960c-695466268875" type="checkbox" ><label class="sk-toggleable__label" for="3a2df016-3631-4cc6-960c-695466268875">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>knn</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5aaa1a50-3954-43c0-802d-0679ecfaaa5f" type="checkbox" ><label class="sk-toggleable__label" for="5aaa1a50-3954-43c0-802d-0679ecfaaa5f">select: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('pass','mean texture'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="836d9068-cfb3-4545-a714-6f349403d567" type="checkbox" ><label class="sk-toggleable__label" for="836d9068-cfb3-4545-a714-6f349403d567">pass</label><div class="sk-toggleable__content"><pre>['mean radius','mean texture']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="0c9e6b1f-0f96-4d6f-8efc-e3bada46d6a7" type="checkbox" ><label class="sk-toggleable__label" for="0c9e6b1f-0f96-4d6f-8efc-e3bada46d6a7">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="56bfccc3-bca0-4d87-a377-a81913e4098c" type="checkbox" ><label class="sk-toggleable__label" for="56bfccc3-bca0-4d87-a377-a81913e4098c">KNeighborsClassifier</label><div class="sk-toggleable__content"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="57737187-5f4c-4186-ad65-e68cecfe14e8" type="checkbox" ><label class="sk-toggleable__label" for="57737187-5f4c-4186-ad65-e68cecfe14e8">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div>
<!-- Second approach diagram: -->
<div class="sk-top-container"><div class="sk-container"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="034d2534-0d63-4319-bfbf-3b0a7117e00f" type="checkbox" ><label class="sk-toggleable__label" for="034d2534-0d63-4319-bfbf-3b0a7117e00f">StackingClassifier</label><div class="sk-toggleable__content"><pre>StackingClassifier(estimators=[('tree',KNeighborsClassifier())]))],final_estimator=Pipeline(steps=[('select',ColumnTransformer(transformers=[('select',LogisticRegression())]),passthrough=True)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>tree</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="50985202-3021-4333-877c-034e62c6e07a" type="checkbox" ><label class="sk-toggleable__label" for="50985202-3021-4333-877c-034e62c6e07a">select: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('pass','mean texture'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8132ce46-3e0b-42d2-b42b-f0a53d192c07" type="checkbox" ><label class="sk-toggleable__label" for="8132ce46-3e0b-42d2-b42b-f0a53d192c07">pass</label><div class="sk-toggleable__content"><pre>['mean radius','mean texture']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e1970d86-b28e-41d0-8297-4d2ed67b4b50" type="checkbox" ><label class="sk-toggleable__label" for="e1970d86-b28e-41d0-8297-4d2ed67b4b50">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="fafe6dec-d6a7-4c00-b561-17f3307e4bde" type="checkbox" ><label class="sk-toggleable__label" for="fafe6dec-d6a7-4c00-b561-17f3307e4bde">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>knn</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8d608e7c-c318-4a67-a9b7-26995a77bcc6" type="checkbox" ><label class="sk-toggleable__label" for="8d608e7c-c318-4a67-a9b7-26995a77bcc6">select: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('pass','mean texture'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="12515389-377c-4fd1-9b50-cf0515dc1919" type="checkbox" ><label class="sk-toggleable__label" for="12515389-377c-4fd1-9b50-cf0515dc1919">pass</label><div class="sk-toggleable__content"><pre>['mean radius','mean texture']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4fa202f1-74c0-47ba-b34d-dbc4e460eff9" type="checkbox" ><label class="sk-toggleable__label" for="4fa202f1-74c0-47ba-b34d-dbc4e460eff9">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="0ac195fa-6584-4220-a00e-8da1dd09b5de" type="checkbox" ><label class="sk-toggleable__label" for="0ac195fa-6584-4220-a00e-8da1dd09b5de">KNeighborsClassifier</label><div class="sk-toggleable__content"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ede1e052-c7af-4bd3-9da9-2fcc69dd8c86" type="checkbox" ><label class="sk-toggleable__label" for="ede1e052-c7af-4bd3-9da9-2fcc69dd8c86">select: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('select',5])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8ad23c33-3176-45c6-9504-98299d187eda" type="checkbox" ><label class="sk-toggleable__label" for="8ad23c33-3176-45c6-9504-98299d187eda">select</label><div class="sk-toggleable__content"><pre>[0,5]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9fa0446e-ea2b-4c32-b32b-07c9b2643717" type="checkbox" ><label class="sk-toggleable__label" for="9fa0446e-ea2b-4c32-b32b-07c9b2643717">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="450a759e-b194-4bf9-a92b-b296f6c9f527" type="checkbox" ><label class="sk-toggleable__label" for="450a759e-b194-4bf9-a92b-b296f6c9f527">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div></div>
查看有关威斯康星州乳腺癌数据集in this notebook的全部内容。
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