英文:
Using sample_weight param with XGBoost through a pipeline
问题
我想在xgboost包的XGBClassifier中使用sample_weight参数。
问题出现在我尝试在sklearn.pipeline中使用它时。
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
clf = XGBClassifier(**params)
steps = [('scaler', MinMaxScaler()), ('classifier', clf)]
pipeline = Pipeline(steps)
当我运行pipeline.fit(x, y, sample_weight=sample_weight)，其中sample_weight只是一个包含整数表示权重的字典时，我遇到了以下错误：
ValueError: Pipeline.fit不接受sample_weight参数。
我该如何解决这个问题？有没有解决方法？我已经看到有一个问题存在。
英文:
I want to use the sample_weight parameter with XGBClassifier from the xgboost package.
The problem happen when I want to use it inside a pipeline from sklearn.pipeline.
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
clf = XGBClassifier(**params)
steps = [ ('scaler', MinMaxScaler() ), ('classifier', clf ) ]
pipeline = Pipeline( steps )
When I run pipeline.fit(x, y, sample_weight=sample_weight) where sample_weight is just a dictionary with int representing weights, I have the following error:
> ValueError: Pipeline.fit does not accept the sample_weight parameter.
How can I solve this problem? Is there a workaround? I have seen that an issue already exists.
答案1
得分: 2
The value error message is factually correct - the Pipeline class does not contain any business logic dealing with sample weights.
However, your pipeline has two steps. And one of the step components - the XGBoost classifier - supports sample weights.
So, the solution is to address the sample weights parameter directly to the classifier step. According to Scikit-Learn conventions, you can do so by prepending the classifier__ prefix (reads "classifier" plus two underscore characters) to your fit param name.
In short:
pipeline = Pipeline( steps )
pipeline.fit(X, y, classifier__sample_weights = weights)
英文:
The value error message is factually correct - the Pipeline class does not contain any business logic dealing with sample weights.
However, your pipeline has two steps. And one of the step components - the XGBoost classifier - supports sample weights.
So, the solution is to address the sample weights parameter directly to the classifier step. According to Scikit-Learn conventions, you can do so by prepending the classifier__ prefix (reads "classifier" plus two underscore characters) to your fit param name.
In short:
pipeline = Pipeline( steps )
pipeline.fit(X, y, classifier__sample_weights = weights)
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