使用KNeighbors分类器的SKLearning管道

SKlearn pipeline using KNeighborsClassifier(使用KNeighbors分类器的SKLearning管道)

问题描述

我正在尝试使用KNeighbors分类器和支持向量机在sklear中构建一个GridSearchCV管道。到目前为止，我已经尝试了以下代码：

from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
from sklearn import svm
from sklearn.svm import SVC
clf = SVC(kernel='linear')
pipeline = Pipeline([ ('knn',neigh), ('sVM', clf)]) # Code breaks here
weight_options = ['uniform','distance']
param_knn = {'weights':weight_options}
param_svc = {'kernel':('linear', 'rbf'), 'C':[1,5,10]}
grid = GridSearchCV(pipeline, param_knn, param_svc, cv=5, scoring='accuracy')

但我收到以下错误：

TypeError: All intermediate steps should be transformers and implement fit and transform. 'KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=3, p=2,
           weights='uniform')' (type <class 'sklearn.neighbors.classification.KNeighborsClassifier'>) doesn't

谁能帮帮我，我哪里做错了，怎么改正？我认为最后一行也有问题，re parms。

推荐答案

错误清楚地表明KNeighbors分类器没有转换方法KNN只有FIT方法，而AS SVM有FIT_Transform()方法。对于管道，我们可以向它传递n个参数。但是所有的参数都应该有转换器方法。请参考下面的链接

http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html

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