python - 如何在 python 中使用 GridSearchCV 以及管道和超参数

Question

我使用了两个估计器，Randomforest 和 SVM

random_forest_pipeline=Pipeline([   
    ('vectorizer',CountVectorizer(stop_words='english')),
    ('random_forest',RandomForestClassifier())
])
svm_pipeline=Pipeline([
    ('vectorizer',CountVectorizer(stop_words='english')),
    ('svm',LinearSVC())
])

我想首先对数据进行矢量化，然后使用估计器，我正在浏览这个在线 tutorial 。然后我使用超参数如下

parameters=[
    {
        'vectorizer__max_features':[500,1000,1500],
        'random_forest__min_samples_split':[50,100,250,500]
    },
    {
        'vectorizer__max_features':[500,1000,1500],
        'svm__C':[1,3,5]
    }
]

并传递给 GridSearchCV

pipelines=[random_forest_pipeline,svm_pipeline]
grid_search=GridSearchCV(pipelines,param_grid=parameters,cv=3,n_jobs=-1)
grid_search.fit(x_train,y_train)

但是，当我运行代码时出现错误

TypeError: estimator should be an estimator implementing 'fit' method

不知道为什么我收到这个错误

Answer 1

问题是 pipelines=[random_forest_pipeline,svm_pipeline] 是一个没有 fit 方法的列表。

即使您可以以这种方式工作，在某些时候 'random_forest__min_samples_split':[50,100,250,500] 也会在 svm_pipeline 中传递，这会引发错误。

ValueError: Invalid parameter svm for estimator Pipeline

您不能以这种方式混合 2 个管道，因为在某些时候您要求使用 svm_pipeline 的值评估 random_forest__min_samples_split，这是无效的。

解决方案:Fit a GridSearch object for the Random forest model and another GridSearch object for the SVC model

pipelines=[random_forest_pipeline,svm_pipeline]

grid_search_1=GridSearchCV(pipelines[0],param_grid=parameters[0],cv=3,n_jobs=-1)
grid_search_1.fit(X,y)

grid_search_2=GridSearchCV(pipelines[1],param_grid=parameters[1],cv=3,n_jobs=-1)
grid_search_2.fit(X,y)

完整代码:

random_forest_pipeline=Pipeline([   
    ('vectorizer',CountVectorizer(stop_words='english')),
    ('random_forest',RandomForestClassifier())
])
svm_pipeline=Pipeline([
    ('vectorizer',CountVectorizer(stop_words='english')),
    ('svm',LinearSVC())
])

parameters=[
    {
        'vectorizer__max_features':[500,1000,1500],
        'random_forest__min_samples_split':[50,100,250,500]
    },
    {
        'vectorizer__max_features':[500,1000,1500],
        'svm__C':[1,3,5]
    }
]

pipelines=[random_forest_pipeline,svm_pipeline]

# gridsearch only for the Random Forest model
grid_search_1 =GridSearchCV(pipelines[0],param_grid=parameters[0],cv=3,n_jobs=-1)
grid_search_1.fit(X,y)

# gridsearch only for the SVC model
grid_search_2 =GridSearchCV(pipelines[1],param_grid=parameters[1],cv=3,n_jobs=-1)
grid_search_2.fit(X,y)

编辑

如果您将模型明确定义到 param_grid 列表中，则可以基于文档。

链接: https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html?highlight=pipeline%20gridsearch

来自文档的代码:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.decomposition import PCA, NMF
from sklearn.feature_selection import SelectKBest, chi2

print(__doc__)

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
    },
]
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)