使用GridSearchCV的随机森林 - param_grid出错

Question

我试图用GridSearchCV创建一个随机森林模型但是得到一个与param_grid有关的错误:"ValueError:估算器管道的参数max_features无效.用`estimator.get_params().keys()"检查可用参数列表.我正在对文档进行分类,所以我也将tf-idf矢量化器推送到管道.这是代码:

from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, f1_score, accuracy_score, precision_score, confusion_matrix
from sklearn.pipeline import Pipeline

 #Classifier Pipeline
pipeline = Pipeline([
    ('tfidf', TfidfVectorizer()),
    ('classifier', RandomForestClassifier())
])
# Params for classifier
params = {"max_depth": [3, None],
              "max_features": [1, 3, 10],
              "min_samples_split": [1, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              # "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

# Grid Search Execute
rf_grid = GridSearchCV(estimator=pipeline , param_grid=params) #cv=10
rf_detector = rf_grid.fit(X_train, Y_train)
print(rf_grid.grid_scores_)

我无法弄清楚错误显示的原因.当我使用GridSearchCV运行决策树时,正在发生相同的顺便说一句.(Scikit-learn 0.17)

Answer 1

您必须将参数分配给管道中的命名步骤.在你的情况下classifier.尝试classifier__在参数名称前 加上. 样品管道

params = {"classifier__max_depth": [3, None],
              "classifier__max_features": [1, 3, 10],
              "classifier__min_samples_split": [1, 3, 10],
              "classifier__min_samples_leaf": [1, 3, 10],
              # "bootstrap": [True, False],
              "classifier__criterion": ["gini", "entropy"]}

Answer 2

尝试get_params()在最终的管道对象上运行,而不仅仅是估算器.这样它就会为网格参数生成所有可用的管道项唯一键.

sorted(pipeline.get_params().keys())

['classifier','classifier__bootstrap','classifier__class_weight','classifier__criterion','classifier__max_depth', 'classifier__max_features', 'classifier__max_leaf_nodes','classifier__min_impurity_split','classifier__min_samples_leaf','classifier__min_samples_split','classifier__min_weight_fraction_leaf','classifier__n_estimators',' classifier__n_jobs','classifier__oob_score','classifier__random_state','classifier__verbose','classifier__warm_start','steps','tfidf','tfidf__analyzer','tfidf__binary','tfidf__decode_error','tfidf__dtype','tfidf__encoding','tfidf__input' , 'tfidf__lowercase', 'tfidf__max_df', 'tfidf__max_features', 'tfidf__min_df', 'tfidf__ngram_range', 'tfidf__norm', 'tfidf__preprocessor', 'tfidf__smooth_idf', 'tfidf__stop_words', 'tfidf__strip_accents', 'tfidf__sublinear_tf', 'tfidf__token_pattern',' tfidf__tokenizer','tfidf__use_idf','tfidf__vocabulary']

当您使用make_pipeline()Piplines的短语法时,这非常有用,您不需要为管道项标签烦恼:

pipeline = make_pipeline(TfidfVectorizer(), RandomForestClassifier())
sorted(pipeline.get_params().keys())

[ 'randomforestclassifier', 'randomforestclassifier__bootstrap', 'randomforestclassifier__class_weight', 'randomforestclassifier__criterion', 'randomforestclassifier__max_depth', 'randomforestclassifier__max_features', 'randomforestclassifier__max_leaf_nodes', 'randomforestclassifier__min_impurity_split', 'randomforestclassifier__min_samples_leaf', 'randomforestclassifier__min_samples_split', 'randomforestclassifier__min_weight_fraction_leaf', 'randomforestclassifier__n_estimators',' randomforestclassifier__n_jobs', 'randomforestclassifier__oob_score', 'randomforestclassifier__random_state', 'randomforestclassifier__verbose', 'randomforestclassifier__warm_start', '步骤', 'tfidfvectorizer', 'tfidfvectorizer__analyzer', 'tfidfvectorizer__binary', 'tfidfvectorizer__decode_error', 'tfidfvectorizer__dtype', 'tfidfvectorizer__encoding', 'tfidfvectorizer__input' ,'tfidfvectorizer__lowercase','tfidfvectorizer__max_df','tfidfvectorizer__max_features','tfidfvectorizer__min_df','tfidfvectoriz er__ngram_range", 'tfidfvectorizer__norm', 'tfidfvectorizer__preprocessor', 'tfidfvectorizer__smooth_idf', 'tfidfvectorizer__stop_words', 'tfidfvectorizer__strip_accents', 'tfidfvectorizer__sublinear_tf', 'tfidfvectorizer__token_pattern', 'tfidfvectorizer__tokenizer', 'tfidfvectorizer__use_idf', 'tfidfvectorizer__vocabulary']