Question

我正在尝试了解如何获取GridSearchCV的得分者的值。下面的示例代码在文本数据上设置了一个小管道。

然后它在不同的ngrams上设置网格搜索。

评分是通过f1措施完成的：

#setup the pipeline
tfidf_vec = TfidfVectorizer(analyzer='word', min_df=0.05, max_df=0.95)
linearsvc = LinearSVC()
clf = Pipeline([('tfidf_vec', tfidf_vec), ('linearsvc', linearsvc)])

# setup the grid search
parameters = {'tfidf_vec__ngram_range': [(1, 1), (1, 2)]}
gs_clf = GridSearchCV(clf, parameters, n_jobs=-1, scoring='f1')
gs_clf = gs_clf.fit(docs_train, y_train)

现在我可以打印分数：

print gs_clf.grid_scores _

[mean: 0.81548, std: 0.01324, params: {'tfidf_vec__ngram_range': (1, 1)},
 mean: 0.82143, std: 0.00538, params: {'tfidf_vec__ngram_range': (1, 2)}]

print gs_clf.grid_scores_ [0] .cv_validation_scores

array([ 0.83234714,  0.8       ,  0.81409002])

我不清楚documentation：

gs_clf.grid_scores_ [0] .cv_validation_scores一个数组，其中每个折叠通过评分参数定义得分（在这种情况下，每次折叠f1指数）？如果没有，那么它是什么？
如果我改为选择其他metric，例如评分=＆＃39; f1_micro＆＃39;，则gs_clf.grid_scores_ [i] .cv_validation_scores中的每个数组都将包含折叠的f1_micro指标特定的网格搜索参数选择？

Answer 1

我编写了以下函数将grid_scores_对象转换为pandas.DataFrame。希望数据框视图有助于消除您的困惑，因为它是一种更直观的格式：

def grid_scores_to_df(grid_scores):
    """
    Convert a sklearn.grid_search.GridSearchCV.grid_scores_ attribute to a tidy
    pandas DataFrame where each row is a hyperparameter-fold combinatination.
    """
    rows = list()
    for grid_score in grid_scores:
        for fold, score in enumerate(grid_score.cv_validation_scores):
            row = grid_score.parameters.copy()
            row['fold'] = fold
            row['score'] = score
            rows.append(row)
    df = pd.DataFrame(rows)
    return df

您必须进行以下导入才能实现此目的：import pandas as pd。

GridSearchCV评分和grid_scores_

1 个答案: