Question

我需要对下面列出的用于Logistic回归分类器的参数执行网格搜索，并使用召回率进行三次评分和交叉验证。

数据位于csv文件（11.1 MB）中，此下载链接为：https://drive.google.com/file/d/1cQFp7HteaaL37CefsbMNuHqPzkINCVzs/view?usp=sharing

我有grid_values = {'gamma':[0.01, 0.1, 1, 10, 100]} 我需要在Logistic回归中应用惩罚L1和L2

由于存在以下错误，我无法验证分数是否会运行：估算器LogisticRegression的参数gamma无效。使用estimator.get_params().keys()检查可用参数列表。

这是我的代码：

from sklearn.model_selection import train_test_split

df = pd.read_csv('fraud_data.csv')

X = df.iloc[:,:-1]
y = df.iloc[:,-1]

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)




def LogisticR_penalty():    
    from sklearn.model_selection import GridSearchCV
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import cross_val_score

    grid_values = {'gamma':[0.01, 0.1, 1, 10, 100]}


    #train de model with many parameters for "C" and penalty='l1'
    lr_l1 = LogisticRegression(penalty='l1')
    grid_lr_l1 = GridSearchCV(lr_l1, param_grid = grid_values, cv=3, scoring = 'recall')
    grid_lr_l1.fit(X_train, y_train)
    y_decision_fn_scores_recall = grid_lr_l1.decision_function(X_test)


    lr_l2 = LogisticRegression(penalty='l2')
    grid_lr_l2 = GridSearchCV(lr_l2, param_grid = grid_values, cv=3 , scoring = 'recall')
    grid_lr_l2.fit(X_train, y_train)
    y_decision_fn_scores_recall = grid_lr_l2.decision_function(X_test)



    #The precision, recall, and accuracy scores for every combination 
    #of the parameters in param_grid are stored in cv_results_
    results = pd.DataFrame()

    results['l1_results'] = pd.DataFrame(grid_lr_l1.cv_results_)
    results['l1_results'] = results['l2_results'].sort_values(by='mean_test_precision_score', ascending=False)

    results['l2_results'] = pd.DataFrame(grid_lr_l2.cv_results_)
    results['l2_results'] = results['l2_results'].sort_values(by='mean_test_precision_score', ascending=False)


    return results
LogisticR_penalty()

我期望从.cv_results_中获得我应该在此处提供的每个参数组合的平均测试成绩：mean_test_precision_score但不确定

输出为：ValueError：估计器LogisticRegression的参数gamma无效。使用estimator.get_params().keys()检查可用参数列表。

Answer 1

在scikit-learn's documentation中，LogisticRegression没有参数gamma，但是有参数C用于正则化权重。

如果您将grid_values = {'gamma':[0.01, 0.1, 1, 10, 100]}的{{1}}更改为您的代码，则可以使用。

Answer 2

错误消息包含您问题的答案。您可以使用函数estimator.get_params().keys()来查看估算器的所有可用参数：

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression()

print(lr.get_params().keys())

输出：

dict_keys(['C', 'class_weight', 'dual', 'fit_intercept', 'intercept_scaling', 'l1_ratio', 'max_iter', 'multi_class', 'n_jobs', 'penalty', 'random_state', 'solver', 'tol', 'verbose', 'warm_start'])

Answer 3

我的代码包含一些错误，主要错误是不正确地使用了param_grid。我必须应用伽玛值为0.01、0.1、1、10、100的L1和L2罚分。正确的方法是：

grid_values = {'penalty': ['l1', 'l2'], 'C': [0.01, 0.1, 1, 10, 100]}

然后，有必要更正我训练逻辑回归的方式，并更正我检索cv_results_中得分并平均这些得分的方式。遵循我的代码：

from sklearn.model_selection import train_test_split

df = pd.read_csv('fraud_data.csv')

X = df.iloc[:,:-1]
y = df.iloc[:,-1]

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

def LogisticR_penalty():    
    from sklearn.model_selection import GridSearchCV
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import cross_val_score

    grid_values = {'penalty': ['l1', 'l2'], 'C': [0.01, 0.1, 1, 10, 100]}


    #train de model with many parameters for "C" and penalty='l1'

    lr = LogisticRegression()
    # We use GridSearchCV to find the value of the range that optimizes a given measurement metric.
    grid_lr_recall = GridSearchCV(lr, param_grid = grid_values, cv=3, scoring = 'recall')
    grid_lr_recall.fit(X_train, y_train)
    y_decision_fn_scores_recall = grid_lr_recall.decision_function(X_test)

    ##The precision, recall, and accuracy scores for every combination 
    #of the parameters in param_grid are stored in cv_results_
    CVresults = []
    CVresults = pd.DataFrame(grid_lr_recall.cv_results_)

    #test scores and mean of them
    split_test_scores = np.vstack((CVresults['split0_test_score'], CVresults['split1_test_score'], CVresults['split2_test_score']))
    mean_scores = split_test_scores.mean(axis=0).reshape(5, 2)

    return mean_scores
LogisticR_penalty()

带有无效参数伽玛的GridSearchCV用于估算器LogisticRegression

3 个答案: