Python 遍历函数并在数据帧中输出结果

希望输出一个干净的数据框，显示模型名称、模型中使用的参数以及结果评分指标。如果有一种更智能的方法来迭代度量函数（给定不同的参数），则效果会更好

以下是我目前掌握的情况：

def train_predict_score(clf, X_train, y_train, X_test, y_test):
    clf = clf.fit(X_train, y_train)
    y_pred_train = clf.predict(X_train)
    y_pred_test = clf.predict(X_test)

    result = []
    result.append(roc_auc_score(y_train, y_pred_train))
    result.append(roc_auc_score(y_test, y_pred_test))
    result.append(cohen_kappa_score(y_train, y_pred_train))
    result.append(cohen_kappa_score(y_test, y_pred_test))
    result.append(f1_score(y_train, y_pred_train, pos_label=1))
    result.append(f1_score(y_test, y_pred_test, pos_label=1))
    result.append(precision_score(y_train, y_pred_train, pos_label=1))
    result.append(precision_score(y_test, y_pred_test, pos_label=1))
    result.append(recall_score(y_train, y_pred_train, pos_label=1))
    result.append(recall_score(y_test, y_pred_test, pos_label=1))

    return result

# Initialize default models
clf1 = LogisticRegression(random_state=0)
clf2 = DecisionTreeClassifier(random_state=0)
clf3 = RandomForestClassifier(random_state=0)
clf4 = GradientBoostingClassifier(random_state=0)

results = []

# Build initial models
for clf in [clf1, clf2, clf3, clf4]:
    result = []
    result.append(clf) # name and parameters - how can I show all info? it gets truncated
    result.append(train_predict_score(clf, X_train, y_train, X_test, y_test)) # how to parse this out into individual columns?
    results.append(result)

results = pd.DataFrame(results, columns=['clf', 'auc_train', 'auc_test', 'f1_train', 'f1_test', 'prec_train',
                                         'prec_test', 'recall_train', 'recall_test'])
results

迭代函数 因为函数是对象，所以您可以将它们列成一个列表，然后简单地对其进行迭代。例如：

def add1(x):
    return x+1
def sub1(x):
    return x-1
for func in [add1, sub1]:
    print(func(10))

屈服

11
9

获取模型名称和参数 据我所知，您希望在不同的列中存储模型的名称（例如LogisticRegression）及其参数。 首先，您可以获得如下参数：

clf.get_params()

这会将所有模型参数作为字典返回。 要获取模型名称，可以获取模型的字符串表示形式，并在“（”上拆分一次。结果列表的第一个元素是模型的名称。因此

>>>clf
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

变成

>>>str(clf).split('(',1)[0]
LogisticRegression

例子 下面是一个小示例，它可以满足您的需要。它在sklearn的乳腺癌数据集上训练3个不同的分类器，并将训练集和测试集上的

roc\u auc

、

f1

、

精度

和

召回

分数作为数据帧返回：

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

#load and split example dataset
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

#classifiers with default parameters
clf1 = LogisticRegression()
clf2 = RandomForestClassifier()
clf3 = SVC()
clf_list = [clf1, clf2, clf3]

results_list = []

for clf in clf_list:
    clf.fit(X_train, y_train)
    res = {}
    #extract the model name from the object string
    res['Model'] = str(clf).split('(', 1)[0]
    #get parameters via get_params() method
    res['Parameters'] = clf.get_params()

    #for every metric, record performance on train and test set
    for metric_score in [roc_auc_score, f1_score, precision_score, recall_score]:
        metric_name = metric_score.__name__
        res[metric_name + '_train'] = metric_score(y_train, clf.predict(X_train))
        res[metric_name + '_test'] = metric_score(y_test, clf.predict(X_test))

    results_list.append(res)

results_df = pd.DataFrame(results_list)

print(results_df.to_string())

                    Model                                         Parameters   f1_test  f1_train  precision_test  precision_train  recall_test  recall_train  roc_au_test  roc_au_train
0      LogisticRegression  {'fit_intercept': True, 'warm_start': False, '...  0.922384  0.969697        0.922384         0.966038     0.922384      0.973384     0.922384      0.959085
1  RandomForestClassifier  {'criterion': 'gini', 'warm_start': False, 'n_...  0.928137  0.998095        0.928137         1.000000     0.928137      0.996198     0.928137      0.998099
2                     SVC  {'decision_function_shape': None, 'verbose': F...  0.500000  1.000000        0.500000         1.000000     0.500000      1.000000     0.500000      1.000000

生成的数据帧：

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

#load and split example dataset
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

#classifiers with default parameters
clf1 = LogisticRegression()
clf2 = RandomForestClassifier()
clf3 = SVC()
clf_list = [clf1, clf2, clf3]

results_list = []

for clf in clf_list:
    clf.fit(X_train, y_train)
    res = {}
    #extract the model name from the object string
    res['Model'] = str(clf).split('(', 1)[0]
    #get parameters via get_params() method
    res['Parameters'] = clf.get_params()

    #for every metric, record performance on train and test set
    for metric_score in [roc_auc_score, f1_score, precision_score, recall_score]:
        metric_name = metric_score.__name__
        res[metric_name + '_train'] = metric_score(y_train, clf.predict(X_train))
        res[metric_name + '_test'] = metric_score(y_test, clf.predict(X_test))

    results_list.append(res)

results_df = pd.DataFrame(results_list)

print(results_df.to_string())

                    Model                                         Parameters   f1_test  f1_train  precision_test  precision_train  recall_test  recall_train  roc_au_test  roc_au_train
0      LogisticRegression  {'fit_intercept': True, 'warm_start': False, '...  0.922384  0.969697        0.922384         0.966038     0.922384      0.973384     0.922384      0.959085
1  RandomForestClassifier  {'criterion': 'gini', 'warm_start': False, 'n_...  0.928137  0.998095        0.928137         1.000000     0.928137      0.996198     0.928137      0.998099
2                     SVC  {'decision_function_shape': None, 'verbose': F...  0.500000  1.000000        0.500000         1.000000     0.500000      1.000000     0.500000      1.000000

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注意：因为您在问题中提到数据框内容被截断：例如，当您尝试在控制台中打印DF时，这种情况仅用于显示目的，就像我上面所做的那样。当您直接访问相应的单元格时，内容仍然存在。

请用您想要的数据框的示例编辑您的帖子看起来像。刚刚添加了一个示例输出的链接。你说“它不工作”是什么意思？这是指我编辑过的旧版本。它现在运行，但不正确。我将更改帖子感谢这个很棒、全面的示例！