DecisionTreeClassifier的查全率曲线下的面积是正方形。

Question

我正在使用DecisionTreeClassifier -学习分类的一些数据。我也在使用其他算法，为了比较它们，我使用了精确召回度量下的区域。问题是，DecisionTreeClassifier的形状是正方形，而不是您所期望的该度量的通常形状。

以下是我如何计算DecisionTreeClassifier的AUPRC。我在计算这方面有一些困难，因为DecisionTreeClassifer和其他分类器(如LogisticRegression )一样，没有decision_function()。

这些都是我在支持向量机、Logistic回归和DecisionTreeClassifier中得到的结果。

下面是我如何计算DecisionTreeClassifier的AUPRC

def execute(X_train, y_train, X_test, y_test):
    tree = DecisionTreeClassifier(class_weight='balanced')
    tree_y_score = tree.fit(X_train, y_train).predict(X_test)

    tree_ap_score = average_precision_score(y_test, tree_y_score)

    precision, recall, _ = precision_recall_curve(y_test, tree_y_score)
    values = {'ap_score': tree_ap_score, 'precision': precision, 'recall': recall}
    return values

下面是我如何计算支持向量机的AUPRC：

def execute(X_train, y_train, X_test, y_test):
    svm = SVC(class_weight='balanced')
    svm.fit(X_train, y_train.values.ravel())
    svm_y_score = svm.decision_function(X_test)

    svm_ap_score = average_precision_score(y_test, svm_y_score)

    precision, recall, _ = precision_recall_curve(y_test, svm_y_score)
    values = {'ap_score': svm_ap_score, 'precision': precision, 'recall': recall}
    return values

下面是我如何计算LogisticRegression的AUPRC：

def execute(X_train, y_train, X_test, y_test):
    lr = LogisticRegression(class_weight='balanced')
    lr.fit(X_train, y_train.values.ravel())
    lr_y_score = lr.decision_function(X_test)

    lr_ap_score = average_precision_score(y_test, lr_y_score)

    precision, recall, _ = precision_recall_curve(y_test, lr_y_score)
    values = {'ap_score': lr_ap_score, 'precision': precision, 'recall': recall}
    return values

然后，我将它们称为方法，并绘制如下结果：

import LogReg_AP_Harness as lrApTest
import SVM_AP_Harness as svmApTest
import DecTree_AP_Harness as dtApTest
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
import matplotlib.pyplot as plt


def do_work(df):
    X = df.ix[:, df.columns != 'Class']
    y = df.ix[:, df.columns == 'Class']

    y_binarized = label_binarize(y, classes=[0, 1])
    n_classes = y_binarized.shape[1]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=0)

    _, _, y_train_binarized, y_test_binarized = train_test_split(X, y_binarized, test_size=.3, random_state=0)

    print('Executing Logistic Regression')
    lr_values = lrApTest.execute(X_train, y_train, X_test, y_test)
    print('Executing Decision Tree')
    dt_values = dtApTest.execute(X_train, y_train_binarized, X_test, y_test_binarized)
    print('Executing SVM')
    svm_values = svmApTest.execute(X_train, y_train, X_test, y_test)

    plot_aupr_curves(lr_values, svm_values, dt_values)


def plot_aupr_curves(lr_values, svm_values, dt_values):
    lr_ap_score = lr_values['ap_score']
    lr_precision = lr_values['precision']
    lr_recall = lr_values['recall']

    svm_ap_score = svm_values['ap_score']
    svm_precision = svm_values['precision']
    svm_recall = svm_values['recall']

    dt_ap_score = dt_values['ap_score']
    dt_precision = dt_values['precision']
    dt_recall = dt_values['recall']

    plt.step(svm_recall, svm_precision, color='g', alpha=0.2,where='post')
    plt.fill_between(svm_recall, svm_precision, step='post', alpha=0.2, color='g')

    plt.step(lr_recall, lr_precision, color='b', alpha=0.2, where='post')
    plt.fill_between(lr_recall, lr_precision, step='post', alpha=0.2, color='b')

    plt.step(dt_recall, dt_precision, color='r', alpha=0.2, where='post')
    plt.fill_between(dt_recall, dt_precision, step='post', alpha=0.2, color='r')

    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title('SVM (Green): Precision-Recall curve: AP={0:0.2f}'.format(svm_ap_score) + '\n' +
              'Logistic Regression (Blue): Precision-Recall curve: AP={0:0.2f}'.format(lr_ap_score) + '\n' +
              'Decision Tree (Red): Precision-Recall curve: AP={0:0.2f}'.format(dt_ap_score))
    plt.show()

在do_work()方法中，我不得不对y进行二进制化，因为DecisionTreeClassifier没有descision_function()。我从here那里得到了这个方法。

这就是情节：

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我想归结起来是我错误地计算了DecisionTreeClassifier的AUPRC。

Answer 1

对于DecisionTreeClassifier，将predict替换为pred_proba；pred_proba与decision_function具有相同的角色。