该ROC曲线的图形看起来很奇怪（sklearn SVC）

Question

因此，我将scikit-learns支持向量分类器（svm.SVC）与流水线和Grid Search结合使用构建了一个小示例。 拟合和评估后，我得到一个看起来非常有趣的ROC曲线：它仅弯曲一次。

我以为我会在这里得到更多的曲线。 谁能解释这种行为？ 最少的工作示例代码：

# Imports
import sklearn as skl
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn import preprocessing
from sklearn import svm
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn import metrics
from tempfile import mkdtemp
from shutil import rmtree
from sklearn.externals.joblib import Memory


def plot_roc(y_test, y_pred):
    fpr, tpr, thresholds = skl.metrics.roc_curve(y_test, y_pred, pos_label=1)
    roc_auc = skl.metrics.auc(fpr, tpr)
    plt.figure()
    lw = 2
    plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area ={0:.2f})'.format(roc_auc))
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show();

# Generate a random dataset
X, y = skl.datasets.make_classification(n_samples=1400, n_features=11,  n_informative=5, n_classes=2, weights=[0.94, 0.06], flip_y=0.05, random_state=42)
X_train, X_test, y_train, y_test = skl.model_selection.train_test_split(X, y, test_size=0.3, random_state=42)

#Instantiate Classifier
normer = preprocessing.Normalizer()
svm1 = svm.SVC(probability=True, class_weight={1: 10})

cached = mkdtemp()
memory = Memory(cachedir=cached, verbose=3)
pipe_1 = Pipeline(steps=[('normalization', normer), ('svm', svm1)], memory=memory)

cv = skl.model_selection.KFold(n_splits=5, shuffle=True, random_state=42)

param_grid = [ {"svm__kernel": ["linear"], "svm__C": [1, 10, 100, 1000]}, {"svm__kernel": ["rbf"], "svm__C": [1, 10, 100, 1000], "svm__gamma": [0.001, 0.0001]} ]
grd = GridSearchCV(pipe_1, param_grid, scoring='roc_auc', cv=cv)

#Training
y_pred = grd.fit(X_train, y_train).predict(X_test)
rmtree(cached)

#Evaluation
confmatrix = skl.metrics.confusion_matrix(y_test, y_pred)
print(confmatrix)
plot_roc(y_test, y_pred)

Answer 1

您的plot_roc(y_test, y_pred)函数在内部调用roc_curve 。

根据roc_curve的文档 ：

y_score：数组，形状= [n_samples]

目标分数可以是肯定类别的概率估计值，置信度值或决策的非阈值度量（如某些分类器上的“ decision_function”所返回）。

因此，当y_pred是正类别的概率而不是硬预测类别的概率最大时，此方法最有效。

尝试以下代码：

y_pred = grd.fit(X_train, y_train).predict_proba(X_test)[:,1] 

然后将y_pred发送到plot方法。