sklearn：auc、roc_curve、roc_auc_score
sklearn.metrics.auc
作用：計算AUC(Area Under the Curve)
metrics.roc_curve
作用：計算 ROC(Receiver operating characteristic)
注意: this implementation is restricted to the binary classification task
sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)
parameter :
-
y_true: array, shape = [n_samples] True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given -
y_score: array, shape = [n_samples] -
pos_label:int or str, default=None, Label considered as positive and others are considered negative.
Returns
fpr: false positive ratestpr: true positive ratesthresholds: array, shape = [n_thresholds]
例子：
pos_label = 1即表示標籤為1的是正樣本，其餘的都是負樣本，因為這個只能做二分類。
import numpy as np from sklearn import metrics y = np.array([1, 1, 2, 2,3,3]) pred = np.array([0.1, 0.4, 0.35, 0.8,0.1,0.8]) fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label = 1) metrics.auc(fpr, tpr) 0.3125
sklearn.metrics.roc_auc_score
作用：Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores
注意：this implementation is restricted to the binary classification task or multilabel classification task inlabel indicator format.
sklearn.metrics.roc_auc_score(y_true, y_score, average=’macro’, sample_weight=None, max_fpr=None)
Parameters:
y_true : array, shape = [n_samples] or [n_samples, n_classes]
y_score : array, shape = [n_samples] or [n_samples, n_classes]
average : string, [None, ‘micro’, ‘macro’ (default), ‘samples’, ‘weighted’]，If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data。
Returns:
auc : float
### roc_auc_score
import numpy as np
from sklearn.metrics import roc_auc_score
y_true = np.array([0, 0, 1, 1])
y_scores = np.array([0.1, 0.4, 0.35, 0.8])
roc_auc_score(y_true, y_scores)
0.75
roc_auc_score 是 預測得分曲線下的 auc，在計算的時候呼叫了 auc；
def _binary_roc_auc_score(y_true, y_score, sample_weight=None):
if len(np.unique(y_true)) != 2:
raise ValueError("Only one class present in y_true. ROC AUC score "
"is not defined in that case.")
fpr, tpr, tresholds = roc_curve(y_true, y_score,
sample_weight=sample_weight)
return auc(fpr, tpr, reorder=True)
所以不能用在多分類問題上。
多分類問題的auc計算例子：
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm,datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
匯入資料：
iris = datasets.load_iris()
X = iris.data
y = iris.target
對訓練標籤做標籤二值化運算（one-hot編碼）：
# Binarize the output
y = label_binarize(y,classes=[0,1,2])
n_classes = y.shape[1]
n_classes
3
對每個資料在尾部加入噪音：
# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X,random_state.randn(n_samples,200 * n_features)]
注：np.c_
np.c_[random_state.randn(2,2),[[0,0],[1,1]]]
array([[ 0.73381936, 0.26909417, 0. , 0. ],
[ 1.07274021, -0.9826661 , 1. , 1. ]])
劃分資料集：
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.5,random_state=0)
fit一個分類器：
# Learn to predict each class against the other
classifier = OneVsRestClassifier(svm.SVC(kernel='linear',probability=True,
random_state=random_state))
y_score = classifier.fit(X_train,y_train).decision_function(X_test)
注：decision_function(X)：
Returns the distance of each sample from the decision boundary for each class.
計算每一個類別的ROC與AUC：
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
# 取出來的是各個類的測試值和預測值
fpr[i], tpr[i],_ = roc_curve(y_test[:, i],y_score[:,i])
roc_auc[i] = auc(fpr[i], tpr[i])
#Compute micro-average ROC curve and ROC area
#類總和的基礎上平均的ROC 和 AUC
fpr["micro"],tpr["micro"],_ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"],tpr["micro"])
繪圖：
plt.rcParams['savefig.dpi'] = 300 #圖片畫素
plt.rcParams['figure.dpi'] = 300 #解析度
plt.figure()
# linewidth
lw = 2
plt.plot(fpr[2], tpr[2], color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
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()
# Compute macro-average ROC curve and ROC area
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
label='micro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc["micro"]),
color='deeppink', linestyle=':', linewidth=4)
plt.plot(fpr["macro"], tpr["macro"],
label='macro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc["macro"]),
color='navy', linestyle=':', linewidth=4)
colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
plt.plot(fpr[i], tpr[i], color=color, lw=lw,
label='ROC curve of class {0} (area = {1:0.2f})'
''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()
