import numpy as np
from sklearn import datasets

digits = datasets.load_digits()
X = digits.data
y = digits.target.copy()

y[digits.target==9] = 1
y[digits.target!=9] = 0    # 产生极度偏斜的数据

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

from sklearn.linear_model import LogisticRegression

log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)

准度度

log_reg.score(X_test, y_test)

输出：0.9755555555555555

混淆矩阵

y_log_predict = log_reg.predict(X_test)

def TN(y_true, y_predict):
    assert len(y_true) == len(y_predict)
    return np.sum((y_true == 0) & (y_predict==0))   # 注意这里是一个‘&’

TN(y_test, y_log_predict)   # 403

def FP(y_true, y_predict):
    assert len(y_true) == len(y_predict)
    return np.sum((y_true == 0) & (y_predict==1))

FP(y_test, y_log_predict)   # 2

def FN(y_true, y_predict):
    assert len(y_true) == len(y_predict)
    return np.sum((y_true == 1) & (y_predict==0))

FN(y_test, y_log_predict)   # 9

def TP(y_true, y_predict):
    assert len(y_true) == len(y_predict)
    return np.sum((y_true == 1) & (y_predict==1))

TP(y_test, y_log_predict)   # 36

def confusion_matrix(y_true, y_predict):
    return np.array([
        [TN(y_true, y_predict), FP(y_true, y_predict)],
        [FN(y_true, y_predict), TP(y_true, y_predict)]
    ])

confusion_matrix(y_test, y_log_predict)

输出结果：
array([[403, 2], [ 9, 36]])

精准率

def precision_score(y_true, y_predict):
    tp = TP(y_true, y_predict)
    fp = FP(y_true, y_predict)
    try:
        return tp / (tp + fp)
    except:   # 处理分母为0的情况
        return 0.0

precision_score(y_test, y_log_predict)

输出结果：0.9473684210526315

召回率

def recall_score(y_true, y_predict):
    tp = TP(y_true, y_predict)
    fn = FN(y_true, y_predict)

    try:
        return tp / (tp + fn)
    except:
        return 0.0

recall_score(y_test, y_log_predict)

输出结果：0.8

scikit-learn中的混淆矩阵、精准率、召回率

from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_log_predict)

from sklearn.metrics import precision_score
precision_score(y_test, y_log_predict)

from sklearn.metrics import recall_score
recall_score(y_test, y_log_predict)