Implement Confusion Matrix Precision Recall
May 18, 2019
实现混淆矩阵，精准率和召回率
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
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)
log_reg.score(X_test, y_test)
0.9755555555555555
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))
def FP(y_true, y_predict):
assert len(y_true) == len(y_predict)
return np.sum((y_true == 0) & (y_predict == 1))
def FN(y_true, y_predict):
assert len(y_true) == len(y_predict)
return np.sum((y_true == 1) & (y_predict == 0))
def TP(y_true, y_predict):
assert len(y_true) == len(y_predict)
return np.sum((y_true == 1) & (y_predict == 1))
TN(y_test, y_log_predict)
403
FP(y_test, y_log_predict)
2
FN(y_test, y_log_predict)
9
TP(y_test, y_log_predict)
36
def confusion_matrix(y_true, y_predict):
return np.array([
[TN(y_test, y_predict), FP(y_test, y_predict)],
[FN(y_test, y_predict), TP(y_test, y_predict)]
])
confusion_matrix(y_test, y_log_predict)
array([[403, 2], [ 9, 36]])
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:
return 0.0
precision_score(y_test, y_log_predict)
0.9473684210526315
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)
0.8
scikit-learn 中的实现
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
confusion_matrix(y_test, y_log_predict)
array([[403, 2], [ 9, 36]], dtype=int64)
precision_score(y_test, y_log_predict)
0.9473684210526315
recall_score(y_test, y_log_predict)
0.8