sklearn评估模型的方法

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一、acc、recall、F1、混淆矩阵、分类综合报告

1、准确率

第一种方式：accuracy_score

# 准确率import numpy as np
from sklearn.metrics import accuracy_score
y_pred = [0, 2, 1, 3,9,9,8,5,8]
y_true = [0, 1, 2, 3,2,6,3,5,9]

accuracy_score(y_true, y_pred)
Out[127]: 0.33333333333333331

accuracy_score(y_true, y_pred, normalize=False)  # 类似海明距离，每个类别求准确后，再求微平均
Out[128]: 3

第二种方式：metrics

宏平均比微平均更合理，但也不是说微平均一无是处，具体使用哪种评测机制，还是要取决于数据集中样本分布

宏平均（Macro-averaging），是先对每一个类统计指标值，然后在对所有类求算术平均值。
微平均（Micro-averaging），是对数据集中的每一个实例不分类别进行统计建立全局混淆矩阵，然后计算相应指标。（来源：谈谈评价指标中的宏平均和微平均）

from sklearn import metrics
metrics.precision_score(y_true, y_pred, average='micro')  # 微平均，精确率
Out[130]: 0.33333333333333331

metrics.precision_score(y_true, y_pred, average='macro')  # 宏平均，精确率
Out[131]: 0.375

metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro')  # 指定特定分类标签的精确率
Out[133]: 0.5

其中average参数有五种：(None, ‘micro’, ‘macro’, ‘weighted’, ‘samples’)
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2、召回率

metrics.recall_score(y_true, y_pred, average='micro')
Out[134]: 0.33333333333333331

metrics.recall_score(y_true, y_pred, average='macro')
Out[135]: 0.3125

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3、F1

metrics.f1_score(y_true, y_pred, average='weighted')
Out[136]: 0.37037037037037035

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4、混淆矩阵

# 混淆矩阵
from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)

Out[137]:
array([[1, 0, 0, ..., 0, 0, 0],
       [0, 0, 1, ..., 0, 0, 0],
       [0, 1, 0, ..., 0, 0, 1],
       ...,
       [0, 0, 0, ..., 0, 0, 1],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 1, 0]])

横为true label 竖为predict

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5、 分类报告

# 分类报告：precision/recall/fi-score/均值/分类个数from sklearn.metrics import classification_report
 y_true = [0, 1, 2, 2, 0]
 y_pred = [0, 0, 2, 2, 0]
 target_names = ['class 0', 'class 1', 'class 2']
 print(classification_report(y_true, y_pred, target_names=target_names))

其中的结果：

             precision    recall  f1-score   support

    class 0       0.67      1.00      0.80         2class 1       0.00      0.00      0.00         1class 2       1.00      1.00      1.00         2

avg / total       0.670.800.725

包含：precision/recall/fi-score/均值/分类个数
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6、 kappa score

kappa score是一个介于(-1, 1)之间的数. score>0.8意味着好的分类；0或更低意味着不好（实际是随机标签）

 from sklearn.metrics import cohen_kappa_score
 y_true = [2, 0, 2, 2, 0, 1]
 y_pred = [0, 0, 2, 2, 0, 2]
 cohen_kappa_score(y_true, y_pred)

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二、ROC

1、计算ROC值

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)

2、ROC曲线

 y = np.array([1, 1, 2, 2])
 scores = np.array([0.1, 0.4, 0.35, 0.8])
 fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)

来看一个官网例子，贴部分代码，全部的code见：Receiver Operating Characteristic (ROC)

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

# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 画图
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()

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三、距离

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1、海明距离

from sklearn.metrics import hamming_loss
 y_pred = [1, 2, 3, 4]
 y_true = [2, 2, 3, 4]
 hamming_loss(y_true, y_pred)
0.25

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2、Jaccard距离

import numpy as np
 from sklearn.metrics import jaccard_similarity_score
 y_pred = [0, 2, 1, 3,4]
 y_true = [0, 1, 2, 3,4]
 jaccard_similarity_score(y_true, y_pred)
0.5
 jaccard_similarity_score(y_true, y_pred, normalize=False)
2

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四、回归

1、 可释方差值（Explained variance score）

 from sklearn.metrics import explained_variance_score
y_true = [3, -0.5, 2, 7]
 y_pred = [2.5, 0.0, 2, 8]
 explained_variance_score(y_true, y_pred)  

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2、 平均绝对误差（Mean absolute error）

from sklearn.metrics import mean_absolute_error
 y_true = [3, -0.5, 2, 7]
 y_pred = [2.5, 0.0, 2, 8]
 mean_absolute_error(y_true, y_pred)

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3、 均方误差（Mean squared error）

 from sklearn.metrics import mean_squared_error
 y_true = [3, -0.5, 2, 7]
 y_pred = [2.5, 0.0, 2, 8]
 mean_squared_error(y_true, y_pred)

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 from sklearn.metrics import median_absolute_error
 y_true = [3, -0.5, 2, 7]
 y_pred = [2.5, 0.0, 2, 8]
 median_absolute_error(y_true, y_pred)

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5、 R方值，确定系数

 from sklearn.metrics import r2_score
 y_true = [3, -0.5, 2, 7]
 y_pred = [2.5, 0.0, 2, 8]
 r2_score(y_true, y_pred)  

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