python + sklearn ︱分类效果评估——acc、recall、F1、ROC、回归、距离

电玩女神 2022-04-24 13:10 266阅读 0赞

# 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。

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’)

## 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

## 3、F1 ##

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

## 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predict 竖为true label

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70]

## 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         2
        class 1       0.00      0.00      0.00         1
        class 2       1.00      1.00      1.00         2
    
    avg / total       0.67      0.80      0.72         5

包含：precision/recall/fi-score/均值/分类个数

## 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)

# 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)][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()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70 1]

# 三、距离 #

## 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

## 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

# 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)

## 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)

## 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)

## 4、中值绝对误差（Median absolute error） ##

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)

## 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)

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70]: /images/20220215/64668abd950b49ceb25e96f8cd455db3.png
[Receiver Operating Characteristic _ROC]: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM4NDEwNDI4_size_16_color_FFFFFF_t_70 1]: /images/20220215/1c3d32f5736843c29008e873e2f9ee18.png