机器学习pycm--史上最强多分类性能评估库
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📚文章目录
🍓1 情景引入
在普通分类问题上，我们一般通过sklearn.metrics库评估模型，比如使用混淆矩阵
from sklearn.metrics import confusion_matrix y_true = [2, 0, 2, 2, 0, 1] y_pred = [0, 1, 2, 2, 0, 2] cm=confusion_matrix(y_true, y_pred) print(cm)结果如下：
[[1 1 0] [0 0 1] [1 0 2]]
还可以结合热力图进行可视化
from matplotlib import pyplot as plt plt.figure(figsize=(8, 6)) sns.heatmap(cm, annot=True, cmap='Blues') plt.xlabel('Predicted labels') plt.ylabel('True labels') plt.show()结果如下：
在需要依据多个指标评价模型时，classification_report也是个不错的选择
from sklearn.metrics import classification_report y_true = [2, 0, 2, 2, 0, 1] y_pred = [0, 1, 2, 2, 0, 2] report=classification_report(y_true,y_pred) print(report)结果如下：
precision recall f1-score support 0 0.50 0.50 0.50 2 1 0.00 0.00 0.00 1 2 0.67 0.67 0.67 3 accuracy 0.50 6 macro avg 0.39 0.39 0.39 6 weighted avg 0.50 0.50 0.50 6
不过，当我们需要更多指标进行模型评估时，该怎么办呢？
我们通常会从sklearn.metrics导入我们需要的指标，再分别调用，进行分析
from sklearn.metrics import cohen_kappa_score,hamming_loss,jaccard_score,accuracy_score acc=accuracy_score(test_labels, pred_labels) # 1.0 kappa = cohen_kappa_score(test_labels, pred_labels) # 1.0 ham_distance = hamming_loss(test_labels, pred_labels) # 0.0 jaccrd_score = jaccard_score(test_labels, pred_labels,average='micro') # 1.0 print(f'acc is acc') print(f'kappa is kappa') print(f'ham_distance is ham_distance') print(f'jaccrd_score is jaccrd_score')这不禁让我思考，是否存在更方便的方法呢？🎈
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🍓2 pycm介绍
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of classifiers.
总结一下，就是说pycm是一个python库，适用于多分类模型的评估。
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🍓3 pycm安装
⚠️ PyCM 2.4 is the last version to support Python 2.7 & Python 3.4
⚠️ Plotting capability requires Matplotlib (>= 3.0.0) or Seaborn (>= 0.9.1)
Source code
- Download Version 3.3 or Latest Source
- Run
pip install -r requirements.txtorpip3 install -r requirements.txt(Need root access)- Run
python3 setup.py installorpython setup.py install(Need root access)PyPI
- Check Python Packaging User Guide
- Run
pip install pycm==3.3orpip3 install pycm==3.3(Need root access)Conda
- Check Conda Managing Package
- Update Conda using
conda update conda(Need root access)- Run
conda install -c sepandhaghighi pycm(Need root access)Easy install
- Run
easy_install --upgrade pycm(Need root access)
总结一下，pycm2.4需要python版本在2.4以上，且画图部分对Matplotlib和Seaborn的版本也有要求。推荐大家使用pip或conda安装，比较常用，遇到问题也容易解决。
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🍓4 pycm使用
🍎4.1 输入向量
直接输入真实的类向量和预测的类向量
from pycm import * y_true = [0,1,2,0,1,2,0,1,2] y_pred = [2,1,2,1,0,1,2,1,0] cm = ConfusionMatrix(actual_vector=y_true, predict_vector=y_pred) print(cm)输出结果分为3部分：
🍊4.1.1 混淆矩阵
Predict 0 1 2 Actual 0 0 1 2 1 1 2 0 2 1 1 1🍊4.1.2 总体指标
Overall Statistics : 95% CI (0.02535,0.64132) ACC Macro 0.55556 ARI -0.07143 AUNP 0.5 AUNU 0.5 Bennett S 0.0 CBA 0.27778 CSI -0.38889 Chi-Squared 3.5 Chi-Squared DF 4 Conditional Entropy 1.14052 Cramer V 0.44096 Cross Entropy 1.6416 F1 Macro 0.30159 F1 Micro 0.33333 FNR Macro 0.66667 FNR Micro 0.66667 FPR Macro 0.33333 FPR Micro 0.33333 Gwet AC1 0.00461 Hamming Loss 0.66667 Joint Entropy 2.72548 KL Divergence 0.05664 Kappa 0.0 Kappa 95% CI (-0.46198,0.46198) Kappa No Prevalence -0.33333 Kappa Standard Error 0.2357 Kappa Unbiased -0.00935 Lambda A 0.33333 Lambda B 0.2 Mutual Information 0.38998 NIR 0.33333 Overall ACC 0.33333 Overall CEN 0.73254 Overall J (0.6,0.2) Overall MCC 0.0 Overall MCEN 0.79544 Overall RACC 0.33333 Overall RACCU 0.33951 P-Value 0.62282 PPV Macro 0.27778 PPV Micro 0.33333 Pearson C 0.52915 Phi-Squared 0.38889 RCI 0.24605 RR 3.0 Reference Entropy 1.58496 Response Entropy 1.53049 SOA1(Landis & Koch) Slight SOA2(Fleiss) Poor SOA3(Altman) Poor SOA4(Cicchetti) Poor SOA5(Cramer) Relatively Strong SOA6(Matthews) Negligible Scott PI -0.00935 Standard Error 0.15713 TNR Macro 0.66667 TNR Micro 0.66667 TPR Macro 0.33333 TPR Micro 0.33333 Zero-one Loss 6🍊4.1.3 各类指标
Class Statistics : Classes 0 1 2 ACC(Accuracy) 0.44444 0.66667 0.55556 AGF(Adjusted F-score) 0.0 0.69338 0.4714 AGM(Adjusted geometric mean) 0 0.66667 0.54951 AM(Difference between automatic and manual classification) -1 1 0 AUC(Area under the ROC curve) 0.33333 0.66667 0.5 AUCI(AUC value interpretation) Poor Fair Poor AUPR(Area under the PR curve) 0.0 0.58333 0.33333 BCD(Bray-Curtis dissimilarity) 0.05556 0.05556 0.0 BM(Informedness or bookmaker informedness) -0.33333 0.33333 0.0 CEN(Confusion entropy) 0.96096 0.60158 0.69499 DOR(Diagnostic odds ratio) 0.0 4.0 1.0 DP(Discriminant power) None 0.33193 -0.0 DPI(Discriminant power interpretation) None Poor Poor ERR(Error rate) 0.55556 0.33333 0.44444 F0.5(F0.5 score) 0.0 0.52632 0.33333 F1(F1 score - harmonic mean of precision and sensitivity) 0.0 0.57143 0.33333 F2(F2 score) 0.0 0.625 0.33333 FDR(False discovery rate) 1.0 0.5 0.66667 FN(False negative/miss/type 2 error) 3 1 2 FNR(Miss rate or false negative rate) 1.0 0.33333 0.66667 FOR(False omission rate) 0.42857 0.2 0.33333 FP(False positive/type 1 error/false alarm) 2 2 2 FPR(Fall-out or false positive rate) 0.33333 0.33333 0.33333 G(G-measure geometric mean of precision and sensitivity) 0.0 0.57735 0.33333 GI(Gini index) -0.33333 0.33333 0.0 GM(G-mean geometric mean of specificity and sensitivity) 0.0 0.66667 0.4714 IBA(Index of balanced accuracy) 0.0 0.44444 0.14815 ICSI(Individual classification success index) -1.0 0.16667 -0.33333 IS(Information score) None 0.58496 0.0 J(Jaccard index) 0.0 0.4 0.2 LS(Lift score) 0.0 1.5 1.0 MCC(Matthews correlation coefficient) -0.37796 0.31623 0.0 MCCI(Matthews correlation coefficient interpretation) Negligible Weak Negligible MCEN(Modified confusion entropy) 0.96096 0.69658 0.72877 MK(Markedness) -0.42857 0.3 0.0 N(Condition negative) 6 6 6 NLR(Negative likelihood ratio) 1.5 0.5 1.0 NLRI(Negative likelihood ratio interpretation) Negligible Negligible Negligible NPV(Negative predictive value) 0.57143 0.8 0.66667 OC(Overlap coefficient) 0.0 0.66667 0.33333 OOC(Otsuka-Ochiai coefficient) 0.0 0.57735 0.33333 OP(Optimized precision) -0.55556 0.66667 0.22222 P(Condition positive or support) 3 3 3 PLR(Positive likelihood ratio) 0.0 2.0 1.0 PLRI(Positive likelihood ratio interpretation) Negligible Poor Negligible POP(Population) 9 9 9 PPV(Precision or positive predictive value) 0.0 0.5 0.33333 PRE(Prevalence) 0.33333 0.33333 0.33333 Q(Yule Q - coefficient of colligation) -1.0 0.6 0.0 QI(Yule Q interpretation) Negligible Moderate Negligible RACC(Random accuracy) 0.07407 0.14815 0.11111 RACCU(Random accuracy unbiased) 0.07716 0.15123 0.11111 TN(True negative/correct rejection) 4 4 4 TNR(Specificity or true negative rate) 0.66667 0.66667 0.66667 TON(Test outcome negative) 7 5 6 TOP(Test outcome positive) 2 4 3 TP(True positive/hit) 0 2 1 TPR(Sensitivity, recall, hit rate, or true positive rate) 0.0 0.66667 0.33333 Y(Youden index) -0.33333 0.33333 0.0 dInd(Distance index) 1.05409 0.4714 0.74536 sInd(Similarity index) 0.25464 0.66667 0.47295
可以看到，大部分总体指标比如F1 score、Kappa等都被包含在内，各类指标如基尼指数、AUC也在内。
🍎4.2 输入矩阵
from pycm import * cm = ConfusionMatrix(matrix="Class1": "Class1": 1, "Class2":2, "Class2": "Class1": 3, "Class2": 4) print(cm)结果如下：
Predict Class1 Class2 Actual Class1 1 2 Class2 3 4其余指标与4.1相同。
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🍓5 进阶用法
🍎5.1 获取各类指标
- 通过cm.print_matrix()打印混淆矩阵
- 通过cm.print_normalized_matrix()打印归一化后的混淆矩阵
- 通过cm.plot()作热力图，可以通过修改cmap=plt.cm.Greens参数自定义颜色
- 通过cm.overall_stat,cm.class_stat分别获取总体指标与各类指标的字典
- 通过cm.overall_stat['Kappa']的形式获取某一个具体指标
🍎5.2 比较器
这里给出一个官方的用例：
>>> cm2 = ConfusionMatrix(matrix=0:0:2,1:50,2:6,1:0:5,1:50,2:3,2:0:1,1:7,2:50) >>> cm3 = ConfusionMatrix(matrix=0:0:50,1:2,2:6,1:0:50,1:5,2:3,2:0:1,1:55,2:2) >>> cp = Compare("cm2":cm2,"cm3":cm3) >>> print(cp) Best : cm2 Rank Name Class-Score Overall-Score 1 cm2 9.05 2.55 2 cm3 6.05 1.98333 >>> cp.best pycm.ConfusionMatrix(classes: [0, 1, 2]) >>> cp.sorted ['cm2', 'cm3'] >>> cp.best_name 'cm2'
🍎 5.3 配合pyQt搭建GUI
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🍓6 结语
以后再遇到分类问题，就不用为寻找评估指标发愁啦，一键使用pycm，直接给出大量指标，还可以通过比较器选出最优预测结果，高效！
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