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二分类.py
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二分类.py
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import warnings
warnings.filterwarnings("ignore")
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import confusion_matrix
#二分类机器学习类
class ML():
def __init__(self,algorithm='randomforest',n_splits=10,scoring='accuracy',penalty='l1'):
'''
x是自变量，y是因变量
algorithm为使用的机器学习算法，默认随机森林
目前支持几种：
'randomforest':随机森林
'logistic':logistic回归
如果指定为logistic回归，还可通过penalty参数指定正则化方式，默认为l1正则化
'xgboost':xgboost
'SVM':支持向量机
n_splits是交叉验证的折数，默认为10，如果设置为0，代表使用留一法
scoring是模型选择的评估指标，默认为准确度
目前支持以下几种：
'accuracy':准确性
'roc_auc':ROC 曲线下方的面积,也被称为AUC
'average_precision':准确率-召回率曲线下方的面积
'''
self.algorithm = algorithm
np.save('n_splits.npy',n_splits)
np.save('scoring.npy',scoring)
np.save('penalty.npy',penalty)
self.x_test = np.load('x_test.npy')
self.y_test = np.load('y_test.npy')
def run(self):
if self.algorithm == 'logistic':
import logistic
self.model = logistic.run()
self.name = 'LogisticRegression'
elif self.algorithm == 'randomforest':
import randomforest
self.model = randomforest.run()
self.name = 'RandomForest'
elif self.algorithm == 'xgboost':
import XGboost
self.model = XGboost.run()
self.name = 'xgboost'
elif self.algorithm == 'SVM':
import SVM
self.model = SVM.run()
self.x_test_scaled = np.load('x_test_scaled.npy')
self.name = 'SVM'
else:
print('算法参数有误')
if self.algorithm == 'SVM':
self.y_pred_prob = self.model.decision_function(self.x_test_scaled)
self.y_pred = self.model.predict(self.x_test_scaled)
else:
self.y_pred_prob = self.model.predict_proba(self.x_test)[:,1]
self.y_pred = self.model.predict(self.x_test)
def result_analysis(self):
#对结果进行分析
#混淆矩阵
self.confusion_matrix = confusion_matrix(self.y_test,self.y_pred)
self.TP = TP = self.confusion_matrix[1,1] #真阳性
self.FN = FN = self.confusion_matrix[1,0] #假阴性
self.FP = FP = self.confusion_matrix[0,1] #假阳性
self.TN = TN = self.confusion_matrix[0,0] #真阴性
self.accuracy = (TP + TN) / (TP + TN + FP + FN)
print("模型的准确率(accuracy)为:",end='')
print(self.accuracy)
self.precision = TP / (TP + FP) #精确率
print("模型的精确率(precision)为:",end='')
print(self.precision)
self.recall = TP / (TP + FN) #召回率
print("模型的召回率(recall)为:",end='')
print(self.recall)
self.PPV = self.precision #阳性预测值
print("模型的阳性预测值(PPV)为:",end='')
print(self.PPV)
self.NPV = TN / (TN + FN) #阴性预测值
print("模型的阴性预测值(NPV)为:",end='')
print(self.NPV)
self.sensitivity = self.recall #灵敏度
print("模型的灵敏度(sensitivity)为:",end='')
print(self.sensitivity)
self.specificity = TN / (TN + FP) #特异度
print("模型的特异度(specificity)为:",end='')
print(self.specificity)
def plot_importance(self):
#画出重要性排序图
if self.algorithm == 'logistic':
import logistic
logistic.plot(self.model)
elif self.algorithm == 'randomforest':
import randomforest
randomforest.plot(self.model)
elif self.algorithm == 'xgboost':
import XGboost
XGboost.plot(self.model)
elif self.algorithm == 'SVM':
print('支持向量机算法不能画出重要性排序图')
def plot_roc(self):
#画出roc曲线
fpr,tpr,thresholds = roc_curve(self.y_test,self.y_pred_prob)
self.roc_auc = auc(fpr,tpr)
label = self.name+ '(auc=' + str(round(self.roc_auc,3)) + ')'
plt.plot(fpr,tpr,label=label)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.plot([0,1],[0,1],'r--',color='navy')
plt.title('Receiver operating characteristic example')
plt.legend(loc='best')
print("模型的AUROC为:",end='')
print(self.roc_auc)
def plot_PR(self):
#画出PR曲线
precision,recall,thresholds = precision_recall_curve(self.y_test,self.y_pred_prob)
self.PR_auc = auc(recall,precision)
label = self.name+ '(auc=' + str(round(self.PR_auc,3)) + ')'
plt.plot(recall,precision,label=label)
plt.xlabel('Recall Rate')
plt.ylabel('Precision Rate')
plt.plot([0,1],[0,1],'r--',color='navy')
plt.title('Precision-recall curve')
plt.legend(loc='best')
print("模型的AUPRC为:",end='')
print(self.PR_auc)
if __name__ == '__main__':
n_splits = 5
scoring = 'precision'
model1 = ML('logistic',n_splits=n_splits,scoring=scoring)
model1.run()
print("以下为logistic回归模型结果:")
model1.result_analysis()
model1.plot_importance()
plt.savefig('logistic_重要性')
plt.clf()
model2 = ML(n_splits=n_splits,scoring=scoring)
model2.run()
print("以下为随机森林模型结果:")
model2.result_analysis()
model2.plot_importance()
plt.savefig('随进森林_重要性')
plt.clf()
model3 = ML('SVM',n_splits=n_splits,scoring=scoring)
model3.run()
print("以下为SVM模型结果:")
model3.result_analysis()
model3.plot_importance()
model4 = ML('xgboost',n_splits=n_splits,scoring=scoring)
model4.run()
print("以下为xgboost模型结果:")
model4.result_analysis()
model4.plot_importance()
plt.savefig('xgboost_重要性')
plt.clf()
model1.plot_roc()
model2.plot_roc()
model3.plot_roc()
model4.plot_roc()
plt.savefig('AUROC')
plt.clf()
model1.plot_PR()
model2.plot_PR()
model3.plot_PR()
model4.plot_PR()
plt.savefig('AUPRC')
plt.clf()