人工智能大作业.zip

import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap #from sklearn.model_selection import train_test_split #废弃！！ from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler #from sklearn.neural_network import BernoulliRBM from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier from sklearn.naive_bayes import GaussianNB from sklearn import metrics from sklearn import linear_model from sklearn.neural_network import MLPClassifier from sklearn.metrics import confusion_matrix from sklearn.decomposition import PCA from imblearn.over_sampling import RandomOverSampler from imblearn.under_sampling import ClusterCentroids,RandomUnderSampler from imblearn.over_sampling import SMOTE, ADASYN from collections import Counter import math h=0.2 # 读取训练数据 def readdata(): fake = pd.read_csv('fake.csv') real = pd.read_csv('real.csv') ALL=fake.append(real) all=ALL.drop(['Unnamed: 0'],axis=1) X=np.array(ALL) #X = StandardScaler().fit_transform(X) y=X[:,24] #y=np.append(np.zeros(fake.shape[0]),np.ones(real.shape[0])) X=X[:,1:-1] return X,y def allmodel(): classifiers = [ linear_model.LogisticRegression(C=1e5),#1 KNeighborsClassifier(5),#2 SVC(kernel="linear", C=0.025),#3 SVC(gamma='auto', C=1),#4 DecisionTreeClassifier(max_depth=5),#5 RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),#6 AdaBoostClassifier(),#7 GaussianNB(),#8 MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1),#9 ] names = ['LogisticRegression',#1 "Nearest Neighbors", #2 "Linear SVM", #3 "RBF SVM",#4 "Decision Tree",#5 "Random Forest", #6 "AdaBoost",#7 "Naive Bayes",#8 'MLPClassifier',#9 ] index=['origional', "overSampler", 'underSampler', 'smotesampler', 'adasynsampler' ] return classifiers,names,index ######################## #print('%-15s %-15s %-15s'%('name','score','roc_auc','wrong')) def runmodel(input_x,input_y,index_name): X_train, X_test, y_train, y_test = train_test_split(input_x,input_y, test_size=.5, random_state=1) print("Method ACC AUC RECALL") for name, clf in zip(names, classifiers): # ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) #confusion=confusion_matrix(y_test, clf.predict(X_test)) # re=metrics.recall_score(y_test, clf.predict(X_test), average=None)[0] #metrics.f1_score(y_test, clf.predict(X_test)) fpr, tpr, thresholds = metrics.roc_curve(y_test, clf.predict(X_test)) roc_auc = metrics.auc(fpr, tpr) print('%-20s, %f, %f, %f '%(name,score,roc_auc,re)) pre_score[name][index_name]=np.array([score,roc_auc,re]) # print('%-15s %-15s %-15s %-15s '%(name,score,roc_auc,sum(abs(clf.predict(X_test)-y_test)))) #,confusion) #pred = #print(metrics.classification_report(y_test, clf.predict(X_test))) ####################### def runmodel_tra(input_x,input_y,index_name): X_train, X_test, y_train, y_test = train_test_split(input_x,input_y, test_size=.5, random_state=1) X_test,y_test=X_train,y_train print("Method ACC AUC RECALL") for name, clf in zip(names, classifiers): # ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) #confusion=confusion_matrix(y_test, clf.predict(X_test)) # re=metrics.recall_score(y_test, clf.predict(X_test), average=None)[0] #metrics.f1_score(y_test, clf.predict(X_test)) fpr, tpr, thresholds = metrics.roc_curve(y_test, clf.predict(X_test)) roc_auc = metrics.auc(fpr, tpr) print('%-20s, %f, %f, %f '%(name,score,roc_auc,re)) tra_score[name][index_name]=np.array([score,roc_auc,re]) ##################### def plot2(X,y,title): pca = PCA(n_components=2) newData=pca.fit_transform(X) cValue=[] for i in range(y.size): if y[i]==0: cValue.append('r') else: cValue.append('b') plt.scatter(newData[:,0], newData[:,1] , c=cValue, marker='o') #cmap=plt.cm.Paired title=title+str(sorted(Counter(y.astype(int)).items())) plt.title(title) plt.show() ################## def overSampler(X,y): ros = RandomOverSampler(random_state=0) X_resampled, y_resampled = ros.fit_sample(X, y) return X_resampled,y_resampled ################## def underSampler(X,y): cc = RandomUnderSampler(random_state=0) X_resampled, y_resampled = cc.fit_sample(X, y) return X_resampled,y_resampled ###################### def smotesampler(X,y): X_resampled_smote, y_resampled_smote = SMOTE().fit_sample(X, y) return X_resampled_smote, y_resampled_smote def adasynsampler(X,y): X_resampled_adasyn, y_resampled_adasyn = ADASYN().fit_sample(X, y) return X_resampled_adasyn, y_resampled_adasyn ####################### #sorted(Counter(y).items()) if __name__ == '__main__': classifiers,names,index=allmodel() X,y=readdata() X_resampled_over,y_resampled_over=overSampler(X,y) X_resampled_under,y_resampled_under=underSampler(X,y) X_resampled_smote, y_resampled_smote=smotesampler(X,y) X_resampled_adasyn, y_resampled_adasyn=adasynsampler(X,y) plot2(X,y,"All DATA(red=fake,blue=real)") plot2(X_resampled_over,y_resampled_over,"overSampler(red=fake,blue=real)") plot2(X_resampled_under,y_resampled_under,"underSampler(red=fake,blue=real)") plot2(X_resampled_smote, y_resampled_smote,"smote(red=fake,blue=real)") plot2(X_resampled_adasyn, y_resampled_adasyn,"adasyn(red=fake,blue=real)") pre_score=pd.DataFrame(index=index,columns=names) tra_score=pd.DataFrame(index=index,columns=names) runmodel(X,y,index[0]) runmodel(X_resampled_over,y_resampled_over,index[1]) runmodel(X_resampled_under,y_resampled_under,index[2]) runmodel(X_resampled_smote, y_resampled_smote,index[3]) runmodel(X_resampled_adasyn, y_resampled_adasyn,index[4]) runmodel_tra(X,y,index[0]) runmodel_tra(X_resampled_over,y_resampled_over,index[1]) runmodel_tra(X_resampled_under,y_resampled_under,index[2]) runmodel_tra(X_resampled_smote, y_resampled_smote,index[3]) runmodel_tra(X_resampled_adasyn, y_resampled_adasyn,index[4]) for name, clf in zip(names, classifiers): print('\n%-20s, %-15s, %s, %s '%(name,'pre_auc','tra_auc','alpha')) for index_name in index: pre_auc=pre_score[name][index_name][1] tra_auc=tra_score[name][index_name][1] myscore=-math.log(pre_auc/tra_auc) print('%-20s, %-15s, %f, %f, %f '%(name,index_name,pre_auc,tra_auc,myscore))