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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import recall_score
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import MinMaxScaler
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier as RFC
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression as LR
import xgboost as xgb
from sklearn.neural_network import MLPClassifier as DNN
import keras
#from keras.models import Sequential # intitialize the ANN
#Stacking
from mlxtend.classifier import StackingClassifier
#from tensorflow.keras
#from keras.layers import Dense # create layers
from keras.layers import Dense
from keras.models import Sequential
data=pd.read_csv('your data path')
Xtrain,Xtest,Ytrain,Ytest=train_test_split(X,Y,test_size=0.3,random_state=0)
#decision tree
test = []
for i in range(10):
clf = tree.DecisionTreeClassifier(max_depth=i+1
,criterion="entropy"
,random_state=0
,splitter="random"
)
clf = clf.fit(Xtrain, Ytrain)
score = clf.score(Xtest, Ytest)
test.append(score)
plt.plot(range(1,11),test,color="red",label="max_depth")
plt.legend()
plt.show()
treeclf = tree.DecisionTreeClassifier(max_depth=8
,criterion="entropy"
,random_state=0
,splitter="random"
).fit(Xtrain,Ytrain)
tpredict=treeclf.predict_proba(Xtest)
#Random forest
scorel=[]
Y=np.array(Y).ravel()#1D标签，随机森林
for i in range(50,150):
rfc=RFC(n_estimators=i+1,n_jobs=-1,random_state=0)
score=cross_val_score(rfc,X,Y,cv=5).mean()
scorel.append(score)
print(max(scorel),(scorel.index(max(scorel)))+51)
plt.plot(range(51,151),scorel)
plt.show()
param_grid={'max_depth':np.arange(1,20,1)}
rfc=RFC(n_estimators=58,n_jobs=-1,random_state=0)
GS=GridSearchCV(rfc,param_grid,cv=5)
GS.fit(X,Y)
GS.best_score_
GS.best_params_
Ytrain=np.array(Ytrain).ravel()
rfc=RFC(n_estimators=58,max_depth=4
,class_weight='balanced'
,random_state=0)
rfc=rfc.fit(Xtrain,Ytrain)
rfcpredict_2=rfc.predict_proba(Xtest)
# SVM
Kernel=['linear','poly','rbf','sigmoid']
Ytrain=np.array(Ytrain).ravel()#1D
for kernel in Kernel:
clf=SVC(kernel=kernel,gamma='auto'
,degree=1,cache_size=5000).fit(Xtrain,Ytrain)
score=clf.score(Xtest,Ytest)
recall=recall_score(Ytest,clf.predict(Xtest))
auc=roc_auc_score(Ytest,clf.decision_function(Xtest))
print("%s:testing accuracy %f, recall is %f', auc is %f" %
(kernel,score,recall,auc))
gamma_range=np.linspace(0.0001,0.005,80)
score=[]
recallscore=[]
aucscore=[]
for gamma in gamma_range:
clf=SVC(kernel='rbf',gamma=gamma
,cache_size=5000,C=1).fit(Xtrain,Ytrain)
result=clf.predict(Xtest)
score_1=clf.score(Xtest,Ytest)
recall_1=recall_score(Ytest,result)
auc_1=roc_auc_score(Ytest,clf.decision_function(Xtest))
score.append(score_1)
recallscore.append(recall_1)
aucscore.append(auc_1)
recall=np.array(recall)
plt.figure()
plt.plot(gamma_range,score,c='red',label='score')
plt.plot(gamma_range,recallscore,c='b',label='recall')
plt.plot(gamma_range,aucscore,c='green',label='auc')
plt.legend()
plt.show()
svmclf=SVC(kernel='rbf',gamma=0.003
,C=1,cache_size=5000
#,class_weight='balanced'
,probability=True).fit(Xtrain,Ytrain)
spredict=svmclf.predict_proba(Xtest)
# logsitic regression
scorel1=[]
scorel2=[]
Ytrain=np.array(Ytrain).ravel()
C=np.arange(0.001,0.5,0.05)
for i in np.arange(0.001,0.5,0.05):
lrl1=LR(penalty='l1',solver='liblinear',C=i,max_iter=1000)
lrl2=LR(penalty='l2',solver='liblinear',C=i,max_iter=1000)
scoreL1=cross_val_score(lrl1,Xtrain,Ytrain,cv=5).mean()
scoreL2=cross_val_score(lrl2,Xtrain,Ytrain,cv=5).mean()
scorel1.append(scoreL1)
scorel2.append(scoreL2)
plt.plot(np.arange(0,0.5,0.05),scorel1,color='red',label='L1')
plt.plot(np.arange(0,0.5,0.05),scorel2,color='black',label='L2')
plt.legend()
plt.show()
print(max(scorel1),C[scorel1.index(max(scorel1))])
print(max(scorel2),C[scorel2.index(max(scorel2))])
from sklearn.linear_model import LogisticRegression as LR
score=[]
C=np.arange(100,200)
for i in np.arange(100,200):#新版最低100
lrl2=LR(penalty='l2',solver='liblinear',C=0.1,max_iter=i)
once=cross_val_score(lrl2,X,Y.values.ravel(),cv=5).mean()
score.append(once)
plt.plot(np.arange(0,100),score)
plt.show()
print(max(score),C[score.index(max(score))])
Ytrain=np.array(Ytrain).ravel()
lrl2=LR(penalty='l2',solver='liblinear',C=0.1,max_iter=10).fit(Xtrain,Ytrain)
lpredict=lrl2.predict_proba(Xtest)
# XGBOOST
dfull=xgb.DMatrix(X,Y)
param1={'silent':True
,'subsample':1
,'obj':'binary:log_loss'
,'max_depth':3
,'eta':0.1
,'gamma':0
,'lambda':1
,'alpha':0
,'colsample_bytree':1
,'colsample_bylevel':1
,'colsample_bynode':1
,'nfold':5}
#默认
num_round=200
cvresult1=xgb.cv(param1,dfull,num_round)
fig,ax=plt.subplots(1,figsize=(15,8))
ax.set_ylim(top=0.6)
ax.plot(range(1,201),cvresult1.iloc[:,0],c='red'
,label='train,original')
ax.plot(range(1,201),cvresult1.iloc[:,2],c='orange'
,label='test,original')
param2={'silent':True
,'subsample':1
,'obj':'binary:log_loss'
,'max_depth':3
,'eta':0.1
,'gamma':0
,'lambda':1
,'alpha':0
,'colsample_bytree':1
,'colsample_bylevel':1
,'colsample_bynode':1
,'nfold':5}
param3={'silent':True
,'subsample':1
,'obj':'binary:log_loss'
,'max_depth':3
,'eta':0.1
,'gamma':0
,'lambda':1
,'alpha':0
,'colsample_bytree':1
,'colsample_bylevel':1
,'colsample_bynode':1
,'nfold':5}
#现在正在调整的参数的影响
cvresult2=xgb.cv(param3,dfull,num_round)
cvresult3=xgb.cv(param1,dfull,num_round)
ax.plot(range(1,201),cvresult2.iloc[:,0],c='green'
,label='train,last')
ax.plot(range(1,201),cvresult2.iloc[:,2],c='blue'
,label='test,last')
ax.plot(range(1,201),cvresult3.iloc[:,0],c='gray'
,label='train,this')
ax.plot(range(1,201),cvresult3.iloc[:,2],c='pink'
,label='test,this')
ax.legend(fontsize='xx-large')
plt.show()
predictdata_1=xgb.DMatrix(predictdata)
xgboostpredict=xgbc.predict(predictdata_1)
xgboostpredict=pd.DataFrame(xgboostpredict)
#ANN
# Initialising the NN
model = Sequential()
from keras import layers
# layers
model.add(Dense(units = 50, kernel_initializer = 'uniform'
, activation = 'relu', input_dim = 5))
model.add(layers.Dropout(0.5))
model.add(Dense(units = 50, kernel_initializer = 'uniform'
, activation = 'relu'))
model.add(layers.Dropout(0.5))
model.add(Dense(units = 25, kernel_initializer = 'uniform'
, activation = 'relu'))
model.add(layers.Dropout(0.5))
model.add(Dense(units = 1, kernel_initializer = 'uniform'
, activation = 'sigmoid'))
# Compiling the ANN
model.compile(optimizer = 'adam'
, loss = 'binary_crossentropy'
, metrics = ['accuracy'])
# Train the ANN
model.fit(Xtrain,Ytrain,batch_size = 60, epochs = 200)
#stacking
from mlxtend.classifier import StackingClassifier
from sklearn.ensemble import RandomForestClassifier as RFC
from xgboost import XGBClassifier as xgb
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression as LR
from sklearn.model_selection import cross_val_score
clf1 = RFC(n_estimators=58,n_jobs=-1,random_state=0)
clf2 = xgb(n_estimators=200,learning_rate=0.1,max_depth=3,random_state=0)
clf3 = SVC(kernel='rbf',gamma=0.003,C=1,cache_size=5000,probability=True,random_state=0)
clf4=LR(penalty='l2',solver='liblinear',C=0.1,max_iter=10,random_state=0)
sclf = StackingClassifier(classifiers=[clf1
,clf2
,clf3]
,meta_classifier=clf2)
Y=np.array(Y).ravel()
cross_val_score(sclf, X, Y, cv=5).mean()
sclfpredict_2=sclf.fit(Xtrain,Ytrain).predict_proba(Xtest)
#ROC
from sklearn.metrics import roc_curve
FPR_2=[]
recall_2=[]
thresholds_2=[]
clfdf=[tpredict#决策树
,rfcpredict_2[:,1]#随机森林
,spredict[:,1]#SVM
,lpredict[:,1]#逻辑回归
,sclfpredict_4[:,1]#stacking
]
for i in range(5):
FPR_1,recall_1,thresholds_1=roc_curve(Ytest,clfdf[i],pos_label=1)
FPR_2.append(FPR_1)
recall_2.append(recall_1)
thresholds_2.append(thresholds_1)
color=('red','black','gray','green','blue'
#,'orange','pink'
)
for i in range(5):
figsize=(10,10)
plt.plot(FPR_2[i],recall_2[i],color[i])
plt.plot([0,1],[0,1],linestyle='--')
plt.xlim(-0.1,1.1)
plt.ylim(-0.1,1.1)