sklearn 各种分类 简单实现

 

import numpy as np
import pandas 
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso,LassoCV,LassoLarsCV
from sklearn.svm import SVC
from sklearn.svm import LinearSVC  #支持向量机
from sklearn.naive_bayes import MultinomialNB #朴素也贝斯
from sklearn.tree import DecisionTreeClassifier #决策树
from sklearn.ensemble import RandomForestClassifier #随机森铃
from sklearn.ensemble import GradientBoostingClassifier #GBDT
from xgboost import XGBClassifier #xgboost

def modelReturn(model,name):
    
    model =  RandomForestClassifier();
    model.fit(x_train,y_train);
    predict = model.predict(x_test);
    trueNum = 0;
    for i in range(len(y_test)):
        if(y_test[i]==predict[i]):
            trueNum+=1;
    print(name,":",trueNum/len(y_test));

    

dataframe = pandas.read_csv("export.csv");
#获取 CVS中的值
dataset = dataframe.values;
#本身数据有53列  下标我0开始 取52列  53列是标签
#X = dataset[:,0:53].astype(np.float);

#Y =dataset[:,53];
X = dataset[:,3:6].astype(np.float);

Y =dataset[:,53];
x_train,x_test,y_train,y_test = train_test_split(X,Y);

#xgboost 46 50
model =XGBClassifier();
modelReturn(model,"xgboost")
#GBDT 40 -48
model =GradientBoostingClassifier();
modelReturn(model,"GBDT")

#随机森林  44-46
model =RandomForestClassifier();
modelReturn(model,"随机森林")

#决策树  36-39
model =  DecisionTreeClassifier();
modelReturn(model,"决策树")

#朴素也贝斯 44-51
model =  MultinomialNB();
modelReturn(model,"朴素也贝斯")

#支持向量机  45-48
model = LinearSVC();
modelReturn(model,"支持向量机")

#SVM  48-52
model = SVC()
modelReturn(model,"SVM")

#laoss   68-73%
model = Lasso(alpha=0.005);  #调节aplha 可以实现对拟合的。的程度
modelReturn(model,"laoss")

"""
model.fit(x_train,y_train);

predict =model.predict(x_test);

trueNum =0;

print(predict)

for i  in range(len(y_test)):
    if ((abs(y_test[i])-abs(predict[i])< 0.5)):
        trueNum += 1;
        
        
print(trueNum/len(y_test));
"""
"""
pca = PCA(n_components=27);
xTrainPca = pca.fit_transform(x_train);
xTestPca = pca.fit_transform(x_test);

log =LogisticRegression();
log.fit(xTrainPca,y_train);

print("准确率:",log.score(xTestPca,y_test));
"""

"""
#降到10个维度
pca = PCA(n_components=50);

xTrainPca = pca.fit_transform(x_train);
xTestPca = pca.fit_transform(x_test);

knn = KNeighborsClassifier(n_neighbors=11);
knn.fit(xTrainPca,y_train);

print(knn.score(xTestPca,y_test))
"""