决策树_线性分类

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn import preprocessing
from sklearn import tree
from sklearn.feature_extraction  import DictVectorizer
import csv
from sklearn import preprocessing
from numpy import genfromtxt
from sklearn.metrics import *

def draw(x_data,y_data,model,plot_=0):
    if plot_ == 0 :
        x_min,x_max=x_data[:,0].min()-1,x_data[:,0].max()+1
        y_min,y_max=x_data[:,1].min()-1,x_data[:,1].max()+1
        xx,yy = np.meshgrid(np.arange(x_min,x_max,0.02),np.arange(y_min,y_max,0.02))

        z = model.predict(np.c_[xx.ravel(),yy.ravel()])
        z = z.reshape(xx.shape)

        cs = plt.contourf(xx,yy,z)
        plt.scatter(x_data[:,0],x_data[:,1],c=y_data)
        plt.show()
        predictions = model.predict(x_data)
        return (classification_report(predictions,y_data))

def graphviz(model):
    import graphviz
    dot_data = tree.export_graphviz(model,out_file=None,feature_names = ["x","y"],class_names=["l1","l2"],filled=True,rounded=True,special_characters=True)
    graph = graphviz.Source(dot_data)
    graph.render("new")
      
def main():
    plot_=0
    data = genfromtxt("LR-testSet.csv",delimiter=",")
    x_data = data[:,:-1]
    y_data = data[:,-1]
    plt.scatter(x_data[:,0],x_data[:,1],c=y_data)
    plt.show()
    model = tree.DecisionTreeClassifier()#基尼系数
    model.fit(x_data,y_data)
    graphviz(model)
    result=draw(x_data,y_data,model)
    print ("result:",result)
    print ("end".center(10,"-"))
     

main()

数据集