python 一个决策树分析的案例

女爷i 2022-12-25 03:57 150阅读 0赞

#23 pythoh descison tree
    from sklearn.feature_extraction import DictVectorizer
    import csv
    import pandas as pd
    from sklearn import preprocessing
    from sklearn import tree
    from sklearn.externals.six import StringIO
    
    data1=pd.read_csv('D://allelectronics.csv')
    data1
    data1.shape[0]
    column_list=data1.columns.tolist()
    column_list=column_list[0].split(';')
    column_list
    data2=data1.iloc[:,0].str.split(';',expand=True)
    data2.columns=column_list
    data2
    
    
    featrue_list=[]
    label_list=[]
    
    label_list=data2['class:buys_computer']
    label_list
    len(data2)
    t=0
    while t<= len(data2):
        rowdict={}
        for i in range(1,len(data2.columns)-1):
            print(i)
            rowdict[data2.columns[i]]=data2.iloc[t,i]
        featrue_list.append(rowdict)
        t+=1
    featrue_list
    
    vec=DictVectorizer()
    dummy_x=vec.fit_transform(featrue_list).toarray()
    
    print('dummyx'+str(dummy_x))
    print(vec.get_feature_names())
    
    lb=preprocessing.LabelBinarizer()
    dumm_y=lb.fit_transform(label_list)
    dumm_y
    
    clf=tree.DecisionTreeClassifier(criterion='entropy')
    clf.fit(dummy_x,dumm_y)
    clf
    # from IPython.display import Image
    # dot_data = tree.export_graphviz(clf, out_file=None,
    #                          feature_names=featrue_list,
    #                          class_names=label_list,
    #                          )
    # graph = pydotplus.graph_from_dot_data(dot_data)
    # Image(graph.create_png())
    with open('x1.dot','w') as f:
        f=tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)
    # import pydotplus
    # graph=pydotplus.graph_from_dot_data(f)
    # graph.write_pdf('1.pdf')
    onerow=dummy_x[0,:]
    onerow
    print('onerowx'+str(onerow))
    
    onerow[0]=1
    onerow[1]=0
    import  numpy as np
    onerow=np.array(onerow).reshape((1,-1))
    PredictedY=clf.predict(onerow)
    PredictedY

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