iris数据集 决策树实现分类并画出决策树

 # coding=utf-8

 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn import tree
 from sklearn.metrics import precision_recall_curve  #准确率与召回率
 import numpy as np
 #import graphviz

 import os
 os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'

 def get_data():
     file_path = "Iris.xlsx"

     data = pd.read_excel(file_path)
     loandata = pd.DataFrame(data)
     ncol = (len(loandata.keys()))
     print(ncol)
     # l = list(data.head(0))  #获取表头
     # print(l)

     feature1 = []
     for i in range(ncol-1):
         feature1.append("feature"+str(i))
     print(feature1)
     iris_x = data.iloc[1:, :ncol-1]#此处有冒号，不显示最后一列
     iris_y = data.iloc[1:,ncol-1]#此处没有冒号，直接定位

     '''计算到底有几个类别'''
     from collections import Counter
     counter = Counter(iris_y)
     con = len(counter)
     print(counter.keys())
     class_names = []
     for i in range(con):
         class_names.append(list(counter.keys())[i])
     x_train, x_test, y_train, y_test = train_test_split(iris_x,iris_y)
     print(x_train)
     print(y_test)
    # return x_train, x_test, y_train, y_test

 #def dtfit(x_train, x_test, y_train, y_test):

     clf = tree.DecisionTreeClassifier()
     clf = clf.fit(x_train,y_train)
     predict_data = clf.predict(x_test)
     predict_proba = clf.predict_proba(x_test)
     from sklearn import metrics
     # Do classification task,
     # then get the ground truth and the predict label named y_true and y_pred
     classify_report = metrics.classification_report(y_test, clf.predict(x_test))
     confusion_matrix = metrics.confusion_matrix(y_train, clf.predict(x_train))
     overall_accuracy = metrics.accuracy_score(y_train, clf.predict(x_train))
     acc_for_each_class = metrics.precision_score(y_train,clf.predict(x_train), average=None)
     overall_accuracy = np.mean(acc_for_each_class)
     print(classify_report)

     import pydotplus
     dot_data = tree.export_graphviz(clf, out_file=None,feature_names=feature1, filled=True, rounded=True, special_characters=True,precision = 4)
     graph = pydotplus.graph_from_dot_data(dot_data)
     graph.write_pdf("workiris.pdf")
     return classify_report

 if __name__ == "__main__":
     x = get_data()
     #dtfit(x_train, x_test, y_train, y_test)

数据地址：http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

保存后注意填写表头