python?sklearn?畫出決策樹并保存為PDF的實(shí)現(xiàn)過程
發(fā)布日期:2022-07-20 19:07 | 文章來源:源碼中國
利用sklearn畫出決策樹并保存為PDF
下載Graphviz
進(jìn)入官網(wǎng)下載并安裝:
https://graphviz.gitlab.io/_pages/Download/Download_windows.html
并將下列路徑配置為環(huán)境變量:
- D:\software\Graphviz\bin
在cmd中測試:
dot -version
python代碼
import numpy as np import pandas as pd from sklearn import tree import graphviz # x,y是sklearn中需要擬合的數(shù)據(jù) x = np.array(exam_train) y = np.array(classes_train) clf = tree.DecisionTreeClassifier(criterion='entropy', class_weight='balanced', max_depth=25) clf = clf.fit(x, y) dot_data = tree.export_graphviz(clf, out_file=None, feature_names=None, filled=True, rounded=True) # 重要參數(shù)可定制 graph = graphviz.Source(dot_data) graph.render(view=True, format="pdf", filename="decisiontree_pdf")
可以生成一張賊帥的決策樹PDF:
python sklearn 決策樹運(yùn)用
數(shù)據(jù)形式(tree.csv)
age look income orderly target older ugly low yes no young ugly high no no young handsome low no no young handsome high yes yes young handsome medium yes yes young handsome medium no no
python源代碼:
# -*- coding:utf-8*- # 將字典 轉(zhuǎn)化為 sklearn 用的數(shù)據(jù)形式 數(shù)據(jù)型 矩陣 from sklearn.feature_extraction import DictVectorizer import csv from sklearn import preprocessing from sklearn import tree allElectronicsData = open('c:/pic/data/tree.csv','rb') reader = csv.reader(allElectronicsData) header = reader.next() # print header ## 數(shù)據(jù)預(yù)處理 featureList = [] labelList = [] for row in reader: # print row[-1] labelList.append(row[-1]) # 下面這幾步的目的是為了讓特征值轉(zhuǎn)化成一種字典的形式,就可以調(diào)用sk-learn里面的DictVectorizer,直接將特征的類別值轉(zhuǎn)化成0,1值 rowDict = {} for i in range(1, len(row) - 1): rowDict[header[i]] = row[i] featureList.append(rowDict) for each in featureList: print each # Vectorize features vec = DictVectorizer() dummyX = vec.fit_transform(featureList).toarray() print("dummyX:"+str(dummyX)) print(vec.get_feature_names()) # label的轉(zhuǎn)化,直接用preprocessing的LabelBinarizer方法 lb = preprocessing.LabelBinarizer() dummyY = lb.fit_transform(labelList) print("dummyY:"+str(dummyY)) print("labelList:"+str(labelList)) #criterion是選擇決策樹節(jié)點(diǎn)的 標(biāo)準(zhǔn) ,這里是按照“熵”為標(biāo)準(zhǔn),即ID3算法;默認(rèn)標(biāo)準(zhǔn)是gini index,即CART算法。 clf = tree.DecisionTreeClassifier() clf = clf.fit(dummyX,dummyY) print("clf:"+str(clf)) # 導(dǎo)入相關(guān)函數(shù),可視化決策樹 # 導(dǎo)出的結(jié)果是一個(gè)dot文件(在系統(tǒng)默認(rèn)路勁),需要安裝Graphviz才能將它住哪華為PDF或png格式 # 輸出的dot文件可以使用graphvize軟件轉(zhuǎn)為PDF,graphvize安裝目錄中的bin目錄放入到環(huán)境變量的Path中 # 使用如下命令 #cmd # dot -Tpdf c:/tree.dot -o c:/tree.pdf #下載地址:http://www.graphviz.org/Download_windows.php #生成dot文件 with open("c:/tree.dot",'w') as f: f = tree.export_graphviz(clf, feature_names= vec.get_feature_names(),out_file= f)
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