python+决策树1

#pandas库(Python Data Analysis的简写)是用来加载、管理和处理数据的。它在后台处理数据结构，支持计算均值等分析方法。 import pandas as pdfrom sklearn.model_selection._validation import cross_val_score   #交叉检验,计算平均正确率from sklearn.tree import DecisionTreeClassifierimport numpy as npclf = DecisionTreeClassifier(random_state=14)filename = "dicision_trees_sample.csv"#修复参数，将Date列的值由字符串改为日期类型。dataset = pd.read_csv(filename, parse_dates=["Date"])  #定义表头即定义属性列。 dataset.columns = ["Date","StartTime","VistorTeam","VisitorPTS","HomeTeam","HomePTS","ScoreType","OT?","Notes"]#添加新特征,主场获胜与否(1表示主场获胜，0表示主场未获胜)，作为预测的结果是否正确的标准。dataset["HomeWin"] = dataset["VisitorPTS"] < dataset["HomePTS"]x_c = dataset["HomeWin"].valuesfrom collections import defaultdictwon_last = defaultdict(int)#dataset["HomeLastWin"]和dataset["VistorLastWin"]必须要定义，否则下面循环里的row赋值将没有作用。#球队上场的输赢作为判断依据（思路是这样的：如果上一次比赛球队A获胜，则预测下一场比赛A也会获胜）dataset["HomeLastWin"] = 0     #默认每一个主场球队在第一次出现的时候都是输的dataset["VistorLastWin"] = 0   #默认每一个客场球队在第一次出现的时候都是输的#dataset.sort_values("Date"),如果报错,可以将sort_values改为sort（pandas版本问题）,数据按照时间排序，因为上一次比赛与下一场比赛是通过时间进行区分的。for index, row in dataset.sort_values("Date").iterrows():    homeTeam = row["HomeTeam"]    visitorTeam = row["VistorTeam"]    row["HomeLastWin"] = won_last[homeTeam]    row["VistorLastWin"] = won_last[visitorTeam]    dataset.ix[index] = row    won_last[homeTeam] = row["HomeWin"]          won_last[visitorTeam] = not row["HomeWin"] #属性列HomeLastWin和VistorLastWin的取值作为预测球队下场是否能获胜的依据x_previouswin = dataset[["HomeLastWin", "VistorLastWin"]].valuesscores = cross_val_score(clf, x_previouswin, x_c, scoring="accuracy")print("使用上场比赛的结果预测下场比赛，Accuracy: {0:.1f}%".format(np.mean(scores) * 100)) #创建新特征，提高准确率，球队排名作为预测是否获胜的依据dataset["HomeTeamRanksHiger"] = 0standings_filename = "leagues_NBA_2013_standings_expanded-standings.csv"standings = pd.read_csv(standings_filename)for index, row in dataset.sort_values("Date").iterrows():    homeTeam = row["HomeTeam"]    visitorTeam = row["VistorTeam"]    #处理有些球队更名问题    if homeTeam == "New Orleans Pelicans":        homeTeam = "New Orleans Hornets"    elif visitorTeam == "New Orleans Pelicans":        visitorTeam = "New Orleans Hornets"    #standings[ standings["Team"]== homeTeam ],首先在standings筛选出homeTeam,然后得到它的排名    homeRank = standings[ standings["Team"]== homeTeam ]["Rk"].values[0]   #存放主场球队排名    visitorRank = standings[ standings["Team"]== visitorTeam]["Rk"].values[0]  #存放客场球队排名    row["HomeTeamRanksHiger"] = int(homeRank > visitorRank)    dataset.ix[index] = rowx_homehigher = dataset[["HomeLastWin", "VistorLastWin","HomeTeamRanksHiger"]].valuesscores = cross_val_score(clf, x_homehigher, x_c, scoring="accuracy")print("使用上场比赛的结果+球队排名预测下场比赛，Accuracy: {0:.1f}%".format(np.mean(scores) * 100)) 

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