Python資料探勘入門與實踐---用決策樹預測獲勝球隊
阿新 • • 發佈:2019-01-06
參考書籍:《Python資料探勘入門與實踐》
1.載入資料集:
使用pandas載入資料集,有1319行資料, 8個特徵, 檢視前5項資料集,並查詢是否有重複資料
#coding=gbk #使用決策樹來預測獲勝球隊 import time start = time.clock() #載入資料集 import pandas as pd file_name = r'D:\datasets\NBA_2014_games.csv' data = pd.read_csv(file_name) print(data.head()) #讀取前5項資料集 # Date Unnamed: 1 Visitor/Neutral PTS Home/Neutral \..... # 0 Tue Oct 29 2013 Box Score Orlando Magic 87 Indiana Pacers # 1 Tue Oct 29 2013 Box Score Los Angeles Clippers 103 Los Angeles Lakers # 2 Tue Oct 29 2013 Box Score Chicago Bulls 95 Miami Heat # 3 Wed Oct 30 2013 Box Score Brooklyn Nets 94 Cleveland Cavaliers # 4 Wed Oct 30 2013 Box Score Atlanta Hawks 109 Dallas Mavericks print(data.shape) # (1319, 8) print(data[data.duplicated()]) # Empty DataFrame 沒有重複元素
資料集清洗:1.第一列資料日期是字串格式,改為日期格式; 2.修改表頭。
#修復表頭資料引數 data = pd.read_csv(file_name, parse_dates= ['Date']) #skiprows 忽略的行數 data.columns = ['Date','Score Type', 'Visitor Team', 'VisitorPts', 'Home Team', 'HomePts', 'OT?', 'Notes'] print(data.head()) #重命名錶頭 # Date Score Type Visitor Team VisitorPts \。。。。 # 0 2013-10-29 Box Score Orlando Magic 87 # 1 2013-10-29 Box Score Los Angeles Clippers 103 # 2 2013-10-29 Box Score Chicago Bulls 95 # 3 2013-10-30 Box Score Brooklyn Nets 94 # 4 2013-10-30 Box Score Atlanta Hawks 109 print('-----') # print(data.ix[1] ) #打印出第2行的資料
提取新特徵:通過現有的資料抽取特徵, 首先確定類別,籃球只有勝負之分, 不像足球還有 平,局, 以1 代表球隊取勝,0為失敗。
#提取新特徵 #找出獲勝的球隊 data['HomeWin'] = data['VisitorPts'] < data['HomePts'] y_true = data['HomeWin'].values print(y_true[:5]) #[ True True True True True] 是 numpy 陣列 # print(data.head()) #建立2個新特徵, 分別是這兩隻球隊的上一場比賽的勝負情況 #建立字典,存放上次比賽結果 from collections import defaultdict won_last = defaultdict(int) data['HomeLastWin'] = None data['VisitorLastWin'] = None #此兩行程式碼原書上沒有,應該增加這2列,否則下面的迴圈不能建立這2列 for index, row in data.iterrows(): home_team = row['Home Team'] visitor_team = row['Visitor Team'] #迴圈獲得球隊名稱 row['HomeLastWin'] = won_last[home_team] row['VisitorLastWin'] = won_last[visitor_team] data.ix[index] = row #更新行數 won_last[home_team] = row['HomeWin'] #判斷上一場是否獲勝 won_last[visitor_team] =not row['HomeWin'] print('----') # print(data.ix[20:25]) # Home Team HomePts OT? Notes HomeWin HomeLastWin VisitorLastWin # 20 Boston Celtics 98 NaN NaN False False False # 21 Brooklyn Nets 101 NaN NaN True False False # 22 Charlotte Bobcats 90 NaN NaN True False True # 23 Denver Nuggets 98 NaN NaN False False False # 24 Houston Rockets 113 NaN NaN True True True # 25 Los Angeles Lakers 85 NaN NaN False False True
一些練習測試程式碼:defaultdict 和 iterrows()的使用方法
won_last['jj'] = 12
dd = won_last['Indiana Pacers'] #defaultdict的作用是在於,當字典裡的key不存在但被查詢時,返回的不是keyError而是一個預設值
print(dd) # 0
print(won_last) # defaultdict(<class 'int'>, {'Indiana Pacers': 0, 'jj': 12}) 返回的是defaultdict型別
dataset = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]])
print(dataset)
for index, row in dataset.iterrows():
print(index) # 0, 1, 2 打印出行號
print(row) #打印出第 1, 2, 3 行的全部元素
2.使用決策樹
這裡直接使用決策樹, 沒有刻意地去調引數,可能是作者為了對比不同特徵的優劣吧。
從資料集中構建有效的特徵, (Feature Engineering 特徵工程)是資料探勘的難點所在, 好的特徵直接關係到結果的正確率, -------甚至比選擇合適的演算法更重要。
#使用決策樹
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state =14) #設定隨機種子,使結果復現,。。。 但是還是不同。
X_previousWins = data[['HomeLastWin', 'VisitorLastWin']].values #使用新建立的2個特徵作為輸入
from sklearn.model_selection import cross_val_score # 使 用交叉驗證模型平均得分
import numpy as np
scores = cross_val_score(clf, X_previousWins, y_true, scoring='accuracy')
mean_score = np.mean(scores) *100
print('the accuracy is %0.2f'%mean_score+'%') #準確率為 the accuracy is 57.47%
使用另一資料集:13年NBA 排名情況
#讀取2013年球隊排名情況
file_name2 = r'D:\datasets\NBA_2013_stangdings.csv'
standings = pd.read_csv(file_name2)
# print(standings.head())
# Rk Team Overall Home Road E W A C \....
# 0 1 Miami Heat 66-16 37-4 29-12 41-11 25-5 14-4 12-6
# 1 2 Oklahoma City Thunder 60-22 34-7 26-15 21-9 39-13 7-3 8-2
# 2 3 San Antonio Spurs 58-24 35-6 23-18 25-5 33-19 8-2 9-1
# 3 4 Denver Nuggets 57-25 38-3 19-22 19-11 38-14 5-5 10-0
# 4 5 Los Angeles Clippers 56-26 32-9 24-17 21-9 35-17 7-3 8-2
# print(standings.shape) # (30, 24) 有30只球隊
建立一個新特徵值, 主場球隊是否比對手排名高。然後使用建立的3個特徵去 fit 模型
#建立一個新特徵值, 主場球隊是否比對手排名高
data['HomeTeamRanksHigher'] = 0
for index, row in data.iterrows():
home_team = row['Home Team']
visitor_team = row['Visitor Team']
if home_team =='New Orleans Pelicans': #更換了名字的球隊
home_team ='New Orleans Hornets'
elif visitor_team == 'New Orleans Pelicans':
visitor_team='New Orleans Hornets'
#比較排名, 更新特徵值
home_rank = standings[standings['Team']== home_team]['Rk'].values[0]
visitor_rank = standings[standings['Team']== visitor_team]['Rk'].values[0]
row['HomeTeamRanksHigher'] = int(home_rank > visitor_rank)
data.ix[index] = row
X_homehigher = data[['HomeLastWin', 'VisitorLastWin', 'HomeTeamRanksHigher']].values
# clf1 = DecisionTreeClassifier(random_state=14)
# scores = cross_val_score(clf1, X_homehigher, y_true, scoring='accuracy')
# mean_score1 = np.mean(scores) *100
# print('the new accuracy is %.2f'%mean_score1 + '%') #the new accuracy is 59.67%
再建立新特徵, 對比比賽的2隊上一場2隊比賽的結果
#再建立新特徵, 對比比賽的2隊上一場2隊比賽的結果
last_match_winner = defaultdict(int)
data['HomeTeamWonLast'] = 0
for index, row in data.iterrows():
home_team = row['Home Team']
visitor_team = row['Visitor Team']
teams = tuple(sorted([home_team, visitor_team]))
row['HomeTeamWonLast'] = 1 if last_match_winner[teams] == row['Home Team'] else 0
data.ix[index] = row
winner = row['Home Team'] if row['HomeWin'] else row['Visitor Team']
last_match_winner[teams] = winner
X_lastwinner = data[['HomeTeamWonLast', 'HomeTeamRanksHigher']]
# clf2 = DecisionTreeClassifier(random_state=14)
# scores = cross_val_score(clf2, X_lastwinner, y_true, scoring='accuracy')
# mean_score2 = np.mean(scores) *100
# print('the accuracy is %.2f'%mean_score2 + '%') # the accuracy is 57.85%
觀察決策樹在訓練資料量很大的情況下, 能否得到有效的模型,使用球隊,並對其編碼
#使用LabelEncoder 轉換器把字串型別的隊名轉換成整型
from sklearn.preprocessing import LabelEncoder
encoding = LabelEncoder()
encoding.fit(data['Home Team'].values) #將主隊名稱轉換成整型
home_teams = encoding.transform(data['Home Team'].values)
visitor_teams = encoding.transform(data['Visitor Team'].values)
X_teams = np.vstack([home_teams, visitor_teams]).T
from sklearn.preprocessing import OneHotEncoder
onehot = OneHotEncoder()
X_teams_expanded = onehot.fit_transform(X_teams).todense()
clf3 = DecisionTreeClassifier(random_state=14)
# scores = cross_val_score(clf3, X_teams_expanded, y_true, scoring='accuracy')
# mean_score3 = np.mean(scores) *100
# print('the accuracy is %.2f'%mean_score3+'%') # the accuracy is 59.52%
3.使用隨機森林
print('----rf-----')
#使用隨機森林進行預測
from sklearn.ensemble import RandomForestClassifier
# rf = RandomForestClassifier(random_state = 14, n_jobs =-1) #最好調下決策樹的引數
# rf_scores = cross_val_score(rf, X_teams, y_true, scoring='accuracy')
# mean_rf_score = np.mean(rf_scores) *100
# print('the randforestclassifier accuracy is %.2f'%mean_rf_score+'%') #the randforestclassifier accuracy is 58.38%
#多使用幾個特徵
print('使用多個引數')
X_all = np.hstack([X_homehigher, X_teams])
# rf_clf2 = RandomForestClassifier(random_state = 14, n_jobs=-1)
# rf_scores2 = cross_val_score(rf_clf2, X_all, y_true, scoring='accuracy')
# mean_rf_score2 = np.mean(rf_scores2) *100
# print('the accuracy is %.2f'%mean_rf_score2+'%') # the accuracy is 57.62%
使用網格搜尋查詢最佳的模型, 並檢視使用的引數。
#調引數, 使用網格搜尋
from sklearn.model_selection import GridSearchCV
param_grid = {
'max_features':[2,3,'auto'],
'n_estimators': [100,110,120 ],
'criterion': ['gini', 'entropy'],
"min_samples_leaf": [2, 4, 6]
}
clf = RandomForestClassifier(random_state=14, n_jobs=-1)
grid = GridSearchCV(clf, param_grid)
grid.fit(X_all, y_true)
score = grid.best_score_ *100
print('the accuracy is %.2f'%score +'%') #the accuracy is 62.02%
something= str(grid.best_estimator_)
print(something) #輸出網格搜尋找到的最佳模型
print(grid.best_params_) #輸出返回最好的引數
# the accuracy is 62.02%
# RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',
# max_depth=None, max_features=3, max_leaf_nodes=None,
# min_impurity_decrease=0.0, min_impurity_split=None,
# min_samples_leaf=2, min_samples_split=2,
# min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,
# oob_score=False, random_state=14, verbose=0, warm_start=False)
# {'n_estimators': 100, 'criterion': 'entropy', 'max_features': 3, 'min_samples_leaf': 2}
# 所花費的時間 : 117.93s
end = time.clock()
time = end - start
print('所花費的時間 : %.2f'%time + 's')