def findPlayerFeatures(year,seasonStatsYear,careerData,nonRookiesTrain,
rookiesTrain,nonRookiesScaler,rookiesScaler):
"""
This module finds all player features.
-nonRookies have >1 year of NBA experience
-rookies have <= 1 year of NBA experience
"""
nonRookies = pd.DataFrame(columns=['URL','Player','Features','3PA'])
rookies = pd.DataFrame(columns=['URL','Player','Features','3PA'])
for index,row in seasonStatsYear.iterrows():
last2digits=str(year)[-2:]
season = str((datetime.datetime(year,1,1)-datetime.timedelta(days=365)).year)+'-'+last2digits
seasonIndex = careerData[row['URL']][careerData[row['URL']]['Season']==season].index[0]
if seasonIndex <= 1:
rookieFeatures = getFeatures.getRookieFeatures(row['URL'])
if isinstance(rookieFeatures,int):
if rookieFeatures==1: rookies.loc[len(rookies)] = [row['URL'],row['Player'],'No data available',np.nan]
elif rookieFeatures==2: rookies.loc[len(rookies)] = [row['URL'],row['Player'],'Low-Volume 3-Point Shooter',np.nan]
else:
scaledRookieFeats = rookiesScaler.transform(rookieFeatures)
rookies.loc[len(rookies)] = [row['URL'],row['Player'],scaledRookieFeats,rookieFeatures[2]]
else:
if np.sum(careerData[row['URL']].ix[:seasonIndex-1]['3PA'])/(seasonIndex-1) > 10:
feat = getFeatures.getNonRookieFeatures(careerData,row,seasonIndex)
for i in range(len(feat)):
if np.isnan(feat[i]): feat[i] = np.mean(nonRookiesTrain['X'][:,i])
scaledFeat = nonRookiesScaler.transform(feat)
nonRookies.loc[len(nonRookies)] = [row['URL'],row['Player'],scaledFeat,np.sum(careerData[row['URL']].ix[:seasonIndex-1]['3PA'])]
## print(nonRookies.loc[len(nonRookies)])
else: nonRookies.loc[len(nonRookies)] = [row['URL'],row['Player'],'Low-Volume 3-Point Shooter',np.nan]
return(nonRookies,rookies)
def getCrossVal(allData,careerData):
"""This module validates different techniques for 3P% prediction for veterans
using 2010-2014 seasons as test sets.
"""
res = {}
preds = {}
for year in range(2010,2015):
print(year)
try:
seasonStats = getData.seasonStatsOnline(year)
except:
print("Webpage didn't read properly, waiting 30 seconds then trying again")
time.sleep(30)
X = allData[2000]['X']
y = allData[2000]['y']
for i in range(2001,year):
X = np.vstack((X,allData[i]['X']))
y= y+allData[i]['y']
train = {'X': X,'y':np.array(y)}
test = {'X':allData[year]['X'],'y':np.array(allData[year]['y'])}
scaler = preprocessing.StandardScaler()
scaledTrain = {'X':scaler.fit_transform(train['X']),'y':train['y']}
scaledTest = {'X':scaler.transform(test['X']),'y':test['y']}
knnGrid = grid_search.GridSearchCV(neighbors.KNeighborsRegressor(),
param_grid={'n_neighbors':[100],'leaf_size':[1]},
scoring='mean_squared_error')
svmGrid = grid_search.GridSearchCV(svm.SVR(),
param_grid={'C':[.15],'gamma':[.015],'epsilon':[.05]},
scoring='mean_squared_error')
rfGrid = grid_search.GridSearchCV(ensemble.RandomForestRegressor(),
param_grid={'n_estimators':[500],'min_samples_split':[125]},
scoring='mean_squared_error')
knnGrid.fit(scaledTrain['X'],scaledTrain['y'])
svmGrid.fit(scaledTrain['X'],scaledTrain['y'])
rfGrid.fit(scaledTrain['X'],scaledTrain['y'])
print(knnGrid.best_estimator_)
print(svmGrid.best_estimator_)
print(rfGrid.best_estimator_)
knnPreds = knnGrid.predict(scaledTest['X'])
svmPreds = svmGrid.predict(scaledTest['X'])
rfPreds = rfGrid.predict(scaledTest['X'])
career = []; lastYear = []; ThreePA = []; leagueAverage=np.array([np.mean(train['X'][:,0])]*len(test['y']))
for index,row in seasonStats.iterrows():
last2digits=str(year)[-2:]
season = str((datetime.datetime(year,1,1)-datetime.timedelta(days=365)).year)+'-'+last2digits
seasonIndex = careerData[row['URL']][careerData[row['URL']]['Season']==season].index[0]
if seasonIndex <= 1:
continue#yearFeatures.append([np.nan for i in range(7)])
else:
rowData = getFeatures.getNonRookieFeatures(careerData,row,seasonIndex)
if np.sum(careerData[row['URL']].ix[:seasonIndex-1]['3PA'])/(seasonIndex-1) > 10 and not np.isnan(careerData[row['URL']].ix[seasonIndex]['3P%']):
career.append(rowData[0])
lastYear.append(rowData[1])
ThreePA.append(np.sum(careerData[row['URL']].ix[:seasonIndex-1]['3PA']))
career = np.array(career); lastYear=np.array(lastYear);ThreePA=np.array(ThreePA)
preds[year] = {'knn':knnPreds,'svm':svmPreds,'rf':rfPreds,
'career':career,'lastYear':lastYear,'leagueAverage':leagueAverage,
'actual':scaledTest['y'],'3PA':ThreePA}
res[year] = {'knn':{'MSE':metrics.mean_squared_error(scaledTest['y']*100,knnPreds*100),
'MAE':metrics.mean_absolute_error(scaledTest['y']*100,knnPreds*100)},
'svm':{'MSE':metrics.mean_squared_error(scaledTest['y']*100,svmPreds*100),
'MAE':metrics.mean_absolute_error(scaledTest['y']*100,svmPreds*100)},
'rf':{'MSE':metrics.mean_squared_error(scaledTest['y']*100,rfPreds*100),
'MAE':metrics.mean_absolute_error(scaledTest['y']*100,rfPreds*100)},
'career':{'MSE':metrics.mean_squared_error(scaledTest['y']*100,career*100),
'MAE':metrics.mean_absolute_error(scaledTest['y']*100,career*100)},
'leagueAverage': {'MSE':metrics.mean_squared_error(scaledTest['y']*100,leagueAverage*100),
'MAE':metrics.mean_absolute_error(scaledTest['y']*100,leagueAverage*100)},
'lastYear':{'MSE':metrics.mean_squared_error(scaledTest['y']*100,lastYear*100),
'MAE':metrics.mean_absolute_error(scaledTest['y']*100,lastYear*100)}
}
print(writeResToPandas({year:res[year]},'nonRookies'))
return(res,preds)
