#training #data2: combine 2014, first 80% for train, 20% for test data2013 = list(DictReader(open("pbp-2013.csv", 'r'))) data2014 = list(DictReader(open("pbp-2014.csv", 'r'))) data2015 = list(DictReader(open("pbp-2015.csv", 'r'))) dataList = [data2013, data2014, data2015] dataName = ["2013","2014","2015"] o = DictWriter(open("DecisionTreeClassifier-mc.csv", 'w'), ["dataName", "classifier", "percent", "score", "OmniScore", "Type1-A/A/Good","Type2-A/B/Bad", "Type3-A/B/Good", "Type4-A/A/Bad"]) o.writeheader() #---------------------------------# for dataindex in range(len(dataList)): pbp2014 = NewPbpExtractor() pbp2014.buildFormationList(dataList[dataindex]) feature, target = pbp2014.extract4Classifier(dataList[dataindex]) dataLength = feature.shape[0] dataLength80 = round(dataLength * 0.8) X_train = feature[0:dataLength80,:] X_test = feature[(dataLength80+1):dataLength,:] y_train = target[0:dataLength80] y_test = target[(dataLength80+1):dataLength] class2014 = classifierEvaluation() Bscore, Bnum, BtypeNum, BomniScore = class2014.Score(y_test, y_test) baseline = {'dataName': dataName[dataindex] ,'classifier': 'baseline', 'percent': Bscore/float(BomniScore),'score': Bscore,'OmniScore': BomniScore, 'Type1-A/A/Good': BtypeNum[0], 'Type2-A/B/Bad': BtypeNum[1], 'Type3-A/B/Good': BtypeNum[2], 'Type4-A/A/Bad': BtypeNum[3]} o.writerow(baseline)
#--------------------------------------------------# #training #data2: combine 2014, first 80% for train, 20% for test data2013 = list(DictReader(open("pbp-2013.csv", 'r'))) data2014 = list(DictReader(open("pbp-2014.csv", 'r'))) data2015 = list(DictReader(open("pbp-2015.csv", 'r'))) dataList = [data2013, data2014, data2015] dataName = ["2013","2014","2015"] #train data 2015 data13and14 = data2013+data2014 pbp2014 = NewPbpExtractor() pbp2014.buildFormationList(data13and14) feature, target = pbp2014.extract4Classifier(data13and14) X_train = feature y_train = target #test data 2015 pbp2014 = NewPbpExtractor() pbp2014.buildFormationList(data2015) feature, target = pbp2014.extract4Classifier(data2015) X_test = feature y_test = target o = DictWriter(open("Bayes.csv", 'w'), ["dataName", "classifier", "percent", "score", "OmniScore", "Type1-A/A/Good","Type2-A/B/Bad", "Type3-A/B/Good", "Type4-A/A/Bad"]) o.writeheader() class2014 = classifierEvaluation()
#training #data2: combine 2014, first 80% for train, 20% for test data2013 = list(DictReader(open("pbp-2013.csv", 'r'))) data2014 = list(DictReader(open("pbp-2014.csv", 'r'))) data2015 = list(DictReader(open("pbp-2015.csv", 'r'))) dataList = [data2013, data2014, data2015] dataName = ["2013","2014","2015"] o = DictWriter(open("rfClassifier-mc.csv", 'w'), ["dataName", "classifier", "percent", "score", "OmniScore", "Type1-A/A/Good","Type2-A/B/Bad", "Type3-A/B/Good", "Type4-A/A/Bad"]) o.writeheader() #---------------------------------# for dataindex in range(len(dataList)): pbp2014 = NewPbpExtractor() pbp2014.buildFormationList(dataList[dataindex]) feature, target = pbp2014.extract4Classifier(dataList[dataindex]) dataLength = feature.shape[0] dataLength80 = round(dataLength * 0.8) X_train = feature[0:dataLength80,:] X_test = feature[(dataLength80+1):dataLength,:] y_train = target[0:dataLength80] y_test = target[(dataLength80+1):dataLength] class2014 = classifierEvaluation() Bscore, Bnum, BtypeNum, BomniScore = class2014.Score(y_test, y_test) baseline = {'dataName': dataName[dataindex] ,'classifier': 'baseline', 'percent': Bscore/float(BomniScore),'score': Bscore,'OmniScore': BomniScore, 'Type1-A/A/Good': BtypeNum[0], 'Type2-A/B/Bad': BtypeNum[1], 'Type3-A/B/Good': BtypeNum[2], 'Type4-A/A/Bad': BtypeNum[3]} o.writerow(baseline)
from sklearn.cross_validation import train_test_split #--------------------------------------------------# #training #data2: combine 2014, first 80% for train, 20% for test data2013 = list(DictReader(open("pbp-2013.csv", 'r'))) data2014 = list(DictReader(open("pbp-2014.csv", 'r'))) data2015 = list(DictReader(open("pbp-2015.csv", 'r'))) dataList = [data2013, data2014, data2015] dataName = ["2013", "2014", "2015"] #train data 2015 data13and14 = data2013 + data2014 pbp2014 = NewPbpExtractor() pbp2014.buildFormationList(data13and14) feature, target = pbp2014.extract4Classifier(data13and14) X_train = feature y_train = target #test data 2015 pbp2014 = NewPbpExtractor() pbp2014.buildFormationList(data2015) feature, target = pbp2014.extract4Classifier(data2015) X_test = feature y_test = target o = DictWriter(open("Bayes.csv", 'w'), [ "dataName", "classifier", "percent", "score", "OmniScore", "Type1-A/A/Good", "Type2-A/B/Bad", "Type3-A/B/Good", "Type4-A/A/Bad" ]) o.writeheader()