# 4. test all data, output 3 ans as features
# D:\Kaggle\Telstra\004_one_hot_resource_type\(Xgboost)_(2016-02-06_11_14_31).model
# D:\Kaggle\Telstra\004_one_hot_resource_type\(Random_Forest)_(2016-02-06_11_24_09).model
# D:\Kaggle\Telstra\004_one_hot_resource_type\(Extra_Trees)_(2016-02-06_11_30_52).model
# D:\Kaggle\Telstra\004_one_hot_resource_type\(K_NN)_(2016-02-06_11_40_10).model
modelFolder = _basePath + "models" + Config.osSep + "stacked" + Config.osSep
clfNameList = []
clfNameList.append("Extra_Trees")
clfNameList.append("K_NN")
clfNameList.append("Random_Forest")
clfNameList.append("Xgboost")
clfNameList.append("Logistic_Regression")
testCsv = _basePath + "010_train_tobe.csv"
dr = DataReader()
newX, testY = dr.cvtPathListToDfList(testCsv, "train")
for curModel in clfNameList:
modelPath = modelFolder + str(getMatchNameModelPath(modelFolder, curModel))
tmpOutPath = _basePath + expNo + "_blender" + curModel + "_train.csv"
tmpClf = loadModel( modelPath)
log(tmpClf.predict_proba(newX))
#outDf = pd.concat([newX, pd.DataFrame(tmpClf.predict_proba(newX))], axis=1)
outDf = pd.DataFrame(tmpClf.predict_proba(newX))
outDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
#musicAlarm()
# 1. read in data
expNo = "008"
expInfo = expNo + "_blender"
_basePath = Config.FolderBasePath + expInfo + Config.osSep
doTestFlag = False
path = _basePath + expNo + "_train_tobe.csv"
testPath = _basePath + expNo + "_test_tobe.csv"
outputPath = _basePath + expNo + "blender_train.csv"
# 1. read data
dr = DataReader()
tmpDfList = []
tmpPath = _basePath + "008_submission_1_train_Extra_Trees.csv"
newX, newY = dr.cvtPathListToDfList(tmpPath, "train")
tmpDfList.append(newX)
tmpPath = _basePath + "008_submission_1_train_K_NN.csv"
newX, newY = dr.cvtPathListToDfList(tmpPath, "train")
tmpDfList.append(newX)
tmpPath = _basePath + "008_submission_1_train_Random_Forest.csv"
newX, newY = dr.cvtPathListToDfList(tmpPath, "train")
tmpDfList.append(newX)
tmpPath = _basePath + "008_submission_1_train_Xgboost.csv"
newX, newY = dr.cvtPathListToDfList(tmpPath, "train")
tmpDfList.append(newX)
clfNameList = []
[0.00310559, 0.53881988, 0.03416149, 0.4052795, 0.01863354],
[0.13333333, 0.01333333, 0.01142857, 0.73142857, 0.11047619],
[0.08222222, 0.00222222, 0.00222222, 0.00222222, 0.91111111],
]
# for tmpFeature in featureList:
# dr = DataReader()
# tmpPath = _basePath + "010_blender_" + tmpFeature + "_train.csv"
# newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
# tmpDf = pd.concat([tmpDf, newX], axis=1)
#
# tmpDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
tmpI, tmpJ = 0, 0
dr = DataReader()
baseDf, ansY = dr.cvtPathListToDfList(_basePath + "010_blenderXgboost_train.csv", "train")
tmpOutPath = _basePath + "010_train_last_blender.csv"
tmpFeatureBlendedAns = pd.DataFrame()
baseDf = pd.DataFrame()
tmpDfList = []
for tmpClfName in clfNameList:
dr = DataReader()
tmpPath = _basePath + "010_" + "blender" + tmpClfName + "_train.csv"
newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
tmpDfList.append(newX)
b1 = Blender(clfNameList, tmpDfList, ansY)
b1.autoFlow(2000, tmpOutPath)
# test
for i2 in range(2,len(bin(i))):
flagList.append(int(bin(i)[i2]))
for j in range(0,5):
if flagList[j] ==1:
tmpCurFeatureList.append(featureList[j])
log(tmpCurFeatureList)
newX = pd.DataFrame()
for tmpFeature in tmpCurFeatureList:
path = _basePath + tmpFeature + "_train.csv"
dr = DataReader()
tmpX = dr.cvtPathListToDfList(path, "test")
newX = pd.concat([newX, tmpX], axis=1)
#log("feature len: " , len(newX))
# Get all best model from newX
fab = ModelFactory()
fab._setXgboostTheradToOne = True
fab._gridSearchFlag = True
fab._onlyTreeBasedModels = True
fab._subFolderName = "one_hot_each_" + str(i)
fab._n_iter_search = 30
fab._expInfo = expInfo
# fab.getAllModels(newX, newY)
fab.getRandomForestClf(newX, newY)
# fab.getXgboostClf(newX, newY)
log ( i , "/32 done..." )
[0.08222222, 0.00222222, 0.00222222, 0.00222222, 0.91111111]
]
tmpOutPath = _basePath + "010_test_tobe.csv"
# for tmpFeature in featureList:
# dr = DataReader()
# tmpPath = _basePath + "010_blender_" + tmpFeature + "_train.csv"
# newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
# tmpDf = pd.concat([tmpDf, newX], axis=1)
#
# tmpDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
tmpI, tmpJ = 0, 0
dr = DataReader()
baseDf, tmpY = dr.cvtPathListToDfList(_basePath + "010_test_asis.csv",
"test")
for tmpFeature in featureList:
outputPath = _basePath + expNo + "_blender_" + tmpFeature + "_test.csv"
#ansPath = _basePath + "010_Extra_Trees_stack_event_type.csv"
#dr = DataReader()
#tmpX, ansY = dr.cvtPathListToDfList(ansPath, "train")
#tmpDfList = []
tmpFeatureBlendedAns = pd.DataFrame()
for tmpClfName in clfNameList:
dr = DataReader()
tmpPath = _basePath + "010_" + tmpClfName + "_stack_" + tmpFeature + "_test.csv"
newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
tmpWight = tmpWeightList[tmpI][tmpJ]
newX = newX.multiply(tmpWight)
# 1. read in data expNo = "008" expInfo = expNo + "_blender" _basePath = Config.FolderBasePath + expInfo + Config.osSep doTestFlag = False path = _basePath + expNo + "_train_tobe.csv" testPath = _basePath + expNo + "_test_tobe.csv" outputPath = _basePath + expNo + "blender_train.csv" # 1. read data dr = DataReader() tmpDfList = [] tmpPath = _basePath + "008_submission_1_train_Extra_Trees.csv" newX, newY = dr.cvtPathListToDfList(tmpPath, "train") tmpDfList.append(newX) tmpPath = _basePath + "008_submission_1_train_K_NN.csv" newX, newY = dr.cvtPathListToDfList(tmpPath, "train") tmpDfList.append(newX) tmpPath = _basePath + "008_submission_1_train_Random_Forest.csv" newX, newY = dr.cvtPathListToDfList(tmpPath, "train") tmpDfList.append(newX) tmpPath = _basePath + "008_submission_1_train_Xgboost.csv" newX, newY = dr.cvtPathListToDfList(tmpPath, "train") tmpDfList.append(newX) clfNameList = []
[0.00310559, 0.53881988, 0.03416149, 0.4052795, 0.01863354],
[0.13333333, 0.01333333, 0.01142857, 0.73142857, 0.11047619],
[0.08222222, 0.00222222, 0.00222222, 0.00222222, 0.91111111]
]
# for tmpFeature in featureList:
# dr = DataReader()
# tmpPath = _basePath + "010_blender_" + tmpFeature + "_train.csv"
# newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
# tmpDf = pd.concat([tmpDf, newX], axis=1)
#
# tmpDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
tmpI, tmpJ = 0, 0
dr = DataReader()
baseDf, ansY = dr.cvtPathListToDfList(
_basePath + "010_blenderXgboost_train.csv", "train")
tmpOutPath = _basePath + "010_train_last_blender.csv"
tmpFeatureBlendedAns = pd.DataFrame()
baseDf = pd.DataFrame()
tmpDfList = []
for tmpClfName in clfNameList:
dr = DataReader()
tmpPath = _basePath + "010_" + "blender" + tmpClfName + "_train.csv"
newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
tmpDfList.append(newX)
b1 = Blender(clfNameList, tmpDfList, ansY)
b1.autoFlow(2000, tmpOutPath)
# test
# Get all best model from newX
fab = ModelFactory()
fab._gridSearchFlag = True
# fab._subFolderName = "stacked"
fab._n_iter_search = 100
fab._expInfo = expInfo
fab.getAllModels(newX, newY)
# Test all data
modelList = ["Xgboost","Random_Forest","Extra_Trees", "K_NN", "Logistic_Regression"]
# featureList = ["event_type", "log_feature", "resource_type", "severity_type"]
# for tmpFeature in featureList:
modelFolder = _basePath + "models" + Config.osSep
for tmpModel in modelList:
curModel = tmpModel
dr = DataReader()
newX = dr.cvtPathListToDfList(testPath, "test")
modelPath = modelFolder + str(getMatchNameModelPath(modelFolder, curModel))
tmpOutPath = _basePath + "011_" + curModel + "_test_ans.csv"
tmpClf = loadModel( modelPath)
log(tmpClf.predict_proba(newX))
outDf = pd.concat([newX, pd.DataFrame(tmpClf.predict_proba(newX))], axis=1)
outDf = pd.DataFrame(tmpClf.predict_proba(newX))
outDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
#musicAlarm()
# log("004 Done")
[0.08222222, 0.00222222, 0.00222222, 0.00222222, 0.91111111],
]
tmpOutPath = _basePath + "010_test_tobe.csv"
# for tmpFeature in featureList:
# dr = DataReader()
# tmpPath = _basePath + "010_blender_" + tmpFeature + "_train.csv"
# newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
# tmpDf = pd.concat([tmpDf, newX], axis=1)
#
# tmpDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
tmpI, tmpJ = 0, 0
dr = DataReader()
baseDf, tmpY = dr.cvtPathListToDfList(_basePath + "010_test_asis.csv", "test")
for tmpFeature in featureList:
outputPath = _basePath + expNo + "_blender_" + tmpFeature + "_test.csv"
# ansPath = _basePath + "010_Extra_Trees_stack_event_type.csv"
# dr = DataReader()
# tmpX, ansY = dr.cvtPathListToDfList(ansPath, "train")
# tmpDfList = []
tmpFeatureBlendedAns = pd.DataFrame()
for tmpClfName in clfNameList:
dr = DataReader()
tmpPath = _basePath + "010_" + tmpClfName + "_stack_" + tmpFeature + "_test.csv"
newX, tmpY = dr.cvtPathListToDfList(tmpPath, "train")
tmpWight = tmpWeightList[tmpI][tmpJ]
newX = newX.multiply(tmpWight)
# 4. test all data, output 3 ans as features
# D:\Kaggle\Telstra\004_one_hot_resource_type\(Xgboost)_(2016-02-06_11_14_31).model
# D:\Kaggle\Telstra\004_one_hot_resource_type\(Random_Forest)_(2016-02-06_11_24_09).model
# D:\Kaggle\Telstra\004_one_hot_resource_type\(Extra_Trees)_(2016-02-06_11_30_52).model
# D:\Kaggle\Telstra\004_one_hot_resource_type\(K_NN)_(2016-02-06_11_40_10).model
modelFolder = _basePath + "models" + Config.osSep + "stacked" + Config.osSep
clfNameList = []
clfNameList.append("Extra_Trees")
clfNameList.append("K_NN")
clfNameList.append("Random_Forest")
clfNameList.append("Xgboost")
clfNameList.append("Logistic_Regression")
testCsv = _basePath + "010_train_tobe.csv"
dr = DataReader()
newX, testY = dr.cvtPathListToDfList(testCsv, "train")
for curModel in clfNameList:
modelPath = modelFolder + str(
getMatchNameModelPath(modelFolder, curModel))
tmpOutPath = _basePath + expNo + "_blender" + curModel + "_train.csv"
tmpClf = loadModel(modelPath)
log(tmpClf.predict_proba(newX))
#outDf = pd.concat([newX, pd.DataFrame(tmpClf.predict_proba(newX))], axis=1)
outDf = pd.DataFrame(tmpClf.predict_proba(newX))
outDf.to_csv(tmpOutPath, sep=',', encoding='utf-8')
#musicAlarm()