def test_GBMGrid_basic_prostate(self):
h2o.beta_features = True
csvFilename = "prostate.csv"
print "\nStarting", csvFilename
# columns start at 0
csvPathname = 'logreg/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=csvFilename + ".hex",
schema='put')
colNames = [
'ID', 'CAPSULE', 'AGE', 'RACE', 'DPROS', 'DCAPS', 'PSA', 'VOL',
'GLEASON'
]
modelKey = 'GBMGrid_prostate'
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
'destination_key': modelKey,
'ignored_cols_by_name': 'ID',
'learn_rate': '.1,.2',
'ntrees': '1:3:1',
'max_depth': '8,9',
'min_rows': '1:5:2',
'response': 'CAPSULE',
'classification': 1 if DO_CLASSIFICATION else 0,
}
kwargs = params.copy()
timeoutSecs = 1800
start = time.time()
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, **kwargs)
elapsed = time.time() - start
print "GBM training completed in", elapsed, "seconds."
h2o_gbm.showGBMGridResults(GBMResult, 15)
def test_GBMGrid_basic_prostate(self):
csvFilename = "prostate.csv"
print "\nStarting", csvFilename
# columns start at 0
csvPathname = 'logreg/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=csvFilename + ".hex", schema='put')
colNames = ['ID','CAPSULE','AGE','RACE','DPROS','DCAPS','PSA','VOL','GLEASON']
modelKey = 'GBMGrid_prostate'
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
'destination_key': modelKey,
'ignored_cols_by_name': 'ID',
'learn_rate': '.1,.2',
'ntrees': '1:3:1',
'max_depth': '8,9',
'min_rows': '1:5:2',
'response': 'CAPSULE',
'classification': 1 if DO_CLASSIFICATION else 0,
}
kwargs = params.copy()
timeoutSecs = 1800
start = time.time()
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, **kwargs)
elapsed = time.time() - start
print "GBM training completed in", elapsed, "seconds."
h2o_gbm.showGBMGridResults(GBMResult, 15)
def test_GBMGrid_basic_benign(self):
csvFilename = "benign.csv"
print "\nStarting", csvFilename
csvPathname = 'logreg/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=csvFilename + ".hex", schema='put')
# columns start at 0
# cols 0-13. 3 is output
# no member id in this one
# check the first in the models list. It should be the best
colNames = [ 'STR','OBS','AGMT','FNDX','HIGD','DEG','CHK', 'AGP1','AGMN','NLV','LIV','WT','AGLP','MST' ]
modelKey = 'GBMGrid_benign'
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
'destination_key': modelKey,
'ignored_cols_by_name': 'STR',
'learn_rate': '.1,.2,.25',
'ntrees': '3:5:1',
'max_depth': '5,7',
'min_rows': '1,2',
'response': 'FNDX',
'classification': 1 if DO_CLASSIFICATION else 0,
}
kwargs = params.copy()
timeoutSecs = 1800
start = time.time()
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, **kwargs)
elapsed = time.time() - start
print "GBM training completed in", elapsed, "seconds."
h2o_gbm.showGBMGridResults(GBMResult, 0)
def test_GBMGrid_basic_prostate(self):
csvFilename = "prostate.csv"
print "\nStarting", csvFilename
# columns start at 0
csvPathname = "logreg/" + csvFilename
parseResult = h2i.import_parse(bucket="smalldata", path=csvPathname, hex_key=csvFilename + ".hex", schema="put")
colNames = ["ID", "CAPSULE", "AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"]
modelKey = "GBMGrid_prostate"
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
"destination_key": modelKey,
"ignored_cols_by_name": "ID",
"learn_rate": ".1,.2",
"ntrees": "1:3:1",
"max_depth": "8,9",
"min_rows": "1:5:2",
"response": "CAPSULE",
"classification": 1 if DO_CLASSIFICATION else 0,
}
kwargs = params.copy()
timeoutSecs = 1800
start = time.time()
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, **kwargs)
elapsed = time.time() - start
print "GBM training completed in", elapsed, "seconds."
h2o_gbm.showGBMGridResults(GBMResult, 10)
def test_GBMGrid_basic_many(self):
h2o.beta_features = True
csvFilename = "prostate.csv"
print "\nStarting", csvFilename
# columns start at 0
csvPathname = 'logreg/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=csvFilename + ".hex", schema='put')
colNames = ['ID','CAPSULE','AGE','RACE','DPROS','DCAPS','PSA','VOL','GLEASON']
modelKey = 'GBMGrid_prostate'
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
'destination_key': modelKey,
'ignored_cols_by_name': 'ID',
'learn_rate': '.1,.2',
'ntrees': '8,10',
'max_depth': '8,9',
'min_rows': '1,2',
'response': 'CAPSULE',
'classification': 1 if DO_CLASSIFICATION else 0,
'grid_parallelism': 1,
}
kwargs = params.copy()
timeoutSecs = 1800
jobs = []
# kick off 5 of these GBM grid jobs, with different tree choices
start = time.time()
totalGBMGridJobs = 0
# for more in range(8):
# fast
# for more in range(9):
for i in range(5):
kwargs = params.copy()
kwargs['min_rows'] = '1,2,3'
if DO_FROM_TO_STEP:
kwargs['max_depth'] = '5:10:1'
else:
kwargs['max_depth'] = '5,6,10'
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, noPoll=True, **kwargs)
# print "GBMResult:", h2o.dump_json(GBMResult)
job_key = GBMResult['job_key']
model_key = GBMResult['destination_key']
jobs.append( (job_key, model_key) )
totalGBMGridJobs += 1
h2o_jobs.pollWaitJobs(timeoutSecs=300)
elapsed = time.time() - start
for job_key, model_key in jobs:
GBMResult = h2o.nodes[0].gbm_grid_view(job_key=job_key, destination_key=model_key)
h2o_gbm.showGBMGridResults(GBMResult, 15)
print "All GBM jobs completed in", elapsed, "seconds."
print "totalGBMGridJobs:", totalGBMGridJobs
def test_GBMGrid_basic_many(self):
csvFilename = "prostate.csv"
print "\nStarting", csvFilename
# columns start at 0
csvPathname = 'logreg/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=csvFilename + ".hex", schema='put')
colNames = ['ID','CAPSULE','AGE','RACE','DPROS','DCAPS','PSA','VOL','GLEASON']
modelKey = 'GBMGrid_prostate'
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
'destination_key': modelKey,
'ignored_cols_by_name': 'ID',
'learn_rate': '.1,.2',
'ntrees': '8,10',
'max_depth': '8,9',
'min_rows': '1,2',
'response': 'CAPSULE',
'classification': 1 if DO_CLASSIFICATION else 0,
'grid_parallelism': 1,
}
kwargs = params.copy()
timeoutSecs = 1800
jobs = []
# kick off 5 of these GBM grid jobs, with different tree choices
start = time.time()
totalGBMGridJobs = 0
# for more in range(8):
# fast
# for more in range(9):
for i in range(50 if DO_FAIL_CASE else 10):
kwargs = params.copy()
kwargs['min_rows'] = '1,2,3'
if DO_FROM_TO_STEP:
kwargs['max_depth'] = '5:10:1'
else:
kwargs['max_depth'] = '5,6,10'
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, noPoll=True, **kwargs)
# print "GBMResult:", h2o.dump_json(GBMResult)
job_key = GBMResult['job_key']
model_key = GBMResult['destination_key']
jobs.append( (job_key, model_key) )
totalGBMGridJobs += 1
h2o_jobs.pollWaitJobs(timeoutSecs=300)
elapsed = time.time() - start
for job_key, model_key in jobs:
GBMResult = h2o.nodes[0].gbm_grid_view(job_key=job_key, destination_key=model_key)
h2o_gbm.showGBMGridResults(GBMResult, 15)
print "All GBM jobs completed in", elapsed, "seconds."
print "totalGBMGridJobs:", totalGBMGridJobs
def test_GBMGrid_basic_benign(self):
csvFilename = "benign.csv"
print "\nStarting", csvFilename
csvPathname = "logreg/" + csvFilename
parseResult = h2i.import_parse(bucket="smalldata", path=csvPathname, hex_key=csvFilename + ".hex", schema="put")
# columns start at 0
# cols 0-13. 3 is output
# no member id in this one
# check the first in the models list. It should be the best
colNames = [
"STR",
"OBS",
"AGMT",
"FNDX",
"HIGD",
"DEG",
"CHK",
"AGP1",
"AGMN",
"NLV",
"LIV",
"WT",
"AGLP",
"MST",
]
modelKey = "GBMGrid_benign"
# 'cols', 'ignored_cols_by_name', and 'ignored_cols' have to be exclusive
params = {
"destination_key": modelKey,
"ignored_cols_by_name": "STR",
"learn_rate": ".1,.2,.25",
"ntrees": "3:5:1",
"max_depth": "5,7",
"min_rows": "1,2",
"response": "FNDX",
"classification": 1 if DO_CLASSIFICATION else 0,
}
kwargs = params.copy()
timeoutSecs = 1800
start = time.time()
GBMResult = h2o_cmd.runGBM(parseResult=parseResult, **kwargs)
elapsed = time.time() - start
print "GBM training completed in", elapsed, "seconds."
h2o_gbm.showGBMGridResults(GBMResult, 0)
