def test_GLM_poisson_1(self):
csvFilename = 'covtype.data'
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=10)
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(inspect['num_rows']), \
" num_cols:", "{:,}".format(inspect['num_cols'])
if (1 == 0):
print "WARNING: just doing the first 33 features, for comparison to ??? numbers"
# pythonic!
x = ",".join(map(str, range(33)))
else:
x = ""
print "WARNING: max_iter set to 8 for benchmark comparisons"
max_iter = 8
y = "54"
kwargs = {
'x': x,
'y': y,
'family': 'poisson',
'link': 'log',
'n_folds': 0,
'max_iter': max_iter,
'beta_epsilon': 1e-3
}
timeoutSecs = 120
# L2
start = time.time()
kwargs.update({'alpha': 0, 'lambda': 0})
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
print "glm (L2) end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 13, **kwargs)
# Elastic
kwargs.update({'alpha': 0.5, 'lambda': 1e-4})
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
print "glm (Elastic) end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 13, **kwargs)
# L1
kwargs.update({'alpha': 1, 'lambda': 1e-4})
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
print "glm (L1) end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 13, **kwargs)
def test_tnc3_ignore(self):
csvFilename = 'tnc3_10.csv'
csvPathname = h2o.find_file('smalldata/' + csvFilename)
print "\n" + csvPathname
key2 = "tnc3.hex"
h2b.browseTheCloud()
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=key2, timeoutSecs=10)
print "Parse result['Key']:", parseKey['destination_key']
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(10)
if (1==0):
lenNodes = len(h2o.nodes)
colResultList = h2e.exec_expr_list_across_cols(lenNodes, numExprList, key2, maxCol=10,
incrementingResult=False, timeoutSecs=10)
print "\ncolResultList after num swap", colResultList
if (1==1):
start = time.time()
kwargs = {'y': 13, 'num_cross_validation_folds': 6}
# hmm. maybe we should update to use key as input
# in case exec is used to change the parseKey
# in any case, the destination_key in parseKey was what was updated
# so if we Exec, it's correct.
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=300, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
#******************
if (1==0):
colResultList = h2e.exec_expr_list_across_cols(lenNodes, charExprList, key2, maxCol=10,
incrementingResult=False, timeoutSecs=10)
print "\ncolResultList after char swap", colResultList
if (1==1):
start = time.time()
kwargs = {'y': 13, 'num_cross_validation_folds': 6}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=300, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
if not h2o.browse_disable:
### print "\n <ctrl-C> to quit sleeping here"
### time.sleep(1500)
pass
def test_GLM_covtype(self):
csvFilename = 'covtype.data'
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname,timeoutSecs=10)
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(inspect['num_rows']), \
" num_cols:", "{:,}".format(inspect['num_cols'])
if (1==0):
print "WARNING: just doing the first 33 features, for comparison to allstate numbers"
# pythonic!
x = ",".join(map(str,range(33)))
else:
x = ""
print "WARNING: max_iter set to 8 for benchmark comparisons"
max_iter = 8
y = "54"
# L2
args = {
'x': x,
'y': y,
'family': 'binomial',
'link': 'logit',
'num_cross_validation_folds': 0,
'case_mode': '=',
'case': 1,
'max_iter': max_iter,
'beta_eps': 1e-3}
timeoutSecs = 120
start = time.time()
kwargs.update({'alpha': 0, 'lambda': 0})
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm (L2) end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 13, **kwargs)
# Elastic
kwargs.update({'alpha': 0.5, 'lambda': 1e-4})
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm (Elastic) end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 13, **kwargs)
# L1
kwargs.update({'alpha': 1, 'lambda': 1e-4})
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm (L1) end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 13, **kwargs)
def test_C_prostate(self):
print "\nStarting prostate.csv"
# columns start at 0
y = "1"
x = ""
csvFilename = "prostate.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
for appendx in xrange(9):
if (appendx == 0):
print "\n0 is member ID. not used"
elif (appendx == 1):
print "\n1 is output."
else:
if x == "":
x = str(appendx)
else:
x = x + "," + str(appendx)
sys.stdout.write('.')
sys.stdout.flush()
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y, 'num_cross_validation_folds': 5}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=2, **kwargs)
# ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON
h2o_glm.simpleCheckGLM(self, glm, 'AGE', **kwargs)
def test_B_benign(self):
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
# columns start at 0
y = "3"
x = ""
# cols 0-13. 3 is output
# no member id in this one
for appendx in xrange(14):
if (appendx == 3):
print "\n3 is output."
else:
if x == "":
x = str(appendx)
else:
x = x + "," + str(appendx)
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y}
# fails with num_cross_validation_folds
print "Not doing num_cross_validation_folds with benign. Fails with 'unable to solve?'"
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=5, **kwargs)
# no longer look at STR?
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
def test_GLM_model_key_unique(self):
modelKeyDict = {}
for trial in range (1,5):
csvPathname = 'iris/iris2.csv'
start = time.time()
# h2o.py now sets destination_key for a fixed default model name,
# we want h2o to create model names for this test, so use none here
kwargs = {'destination_key': None, 'y':4, 'family': 'binomial', 'case': 1, 'case_mode': '>'}
# make sure each parse is unique dest key (not in use
hex_key = "iris2_" + str(trial) + ".hex"
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=hex_key, schema='put',
timeoutSecs=10, noPoll=True, **kwargs )
glmResult = h2o_cmd.runGLMOnly(parseResult=parseResutl, timeoutSecs=10, noPoll=True, **kwargs )
print "GLM #%d" % trial, "started on ", csvPathname, 'took', time.time() - start, 'seconds'
model_key = glmResult['destination_key']
print "GLM model_key:", model_key
if model_key in modelKeyDict:
raise Exception("same model_key used in GLM #%d that matches prior GLM #%d" % (trial, modelKeyDict[model_key]))
modelKeyDict[model_key] = trial
# just show the jobs still going, if any. maybe none, because short (iris)
a = h2o.nodes[0].jobs_admin()
h2o.verboseprint("jobs_admin():", h2o.dump_json(a))
def test_GLM_params_rand2_8977501266014959103(self):
# csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
csvPathname = h2o.find_file('smalldata/covtype/covtype.20k.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname)
# for determinism, I guess we should spit out the seed?
# random.seed(SEED)
# SEED = random.randint(0, sys.maxint)
SEED = 8977501266014959103
# if you have to force to redo a test
# SEED =
random.seed(SEED)
print "\nUsing random seed:", SEED
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
params = {
'y': 54,
'alpha': 0,
'lambda': 0,
'case': 1,
'n_folds': 1
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=70,
parseKey=parseKey,
**kwargs)
# pass the kwargs with all the params, so we know what we asked for!
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.check_sandbox_for_errors()
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_GLM_syn_2659x1049x2enum(self):
csvFilename = "syn_2659x1049x2enum.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
kwargs = params
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=240, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
def test_C_prostate(self):
print "\nStarting prostate.csv"
# columns start at 0
y = "1"
csvFilename = "prostate.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
for maxx in range(2,9):
x = range(maxx)
x.remove(0) # 0 is member ID. not used
x.remove(1) # 1 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
# solver can be ADMM. standardize normalizes the data.
kwargs = {'x': x, 'y': y, 'n_folds': 5,\
'expert': 1, 'lsm_solver': 'GenGradient', 'standardize':1}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=30, **kwargs)
# ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON
h2o_glm.simpleCheckGLM(self, glm, 'AGE', **kwargs)
h2o.check_sandbox_for_errors()
sys.stdout.write('.')
sys.stdout.flush()
def test_GLM_params_rand2_4082088627997819015(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key='covtype')
paramDict = define_params()
for trial in range(40):
# params is mutable. This is default.
params = {
'y': 54,
'n_folds': 3,
'family': 'binomial',
'max_iter': 5,
'case': 1,
'alpha': 0,
'lambda': 0
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
timeoutSecs = max(150,
params['n_folds'] * 10 + params['max_iter'] * 10)
glm = h2o_cmd.runGLMOnly(timeoutSecs=timeoutSecs,
parseKey=parseKey,
**kwargs)
elapsed = time.time() - start
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
# FIX! I suppose we have the problem of stdout/stderr not having flushed?
# should hook in some way of flushing the remote node stdout/stderr
h2o.check_sandbox_for_errors()
print "glm end on ", csvPathname, 'took', elapsed, 'seconds.',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "Trial #", trial, "completed\n"
def test_C_prostate(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting prostate.csv"
# columns start at 0
y = "1"
x = ""
csvFilename = "prostate.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
for maxx in range(2,6):
x = range(maxx)
x.remove(0) # 0 is member ID. not used
x.remove(1) # 1 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y, 'n_folds': 5}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=15, **kwargs)
# ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON
h2o_glm.simpleCheckGLM(self, glm, 'AGE', **kwargs)
sys.stdout.write('.')
sys.stdout.flush()
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
def test_B_benign(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
# columns start at 0
y = "3"
# cols 0-13. 3 is output
# no member id in this one
for maxx in range(11,14):
x = range(maxx)
x.remove(3) # 3 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=15, **kwargs)
# no longer look at STR?
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
sys.stdout.write('.')
sys.stdout.flush()
def test_glm_covtype_single_cols(self):
timeoutSecs = 10
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
print "\n" + csvPathname
# columns start at 0
y = "54"
x = ""
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=15)
print "GLM binomial wth 1 X column at a time"
print "Result check: abs. value of coefficient and intercept returned are bigger than zero"
for colX in xrange(54):
if x == "":
x = str(colX)
else:
# x = x + "," + str(colX)
x = str(colX)
sys.stdout.write('.')
sys.stdout.flush()
print "\nx:", x
print "y:", y
start = time.time()
kwargs = {'x': x, 'y': y, 'n_folds': 6, 'case': 2}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
h2o_glm.simpleCheckGLM(self, glm, colX, **kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
def test_B_importFolder_files(self):
# just do the import folder once
importFolderPath = "/home/0xdiag/datasets/standard"
h2i.setupImportFolder(None, importFolderPath)
timeoutSecs = 1500
csvFilenameAll = [
# quick test first
"covtype.data",
# then the real thing
"billion_rows.csv.gz",
]
# csvFilenameList = random.sample(csvFilenameAll,1)
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
### h2b.browseTheCloud()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in importFolder dir
start = time.time()
parseKey = h2i.parseImportFolderFile(None,
csvFilename,
importFolderPath,
timeoutSecs=timeoutSecs,
pollTimeoutSecs=60)
elapsed = time.time() - start
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey[
'destination_key']
print csvFilename, "completed in", elapsed, "seconds.", "%d pct. of timeout" % (
(elapsed * 100) / timeoutSecs)
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(key=parseKey['destination_key'])
print "\n" + csvFilename
kwargs = {
'x': 0,
'y': 1,
'n_folds': 0,
'case_mode': '=',
'case': 1
}
# one coefficient is checked a little more
colX = 0
# L2
kwargs.update({'alpha': 0, 'lambda': 0})
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
elapsed = time.time() - start
print "glm (L2) end on ", csvFilename, 'took', elapsed, 'seconds.', "%d pct. of timeout" % (
(elapsed / timeoutSecs) * 100)
h2o_glm.simpleCheckGLM(self, glm, colX, **kwargs)
sys.stdout.write('\n.')
sys.stdout.flush()
def test_GLM_syn_2659x1049x2enum(self):
csvFilename = "syn_2659x1049x2enum.csv"
csvPathname = 'logreg' + '/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=csvFilename + ".hex", schema='put')
kwargs = params
glm = h2o_cmd.runGLMOnly(parseResult=parseResult, timeoutSecs=240, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
def test_B_benign(self):
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
# columns start at 0
y = "3"
# cols 0-13. 3 is output
# no member id in this one
for maxx in range(4,14):
x = range(maxx)
x.remove(3) # 3 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
# solver can be ADMM
kwargs = {'x': x, 'y': y,\
'expert': 1, 'lsm_solver': 'GenGradient', 'standardize': 1, 'n_folds': 1}
# fails with n_folds
print "Not doing n_folds with benign. Fails with 'unable to solve?'"
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=30, **kwargs)
# no longer look at STR?
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.check_sandbox_for_errors()
sys.stdout.write('.')
sys.stdout.flush()
def glm_doit(self, csvFilename, bucket, csvPathname, timeoutSecs=30):
print "\nStarting GLM of", csvFilename
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, hex_key=csvFilename + ".hex", schema='put', timeoutSecs=30)
y = "10"
x = ""
# Took n_folds out, because GLM doesn't include n_folds time and it's slow
# wanted to compare GLM time to my measured time
# hastie has two values 1,-1. need to specify case
kwargs = {'x': x, 'y': y, 'case': -1, 'thresholds': 0.5}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
print "GLM in", (time.time() - start), "secs (python)"
h2o_glm.simpleCheckGLM(self, glm, 7, **kwargs)
# compare this glm to the first one. since the files are replications, the results
# should be similar?
GLMModel = glm['GLMModel']
validationsList = glm['GLMModel']['validations']
validations = validationsList[0]
# validations['err']
if self.validations1:
h2o_glm.compareToFirstGlm(self, 'err', validations, self.validations1)
else:
self.validations1 = copy.deepcopy(validations)
def test_GLM_from_import_hosts(self):
if localhost:
csvFilenameList = [
'covtype.data',
]
else:
csvFilenameList = [
'covtype200x.data',
'covtype200x.data',
'covtype.data',
'covtype.data',
'covtype20x.data',
'covtype20x.data',
]
# a browser window too, just because we can
## h2b.browseTheCloud()
importFolderPath = "standard"
validations1= {}
coefficients1= {}
for csvFilename in csvFilenameList:
# have to re-import each iteration now, since the source key
# is removed and if we re-parse it, it's not there
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets', path=csvPathname, timeoutSecs=2000)
print csvFilename, 'parse time:', parseResult['response']['time']
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
print "\n" + csvFilename
start = time.time()
# can't pass lamba as kwarg because it's a python reserved word
# FIX! just look at X=0:1 for speed, for now
kwargs = {'y': 54, 'n_folds': 2, 'family': "binomial", 'case': 1}
glm = h2o_cmd.runGLMOnly(parseResult=parseResult, timeoutSecs=2000, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.verboseprint("\nglm:", glm)
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
GLMModel = glm['GLMModel']
coefficients = GLMModel['coefficients']
validationsList = GLMModel['validations']
validations = validationsList.pop()
# validations['err']
if validations1:
h2o_glm.compareToFirstGlm(self, 'err', validations, validations1)
else:
validations1 = copy.deepcopy(validations)
if coefficients1:
h2o_glm.compareToFirstGlm(self, '0', coefficients, coefficients1)
else:
coefficients1 = copy.deepcopy(coefficients)
sys.stdout.write('.')
sys.stdout.flush()
def test_GLM_princeton(self):
# filename, y, timeoutSecs
# these are all counts? using gaussian?
csvFilenameList = [
('cuse.dat', 'gaussian', 3, 5), # notUsing
('cuse.dat', 'gaussian', 4, 5), # using
('copen.dat', 'gaussian', 4, 5),
('housing.raw', 'gaussian', 4, 5),
]
trial = 0
for (csvFilename, family, y, timeoutSecs) in csvFilenameList:
csvPathname1 = 'logreg/princeton/' + csvFilename
fullPathname1 = h2i.find_folder_and_filename('smalldata', csvPathname1, returnFullPath=True)
csvPathname2 = SYNDATASETS_DIR + '/' + csvFilename + '_stripped.csv'
h2o_util.file_strip_trailing_spaces(fullPathname1, csvPathname2)
kwargs = {'n_folds': 0, 'family': family, 'link': 'familyDefault', 'y': y}
parseResult = h2i.import_parse(path=csvPathname2, schema='put', timeoutSecs=timeoutSecs, **kwargs)
start = time.time()
glm = h2o_cmd.runGLMOnly(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end (w/check) on ", csvPathname2, 'took', time.time() - start, 'seconds'
trial += 1
print "\nTrial #", trial
def test_C_hhp_107_01(self):
csvPathname = h2o.find_file("smalldata/hhp_107_01.data.gz")
print "\n" + csvPathname
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=15)
# pop open a browser on the cloud
h2b.browseTheCloud()
# build up the parameter string in X
y = "106"
x = ""
# go right to the big X and iterate on that case
### for trial in range(2):
for trial in range(2):
print "\nTrial #", trial, "start"
print "\nx:", x
print "y:", y
start = time.time()
kwargs = {'y': y}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=200, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, 57, **kwargs)
h2o.check_sandbox_for_errors()
### h2b.browseJsonHistoryAsUrlLastMatch("GLM")
print "\nTrial #", trial
def test_GLM_big1_nopoll(self):
csvPathname = h2o.find_file("smalldata/hhp_107_01.data.gz")
print "\n" + csvPathname
y = "106"
x = ""
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=15)
glmInitial = []
# dispatch multiple jobs back to back
start = time.time()
for jobDispatch in range(40):
kwargs = {'x': x, 'y': y, 'n_folds': 1}
# FIX! what model keys do these get?
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=300, noPoll=True, **kwargs)
glmInitial.append(glm)
print "glm job dispatch end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "\njobDispatch #", jobDispatch
timeoutSecs = 200
h2o_jobs.pollWaitJobs(pattern='GLMModel', timeoutSecs=timeoutSecs, retryDelaySecs=10)
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
# we saved the initial response?
# if we do another poll they should be done now, and better to get it that
# way rather than the inspect (to match what simpleCheckGLM is expected
for glm in glmInitial:
print "Checking completed job, with no polling:", glm
a = h2o.nodes[0].poll_url(glm['response'], noPoll=True)
h2o_glm.simpleCheckGLM(self, a, 57, **kwargs)
def test_C_hhp_107_01(self):
csvPathname = h2o.find_file("smalldata/hhp_107_01.data.gz")
print "\n" + csvPathname
y = "106"
x = ""
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=15)
for trial in xrange(3):
sys.stdout.write('.')
sys.stdout.flush()
print "\nx:", x
print "y:", y
start = time.time()
kwargs = {'x': x, 'y': y, 'n_folds': 6}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=300,
**kwargs)
# pass the kwargs with all the params, so we know what we asked for!
h2o_glm.simpleCheckGLM(self, glm, 57, **kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
print "\nTrial #", trial
def test_GLM_from_import_hosts(self):
if localhost:
csvFilenameList = ["covtype.data"]
else:
csvFilenameList = [
"covtype200x.data",
"covtype200x.data",
"covtype.data",
"covtype.data",
"covtype20x.data",
"covtype20x.data",
]
# a browser window too, just because we can
h2b.browseTheCloud()
importFolderPath = "/home/0xdiag/datasets/standard"
validations1 = {}
coefficients1 = {}
for csvFilename in csvFilenameList:
# have to re-import each iteration now, since the source key
# is removed and if we re-parse it, it's not there
h2i.setupImportFolder(None, importFolderPath, timeoutSecs=60)
# creates csvFilename.hex from file in importFolder dir
parseKey = h2i.parseImportFolderFile(None, csvFilename, importFolderPath, timeoutSecs=2000)
print csvFilename, "parse time:", parseKey["response"]["time"]
print "Parse result['destination_key']:", parseKey["destination_key"]
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(key=parseKey["destination_key"])
print "\n" + csvFilename
start = time.time()
# can't pass lamba as kwarg because it's a python reserved word
# FIX! just look at X=0:1 for speed, for now
kwargs = {"y": 54, "n_folds": 2, "family": "binomial", "case": 1}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=2000, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.verboseprint("\nglm:", glm)
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
GLMModel = glm["GLMModel"]
coefficients = GLMModel["coefficients"]
validationsList = GLMModel["validations"]
validations = validationsList.pop()
# validations['err']
if validations1:
h2o_glm.compareToFirstGlm(self, "err", validations, validations1)
else:
validations1 = copy.deepcopy(validations)
if coefficients1:
h2o_glm.compareToFirstGlm(self, "0", coefficients, coefficients1)
else:
coefficients1 = copy.deepcopy(coefficients)
sys.stdout.write(".")
sys.stdout.flush()
def test_C_prostate(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting prostate.csv"
# columns start at 0
y = "1"
x = ""
csvFilename = "prostate.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
for maxx in range(2,6):
x = range(maxx)
x.remove(0) # 0 is member ID. not used
x.remove(1) # 1 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y, 'n_folds': 5}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=15, **kwargs)
# ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON
h2o_glm.simpleCheckGLM(self, glm, 'AGE', **kwargs)
sys.stdout.write('.')
sys.stdout.flush()
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
def test_C_hhp_107_01(self):
csvPathname = h2o.find_file("smalldata/hhp_107_01.data.gz")
print "\n" + csvPathname
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=15)
# pop open a browser on the cloud
h2b.browseTheCloud()
# build up the parameter string in X
y = "106"
x = ""
# go right to the big X and iterate on that case
### for trial in range(2):
for trial in range(2):
print "\nTrial #", trial, "start"
print "\nx:", x
print "y:", y
start = time.time()
kwargs = {'y': y}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=200,
**kwargs)
h2o_glm.simpleCheckGLM(self, glm, 57, **kwargs)
h2o.check_sandbox_for_errors()
### h2b.browseJsonHistoryAsUrlLastMatch("GLM")
print "\nTrial #", trial
def test_B_benign(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
# columns start at 0
y = "3"
# cols 0-13. 3 is output
# no member id in this one
for maxx in range(11,14):
x = range(maxx)
x.remove(3) # 3 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y}
# fails with n_folds
print "Not doing n_folds with benign. Fails with 'unable to solve?'"
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=15, **kwargs)
# no longer look at STR?
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
sys.stdout.write('.')
sys.stdout.flush()
def test_GLM_params_rand2_newargs(self):
# csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
csvPathname = h2o.find_file('smalldata/covtype/covtype.20k.data')
key = 'covtype.20k'
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key=key)
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
params = {
'y': 54,
'case': 1,
'lambda': 0,
'alpha': 0,
'n_folds': 1
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=70,
parseKey=parseKey,
**kwargs)
# pass the kwargs with all the params, so we know what we asked for!
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.check_sandbox_for_errors()
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_B_benign(self):
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
# columns start at 0
y = "3"
# cols 0-13. 3 is output
# no member id in this one
for maxx in range(4,14):
x = range(maxx)
x.remove(3) # 3 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
# solver can be ADMM
kwargs = {'x': x, 'y': y,\
'expert': 1, 'lsm_solver': 'GenGradient', 'standardize': 1, 'n_folds': 1}
# fails with n_folds
print "Not doing n_folds with benign. Fails with 'unable to solve?'"
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=30, **kwargs)
# no longer look at STR?
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.check_sandbox_for_errors()
sys.stdout.write('.')
sys.stdout.flush()
def test_GLM_gaussian_rand2(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname)
# for determinism, I guess we should spit out the seed?
# random.seed(SEED)
SEED = random.randint(0, sys.maxint)
# if you have to force to redo a test
# SEED =
random.seed(SEED)
print "\nUsing random seed:", SEED
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
params = {
'y': 54,
'n_folds': 3,
'family': "gamma",
'alpha': 0.5,
'lambda': 1e-4,
'max_iter': 10
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=120,
parseKey=parseKey,
**kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "Trial #", trial, "completed\n"
def glm_doit(self, csvFilename, csvPathname, timeoutSecs=30):
print "\nStarting GLM of", csvFilename
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname,
key2=csvFilename + ".hex",
timeoutSecs=10)
y = "10"
x = ""
# Took n_folds out, because GLM doesn't include n_folds time and it's slow
# wanted to compare GLM time to my measured time
# hastie has two values, 1 and -1. need to use case for one of them
kwargs = {'x': x, 'y': y, 'case': -1}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
print "GLM in", (time.time() - start), "secs (python measured)"
h2o_glm.simpleCheckGLM(self, glm, 7, **kwargs)
# compare this glm to the first one. since the files are replications, the results
# should be similar?
GLMModel = glm['GLMModel']
validationsList = glm['GLMModel']['validations']
validations = validationsList[0]
# validations['err']
if self.validations1:
h2o_glm.compareToFirstGlm(self, 'err', validations, self.validations1)
else:
self.validations1 = copy.deepcopy(validations)
def test_GLM_params_rand2_4082088627997819015(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key='covtype')
paramDict = define_params()
for trial in range(40):
# params is mutable. This is default.
params = {
'y': 54,
'n_folds' : 3,
'family' : 'binomial',
'max_iter' : 5,
'case': 1,
'alpha': 0,
'lambda': 0
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
timeoutSecs = max(150, params['n_folds']*10 + params['max_iter']*10)
glm = h2o_cmd.runGLMOnly(timeoutSecs=timeoutSecs, parseKey=parseKey, **kwargs)
elapsed = time.time() - start
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
# FIX! I suppose we have the problem of stdout/stderr not having flushed?
# should hook in some way of flushing the remote node stdout/stderr
h2o.check_sandbox_for_errors()
print "glm end on ", csvPathname, 'took', elapsed, 'seconds.',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "Trial #", trial, "completed\n"
def test_many_cols_with_syn(self):
### h2b.browseTheCloud()
csvFilename = "logreg_trisum_int_cat_10000x10.csv"
csvPathname = "smalldata/logreg/" + csvFilename
key2 = csvFilename + ".hex"
parseKey = h2o_cmd.parseFile(None, h2o.find_file(csvPathname), key2=key2, timeoutSecs=10)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
paramDict = define_params()
paramDict2 = {}
for k in paramDict:
# sometimes we have a list to pick from in the value. now it's just list of 1.
paramDict2[k] = paramDict[k][0]
y = 10
# FIX! what should we have for case? 1 should be okay because we have 1's in output col
kwargs = {'y': y, 'max_iter': 50}
kwargs.update(paramDict2)
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=20, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 8, **kwargs)
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
time.sleep(5)
def test_loop_random_param_covtype(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key='covtype')
# for determinism, I guess we should spit out the seed?
# random.seed(SEED)
SEED = random.randint(0, sys.maxint)
# if you have to force to redo a test
# SEED =
random.seed(SEED)
print "\nUsing random seed:", SEED
paramDict = define_params()
for trial in range(40):
# params is mutable. This is default.
params = {
'y': 54,
'num_cross_validation_folds' : 3,
'family' : 'binomial',
'max_iter' : 5,
'case': 1,
'alpha': 0,
'lambda': 0
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=150, parseKey=parseKey, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
# FIX! I suppose we have the problem of stdout/stderr not having flushed?
# should hook in some way of flushing the remote node stdout/stderr
h2o.check_sandbox_for_errors()
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_GLM_gaussian_rand2(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname)
# for determinism, I guess we should spit out the seed?
# random.seed(SEED)
SEED = random.randint(0, sys.maxint)
# if you have to force to redo a test
# SEED =
random.seed(SEED)
print "\nUsing random seed:", SEED
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
params = {'y': 54, 'num_cross_validation_folds': 3, 'family': "gaussian", 'alpha': 0.5, 'lambda': 1e-4, 'max_iter': 30}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=120, parseKey=parseKey, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
start = time.time()
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "simpleCheckGLM end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_C_prostate(self):
print "\nStarting prostate.csv"
# columns start at 0
y = "1"
csvFilename = "prostate.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
for maxx in range(2,9):
x = range(maxx)
x.remove(0) # 0 is member ID. not used
x.remove(1) # 1 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
# solver can be ADMM. standardize normalizes the data.
kwargs = {'x': x, 'y': y, 'n_folds': 5,\
'expert': 1, 'lsm_solver': 'GenGradient', 'standardize':1}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=30, **kwargs)
# ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON
h2o_glm.simpleCheckGLM(self, glm, 'AGE', **kwargs)
h2o.check_sandbox_for_errors()
sys.stdout.write('.')
sys.stdout.flush()
def test_GLM_params_rand2(self):
# csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
csvPathname = h2o.find_file('smalldata/covtype/covtype.20k.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key="covtype.20k")
# for determinism, I guess we should spit out the seed?
# random.seed(SEED)
SEED = random.randint(0, sys.maxint)
# if you have to force to redo a test
# SEED =
random.seed(SEED)
print "\nUsing random seed:", SEED
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
params = {'y': 54, 'case': 1, 'alpha': 0, 'lambda': 0, 'n_folds': 1}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=70, parseKey=parseKey, **kwargs)
# pass the kwargs with all the params, so we know what we asked for!
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
h2o.check_sandbox_for_errors()
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_glm_covtype_single_cols(self):
timeoutSecs = 10
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
print "\n" + csvPathname
# columns start at 0
y = "54"
x = ""
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, timeoutSecs=15)
print "GLM binomial wth 1 X column at a time"
print "Result check: abs. value of coefficient and intercept returned are bigger than zero"
for colX in xrange(54):
if x == "":
x = str(colX)
else:
# x = x + "," + str(colX)
x = str(colX)
sys.stdout.write('.')
sys.stdout.flush()
print "\nx:", x
print "y:", y
start = time.time()
kwargs = {'x': x, 'y': y, 'n_folds': 6, 'case': 2}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, colX, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
def glm_doit(self, csvFilename, csvPathname, timeoutSecs=30):
print "\nStarting GLM of", csvFilename
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex", timeoutSecs=10)
y = "10"
x = ""
# Took num_cross_validation_folds out, because GLM doesn't include num_cross_validation_folds time and it's slow
# wanted to compare GLM time to my measured time
# hastie has two values, 1 and -1. need to use case for one of them
kwargs = {'x': x, 'y': y, 'case': -1}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "GLM in", (time.time() - start), "secs (python measured)"
h2o_glm.simpleCheckGLM(self, glm, 7, **kwargs)
# compare this glm to the first one. since the files are replications, the results
# should be similar?
GLMModel = glm['GLMModel']
validationsList = glm['GLMModel']['validations']
validations = validationsList[0]
# validations['err']
if self.validations1:
h2o_glm.compareToFirstGlm(self, 'err', validations, self.validations1)
else:
self.validations1 = copy.deepcopy(validations)
def test_C_prostate_w_predict(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting prostate.csv"
# columns start at 0
y = "1"
x = ""
csvFilename = "prostate.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
for maxx in range(2,6):
x = range(maxx)
x.remove(0) # 0 is member ID. not used
x.remove(1) # 1 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y, 'n_folds': 5}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=15, **kwargs)
# ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON
h2o_glm.simpleCheckGLM(self, glm, 'AGE', **kwargs)
GLMModel = glm['GLMModel']
modelKey = GLMModel['model_key']
print "Doing predict with same dataset, and the GLM model"
h2o.nodes[0].generate_predictions(model_key=modelKey, data_key=parseKey['destination_key'])
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
def test_B_benign_w_predict(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = h2o.find_file('smalldata/logreg' + '/' + csvFilename)
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=csvFilename + ".hex")
# columns start at 0
y = "3"
# cols 0-13. 3 is output
# no member id in this one
for maxx in range(11,14):
x = range(maxx)
x.remove(3) # 3 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y}
# fails with n_folds
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=15, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
GLMModel = glm['GLMModel']
modelKey = GLMModel['model_key']
print "Doing predict with same dataset, and the GLM model"
h2o.nodes[0].generate_predictions(model_key=modelKey, data_key=parseKey['destination_key'])
def test_GLM_gamma_rand2(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname)
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
params = {
'y': 54,
'n_folds': 3,
'family': "gamma",
'alpha': 0.5,
'lambda': 1e-4,
'max_iter': 24
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=120,
parseKey=parseKey,
**kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "Trial #", trial, "completed\n"
def test_B_benign(self):
h2o.nodes[0].log_view()
namelist = h2o.nodes[0].log_download()
print "\nStarting benign.csv"
csvFilename = "benign.csv"
csvPathname = 'logreg' + '/' + csvFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=csvFilename + ".hex", schema='put')
# columns start at 0
y = "3"
# cols 0-13. 3 is output
# no member id in this one
for maxx in range(11,14):
x = range(maxx)
x.remove(3) # 3 is output
x = ",".join(map(str,x))
print "\nx:", x
print "y:", y
kwargs = {'x': x, 'y': y}
glm = h2o_cmd.runGLMOnly(parseResult=parseResult, timeoutSecs=15, **kwargs)
# no longer look at STR?
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
sys.stdout.write('.')
sys.stdout.flush()
def test_loop_random_param_covtype(self):
csvPathname = h2o.find_dataset('UCI/UCI-large/covtype/covtype.data')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname)
paramDict = define_params()
for trial in range(20):
# params is mutable. This is default.
# FIX! does it never end if we don't have alpha specified?
params = {
'y': 54,
'n_folds': 3,
'family': "poisson",
'alpha': 0.5,
'lambda': 1e-4,
'beta_epsilon': 0.001,
'max_iter': 15,
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
# make timeout bigger with xvals
timeoutSecs = 60 + (kwargs['n_folds']*20)
# or double the 4 seconds per iteration (max_iter+1 worst case?)
timeoutSecs = max(timeoutSecs, (8 * (kwargs['max_iter']+1)))
start = time.time()
glm = h2o_cmd.runGLMOnly(timeoutSecs=timeoutSecs, parseKey=parseKey, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "Trial #", trial, "completed\n"
def test_GLM_many_cols_int2cat(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(10000, 10, 'cA.hex', 100),
(10000, 20, 'cB.hex', 200),
(10000, 30, 'cC.hex', 300),
(10000, 40, 'cD.hex', 400),
(10000, 50, 'cE.hex', 500),
]
### h2b.browseTheCloud()
# we're going to do a special exec across all the columns to turn them into enums
# including the duplicate of the output!
exprList = [
'<keyX>= colSwap(<keyX>,<col1>,factor(<keyX>[<col1>]))',
### '<keyX>= colSwap(<keyX>,<col1>,<keyX>[<col1>])',
]
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "\nCreating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
parseKey = h2o_cmd.parseFile(None, csvPathname, key2=key2, timeoutSecs=90)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
print "\nNow running the int 2 enum exec command across all input cols"
colResultList = h2e.exec_expr_list_across_cols(None, exprList, key2, maxCol=colCount,
timeoutSecs=90, incrementingResult=False)
print "\nexec colResultList", colResultList
paramDict2 = {}
for k in paramDict:
paramDict2[k] = paramDict[k][0]
# since we add the output twice, it's no longer colCount-1
y = colCount
kwargs = {'y': y, 'max_iter': 50, 'case': 1}
kwargs.update(paramDict2)
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
# only col y-1 (next to last)doesn't get renamed in coefficients
# due to enum/categorical expansion
print "y:", y
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
time.sleep(3)
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
time.sleep(3)
def test_GLM_with_logit_result_1(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(100000, 5, 'cA', 300),
]
### h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(
rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, \
"using random coefficients and intercept and logit eqn. for output"
(coefficients,
intercept) = gen_rand_equation(colCount, SEEDPERFILE)
print coefficients, intercept
write_syn_dataset(csvPathname, rowCount, colCount, coefficients,
intercept, SEEDPERFILE)
parseKey = h2o_cmd.parseFile(None,
csvPathname,
key2=key2,
timeoutSecs=10)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey[
'destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
y = colCount
kwargs = {
'y': y,
'max_iter': 60,
'lambda': 1e-4,
'alpha': 0,
'weight': 1.0,
'n_folds': 3,
'beta_epsilon': 1e-4,
'thresholds': 0.5,
}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
(warnings, coefficients,
intercept) = h2o_glm.simpleCheckGLM(self, glm, 0, **kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
time.sleep(5)
def test_many_cols_real(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(1000, 100, 'cA', 300),
(1000, 200, 'cB', 300),
(1000, 300, 'cC', 300),
(1000, 400, 'cD', 300),
(1000, 500, 'cE', 300),
(1000, 1000, 'cJ', 300),
]
### h2b.browseTheCloud()
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(
rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
parseKey = h2o_cmd.parseFile(None,
csvPathname,
key2=key2,
timeoutSecs=10)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey[
'destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
y = colCount
kwargs = {
'y': y,
'max_iter': 50,
'case': '1',
'case_mode': '=',
'lambda': 1e-4,
'alpha': 0.6
}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
time.sleep(5)
# try new offset/view
inspect = h2o_cmd.runInspect(None,
parseKey['destination_key'],
offset=100,
view=100)
def test_GLM_from_import_hosts(self):
if localhost:
csvFilenameList = [
'YearPredictionMSD.txt'
]
else:
csvFilenameList = [
'YearPredictionMSD.txt'
]
# a browser window too, just because we can
h2b.browseTheCloud()
importFolderPath = '/home/0xdiag/datasets'
h2i.setupImportFolder(None, importFolderPath)
validations1= {}
coefficients1= {}
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in importFolder dir
parseKey = h2i.parseImportFolderFile(None, csvFilename, importFolderPath, timeoutSecs=120)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(key=parseKey['destination_key'])
print "\n" + csvFilename
start = time.time()
# can't pass lamba as kwarg because it's a python reserved word
# FIX! just look at X=0:1 for speed, for now
kwargs = {'y': 54, 'n_folds': 2, 'family': "binomial", 'case': 1}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=2000, **kwargs)
# different when n_foldsidation is used? No trainingErrorDetails?
h2o.verboseprint("\nglm:", glm)
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
GLMModel = glm['GLMModel']
print "GLM time", GLMModel['time']
coefficients = GLMModel['coefficients']
validationsList = GLMModel['validations']
validations = validationsList.pop()
# validations['err']
if validations1:
h2o_glm.compareToFirstGlm(self, 'err', validations, validations1)
else:
validations1 = copy.deepcopy(validations)
if coefficients1:
h2o_glm.compareToFirstGlm(self, '0', coefficients, coefficients1)
else:
coefficients1 = copy.deepcopy(coefficients)
sys.stdout.write('.')
sys.stdout.flush()
def test_GLM_hdfs_YearPredictionMSD(self):
if localhost:
csvFilenameList = [
'YearPredictionMSD.txt',
'YearPredictionMSD.txt'
]
else:
csvFilenameList = [
'YearPredictionMSD.txt',
'YearPredictionMSD.txt'
]
# a browser window too, just because we can
h2b.browseTheCloud()
validations1= {}
coefficients1= {}
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in importFolder dir
h2i.setupImportHdfs()
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename, path='/datasets', timeoutSecs=60)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(key=parseKey['destination_key'])
print "\n" + csvFilename
start = time.time()
# can't pass lamba as kwarg because it's a python reserved word
# FIX! just look at X=0:1 for speed, for now
kwargs = {'y': 54, 'n_folds': 2, 'family': "binomial", 'case': 1}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=500, **kwargs)
# different when n_foldsidation is used? No trainingErrorDetails?
h2o.verboseprint("\nglm:", glm)
### h2b.browseJsonHistoryAsUrlLastMatch("GLM")
GLMModel = glm['GLMModel']
print "GLM time", GLMModel['time']
coefficients = GLMModel['coefficients']
validationsList = GLMModel['validations']
validations = validationsList.pop()
# validations['err']
if validations1:
h2o_glm.compareToFirstGlm(self, 'err', validations, validations1)
else:
validations1 = copy.deepcopy(validations)
if coefficients1:
h2o_glm.compareToFirstGlm(self, '0', coefficients, coefficients1)
else:
coefficients1 = copy.deepcopy(coefficients)
sys.stdout.write('.')
sys.stdout.flush()
def test_categorical_expand_and_probability_output(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
translateList = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm',
'n', 'o', 'p', 'q', 'r', 's', 't', 'u'
]
tryList = [
(7919, 53, 'cA', 600),
# translated to enums, 4 per col, so don't go above 2k effective cols or too slow!
(2659, 400, 'cB', 600),
]
### h2b.browseTheCloud()
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(
rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "\nCreating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE,
translateList)
print "\nUpload and parse", csvPathname
parseKey = h2o_cmd.parseFile(None,
csvPathname,
key2=key2,
timeoutSecs=240,
retryDelaySecs=0.5)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey[
'destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
paramDict2 = {}
for k in paramDict:
paramDict2[k] = paramDict[k][0]
y = colCount
kwargs = {'y': y, 'max_iter': 20}
kwargs.update(paramDict2)
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
print "glm end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
# only col Y-1 (next to last)doesn't get renamed in coefficients due to enum/categorical expansion
print "y:", y
# FIX! bug was dropped coefficients if constant column is dropped
### h2o_glm.simpleCheckGLM(self, glm, Y-2, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
def test_short(self):
csvFilename = 'part-00000b'
### csvFilename = 'short'
importFolderPath = '/home/hduser/data'
importFolderResult = h2i.setupImportFolder(None, importFolderPath)
csvPathname = importFolderPath + "/" + csvFilename
# FIX! does 'separator=' take ints or ?? hex format
# looks like it takes the hex string (two chars)
start = time.time()
# hardwire TAB as a separator, as opposed to white space (9)
parseKey = h2i.parseImportFolderFile(None, csvFilename, importFolderPath,
timeoutSecs=500, separator=9)
print "Parse of", parseKey['destination_key'], "took", time.time() - start, "seconds"
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
start = time.time()
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'], timeoutSecs=500)
print "Inspect:", parseKey['destination_key'], "took", time.time() - start, "seconds"
h2o_cmd.infoFromInspect(inspect, csvPathname)
# num_rows = inspect['num_rows']
# num_cols = inspect['num_cols']
keepPattern = "oly_|mt_|b_"
y = "is_purchase"
print "y:", y
# don't need the intermediate Dicts produced from columnInfoFromInspect
x = h2o_glm.goodXFromColumnInfo(y, keepPattern=keepPattern, key=parseKey['destination_key'], timeoutSecs=300)
print "x:", x
kwargs = {
'x': x,
'y': y,
# 'case_mode': '>',
# 'case': 0,
'family': 'binomial',
'lambda': 1.0E-5,
'alpha': 0.5,
'max_iter': 5,
'thresholds': 0.5,
'n_folds': 1,
'weight': 100,
'beta_epsilon': 1.0E-4,
}
timeoutSecs = 1800
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, pollTimeoutsecs=60, **kwargs)
elapsed = time.time() - start
print "glm completed in", elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
def test_loop_random_param_covtype(self):
csvPathname = h2o.find_file('smalldata/poisson/Goalies.csv')
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname)
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
# need more info about the dataset for debug
h2o_cmd.infoFromInspect(inspect, csvPathname)
# for determinism, I guess we should spit out the seed?
# random.seed(SEED)
SEED = random.randint(0, sys.maxint)
# if you have to force to redo a test
# SEED =
random.seed(SEED)
paramDict = define_params()
print "\nUsing random seed:", SEED
for trial in range(5):
# params is mutable. This is default.
# FIX! does it never end if we don't have alpha specified?
params = {
'y': 5,
'n_folds': 1,
'family': "poisson",
'alpha': 0.0,
'lambda': 0,
'beta_epsilon': 0.001,
'max_iter': 3,
'standardize': 1,
'expert': 1,
'lsm_solver': 'GenGradient',
}
colX = h2o_glm.pickRandGlmParams(paramDict, params)
kwargs = params.copy()
# make timeout bigger with xvals
timeoutSecs = 180 + (kwargs['n_folds'] * 30)
# or double the 4 seconds per iteration (max_iter+1 worst case?)
timeoutSecs = max(timeoutSecs, (8 * (kwargs['max_iter'] + 1)))
start = time.time()
print "May not solve. Expanded categorical columns causing a large # cols, small # of rows"
glm = h2o_cmd.runGLMOnly(timeoutSecs=timeoutSecs,
parseKey=parseKey,
**kwargs)
elapsed = time.time() - start
print "glm end on ", csvPathname, "Trial #", trial, "completed in", elapsed, "seconds.",\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
start = time.time()
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "simpleCheckGLM end on ", csvPathname, 'took', time.time(
) - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_GLM_many_cols_tridist(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(10000, 10, 'cA', 300),
(10000, 20, 'cB', 300),
(10000, 30, 'cC', 300),
(10000, 40, 'cD', 300),
(10000, 50, 'cE', 300),
(10000, 60, 'cF', 300),
(10000, 70, 'cG', 300),
(10000, 80, 'cH', 300),
(10000, 90, 'cI', 300),
(10000, 100, 'cJ', 300),
(10000, 200, 'cK', 300),
(10000, 300, 'cL', 300),
(10000, 400, 'cM', 300),
(10000, 500, 'cN', 300),
(10000, 600, 'cO', 300),
]
### h2b.browseTheCloud()
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "\nCreating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
parseKey = h2o_cmd.parseFile(None, csvPathname, key2=key2, timeoutSecs=30)
print csvFilename, 'parse time:', parseKey['response']['time']
print "\nParse result['destination_key']:", parseKey['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
paramDict2 = {}
for k in paramDict:
paramDict2[k] = paramDict[k][0]
y = colCount
kwargs = {'y': y}
kwargs.update(paramDict2)
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, 8, **kwargs)
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
time.sleep(5)
def test_GLM_many_cols_enum(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
translateList = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u']
tryList = [
(10000, 100, 'cA', 100),
(10000, 200, 'cB', 200),
(10000, 300, 'cC', 300),
]
### h2b.browseTheCloud()
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "\nCreating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE, translateList)
parseKey = h2o_cmd.parseFile(None, csvPathname, key2=key2, timeoutSecs=30)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
y = colCount
kwargs = {
'y': y,
'max_iter': 50,
'case': 1,
'family': 'binomial',
'lambda': 0,
'alpha': 0,
'max_iter': 50,
'weight': 1.0,
'thresholds': 0.5,
'n_folds': 2,
'beta_epsilon':1.0E-4,
}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "y:", y
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
time.sleep(10)
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
time.sleep(10)
def test_GLM_many_cols(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
if localhost:
tryList = [
(10000, 100, 'cA', 300),
(10000, 1000, 'cB', 300),
(10000, 3000, 'cC', 500),
]
else:
tryList = [
# (10000, 10, 'cB', 300),
# (10000, 50, 'cC', 300),
(10000, 100, 'cD', 300),
(10000, 200, 'cE', 300),
(10000, 300, 'cF', 300),
(10000, 400, 'cG', 300),
(10000, 500, 'cH', 300),
(10000, 1000, 'cI', 300),
]
### h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
# csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
parseKey = h2o_cmd.parseFile(None, csvPathname, key2=key2, timeoutSecs=10)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvFilename
y = colCount
# normally we dno't create x and rely on the default
# create the big concat'ed x like the browser, to see what happens
x = ','.join(map(str, range(colCount)))
kwargs = {'x': x, 'y': y, 'max_iter': 10, 'n_folds': 1, 'alpha': 0.2, 'lambda': 1e-5}
start = time.time()
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=timeoutSecs, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)