def test_parse_covtype_2(self):
tryList = [
('covtype.data', 1, 30),
# ('covtype20x.data', 20, 120),
]
for (csvFilename, multiplyExpected, timeoutSecs) in tryList:
for trial in range(16,24):
# import_result = a_node.import_files(path=find_file("smalldata/logreg/prostate.csv"))
importFolderPath = "standard"
hex_key = 'covtype.hex'
csvPathname = importFolderPath + "/" + csvFilename
chunk_size = 2**trial
print "Trial %s. Trying chunk_size %s (power of 2)" % (trial, chunk_size)
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets', path=csvPathname, schema='local',
timeoutSecs=timeoutSecs, hex_key=hex_key,
chunk_size=chunk_size, doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
for i in range(1):
co = h2o_cmd.runSummary(key=hex_key, column=i)
k = parseResult['frames'][0]['frame_id']['name']
# print "parseResult:", dump_json(parseResult)
a_node = h2o.nodes[0]
frames_result = a_node.frames(key=k, row_count=5)
# print "frames_result from the first parseResult key", dump_json(frames_result)
parseKeyIndexedCheck(frames_result, multiplyExpected)
def test_parse_nfs(self):
print "run as user 0xcustomer on machine with nfs /mnt/0xcustomer-datasets/c1"
tryList = [
('iris2.csv', 'iris2.hex', 1, 30),
]
for (csvFilename, hex_key, multiplyExpected, timeoutSecs) in tryList:
importFolderPath = "/mnt/0xcustomer-datasets/c1"
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(path=csvPathname,
schema='local',
timeoutSecs=timeoutSecs,
hex_key=hex_key,
chunk_size=4194304 / 2,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=150 * multiplyExpected,
expectedNumCols=5,
expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
for i in range(0):
print "Summary on column", i
co = h2o_cmd.runSummary(key=hex_key, column=i)
k = parseResult['frames'][0]['frame_id']['name']
frames_result = h2o.nodes[0].frames(key=k, row_count=5)
# print "frames_result from the first parseResult key", dump_json(frames_result)
parseKeyIndexedCheck(frames_result, multiplyExpected)
def test_delete_all_keys(self):
# FIX! should have some model keys in here too, from RF etc.
importFolderPath = 'standard'
timeoutSecs = 500
csvFilenameAll = [
"covtype.data",
"covtype20x.data",
]
# csvFilenameList = random.sample(csvFilenameAll,1)
csvFilenameList = csvFilenameAll
for trial in range(2):
for csvFilename in csvFilenameList:
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets',
path=csvPathname,
timeoutSecs=500)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
h2i.delete_keys_at_all_nodes()
print "Delete all keys. Shouldn't be any more?"
h2o.nodes[0].remove_all_keys()
print "\nTrial", trial, "completed\n"
def test_parse_100k_cols(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(10, 1000, 'cA', 200, 200),
(10, 2000, 'cA', 200, 200),
(10, 4000, 'cA', 200, 200),
(10, 8000, 'cA', 200, 200),
(10, 9000, 'cA', 200, 200),
(10, 10000, 'cA', 200, 200),
(10, 100000, 'cA', 200, 200),
# (10, 200000, 'cB', 200, 200),
# (10, 300000, 'cB', 200, 200),
# we timeout/fail on 500k? stop at 200k
# (10, 500000, 'cC', 200, 200),
# (10, 1000000, 'cD', 200, 360),
# (10, 1100000, 'cE', 60, 100),
# (10, 1200000, 'cF', 60, 120),
]
# h2b.browseTheCloud()
for (rowCount, colCount, hex_key, timeoutSecs,
timeoutSecs2) 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)
parseResult = h2i.import_parse(
path=csvPathname,
schema='local',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
doSummary=False,
columnNames=None,
intermediateResults=DO_INTERMEDIATE_RESULTS)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
print pA.numRows
print pA.numCols
print pA.parse_key
# this guy can take json object as first thing, or re-read with key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
print "Skipping the delete keys for now"
if 1 == 0:
# if not h2o.browse_disable:
# h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
# time.sleep(5)
h2i.delete_keys_at_all_nodes()
def test_split_frame(self):
csvFilename = 'iris.csv'
csvPathname = 'iris/' + csvFilename
hex_key = "iris.hex"
parseResultA = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=hex_key,
timeoutSecs=10)
print "Just split away and see if anything blows up"
splitMe = hex_key
pA = h2o_cmd.ParseObj(parseResultA)
print pA.numRows
print pA.numCols
print pA.parse_key
print "Just split away and see if anything blows up"
splitMe = hex_key
iA = h2o_cmd.InspectObj(splitMe)
origNumRows = iA.numRows
origNumCols = iA.numCols
for s in range(10):
iA = h2o_cmd.InspectObj(splitMe)
numRows = iA.numRows
fsResult = h2o.n0.split_frame(dataset=splitMe, ratios='[0.5]')
fs = OutputObj(fsResult, 'split_frame')
d = fs.jobs[0].destination_frames
# modelResult = h2o.n0.models(key=model_key)
# model = OutputObj(modelResult['models'][0]['output'], 'split_frame')
# print "model:", dump_json(model)
split_keys = [split.name for split in d]
# modelResult = h2o.n0.models(key=model_key)
# model = OutputObj(modelResult['models'][0]['output'], 'split_frame')
# print "model:", dump_json(model)
# split_keys = [split._key.name for split in model.splits]
iB = h2o_cmd.InspectObj(split_keys[0])
iC = h2o_cmd.InspectObj(split_keys[1])
numCols = iB.numCols
split0_rows = iB.numRows
split1_rows = iC.numRows
# print "Iteration", s, "split0_rows:", split0_rows, "split1_rows:", split1_rows
splitMe = split_keys[1]
# split should be within 1 row accuracy. let's say within 20 for now
self.assertLess(abs(split1_rows - split0_rows), 2)
self.assertEqual(numRows, (split1_rows + split0_rows))
self.assertEqual(numCols, origNumCols)
def test_frame_split(self):
csvFilename = 'covtype.data'
csvPathname = 'standard/' + csvFilename
hex_key = "covtype.hex"
parseResultA = h2i.import_parse(bucket='home-0xdiag-datasets',
path=csvPathname,
hex_key=hex_key,
timeoutSecs=20)
pA = h2o_cmd.ParseObj(parseResultA)
print pA.numRows
print pA.numCols
print pA.parse_key
print "Just split away and see if anything blows up"
splitMe = hex_key
iA = h2o_cmd.InspectObj(splitMe)
origNumRows = iA.numRows
origNumCols = iA.numCols
for s in range(20):
iA = h2o_cmd.InspectObj(splitMe)
numRows = iA.numRows
fsResult = h2o.n0.frame_split(training_frame=splitMe,
ratios='[0.5]')
fs = OutputObj(fsResult, 'frame_split')
model_key = fs.jobs[0].dest.name
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'],
'frame_split')
# print "model:", dump_json(model)
split_keys = [split._key.name for split in model.splits]
iB = h2o_cmd.InspectObj(split_keys[0])
iC = h2o_cmd.InspectObj(split_keys[1])
numCols = iB.numCols
split0_rows = iB.numRows
split1_rows = iC.numRows
# print "Iteration", s, "split0_rows:", split0_rows, "split1_rows:", split1_rows
splitMe = split_keys[1]
# split should be within 1 row accuracy. let's say within 20 for now
self.assertLess(abs(split1_rows - split0_rows), 2)
self.assertEqual(numRows, (split1_rows + split0_rows))
self.assertEqual(numCols, origNumCols)
if split1_rows <= 1:
break
def test_exec2_sum(self):
print "Replicating covtype.data by 2x for results comparison to 1x"
filename1x = 'covtype.data'
pathname1x = h2i.find_folder_and_filename('home-0xdiag-datasets',
'standard/covtype.data',
returnFullPath=True)
filename2x = "covtype_2x.data"
pathname2x = SYNDATASETS_DIR + '/' + filename2x
h2o_util.file_cat(pathname1x, pathname1x, pathname2x)
csvAll = [
(pathname1x, "cA", 5, 1),
(pathname2x, "cB", 5, 2),
(pathname2x, "cC", 5, 2),
]
# h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
firstDone = False
for (csvPathname, hex_key, timeoutSecs, resultMult) in csvAll:
parseResultA = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key)
pA = h2o_cmd.ParseObj(parseResultA)
print pA.numRows
print pA.numCols
print pA.parse_key
iA = h2o_cmd.InspectObj(pA.parse_key)
k = Key(hex_key)
colResultList = []
for i in range(pA.numCols):
result = Expr(Fcn('sum', k[:, i], True)).result
colResultList.append(result)
print "\ncolResultList", colResultList
if not firstDone:
colResultList0 = list(colResultList)
good = [float(x) for x in colResultList0]
firstDone = True
else:
print "\n", colResultList0, "\n", colResultList
# create the expected answer...i.e. N * first
compare = [float(x) / resultMult for x in colResultList]
print "\n", good, "\n", compare
self.assertEqual(
good, compare,
'compare is not equal to good (first try * resultMult)')
def test_parse_syn_gz_cat(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# summary fails with 100000 cols
# overwrite the key each time to save space?
(100, 100, 'cF', 600),
(100, 5000, 'cF', 600),
(100, 10000, 'cF', 600),
# (100, 12000, 'cF', 600),
# (100, 15000, 'cF', 600),
# (100, 17000, 'cF', 600),
(100, 20000, 'cF', 600),
(100, 40000, 'cF', 600),
]
# h2b.browseTheCloud()
for (rowCount, colCount, hex_key, 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)
csvFilenamegz = csvFilename + ".gz"
csvPathnamegz = SYNDATASETS_DIR + '/' + csvFilenamegz
h2o_util.file_gzip(csvPathname, csvPathnamegz)
parseResult = h2i.import_parse(path=csvPathnamegz,
schema='put',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
doSummary=DOSUMMARY)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
print pA.numRows
print pA.numCols
print pA.parse_key
# this guy can take json object as first thing, or re-read with key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
def test_parse_covtype(self):
tryList = [
('covtype.data', 1, 30),
('covtype20x.data', 20, 120),
]
for (csvFilename, multiplyExpected, timeoutSecs) in tryList:
# h2o-dev doesn't take ../.. type paths? make find_file return absolute pathj
a_node = h2o.nodes[0]
importFolderPath = os.path.expanduser(
"~/home-0xdiag-datasets/standard")
csvPathname = importFolderPath + "/" + csvFilename
importResult = a_node.import_files(path=csvPathname)
# print "importResult:", dump_json(importResult)
hex_key = importResult['destination_frames'][0]
if CAUSE_FAIL:
frames_result = a_node.frames(key=k,
row_count=5,
timeoutSecs=timeoutSecs)
# print "frames_result from the first importResult key", dump_json(frames_result)
parseResult = a_node.parse(key=hex_key,
timeoutSecs=timeoutSecs,
chunk_size=4194304 * 4)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=581012 * multiplyExpected,
expectedNumCols=55,
expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
for i in range(0):
print "Summary on column", i
co = h2o_cmd.runSummary(key=hex_key, column=i)
k = parseResult['frames'][0]['frame_id']['name']
# print "parseResult:", dump_json(parseResult)
frames_result = a_node.frames(key=k, row_count=5)
# print "frames_result from the first parseResult key", dump_json(frames_result)
parseKeyIndexedCheck(frames_result, multiplyExpected)
def test_plot_remove_keys(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(100000, 100, 'cG', 400),
(200000, 100, 'cH', 400),
(400000, 100, 'cI', 400),
(800000, 100, 'cJ', 400),
(1000000, 100, 'cK', 400),
]
xList = []
eList = []
fList = []
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
NUM_CASES = h2o_util.fp_format()
sel = random.randint(0, NUM_CASES - 1)
csvFilename = "syn_%s_%s_%s_%s.csv" % (SEEDPERFILE, sel, rowCount,
colCount)
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE,
sel)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
iA = h2o_cmd.InspectObj(pA.parse_key)
parseElapsed = pA.python_elapsed
parse_key = pA.parse_key
byteSize = pA.byteSize
numRows = iA.numRows
numCols = iA.numCols
print parse_key, parseElapsed, byteSize, numRows, numCols
labelList = iA.labelList
node = h2o.nodes[0]
print "Deleting", hex_key, "at", node.http_addr, "Shouldn't matter what node the delete happens at..global?"
start = time.time()
node.remove_key(hex_key, timeoutSecs=30)
removeElapsed = time.time() - start
print "Deleting", hex_key, "took", removeElapsed, "seconds"
# xList.append(ntrees)
xList.append(byteSize)
eList.append(parseElapsed)
fList.append(removeElapsed)
# just plot the last one
if 1 == 1:
xLabel = 'byteSize'
eLabel = 'parseElapsed'
fLabel = 'removeElapsed'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel,
fListTitle, fList, fLabel)
def test_GLM_basic_1(self):
importFolderPath = "logreg"
csvFilename = "benign.csv"
hex_key = "benign.hex"
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=hex_key,
check_header=1,
timeoutSecs=180,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
expected = []
allowedDelta = 0
# loop, to see if we get same centers
labelListUsed = list(labelList)
labelListUsed.remove('STR')
labelListUsed.remove('FNDX') # response removed also
numColsUsed = numCols - 2
for trial in range(1):
# family [u'gaussian', u'binomial', u'poisson', u'gamma', u'tweedie']
# link [u'family_default', u'identity', u'logit', u'log', u'inverse', u'tweedie']
# can we do classification with probabilities?
# are only lambda and alpha grid searchable?
# glm parameters:
# model_id Key<Model> False None []
# training_frame Key<Frame> False None []
# validation_frame Key<Frame> False None []
# ignored_columns string[] False None []
# drop_na20_cols boolean False False []
# score_each_iteration boolean False False []
# response_column VecSpecifier False None []
# balance_classes boolean False False []
# class_sampling_factors float[] False None []
# max_after_balance_size float False 5.0 []
# max_confusion_matrix_size int False 20 []
# max_hit_ratio_k int False 10 []
# family enum False gaussian [u'gaussian', u'binomial', u'poisson', u'gamma']
# solver enum False IRLSM [u'AUTO', u'IRLSM', u'L_BFGS']
# alpha double[] False None []
# lambda double[] False None []
# lambda_search boolean False False []
# lambda_min_ratio double False -1.0 []
# nlambdas int False -1 []
# standardize boolean False True []
# max_iterations int False -1 []
# beta_epsilon double False 0.0001 []
# link enum False family_default [u'family_default', u'identity', u'logit', u'log', u'inverse', u'tweedie']
# prior double False -1.0 []
# use_all_factor_levels boolean False False []
# beta_constraints Key<Frame> False None []
# max_active_predictors int False -1 []
parameters = {
'ignored_columns': '["STR"]',
'response_column': 'FNDX',
# FIX! when is this needed? redundant for binomial?
'balance_classes': False,
'max_after_balance_size': None,
'standardize': False,
'family': 'binomial',
'link': None,
'alpha': '[1e-4]',
'lambda': '[0.5]',
'prior1': None,
'lambda_search': None,
'nlambdas': None,
'lambda_min_ratio': None,
# 'use_all_factor_levels': False,
}
model_key = 'benign_glm.hex'
bmResult = h2o.n0.build_model(algo='glm',
model_id=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=10)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
h2o_glm.simpleCheckGLM(self, model, parameters, labelList,
labelListUsed)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mcms = OutputObj({'data': cmm.max_criteria_and_metric_scores.data},
'mcms')
m1 = mcms.data[1:]
h0 = mcms.data[0]
print "\nmcms", tabulate(m1, headers=h0)
thms = OutputObj(cmm.thresholds_and_metric_scores, 'thms')
if 1 == 0:
cmms = OutputObj({'cm': cmm.confusion_matrices}, 'cmms')
print ""
for i, c in enumerate(cmms.cm):
print "\ncmms.cm[%s]" % i, tabulate(c)
print ""
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
def test_w2v_basic_1(self):
global SYNDATASETS_DIR
SYNDATASETS_DIR = h2o.make_syn_dir()
n = 500000
tryList = [
(n, 1, 'cD', 300),
(n, 2, 'cE', 300),
(n, 3, 'cF', 300),
(n, 4, 'cG', 300),
(n, 5, 'cH', 300),
(n, 6, 'cI', 300),
(n, 7, 'cJ', 300),
(n, 9, 'cK', 300),
]
### h2b.browseTheCloud()
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
csvPathname = create_file_with_seps(rowCount, colCount)
# just parse to make sure it's good
parseResult = h2i.import_parse(path=csvPathname,
checkHeader=1,
delete_on_done=0,
timeoutSecs=180,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
cA = h2o_test.OutputObj(iA.columns[0], "inspect_column")
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
for i in range(colCount):
print cA.type, cA.missing
self.assertEqual(
0, cA.missing,
"Column %s Expected %s. missing: %s is incorrect" %
(i, 0, cA.missing))
self.assertEqual(
'string', cA.type,
"Column %s Expected %s. type: %s is incorrect" %
(i, 0, cA.type))
if DO_SUMMARY:
for i in range(colCount):
co = h2o_cmd.runSummary(key=parse_key, column=i)
print co.label, co.type, co.missing, co.domain, sum(
co.bins)
self.assertEqual(
0, co.missing,
"Column %s Expected %s. missing: %s is incorrect" %
(i, 0, co.missing))
self.assertEqual(
'String', co.type,
"Column %s Expected %s. type: %s is incorrect" %
(i, 0, co.type))
# no cols ignored
labelListUsed = list(labelList)
numColsUsed = numCols
for trial in range(1):
parameters = {
'validation_frame': parse_key, # KeyIndexed False []
'ignored_columns': None, # string[] None []
'score_each_iteration': None, # boolean false []
'minWordFreq': 5, # int 5 []
'wordModel': 'SkipGram', # enum [u'CBOW', u'SkipGram']
'normModel': 'HSM', # enum # [u'HSM', u'NegSampling']
'negSampleCnt': 5, # int 5 []
'vecSize': 100, # int 100
'windowSize': 5, # int 5
'sentSampleRate': 0.001, # float 0.001
'initLearningRate': 0.05, # float 0.05
'epochs': 1, # int 5
}
model_key = 'benign_w2v.hex'
bmResult = h2o.n0.build_model(algo='word2vec',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
# not implemented?
# prResult = h2o.n0.predict(model=model_key, frame=parse_key, timeoutSecs=60)
# pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
def test_bayes_basic(self):
bucket = 'home-0xdiag-datasets'
importFolderPath = 'standard'
trainFilename = 'covtype.shuffled.90pct.data'
train_key = 'covtype.train.hex'
b = Key(train_key)
model_key = 'bayesModelKey'
timeoutSecs = 1800
csvPathname = importFolderPath + "/" + trainFilename
# FIX! do I need to force enum for classification? what if I do regression after this?
columnTypeDict = {54: 'Enum'}
parseResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
columnTypeDict=columnTypeDict,
schema='local',
chunk_size=4194304,
hex_key=train_key,
timeoutSecs=timeoutSecs)
# don't have to make it enum, if 0/1 (can't operate on enums like this)
# make 1-7 go to 0-6. 0 isn't there.
# make 1 thru 6 go to 1
# change columnTypeDict to None above if I do this
# Assign(b[:,54], b[:,54]-1)
# Assign(b[:,54], b[:,54]!=0)
# now we have just 0 and 1
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
labelListUsed = list(labelList)
numColsUsed = numCols
# run through a couple of parameter sets
parameters = []
parameters.append({
'response_column': 'C55', # still 1-55 on colnames
}) # just default
model_key = 'covtype_bayes.hex'
for p in parameters:
bmResult = h2o.n0.build_model(algo='naivebayes',
destination_key=model_key,
training_frame=train_key,
validation_frame=train_key,
parameters=p,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mmResultShort = mmResult['model_metrics'][0]
del mmResultShort['frame'] # too much!
mm = OutputObj(mmResultShort, 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
def glm_doit(self, csvFilename, bucket, csvPathname, timeoutSecs=30):
print "\nStarting GLM of", csvFilename
# we can force a col type to enum now? with param columnTypes
# "Numeric"
# make the last column enum
# Instead of string for parse, make this a dictionary, with column index, value
# that's used for updating the ColumnTypes array before making it a string for parse
columnTypeDict = {10: 'Enum'}
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, columnTypeDict=columnTypeDict,
hex_key=csvFilename + ".hex", schema='put', timeoutSecs=30)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
for i in range(10):
print "Summary on column", i
# FIX! how come only 0 works here for column
co = h2o_cmd.runSummary(key=parse_key, column=i)
for k,v in co:
print k, v
expected = []
allowedDelta = 0
labelListUsed = list(labelList)
labelListUsed.remove('C11')
numColsUsed = numCols - 1
parameters = {
'validation_frame': parse_key,
'ignored_columns': None,
# FIX! for now just use a column that's binomial
'response_column': 'C11',
# FIX! when is this needed? redundant for binomial?
'balance_classes': False,
'max_after_balance_size': None,
'standardize': False,
'family': 'binomial',
'link': None,
'tweedie_variance_power': None,
'tweedie_link_power': None,
'alpha': '[1e-4]',
'lambda': '[0.5,0.25, 0.1]',
'prior1': None,
'lambda_search': None,
'nlambdas': None,
'lambda_min_ratio': None,
'use_all_factor_levels': False,
'n_folds': 1,
}
start = time.time()
model_key = 'hastie_glm.hex'
bmResult = h2o.n0.build_model(
algo='glm',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
h2o_glm.simpleCheckGLM(self, model, parameters, labelList, labelListUsed)
cmmResult = h2o.n0.compute_model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
mm = OutputObj(mmResult, 'mm')
prResult = h2o.n0.predict(model=model_key, frame=parse_key, timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
# compare this glm to the first one. since the files are replications, the results
# should be similar?
if self.validation1:
h2o_glm.compareToFirstGlm(self, 'AUC', validation, self.validation1)
else:
# self.validation1 = copy.deepcopy(validation)
self.validation1 = None
def test_GLM_error1(self):
importFolderPath = "covtype"
csvFilename = "covtype.20k.data"
hex_key = "covtype20k.hex"
binomial_key = "covtype20k.b.hex"
b = Key(hex_key)
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=hex_key,
check_header=1,
timeoutSecs=180,
doSummary=False)
## columnTypeDict = {54: 'Enum'}
columnTypeDict = None
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=binomial_key,
columnTypeDict=columnTypeDict,
check_header=1,
timeoutSecs=180,
doSummary=False)
# don't have to make it enum, if 0/1 (can't operate on enums like this)
# make 1-7 go to 0-6. 0 isn't there.
Assign(b[:, 54], b[:, 54] - 1)
# make 1 thru 6 go to 1
Assign(b[:, 54], b[:, 54] != 0)
# now we have just 0 and 1
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
expected = []
allowedDelta = 0
# loop, to see if we get same centers
labelListUsed = list(labelList)
numColsUsed = numCols
for trial in range(5):
parameters = {
'response_column': 'C55',
'max_iterations': 3,
'solver': 'L_BFGS',
'ignored_columns': '["C1"]',
'alpha': '[0.1]',
'max_after_balance_size': 1000.0,
'class_sampling_factors': '[0.2]',
# 'use_all_factor_levels': None,
'lambda': '[0]',
}
bHack = hex_key
co = h2o_cmd.runSummary(key=binomial_key, column=54)
print "binomial_key summary:", co.label, co.type, co.missing_count, co.domain, sum(
co.histogram_bins)
co = h2o_cmd.runSummary(key=hex_key, column=54)
print "hex_key summary:", co.label, co.type, co.missing_count, co.domain, sum(
co.histogram_bins)
model_key = 'rand_glm.hex'
bmResult = h2o.n0.build_model(algo='glm',
model_id=model_key,
training_frame=bHack,
parameters=parameters,
timeoutSecs=10)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
h2o_glm.simpleCheckGLM(self,
model,
parameters,
labelList,
labelListUsed,
allowNaN=True)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
# FIX! when is this legal
doClassification = False
if doClassification:
mcms = OutputObj(
{'data': cmm.max_criteria_and_metric_scores.data}, 'mcms')
m1 = mcms.data[1:]
h0 = mcms.data[0]
print "\nmcms", tabulate(m1, headers=h0)
if doClassification:
thms = OutputObj(cmm.thresholds_and_metric_scores, 'thms')
cmms = OutputObj({'cm': cmm.confusion_matrices}, 'cmms')
if 1 == 0:
print ""
for i, c in enumerate(cmms.cm):
print "\ncmms.cm[%s]" % i, tabulate(c)
print ""
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
def test_GBMGrid_basic_many(self):
trainFilename = 'prostate.csv'
train_key = 'prostate.hex'
timeoutSecs = 300
csvPathname = "logreg/" + trainFilename
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=train_key, schema='put')
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
labelListUsed = list(labelList)
numColsUsed = numCols
parameters = {
'validation_frame': train_key,
'ignored_columns': "['ID']", # this has to have []
'response_column': 'CAPSULE',
# 'balance_classes':
# 'max_after_balance_size':
# ??
# 'ntrees': '[8, 10]',
'ntrees': 8,
# 'max_depth': '[8, 9]',
'max_depth': 8,
# ??
# 'min_rows': '[1, 2]',
'min_rows': 1,
'nbins': 40,
# ??
# 'learn_rate': "[0.1, 0.2]",
'learn_rate': 0.1,
# FIX! doesn't like it?
# 'loss': 'Bernoulli',
# FIX..no variable importance for GBM yet?
# 'variable_importance': False,
# 'seed':
}
jobs = []
# kick off 5 of these GBM grid jobs, with different tree choices
start = time.time()
totalGBMGridJobs = 0
for i in range(5):
modelKey = 'GBMGrid_prostate_%s', i
bmResult = h2o.n0.build_model(
algo='gbm',
destination_key=modelKey,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
print "GBMResult:", h2o.dump_json(bm)
# FIX! is this right for gridded?
job_key = bm.jobs[0].key.name
# FIX! this isn't a full formed name (%)
model_key = bm.jobs[0].dest.name
jobs.append( (job_key, model_key) )
totalGBMGridJobs += 1
h2o_jobs.pollWaitJobs(timeoutSecs=300)
elapsed = time.time() - start
print "All GBM jobs completed in", elapsed, "seconds."
print "totalGBMGridJobs:", totalGBMGridJobs
for job_key, model_key in jobs:
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
cmmResult = h2o.n0.compute_model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
print "\nLook!, can use dot notation: cmm.cm.confusion.matrix", cmm.cm.confusion_matrix, "\n"
mmResult = h2o.n0.model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
mmResultShort = mmResult['model_metrics'][0]
del mmResultShort['frame'] # too much!
mm = OutputObj(mmResultShort, 'mm')
prResult = h2o.n0.predict(model=model_key, frame=parse_key, timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
def test_GLM_many_cols_4(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 = [
(100000, 10, 'cA', 600),
(100000, 100, 'cA', 600),
]
### h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
for (rowCount, colCount, hex_key, 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,
translateList)
parseResult = h2i.import_parse(path=csvPathname,
hex_key=hex_key,
timeoutSecs=180,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
expected = []
allowedDelta = 0
labelListUsed = list(labelList)
print "labelListUsed", labelListUsed
response = labelListUsed[-1]
labelListUsed.remove(response)
numColsUsed = numCols - 1
for trial in range(1):
# family [u'gaussian', u'binomial', u'poisson', u'gamma', u'tweedie']
# link [u'family_default', u'identity', u'logit', u'log', u'inverse', u'tweedie']
# can we do classification with probabilities?
# are only lambda and alpha grid searchable?
parameters = {
'validation_frame': parse_key,
'ignored_columns': None,
# FIX! for now just use a column that's binomial
'response_column': response, # can't take index now?
# FIX! when is this needed? redundant for binomial?
'balance_classes': False,
'max_after_balance_size': None,
'standardize': False,
'family': 'binomial',
'link': None,
'tweedie_variance_power': None,
'tweedie_link_power': None,
'alpha': '[1e-4]',
'lambda': '[0.5,0.25, 0.1]',
'prior1': None,
'lambda_search': None,
'nlambdas': None,
'lambda_min_ratio': None,
'use_all_factor_levels': False,
'n_folds': 1,
}
model_key = 'many_cols_glm.hex'
bmResult = h2o.n0.build_model(algo='glm',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
h2o_glm.simpleCheckGLM(self, model, parameters, labelList,
labelListUsed)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult, 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'],
'pr')
def test_DL_basic(self):
h2o.nodes[0].remove_all_keys()
importFolderPath = "logreg"
csvFilename = "benign.csv"
hex_key = "benign.hex"
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
hex_key=hex_key,
check_header=1,
timeoutSecs=180,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
expected = []
allowedDelta = 0
# no cols ignored
labelListUsed = list(labelList)
labelListUsed.remove('STR')
numColsUsed = numCols - 1
for trial in range(1):
parameters = {
# required now
# loss enum True None [u'MeanSquare', u'CrossEntropy']
'loss': 'CrossEntropy',
'validation_frame': parse_key, # KeyIndexed None
'ignored_columns': '["STR"]', # string[] None
'response_column': 'FNDX', # string None
'balance_classes': None, # boolean false
'max_after_balance_size': None, # float Infinity
'keep_cross_validation_splits': None, # boolean false
'checkpoint': None, # Key None
'overwrite_with_best_model': None, # boolean true
'expert_mode': None, # boolean false
'autoencoder': None, # boolean false
# 'use_all_factor_levels': None, # boolean true
# [u'Tanh', u'TanhWithDropout', u'Rectifier', u'RectifierWithDropout', u'Maxout', u'MaxoutWithDropout']
'activation': None, # enum Rectifier
'hidden': None, # int[] [200, 200]
'epochs': None, # double 10.0
'train_samples_per_iteration': None, # long -2
'target_ratio_comm_to_comp': None, # double 0.02
'seed': None, # long 1679194146842485659
'adaptive_rate': None, # boolean true
'rho': None, # double 0.99
'epsilon': None, # double 1.0E-8
'rate': None, # double 0.005
'rate_annealing': None, # double 1.0E-6
'rate_decay': None, # double 1.0
'momentum_start': None, # double 0.0
'momentum_ramp': None, # double 1000000.0
'momentum_stable': None, # double 0.0
'nesterov_accelerated_gradient': None, # boolean true
'input_dropout_ratio': None, # double 0.0
'hidden_dropout_ratios':
None, # double[] None (this can grid?)
'l1': None, # double 0.0
'l2': None, # double 0.0
'max_w2': None, # float Infinity
'initial_weight_distribution':
None, # enum UniformAdaptive [u'UniformAdaptive', u'Uniform', u'Normal']
'initial_weight_scale': None, # double 1.0
'loss':
None, # enum MeanSquare [u'Automatic', u'MeanSquare', u'CrossEntropy']
'score_interval': None, # double 5.0
'score_training_samples': None, # long 10000
'score_validation_samples': None, # long 0
'score_duty_cycle': None, # double 0.1
'classification_stop': None, # double 0.0
'regression_stop': None, # double 1.0E-6
'quiet_mode': None, # boolean false
'max_confusion_matrix_size': None, # int 20
'max_hit_ratio_k': None, # int 10
'balance_classes': None, # boolean false
'class_sampling_factors': None, # float[] None
'max_after_balance_size': None, # float Infinity
'score_validation_sampling':
None, # enum Uniform [u'Uniform', u'Stratified']
'diagnostics': None, # boolean true
'variable_importances': None, # boolean false
'fast_mode': None, # boolean true
'ignore_const_cols': None, # boolean true
'force_load_balance': None, # boolean true
'replicate_training_data': None, # boolean false
'single_node_mode': None, # boolean false
'shuffle_training_data': None, # boolean false
'missing_values_handling':
None, # enum MeanImputation [u'Skip', u'MeanImputation']
'sparse': None, # boolean false
'col_major': None, # boolean false
'average_activation': None, # double 0.0
'sparsity_beta': None, # double 0.0
}
model_key = 'benign_dl.hex'
bmResult = h2o.n0.build_model(algo='deeplearning',
model_id=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=10)
print "bmResult:", dump_json(bmResult)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
print "model:", dump_json(model)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
def test_billion_rows(self):
# just do the import folder once
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()
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets',
path='standard/' + csvFilename,
timeoutSecs=timeoutSecs,
pollTimeoutSecs=60)
elapsed = time.time() - start
print csvFilename, "completed in", elapsed, "seconds.", "%d pct. of timeout" % (
(elapsed * 100) / timeoutSecs)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
parameters = {
'response_column': 1,
'n_folds': 0,
'alpha': 0,
'lambda': 0,
}
model_key = 'B.hex'
bmResult = h2o.n0.build_model(algo='glm',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=10)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
h2o_glm.simpleCheckGLM(self, model, parameters, labelList,
labelListUsed)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult, 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
labelListUsed = labelList
h2o_glm.simpleCheckGLM(self, model, parameters, labelList,
labelListUsed)
def test_GBM_basic(self):
bucket = 'home-0xdiag-datasets'
importFolderPath = 'standard'
trainFilename = 'covtype.shuffled.90pct.data'
train_key = 'covtype.train.hex'
model_key = 'GBMModelKey'
timeoutSecs = 1800
csvPathname = importFolderPath + "/" + trainFilename
parseResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
schema='local',
hex_key=train_key,
timeoutSecs=timeoutSecs)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
labelListUsed = list(labelList)
numColsUsed = numCols
parameters = {
'validation_frame': train_key,
'ignored_columns': None,
'score_each_iteration': True,
'response_column': 'C55',
'do_classification': True,
# 'balance_classes':
# 'max_after_balance_size':
'ntrees': 2,
'max_depth': 10,
'min_rows': 3,
'nbins': 40,
'learn_rate': 0.2,
# FIX! doesn't like it?
# 'loss': 'Bernoulli',
# FIX..no variable importance for GBM yet?
'variable_importance': False,
# 'seed':
}
model_key = 'covtype_gbm.hex'
bmResult = h2o.n0.build_model(algo='gbm',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
print "\nLook!, can use dot notation: cmm.cm.confusion.matrix", cmm.cm.confusion_matrix, "\n"
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mmResultShort = mmResult['model_metrics'][0]
del mmResultShort['frame'] # too much!
mm = OutputObj(mmResultShort, 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
def test_parse_rand_enum_compress(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
if DEBUG:
n = 20
else:
n = 1000000
# from command line arg -long
if h2o_args.long_test_case:
repeat = 1000
scale = 10 # scale up the # of rows
tryList = [
(n * scale, 1, 'cI', 300),
(n * scale, 1, 'cI', 300),
(n * scale, 1, 'cI', 300),
]
else:
repeat = 1
scale = 1
tryList = [
(n, 3, 'cI', 300),
(n, 3, 'cI', 300),
(n, 3, 'cI', 300),
]
lastcolsHistory = []
enumList = create_enum_list(listSize=ENUMS_NUM)
for r in range(repeat):
SEED_PER_FILE = random.randint(0, sys.maxint)
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
# using the comma is nice to ensure no craziness
colSepHexString = '2c' # comma
colSepChar = colSepHexString.decode('hex')
colSepInt = int(colSepHexString, base=16)
print "colSepChar:", colSepChar
rowSepHexString = '0a' # newline
rowSepChar = rowSepHexString.decode('hex')
print "rowSepChar:", rowSepChar
csvFilename = 'syn_enums_' + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
# same enum list/mapping, but different dataset?
start = time.time()
lastcols = write_syn_dataset(csvPathname,
enumList,
rowCount,
colCount,
scale=1,
colSepChar=colSepChar,
rowSepChar=rowSepChar,
SEED=SEED_PER_FILE)
elapsed = time.time() - start
print "took %s seconds to create %s" % (elapsed, csvPathname)
# why are we saving this?
lastcolsHistory.append(lastcols)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
check_header=0,
timeoutSecs=30,
separator=colSepInt,
doSummary=DO_SUMMARY)
parseResultA = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key)
# optional. only needed to extract parse_key?
pA = h2o_cmd.ParseObj(parseResultA,
expectedNumRows=rowCount,
expectedNumCols=colCount)
print pA.numRows
print pA.numCols
print pA.parse_key
# this guy can take json object as first thing, or re-read with key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
self.assertEqual(rowCount, iA.numRows)
self.assertEqual(colCount, iA.numCols)
def test_w2v_basic_2(self):
global SYNDATASETS_DIR
SYNDATASETS_DIR = h2o.make_syn_dir()
n = 100
tryList = [
# (n, 1, 'cD', 300),
(n, 2, 'cE', 300),
(n, 3, 'cF', 300),
(n, 4, 'cG', 300),
(n, 5, 'cH', 300),
(n, 6, 'cI', 300),
(n, 7, 'cJ', 300),
(n, 9, 'cK', 300),
]
### h2b.browseTheCloud()
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
csvPathname = create_file_with_seps(rowCount, colCount)
hex_key = "not_used.hex"
# just parse to make sure it's good
parseResult = h2i.import_parse(path=csvPathname,
check_header=1,
delete_on_done=0,
timeoutSecs=180,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
src_key = h2i.find_key('syn_.*csv')
# no cols ignored
labelListUsed = list(labelList)
numColsUsed = numCols
for trial in range(1):
parameters = {
'validation_frame': parse_key, # KeyIndexed False []
'ignored_columns': None, # string[] None []
'minWordFreq': 1, # int 5 []
'wordModel': 'CBOW', # enum [u'CBOW', u'SkipGram']
'normModel':
'NegSampling', # enum # [u'HSM', u'NegSampling']
'negSampleCnt': 1, # int 5 []
'vecSize': 10, # int 100
'windowSize': 2, # int 5
'sentSampleRate': 0.001, # float 0.001
'initLearningRate': 0.05, # float 0.05
'epochs': 1, # int 5
}
model_key = 'benign_w2v.hex'
bmResult = h2o.n0.build_model(algo='word2vec',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=10)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'],
'pr')
h2o_cmd.runStoreView()
def test_summary2_NY0(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
choicesList = [
('N', 'Y', '0'),
('n', 'y', '0'),
('F', 'T', '0'),
('f', 't', '0'),
(' N', ' Y', ' 0'),
(' n', ' y', ' 0'),
(' F', ' T', ' 0'),
(' f', ' t', ' 0'),
]
# white space is stripped
expectedList = [
('N', 'Y', '0'),
('n', 'y', '0'),
('F', 'T', '0'),
('f', 't', '0'),
('N', 'Y', '0'),
('n', 'y', '0'),
('F', 'T', '0'),
('f', 't', '0'),
]
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(100, 200, 'x.hex', choicesList[4], expectedList[4]),
(100, 200, 'x.hex', choicesList[5], expectedList[5]),
(100, 200, 'x.hex', choicesList[6], expectedList[6]),
(100, 200, 'x.hex', choicesList[7], expectedList[7]),
(100, 200, 'x.hex', choicesList[3], expectedList[3]),
(1000, 200, 'x.hex', choicesList[2], expectedList[2]),
(10000, 200, 'x.hex', choicesList[1], expectedList[1]),
(100000, 200, 'x.hex', choicesList[0], expectedList[0]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, choices, expected) in tryList:
# max error = half the bin size?
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
print "Creating random", csvPathname
expectedNaCnt = write_syn_dataset(csvPathname, rowCount, colCount,
SEEDPERFILE, choices)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=10,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
print pA.numRows, pA.numCols, pA.parse_key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
for i in range(colCount):
# walks across the columns triggering a summary on the col desired
# runSummary returns a column object now. inspect and parse don't. They return json.
# maybe eventually will make them return object? But I also pass expected stuff to them
# should I pass expected to summary? no, more complex?
co = h2o_cmd.runSummary(key=hex_key, column=i)
print co.label, co.type, co.missing_count, co.domain, sum(
co.histogram_bins)
print "\nComparing column %s to expected" % i
self.assertEqual(expectedNaCnt[i], co.missing_count, "Column %s Expected %s. missing: %s is incorrect" % \
(i, expectedNaCnt[i], co.missing_count))
self.assertEqual(rowCount - expectedNaCnt[i],
sum(co.histogram_bins))
h2p.green_print("\nDone with trial", trial)
trial += 1
h2i.delete_keys_at_all_nodes()
def test_summary2_exp(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# co.label, (min, 25th, 50th, 75th, max)
# parse setup error ? supposedly fixed now
# (1, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(10, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(100, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(1000, 1, 'x.hex', -5000, 0, ['C1', None, None, None, None, None]),
(10000, 1, 'x.hex', -100000, 100000,
['C1', None, None, None, None, None]),
(100000, 1, 'x.hex', -1, 1, ['C1', None, None, None, None, None]),
(1000000, 1, 'A.hex', 1, 100, ['C1', None, None, None, None,
None]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, rangeMin, rangeMax,
expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(csvPathname,
rowCount,
colCount,
lambd=LAMBD,
SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxErr = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxErr = 1.05 * maxErr
expected[1] = expectedMin
expected[5] = expectedMax
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
print pA.numRows, pA.numCols, pA.parse_key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
# column 0 not used here
assert len(expected) == 6
co = h2o_cmd.runSummary(key=hex_key,
column=0,
expected=expected[1:],
maxDelta=maxErr)
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
print "maxErr", maxErr
if co.label != '' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=False,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.99,
h2oSummary2=co.percentiles[5 if DO_MEDIAN else 9],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
def test_parse_multi_header_single(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_ints.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
headerData = "ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON,output"
# cols must be 9 to match the header above, otherwise a different bug is hit
# extra output is added, so it's 10 total
tryList = [
(57, 300, 9, 'cA', 60, 0),
# try with 1-3 data lines in the header file too
(57, 300, 9, 'cB', 60, 1),
(57, 300, 9, 'cC', 60, 2),
(57, 300, 9, 'cD', 60, 3),
]
trial = 0
for (fileNum, rowCount, colCount, hex_key, timeoutSecs,
dataRowsWithHeader) in tryList:
trial += 1
# FIX! should we add a header to them randomly???
print "Wait while", fileNum, "synthetic files are created in", SYNDATASETS_DIR
rowxcol = str(rowCount) + 'x' + str(colCount)
totalCols = colCount + 1 # 1 extra for output
totalDataRows = 0
for fileN in range(fileNum):
csvFilename = 'syn_' + str(fileN) + "_" + str(
SEED) + "_" + rowxcol + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rList = rand_rowData(colCount)
dataRowsDone = write_syn_dataset(csvPathname,
rowCount,
headerData=None,
rList=rList)
totalDataRows += dataRowsDone
# create the header file
# can make it pass by not doing this
if HEADER:
csvFilename = 'syn_header_' + str(
SEED) + "_" + rowxcol + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
dataRowsDone = write_syn_dataset(csvPathname,
dataRowsWithHeader,
headerData, rList)
totalDataRows += dataRowsDone
# make sure all key names are unique, when we re-put and re-parse (h2o caching issues)
src_key = "syn_" + str(trial)
hex_key = "syn_" + str(trial) + ".hex"
# DON"T get redirected to S3! (EC2 hack in config, remember!)
# use it at the node level directly (because we gen'ed the files.
# I suppose we could force the redirect state bits in h2o.nodes[0] to False, instead?
# put them, rather than using import files, so this works if remote h2o is used
# and python creates the files locally
fileList = os.listdir(SYNDATASETS_DIR)
for f in fileList:
h2i.import_only(path=SYNDATASETS_DIR + "/" + f,
schema='put',
noPrint=True)
print f
# fix. should we have a h2o.n0 for brevity? or h2o.n. ? so we can change it around if multi-node?
# frames = h2o.nodes[0].frames()['frames']
frames = h2o.n0.frames()['frames']
frames_dict = h2o_util.list_to_dict(frames, 'key/name')
# print "frames:", dump_json(frames)
# print "frames_dict:", dump_json(frames_dict)
if HEADER:
header = h2i.find_key('syn_header')
if not header:
raise Exception(
"Didn't find syn_header* key in the import")
# use regex. the only files in the dir will be the ones we just created with *fileN* match
print "Header Key = " + header
start = time.time()
# does h2o-dev take a regex? or do we need to glob
parseResult = h2i.parse_only(
pattern='*' + rowxcol + '*',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
checkHeader="1") # header_from_file=header
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=totalDataRows,
expectedNumCols=totalCols)
print pA.numRows
print pA.numCols
print pA.parse_key
expectedLabelList = headerData.split(",")
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=totalDataRows,
expectedNumCols=totalCols,
expectedMissinglist=[],
expectedLabelList=expectedLabelList)
if DO_RF:
# put in an ignore param, that will fail unless headers were parsed correctly
if HEADER:
kwargs = {
'sample_rate': 0.75,
'max_depth': 25,
'ntrees': 1,
'ignored_cols_by_name': 'ID,CAPSULE'
}
else:
kwargs = {
'sample_rate': 0.75,
'max_depth': 25,
'ntrees': 1
}
rfv = h2o_cmd.runRF(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
h2o.check_sandbox_for_errors()
def test_kmeans_sphere100(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = 'syn_spheres100.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
centersList = write_spheres_dataset(csvPathname, CLUSTERS, SPHERE_PTS)
if SHUFFLE_SPHERES:
# since we create spheres in order
csvFilename2 = 'syn_spheres100_shuffled.csv'
csvPathname2 = SYNDATASETS_DIR + '/' + csvFilename2
h2o_util.file_shuffle(csvPathname, csvPathname2)
else:
csvFilename2 = csvFilename
csvPathname2 = csvPathname
print "\nStarting", csvFilename
parseResult = h2i.import_parse(path=csvPathname2, schema='put', hex_key=csvFilename2 + ".hex")
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
numColsUsed = numCols
labelListUsed = labelList
### h2b.browseTheCloud()
# try 5 times, to see if all inits by h2o are good
# does it break if cols is not specified?
destination_key = 'syn_spheres100.hex'
cols = ",".join(map(str,range(DIMENSIONS)))
for trial in range(2):
parameters = {
'validation_frame': parse_key,
'ignored_columns': None,
'score_each_iteration': False,
'k': CLUSTERS,
'max_iterations': 50,
'standardize': False,
# 'seed': kmeansSeed,
'init': 'Furthest',
}
timeoutSecs = 100
model_key = 'sphere100_k.hex'
kmeansResult = h2o.n0.build_model(
algo='kmeans',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=timeoutSecs)
modelResult = h2o.n0.models(key=model_key)
km = h2o_kmeans.KMeansObj(modelResult, parameters, numRows, numColsUsed, labelListUsed)
# no expected row/error?
expected = [(None, c, None, None) for c in centersList]
expected.sort(key=lambda tup: sum(tup[1]))
h2o_kmeans.compareResultsToExpected(km.tuplesSorted, expected, allowedDelta=[.01, .01, .01])
print "Trial #", trial, "completed"
def test_DL_mnist(self):
h2o.nodes[0].remove_all_keys()
csvPathname_train = 'laptop/mnist/train.csv.gz'
csvPathname_test = 'laptop/mnist/test.csv.gz'
hex_key = 'mnist_train.hex'
validation_key = 'mnist_test.hex'
timeoutSecs = 60
parseResult = h2i.import_parse(bucket='bigdata',
path=csvPathname_train,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
numCols = iA.numCols
labelList = iA.labelList
parseResultV = h2i.import_parse(bucket='bigdata',
path=csvPathname_test,
hex_key=validation_key,
timeoutSecs=timeoutSecs,
doSummary=False)
response = numCols - 1
#Making random id
identifier = ''.join(
random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'deeplearning_' + identifier + '.hex'
parameters = {
'validation_frame': validation_key, # KeyIndexed None
'ignored_columns': None, # string[] None
'response_column': labelList[response], # string None
'balance_classes': None, # boolean false
'max_after_balance_size': None, # float Infinity
'keep_cross_validation_splits': None, # boolean false
'checkpoint': None, # Key None
'overwrite_with_best_model': None, # boolean true
'expert_mode': None, # boolean false
'autoencoder': None, # boolean false
'use_all_factor_levels': None, # boolean true
# [u'Tanh', u'TanhWithDropout', u'Rectifier', u'RectifierWithDropout', u'Maxout', u'MaxoutWithDropout']
'activation': 'RectifierWithDropout', # enum Rectifier
'hidden': '[117,131,129]', # int[] [200, 200]
'epochs': 2.0, # double 10.0
'train_samples_per_iteration': None, # long -2
'target_ratio_comm_to_comp': None, # double 0.02
'seed': None, # long 1679194146842485659
'adaptive_rate': False, # boolean true
'rho': None, # double 0.99
'epsilon': None, # double 1.0E-8
'rate': None, # double 0.005
'rate_annealing': None, # double 1.0E-6
'rate_decay': None, # double 1.0
'momentum_start': 0.5, # double 0.0
'momentum_ramp': 100000, # double 1000000.0
'momentum_stable': 0.9, # double 0.0
'nesterov_accelerated_gradient': None, # boolean true
'input_dropout_ratio': 0.2, # double 0.0
'hidden_dropout_ratios': None, # double[] None (this can grid?)
'l1': 1e-5, # double 0.0
'l2': 1e-7, # double 0.0
'max_w2': 15, # float Infinity
'initial_weight_distribution':
None, # enum UniformAdaptive [u'UniformAdaptive', u'Uniform', u'Normal']
'initial_weight_scale': None, # double 1.0
'loss':
'CrossEntropy', # enum MeanSquare [u'Automatic', u'MeanSquare', u'CrossEntropy']
'score_interval': None, # double 5.0
'score_training_samples': None, # long 10000
'score_validation_samples': None, # long 0
'score_duty_cycle': None, # double 0.1
'classification_stop': None, # double 0.0
'regression_stop': None, # double 1.0E-6
'quiet_mode': None, # boolean false
'max_confusion_matrix_size': None, # int 20
'max_hit_ratio_k': None, # int 10
'balance_classes': None, # boolean false
'class_sampling_factors': None, # float[] None
'max_after_balance_size': None, # float Infinity
'score_validation_sampling':
None, # enum Uniform [u'Uniform', u'Stratified']
'diagnostics': None, # boolean true
'variable_importances': None, # boolean false
'fast_mode': None, # boolean true
'ignore_const_cols': None, # boolean true
'force_load_balance': None, # boolean true
'replicate_training_data': None, # boolean false
'single_node_mode': None, # boolean false
'shuffle_training_data': None, # boolean false
'missing_values_handling':
None, # enum MeanImputation [u'Skip', u'MeanImputation']
'sparse': None, # boolean false
'col_major': None, # boolean false
'average_activation': None, # double 0.0
'sparsity_beta': None, # double 0.0
}
expectedErr = 0.057 ## expected validation error for the above model
relTol = 0.20 ## 20% rel. error tolerance due to Hogwild!
timeoutSecs = 60
start = time.time()
bmResult = h2o.n0.build_model(algo='deeplearning',
model_id=model_key,
training_frame=hex_key,
parameters=parameters,
timeoutSecs=timeoutSecs)
bm = OutputObj(bmResult, 'bm')
print 'deep learning took', time.time() - start, 'seconds'
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
# print "model:", dump_json(model)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=validation_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=validation_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=validation_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
actualErr = model['errors']['valid_err']
print "expected classification error: " + format(expectedErr)
print "actual classification error: " + format(actualErr)
if actualErr != expectedErr and abs(
(expectedErr - actualErr) / expectedErr) > relTol:
raise Exception(
"Scored classification error of %s is not within %s %% relative error of %s"
% (actualErr, float(relTol) * 100, expectedErr))
def test_GLM_covtype(self):
importFolderPath = "standard"
csvFilename = "covtype.data"
hex_key = "covtype.hex"
bucket = "home-0xdiag-datasets"
csvPathname = importFolderPath + "/" + csvFilename
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, hex_key=hex_key,
check_header=1, timeoutSecs=180, doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
expected = []
allowedDelta = 0
labelListUsed = list(labelList)
labelListUsed.remove('C54')
numColsUsed = numCols - 1
for trial in range(1):
# family [u'gaussian', u'binomial', u'poisson', u'gamma', u'tweedie']
# link [u'family_default', u'identity', u'logit', u'log', u'inverse', u'tweedie']
# can we do classification with probabilities?
# are only lambda and alpha grid searchable?
parameters = {
'validation_frame': parse_key,
'ignored_columns': None,
# FIX! for now just use a column that's binomial
'response_column': 'C54',
# FIX! when is this needed? redundant for binomial?
'balance_classes': False,
'max_after_balance_size': None,
'standardize': False,
'family': 'binomial',
'link': None,
'tweedie_variance_power': None,
'tweedie_link_power': None,
'alpha': '[1e-4]',
'lambda': '[0.5,0.25, 0.1]',
'prior1': None,
'lambda_search': None,
'nlambdas': None,
'lambda_min_ratio': None,
'use_all_factor_levels': False,
# NPE with n_folds 2?
'n_folds': 1,
}
model_key = 'covtype_glm.hex'
bmResult = h2o.n0.build_model(
algo='glm',
destination_key=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=60)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
h2o_glm.simpleCheckGLM(self, model, parameters, labelList, labelListUsed)
cmmResult = h2o.n0.compute_model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mcms = OutputObj({'data': cmm.max_criteria_and_metric_scores.data}, 'mcms')
m1 = mcms.data[1:]
h0 = mcms.data[0]
print "\nmcms", tabulate(m1, headers=h0)
thms = OutputObj(cmm.thresholds_and_metric_scores, 'thms')
cmms = OutputObj({'cm': cmm.confusion_matrices}, 'cmms')
if 1==0:
print ""
for i,c in enumerate(cmms.cm):
print "\ncmms.cm[%s]" % i, tabulate(c)
print ""
mmResult = h2o.n0.model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key, frame=parse_key, timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
def test_DL_airlines_small(self):
h2o.nodes[0].remove_all_keys()
csvPathname_train = 'airlines/AirlinesTrain.csv.zip'
csvPathname_test = 'airlines/AirlinesTest.csv.zip'
hex_key = 'train.hex'
validation_key = 'validation.hex'
timeoutSecs = 60
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname_train,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parseResultV = h2i.import_parse(bucket='smalldata',
path=csvPathname_test,
hex_key=validation_key,
timeoutSecs=timeoutSecs,
doSummary=False)
pAV = h2o_cmd.ParseObj(parseResultV)
iAV = h2o_cmd.InspectObj(pAV.parse_key)
#Making random id
identifier = ''.join(
random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'deeplearning_' + identifier + '.hex'
parameters = {
'validation_frame': validation_key, # KeyIndexed None
'ignored_columns': "['IsDepDelayed_REC']", # string[] None
'response_column': 'IsDepDelayed', # string None
'loss': 'CrossEntropy'
}
expectedErr = 0.32 ## expected validation error for the above model
relTol = 0.15 ## 15% rel. error tolerance due to Hogwild!
timeoutSecs = 60
start = time.time()
bmResult = h2o.n0.build_model(algo='deeplearning',
model_id=model_key,
training_frame=hex_key,
parameters=parameters,
timeoutSecs=timeoutSecs)
bm = OutputObj(bmResult, 'bm')
print 'deep learning took', time.time() - start, 'seconds'
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
# print "model:", dump_json(model)
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=validation_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
mmResult = h2o.n0.model_metrics(model=model_key,
frame=validation_key,
timeoutSecs=60)
mm = OutputObj(mmResult['model_metrics'][0], 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=validation_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
actualErr = model['errors']['valid_err']
print "expected classification error: " + format(expectedErr)
print "actual classification error: " + format(actualErr)
if actualErr != expectedErr and abs(
(expectedErr - actualErr) / expectedErr) > relTol:
raise Exception(
"Scored classification error of %s is not within %s %% relative error of %s"
% (actualErr, float(relTol) * 100, expectedErr))
def test_GBM_airlines(self):
files = [
('datasets', 'airlines_all.05p.csv', 'airlines_all.05p.hex', 1800,
'IsDepDelayed'),
# ('datasets', 'airlines_all.csv', 'airlines_all.hex', 1800, 'IsDepDelayed')
]
for importFolderPath, csvFilename, trainKey, timeoutSecs, response in files:
# PARSE train****************************************
csvPathname = importFolderPath + "/" + csvFilename
model_key = 'GBMModelKey'
# IsDepDelayed might already be enum, but just to be sure
parseResult = h2i.import_parse(
path=csvPathname,
schema='hdfs',
hex_key=trainKey,
columnTypeDict={'IsDepDelayed': 'Enum'},
timeoutSecs=timeoutSecs)
pA = h2o_cmd.ParseObj(parseResult)
iA = h2o_cmd.InspectObj(pA.parse_key)
parse_key = pA.parse_key
numRows = iA.numRows
numCols = iA.numCols
labelList = iA.labelList
labelListUsed = list(labelList)
numColsUsed = numCols
parameters = {
'validation_frame': trainKey,
# 'ignored_columns': '[CRSDepTime,CRSArrTime,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay,IsArrDelayed]',
'response_column': response,
# 'balance_classes':
# 'max_after_balance_size':
'ntrees': 2,
'max_depth': 10,
'min_rows': 3,
'nbins': 40,
'learn_rate': 0.2,
# 'loss': 'multinomial',
# FIX! doesn't like it?
# 'loss': 'Bernoulli',
# FIX..no variable importance for GBM yet?
# 'variable_importance': False,
# 'seed':
}
bmResult = h2o.n0.build_model(algo='gbm',
model_id=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=360)
bm = OutputObj(bmResult, 'bm')
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0]['output'], 'model')
cmmResult = h2o.n0.compute_model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
cmm = OutputObj(cmmResult, 'cmm')
# print "\nLook!, can use dot notation: cmm.cm.confusion_matrix", cmm.cm.confusion_matrix, "\n"
mmResult = h2o.n0.model_metrics(model=model_key,
frame=parse_key,
timeoutSecs=60)
mmResultShort = mmResult['model_metrics'][0]
del mmResultShort['frame'] # too much!
mm = OutputObj(mmResultShort, 'mm')
prResult = h2o.n0.predict(model=model_key,
frame=parse_key,
timeoutSecs=60)
pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
