def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameList = [
"airlines_88_08_100lines.csv",
]
h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename, path='/datasets', timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,parseKey=parseKey,timeoutSecs=2000)
def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameList = [
"airlines_88_08_100lines.csv",
]
h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename,
path='/datasets',
timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,
parseKey=parseKey,
timeoutSecs=2000)
def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"TEST-poker1000.csv",
"leads.csv",
"and-testing.data",
"arcene2_train.both",
"arcene_train.both",
# these can't RF ..output classes not integer?
# "bestbuy_test.csv",
# "bestbuy_train.csv",
"covtype.data",
"covtype.4x.shuffle.data",
"covtype4x.shuffle.data",
"covtype.13x.data",
"covtype.13x.shuffle.data",
# "covtype.169x.data",
# "prostate_2g.csv",
# "prostate_long.csv.gz",
"prostate_long_1G.csv",
"hhp.unbalanced.012.1x11.data.gz",
"hhp.unbalanced.012.data.gz",
"hhp.unbalanced.data.gz",
"hhp2.os.noisy.0_1.data",
"hhp2.os.noisy.9_4.data",
"hhp_9_14_12.data",
# "poker_c1s1_testing_refresh.csv",
# "3G_poker_shuffle",
# "billion_rows.csv.gz",
# "poker-hand.1244M.shuffled311M.full.txt",
]
# pick 8 randomly!
if (1==0):
csvFilenameList = random.sample(csvFilenameAll,8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename, path='/datasets', timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,parseKey=parseKey,timeoutSecs=2000)
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_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_hdfs_multi_bad_csv(self):
print "\nUse the new regex capabilities for selecting hdfs: try *csv* at /datasets"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
parseKey = h2o.nodes[0].parse('*airlines_all*csv', key2='random_csv.hex',
exclude=None, header=None, timeoutSecs=600)
print "*csv* regex to hdfs /datasets", 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
sys.stdout.flush()
def test_hdfs_multi_copies(self):
print "\nUse the new regex capabilities for selecting hdfs: try *copies* at /datasets"
print "This should match to a folder with about twenty covtype10x?"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
parseKey = h2i.parseImportHdfsFile(csvFilename='*covtype10x_copies*', key2='copies.hex',
exclude=None, header=None, timeoutSecs=600)
print "*copies* regex to hdfs /datasets", 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
sys.stdout.flush()
def test_hdfs_multi_copies(self):
print "\nUse the new regex capabilities for selecting hdfs: try *copies* at /datasets"
print "This should match to a folder with about twenty covtype10x?"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
parseKey = h2o.nodes[0].parse(
"*covtype10x_copies*", key2="copies.hex", exclude=None, header=None, timeoutSecs=600
)
print "*copies* regex to hdfs /datasets", "parse time:", parseKey["response"]["time"]
print "parse result:", parseKey["destination_key"]
sys.stdout.flush()
def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"allyears2k.csv",
"billion_rows.csv.gz",
"covtype.data",
"covtype.shuffled.data",
"covtype200x.data",
"covtype20x.data",
"kddcup_1999.data.gz",
"rand_logreg_100000000x70.csv.gz",
]
# pick 8 randomly!
if (1==0):
csvFilenameList = random.sample(csvFilenameAll,8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
# h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs(
path='/datasets/standard',
schema='maprfs')
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(
csvFilename=csvFilename,
path='/datasets/standard',
schema='maprfs',
timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,parseKey=parseKey,timeoutSecs=2000)
def test_hdfs_multi_bad_csv(self):
print "\nUse the new regex capabilities for selecting hdfs: try *csv* at /datasets"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
parseKey = h2o.nodes[0].parse('*airlines_all*csv',
key2='random_csv.hex',
exclude=None,
header=None,
timeoutSecs=600)
print "*csv* regex to hdfs /datasets", 'parse time:', parseKey[
'response']['time']
print "parse result:", parseKey['destination_key']
sys.stdout.flush()
def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"allyears2k.csv",
"billion_rows.csv.gz",
"covtype.data",
"covtype.shuffled.data",
"covtype200x.data",
"covtype20x.data",
"kddcup_1999.data.gz",
"rand_logreg_100000000x70.csv.gz",
]
# pick 8 randomly!
if (1 == 0):
csvFilenameList = random.sample(csvFilenameAll, 8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
# h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs(path='/datasets/standard', schema='maprfs')
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename,
path='/datasets/standard',
schema='maprfs',
timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,
parseKey=parseKey,
timeoutSecs=2000)
def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"TEST-poker1000.csv",
]
# pick 8 randomly!
if (1==0):
csvFilenameList = random.sample(csvFilenameAll,8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
# h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs(
path='/datasets',
schema='maprfs')
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(
csvFilename=csvFilename,
path='/datasets',
schema='maprfs',
timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,parseKey=parseKey,timeoutSecs=2000)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
# wait in case it recomputes it
time.sleep(10)
sys.stdout.write('.')
sys.stdout.flush()
def test_hdfs_multi_copies(self):
print "\nUse the new regex capabilities for selecting hdfs: try *copies* at /datasets"
print "This should match to a folder with about twenty covtype10x?"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
parseKey = h2o.nodes[0].parse('*covtype10x_copies*',
key2='copies.hex',
exclude=None,
header=None,
timeoutSecs=600)
print "*copies* regex to hdfs /datasets", 'parse time:', parseKey[
'response']['time']
print "parse result:", parseKey['destination_key']
sys.stdout.flush()
def test_import_nflx_parse_loop(self):
print "Using the -.gz files from hdfs"
# hdfs://<name node>/datasets/manyfiles-nflx-gz/file_1.dat.gz
csvFilename = "file_10.dat.gz"
csvFilepattern = "file_1[0-9].dat.gz"
trialMax = 2
for tryHeap in [24]:
print "\n", tryHeap,"GB heap, 1 jvm per host, import 192.168.1.176 hdfs, then parse"
localhost = h2o.decide_if_localhost()
if (localhost):
h2o.build_cloud(node_count=1, java_heap_GB=tryHeap,
use_hdfs=True, hdfs_name_node='192.168.1.176', hdfs_version='cdh3')
else:
h2o_hosts.build_cloud_with_hosts(node_count=1, java_heap_GB=tryHeap,
use_hdfs=True, hdfs_name_node='192.168.1.176', hdfs_version='cdh3')
# don't raise exception if we find something bad in h2o stdout/stderr?
# h2o.nodes[0].sandbox_ignore_errors = True
timeoutSecs = 500
importFolderPath = "/datasets/manyfiles-nflx-gz"
for trial in range(trialMax):
# since we delete the key, we have to re-import every iteration, to get it again
importHdfsResult = h2i.setupImportHdfs(path=importFolderPath)
hdfsFullList = importHdfsResult['succeeded']
for k in hdfsFullList:
key = k['key']
# just print the first tile
if 'nflx' in key and 'file_1.dat.gz' in key:
# should be hdfs://home-0xdiag-datasets/manyfiles-nflx-gz/file_1.dat.gz
print "example file we'll use:", key
### print "hdfsFullList:", h2o.dump_json(hdfsFullList)
# error if none?
self.assertGreater(len(hdfsFullList),8,"Didn't see more than 8 files in hdfs?")
key2 = csvFilename + "_" + str(trial) + ".hex"
csvFilePattern = 'file_1.dat.gz'
# "key": "hdfs://192.168.1.176/datasets/manyfiles-nflx-gz/file_99.dat.gz",
time.sleep(5)
print "Loading from hdfs:", importFolderPath + "/" + csvFilePattern
start = time.time()
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilePattern, path=importFolderPath,
key2=key2, timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
elapsed = time.time() - start
print hdfsKey, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_hdfs_multi_bad_csv(self):
print "\nUse the new regex capabilities for selecting hdfs: try *csv* at /datasets"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
# path should default to /datasets
# One .gz in with non .gz seems to cause a stack trace..so don't match to all (*airlines*).
# no..maybe it's just the zero length gz file?. No it doesn't show up in the list of keys?
# drwxr-xr-x - earl supergroup 0 2013-07-24 17:55 /datasets/airline.gz
# -rw-r--r-- 3 hduser supergroup 12155501626 2013-02-22 17:13 /datasets/airline_116M.csv
# -rw-r--r-- 3 hduser supergroup 11349125129 2013-05-03 15:45 /datasets/airlines_1988_2008.csv
# -rw-r--r-- 3 hduser supergroup 11349125429 2013-05-01 12:52 /datasets/airlines_1988_2008_shuffled.csv
# -rw-r--r-- 3 hduser supergroup 9936 2013-05-01 11:49 /datasets/airlines_88_08_100lines.csv
# -rw-r--r-- 3 hduser supergroup 12155501626 2013-02-23 15:59 /datasets/airlines_all.csv
# -rw-r--r-- 3 hduser supergroup 133710514626 2013-02-23 15:21 /datasets/airlines_all_11x.csv
parseKey = h2i.parseImportHdfsFile(csvFilename="airline_116M.csv", key2="random_csv.hex", timeoutSecs=600)
print "*csv* regex to hdfs /datasets", "parse time:", parseKey["response"]["time"]
print "parse result:", parseKey["destination_key"]
sys.stdout.flush()
def test_hdfs_multi_bad_csv(self):
print "\nUse the new regex capabilities for selecting hdfs: try *csv* at /datasets"
# pop open a browser on the cloud
h2b.browseTheCloud()
# defaults to /datasets
h2i.setupImportHdfs()
# path should default to /datasets
# One .gz in with non .gz seems to cause a stack trace..so don't match to all (*airlines*).
# no..maybe it's just the zero length gz file?. No it doesn't show up in the list of keys?
# drwxr-xr-x - earl supergroup 0 2013-07-24 17:55 /datasets/airline.gz
# -rw-r--r-- 3 hduser supergroup 12155501626 2013-02-22 17:13 /datasets/airline_116M.csv
# -rw-r--r-- 3 hduser supergroup 11349125129 2013-05-03 15:45 /datasets/airlines_1988_2008.csv
# -rw-r--r-- 3 hduser supergroup 11349125429 2013-05-01 12:52 /datasets/airlines_1988_2008_shuffled.csv
# -rw-r--r-- 3 hduser supergroup 9936 2013-05-01 11:49 /datasets/airlines_88_08_100lines.csv
# -rw-r--r-- 3 hduser supergroup 12155501626 2013-02-23 15:59 /datasets/airlines_all.csv
# -rw-r--r-- 3 hduser supergroup 133710514626 2013-02-23 15:21 /datasets/airlines_all_11x.csv
parseKey = h2i.parseImportHdfsFile(csvFilename='airline_116M.csv', key2='random_csv.hex', timeoutSecs=600)
print "*csv* regex to hdfs /datasets", 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
sys.stdout.flush()
def test_KMeans_sphere15_180GB(self):
csvFilename = 'syn_sphere15_2711545732row_6col_180GB_from_7x.csv'
totalBytes = 183538602156
if FROM_HDFS:
importFolderPath = "/datasets/kmeans_big"
csvPathname = "hdfs://" + importFolderPath + '/' + csvFilename
else:
importFolderPath = "/home3/0xdiag/datasets/kmeans_big"
csvPathname = importFolderPath + '/' + csvFilename
# FIX! put right values in
# will there be different expected for random vs the other inits?
expected = [
([
0.0, -113.00566692375459, -89.99595447985321,
-455.9970643424373, 4732.0, 49791778.0, 36800.0
], 248846122, 1308149283316.2988),
([
0.0, 1.0, 1.0, -525.0093818313685, 2015.001629398412,
25654042.00592703, 28304.0
], 276924291, 1800760152555.98),
([
0.0, 5.0, 2.0, 340.0, 1817.995920197288, 33970406.992053084,
31319.99486705394
], 235089554, 375419158808.3253),
([
0.0, 10.0, -72.00113070337981, -171.0198611715457,
4430.00952228909, 37007399.0, 29894.0
], 166180630, 525423632323.6474),
([
0.0, 11.0, 3.0, 578.0043558141306, 1483.0163188052604,
22865824.99639042, 5335.0
], 167234179, 1845362026223.1094),
([
0.0, 12.0, 3.0, 168.0, -4066.995950679284, 41077063.00269915,
-47537.998050740985
], 195420925, 197941282992.43475),
([
0.0, 19.00092954923767, -10.999565572612255, 90.00028669073289,
1928.0, 39967190.0, 27202.0
], 214401768, 11868360232.658035),
([
0.0, 20.0, 0.0, 141.0, -3263.0030236302937, 6163210.990273981,
30712.99115201907
], 258853406, 598863991074.3276),
([
0.0, 21.0, 114.01584574295777, 242.99690338815898,
1674.0029079209912, 33089556.0, 36415.0
], 190979054, 1505088759456.314),
([
0.0, 25.0, 1.0, 614.0032787274755, -2275.9931284021022,
-48473733.04122273, 47343.0
], 87794427, 1124697008162.3955),
([
0.0, 39.0, 3.0, 470.0, -3337.9880599007597, 28768057.98852736,
16716.003410920028
], 78226988, 1151439441529.0215),
([
0.0, 40.0, 1.0, 145.0, 950.9990795199593, 14602680.991458317,
-14930.007919032574
], 167273589, 693036940951.0249),
([
0.0, 42.0, 4.0, 479.0, -3678.0033024834297, 8209673.001421165,
11767.998552236539
], 148426180, 35942838893.32379),
([
0.0, 48.0, 4.0, 71.0, -951.0035145455234, 49882273.00063991,
-23336.998167498707
], 157533313, 88431531357.62982),
([
0.0, 147.00394564757505, 122.98729664236723, 311.0047920137008,
2320.0, 46602185.0, 11212.0
], 118361306, 1111537045743.7646),
]
benchmarkLogging = ['cpu', 'disk', 'network', 'iostats', 'jstack']
benchmarkLogging = ['cpu', 'disk', 'network', 'iostats']
# IOStatus can hang?
benchmarkLogging = ['cpu', 'disk', 'network']
benchmarkLogging = []
for trial in range(6):
# IMPORT**********************************************
# since H2O deletes the source key, re-import every iteration.
if FROM_HDFS:
importFolderResult = h2i.setupImportHdfs(
None, importFolderPath)
else:
importFolderResult = h2i.setupImportFolder(
None, importFolderPath)
# PARSE ****************************************
print "Parse starting: " + csvFilename
key2 = csvFilename + "_" + str(trial) + ".hex"
start = time.time()
timeoutSecs = 2 * 3600
kwargs = {}
if FROM_HDFS:
parseKey = h2i.parseImportHdfsFile(
None,
csvFilename,
importFolderPath,
key2=key2,
timeoutSecs=timeoutSecs,
pollTimeoutsecs=60,
retryDelaySecs=2,
benchmarkLogging=benchmarkLogging,
**kwargs)
else:
parseKey = h2i.parseImportFolderFile(
None,
csvFilename,
importFolderPath,
key2=key2,
timeoutSecs=timeoutSecs,
pollTimeoutsecs=60,
retryDelaySecs=2,
benchmarkLogging=benchmarkLogging,
**kwargs)
elapsed = time.time() - start
fileMBS = (totalBytes / 1e6) / elapsed
l = '{!s} jvms, {!s}GB heap, {:s} {:s} {:6.2f} MB/sec for {:.2f} secs'.format(
len(h2o.nodes), h2o.nodes[0].java_heap_GB, 'Parse',
csvPathname, fileMBS, elapsed)
print "\n" + l
h2o.cloudPerfH2O.message(l)
# KMeans ****************************************
print "col 0 is enum in " + csvFilename + " but KMeans should skip that automatically?? or no?"
kwargs = {
'k': 15,
'initialization': 'Furthest',
'epsilon': 1e-6,
'cols': None,
'destination_key': 'junk.hex',
# reuse the same seed, to get deterministic results
'seed': 265211114317615310,
}
if (trial % 3) == 0:
kwargs['initialization'] = 'PlusPlus'
elif (trial % 3) == 1:
kwargs['initialization'] = 'Furthest'
else:
kwargs['initialization'] = None
timeoutSecs = 4 * 3600
params = kwargs
paramsString = json.dumps(params)
start = time.time()
kmeans = h2o_cmd.runKMeansOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
benchmarkLogging=benchmarkLogging,
**kwargs)
elapsed = time.time() - start
print "kmeans end on ", csvPathname, 'took', elapsed, 'seconds.', "%d pct. of timeout" % (
(elapsed / timeoutSecs) * 100)
l = '{!s} jvms, {!s}GB heap, {:s} {:s} {:s} for {:.2f} secs {:s}'.format(
len(h2o.nodes), h2o.nodes[0].java_heap_GB, "KMeans",
"trial " + str(trial), csvFilename, elapsed, paramsString)
print l
h2o.cloudPerfH2O.message(l)
(centers, tupleResultList) = h2o_kmeans.bigCheckResults(
self, kmeans, csvPathname, parseKey, 'd', **kwargs)
# all are multipliers of expected tuple value
allowedDelta = (0.01, 0.01, 0.01)
h2o_kmeans.compareResultsToExpected(self,
tupleResultList,
expected,
allowedDelta,
allowError=True,
trial=trial)
def test_B_load_hdfs_and_store_hex_to_hdfs(self):
print "\nLoad a list of files from 0xdata hdfs, parse, and store the .hex to hdfs"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"covtype.data",
"TEST-poker1000.csv",
"leads.csv",
"and-testing.data",
"arcene2_train.both",
"arcene_train.both",
"bestbuy_test.csv",
"bestbuy_train.csv",
"covtype.4x.shuffle.data",
"covtype4x.shuffle.data",
"covtype.13x.data",
"covtype.13x.shuffle.data",
"covtype.169x.data",
"prostate_2g.csv",
"prostate_long.csv.gz",
"prostate_long_1G.csv",
"hhp.unbalanced.012.1x11.data.gz",
"hhp.unbalanced.012.data.gz",
"hhp.unbalanced.data.gz",
"hhp2.os.noisy.0_1.data",
"hhp2.os.noisy.9_4.data",
"hhp_9_14_12.data",
"poker_c1s1_testing_refresh.csv",
"3G_poker_shuffle",
"billion_rows.csv.gz",
"poker-hand.1244M.shuffled311M.full.txt",
]
# pick 8 randomly!
if (1==0):
csvFilenameList = random.sample(csvFilenameAll,8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename, path='/datasets', timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
print "Storing", parseKey['destination_key'], 'to HDFS'
### print "FIX! temporarily disabling since it causes HDFS corruption"
storeKey = h2o_cmd.runStore2HDFS(key=parseKey['destination_key'], timeoutSecs=1000)
h2b.browseJsonHistoryAsUrlLastMatch("Parse")
sys.stdout.write('.')
sys.stdout.flush()
def test_B_hdfs_files(self):
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"3G_poker_shuffle",
"TEST-poker1000.csv",
# corrupt zip file?
# "allstate_claim_prediction_train_set.zip",
"and-testing.data",
"arcene2_train.both",
"arcene_train.both",
"bestbuy_test.csv",
"bestbuy_train.csv",
"billion_rows.csv.gz",
"covtype.13x.data",
"covtype.13x.shuffle.data",
"covtype.169x.data",
"covtype.4x.shuffle.data",
"covtype.data",
"covtype4x.shuffle.data",
"hhp.unbalanced.012.1x11.data.gz",
"hhp.unbalanced.012.data.gz",
"hhp.unbalanced.data.gz",
"hhp2.os.noisy.0_1.data",
"hhp2.os.noisy.9_4.data",
"hhp_9_14_12.data",
"leads.csv",
"prostate_long_1G.csv",
]
# pick 8 randomly!
if (1==0):
csvFilenameList = random.sample(csvFilenameAll,8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
h2b.browseTheCloud()
timeoutSecs = 1000
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
start = time.time()
print 'Parsing', csvFilename
parseKey = h2i.parseImportHdfsFile(
csvFilename=csvFilename, path='/datasets', timeoutSecs=timeoutSecs, retryDelaySecs=1.0)
print csvFilename, '\nparse time (python)', time.time() - start, 'seconds'
print csvFilename, '\nparse time (h2o):', parseKey['response']['time']
### print h2o.dump_json(parseKey['response'])
print "parse result:", parseKey['destination_key']
# I use this if i want the larger set in my localdir
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
### print h2o.dump_json(inspect)
cols = inspect['cols']
# look for nonzero num_missing_values count in each col
for i, colDict in enumerate(cols):
num_missing_values = colDict['num_missing_values']
if num_missing_values != 0:
### print "%s: col: %d, num_missing_values: %d" % (csvFilename, i, num_missing_values)
pass
### print h2o.dump_json(cols[0])
num_cols = inspect['num_cols']
num_rows = inspect['num_rows']
row_size = inspect['row_size']
ptype = inspect['type']
value_size_bytes = inspect['value_size_bytes']
response = inspect['response']
ptime = response['time']
print "num_cols: %s, num_rows: %s, row_size: %s, ptype: %s, \
value_size_bytes: %s, response: %s, time: %s" % \
(num_cols, num_rows, row_size, ptype, value_size_bytes, response, ptime)
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
print "\n" + csvFilename
# start = time.time()
# RFview = h2o_cmd.runRFOnly(trees=1,parseKey=parseKey,timeoutSecs=2000)
# h2b.browseJsonHistoryAsUrlLastMatch("RFView")
# # wait in case it recomputes it
# time.sleep(10)
sys.stdout.write('.')
sys.stdout.flush()
def test_KMeans_sphere15_180GB(self):
csvFilename = 'syn_sphere15_2711545732row_6col_180GB_from_7x.csv'
totalBytes = 183538602156
if FROM_HDFS:
importFolderPath = "/datasets/kmeans_big"
csvPathname = "hdfs://" + importFolderPath + '/' + csvFilename
else:
importFolderPath = "/home3/0xdiag/datasets/kmeans_big"
csvPathname = importFolderPath + '/' + csvFilename
# FIX! put right values in
# will there be different expected for random vs the other inits?
expected = [
([0.0, -113.00566692375459, -89.99595447985321, -455.9970643424373, 4732.0, 49791778.0, 36800.0], 248846122, 1308149283316.2988) ,
([0.0, 1.0, 1.0, -525.0093818313685, 2015.001629398412, 25654042.00592703, 28304.0], 276924291, 1800760152555.98) ,
([0.0, 5.0, 2.0, 340.0, 1817.995920197288, 33970406.992053084, 31319.99486705394], 235089554, 375419158808.3253) ,
([0.0, 10.0, -72.00113070337981, -171.0198611715457, 4430.00952228909, 37007399.0, 29894.0], 166180630, 525423632323.6474) ,
([0.0, 11.0, 3.0, 578.0043558141306, 1483.0163188052604, 22865824.99639042, 5335.0], 167234179, 1845362026223.1094) ,
([0.0, 12.0, 3.0, 168.0, -4066.995950679284, 41077063.00269915, -47537.998050740985], 195420925, 197941282992.43475) ,
([0.0, 19.00092954923767, -10.999565572612255, 90.00028669073289, 1928.0, 39967190.0, 27202.0], 214401768, 11868360232.658035) ,
([0.0, 20.0, 0.0, 141.0, -3263.0030236302937, 6163210.990273981, 30712.99115201907], 258853406, 598863991074.3276) ,
([0.0, 21.0, 114.01584574295777, 242.99690338815898, 1674.0029079209912, 33089556.0, 36415.0], 190979054, 1505088759456.314) ,
([0.0, 25.0, 1.0, 614.0032787274755, -2275.9931284021022, -48473733.04122273, 47343.0], 87794427, 1124697008162.3955) ,
([0.0, 39.0, 3.0, 470.0, -3337.9880599007597, 28768057.98852736, 16716.003410920028], 78226988, 1151439441529.0215) ,
([0.0, 40.0, 1.0, 145.0, 950.9990795199593, 14602680.991458317, -14930.007919032574], 167273589, 693036940951.0249) ,
([0.0, 42.0, 4.0, 479.0, -3678.0033024834297, 8209673.001421165, 11767.998552236539], 148426180, 35942838893.32379) ,
([0.0, 48.0, 4.0, 71.0, -951.0035145455234, 49882273.00063991, -23336.998167498707], 157533313, 88431531357.62982) ,
([0.0, 147.00394564757505, 122.98729664236723, 311.0047920137008, 2320.0, 46602185.0, 11212.0], 118361306, 1111537045743.7646) ,
]
benchmarkLogging = ['cpu','disk', 'network', 'iostats', 'jstack']
benchmarkLogging = ['cpu','disk', 'network', 'iostats']
# IOStatus can hang?
benchmarkLogging = ['cpu', 'disk', 'network']
benchmarkLogging = []
for trial in range(6):
# IMPORT**********************************************
# since H2O deletes the source key, re-import every iteration.
if FROM_HDFS:
importFolderResult = h2i.setupImportHdfs(None, importFolderPath)
else:
importFolderResult = h2i.setupImportFolder(None, importFolderPath)
# PARSE ****************************************
print "Parse starting: " + csvFilename
key2 = csvFilename + "_" + str(trial) + ".hex"
start = time.time()
timeoutSecs = 2 * 3600
kwargs = {}
if FROM_HDFS:
parseKey = h2i.parseImportHdfsFile(None, csvFilename, importFolderPath, key2=key2,
timeoutSecs=timeoutSecs, pollTimeoutsecs=60, retryDelaySecs=2,
benchmarkLogging=benchmarkLogging, **kwargs)
else:
parseKey = h2i.parseImportFolderFile(None, csvFilename, importFolderPath, key2=key2,
timeoutSecs=timeoutSecs, pollTimeoutsecs=60, retryDelaySecs=2,
benchmarkLogging=benchmarkLogging, **kwargs)
elapsed = time.time() - start
fileMBS = (totalBytes/1e6)/elapsed
l = '{!s} jvms, {!s}GB heap, {:s} {:s} {:6.2f} MB/sec for {:.2f} secs'.format(
len(h2o.nodes), h2o.nodes[0].java_heap_GB, 'Parse', csvPathname, fileMBS, elapsed)
print "\n"+l
h2o.cloudPerfH2O.message(l)
# KMeans ****************************************
print "col 0 is enum in " + csvFilename + " but KMeans should skip that automatically?? or no?"
kwargs = {
'k': 15,
'initialization': 'Furthest',
'epsilon': 1e-6,
'cols': None,
'destination_key': 'junk.hex',
# reuse the same seed, to get deterministic results
'seed': 265211114317615310,
}
if (trial%3)==0:
kwargs['initialization'] = 'PlusPlus'
elif (trial%3)==1:
kwargs['initialization'] = 'Furthest'
else:
kwargs['initialization'] = None
timeoutSecs = 4 * 3600
params = kwargs
paramsString = json.dumps(params)
start = time.time()
kmeans = h2o_cmd.runKMeansOnly(parseKey=parseKey, timeoutSecs=timeoutSecs,
benchmarkLogging=benchmarkLogging, **kwargs)
elapsed = time.time() - start
print "kmeans end on ", csvPathname, 'took', elapsed, 'seconds.', "%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
l = '{!s} jvms, {!s}GB heap, {:s} {:s} {:s} for {:.2f} secs {:s}' .format(
len(h2o.nodes), h2o.nodes[0].java_heap_GB, "KMeans", "trial "+str(trial), csvFilename, elapsed, paramsString)
print l
h2o.cloudPerfH2O.message(l)
(centers, tupleResultList) = h2o_kmeans.bigCheckResults(self, kmeans, csvPathname, parseKey, 'd', **kwargs)
# all are multipliers of expected tuple value
allowedDelta = (0.01, 0.01, 0.01)
h2o_kmeans.compareResultsToExpected(self, tupleResultList, expected, allowedDelta, allowError=True, trial=trial)
def test_B_load_hdfs_and_store_hex_to_hdfs(self):
print "\nLoad a list of files from 0xdata hdfs, parse, and store the .hex to hdfs"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"covtype.data",
"TEST-poker1000.csv",
"leads.csv",
"and-testing.data",
"arcene2_train.both",
"arcene_train.both",
"bestbuy_test.csv",
"bestbuy_train.csv",
"covtype.4x.shuffle.data",
"covtype4x.shuffle.data",
"covtype.13x.data",
"covtype.13x.shuffle.data",
"covtype.169x.data",
"prostate_2g.csv",
"prostate_long.csv.gz",
"prostate_long_1G.csv",
"hhp.unbalanced.012.1x11.data.gz",
"hhp.unbalanced.012.data.gz",
"hhp.unbalanced.data.gz",
"hhp2.os.noisy.0_1.data",
"hhp2.os.noisy.9_4.data",
"hhp_9_14_12.data",
"poker_c1s1_testing_refresh.csv",
"3G_poker_shuffle",
"billion_rows.csv.gz",
"poker-hand.1244M.shuffled311M.full.txt",
]
# pick 8 randomly!
if (1 == 0):
csvFilenameList = random.sample(csvFilenameAll, 8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename,
path='/datasets',
timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
print "Storing", parseKey['destination_key'], 'to HDFS'
### print "FIX! temporarily disabling since it causes HDFS corruption"
storeKey = h2o_cmd.runStore2HDFS(key=parseKey['destination_key'],
timeoutSecs=1000)
h2b.browseJsonHistoryAsUrlLastMatch("Parse")
sys.stdout.write('.')
sys.stdout.flush()
def test_B_hdfs_files(self):
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"3G_poker_shuffle",
"TEST-poker1000.csv",
# corrupt zip file?
# "allstate_claim_prediction_train_set.zip",
"and-testing.data",
"arcene2_train.both",
"arcene_train.both",
"bestbuy_test.csv",
"bestbuy_train.csv",
"billion_rows.csv.gz",
"covtype.13x.data",
"covtype.13x.shuffle.data",
"covtype.169x.data",
"covtype.4x.shuffle.data",
"covtype.data",
"covtype4x.shuffle.data",
"hhp.unbalanced.012.1x11.data.gz",
"hhp.unbalanced.012.data.gz",
"hhp.unbalanced.data.gz",
"hhp2.os.noisy.0_1.data",
"hhp2.os.noisy.9_4.data",
"hhp_9_14_12.data",
"leads.csv",
"prostate_long_1G.csv",
]
# pick 8 randomly!
if (1 == 0):
csvFilenameList = random.sample(csvFilenameAll, 8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
h2b.browseTheCloud()
timeoutSecs = 1000
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
start = time.time()
print 'Parsing', csvFilename
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename,
path='/datasets',
timeoutSecs=timeoutSecs,
retryDelaySecs=1.0)
print csvFilename, '\nparse time (python)', time.time(
) - start, 'seconds'
print csvFilename, '\nparse time (h2o):', parseKey['response'][
'time']
### print h2o.dump_json(parseKey['response'])
print "parse result:", parseKey['destination_key']
# I use this if i want the larger set in my localdir
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
### print h2o.dump_json(inspect)
cols = inspect['cols']
# look for nonzero num_missing_values count in each col
for i, colDict in enumerate(cols):
num_missing_values = colDict['num_missing_values']
if num_missing_values != 0:
### print "%s: col: %d, num_missing_values: %d" % (csvFilename, i, num_missing_values)
pass
### print h2o.dump_json(cols[0])
num_cols = inspect['num_cols']
num_rows = inspect['num_rows']
row_size = inspect['row_size']
ptype = inspect['type']
value_size_bytes = inspect['value_size_bytes']
response = inspect['response']
ptime = response['time']
print "num_cols: %s, num_rows: %s, row_size: %s, ptype: %s, \
value_size_bytes: %s, response: %s, time: %s" % \
(num_cols, num_rows, row_size, ptype, value_size_bytes, response, ptime)
# h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
print "\n" + csvFilename
def test_import_nflx_parse_loop(self):
print "Using the -.gz files from hdfs"
# hdfs://<name node>/datasets/manyfiles-nflx-gz/file_1.dat.gz
csvFilename = "file_10.dat.gz"
csvFilepattern = "file_1[0-9].dat.gz"
trialMax = 2
for tryHeap in [24]:
print "\n", tryHeap, "GB heap, 1 jvm per host, import 192.168.1.176 hdfs, then parse"
localhost = h2o.decide_if_localhost()
if (localhost):
h2o.build_cloud(node_count=1,
java_heap_GB=tryHeap,
use_hdfs=True,
hdfs_name_node='192.168.1.176',
hdfs_version='cdh3')
else:
h2o_hosts.build_cloud_with_hosts(
node_count=1,
java_heap_GB=tryHeap,
use_hdfs=True,
hdfs_name_node='192.168.1.176',
hdfs_version='cdh3')
# don't raise exception if we find something bad in h2o stdout/stderr?
# h2o.nodes[0].sandbox_ignore_errors = True
timeoutSecs = 500
importFolderPath = "/datasets/manyfiles-nflx-gz"
for trial in range(trialMax):
# since we delete the key, we have to re-import every iteration, to get it again
importHdfsResult = h2i.setupImportHdfs(path=importFolderPath)
hdfsFullList = importHdfsResult['succeeded']
for k in hdfsFullList:
key = k['key']
# just print the first tile
if 'nflx' in key and 'file_1.dat.gz' in key:
# should be hdfs://home-0xdiag-datasets/manyfiles-nflx-gz/file_1.dat.gz
print "example file we'll use:", key
### print "hdfsFullList:", h2o.dump_json(hdfsFullList)
# error if none?
self.assertGreater(len(hdfsFullList), 8,
"Didn't see more than 8 files in hdfs?")
key2 = csvFilename + "_" + str(trial) + ".hex"
csvFilePattern = 'file_1.dat.gz'
# "key": "hdfs://192.168.1.176/datasets/manyfiles-nflx-gz/file_99.dat.gz",
time.sleep(5)
print "Loading from hdfs:", importFolderPath + "/" + csvFilePattern
start = time.time()
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilePattern,
path=importFolderPath,
key2=key2,
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=60)
elapsed = time.time() - start
print hdfsKey, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_B_hdfs_files(self):
print "\nLoad a list of files from HDFS, parse and do 1 RF tree"
print "\nYou can try running as hduser/hduser if fail"
# larger set in my local dir
# fails because classes aren't integers
# "allstate_claim_prediction_train_set.zip",
csvFilenameAll = [
"TEST-poker1000.csv",
"leads.csv",
"and-testing.data",
"arcene2_train.both",
"arcene_train.both",
# these can't RF ..output classes not integer?
# "bestbuy_test.csv",
# "bestbuy_train.csv",
"covtype.data",
"covtype.4x.shuffle.data",
"covtype4x.shuffle.data",
"covtype.13x.data",
"covtype.13x.shuffle.data",
# "covtype.169x.data",
# "prostate_2g.csv",
# "prostate_long.csv.gz",
"prostate_long_1G.csv",
"hhp.unbalanced.012.1x11.data.gz",
"hhp.unbalanced.012.data.gz",
"hhp.unbalanced.data.gz",
"hhp2.os.noisy.0_1.data",
"hhp2.os.noisy.9_4.data",
"hhp_9_14_12.data",
# "poker_c1s1_testing_refresh.csv",
# "3G_poker_shuffle",
# "billion_rows.csv.gz",
# "poker-hand.1244M.shuffled311M.full.txt",
]
# pick 8 randomly!
if (1 == 0):
csvFilenameList = random.sample(csvFilenameAll, 8)
# Alternatively: do the list in order! Note the order is easy to hard
else:
csvFilenameList = csvFilenameAll
# pop open a browser on the cloud
h2b.browseTheCloud()
timeoutSecs = 200
# save the first, for all comparisions, to avoid slow drift with each iteration
firstglm = {}
h2i.setupImportHdfs()
for csvFilename in csvFilenameList:
# creates csvFilename.hex from file in hdfs dir
print "Loading", csvFilename, 'from HDFS'
parseKey = h2i.parseImportHdfsFile(csvFilename=csvFilename,
path='/datasets',
timeoutSecs=1000)
print csvFilename, 'parse time:', parseKey['response']['time']
print "parse result:", parseKey['destination_key']
print "\n" + csvFilename
start = time.time()
RFview = h2o_cmd.runRFOnly(trees=1,
parseKey=parseKey,
timeoutSecs=2000)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
# wait in case it recomputes it
time.sleep(10)
sys.stdout.write('.')
sys.stdout.flush()
