def parseit(n, pattern, hex_key, timeoutSecs=60, retryDelaySecs=1, pollTimeoutSecs=30):
h2i.parse_only(
node=h2o.nodes[n],
pattern=pattern,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
retryDelaySecs=retryDelaySecs,
pollTimeoutSecs=pollTimeoutSecs,
noPoll=PARSE_NOPOLL,
)
print pattern, "started in parseit (nopoll)"
return "Done"
def parseit(n,
pattern,
hex_key,
timeoutSecs=60,
retryDelaySecs=1,
pollTimeoutSecs=30):
h2i.parse_only(node=h2o.nodes[n],
pattern=pattern,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
retryDelaySecs=retryDelaySecs,
pollTimeoutSecs=pollTimeoutSecs,
noPoll=PARSE_NOPOLL)
print pattern, "started in parseit (nopoll)"
return 'Done'
def test_exec2_fast_locks_fail(self):
csvPathname = 'iris/iris2.csv'
src_key = 'iris.csv'
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(bucket='smalldata',
path=csvPathname,
schema='put',
src_key=src_key,
timeoutSecs=10)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
y = 4
for trial in range(1, 5):
# make sure each parse is unique dest key (not in use)
hex_key = "iris2_" + str(trial) + ".hex"
# what if we kicked off another parse without waiting for it? I think the src key gets locked
# so we'd get lock issues on the src_key
parseResult = h2i.parse_only(pattern=src_key,
delete_on_done=0,
hex_key=hex_key,
timeoutSecs=10)
execExpr = "%s[,%s]=(%s[,%s]==%s)" % (hex_key, y + 1, hex_key,
y + 1, 1)
h2e.exec_expr(execExpr=execExpr, timeoutSecs=10)
# just show the jobs still going, if any. maybe none, because short (iris)
a = h2o.nodes[0].jobs_admin()
h2o.verboseprint("jobs_admin():", h2o.dump_json(a))
def test_exec2_fast_locks(self):
csvPathname = 'iris/iris2.csv'
src_key='iris.csv'
if not AVOID_BUG:
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(bucket='smalldata', path=csvPathname, schema='put',
src_key=src_key, timeoutSecs=10)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
y = 4
for trial in range (1, 100):
if AVOID_BUG:
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(bucket='smalldata', path=csvPathname, schema='put',
src_key=src_key, timeoutSecs=10)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
# make sure each parse is unique dest key (not in use)
hex_key = "iris2_" + str(trial) + ".hex"
# what if we kicked off another parse without waiting for it? I think the src key gets locked
# so we'd get lock issues on the src_key
parseResult = h2i.parse_only(pattern=src_key, hex_key=hex_key,
delete_on_done=1 if AVOID_BUG else 0, timeoutSecs=10)
execExpr="%s[,%s]=(%s[,%s]==%s)" % (hex_key, y+1, hex_key, y+1, 1)
h2e.exec_expr(execExpr=execExpr, timeoutSecs=10)
# just show the jobs still going, if any. maybe none, because short (iris)
a = h2o.nodes[0].jobs_admin()
h2o.verboseprint("jobs_admin():", h2o.dump_json(a))
def test_parse_airline_multi_hdfs(self):
csvFilename = "hex_10"
csvFilePattern = '*' # all files in the folder
trialMax = 2
for tryHeap in [24]:
print "\n", tryHeap,"GB heap, 1 jvm per host, import mr-0x6 hdfs, then parse"
h2o.init(java_heap_GB=tryHeap, random_udp_drop=RANDOM_UDP_DROP, disable_assertions=DISABLE_ASSERTIONS,
use_hdfs=True, hdfs_name_node=NAME_NODE, hdfs_version=VERSION)
timeoutSecs = 3600
importFolderPath = "datasets/airlines_multi"
for trial in range(trialMax):
hex_key = csvFilename + "_" + str(trial) + ".hex"
csvPathname = importFolderPath + "/" + csvFilePattern
start = time.time()
importResult = h2i.import_only(path=csvPathname, schema='hdfs',
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
print "importResult:", h2o.dump_json(importResult)
parseResult = h2i.parse_only(pattern='*csv', hex_key=hex_key,
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=120)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
# we don't delete the hex key. it will start spilling? slow
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_parse_airline_multi_hdfs_many(self):
# default
csvFilename = "hex_10"
csvFilePattern = '*' # all files in the folder
for tryHeap in [24]:
print "\n", tryHeap, "GB heap, 1 jvm per host, import mr-0x6 hdfs, then parse"
h2o.init(java_heap_GB=tryHeap,
random_udp_drop=RANDOM_UDP_DROP,
use_hdfs=True,
hdfs_name_node=NAME_NODE,
hdfs_version=VERSION)
# don't raise exception if we find something bad in h2o stdout/stderr?
# h2o.nodes[0].sandboxIgnoreErrors = True
timeoutSecs = 500
importFolderPath = "datasets/airlines_multi"
csvPathname = importFolderPath + "/" + csvFilePattern
parseResult = h2i.import_only(path=csvPathname,
schema='hdfs',
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=60)
for trial in range(TRIAL_MAX):
# each parse now just does one
csvFilePattern = "*%s.csv" % trial
# if we want multifile
# csvFilePattern = "*"
hex_key = csvFilename + "_" + str(trial) + ".hex"
csvPathname = importFolderPath + "/" + csvFilePattern
start = time.time()
# print "Don't wait for completion. Just load things up!"
print "Drat. the source file is locked if we noPoll. Would have to increment across the individual files?"
print "Drat. We can't re-import the folder, if there's a parse using one of the source files?"
parseResult = h2i.parse_only(pattern=csvFilePattern,
hex_key=hex_key,
noPoll=True,
delete_on_done=0,
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=60)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
# we don't delete the hex key. it will start spilling? slow
h2j.pollWaitJobs(timeoutSecs=300, pollTimeoutSecs=30)
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_parse_all_s3n_thru_hdfs(self):
print "\nLoad a list of files from s3n, parse it thru HDFS"
print "In EC2, michal's config always passes the right config xml"
print "as arg to the java -jar h2o.jar. Only works in EC2"
bucket = 'home-0xdiag-datasets'
csvPathname = 'standard/*'
importResult = h2i.import_only(bucket=bucket, path=csvPathname, schema='s3n')
s3nFullList = importResult['succeeded']
print "s3nFullList:", h2o.dump_json(s3nFullList)
self.assertGreater(len(s3nFullList),1,"Didn't see more than 1 files in s3n?")
s3nList = random.sample(s3nFullList,8)
timeoutSecs = 500
for s in s3nList:
s3nKey = s['key']
s3nFilename = s['file']
# there is some non-file key names returned? s3n metadata?
# only use the keys with csv in their name
if ('csv' not in s3nKey) or ('syn_dataset' in s3nKey) or ('.gz' in s3nKey):
continue
# creates csvFilename.hex from file in hdfs dir
print "Loading s3n key: ", s3nKey, 'thru HDFS'
parseResult = h2i.parse_only(pattern=s3nKey, hex_key=s3nFilename + ".hex",
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
print "parse result:", parseResult['destination_key']
start = time.time()
sys.stdout.flush()
def test_parse_cust(self):
# run as user 0xcustomer to get access (with .json config and ssh key file specified)
importFolderPath = '/mnt/0xcustomer-datasets'
pollTimeoutSecs = 120
retryDelaySecs = 30
timeoutSecs = 300
(importResult, importPattern) = h2i.import_only(path=importFolderPath + "/*")
importFileList = importResult['files']
importFailList = importResult['fails']
importKeyList = importResult['keys']
importDelList = importResult['dels']
if len(importDelList)!=0:
raise Exception("import shouldn't have any deletes. importDelList: %s" % h2o.dump_json(importDelList))
if len(importFileList)<MINFILES:
raise Exception("Didn't import successfully. importFileList: %s" % h2o.dump_json(importFileList))
if len(importKeyList)<MINFILES:
raise Exception("Didn't import successfully. importKeyList: %s" % h2o.dump_json(importKeyList))
if len(importFailList)!=0:
raise Exception("Didn't import successfully. importFailList: %s" % h2o.dump_json(importFailList))
# only parse files with .csv or .tsv in their name (no dirs like that?)
goodKeyList = [key for key in importKeyList if ('.csv' in key or '.tsv' in key)]
trial = 0
# just do 1?
for i, importKey in enumerate(random.sample(goodKeyList,3)):
print "importKey:", importKey
trial +=1
start = time.time()
# some data has ,, in the header row. can't have multiple NAs. h2o doesn't like
# force header=0..should mean headers get treated as NAs
parseResult = h2i.parse_only(pattern=importKey, header=0,
timeoutSecs=timeoutSecs, retryDelaySecs=retryDelaySecs, pollTimeoutSecs=pollTimeoutSecs)
elapsed = time.time() - start
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "Parse result['destination_key']:", parseResult['destination_key']
origKey = parseResult['destination_key']
inspect = h2o_cmd.runInspect(key=origKey)
h2o_cmd.infoFromInspect(inspect, origKey)
execExpr = 'newKey = '+origKey+'[1,1]'
h2e.exec_expr(h2o.nodes[0], execExpr, "a", timeoutSecs=30)
newParseKey = {'destination_key': 'newKey'}
h2o_cmd.checkKeyDistribution()
h2o.nodes[0].remove_key(key=origKey)
# a key isn't created for a scalar
# h2o.nodes[0].remove_key(key='newKey')
self.assertGreater(trial, MINDONE-1, msg="There should be more than %s parsed files" % MINDONE)
def test_parse_nflx_loop_hdfs_fvec(self):
h2o.beta_features = True
print "Using the -.gz files from hdfs"
# hdfs://<name node>/datasets/manyfiles-nflx-gz/file_1.dat.gz
# default
csvFilename = "hex_10"
csvFilePattern = '*' # all files in the folder
for tryHeap in [24]:
print "\n", tryHeap,"GB heap, 1 jvm per host, import mr-0x6 hdfs, then parse"
localhost = h2o.decide_if_localhost()
if (localhost):
h2o.build_cloud(java_heap_GB=tryHeap, random_udp_drop=RANDOM_UDP_DROP, base_port=55930,
use_hdfs=True, hdfs_name_node=NAME_NODE, hdfs_version=VERSION)
else:
h2o_hosts.build_cloud_with_hosts(java_heap_GB=tryHeap, random_udp_drop=RANDOM_UDP_DROP, base_port=55600,
use_hdfs=True, hdfs_name_node=NAME_NODE, hdfs_version=VERSION)
# don't raise exception if we find something bad in h2o stdout/stderr?
# h2o.nodes[0].sandboxIgnoreErrors = True
timeoutSecs = 500
importFolderPath = "datasets/airlines_multi"
csvPathname = importFolderPath + "/" + csvFilePattern
parseResult = h2i.import_only(path=csvPathname, schema='hdfs',
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
for trial in range(TRIAL_MAX):
# each parse now just does one
csvFilePattern = "*%s.csv" % trial
# if we want multifile
# csvFilePattern = "*"
hex_key = csvFilename + "_" + str(trial) + ".hex"
csvPathname = importFolderPath + "/" + csvFilePattern
start = time.time()
# print "Don't wait for completion. Just load things up!"
print "Drat. the source file is locked if we noPoll. Would have to increment across the individual files?"
print "Drat. We can't re-import the folder, if there's a parse using one of the source files?"
parseResult = h2i.parse_only(pattern=csvFilePattern, hex_key=hex_key, noPoll=True, delete_on_done=0,
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
# we don't delete the hex key. it will start spilling? slow
h2j.pollWaitJobs(timeoutSecs=300, pollTimeoutSecs=30)
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_parse_multi_exclude_fvec(self):
h2o.beta_features = True
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 = [
(300, 100, 'cA', 60, '*x[2-5]*'),
(310, 200, 'cB', 60, '*x[1,3-5]*'),
(320, 300, 'cC', 60, '*x[1-2,4-5]*'),
(330, 400, 'cD', 60, '*x[1-3-5]*'),
(340, 500, 'cE', 60, '*x[1-4]*'),
]
## h2b.browseTheCloud()
cnum = 0
# create them all first
for (rowCount, colCount, hex_key, timeoutSecs, excludePattern) in tryList:
cnum += 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)
for fileN in range(FILENUM):
csvFilename = 'syn_' + str(fileN) + "_" + str(SEED) + "_" + rowxcol + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
write_syn_dataset(csvPathname, rowCount, colCount, SEED, translateList)
for (rowCount, colCount, hex_key, timeoutSecs, excludePattern) in tryList:
cnum += 1
# 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:
print f
h2i.import_only(path=SYNDATASETS_DIR + "/" + f)
# pattern match all, then use exclude
parseResult = h2i.parse_only(pattern="*/syn_*",
hex_key=hex_key, exclude=excludePattern, header=1, timeoutSecs=timeoutSecs)
print "parseResult['destination_key']: " + parseResult['destination_key']
print 'parse time:', parseResult['response']['time']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
# FIX! h2o strips one of the headers, but treats all the other files with headers as data
numRows = inspect['numRows']
numCols = inspect['numCols']
print "\n" + parseResult['destination_key'] + ":", \
" numRows:", "{:,}".format(numRows), \
" numCols:", "{:,}".format(numCols)
# all should have rowCount rows (due to the excludePattern
self.assertEqual(numRows, rowCount*FILENUM, msg=("got numRows: %s. Should be rowCount: %s * FILENUM: %s" % \
(numRows, rowCount, FILENUM)))
def test_cols_enum_multi_import(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 = [
(300, 100, 'cA', 60, '*x[2-5]*'),
(310, 200, 'cB', 60, '*x[1,3-5]*'),
(320, 300, 'cC', 60, '*x[1-2,4-5]*'),
(330, 400, 'cD', 60, '*x[1-3-5]*'),
(340, 500, 'cE', 60, '*x[1-4]*'),
]
## h2b.browseTheCloud()
cnum = 0
# create them all first
for (rowCount, colCount, hex_key, timeoutSecs, excludePattern) in tryList:
cnum += 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)
for fileN in range(FILENUM):
csvFilename = 'syn_' + str(fileN) + "_" + str(SEED) + "_" + rowxcol + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
write_syn_dataset(csvPathname, rowCount, colCount, SEED, translateList)
for (rowCount, colCount, hex_key, timeoutSecs, excludePattern) in tryList:
cnum += 1
# 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:
print f
h2i.import_only(path=SYNDATASETS_DIR + "/" + f)
# pattern match all, then use exclude
parseResult = h2i.parse_only(pattern="*/syn_*",
hex_key=hex_key, exclude=excludePattern, header=1, timeoutSecs=timeoutSecs)
print "parseResult['destination_key']: " + parseResult['destination_key']
print 'parse time:', parseResult['response']['time']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
# FIX! h2o strips one of the headers, but treats all the other files with headers as data
num_rows = inspect['num_rows']
num_cols = inspect['num_cols']
print "\n" + parseResult['destination_key'] + ":", \
" num_rows:", "{:,}".format(num_rows), \
" num_cols:", "{:,}".format(num_cols)
# all should have rowCount rows (due to the excludePattern
self.assertEqual(num_rows, rowCount*FILENUM, msg=("got num_rows: %s. Should be rowCount: %s * FILENUM: %s" % \
(num_rows, rowCount, FILENUM)))
def test_exec2_fast_locks_overlap(self):
csvPathname = "iris/iris2.csv"
src_key = "iris.csv"
if not AVOID_BUG:
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(
bucket="smalldata", path=csvPathname, schema="put", src_key=src_key, timeoutSecs=10
)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
y = 4
lastHexKey = None
for trial in range(1, 100):
if AVOID_BUG:
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(
bucket="smalldata", path=csvPathname, schema="put", src_key=src_key, timeoutSecs=10
)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
# make sure each parse is unique dest key (not in use)
hex_key = "iris2_" + str(trial) + ".hex"
# what if we kicked off another parse without waiting for it? I think the src key gets locked
# so we'd get lock issues on the src_key
parseResult = h2i.parse_only(
pattern=src_key, hex_key=hex_key, noPoll=True, delete_on_done=1 if AVOID_BUG else 0, timeoutSecs=10
)
# wait until iteration 2, when lastHexKey is available, so you can operate on that
if lastHexKey:
execExpr = "%s[,%s]=(%s[,%s]==%s)" % (lastHexKey, y + 1, lastHexKey, y + 1, 1)
h2e.exec_expr(execExpr=execExpr, timeoutSecs=10)
lastHexKey = hex_key
# since we are using the same source file, and potentially re-uploading if AVOID_BUG
# we have to synchronize here. I guess we have to make sure the parse is done too, since we're going to
# use it next iteration
h2o_jobs.pollWaitJobs(timeoutSecs=10)
# just show the jobs still going. Shouldn't be any
a = h2o.nodes[0].jobs_admin()
h2o.verboseprint("jobs_admin():", h2o.dump_json(a))
def test_parse_airline_multi_hdfs(self):
csvFilename = "hex_10"
csvFilePattern = '*' # all files in the folder
trialMax = 2
for tryHeap in [24]:
print "\n", tryHeap, "GB heap, 1 jvm per host, import mr-0x6 hdfs, then parse"
h2o.init(java_heap_GB=tryHeap,
random_udp_drop=RANDOM_UDP_DROP,
disable_assertions=DISABLE_ASSERTIONS,
use_hdfs=True,
hdfs_name_node=NAME_NODE,
hdfs_version=VERSION)
timeoutSecs = 3600
importFolderPath = "datasets/airlines_multi"
for trial in range(trialMax):
hex_key = csvFilename + "_" + str(trial) + ".hex"
csvPathname = importFolderPath + "/" + csvFilePattern
start = time.time()
importResult = h2i.import_only(path=csvPathname,
schema='hdfs',
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=60)
print "importResult:", h2o.dump_json(importResult)
parseResult = h2i.parse_only(pattern='*csv',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=120)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
# we don't delete the hex key. it will start spilling? slow
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_parse_airline_multi_hdfs(self):
h2o.beta_features = True
csvFilename = "hex_10"
csvFilePattern = '*' # all files in the folder
trialMax = 2
for tryHeap in [24]:
print "\n", tryHeap,"GB heap, 1 jvm per host, import mr-0x6 hdfs, then parse"
localhost = h2o.decide_if_localhost()
if (localhost):
h2o.build_cloud(java_heap_GB=tryHeap, random_udp_drop=RANDOM_UDP_DROP, base_port=55930, disable_assertions=DISABLE_ASSERTIONS,
use_hdfs=True, hdfs_name_node=NAME_NODE, hdfs_version=VERSION)
else:
# why is 55609 already in use??
h2o_hosts.build_cloud_with_hosts(sandbox_ignore_errors=True, force_tcp=True, java_heap_GB=tryHeap, random_udp_drop=RANDOM_UDP_DROP, base_port=55604, disable_assertions=DISABLE_ASSERTIONS,
use_hdfs=True, hdfs_name_node=NAME_NODE, hdfs_version=VERSION)
# don't raise exception if we find something bad in h2o stdout/stderr?
# h2o.nodes[0].sandboxIgnoreErrors = True
timeoutSecs = 3600
importFolderPath = "datasets/airlines_multi"
for trial in range(trialMax):
hex_key = csvFilename + "_" + str(trial) + ".hex"
csvPathname = importFolderPath + "/" + csvFilePattern
start = time.time()
importResult = h2i.import_only(path=csvPathname, schema='hdfs',
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
print "importResult:", h2o.dump_json(importResult)
parseResult = h2i.parse_only(pattern='*csv', hex_key=hex_key,
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=120)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
# we don't delete the hex key. it will start spilling? slow
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_exec2_fast_locks_overlap(self):
csvPathname = 'iris/iris2.csv'
src_key='iris.csv'
if not AVOID_BUG:
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(bucket='smalldata', path=csvPathname, schema='put',
src_key=src_key, timeoutSecs=10)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
y = 4
lastHexKey = None
for trial in range (1, 100):
if AVOID_BUG:
# need the key name (pattern) to feed to parse)
(importResult, importPattern) = h2i.import_only(bucket='smalldata', path=csvPathname, schema='put',
src_key=src_key, timeoutSecs=10)
# just as a reminder of what these returns look like
print "importResult:", h2o.dump_json(importResult)
print "importPattern:", h2o.dump_json(importPattern)
# make sure each parse is unique dest key (not in use)
hex_key = "iris2_" + str(trial) + ".hex"
# what if we kicked off another parse without waiting for it? I think the src key gets locked
# so we'd get lock issues on the src_key
parseResult = h2i.parse_only(pattern=src_key, hex_key=hex_key, noPoll=True,
delete_on_done=1 if AVOID_BUG else 0, timeoutSecs=10)
# wait until iteration 2, when lastHexKey is available, so you can operate on that
if lastHexKey:
execExpr="%s[,%s]=(%s[,%s]==%s)" % (lastHexKey, y+1, lastHexKey, y+1, 1)
h2e.exec_expr(execExpr=execExpr, timeoutSecs=10)
lastHexKey = hex_key
# since we are using the same source file, and potentially re-uploading if AVOID_BUG
# we have to synchronize here. I guess we have to make sure the parse is done too, since we're going to
# use it next iteration
h2o_jobs.pollWaitJobs(timeoutSecs=10)
# just show the jobs still going. Shouldn't be any
a = h2o.nodes[0].jobs_admin()
h2o.verboseprint("jobs_admin():", h2o.dump_json(a))
def test_parse_all_s3n_thru_hdfs(self):
print "\nLoad a list of files from s3n, parse it thru HDFS"
print "In EC2, michal's config always passes the right config xml"
print "as arg to the java -jar h2o.jar. Only works in EC2"
bucket = 'home-0xdiag-datasets'
csvPathname = 'standard/*'
importResult = h2i.import_only(bucket=bucket,
path=csvPathname,
schema='s3n')
s3nFullList = importResult['succeeded']
print "s3nFullList:", h2o.dump_json(s3nFullList)
self.assertGreater(len(s3nFullList), 1,
"Didn't see more than 1 files in s3n?")
s3nList = random.sample(s3nFullList, 8)
timeoutSecs = 500
for s in s3nList:
s3nKey = s['key']
s3nFilename = s['file']
# there is some non-file key names returned? s3n metadata?
# only use the keys with csv in their name
if ('csv' not in s3nKey) or ('syn_dataset'
in s3nKey) or ('.gz' in s3nKey):
continue
# creates csvFilename.hex from file in hdfs dir
print "Loading s3n key: ", s3nKey, 'thru HDFS'
parseResult = h2i.parse_only(pattern=s3nKey,
hex_key=s3nFilename + ".hex",
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=60)
print s3nFilename, 'parse time:', parseResult['response']['time']
print "parse result:", parseResult['destination_key']
start = time.time()
sys.stdout.flush()
def test_parse_multi_header_single_fvec(self):
h2o.beta_features = True
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?:w
# 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
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()
parseResult = h2i.parse_only(pattern='*' + rowxcol + '*',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
header="1",
header_from_file=header)
print "parseResult['destination_key']: " + parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
# should match # of cols in header or ??
self.assertEqual(
inspect['numCols'], totalCols,
"parse created result with the wrong number of cols %s %s" %
(inspect['numCols'], totalCols))
self.assertEqual(inspect['numRows'], totalDataRows,
"parse created result with the wrong number of rows (header shouldn't count) %s %s" % \
(inspect['numRows'], totalDataRows))
# 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}
start = time.time()
rfv = h2o_cmd.runRF(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed / timeoutSecs) * 100)
print "trial #", trial, "totalDataRows:", totalDataRows, "parse end on ", csvFilename, \
'took', time.time() - start, 'seconds'
h2o.check_sandbox_for_errors()
def test_exec_enums_rand_cut2(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
n = ROWS
tryList = [
# (n, 10, 9, 'cE', 300),
(n, 1, 1, 'cE', 300),
]
# create key names to use for exec
eKeys = ['e%s' % i for i in range(10)]
# h2b.browseTheCloud()
trial = 0
for (rowCount, iColCount, oColCount, hex_key, timeoutSecs) in tryList:
colCount = iColCount + oColCount
hex_key = 'p'
colEnumList = create_col_enum_list(iColCount)
# create 100 possible cut expressions here, so we don't waste time below
rowExprList = []
print "Creating", CUT_EXPR_CNT, 'cut expressions'
for j in range(CUT_EXPR_CNT):
# init cutValue. None means no compare
cutValue = [None for i in range(iColCount)]
# build up a random cut expression
MAX_COLS_IN_EXPR = iColCount
cols = random.sample(range(MAX_COLS_IN_EXPR),
random.randint(1, MAX_COLS_IN_EXPR))
for c in cols:
# possible choices within the column
cel = colEnumList[c]
# for now the cutValues are numbers for the enum mappings
if 1 == 1:
# FIX! hack. don't use encoding 0, maps to NA here? h2o doesn't like
celChoice = str(random.choice(range(len(cel))))
else:
celChoice = random.choice(cel)
cutValue[c] = celChoice
cutExprList = []
for i, c in enumerate(cutValue):
if c is None:
continue
else:
# new ...ability to reference cols
# src[ src$age<17 && src$zip=95120 && ... , ]
# randomly pick == or !=
if random.randint(0, 1) == 0:
cutExprList.append('p$C' + str(i + 1) + '!=' + c)
else:
cutExprList.append('p$C' + str(i + 1) + '==' + c)
cutExpr = ' & '.join(cutExprList)
# print "cutExpr:", cutExpr
# just extract one output col (the first one)
rowExpr = '%s[%s,%s];' % (hex_key, cutExpr, iColCount + 1)
# print "rowExpr:", rowExpr
print rowExpr
rowExprList.append(rowExpr)
# CREATE DATASET*******************************************
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_enums_' + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname,
rowCount,
iColCount,
oColCount,
SEEDPERFILE,
colEnumList=colEnumList)
# PARSE*******************************************************
src_key = csvFilename
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='A' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='B' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='C' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='D' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='E' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='F' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='G' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='H' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='I' + src_key,
timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname,
schema='put',
src_key='J' + src_key,
timeoutSecs=200)
parseResult = h2i.parse_only(pattern='*' + src_key,
hex_key=hex_key,
timeoutSecs=800)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
pNumRows = inspect['numRows']
pNumCols = inspect['numCols']
# print h2o.dump_json(inspect)
levels = h2o.nodes[0].levels(source=hex_key)
print "levels result:", h2o.dump_json(levels)
(missingValuesDict, constantValuesDict, enumSizeDict, colTypeDict, colNameDict) = \
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'], exceptionOnMissingValues=False)
# error if any col has constant values
if len(constantValuesDict) != 0:
raise Exception(
"Probably got a col NA'ed and constant values as a result %s"
% constantValuesDict)
# INIT all possible key names used***************************
# remember. 1 indexing!
# is this needed?
if 1 == 1:
a = 'a=c(1,2,3);' + ';'.join(
['a[,%s]=a[,%s-1]' % (i, i) for i in range(2, colCount)])
print a
for eKey in eKeys:
# build up the columns
e = h2o.nodes[0].exec_query(str='%s;%s=a' % (a, eKey),
print_params=False)
## print h2o.dump_json(e)
xList = []
eList = []
fList = []
for repeat in range(CUT_LOOP_CNT):
# EXEC*******************************************************
# don't use exec_expr to avoid issues with Inspect following etc.
randICol = random.randint(0, iColCount - 1)
randOCol = random.randint(iColCount, iColCount + oColCount - 1)
# should be two different keys in the sample
e = random.sample(eKeys, 2)
fKey = e[0]
eKey = e[1]
start = time.time()
h2o.nodes[0].exec_query(str="%s=%s" %
(fKey, random.choice(rowExprList)))
elapsed = time.time() - start
execTime = elapsed
print "exec 2 took", elapsed, "seconds."
inspect = h2o_cmd.runInspect(key=fKey)
h2o_cmd.infoFromInspect(inspect, fKey)
numRows = inspect['numRows']
numCols = inspect['numCols']
if numRows == 0 or numCols != colCount:
h2p.red_print("Warning: Cut resulted in", numRows,
"rows and", numCols,
"cols. Quantile will abort")
# QUANTILE*******************************************************
quantile = 0.5 if DO_MEDIAN else .999
# first output col. always fed by an exec cut, so 0?
column = iColCount
column = 0
start = time.time()
q = h2o.nodes[0].quantiles(source_key=fKey,
column=column,
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=MULTI_PASS)
h2p.red_print("quantile", quantile, q['result'])
elapsed = time.time() - start
print "quantile end on ", fKey, 'took', elapsed, 'seconds.'
quantileTime = elapsed
# remove all keys*******************************************************
# what about hex_key?
if 1 == 0:
start = time.time()
h2o.nodes[0].remove_all_keys()
elapsed = time.time() - start
print "remove all keys end on ", csvFilename, 'took', elapsed, 'seconds.'
trial += 1
xList.append(trial)
eList.append(execTime)
fList.append(quantileTime)
#****************************************************************
# QUANTILE APPROX. BASELINE FOR SINGLE COL WALK FULL DATASET
print "QUANTILE APPROX. BASELINE FOR SINGLE COL WALK FULL DATASET. Although it's a real col, not an enum col"
quantile = 0.5 if DO_MEDIAN else .999
# first output col. always fed by an exec cut, so 0?
column = iColCount
start = time.time()
q = h2o.nodes[0].quantiles(source_key=hex_key,
column='C' + str(iColCount + 1),
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=0)
elapsed = time.time() - start
h2p.red_print(
hex_key, pNumRows,
"rows Baseline: quantile single col (C" + str(iColCount + 1) + ")",
"one iteration", elapsed, "secs. threshold:", quantile,
q['result'])
print "quantile single col 1 iteration end on", hex_key, "took", elapsed, 'seconds.'
quantileTime = elapsed
#****************************************************************
# PLOTS. look for eplot.jpg and fplot.jpg in local dir?
if DO_PLOT:
xLabel = 'trial'
eLabel = 'exec cut time'
fLabel = 'quantile time'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList,
xLabel,
eListTitle,
eList,
eLabel,
fListTitle,
fList,
fLabel,
server=True)
def test_parse_multi_header_rand_fvec(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_ints.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
allowedLetters = 'abcdeABCDE01234[]'
headerChoices = []
for n in range(500): # max # of cols below is 500
done = False
while not done:
l = random.randint(1,64) # random length headers
headerName = ''.join([random.choice(allowedLetters) for _ in range(l)])
# we keep trying if we already have that header name. Has to be unique.
done = headerName not in headerChoices
headerChoices.append(headerName)
tryList = [
(3, 5, 9, 'cA', 60, 0),
# (3, 5, 25, 'cA', 60, 0),
# (10, 100, 500, 'cA', 60, 0),
]
for trial in range(20):
(fileNum, rowCount, colCount, hex_key, timeoutSecs, dataRowsWithHeader) = random.choice(tryList)
print fileNum, rowCount, colCount, hex_key, timeoutSecs, dataRowsWithHeader
# 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
totalHeaderRows = 0
# random selection of parse param choices
# HEADER_HAS_HDR_ROW = random.randint(0,1)
HEADER_HAS_HDR_ROW = 1
DATA_HAS_HDR_ROW = random.randint(0,1)
PARSE_PATTERN_INCLUDES_HEADER = random.randint(0,1)
# DATA_FIRST_IS_COMMENT = random.randint(0,1)
# HEADER_FIRST_IS_COMMENT = random.randint(0,1)
# FIX! doesn't seem to like just comment in the header file
DATA_FIRST_IS_COMMENT = 0
HEADER_FIRST_IS_COMMENT = 0
GZIP_DATA = random.randint(0,1)
GZIP_HEADER = random.randint(0,1)
SEP_CHAR_GEN = random.choice(paramsDict['separator'])
HEADER_SEP_CHAR_GEN = random.choice(paramsDict['hdr_separator'])
if HEADER_SEP_CHAR_GEN == 'same':
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
# don't put a header in a data file with a different separator?
if DATA_HAS_HDR_ROW and HEADER_HAS_HDR_ROW:
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
# Hack: if both data and header files have a header, then, just in case
# the header and data files should have the same separator
# if they don't, make header match data
if DATA_HAS_HDR_ROW and HEADER_HAS_HDR_ROW:
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
# New for fvec? if separators are not the same, then the header separator needs to be comma
if HEADER_SEP_CHAR_GEN != SEP_CHAR_GEN:
HEADER_SEP_CHAR_GEN = ','
# screw it. make them always match
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
if HEADER_SEP_CHAR_GEN in (',', ' '):
pass
# extra spaces? Don't add any
# if random.randint(0,1):
# HEADER_SEP_CHAR_GEN = " " + HEADER_SEP_CHAR_GEN
# if random.randint(0,1):
# HEADER_SEP_CHAR_GEN = HEADER_SEP_CHAR_GEN + " "
kwargs = {}
for k,v in paramsDict.items():
kwargs[k] = random.choice(v)
kwargs['separator'] = SEP_CHAR_GEN
# parse doesn't auto-detect tab. will autodetect space and comma
if SEP_CHAR_GEN==" " or SEP_CHAR_GEN==",":
del kwargs['separator']
else:
kwargs['separator'] = ord(SEP_CHAR_GEN)
# randomly add leading and trailing white space
# we have to do this after we save the single char HEADER_SEP_CHAR_GEN
if SEP_CHAR_GEN in (',', ' '):
if random.randint(0,1):
SEP_CHAR_GEN = " " + SEP_CHAR_GEN
if random.randint(0,1):
SEP_CHAR_GEN = SEP_CHAR_GEN + " "
print '\nHEADER_HAS_HDR_ROW:', HEADER_HAS_HDR_ROW
print 'DATA_HAS_HDR_ROW:', DATA_HAS_HDR_ROW
print 'PARSE_PATTERN_INCLUDES_HEADER', PARSE_PATTERN_INCLUDES_HEADER
print 'DATA_FIRST_IS_COMMENT:', DATA_FIRST_IS_COMMENT
print 'HEADER_FIRST_IS_COMMENT:', HEADER_FIRST_IS_COMMENT
print 'SEP_CHAR_GEN:', "->" + SEP_CHAR_GEN + "<-"
print 'HEADER_SEP_CHAR_GEN:', "->" + HEADER_SEP_CHAR_GEN + "<-"
print 'GZIP_DATA:', GZIP_DATA
print 'GZIP_HEADER:', GZIP_HEADER
# they need to both use the same separator (h2o rule)
# can't have duplicates
hfhList = random.sample(headerChoices, colCount) + ["output"]
# UPDATE: always use comma or space for header separator?? it should work no matter what
# separator the data uses?
headerForHeader = HEADER_SEP_CHAR_GEN.join(hfhList)
print "headerForHeader:", headerForHeader
# make these different
# hfdList = [random.choice(headerChoices) for h in range(colCount)] + ["output"]
# FIX! keep them the same for now to avoid some odd cases on what header gets used to RF
hfdList = hfhList
headerForData = SEP_CHAR_GEN.join(hfdList)
# create data files
for fileN in range(fileNum):
csvFilenameSuffix = str(fileN) + "_" + str(SEED) + "_" + str(trial) + "_" + rowxcol + '_csv'
csvFilename = 'syn_data_' + csvFilenameSuffix
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rList = rand_rowData(colCount, sepChar=SEP_CHAR_GEN)
(headerRowsDone, dataRowsDone) = write_syn_dataset(csvPathname, rowCount,
headerString=(headerForData if DATA_HAS_HDR_ROW else None), rList=rList,
commentFirst=DATA_FIRST_IS_COMMENT, sepChar=SEP_CHAR_GEN)
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
if GZIP_DATA:
csvPathnamegz = csvPathname + ".gz"
print "gzipping to", csvPathnamegz
h2o_util.file_gzip(csvPathname, csvPathnamegz)
os.rename(csvPathname, SYNDATASETS_DIR + "/not_used_data_" + csvFilenameSuffix)
# pattern match should find the right key with csvPathname
# create the header file
hdrFilenameSuffix = str(SEED) + "_" + str(trial) + "_" + rowxcol + '_csv'
hdrFilename = 'syn_header_' + hdrFilenameSuffix
hdrPathname = SYNDATASETS_DIR + '/' + hdrFilename
# dataRowsWithHeader = 0 # temp hack
(headerRowsDone, dataRowsDone) = write_syn_dataset(hdrPathname, dataRowsWithHeader,
headerString=(headerForHeader if HEADER_HAS_HDR_ROW else None), rList=rList,
commentFirst=HEADER_FIRST_IS_COMMENT, sepChar=SEP_CHAR_GEN)
# only include header file data rows if the parse pattern includes it
if PARSE_PATTERN_INCLUDES_HEADER:
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
if GZIP_HEADER:
hdrPathnamegz = hdrPathname + ".gz"
print "gzipping to", hdrPathnamegz
h2o_util.file_gzip(hdrPathname, hdrPathnamegz)
os.rename(hdrPathname, SYNDATASETS_DIR + "/not_used_header_" + hdrFilenameSuffix)
# pattern match should find the right key with hdrPathnameh
# make sure all key names are unique, when we re-put and re-parse (h2o caching issues)
hex_key = "syn_dst" + 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?:w
# 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)
h2o_cmd.runStoreView()
headerKey = h2i.find_key(hdrFilename)
dataKey = h2i.find_key(csvFilename)
# use regex. the only files in the dir will be the ones we just created
# with *fileN* match
print "Header Key =", headerKey
# put the right name in
if kwargs['header_from_file'] == 'header':
# do we need to add the .hex suffix we know h2o will append
kwargs['header_from_file'] = headerKey
# use one of the data files?
elif kwargs['header_from_file'] == 'data':
# do we need to add the .hex suffix we know h2o will append
kwargs['header_from_file'] = dataKey
# if there's no header in the header file, turn off the header_from_file
if not HEADER_HAS_HDR_ROW:
kwargs['header_from_file'] = None
if HEADER_HAS_HDR_ROW and (kwargs['header_from_file'] == headerKey):
ignoreForRf = hfhList[0]
elif DATA_HAS_HDR_ROW:
ignoreForRf = hfdList[0]
else:
ignoreForRf = None
print "If header_from_file= , required to force header=1 for h2o"
if kwargs['header_from_file']:
kwargs['header'] = 1
# if we have a header in a data file, tell h2o (for now)
elif DATA_HAS_HDR_ROW:
kwargs['header'] = 1
else:
kwargs['header'] = 0
# may have error if h2o doesn't get anything!
start = time.time()
if PARSE_PATTERN_INCLUDES_HEADER and HEADER_HAS_HDR_ROW:
pattern = 'syn_*'+str(trial)+"_"+rowxcol+'*'
else:
pattern = 'syn_data_*'+str(trial)+"_"+rowxcol+'*'
# don't pass to parse
kwargs.pop('hdr_separator', None)
parseResult = h2i.parse_only(pattern=pattern, hex_key=hex_key, timeoutSecs=timeoutSecs, **kwargs)
print "parseResult['destination_key']: " + parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
# more reporting: (we can error here if extra col in header,
# causes all NA for missing col of data)
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'], exceptionOnMissingValues=False)
# should match # of cols in header or ??
self.assertEqual(inspect['numCols'], totalCols, \
"parse created result with the wrong number of cols %s %s" % (inspect['numCols'], totalCols))
# do we end up parsing one data rows as a header because of mismatch in gen/param
h2oLosesOneData = (headerRowsDone==0) and (kwargs['header']==1) and not DATA_HAS_HDR_ROW
# header in data file gets treated as data
h2oGainsOneData = (headerRowsDone!=0) and (kwargs['header']==1) and \
DATA_HAS_HDR_ROW and (kwargs['header_from_file'] is not None)
h2oGainsOneData = False
print "h2oLosesOneData:", h2oLosesOneData
print "h2oGainsOneData:", h2oGainsOneData
if h2oLosesOneData:
totalDataRows -= 1
if h2oGainsOneData:
totalDataRows += 1
if 1==0: # FIX! don't check for now
self.assertEqual(inspect['numRows'], totalDataRows,
"parse created result with the wrong number of rows h2o %s gen'ed: %s" % \
(inspect['numRows'], totalDataRows))
# put in an ignore param, that will fail unless headers were parsed correctly
# doesn't matter if the header got a comment, should see it
kwargs = {'sample': 100, 'depth': 25, 'ntree': 2, 'ignore': ignoreForRf}
start = time.time()
# h2o_cmd.runRF(parseResult=parseResult, timeoutSecs=10, **kwargs)
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
print "trial #", trial, "totalDataRows:", totalDataRows, "parse end on ", csvFilename, \
'took', time.time() - start, 'seconds'
h2o.check_sandbox_for_errors()
h2i.delete_keys_at_all_nodes(pattern='syn_datasets')
def test_parse_multi_header_rand(self):
### h2b.browseTheCloud()
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_ints.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
allowedLetters = 'abcdeABCDE01234[]'
headerChoices = []
for n in range(20):
l = random.randint(1, 64) # random length headers
headerName = ''.join(
[random.choice(allowedLetters) for _ in range(l)])
headerChoices.append(headerName)
# 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 = [
# FIX! one fails count for now
# (1, 5, 9, 'cA', 60, 0),
(1, 5, 9, 'cA', 60, 0),
(1, 5, 25, 'cA', 60, 0),
# try with col mismatch on header.
# FIX! causes exception? don't test for now
# (7, 300, 10, 'cA', 60, 0),
# (7, 300, 10, 'cB', 60, 1),
# (7, 300, 10, 'cC', 60, 2),
# (7, 300, 10, 'cD', 60, 3),
# (7, 300, 8, 'cA', 60, 0),
# (7, 300, 8, 'cB', 60, 1),
# (7, 300, 8, 'cC', 60, 2),
# (7, 300, 8, 'cD', 60, 3),
]
# so many random combos..rather than walk tryList, just do random for some amount of time
for trial in range(50):
(fileNum, rowCount, colCount, hex_key, timeoutSecs,
dataRowsWithHeader) = random.choice(tryList)
print fileNum, rowCount, colCount, hex_key, timeoutSecs, dataRowsWithHeader
# 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
totalHeaderRows = 0
# HEADER_HAS_HDR_ROW = random.randint(0,1)
HEADER_HAS_HDR_ROW = 1
# DATA_HAS_HDR_ROW = random.randint(0,1)
DATA_HAS_HDR_ROW = 0
# PARSE_PATTERN_INCLUDES_HEADER = random.randint(0,1)
PARSE_PATTERN_INCLUDES_HEADER = 0
## DATA_FIRST_IS_COMMENT = random.randint(0,1)
## HEADER_FIRST_IS_COMMENT = random.randint(0,1)
print "TEMPORARY: don't put any comments in"
DATA_FIRST_IS_COMMENT = 0
HEADER_FIRST_IS_COMMENT = 0
# none is not legal
# SEP_CHAR_GEN = random.choice(paramsDict['separator'])
SEP_CHAR_GEN = "\t"
print '\nHEADER_HAS_HDR_ROW:', HEADER_HAS_HDR_ROW
print 'DATA_HAS_HDR_ROW:', DATA_HAS_HDR_ROW
print 'PARSE_PATTERN_INCLUDES_HEADER', PARSE_PATTERN_INCLUDES_HEADER
print 'DATA_FIRST_IS_COMMENT:', DATA_FIRST_IS_COMMENT
print 'HEADER_FIRST_IS_COMMENT:', HEADER_FIRST_IS_COMMENT
print 'SEP_CHAR_GEN:', SEP_CHAR_GEN
# they need to both use the same separator (h2o rule)
hh = [random.choice(headerChoices)
for h in range(colCount)] + ["output"]
print hh
print "UPDATE: always use comma (space legal also?) for header separator?? it should work no matter what separator the data uses?"
headerForHeader = ",".join(hh)
# make these different
hh = [random.choice(headerChoices)
for h in range(colCount)] + ["output"]
headerForData = SEP_CHAR_GEN.join(hh)
# random selection of parse param choices
kwargs = {}
for k, v in paramsDict.items():
aChoice = random.choice(v)
# can tell h2o something different compared to what we actually used!
if k == 'separator':
if aChoice:
sepChar = aChoice
sepCharInt = ord(aChoice) # make it an integer for h2o
else:
sepChar = ',' # default char for None, need it for header/data file creation
sepCharInt = None
aChoice = sepCharInt
kwargs[k] = aChoice
# FOR NOW: ..override the rand choice if it exists, so we can parse and expect 'A' to be found
# match what was gen'ed if choice is not None
if kwargs['separator']:
if SEP_CHAR_GEN == " " or SEP_CHAR_GEN == ",": # parse doesn't auto-detect tab. will autodetect space and comma
del kwargs['separator']
else:
kwargs['separator'] = ord(SEP_CHAR_GEN)
# create data files
for fileN in range(fileNum):
csvFilename = 'syn_data_' + str(fileN) + "_" + str(
SEED) + "_" + str(trial) + "_" + rowxcol + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rList = rand_rowData(colCount, sepChar=SEP_CHAR_GEN)
(headerRowsDone, dataRowsDone) = write_syn_dataset(
csvPathname,
rowCount,
headerString=(headerForData if DATA_HAS_HDR_ROW else None),
rList=rList,
commentFirst=DATA_FIRST_IS_COMMENT,
sepChar=SEP_CHAR_GEN)
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
# create the header file
hdrFilename = 'syn_header_' + str(SEED) + "_" + str(
trial) + "_" + rowxcol + '.csv'
hdrPathname = SYNDATASETS_DIR + '/' + hdrFilename
# dataRowsWithHeader = 0 # temp hack
(headerRowsDone, dataRowsDone) = write_syn_dataset(
hdrPathname,
dataRowsWithHeader,
headerString=(headerForHeader if HEADER_HAS_HDR_ROW else None),
rList=rList,
commentFirst=HEADER_FIRST_IS_COMMENT,
sepChar=SEP_CHAR_GEN)
if PARSE_PATTERN_INCLUDES_HEADER: # only include header file data rows if the parse pattern includes it
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
# make sure all key names are unique, when we re-put and re-parse (h2o caching issues)
hex_key = "syn_dst" + 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?:w
xs = h2o.nodes[0].import_files(SYNDATASETS_DIR)['keys']
headerKey = [x for x in xs if hdrFilename in x][0]
dataKey = [x for x in xs if csvFilename not in x][0]
# use regex. the only files in the dir will be the ones we just created with *fileN* match
print "Header Key =", headerKey
# put the right name in
if kwargs['header_from_file'] == 'syn_header':
kwargs['header_from_file'] = headerKey
# use one of the data files?
elif kwargs['header_from_file'] == 'syn_data':
kwargs['header_from_file'] = dataKey
# if there's no header in the header file, turn off the header_from_file
if not HEADER_HAS_HDR_ROW:
kwargs['header_from_file'] = None
print "If header_from_file= is used, we are currently required to force header=1 for h2o"
if kwargs['header_from_file']:
kwargs['header'] = 1
# if we have a header in a data file, tell h2o (for now)
elif DATA_HAS_HDR_ROW:
kwargs['header'] = 1
else:
kwargs['header'] = 0
# may have error if h2o doesn't get anything!
start = time.time()
if PARSE_PATTERN_INCLUDES_HEADER and HEADER_HAS_HDR_ROW:
pattern = '*syn_*' + str(trial) + "_" + rowxcol + '*'
else:
pattern = '*syn_data_*' + str(trial) + "_" + rowxcol + '*'
parseResult = h2i.parse_only(pattern=pattern,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
**kwargs)
print "parseResult['destination_key']: " + parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
# more reporting: (we can error here if extra col in header, causes all NA for missing col of data)
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'],
exceptionOnMissingValues=False)
# should match # of cols in header or ??
self.assertEqual(inspect['numCols'], totalCols, \
"parse created result with the wrong number of cols %s %s" % (inspect['numCols'], totalCols))
# do we end up parsing one data rows as a header because of mismatch in gen/param
h2oLosesOneData = (headerRowsDone
== 0) and (kwargs['header']
== 1) and not DATA_HAS_HDR_ROW
# header in data file gets treated as data
h2oGainsOneData = (headerRowsDone!=0) and (kwargs['header']==1) and \
DATA_HAS_HDR_ROW and (kwargs['header_from_file'] is not None)
print "h2oLosesOneData:", h2oLosesOneData
print "h2oGainsOneData:", h2oGainsOneData
### print (headerRowsDone!=0), (kwargs['header']==1), DATA_HAS_HDR_ROW, (kwargs['header_from_file'] is not None)
if h2oLosesOneData:
totalDataRows -= 1
if h2oGainsOneData:
totalDataRows += 1
self.assertEqual(inspect['numRows'], totalDataRows,
"parse created result with the wrong number of rows (header rows don't count) h2o: %s gen'ed: %s" % \
(inspect['numRows'], totalDataRows))
# put in an ignore param, that will fail unless headers were parsed correctly
# doesn't matter if the header got a comment, should see it
h2oShouldSeeHeader = (HEADER_HAS_HDR_ROW and
(kwargs['header_from_file']
is not None)) or DATA_HAS_HDR_ROW
if h2oShouldSeeHeader:
kwargs = {'sample': 75, 'depth': 25, 'ntree': 1, 'ignore': 'A'}
else:
kwargs = {'sample': 75, 'depth': 25, 'ntree': 1}
start = time.time()
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed / timeoutSecs) * 100)
print "trial #", trial, "totalDataRows:", totalDataRows, "parse end on ", csvFilename, \
'took', time.time() - start, 'seconds'
h2o.check_sandbox_for_errors()
def test_parse_airline_multi_hdfs(self):
h2o.beta_features = True
csvFilename = "hex_10"
csvFilePattern = '*' # all files in the folder
trialMax = 2
for tryHeap in [24]:
print "\n", tryHeap, "GB heap, 1 jvm per host, import mr-0x6 hdfs, then parse"
localhost = h2o.decide_if_localhost()
if (localhost):
h2o.build_cloud(java_heap_GB=tryHeap,
random_udp_drop=RANDOM_UDP_DROP,
base_port=55930,
disable_assertions=DISABLE_ASSERTIONS,
use_hdfs=True,
hdfs_name_node=NAME_NODE,
hdfs_version=VERSION)
else:
# why is 55609 already in use??
h2o_hosts.build_cloud_with_hosts(
sandbox_ignore_errors=True,
force_tcp=True,
java_heap_GB=tryHeap,
random_udp_drop=RANDOM_UDP_DROP,
base_port=55604,
disable_assertions=DISABLE_ASSERTIONS,
use_hdfs=True,
hdfs_name_node=NAME_NODE,
hdfs_version=VERSION)
# don't raise exception if we find something bad in h2o stdout/stderr?
# h2o.nodes[0].sandboxIgnoreErrors = True
timeoutSecs = 3600
importFolderPath = "datasets/airlines_multi"
for trial in range(trialMax):
hex_key = csvFilename + "_" + str(trial) + ".hex"
csvPathname = importFolderPath + "/" + csvFilePattern
start = time.time()
importResult = h2i.import_only(path=csvPathname,
schema='hdfs',
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=60)
print "importResult:", h2o.dump_json(importResult)
parseResult = h2i.parse_only(pattern='*csv',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
retryDelaySecs=10,
pollTimeoutSecs=120)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Parse #", trial, "completed in", "%6.2f" % elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
h2o_cmd.runStoreView()
# we don't delete the hex key. it will start spilling? slow
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
def test_parse_multi_header_single_fvec(self):
h2o.beta_features = True
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?:w
# 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
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()
parseResult = h2i.parse_only(pattern='*'+rowxcol+'*',
hex_key=hex_key, timeoutSecs=timeoutSecs, header="1", header_from_file=header)
print "parseResult['destination_key']: " + parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
# should match # of cols in header or ??
self.assertEqual(inspect['numCols'], totalCols,
"parse created result with the wrong number of cols %s %s" % (inspect['numCols'], totalCols))
self.assertEqual(inspect['numRows'], totalDataRows,
"parse created result with the wrong number of rows (header shouldn't count) %s %s" % \
(inspect['numRows'], totalDataRows))
# 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}
start = time.time()
rfv = h2o_cmd.runRF(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
print "trial #", trial, "totalDataRows:", totalDataRows, "parse end on ", csvFilename, \
'took', time.time() - start, 'seconds'
h2o.check_sandbox_for_errors()
def test_parse_multi_header_rand(self):
### h2b.browseTheCloud()
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_ints.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
allowedLetters = 'abcdeABCDE01234[]'
headerChoices = []
for n in range(20):
l = random.randint(1,64) # random length headers
headerName = ''.join([random.choice(allowedLetters) for _ in range(l)])
headerChoices.append(headerName)
# 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 = [
# FIX! one fails count for now
# (1, 5, 9, 'cA', 60, 0),
(1, 5, 9, 'cA', 60, 0),
(1, 5, 25, 'cA', 60, 0),
# try with col mismatch on header.
# FIX! causes exception? don't test for now
# (7, 300, 10, 'cA', 60, 0),
# (7, 300, 10, 'cB', 60, 1),
# (7, 300, 10, 'cC', 60, 2),
# (7, 300, 10, 'cD', 60, 3),
# (7, 300, 8, 'cA', 60, 0),
# (7, 300, 8, 'cB', 60, 1),
# (7, 300, 8, 'cC', 60, 2),
# (7, 300, 8, 'cD', 60, 3),
]
# so many random combos..rather than walk tryList, just do random for some amount of time
for trial in range(50):
(fileNum, rowCount, colCount, hex_key, timeoutSecs, dataRowsWithHeader) = random.choice(tryList)
print fileNum, rowCount, colCount, hex_key, timeoutSecs, dataRowsWithHeader
# 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
totalHeaderRows = 0
# HEADER_HAS_HDR_ROW = random.randint(0,1)
HEADER_HAS_HDR_ROW = 1
# DATA_HAS_HDR_ROW = random.randint(0,1)
DATA_HAS_HDR_ROW = 0
# PARSE_PATTERN_INCLUDES_HEADER = random.randint(0,1)
PARSE_PATTERN_INCLUDES_HEADER = 0
## DATA_FIRST_IS_COMMENT = random.randint(0,1)
## HEADER_FIRST_IS_COMMENT = random.randint(0,1)
print "TEMPORARY: don't put any comments in"
DATA_FIRST_IS_COMMENT = 0
HEADER_FIRST_IS_COMMENT = 0
# none is not legal
# SEP_CHAR_GEN = random.choice(paramsDict['separator'])
SEP_CHAR_GEN = "\t"
print '\nHEADER_HAS_HDR_ROW:', HEADER_HAS_HDR_ROW
print 'DATA_HAS_HDR_ROW:', DATA_HAS_HDR_ROW
print 'PARSE_PATTERN_INCLUDES_HEADER', PARSE_PATTERN_INCLUDES_HEADER
print 'DATA_FIRST_IS_COMMENT:', DATA_FIRST_IS_COMMENT
print 'HEADER_FIRST_IS_COMMENT:', HEADER_FIRST_IS_COMMENT
print 'SEP_CHAR_GEN:', SEP_CHAR_GEN
# they need to both use the same separator (h2o rule)
hh = [random.choice(headerChoices) for h in range(colCount)] + ["output"]
print hh
print "UPDATE: always use comma (space legal also?) for header separator?? it should work no matter what separator the data uses?"
headerForHeader = ",".join(hh)
# make these different
hh = [random.choice(headerChoices) for h in range(colCount)] + ["output"]
headerForData = SEP_CHAR_GEN.join(hh)
# random selection of parse param choices
kwargs = {}
for k,v in paramsDict.items():
aChoice = random.choice(v)
# can tell h2o something different compared to what we actually used!
if k == 'separator':
if aChoice:
sepChar = aChoice
sepCharInt = ord(aChoice) # make it an integer for h2o
else:
sepChar = ',' # default char for None, need it for header/data file creation
sepCharInt = None
aChoice = sepCharInt
kwargs[k] = aChoice
# FOR NOW: ..override the rand choice if it exists, so we can parse and expect 'A' to be found
# match what was gen'ed if choice is not None
if kwargs['separator']:
if SEP_CHAR_GEN==" " or SEP_CHAR_GEN==",": # parse doesn't auto-detect tab. will autodetect space and comma
del kwargs['separator']
else:
kwargs['separator'] = ord(SEP_CHAR_GEN)
# create data files
for fileN in range(fileNum):
csvFilename = 'syn_data_' + str(fileN) + "_" + str(SEED) + "_" + str(trial) + "_" + rowxcol + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rList = rand_rowData(colCount, sepChar=SEP_CHAR_GEN)
(headerRowsDone, dataRowsDone) = write_syn_dataset(csvPathname, rowCount,
headerString=(headerForData if DATA_HAS_HDR_ROW else None), rList=rList,
commentFirst=DATA_FIRST_IS_COMMENT, sepChar=SEP_CHAR_GEN)
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
# create the header file
hdrFilename = 'syn_header_' + str(SEED) + "_" + str(trial) + "_" + rowxcol + '.csv'
hdrPathname = SYNDATASETS_DIR + '/' + hdrFilename
# dataRowsWithHeader = 0 # temp hack
(headerRowsDone, dataRowsDone) = write_syn_dataset(hdrPathname, dataRowsWithHeader,
headerString=(headerForHeader if HEADER_HAS_HDR_ROW else None), rList=rList,
commentFirst=HEADER_FIRST_IS_COMMENT, sepChar=SEP_CHAR_GEN)
if PARSE_PATTERN_INCLUDES_HEADER: # only include header file data rows if the parse pattern includes it
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
# make sure all key names are unique, when we re-put and re-parse (h2o caching issues)
hex_key = "syn_dst" + 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?:w
xs = h2o.nodes[0].import_files(SYNDATASETS_DIR)['keys']
headerKey = [x for x in xs if hdrFilename in x][0]
dataKey = [x for x in xs if csvFilename not in x][0]
# use regex. the only files in the dir will be the ones we just created with *fileN* match
print "Header Key =", headerKey
# put the right name in
if kwargs['header_from_file'] == 'syn_header':
kwargs['header_from_file'] = headerKey
# use one of the data files?
elif kwargs['header_from_file'] == 'syn_data':
kwargs['header_from_file'] = dataKey
# if there's no header in the header file, turn off the header_from_file
if not HEADER_HAS_HDR_ROW:
kwargs['header_from_file'] = None
print "If header_from_file= is used, we are currently required to force header=1 for h2o"
if kwargs['header_from_file']:
kwargs['header'] = 1
# if we have a header in a data file, tell h2o (for now)
elif DATA_HAS_HDR_ROW:
kwargs['header'] = 1
else:
kwargs['header'] = 0
# may have error if h2o doesn't get anything!
start = time.time()
if PARSE_PATTERN_INCLUDES_HEADER and HEADER_HAS_HDR_ROW:
pattern = '*syn_*'+str(trial)+"_"+rowxcol+'*'
else:
pattern = '*syn_data_*'+str(trial)+"_"+rowxcol+'*'
parseResult = h2i.parse_only(pattern=pattern, hex_key=hex_key, timeoutSecs=timeoutSecs, **kwargs)
print "parseResult['destination_key']: " + parseResult['destination_key']
print 'parse time:', parseResult['response']['time']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(inspect['num_rows']), \
" num_cols:", "{:,}".format(inspect['num_cols'])
# more reporting: (we can error here if extra col in header, causes all NA for missing col of data)
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'], exceptionOnMissingValues=False)
# should match # of cols in header or ??
self.assertEqual(inspect['num_cols'], totalCols, \
"parse created result with the wrong number of cols %s %s" % (inspect['num_cols'], totalCols))
# do we end up parsing one data rows as a header because of mismatch in gen/param
h2oLosesOneData = (headerRowsDone==0) and (kwargs['header']==1) and not DATA_HAS_HDR_ROW
# header in data file gets treated as data
h2oGainsOneData = (headerRowsDone!=0) and (kwargs['header']==1) and \
DATA_HAS_HDR_ROW and (kwargs['header_from_file'] is not None)
print "h2oLosesOneData:", h2oLosesOneData
print "h2oGainsOneData:", h2oGainsOneData
### print (headerRowsDone!=0), (kwargs['header']==1), DATA_HAS_HDR_ROW, (kwargs['header_from_file'] is not None)
if h2oLosesOneData:
totalDataRows -= 1
if h2oGainsOneData:
totalDataRows += 1
self.assertEqual(inspect['num_rows'], totalDataRows,
"parse created result with the wrong number of rows (header rows don't count) h2o: %s gen'ed: %s" % \
(inspect['num_rows'], totalDataRows))
# put in an ignore param, that will fail unless headers were parsed correctly
# doesn't matter if the header got a comment, should see it
h2oShouldSeeHeader = (HEADER_HAS_HDR_ROW and (kwargs['header_from_file'] is not None)) or DATA_HAS_HDR_ROW
if h2oShouldSeeHeader:
kwargs = {'sample': 75, 'depth': 25, 'ntree': 1, 'ignore': 'A'}
else:
kwargs = {'sample': 75, 'depth': 25, 'ntree': 1}
start = time.time()
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
print "trial #", trial, "totalDataRows:", totalDataRows, "parse end on ", csvFilename, \
'took', time.time() - start, 'seconds'
h2o.check_sandbox_for_errors()
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_exec_enums_rand_cut2(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
n = ROWS
tryList = [
# (n, 10, 9, 'cE', 300),
(n, 1, 1, 'cE', 300),
]
# create key names to use for exec
eKeys = ['e%s' % i for i in range(10)]
# h2b.browseTheCloud()
trial = 0
for (rowCount, iColCount, oColCount, hex_key, timeoutSecs) in tryList:
colCount = iColCount + oColCount
hex_key = 'p'
colEnumList = create_col_enum_list(iColCount)
# create 100 possible cut expressions here, so we don't waste time below
rowExprList = []
print "Creating", CUT_EXPR_CNT, 'cut expressions'
for j in range(CUT_EXPR_CNT):
# init cutValue. None means no compare
cutValue = [None for i in range(iColCount)]
# build up a random cut expression
MAX_COLS_IN_EXPR = iColCount
cols = random.sample(range(MAX_COLS_IN_EXPR), random.randint(1,MAX_COLS_IN_EXPR))
for c in cols:
# possible choices within the column
cel = colEnumList[c]
# for now the cutValues are numbers for the enum mappings
if 1==1:
# FIX! hack. don't use encoding 0, maps to NA here? h2o doesn't like
celChoice = str(random.choice(range(len(cel))))
else:
celChoice = random.choice(cel)
cutValue[c] = celChoice
cutExprList = []
for i,c in enumerate(cutValue):
if c is None:
continue
else:
# new ...ability to reference cols
# src[ src$age<17 && src$zip=95120 && ... , ]
# randomly pick == or !=
if random.randint(0,1)==0:
cutExprList.append('p$C'+str(i+1)+'!='+c)
else:
cutExprList.append('p$C'+str(i+1)+'=='+c)
cutExpr = ' & '.join(cutExprList)
# print "cutExpr:", cutExpr
# just extract one output col (the first one)
rowExpr = '%s[%s,%s];' % (hex_key, cutExpr, iColCount+1)
# print "rowExpr:", rowExpr
print rowExpr
rowExprList.append(rowExpr)
# CREATE DATASET*******************************************
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_enums_' + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, iColCount, oColCount, SEEDPERFILE, colEnumList=colEnumList)
# PARSE*******************************************************
src_key = csvFilename
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='A'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='B'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='C'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='D'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='E'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='F'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='G'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='H'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='I'+src_key, timeoutSecs=200)
parseResult = h2i.import_only(path=csvPathname, schema='put', src_key='J'+src_key, timeoutSecs=200)
parseResult = h2i.parse_only(pattern='*'+src_key, hex_key=hex_key, timeoutSecs=800)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
pNumRows = inspect['numRows']
pNumCols = inspect['numCols']
# print h2o.dump_json(inspect)
levels = h2o.nodes[0].levels(source=hex_key)
print "levels result:", h2o.dump_json(levels)
(missingValuesDict, constantValuesDict, enumSizeDict, colTypeDict, colNameDict) = \
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'], exceptionOnMissingValues=False)
# error if any col has constant values
if len(constantValuesDict) != 0:
raise Exception("Probably got a col NA'ed and constant values as a result %s" % constantValuesDict)
# INIT all possible key names used***************************
# remember. 1 indexing!
# is this needed?
if 1==1:
a = 'a=c(1,2,3);' + ';'.join(['a[,%s]=a[,%s-1]'% (i,i) for i in range(2,colCount)])
print a
for eKey in eKeys:
# build up the columns
e = h2o.nodes[0].exec_query(str='%s;%s=a' % (a, eKey), print_params=False)
## print h2o.dump_json(e)
xList = []
eList = []
fList = []
for repeat in range(CUT_LOOP_CNT):
# EXEC*******************************************************
# don't use exec_expr to avoid issues with Inspect following etc.
randICol = random.randint(0,iColCount-1)
randOCol = random.randint(iColCount, iColCount+oColCount-1)
# should be two different keys in the sample
e = random.sample(eKeys,2)
fKey = e[0]
eKey = e[1]
start = time.time()
h2o.nodes[0].exec_query(str="%s=%s" % (fKey, random.choice(rowExprList)))
elapsed = time.time() - start
execTime = elapsed
print "exec 2 took", elapsed, "seconds."
inspect = h2o_cmd.runInspect(key=fKey)
h2o_cmd.infoFromInspect(inspect, fKey)
numRows = inspect['numRows']
numCols = inspect['numCols']
if numRows==0 or numCols!=colCount:
h2p.red_print("Warning: Cut resulted in", numRows, "rows and", numCols, "cols. Quantile will abort")
# QUANTILE*******************************************************
quantile = 0.5 if DO_MEDIAN else .999
# first output col. always fed by an exec cut, so 0?
column = iColCount
column = 0
start = time.time()
q = h2o.nodes[0].quantiles(source_key=fKey, column=column,
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=MULTI_PASS)
h2p.red_print("quantile", quantile, q['result'])
elapsed = time.time() - start
print "quantile end on ", fKey, 'took', elapsed, 'seconds.'
quantileTime = elapsed
# remove all keys*******************************************************
# what about hex_key?
if 1==0:
start = time.time()
h2o.nodes[0].remove_all_keys()
elapsed = time.time() - start
print "remove all keys end on ", csvFilename, 'took', elapsed, 'seconds.'
trial += 1
xList.append(trial)
eList.append(execTime)
fList.append(quantileTime)
#****************************************************************
# QUANTILE APPROX. BASELINE FOR SINGLE COL WALK FULL DATASET
print "QUANTILE APPROX. BASELINE FOR SINGLE COL WALK FULL DATASET. Although it's a real col, not an enum col"
quantile = 0.5 if DO_MEDIAN else .999
# first output col. always fed by an exec cut, so 0?
column = iColCount
start = time.time()
q = h2o.nodes[0].quantiles(source_key=hex_key, column='C'+str(iColCount+1),
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=0)
elapsed = time.time() - start
h2p.red_print(hex_key, pNumRows, "rows Baseline: quantile single col (C" + str(iColCount+1) + ")", "one iteration", elapsed, "secs. threshold:", quantile, q['result'])
print "quantile single col 1 iteration end on", hex_key, "took", elapsed, 'seconds.'
quantileTime = elapsed
#****************************************************************
# PLOTS. look for eplot.jpg and fplot.jpg in local dir?
if DO_PLOT:
xLabel = 'trial'
eLabel = 'exec cut time'
fLabel = 'quantile time'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel, fListTitle, fList, fLabel, server=True)
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_parse_multi_header_rand_fvec(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_ints.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
allowedLetters = 'abcdeABCDE01234[]'
headerChoices = []
for n in range(500): # max # of cols below is 500
done = False
while not done:
l = random.randint(1, 64) # random length headers
headerName = ''.join(
[random.choice(allowedLetters) for _ in range(l)])
# we keep trying if we already have that header name. Has to be unique.
done = headerName not in headerChoices
headerChoices.append(headerName)
tryList = [
(3, 5, 9, 'cA', 60, 0),
# (3, 5, 25, 'cA', 60, 0),
# (10, 100, 500, 'cA', 60, 0),
]
for trial in range(20):
(fileNum, rowCount, colCount, hex_key, timeoutSecs,
dataRowsWithHeader) = random.choice(tryList)
print fileNum, rowCount, colCount, hex_key, timeoutSecs, dataRowsWithHeader
# 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
totalHeaderRows = 0
# random selection of parse param choices
# HEADER_HAS_HDR_ROW = random.randint(0,1)
HEADER_HAS_HDR_ROW = 1
DATA_HAS_HDR_ROW = random.randint(0, 1)
PARSE_PATTERN_INCLUDES_HEADER = random.randint(0, 1)
# DATA_FIRST_IS_COMMENT = random.randint(0,1)
# HEADER_FIRST_IS_COMMENT = random.randint(0,1)
# FIX! doesn't seem to like just comment in the header file
DATA_FIRST_IS_COMMENT = 0
HEADER_FIRST_IS_COMMENT = 0
GZIP_DATA = random.randint(0, 1)
GZIP_HEADER = random.randint(0, 1)
SEP_CHAR_GEN = random.choice(paramsDict['separator'])
HEADER_SEP_CHAR_GEN = random.choice(paramsDict['hdr_separator'])
if HEADER_SEP_CHAR_GEN == 'same':
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
# don't put a header in a data file with a different separator?
if DATA_HAS_HDR_ROW and HEADER_HAS_HDR_ROW:
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
# Hack: if both data and header files have a header, then, just in case
# the header and data files should have the same separator
# if they don't, make header match data
if DATA_HAS_HDR_ROW and HEADER_HAS_HDR_ROW:
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
# New for fvec? if separators are not the same, then the header separator needs to be comma
if HEADER_SEP_CHAR_GEN != SEP_CHAR_GEN:
HEADER_SEP_CHAR_GEN = ','
# screw it. make them always match
HEADER_SEP_CHAR_GEN = SEP_CHAR_GEN
if HEADER_SEP_CHAR_GEN in (',', ' '):
pass
# extra spaces? Don't add any
# if random.randint(0,1):
# HEADER_SEP_CHAR_GEN = " " + HEADER_SEP_CHAR_GEN
# if random.randint(0,1):
# HEADER_SEP_CHAR_GEN = HEADER_SEP_CHAR_GEN + " "
kwargs = {}
for k, v in paramsDict.items():
kwargs[k] = random.choice(v)
kwargs['separator'] = SEP_CHAR_GEN
# parse doesn't auto-detect tab. will autodetect space and comma
if SEP_CHAR_GEN == " " or SEP_CHAR_GEN == ",":
del kwargs['separator']
else:
kwargs['separator'] = ord(SEP_CHAR_GEN)
# randomly add leading and trailing white space
# we have to do this after we save the single char HEADER_SEP_CHAR_GEN
if SEP_CHAR_GEN in (',', ' '):
if random.randint(0, 1):
SEP_CHAR_GEN = " " + SEP_CHAR_GEN
if random.randint(0, 1):
SEP_CHAR_GEN = SEP_CHAR_GEN + " "
print '\nHEADER_HAS_HDR_ROW:', HEADER_HAS_HDR_ROW
print 'DATA_HAS_HDR_ROW:', DATA_HAS_HDR_ROW
print 'PARSE_PATTERN_INCLUDES_HEADER', PARSE_PATTERN_INCLUDES_HEADER
print 'DATA_FIRST_IS_COMMENT:', DATA_FIRST_IS_COMMENT
print 'HEADER_FIRST_IS_COMMENT:', HEADER_FIRST_IS_COMMENT
print 'SEP_CHAR_GEN:', "->" + SEP_CHAR_GEN + "<-"
print 'HEADER_SEP_CHAR_GEN:', "->" + HEADER_SEP_CHAR_GEN + "<-"
print 'GZIP_DATA:', GZIP_DATA
print 'GZIP_HEADER:', GZIP_HEADER
# they need to both use the same separator (h2o rule)
# can't have duplicates
hfhList = random.sample(headerChoices, colCount) + ["output"]
# UPDATE: always use comma or space for header separator?? it should work no matter what
# separator the data uses?
headerForHeader = HEADER_SEP_CHAR_GEN.join(hfhList)
print "headerForHeader:", headerForHeader
# make these different
# hfdList = [random.choice(headerChoices) for h in range(colCount)] + ["output"]
# FIX! keep them the same for now to avoid some odd cases on what header gets used to RF
hfdList = hfhList
headerForData = SEP_CHAR_GEN.join(hfdList)
# create data files
for fileN in range(fileNum):
csvFilenameSuffix = str(fileN) + "_" + str(SEED) + "_" + str(
trial) + "_" + rowxcol + '_csv'
csvFilename = 'syn_data_' + csvFilenameSuffix
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rList = rand_rowData(colCount, sepChar=SEP_CHAR_GEN)
(headerRowsDone, dataRowsDone) = write_syn_dataset(
csvPathname,
rowCount,
headerString=(headerForData if DATA_HAS_HDR_ROW else None),
rList=rList,
commentFirst=DATA_FIRST_IS_COMMENT,
sepChar=SEP_CHAR_GEN)
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
if GZIP_DATA:
csvPathnamegz = csvPathname + ".gz"
print "gzipping to", csvPathnamegz
h2o_util.file_gzip(csvPathname, csvPathnamegz)
os.rename(
csvPathname, SYNDATASETS_DIR + "/not_used_data_" +
csvFilenameSuffix)
# pattern match should find the right key with csvPathname
# create the header file
hdrFilenameSuffix = str(SEED) + "_" + str(
trial) + "_" + rowxcol + '_csv'
hdrFilename = 'syn_header_' + hdrFilenameSuffix
hdrPathname = SYNDATASETS_DIR + '/' + hdrFilename
# dataRowsWithHeader = 0 # temp hack
(headerRowsDone, dataRowsDone) = write_syn_dataset(
hdrPathname,
dataRowsWithHeader,
headerString=(headerForHeader if HEADER_HAS_HDR_ROW else None),
rList=rList,
commentFirst=HEADER_FIRST_IS_COMMENT,
sepChar=SEP_CHAR_GEN)
# only include header file data rows if the parse pattern includes it
if PARSE_PATTERN_INCLUDES_HEADER:
totalDataRows += dataRowsDone
totalHeaderRows += headerRowsDone
if GZIP_HEADER:
hdrPathnamegz = hdrPathname + ".gz"
print "gzipping to", hdrPathnamegz
h2o_util.file_gzip(hdrPathname, hdrPathnamegz)
os.rename(
hdrPathname,
SYNDATASETS_DIR + "/not_used_header_" + hdrFilenameSuffix)
# pattern match should find the right key with hdrPathnameh
# make sure all key names are unique, when we re-put and re-parse (h2o caching issues)
hex_key = "syn_dst" + 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?:w
# 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)
h2o_cmd.runStoreView()
headerKey = h2i.find_key(hdrFilename)
dataKey = h2i.find_key(csvFilename)
# use regex. the only files in the dir will be the ones we just created
# with *fileN* match
print "Header Key =", headerKey
# put the right name in
if kwargs['header_from_file'] == 'header':
# do we need to add the .hex suffix we know h2o will append
kwargs['header_from_file'] = headerKey
# use one of the data files?
elif kwargs['header_from_file'] == 'data':
# do we need to add the .hex suffix we know h2o will append
kwargs['header_from_file'] = dataKey
# if there's no header in the header file, turn off the header_from_file
if not HEADER_HAS_HDR_ROW:
kwargs['header_from_file'] = None
if HEADER_HAS_HDR_ROW and (kwargs['header_from_file']
== headerKey):
ignoreForRf = hfhList[0]
elif DATA_HAS_HDR_ROW:
ignoreForRf = hfdList[0]
else:
ignoreForRf = None
print "If header_from_file= , required to force header=1 for h2o"
if kwargs['header_from_file']:
kwargs['header'] = 1
# if we have a header in a data file, tell h2o (for now)
elif DATA_HAS_HDR_ROW:
kwargs['header'] = 1
else:
kwargs['header'] = 0
# may have error if h2o doesn't get anything!
start = time.time()
if PARSE_PATTERN_INCLUDES_HEADER and HEADER_HAS_HDR_ROW:
pattern = 'syn_*' + str(trial) + "_" + rowxcol + '*'
else:
pattern = 'syn_data_*' + str(trial) + "_" + rowxcol + '*'
# don't pass to parse
kwargs.pop('hdr_separator', None)
parseResult = h2i.parse_only(pattern=pattern,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
**kwargs)
print "parseResult['destination_key']: " + parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
# more reporting: (we can error here if extra col in header,
# causes all NA for missing col of data)
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'],
exceptionOnMissingValues=False)
# should match # of cols in header or ??
self.assertEqual(inspect['numCols'], totalCols, \
"parse created result with the wrong number of cols %s %s" % (inspect['numCols'], totalCols))
# do we end up parsing one data rows as a header because of mismatch in gen/param
h2oLosesOneData = (headerRowsDone
== 0) and (kwargs['header']
== 1) and not DATA_HAS_HDR_ROW
# header in data file gets treated as data
h2oGainsOneData = (headerRowsDone!=0) and (kwargs['header']==1) and \
DATA_HAS_HDR_ROW and (kwargs['header_from_file'] is not None)
h2oGainsOneData = False
print "h2oLosesOneData:", h2oLosesOneData
print "h2oGainsOneData:", h2oGainsOneData
if h2oLosesOneData:
totalDataRows -= 1
if h2oGainsOneData:
totalDataRows += 1
if 1 == 0: # FIX! don't check for now
self.assertEqual(inspect['numRows'], totalDataRows,
"parse created result with the wrong number of rows h2o %s gen'ed: %s" % \
(inspect['numRows'], totalDataRows))
# put in an ignore param, that will fail unless headers were parsed correctly
# doesn't matter if the header got a comment, should see it
kwargs = {
'sample': 100,
'depth': 25,
'ntree': 2,
'ignore': ignoreForRf
}
start = time.time()
# h2o_cmd.runRF(parseResult=parseResult, timeoutSecs=10, **kwargs)
elapsed = time.time() - start
print "%d pct. of timeout" % ((elapsed / timeoutSecs) * 100)
print "trial #", trial, "totalDataRows:", totalDataRows, "parse end on ", csvFilename, \
'took', time.time() - start, 'seconds'
h2o.check_sandbox_for_errors()
h2i.delete_keys_at_all_nodes(pattern='syn_datasets')
