def test_build_for_clone(self):
# python gets confused about which 'start' if I used start here
elapsed = time.time() - beginning
print "\n%0.2f seconds to get here from start" % elapsed
# might as well open a browser on it? (because the ip/port will vary
# maybe just print the ip/port for now
## h2b.browseTheCloud()
maxTime = 4*3600
totalTime = 0
incrTime = 60
h2p.purple_print("\nSleeping for total of", (maxTime+0.0)/3600, "hours.")
print "Will check h2o logs every", incrTime, "seconds"
print "Should be able to run another test using h2o-nodes.json to clone cloud"
print "i.e. h2o.build_cloud_with_json()"
print "Bad test if a running test shuts down the cloud. I'm supposed to!\n"
h2p.green_print("To watch cloud in browser follow address:")
h2p.green_print(" http://{0}:{1}/Cloud.html".format(h2o.nodes[0].http_addr, h2o.nodes[0].port))
h2p.blue_print("You can start a test (or tests) now!")
h2p.blue_print("Will Check cloud status every %s secs and kill cloud if wrong or no answer" % incrTime)
if CHECK_WHILE_SLEEPING:
h2p.blue_print("Will also look at redirected stdout/stderr logs in sandbox every %s secs" % incrTime)
h2p.red_print("No checking of logs while sleeping, or check of cloud status")
h2p.yellow_print("So if H2O stack traces, it's up to you to kill me if 4 hours is too long")
h2p.yellow_print("ctrl-c will cause all jvms to die(thru psutil terminate, paramiko channel death or h2o shutdown...")
while (totalTime<maxTime): # die after 4 hours
h2o.sleep(incrTime)
totalTime += incrTime
# good to touch all the nodes to see if they're still responsive
# give them up to 120 secs to respond (each individually)
h2o.verify_cloud_size(timeoutSecs=120)
if CHECK_WHILE_SLEEPING:
print "Checking sandbox log files"
h2o.check_sandbox_for_errors(cloudShutdownIsError=True)
else:
print str(datetime.datetime.now()), h2o.python_cmd_line, "still here", totalTime, maxTime, incrTime
# don't do this, as the cloud may be hung?
if 1==0:
print "Shutting down cloud, but first delete all keys"
start = time.time()
h2i.delete_keys_at_all_nodes()
elapsed = time.time() - start
print "delete_keys_at_all_nodes(): took", elapsed, "secs"
def test_build_for_clone(self):
# python gets confused about which 'start' if I used start here
elapsed = time.time() - beginning
print "\n%0.2f seconds to get here from start" % elapsed
# might as well open a browser on it? (because the ip/port will vary
# maybe just print the ip/port for now
## h2b.browseTheCloud()
maxTime = 4 * 3600
totalTime = 0
incrTime = 60
h2p.purple_print("\nSleeping for total of", (maxTime + 0.0) / 3600,
"hours.")
print "Will check h2o logs every", incrTime, "seconds"
print "Should be able to run another test using h2o-nodes.json to clone cloud"
print "i.e. h2o.build_cloud_with_json()"
print "Bad test if a running test shuts down the cloud. I'm supposed to!\n"
h2p.green_print("To watch cloud in browser follow address:")
h2p.green_print(" http://{0}:{1}/Cloud.html".format(
h2o.nodes[0].http_addr, h2o.nodes[0].port))
h2p.blue_print("You can start a test (or tests) now!")
h2p.blue_print(
"Will spin looking at redirected stdout/stderr logs in sandbox for h2o errors every %s secs"
% incrTime)
h2p.red_print("This is just for fun")
h2p.yellow_print("So is this")
while (totalTime < maxTime): # die after 4 hours
h2o.sleep(incrTime)
totalTime += incrTime
# good to touch all the nodes to see if they're still responsive
# give them up to 120 secs to respond (each individually)
h2o.verify_cloud_size(timeoutSecs=120)
print "Checking sandbox log files"
h2o.check_sandbox_for_errors(cloudShutdownIsError=True)
start = time.time()
h2i.delete_keys_at_all_nodes()
elapsed = time.time() - start
print "delete_keys_at_all_nodes(): took", elapsed, "secs"
def test_build_for_clone(self):
# python gets confused about which 'start' if I used start here
elapsed = time.time() - beginning
print "\n%0.2f seconds to get here from start" % elapsed
# might as well open a browser on it? (because the ip/port will vary
# maybe just print the ip/port for now
## h2b.browseTheCloud()
maxTime = 4*3600
totalTime = 0
incrTime = 60
h2p.purple_print("\nSleeping for total of", (maxTime+0.0)/3600, "hours.")
print "Will check h2o logs every", incrTime, "seconds"
print "Should be able to run another test using h2o-nodes.json to clone cloud"
print "i.e. h2o.build_cloud_with_json()"
print "Bad test if a running test shuts down the cloud. I'm supposed to!\n"
h2p.green_print("To watch cloud in browser follow address:")
h2p.green_print(" http://{0}:{1}/Cloud.html".format(h2o.nodes[0].http_addr, h2o.nodes[0].port))
h2p.blue_print("You can start a test (or tests) now!")
h2p.blue_print("Will spin looking at redirected stdout/stderr logs in sandbox for h2o errors every %s secs" % incrTime)
h2p.red_print("This is just for fun")
h2p.yellow_print("So is this")
while (totalTime<maxTime): # die after 4 hours
h2o.sleep(incrTime)
totalTime += incrTime
# good to touch all the nodes to see if they're still responsive
# give them up to 120 secs to respond (each individually)
h2o.verify_cloud_size(timeoutSecs=120)
print "Checking sandbox log files"
h2o.check_sandbox_for_errors(cloudShutdownIsError=True)
start = time.time()
h2i.delete_keys_at_all_nodes()
elapsed = time.time() - start
print "delete_keys_at_all_nodes(): took", elapsed, "secs"
def tearDownClass(cls):
h2o.sleep(3600)
h2o.tear_down_cloud(h2o.nodes)
def test_c5_KMeans_sphere_26GB_fvec(self):
h2o.beta_features = True
# a kludge
h2o.setup_benchmark_log()
# csvFilename = 'syn_sphere15_2711545732row_6col_180GB_from_7x.csv'
csvFilename = 'syn_sphere15_gen_26GB.csv'
# csvFilename = 'syn_sphere_gen_h1m.csv'
# csvFilename = 'syn_sphere_gen_real_1.49M.csv'
# csvFilename = 'syn_sphere_gen_h1m_no_na.csv'
totalBytes = 183538602156
if FROM_HDFS:
importFolderPath = "datasets/kmeans_big"
csvPathname = 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?
if NA_COL_BUG:
expected = [
# the centers are the same for the 26GB and 180GB. The # of rows is right for 180GB,
# so shouldn't be used for 26GB
# or it should be divided by 7
# the distribution is the same, obviously.
([
-113.00566692375459, -89.99595447985321,
-455.9970643424373, 4732.0, 49791778.0, 36800.0
], 248846122, 1308149283316.2988),
([
1.0, 1.0, -525.0093818313685, 2015.001629398412,
25654042.00592703, 28304.0
], 276924291, 1800760152555.98),
([
5.0, 2.0, 340.0, 1817.995920197288, 33970406.992053084,
31319.99486705394
], 235089554, 375419158808.3253),
([
10.0, -72.00113070337981, -171.0198611715457,
4430.00952228909, 37007399.0, 29894.0
], 166180630, 525423632323.6474),
([
11.0, 3.0, 578.0043558141306, 1483.0163188052604,
22865824.99639042, 5335.0
], 167234179, 1845362026223.1094),
([
12.0, 3.0, 168.0, -4066.995950679284, 41077063.00269915,
-47537.998050740985
], 195420925, 197941282992.43475),
([
19.00092954923767, -10.999565572612255, 90.00028669073289,
1928.0, 39967190.0, 27202.0
], 214401768, 11868360232.658035),
([
20.0, 0.0, 141.0, -3263.0030236302937, 6163210.990273981,
30712.99115201907
], 258853406, 598863991074.3276),
([
21.0, 114.01584574295777, 242.99690338815898,
1674.0029079209912, 33089556.0, 36415.0
], 190979054, 1505088759456.314),
([
25.0, 1.0, 614.0032787274755, -2275.9931284021022,
-48473733.04122273, 47343.0
], 87794427, 1124697008162.3955),
([
39.0, 3.0, 470.0, -3337.9880599007597, 28768057.98852736,
16716.003410920028
], 78226988, 1151439441529.0215),
([
40.0, 1.0, 145.0, 950.9990795199593, 14602680.991458317,
-14930.007919032574
], 167273589, 693036940951.0249),
([
42.0, 4.0, 479.0, -3678.0033024834297, 8209673.001421165,
11767.998552236539
], 148426180, 35942838893.32379),
([
48.0, 4.0, 71.0, -951.0035145455234, 49882273.00063991,
-23336.998167498707
], 157533313, 88431531357.62982),
([
147.00394564757505, 122.98729664236723, 311.0047920137008,
2320.0, 46602185.0, 11212.0
], 118361306, 1111537045743.7646),
]
else:
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.
# PARSE ****************************************
print "Parse starting: " + csvFilename
hex_key = csvFilename + "_" + str(trial) + ".hex"
start = time.time()
timeoutSecs = 2 * 3600
kwargs = {}
if FROM_HDFS:
parseResult = h2i.import_parse(
path=csvPathname,
schema='hdfs',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
pollTimeoutSecs=60,
retryDelaySecs=2,
benchmarkLogging=benchmarkLogging,
doSummary=False,
**kwargs)
else:
parseResult = h2i.import_parse(
path=csvPathname,
schema='local',
hex_key=hex_key,
timeoutSecs=timeoutSecs,
pollTimeoutSecs=60,
retryDelaySecs=2,
benchmarkLogging=benchmarkLogging,
doSummary=False,
**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)
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'],
timeoutSecs=300)
numRows = inspect['numRows']
numCols = inspect['numCols']
summary = h2o_cmd.runSummary(key=parseResult['destination_key'],
numRows=numRows,
numCols=numCols,
timeoutSecs=300)
h2o_cmd.infoFromSummary(summary)
# KMeans ****************************************
if not DO_KMEANS:
continue
print "col 0 is enum in " + csvFilename + " but KMeans should skip that automatically?? or no?"
kwargs = {
'k': 15,
'max_iter': 500,
# 'normalize': 1,
'normalize': 0, # temp try
'initialization': 'Furthest',
'destination_key': 'junk.hex',
# we get NaNs if whole col is NA
'ignored_cols': 'C1',
'normalize': 0,
# 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()
kmeansResult = h2o_cmd.runKMeans(parseResult=parseResult,
timeoutSecs=timeoutSecs,
benchmarkLogging=benchmarkLogging,
**kwargs)
elapsed = time.time() - start
print "kmeans end on ", csvPathname, 'took', elapsed, 'seconds.', "%d pct. of timeout" % (
(elapsed / timeoutSecs) * 100)
print "kmeans result:", h2o.dump_json(kmeansResult)
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)
# his does predict
(centers, tupleResultList) = h2o_kmeans.bigCheckResults(
self, kmeansResult, csvPathname, parseResult, 'd', **kwargs)
# all are multipliers of expected tuple value
allowedDelta = (0.01, 0.01, 0.01)
# these clusters were sorted compared to the cluster order in training
h2o_kmeans.showClusterDistribution(self,
tupleResultList,
expected,
trial=trial)
# why is the expected # of rows not right in KMeans2. That means predictions are wrong
h2o_kmeans.compareResultsToExpected(self,
tupleResultList,
expected,
allowedDelta,
allowError=False,
allowRowError=True,
trial=trial)
# the tupleResultList has the size during predict? compare it to the sizes during training
# I assume they're in the same order.
model = kmeansResult['model']
size = model['size']
size2 = [t[1] for t in tupleResultList]
if 1 == 1: # debug
print "training size:", size
print "predict size2:", size2
print "training sorted(size):", sorted(size)
print "predict sorted(size2):", sorted(size2)
print h2o.nodes[0].http_addr
print h2o.nodes[0].port
if 1 == 0: # debug
print "sleeping"
h2o.sleep(3600)
clusters = model["centers"]
cluster_variances = model["within_cluster_variances"]
error = model["total_within_SS"]
iterations = model["iterations"]
normalized = model["normalized"]
max_iter = model["max_iter"]
print "iterations", iterations
if iterations >= (
max_iter -
1): # h2o hits the limit at max_iter-1..shouldn't hit it
raise Exception(
"trial: %s KMeans unexpectedly took %s iterations..which was the full amount allowed by max_iter %s",
(trial, iterations, max_iter))
# this size stuff should be compared now in compareResultsToExpected()..leave it here to make sure
# can't do this compare, because size2 is sorted by center order..
# so we don't know how to reorder size the same way
# we could just sort the two of them, for some bit of comparison.
if sorted(size) != sorted(size2):
raise Exception(
"trial: %s training cluster sizes: %s not the same as predict on same data: %s"
% (trial, size, size2))
# our expected result is sorted by cluster center ordered. but the sizes are from the predicted histogram
expectedSize = [t[1] / SCALE_SIZE for t in expected]
if size2 != expectedSize:
raise Exception(
"trial: %s training cluster sizes: %s not the same as expected: %s"
% (trial, size, expectedSize))
if DELETE_KEYS_EACH_ITER:
h2i.delete_keys_at_all_nodes()
def test_c5_KMeans_sphere_26GB_fvec(self):
h2o.beta_features = True
# a kludge
h2o.setup_benchmark_log()
# csvFilename = 'syn_sphere15_2711545732row_6col_180GB_from_7x.csv'
csvFilename = 'syn_sphere15_gen_26GB.csv'
# csvFilename = 'syn_sphere_gen_h1m.csv'
# csvFilename = 'syn_sphere_gen_real_1.49M.csv'
# csvFilename = 'syn_sphere_gen_h1m_no_na.csv'
totalBytes = 183538602156
if FROM_HDFS:
importFolderPath = "datasets/kmeans_big"
csvPathname = 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?
if NA_COL_BUG:
expected = [
# the centers are the same for the 26GB and 180GB. The # of rows is right for 180GB,
# so shouldn't be used for 26GB
# or it should be divided by 7
# the distribution is the same, obviously.
([-113.00566692375459, -89.99595447985321, -455.9970643424373, 4732.0, 49791778.0, 36800.0], 248846122, 1308149283316.2988) ,
([1.0, 1.0, -525.0093818313685, 2015.001629398412, 25654042.00592703, 28304.0], 276924291, 1800760152555.98) ,
([5.0, 2.0, 340.0, 1817.995920197288, 33970406.992053084, 31319.99486705394], 235089554, 375419158808.3253) ,
([10.0, -72.00113070337981, -171.0198611715457, 4430.00952228909, 37007399.0, 29894.0], 166180630, 525423632323.6474) ,
([11.0, 3.0, 578.0043558141306, 1483.0163188052604, 22865824.99639042, 5335.0], 167234179, 1845362026223.1094) ,
([12.0, 3.0, 168.0, -4066.995950679284, 41077063.00269915, -47537.998050740985], 195420925, 197941282992.43475) ,
([19.00092954923767, -10.999565572612255, 90.00028669073289, 1928.0, 39967190.0, 27202.0], 214401768, 11868360232.658035) ,
([20.0, 0.0, 141.0, -3263.0030236302937, 6163210.990273981, 30712.99115201907], 258853406, 598863991074.3276) ,
([21.0, 114.01584574295777, 242.99690338815898, 1674.0029079209912, 33089556.0, 36415.0], 190979054, 1505088759456.314) ,
([25.0, 1.0, 614.0032787274755, -2275.9931284021022, -48473733.04122273, 47343.0], 87794427, 1124697008162.3955) ,
([39.0, 3.0, 470.0, -3337.9880599007597, 28768057.98852736, 16716.003410920028], 78226988, 1151439441529.0215) ,
([40.0, 1.0, 145.0, 950.9990795199593, 14602680.991458317, -14930.007919032574], 167273589, 693036940951.0249) ,
([42.0, 4.0, 479.0, -3678.0033024834297, 8209673.001421165, 11767.998552236539], 148426180, 35942838893.32379) ,
([48.0, 4.0, 71.0, -951.0035145455234, 49882273.00063991, -23336.998167498707], 157533313, 88431531357.62982) ,
([147.00394564757505, 122.98729664236723, 311.0047920137008, 2320.0, 46602185.0, 11212.0], 118361306, 1111537045743.7646) ,
]
else:
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.
# PARSE ****************************************
print "Parse starting: " + csvFilename
hex_key = csvFilename + "_" + str(trial) + ".hex"
start = time.time()
timeoutSecs = 2 * 3600
kwargs = {}
if FROM_HDFS:
parseResult = h2i.import_parse(path=csvPathname, schema='hdfs', hex_key=hex_key,
timeoutSecs=timeoutSecs, pollTimeoutSecs=60, retryDelaySecs=2,
benchmarkLogging=benchmarkLogging, doSummary=False, **kwargs)
else:
parseResult = h2i.import_parse(path=csvPathname, schema='local', hex_key=hex_key,
timeoutSecs=timeoutSecs, pollTimeoutSecs=60, retryDelaySecs=2,
benchmarkLogging=benchmarkLogging, doSummary=False, **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)
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'], timeoutSecs=300)
numRows = inspect['numRows']
numCols = inspect['numCols']
summary = h2o_cmd.runSummary(key=parseResult['destination_key'], numRows=numRows, numCols=numCols,
timeoutSecs=300)
h2o_cmd.infoFromSummary(summary)
# KMeans ****************************************
if not DO_KMEANS:
continue
print "col 0 is enum in " + csvFilename + " but KMeans should skip that automatically?? or no?"
kwargs = {
'k': 15,
'max_iter': 500,
# 'normalize': 1,
'normalize': 0, # temp try
'initialization': 'Furthest',
'destination_key': 'junk.hex',
# we get NaNs if whole col is NA
'ignored_cols': 'C1',
'normalize': 0,
# 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()
kmeansResult = h2o_cmd.runKMeans(parseResult=parseResult, timeoutSecs=timeoutSecs,
benchmarkLogging=benchmarkLogging, **kwargs)
elapsed = time.time() - start
print "kmeans end on ", csvPathname, 'took', elapsed, 'seconds.', "%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
print "kmeans result:", h2o.dump_json(kmeansResult)
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)
# his does predict
(centers, tupleResultList) = h2o_kmeans.bigCheckResults(self, kmeansResult, csvPathname, parseResult, 'd', **kwargs)
# all are multipliers of expected tuple value
allowedDelta = (0.01, 0.01, 0.01)
# these clusters were sorted compared to the cluster order in training
h2o_kmeans.showClusterDistribution(self, tupleResultList, expected, trial=trial)
# why is the expected # of rows not right in KMeans2. That means predictions are wrong
h2o_kmeans.compareResultsToExpected(self, tupleResultList, expected, allowedDelta, allowError=False,
allowRowError=True, trial=trial)
# the tupleResultList has the size during predict? compare it to the sizes during training
# I assume they're in the same order.
model = kmeansResult['model']
size = model['size']
size2 = [t[1] for t in tupleResultList]
if 1==1: # debug
print "training size:", size
print "predict size2:", size2
print "training sorted(size):", sorted(size)
print "predict sorted(size2):", sorted(size2)
print h2o.nodes[0].http_addr
print h2o.nodes[0].port
if 1==0: # debug
print "sleeping"
h2o.sleep(3600)
clusters = model["centers"]
cluster_variances = model["within_cluster_variances"]
error = model["total_within_SS"]
iterations = model["iterations"]
normalized = model["normalized"]
max_iter = model["max_iter"]
print "iterations", iterations
if iterations >= (max_iter-1): # h2o hits the limit at max_iter-1..shouldn't hit it
raise Exception("trial: %s KMeans unexpectedly took %s iterations..which was the full amount allowed by max_iter %s",
(trial, iterations, max_iter))
# this size stuff should be compared now in compareResultsToExpected()..leave it here to make sure
# can't do this compare, because size2 is sorted by center order..
# so we don't know how to reorder size the same way
# we could just sort the two of them, for some bit of comparison.
if sorted(size)!=sorted(size2):
raise Exception("trial: %s training cluster sizes: %s not the same as predict on same data: %s" % (trial, size, size2))
# our expected result is sorted by cluster center ordered. but the sizes are from the predicted histogram
expectedSize = [t[1]/SCALE_SIZE for t in expected]
if size2!=expectedSize:
raise Exception("trial: %s training cluster sizes: %s not the same as expected: %s" % (trial, size, expectedSize))
if DELETE_KEYS_EACH_ITER:
h2i.delete_keys_at_all_nodes()
