def exec_expr_list_across_cols(lenNodes, exprList, keyX,
minCol=0, maxCol=54, timeoutSecs=10, incrementingResult=True):
colResultList = []
for colX in range(minCol, maxCol):
for i, exprTemplate in enumerate(exprList):
# do each expression at a random node, to facilate key movement
# UPDATE: all execs are to a single node. No mixed node streams
# eliminates some store/store race conditions that caused problems.
# always go to node 0 (forever?)
if lenNodes is None:
execNode = 0
else:
### execNode = random.randint(0,lenNodes-1)
### print execNode
execNode = 0
execExpr = fill_in_expr_template(exprTemplate, colX, colX, 0, keyX)
if incrementingResult: # the Result<col> pattern
resultKey = "Result"+str(colX)
else: # assume it's a re-assign to self
resultKey = keyX
# kbn
# v1
# execResultInspect = exec_expr(h2o.nodes[execNode], execExpr, resultKey, timeoutSecs)
# v2
execResultInspect = exec_expr(h2o.nodes[execNode], execExpr, None, timeoutSecs)
print "\nexecResult:", h2o.dump_json(execResultInspect)
execResultKey = execResultInspect[0]['key']
# v2: Exec2 'apply' can have no key field? (null) maybe just use keyX then
if execResultKey:
resultInspect = h2o_cmd.runInspect(None, execResultKey)
else:
resultInspect = h2o_cmd.runInspect(None, keyX)
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
# min is keyword. shouldn't use.
if incrementingResult: # a col will have a single min
min_value = checkScalarResult(execResultInspect, resultKey)
h2o.verboseprint("min_value: ", min_value, "col:", colX)
print "min_value: ", min_value, "col:", colX
else:
min_value = None
sys.stdout.write('.')
sys.stdout.flush()
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
# slows things down to check every iteration, but good for isolation
if (h2o.check_sandbox_for_errors()):
raise Exception(
"Found errors in sandbox stdout or stderr, on trial #%s." % trial)
print "Column #", colX, "completed\n"
colResultList.append(min_value)
return colResultList
def simpleCheckGLMGrid(self, glmGridResult, colX=None, allowFailWarning=False, **kwargs):
destination_key = glmGridResult["destination_key"]
inspectGG = h2o_cmd.runInspect(None, destination_key)
h2o.verboseprint("Inspect of destination_key", destination_key, ":\n", h2o.dump_json(inspectGG))
# FIX! currently this is all unparsed!
type = inspectGG["type"]
if "unparsed" in type:
print "Warning: GLM Grid result destination_key is unparsed, can't interpret. Ignoring for now"
print "Run with -b arg to look at the browser output, for minimal checking of result"
### cols = inspectGG['cols']
response = inspectGG["response"] # dict
### rows = inspectGG['rows']
value_size_bytes = inspectGG["value_size_bytes"]
model0 = glmGridResult["models"][0]
alpha = model0["alpha"]
area_under_curve = model0["area_under_curve"]
error_0 = model0["error_0"]
error_1 = model0["error_1"]
key = model0["key"]
print "best GLM model key:", key
glm_lambda = model0["lambda"]
# now indirect to the GLM result/model that's first in the list (best)
inspectGLM = h2o_cmd.runInspect(None, key)
h2o.verboseprint("GLMGrid inspectGLM:", h2o.dump_json(inspectGLM))
simpleCheckGLM(self, inspectGLM, colX, allowFailWarning=allowFailWarning, **kwargs)
def test_frame_split(self):
h2o.beta_features = True
csvFilename = 'covtype.data'
csvPathname = 'standard/' + csvFilename
hex_key = "covtype.hex"
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets', path=csvPathname, hex_key=hex_key, schema='local', timeoutSecs=10)
print "Just split away and see if anything blows up"
splitMe = hex_key
inspect = h2o_cmd.runInspect(key=splitMe)
origNumRows = inspect['numRows']
origNumCols = inspect['numCols']
for s in range(20):
inspect = h2o_cmd.runInspect(key=splitMe)
numRows = inspect['numRows']
numCols = inspect['numCols']
fs = h2o.nodes[0].frame_split(source=splitMe, ratios=0.5)
split0_key = fs['split_keys'][0]
split1_key = fs['split_keys'][1]
split0_rows = fs['split_rows'][0]
split1_rows = fs['split_rows'][1]
split0_ratio = fs['split_ratios'][0]
split1_ratio = fs['split_ratios'][1]
print "Iteration", s, "split0_rows:", split0_rows, "split1_rows:", split1_rows
splitMe = split1_key
# split should be within 1 row accuracy. let's say within 20 for now
self.assertLess(abs(split1_rows - split0_rows), 2)
self.assertEqual(numRows, (split1_rows + split0_rows))
self.assertEqual(numCols, origNumCols)
if split0_rows <= 2:
break
def test_parse_1m_cols(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [(10, 65000, "cH", 30)]
h2b.browseTheCloud()
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = "syn_" + str(SEEDPERFILE) + "_" + str(rowCount) + "x" + str(colCount) + ".csv"
csvPathname = SYNDATASETS_DIR + "/" + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
start = time.time()
print "Summary should work with 65k"
parseResult = h2i.import_parse(
path=csvPathname, schema="put", hex_key=hex_key, timeoutSecs=timeoutSecs, doSummary=True
)
print csvFilename, "parse time:", parseResult["response"]["time"]
print "Parse and summary:", parseResult["destination_key"], "took", time.time() - start, "seconds"
# We should be able to see the parse result?
start = time.time()
inspect = h2o_cmd.runInspect(None, parseResult["destination_key"], timeoutSecs=timeoutSecs)
print "Inspect:", parseResult["destination_key"], "took", time.time() - start, "seconds"
h2o_cmd.infoFromInspect(inspect, csvPathname)
print "\n" + csvPathname, " num_rows:", "{:,}".format(
inspect["num_rows"]
), " num_cols:", "{:,}".format(inspect["num_cols"])
# should match # of cols in header or ??
self.assertEqual(
inspect["num_cols"],
colCount,
"parse created result with the wrong number of cols %s %s" % (inspect["num_cols"], colCount),
)
self.assertEqual(
inspect["num_rows"],
rowCount,
"parse created result with the wrong number of rows (header shouldn't count) %s %s"
% (inspect["num_rows"], rowCount),
)
# we should obey max_column_display
column_limits = [25, 25000, 50000]
for column_limit in column_limits:
inspect = h2o_cmd.runInspect(
None, parseResult["destination_key"], max_column_display=column_limit, timeoutSecs=timeoutSecs
)
self.assertEqual(
len(inspect["cols"]), column_limit, "inspect obeys max_column_display = " + str(column_limit)
)
for r in range(0, len(inspect["rows"])):
# NB: +1 below because each row includes a row header row: #{row}
self.assertEqual(
len(inspect["rows"][r]),
column_limit + 1,
"inspect data rows obeys max_column_display = " + str(column_limit),
)
def test_rapids_basic(self):
bucket = 'home-0xdiag-datasets'
csvPathname = 'standard/covtype.data'
hexKey = 'p'
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, schema='put', hex_key=hexKey)
keys = []
for execExpr in exprList:
r = re.match ('\(= \!([a-zA-Z0-9_]+) ', execExpr)
resultKey = r.group(1)
execResult, result = h2e.exec_expr(h2o.nodes[0], execExpr, resultKey=resultKey, timeoutSecs=4)
if DO_ROLLUP:
h2o_cmd.runInspect(key=resultKey)
# rows might be zero!
if execResult['num_rows'] or execResult['num_cols']:
keys.append(execExpr)
else:
h2p.yellow_print("\nNo key created?\n", dump_json(execResult))
print "\nExpressions that created keys. Shouldn't all of these expressions create keys"
for k in keys:
print k
h2o.check_sandbox_for_errors()
def createTestTrain(srcKey, trainDstKey, testDstKey, percent, outputClass, numCols):
# will have to live with random extract. will create variance
print "train: get random %. change class 4 to 1, everything else to 0. factor() to turn real to int (for rf)"
# Create complexity for no good reason!. Do the same thing 5 times in the single exec expressions
execExpr = ""
STUPID_REPEAT = 20
for i in range(STUPID_REPEAT):
execExpr += "a.hex=runif(%s);" % srcKey
execExpr += "%s=%s[a.hex%s,];" % (trainDstKey, srcKey, '<=0.9')
if not DO_MULTINOMIAL:
execExpr += "%s[,%s]=%s[,%s]==%s;" % (trainDstKey, numCols, trainDstKey, numCols, outputClass)
execExpr += "factor(%s[, %s]);" % (trainDstKey, numCols)
h2o_exec.exec_expr(None, execExpr, resultKey=trainDstKey, timeoutSecs=STUPID_REPEAT * 15)
inspect = h2o_cmd.runInspect(key=trainDstKey)
h2o_cmd.infoFromInspect(inspect, "%s after mungeDataset on %s" % (trainDstKey, srcKey) )
print "test: same, but use the same runif() random result, complement"
execExpr = "a.hex=runif(%s);" % srcKey
execExpr += "%s=%s[a.hex%s,];" % (testDstKey, srcKey, '>0.9')
if not DO_MULTINOMIAL:
execExpr += "%s[,%s]=%s[,%s]==%s;" % (testDstKey, numCols, testDstKey, numCols, outputClass)
execExpr += "factor(%s[, %s])" % (testDstKey, numCols)
h2o_exec.exec_expr(None, execExpr, resultKey=testDstKey, timeoutSecs=10)
inspect = h2o_cmd.runInspect(key=testDstKey)
h2o_cmd.infoFromInspect(inspect, "%s after mungeDataset on %s" % (testDstKey, srcKey) )
def test_one_hot_expand_fvec(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(100, 1100, 'cA', 5),
(100, 1000, 'cB', 5),
(100, 900, 'cC', 5),
(100, 800, 'cD', 5),
(100, 700, 'cE', 5),
(100, 600, 'cF', 5),
(100, 500, 'cG', 5),
]
### h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
cnum = 0
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
cnum += 1
csvFilename = 'syn_' + str(SEED) + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEED)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=10)
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
# does it modify the original or ?
oneHotResult = h2o.nodes[0].one_hot(source=parseResult['destination_key'])
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
def simpleCheckGBMGrid(self, glmGridResult, colX=None, allowFailWarning=False, **kwargs):
destination_key = glmGridResult['destination_key']
inspectGG = h2o_cmd.runInspect(None, destination_key)
h2o.verboseprint("Inspect of destination_key", destination_key,":\n", h2o.dump_json(inspectGG))
# FIX! currently this is all unparsed!
#type = inspectGG['type']
#if 'unparsed' in type:
# print "Warning: GBM Grid result destination_key is unparsed, can't interpret. Ignoring for now"
# print "Run with -b arg to look at the browser output, for minimal checking of result"
### cols = inspectGG['cols']
response = inspectGG['response'] # dict
### rows = inspectGG['rows']
#value_size_bytes = inspectGG['value_size_bytes']
model0 = glmGridResult['models'][0]
alpha = model0['alpha']
area_under_curve = model0['area_under_curve']
error_0 = model0['error_0']
error_1 = model0['error_1']
model_key = model0['key']
print "best GBM model key:", model_key
glm_lambda = model0['lambda']
# now indirect to the GBM result/model that's first in the list (best)
inspectGBM = h2o_cmd.runInspect(None, model_key)
h2o.verboseprint("GBMGrid inspectGBM:", h2o.dump_json(inspectGBM))
simpleCheckGBM(self, inspectGBM, colX, allowFailWarning=allowFailWarning, **kwargs)
def test_exec2_na_chop(self):
bucket = 'home-0xdiag-datasets'
csvPathname = 'airlines/year2013.csv'
hexKey = 'i.hex'
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, schema='put', hex_key=hexKey)
inspect = h2o_cmd.runInspect(key='i.hex')
print "\nr.hex" \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
numRows1 = inspect['numRows']
numCols = inspect['numCols']
for resultKey, execExpr in initList:
h2e.exec_expr(h2o.nodes[0], execExpr, resultKey=None, timeoutSecs=10)
start = time.time()
h2e.exec_expr_list_rand(len(h2o.nodes), exprList, keyX='s.hex', maxTrials=200, timeoutSecs=30, maxCol=numCols-1)
inspect = h2o_cmd.runInspect(key='s.hex')
print "\ns.hex" \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
numRows2 = inspect['numRows']
print numRows1, numRows2
h2o.check_sandbox_for_errors()
print "exec end on ", "operators" , 'took', time.time() - start, 'seconds'
def test_nfold_frame_extract(self):
csvFilename = 'covtype.data'
csvPathname = 'standard/' + csvFilename
hex_key = "covtype.hex"
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets', path=csvPathname, hex_key=hex_key, schema='local', timeoutSecs=30)
print "Just nfold_frame_extract away and see if anything blows up"
splitMe = hex_key
inspect = h2o_cmd.runInspect(key=splitMe)
origNumRows = inspect['numRows']
origNumCols = inspect['numCols']
for s in range(20):
inspect = h2o_cmd.runInspect(key=splitMe)
numRows = inspect['numRows']
numCols = inspect['numCols']
# FIX! should check if afold is outside of nfold range allowance
fs = h2o.nodes[0].nfold_frame_extract(source=splitMe, nfolds=2, afold=random.randint(0,1))
print "fs", h2o.dump_json(fs)
split0_key = fs['split_keys'][0]
split1_key = fs['split_keys'][1]
split0_rows = fs['split_rows'][0]
split1_rows = fs['split_rows'][1]
print "Iteration", s, "split0_rows:", split0_rows, "split1_rows:", split1_rows
splitMe = split0_key
if split0_rows<=2:
break
print "Iteration", s
def bigCheckResults(self, kmeans, csvPathname, parseKey, applyDestinationKey, **kwargs):
simpleCheckKMeans(self, kmeans, **kwargs)
model_key = kmeans['destination_key']
kmeansResult = h2o_cmd.runInspect(key=model_key)
centers = kmeansResult['KMeansModel']['clusters']
kmeansApplyResult = h2o.nodes[0].kmeans_apply(
data_key=parseKey['destination_key'], model_key=model_key,
destination_key=applyDestinationKey)
inspect = h2o_cmd.runInspect(None, applyDestinationKey)
h2o_cmd.infoFromInspect(inspect, csvPathname)
kmeansScoreResult = h2o.nodes[0].kmeans_score(
key=parseKey['destination_key'], model_key=model_key)
score = kmeansScoreResult['score']
rows_per_cluster = score['rows_per_cluster']
sqr_error_per_cluster = score['sqr_error_per_cluster']
tupleResultList = []
for i,c in enumerate(centers):
print "\ncenters["+str(i)+"]: ", centers[i]
print "rows_per_cluster["+str(i)+"]: ", rows_per_cluster[i]
print "sqr_error_per_cluster["+str(i)+"]: ", sqr_error_per_cluster[i]
tupleResultList.append( (centers[i], rows_per_cluster[i], sqr_error_per_cluster[i]) )
return (centers, tupleResultList)
def simpleCheckGLMGrid(self, glmGridResult, colX=None, allowFailWarning=False, **kwargs):
destination_key = glmGridResult['destination_key']
inspectGG = h2o_cmd.runInspect(None, destination_key)
h2o.verboseprint("Inspect of destination_key", destination_key,":\n", h2o.dump_json(inspectGG))
# FIX! currently this is all unparsed!
#type = inspectGG['type']
#if 'unparsed' in type:
# print "Warning: GLM Grid result destination_key is unparsed, can't interpret. Ignoring for now"
# print "Run with -b arg to look at the browser output, for minimal checking of result"
### cols = inspectGG['cols']
response = inspectGG['response'] # dict
### rows = inspectGG['rows']
#value_size_bytes = inspectGG['value_size_bytes']
# FIX! does error_0/1 only exist for binomial?
for m, model in enumerate(glmGridResult['models']):
alpha = model['alpha']
area_under_curve = model['area_under_curve']
# FIX! should check max error?
error_0 = model['error_0']
error_1 = model['error_1']
model_key = model['key']
print "#%s GLM model key: %s" % (m, model_key)
glm_lambda = model['lambda']
# now indirect to the GLM result/model that's first in the list (best)
inspectGLM = h2o_cmd.runInspect(None, glmGridResult['models'][0]['key'])
h2o.verboseprint("GLMGrid inspect GLMGrid model 0(best):", h2o.dump_json(inspectGLM))
g = simpleCheckGLM(self, inspectGLM, colX, allowFailWarning=allowFailWarning, **kwargs)
return g
def test_parse_fs_schmoo_fvec(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_prostate.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
headerData = "ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON"
# rowData = "1,0,65,1,2,1,1.4,0,6"
rowData = "1,0,65,1,2,1,1,0,6"
totalRows = 99860
write_syn_dataset(csvPathname, totalRows, headerData, rowData)
print "This is the same format/data file used by test_same_parse, but the non-gzed version"
print "\nSchmoo the # of rows"
print "Updating the key and hex_key names for each trial"
for trial in range (200):
append_syn_dataset(csvPathname, rowData)
totalRows += 1
start = time.time()
key = csvFilename + "_" + str(trial)
hex_key = csvFilename + "_" + str(trial) + ".hex"
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key)
print "trial #", trial, "totalRows:", totalRows, "parse end on ", \
csvFilename, 'took', time.time() - start, 'seconds'
h2o_cmd.runInspect(key=hex_key)
# only used this for debug to look at parse (red last row) on failure
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
h2o.check_sandbox_for_errors()
def simpleCheckGLMGrid(self, glmGridResult, colX=None, allowFailWarning=False, **kwargs):
# "grid": {
# "destination_keys": [
# "GLMGridResults__8222a49156af52532a34fb3ce4304308_0",
# "GLMGridResults__8222a49156af52532a34fb3ce4304308_1",
# "GLMGridResults__8222a49156af52532a34fb3ce4304308_2"
# ]
# },
if h2o.beta_features:
destination_key = glmGridResult['grid']['destination_keys'][0]
inspectGG = h2o.nodes[0].glm_view(destination_key)
models = inspectGG['glm_model']['submodels']
h2o.verboseprint("GLMGrid inspect GLMGrid model 0(best):", h2o.dump_json(models[0]))
g = simpleCheckGLM(self, inspectGG, colX, allowFailWarning=allowFailWarning, **kwargs)
else:
destination_key = glmGridResult['destination_key']
inspectGG = h2o_cmd.runInspect(None, destination_key)
h2o.verboseprint("Inspect of destination_key", destination_key,":\n", h2o.dump_json(inspectGG))
models = glmGridResult['models']
for m, model in enumerate(models):
alpha = model['alpha']
area_under_curve = model['area_under_curve']
# FIX! should check max error?
error_0 = model['error_0']
error_1 = model['error_1']
model_key = model['key']
print "#%s GLM model key: %s" % (m, model_key)
glm_lambda = model['lambda']
# now indirect to the GLM result/model that's first in the list (best)
inspectGLM = h2o_cmd.runInspect(None, glmGridResult['models'][0]['key'])
h2o.verboseprint("GLMGrid inspect GLMGrid model 0(best):", h2o.dump_json(inspectGLM))
g = simpleCheckGLM(self, inspectGLM, colX, allowFailWarning=allowFailWarning, **kwargs)
return g
def test_exec2_sum(self):
bucket = 'home-0xdiag-datasets'
# csvPathname = 'airlines/year2013.csv'
if getpass.getuser()=='jenkins':
csvPathname = 'standard/billion_rows.csv.gz'
else:
csvPathname = '1B/reals_100000x1000_15f.data'
csvPathname = '1B/reals_1B_15f.data'
csvPathname = '1B/reals_1000000x1000_15f.data'
hex_key = 'r1'
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, schema='local',
hex_key=hex_key, timeoutSecs=3000, retryDelaySecs=2)
inspect = h2o_cmd.runInspect(key=hex_key)
print "numRows:", inspect['numRows']
print "numCols:", inspect['numCols']
inspect = h2o_cmd.runInspect(key=hex_key, offset=-1)
print "inspect offset = -1:", h2o.dump_json(inspect)
for execExpr in exprList:
start = time.time()
execResult, result = h2e.exec_expr(h2o.nodes[0], execExpr, resultKey=None, timeoutSecs=300)
print 'exec took', time.time() - start, 'seconds'
print "result:", result
h2o.check_sandbox_for_errors()
def test_sort_of_prostate_with_row_schmoo(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_prostate.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
headerData = "ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON"
rowData = "1,0,65,1,2,1,1.4,0,6"
write_syn_dataset(csvPathname, 99860, headerData, rowData)
print "This is the same format/data file used by test_same_parse, but the non-gzed version"
print "\nSchmoo the # of rows"
print "Updating the key and key2 names for each trial"
for trial in range (200):
append_syn_dataset(csvPathname, rowData)
### start = time.time()
# this was useful to cause failures early on. Not needed eventually
### key = h2o_cmd.parseFile(csvPathname=h2o.find_file("smalldata/logreg/prostate.csv"))
### print "Trial #", trial, "parse end on ", "prostate.csv" , 'took', time.time() - start, 'seconds'
start = time.time()
key = csvFilename + "_" + str(trial)
key2 = csvFilename + "_" + str(trial) + ".hex"
key = h2o_cmd.parseFile(csvPathname=csvPathname, key=key, key2=key2)
print "trial #", trial, "parse end on ", csvFilename, 'took', time.time() - start, 'seconds'
h2o_cmd.runInspect(key=key2)
# only used this for debug to look at parse (red last row) on failure
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
h2o.check_sandbox_for_errors()
def test_parse_summary_c21(self):
importFolderPath = '/mnt/0xcustomer-datasets/c21'
timeoutSecs = 300
csvPathname_train = importFolderPath + '/persona_clean_deep.tsv.zip'
hex_key = 'train.hex'
parseResult = h2i.import_parse(path=csvPathname_train, hex_key=hex_key, timeoutSecs=timeoutSecs)
inspect = h2o_cmd.runInspect(key=hex_key)
missingValuesList = h2o_cmd.infoFromInspect(inspect, csvPathname_train)
# self.assertEqual(missingValuesList, [], "%s should have 3 cols of NAs: %s" % (csvPathname_train, missingValuesList)
numCols = inspect['numCols']
numRows = inspect['numRows']
rSummary = h2o_cmd.runSummary(key=hex_key, rows=numRows, cols=numCols)
h2o_cmd.infoFromSummary(rSummary)
csvPathname_test = importFolderPath + '/persona_clean_deep.tsv.zip'
validation_key = 'test.hex'
parseResult = h2i.import_parse(path=csvPathname_test, hex_key=validation_key, timeoutSecs=timeoutSecs)
inspect = h2o_cmd.runInspect(key=hex_key)
missingValuesList = h2o_cmd.infoFromInspect(inspect, csvPathname_test)
# self.assertEqual(missingValuesList, [], "%s should have 3 cols of NAs: %s" % (csvPathname_test, missingValuesList)
numCols = inspect['numCols']
numRows = inspect['numRows']
rSummary = h2o_cmd.runSummary(key=hex_key, rows=numRows, cols=numCols)
h2o_cmd.infoFromSummary(rSummary)
def test_NOPASS_create_frame_fail(self):
h2o.beta_features = True
for trial in range(20):
kwargs = {'integer_range': None, 'missing_fraction': 0.1, 'cols': 10, 'response_factors': 1, 'seed': 1234, 'randomize': 1, 'categorical_fraction': 0, 'rows': 1, 'factors': 0, 'real_range': 0, 'value': None, 'integer_fraction': 0}
print kwargs
timeoutSecs = 300
parseResult = h2i.import_parse(bucket='smalldata', path='poker/poker1000', hex_key='temp1000.hex',
schema='put', timeoutSecs=timeoutSecs)
cfResult = h2o.nodes[0].create_frame(key='temp1000.hex', timeoutSecs=timeoutSecs, **kwargs)
if DO_DOWNLOAD:
csvPathname = SYNDATASETS_DIR + '/' + 'temp1000.csv'
h2o.nodes[0].csv_download(src_key='temp1000.hex', csvPathname=csvPathname, timeoutSecs=60)
if DO_INSPECT:
h2o_cmd.runInspect(key='temp1000.hex')
rSummary = h2o_cmd.runSummary(key='temp1000.hex', cols=10)
h2o_cmd.infoFromSummary(rSummary)
print h2o.dump_json(cfResult)
print "Trial #", trial, "completed"
def test_GLM2_tnc3_10(self):
h2o.beta_features = True
csvFilename = 'tnc3_10.csv'
print "\n" + csvFilename
hex_key = "tnc3.hex"
h2b.browseTheCloud()
parseResult = h2i.import_parse(bucket='smalldata', path=csvFilename, schema='put', hex_key=hex_key, timeoutSecs=10)
print "Parse result['Key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(10)
if (1==0):
lenNodes = len(h2o.nodes)
colResultList = h2e.exec_expr_list_across_cols(lenNodes, numExprList, hex_key, maxCol=10,
incrementingResult=False, timeoutSecs=10)
print "\ncolResultList after num swap", colResultList
if (1==1):
start = time.time()
kwargs = {'response': 13, 'n_folds': 6}
# hmm. maybe we should update to use key as input
# in case exec is used to change the parseResult
# in any case, the destination_key in parseResult was what was updated
# so if we Exec, it's correct.
glm = h2o_cmd.runGLM(parseResult=parseResult, timeoutSecs=300, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end on ", csvFilename, 'took', time.time() - start, 'seconds'
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
#******************
if (1==0):
colResultList = h2e.exec_expr_list_across_cols(lenNodes, charExprList, hex_key, maxCol=10,
incrementingResult=False, timeoutSecs=10)
print "\ncolResultList after char swap", colResultList
if (1==1):
start = time.time()
kwargs = {'response': 13, 'n_folds': 6}
glm = h2o_cmd.runGLM(parseResult=parseResult, timeoutSecs=300, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end on ", csvFilename, 'took', time.time() - start, 'seconds'
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
if not h2o.browse_disable:
### print "\n <ctrl-C> to quit sleeping here"
### time.sleep(1500)
pass
def test_tnc3_ignore(self):
csvFilename = 'tnc3_10.csv'
csvPathname = h2o.find_file('smalldata/' + csvFilename)
print "\n" + csvPathname
key2 = "tnc3.hex"
h2b.browseTheCloud()
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=key2, timeoutSecs=10)
print "Parse result['Key']:", parseKey['destination_key']
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(10)
if (1==0):
lenNodes = len(h2o.nodes)
colResultList = h2e.exec_expr_list_across_cols(lenNodes, numExprList, key2, maxCol=10,
incrementingResult=False, timeoutSecs=10)
print "\ncolResultList after num swap", colResultList
if (1==1):
start = time.time()
kwargs = {'y': 13, 'num_cross_validation_folds': 6}
# hmm. maybe we should update to use key as input
# in case exec is used to change the parseKey
# in any case, the destination_key in parseKey was what was updated
# so if we Exec, it's correct.
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=300, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
#******************
if (1==0):
colResultList = h2e.exec_expr_list_across_cols(lenNodes, charExprList, key2, maxCol=10,
incrementingResult=False, timeoutSecs=10)
print "\ncolResultList after char swap", colResultList
if (1==1):
start = time.time()
kwargs = {'y': 13, 'num_cross_validation_folds': 6}
glm = h2o_cmd.runGLMOnly(parseKey=parseKey, timeoutSecs=300, **kwargs)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
print "glm end on ", csvPathname, 'took', time.time() - start, 'seconds'
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
if not h2o.browse_disable:
### print "\n <ctrl-C> to quit sleeping here"
### time.sleep(1500)
pass
def bigCheckResults(self, kmeans, csvPathname, parseResult, applyDestinationKey, **kwargs):
simpleCheckKMeans(self, kmeans, **kwargs)
if h2o.beta_features:
# can't use inspect on a model key? now?
model = kmeans["model"]
model_key = model["_key"]
centers = model["centers"]
cluster_variances = model["within_cluster_variances"]
error = model["total_within_SS"]
kmeansResult = kmeans
else:
model_key = kmeans["destination_key"]
kmeansResult = h2o_cmd.runInspect(key=model_key)
h2o.verboseprint("kmeans result:", h2o.dump_json(kmeansResult))
model = kmeansResult["KMeansModel"]
centers = model["clusters"]
error = model["error"]
if h2o.beta_features:
# need to use Predict2?
pass
# no scoring on Kmeans2?..just reuse
# cols/max_ncols params?
predictKey = applyDestinationKey
predictResult = h2o.nodes[0].generate_predictions(
data_key=parseResult["destination_key"], model_key=model_key, destination_key=predictKey
)
summaryResult = h2o.nodes[0].summary_page(key=predictKey)
hcnt = summaryResult["summaries"][0]["hcnt"] # histogram
rows_per_cluster = hcnt
# FIX! does the cluster order/naming match, compared to cluster variances
sqr_error_per_cluster = cluster_variances
else:
kmeansApplyResult = h2o.nodes[0].kmeans_apply(
data_key=parseResult["destination_key"], model_key=model_key, destination_key=applyDestinationKey
)
inspect = h2o_cmd.runInspect(None, applyDestinationKey)
h2o_cmd.infoFromInspect(inspect, csvPathname)
# this was failing
summaryResult = h2o_cmd.runSummary(key=applyDestinationKey)
h2o_cmd.infoFromSummary(summaryResult, noPrint=False)
kmeansScoreResult = h2o.nodes[0].kmeans_score(key=parseResult["destination_key"], model_key=model_key)
score = kmeansScoreResult["score"]
rows_per_cluster = score["rows_per_cluster"]
sqr_error_per_cluster = score["sqr_error_per_cluster"]
tupleResultList = []
print "\nerror: ", error
for i, c in enumerate(centers):
print "\ncenters[" + str(i) + "]: ", [round(c, 2) for c in centers[i]]
print "rows_per_cluster[" + str(i) + "]: ", rows_per_cluster[i]
print "sqr_error_per_cluster[" + str(i) + "]: ", sqr_error_per_cluster[i]
tupleResultList.append((centers[i], rows_per_cluster[i], sqr_error_per_cluster[i]))
return (centers, tupleResultList)
def bigCheckResults(self, kmeans, csvPathname, parseResult, applyDestinationKey, **kwargs):
simpleCheckKMeans(self, kmeans, **kwargs)
if h2o.beta_features:
model_key = kmeans["model"]["_selfKey"]
# Exception: rjson error in inspect: Argument 'src_key' error: benign_k.hex:Key is not a Frame
# can't use inspect on a model key? now?
kmeansResult = kmeans
model = kmeansResult["model"]
centers = model["clusters"]
error = model["error"]
else:
model_key = kmeans["destination_key"]
kmeansResult = h2o_cmd.runInspect(key=model_key)
model = kmeansResult["KMeansModel"]
centers = model["clusters"]
error = model["error"]
if h2o.beta_features:
# need to use Predict2?
pass
# no scoring on Kmeans2?..just reuse
# cols/max_ncols params?
predictKey = applyDestinationKey
predictResult = h2o.nodes[0].generate_predictions(
data_key=parseResult["destination_key"], model_key=model_key, destination_key=predictKey
)
summaryResult = h2o.nodes[0].summary_page(key=predictKey)
hcnt = summaryResult["summaries"][0]["hcnt"] # histogram
rows_per_cluster = hcnt
# have to figure out how to get this with fvec
sqr_error_per_cluster = [0 for h in hcnt]
else:
kmeansApplyResult = h2o.nodes[0].kmeans_apply(
data_key=parseResult["destination_key"], model_key=model_key, destination_key=applyDestinationKey
)
inspect = h2o_cmd.runInspect(None, applyDestinationKey)
h2o_cmd.infoFromInspect(inspect, csvPathname)
# this was failing
summaryResult = h2o_cmd.runSummary(key=applyDestinationKey)
h2o_cmd.infoFromSummary(summaryResult, noPrint=False)
kmeansScoreResult = h2o.nodes[0].kmeans_score(key=parseResult["destination_key"], model_key=model_key)
score = kmeansScoreResult["score"]
rows_per_cluster = score["rows_per_cluster"]
sqr_error_per_cluster = score["sqr_error_per_cluster"]
tupleResultList = []
print "\nerror: ", error
for i, c in enumerate(centers):
print "\ncenters[" + str(i) + "]: ", centers[i]
print "rows_per_cluster[" + str(i) + "]: ", rows_per_cluster[i]
print "sqr_error_per_cluster[" + str(i) + "]: ", sqr_error_per_cluster[i]
tupleResultList.append((centers[i], rows_per_cluster[i], sqr_error_per_cluster[i]))
return (centers, tupleResultList)
def test_rf_float_bigexp_fvec(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_prostate.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
headerData = "ID,CAPSULE,AGE,RACE,DPROS,DCAPS,PSA,VOL,GLEASON"
totalRows = 1000
colCount = 7
write_syn_dataset(csvPathname, totalRows, colCount, headerData)
for trial in range (5):
# grow the data set
rowData = rand_rowData(colCount)
num = random.randint(4096, 10096)
append_syn_dataset(csvPathname, colCount, num)
totalRows += num
# make sure all key names are unique, when we re-put and re-parse (h2o caching issues)
hex_key = csvFilename + "_" + str(trial) + ".hex"
# On EC2 once we get to 30 trials or so, do we see polling hang? GC or spill of heap or ??
ntree = 2
kwargs = {
'ntrees': ntree,
'mtries': None,
'max_depth': 20,
'sample_rate': 0.67,
'destination_key': None,
'nbins': 1024,
'seed': 784834182943470027,
}
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, doSummary=True)
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
numCols = inspect['numCols']
start = time.time()
rfView = h2o_cmd.runRF(parseResult=parseResult, timeoutSecs=15, pollTimeoutSecs=5, **kwargs)
print "trial #", trial, "totalRows:", totalRows, "num:", num, "RF end on ", csvFilename, \
'took', time.time() - start, 'seconds'
(classification_error, classErrorPctList, totalScores) = h2o_rf.simpleCheckRFView(rfv=rfView, ntree=ntree)
# cm0 = rfView['drf_model']['cms'][0]['_arr']
# print cm0
# self.assertEqual(len(cm0), numCols,
# msg="%s cols in cm, means rf must have ignored some cols. I created data with %s cols" % (len(cm0), numCols-1))
inspect = h2o_cmd.runInspect(key=hex_key)
cols = inspect['cols']
numCols = inspect['numCols']
for i,c in enumerate(cols):
if i < (numCols-1): # everything except the last col (output) should be 8 byte float
colType = c['type']
self.assertEqual(colType, 'Real', msg="col %d should be type Real: %s" % (i, colType))
### h2o_cmd.runInspect(key=hex_key)
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
h2o.check_sandbox_for_errors()
def test_exec2_xorsum(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(10000, 1, 'r1', 0, 10, None),
]
ullResultList = []
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
# dynamic range of the data may be useful for estimating error
maxDelta = expectedMax - expectedMin
csvFilename = 'syn_real_' + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
print "Creating random", csvPathname
(expectedUll, expectedFpSum) = write_syn_dataset(csvPathname,
rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
parseResult = h2i.import_parse(path=csvPathname, schema='local', hex_key=hex_key,
timeoutSecs=3000, retryDelaySecs=2)
inspect = h2o_cmd.runInspect(key=hex_key)
print "numRows:", inspect['numRows']
print "numCols:", inspect['numCols']
inspect = h2o_cmd.runInspect(key=hex_key, offset=-1)
print "inspect offset = -1:", h2o.dump_json(inspect)
# looking at the 8 bytes of bits for the h2o doubles
# xorsum will zero out the sign and exponent
for execExpr in exprList:
start = time.time()
(execResult, fpResult) = h2e.exec_expr(h2o.nodes[0], execExpr,
resultKey=None, timeoutSecs=300)
print 'exec took', time.time() - start, 'seconds'
print "execResult:", h2o.dump_json(execResult)
print ""
print "%30s" % "fpResult:", "%.15f" % fpResult
ullResult = h2o_util.doubleToUnsignedLongLong(fpResult)
print "%30s" % "bitResult (0.16x):", "0x%0.16x" % ullResult
print "%30s" % "expectedUll (0.16x):", "0x%0.16x" % expectedUll
# print "%30s" % "hex(bitResult):", hex(ullResult)
ullResultList.append((ullResult, fpResult))
h2o.check_sandbox_for_errors()
print "first result was from a sum. others are xorsum"
print "ullResultList:"
for ullResult, fpResult in ullResultList:
print "%30s" % "ullResult (0.16x):", "0x%0.16x %s" % (ullResult, fpResult)
expectedUllAsDouble = h2o_util.unsignedLongLongToDouble(expectedUll)
print "%30s" % "expectedUll (0.16x):", "0x%0.16x %s" % (expectedUll, expectedUllAsDouble)
expectedFpSumAsLongLong = h2o_util.doubleToUnsignedLongLong(expectedFpSum)
print "%30s" % "expectedFpSum (0.16x):", "0x%0.16x %s" % (expectedFpSumAsLongLong, expectedFpSum)
def test_KMeans_params_rand2(self):
SEED = random.randint(0, sys.maxint)
# if you have to force to redo a test
# SEED =
random.seed(SEED)
print "\nUsing random seed:", SEED
if localhost:
csvFilenameList = [
# ('covtype.data', 60),
('covtype20x.data', 400),
]
else:
csvFilenameList = [
('covtype20x.data', 400),
('covtype200x.data', 2000),
]
importFolderPath = '/home/0xdiag/datasets/standard'
h2i.setupImportFolder(None, importFolderPath)
for csvFilename, timeoutSecs in csvFilenameList:
# creates csvFilename.hex from file in importFolder dir
parseKey = h2i.parseImportFolderFile(None, csvFilename, importFolderPath,
timeoutSecs=2000, pollTimeoutSecs=60)
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
csvPathname = importFolderPath + "/" + csvFilename
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(inspect['num_rows']), \
" num_cols:", "{:,}".format(inspect['num_cols'])
paramDict = define_params()
for trial in range(3):
randomV = paramDict['k']
k = random.choice(randomV)
randomV = paramDict['epsilon']
epsilon = random.choice(randomV)
randomV = paramDict['cols']
cols = random.choice(randomV)
kwargs = {'k': k, 'epsilon': epsilon, 'cols': cols,
'destination_key': csvFilename + "_" + str(trial) + '.hex'}
start = time.time()
kmeans = h2o_cmd.runKMeansOnly(parseKey=parseKey, \
timeoutSecs=timeoutSecs, retryDelaySecs=2, pollTimeoutSecs=60, **kwargs)
elapsed = time.time() - start
print "kmeans end on ", csvPathname, 'took', elapsed, 'seconds.', \
"%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
h2o_kmeans.simpleCheckKMeans(self, kmeans, **kwargs)
### print h2o.dump_json(kmeans)
inspect = h2o_cmd.runInspect(None,key=kmeans['destination_key'])
print h2o.dump_json(inspect)
print "Trial #", trial, "completed\n"
def test_exec2_row_range(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [(1000000, 5, "cA", 200)]
# h2b.browseTheCloud()
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = "syn_" + str(SEEDPERFILE) + "_" + str(rowCount) + "x" + str(colCount) + ".csv"
csvPathname = SYNDATASETS_DIR + "/" + csvFilename
print "\nCreating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
start = time.time()
parseResult = h2i.import_parse(
path=csvPathname, schema="put", hex_key=hex_key, timeoutSecs=timeoutSecs, doSummary=False
)
print "Parse:", parseResult["destination_key"], "took", time.time() - start, "seconds"
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"],
colCount,
"parse created result with the wrong number of cols %s %s" % (inspect["numCols"], colCount),
)
self.assertEqual(
inspect["numRows"],
rowCount,
"parse created result with the wrong number of rows (header shouldn't count) %s %s"
% (inspect["numRows"], rowCount),
)
REPEAT = 1
for i in range(REPEAT):
hex_key_i = hex_key + "_" + str(i)
execExpr = "%s=%s[1,]" % (hex_key_i, hex_key)
resultExec, result = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
execExpr = "%s=%s[1:%s,]" % (hex_key_i, hex_key, 100)
resultExec, result = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
execExpr = "%s=%s[1:%s,]" % (hex_key_i, hex_key, rowCount - 10)
resultExec, result = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
inspect = h2o_cmd.runInspect(None, hex_key_i, timeoutSecs=timeoutSecs)
h2o_cmd.infoFromInspect(inspect, hex_key_i)
print "\n" + hex_key_i, " numRows:", "{:,}".format(
inspect["numRows"]
), " numCols:", "{:,}".format(inspect["numCols"])
def test_rf_hhp_2a_fvec(self):
h2o.beta_features = True
csvFilenameList = {
'hhp.cut3.214.data.gz',
}
for csvFilename in csvFilenameList:
csvPathname = csvFilename
print "RF start on ", csvPathname
dataKeyTrain = 'rTrain.hex'
start = time.time()
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=dataKeyTrain, schema='put',
timeoutSecs=120)
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
numCols = inspect['numCols']
# we want the last col. Should be values 0 to 14. 14 most rare
# from the cut3 set
# 84777 0
# 13392 1
# 6546 2
# 5716 3
# 4210 4
# 3168 5
# 2009 6
# 1744 7
# 1287 8
# 1150 9
# 1133 10
# 780 11
# 806 12
# 700 13
# 345 14
# 3488 15
execExpr = "%s[,%s] = %s[,%s]==14" % (dataKeyTrain, numCols, dataKeyTrain, numCols)
h2o_exec.exec_expr(None, execExpr, resultKey=dataKeyTrain, timeoutSecs=10)
inspect = h2o_cmd.runInspect(key=dataKeyTrain)
h2o_cmd.infoFromInspect(inspect, "going into RF")
execResult = {'destination_key': dataKeyTrain}
kwargs = {
'ntrees': 20,
'max_depth': 20,
'nbins': 50,
}
rfView = h2o_cmd.runRF(parseResult=execResult, timeoutSecs=900, retryDelaySecs=10, **kwargs)
print "RF end on ", csvPathname, 'took', time.time() - start, 'seconds'
(error, classErrorPctList, totalScores) = h2o_rf.simpleCheckRFView(rfv=rfView)
def test_create_frame_rand1(self):
h2o.beta_features = True
# default
params = {
'rows': 1,
'cols': 1
}
for trial in range(20):
h2o_util.pickRandParams(paramDict, params)
i = params.get('integer_fraction', None)
c = params.get('categorical_fraction', None)
r = params.get('randomize', None)
v = params.get('value', None)
# h2o does some strick checking on the combinations of these things
# fractions have to add up to <= 1 and only be used if randomize
# h2o default randomize=1?
if r:
if not i:
i = 0
if not c:
c = 0
if (i and c) and (i + c) >= 1.0:
c = 1.0 - i
params['integer_fraction'] = i
params['categorical_fraction'] = c
params['value'] = None
else:
params['randomize'] = 0
params['integer_fraction'] = 0
params['categorical_fraction'] = 0
kwargs = params.copy()
print kwargs
timeoutSecs = 300
parseResult = h2i.import_parse(bucket='smalldata', path='poker/poker1000', hex_key='temp1000.hex',
schema='put', timeoutSecs=timeoutSecs)
cfResult = h2o.nodes[0].create_frame(key='temp1000.hex', timeoutSecs=timeoutSecs, **kwargs)
if DO_DOWNLOAD:
csvPathname = SYNDATASETS_DIR + '/' + 'temp1000.csv'
h2o.nodes[0].csv_download(src_key='temp1000.hex', csvPathname=csvPathname, timeoutSecs=60)
if DO_INSPECT:
h2o_cmd.runInspect(key='temp1000.hex')
h2o_cmd.runSummary(key='temp1000.hex')
print h2o.dump_json(cfResult)
print "Trial #", trial, "completed"
def test_frame_split_balance(self):
h2o.beta_features = True
csvFilename = 'covtype.data'
csvPathname = 'standard/' + csvFilename
hex_key = "covtype.hex"
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets', path=csvPathname, hex_key=hex_key, schema='local', timeoutSecs=20)
print "Just split away and see if anything blows up"
splitMe = hex_key
inspect = h2o_cmd.runInspect(key=splitMe)
origNumRows = inspect['numRows']
origNumCols = inspect['numCols']
for s in range(20):
inspect = h2o_cmd.runInspect(key=splitMe)
numRows = inspect['numRows']
numCols = inspect['numCols']
fs = h2o.nodes[0].frame_split(source=splitMe, ratios=0.5)
split0_key = fs['split_keys'][0]
split1_key = fs['split_keys'][1]
split0_rows = fs['split_rows'][0]
split1_rows = fs['split_rows'][1]
split0_ratio = fs['split_ratios'][0]
split1_ratio = fs['split_ratios'][1]
print "Iteration", s, "split0_rows:", split0_rows, "split1_rows:", split1_rows
splitMe = split1_key
# split should be within 1 row accuracy. let's say within 20 for now
self.assertLess(abs(split1_rows - split0_rows), 2)
self.assertEqual(numRows, (split1_rows + split0_rows))
self.assertEqual(numCols, origNumCols)
if split0_rows <= 2:
break
print "Now do some rebalancing on the split frames"
for trial in range(2):
rb_key = "rb_%s_%s" % (trial, splitMe)
SEEDPERFILE = random.randint(0, sys.maxint)
randChunks = random.randint(1, 100)
start = time.time()
print "Trial %s: Rebalancing %s to %s with %s chunks" % (trial, splitMe, rb_key, randChunks)
rebalanceResult = h2o.nodes[0].rebalance(source=hex_key, after=rb_key, seed=SEEDPERFILE, chunks=randChunks)
elapsed = time.time() - start
print "rebalance end on ", csvFilename, 'took', elapsed, 'seconds',\
h2o_cmd.runSummary(key=rb_key)
print "\nInspecting the original parsed result"
inspect = h2o_cmd.runInspect(key=hex_key)
h2o_cmd.infoFromInspect(inspect=inspect)
print "\nInspecting the rebalanced result with %s forced chunks" % randChunks
inspect = h2o_cmd.runInspect(key=rb_key)
h2o_cmd.infoFromInspect(inspect=inspect)
def test_tnc3_ignore(self):
csvFilename = "tnc3.csv"
csvPathname = h2o.find_file("smalldata/" + csvFilename)
print "\n" + csvPathname
key2 = "tnc3.hex"
h2b.browseTheCloud()
parseKey = h2o_cmd.parseFile(csvPathname=csvPathname, key2=key2, timeoutSecs=10, header=1)
print "Parse result['Key']:", parseKey["destination_key"]
inspect = h2o_cmd.runInspect(None, parseKey["destination_key"])
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(10)
if 1 == 0:
lenNodes = len(h2o.nodes)
colResultList = h2e.exec_expr_list_across_cols(
lenNodes, numExprList, key2, maxCol=10, incrementingResult=False, timeoutSecs=10
)
print "\ncolResultList after num swap", colResultList
if 1 == 1:
print "\nWe're not CM data getting back from RFView.json that we can check!. so look at the browser"
print 'The good case with ignore="boat,body"'
rfv = h2o_cmd.runRF(trees=5, timeoutSecs=10, ignore="boat,body", csvPathname=csvPathname)
inspect = h2o_cmd.runInspect(None, parseKey["destination_key"])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
# ******************
if 1 == 0:
colResultList = h2e.exec_expr_list_across_cols(
lenNodes, charExprList, key2, maxCol=10, incrementingResult=False, timeoutSecs=10
)
print "\ncolResultList after char swap", colResultList
if 1 == 1:
print "\nNow the bad case (no ignore)"
rfv = h2o_cmd.runRF(trees=5, timeoutSecs=10, csvPathname=csvPathname)
inspect = h2o_cmd.runInspect(None, parseKey["destination_key"])
### h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
### time.sleep(3600)
h2b.browseJsonHistoryAsUrlLastMatch("RFView")
if not h2o.browse_disable:
### print "\n <ctrl-C> to quit sleeping here"
### time.sleep(1500)
pass
def test_parse_bounds_libsvm (self):
print "Random 0/1 for col1. Last has max col = 1, All have zeros for class."
## h2b.browseTheCloud()
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(100, 100, 'cA', 300),
(100000, 100, 'cB', 300),
(100, 100000, 'cC', 300),
]
# h2b.browseTheCloud()
for (rowCount, colCount, key2, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = "syn_%s_%s_%s.csv" % (SEEDPERFILE, rowCount, colCount)
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
# dict of col sums for comparison to exec col sums below
(colNumberMax, synColSumDict) = write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
parseKey = h2o_cmd.parseFile(None, csvPathname, key2=key2, timeoutSecs=timeoutSecs, doSummary=False)
print "Parse result['destination_key']:", parseKey['destination_key']
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'], timeoutSecs=timeoutSecs)
num_cols = inspect['num_cols']
num_rows = inspect['num_rows']
row_size = inspect['row_size']
value_size_bytes = inspect['value_size_bytes']
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(num_rows), \
" num_cols:", "{:,}".format(num_cols), \
" value_size_bytes:", "{:,}".format(value_size_bytes), \
" row_size:", "{:,}".format(row_size)
expectedRowSize = num_cols * 1 # plus output
expectedValueSize = expectedRowSize * num_rows
self.assertEqual(row_size, expectedRowSize,
msg='row_size %s is not expected num_cols * 1 byte: %s' % \
(row_size, expectedRowSize))
self.assertEqual(value_size_bytes, expectedValueSize,
msg='value_size_bytes %s is not expected row_size * rows: %s' % \
(value_size_bytes, expectedValueSize))
summaryResult = h2o_cmd.runSummary(key=key2, timeoutSecs=timeoutSecs)
h2o_cmd.infoFromSummary(summaryResult, noPrint=True)
self.assertEqual(colNumberMax+1, num_cols,
msg="generated %s cols (including output). parsed to %s cols" % (colNumberMax+1, num_cols))
self.assertEqual(rowCount, num_rows,
msg="generated %s rows, parsed to %s rows" % (rowCount, num_rows))
summary = summaryResult['summary']
columnsList = summary['columns']
self.assertEqual(colNumberMax+1, len(columnsList),
msg="generated %s cols (including output). summary has %s columns" % (colNumberMax+1, len(columnsList)))
for columns in columnsList:
N = columns['N']
# self.assertEqual(N, rowCount)
name = columns['name']
stype = columns['type']
histogram = columns['histogram']
bin_size = histogram['bin_size']
bin_names = histogram['bin_names']
bins = histogram['bins']
nbins = histogram['bins']
# definitely not enums
zeros = columns['zeros']
na = columns['na']
smax = columns['max']
smin = columns['min']
mean = columns['mean']
sigma = columns['sigma']
# a single 1 in the last col
if name == "V" + str(colNumberMax): # h2o puts a "V" prefix
synZeros = num_rows - 1
synSigma = None # not sure..depends on the # rows somehow (0 count vs 1 count)
synMean = 1.0/num_rows # why does this need to be a 1 entry list
synMin = [0.0, 1.0]
synMax = [1.0, 0.0]
elif name == ("V1"):
# can reverse-engineer the # of zeroes, since data is always 1
synSum = synColSumDict[1] # could get the same sum for all ccols
synZeros = num_rows - synSum
synSigma = 0.50
synMean = (synSum + 0.0)/num_rows
synMin = [0.0, 1.0]
synMax = [1.0, 0.0]
else:
synZeros = num_rows
synSigma = 0.0
synMean = 0.0
synMin = [0.0]
synMax = [0.0]
# print zeros, synZeros
self.assertAlmostEqual(float(mean), synMean, places=6,
msg='col %s mean %s is not equal to generated mean %s' % (name, mean, 0))
# why are min/max one-entry lists in summary result. Oh..it puts N min, N max
self.assertEqual(smin, synMin,
msg='col %s min %s is not equal to generated min %s' % (name, smin, synMin))
# reverse engineered the number of zeroes, knowing data was always 1 if present?
if name == "V65536" or name == "V65537":
print "columns around possible zeros mismatch:", h2o.dump_json(columns)
self.assertEqual(zeros, synZeros,
msg='col %s zeros %s is not equal to generated zeros count %s' % (name, zeros, synZeros))
self.assertEqual(stype, 'number',
msg='col %s type %s is not equal to %s' % (name, stype, 'number'))
# our random generation will have some variance for col 1. so just check to 2 places
if synSigma:
self.assertAlmostEqual(float(sigma), synSigma, delta=0.03,
msg='col %s sigma %s is not equal to generated sigma %s' % (name, sigma, synSigma))
if CHECK_MAX:
self.assertEqual(smax, synMax,
msg='col %s max %s is not equal to generated max %s' % (name, smax, synMax))
self.assertEqual(0, na,
msg='col %s num_missing_values %d should be 0' % (name, na))
def test_exec2_enums_rand_cut(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
n = ROWS
tryList = [
(n, 10, 9, '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
cols = random.sample(range(iColCount),
random.randint(1, iColCount))
for c in cols:
# possible choices within the column
cel = colEnumList[c]
# for now the cutValues are numbers for the enum mappings
# FIX! hack. don't use encoding 0, maps to NA here? h2o doesn't like
# celChoice = str(random.choice(range(len(cel))))
celChoice = random.choice(range(len(cel)))
cutValue[c] = celChoice
cutExprList = []
pKey = Key('p')
for i, c in enumerate(cutValue):
if c is None:
continue
else:
# new ...ability to reference cols
# src[ src$age<17 && src$zip=95120 && ... , ]
# cutExprList.append('p$C'+str(i+1)+'=='+c)
# all column indexing in h2o-dev is with number
e = Fcn('==', c, pKey[:, i])
cutExprList.append(e)
cutExpr = None
for ce in cutExprList:
if cutExpr:
cutExpr = Fcn('&', cutExpr, ce)
else:
cutExpr = ce
print "cutExpr:", cutExpr
# should be two different keys in the sample
e = random.sample(eKeys, 2)
fKey = e[0]
eKey = e[1]
# rowExpr = '%s[%s,];' % (hex_key, cutExpr)
hKey = Key(hex_key)
rowExpr = hKey[cutExpr, :]
print "rowExpr:", 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*******************************************************
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30)
numRows, numCols, parse_key = h2o_cmd.infoFromParse(parseResult)
inspect = h2o_cmd.runInspect(key=parse_key)
missingList, valueList, numRows, numCols = h2o_cmd.infoFromInspect(
inspect)
# print h2o.dump_json(inspect)
# (missingValuesDict, constantValuesDict, enumSizeDict, colTypeDict, colNameDict) = \
# h2o_cmd.columnInfoFromInspect(parse_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!
# build up the columns
Assign('b', [1, 2, 3])
# could also append 1 col at a time, by assigning to the next col number?
Assign('a', Cbind(['b' for i in range(colCount)]))
for eKey in eKeys:
Assign(eKey, 'a')
## print h2o.dump_json(e)
xList = []
eList = []
fList = []
for repeat in range(200):
# 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]
if 1 == 1:
start = time.time()
Assign(fKey, random.choice(rowExprList)).do()
elapsed = time.time() - start
execTime = elapsed
print "exec 2 took", elapsed, "seconds."
inspect = h2o_cmd.runInspect(key=fKey)
missingList, valueList, numRows, numCols = h2o_cmd.infoFromInspect(
inspect)
if numRows == 0 or numCols != colCount:
h2p.red_print("Warning: Cut resulted in", numRows,
"rows and", numCols,
"cols. Quantile will abort")
# FIX! put quantile back in?
quantileTime = 0
# 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)
# just get a plot of the last one (biggest)
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)
def test_NN2_mnist_multi(self):
#h2b.browseTheCloud()
h2o.beta_features = True
csvPathname_train = 'mnist/train.csv.gz'
csvPathname_test = 'mnist/test.csv.gz'
hex_key = 'mnist_train.hex'
validation_key = 'mnist_test.hex'
timeoutSecs = 90
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname_train,
schema='put',
hex_key=hex_key,
timeoutSecs=timeoutSecs)
parseResultV = h2i.import_parse(bucket='smalldata',
path=csvPathname_test,
schema='put',
hex_key=validation_key,
timeoutSecs=timeoutSecs)
inspect = h2o_cmd.runInspect(None, hex_key)
print "\n" + csvPathname_train, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
response = inspect['numCols'] - 1
#Making random id
identifier = ''.join(
random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'nn_' + identifier + '.hex'
kwargs = {
'ignored_cols': None,
'response': response,
'classification': 1,
'activation': 'RectifierWithDropout',
'input_dropout_ratio': 0.2,
'hidden': '117,131,129',
'rate': 0.005,
'rate_annealing': 1e-6,
'momentum_start': 0.5,
'momentum_ramp': 100000,
'momentum_stable': 0.9,
'l1': 0.00001,
'l2': 0.0000001,
'seed': 98037452452,
'loss': 'CrossEntropy',
'max_w2': 15,
'initial_weight_distribution': 'UniformAdaptive',
#'initial_weight_scale' : 0.01,
'epochs': 20.0,
'destination_key': model_key,
'validation': validation_key,
}
###expectedErr = 0.0362 ## from single-threaded mode
expectedErr = 0.03 ## observed actual value with Hogwild
timeoutSecs = 600
start = time.time()
nn = h2o_cmd.runDeepLearning(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
print "neural net end on ", csvPathname_train, " and ", csvPathname_test, 'took', time.time(
) - start, 'seconds'
#### Now score using the model, and check the validation error
expectedErr = 0.04
relTol = 0.01
predict_key = 'Predict.hex'
kwargs = {
'data_key': validation_key,
'destination_key': predict_key,
'model_key': model_key
}
predictResult = h2o_cmd.runPredict(timeoutSecs=timeoutSecs, **kwargs)
h2o_cmd.runInspect(key=predict_key, verbose=True)
kwargs = {}
predictCMResult = h2o.nodes[0].predict_confusion_matrix(
actual=validation_key,
vactual=response,
predict=predict_key,
vpredict='predict',
timeoutSecs=timeoutSecs,
**kwargs)
cm = predictCMResult['cm']
print h2o_gbm.pp_cm(cm)
actualErr = h2o_gbm.pp_cm_summary(cm) / 100.
print "actual classification error:" + format(actualErr)
print "expected classification error:" + format(expectedErr)
if actualErr != expectedErr and abs(
(expectedErr - actualErr) / expectedErr) > relTol:
raise Exception(
"Scored classification error of %s is not within %s %% relative error of %s"
% (actualErr, float(relTol) * 100, expectedErr))
start = time.time()
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, schema='s3n', hex_key=hex_key,
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60))
elapsed = time.time() - start
print "parse end on ", hex_key, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "parse result:", parseResult['destination_key']
kwargs = {
'cols': None,
'initialization': 'Furthest',
'k': 12
}
start = time.time()
kmeans = h2o_cmd.runKMeans(parseResult=parseResult, \
timeoutSecs=timeoutSecs, retryDelaySecs=2, pollTimeoutSecs=120, **kwargs)
elapsed = time.time() - start
print "kmeans end on ", csvFilename, 'took', elapsed, 'seconds.', \
"%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
h2o_kmeans.simpleCheckKMeans(self, kmeans, **kwargs)
### print h2o.dump_json(kmeans)
inspect = h2o_cmd.runInspect(None,key=kmeans['destination_key'])
print h2o.dump_json(inspect)
print "Trial #", trial, "completed in", time.time() - trialStart, "seconds.", \
if __name__ == '__main__':
h2o.unit_main()
def test_many_fp_formats_libsvm(self):
h2b.browseTheCloud()
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(10, 10, 'cA', 30, 'sparse50'),
(100, 10, 'cB', 30, 'sparse'),
(100000, 100, 'cC', 30, 'sparse'),
(1000, 10, 'cD', 30, 'sparse50'),
(100, 100, 'cE', 30, 'sparse'),
(100, 100, 'cF', 30, 'sparse50'),
]
# h2b.browseTheCloud()
for (rowCount, colCount, key2, timeoutSecs, distribution) in tryList:
# for sel in range(48): # len(caseList)
for sel in [random.randint(0, 47)]: # len(caseList)
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = "syn_%s_%s_%s_%s.csv" % (SEEDPERFILE, sel,
rowCount, colCount)
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
# dict of col sums for comparison to exec col sums below
(synColSumDict,
colNumberMax) = write_syn_dataset(csvPathname, rowCount,
colCount, SEEDPERFILE, sel,
distribution)
selKey2 = key2 + "_" + str(sel)
parseKey = h2o_cmd.parseFile(None,
csvPathname,
key2=selKey2,
timeoutSecs=timeoutSecs)
print csvFilename, 'parse time:', parseKey['response']['time']
print "Parse result['destination_key']:", parseKey[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
num_cols = inspect['num_cols']
num_rows = inspect['num_rows']
print "\n" + csvFilename
# SUMMARY****************************************
# gives us some reporting on missing values, constant values,
# to see if we have x specified well
# figures out everything from parseKey['destination_key']
# needs y to avoid output column (which can be index or name)
# assume all the configs have the same y..just check with the firs tone
goodX = h2o_glm.goodXFromColumnInfo(
y=0, key=parseKey['destination_key'], timeoutSecs=300)
if DO_SUMMARY:
summaryResult = h2o_cmd.runSummary(key=selKey2,
timeoutSecs=360)
h2o_cmd.infoFromSummary(summaryResult, noPrint=True)
# we might have added some zeros at the end, that our colNumberMax won't include
print synColSumDict.keys(), colNumberMax
self.assertEqual(
colNumberMax + 1,
num_cols,
msg=
"generated %s cols (including output). parsed to %s cols"
% (colNumberMax + 1, num_cols))
# Exec (column sums)*************************************************
h2e.exec_zero_list(zeroList)
# how do we know the max dimension (synthetic may not generate anything for the last col)
# use num_cols?. num_cols should be <= colCount.
colSumList = h2e.exec_expr_list_across_cols(
None,
exprList,
selKey2,
maxCol=colNumberMax + 1,
timeoutSecs=timeoutSecs)
self.assertEqual(rowCount,
num_rows,
msg="generated %s rows, parsed to %s rows" %
(rowCount, num_rows))
# need to fix this for compare to expected
# we should be able to keep the list of fp sums per col above
# when we generate the dataset
print "\ncolSumList:", colSumList
print "\nsynColSumDict:", synColSumDict
for k, v in synColSumDict.iteritems():
if k > colNumberMax: # ignore any extra 0 cols at the end
continue
# k should be integers that match the number of cols
self.assertTrue(
k >= 0 and k < len(colSumList),
msg="k: %s len(colSumList): %s num_cols: %s" %
(k, len(colSumList), num_cols))
syn = {}
if k == 0:
syn['name'] = "Target"
syn['size'] = {
1, 2
} # can be two if we actually used the full range 0-255 (need extra for h2o NA)
syn['type'] = {'int'}
syn['min'] = classMin
syn['max'] = classMax
# don't check these for the col 0 'Target'
syn['scale'] = {1}
# syn['base'] = 0
# syn['variance'] = 0
elif k == 1: # we forced this to always be 0
syn['name'] = "V" + str(k)
syn['size'] = {1}
syn['type'] = {'int'}
syn['min'] = 0
syn['max'] = 0
syn['scale'] = {1}
syn['base'] = 0
syn['variance'] = 0
else:
syn['name'] = "V" + str(k)
syn['size'] = {
1, 2, 4, 8
} # can be 2, 4 or 8? maybe make this a set for membership check
syn['type'] = {'int', 'float'}
syn['min'] = valMin
syn['max'] = valMax
syn['scale'] = {1, 10, 100, 1000}
# syn['base'] = 0
# syn['variance'] = 0
syn['num_missing_values'] = 0
syn['enum_domain_size'] = 0
# syn['min'] = 0
# syn['max'] = 0
# syn['mean'] = 0
cols = inspect['cols'][k]
for synKey in syn:
# we may not see the min/max range of values that was bounded by our gen, but
# we can check that it's a subset of the allowed range
if synKey == 'min':
self.assertTrue(
syn[synKey] <= cols[synKey],
msg='col %s %s %s should be <= %s' %
(k, synKey, cols[synKey], syn[synKey]))
elif synKey == 'max':
self.assertTrue(
syn[synKey] >= cols[synKey],
msg='col %s %s %s should be >= %s' %
(k, synKey, cols[synKey], syn[synKey]))
elif synKey == 'size' or synKey == 'scale' or synKey == 'type':
if cols[synKey] not in syn[synKey]:
# for debug of why it was a bad size
print "cols size/min/max:", cols['size'], cols[
'min'], cols['max']
print "syn size/min/max:", syn['size'], syn[
'min'], syn['max']
raise Exception(
'col %s %s %s should be in this allowed %s'
% (k, synKey, cols[synKey], syn[synKey]))
else:
self.assertEqual(
syn[synKey],
cols[synKey],
msg='col %s %s %s should be %s' %
(k, synKey, cols[synKey], syn[synKey]))
colSum = colSumList[k]
print "\nComparing col", k, "sums:", v, colSum
# Even though we're comparing floating point sums, the operations probably should have
# been done in same order, so maybe the comparison can be exact (or not!)
self.assertAlmostEqual(
float(v),
colSum,
places=0,
msg='%0.6f col sum is not equal to expected %0.6f' %
(v, colSum))
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, schema='put')
# 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']
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_rf_covtype_train_oobe2(self):
print "\nUse randomBitVector and filter to separate the dataset randomly"
importFolderPath = "/home/0xdiag/datasets/standard"
csvFilename = 'covtype.data'
csvPathname = importFolderPath + "/" + csvFilename
key2 = csvFilename + ".hex"
h2i.setupImportFolder(None, importFolderPath)
print "\nUsing header=0 on the normal covtype.data"
parseKey = h2i.parseImportFolderFile(None,
csvFilename,
importFolderPath,
key2=key2,
header=0,
timeoutSecs=100)
inspect = h2o_cmd.runInspect(None, parseKey['destination_key'])
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(inspect['num_rows']), \
" num_cols:", "{:,}".format(inspect['num_cols'])
# how many rows for each pct?
num_rows = inspect['num_rows']
pct10 = int(num_rows * .1)
rowsForPct = [i * pct10 for i in range(0, 11)]
# this can be slightly less than 10%
last10 = num_rows - rowsForPct[9]
rowsForPct[10] = last10
# use mod below for picking "rows-to-do" in case we do more than 9 trials
# use 10 if 0 just to see (we copied 10 to 0 above)
rowsForPct[0] = rowsForPct[10]
expectTrainPctRightList = [
0, 85.16, 88.45, 90.24, 91.27, 92.03, 92.64, 93.11, 93.48, 93.79
]
expectScorePctRightList = [
0, 88.81, 91.72, 93.06, 94.02, 94.52, 95.09, 95.41, 95.77, 95.78
]
print "Creating the key of the last 10% data, for scoring"
dataKeyTest = "rTest"
dataKeyTrain = "rTrain"
# start at 90% rows + 1
# randomBitVector(size,selected)
# randomFilter(srcFrame,rows,seed)
# filter(srcFrame,bitVect)
# odd. output is byte, all other exec outputs are 8 byte? (at least the ones below?)
execExpr = "rbv=randomBitVector(" + str(num_rows) + "," + str(
last10) + ",12345)"
h2o_exec.exec_expr(None, execExpr, resultKey="rbv", timeoutSecs=10)
# complement the bit vector
execExpr = "not_rbv=colSwap(rbv,0,rbv[0]==0?1:0)"
h2o_exec.exec_expr(None, execExpr, resultKey="not_rbv", timeoutSecs=10)
execExpr = dataKeyTest + "=filter(" + key2 + ",rbv)"
h2o_exec.exec_expr(None,
execExpr,
resultKey=dataKeyTest,
timeoutSecs=10)
execExpr = dataKeyTrain + "=filter(" + key2 + ",not_rbv)"
h2o_exec.exec_expr(None,
execExpr,
resultKey=dataKeyTrain,
timeoutSecs=10)
### time.sleep(3600)
# keep the 0 entry empty
actualTrainPctRightList = [0]
actualScorePctRightList = [0]
for trial in range(1, 10):
# always slice from the beginning
rowsToUse = rowsForPct[trial % 10]
resultKey = "r" + str(trial)
execExpr = resultKey + "=slice(" + dataKeyTrain + ",1," + str(
rowsToUse) + ")"
# execExpr = resultKey + "=slice(" + dataKeyTrain + ",1)"
h2o_exec.exec_expr(None,
execExpr,
resultKey=resultKey,
timeoutSecs=10)
parseKey['destination_key'] = resultKey
# adjust timeoutSecs with the number of trees
# seems ec2 can be really slow
kwargs = paramDict.copy()
timeoutSecs = 30 + kwargs['ntree'] * 20
start = time.time()
# do oobe
kwargs['out_of_bag_error_estimate'] = 1
kwargs['model_key'] = "model_" + str(trial)
rfv = h2o_cmd.runRFOnly(parseKey=parseKey,
timeoutSecs=timeoutSecs,
**kwargs)
elapsed = time.time() - start
print "RF end on ", csvPathname, 'took', elapsed, 'seconds.', \
"%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
oobeTrainPctRight = 100 * (
1.0 - rfv['confusion_matrix']['classification_error'])
self.assertAlmostEqual(oobeTrainPctRight, expectTrainPctRightList[trial],
msg="OOBE: pct. right for %s pct. training not close enough %6.2f %6.2f"% \
((trial*10), oobeTrainPctRight, expectTrainPctRightList[trial]),
delta=0.2)
actualTrainPctRightList.append(oobeTrainPctRight)
print "Now score on the last 10%"
# pop the stuff from kwargs that were passing as params
model_key = rfv['model_key']
kwargs.pop('model_key', None)
data_key = rfv['data_key']
kwargs.pop('data_key', None)
ntree = rfv['ntree']
kwargs.pop('ntree', None)
# scoring
# RFView.html?
# dataKeyTest=a5m.hex&
# model_key=__RFModel_81c5063c-e724-4ebe-bfc1-3ac6838bc628&
# response_variable=1&
# ntree=50&
# class_weights=-1%3D1.0%2C0%3D1.0%2C1%3D1.0&
# out_of_bag_error_estimate=1&
# http://192.168.1.28:54321/GeneratePredictionsPage.html?model_key=__RFModel_0e2531bc-2552-4f65-8a4a-843031b0cb99&key=iris
# http://192.168.1.28:54321/RFView.html?data_key=iris.hex&model_key=__RFModel_0e2531bc-2552-4f65-8a4a-843031b0cb99&ntree=50&response_variable=4&class_weights=Iris-setosa%3D1.0%2CIris-versicolor%3D1.0%2CIris-virginica%3D1.0&out_of_bag_error_estimate=true&iterative_cm=true
kwargs['iterative_cm'] = 1
# do full scoring
kwargs['out_of_bag_error_estimate'] = 0
rfv = h2o_cmd.runRFView(None,
dataKeyTest,
model_key,
ntree,
timeoutSecs,
retryDelaySecs=1,
print_params=True,
**kwargs)
h2o.nodes[0].generate_predictions(model_key=model_key,
data_key=dataKeyTest)
fullScorePctRight = 100 * (
1.0 - rfv['confusion_matrix']['classification_error'])
self.assertAlmostEqual(fullScorePctRight,expectScorePctRightList[trial],
msg="Full: pct. right for scoring after %s pct. training not close enough %6.2f %6.2f"% \
((trial*10), fullScorePctRight, expectScorePctRightList[trial]),
delta=0.2)
actualScorePctRightList.append(fullScorePctRight)
print "Trial #", trial, "completed", "using %6.2f" % (
rowsToUse * 100.0 / num_rows), "pct. of all rows"
actualDelta = [
abs(a - b)
for a, b in zip(expectTrainPctRightList, actualTrainPctRightList)
]
niceFp = ["{0:0.2f}".format(i) for i in actualTrainPctRightList]
print "maybe should update with actual. Remove single quotes"
print "actualTrainPctRightList =", niceFp
niceFp = ["{0:0.2f}".format(i) for i in actualDelta]
print "actualDelta =", niceFp
actualDelta = [
abs(a - b)
for a, b in zip(expectScorePctRightList, actualScorePctRightList)
]
niceFp = ["{0:0.2f}".format(i) for i in actualScorePctRightList]
print "maybe should update with actual. Remove single quotes"
print "actualScorePctRightList =", niceFp
niceFp = ["{0:0.2f}".format(i) for i in actualDelta]
print "actualDelta =", niceFp
def test_RF(self):
h2o.beta_features = True
if h2o.beta_features:
paramsTrainRF = {
'ntrees': 3,
'max_depth': 10,
'nbins': 50,
'timeoutSecs': 600,
'response': 'C54',
'classification': 1,
}
paramsScoreRF = {
'vactual': 'C54',
'timeoutSecs': 600,
}
else:
paramsTrainRF = {
'use_non_local_data': 1,
'ntree': 10,
'depth': 300,
'bin_limit': 20000,
'stat_type': 'ENTROPY',
'out_of_bag_error_estimate': 1,
'exclusive_split_limit': 0,
'timeoutSecs': 60,
}
paramsScoreRF = {
# scoring requires the response_variable. it defaults to last, so normally
# we don't need to specify. But put this here and (above if used)
# in case a dataset doesn't use last col
'response_variable': None,
'timeoutSecs': 60,
'out_of_bag_error_estimate': 0,
}
# train1
trainKey1 = self.loadData(trainDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, **kwargs)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsScoreRF.copy()
h2o_cmd.runInspect(key='scoreDS1.hex', verbose=True)
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
h2o_cmd.runInspect(key='Predict.hex', verbose=True)
print "\nTrain1\n=========="
h2o_rf.simpleCheckRFScore(node=None,
rfv=trainResult1,
noPrint=False,
**kwargs)
print "\nScore1\n=========+"
print h2o.dump_json(scoreResult1)
h2o_rf.simpleCheckRFScore(node=None,
rfv=scoreResult1,
noPrint=False,
**kwargs)
# train2
trainKey2 = self.loadData(trainDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, **kwargs)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsScoreRF.copy()
h2o_cmd.runInspect(key='scoreDS2.hex', verbose=True)
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
h2o_cmd.runInspect(key='Predict.hex', verbose=True)
print "\nTrain2\n=========="
h2o_rf.simpleCheckRFScore(node=None,
rfv=trainResult2,
noPrint=False,
**kwargs)
print "\nScore2\n=========="
h2o_rf.simpleCheckRFScore(node=None,
rfv=scoreResult2,
noPrint=False,
**kwargs)
if 1 == 0:
print "\nTraining: JsonDiff sorted data results, to non-sorted results (json responses)"
df = h2o_util.JsonDiff(trainResult1,
trainResult2,
with_values=True)
print "df.difference:", h2o.dump_json(df.difference)
print "\nScoring: JsonDiff sorted data results, to non-sorted results (json responses)"
df = h2o_util.JsonDiff(scoreResult1,
scoreResult2,
with_values=True)
print "df.difference:", h2o.dump_json(df.difference)
def test_xl_basic(self):
bucket = 'smalldata'
csvPathname = 'iris/iris_wheader.csv'
hexDF = 'v'
parseResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
schema='put',
hex_key=hexDF)
# uses h2o_xl to do magic with Rapids
# does this DFInit to rows=0 now?
a = DF('a1') # knon_* key
assert isinstance(a, DF)
assert isinstance(a, Key)
assert isinstance(a, Xbase)
assert not isinstance(a, KeyIndexed)
assert not isinstance(a, Fcn)
assert not isinstance(a, Assign)
# look at our secret stash in the base class. Should see the DFInit?
print "Does the lastExecResult stash work?", dump_json(
h2o_xl.Xbase.lastExecResult)
# this should work if str(DF) returns DF.frame
inspect = h2o_cmd.runInspect(key=a)
# print "inspect a", dump_json(inspect)
b = DF('b1')
assert isinstance(b, DF)
inspect = h2o_cmd.runInspect(key=b)
# print "inspect b", dump_json(inspect)
Assign(a, [0, 0, 0])
assert isinstance(a, Key)
b <<= [0, 0, 0]
assert isinstance(b, Key)
# FIX! how come I have to create c here first for python
# see here
# http://eli.thegreenplace.net/2011/05/15/understanding-unboundlocalerror-in-python
# is it too much to require c to exist first?
# c = DF()
# c <<= a + b
# this will trigger ok?
c = DF('c1')
c <<= [0, 0, 0]
assert isinstance(c, Key)
# c[0] <<= a + b
# Assign(lhs=c[0], rhs=(a + b))
rhs = a + b
Assign(c, rhs)
ast = h2o_xl.Xbase.lastExecResult['ast']
astExpected = "(= !c1 (+ %a1 %b1))"
assert ast == astExpected, "Actual: %s Expected: %s" % (ast,
astExpected)
rhs = a[0] + b[0]
Assign(c[0], rhs)
ast = h2o_xl.Xbase.lastExecResult['ast']
astExpected = "(= ([ %c1 #0 #0) (+ ([ %a1 #0 #0) ([ %b1 #0 #0)))"
assert ast == astExpected, "Actual: %s Expected: %s" % (ast,
astExpected)
Assign(c[1], (a[2] + b[2]))
ast = h2o_xl.Xbase.lastExecResult['ast']
astExpected = "(= ([ %c1 #1 #0) (+ ([ %a1 #2 #0) ([ %b1 #2 #0)))"
assert ast == astExpected, "Actual: %s Expected: %s" % (ast,
astExpected)
# assert ast = "(= !b1 (is.na (c {#0})))"
assert isinstance(c, Key), type(c)
inspect = h2o_cmd.runInspect(key=c)
# # print "inspect c", dump_json(inspect)
# DF inits the frame
# if you just want an existing Key, say existing=True
a = DF('a2') # named data frame
assert isinstance(a, DF)
b = DF('b2')
c = DF('c2')
inspect = h2o_cmd.runInspect(key=c)
# # print "inspect c", dump_json(inspect)
a <<= 3
b <<= 3
c <<= 3
c[0] <<= a[0] + b[0]
assert isinstance(c, Key)
inspect = h2o_cmd.runInspect(key=c)
# print "inspect c", dump_json(inspect)
a = DF('a3') # named data frame
b = DF('b3')
c = DF('c3')
a <<= 4
b <<= 4
c <<= 4
c[0] <<= a[0] - b[0]
assert isinstance(c, Key)
c[0] <<= a[0] * b[0]
assert isinstance(c, Key)
a = DF('a4') # named data frame
b = DF('b4')
c = DF('c4')
a <<= 5
b <<= 5
c <<= 5
c[0] <<= (a[0] - b[0])
assert isinstance(c, Key)
inspect = h2o_cmd.runInspect(key=c)
# print "inspect c", dump_json(inspect)
c[0] <<= (a[0] & b[0]) | a[0]
assert isinstance(c, Key)
inspect = h2o_cmd.runInspect(key=c)
# print "inspect c", dump_json(inspect)
# print "\nDoes the keyWriteHistoryList work?"
for k in Xbase.keyWriteHistoryList:
print k
h2o.check_sandbox_for_errors()
def test_GBM_manyfiles_train_test(self):
bucket = 'home-0xdiag-datasets'
modelKey = 'GBMModelKey'
if localhost:
files = [
# None forces num_cols to be used. assumes you set it from Inspect
('manyfiles-nflx-gz', 'file_1[0-9][0-9].dat.gz',
'file_100.hex', 1800, None, 'file_1.dat.gz', 'file_1_test.hex'
)
]
else:
files = [
# None forces num_cols to be used. assumes you set it from Inspect
('manyfiles-nflx-gz', 'file_[0-9].dat.gz', 'file_10.hex', 1800,
None, 'file_1[0-9].dat.gz', 'file_10_test.hex')
]
# if I got to hdfs, it's here
# hdfs://192.168.1.176/datasets/manyfiles-nflx-gz/file_99.dat.gz
# h2b.browseTheCloud()
for (importFolderPath, trainFilename, trainKey, timeoutSecs, response,
testFilename, testKey) in files:
h2o.beta_features = False #turn off beta_features
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
# Parse (train)****************************************
if h2o.beta_features:
print "Parsing to fvec directly! Have to noPoll=true!, and doSummary=False!"
csvPathname = importFolderPath + "/" + trainFilename
parseTrainResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
schema='s3n',
hex_key=trainKey,
timeoutSecs=timeoutSecs,
noPoll=h2o.beta_features,
doSummary=False)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs,
pollTimeoutSecs=timeoutSecs)
print "Filling in the parseTrainResult['destination_key'] for h2o"
parseTrainResult['destination_key'] = trainKey
elapsed = time.time() - start
print "train parse end on ", trainFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", parseTrainResult['destination_key']
### h2o_cmd.runSummary(key=parsTraineResult['destination_key'])
# if you set beta_features here, the fvec translate will happen with the Inspect not the GBM
# h2o.beta_features = True
inspect = h2o_cmd.runInspect(
key=parseTrainResult['destination_key'])
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(inspect['num_rows']), \
" num_cols:", "{:,}".format(inspect['num_cols'])
num_rows = inspect['num_rows']
num_cols = inspect['num_cols']
# Make col 378 it something we can do binomial regression on!
execExpr = '%s[,378] = %s[,378]>15 ? 1 : 0' % (trainKey, trainKey)
resultExec = h2o_cmd.runExec(str=execExpr, timeoutSecs=500)
# Parse (test)****************************************
if h2o.beta_features:
print "Parsing to fvec directly! Have to noPoll=true!, and doSummary=False!"
parseTestResult = h2i.import_parse(bucket=bucket,
path=importFolderPath + "/" +
testFilename,
schema='local',
hex_key=testKey,
timeoutSecs=timeoutSecs,
noPoll=h2o.beta_features,
doSummary=False)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs,
pollTimeoutSecs=timeoutSecs)
print "Filling in the parseTestResult['destination_key'] for h2o"
parseTestResult['destination_key'] = testKey
elapsed = time.time() - start
print "test parse end on ", testFilename, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "test parse result:", parseTestResult['destination_key']
# Make col 378 it something we can do binomial regression on!
print "Slow! exec is converting all imported keys?, not just what was parsed"
execExpr = '%s[,378] = %s[,378]>15 ? 1 : 0' % (testKey, testKey,
testKey)
resultExec = h2o_cmd.runExec(str=execExpr, timeoutSecs=300)
# Note ..no inspect of test data here..so translate happens later?
# GBM (train iterate)****************************************
# if not response:
# response = num_cols - 1
response = 378
print "Using the same response %s for train and test (which should have a output value too)" % response
ntrees = 10
for max_depth in [5, 10, 20, 40]:
params = {
'learn_rate': .2,
'nbins': 1024,
'ntrees': ntrees,
'max_depth': max_depth,
'min_rows': 10,
'response': response,
# 'ignored_cols':
}
print "Using these parameters for GBM: ", params
kwargs = params.copy()
h2o.beta_features = True
# GBM train****************************************
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
noPoll=True,
timeoutSecs=timeoutSecs,
destination_key=modelKey,
**kwargs)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs,
pollTimeoutSecs=timeoutSecs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", trainFilename
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
cm = gbmTrainView['gbm_model']['cm']
pctWrongTrain = h2o_gbm.pp_cm_summary(cm)
print "Last line of this cm might be NAs, not CM"
print "\nTrain\n==========\n"
print h2o_gbm.pp_cm(cm)
# GBM test****************************************
if doPredict:
predictKey = 'Predict.hex'
### h2o_cmd.runInspect(key=parseTestResult['destination_key'])
start = time.time()
gbmTestResult = h2o_cmd.runPredict(
data_key=parseTestResult['destination_key'],
model_key=modelKey,
destination_key=predictKey,
timeoutSecs=timeoutSecs)
# hack
if h2o.beta_features:
h2j.pollWaitJobs(timeoutSecs=timeoutSecs,
pollTimeoutSecs=timeoutSecs)
elapsed = time.time() - start
print "GBM predict completed in", elapsed, "seconds. On dataset: ", testFilename
print "This is crazy!"
gbmPredictCMResult = h2o.nodes[0].predict_confusion_matrix(
actual=parseTestResult['destination_key'],
vactual=response,
predict=predictKey,
vpredict='predict', # choices are 0 and 'predict'
)
# errrs from end of list? is that the last tree?
# all we get is cm
cm = gbmPredictCMResult['cm']
# These will move into the h2o_gbm.py
pctWrong = h2o_gbm.pp_cm_summary(cm)
print "Last line of this cm is really NAs, not CM"
print "\nTest\n==========\n"
print h2o_gbm.pp_cm(cm)
# xList.append(ntrees)
xList.append(max_depth)
eList.append(pctWrong)
fList.append(trainElapsed)
h2o.beta_features = False
if doPredict:
xLabel = 'max_depth'
eLabel = 'pctWrong'
fLabel = 'trainElapsed'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel,
fListTitle, fList, fLabel)
def test_GLM_convergence_2(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(100, 1, 'cD', 300),
# (100, 100, 'cE', 300),
# (100, 200, 'cF', 300),
# (100, 300, 'cG', 300),
# (100, 400, 'cH', 300),
# (100, 500, 'cI', 300),
]
### h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
USEKNOWNFAILURE = False
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_%s_%sx%s.csv' % (SEEDPERFILE, rowCount,
colCount)
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "\nCreating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
if USEKNOWNFAILURE:
csvFilename = 'failtoconverge_100x50.csv'
csvPathname = 'logreg/' + csvFilename
parseResult = h2i.import_parse(path=csvPathname,
hex_key=hex_key,
timeoutSecs=10,
schema='put')
print csvFilename, 'parse time:', parseResult['response']['time']
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
y = colCount
kwargs = {
'max_iter': 40,
'lambda': 1e0,
'alpha': 0.5,
'link': 'familyDefault',
'n_folds': 0,
'beta_epsilon': 1e-4,
'thresholds': '0:1:0.01',
}
if USEKNOWNFAILURE:
kwargs['y'] = 50
else:
kwargs['y'] = y
emsg = None
for i in range(3):
start = time.time()
glm = h2o_cmd.runGLM(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
print 'glm #', i, 'end on', csvPathname, 'took', time.time(
) - start, 'seconds'
# we can pass the warning, without stopping in the test, so we can
# redo it in the browser for comparison
(warnings, coefficients,
intercept) = h2o_glm.simpleCheckGLM(self,
glm,
None,
allowFailWarning=True,
**kwargs)
if 1 == 0:
print "\n", "\ncoefficients in col order:"
# since we're loading the x50 file all the time..the real colCount
# should be 50 (0 to 49)
if USEKNOWNFAILURE:
showCols = 50
else:
showCols = colCount
for c in range(showCols):
print "%s:\t%s" % (c, coefficients[c])
print "intercept:\t", intercept
# gets the failed to converge, here, after we see it in the browser too
x = re.compile("[Ff]ailed")
if warnings:
print "warnings:", warnings
for w in warnings:
print "w:", w
if (re.search(x, w)):
# first
if emsg is None: emsg = w
print w
if emsg: break
if not h2o.browse_disable:
h2b.browseJsonHistoryAsUrlLastMatch("Inspect")
time.sleep(5)
h2b.browseJsonHistoryAsUrlLastMatch("GLM")
time.sleep(5)
# gets the failed to converge, here, after we see it in the browser too
if emsg is not None:
raise Exception(emsg)
def test_GLM_100Mx70_hosts(self):
# enable this if you need to re-create the file
if 1 == 0:
SYNDATASETS_DIR = h2o.make_syn_dir()
createList = [
(100000000, 70, 'cA', 10000),
]
for (rowCount, colCount, hex_key, timeoutSecs) in createList:
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(
rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
# Have to copy it to /home/0xdiag/datasets!
if localhost:
csvFilenameList = [
# ('rand_logreg_500Kx70.csv.gz', 500, 'rand_500Kx70'),
# ('rand_logreg_1Mx70.csv.gz', 500, 'rand_1Mx70'),
('rand_logreg_100000000x70.csv', 500, 'rand_100Mx70.hex'),
]
else:
# None is okay for hex_key
csvFilenameList = [
# ('rand_logreg_500Kx70.csv.gz', 500, 'rand_500Kx70'),
# ('rand_logreg_1Mx70.csv.gz', 500, 'rand_1Mx70'),
('rand_logreg_100000000x70.csv', 500, 'rand_100Mx70.hex'),
]
### h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
for csvFilename, timeoutSecs, hex_key in csvFilenameList:
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=2000,
retryDelaySecs=5,
initialDelaySecs=10,
pollTimeoutSecs=60)
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
csvPathname = importFolderPath + "/" + csvFilename
num_rows = inspect['num_rows']
num_cols = inspect['num_cols']
print "\n" + csvPathname, \
" num_rows:", "{:,}".format(num_rows), \
" num_cols:", "{:,}".format(num_cols)
y = num_cols - 1
kwargs = {
'family': 'binomial',
'link': 'logit',
'y': y,
'max_iter': 8,
'n_folds': 0,
'beta_epsilon': 1e-4,
'alpha': 0,
'lambda': 0
}
for trial in range(3):
start = time.time()
glm = h2o_cmd.runGLM(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
elapsed = time.time() - start
print "glm", trial, "end on ", csvPathname, 'took', elapsed, 'seconds.',
print "%d pct. of timeout" % ((elapsed / timeoutSecs) * 100)
h2o_glm.simpleCheckGLM(self, glm, None, **kwargs)
def test_simple2(self):
# h2o-dev doesn't take ../.. type paths? make find_file return absolute path
# csvPathname = find_file("bigdata/laptop/poker-hand-testing.data")
csvPathname = find_file("smalldata/logreg/prostate.csv")
import_result = h2o.n0.import_files(path=csvPathname)
# print dump_json(import_result)
k = import_result['keys'][0]
frames_result = h2o.n0.frames(key=k)
frame = frames_result['frames'][0]
rows = frame['rows']
columns = frame['columns']
for c in columns:
label = c['label']
missing = c['missing_count']
stype = c['type']
domain = c['domain']
# print dump_json(frame)
# let's see what ray's util does
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)
for k, v in frames_dict.items():
print "frames_dict key:", k
# interesting. we can do dictionary comprehensions
# { k:v for k,v in my_dict.items() if 'Peter' in k }
# how do you parse multiple files
parse_result = h2o.n0.parse(
key=k, intermediateResults=DO_INTERMEDIATE_RESULTS)
frame = parse_result['frames'][0]
hex_key = frame['key']['name']
colCount = 9
rowCount = 380
# colCount = 11
# rowCount = 1000000
start = time.time()
inspect = h2o_cmd.runInspect(None, hex_key)
print "Inspect:", hex_key, "took", time.time() - start, "seconds"
numCols = len(inspect['frames'][0]['columns'])
numRows = inspect['frames'][0]['rows']
print "\n" + csvPathname, \
" rows:", "{:,}".format(numRows), \
" len(columns):", "{:,}".format(numCols)
# should match # of cols in header or ??
self.assertEqual(
numCols, colCount,
"parse created result with the wrong number of cols %s %s" %
(numCols, colCount))
self.assertEqual(numRows, rowCount,
"parse created result with the wrong number of rows (header shouldn't count) %s %s" % \
(numRows, rowCount))
verboseprint(hex_key, ":", dump_json(parse_result))
def test_GBM_many_cols(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
if h2o.localhost:
tryList = [
(10000, 100, 'cA', 300),
]
else:
tryList = [
# (10000, 10, 'cB', 300),
# (10000, 50, 'cC', 300),
(10000, 100, 'cD', 300),
(10000, 200, 'cE', 300),
(10000, 300, 'cF', 300),
(10000, 400, 'cG', 300),
(10000, 500, 'cH', 300),
(10000, 1000, 'cI', 300),
]
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
# csvFilename = 'syn_' + str(SEEDPERFILE) + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
hdrFilename = 'hdr_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
hdrPathname = SYNDATASETS_DIR + '/' + hdrFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, SEEDPERFILE)
# PARSE train****************************************
start = time.time()
xList = []
eList = []
fList = []
modelKey = 'GBMModelKey'
# Parse (train)****************************************
parseTrainResult = h2i.import_parse(bucket=None, path=csvPathname, schema='put',
hex_key=hex_key, timeoutSecs=timeoutSecs,
doSummary=False)
# hack
elapsed = time.time() - start
print "train parse end on ", csvPathname, 'took', elapsed, 'seconds',\
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
print "train parse result:", parseTrainResult['destination_key']
# Logging to a benchmark file
algo = "Parse"
l = '{:d} jvms, {:d}GB heap, {:s} {:s} {:6.2f} secs'.format(
len(h2o.nodes), h2o.nodes[0].java_heap_GB, algo, csvFilename, elapsed)
print l
h2o.cloudPerfH2O.message(l)
inspect = h2o_cmd.runInspect(key=parseTrainResult['destination_key'])
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
numRows = inspect['numRows']
numCols = inspect['numCols']
### h2o_cmd.runSummary(key=parsTraineResult['destination_key'])
# GBM(train iterate)****************************************
ntrees = 5
prefixList = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
# for max_depth in [5,10,20,40]:
for max_depth in [5, 10, 20]:
# PARSE a new header****************************************
print "Creating new header", hdrPathname
prefix = prefixList.pop(0)
write_syn_header(hdrPathname, rowCount, colCount, prefix)
# upload and parse the header to a hex
hdr_hex_key = prefix + "_hdr.hex"
parseHdrResult = h2i.import_parse(bucket=None, path=hdrPathname, schema='put',
header=1, # REQUIRED! otherwise will interpret as enums
hex_key=hdr_hex_key, timeoutSecs=timeoutSecs, doSummary=False)
# Set Column Names (before autoframe is created)
h2o.nodes[0].set_column_names(source=hex_key, copy_from=hdr_hex_key)
# GBM
print "response col name is changing each iteration: parsing a new header"
params = {
'learn_rate': .2,
'nbins': 1024,
'ntrees': ntrees,
'max_depth': max_depth,
'min_rows': 10,
'response': prefix + "_response",
'ignored_cols_by_name': None,
}
print "Using these parameters for GBM: ", params
kwargs = params.copy()
trainStart = time.time()
gbmTrainResult = h2o_cmd.runGBM(parseResult=parseTrainResult,
timeoutSecs=timeoutSecs, destination_key=modelKey, **kwargs)
trainElapsed = time.time() - trainStart
print "GBM training completed in", trainElapsed, "seconds. On dataset: ", csvPathname
# Logging to a benchmark file
algo = "GBM " + " ntrees=" + str(ntrees) + " max_depth=" + str(max_depth)
l = '{:d} jvms, {:d}GB heap, {:s} {:s} {:6.2f} secs'.format(
len(h2o.nodes), h2o.nodes[0].java_heap_GB, algo, csvFilename, trainElapsed)
print l
h2o.cloudPerfH2O.message(l)
gbmTrainView = h2o_cmd.runGBMView(model_key=modelKey)
# errrs from end of list? is that the last tree?
errsLast = gbmTrainView['gbm_model']['errs'][-1]
print "GBM 'errsLast'", errsLast
cm = gbmTrainView['gbm_model']['cms'][-1]['_arr'] # use the last one
pctWrongTrain = h2o_gbm.pp_cm_summary(cm);
print "\nTrain\n==========\n"
print h2o_gbm.pp_cm(cm)
# xList.append(ntrees)
xList.append(max_depth)
eList.append(pctWrongTrain)
fList.append(trainElapsed)
# works if you delete the autoframe
### h2o_import.delete_keys_at_all_nodes(pattern='autoframe')
# just plot the last one
if DO_PLOT:
xLabel = 'max_depth'
eLabel = 'pctWrong'
fLabel = 'trainElapsed'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel, fListTitle, fList, fLabel)
def test_quantile_cmp_uniform(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(500000, 1, 'x.hex', 1, 20000, ('C1', 1.10, 5000.0, 10000.0, 15000.0, 20000.00)),
(500000, 1, 'x.hex', -5000, 0, ('C1', -5001.00, -3750.0, -2445, -1200.0, 99)),
(100000, 1, 'x.hex', -100000, 100000, ('C1', -100001.0, -50000.0, 1613.0, 50000.0, 100000.0)),
(100000, 1, 'x.hex', -1, 1, ('C1', -1.05, -0.48, 0.0087, 0.50, 1.00)),
(100000, 1, 'A.hex', 1, 100, ('C1', 1.05, 26.00, 51.00, 76.00, 100.0)),
(100000, 1, 'A.hex', -99, 99, ('C1', -99, -50.0, 0, 50.00, 99)),
(100000, 1, 'B.hex', 1, 10000, ('C1', 1.05, 2501.00, 5001.00, 7501.00, 10000.00)),
(100000, 1, 'B.hex', -100, 100, ('C1', -100.10, -50.0, 0.85, 51.7, 100,00)),
(100000, 1, 'C.hex', 1, 100000, ('C1', 1.05, 25002.00, 50002.00, 75002.00, 100000.00)),
(100000, 1, 'C.hex', -101, 101, ('C1', -100.10, -50.45, -1.18, 49.28, 100.00)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
# max error = half the bin size?
maxDelta = ((expectedMax - expectedMin)/20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
h2o.beta_features = False
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
h2o.beta_features = False
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=10, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["num_rows"]
numCols = inspect["num_cols"]
h2o.beta_features = True
summaryResult = h2o_cmd.runSummary(key=hex_key)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats['zeros']
mins = stats['mins']
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg='min is not approx. expected')
maxs = stats['maxs']
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg='max is not approx. expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [0.001, 0.01, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.99, 0.999]
pctile = stats['pctile']
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxDelta, msg='25th percentile is not approx. expected')
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxDelta, msg='50th percentile (median) is not approx. expected')
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxDelta, msg='75th percentile is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows/len(hcnt)
# apparently we're not able to estimate for these datasets
# self.assertAlmostEqual(b, rowCount/len(hcnt), delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", compareActual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2p.blue_print("\nTrying exec quantile")
# thresholds = "c(0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99)"
# do the equivalent exec quantile?
# execExpr = "quantile(%s[,1],%s);" % (hex_key, thresholds)
print "Comparing (two places) each of the summary2 threshold quantile results, to single exec quantile"
h2o.beta_features = True
for i, threshold in enumerate(thresholds):
# FIX! do two of the same?..use same one for the 2nd
if i!=0:
# execExpr = "r2=c(1); r2=quantile(%s[,4],c(0,.05,0.3,0.55,0.7,0.95,0.99))" % hex_key
execExpr = "r2=c(1); r2=quantile(%s[,1], c(%s,%s));" % (hex_key, threshold, threshold)
(resultExec, result) = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
h2p.green_print("\nresultExec: %s" % h2o.dump_json(resultExec))
h2p.blue_print("\nthreshold: %.2f Exec quantile: %s Summary2: %s" % (threshold, result, pt[i]))
if not result:
raise Exception("exec result: %s for quantile: %s is bad" % (result, threshold))
h2o_util.assertApproxEqual(result, pctile[i], tol=maxDelta,
msg='exec percentile: %s too different from expected: %s' % (result, pctile[i]))
# for now, do one with all, but no checking
else:
# This seemed to "work" but how do I get the key name for the list of values returned
# the browser result field seemed right, but nulls in the key
execExpr = "r2=c(1); r2=quantile(%s[,1], c(%s));" % (hex_key, ",".join(map(str,thresholds)))
(resultExec, result) = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
inspect = h2o_cmd.runInspect(key='r2')
numCols = inspect['numCols']
numRows = inspect['numRows']
self.assertEqual(numCols,1)
self.assertEqual(numRows,len(thresholds))
# FIX! should run thru the values in the col? how to get
# compare the last one
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=thresholds[-1],
# h2oSummary2=pctile[-1],
# h2oQuantilesApprox=result, # from exec
h2oExecQuantiles=result,
)
h2o.nodes[0].remove_all_keys()
def test_c10_rel_glm(self):
h2o.beta_features = False
print "Since the python is not necessarily run as user=0xcust..., can't use a schema='put' here"
print "Want to be able to run python as jenkins"
print "I guess for big 0xcust files, we don't need schema='put'"
print "For files that we want to put (for testing put), we can get non-private files"
# Parse Train***********************************************************
importFolderPath = '/mnt/0xcustomer-datasets/c3'
csvFilename = 'classification1Train.txt'
csvPathname = importFolderPath + "/" + csvFilename
start = time.time()
parseResult = h2i.import_parse(path=csvPathname, schema='local', timeoutSecs=500, doSummary=False)
print "Parse of", parseResult['destination_key'], "took", time.time() - start, "seconds"
print "Parse result['destination_key']:", parseResult['destination_key']
start = time.time()
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'], timeoutSecs=500)
print "Inspect:", parseResult['destination_key'], "took", time.time() - start, "seconds"
h2o_cmd.infoFromInspect(inspect, csvPathname)
# num_rows = inspect['num_rows']
# num_cols = inspect['num_cols']
# do summary of the parsed dataset last, since we know it fails on this dataset
summaryResult = h2o_cmd.runSummary(key=parseResult['destination_key'])
h2o_cmd.infoFromSummary(summaryResult, noPrint=False)
# keepList = []
# h2o_glm.findXFromColumnInfo(key=parseResult['destination_key'], keepList=keepList)
# see README.txt in 0xcustomer-datasets/c3 for the col names to use in keepList above, to get the indices
# since we're no long zero based, increment by 1
x_from_zero = [6,7,8,10,12,31,32,33,34,35,36,37,40,41,42,43,44,45,46,47,49,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70]
x = ['C' + str(i + 1) for i in x_from_zero]
# GLM Train***********************************************************
keepPattern = None
# don't need the intermediate Dicts produced from columnInfoFromInspect
x = h2o_glm.goodXFromColumnInfo(y, keepPattern=keepPattern, key=parseResult['destination_key'], timeoutSecs=300)
print "from goodX (not used) x:", x
print "y:", 'C1'
# have to use named cols, and they start with 1
kwargs = {
'x' x,
'y': 'C1',
# 'case_mode': '>',
# 'case': 0,
'family': 'binomial',
'lambda': 1.0E-5,
'alpha': 0.5,
'max_iter': 4,
'thresholds': 0.5,
'n_folds': 1,
'weight': 100,
'beta_epsilon': 1.0E-4,
}
def test_exec2_xorsum(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(ROWS, 1, 'r1', 0, 10, None),
]
for trial in range(10):
ullResultList = []
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
# dynamic range of the data may be useful for estimating error
maxDelta = expectedMax - expectedMin
csvFilename = 'syn_real_' + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
print "Creating random", csvPathname
(expectedUllSum, expectedFpSum) = write_syn_dataset(csvPathname,
rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
expectedUllSumAsDouble = h2o_util.unsignedLongLongToDouble(expectedUllSum)
expectedFpSumAsLongLong = h2o_util.doubleToUnsignedLongLong(expectedFpSum)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key,
timeoutSecs=3000, retryDelaySecs=2)
numRows, numCols, parse_key = h2o_cmd.infoFromParse(parseResult)
assert parse_key == hex_key
assert numCols == colCount
assert numRows == rowCount
inspect = h2o_cmd.runInspect(key=hex_key)
missingList, labelList, numRows, numCols = h2o_cmd.infoFromInspect(inspect)
assert len(missingList) == 0
# looking at the 8 bytes of bits for the h2o doubles
# xorsum will zero out the sign and exponent
for execExpr in exprList:
for r in range(10):
if 1==0:
execResult = h2o_cmd.runExec(ast=execExpr, timeoutSecs=30)
fpResult = execResult['scalar']
else:
(execResult, fpResult) = h2e.exec_expr(h2o.nodes[0], execExpr, resultKey='x', timeoutSecs=300)
# print dump_json(h2o.n0.frames(key="h"))
# (execResult, fpResult) = h2e.exec_expr(h2o.nodes[0], execExpr, resultKey='h', timeoutSecs=300)
# print dump_json(h2o.n0.frames(key="r1"))
print r, "execResult:", h2o.dump_json(execResult)
h2o_cmd.runStoreView()
ullResult = h2o_util.doubleToUnsignedLongLong(fpResult)
ullResultList.append((ullResult, fpResult))
print "%30s" % "ullResult (0.16x):", "0x%0.16x %s" % (ullResult, fpResult)
print "%30s" % "expectedUllSum (0.16x):", "0x%0.16x %s" % (expectedUllSum, expectedUllSumAsDouble)
print "%30s" % "expectedFpSum (0.16x):", "0x%0.16x %s" % (expectedFpSumAsLongLong, expectedFpSum)
# allow diff of the lsb..either way
# if ullResult!=expectedUllSum and abs((ullResult-expectedUllSum)>3):
if ullResult!=expectedUllSum:
raise Exception("h2o didn't get the same xorsum as python. 0x%0.16x 0x%0.16x" % \
(ullResult, expectedUllSum))
print "h2o didn't get the same xorsum as python. 0x%0.16x 0x%0.16x" % \
(ullResult, expectedUllSum)
h2o.check_sandbox_for_errors()
print "first result was from a sum. others are xorsum"
print "ullResultList:"
for ullResult, fpResult in ullResultList:
print "%30s" % "ullResult (0.16x):", "0x%0.16x %s" % (ullResult, fpResult)
print "%30s" % "expectedUllSum (0.16x):", "0x%0.16x %s" % (expectedUllSum, expectedUllSumAsDouble)
print "%30s" % "expectedFpSum (0.16x):", "0x%0.16x %s" % (expectedFpSumAsLongLong, expectedFpSum)
def test_exec2_log_like_R(self):
h2o.beta_features = True
bucket = 'home-0xdiag-datasets'
csvPathname = 'airlines/year2013.csv'
# csvPathname = '1B/reals_100000x1000_15f.data'
# csvPathname = '1B/reals_1000000x1000_15f.data'
# csvPathname = '1B/reals_1000000x1_15f.data'
# csvPathname = '1B/reals_1B_15f.data'
# csvPathname = '1B/reals_100M_15f.data'
hex_key = 'r1'
parseResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
schema='local',
hex_key=hex_key,
timeoutSecs=3000,
retryDelaySecs=2,
doSummary=False)
inspect = h2o_cmd.runInspect(key=hex_key)
print "numRows:", inspect['numRows']
print "numCols:", inspect['numCols']
inspect = h2o_cmd.runInspect(key=hex_key, offset=-1)
print "inspect offset = -1:", h2o.dump_json(inspect)
xList = []
eList = []
fList = []
for execExpr in initList:
execResult, result = h2e.exec_expr(h2o.nodes[0],
execExpr,
resultKey=None,
timeoutSecs=300)
for trial in range(300):
for execExpr in exprList:
# put the trial number into the temp for uniqueness
execExpr = re.sub('Last.value', 'Last.value%s' % trial,
execExpr)
start = time.time()
execResult, result = h2e.exec_expr(h2o.nodes[0],
execExpr,
resultKey=None,
timeoutSecs=300)
execTime = time.time() - start
print 'exec took', execTime, 'seconds'
c = h2o.nodes[0].get_cloud()
c = c['nodes']
# print (h2o.dump_json(c))
k = [i['num_keys'] for i in c]
v = [i['value_size_bytes'] for i in c]
print "keys: %s" % " ".join(map(str, k))
print "value_size_bytes: %s" % " ".join(map(str, v))
# print "result:", result
if DO_ORIG:
if 'r1' in execExpr:
xList.append(trial)
eList.append(execTime)
if 'log' in execExpr:
fList.append(execTime)
else:
xList.append(trial)
eList.append(execTime)
fList.append(execTime)
h2o.check_sandbox_for_errors()
# PLOTS. look for eplot.jpg and fplot.jpg in local dir?
if DO_PLOT:
xLabel = 'trial'
if DO_ORIG:
eLabel = 'time: Last.value<trial>.4 = r1[,c(1)]'
fLabel = 'time: Last.value<trial>.7 = log(Last.value<trial>.6)'
else:
eLabel = 'time: Last.value.3 = r2+1'
fLabel = 'time: Last.value.3 = r2+1'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList,
xLabel,
eListTitle,
eList,
eLabel,
fListTitle,
fList,
fLabel,
server=True)
def test_rf_libsvm_fvec(self):
h2o.beta_features = True
# just do the import folder once
# make the timeout variable per dataset. it can be 10 secs for covtype 20x (col key creation)
# so probably 10x that for covtype200
csvFilenameList = [
("gisette_scale.svm", "cF", 30, 1, 0),
("covtype.binary.svm", "cC", 30, 1, 1),
("mnist_train.svm", "cM", 30, 1, 1),
# FIX! fails KMeansScore
# not integer output
# ("colon-cancer.svm", "cA", 30, 1, 1),
("connect4.svm", "cB", 30, 1, 1),
# ("syn_6_1000_10.svm", "cK", 30, 1, 0), Bad libsvm file has the same column multiple times.
# float response requires regression
("syn_0_100_1000.svm", "cL", 30, 1, 0),
("mushrooms.svm", "cG", 30, 1, 1),
# rf doesn't like reals
# ("duke.svm", "cD", 30, 1, 1),
# too many features? 150K inspect timeout?
# ("E2006.train.svm", "cE", 30, 1, 1),
# too big for rf (memory error)
# ("news20.svm", "cH", 30, 1, 1),
# multiclass format ..don't support
# ("tmc2007_train.svm", "cJ", 30, 1, 1),
# normal csv
]
### csvFilenameList = random.sample(csvFilenameAll,1)
# h2b.browseTheCloud()
lenNodes = len(h2o.nodes)
firstDone = False
for (csvFilename, hex_key, timeoutSecs, resultMult,
classification) in csvFilenameList:
# have to import each time, because h2o deletes source after parse
bucket = "home-0xdiag-datasets"
csvPathname = "libsvm/" + csvFilename
# PARSE******************************************
parseResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=2000)
print "Parse result['destination_key']:", parseResult[
'destination_key']
# INSPECT******************************************
start = time.time()
inspect = h2o_cmd.runInspect(None,
parseResult['destination_key'],
timeoutSecs=360)
print "Inspect:", parseResult[
'destination_key'], "took", time.time() - start, "seconds"
h2o_cmd.infoFromInspect(inspect, csvFilename)
# RF******************************************
kwargs = {
'ntrees': 1,
'response': 0,
'classification': classification,
'importance': 0,
}
timeoutSecs = 600
start = time.time()
rf = h2o_cmd.runRF(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
elapsed = time.time() - start
print "rf end on ", csvPathname, 'took', elapsed, 'seconds.', \
"%d pct. of timeout" % ((elapsed/timeoutSecs) * 100)
def test_summary2_int2B(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(100000, 1, 'B.hex', 2533255332, 2633256000, ('C1', None, None,
None, None, None)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax,
expected) in tryList:
# max error = half the bin size?
maxDelta = ((expectedMax - expectedMin) / (MAX_QBINS + 0.0))
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
# also need to add some variance due to random distribution?
# maybe a percentage of the mean
distMean = (expectedMax - expectedMin) / 2
maxShift = distMean * .01
maxDelta = maxDelta + maxShift
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin,
expectedMax, SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=60,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key,
max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
if expected[0]:
self.assertEqual(colname, expected[0])
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [
0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99
]
pctile = stats['pctile']
if expected[1]:
h2o_util.assertApproxEqual(mins[0],
expected[1],
tol=maxDelta,
msg='min is not approx. expected')
h2o_util.assertApproxEqual(
pctile[3],
expected[2],
tol=maxDelta,
msg='25th percentile is not approx. expected')
h2o_util.assertApproxEqual(
pctile[5],
expected[3],
tol=maxDelta,
msg='50th percentile (median) is not approx. expected')
h2o_util.assertApproxEqual(
pctile[7],
expected[4],
tol=maxDelta,
msg='75th percentile is not approx. expected')
h2o_util.assertApproxEqual(maxs[0],
expected[5],
tol=maxDelta,
msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(
hcnt
) # expect 21 thresholds, so 20 bins. each 5% of rows (uniform distribution)
# apparently we can't estimate any more
# self.assertAlmostEqual(b, rowCount/len(hcnt), delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname,
"(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
scipyCol = 0
def test_GLM2grid_covtype_many(self):
h2o.beta_features = True
csvFilename = 'covtype.data'
csvPathname = 'standard/' + csvFilename
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets',
path=csvPathname,
schema='put',
timeoutSecs=10)
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
print "WARNING: max_iter set to 8 for benchmark comparisons"
max_iter = 8
y = "54"
kwargs = {
'response': y,
'family': 'gaussian',
'n_folds': 2,
'max_iter': max_iter,
'beta_epsilon': 1e-3,
'lambda': '0,0.5,0.8',
'alpha': '0,1e-8,1e-4',
}
start = time.time()
jobs = []
totalGLMGridJobs = 0
for i in range(3):
glmResult = h2o_cmd.runGLM(parseResult=parseResult,
timeoutSecs=300,
noPoll=True,
**kwargs)
# print "glmResult:", h2o.dump_json(glmResult)
# assuming it doesn't complete right away, this is the first response
# it differs for the last response
job_key = glmResult['job_key']
grid_key = glmResult['destination_key']
jobs.append((job_key, grid_key))
totalGLMGridJobs += 1
# do some parse work in parallel. Don't poll for parse completion
# don't bother checking the parses when they are completed (pollWaitJobs looks at all)
for i in range(4):
time.sleep(3)
hex_key = str(i) + ".hex"
src_key = str(i) + ".src"
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets',
path=csvPathname,
schema='put',
src_key=src_key,
hex_key=hex_key,
timeoutSecs=10,
noPoll=True,
doSummary=False)
h2o_jobs.pollWaitJobs(timeoutSecs=300)
elapsed = time.time() - start
# 2/GLMGridView.html?grid_key=asd
# 2/GLMModelView.html?_modelKey=asd_0&lambda=NaN
# 2/SaveModel.html?model=GLMGridResults__9a29646b78dd988aacd4f88e4d864ccd_1&path=adfs&force=1
for job_key, grid_key in jobs:
gridResult = h2o.nodes[0].glm_grid_view(grid_key=grid_key)
h2o_glm.simpleCheckGLMGrid(self, gridResult, **kwargs)
print "All GLMGrid jobs completed in", elapsed, "seconds."
print "totalGLMGridJobs:", totalGLMGridJobs