def test_NN2_params_rand2(self):
csvPathname = 'covtype/covtype.20k.data'
hex_key = 'covtype.20k.hex'
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=hex_key, schema='put')
paramDict = define_params()
for trial in range(5):
# params is mutable. This is default.
params = {'response': 'C55'}
h2o_nn.pickRandDeepLearningParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runDeepLearning(timeoutSecs=300, parseResult=parseResult, **kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
# FIX! simple check?
print "Deep Learning end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_NN2_params_rand2(self):
h2o.beta_features = True
csvPathname = 'covtype/covtype.20k.data'
hex_key = 'covtype.20k.hex'
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname, hex_key=hex_key, schema='put')
paramDict = define_params()
for trial in range(3):
# params is mutable. This is default.
params = {'response': 'C55', 'epochs': '1'}
h2o_nn.pickRandDeepLearningParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runDeepLearning(timeoutSecs=500, parseResult=parseResult, **kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
deeplearning_model = nn['deeplearning_model']
errors = deeplearning_model['errors']
# print "errors", h2o.dump_json(errors)
# print "errors, classification", errors['classification']
# assert 1==0
# unstable = nn['model_info']['unstable']
# unstable case caused by :
# normal initial distribution with amplitude 1 and input_dropout_ratio=1.
# blowing up numerically during propagation of all zeroes as input repeatedly.
# arnon added logging to stdout in addition to html in 7899b92ad67.
# Will have to check that first before making predictions.
# print "unstable:", unstable
# FIX! simple check?
print "Deep Learning end on ", csvPathname, 'took', time.time() - start, 'seconds'
print "Trial #", trial, "completed\n"
def test_DeepLearning_mnist(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 = 300
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 = 'deeplearning_' + identifier + '.hex'
kwargs = {
'ignored_cols' : None,
'response' : response,
'classification' : 1,
'activation' : 'RectifierWithDropout',
'input_dropout_ratio' : 0.2,
'hidden' : '1024,1024,2048',
'adaptive_rate' : 1,
'rho' : 0.99,
'epsilon' : 1e-8,
'train_samples_per_iteration' : -1, ## 0: better accuracy! -1: best scalability! 10000: best accuracy?
# 'rate' : 0.01,
# 'rate_annealing' : 1e-6,
# 'momentum_start' : 0.5,
# 'momentum_ramp' : 1800000,
# 'momentum_stable' : 0.99,
'l1' : 1e-5,
'l2' : 0.0,
'seed' : 98037452452,
'loss' : 'CrossEntropy',
'max_w2' : 15,
'initial_weight_distribution' : 'UniformAdaptive',
'epochs' : 128, #enough for 64 nodes
'destination_key' : model_key,
'validation' : validation_key,
'score_interval' : 10000 #don't score until the end
}
timeoutSecs = 7200
start = time.time()
deeplearning = h2o_cmd.runDeepLearning(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
print "neural net end on ", csvPathname_train, " and ", csvPathname_test, 'took', time.time() - start, 'seconds'
predict_key = 'score_' + identifier + '.hex'
kwargs = {
'data_key': validation_key,
'destination_key': predict_key,
'model_key': model_key
}
h2o.beta_features = True
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)
h2o.beta_features = False
def test_DeepLearning_c21(self):
importFolderPath = '/mnt/0xcustomer-datasets/c21'
csvPathname_train = importFolderPath + '/persona_clean_deep.tsv.zip'
csvPathname_test = importFolderPath + '/persona_clean_deep.tsv.zip'
hex_key = 'train.hex'
validation_key = 'test.hex'
timeoutSecs = 300
parseResult = h2i.import_parse(path=csvPathname_train, hex_key=hex_key,
timeoutSecs=timeoutSecs, doSummary=DO_SUMMARY)
parseResultV = h2i.import_parse(path=csvPathname_test, hex_key=validation_key,
timeoutSecs=timeoutSecs, doSummary=DO_SUMMARY)
inspect = h2o_cmd.runInspect(None, hex_key)
print "\n" + csvPathname_train, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
response = 'any_response'
#Making random id
identifier = ''.join(random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'nn_' + identifier + '.hex'
# use defaults otherwise
# need to change epochs otherwise it takes too long
kwargs = {
'epochs' : 0.001,
'response' : response,
'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 = 0.046
relTol = 0.35 # allow 35% tolerance. kbn
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))
def test_NN_airlines_small(self):
#h2b.browseTheCloud()
csvPathname_train = 'airlines/AirlinesTrain.csv.zip'
csvPathname_test = 'airlines/AirlinesTest.csv.zip'
hex_key = 'airlines_train.hex'
validation_key = 'airlines_test.hex'
timeoutSecs = 30
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'])
# this gives the last col number, which is IsDepDelayed_REC (1 or -1)
# response = inspect['numCols'] - 1
# this is "YES"/"NO"
response = 'IsDepDelayed'
#Making random id
identifier = ''.join(
random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'nn_' + identifier + '.hex'
# get the column names
colNames = [c['name'] for c in inspect['cols']]
print "colNames:", colNames
usedCols = ("Origin", "Dest", "fDayofMonth", "fYear", "UniqueCarrier",
"fDayOfWeek", "fMonth", "DepTime", "ArrTime", "Distance")
ignoredCols = []
for c in colNames:
# don't put the response in the ignore list (is there a problem if so?)
if c not in usedCols and c != response:
ignoredCols.append(c)
ignoredColsString = ",".join(ignoredCols)
print "Telling h2o to ignore these cols:"
print ignoredColsString
kwargs = {
'ignored_cols': ignoredColsString,
'response': response,
'classification': 1,
'destination_key': model_key,
}
expectedErr = 0.45 ## expected validation error for the above model
relTol = 0.50 ## 20% rel. error tolerance due to 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'
predict_key = 'score_' + identifier + '.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))
def test_DeepLearning_twovalues(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_twovalues.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rowDataTrue = "1, 0, 65, 1, 2, 1, 1, 4, 1, 4, 1, 4"
rowDataFalse = "0, 1, 0, -1, -2, -1, -1, -4, -1, -4, -1, -4"
twoValueList = [
('A', 'B', 0, 14),
('A', 'B', 1, 14),
(0, 1, 0, 12),
(0, 1, 1, 12),
(0, 1, 'NaN', 12),
(1, 0, 'NaN', 12),
(-1, 1, 0, 12),
(-1, 1, 1, 12),
(-1e1, 1e1, 1e1, 12),
(-1e1, 1e1, -1e1, 12),
]
trial = 0
for (outputTrue, outputFalse, case, coeffNum) in twoValueList:
write_syn_dataset(csvPathname, 20, rowDataTrue, rowDataFalse,
str(outputTrue), str(outputFalse))
start = time.time()
hex_key = csvFilename + "_" + str(trial)
model_key = 'trial_' + str(trial) + '.hex'
validation_key = hex_key
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key)
print "using outputTrue: %s outputFalse: %s" % (outputTrue,
outputFalse)
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
response = inspect['numCols'] - 1
kwargs = {
'ignored_cols': None,
'response': 'C' + str(response),
'classification': 1,
'activation': 'Tanh',
#'input_dropout_ratio' : 0.2,
'hidden': '500',
'rate': 0.01,
'rate_annealing': 1e-6,
'momentum_start': 0,
'momentum_stable': 0,
'l1': 0.0,
'l2': 1e-4,
'seed': 80023842348,
'loss': 'CrossEntropy',
#'max_w2' : 15,
#'warmup_samples' : 0,
'initial_weight_distribution': 'UniformAdaptive',
#'initial_weight_scale' : 0.01,
'epochs': 1.0,
'destination_key': model_key,
'validation': hex_key,
}
timeoutSecs = 60
start = time.time()
h2o_cmd.runDeepLearning(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
print "trial #", trial, "Deep Learning end on ", csvFilename, ' took', time.time(
) - start, 'seconds'
#### Now score using the model, and check the validation error
expectedErr = 0.001
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))
trial += 1
def test_DeepLearning_mnist(self):
#h2b.browseTheCloud()
csvPathname_train = 'mnist/train.csv.gz'
csvPathname_test = 'mnist/test.csv.gz'
hex_key = 'mnist_train.hex'
validation_key = 'mnist_test.hex'
timeoutSecs = 300
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 = 'deeplearning_' + identifier + '.hex'
kwargs = {
'ignored_cols' : None,
'response' : response,
'classification' : 1,
'activation' : 'RectifierWithDropout',
'input_dropout_ratio' : 0.2,
'hidden' : '1024,1024,2048',
'adaptive_rate' : 1,
'rho' : 0.99,
'epsilon' : 1e-8,
'train_samples_per_iteration' : -1, ## 0: better accuracy! -1: best scalability! 10000: best accuracy?
# 'rate' : 0.01,
# 'rate_annealing' : 1e-6,
# 'momentum_start' : 0.5,
# 'momentum_ramp' : 1800000,
# 'momentum_stable' : 0.99,
'l1' : 1e-5,
'l2' : 0.0,
'seed' : 98037452452,
'loss' : 'CrossEntropy',
'max_w2' : 15,
'initial_weight_distribution' : 'UniformAdaptive',
'epochs' : 128, #enough for 64 nodes
'destination_key' : model_key,
'validation' : validation_key,
'score_interval' : 10000 #don't score until the end
}
timeoutSecs = 7200
start = time.time()
deeplearning = h2o_cmd.runDeepLearning(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
print "neural net end on ", csvPathname_train, " and ", csvPathname_test, 'took', time.time() - start, 'seconds'
predict_key = 'score_' + identifier + '.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)
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 = 60
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
kwargs = {
'source' : validation_key,
'max_rows': 0,
'response': response,
'ignored_cols': None, # this is not consistent with ignored_cols_by_name
'classification': 1,
'destination_key': 'score_' + identifier + '.hex',
'model': model_key,
}
nnScoreResult = h2o_cmd.runDeepLearningScore(key=parseResult['destination_key'], timeoutSecs=timeoutSecs, **kwargs)
h2o_nn.checkScoreResult(self, nnScoreResult, expectedErr, relTol, **kwargs)
def test_anomaly_uniform_w_NA(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, COLS, 'x.hex', 1, 20000),
(ROWS, COLS, 'x.hex', -5000, 0),
(ROWS, COLS, 'x.hex', -100000, 100000),
(ROWS, COLS, 'x.hex', -1, 1),
(ROWS, COLS, 'A.hex', 1, 100),
(ROWS, COLS, 'A.hex', -99, 99),
(ROWS, COLS, 'B.hex', 1, 10000),
(ROWS, COLS, 'B.hex', -100, 100),
(ROWS, COLS, 'C.hex', 1, 100000),
(ROWS, COLS, 'C.hex', -101, 101),
]
trial = 1
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
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'])
numRows = inspect["numRows"]
numCols = inspect["numCols"]
print "numRows:", numRows, "numCols:", numCols
model_key = "m.hex"
kwargs = {
'ignored_cols' : None,
'response' : numCols-1,
'classification' : 0,
'activation' : 'RectifierWithDropout',
'input_dropout_ratio' : 0.2,
'hidden' : '117',
'adaptive_rate' : 0,
'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' : 2.0,
'destination_key' : model_key,
# 'validation' : None,
'score_interval' : 10000,
'autoencoder' : 1,
}
timeoutSecs = 600
start = time.time()
nn = h2o_cmd.runDeepLearning(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
print "neural net end. took", time.time() - start, "seconds"
kwargs = {
'destination_key': "a.hex",
'source': parseResult['destination_key'],
'dl_autoencoder_model': model_key,
'thresh': 1.0
}
anomaly = h2o.nodes[0].anomaly(timeoutSecs=30, **kwargs)
inspect = h2o_cmd.runInspect(None, "a.hex")
numRows = inspect["numRows"]
numCols = inspect["numCols"]
print "anomaly: numRows:", numRows, "numCols:", numCols
self.assertEqual(numCols,1)
# twice as many rows because of NA injection
self.assertEqual(numRows,rowCount*(1 + NA_ROW_RATIO))
# first col has the anomaly info. other cols are the same as orig data
aSummary = h2o_cmd.runSummary(key='a.hex', cols=0)
h2o_cmd.infoFromSummary(aSummary)
print "anomaly:", h2o.dump_json(anomaly)
trial += 1
h2i.delete_keys_at_all_nodes()
def test_NN_mnist(self):
#h2b.browseTheCloud()
csvPathname_train = 'standard/covtype.shuffled.90pct.data'
csvPathname_test = 'standard/covtype.shuffled.10pct.data'
hex_key = 'covtype.hex'
validation_key = 'covtype.hex'
timeoutSecs = 30
parseResult = h2i.import_parse(bucket='home-0xdiag-datasets',
path=csvPathname_train,
schema='local',
hex_key=hex_key,
timeoutSecs=timeoutSecs)
parseResultV = h2i.import_parse(bucket='home-0xdiag-datasets',
path=csvPathname_test,
schema='local',
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',
'adaptive_rate': 0,
'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': 96.0,
'destination_key': model_key,
'validation': validation_key,
'score_interval': 10000
}
expectedErr = 0.24 ## expected validation error for the above model
relTol = 0.20 ## 20% rel. error tolerance due to 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'
predict_key = 'score_' + identifier + '.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))
def test_DeepLearning_c21(self):
importFolderPath = '/mnt/0xcustomer-datasets/c21'
csvPathname_train = importFolderPath + '/persona_clean_deep.tsv.zip'
csvPathname_test = importFolderPath + '/persona_clean_deep.tsv.zip'
hex_key = 'train.hex'
validation_key = 'test.hex'
timeoutSecs = 300
parseResult = h2i.import_parse(path=csvPathname_train,
hex_key=hex_key,
timeoutSecs=timeoutSecs,
doSummary=DO_SUMMARY)
parseResultV = h2i.import_parse(path=csvPathname_test,
hex_key=validation_key,
timeoutSecs=timeoutSecs,
doSummary=DO_SUMMARY)
inspect = h2o_cmd.runInspect(None, hex_key)
print "\n" + csvPathname_train, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
response = 'any_response'
#Making random id
identifier = ''.join(
random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'nn_' + identifier + '.hex'
# use defaults otherwise
# need to change epochs otherwise it takes too long
kwargs = {
'epochs': 0.001,
'response': response,
'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 = 0.046
relTol = 0.35 # allow 35% tolerance. kbn
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))
def test_NN_airlines_small(self):
#h2b.browseTheCloud()
h2o.beta_features = True
csvPathname_train = 'airlines/AirlinesTrain.csv.zip'
csvPathname_test = 'airlines/AirlinesTest.csv.zip'
hex_key = 'airlines_train.hex'
validation_key = 'airlines_test.hex'
timeoutSecs = 30
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'])
# this gives the last col number, which is IsDepDelayed_REC (1 or -1)
# response = inspect['numCols'] - 1
# this is "YES"/"NO"
response = 'IsDepDelayed'
#Making random id
identifier = ''.join(random.sample(string.ascii_lowercase + string.digits, 10))
model_key = 'nn_' + identifier + '.hex'
# get the column names
colNames = [c['name'] for c in inspect['cols']]
print "colNames:", colNames
usedCols = ("Origin", "Dest", "fDayofMonth", "fYear", "UniqueCarrier", "fDayOfWeek", "fMonth", "DepTime", "ArrTime", "Distance")
ignoredCols = []
for c in colNames:
# don't put the response in the ignore list (is there a problem if so?)
if c not in usedCols and c != response:
ignoredCols.append(c)
ignoredColsString = ",".join(ignoredCols)
print "Telling h2o to ignore these cols:"
print ignoredColsString
kwargs = {
'ignored_cols' : ignoredColsString,
'response' : response,
'classification' : 1,
'destination_key' : model_key,
}
expectedErr = 0.45 ## expected validation error for the above model
relTol = 0.50 ## 20% rel. error tolerance due to 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'
predict_key = 'score_' + identifier + '.hex'
kwargs = {
'data_key': validation_key,
'destination_key': predict_key,
'model_key': model_key
}
h2o.beta_features = True
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))
h2o.beta_features = False
def test_anomaly_uniform_w_NA(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, COLS, 'x.hex', 1, 20000),
(ROWS, COLS, 'x.hex', -5000, 0),
(ROWS, COLS, 'x.hex', -100000, 100000),
(ROWS, COLS, 'x.hex', -1, 1),
(ROWS, COLS, 'A.hex', 1, 100),
(ROWS, COLS, 'A.hex', -99, 99),
(ROWS, COLS, 'B.hex', 1, 10000),
(ROWS, COLS, 'B.hex', -100, 100),
(ROWS, COLS, 'C.hex', 1, 100000),
(ROWS, COLS, 'C.hex', -101, 101),
]
trial = 1
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin,
expectedMax, SEEDPERFILE)
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'])
numRows = inspect["numRows"]
numCols = inspect["numCols"]
print "numRows:", numRows, "numCols:", numCols
model_key = "m.hex"
kwargs = {
'ignored_cols': None,
'response': numCols - 1,
'classification': 0,
'activation': 'RectifierWithDropout',
'input_dropout_ratio': 0.2,
'hidden': '117',
'adaptive_rate': 0,
'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': 2.0,
'destination_key': model_key,
# 'validation' : None,
'score_interval': 10000,
'autoencoder': 1,
}
timeoutSecs = 600
start = time.time()
nn = h2o_cmd.runDeepLearning(parseResult=parseResult,
timeoutSecs=timeoutSecs,
**kwargs)
print "neural net end. took", time.time() - start, "seconds"
kwargs = {
'destination_key': "a.hex",
'source': parseResult['destination_key'],
'dl_autoencoder_model': model_key,
'thresh': 1.0
}
anomaly = h2o.nodes[0].anomaly(timeoutSecs=30, **kwargs)
inspect = h2o_cmd.runInspect(None, "a.hex")
numRows = inspect["numRows"]
numCols = inspect["numCols"]
print "anomaly: numRows:", numRows, "numCols:", numCols
self.assertEqual(numCols, 1)
# twice as many rows because of NA injection
self.assertEqual(numRows, rowCount * (1 + NA_ROW_RATIO))
# first col has the anomaly info. other cols are the same as orig data
aSummary = h2o_cmd.runSummary(key='a.hex', cols=0)
h2o_cmd.infoFromSummary(aSummary)
print "anomaly:", h2o.dump_json(anomaly)
trial += 1
h2i.delete_keys_at_all_nodes()
def test_NN_mnist(self):
#h2b.browseTheCloud()
csvPathname_train = 'mnist/train.csv.gz'
csvPathname_test = 'mnist/test.csv.gz'
hex_key = 'mnist_train.hex'
validation_key = 'mnist_test.hex'
timeoutSecs = 30
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',
'adaptive_rate' : 0,
'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' : 2.0,
'destination_key' : model_key,
'validation' : validation_key,
'score_interval' : 10000
}
expectedErr = 0.057 ## expected validation error for the above model
relTol = 0.20 ## 20% rel. error tolerance due to 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'
predict_key = 'score_' + identifier + '.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))
def test_DeepLearning_twovalues(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
csvFilename = "syn_twovalues.csv"
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
rowDataTrue = "1, 0, 65, 1, 2, 1, 1, 4, 1, 4, 1, 4"
rowDataFalse = "0, 1, 0, -1, -2, -1, -1, -4, -1, -4, -1, -4"
twoValueList = [
('A','B',0, 14),
('A','B',1, 14),
(0,1,0, 12),
(0,1,1, 12),
(0,1,'NaN', 12),
(1,0,'NaN', 12),
(-1,1,0, 12),
(-1,1,1, 12),
(-1e1,1e1,1e1, 12),
(-1e1,1e1,-1e1, 12),
]
trial = 0
for (outputTrue, outputFalse, case, coeffNum) in twoValueList:
write_syn_dataset(csvPathname, 20,
rowDataTrue, rowDataFalse, str(outputTrue), str(outputFalse))
start = time.time()
hex_key = csvFilename + "_" + str(trial)
model_key = 'trial_' + str(trial) + '.hex'
validation_key = hex_key
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key)
print "using outputTrue: %s outputFalse: %s" % (outputTrue, outputFalse)
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvPathname, \
" numRows:", "{:,}".format(inspect['numRows']), \
" numCols:", "{:,}".format(inspect['numCols'])
response = inspect['numCols']
response = 'C' + str(response)
kwargs = {
'ignored_cols' : None,
'response' : response,
'classification' : 1,
'activation' : 'Tanh',
#'input_dropout_ratio' : 0.2,
'hidden' : '113,71,54',
'rate' : 0.01,
'rate_annealing' : 1e-6,
'momentum_start' : 0,
'momentum_stable' : 0,
'l1' : 0.0,
'l2' : 1e-6,
'seed' : 80023842348,
'loss' : 'CrossEntropy',
#'max_w2' : 15,
'initial_weight_distribution' : 'UniformAdaptive',
#'initial_weight_scale' : 0.01,
'epochs' : 100,
'destination_key' : model_key,
'validation' : hex_key,
}
timeoutSecs = 60
start = time.time()
h2o_cmd.runDeepLearning(parseResult=parseResult, timeoutSecs=timeoutSecs, **kwargs)
print "trial #", trial, "Deep Learning end on ", csvFilename, ' took', time.time() - start, 'seconds'
#### Now score using the model, and check the validation error
expectedErr = 0.00
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))
trial += 1
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 = 0.046
relTol = 0.1
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))
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 = 60
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'
relTol = 0.10 ### 10% relative error is acceptable for Hogwild
h2o_nn.checkLastValidationError(self, nn['neuralnet_model'],
inspect['numRows'], expectedErr,
relTol, **kwargs)
### Now score using the model, and check the validation error
kwargs = {
'source': validation_key,
'max_rows': 0,
'response': response,
'ignored_cols':
None, # this is not consistent with ignored_cols_by_name
'classification': 1,
'destination_key': 'score_' + identifier + '.hex',
'model': model_key,
}
nnScoreResult = h2o_cmd.runDeepLearningScore(
key=parseResult['destination_key'],
timeoutSecs=timeoutSecs,
**kwargs)
h2o_nn.checkScoreResult(self, nnScoreResult, expectedErr, relTol,
**kwargs)
h2o.beta_features = False
