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.pickRandNNParams(paramDict, params)
kwargs = params.copy()
start = time.time()
nn = h2o_cmd.runNNet2(timeoutSecs=70, parseResult=parseResult, **kwargs)
print "nn result:", h2o.dump_json(nn)
h2o.check_sandbox_for_errors()
# FIX! simple check?
print "NN2 end on ", csvPathname, "took", time.time() - start, "seconds"
print "Trial #", trial, "completed\n"
def test_NN_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 = 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',
'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.runNNet2(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
