def test_RF(self):
h2o.beta_features = True
paramsTrainRF = {"ntrees": 2, "max_depth": 300, "nbins": 200, "timeoutSecs": 600, "response": "C55"}
paramsScoreRF = {"vactual": "C55", "timeoutSecs": 600}
trainKey1 = self.loadData(trainDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, **kwargs)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
trainKey2 = self.loadData(trainDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, **kwargs)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
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_RF(self):
trainKey1 = self.loadData(trainDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, **kwargs)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
print "\nTrain1\n=========={0}".format(
h2o_rf.pp_rf_result(trainResult1))
print "\nScore1\n========={0}".format(
h2o_rf.pp_rf_result(scoreResult1))
trainKey2 = self.loadData(trainDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, **kwargs)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
print "\nTrain2\n=========={0}".format(
h2o_rf.pp_rf_result(trainResult2))
print "\nScore2\n========={0}".format(
h2o_rf.pp_rf_result(scoreResult2))
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_RF(self):
trainKey1 = self.loadData(trainDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, **kwargs)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
print "\nTrain1\n=========={0}".format(h2o_rf.pp_rf_result(trainResult1))
print "\nScore1\n========={0}".format(h2o_rf.pp_rf_result(scoreResult1))
trainKey2 = self.loadData(trainDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, **kwargs)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
print "\nTrain2\n=========={0}".format(h2o_rf.pp_rf_result(trainResult2))
print "\nScore2\n========={0}".format(h2o_rf.pp_rf_result(scoreResult2))
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_RF(self):
normalRF = False
#normalRF = True
print """
Normal RF : {0}
Train data: {1}
Test data : {2}""".format(normalRF, trainDS['filename'], scoreDS['filename'])
print "Loading data...."
trainKey = self.loadTrainData()
kwargs = paramsTrainRF.copy()
print "Running normal RF: {0}".format(normalRF)
if normalRF:
trainResult = h2o_rf.trainRF(trainKey, model_key="rfm_normal", **kwargs)
else:
trainResult = h2o_rf.trainRF(trainKey, refine=1, model_key="rfm_refined", **kwargs)
scoreKey = self.loadScoreData()
kwargs = paramsScoreRF.copy()
scoreResult = h2o_rf.scoreRF(scoreKey, trainResult, **kwargs)
print """
Normal RF : {0}
Train data: {1}
Test data : {2}""".format(normalRF, trainDS['filename'], scoreDS['filename'])
print "\nTrain\n=========={0}".format(h2o_rf.pp_rf_result(trainResult))
print "\nScoring\n========={0}".format(h2o_rf.pp_rf_result(scoreResult))
def test_RF(self):
h2o.beta_features = True
paramsTrainRF = {
'seed': '1234567890',
'ntrees': 1,
'max_depth': 10,
# 'sample_rate': 1.0,
'sample_rate': 1.0,
'nbins': 50,
'timeoutSecs': 600,
'response': 'C55',
'classification': 1,
}
paramsScoreRF = {
'vactual': 'C55',
'timeoutSecs': 600,
}
# train1
trainKey1 = self.loadData(trainDS1)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, scoreKey1, **kwargs)
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)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, scoreKey2, **kwargs)
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)
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)
# should only be two diffs
if len(df.difference) > 2:
raise Exception ("Too many diffs in JsonDiff sorted vs non-sorted %s" % len(df.difference))
def test_RF(self):
h2o.beta_features = True
paramsTrainRF = {
'seed': '1234567890',
# if I use 100, and just one tree, I should get same results for sorted/shuffled?
# i.e. the bagging always sees everything. Means oobe will be messed up
# so will specify validation = the 10pct holdout data (could reuse the training data?)
'sample_rate': 1.0,
'ntrees': 3,
'max_depth': 300,
'nbins': 200,
'timeoutSecs': 600,
'response': 'C55',
}
paramsScoreRF = {
'vactual': 'C55',
'timeoutSecs': 600,
}
# 90% data
trainKey1 = self.loadData(trainDS1)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, scoreKey1, **kwargs)
(classification_error1, classErrorPctList1, totalScores1) = h2o_rf.simpleCheckRFView(rfv=trainResult1)
self.assertEqual(4.29, classification_error1)
self.assertEqual([4.17, 2.98, 4.09, 14.91, 21.12, 15.38, 5.22], classErrorPctList1)
self.assertEqual(58101, totalScores1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
# 10% data
trainKey2 = self.loadData(trainDS2)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, scoreKey2, **kwargs)
(classification_error2, classErrorPctList2, totalScores2) = h2o_rf.simpleCheckRFView(rfv=trainResult2)
self.assertEqual(4.29, classification_error2)
self.assertEqual([4.17, 2.98, 4.09, 14.91, 21.12, 15.38, 5.22], classErrorPctList2)
self.assertEqual(58101, totalScores2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
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)
# should only be two diffs
if len(df.difference) > 2:
raise Exception ("Too many diffs in JsonDiff sorted vs non-sorted %s" % len(df.difference))
def test_c8_rf_airlines_hdfs(self):
h2o.beta_features = True
trainParseResult = self.loadTrainData()
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainParseResult, **kwargs)
scoreParseResult = self.loadScoreData()
kwargs = paramsScoreRF.copy()
scoreResult = h2o_rf.scoreRF(scoreParseResult, trainResult, **kwargs)
def test_c8_rf_airlines_hdfs(self):
trainParseResult = self.loadTrainData()
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainParseResult, **kwargs)
scoreParseResult = self.loadScoreData()
kwargs = paramsScoreRF.copy()
scoreResult = h2o_rf.scoreRF(scoreParseResult, trainResult, **kwargs)
h2i.delete_keys_at_all_nodes(timeoutSecs=600)
def test_RF(self):
trainKey = self.loadTrainData()
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainKey, **kwargs)
scoreKey = self.loadScoreData()
kwargs = paramsScoreRF.copy()
scoreResult = h2o_rf.scoreRF(scoreKey, trainResult, **kwargs)
print "\nTrain\n=========={0}".format(h2o_rf.pp_rf_result(trainResult))
print "\nScoring\n========={0}".format(h2o_rf.pp_rf_result(scoreResult))
def test_RF(self):
trainKey = self.loadTrainData()
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainKey, **kwargs)
scoreKey = self.loadScoreData()
kwargs = paramsScoreRF.copy()
scoreResult = h2o_rf.scoreRF(scoreKey, trainResult, **kwargs)
print "\nTrain\n=========={0}".format(h2o_rf.pp_rf_result(trainResult))
print "\nScoring\n========={0}".format(h2o_rf.pp_rf_result(scoreResult))
def test_c8_rf_airlines_hdfs(self):
trainParseResult = self.loadTrainData()
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainParseResult, **kwargs)
scoreParseResult = self.loadScoreData()
kwargs = paramsScoreRF.copy()
scoreResult = h2o_rf.scoreRF(scoreParseResult, trainResult, **kwargs)
print "\nTrain\n=========={0}".format(h2o_rf.pp_rf_result(trainResult))
print "\nScoring\n========={0}".format(h2o_rf.pp_rf_result(scoreResult))
def test_rf_iris(self):
# Train RF
trainParseResult = h2i.import_parse(bucket='smalldata', path='iris/iris2.csv', hex_key='train_iris2.hex', schema='put')
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainParseResult, **kwargs)
scoreParseResult = h2i.import_parse(bucket='smalldata', path='iris/iris2.csv', hex_key='score_iris2.hex', schema='put')
kwargs = paramsTestRF.copy()
scoreResult = h2o_rf.scoreRF(scoreParseResult, trainResult, **kwargs)
print "\nTrain\n=========={0}".format(h2o_rf.pp_rf_result(trainResult))
print "\nScoring\n========={0}".format(h2o_rf.pp_rf_result(scoreResult))
def test_RF(self):
trainKey = self.loadTrainData()
scoreKey = self.loadScoreData()
#time.sleep(3600)
executeNormalRF = True
executeNormalRF = False
if executeNormalRF:
kwargs = paramsTrainRF.copy()
trainResultNormal = h2o_rf.trainRF(trainKey, model_key="rfm_normal", **kwargs)
#print h2o_rf.pp_rf_result(trainResultNormal)
kwargs = paramsScoreRF.copy()
scoreResultNormal = h2o_rf.scoreRF(scoreKey, trainResultNormal, **kwargs)
print "\nScoring normal forest\n========={0}".format(h2o_rf.pp_rf_result(scoreResultNormal))
kwargs = paramsTrainRF.copy()
trainResultRefined = h2o_rf.trainRF(trainKey, refine=1, model_key="rfm_refined", **kwargs)
#print h2o_rf.pp_rf_result(trainResultRefined)
kwargs = paramsScoreRF.copy()
scoreResultRefined = h2o_rf.scoreRF(scoreKey, trainResultRefined, **kwargs)
print "\nScoring refined forest\n========={0}".format(h2o_rf.pp_rf_result(scoreResultRefined))
time.sleep(3600)
def test_rf_iris(self):
# Train RF
trainParseResult = h2i.import_parse(bucket='smalldata',
path='iris/iris2.csv',
hex_key='train_iris2.hex',
schema='put')
kwargs = paramsTrainRF.copy()
trainResult = h2o_rf.trainRF(trainParseResult, **kwargs)
scoreParseResult = h2i.import_parse(bucket='smalldata',
path='iris/iris2.csv',
hex_key='score_iris2.hex',
schema='put')
kwargs = paramsTestRF.copy()
scoreResult = h2o_rf.scoreRF(scoreParseResult, trainResult, **kwargs)
print "\nTrain\n=========={0}".format(h2o_rf.pp_rf_result(trainResult))
print "\nScoring\n========={0}".format(
h2o_rf.pp_rf_result(scoreResult))
def test_RF(self):
h2o.beta_features = True
if h2o.beta_features:
paramsTrainRF = {
'ntrees': 10,
'max_depth': 300,
'nbins': 200,
'timeoutSecs': 600,
'response': 'C55',
}
paramsScoreRF = {
'vactual': 'C55',
'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,
}
trainKey1 = self.loadData(trainDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, **kwargs)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
trainKey2 = self.loadData(trainDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, **kwargs)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
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_RF(self):
h2o.beta_features = True
if h2o.beta_features:
paramsTrainRF = {
'ntrees': 10,
'max_depth': 300,
'nbins': 200,
'timeoutSecs': 600,
'response': 'C55',
}
paramsScoreRF = {
'vactual': 'C55',
'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,
}
trainKey1 = self.loadData(trainDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, **kwargs)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
trainKey2 = self.loadData(trainDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, **kwargs)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
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_RF(self):
h2o.beta_features = True
paramsTrainRF = {
'seed': '1234567890',
# if I use 100, and just one tree, I should get same results for sorted/shuffled?
# i.e. the bagging always sees everything. Means oobe will be messed up
# so will specify validation = the 10pct holdout data (could reuse the training data?)
'sample_rate': 1.0,
'ntrees': 3,
'max_depth': 300,
'nbins': 200,
'timeoutSecs': 600,
'response': 'C55',
}
paramsScoreRF = {
'vactual': 'C55',
'timeoutSecs': 600,
}
# 90% data
trainKey1 = self.loadData(trainDS1)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, scoreKey1, **kwargs)
(classification_error1, classErrorPctList1, totalScores1) = h2o_rf.simpleCheckRFView(rfv=trainResult1)
# self.assertEqual(4.29, classification_error1)
# self.assertEqual([4.17, 2.98, 4.09, 14.91, 21.12, 15.38, 5.22], classErrorPctList1)
# with new RNG 9/26/14
self.assertEqual(4.4, classification_error1)
self.assertEqual([3.71, 3.56, 4.32, 18.55, 21.22, 13.51, 5.82], classErrorPctList1)
self.assertEqual(58101, totalScores1)
kwargs = paramsScoreRF.copy()
scoreResult1 = h2o_rf.scoreRF(scoreKey1, trainResult1, **kwargs)
# 10% data
trainKey2 = self.loadData(trainDS2)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, scoreKey2, **kwargs)
(classification_error2, classErrorPctList2, totalScores2) = h2o_rf.simpleCheckRFView(rfv=trainResult2)
# self.assertEqual(4.29, classification_error2)
# self.assertEqual([4.17, 2.98, 4.09, 14.91, 21.12, 15.38, 5.22], classErrorPctList2)
# with new RNG 9/26/14
self.assertEqual(4.4, classification_error1)
self.assertEqual([3.71, 3.56, 4.32, 18.55, 21.22, 13.51, 5.82], classErrorPctList1)
self.assertEqual(58101, totalScores2)
kwargs = paramsScoreRF.copy()
scoreResult2 = h2o_rf.scoreRF(scoreKey2, trainResult2, **kwargs)
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)
# should only be two diffs
if len(df.difference) > 2:
raise Exception ("Too many diffs in JsonDiff sorted vs non-sorted %s" % len(df.difference))
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=========+"
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)
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_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_RF(self):
h2o.beta_features = True
if h2o.beta_features:
paramsTrainRF = {
"ntrees": 3,
"max_depth": 10,
"nbins": 50,
"timeoutSecs": 600,
"response": "C55",
"classification": 1,
}
paramsScoreRF = {"vactual": "C55", "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_RF(self):
paramsTrainRF = {
'seed': '1234567890',
'ntrees': 1,
'max_depth': 10,
# 'sample_rate': 1.0,
'sample_rate': 1.0,
'nbins': 50,
'timeoutSecs': 600,
'response': 'C55',
'classification': 1,
}
paramsScoreRF = {
'vactual': 'C55',
'timeoutSecs': 600,
}
# train1
trainKey1 = self.loadData(trainDS1)
scoreKey1 = self.loadData(scoreDS1)
kwargs = paramsTrainRF.copy()
trainResult1 = h2o_rf.trainRF(trainKey1, scoreKey1, **kwargs)
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)
scoreKey2 = self.loadData(scoreDS2)
kwargs = paramsTrainRF.copy()
trainResult2 = h2o_rf.trainRF(trainKey2, scoreKey2, **kwargs)
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)
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)
# should only be two diffs
if len(df.difference) > 2:
raise Exception(
"Too many diffs in JsonDiff sorted vs non-sorted %s" %
len(df.difference))
