def build_and_test(a_node, pp, datasets, algos, algo_additional_default_params):
####################################################################################################
# Build and do basic validation checks on models
####################################################################################################
models_to_build = [
ModelSpec.for_dataset('kmeans_prostate', 'kmeans', datasets['prostate_clustering'], { 'k': 2 } ),
ModelSpec.for_dataset('glm_prostate_regression', 'glm', datasets['prostate_regression'], {'family': 'gaussian'} ),
ModelSpec.for_dataset('glm_prostate_binomial', 'glm', datasets['prostate_binomial'], {'family': 'binomial'} ),
ModelSpec.for_dataset('glm_airlines_binomial', 'glm', datasets['airlines_binomial'], {'response_column': 'IsDepDelayed', 'family': 'binomial' } ),
ModelSpec.for_dataset('glm_iris_multinomial', 'glm', datasets['iris_multinomial'], {'response_column': 'class', 'family': 'multinomial' } ),
ModelSpec.for_dataset('deeplearning_prostate_regression', 'deeplearning', datasets['prostate_regression'], { 'epochs': 1, 'loss': 'Quadratic' } ),
ModelSpec.for_dataset('deeplearning_prostate_binomial', 'deeplearning', datasets['prostate_binomial'], { 'epochs': 1, 'hidden': [20, 20], 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('deeplearning_airlines_binomial', 'deeplearning', datasets['airlines_binomial'], { 'epochs': 1, 'hidden': [10, 10], 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('deeplearning_iris_multinomial', 'deeplearning', datasets['iris_multinomial'], { 'epochs': 1, 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('gbm_prostate_regression', 'gbm', datasets['prostate_regression'], { 'ntrees': 5, 'distribution': 'gaussian' } ),
ModelSpec.for_dataset('gbm_prostate_binomial', 'gbm', datasets['prostate_binomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
ModelSpec.for_dataset('gbm_airlines_binomial', 'gbm', datasets['airlines_binomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
ModelSpec.for_dataset('gbm_iris_multinomial', 'gbm', datasets['iris_multinomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
]
# For grid testing, don't build any non-grid models:
# models_to_build = []
built_models = {}
for model_spec in models_to_build:
model = model_spec.build_and_validate_model(a_node)
built_models[model_spec['dest_key']] = model
grids_to_build = [
GridSpec.for_dataset('kmeans_prostate_grid', 'kmeans', datasets['prostate_clustering'], { }, { 'k': [2, 3, 4] } ),
GridSpec.for_dataset('glm_prostate_regression_grid', 'glm', datasets['prostate_regression'], {'family': 'gaussian'}, { 'lambda': [0.0001, 0.001, 0.01, 0.1] } ),
GridSpec.for_dataset('glm_prostate_binomial_grid', 'glm', datasets['prostate_binomial'], {'family': 'binomial'}, { 'lambda': [0.0001, 0.001, 0.01, 0.1] } ),
GridSpec.for_dataset('glm_airlines_binomial_grid', 'glm', datasets['airlines_binomial'], {'response_column': 'IsDepDelayed', 'family': 'binomial'}, { 'lambda': [0.0001, 0.001, 0.01, 0.025] } ),
GridSpec.for_dataset('glm_iris_multinomial_grid', 'glm', datasets['iris_multinomial'], {'response_column': 'class', 'family': 'multinomial'}, { 'lambda': [0.0001, 0.001, 0.01, 0.025] } ),
GridSpec.for_dataset('deeplearning_prostate_regression_grid', 'deeplearning', datasets['prostate_regression'], { 'loss': 'Quadratic' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('deeplearning_prostate_binomial_grid', 'deeplearning', datasets['prostate_binomial'], { 'hidden': [20, 20], 'loss': 'CrossEntropy' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('deeplearning_airlines_binomial_grid', 'deeplearning', datasets['airlines_binomial'], { 'hidden': [10, 10], 'loss': 'CrossEntropy' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('deeplearning_iris_multinomial_grid', 'deeplearning', datasets['iris_multinomial'], { 'loss': 'CrossEntropy' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('gbm_prostate_regression_grid', 'gbm', datasets['prostate_regression'], { 'max_depth': 3 }, { 'ntrees': [1, 5, 10], 'distribution': ["gaussian", "poisson", "gamma", "tweedie"] } ),
GridSpec.for_dataset('gbm_prostate_binomial_grid', 'gbm', datasets['prostate_binomial'], { }, { 'ntrees': [5, 7], 'max_depth': [1, 3, 5] } ),
GridSpec.for_dataset('gbm_airlines_binomial_grid', 'gbm', datasets['airlines_binomial'], { 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
GridSpec.for_dataset('gbm_iris_multinomial_grid', 'gbm', datasets['iris_multinomial'], { 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
# TODO: this should trigger a parameter validation error, but instead the non-grid ntrees silently overrides the grid values: GridSpec.for_dataset('gbm_iris_multinomial_grid', 'gbm', datasets['iris_multinomial'], { 'ntrees': 5, 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
# Test stopping criteria:
GridSpec.for_dataset('gbm_prostate_regression_grid_max_3', 'gbm', datasets['prostate_regression'], { 'max_depth': 3 }, { 'ntrees': [1, 2, 4], 'distribution': ["gaussian", "poisson", "gamma", "tweedie"] }, { 'strategy': "RandomDiscrete", 'max_models': 3 } ),
GridSpec.for_dataset('gbm_prostate_regression_grid_max_20mS', 'gbm', datasets['prostate_regression'], { 'max_depth': 3 }, { 'ntrees': [1, 2, 4], 'distribution': ["gaussian", "poisson", "gamma", "tweedie"] }, { 'strategy': "RandomDiscrete", 'max_runtime_secs': 0.020 } ),
]
for grid_spec in grids_to_build:
grid = grid_spec.build_and_validate_grid(a_node)
for model_key in grid['model_ids']:
model_key = model_key['name']
built_models[model_key] = a_node.models(key=model_key)
# test search limits: max_models
grid = a_node.grid(key='gbm_prostate_regression_grid_max_3')
assert len(grid['model_ids']) == 3, "FAIL: using max_models, expected a max of 3 models, got: " + str(len(grid['model_ids']))
# test search limits: max_runtime_secs
grid = a_node.grid(key='gbm_prostate_regression_grid_max_20mS')
assert len(grid['model_ids']) < 12, "FAIL: using max_runtime_secs, expected less than 12 models, got: " + str(len(grid['model_ids']))
# grid = a_node.grid(key='kmeans_prostate_grid', sort_by='', decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='totss', decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='tot_withinss', decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='betweenss', decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='totss', decreasing=False)
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='tot_withinss', decreasing=False)
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='betweenss', decreasing=False)
# import sys
# sys.exit(0)
#######################################
# Test default parameters validation for each model builder
#
if h2o_test_utils.isVerbose(): print 'Testing ModelBuilder default parameters. . .'
model_builders = a_node.model_builders(timeoutSecs=240)['model_builders']
# Do we know about all of them?
server_algos = model_builders.keys()
assert len(set(server_algos) - set(algos)) == 0, "FAIL: Our set of algos doesn't match what the server knows about. Ours: " + repr(algos) + "; server's: " + repr(server_algos)
for algo, model_builder in model_builders.iteritems():
parameters_list = model_builder['parameters']
test_parameters = { value['name'] : value['default_value'] for value in parameters_list } # collect default parameters
if algo in algo_additional_default_params:
test_parameters.update(algo_additional_default_params[algo])
if h2o_test_utils.isVerboser(): print 'Testing ' + algo + ' with params: ' + repr(test_parameters)
parameters_validation = a_node.validate_model_parameters(algo=algo, training_frame=None, parameters=test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
expected_count = 0
if expected_count != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert expected_count == parameters_validation['error_count'], "FAIL: " + str(expected_count) + " != error_count in good-parameters parameters validation result."
#######################################
# Test DeepLearning parameters validation
#
# Default parameters:
if h2o_test_utils.isVerbose(): print 'Testing DeepLearning default parameters. . .'
model_builder = a_node.model_builders(algo='deeplearning', timeoutSecs=240)['model_builders']['deeplearning']
dl_test_parameters_list = model_builder['parameters']
dl_test_parameters = {value['name'] : value['default_value'] for value in dl_test_parameters_list}
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert 0 == parameters_validation['error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Good parameters (note: testing with null training_frame):
if h2o_test_utils.isVerbose(): print 'Testing DeepLearning good parameters. . .'
dl_test_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]" }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert 0 == parameters_validation['error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Bad parameters (hidden is null):
# (note: testing with null training_frame)
if h2o_test_utils.isVerbose(): print 'Testing DeepLearning bad parameters, null training_frame. . .'
dl_test_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]", 'input_dropout_ratio': 27 }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (input_dropout_ratio)."
h2o.H2O.verboseprint("Good params validation messages: ", repr(parameters_validation))
assert 0 != parameters_validation['error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(parameters_validation)
found_expected_error = False
for validation_message in parameters_validation['messages']:
if validation_message['message_type'] == 'ERRR' and validation_message['field_name'] == 'input_dropout_ratio':
found_expected_error = True
assert found_expected_error, "FAIL: Failed to find error message about input_dropout_ratio in the validation messages."
# Bad parameters (no response_column):
if h2o_test_utils.isVerbose(): print 'Testing DeepLearning bad parameters, null response_column. . .'
dl_test_parameters = {'hidden': "[10, 20, 10]" }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame='prostate_binomial', parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (response_column)."
h2o.H2O.verboseprint("Good params validation messages: ", repr(parameters_validation))
assert 0 != parameters_validation['error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(parameters_validation)
#######################################
# Try to build DeepLearning model for Prostate but with bad parameters; we should get a ModelParametersSchema with the error.
if h2o_test_utils.isVerbose(): print 'About to try to build a DeepLearning model with bad parameters. . .'
dl_prostate_bad_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]", 'input_dropout_ratio': 27 }
parameters_validation = a_node.build_model(algo='deeplearning', model_id='deeplearning_prostate_binomial_bad', training_frame='prostate_binomial', parameters=dl_prostate_bad_parameters, timeoutSecs=240) # synchronous
h2o_test_utils.validate_validation_messages(parameters_validation, ['input_dropout_ratio'])
assert parameters_validation['__http_response']['status_code'] == requests.codes.precondition_failed, "FAIL: expected 412 Precondition Failed from a bad build request, got: " + str(parameters_validation['__http_response']['status_code'])
if h2o_test_utils.isVerbose(): print 'Done trying to build DeepLearning model with bad parameters.'
#####################################
# Early test of predict()
# TODO: remove after we remove the early exit
p = a_node.predict(model='deeplearning_airlines_binomial', frame='airlines_binomial', predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_predictions(a_node, p, 'deeplearning_airlines_binomial', 'airlines_binomial', 43978, predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_frame_exists(a_node, 'deeplearning_airlines_binomial_predictions')
h2o.H2O.verboseprint("Predictions for scoring: ", 'deeplearning_airlines_binomial', " on: ", 'airlines_binomial', ": ", repr(p))
def build_and_test(a_node, pp, datasets, algos, algo_additional_default_params):
####################################################################################################
# Build and do basic validation checks on models
####################################################################################################
models_to_build = [
ModelSpec.for_dataset('kmeans_prostate', 'kmeans', datasets['prostate_clustering'], { 'k': 2 } ),
ModelSpec.for_dataset('glm_prostate_regression', 'glm', datasets['prostate_regression'], {'family': 'gaussian'} ),
ModelSpec.for_dataset('glm_prostate_binomial', 'glm', datasets['prostate_binomial'], {'family': 'binomial'} ),
# TODO: Crashes: ModelSpec('glm_airlines_binomial', 'glm', 'airlines_binomial', {'response_column': 'IsDepDelayed', 'do_classification': True, 'family': 'binomial'}, 'Binomial'),
# Multinomial doesn't make sense for glm: ModelSpec('glm_iris_multinomial', 'glm', iris_multinomial, {'response_column': 'class', 'do_classification': True, 'family': 'gaussian'}, 'Regression'),
ModelSpec.for_dataset('deeplearning_prostate_regression', 'deeplearning', datasets['prostate_regression'], { 'epochs': 1, 'loss': 'Automatic' } ),
# TODO: add toEnum of the response column and put back: ModelSpec.for_dataset('deeplearning_prostate_binomial', 'deeplearning', datasets['prostate_binomial'], { 'epochs': 1, 'hidden': [20, 20], 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('deeplearning_airlines_binomial', 'deeplearning', datasets['airlines_binomial'], { 'epochs': 1, 'hidden': [10, 10], 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('deeplearning_iris_multinomial', 'deeplearning', datasets['iris_multinomial'], { 'epochs': 1, 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('gbm_prostate_regression', 'gbm', datasets['prostate_regression'], { 'ntrees': 5, 'distribution': 'gaussian' } ),
# TODO: add toEnum of the response column and put back: ModelSpec.for_dataset('gbm_prostate_binomial', 'gbm', datasets['prostate_binomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
ModelSpec.for_dataset('gbm_airlines_binomial', 'gbm', datasets['airlines_binomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
ModelSpec.for_dataset('gbm_iris_multinomial', 'gbm', datasets['iris_multinomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
]
built_models = {}
for model_spec in models_to_build:
model = model_spec.build_and_validate_model(a_node)
built_models[model_spec['dest_key']] = model
#######################################
# Test default parameters validation for each model builder
#
if h2o_test_utils.isVerbose(): print 'Testing ModelBuilder default parameters. . .'
model_builders = a_node.model_builders(timeoutSecs=240)['model_builders']
# Do we know about all of them?
server_algos = model_builders.keys()
assert len(set(server_algos) - set(algos)) == 0, "FAIL: Our set of algos doesn't match what the server knows about. Ours: " + repr(algos) + "; server's: " + repr(server_algos)
for algo, model_builder in model_builders.iteritems():
parameters_list = model_builder['parameters']
test_parameters = { value['name'] : value['default_value'] for value in parameters_list } # collect default parameters
if algo in algo_additional_default_params:
test_parameters.update(algo_additional_default_params[algo])
parameters_validation = a_node.validate_model_parameters(algo=algo, training_frame=None, parameters=test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
expected_count = 0
if expected_count != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert expected_count == parameters_validation['error_count'], "FAIL: " + str(expected_count) + " != error_count in good-parameters parameters validation result."
#######################################
# Test DeepLearning parameters validation
#
# Default parameters:
model_builder = a_node.model_builders(algo='deeplearning', timeoutSecs=240)['model_builders']['deeplearning']
dl_test_parameters_list = model_builder['parameters']
dl_test_parameters = {value['name'] : value['default_value'] for value in dl_test_parameters_list}
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert 0 == parameters_validation['error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Good parameters (note: testing with null training_frame):
dl_test_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]" }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert 0 == parameters_validation['error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Bad parameters (hidden is null):
# (note: testing with null training_frame)
dl_test_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]", 'input_dropout_ratio': 27 }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (input_dropout_ratio)."
h2o.H2O.verboseprint("Good params validation messages: ", repr(parameters_validation))
assert 0 != parameters_validation['error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(parameters_validation)
found_expected_error = False
for validation_message in parameters_validation['messages']:
if validation_message['message_type'] == 'ERROR' and validation_message['field_name'] == 'input_dropout_ratio':
found_expected_error = True
assert found_expected_error, "FAIL: Failed to find error message about input_dropout_ratio in the validation messages."
# Bad parameters (no response_column):
dl_test_parameters = {'hidden': "[10, 20, 10]" }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame='prostate_binomial', parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (response_column)."
h2o.H2O.verboseprint("Good params validation messages: ", repr(parameters_validation))
assert 0 != parameters_validation['error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(parameters_validation)
#######################################
# Try to build DeepLearning model for Prostate but with bad parameters; we should get a ModelParametersSchema with the error.
if h2o_test_utils.isVerbose(): print 'About to try to build a DeepLearning model with bad parameters. . .'
dl_prostate_bad_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]", 'input_dropout_ratio': 27 }
parameters_validation = a_node.build_model(algo='deeplearning', model_id='deeplearning_prostate_binomial_bad', training_frame='prostate_binomial', parameters=dl_prostate_bad_parameters, timeoutSecs=240) # synchronous
h2o_test_utils.validate_validation_messages(parameters_validation, ['input_dropout_ratio'])
assert parameters_validation['__http_response']['status_code'] == requests.codes.precondition_failed, "FAIL: expected 412 Precondition Failed from a bad build request, got: " + str(parameters_validation['__http_response']['status_code'])
if h2o_test_utils.isVerbose(): print 'Done trying to build DeepLearning model with bad parameters.'
#####################################
# Early test of predict()
# TODO: remove after we remove the early exit
p = a_node.predict(model='deeplearning_airlines_binomial', frame='airlines_binomial', predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_predictions(a_node, p, 'deeplearning_airlines_binomial', 'airlines_binomial', 43978, predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_frame_exists(a_node, 'deeplearning_airlines_binomial_predictions')
h2o.H2O.verboseprint("Predictions for scoring: ", 'deeplearning_airlines_binomial', " on: ", 'airlines_binomial', ": ", repr(p))
def build_and_test(a_node, pp, datasets, algos, algo_additional_default_params):
####################################################################################################
# Build and do basic validation checks on models
####################################################################################################
models_to_build = [
ModelSpec.for_dataset('kmeans_prostate', 'kmeans', datasets['prostate_clustering'], { 'k': 2 } ),
ModelSpec.for_dataset('glm_prostate_regression', 'glm', datasets['prostate_regression'], {'family': 'gaussian'} ),
ModelSpec.for_dataset('glm_prostate_binomial', 'glm', datasets['prostate_binomial'], {'family': 'binomial'} ),
ModelSpec.for_dataset('glm_airlines_binomial', 'glm', datasets['airlines_binomial'], {'response_column': 'IsDepDelayed', 'family': 'binomial' } ),
ModelSpec.for_dataset('glm_iris_multinomial', 'glm', datasets['iris_multinomial'], {'response_column': 'class', 'family': 'multinomial' } ),
ModelSpec.for_dataset('deeplearning_prostate_regression', 'deeplearning', datasets['prostate_regression'], { 'epochs': 1, 'loss': 'Quadratic' } ),
ModelSpec.for_dataset('deeplearning_prostate_binomial', 'deeplearning', datasets['prostate_binomial'], { 'epochs': 1, 'hidden': [20, 20], 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('deeplearning_airlines_binomial', 'deeplearning', datasets['airlines_binomial'], { 'epochs': 1, 'hidden': [10, 10], 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('deeplearning_iris_multinomial', 'deeplearning', datasets['iris_multinomial'], { 'epochs': 1, 'loss': 'CrossEntropy' } ),
ModelSpec.for_dataset('gbm_prostate_regression', 'gbm', datasets['prostate_regression'], { 'ntrees': 5, 'distribution': 'gaussian' } ),
ModelSpec.for_dataset('gbm_prostate_binomial', 'gbm', datasets['prostate_binomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
ModelSpec.for_dataset('gbm_airlines_binomial', 'gbm', datasets['airlines_binomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
ModelSpec.for_dataset('gbm_iris_multinomial', 'gbm', datasets['iris_multinomial'], { 'ntrees': 5, 'distribution': 'multinomial' } ),
]
built_models = {}
for model_spec in models_to_build:
model = model_spec.build_and_validate_model(a_node)
built_models[model_spec['dest_key']] = model
grids_to_build = [
GridSpec.for_dataset('kmeans_prostate_grid', 'kmeans', datasets['prostate_clustering'], { }, { 'k': [2, 3, 4] } ),
GridSpec.for_dataset('glm_prostate_regression_grid', 'glm', datasets['prostate_regression'], {'family': 'gaussian'}, { 'lambda': [0.0001, 0.001, 0.01, 0.1] } ),
GridSpec.for_dataset('glm_prostate_binomial_grid', 'glm', datasets['prostate_binomial'], {'family': 'binomial'}, { 'lambda': [0.0001, 0.001, 0.01, 0.1] } ),
GridSpec.for_dataset('glm_airlines_binomial_grid', 'glm', datasets['airlines_binomial'], {'response_column': 'IsDepDelayed', 'family': 'binomial'}, { 'lambda': [0.0001, 0.001, 0.01, 0.025] } ),
GridSpec.for_dataset('glm_iris_multinomial_grid', 'glm', datasets['iris_multinomial'], {'response_column': 'class', 'family': 'multinomial'}, { 'lambda': [0.0001, 0.001, 0.01, 0.025] } ),
GridSpec.for_dataset('deeplearning_prostate_regression_grid', 'deeplearning', datasets['prostate_regression'], { 'loss': 'Quadratic' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('deeplearning_prostate_binomial_grid', 'deeplearning', datasets['prostate_binomial'], { 'hidden': [20, 20], 'loss': 'CrossEntropy' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('deeplearning_airlines_binomial_grid', 'deeplearning', datasets['airlines_binomial'], { 'hidden': [10, 10], 'loss': 'CrossEntropy' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('deeplearning_iris_multinomial_grid', 'deeplearning', datasets['iris_multinomial'], { 'loss': 'CrossEntropy' }, { 'epochs': [0.1, 0.5, 1] } ),
GridSpec.for_dataset('gbm_prostate_regression_grid', 'gbm', datasets['prostate_regression'], { 'max_depth': 3 }, { 'ntrees': [1, 5, 10], 'distribution': ["gaussian", "poisson", "gamma", "tweedie"] } ),
GridSpec.for_dataset('gbm_prostate_binomial_grid', 'gbm', datasets['prostate_binomial'], { }, { 'ntrees': [5, 7], 'max_depth': [1, 3, 5] } ),
GridSpec.for_dataset('gbm_airlines_binomial_grid', 'gbm', datasets['airlines_binomial'], { 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
GridSpec.for_dataset('gbm_iris_multinomial_grid', 'gbm', datasets['iris_multinomial'], { 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
# TODO: this should trigger a parameter validation error, but instead the non-grid ntrees silently overrides the drid values: GridSpec.for_dataset('gbm_iris_multinomial_grid', 'gbm', datasets['iris_multinomial'], { 'ntrees': 5, 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
]
for grid_spec in grids_to_build:
grid = grid_spec.build_and_validate_grid(a_node)
for model_key in grid['model_ids']:
model_key = model_key['name']
built_models[model_key] = a_node.models(key=model_key)
# grid = a_node.grid(key='kmeans_prostate_grid', sort_by='', sort_order='desc')
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='totss', sort_order='desc')
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='tot_withinss', sort_order='desc')
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='betweenss', sort_order='desc')
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='totss', sort_order='asc')
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='tot_withinss', sort_order='asc')
h2o_test_utils.fetch_and_validate_grid_sort(a_node, key='kmeans_prostate_grid', sort_by='betweenss', sort_order='asc')
#######################################
# Test default parameters validation for each model builder
#
if h2o_test_utils.isVerbose(): print 'Testing ModelBuilder default parameters. . .'
model_builders = a_node.model_builders(timeoutSecs=240)['model_builders']
# Do we know about all of them?
server_algos = model_builders.keys()
assert len(set(server_algos) - set(algos)) == 0, "FAIL: Our set of algos doesn't match what the server knows about. Ours: " + repr(algos) + "; server's: " + repr(server_algos)
for algo, model_builder in model_builders.iteritems():
parameters_list = model_builder['parameters']
test_parameters = { value['name'] : value['default_value'] for value in parameters_list } # collect default parameters
if algo in algo_additional_default_params:
test_parameters.update(algo_additional_default_params[algo])
parameters_validation = a_node.validate_model_parameters(algo=algo, training_frame=None, parameters=test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
expected_count = 0
if expected_count != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert expected_count == parameters_validation['error_count'], "FAIL: " + str(expected_count) + " != error_count in good-parameters parameters validation result."
#######################################
# Test DeepLearning parameters validation
#
# Default parameters:
model_builder = a_node.model_builders(algo='deeplearning', timeoutSecs=240)['model_builders']['deeplearning']
dl_test_parameters_list = model_builder['parameters']
dl_test_parameters = {value['name'] : value['default_value'] for value in dl_test_parameters_list}
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert 0 == parameters_validation['error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Good parameters (note: testing with null training_frame):
dl_test_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]" }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ", repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print "validation errors: "
pp.pprint(parameters_validation)
assert 0 == parameters_validation['error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Bad parameters (hidden is null):
# (note: testing with null training_frame)
dl_test_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]", 'input_dropout_ratio': 27 }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame=None, parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (input_dropout_ratio)."
h2o.H2O.verboseprint("Good params validation messages: ", repr(parameters_validation))
assert 0 != parameters_validation['error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(parameters_validation)
found_expected_error = False
for validation_message in parameters_validation['messages']:
if validation_message['message_type'] == 'ERRR' and validation_message['field_name'] == 'input_dropout_ratio':
found_expected_error = True
assert found_expected_error, "FAIL: Failed to find error message about input_dropout_ratio in the validation messages."
# Bad parameters (no response_column):
dl_test_parameters = {'hidden': "[10, 20, 10]" }
parameters_validation = a_node.validate_model_parameters(algo='deeplearning', training_frame='prostate_binomial', parameters=dl_test_parameters, timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (response_column)."
h2o.H2O.verboseprint("Good params validation messages: ", repr(parameters_validation))
assert 0 != parameters_validation['error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(parameters_validation)
#######################################
# Try to build DeepLearning model for Prostate but with bad parameters; we should get a ModelParametersSchema with the error.
if h2o_test_utils.isVerbose(): print 'About to try to build a DeepLearning model with bad parameters. . .'
dl_prostate_bad_parameters = {'response_column': 'CAPSULE', 'hidden': "[10, 20, 10]", 'input_dropout_ratio': 27 }
parameters_validation = a_node.build_model(algo='deeplearning', model_id='deeplearning_prostate_binomial_bad', training_frame='prostate_binomial', parameters=dl_prostate_bad_parameters, timeoutSecs=240) # synchronous
h2o_test_utils.validate_validation_messages(parameters_validation, ['input_dropout_ratio'])
assert parameters_validation['__http_response']['status_code'] == requests.codes.precondition_failed, "FAIL: expected 412 Precondition Failed from a bad build request, got: " + str(parameters_validation['__http_response']['status_code'])
if h2o_test_utils.isVerbose(): print 'Done trying to build DeepLearning model with bad parameters.'
#####################################
# Early test of predict()
# TODO: remove after we remove the early exit
p = a_node.predict(model='deeplearning_airlines_binomial', frame='airlines_binomial', predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_predictions(a_node, p, 'deeplearning_airlines_binomial', 'airlines_binomial', 43978, predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_frame_exists(a_node, 'deeplearning_airlines_binomial_predictions')
h2o.H2O.verboseprint("Predictions for scoring: ", 'deeplearning_airlines_binomial', " on: ", 'airlines_binomial', ": ", repr(p))
def build_and_test(a_node, pp, datasets, algos,
algo_additional_default_params):
####################################################################################################
# Build and do basic validation checks on models
####################################################################################################
models_to_build = [
ModelSpec.for_dataset('kmeans_prostate', 'kmeans',
datasets['prostate_clustering'], {'k': 2}),
ModelSpec.for_dataset('glm_prostate_regression', 'glm',
datasets['prostate_regression'],
{'family': 'gaussian'}),
ModelSpec.for_dataset('glm_prostate_binomial', 'glm',
datasets['prostate_binomial'],
{'family': 'binomial'}),
ModelSpec.for_dataset('glm_airlines_binomial', 'glm',
datasets['airlines_binomial'], {
'response_column': 'IsDepDelayed',
'family': 'binomial'
}),
ModelSpec.for_dataset('glm_iris_multinomial', 'glm',
datasets['iris_multinomial'], {
'response_column': 'class',
'family': 'multinomial'
}),
ModelSpec.for_dataset('deeplearning_prostate_regression',
'deeplearning', datasets['prostate_regression'],
{
'epochs': 1,
'loss': 'Quadratic'
}),
ModelSpec.for_dataset('deeplearning_prostate_binomial', 'deeplearning',
datasets['prostate_binomial'], {
'epochs': 1,
'hidden': [20, 20],
'loss': 'CrossEntropy'
}),
ModelSpec.for_dataset('deeplearning_airlines_binomial', 'deeplearning',
datasets['airlines_binomial'], {
'epochs': 1,
'hidden': [10, 10],
'loss': 'CrossEntropy'
}),
ModelSpec.for_dataset('deeplearning_iris_multinomial', 'deeplearning',
datasets['iris_multinomial'], {
'epochs': 1,
'loss': 'CrossEntropy'
}),
ModelSpec.for_dataset('gbm_prostate_regression', 'gbm',
datasets['prostate_regression'], {
'ntrees': 5,
'distribution': 'gaussian'
}),
ModelSpec.for_dataset('gbm_prostate_binomial', 'gbm',
datasets['prostate_binomial'], {
'ntrees': 5,
'distribution': 'multinomial'
}),
ModelSpec.for_dataset('gbm_airlines_binomial', 'gbm',
datasets['airlines_binomial'], {
'ntrees': 5,
'distribution': 'multinomial'
}),
ModelSpec.for_dataset('gbm_iris_multinomial', 'gbm',
datasets['iris_multinomial'], {
'ntrees': 5,
'distribution': 'multinomial'
}),
]
# For grid testing, don't build any non-grid models:
# models_to_build = []
built_models = {}
for model_spec in models_to_build:
model = model_spec.build_and_validate_model(a_node)
built_models[model_spec['dest_key']] = model
grids_to_build = [
# setting a hyperparameter in both places:
# GridSpec.for_dataset('kmeans_prostate_grid', 'kmeans', datasets['prostate_clustering'], { 'k': 6 }, { 'k': [2, 3, 4] } ),
GridSpec.for_dataset('kmeans_prostate_grid', 'kmeans',
datasets['prostate_clustering'], {},
{'k': [2, 3, 4]}),
GridSpec.for_dataset('glm_prostate_regression_grid', 'glm',
datasets['prostate_regression'],
{'family': 'gaussian'},
{'lambda': [0.0001, 0.001, 0.01, 0.1]}),
GridSpec.for_dataset('glm_prostate_binomial_grid', 'glm',
datasets['prostate_binomial'],
{'family': 'binomial'},
{'lambda': [0.0001, 0.001, 0.01, 0.1]}),
GridSpec.for_dataset('glm_airlines_binomial_grid', 'glm',
datasets['airlines_binomial'], {
'response_column': 'IsDepDelayed',
'family': 'binomial'
}, {'lambda': [0.0001, 0.001, 0.01, 0.025]}),
GridSpec.for_dataset('glm_iris_multinomial_grid', 'glm',
datasets['iris_multinomial'], {
'response_column': 'class',
'family': 'multinomial'
}, {'lambda': [0.0001, 0.001, 0.01, 0.025]}),
GridSpec.for_dataset('deeplearning_prostate_regression_grid',
'deeplearning', datasets['prostate_regression'],
{'loss': 'Quadratic'}, {'epochs': [0.1, 0.5, 1]}),
GridSpec.for_dataset('deeplearning_prostate_binomial_grid',
'deeplearning', datasets['prostate_binomial'], {
'hidden': [20, 20],
'loss': 'CrossEntropy'
}, {'epochs': [0.1, 0.5, 1]}),
GridSpec.for_dataset('deeplearning_airlines_binomial_grid',
'deeplearning', datasets['airlines_binomial'], {
'hidden': [10, 10],
'loss': 'CrossEntropy'
}, {'epochs': [0.1, 0.5, 1]}),
GridSpec.for_dataset('deeplearning_iris_multinomial_grid',
'deeplearning', datasets['iris_multinomial'],
{'loss': 'CrossEntropy'},
{'epochs': [0.1, 0.5, 1]}),
GridSpec.for_dataset(
'gbm_prostate_regression_grid', 'gbm',
datasets['prostate_regression'], {'max_depth': 3}, {
'ntrees': [1, 5, 10],
'distribution': ["gaussian", "poisson", "gamma", "tweedie"]
}),
GridSpec.for_dataset('gbm_prostate_binomial_grid', 'gbm',
datasets['prostate_binomial'], {}, {
'ntrees': [5, 7],
'max_depth': [1, 3, 5]
}),
GridSpec.for_dataset('gbm_airlines_binomial_grid', 'gbm',
datasets['airlines_binomial'],
{'distribution': 'multinomial'}, {
'ntrees': [1, 5, 10],
'max_depth': [1, 3, 5]
}),
GridSpec.for_dataset('gbm_iris_multinomial_grid', 'gbm',
datasets['iris_multinomial'],
{'distribution': 'multinomial'}, {
'ntrees': [1, 5, 10],
'max_depth': [1, 3, 5]
}),
# TODO: this should trigger a parameter validation error, but instead the non-grid ntrees silently overrides the grid values: GridSpec.for_dataset('gbm_iris_multinomial_grid', 'gbm', datasets['iris_multinomial'], { 'ntrees': 5, 'distribution': 'multinomial' }, { 'ntrees': [1, 5, 10], 'max_depth': [1, 3, 5] } ),
# Test stopping criteria:
GridSpec.for_dataset(
'gbm_prostate_regression_grid_max_3', 'gbm',
datasets['prostate_regression'], {'max_depth': 3}, {
'ntrees': [1, 2, 4],
'distribution': ["gaussian", "poisson", "gamma", "tweedie"]
}, {
'strategy': "RandomDiscrete",
'max_models': 3
}),
GridSpec.for_dataset(
'gbm_prostate_regression_grid_max_20mS', 'gbm',
datasets['prostate_regression'], {'max_depth': 3}, {
'ntrees': [1, 2, 4],
'distribution': ["gaussian", "poisson", "gamma", "tweedie"]
}, {
'strategy': "RandomDiscrete",
'max_runtime_secs': 0.020
}),
GridSpec.for_dataset(
'gbm_prostate_regression_grid_stopping_deviance', 'gbm',
datasets['prostate_regression'], {}, {
'max_depth': [1, 2, 3, 4, 5, 6, 7],
'ntrees': [1, 2, 3, 4, 5, 6],
'distribution': ["gaussian", "poisson", "gamma"]
}, {
'strategy': "RandomDiscrete",
'seed': 42,
'stopping_metric': 'deviance',
'stopping_tolerance': 0.00001,
'stopping_rounds': 5
}),
GridSpec.for_dataset(
'gbm_prostate_regression_grid_stopping_auto', 'gbm',
datasets['prostate_regression'], {}, {
'max_depth': [1, 2, 3, 4, 5, 6, 7],
'ntrees': [1, 2, 3, 4, 5, 6],
'distribution': ["gaussian", "poisson", "gamma"]
}, {
'strategy': "RandomDiscrete",
'seed': 42,
'stopping_metric': 'AUTO',
'stopping_tolerance': 0.00001,
'stopping_rounds': 5
}),
]
for grid_spec in grids_to_build:
grid = grid_spec.build_and_validate_grid(a_node)
for model_key in grid['model_ids']:
model_key = model_key['name']
built_models[model_key] = a_node.models(key=model_key)
# test search limits: max_models
grid = a_node.grid(key='gbm_prostate_regression_grid_max_3')
assert len(
grid['model_ids']
) == 3, "FAIL: using max_models, expected a max of 3 models, got: " + str(
len(grid['model_ids']))
# test search limits: max_runtime_secs
grid = a_node.grid(key='gbm_prostate_regression_grid_max_20mS')
assert len(
grid['model_ids']
) < 12, "FAIL: using max_runtime_secs, expected less than 12 models, got: " + str(
len(grid['model_ids']))
# test search limits: stopping_deviance
grid = a_node.grid(key='gbm_prostate_regression_grid_stopping_deviance')
deviance_model_count = len(grid['model_ids'])
assert len(
grid['model_ids']
) < 126, "FAIL: using asymptotic deviance stopping criterion, expected less than 126 models, got: " + str(
len(grid['model_ids']))
# test search limits: stopping_auto
grid = a_node.grid(key='gbm_prostate_regression_grid_stopping_auto')
auto_model_count = len(grid['model_ids'])
assert len(
grid['model_ids']
) < 126, "FAIL: using asymptotic auto stopping criterion, expected less than 126 models, got: " + str(
len(grid['model_ids']))
# test that AUTO gave the same answer as deviance
assert deviance_model_count == auto_model_count, "FAIL: using asymptotic auto stopping criterion, expected the same number of models as deviance, got: " + str(
auto_model_count) + " instead of: " + str(deviance_model_count)
# grid = a_node.grid(key='kmeans_prostate_grid', sort_by='', decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node,
key='kmeans_prostate_grid',
sort_by='totss',
decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node,
key='kmeans_prostate_grid',
sort_by='tot_withinss',
decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node,
key='kmeans_prostate_grid',
sort_by='betweenss',
decreasing=True)
h2o_test_utils.fetch_and_validate_grid_sort(a_node,
key='kmeans_prostate_grid',
sort_by='totss',
decreasing=False)
h2o_test_utils.fetch_and_validate_grid_sort(a_node,
key='kmeans_prostate_grid',
sort_by='tot_withinss',
decreasing=False)
h2o_test_utils.fetch_and_validate_grid_sort(a_node,
key='kmeans_prostate_grid',
sort_by='betweenss',
decreasing=False)
# import sys
# sys.exit(0)
#######################################
# Test default parameters validation for each model builder
#
if h2o_test_utils.isVerbose():
print('Testing ModelBuilder default parameters. . .')
model_builders = a_node.model_builders(timeoutSecs=240)['model_builders']
# Do we know about all of them?
server_algos = model_builders.keys()
assert len(
set(server_algos) - set(algos)
) == 0, "FAIL: Our set of algos doesn't match what the server knows about. Ours: " + repr(
algos) + "; server's: " + repr(server_algos)
for algo, model_builder in model_builders.iteritems():
parameters_list = model_builder['parameters']
test_parameters = {
value['name']: value['default_value']
for value in parameters_list
} # collect default parameters
if algo in algo_additional_default_params:
test_parameters.update(algo_additional_default_params[algo])
if h2o_test_utils.isVerboser():
print('Testing ' + algo + ' with params: ' + repr(test_parameters))
parameters_validation = a_node.validate_model_parameters(
algo=algo,
training_frame=None,
parameters=test_parameters,
timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ",
repr(parameters_validation))
expected_count = 0
if expected_count != parameters_validation['error_count']:
print("validation errors: ")
pp.pprint(parameters_validation)
assert expected_count == parameters_validation[
'error_count'], "FAIL: " + str(
expected_count
) + " != error_count in good-parameters parameters validation result."
#######################################
# Test DeepLearning parameters validation
#
# Default parameters:
if h2o_test_utils.isVerbose():
print('Testing DeepLearning default parameters. . .')
model_builder = a_node.model_builders(
algo='deeplearning', timeoutSecs=240)['model_builders']['deeplearning']
dl_test_parameters_list = model_builder['parameters']
dl_test_parameters = {
value['name']: value['default_value']
for value in dl_test_parameters_list
}
parameters_validation = a_node.validate_model_parameters(
algo='deeplearning',
training_frame=None,
parameters=dl_test_parameters,
timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ",
repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print("validation errors: ")
pp.pprint(parameters_validation)
assert 0 == parameters_validation[
'error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Good parameters (note: testing with null training_frame):
if h2o_test_utils.isVerbose():
print('Testing DeepLearning good parameters. . .')
dl_test_parameters = {
'response_column': 'CAPSULE',
'hidden': "[10, 20, 10]"
}
parameters_validation = a_node.validate_model_parameters(
algo='deeplearning',
training_frame=None,
parameters=dl_test_parameters,
timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in good-parameters parameters validation result."
h2o.H2O.verboseprint("Bad params validation messages: ",
repr(parameters_validation))
if 0 != parameters_validation['error_count']:
print("validation errors: ")
pp.pprint(parameters_validation)
assert 0 == parameters_validation[
'error_count'], "FAIL: 0 != error_count in good-parameters parameters validation result."
# Bad parameters (hidden is null):
# (note: testing with null training_frame)
if h2o_test_utils.isVerbose():
print('Testing DeepLearning bad parameters, null training_frame. . .')
dl_test_parameters = {
'response_column': 'CAPSULE',
'hidden': "[10, 20, 10]",
'input_dropout_ratio': 27
}
parameters_validation = a_node.validate_model_parameters(
algo='deeplearning',
training_frame=None,
parameters=dl_test_parameters,
timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (input_dropout_ratio)."
h2o.H2O.verboseprint("Good params validation messages: ",
repr(parameters_validation))
assert 0 != parameters_validation[
'error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(
parameters_validation)
found_expected_error = False
for validation_message in parameters_validation['messages']:
if validation_message['message_type'] == 'ERRR' and validation_message[
'field_name'] == 'input_dropout_ratio':
found_expected_error = True
assert found_expected_error, "FAIL: Failed to find error message about input_dropout_ratio in the validation messages."
# Bad parameters (no response_column):
if h2o_test_utils.isVerbose():
print('Testing DeepLearning bad parameters, null response_column. . .')
dl_test_parameters = {'hidden': "[10, 20, 10]"}
parameters_validation = a_node.validate_model_parameters(
algo='deeplearning',
training_frame='prostate_binomial',
parameters=dl_test_parameters,
timeoutSecs=240) # synchronous
assert 'error_count' in parameters_validation, "FAIL: Failed to find error_count in bad-parameters parameters validation result (response_column)."
h2o.H2O.verboseprint("Good params validation messages: ",
repr(parameters_validation))
assert 0 != parameters_validation[
'error_count'], "FAIL: 0 == error_count in bad-parameters parameters validation result: " + repr(
parameters_validation)
#######################################
# Try to build DeepLearning model for Prostate but with bad parameters; we should get a ModelParametersSchema with the error.
if h2o_test_utils.isVerbose():
print(
'About to try to build a DeepLearning model with bad parameters. . .'
)
dl_prostate_bad_parameters = {
'response_column': 'CAPSULE',
'hidden': "[10, 20, 10]",
'input_dropout_ratio': 27
}
parameters_validation = a_node.build_model(
algo='deeplearning',
model_id='deeplearning_prostate_binomial_bad',
training_frame='prostate_binomial',
parameters=dl_prostate_bad_parameters,
timeoutSecs=240) # synchronous
h2o_test_utils.validate_validation_messages(parameters_validation,
['input_dropout_ratio'])
assert parameters_validation['__http_response'][
'status_code'] == requests.codes.precondition_failed, "FAIL: expected 412 Precondition Failed from a bad build request, got: " + str(
parameters_validation['__http_response']['status_code'])
if h2o_test_utils.isVerbose():
print('Done trying to build DeepLearning model with bad parameters.')
#####################################
# Early test of predict()
# TODO: remove after we remove the early exit
p = a_node.predict(
model='deeplearning_airlines_binomial',
frame='airlines_binomial',
predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_predictions(
a_node,
p,
'deeplearning_airlines_binomial',
'airlines_binomial',
43978,
predictions_frame='deeplearning_airlines_binomial_predictions')
h2o_test_utils.validate_frame_exists(
a_node, 'deeplearning_airlines_binomial_predictions')
h2o.H2O.verboseprint("Predictions for scoring: ",
'deeplearning_airlines_binomial', " on: ",
'airlines_binomial', ": ", repr(p))