def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source and <number_of_models> models ensembles to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the models in the ensembles have been created
Then I check that the predictions are ready
Examples:
|scenario | kwargs |number_of_models |test | output |
| scenario_mle_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_mle_1/predictions.csv", "test": "../data/test_multilabel.csv", "number_of_models": 10} |10| ../data/test_multilabel.csv | ./scenario_mle_2/predictions.csv
"""
print self.test_scenario2.__doc__
examples = [[
'scenario_mle_1',
'{"tag": "my_multilabel_1%s", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_mle_1/predictions.csv", "test": "data/test_multilabel.csv", "number_of_models": 10}'
% PY3, '10', 'data/test_multilabel.csv',
'scenario_mle_2/predictions.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
ml_pred.i_create_resources_and_ensembles_from_source(
self,
multi_label='multi-label ',
number_of_models=example[2],
test=example[3],
output=example[4])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models_in_ensembles(self,
in_ensemble=True)
test_pred.i_check_create_predictions(self)
def test_scenario3(self):
"""
Scenario: Successfully building test predictions from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using dataset and <number_of_models> models ensembles to test "<test>" and log predictions in "<output>"
And I check that the models in the ensembles have been created
Then I check that the predictions are ready
Examples:
|scenario | kwargs | number_of_models |test | output |
| scenario_mle_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_mle_1/predictions.csv", "test": "../data/test_multilabel.csv", "number_of_models": 10} |10| ../data/test_multilabel.csv | ./scenario_mle_3/predictions.csv
"""
print self.test_scenario3.__doc__
examples = [
['scenario_mle_1', '{"tag": "my_multilabel_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_mle_1/predictions.csv", "test": "data/test_multilabel.csv", "number_of_models": 10}', '10', 'data/test_multilabel.csv', 'scenario_mle_3/predictions.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
ml_pred.i_create_resources_and_ensembles_from_source(self, multi_label='multi-label', number_of_models=example[2], test=example[3], output=example[4])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True)
test_pred.i_check_create_predictions(self)