def setup_scenario1(self):
"""
Scenario: Successfully building multi-label test predictions from start:
Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator and <number_of_models> models ensembles to test "<test>" and log predictions in "<output>"
And I check that the source has been created
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:
|tag |label_separator |number_of_labels | data |training_separator |number_of_models | test | output |
|my_multilabel_1|:|7| ../data/multilabel.csv |,|10| ../data/test_multilabel.csv | ./scenario_mle_1/predictions.csv
"""
print self.setup_scenario1.__doc__
examples = [[
'my_multilabel_1%s' % PY3, ':', '7', 'data/multilabel.csv', ',',
'10', 'data/test_multilabel.csv', 'scenario_mle_1/predictions.csv'
]]
for example in examples:
print "\nTesting with:\n", example
ml_pred.i_create_all_ml_resources_and_ensembles(
self,
tag=example[0],
label_separator=example[1],
number_of_labels=example[2],
data=example[3],
training_separator=example[4],
number_of_models=example[5],
test=example[6],
output=example[7])
test_pred.i_check_create_source(self)
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_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_scenario21(self):
"""
Scenario 1: Successfully building test predictions from ensemble
And I create BigML resources from "<data>" using ensemble of <number_of_models> models to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the ensemble has been created
And I check that the models in the ensembles have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|data | number_of_models | test | output | predictions_file |
"""
examples = [
['data/grades.csv', '5', 'data/test_grades.csv', 'scenario21/predictions.csv', 'check_files/predictions_grades_e.csv']]
show_doc(self.test_scenario21, examples)
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_resources_in_prod_from_ensemble( \
self, data=example[0], number_of_models=example[1],
test=example[2], output=example[3])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_ensemble(self)
test_pred.i_check_create_models_in_ensembles(self,
in_ensemble=True)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario21(self):
"""
Scenario 1: Successfully building test predictions from ensemble
And I create BigML resources from "<data>" using ensemble of <number_of_models> models to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the ensemble has been created
And I check that the models in the ensembles have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|data | number_of_models | test | output | predictions_file |
"""
examples = [[
'data/grades.csv', '5', 'data/test_grades.csv',
'scenario21/predictions.csv',
'check_files/predictions_grades_e.csv'
]]
show_doc(self.test_scenario21, examples)
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_resources_in_prod_from_ensemble( \
self, data=example[0], number_of_models=example[1],
test=example[2], output=example[3])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_ensemble(self)
test_pred.i_check_create_models_in_ensembles(self,
in_ensemble=True)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
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)
def test_scenario1(self):
"""
Scenario 1: Successfully building test predictions from ensemble
Given I want to use api in DEV mode
And I create BigML resources in DEV from "<data>" using ensemble of <number_of_models> models to test "<test>" and log predictions in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the ensemble has been created
And I check that the models in the ensembles have been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|data | number_of_models | test | output | predictions_file |
| ../data/grades.csv| 5 | ../data/test_grades.csv | ./scenario1_dev/predictions.csv | ./check_files/predictions_grades_e.csv |
"""
print self.test_scenario1.__doc__
examples = [
[
"data/grades.csv",
"5",
"data/test_grades.csv",
"scenario1_dev/predictions.csv",
"check_files/predictions_grades_e.csv",
]
]
for example in examples:
print "\nTesting with:\n", example
common.i_want_api_dev_mode(self)
test_pred.i_create_resources_in_dev_from_ensemble(
self, data=example[0], number_of_models=example[1], test=example[2], output=example[3]
)
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_ensemble(self)
test_pred.i_check_create_models_in_ensembles(self, in_ensemble=True)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def setup_scenario1(self):
"""
Scenario: Successfully building multi-label test predictions from start:
Given I create BigML multi-label resources tagged as "<tag>" with "<label_separator>" label separator and <number_of_labels> labels uploading train "<data>" file with "<training_separator>" field separator and <number_of_models> models ensembles to test "<test>" and log predictions in "<output>"
And I check that the source has been created
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:
|tag |label_separator |number_of_labels | data |training_separator |number_of_models | test | output |
|my_multilabel_1|:|7| ../data/multilabel.csv |,|10| ../data/test_multilabel.csv | ./scenario_mle_1/predictions.csv
"""
print self.setup_scenario1.__doc__
examples = [
['my_multilabel_1', ':', '7', 'data/multilabel.csv', ',', '10', 'data/test_multilabel.csv', 'scenario_mle_1/predictions.csv']]
for example in examples:
print "\nTesting with:\n", example
ml_pred.i_create_all_ml_resources_and_ensembles(self, tag=example[0], label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], number_of_models=example[5], test=example[6], output=example[7])
test_pred.i_check_create_source(self)
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)
