def test_scenario2(self):
"""
Scenario: Successfully building multi-label evaluations from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source to evaluate and log evaluation in "<output>"
And I check that the dataset has been created
And I check that the models have been created
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | number_of_labels | output |json_evaluation_file |
| scenario_ml_e1| {"tag": "my_multilabel_e_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_e1/evaluation"} | 7 | ./scenario_ml_e2/evaluation | ./check_files/evaluation_ml.json |
"""
print self.test_scenario2.__doc__
examples = [[
'scenario_ml_e1',
'{"tag": "my_multilabel_e_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_e1/evaluation"}',
'7', 'scenario_ml_e2/evaluation', 'check_files/evaluation_ml.json'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
ml_eval.i_create_ml_evaluations_from_source(self,
output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(
self, number_of_evaluations=example[2])
ml_eval.i_check_evaluation_ready(self)
evaluation.then_the_evaluation_file_is_like(self, example[4])
def test_scenario5(self):
"""
Scenario: Successfully building multi-label evaluations from models retrieved by tag
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using models tagged as "<tag>" to evaluate and log evaluation in "<output>"
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation key "<key>" value for the model is greater than <value>
Examples:
|scenario | kwargs | tag | number_of_labels | output |key | value
| scenario_ml_e1| {"tag": "my_multilabel_e_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_e1/evaluation"} | my_multilabel_e_1 | 7 | ./scenario_ml_e5/evaluation | average_phi | 0.8180
"""
print self.test_scenario5.__doc__
examples = [[
'scenario_ml_e1',
'{"tag": "my_multilabel_e_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_e1/evaluation"}',
'my_multilabel_e_1', '7', 'scenario_ml_e5/evaluation',
'average_phi', '0.8180'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
ml_eval.i_create_ml_evaluations_from_tagged_models(
self, tag=example[2], output=example[4])
test_pred.i_check_create_evaluations(
self, number_of_evaluations=example[3])
ml_eval.i_check_evaluation_ready(self)
evaluation.i_check_evaluation_key(self,
key=example[5],
value=example[6])
def setup_scenario1(self):
"""
Scenario: Successfully building multi-label evaluations from scratch
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 to evaluate and log evaluation 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 have been created
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|tag |label_separator |number_of_labels| data |training_separator | output |json_evaluation_file
|my_multilabel_e_1|:|7| ../data/multilabel.csv |,| ./scenario_ml_e1/evaluation | ./check_files/evaluation_ml.json
"""
print self.setup_scenario1.__doc__
examples = [
['my_multilabel_e_1', ':', '7', 'data/multilabel.csv', ',', 'scenario_ml_e1/evaluation', 'check_files/evaluation_ml.json']]
for example in examples:
print "\nTesting with:\n", example
ml_eval.i_create_all_ml_evaluations(self, tag=example[0], label_separator=example[1], number_of_labels=example[2], data=example[3], training_separator=example[4], output=example[5])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(self, number_of_evaluations=example[2])
ml_eval.i_check_evaluation_ready(self)
evaluation.then_the_evaluation_file_is_like(self, example[6])
def test_scenario2(self):
"""
Scenario: Successfully building multi-label evaluations from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using source to evaluate and log evaluation in "<output>"
And I check that the dataset has been created
And I check that the models have been created
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | number_of_labels | output |json_evaluation_file |
| scenario_ml_e1| {"tag": "my_multilabel_e_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_e1/evaluation"} | 7 | ./scenario_ml_e2/evaluation | ./check_files/evaluation_ml.json |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_ml_e1', '{"tag": "my_multilabel_e_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_e1/evaluation"}', '7', 'scenario_ml_e2/evaluation', 'check_files/evaluation_ml.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
ml_eval.i_create_ml_evaluations_from_source(self, output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_evaluations(self, number_of_evaluations=example[2])
ml_eval.i_check_evaluation_ready(self)
evaluation.then_the_evaluation_file_is_like(self, example[4])
def test_scenario4(self):
"""
Scenario: Successfully building multi-label evaluations from models file
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML multi-label resources using models in file "<models_file>" to evaluate and log evaluation in "<output>"
And I check that the <number_of_labels> evaluations have been created
And I check that the evaluation is ready
Then the evaluation key "<key>" value for the model is greater than <value>
Examples:
|scenario | kwargs | models_file | number_of_labels | output |key | value
| scenario_ml_e1| {"tag": "my_multilabel_e_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_e1/evaluation"} | ./scenario_ml_e1/models | 7 | ./scenario_ml_e4/evaluation | average_phi | 0.8180
"""
print self.test_scenario4.__doc__
examples = [
['scenario_ml_e1', '{"tag": "my_multilabel_e_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_e1/evaluation"}', 'scenario_ml_e1/models', '7', 'scenario_ml_e4/evaluation', 'average_phi', '0.8180']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
ml_eval.i_create_ml_evaluations_from_models(self, models_file=example[2], output=example[4])
test_pred.i_check_create_evaluations(self, number_of_evaluations=example[3])
ml_eval.i_check_evaluation_ready(self)
evaluation.i_check_evaluation_key(self, key=example[5], value=example[6])
