def test_scenario3(self):
"""
Scenario: Successfully building evaluations from start:
Given I create BigML resources uploading train "<data>" file to create model and log 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 model has been created
And I evaluate "<test>" with proportional missing strategy
And I check that the source has been created
And I check that the dataset has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | test | output | json_evaluation_file |
| ../data/iris.csv | ../data/iris_nulls.csv | ./scenario_mis_3/evaluation | ./check_files/evaluation_iris_nulls.json |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', 'data/iris_nulls.csv', 'scenario_mis_3/evaluation', 'check_files/evaluation_iris_nulls.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_to_model(self, data=example[0], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
evaluation.i_create_proportional_to_evaluate(self, test=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[3])
def setup_scenario1(self):
"""
Scenario: Successfully building evaluations from start:
Given I create BigML resources uploading train "<data>" file 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 model has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | json_evaluation_file |
| ../data/iris.csv | ./scenario_e1/evaluation | ./check_files/evaluation_iris.json |
"""
print self.setup_scenario1.__doc__
examples = [[
'data/iris.csv', 'scenario_e1/evaluation',
'check_files/evaluation_iris.json'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_to_evaluate(self,
data=example[0],
output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[2])
def test_scenario2(self):
"""
Scenario: Successfully building evaluations from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using source to evaluate and log evaluation in "<output>"
And I check that the dataset has been created
And I check that the model has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | output | json_evaluation_file |
| scenario_e1| {"data": "../data/iris.csv", "output": "./scenario_e1/predictions.csv"} |./scenario_e2/evaluation | ./check_files/evaluation_iris.json |
"""
print self.test_scenario2.__doc__
examples = [[
'scenario_e1',
'{"data": "data/iris.csv", "output": "scenario_e1/predictions.csv"}',
'scenario_e2/evaluation', 'check_files/evaluation_iris.json'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
evaluation.given_i_create_bigml_resources_using_source_to_evaluate(
self, output=example[2])
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[3])
def test_scenario2(self):
"""
Scenario: Successfully building evaluations from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using source to evaluate and log evaluation in "<output>"
And I check that the dataset has been created
And I check that the model has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | output | json_evaluation_file |
| scenario_e1| {"data": "../data/iris.csv", "output": "./scenario_e1/predictions.csv"} |./scenario_e2/evaluation | ./check_files/evaluation_iris.json |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_e1', '{"data": "data/iris.csv", "output": "scenario_e1/predictions.csv"}', 'scenario_e2/evaluation', 'check_files/evaluation_iris.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
evaluation.given_i_create_bigml_resources_using_source_to_evaluate(self, output=example[2])
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[3])
def test_scenario5(self):
"""
Scenario: Successfully building evaluation from model and test file with data map
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using test file "<test>" and a fields map "<fields_map>" to evaluate a model 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 evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | test | fields_map | output | json_evaluation_file |
| scenario_e1| {"data": "../data/iris.csv", "output": "./scenario_e1/predictions.csv"} | ../data/iris_permuted.csv | ../data/fields_map.csv | ./scenario_e7/evaluation | ./check_files/evaluation_iris2.json |
"""
print self.test_scenario5.__doc__
examples = [[
'scenario_e1',
'{"data": "data/iris.csv", "output": "scenario_e1/predictions.csv"}',
'data/iris_permuted.csv', 'data/fields_map.csv',
'scenario_e7/evaluation', 'check_files/evaluation_iris2.json'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
evaluation.i_create_all_resources_to_evaluate_with_model_and_map(
self,
data=example[2],
fields_map=example[3],
output=example[4])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[5])
def test_scenario7(self):
"""
Scenario: Successfully building ensemble evaluations from start and test-split:
Given I create BigML resources uploading train "<data>" file to evaluate an ensemble of <number_of_models> models with test-split <split> 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 train dataset has been created
And I check that the test dataset has been created
And I check that the ensemble has been created
And I check that the evaluation has been created
Then the evaluation key "<key>" value for the model is greater than <value>
And I evaluate the ensemble in directory "<directory>" with the dataset in directory "<directory>" and log evaluation in "<output2>"
And I check that the evaluation has been created
Then the evaluation key "<key>" value for the model is greater than <value>
Examples:
| data | output | split | number_of_models | key | value | directory | output2
| ../data/iris.csv | ./scenario_e8/evaluation | 0.2 | 5 | average_phi | 0.94 | ./scenario_e8/ | ./scenario_e9/evaluation
"""
print self.test_scenario7.__doc__
examples = [
['data/iris.csv', 'scenario_e8/evaluation', '0.2', '5', 'average_phi', '0.94', 'scenario_e8', 'scenario_e9/evaluation']]
for example in examples:
print "\nTesting with:\n", example
evaluation.i_create_with_split_to_evaluate_ensemble(self, data=example[0], number_of_models=example[3], split=example[2], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_dataset(self, suffix='train ')
test_pred.i_check_create_dataset(self, suffix='test ')
test_pred.i_check_create_ensemble(self)
test_pred.i_check_create_evaluation(self)
evaluation.i_check_evaluation_key(self, key=example[4], value=example[5])
evaluation.i_evaluate_ensemble_with_dataset(self, ensemble_dir=example[6], dataset_dir=example[6], output=example[7])
test_pred.i_check_create_evaluation(self)
evaluation.i_check_evaluation_key(self, key=example[4], value=example[5])
def test_scenario6(self):
"""
Scenario: Successfully building evaluations from start and test-split:
Given I create BigML resources uploading train "<data>" file to evaluate with test-split <split> 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 train dataset has been created
And I check that the test dataset has been created
And I check that the model has been created
And I check that the evaluation has been created
Then the evaluation key "<key>" value for the model is greater than <value>
Examples:
| data | output | split | key | value |
| ../data/iris.csv | ./scenario_e6/evaluation | 0.2 | average_phi | 0.85 |
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', 'scenario_e6/evaluation', '0.2', 'average_phi', '0.85']]
for example in examples:
print "\nTesting with:\n", example
evaluation.i_create_with_split_to_evaluate(self, data=example[0], split=example[2], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_dataset(self, suffix='train ')
test_pred.i_check_create_dataset(self, suffix='test ')
test_pred.i_check_create_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.i_check_evaluation_key(self, key=example[3], value=example[4])
def test_scenario5(self):
"""
Scenario: Successfully building evaluation from model and test file with data map
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using test file "<test>" and a fields map "<fields_map>" to evaluate a model 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 evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
|scenario | kwargs | test | fields_map | output | json_evaluation_file |
| scenario_e1| {"data": "../data/iris.csv", "output": "./scenario_e1/predictions.csv"} | ../data/iris_permuted.csv | ../data/fields_map.csv | ./scenario_e7/evaluation | ./check_files/evaluation_iris2.json |
"""
print self.test_scenario5.__doc__
examples = [
['scenario_e1', '{"data": "data/iris.csv", "output": "scenario_e1/predictions.csv"}', 'data/iris_permuted.csv', 'data/fields_map.csv', 'scenario_e7/evaluation', 'check_files/evaluation_iris2.json']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
evaluation.i_create_all_resources_to_evaluate_with_model_and_map(self, data=example[2], fields_map=example[3], output=example[4])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[5])
def test_scenario11(self):
"""
Scenario: Successfully building evaluations for deepnets from start:
Given I create BigML deepnet resources uploading train "<data>" file 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 deepnet has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | json_evaluation_file |
| ../data/iris.csv | ./scenario_e11/evaluation | ./check_files/evaluation_iris_dn.json |
"""
print self.test_scenario11.__doc__
examples = [
['data/iris.csv', 'scenario_e11/evaluation', 'check_files/evaluation_iris_dn.json']]
for example in examples:
print "\nTesting with:\n", example
dn_pred.i_create_all_dn_resources_to_evaluate(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dn_pred.i_check_create_dn_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[2])
def test_scenario7(self):
"""
Scenario: Successfully building ensemble evaluations from start and test-split:
Given I create BigML resources uploading train "<data>" file to evaluate an ensemble of <number_of_models> models with test-split <split> 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 train dataset has been created
And I check that the test dataset has been created
And I check that the ensemble has been created
And I check that the evaluation has been created
Then the evaluation key "<key>" value for the model is greater than <value>
And I evaluate the ensemble in directory "<directory>" with the dataset in directory "<directory>" and log evaluation in "<output2>"
And I check that the evaluation has been created
Then the evaluation key "<key>" value for the model is greater than <value>
Examples:
| data | output | split | number_of_models | key | value | directory | output2
| ../data/iris.csv | ./scenario_e8/evaluation | 0.2 | 5 | average_phi | 0.94 | ./scenario_e8/ | ./scenario_e9/evaluation
"""
print self.test_scenario7.__doc__
examples = [[
'data/iris.csv', 'scenario_e8/evaluation', '0.2', '5',
'average_phi', '0.94', 'scenario_e8', 'scenario_e9/evaluation'
]]
for example in examples:
print "\nTesting with:\n", example
evaluation.i_create_with_split_to_evaluate_ensemble(
self,
data=example[0],
number_of_models=example[3],
split=example[2],
output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_dataset(self, suffix='train ')
test_pred.i_check_create_dataset(self, suffix='test ')
test_pred.i_check_create_ensemble(self)
test_pred.i_check_create_evaluation(self)
evaluation.i_check_evaluation_key(self,
key=example[4],
value=example[5])
evaluation.i_evaluate_ensemble_with_dataset(
self,
ensemble_dir=example[6],
dataset_dir=example[6],
output=example[7])
test_pred.i_check_create_evaluation(self)
evaluation.i_check_evaluation_key(self,
key=example[4],
value=example[5])
def test_scenario04(self):
"""
Scenario: Successfully building evaluation from fusion
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML fusion resources using model built from "<train>" to test "<test>" as an evaluation and log predictions in "<output>"
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | json_evaluation_file |
| ../data/iris.csv | ./scenario_fs_e1/evaluation | ./check_files/evaluation_iris_dn.json |
"""
print self.test_scenario04.__doc__
examples = [
['scenario_fs_e1/evaluation', 'check_files/evaluation_iris_fs.json']]
for example in examples:
print "\nTesting with:\n", example
fs_pred.i_create_fs_resources_from_mode_and_evaluate(self, output=example[0])
fs_pred.i_check_create_fusion(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[1])
def test_scenario6(self):
"""
Scenario: Successfully building evaluations from start and test-split:
Given I create BigML resources uploading train "<data>" file to evaluate with test-split <split> 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 train dataset has been created
And I check that the test dataset has been created
And I check that the model has been created
And I check that the evaluation has been created
Then the evaluation key "<key>" value for the model is greater than <value>
Examples:
| data | output | split | key | value |
| ../data/iris.csv | ./scenario_e6/evaluation | 0.2 | average_phi | 0.85 |
"""
print self.test_scenario6.__doc__
examples = [[
'data/iris.csv', 'scenario_e6/evaluation', '0.2', 'average_phi',
'0.85'
]]
for example in examples:
print "\nTesting with:\n", example
evaluation.i_create_with_split_to_evaluate(self,
data=example[0],
split=example[2],
output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_dataset(self, suffix='train ')
test_pred.i_check_create_dataset(self, suffix='test ')
test_pred.i_check_create_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.i_check_evaluation_key(self,
key=example[3],
value=example[4])
