def test_scenario1(self):
"""
Scenario: Successfully building feature selection from dataset in dev mode:
Given I want to use api in DEV mode
And I create BigML dataset in dev mode uploading train "<data>" file in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I create BigML feature selection <kfold>-fold cross-validations improving "<metric>" in dev mode
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best feature selection is "<selection>", with "<metric>" of <metric_value>
Examples:
| data | output | kfold | metric | selection | metric_value |
| ../data/iris_2f.csv | ./scenario_a_2/evaluation | 2 | accuracy | petal width | 100.00% |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris_2f.csv', 'scenario_a_2/evaluation', '2', 'accuracy', 'petal width', '100.00%']]
for example in examples:
print "\nTesting with:\n", example
common.i_want_api_dev_mode(self)
test_pred.i_create_dev_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_in_dev(self, k_folds=example[2], metric=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_feature_selection(self, example[4], example[3], example[5])
def test_scenario1(self):
"""
Scenario: Successfully building feature selection from dataset in dev mode:
Given I want to use api in DEV mode
And I create BigML dataset in dev mode uploading train "<data>" file in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I create BigML feature selection <kfold>-fold cross-validations improving "<metric>" in dev mode
And I check that the <kfold>-datasets have been created
And I check that the <kfold>-models have been created
And I check that all the <kfold>-fold cross-validations have been created
Then the best feature selection is "<selection>", with "<metric>" of <metric_value>
Examples:
| data | output | kfold | metric | selection | metric_value |
| ../data/iris_2f.csv | ./scenario_a_2/evaluation | 2 | accuracy | petal width | 100.00% |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris_2f.csv', 'scenario_a_2/evaluation', '2', 'accuracy', 'petal width', '100.00%']]
for example in examples:
print "\nTesting with:\n", example
common.i_want_api_dev_mode(self)
test_pred.i_create_dev_dataset(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_pred.i_create_kfold_cross_validation_in_dev(self, k_folds=example[2], metric=example[3])
test_pred.i_check_create_kfold_datasets(self, example[2])
test_pred.i_check_create_kfold_models(self, example[2])
test_pred.i_check_create_all_kfold_cross_validations(self, example[2])
test_pred.i_check_feature_selection(self, example[4], example[3], example[5])
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])
