def test_scenario03(self):
"""
Scenario: Successfully building test predictions from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using source to test "<test>" and log predictions 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 predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario2/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario03.__doc__
examples = [
['scenario1', '{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario2/predictions.csv', 'check_files/predictions_iris.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_resources_from_source(self, None, test=example[2], output=example[3])
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario03(self):
"""
Scenario: Successfully building test predictions from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using source to test "<test>" and log predictions 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 predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario2/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario03.__doc__
examples = [[
'scenario1',
'{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}',
'data/test_iris.csv', 'scenario2/predictions.csv',
'check_files/predictions_iris.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
test_pred.i_create_resources_from_source(self,
None,
test=example[2],
output=example[3])
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
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 to test "<test>" and log predictions 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 predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario_ml_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_1/predictions.csv", "test": "../data/test_multilabel.csv"} | ../data/test_multilabel.csv | ./scenario_ml_2/predictions.csv | ./check_files/predictions_ml_comma.csv |
"""
print self.test_scenario2.__doc__
examples = [[
'scenario_ml_1',
'{"tag": "my_multilabel_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_1/predictions.csv", "test": "data/test_multilabel.csv"}',
'data/test_multilabel.csv', 'scenario_ml_2/predictions.csv',
'check_files/predictions_ml_comma.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
test_pred.i_create_resources_from_source(self,
multi_label='multi-label',
test=example[2],
output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
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 to test "<test>" and log predictions 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 predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario_ml_1| {"tag": "my_multilabel_1", "data": "../data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "./scenario_ml_1/predictions.csv", "test": "../data/test_multilabel.csv"} | ../data/test_multilabel.csv | ./scenario_ml_2/predictions.csv | ./check_files/predictions_ml_comma.csv |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_ml_1', '{"tag": "my_multilabel_1", "data": "data/multilabel.csv", "label_separator": ":", "number_of_labels": 7, "training_separator": ",", "output": "scenario_ml_1/predictions.csv", "test": "data/test_multilabel.csv"}', 'data/test_multilabel.csv', 'scenario_ml_2/predictions.csv', 'check_files/predictions_ml_comma.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_resources_from_source(self, multi_label='multi-label', test=example[2], output=example[3])
test_pred.i_check_create_dataset(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
