def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source using datasets with max categories
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources with <max_categories> as categories limit and <objective> as objective field using source to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the max_categories datasets have 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 |max_categories| objective | test | output |predictions_file |
| scenario_mc_1| {"data": "../data/iris.csv", "max_categories": "1", "objective": "species", "output": "./scenario_mc_1/predictions.csv", "test": "../data/test_iris.csv"} |1| species | ../data/test_iris.csv | ./scenario_mc_2/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_mc_1', '{"data": "data/iris.csv", "max_categories": "1", "objective": "species", "output": "scenario_mc_1/predictions.csv", "test": "data/test_iris.csv"}', '1', 'species', 'data/test_iris.csv', 'scenario_mc_2/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
max_cat.i_create_all_mc_resources_from_source(self, max_categories=example[2], objective=example[3], test=example[4], output=example[5])
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[6])
def test_scenario2(self):
"""
Scenario: Successfully building test predictions from source using datasets with max categories
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources with <max_categories> as categories limit and <objective> as objective field using source to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the max_categories datasets have 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 |max_categories| objective | test | output |predictions_file |
| scenario_mc_1| {"data": "../data/iris.csv", "max_categories": "1", "objective": "species", "output": "./scenario_mc_1/predictions.csv", "test": "../data/test_iris.csv"} |1| species | ../data/test_iris.csv | ./scenario_mc_2/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.test_scenario2.__doc__
examples = [
['scenario_mc_1', '{"data": "data/iris.csv", "max_categories": "1", "objective": "species", "output": "scenario_mc_1/predictions.csv", "test": "data/test_iris.csv"}', '1', 'species', 'data/test_iris.csv', 'scenario_mc_2/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
max_cat.i_create_all_mc_resources_from_source(self, max_categories=example[2], objective=example[3], test=example[4], output=example[5])
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[6])
def setup_scenario1(self):
"""
Scenario: Successfully building test predictions from training data using datasets with max categories
Given I create BigML resources from "<data>" with <max_categories> as categories limit and <objective> as objective field 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 max_categories datasets have 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:
|data |max_categories | objective | test | output |predictions_file |
|../data/iris.csv |1| species |../data/test_iris.csv | ./scenario_mc_1/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.setup_scenario1.__doc__
examples = [
['data/iris.csv', '1', 'species', 'data/test_iris.csv', 'scenario_mc_1/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
max_cat.i_create_all_mc_resources(self, example[0], max_categories=example[1], objective=example[2], test=example[3], output=example[4])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[5])
def setup_scenario1(self):
"""
Scenario: Successfully building test predictions from training data using datasets with max categories
Given I create BigML resources from "<data>" with <max_categories> as categories limit and <objective> as objective field 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 max_categories datasets have 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:
|data |max_categories | objective | test | output |predictions_file |
|../data/iris.csv |1| species |../data/test_iris.csv | ./scenario_mc_1/predictions.csv | ./check_files/predictions_mc.csv |
"""
print self.setup_scenario1.__doc__
examples = [
['data/iris.csv', '1', 'species', 'data/test_iris.csv', 'scenario_mc_1/predictions.csv', 'check_files/predictions_mc.csv']]
for example in examples:
print "\nTesting with:\n", example
max_cat.i_create_all_mc_resources(self, example[0], max_categories=example[1], objective=example[2], test=example[3], output=example[4])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[5])
def test_scenario5(self):
"""
Scenario: Successfully building test predictions from dataset using datasets and model fields with max categories
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources with <max_categories> as categories limit and <objective> as objective field and model fields "<model_fields>" using dataset to test "<test>" and log predictions in "<output>"
And I check that the max_categories datasets have 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 |max_categories|objective | model_fields | test | output |predictions_file |
| scenario_mc_1| {"data": "../data/iris.csv", "max_categories": "1", "objective": "species", "output": "./scenario_mc_1/predictions.csv", "test": "../data/test_iris.csv"} |1| species |sepal length,sepal width |../data/test_iris.csv | ./scenario_mc_5/predictions.csv | ./check_files/predictions_mc2.csv |
"""
print self.test_scenario5.__doc__
examples = [
[
"scenario_mc_1",
'{"data": "data/iris.csv", "max_categories": "1", "objective": "species", "output": "scenario_mc_1/predictions.csv", "test": "data/test_iris.csv"}',
"1",
"species",
"sepal length,sepal width",
"data/test_iris.csv",
"scenario_mc_5/predictions.csv",
"check_files/predictions_mc2.csv",
]
]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
max_cat.i_create_all_mc_resources_from_dataset_with_model_fields(
self,
max_categories=example[2],
objective=example[3],
model_fields=example[4],
test=example[5],
output=example[6],
)
max_cat.i_check_create_max_categories_datasets(self)
test_pred.i_check_create_models(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[7])
