def test_scenario6(self):
"""
Scenario: Successfully comparing predictions for ensembles:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '120', '{"Midterm": 20}', '000005', 46.261364, '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-setosa', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
"""
examples = [
[
'data/iris_unbalanced.csv', '30', '30', '120',
'{"petal width": 4}', '000004', 'Iris-virginica',
'{"boosting": {"iterations": 5}, "number_of_models": 5}'
],
[
'data/grades.csv', '30', '30', '120', '{"Midterm": 20}',
'000005', 61.61036,
'{"boosting": {"iterations": 5}, "number_of_models": 5}'
]
]
show_doc(self.test_scenario6, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
ensemble_create.i_create_an_ensemble_with_params(self, example[7])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_ensemble_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario6(self):
"""
Scenario: Successfully comparing predictions for ensembles:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an ensemble with "<params>"
And I wait until the ensemble is ready less than <time_3> secs
And I create a local ensemble
When I create a prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '120', '{"Midterm": 20}', '000005', 46.261364, '{"number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal width": 0.5}', '000004', 'Iris-setosa', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 6, "petal width": 2}', '000004', 'Iris-virginica', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/iris.csv', '10', '10', '120', '{"petal length": 4, "petal width": 1.5}', '000004', 'Iris-versicolor', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
"""
examples = [
['data/iris_unbalanced.csv', '30', '30', '120', '{"petal width": 4}', '000004', 'Iris-virginica', '{"boosting": {"iterations": 5}, "number_of_models": 5}'],
['data/grades.csv', '30', '30', '120', '{"Midterm": 20}', '000005', 61.61036, '{"boosting": {"iterations": 5}, "number_of_models": 5}']]
show_doc(self.test_scenario6, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
ensemble_create.i_create_an_ensemble_with_params(self, example[7])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_ensemble_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario8(self):
"""
Scenario: Successfully comparing predictions for ensembles:
Given I create a local ensemble predictor from "<directory>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| directory | data_input | prediction
"""
examples = [
['bigml/tests/my_ensemble', '{"petal width": 4}', 68.1258030739]]
show_doc(self.test_scenario6, examples)
for example in examples:
print "\nTesting with:\n", example
ensemble_create.create_local_ensemble_predictor(self, example[0])
prediction_compare.i_create_a_local_ensemble_prediction(self, example[1])
prediction_compare.the_local_ensemble_prediction_is(self, example[2])
def test_scenario8(self):
"""
Scenario: Successfully comparing predictions for ensembles:
Given I create a local ensemble predictor from "<directory>"
And I create a local prediction for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| directory | data_input | prediction
"""
examples = [
['bigml/tests/my_ensemble', '{"petal width": 4}', 68.1258030739]]
show_doc(self.test_scenario6, examples)
for example in examples:
print "\nTesting with:\n", example
ensemble_create.create_local_ensemble_predictor(self, example[0])
prediction_compare.i_create_a_local_ensemble_prediction(self, example[1])
prediction_compare.the_local_ensemble_prediction_is(self, example[2])