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', '10', '10', '120',
'{"petal width": 4}', '000004', 'Iris-virginica',
'{"boosting": {"iterations": 5}, "number_of_models": 5}'
],
[
'data/grades.csv', '10', '10', '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_scenario9(self):
"""
Scenario: Successfully comparing predictions for ensembles with proportional missing strategy in a supervised model:
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 esemble 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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | params
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'],
"""
examples = [[
'data/iris.csv', '10', '10', '50', '{}', '000004',
'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}', {}
],
[
'data/iris.csv', '10', '10', '50', '{}', '000004',
'Iris-versicolor', '0.27261',
'{"number_of_models": 5"}', {
"operating_kind": "confidence"
}
]]
show_doc(self.test_scenario7, 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[8])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[3])
ensemble_create.create_local_supervised_ensemble(self)
prediction_create.i_create_an_ensemble_proportional_prediction(
self, example[4], example[9])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_create.create_local_ensemble_proportional_prediction_with_confidence(
self, example[4], example[9])
prediction_compare.the_local_ensemble_prediction_is(
self, example[6])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
def test_scenario7(self):
"""
Scenario: Successfully comparing predictions for ensembles with proportional missing strategy:
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 esemble 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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | params
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'],
"""
examples = [
['data/iris.csv', '30', '30', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}', {}],
['data/iris.csv', '30', '30', '50', '{}', '000004', 'Iris-versicolor', '0.27261', '{"number_of_models": 5"}', {"operating_kind": "confidence"}],
['data/grades.csv', '30', '30', '50', '{}', '000005', '70.505792', '30.7161', '{"number_of_models": 5}', {}],
['data/grades.csv', '30', '30', '50', '{"Midterm": 20}', '000005', '54.82214', '25.89672', '{"number_of_models": 5}', {"operating_kind": "confidence"}],
['data/grades.csv', '30', '30', '50', '{"Midterm": 20}', '000005', '45.4573', '29.58403', '{"number_of_models": 5}', {}],
['data/grades.csv', '30', '30', '50', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '42.814', '31.51804', '{"number_of_models": 5}', {}]]
show_doc(self.test_scenario7, 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[8])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_proportional_prediction(self, example[4], example[9])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_create.create_local_ensemble_proportional_prediction_with_confidence(self, example[4], example[9])
prediction_compare.the_local_ensemble_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
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_scenario7(self):
"""
Scenario: Successfully comparing predictions for ensembles with proportional missing strategy:
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 esemble 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 proportional missing strategy prediction for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And the confidence for the prediction is "<confidence>"
And I create a proportional missing strategy local prediction for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence | params
"""
examples = [
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-virginica', '0.33784', '{"boosting": {"iterations": 5}}'],
['data/iris.csv', '10', '10', '50', '{}', '000004', 'Iris-versicolor', '0.3174', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '50', '{}', '000005', '70.505792', '30.7161', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '50', '{"Midterm": 20}', '000005', '45.4573', '29.58403', '{"number_of_models": 5}'],
['data/grades.csv', '10', '10', '50', '{"Midterm": 20, "Tutorial": 90, "TakeHome": 100}', '000005', '42.814', '31.51804', '{"number_of_models": 5}']]
show_doc(self.test_scenario7, 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[8])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.i_create_an_ensemble_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_create.create_local_ensemble_proportional_prediction_with_confidence(self, example[4])
prediction_compare.the_local_ensemble_prediction_is(self, example[6])
prediction_compare.the_local_prediction_confidence_is(self, example[7])