def test_scenario1(self):
"""
Scenario: Successfully creating a local prediction from an Ensemble:
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 of <number_of_models> models and <tlp> tlp
And I wait until the ensemble is ready less than <time_3> secs
And I create a local Ensemble
When I create a local ensemble prediction with confidence for "<data_input>"
Then the local prediction is "<prediction>"
And the local prediction's confidence is "<confidence>"
And the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | number_of_models | tlp | data_input |prediction | confidence
| ../data/iris.csv | 10 | 10 | 50 | 5 | 1 | {"petal width": 0.5} | Iris-versicolor | 0.3687 | [0.3403, 0.4150, 0.2447]
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '10', '10', '50', '5', '1', '{"petal width": 0.5}', 'Iris-versicolor', '0.415', '["0.3403", "0.4150", "0.2447"]' ]]
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(self, example[4], example[5])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[3])
ensemble_create.create_local_ensemble(self)
prediction_create.create_local_ensemble_prediction_with_confidence(self, example[6])
compare_pred.the_local_prediction_is(self, example[7])
compare_pred.the_local_prediction_confidence_is(self, example[8])
compare_pred.the_local_probabilities_are(self, example[9])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy and balanced models:
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 a balanced model
And I wait until the model is ready less than <time_3> secs
And I create a local model
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>"
And I create local probabilities for "<data_input>"
Then the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
[
'data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004',
'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'
],
[
'data/iris_unbalanced.csv', '10', '10', '10',
'{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}',
'000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]'
]
]
show_doc(self.test_scenario10, 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])
model_create.i_create_a_balanced_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(
self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(
self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.the_local_prediction_confidence_is(
self, example[7])
prediction_compare.i_create_local_probabilities(self, example[4])
prediction_compare.the_local_probabilities_are(self, example[8])
def test_scenario10(self):
"""
Scenario: Successfully comparing predictions with proportional missing strategy and balanced models:
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 a balanced model
And I wait until the model is ready less than <time_3> secs
And I create a local model
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>"
And I create local probabilities for "<data_input>"
Then the local probabilities are "<probabilities>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | confidence |
"""
examples = [
['data/iris_unbalanced.csv', '10', '10', '10', '{}', '000004', 'Iris-setosa', '0.25284', '[0.33333, 0.33333, 0.33333]'],
['data/iris_unbalanced.csv', '10', '10', '10', '{"petal length":1, "sepal length":1, "petal width": 1, "sepal width": 1}', '000004', 'Iris-setosa', '0.7575', '[1.0, 0.0, 0.0]']]
show_doc(self.test_scenario10, 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])
model_create.i_create_a_balanced_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_model(self)
prediction_create.i_create_a_proportional_prediction(self, example[4])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_proportional_local_prediction(self, example[4])
prediction_compare.the_local_prediction_is(self, example[6])
prediction_create.the_confidence_is(self, example[7])
prediction_compare.the_local_prediction_confidence_is(self, example[7])
prediction_compare.i_create_local_probabilities(self, example[4])
prediction_compare.the_local_probabilities_are(self, example[8])
