def test_scenario3(self):
"""
Scenario: Successfully comparing predictions for deepnets with operating point:
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 deepnet with objective "<objective>" and "<params>"
And I wait until the deepnet is ready less than <time_3> secs
And I create a local deepnet
When I create a prediction with operating point "<operating_point>" for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction with operating point "<operating_point>" for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params | operating_point,
"""
examples = [[
'data/iris.csv', '10', '50', '30000', '{"petal width": 4}',
'000004', 'Iris-versicolor', '{}', {
"kind": "probability",
"threshold": 1,
"positive_class": "Iris-virginica"
}
]]
show_doc(self.test_scenario3, 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_deepnet_with_objective_and_params(
self, example[5], example[7])
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction_with_op(
self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction_with_op(
self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario3(self):
"""
Scenario: Successfully comparing predictions for deepnets with operating point:
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 deepnet with objective "<objective>" and "<params>"
And I wait until the deepnet is ready less than <time_3> secs
And I create a local deepnet
When I create a prediction with operating point "<operating_point>" for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction with operating point "<operating_point>" for "<data_input>"
Then the local prediction is "<prediction>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | objective | prediction | params | operating_point,
"""
examples = [
['data/iris.csv', '10', '50', '30000', '{"petal width": 4}', '000004', 'Iris-versicolor', '{}', {"kind": "probability", "threshold": 1, "positive_class": "Iris-virginica"}]]
show_doc(self.test_scenario3, 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_deepnet_with_objective_and_params(self, example[5], example[7])
model_create.the_deepnet_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_deepnet(self)
prediction_create.i_create_a_deepnet_prediction_with_op(self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_deepnet_prediction_with_op(self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])