def test_scenario9(self):
"""
Scenario: Successfully comparing predictions for logistic regressions with operating kind and 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 a logistic regression with objective "<objective>"
And I wait until the logistic regression is ready less than <time_3> secs
And I create a local supervised model
When I create a prediction with operating kind "<operating_kind>" for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction with operating point "<operating_kind>" 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 length": 5}',
'000004', 'Iris-versicolor', '{}', "probability"
],
[
'data/iris.csv', '10', '50', '30000',
'{"petal length": 2}', '000004', 'Iris-setosa', '{}',
"probability"
]]
show_doc(self.test_scenario9, 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_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_supervised_model(
self, model_type="logistic_regression")
prediction_create.i_create_a_logistic_prediction_with_op_kind(
self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_logistic_prediction_op_kind(
self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])
def test_scenario9(self):
"""
Scenario: Successfully comparing predictions for logistic regressions with operating kind and 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 a logistic regression with objective "<objective>"
And I wait until the logistic regression is ready less than <time_3> secs
And I create a local supervised model
When I create a prediction with operating kind "<operating_kind>" for "<data_input>"
Then the prediction for "<objective>" is "<prediction>"
And I create a local prediction with operating point "<operating_kind>" 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 length": 5}', '000004', 'Iris-versicolor', '{}', "probability"],
['data/iris.csv', '10', '50', '30000', '{"petal length": 2}', '000004', 'Iris-setosa', '{}', "probability"]]
show_doc(self.test_scenario9, 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_logistic_model(self)
model_create.the_logistic_model_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_supervised_model(self, model_type="logistic_regression")
prediction_create.i_create_a_logistic_prediction_with_op_kind(self, example[4], example[8])
prediction_create.the_prediction_is(self, example[5], example[6])
prediction_compare.i_create_a_local_logistic_prediction_op_kind(self, example[4], example[8])
prediction_compare.the_local_prediction_is(self, example[6])