def test_scenario1(self):
"""
Scenario: Successfully creating a model from a dataset list:
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 store the dataset id in a list
And I create a dataset
And I wait until the dataset is ready less than <time_3> secs
And I store the dataset id in a list
Then I create a model from a dataset list
And I wait until the model is ready less than <time_4> secs
And I check the model stems from the original dataset list
Examples:
| data | time_1 | time_2 | time_3 | time_4 |
| ../data/iris.csv | 10 | 10 | 10 | 10
"""
print self.test_scenario1.__doc__
examples = [["data/iris.csv", "10", "10", "10", "10"]]
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])
multimodel_create.i_store_dataset_id(self)
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[3])
multimodel_create.i_store_dataset_id(self)
model_create.i_create_a_model_from_dataset_list(self)
model_create.the_model_is_finished_in_less_than(self, example[4])
multimodel_create.i_check_model_datasets_and_datasets_ids(self)
def test_scenario1(self):
"""
Scenario: Successfully creating an anomaly detector from a dataset and a dataset list:
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
Then I create an anomaly detector from a dataset
And I wait until the anomaly detector is ready less than <time_4> secs
And I check the anomaly detector stems from the original dataset
And I store the dataset id in a list
And I create a dataset
And I wait until the dataset is ready less than <time_3> secs
And I store the dataset id in a list
Then I create an anomaly detector from a dataset list
And I wait until the anomaly detector is ready less than <time_4> secs
And I check the anomaly detector stems from the original dataset list
Examples:
| data | time_1 | time_2 | time_3 | time_4 |
| ../data/tiny_kdd.csv | 40 | 40 | 80 | 100
"""
print self.test_scenario1.__doc__
examples = [
['data/tiny_kdd.csv', '40', '40', '40', '100']]
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])
anomaly_create.i_create_an_anomaly_from_dataset(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self,
example[4])
anomaly_create.i_check_anomaly_dataset_and_datasets_ids(self)
mm_create.i_store_dataset_id(self)
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self,
example[3])
mm_create.i_store_dataset_id(self)
anomaly_create.i_create_an_anomaly_from_dataset_list(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self,
example[4])
anomaly_create.i_check_anomaly_datasets_and_datasets_ids(self)
def test_scenario1(self):
"""
Scenario: Successfully creating an anomaly detector from a dataset and a dataset list:
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
Then I create an anomaly detector from a dataset
And I wait until the anomaly detector is ready less than <time_4> secs
And I check the anomaly detector stems from the original dataset
And I store the dataset id in a list
And I create a dataset
And I wait until the dataset is ready less than <time_3> secs
And I store the dataset id in a list
Then I create an anomaly detector from a dataset list
And I wait until the anomaly detector is ready less than <time_4> secs
And I check the anomaly detector stems from the original dataset list
Examples:
| data | time_1 | time_2 | time_3 | time_4 |
| ../data/tiny_kdd.csv | 40 | 40 | 40 | 100
"""
print self.test_scenario1.__doc__
examples = [
['data/tiny_kdd.csv', '40', '40', '40', '100']]
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])
anomaly_create.i_create_an_anomaly_from_dataset(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self,
example[4])
anomaly_create.i_check_anomaly_dataset_and_datasets_ids(self)
mm_create.i_store_dataset_id(self)
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self,
example[3])
mm_create.i_store_dataset_id(self)
anomaly_create.i_create_an_anomaly_from_dataset_list(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self,
example[4])
anomaly_create.i_check_anomaly_datasets_and_datasets_ids(self)
