def test_scenario4(self):
"""
Scenario: Successfully creating a source from a batch prediction:
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 model
And I wait until the model is ready less than <time_3> secs
When I create a batch prediction for the dataset with the model
And I wait until the batch prediction is ready less than <time_4> secs
Then I create a source from the batch prediction
And I wait until the source is ready less than <time_1> secs
Examples:
| data | time_1 | time_2 | time_3 | time_4 |
| ../data/iris.csv | 30 | 30 | 50 | 50 |
"""
print self.test_scenario4.__doc__
examples = [['data/diabetes.csv', '30', '30', '50', '50']]
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_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(
self, example[4])
batch_pred_create.i_create_a_source_from_batch_prediction(self)
source_create.the_source_is_finished(self, example[1])
def test_scenario4(self):
"""
Scenario: Successfully creating a source from a batch prediction:
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 model
And I wait until the model is ready less than <time_3> secs
When I create a batch prediction for the dataset with the model
And I wait until the batch prediction is ready less than <time_4> secs
Then I create a source from the batch prediction
And I wait until the source is ready less than <time_1> secs
Examples:
| data | time_1 | time_2 | time_3 | time_4 |
| ../data/iris.csv | 30 | 30 | 50 | 50 |
"""
print self.test_scenario4.__doc__
examples = [
['data/diabetes.csv', '30', '30', '50', '50']]
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_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[4])
batch_pred_create.i_create_a_source_from_batch_prediction(self)
source_create.the_source_is_finished(self, example[1])
def test_scenario1(self):
"""
Scenario: Successfully creating a batch prediction:
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 model
And I wait until the model is ready less than <time_3> secs
When I create a batch prediction for the dataset with the model
And I wait until the batch prediction is ready less than <time_4> secs
And I download the created predictions file to "<local_file>"
Then the batch prediction file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | local_file | predictions_file |
| ../data/iris.csv | 30 | 30 | 50 | 50 | ./tmp/batch_predictions.csv |./data/batch_predictions.csv |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '30', '30', '50', '50', 'tmp/batch_predictions.csv', 'data/batch_predictions.csv']]
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_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[4])
batch_pred_create.i_download_predictions_file(self, example[5])
batch_pred_create.i_check_predictions(self, example[6])
def test_scenario1(self):
"""
Scenario: Successfully creating a batch prediction:
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 model
And I wait until the model is ready less than <time_3> secs
When I create a batch prediction for the dataset with the model
And I wait until the batch prediction is ready less than <time_4> secs
And I download the created predictions file to "<local_file>"
Then the batch prediction file is like "<predictions_file>"
Examples:
| data | time_1 | time_2 | time_3 | time_4 | local_file | predictions_file |
| ../data/iris.csv | 30 | 30 | 50 | 50 | ./tmp/batch_predictions.csv |./data/batch_predictions.csv |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '30', '30', '50', '50', 'tmp/batch_predictions.csv', 'data/batch_predictions.csv']]
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_model(self)
model_create.the_model_is_finished_in_less_than(self, example[3])
batch_pred_create.i_create_a_batch_prediction(self)
batch_pred_create.the_batch_prediction_is_finished_in_less_than(self, example[4])
batch_pred_create.i_download_predictions_file(self, example[5])
batch_pred_create.i_check_predictions(self, example[6])
