def test_scenario1(self):
"""
Scenario: Successfully creating an evaluation:
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 an evaluation for the model with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 30 | 30 | average_phi | 1 |
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris.csv', '50', '50', '50', '50', 'average_phi', '1'
]]
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])
evaluation_create.i_create_an_evaluation(self)
evaluation_create.the_evaluation_is_finished_in_less_than(
self, example[4])
evaluation_create.the_measured_measure_is_value(
self, example[5], example[6])
def test_scenario2(self):
"""
Scenario: Successfully creating an evaluation for 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
When I create an evaluation for the ensemble with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | number_of_models | tlp | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 5 | 1 | 50 | 30 | average_phi | 0.98029 |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '50', '50', '5', '1', '80', '80', 'average_phi', '0.98029']]
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[3], example[4])
ensemble_create.the_ensemble_is_finished_in_less_than(self, example[5])
evaluation_create.i_create_an_evaluation_ensemble(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[6])
evaluation_create.the_measured_measure_is_value(self, example[7], example[8])
def test_scenario1(self):
"""
Scenario: Successfully creating an evaluation:
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 an evaluation for the model with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 30 | 30 | average_phi | 1 |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', '50', '50', '50', '50', 'average_phi', '1']]
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])
evaluation_create.i_create_an_evaluation(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[4])
evaluation_create.the_measured_measure_is_value(self, example[5], example[6])
def test_scenario2(self):
"""
Scenario 2: Successfully creating a fusion:
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 with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I update the fusion name to "<fusion_name>"
When I wait until the fusion is ready less than <time_5> secs
And I create a prediction for "<data_input>"
Then the fusion name is "<fusion_name>"
And the prediction for "<objective>" is "<prediction>"
And I create an evaluation for the fusion with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | time_3 | time_4 | fusion_name | data_input | objective | prediction
| ../data/iris.csv | 10 | 10 | 20 | 20 | my new fusion name | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa"
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20', 'my new fusion name',
'{"tags":["my_fusion_2_tag"]}', 'my_fusion_2_tag',
'{"petal width": 1.75, "petal length": 2.45}', "000004",
"Iris-setosa", 'average_phi', '1.0']]
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_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[7])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[3])
model_create.i_update_fusion_name(self, example[5])
model_create.the_fusion_is_finished_in_less_than(self, example[4])
model_create.i_check_fusion_name(self, example[5])
prediction_create.i_create_a_fusion_prediction(self, example[8])
prediction_create.the_prediction_is(self, example[9], example[10])
evaluation_create.i_create_an_evaluation_fusion(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[3])
evaluation_create.the_measured_measure_is_value(self, example[11], example[12])
def test_scenario2(self):
"""
Scenario 2: Successfully creating a fusion:
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 with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I create a model with "<params>"
And I wait until the model is ready less than <time_3> secs
And I retrieve a list of remote models tagged with "<tag>"
And I create a fusion from a list of models
And I wait until the fusion is ready less than <time_4> secs
And I update the fusion name to "<fusion_name>"
When I wait until the fusion is ready less than <time_5> secs
And I create a prediction for "<data_input>"
Then the fusion name is "<fusion_name>"
And the prediction for "<objective>" is "<prediction>"
And I create an evaluation for the fusion with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | time_3 | time_4 | fusion_name | data_input | objective | prediction
| ../data/iris.csv | 10 | 10 | 20 | 20 | my new fusion name | {"petal length": 1, "petal width": 1} | "000004" | "Iris-setosa"
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '10', '10', '20', '20', 'my new fusion name',
'{"tags":["my_fusion_2_tag"]}', 'my_fusion_2_tag',
'{"petal width": 1.75, "petal length": 2.45}', "000004",
"Iris-setosa", 'average_phi', '1.0']]
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_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
model_create.i_create_a_model_with(self, example[6])
model_create.the_model_is_finished_in_less_than(self, example[3])
compare_pred.i_retrieve_a_list_of_remote_models(self, example[7])
model_create.i_create_a_fusion(self)
model_create.the_fusion_is_finished_in_less_than(self, example[3])
model_create.i_update_fusion_name(self, example[5])
model_create.the_fusion_is_finished_in_less_than(self, example[4])
model_create.i_check_fusion_name(self, example[5])
prediction_create.i_create_a_fusion_prediction(self, example[8])
prediction_create.the_prediction_is(self, example[9], example[10])
evaluation_create.i_create_an_evaluation_fusion(self)
evaluation_create.the_evaluation_is_finished_in_less_than(self, example[3])
evaluation_create.the_measured_measure_is_value(self, example[11], example[12])
def test_scenario2(self):
"""
Scenario: Successfully creating an evaluation for 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
When I create an evaluation for the ensemble with the dataset
And I wait until the evaluation is ready less than <time_4> secs
Then the measured "<measure>" is <value>
Examples:
| data | time_1 | time_2 | number_of_models | tlp | time_3 | time_4 | measure | value |
| ../data/iris.csv | 30 | 30 | 5 | 1 | 50 | 30 | average_phi | 0.98029 |
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris.csv', '50', '50', '5', '1', '80', '80', 'average_phi',
'0.98029'
]]
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[3], example[4])
ensemble_create.the_ensemble_is_finished_in_less_than(
self, example[5])
evaluation_create.i_create_an_evaluation_ensemble(self)
evaluation_create.the_evaluation_is_finished_in_less_than(
self, example[6])
evaluation_create.the_measured_measure_is_value(
self, example[7], example[8])
