def test_scenario1(self):
"""
Scenario: Successfully retraining a balanced model
Given I create a BigML balanced model from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
And I retrain the model from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
Then I check that the model has doubled its rows
And I check that the model is balanced
Examples:
|data |output_dir | output_dir_ret
|../data/iris.csv | ./scenario_rt_1 |./scenario_rt_1b |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris.csv', 'scenario_rt_1', 'scenario_rt_1b']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_balanced_model(self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_retrain_model(self, data=example[0], output_dir=example[2])
test_pred.i_check_create_source(self)
execute_steps.i_check_create_execution(self, number_of_executions=2)
test_pred.i_check_create_model_in_execution(self)
test_pred.i_check_model_double(self)
test_pred.i_check_model_is_balanced(self)
def test_scenario2(self):
"""
Scenario: Successfully retraining from a model using sampled dataset
Given I create a BigML balanced model from "<data>" sampling 50% of data and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
And I retrain the model from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
Then I check that the model has doubled its rows
And I check that the model is balanced
Examples:
|data |output_dir | output_dir_ret
|../data/iris.csv | ./scenario_rt_2 |./scenario_rt_2b |
"""
print self.test_scenario2.__doc__
examples = [['data/iris.csv', 'scenario_rt_2', 'scenario_rt_2b'],
[
'https://static.bigml.com/csv/iris.csv',
'scenario_rt_2c', 'scenario_rt_2d'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_balanced_model_from_sample(
self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_dataset(self, suffix='gen ')
test_pred.i_check_create_model(self)
test_pred.i_retrain_model(self,
data=example[0],
output_dir=example[2])
if not example[0].startswith("https"):
test_pred.i_check_create_source(self)
execute_steps.i_check_create_execution(self,
number_of_executions=2)
test_pred.i_check_create_model_in_execution(self)
test_pred.i_check_model_double(self)
test_pred.i_check_model_is_balanced(self)
def test_scenario2(self):
"""
Scenario: Successfully retraining from a model using sampled dataset
Given I create a BigML balanced model from "<data>" sampling 50% of data and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
And I retrain the model from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
Then I check that the model has doubled its rows
And I check that the model is balanced
Examples:
|data |output_dir | output_dir_ret
|../data/iris.csv | ./scenario_rt_2 |./scenario_rt_2b |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', 'scenario_rt_2', 'scenario_rt_2b'],
['https://static.bigml.com/csv/iris.csv', 'scenario_rt_2c',
'scenario_rt_2d']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_balanced_model_from_sample(self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_dataset(self, suffix='gen ')
test_pred.i_check_create_model(self)
test_pred.i_retrain_model(self, data=example[0], output_dir=example[2])
if not example[0].startswith("https"):
test_pred.i_check_create_source(self)
execute_steps.i_check_create_execution(self, number_of_executions=2)
test_pred.i_check_create_model_in_execution(self)
test_pred.i_check_model_double(self)
test_pred.i_check_model_is_balanced(self)
def test_scenario1(self):
"""
Scenario: Successfully retraining a balanced model
Given I create a BigML balanced model from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
And I retrain the model from "<data>" and store logs in "<output_dir>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the model has been created
Then I check that the model has doubled its rows
And I check that the model is balanced
Examples:
|data |output_dir | output_dir_ret
|../data/iris.csv | ./scenario_rt_1 |./scenario_rt_1b |
"""
print self.test_scenario1.__doc__
examples = [['data/iris.csv', 'scenario_rt_1', 'scenario_rt_1b']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_balanced_model(self,
data=example[0],
output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_pred.i_retrain_model(self,
data=example[0],
output_dir=example[2])
test_pred.i_check_create_source(self)
execute_steps.i_check_create_execution(self,
number_of_executions=2)
test_pred.i_check_create_model_in_execution(self)
test_pred.i_check_model_double(self)
test_pred.i_check_model_is_balanced(self)