def test_scenario01(self):
"""
Scenario: Successfully creating an execution from source code:
Given I create BigML execution resources from source code "<code>" and log results in "<output_dir>"
And I check that the script has been created
And I check that the execution has been created
And I check that the result is ready
Then the result file is like "<result_file>"
Examples:
| code | output_dir | result_file
| (+ 1 1) | scenario1_exe | check_files/results_s1exe.json
"""
print self.test_scenario01.__doc__
examples = [[
'(+ 1 1)', 'scenario1_exe', 'check_files/results_s1exe.json'
]]
for example in examples:
print "\nTesting with:\n", example
execute.i_create_all_execution_resources(self, example[0],
example[1])
execute.i_check_create_script(self)
execute.i_check_create_execution(self)
execute.i_check_create_result(self)
execute.i_check_result_is(self, example[2])
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_scenario03(self):
"""
Scenario: Successfully creating an execution with input/outputs from a code file:
Given I create BigML execution resources from code in file "<code_file>" with inputs "<inputs_dec>", outputs "<outputs_dec>" and inputs "<inputs>" and log results in "<output_dir>"
And I check that the script has been created
And I check that the execution has been created
And I check that the result is ready
Then the result file is like "<result_file>"
Examples:
| code_file | output_dir | inputs_dec | outputs_dec | inputs | result_file
| code.whizzml | scenario3_exe | data/inputs_dec.json | data/outputs_dec.json | data/inputs.json | check_files/results_s3exe.json
"""
print self.test_scenario03.__doc__
examples = [[
'data/whizzml/code.whizzml', 'scenario3_exe',
'data/inputs_dec.json', 'data/outputs_dec.json',
'data/inputs.json', 'check_files/results_s3exe.json'
]]
for example in examples:
print "\nTesting with:\n", example
execute.i_create_all_execution_with_io_resources(
self, example[0], example[1], example[2], example[3],
example[4])
execute.i_check_create_script(self)
execute.i_check_create_execution(self)
execute.i_check_create_result(self)
execute.i_check_result_is(self, example[5])
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)
def test_scenario01(self):
"""
Scenario: Successfully creating an execution from source code:
Given I create BigML execution resources from source code "<code>" and log results in "<output_dir>"
And I check that the script has been created
And I check that the execution has been created
And I check that the result is ready
Then the result file is like "<result_file>"
Examples:
| code | output_dir | result_file
| (+ 1 1) | scenario1_exe | check_files/results_s1exe.json
"""
print self.test_scenario01.__doc__
examples = [
['(+ 1 1)', 'scenario1_exe', 'check_files/results_s1exe.json' ]]
for example in examples:
print "\nTesting with:\n", example
execute.i_create_all_execution_resources(self, example[0], example[1])
execute.i_check_create_script(self)
execute.i_check_create_execution(self)
execute.i_check_create_result(self)
execute.i_check_result_is(self, example[2])
def test_scenario03(self):
"""
Scenario: Successfully creating an execution with input/outputs from a code file:
Given I create BigML execution resources from code in file "<code_file>" with inputs "<inputs_dec>", outputs "<outputs_dec>" and inputs "<inputs>" and log results in "<output_dir>"
And I check that the script has been created
And I check that the execution has been created
And I check that the result is ready
Then the result file is like "<result_file>"
Examples:
| code_file | output_dir | inputs_dec | outputs_dec | inputs | result_file
| code.whizzml | scenario3_exe | data/inputs_dec.json | data/outputs_dec.json | data/inputs.json | check_files/results_s3exe.json
"""
print self.test_scenario03.__doc__
examples = [
['data/whizzml/code.whizzml', 'scenario3_exe', 'data/inputs_dec.json', 'data/outputs_dec.json', 'data/inputs.json', 'check_files/results_s3exe.json']]
for example in examples:
print "\nTesting with:\n", example
execute.i_create_all_execution_with_io_resources(self, example[0], example[1], example[2], example[3], example[4])
execute.i_check_create_script(self)
execute.i_check_create_execution(self)
execute.i_check_create_result(self)
execute.i_check_result_is(self, example[5])
