def setup_scenario02(self):
"""
Scenario: Successfully building test predictions from start:
Given I create BigML logistic regression resources uploading train "<data>" file to test "<test>" and log predictions in "<output>"
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 check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
| data | test | output |predictions_file |
| ../data/iris.csv | ../data/test_iris.csv | ./scenario1_lr/predictions.csv | ./check_files/predictions_iris_lr.csv |
"""
print self.setup_scenario02.__doc__
examples = [[
'data/iris.csv', 'data/test_iris.csv',
'scenario1_lr/predictions.csv',
'check_files/predictions_iris_lr.csv'
]]
for example in examples:
print "\nTesting with:\n", example
lr_pred.i_create_all_lr_resources(self, example[0], example[1],
example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
lr_pred.i_check_create_lr_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario10(self):
"""
Scenario: Successfully building logistic regression from a sampled dataset
Given I create a BigML dataset 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 create a BigML logistic regression with params "<params>" from dataset in "<output_dir>"
And I check that the logistic regression has been created
And the logistic regression params are "<params_json>"
Examples:
|data |output_dir | params | params_json
|../data/iris.csv | ./scenario_d_10 | "--sample-rate 0.2 --replacement" | {"sample-rate": 0.2, "replacement": true}
"""
print self.test_scenario10.__doc__
examples = [[
'data/iris.csv', 'scenario_d_10',
'--sample-rate 0.2 --replacement',
'{"sample_rate": 0.2, "replacement": true}'
]]
for example in examples:
print "\nTesting with:\n", example
dataset_adv.i_create_dataset(self,
data=example[0],
output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dataset_adv.i_create_logistic_with_params_from_dataset( \
self, params=example[2], output_dir=example[1])
test_logistic.i_check_create_lr_model(self)
dataset_adv.i_check_logistic_params(self, params_json=example[3])
def test_scenario04(self):
"""
Scenario: Successfully building test predictions from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML logistic regression resources using dataset to test "<test>" and log predictions in "<output>"
And I check that the model has been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario3/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario04.__doc__
examples = [[
'scenario1_lr',
'{"data": "data/iris.csv", "output": "scenario1_lr/predictions.csv", "test": "data/test_iris.csv"}',
'data/test_iris.csv', 'scenario3_lr/predictions.csv',
'check_files/predictions_iris_lr.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
lr_pred.i_create_lr_resources_from_dataset(self,
None,
test=example[2],
output=example[3])
lr_pred.i_check_create_lr_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def setup_scenario02(self):
"""
Scenario: Successfully building test predictions from start:
Given I create BigML logistic regression resources uploading train "<data>" file to test "<test>" and log predictions in "<output>"
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 check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
| data | test | output |predictions_file |
| ../data/iris.csv | ../data/test_iris.csv | ./scenario1_lr/predictions.csv | ./check_files/predictions_iris_lr.csv |
"""
print self.setup_scenario02.__doc__
examples = [
['data/iris.csv', 'data/test_iris.csv', 'scenario1_lr/predictions.csv', 'check_files/predictions_iris_lr.csv']]
for example in examples:
print "\nTesting with:\n", example
lr_pred.i_create_all_lr_resources(self, example[0], example[1], example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
lr_pred.i_check_create_lr_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario03(self):
"""
Scenario: Successfully building test predictions from source
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML logistic regression resources using source to test "<test>" and log predictions in "<output>"
And I check that the dataset has been created
And I check that the model has been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test | output |predictions_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1_lr/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario2/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario03.__doc__
examples = [
['scenario1_lr', '{"data": "data/iris.csv", "output": "scenario1_lr/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario2_lr/predictions.csv', 'check_files/predictions_iris_lr.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
lr_pred.i_create_lr_resources_from_source(self, None, test=example[2], output=example[3])
test_pred.i_check_create_dataset(self, suffix=None)
lr_pred.i_check_create_lr_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario8(self):
"""
Scenario: Successfully building evaluations for logistic regression from start:
Given I create BigML logistic regression resources uploading train "<data>" file to evaluate and log evaluation in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the logistic regression has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | json_evaluation_file |
| ../data/iris.csv | ./scenario_e8/evaluation | ./check_files/evaluation_iris_lr.json |
"""
print self.test_scenario8.__doc__
examples = [[
'data/iris.csv', 'scenario_e8/evaluation',
'check_files/evaluation_iris_lr.json'
]]
for example in examples:
print "\nTesting with:\n", example
lr_pred.i_create_all_lr_resources_to_evaluate(self,
data=example[0],
output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
lr_pred.i_check_create_lr_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[2])
def test_scenario10(self):
"""
Scenario: Successfully building logistic regression from a sampled dataset
Given I create a BigML dataset 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 create a BigML logistic regression with params "<params>" from dataset in "<output_dir>"
And I check that the logistic regression has been created
And the logistic regression params are "<params_json>"
Examples:
|data |output_dir | params | params_json
|../data/iris.csv | ./scenario_d_10 | "--sample-rate 0.2 --replacement" | {"sample-rate": 0.2, "replacement": true}
"""
print self.test_scenario10.__doc__
examples = [
['data/iris.csv', 'scenario_d_10',
'--sample-rate 0.2 --replacement',
'{"sample_rate": 0.2, "replacement": true}']]
for example in examples:
print "\nTesting with:\n", example
dataset_adv.i_create_dataset(self, data=example[0],
output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
dataset_adv.i_create_logistic_with_params_from_dataset( \
self, params=example[2], output_dir=example[1])
test_logistic.i_check_create_lr_model(self)
dataset_adv.i_check_logistic_params(self, params_json=example[3])
def test_scenario8(self):
"""
Scenario: Successfully building evaluations for logistic regression from start:
Given I create BigML logistic regression resources uploading train "<data>" file to evaluate and log evaluation in "<output>"
And I check that the source has been created
And I check that the dataset has been created
And I check that the logistic regression has been created
And I check that the evaluation has been created
Then the evaluation file is like "<json_evaluation_file>"
Examples:
| data | output | json_evaluation_file |
| ../data/iris.csv | ./scenario_e8/evaluation | ./check_files/evaluation_iris_lr.json |
"""
print self.test_scenario8.__doc__
examples = [
['data/iris.csv', 'scenario_e8/evaluation', 'check_files/evaluation_iris_lr.json']]
for example in examples:
print "\nTesting with:\n", example
lr_pred.i_create_all_lr_resources_to_evaluate(self, data=example[0], output=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
lr_pred.i_check_create_lr_model(self)
test_pred.i_check_create_evaluation(self)
evaluation.then_the_evaluation_file_is_like(self, example[2])
