def setup_scenario02(self):
"""
Scenario: Successfully building test predictions from start:
Given I create BigML linear 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/grades.csv | ../data/test_grades.csv | ./scenario1_lrr/predictions.csv | ./check_files/predictions_grades_lrr.csv |
"""
print self.setup_scenario02.__doc__
examples = [
['data/grades.csv', 'data/test_grades_no_missings.csv', 'scenario1_lrr/predictions.csv', 'check_files/predictions_grades_lrr.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 setup_scenario02(self):
"""
Scenario: Successfully building test predictions from start:
Given I create BigML linear 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/grades.csv | ../data/test_grades.csv | ./scenario1_lrr/predictions.csv | ./check_files/predictions_grades_lrr.csv |
"""
print self.setup_scenario02.__doc__
examples = [[
'data/grades.csv', 'data/test_grades_no_missings.csv',
'scenario1_lrr/predictions.csv',
'check_files/predictions_grades_lrr.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 linear 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/grades.csv", "output": "./scenario1_lrr/predictions.csv", "test": "../data/test_grades.csv"} | ../data/test_grades.csv | ./scenario2/predictions.csv | ./check_files/predictions_grades.csv |
"""
print self.test_scenario03.__doc__
examples = [
['scenario1_lrr', '{"data": "data/grades.csv", "output": "scenario1_lrr/predictions.csv", "test": "data/test_grades_no_missings.csv"}', 'data/test_grades_no_missings.csv', 'scenario2_lrr/predictions.csv', 'check_files/predictions_grades_lrr.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_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 linear 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_lrr',
'{"data": "data/grades.csv", "output": "scenario1_lrr/predictions.csv", "test": "data/test_grades_no_missings.csv"}',
'data/test_grades_no_missings.csv',
'scenario3_lrr/predictions.csv',
'check_files/predictions_grades_lrr.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])