def test_scenario1(self):
"""
Scenario: Successfully building test predictions from dataset specifying objective field and model fields
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 BigML resources using dataset, objective field <objective> and model fields <fields> 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:
|data | output_dir | test | output |predictions_file | objective | fields |
| ../data/iris_2fb.csv| ./scénario1 | ../data/test_iris2fb.csv | ./scénario1/predictions.csv | ./check_files/predictions_iris_2fb.csv | spécies | "pétal width" |
"""
print self.test_scenario1.__doc__
examples = [
['data/iris_2fb.csv', u'scénario1', 'data/test_iris2fb.csv', u'scénario1/predictions.csv', 'check_files/predictions_iris_2fb.csv', u'spécies', u'"pétal width"']]
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)
test_pred.i_create_resources_from_dataset_objective_model(self, objective=example[5], fields=example[6], test=example[2], output=example[3])
test_pred.i_check_create_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario11(self):
"""
Scenario: Successfully building test predictions from dataset specifying objective field and model fields
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using dataset, objective field <objective> and model fields <fields> 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 | objective | fields |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario11/predictions.csv | ./check_files/predictions_iris_b.csv | 0 | "petal length","petal width" |
"""
print self.test_scenario11.__doc__
examples = [[
'scenario1',
'{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}',
'data/test_iris.csv', 'scenario11/predictions.csv',
'check_files/predictions_iris_b.csv', '0',
'"petal length","petal width"'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
test_pred.i_create_resources_from_dataset_objective_model(
self,
objective=example[5],
fields=example[6],
test=example[2],
output=example[3])
test_pred.i_check_create_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario1(self):
"""
Scenario: Successfully building test predictions from dataset specifying objective field and model fields
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 BigML resources using dataset, objective field <objective> and model fields <fields> 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:
|data | output_dir | test | output |predictions_file | objective | fields |
| ../data/iris_2fb.csv| ./scénario1 | ../data/test_iris2fb.csv | ./scénario1/predictions.csv | ./check_files/predictions_iris_2fb.csv | spécies | "pétal width" |
"""
print self.test_scenario1.__doc__
examples = [[
'data/iris_2fb.csv', u'scénario1', 'data/test_iris2fb.csv',
u'scénario1/predictions.csv',
'check_files/predictions_iris_2fb.csv', u'spécies',
u'"pétal width"'
]]
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)
test_pred.i_create_resources_from_dataset_objective_model(
self,
objective=example[5],
fields=example[6],
test=example[2],
output=example[3])
test_pred.i_check_create_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario11(self):
"""
Scenario: Successfully building test predictions from dataset specifying objective field and model fields
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using dataset, objective field <objective> and model fields <fields> 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 | objective | fields |
"""
examples = [
['scenario1', '{"data": "data/iris.csv", "output": "scenario1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario11/predictions.csv', 'check_files/predictions_iris_b.csv', '0', '"petal length","petal width"']]
show_doc(self.test_scenario11, examples)
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_pred.i_create_resources_from_dataset_objective_model(self, objective=example[5], fields=example[6], test=example[2], output=example[3])
test_pred.i_check_create_model(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
