def test_scenario06(self):
"""
Scenario: Successfully building batch test predictions from model
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML logistic regression resources using model to test "<test>" as a batch prediction and log predictions in "<output>"
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 | ./scenario4/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario06.__doc__
examples = [[
'scenario1_lr',
'{"data": "data/iris.csv", "output": "scenario1_lr/predictions.csv", "test": "data/test_iris.csv"}',
'data/test_iris.csv', 'scenario5_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_model_remote(self,
test=example[2],
output=example[3])
batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def setup_scenario2(self):
"""
Scenario 2: Successfully building test predictions from scratch:
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely 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 source has been created from the test file
And I check that the dataset has been created from the test file
And I check that the batch prediction 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 | ./scenario_r1/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.setup_scenario2.__doc__
examples = [
['data/iris.csv', 'data/test_iris.csv', 'scenario_r1/predictions.csv', 'check_files/predictions_iris.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_batch(self, data=example[0], test=example[1], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_batch_pred.i_check_create_test_source(self)
test_batch_pred.i_check_create_test_dataset(self)
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario5(self):
"""
Scenario 5: Successfully building test predictions from dataset and prediction format info
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using a model to test the previous test dataset remotely with prediction headers and fields "<fields>" and log predictions in "<output>"
And I check that the batch prediction has been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | fields | output |predictions_file |
| scenario_r1| {"data": "../data/iris.csv", "output": "./scenario_r1/predictions.csv", "test": "../data/test_iris.csv"} | sepal length,sepal width | ./scenario_r4/predictions.csv | ./check_files/predictions_iris_format.csv |
"""
print self.test_scenario5.__doc__
examples = [
['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'sepal length,sepal width', 'scenario_r4/predictions.csv', 'check_files/predictions_iris_format.csv']]
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_model_batch(self, fields=example[2], output=example[3])
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario6(self):
"""
Scenario 6: Successfully building remote test predictions from scratch to a dataset:
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely to a dataset with no CSV output and log resources 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 check that the source has been created from the test file
And I check that the dataset has been created from the test file
And I check that the batch prediction has been created
Then I check that the batch predictions dataset exists
And no local CSV file is created
Examples:
| data | test | output_dir |
| ../data/iris.csv | ../data/test_iris.csv | ./scenario_r5 |
"""
print self.test_scenario6.__doc__
examples = [
['data/iris.csv', 'data/test_iris.csv', 'scenario_r5']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_batch_to_dataset(self, data=example[0], test=example[1], output_dir=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_batch_pred.i_check_create_test_source(self)
test_batch_pred.i_check_create_test_dataset(self)
test_batch_pred.i_check_create_batch_prediction(self)
test_batch_pred.i_check_create_batch_predictions_dataset(self)
anomaly_pred.i_check_no_local_CSV(self)
def test_scenario07(self):
"""
Scenario: Successfully building batch test predictions from model with customized output
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML deepnet resources using model to test "<test>" as a batch prediction with output format "<batch-output>" and log predictions in "<output>"
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | test |batch_output | output |predictions_file |
| scenario1| {"data": "../data/iris.csv", "output": "./scenario1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ../data/batch_output.json | ./scenario6_dn/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario07.__doc__
examples = [[
'scenario1_dn',
'{"data": "data/iris.csv", "output": "scenario1_dn/predictions.csv", "test": "data/test_iris.csv"}',
'data/test_iris.csv', 'data/batch_output.json',
'scenario6_dn/predictions.csv',
'check_files/predictions_iris_dn_prob.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(
self, example[0], example[1])
dn_pred.i_create_dn_resources_from_model_remote_with_options(
self,
test=example[2],
output=example[4],
options_file=example[3])
batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[5])
def test_scenario4(self):
"""
Scenario 4: Successfully building test predictions from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using dataset to test the previous test dataset remotely and log predictions in "<output>"
And I check that the model has been created
And I check that the batch prediction 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 |
| scenario_r1| {"data": "../data/iris.csv", "output": "./scenario_r1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario_r3/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario4.__doc__
examples = [
['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'scenario_r3/predictions.csv', 'check_files/predictions_iris.csv']]
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_batch(self, output=example[2])
test_pred.i_check_create_model(self)
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario5(self):
"""
Scenario 5: Successfully building test predictions from dataset and prediction format info
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using a model to test the previous test dataset remotely with prediction headers and fields "<fields>" and log predictions in "<output>"
And I check that the batch prediction has been created
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs | fields | output |predictions_file |
| scenario_r1| {"data": "../data/iris.csv", "output": "./scenario_r1/predictions.csv", "test": "../data/test_iris.csv"} | sepal length,sepal width | ./scenario_r4/predictions.csv | ./check_files/predictions_iris_format.csv |
"""
print self.test_scenario5.__doc__
examples = [[
'scenario_r1',
'{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}',
'sepal length,sepal width', 'scenario_r4/predictions.csv',
'check_files/predictions_iris_format.csv'
]]
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_model_batch(self,
fields=example[2],
output=example[3])
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario6(self):
"""
Scenario 6: Successfully building remote test predictions from scratch to a dataset:
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely to a dataset with no CSV output and log resources 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 check that the source has been created from the test file
And I check that the dataset has been created from the test file
And I check that the batch prediction has been created
Then I check that the batch predictions dataset exists
And no local CSV file is created
Examples:
| data | test | output_dir |
| ../data/iris.csv | ../data/test_iris.csv | ./scenario_r5 |
"""
print self.test_scenario6.__doc__
examples = [['data/iris.csv', 'data/test_iris.csv', 'scenario_r5']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_batch_to_dataset(
self, data=example[0], test=example[1], output_dir=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_batch_pred.i_check_create_test_source(self)
test_batch_pred.i_check_create_test_dataset(self)
test_batch_pred.i_check_create_batch_prediction(self)
test_batch_pred.i_check_create_batch_predictions_dataset(self)
anomaly_pred.i_check_no_local_CSV(self)
def test_scenario4(self):
"""
Scenario 4: Successfully building test predictions from dataset
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using dataset to test the previous test dataset remotely and log predictions in "<output>"
And I check that the model has been created
And I check that the batch prediction 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 |
| scenario_r1| {"data": "../data/iris.csv", "output": "./scenario_r1/predictions.csv", "test": "../data/test_iris.csv"} | ../data/test_iris.csv | ./scenario_r3/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario4.__doc__
examples = [[
'scenario_r1',
'{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}',
'scenario_r3/predictions.csv', 'check_files/predictions_iris.csv'
]]
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_batch(self,
output=example[2])
test_pred.i_check_create_model(self)
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def setup_scenario2(self):
"""
Scenario 2: Successfully building test predictions from scratch:
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely 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 source has been created from the test file
And I check that the dataset has been created from the test file
And I check that the batch prediction 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 | ./scenario_r1/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.setup_scenario2.__doc__
examples = [
['data/iris.csv', 'data/test_iris.csv', 'scenario_r1/predictions.csv', 'check_files/predictions_iris.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_batch(self, data=example[0], test=example[1], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self, suffix=None)
test_pred.i_check_create_model(self)
test_batch_pred.i_check_create_test_source(self)
test_batch_pred.i_check_create_test_dataset(self)
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario7(self):
"""
Scenario: Successfully building test predictions from model with operating point
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using model with operating point "<operating_point>" to test "<test>" and log predictions in "<output>"
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs |
operating_point | test | output |predictions_file |
"""
examples = [
['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario_r7/predictions_p.csv', 'check_files/predictions_iris_op_prob.csv', "data/operating_point_prob.json"],
['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario_r7/predictions_c.csv', 'check_files/predictions_iris_op_conf.csv', "data/operating_point_conf.json"]]
print self.test_scenario7.__doc__
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_batch_pred.i_create_resources_from_model_with_op_remote(self, operating_point=example[5], test=example[2], output=example[3])
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario7(self):
"""
Scenario: Successfully building test predictions from model with operating point
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML resources using model with operating point "<operating_point>" to test "<test>" and log predictions in "<output>"
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
Examples:
|scenario | kwargs |
operating_point | test | output |predictions_file |
"""
examples = [
['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario_r7/predictions_p.csv', 'check_files/predictions_iris_op_prob.csv', "data/operating_point_prob.json"],
['scenario_r1', '{"data": "data/iris.csv", "output": "scenario_r1/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario_r7/predictions_c.csv', 'check_files/predictions_iris_op_conf.csv', "data/operating_point_conf.json"]]
print self.test_scenario7.__doc__
for example in examples:
print "\nTesting with:\n", example
test_pred.i_have_previous_scenario_or_reproduce_it(self, example[0], example[1])
test_batch_pred.i_create_resources_from_model_with_op_remote(self, operating_point=example[5], test=example[2], output=example[3])
test_batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
def test_scenario02(self):
"""
Scenario: Successfully building batch test predictions from model
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML fusion resources using model built from "<train>" to test "<test>" as a batch prediction and log predictions in "<output>"
And I check that the predictions are ready
Then the local prediction file is like "<predictions_file>"
"""
print self.test_scenario02.__doc__
examples = [
['data/test_iris.csv', 'scenario2_fs/predictions.csv', 'check_files/predictions_iris_fs.csv']]
for example in examples:
print "\nTesting with:\n", example
fs_pred.i_create_fs_resources_from_model_remote(self, test=example[0], output=example[1])
fs_pred.i_check_create_fusion(self)
batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[2])
def test_scenario06(self):
"""
Scenario: Successfully building batch test predictions from model
Given I have previously executed "<scenario>" or reproduce it with arguments <kwargs>
And I create BigML logistic regression resources using model to test "<test>" as a batch prediction and log predictions in "<output>"
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 | ./scenario4/predictions.csv | ./check_files/predictions_iris.csv |
"""
print self.test_scenario06.__doc__
examples = [
['scenario1_lr', '{"data": "data/iris.csv", "output": "scenario1_lr/predictions.csv", "test": "data/test_iris.csv"}', 'data/test_iris.csv', 'scenario5_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_model_remote(self, test=example[2], output=example[3])
batch_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[4])
