def test_scenario2(self):
"""
Scenario: Successfully building remote test centroid predictions from scratch to dataset:
Given I create BigML resources uploading train "<data>" file to find centroids for "<test>" remotely to dataset with no CSV 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 cluster 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 centroid prediction has been created
Then I check that the batch centroids dataset exists
And no local CSV file is created
Examples:
| data | test | output_dir |
| ../data/grades.csv | ../data/test_grades.csv | ./scenario_cb_2 |
"""
print self.test_scenario2.__doc__
examples = [
['data/grades.csv', 'data/test_grades.csv', 'scenario_cb_2']]
for example in examples:
print "\nTesting with:\n", example
test_cluster.i_create_all_cluster_resources_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_cluster(self)
test_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
batch_pred.i_check_create_batch_centroid(self)
batch_pred.i_check_create_batch_centroids_dataset(self)
test_anomaly.i_check_no_local_CSV(self)
def test_scenario2(self):
"""
Scenario: Successfully building remote test centroid predictions from scratch to dataset:
Given I create BigML resources uploading train "<data>" file to find centroids for "<test>" remotely to dataset with no CSV 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 cluster 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 centroid prediction has been created
Then I check that the batch centroids dataset exists
And no local CSV file is created
Examples:
| data | test | output_dir |
| ../data/grades.csv | ../data/test_grades.csv | ./scenario_cb_2 |
"""
print self.test_scenario2.__doc__
examples = [[
'data/grades.csv', 'data/test_grades.csv', 'scenario_cb_2'
]]
for example in examples:
print "\nTesting with:\n", example
test_cluster.i_create_all_cluster_resources_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_cluster(self)
test_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
batch_pred.i_check_create_batch_centroid(self)
batch_pred.i_check_create_batch_centroids_dataset(self)
test_anomaly.i_check_no_local_CSV(self)
def test_scenario4(self):
"""
Scenario: Successfully building test anomaly score predictions from training set as a dataset:
Given I create BigML resources uploading train "<data>" file to find anomaly scores for the training set remotely saved to 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 anomaly detector has been created
And I check that the batch anomaly scores prediction has been created
Then I check that the batch anomaly scores dataset exists
And no local CSV file is created
Examples:
| data | output_dir |
| ../data/iris.csv | ./scenario_ab_4 |
"""
print self.test_scenario3.__doc__
examples = [
['data/iris.csv', 'scenario_ab_4']]
for example in examples:
print "\nTesting with:\n", example
test_anomaly.i_create_all_anomaly_resources_without_test_split(self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_anomaly.i_check_create_anomaly(self)
test_batch.i_check_create_batch_anomaly_scores(self)
test_anomaly.i_check_create_batch_anomaly_score_dataset(self)
test_anomaly.i_check_no_local_CSV(self)
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_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: Successfully building test anomaly score predictions from training set as a dataset:
Given I create BigML resources uploading train "<data>" file to find anomaly scores for the training set remotely saved to 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 anomaly detector has been created
And I check that the batch anomaly scores prediction has been created
Then I check that the batch anomaly scores dataset exists
And no local CSV file is created
Examples:
| data | output_dir |
| ../data/iris.csv | ./scenario_ab_4 |
"""
print self.test_scenario3.__doc__
examples = [['data/iris.csv', 'scenario_ab_4']]
for example in examples:
print "\nTesting with:\n", example
test_anomaly.i_create_all_anomaly_resources_without_test_split(
self, data=example[0], output_dir=example[1])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_anomaly.i_check_create_anomaly(self)
test_batch.i_check_create_batch_anomaly_scores(self)
test_anomaly.i_check_create_batch_anomaly_score_dataset(self)
test_anomaly.i_check_no_local_CSV(self)
