def test_scenario2(self):
"""
Scenario: Successfully building test anomaly score predictions from test split:
Given I create BigML resources uploading train "<data>" file to find anomaly scores with test split "<test_split>" 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 anomaly detector has been created
And I check that the train dataset has been created
And I check that the dataset has been created from the test file
And I check that the batch anomaly scores prediction has been created
And I check that the anomaly scores are ready
Then the local anomaly scores file is like "<predictions_file>"
Examples:
| data | test_split | output |predictions_file |
| ../data/iris.csv | 0.2 | ./scenario_ab_2/anomalies.csv | ./check_files/anomaly_scores_iris.csv |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '0.2', 'scenario_ab_2/anomalies.csv', 'check_files/anomaly_scores_iris.csv']]
for example in examples:
print "\nTesting with:\n", example
test_anomaly.i_create_all_anomaly_resources_with_test_split(self, data=example[0], test_split=example[1], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_anomaly.i_check_create_anomaly(self)
test_pred.i_check_create_dataset(self, suffix='train ')
test_pred.i_check_create_dataset(self, suffix='test ')
test_batch.i_check_create_batch_anomaly_scores(self)
test_anomaly.i_check_create_anomaly_scores(self)
test_anomaly.i_check_anomaly_scores(self, example[3])
def test_scenario1(self):
"""
Scenario: Successfully building test anomaly score predictions from scratch:
Given I create BigML resources uploading train "<data>" file to find anomaly scores for "<test>" remotely with mapping file "<fields_map>" 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 anomaly detector 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 anomaly scores prediction has been created
And I check that the anomaly scores are ready
Then the local anomaly scores file is like "<predictions_file>"
Examples:
| data | test | fields_map | output |predictions_file |
| ../data/grades.csv | ../data/grades_perm.csv | ../data/grades_fields_map_perm.csv | ./scenario_ab_1_r/anomalies.csv | ./check_files/anomaly_scores_grades.csv |
"""
print self.test_scenario1.__doc__
examples = [
['data/grades.csv', 'data/grades_perm.csv', 'data/grades_fields_map_perm.csv', 'scenario_ab_1_r/anomalies.csv', 'check_files/anomaly_scores_grades.csv']]
for example in examples:
print "\nTesting with:\n", example
test_anomaly.i_create_all_anomaly_resources_with_mapping(self, data=example[0], test=example[1], fields_map=example[2], output=example[3])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_anomaly.i_check_create_anomaly(self)
test_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
test_batch.i_check_create_batch_anomaly_scores(self)
test_anomaly.i_check_create_anomaly_scores(self)
test_anomaly.i_check_anomaly_scores(self, example[4])
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_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_scenario2(self):
"""
Scenario: Successfully building test anomaly score predictions from test split:
Given I create BigML resources uploading train "<data>" file to find anomaly scores with test split "<test_split>" 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 anomaly detector has been created
And I check that the train dataset has been created
And I check that the dataset has been created from the test file
And I check that the batch anomaly scores prediction has been created
And I check that the anomaly scores are ready
Then the local anomaly scores file is like "<predictions_file>"
Examples:
| data | test_split | output |predictions_file |
| ../data/iris.csv | 0.2 | ./scenario_ab_2/anomalies.csv | ./check_files/anomaly_scores_iris.csv |
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', '0.2', 'scenario_ab_2/anomalies.csv', 'check_files/anomaly_scores_iris.csv']]
for example in examples:
print "\nTesting with:\n", example
test_anomaly.i_create_all_anomaly_resources_with_test_split(self, data=example[0], test_split=example[1], output=example[2])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_anomaly.i_check_create_anomaly(self)
test_pred.i_check_create_dataset(self, suffix='train ')
test_pred.i_check_create_dataset(self, suffix='test ')
test_batch.i_check_create_batch_anomaly_scores(self)
test_anomaly.i_check_create_anomaly_scores(self)
test_anomaly.i_check_anomaly_scores(self, example[3])
def test_scenario1(self):
"""
Scenario: Successfully building test anomaly score predictions from scratch:
Given I create BigML resources uploading train "<data>" file to find anomaly scores for "<test>" remotely with mapping file "<fields_map>" 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 anomaly detector 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 anomaly scores prediction has been created
And I check that the anomaly scores are ready
Then the local anomaly scores file is like "<predictions_file>"
Examples:
| data | test | fields_map | output |predictions_file |
| ../data/grades.csv | ../data/grades_perm.csv | ../data/grades_fields_map_perm.csv | ./scenario_ab_1_r/anomalies.csv | ./check_files/anomaly_scores_grades.csv |
"""
print self.test_scenario1.__doc__
examples = [
['data/grades.csv', 'data/grades_perm.csv', 'data/grades_fields_map_perm.csv', 'scenario_ab_1_r/anomalies.csv', 'check_files/anomaly_scores_grades.csv']]
for example in examples:
print "\nTesting with:\n", example
test_anomaly.i_create_all_anomaly_resources_with_mapping(self, data=example[0], test=example[1], fields_map=example[2], output=example[3])
test_pred.i_check_create_source(self)
test_pred.i_check_create_dataset(self)
test_anomaly.i_check_create_anomaly(self)
test_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
test_batch.i_check_create_batch_anomaly_scores(self)
test_anomaly.i_check_create_anomaly_scores(self)
test_anomaly.i_check_anomaly_scores(self, example[4])
