def test_scenario2(self):
"""
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely with proportional missing strategy 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_nulls.csv | ./scenario_mis_2/predictions.csv | ./check_files/predictions_iris_nulls.csv
"""
print self.test_scenario2.__doc__
examples = [
['data/iris.csv', 'data/test_iris_nulls.csv', 'scenario_mis_2/predictions.csv', 'check_files/predictions_iris_nulls.csv']]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_remote_proportional(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_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
test_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario2(self):
"""
Scenario: Successfully building test predictions from scratch:
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely with a missing-splits model 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_missing.csv | ../data/test_iris_missing.csv | ./scenario_mspl_2/predictions.csv | ./check_files/predictions_iris_missing.csv
"""
print self.test_scenario2.__doc__
examples = [[
'data/iris_missing.csv', 'data/test_iris_missing.csv',
'scenario_mspl_2/predictions.csv',
'check_files/predictions_iris_missing.csv'
]]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_remote_missing_splits(
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_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
test_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
def test_scenario1(self):
"""
Scenario: Successfully building test centroid predictions from scratch:
Given I create BigML resources uploading train "<data>" file to find centroids 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 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
And I check that the centroids are ready
Then the local centroids 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_cb_1_r/centroids.csv | ./check_files/centroids_grades.csv |
"""
print self.test_scenario1.__doc__
examples = [
['data/grades.csv', 'data/grades_perm.csv', 'data/grades_fields_map_perm.csv', 'scenario_cb_1_r/centroids.csv', 'check_files/centroids_grades.csv']]
for example in examples:
print "\nTesting with:\n", example
test_cluster.i_create_all_cluster_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, 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)
test_cluster.i_check_create_centroids(self)
test_pred.i_check_predictions(self, example[4])
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_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_scenario1(self):
"""
Scenario: Successfully building test centroid predictions from scratch:
Given I create BigML resources uploading train "<data>" file to find centroids 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 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
And I check that the centroids are ready
Then the local centroids 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_cb_1_r/centroids.csv | ./check_files/centroids_grades.csv |
"""
print self.test_scenario1.__doc__
examples = [
['data/grades.csv', 'data/grades_perm.csv', 'data/grades_fields_map_perm.csv', 'scenario_cb_1_r/centroids.csv', 'check_files/centroids_grades.csv']]
for example in examples:
print "\nTesting with:\n", example
test_cluster.i_create_all_cluster_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, 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)
test_cluster.i_check_create_centroids(self)
test_pred.i_check_predictions(self, example[4])
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_scenario2(self):
"""
Scenario: Successfully building test predictions from scratch:
Given I create BigML resources uploading train "<data>" file to test "<test>" remotely with a missing-splits model 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_missing.csv | ../data/test_iris_missing.csv | ./scenario_mspl_2/predictions.csv | ./check_files/predictions_iris_missing.csv
"""
print self.test_scenario2.__doc__
examples = [
[
"data/iris_missing.csv",
"data/test_iris_missing.csv",
"scenario_mspl_2/predictions.csv",
"check_files/predictions_iris_missing.csv",
]
]
for example in examples:
print "\nTesting with:\n", example
test_pred.i_create_all_resources_remote_missing_splits(
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_pred.i_check_create_test_source(self)
test_pred.i_check_create_test_dataset(self)
test_pred.i_check_create_batch_prediction(self)
test_pred.i_check_create_predictions(self)
test_pred.i_check_predictions(self, example[3])
