def test_scenario6(self):
"""
Scenario: Successfully creating an anomaly score:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector from a dataset
And I wait until the anomaly detector is ready less than <time_3> secs
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | score |
| ../data/tiny_kdd.csv | 10 | 10 | 100 | {"src_bytes": 350} | 0.92618 |
| ../data/iris_sp_chars.csv | 10 | 10 | 100 | {"pétal&width\u0000": 300} | 0.90198 |
"""
print self.test_scenario5.__doc__
examples = [
['data/tiny_kdd.csv', '10', '10', '100', '{"src_bytes": 350}', '0.92618', 'data/iris_sp_chars.csv', '10', '10', '100', '{"pétal&width\u0000": 300}', '0.90198'],
['data/iris_sp_chars.csv', '10', '10', '100', '{"pétal&width\u0000": 300}', '0.90198']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self, example[3])
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
def test_scenario6(self):
"""
Scenario: Successfully creating an anomaly score:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector from a dataset
And I wait until the anomaly detector is ready less than <time_3> secs
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | score |
| ../data/tiny_kdd.csv | 10 | 10 | 100 | {"src_bytes": 350} | 0.92618 |
| ../data/iris_sp_chars.csv | 10 | 10 | 100 | {"pétal&width\u0000": 300} | 0.90198 |
"""
print self.test_scenario6.__doc__
examples = [
['data/tiny_kdd.csv', '10', '10', '100', '{"src_bytes": 350}', '0.92846'],
['data/iris_sp_chars.csv', '10', '10', '100', '{"pétal&width\u0000": 300}', '0.89313']]
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self, example[3])
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
def test_scenario3(self):
"""
Scenario: Successfully comparing scores from anomaly detectors:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector
And I wait until the anomaly detector is ready less than <time_3> secs
And I create a local anomaly detector
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
And I create a local anomaly score for "<data_input>"
Then the local anomaly score is "<score>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | score |
"""
examples = [
['data/tiny_kdd.csv', '30', '30', '30', '{"000020": 255.0, "000004": 183.0, "000016": 4.0, "000024": 0.04, "000025": 0.01, "000026": 0.0, "000019": 0.25, "000017": 4.0, "000018": 0.25, "00001e": 0.0, "000005": 8654.0, "000009": "0", "000023": 0.01, "00001f": 123.0}', '0.69802']]
show_doc(self.test_scenario3, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_anomaly(self)
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
prediction_compare.i_create_a_local_anomaly_score(self, example[4])
prediction_compare.the_local_anomaly_score_is(self, example[5])
def test_scenario3(self):
"""
Scenario: Successfully comparing scores from anomaly detectors:
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector
And I wait until the anomaly detector is ready less than <time_3> secs
And I create a local anomaly detector
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
And I create a local anomaly score for "<data_input>"
Then the local anomaly score is "<score>"
Examples:
| data | time_1 | time_2 | time_3 | data_input | score |
"""
examples = [
['data/tiny_kdd.csv', '20', '20', '30', '{"000020": 255.0, "000004": 183.0, "000016": 4.0, "000024": 0.04, "000025": 0.01, "000026": 0.0, "000019": 0.25, "000017": 4.0, "000018": 0.25, "00001e": 0.0, "000005": 8654.0, "000009": "0", "000023": 0.01, "00001f": 123.0}', '0.69802']]
show_doc(self.test_scenario3, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(self, example[3])
prediction_compare.i_create_a_local_anomaly(self)
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
prediction_compare.i_create_a_local_anomaly_score(self, example[4])
prediction_compare.the_local_anomaly_score_is(self, example[5])
def test_scenario1c(self):
"""
Scenario: Successfully comparing remote and local predictions
with raw date input for anomaly detectors
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector
And I wait until the anomaly detector is ready less
than <time_3> secs
And I create a local anomaly detector
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
And I create a local anomaly score for "<data_input>"
Then the local anomaly score is "<score>"
Examples:
|data|time_1|time_2|time_3|data_input|score|
"""
examples = [
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"2001-01-05T23:04:04.693","cat-0":"cat2","target-2":0.01}',
0.54911
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"2011-04-01T00:16:45.747","cat-0":"cat2","target-2":0.32}',
0.52477
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1969-W29-1T17:36:39Z","cat-0":"cat1","target-2":0.87}',
0.93678
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"Mon Jul 14 17:36 +0000 1969","cat-0":"cat1","target-2":0}',
0.93717
]
]
show_doc(self.test_scenario1c, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_anomaly(self)
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
prediction_compare.i_create_a_local_anomaly_score(self, example[4])
prediction_compare.the_local_anomaly_score_is(self, example[5])
def test_scenario1b(self):
"""
Scenario: Successfully comparing remote and local predictions
with raw date input for anomaly detectors
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector
And I wait until the anomaly detector is ready less
than <time_3> secs
And I create a local anomaly detector
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
And I create a local anomaly score for "<data_input>"
Then the local anomaly score is "<score>"
Examples:
|data|time_1|time_2|time_3|data_input|score|
"""
examples = [
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1932-01-30T19:24:11.440","cat-0":"cat2","target-2":0.1}',
0.54343
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1950-11-06T05:34:05.602","cat-0":"cat1" ,"target-2":0.9}',
0.5202
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1969-7-14 17:36","cat-0":"cat2","target-2":0.9}',
0.93639
]
]
show_doc(self.test_scenario1b, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_anomaly(self)
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
prediction_compare.i_create_a_local_anomaly_score(self, example[4])
prediction_compare.the_local_anomaly_score_is(self, example[5])
def test_scenario1(self):
"""
Scenario: Successfully comparing remote and local predictions
with raw date input for anomaly detectors
Given I create a data source uploading a "<data>" file
And I wait until the source is ready less than <time_1> secs
And I create a dataset
And I wait until the dataset is ready less than <time_2> secs
And I create an anomaly detector
And I wait until the anomaly detector is ready less
than <time_3> secs
And I create a local anomaly detector
When I create an anomaly score for "<data_input>"
Then the anomaly score is "<score>"
And I create a local anomaly score for "<data_input>"
Then the local anomaly score is "<score>"
Examples:
|data|time_1|time_2|time_3|data_input|score|
"""
examples = [
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1910-05-08T19:10:23.106","cat-0":"cat2","target-2":0.4}',
0.52477
],
[
'data/dates2.csv', '20', '30', '60',
'{"time-1":"1920-06-30T20:21:20.320","cat-0":"cat1","target-2":0.2}',
0.50654
]
]
show_doc(self.test_scenario1, examples)
for example in examples:
print "\nTesting with:\n", example
source_create.i_upload_a_file(self, example[0])
source_create.the_source_is_finished(self, example[1])
dataset_create.i_create_a_dataset(self)
dataset_create.the_dataset_is_finished_in_less_than(
self, example[2])
anomaly_create.i_create_an_anomaly(self)
anomaly_create.the_anomaly_is_finished_in_less_than(
self, example[3])
prediction_compare.i_create_a_local_anomaly(self)
prediction_create.i_create_an_anomaly_score(self, example[4])
prediction_create.the_anomaly_score_is(self, example[5])
prediction_compare.i_create_a_local_anomaly_score(self, example[4])
prediction_compare.the_local_anomaly_score_is(self, example[5])
