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import unittest
import pandas as pd
from detectors import pattern_detector, threshold_detector, anomaly_detector
from analytic_types.detector_typing import DetectionResult, ProcessingResult, Bound
from analytic_types.segment import Segment
from tests.test_dataset import create_dataframe, create_list_of_timestamps
from utils import convert_pd_timestamp_to_ms
class TestPatternDetector(unittest.TestCase):
def test_small_dataframe(self):
data = [[0,1], [1,2]]
dataframe = pd.DataFrame(data, columns=['timestamp', 'values'])
cache = { 'windowSize': 10 }
detector = pattern_detector.PatternDetector('GENERAL', 'test_id')
with self.assertRaises(ValueError):
detector.detect(dataframe, cache)
def test_only_negative_segments(self):
data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1]
data_ind = [1523889000000 + i for i in range(len(data_val))]
data = {'timestamp': data_ind, 'value': data_val}
dataframe = pd.DataFrame(data = data)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': False, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': False}]
segments = [Segment.from_json(segment) for segment in segments]
cache = {}
detector = pattern_detector.PatternDetector('PEAK', 'test_id')
excepted_error_message = 'test_id has no positive labeled segments. Pattern detector needs at least 1 positive labeled segment'
try:
detector.train(dataframe, segments, cache)
except ValueError as e:
self.assertEqual(str(e), excepted_error_message)
def test_positive_and_negative_segments(self):
data_val = [1.0, 1.0, 1.0, 2.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 5.0, 7.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000004, 'to': 1523889000006, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000001, 'to': 1523889000003, 'labeled': False, 'deleted': False}]
segments = [Segment.from_json(segment) for segment in segments]
cache = {}
detector = pattern_detector.PatternDetector('PEAK', 'test_id')
try:
detector.train(dataframe, segments, cache)
except Exception as e:
self.fail('detector.train fail with error {}'.format(e))
class TestThresholdDetector(unittest.TestCase):
def test_invalid_cache(self):
detector = threshold_detector.ThresholdDetector('test_id')
with self.assertRaises(ValueError):
detector.detect([], None)
with self.assertRaises(ValueError):
detector.detect([], {})
class TestAnomalyDetector(unittest.TestCase):
def test_detect(self):
data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1]
data_ind = [1523889000000 + i for i in range(len(data_val))]
data = {'timestamp': data_ind, 'value': data_val}
dataframe = pd.DataFrame(data = data)
dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms')
cache = {
'confidence': 2,
'alpha': 0.1,
'enableBounds': 'ALL',
'timeStep': 1
}
detector = anomaly_detector.AnomalyDetector('test_id')
detect_result: DetectionResult = detector.detect(dataframe, cache)
detected_segments = list(map(lambda s: {'from': s.from_timestamp, 'to': s.to_timestamp}, detect_result.segments))
result = [{ 'from': 1523889000005.0, 'to': 1523889000005.0 }]
self.assertEqual(result, detected_segments)
cache = {
'confidence': 2,
'alpha': 0.1,
'enableBounds': 'ALL',
'timeStep': 1,
'seasonality': 4,
'segments': [{ 'from': 1523889000001, 'to': 1523889000002, 'data': [10] }]
}
detect_result: DetectionResult = detector.detect(dataframe, cache)
detected_segments = list(map(lambda s: {'from': s.from_timestamp, 'to': s.to_timestamp}, detect_result.segments))
result = []
self.assertEqual(result, detected_segments)
def test_process_data(self):
data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1]
data_ind = [1523889000000 + i for i in range(len(data_val))]
data = {'timestamp': data_ind, 'value': data_val}
dataframe = pd.DataFrame(data = data)
dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms')
cache = {
'confidence': 2,
'alpha': 0.1,
'enableBounds': 'ALL',
'timeStep': 1
}
detector = anomaly_detector.AnomalyDetector('test_id')
detect_result: ProcessingResult = detector.process_data(dataframe, cache)
expected_result = {
'lowerBound': [
(1523889000000, -2.0),
(1523889000001, -1.9),
(1523889000002, -1.71),
(1523889000003, -1.6389999999999998),
(1523889000004, -1.4750999999999999),
(1523889000005, -0.5275899999999998),
(1523889000006, -0.5748309999999996),
(1523889000007, -0.5173478999999996),
(1523889000008, -0.5656131099999995)
],
'upperBound': [
(1523889000000, 2.0),
(1523889000001, 2.1),
(1523889000002, 2.29),
(1523889000003, 2.361),
(1523889000004, 2.5249),
(1523889000005, 3.47241),
(1523889000006, 3.4251690000000004),
(1523889000007, 3.4826521),
(1523889000008, 3.4343868900000007)
]}
self.assertEqual(detect_result.to_json(), expected_result)
cache = {
'confidence': 2,
'alpha': 0.1,
'enableBounds': 'ALL',
'timeStep': 1,
'seasonality': 5,
'segments': [{ 'from': 1523889000001, 'to': 1523889000002,'data': [1] }]
}
detect_result: ProcessingResult = detector.process_data(dataframe, cache)
expected_result = {
'lowerBound': [
(1523889000000, -2.0),
(1523889000001, -2.9),
(1523889000002, -1.71),
(1523889000003, -1.6389999999999998),
(1523889000004, -1.4750999999999999),
(1523889000005, -0.5275899999999998),
(1523889000006, -1.5748309999999996),
(1523889000007, -0.5173478999999996),
(1523889000008, -0.5656131099999995)
],
'upperBound': [
(1523889000000, 2.0),
(1523889000001, 3.1),
(1523889000002, 2.29),
(1523889000003, 2.361),
(1523889000004, 2.5249),
(1523889000005, 3.47241),
(1523889000006, 4.425169),
(1523889000007, 3.4826521),
(1523889000008, 3.4343868900000007)
]}
self.assertEqual(detect_result.to_json(), expected_result)
def test_get_seasonality_offset(self):
detector = anomaly_detector.AnomalyDetector('test_id')
from_timestamp = 1573700973027
seasonality = 3600000
data_start_time = 1573698780000
time_step = 30000
detected_offset = detector.get_seasonality_offset(from_timestamp, seasonality, data_start_time, time_step)
expected_offset = 74
self.assertEqual(detected_offset, expected_offset)
def test_segment_generator(self):
detector = anomaly_detector.AnomalyDetector('test_id')
data = [1, 1, 5, 1, -4, 5, 5, 5, -3, 1]
timestamps = create_list_of_timestamps(len(data))
dataframe = create_dataframe(data)
upper_bound = pd.Series([2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
lower_bound = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
segments = list(detector.detections_generator(dataframe, upper_bound, lower_bound, enabled_bounds=Bound.ALL))
segments_borders = list(map(lambda s: [s.from_timestamp, s.to_timestamp], segments))
self.assertEqual(segments_borders, [[timestamps[2], timestamps[2]], [timestamps[4], timestamps[8]]])
if __name__ == '__main__':
unittest.main()