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265 lines
12 KiB
265 lines
12 KiB
import unittest |
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import pandas as pd |
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from detectors import pattern_detector, threshold_detector, anomaly_detector |
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from analytic_types.detector_typing import DetectionResult, ProcessingResult, Bound |
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from analytic_types.segment import Segment |
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from tests.test_dataset import create_dataframe, create_list_of_timestamps |
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from utils import convert_pd_timestamp_to_ms |
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class TestPatternDetector(unittest.TestCase): |
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def test_small_dataframe(self): |
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data = [[0,1], [1,2]] |
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dataframe = pd.DataFrame(data, columns=['timestamp', 'values']) |
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cache = { 'windowSize': 10 } |
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detector = pattern_detector.PatternDetector('GENERAL', 'test_id') |
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with self.assertRaises(ValueError): |
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detector.detect(dataframe, cache) |
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def test_only_negative_segments(self): |
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data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1] |
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data_ind = [1523889000000 + i for i in range(len(data_val))] |
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data = {'timestamp': data_ind, 'value': data_val} |
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dataframe = pd.DataFrame(data = data) |
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segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': False, 'deleted': False}, |
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{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': False}] |
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segments = [Segment.from_json(segment) for segment in segments] |
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cache = {} |
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detector = pattern_detector.PatternDetector('PEAK', 'test_id') |
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excepted_error_message = 'test_id has no positive labeled segments. Pattern detector needs at least 1 positive labeled segment' |
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try: |
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detector.train(dataframe, segments, cache) |
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except ValueError as e: |
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self.assertEqual(str(e), excepted_error_message) |
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def test_positive_and_negative_segments(self): |
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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] |
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dataframe = create_dataframe(data_val) |
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segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000004, 'to': 1523889000006, 'labeled': True, 'deleted': False}, |
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{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000001, 'to': 1523889000003, 'labeled': False, 'deleted': False}] |
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segments = [Segment.from_json(segment) for segment in segments] |
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cache = {} |
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detector = pattern_detector.PatternDetector('PEAK', 'test_id') |
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try: |
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detector.train(dataframe, segments, cache) |
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except Exception as e: |
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self.fail('detector.train fail with error {}'.format(e)) |
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class TestThresholdDetector(unittest.TestCase): |
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def test_invalid_cache(self): |
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detector = threshold_detector.ThresholdDetector('test_id') |
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with self.assertRaises(ValueError): |
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detector.detect([], None) |
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with self.assertRaises(ValueError): |
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detector.detect([], {}) |
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class TestAnomalyDetector(unittest.TestCase): |
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def test_detect(self): |
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data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1] |
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data_ind = [1523889000000 + i for i in range(len(data_val))] |
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data = {'timestamp': data_ind, 'value': data_val} |
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dataframe = pd.DataFrame(data = data) |
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dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms') |
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cache = { |
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'confidence': 2, |
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'alpha': 0.1, |
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'enableBounds': 'ALL', |
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'timeStep': 1 |
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} |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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detect_result: DetectionResult = detector.detect(dataframe, cache) |
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detected_segments = list(map(lambda s: {'from': s.from_timestamp, 'to': s.to_timestamp}, detect_result.segments)) |
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result = [{ 'from': 1523889000005.0, 'to': 1523889000005.0 }] |
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self.assertEqual(result, detected_segments) |
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cache = { |
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'confidence': 2, |
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'alpha': 0.1, |
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'enableBounds': 'ALL', |
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'timeStep': 1, |
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'seasonality': 4, |
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'segments': [{ 'from': 1523889000001, 'to': 1523889000002, 'data': [10] }] |
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} |
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detect_result: DetectionResult = detector.detect(dataframe, cache) |
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detected_segments = list(map(lambda s: {'from': s.from_timestamp, 'to': s.to_timestamp}, detect_result.segments)) |
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result = [] |
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self.assertEqual(result, detected_segments) |
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def test_process_data(self): |
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data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1] |
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data_ind = [1523889000000 + i for i in range(len(data_val))] |
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data = {'timestamp': data_ind, 'value': data_val} |
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dataframe = pd.DataFrame(data = data) |
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dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms') |
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cache = { |
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'confidence': 2, |
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'alpha': 0.1, |
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'enableBounds': 'ALL', |
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'timeStep': 1 |
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} |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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detect_result: ProcessingResult = detector.process_data(dataframe, cache) |
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expected_result = { |
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'lowerBound': [ |
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(1523889000000, -2.0), |
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(1523889000001, -1.9), |
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(1523889000002, -1.71), |
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(1523889000003, -1.6389999999999998), |
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(1523889000004, -1.4750999999999999), |
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(1523889000005, -0.5275899999999998), |
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(1523889000006, -0.5748309999999996), |
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(1523889000007, -0.5173478999999996), |
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(1523889000008, -0.5656131099999995) |
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], |
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'upperBound': [ |
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(1523889000000, 2.0), |
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(1523889000001, 2.1), |
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(1523889000002, 2.29), |
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(1523889000003, 2.361), |
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(1523889000004, 2.5249), |
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(1523889000005, 3.47241), |
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(1523889000006, 3.4251690000000004), |
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(1523889000007, 3.4826521), |
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(1523889000008, 3.4343868900000007) |
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]} |
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self.assertEqual(detect_result.to_json(), expected_result) |
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cache = { |
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'confidence': 2, |
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'alpha': 0.1, |
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'enableBounds': 'ALL', |
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'timeStep': 1, |
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'seasonality': 5, |
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'segments': [{ 'from': 1523889000001, 'to': 1523889000002,'data': [1] }] |
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} |
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detect_result: ProcessingResult = detector.process_data(dataframe, cache) |
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expected_result = { |
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'lowerBound': [ |
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(1523889000000, -2.0), |
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(1523889000001, -2.9), |
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(1523889000002, -1.71), |
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(1523889000003, -1.6389999999999998), |
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(1523889000004, -1.4750999999999999), |
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(1523889000005, -0.5275899999999998), |
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(1523889000006, -1.5748309999999996), |
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(1523889000007, -0.5173478999999996), |
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(1523889000008, -0.5656131099999995) |
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], |
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'upperBound': [ |
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(1523889000000, 2.0), |
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(1523889000001, 3.1), |
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(1523889000002, 2.29), |
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(1523889000003, 2.361), |
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(1523889000004, 2.5249), |
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(1523889000005, 3.47241), |
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(1523889000006, 4.425169), |
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(1523889000007, 3.4826521), |
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(1523889000008, 3.4343868900000007) |
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]} |
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self.assertEqual(detect_result.to_json(), expected_result) |
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def test_get_seasonality_offset(self): |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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from_timestamp = 1573700973027 |
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seasonality = 3600000 |
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data_start_time = 1573698780000 |
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time_step = 30000 |
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detected_offset = detector.get_seasonality_offset(from_timestamp, seasonality, data_start_time, time_step) |
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expected_offset = 74 |
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self.assertEqual(detected_offset, expected_offset) |
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def test_segment_generator(self): |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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data = [1, 1, 5, 1, -4, 5, 5, 5, -3, 1] |
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timestamps = create_list_of_timestamps(len(data)) |
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dataframe = create_dataframe(data) |
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upper_bound = pd.Series([2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) |
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lower_bound = pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) |
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segments = list(detector.detections_generator(dataframe, upper_bound, lower_bound, enabled_bounds=Bound.ALL)) |
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segments_borders = list(map(lambda s: [s.from_timestamp, s.to_timestamp], segments)) |
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self.assertEqual(segments_borders, [[timestamps[2], timestamps[2]], [timestamps[4], timestamps[8]]]) |
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def test_consume_data(self): |
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cache = { |
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'confidence': 2, |
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'alpha': 0.1, |
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'enableBounds': 'ALL', |
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'timeStep': 1 |
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} |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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detect_result: DetectionResult = None |
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for val in range(22): |
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value = 1 if val != 10 else 5 |
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dataframe = pd.DataFrame({'value': [value], 'timestamp': [1523889000000 + val]}) |
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dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms') |
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detect_result = detector.consume_data(dataframe, cache) |
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detected_segments = list(map(lambda s: {'from': s.from_timestamp, 'to': s.to_timestamp}, detect_result.segments)) |
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result = [{ 'from': 1523889000010, 'to': 1523889000010 }] |
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self.assertEqual(result, detected_segments) |
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def test_get_segment_bound(self): |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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peak_segment = pd.Series([1,2,3,4,3,2,1]) |
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trough_segment = pd.Series([4,3,2,1,2,3,4]) |
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expected_peak_segment_results = { |
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'max_value': 3, |
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'min_value': 1.5 |
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} |
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expected_trough_segment_results = { |
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'max_value': 3.5, |
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'min_value': 2.75 |
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} |
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peak_detector_result_upper = detector.get_segment_bound(peak_segment, Bound.UPPER) |
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peak_detector_result_lower = detector.get_segment_bound(peak_segment, Bound.LOWER) |
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trough_detector_result_upper = detector.get_segment_bound(trough_segment, Bound.UPPER) |
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trough_detector_result_lower = detector.get_segment_bound(trough_segment, Bound.LOWER) |
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self.assertGreaterEqual( |
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max(peak_detector_result_upper), |
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expected_peak_segment_results['max_value'] |
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) |
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self.assertLessEqual( |
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max(peak_detector_result_lower), |
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expected_peak_segment_results['min_value'] |
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) |
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self.assertGreaterEqual( |
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max(trough_detector_result_upper), |
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expected_trough_segment_results['max_value'] |
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) |
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self.assertLessEqual( |
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max(trough_detector_result_lower), |
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expected_trough_segment_results['min_value'] |
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) |
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def test_get_segment_bound_corner_cases(self): |
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detector = anomaly_detector.AnomalyDetector('test_id') |
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empty_segment = pd.Series([]) |
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same_values_segment = pd.Series([2,2,2,2,2,2]) |
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empty_detector_result_upper = detector.get_segment_bound(empty_segment, Bound.UPPER) |
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empty_detector_result_lower = detector.get_segment_bound(empty_segment, Bound.LOWER) |
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same_values_detector_result_upper = detector.get_segment_bound(same_values_segment, Bound.UPPER) |
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same_values_detector_result_lower = detector.get_segment_bound(same_values_segment, Bound.LOWER) |
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self.assertEqual(len(empty_detector_result_upper), 0) |
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self.assertEqual(len(empty_detector_result_lower), 0) |
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self.assertEqual(min(same_values_detector_result_upper), 0) |
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self.assertEqual(max(same_values_detector_result_upper), 0) |
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self.assertEqual(min(same_values_detector_result_lower), 0) |
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self.assertEqual(max(same_values_detector_result_lower), 0) |
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if __name__ == '__main__': |
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unittest.main()
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