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116 lines
4.3 KiB
116 lines
4.3 KiB
6 years ago
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from models import PeakModel, DropModel, TroughModel, JumpModel, GeneralModel
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import aiounittest
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from analytic_unit_manager import AnalyticUnitManager
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from collections import namedtuple
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TestData = namedtuple('TestData', ['uid', 'type', 'values', 'segments'])
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def get_random_id() -> str:
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return str(id(list()))
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class TestDataset(aiounittest.AsyncTestCase):
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timestep = 50 #ms
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def _fill_task(self, uid, data, task_type, analytic_unit_type, segments=None, cache=None):
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task = {
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'analyticUnitId': uid,
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'type': task_type,
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'payload': {
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'data': data,
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'from': data[0][0],
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'to': data[-1][0],
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'analyticUnitType': analytic_unit_type,
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'detector': 'pattern',
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'cache': cache
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},
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'_id': get_random_id()
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}
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if segments: task['payload']['segments'] = segments
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return task
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def _convert_values(self, values) -> list:
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from_t = 0
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to_t = len(values) * self.timestep
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return list(zip(range(from_t, to_t, self.timestep), values))
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def _index_to_test_time(self, idx) -> int:
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return idx * self.timestep
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def _get_learn_task(self, test_data):
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uid, analytic_unit_type, values, segments = test_data
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data = self._convert_values(values)
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segments = [{
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'analyticUnitId': uid,
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'from': self._index_to_test_time(s[0]),
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'to': self._index_to_test_time(s[1]),
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'labeled': True,
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'deleted': False
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} for s in segments]
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return self._fill_task(uid, data, 'LEARN', analytic_unit_type, segments=segments)
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def _get_detect_task(self, test_data, cache):
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uid, analytic_unit_type, values, _ = test_data
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data = self._convert_values(values)
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return self._fill_task(uid, data, 'DETECT', analytic_unit_type, cache=cache)
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def _get_test_dataset(self, pattern) -> tuple:
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"""
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pattern name: ([dataset values], [list of segments])
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segment - (begin, end) - indexes in dataset values
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returns dataset in format (data: List[int], segments: List[List[int]])
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"""
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datasets = {
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'PEAK': ([0, 0, 1, 2, 3, 4, 3, 2, 1, 0, 0], [[2, 8]]),
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'JUMP': ([0, 0, 1, 2, 3, 4, 4, 4], [[1, 6]]),
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'DROP': ([4, 4, 4, 3, 2, 1, 0, 0], [[1, 6]]),
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'TROUGH': ([4, 4, 3, 2, 1, 0, 1, 2, 3, 4, 4], [[1, 9]]),
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'GENERAL': ([0, 0, 1, 2, 3, 4, 3, 2, 1, 0, 0], [[2, 8]])
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}
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return datasets[pattern]
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async def _learn(self, task, manager=None) -> dict:
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if not manager: manager = AnalyticUnitManager()
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result = await manager.handle_analytic_task(task)
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return result['payload']['cache']
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async def _detect(self, task, manager=None) -> dict:
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if not manager: manager = AnalyticUnitManager()
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result = await manager.handle_analytic_task(task)
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return result
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async def _test_detect(self, test_data, manager=None):
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learn_task = self._get_learn_task(test_data)
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cache = await self._learn(learn_task, manager)
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detect_task = self._get_detect_task(test_data, cache)
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result = await self._detect(detect_task, manager)
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return result
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async def test_unit_manager(self):
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test_data = TestData(get_random_id(), 'PEAK', [0,1,2,5,10,5,2,1,1,1,0,0,0,0], [[1,7]])
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manager = AnalyticUnitManager()
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with_manager = await self._test_detect(test_data, manager)
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without_manager = await self._test_detect(test_data)
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self.assertEqual(with_manager, without_manager)
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async def test_cache(self):
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cache_attrs = {
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'PEAK': PeakModel().state.keys(),
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'JUMP': JumpModel().state.keys(),
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'DROP': DropModel().state.keys(),
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'TROUGH': TroughModel().state.keys(),
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'GENERAL': GeneralModel().state.keys()
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}
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for pattern, attrs in cache_attrs.items():
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test_data = TestData(get_random_id(), pattern, *self._get_test_dataset(pattern))
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learn_task = self._get_learn_task(test_data)
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cache = await self._learn(learn_task)
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for a in attrs:
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self.assertTrue(a in cache.keys(), msg='{} not in cache keys: {}'.format(a, cache.keys()))
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