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import unittest
import pandas as pd
import numpy as np
from utils import prepare_data
import models
import random
import scipy.signal
class TestDataset(unittest.TestCase):
def test_models_with_corrupted_dataframe(self):
data = [[1523889000000 + i, float('nan')] for i in range(10)]
dataframe = pd.DataFrame(data, columns=['timestamp', 'value'])
segments = []
model_instances = [
models.JumpModel(),
models.DropModel(),
models.GeneralModel(),
models.PeakModel(),
models.TroughModel()
]
for model in model_instances:
model_name = model.__class__.__name__
model.state = model.get_state(None)
with self.assertRaises(AssertionError):
model.fit(dataframe, segments, 'test')
def test_peak_antisegments(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': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000003, 'to': 1523889000005, 'labeled': False, 'deleted': True}]
try:
model = models.PeakModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_jump_antisegments(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 9.0, 1.0, 1.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000016, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': True}]
try:
model = models.JumpModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_trough_antisegments(self):
data_val = [9.0, 9.0, 9.0, 9.0, 7.0, 4.0, 7.0, 9.0, 9.0, 9.0, 5.0, 1.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000003, 'to': 1523889000005, 'labeled': False, 'deleted': True}]
try:
model = models.TroughModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_drop_antisegments(self):
data_val = [9.0, 9.0, 9.0, 9.0, 9.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000016, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': True}]
try:
model = models.DropModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_general_antisegments(self):
data_val = [1.0, 2.0, 1.0, 2.0, 5.0, 6.0, 3.0, 2.0, 1.0, 1.0, 8.0, 9.0, 8.0, 1.0, 2.0, 3.0, 2.0, 1.0, 1.0, 2.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000003, 'to': 1523889000005, 'labeled': False, 'deleted': True}]
try:
model = models.GeneralModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_jump_empty_segment(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': True, 'deleted': False}]
try:
model = models.JumpModel()
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_drop_empty_segment(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': True, 'deleted': False},
{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': True, 'deleted': False}]
try:
model = models.DropModel()
model.state = model.get_state(None)
model_name = model.__class__.__name__
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_value_error_dataset_input_should_have_multiple_elements(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 4.0, 5.0, 5.0, 6.0, 5.0, 1.0, 2.0, 3.0, 4.0, 5.0,3.0,3.0,2.0,7.0,8.0,9.0,8.0,7.0,6.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000007, 'to': 1523889000011, 'labeled': True, 'deleted': False}]
try:
model = models.JumpModel()
model.state = model.get_state(None)
model_name = model.__class__.__name__
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_prepare_data_for_nonetype(self):
data = [[1523889000000, None], [1523889000001, None], [1523889000002, None]]
try:
data = prepare_data(data)
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_prepare_data_for_nan(self):
data = [[1523889000000, np.nan], [1523889000001, np.nan], [1523889000002, np.nan]]
try:
data = prepare_data(data)
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_prepare_data_output_fon_nan(self):
data_nan = [[1523889000000, np.nan], [1523889000001, np.nan], [1523889000002, np.nan]]
data_none = [[1523889000000, None], [1523889000001, None], [1523889000002, None]]
return_data_nan = prepare_data(data_nan)
return_data_none = prepare_data(data_none)
for item in return_data_nan.value:
self.assertTrue(np.isnan(item))
for item in return_data_none.value:
self.assertTrue(np.isnan(item))
def test_three_value_segment(self):
data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 2.0, 3.0, 4.0, 5.0, 4.0, 2.0, 1.0, 3.0, 4.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000004, 'to': 1523889000006, 'labeled': True, 'deleted': False}]
model_instances = [
models.GeneralModel(),
models.PeakModel(),
]
try:
for model in model_instances:
model_name = model.__class__.__name__
model.state = model.get_state(None)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_general_for_two_labeling(self):
data_val = [1.0, 2.0, 5.0, 2.0, 1.0, 1.0, 3.0, 6.0, 4.0, 2.0, 1.0, 0, 0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000001, 'to': 1523889000003, 'labeled': True, 'deleted': False}]
model = models.GeneralModel()
model.state = model.get_state(None)
model.fit(dataframe, segments,'test')
result = len(data_val) + 1
for _ in range(2):
model.do_detect(dataframe)
max_pattern_index = max(model.do_detect(dataframe))
self.assertLessEqual(max_pattern_index[0], result)
def test_peak_model_for_cache(self):
cache = {
'patternCenter': [1, 6],
'patternModel': [1, 4, 0],
'confidence': 2,
'convolveMax': 8,
'convolveMin': 7,
'windowSize': 1,
'convDelMin': 0,
'convDelMax': 0,
'heightMax': 4,
'heightMin': 4,
}
data_val = [2.0, 5.0, 1.0, 1.0, 1.0, 2.0, 5.0, 1.0, 1.0, 2.0, 3.0, 7.0, 1.0, 1.0, 1.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False}]
model = models.PeakModel()
model.state = model.get_state(cache)
result = model.fit(dataframe, segments, 'test')
self.assertEqual(len(result.pattern_center), 3)
def test_trough_model_for_cache(self):
cache = {
'patternCenter': [2, 6],
'patternModel': [5, 0.5, 4],
'confidence': 2,
'convolveMax': 8,
'convolveMin': 7,
'window_size': 1,
'convDelMin': 0,
'convDelMax': 0,
}
data_val = [5.0, 5.0, 1.0, 4.0, 5.0, 5.0, 0.0, 4.0, 5.0, 5.0, 6.0, 1.0, 5.0, 5.0, 5.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False}]
model = models.TroughModel()
model.state = model.get_state(cache)
result = model.fit(dataframe, segments, 'test')
self.assertEqual(len(result.pattern_center), 3)
def test_jump_model_for_cache(self):
cache = {
'patternCenter': [2, 6],
'patternModel': [5, 0.5, 4],
'confidence': 2,
'convolveMax': 8,
'convolveMin': 7,
'window_size': 1,
'convDelMin': 0,
'convDelMax': 0,
}
data_val = [1.0, 1.0, 1.0, 4.0, 4.0, 0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 4.0, 4.0, 4.0, 4.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 152388900009, 'to': 1523889000013, 'labeled': True, 'deleted': False}]
model = models.JumpModel()
model.state = model.get_state(cache)
result = model.fit(dataframe, segments, 'test')
self.assertEqual(len(result.pattern_center), 3)
def test_models_for_pattern_model_cache(self):
cache = {
'patternCenter': [4, 12],
'patternModel': [],
'confidence': 2,
'convolveMax': 8,
'convolveMin': 7,
'window_size': 2,
'convDelMin': 0,
'convDelMax': 0,
}
data_val = [5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 0, 0, 0, 0, 0, 0, 6.0, 6.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]
dataframe = create_dataframe(data_val)
segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000024, 'labeled': True, 'deleted': False}]
try:
model = models.DropModel()
model_name = model.__class__.__name__
model.state = model.get_state(cache)
model.fit(dataframe, segments, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly'.format(model_name))
def test_problem_data_for_random_model(self):
problem_data = [2.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0,
3.0, 3.0, 3.0, 5.0, 5.0, 5.0, 5.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0,
3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 6.0, 7.0, 8.0, 8.0, 4.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0,
4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0,
4.0, 4.0, 4.0, 4.0, 4.0, 6.0, 5.0, 4.0, 4.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 2.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
2.0, 8.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]
data = create_dataframe(problem_data)
cache = {
'patternCenter': [5, 50],
'patternModel': [],
'windowSize': 2,
'convolveMin': 0,
'convolveMax': 0,
'convDelMin': 0,
'convDelMax': 0,
}
max_ws = 20
iteration = 1
for ws in range(1, max_ws):
for _ in range(iteration):
pattern_model = create_random_model(ws)
convolve = scipy.signal.fftconvolve(pattern_model, pattern_model)
cache['windowSize'] = ws
cache['patternModel'] = pattern_model
cache['convolveMin'] = max(convolve)
cache['convolveMax'] = max(convolve)
try:
model = models.GeneralModel()
model.state = model.get_state(cache)
model_name = model.__class__.__name__
model.detect(data, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly with av_model {} and window size {}'.format(model_name, pattern_model, ws))
def test_random_dataset_for_random_model(self):
data = create_random_model(random.randint(1, 100))
data = create_dataframe(data)
model_instances = [
models.PeakModel(),
models.TroughModel()
]
cache = {
'patternCenter': [5, 50],
'patternModel': [],
'windowSize': 2,
'convolveMin': 0,
'convolveMax': 0,
'confidence': 0,
'heightMax': 0,
'heightMin': 0,
'convDelMin': 0,
'convDelMax': 0,
}
ws = random.randint(1, int(len(data['value']/2)))
pattern_model = create_random_model(ws)
convolve = scipy.signal.fftconvolve(pattern_model, pattern_model)
confidence = 0.2 * (data['value'].max() - data['value'].min())
cache['windowSize'] = ws
cache['patternModel'] = pattern_model
cache['convolveMin'] = max(convolve)
cache['convolveMax'] = max(convolve)
cache['confidence'] = confidence
cache['heightMax'] = data['value'].max()
cache['heightMin'] = confidence
try:
for model in model_instances:
model_name = model.__class__.__name__
model.state = model.get_state(cache)
model.detect(data, 'test')
except ValueError:
self.fail('Model {} raised unexpectedly with dataset {} and cache {}'.format(model_name, data['value'], cache))
if __name__ == '__main__':
unittest.main()
def create_dataframe(data_val: list) -> pd.DataFrame:
data_ind = [1523889000000 + i for i in range(len(data_val))]
data = {'timestamp': data_ind, 'value': data_val}
dataframe = pd.DataFrame(data)
dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms')
return dataframe
def create_random_model(window_size: int) -> list:
return [random.randint(0, 100) for _ in range(window_size * 2 + 1)]