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import sys
ANALYTICS_PATH = '../analytics'
TESTS_PATH = '../tests'
sys.path.extend([ANALYTICS_PATH, TESTS_PATH])
import pandas as pd
import numpy as np
import utils
import models
import test_dataset
# TODO: get_dataset
# TODO: get_segment
PEAK_DATASETS = []
# dataset with 3 peaks
TEST_DATA = test_dataset.create_dataframe([0, 3, 5, 7, 5, 3, 0, 0, 1, 0, 1, 4, 6, 8, 6, 4, 1, 0, 0, 0, 1, 0, 3, 5, 7, 5, 3, 0, 1, 1])
POSITIVE_SEGMENTS = [(1523889000000, 1523889000006), (1523889000021, 1523889000027)]
NEGATIVE_SEGMENTS = [(1523889000009, 1523889000017)]
class Segment():
def __init__(self, start: int, end: int, labeled: bool):
self.start = start
self.end = end
self.labeled = labeled
def get_segment(self):
return {
'_id': 'q',
'analyticUnitId': 'q',
'from': self.start,
'to': self.end,
'labeled': self.labeled,
'deleted': not self.labeled
}
class Metric():
def __init__(self, true_result, model_result):
self.true_result = true_result
self.model_result = model_result['segments']
def get_amount(self):
return len(self.model_result) / len(self.true_result)
def get_accuracy(self):
correct_segment = 0
invalid_segment = 0
for segment in self.model_result:
current_cs = correct_segment
for pattern in self.true_result:
if pattern[0] <= segment[0] and pattern[1] >= segment[1]:
correct_segment += 1
break
if correct_segment == current_cs:
invalid_segment += 1
non_detected = len(self.true_result) - correct_segment
return (correct_segment, invalid_segment, non_detected)
class ModelData():
def __init__(self, frame: pd.DataFrame, positive_segments, negative_segments, model_type: str):
self.frame = frame
self.positive_segments = positive_segments
self.negative_segments = negative_segments
self.model_type = model_type
def get_segments_for_detection(self, positive_amount, negative_amount):
segments = []
for idx, bounds in enumerate(self.positive_segments):
if idx >= positive_amount:
break
segments.append(Segment(bounds[0], bounds[1], True).get_segment())
for idx, bounds in enumerate(self.negative_segments):
if idx >= negative_amount:
break
segments.append(Segment(bounds[0], bounds[1], False).get_segment())
return segments
def get_all_correct_segments(self):
return self.positive_segments
PEAK_DATA_1 = ModelData(TEST_DATA, POSITIVE_SEGMENTS, NEGATIVE_SEGMENTS, 'peak')
PEAK_DATASETS.append(PEAK_DATA_1)
def main(model_type: str) -> None:
table_metric = []
if model_type == 'peak':
for data in PEAK_DATASETS:
dataset = data.frame
segments = data.get_segments_for_detection(1, 0)
model = models.PeakModel()
cache = model.fit(dataset, segments, 'test', {})
detect_result = model.detect(dataset, 'test', cache)
peak_metric = Metric(data.get_all_correct_segments(), detect_result)
table_metric.append((peak_metric.get_amount(), peak_metric.get_accuracy()))
return table_metric
if __name__ == '__main__':
'''
This tool applies the model on datasets and verifies that the detection result corresponds to the correct values.
sys.argv[1] expects one of the models name -> see correct_name
'''
# TODO: use enum
correct_name = ['peak', 'trough', 'jump', 'drop', 'general']
if len(sys.argv) < 2:
print('Enter one of models name: {}'.format(correct_name))
sys.exit(1)
model_type = str(sys.argv[1]).lower()
if model_type in correct_name:
print(main(model_type))
else:
print('Enter one of models name: {}'.format(correct_name))