Fix analytic model tester tool (#885)

* model-> detector

* train result

* fix

* add todo

Co-Authored-By: rozetko <rozetko@hastic.io>

Co-authored-by: rozetko <rozetko@hastic.io>

analytics/tools/analytic_model_tester.py

@@ -6,18 +6,20 @@ sys.path.extend([ANALYTICS_PATH, TESTS_PATH])
 import pandas as pd
 import numpy as np
 import utils
-import models
 import test_dataset
+from analytic_types.segment import Segment
+from detectors import pattern_detector, threshold_detector, anomaly_detector
 
 # 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])
+TEST_DATA = test_dataset.create_dataframe([0, 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)]
+# TODO: more convenient way to specify labeled segments
-NEGATIVE_SEGMENTS = [(1523889000009, 1523889000017)]
+POSITIVE_SEGMENTS = [{'from': 1523889000001, 'to': 1523889000007}, {'from': 1523889000022, 'to': 1523889000028}]
+NEGATIVE_SEGMENTS = [{'from': 1523889000011, 'to': 1523889000017}]
 
-class Segment():
+class TesterSegment():
 
     def __init__(self, start: int, end: int, labeled: bool):
         self.start = start
@@ -36,25 +38,25 @@ class Segment():
 
 class Metric():
 
-    def __init__(self, true_result, model_result):
+    def __init__(self, expected_result, detector_result):
-        self.true_result = true_result
+        self.expected_result = expected_result
-        self.model_result = model_result['segments']
+        self.detector_result = detector_result['segments']
 
     def get_amount(self):
-        return len(self.model_result) / len(self.true_result)
+        return len(self.detector_result) / len(self.expected_result)
 
     def get_accuracy(self):
         correct_segment = 0
         invalid_segment = 0
-        for segment in self.model_result:
+        for segment in self.detector_result:
             current_cs = correct_segment
-            for pattern in self.true_result:
+            for pattern in self.expected_result:
-                if pattern[0] <= segment[0] and pattern[1] >= segment[1]:
+                if pattern['from'] <= segment['from'] and pattern['to'] >= segment['to']:
                     correct_segment += 1
                     break
             if correct_segment == current_cs:
                 invalid_segment += 1
-        non_detected = len(self.true_result) - correct_segment
+        non_detected = len(self.expected_result) - correct_segment
         return (correct_segment, invalid_segment, non_detected)
 
 class ModelData():
@@ -70,12 +72,12 @@ class ModelData():
         for idx, bounds in enumerate(self.positive_segments):
             if idx >= positive_amount:
                 break
-            segments.append(Segment(bounds[0], bounds[1], True).get_segment())
+            segments.append(TesterSegment(bounds['from'], bounds['to'], 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())
+            segments.append(TesterSegment(bounds['from'], bounds['to'], False).get_segment())
 
         return segments
 
@@ -91,9 +93,12 @@ def main(model_type: str) -> None:
         for data in PEAK_DATASETS:
             dataset = data.frame
             segments = data.get_segments_for_detection(1, 0)
-            model = models.PeakModel()
+            segments = [Segment.from_json(segment) for segment in segments]
-            cache = model.fit(dataset, segments, 'test', {})
+            detector = pattern_detector.PatternDetector('PEAK', 'test_id')
-            detect_result = model.detect(dataset, 'test', cache)
+            training_result = detector.train(dataset, segments, {})
+            cache = training_result['cache']
+            detect_result = detector.detect(dataset, cache)
+            detect_result = detect_result.to_json()
             peak_metric = Metric(data.get_all_correct_segments(), detect_result)
             table_metric.append((peak_metric.get_amount(), peak_metric.get_accuracy()))
     return table_metric
@@ -115,4 +120,3 @@ if __name__ == '__main__':
         print('Enter one of models name: {}'.format(correct_name))
 
 
-