"Error: Need at least 1 labeled segment" when labeling only negative segment #790 (#791)

analytics/analytics/analytic_types/segment.py

@@ -1,20 +1,29 @@
 from typing import Optional
 
+import utils.meta
+
+@utils.meta.JSONClass
 class Segment:
     '''
     Used for segment manipulation instead of { 'from': ..., 'to': ... } dict
     '''
 
-    def __init__(self, from_timestamp: int, to_timestamp: int, message: str = None):
+    def __init__(
+        self,
+        from_timestamp: int,
+        to_timestamp: int,
+        _id: Optional[str] = None,
+        analytic_unit_id: Optional[str] = None,
+        labeled: Optional[bool] = None,
+        deleted: Optional[bool] = None,
+        message: Optional[str] = None
+    ):
         if to_timestamp < from_timestamp:
             raise ValueError(f'Can`t create segment with to < from: {to_timestamp} < {from_timestamp}')
         self.from_timestamp = from_timestamp
         self.to_timestamp = to_timestamp
+        self._id = _id
+        self.analytic_unit_id = analytic_unit_id
+        self.labeled = labeled
+        self.deleted = deleted
         self.message = message
-
-    def to_json(self):
-        return {
-            'from': self.from_timestamp,
-            'to': self.to_timestamp,
-            'message': self.message
-        }

analytics/analytics/analytic_unit_manager.py

@@ -5,6 +5,7 @@ from concurrent.futures import Executor, ThreadPoolExecutor
 
 from analytic_unit_worker import AnalyticUnitWorker
 from analytic_types import AnalyticUnitId, ModelCache
+from analytic_types.segment import Segment
 import detectors
 
 
@@ -66,7 +67,9 @@ class AnalyticUnitManager:
             return res
         elif task['type'] == 'LEARN':
             if 'segments' in payload:
-                return await worker.do_train(payload['segments'], data, payload['cache'])
+                segments = payload['segments']
+                segments = [Segment.from_json(segment) for segment in segments]
+                return await worker.do_train(segments, data, payload['cache'])
             elif 'threshold' in payload:
                 return await worker.do_train(payload['threshold'], data, payload['cache'])
             elif 'anomaly' in payload:

analytics/analytics/detectors/pattern_detector.py

@@ -46,8 +46,14 @@ class PatternDetector(Detector):
         self.model = resolve_model_by_pattern(self.pattern_type)
         self.bucket = DataBucket()
 
-    def train(self, dataframe: pd.DataFrame, segments: List[dict], cache: Optional[ModelCache]) -> ModelCache:
+    def train(self, dataframe: pd.DataFrame, segments: List[Segment], cache: Optional[ModelCache]) -> ModelCache:
         # TODO: pass only part of dataframe that has segments
+
+        if self.contains_labeled_segments(segments) == False:
+            msg = f'{self.analytic_unit_id} has no positive labeled segments. Pattern detector needs at least 1 positive labeled segment'
+            logger.error(msg)
+            raise ValueError(msg)
+
         self.model.state: models.ModelState = self.model.get_state(cache)
         new_cache: models.ModelState = self.model.fit(dataframe, segments, self.analytic_unit_id)
 
@@ -134,3 +140,8 @@ class PatternDetector(Detector):
         # TODO: windowSize -> window_size
         return cache.get('windowSize', self.DEFAULT_WINDOW_SIZE)
 
+    def contains_labeled_segments(self, segments: List[Segment]) -> bool:
+        for segment in segments:
+            if segment.labeled == True:
+                return True
+        return False

analytics/analytics/models/model.py

@@ -26,14 +26,23 @@ class AnalyticSegment(Segment):
         self,
         from_timestamp: int,
         to_timestamp: int,
+        _id: str,
+        analytic_unit_id: str,
         labeled: bool,
         deleted: bool,
+        message: str,
         dataframe: pd.DataFrame,
         center_finder = None
     ):
-        super().__init__(from_timestamp, to_timestamp)
-        self.labeled = labeled
-        self.deleted = deleted
+        super().__init__(
+            from_timestamp,
+            to_timestamp,
+            _id,
+            analytic_unit_id,
+            labeled,
+            deleted,
+            message
+        )
 
         self.from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(self.from_timestamp, unit='ms'))
         self.to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(self.to_timestamp, unit='ms'))
@@ -119,19 +128,22 @@ class Model(ABC):
     def get_state(self, cache: Optional[ModelCache] = None) -> ModelState:
         pass
 
-    def fit(self, dataframe: pd.DataFrame, segments: List[dict], id: AnalyticUnitId) -> ModelState:
+    def fit(self, dataframe: pd.DataFrame, segments: List[Segment], id: AnalyticUnitId) -> ModelState:
         logging.debug('Start method fit for analytic unit {}'.format(id))
         data = dataframe['value']
         max_length = 0
         labeled = []
         deleted = []
         for segment_map in segments:
-            if segment_map['labeled'] or segment_map['deleted']:
+            if segment_map.labeled or segment_map.deleted:
                 segment = AnalyticSegment(
-                    segment_map['from'],
-                    segment_map['to'],
-                    segment_map['labeled'],
-                    segment_map['deleted'],
+                    segment_map.from_timestamp,
+                    segment_map.to_timestamp,
+                    segment_map._id,
+                    segment_map.analytic_unit_id,
+                    segment_map.labeled,
+                    segment_map.deleted,
+                    segment_map.message,
                     dataframe,
                     self.find_segment_center
                 )

analytics/analytics/utils/meta.py

@@ -8,9 +8,14 @@ CAMEL_REGEX = re.compile(r'([A-Z])')
 UNDERSCORE_REGEX = re.compile(r'_([a-z])')
 
 def camel_to_underscore(name):
+    #TODO: need to rename 'from'/'to' to 'from_timestamp'/'to_timestamp' everywhere(in analytics, server, panel)
+    if name == 'from' or name == 'to':
+        name += '_timestamp'
     return CAMEL_REGEX.sub(lambda x: '_' + x.group(1).lower(), name)
 
 def underscore_to_camel(name):
+    if name == 'from_timestamp' or name == 'to_timestamp':
+        name = name.replace('_timestamp', '')
     return UNDERSCORE_REGEX.sub(lambda x: x.group(1).upper(), name)
 
 def is_field_private(field_name: str) -> Optional[str]:

analytics/tests/test_dataset.py

@@ -6,6 +6,8 @@ import models
 import random
 import scipy.signal
 
+from analytic_types.segment import Segment
+
 class TestDataset(unittest.TestCase):
 
     def test_models_with_corrupted_dataframe(self):
@@ -32,6 +34,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.PeakModel()
@@ -46,6 +49,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.JumpModel()
@@ -60,6 +64,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.TroughModel()
@@ -74,6 +79,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.DropModel()
@@ -88,6 +94,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.GeneralModel()
@@ -102,6 +109,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.JumpModel()
@@ -116,6 +124,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.DropModel()
@@ -129,6 +138,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         try:
             model = models.JumpModel()
@@ -166,6 +176,7 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
 
         model_instances = [
             models.GeneralModel(),
@@ -183,6 +194,8 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
+    
         model = models.GeneralModel()
         model.state = model.get_state(None)
         model.fit(dataframe, segments,'test')
@@ -209,6 +222,8 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
+
         model = models.PeakModel()
         model.state = model.get_state(cache)
         result = model.fit(dataframe, segments, 'test')
@@ -228,6 +243,8 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
+
         model = models.TroughModel()
         model.state = model.get_state(cache)
         result = model.fit(dataframe, segments, 'test')
@@ -247,6 +264,8 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
+
         model = models.JumpModel()
         model.state = model.get_state(cache)
         result = model.fit(dataframe, segments, 'test')
@@ -266,6 +285,8 @@ class TestDataset(unittest.TestCase):
         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}]
+        segments = [Segment.from_json(segment) for segment in segments]
+
         try:
             model = models.DropModel()
             model_name = model.__class__.__name__

analytics/tests/test_detectors.py

@@ -3,6 +3,8 @@ import pandas as pd
 
 from detectors import pattern_detector, threshold_detector, anomaly_detector
 from analytic_types.detector_typing import DetectionResult, ProcessingResult
+from analytic_types.segment import Segment
+from tests.test_dataset import create_dataframe
 
 class TestPatternDetector(unittest.TestCase):
 
@@ -16,6 +18,35 @@ class TestPatternDetector(unittest.TestCase):
         with self.assertRaises(ValueError):
             detector.detect(dataframe, cache)
 
+    def test_only_negative_segments(self):
+        data_val = [0, 1, 2, 1, 2, 10, 1, 2, 1]
+        data_ind = [1523889000000 + i for i in range(len(data_val))]
+        data = {'timestamp': data_ind, 'value': data_val}
+        dataframe = pd.DataFrame(data = data)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': False, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': False}]
+        segments = [Segment.from_json(segment) for segment in segments]
+        cache = {}
+        detector = pattern_detector.PatternDetector('PEAK', 'test_id')
+        excepted_error_message = 'test_id has no positive labeled segments. Pattern detector needs at least 1 positive labeled segment'
+
+        try:
+            detector.train(dataframe, segments, cache)
+        except ValueError as e:
+            self.assertEqual(str(e), excepted_error_message)
+
+    def test_positive_and_negative_segments(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': 1523889000004, 'to': 1523889000006, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000001, 'to': 1523889000003, 'labeled': False, 'deleted': False}]
+        segments = [Segment.from_json(segment) for segment in segments]
+        cache = {}
+        detector = pattern_detector.PatternDetector('PEAK', 'test_id')
+        try:
+            detector.train(dataframe, segments, cache)
+        except Exception as e:
+            self.fail('detector.train fail with error {}'.format(e))
 
 class TestThresholdDetector(unittest.TestCase):
 

server/src/controllers/analytics_controller.ts

@@ -272,7 +272,7 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId, from?: number
 
     switch(detector) {
       case AnalyticUnit.DetectorType.PATTERN:
-        let segments = await Segment.findMany(id, { labeled: true });
+        let segments = await Segment.findMany(id, { $or: [{ labeled: true }, { deleted: true }]} );
         if(segments.length === 0) {
           throw new Error('Need at least 1 labeled segment');
         }

server/src/services/data_layer/mongodb.ts

@@ -122,10 +122,10 @@ export class MongoDbQueryWrapper implements DbQueryWrapper {
 }
 
 function convertQueryToMongoFormat(query: any): object {
-  if(query.$or !== undefined) {
+  if(query.$or !== undefined && typeof query.$or === 'object') {
     query.$or = convertQueryFieldToMongoFormat(query.$or);
   }
-  if(query.$and !== undefined) {
+  if(query.$and !== undefined && typeof query.$or === 'object') {
     query.$and = convertQueryFieldToMongoFormat(query.$and);
   }
   return query;