Endpoint for smoothing data #612 (#639)

analytics/analytics/analytic_types/detector_typing.py

@@ -1,8 +1,9 @@
 from analytic_types import ModelCache
 from analytic_types.segment import Segment
 
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
+import utils.meta
 
 class DetectionResult:
 
@@ -27,3 +28,14 @@ class DetectionResult:
             'segments': list(map(lambda segment: segment.to_json(), self.segments)),
             'lastDetectionTime': self.last_detection_time
         }
+
+@utils.meta.JSONClass
+class ProcessingResult():
+
+    def __init__(
+        self,
+        data: Optional[List[Tuple[int, int]]] = None
+    ):
+        if data is None:
+            data = []
+        self.data = data

analytics/analytics/analytic_unit_manager.py

@@ -19,7 +19,7 @@ def get_detector_by_type(
     elif detector_type == 'threshold':
         return detectors.ThresholdDetector()
     elif detector_type == 'anomaly':
-        return detectors.AnomalyDetector()
+        return detectors.AnomalyDetector(analytic_unit_id)
 
     raise ValueError('Unknown detector type "%s"' % detector_type)
 
@@ -69,10 +69,14 @@ class AnalyticUnitManager:
                 return await worker.do_train(payload['segments'], data, payload['cache'])
             elif 'threshold' in payload:
                 return await worker.do_train(payload['threshold'], data, payload['cache'])
+            elif 'anomaly' in payload:
+                return await worker.do_train(payload['anomaly'], data, payload['cache'])
             else:
                 raise ValueError('No segments or threshold in LEARN payload')
         elif task['type'] == 'DETECT':
             return await worker.do_detect(data, payload['cache'])
+        elif task['type'] == 'PROCESS':
+            return await worker.process_data(data, payload['cache'])
 
         raise ValueError('Unknown task type "%s"' % task['type'])
 

analytics/analytics/analytic_unit_worker.py

@@ -2,14 +2,14 @@ import config
 import detectors
 import logging
 import pandas as pd
-from typing import Optional, Union, Generator, List
+from typing import Optional, Union, Generator, List, Tuple
 import concurrent.futures
 import asyncio
 import utils
 from utils import get_intersected_chunks, get_chunks, prepare_data
 
 from analytic_types import ModelCache
-from analytic_types.detector_typing import DetectionResult
+from analytic_types.detector_typing import DetectionResult, ProcessingResult
 
 logger = logging.getLogger('AnalyticUnitWorker')
 
@@ -52,41 +52,63 @@ class AnalyticUnitWorker:
         chunk_size = window_size * self.CHUNK_WINDOW_SIZE_FACTOR
         chunk_intersection = window_size * self.CHUNK_INTERSECTION_FACTOR
 
-        detection_result = DetectionResult()
+        detections: List[DetectionResult] = []
+        chunks = []
+        # XXX: get_chunks(data, chunk_size) == get_intersected_chunks(data, 0, chunk_size)
+        if self._detector.is_detection_intersected():
+            chunks = get_intersected_chunks(data, chunk_intersection, chunk_size)
+        else:
+            chunks = get_chunks(data, chunk_size)
 
-        for chunk in get_intersected_chunks(data, chunk_intersection, chunk_size):
+        for chunk in chunks:
             await asyncio.sleep(0)
             chunk_dataframe = prepare_data(chunk)
-            detected = self._detector.detect(chunk_dataframe, cache)
+            detected: DetectionResult = self._detector.detect(chunk_dataframe, cache)
-            self.__append_detection_result(detection_result, detected)
+            detections.append(detected)
-        detection_result.segments = self._detector.merge_segments(detection_result.segments)
+
+        if len(detections) == 0:
+            raise RuntimeError(f'do_detect for {self.analytic_unit_id} got empty detection results')
+
+        detection_result = self._detector.concat_detection_results(detections)
         return detection_result.to_json()
 
     def cancel(self):
         if self._training_future is not None:
             self._training_future.cancel()
 
-    async def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[DetectionResult]:
+    async def consume_data(self, data: list, cache: Optional[ModelCache]) -> Optional[dict]:
         window_size = self._detector.get_window_size(cache)
 
-        detection_result = DetectionResult()
+        detections: List[DetectionResult] = []
 
         for chunk in get_chunks(data, window_size * self.CHUNK_WINDOW_SIZE_FACTOR):
             await asyncio.sleep(0)
             chunk_dataframe = prepare_data(chunk)
             detected = self._detector.consume_data(chunk_dataframe, cache)
-            self.__append_detection_result(detection_result, detected)
+            if detected is not None:
+                detections.append(detected)
 
-        detection_result.segments = self._detector.merge_segments(detection_result.segments)
+        if len(detections) == 0:
-
-        if detection_result.last_detection_time is None:
             return None
         else:
+            detection_result = self._detector.concat_detection_results(detections)
             return detection_result.to_json()
 
-    # TODO: move result concatenation to Detectors
+    async def process_data(self, data: list, cache: ModelCache) -> dict:
-    def __append_detection_result(self, detection_result: DetectionResult, new_chunk: DetectionResult):
+        assert isinstance(self._detector, detectors.ProcessingDetector), f'{self.analytic_unit_id} detector is not ProcessingDetector, can`t process data'
-        if new_chunk is not None:
+        assert cache is not None, f'{self.analytic_unit_id} got empty cache for processing data'
-            detection_result.cache = new_chunk.cache
+
-            detection_result.last_detection_time = new_chunk.last_detection_time
+        processed_chunks: List[ProcessingResult] = []
-            detection_result.segments.extend(new_chunk.segments)
+        window_size = self._detector.get_window_size(cache)
+        for chunk in get_chunks(data, window_size * self.CHUNK_WINDOW_SIZE_FACTOR):
+            await asyncio.sleep(0)
+            chunk_dataframe = prepare_data(chunk)
+            processed: ProcessingResult = self._detector.process_data(chunk_dataframe, cache)
+            if processed is not None:
+                processed_chunks.append(processed)
+
+        if len(processed_chunks) == 0:
+            raise RuntimeError(f'process_data for {self.analytic_unit_id} got empty processing results')
+
+        result = self._detector.concat_processing_results(processed_chunks)
+        return result.to_json()

analytics/analytics/detectors/__init__.py

@@ -1,4 +1,4 @@
-from detectors.detector import Detector
+from detectors.detector import Detector, ProcessingDetector
 from detectors.pattern_detector import PatternDetector
 from detectors.threshold_detector import ThresholdDetector
 from detectors.anomaly_detector import AnomalyDetector

analytics/analytics/detectors/anomaly_detector.py

@@ -1,12 +1,13 @@
 import logging
+import numpy as np
 import pandas as pd
 from typing import Optional, Union, List, Tuple
 
 from analytic_types import AnalyticUnitId, ModelCache
-from analytic_types.detector_typing import DetectionResult
+from analytic_types.detector_typing import DetectionResult, ProcessingResult
 from analytic_types.data_bucket import DataBucket
 from analytic_types.segment import Segment
-from detectors import Detector
+from detectors import Detector, ProcessingDetector
 import utils
 
 MAX_DEPENDENCY_LEVEL = 100
@@ -14,9 +15,10 @@ MIN_DEPENDENCY_FACTOR = 0.1
 logger = logging.getLogger('ANOMALY_DETECTOR')
 
 
-class AnomalyDetector(Detector):
+class AnomalyDetector(ProcessingDetector):
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, analytic_unit_id: AnalyticUnitId):
+        self.analytic_unit_id = analytic_unit_id
         self.bucket = DataBucket()
 
     def train(self, dataframe: pd.DataFrame, payload: Union[list, dict], cache: Optional[ModelCache]) -> ModelCache:
@@ -27,15 +29,16 @@ class AnomalyDetector(Detector):
             }
         }
 
+    # TODO: ModelCache -> ModelState
     def detect(self, dataframe: pd.DataFrame, cache: Optional[ModelCache]) -> DetectionResult:
         data = dataframe['value']
-        last_values = None
+        last_value = None
         if cache is not None:
-            last_values = cache.get('last_values')
+            last_value = cache.get('last_value')
 
-        smothed_data = utils.exponential_smoothing(data, cache['alpha'])
+        smoothed_data = utils.exponential_smoothing(data, cache['alpha'], last_value)
-        upper_bound = smothed_data + cache['confidence']
+        upper_bound = smoothed_data + cache['confidence']
-        lower_bound = smothed_data - cache['confidence']
+        lower_bound = smoothed_data - cache['confidence']
 
         anomaly_indexes = []
         for idx, val in enumerate(data.values):
@@ -48,13 +51,34 @@ class AnomalyDetector(Detector):
             utils.convert_pd_timestamp_to_ms(dataframe['timestamp'][segment[0]]),
             utils.convert_pd_timestamp_to_ms(dataframe['timestamp'][segment[1]]),
         ) for segment in segments]
+
         last_dataframe_time = dataframe.iloc[-1]['timestamp']
         last_detection_time = utils.convert_pd_timestamp_to_ms(last_dataframe_time)
+        # TODO: ['lastValue'] -> .last_value
+        cache['lastValue'] = smoothed_data.values[-1]
+
         return DetectionResult(cache, segments, last_detection_time)
 
     def consume_data(self, data: pd.DataFrame, cache: Optional[ModelCache]) -> Optional[DetectionResult]:
-        self.detect(data, cache)
+        if cache is None:
+            msg = f'consume_data got invalid cache {cache} for task {self.analytic_unit_id}'
+            logging.debug(msg)
+            raise ValueError(msg)
+
+        data_without_nan = data.dropna()
+
+        if len(data_without_nan) == 0:
+            return None
 
+        self.bucket.receive_data(data_without_nan)
+
+        if len(self.bucket.data) >= self.get_window_size(cache):
+            return self.detect(self.bucket, cache)
+
+        return None
+
+    def is_detection_intersected(self) -> bool:
+        return False
 
     def get_window_size(self, cache: Optional[ModelCache]) -> int:
         '''
@@ -69,6 +93,24 @@ class AnomalyDetector(Detector):
                 break
         return level
 
+    def concat_detection_results(self, detections: List[DetectionResult]) -> DetectionResult:
+        result = DetectionResult()
+        for detection in detections:
+            result.segments.extend(detection.segments)
+            result.last_detection_time = detection.last_detection_time
+            result.cache = detection.cache
+        result.segments = utils.merge_intersecting_segments(result.segments)
+        return result
+
+    # TODO: ModelCache -> ModelState
+    def process_data(self, data: pd.DataFrame, cache: ModelCache) -> ProcessingResult:
+        # TODO: exponential_smoothing should return dataframe with related timestamps
+        smoothed = utils.exponential_smoothing(data['value'], cache['alpha'], cache.get('lastValue'))
+        timestamps = utils.convert_series_to_timestamp_list(data.timestamp)
+        smoothed_dataset = list(zip(timestamps, smoothed.values.tolist()))
+        result = ProcessingResult(smoothed_dataset)
+        return result
+
     def merge_segments(self, segments: List[Segment]) -> List[Segment]:
         segments = utils.merge_intersecting_segments(segments)
         return segments

analytics/analytics/detectors/detector.py

@@ -3,7 +3,7 @@ from pandas import DataFrame
 from typing import Optional, Union, List
 
 from analytic_types import ModelCache
-from analytic_types.detector_typing import DetectionResult
+from analytic_types.detector_typing import DetectionResult, ProcessingResult
 from analytic_types.segment import Segment
 
 
@@ -28,5 +28,32 @@ class Detector(ABC):
     def get_window_size(self, cache: Optional[ModelCache]) -> int:
         pass
 
+    def is_detection_intersected(self) -> bool:
+        return True
+
+    def concat_detection_results(self, detections: List[DetectionResult]) -> DetectionResult:
+        result = DetectionResult()
+        for detection in detections:
+            result.segments.extend(detection.segments)
+            result.last_detection_time = detection.last_detection_time
+            result.cache = detection.cache
+        return result
+
     def merge_segments(self, segments: List[Segment]) -> List[Segment]:
         return segments
+
+class ProcessingDetector(Detector):
+
+    @abstractmethod
+    def process_data(self, data, cache: Optional[ModelCache]) -> ProcessingResult:
+        pass
+
+    def concat_processing_results(self, processing_results: List[ProcessingResult]) -> Optional[ProcessingResult]:
+        if len(processing_results) == 0:
+            return None
+
+        united_result = ProcessingResult()
+        for result in processing_results:
+            united_result.data.extend(result.data)
+
+        return united_result

analytics/analytics/detectors/threshold_detector.py

@@ -44,7 +44,6 @@ class ThresholdDetector(Detector):
             current_value = row['value']
             current_timestamp = convert_pd_timestamp_to_ms(row['timestamp'])
             segment = Segment(current_timestamp, current_timestamp)
-
             # TODO: merge segments
             if pd.isnull(current_value):
                 if condition == 'NO_DATA':

analytics/analytics/utils/time.py

@@ -1,4 +1,5 @@
 import pandas as pd
+from typing import List
 
 def convert_sec_to_ms(sec) -> int:
     return int(sec) * 1000
@@ -6,3 +7,7 @@ def convert_sec_to_ms(sec) -> int:
 def convert_pd_timestamp_to_ms(timestamp: pd.Timestamp) -> int:
     # TODO: convert from nanoseconds to millisecond in a better way: not by dividing by 10^6
     return int(timestamp.value) / 1000000
+
+def convert_series_to_timestamp_list(series: pd.Series) -> List[int]:
+    timestamps = map(lambda value: convert_pd_timestamp_to_ms(value), series)
+    return list(timestamps)

analytics/tests/test_detectors.py

@@ -42,7 +42,7 @@ class TestAnomalyDetector(unittest.TestCase):
             'confidence': 2,
             'alpha': 0.1,
         }
-        detector = anomaly_detector.AnomalyDetector()
+        detector = anomaly_detector.AnomalyDetector('test_id')
         detect_result = detector.detect(dataframe, cache)
         result = [{ 'from': 1523889000005.0, 'to': 1523889000005.0 }]
         self.assertEqual(result, detect_result.to_json()['segments'])

server/src/controllers/analytics_controller.ts

@@ -17,6 +17,7 @@ import { queryByMetric, GrafanaUnavailable, DatasourceUnavailable } from 'grafan
 
 import * as _ from 'lodash';
 import { WebhookType } from '../services/notification_service';
+import { AnomalyAnalyticUnit } from '../models/analytic_units/anomaly_analytic_unit_model';
 
 const SECONDS_IN_MINUTE = 60;
 
@@ -105,7 +106,7 @@ export function terminate() {
   alertService.stopAlerting();
 }
 
-async function runTask(task: AnalyticsTask): Promise<TaskResult> {
+export async function runTask(task: AnalyticsTask): Promise<TaskResult> {
   return new Promise<TaskResult>((resolver: TaskResolver) => {
     taskResolvers.set(task.id, resolver); // it will be resolved in onTaskResult()
     analyticsService.sendTask(task);      // we dont wait for result here
@@ -126,7 +127,10 @@ async function getQueryRange(
     return getQueryRangeForLearningBySegments(segments);
   }
 
-  if(detectorType === AnalyticUnit.DetectorType.THRESHOLD) {
+  if(
+    detectorType === AnalyticUnit.DetectorType.THRESHOLD ||
+    detectorType === AnalyticUnit.DetectorType.ANOMALY
+  ) {
     const now = Date.now();
     return {
       from: now - 5 * SECONDS_IN_MINUTE * 1000,
@@ -215,23 +219,34 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId, from?: number
     const detector = analyticUnit.detectorType;
     let taskPayload: any = { detector, analyticUnitType, cache: oldCache };
 
-    if(detector === AnalyticUnit.DetectorType.PATTERN) {
+    switch(detector) {
-      let segments = await Segment.findMany(id, { labeled: true });
+      case AnalyticUnit.DetectorType.PATTERN:
-      if(segments.length === 0) {
+        let segments = await Segment.findMany(id, { labeled: true });
-        throw new Error('Need at least 1 labeled segment');
+        if(segments.length === 0) {
-      }
+          throw new Error('Need at least 1 labeled segment');
-
+        }
-      let segmentObjs = segments.map(s => s.toObject());
+
-
+        let segmentObjs = segments.map(s => s.toObject());
-      let deletedSegments = await Segment.findMany(id, { deleted: true });
+
-      let deletedSegmentsObjs = deletedSegments.map(s => s.toObject());
+        let deletedSegments = await Segment.findMany(id, { deleted: true });
-      segmentObjs = _.concat(segmentObjs, deletedSegmentsObjs);
+        let deletedSegmentsObjs = deletedSegments.map(s => s.toObject());
-      taskPayload.segments = segmentObjs;
+        segmentObjs = _.concat(segmentObjs, deletedSegmentsObjs);
-    } else if(detector === AnalyticUnit.DetectorType.THRESHOLD) {
+        taskPayload.segments = segmentObjs;
-      taskPayload.threshold = {
+        break;
-        value: (analyticUnit as ThresholdAnalyticUnit).value,
+      case AnalyticUnit.DetectorType.THRESHOLD:
-        condition: (analyticUnit as ThresholdAnalyticUnit).condition
+        taskPayload.threshold = {
-      };
+          value: (analyticUnit as ThresholdAnalyticUnit).value,
+          condition: (analyticUnit as ThresholdAnalyticUnit).condition
+        };
+        break;
+      case AnalyticUnit.DetectorType.ANOMALY:
+        taskPayload.anomaly = {
+          alpha: (analyticUnit as AnomalyAnalyticUnit).alpha,
+          confidence: (analyticUnit as AnomalyAnalyticUnit).confidence
+        };
+        break;
+      default:
+        throw new Error(`Unknown type of detector: ${detector}`);
     }
 
     let range: TimeRange;
@@ -558,3 +573,46 @@ async function runDetectionOnExtendedSpan(
   await Detection.insertSpan(detection);
   return detection;
 }
+
+export async function getHSR(analyticUnit: AnalyticUnit.AnalyticUnit, from: number, to: number) {
+  try {
+    const grafanaUrl = getGrafanaUrl(analyticUnit.grafanaUrl);
+    const data = await queryByMetric(analyticUnit.metric, grafanaUrl, from, to, HASTIC_API_KEY);
+
+    if(analyticUnit.detectorType !== AnalyticUnit.DetectorType.ANOMALY) {
+      return data;
+    } else {
+      let cache = await AnalyticUnitCache.findById(analyticUnit.id);
+      if(
+        cache === null ||
+        cache.data.alpha !== (analyticUnit as AnalyticUnit.AnomalyAnalyticUnit).alpha ||
+        cache.data.confidence !== (analyticUnit as AnalyticUnit.AnomalyAnalyticUnit).confidence
+      ) {
+        await runLearning(analyticUnit.id, from, to);
+        cache = await AnalyticUnitCache.findById(analyticUnit.id);
+      }
+
+      cache = cache.data;
+    
+      const analyticUnitType = analyticUnit.type;
+      const detector = analyticUnit.detectorType;
+      const payload = {
+        data: data.values,
+        analyticUnitType,
+        detector,
+        cache
+      };
+
+      const processingTask = new AnalyticsTask(analyticUnit.id, AnalyticsTaskType.PROCESS, payload);
+      const result = await runTask(processingTask);
+      if(result.status !== AnalyticUnit.AnalyticUnitStatus.SUCCESS) {
+        throw new Error(`Data processing error: ${result.error}`);
+      }
+      return { values: result.payload.data, columns: data.columns }
+    }
+  } catch (err) {
+    const message = err.message || JSON.stringify(err);
+    await AnalyticUnit.setStatus(analyticUnit.id, AnalyticUnit.AnalyticUnitStatus.FAILED, message);
+    throw new Error(message);
+  }
+}

server/src/models/analytic_units/anomaly_analytic_unit_model.ts

@@ -35,7 +35,7 @@ export class AnomalyAnalyticUnit extends AnalyticUnit {
       error,
       labeledColor,
       deletedColor,
-      DetectorType.THRESHOLD,
+      DetectorType.ANOMALY,
       visible
     );
   }

server/src/models/analytic_units/index.ts

@@ -3,6 +3,7 @@ import { AnalyticUnitId, AnalyticUnitStatus, DetectorType, ANALYTIC_UNIT_TYPES }
 import { AnalyticUnit } from './analytic_unit_model';
 import { PatternAnalyticUnit } from './pattern_analytic_unit_model';
 import { ThresholdAnalyticUnit } from './threshold_analytic_unit_model';
+import { AnomalyAnalyticUnit } from './anomaly_analytic_unit_model';
 import {
   findById,
   findMany,
@@ -17,7 +18,7 @@ import {
 
 
 export {
-  AnalyticUnit, PatternAnalyticUnit, ThresholdAnalyticUnit,
+  AnalyticUnit, PatternAnalyticUnit, ThresholdAnalyticUnit, AnomalyAnalyticUnit,
   AnalyticUnitId, AnalyticUnitStatus, DetectorType, ANALYTIC_UNIT_TYPES,
   createAnalyticUnitFromObject,
   findById, findMany,

server/src/models/analytics_task_model.ts

@@ -10,7 +10,8 @@ export enum AnalyticsTaskType {
   LEARN = 'LEARN',
   DETECT = 'DETECT',
   CANCEL = 'CANCEL',
-  PUSH = 'PUSH'
+  PUSH = 'PUSH',
+  PROCESS = 'PROCESS'
 };
 
 export class AnalyticsTask {

server/src/routes/data_router.ts

@@ -1,8 +1,5 @@
 import * as AnalyticUnit from '../models/analytic_units';
-import { HASTIC_API_KEY } from '../config';
+import * as AnalyticsController from '../controllers/analytics_controller';
-import { getGrafanaUrl } from '../utils/grafana';
-
-import { queryByMetric } from 'grafana-datasource-kit';
 
 import * as Router from 'koa-router';
 
@@ -46,8 +43,7 @@ async function query(ctx: Router.IRouterContext) {
     throw new Error(`can't find analytic unit ${analyticUnitId}`);
   }
 
-  const grafanaUrl = getGrafanaUrl(analyticUnit.grafanaUrl);
+  const results = await AnalyticsController.getHSR(analyticUnit, from, to);
-  const results = await queryByMetric(analyticUnit.metric, grafanaUrl, from, to, HASTIC_API_KEY);
   ctx.response.body = { results };
 }
 

server/src/services/alert_service.ts

@@ -134,6 +134,7 @@ export class AlertService {
 
     alertsType[AnalyticUnit.DetectorType.THRESHOLD] = ThresholdAlert;
     alertsType[AnalyticUnit.DetectorType.PATTERN] = PatternAlert;
+    alertsType[AnalyticUnit.DetectorType.ANOMALY] = Alert;
 
     this._alerts[analyticUnit.id] = new alertsType[detector](analyticUnit);
   }