Make all models work && add reverse peak model (#124)

- Subtract min value from dataset before passing to model

- Rename StepModel -> DropModel

- Use cache to save state in all models

- Return `Segment { 'from': <timestamp>, 'to': <timestamp>}` instead of `Segment { 'from': <index>, 'to': <index>}` in all models

- Integrate new peaks model (from https://github.com/hastic/hastic-server/pull/123)

- Integrate new reverse-peaks model (from https://github.com/hastic/hastic-server/pull/123)

- Refactor: make `predict` method in `Model` not abstract and remove it from all children

- Refactor: add abstract `do_predict` method to models

analytics/analytic_unit_manager.py

@@ -33,12 +33,11 @@ async def handle_analytic_task(task):
 
         worker = ensure_worker(task['analyticUnitId'], payload['pattern'])
 
-        data = pd.DataFrame(payload['data'], columns=['timestamp', 'value'])
+        data = prepare_data(payload['data'])
-        data['timestamp'] = pd.to_datetime(data['timestamp'])
         result_payload = {}
-        if task['type'] == "LEARN":
+        if task['type'] == 'LEARN':
             result_payload = await worker.do_learn(payload['segments'], data, payload['cache'])
-        elif task['type'] == "PREDICT":
+        elif task['type'] == 'PREDICT':
             result_payload = await worker.do_predict(data, payload['cache'])
         else:
             raise ValueError('Unknown task type "%s"' % task['type'])
@@ -52,8 +51,20 @@ async def handle_analytic_task(task):
         logger.error("handle_analytic_task exception: '%s'" % error_text)
         # TODO: move result to a class which renders to json for messaging to analytics
         return {
-            'status': "FAILED",
+            'status': 'FAILED',
             'error': str(e)
         }
 
+def prepare_data(data: list):
+    """
+        Takes list
+        - converts it into pd.DataFrame,
+        - converts 'timestamp' column to pd.Datetime,
+        - subtracts min value from dataset
+    """
+    data = pd.DataFrame(data, columns=['timestamp', 'value'])
 
+    data['timestamp'] = pd.to_datetime(data['timestamp'])
+    data['value'] = data['value'] - min(data['value'])
+
+    return data

analytics/analytic_unit_worker.py

@@ -2,6 +2,8 @@ import config
 import detectors
 import logging
 import pandas as pd
+from typing import Optional
+from models import AnalyticUnitCache
 
 
 logger = logging.getLogger('AnalyticUnitWorker')
@@ -13,8 +15,8 @@ class AnalyticUnitWorker:
         self.analytic_unit_id = analytic_unit_id
         self.detector = detector
 
-    async def do_learn(self, segments: list, data: pd.DataFrame, cache: dict) -> dict:
+    async def do_learn(self, segments: list, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
         return await self.detector.train(data, segments, cache)
 
-    async def do_predict(self, data: pd.DataFrame, cache: dict) -> dict:
+    async def do_predict(self, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
         return await self.detector.predict(data, cache)

analytics/detectors/detector.py

@@ -1,13 +1,15 @@
+from models import AnalyticUnitCache
 from abc import ABC, abstractmethod
 from pandas import DataFrame
+from typing import Optional
 
 
 class Detector(ABC):
 
     @abstractmethod
-    async def train(self, dataframe: DataFrame, segments: list, cache: dict) -> dict:
+    async def train(self, dataframe: DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
         pass
 
     @abstractmethod
-    async def predict(self, dataframe: DataFrame, cache: dict) -> dict:
+    async def predict(self, dataframe: DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
         pass

analytics/detectors/general_detector/general_detector.py

@@ -1,11 +1,13 @@
 from detectors.general_detector.supervised_algorithm import SupervisedAlgorithm
 from detectors import Detector
+from models import AnalyticUnitCache
 import utils
 
 import pandas as pd
 import logging
 import config
 import json
+from typing import Optional
 
 
 NANOSECONDS_IN_MS = 1000000
@@ -18,7 +20,7 @@ class GeneralDetector(Detector):
     def __init__(self):
         self.model = None
 
-    async def train(self, dataframe: pd.DataFrame, segments: list, cache: dict):
+    async def train(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
 
         confidence = 0.02
         start_index, stop_index = 0, len(dataframe)
@@ -43,9 +45,9 @@ class GeneralDetector(Detector):
             last_prediction_time = 0
 
         logger.info("Learning is finished for anomaly_name='%s'" % self.anomaly_name)
-        return last_prediction_time
+        return cache
 
-    async def predict(self, dataframe: pd.DataFrame, cache: dict):
+    async def predict(self, dataframe: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
         logger.info("Start to predict for anomaly type='%s'" % self.anomaly_name)
         last_prediction_time = pd.to_datetime(last_prediction_time, unit='ms')
 

analytics/detectors/pattern_detector.py

@@ -4,6 +4,7 @@ import logging
 import config
 
 import pandas as pd
+from typing import Optional
 
 from detectors import Detector
 
@@ -13,9 +14,11 @@ logger = logging.getLogger('PATTERN_DETECTOR')
 
 def resolve_model_by_pattern(pattern: str) -> models.Model:
     if pattern == 'PEAK':
-        return models.PeaksModel()
+        return models.PeakModel()
+    if pattern == 'REVERSE_PEAK':
+        return models.ReversePeakModel()
     if pattern == 'DROP':
-        return models.StepModel()
+        return models.DropModel()
     if pattern == 'JUMP':
         return models.JumpModel()
     if pattern == 'CUSTOM':
@@ -30,23 +33,24 @@ class PatternDetector(Detector):
         self.model = resolve_model_by_pattern(self.pattern_type)
         window_size = 100
 
-    async def train(self, dataframe: pd.DataFrame, segments: list, cache: dict):
+    async def train(self, dataframe: pd.DataFrame, segments: list, cache: Optional[models.AnalyticUnitCache]) -> models.AnalyticUnitCache:
         # TODO: pass only part of dataframe that has segments
-        self.model.fit(dataframe, segments, cache)
+        new_cache = self.model.fit(dataframe, segments, cache)
-        # TODO: save model after fit
         return {
-            'cache': cache
+            'cache': new_cache
         }
 
-    async def predict(self, dataframe: pd.DataFrame, cache: dict):
+    async def predict(self, dataframe: pd.DataFrame, cache: Optional[models.AnalyticUnitCache]) -> dict:
-        predicted = await self.model.predict(dataframe, cache)
+        # TODO: split and sleep (https://github.com/hastic/hastic-server/pull/124#discussion_r214085643)
+        predicted = self.model.predict(dataframe, cache)
 
-        segments = [{ 'from': segment[0], 'to': segment[1] } for segment in predicted]
+        segments = [{ 'from': segment[0], 'to': segment[1] } for segment in predicted['segments']]
+        newCache = predicted['cache']
 
         last_dataframe_time = dataframe.iloc[-1]['timestamp']
         last_prediction_time = last_dataframe_time.value
         return {
-            'cache': cache,
+            'cache': newCache,
             'segments': segments,
             'lastPredictionTime': last_prediction_time
         }

analytics/models/__init__.py

@@ -1,5 +1,8 @@
-from models.model import Model
+from models.model import Model, AnalyticUnitCache
-from models.step_model import StepModel
+from models.drop_model import DropModel
-from models.peaks_model import PeaksModel
+from models.peak_model import PeakModel
 from models.jump_model import JumpModel
 from models.custom_model import CustomModel
+
+from models.custom_model import CustomModel
+from models.reverse_peak_model import ReversePeakModel

analytics/models/custom_model.py

@@ -1,6 +1,7 @@
 from models import Model
 import utils
 import pandas as pd
+from typing import Optional
 
 # Paste your model here:
 class CustomModel(Model):
@@ -11,8 +12,8 @@ class CustomModel(Model):
         # It will be saved in filesystem and loaded after server restart
         self.state = {}
     
-    def fit(self, dataframe: pd.DataFrame, segments: list, cache: dict) -> dict:
+    def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[dict]) -> dict:
         pass
     
-    def predict(self, dataframe, cache: dict):
+    def predict(self, dataframe, cache: Optional[dict]):
         return []

analytics/models/drop_model.py

@@ -1,4 +1,4 @@
-from models import Model
+from models import Model, AnalyticUnitCache
 
 import scipy.signal
 from scipy.fftpack import fft
@@ -8,10 +8,11 @@ from scipy.stats import gaussian_kde
 import utils
 import numpy as np
 import pandas as pd
+from typing import Optional
 
 WINDOW_SIZE = 400
 
-class StepModel(Model):
+class DropModel(Model):
     def __init__(self):
         super()
         self.segments = []
@@ -23,13 +24,12 @@ class StepModel(Model):
             'DROP_LENGTH': 1,
         }
 
-    def fit(self, dataframe: pd.DataFrame, segments: list, cache: dict) -> dict:
+    def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
+        if type(cache) is AnalyticUnitCache:
+            self.state = cache
         self.segments = segments
-        d_min = min(dataframe['value'])
-        for i in range(0,len(dataframe['value'])):
-            dataframe.loc[i, 'value'] = dataframe.loc[i, 'value'] - d_min
-        data = dataframe['value']
 
+        data = dataframe['value']
         confidences = []
         convolve_list = []
         drop_height_list = []
@@ -76,46 +76,34 @@ class StepModel(Model):
                 convolve_list.append(max(convolve))
 
         if len(confidences) > 0:
-            self.state['confidence'] = min(confidences)
+            self.state['confidence'] = float(min(confidences))
         else:
             self.state['confidence'] = 1.5
 
         if len(convolve_list) > 0:
-            self.state['convolve_max'] = max(convolve_list)
+            self.state['convolve_max'] = float(max(convolve_list))
         else:
             self.state['convolve_max'] = WINDOW_SIZE
 
         if len(drop_height_list) > 0:
-            self.state['DROP_HEIGHT'] = min(drop_height_list)
+            self.state['DROP_HEIGHT'] = int(min(drop_height_list))
         else:
             self.state['DROP_HEIGHT'] = 1
 
         if len(drop_length_list) > 0:
-            self.state['DROP_LENGTH'] = max(drop_length_list)
+            self.state['DROP_LENGTH'] = int(max(drop_length_list))
         else:
             self.state['DROP_LENGTH'] = 1
 
-    async def predict(self, dataframe: pd.DataFrame, cache: dict) -> dict:
+        return self.state
-        d_min = min(dataframe['value'])
-        for i in range(0,len(dataframe['value'])):
-            dataframe.loc[i, 'value'] = dataframe.loc[i, 'value'] - d_min
-
-        result = await self.__predict(dataframe)
-
-        if len(self.segments) > 0:
-            return [segment for segment in result if not utils.is_intersect(segment, self.segments)]
 
-    async def __predict(self, dataframe):
+    def do_predict(self, dataframe: pd.DataFrame):
-        #window_size = 24
-        #all_max_flatten_data = data.rolling(window=window_size).mean()
-        #all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
-        #print(self.state['DROP_HEIGHT'],self.state['DROP_LENGTH'])
         data = dataframe['value']
         possible_drops = utils.find_drop(data, self.state['DROP_HEIGHT'], self.state['DROP_LENGTH'] + 1)
         filtered = self.__filter_prediction(possible_drops, data)
         return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
 
-    def __filter_prediction(self, segments, data):
+    def __filter_prediction(self, segments: list, data: list):
         delete_list = []
         variance_error = int(0.004 * len(data))
         if variance_error > 50:

analytics/models/jump_model.py

@@ -1,4 +1,4 @@
-from models import Model
+from models import Model, AnalyticUnitCache
 
 import utils
 import numpy as np
@@ -9,6 +9,7 @@ from scipy.signal import argrelextrema
 import math
 from scipy.stats import gaussian_kde
 from scipy.stats import norm
+from typing import Optional
 
 
 WINDOW_SIZE = 400
@@ -26,8 +27,11 @@ class JumpModel(Model):
             'JUMP_LENGTH': 1,
         }
 
-    def fit(self, dataframe: pd.DataFrame, segments: list, cache: dict) -> dict:
+    def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
+        if type(cache) is AnalyticUnitCache:
+            self.state = cache
         self.segments = segments
+
         data = dataframe['value']
         confidences = []
         convolve_list = []
@@ -35,13 +39,17 @@ class JumpModel(Model):
         jump_length_list = []
         for segment in segments:
             if segment['labeled']:
-                segment_data = data.loc[segment['from'] : segment['to'] + 1].reset_index(drop=True)
+                segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from']))
+                segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to']))
+
+                segment_data = data.loc[segment_from_index : segment_to_index + 1].reset_index(drop=True)
                 segment_min = min(segment_data)
                 segment_max = max(segment_data)
                 confidences.append(0.20 * (segment_max - segment_min))
                 flat_segment = segment_data.rolling(window=5).mean()
-                pdf = gaussian_kde(flat_segment.dropna())
+                flat_segment_dropna = flat_segment.dropna()
-                x = np.linspace(flat_segment.dropna().min() - 1, flat_segment.dropna().max() + 1, len(flat_segment.dropna()))
+                pdf = gaussian_kde(flat_segment_dropna)
+                x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
                 y = pdf(x)
                 ax_list = []
                 for i in range(len(x)):
@@ -56,12 +64,12 @@ class JumpModel(Model):
                 segment_max_line = ax_list[max_peak_index, 0]
                 jump_height = 0.9 * (segment_max_line - segment_min_line)
                 jump_height_list.append(jump_height)
-                jump_lenght = utils.find_jump_length(segment_data, segment_min_line, segment_max_line)
+                jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line)
-                jump_length_list.append(jump_lenght)
+                jump_length_list.append(jump_length)
                 cen_ind = utils.intersection_segment(flat_segment, segment_median) #finds all interseprions with median
                 #cen_ind =  utils.find_ind_median(segment_median, flat_segment)
                 jump_center = cen_ind[0]
-                segment_cent_index = jump_center - 5 + segment['from']
+                segment_cent_index = jump_center - 5 + segment_from_index
                 self.ijumps.append(segment_cent_index)
                 labeled_drop = data[segment_cent_index - WINDOW_SIZE : segment_cent_index + WINDOW_SIZE]
                 labeled_min = min(labeled_drop)
@@ -71,41 +79,33 @@ class JumpModel(Model):
                 convolve_list.append(max(convolve))
 
         if len(confidences) > 0:
-            self.state['confidence'] = min(confidences)
+            self.state['confidence'] = float(min(confidences))
         else:
             self.state['confidence'] = 1.5
 
         if len(convolve_list) > 0:
-            self.state['convolve_max'] = max(convolve_list)
+            self.state['convolve_max'] = float(max(convolve_list))
         else:
             self.state['convolve_max'] = WINDOW_SIZE
 
         if len(jump_height_list) > 0:
-            self.state['JUMP_HEIGHT'] = min(jump_height_list)
+            self.state['JUMP_HEIGHT'] = int(min(jump_height_list))
         else:
             self.state['JUMP_HEIGHT'] = 1
 
         if len(jump_length_list) > 0:
-            self.state['JUMP_LENGTH'] = max(jump_length_list)
+            self.state['JUMP_LENGTH'] = int(max(jump_length_list))
         else:
             self.state['JUMP_LENGTH'] = 1  
 
-    def predict(self, dataframe: pd.DataFrame, cache: dict) -> dict:
+        return self.state
-        data = dataframe['value']
-
-        result = self.__predict(data)
-        result.sort()
-        if len(self.segments) > 0:
-            result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
-        return result
 
-    def __predict(self, data):
+    def do_predict(self, dataframe: pd.DataFrame):
-        #window_size = 24
+        data = dataframe['value']
-        #all_max_flatten_data = data.rolling(window=window_size).mean()
-        #all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
         possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1)
+        filtered = self.__filter_prediction(possible_jumps, data)
 
-        return [(x - 1, x + 1) for x in self.__filter_prediction(possible_jumps, data)]
+        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
 
     def __filter_prediction(self, segments, data):
         delete_list = []
@@ -138,5 +138,4 @@ class JumpModel(Model):
         for ijump in self.ijumps:
             segments.append(ijump)
 
-
         return segments

analytics/models/model.py

@@ -1,13 +1,31 @@
+import utils
+
 from abc import ABC, abstractmethod
 from pandas import DataFrame
+from typing import Optional
 
+AnalyticUnitCache = dict
 
 class Model(ABC):
 
     @abstractmethod
-    def fit(self, dataframe: DataFrame, segments: list, cache: dict) -> dict:
+    def fit(self, dataframe: DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
         pass
 
     @abstractmethod
-    def predict(self, dataframe: DataFrame, cache: dict) -> dict:
+    def do_predict(self, dataframe: DataFrame):
         pass
+
+    def predict(self, dataframe: DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
+        if type(cache) is AnalyticUnitCache:
+            self.state = cache
+
+        result = self.do_predict(dataframe)
+        result.sort()
+
+        if len(self.segments) > 0:
+            result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
+        return {
+            'segments': result,
+            'cache': self.state
+        }

analytics/models/peak_model.py

@@ -0,0 +1,113 @@
+from models import Model, AnalyticUnitCache
+
+import scipy.signal
+from scipy.fftpack import fft
+from scipy.signal import argrelextrema
+
+import utils
+import numpy as np
+import pandas as pd
+from typing import Optional
+
+WINDOW_SIZE = 240
+
+
+class PeakModel(Model):
+
+    def __init__(self):
+        super()
+        self.segments = []
+        self.ipeaks = []
+        self.state = {
+            'confidence': 1.5,
+            'convolve_max': 570000
+        }
+
+    def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
+        if type(cache) is AnalyticUnitCache:
+            self.state = cache
+
+        self.segments = segments
+        data = dataframe['value']
+
+        confidences = []
+        convolve_list = []
+        for segment in segments:
+            if segment['labeled']:
+                segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from']))
+                segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to']))
+
+                segment_data = data[segment_from_index: segment_to_index + 1]
+                segment_min = min(segment_data)
+                segment_max = max(segment_data)
+                confidences.append(0.2 * (segment_max - segment_min))
+                flat_segment = segment_data.rolling(window=5).mean()
+                flat_segment = flat_segment.dropna()
+                segment_max_index = flat_segment.idxmax()  # + segment['start']
+                self.ipeaks.append(segment_max_index)
+                labeled_drop = data[segment_max_index - WINDOW_SIZE: segment_max_index + WINDOW_SIZE]
+                labeled_min = min(labeled_drop)
+                for value in labeled_drop:
+                    value = value - labeled_min
+                convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
+                convolve_list.append(max(convolve))
+
+        if len(confidences) > 0:
+            self.state['confidence'] = float(min(confidences))
+        else:
+            self.state['confidence'] = 1.5
+
+        if len(convolve_list) > 0:
+            self.state['convolve_max'] = float(max(convolve_list))
+        else:
+            self.state['convolve_max'] = 570000
+
+        return self.state
+
+    def do_predict(self, dataframe: pd.DataFrame):
+        data = dataframe['value']
+        window_size = 24
+        all_max_flatten_data = data.rolling(window=window_size).mean()
+        all_maxs = argrelextrema(np.array(all_max_flatten_data), np.greater)[0]
+
+        extrema_list = []
+        for i in utils.exponential_smoothing(data + self.state['confidence'], 0.02):
+            extrema_list.append(i)
+
+        segments = []
+        for i in all_maxs:
+            if all_max_flatten_data[i] > extrema_list[i]:
+                segments.append(i+12)
+
+        filtered = self.__filter_prediction(segments, data)
+        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+
+    def __filter_prediction(self, segments: list, all_max_flatten_data: list):
+        delete_list = []
+        variance_error = int(0.004 * len(all_max_flatten_data))
+        if variance_error > 100:
+            variance_error = 100
+        for i in range(1, len(segments)):
+            if segments[i] < segments[i - 1] + variance_error:
+                delete_list.append(segments[i])
+        for item in delete_list:
+            segments.remove(item)
+
+        delete_list = []
+        if len(segments) == 0 or len(self.ipeaks) == 0:
+            segments = []
+            return segments
+
+        pattern_data = all_max_flatten_data[self.ipeaks[0] - WINDOW_SIZE: self.ipeaks[0] + WINDOW_SIZE]
+        for segment in segments:
+            if segment > WINDOW_SIZE:
+                convol_data = all_max_flatten_data[segment - WINDOW_SIZE: segment + WINDOW_SIZE]
+                conv = scipy.signal.fftconvolve(pattern_data, convol_data)
+                if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_max'] * 0.8:
+                    delete_list.append(segment)
+            else:
+                delete_list.append(segment)
+        for item in delete_list:
+            segments.remove(item)
+
+        return segments

analytics/models/peaks_model.py

@@ -1,59 +0,0 @@
-from models import Model
-
-import utils
-from scipy import signal
-import numpy as np
-import pandas as pd
-
-
-class PeaksModel(Model):
-
-    def __init__(self):
-        super()
-
-    def fit(self, dataframe: pd.DataFrame, segments: list, cache: dict) -> dict:
-        pass
-
-    def predict(self, dataframe: pd.DataFrame, cache: dict) -> dict:
-        array = dataframe['value'].as_matrix()
-        window_size = 20
-        # window = np.ones(101)
-        # mean_filtered = signal.fftconvolve(
-        #     np.concatenate([np.zeros(window_size), array, np.zeros(window_size)]),
-        #     window,
-        #     mode='valid'
-        # )
-        # filtered = np.divide(array, mean_filtered / 101)
-
-        window = signal.general_gaussian(2 * window_size + 1, p=0.5, sig=5)
-        #print(window)
-        filtered = signal.fftconvolve(array, window, mode='valid')
-
-        # filtered = np.concatenate([
-        #     np.zeros(window_size),
-        #     filtered,
-        #     np.zeros(window_size)
-        # ])
-        filtered = filtered / np.sum(window)
-        array = array[window_size:-window_size]
-        filtered = np.subtract(array, filtered)
-
-        # filtered = np.convolve(array, step, mode='valid')
-        # print(len(array))
-        # print(len(filtered))
-
-        # step = np.hstack((np.ones(window_size), 0, -1*np.ones(window_size)))
-        #
-        # conv = np.convolve(array, step, mode='valid')
-        #
-        # conv = np.concatenate([
-        #     np.zeros(window_size),
-        #     conv,
-        #     np.zeros(window_size)])
-
-        #data = step_detect.t_scan(array, window=window_size)
-        data = filtered
-        data /= data.max()
-
-        result = utils.find_steps(data, 0.1)
-        return [(dataframe.index[x], dataframe.index[x + window_size]) for x in result]

analytics/models/reverse_peak_model.py

@@ -0,0 +1,112 @@
+from models import Model, AnalyticUnitCache
+
+import scipy.signal
+from scipy.fftpack import fft
+from scipy.signal import argrelextrema
+
+import utils
+import numpy as np
+import pandas as pd
+from typing import Optional
+
+WINDOW_SIZE = 240
+
+class ReversePeakModel(Model):
+
+    def __init__(self):
+        super()
+        self.segments = []
+        self.ipeaks = []
+        self.state = {
+            'confidence': 1.5,
+            'convolve_max': 570000
+        }
+
+    def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
+        if type(cache) is AnalyticUnitCache:
+            self.state = cache
+
+        self.segments = segments 
+        data = dataframe['value']
+    
+        confidences = []
+        convolve_list = []
+        for segment in segments:
+            if segment['labeled']:
+                segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from']))
+                segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to']))
+
+                segment_data = data[segment_from_index: segment_to_index + 1]
+                segment_min = min(segment_data)
+                segment_max = max(segment_data)
+                confidences.append(0.2 * (segment_max - segment_min))
+                flat_segment = segment_data.rolling(window=5).mean()
+                flat_segment = flat_segment.dropna()
+                segment_min_index = flat_segment.idxmin() #+ segment['start']
+                self.ipeaks.append(segment_min_index)
+                labeled_drop = data[segment_min_index - WINDOW_SIZE : segment_min_index + WINDOW_SIZE]
+                labeled_min = min(labeled_drop)
+                for value in labeled_drop:
+                    value = value - labeled_min
+                convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
+                convolve_list.append(max(convolve))
+
+        if len(confidences) > 0:
+            self.state['confidence'] = min(confidences)
+        else:
+            self.state['confidence'] = 1.5
+
+        if len(convolve_list) > 0:
+            self.state['convolve_max'] = max(convolve_list)
+        else:
+            self.state['convolve_max'] = 570000
+        
+        return self.state
+
+    def do_predict(self, dataframe: pd.DataFrame):
+        data = dataframe['value']
+        window_size = 24
+        all_max_flatten_data = data.rolling(window=window_size).mean()
+        all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
+        
+        extrema_list = []
+        for i in utils.exponential_smoothing(data - self.state['confidence'], 0.02):
+            extrema_list.append(i)
+
+        segments = []
+        for i in all_mins:
+            if all_max_flatten_data[i] < extrema_list[i]:
+                segments.append(i + 12)
+        
+        filtered = self.__filter_prediction(segments, data)
+        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+
+    def __filter_prediction(self, segments: list, all_max_flatten_data: list):
+        delete_list = []
+        variance_error = int(0.004 * len(all_max_flatten_data))
+        if variance_error > 100:
+            variance_error = 100
+        for i in range(1, len(segments)):
+            if segments[i] < segments[i - 1] + variance_error:
+                delete_list.append(segments[i])
+        for item in delete_list:
+            segments.remove(item)
+
+        delete_list = []
+        if len(segments) == 0 or len(self.ipeaks) == 0 :
+            segments = []
+            return segments
+            
+        pattern_data = all_max_flatten_data[self.ipeaks[0] - WINDOW_SIZE : self.ipeaks[0] + WINDOW_SIZE]
+        for segment in segments:
+            if segment > WINDOW_SIZE:
+                convol_data = all_max_flatten_data[segment - WINDOW_SIZE : segment + WINDOW_SIZE]
+                conv = scipy.signal.fftconvolve(pattern_data, convol_data)
+                if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_max'] * 0.8:
+                    delete_list.append(segment)
+            else:
+                delete_list.append(segment)
+        for item in delete_list:
+            segments.remove(item)
+
+        return segments

analytics/utils/__init__.py

@@ -148,7 +148,8 @@ def find_jump_length(segment_data, min_line, max_line):
     if (idl[0] - idx[-1] + 1) > 0:
         return idl[0] - idx[-1] + 1
     else:
-        return print("retard alert!")
+        print("retard alert!")
+        return 0
     
 def find_jump(data, height, lenght):
     j_list = []
@@ -192,10 +193,10 @@ def drop_intersection(segment_data, median_line):
     idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0 
     return idx
 
-def find_drop(data, height, lenght):
+def find_drop(data, height, length):
     d_list = []
-    for i in range(len(data)-lenght-1):
+    for i in range(len(data)-length-1):
-        for x in range(1, lenght):
+        for x in range(1, length):
             if(data[i+x] < data[i] - height):
                 d_list.append(i+36)
     return(d_list)

server/src/controllers/analytics_controller.ts

@@ -112,6 +112,8 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId) {
     let oldCache = await AnalyticUnitCache.findById(id);
     if(oldCache !== null) {
       oldCache = oldCache.data;
+    } else {
+      await AnalyticUnitCache.create(id);
     }
     let task = new AnalyticsTask(
       id, AnalyticsTaskType.LEARN, { pattern, segments: segmentObjs, data, cache: oldCache }
@@ -121,7 +123,7 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId) {
     if(result.status !== AnalyticUnit.AnalyticUnitStatus.SUCCESS) {
       throw new Error(result.error)
     }
-    AnalyticUnitCache.setData(id, result.payload.cache);
+    await AnalyticUnitCache.setData(id, result.payload.cache);
   } catch (err) {
     let message = err.message || JSON.stringify(err);
     await AnalyticUnit.setStatus(id, AnalyticUnit.AnalyticUnitStatus.FAILED, message);
@@ -129,34 +131,6 @@ export async function runLearning(id: AnalyticUnit.AnalyticUnitId) {
 
 }
 
-function processPredictionResult(analyticUnitId: AnalyticUnit.AnalyticUnitId, taskResult: any): {
-  lastPredictionTime: number,
-  segments: Segment.Segment[],
-  cache: any
-} {
-  let payload = taskResult.payload;
-  if(payload === undefined) {
-    throw new Error(`Missing payload in result: ${taskResult}`);
-  }
-  if(payload.segments === undefined || !Array.isArray(payload.segments)) {
-    throw new Error(`Missing segments in result or it is corrupted: ${JSON.stringify(payload)}`);
-  }
-  if(payload.lastPredictionTime === undefined || isNaN(+payload.lastPredictionTime)) {
-    throw new Error(
-      `Missing lastPredictionTime is result or it is corrupted: ${JSON.stringify(payload)}`
-    );
-  }
-
-  let segments = payload.segments.map(segment => new Segment.Segment(analyticUnitId, segment.from, segment.to, false));
-
-  return {
-    lastPredictionTime: payload.lastPredictionTime,
-    segments: segments,
-    cache: {}
-  };
-
-}
-
 export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
   let previousLastPredictionTime: number = undefined;
 
@@ -166,20 +140,26 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
     let pattern = unit.type;
 
     let segments = await Segment.findMany(id, { labeled: true });
-    if (segments.length < 2) {
+    if(segments.length < 2) {
       throw new Error('Need at least 2 labeled segments');
     }
 
     let { from, to } = getQueryRangeForLearningBySegments(segments);
     let data = await queryByMetric(unit.metric, unit.panelUrl, from, to);
-    if (data.length === 0) {
+    if(data.length === 0) {
       throw new Error('Empty data to predict on');
     }
 
+    let oldCache = await AnalyticUnitCache.findById(id);
+    if(oldCache !== null) {
+      oldCache = oldCache.data;
+    } else {
+      await AnalyticUnitCache.create(id);
+    }
     let task = new AnalyticsTask(
       id,
       AnalyticsTaskType.PREDICT,
-      { pattern, lastPredictionTime: unit.lastPredictionTime, data, cache: {} }
+      { pattern, lastPredictionTime: unit.lastPredictionTime, data, cache: oldCache }
     );
     let result = await runTask(task);
     if(result.status === AnalyticUnit.AnalyticUnitStatus.FAILED) {
@@ -200,6 +180,7 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
     }
 
     Segment.insertSegments(payload.segments);
+    AnalyticUnitCache.setData(id, payload.cache);
     AnalyticUnit.setPredictionTime(id, payload.lastPredictionTime);
     AnalyticUnit.setStatus(id, AnalyticUnit.AnalyticUnitStatus.READY);
   } catch(err) {
@@ -211,6 +192,34 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
   }
 }
 
+function processPredictionResult(analyticUnitId: AnalyticUnit.AnalyticUnitId, taskResult: any): {
+  lastPredictionTime: number,
+  segments: Segment.Segment[],
+  cache: any
+} {
+  let payload = taskResult.payload;
+  if (payload === undefined) {
+    throw new Error(`Missing payload in result: ${taskResult}`);
+  }
+  if (payload.segments === undefined || !Array.isArray(payload.segments)) {
+    throw new Error(`Missing segments in result or it is corrupted: ${JSON.stringify(payload)}`);
+  }
+  if (payload.lastPredictionTime === undefined || isNaN(+payload.lastPredictionTime)) {
+    throw new Error(
+      `Missing lastPredictionTime is result or it is corrupted: ${JSON.stringify(payload)}`
+    );
+  }
+
+  let segments = payload.segments.map(segment => new Segment.Segment(analyticUnitId, segment.from, segment.to, false));
+
+  return {
+    lastPredictionTime: payload.lastPredictionTime,
+    segments: segments,
+    cache: payload.cache
+  };
+
+}
+
 export function isAnalyticReady(): boolean {
   return analyticsService.ready;
 }

server/src/models/analytic_unit_cache_model.ts

@@ -5,40 +5,32 @@ import { Collection, makeDBQ } from '../services/data_service';
 let db = makeDBQ(Collection.ANALYTIC_UNIT_CACHES);
 
 
-export type AnalyticUnitCacheId = string;
-
 export class AnalyticUnitCache {
   public constructor(
-    public analyticUnitId: AnalyticUnitId,
+    public id: AnalyticUnitId,
-    public data?: any,
+    public data?: any
-    public id?: AnalyticUnitCacheId,
   ) {
-    if(analyticUnitId === undefined) {
+    if(id === undefined) {
-      throw new Error(`Missing field "analyticUnitId"`);
+      throw new Error(`Missing field "id"`);
     }
   }
 
   public toObject() {
     return {
-      _id: this.id,
+      data: this.data || null,
-      analyticUnitId: this.analyticUnitId,
+      _id: this.id
-      data: this.data
     };
   }
 
   static fromObject(obj: any): AnalyticUnitCache {
-    if(obj.method === undefined) {
-      throw new Error('No method in obj:' + obj);
-    }
     return new AnalyticUnitCache(
-      obj.method,
+      obj._id,
       obj.data,
-      obj._id
     );
   }
 }
 
-export async function findById(id: AnalyticUnitCacheId): Promise<AnalyticUnitCache> {
+export async function findById(id: AnalyticUnitId): Promise<AnalyticUnitCache> {
   let obj = await db.findOne(id);
   if(obj === null) {
     return null;
@@ -46,15 +38,15 @@ export async function findById(id: AnalyticUnitCacheId): Promise<AnalyticUnitCac
   return AnalyticUnitCache.fromObject(obj);
 }
 
-export async function create(unit: AnalyticUnitCache): Promise<AnalyticUnitCacheId> {
+export async function create(id: AnalyticUnitId): Promise<AnalyticUnitId> {
-  let obj = unit.toObject();
+  let cache = new AnalyticUnitCache(id);
-  return db.insertOne(obj);
+  return db.insertOne(cache.toObject());
 }
 
-export async function setData(id: AnalyticUnitCacheId, data: any) {
+export async function setData(id: AnalyticUnitId, data: any) {
   return db.updateOne(id, { data });
 }
 
-export async function remove(id: AnalyticUnitCacheId): Promise<void> {
+export async function remove(id: AnalyticUnitId): Promise<void> {
   await db.removeOne(id);
 }