general predictor -> general model (#130)

analytics/analytic_unit_manager.py

@@ -12,11 +12,7 @@ analytic_workers: Dict[AnalyticUnitId, AnalyticUnitWorker] = dict()
 
 
 def get_detector_by_type(analytic_unit_type) -> detectors.Detector:
-    if analytic_unit_type == 'GENERAL':
+    return detectors.PatternDetector(analytic_unit_type)
-        detector = detectors.GeneralDetector()
-    else:
-        detector = detectors.PatternDetector(analytic_unit_type)
-    return detector
 
 def ensure_worker(analytic_unit_id, analytic_unit_type) -> AnalyticUnitWorker:
     if analytic_unit_id in analytic_workers:

analytics/detectors/__init__.py

@@ -1,3 +1,2 @@
 from detectors.detector import Detector
 from detectors.pattern_detector import PatternDetector
-from detectors.general_detector import GeneralDetector

analytics/detectors/general_detector/__init__.py

@@ -1 +0,0 @@
-from detectors.general_detector.general_detector import GeneralDetector

analytics/detectors/general_detector/general_detector.py

@@ -1,80 +0,0 @@
-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
-
-logger = logging.getLogger('GENERAL_DETECTOR')
-
-
-class GeneralDetector(Detector):
-
-    def __init__(self):
-        self.model = None
-
-    async def train(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
-
-        confidence = 0.02
-        start_index, stop_index = 0, len(dataframe)
-        if len(segments) > 0:
-            confidence = 0.0
-            min_time, max_time = utils.segments_box(segments)
-            dataframe = dataframe[dataframe['timestamp'] <= max_time]
-            dataframe = dataframe[dataframe['timestamp'] >= min_time]
-
-        train_augmented = self.preprocessor.get_augmented_data(
-            dataframe.index[0],
-            dataframe.index[-1],
-            segments
-        )
-
-        self.model = SupervisedAlgorithm()
-        await self.model.fit(train_augmented, confidence)
-        if len(segments) > 0:
-            last_dataframe_time = dataframe.iloc[-1]['timestamp']
-            last_prediction_time = int(last_dataframe_time.timestamp() * 1000)
-        else:
-            last_prediction_time = 0
-
-        logger.info("Learning is finished for anomaly_name='%s'" % self.anomaly_name)
-        return cache
-
-    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')
-
-        start_index = self.data_prov.get_upper_bound(last_prediction_time)
-        stop_index = self.data_prov.size()
-        last_prediction_time = int(last_prediction_time.value / NANOSECONDS_IN_MS)
-
-        predicted_anomalies = []
-        if start_index < stop_index:
-            max_chunk_size = 50000
-            predicted = pd.Series()
-            for index in range(start_index, stop_index, max_chunk_size):
-                chunk_start = index
-                chunk_finish = min(index + max_chunk_size, stop_index)
-                predict_augmented = self.preprocessor.get_augmented_data(chunk_start, chunk_finish)
-
-                assert(len(predict_augmented) == chunk_finish - chunk_start)
-
-                predicted_current = await self.model.predict(predict_augmented)
-                predicted = pd.concat([predicted, predicted_current])
-            predicted_anomalies = self.preprocessor.inverse_transform_anomalies(predicted)
-
-            last_row = self.data_prov.get_data_range(stop_index - 1, stop_index)
-
-            last_dataframe_time = last_row.iloc[0]['timestamp']
-            predicted_anomalies = utils.anomalies_to_timestamp(predicted_anomalies)
-            last_prediction_time = int(last_dataframe_time.timestamp() * 1000)
-
-        logger.info("Predicting is finished for anomaly type='%s'" % self.anomaly_name)
-        return predicted_anomalies, last_prediction_time

analytics/detectors/general_detector/supervised_algorithm.py

@@ -1,61 +0,0 @@
-import pickle
-from tsfresh.transformers.feature_selector import FeatureSelector
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.ensemble import IsolationForest
-import pandas as pd
-
-
-class SupervisedAlgorithm(object):
-    frame_size = 16
-    good_features = [
-        #"value__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\"",
-        # "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20",
-        # "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5",
-        # "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10",
-        # "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20",
-        # "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20",
-        # "value__fft_coefficient__coeff_3__attr_\"abs\"",
-        "time_of_day_column_x",
-        "time_of_day_column_y",
-        "value__abs_energy",
-        # "value__absolute_sum_of_changes",
-        # "value__sum_of_reoccurring_data_points",
-    ]
-    clf = None
-    scaler = None
-
-    def __init__(self):
-        self.features = []
-        self.col_to_max, self.col_to_min, self.col_to_median = None, None, None
-        self.augmented_path = None
-
-    async def fit(self, dataset, contamination=0.005):
-        dataset = dataset[self.good_features]
-        dataset = dataset[-100000:]
-
-        self.scaler = MinMaxScaler(feature_range=(-1, 1))
-        # self.clf = svm.OneClassSVM(nu=contamination, kernel="rbf", gamma=0.1)
-        self.clf = IsolationForest(contamination=contamination)
-
-        self.scaler.fit(dataset)
-
-        dataset = self.scaler.transform(dataset)
-        self.clf.fit(dataset)
-
-    async def predict(self, dataframe):
-        dataset = dataframe[self.good_features]
-        dataset = self.scaler.transform(dataset)
-        prediction = self.clf.predict(dataset)
-
-        # for i in range(len(dataset)):
-        #     print(str(dataset[i]) + " " + str(prediction[i]))
-
-        prediction = [x < 0.0 for x in prediction]
-        return pd.Series(prediction, index=dataframe.index)
-
-    def __select_features(self, x, y):
-        # feature_selector = FeatureSelector()
-        feature_selector = FeatureSelector()
-
-        feature_selector.fit(x, y)
-        return feature_selector.relevant_features

analytics/detectors/pattern_detector.py

@@ -13,6 +13,8 @@ logger = logging.getLogger('PATTERN_DETECTOR')
 
 
 def resolve_model_by_pattern(pattern: str) -> models.Model:
+    if pattern == 'GENERAL':
+        return models.GeneralModel()
     if pattern == 'PEAK':
         return models.PeakModel()
     if pattern == 'REVERSE_PEAK':

analytics/models/__init__.py

@@ -2,7 +2,6 @@ from models.model import Model, AnalyticUnitCache
 from models.drop_model import DropModel
 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
+from models.general_model import GeneralModel

analytics/models/general_model.py

@@ -0,0 +1,97 @@
+from models import Model, AnalyticUnitCache
+
+import utils
+import numpy as np
+import pandas as pd
+import scipy.signal
+from scipy.fftpack import fft
+from scipy.signal import argrelextrema
+import math
+from scipy.stats import gaussian_kde
+from scipy.stats import norm
+from typing import Optional
+
+
+WINDOW_SIZE = 350
+
+
+class GeneralModel(Model):
+
+    def __init__(self):
+        super()
+        self.segments = []
+        self.ipats = []
+        self.state = {
+            'convolve_max': WINDOW_SIZE,
+        }
+        self.all_conv = []
+
+    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']
+        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]
+                if len(segment_data) == 0:
+                    continue
+                self.ipats.append(segment_from_index + int((segment_to_index - segment_from_index) / 2))
+                segment_min = min(segment_data)
+                segment_data = segment_data - segment_min
+                segment_max = max(segment_data)
+                segment_data = segment_data / segment_max
+
+                convolve = scipy.signal.fftconvolve(segment_data, segment_data)
+                convolve_list.append(max(convolve))
+
+        if len(convolve_list) > 0:
+            self.state['convolve_max'] = float(max(convolve_list))
+        else:
+            self.state['convolve_max'] = WINDOW_SIZE / 3
+
+        return self.state
+
+    def do_predict(self, dataframe: pd.DataFrame):
+        data = dataframe['value']
+        pat_data = data[self.ipats[0] - WINDOW_SIZE: self.ipats[0] + WINDOW_SIZE]
+        x = min(pat_data)
+        pat_data = pat_data - x
+        y = max(pat_data)
+        pat_data = pat_data / y
+
+        for i in range(WINDOW_SIZE * 2, len(data)):
+            watch_data = data[i - WINDOW_SIZE * 2: i]
+            w = min(watch_data)
+            watch_data = watch_data - w
+            r = max(watch_data)
+            if r < y:
+                watch_data = watch_data / y
+            else:
+                watch_data = watch_data / r
+            conv = scipy.signal.fftconvolve(pat_data, watch_data)
+            self.all_conv.append(max(conv))
+        all_conv_peaks = utils.peak_finder(self.all_conv, WINDOW_SIZE * 2)
+
+        filtered = self.__filter_prediction(all_conv_peaks, data)
+        return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
+
+    def __filter_prediction(self, segments: list, data: list):
+        if len(segments) == 0 or len(self.ipats) == 0:
+            segments = []
+            return segments
+        delete_list = []
+
+        for val in segments:
+            if self.all_conv[val] < self.state['convolve_max'] * 0.8:
+                delete_list.append(val)
+
+        for item in delete_list:
+            segments.remove(item)
+
+        return segments

analytics/utils/__init__.py

@@ -207,3 +207,10 @@ def timestamp_to_index(dataframe, timestamp):
     for i in range(len(data)):
         if data[i] >= timestamp:
             return i
+
+def peak_finder(data, size):
+    all_max = []
+    for i in range(size, len(data) - size):
+        if data[i] == max(data[i - size: i + size]) and data[i] > data[i + 1]:
+            all_max.append(i)
+    return all_max