detectors cleanup & jump_detector integration

analytics/analytic_unit_worker.py

@@ -81,6 +81,6 @@ class AnalyticUnitWorker(object):
             if pattern_type == 'general':
                 model = detectors.GeneralDetector(analytic_unit_id)
             else:
-                model = detectors.PatternDetectionModel(analytic_unit_id, pattern_type)
+                model = detectors.PatternDetector(analytic_unit_id, pattern_type)
             self.models_cache[analytic_unit_id] = model
         return self.models_cache[analytic_unit_id]

analytics/detectors/__init__.py

@@ -1,5 +1,5 @@
 from detectors.general_detector import GeneralDetector
-from detectors.pattern_detection_model import PatternDetectionModel
+from detectors.pattern_detector import PatternDetector
 from detectors.peaks_detector import PeaksDetector
 from detectors.step_detector import StepDetector
 from detectors.jump_detector import Jumpdetector

analytics/detectors/general_detector.py

@@ -75,7 +75,7 @@ class GeneralDetector:
         )
 
         self.model = self.create_algorithm()
-        self.model.fit(train_augmented, confidence)
+        await self.model.fit(train_augmented, confidence)
         if len(anomalies) > 0:
             last_dataframe_time = dataframe.iloc[-1]['timestamp']
             last_prediction_time = int(last_dataframe_time.timestamp() * 1000)

analytics/detectors/jump_detector.py

@@ -5,6 +5,7 @@ from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 import math
 
+
 def is_intersect(target_segment, segments):
     for segment in segments:
         start = max(segment['start'], target_segment[0])
@@ -21,8 +22,7 @@ def exponential_smoothing(series, alpha):
 
 class Jumpdetector:
 
-    def __init__(self, pattern):
+    def __init__(self):
-        self.pattern = pattern
         self.segments = []
         self.confidence = 1.5
         self.convolve_max = 120
@@ -30,11 +30,11 @@ class Jumpdetector:
     def intersection_segment(self, data, median):
         cen_ind = []
         for i in range(1, len(data)-1):
-            if data[i-1] < median and data[i+1] > median:
+            if data[i - 1] < median and data[i + 1] > median:
                 cen_ind.append(i)
         del_ind = []
         for i in range(1,len(cen_ind)):
-            if cen_ind[i] == cen_ind[i-1]+1:
+            if cen_ind[i] == cen_ind[i - 1] + 1:
                 del_ind.append(i - 1)
         del_ind = del_ind[::-1]
         for i in del_ind:
@@ -47,71 +47,75 @@ class Jumpdetector:
             F = 1 * height / (1 + math.exp(-i * alpha))
             distribution.append(F)
         return distribution
-    def alpha_finder(self, data, ):
+    
-        # поиск альфы для логистической сигмоиды
+    def alpha_finder(self, data):
-
+        """
-
+        поиск альфы для логистической сигмоиды
-    def fit(self, dataframe, segments):
+        """
-            data = dataframe['value']
+        pass
-            confidences = []
+
-            convolve_list = []
+
-            for segment in segments:
+    async def fit(self, dataframe, segments):
-                if segment['labeled']:
+        data = dataframe['value']
-                    segment_data = data[segment['start'] : segment['finish'] + 1]
+        confidences = []
-                    segment_min = min(segment_data)
+        convolve_list = []
-                    segment_max = max(segment_data)
+        for segment in segments:
-                    confidences.append(0.20 * (segment_max - segment_min))
+            if segment['labeled']:
-                    flat_segment = segment_data.rolling(window=4).mean() #сглаживаем сегмент
+                segment_data = data[segment['start'] : segment['finish'] + 1]
-                    kde_segment = flat_data.dropna().plot.kde() # distribution density
+                segment_min = min(segment_data)
-                    ax = flat_data.dropna().plot.kde()
+                segment_max = max(segment_data)
-                    ax_list = ax.get_lines()[0].get_xydata()
+                confidences.append(0.20 * (segment_max - segment_min))
-                    mids = argrelextrema(np.array(ax_list), np.less)[0]
+                flat_segment = segment_data.rolling(window=4).mean() #сглаживаем сегмент
-                    maxs = argrelextrema(np.array(ax_list), np.greater)[0]
+                kde_segment = flat_data.dropna().plot.kde() # distribution density
-                    min_peak = maxs[0]
+                ax = flat_data.dropna().plot.kde()
-                    max_peak = maxs[1]
+                ax_list = ax.get_lines()[0].get_xydata()
-                    min_line = ax_list[min_peak, 0]
+                mids = argrelextrema(np.array(ax_list), np.less)[0]
-                    max_line = ax_list[max_peak, 0]
+                maxs = argrelextrema(np.array(ax_list), np.greater)[0]
-                    sigm_heidht = max_line - min_line
+                min_peak = maxs[0]
-                    pat_sigm = logistic_sigmoid(-120, 120, 1, sigm_heidht)
+                max_peak = maxs[1]
-                    for i in range(0, len(pat_sigm)):
+                min_line = ax_list[min_peak, 0]
-                        pat_sigm[i] = pat_sigm[i] + min_line 
+                max_line = ax_list[max_peak, 0]
-                    cen_ind = self.intersection_segment(flat_segment, mids[0])
+                sigm_heidht = max_line - min_line
-                    c = []
+                pat_sigm = logistic_sigmoid(-120, 120, 1, sigm_heidht)
-                    for i in range(len(cen_ind)):
+                for i in range(0, len(pat_sigm)):
-                        x = cen_ind[i]
+                    pat_sigm[i] = pat_sigm[i] + min_line 
-                        cx = scipy.signal.fftconvolve(pat_sigm, flat_data[x-120:x+120])
+                cen_ind = self.intersection_segment(flat_segment, mids[0])
-                        c.append(cx[240])
+                c = []
-                    
+                for i in range(len(cen_ind)):
-                    # в идеале нужно посмотреть гистограмму сегмента и выбрать среднее значение,
+                    x = cen_ind[i]
-                    # далее от него брать + -120 
+                    cx = scipy.signal.fftconvolve(pat_sigm, flat_data[x-120:x+120])
-                    segment_summ = 0
+                    c.append(cx[240])
-                    for val in flat_segment:
+                
-                        segment_summ += val
+                # в идеале нужно посмотреть гистограмму сегмента и выбрать среднее значение,
-                    segment_mid = segment_summ /  len(flat_segment) #посчитать нормально среднее значение/медиану
+                # далее от него брать + -120 
-                    for ind in range(1, len(flat_segment) - 1):
+                segment_summ = 0
-                        if flat_segment[ind + 1] > segment_mid and flat_segment[ind - 1] < segment_mid:
+                for val in flat_segment:
-                            flat_mid_index = ind   # найти пересечение средней и графика, получить его индекс
+                    segment_summ += val
-                    segment_mid_index = flat_mid_index - 5
+                segment_mid = segment_summ /  len(flat_segment) #посчитать нормально среднее значение/медиану
-                    labeled_drop = data[segment_mid_index - 120 : segment_mid_index + 120]
+                for ind in range(1, len(flat_segment) - 1):
-                    labeled_min = min(labeled_drop) 
+                    if flat_segment[ind + 1] > segment_mid and flat_segment[ind - 1] < segment_mid:
-                    for value in labeled_drop: # обрезаем
+                        flat_mid_index = ind   # найти пересечение средней и графика, получить его индекс
-                        value = value - labeled_min
+                segment_mid_index = flat_mid_index - 5
-                    labeled_max = max(labeled_drop)
+                labeled_drop = data[segment_mid_index - 120 : segment_mid_index + 120]
-                    for value in labeled_drop: # нормируем
+                labeled_min = min(labeled_drop) 
-                        value = value / labeled_max
+                for value in labeled_drop: # обрезаем
-                    convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
+                    value = value - labeled_min
-                    convolve_list.append(max(convolve)) # сворачиваем паттерн
+                labeled_max = max(labeled_drop)
-                    # плюс надо впихнуть сюда логистическую сигмоиду и поиск альфы
+                for value in labeled_drop: # нормируем
-
+                    value = value / labeled_max
-            if len(confidences) > 0:
+                convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
-                self.confidence = min(confidences)
+                convolve_list.append(max(convolve)) # сворачиваем паттерн
-            else:
+                # плюс надо впихнуть сюда логистическую сигмоиду и поиск альфы
-                self.confidence = 1.5
+
-
+        if len(confidences) > 0:
-            if len(convolve_list) > 0:
+            self.confidence = min(confidences)
-                self.convolve_max = max(convolve_list)
+        else:
-            else:
+            self.confidence = 1.5
-                self.convolve_max = 120 # макс метрика свертки равна отступу(120), вау!
+
+        if len(convolve_list) > 0:
+            self.convolve_max = max(convolve_list)
+        else:
+            self.convolve_max = 120 # макс метрика свертки равна отступу(120), вау!
         
     def logistic_sigmoid(x1, x2, alpha, height):
         distribution = []
@@ -131,20 +135,20 @@ class Jumpdetector:
         return result
 
     def __predict(self, data):
-            window_size = 24
+        window_size = 24
-            all_max_flatten_data = data.rolling(window=window_size).mean()
+        all_max_flatten_data = data.rolling(window=window_size).mean()
-            extrema_list = []
+        extrema_list = []
-            # добавить все пересечения экспоненты со сглаженным графиком
+        # добавить все пересечения экспоненты со сглаженным графиком
-            
+        
-            for i in exponential_smoothing(data + self.confidence, 0.02):
+        for i in exponential_smoothing(data + self.confidence, 0.02):
-                extrema_list.append(i)
+            extrema_list.append(i)
 
-            segments = []
+        segments = []
-            for i in all_mins:
+        for i in all_mins:
-                if all_max_flatten_data[i] > extrema_list[i]:
+            if all_max_flatten_data[i] > extrema_list[i]:
-                    segments.append(i - window_size)
+                segments.append(i - window_size)
 
-            return [(x - 1, x + 1) for x in self.__filter_prediction(segments, all_max_flatten_data)]
+        return [(x - 1, x + 1) for x in self.__filter_prediction(segments, all_max_flatten_data)]
 
     def __filter_prediction(self, segments, all_max_flatten_data):
         delete_list = []
@@ -179,4 +183,4 @@ class Jumpdetector:
             with open(model_filename, 'rb') as file:
                 (self.confidence, self.convolve_max) = pickle.load(file)
         except:
-            pass
+            pass

analytics/detectors/pattern_detector.py

@@ -1,5 +1,4 @@
-from detectors.step_detector import StepDetector
+import detectors
-from detectors.peaks_detector import PeaksDetector
 
 from grafana_data_provider import GrafanaDataProvider
 
@@ -25,8 +24,17 @@ def segments_box(segments):
     max_time = pd.to_datetime(max_time, unit='ms')
     return min_time, max_time
 
+def resolve_detector_by_pattern(pattern):
+    if pattern == "peak":
+        return detectors.PeaksDetector()
+    if pattern == "drop":
+        return detectors.StepDetector()
+    if pattern == "jump":
+        return detectors.Jumpdetector()
+    raise ValueError('Unknown pattern "%s"' % pattern)
 
-class PatternDetectionModel:
+
+class PatternDetector:
 
     def __init__(self, analytic_unit_id, pattern_type):
         self.analytic_unit_id = analytic_unit_id
@@ -53,14 +61,14 @@ class PatternDetectionModel:
         self.__load_model(pattern_type)
 
     async def learn(self, segments):
-        self.model = self.__create_model(self.pattern_type)
+        self.model = resolve_detector_by_pattern(self.pattern_type)
         window_size = 200
 
         dataframe = self.data_prov.get_dataframe()
 
         segments = self.data_prov.transform_anomalies(segments)
         # TODO: pass only part of dataframe that has segments
-        self.model.fit(dataframe, segments)
+        await self.model.fit(dataframe, segments)
         self.__save_model()
         return 0
 
@@ -88,7 +96,7 @@ class PatternDetectionModel:
                 'finish': max(ts1, ts2)
             })
 
-        last_dataframe_time = dataframe.iloc[- 1]['timestamp']
+        last_dataframe_time = dataframe.iloc[-1]['timestamp']
         last_prediction_time = int(last_dataframe_time.timestamp() * 1000)
         return segments, last_prediction_time
         # return predicted_anomalies, last_prediction_time
@@ -96,13 +104,6 @@ class PatternDetectionModel:
     def synchronize_data(self):
         self.data_prov.synchronize()
 
-    def __create_model(self, pattern):
-        if pattern == "peak":
-            return PeaksDetector()
-        if pattern == "jump" or pattern == "drop":
-            return StepDetector(pattern)
-        raise ValueError('Unknown pattern "%s"' % pattern)
-
     def __load_anomaly_config(self):
         with open(os.path.join(config.ANALYTIC_UNITS_FOLDER, self.analytic_unit_id + ".json"), 'r') as config_file:
             self.anomaly_config = json.load(config_file)
@@ -116,5 +117,5 @@ class PatternDetectionModel:
         logger.info("Load model '%s'" % self.analytic_unit_id)
         model_filename = os.path.join(config.MODELS_FOLDER, self.pattern_type + ".m")
         if os.path.exists(model_filename):
-            self.model = self.__create_model(pattern)
+            self.model = resolve_detector_by_pattern(pattern)
             self.model.load(model_filename)

analytics/detectors/peaks_detector.py

@@ -1,14 +1,42 @@
-import detectors.step_detect
-
 from scipy import signal
 import numpy as np
 
 
+def find_steps(array, threshold):
+    """
+    Finds local maxima by segmenting array based on positions at which
+    the threshold value is crossed. Note that this thresholding is
+    applied after the absolute value of the array is taken. Thus,
+    the distinction between upward and downward steps is lost. However,
+    get_step_sizes can be used to determine directionality after the
+    fact.
+    Parameters
+    ----------
+    array : numpy array
+        1 dimensional array that represents time series of data points
+    threshold : int / float
+        Threshold value that defines a step
+    Returns
+    -------
+    steps : list
+        List of indices of the detected steps
+    """
+    steps        = []
+    array        = np.abs(array)
+    above_points = np.where(array > threshold, 1, 0)
+    ap_dif       = np.diff(above_points)
+    cross_ups    = np.where(ap_dif == 1)[0]
+    cross_dns    = np.where(ap_dif == -1)[0]
+    for upi, dni in zip(cross_ups,cross_dns):
+        steps.append(np.argmax(array[upi:dni]) + upi)
+    return steps
+
+
 class PeaksDetector:
     def __init__(self):
         pass
 
-    def fit(self, dataset, contamination=0.005):
+    async def fit(self, dataset, contamination=0.005):
         pass
 
     async def predict(self, dataframe):
@@ -52,7 +80,7 @@ class PeaksDetector:
         data = filtered
         data /= data.max()
 
-        result = step_detect.find_steps(data, 0.1)
+        result = find_steps(data, 0.1)
         return [(dataframe.index[x], dataframe.index[x + window_size]) for x in result]
 
     def save(self, model_filename):

analytics/detectors/step_detect.py

@@ -1,231 +0,0 @@
-
-"""
-Thomas Kahn
-thomas.b.kahn@gmail.com
-"""
-from __future__ import absolute_import
-from math import sqrt
-import multiprocessing as mp
-import numpy as np
-from six.moves import range
-from six.moves import zip
-
-
-def t_scan(L, window = 1e3, num_workers = -1):
-    """
-    Computes t statistic for i to i+window points versus i-window to i
-    points for each point i in input array. Uses multiple processes to
-    do this calculation asynchronously. Array is decomposed into window
-    number of frames, each consisting of points spaced at window
-    intervals. This optimizes the calculation, as the drone function
-    need only compute the mean and variance for each set once.
-    Parameters
-    ----------
-    L : numpy array
-        1 dimensional array that represents time series of datapoints
-    window : int / float
-        Number of points that comprise the windows of data that are
-        compared
-    num_workers : int
-        Number of worker processes for multithreaded t_stat computation
-        Defult value uses num_cpu - 1 workers
-    Returns
-    -------
-    t_stat : numpy array
-        Array which holds t statistic values for each point. The first
-        and last (window) points are replaced with zero, since the t
-        statistic calculation cannot be performed in that case.
-    """
-    size    = L.size
-    window  = int(window)
-    frames  = list(range(window))
-    n_cols  = (size // window) - 1
-
-    t_stat  = np.zeros((window, n_cols))
-
-    if num_workers == 1:
-        results = [_t_scan_drone(L, n_cols, frame, window) for frame in frames]
-    else:
-        if num_workers == -1:
-            num_workers = mp.cpu_count() - 1
-        pool    = mp.Pool(processes = num_workers)
-        results = [pool.apply_async(_t_scan_drone, args=(L, n_cols, frame, window)) for frame in frames]
-        results = [r.get() for r in results]
-        pool.close()
-
-    for index, row in results:
-        t_stat[index] = row
-
-    t_stat  = np.concatenate((
-        np.zeros(window),
-        t_stat.transpose().ravel(order='C'),
-        np.zeros(size % window)
-    ))
-
-    return t_stat
-
-
-def _t_scan_drone(L, n_cols, frame, window=1e3):
-    """
-    Drone function for t_scan. Not Intended to be called manually.
-    Computes t_scan for the designated frame, and returns result as
-    array along with an integer tag for proper placement in the
-    aggregate array
-    """
-    size   = L.size
-    window = int(window)
-    root_n = sqrt(window)
-
-    output = np.zeros(n_cols)
-    b      = L[frame:window+frame]
-    b_mean = b.mean()
-    b_var  = b.var()
-    for i in range(window+frame, size-window, window):
-        a = L[i:i+window]
-        a_mean = a.mean()
-        a_var  = a.var()
-        output[i // window - 1] = root_n * (a_mean - b_mean) / sqrt(a_var + b_var)
-        b_mean, b_var = a_mean, a_var
-
-    return frame, output
-
-
-def mz_fwt(x, n=2):
-    """
-    Computes the multiscale product of the Mallat-Zhong discrete forward
-    wavelet transform up to and including scale n for the input data x.
-    If n is even, the spikes in the signal will be positive. If n is odd
-    the spikes will match the polarity of the step (positive for steps
-    up, negative for steps down).
-    This function is essentially a direct translation of the MATLAB code
-    provided by Sadler and Swami in section A.4 of the following:
-    http://www.dtic.mil/dtic/tr/fulltext/u2/a351960.pdf
-    Parameters
-    ----------
-    x : numpy array
-        1 dimensional array that represents time series of data points
-    n : int
-        Highest scale to multiply to
-    Returns
-    -------
-    prod : numpy array
-        The multiscale product for x
-    """
-    N_pnts   = x.size
-    lambda_j = [1.5, 1.12, 1.03, 1.01][0:n]
-    if n > 4:
-        lambda_j += [1.0]*(n-4)
-
-    H = np.array([0.125, 0.375, 0.375, 0.125])
-    G = np.array([2.0, -2.0])
-
-    Gn = [2]
-    Hn = [3]
-    for j in range(1,n):
-        q = 2**(j-1)
-        Gn.append(q+1)
-        Hn.append(3*q+1)
-
-    S    = np.concatenate((x[::-1], x))
-    S    = np.concatenate((S, x[::-1]))
-    prod = np.ones(N_pnts)
-    for j in range(n):
-        n_zeros = 2**j - 1
-        Gz      = _insert_zeros(G, n_zeros)
-        Hz      = _insert_zeros(H, n_zeros)
-        current = (1.0/lambda_j[j])*np.convolve(S,Gz)
-        current = current[N_pnts+Gn[j]:2*N_pnts+Gn[j]]
-        prod    *= current
-        if j == n-1:
-            break
-        S_new   = np.convolve(S, Hz)
-        S_new   = S_new[N_pnts+Hn[j]:2*N_pnts+Hn[j]]
-        S       = np.concatenate((S_new[::-1], S_new))
-        S       = np.concatenate((S, S_new[::-1]))
-    return prod
-
-
-def _insert_zeros(x, n):
-    """
-    Helper function for mz_fwt. Splits input array and adds n zeros
-    between values.
-    """
-    newlen       = (n+1)*x.size
-    out          = np.zeros(newlen)
-    indices      = list(range(0, newlen-n, n+1))
-    out[indices] = x
-    return out
-
-
-def find_steps(array, threshold):
-    """
-    Finds local maxima by segmenting array based on positions at which
-    the threshold value is crossed. Note that this thresholding is
-    applied after the absolute value of the array is taken. Thus,
-    the distinction between upward and downward steps is lost. However,
-    get_step_sizes can be used to determine directionality after the
-    fact.
-    Parameters
-    ----------
-    array : numpy array
-        1 dimensional array that represents time series of data points
-    threshold : int / float
-        Threshold value that defines a step
-    Returns
-    -------
-    steps : list
-        List of indices of the detected steps
-    """
-    steps        = []
-    array        = np.abs(array)
-    above_points = np.where(array > threshold, 1, 0)
-    ap_dif       = np.diff(above_points)
-    cross_ups    = np.where(ap_dif == 1)[0]
-    cross_dns    = np.where(ap_dif == -1)[0]
-    for upi, dni in zip(cross_ups,cross_dns):
-        steps.append(np.argmax(array[upi:dni]) + upi)
-    return steps
-
-
-def get_step_sizes(array, indices, window=1000):
-    """
-    Calculates step size for each index within the supplied list. Step
-    size is determined by averaging over a range of points (specified
-    by the window parameter) before and after the index of step
-    occurrence. The directionality of the step is reflected by the sign
-    of the step size (i.e. a positive value indicates an upward step,
-    and a negative value indicates a downward step). The combined
-    standard deviation of both measurements (as a measure of uncertainty
-    in step calculation) is also provided.
-    Parameters
-    ----------
-    array : numpy array
-        1 dimensional array that represents time series of data points
-    indices : list
-        List of indices of the detected steps (as provided by
-        find_steps, for example)
-    window : int, optional
-        Number of points to average over to determine baseline levels
-        before and after step.
-    Returns
-    -------
-    step_sizes : list
-        List of the calculated sizes of each step
-    step_error : list
-    """
-    step_sizes = []
-    step_error = []
-    indices    = sorted(indices)
-    last       = len(indices) - 1
-    for i, index in enumerate(indices):
-        if i == 0:
-            q = min(window, indices[i+1]-index)
-        elif i == last:
-            q = min(window, index - indices[i-1])
-        else:
-            q = min(window, index-indices[i-1], indices[i+1]-index)
-        a = array[index:index+q]
-        b = array[index-q:index]
-        step_sizes.append(a.mean() - b.mean())
-        step_error.append(sqrt(a.var()+b.var()))
-    return step_sizes, step_error

analytics/detectors/step_detector.py

@@ -23,13 +23,12 @@ def exponential_smoothing(series, alpha):
 
 class StepDetector:
 
-    def __init__(self, pattern):
+    def __init__(self):
-        self.pattern = pattern
         self.segments = []
         self.confidence = 1.5
         self.convolve_max = 570000
 
-    def fit(self, dataframe, segments):
+    async def fit(self, dataframe, segments):
         data = dataframe['value']
         confidences = []
         convolve_list = []

analytics/supervised_algorithm.py

@@ -31,7 +31,7 @@ class supervised_algorithm(object):
         self.col_to_max, self.col_to_min, self.col_to_median = None, None, None
         self.augmented_path = None
 
-    def fit(self, dataset, contamination=0.005):
+    async def fit(self, dataset, contamination=0.005):
         dataset = dataset[self.good_features]
         dataset = dataset[-100000:]