diff options
Diffstat (limited to 'resources/tools/presentation/utils.py')
| -rw-r--r-- | resources/tools/presentation/utils.py | 161 |
1 files changed, 127 insertions, 34 deletions
diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py index 8365bfad5c..0a9d985a88 100644 --- a/resources/tools/presentation/utils.py +++ b/resources/tools/presentation/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2017 Cisco and/or its affiliates. +# Copyright (c) 2018 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: @@ -14,6 +14,7 @@ """General purpose utilities. """ +import multiprocessing import subprocess import numpy as np import pandas as pd @@ -21,7 +22,7 @@ import logging from os import walk, makedirs, environ from os.path import join, isdir -from shutil import copy, Error +from shutil import move, Error from math import sqrt from errors import PresentationError @@ -68,58 +69,69 @@ def relative_change(nr1, nr2): return float(((nr2 - nr1) / nr1) * 100) -def remove_outliers(input_data, outlier_const): - """ +def remove_outliers(input_list, outlier_const=1.5, window=14): + """Return list with outliers removed, using split_outliers. - :param input_data: Data from which the outliers will be removed. + :param input_list: Data from which the outliers will be removed. :param outlier_const: Outlier constant. - :type input_data: list + :param window: How many preceding values to take into account. + :type input_list: list of floats :type outlier_const: float + :type window: int :returns: The input list without outliers. - :rtype: list + :rtype: list of floats """ - data = np.array(input_data) + data = np.array(input_list) upper_quartile = np.percentile(data, 75) lower_quartile = np.percentile(data, 25) iqr = (upper_quartile - lower_quartile) * outlier_const quartile_set = (lower_quartile - iqr, upper_quartile + iqr) result_lst = list() - for y in data.tolist(): + for y in input_list: if quartile_set[0] <= y <= quartile_set[1]: result_lst.append(y) return result_lst -def find_outliers(input_data, outlier_const=1.5): +def split_outliers(input_series, outlier_const=1.5, window=14): """Go through the input data and generate two pandas series: - - input data without outliers + - input data with outliers replaced by NAN - outliers. The function uses IQR to detect outliers. - :param input_data: Data to be examined for outliers. + :param input_series: Data to be examined for outliers. :param outlier_const: Outlier constant. - :type input_data: pandas.Series + :param window: How many preceding values to take into account. + :type input_series: pandas.Series :type outlier_const: float - :returns: Tuple: input data with outliers removed; Outliers. - :rtype: tuple (trimmed_data, outliers) + :type window: int + :returns: Input data with NAN outliers and Outliers. + :rtype: (pandas.Series, pandas.Series) """ - upper_quartile = input_data.quantile(q=0.75) - lower_quartile = input_data.quantile(q=0.25) - iqr = (upper_quartile - lower_quartile) * outlier_const - low = lower_quartile - iqr - high = upper_quartile + iqr + list_data = list(input_series.items()) + head_size = min(window, len(list_data)) + head_list = list_data[:head_size] trimmed_data = pd.Series() outliers = pd.Series() - for item in input_data.items(): - item_pd = pd.Series([item[1], ], index=[item[0], ]) - if low <= item[1] <= high: + for item_x, item_y in head_list: + item_pd = pd.Series([item_y, ], index=[item_x, ]) + trimmed_data = trimmed_data.append(item_pd) + for index, (item_x, item_y) in list(enumerate(list_data))[head_size:]: + y_rolling_list = [y for (x, y) in list_data[index - head_size:index]] + y_rolling_array = np.array(y_rolling_list) + q1 = np.percentile(y_rolling_array, 25) + q3 = np.percentile(y_rolling_array, 75) + iqr = (q3 - q1) * outlier_const + low = q1 - iqr + item_pd = pd.Series([item_y, ], index=[item_x, ]) + if low <= item_y: trimmed_data = trimmed_data.append(item_pd) else: - trimmed_data = trimmed_data.append(pd.Series([np.nan, ], - index=[item[0], ])) outliers = outliers.append(item_pd) + nan_pd = pd.Series([np.nan, ], index=[item_x, ]) + trimmed_data = trimmed_data.append(nan_pd) return trimmed_data, outliers @@ -129,7 +141,7 @@ def get_files(path, extension=None, full_path=True): :param path: Path to files. :param extension: Extension of files to process. If it is the empty string, - all files will be processed. + all files will be processed. :param full_path: If True, the files with full path are generated. :type path: str :type extension: str @@ -187,8 +199,10 @@ def execute_command(cmd): stdout, stderr = proc.communicate() - logging.info(stdout) - logging.info(stderr) + if stdout: + logging.info(stdout) + if stderr: + logging.info(stderr) if proc.returncode != 0: logging.error(" Command execution failed.") @@ -239,10 +253,7 @@ def archive_input_data(spec): logging.info(" Archiving the input data files ...") - if spec.is_debug: - extension = spec.debug["input-format"] - else: - extension = spec.input["file-format"] + extension = spec.input["file-format"] data_files = get_files(spec.environment["paths"]["DIR[WORKING,DATA]"], extension=extension) dst = spec.environment["paths"]["DIR[STATIC,ARCH]"] @@ -253,11 +264,93 @@ def archive_input_data(spec): makedirs(dst) for data_file in data_files: - logging.info(" Copying the file: {0} ...".format(data_file)) - copy(data_file, dst) + logging.info(" Moving the file: {0} ...".format(data_file)) + move(data_file, dst) except (Error, OSError) as err: raise PresentationError("Not possible to archive the input data.", str(err)) logging.info(" Done.") + + +def classify_anomalies(data, window): + """Evaluates if the sample value is an outlier, regression, normal or + progression compared to the previous data within the window. + We use the intervals defined as: + - regress: less than trimmed moving median - 3 * stdev + - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev + - progress: more than trimmed moving median + 3 * stdev + where stdev is trimmed moving standard deviation. + + :param data: Full data set with the outliers replaced by nan. + :param window: Window size used to calculate moving average and moving + stdev. + :type data: pandas.Series + :type window: int + :returns: Evaluated results. + :rtype: list + """ + + if data.size < 3: + return None + + win_size = data.size if data.size < window else window + tmm = data.rolling(window=win_size, min_periods=2).median() + tmstd = data.rolling(window=win_size, min_periods=2).std() + + classification = ["normal", ] + first = True + for build, value in data.iteritems(): + if first: + first = False + continue + if np.isnan(value) or np.isnan(tmm[build]) or np.isnan(tmstd[build]): + classification.append("outlier") + elif value < (tmm[build] - 3 * tmstd[build]): + classification.append("regression") + elif value > (tmm[build] + 3 * tmstd[build]): + classification.append("progression") + else: + classification.append("normal") + return classification + + +class Worker(multiprocessing.Process): + """Worker class used to process tasks in separate parallel processes. + """ + + def __init__(self, work_queue, data_queue, func): + """Initialization. + + :param work_queue: Queue with items to process. + :param data_queue: Shared memory between processes. Queue which keeps + the result data. This data is then read by the main process and used + in further processing. + :param func: Function which is executed by the worker. + :type work_queue: multiprocessing.JoinableQueue + :type data_queue: multiprocessing.Manager().Queue() + :type func: Callable object + """ + super(Worker, self).__init__() + self._work_queue = work_queue + self._data_queue = data_queue + self._func = func + + def run(self): + """Method representing the process's activity. + """ + + while True: + try: + self.process(self._work_queue.get()) + finally: + self._work_queue.task_done() + + def process(self, item_to_process): + """Method executed by the runner. + + :param item_to_process: Data to be processed by the function. + :type item_to_process: tuple + """ + self._func(self.pid, self._data_queue, *item_to_process) |