path: root/bin/Proof_Of_Concept_PELT.py

import matplotlib.pyplot as plt
import json
from kneed import KneeLocator
import ruptures as rpt
import time
from multiprocessing import Pool, Manager
import numpy as np
import sys
import getopt
import re
from dfatool.dfatool import RawData

from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage

# py bin\Proof_Of_Concept_PELT.py --filename="..\data\TX.json" --jump=1 --pen_override=10 --refinement_thresh=100


def plot_data_from_json(filename, trace_num, x_axis, y_axis):
    with open(filename, 'r') as f:
        tx_data = json.load(f)
    print(tx_data[trace_num]['parameter'])
    plt.plot(tx_data[trace_num]['offline'][0]['uW'])
    plt.xlabel(x_axis)
    plt.ylabel(y_axis)
    plt.show()


def plot_data_vs_mean(signal, x_axis, y_axis):
    plt.plot(signal)
    average = np.mean(signal)
    plt.hlines(average, 0, len(signal))
    plt.xlabel(x_axis)
    plt.ylabel(y_axis)
    plt.show()


def plot_data_vs_data_vs_means(signal1, signal2, x_axis, y_axis):
    plt.plot(signal1)
    lens = max(len(signal1), len(signal2))
    average = np.mean(signal1)
    plt.hlines(average, 0, lens, color='red')
    plt.vlines(len(signal1), 0, 100000, color='red', linestyles='dashed')
    plt.plot(signal2)
    average = np.mean(signal2)
    plt.hlines(average, 0, lens, color='green')
    plt.vlines(len(signal2), 0, 100000, color='green', linestyles='dashed')
    plt.xlabel(x_axis)
    plt.ylabel(y_axis)
    plt.show()


def get_bkps(algo, pen, q):
    res = pen, len(algo.predict(pen=pen))
    q.put(pen)
    return res


def find_knee_point(data_x, data_y, S=1.0, curve='convex', direction='decreasing', plotting=False):
    kneedle = KneeLocator(data_x, data_y, S=S, curve=curve, direction=direction)
    if plotting:
        kneedle.plot_knee()
    kneepoint = (kneedle.knee, kneedle.knee_y)
    return kneepoint


def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=0, range_max=50, num_processes=8, refresh_delay=1,
              refresh_thresh=5, S=1.0, pen_override=None, pen_modifier=None, plotting=False):
    # default params in Function
    if model is None:
        model = 'l1'
    if jump is None:
        jump = 5
    if min_dist is None:
        min_dist = 2
    if range_min is None:
        range_min = 0
    if range_max is None:
        range_max = 50
    if num_processes is None:
        num_processes = 8
    if refresh_delay is None:
        refresh_delay = 1
    if refresh_thresh is None:
        refresh_thresh = 5
    if S is None:
        S = 1.0
    if plotting is None:
        plotting = False
    if pen_modifier is None:
        pen_modifier = 1
    # change point detection. best fit seemingly with l1. rbf prods. RuntimeErr for pen > 30
    # https://ctruong.perso.math.cnrs.fr/ruptures-docs/build/html/costs/index.html
    # model = "l1"   #"l1"  # "l2", "rbf"
    algo = rpt.Pelt(model=model, jump=jump, min_size=min_dist).fit(signal)

    ### CALC BKPS WITH DIFF PENALTYS
    if pen_override is None and range_max != range_min:
        # building args array for parallelizing
        args = []
        # for displaying progression
        m = Manager()
        q = m.Queue()

        for i in range(range_min, range_max + 1):
            args.append((algo, i, q))

        print_info('starting kneepoint calculation.')
        # init Pool with num_proesses
        with Pool(num_processes) as p:
            # collect results from pool
            result = p.starmap_async(get_bkps, args)
            # monitor loop
            percentage = -100  # Force display of 0%
            i = 0
            while True:
                if result.ready():
                    break
                else:
                    size = q.qsize()
                    last_percentage = percentage
                    percentage = round(size / (range_max - range_min) * 100, 2)
                    if percentage >= last_percentage + 2 or i >= refresh_thresh:
                        print_info('Current progress: ' + str(percentage) + '%')
                        i = 0
                    else:
                        i += 1
                    time.sleep(refresh_delay)
            res = result.get()
        print_info("Finished kneepoint calculation.")
        # DECIDE WHICH PENALTY VALUE TO CHOOSE ACCORDING TO ELBOW/KNEE APPROACH
        # split x and y coords to pass to kneedle
        pen_val = [x[0] for x in res]
        fitted_bkps_val = [x[1] for x in res]
        # # plot to look at res
        knee = find_knee_point(pen_val, fitted_bkps_val, S=S, plotting=plotting)
        # plt.xlabel('Penalty')
        # plt.ylabel('Number of Changepoints')
        # plt.plot(pen_val, fitted_bkps_val)
        # plt.vlines(knee[0], 0, max(fitted_bkps_val), linestyles='dashed')
        # print("knee: " + str(knee[0]))
        # plt.show()
        # modify knee according to options. Defaults to 1 * knee
        knee = (knee[0] * pen_modifier, knee[1])
    else:
        # use forced pen value for plotting if specified. Else use only pen in range
        if pen_override is not None:
            knee = (pen_override, None)
        else:
            knee = (range_min, None)
    print_info("" + str(knee[0]) + " has been selected as kneepoint.")
    # plt.plot(pen_val, fittet_bkps_val)
    if knee[0] is not None:
        bkps = algo.predict(pen=knee[0])
        if plotting:
            fig, ax = rpt.display(signal, bkps)
            plt.show()
        return bkps
    else:
        print_error('With the current thresh-hold S=' + str(S) + ' it is not possible to select a penalty value.')
        exit()


# very short benchmark yielded approx. 1/3 of speed compared to solution with sorting
def needs_refinement_no_sort(signal, mean, thresh):
    # linear search for the top 10%/ bottom 10%
    # should be sufficient
    length_of_signal = len(signal)
    percentile_size = int()
    percentile_size = length_of_signal // 100
    upper_percentile = [None] * percentile_size
    lower_percentile = [None] * percentile_size
    fill_index_upper = percentile_size - 1
    fill_index_lower = percentile_size - 1
    index_smallest_val = fill_index_upper
    index_largest_val = fill_index_lower

    for x in signal:
        if x > mean:
            # will be in upper percentile
            if fill_index_upper >= 0:
                upper_percentile[fill_index_upper] = x
                if x < upper_percentile[index_smallest_val]:
                    index_smallest_val = fill_index_upper
                fill_index_upper = fill_index_upper - 1
                continue

            if x > upper_percentile[index_smallest_val]:
                # replace smallest val. Find next smallest val
                upper_percentile[index_smallest_val] = x
                index_smallest_val = 0
                i = 0
                for y in upper_percentile:
                    if upper_percentile[i] < upper_percentile[index_smallest_val]:
                        index_smallest_val = i
                    i = i + 1

        else:
            if fill_index_lower >= 0:
                lower_percentile[fill_index_lower] = x
                if x > lower_percentile[index_largest_val]:
                    index_largest_val = fill_index_upper
                fill_index_lower = fill_index_lower - 1
                continue
            if x < lower_percentile[index_largest_val]:
                # replace smallest val. Find next smallest val
                lower_percentile[index_largest_val] = x
                index_largest_val = 0
                i = 0
                for y in lower_percentile:
                    if lower_percentile[i] > lower_percentile[index_largest_val]:
                        index_largest_val = i
                    i = i + 1

    # should have the percentiles
    lower_percentile_mean = np.mean(lower_percentile)
    upper_percentile_mean = np.mean(upper_percentile)
    dist = mean - lower_percentile_mean
    if dist > thresh:
        return True
    dist = upper_percentile_mean - mean
    if dist > thresh:
        return True
    return False


# Very short benchmark yielded approx. 3 times the speed of solution not using sort
# TODO: Decide whether median is really the better baseline than mean
def needs_refinement(signal, thresh):
    sorted_signal = sorted(signal)
    length_of_signal = len(signal)
    percentile_size = int()
    percentile_size = length_of_signal // 100
    lower_percentile = sorted_signal[0:percentile_size]
    upper_percentile = sorted_signal[length_of_signal - percentile_size: length_of_signal]
    lower_percentile_mean = np.mean(lower_percentile)
    upper_percentile_mean = np.mean(upper_percentile)
    median = np.median(sorted_signal)
    dist = median - lower_percentile_mean
    if dist > thresh:
        return True
    dist = upper_percentile_mean - median
    if dist > thresh:
        return True
    return False


def print_info(str):
    str_lst = str.split(sep='\n')
    for str in str_lst:
        print("[INFO]" + str)


def print_warning(str):
    str_lst = str.split(sep='\n')
    for str in str_lst:
        print("[WARNING]" + str)


def print_error(str):
    str_lst = str.split(sep='\n')
    for str in str_lst:
        print("[ERROR]" + str, file=sys.stderr)


if __name__ == '__main__':
    # OPTION RECOGNITION
    opt = dict()

    optspec = (
        "filename= "
        "v "
        "model= "
        "jump= "
        "min_dist= "
        "range_min= "
        "range_max= "
        "num_processes= "
        "refresh_delay= "
        "refresh_thresh= "
        "S= "
        "pen_override= "
        "pen_modifier= "
        "plotting= "
        "refinement_thresh= "
    )
    opt_filename = None
    opt_verbose = False
    opt_model = None
    opt_jump = None
    opt_min_dist = None
    opt_range_min = None
    opt_range_max = None
    opt_num_processes = None
    opt_refresh_delay = None
    opt_refresh_thresh = None
    opt_S = None
    opt_pen_override = None
    opt_pen_modifier = None
    opt_plotting = False
    opt_refinement_thresh = None
    try:
        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))

        for option, parameter in raw_opts:
            optname = re.sub(r"^--", "", option)
            opt[optname] = parameter

        if 'filename' not in opt:
            print_error("No file specified!")
            sys.exit(2)
        else:
            opt_filename = opt['filename']
        if 'v' in opt:
            opt_verbose = True
            opt_plotting = True
        if 'model' in opt:
            opt_model = opt['model']
        if 'jump' in opt:
            try:
                opt_jump = int(opt['jump'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'min_dist' in opt:
            try:
                opt_min_dist = int(opt['min_dist'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'range_min' in opt:
            try:
                opt_range_min = int(opt['range_min'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'range_max' in opt:
            try:
                opt_range_max = int(opt['range_max'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'num_processes' in opt:
            try:
                opt_num_processes = int(opt['num_processes'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'refresh_delay' in opt:
            try:
                opt_refresh_delay = int(opt['refresh_delay'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'refresh_thresh' in opt:
            try:
                opt_refresh_thresh = int(opt['refresh_thresh'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'S' in opt:
            try:
                opt_S = float(opt['S'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'pen_override' in opt:
            try:
                opt_pen_override = int(opt['pen_override'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'pen_modifier' in opt:
            try:
                opt_pen_modifier = float(opt['pen_modifier'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
        if 'refinement_thresh' in opt:
            try:
                opt_refinement_thresh = int(opt['refinement_thresh'])
            except ValueError as verr:
                print(verr, file=sys.stderr)
                sys.exit(2)
    except getopt.GetoptError as err:
        print(err, file=sys.stderr)
        sys.exit(2)

    # OPENING DATA
    if ".json" in opt_filename:
        # open file with trace data from json
        print_info(" Will only refine the state which is present in " + opt_filename + " if necessary.")
        with open(opt_filename, 'r') as f:
            states = json.load(f)
        # loop through all traces check if refinement is necessary
        print_info("Checking if refinement is necessary...")
        for measurements_by_state in states:
            # loop through all occurrences of the looked at state
            print_info("Looking at state '" + measurements_by_state['name'] + "' with params: "
                       + str(measurements_by_state['parameter']))
            refine = False
            for measurement in measurements_by_state['offline']:
                # loop through measurements of particular state
                # an check if state needs refinement
                signal = measurement['uW']
                # mean = measurement['uW_mean']
                # TODO: Decide if median is really the better baseline than mean
                if needs_refinement(signal, opt_refinement_thresh):
                    print_info("Refinement is necessary!")
                    refine = True
                    break
            if not refine:
                print_info("No refinement necessary for state '" + measurements_by_state['name'] + "'")
            else:
                # calc and save all bkpts for the given state and param config
                raw_states_list = list()
                for measurement in measurements_by_state['offline']:
                    signal = np.array(measurement['uW'])
                    normed_signal = np.zeros(shape=len(signal))
                    for i in range(0, len(signal)):
                        normed_signal[i] = signal[i] / 1000
                    bkpts = calc_pelt(normed_signal, model=opt_model, range_min=opt_range_min, range_max=opt_range_max,
                                      num_processes=opt_num_processes, jump=opt_jump, S=opt_S,
                                      pen_override=opt_pen_override, pen_modifier=opt_pen_modifier)
                    calced_states = list()
                    start_time = 0
                    end_time = 0
                    for bkpt in bkpts:
                        # start_time of state is end_time of previous one(Transitions are instantaneous)
                        start_time = end_time
                        end_time = bkpt
                        power_vals = signal[start_time: end_time]
                        mean_power = np.mean(power_vals)
                        std_dev = np.std(power_vals)
                        calced_state = (start_time, end_time, mean_power, std_dev)
                        calced_states.append(calced_state)
                    num = 0
                    new_avg_std = 0
                    for s in calced_states:
                        print_info("State " + str(num) + " starts at t=" + str(s[0]) + " and ends at t=" + str(s[1])
                                   + " while using " + str(s[2]) + "uW with  sigma=" + str(s[3]))
                        num = num + 1
                        new_avg_std = new_avg_std + s[3]
                    new_avg_std = new_avg_std / len(calced_states)
                    change_avg_std = measurement['uW_std'] - new_avg_std
                    print_info("The average standard deviation for the newly found states is " + str(new_avg_std))
                    print_info("That is a reduction of " + str(change_avg_std))
                    raw_states_list.append(calced_states)
                num_states_array = [int()] * len(raw_states_list)
                i = 0
                for x in raw_states_list:
                    num_states_array[i] = len(x)
                    i = i + 1
                avg_num_states = np.mean(num_states_array)
                num_states_dev = np.std(num_states_array)
                print_info("On average " + str(avg_num_states) + " States have been found. The standard deviation"
                           + " is " + str(num_states_dev))
                # TODO: MAGIC NUMBER
                if num_states_dev > 1:
                    print_warning("The number of states varies strongly across measurements. Consider choosing a "
                                  "larger value for S or using the pen_modifier option.")
                    time.sleep(5)
                # TODO: Wie bekomme ich da jetzt raus, was die Wahrheit ist?
                # Einfach Durchschnitt nehmen?
                # Preliminary decision: Further on only use the traces, which have the most frequent state count
                counts = np.bincount(num_states_array)
                num_raw_states = np.argmax(counts)
                print_info("Choose " + str(num_raw_states) + " as number of raw_states.")
                i = 0
                cluster_labels_list = []
                num_cluster_list = []
                for raw_states in raw_states_list:
                    # iterate through raw states from measurements
                    if len(raw_states) == num_raw_states:
                        # build array with power values to cluster these
                        value_to_cluster = np.zeros((num_raw_states, 2))
                        j = 0
                        for s in raw_states:
                            value_to_cluster[j][0] = s[2]
                            value_to_cluster[j][1] = 0
                            j = j + 1
                        # linked = linkage(value_to_cluster, 'single')
                        #
                        # labelList = range(1, 11)
                        #
                        # plt.figure(figsize=(10, 7))
                        # dendrogram(linked,
                        #            orientation='top',
                        #            distance_sort='descending',
                        #            show_leaf_counts=True)
                        # plt.show()
                        # TODO: Automatic detection of number of clusters. Aktuell noch MAGIC NUMBER
                        # cluster = AgglomerativeClustering(n_clusters=None, compute_full_tree=True, affinity='euclidean',
                        #                                  linkage='ward', distance_threshold=opt_refinement_thresh)
                        cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
                        cluster.fit_predict(value_to_cluster)
                        print_info("Cluster labels:\n" + str(cluster.labels_))
                        # plt.scatter(value_to_cluster[:, 0], value_to_cluster[:, 1], c=cluster.labels_, cmap='rainbow')
                        # plt.show()
                        # TODO: Problem: Der Algorithmus nummeriert die Zustände nicht immer gleich... also bspw.:
                        # mal ist das tatsächliche Transmit mit 1 belabelt und mal mit 3
                        cluster_labels_list.append(cluster.labels_)
                        num_cluster_list.append(cluster.n_clusters_)
                        i = i + 1
                if i != len(raw_states_list):
                    print_info("Used " + str(i) + "/" + str(len(raw_states_list))
                               + " Measurements for state clustering. "
                                 "Others did not recognize number of states correctly.")
                num_states = np.argmax(np.bincount(num_cluster_list))
                resulting_sequence = [None] * num_raw_states
                i = 0
                for x in resulting_sequence:
                    j = 0
                    test_list = []
                    for arr in cluster_labels_list:
                        if num_cluster_list[j] != num_states:
                            j = j + 1
                        else:
                            test_list.append(arr[i])
                            j = j + 1
                    resulting_sequence[i] = np.argmax(np.bincount(test_list))
                    i = i + 1
                print(resulting_sequence)

                # TODO: TESTING PURPOSES
                exit()

    elif ".tar" in opt_filename:
        # open with dfatool
        raw_data_args = list()
        raw_data_args.append(opt_filename)
        raw_data = RawData(
            raw_data_args, with_traces=True
        )
        print_info("Preprocessing file. Depending on its size, this could take a while.")
        preprocessed_data = raw_data.get_preprocessed_data()
        print_info("File fully preprocessed")

        # TODO: Mal schauen, wie ich das mache. Erstmal nur mit json
    else:
        print_error("Unknown dataformat")
        sys.exit(2)

    # print(tx_data[1]['parameter'])
    # # parse json to array for PELT
    # signal = np.array(tx_data[1]['offline'][0]['uW'])
    #
    # for i in range(0, len(signal)):
    #     signal[i] = signal[i]/1000
    # bkps = calc_pelt(signal, model=opt_model, range_max=opt_range_max, num_processes=opt_num_processes, jump=opt_jump, S=opt_S)
    # fig, ax = rpt.display(signal, bkps)
    # plt.xlabel('Time [us]')
    # plt.ylabel('Power [mW]')
    # plt.show()