diff options
Diffstat (limited to 'bin')
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 130 |
1 files changed, 101 insertions, 29 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index 452ff3f..d4878c1 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -59,7 +59,7 @@ def find_knee_point(data_x, data_y, S=1.0, curve='convex', direction='decreasing return kneepoint -def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, num_processes=8, refresh_delay=1, +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, plotting=False): # default params in Function if model is None: @@ -69,7 +69,7 @@ def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, if min_dist is None: min_dist = 2 if range_min is None: - range_min = 1 + range_min = 0 if range_max is None: range_max = 50 if num_processes is None: @@ -82,24 +82,23 @@ def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, S = 1.0 if plotting is None: plotting = False - # 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: + 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): + for i in range(range_min, range_max + 1): args.append((algo, i, q)) - print('starting kneepoint calculation') + print('[INFO]starting kneepoint calculation.') # init Pool with num_proesses with Pool(num_processes) as p: # collect results from pool @@ -115,30 +114,32 @@ def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, last_percentage = percentage percentage = round(size / (range_max - range_min) * 100, 2) if percentage >= last_percentage + 2 or i >= refresh_thresh: - print('Current progress: ' + str(percentage) + '%') + 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() + # 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() else: - # use forced pen value for plotting - knee = (pen_override, None) - + # 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]) @@ -147,7 +148,8 @@ def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, plt.show() return bkps else: - print('With the current thresh-hold S=' + str(S) + ' it is not possible to select a penalty value.') + 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 @@ -221,7 +223,7 @@ def needs_refinement(signal, thresh): 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] + 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) @@ -234,6 +236,18 @@ def needs_refinement(signal, thresh): return False +def print_info(str): + print("[INFO]" + str) + + +def print_warning(str): + print("[WARNING]" + str) + + +def print_error(str): + print("ERROR" + str, file=sys.stderr) + + if __name__ == '__main__': # OPTION RECOGNITION opt = dict() @@ -276,7 +290,7 @@ if __name__ == '__main__': opt[optname] = parameter if 'filename' not in opt: - print("No file specified!", file=sys.stderr) + print_error("No file specified!") sys.exit(2) else: opt_filename = opt['filename'] @@ -352,15 +366,16 @@ if __name__ == '__main__': # 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.") + 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("Checking if refinement is necessary...") - res = False + print_info("Checking if refinement is necessary...") for measurements_by_state in states: # loop through all occurrences of the looked at state - print("Looking at state '" + measurements_by_state['name'] + "'") + 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 @@ -368,8 +383,65 @@ if __name__ == '__main__': # mean = measurement['uW_mean'] # TODO: Decide if median is really the better baseline than mean if needs_refinement(signal, opt_refinement_thresh): - print("Refinement is necessary!") + 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 + state_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) + 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) + + ".\n[INFO]That is a reduction of " + str(change_avg_std)) + state_list.append(calced_states) + num_states_array = np.zeros(shape=len(measurements_by_state['offline'])) + i = 0 + for x in state_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.") + time.sleep(5) + # TODO: Wie bekomme ich da jetzt raus, was die Wahrheit ist? + # Einfach Durchschnitt nehmen? + # TODO: TESTING PURPOSES + exit() + elif ".tar" in opt_filename: # open with dfatool raw_data_args = list() @@ -377,13 +449,13 @@ if __name__ == '__main__': raw_data = RawData( raw_data_args, with_traces=True ) - print("Preprocessing file. Depending on its size, this could take a while.") + print_info("Preprocessing file. Depending on its size, this could take a while.") preprocessed_data = raw_data.get_preprocessed_data() - print("File fully preprocessed") + print_info("File fully preprocessed") # TODO: Mal schauen, wie ich das mache. Erstmal nur mit json else: - print("Unknown dataformat", file=sys.stderr) + print_error("Unknown dataformat") sys.exit(2) # print(tx_data[1]['parameter']) |