diff options
Diffstat (limited to 'bin/Proof_Of_Concept_PELT.py')
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 98 |
1 files changed, 76 insertions, 22 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index dde99d8..0d5be54 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -504,17 +504,18 @@ if __name__ == '__main__': 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.") + "Will only refine the state which is present in " + opt_filename + " if necessary.") with open(opt_filename, 'r') as f: - states = json.load(f) + configurations = 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: + resulting_sequence_list = [] + for num_config, measurements_by_configuration in enumerate(configurations): # 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'])) + print_info("Looking at state '" + measurements_by_configuration['name'] + "' with params: " + + str(measurements_by_configuration['parameter'])) refine = False - for measurement in measurements_by_state['offline']: + print_info("Checking if refinement is necessary...") + for measurement in measurements_by_configuration['offline']: # loop through measurements of particular state # an check if state needs refinement signal = measurement['uW'] @@ -524,13 +525,13 @@ if __name__ == '__main__': print_info("Refinement is necessary!") refine = True if not refine: - print_info("No refinement necessary for state '" + measurements_by_state['name'] - + "' with params: " + str(measurements_by_state['parameter'])) + print_info("No refinement necessary for state '" + measurements_by_configuration['name'] + + "' with params: " + str(measurements_by_configuration['parameter'])) else: # assume that all measurements of the same param configuration are fundamentally # similar -> calculate penalty for first measurement, use it for all if opt_pen_override is None: - signal = np.array(measurements_by_state['offline'][0]['uW']) + signal = np.array(measurements_by_configuration['offline'][0]['uW']) normed_signal = norm_signal(signal) penalty = calculate_penalty_value(normed_signal, model=opt_model, range_min=opt_range_min, @@ -544,11 +545,11 @@ if __name__ == '__main__': # build arguments for parallel excecution print_info("Starting raw_states calculation.") raw_states_calc_args = [] - for num_measurement, measurement in enumerate(measurements_by_state['offline']): + for num_measurement, measurement in enumerate(measurements_by_configuration['offline']): raw_states_calc_args.append((num_measurement, measurement, penalty, opt_model, opt_jump)) - raw_states_list = [None] * len(measurements_by_state['offline']) + raw_states_list = [None] * len(measurements_by_configuration['offline']) raw_states_res = calc_raw_states(raw_states_calc_args, opt_num_processes) # extracting result and putting it in correct order -> index of raw_states_list # entry still corresponds with index of measurement in measurements_by_states @@ -629,6 +630,7 @@ if __name__ == '__main__': else: print_info("Discarding measurement No. " + str(num_trace) + " because it " + "did not recognize the number of raw_states correctly.") + num_used_measurements = len(raw_states_list) if i != len(raw_states_list): if i / len(raw_states_list) <= 0.5: print_warning("Only used " + str(i) + "/" + str(len(raw_states_list)) @@ -639,6 +641,7 @@ if __name__ == '__main__': print_info("Used " + str(i) + "/" + str(len(raw_states_list)) + " Measurements for refinement. " "Others did not recognize number of states correctly.") + num_used_measurements = i # TODO: DEBUG Kram sys.exit(0) else: @@ -655,16 +658,24 @@ if __name__ == '__main__': for num_label, label in enumerate(labels): count_per_state[label] = count_per_state[label] + 1 avg_per_state[label] = avg_per_state[label] + raw_states[num_label][2] - for i,_ in enumerate(avg_per_state): + for i, _ in enumerate(avg_per_state): avg_per_state[i] = avg_per_state[i] / count_per_state[i] avg_per_state_list[number] = avg_per_state used_clusters = used_clusters + 1 - + else: + # hopefully this does not happen regularly + print_info("Discarding measurement " + str(number) + + " because the clustering yielded not matching results.") + num_used_measurements = num_used_measurements - 1 + if num_used_measurements == 0: + print_error("Something went terribly wrong. Discarded all measurements.") + # continue + sys.exit(-1) # flattend version for clustering: values_to_cluster = np.zeros((num_states * used_clusters, 2)) index = 0 for avg_per_state in avg_per_state_list: - if None not in avg_per_state: + if avg_per_state is not None: for avg in avg_per_state: values_to_cluster[index][0] = avg values_to_cluster[index][1] = 0 @@ -673,30 +684,73 @@ if __name__ == '__main__': # plt.show() cluster = AgglomerativeClustering(n_clusters=num_states) cluster.fit_predict(values_to_cluster) - # HIER WEITER: # Aktuell hast du hier ein plattes Array mit labels. Jetzt also das wieder auf die # ursprünglichen Labels abbilden, die dann verändern mit den hier gefundenen Labels. # Alle identischen Zustände haben identische Labels. Dann vllt bei resulting # sequence ausgeben, wie groß die übereinstimmung bei der Stateabfolge ist. - + new_labels_list = [] + new_labels = [] + i = 0 + for label in cluster.labels_: + new_labels.append(label) + i = i + 1 + if i == num_states: + new_labels_list.append(new_labels) + new_labels = [] + i = 0 + # only the selected measurements are present in new_labels. + # new_labels_index should not be incremented, if not selected_measurement is skipped + new_labels_index = 0 + # cluster_labels_list contains all measurements -> if measurement is skipped + # still increment the index + index = 0 + for elem in avg_per_state_list: + if elem is not None: + for number, label in enumerate(cluster_labels_list[index][1]): + cluster_labels_list[index][1][number] = \ + new_labels_list[new_labels_index][label] + new_labels_index = new_labels_index + 1 + else: + # override not selected measurement labels to avoid choosing the wrong ones. + for number, label in enumerate(cluster_labels_list[index][1]): + cluster_labels_list[index][1][number] = -1 + index = index + 1 resulting_sequence = [None] * num_raw_states i = 0 + confidence = 0 for x in resulting_sequence: j = 0 test_list = [] for arr in [elem[1] for elem in cluster_labels_list]: if num_cluster_list[j][1] != num_states: - # hopefully this does not happen regularly - print_info("Discarding measurement " + str(j) - + " because the clustering yielded not matching results.") j = j + 1 else: + if -1 in arr: + print_error("Bei Janis beschweren! Fehler beim Umbenennen der" + " Zustände wahrscheinlich.") + sys.exit(-1) test_list.append(arr[i]) j = j + 1 - resulting_sequence[i] = np.argmax(np.bincount(test_list)) + bincount = np.bincount(test_list) + resulting_sequence[i] = np.argmax(bincount) + confidence = confidence + bincount[resulting_sequence[i]] / np.sum(bincount) i = i + 1 + confidence = confidence / len(resulting_sequence) + print_info("Confidence of resulting sequence is " + str(confidence) + + " while using " + str(num_used_measurements) + "/" + + str(len(raw_states_list)) + " measurements.") print(resulting_sequence) - sys.exit() + resulting_sequence_list.append((num_config, resulting_sequence)) + # TODO: Was jetzt? Hier habe ich jetzt pro Konfiguration eine Zustandsfolge. Daraus Automat + # erzeugen. Aber wie? Oder erst parametrisieren? Eigentlich brauche ich vorher die + # Loops. Wie erkenne ich die? Es können beliebig viele Loops an beliebigen Stellen + # auftreten. + # TODO: Die Zustandsfolgen werden sich nicht einfach in isomorphe(-einzelne wegfallende bzw. + # hinzukommende Zustände) Automaten übersetzten lassen. Basiert alles auf dem Problem: + # wie erkenne ich, dass zwei Zustände die selben sind und nicht nur einfach eine ähnliche + # Leistungsaufnahme haben?! Vllt Zustände 2D clustern? 1Dim = Leistungsaufnahme, + # 2Dim=Dauer? Zumindest innerhalb einer Paramkonfiguration sollte sich die Dauer eines + # Zustands ja nicht mehr ändern. elif ".tar" in opt_filename: # open with dfatool |