diff options
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 52 |
1 files changed, 38 insertions, 14 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index 4819f64..ac32d88 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -4,6 +4,7 @@ import time import sys import getopt import re +import pprint from multiprocessing import Pool, Manager, cpu_count from kneed import KneeLocator from sklearn.cluster import AgglomerativeClustering @@ -21,7 +22,8 @@ from dfatool.utils import by_name_to_by_param # from scipy.cluster.hierarchy import dendrogram, linkage # for graphical display # py bin\Proof_Of_Concept_PELT.py --filename="..\data\TX.json" --jump=1 --pen_override=28 --refinement_thresh=100 -# py bin\Proof_Of_Concept_PELT.py --filename="..\data\TX.json" --jump=1 --pen_override=28 --refinement_thresh=100 --cache_dicts --cache_loc="..\data\TX_cache" +# py bin\Proof_Of_Concept_PELT.py --filename="..\data\TX.json" --jump=1 --pen_override=28 --refinement_thresh=100 --cache_dicts --cache_loc="..\data\TX2_cache" +from dfatool.validation import CrossValidator def plot_data_from_json(filename, trace_num, x_axis, y_axis): @@ -98,7 +100,7 @@ def calc_pelt(signal, penalty, model="l1", jump=5, min_dist=2, plotting=False): def calculate_penalty_value(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_modifier=None): + pen_modifier=None, show_plots=False): # default params in Function if model is None: model = "l1" @@ -138,7 +140,7 @@ def calculate_penalty_value(signal, model="l1", jump=5, min_dist=2, range_min=0, print_info("starting kneepoint calculation.") # init Pool with num_proesses - with Pool(num_processes) as p: + with Pool(min(num_processes, len(args))) as p: # collect results from pool result = p.starmap_async(get_bkps, args) # monitor loop @@ -199,18 +201,24 @@ def calculate_penalty_value(signal, model="l1", jump=5, min_dist=2, range_min=0, if i == len(fitted_bkps_val[knee[0]:]) - 1: # end sequence with last value end_index = i + # # since it is not guaranteed that this is the end of the plateau, assume the mid + # # of the plateau was hit. + # size = end_index - start_index + # end_index = end_index + size + # However this is not the clean solution. Better if search interval is widened if end_index - start_index > longest_end - longest_start: # last found sequence is the longest found yet longest_start = start_index longest_end = end_index start_index = i prev_val = num_bkpts - # plt.xlabel('Penalty') - # plt.ylabel('Number of Changepoints') - # plt.plot(pen_val, fitted_bkps_val) - # plt.vlines(longest_start + knee[0], 0, max(fitted_bkps_val), linestyles='dashed') - # plt.vlines(longest_end + knee[0], 0, max(fitted_bkps_val), linestyles='dashed') - # plt.show() + if show_plots: + plt.xlabel('Penalty') + plt.ylabel('Number of Changepoints') + plt.plot(pen_val, fitted_bkps_val) + plt.vlines(longest_start + knee[0], 0, max(fitted_bkps_val), linestyles='dashed') + plt.vlines(longest_end + knee[0], 0, max(fitted_bkps_val), linestyles='dashed') + plt.show() # choosing pen from plateau mid_of_plat = longest_start + (longest_end - longest_start) // 2 knee = (mid_of_plat + knee[0], fitted_bkps_val[mid_of_plat + knee[0]]) @@ -331,7 +339,7 @@ def calc_raw_states_func(num_measurement, measurement, penalty, model, jump): def calc_raw_states(arg_list, num_processes=8): m = Manager() - with Pool(processes=num_processes) as p: + with Pool(processes=min(num_processes, len(arg_list))) as p: # collect results from pool result = p.starmap(calc_raw_states_func, arg_list) return result @@ -375,7 +383,7 @@ def print_error(str_to_prt): print("[ERROR]" + str_prt, file=sys.stderr) -def norm_signal(signal, scaler=50): +def norm_signal(signal, scaler=25): # TODO: maybe refine normalisation of signal max_val = max(signal) normed_signal = np.zeros(shape=len(signal)) @@ -656,6 +664,10 @@ if __name__ == '__main__': + "measurement No. " + str(num_measurement) + " is " + str( avg_std)) print_info("That is a reduction of " + str(change_avg_std)) + # l_signal = measurements_by_config['offline'][num_measurement]['uW'] + # l_bkpts = [s[1] for s in raw_states] + # fig, ax = rpt.display(np.array(l_signal), l_bkpts) + # plt.show() print_info("Finished raw_states calculation.") num_states_array = [int()] * len(raw_states_list) i = 0 @@ -787,6 +799,10 @@ if __name__ == '__main__': print_info("All configs usable.") else: print_info("Using only " + str(usable_configs) + " Configs.") + if num_raw_states == 1: + print_info("Upon further inspection it is clear that no refinement is necessary." + " The macromodel is usable.") + sys.exit(-1) by_name = {} usable_configs_2 = len(state_consumptions_by_config) for i in range(num_raw_states): @@ -1034,9 +1050,17 @@ if __name__ == '__main__': model_function = model_function.replace(replace_string, str(arg)) print("Duration-Function for state " + state_name + ": " + model_function) - model = PTAModel(new_by_name, param_names, dict()) - model_json = model.to_json() - print(model_json) + # model = PTAModel(new_by_name, param_names, dict()) + # model_json = model.to_json() + # param_model, _ = model.get_fitted() + # param_quality = model.assess(param_model) + # pprint.pprint(param_quality) + # # model = PTAModel(by_name, ...) + # # validator = CrossValidator(PTAModel, by_name, ...) + # # param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10) + # validator = CrossValidator(PTAModel, new_by_name, param_names, dict()) + # param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10) + # pprint.pprint(param_quality) if not_accurate: print_warning( "THIS RESULT IS NOT ACCURATE. SEE WARNINGLOG TO GET A BETTER UNDERSTANDING" |