diff options
Diffstat (limited to 'bin/merge.py')
| -rwxr-xr-x | bin/merge.py | 147 |
1 files changed, 94 insertions, 53 deletions
diff --git a/bin/merge.py b/bin/merge.py index 1ff7e74..b0d7d22 100755 --- a/bin/merge.py +++ b/bin/merge.py @@ -9,6 +9,7 @@ import sys import plotter from copy import deepcopy from dfatool import aggregate_measures, regression_measures, is_numeric, powerset +from dfatool import append_if_set, mean_or_none from matplotlib.patches import Polygon from scipy import optimize @@ -42,17 +43,21 @@ def mimosa_data(elem): substate_thresholds = [] substate_data = [] timeouts = [] - rel_energies = [] + rel_energies_prev = [] + rel_energies_next = [] if 'timeout' in elem['offline'][0]: timeouts = [x['timeout'] for x in elem['offline']] - if 'uW_mean_delta' in elem['offline'][0]: - rel_energies = [x['uW_mean_delta'] * (x['us'] - 20) for x in elem['offline']] + if 'uW_mean_delta_prev' in elem['offline'][0]: + rel_energies_prev = [x['uW_mean_delta_prev'] * (x['us'] - 20) for x in elem['offline']] + if 'uW_mean_delta_next' in elem['offline'][0]: + rel_energies_next = [x['uW_mean_delta_next'] * (x['us'] - 20) for x in elem['offline']] for x in elem['offline']: if 'substates' in x: substate_thresholds.append(x['substates']['threshold']) substate_data.append(x['substates']['states']) - return means, stds, durations, energies, rel_energies, clips, timeouts, substate_thresholds + return (means, stds, durations, energies, rel_energies_prev, + rel_energies_next, clips, timeouts, substate_thresholds) def online_data(elem): means = [int(x['power']) for x in elem['online']] @@ -254,8 +259,8 @@ def xv_assess_function(name, funbase, what, validation, mae, smape): mae[name] = [] if not name in smape: smape[name] = [] - mae[name].append(goodness['mae']) - smape[name].append(goodness['smape']) + append_if_set(mae, goodness, 'mae') + append_if_set(smape, goodness, 'smape') def xv2_assess_function(name, funbase, what, validation, mae, smape, rmsd): goodness = assess_function(funbase, name, what, parameters, validation) @@ -311,15 +316,19 @@ def fake_add_data_to_aggregate(aggregate, key, isa, database, idx): timeout_val = [] if len(database['timeouts']): timeout_val = [database['timeouts'][idx]] - rel_energy_val = [] - if len(database['rel_energies']): - rel_energy_val = [database['rel_energies'][idx]] + rel_energy_p_val = [] + if len(database['rel_energies_prev']): + rel_energy_p_val = [database['rel_energies_prev'][idx]] + rel_energy_n_val = [] + if len(database['rel_energies_next']): + rel_energy_n_val = [database['rel_energies_next'][idx]] add_data_to_aggregate(aggregate, key, isa, { 'means' : [database['means'][idx]], 'stds' : [database['stds'][idx]], 'durations' : [database['durations'][idx]], 'energies' : [database['energies'][idx]], - 'rel_energies' : rel_energy_val, + 'rel_energies_prev' : rel_energy_p_val, + 'rel_energies_next' : rel_energy_n_val, 'clip_rate' : [database['clip_rate'][idx]], 'timeouts' : timeout_val, }) @@ -377,7 +386,7 @@ def mean_std_by_trace_part(data, transitions, name, what): def load_run_elem(index, element, trace, by_name, by_param, by_trace): - means, stds, durations, energies, rel_energies, clips, timeouts, sub_thresholds = mimosa_data(element) + means, stds, durations, energies, rel_energies_prev, rel_energies_next, clips, timeouts, sub_thresholds = mimosa_data(element) online_means = [] online_durations = [] @@ -394,7 +403,8 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace): 'stds' : stds, 'durations' : durations, 'energies' : energies, - 'rel_energies' : rel_energies, + 'rel_energies_prev' : rel_energies_prev, + 'rel_energies_next' : rel_energies_next, 'clip_rate' : clips, 'timeouts' : timeouts, 'sub_thresholds' : sub_thresholds, @@ -407,7 +417,8 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace): 'stds' : stds, 'durations' : durations, 'energies' : energies, - 'rel_energies' : rel_energies, + 'rel_energies_prev' : rel_energies_prev, + 'rel_energies_next' : rel_energies_next, 'clip_rate' : clips, 'timeouts' : timeouts, 'sub_thresholds' : sub_thresholds, @@ -419,7 +430,8 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace): 'stds' : stds, 'durations' : durations, 'energies' : energies, - 'rel_energies' : rel_energies, + 'rel_energies_prev' : rel_energies_prev, + 'rel_energies_next' : rel_energies_next, 'clip_rate' : clips, 'timeouts' : timeouts, 'sub_thresholds' : sub_thresholds, @@ -487,16 +499,14 @@ def param_measures(name, paramdata, key, fun): # Mean ist besseres für SSR. Da least_squares SSR optimiert # nutzen wir hier auch Mean. goodness = aggregate_measures(fun(pval[key]), pval[key]) - mae.append(goodness['mae']) - rmsd.append(goodness['rmsd']) - if 'smape' in goodness: - smape.append(goodness['smape']) + append_if_set(mae, goodness, 'mae') + append_if_set(rmsd, goodness, 'rmsd') + append_if_set(smape, goodness, 'smape') ret = { - 'mae' : np.mean(mae), - 'rmsd' : np.mean(rmsd) + 'mae' : mean_or_none(mae), + 'rmsd' : mean_or_none(rmsd), + 'smape' : mean_or_none(smape) } - if len(smape): - ret['smape'] = np.mean(smape) return ret @@ -548,10 +558,9 @@ def val_run(aggdata, split_fun, count): validation = aggdata[pairs[i][1]] median = np.median(training) goodness = aggregate_measures(median, validation) - mae.append(goodness['mae']) - rmsd.append(goodness['rmsd']) - if 'smape' in goodness: - smape.append(goodness['smape']) + append_if_set(mae, goodness, 'mae') + append_if_set(rmsd, goodness, 'rmsd') + append_if_set(smape, goodness, 'smape') mae_mean = np.mean(mae) rmsd_mean = np.mean(rmsd) @@ -628,7 +637,8 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters): isa = by_name[name]['isa'] by_name[name]['means'] = np.array(by_name[name]['means']) by_name[name]['energies'] = np.array(by_name[name]['energies']) - by_name[name]['rel_energies'] = np.array(by_name[name]['rel_energies']) + by_name[name]['rel_energies_prev'] = np.array(by_name[name]['rel_energies_prev']) + by_name[name]['rel_energies_next'] = np.array(by_name[name]['rel_energies_next']) by_name[name]['durations'] = np.array(by_name[name]['durations']) if isa == 'state': @@ -641,10 +651,14 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters): print('%16s, static energy, Monte Carlo: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) mae_mean, smape_mean, rms_mean = val_run(by_name[name]['energies'], splitidx_kfold, 10) print('%16s, static energy, 10-fold sys: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) - mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies'], splitidx_srs, 200) - print('%16s, static rel_energy, Monte Carlo: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) - mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies'], splitidx_kfold, 10) - print('%16s, static rel_energy, 10-fold sys: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) + mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_prev'], splitidx_srs, 200) + print('%16s, static rel_energy_p, Monte Carlo: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) + mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_prev'], splitidx_kfold, 10) + print('%16s, static rel_energy_p, 10-fold sys: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) + mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_next'], splitidx_srs, 200) + print('%16s, static rel_energy_n, Monte Carlo: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) + mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_next'], splitidx_kfold, 10) + print('%16s, static rel_energy_n, 10-fold sys: MAE %8.f pJ, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) mae_mean, smape_mean, rms_mean = val_run(by_name[name]['durations'], splitidx_srs, 200) print('%16s, static duration, Monte Carlo: MAE %8.f µs, SMAPE %6.2f%%, RMS %d' % (name, mae_mean, smape_mean, rms_mean)) mae_mean, smape_mean, rms_mean = val_run(by_name[name]['durations'], splitidx_kfold, 10) @@ -701,11 +715,16 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters): val_run_funs(by_name, by_trace, name, 'energies', 'energy', 'user', 'pJ') if 'estimate' in model[isa][name]['energy']['function']: val_run_funs(by_name, by_trace, name, 'energies', 'energy', 'estimate', 'pJ') - if 'rel_energy' in model[isa][name] and 'function' in model[isa][name]['rel_energy']: - if 'user' in model[isa][name]['rel_energy']['function']: - val_run_funs(by_name, by_trace, name, 'rel_energies', 'rel_energy', 'user', 'pJ') - if 'estimate' in model[isa][name]['rel_energy']['function']: - val_run_funs(by_name, by_trace, name, 'rel_energies', 'rel_energy', 'estimate', 'pJ') + if 'rel_energy_prev' in model[isa][name] and 'function' in model[isa][name]['rel_energy_prev']: + if 'user' in model[isa][name]['rel_energy_prev']['function']: + val_run_funs(by_name, by_trace, name, 'rel_energies_prev', 'rel_energy_prev', 'user', 'pJ') + if 'estimate' in model[isa][name]['rel_energy_prev']['function']: + val_run_funs(by_name, by_trace, name, 'rel_energies_prev', 'rel_energy_prev', 'estimate', 'pJ') + if 'rel_energy_next' in model[isa][name] and 'function' in model[isa][name]['rel_energy_next']: + if 'user' in model[isa][name]['rel_energy_next']['function']: + val_run_funs(by_name, by_trace, name, 'rel_energies_next', 'rel_energy_next', 'user', 'pJ') + if 'estimate' in model[isa][name]['rel_energy_next']['function']: + val_run_funs(by_name, by_trace, name, 'rel_energies_next', 'rel_energy_next', 'estimate', 'pJ') return for i, param in enumerate(parameters): @@ -819,11 +838,18 @@ def validate(by_name, by_param, parameters): 'std_inner' : np.std(val['energies']), 'function' : {}, }, - 'rel_energy' : { - 'goodness' : aggregate_measures(model['rel_energy']['static'], val['rel_energies']), - 'median' : np.median(val['rel_energies']), - 'mean' : np.mean(val['rel_energies']), - 'std_inner' : np.std(val['rel_energies']), + 'rel_energy_prev' : { + 'goodness' : aggregate_measures(model['rel_energy_prev']['static'], val['rel_energies_prev']), + 'median' : np.median(val['rel_energies_prev']), + 'mean' : np.mean(val['rel_energies_prev']), + 'std_inner' : np.std(val['rel_energies_prev']), + 'function' : {}, + }, + 'rel_energy_next' : { + 'goodness' : aggregate_measures(model['rel_energy_next']['static'], val['rel_energies_next']), + 'median' : np.median(val['rel_energies_next']), + 'mean' : np.mean(val['rel_energies_next']), + 'std_inner' : np.std(val['rel_energies_next']), 'function' : {}, }, 'clip' : { @@ -874,7 +900,8 @@ def analyze(by_name, by_param, by_trace, parameters): aggval['power']['std_outer'] = np.mean(val['stds']) if isa == 'transition': - aggval['rel_energy'] = keydata(name, val, by_param, by_trace, 'rel_energies') + aggval['rel_energy_prev'] = keydata(name, val, by_param, by_trace, 'rel_energies_prev') + aggval['rel_energy_next'] = keydata(name, val, by_param, by_trace, 'rel_energies_next') if isa == 'transition' and 'function' in data['model']['transition'][name]['timeout']: aggval['timeout'] = keydata(name, val, by_param, by_trace, 'timeouts') @@ -898,10 +925,14 @@ def analyze(by_name, by_param, by_trace, parameters): by_param, allvalues, name, 'energies', i) if aggval['energy']['std_by_param'][param] > 0 and aggval['energy']['std_param'] / aggval['energy']['std_by_param'][param] < 0.6: aggval['energy']['fit_guess'][param] = try_fits(name, 'energies', i, by_param) - aggval['rel_energy']['std_by_param'][param] = mean_std_by_param( - by_param, allvalues, name, 'rel_energies', i) - if aggval['rel_energy']['std_by_param'][param] > 0 and aggval['rel_energy']['std_param'] / aggval['rel_energy']['std_by_param'][param] < 0.6: - aggval['rel_energy']['fit_guess'][param] = try_fits(name, 'rel_energies', i, by_param) + aggval['rel_energy_prev']['std_by_param'][param] = mean_std_by_param( + by_param, allvalues, name, 'rel_energies_prev', i) + if aggval['rel_energy_prev']['std_by_param'][param] > 0 and aggval['rel_energy_prev']['std_param'] / aggval['rel_energy_prev']['std_by_param'][param] < 0.6: + aggval['rel_energy_prev']['fit_guess'][param] = try_fits(name, 'rel_energies_prev', i, by_param) + aggval['rel_energy_next']['std_by_param'][param] = mean_std_by_param( + by_param, allvalues, name, 'rel_energies_next', i) + if aggval['rel_energy_next']['std_by_param'][param] > 0 and aggval['rel_energy_next']['std_param'] / aggval['rel_energy_next']['std_by_param'][param] < 0.6: + aggval['rel_energy_next']['fit_guess'][param] = try_fits(name, 'rel_energies_next', i, by_param) if isa == 'transition' and 'function' in data['model']['transition'][name]['timeout']: aggval['timeout']['std_by_param'][param] = mean_std_by_param( by_param, allvalues, name, 'timeouts', i) @@ -926,7 +957,9 @@ def analyze(by_name, by_param, by_trace, parameters): 'estimated %s duration [µs]' % name) fguess_to_function(name, 'energies', aggval['energy'], parameters, by_param, 'estimated %s energy [pJ]' % name) - fguess_to_function(name, 'rel_energies', aggval['rel_energy'], parameters, by_param, + fguess_to_function(name, 'rel_energies_prev', aggval['rel_energy_prev'], parameters, by_param, + 'estimated relative %s energy [pJ]' % name) + fguess_to_function(name, 'rel_energies_next', aggval['rel_energy_next'], parameters, by_param, 'estimated relative %s energy [pJ]' % name) if 'function' in model['duration'] and 'user' in model['duration']['function']: aggval['duration']['function']['user'] = { @@ -944,14 +977,22 @@ def analyze(by_name, by_param, by_trace, parameters): fit_function( aggval['energy']['function']['user'], name, 'energies', parameters, by_param, yaxis='%s energy [pJ]' % name) - if 'function' in model['rel_energy'] and 'user' in model['rel_energy']['function']: - aggval['rel_energy']['function']['user'] = { - 'raw' : model['rel_energy']['function']['user']['raw'], - 'params' : model['rel_energy']['function']['user']['params'], + if 'function' in model['rel_energy_prev'] and 'user' in model['rel_energy_prev']['function']: + aggval['rel_energy_prev']['function']['user'] = { + 'raw' : model['rel_energy_prev']['function']['user']['raw'], + 'params' : model['rel_energy_prev']['function']['user']['params'], + } + fit_function( + aggval['rel_energy_prev']['function']['user'], name, 'rel_energies_prev', parameters, by_param, + yaxis='%s rel_energy_prev [pJ]' % name) + if 'function' in model['rel_energy_next'] and 'user' in model['rel_energy_next']['function']: + aggval['rel_energy_next']['function']['user'] = { + 'raw' : model['rel_energy_next']['function']['user']['raw'], + 'params' : model['rel_energy_next']['function']['user']['params'], } fit_function( - aggval['rel_energy']['function']['user'], name, 'rel_energies', parameters, by_param, - yaxis='%s rel_energy [pJ]' % name) + aggval['rel_energy_next']['function']['user'], name, 'rel_energies_next', parameters, by_param, + yaxis='%s rel_energy_next [pJ]' % name) if 'function' in model['timeout'] and 'user' in model['timeout']['function']: fguess_to_function(name, 'timeouts', aggval['timeout'], parameters, by_param, 'estimated %s timeout [µs]' % name) |