diff options
Diffstat (limited to 'lib/dfatool.py')
| -rwxr-xr-x | lib/dfatool.py | 79 |
1 files changed, 66 insertions, 13 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index e56e0b0..0340427 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -47,6 +47,10 @@ def aggregate_measures(aggregate, actual): return regression_measures(aggregate_array, np.array(actual)) def regression_measures(predicted, actual): + if type(predicted) != np.ndarray: + raise ValueError('first arg must be ndarray, is {}'.format(type(predicted))) + if type(actual) != np.ndarray: + raise ValueError('second arg must be ndarray, is {}'.format(type(actual))) deviations = predicted - actual if len(deviations) == 0: return {} @@ -204,20 +208,21 @@ class RawData: if not 'offline_aggregates' in online_trace_part: online_trace_part['offline_aggregates'] = { - 'power_mean' : [], + 'power' : [], 'duration' : [], 'power_std' : [], 'energy' : [], 'clipping' : [], - 'timeout' : [], - 'rel_energy_prev' : [], - 'rel_energy_next' : [] } + if online_trace_part['isa'] == 'transition': + online_trace_part['offline_aggregates']['timeout'] = [] + online_trace_part['offline_aggregates']['rel_energy_prev'] = [] + online_trace_part['offline_aggregates']['rel_energy_next'] = [] # Note: All state/transitions are 20us "too long" due to injected # active wait states. These are needed to work around MIMOSA's # relatively low sample rate of 100 kHz (10us) and removed here. - online_trace_part['offline_aggregates']['power_mean'].append( + online_trace_part['offline_aggregates']['power'].append( offline_trace_part['uW_mean']) online_trace_part['offline_aggregates']['duration'].append( offline_trace_part['us'] - 20) @@ -282,7 +287,7 @@ class RawData: 'num_valid' : num_valid } -class Analysis: +class EnergyModel: def __init__(self, preprocessed_data): self.traces = preprocessed_data @@ -290,6 +295,20 @@ class Analysis: self.by_arg = {} self.by_param = {} self.by_trace = {} + np.seterr('raise') + for runidx, run in enumerate(self.traces): + # if opts['ignore-trace-idx'] != runidx + for i, elem in enumerate(run['trace']): + if elem['name'] != 'UNINITIALIZED': + self._load_run_elem(i, elem) + self._aggregate_to_ndarray(self.by_name) + + def _aggregate_to_ndarray(self, aggregate): + for elem in aggregate.values(): + for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: + if key in elem: + elem[key] = np.array(elem[key]) + def _add_data_to_aggregate(self, aggregate, key, element): if not key in aggregate: @@ -304,13 +323,47 @@ class Analysis: def _load_run_elem(self, i, elem): self._add_data_to_aggregate(self.by_name, elem['name'], elem) - def analyze(self): - for runidx, run in enumerate(self.traces): - # if opts['ignore-trace-idx'] != runidx - for i, elem in enumerate(run['trace']): - if elem['name'] != 'UNINITIALIZED': - self._load_run_elem(i, elem) - return self.by_name + def get_static(self): + static_model = {} + for name, elem in self.by_name.items(): + static_model[name] = {} + for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: + if key in elem: + try: + static_model[name][key] = np.mean(elem[key]) + except RuntimeWarning: + print('[W] Got no data for {} {}'.format(name, key)) + except FloatingPointError as fpe: + print('[W] Got no data for {} {}: {}'.format(name, key, fpe)) + + def getter(name, key, **kwargs): + return static_model[name][key] + + return getter + + def states(self): + return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'state', self.by_name.keys()))) + + def transitions(self): + return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'transition', self.by_name.keys()))) + + def assess(self, model_function): + for name, elem in sorted(self.by_name.items()): + print('{}:'.format(name)) + if elem['isa'] == 'state': + predicted_data = np.array(list(map(lambda x: model_function(name, 'power'), elem['power']))) + measures = regression_measures(predicted_data, elem['power']) + print(' power: {:.2f}% / {:.0f} µW'.format( + measures['smape'], measures['mae'] + )) + else: + for key in ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next']: + predicted_data = np.array(list(map(lambda x: model_function(name, key), elem[key]))) + measures = regression_measures(predicted_data, elem[key]) + print(' {:10s}: {:.2f}% / {:.0f}'.format( + key, measures['smape'], measures['mae'] + )) + class MIMOSA: |