diff options
| -rwxr-xr-x | lib/dfatool.py | 97 |
1 files changed, 59 insertions, 38 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 422b838..41bb76c 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -122,6 +122,33 @@ class Keysight: currents[i] = float(row[2]) * -1 return timestamps, currents +def _xv_partitions_kfold(length, num_slices): + pairs = [] + indexes = np.arange(length) + for i in range(0, num_slices): + training = np.delete(indexes, slice(i, None, num_slices)) + validation = indexes[i::num_slices] + pairs.append((training, validation)) + return pairs + +def _xv_partitions_montecarlo(length, num_slices): + pairs = [] + for i in range(0, num_slices): + shuffled = np.random.permutation(np.arange(length)) + border = int(length * float(2) / 3) + training = shuffled[:border] + validation = shuffled[border:] + pairs.append((training, validation)) + return pairs + +class CrossValidation: + + def __init__(self, em, num_partitions): + self._em = em + self._num_partitions = num_partitions + x = EnergyModel.from_model(em.by_name, em._parameter_names) + + def _preprocess_measurement(measurement): setup = measurement['setup'] mim = MIMOSA(float(setup['mimosa_voltage']), int(setup['mimosa_shunt'])) @@ -287,9 +314,9 @@ class RawData: 'param': [], } 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'] = [] + online_trace_part['offline_aggregates']['timeout'] = [] # Note: All state/transitions are 20us "too long" due to injected # active wait states. These are needed to work around MIMOSA's @@ -305,12 +332,12 @@ class RawData: online_trace_part['offline_aggregates']['paramkeys'].append(paramkeys) online_trace_part['offline_aggregates']['param'].append(paramvalue) if online_trace_part['isa'] == 'transition': - online_trace_part['offline_aggregates']['timeout'].append( - offline_trace_part['timeout']) online_trace_part['offline_aggregates']['rel_energy_prev'].append( offline_trace_part['uW_mean_delta_prev'] * (offline_trace_part['us'] - 20)) online_trace_part['offline_aggregates']['rel_energy_next'].append( offline_trace_part['uW_mean_delta_next'] * (offline_trace_part['us'] - 20)) + online_trace_part['offline_aggregates']['timeout'].append( + offline_trace_part['timeout']) def _concatenate_analyzed_traces(self): self.traces = [] @@ -683,7 +710,7 @@ def _compute_param_statistics(by_name, by_param, parameter_names, num_args, stat for param_idx, param in enumerate(parameter_names): ret['std_by_param'][param] = _mean_std_by_param(by_param, state_or_trans, key, param_idx) - if arg_support_enabled and by_name[state_or_trans]['isa'] == 'transition': + if arg_support_enabled and state_or_trans in num_args: for arg_index in range(num_args[state_or_trans]): ret['std_by_arg'].append(_mean_std_by_param(by_param, state_or_trans, key, len(parameter_names) + arg_index)) @@ -718,6 +745,7 @@ class EnergyModel: self.stats = {} np.seterr('raise') self._parameter_names = sorted(self.traces[0]['trace'][0]['parameter'].keys()) + self._epilogue_functions = set() self._num_args = {} for run in self.traces: if ignore_trace_indexes == None or int(run['id']) not in ignore_trace_indexes: @@ -726,8 +754,9 @@ class EnergyModel: self._load_run_elem(i, elem) if elem['isa'] == 'transition' and not elem['name'] in self._num_args and 'args' in elem: self._num_args[elem['name']] = len(elem['args']) - else: - print('[I] ignored trace index #{:d}'.format(int(run['id']))) + #if elem['isa'] == 'transition' and elem['plan']['level'] == 'epilogue': + # self._epilogue_functions.add(elem['name']) + print(self._epilogue_functions) self._aggregate_to_ndarray(self.by_name) self._compute_all_param_statistics() @@ -735,7 +764,7 @@ class EnergyModel: queue = [] for state_or_trans in self.by_name.keys(): self.stats[state_or_trans] = {} - for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: + for key in self.by_name[state_or_trans]['attributes']: if key in self.by_name[state_or_trans]: self.stats[state_or_trans][key] = _compute_param_statistics(self.by_name, self.by_param, self._parameter_names, self._num_args, state_or_trans, key) #queue.append({ @@ -768,18 +797,24 @@ class EnergyModel: def _aggregate_to_ndarray(self, aggregate): for elem in aggregate.values(): - for key in ['power', 'power_std', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: - if key in elem: - elem[key] = np.array(elem[key]) + for key in elem['attributes']: + elem[key] = np.array(elem[key]) def _add_data_to_aggregate(self, aggregate, key, element): if not key in aggregate: aggregate[key] = { - 'isa' : element['isa'] + 'isa' : element['isa'], + 'attributes' : list(element['offline_aggregates'].keys()) } for datakey in element['offline_aggregates'].keys(): aggregate[key][datakey] = [] + if element['isa'] == 'state': + aggregate[key]['attributes'] = ['power'] + else: + aggregate[key]['attributes'] = ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next'] + if element['plan']['level'] == 'epilogue': + aggregate[key]['attributes'].insert(0, 'timeout') for datakey, dataval in element['offline_aggregates'].items(): aggregate[key][datakey].extend(dataval) @@ -822,14 +857,13 @@ class EnergyModel: model = {} for name, elem in model_dict.items(): model[name] = {} - for key in ['power', 'energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next']: - if key in elem: - try: - model[name][key] = model_function(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)) + for key in elem['attributes']: + try: + model[name][key] = model_function(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)) return model def get_static(self): @@ -867,11 +901,7 @@ class EnergyModel: for state_or_tran in self.by_name.keys(): param_keys = filter(lambda k: k[0] == state_or_tran, self.by_param.keys()) param_subdict = dict(map(lambda k: [k, self.by_param[k]], param_keys)) - if self.by_name[state_or_tran]['isa'] == 'state': - attributes = ['power'] - else: - attributes = ['energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next'] - for model_attribute in attributes: + for model_attribute in self.by_name[state_or_tran]['attributes']: fit_results = {} for parameter_index, parameter_name in enumerate(self._parameter_names): if self.param_dependence_ratio(state_or_tran, model_attribute, parameter_name) > 0.5: @@ -898,11 +928,7 @@ class EnergyModel: num_args = 0 if arg_support_enabled and self.by_name[state_or_tran]['isa'] == 'transition': num_args = self._num_args[state_or_tran] - if self.by_name[state_or_tran]['isa'] == 'state': - attributes = ['power'] - else: - attributes = ['energy', 'duration', 'timeout', 'rel_energy_prev', 'rel_energy_next'] - for model_attribute in attributes: + for model_attribute in self.by_name[state_or_tran]['attributes']: fit_results = {} for result in all_fit_results: if result['key'][0] == state_or_tran and result['key'][1] == model_attribute: @@ -956,15 +982,10 @@ class EnergyModel: results = {} for name, elem in sorted(self.by_name.items()): results[name] = {} - if elem['isa'] == 'state': - predicted_data = np.array(list(map(lambda i: model_function(name, 'power', param=elem['param'][i]), range(len(elem['power']))))) - measures = regression_measures(predicted_data, elem['power']) - results[name]['power'] = measures - else: - for key in ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next', 'timeout']: - predicted_data = np.array(list(map(lambda i: model_function(name, key, param=elem['param'][i]), range(len(elem[key]))))) - measures = regression_measures(predicted_data, elem[key]) - results[name][key] = measures + for key in elem['attributes']: + predicted_data = np.array(list(map(lambda i: model_function(name, key, param=elem['param'][i]), range(len(elem[key]))))) + measures = regression_measures(predicted_data, elem[key]) + results[name][key] = measures return results |