#!/usr/bin/env python3 import csv from itertools import chain, combinations import io import json import numpy as np import os from scipy import optimize from gplearn.genetic import SymbolicRegressor from sklearn.metrics import r2_score import struct import sys import tarfile from multiprocessing import Pool arg_support_enabled = True def running_mean(x, N): cumsum = np.cumsum(np.insert(x, 0, 0)) return (cumsum[N:] - cumsum[:-N]) / N def is_numeric(n): if n == None: return False try: int(n) return True except ValueError: return False def soft_cast_int(n): if n == None or n == '': return None try: return int(n) except ValueError: return n def float_or_nan(n): if n == None: return np.nan try: return float(n) except ValueError: return np.nan def _elem_param_and_arg_list(elem): param_dict = elem['parameter'] paramkeys = sorted(param_dict.keys()) paramvalue = [soft_cast_int(param_dict[x]) for x in paramkeys] if arg_support_enabled and 'args' in elem: paramvalue.extend(map(soft_cast_int, elem['args'])) return paramvalue def _arg_name(arg_index): return '~arg{:02}'.format(arg_index) def append_if_set(aggregate, data, key): if key in data: aggregate.append(data[key]) def mean_or_none(arr): if len(arr): return np.mean(arr) return -1 def aggregate_measures(aggregate, actual): aggregate_array = np.array([aggregate] * len(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 mean = np.mean(actual) if len(deviations) == 0: return {} measures = { 'mae' : np.mean(np.abs(deviations), dtype=np.float64), 'msd' : np.mean(deviations**2, dtype=np.float64), 'rmsd' : np.sqrt(np.mean(deviations**2), dtype=np.float64), 'ssr' : np.sum(deviations**2, dtype=np.float64), 'rsq' : r2_score(actual, predicted), } #rsq_quotient = np.sum((actual - mean)**2, dtype=np.float64) * np.sum((predicted - mean)**2, dtype=np.float64) if np.all(actual != 0): measures['mape'] = np.mean(np.abs(deviations / actual)) * 100 # bad measure if np.all(np.abs(predicted) + np.abs(actual) != 0): measures['smape'] = np.mean(np.abs(deviations) / (( np.abs(predicted) + np.abs(actual)) / 2 )) * 100 #if np.all(rsq_quotient != 0): # measures['rsq'] = (np.sum((actual - mean) * (predicted - mean), dtype=np.float64)**2) / rsq_quotient return measures def powerset(iterable): s = list(iterable) return chain.from_iterable(combinations(s, r) for r in range(len(s)+1)) class Keysight: def __init__(self): pass def load_data(self, filename): with open(filename) as f: for i, l in enumerate(f): pass timestamps = np.ndarray((i-3), dtype=float) currents = np.ndarray((i-3), dtype=float) # basically seek back to start with open(filename) as f: for _ in range(4): next(f) reader = csv.reader(f, delimiter=',') for i, row in enumerate(reader): timestamps[i] = float(row[0]) 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'])) charges, triggers = mim.load_data(measurement['content']) trigidx = mim.trigger_edges(triggers) triggers = [] cal_edges = mim.calibration_edges(running_mean(mim.currents_nocal(charges[0:trigidx[0]]), 10)) calfunc, caldata = mim.calibration_function(charges, cal_edges) vcalfunc = np.vectorize(calfunc, otypes=[np.float64]) processed_data = { 'fileno' : measurement['fileno'], 'info' : measurement['info'], 'triggers' : len(trigidx), 'first_trig' : trigidx[0] * 10, 'calibration' : caldata, 'trace' : mim.analyze_states(charges, trigidx, vcalfunc) } return processed_data class RawData: def __init__(self, filenames): self.filenames = filenames.copy() self.traces_by_fileno = [] self.setup_by_fileno = [] self.version = 0 self.preprocessed = False self._parameter_names = None def _state_is_too_short(self, online, offline, state_duration, next_transition): # We cannot control when an interrupt causes a state to be left if next_transition['plan']['level'] == 'epilogue': return False # Note: state_duration is stored as ms, not us return offline['us'] < state_duration * 500 def _state_is_too_long(self, online, offline, state_duration, prev_transition): # If the previous state was left by an interrupt, we may have some # waiting time left over. So it's okay if the current state is longer # than expected. if prev_transition['plan']['level'] == 'epilogue': return False # state_duration is stored as ms, not us return offline['us'] > state_duration * 1500 def _measurement_is_valid(self, processed_data): setup = self.setup_by_fileno[processed_data['fileno']] traces = self.traces_by_fileno[processed_data['fileno']] state_duration = setup['state_duration'] # Check trigger count sched_trigger_count = 0 for run in traces: sched_trigger_count += len(run['trace']) if sched_trigger_count != processed_data['triggers']: processed_data['error'] = 'got {got:d} trigger edges, expected {exp:d}'.format( got = processed_data['triggers'], exp = sched_trigger_count ) return False # Check state durations. Very short or long states can indicate a # missed trigger signal which wasn't detected due to duplicate # triggers elsewhere online_datapoints = [] for run_idx, run in enumerate(traces): for trace_part_idx in range(len(run['trace'])): online_datapoints.append((run_idx, trace_part_idx)) for offline_idx, online_ref in enumerate(online_datapoints): online_run_idx, online_trace_part_idx = online_ref offline_trace_part = processed_data['trace'][offline_idx] online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx] if self._parameter_names == None: self._parameter_names = sorted(online_trace_part['parameter'].keys()) if sorted(online_trace_part['parameter'].keys()) != self._parameter_names: processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) has inconsistent paramete set: should be {param_want:s}, is {param_is:s}'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], param_want = self._parameter_names, param_is = sorted(online_trace_part['parameter'].keys()) ) if online_trace_part['isa'] != offline_trace_part['isa']: processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) claims to be {off_isa:s}, but should be {on_isa:s}'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], off_isa = offline_trace_part['isa'], on_isa = online_trace_part['isa']) return False # Clipping in UNINITIALIZED (offline_idx == 0) can happen during # calibration and is handled by MIMOSA if offline_idx != 0 and offline_trace_part['clip_rate'] != 0: processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) was clipping {clip:f}% of the time'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], clip = offline_trace_part['clip_rate'] * 100, ) return False if online_trace_part['isa'] == 'state' and online_trace_part['name'] != 'UNINITIALIZED': online_prev_transition = traces[online_run_idx]['trace'][online_trace_part_idx-1] online_next_transition = traces[online_run_idx]['trace'][online_trace_part_idx+1] try: if self._state_is_too_short(online_trace_part, offline_trace_part, state_duration, online_next_transition): processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too short (duration = {dur:d} us)'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], dur = offline_trace_part['us']) return False if self._state_is_too_long(online_trace_part, offline_trace_part, state_duration, online_prev_transition): processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too long (duration = {dur:d} us)'.format( off_idx = offline_idx, on_idx = online_run_idx, on_sub = online_trace_part_idx, on_name = online_trace_part['name'], dur = offline_trace_part['us']) return False except KeyError: pass # TODO es gibt next_transitions ohne 'plan' return True def _merge_measurement_into_online_data(self, measurement): online_datapoints = [] traces = self.traces_by_fileno[measurement['fileno']] for run_idx, run in enumerate(traces): for trace_part_idx in range(len(run['trace'])): online_datapoints.append((run_idx, trace_part_idx)) for offline_idx, online_ref in enumerate(online_datapoints): online_run_idx, online_trace_part_idx = online_ref offline_trace_part = measurement['trace'][offline_idx] online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx] if not 'offline' in online_trace_part: online_trace_part['offline'] = [offline_trace_part] else: online_trace_part['offline'].append(offline_trace_part) paramkeys = sorted(online_trace_part['parameter'].keys()) paramvalue = [soft_cast_int(online_trace_part['parameter'][x]) for x in paramkeys] # NB: Unscheduled transitions do not have an 'args' field set. # However, they should only be caused by interrupts, and # interrupts don't have args anyways. if arg_support_enabled and 'args' in online_trace_part: paramvalue.extend(map(soft_cast_int, online_trace_part['args'])) if not 'offline_aggregates' in online_trace_part: online_trace_part['offline_attributes'] = ['power', 'duration', 'energy'] online_trace_part['offline_aggregates'] = { 'power' : [], 'duration' : [], 'power_std' : [], 'energy' : [], 'paramkeys' : [], 'param': [], } if online_trace_part['isa'] == 'transition': online_trace_part['offline_attributes'].extend(['rel_energy_prev', 'rel_energy_next', '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 # relatively low sample rate of 100 kHz (10us) and removed here. online_trace_part['offline_aggregates']['power'].append( offline_trace_part['uW_mean']) online_trace_part['offline_aggregates']['duration'].append( offline_trace_part['us'] - 20) online_trace_part['offline_aggregates']['power_std'].append( offline_trace_part['uW_std']) online_trace_part['offline_aggregates']['energy'].append( offline_trace_part['uW_mean'] * (offline_trace_part['us'] - 20)) 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']['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 = [] for trace in self.traces_by_fileno: self.traces.extend(trace) def get_preprocessed_data(self, verbose = True): self.verbose = verbose if self.preprocessed: return self.traces if self.version == 0: self.preprocess_0() self.preprocessed = True return self.traces # Loads raw MIMOSA data and turns it into measurements which are ready to # be analyzed. def preprocess_0(self): mim_files = [] for i, filename in enumerate(self.filenames): with tarfile.open(filename) as tf: self.setup_by_fileno.append(json.load(tf.extractfile('setup.json'))) self.traces_by_fileno.append(json.load(tf.extractfile('src/apps/DriverEval/DriverLog.json'))) for member in tf.getmembers(): _, extension = os.path.splitext(member.name) if extension == '.mim': mim_files.append({ 'content' : tf.extractfile(member).read(), 'fileno' : i, 'info' : member, 'setup' : self.setup_by_fileno[i], 'traces' : self.traces_by_fileno[i], }) with Pool() as pool: measurements = pool.map(_preprocess_measurement, mim_files) num_valid = 0 for measurement in measurements: if self._measurement_is_valid(measurement): self._merge_measurement_into_online_data(measurement) num_valid += 1 elif self.verbose: print('[W] Skipping {ar:s}/{m:s}: {e:s}'.format( ar = self.filenames[measurement['fileno']], m = measurement['info'].name, e = measurement['error'])) if self.verbose: print('[I] {num_valid:d}/{num_total:d} measurements are valid'.format( num_valid = num_valid, num_total = len(measurements))) self._concatenate_analyzed_traces() self.preprocessing_stats = { 'num_runs' : len(measurements), 'num_valid' : num_valid } def _param_slice_eq(a, b, index): if (*a[1][:index], *a[1][index+1:]) == (*b[1][:index], *b[1][index+1:]) and a[0] == b[0]: return True return False class ParamFunction: def __init__(self, param_function, validation_function, num_vars): self._param_function = param_function self._validation_function = validation_function self._num_variables = num_vars def is_valid(self, arg): return self._validation_function(arg) def eval(self, param, args): return self._param_function(param, args) def error_function(self, P, X, y): return self._param_function(P, X) - y class AnalyticFunction: def __init__(self, function_str, num_vars, parameters, num_args): self._parameter_names = parameters self._num_args = num_args self._model_str = function_str rawfunction = function_str self._dependson = [False] * (len(parameters) + num_args) self.fit_success = False for i in range(len(parameters)): if rawfunction.find('parameter({})'.format(parameters[i])) >= 0: self._dependson[i] = True rawfunction = rawfunction.replace('parameter({})'.format(parameters[i]), 'model_param[{:d}]'.format(i)) for i in range(0, num_args): if rawfunction.find('function_arg({:d})'.format(i)) >= 0: self._dependson[len(parameters) + i] = True rawfunction = rawfunction.replace('function_arg({:d})'.format(i), 'model_param[{:d}]'.format(len(parameters) + i)) for i in range(num_vars): rawfunction = rawfunction.replace('regression_arg({:d})'.format(i), 'reg_param[{:d}]'.format(i)) self._function_str = rawfunction self._function = eval('lambda reg_param, model_param: ' + rawfunction); self._regression_args = list(np.ones((num_vars))) def _get_fit_data(self, by_param, state_or_tran, model_attribute): dimension = len(self._parameter_names) + self._num_args X = [[] for i in range(dimension)] Y = [] num_valid = 0 num_total = 0 for key, val in by_param.items(): if key[0] == state_or_tran and len(key[1]) == dimension: valid = True num_total += 1 for i in range(dimension): if self._dependson[i] and not is_numeric(key[1][i]): valid = False if valid: num_valid += 1 Y.extend(val[model_attribute]) for i in range(dimension): if self._dependson[i]: X[i].extend([float(key[1][i])] * len(val[model_attribute])) else: X[i].extend([np.nan] * len(val[model_attribute])) elif key[0] == state_or_tran and len(key[1]) != dimension: print('[W] Invalid parameter key length while gathering fit data for {}/{}. is {}, want {}.'.format(state_or_tran, model_attribute, len(key[1]), dimension)) X = np.array(X) Y = np.array(Y) return X, Y, num_valid, num_total def fit(self, by_param, state_or_tran, model_attribute): X, Y, num_valid, num_total = self._get_fit_data(by_param, state_or_tran, model_attribute) if num_valid > 2: error_function = lambda P, X, y: self._function(P, X) - y try: res = optimize.least_squares(error_function, self._regression_args, args=(X, Y), xtol=2e-15) except ValueError as err: print('[W] Fit failed for {}/{}: {} (function: {})'.format(state_or_tran, model_attribute, err, self._model_str)) return if res.status > 0: self._regression_args = res.x self.fit_success = True else: print('[W] Fit failed for {}/{}: {} (function: {})'.format(state_or_tran, model_attribute, res.message, self._model_str)) else: print('[W] Insufficient amount of valid parameter keys, cannot fit {}/{}'.format(state_or_tran, model_attribute)) def is_predictable(self, param_list): for i, param in enumerate(param_list): if self._dependson[i] and not is_numeric(param): return False return True def eval(self, param_list): return self._function(self._regression_args, param_list) class analytic: _num0_8 = np.vectorize(lambda x: 8 - bin(int(x)).count("1")) _num0_16 = np.vectorize(lambda x: 16 - bin(int(x)).count("1")) _num1 = np.vectorize(lambda x: bin(int(x)).count("1")) _function_map = { 'linear' : lambda x: x, 'logarithmic' : np.log, 'logarithmic1' : lambda x: np.log(x + 1), 'exponential' : np.exp, 'square' : lambda x : x ** 2, 'fractional' : lambda x : 1 / x, 'sqrt' : np.sqrt, 'num0_8' : _num0_8, 'num0_16' : _num0_16, 'num1' : _num1, } def functions(): functions = { 'linear' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * model_param, lambda model_param: True, 2 ), 'logarithmic' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.log(model_param), lambda model_param: model_param > 0, 2 ), 'logarithmic1' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.log(model_param + 1), lambda model_param: model_param > -1, 2 ), 'exponential' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.exp(model_param), lambda model_param: model_param <= 64, 2 ), #'polynomial' : lambda reg_param, model_param: reg_param[0] + reg_param[1] * model_param + reg_param[2] * model_param ** 2, 'square' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * model_param ** 2, lambda model_param: True, 2 ), 'fractional' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] / model_param, lambda model_param: model_param != 0, 2 ), 'sqrt' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.sqrt(model_param), lambda model_param: model_param >= 0, 2 ), 'num0_8' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._num0_8(model_param), lambda model_param: True, 2 ), 'num0_16' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._num0_16(model_param), lambda model_param: True, 2 ), 'num1' : ParamFunction( lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._num1(model_param), lambda model_param: True, 2 ), } return functions def _fmap(reference_type, reference_name, function_type): ref_str = '{}({})'.format(reference_type,reference_name) if function_type == 'linear': return ref_str if function_type == 'logarithmic': return 'np.log({})'.format(ref_str) if function_type == 'logarithmic1': return 'np.log({} + 1)'.format(ref_str) if function_type == 'exponential': return 'np.exp({})'.format(ref_str) if function_type == 'exponential': return 'np.exp({})'.format(ref_str) if function_type == 'square': return '({})**2'.format(ref_str) if function_type == 'fractional': return '1/({})'.format(ref_str) if function_type == 'sqrt': return 'np.sqrt({})'.format(ref_str) return 'analytic._{}({})'.format(function_type, ref_str) def function_powerset(function_descriptions, parameter_names, num_args): buf = '0' arg_idx = 0 for combination in powerset(function_descriptions.items()): buf += ' + regression_arg({:d})'.format(arg_idx) arg_idx += 1 for function_item in combination: if arg_support_enabled and is_numeric(function_item[0]): buf += ' * {}'.format(analytic._fmap('function_arg', function_item[0], function_item[1]['best'])) else: buf += ' * {}'.format(analytic._fmap('parameter', function_item[0], function_item[1]['best'])) return AnalyticFunction(buf, arg_idx, parameter_names, num_args) #def function_powerset(function_descriptions): # function_buffer = lambda param, arg: 0 # param_idx = 0 # for combination in powerset(function_descriptions): # new_function = lambda param, arg: param[param_idx] # param_idx += 1 # for function_name in combination: # new_function = lambda param, arg: new_function(param, arg) * analytic._function_map[function_name](arg) # new_function = lambda param, arg: param[param_idx] * # function_buffer = lambda param, arg: function_buffer(param, arg) + def _try_fits_parallel(arg): return { 'key' : arg['key'], 'result' : _try_fits(*arg['args']) } def _try_fits(by_param, state_or_tran, model_attribute, param_index): functions = analytic.functions() for param_key in filter(lambda x: x[0] == state_or_tran, by_param.keys()): # We might remove elements from 'functions' while iterating over # its keys. A generator will not allow this, so we need to # convert to a list. function_names = list(functions.keys()) for function_name in function_names: function_object = functions[function_name] if is_numeric(param_key[1][param_index]) and not function_object.is_valid(param_key[1][param_index]): functions.pop(function_name, None) raw_results = {} ref_results = { 'mean' : [], 'median' : [] } results = {} for param_key in filter(lambda x: x[0] == state_or_tran, by_param.keys()): X = [] Y = [] num_valid = 0 num_total = 0 for k, v in by_param.items(): if _param_slice_eq(k, param_key, param_index): num_total += 1 if is_numeric(k[1][param_index]): num_valid += 1 X.extend([float(k[1][param_index])] * len(v[model_attribute])) Y.extend(v[model_attribute]) if num_valid > 2: X = np.array(X) Y = np.array(Y) for function_name, param_function in functions.items(): raw_results[function_name] = {} error_function = param_function.error_function res = optimize.least_squares(error_function, [0, 1], args=(X, Y), xtol=2e-15) measures = regression_measures(param_function.eval(res.x, X), Y) for measure, error_rate in measures.items(): if not measure in raw_results[function_name]: raw_results[function_name][measure] = [] raw_results[function_name][measure].append(error_rate) #print(function_name, res, measures) mean_measures = aggregate_measures(np.mean(Y), Y) ref_results['mean'].append(mean_measures['rmsd']) median_measures = aggregate_measures(np.median(Y), Y) ref_results['median'].append(median_measures['rmsd']) best_fit_val = np.inf best_fit_name = None for function_name, result in raw_results.items(): if len(result) > 0: results[function_name] = {} for measure in result.keys(): results[function_name][measure] = np.mean(result[measure]) rmsd = results[function_name]['rmsd'] if rmsd < best_fit_val: best_fit_val = rmsd best_fit_name = function_name return { 'best' : best_fit_name, 'best_rmsd' : best_fit_val, 'mean_rmsd' : np.mean(ref_results['mean']), 'median_rmsd' : np.mean(ref_results['median']), 'results' : results } def _compute_param_statistics_parallel(args): return { 'state_or_trans' : args['state_or_trans'], 'key' : args['key'], 'result' : _compute_param_statistics(*args['args']) } def _compute_param_statistics(by_name, by_param, parameter_names, num_args, state_or_trans, key): ret = { 'std_static' : np.std(by_name[state_or_trans][key]), 'std_param_lut' : np.mean([np.std(by_param[x][key]) for x in by_param.keys() if x[0] == state_or_trans]), 'std_by_param' : {}, 'std_by_arg' : [], } 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 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)) return ret # returns the mean standard deviation of all measurements of 'what' # (e.g. power consumption or timeout) for state/transition 'name' where # parameter 'index' is dynamic and all other parameters are fixed. # I.e., if parameters are a, b, c ∈ {1,2,3} and 'index' corresponds to b', then # this function returns the mean of the standard deviations of (a=1, b=*, c=1), # (a=1, b=*, c=2), and so on def _mean_std_by_param(by_param, state_or_tran, key, param_index): partitions = [] for param_value in filter(lambda x: x[0] == state_or_tran, by_param.keys()): param_partition = [] for k, v in by_param.items(): if _param_slice_eq(k, param_value, param_index): param_partition.extend(v[key]) if len(param_partition): partitions.append(param_partition) else: print('[W] parameter value partition for {} is empty'.format(param_value)) return np.mean([np.std(partition) for partition in partitions]) class EnergyModel: def __init__(self, preprocessed_data, ignore_trace_indexes = None, discard_outliers = None): self.traces = preprocessed_data self.by_name = {} self.by_param = {} self.by_trace = {} self.stats = {} np.seterr('raise') self._parameter_names = sorted(self.traces[0]['trace'][0]['parameter'].keys()) self._num_args = {} self._outlier_threshold = discard_outliers if discard_outliers != None: self._compute_outlier_stats(ignore_trace_indexes, discard_outliers) for run in self.traces: if ignore_trace_indexes == None or int(run['id']) not in ignore_trace_indexes: for i, elem in enumerate(run['trace']): if elem['name'] != 'UNINITIALIZED': 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']) self._aggregate_to_ndarray(self.by_name) self._compute_all_param_statistics() def _compute_outlier_stats(self, ignore_trace_indexes, threshold): tmp_by_param = {} self.median_by_param = {} for run in self.traces: if ignore_trace_indexes == None or int(run['id']) not in ignore_trace_indexes: for i, elem in enumerate(run['trace']): key = (elem['name'], tuple(_elem_param_and_arg_list(elem))) if not key in tmp_by_param: tmp_by_param[key] = {} for attribute in elem['offline_attributes']: tmp_by_param[key][attribute] = [] for attribute in elem['offline_attributes']: tmp_by_param[key][attribute].extend(elem['offline_aggregates'][attribute]) for key, elem in tmp_by_param.items(): if not key in self.median_by_param: self.median_by_param[key] = {} for attribute in tmp_by_param[key].keys(): self.median_by_param[key][attribute] = np.median(tmp_by_param[key][attribute]) def _compute_all_param_statistics(self): queue = [] for state_or_trans in self.by_name.keys(): self.stats[state_or_trans] = {} 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({ # 'state_or_trans' : state_or_trans, # 'key' : key, # 'args' : [self.by_name, self.by_param, self._parameter_names, self._num_args, state_or_trans, key] #}) # IPC overhead for by_name/by_param (un)pickling is higher than # multiprocessing speedup... so let's not do this. #with Pool() as pool: # results = pool.map(_compute_param_statistics_parallel, queue) #for ret in results: # self.stats[ret['state_or_trans']][ret['key']] = ret['result'] @classmethod def from_model(self, model_data, parameter_names): self.by_name = {} self.by_param = {} self.stats = {} np.seterr('raise') self._parameter_names = parameter_names for state_or_tran, values in model_data.items(): for elem in values: self._load_agg_elem(state_or_tran, elem) #if elem['isa'] == 'transition' and not state_or_tran in self._num_args and 'args' in elem: # self._num_args = len(elem['args']) self._aggregate_to_ndarray(self.by_name) self._compute_all_param_statistics() def _aggregate_to_ndarray(self, aggregate): for elem in aggregate.values(): for key in elem['attributes']: elem[key] = np.array(elem[key]) def _prune_outliers(self, key, attribute, data): if self._outlier_threshold == None: return data median = self.median_by_param[key][attribute] if np.median(np.abs(data - median)) == 0: return data pruned_data = list(filter(lambda x: np.abs(0.6745 * (x - median) / np.median(np.abs(data - median))) > self._outlier_threshold, data )) if len(pruned_data): print('[I] Pruned outliers from ({}) {}: {}'.format(key, attribute, pruned_data)) data = list(filter(lambda x: np.abs(0.6745 * (x - median) / np.median(np.abs(data - median))) <= self._outlier_threshold, data )) return data def _add_data_to_aggregate(self, aggregate, key, element): if not key in aggregate: aggregate[key] = { '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(): if datakey in element['offline_attributes']: dataval = self._prune_outliers((element['name'], tuple(_elem_param_and_arg_list(element))), datakey, dataval) aggregate[key][datakey].extend(dataval) def _load_agg_elem(self, name, elem): self._add_data_to_aggregate(self.by_name, name, elem) self._add_data_to_aggregate(self.by_param, (name, tuple(elem['param'])), elem) def _load_run_elem(self, i, elem): self._add_data_to_aggregate(self.by_name, elem['name'], elem) self._add_data_to_aggregate(self.by_param, (elem['name'], tuple(_elem_param_and_arg_list(elem))), elem) def generic_param_independence_ratio(self, state_or_trans, key): statistics = self.stats[state_or_trans][key] if statistics['std_static'] == 0: return 0 return statistics['std_param_lut'] / statistics['std_static'] def generic_param_dependence_ratio(self, state_or_trans, key): return 1 - self.generic_param_independence_ratio(state_or_trans, key) def param_independence_ratio(self, state_or_trans, key, param): statistics = self.stats[state_or_trans][key] if statistics['std_by_param'][param] == 0: return 0 return statistics['std_param_lut'] / statistics['std_by_param'][param] def param_dependence_ratio(self, state_or_trans, key, param): return 1 - self.param_independence_ratio(state_or_trans, key, param) def arg_independence_ratio(self, state_or_trans, key, arg_index): statistics = self.stats[state_or_trans][key] if statistics['std_by_arg'][arg_index] == 0: return 0 return statistics['std_param_lut'] / statistics['std_by_arg'][arg_index] def arg_dependence_ratio(self, state_or_trans, key, arg_index): return 1 - self.arg_independence_ratio(state_or_trans, key, arg_index) def _get_model_from_dict(self, model_dict, model_function): model = {} for name, elem in model_dict.items(): model[name] = {} 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): static_model = self._get_model_from_dict(self.by_name, np.median) def static_median_getter(name, key, **kwargs): return static_model[name][key] return static_median_getter def get_static_using_mean(self): static_model = self._get_model_from_dict(self.by_name, np.mean) def static_mean_getter(name, key, **kwargs): return static_model[name][key] return static_mean_getter def get_param_lut(self): lut_model = self._get_model_from_dict(self.by_param, np.median) def lut_median_getter(name, key, param, arg = [], **kwargs): param.extend(map(soft_cast_int, arg)) return lut_model[(name, tuple(param))][key] return lut_median_getter def get_param_analytic(self): static_model = self._get_model_from_dict(self.by_name, np.median) def get_fitted(self): static_model = self._get_model_from_dict(self.by_name, np.median) param_model = dict([[state_or_tran, {}] for state_or_tran in self.by_name.keys()]) fit_queue = [] 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)) 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: fit_queue.append({ 'key' : [state_or_tran, model_attribute, parameter_name], 'args' : [self.by_param, state_or_tran, model_attribute, parameter_index] }) #fit_results[parameter_name] = _try_fits(self.by_param, state_or_tran, model_attribute, parameter_index) #print('{} {} is {}'.format(state_or_tran, parameter_name, fit_results[parameter_name]['best'])) if arg_support_enabled and self.by_name[state_or_tran]['isa'] == 'transition': for arg_index in range(self._num_args[state_or_tran]): if self.arg_dependence_ratio(state_or_tran, model_attribute, arg_index) > 0.5: fit_queue.append({ 'key' : [state_or_tran, model_attribute, arg_index], 'args' : [param_subdict, state_or_tran, model_attribute, len(self._parameter_names) + arg_index] }) #fit_results[_arg_name(arg_index)] = _try_fits(self.by_param, state_or_tran, model_attribute, len(self._parameter_names) + arg_index) #if 'args' in self.by_name[state_or_tran]: # for i, arg in range(len(self.by_name with Pool() as pool: all_fit_results = pool.map(_try_fits_parallel, fit_queue) for state_or_tran in self.by_name.keys(): num_args = 0 if arg_support_enabled and self.by_name[state_or_tran]['isa'] == 'transition': num_args = self._num_args[state_or_tran] 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: fit_result = result['result'] if fit_result['best_rmsd'] >= min(fit_result['mean_rmsd'], fit_result['median_rmsd']): print('[I] Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})'.format( state_or_tran, model_attribute, result['key'][2], fit_result['best_rmsd'], fit_result['mean_rmsd'], fit_result['median_rmsd'])) elif fit_result['best_rmsd'] >= 0.5 * min(fit_result['mean_rmsd'], fit_result['median_rmsd']): print('[I] Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ({:.0f}, {:.0f})'.format( state_or_tran, model_attribute, result['key'][2], fit_result['best_rmsd'], fit_result['mean_rmsd'], fit_result['median_rmsd'])) else: fit_results[result['key'][2]] = fit_result if len(fit_results.keys()): x = analytic.function_powerset(fit_results, self._parameter_names, num_args) x.fit(self.by_param, state_or_tran, model_attribute) if x.fit_success: param_model[state_or_tran][model_attribute] = { 'fit_result': fit_results, 'function' : x } def model_getter(name, key, **kwargs): if key in param_model[name]: param_list = kwargs['param'] param_function = param_model[name][key]['function'] if param_function.is_predictable(param_list): return param_function.eval(param_list) return static_model[name][key] def info_getter(name, key): if key in param_model[name]: return param_model[name][key] return None return model_getter, info_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 parameters(self): return self._parameter_names def assess(self, model_function): results = {} for name, elem in sorted(self.by_name.items()): results[name] = {} 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 class MIMOSA: def __init__(self, voltage, shunt): self.voltage = voltage self.shunt = shunt self.r1 = 984 # "1k" self.r2 = 99013 # "100k" def charge_to_current_nocal(self, charge): ua_max = 1.836 / self.shunt * 1000000 ua_step = ua_max / 65535 return charge * ua_step def _load_tf(self, tf): num_bytes = tf.getmember('/tmp/mimosa//mimosa_scale_1.tmp').size charges = np.ndarray(shape=(int(num_bytes / 4)), dtype=np.int32) triggers = np.ndarray(shape=(int(num_bytes / 4)), dtype=np.int8) with tf.extractfile('/tmp/mimosa//mimosa_scale_1.tmp') as f: content = f.read() iterator = struct.iter_unpack('> 4) triggers[i] = (word[0] & 0x08) >> 3 i += 1 return charges, triggers def load_data(self, raw_data): with io.BytesIO(raw_data) as data_object: with tarfile.open(fileobj = data_object) as tf: return self._load_tf(tf) def currents_nocal(self, charges): ua_max = 1.836 / self.shunt * 1000000 ua_step = ua_max / 65535 return charges.astype(np.double) * ua_step def trigger_edges(self, triggers): trigidx = [] prevtrig = triggers[0] # the device is reset for MIMOSA calibration in the first 10s and may # send bogus interrupts -> bogus triggers for i in range(1000000, triggers.shape[0]): trig = triggers[i] if trig != prevtrig: # Due to MIMOSA's integrate-read-reset cycle, the trigger # appears two points (20µs) before the corresponding data trigidx.append(i+2) prevtrig = trig return trigidx def calibration_edges(self, currents): r1idx = 0 r2idx = 0 ua_r1 = self.voltage / self.r1 * 1000000 # first second may be bogus for i in range(100000, len(currents)): if r1idx == 0 and currents[i] > ua_r1 * 0.6: r1idx = i elif r1idx != 0 and r2idx == 0 and i > (r1idx + 180000) and currents[i] < ua_r1 * 0.4: r2idx = i # 2s disconnected, 2s r1, 2s r2 with r1 < r2 -> ua_r1 > ua_r2 # allow 5ms buffer in both directions to account for bouncing relais contacts return r1idx - 180500, r1idx - 500, r1idx + 500, r2idx - 500, r2idx + 500, r2idx + 180500 def calibration_function(self, charges, cal_edges): dis_start, dis_end, r1_start, r1_end, r2_start, r2_end = cal_edges if dis_start < 0: dis_start = 0 chg_r0 = charges[dis_start:dis_end] chg_r1 = charges[r1_start:r1_end] chg_r2 = charges[r2_start:r2_end] cal_0_mean = np.mean(chg_r0) cal_0_std = np.std(chg_r0) cal_r1_mean = np.mean(chg_r1) cal_r1_std = np.std(chg_r1) cal_r2_mean = np.mean(chg_r2) cal_r2_std = np.std(chg_r2) ua_r1 = self.voltage / self.r1 * 1000000 ua_r2 = self.voltage / self.r2 * 1000000 if cal_r2_mean > cal_0_mean: b_lower = (ua_r2 - 0) / (cal_r2_mean - cal_0_mean) else: print('[W] 0 uA == %.f uA during calibration' % (ua_r2)) b_lower = 0 b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean) b_total = (ua_r1 - 0) / (cal_r1_mean - cal_0_mean) a_lower = -b_lower * cal_0_mean a_upper = -b_upper * cal_r2_mean a_total = -b_total * cal_0_mean if self.shunt == 680: # R1 current is higher than shunt range -> only use R2 for calibration def calfunc(charge): if charge < cal_0_mean: return 0 else: return charge * b_lower + a_lower else: def calfunc(charge): if charge < cal_0_mean: return 0 if charge <= cal_r2_mean: return charge * b_lower + a_lower else: return charge * b_upper + a_upper + ua_r2 caldata = { 'edges' : [x * 10 for x in cal_edges], 'offset': cal_0_mean, 'offset2' : cal_r2_mean, 'slope_low' : b_lower, 'slope_high' : b_upper, 'add_low' : a_lower, 'add_high' : a_upper, 'r0_err_uW' : np.mean(self.currents_nocal(chg_r0)) * self.voltage, 'r0_std_uW' : np.std(self.currents_nocal(chg_r0)) * self.voltage, 'r1_err_uW' : (np.mean(self.currents_nocal(chg_r1)) - ua_r1) * self.voltage, 'r1_std_uW' : np.std(self.currents_nocal(chg_r1)) * self.voltage, 'r2_err_uW' : (np.mean(self.currents_nocal(chg_r2)) - ua_r2) * self.voltage, 'r2_std_uW' : np.std(self.currents_nocal(chg_r2)) * self.voltage, } #print("if charge < %f : return 0" % cal_0_mean) #print("if charge <= %f : return charge * %f + %f" % (cal_r2_mean, b_lower, a_lower)) #print("else : return charge * %f + %f + %f" % (b_upper, a_upper, ua_r2)) return calfunc, caldata def calcgrad(self, currents, threshold): grad = np.gradient(running_mean(currents * self.voltage, 10)) # len(grad) == len(currents) - 9 subst = [] lastgrad = 0 for i in range(len(grad)): # minimum substate duration: 10ms if np.abs(grad[i]) > threshold and i - lastgrad > 50: # account for skew introduced by running_mean and current # ramp slope (parasitic capacitors etc.) subst.append(i+10) lastgrad = i if lastgrad != i: subst.append(i+10) return subst # TODO konfigurierbare min/max threshold und len(gradidx) > X, binaere # Sache nach noetiger threshold. postprocessing mit # "zwei benachbarte substates haben sehr aehnliche werte / niedrige stddev" -> mergen # ... min/max muessen nicht vorgegeben werden, sind ja bekannt (0 / np.max(grad)) # TODO bei substates / index foo den offset durch running_mean beachten # TODO ggf. clustering der 'abs(grad) > threshold' und bestimmung interessanter # uebergaenge dadurch? def gradfoo(self, currents): gradients = np.abs(np.gradient(running_mean(currents * self.voltage, 10))) gradmin = np.min(gradients) gradmax = np.max(gradients) threshold = np.mean([gradmin, gradmax]) gradidx = self.calcgrad(currents, threshold) num_substates = 2 while len(gradidx) != num_substates: if gradmax - gradmin < 0.1: # We did our best return threshold, gradidx if len(gradidx) > num_substates: gradmin = threshold else: gradmax = threshold threshold = np.mean([gradmin, gradmax]) gradidx = self.calcgrad(currents, threshold) return threshold, gradidx def analyze_states(self, charges, trigidx, ua_func): previdx = 0 is_state = True iterdata = [] for idx in trigidx: range_raw = charges[previdx:idx] range_ua = ua_func(range_raw) substates = {} if previdx != 0 and idx - previdx > 200: thr, subst = 0, [] #self.gradfoo(range_ua) if len(subst): statelist = [] prevsubidx = 0 for subidx in subst: statelist.append({ 'duration': (subidx - prevsubidx) * 10, 'uW_mean' : np.mean(range_ua[prevsubidx : subidx] * self.voltage), 'uW_std' : np.std(range_ua[prevsubidx : subidx] * self.voltage), }) prevsubidx = subidx substates = { 'threshold' : thr, 'states' : statelist, } isa = 'state' if not is_state: isa = 'transition' data = { 'isa': isa, 'clip_rate' : np.mean(range_raw == 65535), 'raw_mean': np.mean(range_raw), 'raw_std' : np.std(range_raw), 'uW_mean' : np.mean(range_ua * self.voltage), 'uW_std' : np.std(range_ua * self.voltage), 'us' : (idx - previdx) * 10, } if 'states' in substates: data['substates'] = substates ssum = np.sum(list(map(lambda x : x['duration'], substates['states']))) if ssum != data['us']: print("ERR: duration %d vs %d" % (data['us'], ssum)) if isa == 'transition': # subtract average power of previous state # (that is, the state from which this transition originates) data['uW_mean_delta_prev'] = data['uW_mean'] - iterdata[-1]['uW_mean'] # placeholder to avoid extra cases in the analysis data['uW_mean_delta_next'] = data['uW_mean'] data['timeout'] = iterdata[-1]['us'] elif len(iterdata) > 0: # subtract average power of next state # (the state into which this transition leads) iterdata[-1]['uW_mean_delta_next'] = iterdata[-1]['uW_mean'] - data['uW_mean'] iterdata.append(data) previdx = idx is_state = not is_state return iterdata