diff options
| -rw-r--r-- | lib/utils.py | 51 |
1 files changed, 48 insertions, 3 deletions
diff --git a/lib/utils.py b/lib/utils.py index d8df9cd..e3eda16 100644 --- a/lib/utils.py +++ b/lib/utils.py @@ -83,10 +83,11 @@ def parse_conf_str(conf_str): def remove_index_from_tuple(parameters, index): """ - Remove the element at `index` from tuple `parameters` (edited in-place). + Remove the element at `index` from tuple `parameters`. - :param parameters: tuple (edited in-place) + :param parameters: tuple :param index: index of element which is to be removed + :returns: parameters tuple without the element at index """ return (*parameters[:index], *parameters[index+1:]) @@ -202,7 +203,7 @@ def compute_param_statistics(by_name, by_param, parameter_names, arg_count, stat :param attribute: model attribute, e.g. 'power' or 'duration' :param verbose: print warning if some parameter partitions are too small for fitting - :return: a dict with the following content: + :returns: a dict with the following content: std_static -- static parameter-unaware model error: stddev of by_name[state_or_trans][attribute] std_param_lut -- static parameter-aware model error: mean stddev of by_param[(state_or_trans, *)][attribute] std_by_param -- static parameter-aware model error ignoring a single parameter. @@ -237,6 +238,26 @@ def compute_param_statistics(by_name, by_param, parameter_names, arg_count, stat return ret +def _param_values(by_param, state_or_tran): + """ + Return the distinct values of each parameter in by_param. + + E.g. if by_param.keys() contains the distinct parameter values (1, 1), (1, 2), (1, 3), (0, 3), + this function returns [[1, 0], [1, 2, 3]]. + Note that the order is not deterministic at the moment. + """ + param_tuples = list(map(lambda x: x[1], filter(lambda x: x[0] == state_or_tran, by_param.keys()))) + distinct_values = [set() for i in range(len(param_tuples[0]))] + for param_tuple in param_tuples: + for i in range(len(param_tuple)): + distinct_values[i].add(param_tuple[i]) + + # TODO returned values must have a deterministic order + + # Convert sets to lists + distinct_values = list(map(list, distinct_values)) + return distinct_values + def _mean_std_by_param(by_param, state_or_tran, attribute, param_index, verbose = False): u""" Calculate the mean standard deviation for a static model where all parameters but param_index are constant. @@ -254,8 +275,32 @@ def _mean_std_by_param(by_param, state_or_tran, attribute, param_index, verbose 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. + Also returns an (n-1)-dimensional array (where n is the number of parameters) + giving the standard deviation of each individual partition. E.g. for + param_index == 2 and 4 parameters, array[a][b][d] is the + stddev of measurements with param0 == a, param1 == b, param2 variable, + and param3 == d. """ partitions = [] + + # TODO precalculate or cache info_shape (it only depends on state_or_tran) + param_values = list(remove_index_from_tuple(_param_values(by_param, state_or_tran), param_index)) + info_shape = tuple(map(len, param_values)) + stddev_matrix = np.full(info_shape, np.nan) + + for param_value in itertools.product(*param_values): + param_partition = list() + for k, v in by_param.items(): + if k[0] == state_or_tran and (*k[1][:param_index], *k[1][param_index+1:]) == param_value: + param_partition.extend(v[attribute]) + + if len(param_partition) > 1: + matrix_index = list(range(len(param_value))) + for i in range(len(param_value)): + matrix_index[i] = param_values[i].index(param_value[i]) + matrix_index = tuple(matrix_index) + stddev_matrix[matrix_index] = np.std(param_partition) + for param_value in filter(lambda x: x[0] == state_or_tran, by_param.keys()): param_partition = [] for k, v in by_param.items(): |