diff options
| -rw-r--r-- | lib/parameters.py | 49 |
1 files changed, 48 insertions, 1 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index ac67c2a..d2fa253 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -95,7 +95,7 @@ def _std_by_param(n_by_param, all_param_values, param_index): :param param_index: index of variable parameter :returns: (stddev matrix, mean stddev, LUT matrix) *stddev matrix* is an ((number of parameters)-1)-dimensional matrix giving the standard deviation of each individual parameter variation partition. - E.g. for param_index == 2 and 4 parameters, stddev matrix[a][b][d] is the stddev of + E.g. for param_index == 2 and 4 parameters, stddev matrix[a, b, d] is the stddev of measurements with param0 == all_param_values[0][a], param1 == all_param_values[1][b], param2 variable, and param3 == all_param_values[3][d]. @@ -261,6 +261,8 @@ def _compute_param_statistics( ret["_depends_on_param"] = dict() ret["_depends_on_arg"] = list() + ret["codependent_param_pair"] = _codepenent_param_pair(param_tuples) + np.seterr("raise") for param_idx, param in enumerate(param_names): @@ -306,6 +308,51 @@ def _compute_param_statistics( return ret +def _codepenent_param_pair(param_values): + lut = [dict() for i in param_values[0]] + for param_index in range(len(param_values[0])): + uniqs = set(map(lambda param_tuple: param_tuple[param_index], param_values)) + for uniq_index, uniq in enumerate(uniqs): + lut[param_index][uniq] = uniq_index + + normed_param_values = list() + for param_tuple in param_values: + normed_param_values.append( + tuple(map(lambda ipv: lut[ipv[0]][ipv[1]], enumerate(param_tuple))) + ) + + normed_param_values = np.array(normed_param_values) + + std_by_param = list() + std_by_param_pair = dict() + + ret = dict() + + for param1_i in range(len(lut)): + std_by_param.append(np.std(normed_param_values[:, param1_i])) + for param2_i in range(param1_i + 1, len(lut)): + stds = list() + for param1_value in range(len(lut[param1_i])): + tt = normed_param_values[:, param1_i] == param1_value + values = normed_param_values[tt, param2_i] + if len(values) <= 1: + stds.append(0.0) + else: + stds.append(np.std(values)) + std_by_param_pair[(param1_i, param2_i)] = np.mean(stds) + + for param1_i in range(len(lut)): + for param2_i in range(param1_i + 1, len(lut)): + if std_by_param[param1_i] > 0 and std_by_param[param2_i] > 0: + if std_by_param_pair[(param1_i, param2_i)] == 0: + ret[(param1_i, param2_i)] = True + logger.warning( + f"parameters ({param1_i}, {param2_i}) are codependent" + ) + + return ret + + def _compute_param_statistics_parallel(arg): return {"key": arg["key"], "dict": _compute_param_statistics(*arg["args"])} |