path: root/lib/parameters.py

import itertools
import numpy as np
from utils import remove_index_from_tuple, is_numeric

def compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_trans, attribute, verbose = False):
    """
    Compute standard deviation and correlation coefficient for various data partitions.

    It is strongly recommended to vary all parameter values evenly across partitions.
    For instance, given two parameters, providing only the combinations
    (1, 1), (5, 1), (7, 1,) (10, 1), (1, 2), (1, 6) will lead to bogus results.
    It is better to provide (1, 1), (5, 1), (1, 2), (5, 2), ... (i.e. a cross product of all individual parameter values)

    :param by_name: ground truth partitioned by state/transition name.
        by_name[state_or_trans][attribute] must be a list or 1-D numpy array.
        by_name[state_or_trans]['param'] must be a list of parameter values
        corresponding to the ground truth, e.g. [[1, 2, 3], ...] if the
        first ground truth element has the (lexically) first parameter set to 1,
        the second to 2 and the third to 3.
    :param by_param: ground truth partitioned by state/transition name and parameters.
        by_name[(state_or_trans, *)][attribute] must be a list or 1-D numpy array.
    :param parameter_names: list of parameter names, must have the same order as the parameter
        values in by_param (lexical sorting is recommended).
    :param arg_count: dict providing the number of functions args ("local parameters") for each function.
    :param state_or_trans: state or transition name, e.g. 'send' or 'TX'
    :param attribute: model attribute, e.g. 'power' or 'duration'
    :param verbose: print warning if some parameter partitions are too small for fitting

    :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.
        dictionary with one key per parameter. The value is the mean stddev
        of measurements where all other parameters are fixed and the parameter
        in question is variable. E.g. std_by_param['X'] is the mean stddev of
        by_param[(state_or_trans, (X=*, Y=..., Z=...))][attribute].
    std_by_arg -- same, but ignoring a single function argument
        Only set if state_or_trans appears in arg_count, empty dict otherwise.
    corr_by_param -- correlation coefficient
    corr_by_arg -- same, but ignoring a single function argument
        Only set if state_or_trans appears in arg_count, empty dict otherwise.
    """
    ret = {
        'std_static' : np.std(by_name[state_or_trans][attribute]),
        'std_param_lut' : np.mean([np.std(by_param[x][attribute]) for x in by_param.keys() if x[0] == state_or_trans]),
        'std_by_param' : {},
        'std_by_param_values' : {},
        'lut_by_param_values' : {},
        'std_by_arg' : [],
        'std_by_arg_values' : [],
        'lut_by_arg_values' : [],
        'corr_by_param' : {},
        'corr_by_arg' : [],
    }

    np.seterr('raise')

    param_values = distinct_param_values(by_name, state_or_trans)

    for param_idx, param in enumerate(parameter_names):
        std_matrix, mean_std, lut_matrix = _std_by_param(by_param, param_values, state_or_trans, attribute, param_idx, verbose)
        ret['std_by_param'][param] = mean_std
        ret['std_by_param_values'][param] = std_matrix
        ret['lut_by_param_values'][param] = lut_matrix
        ret['corr_by_param'][param] = _corr_by_param(by_name, state_or_trans, attribute, param_idx)
    if state_or_trans in arg_count:
        for arg_index in range(arg_count[state_or_trans]):
            std_matrix, mean_std, lut_matrix = _std_by_param(by_param, param_values, state_or_trans, attribute, len(parameter_names) + arg_index, verbose)
            ret['std_by_arg'].append(mean_std)
            ret['std_by_arg_values'].append(std_matrix)
            ret['lut_by_arg_values'].append(lut_matrix)
            ret['corr_by_arg'].append(_corr_by_param(by_name, state_or_trans, attribute, len(parameter_names) + arg_index))

    return ret

def distinct_param_values(by_name, state_or_tran):
    """
    Return the distinct values of each parameter in by_name[state_or_tran].

    E.g. if by_name[state_or_tran]['param'] contains the distinct entries (1, 1), (1, 2), (1, 3), (0, 3),
    this function returns [[1, 0], [1, 2, 3]].
    Note that the order is not guaranteed to be deterministic at the moment.

    Also note that this function deliberately also consider None
    (uninitialized parameter with unknown value) as a distinct value. Benchmarks
    and drivers must ensure that a parameter is only None when its value is
    not important yet, e.g. a packet length parameter must only be None when
    write() or similar has not been called yet. Other parameters should always
    be initialized when leaving UNINITIALIZED.
    """
    # TODO a set() is an _unordered_ collection, so this must be converted to
    # an OrderedDict or a list with a duplicate-pruning step
    distinct_values = [set() for i in range(len(by_name[state_or_tran]['param'][0]))]
    for param_tuple in by_name[state_or_tran]['param']:
        for i in range(len(param_tuple)):
            distinct_values[i].add(param_tuple[i])

    # Convert sets to lists
    distinct_values = list(map(list, distinct_values))
    return distinct_values

def _std_by_param(by_param, all_param_values, state_or_tran, attribute, param_index, verbose = False):
    u"""
    Calculate standard deviations for a static model where all parameters but param_index are constant.

    :param by_param: measurements sorted by key/transition name and parameter values
    :param state_or_tran: state or transition name (-> by_param[(state_or_tran, *)])
    :param attribute: model attribute, e.g. 'power' or 'duration'
           (-> by_param[(state_or_tran, *)][attribute])
    :param param_index: index of variable parameter
    :returns: (stddev matrix, mean stddev)

    Returns the mean standard deviation of all measurements of 'attribute'
    (e.g. power consumption or timeout) for state/transition 'state_or_tran' where
    parameter 'param_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.
    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.
    """
    param_values = list(remove_index_from_tuple(all_param_values, param_index))
    info_shape = tuple(map(len, param_values))

    # We will calculate the mean over the entire matrix later on. We cannot
    # guarantee that each entry will be filled in this loop (e.g. transitions
    # whose arguments are combined using 'zip' rather than 'cartesian' always
    # have missing parameter combinations), we pre-fill it with NaN and use
    # np.nanmean to skip those when calculating the mean.
    stddev_matrix = np.full(info_shape, np.nan)
    lut_matrix = np.full(info_shape, np.nan)

    for param_value in itertools.product(*param_values):
        param_partition = list()
        std_list = 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])
                std_list.append(np.std(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)
            lut_matrix[matrix_index] = np.mean(std_list)
        # This can (and will) happen in normal operation, e.g. when a transition's
        # arguments are combined using 'zip' rather than 'cartesian'.
        #elif len(param_partition) == 1:
        #    vprint(verbose, '[W] parameter value partition for {} contains only one element -- skipping'.format(param_value))
        #else:
        #    vprint(verbose, '[W] parameter value partition for {} is empty'.format(param_value))

    if np.all(np.isnan(stddev_matrix)):
        vprint(verbose, '[W] {}/{} parameter #{} has no data partitions -- how did this even happen?'.format(state_or_tran, attribute, param_index))
        vprint(verbose, 'stddev_matrix = {}'.format(stddev_matrix))
        return stddev_matrix, 0.

    return stddev_matrix, np.nanmean(stddev_matrix), lut_matrix #np.mean([np.std(partition) for partition in partitions])

def _corr_by_param(by_name, state_or_trans, attribute, param_index):
    if _all_params_are_numeric(by_name[state_or_trans], param_index):
        param_values = np.array(list((map(lambda x: x[param_index], by_name[state_or_trans]['param']))))
        try:
            return np.corrcoef(by_name[state_or_trans][attribute], param_values)[0, 1]
        except FloatingPointError:
            # Typically happens when all parameter values are identical.
            # Building a correlation coefficient is pointless in this case
            # -> assume no correlation
            return 0.
        except ValueError:
            print('[!] Exception in _corr_by_param(by_name, state_or_trans={}, attribute={}, param_index={})'.format(state_or_trans, attribute, param_index))
            print('[!] while executing np.corrcoef(by_name[{}][{}]={}, {}))'.format(state_or_trans, attribute, by_name[state_or_trans][attribute], param_values))
            raise
    else:
        return 0.

def _all_params_are_numeric(data, param_idx):
    """Check if all `data['param'][*][param_idx]` elements are numeric, as reported by `utils.is_numeric`."""
    param_values = list(map(lambda x: x[param_idx], data['param']))
    if len(list(filter(is_numeric, param_values))) == len(param_values):
        return True
    return False

def prune_dependent_parameters(by_name, parameter_names, correlation_threshold = 0.5):
    """
    Remove dependent parameters from aggregate.

    :param by_name: measurements partitioned by state/transition/... name and attribute, edited in-place.
        by_name[name][attribute] must be a list or 1-D numpy array.
        by_name[stanamete_or_trans]['param'] must be a list of parameter values.
        Other dict members are left as-is
    :param parameter_names: List of parameter names in the order they are used in by_name[name]['param'], edited in-place.
    :param correlation_threshold: Remove parameter if absolute correlation exceeds this threshold (default: 0.5)

    Model generation (and its components, such as relevant parameter detection and least squares optimization) only works if input variables (i.e., parameters)
    are independent of each other. This function computes the correlation coefficient for each pair of parameters and removes those which depend on each other.
    For each pair of dependent parameters, the lexically greater one is removed (e.g. "a" and "b" -> "b" is removed).
    """

    parameter_indices_to_remove = list()
    for parameter_combination in itertools.product(range(len(parameter_names)), range(len(parameter_names))):
        index_1, index_2 = parameter_combination
        if index_1 >= index_2:
            continue
        parameter_values = [list(), list()] # both parameters have a value
        parameter_values_1 = list() # parameter 1 has a value
        parameter_values_2 = list() # parameter 2 has a value
        for name in by_name:
            for measurement in by_name[name]['param']:
                value_1 = measurement[index_1]
                value_2 = measurement[index_2]
                if is_numeric(value_1):
                    parameter_values_1.append(value_1)
                if is_numeric(value_2):
                    parameter_values_2.append(value_2)
                if is_numeric(value_1) and is_numeric(value_2):
                    parameter_values[0].append(value_1)
                    parameter_values[1].append(value_2)
        if len(parameter_values[0]):
            # Calculating the correlation coefficient only makes sense when neither value is constant
            if np.std(parameter_values_1) != 0 and np.std(parameter_values_2) != 0:
                correlation = np.corrcoef(parameter_values)[0][1]
                if correlation != np.nan and np.abs(correlation) > correlation_threshold:
                    print('[!] Parameters {} <-> {} are correlated with coefficcient {}'.format(parameter_names[index_1], parameter_names[index_2], correlation))
                    if len(parameter_values_1) < len(parameter_values_2):
                        index_to_remove = index_1
                    else:
                        index_to_remove = index_2
                    print('    Removing parameter {}'.format(parameter_names[index_to_remove]))
                    parameter_indices_to_remove.append(index_to_remove)
    remove_parameters_by_indices(by_name, parameter_names, parameter_indices_to_remove)

def remove_parameters_by_indices(by_name, parameter_names, parameter_indices_to_remove):
    """
    Remove parameters listed in `parameter_indices` from aggregate `by_name` and `parameter_names`.

    :param by_name: measurements partitioned by state/transition/... name and attribute, edited in-place.
        by_name[name][attribute] must be a list or 1-D numpy array.
        by_name[stanamete_or_trans]['param'] must be a list of parameter values.
        Other dict members are left as-is
    :param parameter_names: List of parameter names in the order they are used in by_name[name]['param'], edited in-place.
    :param parameter_indices_to_remove: List of parameter indices to be removed
    """

    # Start removal from the end of the list to avoid renumbering of list elemenets
    for parameter_index in sorted(parameter_indices_to_remove, reverse = True):
        for name in by_name:
            for measurement in by_name[name]['param']:
                measurement.pop(parameter_index)
        parameter_names.pop(parameter_index)

class ParamStats:

    def __init__(self, by_name, by_param, parameter_names, arg_count, use_corrcoef = False, verbose = False):
        """
        Compute standard deviation and correlation coefficient on parameterized data partitions.

        It is strongly recommended to vary all parameter values evenly.
        For instance, given two parameters, providing only the combinations
        (1, 1), (5, 1), (7, 1,) (10, 1), (1, 2), (1, 6) will lead to bogus results.
        It is better to provide (1, 1), (5, 1), (1, 2), (5, 2), ... (i.e. a cross product of all individual parameter values)

        arguments:
        by_name -- ground truth partitioned by state/transition name.
            by_name[state_or_trans][attribute] must be a list or 1-D numpy array.
            by_name[state_or_trans]['param'] must be a list of parameter values
            corresponding to the ground truth, e.g. [[1, 2, 3], ...] if the
            first ground truth element has the (lexically) first parameter set to 1,
            the second to 2 and the third to 3.
        by_param -- ground truth partitioned by state/transition name and parameters.
            by_name[(state_or_trans, *)][attribute] must be a list or 1-D numpy array.
        parameter_names -- list of parameter names, must have the same order as the parameter
            values in by_param (lexical sorting is recommended).
        arg_count -- dict providing the number of functions args ("local parameters") for each function.
        use_corrcoef -- use correlation coefficient instead of stddev heuristic for parameter detection
        """
        self.stats = dict()
        self.use_corrcoef = use_corrcoef
        self._parameter_names = parameter_names
        # Note: This is deliberately single-threaded. The overhead incurred
        # by multiprocessing is higher than the speed gained by parallel
        # computation of statistics measures.
        for state_or_tran in by_name.keys():
            self.stats[state_or_tran] = dict()
            for attribute in by_name[state_or_tran]['attributes']:
                self.stats[state_or_tran][attribute] = compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_tran, attribute, verbose = verbose)

    def _generic_param_independence_ratio(self, state_or_trans, attribute):
        """
        Return the heuristic ratio of parameter independence for state_or_trans and attribute.

        This is not supported if the correlation coefficient is used.
        A value close to 1 means no influence, a value close to 0 means high probability of influence.
        """
        statistics = self.stats[state_or_trans][attribute]
        if self.use_corrcoef:
            # not supported
            raise ValueError
        if statistics['std_static'] == 0:
            return 0
        return statistics['std_param_lut'] / statistics['std_static']

    def generic_param_dependence_ratio(self, state_or_trans, attribute):
        """
        Return the heuristic ratio of parameter dependence for state_or_trans and attribute.

        This is not supported if the correlation coefficient is used.
        A value close to 0 means no influence, a value close to 1 means high probability of influence.
        """
        return 1 - self._generic_param_independence_ratio(state_or_trans, attribute)

    def _reduce_param_matrix(self, matrix: np.ndarray, parameter_names: list) -> list:
        """
        :param matrix: parameter dependence matrix, M[(...)] == 1 iff (model attribute) is influenced by (parameter) for other parameter value indxe == (...)
        :param parameter_names: names of parameters in the order in which they appear in the matrix index. The first entry corresponds to the first axis, etc.
        :returns: parameters which determine whether (parameter) has an effect on (model attribute). If a parameter is not part of this list, its value does not
            affect (parameter)'s influence on (model attribute) -- it either always or never has an influence
        """
        if np.all(matrix == True) or np.all(matrix == False):
            return list()

        if not is_power_of_two(np.count_nonzero(matrix)):
            # cannot be reliably reduced to a list of parameters
            return list()

        if np.count_nonzero(matrix) == 1:
            influential_parameters = list()
            for i, parameter_name in enumerate(parameter_names):
                if matrix.shape[i] > 1:
                    influential_parameters.append(parameter_name)
            return influential_parameters

        for axis in range(matrix.ndim):
            candidate = self._reduce_param_matrix(np.all(matrix, axis=axis), remove_index_from_tuple(parameter_names, axis))
            if len(candidate):
                return candidate

        return list()

    def _get_codependent_parameters(self, stats, param):
        """
        Return list of parameters which affect whether `param` influences the model attribute described in `stats` or not.
        """
        safe_div = np.vectorize(lambda x,y: 0. if x == 0 else 1 - x/y)
        ratio_by_value = safe_div(stats['lut_by_param_values'][param], stats['std_by_param_values'][param])
        err_mode = np.seterr('ignore')
        dep_by_value = ratio_by_value > 0.5
        np.seterr(**err_mode)

        other_param_list = list(filter(lambda x: x != param, self._parameter_names))
        influencer_parameters = self._reduce_param_matrix(dep_by_value, other_param_list)
        return influencer_parameters

    def _param_independence_ratio(self, state_or_trans: str, attribute: str, param: str) -> float:
        """
        Return the heuristic ratio of parameter independence for state_or_trans, attribute, and param.

        A value close to 1 means no influence, a value close to 0 means high probability of influence.
        """
        statistics = self.stats[state_or_trans][attribute]
        if self.use_corrcoef:
            return 1 - np.abs(statistics['corr_by_param'][param])
        if statistics['std_by_param'][param] == 0:
            if statistics['std_param_lut'] != 0:
                raise RuntimeError("wat")
            # In general, std_param_lut < std_by_param. So, if std_by_param == 0, std_param_lut == 0 follows.
            # This means that the variation of param does not affect the model quality -> no influence, return 1
            return 1.

        return statistics['std_param_lut'] / statistics['std_by_param'][param]

    def param_dependence_ratio(self, state_or_trans: str, attribute: str, param: str) -> float:
        """
        Return the heuristic ratio of parameter dependence for state_or_trans, attribute, and param.

        A value close to 0 means no influence, a value close to 1 means high probability of influence.

        :param state_or_trans: state or transition name
        :param attribute: model attribute
        :param param: parameter name

        :returns: parameter dependence (float between 0 == no influence and 1 == high probability of influence)
        """
        return 1 - self._param_independence_ratio(state_or_trans, attribute, param)

    def reverse_dependent_parameters(self, state_or_trans: str, attribute: str, param: str) -> list:
        """
        Return parameters whose value influences whether `attribute` of `state_or_trans` depends on `param` or not.

        For example, a radio's TX POWER is only influenced by the packet length if dynamically sized payloads are enabled.
        So reverse_dependent_parameters('TX', 'POWER', 'packet_length') == ['dynamic_payload_size'].

        :param state_or_trans: state or transition name
        :param attribute: model attribute
        :param param: parameter name

        :returns: list of parameters
        """
        return self._get_codependent_parameters(self.stats[state_or_trans][attribute], param)

    def _arg_independence_ratio(self, state_or_trans, attribute, arg_index):
        statistics = self.stats[state_or_trans][attribute]
        if self.use_corrcoef:
            return 1 - np.abs(statistics['corr_by_arg'][arg_index])
        if statistics['std_by_arg'][arg_index] == 0:
            if statistics['std_param_lut'] != 0:
                raise RuntimeError("wat")
            # In general, std_param_lut < std_by_arg. So, if std_by_arg == 0, std_param_lut == 0 follows.
            # This means that the variation of arg does not affect the model quality -> no influence, return 1
            return 1
        return statistics['std_param_lut'] / statistics['std_by_arg'][arg_index]

    def arg_dependence_ratio(self, state_or_trans: str, attribute: str, arg_index: int) -> float:
        return 1 - self._arg_independence_ratio(state_or_trans, attribute, arg_index)

    # This heuristic is very similar to the "function is not much better than
    # median" checks in get_fitted. So far, doing it here as well is mostly
    # a performance and not an algorithm quality decision.
    # --df, 2018-04-18
    def depends_on_param(self, state_or_trans, attribute, param):
        """Return whether attribute of state_or_trans depens on param."""
        if self.use_corrcoef:
            return self.param_dependence_ratio(state_or_trans, attribute, param) > 0.1
        else:
            return self.param_dependence_ratio(state_or_trans, attribute, param) > 0.5

    # See notes on depends_on_param
    def depends_on_arg(self, state_or_trans, attribute, arg_index):
        """Return whether attribute of state_or_trans depens on arg_index."""
        if self.use_corrcoef:
            return self.arg_dependence_ratio(state_or_trans, attribute, arg_index) > 0.1
        else:
            return self.arg_dependence_ratio(state_or_trans, attribute, arg_index) > 0.5