def _calculate_adjustment(
        lcm, choosers, alternatives, alt_segmenter,
        clip_change_low, clip_change_high, multiplier_func=None):
    """
    Calculate adjustments to prices to compensate for
    supply and demand effects.

    Parameters
    ----------
    lcm : LocationChoiceModel
        Used to calculate the probability of agents choosing among
        alternatives. Must be fully configured and fitted.
    choosers : pandas.DataFrame
    alternatives : pandas.DataFrame
    alt_segmenter : pandas.Series
        Will be used to segment alternatives and probabilities to do
        comparisons of supply and demand by submarket.
    clip_change_low : float
        The minimum amount by which to multiply prices each iteration.
    clip_change_high : float
        The maximum amount by which to multiply prices each iteration.
    multiplier_func : function (returns Series, boolean)
        A function which takes separate demand and supply Series
        and returns a tuple where the first item is a Series with the
        ratio of new price to old price (all indexes should be the same) -
        by default the ratio of demand to supply is the ratio of the new
        price to the old price.  The second return value is a
        boolean which when True tells this module to stop looping (that
        convergence has been satisfied)

    Returns
    -------
    alts_muliplier : pandas.Series
        Same index as `alternatives`, values clipped to `clip_change_low`
        and `clip_change_high`.
    submarkets_multiplier : pandas.Series
        Index is unique values from `alt_segmenter`, values are the ratio
        of demand / supply for each segment in `alt_segmenter`.
    finished : boolean
        boolean indicator that this adjustment should be considered the
        final adjustment (if True).  If false, the iterative algorithm
        should continue.

    """
    logger.debug('start: calculate supply and demand price adjustment ratio')
    # probabilities of agents choosing * number of agents = demand
    demand = lcm.summed_probabilities(choosers, alternatives)
    # group by submarket
    demand = demand.groupby(alt_segmenter.loc[demand.index].values).sum()

    # number of alternatives
    supply = alt_segmenter.value_counts()

    if multiplier_func is not None:
        multiplier, finished = multiplier_func(demand, supply)
    else:
        multiplier, finished = (demand / supply), False
    multiplier = multiplier.clip(clip_change_low, clip_change_high)

    # broadcast multiplier back to alternatives index
    alts_muliplier = multiplier.loc[alt_segmenter]
    alts_muliplier.index = alt_segmenter.index

    logger.debug(
        ('finish: calculate supply and demand price adjustment multiplier '
         'with mean multiplier {}').format(multiplier.mean()))
    return alts_muliplier, multiplier, finished
def _calculate_adjustment ( lcm , choosers , alternatives , alt_segmenter , clip_change_low , clip_change_high , multiplier_func = None ) : logger . debug ( 'start: calculate supply and demand price adjustment ratio' ) demand = lcm . summed_probabilities ( choosers , alternatives ) demand = demand . groupby ( alt_segmenter . loc [ demand . index ] . values ) . sum ( ) supply = alt_segmenter . value_counts ( ) if multiplier_func is not None : multiplier , finished = multiplier_func ( demand , supply ) else : multiplier , finished = ( demand / supply ) , False multiplier = multiplier . clip ( clip_change_low , clip_change_high ) alts_muliplier = multiplier . loc [ alt_segmenter ] alts_muliplier . index = alt_segmenter . index logger . debug ( ( 'finish: calculate supply and demand price adjustment multiplier ' 'with mean multiplier {}' ) . format ( multiplier . mean ( ) ) ) return alts_muliplier , multiplier , finished
Calculate adjustments to prices to compensate for
    supply and demand effects.

    Parameters
    ----------
    lcm : LocationChoiceModel
        Used to calculate the probability of agents choosing among
        alternatives. Must be fully configured and fitted.
    choosers : pandas.DataFrame
    alternatives : pandas.DataFrame
    alt_segmenter : pandas.Series
        Will be used to segment alternatives and probabilities to do
        comparisons of supply and demand by submarket.
    clip_change_low : float
        The minimum amount by which to multiply prices each iteration.
    clip_change_high : float
        The maximum amount by which to multiply prices each iteration.
    multiplier_func : function (returns Series, boolean)
        A function which takes separate demand and supply Series
        and returns a tuple where the first item is a Series with the
        ratio of new price to old price (all indexes should be the same) -
        by default the ratio of demand to supply is the ratio of the new
        price to the old price.  The second return value is a
        boolean which when True tells this module to stop looping (that
        convergence has been satisfied)

    Returns
    -------
    alts_muliplier : pandas.Series
        Same index as `alternatives`, values clipped to `clip_change_low`
        and `clip_change_high`.
    submarkets_multiplier : pandas.Series
        Index is unique values from `alt_segmenter`, values are the ratio
        of demand / supply for each segment in `alt_segmenter`.
    finished : boolean
        boolean indicator that this adjustment should be considered the
        final adjustment (if True).  If false, the iterative algorithm
        should continue.