def columns_used(self):
"""
Returns all the columns used across all models in the group
for filtering and in the model expression.
"""
return list(tz.unique(tz.concat(
m.columns_used() for m in self.models.values())))
def columns_used(self):
"""
Returns all the columns used across all models in the group
for filtering and in the model expression.
"""
return list(
tz.unique(tz.concat(m.columns_used()
for m in self.models.values())))
def _column_names_from_metadata(dicts):
"""
Get the unique set of keys from a list of dictionaries.
Parameters
----------
dicts : iterable
Sequence of dictionaries.
Returns
-------
keys : list
Unique set of keys.
"""
return list(tz.unique(tz.concat(dicts)))
def _column_names_from_metadata(dicts):
"""
Get the unique set of keys from a list of dictionaries.
Parameters
----------
dicts : iterable
Sequence of dictionaries.
Returns
-------
keys : list
Unique set of keys.
"""
return list(tz.unique(tz.concat(dicts)))
def initial_household_utilities(utilities, people, hh_id_col):
"""
Create initial household utilities by grouping and summing utilities
from individual household members.
Parameters
----------
utilities : pandas.DataFrame
Should have the index of `people` and columns for each alternative.
people : pandas.DataFrame
DataFrame of individual people data.
hh_id_col : str
Name of the column in `people` that has their household ID.
Returns
-------
hh_util : dict of pandas.Series
Keys will be household IDs and values will be Series
mapping alternative choices to their utility.
"""
hh_util = {}
alts = utilities.columns
combo_cache = {}
for hh_id, df in people.groupby(hh_id_col, sort=False):
hh_size = len(df)
utils = utilities.loc[df.index].as_matrix()
if hh_size in combo_cache:
ncombos, combos, flat_combos, tiled = combo_cache[hh_size]
else:
combos = list(itertools.product(alts, repeat=hh_size))
flat_combos = list(
tz.concat(itertools.product(range(len(alts)), repeat=hh_size)))
ncombos = len(combos)
tiled = np.tile(np.arange(hh_size), ncombos)
combo_cache[hh_size] = (ncombos, combos, flat_combos, tiled)
u = utils[tiled, flat_combos].reshape((ncombos, hh_size)).sum(axis=1)
hh_util[hh_id] = pd.Series(u, index=combos)
return hh_util
def household_choices_to_people(hh_choices, people):
"""
Map household choices to people so that we know the activity pattern
for individuals.
Parameters
----------
hh_choices : pandas.Series
Maps household ID to chosen alternative, where the alternative
is a tuple of individual utilities.
people : pandas.DataFrame
DataFrame of individual people data.
Returns
-------
choices : pandas.Series
Maps index of `people` to their activity pattern choice.
"""
return pd.Series(
gen(tz.concat(hh_choices.values)), index=people.index)