def other_than(groups, bools):
"""
Construct a Series that has booleans indicating the presence of
something- or someone-else with a certain property within a group.
Parameters
----------
groups : pandas.Series
A column with the same index as `bools` that defines the grouping
of `bools`. The `bools` Series will be used to index `groups` and
then the grouped values will be counted.
bools : pandas.Series
A boolean Series indicating where the property of interest is present.
Should have the same index as `groups`.
Returns
-------
others : pandas.Series
A boolean Series with the same index as `groups` and `bools`
indicating whether there is something- or something-else within
a group with some property (as indicated by `bools`).
"""
counts = groups[bools].value_counts()
merge_col = groups.to_frame(name="right")
pipeline = tz.compose(
tz.curry(pd.Series.fillna, value=False),
itemgetter("left"),
tz.curry(pd.DataFrame.merge, right=merge_col, how="right", left_index=True, right_on="right"),
tz.curry(pd.Series.to_frame, name="left"),
)
gt0 = pipeline(counts > 0)
gt1 = pipeline(counts > 1)
return gt1.where(bools, other=gt0)
def make_household_choices(hh_util):
"""
Decide on the activity pattern for each household.
Parameters
----------
hh_util : dict of pandas.Series
Keys will be household IDs and values will be Series
mapping alternative choices to their utility.
Returns
-------
choices : pandas.Series
Maps household ID to chosen alternative, where the alternative
is a tuple of individual utilities.
"""
# convert hh_util dict to a few DFs with alternatives in the columns
# and household IDs in the index
df_func = tz.compose(
pd.DataFrame.transpose,
pd.DataFrame.from_dict)
grouped_by_size = (
tz.valfilter(lambda x: len(x) == l, hh_util)
for l in tz.unique(tz.map(len, hh_util.values())))
dfs = tz.map(df_func, grouped_by_size)
# go over all the DFs and do utils_to_probs and make_choices
choices = (
pd.Series(
df.columns[mnl.make_choices(mnl.utils_to_probs(df))].values,
index=df.index)
for df in dfs)
# concat all the resulting Series
return pd.concat(choices)
def other_than(groups, bools):
"""
Construct a Series that has booleans indicating the presence of
something- or someone-else with a certain property within a group.
Parameters
----------
groups : pandas.Series
A column with the same index as `bools` that defines the grouping
of `bools`. The `bools` Series will be used to index `groups` and
then the grouped values will be counted.
bools : pandas.Series
A boolean Series indicating where the property of interest is present.
Should have the same index as `groups`.
Returns
-------
others : pandas.Series
A boolean Series with the same index as `groups` and `bools`
indicating whether there is something- or something-else within
a group with some property (as indicated by `bools`).
"""
counts = groups[bools].value_counts()
merge_col = groups.to_frame(name='right')
pipeline = tz.compose(
tz.curry(pd.Series.fillna, value=False), itemgetter('left'),
tz.curry(pd.DataFrame.merge,
right=merge_col,
how='right',
left_index=True,
right_on='right'), tz.curry(pd.Series.to_frame, name='left'))
gt0 = pipeline(counts > 0)
gt1 = pipeline(counts > 1)
return gt1.where(bools, other=gt0)
