def all_subaccounts(self):
"""
Returns an iterator of all subaccounts that have a recorded transaction
with the account.
"""
return tz.unique(t.subaccount for t in self.transactions)
def choosers_columns_used(self):
"""
Columns from the choosers table that are used for filtering.
"""
return list(tz.unique(tz.concatv(
util.columns_in_filters(self.choosers_predict_filters),
util.columns_in_filters(self.choosers_fit_filters))))
def columns_in_filters(filters):
"""
Returns a list of the columns used in a set of query filters.
Parameters
----------
filters : list of str or str
List of the filters as passed passed to ``apply_filter_query``.
Returns
-------
columns : list of str
List of all the strings mentioned in the filters.
"""
if not filters:
return []
if not isinstance(filters, str):
filters = ' '.join(filters)
columns = []
reserved = {'and', 'or', 'in', 'not'}
for toknum, tokval, _, _, _ in generate_tokens(StringIO(filters).readline):
if toknum == NAME and tokval not in reserved:
columns.append(tokval)
return list(tz.unique(columns))
def columns_in_filters(filters):
"""
Returns a list of the columns used in a set of query filters.
Parameters
----------
filters : list of str or str
List of the filters as passed passed to ``apply_filter_query``.
Returns
-------
columns : list of str
List of all the strings mentioned in the filters.
"""
if not filters:
return []
if not isinstance(filters, str):
filters = ' '.join(filters)
columns = []
reserved = {'and', 'or', 'in', 'not'}
for toknum, tokval, _, _, _ in generate_tokens(StringIO(filters).readline):
if toknum == NAME and tokval not in reserved:
columns.append(tokval)
return list(tz.unique(columns))
def all_subaccounts(self):
"""
Returns an iterator of all subaccounts that have a recorded transaction
with the account.
"""
return tz.unique(t.subaccount for t in self.transactions)
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):
"""
Columns from any table used in the model. May come from either
the choosers or alternatives tables.
"""
return list(tz.unique(tz.concatv(
self.choosers_columns_used(),
#self.alts_columns_used(),
self.interaction_columns_used())))
def interaction_columns_used(self):
"""
Columns from the interaction dataset used for filtering and in
the model. These may come originally from either the choosers or
alternatives tables.
"""
return list(tz.unique(tz.concatv(
util.columns_in_filters(self.interaction_predict_filters),
util.columns_in_formula(self.model_expression))))
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 in this model for filtering
and in the model expression.
"""
return list(tz.unique(tz.concatv(
util.columns_in_filters(self.fit_filters),
util.columns_in_filters(self.predict_filters),
util.columns_in_formula(self.model_expression))))
def columns_used(self):
"""
Returns all the columns used in this model for filtering
and in the model expression.
"""
return list(
tz.unique(
tz.concatv(util.columns_in_filters(self.fit_filters),
util.columns_in_filters(self.predict_filters),
util.columns_in_formula(self.model_expression))))
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.concatv(
util.columns_in_filters(self.fit_filters),
util.columns_in_filters(self.predict_filters),
util.columns_in_formula(self.default_model_expr),
self._group.columns_used(),
[self.segmentation_col])))
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.concatv(util.columns_in_filters(self.fit_filters),
util.columns_in_filters(self.predict_filters),
util.columns_in_formula(self.default_model_expr),
self._group.columns_used(),
[self.segmentation_col])))
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 columns_in_formula(formula):
"""
Returns the names of all the columns used in a patsy formula.
Parameters
----------
formula : str, iterable, or dict
Any formula construction supported by ``str_model_expression``.
Returns
-------
columns : list of str
"""
if formula is None:
return []
formula = str_model_expression(formula, add_constant=False)
columns = []
tokens = map(
lambda x: x.extra,
tz.remove(
lambda x: x.extra is None,
_tokens_from_patsy(patsy.parse_formula.parse_formula(formula))))
for tok in tokens:
# if there are parentheses in the expression we
# want to drop them and everything outside
# and start again from the top
if '(' in tok:
start = tok.find('(') + 1
fin = tok.rfind(')')
columns.extend(columns_in_formula(tok[start:fin]))
else:
for toknum, tokval, _, _, _ in generate_tokens(
StringIO(tok).readline):
if toknum == NAME:
columns.append(tokval)
return list(tz.unique(columns))
def columns_in_formula(formula):
"""
Returns the names of all the columns used in a patsy formula.
Parameters
----------
formula : str, iterable, or dict
Any formula construction supported by ``str_model_expression``.
Returns
-------
columns : list of str
"""
if formula is None:
return []
formula = str_model_expression(formula, add_constant=False)
columns = []
tokens = map(
lambda x: x.extra,
tz.remove(
lambda x: x.extra is None,
_tokens_from_patsy(patsy.parse_formula.parse_formula(formula))))
for tok in tokens:
# if there are parentheses in the expression we
# want to drop them and everything outside
# and start again from the top
if '(' in tok:
start = tok.find('(') + 1
fin = tok.rfind(')')
columns.extend(columns_in_formula(tok[start:fin]))
else:
for toknum, tokval, _, _, _ in generate_tokens(
StringIO(tok).readline):
if toknum == NAME:
columns.append(tokval)
return list(tz.unique(columns))
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)