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Merge pull request #788 from jpn--/develop-merge
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Merge missing changes from main back to develop
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@jpn--
jpn-- authored Jan 31, 2024
2 parents fe6bd6e + e161158 commit 3d66176
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Showing 2 changed files with 519 additions and 2 deletions.
230 changes: 228 additions & 2 deletions activitysim/abm/models/util/cdap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -313,6 +313,20 @@ def get_cached_spec(state: workflow.State, hhsize):
return None


def get_cached_joint_spec(hhsize):

spec_name = cached_joint_spec_name(hhsize)

spec = inject.get_injectable(spec_name, None)
if spec is not None:
logger.debug(
"build_cdap_joint_spec returning cached injectable spec %s", spec_name
)
return spec

return None


def get_cached_joint_spec(state: workflow.State, hhsize):

spec_name = cached_joint_spec_name(hhsize)
Expand Down Expand Up @@ -345,6 +359,12 @@ def cache_joint_spec(hhsize, spec):
inject.add_injectable(spec_name, spec)


def cache_joint_spec(hhsize, spec):
spec_name = cached_joint_spec_name(hhsize)
# cache as injectable
inject.add_injectable(spec_name, spec)


def build_cdap_spec(
state: workflow.State,
interaction_coefficients,
Expand Down Expand Up @@ -550,6 +570,207 @@ def build_cdap_spec(
return spec


def build_cdap_joint_spec(
joint_tour_coefficients, hhsize, trace_spec=False, trace_label=None, cache=True
):
"""
Build a spec file for computing joint tour utilities of alternative household member for households of specified size.
We generate this spec automatically from a table of rules and coefficients because the
interaction rules are fairly simple and can be expressed compactly whereas
there is a lot of redundancy between the spec files for different household sizes, as well as
in the vectorized expression of the interaction alternatives within the spec file itself
joint_tour_coefficients has five columns:
label
label of the expression
description
description of the expression
dependency
if the expression is dependent on alternative, and which alternative is it dependent on
(e.g. M_px, N_px, H_px)
expression
expression of the utility term
coefficient
The coefficient to apply for the alternative
The generated spec will have the eval expression in the index, and a utility column for each
alternative (e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN', 'MMJ', 'MNJ', 'NMJ', 'NNJ'] for hhsize 2 with joint alts)
Parameters
----------
joint_tour_coefficients : pandas.DataFrame
Rules and coefficients for generating joint tour specs for different household sizes
hhsize : int
household size for which the spec should be built.
Returns
-------
spec: pandas.DataFrame
"""

t0 = tracing.print_elapsed_time()

# cdap joint spec is same for all households of MAX_HHSIZE and greater
hhsize = min(hhsize, MAX_HHSIZE)

if cache:
spec = get_cached_joint_spec(hhsize)
if spec is not None:
return spec

expression_name = "Expression"

# generate a list of activity pattern alternatives for this hhsize
# e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN'] for hhsize=2
alternatives = ["".join(tup) for tup in itertools.product("HMN", repeat=hhsize)]

joint_alternatives = [
"".join(tup) + "J"
for tup in itertools.product("HMN", repeat=hhsize)
if tup.count("M") + tup.count("N") >= 2
]
alternatives = alternatives + joint_alternatives

# spec df has expression column plus a column for each alternative
spec = pd.DataFrame(columns=[expression_name] + alternatives)

# Before processing the interaction_coefficients, we add add rows to the spec to carry
# the alternative utilities previously computed for each individual into all hh alternative
# columns in which the individual assigned that alternative. The Expression column contains
# the name of the choosers column with that individuals utility for the individual alternative
# and the hh alternative columns that should receive that utility are given a value of 1
# e.g. M_p1 is a column in choosers with the individual utility to person p1 of alternative M
# Expression MM MN MH NM NN NH HM HN HH
# M_p1 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
# N_p1 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0
for pnum in range(1, hhsize + 1):
for activity in ["M", "N", "H"]:

new_row_index = len(spec)
spec.loc[new_row_index, expression_name] = add_pn(activity, pnum)

# list of alternative columns where person pnum has expression activity
# e.g. for M_p1 we want the columns where activity M is in position p1
alternative_columns = [
alt for alt in alternatives if alt[pnum - 1] == activity
]
spec.loc[new_row_index, alternative_columns] = 1

# for each row in the joint util table
for row in joint_tour_coefficients.itertuples():

# if there is no dependencies
if row.dependency is np.nan:
expression = row.Expression
# add a new row to spec
new_row_index = len(spec)
spec.loc[new_row_index, expression_name] = expression
spec.loc[new_row_index, alternatives] = row.coefficient
# if there is dependencies
else:
dependency_name = row.dependency
expression = row.Expression
coefficient = row.coefficient
if dependency_name in ["M_px", "N_px", "H_px"]:
if "_pxprod" in expression:
prod_conds = row.Expression.split("|")
expanded_expressions = [
tup
for tup in itertools.product(
range(len(prod_conds)), repeat=hhsize
)
]
for expression_tup in expanded_expressions:
expression_list = []
dependency_list = []
for counter in range(len(expression_tup)):
expression_list.append(
prod_conds[expression_tup[counter]].replace(
"xprod", str(counter + 1)
)
)
if expression_tup[counter] == 0:
dependency_list.append(
dependency_name.replace("x", str(counter + 1))
)

expression_value = "&".join(expression_list)
dependency_value = pd.Series(
np.ones(len(alternatives)), index=alternatives
)
if len(dependency_list) > 0:
for dependency in dependency_list:
# temp = spec.loc[spec[expression_name]==dependency, alternatives].squeeze().fillna(0)
dependency_value *= (
spec.loc[
spec[expression_name] == dependency,
alternatives,
]
.squeeze()
.fillna(0)
)

# add a new row to spec
new_row_index = len(spec)
spec.loc[new_row_index] = dependency_value
spec.loc[new_row_index, expression_name] = expression_value
spec.loc[new_row_index, alternatives] = (
spec.loc[new_row_index, alternatives] * coefficient
)

elif "_px" in expression:
for pnum in range(1, hhsize + 1):
dependency_name = row.dependency.replace("x", str(pnum))
expression = row.Expression.replace("x", str(pnum))

# add a new row to spec
new_row_index = len(spec)
spec.loc[new_row_index] = spec.loc[
spec[expression_name] == dependency_name
].squeeze()
spec.loc[new_row_index, expression_name] = expression
spec.loc[new_row_index, alternatives] = (
spec.loc[new_row_index, alternatives] * coefficient
)

# drop dependency rows
spec = spec[~spec[expression_name].str.startswith(("M_p", "N_p", "H_p"))]

# eval expression goes in the index
spec.set_index(expression_name, inplace=True)

for c in spec.columns:
spec[c] = spec[c].fillna(0)

simulate.uniquify_spec_index(spec)

# make non-joint alts 0
for c in alternatives:
if c.endswith("J"):
continue
else:
spec[c] = 0

if trace_spec:
tracing.trace_df(
spec,
"%s.hhsize%d_joint_spec" % (trace_label, hhsize),
transpose=False,
slicer="NONE",
)

if trace_spec:
tracing.trace_df(
spec,
"%s.hhsize%d_joint_spec_patched" % (trace_label, hhsize),
transpose=False,
slicer="NONE",
)

if cache:
cache_joint_spec(hhsize, spec)

t0 = tracing.print_elapsed_time("build_cdap_joint_spec hh_size %s" % hhsize, t0)

return spec


def build_cdap_joint_spec(
state: workflow.State,
joint_tour_coefficients,
Expand Down Expand Up @@ -992,8 +1213,6 @@ def household_activity_choices(
# add joint util to util
utils = utils.add(joint_tour_utils)

probs = logit.utils_to_probs(state, utils, trace_label=trace_label)

# select an activity pattern alternative for each household based on probability
# result is a series indexed on _hh_index_ with the (0 based) index of the column from probs
idx_choices, rands = logit.make_choices(state, probs, trace_label=trace_label)
Expand Down Expand Up @@ -1287,6 +1506,13 @@ def _run_cdap(
lambda x: 1 if "J" in x else 0
)

# return household joint tour flag
if add_joint_tour_utility:
hh_activity_choices = hh_activity_choices.to_frame(name="hh_choices")
hh_activity_choices["has_joint_tour"] = hh_activity_choices["hh_choices"].apply(
lambda x: 1 if "J" in x else 0
)

# if DUMP:
# state.tracing.trace_df(hh_activity_choices, '%s.DUMP.hh_activity_choices' % trace_label,
# transpose=False, slicer='NONE')
Expand Down
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