Poisson sampling methodology for EET

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
     args: ["--profile", "black", "--filter-files"]
 
 - repo: https://github.com/psf/black
-  rev: 22.6.0
+  rev: 22.12.0
   hooks:
     - id: black
       language_version: python3.13

activitysim/abm/models/location_choice.py

@@ -17,6 +17,7 @@
 )
 from activitysim.core.interaction_sample import interaction_sample
 from activitysim.core.interaction_sample_simulate import interaction_sample_simulate
+from activitysim.core.logit import AltsContext
 from activitysim.core.util import reindex
 from activitysim.core.exceptions import DuplicateWorkflowTableError
 
@@ -603,6 +604,7 @@ def run_location_simulate(
     chunk_tag,
     trace_label,
     skip_choice=False,
+    alts_context: AltsContext | None = None,
 ):
     """
     run location model on location_sample annotated with mode_choice logsum
@@ -712,6 +714,7 @@ def run_location_simulate(
         compute_settings=model_settings.compute_settings.subcomponent_settings(
             "simulate"
         ),
+        alts_context=alts_context,
     )
 
     if not want_logsums:
@@ -788,6 +791,13 @@ def run_location_choice(
         if choosers.shape[0] == 0:
             logger.info(f"{trace_label} skipping segment {segment_name}: no choosers")
             continue
+        # using land use rather than size terms in case something goes 0 base -> nonzero project, double
+        # check if that would be in dest_size_terms as a zero
+        alts_context = AltsContext.from_series(
+            dest_size_terms.index
+        )  # index zone_id, not ALT_DEST_COL_NAME
+        # assumes that dest_size_terms will always contain zeros for non-attractive zones, i.e. it will have the
+        # same length as land_use
 
         # - location_sample
         location_sample_df = run_location_sample(
@@ -841,6 +851,7 @@ def run_location_choice(
                 trace_label, "simulate.%s" % segment_name
             ),
             skip_choice=skip_choice,
+            alts_context=alts_context,
         )
 
         if estimator:
@@ -1019,6 +1030,10 @@ def iterate_location_choice(
     ) = None  # initialize to None, will be populated in first iteration
 
     for iteration in range(1, max_iterations + 1):
+        # Force reset the setting at the start of each shadow price iteration for consistency
+        logger.info("Resetting random number seeds for iteration {}".format(iteration))
+        state.get_rn_generator().end_step(trace_label)
+        state.get_rn_generator().begin_step(trace_label)
         persons_merged_df_ = persons_merged_df.copy()
 
         if spc.use_shadow_pricing and iteration > 1:

activitysim/abm/models/parking_location_choice.py

@@ -21,6 +21,7 @@
 from activitysim.core.configuration.base import PreprocessorSettings
 from activitysim.core.configuration.logit import LogitComponentSettings
 from activitysim.core.interaction_sample_simulate import interaction_sample_simulate
+from activitysim.core.logit import AltsContext
 from activitysim.core.tracing import print_elapsed_time
 from activitysim.core.util import assign_in_place, drop_unused_columns
 from activitysim.core.exceptions import DuplicateWorkflowTableError
@@ -112,6 +113,7 @@ def parking_destination_simulate(
     chunk_size,
     trace_hh_id,
     trace_label,
+    alts_context: AltsContext | None = None,
 ):
     """
     Chose destination from destination_sample (with od_logsum and dp_logsum columns added)
@@ -150,6 +152,7 @@ def parking_destination_simulate(
         trace_label=trace_label,
         trace_choice_name="parking_loc",
         explicit_chunk_size=model_settings.explicit_chunk,
+        alts_context=alts_context,
     )
 
     # drop any failed zero_prob destinations
@@ -211,6 +214,9 @@ def choose_parking_location(
     )
     destination_sample.index = np.repeat(trips.index.values, len(alternatives))
     destination_sample.index.name = trips.index.name
+    # using destination_sample would also be right because destination_sample isn't a sample here,
+    # but that could change
+    alts_context = AltsContext.from_series(alternatives[alt_dest_col_name])
 
     destinations = parking_destination_simulate(
         state,
@@ -223,6 +229,7 @@ def choose_parking_location(
         chunk_size=chunk_size,
         trace_hh_id=trace_hh_id,
         trace_label=trace_label,
+        alts_context=alts_context,
     )
 
     if want_sample_table:

activitysim/abm/models/trip_destination.py

@@ -32,6 +32,7 @@
 from activitysim.core.configuration.logit import LocationComponentSettings
 from activitysim.core.interaction_sample import interaction_sample
 from activitysim.core.interaction_sample_simulate import interaction_sample_simulate
+from activitysim.core.logit import AltsContext
 from activitysim.core.skim_dictionary import DataFrameMatrix
 from activitysim.core.tracing import print_elapsed_time
 from activitysim.core.util import assign_in_place, reindex
@@ -950,6 +951,7 @@ def trip_destination_simulate(
     skim_hotel,
     estimator,
     trace_label,
+    alts_context: AltsContext | None = None,
 ):
     """
     Chose destination from destination_sample (with od_logsum and dp_logsum columns added)
@@ -1036,6 +1038,7 @@ def trip_destination_simulate(
         trace_choice_name="trip_dest",
         estimator=estimator,
         explicit_chunk_size=model_settings.explicit_chunk,
+        alts_context=alts_context,
     )
 
     if not want_logsums:
@@ -1126,7 +1129,10 @@ def choose_trip_destination(
         destination_sample["dp_logsum"] = 0.0
 
     t0 = print_elapsed_time("%s.compute_logsums" % trace_label, t0, debug=True)
-
+    alt_dest_col_name = model_settings.ALT_DEST_COL_NAME
+    alts = alternatives.index
+    assert alts.name == alt_dest_col_name
+    alts_context = AltsContext.from_series(alts)
     destinations = trip_destination_simulate(
         state,
         primary_purpose=primary_purpose,
@@ -1138,6 +1144,7 @@ def choose_trip_destination(
         skim_hotel=skim_hotel,
         estimator=estimator,
         trace_label=trace_label,
+        alts_context=alts_context,
     )
 
     dropped_trips = ~trips.index.isin(destinations.index)

activitysim/abm/models/trip_scheduling_choice.py

@@ -20,6 +20,7 @@
 )
 from activitysim.core.configuration.logit import LogitComponentSettings
 from activitysim.core.interaction_sample_simulate import _interaction_sample_simulate
+from activitysim.core.logit import AltsContext
 from activitysim.core.skim_dataset import SkimDataset
 from activitysim.core.skim_dictionary import SkimDict
 
@@ -314,6 +315,9 @@ def run_trip_scheduling_choice(
                 estimator=None,
                 chunk_sizer=chunk_sizer,
                 compute_settings=model_settings.compute_settings,
+                alts_context=AltsContext(
+                    schedules[SCHEDULE_ID].min(), schedules[SCHEDULE_ID].max()
+                ),
             )
 
             assert len(choices.index) == len(choosers.index)

activitysim/abm/tables/shadow_pricing.py

@@ -171,6 +171,7 @@ def __init__(
 
         self.num_processes = num_processes
         self.use_shadow_pricing = bool(state.settings.use_shadow_pricing)
+        logger.info("Use shadow pricing: %s", self.use_shadow_pricing)
         self.saved_shadow_price_file_path = (
             None  # set by read_saved_shadow_prices if loaded
         )

activitysim/core/chunk.py

@@ -1244,6 +1244,7 @@ def adaptive_chunked_choosers(
             chunk_size = math.ceil(num_choosers * explicit_chunk_size)
         else:
             chunk_size = math.ceil(explicit_chunk_size / num_processes)
+        logger.info(f"Running {trace_label} with {chunk_size} choosers")
     elif chunk_size is None:
         chunk_size = state.settings.chunk_size