BayDAG Contribution #11: School Escorting Estimation Updates

activitysim/abm/models/school_escorting.py

@@ -58,7 +58,14 @@ def determine_escorting_participants(
         & (persons.cdap_activity == "M")
     ]
     households_with_escortees = escortees["household_id"]
-    choosers = choosers[choosers.index.isin(households_with_escortees)]
+    if len(households_with_escortees) == 0:
+        logger.warning("No households with escortees found!")
+    else:
+        tot_households = len(choosers)
+        choosers = choosers[choosers.index.isin(households_with_escortees)]
+        logger.info(
+            f"Proceeding with {len(choosers)} households with escortees out of {tot_households} total households"
+        )
 
     # can specify different weights to determine chaperones
     persontype_weight = model_settings.PERSON_WEIGHT
@@ -140,7 +147,7 @@ def add_prev_choices_to_choosers(
             stage_alts,
             how="left",
             left_on=escorting_choice,
-            right_on=stage_alts.index.name,
+            right_index=True,
         )
         .set_index("household_id")
     )
@@ -216,8 +223,12 @@ def create_school_escorting_bundles_table(choosers, tours, stage):
     bundles : pd.DataFrame
         one school escorting bundle per row
     """
-    # making a table of bundles
-    choosers = choosers.reset_index()
+    # want to keep household_id in columns, which is already there if running in estimation mode
+    if "household_id" in choosers.columns:
+        choosers = choosers.reset_index(drop=True)
+    else:
+        choosers = choosers.reset_index()
+    # creating a row for every school escorting bundle
     choosers = choosers.loc[choosers.index.repeat(choosers["nbundles"])]
 
     bundles = pd.DataFrame()
@@ -460,7 +471,11 @@ def school_escorting(
 
     trace_hh_id = state.settings.trace_hh_id
 
-    alts = simulate.read_model_alts(state, model_settings.ALTS, set_index="Alt")
+    # FIXME setting index as "Alt" causes crash in estimation mode...
+    # happens in joint_tour_frequency_composition too!
+    # alts = simulate.read_model_alts(state, model_settings.ALTS, set_index="Alt")
+    alts = simulate.read_model_alts(state, model_settings.ALTS, set_index=None)
+    alts.index = alts["Alt"].values
 
     choosers, participant_columns = determine_escorting_participants(
         households_merged, persons, model_settings
@@ -478,7 +493,9 @@ def school_escorting(
     for stage_num, stage in enumerate(school_escorting_stages):
         stage_trace_label = trace_label + "_" + stage
         estimator = estimation.manager.begin_estimation(
-            state, "school_escorting_" + stage
+            state,
+            model_name="school_escorting_" + stage,
+            bundle_name="school_escorting",
         )
 
         model_spec_raw = state.filesystem.read_model_spec(
@@ -533,9 +550,26 @@ def school_escorting(
 
         if estimator:
             estimator.write_model_settings(model_settings, model_settings_file_name)
-            estimator.write_spec(model_settings)
-            estimator.write_coefficients(coefficients_df, model_settings)
+            estimator.write_spec(model_settings, tag=stage.upper() + "_SPEC")
+            estimator.write_coefficients(
+                coefficients_df, file_name=stage.upper() + "_COEFFICIENTS"
+            )
             estimator.write_choosers(choosers)
+            estimator.write_alternatives(alts, bundle_directory=True)
+
+            # FIXME #interaction_simulate_estimation_requires_chooser_id_in_df_column
+            #  shuold we do it here or have interaction_simulate do it?
+            # chooser index must be duplicated in column or it will be omitted from interaction_dataset
+            # estimation requires that chooser_id is either in index or a column of interaction_dataset
+            # so it can be reformatted (melted) and indexed by chooser_id and alt_id
+            assert choosers.index.name == "household_id"
+            assert "household_id" not in choosers.columns
+            choosers["household_id"] = choosers.index
+
+            # FIXME set_alt_id - do we need this for interaction_simulate estimation bundle tables?
+            estimator.set_alt_id("alt_id")
+
+            estimator.set_chooser_id(choosers.index.name)
 
         log_alt_losers = state.settings.log_alt_losers
 
@@ -580,47 +614,74 @@ def school_escorting(
 
         if stage_num >= 1:
             choosers["Alt"] = choices
-            choosers = choosers.join(alts, how="left", on="Alt")
+            choosers = choosers.join(alts.set_index("Alt"), how="left", on="Alt")
             bundles = create_school_escorting_bundles_table(
                 choosers[choosers["Alt"] > 1], tours, stage
             )
             escort_bundles.append(bundles)
 
     escort_bundles = pd.concat(escort_bundles)
-    escort_bundles["bundle_id"] = (
-        escort_bundles["household_id"] * 10
-        + escort_bundles.groupby("household_id").cumcount()
-        + 1
-    )
-    escort_bundles.sort_values(
-        by=["household_id", "school_escort_direction"],
-        ascending=[True, False],
-        inplace=True,
-    )
 
-    school_escort_tours = school_escort_tours_trips.create_pure_school_escort_tours(
-        state, escort_bundles
-    )
-    chauf_tour_id_map = {
-        v: k for k, v in school_escort_tours["bundle_id"].to_dict().items()
-    }
-    escort_bundles["chauf_tour_id"] = np.where(
-        escort_bundles["escort_type"] == "ride_share",
-        escort_bundles["first_mand_tour_id"],
-        escort_bundles["bundle_id"].map(chauf_tour_id_map),
-    )
-    assert (
-        escort_bundles["chauf_tour_id"].notnull().all()
-    ), f"chauf_tour_id is null for {escort_bundles[escort_bundles['chauf_tour_id'].isna()]}. Check availability conditions."
+    # Only want to create bundles and tours and trips if at least one household has school escorting
+    if len(escort_bundles) > 0:
+        escort_bundles["bundle_id"] = (
+            escort_bundles["household_id"] * 10
+            + escort_bundles.groupby("household_id").cumcount()
+            + 1
+        )
+        escort_bundles.sort_values(
+            by=["household_id", "school_escort_direction"],
+            ascending=[True, False],
+            inplace=True,
+        )
 
-    tours = school_escort_tours_trips.add_pure_escort_tours(tours, school_escort_tours)
-    tours = school_escort_tours_trips.process_tours_after_escorting_model(
-        state, escort_bundles, tours
-    )
+        school_escort_tours = school_escort_tours_trips.create_pure_school_escort_tours(
+            state, escort_bundles
+        )
+        chauf_tour_id_map = {
+            v: k for k, v in school_escort_tours["bundle_id"].to_dict().items()
+        }
+        escort_bundles["chauf_tour_id"] = np.where(
+            escort_bundles["escort_type"] == "ride_share",
+            escort_bundles["first_mand_tour_id"],
+            escort_bundles["bundle_id"].map(chauf_tour_id_map),
+        )
 
-    school_escort_trips = school_escort_tours_trips.create_school_escort_trips(
-        escort_bundles
-    )
+        assert (
+            escort_bundles["chauf_tour_id"].notnull().all()
+        ), f"chauf_tour_id is null for {escort_bundles[escort_bundles['chauf_tour_id'].isna()]}. Check availability conditions."
+
+        tours = school_escort_tours_trips.add_pure_escort_tours(
+            tours, school_escort_tours
+        )
+        tours = school_escort_tours_trips.process_tours_after_escorting_model(
+            state, escort_bundles, tours
+        )
+        school_escort_trips = school_escort_tours_trips.create_school_escort_trips(
+            escort_bundles
+        )
+
+    else:
+        # create empty school escort tours & trips tables to be used downstream
+        tours["school_esc_outbound"] = pd.NA
+        tours["school_esc_inbound"] = pd.NA
+        tours["school_escort_direction"] = pd.NA
+        tours["next_pure_escort_start"] = pd.NA
+        school_escort_tours = pd.DataFrame(columns=tours.columns)
+        trip_cols = [
+            "household_id",
+            "person_id",
+            "tour_id",
+            "trip_id",
+            "outbound",
+            "depart",
+            "purpose",
+            "destination",
+            "escort_participants",
+            "chauf_tour_id",
+            "primary_purpose",
+        ]
+        school_escort_trips = pd.DataFrame(columns=trip_cols)
 
     school_escort_trips["primary_purpose"] = school_escort_trips[
         "primary_purpose"

activitysim/abm/models/util/school_escort_tours_trips.py

@@ -405,6 +405,19 @@ def merge_school_escort_trips_into_pipeline(state: workflow.State):
     tours = state.get_dataframe("tours")
     trips = state.get_dataframe("trips")
 
+    # checking to see if there are school escort trips to merge in
+    if len(school_escort_trips) == 0:
+        # if no trips, fill escorting columns with NA
+        trips[
+            [
+                "escort_participants",
+                "school_escort_direction",
+                "school_escort_trip_id",
+            ]
+        ] = pd.NA
+        state.add_table("trips", trips)
+        return trips
+
     # want to remove stops if school escorting takes place on that half tour so we can replace them with the actual stops
     out_se_tours = tours[
         tours["school_esc_outbound"].isin(["pure_escort", "ride_share"])
@@ -643,6 +656,10 @@ def force_escortee_tour_modes_to_match_chauffeur(state: workflow.State, tours):
     # Does it even matter if trip modes are getting matched later?
     escort_bundles = state.get_dataframe("escort_bundles")
 
+    if len(escort_bundles) == 0:
+        # do not need to do anything if no escorting
+        return tours
+
     # grabbing the school tour ids for each school escort bundle
     se_tours = escort_bundles[["school_tour_ids", "chauf_tour_id"]].copy()
     # merging in chauffeur tour mode