Workplace Location Logsum Overwritten in Estimation

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None

activitysim/abm/models/location_choice.py

@@ -950,34 +950,6 @@ def run_location_choice(
  )
  estimator.write_override_choices(choices_df.choice)
 
- if want_logsums:
- # if we override choices, we need to to replace choice logsum with ologsim for override location
- # fortunately, as long as we aren't sampling dest alts, the logsum will be in location_sample_df
-
- # if we start sampling dest alts, we will need code below to compute override location logsum
- assert estimator.want_unsampled_alternatives
-
- # merge mode_choice_logsum for the overridden location
- # alt_logsums columns: ['person_id', 'choice', 'logsum']
- alt_dest_col = model_settings.ALT_DEST_COL_NAME
- alt_logsums = (
- location_sample_df[[alt_dest_col, ALT_LOGSUM]]
- .rename(columns={alt_dest_col: "choice", ALT_LOGSUM: "logsum"})
- .reset_index()
- )
-
- # choices_df columns: ['person_id', 'choice']
- choices_df = choices_df[["choice"]].reset_index()
-
- # choices_df columns: ['person_id', 'choice', 'logsum']
- choices_df = pd.merge(choices_df, alt_logsums, how="left").set_index(
- "person_id"
- )
-
- logger.debug(
- f"{trace_label} segment {segment_name} estimation: override logsums"
- )
-
  if state.settings.trace_hh_id:
  estimation_trace_label = tracing.extend_trace_label(
  trace_label, f"estimation.{segment_name}.survey_choices"

activitysim/abm/test/test_location_choice.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pandas as pd
+import pandas.testing as pdt
+
+from activitysim.abm.models import location_choice
+
+
+def test_estimation_override_preserves_destination_choice_logsum(monkeypatch):
+ """Survey overrides should only change the chosen destination's mode logsum."""
+ person_index = pd.Index([1], name="person_id")
+ location_sample = pd.DataFrame(
+ {
+ "alt_dest": [101, 202],
+ location_choice.ALT_LOGSUM: [5.0, 7.0],
+ },
+ index=pd.Index([1, 1], name="person_id"),
+ )
+ modeled_choices = pd.DataFrame(
+ {"choice": [101], "logsum": [-1.5]}, index=person_index
+ )
+
+ # Keep the test focused on the estimation override and final logsum merge.
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_sample",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_logsums",
+ lambda *args, **kwargs: location_sample.copy(),
+ )
+ monkeypatch.setattr(
+ location_choice,
+ "run_location_simulate",
+ lambda *args, **kwargs: modeled_choices.copy(),
+ )
+
+ estimator = Mock()
+ estimator.get_survey_values.return_value = pd.Series(
+ [202], index=person_index, name="choice"
+ )
+ shadow_price_calculator = Mock()
+ shadow_price_calculator.dest_size_terms.return_value = pd.Series(
+ [1.0, 1.0], index=[101, 202]
+ )
+ model_settings = SimpleNamespace(
+ ALT_DEST_COL_NAME="alt_dest",
+ CHOOSER_SEGMENT_COLUMN_NAME="segment",
+ DEST_CHOICE_COLUMN_NAME="workplace_zone_id",
+ LOGSUM_SETTINGS="tour_mode_choice.yaml",
+ SEGMENT_IDS={"workers": 1},
+ )
+ state = SimpleNamespace(
+ settings=SimpleNamespace(
+ sample_method="monte_carlo",
+ trace_hh_id=None,
+ use_explicit_error_terms=False,
+ )
+ )
+ persons = pd.DataFrame({"segment": [1]}, index=person_index)
+
+ choices, sample = location_choice.run_location_choice(
+ state=state,
+ persons_merged_df=persons,
+ network_los=Mock(),
+ shadow_price_calculator=shadow_price_calculator,
+ want_logsums=True,
+ want_sample_table=False,
+ estimator=estimator,
+ model_settings=model_settings,
+ chunk_size=0,
+ chunk_tag="workplace_location",
+ trace_label="workplace_location",
+ )
+
+ expected = pd.DataFrame(
+ {
+ "choice": [202],
+ "logsum": [-1.5],
+ location_choice.ALT_LOGSUM: [7.0],
+ },
+ index=person_index,
+ )
+ pdt.assert_frame_equal(choices, expected)
+ assert sample is None