Make global constants available to interaction-based model expressions

activitysim/core/interaction_simulate.py

@@ -100,7 +100,8 @@ def eval_interaction_utilities(
         assert len(spec.columns) == 1
 
         # avoid altering caller's passed-in locals_d parameter (they may be looping)
-        locals_d = locals_d.copy() if locals_d is not None else {}
+        # global constants are always available, but can be overridden by locals_d
+        locals_d = {**state.get_global_constants(), **(locals_d or {})}
 
         utilities = None
 

activitysim/core/test/test_interaction_sample_simulate.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import pytest
 
-from activitysim.core import interaction_sample_simulate, workflow
+from activitysim.core import interaction_sample, interaction_sample_simulate, workflow
 from activitysim.core.logit import AltsContext
 
 
@@ -18,6 +18,64 @@ def state() -> workflow.State:
     return state
 
 
+def test_global_constants_available_in_sampling_and_simulation(tmp_path):
+    """Global constants are available in both destination-choice substeps."""
+    configs_dir = tmp_path.joinpath("configs")
+    configs_dir.mkdir()
+    configs_dir.joinpath("constants.yaml").write_text(
+        "SAMPLE_SCALE: 3.0\nSIMULATE_SCALE: 2.0\n"
+    )
+    tmp_path.joinpath("data").mkdir()
+
+    state = workflow.State()
+    state.initialize_filesystem(
+        working_dir=tmp_path, configs_dir=("configs",)
+    ).default_settings()
+    state.settings.check_for_variability = False
+
+    choosers = pd.DataFrame(
+        {"chooser_attr": [1.0, 2.0]},
+        index=pd.Index([0, 1], name="person_id"),
+    )
+    alternatives = pd.DataFrame(
+        {"alt_attr": [1.0, 2.0]},
+        index=pd.Index([10, 20], name="alt_id"),
+    )
+
+    # Sampling and simulation use separate specifications in location and
+    # destination choice, so exercise each expression-evaluation path.
+    sample_spec = pd.DataFrame(
+        {"coefficient": [1.0]},
+        index=pd.Index(["alt_attr * SAMPLE_SCALE"], name="Expression"),
+    )
+    sample = interaction_sample.interaction_sample(
+        state,
+        choosers,
+        alternatives,
+        sample_spec,
+        sample_size=0,
+        alt_col_name="alt_id",
+    )
+    sampled_alternatives = sample.join(alternatives, on="alt_id")
+
+    simulate_spec = pd.DataFrame(
+        {"coefficient": [1.0]},
+        index=pd.Index(["alt_attr * SIMULATE_SCALE"], name="Expression"),
+    )
+    results = interaction_sample_simulate.interaction_sample_simulate(
+        state,
+        choosers,
+        sampled_alternatives,
+        simulate_spec,
+        choice_column="alt_id",
+        want_logsums=True,
+        skip_choice=True,
+    )
+
+    expected_logsum = np.logaddexp(2.0, 4.0)
+    np.testing.assert_allclose(results["logsums"], expected_logsum)
+
+
 def test_interaction_sample_simulate_parity(state):
     # Run interaction_sample_simulate with and without explicit error terms and check that results are similar.
 

activitysim/core/test/test_interaction_simulate.py

@@ -1,6 +1,8 @@
 # ActivitySim
 # See full license in LICENSE.txt.
 
+from __future__ import annotations
+
 import numpy as np
 import pandas as pd
 import pytest
@@ -172,3 +174,73 @@ def test_interaction_simulate_eet_large_utilities(state):
     assert not choices_eet.isna().any()
     # With such a large difference, Alt 1 should be the dominant choice
     assert (choices_eet == 1).all()
+
+
+def test_eval_interaction_utilities_global_constants(tmp_path):
+    # global constants (from constants.yaml) should be available to expressions
+    # evaluated for interaction models (e.g. location choice, destination choice,
+    # tour scheduling), see issue #1015
+
+    configs_dir = tmp_path.joinpath("configs")
+    configs_dir.mkdir()
+    configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+    tmp_path.joinpath("data").mkdir()
+
+    state = workflow.State()
+    state.initialize_filesystem(
+        working_dir=tmp_path, configs_dir=("configs",)
+    ).default_settings()
+    state.settings.check_for_variability = False
+
+    df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+    spec = pd.DataFrame(
+        {"coefficient": [1.0]},
+        index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+    )
+
+    utilities, _ = interaction_simulate.eval_interaction_utilities(
+        state,
+        spec,
+        df,
+        locals_d=None,
+        trace_label="test_global_constants",
+        trace_rows=None,
+    )
+
+    np.testing.assert_allclose(
+        utilities.utility.to_numpy(), df.distance_km.to_numpy() * 0.621371
+    )
+
+
+def test_eval_interaction_utilities_locals_override_global_constants(tmp_path):
+    # values passed in locals_d take precedence over global constants
+
+    configs_dir = tmp_path.joinpath("configs")
+    configs_dir.mkdir()
+    configs_dir.joinpath("constants.yaml").write_text("KM_TO_MILE: 0.621371\n")
+    tmp_path.joinpath("data").mkdir()
+
+    state = workflow.State()
+    state.initialize_filesystem(
+        working_dir=tmp_path, configs_dir=("configs",)
+    ).default_settings()
+    state.settings.check_for_variability = False
+
+    df = pd.DataFrame({"distance_km": [1.0, 10.0]}, index=[0, 1])
+
+    spec = pd.DataFrame(
+        {"coefficient": [1.0]},
+        index=pd.Index(["distance_km * KM_TO_MILE"], name="Expression"),
+    )
+
+    utilities, _ = interaction_simulate.eval_interaction_utilities(
+        state,
+        spec,
+        df,
+        locals_d={"KM_TO_MILE": 1.0},
+        trace_label="test_global_constants_override",
+        trace_rows=None,
+    )
+
+    np.testing.assert_allclose(utilities.utility.to_numpy(), df.distance_km.to_numpy())