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Fix for simulation-based shadow pricing segmentation #1083

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Fix for simulation-based shadow pricing segmentation #1083

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440fd32
take segmentation from model setting
i-am-sijia Jun 16, 2026
f82f6e1
deprecate `SEGMENT_TO_NAME`
i-am-sijia Jun 16, 2026
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34 changes: 19 additions & 15 deletions activitysim/abm/tables/shadow_pricing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,12 +62,6 @@
TALLY_CHECKOUT = (1, -1)
TALLY_PENDING_PERSONS = (2, -1)

default_segment_to_name_dict = {
# model_selector : persons_segment_name
"school": "school_segment",
"workplace": "income_segment",
}


def size_table_name(model_selector):
"""
Expand Down Expand Up @@ -134,12 +128,6 @@ class ShadowPriceSettings(PydanticReadable, extra="forbid"):

WRITE_ITERATION_CHOICES: bool = False

SEGMENT_TO_NAME: dict[str, str] = {
"school": "school_segment",
"workplace": "income_segment",
} # pydantic uses deep copy, so mutable default value is ok here
"""Mapping from model_selector to persons_segment_name."""


class ShadowPriceCalculator:
def __init__(
Expand Down Expand Up @@ -176,6 +164,7 @@ def __init__(
)

self.model_selector = model_settings.MODEL_SELECTOR
self.chooser_segment_column = model_settings.CHOOSER_SEGMENT_COLUMN_NAME

if (self.num_processes > 1) and not state.settings.fail_fast:
# if we are multiprocessing, then fail_fast should be true or we will wait forever for failed processes
Expand Down Expand Up @@ -826,9 +815,24 @@ def update_shadow_prices(self, state):
sampled_persons = pd.DataFrame()
persons_merged = state.get_dataframe("persons_merged")

# need to join the segment to the choices to sample correct persons
segment_to_name_dict = self.shadow_settings.SEGMENT_TO_NAME
segment_name = segment_to_name_dict[self.model_selector]
# Use the model chooser segmentation to keep shadow-pricing resampling
# consistent with segment_ids in location choice settings.
segment_name = self.chooser_segment_column
if segment_name not in persons_merged.columns:
raise SystemConfigurationError(
f"Missing chooser segment column '{segment_name}' in persons_merged "
f"for {self.model_selector} simulation shadow pricing"
)

# Fail fast on obvious misconfiguration instead of silently sampling no one.
segment_values = set(self.segment_ids.values())
chooser_values = set(persons_merged[segment_name].dropna().unique())
if not segment_values.intersection(chooser_values):
raise SystemConfigurationError(
f"No overlap between SEGMENT_IDS values ({sorted(segment_values)}) and "
f"persons_merged['{segment_name}'] values for {self.model_selector} "
"simulation shadow pricing"
)

if type(self.choices_synced) != pd.DataFrame:
self.choices_synced = self.choices_synced.to_frame()
Expand Down
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