Shadow Pricing Enhancements

activitysim/abm/models/location_choice.py

@@ -227,7 +227,7 @@ def location_sample(
 DEST_MAZ = "dest_MAZ"
 
 
-def aggregate_size_terms(dest_size_terms, network_los):
+def aggregate_size_terms(dest_size_terms, network_los, model_settings):
  #
  # aggregate MAZ_size_terms to TAZ_size_terms
  #
@@ -261,6 +261,21 @@ def aggregate_size_terms(dest_size_terms, network_los):
  for c in weighted_average_cols:
  TAZ_size_terms[c] /= TAZ_size_terms["size_term"] # weighted average
 
+ spc = shadow_pricing.load_shadow_price_calculator(model_settings)
+ if spc.use_shadow_pricing and (
+ spc.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation"
+ ):
+ # allow TAZs with at least one underassigned MAZ in them, therefore with a shadowprice larger than -999, to be selected again
+ TAZ_size_terms["shadow_price_utility_adjustment"] = np.where(
+ TAZ_size_terms["shadow_price_utility_adjustment"] > -999, 0, -999
+ )
+ # now, negative size term means shadow price is -999. Setting size_term to 0 so the prob of that MAZ being selected becomes 0
+ MAZ_size_terms["size_term"] = np.where(
+ MAZ_size_terms["shadow_price_utility_adjustment"] < 0,
+ 0,
+ MAZ_size_terms["size_term"],
+ )
+
  if TAZ_size_terms.isna().any(axis=None):
  logger.warning(
  f"TAZ_size_terms with NAN values\n{TAZ_size_terms[TAZ_size_terms.isna().any(axis=1)]}"
@@ -308,7 +323,9 @@ def location_presample(
  alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]
  assert DEST_TAZ != alt_dest_col_name
 
- MAZ_size_terms, TAZ_size_terms = aggregate_size_terms(dest_size_terms, network_los)
+ MAZ_size_terms, TAZ_size_terms = aggregate_size_terms(
+ dest_size_terms, network_los, model_settings
+ )
 
  # convert MAZ zone_id to 'TAZ' in choosers (persons_merged)
  # persons_merged[HOME_TAZ] = persons_merged[HOME_MAZ].map(maz_to_taz)
@@ -856,6 +873,7 @@ def iterate_location_choice(
 
  # chooser segmentation allows different sets coefficients for e.g. different income_segments or tour_types
  chooser_segment_column = model_settings["CHOOSER_SEGMENT_COLUMN_NAME"]
+ segment_ids = model_settings["SEGMENT_IDS"]
 
  assert (
  chooser_segment_column in persons_merged_df
@@ -869,11 +887,38 @@ def iterate_location_choice(
 
  for iteration in range(1, max_iterations + 1):
 
+ persons_merged_df_ = persons_merged_df.copy()
+
  if spc.use_shadow_pricing and iteration > 1:
  spc.update_shadow_prices()
 
- choices_df, save_sample_df = run_location_choice(
- persons_merged_df,
+ if spc.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation":
+ # filter from the sampled persons
+ persons_merged_df_ = persons_merged_df_[
+ persons_merged_df_.index.isin(spc.sampled_persons.index)
+ ]
+ # handle cases where a segment has persons but no zones to receive them
+ desired_size_sum = spc.desired_size[
+ spc.desired_size.index.isin(
+ spc.shadow_prices[spc.shadow_prices.iloc[:, 0] != -999].index
+ )
+ ].sum()
+ zero_desired_size_segments = [
+ i for i in desired_size_sum.index if desired_size_sum[i] == 0
+ ]
+ zero_desired_size_segments_ids = [
+ segment_ids[key] for key in zero_desired_size_segments
+ ]
+ persons_merged_df_ = persons_merged_df_[
+ ~persons_merged_df_[chooser_segment_column].isin(
+ zero_desired_size_segments_ids
+ )
+ ]
+
+ persons_merged_df_ = persons_merged_df_.sort_index()
+
+ choices_df_, save_sample_df = run_location_choice(
+ persons_merged_df_,
  network_los,
  shadow_price_calculator=spc,
  want_logsums=logsum_column_name is not None,
@@ -886,10 +931,35 @@ def iterate_location_choice(
  trace_label=tracing.extend_trace_label(trace_label, "i%s" % iteration),
  )
 
- # choices_df is a pandas DataFrame with columns 'choice' and (optionally) 'logsum'
- if choices_df is None:
+ # choices_df is a pandas DataFrame with columns "choice" and (optionally) "logsum"
+ if choices_df_ is None:
  break
 
+ if spc.use_shadow_pricing:
+ # handle simulation method
+ if (
+ spc.shadow_settings["SHADOW_PRICE_METHOD"] == "simulation"
+ and iteration > 1
+ ):
+ # if a process ends up with no sampled workers in it, hence an empty choice_df_, then choice_df wil be what it was previously
+ if len(choices_df_) == 0:
+ choices_df = choices_df
+ else:
+ choices_df = pd.concat([choices_df, choices_df_], axis=0)
+ choices_df_index = choices_df_.index.name
+ choices_df = choices_df.reset_index()
+ # update choices of workers/students
+ choices_df = choices_df.drop_duplicates(
+ subset=[choices_df_index], keep="last"
+ )
+ choices_df = choices_df.set_index(choices_df_index)
+ choices_df = choices_df.sort_index()
+ else:
+ choices_df = choices_df_.copy()
+
+ else:
+ choices_df = choices_df_
+
  spc.set_choices(
  choices=choices_df["choice"],
  segment_ids=persons_merged_df[chooser_segment_column].reindex(

activitysim/abm/models/trip_purpose.py

@@ -71,9 +71,7 @@ def choose_intermediate_trip_purpose(
 
  # probs should sum to 1 across rows
  sum_probs = probs_spec[purpose_cols].sum(axis=1)
- probs_spec.loc[:, purpose_cols] = probs_spec.loc[:, purpose_cols].div(
- sum_probs, axis=0
- )
+ probs_spec[purpose_cols] = probs_spec[purpose_cols].div(sum_probs, axis=0)
 
  # left join trips to probs (there may be multiple rows per trip for multiple depart ranges)
  choosers = pd.merge(