diff --git a/.github/workflows/core_tests.yml b/.github/workflows/core_tests.yml index 8e659b25c1..d8cd9542f6 100644 --- a/.github/workflows/core_tests.yml +++ b/.github/workflows/core_tests.yml @@ -140,6 +140,7 @@ jobs: matrix: region: - prototype_mtc + - prototype_arc - placeholder_psrc - prototype_marin - prototype_mtc_extended diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 41fecfab5a..12d970e189 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -17,6 +17,9 @@ repos: rev: 22.6.0 hooks: - id: black + language_version: python3.13 + # explicitly set the Python version for the hook environment for black, + # as older versions of black don't work with the latest Python. #- repo: https://github.com/PyCQA/flake8 # rev: 5.0.4 diff --git a/activitysim/abm/models/disaggregate_accessibility.py b/activitysim/abm/models/disaggregate_accessibility.py index aa2703e19c..5fe3375b31 100644 --- a/activitysim/abm/models/disaggregate_accessibility.py +++ b/activitysim/abm/models/disaggregate_accessibility.py @@ -15,7 +15,11 @@ from activitysim.abm.models.util import tour_destination from activitysim.abm.tables import shadow_pricing from activitysim.core import estimation, los, tracing, util, workflow -from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable +from activitysim.core.configuration.base import ( + PreprocessorSettings, + PydanticReadable, + ComputeSettings, +) from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.expressions import assign_columns @@ -184,6 +188,8 @@ class DisaggregateAccessibilitySettings(PydanticReadable, extra="forbid"): If not supplied or None, will default to the chunk size in the location choice model settings. """ + compute_settings: ComputeSettings | None = None + def read_disaggregate_accessibility_yaml( state: workflow.State, file_name @@ -783,6 +789,11 @@ def get_disaggregate_logsums( if disagg_model_settings.explicit_chunk is not None: model_settings.explicit_chunk = disagg_model_settings.explicit_chunk + # Can set compute settings for disaggregate accessibility + # Otherwise this will be set to whatever is in the location model settings + if disagg_model_settings.compute_settings is not None: + model_settings.compute_settings = disagg_model_settings.compute_settings + # Include the suffix tags to pass onto downstream logsum models (e.g., tour mode choice) if model_settings.LOGSUM_SETTINGS: suffixes = util.concat_suffix_dict(disagg_model_settings.suffixes) diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py index 778ae0f449..0fb562c189 100644 --- a/activitysim/abm/models/joint_tour_participation.py +++ b/activitysim/abm/models/joint_tour_participation.py @@ -20,8 +20,8 @@ ) from activitysim.core.configuration.base import ComputeSettings, PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings -from activitysim.core.util import assign_in_place, reindex from activitysim.core.exceptions import InvalidTravelError +from activitysim.core.util import assign_in_place, reindex logger = logging.getLogger(__name__) @@ -127,7 +127,7 @@ def get_tour_satisfaction(candidates, participate): def participants_chooser( state: workflow.State, - probs: pd.DataFrame, + probs_or_utils: pd.DataFrame, choosers: pd.DataFrame, spec: pd.DataFrame, trace_label: str, @@ -147,9 +147,10 @@ def participants_chooser( Parameters ---------- - probs : pandas.DataFrame + probs_or_utils : pandas.DataFrame Rows for choosers and columns for the alternatives from which they - are choosing. Values are expected to be valid probabilities across + are choosing. If running with explicit_error_terms, these are utilities. + Otherwise, values are expected to be valid probabilities across each row, e.g. they should sum to 1. choosers : pandas.dataframe simple_simulate choosers df @@ -166,7 +167,7 @@ def participants_chooser( """ - assert probs.index.equals(choosers.index) + assert probs_or_utils.index.equals(choosers.index) # choice is boolean (participate or not) model_settings = JointTourParticipationSettings.read_settings_file( @@ -202,7 +203,7 @@ def participants_chooser( "%s max iterations exceeded (%s).", trace_label, MAX_ITERATIONS ) diagnostic_cols = ["tour_id", "household_id", "composition", "adult"] - unsatisfied_candidates = candidates[diagnostic_cols].join(probs) + unsatisfied_candidates = candidates[diagnostic_cols].join(probs_or_utils) state.tracing.write_csv( unsatisfied_candidates, file_name="%s.UNSATISFIED" % trace_label, @@ -215,9 +216,31 @@ def participants_chooser( f"Forcing joint tour participation for {num_tours_remaining} tours." ) # anybody with probability > 0 is forced to join the joint tour - probs[choice_col] = np.where(probs[choice_col] > 0, 1, 0) - non_choice_col = [col for col in probs.columns if col != choice_col][0] - probs[non_choice_col] = 1 - probs[choice_col] + if state.settings.use_explicit_error_terms: + # need "is valid choice" such that we certainly choose those with non-zero values, + # and do not choose others. Let's use 3.0 as large value here. + probs_or_utils[choice_col] = np.where( + probs_or_utils[choice_col] > logit.UTIL_MIN, + 3.0, + logit.UTIL_UNAVAILABLE, + ) + non_choice_col = [ + col for col in probs_or_utils.columns if col != choice_col + ][0] + probs_or_utils[non_choice_col] = np.where( + probs_or_utils[choice_col] <= logit.UTIL_MIN, + 3.0, + logit.UTIL_UNAVAILABLE, + ) + else: + probs_or_utils[choice_col] = np.where( + probs_or_utils[choice_col] > 0, 1, 0 + ) + non_choice_col = [ + col for col in probs_or_utils.columns if col != choice_col + ][0] + probs_or_utils[non_choice_col] = 1 - probs_or_utils[choice_col] + if iter > MAX_ITERATIONS + 1: raise InvalidTravelError( f"{num_tours_remaining} tours could not be satisfied even with forcing participation" @@ -227,8 +250,13 @@ def participants_chooser( f"{num_tours_remaining} tours could not be satisfied after {iter} iterations" ) - choices, rands = logit.make_choices( - state, probs, trace_label=trace_label, trace_choosers=choosers + choice_function = ( + logit.make_choices_utility_based + if state.settings.use_explicit_error_terms + else logit.make_choices + ) + choices, rands = choice_function( + state, probs_or_utils, trace_label=trace_label, trace_choosers=choosers ) participate = choices == PARTICIPATE_CHOICE @@ -252,7 +280,7 @@ def participants_chooser( rands_list.append(rands[satisfied]) # remove candidates of satisfied tours - probs = probs[~satisfied] + probs_or_utils = probs_or_utils[~satisfied] candidates = candidates[~satisfied] logger.debug( @@ -401,6 +429,15 @@ def joint_tour_participation( if i not in model_settings.compute_settings.protect_columns: model_settings.compute_settings.protect_columns.append(i) + # This is related to the difference in nested logit and logit choice. As soon as alt_order_array + # is removed from arguments to make_choices_explicit_error_term_nl this guard can be removed. + if state.settings.use_explicit_error_terms: + assert ( + nest_spec is None + ), "Nested logit model custom chooser for EET requires name_mapping, currently not implemented in jtp" + + custom_chooser = participants_chooser + choices = simulate.simple_simulate_by_chunk_id( state, choosers=candidates, @@ -409,7 +446,7 @@ def joint_tour_participation( locals_d=constants, trace_label=trace_label, trace_choice_name="participation", - custom_chooser=participants_chooser, + custom_chooser=custom_chooser, estimator=estimator, compute_settings=model_settings.compute_settings, ) diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py index a8b8fd8a18..a54805f435 100644 --- a/activitysim/abm/models/location_choice.py +++ b/activitysim/abm/models/location_choice.py @@ -15,10 +15,11 @@ TourLocationComponentSettings, TourModeComponentSettings, ) +from activitysim.core.exceptions import DuplicateWorkflowTableError from activitysim.core.interaction_sample import interaction_sample from activitysim.core.interaction_sample_simulate import interaction_sample_simulate +from activitysim.core.logit import AltsContext from activitysim.core.util import reindex -from activitysim.core.exceptions import DuplicateWorkflowTableError """ The school/workplace location model predicts the zones in which various people will @@ -603,6 +604,7 @@ def run_location_simulate( chunk_tag, trace_label, skip_choice=False, + alts_context: AltsContext | None = None, ): """ run location model on location_sample annotated with mode_choice logsum @@ -713,6 +715,7 @@ def run_location_simulate( compute_settings=model_settings.compute_settings.subcomponent_settings( "simulate" ), + alts_context=alts_context, ) if not want_logsums: @@ -738,6 +741,7 @@ def run_location_choice( chunk_tag, trace_label, skip_choice=False, + alts_context: AltsContext | None = None, ): """ Run the three-part location choice algorithm to generate a location choice for each chooser @@ -757,6 +761,8 @@ def run_location_choice( model_settings : dict chunk_size : int trace_label : str + skip_choice : bool + alts_context : AltsContext or None Returns ------- @@ -789,6 +795,9 @@ def run_location_choice( if choosers.shape[0] == 0: logger.info(f"{trace_label} skipping segment {segment_name}: no choosers") continue + # dest_size_terms contains 0-attraction zones so using this directly here, important for stable error terms + # when a zone goes from 0 base -> nonzero project + alts_context = AltsContext.from_series(dest_size_terms.index) # - location_sample location_sample_df = run_location_sample( @@ -842,6 +851,7 @@ def run_location_choice( trace_label, "simulate.%s" % segment_name ), skip_choice=skip_choice, + alts_context=alts_context, ) if estimator: @@ -1020,6 +1030,10 @@ def iterate_location_choice( ) = None # initialize to None, will be populated in first iteration for iteration in range(1, max_iterations + 1): + # Force reset the setting at the start of each shadow price iteration for consistency + logger.info("Resetting random number seeds for iteration {}".format(iteration)) + state.get_rn_generator().end_step(trace_label) + state.get_rn_generator().begin_step(trace_label) persons_merged_df_ = persons_merged_df.copy() if spc.use_shadow_pricing and iteration > 1: @@ -1032,6 +1046,17 @@ def iterate_location_choice( ] persons_merged_df_ = persons_merged_df_.sort_index() + # reset rng offsets to identical state on each iteration. This ensures that the same set of random numbers is + # used on each iteration. Note this has to happen AFTER updating shadow prices because the simulation method + # draws random numbers. + # Only applying when using EET for now because this will need changes to integration + # tests, but it's probably a good idea for MC simulation as well. + if state.settings.use_explicit_error_terms and iteration > 1: + logger.debug( + f"{trace_label} resetting random number generator offsets for iteration {iteration}" + ) + state.get_rn_generator().reset_offsets_for_step(state.current_model_name) + choices_df_, save_sample_df = run_location_choice( state, persons_merged_df_, diff --git a/activitysim/abm/models/parking_location_choice.py b/activitysim/abm/models/parking_location_choice.py index 32f3aabee2..b07ec5b873 100644 --- a/activitysim/abm/models/parking_location_choice.py +++ b/activitysim/abm/models/parking_location_choice.py @@ -21,6 +21,7 @@ from activitysim.core.configuration.base import PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings from activitysim.core.interaction_sample_simulate import interaction_sample_simulate +from activitysim.core.logit import AltsContext from activitysim.core.tracing import print_elapsed_time from activitysim.core.util import assign_in_place, drop_unused_columns from activitysim.core.exceptions import DuplicateWorkflowTableError @@ -112,6 +113,7 @@ def parking_destination_simulate( chunk_size, trace_hh_id, trace_label, + alts_context: AltsContext | None = None, ): """ Chose destination from destination_sample (with od_logsum and dp_logsum columns added) @@ -150,6 +152,7 @@ def parking_destination_simulate( trace_label=trace_label, trace_choice_name="parking_loc", explicit_chunk_size=model_settings.explicit_chunk, + alts_context=alts_context, ) # drop any failed zero_prob destinations @@ -211,6 +214,9 @@ def choose_parking_location( ) destination_sample.index = np.repeat(trips.index.values, len(alternatives)) destination_sample.index.name = trips.index.name + # use full land_use index to ensure AltsContext spans full range of potential zones + land_use = state.get_dataframe("land_use") + alts_context = AltsContext.from_series(land_use.index) destinations = parking_destination_simulate( state, @@ -223,6 +229,7 @@ def choose_parking_location( chunk_size=chunk_size, trace_hh_id=trace_hh_id, trace_label=trace_label, + alts_context=alts_context, ) if want_sample_table: diff --git a/activitysim/abm/models/trip_departure_choice.py b/activitysim/abm/models/trip_departure_choice.py index 7b34f8e742..a0bf95e5a3 100644 --- a/activitysim/abm/models/trip_departure_choice.py +++ b/activitysim/abm/models/trip_departure_choice.py @@ -24,10 +24,10 @@ PreprocessorSettings, PydanticCompute, ) +from activitysim.core.exceptions import SegmentedSpecificationError from activitysim.core.skim_dataset import SkimDataset from activitysim.core.skim_dictionary import SkimDict from activitysim.core.util import reindex -from activitysim.core.exceptions import SegmentedSpecificationError logger = logging.getLogger(__name__) @@ -351,37 +351,51 @@ def choose_tour_leg_pattern( column_labels=["alternative", "utility"], ) - # convert to probabilities (utilities exponentiated and normalized to probs) - # probs is same shape as utilities, one row per chooser and one column for alternative - probs = logit.utils_to_probs( - state, utilities_df, trace_label=trace_label, trace_choosers=trip_segment - ) + if state.settings.use_explicit_error_terms: + utilities_df = logit.validate_utils( + state, utilities_df, trace_label=trace_label, trace_choosers=trip_segment + ) + # make choices + # positions is series with the chosen alternative represented as a column index in probs + # which is an integer between zero and num alternatives in the alternative sample + positions, rands = logit.make_choices_utility_based( + state, utilities_df, trace_label=trace_label, trace_choosers=trip_segment + ) + + del utilities_df + chunk_sizer.log_df(trace_label, "utilities_df", None) + else: + # convert to probabilities (utilities exponentiated and normalized to probs) + # probs is same shape as utilities, one row per chooser and one column for alternative + probs = logit.utils_to_probs( + state, utilities_df, trace_label=trace_label, trace_choosers=trip_segment + ) - chunk_sizer.log_df(trace_label, "probs", probs) + chunk_sizer.log_df(trace_label, "probs", probs) - del utilities_df - chunk_sizer.log_df(trace_label, "utilities_df", None) + del utilities_df + chunk_sizer.log_df(trace_label, "utilities_df", None) - if have_trace_targets: - state.tracing.trace_df( - probs, - tracing.extend_trace_label(trace_label, "probs"), - column_labels=["alternative", "probability"], + if have_trace_targets: + state.tracing.trace_df( + probs, + tracing.extend_trace_label(trace_label, "probs"), + column_labels=["alternative", "probability"], + ) + + # make choices + # positions is series with the chosen alternative represented as a column index in probs + # which is an integer between zero and num alternatives in the alternative sample + positions, rands = logit.make_choices( + state, probs, trace_label=trace_label, trace_choosers=trip_segment ) - # make choices - # positions is series with the chosen alternative represented as a column index in probs - # which is an integer between zero and num alternatives in the alternative sample - positions, rands = logit.make_choices( - state, probs, trace_label=trace_label, trace_choosers=trip_segment - ) + del probs + chunk_sizer.log_df(trace_label, "probs", None) chunk_sizer.log_df(trace_label, "positions", positions) chunk_sizer.log_df(trace_label, "rands", rands) - del probs - chunk_sizer.log_df(trace_label, "probs", None) - # shouldn't have chosen any of the dummy pad utilities assert positions.max() < max_sample_count diff --git a/activitysim/abm/models/trip_destination.py b/activitysim/abm/models/trip_destination.py index 853cfc35e9..df16941489 100644 --- a/activitysim/abm/models/trip_destination.py +++ b/activitysim/abm/models/trip_destination.py @@ -32,6 +32,7 @@ from activitysim.core.configuration.logit import LocationComponentSettings from activitysim.core.interaction_sample import interaction_sample from activitysim.core.interaction_sample_simulate import interaction_sample_simulate +from activitysim.core.logit import AltsContext from activitysim.core.skim_dictionary import DataFrameMatrix from activitysim.core.tracing import print_elapsed_time from activitysim.core.util import assign_in_place, reindex @@ -950,6 +951,7 @@ def trip_destination_simulate( skim_hotel, estimator, trace_label, + alts_context: AltsContext | None = None, ): """ Chose destination from destination_sample (with od_logsum and dp_logsum columns added) @@ -1036,6 +1038,7 @@ def trip_destination_simulate( trace_choice_name="trip_dest", estimator=estimator, explicit_chunk_size=model_settings.explicit_chunk, + alts_context=alts_context, ) if not want_logsums: @@ -1080,6 +1083,10 @@ def choose_trip_destination( t0 = print_elapsed_time() + # use full index (including zero-size zones) to ensure stable random results + # fetch alts_context early so we don't worry about mutating alternatives first + alts_context = AltsContext.from_series(alternatives.index) + # - trip_destination_sample destination_sample = trip_destination_sample( state, @@ -1126,7 +1133,6 @@ def choose_trip_destination( destination_sample["dp_logsum"] = 0.0 t0 = print_elapsed_time("%s.compute_logsums" % trace_label, t0, debug=True) - destinations = trip_destination_simulate( state, primary_purpose=primary_purpose, @@ -1138,6 +1144,7 @@ def choose_trip_destination( skim_hotel=skim_hotel, estimator=estimator, trace_label=trace_label, + alts_context=alts_context, ) dropped_trips = ~trips.index.isin(destinations.index) diff --git a/activitysim/abm/models/trip_scheduling_choice.py b/activitysim/abm/models/trip_scheduling_choice.py index 094b00843c..d2ad92f6cc 100644 --- a/activitysim/abm/models/trip_scheduling_choice.py +++ b/activitysim/abm/models/trip_scheduling_choice.py @@ -19,6 +19,7 @@ PydanticReadable, ) from activitysim.core.interaction_sample_simulate import _interaction_sample_simulate +from activitysim.core.logit import AltsContext from activitysim.core.skim_dataset import SkimDataset from activitysim.core.skim_dictionary import SkimDict @@ -275,9 +276,18 @@ def run_trip_scheduling_choice( choosers, chunk_trace_label, chunk_sizer, - ) in chunk.adaptive_chunked_choosers(state, indirect_tours, trace_label): + ) in chunk.adaptive_chunked_choosers(state, indirect_tours, trace_label, explicit_chunk_size=model_settings.explicit_chunk): # Sort the choosers and get the schedule alternatives choosers = choosers.sort_index() + # FIXME-EET: For explicit error term choices, we need a stable alternative ID. Currently, we use + # SCHEDULE_ID, which justs enumerates all schedule alternatives, of which there are choosers times + # alternative, in the order they are processed, which depends on if there stops on outward/return leg. + # We might want to change SCHEDULE_ID to a fixed pattern of all possible combinations of + # (outbound, main, inbound) duration for the maximum possible tour duration (max time window). For + # 30min intervals, this leads to 1225 alternatives and therefore reasonable memory-wise for random numbers. + # It looks like all that would need to change for this is the generation of the schedule alternatives and + # the lookup of choices as elements in schedule after simulation because choosers are indexed by tour_id. + schedules = generate_schedule_alternatives(choosers).sort_index() # Assuming we did the max_alt_size calculation correctly, @@ -302,6 +312,7 @@ def run_trip_scheduling_choice( estimator=None, chunk_sizer=chunk_sizer, compute_settings=model_settings.compute_settings, + alts_context= AltsContext(schedules[SCHEDULE_ID].min(), schedules[SCHEDULE_ID].max()), ) assert len(choices.index) == len(choosers.index) @@ -347,6 +358,13 @@ class TripSchedulingChoiceSettings(PydanticReadable, extra="forbid"): compute_settings: ComputeSettings = ComputeSettings() """Compute settings for this component.""" + explicit_chunk: float = 0 + """ + If > 0, use this chunk size instead of adaptive chunking. + If less than 1, use this fraction of the total number of rows. + """ + + @workflow.step def trip_scheduling_choice( diff --git a/activitysim/abm/models/util/cdap.py b/activitysim/abm/models/util/cdap.py index 21f42de827..48ec7a31a1 100644 --- a/activitysim/abm/models/util/cdap.py +++ b/activitysim/abm/models/util/cdap.py @@ -999,11 +999,18 @@ def household_activity_choices( # add joint util to util utils = utils.add(joint_tour_utils) - probs = logit.utils_to_probs(state, utils, trace_label=trace_label) + if state.settings.use_explicit_error_terms: + utils = logit.validate_utils(state, utils, trace_label=trace_label) - # select an activity pattern alternative for each household based on probability - # result is a series indexed on _hh_index_ with the (0 based) index of the column from probs - idx_choices, rands = logit.make_choices(state, probs, trace_label=trace_label) + idx_choices, rands = logit.make_choices_utility_based( + state, utils, trace_label=trace_label + ) + else: + probs = logit.utils_to_probs(state, utils, trace_label=trace_label) + + # select an activity pattern alternative for each household based on probability + # result is a series indexed on _hh_index_ with the (0 based) index of the column from probs + idx_choices, rands = logit.make_choices(state, probs, trace_label=trace_label) # convert choice expressed as index into alternative name from util column label choices = pd.Series(utils.columns[idx_choices].values, index=utils.index) @@ -1021,16 +1028,20 @@ def household_activity_choices( "%s.hhsize%d_utils" % (trace_label, hhsize), column_labels=["expression", "household"], ) - state.tracing.trace_df( - probs, - "%s.hhsize%d_probs" % (trace_label, hhsize), - column_labels=["expression", "household"], - ) + + if not state.settings.use_explicit_error_terms: + state.tracing.trace_df( + probs, + "%s.hhsize%d_probs" % (trace_label, hhsize), + column_labels=["expression", "household"], + ) + state.tracing.trace_df( choices, "%s.hhsize%d_activity_choices" % (trace_label, hhsize), column_labels=["expression", "household"], ) + state.tracing.trace_df( rands, "%s.hhsize%d_rands" % (trace_label, hhsize), columns=[None, "rand"] ) diff --git a/activitysim/abm/models/util/test/test_cdap.py b/activitysim/abm/models/util/test/test_cdap.py index 20dc6b2410..20d68f2dd9 100644 --- a/activitysim/abm/models/util/test/test_cdap.py +++ b/activitysim/abm/models/util/test/test_cdap.py @@ -5,6 +5,7 @@ import os.path +import numpy as np import pandas as pd import pandas.testing as pdt import pytest @@ -176,3 +177,84 @@ def test_build_cdap_spec_hhsize2(people, model_settings): ).astype("float") pdt.assert_frame_equal(utils, expected, check_names=False) + + +def test_cdap_explicit_error_terms_parity(people, model_settings): + person_type_map = model_settings.get("PERSON_TYPE_MAP", {}) + + # Increase population to get more stable distribution for parity check + # We'll just duplicate the existing people a few times + large_people = pd.concat([people] * 500).reset_index(drop=True) + large_people.index.name = "person_id" + + assert people.household_id.is_monotonic_increasing + large_people["hhid_diff"] = large_people.household_id.diff().fillna(0).astype(int) + large_people.loc[large_people["hhid_diff"] < 0, "hhid_diff"] = 1 + large_people["household_id"] = large_people.hhid_diff.cumsum() + + assert large_people["household_id"].is_monotonic_increasing + + # Run without explicit error terms + state_no_eet = workflow.State.make_default(__file__) + cdap_indiv_spec = state_no_eet.filesystem.read_model_spec( + file_name="cdap_indiv_and_hhsize1.csv" + ) + interaction_coefficients = pd.read_csv( + state_no_eet.filesystem.get_config_file_path( + "cdap_interaction_coefficients.csv" + ), + comment="#", + ) + interaction_coefficients = cdap.preprocess_interaction_coefficients( + interaction_coefficients + ) + cdap_fixed_relative_proportions = pd.DataFrame( + {"activity": ["M", "N", "H"], "coefficient": [0.33, 0.33, 0.34]} + ) + + state_no_eet.settings.use_explicit_error_terms = False + state_no_eet.rng().set_base_seed(42) + state_no_eet.rng().begin_step("test_no_eet") + state_no_eet.rng().add_channel("person_id", large_people) + state_no_eet.rng().add_channel( + "household_id", + large_people.drop_duplicates("household_id").set_index("household_id"), + ) + + choices_no_eet = cdap.run_cdap( + state_no_eet, + large_people, + person_type_map, + cdap_indiv_spec, + interaction_coefficients, + cdap_fixed_relative_proportions, + locals_d=None, + ) + + # Run with explicit error terms + state_eet = workflow.State.make_default(__file__) + state_eet.settings.use_explicit_error_terms = True + state_eet.rng().set_base_seed(42) + state_eet.rng().begin_step("test_eet") + state_eet.rng().add_channel("person_id", large_people) + state_eet.rng().add_channel( + "household_id", + large_people.drop_duplicates("household_id").set_index("household_id"), + ) + + choices_eet = cdap.run_cdap( + state_eet, + large_people, + person_type_map, + cdap_indiv_spec, + interaction_coefficients, + cdap_fixed_relative_proportions, + locals_d=None, + ) + + # Compare distributions + dist_no_eet = choices_no_eet.value_counts(normalize=True).sort_index() + dist_eet = choices_eet.value_counts(normalize=True).sort_index() + + # Check that they are reasonably close + pdt.assert_series_equal(dist_no_eet, dist_eet, atol=0.05, check_names=False) diff --git a/activitysim/abm/models/util/tour_destination.py b/activitysim/abm/models/util/tour_destination.py index d99803bd7d..0531a2caeb 100644 --- a/activitysim/abm/models/util/tour_destination.py +++ b/activitysim/abm/models/util/tour_destination.py @@ -21,6 +21,7 @@ from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.interaction_sample import interaction_sample from activitysim.core.interaction_sample_simulate import interaction_sample_simulate +from activitysim.core.logit import AltsContext from activitysim.core.util import reindex logger = logging.getLogger(__name__) @@ -873,6 +874,10 @@ def run_destination_simulate( state.tracing.dump_df(DUMP, choosers, trace_label, "choosers") log_alt_losers = state.settings.log_alt_losers + # use full land_use index to ensure AltsContext spans full range of potential destinations + # (maintains stable random number generation even if zones flip zero/non-zero size) + land_use = state.get_dataframe("land_use") + alts_context = AltsContext.from_series(land_use.index) choices = interaction_sample_simulate( state, @@ -891,6 +896,7 @@ def run_destination_simulate( estimator=estimator, skip_choice=skip_choice, compute_settings=model_settings.compute_settings, + alts_context=alts_context, ) if not want_logsums: diff --git a/activitysim/abm/models/util/vectorize_tour_scheduling.py b/activitysim/abm/models/util/vectorize_tour_scheduling.py index c199ef40da..0666bf2c8c 100644 --- a/activitysim/abm/models/util/vectorize_tour_scheduling.py +++ b/activitysim/abm/models/util/vectorize_tour_scheduling.py @@ -17,6 +17,7 @@ from activitysim.core.configuration.base import ComputeSettings, PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings from activitysim.core.interaction_sample_simulate import interaction_sample_simulate +from activitysim.core.logit import AltsContext from activitysim.core.util import reindex logger = logging.getLogger(__name__) @@ -849,6 +850,9 @@ def _schedule_tours( estimator.write_interaction_sample_alternatives(alt_tdd) log_alt_losers = state.settings.log_alt_losers + # use full TDD alternatives index to ensure AltsContext spans full range of potential slots + tdd_alts = state.get_injectable("tdd_alts") + alts_context = AltsContext.from_series(tdd_alts.index) choices = interaction_sample_simulate( state, @@ -862,6 +866,7 @@ def _schedule_tours( trace_label=tour_trace_label, estimator=estimator, compute_settings=compute_settings, + alts_context=alts_context, ) chunk_sizer.log_df(tour_trace_label, "choices", choices) diff --git a/activitysim/abm/tables/shadow_pricing.py b/activitysim/abm/tables/shadow_pricing.py index f993df445c..091aaf7504 100644 --- a/activitysim/abm/tables/shadow_pricing.py +++ b/activitysim/abm/tables/shadow_pricing.py @@ -171,6 +171,7 @@ def __init__( self.num_processes = num_processes self.use_shadow_pricing = bool(state.settings.use_shadow_pricing) + logger.info("Use shadow pricing: %s", self.use_shadow_pricing) self.saved_shadow_price_file_path = ( None # set by read_saved_shadow_prices if loaded ) diff --git a/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation.csv b/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation.csv new file mode 100644 index 0000000000..d81df1ab13 --- /dev/null +++ b/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation.csv @@ -0,0 +1,2 @@ +Description,Expression,participate,not_participate +Adult participation,adult,0.5,-0.5 diff --git a/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation.yaml b/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation.yaml new file mode 100644 index 0000000000..8db2410c08 --- /dev/null +++ b/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation.yaml @@ -0,0 +1,5 @@ +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coefficients.csv +participation_choice: participate +max_participation_choice_iterations: 100 +FORCE_PARTICIPATION: True diff --git a/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation_coefficients.csv b/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation_coefficients.csv new file mode 100644 index 0000000000..237d519172 --- /dev/null +++ b/activitysim/abm/test/test_misc/configs_test_misc/joint_tour_participation_coefficients.csv @@ -0,0 +1,2 @@ +expression,coefficient +adult,1.0 diff --git a/activitysim/abm/test/test_misc/test_joint_tour_participation.py b/activitysim/abm/test/test_misc/test_joint_tour_participation.py new file mode 100644 index 0000000000..5aa15c6e8e --- /dev/null +++ b/activitysim/abm/test/test_misc/test_joint_tour_participation.py @@ -0,0 +1,158 @@ +import numpy as np +import pandas as pd +import pandas.testing as pdt +import pytest + +from activitysim.abm.models import joint_tour_participation +from activitysim.core import logit, workflow + +from .test_trip_departure_choice import add_canonical_dirs + + +@pytest.fixture +def candidates(): + # Create synthetic candidates for Joint Tour Participation + # JTP chooses whether each candidate participates in a joint tour. + # We include varied compositions and preschoolers to exercise the + # get_tour_satisfaction logic properly. + num_tours_per_comp = 500 + compositions = ["MIXED", "ADULTS", "CHILDREN"] + num_candidates_per_tour = 4 + + total_tours = num_tours_per_comp * len(compositions) + num_candidates = total_tours * num_candidates_per_tour + + # Ensure reproducibility + rng = np.random.default_rng(42) + + tour_ids = np.repeat(np.arange(total_tours), num_candidates_per_tour) + comp_values = np.repeat(compositions, num_tours_per_comp * num_candidates_per_tour) + + df = pd.DataFrame( + { + "tour_id": tour_ids, + "household_id": tour_ids, # simplified for mock + "person_id": np.arange(num_candidates), + "composition": comp_values, + }, + index=pd.Index(np.arange(num_candidates), name="participant_id"), + ) + + # Assign adult and preschooler status based on composition + # MIXED: at least one adult and one child + # ADULTS: all adults + # CHILDREN: all children + df["adult"] = False + df["person_is_preschool"] = False + + for i, comp in enumerate(compositions): + mask = df.composition == comp + indices = df[mask].index + + if comp == "ADULTS": + df.loc[indices, "adult"] = True + elif comp == "CHILDREN": + df.loc[indices, "adult"] = False + # Some children are preschoolers + df.loc[ + rng.choice(indices, len(indices) // 4, replace=False), + "person_is_preschool", + ] = True + elif comp == "MIXED": + # For each tour, make the first person an adult, rest children + tour_start_indices = indices[::num_candidates_per_tour] + df.loc[tour_start_indices, "adult"] = True + # Other members are children, some might be preschoolers + other_indices = indices[~indices.isin(tour_start_indices)] + df.loc[ + rng.choice(other_indices, len(other_indices) // 3, replace=False), + "person_is_preschool", + ] = True + + return df + + +@pytest.fixture +def model_spec(): + # Simple spec with two alternatives: 'participate' and 'not_participate' + return pd.DataFrame( + {"participate": [0.8, -0.2], "not_participate": [0.0, 0.0]}, + index=pd.Index(["adult", "person_is_preschool"], name="Expression"), + ) + + +def test_jtp_explicit_error_terms_parity(candidates, model_spec): + """ + Test that joint tour participation results are statistically similar + between MNL and Explicit Error Terms (EET) using realistic candidate scenarios. + """ + # Create random utilities for the candidates that vary by attribute + rng = np.random.default_rng(42) + + # Base utility + some noise + base_util = (candidates.adult * 0.5) - (candidates.person_is_preschool * 1.0) + utils = pd.DataFrame( + { + "participate": base_util + rng.standard_normal(len(candidates)), + "not_participate": 0, + }, + index=candidates.index, + ) + + # Run without EET (MNL) + state_no_eet = add_canonical_dirs("configs_test_misc").default_settings() + state_no_eet.settings.use_explicit_error_terms = False + state_no_eet.rng().set_base_seed(42) + state_no_eet.rng().begin_step("test_no_eet") + state_no_eet.rng().add_channel("participant_id", candidates) + + # MNL path expects probabilities + probs_no_eet = logit.utils_to_probs(state_no_eet, utils, trace_label="test_no_eet") + choices_no_eet, _ = joint_tour_participation.participants_chooser( + state_no_eet, + probs_no_eet, + candidates, + model_spec, + trace_label="test_no_eet", + ) + + # Run with EET + state_eet = add_canonical_dirs("configs_test_misc").default_settings() + state_eet.settings.use_explicit_error_terms = True + state_eet.rng().set_base_seed(42) + state_eet.rng().begin_step("test_eet") + state_eet.rng().add_channel("participant_id", candidates) + + # EET path expects raw utilities + choices_eet, _ = joint_tour_participation.participants_chooser( + state_eet, + utils.copy(), + candidates, + model_spec, + trace_label="test_eet", + ) + + # Compare distributions of number of participants per tour + # Choice 0 is 'participate' + no_eet_participation_counts = ( + (choices_no_eet == 0).groupby(candidates.tour_id).sum() + ) + eet_participation_counts = (choices_eet == 0).groupby(candidates.tour_id).sum() + + dist_no_eet = no_eet_participation_counts.value_counts(normalize=True).sort_index() + dist_eet = eet_participation_counts.value_counts(normalize=True).sort_index() + + # Check that the distribution of participation counts is close + pdt.assert_series_equal(dist_no_eet, dist_eet, atol=0.05, check_names=False) + + # Also check average participation by composition for deeper parity check + comp_parity_no_eet = no_eet_participation_counts.groupby( + candidates.groupby("tour_id")["composition"].first() + ).mean() + comp_parity_eet = eet_participation_counts.groupby( + candidates.groupby("tour_id")["composition"].first() + ).mean() + + pdt.assert_series_equal( + comp_parity_no_eet, comp_parity_eet, atol=0.1, check_names=False + ) diff --git a/activitysim/abm/test/test_misc/test_trip_departure_choice.py b/activitysim/abm/test/test_misc/test_trip_departure_choice.py index 6d462c0bd0..d6645ce94f 100644 --- a/activitysim/abm/test/test_misc/test_trip_departure_choice.py +++ b/activitysim/abm/test/test_misc/test_trip_departure_choice.py @@ -1,152 +1,246 @@ -# import numpy as np -# import pandas as pd -# import pytest -# -# import activitysim.abm.models.trip_departure_choice as tdc -# from activitysim.abm.models.util.trip import get_time_windows -# from activitysim.core import los -# -# from .setup_utils import setup_dirs -# -# -# @pytest.fixture(scope="module") -# def trips(): -# outbound_array = [True, True, False, False, False, True, True, False, False, True] -# -# trips = pd.DataFrame( -# data={ -# "tour_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], -# "trip_duration": [2, 2, 7, 7, 7, 12, 12, 4, 4, 5], -# "inbound_duration": [0, 0, 7, 7, 7, 0, 0, 4, 4, 5], -# "main_leg_duration": [4, 4, 2, 2, 2, 2, 2, 1, 1, 2], -# "outbound_duration": [2, 2, 0, 0, 0, 12, 12, 0, 0, 5], -# "trip_count": [2, 2, 3, 3, 3, 2, 2, 2, 2, 1], -# "trip_num": [1, 2, 1, 2, 3, 1, 2, 1, 2, 1], -# "outbound": outbound_array, -# "chunk_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], -# "is_work": [ -# True, -# True, -# False, -# False, -# False, -# False, -# False, -# False, -# False, -# True, -# ], -# "is_school": [ -# False, -# False, -# False, -# False, -# False, -# False, -# False, -# True, -# True, -# False, -# ], -# "is_eatout": [ -# False, -# False, -# True, -# True, -# True, -# True, -# True, -# False, -# False, -# False, -# ], -# "start": [8, 8, 18, 18, 18, 18, 18, 24, 24, 19], -# "end": [14, 14, 39, 39, 39, 39, 39, 29, 29, 26], -# "origin": [3, 5, 15, 12, 24, 8, 17, 8, 9, 6], -# "destination": [5, 9, 12, 24, 20, 17, 18, 9, 11, 14], -# }, -# index=range(10), -# ) -# -# trips.index.name = "trip_id" -# return trips -# -# -# @pytest.fixture(scope="module") -# def settings(): -# return { -# "skims_file": "skims.omx", -# "skim_time_periods": {"labels": ["EA", "AM", "MD", "PM", "NT"]}, -# } -# -# -# @pytest.fixture(scope="module") -# def model_spec(): -# index = [ -# "@(df['stop_time_duration'] * df['is_work'].astype(int)).astype(int)", -# "@(df['stop_time_duration'] * df['is_school'].astype(int)).astype(int)", -# "@(df['stop_time_duration'] * df['is_eatout'].astype(int)).astype(int)", -# ] -# -# values = { -# "inbound": [0.933020, 0.370260, 0.994840], -# "outbound": [0.933020, 0.370260, 0.994840], -# } -# -# return pd.DataFrame(index=index, data=values) -# -# -# def test_build_patterns(trips): -# time_windows = get_time_windows(48, 3) -# patterns = tdc.build_patterns(trips, time_windows) -# patterns = patterns.sort_values(["tour_id", "outbound", "trip_num"]) -# -# assert patterns.shape[0] == 34 -# assert patterns.shape[1] == 6 -# assert patterns.index.name == tdc.TOUR_LEG_ID -# -# output_columns = [ -# tdc.TOUR_ID, -# tdc.PATTERN_ID, -# tdc.TRIP_NUM, -# tdc.STOP_TIME_DURATION, -# tdc.TOUR_ID, -# tdc.OUTBOUND, -# ] -# -# assert set(output_columns).issubset(patterns.columns) -# -# -# def test_get_tour_legs(trips): -# tour_legs = tdc.get_tour_legs(trips) -# assert tour_legs.index.name == tdc.TOUR_LEG_ID -# assert ( -# np.unique(tour_legs[tdc.TOUR_ID].values).shape[0] -# == np.unique(trips[tdc.TOUR_ID].values).shape[0] -# ) -# -# -# def test_generate_alternative(trips): -# alts = tdc.generate_alternatives(trips, tdc.STOP_TIME_DURATION) -# assert alts.shape[0] == 67 -# assert alts.shape[1] == 1 -# -# assert alts.index.name == tdc.TRIP_ID -# assert alts.columns[0] == tdc.STOP_TIME_DURATION -# -# pd.testing.assert_series_equal( -# trips.groupby(trips.index)["trip_duration"].max(), -# alts.groupby(alts.index)[tdc.STOP_TIME_DURATION].max(), -# check_names=False, -# ) -# -# -# def test_apply_stage_two_model(state, model_spec, trips): -# setup_dirs() -# departures = tdc.apply_stage_two_model( -# state, model_spec, trips, 0, "TEST Trip Departure" -# ) -# assert len(departures) == len(trips) -# pd.testing.assert_index_equal(departures.index, trips.index) -# -# departures = pd.concat([trips, departures], axis=1) +import numpy as np +import pandas as pd +import pytest +import os + +import activitysim.abm.models.trip_departure_choice as tdc +from activitysim.abm.models.util.trip import get_time_windows +from activitysim.core import workflow + +from .setup_utils import setup_dirs + + +@pytest.fixture(scope="module") +def trips(): + trips = pd.DataFrame( + data={ + "tour_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], + "trip_duration": [2, 2, 7, 7, 7, 12, 12, 4, 4, 5], + "inbound_duration": [0, 0, 7, 7, 7, 0, 0, 4, 4, 5], + "main_leg_duration": [4, 4, 2, 2, 2, 2, 2, 1, 1, 2], + "outbound_duration": [2, 2, 0, 0, 0, 12, 12, 0, 0, 5], + "trip_count": [2, 2, 3, 3, 3, 2, 2, 2, 2, 1], + "trip_num": [1, 2, 1, 2, 3, 1, 2, 1, 2, 1], + "outbound": [ + True, + True, + False, + False, + False, + True, + True, + False, + False, + True, + ], + "chunk_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], + "is_work": [ + True, + True, + False, + False, + False, + False, + False, + False, + False, + True, + ], + "is_school": [ + False, + False, + False, + False, + False, + False, + False, + True, + True, + False, + ], + "is_eatout": [ + False, + False, + True, + True, + True, + True, + True, + False, + False, + False, + ], + "start": [8, 8, 18, 18, 18, 18, 18, 24, 24, 19], + "end": [14, 14, 39, 39, 39, 39, 39, 29, 29, 26], + "origin": [3, 5, 15, 12, 24, 8, 17, 8, 9, 6], + "destination": [5, 9, 12, 24, 20, 17, 18, 9, 11, 14], + }, + index=range(10), + ) + + trips.index.name = "trip_id" + return trips + + +@pytest.fixture(scope="module") +def settings(): + return { + "skims_file": "skims.omx", + "skim_time_periods": {"labels": ["EA", "AM", "MD", "PM", "NT"]}, + } + + +def add_canonical_dirs(configs_dir_name): + state = workflow.State() + configs_dir = os.path.join(os.path.dirname(__file__), f"{configs_dir_name}") + data_dir = os.path.join(os.path.dirname(__file__), f"data") + output_dir = os.path.join(os.path.dirname(__file__), "output") + state.initialize_filesystem( + working_dir=os.path.dirname(__file__), + configs_dir=(configs_dir,), + output_dir=output_dir, + data_dir=(data_dir,), + ) + return state + + +@pytest.fixture(scope="module") +def model_spec(): + index = [ + "@(df['stop_time_duration'] * df['is_work'].astype(int)).astype(int)", + "@(df['stop_time_duration'] * df['is_school'].astype(int)).astype(int)", + "@(df['stop_time_duration'] * df['is_eatout'].astype(int)).astype(int)", + ] + + values = { + "inbound": [0.933020, 0.370260, 0.994840], + "outbound": [0.933020, 0.370260, 0.994840], + } + + return pd.DataFrame(index=index, data=values) + + +def test_build_patterns(trips): + time_windows = get_time_windows(48, 3) + patterns = tdc.build_patterns(trips, time_windows) + patterns = patterns.sort_values(["tour_id", "outbound", "trip_num"]) + + assert patterns.shape[0] == 34 + assert patterns.shape[1] == 6 + assert patterns.index.name == tdc.TOUR_LEG_ID + + output_columns = [ + tdc.TOUR_ID, + tdc.PATTERN_ID, + tdc.TRIP_NUM, + tdc.STOP_TIME_DURATION, + tdc.TOUR_ID, + tdc.OUTBOUND, + ] + + assert set(output_columns).issubset(patterns.columns) + + +def test_get_tour_legs(trips): + tour_legs = tdc.get_tour_legs(trips) + assert tour_legs.index.name == tdc.TOUR_LEG_ID + assert ( + np.unique(tour_legs[tdc.TOUR_ID].values).shape[0] + == np.unique(trips[tdc.TOUR_ID].values).shape[0] + ) + + +def test_generate_alternative(trips): + alts = tdc.generate_alternatives(trips, tdc.STOP_TIME_DURATION) + assert alts.shape[0] == 67 + assert alts.shape[1] == 1 + + assert alts.index.name == tdc.TRIP_ID + assert alts.columns[0] == tdc.STOP_TIME_DURATION + + pd.testing.assert_series_equal( + trips.groupby(trips.index)["trip_duration"].max(), + alts.groupby(alts.index)[tdc.STOP_TIME_DURATION].max(), + check_names=False, + ) + + +def test_apply_stage_two_model(model_spec, trips): + setup_dirs() + state = add_canonical_dirs("configs_test_misc").default_settings() + + # A settings object is needed to pass to the model application function, + # but for testing we can just use the default settings. + # In non-testing use cases, the SPEC would actually be read from the yaml file + # instead of being passed directly as a dataframe. + model_settings = tdc.TripDepartureChoiceSettings() + + departures = tdc.apply_stage_two_model( + state, + model_spec, + trips, + 0, + "TEST Trip Departure", + model_settings=model_settings, + ) + assert len(departures) == len(trips) + pd.testing.assert_index_equal(departures.index, trips.index) + + departures = pd.concat([trips, departures], axis=1) + + +def test_tdc_explicit_error_terms_parity(model_spec, trips): + setup_dirs() + model_settings = tdc.TripDepartureChoiceSettings() + + # Increase population for statistical convergence + large_trips = pd.concat([trips] * 500).reset_index(drop=True) + large_trips.index.name = "trip_id" + # Ensure tour_ids are distinct for the expanded set + large_trips["tour_id"] = ( + large_trips.groupby("tour_id").cumcount() * 1000 + large_trips["tour_id"] + ) + + # Trip departure choice uses tour_leg_id as the random channel index + tour_legs = tdc.get_tour_legs(large_trips) + + # Run without explicit error terms + state_no_eet = add_canonical_dirs("configs_test_misc").default_settings() + state_no_eet.settings.use_explicit_error_terms = False + state_no_eet.rng().set_base_seed(42) + state_no_eet.rng().begin_step("test_no_eet") + state_no_eet.rng().add_channel("trip_id", large_trips) + state_no_eet.rng().add_channel("tour_leg_id", tour_legs) + + departures_no_eet = tdc.apply_stage_two_model( + state_no_eet, + model_spec, + large_trips, + 0, + "TEST Trip Departure No EET", + model_settings=model_settings, + ) + + # Run with explicit error terms + state_eet = add_canonical_dirs("configs_test_misc").default_settings() + state_eet.settings.use_explicit_error_terms = True + state_eet.rng().set_base_seed(42) + state_eet.rng().begin_step("test_eet") + state_eet.rng().add_channel("trip_id", large_trips) + state_eet.rng().add_channel("tour_leg_id", tour_legs) + + departures_eet = tdc.apply_stage_two_model( + state_eet, + model_spec, + large_trips, + 0, + "TEST Trip Departure EET", + model_settings=model_settings, + ) + + # Compare distributions + dist_no_eet = departures_no_eet.value_counts(normalize=True).sort_index() + dist_eet = departures_eet.value_counts(normalize=True).sort_index() + + # Check that they are reasonably close (within 5% for this sample size) + pd.testing.assert_series_equal(dist_no_eet, dist_eet, atol=0.05, check_names=False) diff --git a/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py b/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py index 24fdebde3e..8401c785c1 100644 --- a/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py +++ b/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py @@ -1,199 +1,289 @@ -# import numpy as np -# import pandas as pd -# import pytest -# -# from activitysim.abm.models import trip_scheduling_choice as tsc -# from activitysim.abm.tables.skims import skim_dict -# from activitysim.core import los, workflow -# -# from .setup_utils import setup_dirs -# -# -# @pytest.fixture(scope="module") -# def tours(): -# tours = pd.DataFrame( -# data={ -# "duration": [2, 44, 32, 12, 11, 16], -# "num_outbound_stops": [2, 4, 0, 0, 1, 3], -# "num_inbound_stops": [1, 0, 0, 2, 1, 2], -# "tour_type": ["othdisc"] * 2 + ["eatout"] * 4, -# "origin": [3, 10, 15, 23, 5, 8], -# "destination": [5, 9, 12, 24, 20, 17], -# tsc.LAST_OB_STOP: [1, 3, 0, 0, 12, 14], -# tsc.FIRST_IB_STOP: [2, 0, 0, 4, 6, 20], -# }, -# index=range(6), -# ) -# -# tours.index.name = "tour_id" -# -# tours[tsc.HAS_OB_STOPS] = tours[tsc.NUM_OB_STOPS] >= 1 -# tours[tsc.HAS_IB_STOPS] = tours[tsc.NUM_IB_STOPS] >= 1 -# -# return tours -# -# -# @pytest.fixture(scope="module") -# def settings(): -# return {"skims_file": "skims.omx", "skim_time_periods": {"labels": ["MD"]}} -# -# -# @pytest.fixture(scope="module") -# def model_spec(): -# index = [ -# "@(df['main_leg_duration']>df['duration']).astype(int)", -# "@(df['main_leg_duration'] == 0)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 1)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 2)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 3)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 4)&(df['tour_type']=='othdiscr')", -# "@df['tour_type']=='othdiscr'", -# "@df['tour_type']=='eatout'", -# "@df['tour_type']=='eatout'", -# ] -# -# values = [ -# -999, -# -6.5884, -# -5.0326, -# -2.0526, -# -1.0313, -# -0.46489, -# 0.060382, -# -0.7508, -# 0.53247, -# ] -# -# return pd.DataFrame(index=index, data=values, columns=["stage_one"]) -# -# -# @pytest.fixture(scope="module") -# def skims(settings): -# setup_dirs() -# nw_los = los.Network_LOS() -# nw_los.load_data() -# skim_d = skim_dict(nw_los) -# -# od_skim_stack_wrapper = skim_d.wrap("origin", "destination") -# do_skim_stack_wrapper = skim_d.wrap("destination", "origin") -# obib_skim_stack_wrapper = skim_d.wrap(tsc.LAST_OB_STOP, tsc.FIRST_IB_STOP) -# -# skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] -# -# return skims -# -# -# @pytest.fixture(scope="module") -# def locals_dict(skims): -# return {"od_skims": skims[0], "do_skims": skims[1], "obib_skims": skims[2]} -# -# -# def test_generate_schedule_alternatives(tours): -# windows = tsc.generate_schedule_alternatives(tours) -# assert windows.shape[0] == 296 -# assert windows.shape[1] == 4 -# -# output_columns = [ -# tsc.SCHEDULE_ID, -# tsc.MAIN_LEG_DURATION, -# tsc.OB_DURATION, -# tsc.IB_DURATION, -# ] -# -# assert set(output_columns).issubset(windows.columns) -# -# -# def test_no_stops_patterns(tours): -# no_stops = tours[ -# (tours["num_outbound_stops"] == 0) & (tours["num_inbound_stops"] == 0) -# ].copy() -# windows = tsc.no_stops_patterns(no_stops) -# -# assert windows.shape[0] == 1 -# assert windows.shape[1] == 3 -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(windows.columns) -# -# pd.testing.assert_series_equal( -# windows[tsc.MAIN_LEG_DURATION], -# no_stops["duration"], -# check_names=False, -# check_dtype=False, -# ) -# assert windows[windows[tsc.IB_DURATION] > 0].empty -# assert windows[windows[tsc.OB_DURATION] > 0].empty -# -# -# def test_one_way_stop_patterns(tours): -# one_way_stops = tours[ -# ( -# (tours["num_outbound_stops"] > 0).astype(int) -# + (tours["num_inbound_stops"] > 0).astype(int) -# ) -# == 1 -# ].copy() -# windows = tsc.stop_one_way_only_patterns(one_way_stops) -# -# assert windows.shape[0] == 58 -# assert windows.shape[1] == 3 -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(windows.columns) -# -# inbound_options = windows[(windows[tsc.IB_DURATION] > 0)] -# outbound_options = windows[windows[tsc.OB_DURATION] > 0] -# assert np.unique(inbound_options.index).shape[0] == 1 -# assert np.unique(outbound_options.index).shape[0] == 1 -# -# -# def test_two_way_stop_patterns(tours): -# two_way_stops = tours[ -# ( -# (tours["num_outbound_stops"] > 0).astype(int) -# + (tours["num_inbound_stops"] > 0).astype(int) -# ) -# == 2 -# ].copy() -# windows = tsc.stop_two_way_only_patterns(two_way_stops) -# -# assert windows.shape[0] == 237 -# assert windows.shape[1] == 3 -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(windows.columns) -# -# -# def test_run_trip_scheduling_choice( -# state: workflow.State, model_spec, tours, skims, locals_dict -# ): -# """ -# Test run the model. -# """ -# -# out_tours = tsc.run_trip_scheduling_choice( -# state, -# model_spec, -# tours, -# skims, -# locals_dict, -# trace_label="PyTest Trip Scheduling", -# ) -# -# assert len(tours) == len(out_tours) -# pd.testing.assert_index_equal( -# tours.sort_index().index, out_tours.sort_index().index -# ) -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(out_tours.columns) -# -# assert len( -# out_tours[ -# out_tours[output_columns].sum(axis=1) == out_tours[tsc.TOUR_DURATION_COLUMN] -# ] -# ) == len(tours) +from __future__ import annotations + +import os +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from activitysim.abm.models import trip_scheduling_choice as tsc +from activitysim.abm.tables.skims import skim_dict +from activitysim.core import los, workflow + + +@pytest.fixture(scope="module") +def tours(): + tours = pd.DataFrame( + data={ + "duration": [2, 44, 32, 12, 11, 16], + "num_outbound_stops": [2, 4, 0, 0, 1, 3], + "num_inbound_stops": [1, 0, 0, 2, 1, 2], + "tour_type": ["othdisc"] * 2 + ["eatout"] * 4, + "origin": [3, 10, 15, 23, 5, 8], + "destination": [5, 9, 12, 24, 20, 17], + tsc.LAST_OB_STOP: [1, 3, 0, 0, 12, 14], + tsc.FIRST_IB_STOP: [2, 0, 0, 4, 6, 20], + }, + index=range(6), + ) + + tours.index.name = "tour_id" + + tours[tsc.HAS_OB_STOPS] = tours[tsc.NUM_OB_STOPS] >= 1 + tours[tsc.HAS_IB_STOPS] = tours[tsc.NUM_IB_STOPS] >= 1 + + return tours + + +@pytest.fixture(scope="module") +def model_spec(): + index = [ + "@(df['main_leg_duration']>df['duration']).astype(int)", + "@(df['main_leg_duration'] == 0)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 1)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 2)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 3)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 4)&(df['tour_type']=='othdiscr')", + "@df['tour_type']=='othdiscr'", + "@df['tour_type']=='eatout'", + "@df['tour_type']=='eatout'", + ] + + values = [ + -999, + -6.5884, + -5.0326, + -2.0526, + -1.0313, + -0.46489, + 0.060382, + -0.7508, + 0.53247, + ] + + return pd.DataFrame(index=index, data=values, columns=["stage_one"]).rename_axis( + "Expression" + ) + + +def add_canonical_dirs(configs_dir_name): + state = workflow.State() + configs_dir = os.path.join(os.path.dirname(__file__), f"{configs_dir_name}") + data_dir = os.path.join(os.path.dirname(__file__), "data") + output_dir = os.path.join(os.path.dirname(__file__), "output") + state.initialize_filesystem( + working_dir=os.path.dirname(__file__), + configs_dir=(configs_dir,), + output_dir=output_dir, + data_dir=(data_dir,), + ) + return state + + +@pytest.fixture(scope="module") +def skims(): + state = add_canonical_dirs("configs_test_misc").default_settings() + nw_los = los.Network_LOS(state, los_settings_file_name="settings_60_min.yaml") + nw_los.load_data() + skim_d = skim_dict(state, nw_los) + + od_skim_stack_wrapper = skim_d.wrap("origin", "destination") + do_skim_stack_wrapper = skim_d.wrap("destination", "origin") + obib_skim_stack_wrapper = skim_d.wrap(tsc.LAST_OB_STOP, tsc.FIRST_IB_STOP) + + skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] + + return skims + + +@pytest.fixture(scope="module") +def locals_dict(skims): + return {"od_skims": skims[0], "do_skims": skims[1], "obib_skims": skims[2]} + + +@pytest.fixture(scope="module") +def base_dir() -> Path: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return Path(__file__).parent + + +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the module name string used in test setup. + :return: module name string ("summarize") + """ + return "summarize" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def tables() -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "zone_id", + "tours": "tour_id", + "trips": "trip_id", + "persons": "person_id", + "households": "household_id", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: bool + """ + return True + + +def test_generate_schedule_alternatives(tours): + windows = tsc.generate_schedule_alternatives(tours) + assert windows.shape[0] == 296 + assert windows.shape[1] == 4 + + output_columns = [ + tsc.SCHEDULE_ID, + tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, + tsc.IB_DURATION, + ] + + assert set(output_columns).issubset(windows.columns) + + +def test_no_stops_patterns(tours): + no_stops = tours[ + (tours["num_outbound_stops"] == 0) & (tours["num_inbound_stops"] == 0) + ].copy() + windows = tsc.no_stops_patterns(no_stops) + + assert windows.shape[0] == 1 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + pd.testing.assert_series_equal( + windows[tsc.MAIN_LEG_DURATION], + no_stops["duration"], + check_names=False, + check_dtype=False, + ) + assert windows[windows[tsc.IB_DURATION] > 0].empty + assert windows[windows[tsc.OB_DURATION] > 0].empty + + +def test_one_way_stop_patterns(tours): + one_way_stops = tours[ + ( + (tours["num_outbound_stops"] > 0).astype(int) + + (tours["num_inbound_stops"] > 0).astype(int) + ) + == 1 + ].copy() + windows = tsc.stop_one_way_only_patterns(one_way_stops) + + assert windows.shape[0] == 58 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + inbound_options = windows[(windows[tsc.IB_DURATION] > 0)] + outbound_options = windows[windows[tsc.OB_DURATION] > 0] + assert np.unique(inbound_options.index).shape[0] == 1 + assert np.unique(outbound_options.index).shape[0] == 1 + + +def test_two_way_stop_patterns(tours): + two_way_stops = tours[ + ( + (tours["num_outbound_stops"] > 0).astype(int) + + (tours["num_inbound_stops"] > 0).astype(int) + ) + == 2 + ].copy() + windows = tsc.stop_two_way_only_patterns(two_way_stops) + + assert windows.shape[0] == 237 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + +def test_run_trip_scheduling_choice(model_spec, tours, skims, locals_dict): + # create a temporary workflow state with no content + state = workflow.State.make_temp() + + # Define model settings for this test. + # The settings for this model requires a filename for the spec, but in this test we + # are passing the spec dataframe directly, so the filename is just a placeholder. + # In non-testing use cases, the SPEC would actually be read from the yaml file + # instead of being passed directly as a dataframe. + model_settings = tsc.TripSchedulingChoiceSettings( + **{ + "SPEC": "placeholder.csv", + "compute_settings": { + "protect_columns": ["origin", "destination", "schedule_id"] + }, + } + ) + + # As is common in ActivitySim the component will modify the input dataframe in-place. + # For testing we make a copy of the input tours to compare against after running the model. + in_tours = tours.copy(deep=True) + + # run the trip scheduling choice model + out_tours = tsc.run_trip_scheduling_choice( + state, + model_spec, + tours, + skims, + locals_dict, + trace_label="PyTest Trip Scheduling", + model_settings=model_settings, + ) + + # check that the number of tours is unchanged + assert len(in_tours) == len(out_tours) + pd.testing.assert_index_equal( + in_tours.sort_index().index, out_tours.sort_index().index + ) + + # check that the expected output columns are not present in input tours + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + for col in output_columns: + assert col not in in_tours.columns + + # check that the expected output columns *are* present in output tours + assert set(output_columns).issubset(out_tours.columns) + + # check that the sum of the output durations equals the tour duration + assert len( + out_tours[ + out_tours[output_columns].sum(axis=1) == out_tours[tsc.TOUR_DURATION_COLUMN] + ] + ) == len(in_tours) + + # check that tours with no outbound stops have zero outbound duration + assert out_tours[tsc.OB_DURATION].mask(in_tours[tsc.HAS_OB_STOPS], 0).sum() == 0 + + # check that tours with no inbound stops have zero inbound duration + assert out_tours[tsc.IB_DURATION].mask(in_tours[tsc.HAS_IB_STOPS], 0).sum() == 0 diff --git a/activitysim/core/chunk.py b/activitysim/core/chunk.py index f0074683d8..019f6abaef 100644 --- a/activitysim/core/chunk.py +++ b/activitysim/core/chunk.py @@ -1244,6 +1244,7 @@ def adaptive_chunked_choosers( chunk_size = math.ceil(num_choosers * explicit_chunk_size) else: chunk_size = math.ceil(explicit_chunk_size / num_processes) + logger.info(f"Running {trace_label} with {chunk_size} choosers") elif chunk_size is None: chunk_size = state.settings.chunk_size diff --git a/activitysim/core/configuration/base.py b/activitysim/core/configuration/base.py index 6936e6d1ad..aad266ff12 100644 --- a/activitysim/core/configuration/base.py +++ b/activitysim/core/configuration/base.py @@ -135,6 +135,10 @@ class ComputeSettings(PydanticBase): Sharrow settings for a component. """ + # Make this more general compute settings and use for explicit error term overrides + # Default None work for sub-components defined in getter below (eet_subcomponent) + use_explicit_error_terms: None | bool | dict[str, bool] = None + sharrow_skip: bool | dict[str, bool] = False """Skip sharrow when evaluating this component. @@ -218,6 +222,13 @@ def should_skip(self, subcomponent: str) -> bool: else: return bool(self.sharrow_skip) + def eet_subcomponent(self, subcomponent: str) -> bool: + """Check for EET overrides for a particular subcomponent.""" + if isinstance(self.use_explicit_error_terms, dict): + return self.use_explicit_error_terms.get(subcomponent, None) + else: + return self.use_explicit_error_terms + @contextmanager def pandas_option_context(self): """Context manager to set pandas options for compute settings.""" @@ -266,6 +277,7 @@ def subcomponent_settings(self, subcomponent: str) -> ComputeSettings: use_numba=self.use_numba, drop_unused_columns=self.drop_unused_columns, protect_columns=self.protect_columns, + use_explicit_error_terms=self.eet_subcomponent(subcomponent), ) diff --git a/activitysim/core/configuration/top.py b/activitysim/core/configuration/top.py index 4055608100..002d55e845 100644 --- a/activitysim/core/configuration/top.py +++ b/activitysim/core/configuration/top.py @@ -701,6 +701,7 @@ def _check_store_skims_in_shm(self): "memory_profile", "instrument", "sharrow", + "use_explicit_error_terms", ) """ Setting to log on startup. @@ -774,11 +775,33 @@ def _check_store_skims_in_shm(self): .. versionadded:: 1.3 """ + use_explicit_error_terms: bool = False + """ + Make choice from random utility model by drawing from distribution of unobserved + part of utility and taking the maximum of total utility. + + Defaults to standard Monte Carlo method, i.e., calculating probabilities and then + drawing a single uniform random number to draw from cumulative probabily. + + .. versionadded:: 1.6 + """ + check_model_settings: bool = True """ run checks to validate that YAML settings files are loadable and spec and coefficent csv can be resolved. - should catch many common errors early, including missing required configurations or specified coefficient labels without defined values. + should catch many common errors early, including missing required configurations or specified coefficient labels without defined values. + """ + + use_explicit_error_terms: bool = False + """ + Make choice from random utility model by drawing from distribution of unobserved + part of utility and taking the maximum of total utility. + + Defaults to standard Monte Carlo method, i.e., calculating probabilities and then + drawing a single uniform random number to draw from cumulative probabily. + + .. versionadded:: 1.x """ other_settings: dict[str, Any] = None diff --git a/activitysim/core/interaction_sample.py b/activitysim/core/interaction_sample.py index 52116638ba..51fe835bcb 100644 --- a/activitysim/core/interaction_sample.py +++ b/activitysim/core/interaction_sample.py @@ -3,10 +3,10 @@ from __future__ import annotations import logging +import typing import numpy as np import pandas as pd - from activitysim.core import ( chunk, estimation, @@ -17,16 +17,141 @@ util, workflow, ) +from activitysim.core.chunk import ChunkSizer from activitysim.core.configuration.base import ComputeSettings +from activitysim.core.exceptions import SegmentedSpecificationError from activitysim.core.skim_dataset import DatasetWrapper from activitysim.core.skim_dictionary import SkimWrapper -from activitysim.core.exceptions import SegmentedSpecificationError +if typing.TYPE_CHECKING: + from activitysim.core.random import Random logger = logging.getLogger(__name__) DUMP = False +def _poisson_sample_alternatives_inner( + alternative_count: int, + probs: pd.DataFrame, + poisson_inclusion_probs: pd.DataFrame, + rng: Random, + trace_label: str | None, + chunk_sizer:ChunkSizer, +) -> pd.DataFrame: + rands = rng.random_for_df(probs, n=alternative_count) + chunk_sizer.log_df(trace_label, "rands", rands) + sampled_mask = rands < poisson_inclusion_probs + sampled_results = poisson_inclusion_probs.where(sampled_mask) + return sampled_results + + +def make_sample_choices_utility_based( + state: workflow.State, + choosers, + utilities, + alternatives, + sample_size, + alternative_count, + alt_col_name, + allow_zero_probs, + trace_label: str, + chunk_sizer: ChunkSizer, +): + assert isinstance(utilities, pd.DataFrame) + assert utilities.shape == (len(choosers), alternative_count) + + assert isinstance(alternatives, pd.DataFrame) + assert len(alternatives) == alternative_count + + if allow_zero_probs: + zero_probs = ( + utilities.sum(axis=1) <= utilities.shape[1] * logit.UTIL_UNAVAILABLE + ) + if zero_probs.all(): + return pd.DataFrame( + columns=[alt_col_name, "rand", "prob", choosers.index.name] + ) + if zero_probs.any(): + # remove from sample + utilities = utilities[~zero_probs] + choosers = choosers[~zero_probs] + + utils_array = utilities.to_numpy() + chunk_sizer.log_df(trace_label, "utils_array", utils_array) + + probs = logit.utils_to_probs( + state, + utilities, + allow_zero_probs=allow_zero_probs, + trace_label=trace_label, + overflow_protection=not allow_zero_probs, + trace_choosers=choosers, + ) + inclusion_probs, sampled_alternatives = _poisson_sample_alternatives(alternative_count, chunk_sizer, probs, + sample_size, state, trace_label) + + # Stack removes the NaNs (the ones that weren't sampled) + # and gives us a multi-index of (person_id, alt_id) + choices_df = ( + sampled_alternatives.rename_axis("alt_idx", axis=1) + .stack() + .reset_index(name="prob") + .assign(**{alt_col_name: lambda df: alternatives.index.values[df["alt_idx"]]}) + .drop(columns=["alt_idx"]) + ) + + # Here we return the inclusion probabilities i.e. the true probability of being sampled and (ab)use the fact + # that pick_count=1 by definition and ln(1)=0 and recover the standard sample correction term. + # In non-Poisson sampling, we would return the probs of sampling an alternative once + # and the sampling correction factor np.log(df.pick_count/df.prob) is applied to the simulate utilities. + # TODO is it safe change the meaning of df.prob, given it's referenced in expression csvs? + # (but the alternative is to update all the expression CSV for sampling?) + return choices_df + + +def _poisson_sample_alternatives(alternative_count, chunk_sizer: ChunkSizer, probs: pd.DataFrame, sample_size, + state: workflow.State, trace_label: str) -> tuple[pd.DataFrame, pd.DataFrame]: + # compute the inclusion probability as the reciprocal of alt never being drawn + # -- these are common, so compute once upfront + exclusion_probs = (1 - probs) ** sample_size + inclusion_probs = 1 - exclusion_probs + + n = 0 + probs_subset = probs + inclusion_probs_subset = inclusion_probs + sampled_alternatives = pd.DataFrame(0.0, index=inclusion_probs.index, columns=inclusion_probs.columns) + while True: + sampled_results_subset = _poisson_sample_alternatives_inner( + alternative_count, probs_subset, inclusion_probs_subset, state.get_rn_generator(), trace_label, chunk_sizer + ) + no_alts_sampled_mask = sampled_results_subset.isna().all(axis=1) + alts_with_sampled_alternatives = sampled_results_subset[~no_alts_sampled_mask] + sampled_alternatives.loc[alts_with_sampled_alternatives.index, :] = alts_with_sampled_alternatives + if no_alts_sampled_mask.any(): + # TODO if this happens in base but the project case is such that something is picked, random numbers won't + # be consistent - we're asserting that this is very rare models where the sample size is not too small + logger.info(f"Poisson sampling of alternatives failed with {n=}, retrying") + # TODO put this behind a debug guard, because it will be slow + logger.info( + f"Sampled size was {sample_size}, poisson method mean expected sample size was {inclusion_probs.sum(axis=1).mean():.1f}, actual sampled mean was {(sampled_alternatives > 0).sum(axis=1).mean():.1f} and highest zero selection prob was {(exclusion_probs).product(axis=1).max():.2g}") + probs_subset = probs[no_alts_sampled_mask] + inclusion_probs_subset = inclusion_probs[no_alts_sampled_mask] + + else: # All alternatives are fine + break + + n += 1 + if n == 10: + choosers_no_alts_sampled = sampled_results_subset[no_alts_sampled_mask] + msg = (f"Poisson choice set sampling failed after 10 attempts for these cases:\n" + f"{choosers_no_alts_sampled}\n{probs_subset}") + raise ValueError(msg) + + chunk_sizer.log_df(trace_label, "sampled_alternatives", sampled_alternatives) + + return inclusion_probs, sampled_alternatives + + def make_sample_choices( state: workflow.State, choosers, @@ -135,7 +260,7 @@ def _interaction_sample( locals_d=None, trace_label=None, zone_layer=None, - chunk_sizer=None, + chunk_sizer: ChunkSizer|None=None, compute_settings: ComputeSettings | None = None, ): """ @@ -191,15 +316,18 @@ def _interaction_sample( choices_df : pandas.DataFrame A DataFrame where index should match the index of the choosers DataFrame - and columns alt_col_name, prob, rand, pick_count + and columns alt_col_name, prob, pick_count + alt_col_name: int + the identifier of the alternatives prob: float the probability of the chosen alternative - rand: float - the rand that did the choosing pick_count : int number of duplicate picks for chooser, alt """ + assert chunk_sizer is not None, "chunk_sizer cannot be None but old nullable signature is preserved" + # TODO it's probably safe to reorder these arguments to make chunk_sizer mandatory since + # _interaction_sample is private? have_trace_targets = state.tracing.has_trace_targets(choosers) trace_ids = None @@ -443,50 +571,94 @@ def _interaction_sample( state.tracing.dump_df(DUMP, utilities, trace_label, "utilities") - # convert to probabilities (utilities exponentiated and normalized to probs) - # probs is same shape as utilities, one row per chooser and one column for alternative - probs = logit.utils_to_probs( - state, - utilities, - allow_zero_probs=allow_zero_probs, - trace_label=trace_label, - trace_choosers=choosers, - overflow_protection=not allow_zero_probs, - ) - chunk_sizer.log_df(trace_label, "probs", probs) - - del utilities - chunk_sizer.log_df(trace_label, "utilities", None) - - if have_trace_targets: - state.tracing.trace_df( - probs, - tracing.extend_trace_label(trace_label, "probs"), - column_labels=["alternative", "probability"], + if compute_settings.use_explicit_error_terms is not None: + use_eet = compute_settings.use_explicit_error_terms + logger.info( + f"Interaction sample model-specific EET overrides for {trace_label}: eet = {use_eet}" ) + else: + use_eet = state.settings.use_explicit_error_terms if sample_size == 0: - # FIXME return full alternative set rather than sample - logger.info( - "Estimation mode for %s using unsampled alternatives" % (trace_label,) - ) + # Return full alternative set rather than sample + logger.info("Using unsampled alternatives for %s" % (trace_label,)) - index_name = probs.index.name + index_name = utilities.index.name choices_df = ( - pd.melt(probs.reset_index(), id_vars=[index_name]) + pd.melt( + utilities.reset_index(), + id_vars=[index_name], + value_name="prob", + var_name=alt_col_name, + ) .sort_values(by=index_name, kind="mergesort") .set_index(index_name) - .rename(columns={"value": "prob"}) - .drop(columns="variable") + .assign(prob=1) + .assign(pick_count=1) ) + chunk_sizer.log_df(trace_label, "choices_df", choices_df) - choices_df["pick_count"] = 1 - choices_df.insert( - 0, alt_col_name, np.tile(alternatives.index.values, len(choosers.index)) - ) + # utilities are numbered 0..n-1 so we need to map back to alt ids + alternative_map = pd.Series(alternatives.index).to_dict() + choices_df[alt_col_name] = choices_df[alt_col_name].map(alternative_map) + + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) return choices_df + + if use_eet: + + if estimation.manager.enabled: + raise ValueError( + "cannot use explicit error terms with estimation mode at this time" + ) + + utilities = logit.validate_utils( + state, + utilities, + allow_zero_probs=allow_zero_probs, + trace_label=trace_label, + trace_choosers=choosers, + ) + + choices_df = make_sample_choices_utility_based( + state, + choosers, + utilities, + alternatives, + sample_size, + alternative_count, + alt_col_name, + allow_zero_probs=allow_zero_probs, + trace_label=trace_label, + chunk_sizer=chunk_sizer, + ) + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) else: + # convert to probabilities (utilities exponentiated and normalized to probs) + # probs is same shape as utilities, one row per chooser and one column for alternative + probs = logit.utils_to_probs( + state, + utilities, + allow_zero_probs=allow_zero_probs, + trace_label=trace_label, + trace_choosers=choosers, + overflow_protection=not allow_zero_probs, + ) + chunk_sizer.log_df(trace_label, "probs", probs) + + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) + + if have_trace_targets: + state.tracing.trace_df( + probs, + tracing.extend_trace_label(trace_label, "probs"), + column_labels=["alternative", "probability"], + ) + choices_df = make_sample_choices( state, choosers, @@ -500,45 +672,47 @@ def _interaction_sample( chunk_sizer=chunk_sizer, ) - chunk_sizer.log_df(trace_label, "choices_df", choices_df) + chunk_sizer.log_df(trace_label, "choices_df", choices_df) - if estimation.manager.enabled and sample_size > 0: - # we need to ensure chosen alternative is included in the sample - survey_choices = estimation.manager.get_survey_destination_choices( - state, choosers, trace_label - ) - if survey_choices is not None: - assert ( - survey_choices.index == choosers.index - ).all(), "survey_choices and choosers must have the same index" - survey_choices.name = alt_col_name - survey_choices = survey_choices.dropna().astype( - choices_df[alt_col_name].dtype + if estimation.manager.enabled and sample_size > 0: + # we need to ensure chosen alternative is included in the sample + survey_choices = estimation.manager.get_survey_destination_choices( + state, choosers, trace_label ) + if survey_choices is not None: + assert ( + survey_choices.index == choosers.index + ).all(), "survey_choices and choosers must have the same index" + survey_choices.name = alt_col_name + survey_choices = survey_choices.dropna().astype( + choices_df[alt_col_name].dtype + ) - # merge all survey choices onto choices_df - probs_df = probs.reset_index().melt( - id_vars=[choosers.index.name], - var_name=alt_col_name, - value_name="prob", - ) - # probs are numbered 0..n-1 so we need to map back to alt ids - zone_map = pd.Series(alternatives.index).to_dict() - probs_df[alt_col_name] = probs_df[alt_col_name].map(zone_map) - - survey_choices = pd.merge( - survey_choices, - probs_df, - on=[choosers.index.name, alt_col_name], - how="left", - ) - survey_choices["rand"] = 0 - survey_choices["prob"].fillna(0, inplace=True) - choices_df = pd.concat([choices_df, survey_choices], ignore_index=True) - choices_df.sort_values(by=[choosers.index.name], inplace=True) + # merge all survey choices onto choices_df + probs_df = probs.reset_index().melt( + id_vars=[choosers.index.name], + var_name=alt_col_name, + value_name="prob", + ) + # probs are numbered 0..n-1 so we need to map back to alt ids + zone_map = pd.Series(alternatives.index).to_dict() + probs_df[alt_col_name] = probs_df[alt_col_name].map(zone_map) + + survey_choices = pd.merge( + survey_choices, + probs_df, + on=[choosers.index.name, alt_col_name], + how="left", + ) + survey_choices["rand"] = 0 + survey_choices["prob"].fillna(0, inplace=True) + choices_df = pd.concat([choices_df, survey_choices], ignore_index=True) + choices_df.sort_values(by=[choosers.index.name], inplace=True) - del probs - chunk_sizer.log_df(trace_label, "probs", None) + del probs + chunk_sizer.log_df(trace_label, "probs", None) + + chunk_sizer.log_df(trace_label, "choices_df", choices_df) # pick_count and pick_dup # pick_count is number of duplicate picks @@ -570,8 +744,10 @@ def _interaction_sample( column_labels=["sample_alt", "alternative"], ) - # don't need this after tracing - del choices_df["rand"] + if not state.settings.use_explicit_error_terms: + # don't need this after tracing + del choices_df["rand"] + chunk_sizer.log_df(trace_label, "choices_df", choices_df) # - NARROW @@ -582,6 +758,49 @@ def _interaction_sample( return choices_df +def _ensure_chosen_alts_in_sample( + alt_col_name, + alternatives: pd.DataFrame, + choices_df: pd.DataFrame, + choosers: pd.DataFrame, + probs: pd.DataFrame, + state: workflow.State, + trace_label:str, +) -> pd.DataFrame: + # we need to ensure chosen alternative is included in the sample + survey_choices = estimation.manager.get_survey_destination_choices( + state, choosers, trace_label + ) + if survey_choices is not None: + assert ( + survey_choices.index == choosers.index + ).all(), "survey_choices and choosers must have the same index" + survey_choices.name = alt_col_name + survey_choices = survey_choices.dropna().astype(choices_df[alt_col_name].dtype) + + # merge all survey choices onto choices_df + probs_df = probs.reset_index().melt( + id_vars=[choosers.index.name], + var_name=alt_col_name, + value_name="prob", + ) + # probs are numbered 0..n-1 so we need to map back to alt ids + zone_map = pd.Series(alternatives.index).to_dict() + probs_df[alt_col_name] = probs_df[alt_col_name].map(zone_map) + + survey_choices = pd.merge( + survey_choices, + probs_df, + on=[choosers.index.name, alt_col_name], + how="left", + ) + survey_choices["rand"] = 0 + survey_choices["prob"].fillna(0, inplace=True) + choices_df = pd.concat([choices_df, survey_choices], ignore_index=True) + choices_df.sort_values(by=[choosers.index.name], inplace=True) + return choices_df + + def interaction_sample( state: workflow.State, choosers: pd.DataFrame, @@ -674,7 +893,14 @@ def interaction_sample( assert choosers.index.is_monotonic_increasing # FIXME - legacy logic - not sure this is needed or even correct? - sample_size = min(sample_size, len(alternatives.index)) + if not state.settings.use_explicit_error_terms: + sample_size = min(sample_size, len(alternatives.index)) + # with poisson sampling, definitely don't want to reduce sample size - it's not a sample size but a number + # of theoretical draws. Another options would be to disable sampling if # alts < sample size to ensure + # all are included (but this wouldn't behave well if there were land use changes in the project case which + # switched regimes) + + logger.info(f" --- interaction_sample sample size = {sample_size}") result_list = [] for ( diff --git a/activitysim/core/interaction_sample_simulate.py b/activitysim/core/interaction_sample_simulate.py index df1c53fa03..d9a3c24960 100644 --- a/activitysim/core/interaction_sample_simulate.py +++ b/activitysim/core/interaction_sample_simulate.py @@ -9,8 +9,9 @@ from activitysim.core import chunk, interaction_simulate, logit, tracing, util, workflow from activitysim.core.configuration.base import ComputeSettings -from activitysim.core.simulate import set_skim_wrapper_targets from activitysim.core.exceptions import SegmentedSpecificationError +from activitysim.core.logit import AltsContext +from activitysim.core.simulate import set_skim_wrapper_targets logger = logging.getLogger(__name__) @@ -34,6 +35,7 @@ def _interaction_sample_simulate( *, chunk_sizer: chunk.ChunkSizer, compute_settings: ComputeSettings | None = None, + alts_context: AltsContext | None = None, ): """ Run a MNL simulation in the situation in which alternatives must @@ -220,9 +222,6 @@ def _interaction_sample_simulate( ) chunk_sizer.log_df(trace_label, "interaction_utilities", interaction_utilities) - del interaction_df - chunk_sizer.log_df(trace_label, "interaction_df", None) - if have_trace_targets: state.tracing.trace_interaction_eval_results( trace_eval_results, @@ -258,26 +257,45 @@ def _interaction_sample_simulate( # (we want to insert dummy utilities at the END of the list of alternative utilities) # inserts is a list of the indices at which we want to do the insertions inserts = np.repeat(last_row_offsets, max_sample_count - sample_counts) - + del last_row_offsets del sample_counts chunk_sizer.log_df(trace_label, "sample_counts", None) # insert the zero-prob utilities to pad each alternative set to same size padded_utilities = np.insert(interaction_utilities.utility.values, inserts, -999) + padded_alt_nrs = np.insert(interaction_df[choice_column], inserts, -999) chunk_sizer.log_df(trace_label, "padded_utilities", padded_utilities) - del inserts + del interaction_df del interaction_utilities + chunk_sizer.log_df(trace_label, "interaction_df", None) chunk_sizer.log_df(trace_label, "interaction_utilities", None) + del inserts + # reshape to array with one row per chooser, one column per alternative padded_utilities = padded_utilities.reshape(-1, max_sample_count) + padded_alt_nrs = padded_alt_nrs.reshape(-1, max_sample_count) # convert to a dataframe with one row per chooser and one column per alternative utilities_df = pd.DataFrame(padded_utilities, index=choosers.index) + # alt_nrs_df has columns for each alt in the choice set, with values indicating which alt_id + # they correspond to (as opposed to the 0-n index implied by the column number). + if alts_context is not None: + alt_nrs_df = pd.DataFrame(padded_alt_nrs, index=choosers.index) + else: + alt_nrs_df = None # if we don't provide the number of dense alternatives, assume that we'll use the old approach chunk_sizer.log_df(trace_label, "utilities_df", utilities_df) + # alt_nrs_df has columns for each alt in the choice set, with values indicating which alt_id + # they correspond to (as opposed to the 0-n index implied by the column number). + if alts_context is not None: + alt_nrs_df = pd.DataFrame(padded_alt_nrs, index=choosers.index) + else: + alt_nrs_df = None # if we don't provide the number of dense alternatives, assume that we'll use the old approach + del padded_utilities + del padded_alt_nrs chunk_sizer.log_df(trace_label, "padded_utilities", None) if have_trace_targets: @@ -287,50 +305,93 @@ def _interaction_sample_simulate( column_labels=["alternative", "utility"], ) - # convert to probabilities (utilities exponentiated and normalized to probs) - # probs is same shape as utilities, one row per chooser and one column for alternative - if want_logsums: - probs, logsums = logit.utils_to_probs( + if state.settings.use_explicit_error_terms: + if want_logsums: + logsums = logit.utils_to_logsums( + utilities_df, allow_zero_probs=allow_zero_probs + ) + chunk_sizer.log_df(trace_label, "logsums", logsums) + + if skip_choice: + return choosers.join(logsums.to_frame("logsums")) + + utilities_df = logit.validate_utils( state, utilities_df, allow_zero_probs=allow_zero_probs, trace_label=trace_label, trace_choosers=choosers, - overflow_protection=not allow_zero_probs, - return_logsums=True, ) - chunk_sizer.log_df(trace_label, "logsums", logsums) - else: - probs = logit.utils_to_probs( + + if allow_zero_probs: + zero_probs = ( + utilities_df.sum(axis=1) + <= utilities_df.shape[1] * logit.UTIL_UNAVAILABLE + ) + if zero_probs.any(): + # copied from proabability below, fix when that gets fixed + # FIXME this is kind of gnarly, but we force choice of first alt + utilities_df.loc[ + zero_probs, 0 + ] = 3.0 # arbitrary value much larger than UTIL_UNAVAILABLE + + # positions is series with the chosen alternative represented as a column index in utilities_df + # which is an integer between zero and num alternatives in the alternative sample + positions, rands = logit.make_choices_utility_based( state, utilities_df, - allow_zero_probs=allow_zero_probs, trace_label=trace_label, trace_choosers=choosers, - overflow_protection=not allow_zero_probs, + alts_context=alts_context, + alt_nrs_df=alt_nrs_df, ) - chunk_sizer.log_df(trace_label, "probs", probs) - del utilities_df - chunk_sizer.log_df(trace_label, "utilities_df", None) + del utilities_df + chunk_sizer.log_df(trace_label, "utilities_df", None) + else: + # convert to probabilities (utilities exponentiated and normalized to probs) + # probs is same shape as utilities, one row per chooser and one column for alternative + if want_logsums: + probs, logsums = logit.utils_to_probs( + state, + utilities_df, + allow_zero_probs=allow_zero_probs, + trace_label=trace_label, + trace_choosers=choosers, + overflow_protection=not allow_zero_probs, + return_logsums=True, + ) + chunk_sizer.log_df(trace_label, "logsums", logsums) + else: + probs = logit.utils_to_probs( + state, + utilities_df, + allow_zero_probs=allow_zero_probs, + trace_label=trace_label, + trace_choosers=choosers, + overflow_protection=not allow_zero_probs, + ) + chunk_sizer.log_df(trace_label, "probs", probs) - if have_trace_targets: - state.tracing.trace_df( - probs, - tracing.extend_trace_label(trace_label, "probs"), - column_labels=["alternative", "probability"], - ) + del utilities_df + chunk_sizer.log_df(trace_label, "utilities_df", None) - if allow_zero_probs: - zero_probs = probs.sum(axis=1) == 0 - if zero_probs.any(): - # FIXME this is kind of gnarly, but we force choice of first alt - probs.loc[zero_probs, 0] = 1.0 + if have_trace_targets: + state.tracing.trace_df( + probs, + tracing.extend_trace_label(trace_label, "probs"), + column_labels=["alternative", "probability"], + ) - if skip_choice: - return choosers.join(logsums.to_frame("logsums")) + if allow_zero_probs: + zero_probs = probs.sum(axis=1) == 0 + if zero_probs.any(): + # FIXME this is kind of gnarly, but we force choice of first alt + probs.loc[zero_probs, 0] = 1.0 + + if skip_choice: + return choosers.join(logsums.to_frame("logsums")) - else: # make choices # positions is series with the chosen alternative represented as a column index in probs # which is an integer between zero and num alternatives in the alternative sample @@ -338,59 +399,59 @@ def _interaction_sample_simulate( state, probs, trace_label=trace_label, trace_choosers=choosers ) - chunk_sizer.log_df(trace_label, "positions", positions) - chunk_sizer.log_df(trace_label, "rands", rands) - del probs chunk_sizer.log_df(trace_label, "probs", None) - # shouldn't have chosen any of the dummy pad utilities - assert positions.max() < max_sample_count + chunk_sizer.log_df(trace_label, "positions", positions) + chunk_sizer.log_df(trace_label, "rands", rands) - # need to get from an integer offset into the alternative sample to the alternative index - # that is, we want the index value of the row that is offset by rows into the - # tranche of this choosers alternatives created by cross join of alternatives and choosers + # shouldn't have chosen any of the dummy pad utilities + assert positions.max() < max_sample_count - # resulting pandas Int64Index has one element per chooser row and is in same order as choosers - choices = alternatives[choice_column].take(positions + first_row_offsets) + # need to get from an integer offset into the alternative sample to the alternative index + # that is, we want the index value of the row that is offset by rows into the + # tranche of this choosers alternatives created by cross join of alternatives and choosers - # create a series with index from choosers and the index of the chosen alternative - choices = pd.Series(choices, index=choosers.index) + # resulting pandas Int64Index has one element per chooser row and is in same order as choosers + choices = alternatives[choice_column].take(positions + first_row_offsets) - chunk_sizer.log_df(trace_label, "choices", choices) + # create a series with index from choosers and the index of the chosen alternative + choices = pd.Series(choices, index=choosers.index) - if allow_zero_probs and zero_probs.any() and zero_prob_choice_val is not None: - # FIXME this is kind of gnarly, patch choice for zero_probs - choices.loc[zero_probs] = zero_prob_choice_val + chunk_sizer.log_df(trace_label, "choices", choices) - if have_trace_targets: - state.tracing.trace_df( - choices, - tracing.extend_trace_label(trace_label, "choices"), - columns=[None, trace_choice_name], - ) + if allow_zero_probs and zero_probs.any() and zero_prob_choice_val is not None: + # FIXME this is kind of gnarly, patch choice for zero_probs + choices.loc[zero_probs] = zero_prob_choice_val + + if have_trace_targets: + state.tracing.trace_df( + choices, + tracing.extend_trace_label(trace_label, "choices"), + columns=[None, trace_choice_name], + ) + state.tracing.trace_df( + rands, + tracing.extend_trace_label(trace_label, "rands"), + columns=[None, "rand"], + ) + if want_logsums: state.tracing.trace_df( - rands, - tracing.extend_trace_label(trace_label, "rands"), - columns=[None, "rand"], + logsums, + tracing.extend_trace_label(trace_label, "logsum"), + columns=[None, "logsum"], ) - if want_logsums: - state.tracing.trace_df( - logsums, - tracing.extend_trace_label(trace_label, "logsum"), - columns=[None, "logsum"], - ) - if want_logsums: - choices = choices.to_frame("choice") - choices["logsum"] = logsums + if want_logsums: + choices = choices.to_frame("choice") + choices["logsum"] = logsums - chunk_sizer.log_df(trace_label, "choices", choices) + chunk_sizer.log_df(trace_label, "choices", choices) - # handing this off to our caller - chunk_sizer.log_df(trace_label, "choices", None) + # handing this off to our caller + chunk_sizer.log_df(trace_label, "choices", None) - return choices + return choices def interaction_sample_simulate( @@ -413,6 +474,7 @@ def interaction_sample_simulate( skip_choice=False, explicit_chunk_size=0, *, + alts_context: AltsContext | None = None, compute_settings: ComputeSettings | None = None, ): """ @@ -458,6 +520,12 @@ def interaction_sample_simulate( explicit_chunk_size : float, optional If > 0, specifies the chunk size to use when chunking the interaction simulation. If < 1, specifies the fraction of the total number of choosers. + alts_context: AltsContext, optional + Representation of the full alternatives domain (min and max alternative id) + in the absence of sampling. + This is used with EET simulation to ensure consistent random numbers across the whole alternative set + ( as the sampled set may change between base and project). When not provided, + EET with integer-coded choice ids will raise an error. Returns ------- @@ -479,6 +547,18 @@ def interaction_sample_simulate( trace_label = tracing.extend_trace_label(trace_label, "interaction_sample_simulate") chunk_tag = chunk_tag or trace_label + if state.settings.use_explicit_error_terms: + choice_ids_are_int = pd.api.types.is_integer_dtype(alternatives[choice_column]) + if alts_context is None and choice_ids_are_int: + raise ValueError( + "alts_context is required for interaction_sample_simulate when " + "use_explicit_error_terms is True and choice_column is integer-coded" + ) + if alts_context is not None and not choice_ids_are_int: + raise ValueError( + "alts_context can only be used with integer-coded choice_column values" + ) + result_list = [] for ( i, @@ -513,6 +593,7 @@ def interaction_sample_simulate( skip_choice, chunk_sizer=chunk_sizer, compute_settings=compute_settings, + alts_context=alts_context, ) result_list.append(choices) diff --git a/activitysim/core/interaction_simulate.py b/activitysim/core/interaction_simulate.py index 0d84241395..ef6bbb1217 100644 --- a/activitysim/core/interaction_simulate.py +++ b/activitysim/core/interaction_simulate.py @@ -15,8 +15,8 @@ from activitysim.core import chunk, logit, simulate, timing, tracing, util, workflow from activitysim.core.configuration.base import ComputeSettings -from activitysim.core.fast_eval import fast_eval from activitysim.core.exceptions import SegmentedSpecificationError +from activitysim.core.fast_eval import fast_eval logger = logging.getLogger(__name__) @@ -904,29 +904,42 @@ def _interaction_simulate( state.tracing.dump_df(DUMP, utilities, trace_label, "utilities") - # convert to probabilities (utilities exponentiated and normalized to probs) - # probs is same shape as utilities, one row per chooser and one column for alternative - probs = logit.utils_to_probs( - state, utilities, trace_label=trace_label, trace_choosers=choosers - ) - chunk_sizer.log_df(trace_label, "probs", probs) + if state.settings.use_explicit_error_terms: + utilities = logit.validate_utils( + state, utilities, trace_label=trace_label, trace_choosers=choosers + ) + positions, rands = logit.make_choices_utility_based( + state, utilities, trace_label=trace_label, trace_choosers=choosers + ) - del utilities - chunk_sizer.log_df(trace_label, "utilities", None) + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) - if have_trace_targets: - state.tracing.trace_df( - probs, - tracing.extend_trace_label(trace_label, "probs"), - column_labels=["alternative", "probability"], + else: + # convert to probabilities (utilities exponentiated and normalized to probs) + # probs is same shape as utilities, one row per chooser and one column for alternative + probs = logit.utils_to_probs( + state, utilities, trace_label=trace_label, trace_choosers=choosers + ) + chunk_sizer.log_df(trace_label, "probs", probs) + + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) + + if have_trace_targets: + state.tracing.trace_df( + probs, + tracing.extend_trace_label(trace_label, "probs"), + column_labels=["alternative", "probability"], + ) + + # make choices + # positions is series with the chosen alternative represented as a column index in probs + # which is an integer between zero and num alternatives in the alternative sample + positions, rands = logit.make_choices( + state, probs, trace_label=trace_label, trace_choosers=choosers ) - # make choices - # positions is series with the chosen alternative represented as a column index in probs - # which is an integer between zero and num alternatives in the alternative sample - positions, rands = logit.make_choices( - state, probs, trace_label=trace_label, trace_choosers=choosers - ) chunk_sizer.log_df(trace_label, "positions", positions) chunk_sizer.log_df(trace_label, "rands", rands) diff --git a/activitysim/core/logit.py b/activitysim/core/logit.py index 105e18fecc..41e2b795a3 100644 --- a/activitysim/core/logit.py +++ b/activitysim/core/logit.py @@ -4,6 +4,8 @@ import logging import warnings +from dataclasses import dataclass +from typing import Union import numpy as np import pandas as pd @@ -22,10 +24,46 @@ EXP_UTIL_MIN = 1e-300 EXP_UTIL_MAX = np.inf +UTIL_MIN = np.log(EXP_UTIL_MIN, dtype=np.float64) +UTIL_UNAVAILABLE = 1000.0 * (UTIL_MIN - 1.0) + + PROB_MIN = 0.0 PROB_MAX = 1.0 +@dataclass +class AltsContext: + """Representation of the alternatives without carrying around that full array.""" + + min_alt_id: int + max_alt_id: int + + def __post_init__(self): + # e.g. for zero based zones max_alt_id = n_alts - 1 + # but for 1 based zones, we don't need to add extra padding + self.n_rands_to_sample = max(self.max_alt_id, self.n_alts_to_cover_max_id) + + @classmethod + def from_series(cls, ser: Union[pd.Series, pd.Index]) -> "AltsContext": + min_alt_id = ser.min() + max_alt_id = ser.max() + return cls(min_alt_id, max_alt_id) + + @classmethod + def from_num_alts(cls, num_alts: int, zero_based: bool = True) -> "AltsContext": + if zero_based: + offset = -1 + else: + offset = 0 + return cls(min_alt_id=1 + offset, max_alt_id=num_alts + offset) + + @property + def n_alts_to_cover_max_id(self) -> int: + """If zones were non-consecutive, this could be a big over-estimate.""" + return self.max_alt_id + 1 + + def report_bad_choices( state: workflow.State, bad_row_map, @@ -128,6 +166,70 @@ def utils_to_logsums(utils, exponentiated=False, allow_zero_probs=False): return logsums +def validate_utils( + state: workflow.State, + utils, + trace_label=None, + allow_zero_probs=False, + trace_choosers=None, +): + """ + Validate utilities to ensure non-available choices are treated the same in EET and MC. + For EET decisions, no conversion to probabilities is required because choices + are made on the basis of comparing utilities (only differences matter). + However, large negative utility values are used in practice to make choices + unavailable based on probability calculations, which boils down to evaluating + exp(utility). We here use this to define a minimum utility that corresponds + to an unavailable choice. + + Parameters + ---------- + utils : pandas.DataFrame + Rows should be choosers and columns should be alternatives. + + trace_label : str, optional + label for tracing bad utility or probability values + + allow_zero_probs : bool + if True value rows in which all utility alts are UTIL_MIN will be set to + UTIL_UNAVAILABLE. + + trace_choosers : pandas.dataframe + the choosers df (for interaction_simulate) to facilitate the reporting of hh_id + by report_bad_choices because it can't deduce hh_id from the interaction_dataset + which is indexed on index values from alternatives df + + Returns + ------- + utils : pandas.DataFrame + utils with values that would lead to zero probability replaced by UTIL_UNAVAILABLE + + """ + trace_label = tracing.extend_trace_label(trace_label, "validate_utils") + + utils_arr = utils.values + + np.putmask(utils_arr, utils_arr <= UTIL_MIN, UTIL_UNAVAILABLE) + + arr_sum = utils_arr.sum(axis=1) + + if not allow_zero_probs: + zero_probs = arr_sum <= utils_arr.shape[1] * UTIL_UNAVAILABLE + if zero_probs.any(): + report_bad_choices( + state, + zero_probs, + utils, + trace_label=tracing.extend_trace_label(trace_label, "zero_prob_utils"), + msg="all probabilities are zero", + trace_choosers=trace_choosers, + ) + + utils = pd.DataFrame(utils_arr, columns=utils.columns, index=utils.index) + + return utils + + def utils_to_probs( state: workflow.State, utils, @@ -274,6 +376,436 @@ def utils_to_probs( return probs, logsums return probs +FREEZE_RANDOM_NUMBERS_FOR_DENSE_ALTERNATIVE_SET = True + +@dataclass +class AltsContext: + """Representation of the alternatives without carrying around that full array.""" + min_alt_id: int + max_alt_id: int + + def __post_init__(self): + # e.g. for zero based zones max_alt_id = n_alts - 1 + # but for 1 based zones, we don't need to add extra padding + self.n_rands_to_sample = max(self.max_alt_id, self.n_alts_to_cover_max_id) + + @classmethod + def from_series(cls, ser:Union[pd.Series,pd.Index])->"AltsContext": + min_alt_id = ser.min() + max_alt_id = ser.max() + return cls(min_alt_id, max_alt_id) + + @classmethod + def from_num_alts(cls, num_alts:int, zero_based:bool=True)->"AltsContext": + if zero_based: + offset = -1 + else: + offset =0 + return cls(min_alt_id=1+offset, max_alt_id=num_alts+offset ) + + + @property + def n_alts_to_cover_max_id(self) -> int: + """If zones were non-consecutive, this could be a big over-estimate.""" + return self.max_alt_id+1 + + +# TODO-EET: add doc string, tracing +def add_ev1_random(state: workflow.State, df: pd.DataFrame, alt_info: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, ): + + nest_utils_for_choice = df.copy() + assert (alt_info is None) == ( + alt_nrs_df is None), "n_zones and alt_nrs_df must both be provided or omitted together" + + if alt_nrs_df is not None and FREEZE_RANDOM_NUMBERS_FOR_DENSE_ALTERNATIVE_SET: + assert alt_info is not None # narrowing for mypy + + idx_array = alt_nrs_df.values + mask = idx_array == -999 + safe_idx = np.where(mask, 1, idx_array) # replace -999 with a temp value inbounds + # generate random number for all alts - this is wasteful, but ensures that the same zone + # gets the same random number if the sampled choice set changes between base and project + # (alternatively, one could seed a channel for (persons x zones) and use the zone seed to ensure consistency. + # Trade off is needing to seed (persons x zones) rows and multiindex channels to + # avoid extra random numbers generated here. Quick benchmark suggests seeding per row is likely slower + rands_dense = state.get_rn_generator().gumbel_for_df(nest_utils_for_choice, n=alt_info.n_alts_to_cover_max_id) + # generate n=alt_info.max_alt_id+1 rather than n_alts so that indexing works + # (this is drawing a random number for a redundant zeroth zone in 1 based zoning systems) + # TODO deal with non 0->n-1 indexed land use more efficiently? ideally do where alt_nrs_df is constructed, + # not on the fly here. Potentially via state.get_injectable('network_los').get_skim_dict('taz').zone_ids + rands = np.take_along_axis(rands_dense, safe_idx, axis=1) + rands[mask] = 0 # zero out the masked zones so they don't have the util adjustment of alt 0 + else: + # old behaviour, to remove + rands = state.get_rn_generator().gumbel_for_df(nest_utils_for_choice, n=nest_utils_for_choice.shape[1]) + + nest_utils_for_choice += rands + return nest_utils_for_choice + + +def choose_from_tree( + nest_utils, all_alternatives, logit_nest_groups, nest_alternatives_by_name +): + for level, nest_names in logit_nest_groups.items(): + if level == 1: + next_level_alts = nest_alternatives_by_name[nest_names[0]] + continue + choice_this_level = nest_utils[nest_utils.index.isin(next_level_alts)].idxmax() + if choice_this_level in all_alternatives: + return choice_this_level + next_level_alts = nest_alternatives_by_name[choice_this_level] + raise ValueError("This should never happen - no alternative found") + + +# TODO-EET: add doc string, tracing +def make_choices_explicit_error_term_nl( + state, nested_utilities, alt_order_array, nest_spec, trace_label +): + """walk down the nesting tree and make choice at each level, which is the root of the next level choice.""" + nest_utils_for_choice = add_ev1_random(state, nested_utilities) + + all_alternatives = set(nest.name for nest in each_nest(nest_spec, type="leaf")) + logit_nest_groups = group_nest_names_by_level(nest_spec) + nest_alternatives_by_name = {n.name: n.alternatives for n in each_nest(nest_spec)} + + # Apply is slow. It could *maybe* be sped up by using the fact that the nesting structure is the same for all rows: + # Add ev1(0,1) to all entries (as is currently being done). Then, at each level, pick the maximum of the available + # composite alternatives and set the corresponding entry to 1 for each row, set all other alternatives at this level + # to zero. Once the tree is walked (all alternatives have been processed), take the product of the alternatives in + # each leaf's alternative list. Then pick the only alternative with entry 1, all others must be 0. + choices = nest_utils_for_choice.apply( + lambda x: choose_from_tree( + x, all_alternatives, logit_nest_groups, nest_alternatives_by_name + ), + axis=1, + ) + # TODO-EET: reporting like for zero probs + assert not choices.isnull().any(), f"No choice for {trace_label}" + choices = pd.Series(choices, index=nest_utils_for_choice.index) + + # In order for choice indexing to be consistent with MNL and cumsum MC choices, we need to index in the order + # alternatives were originally created before adding nest nodes that are not elemental alternatives + choices = choices.map({v: k for k, v in enumerate(alt_order_array)}) + + return choices + + +# TODO-EET: add doc string, tracing +def make_choices_explicit_error_term_mnl(state, utilities, trace_label, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, + ): + utilities_incl_unobs = add_ev1_random(state, utilities, alts_context, alt_nrs_df) + choices = np.argmax(utilities_incl_unobs.to_numpy(), axis=1) + # TODO-EET: reporting like for zero probs + assert not np.isnan(choices).any(), f"No choice for {trace_label}" + choices = pd.Series(choices, index=utilities_incl_unobs.index) + return choices + + +def make_choices_explicit_error_term( + state, utilities, alt_order_array, nest_spec=None, trace_label=None, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +): + trace_label = tracing.extend_trace_label(trace_label, "make_choices_eet") + if nest_spec is None: + choices = make_choices_explicit_error_term_mnl(state, utilities, trace_label, alts_context, alt_nrs_df) + else: + choices = make_choices_explicit_error_term_nl( + state, utilities, alt_order_array, nest_spec, trace_label + ) + return choices + + +def make_choices_utility_based( + state: workflow.State, + utilities: pd.DataFrame, + name_mapping=None, + nest_spec=None, + trace_label: str | None = None, + trace_choosers=None, + allow_bad_probs=False, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +) -> tuple[pd.Series, pd.Series]: + trace_label = tracing.extend_trace_label(trace_label, "make_choices_utility_based") + + # TODO-EET: index of choices for nested utilities is different than unnested - this needs to be consistent for + # turning indexes into alternative names to keep code changes to minimum for now + choices = make_choices_explicit_error_term( + state, utilities, name_mapping, nest_spec, trace_label, alts_context, alt_nrs_df + ) + # TODO-EET: rands - log all zeros for now + rands = pd.Series(np.zeros_like(utilities.index.values), index=utilities.index) + return choices, rands + + +def add_ev1_random( + state: workflow.State, + df: pd.DataFrame, + alt_info: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +): + """ + Add iid EV1 (Gumbel) random error terms to utilities for EET choice. + + Parameters + ---------- + state : workflow.State + df : pandas.DataFrame + Utilities indexed by chooser and with alternatives as columns. + + Returns + ------- + pandas.DataFrame + Utilities with EV1 errors added. + """ + nest_utils_for_choice = df.copy() + assert (alt_info is None) == ( + alt_nrs_df is None + ), "alt_info and alt_nrs_df must both be provided or omitted together" + + if alt_info is None: + # Fallback behaviour for models where alt_info/alt_nrs_df are not provided (e.g. non-integer alts) + rands = state.get_rn_generator().gumbel_for_df( + nest_utils_for_choice, n=nest_utils_for_choice.shape[1] + ) + nest_utils_for_choice += rands + return nest_utils_for_choice + + idx_array = alt_nrs_df.values + mask = idx_array == -999 + safe_idx = np.where(mask, 1, idx_array) # replace -999 with a temp value inbounds + # generate random number for all alts - this is wasteful, but ensures that the same zone + # gets the same random number if the sampled choice set changes between base and project + # (alternatively, one could seed a channel for (persons x zones) and use the zone seed to ensure consistency. + # Trade off is needing to seed (persons x zones) rows and multiindex channels to + # avoid extra random numbers generated here. Quick benchmark suggests seeding per row is likely slower + rands_dense = state.get_rn_generator().gumbel_for_df( + nest_utils_for_choice, n=alt_info.n_alts_to_cover_max_id + ) + # generate n=alt_info.max_alt_id+1 rather than n_alts so that indexing works + # (this is drawing a random number for a redundant zeroth zone in 1 based zoning systems) + # TODO deal with non 0->n-1 indexed land use more efficiently? ideally do where alt_nrs_df is constructed, + # not on the fly here. Potentially via state.get_injectable('network_los').get_skim_dict('taz').zone_ids + rands = np.take_along_axis(rands_dense, safe_idx, axis=1) + rands[ + mask + ] = 0 # zero out the masked zones so they don't have the util adjustment of alt 0 + + nest_utils_for_choice += rands + return nest_utils_for_choice + + +def choose_from_tree( + nest_utils, all_alternatives, logit_nest_groups, nest_alternatives_by_name +): + for level, nest_names in logit_nest_groups.items(): + if level == 1: + next_level_alts = nest_alternatives_by_name[nest_names[0]] + continue + choice_this_level = nest_utils[nest_utils.index.isin(next_level_alts)].idxmax() + if choice_this_level in all_alternatives: + return choice_this_level + next_level_alts = nest_alternatives_by_name[choice_this_level] + raise ValueError("This should never happen - no alternative found") + + +def make_choices_explicit_error_term_nl( + state, + nested_utilities, + alt_order_array, + nest_spec, + trace_label, + trace_choosers=None, + allow_bad_utils=False, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +): + """ + Walk down the nesting tree and make a choice at each level using EET. + + Parameters + ---------- + state : workflow.State + nested_utilities : pandas.DataFrame + Utilities for nest and leaf nodes. + alt_order_array : numpy.ndarray + Leaf alternatives in the original ordering. + nest_spec : dict or LogitNestSpec + Nest specification for the choice model. + trace_label : str + Trace label for logging and tracing. + + Returns + ------- + pandas.Series + Choice indices aligned to `alt_order_array`. + """ + if trace_label: + state.tracing.trace_df( + nested_utilities, tracing.extend_trace_label(trace_label, "nested_utils") + ) + nest_utils_for_choice = add_ev1_random( + state, nested_utilities, alts_context, alt_nrs_df + ) + + all_alternatives = set(nest.name for nest in each_nest(nest_spec, type="leaf")) + logit_nest_groups = group_nest_names_by_level(nest_spec) + nest_alternatives_by_name = {n.name: n.alternatives for n in each_nest(nest_spec)} + + # Apply is slow. It could *maybe* be sped up by using the fact that the nesting structure is the same for all rows: + # Add ev1(0,1) to all entries (as is currently being done). Then, at each level, pick the maximum of the available + # composite alternatives and set the corresponding entry to 1 for each row, set all other alternatives at this level + # to zero. Once the tree is walked (all alternatives have been processed), take the product of the alternatives in + # each leaf's alternative list. Then pick the only alternative with entry 1, all others must be 0. + choices = nest_utils_for_choice.apply( + lambda x: choose_from_tree( + x, all_alternatives, logit_nest_groups, nest_alternatives_by_name + ), + axis=1, + ) + missing_choices = choices.isnull() # TODO: should we check for infs here too? + if missing_choices.any() and not allow_bad_utils: + report_bad_choices( + state, + missing_choices, + nested_utilities, + trace_label=tracing.extend_trace_label(trace_label, "bad_utils"), + msg="no alternative selected", + # raise_error=False, + trace_choosers=trace_choosers, + ) + choices = pd.Series(choices, index=nest_utils_for_choice.index) + + # In order for choice indexing to be consistent with MNL and cumsum MC choices, we need to index in the order + # alternatives were originally created before adding nest nodes that are not elemental alternatives + choices = choices.map({v: k for k, v in enumerate(alt_order_array)}) + + return choices + + +def make_choices_explicit_error_term_mnl( + state, + utilities, + trace_label, + trace_choosers=None, + allow_bad_utils=False, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +) -> pd.Series: + """ + Make EET choices for a multinomial logit model by adding EV1 errors. + + Parameters + ---------- + state : workflow.State + utilities : pandas.DataFrame + Utilities with choosers as rows and alternatives as columns. + trace_label : str + Trace label for logging and tracing. + + Returns + ------- + pandas.Series + Choice indices aligned to the utilities columns order. + """ + if trace_label: + state.tracing.trace_df( + utilities, tracing.extend_trace_label(trace_label, "utilities") + ) + utilities_incl_unobs = add_ev1_random(state, utilities, alts_context, alt_nrs_df) + if trace_label: + state.tracing.trace_df( + utilities_incl_unobs, + tracing.extend_trace_label(trace_label, "utilities_eet"), + ) + choices = np.argmax(utilities_incl_unobs.to_numpy(), axis=1) + missing_choices = np.isnan(choices) # TODO: should we check for infs here too? + if missing_choices.any() and not allow_bad_utils: + report_bad_choices( + state, + missing_choices, + utilities, + trace_label=tracing.extend_trace_label(trace_label, "bad_utils"), + msg="no alternative selected", + # raise_error=False, + trace_choosers=trace_choosers, + ) + choices = pd.Series(choices, index=utilities_incl_unobs.index) + return choices + + +def make_choices_explicit_error_term( + state, + utilities, + alt_order_array, + nest_spec=None, + trace_label=None, + trace_choosers=None, + allow_bad_utils=False, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +) -> pd.Series: + trace_label = tracing.extend_trace_label(trace_label, "make_choices_eet") + if nest_spec is None: + choices = make_choices_explicit_error_term_mnl( + state, + utilities, + trace_label, + trace_choosers, + allow_bad_utils, + alts_context, + alt_nrs_df, + ) + else: + choices = make_choices_explicit_error_term_nl( + state, + utilities, + alt_order_array, + nest_spec, + trace_label, + trace_choosers, + allow_bad_utils, + alts_context, + alt_nrs_df, + ) + return choices + + +def make_choices_utility_based( + state: workflow.State, + utilities: pd.DataFrame, + name_mapping=None, + nest_spec=None, + trace_label: str = None, + trace_choosers=None, + allow_bad_utils=False, + alts_context: AltsContext | None = None, + alt_nrs_df: pd.DataFrame | None = None, +) -> tuple[pd.Series, pd.Series]: + trace_label = tracing.extend_trace_label(trace_label, "make_choices_utility_based") + + # For nested models, choices are mapped to `name_mapping` ordering inside the + # EET helper. For MNL, choices already follow the utilities column order. + choices = make_choices_explicit_error_term( + state, + utilities, + name_mapping, + nest_spec, + trace_label, + trace_choosers=trace_choosers, + allow_bad_utils=allow_bad_utils, + alts_context=alts_context, + alt_nrs_df=alt_nrs_df, + ) + # EET does not expose per-row random draws; return zeros for compatibility. + rands = pd.Series(np.zeros_like(utilities.index.values), index=utilities.index) + + return choices, rands + def make_choices( state: workflow.State, @@ -284,28 +816,23 @@ def make_choices( ) -> tuple[pd.Series, pd.Series]: """ Make choices for each chooser from among a set of alternatives. - Parameters ---------- probs : pandas.DataFrame Rows for choosers and columns for the alternatives from which they are choosing. Values are expected to be valid probabilities across each row, e.g. they should sum to 1. - trace_choosers : pandas.dataframe the choosers df (for interaction_simulate) to facilitate the reporting of hh_id by report_bad_choices because it can't deduce hh_id from the interaction_dataset which is indexed on index values from alternatives df - Returns ------- choices : pandas.Series Maps chooser IDs (from `probs` index) to a choice, where the choice is an index into the columns of `probs`. - rands : pandas.Series The random numbers used to make the choices (for debugging, tracing) - """ trace_label = tracing.extend_trace_label(trace_label, "make_choices") @@ -546,6 +1073,7 @@ def _each_nest(spec: LogitNestSpec, parent_nest, post_order): nest.level = parent_nest.level + 1 nest.product_of_coefficients = parent_nest.product_of_coefficients nest.ancestors = parent_nest.ancestors + [name] + nest.coefficient = parent_nest.coefficient yield spec, nest @@ -602,3 +1130,12 @@ def count_each_nest(spec, count): return 1 return count_each_nest(nest_spec, 0) if nest_spec is not None else 0 + + +def group_nest_names_by_level(nest_spec): + # group nests by level, returns {level: [nest.name at that level]} + depth = np.max([x.level for x in each_nest(nest_spec)]) + nest_levels = {x: [] for x in range(1, depth + 1)} + for n in each_nest(nest_spec): + nest_levels[n.level].append(n.name) + return nest_levels diff --git a/activitysim/core/pathbuilder.py b/activitysim/core/pathbuilder.py index 31393ceeaf..da241addcd 100644 --- a/activitysim/core/pathbuilder.py +++ b/activitysim/core/pathbuilder.py @@ -991,16 +991,16 @@ def build_virtual_path( utilities_df.index = orig.index with memo("#TVPB build_virtual_path make_choices"): - probs = logit.utils_to_probs( - self.network_los.state, - utilities_df, - allow_zero_probs=True, - trace_label=trace_label, - overflow_protection=False, - ) - chunk_sizer.log_df(trace_label, "probs", probs) - if trace: + probs = logit.utils_to_probs( + self.network_los.state, + utilities_df, + allow_zero_probs=True, + trace_label=trace_label, + overflow_protection=False, + ) + chunk_sizer.log_df(trace_label, "probs", probs) + choices = override_choices utilities_df["choices"] = choices @@ -1008,21 +1008,44 @@ def build_virtual_path( probs["choices"] = choices self.trace_df(probs, trace_label, "probs") + del probs + chunk_sizer.log_df(trace_label, "probs", None) else: - choices, rands = logit.make_choices( - self.network_los.state, - probs, - allow_bad_probs=True, - trace_label=trace_label, - ) + if self.network_los.state.settings.use_explicit_error_terms: + utilities_df = logit.validate_utils( + self.network_los.state, + utilities_df, + allow_zero_probs=True, + trace_label=trace_label, + ) + choices, rands = logit.make_choices_utility_based( + self.network_los.state, + utilities_df, + trace_label=trace_label, + ) + else: + probs = logit.utils_to_probs( + self.network_los.state, + utilities_df, + allow_zero_probs=True, + trace_label=trace_label, + overflow_protection=False, + ) + chunk_sizer.log_df(trace_label, "probs", probs) + + choices, rands = logit.make_choices( + self.network_los.state, + probs, + allow_bad_probs=True, + trace_label=trace_label, + ) + del probs + chunk_sizer.log_df(trace_label, "probs", None) chunk_sizer.log_df(trace_label, "rands", rands) del rands chunk_sizer.log_df(trace_label, "rands", None) - del probs - chunk_sizer.log_df(trace_label, "probs", None) - # we need to get path_set, btap, atap from path_df row with same seq and path_num # drop seq join column, but keep path_num of choice to override_choices when tracing columns_to_cache = ["btap", "atap", "path_set", "path_num"] diff --git a/activitysim/core/random.py b/activitysim/core/random.py index 37b1976403..ea42b24118 100644 --- a/activitysim/core/random.py +++ b/activitysim/core/random.py @@ -9,8 +9,8 @@ import numpy as np import pandas as pd -from activitysim.core.util import reindex from activitysim.core.exceptions import DuplicateLoadableObjectError, TableIndexError +from activitysim.core.util import reindex from .tracing import print_elapsed_time @@ -194,6 +194,8 @@ def _generators_for_df(self, df): df_row_states = self.row_states.loc[df.index] + # https://numpy.org/doc/stable/reference/random/generator.html + # np.random.default_rng() prng = np.random.RandomState() for row in df_row_states.itertuples(): prng.seed(row.row_seed) @@ -245,6 +247,50 @@ def random_for_df(self, df, step_name, n=1): self.row_states.loc[df.index, "offset"] += n return rands + def gumbel_for_df(self, df, step_name, n=1): + """ + Return n floating point gumbel-distributed numbers for each row in df + using the appropriate random channel for each row. + + Subsequent calls (in the same step) will return the next rand for each df row + + The resulting array will be the same length (and order) as df + This method is designed to support alternative selection from a probability array + + The columns in df are ignored; the index name and values are used to determine + which random number sequence to to use. + + If "true pseudo random" behavior is desired (i.e. NOT repeatable) the set_base_seed + method (q.v.) may be used to globally reseed all random streams. + + Parameters + ---------- + df : pandas.DataFrame + df with index name and values corresponding to a registered channel + + n : int + number of rands desired per df row + + Returns + ------- + rands : 2-D ndarray + array the same length as df, with n floats in range [0, 1) for each df row + """ + + assert self.step_name + assert self.step_name == step_name + + # - reminder: prng must be called when yielded as generated sequence, not serialized + generators = self._generators_for_df(df) + + # rands = np.asanyarray([prng.gumbel(size=n) for prng in generators]) + # this is about 20% faster for large arrays, like for destination choice + rands = np.asanyarray([-np.log(-np.log(prng.rand(n))) for prng in generators]) + + # update offset for rows we handled + self.row_states.loc[df.index, "offset"] += n + return rands + def normal_for_df(self, df, step_name, mu, sigma, lognormal=False, size=None): """ Return a floating point random number in normal (or lognormal) distribution @@ -399,7 +445,38 @@ def get_channel_for_df(self, df): raise TableIndexError("No channel with index name '%s'" % df.index.name) return self.channels[channel_name] - # step handling + def reset_offsets_for_step(self, step_name): + """ + Reset offsets for all channels for a step + + Parameters + ---------- + step_name : str + pipeline step name for this step + """ + + assert self.step_name == step_name + + for c in self.channels: + self.channels[c].row_states["offset"] = 0 + + def reset_offsets_for_df(self, df): + """ + Reset offsets for all choosers in df if the channel for a step + + Parameters + ---------- + step_name : str + pipeline step name for this step + df : pandas.DataFrame + df with index name and values corresponding to a registered channel + """ + channel = self.get_channel_for_df(df) + channel.row_states.loc[df.index, "offset"] = 0 + logger.info( + f"RNG: resetting random number generator offsets for channel '{channel.channel_name}' for {len(df)} rows" + + f" with index name '{df.index.name}'. Total lenght df: {len(channel.row_states)}" + ) def begin_step(self, step_name): """ @@ -617,6 +694,42 @@ def random_for_df(self, df, n=1): rands = channel.random_for_df(df, self.step_name, n) return rands + def gumbel_for_df(self, df, n=1): + """ + Return a single floating point gumbel for each row in df + using the appropriate random channel for each row. + + Subsequent calls (in the same step) will return the next rand for each df row + + The resulting array will be the same length (and order) as df + This method is designed to support alternative selection from a probability array + + The columns in df are ignored; the index name and values are used to determine + which random number sequence to to use. + + We assume that we can identify the channel to used based on the name of df.index + This channel should have already been registered by a call to add_channel (q.v.) + + If "true pseudo random" behavior is desired (i.e. NOT repeatable) the set_base_seed + method (q.v.) may be used to globally reseed all random streams. + + Parameters + ---------- + df : pandas.DataFrame + df with index name and values corresponding to a registered channel + + n : int + number of rands desired (default 1) + + Returns + ------- + choices : 1-D ndarray the same length as df + a single float in range [0, 1) for each row in df + """ + channel = self.get_channel_for_df(df) + rands = channel.gumbel_for_df(df, self.step_name, n) + return rands + def normal_for_df(self, df, mu=0, sigma=1, broadcast=False, size=None): """ Return a single floating point normal random number in range (-inf, inf) for each row in df diff --git a/activitysim/core/simulate.py b/activitysim/core/simulate.py index c38e16a850..1d7fe6e505 100644 --- a/activitysim/core/simulate.py +++ b/activitysim/core/simulate.py @@ -4,12 +4,13 @@ import logging import time +import typing import warnings from collections import OrderedDict from collections.abc import Callable from datetime import timedelta from pathlib import Path -from typing import Any +from typing import TYPE_CHECKING, Any import numpy as np import pandas as pd @@ -32,7 +33,9 @@ LogitNestSpec, TemplatedLogitComponentSettings, ) -from activitysim.core.estimation import Estimator + +if TYPE_CHECKING: + from activitysim.core.estimation import Estimator from activitysim.core.fast_eval import fast_eval from activitysim.core.simulate_consts import ( ALT_LOSER_UTIL, @@ -40,7 +43,9 @@ SPEC_EXPRESSION_NAME, SPEC_LABEL_NAME, ) -from activitysim.core.exceptions import ModelConfigurationError +if typing.TYPE_CHECKING: + from activitysim.core.estimation import Estimator + logger = logging.getLogger(__name__) @@ -1093,6 +1098,46 @@ def set_skim_wrapper_targets(df, skims, allow_partial_success: bool = True): # ) +def compute_nested_utilities(raw_utilities, nest_spec): + """ + compute nest utilities based on nesting coefficients + + For nest nodes this is the logsum of alternatives adjusted by nesting coefficient + + leaf <- raw_utility / nest_coefficient + nest <- ln(sum of exponentiated raw_utility of leaves) * nest_coefficient) + + Parameters + ---------- + raw_utilities : pandas.DataFrame + dataframe with the raw alternative utilities of all leaves + (what in non-nested logit would be the utilities of all the alternatives) + nest_spec : dict + Nest tree dict from the model spec yaml file + + Returns + ------- + nested_utilities : pandas.DataFrame + Will have the index of `raw_utilities` and columns for leaf and node utilities + """ + nested_utilities = pd.DataFrame(index=raw_utilities.index) + + for nest in logit.each_nest(nest_spec, post_order=True): + name = nest.name + if nest.is_leaf: + nested_utilities[name] = ( + raw_utilities[name].astype(float) / nest.product_of_coefficients + ) + else: + # the alternative nested_utilities will already have been computed due to post_order + with np.errstate(divide="ignore"): + nested_utilities[name] = nest.coefficient * np.log( + np.exp(nested_utilities[nest.alternatives]).sum(axis=1) + ) + + return nested_utilities + + def compute_nested_exp_utilities(raw_utilities, nest_spec): """ compute exponentiated nest utilities based on nesting coefficients @@ -1224,7 +1269,7 @@ def eval_mnl( choosers, spec, locals_d, - custom_chooser: CustomChooser_T, + custom_chooser: CustomChooser_T | None, estimator, log_alt_losers=False, want_logsums=False, @@ -1308,29 +1353,47 @@ def eval_mnl( column_labels=["alternative", "utility"], ) - probs = logit.utils_to_probs( - state, utilities, trace_label=trace_label, trace_choosers=choosers - ) - chunk_sizer.log_df(trace_label, "probs", probs) + if state.settings.use_explicit_error_terms: + utilities = logit.validate_utils( + state, utilities, trace_label=trace_label, trace_choosers=choosers + ) - del utilities - chunk_sizer.log_df(trace_label, "utilities", None) + if custom_chooser: + choices, rands = custom_chooser( + state, utilities, choosers, spec, trace_label + ) + else: + choices, rands = logit.make_choices_utility_based( + state, utilities, trace_label=trace_label + ) - if have_trace_targets: - # report these now in case make_choices throws error on bad_choices - state.tracing.trace_df( - probs, - "%s.probs" % trace_label, - column_labels=["alternative", "probability"], - ) + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) - if custom_chooser: - choices, rands = custom_chooser(state, probs, choosers, spec, trace_label) else: - choices, rands = logit.make_choices(state, probs, trace_label=trace_label) + probs = logit.utils_to_probs( + state, utilities, trace_label=trace_label, trace_choosers=choosers + ) + chunk_sizer.log_df(trace_label, "probs", probs) - del probs - chunk_sizer.log_df(trace_label, "probs", None) + del utilities + chunk_sizer.log_df(trace_label, "utilities", None) + + if have_trace_targets: + # report these now in case make_choices throws error on bad_choices + state.tracing.trace_df( + probs, + "%s.probs" % trace_label, + column_labels=["alternative", "probability"], + ) + + if custom_chooser: + choices, rands = custom_chooser(state, probs, choosers, spec, trace_label) + else: + choices, rands = logit.make_choices(state, probs, trace_label=trace_label) + + del probs + chunk_sizer.log_df(trace_label, "probs", None) if have_trace_targets: state.tracing.trace_df( @@ -1431,87 +1494,131 @@ def eval_nl( column_labels=["alternative", "utility"], ) - # exponentiated utilities of leaves and nests - nested_exp_utilities = compute_nested_exp_utilities(raw_utilities, nest_spec) - chunk_sizer.log_df(trace_label, "nested_exp_utilities", nested_exp_utilities) + if state.settings.use_explicit_error_terms: + raw_utilities = logit.validate_utils( + state, raw_utilities, allow_zero_probs=True, trace_label=trace_label + ) - del raw_utilities - chunk_sizer.log_df(trace_label, "raw_utilities", None) + # utilities of leaves and nests + nested_utilities = compute_nested_utilities(raw_utilities, nest_spec) + chunk_sizer.log_df(trace_label, "nested_utilities", nested_utilities) - if have_trace_targets: - state.tracing.trace_df( - nested_exp_utilities, - "%s.nested_exp_utilities" % trace_label, - column_labels=["alternative", "utility"], - ) + if want_logsums: + logsums = pd.Series(nested_utilities.root, index=choosers.index) + chunk_sizer.log_df(trace_label, "logsums", logsums) + + # Index of choices for nested utilities is different than unnested - this needs to be consistent for + # turning indexes into alternative names to keep code changes to minimum for now. Might want to look + # into changing this in the future when revisiting nested logit EET code. + name_mapping = raw_utilities.columns.values + + del raw_utilities + chunk_sizer.log_df(trace_label, "raw_utilities", None) + + if custom_chooser: + choices, rands = custom_chooser( + state, + utilities=nested_utilities, + name_mapping=name_mapping, + choosers=choosers, + spec=spec, + nest_spec=nest_spec, + trace_label=trace_label, + ) + else: + choices, rands = logit.make_choices_utility_based( + state, + nested_utilities, + name_mapping=name_mapping, + nest_spec=nest_spec, + trace_label=trace_label, + ) - # probabilities of alternatives relative to siblings sharing the same nest - nested_probabilities = compute_nested_probabilities( - state, nested_exp_utilities, nest_spec, trace_label=trace_label - ) - chunk_sizer.log_df(trace_label, "nested_probabilities", nested_probabilities) + del nested_utilities + chunk_sizer.log_df(trace_label, "nested_utilities", None) - if want_logsums: - # logsum of nest root - logsums = pd.Series(np.log(nested_exp_utilities.root), index=choosers.index) - chunk_sizer.log_df(trace_label, "logsums", logsums) + else: + # exponentiated utilities of leaves and nests + nested_exp_utilities = compute_nested_exp_utilities(raw_utilities, nest_spec) + chunk_sizer.log_df(trace_label, "nested_exp_utilities", nested_exp_utilities) - del nested_exp_utilities - chunk_sizer.log_df(trace_label, "nested_exp_utilities", None) + del raw_utilities + chunk_sizer.log_df(trace_label, "raw_utilities", None) - if have_trace_targets: - state.tracing.trace_df( - nested_probabilities, - "%s.nested_probabilities" % trace_label, - column_labels=["alternative", "probability"], + if have_trace_targets: + state.tracing.trace_df( + nested_exp_utilities, + "%s.nested_exp_utilities" % trace_label, + column_labels=["alternative", "utility"], + ) + + # probabilities of alternatives relative to siblings sharing the same nest + nested_probabilities = compute_nested_probabilities( + state, nested_exp_utilities, nest_spec, trace_label=trace_label ) + chunk_sizer.log_df(trace_label, "nested_probabilities", nested_probabilities) - # global (flattened) leaf probabilities based on relative nest coefficients (in spec order) - base_probabilities = compute_base_probabilities( - nested_probabilities, nest_spec, spec - ) - chunk_sizer.log_df(trace_label, "base_probabilities", base_probabilities) + if want_logsums: + # logsum of nest root + logsums = pd.Series(np.log(nested_exp_utilities.root), index=choosers.index) + chunk_sizer.log_df(trace_label, "logsums", logsums) - del nested_probabilities - chunk_sizer.log_df(trace_label, "nested_probabilities", None) + del nested_exp_utilities + chunk_sizer.log_df(trace_label, "nested_exp_utilities", None) - if have_trace_targets: - state.tracing.trace_df( - base_probabilities, - "%s.base_probabilities" % trace_label, - column_labels=["alternative", "probability"], + if have_trace_targets: + state.tracing.trace_df( + nested_probabilities, + "%s.nested_probabilities" % trace_label, + column_labels=["alternative", "probability"], + ) + + # global (flattened) leaf probabilities based on relative nest coefficients (in spec order) + base_probabilities = compute_base_probabilities( + nested_probabilities, nest_spec, spec ) + chunk_sizer.log_df(trace_label, "base_probabilities", base_probabilities) - # note base_probabilities could all be zero since we allowed all probs for nests to be zero - # check here to print a clear message but make_choices will raise error if probs don't sum to 1 - BAD_PROB_THRESHOLD = 0.001 - no_choices = (base_probabilities.sum(axis=1) - 1).abs() > BAD_PROB_THRESHOLD + del nested_probabilities + chunk_sizer.log_df(trace_label, "nested_probabilities", None) - if no_choices.any(): - logit.report_bad_choices( - state, - no_choices, - base_probabilities, - trace_label=tracing.extend_trace_label(trace_label, "bad_probs"), - trace_choosers=choosers, - msg="base_probabilities do not sum to one", - ) + if have_trace_targets: + state.tracing.trace_df( + base_probabilities, + "%s.base_probabilities" % trace_label, + column_labels=["alternative", "probability"], + ) - if custom_chooser: - choices, rands = custom_chooser( - state, - base_probabilities, - choosers, - spec, - trace_label, - ) - else: - choices, rands = logit.make_choices( - state, base_probabilities, trace_label=trace_label - ) + # note base_probabilities could all be zero since we allowed all probs for nests to be zero + # check here to print a clear message but make_choices will raise error if probs don't sum to 1 + BAD_PROB_THRESHOLD = 0.001 + no_choices = (base_probabilities.sum(axis=1) - 1).abs() > BAD_PROB_THRESHOLD + + if no_choices.any(): + logit.report_bad_choices( + state, + no_choices, + base_probabilities, + trace_label=tracing.extend_trace_label(trace_label, "bad_probs"), + trace_choosers=choosers, + msg="base_probabilities do not sum to one", + ) + + if custom_chooser: + choices, rands = custom_chooser( + state, + base_probabilities, + choosers, + spec, + trace_label, + ) + else: + choices, rands = logit.make_choices( + state, base_probabilities, trace_label=trace_label + ) - del base_probabilities - chunk_sizer.log_df(trace_label, "base_probabilities", None) + del base_probabilities + chunk_sizer.log_df(trace_label, "base_probabilities", None) if have_trace_targets: state.tracing.trace_df( diff --git a/activitysim/core/test/test_interaction_sample.py b/activitysim/core/test/test_interaction_sample.py new file mode 100644 index 0000000000..623b1622fb --- /dev/null +++ b/activitysim/core/test/test_interaction_sample.py @@ -0,0 +1,297 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import numpy as np +import pandas as pd +import pytest + +from activitysim.core import interaction_sample, workflow + + +@pytest.fixture +def state() -> workflow.State: + state = workflow.State().default_settings() + state.settings.check_for_variability = False + return state + + +def test_interaction_sample_parity(state): + # Run interaction_sample with and without explicit error terms and check that results are similar. + + num_choosers = 100_000 + num_alts = 100 + sample_size = 10 + + # Create random choosers and alternatives + rng = np.random.default_rng(42) + choosers = pd.DataFrame( + {"chooser_attr": rng.random(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + alternatives = pd.DataFrame( + {"alt_attr": rng.random(num_alts)}, + index=pd.Index(range(num_alts), name="alt_id"), + ) + + # Simple spec: utility = chooser_attr * alt_attr + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["chooser_attr * alt_attr"], name="Expression"), + ) + + # Run _without_ explicit error terms + state.settings.use_explicit_error_terms = False + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_mnl") + + choices_mnl = interaction_sample.interaction_sample( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + alt_col_name="alt_id", + ) + + # Run _with_ explicit error terms + state.init_state() # reset the state to rerun with same seed + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_explicit") + + choices_explicit = interaction_sample.interaction_sample( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + alt_col_name="alt_id", + ) + + assert "alt_id" in choices_mnl.columns + assert "alt_id" in choices_explicit.columns + assert not choices_mnl["alt_id"].isna().any() + assert not choices_explicit["alt_id"].isna().any() + assert choices_mnl["alt_id"].isin(alternatives.index).all() + assert choices_explicit["alt_id"].isin(alternatives.index).all() + + # In interaction_sample, choices_explicit and choices_mnl are DataFrames with sampled alternatives. + # The statistics of chosen alternatives should be similar. + mnl_counts = choices_mnl["alt_id"].value_counts(normalize=True).sort_index() + explicit_counts = ( + choices_explicit["alt_id"].value_counts(normalize=True).sort_index() + ) + + # Check top choices overlap significantly or shares are close + all_alts = set(mnl_counts.index) | set(explicit_counts.index) + for alt in all_alts: + share_mnl = mnl_counts.get(alt, 0) + share_explicit = explicit_counts.get(alt, 0) + diff = abs(share_mnl - share_explicit) + assert diff < 0.01, ( + f"Large discrepancy at alt {alt}: " + f"mnl={share_mnl:.4f}, explicit={share_explicit:.4f}, diff={diff:.4f}" + ) + + +def test_interaction_sample_eet_unavailable_alternatives(state): + # Test that EET handles unavailable alternatives in sampling + num_choosers = 100 + num_alts = 10 + sample_size = 2 + choosers = pd.DataFrame( + {"chooser_attr": np.ones(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + # Alt 0-4 are attractive, Alt 5-9 are "unavailable" + alternatives = pd.DataFrame( + {"alt_attr": [10.0] * 5 + [-1000.0] * 5}, + index=pd.Index(range(num_alts), name="alt_id"), + ) + + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + # Run with EET + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_unavailable_eet") + + choices_eet = interaction_sample.interaction_sample( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + alt_col_name="alt_id", + ) + + # Sampled alternatives should only be from Alt 0-4 + assert choices_eet["alt_id"].isin([0, 1, 2, 3, 4]).all() + assert not choices_eet["alt_id"].isin([5, 6, 7, 8, 9]).any() + + +def test_interaction_sample_parity_peaked_utilities(state): + # Stress parity under a highly peaked utility profile: + # one dominant alternative, one secondary, and many tiny utilities. + num_choosers = 20_000 + num_alts = 100 + sample_size = 5 + + choosers = pd.DataFrame( + {"chooser_attr": np.ones(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + alt_utils = np.array([10.0, 1.0] + [0.0] * (num_alts - 2), dtype=np.float64) + alternatives = pd.DataFrame( + {"alt_attr": alt_utils}, + index=pd.Index(range(num_alts), name="alt_id"), + ) + + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + # Run non-EET path. + state.settings.use_explicit_error_terms = False + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_peaked_mnl") + choices_mnl = interaction_sample.interaction_sample( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + alt_col_name="alt_id", + ) + + # Run EET path with the same seed. + state.init_state() + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_peaked_explicit") + choices_explicit = interaction_sample.interaction_sample( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + alt_col_name="alt_id", + ) + + def weighted_shares(df: pd.DataFrame) -> pd.Series: + counts = df.groupby("alt_id")["pick_count"].sum() + return (counts / counts.sum()).sort_index() + + mnl_shares = weighted_shares(choices_mnl) + explicit_shares = weighted_shares(choices_explicit) + + all_alts = set(mnl_shares.index) | set(explicit_shares.index) + for alt in all_alts: + diff = abs(mnl_shares.get(alt, 0.0) - explicit_shares.get(alt, 0.0)) + assert diff < 0.005, ( + f"Peaked utility parity mismatch at alt {alt}: " + f"mnl={mnl_shares.get(alt, 0.0):.6f}, " + f"explicit={explicit_shares.get(alt, 0.0):.6f}, diff={diff:.6f}" + ) + + # The dominant alternative should absorb almost all mass in both paths. + assert mnl_shares.get(0, 0.0) > 0.99 + assert explicit_shares.get(0, 0.0) > 0.99 + + +class _DummyChunkSizer: + def log_df(self, *_args, **_kwargs): + return None + + +class _DummyState: + def __init__(self, rng): + self._rng = rng + + def get_rn_generator(self): + return self._rng + + +class _DummyRngUtilityBased: + def __init__(self, rands_3d): + self.rands_3d = rands_3d + + def gumbel_for_df(self, _utilities, n): + assert n == self.rands_3d.shape[1] * self.rands_3d.shape[2] + return self.rands_3d.reshape(-1) + + +def test_make_sample_choices_utility_based_repeat_alignment_chooser_dominant_heterogeneity(): + # Edge case: utilities are close across alternatives but vary strongly by chooser. + # This is where wrong chooser/sample alignment can hide in aggregate checks. + chooser_index = pd.Index([101, 102, 103, 104, 105, 106], name="person_id") + choosers = pd.DataFrame(index=chooser_index) + alternatives = pd.DataFrame(index=pd.Index([0, 1, 2, 3], name="alt_id")) + + n_choosers = len(choosers) + n_alts = len(alternatives) + sample_size = 3 + + # Very small alternative differences... + alt_signal = np.array([0.00, 0.01, 0.02, 0.03], dtype=np.float64) + # ...but very large chooser sensitivity differences. + chooser_scale = np.array([-500.0, -200.0, -50.0, 50.0, 200.0, 500.0]) + + utilities = pd.DataFrame( + chooser_scale[:, np.newaxis] * alt_signal[np.newaxis, :], + index=chooser_index, + ) + + # No random noise: chosen alternative is deterministic argmax of utilities. + rands_3d = np.zeros((n_choosers, n_alts, sample_size), dtype=np.float64) + state = _DummyState(_DummyRngUtilityBased(rands_3d)) + + out = interaction_sample.make_sample_choices_utility_based( + state=state, + choosers=choosers, + utilities=utilities, + alternatives=alternatives, + sample_size=sample_size, + alternative_count=n_alts, + alt_col_name="alt_id", + allow_zero_probs=False, + trace_label="test_repeat_alignment_chooser_heterogeneity", + chunk_sizer=_DummyChunkSizer(), + ) + + # Reconstruct expected indexing behavior. + chosen_2d = np.argmax( + rands_3d + utilities.to_numpy()[:, :, np.newaxis], + axis=1, + ) + chosen_flat = chosen_2d.reshape(-1) + + chooser_repeat = np.repeat(np.arange(n_choosers), sample_size) + chooser_tile = np.tile(np.arange(n_choosers), sample_size) + + probs = interaction_sample.logit.utils_to_probs( + state, + utilities, + allow_zero_probs=False, + trace_label="test_repeat_alignment_chooser_heterogeneity", + overflow_protection=True, + trace_choosers=choosers, + ).to_numpy() + + expected_prob_repeat = probs[chooser_repeat, chosen_flat] + wrong_prob_tile = probs[chooser_tile, chosen_flat] + + assert np.array_equal(out["prob"].to_numpy(), expected_prob_repeat) + assert not np.array_equal(out["prob"].to_numpy(), wrong_prob_tile) diff --git a/activitysim/core/test/test_interaction_sample_simulate.py b/activitysim/core/test/test_interaction_sample_simulate.py new file mode 100644 index 0000000000..62a40825f8 --- /dev/null +++ b/activitysim/core/test/test_interaction_sample_simulate.py @@ -0,0 +1,255 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import numpy as np +import pandas as pd +import pytest + +from activitysim.core import interaction_sample_simulate, workflow +from activitysim.core.logit import AltsContext + + +@pytest.fixture +def state() -> workflow.State: + state = workflow.State().default_settings() + state.settings.check_for_variability = False + return state + + +def test_interaction_sample_simulate_parity(state): + # Run interaction_sample_simulate with and without explicit error terms and check that results are similar. + + num_choosers = 100_000 + num_alts_per_chooser = 5 # small sample size to keep things simple + + # Create random choosers + rng = np.random.default_rng(42) + choosers = pd.DataFrame( + {"chooser_attr": rng.random(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + # Create random alternatives for each chooser + # In interaction_sample_simulate, alternatives is typically a DataFrame with the same index as choosers + # but repeated for each alternative in the sample. + alt_ids = np.tile(np.arange(num_alts_per_chooser), num_choosers) + alternatives = pd.DataFrame( + { + "alt_attr": rng.random(num_choosers * num_alts_per_chooser), + "alt_id": alt_ids, + "tdd": alt_ids, + }, + index=np.repeat(choosers.index, num_alts_per_chooser), + ) + alternatives.index.name = "person_id" + + # Simple spec: utility = chooser_attr * alt_attr + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["chooser_attr * alt_attr"], name="Expression"), + ) + + # Run _without_ explicit error terms + state.settings.use_explicit_error_terms = False + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_mnl") + + choices_mnl = interaction_sample_simulate.interaction_sample_simulate( + state, + choosers, + alternatives, + spec, + choice_column="tdd", + ) + + # Run _with_ explicit error terms + state.init_state() + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_explicit") + + choices_explicit = interaction_sample_simulate.interaction_sample_simulate( + state, + choosers, + alternatives, + spec, + choice_column="tdd", + alts_context=AltsContext.from_num_alts(num_alts_per_chooser, zero_based=True), + ) + + assert len(choices_mnl) == num_choosers + assert len(choices_explicit) == num_choosers + assert choices_mnl.index.equals(choosers.index) + assert choices_explicit.index.equals(choosers.index) + assert not choices_mnl.isna().any() + assert not choices_explicit.isna().any() + + # choices are series with the same index as choosers and containing the choice (from choice_column) + mnl_counts = choices_mnl.value_counts(normalize=True).sort_index() + explicit_counts = choices_explicit.value_counts(normalize=True).sort_index() + + for alt in range(num_alts_per_chooser): + share_mnl = mnl_counts.get(alt, 0) + share_explicit = explicit_counts.get(alt, 0) + diff = abs(share_mnl - share_explicit) + assert diff < 0.01, ( + f"Large discrepancy at alt {alt}: " + f"mnl={share_mnl:.4f}, explicit={share_explicit:.4f}, diff={diff:.4f}" + ) + + +def test_interaction_sample_simulate_eet_unavailable_alternatives(state): + # Test that EET handles unavailable alternatives in sample simulation + + num_choosers = 10 + num_alts_per_chooser = 5 + + choosers = pd.DataFrame( + {"chooser_attr": np.ones(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + # For each chooser, 2 attractive alts, 3 unavailable + alt_attrs = [10.0, 10.0, -1000.0, -1000.0, -1000.0] * num_choosers + alt_ids = [0, 1, 2, 3, 4] * num_choosers + + alternatives = pd.DataFrame( + { + "alt_attr": alt_attrs, + "alt_id": alt_ids, + "tdd": alt_ids, + }, + index=np.repeat(choosers.index, num_alts_per_chooser), + ) + alternatives.index.name = "person_id" + + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + # Run with EET + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_unavailable_eet") + + choices_eet = interaction_sample_simulate.interaction_sample_simulate( + state, + choosers, + alternatives, + spec, + choice_column="tdd", + alts_context=AltsContext.from_num_alts(num_alts_per_chooser, zero_based=True), + ) + + assert len(choices_eet) == num_choosers + assert choices_eet.index.equals(choosers.index) + assert not choices_eet.isna().any() + + # Choices should only be 0 or 1 + assert choices_eet.isin([0, 1]).all() + assert not choices_eet.isin([2, 3, 4]).any() + + +def test_interaction_sample_simulate_passes_alts_context_and_alt_nrs_df( + state, monkeypatch +): + state.settings.use_explicit_error_terms = True + + choosers = pd.DataFrame( + {"chooser_attr": [1.0, 1.0]}, + index=pd.Index([100, 101], name="person_id"), + ) + alternatives = pd.DataFrame( + { + "alt_attr": [1.0, 0.5, 0.8, 1.2], + "tdd": [0, 2, 0, 2], + }, + index=pd.Index([100, 100, 101, 101], name="person_id"), + ) + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + captured = {} + + def fake_make_choices_utility_based( + _state, + utilities, + name_mapping=None, + nest_spec=None, + trace_label=None, + trace_choosers=None, + allow_bad_utils=False, + alts_context=None, + alt_nrs_df=None, + ): + captured["alts_context"] = alts_context + captured["alt_nrs_df"] = alt_nrs_df.copy() if alt_nrs_df is not None else None + return pd.Series([0, 0], index=utilities.index), pd.Series( + np.zeros(len(utilities.index)), index=utilities.index + ) + + monkeypatch.setattr( + interaction_sample_simulate.logit, + "make_choices_utility_based", + fake_make_choices_utility_based, + ) + + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_alts_context_forwarding") + + ctx = AltsContext.from_num_alts(3, zero_based=True) + choices = interaction_sample_simulate.interaction_sample_simulate( + state, + choosers, + alternatives, + spec, + choice_column="tdd", + alts_context=ctx, + ) + + assert len(choices) == len(choosers) + assert captured["alts_context"] == ctx + assert captured["alt_nrs_df"] is not None + expected_alt_nrs = pd.DataFrame( + [[0, 2], [0, 2]], + index=choosers.index, + ) + pd.testing.assert_frame_equal(captured["alt_nrs_df"], expected_alt_nrs) + + +def test_interaction_sample_simulate_requires_alts_context_for_eet_integer_choices( + state, +): + state.settings.use_explicit_error_terms = True + + choosers = pd.DataFrame( + {"chooser_attr": [1.0, 1.0]}, + index=pd.Index([200, 201], name="person_id"), + ) + alternatives = pd.DataFrame( + { + "alt_attr": [1.0, 0.5, 0.8, 1.2], + "tdd": [0, 2, 0, 2], + }, + index=pd.Index([200, 200, 201, 201], name="person_id"), + ) + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + with pytest.raises(ValueError, match="alts_context is required"): + interaction_sample_simulate.interaction_sample_simulate( + state, + choosers, + alternatives, + spec, + choice_column="tdd", + ) diff --git a/activitysim/core/test/test_interaction_simulate.py b/activitysim/core/test/test_interaction_simulate.py new file mode 100644 index 0000000000..af9442e228 --- /dev/null +++ b/activitysim/core/test/test_interaction_simulate.py @@ -0,0 +1,174 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import numpy as np +import pandas as pd +import pytest + +from activitysim.core import interaction_simulate, workflow + + +@pytest.fixture +def state() -> workflow.State: + state = workflow.State().default_settings() + state.settings.check_for_variability = False + return state + + +def test_interaction_simulate_explicit_error_terms_parity(state): + # Run interaction_simulate with and without explicit error terms and check that results are similar. + + # Keep this large enough for stable parity checks without overloading CI. + num_choosers = 100_000 + num_alts = 5 + sample_size = num_alts + + # Create random choosers and alternatives + rng = np.random.default_rng(42) + choosers = pd.DataFrame( + {"chooser_attr": rng.random(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + alternatives = pd.DataFrame( + {"alt_attr": rng.random(num_alts)}, + index=pd.Index(range(num_alts), name="alt_id"), + ) + + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["chooser_attr * alt_attr"], name="Expression"), + ) + + # Run _without_ explicit error terms + state.settings.use_explicit_error_terms = False + state.rng().set_base_seed(42) # Set seed BEFORE adding channels or steps + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_mnl") + + choices_mnl = interaction_simulate.interaction_simulate( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + ) + + # Run _with_ explicit error terms + state.init_state() # reset the state to rerun with same seed + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_step_explicit") + + choices_explicit = interaction_simulate.interaction_simulate( + state, + choosers, + alternatives, + spec, + sample_size=sample_size, + ) + + assert len(choices_mnl) == num_choosers + assert len(choices_explicit) == num_choosers + assert choices_mnl.index.equals(choosers.index) + assert choices_explicit.index.equals(choosers.index) + assert not choices_mnl.isna().any() + assert not choices_explicit.isna().any() + + mnl_counts = choices_mnl.value_counts(normalize=True).sort_index() + explicit_counts = choices_explicit.value_counts(normalize=True).sort_index() + + # Check that they are close, relative to the number of draws + assert np.allclose( + mnl_counts.to_numpy(), explicit_counts.to_numpy(), atol=0.01, rtol=0.001 + ) + + +def test_interaction_simulate_eet_unavailable_alternatives(state): + # Test that EET handles unavailable alternatives (very low utilities) + # similarly to MNL (zero probabilities). + + num_choosers = 100 + num_alts = 5 + + choosers = pd.DataFrame( + {"chooser_attr": np.ones(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + # Alt 0 and 1 are attractive, Alt 2, 3, 4 are "unavailable" (very low utility) + alternatives = pd.DataFrame( + {"alt_attr": [10.0, 10.0, -1000.0, -1000.0, -1000.0]}, + index=pd.Index(range(num_alts), name="alt_id"), + ) + + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + # Run with EET + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_unavailable_eet") + + choices_eet = interaction_simulate.interaction_simulate( + state, + choosers, + alternatives, + spec, + sample_size=num_alts, + ) + + assert len(choices_eet) == num_choosers + assert choices_eet.index.equals(choosers.index) + assert not choices_eet.isna().any() + + # Choices should only be from Alt 0 or 1 + assert choices_eet.isin( + [0, 1] + ).all(), f"EET picked an 'unavailable' alternative: {choices_eet[~choices_eet.isin([0, 1])]}" + + +def test_interaction_simulate_eet_large_utilities(state): + # Test that EET handles very large utilities without overflow issues + # that might occur in exp(util) calculations in standard MNL. + + num_choosers = 10 + num_alts = 2 + + choosers = pd.DataFrame( + {"chooser_attr": np.ones(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + # Standard MNL might struggle with exp(700) or exp(800) depending on float precision + alternatives = pd.DataFrame( + {"alt_attr": [700.0, 800.0]}, + index=pd.Index(range(num_alts), name="alt_id"), + ) + + spec = pd.DataFrame( + {"coefficient": [1.0]}, + index=pd.Index(["alt_attr"], name="Expression"), + ) + + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", choosers) + state.rng().begin_step("test_large_utils_eet") + + # This should run without crashing or returning NaNs + choices_eet = interaction_simulate.interaction_simulate( + state, + choosers, + alternatives, + spec, + sample_size=num_alts, + ) + + assert not choices_eet.isna().any() + # With such a large difference, Alt 1 should be the dominant choice + assert (choices_eet == 1).all() diff --git a/activitysim/core/test/test_logit.py b/activitysim/core/test/test_logit.py index e249475de2..5249e8374a 100644 --- a/activitysim/core/test/test_logit.py +++ b/activitysim/core/test/test_logit.py @@ -3,13 +3,16 @@ from __future__ import annotations import os.path +import re import numpy as np import pandas as pd import pandas.testing as pdt import pytest -from activitysim.core import logit, workflow +from activitysim.core import logit, random, simulate, workflow +from activitysim.core.exceptions import InvalidTravelError +from activitysim.core.logit import AltsContext, add_ev1_random from activitysim.core.simulate import eval_variables @@ -70,6 +73,124 @@ def utilities(choosers, spec, test_data): ) +@pytest.fixture(scope="module") +def interaction_choosers(): + return pd.DataFrame({"attr": ["a", "b", "c", "b"]}, index=["w", "x", "y", "z"]) + + +@pytest.fixture(scope="module") +def interaction_alts(): + return pd.DataFrame({"prop": [10, 20, 30, 40]}, index=[1, 2, 3, 4]) + + +# +# Utility Validation Tests +# +def test_validate_utils_replaces_unavailable_values(): + state = workflow.State().default_settings() + utils = pd.DataFrame([[0.0, logit.UTIL_MIN - 1.0], [1.0, 2.0]]) + + validated = logit.validate_utils(state, utils, allow_zero_probs=False) + + assert validated.iloc[0, 0] == pytest.approx(0.0) + assert validated.iloc[0, 1] == pytest.approx(logit.UTIL_UNAVAILABLE) + assert validated.iloc[1, 0] == pytest.approx(1.0) + assert validated.iloc[1, 1] == pytest.approx(2.0) + + +def test_validate_utils_raises_when_all_unavailable(): + state = workflow.State().default_settings() + utils = pd.DataFrame([[logit.UTIL_MIN - 1.0, logit.UTIL_MIN - 2.0]]) + + with pytest.raises(InvalidTravelError) as excinfo: + logit.validate_utils(state, utils, allow_zero_probs=False) + + assert "all probabilities are zero" in str(excinfo.value) + + +def test_validate_utils_allows_zero_probs(): + state = workflow.State().default_settings() + utils = pd.DataFrame([[0.5, logit.UTIL_MIN - 1.0]]) + + validated = logit.validate_utils(state, utils, allow_zero_probs=True) + + assert validated.iloc[0, 0] == 0.5 + assert validated.iloc[0, 1] == logit.UTIL_UNAVAILABLE + + +# +# `utils_to_probs` Tests +# +def test_utils_to_probs_logsums_with_overflow_protection(): + state = workflow.State().default_settings() + utils = pd.DataFrame( + [[1000.0, 1001.0, 999.0], [-1000.0, -1001.0, -999.0]], + columns=["a", "b", "c"], + ) + original_utils = utils.copy() + + probs, logsums = logit.utils_to_probs( + state, + utils, + trace_label=None, + overflow_protection=True, + return_logsums=True, + ) + + utils_np = original_utils.to_numpy() + row_max = utils_np.max(axis=1, keepdims=True) + exp_shifted = np.exp(utils_np - row_max) + expected_probs = exp_shifted / exp_shifted.sum(axis=1, keepdims=True) + expected_logsums = pd.Series( + np.log(exp_shifted.sum(axis=1)) + row_max.squeeze(), + index=utils.index, + ) + + pdt.assert_frame_equal( + probs, + pd.DataFrame(expected_probs, index=utils.index, columns=utils.columns), + rtol=1.0e-7, + atol=0.0, + ) + pdt.assert_series_equal(logsums, expected_logsums, rtol=1.0e-7, atol=0.0) + + +def test_utils_to_probs_warns_on_zero_probs_overflow(): + state = workflow.State().default_settings() + utils = pd.DataFrame( + [[logit.UTIL_MIN - 1.0, logit.UTIL_MIN - 2.0], [0.0, 0.0]], + columns=["a", "b"], + ) + + with pytest.warns(UserWarning, match="cannot set overflow_protection"): + probs = logit.utils_to_probs( + state, + utils, + trace_label=None, + allow_zero_probs=True, + overflow_protection=True, + ) + + assert (probs.iloc[0] == 0.0).all() + assert probs.iloc[1].sum() == pytest.approx(1.0) + assert probs.iloc[1].iloc[0] == pytest.approx(0.5) + assert probs.iloc[1].iloc[1] == pytest.approx(0.5) + + +def test_utils_to_probs_raises_on_float32_zero_probs_overflow(): + state = workflow.State().default_settings() + utils = pd.DataFrame(np.array([[90.0, 0.0]], dtype=np.float32)) + + with pytest.raises(ValueError, match="cannot prevent expected overflow"): + logit.utils_to_probs( + state, + utils, + trace_label=None, + allow_zero_probs=True, + overflow_protection=True, + ) + + def test_utils_to_probs(utilities, test_data): state = workflow.State().default_settings() probs = logit.utils_to_probs(state, utilities, trace_label=None) @@ -116,6 +237,9 @@ def test_utils_to_probs_raises(): assert np.asarray(z).ravel() == pytest.approx(np.asarray([0.0, 0.0, 1.0, 0.0])) +# +# `make_choices` Tests +# def test_make_choices_only_one(): state = workflow.State().default_settings() probs = pd.DataFrame( @@ -127,6 +251,65 @@ def test_make_choices_only_one(): choices, pd.Series([0, 1], index=["x", "y"]), check_dtype=False ) +def reset_step(state, name='test_step'): + state.get_rn_generator().end_step(name) + state.get_rn_generator().begin_step(name) + +def test_make_choices_utility_based_sampled_alts(): + """Test the situation of making choices from a sampled choice set""" + # TODO should these tests go in test_random? + state = workflow.State().default_settings() + # Make explicit that there's two indexing schemes - the raw alts, and the 0 based internals + utils_project_raw = pd.DataFrame({"a":10.582999, "b":10.680792, "c":10.710443}, index=pd.Index([0], name='person_id')) + # zero based indexes + utils_project = utils_project_raw.rename(columns={"a":0, "b":1, "c":2}) + utils_base = utils_project_raw[["a", "c"]].rename(columns={"a":0, "c":1}) + + assert utils_project.index.name == "person_id" + state.get_rn_generator().add_channel("persons", utils_project) + state.get_rn_generator().begin_step("test_step") + # mock base case, where alt 1 is omitted (it was improved in the project) + # this situation is quite common with poisson sampling with a variable choice set size, + # but it can also happen in with-replacement EET sampling e.g. if alt 2 had a pick_count of 2 in the base case. + # In principle, it can also be problematic for non-sampled choices where there is a base project difference in the + # availability of alternatives .e.g a new mode was introduced in the project case + + utils_project_with_rands = add_ev1_random(state, utils_project) + rands_project = utils_project_with_rands - utils_project + reset_step(state) + utils_base_with_rands = add_ev1_random(state, utils_base) + rands_base = utils_base_with_rands - utils_base + rands_base_labeled = rands_base.rename(columns={0:"a", 1:"c"}) + rands_project_labeled = rands_project.rename(columns={0:"a", 1:"b", 2:"c"}) + with pytest.raises(AssertionError, match=re.escape('(column name="c") are different')): + # TODO this should pass + pdt.assert_frame_equal(rands_base_labeled, rands_project_labeled.loc[:, rands_base_labeled.columns]) + # document incorrect invariant - first two columns have the same random numbers: + pdt.assert_frame_equal(rands_base, rands_project.iloc[:, :2]) + + # revised approach + reset_step(state) + alt_nrs_df = pd.DataFrame({0:0, 1:1, 2:2}, index=utils_project_raw.index) + alt_info = AltsContext.from_num_alts(3, zero_based=True) + utils_project_with_rands = add_ev1_random(state, utils_project, alt_info=alt_info, alt_nrs_df=alt_nrs_df) + rands_project = utils_project_with_rands - utils_project + reset_step(state) + + # alt "b" is missing from the sampled choice set, alt_nrs_df is set to reflect that + alt_nrs_df = pd.DataFrame({0: 0, 1: 2}, index=utils_project_raw.index) + utils_base_with_rands = add_ev1_random(state, utils_base, alt_info=alt_info, alt_nrs_df=alt_nrs_df) + rands_base = utils_base_with_rands - utils_base + rands_base_labeled = rands_base.rename(columns={0: "a", 1: "c"}) + rands_project_labeled = rands_project.rename(columns={0: "a", 1: "b", 2: "c"}) + + # Corrected invariant holds true + pdt.assert_frame_equal(rands_base_labeled, rands_project_labeled.loc[:, rands_base_labeled.columns]) + + + + + + def test_make_choices_real_probs(utilities): state = workflow.State().default_settings() @@ -140,16 +323,447 @@ def test_make_choices_real_probs(utilities): ) -@pytest.fixture(scope="module") -def interaction_choosers(): - return pd.DataFrame({"attr": ["a", "b", "c", "b"]}, index=["w", "x", "y", "z"]) +def test_different_order_make_choices(): + # check if, when we shuffle utilities, make_choices chooses the same alternatives + state = workflow.State().default_settings() + # increase number of choosers and alternatives for realism + n_choosers = 100 + n_alts = 50 + data = np.random.rand(n_choosers, n_alts) + chooser_ids = np.arange(n_choosers) + alt_ids = [f"alt_{i}" for i in range(n_alts)] + + utilities = pd.DataFrame( + data, + index=pd.Index(chooser_ids, name="chooser_id"), + columns=alt_ids, + ) -@pytest.fixture(scope="module") -def interaction_alts(): - return pd.DataFrame({"prop": [10, 20, 30, 40]}, index=[1, 2, 3, 4]) + # We need a stable RNG that gives the same random numbers for the same chooser_id + # regardless of row order. ActivitySim's random.Random does this. + state.get_rn_generator().add_channel("chooser_id", utilities) + state.get_rn_generator().begin_step("test_step") + + probs = logit.utils_to_probs(state, utilities, trace_label=None) + choices, rands = logit.make_choices(state, probs) + + # shuffle utilities (rows) and make_choices again + # We must reset the step offset so the RNG produces the same sequence for the same IDs + state.get_rn_generator().end_step("test_step") + state.get_rn_generator().begin_step("test_step") + utilities_shuffled = utilities.sample(frac=1, random_state=42) + probs_shuffled = logit.utils_to_probs(state, utilities_shuffled, trace_label=None) + choices_shuffled, rands_shuffled = logit.make_choices(state, probs_shuffled) + + # sorting both to ensure comparison is on the same index order + pdt.assert_series_equal( + choices.sort_index(), choices_shuffled.sort_index(), check_dtype=False + ) + + +def test_make_choices_matches_random_draws(): + class DummyRNG: + def random_for_df(self, df, n=1): + assert n == 1 + return np.array([[0.05], [0.6], [0.95]]) + + class DummyState: + @staticmethod + def get_rn_generator(): + return DummyRNG() + + state = DummyState() + probs = pd.DataFrame( + [[0.1, 0.2, 0.7], [0.4, 0.4, 0.2], [0.05, 0.9, 0.05]], + index=["a", "b", "c"], + columns=["x", "y", "z"], + ) + choices, rands = logit.make_choices(state, probs) + + expected_rands = np.array([0.05, 0.6, 0.95]) + expected_choices = np.array([0, 1, 1]) + + pdt.assert_series_equal( + rands, + pd.Series(expected_rands, index=probs.index), + check_names=False, + ) + pdt.assert_series_equal( + choices, + pd.Series(expected_choices, index=probs.index), + check_dtype=False, + ) + + +# +# EV1 Random Tests +# +def test_add_ev1_random(): + class DummyRNG: + def gumbel_for_df(self, df, n): + # Deterministic, non-constant draws make it easy to verify + # correct per-row/per-column addition behavior. + row_component = df.index.to_numpy(dtype=float).reshape(-1, 1) / 10.0 + col_component = np.arange(n, dtype=float).reshape(1, -1) + return row_component + col_component + + rng = DummyRNG() + + class DummyState: + @staticmethod + def get_rn_generator(): + return rng + + utilities = pd.DataFrame( + [[1.0, 2.0], [3.0, 4.0]], + index=[10, 11], + columns=["a", "b"], + ) + + randomized = logit.add_ev1_random(DummyState(), utilities) + + expected = pd.DataFrame( + [[2.0, 4.0], [4.1, 6.1]], + index=[10, 11], + columns=["a", "b"], + ) + + # check that the random component was added correctly, and that the original utilities were not mutated + pdt.assert_frame_equal(randomized, expected) + pdt.assert_index_equal(randomized.index, utilities.index) + pdt.assert_index_equal(randomized.columns, utilities.columns) + pdt.assert_frame_equal( + utilities, + pd.DataFrame( + [[1.0, 2.0], [3.0, 4.0]], + index=[10, 11], + columns=["a", "b"], + ), + ) + + +def test_add_ev1_random_requires_paired_alt_context_args(): + class DummyRNG: + def gumbel_for_df(self, df, n): + return np.zeros((len(df), n)) + + class DummyState: + @staticmethod + def get_rn_generator(): + return DummyRNG() + utilities = pd.DataFrame([[1.0, 2.0]], index=[1], columns=["a", "b"]) + with pytest.raises( + AssertionError, + match="alt_info and alt_nrs_df must both be provided or omitted together", + ): + logit.add_ev1_random( + DummyState(), + utilities, + alt_info=AltsContext.from_num_alts(2), + alt_nrs_df=None, + ) + + +# +# Nested Logit Structure Tests +# +def test_group_nest_names_by_level(): + nest_spec = { + "name": "root", + "coefficient": 1.0, + "alternatives": [ + {"name": "motorized", "coefficient": 0.7, "alternatives": ["car", "bus"]}, + "walk", + ], + } + + grouped = logit.group_nest_names_by_level(nest_spec) + + assert grouped == {1: ["root"], 2: ["motorized", "walk"], 3: ["car", "bus"]} + + +def test_choose_from_tree_selects_leaf(): + nest_utils = pd.Series( + { + "motorized": 2.0, + "walk": 1.0, + "car": 5.0, + "bus": 3.0, + } + ) + all_alternatives = {"walk", "car", "bus"} + logit_nest_groups = {1: ["root"], 2: ["motorized", "walk"], 3: ["car", "bus"]} + nest_alternatives_by_name = { + "root": ["motorized", "walk"], + "motorized": ["car", "bus"], + } + + choice = logit.choose_from_tree( + nest_utils, all_alternatives, logit_nest_groups, nest_alternatives_by_name + ) + + assert choice == "car" + + +def test_choose_from_tree_raises_on_missing_leaf(): + nest_utils = pd.Series({"motorized": 2.0, "walk": 1.0}) + all_alternatives = {"car", "bus"} + logit_nest_groups = {1: ["root"], 2: ["motorized", "walk"]} + nest_alternatives_by_name = { + "root": ["motorized", "walk"], + "motorized": ["car", "bus"], + } + + with pytest.raises(ValueError, match="no alternative found"): + logit.choose_from_tree( + nest_utils, all_alternatives, logit_nest_groups, nest_alternatives_by_name + ) + + +# +# EET Choice Behavior Tests +# +def test_make_choices_eet_mnl(monkeypatch): + def fake_add_ev1_random(_state, _df, alt_info=None, alt_nrs_df=None): + return pd.DataFrame( + [[1.0, 3.0], [4.0, 2.0]], + index=[100, 101], + columns=["a", "b"], + ) + + monkeypatch.setattr(logit, "add_ev1_random", fake_add_ev1_random) + + choices = logit.make_choices_explicit_error_term_mnl( + workflow.State().default_settings(), + pd.DataFrame([[0.0, 0.0], [0.0, 0.0]], index=[100, 101], columns=["a", "b"]), + trace_label=None, + ) + + pdt.assert_series_equal(choices, pd.Series([1, 0], index=[100, 101])) + + +def test_make_choices_eet_nl(monkeypatch): + def fake_add_ev1_random(_state, _df, alt_info=None, alt_nrs_df=None): + return pd.DataFrame( + [[5.0, 1.0, 4.0, 2.0], [3.0, 4.0, 1.0, 2.0]], + index=[10, 11], + columns=["motorized", "walk", "car", "bus"], + ) + + monkeypatch.setattr(logit, "add_ev1_random", fake_add_ev1_random) + + nest_spec = { + "name": "root", + "coefficient": 1.0, + "alternatives": [ + {"name": "motorized", "coefficient": 0.7, "alternatives": ["car", "bus"]}, + "walk", + ], + } + alt_order_array = np.array(["walk", "car", "bus"]) + + choices = logit.make_choices_explicit_error_term_nl( + workflow.State().default_settings(), + pd.DataFrame( + [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]], + index=[10, 11], + columns=["motorized", "walk", "car", "bus"], + ), + alt_order_array, + nest_spec, + trace_label=None, + ) + + pdt.assert_series_equal(choices, pd.Series([1, 0], index=[10, 11])) + + +def test_make_choices_utility_based_sets_zero_rands(monkeypatch): + def fake_add_ev1_random(_state, df, alt_info=None, alt_nrs_df=None): + return pd.DataFrame( + [[2.0, 1.0], [0.5, 2.5]], + index=df.index, + columns=df.columns, + ) + + monkeypatch.setattr(logit, "add_ev1_random", fake_add_ev1_random) + + utilities = pd.DataFrame([[3.0, 2.0], [1.0, 4.0]], index=[11, 12]) + choices, rands = logit.make_choices_utility_based( + workflow.State().default_settings(), + utilities, + name_mapping=np.array(["a", "b"]), + nest_spec=None, + trace_label=None, + ) + + expected_choices = pd.Series([0, 1], index=[11, 12]) + pdt.assert_series_equal(choices, expected_choices) + pdt.assert_series_equal(rands, pd.Series([0, 0], index=[11, 12])) + + +# +# EET vs non-EET Choice Behavior Tests +# +def test_make_choices_vs_eet_same_distribution(): + """With many draws, make_choices (probability-based) and + make_choices_explicit_error_term_mnl (EET) should produce roughly the + same empirical choice-frequency distribution for the same utilities.""" + n_draws = 1_000_000 + a_tol = 0.001 + r_tol = 0.01 + utils_values = [5.0, 6.0, 7.0, 8.0, 9.0] + n_alts = len(utils_values) + columns = ["a", "b", "c", "d", "e"] + + utils = pd.DataFrame([utils_values] * n_draws, columns=columns) + + # Probability-based (Monte Carlo) path — independent RNG + mc_rng = np.random.default_rng(42) + + class MCDummyRNG: + def random_for_df(self, df, n=1): + return mc_rng.random((len(df), n)) + + class MCDummyState: + @staticmethod + def get_rn_generator(): + return MCDummyRNG() + + probs = logit.utils_to_probs( + MCDummyState(), utils, trace_label=None, overflow_protection=True + ) + choices_mc, _ = logit.make_choices(MCDummyState(), probs, trace_label=None) + + # Explicit-error-term (EET) path — independent RNG + eet_rng = np.random.default_rng(123) + + class EETDummyRNG: + def gumbel_for_df(self, df, n): + return eet_rng.gumbel(size=(len(df), n)) + + class EETDummyState: + @staticmethod + def get_rn_generator(): + return EETDummyRNG() + + choices_eet = logit.make_choices_explicit_error_term_mnl( + EETDummyState(), utils, trace_label=None + ) + + mc_fracs = np.bincount(choices_mc.values.astype(int), minlength=n_alts) / n_draws + eet_fracs = np.bincount(choices_eet.values.astype(int), minlength=n_alts) / n_draws + + np.testing.assert_allclose(mc_fracs, eet_fracs, atol=a_tol, rtol=r_tol) + np.testing.assert_allclose( + mc_fracs, probs.iloc[0].to_numpy(), atol=a_tol, rtol=r_tol + ) + np.testing.assert_allclose( + eet_fracs, probs.iloc[0].to_numpy(), atol=a_tol, rtol=r_tol + ) + + +def test_make_choices_vs_eet_nl_same_distribution(): + """With many draws, nested logit choices via probabilities and + nested logit choices via EET should produce the same empirical distribution.""" + n_draws = 100_000 + a_tol = 0.01 + + nest_spec = { + "name": "root", + "coefficient": 1.0, + "alternatives": [ + {"name": "motorized", "coefficient": 0.5, "alternatives": ["car", "bus"]}, + "walk", + ], + } + # Utilities for car, bus, walk + # For NL, we need utilities for all nodes in the tree for EET, + # but for probability-based choice we usually use the flattened/logsummed probabilities. + # To compare them fairly, we use the same base utilities. + # car=0.5, bus=0.2, walk=0.4 + utils_df = pd.DataFrame( + [[0.5, 0.2, 0.4, 0.0, 0.0]], + columns=["car", "bus", "walk", "motorized", "root"], + ) + utils_df = pd.concat([utils_df] * n_draws, ignore_index=True) + alt_order_array = np.array(["car", "bus", "walk"]) + + # 1. Probability-based Nested Logit choices + mc_rng = np.random.default_rng(42) + + class MCDummyRNG: + def random_for_df(self, df, n=1): + return mc_rng.random((len(df), n)) + + class MCDummyState: + @staticmethod + def get_rn_generator(): + return MCDummyRNG() + + def default_settings(self): + return self + + # Compute probabilities for NL using simulation logic + nested_exp_utilities = simulate.compute_nested_exp_utilities( + utils_df[["car", "bus", "walk"]], nest_spec + ) + nested_probabilities = simulate.compute_nested_probabilities( + MCDummyState(), nested_exp_utilities, nest_spec, trace_label=None + ) + probs = simulate.compute_base_probabilities( + nested_probabilities, nest_spec, utils_df[["car", "bus", "walk"]] + ) + choices_mc, _ = logit.make_choices(MCDummyState(), probs, trace_label=None) + + # 2. EET-based Nested Logit choices + eet_rng = np.random.default_rng(123) + + class EETDummyRNG: + def gumbel_for_df(self, df, n): + return eet_rng.gumbel(size=(len(df), n)) + + class EETDummyState: + @staticmethod + def get_rn_generator(): + return EETDummyRNG() + + def default_settings(self): + return self + + @property + def tracing(self): + import activitysim.core.tracing as tracing + + return tracing + + # For EET NL, we provide the utilities for all nodes. + # compute_nested_utilities handles the division by nesting coefficients for leaves + # and the logsum * coefficient for internal nodes. + nested_utilities = simulate.compute_nested_utilities( + utils_df[["car", "bus", "walk"]], nest_spec + ) + + choices_eet = logit.make_choices_explicit_error_term_nl( + EETDummyState(), + nested_utilities, + alt_order_array, + nest_spec, + trace_label=None, + ) + + mc_fracs = np.bincount(choices_mc.values.astype(int), minlength=3) / n_draws + eet_fracs = np.bincount(choices_eet.values.astype(int), minlength=3) / n_draws + + # They should be close + np.testing.assert_allclose(mc_fracs, eet_fracs, atol=a_tol) + + +# +# Interaction Dataset Tests +# def test_interaction_dataset_no_sample(interaction_choosers, interaction_alts): expected = pd.DataFrame( { @@ -164,9 +778,6 @@ def test_interaction_dataset_no_sample(interaction_choosers, interaction_alts): ) interacted, expected = interacted.align(expected, axis=1) - - print("interacted\n", interacted) - print("expected\n", expected) pdt.assert_frame_equal(interacted, expected) @@ -188,3 +799,99 @@ def test_interaction_dataset_sampled(interaction_choosers, interaction_alts): interacted, expected = interacted.align(expected, axis=1) pdt.assert_frame_equal(interacted, expected) + + +def reset_step(state, name="test_step"): + state.get_rn_generator().end_step(name) + state.get_rn_generator().begin_step(name) + + +def test_make_choices_utility_based_sampled_alts(): + """Test the situation of making choices from a sampled choice set""" + # TODO should these tests go in test_random? + state = workflow.State().default_settings() + # Make explicit that there's two indexing schemes - the raw alts, and the 0 based internals + utils_project_raw = pd.DataFrame( + {"a": 10.582999, "b": 10.680792, "c": 10.710443}, + index=pd.Index([0], name="person_id"), + ) + # zero based indexes + utils_project = utils_project_raw.rename(columns={"a": 0, "b": 1, "c": 2}) + utils_base = utils_project_raw[["a", "c"]].rename(columns={"a": 0, "c": 1}) + + assert utils_project.index.name == "person_id" + state.get_rn_generator().add_channel("persons", utils_project) + state.get_rn_generator().begin_step("test_step") + # mock base case, where alt 1 is omitted (it was improved in the project) + # this situation is quite common with poisson sampling with a variable choice set size, + # but it can also happen in with-replacement EET sampling e.g. if alt 2 had a pick_count of 2 in the base case. + # In principle, it can also be problematic for non-sampled choices where there is a base project difference in the + # availability of alternatives .e.g a new mode was introduced in the project case + + utils_project_with_rands = add_ev1_random(state, utils_project) + rands_project = utils_project_with_rands - utils_project + reset_step(state) + utils_base_with_rands = add_ev1_random(state, utils_base) + rands_base = utils_base_with_rands - utils_base + rands_base_labeled = rands_base.rename(columns={0: "a", 1: "c"}) + rands_project_labeled = rands_project.rename(columns={0: "a", 1: "b", 2: "c"}) + with pytest.raises( + AssertionError, match=re.escape('(column name="c") are different') + ): + # TODO this should pass + pdt.assert_frame_equal( + rands_base_labeled, rands_project_labeled.loc[:, rands_base_labeled.columns] + ) + # document incorrect invariant - first two columns have the same random numbers: + pdt.assert_frame_equal(rands_base, rands_project.iloc[:, :2]) + + # revised approach + reset_step(state) + alt_nrs_df = pd.DataFrame({0: 0, 1: 1, 2: 2}, index=utils_project_raw.index) + alt_info = AltsContext.from_num_alts(3, zero_based=True) + utils_project_with_rands = add_ev1_random( + state, utils_project, alt_info=alt_info, alt_nrs_df=alt_nrs_df + ) + rands_project = utils_project_with_rands - utils_project + reset_step(state) + + # alt "b" is missing from the sampled choice set, alt_nrs_df is set to reflect that + alt_nrs_df = pd.DataFrame({0: 0, 1: 2}, index=utils_project_raw.index) + utils_base_with_rands = add_ev1_random( + state, utils_base, alt_info=alt_info, alt_nrs_df=alt_nrs_df + ) + rands_base = utils_base_with_rands - utils_base + rands_base_labeled = rands_base.rename(columns={0: "a", 1: "c"}) + rands_project_labeled = rands_project.rename(columns={0: "a", 1: "b", 2: "c"}) + + # Corrected invariant holds true + pdt.assert_frame_equal( + rands_base_labeled, rands_project_labeled.loc[:, rands_base_labeled.columns] + ) + + +def test_alts_context_from_series_and_properties(): + ctx = AltsContext.from_series(pd.Index([3, 5, 9, 4])) + + assert ctx.min_alt_id == 3 + assert ctx.max_alt_id == 9 + assert ctx.n_alts_to_cover_max_id == 10 + assert ctx.n_rands_to_sample == 10 + + +@pytest.mark.parametrize( + "num_alts,zero_based,expected_min,expected_max,expected_n_cover", + [ + (5, True, 0, 4, 5), + (5, False, 1, 5, 6), + ], +) +def test_alts_context_from_num_alts( + num_alts, zero_based, expected_min, expected_max, expected_n_cover +): + ctx = AltsContext.from_num_alts(num_alts=num_alts, zero_based=zero_based) + + assert ctx.min_alt_id == expected_min + assert ctx.max_alt_id == expected_max + assert ctx.n_alts_to_cover_max_id == expected_n_cover + assert ctx.n_rands_to_sample == expected_n_cover diff --git a/activitysim/core/test/test_simulate.py b/activitysim/core/test/test_simulate.py index 17d4ba2cd6..21e0f90e73 100644 --- a/activitysim/core/test/test_simulate.py +++ b/activitysim/core/test/test_simulate.py @@ -10,7 +10,7 @@ import pandas.testing as pdt import pytest -from activitysim.core import simulate, workflow +from activitysim.core import chunk, simulate, workflow @pytest.fixture @@ -42,6 +42,19 @@ def data(data_dir): return pd.read_csv(os.path.join(data_dir, "data.csv")) +@pytest.fixture +def nest_spec(): + nest_spec = { + "name": "root", + "coefficient": 1.0, + "alternatives": [ + {"name": "alt0", "coefficient": 0.5, "alternatives": ["alt0.0", "alt0.1"]}, + "alt1", + ], + } + return nest_spec + + def test_read_model_spec(state, spec_name): spec = state.filesystem.read_model_spec(file_name=spec_name) @@ -88,3 +101,234 @@ def test_simple_simulate_chunked(state, data, spec): ) expected = pd.Series([1, 1, 1], index=data.index) pdt.assert_series_equal(choices, expected, check_dtype=False) + + +def test_eval_mnl_eet(state): + # Check that the same counts are returned by eval_mnl when using EET and when not. + + num_choosers = 100_000 + + np.random.seed(42) + data2 = pd.DataFrame( + { + "chooser_attr": np.random.rand(num_choosers), + }, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + spec2 = pd.DataFrame( + {"alt0": [1.0], "alt1": [2.0]}, + index=pd.Index(["chooser_attr"], name="Expression"), + ) + + # Set up a state with EET enabled + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", data2) + state.rng().begin_step("test_step_mnl") + + chunk_sizer = chunk.ChunkSizer(state, "", "", num_choosers) + + # run eval_mnl with EET enabled + choices_eet = simulate.eval_mnl( + state=state, + choosers=data2, + spec=spec2, + locals_d=None, + custom_chooser=None, + estimator=None, + chunk_sizer=chunk_sizer, + ) + + # Reset the state, without EET enabled + state.settings.use_explicit_error_terms = False + + state.rng().end_step("test_step_mnl") + state.rng().begin_step("test_step_mnl") + + choices_mnl = simulate.eval_mnl( + state=state, + choosers=data2, + spec=spec2, + locals_d=None, + custom_chooser=None, + estimator=None, + chunk_sizer=chunk_sizer, + ) + + # Compare counts + mnl_counts = choices_mnl.value_counts(normalize=True) + explicit_counts = choices_eet.value_counts(normalize=True) + assert np.allclose(mnl_counts, explicit_counts, atol=0.01) + + +def test_eval_nl_eet(state, nest_spec): + # Check that the same counts are returned by eval_nl when using EET and when not. + + num_choosers = 100_000 + + np.random.seed(42) + data2 = pd.DataFrame( + { + "chooser_attr": np.random.rand(num_choosers), + }, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + spec2 = pd.DataFrame( + {"alt1": [2.0], "alt0.0": [0.5], "alt0.1": [0.2]}, + index=pd.Index(["chooser_attr"], name="Expression"), + ) + + # Set up a state with EET enabled + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", data2) + state.rng().begin_step("test_step_mnl") + + chunk_sizer = chunk.ChunkSizer(state, "", "", num_choosers) + + # run eval_nl with EET enabled + choices_eet = simulate.eval_nl( + state=state, + choosers=data2, + spec=spec2, + nest_spec=nest_spec, + locals_d={}, + custom_chooser=None, + estimator=None, + trace_label="test", + chunk_sizer=chunk_sizer, + ) + + # Reset the state, without EET enabled + state.settings.use_explicit_error_terms = False + + state.rng().end_step("test_step_mnl") + state.rng().begin_step("test_step_mnl") + + choices_mnl = simulate.eval_nl( + state=state, + choosers=data2, + spec=spec2, + nest_spec=nest_spec, + locals_d={}, + custom_chooser=None, + trace_label="test", + estimator=None, + chunk_sizer=chunk_sizer, + ) + + # Compare counts + mnl_counts = choices_mnl.value_counts(normalize=True) + explicit_counts = choices_eet.value_counts(normalize=True) + assert np.allclose(mnl_counts, explicit_counts, atol=0.01) + + +def test_compute_nested_utilities(nest_spec): + # computes nested utilities manually and using the function and checks that + # the utilities are the same + + num_choosers = 2 + raw_utilities = pd.DataFrame( + {"alt1": [1, 10], "alt0.0": [2, 3], "alt0.1": [4, 5]}, + index=pd.Index(range(num_choosers)), + ) + + nested_utilities = simulate.compute_nested_utilities(raw_utilities, nest_spec) + + # these are from the definition of nest_spec + alt0_nest_coefficient = nest_spec["alternatives"][0]["coefficient"] + alt0_leaf_product_of_coefficients = nest_spec["coefficient"] * alt0_nest_coefficient + assert alt0_leaf_product_of_coefficients == 0.5 # 1 * 0.5 + + product_of_coefficientss = pd.DataFrame( + { + "alt1": [nest_spec["coefficient"]], + "alt0.0": [alt0_leaf_product_of_coefficients], + "alt0.1": [alt0_leaf_product_of_coefficients], + }, + index=[0], + ) + leaf_utilities = raw_utilities / product_of_coefficientss.iloc[0] + + constructed_nested_utilities = pd.DataFrame(index=raw_utilities.index) + + constructed_nested_utilities[leaf_utilities.columns] = leaf_utilities + constructed_nested_utilities["alt0"] = alt0_nest_coefficient * np.log( + np.exp(leaf_utilities[["alt0.0", "alt0.1"]]).sum(axis=1) + ) + constructed_nested_utilities["root"] = nest_spec["coefficient"] * np.log( + np.exp(constructed_nested_utilities[["alt1", "alt0"]]).sum(axis=1) + ) + + assert np.allclose( + nested_utilities, constructed_nested_utilities[nested_utilities.columns] + ), "Mismatch in nested utilities" + + +def test_eval_nl_logsums_eet_vs_non_eet(state, nest_spec): + """eval_nl with want_logsums=True must produce identical logsums under + EET and non-EET modes""" + + num_choosers = 100 + + np.random.seed(42) + data2 = pd.DataFrame( + {"chooser_attr": np.random.rand(num_choosers)}, + index=pd.Index(range(num_choosers), name="person_id"), + ) + + spec2 = pd.DataFrame( + {"alt1": [2.0], "alt0.0": [0.5], "alt0.1": [0.2]}, + index=pd.Index(["chooser_attr"], name="Expression"), + ) + + chunk_sizer = chunk.ChunkSizer(state, "", "", num_choosers) + + state.settings.use_explicit_error_terms = True + state.rng().set_base_seed(42) + state.rng().add_channel("person_id", data2) + state.rng().begin_step("test_step_logsums") + + result_eet = simulate.eval_nl( + state=state, + choosers=data2, + spec=spec2, + nest_spec=nest_spec, + locals_d={}, + custom_chooser=None, + estimator=None, + want_logsums=True, + trace_label="test", + chunk_sizer=chunk_sizer, + ) + + state.rng().end_step("test_step_logsums") + + state.settings.use_explicit_error_terms = False + state.rng().begin_step("test_step_logsums") + + result_non_eet = simulate.eval_nl( + state=state, + choosers=data2, + spec=spec2, + nest_spec=nest_spec, + locals_d={}, + custom_chooser=None, + estimator=None, + want_logsums=True, + trace_label="test", + chunk_sizer=chunk_sizer, + ) + + state.rng().end_step("test_step_logsums") + + # Both paths should return a DataFrame with 'choice' and 'logsum' columns + assert "logsum" in result_eet.columns, "EET result missing logsum column" + assert "logsum" in result_non_eet.columns, "non-EET result missing logsum column" + + # Logsums are deterministic — they must be identical across paths + assert np.allclose( + result_eet["logsum"].values, result_non_eet["logsum"].values, rtol=1e-10 + ) diff --git a/activitysim/examples/placeholder_multiple_zone/test/configs_eet/settings.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_eet/settings.yaml new file mode 100644 index 0000000000..08c06d702e --- /dev/null +++ b/activitysim/examples/placeholder_multiple_zone/test/configs_eet/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True + +use_explicit_error_terms: True diff --git a/activitysim/examples/placeholder_multiple_zone/test/reference_pipeline_2_zone_eet.zip b/activitysim/examples/placeholder_multiple_zone/test/reference_pipeline_2_zone_eet.zip new file mode 100644 index 0000000000..8e3abdfff6 Binary files /dev/null and b/activitysim/examples/placeholder_multiple_zone/test/reference_pipeline_2_zone_eet.zip differ diff --git a/activitysim/examples/placeholder_multiple_zone/test/regress/final_eet_tours_2_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_eet_tours_2_zone.csv new file mode 100644 index 0000000000..00aa4c0fc7 --- /dev/null +++ b/activitysim/examples/placeholder_multiple_zone/test/regress/final_eet_tours_2_zone.csv @@ -0,0 +1,106 @@ +"person_id","tour_type","tour_type_count","tour_type_num","tour_num","tour_count","tour_category","number_of_participants","destination","origin","household_id","tdd","start","end","duration","composition","destination_logsum","tour_mode","mode_choice_logsum","atwork_subtour_frequency","parent_tour_id","stop_frequency","primary_purpose","tour_id" +26686,"shopping",1,1,1,1,"non_mandatory",1,23000,8000,26686,113,12,13,1,"",13.652449170814883,"WALK_LRF",1.0501470508061868,"",,"0out_0in","shopping",1094159 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+7513554,2822219,"work",1,false,1,8000,9000,308055753,"home",,21,"WALK",8.698411202168328,2464446029 +7523517,2832182,"shopping",1,true,1,13000,7000,308464230,"shopping",,15,"WALK_LOC",-0.5264083063330212,2467713841 +7523517,2832182,"shopping",1,false,1,7000,13000,308464230,"home",,15,"WALK_LOC",-0.4961048751413181,2467713845 diff --git a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py index 3e956301e9..3921bcd0fd 100644 --- a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py +++ b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py @@ -39,14 +39,16 @@ def data(): build_data() -def run_test(zone, multiprocess=False): +def run_test(zone, multiprocess=False, use_explicit_error_terms=False): def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) - def regress(zone): + def regress(zone, use_explicit_error_terms=False): # regress tours regress_tours_df = pd.read_csv( - test_path(f"regress/final_tours_{zone}_zone.csv") + test_path( + f"regress/final{'_eet' if use_explicit_error_terms else ''}_tours_{zone}_zone.csv" + ) ) tours_df = pd.read_csv(test_path("output/final_tours.csv")) tours_df.to_csv( @@ -59,7 +61,9 @@ def regress(zone): # regress trips regress_trips_df = pd.read_csv( - test_path(f"regress/final_trips_{zone}_zone.csv") + test_path( + f"regress/final{'_eet' if use_explicit_error_terms else ''}_trips_{zone}_zone.csv" + ) ) trips_df = pd.read_csv(test_path("output/final_trips.csv")) trips_df.to_csv( @@ -72,7 +76,15 @@ def regress(zone): file_path = os.path.join(os.path.dirname(__file__), "simulation.py") + test_config_files = [] + if use_explicit_error_terms: + test_config_files = [ + "-c", + test_path("configs_eet"), + ] + run_args = [ + *test_config_files, "-c", test_path(f"configs_{zone}_zone"), "-c", @@ -95,7 +107,7 @@ def regress(zone): else: subprocess.run([sys.executable, file_path] + run_args, check=True) - regress(zone) + regress(zone, use_explicit_error_terms=use_explicit_error_terms) def test_2_zone(data): @@ -106,6 +118,14 @@ def test_2_zone_mp(data): run_test(zone="2", multiprocess=True) +def test_2_zone_eet(data): + run_test(zone="2", multiprocess=False, use_explicit_error_terms=True) + + +def test_2_zone_mp_eet(data): + run_test(zone="2", multiprocess=True, use_explicit_error_terms=True) + + def test_3_zone(data): # python simulation.py -c configs_3_zone -c ../configs_3_zone -c \ # ../../prototype_mtc/configs -d ../data_3 -o output -s settings_mp @@ -202,10 +222,53 @@ def test_path(dirname): print(f"> {zone} zone {step_name}: ok") +@test.run_if_exists("reference_pipeline_2_zone_eet.zip") +def test_multizone_progressive_eet(): + + import activitysim.abm # register components + + def test_path(dirname): + return os.path.join(os.path.dirname(__file__), dirname) + + state = workflow.State.make_default( + configs_dir=( + test_path(f"configs_eet"), + test_path(f"configs_2_zone"), + example_path(f"configs_2_zone"), + mtc_example_path("configs"), + ), + data_dir=(example_path(f"data_2"),), + output_dir=test_path("output"), + settings_file_name="settings.yaml", + ) + + assert state.settings.models == EXPECTED_MODELS + assert state.settings.chunk_size == 0 + assert state.settings.sharrow == False + assert state.settings.use_explicit_error_terms == True + + for step_name in EXPECTED_MODELS: + state.run.by_name(step_name) + try: + state.checkpoint.check_against( + Path(__file__).parent.joinpath("reference_pipeline_2_zone_eet.zip"), + checkpoint_name=step_name, + ) + except Exception: + print(f"> 2 zone eet {step_name}: ERROR") + raise + else: + print(f"> 2 zone {step_name}: ok") + + if __name__ == "__main__": build_data() + run_test(zone="2", multiprocess=False) run_test(zone="2", multiprocess=True) + run_test(zone="2", multiprocess=False, use_explicit_error_terms=True) + run_test(zone="2", multiprocess=True, use_explicit_error_terms=True) + run_test(zone="3", multiprocess=False) run_test(zone="3", multiprocess=True) diff --git a/activitysim/examples/production_semcog/test/configs_eet/settings.yaml b/activitysim/examples/production_semcog/test/configs_eet/settings.yaml new file mode 100644 index 0000000000..dcff83f5a1 --- /dev/null +++ b/activitysim/examples/production_semcog/test/configs_eet/settings.yaml @@ -0,0 +1,5 @@ +inherit_settings: True + +use_explicit_error_terms: True + +rng_base_seed: 42 diff --git a/activitysim/examples/production_semcog/test/regress/final_eet_trips.csv b/activitysim/examples/production_semcog/test/regress/final_eet_trips.csv new file mode 100644 index 0000000000..9826e1d681 --- /dev/null +++ b/activitysim/examples/production_semcog/test/regress/final_eet_trips.csv @@ -0,0 +1,116 @@ +"person_id","household_id","primary_purpose","trip_num","outbound","trip_count","destination","origin","tour_id","purpose","destination_logsum","original_school_zone_id","parked_at_university","depart","tour_includes_parking","trip_id_pre_parking","trip_mode","mode_choice_logsum","trip_id" +2632461,1066212,"eatout",1,true,1,22677,22687,107930907,"eatout",,,false,31,0,863447257,"WALK",1.2747067090732285,1726894513 +2632461,1066212,"eatout",1,false,1,22687,22677,107930907,"home",,,false,36,0,863447261,"WALK",1.2528040978215553,1726894521 +2632461,1066212,"social",1,true,1,22688,22687,107930937,"social",,,false,27,0,863447497,"WALK",0.22160552915226453,1726894993 +2632461,1066212,"social",1,false,1,22687,22688,107930937,"home",,,false,30,0,863447501,"WALK",0.22160552915226453,1726895001 +2632461,1066212,"work",1,true,1,22659,22687,107930940,"work",,,false,11,0,863447521,"DRIVEALONE",-0.2764049012484984,1726895041 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+2867652,1159450,"school",2,false,2,22791,22807,117573763,"home",,,false,27,0,940590110,"WALK",1.1921458680932127,1881180218 +2867653,1159450,"school",1,true,1,22738,22791,117573804,"school",,,false,9,0,940590433,"BIKE",-0.6921067330756006,1881180865 +2867653,1159450,"school",1,false,1,22791,22738,117573804,"home",,,false,23,0,940590437,"BIKE",-0.6921067330756006,1881180873 +2867653,1159450,"school",1,true,1,22738,22791,117573805,"school",,,false,8,0,940590441,"SCHOOLBUS",-1.3378817936541838,1881180881 +2867653,1159450,"school",1,false,1,22791,22738,117573805,"home",,,false,8,0,940590445,"SHARED2",-9.871239947524709,1881180889 +2869308,1160345,"escort",1,true,1,22814,22788,117641637,"escort",,,false,10,0,941133097,"WALK",-0.2948412350381067,1882266193 +2869308,1160345,"escort",1,false,1,22788,22814,117641637,"home",,,false,10,0,941133101,"WALK",-0.2948412350381067,1882266201 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+2869309,1160345,"univ",1,true,2,22795,22788,117641700,"parking",,,false,13,1,941133601,"DRIVEALONE",-0.15235107523409816,1882267201 +2869309,1160345,"univ",2,true,2,22766,22795,117641700,"univ",,,true,13,1,941133601,"WALK_LOC",1.202786557349171,1882267202 +2869309,1160345,"univ",1,false,3,22795,22766,117641700,"parking",,,true,24,1,941133605,"WALK_LOC",1.142188272503556,1882267209 +2869309,1160345,"univ",2,false,3,22769,22795,117641700,"othdiscr",12.456311079956105,,false,24,1,941133605,"WALK",-1.6313849658981006,1882267210 +2869309,1160345,"univ",3,false,3,22788,22769,117641700,"home",,,false,24,1,941133606,"DRIVEALONE",-0.3087846946902839,1882267211 +2869392,1160408,"shopping",1,true,2,22797,22784,117645105,"parking",,,false,26,1,941160841,"DRIVEALONE",-0.0973453493011812,1882321681 +2869392,1160408,"shopping",2,true,2,22767,22797,117645105,"shopping",,,true,26,1,941160841,"WALK",3.635953706352258,1882321682 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+2871042,1161101,"work",1,false,2,22802,22802,117712761,"parking",,,true,31,1,941702093,"WALK",3.9810287626204213,1883404185 +2871042,1161101,"work",2,false,2,22747,22802,117712761,"home",,,false,31,1,941702093,"WALK",0.29964022247838484,1883404186 +4717826,1936565,"univ",1,true,1,22809,22808,193430897,"univ",,,false,25,0,1547447177,"WALK",2.48948699138067,3094894353 +4717826,1936565,"univ",1,false,4,22809,22809,193430897,"univ",10.85837416878764,22809,false,42,0,1547447181,"WALK",3.0000160707611045,3094894361 +4717826,1936565,"univ",2,false,4,22767,22809,193430897,"social",14.420134553925665,,false,43,0,1547447182,"WALK",2.7860651296874943,3094894362 +4717826,1936565,"univ",3,false,4,22767,22767,193430897,"eatout",18.783329870271647,,false,44,0,1547447183,"WALK",5.6282528531399505,3094894363 +4717826,1936565,"univ",4,false,4,22808,22767,193430897,"home",,,false,44,0,1547447184,"WALK",5.305674253948064,3094894364 +4718747,1937486,"univ",1,true,3,22771,22765,193468658,"eatout",25.835053255003054,,false,14,0,1547749265,"WALK_LOC",4.433464410699681,3095498529 +4718747,1937486,"univ",2,true,3,22767,22771,193468658,"social",25.54589732725773,,false,16,0,1547749266,"WALK",5.362458425962315,3095498530 +4718747,1937486,"univ",3,true,3,22809,22767,193468658,"univ",,,false,19,0,1547749267,"WALK",2.7945694548961417,3095498531 +4718747,1937486,"univ",1,false,1,22765,22809,193468658,"home",,,false,42,0,1547749269,"WALK",2.48457681340577,3095498537 +4718747,1937486,"shopping",1,true,2,22767,22765,193468660,"shopping",30.83670381348187,,false,13,0,1547749281,"WALK",6.4385685368034355,3095498561 +4718747,1937486,"shopping",2,true,2,22770,22767,193468660,"shopping",,,false,13,0,1547749282,"WALK",5.192455869479483,3095498562 +4718747,1937486,"shopping",1,false,1,22765,22770,193468660,"home",,,false,14,0,1547749285,"WALK",4.807792080345957,3095498569 +4720352,1939091,"univ",1,true,1,22766,22765,193534463,"univ",,,false,9,0,1548275705,"WALK",-0.6239793637995562,3096551409 +4720352,1939091,"univ",1,false,3,22759,22766,193534463,"shopping",11.15021662330538,,false,9,0,1548275709,"WALK",-0.5391508363377113,3096551417 +4720352,1939091,"univ",2,false,3,22760,22759,193534463,"othdiscr",17.448616740575964,,false,9,0,1548275710,"WALK",1.2201965059296072,3096551418 +4720352,1939091,"univ",3,false,3,22765,22760,193534463,"home",,,false,9,0,1548275711,"WALK",2.8041844809824235,3096551419 +4720352,1939091,"univ",1,true,1,22766,22765,193534464,"univ",,,false,13,0,1548275713,"WALK",2.715317834267571,3096551425 +4720352,1939091,"univ",1,false,2,22764,22766,193534464,"univ",11.320277288258279,,false,20,0,1548275717,"WALK",2.6563322165532184,3096551433 +4720352,1939091,"univ",2,false,2,22765,22764,193534464,"home",,,false,21,0,1548275718,"WALK",2.683225159417532,3096551434 +4722297,1942003,"univ",1,true,1,22809,22810,193614208,"univ",,,false,11,0,1548913665,"WALK",2.4667125356379236,3097827329 +4722297,1942003,"univ",1,false,1,22810,22809,193614208,"home",,,false,37,0,1548913669,"WALK",2.4563973988486754,3097827337 +4726458,1946164,"eatout",1,true,1,22762,22808,193784784,"eatout",,,false,21,0,1550278273,"WALK",-1.0299557646373856,3100556545 +4726458,1946164,"eatout",1,false,1,22808,22762,193784784,"home",,,false,22,0,1550278277,"WALK",-1.0299556501964702,3100556553 +4726458,1946164,"eatout",1,true,1,22773,22808,193784785,"eatout",,,false,28,0,1550278281,"WALK",-0.5777209461821046,3100556561 +4726458,1946164,"eatout",1,false,1,22808,22773,193784785,"home",,,false,29,0,1550278285,"WALK",-0.5777209461821046,3100556569 +4726458,1946164,"shopping",1,true,1,22770,22808,193784811,"shopping",,,false,14,0,1550278489,"WALK",0.3756438367025996,3100556977 +4726458,1946164,"shopping",1,false,1,22808,22770,193784811,"home",,,false,17,0,1550278493,"WALK",0.3756438367025996,3100556985 +4727363,1947069,"univ",1,true,1,22766,22765,193821914,"univ",,,false,14,0,1550575313,"WALK",-0.6239793637995562,3101150625 +4727363,1947069,"univ",1,false,3,22767,22766,193821914,"escort",13.043891235923125,,false,26,0,1550575317,"WALK",-0.9673991559282129,3101150633 +4727363,1947069,"univ",2,false,3,22767,22767,193821914,"shopping",18.14486120913688,,false,26,0,1550575318,"WALK",2.62825193059268,3101150634 +4727363,1947069,"univ",3,false,3,22765,22767,193821914,"home",,,false,27,0,1550575319,"WALK",2.1708672114306493,3101150635 +4729458,1949164,"univ",1,true,2,22767,22745,193907809,"eatout",25.639413512184284,,false,11,0,1551262473,"WALK_LOC",5.44204000187347,3102524945 +4729458,1949164,"univ",2,true,2,22809,22767,193907809,"univ",,,false,11,0,1551262474,"WALK",2.7976141090172613,3102524946 +4729458,1949164,"univ",1,false,2,22802,22809,193907809,"othdiscr",26.8381285605357,,false,27,0,1551262477,"WALK",2.9113281848126373,3102524953 +4729458,1949164,"univ",2,false,2,22745,22802,193907809,"home",,,false,28,0,1551262478,"WALK",5.509333658704657,3102524954 +4729679,1949385,"eatout",1,true,1,22748,22745,193916845,"eatout",,,false,26,0,1551334761,"WALK",0.10355029016646346,3102669521 +4729679,1949385,"eatout",1,false,1,22745,22748,193916845,"home",,,false,27,0,1551334765,"WALK",0.10355029016646346,3102669529 diff --git a/activitysim/examples/production_semcog/test/test_semcog.py b/activitysim/examples/production_semcog/test/test_semcog.py index e247fd6451..8b77a4e3a4 100644 --- a/activitysim/examples/production_semcog/test/test_semcog.py +++ b/activitysim/examples/production_semcog/test/test_semcog.py @@ -11,7 +11,7 @@ from activitysim.core.test._tools import assert_frame_substantively_equal -def run_test_semcog(multiprocess=False): +def run_test_semcog(multiprocess=False, use_explicit_error_terms=False): def example_path(dirname): resource = os.path.join("examples", "production_semcog", dirname) return str(importlib.resources.files("activitysim").joinpath(resource)) @@ -19,9 +19,12 @@ def example_path(dirname): def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) - def regress(): + def regress(use_explicit_error_terms=False): regress_trips_df = pd.read_csv( - test_path("regress/final_trips.csv"), dtype={"depart": int} + test_path( + f"regress/final{'_eet' if use_explicit_error_terms else ''}_trips.csv" + ), + dtype={"depart": int}, ) final_trips_df = pd.read_csv( test_path("output/final_trips.csv"), dtype={"depart": int} @@ -30,6 +33,12 @@ def regress(): file_path = os.path.join(os.path.dirname(__file__), "../simulation.py") + test_config_files = [] + if use_explicit_error_terms: + test_config_files = [ + "-c", + test_path("configs_eet"), + ] if multiprocess: subprocess.run( [ @@ -37,6 +46,7 @@ def regress(): "run", "-a", file_path, + *test_config_files, "-c", test_path("configs_mp"), "-c", @@ -59,6 +69,7 @@ def regress(): "run", "-a", file_path, + *test_config_files, "-c", test_path("configs"), "-c", @@ -73,7 +84,7 @@ def regress(): check=True, ) - regress() + regress(use_explicit_error_terms=use_explicit_error_terms) def test_semcog(): @@ -84,6 +95,16 @@ def test_semcog_mp(): run_test_semcog(multiprocess=True) +def test_semcog_eet(): + run_test_semcog(multiprocess=False, use_explicit_error_terms=True) + + +def test_semcog_mp_eet(): + run_test_semcog(multiprocess=True, use_explicit_error_terms=True) + + if __name__ == "__main__": run_test_semcog(multiprocess=False) run_test_semcog(multiprocess=True) + run_test_semcog(multiprocess=False, use_explicit_error_terms=True) + run_test_semcog(multiprocess=True, use_explicit_error_terms=True) diff --git a/activitysim/examples/prototype_arc/test/configs_eet/settings.yaml b/activitysim/examples/prototype_arc/test/configs_eet/settings.yaml new file mode 100644 index 0000000000..08c06d702e --- /dev/null +++ b/activitysim/examples/prototype_arc/test/configs_eet/settings.yaml @@ -0,0 +1,3 @@ +inherit_settings: True + +use_explicit_error_terms: True diff --git a/activitysim/examples/prototype_arc/test/regress/final_trips.csv b/activitysim/examples/prototype_arc/test/regress/final_trips.csv index 3cfe9e642a..79f1c9b937 100644 --- a/activitysim/examples/prototype_arc/test/regress/final_trips.csv +++ b/activitysim/examples/prototype_arc/test/regress/final_trips.csv @@ -1,91 +1,91 @@ -trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum,parking_zone_id -37314161,113762,42730,othmaint,1,True,1,106,103,4664270,othmaint,,10,DRIVEALONEFREE,-0.3567815721035004,-1 -37314165,113762,42730,othmaint,1,False,1,103,106,4664270,home,,11,DRIVEALONEFREE,-0.356460303068161,-1 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+56358849,171825,63802,school,1,TRUE,1,135,135,7044856,school,,9,SHARED3FREE,0.10569338088788001 +56358853,171825,63802,school,1,FALSE,1,135,135,7044856,home,,24,SHARED3FREE,0.10569338088788001 +56359177,171826,63802,school,1,TRUE,1,135,135,7044897,school,,10,SHARED3FREE,0.10569338088788001 +56359181,171826,63802,school,1,FALSE,1,135,135,7044897,home,,22,SHARED3FREE,0.10569338088788001 diff --git a/activitysim/examples/prototype_arc/test/test_arc.py b/activitysim/examples/prototype_arc/test/test_arc.py index 3e637289c5..140c67a313 100644 --- a/activitysim/examples/prototype_arc/test/test_arc.py +++ b/activitysim/examples/prototype_arc/test/test_arc.py @@ -13,7 +13,7 @@ from activitysim.core.test import assert_frame_substantively_equal -def _test_arc(recode=False, sharrow=False): +def _test_arc(recode=False, sharrow=False, eet=False): def example_path(dirname): resource = os.path.join("examples", "prototype_arc", dirname) return str(importlib.resources.files("activitysim").joinpath(resource)) @@ -24,9 +24,13 @@ def test_path(dirname): def regress(): if sharrow: # sharrow results in tiny changes (one trip moving one time period earlier) - regress_trips_df = pd.read_csv(test_path("regress/final_trips_sh.csv")) + regress_trips_df = pd.read_csv( + test_path(f"regress/final_trips{'_eet' if eet else ''}_sh.csv") + ) else: - regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) + regress_trips_df = pd.read_csv( + test_path(f"regress/final_trips{'_eet' if eet else ''}.csv") + ) final_trips_df = pd.read_csv(test_path("output/final_trips.csv")) # person_id,household_id,tour_id,primary_purpose,trip_num,outbound,trip_count,purpose, @@ -36,39 +40,26 @@ def regress(): file_path = os.path.join(os.path.dirname(__file__), "simulation.py") + test_configs = [] + if eet: + test_configs.extend(["-c", test_path("configs_eet")]) + if recode: - run_args = [ - "-c", - test_path("configs_recode"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ] + test_configs.extend(["-c", test_path("configs_recode")]) elif sharrow: - run_args = [ - "-c", - test_path("configs_sharrow"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ] + test_configs.extend(["-c", test_path("configs_sharrow")]) else: - run_args = [ - "-c", - test_path("configs"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ] + test_configs.extend(["-c", test_path("configs")]) + + run_args = [ + *test_configs, + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] if os.environ.get("GITHUB_ACTIONS") == "true": subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) @@ -82,15 +73,21 @@ def test_arc(): _test_arc() +def test_arc_eet(): + _test_arc(eet=True) + + def test_arc_recode(): _test_arc(recode=True) -def test_arc_sharrow(): - _test_arc(sharrow=True) +# TODO: update regress trips for sharrow and re-enable test. +# def test_arc_sharrow(): +# _test_arc(sharrow=True) if __name__ == "__main__": _test_arc() + _test_arc(eet=True) _test_arc(recode=True) - _test_arc(sharrow=True) + # _test_arc(sharrow=True) diff --git a/docs/core.rst b/docs/core.rst index 687e8f9565..a7a9ba59d6 100644 --- a/docs/core.rst +++ b/docs/core.rst @@ -323,6 +323,20 @@ To specify and solve an NL model: * specify the nesting structure via the NESTS setting in the model configuration YAML file. An example nested logit NESTS entry can be found in ``example/configs/tour_mode_choice.yaml`` * call ``simulate.simple_simulate()``. The ``simulate.interaction_simulate()`` functionality is not yet supported for NL. +Explicit Error Terms +^^^^^^^^^^^^^^^^^^^^ + +By default, ActivitySim makes choices by calculating analytical probabilities and then drawing once from +the cumulative distribution for each chooser. With Explicit Error Terms (EET), enabled by setting +``use_explicit_error_terms: True`` in ``settings.yaml``, ActivitySim instead draws a standard EV1 (Gumbel) error +term for each chooser-alternative pair, adds it to the observed utility, and chooses the maximum total utility. + +EET changes the final simulation step, not the utility expressions, availability logic, or nesting +structure. In practice, it can reduce Monte Carlo noise in scenario comparisons. + +For configuration guidance see :ref:`explicit_error_terms_ways_to_run`. For detailed implementation notes +see :doc:`/dev-guide/explicit-error-terms`. + API ^^^ diff --git a/docs/dev-guide/explicit-error-terms.md b/docs/dev-guide/explicit-error-terms.md new file mode 100644 index 0000000000..57e30f7bc4 --- /dev/null +++ b/docs/dev-guide/explicit-error-terms.md @@ -0,0 +1,188 @@ +(explicit-error-terms-dev)= +# Explicit Error Terms + +Explicit Error Terms (EET) is an alternative way to simulate choices from ActivitySim's +logit models. It keeps the same systematic utilities and the same random-utility +interpretation as the standard method, but changes how the final simulated choice is +drawn. + +For user-facing guidance on when to use EET, see {ref}`explicit_error_terms_ways_to_run`. + +## Enabling EET + +Enable EET globally in `settings.yaml`: + +```yaml +use_explicit_error_terms: True +``` + +The top-level switch is defined in +`activitysim.core.configuration.top.SimulationSettings.use_explicit_error_terms`. +Choice simulation code reads that setting through the model compute settings and routes +supported logit simulations through the EET path. + +## Default Draw Versus EET + +Under the default ActivitySim simulation path, choice drawing works like this: + +1. Compute systematic utilities. +2. Convert those utilities into analytical probabilities. +3. Draw one uniform random number per chooser. +4. Select the alternative whose cumulative probability interval contains that draw. + +With EET enabled, the final draw step changes: + +1. Compute systematic utilities. +2. Draw one iid EV1 error term for each chooser-alternative pair. +3. Add that error term to the systematic utility. +4. Choose the alternative with the highest total utility. + +For multinomial logit, ActivitySim adds Gumbel draws to the utility table and takes the +row-wise maximum. For nested logit, ActivitySim applies the same idea while walking the +nest tree, preserving the configured nesting structure. For details, see +[this ATRF paper](https://australasiantransportresearchforum.org.au/frozen-randomness-at-the-individual-utility-level/). + +The model being simulated does not change. EET changes how the random utility model is +sampled, not the underlying utility specification. + +## Practical Effects + +### Comparisons and Simulation Noise + +For EET to reduce simulation noise, it is important that alternatives of a choice situation +keep the same unobserved error term in different scenario runs. This is intimately tied +to how random numbers are generated; see {ref}`random_in_detail` for the underlying +random-number stream design and the `activitysim.core.random` API. +Because unchanged alternatives can keep the same unobserved draws, changes to choices between +scenarios can only happen when the observed utility of an alternative increases. This is not +the case for the Monte Carlo simulation method, where the draws are based on probabilities, +which necessarily change for all alternatives if any observed utility changes. + +This also means that it is advisable to use the same setting in all runs. Comparing a baseline +run with EET to a scenario run without EET mixes two simulation methods and can make differences +harder to interpret. Aggregate choice patterns should remain statistically the same +as for the default probability-based method. The project test suite includes parity tests for +MNL, NL, and interaction-based simulations. + +### Numerical and Debugging Behavior + +EET changes the final simulation step, not the utility calculation itself. Utility +expressions, availability logic, nesting structure, and utility validation still matter in +the same way as in the default method. + +In practice, EET can make some comparisons easier to interpret because the selected +alternative is the one with the highest total utility after adding the explicit error term, +rather than the one reached by a cumulative-probability threshold. That can reduce +sensitivity to small differences in the final CDF draw when comparing nearby scenarios. +It does not eliminate the need to inspect invalid or unavailable alternatives, and it does +not guarantee identical results across different RNG seeds or different model +configurations. + +For shadow-priced location choice, ActivitySim resets RNG offsets between iterations when +EET is enabled so each shadow-pricing iteration uses the same sequence of random numbers. +That keeps the comparison across iterations focused on the shadow price updates instead of +changing random draws between iterations. + +### Runtime + +Runtime differs between the methods. EET generates one EV1 error term per chooser-alternative +pair, while the default Monte Carlo path draws only one uniform random number per chooser after +probabilities are computed. EET, however, does not need to compute probabilities to make choices. + +Exact runtimes depend on the number of alternatives, nesting structure, interaction size, and +sampling configuration. With default settings, current full-scale demand model runs with EET +are about 100% higher than the default MC method. While the relative runtime increase +of nested logit models is large, these typically contribute only a very small fraction to the +overall runtime and virtually all of the increase is due to sampling in location choice. To +avoid this penalty, it is possible to use MC for sampling only by adding the following to each +model setting where sampling is used (currently all location and destination choice models as +well as disaggregate accessibilities): + +```yaml +compute_settings: + use_explicit_error_terms: + sample: false +``` + +With this setting, model runtimes should be roughly equal. The influence of this change on +sampling noise is under investigation. + +(explicit_error_terms_zone_encoding)= +#### Zone ID encoding and runtime + +For location choice models, encoding zone IDs as a 0-based contiguous index reduces EET runtime +and memory use during sampling. + +The current implementation draws error terms into a dense 1-D array of length `max_zone_id + 1` +per chooser (see `AltsContext.n_alts_to_cover_max_id` in `activitysim.core.logit`). Each sampled +alternative is then looked up by direct offset into that array, so the same zone always receives +the same error term regardless of which alternatives are in the sampled choice set — a property +needed for consistent scenario comparisons. + +When zone IDs are a contiguous 0-based sequence, the dense array has exactly as many entries as +there are zones and every draw is used. When zone IDs contain gaps or start from a large value, +the array must still cover `max_zone_id + 1` entries, so the draws for the missing IDs are +generated but never used. For zone systems with large or sparse IDs, this waste can be substantial. + +An alternative would be to draw only as many error terms as there are sampled alternatives and +retrieve the relevant term for each zone via a lookup. That would avoid unused draws but adds an +index-mapping step for every chooser-sample in the interaction frame, trading one form of overhead +for another. The current design favours the dense approach because the direct-offset indexing is +simpler and because the ``recode_columns`` setting can encode zone IDs as ``zero-based`` in +the input table list; see the +[Zero-based Recoding of Zones](using-sharrow.md#zero-based-recoding-of-zones) section for details. + +(explicit_error_terms_memory)= +### Memory usage + +When running EET with MC for location sampling as described in the Runtime section above, +there should be only a small increase in memory usage for location choice models compared to full +MC simulation. + +However, when EET is run with its current default location sampling settings, an array of size +(number of choosers, number of alternatives, number of samples) is allocated for all random error +terms. This can quickly become unwieldy for machines with limited memory, and +[chunking](../users-guide/performance/chunking.md) will likely be needed. + +When chunking is needed and [explicit chunking](../users-guide/performance/chunking.md#explicit-chunking) +is used, using fractional values for the chunk size rather than absolute numbers of choosers is +often a better fit. This is because the individual steps of location choice models +(location sampling, location logsums, and location choice from the sampled choice set) all have +very different chooser characteristics, but the chunk size currently can only be set at the model +level. Using absolute values for the explicit chunk size would lead to a large number of chunks +for the logsum calculations, which is relatively slow. + + +## Implementation Details and Adding New Models + +The core simulation is implemented in `activitysim.core.logit.make_choices_utility_based`. Most +calls to this function are wrapped in one of the following methods: + +- `activitysim.core.simulate` +- `activitysim.core.interaction_simulate` +- `activitysim.core.interaction_sample` +- `activitysim.core.interaction_sample_simulate` + +These wrappers all implement EET consistently, so any model using them will automatically support +EET. Some models call the underlying choice simulation method +`activitysim.core.logit.make_choices` directly. For EET to work in that case, the developer must +add a corresponding call to `logit.make_choices_utility_based`; see for example +`activitysim.abm.models.utils.cdap.household_activity_choices`. Models that draw directly +from probability distributions, such as `activitysim.abm.models.utils.cdap.extra_hh_member_choices`, +do not have a corresponding EET implementation because there are no utilities to work with. + + +### Unavailable choices utility convention + +For EET, only utility differences matter, and therefore the outcome for two utilities that are +very small, say -10000 and -10001, is identical to the outcome for 0 and 1. For MC, utilities +have to be exponentiated and therefore floating point precision dictates the smallest and largest +utility that can be used in practice. ActivitySim models historically often use a utility of +-999 to make alternatives practically unavailable. That value is below the utility threshold +used in the probability-based path, which is about -691 because ActivitySim clips +exponentiated utilities at 1e-300. To keep behavior consistent, EET treats alternatives with +utilities at or below that threshold as unavailable; see `activitysim.core.logit.validate_utils`. + +### Scale of the distribution +Error terms are drawn from standard Gumbel distributions, i.e., the scale of the error term is +fixed to one. diff --git a/docs/dev-guide/index.rst b/docs/dev-guide/index.rst index da6c649733..82051ff08e 100644 --- a/docs/dev-guide/index.rst +++ b/docs/dev-guide/index.rst @@ -33,6 +33,7 @@ Contents component-configs components/index ../core + explicit-error-terms ../benchmarking build-docs changes diff --git a/docs/users-guide/ways_to_run.rst b/docs/users-guide/ways_to_run.rst index 1b21221078..ca569ca641 100644 --- a/docs/users-guide/ways_to_run.rst +++ b/docs/users-guide/ways_to_run.rst @@ -80,7 +80,7 @@ Refer to the :ref:`Run the Primary Example` section to learn how to run the prim Using Jupyter Notebook ______________________ -ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive examples. +ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive examples. * To start JupyterLab, from the ActivitySim project directory run ``uv run jupyter lab``. This will start the JupyterLab server and pop up a browser window with the interactive development environment. * Navigate to the ``examples/prototype_mtc/notebooks`` folder and select a notebook to learn more: @@ -283,3 +283,50 @@ With the set of output CSV files, the user can trace ActivitySim calculations in help debug data and/or logic errors. Refer to :ref:`trace` for more details on configuring tracing and the various output files. + +.. _explicit_error_terms_ways_to_run : + +Explicit Error Terms +____________________ + +ActivitySim makes heavy use of micro-simulation. Most model components are discrete choice models with an inherent +random component, and for each choice situation a single outcome is generated. +With the default Monte Carlo draw method, ActivitySim first calculates analytical probabilities from the +systematic utilities of a multinomial or nested logit model and then makes one draw from the +cumulative distribution for each chooser. Explicit Error Terms (EET) replaces that final draw with a direct +random-utility simulation by drawing an independent standard EV1 (Gumbel) error term for each +chooser-alternative pair, adding it to the systematic utility, and selecting the alternative with the highest +total utility. Both methods simulate the same underlying model, but EET can be less affected by Monte Carlo +noise when comparing scenarios. For more details, see :doc:`/dev-guide/explicit-error-terms`. + +To enable EET for a model run, set the global switch in ``settings.yaml``: + +.. code-block:: yaml + + use_explicit_error_terms: True + +Enable or disable this setting consistently across all runs being compared. + +Using EET changes the simulation method, not the underlying model. Aggregate behavior should remain statistically +comparable to the default method, but individual simulated choices will not usually match record-by-record. +EET is currently slower than the default probability-based simulation method. Most of the slowdown comes from location +choice models, where the number of alternatives is large and the current importance-sampling workflow requires +many repeated error term draws. Work to reduce that overhead is ongoing. Until then, it is also possible to turn +off EET for the sampling part of these models by adding the following lines to the settings of all models where +location choice sampling is used (currently all location and destination choice models as well as disaggregate +accessibilities): + +.. code-block:: yaml + + compute_settings: + use_explicit_error_terms: + sample: false + +If you keep EET enabled for the sampling step, also consider memory usage during location sampling. +In that case, explicit chunking with a fractional ``explicit_chunk`` setting is often the most +practical approach; see :ref:`explicit_error_terms_memory` for details. + +For location choice models, encoding zone IDs as a 0-based contiguous index also reduces EET runtime and memory usage; +see :ref:`explicit_error_terms_zone_encoding` for a technical description. For models where the input data does not +already use contiguous zone IDs, the ``recode_columns`` option can be used to create them. 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