def initialize_transition_tables(self, policy_list: List[SparkPolicy]) -> None:
"""Populate the 'before' transition table and initializes the max_sentence from historical data """
self.transition_tables[MIN_POSSIBLE_POLICY_TS] = TransitionTable(
MIN_POSSIBLE_POLICY_TS, []
)
self.transition_tables[MIN_POSSIBLE_POLICY_TS].generate_transition_table(
TransitionTableType.AFTER, self.historical_outflows
)
policy_time_steps = list({policy.policy_ts for policy in policy_list})
if (
len(policy_time_steps) > 0
and min(policy_time_steps) <= MIN_POSSIBLE_POLICY_TS
):
raise ValueError(
f"Policy ts exceeds minimum allowable value ({MIN_POSSIBLE_POLICY_TS}): {min(policy_time_steps)}"
)
policy_time_steps.append(MIN_POSSIBLE_POLICY_TS)
policy_time_steps.sort()
for ts_idx in range(1, len(policy_time_steps)):
self.transition_tables[policy_time_steps[ts_idx]] = TransitionTable(
policy_time_steps[ts_idx],
SparkPolicy.get_ts_policies(policy_list, policy_time_steps[ts_idx]),
self.transition_tables[policy_time_steps[ts_idx - 1]].get_table(
TransitionTableType.AFTER
),
)
# normalize all tables
for transition_table in self.transition_tables.values():
transition_table.normalize_transitions()
def _initialize_admissions_predictors(
self, constant_admissions: bool, projection_type: Optional[str]
) -> None:
"""Generate the dictionary of one admission predictor per policy time step that defines outflow behaviors"""
policy_time_steps = list({policy.policy_ts for policy in self.policy_list})
if (
len(policy_time_steps) > 0
and min(policy_time_steps) <= MIN_POSSIBLE_POLICY_TS
):
raise ValueError(
f"Policy ts exceeds minimum allowable value ({MIN_POSSIBLE_POLICY_TS}): {min(policy_time_steps)}"
)
# TODO(#6727): inheritance -> composition for SparkCompartment so this reused logic is cleaned up
policy_time_steps.append(MIN_POSSIBLE_POLICY_TS)
policy_time_steps.sort()
self.policy_data[MIN_POSSIBLE_POLICY_TS] = self.outflows_data
# first pass transforms outflows data according to policies
for ts_idx in range(1, len(policy_time_steps)):
# start with a copy of the data from the previous policy data
ts_data = self.policy_data[policy_time_steps[ts_idx - 1]].copy()
for policy in SparkPolicy.get_ts_policies(
self.policy_list, policy_time_steps[ts_idx]
):
ts_data = policy.policy_fn(ts_data)
self.policy_data[policy_time_steps[ts_idx]] = ts_data
# second pass creates admissions predictors from transformed outflows data
for ts, ts_data in self.policy_data.items():
self.admissions_predictors[ts] = PredictedAdmissions(
ts_data, constant_admissions, projection_type
)
def test_get_ts_policies(self) -> None:
expected_list = self.policy_list[1:]
result_list = SparkPolicy.get_ts_policies(self.policy_list,
time_step=1)
self.assertEqual(expected_list, result_list)