From a78ee13510701152d1194d72104859f9a8cca39e Mon Sep 17 00:00:00 2001 From: Robert Drury Date: Fri, 20 Dec 2024 10:55:51 -0800 Subject: [PATCH] Warm start a stratey based on config provided seed conditions (#487) Summary: Pull Request resolved: https://github.com/facebookresearch/aepsych/pull/487 There is a desire to warm start a strategy with data filtered on master table values recorded from previous experiments. Data can be filtered on any data other than master.unique_id. This means that the below are all valid filter criteria: - experiment_name - experiment_description - experiment_id - participant_id - anything stored in extra_metadata Criteria will follow AND logic between fields, but inclusive OR logic within the same field. Data will be further filtered out if it does not meet these criteria: - Each parameter matches on name and type - Each outcome matches on name and type - Stimuli per trial are the same allow-large-files Differential Revision: D66731597 --- aepsych/config.py | 24 +- aepsych/database/data_fetcher.py | 475 ++++++++++++++++++ aepsych/database/db.py | 18 +- .../server/message_handlers/handle_setup.py | 11 +- aepsych/server/server.py | 2 +- aepsych/strategy.py | 15 + aepsych/utils.py | 6 + tests/server/test_server.py | 9 +- tests/test_databases/1000_outcome.db | Bin 0 -> 12734464 bytes tests/test_datafetcher.py | 302 +++++++++++ 10 files changed, 840 insertions(+), 22 deletions(-) create mode 100644 aepsych/database/data_fetcher.py create mode 100644 tests/test_databases/1000_outcome.db create mode 100644 tests/test_datafetcher.py diff --git a/aepsych/config.py b/aepsych/config.py index c7eecbf3b..261b595b3 100644 --- a/aepsych/config.py +++ b/aepsych/config.py @@ -149,16 +149,14 @@ def to_dict(self, deduplicate: bool = True) -> Dict[str, Any]: _dict[section][setting] = self[section][setting] return _dict - # Turn the metadata section into JSON. - def jsonifyMetadata(self, only_extra: bool = False) -> str: - """Return a json string of the metadata section. + def get_metadata(self, only_extra: bool = False) -> Dict[Any, Any]: + """Return a dictionary of the metadata section. Args: - only_extra (bool): Only jsonify the extra meta data. + only_extra (bool, optional): Only gather the extra metadata. Defaults to False. Returns: - str: A json string representing the metadata dictionary or an empty string - if there is no metadata to return. + Dict[Any, Any]: a collection of the metadata stored in this conig. """ configdict = self.to_dict() metadata = configdict["metadata"].copy() @@ -172,7 +170,21 @@ def jsonifyMetadata(self, only_extra: bool = False) -> str: ] for name in default_metadata: metadata.pop(name, None) + + return metadata + + # Turn the metadata section into JSON. + def jsonifyMetadata(self, only_extra: bool = False) -> str: + """Return a json string of the metadata section. + + Args: + only_extra (bool): Only jsonify the extra meta data. + Returns: + str: A json string representing the metadata dictionary or an empty string + if there is no metadata to return. + """ + metadata = self.get_metadata(only_extra) if len(metadata.keys()) == 0: return "" else: diff --git a/aepsych/database/data_fetcher.py b/aepsych/database/data_fetcher.py new file mode 100644 index 000000000..78238649b --- /dev/null +++ b/aepsych/database/data_fetcher.py @@ -0,0 +1,475 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta, Inc. and its affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +from typing import ( + Any, + Dict, + List, + Tuple, + Optional, + Iterable +) + +from aepsych.config import Config, ConfigurableMixin +from aepsych.strategy import Strategy +from aepsych.utils_logging import getLogger +from aepsych.utils import generate_default_outcome_names +from aepsych.database.tables import DBMasterTable + +import torch +import json + +logger = getLogger() + +# !!!!!! WARNING !!!!! +# +# If you modify the parameter order in the query in _get_data ensure you update these to match! +# The order in which you place the parameters in a sql query dictates the placement in the results +# array. +# +# For example: +# select iteration_id, param_name, param_val, outcome_id results in this list: +# [iteration_id, param_name, param_val, outcome_id] +# +# !!!!!! WARNING !!!!! +# "const" variable identifiers to avoid magic number indexing into the query results +ITERATION_ID = 0 +PARAM_NAME_ID = 1 +PARAM_VAL_ID = 2 +OUTCOME_ID = 3 + +class DataFetcher(ConfigurableMixin): + + def __init__(self, exp_names: Optional[List[str]] = None, exp_desc: Optional[List[str]] = None, + exp_ids: Optional[List[str]] = None, par_ids: Optional[List[str]] = None, + ex_data: Optional[Dict[str, str]] = None, **kwargs): + """Initialize the DataFetcher object. + + Args: + exp_names (List[str], optional): A list of experiment names to use as a filter on warm start data. + exp_desc (List[str], optional): A list of experiment descriptions to use as a filter on warm start data. + exp_ids (List[str], optional): A list of experiment ids to use as a filter on warm start data. + par_ids (List[str], optional): A list of participant ids to use as a filter on warm start data. + ex_data (Dict[str, str], optional): A map of key-value pairs to use as a filter on warm start data. + """ + self.experiment_names: Optional[List[str]] = exp_names + self.experiment_desc: Optional[List[str]] = exp_desc + self.experiment_ids: Optional[List[str]] = exp_ids + self.participant_ids: Optional[List[str]] = par_ids + self.extra_metadata: Optional[Dict[str, str]] = ex_data + + @property + def _has_search_criteria(self) -> bool: + """Checks to see if the search criteria necessary for warm starting a strategy exists. + + Returns: + bool: are any filter critera set. + """ + return self.experiment_ids is not None or\ + self.experiment_names is not None or\ + self.experiment_desc is not None or\ + self.participant_ids is not None or\ + self.extra_metadata is not None + + def _build_query(self) -> str: + query_clauses: List[str] = [] + if self.experiment_names is not None: + query_clauses.append(f"experiment_name in {_print_in_params(self.experiment_names)}") + + if self.experiment_ids is not None: + query_clauses.append(f"experiment_id in {_print_in_params(self.experiment_ids)}") + + if self.experiment_desc is not None: + query_clauses.append(f"experiment_description in {_print_in_params(self.experiment_desc)}") + + if self.participant_ids is not None: + query_clauses.append(f"participant_id in {_print_in_params(self.participant_ids)}") + + if self.extra_metadata is not None: + query_clauses.append(f"{_print_like_params('extra_metadata', self.extra_metadata)}") + + query = "select unique_id from master where " + for i, clause in enumerate(query_clauses): + query += clause + if i < len(query_clauses) - 1: + query += " and " + + return query + + def _are_stimuli_valid(self, server, config: Config, id: int) -> bool: + pickeld_stimuli_per_trial = config.get("common", "stimuli_per_trial", fallback=-1) + active_stimuli_per_trial = server.config.get("common", "stimuli_per_trial", fallback=0) + + if pickeld_stimuli_per_trial != active_stimuli_per_trial: + _print_warning( + self, + server.db.get_master_record(id), + f"Data with master id: {id} was matched with {{0}} but is invalid due because it has " +\ + f"{pickeld_stimuli_per_trial} stimuli and the current experiment has {active_stimuli_per_trial} stimuli. " +\ + "Skipping this entry." + ) + + return False #trial stimuli don't match this isn't valid data + + return True + + def _are_parameters_valid(self, server, config: Config, id: int) -> bool: + param_names = set(config.getlist("common", "parnames", element_type=str)) + if len(param_names.intersection(set(server.parnames))) == len(server.parnames): + match = True + for param in param_names: + match = config[param]["par_type"] == server.config[param]["par_type"] + if not match: + break + + if config[param]["par_type"] == "continuous": + pickled_lb = config[param]["lower_bound"] + pickled_ub = config[param]["upper_bound"] + + active_lb = server.config[param]["lower_bound"] + active_ub = server.config[param]["upper_bound"] + if pickled_lb != active_lb or pickled_ub != active_ub: + _print_warning( + self, + server.db.get_master_record(id), + f"Data with master id: {id} was matched with {{0}} but its parameter {param} " +\ + f"has bounds ({pickled_lb}, {pickled_ub}) compared to the active bounds " +\ + f"({active_lb}, {active_ub})." + ) + + if not match: + _print_warning( + self, + server.db.get_master_record(id), + f"Data with master id: {id} was matched with {{0}} but is invalid due to parameter " +\ + f"{param} having the type {config[param]['par_type']} while its active type " +\ + f"is {server.config[param]['par_type']}. Skipping this entry." + ) + return False #param types don't match this isn't valid data + else: + _print_warning( + self, + server.db.get_master_record(id), + f"Data with master id: {id} was matched with {{0}} but is invalid due to " +\ + "a mismatch of parameter names. Skipping this entry." + ) + return False #param names don't match this isn't valid data + + return True + + def _are_outcomes_valid(self, server, config: Config, id: int) -> bool: + pickled_outcome_types = config.getlist("common", "outcome_types", element_type=str) + active_outcome_types = server.config.getlist("common", "outcome_types", element_type=str) + + # this doesn't ensure order i.e. the types may match but could be applied to different outcomes + outcome_types_match = len(pickled_outcome_types) == len(active_outcome_types) + for i, outcome_type in enumerate(pickled_outcome_types): + if not outcome_types_match: + break + + if outcome_type != active_outcome_types[i]: + outcome_types_match = False + + if outcome_types_match: + pickled_outcome_names = set(config.getlist("common", "outcome_names", element_type=str, + fallback=generate_default_outcome_names(len(pickled_outcome_types)))) + + active_outcome_names = set(server.config.getlist("common", "outcome_names", element_type=str, + fallback=generate_default_outcome_names(len(active_outcome_types)))) + + if len(pickled_outcome_names.intersection(active_outcome_names)) != len(active_outcome_names): + _print_warning( + self, + server.db.get_master_record(id), + f"Data with master id: {id} was matched with {{0}} but is invalid due to " +\ + "a mismatch in outcome names. Skipping this entry." + ) + return False #outcome types donot match this isn't valid data + else: + _print_warning( + self, + server.db.get_master_record(id), + f"Data with master id: {id} was matched with {{0}} but is invalid due to " +\ + "a mismatch in outcome types. Skipping this entry." + ) + return False #outcome names dont match this isn't valid data + + return True + + def _is_ex_metadata_match_valid(self, config: Config) -> bool: + pickled_ex_data = config.get_metadata(only_extra= True) + if len(pickled_ex_data) == 0: + return True + + potential_matches = pickled_ex_data.keys() & self.extra_metadata.keys() + for key in potential_matches: + if pickled_ex_data[key] == self.extra_metadata[key] or\ + (isinstance(self.extra_metadata[key], Iterable) and pickled_ex_data[key] in self.extra_metadata[key]): + return True + + # don't warn about skipping this data since it was part of the initial filter criteria and + # we don't warn about any of that filtering. + return False + + def _get_valid_data_ids(self, server) -> List[int]: + """Gets all master table ids associated with the data defined by provided search criteria. + The data is then filtered by the current strategy's properties to see if it would be valid for use. + + Args: + server (AEPsychServer): the instance of the server. + + Returns: + List[int]: a list of master table ids that meed all criteria to be valid for use in the current strategy. + """ + valid_match_ids = [] + query = self._build_query() + potential_match_ids = server.db.execute_sql_query(query, None) + + for id in potential_match_ids: + config: Config = server.db.get_config_for(id[0]) + + # ensure that any data matched on metadata keys actually have matching values in their configs. + if not self._is_ex_metadata_match_valid(config): + continue + + # check that stimuli_per_trial match between current + pickled config + if not self._are_stimuli_valid(server, config, id[0]): + continue + + # check param names + types match between current + pickeld config + if not self._are_parameters_valid(server, config, id[0]): + continue + + # check outcome names + types match between current + pickled config + if not self._are_outcomes_valid(server, config, id[0]): + continue + + # we found a valid match keep a record of it + valid_match_ids.append(id[0]) + + return valid_match_ids + + def _construct_data_query(self, ids: List[int]) -> str: + # !!!!!! WARNING !!!!! + # If you modify the parameter order in the query + # ensure you update the "const" id values to match! + # + # see comment at line 45 + # !!!!!! WARNING !!!!! + return f"""select param_data.iteration_id, param_data.param_name, + param_data.param_value, outcome_data.outcome_value from param_data + inner join outcome_data on param_data.iteration_id = outcome_data.iteration_id + inner join raw_data on param_data.iteration_id = raw_data.unique_id + inner join master on raw_data.master_table_id = master.unique_id + where master.unique_id in {_print_in_params(ids)}""" + + + def _get_data(self, server, ids: List[int]) -> List[Tuple[Any]]: + """Gets the actual data to be fed into the strategy's tensors. + + Args: + server (AEPsychServer): The instance of the server. + ids (List[int]): A list of master table ids used to identify and retrive experiment data. + + Returns: + results (List[Tuple[Any]]): a list of all data to be fed into the strategy's tensors. + """ + query = self._construct_data_query(ids) + results = server.db.execute_sql_query(query, None) + return results if results is not None else [] + + def warm_start_strat(self, server, strat: Strategy): + """Warm start the current strategy with data from previous experiments. + + Args: + server (AEPsychServer): The instance of the server + strat (Strategy): The strategy to warm start. + """ + if not self._has_search_criteria: + return #no data to process + + valid_match_ids = self._get_valid_data_ids(server) + data = self._get_data(server, valid_match_ids) + + # since we append param names with _stimuli + n when writing the record to the database + # we need to parse the param name to ensure it will match the names stored in server.parname + # otherwise server._config_to_tensor will fail. + def trim_param_name(name: str) -> str: + end = name.rfind("_stimuli") + if end == -1: + end = None + + return name[0:end] + + i = 0 + fetch_count = 0 + while i < len(data): + # recreate the config dictionary ensuring that all related stimuli are grouped + # together. This way we can ensure they're being fed into the model correctly. + config = { trim_param_name(data[i][PARAM_NAME_ID]): float(data[i][PARAM_VAL_ID]) } + outcome = torch.tensor(data[i][OUTCOME_ID], dtype= torch.float64) + + # while the parameters iteration id matches keep adding data to the dictionary + # we can do this since all data is pre-sorted by iteration when fed into the database + # and maintains that sorting when pulled from it. + j = 1 + while i + j < len(data) and data[i][ITERATION_ID] == data[i + j][ITERATION_ID]: + config[trim_param_name(data[i + j][PARAM_NAME_ID])] = float(data[i + j][PARAM_VAL_ID]) + j += 1 + + i += j + x = server._config_to_tensor(config) + + # only use data to warm model if it is still defined after passing it through + # the strategy's transforms. + res = strat.transforms.transform(x) + if not (res.isinf().any() or res.isnan().any()): + fetch_count += 1 + strat.pre_warm_model(x, outcome) + + logger.info(f"Strategy {strat.name} was warm started with {fetch_count} rows of data.") + + @classmethod + def get_config_options( + cls, + config: Config, + name: Optional[str] = None, + options: Optional[Dict[str, Any]] = None + ) -> Dict[str, Any]: + """Populate an instance of DataFetcher with search criteria provided in the experiment's config. + + Args: + config (Config): Config to look for options in. + name (str, optional): The name of the strategy to warm start (Not actually optional here.) + options (Dict[str, Any], optional): options are ignored. + + Raises: + ValueError: the name of the strategy is necessary to identify warm start search criteria. + KeyError: the config specified this strategy should be warm started but the associated config section wasn't defined. + + Returns: + Dict[str, Any]: a dictionary of the search criteria described in the experiment's config + """ + if name is None: + raise ValueError("name of strategy must be set to warm start strategy.") + + if not config.has_option(name, "seed_data_conditions"): + return { + "exp_names": None, + "exp_ids": None, + "exp_desc": None, + "par_ids": None, + "ex_data": None + } + + experiment_names = None + experiment_ids = None + experiment_descriptions = None + participant_ids = None + extra_metadata = None + + seed_conds_name = config.get(name, "seed_data_conditions") + if config.has_section(seed_conds_name): + seed_conds_data = config.to_dict()[seed_conds_name].copy() + if "experiment_name" in seed_conds_data: + experiment_names = config.getlist(seed_conds_name, "experiment_name", element_type=str) + seed_conds_data.pop("experiment_name", None) + + if "experiment_id" in seed_conds_data: + experiment_ids = config.getlist(seed_conds_name, "experiment_id", element_type=str) + seed_conds_data.pop("experiment_id", None) + + if "experiment_description" in seed_conds_data: + experiment_descriptions = config.getlist(seed_conds_name, "experiment_description", element_type=str) + seed_conds_data.pop("experiment_description", None) + + if "participant_id" in seed_conds_data: + participant_ids = config.getlist(seed_conds_name, "participant_id", element_type=str) + seed_conds_data.pop("participant_id", None) + + extra_metadata = seed_conds_data if len(seed_conds_data) > 0 else None + else: + raise KeyError(f"config must have section {seed_conds_name} to warm start strategy.") + + return { + "exp_names": experiment_names, + "exp_ids": experiment_ids, + "exp_desc": experiment_descriptions, + "par_ids": participant_ids, + "ex_data": extra_metadata + } + +def _print_in_params(list_to_print: List[Any]) -> str: + # This prints a list of values to be used in a sql in statement + out_string = "(" + for i, val in enumerate(list_to_print): + out_string += f"'{val}'" + if i < len(list_to_print) - 1: + out_string += ", " + + out_string += ")" + return out_string + +def _print_like_params(column_name: str, dict_to_print: Dict[str, str]) -> str: + # This prints a list of values to be in a sequence of sql like statments + # that are combined using a binary or operation. + out_string = "(" + for i, (key, val) in enumerate(dict_to_print.items()): + out_string += f"{column_name} like '%\"{key}\":%'" + if (i < len(dict_to_print) - 1): + out_string += " or " + + out_string += ")" + return out_string + +def _print_warning(fetcher: DataFetcher, query_record: DBMasterTable, format: str): + first_match = True + match_str = "" + if fetcher.experiment_names and query_record.experiment_name in fetcher.experiment_names: + match_str += f"experiment_name = {query_record.experiment_name}" + first_match = False + + if fetcher.experiment_desc and query_record.experiment_description in fetcher.experiment_desc: + if not first_match: + match_str += " and " + + match_str += f"experimend_desc = {query_record.experiment_description}" + first_match = False + + if fetcher.experiment_ids and query_record.experiment_id in fetcher.experiment_ids: + if not first_match: + match_str += " and " + + match_str += f"experimend_id = {query_record.experiment_id}" + first_match = False + + if fetcher.participant_ids and query_record.participant_id in fetcher.participant_ids: + if not first_match: + match_str += " and " + + match_str += f"participant_id = {query_record.participant_id}" + first_match = False + + # assumes that all false positive metadata matches have been filtered out. + if fetcher.extra_metadata and query_record.extra_metadata: + ex_data_str = "" if first_match else " and " + ex_data = json.loads(query_record.extra_metadata) + + first_ex_match = True + for i, (key, val) in enumerate(fetcher.extra_metadata.items()): + if key in ex_data and (val == ex_data[key] or (isinstance(val, Iterable) and ex_data[key] in val)): + if not first_ex_match: + ex_data_str = ex_data_str + ", " + if i == len(fetcher.extra_metadata) - 1: + ex_data_str += " and " + + ex_data_str += f"{key} = {val}" + first_ex_match = False + + if not first_ex_match: + match_str += ex_data_str + + logger.warning(format.format(match_str)) \ No newline at end of file diff --git a/aepsych/database/db.py b/aepsych/database/db.py index a52925f37..624f841ff 100644 --- a/aepsych/database/db.py +++ b/aepsych/database/db.py @@ -128,7 +128,7 @@ def execute_sql_query(self, query: str, vals: Dict[str, str]) -> List[Any]: List[Any]: The results of the query. """ with self.session_scope() as session: - return session.execute(query, vals).fetchall() + return session.execute(query, vals).all() def get_master_records(self) -> List[tables.DBMasterTable]: """Grab the list of master records. @@ -239,14 +239,14 @@ def get_raw_for(self, master_id: int) -> Optional[List[tables.DbRawTable]]: return None - def get_all_params_for(self, master_id: int) -> Optional[List[tables.DbRawTable]]: + def get_all_params_for(self, master_id: int) -> Optional[List[tables.DbParamTable]]: """Get the parameters for all the iterations of a specific experiment. Args: master_id (int): The master id. Returns: - List[tables.DbRawTable] or None: The parameters or None if they don't exist. + List[tables.DbParamTable] or None: The parameters or None if they don't exist. """ warnings.warn( "get_all_params_for is the same as get_param_for since there can only be one instance of any master_id", @@ -266,14 +266,14 @@ def get_all_params_for(self, master_id: int) -> Optional[List[tables.DbRawTable] return None - def get_param_for(self, master_id: int) -> Optional[List[tables.DbRawTable]]: + def get_param_for(self, master_id: int) -> Optional[List[tables.DbParamTable]]: """Get the parameters for a specific iteration of a specific experiment. Args: master_id (int): The master id. Returns: - List[tables.DbRawTable] or None: The parameters or None if they don't exist. + List[tables.DbParamTable] or None: The parameters or None if they don't exist. """ raw_record = self.get_raw_for(master_id) @@ -284,14 +284,14 @@ def get_param_for(self, master_id: int) -> Optional[List[tables.DbRawTable]]: return None - def get_all_outcomes_for(self, master_id: int) -> Optional[List[tables.DbRawTable]]: + def get_all_outcomes_for(self, master_id: int) -> Optional[List[tables.DbOutcomeTable]]: """Get the outcomes for all the iterations of a specific experiment. Args: master_id (int): The master id. Returns: - List[tables.DbRawTable] or None: The outcomes or None if they don't exist. + List[tables.DbOutcomeTable] or None: The outcomes or None if they don't exist. """ warnings.warn( "get_all_outcomes_for is the same as get_outcome_for since there can only be one instance of any master_id", @@ -311,14 +311,14 @@ def get_all_outcomes_for(self, master_id: int) -> Optional[List[tables.DbRawTabl return None - def get_outcome_for(self, master_id: int) -> Optional[List[tables.DbRawTable]]: + def get_outcome_for(self, master_id: int) -> Optional[List[tables.DbOutcomeTable]]: """Get the outcomes for a specific iteration of a specific experiment. Args: master_id (int): The master id. Returns: - List[tables.DbRawTable] or None: The outcomes or None if they don't exist. + List[tables.DbOutcomeTable] or None: The outcomes or None if they don't exist. """ raw_record = self.get_raw_for(master_id) diff --git a/aepsych/server/message_handlers/handle_setup.py b/aepsych/server/message_handlers/handle_setup.py index 312a78b6a..dd4ebebe5 100644 --- a/aepsych/server/message_handlers/handle_setup.py +++ b/aepsych/server/message_handlers/handle_setup.py @@ -11,6 +11,7 @@ from aepsych.config import Config from aepsych.strategy import SequentialStrategy from aepsych.version import __version__ +from aepsych.database.data_fetcher import DataFetcher logger = utils_logging.getLogger(logging.INFO) @@ -33,6 +34,10 @@ def _configure(server, config): server.strat = SequentialStrategy.from_config(config) server.strat_id = server.n_strats - 1 # 0-index strats + for strat in server.strat.strat_list: + fetcher = DataFetcher.from_config(config, strat.name) + fetcher.warm_start_strat(server, strat) + return server.strat_id @@ -88,13 +93,11 @@ def handle_setup(server, request): par_id = tempconfig["metadata"].get("participant_id", fallback=None) # This may be populated when replaying - if server._db_master_record is not None: + exp_id = tempconfig["metadata"].get("experiment_id", fallback=None) + if exp_id is None and server._db_master_record is not None: exp_id = server._db_master_record.experiment_id - else: - exp_id = tempconfig["metadata"].get("experiment_id", fallback=None) extra_metadata = tempconfig.jsonifyMetadata(only_extra=True) - server._db_master_record = server.db.record_setup( description=exp_desc, name=exp_name, diff --git a/aepsych/server/server.py b/aepsych/server/server.py index 2e67ab07f..8211f11e6 100644 --- a/aepsych/server/server.py +++ b/aepsych/server/server.py @@ -61,7 +61,7 @@ def __init__(self, socket=None, database_path=None): self.exit_server_loop = False self._db_master_record = None self._db_raw_record = None - self.db = db.Database(database_path) + self.db: db.Database = db.Database(database_path) self.skip_computations = False self.strat_names = None diff --git a/aepsych/strategy.py b/aepsych/strategy.py index 4a7c2ab39..f19599f80 100644 --- a/aepsych/strategy.py +++ b/aepsych/strategy.py @@ -542,6 +542,21 @@ def n_trials(self) -> int: ) return self.min_asks + def pre_warm_model(self, x: torch.Tensor, y: torch.Tensor) -> None: + """ + Adds new data points to the strategy, and normalizes the inputs. + We speceifically disregard the n return value of normalize_inputs here in order + to stop warm start data from affecting the trials run length. + + Args: + x torch.Tensor: The input data points. + y torch.Tensor: The output data points. + + """ + # warming the model shouldn't affect strategy.n + self.x, self.y, n = self.normalize_inputs(x, y) + self._model_is_fresh = False + def add_data( self, x: Union[np.ndarray, torch.Tensor], y: Union[np.ndarray, torch.Tensor] ) -> None: diff --git a/aepsych/utils.py b/aepsych/utils.py index c1474e711..f09fc5332 100644 --- a/aepsych/utils.py +++ b/aepsych/utils.py @@ -431,3 +431,9 @@ def get_dims(config: Config) -> int: except KeyError: # Likely old style of parameter definition, fallback to looking at a bound return len(config.getlist("common", "lb", element_type=float)) + +def generate_default_outcome_names(count: int) -> List[str]: + if count == 1: + return ["outcome"] + + return ["outcome_" + i for i in range(count)] diff --git a/tests/server/test_server.py b/tests/server/test_server.py index a62de9771..52d9b50a7 100644 --- a/tests/server/test_server.py +++ b/tests/server/test_server.py @@ -57,13 +57,18 @@ class BaseServerTestCase(unittest.TestCase): + #so that this can be overridden for tests that require specific databases. + @property + def database_path(self): + return "./{}_test_server.db".format(str(uuid.uuid4().hex)) + def setUp(self): # setup logger server.logger = utils_logging.getLogger(logging.DEBUG, "logs") # random port socket = server.sockets.PySocket(port=0) # random datebase path name without dashes - database_path = "./{}_test_server.db".format(str(uuid.uuid4().hex)) + database_path = self.database_path self.s = server.AEPsychServer(socket=socket, database_path=database_path) self.db_name = database_path.split("/")[1] self.db_path = database_path @@ -88,7 +93,7 @@ def dummy_create_setup(self, server, request=None): ) -class ServerTestCase(BaseServerTestCase): +class ServerTestCase(BaseServerTestCase): def test_final_strat_serialization(self): setup_request = { "type": "setup", diff --git a/tests/test_databases/1000_outcome.db b/tests/test_databases/1000_outcome.db new file mode 100644 index 0000000000000000000000000000000000000000..c538d788efba83521af9dd037ee3add446bfdfa6 GIT binary patch literal 12734464 zcmeFa349yH6+f=S=gPav9g^6IasUn_LiZ6!LT5f;imMUKugO}pf}})QcAf>FUo!OriIe$|9vys)k?P9G!Fd!zuzZ@JiVEDJI8x( zX5PG=omqYI^7eGp)|OzMk+jWi7-29P4aeJT27_yX!BBI!!C?83!C)}rUnTyP;h*|P zasTBHdDu5zB7>^3>NSJ%7x_mu&slyhy;=Pxf`|SNao`XK4sqZR2M%%I5C;x%;4|UC zR%2!5_`?r3Zd;O$G{>WfOu8k}84b5a(h)sgwJ5Y?!I~wuH47FlUt-gz*y^R~Ojmm{ z6Aibw+D>d-vt-$lkgaj$8e8Kj%a_lxN!2QKSEMs)J9R;5(FqGe^9&R?r`zhaQh82GSB93HxU7+q>h+J$w1t)| 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