Source code for ax.core.experiment

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import logging
from collections import OrderedDict, defaultdict
from datetime import datetime
from functools import reduce
from typing import Any, Dict, List, Optional, Tuple, Type

import pandas as pd
from ax.core.arm import Arm
from ax.core.base import Base
from ax.core.base_trial import BaseTrial, TrialStatus
from ax.core.batch_trial import BatchTrial
from ax.core.data import Data
from ax.core.generator_run import GeneratorRun
from ax.core.metric import Metric
from ax.core.optimization_config import OptimizationConfig
from ax.core.parameter import Parameter
from ax.core.runner import Runner
from ax.core.search_space import SearchSpace
from ax.core.trial import Trial
from ax.utils.common.docutils import copy_doc
from ax.utils.common.logger import get_logger
from ax.utils.common.timeutils import current_timestamp_in_millis


logger: logging.Logger = get_logger(__name__)


# pyre-fixme[13]: Attribute `_search_space` is never initialized.
[docs]class Experiment(Base): """Base class for defining an experiment.""" def __init__( self, search_space: SearchSpace, name: Optional[str] = None, optimization_config: Optional[OptimizationConfig] = None, tracking_metrics: Optional[List[Metric]] = None, runner: Optional[Runner] = None, status_quo: Optional[Arm] = None, description: Optional[str] = None, is_test: bool = False, experiment_type: Optional[str] = None, ) -> None: """Inits Experiment. Args: search_space: Search space of the experiment. name: Name of the experiment. optimization_config: Optimization config of the experiment. tracking_metrics: Additional tracking metrics not used for optimization. runner: Default runner used for trials on this experiment. status_quo: Arm representing existing "control" arm. description: Description of the experiment. is_test: Convenience metadata tracker for the user to mark test experiments. experiment_type: The class of experiments this one belongs to. """ # appease pyre self._search_space: SearchSpace self._status_quo: Optional[Arm] = None self._name = name self.description = description self.runner = runner self.is_test = is_test self._data_by_trial: Dict[int, OrderedDict[int, Data]] = {} self._experiment_type: Optional[str] = experiment_type self._optimization_config = None self._tracking_metrics: Dict[str, Metric] = {} self._time_created: datetime = datetime.now() self._trials: Dict[int, BaseTrial] = {} self._arms_by_signature: Dict[str, Arm] = {} self.add_tracking_metrics(tracking_metrics or []) # call setters defined below self.search_space = search_space self.status_quo = status_quo if optimization_config is not None: self.optimization_config = optimization_config @property def has_name(self) -> bool: """Return true if experiment's name is not None.""" return self._name is not None @property def name(self) -> str: """Get experiment name. Throws if name is None.""" if self._name is None: raise ValueError("Experiment's name is None.") # pyre-fixme[7]: Expected `str` but got `Optional[str]`. return self._name @name.setter def name(self, name: str) -> None: """Set experiment name.""" self._name = name @property def is_simple_experiment(self): """Whether this experiment is a regular Experiment or the subclassing `SimpleExperiment`.""" return False @property def time_created(self) -> datetime: """Creation time of the experiment.""" return self._time_created @property def experiment_type(self) -> Optional[str]: """The type of the experiment.""" return self._experiment_type @experiment_type.setter def experiment_type(self, experiment_type: Optional[str]) -> None: """Set the type of the experiment.""" self._experiment_type = experiment_type @property def search_space(self) -> SearchSpace: """The search space for this experiment. When setting a new search space, all parameter names and types must be preserved. However, if no trials have been created, all modifications are allowed. """ # TODO maybe return a copy here to guard against implicit changes return self._search_space @search_space.setter def search_space(self, search_space: SearchSpace) -> None: # Allow all modifications when no trials present. if len(self.trials) > 0: if len(search_space.parameters) < len(self._search_space.parameters): raise ValueError( "New search_space must contain all parameters in the existing." ) for param_name, parameter in search_space.parameters.items(): if param_name not in self._search_space.parameters: raise ValueError( f"Cannot add new parameter `{param_name}` because " "it is not defined in the existing search space." ) elif ( parameter.parameter_type != self._search_space.parameters[param_name].parameter_type ): raise ValueError( f"Expected parameter `{param_name}` to be of type " f"{self._search_space.parameters[param_name].parameter_type}, " f"got {parameter.parameter_type}." ) self._search_space = search_space @property def status_quo(self) -> Optional[Arm]: """The existing arm that new arms will be compared against.""" return self._status_quo @status_quo.setter def status_quo(self, status_quo: Optional[Arm]) -> None: if status_quo is not None: self.search_space.check_types(status_quo.parameters, raise_error=True) # Compute a unique name if "status_quo" is taken name = "status_quo" sq_idx = 0 arms_by_name = self.arms_by_name while name in arms_by_name: name = f"status_quo_e{sq_idx}" sq_idx += 1 self._name_and_store_arm_if_not_exists(arm=status_quo, proposed_name=name) # If old status_quo not present in any trials, # remove from _arms_by_signature if self._status_quo is not None: persist_old_sq = False for trial in self._trials.values(): # pyre-fixme[16]: `Optional` has no attribute `name`. if self._status_quo.name in trial.arms_by_name: persist_old_sq = True break if not persist_old_sq: # pyre-fixme[16]: `Optional` has no attribute `signature`. self._arms_by_signature.pop(self._status_quo.signature) self._status_quo = status_quo @property def parameters(self) -> Dict[str, Parameter]: """The parameters in the experiment's search space.""" return self.search_space.parameters @property def arms_by_name(self) -> Dict[str, Arm]: """The arms belonging to this experiment, by their name.""" return {arm.name: arm for arm in self._arms_by_signature.values()} @property def arms_by_signature(self) -> Dict[str, Arm]: """The arms belonging to this experiment, by their signature.""" return self._arms_by_signature @property def sum_trial_sizes(self) -> int: """Sum of numbers of arms attached to each trial in this experiment.""" return reduce(lambda a, b: a + len(b.arms_by_name), self._trials.values(), 0) @property def num_abandoned_arms(self) -> int: """How many arms attached to this experiment are abandoned.""" abandoned = set() for trial in self.trials.values(): for x in trial.abandoned_arms: abandoned.add(x) return len(abandoned) @property def optimization_config(self) -> Optional[OptimizationConfig]: """The experiment's optimization config.""" return self._optimization_config @optimization_config.setter def optimization_config(self, optimization_config: OptimizationConfig) -> None: for metric_name in optimization_config.metrics.keys(): if metric_name in self._tracking_metrics: self.remove_tracking_metric(metric_name) self._optimization_config = optimization_config @property def data_by_trial(self) -> Dict[int, OrderedDict]: """Data stored on the experiment, indexed by trial index and storage time. First key is trial index and second key is storage time in milliseconds. For a given trial, data is ordered by storage time, so first added data will appear first in the list. """ return self._data_by_trial
[docs] def add_tracking_metric(self, metric: Metric) -> "Experiment": """Add a new metric to the experiment. Args: metric: Metric to be added. """ if metric.name in self._tracking_metrics: raise ValueError( f"Metric `{metric.name}` already defined on experiment. " "Use `update_tracking_metric` to update an existing metric definition." ) if self.optimization_config and metric.name in self.optimization_config.metrics: raise ValueError( f"Metric `{metric.name}` already present in experiment's " "OptimizationConfig. Set a new OptimizationConfig without this metric " "before adding it to tracking metrics." ) self._tracking_metrics[metric.name] = metric return self
[docs] def add_tracking_metrics(self, metrics: List[Metric]) -> "Experiment": """Add a list of new metrics to the experiment. If any of the metrics are already defined on the experiment, we raise an error and don't add any of them to the experiment Args: metrics: Metrics to be added. """ # Before setting any metrics, we validate none are already on # the experiment for metric in metrics: if metric.name in self._tracking_metrics: raise ValueError( f"Metric `{metric.name}` already defined on experiment. " "Use `update_tracking_metric` to update an existing metric" " definition." ) if ( self.optimization_config and metric.name in self.optimization_config.metrics ): raise ValueError( f"Metric `{metric.name}` already present in experiment's " "OptimizationConfig. Set a new OptimizationConfig without" " this metric before adding it to tracking metrics." ) for metric in metrics: self._tracking_metrics[metric.name] = metric return self
[docs] def update_tracking_metric(self, metric: Metric) -> "Experiment": """Redefine a metric that already exists on the experiment. Args: metric: New metric definition. """ if metric.name not in self._tracking_metrics: raise ValueError(f"Metric `{metric.name}` doesn't exist on experiment.") self._tracking_metrics[metric.name] = metric return self
[docs] def remove_tracking_metric(self, metric_name: str) -> "Experiment": """Remove a metric that already exists on the experiment. Args: metric_name: Unique name of metric to remove. """ if metric_name not in self._tracking_metrics: raise ValueError(f"Metric `{metric_name}` doesn't exist on experiment.") del self._tracking_metrics[metric_name] return self
@property def metrics(self) -> Dict[str, Metric]: """The metrics attached to the experiment.""" optimization_config_metrics: Dict[str, Metric] = {} if self.optimization_config is not None: optimization_config_metrics = self.optimization_config.metrics return {**self._tracking_metrics, **optimization_config_metrics} def _metrics_by_class( self, metrics: Optional[List[Metric]] = None ) -> Dict[Type[Metric], List[Metric]]: metrics_by_class: Dict[Type[Metric], List[Metric]] = defaultdict(list) for metric in metrics or list(self.metrics.values()): metrics_by_class[metric.__class__].append(metric) return metrics_by_class
[docs] def fetch_data(self, metrics: Optional[List[Metric]] = None, **kwargs: Any) -> Data: """Fetches data for all metrics and trials on this experiment. Args: metrics: If provided, fetch data for these metrics instead of the ones defined on the experiment. kwargs: keyword args to pass to underlying metrics' fetch data functions. Returns: Data for the experiment. """ if not self.metrics and not metrics: raise ValueError( "No metrics to fetch data for, as no metrics are defined for " "this experiment, and none were passed in to `fetch_data`." ) try: data_list = [ metric_cls.fetch_experiment_data_multi(self, metric_list, **kwargs) for metric_cls, metric_list in self._metrics_by_class( metrics=metrics ).items() ] # For trials in candidate phase, append any attached data for trial in self.trials.values(): if trial.status == TrialStatus.CANDIDATE: trial_data, _ = self.lookup_data_for_trial(trial_index=trial.index) if not trial_data.df.empty: data_list.append(trial_data) return Data.from_multiple_data(data_list) except NotImplementedError: # If some of the metrics do not implement data fetching, we should # fall back to data that has been attached. return Data.from_multiple_data( [self.lookup_data_for_trial(trial_index=idx)[0] for idx in self.trials] )
@copy_doc(BaseTrial.fetch_data) def _fetch_trial_data( self, trial_index: int, metrics: Optional[List[Metric]] = None, **kwargs: Any ) -> Data: if not self.metrics and not metrics: raise ValueError( "No metrics to fetch data for, as no metrics are defined for " "this experiment, and none were passed in to `fetch_trial_data`." ) trial = self.trials[trial_index] if trial.status == TrialStatus.CANDIDATE: return self.lookup_data_for_trial(trial_index=trial_index)[0] elif not trial.status.expecting_data: return Data() try: return self._fetch_trial_data_no_lookup( trial_index=trial_index, metrics=metrics, **kwargs ) except NotImplementedError: # If some of the metrics do not implement data fetching, we should # fall back to data that has been attached. return self.lookup_data_for_trial(trial_index=trial_index)[0] def _fetch_trial_data_no_lookup( self, trial_index: int, metrics: Optional[List[Metric]], **kwargs: Any ) -> Data: """Fetches data explicitly from metric logic, does not look up attached data on experiment. """ return Data.from_multiple_data( [ metric_cls.fetch_trial_data_multi( self.trials[trial_index], metric_list, **kwargs ) for metric_cls, metric_list in self._metrics_by_class( metrics=metrics ).items() ] )
[docs] def attach_data(self, data: Data, combine_with_last_data: bool = False) -> int: """Attach data to experiment. Stores data in `experiment._data_by_trial`, to be looked up via `experiment.lookup_data_by_trial`. Args: data: Data object to store. combine_with_last_data: By default, when attaching data, it's identified by its timestamp, and `experiment.lookup_data_by_trial` returns data by most recent timestamp. In some cases, however, the goal is to combine all data attached for a trial into a single `Data` object. To achieve that goal, every call to `attach_data` after the initial data is attached to trials, should be set to `True`. Then, the newly attached data will be appended to existing data, rather than stored as a separate object, and `lookup_data_by_trial` will return the combined data object, rather than just the most recently added data. This will validate that the newly added data does not contain observations for the metrics that already have observations in the most recent data stored. Returns: Timestamp of storage in millis. """ cur_time_millis = current_timestamp_in_millis() for trial_index, trial_df in data.df.groupby(data.df["trial_index"]): current_trial_data = ( self._data_by_trial[trial_index] if trial_index in self._data_by_trial else OrderedDict() ) if combine_with_last_data and len(current_trial_data) > 0: last_ts, last_data = list(current_trial_data.items())[-1] merged = pd.merge( last_data.df, trial_df, on=["trial_index", "metric_name", "arm_name"], how="inner", ) if not merged.empty: raise ValueError( f"Last data for trial {trial_index} already contained an " f"observation for metric {merged.head()['metric_name']}." ) current_trial_data[cur_time_millis] = Data.from_multiple_data( [last_data, Data(trial_df)] ) else: current_trial_data[cur_time_millis] = Data(trial_df) self._data_by_trial[trial_index] = current_trial_data return cur_time_millis
[docs] def lookup_data_for_ts(self, timestamp: int) -> Data: """Collect data for all trials stored at this timestamp. Useful when many trials' data was fetched and stored simultaneously and user wants to retrieve same collection of data later. Can also be used to lookup specific data for a single trial when storage time is known. Args: timestamp: Timestamp in millis at which data was stored. Returns: Data object with all data stored at the timestamp. """ trial_datas = [] for _trial_index, ts_to_data in self._data_by_trial.items(): if timestamp in ts_to_data: trial_datas.append(ts_to_data[timestamp]) return Data.from_multiple_data(trial_datas)
[docs] def lookup_data_for_trial(self, trial_index: int) -> Tuple[Data, int]: """Lookup stored data for a specific trial. Returns latest data object, and its storage timestamp, present for this trial. Returns empty data and -1 if no data present. Args: trial_index: The index of the trial to lookup data for. Returns: The requested data object, and its storage timestamp in milliseconds. """ if trial_index not in self._data_by_trial: return (Data(), -1) trial_data_list = list(self._data_by_trial[trial_index].values()) storage_time_list = list(self._data_by_trial[trial_index].keys()) return ( (trial_data_list[-1], storage_time_list[-1]) if len(trial_data_list) > 0 else (Data(), -1) )
@property def num_trials(self) -> int: """How many trials are associated with this experiment.""" return len(self._trials) @property def trials(self) -> Dict[int, BaseTrial]: """The trials associated with the experiment.""" return self._trials @property def trials_by_status(self) -> Dict[TrialStatus, List[BaseTrial]]: """The trials associated with the experiment grouped by trial status.""" output = defaultdict(list) for trial in self.trials.values(): output[trial.status].append(trial) return dict(output)
[docs] def new_trial( self, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, ) -> Trial: """Create a new trial associated with this experiment.""" return Trial( experiment=self, trial_type=trial_type, generator_run=generator_run )
[docs] def new_batch_trial( self, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, optimize_for_power: Optional[bool] = False, ) -> BatchTrial: """Create a new batch trial associated with this experiment.""" return BatchTrial( experiment=self, trial_type=trial_type, generator_run=generator_run, optimize_for_power=optimize_for_power, )
def _attach_trial(self, trial: BaseTrial) -> int: """Attach a trial to this experiment. Should only be called within the trial constructor. Args: trial: The trial to be attached. Returns: The index of the trial within the experiment's trial list. """ if trial.experiment is not self: raise ValueError("BatchTrial does not belong to this experiment.") for existing_trial in self._trials.values(): if existing_trial is trial: raise ValueError("BatchTrial already attached to experiment.") index = 0 if len(self._trials) == 0 else max(self._trials.keys()) + 1 self._trials[index] = trial return index def _name_and_store_arm_if_not_exists(self, arm: Arm, proposed_name: str) -> None: """Tries to lookup arm with same signature, otherwise names and stores it. - Looks up if arm already exists on experiment - If so, name the input arm the same as the existing arm - else name the arm with given name and store in _arms_by_signature Args: arm: The arm object to name. proposed_name: The name to assign if it doesn't have one already. """ # If arm is identical to an existing arm, return that # so that the names match. if arm.signature in self.arms_by_signature: existing_arm = self.arms_by_signature[arm.signature] if arm.has_name: if arm.name != existing_arm.name: raise ValueError( f"Arm already exists with name {existing_arm.name}, " f"which doesn't match given arm name of {arm.name}." ) else: arm.name = existing_arm.name else: if not arm.has_name: arm.name = proposed_name self._arms_by_signature[arm.signature] = arm
[docs] def reset_runners(self, runner: Runner) -> None: """Replace all candidate trials runners. Args: runner: New runner to replace with. """ for trial in self._trials.values(): if trial.status == TrialStatus.CANDIDATE: trial.runner = runner self.runner = runner
def __repr__(self) -> str: return self.__class__.__name__ + f"({self._name})" # --- MultiTypeExperiment convenience functions --- # # Certain functionalities have special behavior for multi-type experiments. # This defines the base behavior for regular experiments that will be # overridden in the MultiTypeExperiment class. @property def default_trial_type(self) -> Optional[str]: """Default trial type assigned to trials in this experiment. In the base experiment class this is always None. For experiments with multiple trial types, use the MultiTypeExperiment class. """ return None
[docs] def runner_for_trial(self, trial: BaseTrial) -> Optional[Runner]: """The default runner to use for a given trial. In the base experiment class, this is always the default experiment runner. For experiments with multiple trial types, use the MultiTypeExperiment class. """ return self.runner
[docs] def supports_trial_type(self, trial_type: Optional[str]) -> bool: """Whether this experiment allows trials of the given type. The base experiment class only supports None. For experiments with multiple trial types, use the MultiTypeExperiment class. """ return trial_type is None
@property def trials_expecting_data(self) -> List[BaseTrial]: """List[BaseTrial]: the list of all trials for which data has arrived or is expected to arrive. """ return [trial for trial in self.trials.values() if trial.status.expecting_data]