Source code for ax.core.observation

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

# pyre-strict

from __future__ import annotations

import json
import warnings
from collections.abc import Iterable
from copy import deepcopy
from logging import Logger

import ax.core.experiment as experiment
import numpy as np
import numpy.typing as npt
import pandas as pd
from ax.core.arm import Arm
from ax.core.base_trial import NON_ABANDONED_STATUSES, TrialStatus
from ax.core.batch_trial import BatchTrial
from ax.core.data import Data
from ax.core.map_data import MapData
from ax.core.map_metric import MapMetric
from ax.core.types import TCandidateMetadata, TParameterization
from ax.utils.common.base import Base
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
from pyre_extensions import none_throws

logger: Logger = get_logger(__name__)

TIME_COLS = {"start_time", "end_time"}

OBS_COLS: set[str] = {
    "arm_name",
    "trial_index",
    "random_split",
    "fidelities",
    *TIME_COLS,
}

OBS_KWARGS: set[str] = {"trial_index", "random_split", *TIME_COLS}


[docs] class ObservationFeatures(Base): """The features of an observation. These include both the arm parameters and the features of the observation found in the Data object: trial index, times, and random split. This object is meant to contain everything needed to represent this observation in a model feature space. It is essentially a row of Data joined with the arm parameters. An ObservationFeatures object would typically have a corresponding ObservationData object that provides the observed outcomes. Attributes: parameters: arm parameters trial_index: trial index start_time: batch start time end_time: batch end time random_split: random split """ def __init__( self, parameters: TParameterization, trial_index: int | None = None, start_time: pd.Timestamp | None = None, end_time: pd.Timestamp | None = None, random_split: int | None = None, metadata: TCandidateMetadata = None, ) -> None: self.parameters = parameters self.trial_index = trial_index self.start_time = start_time self.end_time = end_time self.random_split = random_split self.metadata = metadata
[docs] @staticmethod def from_arm( arm: Arm, trial_index: int | None = None, start_time: pd.Timestamp | None = None, end_time: pd.Timestamp | None = None, random_split: int | None = None, metadata: TCandidateMetadata = None, ) -> ObservationFeatures: """Convert a Arm to an ObservationFeatures, including additional data as specified. """ return ObservationFeatures( # NOTE: Arm.parameters makes a copy of the original dict, so any # modifications to the parameters dict will not be reflected in # the original Arm parameters. parameters=arm.parameters, trial_index=trial_index, start_time=start_time, end_time=end_time, random_split=random_split, metadata=metadata, )
[docs] def update_features(self, new_features: ObservationFeatures) -> ObservationFeatures: """Updates the existing ObservationFeatures with the fields of the the input. Adds all of the new parameters to the existing parameters and overwrites any other fields that are not None on the new input features.""" self.parameters.update(new_features.parameters) if new_features.trial_index is not None: self.trial_index = new_features.trial_index if new_features.start_time is not None: self.start_time = new_features.start_time if new_features.end_time is not None: self.end_time = new_features.end_time if new_features.random_split is not None: self.random_split = new_features.random_split return self
[docs] def clone( self, replace_parameters: TParameterization | None = None ) -> ObservationFeatures: """Make a copy of these ``ObservationFeatures``. Args: replace_parameters: An optimal parameterization, to which to set the parameters of the cloned ``ObservationFeatures``. Useful when transforming observation features in a way that requires a change to parameterization –– for example, while casting it to a hierarchical search space. """ parameters = ( self.parameters if replace_parameters is None else replace_parameters ) return ObservationFeatures( parameters=parameters.copy(), trial_index=self.trial_index, start_time=self.start_time, end_time=self.end_time, random_split=self.random_split, metadata=deepcopy(self.metadata), )
def __repr__(self) -> str: strs = [] for attr in ["trial_index", "start_time", "end_time", "random_split"]: if getattr(self, attr) is not None: strs.append(f", {attr}={getattr(self, attr)}") repr_str = "ObservationFeatures(parameters={parameters}".format( parameters=self.parameters ) repr_str += "".join(strs) + ")" return repr_str def __hash__(self) -> int: parameters = self.parameters.copy() for k, v in parameters.items(): if type(v) is np.int64: parameters[k] = int(v) elif type(v) is np.float32: parameters[k] = float(v) return hash( ( json.dumps(parameters, sort_keys=True), self.trial_index, self.start_time, self.end_time, self.random_split, ) )
[docs] class ObservationData(Base): """Outcomes observed at a point. The "point" corresponding to this ObservationData would be an ObservationFeatures object. Attributes: metric_names: A list of k metric names that were observed means: a k-array of observed means covariance: a (k x k) array of observed covariances """ def __init__( self, metric_names: list[str], means: npt.NDArray, covariance: npt.NDArray, ) -> None: k = len(metric_names) if means.shape != (k,): raise ValueError(f"Shape of means should be {(k,)}, is {(means.shape)}.") if covariance.shape != (k, k): raise ValueError( "Shape of covariance should be {}, is {}.".format( (k, k), (covariance.shape) ) ) self.metric_names = metric_names self.means = means self.covariance = covariance @property def means_dict(self) -> dict[str, float]: """Extract means from this observation data as mapping from metric name to mean. """ return dict(zip(self.metric_names, self.means)) @property def covariance_matrix(self) -> dict[str, dict[str, float]]: """Extract covariance matric from this observation data as mapping from metric name (m1) to mapping of another metric name (m2) to the covariance of the two metrics (m1 and m2). """ return { m1: { m2: float(self.covariance[idx1][idx2]) for idx2, m2 in enumerate(self.metric_names) } for idx1, m1 in enumerate(self.metric_names) } def __repr__(self) -> str: return "ObservationData(metric_names={mn}, means={m}, covariance={c})".format( mn=self.metric_names, m=self.means, c=self.covariance )
[docs] class Observation(Base): """Represents an observation. A set of features (ObservationFeatures) and corresponding measurements (ObservationData). Optionally, an arm name associated with the features. Attributes: features (ObservationFeatures) data (ObservationData) arm_name (Optional[str]) """ def __init__( self, features: ObservationFeatures, data: ObservationData, arm_name: str | None = None, ) -> None: self.features = features self.data = data self.arm_name = arm_name
def _observations_from_dataframe( experiment: experiment.Experiment, df: pd.DataFrame, cols: list[str], arm_name_only: bool, map_keys: Iterable[str], statuses_to_include: set[TrialStatus], statuses_to_include_map_metric: set[TrialStatus], map_keys_as_parameters: bool = False, ) -> list[Observation]: """Helper method for extracting observations grouped by `cols` from `df`. Args: experiment: Experiment with arm parameters. df: DataFrame derived from experiment Data. cols: columns used to group data into different observations. arm_name_only: whether arm_name is the only column in `cols`. map_keys: columns that map dict-like Data e.g. `timestamp` in timeseries data, `epoch` in ML training traces. statuses_to_include: data from non-MapMetrics will only be included for trials with statuses in this set. statuses_to_include_map_metric: data from MapMetrics will only be included for trials with statuses in this set. map_keys_as_parameters: Whether map_keys should be returned as part of the parameters of the Observation objects. Returns: List of Observation objects. """ if len(cols) == 0: return [] observations = [] abandoned_arms_dict = {} for g, d in df.groupby(by=cols if len(cols) > 1 else cols[0]): obs_kwargs = {} if arm_name_only: features = {"arm_name": g} arm_name = g trial_index = None else: features = dict(zip(cols, g)) arm_name = features["arm_name"] trial_index = features.get("trial_index", None) is_arm_abandoned = False trial_status = None if trial_index is not None: trial = experiment.trials[trial_index] trial_status = trial.status metadata = trial._get_candidate_metadata(arm_name) or {} if Keys.TRIAL_COMPLETION_TIMESTAMP not in metadata: if trial._time_completed is not None: metadata[Keys.TRIAL_COMPLETION_TIMESTAMP] = none_throws( trial._time_completed ).timestamp() obs_kwargs[Keys.METADATA] = metadata # Determine if this arm is abandoned. is_arm_abandoned = trial.is_abandoned if isinstance(trial, BatchTrial): if trial.index not in abandoned_arms_dict: # Same abandoned arm names to dict to avoid recomputing them # on creation of every observation. abandoned_arms_dict[trial.index] = trial.abandoned_arm_names if arm_name in abandoned_arms_dict[trial.index]: is_arm_abandoned = True obs_parameters = experiment.arms_by_name[arm_name].parameters.copy() if obs_parameters: obs_kwargs["parameters"] = obs_parameters for f, val in features.items(): if f in OBS_KWARGS: obs_kwargs[f] = val # add start and end time of trial if the start and end time # is the same for all metrics and arms for col in TIME_COLS: if col in d.columns: times = d[col] if times.nunique() == 1 and not times.isnull().any(): obs_kwargs[col] = times.iloc[0] fidelities = features.get("fidelities") if fidelities is not None: obs_parameters.update(json.loads(fidelities)) for map_key in map_keys: if map_key in obs_parameters or map_keys_as_parameters: obs_parameters[map_key] = features[map_key] else: obs_kwargs[Keys.METADATA][map_key] = features[map_key] d = _filter_data_on_status( df=d, experiment=experiment, trial_status=trial_status, is_arm_abandoned=is_arm_abandoned, statuses_to_include=statuses_to_include, statuses_to_include_map_metric=statuses_to_include_map_metric, ) if len(d) == 0: continue observations.append( Observation( features=ObservationFeatures(**obs_kwargs), data=ObservationData( metric_names=d["metric_name"].tolist(), means=d["mean"].values, covariance=np.diag(d["sem"].values ** 2), ), arm_name=arm_name, ) ) return observations def _filter_data_on_status( df: pd.DataFrame, experiment: experiment.Experiment, trial_status: TrialStatus | None, # Arms on a BatchTrial can be abandoned even if the BatchTrial is not. # Data will be filtered out if is_arm_abandoned is True and the corresponding # statuses_to_include does not contain TrialStatus.ABANDONED. is_arm_abandoned: bool, statuses_to_include: set[TrialStatus], statuses_to_include_map_metric: set[TrialStatus], ) -> pd.DataFrame: if "metric_name" not in df.columns: raise ValueError(f"`metric_name` column is missing from {df!r}.") dfs = [] for g, d in df.groupby(by="metric_name"): metric_name = g if metric_name not in experiment.metrics: # Observations can only be made for metrics attached to the experiment. logger.exception( f"Data contains metric {metric_name} that has not been added to the " "experiment. You can either update the `optimization_config` or attach " "it as a tracking metric using `Experiment.add_tracking_metrics` " "or `AxClient.add_tracking_metrics`. Ignoring all data for " f"metric {metric_name}." ) continue metric = experiment.metrics[metric_name] statuses_to_include_metric = ( statuses_to_include_map_metric if isinstance(metric, MapMetric) else statuses_to_include ) if trial_status is not None and trial_status not in statuses_to_include_metric: continue if is_arm_abandoned and TrialStatus.ABANDONED not in statuses_to_include_metric: continue dfs.append(d) if len(dfs) == 0: return pd.DataFrame() df = pd.concat(dfs) return df
[docs] def get_feature_cols(data: Data, is_map_data: bool = False) -> list[str]: feature_cols = OBS_COLS.intersection(data.df.columns) # note we use this check, rather than isinstance, since # only some Modelbridges (e.g. MapTorchModelBridge) # use observations_from_map_data, which is required # to properly handle MapData features (e.g. fidelity). if is_map_data: data = checked_cast(MapData, data) feature_cols = feature_cols.union(data.map_keys) for column in TIME_COLS: if column in feature_cols and len(data.df[column].unique()) > 1: warnings.warn( f"`{column} is not consistent and being discarded from " "observation data", stacklevel=5, ) feature_cols.discard(column) return list(feature_cols)
[docs] def observations_from_data( experiment: experiment.Experiment, data: Data, statuses_to_include: set[TrialStatus] | None = None, statuses_to_include_map_metric: set[TrialStatus] | None = None, ) -> list[Observation]: """Convert Data to observations. Converts a Data object to a list of Observation objects. Pulls arm parameters from from experiment. Overrides fidelity parameters in the arm with those found in the Data object. Uses a diagonal covariance matrix across metric_names. Args: experiment: Experiment with arm parameters. data: Data of observations. statuses_to_include: data from non-MapMetrics will only be included for trials with statuses in this set. Defaults to all statuses except abandoned. statuses_to_include_map_metric: data from MapMetrics will only be included for trials with statuses in this set. Defaults to completed status only. Returns: List of Observation objects. """ if statuses_to_include is None: statuses_to_include = NON_ABANDONED_STATUSES if statuses_to_include_map_metric is None: statuses_to_include_map_metric = {TrialStatus.COMPLETED} feature_cols = get_feature_cols(data) observations = [] arm_name_only = len(feature_cols) == 1 # there will always be an arm name # One DataFrame where all rows have all features. isnull = data.df[feature_cols].isnull() isnull_any = isnull.any(axis=1) incomplete_df_cols = isnull[isnull_any].any() # Get the incomplete_df columns that are complete, and usable as groupby keys. complete_feature_cols = list( OBS_COLS.intersection(incomplete_df_cols.index[~incomplete_df_cols]) ) if set(feature_cols) == set(complete_feature_cols): complete_df = data.df incomplete_df = None else: # The groupby and filter is expensive, so do it only if we have to. grouped = data.df.groupby(by=complete_feature_cols) complete_df = grouped.filter(lambda r: ~r[feature_cols].isnull().any().any()) incomplete_df = grouped.filter(lambda r: r[feature_cols].isnull().any().any()) # Get Observations from complete_df observations.extend( _observations_from_dataframe( experiment=experiment, df=complete_df, cols=feature_cols, arm_name_only=arm_name_only, statuses_to_include=statuses_to_include, statuses_to_include_map_metric=statuses_to_include_map_metric, map_keys=[], ) ) if incomplete_df is not None: # Get Observations from incomplete_df observations.extend( _observations_from_dataframe( experiment=experiment, df=incomplete_df, cols=complete_feature_cols, arm_name_only=arm_name_only, statuses_to_include=statuses_to_include, statuses_to_include_map_metric=statuses_to_include_map_metric, map_keys=[], ) ) return observations
[docs] def observations_from_map_data( experiment: experiment.Experiment, map_data: MapData, statuses_to_include: set[TrialStatus] | None = None, statuses_to_include_map_metric: set[TrialStatus] | None = None, map_keys_as_parameters: bool = False, limit_rows_per_metric: int | None = None, limit_rows_per_group: int | None = None, ) -> list[Observation]: """Convert MapData to observations. Converts a MapData object to a list of Observation objects. Pulls arm parameters from experiment. Overrides fidelity parameters in the arm with those found in the Data object. Uses a diagonal covariance matrix across metric_names. Args: experiment: Experiment with arm parameters. map_data: MapData of observations. statuses_to_include: data from non-MapMetrics will only be included for trials with statuses in this set. Defaults to all statuses except abandoned. statuses_to_include_map_metric: data from MapMetrics will only be included for trials with statuses in this set. Defaults to all statuses except abandoned. map_keys_as_parameters: Whether map_keys should be returned as part of the parameters of the Observation objects. limit_rows_per_metric: If specified, uses MapData.subsample() with `limit_rows_per_metric` equal to the specified value on the first map_key (map_data.map_keys[0]) to subsample the MapData. This is useful in, e.g., cases where learning curves are frequently updated, leading to an intractable number of Observation objects created. limit_rows_per_group: If specified, uses MapData.subsample() with `limit_rows_per_group` equal to the specified value on the first map_key (map_data.map_keys[0]) to subsample the MapData. Returns: List of Observation objects. """ if statuses_to_include is None: statuses_to_include = NON_ABANDONED_STATUSES if statuses_to_include_map_metric is None: statuses_to_include_map_metric = NON_ABANDONED_STATUSES if limit_rows_per_metric is not None or limit_rows_per_group is not None: map_data = map_data.subsample( map_key=map_data.map_keys[0], limit_rows_per_metric=limit_rows_per_metric, limit_rows_per_group=limit_rows_per_group, include_first_last=True, ) feature_cols = get_feature_cols(map_data, is_map_data=True) observations = [] arm_name_only = len(feature_cols) == 1 # there will always be an arm name # One DataFrame where all rows have all features. isnull = map_data.map_df[feature_cols].isnull() isnull_any = isnull.any(axis=1) incomplete_df_cols = isnull[isnull_any].any() # Get the incomplete_df columns that are complete, and usable as groupby keys. obs_cols_and_map = OBS_COLS.union(map_data.map_keys) complete_feature_cols = list( obs_cols_and_map.intersection(incomplete_df_cols.index[~incomplete_df_cols]) ) if set(feature_cols) == set(complete_feature_cols): complete_df = map_data.map_df incomplete_df = None else: # The groupby and filter is expensive, so do it only if we have to. grouped = map_data.map_df.groupby( by=( complete_feature_cols if len(complete_feature_cols) > 1 else complete_feature_cols[0] ) ) complete_df = grouped.filter(lambda r: ~r[feature_cols].isnull().any().any()) incomplete_df = grouped.filter(lambda r: r[feature_cols].isnull().any().any()) # Get Observations from complete_df observations.extend( _observations_from_dataframe( experiment=experiment, df=complete_df, cols=feature_cols, arm_name_only=arm_name_only, map_keys=map_data.map_keys, statuses_to_include=statuses_to_include, statuses_to_include_map_metric=statuses_to_include_map_metric, map_keys_as_parameters=map_keys_as_parameters, ) ) if incomplete_df is not None: # Get Observations from incomplete_df observations.extend( _observations_from_dataframe( experiment=experiment, df=incomplete_df, cols=complete_feature_cols, arm_name_only=arm_name_only, map_keys=map_data.map_keys, statuses_to_include=statuses_to_include, statuses_to_include_map_metric=statuses_to_include_map_metric, map_keys_as_parameters=map_keys_as_parameters, ) ) return observations
[docs] def separate_observations( observations: list[Observation], copy: bool = False ) -> tuple[list[ObservationFeatures], list[ObservationData]]: """Split out observations into features+data. Args: observations: input observations Returns: observation_features: ObservationFeatures observation_data: ObservationData """ if copy: observation_features = [deepcopy(obs.features) for obs in observations] observation_data = [deepcopy(obs.data) for obs in observations] else: observation_features = [obs.features for obs in observations] observation_data = [obs.data for obs in observations] return observation_features, observation_data
[docs] def recombine_observations( observation_features: list[ObservationFeatures], observation_data: list[ObservationData], arm_names: list[str] | None = None, ) -> list[Observation]: """ Construct a list of `Observation`s from the given arguments. In the returned list of `Observation`s, element `i` has `features` from `observation_features[i]`, `data` from `observation_data[i]`, and, if applicable, `arm_name` from `arm_names[i]`. """ if len(observation_features) != len(observation_data): raise ValueError("Got features and data of different lengths") if arm_names is not None and len(observation_features) != len(arm_names): raise ValueError("Got features and arm_names of different lengths") return [ Observation( features=observation_features[i], data=obsd, arm_name=None if arm_names is None else arm_names[i], ) for i, obsd in enumerate(observation_data) ]