#!/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 json
from copy import deepcopy
from typing import List, Optional, Tuple
import numpy as np
import pandas as pd
from ax.core.arm import Arm
from ax.core.base import Base
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.core.types import TParameterization
[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: Optional[np.int64] = None,
# pyre-fixme[11]: Annotation `Timestamp` is not defined as a type.
start_time: Optional[pd.Timestamp] = None,
# pyre-fixme[11]: Annotation `Timestamp` is not defined as a type.
end_time: Optional[pd.Timestamp] = None,
random_split: Optional[np.int64] = 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
[docs] @staticmethod
def from_arm(
arm: Arm,
trial_index: Optional[np.int64] = None,
# pyre-fixme[11]: Annotation `Timestamp` is not defined as a type.
start_time: Optional[pd.Timestamp] = None,
# pyre-fixme[11]: Annotation `Timestamp` is not defined as a type.
end_time: Optional[pd.Timestamp] = None,
random_split: Optional[np.int64] = None,
) -> "ObservationFeatures":
"""Convert a Arm to an ObservationFeatures, including additional
data as specified.
"""
return ObservationFeatures(
parameters=arm.parameters,
trial_index=trial_index,
start_time=start_time,
end_time=end_time,
random_split=random_split,
)
[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
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(", {attr}={val}".format(attr=attr, val=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) # pragma: no cover
elif type(v) is np.float32:
parameters[k] = float(v) # pragma: no cover
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: np.ndarray, covariance: np.ndarray
) -> None:
k = len(metric_names)
if means.shape != (k,):
raise ValueError(
"Shape of means should be {}, is {}.".format((k,), (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
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, a arm name associated with the
features.
Attributes:
features (ObservationFeatures)
data (ObservationData)
arm_name (Optional[str])
"""
def __init__(
self,
features: ObservationFeatures,
data: ObservationData,
arm_name: Optional[str] = None,
) -> None:
self.features = features
self.data = data
self.arm_name = arm_name
[docs]def observations_from_data(experiment: Experiment, data: Data) -> List[Observation]:
"""Convert Data to observations.
Converts a Data 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.
data: Data of observations
Returns: List of Observation objects.
"""
feature_cols = list(
{
"arm_name",
"trial_index",
"start_time",
"end_time",
"random_split",
"fidelities",
}.intersection(data.df.columns)
)
observations = []
for g, d in data.df.groupby(by=feature_cols):
# If g were a single value, zip would transform it into an index,
# and we want the value.
if not isinstance(g, (list, tuple)):
g = [g] # pragma: no cover
features = dict(zip(feature_cols, g))
obs_kwargs = {}
obs_parameters = experiment.arms_by_name[features["arm_name"]].parameters.copy()
if obs_parameters:
obs_kwargs["parameters"] = obs_parameters
for f in ["trial_index", "start_time", "end_time", "random_split"]:
obs_kwargs[f] = features.get(f, None)
if "fidelities" in features:
fidelity_dict = json.loads(features["fidelities"])
for param_name, val in fidelity_dict.items():
obs_parameters[param_name] = val
observations.append(
Observation(
features=ObservationFeatures(**obs_kwargs),
data=ObservationData(
metric_names=list(d["metric_name"].values),
means=d["mean"].values,
covariance=np.diag(d["sem"].values ** 2),
),
arm_name=features["arm_name"],
)
)
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
