# ax.core¶

## Core Classes¶

### Arm¶

class ax.core.arm.Arm(parameters, name=None)[source]

Base class for defining arms.

Randomization in experiments assigns units to a given arm. Thus, the arm encapsulates the parametrization needed by the unit.

clone(clear_name=False)[source]

Create a copy of this arm.

Parameters

clear_name (bool) – whether this cloned copy should set its name to None instead of the name of the arm being cloned. Defaults to False.

Return type

Arm

property has_name

Return true if arm’s name is not None.

Return type

bool

static md5hash(parameters)[source]

Return unique identifier for arm’s parameters.

Parameters

parameters (Dict[str, Union[str, float, int, None]]) – Parameterization; mapping of param name to value.

Return type

str

Returns

Hash of arm’s parameters.

property name

Get arm name. Throws if name is None.

Return type

str

property name_or_short_signature

Returns arm name if exists; else last 4 characters of the hash.

Used for presentation of candidates (e.g. plotting and tables), where the candidates do not yet have names (since names are automatically set upon addition to a trial).

Return type

str

property parameters

Get mapping from parameter names to values.

Return type

Dict[str, Union[str, float, int, None]]

property signature

Get unique representation of a arm.

Return type

str

### Base¶

class ax.core.base.Base[source]

Bases: object

Metaclass for core Ax classes.

### BaseTrial¶

class ax.core.base_trial.BaseTrial(experiment, trial_type=None)[source]

Base class for representing trials.

Trials are containers for arms that are deployed together. There are two types of trials: regular Trial, which only contains a single arm, and BatchTrial, which contains an arbitrary number of arms.

abstract property abandoned_arms

All abandoned arms, associated with this trial.

Return type
property abandoned_reason
Return type
abstract property arms
Return type
abstract property arms_by_name
Return type
assign_runner()[source]

Assigns default experiment runner if trial doesn’t already have one.

Return type

BaseTrial

complete()[source]
Stops the trial if functionality is defined on runner

and marks trial completed.

Return type

BaseTrial

Returns

The trial instance.

property deployed_name

Name of the experiment created in external framework.

This property is derived from the name field in run_metadata.

Return type
property experiment

The experiment this trial belongs to.

Return type

Experiment

fetch_data(metrics=None, **kwargs)[source]

Fetch data for this trial for all metrics on experiment.

Parameters
Return type

Data

Returns

Data for this trial.

property index

The index of this trial within the experiment’s trial list.

Return type

int

property is_abandoned

Whether this trial is abandoned.

Return type

bool

mark_abandoned(reason=None)[source]

Mark trial as abandoned.

Parameters

abandoned_reason – The reason the trial was abandoned.

Return type

BaseTrial

Returns

The trial instance.

mark_completed(allow_repeat_completion=False)[source]

Mark trial as completed.

Parameters

allow_repeat_completion (bool) – If set to True, this function will not raise an error is a trial that has already been marked as completed is being marked as completed again.

Return type

BaseTrial

Returns

The trial instance.

mark_dispatched()[source]

Mark trial as dispatched through the service API to await completion.

Return type

BaseTrial

Returns

The trial instance.

mark_failed()[source]

Mark trial as failed.

Return type

BaseTrial

Returns

The trial instance.

mark_running()[source]

Mark trial has started running.

Return type

BaseTrial

Returns

The trial instance.

mark_staged()[source]

Mark the trial as being staged for running.

Return type

BaseTrial

Returns

The trial instance.

run()[source]

Deploys the trial according to the behavior on the runner.

The runner returns a run_metadata dict containining metadata of the deployment process. It also returns a deployed_name of the trial within the system to which it was deployed. Both these fields are set on the trial.

Return type

BaseTrial

Returns

The trial instance.

property run_metadata

Dict containing metadata from the deployment process.

This is set implicitly during trial.run().

Return type
property runner

The runner object defining how to deploy the trial.

Return type
property status

The status of the trial in the experimentation lifecycle.

Return type

TrialStatus

property time_completed

Completion time of the trial.

Return type
property time_created

Creation time of the trial.

Return type

datetime

property time_run_started

Time the trial was started running (i.e. collecting data).

Return type
property time_staged

Staged time of the trial.

Return type
property trial_type

The type of the trial.

Relevant for experiments containing different kinds of trials (e.g. different deployment types).

Return type
class ax.core.base_trial.TrialStatus[source]

Bases: enum.Enum

Enum of trial status.

General lifecycle of a trial is::

CANDIDATE --> STAGED --> RUNNING --> COMPLETED
------------->         --> FAILED (machine failure)
-------------------------> ABANDONED (human-initiated action)
--> DISPATCHED ---------->


Trials may be abandoned at any time prior to completion or failure via human intervention. The difference between abandonment and failure is that the former is human-directed, while the latter is an internal failure state.

Additionally, when trials are deployed, they may be in an intermediate staged state (e.g. scheduled but waiting for resources) or immediately transition to running.

When used though the service API, Ax proposes trials and expects the client application to compelete them with evaluation data when available. In this case, a trial is set to ‘dispatched’ right after the trial is created, and when user completes the trial with data, its status is set to ‘completed’.

ABANDONED = 5
CANDIDATE = 0
COMPLETED = 3
DISPATCHED = 6
FAILED = 2
RUNNING = 4
STAGED = 1
property expecting_data

True if trial is expecting data.

Return type

bool

property is_deployed

True if trial has been deployed but not completed.

Return type

bool

property is_failed

True if this trial is a failed one.

Return type

bool

property is_terminal

True if trial is completed.

Return type

bool

ax.core.base_trial.immutable_once_run(func)[source]

Decorator for methods that should throw Error when trial is running or has ever run and immutable.

Return type

Callable

### BatchTrial¶

class ax.core.batch_trial.AbandonedArm[source]

Bases: tuple

Tuple storing metadata of arm that has been abandoned within a BatchTrial.

property name

Alias for field number 0

property reason

Alias for field number 2

property time

Alias for field number 1

class ax.core.batch_trial.BatchTrial(experiment, generator_run=None, trial_type=None)[source]
property abandoned_arms

List of arms that have been abandoned within this trial

Return type
property abandoned_arms_metadata
Return type
add_arm(*args, **kwargs)
add_arms_and_weights(*args, **kwargs)
add_generator_run(*args, **kwargs)
property arm_weights

The set of arms and associated weights for the trial.

These are constructed by merging the arms and weights from each generator run that is attached to the trial.

Return type
property arms

All arms contained in the trial.

Return type
property arms_by_name

Map from arm name to object for all arms in trial.

Return type
clone()[source]

Clone the trial.

Return type

BatchTrial

Returns

A new instance of the trial.

property experiment

The experiment this batch belongs to.

Return type

Experiment

property generator_run_structs

List of generator run structs attached to this trial.

Struct holds generator_run object and the weight with which it was added.

Return type
property index

The index of this batch within the experiment’s batch list.

Return type

int

property is_factorial

Return true if the trial’s arms are a factorial design with no linked factors.

Return type

bool

mark_arm_abandoned(arm_name, reason=None)[source]

Mark a arm abandoned.

Usually done after deployment when one arm causes issues but user wants to continue running other arms in the batch.

Parameters
Return type

BatchTrial

Returns

The batch instance.

normalized_arm_weights(total=1, trunc_digits=None)[source]

Returns arms with a new set of weights normalized to the given total.

This method is useful for many runners where we need to normalize weights to a certain total without mutating the weights attached to a trial.

Parameters
• total (float) – The total weight to which to normalize. Default is 1, in which case arm weights can be interpreted as probabilities.

• trunc_digits (Optional[int]) – The number of digits to keep. If the resulting total weight is not equal to total, re-allocate weight in such a way to maintain relative weights as best as possible.

Return type
Returns

Mapping from arms to the new set of weights.

reweight_status_quo(*args, **kwargs)
run()[source]

Deploys the trial according to the behavior on the runner.

The runner returns a run_metadata dict containining metadata of the deployment process. It also returns a deployed_name of the trial within the system to which it was deployed. Both these fields are set on the trial.

Return type

BatchTrial

Returns

The trial instance.

set_status_quo_and_optimize_power(*args, **kwargs)
set_status_quo_with_weight(*args, **kwargs)
property status_quo

The control arm for this batch.

Return type
property weights

Weights corresponding to arms contained in the trial.

Return type
class ax.core.batch_trial.GeneratorRunStruct[source]

Bases: tuple

Stores GeneratorRun object as well as the weight with which it was added.

property generator_run

Alias for field number 0

property weight

Alias for field number 1

### Data¶

class ax.core.data.Data(df=None, description=None)[source]

Class storing data for an experiment.

The dataframe is retrieved via the df property. The data can be stored to gluster for future use by attaching it to an experiment using experiment.add_data() (this requires a description to be set.)

df

DataFrame with underlying data, and required columns.

description

static column_data_types()[source]

Type specification for all supported columns.

Return type

Dict[str, Type[+CT_co]]

property df
Return type

DataFrame

property df_hash

Compute hash of pandas DataFrame.

This first serializes the DataFrame and computes the md5 hash on the resulting string. Note that this may cause performance issue for very large DataFrames.

Parameters

df – The DataFrame for which to compute the hash.

Returns

str: The hash of the DataFrame.

Return type

str

static from_evaluations(evaluations, trial_index, sample_sizes=None)[source]

Convert dict of evaluations to Ax data object.

Parameters
Return type

Data

Returns

Ax Data object.

static from_fidelity_evaluations(evaluations, trial_index, sample_sizes=None)[source]

Convert dict of fidelity evaluations to Ax data object.

Parameters
Return type

Data

Returns

Ax Data object.

static from_multiple_data(data)[source]
Return type

Data

static required_columns()[source]

Names of required columns.

Return type
ax.core.data.custom_data_class(column_data_types=None, required_columns=None, time_columns=None)[source]

Creates a custom data class with additional columns.

All columns and their designations on the base data class are preserved, the inputs here are appended to the definitions on the base class.

Parameters
Return type
Returns

New data subclass with amended column definitions.

ax.core.data.set_single_trial(data)[source]

Returns a new Data object where we set all rows to have the same trial index (i.e. 0). This is meant to be used with our IVW transform, which will combine multiple observations of the same metric.

Return type

Data

### Experiment¶

class ax.core.experiment.Experiment(search_space, name=None, optimization_config=None, tracking_metrics=None, runner=None, status_quo=None, description=None, is_test=False, experiment_type=None)[source]

Base class for defining an experiment.

add_tracking_metric(metric)[source]

Add a new metric to the experiment.

Parameters

metric (Metric) – Metric to be added.

Return type

Experiment

property arms_by_name

The arms belonging to this experiment, by their name.

Return type
property arms_by_signature

The arms belonging to this experiment, by their signature.

Return type
attach_data(data)[source]

Attach data to experiment.

Parameters

data (Data) – Data object to store.

Return type

int

Returns

Timestamp of storage in millis.

property data_by_trial

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 type
property default_trial_type

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 type
property experiment_type

The type of the experiment.

Return type
fetch_data(metrics=None, **kwargs)[source]

Fetches data for all metrics and trials on this experiment.

Parameters
Return type

Data

Returns

Data for the experiment.

property has_name

Return true if experiment’s name is not None.

Return type

bool

property is_simple_experiment

Whether this experiment is a regular Experiment or the subclassing SimpleExperiment.

lookup_data_for_trial(trial_index)[source]

Lookup stored data for a specific trial.

Returns latest data object present for this trial. Returns empty data if no data present.

Parameters

trial_index (int) – The index of the trial to lookup data for.

Return type

Data

Returns

Requested data object.

lookup_data_for_ts(timestamp)[source]

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.

Parameters

timestamp (int) – Timestamp in millis at which data was stored.

Return type

Data

Returns

Data object with all data stored at the timestamp.

property metrics

The metrics attached to the experiment.

Return type
property name

Get experiment name. Throws if name is None.

Return type

str

new_batch_trial(generator_run=None, trial_type=None)[source]

Create a new batch trial associated with this experiment.

Return type

BatchTrial

new_trial(generator_run=None, trial_type=None)[source]

Create a new trial associated with this experiment.

Return type

Trial

property num_abandoned_arms

How many arms attached to this experiment are abandoned.

Return type

int

property num_trials

How many trials are associated with this experiment.

Return type

int

property optimization_config

The experiment’s optimization config.

Return type
property parameters

The parameters in the experiment’s search space.

Return type
remove_tracking_metric(metric_name)[source]

Remove a metric that already exists on the experiment.

Parameters

metric_name (str) – Unique name of metric to remove.

Return type

Experiment

runner_for_trial(trial)[source]

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 type
property search_space

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.

Return type

SearchSpace

property status_quo

The existing arm that new arms will be compared against.

Return type
property sum_trial_sizes

Sum of numbers of arms attached to each trial in this experiment.

Return type

int

supports_trial_type(trial_type)[source]

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 type

bool

property time_created

Creation time of the experiment.

Return type

datetime

property trials

The trials associated with the experiment.

Return type
property trials_expecting_data

the list of all trials for which data has arrived or is expected to arrive.

Type

List[BaseTrial]

Return type
update_tracking_metric(metric)[source]

Redefine a metric that already exists on the experiment.

Parameters

metric (Metric) – New metric definition.

Return type

Experiment

### GeneratorRun¶

class ax.core.generator_run.ArmWeight[source]

Bases: tuple

NamedTuple for tying together arms and weights.

property arm

Alias for field number 0

property weight

Alias for field number 1

class ax.core.generator_run.GeneratorRun(arms, weights=None, optimization_config=None, search_space=None, model_predictions=None, best_arm_predictions=None, type=None, fit_time=None, gen_time=None, model_key=None, model_kwargs=None, bridge_kwargs=None)[source]

An object that represents a single run of a generator.

This object is created each time the gen method of a generator is called. It stores the arms and (optionally) weights that were generated by the run. When we add a generator run to a trial, its arms and weights will be merged with those from previous generator runs that were already attached to the trial.

property arm_weights

Mapping from arms to weights (order matches order in arms property).

Return type
property arms

Returns arms generated by this run.

Return type
property best_arm_predictions
Return type
clone()[source]

Return a deep copy of a GeneratorRun.

Return type

GeneratorRun

property fit_time
Return type
property gen_time
Return type
property generator_run_type

The type of the generator run.

Return type
property index

The index of this generator run within a trial’s list of generator run structs. This field is set when the generator run is added to a trial.

Return type
property model_predictions
Return type
property model_predictions_by_arm
Return type
property optimization_config

The optimization config used during generation of this run.

Return type
property param_df

Constructs a Pandas dataframe with the parameter values for each arm.

Useful for inspecting the contents of a generator run.

Returns

a dataframe with the generator run’s arms.

Return type

pd.DataFrame

property search_space

The search used during generation of this run.

Return type
property time_created

Creation time of the batch.

Return type

datetime

property weights

Returns weights associated with arms generated by this run.

Return type
class ax.core.generator_run.GeneratorRunType[source]

Bases: enum.Enum

Class for enumerating generator run types.

MANUAL = 1
STATUS_QUO = 0
ax.core.generator_run.extract_arm_predictions(model_predictions, arm_idx)[source]

Extract a particular arm from model_predictions.

Parameters
Return type
Returns

(mean, cov) for specified arm.

### Metric¶

class ax.core.metric.Metric(name, lower_is_better=None)[source]

Base class for representing metrics.

lower_is_better

Flag for metrics which should be minimized.

clone()[source]

Create a copy of this Metric.

Return type

Metric

fetch_experiment_data(experiment, **kwargs)[source]

Fetch this metric’s data for an experiment.

Default behavior is to fetch data from all trials expecting data and concatenate the results.

Return type

Data

classmethod fetch_experiment_data_multi(experiment, metrics, **kwargs)[source]

Fetch multiple metrics data for an experiment.

Default behavior calls fetch_trial_data_multi for each trial. Subclasses should override to batch data computation across trials + metrics.

Return type

Data

fetch_trial_data(trial, **kwargs)[source]

Fetch data for one trial.

Return type

Data

classmethod fetch_trial_data_multi(trial, metrics, **kwargs)[source]

Fetch multiple metrics data for one trial.

Default behavior calls fetch_trial_data for each metric. Subclasses should override this to trial data computation for multiple metrics.

Return type

Data

property name

Get name of metric.

Return type

str

### MultiTypeExperiment¶

class ax.core.multi_type_experiment.MultiTypeExperiment(name, search_space, default_trial_type, default_runner, optimization_config=None, status_quo=None, description=None, is_test=False, experiment_type=None)[source]

Class for experiment with multiple trial types.

A canonical use case for this is tuning a large production system with limited evaluation budget and a simulator which approximates evaluations on the main system. Trial deployment and data fetching is separate for the two systems, but the final data is combined and fed into multi-task models.

See the Multi-Task Modeling tutorial for more details.

name

Name of the experiment.

description

Description of the experiment.

add_tracking_metric(metric, trial_type, canonical_name=None)[source]

Add a new metric to the experiment.

Parameters
Return type

MultiTypeExperiment

add_trial_type(trial_type, runner)[source]

Add a new trial_type to be supported by this experiment.

Parameters
Return type

MultiTypeExperiment

property default_trial_type

Default trial type assigned to trials in this experiment.

Return type
property default_trials

Return the indicies for trials of the default type.

Return type
fetch_data(metrics=None, **kwargs)[source]

Fetches data for all metrics and trials on this experiment.

Parameters
Return type

Data

Returns

Data for the experiment.

property metric_to_trial_type

Map metrics to trial types.

Adds in default trial type for OC metrics to custom defined trial types..

Return type
remove_tracking_metric(metric_name)[source]

Remove a metric that already exists on the experiment.

Parameters

metric_name (str) – Unique name of metric to remove.

Return type

MultiTypeExperiment

runner_for_trial(trial)[source]

The default runner to use for a given trial.

Looks up the appropriate runner for this trial type in the trial_type_to_runner.

Return type
supports_trial_type(trial_type)[source]

Whether this experiment allows trials of the given type.

Only trial types defined in the trial_type_to_runner are allowed.

Return type

bool

update_runner(trial_type, runner)[source]

Update the default runner for an existing trial_type.

Parameters
Return type

MultiTypeExperiment

update_tracking_metric(metric, trial_type, canonical_name=None)[source]

Update an existing metric on the experiment.

Parameters
Return type

MultiTypeExperiment

### Objective¶

class ax.core.objective.Objective(metric, minimize=False)[source]

Base class for representing an objective.

minimize

If True, minimize metric.

clone()[source]

Create a copy of the objective.

Return type

Objective

property metric

Get the objective metric.

Return type

Metric

property metrics

Get a list of objective metrics.

Return type
class ax.core.objective.ScalarizedObjective(metrics, weights=None, minimize=False)[source]

Class for an objective composed of a linear scalarization of metrics.

metrics

List of metrics.

weights

Weights for scalarization; default to 1.

clone()[source]

Create a copy of the objective.

Return type

Objective

property metric

Override base method to error.

Return type

Metric

property metric_weights

Get the objective metrics and weights.

Return type
property metrics

Get the objective metrics.

Return type

### Observation¶

class ax.core.observation.Observation(features, data, arm_name=None)[source]

Represents an observation.

A set of features (ObservationFeatures) and corresponding measurements (ObservationData). Optionally, a arm name associated with the features.

features
Type

ObservationFeatures

data
Type

ObservationData

arm_name
Type

Optional[str]

class ax.core.observation.ObservationData(metric_names, means, covariance)[source]

Outcomes observed at a point.

The “point” corresponding to this ObservationData would be an ObservationFeatures object.

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

class ax.core.observation.ObservationFeatures(parameters, trial_index=None, start_time=None, end_time=None, random_split=None)[source]

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.

parameters

arm parameters

trial_index

trial index

start_time

batch start time

end_time

batch end time

random_split

random split

static from_arm(arm, trial_index=None, start_time=None, end_time=None, random_split=None)[source]

Convert a Arm to an ObservationFeatures, including additional data as specified.

Return type

ObservationFeatures

update_features(new_features)[source]

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.

Return type

ObservationFeatures

ax.core.observation.observations_from_data(experiment, data)[source]

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.

Parameters

Returns: List of Observation objects.

Return type
ax.core.observation.separate_observations(observations, copy=False)[source]

Split out observations into features+data.

Parameters

observations (List[Observation]) – input observations

Returns

ObservationFeatures observation_data: ObservationData

Return type

observation_features

### OptimizationConfig¶

class ax.core.optimization_config.OptimizationConfig(objective, outcome_constraints=None)[source]

An optimization configuration, which comprises an objective and outcome constraints.

There is no minimum or maximum number of outcome constraints, but an individual metric can have at most two constraints–which is how we represent metrics with both upper and lower bounds.

clone()[source]

Make a copy of this optimization config.

Return type

OptimizationConfig

property metrics
Return type
property objective

Get objective.

Return type

Objective

property outcome_constraints

Get outcome constraints.

Return type

### OutcomeConstraint¶

class ax.core.outcome_constraint.OutcomeConstraint(metric, op, bound, relative=True)[source]

Base class for representing outcome constraints.

Outcome constraints may of the form metric >= bound or metric <= bound, where the bound can be expressed as an absolute measurement or relative to the status quo (if applicable).

metric

Metric to constrain.

op

Specifies whether metric should be greater or equal to, or less than or equal to, some bound.

bound

The bound in the constraint.

relative

Whether you want to bound on an absolute or relative scale. If relative, bound is the acceptable percent change.

clone()[source]

Create a copy of this OutcomeConstraint.

Return type

OutcomeConstraint

property metric
Return type

Metric

property op
Return type

ComparisonOp

### Parameter¶

class ax.core.parameter.ChoiceParameter(name, parameter_type, values, is_ordered=False, is_task=False, is_fidelity=False, target_value=None)[source]

Parameter object that specifies a discrete set of values.

add_values(values)[source]

Add input list to the set of allowed values for parameter.

Cast all input values to the parameter type.

Parameters

values (List[Union[str, float, int, None]]) – Values being added to the allowed list.

Return type

ChoiceParameter

clone()[source]
Return type

ChoiceParameter

property is_ordered
Return type

bool

property is_task
Return type

bool

property name
Return type

str

property parameter_type
Return type

ParameterType

set_values(values)[source]

Set the list of allowed values for parameter.

Cast all input values to the parameter type.

Parameters

values (List[Union[str, float, int, None]]) – New list of allowed values.

Return type

ChoiceParameter

validate(value)[source]

Checks that the input is in the list of allowed values.

Parameters

value (Union[str, float, int, None]) – Value being checked.

Return type

bool

Returns

True if valid, False otherwise.

property values
Return type

List[Union[str, float, int, None]]

class ax.core.parameter.FixedParameter(name, parameter_type, value, is_fidelity=False, target_value=None)[source]

Parameter object that specifies a single fixed value.

clone()[source]
Return type

FixedParameter

property name
Return type

str

property parameter_type
Return type

ParameterType

set_value(value)[source]
Return type

FixedParameter

validate(value)[source]

Checks that the input is equal to the fixed value.

Parameters

value (Union[str, float, int, None]) – Value being checked.

Return type

bool

Returns

True if valid, False otherwise.

property value
Return type

Union[str, float, int, None]

class ax.core.parameter.Parameter[source]
clone()[source]
Return type

Parameter

property is_fidelity
Return type

bool

property is_numeric
Return type

bool

is_valid_type(value)[source]

Whether a given value’s type is allowed by this parameter.

Return type

bool

abstract property name
Return type

str

abstract property parameter_type
Return type

ParameterType

property python_type

The python type for the corresponding ParameterType enum.

Used primarily for casting values of unknown type to conform to that of the parameter.

Return type
property target_value
Return type

Union[str, float, int, None]

abstract validate(value)[source]
Return type

bool

class ax.core.parameter.ParameterType[source]

Bases: enum.Enum

An enumeration.

BOOL = 0
FLOAT = 2
INT = 1
STRING = 3
property is_numeric
Return type

bool

class ax.core.parameter.RangeParameter(name, parameter_type, lower, upper, log_scale=False, digits=None, is_fidelity=False, target_value=None)[source]

Parameter object that specifies a continuous numerical range of values.

clone()[source]
Return type

RangeParameter

property digits

Number of digits to round values to for float type.

Upper and lower bound are re-cast after this property is changed.

Return type
is_valid_type(value)[source]

Same as default except allows floats whose value is an int for Int parameters.

Return type

bool

property log_scale

Whether to sample in log space when drawing random values of the parameter.

Return type

bool

property lower

Lower bound of the parameter range.

Value is cast to parameter type upon set and also validated to ensure the bound is strictly less than upper bound.

Return type

float

property name
Return type

str

property parameter_type
Return type

ParameterType

set_digits(digits)[source]
Return type

RangeParameter

set_log_scale(log_scale)[source]
Return type

RangeParameter

update_range(lower=None, upper=None)[source]

Set the range to the given values.

If lower or upper is not provided, it will be left at its current value.

Parameters
Return type

RangeParameter

property upper

Upper bound of the parameter range.

Value is cast to parameter type upon set and also validated to ensure the bound is strictly greater than lower bound.

Return type

float

validate(value)[source]

Returns True if input is a valid value for the parameter.

Checks that value is of the right type and within the valid range for the parameter. Returns False if value is None.

Parameters

value (Union[str, float, int, None]) – Value being checked.

Return type

bool

Returns

True if valid, False otherwise.

### ParameterConstraint¶

class ax.core.parameter_constraint.OrderConstraint(lower_parameter, upper_parameter)[source]

Constraint object for specifying one parameter to be smaller than another.

clone()[source]

Clone.

Return type

OrderConstraint

clone_with_transformed_parameters(transformed_parameters)[source]

Clone, but replace parameters with transformed versions.

Return type

OrderConstraint

property constraint_dict

Weights on parameters for linear constraint representation.

Return type
property lower_parameter

Parameter with lower value.

Return type

Parameter

property parameters

Parameters.

Return type
property upper_parameter

Parameter with higher value.

Return type

Parameter

class ax.core.parameter_constraint.ParameterConstraint(constraint_dict, bound)[source]

Base class for linear parameter constraints.

Constraints are expressed using a map from parameter name to weight followed by a bound.

The constraint is satisfied if w * v <= b where:

w is the vector of parameter weights. v is a vector of parameter values. b is the specified bound. * is the dot product operator.

property bound

Get bound of the inequality of the constraint.

Return type

float

check(parameter_dict)[source]

Whether or not the set of parameter values satisfies the constraint.

Does a weighted sum of the parameter values based on the constraint_dict and checks that the sum is less than the bound.

Parameters

parameter_dict (Dict[str, Union[float, int]]) – Map from parameter name to parameter value.

Return type

bool

Returns

Whether the constraint is satisfied.

clone()[source]

Clone.

Return type

ParameterConstraint

clone_with_transformed_parameters(transformed_parameters)[source]

Clone, but replaced parameters with transformed versions.

Return type

ParameterConstraint

property constraint_dict

Get mapping from parameter names to weights.

Return type
class ax.core.parameter_constraint.SumConstraint(parameters, is_upper_bound, bound)[source]

Constraint on the sum of parameters being greater or less than a bound.

clone()[source]

Clone.

Return type

SumConstraint

clone_with_transformed_parameters(transformed_parameters)[source]

Clone, but replace parameters with transformed versions.

Return type

SumConstraint

property constraint_dict

Weights on parameters for linear constraint representation.

Return type
property op

Whether the sum is constrained by a <= or >= inequality.

Return type

ComparisonOp

property parameters

Parameters.

Return type
ax.core.parameter_constraint.validate_constraint_parameters(parameters)[source]

Basic validation of parameters used in a constraint.

Parameters

parameters (List[Parameter]) – Parameters used in constraint.

Raises

ValueError if the parameters are not valid for use.

Return type

None

### Runner¶

class ax.core.runner.Runner[source]

Abstract base class for custom runner classes

abstract run(trial)[source]

Deploys a trial based on custom runner subclass implementation.

Parameters

trial (BaseTrial) – The trial to deploy.

Return type
Returns

Dict of run metadata from the deployment process.

property staging_required

Whether the trial goes to staged or running state once deployed.

Return type

bool

stop(trial)[source]

Stop a trial based on custom runner subclass implementation.

Optional to implement

Parameters

trial (BaseTrial) – The trial to deploy.

Return type

None

### SearchSpace¶

class ax.core.search_space.SearchSpace(parameters, parameter_constraints=None)[source]

Base object for SearchSpace object.

Contains a set of Parameter objects, each of which have a name, type, and set of valid values. The search space also contains a set of ParameterConstraint objects, which can be used to define restrictions across parameters (e.g. p_a < p_b).

add_parameter(parameter)[source]
Return type

None

add_parameter_constraints(parameter_constraints)[source]
Return type

None

cast_arm(arm)[source]

Cast parameterization of given arm to the types in this SearchSpace.

For each parameter in given arm, cast it to the proper type specified in this search space. Throws if there is a mismatch in parameter names. This is mostly useful for int/float, which user can be sloppy with when hand written.

Parameters

arm (Arm) – Arm to cast.

Return type

Arm

Returns

New casted arm.

check_membership(parameterization, raise_error=False)[source]

Whether the given parameterization belongs in the search space.

Checks that the given parameter values have the same name/type as search space parameters, are contained in the search space domain, and satisfy the parameter constraints.

Parameters
Return type

bool

Returns

Whether the parameterization is contained in the search space.

check_types(parameterization, allow_none=True, raise_error=False)[source]

Checks that the given parameterization’s types match the search space.

Checks that the names of the parameterization match those specified in the search space, and the given values are of the correct type.

Parameters
Return type

bool

Returns

Whether the parameterization has valid types.

clone()[source]
Return type

SearchSpace

out_of_design_arm()[source]

Create a default out-of-design arm.

An out of design arm contains values for some parameters which are outside of the search space. In the modeling conversion, these parameters are all stripped down to an empty dictionary, since the point is already outside of the modeled space.

Return type

Arm

Returns

New arm w/ null parameter values.

property parameter_constraints
Return type
property parameters
Return type
set_parameter_constraints(parameter_constraints)[source]
Return type

None

property tunable_parameters
Return type
update_parameter(parameter)[source]
Return type

None

### SimpleExperiment¶

class ax.core.simple_experiment.SimpleExperiment(search_space, name=None, objective_name=None, evaluation_function=<function unimplemented_evaluation_function>, minimize=False, outcome_constraints=None, status_quo=None)[source]

Simplified experiment class with defaults.

Parameters
add_tracking_metric(metric)[source]

Add a new metric to the experiment.

Parameters

metric (Metric) – Metric to be added.

Return type

SimpleExperiment

eval()[source]

Evaluate all arms in the experiment with the evaluation function passed as argument to this SimpleExperiment.

Return type

Data

eval_trial(trial)[source]

Evaluate trial arms with the evaluation function of this experiment.

Parameters

trial (BaseTrial) – trial, whose arms to evaluate.

Return type

Data

property evaluation_function

Get the evaluation function.

Return type
evaluation_function_outer(parameterization, weight=None)[source]
Return type
fetch_data(metrics=None, **kwargs)[source]

Fetches data for all metrics and trials on this experiment.

Parameters
Return type

Data

Returns

Data for the experiment.

property has_evaluation_function

Whether this SimpleExperiment has a valid evaluation function attached.

Return type

bool

property is_simple_experiment

Whether this experiment is a regular Experiment or the subclassing SimpleExperiment.

update_tracking_metric(metric)[source]

Redefine a metric that already exists on the experiment.

Parameters

metric (Metric) – New metric definition.

Return type

SimpleExperiment

ax.core.simple_experiment.unimplemented_evaluation_function(parameterization, weight=None)[source]

Default evaluation function used if none is provided during initialization. The evaluation function must be manually set before use.

Return type

### Trial¶

class ax.core.trial.Trial(experiment, generator_run=None, trial_type=None)[source]

Trial that only has one attached arm and no arm weights.

Parameters
• experiment (Experiment) – experiment, to which this trial is attached

• generator_run (Optional[GeneratorRun]) – generator_run associated with this trial. Trial has only one generator run (and thus arm) attached to it. This can also be set later through add_arm or add_generator_run, but a trial’s associated genetor run is immutable once set.

property abandoned_arms

Abandoned arms attached to this trial.

Return type
add_arm(*args, **kwargs)
add_generator_run(*args, **kwargs)
property arm

The arm associated with this batch.

Return type
property arms

All arms attached to this trial.

Returns

list of a single arm

attached to this trial if there is one, else None.

Return type

arms

property arms_by_name

Dictionary of all arms attached to this trial with their names as keys.

Returns

dictionary of a single

arm name to arm if one is attached to this trial, else None.

Return type

arms

property generator_run

Generator run attached to this trial.

Return type
property objective_mean

Objective mean for the arm attached to this trial.

Return type

## Core Types¶

class ax.core.types.ComparisonOp[source]

Bases: enum.Enum

Class for enumerating comparison operations.

GEQ = 0
LEQ = 1
ax.core.types.merge_model_predict(predict, predict_append)[source]

Append model predictions to an existing set of model predictions.

TModelPredict is of the form:

{metric_name: [mean1, mean2, …], {metric_name: {metric_name: [var1, var2, …]}})

This will append the predictions

Parameters
Return type
Returns

TModelPredict with the new predictions appended.