ax¶
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class
ax.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.
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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
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property
parameters¶ Get mapping from parameter names to values.
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property
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class
ax.BatchTrial(experiment, generator_run=None, trial_type=None, optimize_for_power=False)[source]¶ -
property
abandoned_arms¶ List of arms that have been abandoned within this trial
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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
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property
arms_by_name¶ Map from arm name to object for all arms in trial.
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property
experiment¶ The experiment this batch belongs to.
- Return type
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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
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property
is_factorial¶ Return true if the trial’s arms are a factorial design with no linked factors.
- Return type
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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
- Returns
The batch instance.
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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.
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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
- Returns
The trial instance.
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property
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class
ax.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.
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add_values(values)[source]¶ Add input list to the set of allowed values for parameter.
Cast all input values to the parameter type.
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class
ax.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 an external store for future use by attaching it to an experiment using experiment.attach_data() (this requires a description to be set.)
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df¶ DataFrame with underlying data, and required columns.
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description¶ Human-readable description of data.
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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
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static
from_evaluations(evaluations, trial_index, sample_sizes=None, start_time=None, end_time=None)[source]¶ Convert dict of evaluations to Ax data object.
- Parameters
evaluations (
Dict[str,Dict[str,Tuple[float,Optional[float]]]]) – Map from arm name to metric outcomes (itself a mapping of metric names to tuples of mean and optionally a SEM).trial_index (
int) – Trial index to which this data belongs.sample_sizes (
Optional[Dict[str,int]]) – Number of samples collected for each arm.start_time (
Optional[int]) – Optional start time of run of the trial that produced this data, in milliseconds.end_time (
Optional[int]) – Optional end time of run of the trial that produced this data, in milliseconds.
- Return type
- Returns
Ax Data object.
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static
from_fidelity_evaluations(evaluations, trial_index, sample_sizes=None, start_time=None, end_time=None)[source]¶ Convert dict of fidelity evaluations to Ax data object.
- Parameters
evaluations (
Dict[str,List[Tuple[Dict[str,Union[str,bool,float,int,None]],Dict[str,Tuple[float,Optional[float]]]]]]) – Map from arm name to list of (fidelity, metric outcomes) (where metric outcomes is itself a mapping of metric names to tuples of mean and SEM).trial_index (
int) – Trial index to which this data belongs.sample_sizes (
Optional[Dict[str,int]]) – Number of samples collected for each arm.start_time (
Optional[int]) – Optional start time of run of the trial that produced this data, in milliseconds.end_time (
Optional[int]) – Optional end time of run of the trial that produced this data, in milliseconds.
- Return type
- Returns
Ax Data object.
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class
ax.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.
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add_tracking_metric(metric)[source]¶ Add a new metric to the experiment.
- Parameters
metric (
Metric) – Metric to be added.- Return type
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add_tracking_metrics(metrics)[source]¶ 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
- Parameters
- Return type
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property
arms_by_name¶ The arms belonging to this experiment, by their name.
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property
arms_by_signature¶ The arms belonging to this experiment, by their signature.
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attach_data(data, combine_with_last_data=False)[source]¶ Attach data to experiment. Stores data in experiment._data_by_trial, to be looked up via experiment.lookup_data_by_trial.
- Parameters
data (
Data) – Data object to store.combine_with_last_data (
bool) – 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.
- Return type
- Returns
Timestamp of storage in millis.
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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
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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.
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fetch_data(metrics=None, **kwargs)[source]¶ Fetches data for all metrics and trials on this experiment.
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property
is_simple_experiment¶ Whether this experiment is a regular Experiment or the subclassing SimpleExperiment.
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lookup_data_for_trial(trial_index)[source]¶ 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.
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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.
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new_batch_trial(generator_run=None, trial_type=None, optimize_for_power=False)[source]¶ Create a new batch trial associated with this experiment.
- Return type
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new_trial(generator_run=None, trial_type=None)[source]¶ Create a new trial associated with this experiment.
- Return type
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property
num_abandoned_arms¶ How many arms attached to this experiment are abandoned.
- Return type
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property
optimization_config¶ The experiment’s optimization config.
- Return type
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property
parameters¶ The parameters in the experiment’s search space.
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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
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reset_runners(runner)[source]¶ Replace all candidate trials runners.
- Parameters
runner (
Runner) – New runner to replace with.- Return type
None
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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.
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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
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property
status_quo¶ The existing arm that new arms will be compared against.
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property
sum_trial_sizes¶ Sum of numbers of arms attached to each trial in this experiment.
- Return type
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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
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property
trials_by_status¶ The trials associated with the experiment grouped by trial status.
- Return type
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property
trials_expecting_data¶ the list of all trials for which data has arrived or is expected to arrive.
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class
ax.FixedParameter(name, parameter_type, value, is_fidelity=False, target_value=None)[source]¶ Parameter object that specifies a single fixed value.
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class
ax.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, gen_metadata=None, model_state_after_gen=None, generation_step_index=None)[source]¶ An object that represents a single run of a generator.
This object is created each time the
genmethod 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
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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.
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property
optimization_config¶ The optimization config used during generation of this run.
- Return type
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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
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property
search_space¶ The search used during generation of this run.
- Return type
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property
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class
ax.Metric(name, lower_is_better=None)[source]¶ Base class for representing metrics.
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lower_is_better¶ Flag for metrics which should be minimized.
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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
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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
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class
ax.Models[source]¶ Registry of available models.
Uses MODEL_KEY_TO_MODEL_SETUP to retrieve settings for model and model bridge, by the key stored in the enum value.
To instantiate a model in this enum, simply call an enum member like so: Models.SOBOL(search_space=search_space) or Models.GPEI(experiment=experiment, data=data). Keyword arguments specified to the call will be passed into the model or the model bridge constructors according to their keyword.
For instance, Models.SOBOL(search_space=search_space, scramble=False) will instantiate a RandomModelBridge(search_space=search_space) with a SobolGenerator(scramble=False) underlying model.
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view_defaults()[source]¶ Obtains the default keyword arguments for the model and the modelbridge specified through the Models enum, for ease of use in notebook environment, since models and bridges cannot be inspected directly through the enum.
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class
ax.Objective(metric, minimize=False)[source]¶ Base class for representing an objective.
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minimize¶ If True, minimize metric.
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class
ax.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.
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property
outcome_constraints¶ Get outcome constraints.
- Return type
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property
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class
ax.OptimizationLoop(experiment, total_trials=20, arms_per_trial=1, random_seed=None, wait_time=0, run_async=False, generation_strategy=None)[source]¶ Managed optimization loop, in which Ax oversees deployment of trials and gathering data.
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full_run()[source]¶ Runs full optimization loop as defined in the provided optimization plan.
- Return type
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static
with_evaluation_function(parameters, evaluation_function, experiment_name=None, objective_name=None, minimize=False, parameter_constraints=None, outcome_constraints=None, total_trials=20, arms_per_trial=1, wait_time=0, random_seed=None, generation_strategy=None)[source]¶ Constructs a synchronous OptimizationLoop using an evaluation function.
- Return type
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classmethod
with_runners_and_metrics(parameters, path_to_runner, paths_to_metrics, experiment_name=None, objective_name=None, minimize=False, parameter_constraints=None, outcome_constraints=None, total_trials=20, arms_per_trial=1, wait_time=0, random_seed=None)[source]¶ Constructs an asynchronous OptimizationLoop using Ax runners and metrics.
- Return type
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class
ax.OrderConstraint(lower_parameter, upper_parameter)[source]¶ Constraint object for specifying one parameter to be smaller than another.
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clone_with_transformed_parameters(transformed_parameters)[source]¶ Clone, but replace parameters with transformed versions.
- Return type
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property
constraint_dict¶ Weights on parameters for linear constraint representation.
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class
ax.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).
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metric¶ Metric to constrain.
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op¶ Specifies whether metric should be greater or equal to, or less than or equal to, some bound.
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bound¶ The bound in the constraint.
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relative¶ Whether you want to bound on an absolute or relative scale. If relative, bound is the acceptable percent change.
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class
ax.Parameter[source]¶
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class
ax.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.
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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.
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class
ax.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.
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property
digits¶ Number of digits to round values to for float type.
Upper and lower bound are re-cast after this property is changed.
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is_valid_type(value)[source]¶ Same as default except allows floats whose value is an int for Int parameters.
- Return type
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property
log_scale¶ Whether to sample in log space when drawing random values of the parameter.
- Return type
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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
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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
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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
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property
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class
ax.Runner[source]¶ Abstract base class for custom runner classes
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property
staging_required¶ Whether the trial goes to staged or running state once deployed.
- Return type
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property
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class
ax.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).
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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.
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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
- Returns
Whether the parameterization is contained in the search space.
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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
- Returns
Whether the parameterization has valid types.
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construct_arm(parameters=None, name=None)[source]¶ Construct new arm using given parameters and name. Any missing parameters fallback to the experiment defaults, represented as None
- Return type
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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
- Returns
New arm w/ null parameter values.
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class
ax.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
search_space (
SearchSpace) – parameter spaceobjective_name (
Optional[str]) – which of the metrics computed by the evaluation function is the objectiveevaluation_function (
Callable[[Dict[str,Union[str,bool,float,int,None]],Optional[float]],Union[Dict[str,Tuple[float,Optional[float]]],Tuple[float,Optional[float]],float,List[Tuple[Dict[str,Union[str,bool,float,int,None]],Dict[str,Tuple[float,Optional[float]]]]]]]) – function that evaluates mean and standard error for a parameter configuration. This function should accept a dictionary of parameter names to parameter values (TParametrization) and optionally a weight, and return a dictionary of metric names to a tuple of means and standard errors (TEvaluationOutcome). The function can also return a single tuple, in which case we assume the metric is the objective.minimize (
bool) – whether the objective should be minimized, defaults to Falseoutcome_constraints (
Optional[List[OutcomeConstraint]]) – constraints on the outcome, if anystatus_quo (
Optional[Arm]) – Arm representing existing “control” arm
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add_tracking_metric(metric)[source]¶ Add a new metric to the experiment.
- Parameters
metric (
Metric) – Metric to be added.- Return type
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eval()[source]¶ Evaluate all arms in the experiment with the evaluation function passed as argument to this SimpleExperiment.
- Return type
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property
evaluation_function¶ Get the evaluation function.
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fetch_data(metrics=None, **kwargs)[source]¶ Fetches data for all metrics and trials on this experiment.
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property
has_evaluation_function¶ Whether this SimpleExperiment has a valid evaluation function attached.
- Return type
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property
is_simple_experiment¶ Whether this experiment is a regular Experiment or the subclassing SimpleExperiment.
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class
ax.SumConstraint(parameters, is_upper_bound, bound)[source]¶ Constraint on the sum of parameters being greater or less than a bound.
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clone()[source]¶ Clone.
To use the same constraint, we need to reconstruct the original bound. We do this by re-applying the original bound weighting.
- Return type
-
clone_with_transformed_parameters(transformed_parameters)[source]¶ Clone, but replace parameters with transformed versions.
- Return type
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property
constraint_dict¶ Weights on parameters for linear constraint representation.
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property
op¶ Whether the sum is constrained by a <= or >= inequality.
- Return type
-
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class
ax.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 attachedgenerator_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.
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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
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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
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property
generator_run¶ Generator run attached to this trial.
- Return type
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get_metric_mean(metric_name)[source]¶ Metric mean for the arm attached to this trial, retrieved from the latest data available for the metric for the trial.
- Return type
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property
objective_mean¶ Objective mean for the arm attached to this trial, retrieved from the latest data available for the objective for the trial.
Note: the retrieved objective is the experiment-level objective at the time of the call to objective_mean, which is not necessarily the objective that was set at the time the trial was created or ran.
- Return type
-
ax.optimize(parameters, evaluation_function, experiment_name=None, objective_name=None, minimize=False, parameter_constraints=None, outcome_constraints=None, total_trials=20, arms_per_trial=1, random_seed=None, generation_strategy=None)[source]¶ Construct and run a full optimization loop.
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ax.save(experiment, filepath)¶ Save experiment to file.
Convert Ax experiment to JSON-serializable dictionary.
Write to file.
- Return type
None
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ax.load(filepath)¶ Load experiment from file.
Read file.
Convert dictionary to Ax experiment instance.
- Return type