ax.utils

Common

Base

class ax.utils.common.base.Base[source]

Bases: object

Metaclass for core Ax classes. Provides an equality check and db_id property for SQA storage.

property db_id: int | None
class ax.utils.common.base.SortableBase[source]

Bases: Base

Extension to the base class that also provides an inequality check.

Constants

class ax.utils.common.constants.Keys(value)[source]

Bases: str, Enum

Enum of reserved keys in options dicts etc, alphabetized.

NOTE: Useful for keys in dicts that correspond to kwargs to classes or functions and/or are used in multiple places.

ACQF_KWARGS = 'acquisition_function_kwargs'
BATCH_INIT_CONDITIONS = 'batch_initial_conditions'
CANDIDATE_SET = 'candidate_set'
CANDIDATE_SIZE = 'candidate_size'
COST_AWARE_UTILITY = 'cost_aware_utility'
COST_INTERCEPT = 'cost_intercept'
CURRENT_VALUE = 'current_value'
EXPAND = 'expand'
EXPECTED_ACQF_VAL = 'expected_acquisition_value'
EXPERIMENT_TOTAL_CONCURRENT_ARMS = 'total_concurrent_arms'
FIDELITY_FEATURES = 'fidelity_features'
FIDELITY_WEIGHTS = 'fidelity_weights'
FRAC_RANDOM = 'frac_random'
FULL_PARAMETERIZATION = 'full_parameterization'
IMMUTABLE_SEARCH_SPACE_AND_OPT_CONF = 'immutable_search_space_and_opt_config'
LONG_RUN = 'long_run'
MAXIMIZE = 'maximize'
METADATA = 'metadata'
METRIC_NAMES = 'metric_names'
NUM_FANTASIES = 'num_fantasies'
NUM_INNER_RESTARTS = 'num_inner_restarts'
NUM_RESTARTS = 'num_restarts'
NUM_TRACE_OBSERVATIONS = 'num_trace_observations'
OBJECTIVE = 'objective'
OPTIMIZER_KWARGS = 'optimizer_kwargs'
PAIRWISE_PREFERENCE_QUERY = 'pairwise_pref_query'
PREFERENCE_DATA = 'preference_data'
PROJECT = 'project'
QMC = 'qmc'
RAW_INNER_SAMPLES = 'raw_inner_samples'
RAW_SAMPLES = 'raw_samples'
RESUMED_FROM_STORAGE_TS = 'resumed_from_storage_timestamps'
SAMPLER = 'sampler'
SEED_INNER = 'seed_inner'
SEQUENTIAL = 'sequential'
SHORT_RUN = 'short_run'
STATE_DICT = 'state_dict'
SUBCLASS = 'subclass'
SUBSET_MODEL = 'subset_model'
TASK_FEATURES = 'task_features'
TRIAL_COMPLETION_TIMESTAMP = 'trial_completion_timestamp'
WARM_START_REFITTING = 'warm_start_refitting'
X_BASELINE = 'X_baseline'

Decorator

class ax.utils.common.decorator.ClassDecorator[source]

Bases: ABC

Template for making a decorator work as a class level decorator. That decorator should extend ClassDecorator. It must implement __init__ and decorate_callable. See disable_logger.decorate_callable for an example. decorate_callable should call self._call_func() instead of directly calling func to handle static functions. Note: _call_func is still imperfect and unit tests should be used to ensure everything is working properly. There is a lot of complexity in detecting classmethods and staticmethods and removing the self argument in the right situations. For best results always use keyword args in the decorated class.

DECORATE_PRIVATE can be set to determine whether private methods should be decorated. In the case of a logging decorator, you may only want to decorate things the user calls. But in the case of a disable logging decorator, you may want to decorate everything to ensure no logs escape.

DECORATE_PRIVATE = True
abstract decorate_callable(func: Callable[[...], T]) Callable[[...], T][source]
decorate_class(klass: T) T[source]

Deprecation

Docutils

Support functions for sphinx et. al

ax.utils.common.docutils.copy_doc(src: Callable[[...], Any]) Callable[[_T], _T][source]

A decorator that copies the docstring of another object

Since sphinx actually loads the python modules to grab the docstrings this works with both sphinx and the help function.

class Cat(Mamal):

  @property
  @copy_doc(Mamal.is_feline)
  def is_feline(self) -> true:
      ...

Equality

ax.utils.common.equality.dataframe_equals(df1: DataFrame, df2: DataFrame) bool[source]

Compare equality of two pandas dataframes.

ax.utils.common.equality.datetime_equals(dt1: datetime | None, dt2: datetime | None) bool[source]

Compare equality of two datetimes, up to a difference of one second.

ax.utils.common.equality.equality_typechecker(eq_func: Callable) Callable[source]

A decorator to wrap all __eq__ methods to ensure that the inputs are of the right type.

ax.utils.common.equality.is_ax_equal(one_val: Any, other_val: Any) bool[source]

Check for equality of two values, handling lists, dicts, dfs, floats, dates, and numpy arrays. This method and same_elements function as a recursive unit.

Some special cases: - For datetime objects, the equality is checked up to a tolerance of one second. - For floats, np.isclose is used to check for almost-equality. - For lists (and dict values), same_elements is used. This ignores

the ordering of the elements, and checks that the two lists are subsets of each other (under the assumption that there are no duplicates).

  • If the objects don’t fall into any of the special cases, we use simple equality check and cast the output to a boolean. If the comparison or cast fails, we return False. Example: the comparison of a float with a numpy array (with multiple elements) will return False.

ax.utils.common.equality.object_attribute_dicts_equal(one_dict: dict[str, Any], other_dict: dict[str, Any], skip_db_id_check: bool = False) bool[source]

Utility to check if all items in attribute dicts of two Ax objects are the same.

NOTE: Special-cases some Ax object attributes, like “_experiment” or “_model”, where full equality is hard to check.

Parameters:
  • one_dict – First object’s attribute dict (obj.__dict__).

  • other_dict – Second object’s attribute dict (obj.__dict__).

  • skip_db_id_check – If True, will exclude the db_id attributes from the equality check. Useful for ensuring that all attributes of an object are equal except the ids, with which one or both of them are saved to the database (e.g. if confirming an object before it was saved, to the version reloaded from the DB).

ax.utils.common.equality.object_attribute_dicts_find_unequal_fields(one_dict: dict[str, Any], other_dict: dict[str, Any], fast_return: bool = True, skip_db_id_check: bool = False) tuple[dict[str, tuple[Any, Any]], dict[str, tuple[Any, Any]]][source]

Utility for finding out what attributes of two objects’ attribute dicts are unequal.

Parameters:
  • one_dict – First object’s attribute dict (obj.__dict__).

  • other_dict – Second object’s attribute dict (obj.__dict__).

  • fast_return – Boolean representing whether to return as soon as a single unequal attribute was found or to iterate over all attributes and collect all unequal ones.

  • skip_db_id_check – If True, will exclude the db_id attributes from the equality check. Useful for ensuring that all attributes of an object are equal except the ids, with which one or both of them are saved to the database (e.g. if confirming an object before it was saved, to the version reloaded from the DB).

Returns:

  • attribute name to attribute values of unequal type (as a tuple),

  • attribute name to attribute values of unequal value (as a tuple).

Return type:

Two dictionaries

ax.utils.common.equality.same_elements(list1: list[Any], list2: list[Any]) bool[source]

Compare equality of two lists of core Ax objects.

Assumptions:

– The contents of each list are types that implement __eq__ – The lists do not contain duplicates

Checking equality is then the same as checking that the lists are the same length, and that both are subsets of the other.

Executils

ax.utils.common.executils.execute_with_timeout(partial_func: Callable[[...], T], timeout: float) T[source]

Execute a function in a thread that we can abandon if it takes too long. The thread cannot actually be terminated, so the process will keep executing after timeout, but not on the main thread.

Parameters:
  • partial_func – A partial function to execute. This should either be a function that takes no arguments, or a functools.partial function with all arguments bound.

  • timeout – The timeout in seconds.

Returns:

The return value of the partial function when called.

ax.utils.common.executils.handle_exceptions_in_retries(no_retry_exceptions: tuple[type[Exception], ...], retry_exceptions: tuple[type[Exception], ...], suppress_errors: bool, check_message_contains: str | None, last_retry: bool, logger: Logger | None, wrap_error_message_in: str | None) Generator[None, None, None][source]
ax.utils.common.executils.retry_on_exception(exception_types: tuple[type[Exception], ...] | None = None, no_retry_on_exception_types: tuple[type[Exception], ...] | None = None, check_message_contains: list[str] | None = None, retries: int = 3, suppress_all_errors: bool = False, logger: Logger | None = None, default_return_on_suppression: Any | None = None, wrap_error_message_in: str | None = None, initial_wait_seconds: int | None = None) Any | None[source]

A decorator for instance methods or standalone functions that makes them retry on failure and allows to specify on which types of exceptions the function should and should not retry.

NOTE: If the argument suppress_all_errors is supplied and set to True, the error will be suppressed and default value returned.

Parameters:
  • exception_types – A tuple of exception(s) types to catch in the decorated function. If none is provided, baseclass Exception will be used.

  • no_retry_on_exception_types – Exception types to consider non-retryable even if their supertype appears in exception_types or the only exceptions to not retry on if no exception_types are specified.

  • check_message_contains – A list of strings, against which to match error messages. If the error message contains any one of these strings, the exception will cause a retry. NOTE: This argument works in addition to exception_types; if those are specified, only the specified types of exceptions will be caught and retried on if they contain the strings provided as check_message_contains.

  • retries – Number of retries to perform.

  • suppress_all_errors – If true, after all the retries are exhausted, the error will still be suppressed and default_return_on_suppresion will be returned from the function. NOTE: If using this argument, the decorated function may not actually get fully executed, if it consistently raises an exception.

  • logger – A handle for the logger to be used.

  • default_return_on_suppression – If the error is suppressed after all the retries, then this default value will be returned from the function. Defaults to None.

  • wrap_error_message_in – If raising the error message after all the retries, a string wrapper for the error message (useful for making error messages more user-friendly). NOTE: Format of resulting error will be: “<wrap_error_message_in>: <original_error_type>: <original_error_msg>”, with the stack trace of the original message.

  • initial_wait_seconds – Initial length of time to wait between failures, doubled after each failure up to a maximum of 10 minutes. If unspecified then there is no wait between retries.

FuncEnum

class ax.utils.common.func_enum.FuncEnum(value)[source]

Bases: Enum

A base class for all enums with the following structure: string values that map to names of functions, which reside in the same module as the enum.

Kwargs

ax.utils.common.kwargs.consolidate_kwargs(kwargs_iterable: Iterable[dict[str, Any] | None], keywords: Iterable[str]) dict[str, Any][source]

Combine an iterable of kwargs into a single dict of kwargs, where kwargs by duplicate keys that appear later in the iterable get priority over the ones that appear earlier and only kwargs referenced in keywords will be used. This allows to combine somewhat redundant sets of kwargs, where a user-set kwarg, for instance, needs to override a default kwarg.

>>> consolidate_kwargs(
...     kwargs_iterable=[{'a': 1, 'b': 2}, {'b': 3, 'c': 4, 'd': 5}],
...     keywords=['a', 'b', 'd']
... )
{'a': 1, 'b': 3, 'd': 5}
ax.utils.common.kwargs.filter_kwargs(function: Callable, **kwargs: Any) Any[source]

Filter out kwargs that are not applicable for a given function. Return a copy of given kwargs dict with only the required kwargs.

ax.utils.common.kwargs.get_function_argument_names(function: Callable, omit: list[str] | None = None) list[str][source]

Extract parameter names from function signature.

ax.utils.common.kwargs.get_function_default_arguments(function: Callable) dict[str, Any][source]

Extract default arguments from function signature.

ax.utils.common.kwargs.warn_on_kwargs(callable_with_kwargs: Callable, **kwargs: Any) None[source]

Log a warning when a decoder function receives unexpected kwargs.

NOTE: This mainly caters to the use case where an older version of Ax is used to decode objects, serialized to JSON by a newer version of Ax (and therefore potentially containing new fields). In that case, the decoding function should not fail when encountering those additional fields, but rather just ignore them and log a warning using this function.

Logger

class ax.utils.common.logger.AxOutputNameFilter(name='')[source]

Bases: Filter

This is a filter which sets the record’s output_name, if not configured

filter(record: LogRecord) bool[source]

Determine if the specified record is to be logged.

Returns True if the record should be logged, or False otherwise. If deemed appropriate, the record may be modified in-place.

ax.utils.common.logger.build_file_handler(filepath: str, level: int = 20) StreamHandler[source]

Build a file handle that logs entries to the given file, using the same formatting as the stream handler.

Parameters:
  • filepath – Location of the file to log output to. If the file exists, output will be appended. If it does not exist, a new file will be created.

  • level – The log level. By default, sets level to INFO

Returns:

A logging.FileHandler instance

ax.utils.common.logger.build_stream_handler(level: int = 20) StreamHandler[source]

Build the default stream handler used for most Ax logging. Sets default level to INFO, instead of WARNING.

Parameters:

level – The log level. By default, sets level to INFO

Returns:

A logging.StreamHandler instance

class ax.utils.common.logger.disable_logger(name: str, level: int = 40)[source]

Bases: ClassDecorator

decorate_callable(func: Callable[[...], T]) Callable[[...], T][source]
class ax.utils.common.logger.disable_loggers(names: list[str], level: int = 40)[source]

Bases: ClassDecorator

decorate_callable(func: Callable[[...], T]) Callable[[...], T][source]
ax.utils.common.logger.get_logger(name: str, level: int = 20, force_name: bool = False) Logger[source]

Get an Axlogger.

To set a human-readable “output_name” that appears in logger outputs, add {“output_name”: “[MY_OUTPUT_NAME]”} to the logger’s contextual information. By default, we use the logger’s name

NOTE: To change the log level on particular outputs (e.g. STDERR logs), set the proper log level on the relevant handler, instead of the logger e.g. logger.handers[0].setLevel(INFO)

Parameters:
  • name – The name of the logger.

  • level – The level at which to actually log. Logs below this level of importance will be discarded

  • force_name – If set to false and the module specified is not ultimately a descendent of the ax module specified by name, “ax.” will be prepended to name

Returns:

The logging.Logger object.

ax.utils.common.logger.make_indices_str(indices: Iterable[int]) str[source]

Generate a string representation of an iterable of indices; if indices are contiguous, returns a string formatted like like ‘<min_idx> - <max_idx>’, otherwise a string formatted like ‘[idx_1, idx_2, …, idx_n’].

ax.utils.common.logger.set_stderr_log_level(level: int) None[source]

Set the log level for stream handler, such that logs of given level are printed to STDERR by the root logger

Mock Torch

ax.utils.common.mock.mock_patch_method_original(mock_path: str, original_method: Callable[[...], T]) MagicMock[source]

Context manager for patching a method returning type T on class C, to track calls to it while still executing the original method. There is not a native way to do this with mock.patch.

Random

ax.utils.common.random.set_rng_seed(seed: int) None[source]

Sets seeds for random number generators from numpy, pytorch, and the native random module.

Parameters:

seed – The random number generator seed.

ax.utils.common.random.with_rng_seed(seed: int | None) Generator[None, None, None][source]

Context manager that sets the random number generator seeds to a given value and restores the previous state on exit.

If the seed is None, the context manager does nothing. This makes it possible to use the context manager without having to change the code based on whether the seed is specified.

Parameters:

seed – The random number generator seed.

Result

class ax.utils.common.result.Err(value: E)[source]

Bases: Generic[T, E], Result[T, E]

Contains the error value.

property err: E
is_err() bool[source]
is_ok() bool[source]
map(op: Callable[[T], U]) Result[U, E][source]

Maps a Result[T, E] to Result[U, E] by applying a function to a contained Ok value, leaving an Err value untouched. This function can be used to compose the results of two functions.

map_err(op: Callable[[E], F]) Result[T, F][source]

Maps a Result[T, E] to Result[T, F] by applying a function to a contained Err value, leaving an Ok value untouched. This function can be used to pass through a successful result while handling an error.

map_or(default: U, op: Callable[[T], U]) U[source]

Returns the provided default (if Err), or applies a function to the contained value (if Ok).

map_or_else(default_op: Callable[[], U], op: Callable[[T], U]) U[source]

Maps a Result[T, E] to U by applying fallback function default to a contained Err value, or function op to a contained Ok value. This function can be used to unpack a successful result while handling an error.

property ok: None
unwrap() NoReturn[source]

Returns the contained Ok value.

Because this function may raise an UnwrapError, its use is generally discouraged. Instead, prefer to handle the Err case explicitly, or call unwrap_or, unwrap_or_else, or unwrap_or_default.

unwrap_err() E[source]

Returns the contained Err value.

Because this function may raise an UnwrapError, its use is generally discouraged. Instead, prefer to handle the Err case explicitly, or call unwrap_or, unwrap_or_else, or unwrap_or_default.

unwrap_or(default: T) T[source]

Returns the contained Ok value or a provided default.

unwrap_or_else(op: Callable[[E], T]) T[source]

Returns the contained Ok value or computes it from a Callable.

property value: E
class ax.utils.common.result.ExceptionE(message: str, exception: Exception)[source]

Bases: object

A class that holds an Exception and can be used as the E type in Result[T, E].

exception: Exception
message: str
tb_str() str | None[source]
class ax.utils.common.result.Ok(value: T)[source]

Bases: Generic[T, E], Result[T, E]

Contains the success value.

property err: None
is_err() bool[source]
is_ok() bool[source]
map(op: Callable[[T], U]) Result[U, E][source]

Maps a Result[T, E] to Result[U, E] by applying a function to a contained Ok value, leaving an Err value untouched. This function can be used to compose the results of two functions.

map_err(op: Callable[[E], F]) Result[T, F][source]

Maps a Result[T, E] to Result[T, F] by applying a function to a contained Err value, leaving an Ok value untouched. This function can be used to pass through a successful result while handling an error.

map_or(default: U, op: Callable[[T], U]) U[source]

Returns the provided default (if Err), or applies a function to the contained value (if Ok).

map_or_else(default_op: Callable[[], U], op: Callable[[T], U]) U[source]

Maps a Result[T, E] to U by applying fallback function default to a contained Err value, or function op to a contained Ok value. This function can be used to unpack a successful result while handling an error.

property ok: T
unwrap() T[source]

Returns the contained Ok value.

Because this function may raise an UnwrapError, its use is generally discouraged. Instead, prefer to handle the Err case explicitly, or call unwrap_or, unwrap_or_else, or unwrap_or_default.

unwrap_err() NoReturn[source]

Returns the contained Err value.

Because this function may raise an UnwrapError, its use is generally discouraged. Instead, prefer to handle the Err case explicitly, or call unwrap_or, unwrap_or_else, or unwrap_or_default.

unwrap_or(default: U) T[source]

Returns the contained Ok value or a provided default.

unwrap_or_else(op: Callable[[E], T]) T[source]

Returns the contained Ok value or computes it from a Callable.

property value: T
class ax.utils.common.result.Result[source]

Bases: Generic[T, E], ABC

A minimal implementation of a rusty Result monad. See https://doc.rust-lang.org/std/result/enum.Result.html for more information.

abstract property err: E | None
abstract is_err() bool[source]
abstract is_ok() bool[source]
abstract map(op: Callable[[T], U]) Result[U, E][source]

Maps a Result[T, E] to Result[U, E] by applying a function to a contained Ok value, leaving an Err value untouched. This function can be used to compose the results of two functions.

abstract map_err(op: Callable[[E], F]) Result[T, F][source]

Maps a Result[T, E] to Result[T, F] by applying a function to a contained Err value, leaving an Ok value untouched. This function can be used to pass through a successful result while handling an error.

abstract map_or(default: U, op: Callable[[T], U]) U[source]

Returns the provided default (if Err), or applies a function to the contained value (if Ok).

abstract map_or_else(default_op: Callable[[], U], op: Callable[[T], U]) U[source]

Maps a Result[T, E] to U by applying fallback function default to a contained Err value, or function op to a contained Ok value. This function can be used to unpack a successful result while handling an error.

abstract property ok: T | None
abstract unwrap() T[source]

Returns the contained Ok value.

Because this function may raise an UnwrapError, its use is generally discouraged. Instead, prefer to handle the Err case explicitly, or call unwrap_or, unwrap_or_else, or unwrap_or_default.

abstract unwrap_err() E[source]

Returns the contained Err value.

Because this function may raise an UnwrapError, its use is generally discouraged. Instead, prefer to handle the Err case explicitly, or call unwrap_or, unwrap_or_else, or unwrap_or_default.

abstract unwrap_or(default: T) T[source]

Returns the contained Ok value or a provided default.

abstract unwrap_or_else(op: Callable[[E], T]) T[source]

Returns the contained Ok value or computes it from a Callable.

abstract property value: T | E
exception ax.utils.common.result.UnwrapError[source]

Bases: Exception

Exception that indicates something has gone wrong in an unwrap call.

This should not happen in real world use and indicates a user has impropperly or unsafely used the Result abstraction.

Serialization

class ax.utils.common.serialization.SerializationMixin[source]

Bases: object

Base class for Ax objects that define their JSON serialization and deserialization logic at the class level, e.g. most commonly Runner and Metric subclasses.

NOTE: Using this class for Ax objects that receive other Ax objects as inputs, is recommended only iff the parent object (that would be inheriting from this base class) is not enrolled into CORE_ENCODER/DECODER_REGISTRY. Inheriting from this mixin with an Ax object that is in CORE_ENCODER/DECODER_REGISTRY, will result in a circular dependency, so such classes should inplement their encoding and decoding logic within the json_store module and not on the classes.

For example, TransitionCriterion take TrialStatus as inputs and are defined on the CORE_ENCODER/DECODER_REGISTRY, so TransitionCriterion should not inherit from SerializationMixin and should define custom encoding/decoding logic within the json_store module.

classmethod deserialize_init_args(args: dict[str, Any], decoder_registry: dict[str, type[T] | Callable[[...], T]] | None = None, class_decoder_registry: None | dict[str, Callable[[dict[str, Any]], Any]] = None) dict[str, Any][source]

Given a dictionary, deserialize the properties needed to initialize the object. Used for storage.

classmethod serialize_init_args(obj: SerializationMixin) dict[str, Any][source]

Serialize the properties needed to initialize the object. Used for storage.

ax.utils.common.serialization.callable_from_reference(path: str) Callable[source]

Retrieves a callable by its path.

ax.utils.common.serialization.callable_to_reference(callable: Callable) str[source]

Obtains path to the callable of form <module>.<name>.

ax.utils.common.serialization.extract_init_args(args: dict[str, Any], class_: type) dict[str, Any][source]

Given a dictionary, extract the arguments required for the given class’s constructor.

ax.utils.common.serialization.named_tuple_to_dict(data: Any) Any[source]

Recursively convert NamedTuples to dictionaries.

ax.utils.common.serialization.serialize_init_args(obj: Any, exclude_fields: list[str] | None = None) dict[str, Any][source]

Given an object, return a dictionary of the arguments that are needed by its constructor.

Testutils

Support functions for tests

class ax.utils.common.testutils.TestCase(methodName: str = 'runTest')[source]

Bases: TestCase

The base Ax test case, contains various helper functions to write unittests.

assertAxBaseEqual(first: Base, second: Base, msg: str | None = None, skip_db_id_check: bool = False) None[source]

Check that two Ax objects that subclass Base are equal or raise assertion error otherwise.

Parameters:
  • firstBase-subclassing object to compare to second.

  • secondBase-subclassing object to compare to first.

  • msg – Message to put into the assertion error raised on inequality; if not specified, a default message is used.

  • skip_db_id_check

    If True, will exclude the db_id attributes from the equality check. Useful for ensuring that all attributes of an object are equal except the ids, with which one or both of them are saved to the database (e.g. if confirming an object before it was saved, to the

    version reloaded from the DB).

assertDictsAlmostEqual(a: dict[str, Any], b: dict[str, Any], consider_nans_equal: bool = False) None[source]

Testing utility that checks that 1) the keys of a and b are identical, and that 2) the values of a and b are almost equal if they have a floating point type, considering NaNs as equal, and otherwise just equal.

Parameters:
  • test – The test case object.

  • a – A dictionary.

  • b – Another dictionary.

  • consider_nans_equal – Whether to consider NaNs equal when comparing floating point numbers.

assertEqual(first: Any, second: Any, msg: str | None = None) None[source]

Fail if the two objects are unequal as determined by the ‘==’ operator.

assertRaisesOn(exc: type[Exception], line: str | None = None, regex: str | None = None) AbstractContextManager[None][source]

Assert that an exception is raised on a specific line.

setUp() None[source]

Only show log messages of WARNING or higher while testing.

Ax prints a lot of INFO logs that are not relevant for unit tests.

Also silences a number of common warnings originating from Ax & BoTorch.

static silence_stderr() Generator[None, None, None][source]

A context manager that silences stderr for part of a test.

If any exception passes through this context manager the stderr will be printed, otherwise it will be discarded.

ax.utils.common.testutils.setup_import_mocks(mocked_import_paths: list[str], mock_config_dict: dict[str, Any] | None = None) None[source]

This function mocks expensive modules used in tests. It must be called before those modules are imported or it will not work. Stubbing out these modules will obviously affect the behavior of all tests that use it, so be sure modules being mocked are not important to your test. It will also mock all child modules.

Parameters:
  • mocked_import_paths – List of module paths to mock.

  • mock_config_dict – Dictionary of attributes to mock on the modules being mocked. This is useful if the import is expensive, but there is still some functionality it has the test relies on. These attributes will be set on all modules being mocked.

Timeutils

ax.utils.common.timeutils.current_timestamp_in_millis() int[source]

Grab current timestamp in milliseconds as an int.

ax.utils.common.timeutils.timestamps_in_range(start: datetime, end: datetime, delta: timedelta) Generator[datetime, None, None][source]

Generator of timestamps in range [start, end], at intervals delta.

ax.utils.common.timeutils.to_ds(ts: datetime) str[source]

Convert a datetime to a DS string.

ax.utils.common.timeutils.to_ts(ds: str) datetime[source]

Convert a DS string to a datetime.

ax.utils.common.timeutils.unixtime_to_pandas_ts(ts: float) Timestamp[source]

Convert float unixtime into pandas timestamp (UTC).

Typeutils

ax.utils.common.typeutils.checked_cast(typ: type[T], val: V, exception: Exception | None = None) T[source]

Cast a value to a type (with a runtime safety check).

Returns the value unchanged and checks its type at runtime. This signals to the typechecker that the value has the designated type.

Like typing.cast check_cast performs no runtime conversion on its argument, but, unlike typing.cast, checked_cast will throw an error if the value is not of the expected type. The type passed as an argument should be a python class.

Parameters:
  • typ – the type to cast to

  • val – the value that we are casting

  • exception – override exception to raise if typecheck fails

Returns:

the val argument, unchanged

ax.utils.common.typeutils.checked_cast_dict(key_typ: type[K], value_typ: type[V], d: dict[X, Y]) dict[K, V][source]

Calls checked_cast on all keys and values in the dictionary.

ax.utils.common.typeutils.checked_cast_list(typ: type[T], old_l: list[V]) list[T][source]

Calls checked_cast on all items in a list.

ax.utils.common.typeutils.checked_cast_optional(typ: type[T], val: V | None) T | None[source]

Calls checked_cast only if value is not None.

ax.utils.common.typeutils.checked_cast_to_tuple(typ: tuple[type[V], ...], val: V) T[source]

Cast a value to a union of multiple types (with a runtime safety check). This function is similar to checked_cast, but allows for the type to be defined as a tuple of types, in which case the value is cast as a union of the types in the tuple.

Parameters:
  • typ – the tuple of types to cast to

  • val – the value that we are casting

Returns:

the val argument, unchanged

ax.utils.common.typeutils.not_none(val: T | None, message: str | None = None) T[source]

Unbox an optional type.

Parameters:
  • val – the value to cast to a non None type

  • message – optional override of the default error message

Returns:

val when val is not None

Return type:

V

Throws:

ValueError if val is None

Typeutils Non-Native

ax.utils.common.typeutils_nonnative.numpy_type_to_python_type(value: Any) Any[source]

If value is a Numpy int or float, coerce to a Python int or float. This is necessary because some of our transforms return Numpy values.

Typeutils Torch

ax.utils.common.typeutils_torch.torch_type_from_str(identifier: str, type_name: str) dtype | device | Size[source]
ax.utils.common.typeutils_torch.torch_type_to_str(value: dtype | device | Size) str[source]

Converts torch types, commonly used in Ax, to string representations.

Flake8 Plugins

Docstring Checker

ax.utils.flake8_plugins.docstring_checker.A000(node: AST) Error
class ax.utils.flake8_plugins.docstring_checker.DocstringChecker(tree, filename)[source]

Bases: object

A flake8 plug-in that makes sure all public functions have a docstring

fikename: str
name: str = 'docstring checker'
run()[source]
tree: Module
version: str = '1.0.0'
class ax.utils.flake8_plugins.docstring_checker.DocstringCheckerVisitor[source]

Bases: NodeVisitor

check_A000(node: AST) None[source]
errors: list[Error]
visit_AsyncFunctionDef(node: ClassDef) None[source]
visit_ClassDef(node: ClassDef) None[source]
visit_FunctionDef(node: FunctionDef) None[source]
class ax.utils.flake8_plugins.docstring_checker.Error(lineno, col, message, type)[source]

Bases: NamedTuple

col: int

Alias for field number 1

lineno: int

Alias for field number 0

message: str

Alias for field number 2

type: type

Alias for field number 3

ax.utils.flake8_plugins.docstring_checker.is_copy_doc_call(c)[source]

Tries to guess if this is a call to the copy_doc decorator. This is a purely syntactic check so if the decorator was aliased as another name] or wrapped in another function we will fail.

ax.utils.flake8_plugins.docstring_checker.new_error(errorid: str, msg: str) Callable[[AST], Error][source]
ax.utils.flake8_plugins.docstring_checker.should_check(filename)[source]

Measurement

Synthetic Functions

class ax.utils.measurement.synthetic_functions.Aug_Branin[source]

Bases: SyntheticFunction

Augmented Branin function (3-dimensional with infinitely many global minima).

class ax.utils.measurement.synthetic_functions.Aug_Hartmann6[source]

Bases: Hartmann6

Augmented Hartmann6 function (7-dimensional with 1 global minimum).

class ax.utils.measurement.synthetic_functions.Branin[source]

Bases: SyntheticFunction

Branin function (2-dimensional with 3 global minima).

class ax.utils.measurement.synthetic_functions.FromBotorch(botorch_synthetic_function: SyntheticTestFunction)[source]

Bases: SyntheticFunction

property name: str
class ax.utils.measurement.synthetic_functions.Hartmann6[source]

Bases: SyntheticFunction

Hartmann6 function (6-dimensional with 1 global minimum).

class ax.utils.measurement.synthetic_functions.SyntheticFunction[source]

Bases: ABC

property domain: list[tuple[float, float]]

Domain on which function is evaluated.

The list is of the same length as the dimensionality of the inputs, where each element of the list is a tuple corresponding to the min and max of the domain for that dimension.

f(X: ndarray[Any, dtype[_ScalarType_co]]) float | ndarray[Any, dtype[_ScalarType_co]][source]

Synthetic function implementation.

Parameters:

X (numpy.ndarray) – an n by d array, where n represents the number of observations and d is the dimensionality of the inputs.

Returns:

an n-dimensional array.

Return type:

numpy.ndarray

property fmax: float

Value at global minimum(s).

property fmin: float

Value at global minimum(s).

informative_failure_on_none(attr: T | None) T[source]
property maximums: list[tuple[float, ...]]

List of global minimums.

Each element of the list is a d-tuple, where d is the dimensionality of the inputs. There may be more than one global minimums.

property minimums: list[tuple[float, ...]]

List of global minimums.

Each element of the list is a d-tuple, where d is the dimensionality of the inputs. There may be more than one global minimums.

property name: str
property required_dimensionality: int

Required dimensionality of input to this function.

ax.utils.measurement.synthetic_functions.from_botorch(botorch_synthetic_function: SyntheticTestFunction) SyntheticFunction[source]

Utility to generate Ax synthetic functions from BoTorch synthetic functions.

Notebook

Plotting

ax.utils.notebook.plotting.init_notebook_plotting(offline: bool = False) None[source]

Initialize plotting in notebooks, either in online or offline mode.

ax.utils.notebook.plotting.render(plot_config: AxPlotConfig, inject_helpers: bool = False) None[source]

Render plot config.

Report

Render

ax.utils.report.render.h2_html(text: str) str[source]

Embed text in subheading tag.

ax.utils.report.render.h3_html(text: str) str[source]

Embed text in subsubheading tag.

Embed text and reference address into link tag.

ax.utils.report.render.list_item_html(text: str) str[source]

Embed text in list element tag.

ax.utils.report.render.p_html(text: str) str[source]

Embed text in paragraph tag.

ax.utils.report.render.render_report_elements(experiment_name: str, html_elements: list[str], header: bool = True, offline: bool = False, notebook_env: bool = False) str[source]

Generate Ax HTML report for a given experiment from HTML elements.

Uses Jinja2 for template. Injects Plotly JS for graph rendering.

Example:

html_elements = [
    h2_html("Subsection with plot"),
    p_html("This is an example paragraph."),
    plot_html(plot_fitted(gp_model, 'perf_metric')),
    h2_html("Subsection with table"),
    pandas_html(data.df),
]
html = render_report_elements('My experiment', html_elements)
Parameters:
  • experiment_name – the name of the experiment to use for title.

  • html_elements – list of HTML strings to render in report body.

  • header – if True, render experiment title as a header. Meant to be used for standalone reports (e.g. via email), as opposed to served on the front-end.

  • offline – if True, entire Plotly library is bundled with report.

  • notebook_env – if True, caps the report width to 700px for viewing in a notebook environment.

Returns:

HTML string.

Return type:

str

ax.utils.report.render.table_cell_html(text: str, width: str | None = None) str[source]

Embed text or an HTML element into table cell tag.

ax.utils.report.render.table_heading_cell_html(text: str) str[source]

Embed text or an HTML element into table heading cell tag.

ax.utils.report.render.table_html(table_rows: list[str]) str[source]

Embed list of HTML elements into table tag.

ax.utils.report.render.table_row_html(table_cells: list[str]) str[source]

Embed list of HTML elements into table row tag.

ax.utils.report.render.unordered_list_html(list_items: list[str]) str[source]

Embed list of html elements into an unordered list tag.

Sensitivity

Derivative GP

ax.utils.sensitivity.derivative_gp.get_KXX_inv(gp: Model) Tensor[source]

Get the inverse matrix of K(X,X). :param gp: Botorch model.

Returns:

The inverse of K(X,X).

ax.utils.sensitivity.derivative_gp.get_KxX_dx(gp: Model, x: Tensor, kernel_type: str = 'rbf') Tensor[source]

Computes the analytic derivative of the kernel K(x,X) w.r.t. x. :param gp: Botorch model. :param x: (n x D) Test points. :param kernel_type: Takes “rbf” or “matern”

Returns:

Tensor (n x D) The derivative of the kernel K(x,X) w.r.t. x.

ax.utils.sensitivity.derivative_gp.get_Kxx_dx2(gp: Model, kernel_type: str = 'rbf') Tensor[source]

Computes the analytic second derivative of the kernel w.r.t. the training data :param gp: Botorch model. :param kernel_type: Takes “rbf” or “matern”

Returns:

Tensor (n x D x D) The second derivative of the kernel w.r.t. the training data.

ax.utils.sensitivity.derivative_gp.posterior_derivative(gp: Model, x: Tensor, kernel_type: str = 'rbf') MultivariateNormal[source]

Computes the posterior of the derivative of the GP w.r.t. the given test points x. This follows the derivation used by GIBO in Sarah Muller, Alexander von Rohr, Sebastian Trimpe. “Local policy search with Bayesian optimization”, Advances in Neural Information Processing Systems 34, NeurIPS 2021. :param gp: Botorch model :param x: (n x D) Test points. :param kernel_type: Takes “rbf” or “matern”

Returns:

A Botorch Posterior.

Derivative Measures

class ax.utils.sensitivity.derivative_measures.GpDGSMGpMean(model: Model, bounds: Tensor, derivative_gp: bool = False, kernel_type: str | None = None, Y_scale: float = 1.0, num_mc_samples: int = 10000, input_qmc: bool = False, dtype: dtype = torch.float64, num_bootstrap_samples: int = 1, discrete_features: list[int] | None = None)[source]

Bases: object

aggregation(transform_fun: Callable[[Tensor], Tensor]) Tensor[source]
bootstrap_indices: Tensor | None = None
gradient_absolute_measure() Tensor[source]

Computes the gradient absolute measure:

Returns:

if self.num_bootstrap_samples > 1

Tensor: (values, var_mc, stderr_mc) x dim

else

Tensor: (values) x dim

gradient_measure() Tensor[source]

Computes the gradient measure:

Returns:

if self.num_bootstrap_samples > 1

Tensor: (values, var_mc, stderr_mc) x dim

else

Tensor: (values) x dim

gradients_square_measure() Tensor[source]

Computes the gradient square measure:

Returns:

if num_bootstrap_samples > 1

Tensor: (values, var_mc, stderr_mc) x dim

else

Tensor: (values) x dim

mean_gradients: Tensor | None = None
mean_gradients_btsp: list[Tensor] | None = None
class ax.utils.sensitivity.derivative_measures.GpDGSMGpSampling(model: Model, bounds: Tensor, num_gp_samples: int, derivative_gp: bool = False, kernel_type: str | None = None, Y_scale: float = 1.0, num_mc_samples: int = 10000, input_qmc: bool = False, gp_sample_qmc: bool = False, dtype: dtype = torch.float64, num_bootstrap_samples: int = 1)[source]

Bases: GpDGSMGpMean

aggregation(transform_fun: Callable[[Tensor], Tensor]) Tensor[source]
samples_gradients: Tensor | None = None
samples_gradients_btsp: list[Tensor] | None = None
ax.utils.sensitivity.derivative_measures.compute_derivatives_from_model_list(model_list: list[Model], bounds: Tensor, discrete_features: list[int] | None = None, **kwargs: Any) Tensor[source]

Computes average derivatives of a list of models on a bounded domain. Estimation is according to the GP posterior mean function.

Parameters:
  • model_list – A list of m botorch.models.model.Model types for which to compute the average derivative.

  • bounds – A 2 x d Tensor of lower and upper bounds of the domain of the models.

  • discrete_features – If specified, the inputs associated with the indices in this list are generated using an integer-valued uniform distribution, rather than the default (pseudo-)random continuous uniform distribution.

  • kwargs – Passed along to GpDGSMGpMean.

Returns:

A (m x d) tensor of gradient measures.

ax.utils.sensitivity.derivative_measures.sample_discrete_parameters(input_mc_samples: Tensor, discrete_features: None | list[int], bounds: Tensor, num_mc_samples: int) Tensor[source]

Samples the input parameters uniformly at random for the discrete features.

Parameters:
  • input_mc_samples – The input mc samples tensor to be modified.

  • discrete_features – A list of integers (or None) of indices corresponding to discrete features.

  • bounds – The parameter bounds.

  • num_mc_samples – The number of Monte Carlo grid samples.

Returns:

A modified input mc samples tensor.

Sobol Measures

ax.utils.sensitivity.sobol_measures.GaussianLinkMean(mean: Tensor, var: Tensor) Tensor[source]
ax.utils.sensitivity.sobol_measures.ProbitLinkMean(mean: Tensor, var: Tensor) Tensor[source]
class ax.utils.sensitivity.sobol_measures.SobolSensitivity(bounds: Tensor, input_function: Callable[[Tensor], Tensor] | None = None, num_mc_samples: int = 10000, input_qmc: bool = False, second_order: bool = False, first_order_idcs: Tensor | None = None, num_bootstrap_samples: int = 1, bootstrap_array: bool = False, discrete_features: list[int] | None = None)[source]

Bases: object

evalute_function(f_A_B_ABi: Tensor | None = None) None[source]
evaluates the objective function and devides the evaluation into

torch.Tensors needed for the indices computation.

Parameters:

f_A_B_ABi – Function evaluations on the entire grid of size M(d+2).

first_order_indices() Tensor[source]

Computes the first order Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values,var_mc,stderr_mc)x dim

else

Tensor: (values)x dim

generate_all_input_matrix() Tensor[source]
second_order_indices(first_order_idcs: Tensor | None = None, first_order_idcs_btsp: Tensor | None = None) Tensor[source]

Computes the Second order Sobol indices: :param first_order_idcs: Tensor of previously computed first order indices, where

first_order_idcs.shape = torch.Size([dim]).

Parameters:

first_order_idcs_btsp – Tensor of all first order indices given by bootstrap.

Returns:

if num_bootstrap_samples>1

Tensor: (values,var_mc,stderr_mc)x dim

else

Tensor: (values)x dim

total_order_indices() Tensor[source]

Computes the total Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values,var_mc,stderr_mc)x dim

else

Tensor: (values)x dim

class ax.utils.sensitivity.sobol_measures.SobolSensitivityGPMean(model: ~botorch.models.model.Model, bounds: ~torch.Tensor, num_mc_samples: int = 10000, second_order: bool = False, input_qmc: bool = False, num_bootstrap_samples: int = 1, first_order_idcs: ~torch.Tensor | None = None, link_function: ~collections.abc.Callable[[~torch.Tensor, ~torch.Tensor], ~torch.Tensor] = <function GaussianLinkMean>, mini_batch_size: int = 128, discrete_features: list[int] | None = None)[source]

Bases: object

first_order_indices() Tensor[source]

Computes the first order Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values,var_mc,stderr_mc)x dim

else

Tensor: (values)x dim

second_order_indices() Tensor[source]

Computes the Second order Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values,var_mc,stderr_mc)x dim(dim-1)/2

else

Tensor: (values)x dim(dim-1)/2

total_order_indices() Tensor[source]

Computes the total Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values,var_mc,stderr_mc)x dim

else

Tensor: (values)x dim

class ax.utils.sensitivity.sobol_measures.SobolSensitivityGPSampling(model: Model, bounds: Tensor, num_gp_samples: int = 1000, num_mc_samples: int = 10000, second_order: bool = False, input_qmc: bool = False, gp_sample_qmc: bool = False, num_bootstrap_samples: int = 1, discrete_features: list[int] | None = None)[source]

Bases: object

property dim: int

Returns the input dimensionality of self.model.

first_order_indices() Tensor[source]

Computes the first order Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values, var_gp, stderr_gp, var_mc, stderr_mc) x dim

else

Tensor: (values, var, stderr) x dim

second_order_indices() Tensor[source]

Computes the Second order Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values, var_gp, stderr_gp, var_mc, stderr_mc) x dim(dim-1) / 2

else

Tensor: (values, var, stderr) x dim(dim-1) / 2

total_order_indices() Tensor[source]

Computes the total Sobol indices:

Returns:

if num_bootstrap_samples>1

Tensor: (values, var_gp, stderr_gp, var_mc, stderr_mc) x dim

else

Tensor: (values, var, stderr) x dim

ax.utils.sensitivity.sobol_measures.array_with_string_indices_to_dict(rows: list[str], cols: list[str], A: ndarray[Any, dtype[_ScalarType_co]]) dict[str, dict[str, ndarray[Any, dtype[_ScalarType_co]]]][source]
Parameters:
  • rows – A list of strings with which to index rows of A.

  • cols – A list of strings with which to index columns of A.

  • A – A matrix, with len(rows) rows and len(cols) columns.

Returns:

A dictionary dict that satisfies dict[rows[i]][cols[j]] = A[i, j].

ax.utils.sensitivity.sobol_measures.ax_parameter_sens(model_bridge: TorchModelBridge, metrics: list[str] | None = None, order: str = 'first', signed: bool = True, **sobol_kwargs: Any) dict[str, dict[str, ndarray[Any, dtype[_ScalarType_co]]]][source]

Compute sensitivity for all metrics on an TorchModelBridge.

Sobol measures are always positive regardless of the direction in which the parameter influences f. If signed is set to True, then the Sobol measure for each parameter will be given as its sign the sign of the average gradient with respect to that parameter across the search space. Thus, important parameters that, when increased, decrease f will have large and negative values; unimportant parameters will have values close to 0.

Parameters:
  • model_bridge – A ModelBridge object with models that were fit.

  • metrics – The names of the metrics and outcomes for which to compute sensitivities. This should preferably be metrics with a good model fit. Defaults to model_bridge.outcomes.

  • order – A string specifying the order of the Sobol indices to be computed. Supports “first” and “total” and defaults to “first”.

  • signed – A bool for whether the measure should be signed.

  • sobol_kwargs – keyword arguments passed on to SobolSensitivityGPMean, and if signed, GpDGSMGpMean.

Returns:

{‘parameter_name’ or (parameter_name_1, ‘parameter_name_2’): sensitivity_value}}, where the sensitivity value is cast to a Numpy array in order to be compatible with plot_feature_importance_by_feature.

Return type:

Dictionary {‘metric_name’

ax.utils.sensitivity.sobol_measures.compute_sobol_indices_from_model_list(model_list: list[Model], bounds: Tensor, order: str = 'first', discrete_features: list[int] | None = None, **sobol_kwargs: Any) Tensor[source]

Computes Sobol indices of a list of models on a bounded domain.

Parameters:
  • model_list – A list of botorch.models.model.Model types for which to compute the Sobol indices.

  • bounds – A 2 x d Tensor of lower and upper bounds of the domain of the models.

  • order – A string specifying the order of the Sobol indices to be computed. Supports “first”, “second” and “total” and defaults to “first”. “total” computes the importance of a variable considering its main effect and all of its higher-order interactions, whereas “first” and “second” the variance when altering the variable in isolation or with one other variable, respectively.

  • discrete_features – If specified, the inputs associated with the indices in this list are generated using an integer-valued uniform distribution, rather than the default (pseudo-)random continuous uniform distribution.

  • sobol_kwargs – keyword arguments passed on to SobolSensitivityGPMean.

Returns:

With m GPs, returns a (m x d) tensor of order-order Sobol indices.

Stats

Statstools

ax.utils.stats.statstools.agresti_coull_sem(n_numer: Series | ndarray[Any, dtype[_ScalarType_co]] | int, n_denom: Series | ndarray[Any, dtype[_ScalarType_co]] | int, prior_successes: int = 2, prior_failures: int = 2) ndarray[Any, dtype[_ScalarType_co]] | float[source]

Compute the Agresti-Coull style standard error for a binomial proportion.

Reference: Agresti, Alan, and Brent A. Coull. Approximate Is Better than ‘Exact’ for Interval Estimation of Binomial Proportions.” The American Statistician, vol. 52, no. 2, 1998, pp. 119-126. JSTOR, www.jstor.org/stable/2685469.

ax.utils.stats.statstools.inverse_variance_weight(means: ndarray[Any, dtype[_ScalarType_co]], variances: ndarray[Any, dtype[_ScalarType_co]], conflicting_noiseless: str = 'warn') tuple[float, float][source]

Perform inverse variance weighting.

Parameters:
  • means – The means of the observations.

  • variances – The variances of the observations.

  • conflicting_noiseless – How to handle the case of multiple observations with zero variance but different means. Options are “warn” (default), “ignore” or “raise”.

ax.utils.stats.statstools.marginal_effects(df: DataFrame) DataFrame[source]

This method calculates the relative (in %) change in the outcome achieved by using any individual factor level versus randomizing across all factor levels. It does this by estimating a baseline under the experiment by marginalizing over all factors/levels. For each factor level, then, it conditions on that level for the individual factor and then marginalizes over all levels for all other factors.

Parameters:

df – Dataframe containing columns named mean and sem. All other columns are assumed to be factors for which to calculate marginal effects.

Returns:

A dataframe containing columns “Name”, “Level”, “Beta” and “SE”

corresponding to the factor, level, effect and standard error. Results are relativized as percentage changes.

ax.utils.stats.statstools.positive_part_james_stein(means: ndarray | list[float], sems: ndarray | list[float]) tuple[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]][source]

Estimation method for Positive-part James-Stein estimator.

This method takes a vector of K means (y_i) and standard errors (sigma_i) and calculates the positive-part James Stein estimator.

Resulting estimates are the shrunk means and standard errors. The positive part James-Stein estimator shrinks each constituent average to the grand average:

y_i - phi_i * y_i + phi_i * ybar

The variable phi_i determines the amount of shrinkage. For phi_i = 1, mu_hat is equal to ybar (the mean of all y_i), while for phi_i = 0, mu_hat is equal to y_i. It can be shown that restricting phi_i <= 1 dominates the unrestricted estimator, so this method restricts phi_i in this manner. The amount of shrinkage, phi_i, is determined by:

(K - 3) * sigma2_i / s2

That is, less shrinkage is applied when individual means are estimated with greater precision, and more shrinkage is applied when individual means are very tightly clustered together. We also restrict phi_i to never be larger than 1.

The variance of the mean estimator is:

(1 - phi_i) * sigma2_i + phi * sigma2_i / K + 2 * phi_i ** 2 * (y_i - ybar)^2 / (K - 3)

The first term is the variance component from y_i, the second term is the contribution from the mean of all y_i, and the third term is the contribution from the uncertainty in the sum of squared deviations of y_i from the mean of all y_i.

For more information, see https://ax.dev/docs/models.html#empirical-bayes-and-thompson-sampling.

Parameters:
  • means – Means of each arm

  • sems – Standard errors of each arm

Returns:

Empirical Bayes estimate of each arm’s mean sem_i: Empirical Bayes estimate of each arm’s sem

Return type:

mu_hat_i

ax.utils.stats.statstools.relativize(means_t: ndarray[Any, dtype[_ScalarType_co]] | list[float] | float, sems_t: ndarray[Any, dtype[_ScalarType_co]] | list[float] | float, mean_c: float, sem_c: float, bias_correction: bool = True, cov_means: ndarray[Any, dtype[_ScalarType_co]] | list[float] | float = 0.0, as_percent: bool = False, control_as_constant: bool = False) tuple[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]][source]

Ratio estimator based on the delta method.

This uses the delta method (i.e. a Taylor series approximation) to estimate the mean and standard deviation of the sampling distribution of the ratio between test and control – that is, the sampling distribution of an estimator of the true population value under the assumption that the means in test and control have a known covariance:

(mu_t / mu_c) - 1.

Under a second-order Taylor expansion, the sampling distribution of the relative change in empirical means, which is m_t / m_c - 1, is approximately normally distributed with mean

[(mu_t - mu_c) / mu_c] - [(sigma_c)^2 * mu_t] / (mu_c)^3

and variance

(sigma_t / mu_c)^2 - 2 * mu_t _ sigma_tc / mu_c^3 + [(sigma_c * mu_t)^2 / (mu_c)^4]

as the higher terms are assumed to be close to zero in the full Taylor series. To estimate these parameters, we plug in the empirical means and standard errors. This gives us the estimators:

[(m_t - m_c) / m_c] - [(s_c)^2 * m_t] / (m_c)^3

and

(s_t / m_c)^2 - 2 * m_t * s_tc / m_c^3 + [(s_c * m_t)^2 / (m_c)^4]

Note that the delta method does NOT take as input the empirical standard deviation of a metric, but rather the standard error of the mean of that metric – that is, the standard deviation of the metric after division by the square root of the total number of observations.

Parameters:
  • means_t – Sample means (test)

  • sems_t – Sample standard errors of the means (test)

  • mean_c – Sample mean (control)

  • sem_c – Sample standard error of the mean (control)

  • bias_correction – Whether to apply bias correction when computing relativized metric values. Uses a second-order Taylor expansion for approximating the means and standard errors of the ratios.

  • cov_means – Sample covariance between test and control

  • as_percent – If true, return results in percent (* 100)

  • control_as_constant – If true, control is treated as a constant. bias_correction, sem_c, and cov_means are ignored when this is true.

Returns:

Inferred means of the sampling distribution of

the relative change (mean_t - mean_c) / abs(mean_c)

sem_hat: Inferred standard deviation of the sampling

distribution of rel_hat – i.e. the standard error.

Return type:

rel_hat

ax.utils.stats.statstools.relativize_data(data: Data, status_quo_name: str = 'status_quo', as_percent: bool = False, include_sq: bool = False, bias_correction: bool = True, control_as_constant: bool = False) Data[source]

Relativize a data object w.r.t. a status_quo arm.

Parameters:
  • data – The data object to be relativized.

  • status_quo_name – The name of the status_quo arm.

  • as_percent – If True, return results as percentage change.

  • include_sq – Include status quo in final df.

  • bias_correction – Whether to apply bias correction when computing relativized metric values. Uses a second-order Taylor expansion for approximating the means and standard errors or the ratios, see ax.utils.stats.statstools.relativize for more details.

  • control_as_constant – If true, control is treated as a constant. bias_correction is ignored when this is true.

Returns:

The new data object with the relativized metrics (excluding the

status_quo arm)

ax.utils.stats.statstools.total_variance(means: ndarray[Any, dtype[_ScalarType_co]], variances: ndarray[Any, dtype[_ScalarType_co]], sample_sizes: ndarray[Any, dtype[_ScalarType_co]]) float[source]

Compute total variance.

ax.utils.stats.statstools.unrelativize(means_t: ndarray[Any, dtype[_ScalarType_co]] | list[float] | float, sems_t: ndarray[Any, dtype[_ScalarType_co]] | list[float] | float, mean_c: float, sem_c: float, bias_correction: bool = True, cov_means: ndarray[Any, dtype[_ScalarType_co]] | list[float] | float = 0.0, as_percent: bool = False, control_as_constant: bool = False) tuple[ndarray[Any, dtype[_ScalarType_co]], ndarray[Any, dtype[_ScalarType_co]]][source]

Reverse operation of ax.utils.stats.statstools.relativize.

Parameters:
  • means_t – Relativized sample means (test) to be unrelativized

  • sems_t – Relativized sample SEM of the means (test) to be unrelativized

  • mean_c – Unrelativized control mean

  • sem_c – Unrelativized control SEM of the mean

  • bias_correction – if means_t and sems_t are obtained with bias_correction=True in ax.utils.stats.statstools.relativize

  • cov_means – Sample covariance between the unrelativized test and control

  • as_percent – If true, assuming means_t and sems_t are percentages (i.e., 1 means 1%).

  • control_as_constant – If true, control is treated as a constant. bias_correction, sem_c, and cov_means are ignored when this is true.

Returns:

Inferred sample (test) means in the unrelativized scale s_t: Inferred SEM of sample (test) means in the unrelativized scale

Return type:

m_t

Model Fit Metrics

ax.utils.stats.model_fit_stats.DEFAULT_KDE_BANDWIDTH = 0.1

################################ Model Fit Metrics ###############################

class ax.utils.stats.model_fit_stats.ModelFitMetricProtocol(*args, **kwargs)[source]

Bases: Protocol

Structural type for model fit metrics.

ax.utils.stats.model_fit_stats.coefficient_of_determination(y_obs: ndarray[Any, dtype[_ScalarType_co]], y_pred: ndarray[Any, dtype[_ScalarType_co]], se_pred: ndarray[Any, dtype[_ScalarType_co]] | None = None, eps: float = 1e-12) float[source]

Computes coefficient of determination, the proportion of variance in y_obs accounted for by predictions y_pred.

Parameters:
  • y_obs – An array of observations for a single metric.

  • y_pred – An array of the predicted values corresponding to y_obs.

  • se_pred – Not used, kept for API compatibility.

  • eps – A small constant to add to the denominator for numerical stability.

Returns:

The scalar coefficient of determination, “R squared”.

ax.utils.stats.model_fit_stats.compute_model_fit_metrics(y_obs: Mapping[str, ndarray[Any, dtype[_ScalarType_co]]], y_pred: Mapping[str, ndarray[Any, dtype[_ScalarType_co]]], se_pred: Mapping[str, ndarray[Any, dtype[_ScalarType_co]]], fit_metrics_dict: Mapping[str, ModelFitMetricProtocol]) dict[str, dict[str, float]][source]

Computes the model fit metrics for each experimental metric in the input dicts.

Parameters:
  • y_obs – A dictionary mapping from experimental metric name to observed values.

  • y_pred – A dictionary mapping from experimental metric name to predicted values.

  • se_pred – A dictionary mapping from experimental metric name to predicted standard errors.

  • fit_metrics_dict – A dictionary mapping from model fit metric name to a ModelFitMetricProtocol function that evaluates a model fit metric.

Returns:

A nested dictionary mapping from model fit and experimental metric names to their corresponding model fit metrics values.

ax.utils.stats.model_fit_stats.entropy_of_observations(y_obs: ndarray[Any, dtype[_ScalarType_co]], y_pred: ndarray[Any, dtype[_ScalarType_co]], se_pred: ndarray[Any, dtype[_ScalarType_co]], bandwidth: float = 0.1) float[source]

Computes the entropy of the observations y_obs using a kernel density estimator. This can be used to quantify how “clustered” the outcomes are. NOTE: y_pred and se_pred are not used, but are required for the API.

Parameters:
  • y_obs – An array of observations for a single metric.

  • y_pred – Unused.

  • se_pred – Unused.

  • bandwidth – The kernel bandwidth. Defaults to 0.1, which is a reasonable value for standardized outcomes y_obs. The rank ordering of the results on a set of y_obs data sets is not generally sensitive to the bandwidth, if it is held fixed across the data sets. The absolute value of the results however changes significantly with the bandwidth.

Returns:

The scalar entropy of the observations.

ax.utils.stats.model_fit_stats.mean_of_the_standardized_error(y_obs: ndarray[Any, dtype[_ScalarType_co]], y_pred: ndarray[Any, dtype[_ScalarType_co]], se_pred: ndarray[Any, dtype[_ScalarType_co]]) float[source]

Computes the mean of the error standardized by the predictive standard deviation of the model se_pred. If the model makes good predictions and its uncertainty is quantified well, should be close to 0 and be normally distributed.

NOTE: This assumes that se_pred is the predictive standard deviation of the observations of the objective y, not the predictive standard deviation of the objective f itself. In practice, this will matter for very noisy observations.

Parameters:
  • y_obs – An array of observations for a single metric.

  • y_pred – An array of the predicted values corresponding to y_obs.

  • se_pred – An array of the standard errors of the predicted values.

Returns:

The scalar mean of the standardized error.

ax.utils.stats.model_fit_stats.std_of_the_standardized_error(y_obs: ndarray[Any, dtype[_ScalarType_co]], y_pred: ndarray[Any, dtype[_ScalarType_co]], se_pred: ndarray[Any, dtype[_ScalarType_co]]) float[source]

Standard deviation of the error standardized by the predictive standard deviation of the model se_pred. If the uncertainty is quantified well, should be close to 1.

NOTE: This assumes that se_pred is the predictive standard deviation of the observations of the objective y, not the predictive standard deviation of the objective f itself. In practice, this will matter for very noisy observations.

Parameters:
  • y_obs – An array of observations for a single metric.

  • y_pred – An array of the predicted values corresponding to y_obs.

  • se_pred – An array of the standard errors of the predicted values.

Returns:

The scalar standard deviation of the standardized error.

Testing

Backend Scheduler

Backend Simulator

class ax.utils.testing.backend_simulator.BackendSimulator(options: BackendSimulatorOptions | None = None, queued: list[SimTrial] | None = None, running: list[SimTrial] | None = None, failed: list[SimTrial] | None = None, completed: list[SimTrial] | None = None, verbose_logging: bool = True)[source]

Bases: object

Simulator for a backend deployment with concurrent dispatch and a queue.

property all_trials: list[SimTrial]

All trials on the simulator.

classmethod from_state(state: BackendSimulatorState)[source]

Construct a simulator from a state.

Parameters:

state – A BackendSimulatorState to set the simulator to.

Returns:

A BackendSimulator with the desired state.

get_sim_trial_by_index(trial_index: int) SimTrial | None[source]

Get a SimTrial by trial_index.

Parameters:

trial_index – The index of the trial to return.

Returns:

A SimTrial with the index trial_index or None if not found.

lookup_trial_index_status(trial_index: int) TrialStatus | None[source]

Lookup the trial status of a trial_index.

Parameters:

trial_index – The index of the trial to check.

Returns:

A TrialStatus.

new_trial(trial: SimTrial, status: TrialStatus) None[source]

Register a trial into the simulator.

Parameters:
  • trial – A new trial to add.

  • status – The status of the new trial, either STAGED (add to self._queued) or RUNNING (add to self._running).

property num_completed: int

The number of completed trials.

property num_failed: int

The number of failed trials.

property num_queued: int

The number of queued trials (to run as soon as capacity is available).

property num_running: int

The number of currently running trials.

reset() None[source]

Reset the simulator.

run_trial(trial_index: int, runtime: float) None[source]

Run a simulated trial.

Parameters:
  • trial_index – The index of the trial (usually the Ax trial index)

  • runtime – The runtime of the simulation. Typically sampled from the runtime model of a simulation model.

Internally, the runtime is scaled by the time_scaling factor, so that the simulation can run arbitrarily faster than the underlying evaluation.

state() BackendSimulatorState[source]

Return a BackendSimulatorState containing the state of the simulator.

status() SimStatus[source]

Return the internal status of the simulator.

Returns:

A SimStatus object representing the current simulator state.

stop_trial(trial_index: int) None[source]

Stop a simulated trial by setting the completed time to the current time.

Parameters:

trial_index – The index of the trial to stop.

property time: float

The current time.

update() None[source]

Update the state of the simulator.

property use_internal_clock: bool

Whether or not we are using the internal clock.

class ax.utils.testing.backend_simulator.BackendSimulatorOptions(max_concurrency: int = 1, time_scaling: float = 1.0, failure_rate: float = 0.0, internal_clock: float | None = None, use_update_as_start_time: bool = False)[source]

Bases: object

Settings for the BackendSimulator.

Parameters:
  • max_concurrency – The maximum number of trials that can be run in parallel.

  • time_scaling – The factor to scale down the runtime of the tasks by. If runtime is the actual runtime of a trial, the simulation time will be runtime / time_scaling.

  • failure_rate – The rate at which the trials are failing. For now, trials fail independently with at coin flip based on that rate.

  • internal_clock – The initial state of the internal clock. If None, the simulator uses time.time() as the clock.

  • use_update_as_start_time – Whether the start time of a new trial should be logged as the current time (at time of update) or end time of previous trial. This makes sense when using the internal clock and the BackendSimulator is simulated forward by an external process (such as Scheduler).

failure_rate: float = 0.0
internal_clock: float | None = None
max_concurrency: int = 1
time_scaling: float = 1.0
use_update_as_start_time: bool = False
class ax.utils.testing.backend_simulator.BackendSimulatorState(options: BackendSimulatorOptions, verbose_logging: bool, queued: list[dict[str, float | None]], running: list[dict[str, float | None]], failed: list[dict[str, float | None]], completed: list[dict[str, float | None]])[source]

Bases: object

State of the BackendSimulator.

Parameters:
  • options – The BackendSimulatorOptions associated with this simulator.

  • verbose_logging – Whether the simulator is using verbose logging.

  • queued – Currently queued trials.

  • running – Currently running trials.

  • failed – Currently failed trials.

  • completed – Currently completed trials.

completed: list[dict[str, float | None]]
failed: list[dict[str, float | None]]
options: BackendSimulatorOptions
queued: list[dict[str, float | None]]
running: list[dict[str, float | None]]
verbose_logging: bool
class ax.utils.testing.backend_simulator.SimStatus(queued: list[int], running: list[int], failed: list[int], time_remaining: list[float], completed: list[int])[source]

Bases: object

Container for status of the simulation.

queued

List of indices of queued trials.

Type:

list[int]

running

List of indices of running trials.

Type:

list[int]

failed

List of indices of failed trials.

Type:

list[int]

time_remaining

List of sim time remaining for running trials.

Type:

list[float]

completed

List of indicies of completed trials.

Type:

list[int]

completed: list[int]
failed: list[int]
queued: list[int]
running: list[int]
time_remaining: list[float]
class ax.utils.testing.backend_simulator.SimTrial(trial_index: int, sim_runtime: float, sim_start_time: float | None = None, sim_queued_time: float | None = None, sim_completed_time: float | None = None)[source]

Bases: object

Container for the simulation tasks.

trial_index

The index of the trial (should match Ax trial index).

Type:

int

sim_runtime

The runtime of the trial (sampled at creation).

Type:

float

sim_start_time

When the trial started running (or exits queued state).

Type:

float | None

sim_queued_time

When the trial was initially queued.

Type:

float | None

sim_completed_time

When the trial was marked as completed. Currently, this is used by an early-stopper via stop_trial.

Type:

float | None

sim_completed_time: float | None = None
sim_queued_time: float | None = None
sim_runtime: float
sim_start_time: float | None = None
trial_index: int
ax.utils.testing.backend_simulator.format(trial_list: list[dict[str, float | None]]) str[source]

Helper function for formatting a list.

Benchmark Stubs

class ax.utils.testing.benchmark_stubs.DummyTestFunction(*, outcome_names: list[str] = <factory>, num_outcomes: int = 1, dim: int = 6)[source]

Bases: BenchmarkTestFunction

dim: int = 6
evaluate_true(params: dict[str, float]) Tensor[source]

Evaluate noiselessly.

Returns:

1d tensor of shape (len(outcome_names),).

num_outcomes: int = 1
outcome_names: list[str]
class ax.utils.testing.benchmark_stubs.TestDataset(root: str = '', train: bool = True, download: bool = True, transform: Any | None = None)[source]

Bases: Dataset

ax.utils.testing.benchmark_stubs.get_aggregated_benchmark_result() AggregatedBenchmarkResult[source]
ax.utils.testing.benchmark_stubs.get_benchmark_result() BenchmarkResult[source]
ax.utils.testing.benchmark_stubs.get_moo_surrogate() BenchmarkProblem[source]
ax.utils.testing.benchmark_stubs.get_multi_objective_benchmark_problem(observe_noise_sd: bool = False, num_trials: int = 4, test_problem_class: type[~botorch.test_functions.multi_objective.BraninCurrin] = <class 'botorch.test_functions.multi_objective.BraninCurrin'>, report_inference_value_as_trace: bool = False) BenchmarkProblem[source]
ax.utils.testing.benchmark_stubs.get_single_objective_benchmark_problem(observe_noise_sd: bool = False, num_trials: int = 4, test_problem_kwargs: dict[str, Any] | None = None, report_inference_value_as_trace: bool = False, noise_std: float | list[float] = 0.0) BenchmarkProblem[source]
ax.utils.testing.benchmark_stubs.get_sobol_gpei_benchmark_method() BenchmarkMethod[source]
ax.utils.testing.benchmark_stubs.get_soo_surrogate() BenchmarkProblem[source]
ax.utils.testing.benchmark_stubs.get_soo_surrogate_test_function(lazy: bool = True) SurrogateTestFunction[source]

Core Stubs

class ax.utils.testing.core_stubs.CustomTestMetric(name: str, test_attribute: str, lower_is_better: bool | None = None)[source]

Bases: Metric

class ax.utils.testing.core_stubs.CustomTestRunner(test_attribute: str)[source]

Bases: Runner

run(trial: BaseTrial) dict[str, Any][source]

Deploys a trial based on custom runner subclass implementation.

Parameters:

trial – The trial to deploy.

Returns:

Dict of run metadata from the deployment process.

class ax.utils.testing.core_stubs.DummyEarlyStoppingStrategy(early_stop_trials: dict[int, str | None] | None = None)[source]

Bases: BaseEarlyStoppingStrategy

should_stop_trials_early(trial_indices: set[int], experiment: Experiment, **kwargs: dict[str, Any]) dict[int, str | None][source]

Decide whether to complete trials before evaluation is fully concluded.

Typical examples include stopping a machine learning model’s training, or halting the gathering of samples before some planned number are collected.

Parameters:
  • trial_indices – Indices of candidate trials to stop early.

  • experiment – Experiment that contains the trials and other contextual data.

Returns:

A dictionary mapping trial indices that should be early stopped to (optional) messages with the associated reason.

class ax.utils.testing.core_stubs.DummyGlobalStoppingStrategy(min_trials: int, trial_to_stop: int)[source]

Bases: BaseGlobalStoppingStrategy

A dummy Global Stopping Strategy which stops the optimization after a pre-specified number of trials are completed.

class ax.utils.testing.core_stubs.SpecialGenerationStrategy[source]

Bases: GenerationStrategyInterface

A subclass of GenerationStrategyInterface to be used for testing how methods respond to subtypes other than GenerationStrategy.

clone_reset() SpecialGenerationStrategy[source]

Returns a clone of this generation strategy with all state reset.

gen_for_multiple_trials_with_multiple_models(experiment: Experiment, data: Data | None = None, pending_observations: dict[str, list[ObservationFeatures]] | None = None, n: int | None = None, num_trials: int = 1, arms_per_node: dict[str, int] | None = None) list[list[GeneratorRun]][source]

Produce GeneratorRun-s for multiple trials at once with the possibility of joining GeneratorRun-s from multiple models into one BatchTrial.

Parameters:
  • experimentExperiment, for which the generation strategy is producing a new generator run in the course of gen, and to which that generator run will be added as trial(s). Information stored on the experiment (e.g., trial statuses) is used to determine which model will be used to produce the generator run returned from this method.

  • data – Optional data to be passed to the underlying model’s gen, which is called within this method and actually produces the resulting generator run. By default, data is all data on the experiment.

  • pending_observations – A map from metric name to pending observations for that metric, used by some models to avoid resuggesting points that are currently being evaluated.

  • n – Integer representing how many total arms should be in the generator runs produced by this method. NOTE: Some underlying models may ignore the n and produce a model-determined number of arms. In that case this method will also output generator runs with number of arms that can differ from n.

  • num_trials – Number of trials to generate generator runs for in this call. If not provided, defaults to 1.

  • arms_per_node – An optional map from node name to the number of arms to generate from that node. If not provided, will default to the number of arms specified in the node’s InputConstructors or n if no InputConstructors are defined on the node. We expect either n or arms_per_node to be provided, but not both, and this is an advanced argument that should only be used by advanced users.

Returns:

A list of lists of GeneratorRun-s. Each outer list item represents a (Batch)Trial being suggested, with a list of GeneratorRun-s for that trial.

class ax.utils.testing.core_stubs.TestTrial(experiment: core.experiment.Experiment, trial_type: str | None = None, ttl_seconds: int | None = None, index: int | None = None)[source]

Bases: BaseTrial

Trial class to test unsupported trial type error

abandoned_arms() str[source]

All abandoned arms, associated with this trial.

property arms: list[Arm]
arms_by_name() str[source]
generator_runs() str[source]

All generator runs associated with this trial.

ax.utils.testing.core_stubs.get_abandoned_arm() AbandonedArm[source]
ax.utils.testing.core_stubs.get_acquisition_function_type() type[AcquisitionFunction][source]
ax.utils.testing.core_stubs.get_acquisition_type() type[Acquisition][source]
ax.utils.testing.core_stubs.get_and_early_stopping_strategy() AndEarlyStoppingStrategy[source]
ax.utils.testing.core_stubs.get_arm() Arm[source]
ax.utils.testing.core_stubs.get_arm_weights1() MutableMapping[Arm, float][source]
ax.utils.testing.core_stubs.get_arm_weights2() MutableMapping[Arm, float][source]
ax.utils.testing.core_stubs.get_arms() list[Arm][source]
ax.utils.testing.core_stubs.get_arms_from_dict(arm_weights_dict: MutableMapping[Arm, float]) list[Arm][source]
ax.utils.testing.core_stubs.get_auxiliary_experiment() AuxiliaryExperiment[source]
ax.utils.testing.core_stubs.get_batch_trial(abandon_arm: bool = True, experiment: Experiment | None = None, constrain_search_space: bool = True) BatchTrial[source]
ax.utils.testing.core_stubs.get_batch_trial_with_repeated_arms(num_repeated_arms: int) BatchTrial[source]

Create a batch that contains both new arms and N arms from the last existed trial in the experiment. Where N is equal to the input argument ‘num_repeated_arms’.

ax.utils.testing.core_stubs.get_botorch_model() BoTorchModel[source]
ax.utils.testing.core_stubs.get_botorch_model_with_default_acquisition_class() BoTorchModel[source]
ax.utils.testing.core_stubs.get_botorch_model_with_surrogate_spec() BoTorchModel[source]
ax.utils.testing.core_stubs.get_botorch_model_with_surrogate_specs() BoTorchModel[source]
ax.utils.testing.core_stubs.get_branin_arms(n: int, seed: int) list[Arm][source]
ax.utils.testing.core_stubs.get_branin_data(trial_indices: Iterable[int] | None = None, trials: Iterable[Trial] | None = None) Data[source]
ax.utils.testing.core_stubs.get_branin_data_batch(batch: BatchTrial) Data[source]
ax.utils.testing.core_stubs.get_branin_data_multi_objective(trial_indices: Iterable[int] | None = None, num_objectives: int = 2) Data[source]
ax.utils.testing.core_stubs.get_branin_experiment(has_optimization_config: bool = True, with_batch: bool = False, with_trial: bool = False, with_status_quo: bool = False, with_fidelity_parameter: bool = False, with_choice_parameter: bool = False, with_str_choice_param: bool = False, with_parameter_constraint: bool = False, search_space: SearchSpace | None = None, minimize: bool = False, named: bool = True, num_batch_trial: int = 1, with_completed_batch: bool = False, with_completed_trial: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_branin_experiment_with_multi_objective(has_optimization_config: bool = True, has_objective_thresholds: bool = False, with_batch: bool = False, with_status_quo: bool = False, with_fidelity_parameter: bool = False, num_objectives: int = 2, with_trial: bool = False, with_completed_trial: bool = False, with_relative_constraint: bool = False, with_absolute_constraint: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_branin_experiment_with_status_quo_trials(num_sobol_trials: int = 5, multi_objective: bool = False) tuple[Experiment, ObservationFeatures][source]
ax.utils.testing.core_stubs.get_branin_experiment_with_timestamp_map_metric(with_status_quo: bool = False, rate: float | None = None, map_tracking_metric: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_branin_metric(name: str = 'branin', lower_is_better: bool = True) BraninMetric[source]
ax.utils.testing.core_stubs.get_branin_multi_objective(num_objectives: int = 2) MultiObjective[source]
ax.utils.testing.core_stubs.get_branin_multi_objective_optimization_config(has_objective_thresholds: bool = False, num_objectives: int = 2, with_relative_constraint: bool = False, with_absolute_constraint: bool = False) MultiObjectiveOptimizationConfig[source]
ax.utils.testing.core_stubs.get_branin_objective(name: str = 'branin', minimize: bool = False) Objective[source]
ax.utils.testing.core_stubs.get_branin_optimization_config(minimize: bool = False) OptimizationConfig[source]
ax.utils.testing.core_stubs.get_branin_outcome_constraint(name: str = 'branin') OutcomeConstraint[source]
ax.utils.testing.core_stubs.get_branin_search_space(with_fidelity_parameter: bool = False, with_choice_parameter: bool = False, with_str_choice_param: bool = False, with_parameter_constraint: bool = False) SearchSpace[source]
ax.utils.testing.core_stubs.get_branin_with_multi_task(with_multi_objective: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_chained_input_transform() ChainedInputTransform[source]
ax.utils.testing.core_stubs.get_choice_parameter() ChoiceParameter[source]
ax.utils.testing.core_stubs.get_custom_multi_objective() MultiObjective[source]
ax.utils.testing.core_stubs.get_data(metric_name: str = 'ax_test_metric', trial_index: int = 0, num_non_sq_arms: int = 4, include_sq: bool = True) Data[source]
ax.utils.testing.core_stubs.get_dataset(num_samples: int = 2, d: int = 2, m: int = 2, has_observation_noise: bool = False, feature_names: list[str] | None = None, outcome_names: list[str] | None = None, tkwargs: dict[str, Any] | None = None, seed: int | None = None) SupervisedDataset[source]

Constructs a SupervisedDataset based on the given arguments.

Parameters:
  • num_samples – The number of samples in the dataset.

  • d – The dimension of the features.

  • m – The number of outcomes.

  • has_observation_noise – If True, includes Yvar in the dataset.

  • feature_names – A list of feature names. Defaults to x0, x1…

  • outcome_names – A list of outcome names. Defaults to y0, y1…

  • tkwargs – Optional dictionary of tensor kwargs, such as dtype and device.

  • seed – An optional seed used to generate the data.

ax.utils.testing.core_stubs.get_default_scheduler_options() SchedulerOptions[source]
ax.utils.testing.core_stubs.get_discrete_search_space() SearchSpace[source]
ax.utils.testing.core_stubs.get_experiment(with_status_quo: bool = True, constrain_search_space: bool = True) Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_batch_and_single_trial() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_batch_trial(constrain_search_space: bool = True) Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_custom_runner_and_metric(constrain_search_space: bool = True, immutable: bool = False, multi_objective: bool = False, num_trials: int = 3, has_outcome_constraint: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_data() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_map_data() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_map_data_type() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_multi_objective() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_observations(observations: list[list[float]], minimize: bool = False, scalarized: bool = False, constrained: bool = False, with_tracking_metrics: bool = False, search_space: SearchSpace | None = None) Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_repeated_arms(num_repeated_arms: int) Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_scalarized_objective_and_outcome_constraint() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_trial() Experiment[source]
ax.utils.testing.core_stubs.get_experiment_with_trial_with_ttl() Experiment[source]
ax.utils.testing.core_stubs.get_factorial_experiment(has_optimization_config: bool = True, with_batch: bool = False, with_status_quo: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_factorial_metric(name: str = 'success_metric') FactorialMetric[source]
ax.utils.testing.core_stubs.get_factorial_search_space() SearchSpace[source]
ax.utils.testing.core_stubs.get_fixed_parameter() FixedParameter[source]
ax.utils.testing.core_stubs.get_gamma_prior() GammaPrior[source]
ax.utils.testing.core_stubs.get_generator_run() GeneratorRun[source]
ax.utils.testing.core_stubs.get_generator_run2() GeneratorRun[source]
ax.utils.testing.core_stubs.get_hartmann_metric(name: str = 'hartmann') Hartmann6Metric[source]
ax.utils.testing.core_stubs.get_hartmann_search_space(with_fidelity_parameter: bool = False) SearchSpace[source]
ax.utils.testing.core_stubs.get_hierarchical_search_space(with_fixed_parameter: bool = False) HierarchicalSearchSpace[source]
ax.utils.testing.core_stubs.get_hierarchical_search_space_experiment(num_observations: int = 0) Experiment[source]
ax.utils.testing.core_stubs.get_high_dimensional_branin_experiment(with_batch: bool = False, with_status_quo: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_hss_trials_with_fixed_parameter(exp: Experiment) dict[int, BaseTrial][source]
ax.utils.testing.core_stubs.get_improvement_global_stopping_strategy() ImprovementGlobalStoppingStrategy[source]
ax.utils.testing.core_stubs.get_interval() Interval[source]
ax.utils.testing.core_stubs.get_l2_reg_weight_parameter() RangeParameter[source]
ax.utils.testing.core_stubs.get_large_factorial_search_space(num_levels: int = 10, num_parameters: int = 6) SearchSpace[source]
ax.utils.testing.core_stubs.get_large_ordinal_search_space(n_ordinal_choice_parameters: int, n_continuous_range_parameters: int) SearchSpace[source]
ax.utils.testing.core_stubs.get_lr_parameter() RangeParameter[source]
ax.utils.testing.core_stubs.get_many_branin_objective_opt_config(n_objectives: int) MultiObjectiveOptimizationConfig[source]
ax.utils.testing.core_stubs.get_map_data(trial_index: int = 0) MapData[source]
ax.utils.testing.core_stubs.get_map_key_info() MapKeyInfo[source]
ax.utils.testing.core_stubs.get_map_metric(name: str, rate: float | None = None) BraninTimestampMapMetric[source]
ax.utils.testing.core_stubs.get_map_objective(minimize: bool = False) Objective[source]
ax.utils.testing.core_stubs.get_map_optimization_config() OptimizationConfig[source]
ax.utils.testing.core_stubs.get_metric() Metric[source]
ax.utils.testing.core_stubs.get_mll_type() type[MarginalLogLikelihood][source]
ax.utils.testing.core_stubs.get_model_covariance() dict[str, dict[str, list[float]]][source]
ax.utils.testing.core_stubs.get_model_mean() dict[str, list[float]][source]
ax.utils.testing.core_stubs.get_model_parameter(with_fixed_parameter: bool = False) ChoiceParameter[source]
ax.utils.testing.core_stubs.get_model_predictions() tuple[dict[str, list[float]], dict[str, dict[str, list[float]]]][source]
ax.utils.testing.core_stubs.get_model_predictions_per_arm() dict[str, tuple[dict[str, float], dict[str, dict[str, float]] | None]][source]
ax.utils.testing.core_stubs.get_model_type() type[Model][source]
ax.utils.testing.core_stubs.get_multi_objective() MultiObjective[source]
ax.utils.testing.core_stubs.get_multi_objective_optimization_config(custom_metric: bool = False, relative: bool = True, outcome_constraint: bool = True) MultiObjectiveOptimizationConfig[source]
ax.utils.testing.core_stubs.get_multi_type_experiment(add_trial_type: bool = True, add_trials: bool = False, num_arms: int = 10) MultiTypeExperiment[source]
ax.utils.testing.core_stubs.get_multi_type_experiment_with_multi_objective(add_trials: bool = False) MultiTypeExperiment[source]
ax.utils.testing.core_stubs.get_non_monolithic_branin_moo_data() Data[source]
ax.utils.testing.core_stubs.get_num_boost_rounds_parameter() RangeParameter[source]
ax.utils.testing.core_stubs.get_objective(minimize: bool = False) Objective[source]
ax.utils.testing.core_stubs.get_objective_threshold(metric_name: str = 'm1', bound: float = -0.25, comparison_op: ComparisonOp = ComparisonOp.GEQ) ObjectiveThreshold[source]
ax.utils.testing.core_stubs.get_observations_with_invalid_value(invalid_value: float) list[Observation][source]
ax.utils.testing.core_stubs.get_online_sobol_mbm_generation_strategy(sobol_steps: int = 1) GenerationStrategy[source]

Constructs a GenerationStrategy with Sobol and MBM nodes for simulating online optimization.

ax.utils.testing.core_stubs.get_optimization_config(outcome_constraint: bool = True, relative: bool = True) OptimizationConfig[source]
ax.utils.testing.core_stubs.get_optimization_config_no_constraints(minimize: bool = False) OptimizationConfig[source]
ax.utils.testing.core_stubs.get_or_early_stopping_strategy() OrEarlyStoppingStrategy[source]
ax.utils.testing.core_stubs.get_order_constraint() OrderConstraint[source]
ax.utils.testing.core_stubs.get_ordered_choice_parameter() ChoiceParameter[source]
ax.utils.testing.core_stubs.get_outcome_constraint(metric: Metric | None = None, relative: bool = True, bound: float = -0.25) OutcomeConstraint[source]
ax.utils.testing.core_stubs.get_parameter_constraint(param_x: str = 'x', param_y: str = 'w') ParameterConstraint[source]
ax.utils.testing.core_stubs.get_parameter_distribution() ParameterDistribution[source]
ax.utils.testing.core_stubs.get_pathlib_path() Path[source]
ax.utils.testing.core_stubs.get_percentile_early_stopping_strategy() PercentileEarlyStoppingStrategy[source]
ax.utils.testing.core_stubs.get_percentile_early_stopping_strategy_with_non_objective_metric_name() PercentileEarlyStoppingStrategy[source]
ax.utils.testing.core_stubs.get_range_parameter() RangeParameter[source]
ax.utils.testing.core_stubs.get_range_parameter2() RangeParameter[source]
ax.utils.testing.core_stubs.get_risk_measure() RiskMeasure[source]
ax.utils.testing.core_stubs.get_robust_branin_experiment(risk_measure: RiskMeasure | None = None, optimization_config: OptimizationConfig | None = None, num_sobol_trials: int = 2) Experiment[source]
ax.utils.testing.core_stubs.get_robust_search_space(lb: float = 0.0, ub: float = 5.0, multivariate: bool = False, use_discrete: bool = False, num_samples: int = 4) RobustSearchSpace[source]
ax.utils.testing.core_stubs.get_robust_search_space_environmental(lb: float = 0.0, ub: float = 5.0) RobustSearchSpace[source]
ax.utils.testing.core_stubs.get_scalarized_objective() Objective[source]
ax.utils.testing.core_stubs.get_scalarized_outcome_constraint() ScalarizedOutcomeConstraint[source]
ax.utils.testing.core_stubs.get_scheduler_options_batch_trial() SchedulerOptions[source]
ax.utils.testing.core_stubs.get_search_space(constrain_search_space: bool = True) SearchSpace[source]
ax.utils.testing.core_stubs.get_search_space_for_range_value(min: float = 3.0, max: float = 6.0) SearchSpace[source]
ax.utils.testing.core_stubs.get_search_space_for_range_values(min: float = 3.0, max: float = 6.0) SearchSpace[source]
ax.utils.testing.core_stubs.get_search_space_for_value(val: float = 3.0) SearchSpace[source]
ax.utils.testing.core_stubs.get_search_space_with_choice_parameters(num_ordered_parameters: int = 2, num_unordered_choices: int = 5) SearchSpace[source]
ax.utils.testing.core_stubs.get_sebo_acquisition_class() type[SEBOAcquisition][source]
ax.utils.testing.core_stubs.get_small_discrete_search_space() SearchSpace[source]
ax.utils.testing.core_stubs.get_status_quo() Arm[source]
ax.utils.testing.core_stubs.get_sum_constraint1() SumConstraint[source]
ax.utils.testing.core_stubs.get_sum_constraint2() SumConstraint[source]
ax.utils.testing.core_stubs.get_surrogate() Surrogate[source]
ax.utils.testing.core_stubs.get_surrogate_spec_with_default() SurrogateSpec[source]
ax.utils.testing.core_stubs.get_surrogate_spec_with_lognormal() SurrogateSpec[source]
ax.utils.testing.core_stubs.get_synthetic_runner() SyntheticRunner[source]
ax.utils.testing.core_stubs.get_task_choice_parameter() ChoiceParameter[source]
ax.utils.testing.core_stubs.get_test_map_data_experiment(num_trials: int, num_fetches: int, num_complete: int, map_tracking_metric: bool = False) Experiment[source]
ax.utils.testing.core_stubs.get_threshold_early_stopping_strategy() ThresholdEarlyStoppingStrategy[source]
ax.utils.testing.core_stubs.get_trial() Trial[source]
ax.utils.testing.core_stubs.get_trial_based_criterion() list[TrialBasedCriterion][source]
ax.utils.testing.core_stubs.get_weights() list[float][source]
ax.utils.testing.core_stubs.get_weights_from_dict(arm_weights_dict: MutableMapping[Arm, float]) list[float][source]
ax.utils.testing.core_stubs.get_winsorization_config() WinsorizationConfig[source]
ax.utils.testing.core_stubs.run_branin_experiment_with_generation_strategy(generation_strategy: GenerationStrategy, num_trials: int = 6, kwargs_for_get_branin_experiment: dict[str, Any] | None = None) Experiment[source]

Gets a Branin experiment using any given kwargs and runs num_trials trials using the given generation strategy.

Modeling Stubs

ax.utils.testing.modeling_stubs.get_experiment_for_value() Experiment[source]
ax.utils.testing.modeling_stubs.get_generation_strategy(with_experiment: bool = False, with_callable_model_kwarg: bool = True, with_completion_criteria: int = 0, with_generation_nodes: bool = False) GenerationStrategy[source]
ax.utils.testing.modeling_stubs.get_input_transform_type() type[InputTransform][source]
ax.utils.testing.modeling_stubs.get_legacy_list_surrogate_generation_step_as_dict() dict[str, Any][source]

For use ensuring backwards compatibility loading the now deprecated ListSurrogate.

ax.utils.testing.modeling_stubs.get_observation(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_observation1(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_observation1trans(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_observation2(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_observation2trans(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_observation_features() ObservationFeatures[source]
ax.utils.testing.modeling_stubs.get_observation_status_quo0(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_observation_status_quo1(first_metric_name: str = 'a', second_metric_name: str = 'b') Observation[source]
ax.utils.testing.modeling_stubs.get_outcome_transfrom_type() type[OutcomeTransform][source]
ax.utils.testing.modeling_stubs.get_surrogate_as_dict() dict[str, Any][source]

For use ensuring backwards compatibility when loading Surrogate with input_transform and outcome_transform kwargs.

ax.utils.testing.modeling_stubs.get_surrogate_generation_step() GenerationStep[source]
ax.utils.testing.modeling_stubs.get_surrogate_spec_as_dict(model_class: str | None = None, with_legacy_input_transform: bool = False) dict[str, Any][source]

For use ensuring backwards compatibility when loading SurrogateSpec with input_transform and outcome_transform kwargs.

ax.utils.testing.modeling_stubs.get_to_new_sq_transform_type() type[TransformToNewSQ][source]
ax.utils.testing.modeling_stubs.get_transform_type() type[Transform][source]
ax.utils.testing.modeling_stubs.sobol_gpei_generation_node_gs(with_model_selection: bool = False, with_auto_transition: bool = False, with_previous_node: bool = False, with_input_constructors_all_n: bool = False, with_input_constructors_remaining_n: bool = False, with_input_constructors_repeat_n: bool = False, with_input_constructors_target_trial: bool = False, with_input_constructors_sq_features: bool = False, with_unlimited_gen_mbm: bool = False, with_trial_type: bool = False, with_is_SOO_transition: bool = False) GenerationStrategy[source]

Returns a basic SOBOL+MBM GS using GenerationNodes for testing.

Parameters:

with_model_selection – If True, will add a second ModelSpec in the MBM node. This can be used for testing model selection.

class ax.utils.testing.modeling_stubs.transform_1(search_space: SearchSpace | None = None, observations: list[Observation] | None = None, modelbridge: modelbridge_module.base.ModelBridge | None = None, config: TConfig | None = None)[source]

Bases: Transform

transform_observation_features(observation_features: list[ObservationFeatures]) list[ObservationFeatures][source]

Transform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters:

observation_features – Observation features

Returns: transformed observation features

transform_optimization_config(optimization_config: OptimizationConfig, modelbridge: ModelBridge | None, fixed_features: ObservationFeatures | None) OptimizationConfig[source]

Transform optimization config.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters:

optimization_config – The optimization config

Returns: transformed optimization config.

transform_search_space(search_space: SearchSpace) SearchSpace[source]

Transform search space.

The transforms are typically done in-place. This calls two private methods, _transform_search_space, which transforms the core search space attributes, and _transform_parameter_distributions, which transforms the distributions when using a RobustSearchSpace.

Parameters:

search_space – The search space

Returns: transformed search space.

untransform_observation_features(observation_features: list[ObservationFeatures]) list[ObservationFeatures][source]

Untransform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters:

observation_features – Observation features in the transformed space

Returns: observation features in the original space

class ax.utils.testing.modeling_stubs.transform_2(search_space: SearchSpace | None = None, observations: list[Observation] | None = None, modelbridge: modelbridge_module.base.ModelBridge | None = None, config: TConfig | None = None)[source]

Bases: Transform

transform_observation_features(observation_features: list[ObservationFeatures]) list[ObservationFeatures][source]

Transform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters:

observation_features – Observation features

Returns: transformed observation features

transform_optimization_config(optimization_config: OptimizationConfig, modelbridge: ModelBridge | None, fixed_features: ObservationFeatures | None) OptimizationConfig[source]

Transform optimization config.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters:

optimization_config – The optimization config

Returns: transformed optimization config.

transform_search_space(search_space: SearchSpace) SearchSpace[source]

Transform search space.

The transforms are typically done in-place. This calls two private methods, _transform_search_space, which transforms the core search space attributes, and _transform_parameter_distributions, which transforms the distributions when using a RobustSearchSpace.

Parameters:

search_space – The search space

Returns: transformed search space.

untransform_observation_features(observation_features: list[ObservationFeatures]) list[ObservationFeatures][source]

Untransform observation features.

This is typically done in-place. This class implements the identity transform (does nothing).

Parameters:

observation_features – Observation features in the transformed space

Returns: observation features in the original space

Preference Stubs

ax.utils.testing.preference_stubs.experimental_metric_eval(parameters: dict[str, Any], metric_names: list[str]) dict[str, dict[str, int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None]] | int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None] | list[tuple[dict[str, None | str | bool | float | int], dict[str, int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None]]]] | list[tuple[dict[str, Hashable], dict[str, int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None]]]]][source]

evaluating experimental metrics

Parameters:
  • parameters – Dict of arm name to parameterization

  • metric_names – List of metric names

Returns:

Dict of arm name to metric name to (mean, sem)

ax.utils.testing.preference_stubs.get_pbo_experiment(num_parameters: int = 2, num_experimental_metrics: int = 3, parameter_names: list[str] | None = None, tracking_metric_names: list[str] | None = None, num_experimental_trials: int = 3, num_preference_trials: int = 3, num_preference_trials_w_repeated_arm: int = 5, include_sq: bool = True, partial_data: bool = False, unbounded_search_space: bool = False, experiment_name: str = 'pref_experiment') Experiment[source]

Create synthetic preferential BO experiment

ax.utils.testing.preference_stubs.pairwise_pref_metric_eval(parameters: dict[str, dict[str, None | str | bool | float | int]], utility_func: ~collections.abc.Callable[[dict[str, None | str | bool | float | int]], float] = <function sum_utility>) dict[str, dict[str, int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None]] | int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None] | list[tuple[dict[str, None | str | bool | float | int], dict[str, int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None]]]] | list[tuple[dict[str, Hashable], dict[str, int | float | floating | integer | tuple[int | float | floating | integer, int | float | floating | integer | None]]]]][source]

evaluating pairwise comparisons using utility_func

ax.utils.testing.preference_stubs.sum_utility(parameters: dict[str, None | str | bool | float | int]) float[source]

Test utility function that sums over parameter values

Mocking

ax.utils.testing.mock.mock_botorch_optimize(f: Callable) Callable[source]

Wraps f in mock_botorch_optimize_context_manager for use as a decorator.

ax.utils.testing.mock.mock_botorch_optimize_context_manager(force: bool = False) Generator[None, None, None][source]

A context manager that uses mocks to speed up optimization for testing. Currently, the primary tactic is to force the underlying scipy methods to stop after just one iteration.

This context manager uses BoTorch’s mock_optimize_context_manager, and adds some additional mocks that are not possible to cover in BoTorch due to the need to mock the functions where they are used.

Parameters:

force – If True will not raise an AssertionError if no mocks are called. USE RESPONSIBLY.

ax.utils.testing.mock.skip_fit_gpytorch_mll(f: Callable) Callable[source]

Wraps f in the skip_fit_gpytorch_mll_context_manager for use as a decorator.

ax.utils.testing.mock.skip_fit_gpytorch_mll_context_manager() Generator[None, None, None][source]

A context manager that makes fit_gpytorch_mll a no-op.

This should only be used to speed up slow tests.

Test Init Files

class ax.utils.testing.test_init_files.InitTest(methodName: str = 'runTest')[source]

Bases: TestCase

test_InitFiles() None[source]

__init__.py files are necessary for the inclusion of the directories in pip builds.

Torch Stubs

ax.utils.testing.torch_stubs.get_torch_test_data(dtype: dtype = torch.float32, cuda: bool = False, constant_noise: bool = True, task_features: list[int] | None = None, offset: float = 0.0) tuple[list[Tensor], list[Tensor], list[Tensor], list[tuple[float, float]], list[int], list[str], list[str]][source]

Utils

ax.utils.testing.utils.generic_equals(first: Any, second: Any) bool[source]
ax.utils.testing.utils.run_trials_with_gs(experiment: Experiment, gs: GenerationStrategy, num_trials: int) None[source]

Runs and completes num_trials trials for the given experiment with the given GS. The trials are completed with random metric values between 0 and 1.

Parameters:
  • experiment – The experiment to run trials on. Must have an optimization config.

  • gs – The generation strategy to use.

  • num_trials – The number of trials to run.

Test Metrics

Backend Simulator Map

class ax.utils.testing.metrics.backend_simulator_map.BackendSimulatorTimestampMapMetric(name: str, param_names: Iterable[str], noise_sd: float = 0.0, lower_is_better: bool | None = None, cache_evaluations: bool = True)[source]

Bases: NoisyFunctionMapMetric

A metric that interfaces with an underlying BackendSimulator and returns timestamp map data.

convert_to_timestamps(start_time: float, end_time: float) list[float][source]

Given a starting and current time, get the list of intermediate timestamps at which we have observations.

fetch_trial_data(trial: BaseTrial, noisy: bool = True, **kwargs: Any) Result[MapData, MetricFetchE][source]

Fetch data for one trial.

Branin Backend Map

class ax.utils.testing.metrics.branin_backend_map.BraninBackendMapMetric(name: str, param_names: list[str], noise_sd: float = 0.0, lower_is_better: bool | None = True, cache_evaluations: bool = True, rate: float = 0.5, delta_t: float = 1.0)[source]

Bases: BackendSimulatorTimestampMapMetric, BraninTimestampMapMetric

A Branin BackendSimulatorTimestampMapMetric with a multiplicative factor of 1 - exp(-rate * t) where t is the runtime of the trial.

convert_to_timestamps(start_time: float | None, end_time: float) list[float][source]

Given a starting and current time, get the list of intermediate timestamps at which we have observations.

Tutorials

Neural Net

class ax.utils.tutorials.cnn_utils.CNN[source]

Bases: Module

Convolutional Neural Network.

forward(x: Tensor) Tensor[source]

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

ax.utils.tutorials.cnn_utils.evaluate(net: Module, data_loader: DataLoader, dtype: dtype, device: device) float[source]

Compute classification accuracy on provided dataset.

Parameters:
  • net – trained model

  • data_loader – DataLoader containing the evaluation set

  • dtype – torch dtype

  • device – torch device

Returns:

classification accuracy

Return type:

float

ax.utils.tutorials.cnn_utils.get_partition_data_loaders(train_valid_set: Dataset, test_set: Dataset, downsample_pct: float = 0.5, train_pct: float = 0.8, batch_size: int = 128, num_workers: int = 0, deterministic_partitions: bool = False, downsample_pct_test: float | None = None) tuple[DataLoader, DataLoader, DataLoader][source]
Helper function for partitioning training data into training and validation sets,

downsampling data, and initializing DataLoaders for each partition.

Parameters:
  • train_valid_set – torch.dataset

  • downsample_pct – the proportion of the dataset to use for training, and validation

  • train_pct – the proportion of the downsampled data to use for training

  • batch_size – how many samples per batch to load

  • num_workers – number of workers (subprocesses) for loading data

  • deterministic_partitions – whether to partition data in a deterministic fashion

  • downsample_pct_test – the proportion of the dataset to use for test, default to be equal to downsample_pct

Returns:

training data DataLoader: validation data DataLoader: test data

Return type:

DataLoader

ax.utils.tutorials.cnn_utils.load_mnist(downsample_pct: float = 0.5, train_pct: float = 0.8, data_path: str = './data', batch_size: int = 128, num_workers: int = 0, deterministic_partitions: bool = False, downsample_pct_test: float | None = None) tuple[DataLoader, DataLoader, DataLoader][source]
Load MNIST dataset (download if necessary) and split data into training,

validation, and test sets.

Parameters:
  • downsample_pct – the proportion of the dataset to use for training, validation, and test

  • train_pct – the proportion of the downsampled data to use for training

  • data_path – Root directory of dataset where MNIST/processed/training.pt and MNIST/processed/test.pt exist.

  • batch_size – how many samples per batch to load

  • num_workers – number of workers (subprocesses) for loading data

  • deterministic_partitions – whether to partition data in a deterministic fashion

  • downsample_pct_test – the proportion of the dataset to use for test, default to be equal to downsample_pct

Returns:

training data DataLoader: validation data DataLoader: test data

Return type:

DataLoader

ax.utils.tutorials.cnn_utils.split_dataset(dataset: Dataset, lengths: list[int], deterministic_partitions: bool = False) list[Dataset][source]

Split a dataset either randomly or deterministically.

Parameters:
  • dataset – the dataset to split

  • lengths – the lengths of each partition

  • deterministic_partitions – deterministic_partitions: whether to partition data in a deterministic fashion

Returns:

split datasets

Return type:

List[Dataset]

ax.utils.tutorials.cnn_utils.train(net: Module, train_loader: DataLoader, parameters: dict[str, float], dtype: dtype, device: device) Module[source]

Train CNN on provided data set.

Parameters:
  • net – initialized neural network

  • train_loader – DataLoader containing training set

  • parameters – dictionary containing parameters to be passed to the optimizer. - lr: default (0.001) - momentum: default (0.0) - weight_decay: default (0.0) - num_epochs: default (1)

  • dtype – torch dtype

  • device – torch device

Returns:

trained CNN.

Return type:

nn.Module