neuraxle.metaopt.data.vanilla

Module-level documentation for neuraxle.metaopt.data.vanilla. Here is an inheritance diagram, including dependencies to other base modules of Neuraxle:

Neuraxle’s Base Hyperparameter Repository Classes

Data objects and related repositories used by AutoML.

Classes are splitted like this for the AutoML:

• Projects

• Clients

• Rounds (runs)

• Trials

• TrialSplits

• MetricResults

Functions

as_named_odict(obj, …)
from_json(_json)
object_decoder(obj[, odictify])
object_pairs_decoder(obj)
to_int_if_not_float_or_none(attr)
to_json(obj)
to_str_if_not_none(attr)

Classes

BaseDataclass(*args, **kwds)
BaseTrialDataclassMixin(hyperparams, status, …)	Mixin class for TrialMetadata and TrialSplitMetadata that also must inherit from BaseMetadata.
ClientDataclass(*args, **kwds)
DataclassHasListMixin()
DataclassHasOrderedDictMixin()
MetadataJSONEncoder(*[, skipkeys, …])
MetricResultsDataclass(metric_name, …)	MetricResult object used by AutoML algorithm classes.
ProjectDataclass(*args, **kwds)
RootDataclass(*args, **kwds)
RoundDataclass(*args, **kwds)
ScopedLocation(project_name, client_name, …)	A location in the metadata tree.
TrialDataclass(hyperparams, status, …)	This class is a data structure most often used under AutoML to store information about a trial.
TrialSplitDataclass(hyperparams, status, …)	TrialSplit object used by AutoML algorithm classes.

neuraxle.metaopt.data.vanilla.to_int_if_not_float_or_none(attr) → int

neuraxle.metaopt.data.vanilla.to_str_if_not_none(attr) → str

class neuraxle.metaopt.data.vanilla.ScopedLocation(project_name: str = None, client_name: str = None, round_number: int = None, trial_number: int = None, split_number: int = None, metric_name: str = None)

Bases: neuraxle.base.BaseService

A location in the metadata tree.

project_name = None

client_name = None

round_number = None

trial_number = None

split_number = None

metric_name = None

copy() → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a copy of the ScopedLocation.

with_dc(dc: neuraxle.metaopt.data.vanilla.BaseDataclass) → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a new ScopedLocation with the provided BaseDataclass (dc) type’s id added.

fill_to_dc(dc: neuraxle.metaopt.data.vanilla.BaseDataclass) → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a ScopedLocation with the provided BaseDataclass (dc) type’s id added at the end, with the particularity that if some elements are missing, they are filled with the default null values.

pad_nans() → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a ScopedLocation with the missing elements filled with the default null values.

with_id(_id: Union[str, int]) → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a longer ScopedLocation with the provided id added at the end.

at_dc(dc: neuraxle.metaopt.data.vanilla.BaseDataclass) → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a trimmed ScopedLocation with the provided BaseDataclass (dc) type’s id as the ScopedLocation’s deepest attribute.

static default_full() → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a ScopedLocation with all attributes set to the default non-null value instead of None.

static default(round_number: Optional[int] = None, trial_number: Optional[int] = None, split_number: Optional[int] = None, metric_name: Optional[str] = None) → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns the default ScopedLocation. That is:

peek() → Union[str, int]

Pop without removing the last element: return the last non-None element.

pop() → Union[str, int]

Returns the last not None scoped location attribute and remove it from self.

popped() → neuraxle.metaopt.data.vanilla.ScopedLocation

Returns a new ScopedLocation with the last not None scoped location attribute removed.

as_list(stringify: bool = False) → List[Union[int, str]]

Returns a list of the scoped location attributes. Item that has a value of None are not included in the list.

Parameters

stringify (bool) – If True, the scoped location attributes are converted to strings.

Returns

list of not none scoped location attributes

new_dataclass_from_id()

Creates a new BaseDataclass of the right type with just the provided ID filled.

last_dc_type() → Type[neuraxle.metaopt.data.vanilla.BaseDataclass]

__init__(project_name: str = None, client_name: str = None, round_number: int = None, trial_number: int = None, split_number: int = None, metric_name: str = None) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.BaseDataclass(*args, **kwds)

Bases: typing.Generic, abc.ABC

_validate()

static from_dict(_dict: Dict[str, Any]) → neuraxle.metaopt.data.vanilla.BaseDataclass

to_dict() → Dict[str, Any]

_id_attr_name

_sublocation_attr_name

get_id() → Union[str, int]

set_id(_id: Union[str, int]) → neuraxle.metaopt.data.vanilla.BaseDataclass

get_sublocation() → Union[List[SubDataclassT], OrderedDict[str, SubDataclassT]]

get_sublocation_items() → List[Tuple[Union[str, int], SubDataclassT]]

set_sublocation(sublocation: Union[List[SubDataclassT], OrderedDict[str, SubDataclassT]]) → BaseDataclass

set_sublocation_keys(keys: List[Union[int, str]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

Use this to set a shallow sublocation only from their keys.

_is_key_in_subattr(key_subattr)

store(dc: SubDataclassT) → Union[str, int]

Add a subdataclass to the sublocation, at its proper ID.

shallow() → neuraxle.metaopt.data.vanilla.BaseDataclass

Replaces the sublocation items with None when the sublocation is a BaseDataclass type.

empty() → neuraxle.metaopt.data.vanilla.BaseDataclass

Do empty the sublocation when the sublocation is a BaseDataclass type.

has_sublocation_dataclasses() → bool

get_sublocation_values() → List[SubDataclassT]

get_sublocation_keys() → List[Union[int, str]]

classmethod subdataclass_type() → Type[SubDataclassT]

is_terminal_leaf() → bool

_tree(_list: List[neuraxle.metaopt.data.vanilla.ScopedLocation], parent_scope: neuraxle.metaopt.data.vanilla.ScopedLocation) → List[neuraxle.metaopt.data.vanilla.ScopedLocation]

tree() → List[neuraxle.metaopt.data.vanilla.ScopedLocation]

__init__() → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.DataclassHasOrderedDictMixin

Bases: object

_validate()

get_sublocation() → OrderedDict[ScopedLocationAttrStr, SubDataclassT]

set_sublocation(sublocation: OrderedDict[ScopedLocationAttrStr, SubDataclassT]) → None

set_sublocation_keys(keys: List[Union[int, str]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

set_sublocation_items(items: List[Tuple[Union[str, int], SubDataclassT]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

get_sublocation_values() → List[SubDataclassT]

get_sublocation_keys() → List[str]

store(dc: SubDataclassT) → str

shallow() → neuraxle.metaopt.data.vanilla.BaseDataclass

empty() → neuraxle.metaopt.data.vanilla.BaseDataclass

_sort()

__init__() → None

class neuraxle.metaopt.data.vanilla.DataclassHasListMixin

Bases: object

_validate()

get_sublocation() → List[SubDataclassT]

set_sublocation(sublocation: List[SubDataclassT]) → None

set_sublocation_keys(keys: List[Union[int, str]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

set_sublocation_items(items: List[Tuple[Union[str, int], SubDataclassT]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

get_sublocation_values() → List[SubDataclassT]

get_sublocation_keys() → List[int]

_is_key_in_subattr(key_subattr)

store(dc: SubDataclassT) → int

shallow() → neuraxle.metaopt.data.vanilla.BaseDataclass

empty() → neuraxle.metaopt.data.vanilla.BaseDataclass

get_next_i() → int

__init__() → None

class neuraxle.metaopt.data.vanilla.BaseTrialDataclassMixin(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples = <factory>, status: neuraxle.base.TrialStatus = <TrialStatus.PLANNED: 'PLANNED'>, created_time: datetime.datetime = <factory>, start_time: datetime.datetime = None, end_time: datetime.datetime = None)

Bases: object

Mixin class for TrialMetadata and TrialSplitMetadata that also must inherit from BaseMetadata.

status = 'PLANNED'

start_time = None

end_time = None

_validate()

_validate_date_attr(attr_name) → None

start() → neuraxle.metaopt.data.vanilla.BaseTrialDataclassMixin

end(status: neuraxle.base.TrialStatus) → neuraxle.metaopt.data.vanilla.BaseTrialDataclassMixin

__init__(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples = <factory>, status: neuraxle.base.TrialStatus = <TrialStatus.PLANNED: 'PLANNED'>, created_time: datetime.datetime = <factory>, start_time: datetime.datetime = None, end_time: datetime.datetime = None) → None

class neuraxle.metaopt.data.vanilla.RootDataclass(*args, **kwds)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasOrderedDictMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

_id_attr_name

_sublocation_attr_name

get_id() → Union[str, int]

get_sublocation() → OrderedDict[str, SubDataclassT]

__init__(projects: OrderedDict[str, ProjectDataclass] = <factory>) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.ProjectDataclass(*args, **kwds)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasOrderedDictMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

project_name = 'default_project'

__init__(project_name: str = 'default_project', clients: OrderedDict[str, ClientDataclass] = <factory>) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.ClientDataclass(*args, **kwds)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasListMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

client_name = 'default_client'

__init__(client_name: str = 'default_client', rounds: List[RoundDataclass] = <factory>) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.RoundDataclass(*args, **kwds)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasListMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

round_number = 0

main_metric_name = None

__init__(round_number: int = 0, trials: List[TrialDataclass] = <factory>, main_metric_name: str = None) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.TrialDataclass(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples = <factory>, status: neuraxle.base.TrialStatus = <TrialStatus.PLANNED: 'PLANNED'>, created_time: datetime.datetime = <factory>, start_time: datetime.datetime = None, end_time: datetime.datetime = None, trial_number: int = 0, validation_splits: List[TrialSplitDataclass] = <factory>, retrained_split: Optional[neuraxle.metaopt.data.vanilla.TrialSplitDataclass] = None)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasListMixin, neuraxle.metaopt.data.vanilla.BaseTrialDataclassMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

This class is a data structure most often used under AutoML to store information about a trial. This information is itself managed by the HyperparameterRepository class and the Trial class within the AutoML.

trial_number = 0

retrained_split = None

store(dc: SubDataclassT) → int

Add a subdataclass to the sublocation, at its proper ID.

set_sublocation_keys(keys: List[Union[int, str]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

Use this to set a shallow sublocation only from their keys.

set_sublocation_items(items: List[Tuple[Union[str, int], SubDataclassT]]) → neuraxle.metaopt.data.vanilla.BaseDataclass

shallow() → neuraxle.metaopt.data.vanilla.BaseDataclass

Replaces the sublocation items with None when the sublocation is a BaseDataclass type.

_set_shallow_retrained_split_id()

empty() → neuraxle.metaopt.data.vanilla.BaseDataclass

Do empty the sublocation when the sublocation is a BaseDataclass type.

__init__(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples = <factory>, status: neuraxle.base.TrialStatus = <TrialStatus.PLANNED: 'PLANNED'>, created_time: datetime.datetime = <factory>, start_time: datetime.datetime = None, end_time: datetime.datetime = None, trial_number: int = 0, validation_splits: List[TrialSplitDataclass] = <factory>, retrained_split: Optional[neuraxle.metaopt.data.vanilla.TrialSplitDataclass] = None) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.TrialSplitDataclass(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples = <factory>, status: neuraxle.base.TrialStatus = <TrialStatus.PLANNED: 'PLANNED'>, created_time: datetime.datetime = <factory>, start_time: datetime.datetime = None, end_time: datetime.datetime = None, split_number: int = 0, metric_results: OrderedDict[str, MetricResultsDataclass] = <factory>)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasOrderedDictMixin, neuraxle.metaopt.data.vanilla.BaseTrialDataclassMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

TrialSplit object used by AutoML algorithm classes.

split_number = 0

is_retrain_split() → bool

__init__(hyperparams: neuraxle.hyperparams.space.HyperparameterSamples = <factory>, status: neuraxle.base.TrialStatus = <TrialStatus.PLANNED: 'PLANNED'>, created_time: datetime.datetime = <factory>, start_time: datetime.datetime = None, end_time: datetime.datetime = None, split_number: int = 0, metric_results: OrderedDict[str, MetricResultsDataclass] = <factory>) → None

_abc_impl = <_abc_data object>

class neuraxle.metaopt.data.vanilla.MetricResultsDataclass(metric_name: str = 'main', validation_values: List[float] = <factory>, train_values: List[float] = <factory>, higher_score_is_better: bool = True)

Bases: neuraxle.metaopt.data.vanilla.DataclassHasListMixin, neuraxle.metaopt.data.vanilla.BaseDataclass

MetricResult object used by AutoML algorithm classes.

metric_name = 'main'

higher_score_is_better = True

shallow() → neuraxle.metaopt.data.vanilla.BaseDataclass

Replaces the sublocation items with None when the sublocation is a BaseDataclass type.

empty() → neuraxle.metaopt.data.vanilla.BaseDataclass

Do empty the sublocation when the sublocation is a BaseDataclass type.

__init__(metric_name: str = 'main', validation_values: List[float] = <factory>, train_values: List[float] = <factory>, higher_score_is_better: bool = True) → None

_abc_impl = <_abc_data object>

neuraxle.metaopt.data.vanilla.as_named_odict(obj: Union[neuraxle.metaopt.data.vanilla.BaseDataclass, List[neuraxle.metaopt.data.vanilla.BaseDataclass]]) → OrderedDict[ScopedLocationAttrStr, BaseDataclass]

neuraxle.metaopt.data.vanilla.object_pairs_decoder(obj)

neuraxle.metaopt.data.vanilla.object_decoder(obj, odictify=False)

class neuraxle.metaopt.data.vanilla.MetadataJSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: json.encoder.JSONEncoder

default(o)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

neuraxle.metaopt.data.vanilla.to_json(obj: neuraxle.metaopt.data.vanilla.BaseDataclass) → str

neuraxle.metaopt.data.vanilla.from_json(_json: str) → neuraxle.metaopt.data.vanilla.BaseDataclass