neuraxle.metaopt.repositories.db

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

Neuraxle’s SQLAlchemy Hyperparameter Repository Classes

Data objects and related repositories used by AutoML, SQL version.

Classes

AutoMLLog(**kwargs)
BaseTrialNodeMixin
ClientNode([_dataclass])
DataClassNode([_dataclass])
DataClassTreeNodeUpdater(session, scope, deep)
DatabaseHyperparamRepository(engine, session)
MetricResultsNode([_dataclass])
MetricResultsValuesMixin
PostGreSQLHyperparamsRepository(…[, echo])
ProjectNode([_dataclass])
RoundNode([_dataclass])
SQLLiteHyperparamsRepository(sqllite_db_path)
ScopedLocationTreeNode(dataclass_node[, parent])
TrainMetricResultsValues(value[, _datetime])
TrialNode([_dataclass])
TrialSplitNode([_dataclass])
ValidMetricResultsValues(value[, _datetime])

class neuraxle.metaopt.repositories.db.ScopedLocationTreeNode(dataclass_node, parent=None)

Bases: sqlalchemy.orm.decl_api.Base

id

parent_id

project_name

client_name

round_number

trial_number

split_number

metric_name

subdataclasses

__init__(dataclass_node, parent=None)

dataclass_node

_set_location_from(dataclass_node: neuraxle.metaopt.repositories.db.DataClassNode, parent=None)

loc

id_attr_value

to_dataclass(deep=False)

static query(session: sqlalchemy.orm.session.Session, scope: neuraxle.metaopt.data.vanilla.ScopedLocation, deep=False) → Optional[neuraxle.metaopt.repositories.db.ScopedLocationTreeNode]

_sa_class_manager = {'client_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'dataclass_node': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'metric_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'parent_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'project_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'round_number': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'split_number': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'subdataclasses': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'trial_number': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

class neuraxle.metaopt.repositories.db.DataClassTreeNodeUpdater(session: sqlalchemy.orm.session.Session, scope: neuraxle.metaopt.data.vanilla.ScopedLocation, deep: bool = False)

Bases: object

__init__(session: sqlalchemy.orm.session.Session, scope: neuraxle.metaopt.data.vanilla.ScopedLocation, deep: bool = False)

Initialize self. See help(type(self)) for accurate signature.

add_or_update_dataclass(_dataclass: SubDataclassT, parent: Optional[neuraxle.metaopt.repositories.db.ScopedLocationTreeNode])

Add or update a dataclass in the database.

Possible scenarios: - dataclass is not in the database:

• add it to the database. Recursively:

  • add all subdataclasses

• dataclass is in the database:

  • update it in the database. Recursively, for all subdataclasses wheter found or not:

    • add all subdataclasses

    • update all subdataclasses

This means we have the following methods chaining into each other: - add or update dataclass - update dataclass - add dataclass

update_dataclass(_dataclass: SubDataclassT, node: neuraxle.metaopt.repositories.db.ScopedLocationTreeNode)

add_dataclass(_dataclass: SubDataclassT, parent: Optional[neuraxle.metaopt.repositories.db.ScopedLocationTreeNode])

class neuraxle.metaopt.repositories.db.DataClassNode(_dataclass=None)

Bases: sqlalchemy.orm.decl_api.Base

type

tree_id

tree

logs

__init__(_dataclass=None)

id_attr_value

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.BaseDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.RootDataclass

_sa_class_manager = {'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

class neuraxle.metaopt.repositories.db.ProjectNode(_dataclass=None)

Bases: neuraxle.metaopt.repositories.db.DataClassNode

id

project_name

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.ProjectDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.ProjectDataclass

__init__(_dataclass=None)

_sa_class_manager = {'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'project_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

id_attr_value

logs

tree

tree_id

type

class neuraxle.metaopt.repositories.db.ClientNode(_dataclass=None)

Bases: neuraxle.metaopt.repositories.db.DataClassNode

id

client_name

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.ClientDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.ClientDataclass

__init__(_dataclass=None)

_sa_class_manager = {'client_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

id_attr_value

logs

tree

tree_id

type

class neuraxle.metaopt.repositories.db.RoundNode(_dataclass=None)

Bases: neuraxle.metaopt.repositories.db.DataClassNode

id

round_number

main_metric_name

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.RoundDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.RoundDataclass

__init__(_dataclass=None)

_sa_class_manager = {'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'main_metric_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'round_number': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

id_attr_value

logs

tree

tree_id

type

class neuraxle.metaopt.repositories.db.BaseTrialNodeMixin

Bases: object

hyperparams = Column(None, JSON(), table=None, nullable=False, default=ColumnDefault(<function dict>))

status = Column(None, String(), table=None, nullable=False, default=ColumnDefault('PLANNED'))

created_time = Column(None, DateTime(), table=None, nullable=False, default=ColumnDefault(<sqlalchemy.sql.functions.now at 0x7f57111b8b80; now>))

start_time = Column(None, DateTime(), table=None, default=ColumnDefault(<sqlalchemy.sql.functions.now at 0x7f57111b8f10; now>))

end_time = Column(None, DateTime(), table=None)

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.BaseTrialDataclassMixin)

_attrs_for_to_dataclass() → Dict[str, Any]

class neuraxle.metaopt.repositories.db.TrialNode(_dataclass=None)

Bases: neuraxle.metaopt.repositories.db.BaseTrialNodeMixin, neuraxle.metaopt.repositories.db.DataClassNode

id

trial_number

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.TrialDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.TrialDataclass

__init__(_dataclass=None)

_sa_class_manager = {'created_time': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'end_time': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'hyperparams': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'start_time': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'status': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'trial_number': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

created_time

end_time

hyperparams

id_attr_value

logs

start_time

status

tree

tree_id

type

class neuraxle.metaopt.repositories.db.TrialSplitNode(_dataclass=None)

Bases: neuraxle.metaopt.repositories.db.BaseTrialNodeMixin, neuraxle.metaopt.repositories.db.DataClassNode

id

split_number

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.TrialSplitDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.TrialSplitDataclass

__init__(_dataclass=None)

_sa_class_manager = {'created_time': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'end_time': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'hyperparams': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'split_number': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'start_time': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'status': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

created_time

end_time

hyperparams

id_attr_value

logs

start_time

status

tree

tree_id

type

class neuraxle.metaopt.repositories.db.MetricResultsNode(_dataclass=None)

Bases: neuraxle.metaopt.repositories.db.DataClassNode

id

metric_name

valid_values

train_values

higher_score_is_better

_update_attrs_from_dataclass(_dataclass: neuraxle.metaopt.data.vanilla.MetricResultsDataclass)

to_empty_dataclass() → neuraxle.metaopt.data.vanilla.MetricResultsDataclass

__init__(_dataclass=None)

_sa_class_manager = {'higher_score_is_better': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id_attr_value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'logs': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'metric_name': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'train_values': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'tree_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'type': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'valid_values': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

id_attr_value

logs

tree

tree_id

type

class neuraxle.metaopt.repositories.db.MetricResultsValuesMixin

Bases: object

id = Column(None, Integer(), table=None, primary_key=True, nullable=False, default=Sequence('id_seq', metadata=MetaData()))

__init__()

Initialize self. See help(type(self)) for accurate signature.

value = Column(None, Float(), table=None, nullable=False)

datetime = Column(None, DateTime(), table=None, default=ColumnDefault(<sqlalchemy.sql.functions.now at 0x7f57111e1af0; now>))

class neuraxle.metaopt.repositories.db.TrainMetricResultsValues(value, _datetime=None)

Bases: neuraxle.metaopt.repositories.db.MetricResultsValuesMixin, sqlalchemy.orm.decl_api.Base

metric_results_node_id

metric_results_node

__init__(value, _datetime=None)

_sa_class_manager = {'datetime': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'metric_results_node': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'metric_results_node_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

datetime

id

value

class neuraxle.metaopt.repositories.db.ValidMetricResultsValues(value, _datetime=None)

Bases: neuraxle.metaopt.repositories.db.MetricResultsValuesMixin, sqlalchemy.orm.decl_api.Base

metric_results_node_id

metric_results_node

__init__(value, _datetime=None)

_sa_class_manager = {'datetime': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'metric_results_node': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'metric_results_node_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'value': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

datetime

id

value

class neuraxle.metaopt.repositories.db.AutoMLLog(**kwargs)

Bases: sqlalchemy.orm.decl_api.Base

id

dataclass_node_id

dataclass_node

datetime

log_text

__init__(**kwargs)

A simple constructor that allows initialization from kwargs.

Sets attributes on the constructed instance using the names and values in kwargs.

Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.

_sa_class_manager = {'dataclass_node': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'dataclass_node_id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'datetime': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'id': <sqlalchemy.orm.attributes.InstrumentedAttribute object>, 'log_text': <sqlalchemy.orm.attributes.InstrumentedAttribute object>}

class neuraxle.metaopt.repositories.db._DatabaseLoggerHandlerMixin

Bases: object

Mixin to add a in-memory logging handler to a repository.

add_logging_handler(logger: neuraxle.logging.logging.NeuraxleLogger, scope: neuraxle.metaopt.data.vanilla.ScopedLocation) → neuraxle.metaopt.repositories.repo.HyperparamsRepository

get_log_from_logging_handler(logger: neuraxle.logging.logging.NeuraxleLogger, scope: neuraxle.metaopt.data.vanilla.ScopedLocation) → str

class neuraxle.metaopt.repositories.db.DatabaseHyperparamRepository(engine, session)

Bases: neuraxle.metaopt.repositories.db._DatabaseLoggerHandlerMixin, neuraxle.metaopt.repositories.repo.HyperparamsRepository

__init__(engine, session)

Initialize self. See help(type(self)) for accurate signature.

create_db() → neuraxle.metaopt.repositories.db.DatabaseHyperparamRepository

load(scope: neuraxle.metaopt.data.vanilla.ScopedLocation, deep=False) → SubDataclassT

Get metadata from scope.

The fetched metadata will be the one that is the last item that is not a None in the provided scope.

Parameters

scope (ScopedLocation) – scope to get metadata from.

Returns

metadata from scope.

save(_dataclass: SubDataclassT, scope: neuraxle.metaopt.data.vanilla.ScopedLocation, deep=False) → neuraxle.metaopt.repositories.repo.HyperparamsRepository

Save metadata to scope.

Parameters

• metadata – metadata to save.

• scope (ScopedLocation) – scope to save metadata to.

• deep (bool) – if True, save metadata’s sublocations recursively.

_abc_impl = <_abc_data object>

class neuraxle.metaopt.repositories.db.SQLLiteHyperparamsRepository(sqllite_db_path, echo=False)

Bases: neuraxle.metaopt.repositories.db.DatabaseHyperparamRepository

__init__(sqllite_db_path, echo=False)

Initialize self. See help(type(self)) for accurate signature.

_abc_impl = <_abc_data object>

class neuraxle.metaopt.repositories.db.PostGreSQLHyperparamsRepository(postgresql_db_path, echo=False)

Bases: neuraxle.metaopt.repositories.db.DatabaseHyperparamRepository

_abc_impl = <_abc_data object>

__init__(postgresql_db_path, echo=False)

Initialize self. See help(type(self)) for accurate signature.

get_database_path(user: str, password: str, host: str, dialect: str, driver: str = '')

Parameters

• user (str) –

• password (str) –

• dialect (str) – e.g. mysql

• driver (str) – (Optional) e.g. “pymysql”

Returns