d3m.contrib.primitives.null module

class d3m.contrib.primitives.null.NullDataFrameUnsupervisedLearnerPrimitive(*, hyperparams, random_seed=0, docker_containers=None, volumes=None, temporary_directory=None)

Bases: d3m.primitive_interfaces.unsupervised_learning.UnsupervisedLearnerPrimitiveBase[[d3m.container.pandas.DataFrame, d3m.container.pandas.DataFrame, d3m.contrib.primitives.null.Params], d3m.contrib.primitives.null.Hyperparams]

A primitive which passes through inputs as outputs.

It does not really care if inputs is a Dataframe.

fit(*, timeout=None, iterations=None)

A noop.

Parameters

• timeout (Optional[float]) – A maximum time this primitive should be fitting during this method call, in seconds.

• iterations (Optional[int]) – How many of internal iterations should the primitive do.

Returns

A CallResult with None value.

Return type

CallResult[None]

get_params()

A noop.

Returns

An instance of parameters.

Return type

Params

produce(*, inputs, timeout=None, iterations=None)

Produce primitive’s best choice of the output for each of the inputs.

The output value should be wrapped inside CallResult object before returning.

In many cases producing an output is a quick operation in comparison with fit, but not all cases are like that. For example, a primitive can start a potentially long optimization process to compute outputs. timeout and iterations can serve as a way for a caller to guide the length of this process.

Ideally, a primitive should adapt its call to try to produce the best outputs possible inside the time allocated. If this is not possible and the primitive reaches the timeout before producing outputs, it should raise a TimeoutError exception to signal that the call was unsuccessful in the given time. The state of the primitive after the exception should be as the method call has never happened and primitive should continue to operate normally. The purpose of timeout is to give opportunity to a primitive to cleanly manage its state instead of interrupting execution from outside. Maintaining stable internal state should have precedence over respecting the timeout (caller can terminate the misbehaving primitive from outside anyway). If a longer timeout would produce different outputs, then CallResult’s has_finished should be set to False.

Some primitives have internal iterations (for example, optimization iterations). For those, caller can provide how many of primitive’s internal iterations should a primitive do before returning outputs. Primitives should make iterations as small as reasonable. If iterations is None, then there is no limit on how many iterations the primitive should do and primitive should choose the best amount of iterations on its own (potentially controlled through hyper-parameters). If iterations is a number, a primitive has to do those number of iterations, if possible. timeout should still be respected and potentially less iterations can be done because of that. Primitives with internal iterations should make CallResult contain correct values.

For primitives which do not have internal iterations, any value of iterations means that they should run fully, respecting only timeout.

If primitive should have been fitted before calling this method, but it has not been, primitive should raise a PrimitiveNotFittedError exception.

Parameters

• inputs (DataFrame) – The inputs of shape [num_inputs, …].

• timeout (Optional[float]) – A maximum time this primitive should take to produce outputs during this method call, in seconds.

• iterations (Optional[int]) – How many of internal iterations should the primitive do.

Returns

The outputs of shape [num_inputs, …] wrapped inside CallResult.

Return type

CallResult[DataFrame]

set_params(*, params)

A noop.

Parameters

params (Params) – An instance of parameters.

Return type

None

set_training_data()

A noop.

Return type

None

logger: typing.ClassVar[logging.Logger] = <Logger d3m.primitives.operator.null.DataFrameUnsupervisedLearnerCore (WARNING)>

Primitive’s logger. Available as a class attribute. This gets automatically set to primitive’s logger in metaclass.

metadata: ClassVar[d3m.metadata.base.PrimitiveMetadata] = <d3m.metadata.base.PrimitiveMetadata object>

Primitive’s metadata. Available as a class attribute. Primitive author should provide all fields which cannot be determined automatically inside the code. In this way metadata is close to the code and it is easier for consumers to make sure metadata they are using is really matching the code they are using. PrimitiveMetadata class updates itself with metadata about code and other things it can extract automatically.

class d3m.contrib.primitives.null.NullTransformerPrimitive(*, hyperparams, random_seed=0, docker_containers=None, volumes=None, temporary_directory=None)

Bases: d3m.primitive_interfaces.transformer.TransformerPrimitiveBase[[d3m.container.list.List, d3m.container.list.List], d3m.contrib.primitives.null.Hyperparams]

A primitive which passes through inputs as outputs.

It does not really care if inputs is list.

produce(*, inputs, timeout=None, iterations=None)

Produce primitive’s best choice of the output for each of the inputs.

The output value should be wrapped inside CallResult object before returning.

In many cases producing an output is a quick operation in comparison with fit, but not all cases are like that. For example, a primitive can start a potentially long optimization process to compute outputs. timeout and iterations can serve as a way for a caller to guide the length of this process.

Ideally, a primitive should adapt its call to try to produce the best outputs possible inside the time allocated. If this is not possible and the primitive reaches the timeout before producing outputs, it should raise a TimeoutError exception to signal that the call was unsuccessful in the given time. The state of the primitive after the exception should be as the method call has never happened and primitive should continue to operate normally. The purpose of timeout is to give opportunity to a primitive to cleanly manage its state instead of interrupting execution from outside. Maintaining stable internal state should have precedence over respecting the timeout (caller can terminate the misbehaving primitive from outside anyway). If a longer timeout would produce different outputs, then CallResult’s has_finished should be set to False.

Some primitives have internal iterations (for example, optimization iterations). For those, caller can provide how many of primitive’s internal iterations should a primitive do before returning outputs. Primitives should make iterations as small as reasonable. If iterations is None, then there is no limit on how many iterations the primitive should do and primitive should choose the best amount of iterations on its own (potentially controlled through hyper-parameters). If iterations is a number, a primitive has to do those number of iterations, if possible. timeout should still be respected and potentially less iterations can be done because of that. Primitives with internal iterations should make CallResult contain correct values.

For primitives which do not have internal iterations, any value of iterations means that they should run fully, respecting only timeout.

If primitive should have been fitted before calling this method, but it has not been, primitive should raise a PrimitiveNotFittedError exception.

Parameters

• inputs (List) – The inputs of shape [num_inputs, …].

• timeout (Optional[float]) – A maximum time this primitive should take to produce outputs during this method call, in seconds.

• iterations (Optional[int]) – How many of internal iterations should the primitive do.

Returns

The outputs of shape [num_inputs, …] wrapped inside CallResult.

Return type

CallResult[List]

logger: typing.ClassVar[logging.Logger] = <Logger d3m.primitives.operator.null.TransformerCore (WARNING)>

Primitive’s logger. Available as a class attribute. This gets automatically set to primitive’s logger in metaclass.

metadata: ClassVar[d3m.metadata.base.PrimitiveMetadata] = <d3m.metadata.base.PrimitiveMetadata object>

Primitive’s metadata. Available as a class attribute. Primitive author should provide all fields which cannot be determined automatically inside the code. In this way metadata is close to the code and it is easier for consumers to make sure metadata they are using is really matching the code they are using. PrimitiveMetadata class updates itself with metadata about code and other things it can extract automatically.

class d3m.contrib.primitives.null.NullUnsupervisedLearnerPrimitive(*, hyperparams, random_seed=0, docker_containers=None, volumes=None, temporary_directory=None)

Bases: d3m.primitive_interfaces.unsupervised_learning.UnsupervisedLearnerPrimitiveBase[[d3m.container.list.List, d3m.container.list.List, d3m.contrib.primitives.null.Params], d3m.contrib.primitives.null.Hyperparams]

A primitive which passes through inputs as outputs.

It does not really care if inputs is list.

fit(*, timeout=None, iterations=None)

A noop.

Parameters

• timeout (Optional[float]) – A maximum time this primitive should be fitting during this method call, in seconds.

• iterations (Optional[int]) – How many of internal iterations should the primitive do.

Returns

A CallResult with None value.

Return type

CallResult[None]

get_params()

A noop.

Returns

An instance of parameters.

Return type

Params

produce(*, inputs, timeout=None, iterations=None)

Produce primitive’s best choice of the output for each of the inputs.

The output value should be wrapped inside CallResult object before returning.

In many cases producing an output is a quick operation in comparison with fit, but not all cases are like that. For example, a primitive can start a potentially long optimization process to compute outputs. timeout and iterations can serve as a way for a caller to guide the length of this process.

Ideally, a primitive should adapt its call to try to produce the best outputs possible inside the time allocated. If this is not possible and the primitive reaches the timeout before producing outputs, it should raise a TimeoutError exception to signal that the call was unsuccessful in the given time. The state of the primitive after the exception should be as the method call has never happened and primitive should continue to operate normally. The purpose of timeout is to give opportunity to a primitive to cleanly manage its state instead of interrupting execution from outside. Maintaining stable internal state should have precedence over respecting the timeout (caller can terminate the misbehaving primitive from outside anyway). If a longer timeout would produce different outputs, then CallResult’s has_finished should be set to False.

Some primitives have internal iterations (for example, optimization iterations). For those, caller can provide how many of primitive’s internal iterations should a primitive do before returning outputs. Primitives should make iterations as small as reasonable. If iterations is None, then there is no limit on how many iterations the primitive should do and primitive should choose the best amount of iterations on its own (potentially controlled through hyper-parameters). If iterations is a number, a primitive has to do those number of iterations, if possible. timeout should still be respected and potentially less iterations can be done because of that. Primitives with internal iterations should make CallResult contain correct values.

For primitives which do not have internal iterations, any value of iterations means that they should run fully, respecting only timeout.

If primitive should have been fitted before calling this method, but it has not been, primitive should raise a PrimitiveNotFittedError exception.

Parameters

• inputs (List) – The inputs of shape [num_inputs, …].

• timeout (Optional[float]) – A maximum time this primitive should take to produce outputs during this method call, in seconds.

• iterations (Optional[int]) – How many of internal iterations should the primitive do.

Returns

The outputs of shape [num_inputs, …] wrapped inside CallResult.

Return type

CallResult[List]

set_params(*, params)

A noop.

Parameters

params (Params) – An instance of parameters.

Return type

None

set_training_data()

A noop.

Return type

None

logger: typing.ClassVar[logging.Logger] = <Logger d3m.primitives.operator.null.UnsupervisedLearnerCore (WARNING)>

Primitive’s logger. Available as a class attribute. This gets automatically set to primitive’s logger in metaclass.

metadata: ClassVar[d3m.metadata.base.PrimitiveMetadata] = <d3m.metadata.base.PrimitiveMetadata object>

Primitive’s metadata. Available as a class attribute. Primitive author should provide all fields which cannot be determined automatically inside the code. In this way metadata is close to the code and it is easier for consumers to make sure metadata they are using is really matching the code they are using. PrimitiveMetadata class updates itself with metadata about code and other things it can extract automatically.