ConcatStream

class capymoa.stream.ConcatStream

Bases: Stream[_AnyInstance]

Concatenate multiple streams into a single stream. When the end of a stream is reached, the next stream in the list is used. >>> from capymoa.stream import ConcatStream, NumpyStream >>> import numpy as np >>> X1 = np.array([[1, 2, 3]]) >>> X2 = np.array([[4, 5, 6]]) >>> y1 = np.array([0]) >>> y2 = np.array([0]) >>> stream1 = NumpyStream(X1, y1) >>> stream2 = NumpyStream(X2, y2) >>> concat_stream = ConcatStream([stream1, stream2]) >>> for instance in concat_stream: … print(instance) LabeledInstance(

Schema(No_Name), x=[1. 2. 3.], y_index=0, y_label=’0’

) LabeledInstance(

Schema(No_Name), x=[4. 5. 6.], y_index=0, y_label=’0’

)

CLI_help() → str

Return a help message

__init__(streams: Sequence[Stream])

Construct a ConcatStream object from a list of streams. :param streams: A list of streams to chain together.

__iter__() → Iterator[_AnyInstance]

Get an iterator over the stream.

This will NOT restart the stream if it has already been iterated over. Please use the restart() method to restart the stream.

Yield:

An iterator over the stream.

__next__() → _AnyInstance

Get the next instance in the stream.

Returns:

The next instance in the stream.

has_more_instances() → bool

Return True if the stream have more instances to read.

next_instance() → _AnyInstance

Return the next instance in the stream. :raises ValueError: If the machine learning task is neither a regression

nor a classification task.

Returns:

A labeled instances or a regression depending on the schema.

get_schema() → Schema

Return the schema of the stream.

get_moa_stream() → InstanceStream | None

Get the MOA stream object if it exists.

restart()

Restart the stream to read instances from the beginning.