CSVStream#

class capymoa.stream.CSVStream[source]#

Bases: Stream

__init__(
csv_file_path,
dtypes: list = None,
values_for_nominal_features={},
class_index: int = -1,
values_for_class_label: list = None,
target_attribute_name=None,
target_type: str = None,
skip_header: bool = False,
delimiter=',',
)[source]#

Construct a Stream from a MOA stream object.

Usually, you will want to construct a Stream using the capymoa.stream.stream_from_file() function.

Parameters:

  • moa_stream – The MOA stream object to read instances from. Is None if the stream is created from a numpy array.

  • schema – The schema of the stream. If None, the schema is inferred from the moa_stream.

  • CLI – Additional command line arguments to pass to the MOA stream.

Raises:

  • ValueError – If no schema is provided and no moa_stream is provided.

  • ValueError – If command line arguments are provided without a moa_stream.

CLI_help() → str[source]#

Return cli help string for the stream.

count_number_of_lines()[source]#
has_more_instances()[source]#

Return True if the stream have more instances to read.

next_instance()[source]#

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()[source]#

Return the schema of the stream.

get_moa_stream()[source]#

Get the MOA stream object if it exists.

restart()[source]#

Restart the stream to read instances from the beginning.