21811412795

Committed 09 Feb 2026 03:33AM UTC coverage: 63.304%. First build

Build # 21811412795

Build Type

Pull #330

github

Committed by

web-flow

Commit Message

Merge 2054b9323 into c3f021633

Pull Request Pull Request #330: WIP code coverage

Coverage Stats

1 of 2 new or added lines in 1 file covered. (50.0%)

5196 of 8208 relevant lines covered (63.3%)

1.27 hits per line

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56.67

/src/capymoa/misc.py

from jpype.pickle import JPickler, JUnpickler
from jpype import JException
from typing import BinaryIO, TextIO
from pathlib import Path
from io import RawIOBase, BufferedIOBase
from capymoa.stream._stream import Stream
import tqdm


def save_model(model: object, file: BinaryIO) -> None:
    """Save a model to a jpype pickle file.

    >>> from capymoa.classifier import AdaptiveRandomForestClassifier
    >>> from capymoa.datasets import ElectricityTiny
    >>> from tempfile import TemporaryFile
    >>> stream = ElectricityTiny()
    >>> learner = AdaptiveRandomForestClassifier(schema=stream.get_schema())
    >>> with TemporaryFile() as fd:
    ...     save_model(learner, fd)

    See https://jpype.readthedocs.io/en/latest/api.html#jpype-pickle-module for
    more information.

    :param model: A python object optionally containing Java objects.
    :param file: The file-like object to save the model to.
    """
    if not file.writable():
        raise ValueError("File must be writable.")
    JPickler(file).dump(model)


def load_model(file: BinaryIO) -> object:
    """Load a model from a jpype pickle file.

    See also: :func:`save_model`.

    :param file: The file-like object to load the model from.
    :return: The loaded model.
    """
    if not isinstance(file, (RawIOBase, BufferedIOBase)):
        raise ValueError("File must be opened in binary mode.")
    if not file.readable():
        raise ValueError("File must be readable.")
    try:
        return JUnpickler(file).load()
    except JException as e:
        raise RuntimeError(
            "Exception loading model.\n"
            "If you are trying to load a model saved with a version of CapyMOA < 0.8.2, "
            "use `legacy_load_model` and `save_model` to reformat the model."
        ) from e


def save_stream_arff(file: TextIO | Path | str, stream: Stream) -> None:
    """Save a CapyMOA stream to an ARFF file.

    Usage for classification datastream:

    >>> from capymoa.stream import NumpyStream
    >>> import numpy as np
    >>> from io import StringIO
    >>>
    >>> stream = NumpyStream(
    ...     X=np.array([[0, 1], [1, 0], [0, 0]]),
    ...     y=np.array([0, 1, 0]),
    ...     dataset_name="SimpleDataset",
    ...     target_type="categorical"
    ... )
    >>> fd = StringIO() # You can 'open' a real file instead
    >>> save_stream_arff(fd, stream)
    >>> print(fd.getvalue())
    @relation SimpleDataset
    <BLANKLINE>
    @attribute 0 numeric
    @attribute 1 numeric
    @attribute target {0,1}
    <BLANKLINE>
    @data
    0.0,1.0,0,
    1.0,0.0,1,
    0.0,0.0,0,
    <BLANKLINE>

    Usage for regression datastream:

    >>> stream = NumpyStream(
    ...     X=np.array([[0, 1], [1, 0], [0, 0]]),
    ...     y=np.array([0, 1, 0]),
    ...     dataset_name="SimpleDataset",
    ...     target_type="numeric"
    ... )
    >>> fd = StringIO() # You can 'open' a real file instead
    >>> save_stream_arff(fd, stream)
    >>> print(fd.getvalue())
    @relation SimpleDataset
    <BLANKLINE>
    @attribute 0 numeric
    @attribute 1 numeric
    @attribute target numeric
    <BLANKLINE>
    @data
    0.0,1.0,0.0,
    1.0,0.0,1.0,
    0.0,0.0,0.0,
    <BLANKLINE>


    :param file: A file-like object or path to write the ARFF to.
    :param stream: The stream to save.
    """
    if isinstance(file, (Path, str)):
        with open(file, "w") as fd:
            return save_stream_arff(fd, stream)
    else:
        moa_header = stream.get_schema().get_moa_header()
        file.write(str(moa_header))
        for instance in tqdm.tqdm(stream, desc="Saving to ARFF"):
            file.write(str(instance.java_instance.toString()) + "\n")


def _TODO_REMOVE_ME_I_AM_A_TEST():
    print("This is a test function that should be removed.")

1	from jpype.pickle import JPickler, JUnpickler	2✔
2	from jpype import JException	2✔
3	from typing import BinaryIO, TextIO	2✔
4	from pathlib import Path	2✔
5	from io import RawIOBase, BufferedIOBase	2✔
6	from capymoa.stream._stream import Stream	2✔
7	import tqdm	2✔
8
9
10	def save_model(model: object, file: BinaryIO) -> None:	2✔
11	"""Save a model to a jpype pickle file.
12
13	>>> from capymoa.classifier import AdaptiveRandomForestClassifier
14	>>> from capymoa.datasets import ElectricityTiny
15	>>> from tempfile import TemporaryFile
16	>>> stream = ElectricityTiny()
17	>>> learner = AdaptiveRandomForestClassifier(schema=stream.get_schema())
18	>>> with TemporaryFile() as fd:
19	... save_model(learner, fd)
20
21	See https://jpype.readthedocs.io/en/latest/api.html#jpype-pickle-module for
22	more information.
23
24	:param model: A python object optionally containing Java objects.
25	:param file: The file-like object to save the model to.
26	"""
27	if not file.writable():	2✔
28	raise ValueError("File must be writable.")	×
29	JPickler(file).dump(model)	2✔
30
31
32	def load_model(file: BinaryIO) -> object:	2✔
33	"""Load a model from a jpype pickle file.
34
35	See also: :func:`save_model`.
36
37	:param file: The file-like object to load the model from.
38	:return: The loaded model.
39	"""
40	if not isinstance(file, (RawIOBase, BufferedIOBase)):	2✔
41	raise ValueError("File must be opened in binary mode.")	×
42	if not file.readable():	2✔
43	raise ValueError("File must be readable.")	×
44	try:	2✔
45	return JUnpickler(file).load()	2✔
46	except JException as e:	×
47	raise RuntimeError(	×
48	"Exception loading model.\n"
49	"If you are trying to load a model saved with a version of CapyMOA < 0.8.2, "
50	"use `legacy_load_model` and `save_model` to reformat the model."
51	) from e
52
53
54	def save_stream_arff(file: TextIO \| Path \| str, stream: Stream) -> None:	2✔
55	"""Save a CapyMOA stream to an ARFF file.
56
57	Usage for classification datastream:
58
59	>>> from capymoa.stream import NumpyStream
60	>>> import numpy as np
61	>>> from io import StringIO
62	>>>
63	>>> stream = NumpyStream(
64	... X=np.array([[0, 1], [1, 0], [0, 0]]),
65	... y=np.array([0, 1, 0]),
66	... dataset_name="SimpleDataset",
67	... target_type="categorical"
68	... )
69	>>> fd = StringIO() # You can 'open' a real file instead
70	>>> save_stream_arff(fd, stream)
71	>>> print(fd.getvalue())
72	@relation SimpleDataset
73	<BLANKLINE>
74	@attribute 0 numeric
75	@attribute 1 numeric
76	@attribute target {0,1}
77	<BLANKLINE>
78	@data
79	0.0,1.0,0,
80	1.0,0.0,1,
81	0.0,0.0,0,
82	<BLANKLINE>
83
84	Usage for regression datastream:
85
86	>>> stream = NumpyStream(
87	... X=np.array([[0, 1], [1, 0], [0, 0]]),
88	... y=np.array([0, 1, 0]),
89	... dataset_name="SimpleDataset",
90	... target_type="numeric"
91	... )
92	>>> fd = StringIO() # You can 'open' a real file instead
93	>>> save_stream_arff(fd, stream)
94	>>> print(fd.getvalue())
95	@relation SimpleDataset
96	<BLANKLINE>
97	@attribute 0 numeric
98	@attribute 1 numeric
99	@attribute target numeric
100	<BLANKLINE>
101	@data
102	0.0,1.0,0.0,
103	1.0,0.0,1.0,
104	0.0,0.0,0.0,
105	<BLANKLINE>
106
107
108	:param file: A file-like object or path to write the ARFF to.
109	:param stream: The stream to save.
110	"""
111	if isinstance(file, (Path, str)):	×
112	with open(file, "w") as fd:	×
113	return save_stream_arff(fd, stream)	×
114	else:
115	moa_header = stream.get_schema().get_moa_header()	×
116	file.write(str(moa_header))	×
117	for instance in tqdm.tqdm(stream, desc="Saving to ARFF"):	×
118	file.write(str(instance.java_instance.toString()) + "\n")	×
119
120
121	def _TODO_REMOVE_ME_I_AM_A_TEST():	2✔
NEW 122	print("This is a test function that should be removed.")	×

adaptive-machine-learning / CapyMOA / 21811412795

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