code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def _pkl_filepath(*args, **kwargs):
"""Return filename for Python 3 pickles
args[-1] is expected to be the ".pkl" filename. For compatibility with
older scikit-learn versions, a suffix is inserted before the extension.
_pkl_filepath('/path/to/folder', 'filename.pkl') returns
'/path/to/folder/filen... | Return filename for Python 3 pickles
args[-1] is expected to be the ".pkl" filename. For compatibility with
older scikit-learn versions, a suffix is inserted before the extension.
_pkl_filepath('/path/to/folder', 'filename.pkl') returns
'/path/to/folder/filename_py3.pkl'
| _pkl_filepath | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def _sha256(path):
"""Calculate the sha256 hash of the file at path."""
sha256hash = hashlib.sha256()
chunk_size = 8192
with open(path, "rb") as f:
while True:
buffer = f.read(chunk_size)
if not buffer:
break
sha256hash.update(buffer)
retur... | Calculate the sha256 hash of the file at path. | _sha256 | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def _fetch_remote(remote, dirname=None, n_retries=3, delay=1):
"""Helper function to download a remote dataset.
Fetch a dataset pointed by remote's url, save into path using remote's
filename and ensure its integrity based on the SHA256 checksum of the
downloaded file.
.. versionchanged:: 1.6
... | Helper function to download a remote dataset.
Fetch a dataset pointed by remote's url, save into path using remote's
filename and ensure its integrity based on the SHA256 checksum of the
downloaded file.
.. versionchanged:: 1.6
If the file already exists locally and the SHA256 checksums match... | _fetch_remote | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def _filter_filename(value, filter_dots=True):
"""Derive a name that is safe to use as filename from the given string.
Adapted from the `slugify` function of django:
https://github.com/django/django/blob/master/django/utils/text.py
Convert spaces or repeated dashes to single dashes. Replace characters... | Derive a name that is safe to use as filename from the given string.
Adapted from the `slugify` function of django:
https://github.com/django/django/blob/master/django/utils/text.py
Convert spaces or repeated dashes to single dashes. Replace characters that
aren't alphanumerics, underscores, hyphens o... | _filter_filename | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def fetch_file(
url, folder=None, local_filename=None, sha256=None, n_retries=3, delay=1
):
"""Fetch a file from the web if not already present in the local folder.
If the file already exists locally (and the SHA256 checksums match when
provided), the path to the local file is returned without re-downl... | Fetch a file from the web if not already present in the local folder.
If the file already exists locally (and the SHA256 checksums match when
provided), the path to the local file is returned without re-downloading.
.. versionadded:: 1.6
Parameters
----------
url : str
URL of the file... | fetch_file | python | scikit-learn/scikit-learn | sklearn/datasets/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py | BSD-3-Clause |
def fetch_california_housing(
*,
data_home=None,
download_if_missing=True,
return_X_y=False,
as_frame=False,
n_retries=3,
delay=1.0,
):
"""Load the California housing dataset (regression).
============== ==============
Samples total 20640
Dimensionality ... | Load the California housing dataset (regression).
============== ==============
Samples total 20640
Dimensionality 8
Features real
Target real 0.15 - 5.
============== ==============
Read more in the :ref:`User Guide <california_ho... | fetch_california_housing | python | scikit-learn/scikit-learn | sklearn/datasets/_california_housing.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_california_housing.py | BSD-3-Clause |
def fetch_covtype(
*,
data_home=None,
download_if_missing=True,
random_state=None,
shuffle=False,
return_X_y=False,
as_frame=False,
n_retries=3,
delay=1.0,
):
"""Load the covertype dataset (classification).
Download it if necessary.
================= ============
... | Load the covertype dataset (classification).
Download it if necessary.
================= ============
Classes 7
Samples total 581012
Dimensionality 54
Features int
================= ============
Read more in the... | fetch_covtype | python | scikit-learn/scikit-learn | sklearn/datasets/_covtype.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_covtype.py | BSD-3-Clause |
def fetch_kddcup99(
*,
subset=None,
data_home=None,
shuffle=False,
random_state=None,
percent10=True,
download_if_missing=True,
return_X_y=False,
as_frame=False,
n_retries=3,
delay=1.0,
):
"""Load the kddcup99 dataset (classification).
Download it if necessary.
... | Load the kddcup99 dataset (classification).
Download it if necessary.
================= ====================================
Classes 23
Samples total 4898431
Dimensionality 41
... | fetch_kddcup99 | python | scikit-learn/scikit-learn | sklearn/datasets/_kddcup99.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_kddcup99.py | BSD-3-Clause |
def _fetch_brute_kddcup99(
data_home=None, download_if_missing=True, percent10=True, n_retries=3, delay=1.0
):
"""Load the kddcup99 dataset, downloading it if necessary.
Parameters
----------
data_home : str, default=None
Specify another download and cache folder for the datasets. By defaul... | Load the kddcup99 dataset, downloading it if necessary.
Parameters
----------
data_home : str, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
download_if_missing : bool, default=Tr... | _fetch_brute_kddcup99 | python | scikit-learn/scikit-learn | sklearn/datasets/_kddcup99.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_kddcup99.py | BSD-3-Clause |
def _mkdirp(d):
"""Ensure directory d exists (like mkdir -p on Unix)
No guarantee that the directory is writable.
"""
try:
os.makedirs(d)
except OSError as e:
if e.errno != errno.EEXIST:
raise | Ensure directory d exists (like mkdir -p on Unix)
No guarantee that the directory is writable.
| _mkdirp | python | scikit-learn/scikit-learn | sklearn/datasets/_kddcup99.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_kddcup99.py | BSD-3-Clause |
def _check_fetch_lfw(
data_home=None, funneled=True, download_if_missing=True, n_retries=3, delay=1.0
):
"""Helper function to download any missing LFW data"""
data_home = get_data_home(data_home=data_home)
lfw_home = join(data_home, "lfw_home")
if not exists(lfw_home):
makedirs(lfw_home)
... | Helper function to download any missing LFW data | _check_fetch_lfw | python | scikit-learn/scikit-learn | sklearn/datasets/_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_lfw.py | BSD-3-Clause |
def _fetch_lfw_people(
data_folder_path, slice_=None, color=False, resize=None, min_faces_per_person=0
):
"""Perform the actual data loading for the lfw people dataset
This operation is meant to be cached by a joblib wrapper.
"""
# scan the data folder content to retain people with more that
# ... | Perform the actual data loading for the lfw people dataset
This operation is meant to be cached by a joblib wrapper.
| _fetch_lfw_people | python | scikit-learn/scikit-learn | sklearn/datasets/_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_lfw.py | BSD-3-Clause |
def fetch_lfw_people(
*,
data_home=None,
funneled=True,
resize=0.5,
min_faces_per_person=0,
color=False,
slice_=(slice(70, 195), slice(78, 172)),
download_if_missing=True,
return_X_y=False,
n_retries=3,
delay=1.0,
):
"""Load the Labeled Faces in the Wild (LFW) people data... | Load the Labeled Faces in the Wild (LFW) people dataset (classification).
Download it if necessary.
================= =======================
Classes 5749
Samples total 13233
Dimensionality 5828
Features ... | fetch_lfw_people | python | scikit-learn/scikit-learn | sklearn/datasets/_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_lfw.py | BSD-3-Clause |
def _fetch_lfw_pairs(
index_file_path, data_folder_path, slice_=None, color=False, resize=None
):
"""Perform the actual data loading for the LFW pairs dataset
This operation is meant to be cached by a joblib wrapper.
"""
# parse the index file to find the number of pairs to be able to allocate
... | Perform the actual data loading for the LFW pairs dataset
This operation is meant to be cached by a joblib wrapper.
| _fetch_lfw_pairs | python | scikit-learn/scikit-learn | sklearn/datasets/_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_lfw.py | BSD-3-Clause |
def fetch_lfw_pairs(
*,
subset="train",
data_home=None,
funneled=True,
resize=0.5,
color=False,
slice_=(slice(70, 195), slice(78, 172)),
download_if_missing=True,
n_retries=3,
delay=1.0,
):
"""Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).
Downl... | Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).
Download it if necessary.
================= =======================
Classes 2
Samples total 13233
Dimensionality 5828
Features ... | fetch_lfw_pairs | python | scikit-learn/scikit-learn | sklearn/datasets/_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_lfw.py | BSD-3-Clause |
def fetch_olivetti_faces(
*,
data_home=None,
shuffle=False,
random_state=0,
download_if_missing=True,
return_X_y=False,
n_retries=3,
delay=1.0,
):
"""Load the Olivetti faces data-set from AT&T (classification).
Download it if necessary.
================= =================... | Load the Olivetti faces data-set from AT&T (classification).
Download it if necessary.
================= =====================
Classes 40
Samples total 400
Dimensionality 4096
Features real, between 0 and... | fetch_olivetti_faces | python | scikit-learn/scikit-learn | sklearn/datasets/_olivetti_faces.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_olivetti_faces.py | BSD-3-Clause |
def _retry_with_clean_cache(
openml_path: str,
data_home: Optional[str],
no_retry_exception: Optional[Exception] = None,
) -> Callable:
"""If the first call to the decorated function fails, the local cached
file is removed, and the function is called again. If ``data_home`` is
``None``, then the... | If the first call to the decorated function fails, the local cached
file is removed, and the function is called again. If ``data_home`` is
``None``, then the function is called once. We can provide a specific
exception to not retry on using `no_retry_exception` parameter.
| _retry_with_clean_cache | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _retry_on_network_error(
n_retries: int = 3, delay: float = 1.0, url: str = ""
) -> Callable:
"""If the function call results in a network error, call the function again
up to ``n_retries`` times with a ``delay`` between each call. If the error
has a 412 status code, don't call the function again as... | If the function call results in a network error, call the function again
up to ``n_retries`` times with a ``delay`` between each call. If the error
has a 412 status code, don't call the function again as this is a specific
OpenML error.
The url parameter is used to give more information to the user abou... | _retry_on_network_error | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _open_openml_url(
url: str, data_home: Optional[str], n_retries: int = 3, delay: float = 1.0
):
"""
Returns a resource from OpenML.org. Caches it to data_home if required.
Parameters
----------
url : str
OpenML URL that will be downloaded and cached locally. The path component
... |
Returns a resource from OpenML.org. Caches it to data_home if required.
Parameters
----------
url : str
OpenML URL that will be downloaded and cached locally. The path component
of the URL is used to replicate the tree structure as sub-folders of the local
cache folder.
da... | _open_openml_url | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _get_json_content_from_openml_api(
url: str,
error_message: Optional[str],
data_home: Optional[str],
n_retries: int = 3,
delay: float = 1.0,
) -> Dict:
"""
Loads json data from the openml api.
Parameters
----------
url : str
The URL to load from. Should be an officia... |
Loads json data from the openml api.
Parameters
----------
url : str
The URL to load from. Should be an official OpenML endpoint.
error_message : str or None
The error message to raise if an acceptable OpenML error is thrown
(acceptable error is, e.g., data id not found. O... | _get_json_content_from_openml_api | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _get_data_info_by_name(
name: str,
version: Union[int, str],
data_home: Optional[str],
n_retries: int = 3,
delay: float = 1.0,
):
"""
Utilizes the openml dataset listing api to find a dataset by
name/version
OpenML api function:
https://www.openml.org/api_docs#!/data/get_data... |
Utilizes the openml dataset listing api to find a dataset by
name/version
OpenML api function:
https://www.openml.org/api_docs#!/data/get_data_list_data_name_data_name
Parameters
----------
name : str
name of the dataset
version : int or str
If version is an integer, t... | _get_data_info_by_name | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _get_num_samples(data_qualities: OpenmlQualitiesType) -> int:
"""Get the number of samples from data qualities.
Parameters
----------
data_qualities : list of dict
Used to retrieve the number of instances (samples) in the dataset.
Returns
-------
n_samples : int
The num... | Get the number of samples from data qualities.
Parameters
----------
data_qualities : list of dict
Used to retrieve the number of instances (samples) in the dataset.
Returns
-------
n_samples : int
The number of samples in the dataset or -1 if data qualities are
unavail... | _get_num_samples | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _load_arff_response(
url: str,
data_home: Optional[str],
parser: str,
output_type: str,
openml_columns_info: dict,
feature_names_to_select: List[str],
target_names_to_select: List[str],
shape: Optional[Tuple[int, int]],
md5_checksum: str,
n_retries: int = 3,
delay: float ... | Load the ARFF data associated with the OpenML URL.
In addition of loading the data, this function will also check the
integrity of the downloaded file from OpenML using MD5 checksum.
Parameters
----------
url : str
The URL of the ARFF file on OpenML.
data_home : str
The locati... | _load_arff_response | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def _download_data_to_bunch(
url: str,
sparse: bool,
data_home: Optional[str],
*,
as_frame: bool,
openml_columns_info: List[dict],
data_columns: List[str],
target_columns: List[str],
shape: Optional[Tuple[int, int]],
md5_checksum: str,
n_retries: int = 3,
delay: float = 1... | Download ARFF data, load it to a specific container and create to Bunch.
This function has a mechanism to retry/cache/clean the data.
Parameters
----------
url : str
The URL of the ARFF file on OpenML.
sparse : bool
Whether the dataset is expected to use the sparse ARFF format.
... | _download_data_to_bunch | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def fetch_openml(
name: Optional[str] = None,
*,
version: Union[str, int] = "active",
data_id: Optional[int] = None,
data_home: Optional[Union[str, os.PathLike]] = None,
target_column: Optional[Union[str, List]] = "default-target",
cache: bool = True,
return_X_y: bool = False,
as_fra... | Fetch dataset from openml by name or dataset id.
Datasets are uniquely identified by either an integer ID or by a
combination of name and version (i.e. there might be multiple
versions of the 'iris' dataset). Please give either name or data_id
(not both). In case a name is given, a version can also be
... | fetch_openml | python | scikit-learn/scikit-learn | sklearn/datasets/_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_openml.py | BSD-3-Clause |
def fetch_rcv1(
*,
data_home=None,
subset="all",
download_if_missing=True,
random_state=None,
shuffle=False,
return_X_y=False,
n_retries=3,
delay=1.0,
):
"""Load the RCV1 multilabel dataset (classification).
Download it if necessary.
Version: RCV1-v2, vectors, full sets... | Load the RCV1 multilabel dataset (classification).
Download it if necessary.
Version: RCV1-v2, vectors, full sets, topics multilabels.
================= =====================
Classes 103
Samples total 804414
Dimensionality ... | fetch_rcv1 | python | scikit-learn/scikit-learn | sklearn/datasets/_rcv1.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_rcv1.py | BSD-3-Clause |
def _generate_hypercube(samples, dimensions, rng):
"""Returns distinct binary samples of length dimensions."""
if dimensions > 30:
return np.hstack(
[
rng.randint(2, size=(samples, dimensions - 30)),
_generate_hypercube(samples, 30, rng),
]
... | Returns distinct binary samples of length dimensions. | _generate_hypercube | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_classification(
n_samples=100,
n_features=20,
*,
n_informative=2,
n_redundant=2,
n_repeated=0,
n_classes=2,
n_clusters_per_class=2,
weights=None,
flip_y=0.01,
class_sep=1.0,
hypercube=True,
shift=0.0,
scale=1.0,
shuffle=True,
random_state=None,
... | Generate a random n-class classification problem.
This initially creates clusters of points normally distributed (std=1)
about vertices of an ``n_informative``-dimensional hypercube with sides of
length ``2*class_sep`` and assigns an equal number of clusters to each
class. It introduces interdependence... | make_classification | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_multilabel_classification(
n_samples=100,
n_features=20,
*,
n_classes=5,
n_labels=2,
length=50,
allow_unlabeled=True,
sparse=False,
return_indicator="dense",
return_distributions=False,
random_state=None,
):
"""Generate a random multilabel classification problem.... | Generate a random multilabel classification problem.
For each sample, the generative process is:
- pick the number of labels: n ~ Poisson(n_labels)
- n times, choose a class c: c ~ Multinomial(theta)
- pick the document length: k ~ Poisson(length)
- k times, choose a word: w ~ Multi... | make_multilabel_classification | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_hastie_10_2(n_samples=12000, *, random_state=None):
"""Generate data for binary classification used in Hastie et al. 2009, Example 10.2.
The ten features are standard independent Gaussian and
the target ``y`` is defined by::
y[i] = 1 if np.sum(X[i] ** 2) > 9.34 else -1
Read more in the... | Generate data for binary classification used in Hastie et al. 2009, Example 10.2.
The ten features are standard independent Gaussian and
the target ``y`` is defined by::
y[i] = 1 if np.sum(X[i] ** 2) > 9.34 else -1
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
--------... | make_hastie_10_2 | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_regression(
n_samples=100,
n_features=100,
*,
n_informative=10,
n_targets=1,
bias=0.0,
effective_rank=None,
tail_strength=0.5,
noise=0.0,
shuffle=True,
coef=False,
random_state=None,
):
"""Generate a random regression problem.
The input set can either be... | Generate a random regression problem.
The input set can either be well conditioned (by default) or have a low
rank-fat tail singular profile. See :func:`make_low_rank_matrix` for
more details.
The output is generated by applying a (potentially biased) random linear
regression model with `n_informa... | make_regression | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_circles(
n_samples=100, *, shuffle=True, noise=None, random_state=None, factor=0.8
):
"""Make a large circle containing a smaller circle in 2d.
A simple toy dataset to visualize clustering and classification
algorithms.
Read more in the :ref:`User Guide <sample_generators>`.
Paramete... | Make a large circle containing a smaller circle in 2d.
A simple toy dataset to visualize clustering and classification
algorithms.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int or tuple of shape (2,), dtype=int, default=100
If int, it is... | make_circles | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_moons(n_samples=100, *, shuffle=True, noise=None, random_state=None):
"""Make two interleaving half circles.
A simple toy dataset to visualize clustering and classification
algorithms. Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int or tup... | Make two interleaving half circles.
A simple toy dataset to visualize clustering and classification
algorithms. Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int or tuple of shape (2,), dtype=int, default=100
If int, the total number of points ge... | make_moons | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_blobs(
n_samples=100,
n_features=2,
*,
centers=None,
cluster_std=1.0,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None,
return_centers=False,
):
"""Generate isotropic Gaussian blobs for clustering.
Read more in the :ref:`User Guide <sample_generators>`.
... | Generate isotropic Gaussian blobs for clustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int or array-like, default=100
If int, it is the total number of points equally divided among
clusters.
If array-like, each element of the... | make_blobs | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_friedman1(n_samples=100, n_features=10, *, noise=0.0, random_state=None):
"""Generate the "Friedman #1" regression problem.
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are independent features uniformly distributed on the interval
[0, 1]. The output `y` is created ac... | Generate the "Friedman #1" regression problem.
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are independent features uniformly distributed on the interval
[0, 1]. The output `y` is created according to the formula::
y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] ... | make_friedman1 | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_friedman2(n_samples=100, *, noise=0.0, random_state=None):
"""Generate the "Friedman #2" regression problem.
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are 4 independent features uniformly distributed on the
intervals::
0 <= X[:, 0] <= 100,
40 * pi ... | Generate the "Friedman #2" regression problem.
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are 4 independent features uniformly distributed on the
intervals::
0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= ... | make_friedman2 | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_friedman3(n_samples=100, *, noise=0.0, random_state=None):
"""Generate the "Friedman #3" regression problem.
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are 4 independent features uniformly distributed on the
intervals::
0 <= X[:, 0] <= 100,
40 * pi ... | Generate the "Friedman #3" regression problem.
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are 4 independent features uniformly distributed on the
intervals::
0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= ... | make_friedman3 | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_low_rank_matrix(
n_samples=100,
n_features=100,
*,
effective_rank=10,
tail_strength=0.5,
random_state=None,
):
"""Generate a mostly low rank matrix with bell-shaped singular values.
Most of the variance can be explained by a bell-shaped curve of width
effective_rank: the lo... | Generate a mostly low rank matrix with bell-shaped singular values.
Most of the variance can be explained by a bell-shaped curve of width
effective_rank: the low rank part of the singular values profile is::
(1 - tail_strength) * exp(-1.0 * (i / effective_rank) ** 2)
The remaining singular values... | make_low_rank_matrix | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_sparse_coded_signal(
n_samples,
*,
n_components,
n_features,
n_nonzero_coefs,
random_state=None,
):
"""Generate a signal as a sparse combination of dictionary elements.
Returns matrices `Y`, `D` and `X` such that `Y = XD` where `X` is of shape
`(n_samples, n_components)`, `... | Generate a signal as a sparse combination of dictionary elements.
Returns matrices `Y`, `D` and `X` such that `Y = XD` where `X` is of shape
`(n_samples, n_components)`, `D` is of shape `(n_components, n_features)`, and
each row of `X` has exactly `n_nonzero_coefs` non-zero elements.
Read more in the ... | make_sparse_coded_signal | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_sparse_uncorrelated(n_samples=100, n_features=10, *, random_state=None):
"""Generate a random regression problem with sparse uncorrelated design.
This dataset is described in Celeux et al [1]. as::
X ~ N(0, 1)
y(X) = X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]
Only the fi... | Generate a random regression problem with sparse uncorrelated design.
This dataset is described in Celeux et al [1]. as::
X ~ N(0, 1)
y(X) = X[:, 0] + 2 * X[:, 1] - 2 * X[:, 2] - 1.5 * X[:, 3]
Only the first 4 features are informative. The remaining features are
useless.
Read more in... | make_sparse_uncorrelated | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_spd_matrix(n_dim, *, random_state=None):
"""Generate a random symmetric, positive-definite matrix.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_dim : int
The matrix dimension.
random_state : int, RandomState instance or None, default=None
... | Generate a random symmetric, positive-definite matrix.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_dim : int
The matrix dimension.
random_state : int, RandomState instance or None, default=None
Determines random number generation for dataset cre... | make_spd_matrix | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_sparse_spd_matrix(
n_dim=1,
*,
alpha=0.95,
norm_diag=False,
smallest_coef=0.1,
largest_coef=0.9,
sparse_format=None,
random_state=None,
):
"""Generate a sparse symmetric definite positive matrix.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
... | Generate a sparse symmetric definite positive matrix.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_dim : int, default=1
The size of the random matrix to generate.
.. versionchanged:: 1.4
Renamed from ``dim`` to ``n_dim``.
alpha : flo... | make_sparse_spd_matrix | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_swiss_roll(n_samples=100, *, noise=0.0, random_state=None, hole=False):
"""Generate a swiss roll dataset.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, default=100
The number of sample points on the Swiss Roll.
noise : float, d... | Generate a swiss roll dataset.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, default=100
The number of sample points on the Swiss Roll.
noise : float, default=0.0
The standard deviation of the gaussian noise.
random_state : int... | make_swiss_roll | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_s_curve(n_samples=100, *, noise=0.0, random_state=None):
"""Generate an S curve dataset.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, default=100
The number of sample points on the S curve.
noise : float, default=0.0
T... | Generate an S curve dataset.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, default=100
The number of sample points on the S curve.
noise : float, default=0.0
The standard deviation of the gaussian noise.
random_state : int, Ran... | make_s_curve | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_gaussian_quantiles(
*,
mean=None,
cov=1.0,
n_samples=100,
n_features=2,
n_classes=3,
shuffle=True,
random_state=None,
):
r"""Generate isotropic Gaussian and label samples by quantile.
This classification dataset is constructed by taking a multi-dimensional
standard ... | Generate isotropic Gaussian and label samples by quantile.
This classification dataset is constructed by taking a multi-dimensional
standard normal distribution and defining classes separated by nested
concentric multi-dimensional spheres such that roughly equal numbers of
samples are in each class (qu... | make_gaussian_quantiles | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_biclusters(
shape,
n_clusters,
*,
noise=0.0,
minval=10,
maxval=100,
shuffle=True,
random_state=None,
):
"""Generate a constant block diagonal structure array for biclustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
sha... | Generate a constant block diagonal structure array for biclustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
shape : tuple of shape (n_rows, n_cols)
The shape of the result.
n_clusters : int
The number of biclusters.
noise : float, defaul... | make_biclusters | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def make_checkerboard(
shape,
n_clusters,
*,
noise=0.0,
minval=10,
maxval=100,
shuffle=True,
random_state=None,
):
"""Generate an array with block checkerboard structure for biclustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
... | Generate an array with block checkerboard structure for biclustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
shape : tuple of shape (n_rows, n_cols)
The shape of the result.
n_clusters : int or array-like or shape (n_row_clusters, n_column_clusters)
... | make_checkerboard | python | scikit-learn/scikit-learn | sklearn/datasets/_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py | BSD-3-Clause |
def _load_coverage(F, header_length=6, dtype=np.int16):
"""Load a coverage file from an open file object.
This will return a numpy array of the given dtype
"""
header = [F.readline() for _ in range(header_length)]
make_tuple = lambda t: (t.split()[0], float(t.split()[1]))
header = dict([make_tu... | Load a coverage file from an open file object.
This will return a numpy array of the given dtype
| _load_coverage | python | scikit-learn/scikit-learn | sklearn/datasets/_species_distributions.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_species_distributions.py | BSD-3-Clause |
def _load_csv(F):
"""Load csv file.
Parameters
----------
F : file object
CSV file open in byte mode.
Returns
-------
rec : np.ndarray
record array representing the data
"""
names = F.readline().decode("ascii").strip().split(",")
rec = np.loadtxt(F, skiprows=0,... | Load csv file.
Parameters
----------
F : file object
CSV file open in byte mode.
Returns
-------
rec : np.ndarray
record array representing the data
| _load_csv | python | scikit-learn/scikit-learn | sklearn/datasets/_species_distributions.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_species_distributions.py | BSD-3-Clause |
def fetch_species_distributions(
*,
data_home=None,
download_if_missing=True,
n_retries=3,
delay=1.0,
):
"""Loader for species distribution dataset from Phillips et. al. (2006).
Read more in the :ref:`User Guide <species_distribution_dataset>`.
Parameters
----------
data_home :... | Loader for species distribution dataset from Phillips et. al. (2006).
Read more in the :ref:`User Guide <species_distribution_dataset>`.
Parameters
----------
data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-le... | fetch_species_distributions | python | scikit-learn/scikit-learn | sklearn/datasets/_species_distributions.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_species_distributions.py | BSD-3-Clause |
def load_svmlight_file(
f,
*,
n_features=None,
dtype=np.float64,
multilabel=False,
zero_based="auto",
query_id=False,
offset=0,
length=-1,
):
"""Load datasets in the svmlight / libsvm format into sparse CSR matrix.
This format is a text-based format, with one sample per line... | Load datasets in the svmlight / libsvm format into sparse CSR matrix.
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable
to predict.
This f... | load_svmlight_file | python | scikit-learn/scikit-learn | sklearn/datasets/_svmlight_format_io.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_svmlight_format_io.py | BSD-3-Clause |
def load_svmlight_files(
files,
*,
n_features=None,
dtype=np.float64,
multilabel=False,
zero_based="auto",
query_id=False,
offset=0,
length=-1,
):
"""Load dataset from multiple files in SVMlight format.
This function is equivalent to mapping load_svmlight_file over a list of... | Load dataset from multiple files in SVMlight format.
This function is equivalent to mapping load_svmlight_file over a list of
files, except that the results are concatenated into a single, flat list
and the samples vectors are constrained to all have the same number of
features.
In case the file c... | load_svmlight_files | python | scikit-learn/scikit-learn | sklearn/datasets/_svmlight_format_io.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_svmlight_format_io.py | BSD-3-Clause |
def dump_svmlight_file(
X,
y,
f,
*,
zero_based=True,
comment=None,
query_id=None,
multilabel=False,
):
"""Dump the dataset in svmlight / libsvm file format.
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable... | Dump the dataset in svmlight / libsvm file format.
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable
to predict.
Parameters
----------... | dump_svmlight_file | python | scikit-learn/scikit-learn | sklearn/datasets/_svmlight_format_io.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_svmlight_format_io.py | BSD-3-Clause |
def _download_20newsgroups(target_dir, cache_path, n_retries, delay):
"""Download the 20 newsgroups data and stored it as a zipped pickle."""
train_path = os.path.join(target_dir, TRAIN_FOLDER)
test_path = os.path.join(target_dir, TEST_FOLDER)
os.makedirs(target_dir, exist_ok=True)
logger.info("Do... | Download the 20 newsgroups data and stored it as a zipped pickle. | _download_20newsgroups | python | scikit-learn/scikit-learn | sklearn/datasets/_twenty_newsgroups.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_twenty_newsgroups.py | BSD-3-Clause |
def strip_newsgroup_footer(text):
"""
Given text in "news" format, attempt to remove a signature block.
As a rough heuristic, we assume that signatures are set apart by either
a blank line or a line made of hyphens, and that it is the last such line
in the file (disregarding blank lines at the end)... |
Given text in "news" format, attempt to remove a signature block.
As a rough heuristic, we assume that signatures are set apart by either
a blank line or a line made of hyphens, and that it is the last such line
in the file (disregarding blank lines at the end).
Parameters
----------
text... | strip_newsgroup_footer | python | scikit-learn/scikit-learn | sklearn/datasets/_twenty_newsgroups.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_twenty_newsgroups.py | BSD-3-Clause |
def fetch_20newsgroups(
*,
data_home=None,
subset="train",
categories=None,
shuffle=True,
random_state=42,
remove=(),
download_if_missing=True,
return_X_y=False,
n_retries=3,
delay=1.0,
):
"""Load the filenames and data from the 20 newsgroups dataset \
(classification).
... | Load the filenames and data from the 20 newsgroups dataset (classification).
Download it if necessary.
================= ==========
Classes 20
Samples total 18846
Dimensionality 1
Features text
================= ==========
... | fetch_20newsgroups | python | scikit-learn/scikit-learn | sklearn/datasets/_twenty_newsgroups.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_twenty_newsgroups.py | BSD-3-Clause |
def fetch_20newsgroups_vectorized(
*,
subset="train",
remove=(),
data_home=None,
download_if_missing=True,
return_X_y=False,
normalize=True,
as_frame=False,
n_retries=3,
delay=1.0,
):
"""Load and vectorize the 20 newsgroups dataset (classification).
Download it if necess... | Load and vectorize the 20 newsgroups dataset (classification).
Download it if necessary.
This is a convenience function; the transformation is done using the
default settings for
:class:`~sklearn.feature_extraction.text.CountVectorizer`. For more
advanced usage (stopword filtering, n-gram extracti... | fetch_20newsgroups_vectorized | python | scikit-learn/scikit-learn | sklearn/datasets/_twenty_newsgroups.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_twenty_newsgroups.py | BSD-3-Clause |
def test_20news_length_consistency(fetch_20newsgroups_fxt):
"""Checks the length consistencies within the bunch
This is a non-regression test for a bug present in 0.16.1.
"""
# Extract the full dataset
data = fetch_20newsgroups_fxt(subset="all")
assert len(data["data"]) == len(data.data)
as... | Checks the length consistencies within the bunch
This is a non-regression test for a bug present in 0.16.1.
| test_20news_length_consistency | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_20news.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_20news.py | BSD-3-Clause |
def test_post_process_frame(feature_names, target_names):
"""Check the behaviour of the post-processing function for splitting a dataframe."""
pd = pytest.importorskip("pandas")
X_original = pd.DataFrame(
{
"col_int_as_integer": [1, 2, 3],
"col_int_as_numeric": [1, 2, 3],
... | Check the behaviour of the post-processing function for splitting a dataframe. | test_post_process_frame | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_arff_parser.py | BSD-3-Clause |
def test_load_arff_from_gzip_file_error_parser():
"""An error will be raised if the parser is not known."""
# None of the input parameters are required to be accurate since the check
# of the parser will be carried out first.
err_msg = "Unknown parser: 'xxx'. Should be 'liac-arff' or 'pandas'"
with... | An error will be raised if the parser is not known. | test_load_arff_from_gzip_file_error_parser | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_arff_parser.py | BSD-3-Clause |
def test_pandas_arff_parser_strip_single_quotes(parser_func):
"""Check that we properly strip single quotes from the data."""
pd = pytest.importorskip("pandas")
arff_file = BytesIO(
textwrap.dedent(
"""
@relation 'toy'
@attribute 'cat_single_quote' {'A', 'B', 'C'... | Check that we properly strip single quotes from the data. | test_pandas_arff_parser_strip_single_quotes | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_arff_parser.py | BSD-3-Clause |
def test_pandas_arff_parser_strip_double_quotes(parser_func):
"""Check that we properly strip double quotes from the data."""
pd = pytest.importorskip("pandas")
arff_file = BytesIO(
textwrap.dedent(
"""
@relation 'toy'
@attribute 'cat_double_quote' {"A", "B", "C"... | Check that we properly strip double quotes from the data. | test_pandas_arff_parser_strip_double_quotes | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_arff_parser.py | BSD-3-Clause |
def test_pandas_arff_parser_strip_no_quotes(parser_func):
"""Check that we properly parse with no quotes characters."""
pd = pytest.importorskip("pandas")
arff_file = BytesIO(
textwrap.dedent(
"""
@relation 'toy'
@attribute 'cat_without_quote' {A, B, C}
... | Check that we properly parse with no quotes characters. | test_pandas_arff_parser_strip_no_quotes | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_arff_parser.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_arff_parser.py | BSD-3-Clause |
def test_load_diabetes_raw():
"""Test to check that we load a scaled version by default but that we can
get an unscaled version when setting `scaled=False`."""
diabetes_raw = load_diabetes(scaled=False)
assert diabetes_raw.data.shape == (442, 10)
assert diabetes_raw.target.size == 442
assert len... | Test to check that we load a scaled version by default but that we can
get an unscaled version when setting `scaled=False`. | test_load_diabetes_raw | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_base.py | BSD-3-Clause |
def test_load_boston_error():
"""Check that we raise the ethical warning when trying to import `load_boston`."""
msg = "The Boston housing prices dataset has an ethical problem"
with pytest.raises(ImportError, match=msg):
from sklearn.datasets import load_boston # noqa: F401
# other non-existi... | Check that we raise the ethical warning when trying to import `load_boston`. | test_load_boston_error | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_base.py | BSD-3-Clause |
def test_corrupted_file_error_message(fetch_kddcup99_fxt, tmp_path):
"""Check that a nice error message is raised when cache is corrupted."""
kddcup99_dir = tmp_path / "kddcup99_10-py3"
kddcup99_dir.mkdir()
samples_path = kddcup99_dir / "samples"
with samples_path.open("wb") as f:
f.write(b... | Check that a nice error message is raised when cache is corrupted. | test_corrupted_file_error_message | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_kddcup99.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_kddcup99.py | BSD-3-Clause |
def mock_data_home(tmp_path_factory):
"""Test fixture run once and common to all tests of this module"""
Image = pytest.importorskip("PIL.Image")
data_dir = tmp_path_factory.mktemp("scikit_learn_lfw_test")
lfw_home = data_dir / "lfw_home"
lfw_home.mkdir(parents=True, exist_ok=True)
random_stat... | Test fixture run once and common to all tests of this module | mock_data_home | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_lfw.py | BSD-3-Clause |
def test_fetch_lfw_people_internal_cropping(mock_data_home):
"""Check that we properly crop the images.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/24942
"""
# If cropping was not done properly and we don't resize the images, the images would
# have their origin... | Check that we properly crop the images.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/24942
| test_fetch_lfw_people_internal_cropping | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_lfw.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_lfw.py | BSD-3-Clause |
def test_fetch_openml_as_frame_true(
monkeypatch,
data_id,
dataset_params,
n_samples,
n_features,
n_targets,
parser,
gzip_response,
):
"""Check the behaviour of `fetch_openml` with `as_frame=True`.
Fetch by ID and/or name (depending if the file was previously cached).
"""
... | Check the behaviour of `fetch_openml` with `as_frame=True`.
Fetch by ID and/or name (depending if the file was previously cached).
| test_fetch_openml_as_frame_true | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_as_frame_false(
monkeypatch,
data_id,
dataset_params,
n_samples,
n_features,
n_targets,
parser,
):
"""Check the behaviour of `fetch_openml` with `as_frame=False`.
Fetch both by ID and/or name + version.
"""
pytest.importorskip("pandas")
_monkey_pat... | Check the behaviour of `fetch_openml` with `as_frame=False`.
Fetch both by ID and/or name + version.
| test_fetch_openml_as_frame_false | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_consistency_parser(monkeypatch, data_id):
"""Check the consistency of the LIAC-ARFF and pandas parsers."""
pd = pytest.importorskip("pandas")
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
bunch_liac = fetch_openml(
data_id=data_id,
as_f... | Check the consistency of the LIAC-ARFF and pandas parsers. | test_fetch_openml_consistency_parser | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser):
"""Check the equivalence of the dataset when using `as_frame=False` and
`as_frame=True`.
"""
pytest.importorskip("pandas")
data_id = 61
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
bunch_as... | Check the equivalence of the dataset when using `as_frame=False` and
`as_frame=True`.
| test_fetch_openml_equivalence_array_dataframe | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_iris_pandas(monkeypatch, parser):
"""Check fetching on a numerical only dataset with string labels."""
pd = pytest.importorskip("pandas")
CategoricalDtype = pd.api.types.CategoricalDtype
data_id = 61
data_shape = (150, 4)
target_shape = (150,)
frame_shape = (150, 5)
... | Check fetching on a numerical only dataset with string labels. | test_fetch_openml_iris_pandas | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_forcing_targets(monkeypatch, parser, target_column):
"""Check that we can force the target to not be the default target."""
pd = pytest.importorskip("pandas")
data_id = 61
_monkey_patch_webbased_functions(monkeypatch, data_id, True)
bunch_forcing_target = fetch_openml(
... | Check that we can force the target to not be the default target. | test_fetch_openml_forcing_targets | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_equivalence_frame_return_X_y(monkeypatch, data_id, parser):
"""Check the behaviour of `return_X_y=True` when `as_frame=True`."""
pd = pytest.importorskip("pandas")
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
bunch = fetch_openml(
data_id=data... | Check the behaviour of `return_X_y=True` when `as_frame=True`. | test_fetch_openml_equivalence_frame_return_X_y | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_equivalence_array_return_X_y(monkeypatch, data_id, parser):
"""Check the behaviour of `return_X_y=True` when `as_frame=False`."""
pytest.importorskip("pandas")
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
bunch = fetch_openml(
data_id=data_id,... | Check the behaviour of `return_X_y=True` when `as_frame=False`. | test_fetch_openml_equivalence_array_return_X_y | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_difference_parsers(monkeypatch):
"""Check the difference between liac-arff and pandas parser."""
pytest.importorskip("pandas")
data_id = 1119
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True)
# When `as_frame=False`, the categories will be ordinally en... | Check the difference between liac-arff and pandas parser. | test_fetch_openml_difference_parsers | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def datasets_column_names():
"""Returns the columns names for each dataset."""
return {
61: ["sepallength", "sepalwidth", "petallength", "petalwidth", "class"],
2: [
"family",
"product-type",
"steel",
"carbon",
"hardness",
"... | Returns the columns names for each dataset. | datasets_column_names | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_types_inference(
monkeypatch,
data_id,
parser,
expected_n_categories,
expected_n_floats,
expected_n_ints,
gzip_response,
datasets_column_names,
datasets_missing_values,
):
"""Check that `fetch_openml` infer the right number of categories, integers, and
f... | Check that `fetch_openml` infer the right number of categories, integers, and
floats. | test_fetch_openml_types_inference | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_requires_pandas_error(monkeypatch, params):
"""Check that we raise the proper errors when we require pandas."""
data_id = 1119
try:
check_pandas_support("test_fetch_openml_requires_pandas")
except ImportError:
_monkey_patch_webbased_functions(monkeypatch, data_id, T... | Check that we raise the proper errors when we require pandas. | test_fetch_openml_requires_pandas_error | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_sparse_arff_error(monkeypatch, params, err_msg):
"""Check that we raise the expected error for sparse ARFF datasets and
a wrong set of incompatible parameters.
"""
pytest.importorskip("pandas")
data_id = 292
_monkey_patch_webbased_functions(monkeypatch, data_id, True)
... | Check that we raise the expected error for sparse ARFF datasets and
a wrong set of incompatible parameters.
| test_fetch_openml_sparse_arff_error | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_convert_arff_data_dataframe_warning_low_memory_pandas(monkeypatch):
"""Check that we raise a warning regarding the working memory when using
LIAC-ARFF parser."""
pytest.importorskip("pandas")
data_id = 1119
_monkey_patch_webbased_functions(monkeypatch, data_id, True)
msg = "Could not ... | Check that we raise a warning regarding the working memory when using
LIAC-ARFF parser. | test_convert_arff_data_dataframe_warning_low_memory_pandas | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_iris_warn_multiple_version(monkeypatch, gzip_response):
"""Check that a warning is raised when multiple versions exist and no version is
requested."""
data_id = 61
data_name = "iris"
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response)
msg = re.escape(
... | Check that a warning is raised when multiple versions exist and no version is
requested. | test_fetch_openml_iris_warn_multiple_version | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_no_target(monkeypatch, gzip_response):
"""Check that we can get a dataset without target."""
data_id = 61
target_column = None
expected_observations = 150
expected_features = 5
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response)
data = fetch_openml(
... | Check that we can get a dataset without target. | test_fetch_openml_no_target | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_missing_values_pandas(monkeypatch, gzip_response, parser):
"""check that missing values in categories are compatible with pandas
categorical"""
pytest.importorskip("pandas")
data_id = 42585
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=gzip_response)
penguins = f... | check that missing values in categories are compatible with pandas
categorical | test_missing_values_pandas | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_inactive(monkeypatch, gzip_response, dataset_params):
"""Check that we raise a warning when the dataset is inactive."""
data_id = 40675
_monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response)
msg = "Version 1 of dataset glass2 is inactive,"
with pytest.warns(UserW... | Check that we raise a warning when the dataset is inactive. | test_fetch_openml_inactive | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_overwrite_default_params_read_csv(monkeypatch):
"""Check that we can overwrite the default parameters of `read_csv`."""
pytest.importorskip("pandas")
data_id = 1590
_monkey_patch_webbased_functions(monkeypatch, data_id=data_id, gzip_response=False)
common_params = {
"d... | Check that we can overwrite the default parameters of `read_csv`. | test_fetch_openml_overwrite_default_params_read_csv | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_verify_checksum(monkeypatch, as_frame, tmpdir, parser):
"""Check that the checksum is working as expected."""
if as_frame or parser == "pandas":
pytest.importorskip("pandas")
data_id = 2
_monkey_patch_webbased_functions(monkeypatch, data_id, True)
# create a temporary... | Check that the checksum is working as expected. | test_fetch_openml_verify_checksum | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_with_ignored_feature(monkeypatch, gzip_response, parser):
"""Check that we can load the "zoo" dataset.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/14340
"""
if parser == "pandas":
pytest.importorskip("pandas")
data_id = 62
_monke... | Check that we can load the "zoo" dataset.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/14340
| test_fetch_openml_with_ignored_feature | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_leading_whitespace(monkeypatch):
"""Check that we can strip leading whitespace in pandas parser.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/25311
"""
pd = pytest.importorskip("pandas")
data_id = 1590
_monkey_patch_webbased_functions(mo... | Check that we can strip leading whitespace in pandas parser.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/25311
| test_fetch_openml_leading_whitespace | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_fetch_openml_quotechar_escapechar(monkeypatch):
"""Check that we can handle escapechar and single/double quotechar.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/25478
"""
pd = pytest.importorskip("pandas")
data_id = 42074
_monkey_patch_webbased_funct... | Check that we can handle escapechar and single/double quotechar.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/25478
| test_fetch_openml_quotechar_escapechar | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_openml.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_openml.py | BSD-3-Clause |
def test_make_classification_informative_features():
"""Test the construction of informative features in make_classification
Also tests `n_clusters_per_class`, `n_classes`, `hypercube` and
fully-specified `weights`.
"""
# Create very separate clusters; check that vertices are unique and
# corre... | Test the construction of informative features in make_classification
Also tests `n_clusters_per_class`, `n_classes`, `hypercube` and
fully-specified `weights`.
| test_make_classification_informative_features | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_samples_generator.py | BSD-3-Clause |
def test_make_classification_return_x_y():
"""
Test that make_classification returns a Bunch when return_X_y is False.
Also that bunch.X is the same as X
"""
kwargs = {
"n_samples": 100,
"n_features": 20,
"n_informative": 5,
"n_redundant": 1,
"n_repeated": 1... |
Test that make_classification returns a Bunch when return_X_y is False.
Also that bunch.X is the same as X
| test_make_classification_return_x_y | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_samples_generator.py | BSD-3-Clause |
def _load_svmlight_local_test_file(filename, **kwargs):
"""
Helper to load resource `filename` with `importlib.resources`
"""
data_path = _svmlight_local_test_file_path(filename)
with data_path.open("rb") as f:
return load_svmlight_file(f, **kwargs) |
Helper to load resource `filename` with `importlib.resources`
| _load_svmlight_local_test_file | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_svmlight_format.py | BSD-3-Clause |
def test_load_large_qid():
"""
load large libsvm / svmlight file with qid attribute. Tests 64-bit query ID
"""
data = b"\n".join(
(
"3 qid:{0} 1:0.53 2:0.12\n2 qid:{0} 1:0.13 2:0.1".format(i).encode()
for i in range(1, 40 * 1000 * 1000)
)
)
X, y, qid = loa... |
load large libsvm / svmlight file with qid attribute. Tests 64-bit query ID
| test_load_large_qid | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_svmlight_format.py | BSD-3-Clause |
def test_multilabel_y_explicit_zeros(tmp_path, csr_container):
"""
Ensure that if y contains explicit zeros (i.e. elements of y.data equal to
0) then those explicit zeros are not encoded.
"""
save_path = str(tmp_path / "svm_explicit_zero")
rng = np.random.RandomState(42)
X = rng.randn(3, 5).... |
Ensure that if y contains explicit zeros (i.e. elements of y.data equal to
0) then those explicit zeros are not encoded.
| test_multilabel_y_explicit_zeros | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_svmlight_format.py | BSD-3-Clause |
def test_dump_read_only(tmp_path):
"""Ensure that there is no ValueError when dumping a read-only `X`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/28026
"""
rng = np.random.RandomState(42)
X = rng.randn(5, 2)
y = rng.randn(5)
# Convert to memmap-backed ... | Ensure that there is no ValueError when dumping a read-only `X`.
Non-regression test for:
https://github.com/scikit-learn/scikit-learn/issues/28026
| test_dump_read_only | python | scikit-learn/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/tests/test_svmlight_format.py | BSD-3-Clause |
def get_covariance(self):
"""Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where S**2 contains the explained variances, and sigma2 contains the
noise variances.
Returns
-------
cov : ar... | Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where S**2 contains the explained variances, and sigma2 contains the
noise variances.
Returns
-------
cov : array of shape=(n_features, n_features)... | get_covariance | python | scikit-learn/scikit-learn | sklearn/decomposition/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_base.py | BSD-3-Clause |
def get_precision(self):
"""Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : array, shape=(n_features, n_features)
Estimated... | Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : array, shape=(n_features, n_features)
Estimated precision of data.
| get_precision | python | scikit-learn/scikit-learn | sklearn/decomposition/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_base.py | BSD-3-Clause |
def fit(self, X, y=None):
"""Placeholder for fit. Subclasses should implement this method!
Fit the model with X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples and
`n_featur... | Placeholder for fit. Subclasses should implement this method!
Fit the model with X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples and
`n_features` is the number of features.
... | fit | python | scikit-learn/scikit-learn | sklearn/decomposition/_base.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_base.py | BSD-3-Clause |
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