package sklearn

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package sklearn

sklearn
- Sklearn

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Class type

val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

val clear_data_home : ?data_home:string -> unit -> Py.Object.t

Delete all the content of the data home cache.

Parameters ---------- data_home : str | None The path to scikit-learn data dir.

val dump_svmlight_file : 
  ?zero_based:bool ->
  ?comment:string ->
  ?query_id:[> `ArrayLike ] Np.Obj.t ->
  ?multilabel:bool ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  f:[ `S of string | `File_like_in_binary_mode of Py.Object.t ] ->
  unit ->
  Py.Object.t

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 ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, sparse matrix, shape = n_samples (, n_labels) Target values. Class labels must be an integer or float, or array-like objects of integer or float for multilabel classifications.

f : string or file-like in binary mode If string, specifies the path that will contain the data. If file-like, data will be written to f. f should be opened in binary mode.

zero_based : boolean, optional Whether column indices should be written zero-based (True) or one-based (False).

comment : string, optional Comment to insert at the top of the file. This should be either a Unicode string, which will be encoded as UTF-8, or an ASCII byte string. If a comment is given, then it will be preceded by one that identifies the file as having been dumped by scikit-learn. Note that not all tools grok comments in SVMlight files.

query_id : array-like of shape (n_samples,) Array containing pairwise preference constraints (qid in svmlight format).

multilabel : boolean, optional Samples may have several labels each (see https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)

.. versionadded:: 0.17 parameter *multilabel* to support multilabel datasets.

val fetch_20newsgroups : 
  ?data_home:Py.Object.t ->
  ?subset:string ->
  ?categories:[ `S of string | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  ?shuffle:bool ->
  ?random_state:int ->
  ?remove:Py.Object.t ->
  ?download_if_missing:Py.Object.t ->
  unit ->
  < data : Py.Object.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; filenames : Py.Object.t
  ; descr : string
  ; target_names : Py.Object.t >

Load the filenames and data from the 20 newsgroups dataset (classification).

Download it if necessary.

================= ========== Classes 20 Samples total 18846 Dimensionality 1 Features text ================= ==========

Read more in the :ref:`User Guide <20newsgroups_dataset>`.

Parameters ---------- data_home : optional, default: None Specify a download and cache folder for the datasets. If None, all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

subset : 'train' or 'test', 'all', optional Select the dataset to load: 'train' for the training set, 'test' for the test set, 'all' for both, with shuffled ordering.

categories : None or collection of string or unicode If None (default), load all the categories. If not None, list of category names to load (other categories ignored).

shuffle : bool, optional Whether or not to shuffle the data: might be important for models that make the assumption that the samples are independent and identically distributed (i.i.d.), such as stochastic gradient descent.

random_state : int, RandomState instance, default=None Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

remove : tuple May contain any subset of ('headers', 'footers', 'quotes'). Each of these are kinds of text that will be detected and removed from the newsgroup posts, preventing classifiers from overfitting on metadata.

'headers' removes newsgroup headers, 'footers' removes blocks at the ends of posts that look like signatures, and 'quotes' removes lines that appear to be quoting another post.

'headers' follows an exact standard; the other filters are not always correct.

download_if_missing : optional, True by default If False, raise an IOError if the data is not locally available instead of trying to download the data from the source site.

return_X_y : bool, default=False. If True, returns `(data.data, data.target)` instead of a Bunch object.

.. versionadded:: 0.22

Returns ------- bunch : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : list, length n_samples The data list to learn. target: array, shape n_samples The target labels. filenames: list, length n_samples The path to the location of the data. DESCR: str The full description of the dataset. target_names: list, length n_classes The names of target classes.

(data, target) : tuple if `return_X_y=True` .. versionadded:: 0.22

val fetch_20newsgroups_vectorized : 
  ?subset:string ->
  ?remove:Py.Object.t ->
  ?data_home:Py.Object.t ->
  ?download_if_missing:Py.Object.t ->
  ?normalize:bool ->
  unit ->
  < data : [ `ArrayLike | `Object | `Spmatrix ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; target_names : Py.Object.t
  ; descr : string >

Load the 20 newsgroups dataset and vectorize it into token counts (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 extraction, etc.), combine fetch_20newsgroups with a custom :class:`sklearn.feature_extraction.text.CountVectorizer`, :class:`sklearn.feature_extraction.text.HashingVectorizer`, :class:`sklearn.feature_extraction.text.TfidfTransformer` or :class:`sklearn.feature_extraction.text.TfidfVectorizer`.

The resulting counts are normalized using :func:`sklearn.preprocessing.normalize` unless normalize is set to False.

================= ========== Classes 20 Samples total 18846 Dimensionality 130107 Features real ================= ==========

Read more in the :ref:`User Guide <20newsgroups_dataset>`.

Parameters ---------- subset : 'train' or 'test', 'all', optional Select the dataset to load: 'train' for the training set, 'test' for the test set, 'all' for both, with shuffled ordering.

'headers' removes newsgroup headers, 'footers' removes blocks at the ends of posts that look like signatures, and 'quotes' removes lines that appear to be quoting another post.

data_home : optional, default: None Specify an download and cache folder for the datasets. If None, all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

download_if_missing : optional, True by default If False, raise an IOError if the data is not locally available instead of trying to download the data from the source site.

return_X_y : bool, default=False If True, returns ``(data.data, data.target)`` instead of a Bunch object.

.. versionadded:: 0.20

normalize : bool, default=True If True, normalizes each document's feature vector to unit norm using :func:`sklearn.preprocessing.normalize`.

.. versionadded:: 0.22

Returns ------- bunch : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data: sparse matrix, shape n_samples, n_features The data matrix to learn. target: array, shape n_samples The target labels. target_names: list, length n_classes The names of target classes. DESCR: str The full description of the dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.20

val fetch_california_housing : 
  ?data_home:Py.Object.t ->
  ?download_if_missing:Py.Object.t ->
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; feature_names : string list
  ; descr : string
  ; frame : Py.Object.t >

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_housing_dataset>`.

Parameters ---------- data_home : optional, 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 : optional, default=True If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

return_X_y : boolean, default=False. If True, returns ``(data.data, data.target)`` instead of a Bunch object.

.. versionadded:: 0.20

.. versionadded:: 0.23

Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray, shape (20640, 8) Each row corresponding to the 8 feature values in order. If ``as_frame`` is True, ``data`` is a pandas object. target : numpy array of shape (20640,) Each value corresponds to the average house value in units of 100,000. If ``as_frame`` is True, ``target`` is a pandas object. feature_names : list of length 8 Array of ordered feature names used in the dataset. DESCR : string Description of the California housing dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.20

frame : pandas DataFrame Only present when `as_frame=True`. DataFrame with ``data`` and ``target``.

.. versionadded:: 0.23

Notes -----

This dataset consists of 20,640 samples and 9 features.

val fetch_covtype : 
  ?data_home:string ->
  ?download_if_missing:bool ->
  ?random_state:int ->
  ?shuffle:bool ->
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; descr : string >

Load the covertype dataset (classification).

Download it if necessary.

================= ============ Classes 7 Samples total 581012 Dimensionality 54 Features int ================= ============

Read more in the :ref:`User Guide <covtype_dataset>`.

Parameters ---------- data_home : string, optional 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 : boolean, default=True If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

shuffle : bool, default=False Whether to shuffle dataset.

return_X_y : boolean, default=False. If True, returns ``(data.data, data.target)`` instead of a Bunch object.

.. versionadded:: 0.20

Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : numpy array of shape (581012, 54) Each row corresponds to the 54 features in the dataset. target : numpy array of shape (581012,) Each value corresponds to one of the 7 forest covertypes with values ranging between 1 to 7. DESCR : str Description of the forest covertype dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.20

val fetch_kddcup99 : 
  ?subset:[ `Http | `Smtp | `SF | `SA ] ->
  ?data_home:string ->
  ?shuffle:bool ->
  ?random_state:int ->
  ?percent10:bool ->
  ?download_if_missing:bool ->
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; descr : string >

Load the kddcup99 dataset (classification).

Download it if necessary.

================= ==================================== Classes 23 Samples total 4898431 Dimensionality 41 Features discrete (int) or continuous (float) ================= ====================================

Read more in the :ref:`User Guide <kddcup99_dataset>`.

.. versionadded:: 0.18

Parameters ---------- subset : None, 'SA', 'SF', 'http', 'smtp' To return the corresponding classical subsets of kddcup 99. If None, return the entire kddcup 99 dataset.

data_home : string, optional Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders. .. versionadded:: 0.19

shuffle : bool, default=False Whether to shuffle dataset.

random_state : int, RandomState instance, default=None Determines random number generation for dataset shuffling and for selection of abnormal samples if `subset='SA'`. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

percent10 : bool, default=True Whether to load only 10 percent of the data.

download_if_missing : bool, default=True If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

return_X_y : boolean, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.20

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray of shape (494021, 41) The data matrix to learn. target : ndarray of shape (494021,) The regression target for each sample. DESCR : str The full description of the dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.20

val fetch_lfw_pairs : 
  ?subset:Py.Object.t ->
  ?data_home:Py.Object.t ->
  ?funneled:bool ->
  ?resize:float ->
  ?color:bool ->
  ?slice_:Py.Object.t ->
  ?download_if_missing:Py.Object.t ->
  unit ->
  < data : Py.Object.t
  ; pairs : Py.Object.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; descr : string >

Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).

Download it if necessary.

================= ======================= Classes 2 Samples total 13233 Dimensionality 5828 Features real, between 0 and 255 ================= =======================

In the official `README.txt`_ this task is described as the 'Restricted' task. As I am not sure as to implement the 'Unrestricted' variant correctly, I left it as unsupported for now.

.. _`README.txt`: http://vis-www.cs.umass.edu/lfw/README.txt

The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47.

Read more in the :ref:`User Guide <labeled_faces_in_the_wild_dataset>`.

Parameters ---------- subset : optional, default: 'train' Select the dataset to load: 'train' for the development training set, 'test' for the development test set, and '10_folds' for the official evaluation set that is meant to be used with a 10-folds cross validation.

data_home : optional, default: None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

funneled : boolean, optional, default: True Download and use the funneled variant of the dataset.

resize : float, optional, default 0.5 Ratio used to resize the each face picture.

color : boolean, optional, default False Keep the 3 RGB channels instead of averaging them to a single gray level channel. If color is True the shape of the data has one more dimension than the shape with color = False.

slice_ : optional Provide a custom 2D slice (height, width) to extract the 'interesting' part of the jpeg files and avoid use statistical correlation from the background

download_if_missing : optional, True by default If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray of shape (2200, 5828). Shape depends on ``subset``. Each row corresponds to 2 ravel'd face images of original size 62 x 47 pixels. Changing the ``slice_``, ``resize`` or ``subset`` parameters will change the shape of the output. pairs : ndarray of shape (2200, 2, 62, 47). Shape depends on ``subset`` Each row has 2 face images corresponding to same or different person from the dataset containing 5749 people. Changing the ``slice_``, ``resize`` or ``subset`` parameters will change the shape of the output. target : numpy array of shape (2200,). Shape depends on ``subset``. Labels associated to each pair of images. The two label values being different persons or the same person. DESCR : string Description of the Labeled Faces in the Wild (LFW) dataset.

val fetch_lfw_people : 
  ?data_home:Py.Object.t ->
  ?funneled:bool ->
  ?resize:float ->
  ?min_faces_per_person:int ->
  ?color:bool ->
  ?slice_:Py.Object.t ->
  ?download_if_missing:Py.Object.t ->
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; images : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; descr : string >

Load the Labeled Faces in the Wild (LFW) people dataset (classification).

Download it if necessary.

================= ======================= Classes 5749 Samples total 13233 Dimensionality 5828 Features real, between 0 and 255 ================= =======================

Read more in the :ref:`User Guide <labeled_faces_in_the_wild_dataset>`.

Parameters ---------- data_home : optional, default: None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

funneled : boolean, optional, default: True Download and use the funneled variant of the dataset.

resize : float, optional, default 0.5 Ratio used to resize the each face picture.

min_faces_per_person : int, optional, default None The extracted dataset will only retain pictures of people that have at least `min_faces_per_person` different pictures.

slice_ : optional Provide a custom 2D slice (height, width) to extract the 'interesting' part of the jpeg files and avoid use statistical correlation from the background

download_if_missing : optional, True by default If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

return_X_y : boolean, default=False. If True, returns ``(dataset.data, dataset.target)`` instead of a Bunch object. See below for more information about the `dataset.data` and `dataset.target` object.

.. versionadded:: 0.20

Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : numpy array of shape (13233, 2914) Each row corresponds to a ravelled face image of original size 62 x 47 pixels. Changing the ``slice_`` or resize parameters will change the shape of the output. images : numpy array of shape (13233, 62, 47) Each row is a face image corresponding to one of the 5749 people in the dataset. Changing the ``slice_`` or resize parameters will change the shape of the output. target : numpy array of shape (13233,) Labels associated to each face image. Those labels range from 0-5748 and correspond to the person IDs. DESCR : string Description of the Labeled Faces in the Wild (LFW) dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.20

val fetch_olivetti_faces : 
  ?data_home:Py.Object.t ->
  ?shuffle:bool ->
  ?random_state:int ->
  ?download_if_missing:Py.Object.t ->
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; images : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; descr : string >

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 1 ================= =====================

Read more in the :ref:`User Guide <olivetti_faces_dataset>`.

Parameters ---------- data_home : optional, default: None Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

shuffle : boolean, optional If True the order of the dataset is shuffled to avoid having images of the same person grouped.

random_state : int, RandomState instance or None, default=0 Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

download_if_missing : optional, True by default If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

return_X_y : boolean, default=False. If True, returns `(data, target)` instead of a `Bunch` object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.22

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data: ndarray, shape (400, 4096) Each row corresponds to a ravelled face image of original size 64 x 64 pixels. images : ndarray, shape (400, 64, 64) Each row is a face image corresponding to one of the 40 subjects of the dataset. target : ndarray, shape (400,) Labels associated to each face image. Those labels are ranging from 0-39 and correspond to the Subject IDs. DESCR : str Description of the modified Olivetti Faces Dataset.

(data, target) : tuple if `return_X_y=True` .. versionadded:: 0.22

val fetch_openml : 
  ?name:string ->
  ?version:[ `I of int | `Active ] ->
  ?data_id:int ->
  ?data_home:string ->
  ?target_column:[ `S of string | `Arr of [> `ArrayLike ] Np.Obj.t | `None ] ->
  ?cache:bool ->
  unit ->
  < data : Py.Object.t
  ; target : Py.Object.t
  ; descr : string
  ; feature_names : string list
  ; target_names : string list
  ; categories : Dict.t option
  ; details : Dict.t
  ; frame : Py.Object.t >

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 provided.

Read more in the :ref:`User Guide <openml>`.

.. versionadded:: 0.20

.. note:: EXPERIMENTAL

The API is experimental (particularly the return value structure), and might have small backward-incompatible changes in future releases.

Parameters ---------- name : str or None String identifier of the dataset. Note that OpenML can have multiple datasets with the same name.

version : integer or 'active', default='active' Version of the dataset. Can only be provided if also ``name`` is given. If 'active' the oldest version that's still active is used. Since there may be more than one active version of a dataset, and those versions may fundamentally be different from one another, setting an exact version is highly recommended.

data_id : int or None OpenML ID of the dataset. The most specific way of retrieving a dataset. If data_id is not given, name (and potential version) are used to obtain a dataset.

data_home : string or None, default None Specify another download and cache folder for the data sets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

target_column : string, list or None, default 'default-target' Specify the column name in the data to use as target. If 'default-target', the standard target column a stored on the server is used. If ``None``, all columns are returned as data and the target is ``None``. If list (of strings), all columns with these names are returned as multi-target (Note: not all scikit-learn classifiers can handle all types of multi-output combinations)

cache : boolean, default=True Whether to cache downloaded datasets using joblib.

return_X_y : boolean, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` objects.

as_frame : boolean, default=False If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. The Bunch will contain a ``frame`` attribute with the target and the data. If ``return_X_y`` is True, then ``(data, target)`` will be pandas DataFrames or Series as describe above.

Returns -------

data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : np.array, scipy.sparse.csr_matrix of floats, or pandas DataFrame The feature matrix. Categorical features are encoded as ordinals. target : np.array, pandas Series or DataFrame The regression target or classification labels, if applicable. Dtype is float if numeric, and object if categorical. If ``as_frame`` is True, ``target`` is a pandas object. DESCR : str The full description of the dataset feature_names : list The names of the dataset columns target_names: list The names of the target columns

.. versionadded:: 0.22

categories : dict or None Maps each categorical feature name to a list of values, such that the value encoded as i is ith in the list. If ``as_frame`` is True, this is None. details : dict More metadata from OpenML frame : pandas DataFrame Only present when `as_frame=True`. DataFrame with ``data`` and ``target``.

(data, target) : tuple if ``return_X_y`` is True

.. note:: EXPERIMENTAL

This interface is **experimental** and subsequent releases may change attributes without notice (although there should only be minor changes to ``data`` and ``target``).

Missing values in the 'data' are represented as NaN's. Missing values in 'target' are represented as NaN's (numerical target) or None (categorical target)

val fetch_rcv1 : 
  ?data_home:string ->
  ?subset:[ `Test | `All | `Train ] ->
  ?download_if_missing:bool ->
  ?random_state:int ->
  ?shuffle:bool ->
  unit ->
  < data : Py.Object.t
  ; target : Py.Object.t
  ; sample_id : Py.Object.t
  ; target_names : Py.Object.t
  ; descr : string >

Load the RCV1 multilabel dataset (classification).

Download it if necessary.

Version: RCV1-v2, vectors, full sets, topics multilabels.

================= ===================== Classes 103 Samples total 804414 Dimensionality 47236 Features real, between 0 and 1 ================= =====================

Read more in the :ref:`User Guide <rcv1_dataset>`.

.. versionadded:: 0.17

Parameters ---------- data_home : string, optional Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

subset : string, 'train', 'test', or 'all', default='all' Select the dataset to load: 'train' for the training set (23149 samples), 'test' for the test set (781265 samples), 'all' for both, with the training samples first if shuffle is False. This follows the official LYRL2004 chronological split.

download_if_missing : boolean, default=True If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

shuffle : bool, default=False Whether to shuffle dataset.

.. versionadded:: 0.20

Returns ------- dataset : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : scipy csr array, dtype np.float64, shape (804414, 47236) The array has 0.16% of non zero values. target : scipy csr array, dtype np.uint8, shape (804414, 103) Each sample has a value of 1 in its categories, and 0 in others. The array has 3.15% of non zero values. sample_id : numpy array, dtype np.uint32, shape (804414,) Identification number of each sample, as ordered in dataset.data. target_names : numpy array, dtype object, length (103) Names of each target (RCV1 topics), as ordered in dataset.target. DESCR : string Description of the RCV1 dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.20

val fetch_species_distributions : 
  ?data_home:Py.Object.t ->
  ?download_if_missing:Py.Object.t ->
  unit ->
  < data : Py.Object.t
  ; coverages : [> `ArrayLike ] Np.Obj.t
  ; train : [> `ArrayLike ] Np.Obj.t
  ; test : [> `ArrayLike ] Np.Obj.t
  ; nx : Py.Object.t
  ; x_left_lower_corner : Py.Object.t
  ; grid_size : float >

Loader for species distribution dataset from Phillips et. al. (2006)

Read more in the :ref:`User Guide <datasets>`.

Parameters ---------- data_home : optional, 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 : optional, True by default If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

coverages : array, shape = 14, 1592, 1212 These represent the 14 features measured at each point of the map grid. The latitude/longitude values for the grid are discussed below. Missing data is represented by the value -9999. train : record array, shape = (1624,) The training points for the data. Each point has three fields:

train'species' is the species name
train'dd long' is the longitude, in degrees
train'dd lat' is the latitude, in degrees test : record array, shape = (620,) The test points for the data. Same format as the training data. Nx, Ny : integers The number of longitudes (x) and latitudes (y) in the grid x_left_lower_corner, y_left_lower_corner : floats The (x,y) position of the lower-left corner, in degrees grid_size : float The spacing between points of the grid, in degrees

References ----------

* `'Maximum entropy modeling of species geographic distributions' <http://rob.schapire.net/papers/ecolmod.pdf>`_ S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.

Notes -----

This dataset represents the geographic distribution of species. The dataset is provided by Phillips et. al. (2006).

The two species are:

`'Bradypus variegatus' <http://www.iucnredlist.org/details/3038/0>`_ , the Brown-throated Sloth.

`'Microryzomys minutus' <http://www.iucnredlist.org/details/13408/0>`_ , also known as the Forest Small Rice Rat, a rodent that lives in Peru, Colombia, Ecuador, Peru, and Venezuela.

For an example of using this dataset with scikit-learn, see :ref:`examples/applications/plot_species_distribution_modeling.py <sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.

val get_data_home : ?data_home:string -> unit -> Py.Object.t

Return the path of the scikit-learn data dir.

This folder is used by some large dataset loaders to avoid downloading the data several times.

By default the data dir is set to a folder named 'scikit_learn_data' in the user home folder.

Alternatively, it can be set by the 'SCIKIT_LEARN_DATA' environment variable or programmatically by giving an explicit folder path. The '~' symbol is expanded to the user home folder.

If the folder does not already exist, it is automatically created.

Parameters ---------- data_home : str | None The path to scikit-learn data dir.

val load_boston : 
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; feature_names : string list
  ; descr : string
  ; filename : string >

Load and return the boston house-prices dataset (regression).

============== ============== Samples total 506 Dimensionality 13 Features real, positive Targets real 5. - 50. ============== ==============

Read more in the :ref:`User Guide <boston_dataset>`.

Parameters ---------- return_X_y : bool, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.18

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray of shape (506, 13) The data matrix. target : ndarray of shape (506, ) The regression target. filename : str The physical location of boston csv dataset.

.. versionadded:: 0.20

DESCR : str The full description of the dataset. feature_names : ndarray The names of features

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.18

Notes ----- .. versionchanged:: 0.20 Fixed a wrong data point at 445, 0.

Examples -------- >>> from sklearn.datasets import load_boston >>> X, y = load_boston(return_X_y=True) >>> print(X.shape) (506, 13)

val load_breast_cancer : 
  unit ->
  < data : Py.Object.t
  ; target : Py.Object.t
  ; feature_names : string list
  ; target_names : string list
  ; frame : Py.Object.t
  ; descr : string
  ; filename : string
  ; https : Py.Object.t >

Load and return the breast cancer wisconsin dataset (classification).

The breast cancer dataset is a classic and very easy binary classification dataset.

================= ============== Classes 2 Samples per class 212(M),357(B) Samples total 569 Dimensionality 30 Features real, positive ================= ==============

Read more in the :ref:`User Guide <breast_cancer_dataset>`.

Parameters ---------- return_X_y : bool, default=False If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.18

as_frame : bool, default=False If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If `return_X_y` is True, then (`data`, `target`) will be pandas DataFrames or Series as described below.

.. versionadded:: 0.23

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray, dataframe of shape (569, 30) The data matrix. If `as_frame=True`, `data` will be a pandas DataFrame. target: ndarray, Series of shape (569,) The classification target. If `as_frame=True`, `target` will be a pandas Series. feature_names: list The names of the dataset columns. target_names: list The names of target classes. frame: DataFrame of shape (569, 31) Only present when `as_frame=True`. DataFrame with `data` and `target`.

.. versionadded:: 0.23 DESCR: str The full description of the dataset. filename: str The path to the location of the data.

.. versionadded:: 0.20

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.18

The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is downloaded from: https://goo.gl/U2Uwz2

Examples -------- Let's say you are interested in the samples 10, 50, and 85, and want to know their class name.

>>> from sklearn.datasets import load_breast_cancer >>> data = load_breast_cancer() >>> data.target[10, 50, 85] array(0, 1, 0) >>> list(data.target_names) 'malignant', 'benign'

val load_diabetes : 
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; feature_names : string list
  ; descr : string
  ; data_filename : string
  ; target_filename : string >

Load and return the diabetes dataset (regression).

============== ================== Samples total 442 Dimensionality 10 Features real, -.2 < x < .2 Targets integer 25 - 346 ============== ==================

Read more in the :ref:`User Guide <diabetes_dataset>`.

Parameters ---------- return_X_y : bool, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.18

.. versionadded:: 0.23

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray, dataframe of shape (442, 10) The data matrix. If `as_frame=True`, `data` will be a pandas DataFrame. target: ndarray, Series of shape (442,) The regression target. If `as_frame=True`, `target` will be a pandas Series. feature_names: list The names of the dataset columns. frame: DataFrame of shape (442, 11) Only present when `as_frame=True`. DataFrame with `data` and `target`.

.. versionadded:: 0.23 DESCR: str The full description of the dataset. data_filename: str The path to the location of the data. target_filename: str The path to the location of the target.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.18

val load_digits : 
  ?n_class:[ `Between_0_and_10 of Py.Object.t | `I of int ] ->
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; target_names : string list
  ; feature_names : string list
  ; descr : string
  ; images : [> `ArrayLike ] Np.Obj.t >

Load and return the digits dataset (classification).

Each datapoint is a 8x8 image of a digit.

================= ============== Classes 10 Samples per class ~180 Samples total 1797 Dimensionality 64 Features integers 0-16 ================= ==============

Read more in the :ref:`User Guide <digits_dataset>`.

Parameters ---------- n_class : integer, between 0 and 10, optional (default=10) The number of classes to return.

return_X_y : bool, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.18

.. versionadded:: 0.23

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray, dataframe of shape (1797, 64) The flattened data matrix. If `as_frame=True`, `data` will be a pandas DataFrame. target: ndarray, Series of shape (1797,) The classification target. If `as_frame=True`, `target` will be a pandas Series. feature_names: list The names of the dataset columns. target_names: list The names of target classes.

.. versionadded:: 0.20

frame: DataFrame of shape (1797, 65) Only present when `as_frame=True`. DataFrame with `data` and `target`.

.. versionadded:: 0.23 images: ndarray of shape (1797, 8, 8) The raw image data. DESCR: str The full description of the dataset.

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.18

This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

Examples -------- To load the data and visualize the images::

>>> from sklearn.datasets import load_digits >>> digits = load_digits() >>> print(digits.data.shape) (1797, 64) >>> import matplotlib.pyplot as plt #doctest: +SKIP >>> plt.gray() #doctest: +SKIP >>> plt.matshow(digits.images0) #doctest: +SKIP >>> plt.show() #doctest: +SKIP

val load_files : 
  ?description:string ->
  ?categories:[> `ArrayLike ] Np.Obj.t ->
  ?load_content:bool ->
  ?shuffle:bool ->
  ?encoding:Py.Object.t ->
  ?decode_error:[ `Strict | `Ignore | `Replace ] ->
  ?random_state:int ->
  container_path:string ->
  unit ->
  < data : string list
  ; target : [> `ArrayLike ] Np.Obj.t
  ; target_names : string list
  ; descr : string
  ; filenames : string list >

Load text files with categories as subfolder names.

Individual samples are assumed to be files stored a two levels folder structure such as the following:

container_folder/ category_1_folder/ file_1.txt file_2.txt ... file_42.txt category_2_folder/ file_43.txt file_44.txt ...

The folder names are used as supervised signal label names. The individual file names are not important.

This function does not try to extract features into a numpy array or scipy sparse matrix. In addition, if load_content is false it does not try to load the files in memory.

To use text files in a scikit-learn classification or clustering algorithm, you will need to use the :mod`~sklearn.feature_extraction.text` module to build a feature extraction transformer that suits your problem.

If you set load_content=True, you should also specify the encoding of the text using the 'encoding' parameter. For many modern text files, 'utf-8' will be the correct encoding. If you leave encoding equal to None, then the content will be made of bytes instead of Unicode, and you will not be able to use most functions in :mod:`~sklearn.feature_extraction.text`.

Similar feature extractors should be built for other kind of unstructured data input such as images, audio, video, ...

Read more in the :ref:`User Guide <datasets>`.

Parameters ---------- container_path : string or unicode Path to the main folder holding one subfolder per category

description : string or unicode, optional (default=None) A paragraph describing the characteristic of the dataset: its source, reference, etc.

categories : A collection of strings or None, optional (default=None) If None (default), load all the categories. If not None, list of category names to load (other categories ignored).

load_content : bool, optional (default=True) Whether to load or not the content of the different files. If true a 'data' attribute containing the text information is present in the data structure returned. If not, a filenames attribute gives the path to the files.

shuffle : bool, optional (default=True) Whether or not to shuffle the data: might be important for models that make the assumption that the samples are independent and identically distributed (i.i.d.), such as stochastic gradient descent.

encoding : string or None (default is None) If None, do not try to decode the content of the files (e.g. for images or other non-text content). If not None, encoding to use to decode text files to Unicode if load_content is True.

decode_error : 'strict', 'ignore', 'replace', optional Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. Passed as keyword argument 'errors' to bytes.decode.

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : list of str Only present when `load_content=True`. The raw text data to learn. target : ndarray The target labels (integer index). target_names : list The names of target classes. DESCR : str The full description of the dataset. filenames: ndarray The filenames holding the dataset.

val load_iris : 
  unit ->
  < data : [> `ArrayLike ] Np.Obj.t
  ; target : [> `ArrayLike ] Np.Obj.t
  ; target_names : [> `ArrayLike ] Np.Obj.t
  ; feature_names : string list
  ; descr : string
  ; filename : string >

Load and return the iris dataset (classification).

The iris dataset is a classic and very easy multi-class classification dataset.

================= ============== Classes 3 Samples per class 50 Samples total 150 Dimensionality 4 Features real, positive ================= ==============

Read more in the :ref:`User Guide <iris_dataset>`.

Parameters ---------- return_X_y : bool, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.18

.. versionadded:: 0.23

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray, dataframe of shape (150, 4) The data matrix. If `as_frame=True`, `data` will be a pandas DataFrame. target: ndarray, Series of shape (150,) The classification target. If `as_frame=True`, `target` will be a pandas Series. feature_names: list The names of the dataset columns. target_names: list The names of target classes. frame: DataFrame of shape (150, 5) Only present when `as_frame=True`. DataFrame with `data` and `target`.

.. versionadded:: 0.23 DESCR: str The full description of the dataset. filename: str The path to the location of the data.

.. versionadded:: 0.20

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.18

Notes ----- .. versionchanged:: 0.20 Fixed two wrong data points according to Fisher's paper. The new version is the same as in R, but not as in the UCI Machine Learning Repository.

Examples -------- Let's say you are interested in the samples 10, 25, and 50, and want to know their class name.

>>> from sklearn.datasets import load_iris >>> data = load_iris() >>> data.target[10, 25, 50] array(0, 0, 1) >>> list(data.target_names) 'setosa', 'versicolor', 'virginica'

val load_linnerud : 
  unit ->
  < data : Py.Object.t
  ; target : Py.Object.t
  ; feature_names : string list
  ; target_names : string list
  ; frame : Py.Object.t
  ; descr : string
  ; data_filename : string
  ; target_filename : string >

Load and return the physical excercise linnerud dataset.

This dataset is suitable for multi-ouput regression tasks.

============== ============================ Samples total 20 Dimensionality 3 (for both data and target) Features integer Targets integer ============== ============================

Read more in the :ref:`User Guide <linnerrud_dataset>`.

Parameters ---------- return_X_y : bool, default=False. If True, returns ``(data, target)`` instead of a Bunch object. See below for more information about the `data` and `target` object.

.. versionadded:: 0.18

as_frame : bool, default=False If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target columns. If `return_X_y` is True, then (`data`, `target`) will be pandas DataFrames or Series as described below.

.. versionadded:: 0.23

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

data : ndarray, dataframe of shape (20, 3) The data matrix. If `as_frame=True`, `data` will be a pandas DataFrame. target: ndarray, dataframe of shape (20, 3) The regression targets. If `as_frame=True`, `target` will be a pandas DataFrame. feature_names: list The names of the dataset columns. target_names: list The names of the target columns. frame: DataFrame of shape (20, 6) Only present when `as_frame=True`. DataFrame with `data` and `target`.

.. versionadded:: 0.23 DESCR: str The full description of the dataset. data_filename: str The path to the location of the data. target_filename: str The path to the location of the target.

.. versionadded:: 0.20

(data, target) : tuple if ``return_X_y`` is True

.. versionadded:: 0.18

val load_sample_image : Py.Object.t -> < img : [> `ArrayLike ] Np.Obj.t >

Load the numpy array of a single sample image

Read more in the :ref:`User Guide <sample_images>`.

Parameters ---------- image_name : `china.jpg`, `flower.jpg` The name of the sample image loaded

Returns ------- img : 3D array The image as a numpy array: height x width x color

Examples --------

>>> from sklearn.datasets import load_sample_image >>> china = load_sample_image('china.jpg') # doctest: +SKIP >>> china.dtype # doctest: +SKIP dtype('uint8') >>> china.shape # doctest: +SKIP (427, 640, 3) >>> flower = load_sample_image('flower.jpg') # doctest: +SKIP >>> flower.dtype # doctest: +SKIP dtype('uint8') >>> flower.shape # doctest: +SKIP (427, 640, 3)

val load_sample_images : 
  unit ->
  < data : Py.Object.t
  ; images : Np.Numpy.Ndarray.List.t
  ; filenames : string list
  ; descr : string >

Load sample images for image manipulation.

Loads both, ``china`` and ``flower``.

Read more in the :ref:`User Guide <sample_images>`.

Returns ------- data : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes.

images : list of ndarray of shape (427, 640, 3) The two sample image. filenames : list The filenames for the images. DESCR : str The full description of the dataset.

Examples -------- To load the data and visualize the images:

>>> from sklearn.datasets import load_sample_images >>> dataset = load_sample_images() #doctest: +SKIP >>> len(dataset.images) #doctest: +SKIP 2 >>> first_img_data = dataset.images0 #doctest: +SKIP >>> first_img_data.shape #doctest: +SKIP (427, 640, 3) >>> first_img_data.dtype #doctest: +SKIP dtype('uint8')

val load_svmlight_file : 
  ?n_features:int ->
  ?dtype:Np.Dtype.t ->
  ?multilabel:bool ->
  ?zero_based:[ `Auto | `Bool of bool ] ->
  ?query_id:bool ->
  ?offset:int ->
  ?length:int ->
  f:[ `S of string | `I of int | `File_like of Py.Object.t ] ->
  unit ->
  < x : [ `ArrayLike | `Object | `Spmatrix ] Np.Obj.t
  ; y : Py.Object.t
  ; query_id : [> `ArrayLike ] Np.Obj.t >

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 format is used as the default format for both svmlight and the libsvm command line programs.

Parsing a text based source can be expensive. When working on repeatedly on the same dataset, it is recommended to wrap this loader with joblib.Memory.cache to store a memmapped backup of the CSR results of the first call and benefit from the near instantaneous loading of memmapped structures for the subsequent calls.

In case the file contains a pairwise preference constraint (known as 'qid' in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered.

This implementation is written in Cython and is reasonably fast. However, a faster API-compatible loader is also available at:

https://github.com/mblondel/svmlight-loader

Parameters ---------- f : str, file-like, int (Path to) a file to load. If a path ends in '.gz' or '.bz2', it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. A file-like or file descriptor will not be closed by this function. A file-like object must be opened in binary mode.

n_features : int or None The number of features to use. If None, it will be inferred. This argument is useful to load several files that are subsets of a bigger sliced dataset: each subset might not have examples of every feature, hence the inferred shape might vary from one slice to another. n_features is only required if ``offset`` or ``length`` are passed a non-default value.

dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``.

multilabel : boolean, optional, default False Samples may have several labels each (see https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)

zero_based : boolean or 'auto', optional, default 'auto' Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to 'auto', a heuristic check is applied to determine this from the file contents. Both kinds of files occur 'in the wild', but they are unfortunately not self-identifying. Using 'auto' or True should always be safe when no ``offset`` or ``length`` is passed. If ``offset`` or ``length`` are passed, the 'auto' mode falls back to ``zero_based=True`` to avoid having the heuristic check yield inconsistent results on different segments of the file.

query_id : boolean, default False If True, will return the query_id array for each file.

offset : integer, optional, default 0 Ignore the offset first bytes by seeking forward, then discarding the following bytes up until the next new line character.

length : integer, optional, default -1 If strictly positive, stop reading any new line of data once the position in the file has reached the (offset + length) bytes threshold.

Returns ------- X : scipy.sparse matrix of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), or, in the multilabel a list of tuples of length n_samples.

query_id : array of shape (n_samples,) query_id for each sample. Only returned when query_id is set to True.

See also -------- load_svmlight_files: similar function for loading multiple files in this format, enforcing the same number of features/columns on all of them.

Examples -------- To use joblib.Memory to cache the svmlight file::

from joblib import Memory from .datasets import load_svmlight_file mem = Memory('./mycache')

@mem.cache def get_data(): data = load_svmlight_file('mysvmlightfile') return data0, data1

X, y = get_data()

val load_svmlight_files : 
  ?n_features:int ->
  ?dtype:Np.Dtype.t ->
  ?multilabel:bool ->
  ?zero_based:[ `Auto | `Bool of bool ] ->
  ?query_id:bool ->
  ?offset:int ->
  ?length:int ->
  files:Py.Object.t ->
  unit ->
  Py.Object.t

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.

Parameters ---------- files : iterable over str, file-like, int (Paths of) files to load. If a path ends in '.gz' or '.bz2', it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. File-likes and file descriptors will not be closed by this function. File-like objects must be opened in binary mode.

n_features : int or None The number of features to use. If None, it will be inferred from the maximum column index occurring in any of the files.

This can be set to a higher value than the actual number of features in any of the input files, but setting it to a lower value will cause an exception to be raised.

dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``.

multilabel : boolean, optional Samples may have several labels each (see https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)

zero_based : boolean or 'auto', optional Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to 'auto', a heuristic check is applied to determine this from the file contents. Both kinds of files occur 'in the wild', but they are unfortunately not self-identifying. Using 'auto' or True should always be safe when no offset or length is passed. If offset or length are passed, the 'auto' mode falls back to zero_based=True to avoid having the heuristic check yield inconsistent results on different segments of the file.

query_id : boolean, defaults to False If True, will return the query_id array for each file.

offset : integer, optional, default 0 Ignore the offset first bytes by seeking forward, then discarding the following bytes up until the next new line character.

length : integer, optional, default -1 If strictly positive, stop reading any new line of data once the position in the file has reached the (offset + length) bytes threshold.

Returns ------- X1, y1, ..., Xn, yn where each (Xi, yi) pair is the result from load_svmlight_file(filesi).

If query_id is set to True, this will return instead X1, y1, q1, ..., Xn, yn, qn where (Xi, yi, qi) is the result from load_svmlight_file(filesi)

Notes ----- When fitting a model to a matrix X_train and evaluating it against a matrix X_test, it is essential that X_train and X_test have the same number of features (X_train.shape1 == X_test.shape1). This may not be the case if you load the files individually with load_svmlight_file.

See also -------- make_biclusters

val make_circles : 
  ?n_samples:[ `I of int | `Two_element_tuple of Py.Object.t ] ->
  ?shuffle:bool ->
  ?noise:float ->
  ?random_state:int ->
  ?factor:float ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 two-element tuple, optional (default=100) If int, it is the total number of points generated. For odd numbers, the inner circle will have one point more than the outer circle. If two-element tuple, number of points in outer circle and inner circle.

shuffle : bool, optional (default=True) Whether to shuffle the samples.

noise : double or None (default=None) Standard deviation of Gaussian noise added to the data.

random_state : int, RandomState instance, default=None Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

factor : 0 < double < 1 (default=.8) Scale factor between inner and outer circle.

Returns ------- X : array of shape n_samples, 2 The generated samples.

y : array of shape n_samples The integer labels (0 or 1) for class membership of each sample.

val make_classification : 
  ?n_samples:int ->
  ?n_features:int ->
  ?n_informative:int ->
  ?n_redundant:int ->
  ?n_repeated:int ->
  ?n_classes:int ->
  ?n_clusters_per_class:int ->
  ?weights:[> `ArrayLike ] Np.Obj.t ->
  ?flip_y:float ->
  ?class_sep:float ->
  ?hypercube:bool ->
  ?shift:[ `Arr of [> `ArrayLike ] Np.Obj.t | `None ] ->
  ?scale:[ `Arr of [> `ArrayLike ] Np.Obj.t | `None ] ->
  ?shuffle:bool ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 between these features and adds various types of further noise to the data.

Without shuffling, ``X`` horizontally stacks features in the following order: the primary ``n_informative`` features, followed by ``n_redundant`` linear combinations of the informative features, followed by ``n_repeated`` duplicates, drawn randomly with replacement from the informative and redundant features. The remaining features are filled with random noise. Thus, without shuffling, all useful features are contained in the columns ``X:, :n_informative + n_redundant + n_repeated``.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

n_features : int, optional (default=20) The total number of features. These comprise ``n_informative`` informative features, ``n_redundant`` redundant features, ``n_repeated`` duplicated features and ``n_features-n_informative-n_redundant-n_repeated`` useless features drawn at random.

n_informative : int, optional (default=2) The number of informative features. Each class is composed of a number of gaussian clusters each located around the vertices of a hypercube in a subspace of dimension ``n_informative``. For each cluster, informative features are drawn independently from N(0, 1) and then randomly linearly combined within each cluster in order to add covariance. The clusters are then placed on the vertices of the hypercube.

n_redundant : int, optional (default=2) The number of redundant features. These features are generated as random linear combinations of the informative features.

n_repeated : int, optional (default=0) The number of duplicated features, drawn randomly from the informative and the redundant features.

n_classes : int, optional (default=2) The number of classes (or labels) of the classification problem.

n_clusters_per_class : int, optional (default=2) The number of clusters per class.

weights : array-like of shape (n_classes,) or (n_classes - 1,), (default=None) The proportions of samples assigned to each class. If None, then classes are balanced. Note that if ``len(weights) == n_classes - 1``, then the last class weight is automatically inferred. More than ``n_samples`` samples may be returned if the sum of ``weights`` exceeds 1.

flip_y : float, optional (default=0.01) The fraction of samples whose class is assigned randomly. Larger values introduce noise in the labels and make the classification task harder. Note that the default setting flip_y > 0 might lead to less than n_classes in y in some cases.

class_sep : float, optional (default=1.0) The factor multiplying the hypercube size. Larger values spread out the clusters/classes and make the classification task easier.

hypercube : boolean, optional (default=True) If True, the clusters are put on the vertices of a hypercube. If False, the clusters are put on the vertices of a random polytope.

shift : float, array of shape n_features or None, optional (default=0.0) Shift features by the specified value. If None, then features are shifted by a random value drawn in -class_sep, class_sep.

scale : float, array of shape n_features or None, optional (default=1.0) Multiply features by the specified value. If None, then features are scaled by a random value drawn in 1, 100. Note that scaling happens after shifting.

shuffle : boolean, optional (default=True) Shuffle the samples and the features.

Returns ------- X : array of shape n_samples, n_features The generated samples.

y : array of shape n_samples The integer labels for class membership of each sample.

Notes ----- The algorithm is adapted from Guyon 1 and was designed to generate the 'Madelon' dataset.

References ---------- .. 1 I. Guyon, 'Design of experiments for the NIPS 2003 variable selection benchmark', 2003.

See also -------- make_blobs: simplified variant make_multilabel_classification: unrelated generator for multilabel tasks

val make_friedman1 : 
  ?n_samples:int ->
  ?n_features:int ->
  ?noise:float ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 - 0.5) ** 2 + 10 * X:, 3 + 5 * X:, 4 + noise * N(0, 1).

Out of the `n_features` features, only 5 are actually used to compute `y`. The remaining features are independent of `y`.

The number of features has to be >= 5.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

n_features : int, optional (default=10) The number of features. Should be at least 5.

noise : float, optional (default=0.0) The standard deviation of the gaussian noise applied to the output.

random_state : int, RandomState instance, default=None Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.

Returns ------- X : array of shape n_samples, n_features The input samples.

y : array of shape n_samples The output values.

References ---------- .. 1 J. Friedman, 'Multivariate adaptive regression splines', The Annals of Statistics 19 (1), pages 1-67, 1991.

.. 2 L. Breiman, 'Bagging predictors', Machine Learning 24, pages 123-140, 1996.

val make_friedman2 : 
  ?n_samples:int ->
  ?noise:float ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 <= 11.

The output `y` is created according to the formula::

y(X) = (X:, 0 ** 2 + (X:, 1 * X:, 2 - 1 / (X:, 1 * X:, 3)) ** 2) ** 0.5 + noise * N(0, 1).

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

noise : float, optional (default=0.0) The standard deviation of the gaussian noise applied to the output.

Returns ------- X : array of shape n_samples, 4 The input samples.

y : array of shape n_samples The output values.

References ---------- .. 1 J. Friedman, 'Multivariate adaptive regression splines', The Annals of Statistics 19 (1), pages 1-67, 1991.

.. 2 L. Breiman, 'Bagging predictors', Machine Learning 24, pages 123-140, 1996.

val make_friedman3 : 
  ?n_samples:int ->
  ?noise:float ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 <= 11.

The output `y` is created according to the formula::

y(X) = arctan((X:, 1 * X:, 2 - 1 / (X:, 1 * X:, 3)) / X:, 0) + noise * N(0, 1).

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

noise : float, optional (default=0.0) The standard deviation of the gaussian noise applied to the output.

Returns ------- X : array of shape n_samples, 4 The input samples.

y : array of shape n_samples The output values.

References ---------- .. 1 J. Friedman, 'Multivariate adaptive regression splines', The Annals of Statistics 19 (1), pages 1-67, 1991.

.. 2 L. Breiman, 'Bagging predictors', Machine Learning 24, pages 123-140, 1996.

val make_gaussian_quantiles : 
  ?mean:[> `ArrayLike ] Np.Obj.t ->
  ?cov:float ->
  ?n_samples:int ->
  ?n_features:int ->
  ?n_classes:int ->
  ?shuffle:bool ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 (quantiles of the :math:`\chi^2` distribution).

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- mean : array of shape n_features, optional (default=None) The mean of the multi-dimensional normal distribution. If None then use the origin (0, 0, ...).

cov : float, optional (default=1.) The covariance matrix will be this value times the unit matrix. This dataset only produces symmetric normal distributions.

n_samples : int, optional (default=100) The total number of points equally divided among classes.

n_features : int, optional (default=2) The number of features for each sample.

n_classes : int, optional (default=3) The number of classes

shuffle : boolean, optional (default=True) Shuffle the samples.

Returns ------- X : array of shape n_samples, n_features The generated samples.

y : array of shape n_samples The integer labels for quantile membership of each sample.

Notes ----- The dataset is from Zhu et al 1.

References ---------- .. 1 J. Zhu, H. Zou, S. Rosset, T. Hastie, 'Multi-class AdaBoost', 2009.

val make_hastie_10_2 : 
  ?n_samples:int ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

Generates 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::

yi = 1 if np.sum(Xi ** 2) > 9.34 else -1

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=12000) The number of samples.

Returns ------- X : array of shape n_samples, 10 The input samples.

y : array of shape n_samples The output values.

References ---------- .. 1 T. Hastie, R. Tibshirani and J. Friedman, 'Elements of Statistical Learning Ed. 2', Springer, 2009.

See also -------- make_gaussian_quantiles: a generalization of this dataset approach

val make_low_rank_matrix : 
  ?n_samples:int ->
  ?n_features:int ->
  ?effective_rank:int ->
  ?tail_strength:float ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t

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' tail is fat, decreasing as::

tail_strength * exp(-0.1 * i / effective_rank).

The low rank part of the profile can be considered the structured signal part of the data while the tail can be considered the noisy part of the data that cannot be summarized by a low number of linear components (singular vectors).

This kind of singular profiles is often seen in practice, for instance:

gray level pictures of faces
TF-IDF vectors of text documents crawled from the web

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

n_features : int, optional (default=100) The number of features.

effective_rank : int, optional (default=10) The approximate number of singular vectors required to explain most of the data by linear combinations.

tail_strength : float between 0.0 and 1.0, optional (default=0.5) The relative importance of the fat noisy tail of the singular values profile.

Returns ------- X : array of shape n_samples, n_features The matrix.

val make_moons : 
  ?n_samples:[ `I of int | `Two_element_tuple of Py.Object.t ] ->
  ?shuffle:bool ->
  ?noise:float ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

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 two-element tuple, optional (default=100) If int, the total number of points generated. If two-element tuple, number of points in each of two moons.

shuffle : bool, optional (default=True) Whether to shuffle the samples.

noise : double or None (default=None) Standard deviation of Gaussian noise added to the data.

Returns ------- X : array of shape n_samples, 2 The generated samples.

y : array of shape n_samples The integer labels (0 or 1) for class membership of each sample.

val make_multilabel_classification : 
  ?n_samples:int ->
  ?n_features:int ->
  ?n_classes:int ->
  ?n_labels:int ->
  ?length:int ->
  ?allow_unlabeled:bool ->
  ?sparse:bool ->
  ?return_indicator:[ `False | `Dense | `Sparse ] ->
  ?return_distributions:bool ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t

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 ~ Multinomial(theta_c)

In the above process, rejection sampling is used to make sure that n is never zero or more than `n_classes`, and that the document length is never zero. Likewise, we reject classes which have already been chosen.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

n_features : int, optional (default=20) The total number of features.

n_classes : int, optional (default=5) The number of classes of the classification problem.

n_labels : int, optional (default=2) The average number of labels per instance. More precisely, the number of labels per sample is drawn from a Poisson distribution with ``n_labels`` as its expected value, but samples are bounded (using rejection sampling) by ``n_classes``, and must be nonzero if ``allow_unlabeled`` is False.

length : int, optional (default=50) The sum of the features (number of words if documents) is drawn from a Poisson distribution with this expected value.

allow_unlabeled : bool, optional (default=True) If ``True``, some instances might not belong to any class.

sparse : bool, optional (default=False) If ``True``, return a sparse feature matrix

.. versionadded:: 0.17 parameter to allow *sparse* output.

return_indicator : 'dense' (default) | 'sparse' | False If ``dense`` return ``Y`` in the dense binary indicator format. If ``'sparse'`` return ``Y`` in the sparse binary indicator format. ``False`` returns a list of lists of labels.

return_distributions : bool, optional (default=False) If ``True``, return the prior class probability and conditional probabilities of features given classes, from which the data was drawn.

Returns ------- X : array of shape n_samples, n_features The generated samples.

Y : array or sparse CSR matrix of shape n_samples, n_classes The label sets.

p_c : array, shape n_classes The probability of each class being drawn. Only returned if ``return_distributions=True``.

p_w_c : array, shape n_features, n_classes The probability of each feature being drawn given each class. Only returned if ``return_distributions=True``.

val make_regression : 
  ?n_samples:int ->
  ?n_features:int ->
  ?n_informative:int ->
  ?n_targets:int ->
  ?bias:float ->
  ?effective_rank:int ->
  ?tail_strength:float ->
  ?noise:float ->
  ?shuffle:bool ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t

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_informative` nonzero regressors to the previously generated input and some gaussian centered noise with some adjustable scale.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of samples.

n_features : int, optional (default=100) The number of features.

n_informative : int, optional (default=10) The number of informative features, i.e., the number of features used to build the linear model used to generate the output.

n_targets : int, optional (default=1) The number of regression targets, i.e., the dimension of the y output vector associated with a sample. By default, the output is a scalar.

bias : float, optional (default=0.0) The bias term in the underlying linear model.

effective_rank : int or None, optional (default=None) if not None: The approximate number of singular vectors required to explain most of the input data by linear combinations. Using this kind of singular spectrum in the input allows the generator to reproduce the correlations often observed in practice. if None: The input set is well conditioned, centered and gaussian with unit variance.

tail_strength : float between 0.0 and 1.0, optional (default=0.5) The relative importance of the fat noisy tail of the singular values profile if `effective_rank` is not None.

noise : float, optional (default=0.0) The standard deviation of the gaussian noise applied to the output.

shuffle : boolean, optional (default=True) Shuffle the samples and the features.

coef : boolean, optional (default=False) If True, the coefficients of the underlying linear model are returned.

Returns ------- X : array of shape n_samples, n_features The input samples.

y : array of shape n_samples or n_samples, n_targets The output values.

coef : array of shape n_features or n_features, n_targets, optional The coefficient of the underlying linear model. It is returned only if coef is True.

val make_s_curve : 
  ?n_samples:int ->
  ?noise:float ->
  ?random_state:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

Generate an S curve dataset.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int, optional (default=100) The number of sample points on the S curve.

noise : float, optional (default=0.0) The standard deviation of the gaussian noise.

Returns ------- X : array of shape n_samples, 3 The points.

t : array of shape n_samples The univariate position of the sample according to the main dimension of the points in the manifold.

val make_sparse_coded_signal : 
  ?random_state:int ->
  n_samples:int ->
  n_components:int ->
  n_features:int ->
  n_nonzero_coefs:int ->
  unit ->
  [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t
  * [> `ArrayLike ] Np.Obj.t

Generate a signal as a sparse combination of dictionary elements.

Returns a matrix Y = DX, such as D is (n_features, n_components), X is (n_components, n_samples) and each column of X has exactly n_nonzero_coefs non-zero elements.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- n_samples : int number of samples to generate

n_components : int, number of components in the dictionary

n_features : int number of features of the dataset to generate

n_nonzero_coefs : int number of active (non-zero) coefficients in each sample

Returns ------- data : array of shape n_features, n_samples The encoded signal (Y).

dictionary : array of shape n_features, n_components The dictionary with normalized components (D).

code : array of shape n_components, n_samples The sparse code such that each column of this matrix has exactly n_nonzero_coefs non-zero items (X).

val make_sparse_spd_matrix : 
  ?dim:int ->
  ?alpha:float ->
  ?norm_diag:bool ->
  ?smallest_coef:float ->
  ?largest_coef:float ->
  ?random_state:int ->
  unit ->
  [ `ArrayLike | `Object | `Spmatrix ] Np.Obj.t

Generate a sparse symmetric definite positive matrix.

Read more in the :ref:`User Guide <sample_generators>`.

Parameters ---------- dim : integer, optional (default=1) The size of the random matrix to generate.

alpha : float between 0 and 1, optional (default=0.95) The probability that a coefficient is zero (see notes). Larger values enforce more sparsity.

norm_diag : boolean, optional (default=False) Whether to normalize the output matrix to make the leading diagonal elements all 1

smallest_coef : float between 0 and 1, optional (default=0.1) The value of the smallest coefficient.

largest_coef : float between 0 and 1, optional (default=0.9) The value of the largest coefficient.

Returns ------- prec : sparse matrix of shape (dim, dim) The generated matrix.

Notes ----- The sparsity is actually imposed on the cholesky factor of the matrix. Thus alpha does not translate directly into the filling fraction of the matrix itself.