package sklearn

You can search for identifiers within the package.

in-package search v0.2.0

sklearn
- Sklearn

Legend:
Library
Module
Module type
Parameter
Class
Class type

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?n_features:int ->
  ?input_type:Py.Object.t ->
  ?dtype:Py.Object.t ->
  ?alternate_sign:Py.Object.t ->
  unit ->
  t

Implements feature hashing, aka the hashing trick.

This class turns sequences of symbolic feature names (strings) into scipy.sparse matrices, using a hash function to compute the matrix column corresponding to a name. The hash function employed is the signed 32-bit version of Murmurhash3.

Feature names of type byte string are used as-is. Unicode strings are converted to UTF-8 first, but no Unicode normalization is done. Feature values must be (finite) numbers.

This class is a low-memory alternative to DictVectorizer and CountVectorizer, intended for large-scale (online) learning and situations where memory is tight, e.g. when running prediction code on embedded devices.

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

.. versionadded:: 0.13

Parameters ---------- n_features : integer, optional The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners. input_type : string, optional, default "dict" Either "dict" (the default) to accept dictionaries over (feature_name, value); "pair" to accept pairs of (feature_name, value); or "string" to accept single strings. feature_name should be a string, while value should be a number. In the case of "string", a value of 1 is implied. The feature_name is hashed to find the appropriate column for the feature. The value's sign might be flipped in the output (but see non_negative, below). dtype : numpy type, optional, default np.float64 The type of feature values. Passed to scipy.sparse matrix constructors as the dtype argument. Do not set this to bool, np.boolean or any unsigned integer type. alternate_sign : boolean, optional, default True When True, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection.

Examples -------- >>> from sklearn.feature_extraction import FeatureHasher >>> h = FeatureHasher(n_features=10) >>> D = {'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5} >>> f = h.transform(D) >>> f.toarray() array([ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.], [ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.])

See also -------- DictVectorizer : vectorizes string-valued features using a hash table. sklearn.preprocessing.OneHotEncoder : handles nominal/categorical features.

val fit : ?x:Arr.t -> ?y:Py.Object.t -> t -> t

No-op.

This method doesn't do anything. It exists purely for compatibility with the scikit-learn transformer API.

Parameters ---------- X : array-like

Returns ------- self : FeatureHasher

val fit_transform : 
  ?y:Arr.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:Arr.t ->
  t ->
  Arr.t

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters ---------- X : numpy array of shape n_samples, n_features Training set.

y : numpy array of shape n_samples Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.

val get_params : ?deep:bool -> t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val set_params : ?params:(string * Py.Object.t) list -> t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : raw_X:Py.Object.t -> t -> Arr.t

Transform a sequence of instances to a scipy.sparse matrix.

Parameters ---------- raw_X : iterable over iterable over raw features, length = n_samples Samples. Each sample must be iterable an (e.g., a list or tuple) containing/generating feature names (and optionally values, see the input_type constructor argument) which will be hashed. raw_X need not support the len function, so it can be the result of a generator; n_samples is determined on the fly.

Returns ------- X : sparse matrix of shape (n_samples, n_features) Feature matrix, for use with estimators or further transformers.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.