val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_bins:[ `Int of int | `Ndarray of Ndarray.t ] ->
?encode:[ `Onehot | `Onehot_dense | `Ordinal ] ->
?strategy:[ `Uniform | `Quantile | `Kmeans ] ->
unit ->
tBin continuous data into intervals.
Read more in the :ref:`User Guide <preprocessing_discretization>`.
Parameters ---------- n_bins : int or array-like, shape (n_features,) (default=5) The number of bins to produce. Raises ValueError if ``n_bins < 2``.
encode : 'onehot', 'onehot-dense', 'ordinal', (default='onehot') Method used to encode the transformed result.
onehot Encode the transformed result with one-hot encoding and return a sparse matrix. Ignored features are always stacked to the right. onehot-dense Encode the transformed result with one-hot encoding and return a dense array. Ignored features are always stacked to the right. ordinal Return the bin identifier encoded as an integer value.
strategy : 'uniform', 'quantile', 'kmeans', (default='quantile') Strategy used to define the widths of the bins.
uniform All bins in each feature have identical widths. quantile All bins in each feature have the same number of points. kmeans Values in each bin have the same nearest center of a 1D k-means cluster.
Attributes ---------- n_bins_ : int array, shape (n_features,) Number of bins per feature. Bins whose width are too small (i.e., <= 1e-8) are removed with a warning.
bin_edges_ : array of arrays, shape (n_features, ) The edges of each bin. Contain arrays of varying shapes ``(n_bins_, )`` Ignored features will have empty arrays.
See Also -------- sklearn.preprocessing.Binarizer : Class used to bin values as ``0`` or ``1`` based on a parameter ``threshold``.
Notes ----- In bin edges for feature ``i``, the first and last values are used only for ``inverse_transform``. During transform, bin edges are extended to::
np.concatenate(-np.inf, bin_edges_[i][1:-1], np.inf)
You can combine ``KBinsDiscretizer`` with :class:`sklearn.compose.ColumnTransformer` if you only want to preprocess part of the features.
``KBinsDiscretizer`` might produce constant features (e.g., when ``encode = 'onehot'`` and certain bins do not contain any data). These features can be removed with feature selection algorithms (e.g., :class:`sklearn.feature_selection.VarianceThreshold`).
Examples -------- >>> X = [-2, 1, -4, -1],
... [-1, 2, -3, -0.5],
... [ 0, 3, -2, 0.5],
... [ 1, 4, -1, 2] >>> est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform') >>> est.fit(X) KBinsDiscretizer(...) >>> Xt = est.transform(X) >>> Xt # doctest: +SKIP array([ 0., 0., 0., 0.],
[ 1., 1., 1., 0.],
[ 2., 2., 2., 1.],
[ 2., 2., 2., 2.])
Sometimes it may be useful to convert the data back into the original feature space. The ``inverse_transform`` function converts the binned data into the original feature space. Each value will be equal to the mean of the two bin edges.
>>> est.bin_edges_0 array(-2., -1., 0., 1.) >>> est.inverse_transform(Xt) array([-1.5, 1.5, -3.5, -0.5],
[-0.5, 2.5, -2.5, -0.5],
[ 0.5, 3.5, -1.5, 0.5],
[ 0.5, 3.5, -1.5, 1.5])
val fit : ?y:Py.Object.t -> x:Ndarray.t -> t -> tFit the estimator.
Parameters ---------- X : numeric array-like, shape (n_samples, n_features) Data to be discretized.
y : None Ignored. This parameter exists only for compatibility with :class:`sklearn.pipeline.Pipeline`.
Returns ------- self
val fit_transform :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit 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 -> Py.Object.tGet 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.
Transform discretized data back to original feature space.
Note that this function does not regenerate the original data due to discretization rounding.
Parameters ---------- Xt : numeric array-like, shape (n_sample, n_features) Transformed data in the binned space.
Returns ------- Xinv : numeric array-like Data in the original feature space.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.
Discretize the data.
Parameters ---------- X : numeric array-like, shape (n_samples, n_features) Data to be discretized.
Returns ------- Xt : numeric array-like or sparse matrix Data in the binned space.
val n_bins_ : t -> Py.Object.tAttribute n_bins_: see constructor for documentation
val bin_edges_ : t -> Py.Object.tAttribute bin_edges_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.