KernelDensity

classsklearn.neighbors.KernelDensity(*, bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None)[source]

Kernel Density Estimation.

Read more in the User Guide.

Parameters:

bandwidthfloat or {“scott”, “silverman”}, default=1.0

The bandwidth of the kernel. If bandwidth is a float, it defines the bandwidth of the kernel. If bandwidth is a string, one of the estimation methods is implemented.

algorithm{‘kd_tree’, ‘ball_tree’, ‘auto’}, default=’auto’

The tree algorithm to use.

kernel{‘gaussian’, ‘tophat’, ‘epanechnikov’, ‘exponential’, ‘linear’, ‘cosine’}, default=’gaussian’

The kernel to use.

metricstr, default=’euclidean’

Metric to use for distance computation. See the documentation of scipy.spatial.distance and the metrics listed in distance_metrics for valid metric values.

Not all metrics are valid with all algorithms: refer to the documentation of BallTree and KDTree. Note that the normalization of the density output is correct only for the Euclidean distance metric.

atolfloat, default=0

The desired absolute tolerance of the result. A larger tolerance will generally lead to faster execution.

rtolfloat, default=0

The desired relative tolerance of the result. A larger tolerance will generally lead to faster execution.

breadth_firstbool, default=True

If true (default), use a breadth-first approach to the problem. Otherwise use a depth-first approach.

leaf_sizeint, default=40

Specify the leaf size of the underlying tree. See BallTree or KDTree for details.

metric_paramsdict, default=None

Additional parameters to be passed to the tree for use with the metric. For more information, see the documentation of BallTree or KDTree.

Attributes:

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

tree_BinaryTree instance

The tree algorithm for fast generalized N-point problems.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

bandwidth_float

Value of the bandwidth, given directly by the bandwidth parameter or estimated using the ‘scott’ or ‘silverman’ method.

Added in version 1.0.

See also

sklearn.neighbors.KDTree

K-dimensional tree for fast generalized N-point problems.

sklearn.neighbors.BallTree

Ball tree for fast generalized N-point problems.

Examples

Compute a gaussian kernel density estimate with a fixed bandwidth.

>>> from sklearn.neighbors import KernelDensity
>>> import numpy as np
>>> rng = np.random.RandomState(42)
>>> X = rng.random_sample((100, 3))
>>> kde = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(X)
>>> log_density = kde.score_samples(X[:3])
>>> log_density
array([-1.52955942, -1.51462041, -1.60244657])

fit(X, y=None, sample_weight=None)[source]

Fit the Kernel Density model on the data.

Parameters:

Xarray-like of shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

yNone

Ignored. This parameter exists only for compatibility with Pipeline.

sample_weightarray-like of shape (n_samples,), default=None

List of sample weights attached to the data X.

Added in version 0.20.

Returns:

selfobject

Returns the instance itself.

get_metadata_routing()[source]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

sample(n_samples=1, random_state=None)[source]

Generate random samples from the model.

Currently, this is implemented only for gaussian and tophat kernels.

Parameters:

n_samplesint, default=1

Number of samples to generate.

random_stateint, RandomState instance or None, default=None

Determines random number generation used to generate random samples. Pass an int for reproducible results across multiple function calls. See Glossary.

Returns:

Xarray-like of shape (n_samples, n_features)

List of samples.

score(X, y=None)[source]

Compute the total log-likelihood under the model.

Parameters:

Xarray-like of shape (n_samples, n_features)

List of n_features-dimensional data points. Each row corresponds to a single data point.

yNone

Ignored. This parameter exists only for compatibility with Pipeline.

Returns:

logprobfloat

Total log-likelihood of the data in X. This is normalized to be a probability density, so the value will be low for high-dimensional data.

score_samples(X)[source]

Compute the log-likelihood of each sample under the model.

Parameters:

Xarray-like of shape (n_samples, n_features)

An array of points to query. Last dimension should match dimension of training data (n_features).

Returns:

densityndarray of shape (n_samples,)

Log-likelihood of each sample in X. These are normalized to be probability densities, so values will be low for high-dimensional data.

set_fit_request(*, sample_weight:bool|None|str='$UNCHANGED$') → KernelDensity[source]

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
• False: metadata is not requested and the meta-estimator will not pass it to fit.
• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)[source]

Set the parameters of this estimator.

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

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

Gallery examples

Kernel Density Estimation

Simple 1D Kernel Density Estimation

Kernel Density Estimate of Species Distributions