snowflake.ml.modeling.neighbors.KernelDensity¶

class snowflake.ml.modeling.neighbors.KernelDensity(*, bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True, leaf_size=40, metric_params=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Kernel Density Estimation For more details on this class, see sklearn.neighbors.KernelDensity

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

This parameter is optional and will be ignored during fit. It is present here for API consistency by convention.

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

bandwidth: float 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.

metric: str, 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.

atol: float, default=0

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

rtol: float, default=0

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

breadth_first: bool, default=True

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

leaf_size: int, default=40

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

metric_params: dict, 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.

Methods

fit(dataset)	Fit the Kernel Density model on the data For more details on this function, see sklearn.neighbors.KernelDensity.fit
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
score(dataset)	Compute the total log-likelihood under the model For more details on this function, see sklearn.neighbors.KernelDensity.score
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_sklearn()	Get sklearn.neighbors.KernelDensity object.

Attributes

model_signatures

Returns model signature of current class.