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, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶
Bases:
BaseTransformerKernel Density Estimation For more details on this class, see sklearn.neighbors.KernelDensity
- 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_metricsfor valid metric values.Not all metrics are valid with all algorithms: refer to the documentation of
BallTreeandKDTree. 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
BallTreeorKDTreefor 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
BallTreeorKDTree.- 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 and sample-weight_col parameters are considered input columns.
- label_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).
- 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 mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.
- drop_input_cols: Optional[bool], default=False
If set, the response of predict(), transform() methods will not contain input columns.
Methods
fit(dataset)Fit the Kernel Density model on the data For more details on this function, see sklearn.neighbors.KernelDensity.fit
score(dataset)Compute the total log-likelihood under the model For more details on this function, see sklearn.neighbors.KernelDensity.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.neighbors.KernelDensity object.
Attributes
model_signaturesReturns model signature of current class.