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
andKDTree
. 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
orKDTree
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
orKDTree
.
Base class for all transformers.
Methods
- fit(dataset: Union[DataFrame, DataFrame]) KernelDensity ¶
Fit the Kernel Density model on the data For more details on this function, see sklearn.neighbors.KernelDensity.fit
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]
Snowpark or Pandas DataFrame.
- Returns:
self
- fit_transform(dataset: Union[DataFrame, DataFrame]) Union[Any, ndarray[Any, dtype[Any]]] ¶
- Returns:
Transformed dataset.
- get_input_cols() List[str] ¶
Input columns getter.
- Returns:
Input columns.
- get_label_cols() List[str] ¶
Label column getter.
- Returns:
Label column(s).
- get_output_cols() List[str] ¶
Output columns getter.
- Returns:
Output columns.
- get_params(deep: bool = True) Dict[str, Any] ¶
Get parameters for this transformer.
- Args:
- deep: If True, will return the parameters for this transformer and
contained subobjects that are transformers.
- Returns:
Parameter names mapped to their values.
- get_passthrough_cols() List[str] ¶
Passthrough columns getter.
- Returns:
Passthrough column(s).
- get_sample_weight_col() Optional[str] ¶
Sample weight column getter.
- Returns:
Sample weight column.
- get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) Dict[str, Any] ¶
Get sklearn keyword arguments.
This method enables modifying object parameters for special cases.
- Args:
- default_sklearn_obj: Sklearn object used to get default parameter values. Necessary when
sklearn_added_keyword_to_version_dict is provided.
sklearn_initial_keywords: Initial keywords in sklearn. sklearn_unused_keywords: Sklearn keywords that are unused in snowml. snowml_only_keywords: snowml only keywords not present in sklearn. sklearn_added_keyword_to_version_dict: Added keywords mapped to the sklearn versions in which they were
added.
- sklearn_added_kwarg_value_to_version_dict: Added keyword argument values mapped to the sklearn versions
in which they were added.
- sklearn_deprecated_keyword_to_version_dict: Deprecated keywords mapped to the sklearn versions in which
they were deprecated.
- sklearn_removed_keyword_to_version_dict: Removed keywords mapped to the sklearn versions in which they
were removed.
- Returns:
Sklearn parameter names mapped to their values.
- score(dataset: Union[DataFrame, DataFrame]) float ¶
Compute the total log-likelihood under the model For more details on this function, see sklearn.neighbors.KernelDensity.score
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]
Snowpark or Pandas DataFrame.
- Returns:
Score.
- set_drop_input_cols(drop_input_cols: Optional[bool] = False) None ¶
- set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) KernelDensity ¶
Input columns setter.
- Args:
input_cols: A single input column or multiple input columns.
- Returns:
self
- set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) Base ¶
Label column setter.
- Args:
label_cols: A single label column or multiple label columns if multi task learning.
- Returns:
self
- set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) Base ¶
Output columns setter.
- Args:
output_cols: A single output column or multiple output columns.
- Returns:
self
- set_params(**params: Dict[str, Any]) None ¶
Set the parameters of this transformer.
The method works on simple transformers as well as on nested objects. The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Args:
**params: Transformer parameter names mapped to their values.
- Raises:
SnowflakeMLException: Invalid parameter keys.
- set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) Base ¶
Passthrough columns setter.
- Args:
- passthrough_cols: Column(s) that should not be used or modified by the estimator/transformer.
Estimator/Transformer just passthrough these columns without any modifications.
- Returns:
self
- set_sample_weight_col(sample_weight_col: Optional[str]) Base ¶
Sample weight column setter.
- Args:
sample_weight_col: A single column that represents sample weight.
- Returns:
self
- to_sklearn() Any ¶
Get sklearn.neighbors.KernelDensity object.
Attributes
- model_signatures¶
Returns model signature of current class.
- Raises:
exceptions.SnowflakeMLException: If estimator is not fitted, then model signature cannot be inferred
- Returns:
Dict[str, ModelSignature]: each method and its input output signature