snowflake.ml.modeling.preprocessing.KBinsDiscretizerΒΆ
- class snowflake.ml.modeling.preprocessing.KBinsDiscretizer(*, n_bins: Union[int, List[int]] = 5, encode: str = 'onehot', strategy: str = 'quantile', input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False)ΒΆ
Bases:
BaseTransformer
Bin continuous data into intervals.
- Args:
- n_bins: int or array-like of shape (n_features,), default=5
The number of bins to produce. Raises ValueError if n_bins < 2.
- encode: {βonehotβ, βonehot-denseβ, βordinalβ}, default=βonehotβ
Method used to encode the transformed result.
βonehotβ: Encode the transformed result with one-hot encoding and return a sparse representation.
- βonehot-denseβ: Encode the transformed result with one-hot encoding and return separate column for
each encoded value.
βordinalβ: Return the bin identifier encoded as an integer value.
- strategy: {βuniformβ, βquantileβ}, default=βquantileβ
Strategy used to define the widths of the bins.
βuniformβ: All bins in each feature have identical widths.
βquantileβ: All bins in each feature have the same number of points.
- input_cols: str or Iterable [column_name], default=None
Single or multiple input columns.
- output_cols: str or Iterable [column_name], default=None
Single or multiple output columns.
- passthrough_cols: 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.
- drop_input_cols: boolean, default=False
Remove input columns from output if set True.
- Attributes:
- bin_edges_: ndarray of ndarray of shape (n_features,)
The edges of each bin. Contain arrays of varying shapes (n_bins_, )
- n_bins_: ndarray of shape (n_features,), dtype=np.int_
Number of bins per feature.
Base class for all transformers.
Methods
- fit(dataset: Union[DataFrame, DataFrame]) KBinsDiscretizer ΒΆ
Fit KBinsDiscretizer with dataset.
- Args:
dataset: Input dataset.
- Returns:
Fitted self instance.
- get_input_cols() List[str] ΒΆ
Input columns getter.
- Returns:
Input columns.
- get_label_cols() List[str] ΒΆ
Label column getter.
- Returns:
Label column(s).
- get_output_cols() List[str] ΒΆ
Get output column names. Expand output column names for βonehot-denseβ encoding.
- Returns:
Output column names.
- get_params(deep: bool = True) Dict[str, Any] ΒΆ
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] ΒΆ
Passthrough columns getter.
- Returns:
Passthrough column(s).
- get_sample_weight_col() Optional[str] ΒΆ
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] ΒΆ
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.
- set_drop_input_cols(drop_input_cols: Optional[bool] = False) None ΒΆ
- set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) Base ΒΆ
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 ΒΆ
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 ΒΆ
Output columns setter.
- Args:
output_cols: A single output column or multiple output columns.
- Returns:
self
- set_params(**params: Dict[str, Any]) None ΒΆ
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 itβs 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 ΒΆ
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 ΒΆ
Sample weight column setter.
- Args:
sample_weight_col: A single column that represents sample weight.
- Returns:
self
- to_lightgbm() Any ΒΆ
- to_sklearn() Any ΒΆ
- to_xgboost() Any ΒΆ
- transform(dataset: Union[DataFrame, DataFrame]) Union[DataFrame, DataFrame, csr_matrix] ΒΆ
Discretize the data.
- Args:
dataset: Input dataset.
- Returns:
Discretized output data based on input type. - If input is snowpark DataFrame, returns snowpark DataFrame - If input is a pd.DataFrame and βself.encdoe=onehotβ, returns βcsr_matrixβ - If input is a pd.DataFrame and βself.encode in [βordinalβ, βonehot-denseβ]β, returns βpd.DataFrameβ