snowflake.ml.modeling.preprocessing.OrdinalEncoder¶

class snowflake.ml.modeling.preprocessing.OrdinalEncoder(*, categories: Union[str, Dict[str, Union[ndarray[Any, dtype[int64]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[str_]], ndarray[Any, dtype[bool_]]]]] = 'auto', handle_unknown: str = 'error', unknown_value: Optional[Union[int, float]] = None, encoded_missing_value: Union[int, float] = nan, 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)¶

Bases: BaseTransformer

Encodes categorical features as an integer array.

In other words, each category (i.e., distinct numeric or string value) is assigned an integer value, starting with zero.

For more details on what this transformer does, see sklearn.preprocessing.OrdinalEncoder.

Args:

categories: Union[str, Dict[str, type_utils.LiteralNDArrayType]], default=”auto”

The string ‘auto’ (the default) causes the categories to be extracted from the input columns. To specify the categories yourself, pass a dictionary mapping the column name to an ndarray containing the categories.

handle_unknown: str, default=”error”

Specifies how unknown categories are handled during transformation. Applicable only if categories is not ‘auto’. Valid values are:

• ‘error’: Raise an error if an unknown category is present during transform (default).

• ‘use_encoded_value’: When an unknown category is encountered during transform, the specified

  encoded_missing_value (below) is used.

unknown_value: Optional[Union[int, float]], default=None

When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter is required and will set the encoded value of unknown categories. It has to be distinct from the values used to encode any of the categories in fit.

encoded_missing_value: Union[int, float], default=np.nan

The value to be used to encode unknown categories.

input_cols: Optional[Union[str, List[str]]], default=None

The name(s) of one or more columns in a DataFrame containing a feature to be encoded.

output_cols: Optional[Union[str, List[str]]], default=None

The name(s) of one or more columns in a DataFrame in which results will be stored. The number of columns specified must match the number of input columns.

passthrough_cols: Optional[Union[str, List[str]]], default=None

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: Optional[bool], default=False

Remove input columns from output if set True. False by default.

Attributes:

categories_ (dict of ndarray): List[type_utils.LiteralNDArrayType]

The categories of each feature determined during fitting. Maps input column names to an array of the detected categories. Attributes are valid only after fit() has been called.

Encode categorical features as an integer column.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are converted to ordinal integers. This results in a single column of integers (0 to n_categories - 1) per feature.

Args:

categories: ‘auto’ or dict {column_name: ndarray([category])}, default=’auto’

Categories (unique values) per feature: - ‘auto’: Determine categories automatically from the training data. - dict: categories[column_name] holds the categories expected in

the column provided. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values.

The used categories can be found in the categories_ attribute.

handle_unknown: {‘error’, ‘use_encoded_value’}, default=’error’

When set to ‘error’ an error will be raised in case an unknown categorical feature is present during transform. When set to ‘use_encoded_value’, the encoded value of unknown categories will be set to the value given for the parameter unknown_value.

unknown_value: int or np.nan, default=None

When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter is required and will set the encoded value of unknown categories. It has to be distinct from the values used to encode any of the categories in fit.

encoded_missing_value: Encoded value of missing categories. input_cols: Single or multiple input columns. output_cols: 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 helful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during in training or inference.

drop_input_cols: Remove input columns from output if set True. False by default.

Attributes:

categories_: The categories of each feature determined during fitting.

Methods

fit(dataset: Union[DataFrame, DataFrame]) → OrdinalEncoder¶

Fit the OrdinalEncoder to dataset.

Validates the transformer arguments and derives the list of categories (distinct values) from the data, making this list available as an attribute of the transformer instance (see Attributes).

Returns the transformer instance.

Args:

dataset: Input dataset.

Returns:

Fitted encoder.

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.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) → None¶

set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) → Base¶

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_lightgbm() → Any¶

to_sklearn() → Any¶

to_xgboost() → Any¶

transform(dataset: Union[DataFrame, DataFrame]) → Union[DataFrame, DataFrame]¶

Transform dataset to ordinal codes.

Args:

dataset: Input dataset.

Returns:

Output dataset.