snowflake.ml.modeling.impute.SimpleImputer

class snowflake.ml.modeling.impute.SimpleImputer(*, missing_values: Optional[Union[int, float, str, float64]] = nan, strategy: Optional[str] = 'mean', fill_value: Optional[Union[str, float]] = None, 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

Univariate imputer for completing missing values with simple strategies. Note that the add_indicator parameter is not implemented. For more details on this class, see sklearn.impute.SimpleImputer (https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html).

Parameters:

  • missing_values – int, float, str, np.nan or None, default=np.nan. The values to treat as missing and impute during transform.

  • strategy –

    str, default=”mean”. The imputation strategy.

    • If “mean”, replace missing values using the mean along each column. Can only be used with numeric data.

    • If “median”, replace missing values using the median along each column. Can only be used with numeric data.

    • If “most_frequent”, replace missing using the most frequent value along each column. Can be used with strings or numeric data. If there is more than one such value, only the smallest is returned.

    • If “constant”, replace the missing values with fill_value, including columns that are entirely null. Can be used with strings or numeric data.

  • fill_value – Optional[str] When strategy == “constant”, fill_value is used to replace all occurrences of missing_values. For string or object data types, fill_value must be a string. If None, fill_value will be 0 when imputing numerical data and missing_value for strings and object data types.

  • input_cols – Optional[Union[str, List[str]]] Columns to use as inputs during fit and transform.

  • output_cols – Optional[Union[str, List[str]]] A string or list of strings representing column names that will store the output of transform operation. The length of output_cols must equal the length of input_cols.

  • 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 – bool, default=False Remove input columns from output if set True.

statistics_

dict {input_col: stats_value} Dict containing the imputation fill value for each feature. Computing statistics can result in np.nan values. During transform, features corresponding to np.nan statistics will be discarded.

n_features_in_

int Number of features seen during fit.

feature_names_in_

ndarray of shape (n_features_in,) Names of features seen during fit.

Raises:

SnowflakeMLException – If strategy is invalid, or if fill value is specified for strategy that isn’t “constant”.

Base class for all transformers.

Methods

fit(dataset: DataFrame) → SimpleImputer

Compute values to impute for the dataset according to the strategy.

Parameters:

dataset – Input dataset.

Returns:

Fitted simple imputer.

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.

Parameters:

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.

Parameters:

  • 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.

Parameters:

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.

Parameters:

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.

Parameters:

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.

Parameters:

**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.

Parameters:

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.

Parameters:

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 the input dataset by imputing the computed statistics in the input columns.

Parameters:

dataset – Input dataset.

Returns:

Output dataset.

语言: 中文