gators.imputers._BaseImputer¶
-
class
gators.imputers._BaseImputer(strategy: str, value: Optional[Union[float, str]], columns: List[str], inplace: bool = True)[source]¶ Base imputer transformer class.
- Parameters
- strategystr
Imputation strategy. The possible values are:
constant
most_frequent (only for the FloatImputer class)
mean (only for the FloatImputer class)
median (only for the FloatImputer class)
- value (Union[float, str, None])Imputation value, default None.
used for strategy=constant.
- columnsList[float], default None.
List of columns.
- inplaceList[float], default None.
If True, impute in-place. If False, create new imputed columns.
-
transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame][source]¶ Transform the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- Returns
- XDataFrame
Transformed dataframe.
-
compute_statistics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], value: Optional[Union[float, int, str]]) → Dict[str, Union[float, int, str]][source]¶ Compute the imputation values.
- Parameters
- XDataFrame
Dataframe. used to compute the imputation values.
- valueUnion[float, int, str, None]
Value used for imputation.
- Returns
- statisticsDict[str, Union[float, int, str]]
Imputation value mapping.
-
static
check_array(X: numpy.ndarray)¶ Validate array.
- Parameters
- Xnp.ndarray
Array.
-
check_array_is_numerics(X: numpy.ndarray)¶ Check if array is only numerics.
- Parameters
- Xnp.ndarray
Array.
-
static
check_binary_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not binary.
- Parameters
- ySeries
Target values.
-
static
check_dataframe(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Validate dataframe.
- Parameters
- XDataFrame
Dataframe.
-
static
check_dataframe_contains_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
static
check_dataframe_is_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
check_dataframe_with_objects(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe contains object columns.
- Parameters
- XDataFrame
Dataframe.
-
check_datatype(dtype, accepted_dtypes)¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
static
check_multiclass_target(y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not discrete.
- Parameters
- ySeries
Target values.
-
check_nans(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], columns: List[str])¶ Raise an error if X contains NaN values.
- Parameters
- XDataFrame
Dataframe.
- theta_vecList[float]
List of columns.
-
static
check_regression_target(y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not discrete.
- Parameters
- ySeries
Target values.
-
static
check_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶ Validate target.
- Parameters
- XDataFrame
Dataframe.
- ySeries
Target values.
-
abstract
fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.transformers.transformer.Transformer¶ Fit the transformer on the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- ySeries, default None.
Target values.
- Returns
- selfTransformer
Instance of itself.
-
fit_transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]¶ Fit and Transform the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- ySeries, default None.
Input target.
- Returns
- XDataFrame
Transformed dataframe.
-
static
get_column_names(inplace: bool, columns: List[str], suffix: str)¶ Return the names of the modified columns.
- Parameters
- inplacebool
If True return columns. If False return columns__suffix.
- columnsList[str]
List of columns.
- suffixstr
Suffix used if inplace is False.
- Returns
- List[str]
List of column names.
-
get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
-
set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
-
abstract
transform_numpy(X: Union[pd.Series, ks.Series, dd.Series], y: Union[pd.Series, ks.Series, dd.Series] = None)¶ Transform the array X.
- Parameters
- Xnp.ndarray
Array.
- Returns
- Xnp.ndarray
Transformed array.