gators.imputers.ObjectImputer¶

class gators.imputers.ObjectImputer(strategy: str, value: str = None, columns=None, inplace: bool = True)[source]¶

Impute the categorical columns using the strategy passed by the user.

Parameters

strategystr

Imputation strategy.

Supported imputation strategies are:

‘constant’

‘most_frequent’

valuestr, default None.

Imputation value used for strategy=constant.

inplaceList[float], default None.

If True, impute in-place. If False, create new imputed columns.

See also

gators.imputers.NumericsImputer: Impute numerical columns.

Examples

>>> from gators.imputers import ObjectImputer

The imputation can be done for the selected categorical columns

>>> obj = ObjectImputer(strategy='most_frequent', columns=['A'])

or for all the categorical columns

>>> obj = ObjectImputer(strategy='most_frequent')

The fit, transform, and fit_transform methods accept:

dask dataframes:

>>> import dask.dataframe as dd
>>> import pandas as pd
>>> import numpy as np
>>> X = dd.from_pandas(pd.DataFrame(
... {'A': ['a', 'b', 'a', None],
... 'B': ['c', 'c', 'd', None],
... 'C': [0, 1, 2, np.nan]}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> import numpy as np
>>> X = ks.DataFrame(
... {'A': ['a', 'b', 'a', None],
... 'B': ['c', 'c', 'd', None],
... 'C': [0, 1, 2, np.nan]})

and pandas dataframes:

>>> import pandas as pd
>>> import numpy as np
>>> X = pd.DataFrame(
... {'A': ['a', 'b', 'a', None],
... 'B': ['c', 'c', 'd', None],
... 'C': [0, 1, 2, np.nan]})

The result is a transformed dataframe belonging to the same dataframe library.

imputation done for the selected columns:

>>> obj = ObjectImputer(strategy='most_frequent', columns=['A'])
>>> obj.fit_transform(X)
   A     B    C
0  a     c  0.0
1  b     c  1.0
2  a     d  2.0
3  a  None  NaN

imputation done for all the columns:

>>> X = pd.DataFrame(
... {'A': ['a', 'b', 'a', None],
... 'B': ['c', 'c', 'd', None],
... 'C': [0, 1, 2, np.nan]})
>>> obj = ObjectImputer(strategy='most_frequent')
>>> obj.fit_transform(X)
   A  B    C
0  a  c  0.0
1  b  c  1.0
2  a  d  2.0
3  a  c  NaN

Independly of the dataframe library used to fit the transformer, the tranform_numpy method only accepts NumPy arrays and returns a transformed NumPy array. Note that this transformer should only be used when the number of rows is small e.g. in real-time environment.

>>> X = pd.DataFrame(
... {'A': ['a', 'b', 'a', None],
... 'B': ['c', 'c', 'd', None],
... 'C': [0, 1, 2, np.nan]})
>>> obj.transform_numpy(X.to_numpy())
array([['a', 'c', 0.0],
       ['b', 'c', 1.0],
       ['a', 'd', 2.0],
       ['a', 'c', nan]], dtype=object)

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.imputers.object_imputer.ObjectImputer[source]¶

Fit the transformer on the dataframe X.

Parameters

XDataFrame.: Input dataframe.
ySeries, default None.: Target values.

Returns

self‘ObjectImputer’: Instance of itself.

transform_numpy(X: Union[pd.Series, ks.Series, dd.Series], y: Union[pd.Series, ks.Series, dd.Series] = None)[source]¶

Transform the NumPy array X.

Parameters

Xnp.ndarray:: Input array.

Returns

Xnp.ndarray: Transformed array.

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.

compute_statistics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], value: Optional[Union[float, int, str]]) → Dict[str, Union[float, int, str]]¶

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.

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.

transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]¶

Transform the dataframe X.

Parameters

XDataFrame.: Input dataframe.

Returns

XDataFrame: Transformed dataframe.

gators.imputers.NumericsImputer

Encoders