gators.binning.BinRareCategories¶

class gators.binning.BinRareCategories(min_ratio: float)[source]¶

Replace low occurence categories by the value “OTHERS”.

Use BinRareCategories to reduce the cardinality of high cardinal columns. This transformer is also useful to replace unseen categories by a value which is already taken it account by the encoders.

Parameters

min_ratiofloat: Min occurence ratio per category.

Examples

Imports and initialization:

>>> from gators.binning import BinRareCategories
>>> obj = BinRareCategories(min_ratio=0.5)

The fit, transform, and fit_transform methods accept:

dask dataframes:

>>> import dask.dataframe as dd
>>> import pandas as pd
>>> X = dd.from_pandas(pd.DataFrame({'A': ['a', 'a', 'b'], 'B': ['a', 'b', 'c']}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': ['a', 'a', 'b'], 'B': ['a', 'b', 'c']})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({'A': ['a', 'a', 'b'], 'B': ['a', 'b', 'c']})

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

>>> obj.fit_transform(X)
        A       B
0       a  OTHERS
1       a  OTHERS
2  OTHERS  OTHERS

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.

>>> obj.transform_numpy(X.to_numpy())
array([['a', 'OTHERS'],
       ['a', 'OTHERS'],
       ['OTHERS', 'OTHERS']], dtype=object)

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.binning.bin_rare_categories.BinRareCategories[source]¶

Fit the transformer on the dataframe X.

Parameters

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

Returns

BinRareCategories: Instance of itself.

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.

transform_numpy(X: numpy.ndarray) → numpy.ndarray[source]¶

Transform the array X.

Parameters

Xnp.ndarray: Array.

Returns

Xnp.ndarray: Transformed array.

static compute_categories_to_keep_dict(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], min_ratio: float) → Dict[str, List[str]][source]¶

Compute the category frequency.

Parameters

XDataFrame.: Input dataframe.
min_ratiofloat: Min occurence per category.

Returns

mappingDict[str, List[str]]: Categories to keep.

static get_categories_to_keep_np(categories_to_keep_dict: Dict[str, numpy.ndarray]) → numpy.ndarray[source]¶

Get the categories to keep.

Parameters

categories_to_keep_dictDict[str, np.ndarray]): Categories to keep.

Returns

categories_to_keep_npnp.ndarray: Categories to keep.

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.

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.

Binning

gators.binning.BinSingleTargetClassCategories