gators.encoders.BinnedColumnsEncoder¶

class gators.encoders.BinnedColumnsEncoder(columns: List[str], inplace: bool = True)[source]¶

Encode the categorical variables after running a Gators Binning transformer.

Replace the bins “_X” by X, where X is an integer.

Parameters

dtypetype, default np.float64.: Numerical datatype of the output data.

Examples

Imports and initialization:

>>> from gators.encoders import BinnedColumnsEncoder
>>> obj = BinnedColumnsEncoder(columns=['A', 'B'], inplace=False)

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': ['_0', '_0', '_1'], 'B': ['_1', '_0', '_0']}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': ['_0', '_0', '_1'], 'B': ['_1', '_0', '_0']})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({'A': ['_0', '_0', '_1'], 'B': ['_1', '_0', '_0']})

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

>>> obj.fit_transform(X.copy())
    A   B  A__ordinal  B__ordinal
0  _0  _1         0.0         1.0
1  _0  _0         0.0         0.0
2  _1  _0         1.0         0.0

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([['_0', '_1', 0.0, 1.0],
       ['_0', '_0', 0.0, 0.0],
       ['_1', '_0', 1.0, 0.0]], dtype=object)

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.encoders.binned_columns_encoder.BinnedColumnsEncoder[source]¶

Fit the encoder.

Parameters

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

Returns

BinnedColumnsEncoder:: Instance of itself.

transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → Dict[str, Dict[str, float]][source]¶

Generate the mapping to perform the encoding.

Parameters

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

Returns

XDataFrame: Transformed dataframe.

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.

static decompose_mapping(mapping: Dict[str, Dict[str, float]]) → Tuple[List[str], numpy.ndarray, numpy.ndarray]¶

Decompose the mapping.

Parameters

mappingDict[str, Dict[str, float]]: The dictionary keys are the categorical columns, the keys are the mapping itself for the assocaited column.

Returns

columnsList[float]: List of columns.
values_vecnp.ndarray: Values to encode.
encoded_values_vecnp.ndarray: Values used to encode.

display_mapping(cmap: Union[str, colormap], k=5, decimal=2, title='')¶

Display the encoder mapping in a jupyter notebook. Parameters ———- cmap : Union[str, ‘colormap’]

Matplotlib colormap.

kint, default 5.: Number of mappings displayed.
decimalsint, default 2.: Number of decimal places to use.
titlestr, default ‘’.: Plot title.

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

Transform the array X.

Parameters

Xnp.ndarray: Input array.
Returns
——-
Xnp.ndarray: Encoded array.

gators.encoders.OneHotEncoder

gators.encoders.WOEEncoder