gators.binning.BinSingleTargetClassCategories¶

class gators.binning.BinSingleTargetClassCategories[source]¶

Bin single target class categories.

Ensure that the target class ratio for each categy is between 0 and 1 excluded. Note that this transformer should only be used for binary classification problems.

Examples

Imports and initialization:

>>> from gators.binning import BinSingleTargetClassCategories
>>> obj = BinSingleTargetClassCategories()

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", "_1", "_2", '_2', '_1'],
... "B": ["_1", "_2", "_1", '_1', '_1'],
... "C": ["_0", "_0", "_1", '_2', '_2'],
... "D": ["_0", '_0', '_1', '_1', '_1'],
... "E": [1, 2, 3, 4, 5]}), npartitions=1)
>>> y = dd.from_pandas(pd.Series([0, 1, 1, 0, 0], name='Target'), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({
... "A": ["_0", "_1", "_2", '_2', '_1'],
... "B": ["_1", "_2", "_1", '_1', '_1'],
... "C": ["_0", "_0", "_1", '_2', '_2'],
... "D": ["_0", '_0', '_1', '_1', '_1'],
... "E": [1, 2, 3, 4, 5]})
>>> y = ks.Series([0, 1, 1, 0, 0], name='Target')

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({
... "A": ["_0", "_1", "_2", '_2', '_1'],
... "B": ["_1", "_2", "_1", '_1', '_1'],
... "C": ["_0", "_0", "_1", '_2', '_2'],
... "D": ["_0", '_0', '_1', '_1', '_1'],
... "E": [1, 2, 3, 4, 5]})
>>> y = pd.Series([0, 1, 1, 0, 0], name='Target')

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

>>> obj.fit_transform(X, y)
       A      B         C   D  E
_0|_1  _1|_2  _0|_1|_2  _0  1
_0|_1  _1|_2  _0|_1|_2  _0  2
   _2  _1|_2  _0|_1|_2  _1  3
   _2  _1|_2  _0|_1|_2  _1  4
_0|_1  _1|_2  _0|_1|_2  _1  5

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', '_1|_2', '_0|_1|_2', '_0', 1],
       ['_0|_1', '_1|_2', '_0|_1|_2', '_0', 2],
       ['_2', '_1|_2', '_0|_1|_2', '_1', 3],
       ['_2', '_1|_2', '_0|_1|_2', '_1', 4],
       ['_0|_1', '_1|_2', '_0|_1|_2', '_1', 5]], dtype=object)

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series]) → gators.binning.bin_single_target_class_categories.BinSingleTargetClassCategories[source]¶

Fit the transformer on the dataframe X.

Parameters

XDataFrame.: Input dataframe.
ySeries.: Target values.

Returns

BinSingleTargetClassCategories: 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 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.

gators.binning.BinRareCategories

gators.binning._BaseBinning