gators.binning.QuantileBinning¶

class gators.binning.QuantileBinning(n_bins: int, inplace=False)[source]¶

Bin the columns using quantile-based splits.

The binning can be done inplace or by adding the binned columns to the existing data.

Parameters

n_binsint: Number of bins to use.
inplacebool, default False: If False, return the dataframe with the new binned columns with the names “column_name__bin”). Otherwise, return the dataframe with the existing binned columns.

See also

gators.binning.Binning: Bin using equal splits.
gators.binning.CustomBinning: Bin using the variable quantiles.
gators.binning.TreeBinning: Bin using tree-based splits.

Examples

>>> from gators.binning import QuantileBinning

The binning can be done inplace by modifying the existing columns

>>> obj = QuantileBinning(n_bins=3, inplace=True)

or by adding new binned columns

>>> obj = QuantileBinning(n_bins=3, inplace=True)

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': [-1, 0, 1], 'B': [3, 2, 1]}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': [-1, 0, 1], 'B': [3, 2, 1]})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({'A': [-1, 0, 1], 'B': [3, 2, 1]})

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

with inplace=True

>>> obj = QuantileBinning(n_bins=3, inplace=True)
>>> obj.fit_transform(X)
               A             B
0  (-inf, -0.33)   [2.33, inf)
1  [-0.33, 0.33)  [1.67, 2.33)
2    [0.33, inf)  (-inf, 1.67)

with inplace=False

>>> X = pd.DataFrame({'A': [-1, 0, 1], 'B': [3, 2, 1]})
>>> obj = QuantileBinning(n_bins=3, inplace=False)
>>> obj.fit_transform(X)
   A  B         A__bin        B__bin
0 -1  3  (-inf, -0.33)   [2.33, inf)
1  0  2  [-0.33, 0.33)  [1.67, 2.33)
2  1  1    [0.33, inf)  (-inf, 1.67)

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': [-1, 0, 1], 'B': [3, 2, 1]})
>>> obj.transform_numpy(X.to_numpy())
array([[-1, 3, '(-inf, -0.33)', '[2.33, inf)'],
       [0, 2, '[-0.33, 0.33)', '[1.67, 2.33)'],
       [1, 1, '[0.33, inf)', '(-inf, 1.67)']], dtype=object)

compute_bins(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → Tuple[List[List[float]], numpy.ndarray][source]¶

Compute the bins list and the bins array. The bin list is used for dataframes and the bins array is used for arrays.

Parameters

XDataFrame: Input dataframe.
n_binsint: Number of bins to use.

Returns

binsList[List[float]]: Bin splits definition. The dictionary keys are the column names to bin, its values are the split arrays.
bins_npnp.ndarray: Bin splits definition for NumPy.

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(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

self‘Transformer’: 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.

static get_labels(pretty_bins_dict: Dict[str, numpy.array])¶

Get the labels of the bins.

Parameters

pretty_bins_dictDict[str, np.array]): pretified bins used to generate the labels.

Returns

Dict[str, np.array]: Labels.
np.array: Labels.

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.

transform_numpy(X: numpy.ndarray) → numpy.ndarray¶

Transform the array X.

Parameters

Xnp.ndarray: Array.

Returns

Xnp.ndarray: Transformed array.

gators.binning.Binning

gators.binning.TreeBinning