gators.feature_selection.InformationValue¶

class gators.feature_selection.InformationValue(k: int, regularization: float = 0.1, max_iv: float = 10.0)[source]¶

Feature selection based on the information value.

InformationValue accepts only binary variable targets.

Parameters

kint: Number of features to keep.
regularizationfloat, default 0.5.: Insure that the weights of evidence are finite.
max_ivint, default 10.: Drop columns with an information larger than max_iv.

Examples

Imports and initialization:

>>> from gators.feature_selection import InformationValue
>>> from gators.binning import Binning
>>> obj = InformationValue(k=3)

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', 'b', 'a', 'b', 'c', 'b'],
... "B": ['true', 'true', 'false', 'true', 'false', 'false'],
... "D": ['a', 'b', 'c', 'd', 'e', 'f'],
... "F": ['e', 'f', 'g', 'e', 'f', 'g']}), npartitions=1)
>>> y = dd.from_pandas(pd.Series([1, 1, 1, 0, 0, 0], name='TARGET'), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({
... "A": ['a', 'b', 'a', 'b', 'c', 'b'],
... "B": ['true', 'true', 'false', 'true', 'false', 'false'],
... "D": ['a', 'b', 'c', 'd', 'e', 'f'],
... "F": ['e', 'f', 'g', 'e', 'f', 'g']})
>>> y = ks.Series([1, 1, 1, 0, 0, 0], name='TARGET')

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({
... "A": ['a', 'b', 'a', 'b', 'c', 'b'],
... "B": ['true', 'true', 'false', 'true', 'false', 'false'],
... "D": ['a', 'b', 'c', 'd', 'e', 'f'],
... "F": ['e', 'f', 'g', 'e', 'f', 'g']})
>>> y = pd.Series([1, 1, 1, 0, 0, 0], name='TARGET')

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

>>> obj.fit_transform(X, y)
   A      B  F
a   true  e
b   true  f
a  false  g
b   true  e
c  false  f
b  false  g

>>> X = pd.DataFrame({
... "A": ['a', 'b', 'a', 'b', 'c', 'b'],
... "B": ['true', 'true', 'false', 'true', 'false', 'false'],
... "D": ['a', 'b', 'c', 'd', 'e', 'f'],
... "F": ['e', 'f', 'g', 'e', 'f', 'g']})
>>> _ = obj.fit(X, y)
>>> obj.transform_numpy(X.to_numpy())
array([['a', 'true', 'e'],
       ['b', 'true', 'f'],
       ['a', 'false', 'g'],
       ['b', 'true', 'e'],
       ['c', 'false', 'f'],
       ['b', 'false', 'g']], dtype=object)

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series]) → gators.feature_selection.information_value.InformationValue[source]¶

Fit the transformer on the dataframe X.

Parameters

X: DataFrame: Input dataframe.
y: Series: Target values.
self“InformationValue”: Instance of itself.

static compute_information_value(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series], regularization: float) → pandas.core.series.Series[source]¶

Compute information value.

Parameters

X: DataFrame: Input dataframe.
y: np.ndarray: Target values.

Returns

pd.Series: Information value.

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.

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]¶

Transform the dataframe X.

Parameters

XDataFrame.: Input dataframe.
ynp.ndarray: Target values.

Returns

XDataFrame: Transformed dataframe.

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

Transform the array X.

Parameters

Xnp.ndarray: Input array.

Returns

Xnp.ndarray: Transformed array.

gators.feature_selection.SupervizedCorrelationFilter

gators.feature_selection.SelectFromModel