gators.binning._BaseBinning

class gators.binning._BaseBinning(n_bins: int, inplace: bool)[source]

Base binning transformer class.

Parameters
n_binsint

Number of bins to use.

inplacebool

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.

fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.transformers.transformer.Transformer[source]

Fit the transformer on the dataframe X.

Parameters
XDataFrame

Input dataframe.

ySeries, default None.

Target values.

Returns
self‘Transformer’

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

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.

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.