gators.feature_generation.OneHot¶

class gators.feature_generation.OneHot(categories_dict: Dict[str, List[str]], column_names: List[str] = None)[source]¶

Create new columns based on the one-hot technique.

Parameters

categories_dictDict[str: List[str]].: keys: columns, values: list of category name.

Examples

Imports and initialization:

>>> from gators.feature_generation import OneHot
>>> categories_dict = {'A': ['b', 'c'], 'B': ['z']}
>>> obj = OneHot(categories_dict=categories_dict)

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', 'c'], 'B': ['z', 'a', 'a']}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': ['a', 'b', 'c'], 'B': ['z', 'a', 'a']})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['z', 'a', 'a']})

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

>>> obj.fit_transform(X)
   A  B  A__onehot__b  A__onehot__c  B__onehot__z
0  a  z         False         False          True
1  b  a          True         False         False
2  c  a         False          True         False

>>> X = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['z', 'a', 'a']})
>>> _ = obj.fit(X)
>>> obj.transform_numpy(X.to_numpy())
array([['a', 'z', False, False, True],
       ['b', 'a', True, False, False],
       ['c', 'a', False, True, False]], dtype=object)

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

Fit the dataframe X.

Parameters

XDataFrame.: Input dataframe. y (np.ndarray, optional): labels. Defaults to None.

Returns

selfOneHot: 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: Input 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.feature_generation.PolynomialFeatures

gators.feature_generation.PolynomialObjectFeatures