gators.feature_generation_str.SplitExtract¶

class gators.feature_generation_str.SplitExtract(columns: List[str], str_split_vec: List[int], idx_split_vec: List[int], column_names: List[str] = None)[source]¶

Create new columns based on split strings.

The transformer applies two steps:

split each string element using the given value.
extract the string of the given split list element.

Parameters

theta_vecList[float]: List of columns.
str_split_vecList[int]: List of separators.
idx_split_vecList[int]: List of split indices.
column_namesList[int]: List of new column names.

Examples

Imports and initialization:

>>> from gators.feature_generation_str import SplitExtract
>>> obj = SplitExtract(
... columns=['A', 'A'], str_split_vec=['*', '*'], idx_split_vec=[0, 1])

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': ['qw*e', 'a*qd', 'zxq*'], 'B': [1, 2, 3]}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': ['qw*e', 'a*qd', 'zxq*'], 'B': [1, 2, 3]})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame({'A': ['qw*e', 'a*qd', 'zxq*'], 'B': [1, 2, 3]})

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

>>> obj.fit_transform(X)
      A  B A__split_by_*_idx_0 A__split_by_*_idx_1
0  qw*e  1                  qw                   e
1  a*qd  2                   a                  qd
2  zxq*  3                 zxq
>>> X = pd.DataFrame({'A': ['qw*e', 'a*qd', 'zxq*'], 'B': [1, 2, 3]})
>>> _ = obj.fit(X)
>>> obj.transform_numpy(X.to_numpy())
array([['qw*e', 1, 'qw', 'e'],
       ['a*qd', 2, 'a', 'qd'],
       ['zxq*', 3, 'zxq', '']], dtype=object)

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(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

Xpd.DataFrame: Input dataframe.
ySeries, default None.: Target values.

Returns

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.

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_str._BaseStringFeature

gators.feature_generation_str.Extract