gators.feature_generation_str.Extract

class gators.feature_generation_str.Extract(columns: List[str], i_min_vec: List[int], i_max_vec: List[int], column_names: List[int] = None)[source]

Create new object columns based on substrings.

Parameters
theta_vecList[float]

List of columns.

i_min_vecList[int]

List of indices.

i_max_vecList[int]

List of indices.

column_namesList[int]

List of column names.

Examples

Imports and initialization:

>>> from gators.feature_generation_str import Extract
>>> obj = Extract(columns=['A', 'A'], i_min_vec=[0, 2], i_max_vec=[1, 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': ['qwe', 'asd', 'zxc'], 'B': [1, 2, 3]}), npartitions=1)
  • koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame({'A': ['qwe', 'asd', 'zxc'], 'B': [1, 2, 3]})
  • and pandas dataframes:

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

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

>>> obj.fit_transform(X)
     A  B A__substring_0_to_1 A__substring_2_to_3
0  qwe  1                   q                   e
1  asd  2                   a                   d
2  zxc  3                   z                   c
>>> X = pd.DataFrame({'A': ['qwe', 'asd', 'zxc'], 'B': [1, 2, 3]})
>>> _ = obj.fit(X)
>>> obj.transform_numpy(X.to_numpy())
array([['qwe', 1, 'q', 'e'],
       ['asd', 2, 'a', 'd'],
       ['zxc', 3, 'z', 'c']], dtype=object)
fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.feature_generation_str.extract.Extract[source]

Fit the transformer on the dataframe X.

Parameters
XDataFrame.

Input dataframe.

ySeries, default None.

Target values.

Returns
Extract

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.