gators.feature_generation.PolynomialFeatures¶

class gators.feature_generation.PolynomialFeatures(columns: List[str], degree=2, interaction_only=False)[source]¶

Create new columns based on columns multiplication.

Parameters

theta_vecList[float]: List of columns.
degreeint, default = 2: The degree of polynomial. The default of degree of 2 will produce A * A, B * B, and A * B from features A and B.
interaction_onlybool, default = False: Allows to keep only interaction terms. If true, only A * B will be produced from features A and B.
dtypetype, default np.float64: Numpy dtype of the output data.

Examples

Imports and initialization:

>>> from gators.feature_generation import PolynomialFeatures
>>> obj = PolynomialFeatures(columns=['A', 'B'])

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(
... {'X': [200.0, 210.0], 'Y': [140.0, 160.0], 'Z': [100.0, 125.0]}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> X = ks.DataFrame(
... {'A': [0.0, 3.0, 6.0], 'B': [1.0, 4.0, 7.0], 'C': [2.0, 5.0, 8.0]})

and pandas dataframes:

>>> import pandas as pd
>>> X = pd.DataFrame(
... {'A': [0.0, 3.0, 6.0], 'B': [1.0, 4.0, 7.0], 'C': [2.0, 5.0, 8.0]})

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

>>> obj.fit_transform(X)
     A    B    C  A__x__A  A__x__B  B__x__B
0  0.0  1.0  2.0      0.0      0.0      1.0
1  3.0  4.0  5.0      9.0     12.0     16.0
2  6.0  7.0  8.0     36.0     42.0     49.0

>>> X = pd.DataFrame(
... {'A': [0.0, 3.0, 6.0], 'B': [1.0, 4.0, 7.0], 'C': [2.0, 5.0, 8.0]})
>>> _ = obj.fit(X)
>>> obj.transform_numpy(X.to_numpy())
array([[ 0.,  1.,  2.,  0.,  0.,  1.],
       [ 3.,  4.,  5.,  9., 12., 16.],
       [ 6.,  7.,  8., 36., 42., 49.]])

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

Fit the dataframe X.

Parameters

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

Returns

selfPolynomialFeatures: 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.PlanRotation

gators.feature_generation.OneHot