skoot.feature_extraction.InteractionTermTransformer

class skoot.feature_extraction.InteractionTermTransformer(cols=None, as_df=True, interaction_function=None, sep='_', name_suffix='I')

Create interaction terms between predictors.

This class will compute interaction terms between selected columns. An interaction captures some relationship between two independent variables in the form of \(I_{ij} = x_{i} imes x_{j}\).

Parameters:

cols : array-like, shape=(n_features,), optional (default=None)

The names of the columns on which to apply the transformation. If no column names are provided, the transformer will be fit on the entire frame. Note that the transformation will also only apply to the specified columns, and any other non-specified columns will still be present after transformation.

as_df : bool, optional (default=True)

Whether to return a Pandas DataFrame in the transform method. If False, will return a Numpy ndarray instead. Since most skoot transformers depend on explicitly-named DataFrame features, the as_df parameter is True by default.

interaction_function : callable, optional (default=None)

A callable for interactions. Default None will result in multiplication of two Series objects. Use caution when passing a lambda expression, since they cannot be persisted via pickle!

sep : str or unicode (optional, default=”_”)

The separator between the new feature names. The names will be in the form of:

<left><sep><right><sep><suffix>

For examples, for columns ‘a’ and ‘b’, sep="_" and name_suffix="delta", the new column name would be:

a_b_delta

name_suffix : str, optional (default=’I’)

The suffix to add to the new feature name in the form of <feature_x>_<feature_y>_<suffix>

Attributes

fun_	(callable) The interaction term function
fit_cols_	(list) The list of column names on which the transformer was fit. This is used to validate the presence of the features in the test set during the transform stage.

Examples

The following example interacts the first two columns of the iris dataset using the default _mul function (product).

>>> from skoot.feature_extraction import InteractionTermTransformer
>>> from skoot.datasets import load_iris_df
>>>
>>> X = load_iris_df(include_tgt=False)
>>>
>>> trans = InteractionTermTransformer(cols=X.columns[:2])
>>> X_transform = trans.fit_transform(X)
>>>
>>> assert X_transform.shape[1] == X.shape[1] + 1 # only added 1 col
>>> X_transform[X_transform.columns[-1]].head()
0    17.85
1    14.70
2    15.04
3    14.26
4    18.00
Name: sepal length (cm)_sepal width (cm)_I, dtype: float64

Methods

fit(X[, y])	Fit the interaction term transformer.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform a test matrix given the already-fit transformer.

__init__(cols=None, as_df=True, interaction_function=None, sep='_', name_suffix='I')

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)

Fit the interaction term transformer.

Parameters:

X : pd.DataFrame, shape=(n_samples, n_features)

The Pandas frame to fit. The frame will only be fit on the prescribed cols (see __init__) or all of them if cols is None.

y : array-like or None, shape=(n_samples,), optional (default=None)

Pass-through for sklearn.pipeline.Pipeline.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

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 pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self

transform(X)

Transform a test matrix given the already-fit transformer.

Parameters:

X : pd.DataFrame, shape=(n_samples, n_features)

The Pandas frame to transform. The operation will be applied to a copy of the input data, and the result will be returned.

Returns:

X : Pandas DataFrame

The operation is applied to a copy of X, and the result set is returned.