Feature Extraction¶

The skmine.feature_extraction module provides a set of methods that can be used to extract features in a format supported by machine learning algorithms from datasets containing raw data.

SLIMVectorizer¶

class skmine.feature_extraction.SLIMVectorizer(strategy='codes', k=5, pruning=False, stop_items=None, **kwargs)[source]¶

SLIM mining, turned into a feature extraction step for sklearn

k new itemsets (associations of one or more items) are learned at training time

The model (pattern set) is then used to cover new data, in order of usage. This is similar to one-hot-encoding, except the dimension will be much more concise, because the columns will be patterns learned via an MDL criterion.

Parameters

strategy (str, default="codes") –
If the chosen strategy is set to one-hot, non-zero cells are filled with ones.

If the chosen strategy is left to codes, non-zero cells are filled with code lengths, i.e the probabity of the pattern in the training data.
k (int, default=5) –
Number of non-singleton itemsets to mine. A singleton is an itemset containing a single item.

Calls to .transform will output pandas.DataFrame with k columns
pruning (bool, default=False) – Either to activate pruning or not.
stop_items (iterable, default=None) – Set of items to filter out while ingesting the input data.

Examples

>>> from skmine.feature_extraction import SLIMVectorizer
>>> D = [['bananas', 'milk'], ['milk', 'bananas', 'cookies'], ['cookies', 'butter', 'tea']]
>>> res  = SLIMVectorizer(k=2).fit_transform(D)
>>> print(res.to_string())
   (bananas, milk)  (cookies,)
0              0.4         0.0
1              0.4         0.4
2              0.0         0.4