featuretools.encode_features¶

featuretools.encode_features(feature_matrix, features, top_n=10, include_unknown=True, to_encode=None, inplace=False, drop_first=False, verbose=False)[source]¶

Encode categorical features

Parameters

feature_matrix (pd.DataFrame) – Dataframe of features.
features (list[PrimitiveBase]) – Feature definitions in feature_matrix.
top_n (int or dict[string -> int]) – Number of top values to include. If dict[string -> int] is used, key is feature name and value is the number of top values to include for that feature. If a feature’s name is not in dictionary, a default value of 10 is used.
include_unknown (pd.DataFrame) – Add feature encoding an unknown class. defaults to True
to_encode (list[str]) – List of feature names to encode. features not in this list are unencoded in the output matrix defaults to encode all necessary features.
inplace (bool) – Encode feature_matrix in place. Defaults to False.
drop_first (bool) – Whether to get k-1 dummies out of k categorical levels by removing the first level. defaults to False
verbose (str) – Print progress info.

Returns

encoded feature_matrix, encoded features

Return type

(pd.Dataframe, list)

Example

In [1]: f1 = ft.Feature(es["log"]["product_id"])

In [2]: f2 = ft.Feature(es["log"]["purchased"])

In [3]: f3 = ft.Feature(es["log"]["value"])

In [4]: features = [f1, f2, f3]

In [5]: ids = [0, 1, 2, 3, 4, 5]

In [6]: feature_matrix = ft.calculate_feature_matrix(features, es,
   ...:                                              instance_ids=ids)
   ...: 

In [7]: fm_encoded, f_encoded = ft.encode_features(feature_matrix,
   ...:                                            features)
   ...: 

In [8]: f_encoded
Out[8]: 
[<Feature: product_id = coke zero>,
 <Feature: product_id = car>,
 <Feature: product_id = toothpaste>,
 <Feature: product_id is unknown>,
 <Feature: purchased>,
 <Feature: value>]

In [9]: fm_encoded, f_encoded = ft.encode_features(feature_matrix,
   ...:                                            features, top_n=2)
   ...: 

In [10]: f_encoded
Out[10]: 
[<Feature: product_id = coke zero>,
 <Feature: product_id = car>,
 <Feature: product_id is unknown>,
 <Feature: purchased>,
 <Feature: value>]

In [11]: fm_encoded, f_encoded = ft.encode_features(feature_matrix, features,
   ....:                                            include_unknown=False)
   ....: 

In [12]: f_encoded
Out[12]: 
[<Feature: product_id = coke zero>,
 <Feature: product_id = car>,
 <Feature: product_id = toothpaste>,
 <Feature: purchased>,
 <Feature: value>]

In [13]: fm_encoded, f_encoded = ft.encode_features(feature_matrix, features,
   ....:                                            to_encode=['purchased'])
   ....: 

In [14]: f_encoded
Out[14]: [<Feature: product_id>, <Feature: purchased>, <Feature: value>]

In [15]: fm_encoded, f_encoded = ft.encode_features(feature_matrix, features,
   ....:                                            drop_first=True)
   ....: 

In [16]: f_encoded
Out[16]: 
[<Feature: product_id = coke zero>,
 <Feature: product_id = car>,
 <Feature: product_id is unknown>,
 <Feature: purchased>,
 <Feature: value>]