featuretools.wrappers.DFSTransformer¶
-
class
featuretools.wrappers.
DFSTransformer
(entities=None, relationships=None, entityset=None, target_entity=None, agg_primitives=None, trans_primitives=None, allowed_paths=None, max_depth=2, ignore_entities=None, ignore_variables=None, seed_features=None, drop_contains=None, drop_exact=None, where_primitives=None, max_features=- 1, verbose=False)[source]¶ Transformer using Scikit-Learn interface for Pipeline uses.
-
__init__
(entities=None, relationships=None, entityset=None, target_entity=None, agg_primitives=None, trans_primitives=None, allowed_paths=None, max_depth=2, ignore_entities=None, ignore_variables=None, seed_features=None, drop_contains=None, drop_exact=None, where_primitives=None, max_features=- 1, verbose=False)[source]¶ Creates Transformer
- Parameters
entities (dict[str -> tuple(pd.DataFrame, str, str)]) – Dictionary of entities. Entries take the format {entity id -> (dataframe, id column, (time_column))}.
relationships (list[(str, str, str, str)]) – List of relationships between entities. List items are a tuple with the format (parent entity id, parent variable, child entity id, child variable).
entityset (EntitySet) – An already initialized entityset. Required if entities and relationships are not defined.
target_entity (str) – Entity id of entity on which to make predictions.
agg_primitives (list[str or AggregationPrimitive], optional) –
List of Aggregation Feature types to apply.
- Default: [“sum”, “std”, “max”, “skew”, “min”, “mean”,
”count”, “percent_true”, “num_unique”, “mode”]
trans_primitives (list[str or TransformPrimitive], optional) –
List of Transform Feature functions to apply.
- Default: [“day”, “year”, “month”, “weekday”, “haversine”,
”num_words”, “num_characters”]
allowed_paths (list[list[str]]) – Allowed entity paths on which to make features.
max_depth (int) – Maximum allowed depth of features.
ignore_entities (list[str], optional) – List of entities to blacklist when creating features.
ignore_variables (dict[str -> list[str]], optional) – List of specific variables within each entity to blacklist when creating features.
seed_features (list[
FeatureBase
]) – List of manually defined features to use.drop_contains (list[str], optional) – Drop features that contains these strings in name.
drop_exact (list[str], optional) – Drop features that exactly match these strings in name.
where_primitives (list[str or PrimitiveBase], optional) –
List of Primitives names (or types) to apply with where clauses.
Default:
[“count”]
max_features (int, optional) – Cap the number of generated features to this number. If -1, no limit.
Example
In [1]: import featuretools as ft In [2]: import pandas as pd In [3]: from featuretools.wrappers import DFSTransformer In [4]: from sklearn.pipeline import Pipeline In [5]: from sklearn.ensemble import ExtraTreesClassifier # Get examle data In [6]: n_customers = 3 In [7]: es = ft.demo.load_mock_customer(return_entityset=True, n_customers=5) In [8]: y = [True, False, True] # Build dataset In [9]: pipeline = Pipeline(steps=[ ...: ('ft', DFSTransformer(entityset=es, ...: target_entity="customers", ...: max_features=3)), ...: ('et', ExtraTreesClassifier(n_estimators=100)) ...: ]) ...: # Fit and predict In [10]: pipeline.fit([1, 2, 3], y=y) # fit on first 3 customers Out[10]: Pipeline(steps=[('ft', <featuretools_sklearn_transformer.transformer.DFSTransformer object at 0x7f8407224080>), ('et', ExtraTreesClassifier())]) In [11]: pipeline.predict_proba([4,5]) # predict probability of each class on last 2 Out[11]: array([[0., 1.], [0., 1.]]) In [12]: pipeline.predict([4,5]) # predict on last 2 Out[12]: array([ True, True]) # Same as above, but using cutoff times In [13]: ct = pd.DataFrame() In [14]: ct['customer_id'] = [1, 2, 3, 4, 5] In [15]: ct['time'] = pd.to_datetime(['2014-1-1 04:00', ....: '2014-1-2 17:20', ....: '2014-1-4 09:53', ....: '2014-1-4 13:48', ....: '2014-1-5 15:32']) ....: In [16]: pipeline.fit(ct.head(3), y=y) Out[16]: Pipeline(steps=[('ft', <featuretools_sklearn_transformer.transformer.DFSTransformer object at 0x7f8407224080>), ('et', ExtraTreesClassifier())]) In [17]: pipeline.predict_proba(ct.tail(2)) Out[17]: array([[0.46, 0.54], [0. , 1. ]]) In [18]: pipeline.predict(ct.tail(2)) Out[18]: array([ True, True])
Methods
__init__
([entities, relationships, …])Creates Transformer
fit
(cuttof_time_ids[, y])Wrapper for DFS
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])transform
(cuttof_time_ids)Wrapper for calculate_feature_matix
-