Refactored feature renaming (#821)

docs/releases/unreleased.md

@@ -4,6 +4,11 @@
 
 - Created this module to store estimator unit testing, rather than having it in the `utils` module.
 
+## compose
+
+- Split `compose.Renamer` into `compose.Prefixer` and `compose.Suffixer` that respectively prepend and append a string to the features' name.
+- Changed `compose.Renamer` to allow feature renaming following a mapping.
+
 ## evaluate
 
 - Refactored `evaluate.progressive_validation` to work with `base.AnomalyDetector`s.

river/compose/__init__.py

@@ -8,7 +8,7 @@
 from .grouper import Grouper
 from .pipeline import Pipeline
 from .product import TransformerProduct
-from .rename import Renamer
+from .renamer import Prefixer, Renamer, Suffixer
 from .select import Discard, Select, SelectType
 from .target_transform import TargetTransformRegressor
 from .union import TransformerUnion
@@ -18,9 +18,11 @@
     "FuncTransformer",
     "Grouper",
     "Pipeline",
+    "Prefixer",
     "Renamer",
     "Select",
     "SelectType",
+    "Suffixer",
     "TargetTransformRegressor",
     "TransformerProduct",
     "TransformerUnion",

river/compose/pipeline.py

@@ -282,8 +282,8 @@ class Pipeline(base.Estimator):
     ...     ('A harsh comment', False)
     ... ]
 
-    >>> tfidf = fx.TFIDF() | compose.Renamer(prefix='tfidf_')
-    >>> counts = fx.BagOfWords() | compose.Renamer(prefix='count_')
+    >>> tfidf = fx.TFIDF() | compose.Prefixer('tfidf_')
+    >>> counts = fx.BagOfWords() | compose.Prefixer('count_')
     >>> mnb = naive_bayes.MultinomialNB()
     >>> model = (tfidf + counts) | mnb
 
@@ -296,24 +296,20 @@ class Pipeline(base.Estimator):
     0. Input
     --------
     A positive comment
-    <BLANKLINE>
     1. Transformer union
     --------------------
-        1.0 TFIDF | Renamer
-        -------------------
+        1.0 TFIDF | Prefixer
+        --------------------
         tfidf_comment: 0.47606 (float)
         tfidf_positive: 0.87942 (float)
-    <BLANKLINE>
-        1.1 BagOfWords | Renamer
-        ------------------------
+        1.1 BagOfWords | Prefixer
+        -------------------------
         count_comment: 1 (int)
         count_positive: 1 (int)
-    <BLANKLINE>
     count_comment: 1 (int)
     count_positive: 1 (int)
     tfidf_comment: 0.50854 (float)
     tfidf_positive: 0.86104 (float)
-    <BLANKLINE>
     2. MultinomialNB
     ----------------
     False: 0.19313

river/compose/renamer.py

@@ -0,0 +1,94 @@
+from typing import Dict
+
+from .. import base
+
+__all__ = ["Renamer", "Prefixer", "Suffixer"]
+
+
+class Renamer(base.Transformer):
+    """Renames features following substitution rules.
+
+    Parameters
+    ----------
+    mapping
+        Dictionnary describing substitution rules. Keys in `mapping` that are not a feature's name are silently ignored.
+
+    Examples
+    --------
+
+    >>> from river import compose
+
+    >>> mapping = {'a': 'v', 'c': 'o'}
+    >>> x = {'a': 42, 'b': 12}
+    >>> compose.Renamer(mapping).transform_one(x)
+    {'b': 12, 'v': 42}
+
+    """
+
+    def __init__(self, mapping: Dict[str, str]):
+        self.mapping = mapping
+
+    def transform_one(self, x):
+        for old_key, new_key in self.mapping.items():
+            try:
+                x[new_key] = x.pop(old_key)
+            except KeyError:
+                pass  # Ignoring keys that are not a feature's name
+
+        return x
+
+
+class Prefixer(base.Transformer):
+    """Prepends a prefix on features names.
+
+    Parameters
+    ----------
+    prefix
+
+    Examples
+    --------
+
+    >>> from river import compose
+
+    >>> x = {'a': 42, 'b': 12}
+    >>> compose.Prefixer('prefix_').transform_one(x)
+    {'prefix_a': 42, 'prefix_b': 12}
+
+    """
+
+    def __init__(self, prefix: str):
+        self.prefix = prefix
+
+    def _rename(self, s: str) -> str:
+        return f"{self.prefix}{s}"
+
+    def transform_one(self, x):
+        return {self._rename(i): xi for i, xi in x.items()}
+
+
+class Suffixer(base.Transformer):
+    """Appends a suffix on features names.
+
+    Parameters
+    ----------
+    suffix
+
+    Examples
+    --------
+
+    >>> from river import compose
+
+    >>> x = {'a': 42, 'b': 12}
+    >>> compose.Suffixer('_suffix').transform_one(x)
+    {'a_suffix': 42, 'b_suffix': 12}
+
+    """
+
+    def __init__(self, suffix: str):
+        self.suffix = suffix
+
+    def _rename(self, s: str) -> str:
+        return f"{s}{self.suffix}"
+
+    def transform_one(self, x):
+        return {self._rename(i): xi for i, xi in x.items()}

river/compose/test_product.py

@@ -53,3 +53,21 @@ def test_both_are_pipelines():
         "b_2": 1,
         "a_1": 1,
     }
+
+
+def test_renaming():
+
+    renamer = compose.Renamer(dict(a="z", b="y", c="x"))
+    assert renamer.transform_one(dict(a=1, b=2, d=3)) == dict(z=1, y=2, d=3)
+
+
+def test_prefixing():
+
+    prefixer = compose.Prefixer("x_")
+    assert prefixer.transform_one(dict(a=1, b=2, d=3)) == dict(x_a=1, x_b=2, x_d=3)
+
+
+def test_suffixing():
+
+    suffixer = compose.Suffixer("_x")
+    assert suffixer.transform_one(dict(a=1, b=2, d=3)) == dict(a_x=1, b_x=2, d_x=3)

river/test_estimators.py

@@ -54,6 +54,9 @@ def iter_estimators_which_can_be_tested():
         compose.FuncTransformer,
         compose.Grouper,
         compose.Pipeline,
+        compose.Prefixer,
+        compose.Renamer,
+        compose.Suffixer,
         compose.TargetTransformRegressor,
         ensemble.AdaptiveRandomForestClassifier,
         ensemble.AdaptiveRandomForestRegressor,