Exposing transformations applied by Cleaner and TableVectorizer

skrub/_table_vectorizer.py

@@ -181,6 +181,39 @@ def _get_preprocessors(
     return steps
 
 
+def _list_transformations(estimator):
+    message = ""
+    for step in estimator._pipeline.named_steps:
+        if step == "checkinputdataframe":
+            continue
+        transformer = estimator._pipeline.named_steps[step]
+        match transformer.transformer:
+            case DropUninformative():
+                dropped = set(transformer.all_inputs_) - set(transformer.all_outputs_)
+                if dropped != set():
+                    message += "DropUninformative - " + "\n"
+                    message += f"Dropped columns {dropped}" + "\n"
+                    message += f"Used inputs: {transformer.used_inputs_}" + "\n"
+            case ToFloat():
+                message += "ToFloat - " + "\n"
+                message += (
+                    f"Columns transformed to float: {transformer.used_inputs_}" + "\n"
+                )
+            case ToDatetime():
+                message += "ToDatetime - " + "\n"
+                message += (
+                    f"Columns transformed to datetime: {transformer.used_inputs_}"
+                    + "\n"
+                )
+            case CleanNullStrings():
+                message += "CleanNullStrings - " + "\n"
+                message += (
+                    f"Columns with standardized nulls: {transformer.used_inputs_}"
+                    + "\n"
+                )
+    return message
+
+
 class Cleaner(TransformerMixin, BaseEstimator):
     """Column-wise consistency checks and sanitization of dtypes, null values and dates.
 
@@ -538,6 +571,9 @@ def get_feature_names_out(self, input_features=None):
         check_is_fitted(self, "all_outputs_")
         return np.asarray(self.all_outputs_)
 
+    def list_transformations(self):
+        return _list_transformations(self)
+
 
 class TableVectorizer(TransformerMixin, BaseEstimator):
     """Transform a dataframe to a numeric (vectorized) representation.
@@ -1164,3 +1200,40 @@ def get_feature_names_out(self, input_features=None):
         """
         check_is_fitted(self, "all_outputs_")
         return np.asarray(self.all_outputs_)
+
+    def list_transformations(self):
+        preprocessing_transformations = _list_transformations(self)
+        vectorize_transformations = ""
+        specific_transformations = ""
+
+        all_transformers = self.kind_to_columns_
+        specific = all_transformers.pop("specific")
+
+        for transformer_type, transformer_cols in all_transformers.items():
+            if transformer_cols != []:
+                vectorize_transformations += (
+                    f"{transformer_type} transformer is \
+                        {getattr(self, transformer_type)} \
+                            and was applied to {transformer_cols}."
+                    + "\n"
+                )
+            else:
+                vectorize_transformations += (
+                    f"{transformer_type} transformer is \
+                        {getattr(self, transformer_type)} \
+                            and was applied to nothing."
+                    + "\n"
+                )
+
+        if self.specific_transformers != ():
+            for t in self.specific_transformers:
+                specific_transformations += f"specific transformer \
+                        {t} was applied to {specific}"
+
+        return (
+            preprocessing_transformations
+            + "\n\n"
+            + vectorize_transformations
+            + "\n\n"
+            + specific_transformations
+        )

skrub/tests/test_table_vectorizer.py

@@ -28,6 +28,7 @@
 from skrub._to_float import ToFloat
 from skrub._to_str import ToStr
 from skrub.conftest import _POLARS_INSTALLED
+from skrub.datasets._generating import toy_cities
 
 MSG_PANDAS_DEPRECATED_WARNING = "Skip deprecation warning"
 
@@ -1277,3 +1278,15 @@ def test_duration_to_float(df_module):
     vectorizer = Cleaner()
     transformed = vectorizer.fit_transform(df)
     df_module.assert_column_equal(transformed["duration"], df["duration"])
+
+
+def test_list_transformations(df_module):
+    df = toy_cities()
+
+    vectorizer = TableVectorizer()
+    _ = vectorizer.fit_transform(df)
+    _ = vectorizer.list_transformations()
+
+    vectorizer = Cleaner()
+    _ = vectorizer.fit_transform(df)
+    _ = vectorizer.list_transformations()