changed additional mentions of ColumnTransformer

Author: SebastienMelo

python_scripts/03_categorical_pipeline_column_transformer.py

@@ -74,9 +74,10 @@
 #   categories.
 # * **numerical scaling** numerical features which will be standardized.
 #
-# Now, we create our `ColumnTransfomer` by specifying three values: the
-# preprocessor name, the transformer, and the columns. First, let's create the
-# preprocessors for the numerical and categorical parts.
+# Now, we create our `ColumnTransfomer` using the helper function
+# `make_column_transformer`. We specify two values: the transformer, and the
+# columns. First, let's create the preprocessors for the numerical and
+# categorical parts.
 
 # %%
 from sklearn.preprocessing import OneHotEncoder, StandardScaler
@@ -89,13 +90,11 @@
 # their respective columns.
 
 # %%
-from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_transformer
 
-preprocessor = ColumnTransformer(
-    [
-        ("one-hot-encoder", categorical_preprocessor, categorical_columns),
-        ("standard_scaler", numerical_preprocessor, numerical_columns),
-    ]
+preprocessor = make_column_transformer(
+    (categorical_preprocessor, categorical_columns),
+    (numerical_preprocessor, numerical_columns),
 )
 
 # %% [markdown]
@@ -234,8 +233,8 @@
     handle_unknown="use_encoded_value", unknown_value=-1
 )
 
-preprocessor = ColumnTransformer(
-    [("categorical", categorical_preprocessor, categorical_columns)],
+preprocessor = make_column_transformer(
+    (categorical_preprocessor, categorical_columns),
     remainder="passthrough",
 )
 

python_scripts/parameter_tuning_sol_02.py

@@ -31,21 +31,15 @@
 )
 
 # %%
-from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_transformer
 from sklearn.compose import make_column_selector as selector
 from sklearn.preprocessing import OrdinalEncoder
 
 categorical_preprocessor = OrdinalEncoder(
     handle_unknown="use_encoded_value", unknown_value=-1
 )
-preprocessor = ColumnTransformer(
-    [
-        (
-            "cat_preprocessor",
-            categorical_preprocessor,
-            selector(dtype_include=object),
-        )
-    ],
+preprocessor = make_column_transformer(
+    (categorical_preprocessor, selector(dtype_include=object)),
     remainder="passthrough",
 )
 
@@ -121,3 +115,5 @@
 test_score = model.score(data_test, target_test)
 
 print(f"Test score after the parameter tuning: {test_score:.3f}")
+
+# %%