Target generator

examples/03_complex_models/run_generate_target.py

@@ -0,0 +1,72 @@
+"""
+Target Generation
+=================
+
+This example uses the ``iris`` dataset and tests a regression model in which
+the target variable is generated from some features within the cross-validation
+procedure. We will use the Iris dataset and generate a target variable using
+PCA on the petal features. Then, we will evaluate if a regression model can
+predict the generated target from the sepal features
+
+.. include:: ../../links.inc
+"""
+# Authors: Federico Raimondo <[email protected]>
+# License: AGPL
+
+# %%
+from seaborn import load_dataset
+from julearn import run_cross_validation
+from julearn.pipeline import PipelineCreator
+from julearn.utils import configure_logging
+
+###############################################################################
+# Set the logging level to info to see extra information.
+configure_logging(level="DEBUG")
+
+###############################################################################
+df_iris = load_dataset("iris")
+
+
+###############################################################################
+# As features, we will use the sepal length, width and petal length.
+# We will try to predict the species.
+
+X = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
+y = "__generated__"  # to indicate to julearn that the target will be generated
+
+
+
+# Define our feature types
+X_types = {
+    "sepal": ["sepal_length", "sepal_width"],
+    "petal": ["petal_length", "petal_width"],
+}
+
+# %% reate the pipeline that will generate the features
+target_creator = PipelineCreator(problem_type="transformer", apply_to="petal")
+target_creator.add("pca", n_components=2)
+target_creator.add("pick_columns", keep="pca__pca0")
+
+
+# %% Create the final pipeline
+creator = PipelineCreator(problem_type="regression")
+creator.add("generate_target", apply_to="petal", transformer=target_creator)
+creator.add(
+    "linreg", apply_to="sepal",
+)
+
+# %%
+scores, model = run_cross_validation(
+    X=X,
+    y=y,
+    X_types=X_types,
+    data=df_iris,
+    model=creator,
+    return_estimator="final",
+    cv=2,
+)
+
+print(scores["test_score"])  # type: ignore
+
+# %%
+print(model)

julearn/base/column_types.py

@@ -4,7 +4,7 @@
 #          Sami Hamdan <[email protected]>
 # License: AGPL
 
-from typing import Callable, List, Set, Union
+from typing import Any, Callable, Dict, List, Set, Union
 
 from sklearn.compose import make_column_selector
 
@@ -251,6 +251,22 @@ def __repr__(self):
             f"ColumnTypes<types={self._column_types}; pattern={self.pattern}>"
         )
 
+    def filter(self, X_types: Dict[str, Any]) -> Dict[str, Any]:
+        """Filter the X_types based on the column_types.
+
+        Parameters
+        ----------
+        X_types : dict
+            The types of the columns.
+
+        Returns
+        -------
+        dict:
+            The filtered X_types.
+
+        """
+        return {k: v for k, v in X_types.items() if k in self._column_types}
+
     def copy(self) -> "ColumnTypes":
         """Get a copy of the ColumnTypes.