fix: Fixed incorrect pretrained status check in DualPredictor. (#52)

* fix: incorrect pretrained status check in DualPredictor.

deel/puncc/api/conformalization.py

@@ -280,9 +280,9 @@ def fit(
                 predictor.fit(X_fit, y_fit, **kwargs)  # Fit K-fold predictor
 
             # Make sure that predictor is already trained if train arg is False
-            elif self.train is False and predictor.is_trained is False:
+            elif self.train is False and predictor.get_is_trained() is False:
                 raise RuntimeError(
-                    "'train' argument is set to 'False' but model is not pre-trained"
+                    "'train' argument is set to 'False' but model(s) not pre-trained."
                 )
 
             else:  # Skipping training

deel/puncc/api/prediction.py

@@ -23,7 +23,7 @@
 """
 This module provides standard wrappings for ML models.
 """
-import pkgutil
+import importlib
 from copy import deepcopy
 from typing import Any
 from typing import Iterable
@@ -37,7 +37,7 @@
 from deel.puncc.api.utils import dual_predictor_check
 from deel.puncc.api.utils import supported_types_check
 
-if pkgutil.find_loader("tensorflow") is not None:
+if importlib.util.find_spec("tensorflow") is not None:
     import tensorflow as tf
 
 
@@ -133,6 +133,10 @@ def __init__(self, model: Any, is_trained: bool = False, **compile_kwargs):
         if self.compile_kwargs:
             _ = self.model.compile(**self.compile_kwargs)
 
+    def get_is_trained(self) -> bool:
+        """Get flag that informs if the model is pre-trained."""
+        return self.is_trained
+
     def fit(self, X: Iterable, y: Optional[Iterable] = None, **kwargs) -> None:
         """Fit model to the training data.
 
@@ -190,7 +194,7 @@ def copy(self):
         if (
             "tensorflow" in model_type_str
             or "keras" in model_type_str
-            and pkgutil.find_loader("tensorflow") is not None
+            and importlib.util.find_spec("tensorflow") is not None
         ):
             # pylint: disable=E1101
             model = tf.keras.models.clone_model(self.model)
@@ -319,6 +323,12 @@ def __init__(
         if len(self.compile_args[1].keys()) != 0 and is_trained[1] is False:
             _ = self.models[1].compile(**self.compile_args[1])
 
+    def get_is_trained(self) -> bool:
+        """Get flag that informs if the models are pre-trained.
+        Returns True only when both models are pretrained.
+        """
+        return self.is_trained[0] and self.is_trained[1]
+
     def fit(
         self, X: Iterable, y: Iterable, dictargs: List[dict] = [{}, {}]
     ) -> None:
@@ -392,7 +402,7 @@ def copy(self):
             try:
                 model_copy = deepcopy(model)
             except Exception as e_outer:
-                if pkgutil.find_loader("tensorflow") is not None:
+                if importlib.util.find_spec("tensorflow") is not None:
                     try:
                         # pylint: disable=E1101
                         model_copy = tf.keras.models.clone_model(model)

docs/source/conf.py

@@ -26,7 +26,7 @@
 author = "Mouhcine Mendil, Luca Mossina and Joseba Dalmau"
 
 # The full version, including alpha/beta/rc tags
-release = "0.7.4"
+release = "0.7.6"
 
 
 # -- General configuration ---------------------------------------------------

requirements.txt

@@ -2,5 +2,5 @@ joblib
 matplotlib
 numpy
 pandas
-scikit-learn
+scikit-learn~=1.3.0
 tqdm

setup.py

@@ -61,7 +61,7 @@
 
 setuptools.setup(
     name="puncc",
-    version="0.7.5",
+    version="0.7.6",
     author=", ".join(["Mouhcine Mendil", "Luca Mossina", "Joseba Dalmau"]),
     author_email=", ".join(
         [

tests/api/test_conformalization.py

@@ -33,6 +33,7 @@
 from deel.puncc.api.calibration import BaseCalibrator
 from deel.puncc.api.conformalization import ConformalPredictor
 from deel.puncc.api.prediction import BasePredictor
+from deel.puncc.api.prediction import DualPredictor
 from deel.puncc.api.splitting import KFoldSplitter
 from deel.puncc.api.splitting import RandomSplitter
 
@@ -72,6 +73,12 @@ def setUp(self):
             pred_set_func=prediction_sets.constant_interval,
         )
 
+        # Definition of a dual calibrator
+        self.dual_calibrator = BaseCalibrator(
+            nonconf_score_func=nonconformity_scores.cqr_score,
+            pred_set_func=prediction_sets.cqr_interval,
+        )
+
         # Random splitter
         self.random_splitter = RandomSplitter(ratio=0.2)
 
@@ -166,6 +173,70 @@ def test_pretrained_predictor(self):
             )
             conformal_predictor.fit(self.X_calib, self.y_calib)
 
+    def test_pretrained_dualpredictor(self):
+        # Predictor initialized with trained model
+        model1 = linear_model.LinearRegression()
+        model2 = linear_model.LinearRegression()
+        model1.fit(self.X_fit, self.y_fit)
+        model2.fit(self.X_fit, self.y_fit)
+        trained_predictor = DualPredictor(
+            [model1, model2], is_trained=[True, True]
+        )
+        notrained_predictor = DualPredictor(
+            [model1, model2], is_trained=[False, True]
+        )
+
+        # Conformal predictor (good)
+        conformal_predictor = ConformalPredictor(
+            predictor=trained_predictor,
+            calibrator=self.dual_calibrator,
+            splitter=self.random_splitter,
+            train=False,
+        )
+        # Compute nonconformity scores
+        conformal_predictor.fit(self.X_train, self.y_train)
+
+        # Conformal predictor (bad)
+        with self.assertRaises(RuntimeError):
+            conformal_predictor = ConformalPredictor(
+                predictor=notrained_predictor,
+                calibrator=self.dual_calibrator,
+                splitter=self.random_splitter,
+                train=False,
+            )
+            # Compute nonconformity scores
+            conformal_predictor.fit(self.X_train, self.y_train)
+
+        # Conformalization with no splitter (good)
+        conformal_predictor = ConformalPredictor(
+            predictor=trained_predictor,
+            calibrator=self.dual_calibrator,
+            splitter=None,
+            train=False,
+        )
+        conformal_predictor.fit(self.X_calib, self.y_calib)
+        conformal_predictor.predict(self.X_test, alpha=0.1)
+
+        # Conformalization with no splitter (bad)
+        with self.assertRaises(RuntimeError):
+            conformal_predictor = ConformalPredictor(
+                predictor=notrained_predictor,
+                calibrator=self.dual_calibrator,
+                splitter=None,
+                train=False,
+            )
+            conformal_predictor.fit(self.X_calib, self.y_calib)
+
+        # Conformalization with no splitter and train set to True (bad)
+        with self.assertRaises(RuntimeError):
+            conformal_predictor = ConformalPredictor(
+                predictor=notrained_predictor,
+                calibrator=self.dual_calibrator,
+                splitter=None,
+                train=True,
+            )
+            conformal_predictor.fit(self.X_calib, self.y_calib)
+
     def test_get_nconf_scores_split(self):
         # Conformal predictor
         conformal_predictor = ConformalPredictor(