From 747d9c48d1f9c3d3b6e2b08dcb09b87d2e20e87a Mon Sep 17 00:00:00 2001
From: NAIR BENREKIA Nour Eddine Yassine INNOV/IT-S
 <noureddineyassine.nairbenrekia@orange.com>
Date: Mon, 21 Oct 2024 15:41:17 +0200
Subject: [PATCH] WIP

---
 khiops/samples/samples_sklearn.py | 50 +++++++++++++++++++++++++++++++
 khiops/sklearn/estimators.py      |  6 ++--
 2 files changed, 54 insertions(+), 2 deletions(-)

diff --git a/khiops/samples/samples_sklearn.py b/khiops/samples/samples_sklearn.py
index d61cc49e..e951d74a 100644
--- a/khiops/samples/samples_sklearn.py
+++ b/khiops/samples/samples_sklearn.py
@@ -89,6 +89,53 @@ def khiops_classifier():
     # kh.visualize_report("report.khj")
 
 
+def khiops_classifier_float_target():
+    """Trains a `.KhiopsClassifier` on a monotable dataframe
+    with a float target"""
+    # Imports
+    import os
+    import pandas as pd
+    from khiops import core as kh
+    from khiops.sklearn import KhiopsClassifier
+    from sklearn.model_selection import train_test_split
+
+    # Load the dataset into a pandas dataframe
+    adult_path = os.path.join(kh.get_samples_dir(), "Adult", "Adult.txt")
+    adult_df = pd.read_csv(adult_path, sep="\t")
+    adult_df["class"] = adult_df["class"].replace({"less": 0.0, "more": 1.0})
+
+    # Split the whole dataframe into train and test (70%-30%)
+    adult_train_df, adult_test_df = train_test_split(
+        adult_df, test_size=0.3, random_state=1
+    )
+
+    # Split the dataset into:
+    # - the X feature table
+    # - the y target vector ("class" column)
+    X_train = adult_train_df.drop("class", axis=1)
+    X_test = adult_test_df.drop("class", axis=1)
+    y_train = adult_train_df["class"]
+
+    # Create the classifier object
+    khc = KhiopsClassifier()
+
+    # Train the classifier
+    khc.fit(X_train, y_train)
+
+    # Predict the classes on the test dataset
+    y_test_pred = khc.predict(X_test)
+    print("Predicted classes (first 10):")
+    print(y_test_pred[0:10])
+    print("---")
+
+    # Predict the class probabilities on the test dataset
+    y_test_probas = khc.predict_proba(X_test)
+    print(f"Class order: {khc.classes_}")
+    print("Predicted class probabilities (first 10):")
+    print(y_test_probas[0:10])
+    print("---")
+
+
 def khiops_classifier_multiclass():
     """Trains a multiclass `.KhiopsClassifier` on a monotable dataframe"""
     # Imports
@@ -1061,6 +1108,8 @@ def khiops_classifier_multitable_star_file():
     print(f"Test auc      = {test_auc}")
 
 
+exported_samples = [khiops_classifier_float_target]
+"""
 exported_samples = [
     khiops_classifier,
     khiops_classifier_multiclass,
@@ -1080,6 +1129,7 @@ def khiops_classifier_multitable_star_file():
     khiops_classifier_multitable_list,
     khiops_classifier_multitable_star_file,
 ]
+"""
 
 
 def execute_samples(args):
diff --git a/khiops/sklearn/estimators.py b/khiops/sklearn/estimators.py
index 47bfc16b..2e736b3c 100644
--- a/khiops/sklearn/estimators.py
+++ b/khiops/sklearn/estimators.py
@@ -2259,13 +2259,11 @@ def predict(self, X):
         """
         # Call the parent's method
         y_pred = super().predict(X)
-
         # Adjust the data type according to the original target type
         # Note: String is coerced explictly because astype does not work as expected
         if isinstance(y_pred, pd.DataFrame):
             # Transform to numpy.ndarray
             y_pred = y_pred.to_numpy(copy=False).ravel()
-
             # If integer and string just transform
             if pd.api.types.is_integer_dtype(self._original_target_dtype):
                 y_pred = y_pred.astype(self._original_target_dtype)
@@ -2275,6 +2273,10 @@ def predict(self, X):
                 self._original_target_dtype
             ):
                 y_pred = y_pred.astype(str, copy=False)
+            elif pd.api.types.is_float_dtype(self._original_target_type):
+                print(self._original_target_type)
+                y_pred = y_pred.astype(str, copy=False)
+                print(y_pred)
             # If category first coerce the type to the categories' type
             else:
                 assert isinstance(self._original_target_dtype, pd.CategoricalDtype), (