Merge pull request #14 from soda-inria/features

Features

carte_ai/src/carte_table_to_graph.py

@@ -14,7 +14,6 @@
 )  # Import FeatureHasher from scikit-learn
 from carte_ai.configs.directory import config_directory
 
-
 def _create_edge_index(
     num_nodes: int,
     edge_attr: torch.Tensor,
@@ -66,7 +65,6 @@ def _create_edge_index(
 
     return edge_index, edge_attr_
 
-
 class Table2GraphTransformer(TransformerMixin, BaseEstimator):
     """
     Transformer from tables to a list of graphs.
@@ -123,28 +121,24 @@ def fit(self, X, y=None):
         if not hasattr(self, "lm_model_"):
             self._load_lm_model()
 
-        cat_col_names = (
+        # Use original column names without lowercasing to avoid mismatches
+        self.cat_col_names = list(
             X.select_dtypes(include="object")
             .columns.str.replace("\n", " ", regex=True)
-            .str.lower()
         )
-        self.cat_col_names = list(cat_col_names)
-        num_col_names = (
+        self.num_col_names = list(
             X.select_dtypes(exclude="object")
             .columns.str.replace("\n", " ", regex=True)
-            .str.lower()
         )
-        self.num_col_names = list(num_col_names)
         self.col_names = self.cat_col_names + self.num_col_names
 
         self.num_transformer_ = PowerTransformer().set_output(transform="pandas")
 
-
         # Ensure numerical columns exist before fitting the transformer
-        if self.num_col_names:
-            num_cols_exist = [col for col in self.num_col_names if col in X.columns]
-            if num_cols_exist:
-                self.num_transformer_.fit(X[num_cols_exist])
+        num_cols_exist = [col for col in self.num_col_names if col in X.columns]
+        if num_cols_exist:
+            self.num_transformer_.fit(X[num_cols_exist])
+            #print(f"Numerical columns fitted for normalization: {num_cols_exist}")
 
         self.is_fitted_ = True
         return self
@@ -186,10 +180,17 @@ def transform(self, X, y=None):
         name_dict = {name: idx for idx, name in enumerate(names_total)}
 
         name_attr_total = self._transform_names(names_total)
-        if self.num_col_names:
-            num_cols_exist = [col for col in self.num_col_names if col in X.columns]
-            if num_cols_exist:
-                X_numerical = self._transform_numerical(X_numerical[num_cols_exist])
+
+        # Use the original numerical column names for transformation
+        num_cols_exist = [col for col in self.num_col_names if col in X.columns]
+        if num_cols_exist:
+            X_numerical = self._transform_numerical(X[num_cols_exist])
+            #print(f"Transformed numerical columns: {X_numerical.head()}")
+            # Check mean and variance for each column
+            #for col in num_cols_exist:
+            #    mean = X_numerical[col].mean()
+            #    variance = X_numerical[col].var()
+                #print(f"Column: {col}, Mean: {mean:.6f}, Variance: {variance:.6f}")
 
         data_graph = [
             self._graph_construct(
@@ -223,7 +224,6 @@ def _load_lm_model(self):
             if self.n_components != 300:
                 fasttext.util.reduce_model(self.lm_model_, self.n_components)
 
-
     def _transform_numerical(self, X):
         """
         Transform numerical columns using power transformer.