fix: resolve issue withpredict_proba when used with ctx_data

Author: abon-mostly

mostlyai/engine/_tabular/probability.py

@@ -245,6 +245,7 @@ def _generate_marginal_probs(
     tgt_stats: dict,
     seed_columns: list[str],
     device: torch.device,
+    n_samples: int,
     ctx_data: pd.DataFrame | None = None,
     ctx_stats: dict | None = None,
     fixed_probs: dict | None = None,
@@ -259,14 +260,14 @@ def _generate_marginal_probs(
         tgt_stats: Target statistics
         seed_columns: Seed column names in original format, in correct order
         device: Device for computation
+        n_samples: Number of samples to generate probabilities for
         ctx_data: Optional context data
         ctx_stats: Optional context statistics (required if ctx_data provided)
         fixed_probs: Optional fixed probabilities for rare token handling
 
     Returns:
         DataFrame of shape (n_samples, cardinality) with probabilities and column names
     """
-    n_samples = len(seed_encoded)
     target_stats = tgt_stats["columns"][target_column]
 
     # Build fixed_values dict from seed_encoded
@@ -407,6 +408,7 @@ def predict_proba(
         tgt_stats=tgt_stats,
         seed_columns=seed_columns,
         device=device,
+        n_samples=n_samples,
         ctx_data=ctx_data,
         ctx_stats=ctx_stats,
         fixed_probs=fixed_probs,
@@ -451,10 +453,10 @@ def predict_proba(
         # Build DataFrames for each combo with actual values, then concatenate
         combo_dfs = []
         for combo_idx, prev_combo in enumerate(prev_combos):
-            # Copy extended_seed for this combo
-            df = extended_seed.copy()
+            # Build data dict starting with columns from extended_seed
+            data = {col: extended_seed[col].values for col in extended_seed.columns}
 
-            # Add previous target columns with actual values (no dummy values)
+            # Add previous target columns with actual values
             for i in range(target_idx):
                 prev_target_col = target_columns[i]
                 encoded_val = prev_combo[i]
@@ -466,8 +468,10 @@ def predict_proba(
                         argn_column=prev_target_stats[ARGN_COLUMN],
                         argn_sub_column=sub_col_key,
                     )
-                    df[full_sub_col_name] = encoded_val
+                    data[full_sub_col_name] = encoded_val
 
+            # Create DataFrame with explicit row count
+            df = pd.DataFrame(data, index=range(n_samples))
             combo_dfs.append(df)
 
         # Concatenate all combo DataFrames into single batch
@@ -490,6 +494,7 @@ def predict_proba(
             tgt_stats=tgt_stats,
             seed_columns=extended_seed_columns,
             device=device,
+            n_samples=n_samples * num_prev_combos,
             ctx_data=batched_ctx_data,
             ctx_stats=ctx_stats,
             fixed_probs=fixed_probs,