Update data_generation.py

data_generation.py

@@ -1,6 +1,16 @@
+import torch
 import pandas as pd
 from sdv.single_table import CTGANSynthesizer
 
+# Patch torch.load to remap MPS tensors to CPU for environments without Apple Silicon
+_original_torch_load = torch.load
+
+def _cpu_map_load(*args, **kwargs):
+    kwargs.setdefault('map_location', 'cpu')
+    return _original_torch_load(*args, **kwargs)
+
+torch.load = _cpu_map_load
+
 
 def generate_synthetic_training_data(n=30_000):
     """Generates synthetic training data using pre-trained CTGAN models for each credit score category.
@@ -10,9 +20,9 @@ def generate_synthetic_training_data(n=30_000):
     Returns:
         pd.DataFrame: The generated synthetic training data.
     """
-    good_generator = CTGANSynthesizer.load("models/v4/synth_good.pkl")
-    poor_generator = CTGANSynthesizer.load("models/v4/synth_poor.pkl")
-    standard_generator = CTGANSynthesizer.load("models/v4/synth_standard.pkl")
+    good_generator = CTGANSynthesizer.load("../models/v4/synth_good.pkl")
+    poor_generator = CTGANSynthesizer.load("../models/v4/synth_poor.pkl")
+    standard_generator = CTGANSynthesizer.load("../models/v4/synth_standard.pkl")
 
     synth_good = good_generator.sample(n)
     synth_poor = poor_generator.sample(n)