linreg: Add back full test

Author: jonnor

src/emlearn_linreg/linreg.py

@@ -1,10 +1,13 @@
 
 
+log_prefix = 'emlearn_linreg:'
+
 def train(model, X_train, y_train,
         max_iterations=100, 
         tolerance=1e-6,
         check_interval=10,
         divergence_factor=10.0,
+        score_limit=None,
         verbose=0,
         ):
     """
@@ -25,22 +28,25 @@ def train(model, X_train, y_train,
         change = abs(prev_mse - current_mse)
 
         if verbose >= 2:
-            print(f'Iteration {iteration} mse={current_mse}')        
+            print(log_prefix, f'Iteration {iteration} mse={current_mse}')        
 
         # Check convergence
         converged = change < tolerance and iteration > check_interval * 2
 
+        if score_limit is not None:
+            converged = converged or current_mse <= score_limit
+
         # Check divergence
         diverged = current_mse > prev_mse * divergence_factor or not (current_mse == current_mse)  # NaN check
 
         if converged:
-            if verbose:
-                print(f"Converged at iteration {iteration}")
+            if verbose >= 1:
+                print(log_prefix, f"Converged at iteration {iteration}")
             break
 
         if diverged:
-            if verbose:
-                print(f"Diverged at iteration {iteration}")
+            if verbose >= 1:
+                print(log_prefix, f"Diverged at iteration {iteration}")
             break
 
         prev_mse = current_mse

tests/test_linreg_california.py

@@ -39,7 +39,7 @@ def compare_regularization():
 
         model = emlearn_linreg.new(n_features, alpha, l1_ratio, 0.01)
         emlearn_linreg.train(model, X_subset, y_subset,
-            max_iterations=2000, check_interval=50, verbose=True)
+            max_iterations=2000, check_interval=50, verbose=1)
 
         # Calculate predictions and MSE
         mse = model.score_mse(X_subset, y_subset)
@@ -53,6 +53,63 @@ def compare_regularization():
         print(f"  Non-zero weights: {non_zero}/{n_features}")
         print(f"  Max weight magnitude: {max(abs(w) for w in weights):.6f}")
 
+def test_elasticnet_full():
+    """Test with full dataset."""
+    print("\n=== Full Dataset Test ===")
+    
+    # Load full datasets
+    X_train_shape, X_train_data = load_npy_as_array('X_train.npy')
+    y_train_shape, y_train_data = load_npy_as_array('y_train.npy')
+    X_test_shape, X_test_data = load_npy_as_array('X_test.npy')
+    y_test_shape, y_test_data = load_npy_as_array('y_test.npy')
+    
+    n_features = X_train_shape[1]
+    n_train = y_train_shape[0]
+    n_test = y_test_shape[0]
+    
+    print(f"Train set: {n_train} samples")
+    print(f"Test set: {n_test} samples")
+    print(f"Features: {n_features}")
+    
+    # Create model with different hyperparameters for full dataset
+    print("Creating ElasticNet model...")
+    model = emlearn_linreg.new(n_features, 0.001, 0.5, 0.01)  # Lower learning rate for stability
+    
+    # Train on full dataset
+    print("Training on full dataset...")
+    emlearn_linreg.train(model, X_train_data, y_train_data,
+        max_iterations=2000, check_interval=50, verbose=1, tolerance=0.001, score_limit=0.60)
+    
+    # Get final parameters
+    weights = array.array('f', [0.0] * n_features)
+    model.get_weights(weights)
+    bias = model.get_bias()
+    
+    print(f"Final weights: {list(weights)}")
+    print(f"Final bias: {bias}")
+    
+    # Evaluate on train set
+    print("Calculating training MSE...")    
+    train_mse = model.score_mse(X_train_data, y_train_data)
+    print(f"Training MSE: {train_mse}")
+    
+    # Evaluate on test set
+    print("Calculating test MSE...")
+    test_mse = model.score_mse(X_test_data, y_test_data)
+    print(f"Test MSE: {test_mse}")
+    
+    # Make some sample predictions
+    print("\nSample predictions:")
+    for i in range(min(5, n_test)):
+        start_idx = i * n_features
+        end_idx = start_idx + n_features
+        test_features = array.array('f', X_test_data[start_idx:end_idx])
+        prediction = model.predict(test_features)
+        actual = y_test_data[i]
+        print(f"Sample {i}: predicted={prediction:.3f}, actual={actual:.3f}, error={abs(prediction-actual):.3f}")
+    
+    return model
+
 def main():
     """Main test function."""
     print("ElasticNet MicroPython Module Test")
@@ -62,6 +119,7 @@ def main():
         # Compare regularization approaches
         compare_regularization()
 
+        test_elasticnet_full()        
         print("\n=== All Tests Completed Successfully! ===")
 
     except Exception as e: