linreg: Fixup test

Author: jonnor

src/emlearn_linreg/emlearn_linreg.py

@@ -1,5 +1,11 @@
 
-from emlearn_linreg_c import *
+# When used as external C module, the .py is the top-level import,
+# and we need to merge the native module symbols at import time
+# When used as dynamic native modules (.mpy), .py and native code is merged at build time
+try:
+    from emlearn_linreg_c import *
+except ImportError as e:
+    pass
 
 log_prefix = 'emlearn_linreg:'
 

tests/test_linreg_california.py

@@ -5,7 +5,7 @@
 import gc
 import time
 
-data_dir = './examples/datasets/california'
+data_dir = './examples/datasets/california/'
 
 def load_npy(filename):
     shape, data = npyfile.load(filename)
@@ -16,10 +16,10 @@ def test_elasticnet_full():
     print("\n=== Full Dataset Test ===")
 
     # Load full datasets
-    X_train_shape, X_train_data = load_npy('X_train.npy')
-    y_train_shape, y_train_data = load_npy('y_train.npy')
-    X_test_shape, X_test_data = load_npy('X_test.npy')
-    y_test_shape, y_test_data = load_npy('y_test.npy')
+    X_train_shape, X_train_data = load_npy(data_dir+'X_train.npy')
+    y_train_shape, y_train_data = load_npy(data_dir+'y_train.npy')
+    X_test_shape, X_test_data = load_npy(data_dir+'X_test.npy')
+    y_test_shape, y_test_data = load_npy(data_dir+'y_test.npy')
 
     n_features = X_train_shape[1]
     n_train = y_train_shape[0]
@@ -31,15 +31,15 @@ def test_elasticnet_full():
 
     # Create model with different hyperparameters for full dataset
     # Lower learning rate for stability
-    model = emlearn_linreg.new(n_features, 0.001, 0.5, 0.01)  
+    model = emlearn_linreg.new(n_features, 0.001, 0.5, 0.1)  
 
     train_start = time.ticks_ms()
     # Train on full dataset
     print("Training on full dataset...")
     stop_iter, stop_mse = emlearn_linreg.train(model,
             X_train_data, y_train_data,
-            max_iterations=2000, check_interval=50,
-            verbose=2, tolerance=0.001, score_limit=0.60,
+            max_iterations=1000, check_interval=50,
+            verbose=2, tolerance=0.0001, score_limit=0.60,
     )
     train_duration = time.ticks_diff(time.ticks_ms(), train_start)    
     print('Train time (ms)', train_duration, 'per iter', train_duration/stop_iter)
@@ -48,8 +48,8 @@ def test_elasticnet_full():
     train_mse = model.score_mse(X_train_data, y_train_data)
     test_mse = model.score_mse(X_test_data, y_test_data)
 
-    assert train_mse <= 0.60, train_mse
-    assert test_mse <= 0.60, test_mse
+    assert train_mse <= 0.65, train_mse
+    assert test_mse <= 0.65, test_mse
 
 
 def main():