reshaped tests directory

tests/consistency_tests/conftest.py

@@ -1,8 +1,9 @@
 # tests/consistency_tests/conftest.py
 
 import pytest
+
 from hungarian_net.torch_modules.attention_layer import AttentionLayer
-from hungarian_net.torch_modules.hnet_gru import HNetGRU  
+from hungarian_net.torch_modules.hnet_gru import HNetGRU
 
 # TODO: maybe rewrite docstrings
 

tests/consistency_tests/generate_hnet_training_data/test_consistency_generate_hnet_training_data.py → tests/consistency_tests/generate_hnet_training_data/tests_consistency_generate_hnet_training_data.py

@@ -1,4 +1,4 @@
-# tests/consistency_tests/generate_hnet_training_data/test_generate_hnet_training_data.py
+# tests/consistency_tests/generate_hnet_training_data/tests_consistency_generate_hnet_training_data.py
 
 import numpy as np
 import pytest

tests/consistency_tests/lightning_datamodules/tests_consistency_hungarian_datamodule.py

@@ -0,0 +1 @@
+# tests/consistency_tests/lightning_datamodules/tests_consistency_hungarian_datamodule.py

tests/consistency_tests/lightning_modules/tests_consistency_hnet_gru_lightning.py

@@ -0,0 +1,3 @@
+# tests/consistency_tests/lightning_modules/tests_consistency_hnet_gru_lightning.py
+
+import pytest

tests/consistency_tests/run/tests_consistency_run.py

@@ -0,0 +1,3 @@
+# tests/consistency_tests/run/tests_consistency_run.py
+
+

tests/consistency_tests/torch_modules/tests_consistency_attention_layer.py

@@ -0,0 +1 @@
+# tests/consistency_tests/torch_modules/tests_consistency_attention_layer.py

tests/consistency_tests/model/test_consistency_train_hnet.py → tests/consistency_tests/torch_modules/tests_consistency_hnet_gru.py

@@ -1,16 +1,12 @@
-# tests/consistency_tests/model/test_train_hnet.py
+# tests/consistency_tests/torch_modules/tests_consistency_hnet_gru.py
 
 import pytest
 import torch
-
-from hungarian_net.torch_modules.attention_layer import AttentionLayer
-from hungarian_net.torch_modules.hnet_gru import HNetGRU    
-
-# TODO: maybe rewrite docstrings
+from hungarian_net.torch_modules.hnet_gru import HNetGRU
 
 
 @pytest.mark.consistency
-def test_model_initialization(model, max_doas) -> None:
+def test_HNetGRU_init(model, max_doas) -> None:
     """Test the initialization of the HNetGRU model.
 
     Args:
@@ -25,9 +21,8 @@ def test_model_initialization(model, max_doas) -> None:
         model.max_len == max_doas
     ), f"Expected max_doas {max_doas}, got {model.max_len}"
 
-
 @pytest.mark.consistency
-def test_forward_pass(model, batch_size) -> None:
+def test_HNetGRU_forward(model, batch_size) -> None:
     """Test the forward pass of the HNetGRU model to ensure correct output shapes.
 
     Args:
@@ -55,22 +50,4 @@ def test_forward_pass(model, batch_size) -> None:
     assert output3.shape == (
         batch_size,
         model.max_len,
-    ), f"Expected output3 shape {(batch_size, model.max_len)}, got {output3.shape}"
-
-
-@pytest.mark.consistency
-def test_attention_layer_initialization(attentionLayer) -> None:
-    """Test the initialization of the AttentionLayer.
-
-    Args:
-        attentionLayer (AttentionLayer): The AttentionLayer instance provided by the fixture.
-
-    Returns:
-        None
-    """
-    assert isinstance(
-        attentionLayer, AttentionLayer
-    ), f"AttentionLayer is not an instance of AttentionLayer class, got {attentionLayer.__repr__()}"
-
-
-# TODO: write test for compute_weight_accuracy
+    ), f"Expected output3 shape {(batch_size, model.max_len)}, got {output3.shape}"

tests/nonregression_tests/conftest.py

@@ -1,7 +1,8 @@
 # tests/nonregression_tests/conftest.py
 
 import pytest
-from hungarian_net.torch_modules.hnet_gru import HNetGRU 
+
+from hungarian_net.torch_modules.hnet_gru import HNetGRU
 
 
 @pytest.fixture(params=[256])

tests/nonregression_tests/generate_hnet_training_data/test_nonregression_generate_hnet_training_data.py → tests/nonregression_tests/generate_hnet_training_data/tests_nonregression_generate_hnet_training_data.py

@@ -1,4 +1,4 @@
-# tests/nonregression_tests/generate_hnet_training_data/test_nonregression_generate_hnet_training_data.py
+# tests/nonregression_tests/generate_hnet_training_data/tests_nonregression_generate_hnet_training_data.py
 import pytest
 from pytest_mock import mocker
 

tests/nonregression_tests/lightning_datamodules/tests_nonregression_hungarian_datamodule.py

@@ -0,0 +1 @@
+# tests/nonregression_tests/lightning_datamodules/tests_nonregression_hungarian_datamodule.py

tests/nonregression_tests/lightning_modules/tests_nonregression_hnet_gru_lightning.py

@@ -0,0 +1,3 @@
+# tests/nonregression_tests/lightning_modules/tests_nonregression_hnet_gru_lightning.py
+
+import pytest

tests/nonregression_tests/run/tests_nonregression_run.py

@@ -0,0 +1,20 @@
+# tests/nonregression_tests/run/tests_nonregression_run.py
+
+import os
+
+import pytest
+import torch
+from pytest_mock import mocker
+
+from hungarian_net.torch_modules.hnet_gru import HNetGRU
+from run import main as train_main
+from run import set_seed
+
+# TODO: Performing a non-regression test by directly comparing a newly trained model with a reference model is ineffective due to inherent numerical computation errors that can cause discrepancies.
+# TODO: In future iterations, it would be more effective to assess regression by evaluating the model's individual components (e.g., functions, classes, methods) to ensure each part operates as expected without being affected by numerical inaccuracies.
+
+
+@pytest.mark.nonregression
+def test_non_regression_train_hnet(mocker):
+
+    set_seed()

tests/nonregression_tests/torch_modules/tests_nonregression_attention_layer.py

@@ -0,0 +1 @@
+# tests/nonregression_tests/torch_modules/tests_nonregression_attention_layer.py

tests/nonregression_tests/torch_modules/tests_nonregression_hnet_gru.py

@@ -0,0 +1 @@
+# tests/nonregression_tests/torch_modules/tests_nonregression_hnet_gru.py

tests/scenarios_tests/conftest.py

@@ -2,7 +2,8 @@
 
 import numpy as np
 import pytest
-from hungarian_net.torch_modules.hnet_gru import HNetGRU 
+
+from hungarian_net.torch_modules.hnet_gru import HNetGRU
 
 
 @pytest.fixture(params=[2])

tests/scenarios_tests/generate_hnet_training_data/test_scenarios_generate_hnet_training_data.py → tests/scenarios_tests/generate_hnet_training_data/tests_scenarios_generate_hnet_training_data.py

@@ -1,4 +1,4 @@
-# tests/scenarios_tests/test_generate_hnet_training_data.py
+# tests/scenarios_tests/generate_hnet_training_data/tests_scenarios_generate_hnet_training_data.py
 
 import numpy as np
 import pytest

tests/scenarios_tests/lightning_datamodules/tests_scenarios_hungarian_datamodule.py

@@ -0,0 +1 @@
+# tests/scenarios_tests/lightning_datamodules/tests_scenarios_hungarian_datamodule.py

tests/scenarios_tests/lightning_modules/tests_scenarios_hnet_gru_lightning.py

@@ -0,0 +1,3 @@
+# tests/scenarios_tests/lightning_modules/tests_scenarios_hnet_gru_lightning.py
+
+import pytest

tests/scenarios_tests/model/test_scenarios_train_hnet.py → tests/scenarios_tests/run/tests_scenarios_run.py

@@ -1,7 +1,9 @@
-# tests/scenarios_tests/model/test_train_hnet.py
+# tests/scenarios_tests/run/tests_scenarios_run.py
 
 import re
+
 import pytest
+
 from run import main
 
 
@@ -40,7 +42,7 @@
     ],
 )
 def test_train_model_under_various_distributions(
-    max_doas, batch_size, nb_epochs, training_data, test_data
+    # max_doas, batch_size, nb_epochs, training_data, test_data
 ):
     """
     Train the HNetGRU model with various data distributions.
@@ -53,6 +55,9 @@ def test_train_model_under_various_distributions(
         test_data (str): Path to the testing data file.
     """
 
+    # TODO: to re-work
+    # TODO: next step : train hnet model under various data distributions
+
     # Extract sample ranges from the training_data filename
     match = re.search(r"hung_data_train_DOA\d+_(\d+)-(\d+)-(\d+)", training_data)
     if match:
@@ -63,11 +68,11 @@ def test_train_model_under_various_distributions(
     # Mock nb_epochs to be 1 regardless of the input
     nb_epochs = 1
 
-    main(
-        batch_size=batch_size,
-        nb_epochs=nb_epochs,
-        max_len=max_doas,
-        sample_range_used=sample_range_used,
-        filename_train=training_data,
-        filename_test=test_data,
-    )
+    # main(
+    #     batch_size=batch_size,
+    #     nb_epochs=nb_epochs,
+    #     max_len=max_doas,
+    #     sample_range_used=sample_range_used,
+    #     filename_train=training_data,
+    #     filename_test=test_data,
+    # )

tests/scenarios_tests/torch_modules/tests_scenarios_attention_layer.py

@@ -0,0 +1,22 @@
+# tests/scenarios_tests/torch_modules/tests_scenarios_attention_layer.py
+
+import pytest
+
+from hungarian_net.torch_modules.attention_layer import AttentionLayer
+
+
+@pytest.mark.consistency
+def test_AttentionLayer_init(attentionLayer) -> None:
+    """Test the initialization of the AttentionLayer.
+
+    Args:
+        attentionLayer (AttentionLayer): The AttentionLayer instance provided by the fixture.
+
+    Returns:
+        None
+    """
+    assert isinstance(
+        attentionLayer, AttentionLayer
+    ), f"AttentionLayer is not an instance of AttentionLayer class, got {attentionLayer.__repr__()}"
+
+

tests/scenarios_tests/torch_modules/tests_scenarios_hnet_gru.py

@@ -0,0 +1 @@
+# tests/scenarios_tests/torch_modules/tests_scenarios_hnet_gru.py