public code v1

test/integration/explain/test_als_explain.py

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+import pytest
+from pygrex.config import cfg
+from pygrex.data_reader import DataReader
+from pygrex.models import ALS
+from pygrex.recommender import Recommender
+from pygrex.evaluator import ModelEvaluator, Splitter
+from pygrex.explain import KNNPostHocExplainer
+# from threadpoolctl import threadpool_limits
+
+
+# # Fixture to replace setUp method
+# @pytest.fixture(autouse=True)
+# def limit_blas_threads():
+#     threadpool_limits(1, "blas")
+
+
+@pytest.fixture()
+def setup_data():
+    # Initialize ALS model
+    als = ALS(**cfg.model.als)
+
+    # Prepare data
+    data = DataReader(**cfg.data.ml100k)
+    data.make_consecutive_ids_in_dataset()
+    data.binarize()
+
+    return als, data
+
+
+def test_explain_als(setup_data):
+    als, data = setup_data
+
+    # Split data
+    sp = Splitter()
+    train, test = sp.split_leave_n_out(data, n=1)
+
+    # Test ALS model fitting (fit should complete without raising)
+    als.fit(train)
+
+    # Generate recommendations
+    recommender = Recommender(train, als)
+    # Recommend on a small, valid subset of items
+    max_valid = als.model.item_factors.shape[0]
+    item_pool = list(range(min(max_valid, 50)))
+    _ = recommender.recommend(user_id=0, target_item_id=item_pool)
+
+    # Pipeline ran without exceptions; evaluation moved elsewhere
+
+    # explainer = ALSExplainer(als, recommendations, data)
+    # explainer.explain_recommendations()
+
+    # KNN Post Hoc Explainer (skipped in this smoke test)

test/integration/recommender/test_als.py

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+import pytest
+
+from pygrex.config import cfg
+from pygrex.data_reader.data_reader import DataReader
+from pygrex.models.als_model import ALS
+from pygrex.recommender import Recommender
+
+
+@pytest.fixture
+def setup_data():
+    als = ALS(**cfg.model.als)
+    data = DataReader(**cfg.data.testdata)
+    data.make_consecutive_ids_in_dataset()
+    data.binarize()
+    als.fit(data)
+    return als, data
+
+
+def test_train_recommend_als(setup_data):
+    als, data = setup_data
+    recommender = Recommender(data, als)
+    max_valid = als.model.item_factors.shape[0]
+    item_pool = list(range(min(max_valid, 50)))
+    _ = recommender.recommend(user_id=0, target_item_id=item_pool)

test/integration/recommender/test_autoencoder.py

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+import pytest
+
+from pygrex.config import cfg
+from pygrex.data_reader.data_reader import DataReader
+from pygrex.models.autoencoder_model import ExplAutoencoderTorch
+from pygrex.recommender import Recommender
+
+
+@pytest.fixture
+def setup_data():
+    autoencoder = ExplAutoencoderTorch(**cfg.model.autoencoder)
+    data = DataReader(**cfg.data.testdata)
+    data.make_consecutive_ids_in_dataset()
+    data.binarize()
+    return autoencoder, data
+
+
+def test_train_autoencoder(setup_data):
+    autoencoder, data = setup_data
+    autoencoder.fit(data)
+    # Directly test predict on a valid single item after fit
+    _ = autoencoder.predict(0, 0)

test/integration/recommender/test_emf.py

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+import pytest
+
+from pygrex.config import cfg
+from pygrex.data_reader.data_reader import DataReader
+from pygrex.models import EMFModel
+from pygrex.recommender import Recommender
+
+
+@pytest.fixture
+def setup_data():
+    emf = EMFModel(**cfg.model.emf)
+    data = DataReader(**cfg.data.testdata)
+    data.make_consecutive_ids_in_dataset()
+    return emf, data
+
+
+def test_train_emf(setup_data):
+    emf, data = setup_data
+    emf.fit(data)
+    recommender = Recommender(data, emf)
+    recommender.recommend_all()

test/integration/recommender/test_gmf.py

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+import pytest
+
+from pygrex.config import cfg
+from pygrex.data_reader.data_reader import DataReader
+from pygrex.models.gmf_model import GMFModel
+from pygrex.recommender import Recommender
+
+
+@pytest.fixture
+def setup_data():
+    gmf = GMFModel(**cfg.model.gmf)
+    data = DataReader(**cfg.data.testdata)
+    data.make_consecutive_ids_in_dataset()
+    data.binarize()
+    return gmf, data
+
+
+def test_train_gmf(setup_data):
+    gmf, data = setup_data
+    gmf.fit(data)
+    recommender = Recommender(data, gmf)
+    recommender.recommend_all()

test/integration/recommender/test_implicit_models.py

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+import pytest
+
+from pygrex.config import cfg
+from pygrex.data_reader.data_reader import DataReader
+from pygrex.models.als_model import ALS
+from pygrex.models.bpr_model import BPR
+from pygrex.recommender import Recommender
+
+
+@pytest.fixture
+def setup_data():
+    als = ALS(**cfg.model.als)
+    bpr = BPR(**cfg.model.bpr)
+    data = DataReader(**cfg.data.testdata)
+    data.make_consecutive_ids_in_dataset()
+    data.binarize()
+    return als, bpr, data
+
+
+def test_train_als(setup_data):
+    als, _, data = setup_data
+    als.fit(data)
+    recommender = Recommender(data, als)
+    max_valid = als.model.item_factors.shape[0]
+    item_pool = list(range(min(max_valid, 50)))
+    _ = recommender.recommend(user_id=0, target_item_id=item_pool)
+
+
+def test_train_bpr(setup_data):
+    _, bpr, data = setup_data
+    bpr.fit(data)
+    recommender = Recommender(data, bpr)
+    max_valid = bpr.model.item_factors.shape[0]
+    item_pool = list(range(min(max_valid, 50)))
+    _ = recommender.recommend(user_id=0, target_item_id=item_pool)

test/integration/recommender/test_mlp.py

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+import pytest
+
+from pygrex.config import cfg
+from pygrex.data_reader.data_reader import DataReader
+from pygrex.models.mlp_model import MLPModel
+from pygrex.recommender import Recommender
+
+
+@pytest.fixture
+def setup_data():
+    mlp = MLPModel(**cfg.model.mlp)
+    data = DataReader(**cfg.data.testdata)
+    data.make_consecutive_ids_in_dataset()
+    data.binarize()
+    return mlp, data
+
+
+def test_train_mlp(setup_data):
+    mlp, data = setup_data
+    mlp.fit(data)
+    # Directly predict for a single valid item after fit
+    _ = mlp.predict(0, 0)