Bug in Deeprec tests and adding more tests

tests/ci/azureml_tests/test_groups.py

@@ -204,13 +204,6 @@
     ],
     "group_gpu_001": [  # Total group time: 492.62s
         "tests/unit/examples/test_notebooks_gpu.py::test_gpu_vm",  # 0.76s (Always the first test to check the GPU works)
-        "tests/unit/recommenders/models/test_deeprec_model.py::test_xdeepfm_component_definition",
-        "tests/unit/recommenders/models/test_deeprec_model.py::test_dkn_component_definition",
-        "tests/unit/recommenders/models/test_deeprec_model.py::test_dkn_item2item_component_definition",
-        # "tests/unit/recommenders/models/test_deeprec_model.py::test_slirec_component_definition", # FIXME: Issue #1953
-        # "tests/unit/recommenders/models/test_deeprec_model.py::test_nextitnet_component_definition", # FIXME: Issue #1953
-        # "tests/unit/recommenders/models/test_deeprec_model.py::test_sum_component_definition", # FIXME: Issue #1953
-        "tests/unit/recommenders/models/test_deeprec_model.py::test_lightgcn_component_definition",
         "tests/unit/recommenders/models/test_rbm.py::test_sampling_funct",
         "tests/unit/recommenders/models/test_rbm.py::test_train_param_init",
         "tests/unit/recommenders/models/test_rbm.py::test_save_load",
@@ -237,9 +230,19 @@
         "tests/unit/recommenders/models/test_ncf_dataset.py::test_datafile_missing_column",
         # "tests/unit/recommenders/models/test_sasrec_model.py::test_prepare_data", # FIXME: it takes too long to run
         # "tests/unit/recommenders/models/test_sasrec_model.py::test_sampler", # FIXME: it takes too long to run
-        #"tests/unit/recommenders/models/test_sasrec_model.py::test_sasrec", # FIXME: it takes too long to run
+        # "tests/unit/recommenders/models/test_sasrec_model.py::test_sasrec", # FIXME: it takes too long to run
         # "tests/unit/recommenders/models/test_sasrec_model.py::test_ssept", # FIXME: it takes too long to run
     ],
+    "group_gpu_002": [  # Total group time:
+        "tests/unit/examples/test_notebooks_gpu.py::test_gpu_vm",  # 0.76s (Always the first test to check the GPU works)
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_xdeepfm_component_definition",
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_dkn_component_definition",
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_dkn_item2item_component_definition",
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_slirec_component_definition",
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_nextitnet_component_definition",
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_sum_component_definition",
+        "tests/unit/recommenders/models/test_deeprec_model.py::test_lightgcn_component_definition",
+    ],
     "group_notebooks_gpu_001": [  # Total group time: 563.35s
         "tests/unit/examples/test_notebooks_gpu.py::test_gpu_vm",  # 0.76s (Always the first test to check the GPU works)
         "tests/unit/examples/test_notebooks_gpu.py::test_dkn_quickstart",

tests/unit/recommenders/models/test_deeprec_model.py

@@ -1,8 +1,9 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-import pytest
 import os
+import pytest
+
 from recommenders.datasets import movielens
 from recommenders.datasets.amazon_reviews import (
     download_and_extract,
@@ -36,36 +37,16 @@
     pass  # skip this import if we are in cpu environment
 
 
-@pytest.mark.gpu
-def test_xdeepfm_component_definition(deeprec_resource_path):
-    data_path = os.path.join(deeprec_resource_path, "xdeepfm")
-    yaml_file = os.path.join(data_path, "xDeepFM.yaml")
-
-    if not os.path.exists(yaml_file):
-        download_deeprec_resources(
-            "https://recodatasets.z20.web.core.windows.net/deeprec/",
-            data_path,
-            "xdeepfmresources.zip",
-        )
-
-    hparams = prepare_hparams(yaml_file)
-    model = XDeepFMModel(hparams, FFMTextIterator)
-
-    assert model.logit is not None
-    assert model.update is not None
-    assert model.iterator is not None
-
-
 @pytest.mark.gpu
 @pytest.fixture(scope="module")
 def dkn_files(deeprec_resource_path):
     data_path = os.path.join(deeprec_resource_path, "dkn")
     yaml_file = os.path.join(data_path, "dkn.yaml")
-    news_feature_file = os.path.join(data_path, r"doc_feature.txt")
-    user_history_file = os.path.join(data_path, r"user_history.txt")
-    wordEmb_file = os.path.join(data_path, r"word_embeddings_100.npy")
-    entityEmb_file = os.path.join(data_path, r"TransE_entity2vec_100.npy")
-    contextEmb_file = os.path.join(data_path, r"TransE_context2vec_100.npy")
+    news_feature_file = os.path.join(data_path, "doc_feature.txt")
+    user_history_file = os.path.join(data_path, "user_history.txt")
+    wordEmb_file = os.path.join(data_path, "word_embeddings_100.npy")
+    entityEmb_file = os.path.join(data_path, "TransE_entity2vec_100.npy")
+    contextEmb_file = os.path.join(data_path, "TransE_context2vec_100.npy")
 
     download_deeprec_resources(
         "https://recodatasets.z20.web.core.windows.net/deeprec/",
@@ -83,6 +64,83 @@ def dkn_files(deeprec_resource_path):
     )
 
 
+@pytest.mark.gpu
+@pytest.fixture(scope="module")
+def sequential_files(deeprec_resource_path):
+    data_path = os.path.join(deeprec_resource_path, "slirec")
+    train_file = os.path.join(data_path, "train_data")
+    valid_file = os.path.join(data_path, "valid_data")
+    test_file = os.path.join(data_path, "test_data")
+    user_vocab = os.path.join(data_path, "user_vocab.pkl")
+    item_vocab = os.path.join(data_path, "item_vocab.pkl")
+    cate_vocab = os.path.join(data_path, "category_vocab.pkl")
+
+    reviews_name = "reviews_Movies_and_TV_5.json"
+    meta_name = "meta_Movies_and_TV.json"
+    reviews_file = os.path.join(data_path, reviews_name)
+    meta_file = os.path.join(data_path, meta_name)
+
+    # number of negative instances with a positive instance for validation
+    valid_num_ngs = 4
+    # number of negative instances with a positive instance for testing
+    test_num_ngs = 9
+    # sample a small item set for training and testing here for example
+    sample_rate = 0.01
+
+    input_files = [
+        reviews_file,
+        meta_file,
+        train_file,
+        valid_file,
+        test_file,
+        user_vocab,
+        item_vocab,
+        cate_vocab,
+    ]
+    download_and_extract(reviews_name, reviews_file)
+    download_and_extract(meta_name, meta_file)
+    data_preprocessing(
+        *input_files,
+        sample_rate=sample_rate,
+        valid_num_ngs=valid_num_ngs,
+        test_num_ngs=test_num_ngs
+    )
+
+    return (
+        data_path,
+        user_vocab,
+        item_vocab,
+        cate_vocab,
+    )
+
+
+@pytest.mark.gpu
+def test_xdeepfm_component_definition(deeprec_resource_path):
+    data_path = os.path.join(deeprec_resource_path, "xdeepfm")
+    yaml_file = os.path.join(data_path, "xDeepFM.yaml")
+
+    if not os.path.exists(yaml_file):
+        download_deeprec_resources(
+            "https://recodatasets.z20.web.core.windows.net/deeprec/",
+            data_path,
+            "xdeepfmresources.zip",
+        )
+
+    hparams = prepare_hparams(yaml_file)
+    model = XDeepFMModel(hparams, FFMTextIterator)
+
+    assert model.logit is not None
+    assert model.update is not None
+    assert model.iterator is not None
+    assert model.hparams is not None
+    assert model.hparams.model_type == "xDeepFM"
+    assert model.hparams.epochs == 50
+    assert model.hparams.batch_size == 128
+    assert model.hparams.learning_rate == 0.0005
+    assert model.hparams.loss == "log_loss"
+    assert model.hparams.optimizer == "adam"
+
+
 @pytest.mark.gpu
 def test_dkn_component_definition(dkn_files):
     # Load params from fixture
@@ -107,12 +165,18 @@ def test_dkn_component_definition(dkn_files):
         epochs=1,
         learning_rate=0.0001,
     )
-    assert hparams is not None
 
     model = DKN(hparams, DKNTextIterator)
     assert model.logit is not None
     assert model.update is not None
     assert model.iterator is not None
+    assert model.hparams is not None
+    assert model.hparams.model_type == "dkn"
+    assert model.hparams.epochs == 1
+    assert model.hparams.batch_size == 100
+    assert model.hparams.learning_rate == 0.0001
+    assert model.hparams.loss == "log_loss"
+    assert model.hparams.optimizer == "adam"
 
 
 @pytest.mark.gpu
@@ -143,65 +207,21 @@ def test_dkn_item2item_component_definition(dkn_files):
         use_entity=True,
         use_context=True,
     )
-    assert hparams is not None
 
     hparams.neg_num = 9
     model_item2item = DKNItem2Item(hparams, DKNItem2itemTextIterator)
     assert model_item2item.pred_logits is not None
     assert model_item2item.update is not None
     assert model_item2item.iterator is not None
-
-
-@pytest.mark.gpu
-@pytest.fixture(scope="module")
-def sequential_files(deeprec_resource_path):
-    data_path = os.path.join(deeprec_resource_path, "slirec")
-    train_file = os.path.join(data_path, r"train_data")
-    valid_file = os.path.join(data_path, r"valid_data")
-    test_file = os.path.join(data_path, r"test_data")
-    user_vocab = os.path.join(data_path, r"user_vocab.pkl")
-    item_vocab = os.path.join(data_path, r"item_vocab.pkl")
-    cate_vocab = os.path.join(data_path, r"category_vocab.pkl")
-
-    reviews_name = "reviews_Movies_and_TV_5.json"
-    meta_name = "meta_Movies_and_TV.json"
-    reviews_file = os.path.join(data_path, reviews_name)
-    meta_file = os.path.join(data_path, meta_name)
-    valid_num_ngs = (
-        4  # number of negative instances with a positive instance for validation
-    )
-    test_num_ngs = (
-        9  # number of negative instances with a positive instance for testing
-    )
-    sample_rate = (
-        0.01  # sample a small item set for training and testing here for example
-    )
-
-    input_files = [
-        reviews_file,
-        meta_file,
-        train_file,
-        valid_file,
-        test_file,
-        user_vocab,
-        item_vocab,
-        cate_vocab,
-    ]
-    download_and_extract(reviews_name, reviews_file)
-    download_and_extract(meta_name, meta_file)
-    data_preprocessing(
-        *input_files,
-        sample_rate=sample_rate,
-        valid_num_ngs=valid_num_ngs,
-        test_num_ngs=test_num_ngs
-    )
-
-    return (
-        data_path,
-        user_vocab,
-        item_vocab,
-        cate_vocab,
-    )
+    assert model_item2item.hparams is not None
+    assert model_item2item.hparams.model_type == "dkn"
+    assert model_item2item.hparams.epochs == 1
+    assert model_item2item.hparams.batch_size == 100
+    assert model_item2item.hparams.learning_rate == 0.0005
+    assert model_item2item.hparams.loss == "log_loss"
+    assert model_item2item.hparams.optimizer == "adam"
+    assert model_item2item.hparams.max_grad_norm == 0.5
+    assert model_item2item.hparams.his_size == 20
 
 
 @pytest.mark.gpu
@@ -223,12 +243,22 @@ def test_slirec_component_definition(sequential_files, deeprec_config_path):
         cate_vocab=cate_vocab,
         need_sample=True,
     )
-    assert hparams is not None
 
     model = SLI_RECModel(hparams, SequentialIterator)
     assert model.logit is not None
     assert model.update is not None
     assert model.iterator is not None
+    assert model.hparams is not None
+    assert model.hparams.model_type == "sli_rec"
+    assert model.hparams.epochs == 1
+    assert model.hparams.batch_size == 400
+    assert model.hparams.learning_rate == 0.001
+    assert model.hparams.loss == "softmax"
+    assert model.hparams.optimizer == "adam"
+    assert model.hparams.train_num_ngs == 4
+    assert model.hparams.embed_l2 == 0.0
+    assert model.hparams.layer_l2 == 0.0
+    assert model.hparams.need_sample is True
 
 
 @pytest.mark.gpu
@@ -251,12 +281,22 @@ def test_nextitnet_component_definition(sequential_files, deeprec_config_path):
         cate_vocab=cate_vocab,
         need_sample=True,
     )
-    assert hparams_nextitnet is not None
 
     model_nextitnet = NextItNetModel(hparams_nextitnet, NextItNetIterator)
     assert model_nextitnet.logit is not None
     assert model_nextitnet.update is not None
     assert model_nextitnet.iterator is not None
+    assert model_nextitnet.hparams is not None
+    assert model_nextitnet.hparams.model_type == "NextItNet"
+    assert model_nextitnet.hparams.epochs == 1
+    assert model_nextitnet.hparams.batch_size == 400
+    assert model_nextitnet.hparams.learning_rate == 0.001
+    assert model_nextitnet.hparams.loss == "softmax"
+    assert model_nextitnet.hparams.optimizer == "adam"
+    assert model_nextitnet.hparams.train_num_ngs == 4
+    assert model_nextitnet.hparams.embed_l2 == 0.0
+    assert model_nextitnet.hparams.layer_l2 == 0.0
+    assert model_nextitnet.hparams.need_sample is True
 
 
 @pytest.mark.gpu
@@ -279,12 +319,22 @@ def test_sum_component_definition(sequential_files, deeprec_config_path):
         cate_vocab=cate_vocab,
         need_sample=True,
     )
-    assert hparams_sum is not None
 
     model_sum = SUMModel(hparams_sum, SequentialIterator)
     assert model_sum.logit is not None
     assert model_sum.update is not None
     assert model_sum.iterator is not None
+    assert model_sum.hparams is not None
+    assert model_sum.hparams.model_type == "SUM"
+    assert model_sum.hparams.epochs == 1
+    assert model_sum.hparams.batch_size == 400
+    assert model_sum.hparams.learning_rate == 0.001
+    assert model_sum.hparams.loss == "softmax"
+    assert model_sum.hparams.optimizer == "adam"
+    assert model_sum.hparams.train_num_ngs == 4
+    assert model_sum.hparams.embed_l2 == 0.0
+    assert model_sum.hparams.layer_l2 == 0.0
+    assert model_sum.hparams.need_sample is True
 
 
 @pytest.mark.gpu
@@ -296,16 +346,17 @@ def test_lightgcn_component_definition(deeprec_config_path):
 
     data = ImplicitCF(train=train, test=test)
 
-    embed_size = 64
-    hparams = prepare_hparams(yaml_file, embed_size=embed_size)
+    hparams = prepare_hparams(yaml_file, embed_size=64)
     model = LightGCN(hparams, data)
 
     assert model.norm_adj is not None
-    assert model.ua_embeddings.shape == [data.n_users, embed_size]
-    assert model.ia_embeddings.shape == [data.n_items, embed_size]
+    assert model.ua_embeddings.shape == [943, 64]
+    assert model.ia_embeddings.shape == [1682, 64]
     assert model.u_g_embeddings is not None
     assert model.pos_i_g_embeddings is not None
     assert model.neg_i_g_embeddings is not None
     assert model.batch_ratings is not None
     assert model.loss is not None
     assert model.opt is not None
+    assert model.batch_size == 1024
+    assert model.epochs == 1000