Replace SARSingleNode with SAR (#1768)

recommenders-team · Jul 1, 2022 · 32107f1 · 32107f1
1 parent 7381e40
commit 32107f1
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Showing 4 changed files with 23 additions and 23 deletions.
diff --git a/examples/00_quick_start/sar_movielens_with_azureml.ipynb b/examples/00_quick_start/sar_movielens_with_azureml.ipynb
@@ -341,7 +341,7 @@
     "from recommenders.datasets import movielens\n",
     "from recommenders.datasets.python_splitters import python_stratified_split\n",
     "from recommenders.evaluation.python_evaluation import map_at_k, ndcg_at_k, precision_at_k, recall_at_k\n",
-    "from recommenders.models.sar.sar_singlenode import SARSingleNode\n",
+    "from recommenders.models.sar import SAR\n",
     "\n",
     "\n",
     "logging.basicConfig(level=logging.DEBUG, \n",
@@ -384,7 +384,7 @@
     "train, test = python_stratified_split(data, ratio=0.75, col_user=header[\"col_user\"], col_item=header[\"col_item\"], seed=42)\n",
     "\n",
     "# instantiate the model\n",
-    "model = SARSingleNode(\n",
+    "model = SAR(\n",
     "    similarity_type=\"jaccard\", \n",
     "    time_decay_coefficient=30, \n",
     "    time_now=None, \n",

diff --git a/examples/02_model_collaborative_filtering/sar_deep_dive.ipynb b/examples/02_model_collaborative_filtering/sar_deep_dive.ipynb
@@ -133,7 +133,7 @@
     "from recommenders.datasets import movielens\n",
     "from recommenders.datasets.python_splitters import python_stratified_split\n",
     "from recommenders.evaluation.python_evaluation import map_at_k, ndcg_at_k, precision_at_k, recall_at_k\n",
-    "from recommenders.models.sar.sar_singlenode import SARSingleNode\n",
+    "from recommenders.models.sar import SAR\n",
     "\n",
     "print(\"System version: {}\".format(sys.version))\n",
     "print(\"Pandas version: {}\".format(pd.__version__))"
@@ -339,7 +339,7 @@
     "logging.basicConfig(level=logging.DEBUG, \n",
     "                    format='%(asctime)s %(levelname)-8s %(message)s')\n",
     "\n",
-    "model = SARSingleNode(\n",
+    "model = SAR(\n",
     "    similarity_type=\"jaccard\", \n",
     "    time_decay_coefficient=30, \n",
     "    time_now=None, \n",

diff --git a/examples/06_benchmarks/benchmark_utils.py b/examples/06_benchmarks/benchmark_utils.py
@@ -19,7 +19,7 @@
 )
 from recommenders.utils.timer import Timer
 from recommenders.utils.spark_utils import start_or_get_spark
-from recommenders.models.sar.sar_singlenode import SARSingleNode
+from recommenders.models.sar import SAR
 from recommenders.models.ncf.ncf_singlenode import NCF
 from recommenders.models.ncf.dataset import Dataset as NCFDataset
 from recommenders.models.surprise.surprise_utils import (
@@ -304,7 +304,7 @@ def prepare_training_sar(train, test):
 
 
 def train_sar(params, data):
-    model = SARSingleNode(**params)
+    model = SAR(**params)
     model.set_index(data)
     with Timer() as t:
         model.fit(data)

diff --git a/tests/unit/recommenders/models/test_sar_singlenode.py b/tests/unit/recommenders/models/test_sar_singlenode.py
@@ -12,7 +12,7 @@
 import urllib
 
 from recommenders.utils.constants import DEFAULT_PREDICTION_COL
-from recommenders.models.sar.sar_singlenode import SARSingleNode
+from recommenders.models.sar import SAR
 
 
 def _csv_reader_url(url, delimiter=",", encoding="utf-8"):
@@ -32,7 +32,7 @@ def load_userpred(file, k=10):
 
 
 def test_init(header):
-    model = SARSingleNode(similarity_type="jaccard", **header)
+    model = SAR(similarity_type="jaccard", **header)
 
     assert model.col_user == "UserId"
     assert model.col_item == "MovieId"
@@ -50,7 +50,7 @@ def test_init(header):
     "similarity_type, timedecay_formula", [("jaccard", False), ("lift", True)]
 )
 def test_fit(similarity_type, timedecay_formula, train_test_dummy_timestamp, header):
-    model = SARSingleNode(
+    model = SAR(
         similarity_type=similarity_type, timedecay_formula=timedecay_formula, **header
     )
     trainset, testset = train_test_dummy_timestamp
@@ -63,7 +63,7 @@ def test_fit(similarity_type, timedecay_formula, train_test_dummy_timestamp, hea
 def test_predict(
     similarity_type, timedecay_formula, train_test_dummy_timestamp, header
 ):
-    model = SARSingleNode(
+    model = SAR(
         similarity_type=similarity_type, timedecay_formula=timedecay_formula, **header
     )
     trainset, testset = train_test_dummy_timestamp
@@ -78,7 +78,7 @@ def test_predict(
 
 
 def test_predict_all_items(train_test_dummy_timestamp, header):
-    model = SARSingleNode(**header)
+    model = SAR(**header)
     trainset, _ = train_test_dummy_timestamp
     model.fit(trainset)
 
@@ -110,7 +110,7 @@ def test_sar_item_similarity(
     threshold, similarity_type, file, demo_usage_data, sar_settings, header
 ):
 
-    model = SARSingleNode(
+    model = SAR(
         similarity_type=similarity_type,
         timedecay_formula=False,
         time_decay_coefficient=30,
@@ -160,7 +160,7 @@ def test_sar_item_similarity(
 
 def test_user_affinity(demo_usage_data, sar_settings, header):
     time_now = demo_usage_data[header["col_timestamp"]].max()
-    model = SARSingleNode(
+    model = SAR(
         similarity_type="cooccurrence",
         timedecay_formula=True,
         time_decay_coefficient=30,
@@ -185,7 +185,7 @@ def test_user_affinity(demo_usage_data, sar_settings, header):
 
     # Set time_now to 60 days later
     two_months = 2 * 30 * (24 * 60 * 60)
-    model = SARSingleNode(
+    model = SAR(
         similarity_type="cooccurrence",
         timedecay_formula=True,
         time_decay_coefficient=30,
@@ -217,7 +217,7 @@ def test_recommend_k_items(
     threshold, similarity_type, file, header, sar_settings, demo_usage_data
 ):
     time_now = demo_usage_data[header["col_timestamp"]].max()
-    model = SARSingleNode(
+    model = SAR(
         similarity_type=similarity_type,
         timedecay_formula=True,
         time_decay_coefficient=30,
@@ -250,7 +250,7 @@ def test_recommend_k_items(
 
 def test_get_item_based_topk(header, pandas_dummy):
 
-    sar = SARSingleNode(**header)
+    sar = SAR(**header)
     sar.fit(pandas_dummy)
 
     # test with just items provided
@@ -307,7 +307,7 @@ def test_get_popularity_based_topk(header):
         }
     )
 
-    sar = SARSingleNode(**header)
+    sar = SAR(**header)
     sar.fit(train_df)
 
     expected = pd.DataFrame(dict(MovieId=[4, 1, 2], prediction=[4, 3, 2]))
@@ -337,7 +337,7 @@ def test_get_normalized_scores(header):
         }
     )
 
-    model = SARSingleNode(**header, timedecay_formula=True, normalize=True)
+    model = SAR(**header, timedecay_formula=True, normalize=True)
     model.fit(train)
     actual = model.score(test, remove_seen=True)
     expected = np.array(
@@ -387,7 +387,7 @@ def test_match_similarity_type_from_json_file(header):
 
     params.update(header)
 
-    model = SARSingleNode(**params)
+    model = SAR(**params)
 
     train = pd.DataFrame(
         {
@@ -403,7 +403,7 @@ def test_match_similarity_type_from_json_file(header):
 
 
 def test_dataset_with_duplicates(header):
-    model = SARSingleNode(**header)
+    model = SAR(**header)
     train = pd.DataFrame(
         {
             header["col_user"]: [1, 1, 2, 2, 2],
@@ -416,7 +416,7 @@ def test_dataset_with_duplicates(header):
 
 
 def test_get_topk_most_similar_users(header):
-    model = SARSingleNode(**header)
+    model = SAR(**header)
     # 1, 2, and 4 used the same items, but 1 and 2 have the same ratings also
     train = pd.DataFrame(
         {
@@ -441,7 +441,7 @@ def test_get_topk_most_similar_users(header):
 
 
 def test_item_frequencies(header):
-    model = SARSingleNode(**header)
+    model = SAR(**header)
     train = pd.DataFrame(
         {
             header["col_user"]: [1, 1, 2, 2, 3, 3, 3, 3, 4, 4],
@@ -454,7 +454,7 @@ def test_item_frequencies(header):
 
 
 def test_user_frequencies(header):
-    model = SARSingleNode(**header)
+    model = SAR(**header)
     train = pd.DataFrame(
         {
             header["col_user"]: [1, 1, 2, 2, 3, 3, 3, 3, 4, 4],