Merge pull request #621 from deklanw/slimelastic

add SLIMElastic

recbole/model/general_recommender/__init__.py

@@ -16,6 +16,7 @@
 from recbole.model.general_recommender.neumf import NeuMF
 from recbole.model.general_recommender.ngcf import NGCF
 from recbole.model.general_recommender.pop import Pop
+from recbole.model.general_recommender.slimelastic import SLIMElastic
 from recbole.model.general_recommender.spectralcf import SpectralCF
 from recbole.model.general_recommender.ease import EASE
 from recbole.model.general_recommender.nncf import NNCF

recbole/model/general_recommender/slimelastic.py

@@ -0,0 +1,110 @@
+r"""
+SLIMElastic
+################################################
+Reference:
+    10.1109/ICDM.2011.134
+    https://www.slideshare.net/MarkLevy/efficient-slides
+
+Reference code:
+    https://github.com/KarypisLab/SLIM
+    https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation/blob/master/SLIM_ElasticNet/SLIMElasticNetRecommender.py
+"""
+
+
+from recbole.utils.enum_type import ModelType
+import numpy as np
+import scipy.sparse as sp
+import torch
+import warnings
+from sklearn.linear_model import ElasticNet
+from sklearn.exceptions import ConvergenceWarning
+
+from recbole.utils import InputType
+from recbole.model.abstract_recommender import GeneralRecommender
+
+
+# https://github.com/RUCAIBox/RecBole/issues/622
+def add_noise(t, mag=1e-5):
+    return t + mag * torch.rand(t.shape)
+
+
+class SLIMElastic(GeneralRecommender):
+    input_type = InputType.POINTWISE
+    type = ModelType.TRADITIONAL
+
+    def __init__(self, config, dataset):
+        super().__init__(config, dataset)
+
+        # need at least one param
+        self.dummy_param = torch.nn.Parameter(torch.zeros(1))
+
+        X = dataset.inter_matrix(
+            form='csr').astype(np.float32)
+
+        X = X.tolil()
+        self.interaction_matrix = X
+
+        hide_item = config['hide_item']
+        alpha = config['alpha']
+        l1_ratio = config['l1_ratio']
+        positive_only = config['positive_only']
+
+        model = ElasticNet(alpha=alpha, l1_ratio=l1_ratio,
+                           positive=positive_only,
+                           fit_intercept=False,
+                           copy_X=False,
+                           precompute=True,
+                           selection='random',
+                           max_iter=100,
+                           tol=1e-4)
+
+        item_coeffs = []
+
+        # ignore ConvergenceWarnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=ConvergenceWarning)
+
+            for j in range(X.shape[1]):
+                # target column
+                r = X[:, j]
+
+                if hide_item:
+                    # set item column to 0
+                    X[:, j] = 0
+
+                # fit the model
+                model.fit(X, r.todense().getA1())
+
+                # store the coefficients
+                coeffs = model.sparse_coef_
+
+                item_coeffs.append(coeffs)
+
+                if hide_item:
+                    # reattach column if removed
+                    X[:, j] = r
+
+        self.item_similarity = sp.vstack(item_coeffs).T
+
+    def forward(self):
+        pass
+
+    def calculate_loss(self, interaction):
+        return torch.nn.Parameter(torch.zeros(1))
+
+    def predict(self, interaction):
+        user = interaction[self.USER_ID].cpu().numpy()
+        item = interaction[self.ITEM_ID].cpu().numpy()
+
+        r = torch.from_numpy((self.interaction_matrix[user, :].multiply(
+            self.item_similarity[:, item].T)).sum(axis=1).getA1())
+
+        return add_noise(r)
+
+    def full_sort_predict(self, interaction):
+        user = interaction[self.USER_ID].cpu().numpy()
+
+        r = self.interaction_matrix[user, :] @ self.item_similarity
+        r = torch.from_numpy(r.todense().getA1())
+
+        return add_noise(r)

recbole/properties/model/SLIMElastic.yaml

@@ -0,0 +1,4 @@
+alpha: 0.2
+l1_ratio: 0.02
+positive_only: False
+hide_item: True

run_test_example.py

@@ -134,6 +134,10 @@
         'model': 'LINE',
         'dataset': 'ml-100k',
     },
+    'Test SLIMElastic': {
+        'model': 'SLIMElastic',
+        'dataset': 'ml-100k',
+    },
     'Test EASE': {
         'model': 'EASE',
         'dataset': 'ml-100k',
@@ -359,13 +363,15 @@ def run_test_examples():
     for idx, example in enumerate(test_examples.keys()):
         if example in closed_examples:
             continue
-        print('\n\n Begin to run %d / %d example: %s \n\n' % (idx + 1, n_examples, example))
+        print('\n\n Begin to run %d / %d example: %s \n\n' %
+              (idx + 1, n_examples, example))
         try:
             config_dict = test_examples[example]
             if 'epochs' not in config_dict:
                 config_dict['epochs'] = 1
             run_recbole(config_dict=config_dict, saved=False)
-            print('\n\n Running %d / %d example successfully: %s \n\n' % (idx + 1, n_examples, example))
+            print('\n\n Running %d / %d example successfully: %s \n\n' %
+                  (idx + 1, n_examples, example))
             success_examples.append(example)
         except Exception:
             print(traceback.format_exc())

tests/model/test_model_auto.py

@@ -188,6 +188,12 @@ def test_NNCF(self):
         quick_test(config_dict)
 
 
+    def test_slimelastic(self):
+        config_dict = {
+            'model': 'SLIMElastic',
+        }
+        quick_test(config_dict)
+
 
 class TestContextRecommender(unittest.TestCase):
     # todo: more complex context information should be test, such as criteo dataset
@@ -754,5 +760,6 @@ def test_kgnnls_with_concat(self):
         quick_test(config_dict)
 
 
+
 if __name__ == '__main__':
     unittest.main()