Fix segfault when predicting on a model that hasn't been fitted.

Author: maciejkula

changelog.md

@@ -6,6 +6,7 @@
 ### Fixed
 - recall_at_k and precision_at_k now work correctly at k=1 (thanks to Zank Bennett).
 - Moved Movielens data to data release to prevent grouplens server flakiness from affecting users.
+- Fix segfault when trying to predict from a model that has not been fitted.
 
 ## [1.12][2017-01-26]
 ### Changed

lightfm/lightfm.py

@@ -177,6 +177,25 @@ def _reset_state(self):
         self.user_bias_gradients = None
         self.user_bias_momentum = None
 
+    def _check_initialized(self):
+
+        for var in (self.item_embeddings,
+                    self.item_embedding_gradients,
+                    self.item_embedding_momentum,
+                    self.item_biases,
+                    self.item_bias_gradients,
+                    self.item_bias_momentum,
+                    self.user_embeddings,
+                    self.user_embedding_gradients,
+                    self.user_embedding_momentum,
+                    self.user_biases,
+                    self.user_bias_gradients,
+                    self.user_bias_momentum):
+
+            if var is None:
+                raise ValueError('You must fit the model before '
+                                 'trying to obtain predictions.')
+
     def _initialize(self, no_components, no_item_features, no_user_features):
         """
         Initialise internal latent representations.
@@ -596,6 +615,8 @@ def predict(self, user_ids, item_ids, item_features=None,
             by the inputs.
         """
 
+        self._check_initialized()
+
         if not isinstance(user_ids, np.ndarray):
             user_ids = np.repeat(np.int32(user_ids), len(item_ids))
 
@@ -670,6 +691,8 @@ def predict_rank(self, test_interactions, train_interactions=None,
             input interactions matrix.
         """
 
+        self._check_initialized()
+
         n_users, n_items = test_interactions.shape
 
         (user_features,
@@ -731,6 +754,8 @@ def get_item_representations(self, features=None):
             Biases and latent representations for items.
         """
 
+        self._check_initialized()
+
         if features is None:
             return self.item_biases, self.item_embeddings
 
@@ -758,6 +783,8 @@ def get_user_representations(self, features=None):
             Biases and latent representations for users.
         """
 
+        self._check_initialized()
+
         if features is None:
             return self.user_biases, self.user_embeddings
 

tests/test_api.py

@@ -332,3 +332,21 @@ def test_sklearn_api():
     params['invalid_param'] = 666
     with pytest.raises(ValueError):
         model.set_params(**params)
+
+
+def test_predict_not_fitte():
+
+    model = LightFM()
+
+    with pytest.raises(ValueError):
+        model.predict(np.arange(10),
+                      np.arange(10))
+
+    with pytest.raises(ValueError):
+        model.predict_rank(1)
+
+    with pytest.raises(ValueError):
+        model.get_user_representations()
+
+    with pytest.raises(ValueError):
+        model.get_item_representations()