Including method SVD.get_utility_matrix

funk_svd/svd.py

@@ -253,3 +253,19 @@ def _on_epoch_end(self, start, val_loss=None, val_rmse=None, val_mae=None):
             print('val_mae: {:.2f}'.format(val_mae), end=' - ')
 
         print('took {:.1f} sec'.format(end - start))
+
+    def get_utility_matrix(self, X, fillna = 0):
+        """ Creates an utility matrix based on a [u_id, i_id, rating] dataframe
+
+        Args:
+            X {pd.DataFrame} -- dataframe with columns u_id, i_id and rating
+            fillna {int} -- value to fill the non-existing ratings
+
+        Returns:
+            pd.DataFrame -- utility matrix with users as index and items as 
+                columns
+        """        
+
+        return X.pivot(
+            index='u_id', columns='i_id', values='rating'
+        ).fillna(fillna)

run_experiment.py

@@ -6,7 +6,6 @@
 
 from sklearn.metrics import mean_absolute_error
 
-
 df = fetch_ml_ratings(variant='100k')
 
 train = df.sample(frac=0.8, random_state=7)
@@ -16,9 +15,19 @@
 svd = SVD(learning_rate=0.001, regularization=0.005, n_epochs=100,
           n_factors=15, min_rating=1, max_rating=5)
 
+df_matrix_original = svd.get_utility_matrix(df)
+print ("Original Utility Matrix: \n", df_matrix_original.values)
+
+# Getting all u_id and i_id combinations
+df_user_item = pd.melt(df_matrix_original.reset_index(drop=False), id_vars='u_id')
+
 svd.fit(X=train, X_val=val, early_stopping=True, shuffle=False)
 
-pred = svd.predict(test)
-mae = mean_absolute_error(test["rating"], pred)
+pred_test = svd.predict(test)
+df_user_item["rating"] = svd.predict(df_user_item)
+
+print ("Predicted Utility Matrix: \n", svd.get_utility_matrix(df_user_item).values)
+
+mae = mean_absolute_error(test["rating"], pred_test)
 
 print(f'Test MAE: {mae:.2f}')