Rename load_image() and load_freature() as load_visual_feature() (#371)

cornac/datasets/amazon_clothing.py

@@ -62,8 +62,8 @@ def load_text():
     return texts, ids
 
 
-def load_image():
-    """Load the item image in the form of visual features (extracted from pre-trained CNN)
+def load_visual_feature():
+    """Load item visual features (extracted from pre-trained CNN)
 
     Returns
     -------

cornac/datasets/tradesy.py

@@ -49,8 +49,8 @@ def load_feedback(reader: Reader = None) -> List:
     return reader.read(fpath, fmt='UI', sep=',')
 
 
-def load_feature():
-    """Load the item visual feature
+def load_visual_feature():
+    """Load item visual features
 
     Returns
     -------

examples/vbpr_tradesy.py

@@ -26,7 +26,7 @@ from cornac.eval_methods import RatioSplit
 # VBPR extends Bayesian Personalized Randing to leverage item visual features (extracted from product images using CNN)
 # The necessary data can be loaded as follows
 feedback = tradesy.load_feedback()
-features, item_ids = tradesy.load_feature()  # BIG file
+features, item_ids = tradesy.load_visual_feature()  # BIG file
 
 # Instantiate a ImageModality, it makes it convenient to work with visual auxiliary information
 # For more details, please refer to the tutorial on how to work with auxiliary data

examples/vmf_clothing.py

@@ -22,7 +22,7 @@ from cornac.eval_methods import RatioSplit
 
 # The necessary data can be loaded as follows
 feedback = amazon_clothing.load_feedback()
-features, item_ids = amazon_clothing.load_image()
+features, item_ids = amazon_clothing.load_visual_feature()
 
 # Instantiate a ImageModality, it makes it convenient to work with visual auxiliary information
 # For more details, please refer to the tutorial on how to work with auxiliary data