Use new predict method consistently

lenskit/batch/_predict.py

@@ -68,12 +68,6 @@ def predict(algo, model, pairs, nprocs=None):
             result will also contain a `rating` column.
     """
 
-    pfun = None
-    if not isinstance(algo, Predictor):
-        warnings.warn('non-Perdictor argument to predict deprecated', DeprecationWarning)
-        nprocs = None
-        pfun = algo
-
     if nprocs and nprocs > 1 and mp.get_start_method() == 'fork':
         _logger.info('starting predict process with %d workers', nprocs)
         with MPRecContext(algo, model),  Pool(nprocs) as pool:
@@ -83,11 +77,7 @@ def predict(algo, model, pairs, nprocs=None):
     else:
         results = []
         for user, udf in pairs.groupby('user'):
-            if pfun:
-                res = pfun(user, udf['item'])
-                res = pd.DataFrame({'user': user, 'item': res.index, 'prediction': res.values})
-            else:
-                res = _predict_user(algo, model, user, udf)
+            res = _predict_user(algo, model, user, udf)
             results.append(res)
 
     results = pd.concat(results)

tests/test_als_explicit.py

@@ -123,7 +123,7 @@ def eval(train, test):
         _log.info('running training')
         model = algo.train(train)
         _log.info('testing %d users', test.user.nunique())
-        return batch.predict(algo, test, model=model)
+        return batch.predict(algo, model, test)
 
     folds = xf.partition_users(ratings, 5, xf.SampleFrac(0.2))
     preds = pd.concat(eval(train, test) for (train, test) in folds)

tests/test_batch_predict.py

@@ -14,7 +14,6 @@
 _log = logging.getLogger(__name__)
 
 MLB = namedtuple('MLB', ['ratings', 'algo', 'model'])
-MLB.predictor = property(lambda mlb: partial(mlb.algo.predict, mlb.model))
 
 
 @pytest.fixture
@@ -27,20 +26,7 @@ def mlb():
 
 def test_predict_single(mlb):
     tf = pd.DataFrame({'user': [1], 'item': [31]})
-    res = lkb.predict(mlb.predictor, tf)
-
-    assert len(res) == 1
-    assert all(res.user == 1)
-    assert set(res.columns) == set(['user', 'item', 'prediction'])
-    assert all(res.item == 31)
-
-    expected = mlb.model.mean + mlb.model.items.loc[31] + mlb.model.users.loc[1]
-    assert res.prediction.iloc[0] == pytest.approx(expected)
-
-
-def test_predict_single_model(mlb):
-    tf = pd.DataFrame({'user': [1], 'item': [31]})
-    res = lkb.predict(mlb.algo, tf, mlb.model)
+    res = lkb.predict(mlb.algo, mlb.model, tf)
 
     assert len(res) == 1
     assert all(res.user == 1)
@@ -61,7 +47,7 @@ def test_predict_user(mlb):
     test_items = pd.concat([test_rated, pd.Series(test_unrated)])
 
     tf = pd.DataFrame({'user': uid, 'item': test_items})
-    res = lkb.predict(mlb.predictor, tf)
+    res = lkb.predict(mlb.algo, mlb.model, tf)
 
     assert len(res) == 15
     assert set(res.columns) == set(['user', 'item', 'prediction'])
@@ -83,7 +69,7 @@ def test_predict_two_users(mlb):
     while tf is None or len(set(tf.user)) < 2:
         tf = mlb.ratings[mlb.ratings.user.isin(uids)].loc[:, ('user', 'item')].sample(10)
 
-    res = lkb.predict(mlb.predictor, tf)
+    res = lkb.predict(mlb.algo, mlb.model, tf)
 
     assert len(res) == 10
     assert set(res.user) == set(uids)
@@ -102,7 +88,7 @@ def test_predict_include_rating(mlb):
     while tf is None or len(set(tf.user)) < 2:
         tf = mlb.ratings[mlb.ratings.user.isin(uids)].loc[:, ('user', 'item', 'rating')].sample(10)
 
-    res = lkb.predict(mlb.predictor, tf)
+    res = lkb.predict(mlb.algo, mlb.model, tf)
 
     assert len(res) == 10
     assert set(res.user) == set(uids)
@@ -133,7 +119,7 @@ def eval(train, test):
         _log.info('running training')
         model = algo.train(train)
         _log.info('testing %d users', test.user.nunique())
-        recs = batch.predict(algo, test, model=model, nprocs=ncpus)
+        recs = batch.predict(algo, model, test, nprocs=ncpus)
         return recs
 
     preds = pd.concat((eval(train, test)

tests/test_funksvd.py

@@ -140,7 +140,7 @@ def test_fsvd_known_preds():
     known_preds = pd.read_csv(str(pred_file))
     pairs = known_preds.loc[:, ['user', 'item']]
 
-    preds = batch.predict(algo, pairs, model=model)
+    preds = batch.predict(algo, model, pairs)
     merged = pd.merge(known_preds.rename(columns={'prediction': 'expected'}), preds)
     assert len(merged) == len(preds)
     merged['error'] = merged.expected - merged.prediction
@@ -173,7 +173,7 @@ def eval(train, test):
         _log.info('running training')
         model = algo.train(train)
         _log.info('testing %d users', test.user.nunique())
-        return batch.predict(algo, test, model=model)
+        return batch.predict(algo, model, test)
 
     folds = xf.partition_users(ratings, 5, xf.SampleFrac(0.2))
     preds = pd.concat(eval(train, test) for (train, test) in folds)

tests/test_knn_item_item.py

@@ -448,7 +448,7 @@ def eval(train, test):
         _log.info('running training')
         model = algo.train(train)
         _log.info('testing %d users', test.user.nunique())
-        return batch.predict(lambda u, xs: algo.predict(model, u, xs), test)
+        return batch.predict(algo, model, test)
 
     preds = pd.concat((eval(train, test)
                        for (train, test)
@@ -477,7 +477,7 @@ def test_ii_known_preds():
     known_preds = pd.read_csv(str(pred_file))
     pairs = known_preds.loc[:, ['user', 'item']]
 
-    preds = batch.predict(algo, pairs, model=model)
+    preds = batch.predict(algo, model, pairs)
     merged = pd.merge(known_preds.rename(columns={'prediction': 'expected'}), preds)
     assert len(merged) == len(preds)
     merged['error'] = merged.expected - merged.prediction

tests/test_knn_user_user.py

@@ -184,7 +184,7 @@ def test_uu_known_preds():
     known_preds = pd.read_csv(str(pred_file))
     pairs = known_preds.loc[:, ['user', 'item']]
 
-    preds = batch.predict(algo, pairs, model=model)
+    preds = batch.predict(algo, model, pairs)
     merged = pd.merge(known_preds.rename(columns={'prediction': 'expected'}), preds)
     assert len(merged) == len(preds)
     merged['error'] = merged.expected - merged.prediction
@@ -205,7 +205,7 @@ def __batch_eval(job):
     _log.info('running training')
     model = algo.train(train)
     _log.info('testing %d users', test.user.nunique())
-    return batch.predict(lambda u, xs: algo.predict(model, u, xs), test)
+    return batch.predict(algo, model, test)
 
 
 @mark.slow

tests/test_predict_metrics.py

@@ -170,7 +170,7 @@ def test_batch_rmse():
 
     def eval(train, test):
         model = algo.train(train)
-        preds = batch.predict(lambda u, xs: algo.predict(model, u, xs), test)
+        preds = batch.predict(algo, model, test)
         return preds.set_index(['user', 'item'])
 
     results = pd.concat((eval(train, test)