Merge pull request #350 from mdekstrand/tweak/cleanup-extensions

Clean up legacy extension imports

tests/test_ml20m.py

@@ -5,27 +5,14 @@
 import logging
 from pathlib import Path
 
-import pandas as pd
-import numpy as np
 
 from lenskit.datasets import MovieLens
-from lenskit import crossfold as xf
-from lenskit.metrics import predict as pm
 from lenskit import batch
 from lenskit.algorithms import Recommender
 from lenskit.algorithms.basic import Popular
 from lenskit.algorithms.als import BiasedMF
-from lenskit.algorithms import item_knn as knn
-
-try:
-    import lenskit_tf
-except:
-    lenskit_tf = None
-from lenskit.util import Stopwatch
-from lenskit.util import test as lktu
 
 import pytest
-from pytest import approx
 
 _log = logging.getLogger(__name__)
 
@@ -76,27 +63,3 @@ def test_als_isolate(ml20m, rng):
         assert len(preds) == len(pairs)
     finally:
         ares.close()
-
-
-@pytest.mark.realdata
-@pytest.mark.slow
-@pytest.mark.skip
-@pytest.mark.skipif(
-    lenskit_tf is None or not lenskit_tf.TF_AVAILABLE, reason="TensorFlow not available"
-)
-def test_tf_isvd(ml20m):
-    algo = lenskit_tf.IntegratedBiasMF(20)
-
-    def eval(train, test):
-        _log.info("running training")
-        algo.fit(train)
-        _log.info("testing %d users", test.user.nunique())
-        return batch.predict(algo, test)
-
-    folds = xf.sample_users(ml20m, 2, 5000, xf.SampleFrac(0.2))
-    preds = pd.concat(eval(train, test) for (train, test) in folds)
-    mae = pm.mae(preds.prediction, preds.rating)
-    assert mae == approx(0.60, abs=0.025)
-
-    user_rmse = preds.groupby("user").apply(lambda df: pm.rmse(df.prediction, df.rating))
-    assert user_rmse.mean() == approx(0.92, abs=0.05)