def test__predict__good__disjoint_drop():
"""test the predict() method on disjoint validation pairs with the cold_start='drop' option"""
model = RankFM(factors=2)
model.fit(intx_train_pd_int)
scores = model.predict(intx_valid_disjoint, cold_start='drop')
shape = scores.shape == (5, )
dtype = scores.dtype == np.float32
nmiss = np.sum(np.isnan(scores).astype(np.int32)) == 0
assert shape and dtype and nmiss
def test__predict__good__train():
"""test the predict() method on the training inputs"""
model = RankFM(factors=2)
model.fit(intx_train_pd_int)
scores = model.predict(intx_train_pd_int)
shape = scores.shape == (9, )
dtype = scores.dtype == np.float32
nmiss = np.sum(np.isnan(scores).astype(np.int32)) == 0
assert shape and dtype and nmiss