def test_zero_known_item_scores():
train = get_random_coo_matrix().tocsr()
predictions = np.random.random_sample(train.shape)
r = BaseRecommender()
safe = r._zero_known_item_scores(predictions,train)
num_users,num_items = predictions.shape
for u in xrange(num_users):
for i in xrange(num_items):
if i in train[u].indices:
assert_less_equal(safe[u,i],0)
else:
assert_equal(safe[u,i],predictions[u,i])
def test_zero_known_item_scores():
train = get_random_coo_matrix().tocsr()
predictions = np.random.random_sample(train.shape)
r = BaseRecommender()
safe = r._zero_known_item_scores(predictions, train)
num_users, num_items = predictions.shape
for u in xrange(num_users):
for i in xrange(num_items):
if i in train[u].indices:
assert_less_equal(safe[u, i], 0)
else:
assert_equal(safe[u, i], predictions[u, i])
def test_save_load():
r = save_load(BaseRecommender())
assert_equal(type(r), BaseRecommender)
r = MyRecommender()
r2 = save_load(r)
assert_equal(type(r2), type(r))
assert_array_equal(r2.foo, r.foo)
assert_equal(r2.description, r.description)
def load_recommender(filepath):
"""
Load a recommender model from file after it has been saved by
save_recommender().
Parameters
----------
filepath : str
The filepath to read from.
"""
return BaseRecommender.load(filepath)
def read_recommender_description(filepath):
"""
Read a recommender model description from file after it has
been saved by save_recommender(), without loading all the
associated data into memory.
Parameters
----------
filepath : str
The filepath to read from.
"""
return BaseRecommender.read_recommender_description(filepath)
def test_read_recommender_description():
check_read_description(BaseRecommender())
check_read_description(MyRecommender())
def test_save_filepath_condition():
r = BaseRecommender()
invalid_filepath = 'no suffix'
assert_raises(ValueError, r.save, invalid_filepath)
def check_read_description(r):
f, path = tempfile.mkstemp(suffix='.npz')
r.save(path)
d = BaseRecommender.read_recommender_description(path)
assert_equal(str(r), d)
def save_load(r):
f, path = tempfile.mkstemp(suffix='.npz')
r.save(path)
return BaseRecommender.load(path)
def check_read_description(r):
f,path = tempfile.mkstemp(suffix='.npz')
r.save(path)
d = BaseRecommender.read_recommender_description(path)
assert_equal(str(r),d)
def save_load(r):
f,path = tempfile.mkstemp(suffix='.npz')
r.save(path)
return BaseRecommender.load(path)
