def test_precision_at_k_with_ties(): no_users, no_items = (10, 100) train, test = _generate_data(no_users, no_items) model = LightFM(loss="bpr") model.fit_partial(train) # Make all predictions zero model.user_embeddings = np.zeros_like(model.user_embeddings) model.item_embeddings = np.zeros_like(model.item_embeddings) model.user_biases = np.zeros_like(model.user_biases) model.item_biases = np.zeros_like(model.item_biases) k = 10 precision = evaluation.precision_at_k(model, test, k=k) # Pessimistic precision with all ties assert precision.mean() == 0.0
def test_predict_ranks(): no_users, no_items = (10, 100) train = sp.coo_matrix((no_users, no_items), dtype=np.float32) train = sp.rand(no_users, no_items, format="csr", random_state=42) model = LightFM() model.fit_partial(train) # Compute ranks for all items rank_input = sp.csr_matrix(np.ones((no_users, no_items))) ranks = model.predict_rank(rank_input, num_threads=2).todense() assert np.all(ranks.min(axis=1) == 0) assert np.all(ranks.max(axis=1) == no_items - 1) for row in range(no_users): assert np.all(np.sort(ranks[row]) == np.arange(no_items)) # Train set exclusions. All ranks should be zero # if train interactions is dense. ranks = model.predict_rank(rank_input, train_interactions=rank_input, check_intersections=False).todense() assert np.all(ranks == 0) # Max rank should be num_items - 1 - number of positives # in train in that row ranks = model.predict_rank(rank_input, train_interactions=train, check_intersections=False).todense() assert np.all( np.squeeze(np.array(ranks.max(axis=1))) == no_items - 1 - np.squeeze(np.array(train.getnnz(axis=1)))) # check error is raised when train and test have interactions in common with pytest.raises(ValueError): model.predict_rank(train, train_interactions=train, check_intersections=True) # check error not raised when flag is False model.predict_rank(train, train_interactions=train, check_intersections=False) # check no errors raised when train and test have no interactions in common not_train = sp.rand(no_users, no_items, format="csr", random_state=43) - train not_train.data[not_train.data < 0] = 0 not_train.eliminate_zeros() model.predict_rank(not_train, train_interactions=train, check_intersections=True) # Make sure ranks are computed pessimistically when # there are ties (that is, equal predictions for every # item will assign maximum rank to each). model.user_embeddings = np.zeros_like(model.user_embeddings) model.item_embeddings = np.zeros_like(model.item_embeddings) model.user_biases = np.zeros_like(model.user_biases) model.item_biases = np.zeros_like(model.item_biases) ranks = model.predict_rank(rank_input, num_threads=2).todense() assert np.all(ranks.min(axis=1) == 99) assert np.all(ranks.max(axis=1) == 99) # Wrong input dimensions with pytest.raises(ValueError): model.predict_rank(sp.csr_matrix((5, 5)), num_threads=2)
def test_predict_ranks(): no_users, no_items = (10, 100) train = sp.coo_matrix((no_users, no_items), dtype=np.float32) train = sp.rand(no_users, no_items, format="csr", random_state=42) model = LightFM() model.fit_partial(train) # Compute ranks for all items rank_input = sp.csr_matrix(np.ones((no_users, no_items))) ranks = model.predict_rank(rank_input, num_threads=2).todense() assert np.all(ranks.min(axis=1) == 0) assert np.all(ranks.max(axis=1) == no_items - 1) for row in range(no_users): assert np.all(np.sort(ranks[row]) == np.arange(no_items)) # Train set exclusions. All ranks should be zero # if train interactions is dense. ranks = model.predict_rank( rank_input, train_interactions=rank_input, check_intersections=False ).todense() assert np.all(ranks == 0) # Max rank should be num_items - 1 - number of positives # in train in that row ranks = model.predict_rank( rank_input, train_interactions=train, check_intersections=False ).todense() assert np.all( np.squeeze(np.array(ranks.max(axis=1))) == no_items - 1 - np.squeeze(np.array(train.getnnz(axis=1))) ) # check error is raised when train and test have interactions in common with pytest.raises(ValueError): model.predict_rank(train, train_interactions=train, check_intersections=True) # check error not raised when flag is False model.predict_rank(train, train_interactions=train, check_intersections=False) # check no errors raised when train and test have no interactions in common not_train = sp.rand(no_users, no_items, format="csr", random_state=43) - train not_train.data[not_train.data < 0] = 0 not_train.eliminate_zeros() model.predict_rank(not_train, train_interactions=train, check_intersections=True) # Make sure ranks are computed pessimistically when # there are ties (that is, equal predictions for every # item will assign maximum rank to each). model.user_embeddings = np.zeros_like(model.user_embeddings) model.item_embeddings = np.zeros_like(model.item_embeddings) model.user_biases = np.zeros_like(model.user_biases) model.item_biases = np.zeros_like(model.item_biases) ranks = model.predict_rank(rank_input, num_threads=2).todense() assert np.all(ranks.min(axis=1) == 99) assert np.all(ranks.max(axis=1) == 99) # Wrong input dimensions with pytest.raises(ValueError): model.predict_rank(sp.csr_matrix((5, 5)), num_threads=2)