Python LightFM.predict_rank方法代码示例

# 需要导入模块: from lightfm.lightfm import LightFM [as 别名]
# 或者: from lightfm.lightfm.LightFM import predict_rank [as 别名]
def test_matrix_types():

    mattypes = (sp.coo_matrix, sp.lil_matrix, sp.csr_matrix, sp.csc_matrix)

    dtypes = (np.int32, np.int64, np.float32, np.float64)

    no_users, no_items = (10, 100)
    no_features = 20

    for mattype in mattypes:
        for dtype in dtypes:
            train = mattype((no_users, no_items), dtype=dtype)
            weights = train.tocoo()

            user_features = mattype((no_users, no_features), dtype=dtype)
            item_features = mattype((no_items, no_features), dtype=dtype)

            model = LightFM()
            model.fit_partial(
                train,
                sample_weight=weights,
                user_features=user_features,
                item_features=item_features,
            )

            model.predict(
                np.random.randint(0, no_users, 10).astype(np.int32),
                np.random.randint(0, no_items, 10).astype(np.int32),
                user_features=user_features,
                item_features=item_features,
            )

            model.predict_rank(
                train, user_features=user_features, item_features=item_features
            )

# 需要导入模块: from lightfm.lightfm import LightFM [as 别名]
# 或者: from lightfm.lightfm.LightFM import predict_rank [as 别名]
def test_predict_not_fitted():

    model = LightFM()

    with pytest.raises(ValueError):
        model.predict(np.arange(10), np.arange(10))

    with pytest.raises(ValueError):
        model.predict_rank(1)

    with pytest.raises(ValueError):
        model.get_user_representations()

    with pytest.raises(ValueError):
        model.get_item_representations()

# 需要导入模块: from lightfm.lightfm import LightFM [as 别名]
# 或者: from lightfm.lightfm.LightFM import predict_rank [as 别名]
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)