Python LightFM.fit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 fit [as 别名]
def test_sample_weight():

    model = LightFM()

    train = sp.coo_matrix(np.array([[0, 1], [0, 1]]))

    with pytest.raises(ValueError):
        # Wrong number of weights
        sample_weight = sp.coo_matrix(np.zeros((2, 2)))

        model.fit(train, sample_weight=sample_weight)

    with pytest.raises(ValueError):
        # Wrong shape
        sample_weight = sp.coo_matrix(np.zeros(2))
        model.fit(train, sample_weight=np.zeros(3))

    with pytest.raises(ValueError):
        # Wrong order of entries
        sample_weight = sp.coo_matrix((train.data, (train.row[::-1], train.col[::-1])))
        model.fit(train, sample_weight=np.zeros(3))

    sample_weight = sp.coo_matrix((train.data, (train.row, train.col)))
    model.fit(train, sample_weight=sample_weight)

    model = LightFM(loss="warp-kos")

    with pytest.raises(NotImplementedError):
        model.fit(train, sample_weight=np.ones(1))

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

    no_users, no_items = (1000, 2)

    train = sp.rand(no_users, no_items, format="csr", random_state=42)

    model = LightFM(loss="warp", max_sampled=10)

    model.fit(train)

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

    no_users, no_items = (1000, 1000)

    train = sp.rand(no_users, no_items, format="csr", random_state=42)

    model = LightFM(learning_rate=10000000.0, loss="warp")

    with pytest.raises(ValueError):
        model.fit(train, epochs=10)

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

    model = LightFM(random_state=SEED)

    model.fit(train, epochs=1)

    assert np.mean(model.user_embedding_gradients) > 1.0

    model.fit(train, epochs=0)
    assert np.all(model.user_embedding_gradients == 1.0)

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

    model = LightFM(random_state=SEED)
    model.fit(train, epochs=10)

    train_predictions = model.predict(train.row, train.col)
    test_predictions = model.predict(test.row, test.col)

    assert roc_auc_score(train.data, train_predictions) > 0.84
    assert roc_auc_score(test.data, test_predictions) > 0.76

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

    no_users, no_items = (1000, 1000)

    train = sp.rand(no_users, no_items, format="csr", random_state=42)
    train.data *= np.nan

    model = LightFM(loss="warp")

    with pytest.raises(ValueError):
        model.fit(train)

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

    no_users, no_items = (1000, 1000)

    train = sp.rand(no_users, no_items, format="csr", random_state=42)

    features = sp.identity(no_items)
    features.data *= np.nan

    model = LightFM(loss="warp")

    with pytest.raises(ValueError):
        model.fit(train, epochs=10, user_features=features, item_features=features)

# 需要导入模块: from lightfm.lightfm import LightFM [as 别名]
# 或者: from lightfm.lightfm.LightFM import fit [as 别名]
def test_coo_with_duplicate_entries():
    # Calling .tocsr on a COO matrix with duplicate entries
    # changes its data arrays in-place, leading to out-of-bounds
    # array accesses in the WARP code.
    # Reported in https://github.com/lyst/lightfm/issues/117.

    rows, cols = (1000, 100)
    mat = sp.random(rows, cols)
    mat.data[:] = 1

    # Duplicate entries in the COO matrix
    mat.data = np.concatenate((mat.data, mat.data[:1000]))
    mat.row = np.concatenate((mat.row, mat.row[:1000]))
    mat.col = np.concatenate((mat.col, mat.col[:1000]))

    for loss in ("warp", "bpr", "warp-kos"):
        model = LightFM(loss="warp")
        model.fit(mat)

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

    no_users, no_items = (1000, 1000)

    train = sp.rand(no_users, no_items, format="csr", random_state=42)

    model = LightFM(loss="warp")

    model.fit(train)

    with pytest.raises((ValueError, OverflowError)):
        print(
            model.predict(
                1231241241231241414,
                np.arange(no_items),
                user_features=sp.identity(no_users),
            )
        )

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

    no_users, no_items = (10, 100)

    train = sp.coo_matrix((no_users, no_items), dtype=np.int32)

    model = LightFM()
    assert model.fit_partial(train) is model
    assert model.fit(train) is model