6 test(s) failed, 208 test(s) collected.

Cmd.exe exited with code '1'.

7 test(s) failed, 208 test(s) collected.

Bash exited with code '1'.

moon_dataset = array([[-7.92499569e-02,  9.96854776e-01],
       [ 9.95471923e-01,  9.50560433e-02],
       [ 1.84125353e+00, -4.0640....75819158e-02,  9.98867339e-01],
       [-7.86053095e-01,  6.18158986e-01],
       [ 1.01586596e+00, -4.99874128e-01]])

    @tf_only
    def test_create_model(moon_dataset):
        """test a simple parametric UMAP network"""
        embedder = ParametricUMAP()
>       embedding = embedder.fit_transform(moon_dataset)

umap/tests/test_parametric_umap.py:31: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap/parametric_umap.py:164: in fit_transform
    return super().fit_transform(X, y)
umap/umap_.py:2891: in fit_transform
    self.fit(X, y, force_all_finite)
umap/parametric_umap.py:149: in fit
    return super().fit(X, y)
umap/umap_.py:2784: in fit
    self.embedding_, aux_data = self._fit_embed_data(
umap/parametric_umap.py:299: in _fit_embed_data
    history = self.parametric_model.fit(
/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:122: in error_handler
    raise e.with_traceback(filtered_tb) from None
umap/parametric_umap.py:1002: in compute_loss
    umap_loss = self._umap_loss(y_pred)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <UMAPModel name=umap_model, built=True>
y_pred = {'embedding_from': <tf.Tensor 'umap_model_1/sequential_3/z_1/Add:0' shape=(200, 2) dtype=float32>, 'embedding_to': <tf.Tensor 'umap_model_1/sequential_1/z_1/Add:0' shape=(200, 2) dtype=float32>}
repulsion_strength = 1.0

    def _umap_loss(self, y_pred, repulsion_strength=1.0):
        # split out to/from
        embedding_to = y_pred["embedding_to"]
        embedding_from = y_pred["embedding_from"]
    
        # get negative samples
        embedding_neg_to = ops.repeat(embedding_to, self.negative_sample_rate, axis=0)
        repeat_neg = ops.repeat(embedding_from, self.negative_sample_rate, axis=0)
    
        repeat_neg_batch_dim = ops.shape(repeat_neg)[0]
    
        if keras.config.backend() in ("tensorflow", "jax"):
>           shuffled_indices = tf.random.shuffle(
                ops.arange(repeat_neg_batch_dim), seed=self.seed_generator
            )
E           TypeError: unsupported operand type(s) for %: 'SeedGenerator' and 'int'

umap/parametric_umap.py:1033: TypeError

moon_dataset = array([[-7.92499569e-02,  9.96854776e-01],
       [ 9.95471923e-01,  9.50560433e-02],
       [ 1.84125353e+00, -4.0640....75819158e-02,  9.98867339e-01],
       [-7.86053095e-01,  6.18158986e-01],
       [ 1.01586596e+00, -4.99874128e-01]])

    @tf_only
    def test_global_loss(moon_dataset):
        """test a simple parametric UMAP network"""
        embedder = ParametricUMAP(global_correlation_loss_weight=1.0)
>       embedding = embedder.fit_transform(moon_dataset)

umap/tests/test_parametric_umap.py:41: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap/parametric_umap.py:164: in fit_transform
    return super().fit_transform(X, y)
umap/umap_.py:2891: in fit_transform
    self.fit(X, y, force_all_finite)
umap/parametric_umap.py:149: in fit
    return super().fit(X, y)
umap/umap_.py:2784: in fit
    self.embedding_, aux_data = self._fit_embed_data(
umap/parametric_umap.py:299: in _fit_embed_data
    history = self.parametric_model.fit(
/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:122: in error_handler
    raise e.with_traceback(filtered_tb) from None
umap/parametric_umap.py:1002: in compute_loss
    umap_loss = self._umap_loss(y_pred)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <UMAPModel name=umap_model, built=True>
y_pred = {'embedding_from': <tf.Tensor 'umap_model_1/sequential_1_3/z_1/Add:0' shape=(200, 2) dtype=float32>, 'embedding_to': <tf.Tensor 'umap_model_1/sequential_1_1/z_1/Add:0' shape=(200, 2) dtype=float32>}
repulsion_strength = 1.0

    def _umap_loss(self, y_pred, repulsion_strength=1.0):
        # split out to/from
        embedding_to = y_pred["embedding_to"]
        embedding_from = y_pred["embedding_from"]
    
        # get negative samples
        embedding_neg_to = ops.repeat(embedding_to, self.negative_sample_rate, axis=0)
        repeat_neg = ops.repeat(embedding_from, self.negative_sample_rate, axis=0)
    
        repeat_neg_batch_dim = ops.shape(repeat_neg)[0]
    
        if keras.config.backend() in ("tensorflow", "jax"):
>           shuffled_indices = tf.random.shuffle(
                ops.arange(repeat_neg_batch_dim), seed=self.seed_generator
            )
E           TypeError: unsupported operand type(s) for %: 'SeedGenerator' and 'int'

umap/parametric_umap.py:1033: TypeError

moon_dataset = array([[-7.92499569e-02,  9.96854776e-01],
       [ 9.95471923e-01,  9.50560433e-02],
       [ 1.84125353e+00, -4.0640....75819158e-02,  9.98867339e-01],
       [-7.86053095e-01,  6.18158986e-01],
       [ 1.01586596e+00, -4.99874128e-01]])

    @tf_only
    def test_inverse_transform(moon_dataset):
        """tests inverse_transform"""
    
        def norm(x):
            return (x - np.min(x)) / (np.max(x) - np.min(x))
    
        X = norm(moon_dataset)
        embedder = ParametricUMAP(parametric_reconstruction=True)
>       Z = embedder.fit_transform(X)

umap/tests/test_parametric_umap.py:56: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap/parametric_umap.py:164: in fit_transform
    return super().fit_transform(X, y)
umap/umap_.py:2891: in fit_transform
    self.fit(X, y, force_all_finite)
umap/parametric_umap.py:149: in fit
    return super().fit(X, y)
umap/umap_.py:2784: in fit
    self.embedding_, aux_data = self._fit_embed_data(
umap/parametric_umap.py:299: in _fit_embed_data
    history = self.parametric_model.fit(
/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:122: in error_handler
    raise e.with_traceback(filtered_tb) from None
umap/parametric_umap.py:1002: in compute_loss
    umap_loss = self._umap_loss(y_pred)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <UMAPModel name=umap_model, built=True>
y_pred = {'embedding_from': <tf.Tensor 'umap_model_1/sequential_2_3/z_1/Add:0' shape=(200, 2) dtype=float32>, 'embedding_to': <...float32>, 'reconstruction': <tf.Tensor 'umap_model_1/sequential_3_1/reshape_1/Reshape:0' shape=(200, 2) dtype=float32>}
repulsion_strength = 1.0

    def _umap_loss(self, y_pred, repulsion_strength=1.0):
        # split out to/from
        embedding_to = y_pred["embedding_to"]
        embedding_from = y_pred["embedding_from"]
    
        # get negative samples
        embedding_neg_to = ops.repeat(embedding_to, self.negative_sample_rate, axis=0)
        repeat_neg = ops.repeat(embedding_from, self.negative_sample_rate, axis=0)
    
        repeat_neg_batch_dim = ops.shape(repeat_neg)[0]
    
        if keras.config.backend() in ("tensorflow", "jax"):
>           shuffled_indices = tf.random.shuffle(
                ops.arange(repeat_neg_batch_dim), seed=self.seed_generator
            )
E           TypeError: unsupported operand type(s) for %: 'SeedGenerator' and 'int'

umap/parametric_umap.py:1033: TypeError

moon_dataset = array([[-7.92499569e-02,  9.96854776e-01],
       [ 9.95471923e-01,  9.50560433e-02],
       [ 1.84125353e+00, -4.0640....75819158e-02,  9.98867339e-01],
       [-7.86053095e-01,  6.18158986e-01],
       [ 1.01586596e+00, -4.99874128e-01]])

    @tf_only
    def test_custom_encoder_decoder(moon_dataset):
        """test using a custom encoder / decoder"""
        dims = (2,)
        n_components = 2
        encoder = tf.keras.Sequential(
            [
                tf.keras.layers.Input(shape=dims),
                tf.keras.layers.Flatten(),
                tf.keras.layers.Dense(units=100, activation="relu"),
                tf.keras.layers.Dense(units=100, activation="relu"),
                tf.keras.layers.Dense(units=100, activation="relu"),
                tf.keras.layers.Dense(units=n_components, name="z"),
            ]
        )
    
        decoder = tf.keras.Sequential(
            [
                tf.keras.layers.Input(shape=(n_components,)),
                tf.keras.layers.Dense(units=100, activation="relu"),
                tf.keras.layers.Dense(units=100, activation="relu"),
                tf.keras.layers.Dense(units=100, activation="relu"),
                tf.keras.layers.Dense(
                    units=np.product(dims), name="recon", activation=None
                ),
                tf.keras.layers.Reshape(dims),
            ]
        )
    
        embedder = ParametricUMAP(
            encoder=encoder,
            decoder=decoder,
            dims=dims,
            parametric_reconstruction=True,
            verbose=True,
        )
>       embedding = embedder.fit_transform(moon_dataset)

umap/tests/test_parametric_umap.py:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
umap/parametric_umap.py:164: in fit_transform
    return super().fit_transform(X, y)
umap/umap_.py:2891: in fit_transform
    self.fit(X, y, force_all_finite)
umap/parametric_umap.py:149: in fit
    return super().fit(X, y)
umap/umap_.py:2784: in fit
    self.embedding_, aux_data = self._fit_embed_data(
umap/parametric_umap.py:299: in _fit_embed_data
    history = self.parametric_model.fit(
/opt/hostedtoolcache/Python/3.9.19/x64/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:122: in error_handler
    raise e.with_traceback(filtered_tb) from None
umap/parametric_umap.py:1002: in compute_loss
    umap_loss = self._umap_loss(y_pred)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <UMAPModel name=umap_model, built=True>
y_pred = {'embedding_from': <tf.Tensor 'umap_model_1/sequential_4_3/z_1/Add:0' shape=(200, 2) dtype=float32>, 'embedding_to': <...oat32>, 'reconstruction': <tf.Tensor 'umap_model_1/sequential_5_1/reshape_1_1/Reshape:0' shape=(200, 2) dtype=float32>}
repulsion_strength = 1.0

    def _umap_loss(self, y_pred, repulsion_strength=1.0):
        # split out to/from
        embedding_to = y_pred["embedding_to"]
        embedding_from = y_pred["embedding_from"]
    
        # get negative samples
        embedding_neg_to = ops.repeat(embedding_to, self.negative_sample_rate, axis=0)
        repeat_neg = ops.repeat(embedding_from, self.negative_sample_rate, axis=0)
    
        repeat_neg_batch_dim = ops.shape(repeat_neg)[0]
    
        if keras.config.backend() in ("tensorflow", "jax"):
>           shuffled_indices = tf.random.shuffle(
                ops.arange(repeat_neg_batch_dim), seed=self.seed_generator
            )
E           TypeError: unsupported operand type(s) for %: 'SeedGenerator' and 'int'

umap/parametric_umap.py:1033: TypeError