chemiscope.metatensor_featurizer

README.md

@@ -103,6 +103,12 @@ dependencies with:
 pip install chemiscope[explore]
 ```
 
+To use `chemiscope.metatensor_featurizer` for providing your trained model
+to get the features for `chemiscope.explore`, install the dependencies with:
+```bash
+pip install chemiscope[metatensor]
+```
+
 ## sphinx and sphinx-gallery integration
 
 Chemiscope provides also extensions for `sphinx` and `sphinx-gallery` to

docs/src/python/reference.rst

@@ -26,3 +26,5 @@
 .. autofunction:: chemiscope.ase_tensors_to_ellipsoids
 
 .. autofunction:: chemiscope.explore
+
+.. autofunction:: chemiscope.metatensor_featurizer

pyproject.toml

@@ -75,3 +75,8 @@ explore = [
     "dscribe",
     "scikit-learn",
 ]
+
+metatensor = [
+    "metatensor",
+    "metatensor[torch]"
+]

python/chemiscope/__init__.py

@@ -11,7 +11,7 @@
     extract_properties,
     librascal_atomic_environments,
 )
-from .explore import explore  # noqa: F401
+from .explore import explore, metatensor_featurizer  # noqa: F401
 from .version import __version__  # noqa: F401
 
 from .jupyter import show, show_input  # noqa

python/chemiscope/explore.py → python/chemiscope/explore/__init__.py

@@ -1,6 +1,8 @@
-import os
+from ..jupyter import show
+from ._soap_pca import soap_pca_featurize
+from ._metatensor import metatensor_featurizer
 
-from .jupyter import show
+__all__ = ["explore", "metatensor_featurizer"]
 
 
 def explore(frames, featurize=None, properties=None, environments=None, mode="default"):
@@ -116,96 +118,9 @@ def soap_kpca_featurize(frames, environments):
     # Add dimensionality reduction results to properties
     properties["features"] = X_reduced
 
-    # Return chemiscope widget
     return show(
-        frames=frames, properties=properties, mode=mode, environments=environments
+        frames=frames,
+        properties=properties,
+        environments=environments,
+        mode=mode,
     )
-
-
-def soap_pca_featurize(frames, environments=None):
-    """
-    Computes SOAP features for a given set of atomic structures and performs
-    dimensionality reduction using PCA. Custom featurize functions should
-    have the same signature.
-
-    Note:
-    - The SOAP descriptor parameters are pre-defined.
-    - We use all available CPU cores for parallel computation of SOAP descriptors.
-    """
-
-    # Check if dependencies were installed
-    try:
-        from dscribe.descriptors import SOAP
-        from sklearn.decomposition import PCA
-    except ImportError as e:
-        raise ImportError(
-            f"Required package not found: {str(e)}. Please install dependency "
-            + "using 'pip install chemiscope[explore]'."
-        )
-    centers = None
-
-    # Get the atom indexes from the environments and pick related frames
-    if environments is not None:
-        centers = _extract_environment_indices(environments)
-
-    # Pick frames and properties related to the environments if provided
-    if environments is not None:
-        # Sort environments by structure id and atom id
-        environments = sorted(environments, key=lambda x: (x[0], x[1]))
-
-        # Check structure indexes
-        unique_structures = list({env[0] for env in environments})
-        if any(index >= len(frames) for index in unique_structures):
-            raise IndexError(
-                "Some structure indices in 'environments' are larger than the number of"
-                "frames"
-            )
-
-        if len(unique_structures) != len(frames):
-            # only include frames that are present in the user-provided environments
-            frames = [frames[index] for index in unique_structures]
-
-    # Get global species
-    species = set()
-    for frame in frames:
-        species.update(frame.get_chemical_symbols())
-    species = list(species)
-
-    # Check if periodic
-    is_periodic = all(all(frame.get_pbc()) for frame in frames)
-
-    # Initialize calculator
-    soap = SOAP(
-        species=species,
-        r_cut=4.5,
-        n_max=8,
-        l_max=6,
-        sigma=0.2,
-        rbf="gto",
-        average="outer",
-        periodic=is_periodic,
-        weighting={"function": "pow", "c": 1, "m": 5, "d": 1, "r0": 3.5},
-        compression={"mode": "mu1nu1"},
-    )
-
-    # Calculate descriptors
-    n_jobs = min(len(frames), os.cpu_count())
-    feats = soap.create(frames, centers=centers, n_jobs=n_jobs)
-
-    # Compute pca
-    pca = PCA(n_components=2)
-    return pca.fit_transform(feats)
-
-
-def _extract_environment_indices(envs):
-    """
-    Convert from chemiscope's environements to DScribe's centers selection
-
-    :param: list envs: each element is a list of [env_index, atom_index, cutoff]
-    """
-    grouped_envs = {}
-    for [env_index, atom_index, _cutoff] in envs:
-        if env_index not in grouped_envs:
-            grouped_envs[env_index] = []
-        grouped_envs[env_index].append(atom_index)
-    return list(grouped_envs.values())

python/chemiscope/explore/_metatensor.py

@@ -0,0 +1,148 @@
+import numpy as np
+
+
+def metatensor_featurizer(
+    model,
+    extensions_directory=None,
+    check_consistency=False,
+    device=None,
+):
+    """
+    Create a featurizer function using a `metatensor`_ model to obtain the features from
+    structures. The model must be able to create a ``"feature"`` output.
+
+    :param model: model to use for the calculation. It can be a file path, a Python
+        instance of :py:class:`metatensor.torch.atomistic.MetatensorAtomisticModel`, or
+        the output of :py:func:`torch.jit.script` on
+        :py:class:`metatensor.torch.atomistic.MetatensorAtomisticModel`.
+    :param extensions_directory: a directory where model extensions are located
+    :param check_consistency: should we check the model for consistency when running,
+        defaults to False.
+    :param device: a torch device to use for the calculation. If ``None``, the function
+        will use the options in model's ``supported_device`` attribute.
+
+    :returns: a function that takes a list of frames and returns the features.
+
+    To use this function, additional dependencies are required. They can be installed
+    with the following command:
+
+    .. code:: bash
+
+        pip install chemiscope[metatensor]
+
+    Here is an example using a pre-trained `metatensor`_ model, stored as a ``model.pt``
+    file with the compiled extensions stored in the ``extensions/`` directory. To obtain
+    the details on how to get it, see metatensor `tutorial
+    <https://lab-cosmo.github.io/metatrain/latest/getting-started/usage.html>`_. The
+    frames are obtained by reading structures from a file that `ase <ase-io_>`_ can
+    read.
+
+    .. code-block:: python
+
+        import chemiscope
+        import ase.io
+
+        # Read the structures from the dataset frames =
+        ase.io.read("data/explore_c-gap-20u.xyz", ":")
+
+        # Provide model file ("model.pt") to `metatensor_featurizer`
+        featurizer = chemiscope.metatensor_featurizer(
+            "model.pt", extensions_directory="extensions"
+        )
+
+        chemiscope.explore(frames, featurize=featurizer)
+
+    For more examples, see the related :ref:`documentation
+    <chemiscope-explore-metatensor>`.
+
+    .. _metatensor: https://docs.metatensor.org/latest/index.html
+    .. _chemiscope-explore-metatensor:
+        https://chemiscope.org/docs/examples/7-explore-advanced.html#example-with-metatensor-model
+    """
+
+    # Check if dependencies were installed
+    try:
+        from metatensor.torch.atomistic import ModelOutput
+        from metatensor.torch.atomistic.ase_calculator import MetatensorCalculator
+    except ImportError as e:
+        raise ImportError(
+            f"Required package not found: {e}. Please install the dependency using "
+            "'pip install chemiscope[metatensor]'."
+        )
+
+    # Initialize metatensor calculator
+    CALCULATOR = MetatensorCalculator(
+        model=model,
+        extensions_directory=extensions_directory,
+        check_consistency=check_consistency,
+        device=device,
+    )
+
+    def get_features(atoms, environments):
+        """Run the model on a single atomic structure and extract the features"""
+        outputs = {"features": ModelOutput(per_atom=environments is not None)}
+        selected_atoms = _create_selected_atoms(environments)
+        output = CALCULATOR.run_model(atoms, outputs, selected_atoms)
+
+        return output["features"].block().values.detach().cpu().numpy()
+
+    def featurize(frames, environments):
+        if isinstance(frames, list):
+            envs_per_frame = _get_environments_per_frame(environments, len(frames))
+
+            outputs = [
+                get_features(frame, envs) for frame, envs in zip(frames, envs_per_frame)
+            ]
+            return np.vstack(outputs)
+        else:
+            return get_features(frames, environments)
+
+    return featurize
+
+
+def _get_environments_per_frame(environments, num_frames):
+    """
+    Organize the environments for each frame
+
+    :param list environments: a list of atomic environments
+    :param int num_frames: total number of frames
+    """
+    envs_per_frame = [None] * num_frames
+
+    if environments is None:
+        return envs_per_frame
+
+    frames_dict = {}
+
+    # Group environments by structure_id
+    for env in environments:
+        structure_id = env[0]
+        if structure_id not in frames_dict:
+            frames_dict[structure_id] = []
+        frames_dict[structure_id].append(env)
+
+    # Insert environments to the frame indices
+    for structure_id, envs in frames_dict.items():
+        if structure_id < num_frames:
+            envs_per_frame[structure_id] = envs
+
+    return envs_per_frame
+
+
+def _create_selected_atoms(environments):
+    """
+    Convert environments into ``Labels`` object, to be used as ``selected_atoms``
+
+    :param environments: a list of atom-centered environments
+    """
+    import torch
+    from metatensor.torch import Labels
+
+    if environments is None:
+        return None
+
+    # Extract system and atom indices from environments, overriding the structure id to
+    # be 0 (since we will only give a single frame to the calculator at the same time).
+    values = torch.tensor([(0, atom_id) for _, atom_id, _ in environments])
+
+    return Labels(names=["system", "atom"], values=values)

python/chemiscope/explore/_soap_pca.py

@@ -0,0 +1,90 @@
+import os
+
+
+def soap_pca_featurize(frames, environments=None):
+    """
+    Computes SOAP features for a given set of atomic structures and performs
+    dimensionality reduction using PCA. Custom featurize functions should
+    have the same signature.
+
+    Note:
+    - The SOAP descriptor parameters are pre-defined.
+    - We use all available CPU cores for parallel computation of SOAP descriptors.
+    """
+
+    # Check if dependencies were installed
+    try:
+        from dscribe.descriptors import SOAP
+        from sklearn.decomposition import PCA
+    except ImportError as e:
+        raise ImportError(
+            f"Required package not found: {str(e)}. Please install dependency "
+            + "using 'pip install chemiscope[explore]'."
+        )
+    centers = None
+
+    # Get the atom indexes from the environments and pick related frames
+    if environments is not None:
+        centers = _extract_environment_indices(environments)
+
+    # Pick frames and properties related to the environments if provided
+    if environments is not None:
+        # Sort environments by structure id and atom id
+        environments = sorted(environments, key=lambda x: (x[0], x[1]))
+
+        # Check structure indexes
+        unique_structures = list({env[0] for env in environments})
+        if any(index >= len(frames) for index in unique_structures):
+            raise IndexError(
+                "Some structure indices in 'environments' are larger than the number of"
+                "frames"
+            )
+
+        if len(unique_structures) != len(frames):
+            # only include frames that are present in the user-provided environments
+            frames = [frames[index] for index in unique_structures]
+
+    # Get global species
+    species = set()
+    for frame in frames:
+        species.update(frame.get_chemical_symbols())
+    species = list(species)
+
+    # Check if periodic
+    is_periodic = all(all(frame.get_pbc()) for frame in frames)
+
+    # Initialize calculator
+    soap = SOAP(
+        species=species,
+        r_cut=4.5,
+        n_max=8,
+        l_max=6,
+        sigma=0.2,
+        rbf="gto",
+        average="outer",
+        periodic=is_periodic,
+        weighting={"function": "pow", "c": 1, "m": 5, "d": 1, "r0": 3.5},
+        compression={"mode": "mu1nu1"},
+    )
+
+    # Calculate descriptors
+    n_jobs = min(len(frames), os.cpu_count())
+    feats = soap.create(frames, centers=centers, n_jobs=n_jobs)
+
+    # Compute pca
+    pca = PCA(n_components=2)
+    return pca.fit_transform(feats)
+
+
+def _extract_environment_indices(environments):
+    """
+    Convert from chemiscope's environments to DScribe's centers selection
+
+    :param: list environments: each element is a list of [env_index, atom_index, cutoff]
+    """
+    grouped_envs = {}
+    for [env_index, atom_index, _cutoff] in environments:
+        if env_index not in grouped_envs:
+            grouped_envs[env_index] = []
+        grouped_envs[env_index].append(atom_index)
+    return list(grouped_envs.values())

python/examples/7-explore-advanced.py

@@ -186,6 +186,7 @@ def mace_mp0_tsne(frames, environments):
 fetch_dataset("mace-mp-tsne-m3cd.json.gz")
 chemiscope.show_input("data/mace-mp-tsne-m3cd.json.gz")
 
+
 # %%
 #
 # Example with SOAP, t-SNE and environments