ml4chem package

Subpackages

• ml4chem.atomistic package
  • Subpackages
    • ml4chem.atomistic.features package
      • Submodules
      • ml4chem.atomistic.features.aev module
      • ml4chem.atomistic.features.autoencoders module
      • ml4chem.atomistic.features.base module
      • ml4chem.atomistic.features.cartesian module
      • ml4chem.atomistic.features.coulombmatrix module
      • ml4chem.atomistic.features.cutoff module
      • ml4chem.atomistic.features.gaussian module
      • Module contents
    • ml4chem.atomistic.models package
      • Submodules
      • ml4chem.atomistic.models.autoencoders module
      • ml4chem.atomistic.models.base module
      • ml4chem.atomistic.models.gaussian_process module
      • ml4chem.atomistic.models.kernelridge module
      • ml4chem.atomistic.models.loss module
      • ml4chem.atomistic.models.merger module
      • ml4chem.atomistic.models.neuralnetwork module
      • ml4chem.atomistic.models.se3net module
      • Module contents
  • Submodules
  • ml4chem.atomistic.potentials module
  • Module contents
• ml4chem.backends package
  • Submodules
  • ml4chem.backends.available module
  • ml4chem.backends.operations module
  • Module contents
• ml4chem.data package
  • Submodules
  • ml4chem.data.handler module
  • ml4chem.data.parser module
  • ml4chem.data.preprocessing module
  • ml4chem.data.serialization module
  • ml4chem.data.utils module
  • Module contents
• ml4chem.optim package
  • Submodules
  • ml4chem.optim.LBFGS module
  • ml4chem.optim.handler module
  • Module contents

Submodules

ml4chem.metrics module

ml4chem.metrics.compute_mae(outputs, targets, atoms_per_image=None)

Compute MAE

Useful when using futures.

Parameters

• outputs (list) – List of outputs.

• targets (list) – List if targets.

• atoms_per_image (list) – List of atoms per image.

Returns

mae – Mean absolute error.

Return type

float

ml4chem.metrics.compute_mse(outputs, targets, atoms_per_image=None)

Compute MSE

Useful when using futures.

Parameters

• outputs (list) – List of outputs.

• targets (list) – List if targets.

• atoms_per_image (list) – List of atoms per image.

Returns

mse – Mean squared error.

Return type

float

ml4chem.metrics.compute_rmse(outputs, targets, atoms_per_image=None)

Compute RMSE

Useful when using futures.

Parameters

• outputs (list) – List of outputs.

• targets (list) – List if targets.

• atoms_per_image (list) – List of atoms per image.

Returns

rmse – Root-mean squared error.

Return type

float

ml4chem.utils module

ml4chem.utils.convert_elapsed_time(seconds)

Convert elapsed time in seconds to HH:MM:SS format

ml4chem.utils.dynamic_import(name, package, alt_name=None)

A dynamic module importer

Parameters

• name (str) – Name of the module to be imported.

• package (str) – Path to package. Example: ml4chem.atomistic.features

• alt_name (str) – Alternative module_name.

Returns

_class – An class object.

Return type

obj

ml4chem.utils.get_chunks(sequence, chunk_size, svm=True)

A function that yields a list in chunks

Parameters

• sequence (list or dictionary) – A list or a dictionary to be split.

• chunk_size (int) – Number of elements in each group.

• svm (bool) – Whether or not these chunks are going to be used for kernel methods.

ml4chem.utils.get_hash(image)

Get the SHA1 hash of an image object

Parameters

image (object) – An image to be hashed.

Returns

_hash – Hash of image in string format

Return type

str

ml4chem.utils.get_header_message()

Function that returns ML4Chem header

ml4chem.utils.get_neighborlist(image, cutoff)

Get the list of neighbors

Parameters

image (object) – ASE image.

Returns

Return type

A list of neighbors with offset distances.

ml4chem.utils.get_number_of_parameters(model)

Get the number of parameters

Parameters

model (obj) – Pytorch model to perform forward() and get gradients.

Returns

• (total_params, train_params) tuple with total number of parameters and

• number of trainable parameters.

ml4chem.utils.lod_to_list(data, svm=False, requires_grad=False)

List Of Dict (lod) to list

Parameters

• data (list) – A list with ml4chem dictionaries. Those ones coming from get_chunks()

• svm (bool, optional.) – Whether or not these chunks are going to be used for kernel methods, by default False.

• requires_grad (bool, optional.) – Do we require gradients?, by default False.

Returns

A list of tensors or list of float.

Return type

_list

ml4chem.utils.logger(filename=None, level=None, format=None, filemode='a')

A wrapper to the logging python module

This module is useful for cases where we need to log in a for loop different files. It also will allow more flexibility later on how the logging format could evolve.

Parameters

• filename (str, optional) – Name of logfile. If no filename is provided, we output to stdout.

• level (str, optional) – Level of logging messages, by default ‘info’. Supported are: ‘info’ and ‘debug’.

• format (str, optional) – Format of logging messages, by default ‘%(message)s’.

• filemode (str, optional) – If filename is specified, open the file in this mode. Defaults to “a”. Supported modes are: “r” (read), “w” (write), “a” (append).

Returns

A logger object.

Return type

logger

ml4chem.visualization module

ml4chem.visualization.parity(predictions, true, scores=False, filename=None, **kwargs)

A parity plot function

Parameters

• predictions (list or ndarray) – Model predictions in a list.

• true (list or ndarray) – Targets or true values.

• scores (bool) – Print scores in parity plot.

• filename (str) – A name to save the plot to a file. If filename is non existent, we call plt.show().

Notes

kwargs accepts all valid keyword arguments for matplotlib.pyplot.savefig.

ml4chem.visualization.plot_atomic_features(latent_space, method='PCA', dimensions=2, backend='seaborn', data_only=False, preprocessor=None, backend_kwargs=None, **kwargs)

Plot high dimensional atomic feature vectors

This function can take a feature space dictionary, or a database file and plot the atomic features using PCA or t-SNE.

$ ml4chem –plot tsne –file path.db

Parameters

• latent_space (dict or str) – Dictionary of atomic features of path to database file.

• method (str, optional) – Dimensionality reduction method to employed, by default “PCA”. Supported are: “PCA” and “TSNE”.

• dimensions (int, optional) – Number of dimensions to reduce the high dimensional atomic feature vectors, by default 2.

• backend (str, optional) – Select the backend to plot features. Supported are “plotly” and “seaborn”, by default “plotly”.

• preprocessor (obj) – One of the preprocessors supported by sklearn e.g.: StandardScaler(), Normalizer().

• backend_kwargs (dict) –

  Dictionary with extra keyword arguments to extend functionality of backends that cannot be set with the defaults keyword arguments of the plot_atomic_features function.

  For more information see:

  • https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

  • https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html

• data_only (bool) – If set to True, this function returns only data in a dataframe with the following structure:

ml4chem.visualization.read_log(logfile, metric='loss', refresh=None, data_only=False)

Read the logfile

Parameters

• logfile (str) – Path to logfile.

• metric (str) –

  The keys,values of the dictionary are:

  • ”loss”: Loss function values.

  • ”training”: Training error.

  • ”test”: Test error.

  • ”combined”: training + test errors in same plot.

• refresh (float) – Interval in seconds before refreshing log file plot.

• data_only (bool) – If set to True, this function returns only data in a dataframe with the following structure:

• df.head() (>>>) – epochs loss training test

• 1 33779.46 815.6884 793.3943 (0) –

Returns

If data_only is true we return dataframe, otherwise a figure.

Return type

pandas.DataFrame or matplotlib.pyplot object

Module contents