megnet.models.base module

Implements basic GraphModels.

class GraphModel(model: keras.engine.training.Model, graph_converter: megnet.data.graph.StructureGraph, target_scaler: megnet.utils.preprocessing.Scaler = <megnet.utils.preprocessing.DummyScaler object>, metadata: Optional[Dict] = None, **kwargs)

Bases: object

Composition of keras model and converter class for transfering structure object to input tensors. We add methods to train the model from (structures, targets) pairs

Parameters

• model – (keras model)

• graph_converter – (object) a object that turns a structure to a graph, check megnet.data.crystal

• target_scaler – (object) a scaler object for converting targets, check megnet.utils.preprocessing

• metadata – (dict) An optional dict of metadata associated with the model. Recommended to incorporate some basic information such as units, MAE performance, etc.

check_dimension(graph: Dict) → bool

Check the model dimension against the graph converter dimension :param graph: structure graph

Returns:

classmethod from_file(filename: str) → megnet.models.base.GraphModel

Class method to load model from

filename for keras model filename.json for additional converters

Parameters

filename – (str) model file name

Returns

GraphModel

get_all_graphs_targets(structures: List[pymatgen.core.structure.Structure], targets: List[float], scrub_failed_structures: bool = False) → tuple

Compute the graphs from structures and spit out (graphs, targets) with options to automatically remove structures with failed graph computations

Parameters

• structures – (list) pymatgen structure list

• targets – (list) target property list

• scrub_failed_structures – (bool) whether to scrub those failed structures

Returns

graphs, targets

predict_graph(graph: Dict) → numpy.ndarray

Predict property from graph

Parameters

graph – a graph dictionary, see megnet.data.graph

Returns

predicted target value

predict_graphs(graphs: List[Dict]) → numpy.ndarray

Predict properties from graphs

Parameters

graphs – a list graph dictionary, see megnet.data.graph

Returns

predicted target values

predict_structure(structure: pymatgen.core.structure.Structure) → numpy.ndarray

Predict property from structure

Parameters

structure – pymatgen structure or molecule

Returns

predicted target value

predict_structures(structures: List[pymatgen.core.structure.Structure]) → numpy.ndarray

Predict properties of structure list

Parameters

structures – list of pymatgen Structure/Molecule

Returns

predicted target values

save_model(filename: str) → None

Save the model to a keras model hdf5 and a json config for additional converters

Parameters

filename – (str) output file name

Returns

None

train(train_structures: List[pymatgen.core.structure.Structure], train_targets: List[float], validation_structures: Optional[List[pymatgen.core.structure.Structure]] = None, validation_targets: Optional[List[float]] = None, sample_weights: Optional[List[float]] = None, epochs: int = 1000, batch_size: int = 128, verbose: int = 1, callbacks: Optional[List[keras.callbacks.Callback]] = None, scrub_failed_structures: bool = False, prev_model: Optional[str] = None, save_checkpoint: bool = True, automatic_correction: bool = False, lr_scaling_factor: float = 0.5, patience: int = 500, dirname: str = 'callback', **kwargs) → megnet.models.base.GraphModel

Parameters

• train_structures – (list) list of pymatgen structures

• train_targets – (list) list of target values

• validation_structures – (list) list of pymatgen structures as validation

• validation_targets – (list) list of validation targets

• sample_weights – (list) list of sample weights for training data

• epochs – (int) number of epochs

• batch_size – (int) training batch size

• verbose – (int) keras fit verbose, 0 no progress bar, 1 only at the epoch end and 2 every batch

• callbacks – (list) megnet or keras callback functions for training

• scrub_failed_structures – (bool) whether to scrub structures with failed graph computation

• prev_model – (str) file name for previously saved model

• save_checkpoint – (bool) whether to save checkpoint

• automatic_correction – (bool) correct nan errors

• lr_scaling_factor – (float, less than 1) scale the learning rate down when nan loss encountered

• patience – (int) patience for early stopping

• dirname – (str) the directory in which to save checkpoints, if save_checkpoint=True

• **kwargs –

train_from_graphs(train_graphs: List[Dict], train_targets: List[float], validation_graphs: Optional[List[Dict]] = None, validation_targets: Optional[List[float]] = None, sample_weights: Optional[List[float]] = None, epochs: int = 1000, batch_size: int = 128, verbose: int = 1, callbacks: Optional[List[keras.callbacks.Callback]] = None, prev_model: Optional[str] = None, lr_scaling_factor: float = 0.5, patience: int = 500, save_checkpoint: bool = True, automatic_correction: bool = False, dirname: str = 'callback', **kwargs) → megnet.models.base.GraphModel

Parameters

• train_graphs – (list) list of graph dictionaries

• train_targets – (list) list of target values

• validation_graphs – (list) list of graphs as validation

• validation_targets – (list) list of validation targets

• sample_weights – (list) list of sample weights

• epochs – (int) number of epochs

• batch_size – (int) training batch size

• verbose – (int) keras fit verbose, 0 no progress bar, 1 only at the epoch end and 2 every batch

• callbacks – (list) megnet or keras callback functions for training

• prev_model – (str) file name for previously saved model

• lr_scaling_factor – (float, less than 1) scale the learning rate down when nan loss encountered

• patience – (int) patience for early stopping

• save_checkpoint – (bool) whether to save checkpoint

• automatic_correction – (bool) correct nan errors

• dirname – (str) the directory in which to save checkpoints, if save_checkpoint=True

• **kwargs –