muammar/ml4chem: 0.0.8

These changes are related to ML4Chem's publication: Creation of atomistic module to comply with publication structure. models: All models comply with base class inheritance using mixin. models moved to atomistic.models. Scripts have to be changed as the following example from ml4chem.atomistic.models.neuralnetwork import NeuralNetwork. SVM model refactoring. training class can take checkpoint dictionary to save checkpoints during training. NeuralNetwork and RetentionTime models support training with test error. New get_activations function to get activations of neural network. features: features moved to atomistic.features. Scripts have to be changed as the following example from ml4chem.atomistic.features.gaussian import Gaussian. data.visualization moved to .visualization. read_log() function now supports data_only keyword argument to return only data instead of matplotlib plot. plot_atomic_features supports now backend keyword arguments and preprocessing of features before doing PCA or T-SNE using make_pipeline. Example: backend_kwargs = {"perplexity": 500} dimension = 2 dot_size = 3 plot, df = plot_atomic_features( latent_space, method="tsne", preprocessor=StandardScaler(), dimensions=dimension, backend="plotly", dot_size=dot_size, backend_kwargs=backend_kwargs ) Restructured documentation. AtomicMSELoss supports penalization using a list of uncertainties. Potentials class moved to atomistic module. It has to be imported as from ml4chem.atomistic import Potentials