mdlearn.nn.models.ae.linear

Linear-layer autoencoder model with trainer class.

Classes

LinearAE(*args, **kwargs)

A symmetric autoencoder with all linear layers.

LinearAETrainer([input_dim, latent_dim, ...])

Trainer class to fit a linear autoencoder to a set of feature vectors.
class mdlearn.nn.models.ae.linear.LinearAE(*args: Any, **kwargs: Any)

A symmetric autoencoder with all linear layers. Applies a ReLU activation between encoder and decoder.

__init__(input_dim: int, latent_dim: int = 8, hidden_neurons: List[int] = [128], bias: bool = True, relu_slope: float = 0.0, inplace_activation: bool = False)
Parameters

  • input_dim (int) – Dimension of input tensor (should be flattened).

  • latent_dim (int, default=8) – Dimension of the latent space.

  • hidden_neurons (List[int], default=[128]) – Linear layers in_features.

  • bias (bool, default=True) – Use a bias term in the Linear layers.

  • relu_slope (float, default=0.0) – If greater than 0.0, will use LeakyReLU activiation with negative_slope set to relu_slope.

  • inplace_activation (bool, default=False) – Sets the inplace option for the activation function.

forward(x: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor]

Forward pass of autoencoder.

Parameters

x (torch.Tensor) – Input data.

Returns

Tuple[torch.Tensor, torch.Tensor] – The batch of latent vectors z and the reconstructions recon_x.

recon_loss(x: torch.Tensor, recon_x: torch.Tensor, reduction: str = 'mean') → torch.Tensor

Compute the MSE reconstruction loss between x and recon_x.

Parameters

  • x (torch.Tensor) – The input data.

  • recon_x (torch.Tensor) – The reconstruction of the input data x

  • reduction (str, default=”mean”) – The reduction strategy for the F.mse_loss function.

Returns

torch.Tensor – The reconstruction loss between x and recon_x.

class mdlearn.nn.models.ae.linear.LinearAETrainer(input_dim: int = 40, latent_dim: int = 3, hidden_neurons: List[int] = [32, 16, 8], bias: bool = True, relu_slope: float = 0.0, inplace_activation: bool = False, seed: int = 42, in_gpu_memory: bool = False, num_data_workers: int = 0, prefetch_factor: int = 2, split_pct: float = 0.8, split_method: str = 'random', batch_size: int = 128, shuffle: bool = True, device: str = 'cpu', optimizer_name: str = 'RMSprop', optimizer_hparams: Dict[str, Any] = {'lr': 0.001, 'weight_decay': 1e-05}, scheduler_name: Optional[str] = None, scheduler_hparams: Dict[str, Any] = {}, epochs: int = 100, verbose: bool = False, clip_grad_max_norm: float = 10.0, checkpoint_log_every: int = 10, plot_log_every: int = 10, plot_n_samples: int = 10000, plot_method: Optional[str] = 'TSNE', train_subsample_pct: float = 1.0, valid_subsample_pct: float = 1.0, use_wandb: bool = False)

Trainer class to fit a linear autoencoder to a set of feature vectors.

__init__(input_dim: int = 40, latent_dim: int = 3, hidden_neurons: List[int] = [32, 16, 8], bias: bool = True, relu_slope: float = 0.0, inplace_activation: bool = False, seed: int = 42, in_gpu_memory: bool = False, num_data_workers: int = 0, prefetch_factor: int = 2, split_pct: float = 0.8, split_method: str = 'random', batch_size: int = 128, shuffle: bool = True, device: str = 'cpu', optimizer_name: str = 'RMSprop', optimizer_hparams: Dict[str, Any] = {'lr': 0.001, 'weight_decay': 1e-05}, scheduler_name: Optional[str] = None, scheduler_hparams: Dict[str, Any] = {}, epochs: int = 100, verbose: bool = False, clip_grad_max_norm: float = 10.0, checkpoint_log_every: int = 10, plot_log_every: int = 10, plot_n_samples: int = 10000, plot_method: Optional[str] = 'TSNE', train_subsample_pct: float = 1.0, valid_subsample_pct: float = 1.0, use_wandb: bool = False)
Parameters

  • input_dim (int, default=40) – Dimension of input tensor (should be flattened).

  • latent_dim (int, default=3) – Dimension of the latent space.

  • hidden_neurons (List[int], default=[32, 16, 8]) – Linear layers in_features. Defines the shape of the autoencoder (does not include latent dimension). The encoder and decoder are symmetric.

  • bias (bool, default=True) – Use a bias term in the Linear layers.

  • relu_slope (float, default=0.0) – If greater than 0.0, will use LeakyReLU activiation with negative_slope set to relu_slope.

  • inplace_activation (bool, default=False) – Sets the inplace option for the activation function.

  • seed (int, default=42) – Random seed for torch, numpy, and random module.

  • in_gpu_memory (bool, default=False) – If True, will pre-load the entire data array to GPU memory.

  • num_data_workers (int, default=0) – How many subprocesses to use for data loading. 0 means that the data will be loaded in the main process.

  • prefetch_factor (int, by default=2) – Number of samples loaded in advance by each worker. 2 means there will be a total of 2 * num_workers samples prefetched across all workers.

  • split_pct (float, default=0.8) – Proportion of data set to use for training. The rest goes to validation.

  • split_method (str, default=”random”) – Method to split the data. For random split use “random”, for a simple partition, use “partition”.

  • batch_size (int, default=128) – Mini-batch size for training.

  • shuffle (bool, default=True) – Whether to shuffle training data or not.

  • device (str, default=”cpu”) – Specify training hardware either cpu or cuda for GPU devices.

  • optimizer_name (str, default=”RMSprop”) – Name of the PyTorch optimizer to use. Matches PyTorch optimizer class name.

  • optimizer_hparams (Dict[str, Any], default={“lr”: 0.001, “weight_decay”: 0.00001}) – Dictionary of hyperparameters to pass to the chosen PyTorch optimizer.

  • scheduler_name (Optional[str], default=None) – Name of the PyTorch learning rate scheduler to use. Matches PyTorch optimizer class name.

  • scheduler_hparams (Dict[str, Any], default={}) – Dictionary of hyperparameters to pass to the chosen PyTorch learning rate scheduler.

  • epochs (int, default=100) – Number of epochs to train for.

  • verbose (bool, default False) – If True, will print training and validation loss at each epoch.

  • clip_grad_max_norm (float, default=10.0) – Max norm of the gradients for gradient clipping for more information see: torch.nn.utils.clip_grad_norm_ documentation.

  • checkpoint_log_every (int, default=10) – Epoch interval to log a checkpoint file containing the model weights, optimizer, and scheduler parameters.

  • plot_log_every (int, default=10) – Epoch interval to log a visualization plot of the latent space.

  • plot_n_samples (int, default=10000) – Number of validation samples to use for plotting.

  • plot_method (Optional[str], default=”TSNE”) – The method for visualizing the latent space or if visualization should not be run, set plot_method=None. If using "TSNE", it will attempt to use the RAPIDS.ai GPU implementation and will fallback to the sklearn CPU implementation if RAPIDS.ai is unavailable.

  • train_subsample_pct (float, default=1.0) – Percentage of training data to use during hyperparameter sweeps.

  • valid_subsample_pct (float, default=1.0) – Percentage of validation data to use during hyperparameter sweeps.

  • use_wandb (bool, default=False) – If True, will log results to wandb.

Raises

  • ValueError – split_pct should be between 0 and 1.

  • ValueError – train_subsample_pct should be between 0 and 1.

  • ValueError – valid_subsample_pct should be between 0 and 1.

  • ValueError – Specified device as cuda, but it is unavailable.

fit(X: numpy.ndarray, scalars: Dict[str, numpy.ndarray] = {}, output_path: Union[str, pathlib.Path] = './', checkpoint: Optional[Union[str, pathlib.Path]] = None)

Trains the autoencoder on the input data X.

Parameters

  • X (np.ndarray) – Input features vectors of shape (N, D) where N is the number of data examples, and D is the dimension of the feature vector.

  • scalars (Dict[str, np.ndarray], default={}) – Dictionary of scalar arrays. For instance, the root mean squared deviation (RMSD) for each feature vector can be passed via {"rmsd": np.array(...)}. The dimension of each scalar array should match the number of input feature vectors N.

  • output_path (PathLike, default=”./”) – Path to write training results to. Makes an output_path/checkpoints folder to save model checkpoint files, and output_path/plots folder to store latent space visualizations.

  • checkpoint (Optional[PathLike], default=None) – Path to a specific model checkpoint file to restore training.

Raises

  • TypeError – If scalars is not type dict. A common error is to pass output_path as the second argument.

  • NotImplementedError – If using a learning rate scheduler other than ReduceLROnPlateau, a step function will need to be implemented.

predict(X: numpy.ndarray, inference_batch_size: int = 512, checkpoint: Optional[Union[str, pathlib.Path]] = None) → Tuple[numpy.ndarray, float]

Predict using the LinearAE

Parameters

  • X (np.ndarray) – The input data to predict on.

  • inference_batch_size (int, default=512) – The batch size for inference.

  • checkpoint (Optional[PathLike], default=None) – Path to a specific model checkpoint file.

Returns

Tuple[np.ndarray, float] – The z latent vectors corresponding to the input data X and the average reconstruction loss.