Interface-checkpoint

Module Contents

Functions

fit_MIDAA(input_matrix[, normalization_factor, ...])

Fits the MIDAA model to given input data, using specified architecture and optimization parameters.

training_loop(deepAA, input_matrix, model_matrix, ...)

initialize_params(params_run, A, B)

handle_z(params_run, fix_Z, Z_fix_release_step, steps, ...)

handle_model_matrix(params_run, model_matrix, ...)

calculate_loss(params_run, fix_Z, deepAA, ...)

prepare_final_parameters(params_run, CUDA)

compile_final_dict(params_run, params_input, ...)

load_model_from_state_dict(model, input_matrix, path)

Loads a model's state dictionary from a specified path and updates the model with inferred quantities
Interface-checkpoint.fit_MIDAA(input_matrix, normalization_factor=None, input_types=['NB'], loss_weights_reconstruction=None, side_matrices=None, input_types_side=None, loss_weights_side=None, hidden_dims_dec_common=[256, 512], hidden_dims_dec_last=[1024], hidden_dims_dec_last_side=None, hidden_dims_enc_ind=[1024], hidden_dims_enc_common=[512, 256], hidden_dims_enc_pre_Z=[256, 128], layers_independent_types=None, layers_independent_types_side=None, image_size=[256, 256], narchetypes=10, model_matrix=None, just_VAE=False, linearize_encoder=False, linearize_decoder=False, VAE_steps=None, CUDA=True, lr=0.005, gamma_lr=0.1, steps=2000, fix_Z=False, initialization_B_weight=None, Z_fix_norm=None, Z_fix_release_step=None, reconstruct_input_and_side=False, initialization_input=None, initialization_steps_phase_1=1000, initialization_lr_phase_1=0.001, initialization_steps_phase_2=350, initialization_lr_phase_2=0.0005, torch_seed=3, batch_size=5128, kernel_size=3, stride=1, padding=1, pool_size=2, pool_stride=2)

Fits the MIDAA model to given input data, using specified architecture and optimization parameters.

Parameters: - input_matrix (list of ndarray): Input data matrix, where each entry is a tensor representation

of the input data for a different modality.

  • normalization_factor (list of ndarray, optional): Normalization factors for the input data. Default is None.

  • input_types (list of str, optional): Types of input data. Default is [“NB”].

  • loss_weights_reconstruction (list of float, optional): Weights for the reconstruction loss. Default is None.

  • side_matrices (list of ndarray, optional): Side information matrices. Default is None.

  • input_types_side (list of str, optional): Types of side information. Default is None.

  • loss_weights_side (list of float, optional): Weights for the side information loss. Default is None.

  • hidden_dims_dec_common, hidden_dims_dec_last, hidden_dims_dec_last_side, hidden_dims_enc_ind, hidden_dims_enc_common, hidden_dims_enc_pre_Z (list of int, optional): Dimensions of various layers in the decoder and encoder. Defaults are specified for each.

  • layers_independent_types, layers_independent_types_side (list of str, optional): Types of layers for independent modeling.

  • image_size (list of int, optional): Size of the input images (width, height). Default is [256, 256].

  • narchetypes (int, optional): Number of archetypes to model. Default is 10.

  • model_matrix (ndarray, optional): Matrix representing the model. Default is None.

  • just_VAE (bool, optional): Flag to run only the VAE without the archetypal analysis. Default is False.

  • linearize_encoder, linearize_decoder (bool, optional): Flags to linearize encoder and decoder. Defaults are False.

  • VAE_steps (int, optional): Number of steps to run the VAE. Default is None.

  • CUDA (bool, optional): Flag to use CUDA if available. Default is True.

  • lr (float, optional): Learning rate for the optimizer. Default is 0.005.

  • gamma_lr (float, optional): Learning rate decay factor. Default is 0.1.

  • steps (int, optional): Number of training steps. Default is 2000.

  • fix_Z (bool, optional): Flag to fix Z during training. Default is False.

  • initialization_B_weight (float, optional): Initial weight for B. Default is None.

  • Z_fix_norm (float, optional): Normalization factor for Z when fixed. Default is None.

  • Z_fix_release_step (int, optional): Step to release Z fix. Default is None.

  • reconstruct_input_and_side (bool, optional): Flag to reconstruct both input and side information. Default is False.

  • initialization_input (dict, optional): Initial values for the input. Default is None.

  • initialization_steps_phase_1, initialization_steps_phase_2 (int, optional): Number of steps for the two phases of initialization. Defaults are specified.

  • initialization_lr_phase_1, initialization_lr_phase_2 (float, optional): Learning rates for the two phases of initialization. Defaults are specified.

  • torch_seed (int, optional): Seed for PyTorch’s RNG. Default is 3.

  • batch_size (int, optional): Batch size for training. Default is 5128.

  • kernel_size, stride, padding, pool_size, pool_stride (int, optional): Convolution and pooling parameters. Defaults are specified.

Returns: dict: A dictionary containing the final parameters, input parameters, ELBO list, and the DeepAA instance.

This function configures and trains a DeepAA model based on the specified parameters and data. It handles device setting, seed initialization, data preprocessing, and the training loop, including potential initialization phases for the model. The final output includes training diagnostics and the trained model itself, ready for further analysis or application.

Interface-checkpoint.training_loop(deepAA, input_matrix, model_matrix, normalization_factor, side_matrices, initialization_input, loss_weights_reconstruction, loss_weights_side, initialization_B_weight, batch_size, lr, steps, lrd, VAE_steps, fix_Z, Z_fix_release_step, initialization_mode_step1=False, initialization_mode_step2=False, CUDA=True)
Interface-checkpoint.initialize_params(params_run, A, B)
Interface-checkpoint.handle_z(params_run, fix_Z, Z_fix_release_step, steps, Z, A, B)
Interface-checkpoint.handle_model_matrix(params_run, model_matrix, reconstruct_input_and_side, A, deepAA)
Interface-checkpoint.calculate_loss(params_run, fix_Z, deepAA, input_matrix, model_matrix, normalization_factor, side_matrices, loss_weights_reconstruction, loss_weights_side)
Interface-checkpoint.prepare_final_parameters(params_run, CUDA)
Interface-checkpoint.compile_final_dict(params_run, params_input, elbo_list, deepAA)
Interface-checkpoint.load_model_from_state_dict(model, input_matrix, path, CUDA=False)

Loads a model’s state dictionary from a specified path and updates the model with inferred quantities based on a provided input matrix.

Parameters: - model (dict): A dictionary containing the model structure, including the ‘deepAA_obj’ (the model object)

and ‘hyperparameters’. This dictionary will be updated with ‘inferred_quantities’ based on the input data.

  • input_matrix (list of ndarray): The input data matrix to be used for inference after loading the model, where each entry is a tensor representation of the input data for a different modality.

  • path (str): Path to the file containing the saved state dictionary of the model.

  • CUDA (bool, optional): Indicates whether CUDA (GPU) should be used for loading and inference. If False, operations will be performed on the CPU. Default is False.

The function loads the model’s state dictionary from the specified path, considering whether CUDA is enabled or not. It sets the model to evaluation mode, processes the provided input matrix, and performs inference to obtain and update the model with new inferred quantities such as A, B, and Z matrices. It also updates the model with any final parameters adjustments based on the model’s hyperparameters.

Note: - The ‘model’ dictionary must contain ‘deepAA_obj’, an instance of the model, and ‘hyperparameters’, a dictionary

specifying model configurations like ‘fix_Z’, ‘Z_fix_release_step’, and ‘steps’.

  • After loading the state and performing inference, the function updates the ‘model’ dictionary with ‘inferred_quantities’, which include the results from the inference.