API Overview

Modules

Classes

checkpoint.Checkpoint: Helper class to save the model's parameters and chains at regular intervals during training and to log the
dataset.DatasetDCA: Dataset class for handling multi-sequence alignments data.
utils.Timer: Track recent (time, pearson) points and predict when a target Pearson is reached.

Functions

cobalt.prune_redundant_sequences: Prunes sequences from X such that no sequence has more than 'seqid_th' fraction of its residues identical to any other sequence in the set.
cobalt.run_cobalt: Runs the Cobalt algorithm to split the input MSA into training and test sets.
cobalt.split_train_test: Splits X into two sets, T and S, such that no sequence in S has more than
dca.get_contact_map: Computes the contact map from the model coupling matrix.
dca.get_mf_contact_map: Computes the contact map using the mean-field DCA approximation from the data.
dca.get_seqid: Returns a tensor containing the sequence identities between two sets of one-hot encoded sequences.
dca.get_seqid_stats: - If s2 is provided, computes the mean and the standard deviation of the mean sequence identity between two sets of one-hot encoded sequences.
dca.set_zerosum_gauge: Sets the zero-sum gauge on the coupling matrix.
fasta.compute_weights: Computes the weight to be assigned to each sequence 's' in 'data' as 1 / n_clust, where 'n_clust' is the number of sequences
fasta.decode_sequence: Takes a numeric sequence or list of seqences in input an returns the corresponding string encoding.
fasta.encode_sequence: Encodes a sequence or a list of sequences into a numeric format.
fasta.get_tokens: Converts a known alphabet into the corresponding tokens, otherwise returns the custom alphabet.
fasta.import_from_fasta: Import sequences from a fasta or compressed fasta (.fas.gz) file. The following operations are performed:
fasta.validate_alphabet: Validates that all characters in the sequences are present in the provided alphabet.
fasta.write_fasta: Generate a fasta file with the input sequences.
functional.one_hot: A fast one-hot encoding function faster than the PyTorch one working with torch.int32 and returning a float Tensor.
graph.decimate_graph: Performs one decimation step and updates the parameters and mask.
graph.update_mask_decimation: Updates the mask by removing the n_remove couplings with the smallest Dkl.
graph.update_mask_element_activation: Updates the mask by activating the nactivate couplings with the largest Dkl.
io.load_chains: Loads the sequences from a fasta file and returns the one-hot encoded version.
io.load_params: Import the parameters of the model from a text file.
io.load_params_old: Import the parameters of the model from a file.
io.load_params_oldformat: Import the parameters of the model from a file. Assumes the old DCA format.
io.save_chains: Saves the chains in a fasta file.
io.save_params: Saves the parameters of the model in a file.
io.save_params_oldformat: Saves the parameters of the model in a file. Assumes the old DCA format.
plot.plot_PCA: Makes the scatter plot of the components (pc1, pc2) of the input data and shows the histograms of the components.
plot.plot_autocorrelation: Plots the time-autocorrelation curve of the sequence identity and the generated and data sequence identities.
plot.plot_contact_map: Plots the contact map.
plot.plot_pearson_sampling: Plots the Pearson correlation coefficient over sampling time.
plot.plot_scatter_correlations: Plots the scatter plot of the data and generated Cij and Cijk values.
resampling.compute_mixing_time: Computes the mixing time using the t and t/2 method. The sampling will halt when the mixing time is reached or
sampling.get_sampler: Returns the sampling function corresponding to the chosen method.
sampling.gibbs_sampling: Gibbs sampling. Attempts L * nsweeps mutations to each sequence in 'chains'.
sampling.gibbs_step_independent_sites: Performs a single mutation using the Gibbs sampler. This version selects different random sites for each chain. It is
sampling.gibbs_step_uniform_sites: Performs a single mutation using the Gibbs sampler. In this version, the mutation is attempted at the same sites for all chains.
sampling.metropolis_sampling: Metropolis sampling. Attempts L * nsweeps mutations to each sequence in 'chains'.
sampling.metropolis_step_independent_sites: Performs a single mutation using the Metropolis sampler. This version selects different random sites for each chain. It is
sampling.metropolis_step_uniform_sites: Performs a single mutation using the Metropolis sampler. In this version, the mutation is attempted at the same sites for all chains.
sampling.sampling_profile: Samples from the profile model defined by the local biases only.
statmech.compute_energy: Compute the DCA energy for a batch of sequences.
statmech.compute_entropy: Compute the entropy of the DCA model.
statmech.compute_logZ_exact: Compute the log-partition function of the model.
statmech.compute_log_likelihood: Compute the log-likelihood per residue of the model.
statmech.enumerate_states: Enumerate all possible states of a system of L sites and q states.
statmech.iterate_tap: Iterates the TAP equations until convergence.
stats.extract_Cij_from_freq: Extracts the lower triangular part of the covariance matrices of the natural data and generated data starting from the frequencies.
stats.extract_Cij_from_seqs: Extracts the lower triangular part of the covariance matrices of the natural data and generated data starting from the sequences.
stats.generate_unique_triplets: Generates a set of unique triplets of positions. Used to compute the 3-points statistics.
stats.get_correlation_two_points: Computes the Pearson coefficient and the slope between the two-point frequencies of data and chains.
stats.get_covariance_matrix: Computes the weighted covariance matrix of the input multi sequence alignment.
stats.get_freq_single_point: Computes the single point frequencies of the input MSA.
stats.get_freq_three_points: Computes the 3-body connected correlation statistics of the input MSAs.
stats.get_freq_two_points: Computes the 2-points statistics of the input MSA.
training.train_eaDCA: Fits an eaDCA model on the training data and saves the results in a file.
training.train_edDCA: Fits an edDCA model on the training data and saves the results in a file.
training.train_edgeDCA: Fits an edge activation DCA model (edgeDCA) on the training data and saves the results in a file.
training.train_graph: Trains the model on a given graph until the target Pearson correlation is reached or the maximum number of epochs is exceeded.
training.update_params: Updates the parameters of the model.
training.update_params_edge_activation: Updates the mask and the coupling parameters using the edge-activation algorithm.
utils.get_device: Returns the device where to store the tensors.
utils.get_dtype: Returns the data type of the tensors.
utils.get_mask_save: Returns the mask to save the upper-triangular part of the coupling matrix.
utils.init_chains: Initialize the Markov chains of the DCA model. If 'fi' is provided, the chains are sampled from the
utils.init_parameters: Initialize the parameters of the DCA model. The bias terms are initialized
utils.parse_log_file: Parse a DCA training log file.
utils.resample_sequences: Extracts nextract sequences from data with replacement according to the weights.

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