MSA_next.py

import os
import pickle
from fastcore.script import *
from MSA import *

@call_parse
def gen_MSAs(filepath:Param(help='Path of the input directory',type=str,default='/Iterative_masking-master/examples'),
         filename:Param(help='Name of the input file(s)',type=str,nargs='+',default=["PF00072.fasta"]),
         new_dir:Param(help='Name of the output directory',type=str,default='results_new'),
         pdf:Param(help='Should I sample tokens from the pdf ? (bool)',type=bool_arg,default=False),
         T:Param(help='Which is the sampling Temperature from the pdf ? (only when `pdf` is True)',type=float,default=1),
         sample_all:Param(help='Should I sample all tokens or just the masked ones ? (True = sample all tokens)',type=bool_arg, default=False),
         Iters:Param(help='Number of total iterations to generate the new tokens',type=int,default=20),
         pmask:Param(help='Masking probability',type=float,default=0.1),
         num:Param(help='Size of the batches MSAs which the MSA-Transformer receives as input',type=int,nargs='+',default=[10]),
         depth:Param(help='Number of batches (of size num) that you want to generate',type=int,default=10),
         generate:Param(help='How should I generate sequences ? False (=Batch generation) or Linear with context (=linear-ran/linear-tot-ran), `-ran` means that the context MSA is sampled randomly (once) while `-tot-ran` means that it is sampled randomly each time.',type=str, default=False),
         print_all:Param(help='Should I print the MSA after each iteration ? (bool)',type=bool_arg,default=False),
         range_vals:Param(help='First and last index of the sequences that you want to use as ancestors', type=int,nargs='+',default=False),
         phylo_w:Param(help='Should I sample the starting sequences from the phylogeny weights ? (bool)',type=bool_arg,default=False)
         ):
    "Generate a new MSA either with Batch generation of Context generation. It shuffles the initial MSA and uses different slices as batch MSAs"

    # Create folder
    path = os.getcwd()
    path1 = new_dir
    if new_dir is False:
        path1 = filename[0][:-6]
    try:
        os.mkdir(path + "/" + path1)
    except OSError:
        print("Creation of the directory %s failed" % (path + "/" + path1))
    else:
        print("Successfully created the directory %s " % (path + "/" + path1))

    # Save Input MSA
    print('Tokenize')
    Class = IM_MSA_Transformer(filename=filename,
                               num=num,
                               filepath=filepath)
    idx_list = Class.idx_list
    old_tkn = Class.print_tokens()
    a_file = open(path1 + "/dictionary-tokens.pkl", "wb")
    pickle.dump(idx_list, a_file)
    a_file.close()
    np.save(path1 + "/original-tokens.npy", old_tkn[0])

    add_strs = ""
    if pdf==True:
        add_strs += f"_pdf(T={round(T,3)})"
        print(
            "We are sampling new tokens from the pdf of logits and not taking the mode of the pdf"
        )
    if T!=1 and pdf==False:
        print('To sample with a Temperature you should use pdf=True, otherwise the result is the same')
    if sample_all == False:
        add_strs += "_(only-masked-sampled)"
    if not generate==False:
        add_strs += "_"+generate+"_(context-"+str(num[0])+")"
    if phylo_w:
        add_strs += "_phylo-w"

    print('Generate Class')
    Class = IM_MSA_Transformer(iterations=np.array([Iters]),
                               p_mask=pmask,
                               filename=filename,
                               num=num,
                               filepath=filepath)

    print('Compute results from Class')
    Class.iterations = np.array([Iters])
    Class.p_mask = pmask

    if generate == False:
        print('Generating MSA with same size as the original one')
        old_T, new_T = Class.Batch_MSA(simplified=True,
                                    repetitions=depth,
                                    use_pdf=pdf, sample_all=sample_all, T=T, phylo=phylo_w)
        NNN = min(num[0] * depth, old_T.shape[1])

    elif generate=='linear-ran' or generate=='linear-tot-ran':
        print('Generate MSA with linear context generation')
        orig_tkn = np.load(path + "/" + path1 + "/original-tokens.npy")
        # select ancestor and context
        np.random.seed(0)
        indices = np.random.permutation(orig_tkn.shape[0])
        indexes_context = indices[:num[0]]
        indices = np.random.permutation(orig_tkn.shape[0])
        if depth == -1:
            ind_ancestor = indices
        elif range_vals is False:
            ind_ancestor = indices[:depth]
        else:
            if range_vals[1] == -1 :
                ind_ancestor = indices[range_vals[0]:]
                range_vals[1] = orig_tkn.shape[0]
            else:
                ind_ancestor = indices[range_vals[0]:range_vals[1]]
        ancestor = orig_tkn[ind_ancestor,:]
        context  = orig_tkn[indexes_context,:][None,:,:]
        if generate=='linear-tot-ran':
            context = 'tot-ran'
        old_T, new_T = Class.Context_MSA(None, ancestor, context, use_pdf=pdf, simplified=True, sample_all=sample_all, print_all=print_all, T=T)
        if generate=='linear-tot-ran':
            old_T = ancestor[None,:,:]
        NNN = new_T.shape[2]
    else:
        print('ERROR: Select a generative process')

    # define the name of the directory to be created and create it
    path2 = "Generated" + "_iter-" + str(
        Iters) + "_pmask-" + str(pmask) + "_seqs-" + str(NNN) + add_strs
    try:
        os.mkdir(path + "/" + path1 + "/" + path2)
    except OSError:
        print("Creation of the directory %s failed" % (path + "/" +
              path1 + "/" + path2))
    else:
        print("Successfully created the directory %s " % (path + "/" +
              path1 + "/" + path2))

    # Save data
    if generate == False or generate=='linear-tot-ran':
        np.save(path1 + "/" + path2 + "/shuffled-tokens.npy", old_T[0])
    else:
        np.save(path1 + "/" + path2 + "/context-tokens.npy", old_T[0])
    str_add = ''
    if range_vals is not False:
        str_add = '_range_indx_'+str(range_vals[0])+','+str(range_vals[1])
    np.save(path1 + "/" + path2 + "/new-tokens"+str_add+".npy", new_T[0])

    return 1

print(show_doc(gen_MSAs))