Initial commit

.devcontainer.json

+{
+    "name": "nbdev_template-codespaces",
+    "dockerComposeFile": "docker-compose.yml",
+    "service": "watcher",
+    "settings": {"terminal.integrated.shell.linux": "/bin/bash"},
+    "mounts": [ "source=/var/run/docker.sock,target=/var/run/docker.sock,type=bind" ],
+    "forwardPorts": [4000, 8080],
+    "appPort": [4000, 8080],
+    "extensions": ["ms-python.python",
+                   "ms-azuretools.vscode-docker"],
+    "runServices": ["notebook", "jekyll", "watcher"],
+    "postStartCommand": "pip install -e ."
+}

.gitignore

+.jekyll-cache/
+Gemfile.lock  
+*.bak
+.gitattributes
+.last_checked
+.gitconfig
+*.bak
+*.log
+*~
+~*
+_tmp*
+tmp*
+tags
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode
+*.swp
+
+# osx generated files
+.DS_Store
+.DS_Store?
+.Trashes
+ehthumbs.db
+Thumbs.db
+.idea
+
+# pytest
+.pytest_cache
+
+# tools/trust-doc-nbs
+docs_src/.last_checked
+
+# symlinks to fastai
+docs_src/fastai
+tools/fastai
+
+# link checker
+checklink/cookies.txt
+
+# .gitconfig is now autogenerated
+.gitconfig
+

CONTRIBUTING.md

+# How to contribute
+
+## How to get started
+
+Before anything else, please install the git hooks that run automatic scripts during each commit and merge to strip the notebooks of superfluous metadata (and avoid merge conflicts). After cloning the repository, run the following command inside it:
+```
+nbdev_install_git_hooks
+```
+
+## Did you find a bug?
+
+* Ensure the bug was not already reported by searching on GitHub under Issues.
+* If you're unable to find an open issue addressing the problem, open a new one. Be sure to include a title and clear description, as much relevant information as possible, and a code sample or an executable test case demonstrating the expected behavior that is not occurring.
+* Be sure to add the complete error messages.
+
+#### Did you write a patch that fixes a bug?
+
+* Open a new GitHub pull request with the patch.
+* Ensure that your PR includes a test that fails without your patch, and pass with it.
+* Ensure the PR description clearly describes the problem and solution. Include the relevant issue number if applicable.
+
+## PR submission guidelines
+
+* Keep each PR focused. While it's more convenient, do not combine several unrelated fixes together. Create as many branches as needing to keep each PR focused.
+* Do not mix style changes/fixes with "functional" changes. It's very difficult to review such PRs and it most likely get rejected.
+* Do not add/remove vertical whitespace. Preserve the original style of the file you edit as much as you can.
+* Do not turn an already submitted PR into your development playground. If after you submitted PR, you discovered that more work is needed - close the PR, do the required work and then submit a new PR. Otherwise each of your commits requires attention from maintainers of the project.
+* If, however, you submitted a PR and received a request for changes, you should proceed with commits inside that PR, so that the maintainer can see the incremental fixes and won't need to review the whole PR again. In the exception case where you realize it'll take many many commits to complete the requests, then it's probably best to close the PR, do the work and then submit it again. Use common sense where you'd choose one way over another.
+
+## Do you want to contribute to the documentation?
+
+* Docs are automatically created from the notebooks in the nbs folder.
+

Iterative_masking/__init__.py

+__version__ = "0.0.1"

Iterative_masking/_nbdev.py

+# AUTOGENERATED BY NBDEV! DO NOT EDIT!
+
+__all__ = ["index", "modules", "custom_doc_links", "git_url"]
+
+index = {"IM_MSA_Transformer": "00_core.ipynb",
+         "gen_MSAs": "00_core.ipynb"}
+
+modules = ["core.py"]
+
+doc_url = "https://damiano-sg.github.io/Iterative_masking/"
+
+git_url = "https://github.com/damiano-sg/Iterative_masking/tree/main/"
+
+def custom_doc_links(name): return None

LICENSE

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MANIFEST.in

+include settings.ini
+include LICENSE
+include CONTRIBUTING.md
+include README.md
+recursive-exclude * __pycache__

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))