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.devcontainer.json 0 → 100755
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{
"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 0 → 100755
View file @ 319d9d8b
.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
00_core.ipynb 0 → 100755
View file @ 319d9d8b
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# default_exp core"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterative_masking\n",
"\n",
"> Use MSA Transformer to generate synthetic protein sequences by masking iteratively the same MSA."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# hide\n",
"from nbdev.showdoc import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# export\n",
"\n",
"import numpy as np\n",
"import esm\n",
"from numba import njit, prange\n",
"import torch\n",
"from Bio import SeqIO\n",
"import itertools\n",
"from typing import List, Tuple\n",
"import string\n",
"from warnings import warn\n",
"\n",
"torch.set_grad_enabled(False)\n",
"\n",
"\n",
"# Iterative masking MSA-Transformer\n",
"class IM_MSA_Transformer:\n",
" \"\"\"Class that implement the Iterative masking algorithm\"\"\"\n",
" def __init__(self,\n",
" iterations=None,\n",
" p_mask=None,\n",
" filename=None,\n",
" num=None,\n",
" filepath=None):\n",
"\n",
" self.iterations = iterations # number of iterations used to generate the MSA\n",
" self.p_mask = p_mask # masking probability for the MSA generation\n",
" #---------------------------------------------------------------------------------------\n",
" # Delete lowercase characters and punctuations from a string (input fasta file)\n",
" self.deletekeys = dict.fromkeys(string.ascii_lowercase)\n",
" self.deletekeys[\".\"] = None\n",
" self.deletekeys[\"*\"] = None\n",
" self.translation = str.maketrans(self.deletekeys)\n",
" #---------------------------------------------------------------------------------------\n",
" if filename is None or num is None or filepath is None:\n",
" raise ValueError(\"`filepath`, `filename` and `num` must be specified to import the MSA\")\n",
" # Import Transformer model\n",
" self.msa_transformer, self.msa_alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S()\n",
" self.msa_transformer = self.msa_transformer.eval().cuda()\n",
" self.msa_batch_converter = self.msa_alphabet.get_batch_converter()\n",
" self.idx_list = self.msa_alphabet.tok_to_idx\n",
" print('MSA Transformer model imported')\n",
"\n",
" # If filename is an array then it's the input MSA\n",
" with torch.no_grad():\n",
" if isinstance(filename,np.ndarray):\n",
" self.msa_data = torch.Tensor(filename).type(torch.int64)\n",
" if len(filename.shape) != 3:\n",
" raise ValueError(\"`filename` should be an array with 3 axes\")\n",
" self.msa_batch_tokens = self.msa_data[:, :num[0], :]\n",
" print('Using MSA given in input')\n",
" else:\n",
" if len(num) != len(filename):\n",
" raise ValueError(\"`filename` and `num` must have the same length\")\n",
" #---------------------------------------------------------------------------------------\n",
" # Import MSAs\n",
" self.msa_data = []\n",
" for ff, nn in zip(filename, num):\n",
" self.msa_data += [self.read_msa(filepath + '/' + ff, nn)]\n",
" print('MSA Imported')\n",
" #---------------------------------------------------------------------------------------\n",
" # Create tokens starting from MSA\n",
" self.msa_batch_labels, self.msa_batch_strs, self.msa_batch_tokens = self.msa_batch_converter(\n",
" self.msa_data)\n",
" self.msa_data = (self.msa_batch_tokens).clone()\n",
" print(f'We are using batch MSAs of {num[0]} sequences')\n",
" self.msa_batch_tokens = self.msa_batch_tokens[:, :num[0], :]\n",
"\n",
" # Import tokens into cuda\n",
" self.msa_batch_tokens = self.msa_batch_tokens.cuda()\n",
"\n",
" print('MSA converted into tokens tensor of size and type:')\n",
" print(self.msa_batch_tokens.size(), self.msa_batch_tokens.dtype)\n",
"\n",
" #---------------------------------------------------------------------------------------\n",
" # Useful functions for handling string sequences\n",
" def read_sequence(self, filename: str) -> Tuple[str, str]:\n",
" \"\"\" Reads the first (reference) sequences from a fasta or MSA file.\"\"\"\n",
" record = next(SeqIO.parse(filename, \"fasta\"))\n",
" return record.description, str(record.seq)\n",
"\n",
" def remove_insertions(self, sequence: str) -> str:\n",
" \"\"\" Removes any insertions into the sequence. Needed to load aligned sequences in an MSA. \"\"\"\n",
" return sequence.translate(self.translation)\n",
"\n",
" def read_msa(self, filename: str, nseq: int) -> List[Tuple[str, str]]:\n",
" \"\"\" Reads the first nseq sequences from an MSA file, automatically removes insertions.\"\"\"\n",
" tot = len([elem.id for elem in SeqIO.parse(filename, \"fasta\")])\n",
" print(f'Number of sequences in {filename}: ', tot)\n",
" return [\n",
" (record.description, self.remove_insertions(str(record.seq)))\n",
" for record in itertools.islice(SeqIO.parse(filename, \"fasta\"), tot)]\n",
"\n",
"#-----------------------------------------------------------------------------------------------------------------------\n",
"# USEFUL FUNCTIONS TO RUN THE MSA TRANSFORMER ON INFERENCE MODE\n",
"#-----------------------------------------------------------------------------------------------------------------------\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" def print_tokens(self, tokens=None):\n",
" \"\"\"\n",
" Outputs (on the cpu) the input `tokens` of the MSA, detaching them from the GPU.\n",
" \"\"\"\n",
" with torch.no_grad():\n",
" if tokens is None:\n",
" return ((self.msa_batch_tokens.detach().cpu()).to(\n",
" dtype=torch.int8)).numpy()\n",
" else:\n",
" return ((tokens.detach().cpu()).to(dtype=torch.int8)).numpy()\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" def compute_embeddings(self, tokens=None, lyrs=[12]):\n",
" \"\"\"\n",
" Starting from the `tokens`, use the model to predict their output embeddings and their associated\n",
" logits (when softmaxed they give the probability of each token)\n",
" `lyrs`: list of the layers from which extracting the embeddings (# 12 is the last layer)\n",
" \"\"\"\n",
" with torch.no_grad():\n",
" if tokens is None:\n",
" tokens = self.msa_batch_tokens\n",
" if not tokens.is_cuda:\n",
" tokens = tokens.cuda()\n",
" results = self.msa_transformer(tokens,\n",
" repr_layers=lyrs,\n",
" return_contacts=False)\n",
" token_representations = results[\"representations\"][lyrs[0]].detach().cpu().numpy()\n",
" logits = results[\"logits\"].detach().cpu().numpy()\n",
" del results\n",
" return token_representations, logits\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" def compute_contacts(self, tokens=None):\n",
" \"\"\"\n",
" Starting from the `tokens`, use the model to predict the contact matrix of each MSA\n",
" \"\"\"\n",
" with torch.no_grad():\n",
" if tokens is None:\n",
" tokens = self.msa_batch_tokens\n",
" if not tokens.is_cuda:\n",
" tokens = tokens.cuda()\n",
" msa_contacts = self.msa_transformer.predict_contacts(tokens).cpu()\n",
" return msa_contacts\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" @njit(parallel=True)\n",
" def Weights_Phylogeny(tkn, delta=0.8):\n",
" \"\"\"\n",
" Compute the Phylogeny weights of the sequences\n",
" `tkn`: the 2d array of tokens of one MSA, it should not have the first token (0)\n",
" and it should end before the start of the padding tokens (1).\n",
" `delta`: the phylogeny parameter\n",
" \"\"\"\n",
" depth, length = tkn.shape\n",
"\n",
" def _inner(seq1, seq2):\n",
" return np.sum(seq1 != seq2) / length\n",
"\n",
" weights = np.empty(depth, dtype=np.float64)\n",
" for i in prange(depth):\n",
" dists = np.empty(depth, dtype=np.float64)\n",
" for j in range(depth):\n",
" dists[j] = _inner(tkn[i], tkn[j])\n",
" within_neighbourhood = np.sum(dists < 1 - delta)\n",
" weights[i] = 1 / within_neighbourhood\n",
" return weights\n",
"\n",
"\n",
"#-----------------------------------------------------------------------------------------------------------------------\n",
"# USEFUL FUNCTIONS FOR THE MSA GENERATION WITH THE TRANSFORMER ON INFERENCE MODE\n",
"#-----------------------------------------------------------------------------------------------------------------------\n",
"\n",
"#-------------------------------------------------------------------------------------------------------------------\n",
"# Softmax of the logits tensor\n",
"\n",
" def softmax_tensor(self, x, axis, T=1):\n",
" \"\"\"\n",
" Compute softmax values for each sets of scores in `x` where `x` is the 4-d tensor of logits\n",
" and `T` is the sampling temperature.\n",
" \"\"\"\n",
" return torch.exp(x/T) / torch.sum(torch.exp(x/T), axis=axis)[:, :, :, None]\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" def generate_MSA(self, MSA_tokens, mask_idx=32, use_pdf=False, sample_all=False, T=1):\n",
" \"\"\"\n",
" Generate a new MSA by masking some entries of the original MSA and\n",
" re-predicting them through MSA Transformer.\n",
"\n",
" `MSA_tokens`: input tokens.\n",
"\n",
" `p_mask`: probability that an entry of the MSA is masked.\n",
"\n",
" `mask_idx`: masking index (as interpreted by the model), for MSA-Tr it's 32.\n",
"\n",
" `use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
" `sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
" `T`: Temperature of sampling from the pdf of output logits.\n",
" \"\"\"\n",
" with torch.no_grad():\n",
" if not MSA_tokens.is_cuda:\n",
" MSA_tokens = MSA_tokens.cuda()\n",
" mask = ((torch.rand(MSA_tokens.shape) > self.p_mask).type(\n",
" torch.uint8)).cuda()\n",
" masked_msa_tokens = MSA_tokens * mask + mask_idx * (1 - mask)\n",
" results = self.msa_transformer(masked_msa_tokens,\n",
" repr_layers=[12],\n",
" return_contacts=False)\n",
" msa_logits = self.softmax_tensor(x=results[\"logits\"], axis=3, T=T)\n",
" if use_pdf == False:\n",
" new_msa_tokens = torch.argmax(msa_logits, dim=3)\n",
" else:\n",
" Vals = torch.tensor([\n",
" 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,\n",
" 20, 21, 22, 23, 30\n",
" ],\n",
" dtype=torch.int64)\n",
" maxval = Vals[-1].cuda()\n",
" msa_logits = msa_logits[:, :, :, Vals]\n",
" msa_logits = msa_logits / (torch.sum(msa_logits,\n",
" axis=3)[:, :, :, None])\n",
" cum = torch.cumsum(msa_logits, dim=3)\n",
" idxs = torch.zeros_like(cum, dtype=torch.int64).cuda()\n",
" idxs1 = Vals[None, None, None, :].cuda()\n",
" idxs = idxs + idxs1\n",
" sample = (torch.rand(\n",
" (cum.shape[0], cum.shape[1], cum.shape[2]))).cuda()\n",
" idxs[torch.gt(sample[:, :, :, None], cum)] = 100\n",
" new_msa_tokens = torch.minimum(torch.amin(idxs, axis=3),\n",
" maxval)\n",
" del cum, idxs, idxs1, sample\n",
" if sample_all == False:\n",
" new_msa_tokens = MSA_tokens * mask + new_msa_tokens * (1 - mask)\n",
" new_msa_tokens[:, :, 0] = 0\n",
" del mask, masked_msa_tokens, results, msa_logits\n",
" return new_msa_tokens\n",
"\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" def NEW_MSA(self, use_pdf=False, simplified=False, sample_all=False, T=1):\n",
" \"\"\"\n",
" Generate a new MSA by iteratively calling the masked MSA generator defined in: `self.generate_MSA`.\n",
"\n",
" ---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed\n",
" or contacs), otherwise use `simplified`=True.\n",
"\n",
" The variable `self.iterations` must be a numpy array which specifies when (at which iterations)\n",
" the tokens should be saved. The last element of the array gives the maximum number of iterations that should be done.\n",
"\n",
" `use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
" `sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
" `T`: Temperature of sampling from the pdf of output logits.\n",
" \"\"\"\n",
" if self.iterations is None or self.p_mask is None:\n",
" raise ValueError(\n",
" \"Both `iterations` (numpy array) and `p_mask` (float) must be specified to generate a new MSA\"\n",
" )\n",
" max_iter = self.iterations[-1]\n",
" with torch.no_grad():\n",
" new_msa_tokens = self.msa_batch_tokens.clone()\n",
" all_tokens = torch.zeros(\n",
" (len(self.iterations), self.msa_batch_tokens.shape[0],\n",
" self.msa_batch_tokens.shape[1],\n",
" self.msa_batch_tokens.shape[2]),\n",
" dtype=torch.int64)\n",
" if simplified:\n",
" all_tokens = all_tokens.to(dtype=torch.int8)\n",
" if self.msa_alphabet.mask_idx != 32:\n",
" raise ValueError(\n",
" f\"The token used for masking is {self.msa_alphabet.mask_idx} instead of 32\"\n",
" )\n",
" # Iterate the MSA generation process\n",
" j = 0\n",
" for i in range(max_iter):\n",
" new_msa_tokens = self.generate_MSA(\n",
" MSA_tokens=new_msa_tokens,\n",
" mask_idx=self.msa_alphabet.mask_idx,\n",
" use_pdf=use_pdf, sample_all=sample_all, T=T)\n",
" if np.any((i + 1) == self.iterations):\n",
" # Save the tokens at the specified iterations\n",
" if simplified:\n",
" all_tokens[j,\n",
" ...] = (new_msa_tokens.clone().detach().cpu()).to(\n",
" dtype=torch.int8)\n",
" else:\n",
" all_tokens[j, ...] = new_msa_tokens.clone()\n",
" j += 1\n",
" del new_msa_tokens\n",
" if simplified:\n",
" return all_tokens.numpy()\n",
" else:\n",
" return all_tokens.cuda()\n",
"\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" def Batch_MSA(self, use_pdf=False, simplified=False, repetitions=2, sample_all=False, T=1, phylo=False):\n",
" \"\"\"\n",
" Generate a full MSA by calling with different input MSAs the iterative MSA generator defined\n",
" in: `self.NEW_MSA`.\n",
"\n",
" ---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed\n",
" or contacs), otherwise use `simplified`=True\n",
"\n",
" The variable `self.iterations` must be a numpy array which specifies when (at which iterations)\n",
" the tokens must be saved. The last element of the array gives the maximum number of iterations that should be done.\n",
"\n",
" `repetitions`: the number of times self.NEW_MSA() is repeated with a different input MSA.\n",
"\n",
" `use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
" `sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
" `T`: Temperature of sampling from the pdf of output logits.\n",
"\n",
" `phylo`: if True the start sequences are sampled from phylogeny weights instead of randomly.\n",
" \"\"\"\n",
" with torch.no_grad():\n",
" all_tokens = np.zeros(\n",
" (len(self.iterations), self.msa_batch_tokens.shape[0],\n",
" self.msa_batch_tokens.shape[1] * repetitions,\n",
" self.msa_batch_tokens.shape[2]),\n",
" dtype=np.int64)\n",
" if simplified:\n",
" all_tokens = all_tokens.astype('int8')\n",
" ALL_tokens = self.msa_data\n",
" depth = self.msa_batch_tokens.shape[1]\n",
" if repetitions * depth > ALL_tokens.shape[1]:\n",
" all_tokens = np.zeros(\n",
" (len(self.iterations), self.msa_batch_tokens.shape[0],\n",
" ALL_tokens.shape[1], self.msa_batch_tokens.shape[2]),\n",
" dtype=np.int64)\n",
"\n",
" if not phylo:\n",
" ALL_tokens = ALL_tokens[:, torch.randperm(ALL_tokens.shape[1]), :]\n",
" else:\n",
" _ = self.Weights_Phylogeny(ALL_tokens[0, :20, :], delta=0.8)\n",
" phylo_w = self.Weights_Phylogeny(ALL_tokens[0, :, :], delta=0.8)\n",
" indxs = torch.multinomial(phylo_w, ALL_tokens.shape[1], replacement=True)\n",
" ALL_tokens = ALL_tokens[:, indxs, :]\n",
" for i in range(repetitions):\n",
" ind = torch.arange(i * depth, (i + 1) * depth)\n",
" if (i + 1) * depth > ALL_tokens.shape[1]:\n",
" ind = torch.arange(i * depth, ALL_tokens.shape[1])\n",
" self.msa_batch_tokens = ALL_tokens[:, ind, :]\n",
" self.msa_batch_tokens = self.msa_batch_tokens.cuda()\n",
" all_tokens[:, :,\n",
" ind.numpy(), :] = self.NEW_MSA(use_pdf=use_pdf, simplified=simplified, sample_all=sample_all, T=T)\n",
" if (i + 1) * depth > ALL_tokens.shape[1]:\n",
" break\n",
"\n",
" if simplified:\n",
" return (ALL_tokens[:, :repetitions *\n",
" depth, :].numpy()).astype('int8'), all_tokens\n",
" else:\n",
" return ALL_tokens[:, :repetitions *\n",
" depth, :], torch.from_numpy(all_tokens).cuda()\n",
"\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
"\n",
" def generate_MSA_context(self, ancestor, context, mask_idx=32, use_pdf=False, sample_all=False, T=1):\n",
" \"\"\"\n",
" Generate a new sequence by masking some entries of the original ancestor sequence and\n",
" re-predicting them through the transformer model (mask only `ancestor`, not the `context`).\n",
"\n",
" `ancestor`: input sequence to be masked iteratively.\n",
"\n",
" `context`: context MSA (not masked).\n",
"\n",
" `p_mask`: probability that an entry of the MSA is masked.\n",
"\n",
" `mask_idx`: masking index (as interpreted by the model), for MSA-Tr it's 32.\n",
"\n",
" `use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
" `sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
" `T`: Temperature of sampling from the pdf of output logits.\n",
" \"\"\"\n",
" with torch.no_grad():\n",
"\n",
" if not ancestor.is_cuda:\n",
" ancestor = ancestor.cuda()\n",
" if not context.is_cuda:\n",
" context = context.cuda()\n",
"\n",
" mask = ((torch.rand(ancestor.shape) > self.p_mask).type(torch.uint8)).cuda()\n",
" masked_ancestor = ancestor * mask + mask_idx * (1 - mask)\n",
" \n",
" masked_msa_tokens = torch.zeros((context.shape[0],\n",
" context.shape[1]+1,\n",
" context.shape[2]),\n",
" dtype=torch.int64).cuda()\n",
" masked_msa_tokens[0, 0, :] = masked_ancestor\n",
" masked_msa_tokens[:, 1:, :] = context\n",
"\n",
" results = self.msa_transformer(masked_msa_tokens,\n",
" repr_layers=[12],\n",
" return_contacts=False)\n",
" results1 = results[\"logits\"][:,0,:,:]\n",
" results1 = results1[:,None,:,:]\n",
" msa_logits = self.softmax_tensor(x=results1, axis=3, T=T)\n",
"\n",
" if use_pdf == False:\n",
" new_generation = torch.argmax(msa_logits, dim=3)[0,0,:]\n",
" else:\n",
" Vals = torch.tensor([\n",
" 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,\n",
" 20, 21, 22, 23, 30],dtype=torch.int64)\n",
" maxval = Vals[-1].cuda()\n",
" msa_logits = msa_logits[:, :, :, Vals]\n",
" msa_logits = msa_logits / (torch.sum(msa_logits,\n",
" axis=3)[:, :, :, None])\n",
" cum = torch.cumsum(msa_logits, dim=3)\n",
" idxs = torch.zeros_like(cum, dtype=torch.int64).cuda()\n",
" idxs1 = Vals[None, None, None, :].cuda()\n",
" idxs = idxs + idxs1\n",
" sample = (torch.rand(\n",
" (cum.shape[0], cum.shape[1], cum.shape[2]))).cuda()\n",
" idxs[torch.gt(sample[:, :, :, None], cum)] = 100\n",
" new_generation = torch.minimum(torch.amin(idxs, axis=3),\n",
" maxval)[0,0,:]\n",
" del cum, idxs, idxs1, sample\n",
" \n",
" if sample_all == False:\n",
" new_generation = ancestor * mask + new_generation * (1 - mask)\n",
" new_generation[0] = 0\n",
"\n",
" del mask, masked_msa_tokens, results, results1, msa_logits\n",
" return new_generation\n",
"\n",
" #-------------------------------------------------------------------------------------------------------------------\n",
" # Generate new sequence in a Linear tree by reiterating the function `generate_MSA_context()` starting from the sequence:\n",
" # `ancestor` (original sequence) and using the sequences in `context` as context MSA.\n",
" def Context_MSA(self, depth=None, ancestor=None, context=None, use_pdf=False, simplified=False, sample_all=False, print_all=True, T=1):\n",
" \"\"\"\n",
" Generates a new MSA with context-generation by iterating the masking on the original ancestor sequence\n",
" using: `self.generate_MSA_context`. It masks `ancestor` (original sequence) and uses the sequences in `context` as context MSA.\n",
" \n",
" ---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed\n",
" or contacs), otherwise use `simplified`=True\n",
"\n",
" The variable `self.iterations` must be a numpy array which specifies when (at which iterations)\n",
" the tokens must be saved. The last element of the array gives the maximum number of iterations that should be done.\n",
" If `print_all`=True then it saves the generated sequences at each iteration.\n",
"\n",
" `ancestor`: input sequence to be masked iteratively.\n",
"\n",
" `context`: context MSA (not masked).\n",
"\n",
" `use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
" `sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
" `T`: Temperature of sampling from the pdf of output logits.\n",
"\n",
" `depth`: number of generated sequences, if None the depth is the number of ancestor sequences.\n",
" \"\"\"\n",
" with torch.no_grad():\n",
" total_ran=False\n",
" if ancestor is None and context is None and depth is not None:\n",
" ALL_tokens = self.msa_data\n",
" ALL_tokens = ALL_tokens[:, torch.randperm(ALL_tokens.shape[1]), :]\n",
" ancestor = ALL_tokens[0,:depth,:]\n",
" ALL_tokens = ALL_tokens[:, torch.randperm(ALL_tokens.shape[1]), :]\n",
" context = ALL_tokens[:,:self.msa_batch_tokens.shape[1],:]\n",
" elif depth is None:\n",
" depth = ancestor.shape[0]\n",
" if isinstance(context,np.ndarray):\n",
" total_ran=False\n",
" elif context=='tot-ran':\n",
" total_ran=True\n",
" else:\n",
" print('ERROR, either you give depth or you give ancestor and context')\n",
"\n",
" all_tokens = torch.zeros((self.msa_batch_tokens.shape[0],\n",
" self.iterations[-1]+1,\n",
" depth,\n",
" ancestor.shape[1]),\n",
" dtype=torch.int64).cuda()\n",
"\n",
" ancestor = torch.from_numpy(ancestor).to(dtype=torch.int64)\n",
" if not total_ran:\n",
" context = torch.from_numpy(context).to(dtype=torch.int64)\n",
" if total_ran:\n",
" ALL_tokens = self.msa_data\n",
"\n",
" all_tokens[0, 0, :, :] = ancestor\n",
"\n",
" if simplified:\n",
" all_tokens = all_tokens.to(dtype=torch.int8)\n",
" if self.msa_alphabet.mask_idx != 32:\n",
" raise ValueError(\n",
" f\"The token used for masking is {self.msa_alphabet.mask_idx} instead of 32\"\n",
" )\n",
" \n",
" # Iterate the MSA generation tree\n",
" for j in range(depth):\n",
" new_ancestor = all_tokens[0, 0, j, :]\n",
" for i in range(1,self.iterations[-1]+1):\n",
" if total_ran:\n",
" context = (ALL_tokens[:, torch.randperm(ALL_tokens.shape[1])[:self.msa_batch_tokens.shape[1]], :]).cuda()\n",
" new_ancestor = self.generate_MSA_context(ancestor=new_ancestor,context=context, mask_idx=self.msa_alphabet.mask_idx, use_pdf=use_pdf, sample_all=sample_all, T=T)\n",
" if print_all:\n",
" all_tokens[0, i, j, :] = new_ancestor\n",
" if not print_all:\n",
" all_tokens[0, -1, j, :] = new_ancestor\n",
" # torch.cuda.empty_cache()\n",
"\n",
" if not print_all:\n",
" all_tokens = all_tokens[:,torch.tensor([-1]),:,:]\n",
"\n",
" if simplified:\n",
" return ((context.detach().cpu()).to(dtype=torch.int8)).numpy(), ((all_tokens.detach().cpu()).to(dtype=torch.int8)).numpy()\n",
" else:\n",
" return context.cuda(), all_tokens.cuda()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"<h2 id=\"IM_MSA_Transformer\" class=\"doc_header\"><code>class</code> <code>IM_MSA_Transformer</code><a href=\"\" class=\"source_link\" style=\"float:right\">[source]</a></h2>\n",
"\n",
"> <code>IM_MSA_Transformer</code>(**`iterations`**=*`None`*, **`p_mask`**=*`None`*, **`filename`**=*`None`*, **`num`**=*`None`*, **`filepath`**=*`None`*)\n",
"\n",
"Class that implement the Iterative masking algorithm"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(IM_MSA_Transformer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"<h4 id=\"IM_MSA_Transformer.Batch_MSA\" class=\"doc_header\"><code>IM_MSA_Transformer.Batch_MSA</code><a href=\"__main__.py#L303\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
"\n",
"> <code>IM_MSA_Transformer.Batch_MSA</code>(**`use_pdf`**=*`False`*, **`simplified`**=*`False`*, **`repetitions`**=*`2`*, **`sample_all`**=*`False`*, **`T`**=*`1`*, **`phylo`**=*`False`*)\n",
"\n",
"Generate a full MSA by calling with different input MSAs the iterative MSA generator defined\n",
"in: `self.NEW_MSA`.\n",
"\n",
"---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed\n",
" or contacs), otherwise use `simplified`=True\n",
"\n",
"The variable `self.iterations` must be a numpy array which specifies when (at which iterations)\n",
"the tokens must be saved. The last element of the array gives the maximum number of iterations that should be done.\n",
"\n",
"`repetitions`: the number of times self.NEW_MSA() is repeated with a different input MSA.\n",
"\n",
"`use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
"`sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
"`T`: Temperature of sampling from the pdf of output logits.\n",
"\n",
"`phylo`: if True the start sequences are sampled from phylogeny weights instead of randomly."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(IM_MSA_Transformer.Batch_MSA)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"<h4 id=\"IM_MSA_Transformer.Context_MSA\" class=\"doc_header\"><code>IM_MSA_Transformer.Context_MSA</code><a href=\"__main__.py#L448\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
"\n",
"> <code>IM_MSA_Transformer.Context_MSA</code>(**`depth`**=*`None`*, **`ancestor`**=*`None`*, **`context`**=*`None`*, **`use_pdf`**=*`False`*, **`simplified`**=*`False`*, **`sample_all`**=*`False`*, **`print_all`**=*`True`*, **`T`**=*`1`*)\n",
"\n",
"Generates a new MSA with context-generation by iterating the masking on the original ancestor sequence\n",
"using: `self.generate_MSA_context`. It masks `ancestor` (original sequence) and uses the sequences in `context` as context MSA.\n",
"\n",
"---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed\n",
" or contacs), otherwise use `simplified`=True\n",
"\n",
"The variable `self.iterations` must be a numpy array which specifies when (at which iterations)\n",
"the tokens must be saved. The last element of the array gives the maximum number of iterations that should be done.\n",
"If `print_all`=True then it saves the generated sequences at each iteration.\n",
"\n",
"`ancestor`: input sequence to be masked iteratively.\n",
"\n",
"`context`: context MSA (not masked).\n",
"\n",
"`use_pdf`: if it's True the function sample the token from the logits pdf \n",
" instead of getting the argmax (greedy sampling).\n",
"\n",
"`sample_all`: if True all the new tokens are obtained from the logits (both\n",
" the masked and the non masked), if False the non masked tokens\n",
" are left untouched and only the masked ones are changed.\n",
"\n",
"`T`: Temperature of sampling from the pdf of output logits.\n",
"\n",
"`depth`: number of generated sequences, if None the depth is the number of ancestor sequences."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(IM_MSA_Transformer.Context_MSA)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# export\n",
"import os\n",
"import pickle\n",
"from fastcore.script import *\n",
"\n",
"@call_parse\n",
"def gen_MSAs(filepath:Param(help='Path of the input directory',type=str,default='./'),\n",
" filename:Param(help='Name of the input file(s)',type=str,nargs='+',default=False),\n",
" new_dir:Param(help='Name of the output directory',type=str,default=False),\n",
" pdf:Param(help='Should I sample tokens from the pdf ? (bool)',type=bool_arg,default=False),\n",
" T:Param(help='Which is the sampling Temperature from the pdf ? (only when `pdf` is True)',type=float,default=1),\n",
" sample_all:Param(help='Should I sample all tokens or just the masked ones ? (True = sample all tokens)',type=bool_arg, default=False),\n",
" Iters:Param(help='Number of total iterations to generate the new tokens',type=int,default=10),\n",
" pmask:Param(help='Masking probability',type=float,default=0.1),\n",
" num:Param(help='Size of the batches MSAs which the MSA-Transformer receives as input',type=int,nargs='+',default=100),\n",
" depth:Param(help='Number of batches (of size num) that you want to generate',type=int,default=2),\n",
" 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),\n",
" print_all:Param(help='Should I print the MSA after each iteration ? (bool)',type=bool_arg,default=False),\n",
" range_vals:Param(help='First and last index of the sequences that you want to use as ancestors', type=int,nargs='+',default=False),\n",
" phylo_w:Param(help='Should I sample the starting sequences from the phylogeny weights ? (bool)',type=bool_arg,default=False)\n",
" ):\n",
" \"Generate a new MSA either with Batch generation of Context generation. It shuffles the initial MSA and uses different slices as batch MSAs\"\n",
"\n",
" # Create folder\n",
" path = os.getcwd()\n",
" path1 = new_dir\n",
" if new_dir is False:\n",
" path1 = filename[0][:-6]\n",
" try:\n",
" os.mkdir(path + \"/\" + path1)\n",
" except OSError:\n",
" print(\"Creation of the directory %s failed\" % (path + \"/\" + path1))\n",
" else:\n",
" print(\"Successfully created the directory %s \" % (path + \"/\" + path1))\n",
"\n",
" # Save Input MSA\n",
" print('Tokenize')\n",
" Class = IM_MSA_Transformer(filename=filename,\n",
" num=[-1],\n",
" filepath=filepath)\n",
" idx_list = Class.idx_list\n",
" old_tkn = Class.print_tokens()\n",
" a_file = open(path1 + \"/dictionary-tokens.pkl\", \"wb\")\n",
" pickle.dump(idx_list, a_file)\n",
" a_file.close()\n",
" np.save(path1 + \"/original-tokens.npy\", old_tkn[0])\n",
"\n",
" add_strs = \"\"\n",
" if pdf==True:\n",
" add_strs += f\"_pdf(T={round(T,3)})\"\n",
" print(\n",
" \"We are sampling new tokens from the pdf of logits and not taking the mode of the pdf\"\n",
" )\n",
" if T!=1 and pdf==False:\n",
" print('To sample with a Temperature you should use pdf=True, otherwise the result is the same')\n",
" if sample_all == False:\n",
" add_strs += \"_(only-masked-sampled)\"\n",
" if not generate==False:\n",
" add_strs += \"_\"+generate+\"_(context-\"+str(num[0])+\")\"\n",
" if phylo_w:\n",
" add_strs += \"_phylo-w\"\n",
"\n",
" print('Generate Class')\n",
" Class = IM_MSA_Transformer(iterations=np.array([Iters]),\n",
" p_mask=pmask,\n",
" filename=filename,\n",
" num=num,\n",
" filepath=filepath)\n",
"\n",
" print('Compute results from Class')\n",
" Class.iterations = np.array([Iters])\n",
" Class.p_mask = pmask\n",
"\n",
" if generate == False:\n",
" print('Generating MSA with same size as the original one')\n",
" old_T, new_T = Class.Batch_MSA(simplified=True,\n",
" repetitions=depth,\n",
" use_pdf=pdf, sample_all=sample_all, T=T, phylo=phylo_w)\n",
" NNN = min(num[0] * depth, old_T.shape[1])\n",
"\n",
" elif generate=='linear-ran' or generate=='linear-tot-ran':\n",
" print('Generate MSA with linear context generation')\n",
" orig_tkn = np.load(path + \"/\" + path1 + \"/original-tokens.npy\")\n",
" # select ancestor and context\n",
" np.random.seed(0)\n",
" indices = np.random.permutation(orig_tkn.shape[0])\n",
" indexes_context = indices[:num[0]]\n",
" indices = np.random.permutation(orig_tkn.shape[0])\n",
" if depth == -1:\n",
" ind_ancestor = indices\n",
" elif range_vals is False:\n",
" ind_ancestor = indices[:depth]\n",
" else:\n",
" if range_vals[1] == -1 :\n",
" ind_ancestor = indices[range_vals[0]:]\n",
" range_vals[1] = orig_tkn.shape[0]\n",
" else:\n",
" ind_ancestor = indices[range_vals[0]:range_vals[1]]\n",
" ancestor = orig_tkn[ind_ancestor,:]\n",
" context = orig_tkn[indexes_context,:][None,:,:]\n",
" if generate=='linear-tot-ran':\n",
" context = 'tot-ran'\n",
" 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)\n",
" if generate=='linear-tot-ran':\n",
" old_T = ancestor[None,:,:]\n",
" NNN = new_T.shape[2]\n",
" else:\n",
" print('ERROR: Select a generative process')\n",
"\n",
" # define the name of the directory to be created and create it\n",
" path2 = \"Generated\" + \"_iter-\" + str(\n",
" Iters) + \"_pmask-\" + str(pmask) + \"_seqs-\" + str(NNN) + add_strs\n",
" try:\n",
" os.mkdir(path + \"/\" + path1 + \"/\" + path2)\n",
" except OSError:\n",
" print(\"Creation of the directory %s failed\" % (path + \"/\" +\n",
" path1 + \"/\" + path2))\n",
" else:\n",
" print(\"Successfully created the directory %s \" % (path + \"/\" +\n",
" path1 + \"/\" + path2))\n",
"\n",
" # Save data\n",
" if generate == False or generate=='linear-tot-ran':\n",
" np.save(path1 + \"/\" + path2 + \"/shuffled-tokens.npy\", old_T[0])\n",
" else:\n",
" np.save(path1 + \"/\" + path2 + \"/context-tokens.npy\", old_T[0])\n",
" str_add = ''\n",
" if range_vals is not False:\n",
" str_add = '_range_indx_'+str(range_vals[0])+','+str(range_vals[1])\n",
" np.save(path1 + \"/\" + path2 + \"/new-tokens\"+str_add+\".npy\", new_T[0])\n",
"\n",
" return 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"<h4 id=\"gen_MSAs\" class=\"doc_header\"><code>gen_MSAs</code><a href=\"__main__.py#L6\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
"\n",
"> <code>gen_MSAs</code>(**`filepath`**:\"Path of the input directory\", **`filename`**:\"Name of the input file(s)\", **`new_dir`**:\"Name of the output directory\", **`pdf`**:\"Should I sample tokens from the pdf ? (bool)\", **`T`**:\"Which is the sampling Temperature from the pdf ? (only when `pdf` is True)\", **`sample_all`**:\"Should I sample all tokens or just the masked ones ? (True = sample all tokens)\", **`Iters`**:\"Number of total iterations to generate the new tokens\", **`pmask`**:\"Masking probability\", **`num`**:\"Size of the batches MSAs which the MSA-Transformer receives as input\", **`depth`**:\"Number of batches (of size num) that you want to generate\", **`generate`**:\"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.\", **`print_all`**:\"Should I print the MSA after each iteration ? (bool)\", **`range_vals`**:\"First and last index of the sequences that you want to use as ancestors\", **`phylo_w`**:\"Should I sample the starting sequences from the phylogeny weights ? (bool)\")\n",
"\n",
"Generate a new MSA either with Batch generation of Context generation. It shuffles the initial MSA and uses different slices as batch MSAs"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(gen_MSAs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build library"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converted 00_core.ipynb.\n",
"Converted index.ipynb.\n",
"converting: /home/damiano/Documents/Projects/Iterative_masking/00_core.ipynb\n",
"converting: /home/damiano/Documents/Projects/Iterative_masking/index.ipynb\n",
"converting /home/damiano/Documents/Projects/Iterative_masking/index.ipynb to README.md\n"
]
}
],
"source": [
"# hide\n",
"\n",
"# RUN THIS CELL EVERYTIME YOU CHANGE THE NOTEBOOK SO THAT IT BUILDS THE NEW LIBRARY \n",
"# --------> no need to run nbdev_build_lib on the terminal\n",
"from nbdev.export import notebook2script\n",
"notebook2script()\n",
"# !nbdev_clean_nbs\n",
"!nbdev_build_docs"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
CONTRIBUTING.md 0 → 100755
View file @ 319d9d8b
# 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 0 → 100755
View file @ 319d9d8b
__version__ = "0.0.1"
Iterative_masking/_nbdev.py 0 → 100755
View file @ 319d9d8b
# 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
Iterative_masking/core.py 0 → 100755
View file @ 319d9d8b
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified).
__all__ = ['IM_MSA_Transformer', 'gen_MSAs']
# Cell
import numpy as np
import esm
from numba import njit, prange
import torch
from Bio import SeqIO
import itertools
from typing import List, Tuple
import string
from warnings import warn
torch.set_grad_enabled(False)
# Iterative masking MSA-Transformer
class IM_MSA_Transformer:
"""Class that implement the Iterative masking algorithm"""
def __init__(self,
iterations=None,
p_mask=None,
filename=None,
num=None,
filepath=None):
self.iterations = iterations # number of iterations used to generate the MSA
self.p_mask = p_mask # masking probability for the MSA generation
self.num = num
#---------------------------------------------------------------------------------------
# Delete lowercase characters and punctuations from a string (input fasta file)
self.deletekeys = dict.fromkeys(string.ascii_lowercase)
self.deletekeys["."] = None
self.deletekeys["*"] = None
self.translation = str.maketrans(self.deletekeys)
#---------------------------------------------------------------------------------------
if filename is None or num is None or filepath is None:
raise ValueError(
"`filepath`, `filename` and `num` must be specified to import the MSA"
)
# Import Transformer model
self.msa_transformer, self.msa_alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S(
)
self.msa_transformer = self.msa_transformer.eval().cuda()
self.msa_batch_converter = self.msa_alphabet.get_batch_converter()
self.idx_list = self.msa_alphabet.tok_to_idx
print('MSA Transformer model imported')
# If filename is an array then it's the input MSA
with torch.no_grad():
if isinstance(filename, np.ndarray):
self.msa_data = torch.Tensor(filename).type(torch.int64)
if len(filename.shape) != 3:
raise ValueError(
"`filename` should be an array with 3 axes")
self.msa_batch_tokens = self.msa_data[:, :num[0], :]
print('Using MSA given in input')
else:
if len(num) != len(filename):
raise ValueError(
"`filename` and `num` must have the same length")
#---------------------------------------------------------------------------------------
# Import MSAs
self.msa_data = []
for ff, nn in zip(filename, num):
self.msa_data += [self.read_msa(filepath + '/' + ff, nn)]
print('MSA Imported')
#---------------------------------------------------------------------------------------
# Create tokens starting from MSA
self.msa_batch_labels, self.msa_batch_strs, self.msa_batch_tokens = self.msa_batch_converter(
self.msa_data)
self.msa_data = (self.msa_batch_tokens).clone()
print(f'We are using batch MSAs of {num[0]} sequences')
self.msa_batch_tokens = self.msa_batch_tokens[:, :num[0], :]
# Import tokens into cuda
self.msa_batch_tokens = self.msa_batch_tokens.cuda()
print('MSA converted into tokens tensor of size and type:')
print(self.msa_batch_tokens.size(), self.msa_batch_tokens.dtype)
#---------------------------------------------------------------------------------------
# Useful functions for handling string sequences
def read_sequence(self, filename: str) -> Tuple[str, str]:
""" Reads the first (reference) sequences from a fasta or MSA file."""
record = next(SeqIO.parse(filename, "fasta"))
return record.description, str(record.seq)
def remove_insertions(self, sequence: str) -> str:
""" Removes any insertions into the sequence. Needed to load aligned sequences in an MSA. """
return sequence.translate(self.translation)
def read_msa(self, filename: str, nseq: int) -> List[Tuple[str, str]]:
""" Reads the first nseq sequences from an MSA file, automatically removes insertions."""
tot = len([elem.id for elem in SeqIO.parse(filename, "fasta")])
print(f'Number of sequences in {filename}: ', tot)
return [
(record.description, self.remove_insertions(str(record.seq)))
for record in itertools.islice(SeqIO.parse(filename, "fasta"), tot)
]
#-----------------------------------------------------------------------------------------------------------------------
# USEFUL FUNCTIONS TO RUN THE MSA TRANSFORMER ON INFERENCE MODE
#-----------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------
def print_tokens(self, tokens=None):
"""
Outputs (on the cpu) the input `tokens` of the MSA, detaching them from the GPU.
"""
with torch.no_grad():
if tokens is None:
return ((self.msa_batch_tokens.detach().cpu()).to(
dtype=torch.int8)).numpy()
else:
return ((tokens.detach().cpu()).to(dtype=torch.int8)).numpy()
#-------------------------------------------------------------------------------------------------------------------
def compute_embeddings(self, tokens=None, lyrs=[12]):
"""
Starting from the `tokens`, use the model to predict their output embeddings and their associated
logits (when softmaxed they give the probability of each token)
`lyrs`: list of the layers from which extracting the embeddings (# 12 is the last layer)
"""
with torch.no_grad():
if tokens is None:
tokens = self.msa_batch_tokens
if not tokens.is_cuda:
tokens = tokens.cuda()
results = self.msa_transformer(tokens,
repr_layers=lyrs,
return_contacts=False)
token_representations = results["representations"][
lyrs[0]].detach().cpu().numpy()
logits = results["logits"].detach().cpu().numpy()
del results
return token_representations, logits
#-------------------------------------------------------------------------------------------------------------------
def compute_contacts(self, tokens=None):
"""
Starting from the `tokens`, use the model to predict the contact matrix of each MSA
"""
with torch.no_grad():
if tokens is None:
tokens = self.msa_batch_tokens
if not tokens.is_cuda:
tokens = tokens.cuda()
msa_contacts = self.msa_transformer.predict_contacts(tokens).cpu()
return msa_contacts
#-------------------------------------------------------------------------------------------------------------------
@njit(parallel=True)
def Weights_Phylogeny(tkn, delta=0.8):
"""
Compute the Phylogeny weights of the sequences
`tkn`: the 2d array of tokens of one MSA, it should not have the first token (0)
and it should end before the start of the padding tokens (1).
`delta`: the phylogeny parameter
"""
depth, length = tkn.shape
def _inner(seq1, seq2):
return np.sum(seq1 != seq2) / length
weights = np.empty(depth, dtype=np.float64)
for i in prange(depth):
dists = np.empty(depth, dtype=np.float64)
for j in range(depth):
dists[j] = _inner(tkn[i], tkn[j])
within_neighbourhood = np.sum(dists < 1 - delta)
weights[i] = 1 / within_neighbourhood
return weights
#-----------------------------------------------------------------------------------------------------------------------
# USEFUL FUNCTIONS FOR THE MSA GENERATION WITH THE TRANSFORMER ON INFERENCE MODE
#-----------------------------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------------------------
# Softmax of the logits tensor
def softmax_tensor(self, x, axis, T=1):
"""
Compute softmax values for each sets of scores in `x` where `x` is the 4-d tensor of logits
and `T` is the sampling temperature.
"""
return torch.exp(x / T) / torch.sum(torch.exp(x / T),
axis=axis)[:, :, :, None]
#-------------------------------------------------------------------------------------------------------------------
def generate_MSA(self,
MSA_tokens,
mask_idx=32,
use_pdf=False,
sample_all=False,
T=1):
"""
Generate a new MSA by masking some entries of the original MSA and
re-predicting them through MSA Transformer.
`MSA_tokens`: input tokens.
`p_mask`: probability that an entry of the MSA is masked.
`mask_idx`: masking index (as interpreted by the model), for MSA-Tr it's 32.
`use_pdf`: if it's True the function sample the token from the logits pdf
instead of getting the argmax (greedy sampling).
`sample_all`: if True all the new tokens are obtained from the logits (both
the masked and the non masked), if False the non masked tokens
are left untouched and only the masked ones are changed.
`T`: Temperature of sampling from the pdf of output logits.
"""
with torch.no_grad():
if not MSA_tokens.is_cuda:
MSA_tokens = MSA_tokens.cuda()
mask = ((torch.rand(MSA_tokens.shape) > self.p_mask).type(
torch.uint8)).cuda()
masked_msa_tokens = MSA_tokens * mask + mask_idx * (1 - mask)
results = self.msa_transformer(masked_msa_tokens,
repr_layers=[12],
return_contacts=False)
msa_logits = self.softmax_tensor(x=results["logits"], axis=3, T=T)
if use_pdf == False:
new_msa_tokens = torch.argmax(msa_logits, dim=3)
else:
Vals = torch.tensor([
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 30
],
dtype=torch.int64)
maxval = Vals[-1].cuda()
msa_logits = msa_logits[:, :, :, Vals]
msa_logits = msa_logits / (torch.sum(msa_logits,
axis=3)[:, :, :, None])
cum = torch.cumsum(msa_logits, dim=3)
idxs = torch.zeros_like(cum, dtype=torch.int64).cuda()
idxs1 = Vals[None, None, None, :].cuda()
idxs = idxs + idxs1
sample = (torch.rand(
(cum.shape[0], cum.shape[1], cum.shape[2]))).cuda()
idxs[torch.gt(sample[:, :, :, None], cum)] = 100
new_msa_tokens = torch.minimum(torch.amin(idxs, axis=3),
maxval)
del cum, idxs, idxs1, sample
if sample_all == False:
new_msa_tokens = MSA_tokens * mask + new_msa_tokens * (1 -
mask)
new_msa_tokens[:, :, 0] = 0
del mask, masked_msa_tokens, results, msa_logits
return new_msa_tokens
#-------------------------------------------------------------------------------------------------------------------
def NEW_MSA(self, use_pdf=False, simplified=False, sample_all=False, T=1):
"""
Generate a new MSA by iteratively calling the masked MSA generator defined in: `self.generate_MSA`.
---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed
or contacs), otherwise use `simplified`=True.
The variable `self.iterations` must be a numpy array which specifies when (at which iterations)
the tokens should be saved. The last element of the array gives the maximum number of iterations that should be done.
`use_pdf`: if it's True the function sample the token from the logits pdf
instead of getting the argmax (greedy sampling).
`sample_all`: if True all the new tokens are obtained from the logits (both
the masked and the non masked), if False the non masked tokens
are left untouched and only the masked ones are changed.
`T`: Temperature of sampling from the pdf of output logits.
"""
if self.iterations is None or self.p_mask is None:
raise ValueError(
"Both `iterations` (numpy array) and `p_mask` (float) must be specified to generate a new MSA"
)
max_iter = self.iterations[-1]
with torch.no_grad():
new_msa_tokens = self.msa_batch_tokens.clone()
all_tokens = torch.zeros(
(len(self.iterations), self.msa_batch_tokens.shape[0],
self.msa_batch_tokens.shape[1],
self.msa_batch_tokens.shape[2]),
dtype=torch.int64)
if simplified:
all_tokens = all_tokens.to(dtype=torch.int8)
if self.msa_alphabet.mask_idx != 32:
raise ValueError(
f"The token used for masking is {self.msa_alphabet.mask_idx} instead of 32"
)
# Iterate the MSA generation process
j = 0
for i in range(max_iter):
new_msa_tokens = self.generate_MSA(
MSA_tokens=new_msa_tokens,
mask_idx=self.msa_alphabet.mask_idx,
use_pdf=use_pdf,
sample_all=sample_all,
T=T)
if np.any((i + 1) == self.iterations):
# Save the tokens at the specified iterations
if simplified:
all_tokens[j, ...] = (
new_msa_tokens.clone().detach().cpu()).to(
dtype=torch.int8)
else:
all_tokens[j, ...] = new_msa_tokens.clone()
j += 1
del new_msa_tokens
if simplified:
return all_tokens.numpy()
else:
return all_tokens.cuda()
#-------------------------------------------------------------------------------------------------------------------
def Batch_MSA(self,
use_pdf=False,
simplified=False,
repetitions=2,
sample_all=False,
T=1,
phylo=False):
"""
Generate a full MSA by calling with different input MSAs the iterative MSA generator defined
in: `self.NEW_MSA`.
---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed
or contacs), otherwise use `simplified`=True
The variable `self.iterations` must be a numpy array which specifies when (at which iterations)
the tokens must be saved. The last element of the array gives the maximum number of iterations that should be done.
`repetitions`: the number of times self.NEW_MSA() is repeated with a different input MSA.
`use_pdf`: if it's True the function sample the token from the logits pdf
instead of getting the argmax (greedy sampling).
`sample_all`: if True all the new tokens are obtained from the logits (both
the masked and the non masked), if False the non masked tokens
are left untouched and only the masked ones are changed.
`T`: Temperature of sampling from the pdf of output logits.
`phylo`: if True the start sequences are sampled from phylogeny weights instead of randomly.
"""
with torch.no_grad():
all_tokens = np.zeros(
(len(self.iterations), self.msa_batch_tokens.shape[0],
self.msa_batch_tokens.shape[1] * repetitions,
self.msa_batch_tokens.shape[2]),
dtype=np.int64)
if simplified:
all_tokens = all_tokens.astype('int8')
ALL_tokens = self.msa_data
depth = self.num[0]
if repetitions * depth > ALL_tokens.shape[1]:
all_tokens = np.zeros(
(len(self.iterations), self.msa_batch_tokens.shape[0],
ALL_tokens.shape[1], self.msa_batch_tokens.shape[2]),
dtype=np.int64)
if not phylo:
ALL_tokens = ALL_tokens[:,
torch.randperm(ALL_tokens.shape[1]), :]
else:
_ = self.Weights_Phylogeny(ALL_tokens[0, :20, :], delta=0.8)
phylo_w = self.Weights_Phylogeny(ALL_tokens[0, :, :],
delta=0.8)
indxs = torch.multinomial(phylo_w,
ALL_tokens.shape[1],
replacement=True)
ALL_tokens = ALL_tokens[:, indxs, :]
for i in range(repetitions):
ind = torch.arange(i * depth, (i + 1) * depth)
if (i + 1) * depth > ALL_tokens.shape[1]:
ind = torch.arange(i * depth, ALL_tokens.shape[1])
self.msa_batch_tokens = ALL_tokens[:, ind, :]
self.msa_batch_tokens = self.msa_batch_tokens.cuda()
all_tokens[:, :, ind.numpy(), :] = self.NEW_MSA(
use_pdf=use_pdf,
simplified=simplified,
sample_all=sample_all,
T=T)
if (i + 1) * depth > ALL_tokens.shape[1]:
break
if simplified:
return (ALL_tokens[:, :repetitions *
depth, :].numpy()).astype('int8'), all_tokens
else:
return ALL_tokens[:, :repetitions *
depth, :], torch.from_numpy(all_tokens).cuda()
#-------------------------------------------------------------------------------------------------------------------
def generate_MSA_context(self,
ancestor,
context,
mask_idx=32,
use_pdf=False,
sample_all=False,
T=1):
"""
Generate a new sequence by masking some entries of the original ancestor sequence and
re-predicting them through the transformer model (mask only `ancestor`, not the `context`).
`ancestor`: input sequence to be masked iteratively.
`context`: context MSA (not masked).
`p_mask`: probability that an entry of the MSA is masked.
`mask_idx`: masking index (as interpreted by the model), for MSA-Tr it's 32.
`use_pdf`: if it's True the function sample the token from the logits pdf
instead of getting the argmax (greedy sampling).
`sample_all`: if True all the new tokens are obtained from the logits (both
the masked and the non masked), if False the non masked tokens
are left untouched and only the masked ones are changed.
`T`: Temperature of sampling from the pdf of output logits.
"""
with torch.no_grad():
if not ancestor.is_cuda:
ancestor = ancestor.cuda()
if not context.is_cuda:
context = context.cuda()
mask = ((torch.rand(ancestor.shape) > self.p_mask).type(
torch.uint8)).cuda()
masked_ancestor = ancestor * mask + mask_idx * (1 - mask)
masked_msa_tokens = torch.zeros(
(context.shape[0], context.shape[1] + 1, context.shape[2]),
dtype=torch.int64).cuda()
masked_msa_tokens[0, 0, :] = masked_ancestor
masked_msa_tokens[:, 1:, :] = context
results = self.msa_transformer(masked_msa_tokens,
repr_layers=[12],
return_contacts=False)
results1 = results["logits"][:, 0, :, :]
results1 = results1[:, None, :, :]
msa_logits = self.softmax_tensor(x=results1, axis=3, T=T)
if use_pdf == False:
new_generation = torch.argmax(msa_logits, dim=3)[0, 0, :]
else:
Vals = torch.tensor([
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 30
],
dtype=torch.int64)
maxval = Vals[-1].cuda()
msa_logits = msa_logits[:, :, :, Vals]
msa_logits = msa_logits / (torch.sum(msa_logits,
axis=3)[:, :, :, None])
cum = torch.cumsum(msa_logits, dim=3)
idxs = torch.zeros_like(cum, dtype=torch.int64).cuda()
idxs1 = Vals[None, None, None, :].cuda()
idxs = idxs + idxs1
sample = (torch.rand(
(cum.shape[0], cum.shape[1], cum.shape[2]))).cuda()
idxs[torch.gt(sample[:, :, :, None], cum)] = 100
new_generation = torch.minimum(torch.amin(idxs, axis=3),
maxval)[0, 0, :]
del cum, idxs, idxs1, sample
if sample_all == False:
new_generation = ancestor * mask + new_generation * (1 - mask)
new_generation[0] = 0
del mask, masked_msa_tokens, results, results1, msa_logits
return new_generation
#-------------------------------------------------------------------------------------------------------------------
# Generate new sequence in a Linear tree by reiterating the function `generate_MSA_context()` starting from the sequence:
# `ancestor` (original sequence) and using the sequences in `context` as context MSA.
def Context_MSA(self,
depth=None,
ancestor=None,
context=None,
use_pdf=False,
simplified=False,
sample_all=False,
print_all=True,
T=1):
"""
Generates a new MSA with context-generation by iterating the masking on the original ancestor sequence
using: `self.generate_MSA_context`. It masks `ancestor` (original sequence) and uses the sequences in `context` as context MSA.
---> Use this function with `simplified`=False only if you need tokens in cuda ! (i.e. if you want to compute embed
or contacs), otherwise use `simplified`=True
The variable `self.iterations` must be a numpy array which specifies when (at which iterations)
the tokens must be saved. The last element of the array gives the maximum number of iterations that should be done.
If `print_all`=True then it saves the generated sequences at each iteration.
`ancestor`: input sequence to be masked iteratively.
`context`: context MSA (not masked).
`use_pdf`: if it's True the function sample the token from the logits pdf
instead of getting the argmax (greedy sampling).
`sample_all`: if True all the new tokens are obtained from the logits (both
the masked and the non masked), if False the non masked tokens
are left untouched and only the masked ones are changed.
`T`: Temperature of sampling from the pdf of output logits.
`depth`: number of generated sequences, if None the depth is the number of ancestor sequences.
"""
with torch.no_grad():
total_ran = False
if ancestor is None and context is None and depth is not None:
ALL_tokens = self.msa_data
ALL_tokens = ALL_tokens[:,
torch.randperm(ALL_tokens.shape[1]), :]
ancestor = ALL_tokens[0, :depth, :]
ALL_tokens = ALL_tokens[:,
torch.randperm(ALL_tokens.shape[1]), :]
context = ALL_tokens[:, :self.msa_batch_tokens.shape[1], :]
elif depth is None:
depth = ancestor.shape[0]
if isinstance(context, np.ndarray):
total_ran = False
elif context == 'tot-ran':
total_ran = True
else:
print(
'ERROR, either you give depth or you give ancestor and context'
)
all_tokens = torch.zeros(
(self.msa_batch_tokens.shape[0], self.iterations[-1] + 1,
depth, ancestor.shape[1]),
dtype=torch.int64).cuda()
ancestor = torch.from_numpy(ancestor).to(dtype=torch.int64)
if not total_ran:
context = torch.from_numpy(context).to(dtype=torch.int64)
if total_ran:
ALL_tokens = self.msa_data
all_tokens[0, 0, :, :] = ancestor
if simplified:
all_tokens = all_tokens.to(dtype=torch.int8)
if self.msa_alphabet.mask_idx != 32:
raise ValueError(
f"The token used for masking is {self.msa_alphabet.mask_idx} instead of 32"
)
# Iterate the MSA generation tree
for j in range(depth):
new_ancestor = all_tokens[0, 0, j, :]
for i in range(1, self.iterations[-1] + 1):
if total_ran:
context = (
ALL_tokens[:,
torch.randperm(ALL_tokens.shape[1]
)[:self.msa_batch_tokens.
shape[1]], :]).cuda()
new_ancestor = self.generate_MSA_context(
ancestor=new_ancestor,
context=context,
mask_idx=self.msa_alphabet.mask_idx,
use_pdf=use_pdf,
sample_all=sample_all,
T=T)
if print_all:
all_tokens[0, i, j, :] = new_ancestor
if not print_all:
all_tokens[0, -1, j, :] = new_ancestor
# torch.cuda.empty_cache()
if not print_all:
all_tokens = all_tokens[:, torch.tensor([-1]), :, :]
if simplified:
return ((context.detach().cpu()).to(
dtype=torch.int8)).numpy(), ((all_tokens.detach().cpu()).to(
dtype=torch.int8)).numpy()
else:
return context.cuda(), all_tokens.cuda()
# Cell
import os
import pickle
from fastcore.script import *
@call_parse
def gen_MSAs(filepath: Param(
help='Path of the input directory', type=str, default='./'
), filename: Param(
help='Name of the input file(s)', type=str, nargs='+', default=False
), new_dir: Param(
help='Name of the output directory', type=str, default=False
), 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=10
), 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=100
), depth: Param(
help='Number of batches (of size num) that you want to generate',
type=int,
default=2
), 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=[-1], 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
\ No newline at end of file
LICENSE 0 → 100755
View file @ 319d9d8b
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MANIFEST.in 0 → 100755
View file @ 319d9d8b
include settings.ini
include LICENSE
include CONTRIBUTING.md
include README.md
recursive-exclude * __pycache__
MSA_next.py 0 → 100755
View file @ 319d9d8b
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))
bin/linux64/esm 0 → 100755
View file @ 319d9d8b
File added
data/RubiscoSubfamily3_modifAll.fasta 0 → 100755
View file @ 319d9d8b
This source diff could not be displayed because it is too large. You can view the blob instead.
data_new/RubiscoSubfamily3.fasta 0 → 100755
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This source diff could not be displayed because it is too large. You can view the blob instead.
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