Commit
·
fb7491b
1
Parent(s):
8764e8e
Initial dataset upload with embeddings, model, and calibration files
Browse files- .gitattributes +6 -0
- README.md +86 -0
- protein_vec_models/aspect_vec_ec.ckpt → data/lookup/scope_lookup.fasta +2 -2
- protein_vec_models/aspect_vec_gene3d.ckpt → data/lookup/scope_lookup_embeddings.npy +2 -2
- protein_vec_models/aspect_vec_go_cco.ckpt → data/lookup_embeddings.npy +2 -2
- protein_vec_models/aspect_vec_go_bpo.ckpt → data/lookup_embeddings_meta_data.tsv +2 -2
- protein_vec_models/__pycache__/embed_structure_model.cpython-310.pyc +0 -0
- protein_vec_models/__pycache__/embed_structure_model.cpython-312.pyc +0 -0
- protein_vec_models/__pycache__/model_protein_moe.cpython-310.pyc +0 -0
- protein_vec_models/__pycache__/model_protein_moe.cpython-312.pyc +0 -0
- protein_vec_models/__pycache__/model_protein_vec_single_variable.cpython-310.pyc +0 -0
- protein_vec_models/__pycache__/model_protein_vec_single_variable.cpython-312.pyc +0 -0
- protein_vec_models/__pycache__/utils_search.cpython-310.pyc +0 -0
- protein_vec_models/__pycache__/utils_search.cpython-312.pyc +0 -0
- protein_vec_models/aspect_vec_ec_params.json +3 -14
- protein_vec_models/aspect_vec_gene3d_params.json +3 -14
- protein_vec_models/aspect_vec_go_bpo_params.json +3 -14
- protein_vec_models/aspect_vec_go_cco_params.json +3 -14
- protein_vec_models/aspect_vec_go_mfo.ckpt +0 -3
- protein_vec_models/aspect_vec_go_mfo_params.json +3 -14
- protein_vec_models/aspect_vec_pfam.ckpt +0 -3
- protein_vec_models/aspect_vec_pfam_params.json +3 -14
- protein_vec_models/data_protein_vec.py +3 -144
- protein_vec_models/embed_structure_model.py +3 -113
- protein_vec_models/gh_encode_and_search_new_proteins.ipynb +3 -511
- protein_vec_models/gh_encode_and_search_proteins_w_low_seq_similarity.ipynb +3 -522
- protein_vec_models/launch_pl_worker.bash +3 -43
- protein_vec_models/model_protein_moe.py +3 -319
- protein_vec_models/model_protein_vec_single_variable.py +3 -169
- protein_vec_models/pl_worker.bash +3 -27
- protein_vec_models/protein_vec_params.json +3 -14
- protein_vec_models/tm_vec_swiss_model_large.ckpt +0 -3
- protein_vec_models/tm_vec_swiss_model_large_params.json +3 -11
- protein_vec_models/train_protein_vec.py +3 -156
- protein_vec_models/utils_search.py +3 -72
- results/calibration_probs.csv +1001 -0
- results/fdr_thresholds.csv +101 -0
- results/fnr_thresholds.csv +101 -0
.gitattributes
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.tsv filter=lfs diff=lfs merge=lfs -text
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*.fasta filter=lfs diff=lfs merge=lfs -text
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*.fa filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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protein_vec_models/** filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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task_categories:
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- feature-extraction
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- text-classification
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tags:
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- protein
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- bioinformatics
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- embeddings
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- conformal-prediction
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size_categories:
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- 1M<n<10M
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---
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# Conformal Protein Retrieval - Data Files
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This dataset contains the large data files required to run the [Conformal Protein Retrieval Gradio Space](https://huggingface.co/spaces/LoocasGoose/cpr).
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## Contents
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### 📊 Lookup Databases
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**UniProt Database:**
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- `data/lookup_embeddings.npy` - Pre-embedded UniProt protein sequences (Protein-Vec embeddings)
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- `data/lookup_embeddings_meta_data.tsv` - Metadata for UniProt proteins (Entry, Pfam, Protein names)
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**SCOPE Database:**
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- `data/lookup/scope_lookup_embeddings.npy` - Pre-embedded SCOPE protein domain sequences
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- `data/lookup/scope_lookup.fasta` - FASTA metadata for SCOPE proteins
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### 🎯 Conformal Prediction Files
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- `results/fdr_thresholds.csv` - Precomputed FDR (False Discovery Rate) thresholds
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- `results/fnr_thresholds.csv` - Precomputed FNR (False Negative Rate) thresholds
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- `results/calibration_probs.csv` - Calibration probabilities for Venn-Abers prediction
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### 🧬 Protein-Vec Model
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- `protein_vec_models/protein_vec.ckpt` - Main Protein-Vec model checkpoint
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- `protein_vec_models/protein_vec_params.json` - Model configuration
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- `protein_vec_models/*.py` - Model architecture code files
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## Usage
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These files are automatically loaded by the Gradio Space application. To use them locally:
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```python
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from huggingface_hub import hf_hub_download
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import numpy as np
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# Download a specific file
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embedding_file = hf_hub_download(
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repo_id="LoocasGoose/cpr_data",
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filename="data/lookup_embeddings.npy",
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repo_type="dataset"
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)
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# Load the embeddings
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embeddings = np.load(embedding_file)
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```
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## Citation
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If you use these data files, please cite the original paper:
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```bibtex
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@article{boger2025functional,
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title={Functional protein mining with conformal guarantees},
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author={Boger, Ron S and Chithrananda, Seyone and Angelopoulos, Anastasios N and Yoon, Peter H and Jordan, Michael I and Doudna, Jennifer A},
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journal={Nature Communications},
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volume={16},
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number={1},
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pages={85},
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year={2025},
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publisher={Nature Publishing Group UK London}
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}
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```
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## License
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Apache 2.0
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## Source
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Original data from: [Zenodo](https://zenodo.org/records/14272215)
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protein_vec_models/aspect_vec_ec.ckpt → data/lookup/scope_lookup.fasta
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version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1b945ecd0c51beb3576170e8e82690e414a2eec42145ec31b6e5aabdc43ca2f0
|
| 3 |
+
size 254
|
|
|
|
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|
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|
|
|
protein_vec_models/aspect_vec_pfam.ckpt
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:7e69c4f5528e933b60c7cf95411aaa0c2175be519e9a8f4a6dccb06973e4e6c3
|
| 3 |
-
size 211085207
|
|
|
|
|
|
|
|
|
|
|
|
protein_vec_models/aspect_vec_pfam_params.json
CHANGED
|
@@ -1,14 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
"num_layers": 2,
|
| 5 |
-
"dim_feedforward": 2048,
|
| 6 |
-
"out_dim": 512,
|
| 7 |
-
"dropout": 0.1,
|
| 8 |
-
"activation": "relu",
|
| 9 |
-
"num_variables": 10,
|
| 10 |
-
"vocab": 20,
|
| 11 |
-
"lr0": 0.0001,
|
| 12 |
-
"warmup_steps": 500,
|
| 13 |
-
"p_bernoulli": 0.5
|
| 14 |
-
}
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9c06753c8c1287418b8e99ff7f7b0f244d080337c78467653ea358fd8bfe4584
|
| 3 |
+
size 254
|
|
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|
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|
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|
|
|
|
|
protein_vec_models/data_protein_vec.py
CHANGED
|
@@ -1,144 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
from typing import Union, List, Tuple, Any, Dict, Optional
|
| 5 |
-
import pickle
|
| 6 |
-
import h5py
|
| 7 |
-
import torch
|
| 8 |
-
import numpy as np
|
| 9 |
-
import pandas as pd
|
| 10 |
-
from torch.utils.data import Dataset
|
| 11 |
-
from collections import defaultdict
|
| 12 |
-
import re
|
| 13 |
-
from torch import nn
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
class get_parquet(Dataset):
|
| 18 |
-
"""
|
| 19 |
-
Dataset wrapper for HDF5 files
|
| 20 |
-
"""
|
| 21 |
-
def __init__(self,
|
| 22 |
-
pair_path,
|
| 23 |
-
embedding_path,
|
| 24 |
-
indices: Optional[List[int]] = None
|
| 25 |
-
):
|
| 26 |
-
"""
|
| 27 |
-
Construct the dataset
|
| 28 |
-
:args:
|
| 29 |
-
:filepath - where to read from
|
| 30 |
-
"""
|
| 31 |
-
self.pairs = pd.read_parquet(pair_path)
|
| 32 |
-
self.embedding_path = embedding_path
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
def __len__(self):
|
| 36 |
-
return len(self.pairs)
|
| 37 |
-
|
| 38 |
-
def __getitem__(self, index):
|
| 39 |
-
sample = self.pairs.iloc[index,:]
|
| 40 |
-
|
| 41 |
-
id_var = sample[0]
|
| 42 |
-
id_path = str(self.embedding_path) + "/" + id_var[0:2] + "/" + id_var
|
| 43 |
-
id_embedding = pickle.load(open(id_path, "rb"))
|
| 44 |
-
|
| 45 |
-
total_sample = defaultdict(dict)
|
| 46 |
-
total_sample['id'] = id_embedding
|
| 47 |
-
total_sample['key'] = sample[2]
|
| 48 |
-
total_sample['margin'] = sample[3]
|
| 49 |
-
total_sample['tm'] = sample[4]
|
| 50 |
-
total_sample['tm_score'] = sample[5]
|
| 51 |
-
|
| 52 |
-
positive = sample[1].split(":")[0]
|
| 53 |
-
negative = sample[1].split(":")[1]
|
| 54 |
-
positive_path = str(self.embedding_path) + "/" + positive[0:2] + "/" + positive
|
| 55 |
-
negative_path = str(self.embedding_path) + "/" + negative[0:2] + "/" + negative
|
| 56 |
-
total_sample["positive"] = pickle.load(open(positive_path, "rb"))
|
| 57 |
-
total_sample["negative"] = pickle.load(open(negative_path, "rb"))
|
| 58 |
-
|
| 59 |
-
total_sample = dict(total_sample)
|
| 60 |
-
return total_sample
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
def collate_fn(batch, pad_id = 0):
|
| 64 |
-
all_data_dict = defaultdict(dict)
|
| 65 |
-
batch_size = len(batch)
|
| 66 |
-
|
| 67 |
-
keys = [t['key'] for t in batch]
|
| 68 |
-
all_data_dict['key'] = keys
|
| 69 |
-
|
| 70 |
-
tms = [t['tm'] for t in batch]
|
| 71 |
-
all_data_dict['tm'] = tms
|
| 72 |
-
|
| 73 |
-
tm_scores = [t['tm_score'] for t in batch]
|
| 74 |
-
all_data_dict['tm_scores'] = torch.FloatTensor(tm_scores)
|
| 75 |
-
|
| 76 |
-
margins = [t['margin'] for t in batch]
|
| 77 |
-
all_data_dict['margin'] = torch.FloatTensor(margins)
|
| 78 |
-
|
| 79 |
-
shape_ids = [t['id'][0].shape[0] for t in batch]
|
| 80 |
-
dim = batch[0]['id'].shape[2]
|
| 81 |
-
pos_shapes = [t["positive"][0].shape[0] for t in batch]
|
| 82 |
-
neg_shapes = [t["negative"][0].shape[0] for t in batch]
|
| 83 |
-
shape_ids += pos_shapes
|
| 84 |
-
shape_ids += neg_shapes
|
| 85 |
-
|
| 86 |
-
biggest_shape = np.max(shape_ids)
|
| 87 |
-
pad_tensor = torch.zeros(biggest_shape, dim).type(torch.BoolTensor)
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
id_tensors = [t['id'][0] for t in batch]
|
| 91 |
-
id_tensors.append(pad_tensor)
|
| 92 |
-
padded_ids = torch.nn.utils.rnn.pad_sequence(id_tensors, padding_value=0, batch_first=True)[:-1, :, :]
|
| 93 |
-
id_padding = torch.zeros(padded_ids.shape[0:2]).type(torch.BoolTensor)
|
| 94 |
-
id_padding[padded_ids[:,:,0] == pad_id] = True
|
| 95 |
-
all_data_dict['id'] = padded_ids
|
| 96 |
-
all_data_dict['id_padding'] = id_padding
|
| 97 |
-
|
| 98 |
-
pos_tensor_values = [t["positive"][0] for t in batch]
|
| 99 |
-
pos_tensor_values.append(pad_tensor)
|
| 100 |
-
neg_tensor_values = [t["negative"][0] for t in batch]
|
| 101 |
-
neg_tensor_values.append(pad_tensor)
|
| 102 |
-
|
| 103 |
-
padded_pos_tensors = torch.nn.utils.rnn.pad_sequence(pos_tensor_values, padding_value=0, batch_first=True)[:-1, :, :]
|
| 104 |
-
padded_neg_tensors = torch.nn.utils.rnn.pad_sequence(neg_tensor_values, padding_value=0, batch_first=True)[:-1, :, :]
|
| 105 |
-
|
| 106 |
-
pos_pad_labels = torch.zeros(padded_pos_tensors.shape[0:2]).type(torch.BoolTensor)
|
| 107 |
-
pos_pad_labels[padded_pos_tensors[:,:,0] == pad_id] = True
|
| 108 |
-
neg_pad_labels = torch.zeros(padded_neg_tensors.shape[0:2]).type(torch.BoolTensor)
|
| 109 |
-
neg_pad_labels[padded_neg_tensors[:,:,0] == pad_id] = True
|
| 110 |
-
|
| 111 |
-
all_data_dict["positive"] = padded_pos_tensors
|
| 112 |
-
all_data_dict["negative"] = padded_neg_tensors
|
| 113 |
-
all_data_dict["positive_padding"] = pos_pad_labels
|
| 114 |
-
all_data_dict["negative_padding"] = neg_pad_labels
|
| 115 |
-
|
| 116 |
-
all_data_dict = dict(all_data_dict)
|
| 117 |
-
|
| 118 |
-
return(all_data_dict)
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
#Construct datasets function
|
| 124 |
-
def construct_datasets(pair_path, embedding_path, train_prop=.9, val_prop=.05, test_prop = .05):
|
| 125 |
-
dataset = get_parquet(pair_path, embedding_path)
|
| 126 |
-
total_samples = len(dataset)
|
| 127 |
-
sampleable_values = np.arange(total_samples)
|
| 128 |
-
|
| 129 |
-
train_n_to_sample = int(len(sampleable_values) * train_prop)
|
| 130 |
-
val_n_to_sample = int(len(sampleable_values) * val_prop)
|
| 131 |
-
test_n_to_sample = int(len(sampleable_values) * test_prop)
|
| 132 |
-
|
| 133 |
-
train_indices = np.random.choice(sampleable_values, train_n_to_sample, replace=False)
|
| 134 |
-
sampleable_values = sampleable_values[~np.isin(sampleable_values, train_indices)]
|
| 135 |
-
val_indices = np.random.choice(sampleable_values, val_n_to_sample, replace=False)
|
| 136 |
-
sampleable_values = sampleable_values[~np.isin(sampleable_values, val_indices)]
|
| 137 |
-
test_indices = np.random.choice(sampleable_values, test_n_to_sample, replace=False)
|
| 138 |
-
|
| 139 |
-
#Make train, test, and validation datasets using torch subset
|
| 140 |
-
train_ds = torch.utils.data.Subset(dataset, train_indices)
|
| 141 |
-
val_ds = torch.utils.data.Subset(dataset, val_indices)
|
| 142 |
-
test_ds = torch.utils.data.Subset(dataset, test_indices)
|
| 143 |
-
|
| 144 |
-
return(train_ds, val_ds, test_ds)
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:12d66c4e7b55a0cd14b28b78d293b1d22a584eaaf03e1d87037e1f13f86b2230
|
| 3 |
+
size 6394
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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|
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|
|
|
|
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|
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|
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|
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|
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|
|
protein_vec_models/embed_structure_model.py
CHANGED
|
@@ -1,113 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
from dataclasses import dataclass, asdict
|
| 5 |
-
|
| 6 |
-
import torch
|
| 7 |
-
from torch import nn
|
| 8 |
-
import pytorch_lightning as pl
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
@dataclass
|
| 12 |
-
class Config:
|
| 13 |
-
def isolate(self, config):
|
| 14 |
-
specifics = inspect.signature(config).parameters
|
| 15 |
-
my_specifics = {k: v for k, v in asdict(self).items() if k in specifics}
|
| 16 |
-
return config(**my_specifics)
|
| 17 |
-
|
| 18 |
-
def to_json(self, filename):
|
| 19 |
-
config = json.dumps(asdict(self), indent=2)
|
| 20 |
-
with open(filename, 'w') as f:
|
| 21 |
-
f.write(config)
|
| 22 |
-
|
| 23 |
-
@classmethod
|
| 24 |
-
def from_json(cls, filename):
|
| 25 |
-
with open(filename, 'r') as f:
|
| 26 |
-
js = json.loads(f.read())
|
| 27 |
-
config = cls(**js)
|
| 28 |
-
return config
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
@dataclass
|
| 32 |
-
class trans_basic_block_Config_tmvec(Config):
|
| 33 |
-
d_model: int = 1024
|
| 34 |
-
nhead: int = 4
|
| 35 |
-
num_layers: int = 2
|
| 36 |
-
dim_feedforward: int = 2048
|
| 37 |
-
out_dim: int = 512
|
| 38 |
-
dropout: float = 0.1
|
| 39 |
-
activation: str = 'relu'
|
| 40 |
-
# data params
|
| 41 |
-
lr0: float = 0.0001
|
| 42 |
-
warmup_steps: int = 300
|
| 43 |
-
|
| 44 |
-
def build(self):
|
| 45 |
-
return trans_basic_block_tmvec(self)
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
class trans_basic_block_tmvec(pl.LightningModule):
|
| 49 |
-
"""
|
| 50 |
-
TransformerEncoderLayer with preset parameters followed by global pooling and dropout
|
| 51 |
-
"""
|
| 52 |
-
def __init__(self, config: trans_basic_block_Config_tmvec):
|
| 53 |
-
super().__init__()
|
| 54 |
-
self.config = config
|
| 55 |
-
|
| 56 |
-
# build encoder
|
| 57 |
-
encoder_args = {k: v for k, v in asdict(config).items() if k in inspect.signature(nn.TransformerEncoderLayer).parameters}
|
| 58 |
-
num_layers = config.num_layers
|
| 59 |
-
|
| 60 |
-
encoder_layer = nn.TransformerEncoderLayer(batch_first=True, **encoder_args)
|
| 61 |
-
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
|
| 62 |
-
|
| 63 |
-
self.dropout = nn.Dropout(self.config.dropout)
|
| 64 |
-
self.mlp = nn.Linear(self.config.d_model, self.config.out_dim)
|
| 65 |
-
|
| 66 |
-
self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)
|
| 67 |
-
self.l1_loss = nn.L1Loss(reduction='mean')
|
| 68 |
-
|
| 69 |
-
def forward(self, x, src_mask, src_key_padding_mask):
|
| 70 |
-
x = self.encoder(x, mask=src_mask, src_key_padding_mask=src_key_padding_mask)
|
| 71 |
-
lens = torch.logical_not(src_key_padding_mask).sum(dim=1).float()
|
| 72 |
-
x = x.sum(dim=1) / lens.unsqueeze(1)
|
| 73 |
-
x = self.dropout(x)
|
| 74 |
-
x = self.mlp(x)
|
| 75 |
-
return x
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
def distance_loss_euclidean(self, output_seq1, output_seq2, tm_score):
|
| 79 |
-
pdist_seq = nn.PairwiseDistance(p=2)
|
| 80 |
-
dist_seq = pdist_seq(output_seq1, output_seq2)
|
| 81 |
-
dist_tm = torch.cdist(dist_seq.unsqueeze(0), tm_score.float().unsqueeze(0), p=2)
|
| 82 |
-
return dist_tm
|
| 83 |
-
|
| 84 |
-
def distance_loss_sigmoid(self, output_seq1, output_seq2, tm_score):
|
| 85 |
-
dist_seq = output_seq1 - output_seq2
|
| 86 |
-
dist_seq = torch.sigmoid(dist_seq).mean(1)
|
| 87 |
-
dist_tm = torch.cdist(dist_seq.unsqueeze(0), tm_score.float().unsqueeze(0), p=2)
|
| 88 |
-
return dist_tm
|
| 89 |
-
|
| 90 |
-
def distance_loss(self, output_seq1, output_seq2, tm_score):
|
| 91 |
-
dist_seq = self.cos(output_seq1, output_seq2)
|
| 92 |
-
dist_tm = self.l1_loss(dist_seq.unsqueeze(0), tm_score.float().unsqueeze(0))
|
| 93 |
-
return dist_tm
|
| 94 |
-
|
| 95 |
-
def training_step(self, train_batch, batch_idx):
|
| 96 |
-
sequence_1, sequence_2, pad_mask_1, pad_mask_2, tm_score = train_batch
|
| 97 |
-
out_seq1 = self.forward(sequence_1, src_mask=None, src_key_padding_mask=pad_mask_1)
|
| 98 |
-
out_seq2 = self.forward(sequence_2, src_mask=None, src_key_padding_mask=pad_mask_2)
|
| 99 |
-
loss = self.distance_loss(out_seq1, out_seq2, tm_score)
|
| 100 |
-
self.log('train_loss', loss)
|
| 101 |
-
return loss
|
| 102 |
-
|
| 103 |
-
def validation_step(self, val_batch, batch_idx):
|
| 104 |
-
sequence_1, sequence_2, pad_mask_1, pad_mask_2, tm_score = val_batch
|
| 105 |
-
out_seq1 = self.forward(sequence_1, src_mask=None, src_key_padding_mask=pad_mask_1)
|
| 106 |
-
out_seq2 = self.forward(sequence_2, src_mask=None, src_key_padding_mask=pad_mask_2)
|
| 107 |
-
loss = self.distance_loss(out_seq1, out_seq2, tm_score)
|
| 108 |
-
self.log('val_loss', loss)
|
| 109 |
-
|
| 110 |
-
def configure_optimizers(self):
|
| 111 |
-
optimizer = torch.optim.Adam(self.parameters(), lr=self.config.lr0)
|
| 112 |
-
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
|
| 113 |
-
return [optimizer], [lr_scheduler]
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7f902cd4e365bab3cc0a1b3a81095f5971b006d0cedb38fe7ad41483819efdc0
|
| 3 |
+
size 4338
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|
protein_vec_models/gh_encode_and_search_new_proteins.ipynb
CHANGED
|
@@ -1,511 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 62,
|
| 6 |
-
"id": "d7c2da84-2365-4e17-9c47-75eee1823524",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [],
|
| 9 |
-
"source": [
|
| 10 |
-
"import numpy as np\n",
|
| 11 |
-
"import pandas as pd\n",
|
| 12 |
-
"import torch\n",
|
| 13 |
-
"from torch.utils.data import Dataset\n",
|
| 14 |
-
"from model_protein_moe import trans_basic_block, trans_basic_block_Config\n",
|
| 15 |
-
"from utils_search import *\n",
|
| 16 |
-
"from transformers import T5EncoderModel, T5Tokenizer\n",
|
| 17 |
-
"import re\n",
|
| 18 |
-
"import gc\n",
|
| 19 |
-
"from sklearn.manifold import TSNE\n",
|
| 20 |
-
"import matplotlib.pyplot as plt\n",
|
| 21 |
-
"import seaborn as sns\n",
|
| 22 |
-
"from Bio import SeqIO\n",
|
| 23 |
-
"import pickle\n",
|
| 24 |
-
"\n",
|
| 25 |
-
"from pathlib import Path\n",
|
| 26 |
-
"from dataclasses import dataclass\n",
|
| 27 |
-
"from typing import Union, List, Tuple, Any, Dict, Optional\n",
|
| 28 |
-
"import pickle\n",
|
| 29 |
-
"import h5py\n",
|
| 30 |
-
"import torch\n",
|
| 31 |
-
"import numpy as np\n",
|
| 32 |
-
"import pandas as pd\n",
|
| 33 |
-
"from torch.utils.data import Dataset\n",
|
| 34 |
-
"from collections import defaultdict\n",
|
| 35 |
-
"import re\n",
|
| 36 |
-
"from torch import nn\n",
|
| 37 |
-
"from torch.utils.data import DataLoader\n",
|
| 38 |
-
"import faiss\n",
|
| 39 |
-
"import os\n",
|
| 40 |
-
"from sklearn.metrics import f1_score, precision_score, recall_score\n",
|
| 41 |
-
"\n",
|
| 42 |
-
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
|
| 43 |
-
]
|
| 44 |
-
},
|
| 45 |
-
{
|
| 46 |
-
"cell_type": "code",
|
| 47 |
-
"execution_count": 63,
|
| 48 |
-
"id": "9a600f00-c74b-4637-9c03-2acb3add515e",
|
| 49 |
-
"metadata": {},
|
| 50 |
-
"outputs": [],
|
| 51 |
-
"source": [
|
| 52 |
-
"#Protein-Vec MOE model checkpoint and config\n",
|
| 53 |
-
"vec_model_cpnt = '/mnt/home/thamamsy/ceph/protein_vec/models/model0.0001_dmodel512_nlayer2_moe_all/checkpoints/last-v1.ckpt'\n",
|
| 54 |
-
"vec_model_config = '/mnt/home/thamamsy/ceph/protein_vec/models/model0.0001_dmodel512_nlayer2_moe_all/params.json'"
|
| 55 |
-
]
|
| 56 |
-
},
|
| 57 |
-
{
|
| 58 |
-
"cell_type": "code",
|
| 59 |
-
"execution_count": 64,
|
| 60 |
-
"id": "f7fc0ff9-1315-4f11-a29a-da5fa7f1a32c",
|
| 61 |
-
"metadata": {},
|
| 62 |
-
"outputs": [],
|
| 63 |
-
"source": [
|
| 64 |
-
"#Load the ProtTrans model and ProtTrans tokenizer\n",
|
| 65 |
-
"tokenizer = T5Tokenizer.from_pretrained(\"Rostlab/prot_t5_xl_uniref50\", do_lower_case=False )\n",
|
| 66 |
-
"model = T5EncoderModel.from_pretrained(\"Rostlab/prot_t5_xl_uniref50\")\n",
|
| 67 |
-
"gc.collect()\n",
|
| 68 |
-
"\n",
|
| 69 |
-
"model = model.to(device)\n",
|
| 70 |
-
"model = model.eval()"
|
| 71 |
-
]
|
| 72 |
-
},
|
| 73 |
-
{
|
| 74 |
-
"cell_type": "code",
|
| 75 |
-
"execution_count": 65,
|
| 76 |
-
"id": "5771ef42-4c25-4715-b5bd-ce8d736e803c",
|
| 77 |
-
"metadata": {},
|
| 78 |
-
"outputs": [
|
| 79 |
-
{
|
| 80 |
-
"name": "stderr",
|
| 81 |
-
"output_type": "stream",
|
| 82 |
-
"text": [
|
| 83 |
-
"Lightning automatically upgraded your loaded checkpoint from v1.8.0rc0 to v1.9.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../../../public_www/tm_vec_swiss_model_large.ckpt`\n"
|
| 84 |
-
]
|
| 85 |
-
}
|
| 86 |
-
],
|
| 87 |
-
"source": [
|
| 88 |
-
"#Load the model\n",
|
| 89 |
-
"vec_model_config = trans_basic_block_Config.from_json(vec_model_config)\n",
|
| 90 |
-
"model_deep = trans_basic_block.load_from_checkpoint(vec_model_cpnt, config=vec_model_config)\n",
|
| 91 |
-
"model_deep = model_deep.to(device)\n",
|
| 92 |
-
"model_deep = model_deep.eval()"
|
| 93 |
-
]
|
| 94 |
-
},
|
| 95 |
-
{
|
| 96 |
-
"cell_type": "code",
|
| 97 |
-
"execution_count": 27,
|
| 98 |
-
"id": "5bba4329-557e-44e1-a0f3-19f0ade75326",
|
| 99 |
-
"metadata": {},
|
| 100 |
-
"outputs": [],
|
| 101 |
-
"source": [
|
| 102 |
-
"# Load in uniprot meta data\n",
|
| 103 |
-
"meta_data_new = pd.read_csv('/mnt/home/thamamsy/ceph/protein_vec/data/uniprot_data/uniprotkb_AND_reviewed_true_2023_07_03.tsv', sep='\\t')\n",
|
| 104 |
-
"\n",
|
| 105 |
-
"#These are training proteins\n",
|
| 106 |
-
"combined_outs = np.load('/mnt/home/thamamsy/projects/protein_vec/lib/protein_vec_mixture_of_experts/combined_training_proteins.npy', allow_pickle=True)\n",
|
| 107 |
-
"\n",
|
| 108 |
-
"#Filter for the meta data of training proteins \n",
|
| 109 |
-
"lookups = meta_data_new[meta_data_new['Entry'].isin(combined_outs)]"
|
| 110 |
-
]
|
| 111 |
-
},
|
| 112 |
-
{
|
| 113 |
-
"cell_type": "code",
|
| 114 |
-
"execution_count": 28,
|
| 115 |
-
"id": "c47ba5aa-0e9d-403d-90ef-f62c6b24c83c",
|
| 116 |
-
"metadata": {},
|
| 117 |
-
"outputs": [
|
| 118 |
-
{
|
| 119 |
-
"name": "stdout",
|
| 120 |
-
"output_type": "stream",
|
| 121 |
-
"text": [
|
| 122 |
-
"Max date\n"
|
| 123 |
-
]
|
| 124 |
-
},
|
| 125 |
-
{
|
| 126 |
-
"data": {
|
| 127 |
-
"text/plain": [
|
| 128 |
-
"'2022-05-25'"
|
| 129 |
-
]
|
| 130 |
-
},
|
| 131 |
-
"execution_count": 28,
|
| 132 |
-
"metadata": {},
|
| 133 |
-
"output_type": "execute_result"
|
| 134 |
-
}
|
| 135 |
-
],
|
| 136 |
-
"source": [
|
| 137 |
-
"print(\"Max date\")\n",
|
| 138 |
-
"np.max(lookups['Date of creation'])"
|
| 139 |
-
]
|
| 140 |
-
},
|
| 141 |
-
{
|
| 142 |
-
"cell_type": "code",
|
| 143 |
-
"execution_count": 29,
|
| 144 |
-
"id": "bc03eabf-d1b2-4ca7-aadc-889d1eb8e2ff",
|
| 145 |
-
"metadata": {},
|
| 146 |
-
"outputs": [],
|
| 147 |
-
"source": [
|
| 148 |
-
"#Now filter for the proteins that were newly discovered\n",
|
| 149 |
-
"new_proteins = meta_data_new[meta_data_new['Date of creation'] > '2022-05-25'].reset_index(drop=True)"
|
| 150 |
-
]
|
| 151 |
-
},
|
| 152 |
-
{
|
| 153 |
-
"cell_type": "code",
|
| 154 |
-
"execution_count": 30,
|
| 155 |
-
"id": "aa1d2919-12be-4893-92d8-22c1db7ba41e",
|
| 156 |
-
"metadata": {},
|
| 157 |
-
"outputs": [
|
| 158 |
-
{
|
| 159 |
-
"name": "stdout",
|
| 160 |
-
"output_type": "stream",
|
| 161 |
-
"text": [
|
| 162 |
-
"Number of new proteins deposited after 2022-05-25\n"
|
| 163 |
-
]
|
| 164 |
-
},
|
| 165 |
-
{
|
| 166 |
-
"data": {
|
| 167 |
-
"text/plain": [
|
| 168 |
-
"2350"
|
| 169 |
-
]
|
| 170 |
-
},
|
| 171 |
-
"execution_count": 30,
|
| 172 |
-
"metadata": {},
|
| 173 |
-
"output_type": "execute_result"
|
| 174 |
-
}
|
| 175 |
-
],
|
| 176 |
-
"source": [
|
| 177 |
-
"print('Number of new proteins deposited after 2022-05-25')\n",
|
| 178 |
-
"len(new_proteins)"
|
| 179 |
-
]
|
| 180 |
-
},
|
| 181 |
-
{
|
| 182 |
-
"cell_type": "code",
|
| 183 |
-
"execution_count": 31,
|
| 184 |
-
"id": "6bc961b3-35ab-4b11-b284-6422f6b364b7",
|
| 185 |
-
"metadata": {},
|
| 186 |
-
"outputs": [
|
| 187 |
-
{
|
| 188 |
-
"name": "stdout",
|
| 189 |
-
"output_type": "stream",
|
| 190 |
-
"text": [
|
| 191 |
-
"200\n",
|
| 192 |
-
"400\n",
|
| 193 |
-
"600\n",
|
| 194 |
-
"800\n",
|
| 195 |
-
"1000\n",
|
| 196 |
-
"1200\n",
|
| 197 |
-
"1400\n",
|
| 198 |
-
"1600\n",
|
| 199 |
-
"1800\n",
|
| 200 |
-
"2000\n",
|
| 201 |
-
"2200\n"
|
| 202 |
-
]
|
| 203 |
-
}
|
| 204 |
-
],
|
| 205 |
-
"source": [
|
| 206 |
-
"# This is a forward pass of the Protein-Vec model\n",
|
| 207 |
-
"# Every aspect is turned on (therefore no masks)\n",
|
| 208 |
-
"sampled_keys = np.array(['TM', 'PFAM', 'GENE3D', 'ENZYME', 'MFO', 'BPO', 'CCO'])\n",
|
| 209 |
-
"all_cols = np.array(['TM', 'PFAM', 'GENE3D', 'ENZYME', 'MFO', 'BPO', 'CCO'])\n",
|
| 210 |
-
"masks = [all_cols[k] in sampled_keys for k in range(len(all_cols))]\n",
|
| 211 |
-
"masks = torch.logical_not(torch.tensor(masks, dtype=torch.bool))[None,:]\n",
|
| 212 |
-
"\n",
|
| 213 |
-
"#Pull out sequences for the new proteins\n",
|
| 214 |
-
"flat_seqs = new_proteins['Sequence'].values\n",
|
| 215 |
-
"\n",
|
| 216 |
-
"#Loop through the sequences and embed them using protein-vec\n",
|
| 217 |
-
"i = 0\n",
|
| 218 |
-
"embed_all_sequences = []\n",
|
| 219 |
-
"while i < len(flat_seqs): \n",
|
| 220 |
-
" protrans_sequence = featurize_prottrans(flat_seqs[i:i+1], model, tokenizer, device)\n",
|
| 221 |
-
" embedded_sequence = embed_vec(protrans_sequence, model_deep, masks, device)\n",
|
| 222 |
-
" embed_all_sequences.append(embedded_sequence)\n",
|
| 223 |
-
" i = i + 1\n",
|
| 224 |
-
" \n",
|
| 225 |
-
" if i % 200 == 0:\n",
|
| 226 |
-
" print(i)\n",
|
| 227 |
-
"\n",
|
| 228 |
-
"#Combine the embedding vectors into an array\n",
|
| 229 |
-
"query_embeddings = np.concatenate(embed_all_sequences)"
|
| 230 |
-
]
|
| 231 |
-
},
|
| 232 |
-
{
|
| 233 |
-
"cell_type": "markdown",
|
| 234 |
-
"id": "78713307-6ed2-435a-9389-18bb35902269",
|
| 235 |
-
"metadata": {},
|
| 236 |
-
"source": [
|
| 237 |
-
"Now that we have embeddings for the newly discovered proteins, we can visualize them after performing TSNE, and we can transfer annotations to them as well"
|
| 238 |
-
]
|
| 239 |
-
},
|
| 240 |
-
{
|
| 241 |
-
"cell_type": "code",
|
| 242 |
-
"execution_count": 32,
|
| 243 |
-
"id": "80aa34c5-d700-4320-af0d-d849636a4ce4",
|
| 244 |
-
"metadata": {
|
| 245 |
-
"tags": []
|
| 246 |
-
},
|
| 247 |
-
"outputs": [],
|
| 248 |
-
"source": [
|
| 249 |
-
"#Perform TSNE on the embedding vectors\n",
|
| 250 |
-
"all_X_embedded = TSNE(n_components=2, learning_rate='auto', init='random').fit_transform(query_embeddings)\n",
|
| 251 |
-
"all_X_embedded_df = pd.DataFrame(all_X_embedded)\n",
|
| 252 |
-
"all_X_embedded_df.columns = [\"Dim1\", \"Dim2\"]\n",
|
| 253 |
-
"all_X_embedded_df['Pfam'] = new_proteins['Pfam'].values\n",
|
| 254 |
-
"all_X_embedded_df['EC'] = new_proteins['EC number'].values"
|
| 255 |
-
]
|
| 256 |
-
},
|
| 257 |
-
{
|
| 258 |
-
"cell_type": "code",
|
| 259 |
-
"execution_count": 33,
|
| 260 |
-
"id": "62818905-93bf-45b4-9d21-8842788b4a14",
|
| 261 |
-
"metadata": {
|
| 262 |
-
"tags": []
|
| 263 |
-
},
|
| 264 |
-
"outputs": [
|
| 265 |
-
{
|
| 266 |
-
"data": {
|
| 267 |
-
"text/plain": [
|
| 268 |
-
"<seaborn.axisgrid.FacetGrid at 0x15553b5c7250>"
|
| 269 |
-
]
|
| 270 |
-
},
|
| 271 |
-
"execution_count": 33,
|
| 272 |
-
"metadata": {},
|
| 273 |
-
"output_type": "execute_result"
|
| 274 |
-
},
|
| 275 |
-
{
|
| 276 |
-
"data": {
|
| 277 |
-
"image/png": 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",
|
| 278 |
-
"text/plain": [
|
| 279 |
-
"<Figure size 680.125x500 with 1 Axes>"
|
| 280 |
-
]
|
| 281 |
-
},
|
| 282 |
-
"metadata": {},
|
| 283 |
-
"output_type": "display_data"
|
| 284 |
-
}
|
| 285 |
-
],
|
| 286 |
-
"source": [
|
| 287 |
-
"#For visualization purposes, filter for the top 20 PFam terms\n",
|
| 288 |
-
"top_ranks = list(all_X_embedded_df['Pfam'].value_counts()[0:20].index)\n",
|
| 289 |
-
"sns.lmplot(x=\"Dim1\", y=\"Dim2\", data=all_X_embedded_df[all_X_embedded_df['Pfam'].isin(top_ranks)], hue=\"Pfam\", fit_reg=False)\n"
|
| 290 |
-
]
|
| 291 |
-
},
|
| 292 |
-
{
|
| 293 |
-
"cell_type": "code",
|
| 294 |
-
"execution_count": null,
|
| 295 |
-
"id": "359c2bb2-e4fb-4517-906c-46a5f0b7719d",
|
| 296 |
-
"metadata": {},
|
| 297 |
-
"outputs": [],
|
| 298 |
-
"source": []
|
| 299 |
-
},
|
| 300 |
-
{
|
| 301 |
-
"cell_type": "code",
|
| 302 |
-
"execution_count": 47,
|
| 303 |
-
"id": "91bb6977-b852-4cf8-8c29-62ffe56358d1",
|
| 304 |
-
"metadata": {
|
| 305 |
-
"tags": []
|
| 306 |
-
},
|
| 307 |
-
"outputs": [],
|
| 308 |
-
"source": [
|
| 309 |
-
"################## Load the lookup database of all embeddings (note that we will pull out only embeddings from proteins that were trained on)\n",
|
| 310 |
-
"embeddings = np.load('/mnt/home/thamamsy/projects/protein-vec/data/lookup_embeddings.npy')\n",
|
| 311 |
-
"lookup_proteins_meta = pd.read_csv('/mnt/home/thamamsy/projects/protein-vec/data/lookup_embeddings_meta_data.tsv', sep=\"\\t\")\n"
|
| 312 |
-
]
|
| 313 |
-
},
|
| 314 |
-
{
|
| 315 |
-
"cell_type": "code",
|
| 316 |
-
"execution_count": 50,
|
| 317 |
-
"id": "4c0443c1-bedf-47fc-8a9e-c9981b1ed7bb",
|
| 318 |
-
"metadata": {
|
| 319 |
-
"tags": []
|
| 320 |
-
},
|
| 321 |
-
"outputs": [
|
| 322 |
-
{
|
| 323 |
-
"name": "stdout",
|
| 324 |
-
"output_type": "stream",
|
| 325 |
-
"text": [
|
| 326 |
-
"Maximum date of lookup database protein\n"
|
| 327 |
-
]
|
| 328 |
-
},
|
| 329 |
-
{
|
| 330 |
-
"data": {
|
| 331 |
-
"text/plain": [
|
| 332 |
-
"'2022-05-25'"
|
| 333 |
-
]
|
| 334 |
-
},
|
| 335 |
-
"execution_count": 50,
|
| 336 |
-
"metadata": {},
|
| 337 |
-
"output_type": "execute_result"
|
| 338 |
-
}
|
| 339 |
-
],
|
| 340 |
-
"source": [
|
| 341 |
-
"print(\"Maximum date of lookup database protein\")\n",
|
| 342 |
-
"np.max(lookup_proteins_meta['Date of creation'])"
|
| 343 |
-
]
|
| 344 |
-
},
|
| 345 |
-
{
|
| 346 |
-
"cell_type": "markdown",
|
| 347 |
-
"id": "da53d555-a20b-43c0-8140-15b88637e3a2",
|
| 348 |
-
"metadata": {
|
| 349 |
-
"tags": []
|
| 350 |
-
},
|
| 351 |
-
"source": [
|
| 352 |
-
"We can run search and the nearest neighbor pipeline for any of our available aspects\n",
|
| 353 |
-
" - 'Gene Ontology (biological process)'\n",
|
| 354 |
-
" - 'Gene Ontology (molecular function)' \n",
|
| 355 |
-
" - 'Gene Ontology (cellular component)' \n",
|
| 356 |
-
" - 'Gene3D' \n",
|
| 357 |
-
" - 'Pfam' \n",
|
| 358 |
-
" - 'EC number'"
|
| 359 |
-
]
|
| 360 |
-
},
|
| 361 |
-
{
|
| 362 |
-
"cell_type": "code",
|
| 363 |
-
"execution_count": 51,
|
| 364 |
-
"id": "03cd96ff-0f0a-4af5-a3d6-b599b1608443",
|
| 365 |
-
"metadata": {},
|
| 366 |
-
"outputs": [],
|
| 367 |
-
"source": [
|
| 368 |
-
"#Switch this for whichever aspect you want to perform search for\n",
|
| 369 |
-
"############### User parameter\n",
|
| 370 |
-
"column = 'Pfam'"
|
| 371 |
-
]
|
| 372 |
-
},
|
| 373 |
-
{
|
| 374 |
-
"cell_type": "code",
|
| 375 |
-
"execution_count": 52,
|
| 376 |
-
"id": "ccee8331-342c-4acf-b441-decd61c0b717",
|
| 377 |
-
"metadata": {},
|
| 378 |
-
"outputs": [],
|
| 379 |
-
"source": [
|
| 380 |
-
"# Filter for lookup proteins with annotations for the relavant aspect (don't want to transfer null annotations)\n",
|
| 381 |
-
"col_lookup = lookup_proteins_meta[~lookup_proteins_meta[column].isnull()]\n",
|
| 382 |
-
"col_lookup_embeddings = embeddings[col_lookup.index]\n",
|
| 383 |
-
"col_meta_data = col_lookup[column].values\n",
|
| 384 |
-
"\n",
|
| 385 |
-
"# load database\n",
|
| 386 |
-
"lookup_database = load_database(col_lookup_embeddings)\n",
|
| 387 |
-
"\n",
|
| 388 |
-
"# Query for the 1st nearest neighbor\n",
|
| 389 |
-
"k = 1\n",
|
| 390 |
-
"D, I = query(lookup_database, query_embeddings, k)\n",
|
| 391 |
-
"\n",
|
| 392 |
-
"#Get metadata for the 1st nearest neighbor\n",
|
| 393 |
-
"near_ids = []\n",
|
| 394 |
-
"for i in range(I.shape[0]):\n",
|
| 395 |
-
" meta = col_meta_data[I[i]]\n",
|
| 396 |
-
" near_ids.append(list(meta)) \n",
|
| 397 |
-
"\n",
|
| 398 |
-
"near_ids = np.array(near_ids)"
|
| 399 |
-
]
|
| 400 |
-
},
|
| 401 |
-
{
|
| 402 |
-
"cell_type": "code",
|
| 403 |
-
"execution_count": 56,
|
| 404 |
-
"id": "dbef3ae1-92a8-4618-90d4-7847c022c638",
|
| 405 |
-
"metadata": {
|
| 406 |
-
"tags": []
|
| 407 |
-
},
|
| 408 |
-
"outputs": [
|
| 409 |
-
{
|
| 410 |
-
"name": "stdout",
|
| 411 |
-
"output_type": "stream",
|
| 412 |
-
"text": [
|
| 413 |
-
"Annotations for the nearest neighbors (with aspect annotations) of newly discovered proteins\n",
|
| 414 |
-
"[['PF10645;PF17652;PF03639;']\n",
|
| 415 |
-
" ['PF01583;PF01747;PF14306;']\n",
|
| 416 |
-
" ['PF01370;']\n",
|
| 417 |
-
" ...\n",
|
| 418 |
-
" ['PF04517;']\n",
|
| 419 |
-
" ['PF10484;']\n",
|
| 420 |
-
" ['PF07380;']]\n"
|
| 421 |
-
]
|
| 422 |
-
}
|
| 423 |
-
],
|
| 424 |
-
"source": [
|
| 425 |
-
"print(\"Annotations for the nearest neighbors (with aspect annotations) of newly discovered proteins\")\n",
|
| 426 |
-
"print(near_ids)"
|
| 427 |
-
]
|
| 428 |
-
},
|
| 429 |
-
{
|
| 430 |
-
"cell_type": "code",
|
| 431 |
-
"execution_count": 57,
|
| 432 |
-
"id": "05b49028-2739-4a91-9c2b-4114a70f6a24",
|
| 433 |
-
"metadata": {},
|
| 434 |
-
"outputs": [],
|
| 435 |
-
"source": [
|
| 436 |
-
"# Calculate the recall (sensitivity) performance results for every aspect\n",
|
| 437 |
-
"if column == 'EC number':\n",
|
| 438 |
-
" ground_meta_data = new_proteins[(~new_proteins[column].isnull()) & (~new_proteins[column].astype(str).str.contains('-', regex=False))]\n",
|
| 439 |
-
"else:\n",
|
| 440 |
-
" ground_meta_data = new_proteins[(~new_proteins[column].isnull())]\n",
|
| 441 |
-
"\n",
|
| 442 |
-
"rel_col = ground_meta_data[column].values\n",
|
| 443 |
-
"relevant_indices = np.array(list(ground_meta_data.index))\n",
|
| 444 |
-
"relevant_near_ids = near_ids[relevant_indices]\n",
|
| 445 |
-
"relevant_D = D[relevant_indices]\n",
|
| 446 |
-
"\n",
|
| 447 |
-
"#this calculates exact match\n",
|
| 448 |
-
"intersection = []\n",
|
| 449 |
-
"for i in range(len(rel_col)):\n",
|
| 450 |
-
" p1s = set(rel_col[i].split(\";\"))\n",
|
| 451 |
-
" p1s = {item for item in p1s if item != \"\"}\n",
|
| 452 |
-
" \n",
|
| 453 |
-
" p2s = set(relevant_near_ids[i,0].split(\";\"))\n",
|
| 454 |
-
" p2s = {item for item in p2s if item != \"\"}\n",
|
| 455 |
-
" \n",
|
| 456 |
-
" inter = len(list(p1s & p2s))\n",
|
| 457 |
-
" acc = inter/len(p1s)\n",
|
| 458 |
-
" intersection.append(acc)"
|
| 459 |
-
]
|
| 460 |
-
},
|
| 461 |
-
{
|
| 462 |
-
"cell_type": "code",
|
| 463 |
-
"execution_count": 61,
|
| 464 |
-
"id": "06fe1b00-fed6-44bf-94fc-3912a40af20f",
|
| 465 |
-
"metadata": {},
|
| 466 |
-
"outputs": [
|
| 467 |
-
{
|
| 468 |
-
"name": "stdout",
|
| 469 |
-
"output_type": "stream",
|
| 470 |
-
"text": [
|
| 471 |
-
"PFAM: Recall of protein MOE model for annotating newly discovered proteins:\n",
|
| 472 |
-
"0.8686829147424137\n"
|
| 473 |
-
]
|
| 474 |
-
}
|
| 475 |
-
],
|
| 476 |
-
"source": [
|
| 477 |
-
"print(\"PFAM: Recall of protein MOE model for annotating newly discovered proteins:\")\n",
|
| 478 |
-
"print(np.mean(np.array(intersection)))"
|
| 479 |
-
]
|
| 480 |
-
},
|
| 481 |
-
{
|
| 482 |
-
"cell_type": "code",
|
| 483 |
-
"execution_count": null,
|
| 484 |
-
"id": "6b5fb4f1-3f10-49a8-ba79-4afb7a4f6d3c",
|
| 485 |
-
"metadata": {},
|
| 486 |
-
"outputs": [],
|
| 487 |
-
"source": []
|
| 488 |
-
}
|
| 489 |
-
],
|
| 490 |
-
"metadata": {
|
| 491 |
-
"kernelspec": {
|
| 492 |
-
"display_name": "protein_vec_env",
|
| 493 |
-
"language": "python",
|
| 494 |
-
"name": "protein_vec_env"
|
| 495 |
-
},
|
| 496 |
-
"language_info": {
|
| 497 |
-
"codemirror_mode": {
|
| 498 |
-
"name": "ipython",
|
| 499 |
-
"version": 3
|
| 500 |
-
},
|
| 501 |
-
"file_extension": ".py",
|
| 502 |
-
"mimetype": "text/x-python",
|
| 503 |
-
"name": "python",
|
| 504 |
-
"nbconvert_exporter": "python",
|
| 505 |
-
"pygments_lexer": "ipython3",
|
| 506 |
-
"version": "3.9.15"
|
| 507 |
-
}
|
| 508 |
-
},
|
| 509 |
-
"nbformat": 4,
|
| 510 |
-
"nbformat_minor": 5
|
| 511 |
-
}
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:987c5237abe3da6e507ede895065303e781065924920f44c6ba73d7b352d8fbe
|
| 3 |
+
size 87432
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|
|
protein_vec_models/gh_encode_and_search_proteins_w_low_seq_similarity.ipynb
CHANGED
|
@@ -1,522 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 1,
|
| 6 |
-
"id": "d7c2da84-2365-4e17-9c47-75eee1823524",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [
|
| 9 |
-
{
|
| 10 |
-
"name": "stderr",
|
| 11 |
-
"output_type": "stream",
|
| 12 |
-
"text": [
|
| 13 |
-
"/mnt/home/thamamsy/projects/protein_vec/lib/environment/protein_vec_env/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
| 14 |
-
" from .autonotebook import tqdm as notebook_tqdm\n"
|
| 15 |
-
]
|
| 16 |
-
}
|
| 17 |
-
],
|
| 18 |
-
"source": [
|
| 19 |
-
"import numpy as np\n",
|
| 20 |
-
"import pandas as pd\n",
|
| 21 |
-
"import torch\n",
|
| 22 |
-
"from torch.utils.data import Dataset\n",
|
| 23 |
-
"from model_protein_moe import trans_basic_block, trans_basic_block_Config\n",
|
| 24 |
-
"from utils_search import *\n",
|
| 25 |
-
"from transformers import T5EncoderModel, T5Tokenizer\n",
|
| 26 |
-
"import re\n",
|
| 27 |
-
"import gc\n",
|
| 28 |
-
"from sklearn.manifold import TSNE\n",
|
| 29 |
-
"import matplotlib.pyplot as plt\n",
|
| 30 |
-
"import seaborn as sns\n",
|
| 31 |
-
"from Bio import SeqIO\n",
|
| 32 |
-
"import pickle\n",
|
| 33 |
-
"\n",
|
| 34 |
-
"from pathlib import Path\n",
|
| 35 |
-
"from dataclasses import dataclass\n",
|
| 36 |
-
"from typing import Union, List, Tuple, Any, Dict, Optional\n",
|
| 37 |
-
"import pickle\n",
|
| 38 |
-
"import h5py\n",
|
| 39 |
-
"import torch\n",
|
| 40 |
-
"import numpy as np\n",
|
| 41 |
-
"import pandas as pd\n",
|
| 42 |
-
"from torch.utils.data import Dataset\n",
|
| 43 |
-
"from collections import defaultdict\n",
|
| 44 |
-
"import re\n",
|
| 45 |
-
"from torch import nn\n",
|
| 46 |
-
"from torch.utils.data import DataLoader\n",
|
| 47 |
-
"import faiss\n",
|
| 48 |
-
"import os\n",
|
| 49 |
-
"from sklearn.metrics import f1_score, precision_score, recall_score\n",
|
| 50 |
-
"\n",
|
| 51 |
-
"device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
|
| 52 |
-
]
|
| 53 |
-
},
|
| 54 |
-
{
|
| 55 |
-
"cell_type": "code",
|
| 56 |
-
"execution_count": 2,
|
| 57 |
-
"id": "9a600f00-c74b-4637-9c03-2acb3add515e",
|
| 58 |
-
"metadata": {},
|
| 59 |
-
"outputs": [],
|
| 60 |
-
"source": [
|
| 61 |
-
"#Protein-Vec MOE model checkpoint and config\n",
|
| 62 |
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"vec_model_cpnt = '/mnt/home/thamamsy/ceph/protein_vec/models/model0.0001_dmodel512_nlayer2_moe_all/checkpoints/last-v1.ckpt'\n",
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| 63 |
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"vec_model_config = '/mnt/home/thamamsy/ceph/protein_vec/models/model0.0001_dmodel512_nlayer2_moe_all/params.json'"
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| 64 |
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]
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f7fc0ff9-1315-4f11-a29a-da5fa7f1a32c",
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"metadata": {},
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"outputs": [
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"output_type": "stream",
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"text": [
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"You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
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}
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"source": [
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| 81 |
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"#Load the ProtTrans model and ProtTrans tokenizer\n",
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| 82 |
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"tokenizer = T5Tokenizer.from_pretrained(\"Rostlab/prot_t5_xl_uniref50\", do_lower_case=False )\n",
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| 83 |
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"model = T5EncoderModel.from_pretrained(\"Rostlab/prot_t5_xl_uniref50\")\n",
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| 84 |
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"gc.collect()\n",
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| 85 |
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"\n",
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| 86 |
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"model = model.to(device)\n",
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| 87 |
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"model = model.eval()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5771ef42-4c25-4715-b5bd-ce8d736e803c",
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"metadata": {},
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"outputs": [
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"text": [
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| 100 |
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"Lightning automatically upgraded your loaded checkpoint from v1.8.0rc0 to v1.9.4. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file ../../../public_www/tm_vec_swiss_model_large.ckpt`\n"
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| 101 |
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]
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}
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],
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"source": [
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| 105 |
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"#Load the model\n",
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| 106 |
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"vec_model_config = trans_basic_block_Config.from_json(vec_model_config)\n",
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| 107 |
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"model_deep = trans_basic_block.load_from_checkpoint(vec_model_cpnt, config=vec_model_config)\n",
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| 108 |
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"model_deep = model_deep.to(device)\n",
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| 109 |
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"model_deep = model_deep.eval()"
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| 110 |
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "0bfe593d-87de-47ea-b2e6-f73cce9655ab",
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"metadata": {},
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"outputs": [],
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| 118 |
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"source": [
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| 119 |
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"### Evaluate based on sequence similarity \n",
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| 120 |
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"# Load in uniprot meta data\n",
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| 121 |
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"meta_data_new = pd.read_csv('/mnt/home/thamamsy/ceph/protein_vec/data/uniprot_data/uniprotkb_AND_reviewed_true_2023_07_03.tsv', sep='\\t')\n",
|
| 122 |
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"\n",
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| 123 |
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"#Load \n",
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| 124 |
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"held_out_50 = np.load('/mnt/home/thamamsy/ceph/protein_vec/data/uniprot_data/training_splits/mmseq_splits/held_out_50_proteins.npy', allow_pickle=True)\n",
|
| 125 |
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"held_out_20 = np.load('/mnt/home/thamamsy/ceph/protein_vec/data/uniprot_data/training_splits/mmseq_splits/held_out_20_proteins.npy', allow_pickle=True)\n",
|
| 126 |
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"\n",
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| 127 |
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"held_out_50_proteins = meta_data_new[meta_data_new['Entry'].isin(held_out_50)]\n",
|
| 128 |
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"held_out_20_proteins = meta_data_new[meta_data_new['Entry'].isin(held_out_20)]"
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| 129 |
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]
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},
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| 131 |
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{
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"cell_type": "code",
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| 133 |
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"execution_count": 10,
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| 134 |
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"id": "23b7cf95-94df-49c8-b703-6cbdab9a4086",
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| 135 |
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"metadata": {},
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| 136 |
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"outputs": [],
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| 137 |
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"source": [
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| 138 |
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"####### Can change this here for 50 vs. 20\n",
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| 139 |
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"###############################################\n",
|
| 140 |
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"data_subset = held_out_50_proteins.reset_index()"
|
| 141 |
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]
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| 142 |
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},
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{
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| 144 |
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"cell_type": "code",
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| 145 |
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"execution_count": 11,
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"id": "617da848-560c-4f95-8b0f-fedd31dbcfd9",
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| 147 |
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"metadata": {},
|
| 148 |
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"outputs": [
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| 149 |
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{
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| 150 |
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"name": "stdout",
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| 151 |
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"output_type": "stream",
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| 152 |
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"text": [
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| 153 |
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"Number of proteins\n"
|
| 154 |
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]
|
| 155 |
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},
|
| 156 |
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{
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| 157 |
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"data": {
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| 158 |
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"text/plain": [
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"5191"
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| 160 |
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]
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},
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| 162 |
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"execution_count": 11,
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| 163 |
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"metadata": {},
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| 164 |
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"output_type": "execute_result"
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| 165 |
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}
|
| 166 |
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],
|
| 167 |
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"source": [
|
| 168 |
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"print('Number of proteins')\n",
|
| 169 |
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"len(data_subset)"
|
| 170 |
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]
|
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},
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| 172 |
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{
|
| 173 |
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"cell_type": "code",
|
| 174 |
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"execution_count": 12,
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"id": "33a8b21d-8bf0-4e41-b2c4-3e2d69e330c4",
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"metadata": {
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| 177 |
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"tags": []
|
| 178 |
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},
|
| 179 |
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"outputs": [
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| 182 |
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"output_type": "stream",
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"text": [
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| 184 |
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"/mnt/home/thamamsy/projects/protein_vec/lib/environment/protein_vec_env/lib/python3.9/site-packages/torch/nn/modules/transformer.py:296: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:177.)\n",
|
| 185 |
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" output = torch._nested_tensor_from_mask(output, src_key_padding_mask.logical_not(), mask_check=False)\n"
|
| 186 |
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]
|
| 187 |
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},
|
| 188 |
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{
|
| 189 |
-
"name": "stdout",
|
| 190 |
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"output_type": "stream",
|
| 191 |
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"text": [
|
| 192 |
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"200\n",
|
| 193 |
-
"400\n",
|
| 194 |
-
"600\n",
|
| 195 |
-
"800\n",
|
| 196 |
-
"1000\n",
|
| 197 |
-
"1200\n",
|
| 198 |
-
"1400\n",
|
| 199 |
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"1600\n",
|
| 200 |
-
"1800\n",
|
| 201 |
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"2000\n",
|
| 202 |
-
"2200\n",
|
| 203 |
-
"2400\n",
|
| 204 |
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"2600\n",
|
| 205 |
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"2800\n",
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| 206 |
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"3000\n",
|
| 207 |
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"3200\n",
|
| 208 |
-
"3400\n",
|
| 209 |
-
"3600\n",
|
| 210 |
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"3800\n",
|
| 211 |
-
"4000\n",
|
| 212 |
-
"4200\n",
|
| 213 |
-
"4400\n",
|
| 214 |
-
"4600\n",
|
| 215 |
-
"4800\n",
|
| 216 |
-
"5000\n"
|
| 217 |
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]
|
| 218 |
-
}
|
| 219 |
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],
|
| 220 |
-
"source": [
|
| 221 |
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"# This is a forward pass of the Protein-Vec model\n",
|
| 222 |
-
"# Every aspect turned on\n",
|
| 223 |
-
"sampled_keys = np.array(['TM', 'PFAM', 'GENE3D', 'ENZYME', 'MFO', 'BPO', 'CCO'])\n",
|
| 224 |
-
"all_cols = np.array(['TM', 'PFAM', 'GENE3D', 'ENZYME', 'MFO', 'BPO', 'CCO'])\n",
|
| 225 |
-
"\n",
|
| 226 |
-
"masks = [all_cols[k] in sampled_keys for k in range(len(all_cols))]\n",
|
| 227 |
-
"masks = torch.logical_not(torch.tensor(masks, dtype=torch.bool))[None,:]\n",
|
| 228 |
-
"\n",
|
| 229 |
-
"#Pull out sequences for the new proteins\n",
|
| 230 |
-
"flat_seqs = data_subset['Sequence'].values\n",
|
| 231 |
-
"\n",
|
| 232 |
-
"#Loop through the sequences and embed them using protein-vec\n",
|
| 233 |
-
"i = 0\n",
|
| 234 |
-
"embed_all_sequences = []\n",
|
| 235 |
-
"while i < len(flat_seqs): \n",
|
| 236 |
-
" protrans_sequence = featurize_prottrans(flat_seqs[i:i+1], model, tokenizer, device)\n",
|
| 237 |
-
" embedded_sequence = embed_vec(protrans_sequence, model_deep, masks, device)\n",
|
| 238 |
-
" embed_all_sequences.append(embedded_sequence)\n",
|
| 239 |
-
" i = i + 1\n",
|
| 240 |
-
" \n",
|
| 241 |
-
" if i % 200 == 0:\n",
|
| 242 |
-
" print(i)\n",
|
| 243 |
-
" \n",
|
| 244 |
-
"query_embeddings = np.concatenate(embed_all_sequences)"
|
| 245 |
-
]
|
| 246 |
-
},
|
| 247 |
-
{
|
| 248 |
-
"cell_type": "code",
|
| 249 |
-
"execution_count": null,
|
| 250 |
-
"id": "9ac6038d-d897-4d0c-8a3a-e59248cebdaf",
|
| 251 |
-
"metadata": {},
|
| 252 |
-
"outputs": [],
|
| 253 |
-
"source": []
|
| 254 |
-
},
|
| 255 |
-
{
|
| 256 |
-
"cell_type": "markdown",
|
| 257 |
-
"id": "78713307-6ed2-435a-9389-18bb35902269",
|
| 258 |
-
"metadata": {},
|
| 259 |
-
"source": [
|
| 260 |
-
"Now that we have embeddings for the proteins with low sequence similarity to our training proteins, we can visualize them after performing TSNE, and we can transfer annotations to them as well"
|
| 261 |
-
]
|
| 262 |
-
},
|
| 263 |
-
{
|
| 264 |
-
"cell_type": "code",
|
| 265 |
-
"execution_count": 14,
|
| 266 |
-
"id": "80aa34c5-d700-4320-af0d-d849636a4ce4",
|
| 267 |
-
"metadata": {
|
| 268 |
-
"tags": []
|
| 269 |
-
},
|
| 270 |
-
"outputs": [],
|
| 271 |
-
"source": [
|
| 272 |
-
"#Perform TSNE on the embedding vectors\n",
|
| 273 |
-
"all_X_embedded = TSNE(n_components=2, learning_rate='auto', init='random').fit_transform(query_embeddings)\n",
|
| 274 |
-
"all_X_embedded_df = pd.DataFrame(all_X_embedded)\n",
|
| 275 |
-
"all_X_embedded_df.columns = [\"Dim1\", \"Dim2\"]\n",
|
| 276 |
-
"all_X_embedded_df['Pfam'] = data_subset['Pfam'].values\n",
|
| 277 |
-
"all_X_embedded_df['EC'] = data_subset['EC number'].values"
|
| 278 |
-
]
|
| 279 |
-
},
|
| 280 |
-
{
|
| 281 |
-
"cell_type": "code",
|
| 282 |
-
"execution_count": 15,
|
| 283 |
-
"id": "62818905-93bf-45b4-9d21-8842788b4a14",
|
| 284 |
-
"metadata": {
|
| 285 |
-
"tags": []
|
| 286 |
-
},
|
| 287 |
-
"outputs": [
|
| 288 |
-
{
|
| 289 |
-
"data": {
|
| 290 |
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"text/plain": [
|
| 291 |
-
"<seaborn.axisgrid.FacetGrid at 0x15512aa226d0>"
|
| 292 |
-
]
|
| 293 |
-
},
|
| 294 |
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"execution_count": 15,
|
| 295 |
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"metadata": {},
|
| 296 |
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"output_type": "execute_result"
|
| 297 |
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},
|
| 298 |
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{
|
| 299 |
-
"data": {
|
| 300 |
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"image/png": 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",
|
| 301 |
-
"text/plain": [
|
| 302 |
-
"<Figure size 691x500 with 1 Axes>"
|
| 303 |
-
]
|
| 304 |
-
},
|
| 305 |
-
"metadata": {},
|
| 306 |
-
"output_type": "display_data"
|
| 307 |
-
}
|
| 308 |
-
],
|
| 309 |
-
"source": [
|
| 310 |
-
"#For visualization purposes, filter for the top 20 EC terms\n",
|
| 311 |
-
"top_ranks = list(all_X_embedded_df['EC'].value_counts()[0:20].index)\n",
|
| 312 |
-
"sns.lmplot(x=\"Dim1\", y=\"Dim2\", data=all_X_embedded_df[all_X_embedded_df['EC'].isin(top_ranks)], hue=\"EC\", fit_reg=False)\n"
|
| 313 |
-
]
|
| 314 |
-
},
|
| 315 |
-
{
|
| 316 |
-
"cell_type": "code",
|
| 317 |
-
"execution_count": 16,
|
| 318 |
-
"id": "91bb6977-b852-4cf8-8c29-62ffe56358d1",
|
| 319 |
-
"metadata": {
|
| 320 |
-
"tags": []
|
| 321 |
-
},
|
| 322 |
-
"outputs": [],
|
| 323 |
-
"source": [
|
| 324 |
-
"################## Load the lookup database of all embeddings (note that we will pull out only embeddings from proteins that were trained on)\n",
|
| 325 |
-
"embeddings = np.load('/mnt/home/thamamsy/projects/protein-vec/data/lookup_embeddings.npy')\n",
|
| 326 |
-
"lookup_proteins_meta = pd.read_csv('/mnt/home/thamamsy/projects/protein-vec/data/lookup_embeddings_meta_data.tsv', sep=\"\\t\")\n"
|
| 327 |
-
]
|
| 328 |
-
},
|
| 329 |
-
{
|
| 330 |
-
"cell_type": "markdown",
|
| 331 |
-
"id": "da53d555-a20b-43c0-8140-15b88637e3a2",
|
| 332 |
-
"metadata": {
|
| 333 |
-
"tags": []
|
| 334 |
-
},
|
| 335 |
-
"source": [
|
| 336 |
-
"We can run search and the nearest neighbor pipeline for any of our available aspects\n",
|
| 337 |
-
" - 'Gene Ontology (biological process)'\n",
|
| 338 |
-
" - 'Gene Ontology (molecular function)' \n",
|
| 339 |
-
" - 'Gene Ontology (cellular component)' \n",
|
| 340 |
-
" - 'Gene3D' \n",
|
| 341 |
-
" - 'Pfam' \n",
|
| 342 |
-
" - 'EC number'"
|
| 343 |
-
]
|
| 344 |
-
},
|
| 345 |
-
{
|
| 346 |
-
"cell_type": "code",
|
| 347 |
-
"execution_count": 17,
|
| 348 |
-
"id": "03cd96ff-0f0a-4af5-a3d6-b599b1608443",
|
| 349 |
-
"metadata": {},
|
| 350 |
-
"outputs": [],
|
| 351 |
-
"source": [
|
| 352 |
-
"#Switch this for whichever aspect you want to perform search for\n",
|
| 353 |
-
"############### User parameter\n",
|
| 354 |
-
"column = 'Gene Ontology (biological process)'"
|
| 355 |
-
]
|
| 356 |
-
},
|
| 357 |
-
{
|
| 358 |
-
"cell_type": "code",
|
| 359 |
-
"execution_count": 18,
|
| 360 |
-
"id": "ccee8331-342c-4acf-b441-decd61c0b717",
|
| 361 |
-
"metadata": {},
|
| 362 |
-
"outputs": [],
|
| 363 |
-
"source": [
|
| 364 |
-
"# Filter for lookup proteins with annotations for the relavant aspect (don't want to transfer null annotations)\n",
|
| 365 |
-
"col_lookup = lookup_proteins_meta[~lookup_proteins_meta[column].isnull()]\n",
|
| 366 |
-
"col_lookup_embeddings = embeddings[col_lookup.index]\n",
|
| 367 |
-
"col_meta_data = col_lookup[column].values\n",
|
| 368 |
-
"\n",
|
| 369 |
-
"# load database\n",
|
| 370 |
-
"lookup_database = load_database(col_lookup_embeddings)\n",
|
| 371 |
-
"\n",
|
| 372 |
-
"# Query for the 1st nearest neighbor\n",
|
| 373 |
-
"k = 1\n",
|
| 374 |
-
"D, I = query(lookup_database, query_embeddings, k)\n",
|
| 375 |
-
"\n",
|
| 376 |
-
"#Get metadata for the 1st nearest neighbor\n",
|
| 377 |
-
"near_ids = []\n",
|
| 378 |
-
"for i in range(I.shape[0]):\n",
|
| 379 |
-
" meta = col_meta_data[I[i]]\n",
|
| 380 |
-
" near_ids.append(list(meta)) \n",
|
| 381 |
-
"\n",
|
| 382 |
-
"near_ids = np.array(near_ids)"
|
| 383 |
-
]
|
| 384 |
-
},
|
| 385 |
-
{
|
| 386 |
-
"cell_type": "code",
|
| 387 |
-
"execution_count": 19,
|
| 388 |
-
"id": "dbef3ae1-92a8-4618-90d4-7847c022c638",
|
| 389 |
-
"metadata": {
|
| 390 |
-
"tags": []
|
| 391 |
-
},
|
| 392 |
-
"outputs": [
|
| 393 |
-
{
|
| 394 |
-
"name": "stdout",
|
| 395 |
-
"output_type": "stream",
|
| 396 |
-
"text": [
|
| 397 |
-
"Annotations for the nearest neighbors (with aspect annotations) of newly discovered proteins\n",
|
| 398 |
-
"[['determination of adult lifespan [GO:0008340]; innate immune response [GO:0045087]; magnesium ion homeostasis [GO:0010960]; magnesium ion transport [GO:0015693]; positive regulation of gonad development [GO:1905941]; positive regulation of multicellular organism growth [GO:0040018]; positive regulation of vulval development [GO:0040026]; response to magnesium ion [GO:0032026]']\n",
|
| 399 |
-
" ['fatty acid metabolic process [GO:0006631]; sphingolipid metabolic process [GO:0006665]']\n",
|
| 400 |
-
" ['endoplasmic reticulum unfolded protein response [GO:0030968]; lipoprotein metabolic process [GO:0042157]; lipoprotein transport [GO:0042953]; plasma lipoprotein particle assembly [GO:0034377]']\n",
|
| 401 |
-
" ...\n",
|
| 402 |
-
" ['deoxyribonucleotide catabolic process [GO:0009264]']\n",
|
| 403 |
-
" ['ubiquinone biosynthetic process [GO:0006744]']\n",
|
| 404 |
-
" ['archaeal or bacterial-type flagellum-dependent cell motility [GO:0097588]']]\n"
|
| 405 |
-
]
|
| 406 |
-
}
|
| 407 |
-
],
|
| 408 |
-
"source": [
|
| 409 |
-
"print(\"Annotations for the nearest neighbors (with aspect annotations) of newly discovered proteins\")\n",
|
| 410 |
-
"print(near_ids)"
|
| 411 |
-
]
|
| 412 |
-
},
|
| 413 |
-
{
|
| 414 |
-
"cell_type": "code",
|
| 415 |
-
"execution_count": 21,
|
| 416 |
-
"id": "05b49028-2739-4a91-9c2b-4114a70f6a24",
|
| 417 |
-
"metadata": {},
|
| 418 |
-
"outputs": [],
|
| 419 |
-
"source": [
|
| 420 |
-
"# Calculate the recall (sensitivity) performance results for every aspect\n",
|
| 421 |
-
"if column == 'EC number':\n",
|
| 422 |
-
" ground_meta_data = data_subset[(~data_subset[column].isnull()) & (~data_subset[column].astype(str).str.contains('-', regex=False))]\n",
|
| 423 |
-
"else:\n",
|
| 424 |
-
" ground_meta_data = data_subset[(~data_subset[column].isnull())]\n",
|
| 425 |
-
"\n",
|
| 426 |
-
"rel_col = ground_meta_data[column].values\n",
|
| 427 |
-
"relevant_indices = np.array(list(ground_meta_data.index))\n",
|
| 428 |
-
"relevant_near_ids = near_ids[relevant_indices]\n",
|
| 429 |
-
"relevant_D = D[relevant_indices]\n",
|
| 430 |
-
"\n",
|
| 431 |
-
"#this calculates exact match\n",
|
| 432 |
-
"intersection = []\n",
|
| 433 |
-
"for i in range(len(rel_col)):\n",
|
| 434 |
-
" p1s = set(rel_col[i].split(\";\"))\n",
|
| 435 |
-
" p1s = {item for item in p1s if item != \"\"}\n",
|
| 436 |
-
" p2s = set(relevant_near_ids[i,0].split(\";\"))\n",
|
| 437 |
-
" p2s = {item for item in p2s if item != \"\"}\n",
|
| 438 |
-
" inter = len(list(p1s & p2s))\n",
|
| 439 |
-
" acc = inter/len(p1s)\n",
|
| 440 |
-
" intersection.append(acc)\n",
|
| 441 |
-
"\n",
|
| 442 |
-
" \n",
|
| 443 |
-
"#Exact match accuracy\n",
|
| 444 |
-
"exact_matches = 0\n",
|
| 445 |
-
"for i in range(len(rel_col)):\n",
|
| 446 |
-
" p1s = set(rel_col[i].split(\";\"))\n",
|
| 447 |
-
" p2s = set(relevant_near_ids[i,0].split(\";\"))\n",
|
| 448 |
-
" if p1s == p2s:\n",
|
| 449 |
-
" exact_matches += 1"
|
| 450 |
-
]
|
| 451 |
-
},
|
| 452 |
-
{
|
| 453 |
-
"cell_type": "code",
|
| 454 |
-
"execution_count": 24,
|
| 455 |
-
"id": "06fe1b00-fed6-44bf-94fc-3912a40af20f",
|
| 456 |
-
"metadata": {},
|
| 457 |
-
"outputs": [
|
| 458 |
-
{
|
| 459 |
-
"name": "stdout",
|
| 460 |
-
"output_type": "stream",
|
| 461 |
-
"text": [
|
| 462 |
-
"Gene Ontology (biological process): Recall of protein MOE model for annotating proteins with sequence similarity below 50%\n",
|
| 463 |
-
"0.7533442184946648\n"
|
| 464 |
-
]
|
| 465 |
-
}
|
| 466 |
-
],
|
| 467 |
-
"source": [
|
| 468 |
-
"print(\"Gene Ontology (biological process): Recall of protein MOE model for annotating proteins with sequence similarity below 50%\")\n",
|
| 469 |
-
"print(np.mean(np.array(intersection)))"
|
| 470 |
-
]
|
| 471 |
-
},
|
| 472 |
-
{
|
| 473 |
-
"cell_type": "code",
|
| 474 |
-
"execution_count": 25,
|
| 475 |
-
"id": "6b5fb4f1-3f10-49a8-ba79-4afb7a4f6d3c",
|
| 476 |
-
"metadata": {},
|
| 477 |
-
"outputs": [
|
| 478 |
-
{
|
| 479 |
-
"name": "stdout",
|
| 480 |
-
"output_type": "stream",
|
| 481 |
-
"text": [
|
| 482 |
-
"Gene Ontology (biological process): Exact match accuracy of protein MOE model for annotating proteins with sequence similarity below 50%\n",
|
| 483 |
-
"0.6657317695053544\n"
|
| 484 |
-
]
|
| 485 |
-
}
|
| 486 |
-
],
|
| 487 |
-
"source": [
|
| 488 |
-
"print(\"Gene Ontology (biological process): Exact match accuracy of protein MOE model for annotating proteins with sequence similarity below 50%\")\n",
|
| 489 |
-
"print(exact_matches/len(rel_col))"
|
| 490 |
-
]
|
| 491 |
-
},
|
| 492 |
-
{
|
| 493 |
-
"cell_type": "code",
|
| 494 |
-
"execution_count": null,
|
| 495 |
-
"id": "854ea700-5661-4097-a9fb-223760663102",
|
| 496 |
-
"metadata": {},
|
| 497 |
-
"outputs": [],
|
| 498 |
-
"source": []
|
| 499 |
-
}
|
| 500 |
-
],
|
| 501 |
-
"metadata": {
|
| 502 |
-
"kernelspec": {
|
| 503 |
-
"display_name": "protein_vec_env",
|
| 504 |
-
"language": "python",
|
| 505 |
-
"name": "protein_vec_env"
|
| 506 |
-
},
|
| 507 |
-
"language_info": {
|
| 508 |
-
"codemirror_mode": {
|
| 509 |
-
"name": "ipython",
|
| 510 |
-
"version": 3
|
| 511 |
-
},
|
| 512 |
-
"file_extension": ".py",
|
| 513 |
-
"mimetype": "text/x-python",
|
| 514 |
-
"name": "python",
|
| 515 |
-
"nbconvert_exporter": "python",
|
| 516 |
-
"pygments_lexer": "ipython3",
|
| 517 |
-
"version": "3.9.15"
|
| 518 |
-
}
|
| 519 |
-
},
|
| 520 |
-
"nbformat": 4,
|
| 521 |
-
"nbformat_minor": 5
|
| 522 |
-
}
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9247c129628b1aac6434363f6a13451c4cf480fe19f905befc38df6e7ce1f3ed
|
| 3 |
+
size 84490
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|
|
protein_vec_models/launch_pl_worker.bash
CHANGED
|
@@ -1,43 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
#SBATCH -C a100
|
| 5 |
-
#SBATCH --exclusive
|
| 6 |
-
#SBATCH --tasks-per-node=4
|
| 7 |
-
#SBATCH --cpus-per-task=8
|
| 8 |
-
#SBATCH --mem 800gb
|
| 9 |
-
#SBATCH --gpus-per-node=4
|
| 10 |
-
#SBATCH --job-name=train
|
| 11 |
-
#SBATCH -o slurm-%x.%j.out
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
source /mnt/home/thamamsy/projects/protein_vec/lib/environment/protein_vec_env/bin/activate
|
| 15 |
-
|
| 16 |
-
export METAG_NNODES=$(scontrol show hostnames $SLURM_JOB_NODELIST | wc -l)
|
| 17 |
-
echo "Number of nodes: $METAG_NNODES"
|
| 18 |
-
|
| 19 |
-
export DATA_NAME=moe_all
|
| 20 |
-
#Dataset path
|
| 21 |
-
export METAG_DATA=/mnt/home/thamamsy/ceph/protein_vec/data/uniprot_data/training_splits/combos_training_data.parquet
|
| 22 |
-
#Emnedding folder (pickle files)
|
| 23 |
-
export METAG_EMBEDDING=/mnt/home/thamamsy/ceph/swiss/swiss_protrans/pickles
|
| 24 |
-
|
| 25 |
-
export METAG_DMODEL=512
|
| 26 |
-
export METAG_NLAYER=2
|
| 27 |
-
export METAG_NHEADS=4
|
| 28 |
-
export METAG_IN_DIM=2048
|
| 29 |
-
export METAG_WARMUP_STEPS=500
|
| 30 |
-
export METAG_TRAIN_PROP=0.95
|
| 31 |
-
export METAG_VAL_PROP=0.005
|
| 32 |
-
export METAG_TEST_PROP=0.005
|
| 33 |
-
|
| 34 |
-
set -ex
|
| 35 |
-
export METAG_LR=0.0001
|
| 36 |
-
export METAG_BSIZE=16 #16 for most
|
| 37 |
-
export METAG_SESSION=/mnt/home/thamamsy/ceph/protein_vec/models/model${METAG_LR}_dmodel${METAG_DMODEL}_nlayer${METAG_NLAYER}_${DATA_NAME}
|
| 38 |
-
export EPOCHS=5
|
| 39 |
-
export NCCL_DEBUG=INFO
|
| 40 |
-
|
| 41 |
-
set +x
|
| 42 |
-
|
| 43 |
-
srun pl_worker.bash
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2a822fe831ff36515a28866d18544c3804e36152fb792497785484a88c294d08
|
| 3 |
+
size 1235
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
protein_vec_models/model_protein_moe.py
CHANGED
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@@ -1,319 +1,3 @@
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from dataclasses import dataclass, asdict
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from model_protein_vec_single_variable import trans_basic_block_single, trans_basic_block_Config_single
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from embed_structure_model import trans_basic_block_tmvec, trans_basic_block_Config_tmvec
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import torch
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from torch import nn
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import torch.nn.functional as F
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import pytorch_lightning as pl
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import numpy as np
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import random
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import os
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@dataclass
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class Config:
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def isolate(self, config):
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specifics = inspect.signature(config).parameters
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my_specifics = {k: v for k, v in asdict(self).items() if k in specifics}
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return config(**my_specifics)
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def to_json(self, filename):
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config = json.dumps(asdict(self), indent=2)
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with open(filename, 'w') as f:
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f.write(config)
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@classmethod
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def from_json(cls, filename):
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with open(filename, 'r') as f:
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js = json.loads(f.read())
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config = cls(**js)
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return config
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@dataclass
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class trans_basic_block_Config(Config):
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d_model: int = 512
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nhead: int = 4
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num_layers: int = 2
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dim_feedforward: int = 2048
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out_dim: int = 512
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dropout: float = 0.1
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activation: str = 'relu'
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num_variables: int = 10 #9
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vocab: int = 20
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# data params
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lr0: float = 0.0001
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warmup_steps: int = 300
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p_bernoulli: float = .5
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def build(self):
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return trans_basic_block(self)
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class trans_basic_block(pl.LightningModule):
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"""
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TransformerEncoderLayer with preset parameters followed by global pooling and dropout
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"""
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def __init__(self, config: trans_basic_block_Config):
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super().__init__()
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self.config = config
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#Encoding
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encoder_args = {k: v for k, v in asdict(config).items() if k in inspect.signature(nn.TransformerEncoderLayer).parameters}
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num_layers = config.num_layers
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encoder_layer = nn.TransformerEncoderLayer(batch_first=True, **encoder_args)
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self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
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#Linear and dropout
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self.dropout = nn.Dropout(self.config.dropout)
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# Define 1D convolutional layer
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#2 layer approach:
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self.mlp_1 = nn.Linear(self.config.d_model, self.config.out_dim)
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self.mlp_2 = nn.Linear(self.config.out_dim, self.config.out_dim)
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#Loss functions
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self.trip_margin_loss = nn.TripletMarginLoss(margin=1.0, p=2)
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#embedding lookup
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self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)
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self.l1_loss = nn.L1Loss(reduction='mean')
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self.pdist = nn.PairwiseDistance(p=2)
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################## TM-Vec model
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vec_model_cpnt_tmvec = os.path.join(os.path.dirname(__file__), 'tm_vec_swiss_model_large.ckpt')
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vec_model_config_tmvec = os.path.join(os.path.dirname(__file__), 'tm_vec_swiss_model_large_params.json')
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#Load the model
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vec_model_config_tmvec = trans_basic_block_Config_tmvec.from_json(vec_model_config_tmvec)
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self.model_aspect_tmvec = trans_basic_block_tmvec.load_from_checkpoint(vec_model_cpnt_tmvec, config=vec_model_config_tmvec)
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for param in self.model_aspect_tmvec.parameters():
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param.requires_grad = False
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################## PFam model
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vec_model_cpnt_pfam = os.path.join(os.path.dirname(__file__), 'aspect_vec_pfam.ckpt')
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vec_model_config_pfam = os.path.join(os.path.dirname(__file__), 'aspect_vec_pfam_params.json')
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#Load the model
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vec_model_config_pfam = trans_basic_block_Config_single.from_json(vec_model_config_pfam)
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self.model_aspect_pfam = trans_basic_block_single.load_from_checkpoint(vec_model_cpnt_pfam, config=vec_model_config_pfam)
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for param in self.model_aspect_pfam.parameters():
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param.requires_grad = False
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################## GENE3D model
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vec_model_cpnt_gene3D = os.path.join(os.path.dirname(__file__), 'aspect_vec_gene3d.ckpt')
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vec_model_config_gene3D = os.path.join(os.path.dirname(__file__), 'aspect_vec_gene3d_params.json')
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#Load the model
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vec_model_config_gene3D = trans_basic_block_Config_single.from_json(vec_model_config_gene3D)
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self.model_aspect_gene3D = trans_basic_block_single.load_from_checkpoint(vec_model_cpnt_gene3D, config=vec_model_config_gene3D)
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for param in self.model_aspect_gene3D.parameters():
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param.requires_grad = False
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################## EC model
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vec_model_cpnt_ec = os.path.join(os.path.dirname(__file__), 'aspect_vec_ec.ckpt')
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vec_model_config_ec = os.path.join(os.path.dirname(__file__), 'aspect_vec_ec_params.json')
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#Load the model
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vec_model_config_ec = trans_basic_block_Config_single.from_json(vec_model_config_ec)
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self.model_aspect_ec = trans_basic_block_single.load_from_checkpoint(vec_model_cpnt_ec, config=vec_model_config_ec)
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for param in self.model_aspect_ec.parameters():
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param.requires_grad = False
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################## GO MFO model
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vec_model_cpnt_mfo = os.path.join(os.path.dirname(__file__), 'aspect_vec_go_mfo.ckpt')
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vec_model_config_mfo = os.path.join(os.path.dirname(__file__), 'aspect_vec_go_mfo_params.json')
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#Load the model
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vec_model_config_mfo = trans_basic_block_Config_single.from_json(vec_model_config_mfo)
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self.model_aspect_mfo = trans_basic_block_single.load_from_checkpoint(vec_model_cpnt_mfo, config=vec_model_config_mfo)
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for param in self.model_aspect_mfo.parameters():
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param.requires_grad = False
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################## GO BPO model
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vec_model_cpnt_bpo = os.path.join(os.path.dirname(__file__), 'aspect_vec_go_bpo.ckpt')
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vec_model_config_bpo = os.path.join(os.path.dirname(__file__), 'aspect_vec_go_bpo_params.json')
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#Load the model
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vec_model_config_bpo = trans_basic_block_Config_single.from_json(vec_model_config_bpo)
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self.model_aspect_bpo = trans_basic_block_single.load_from_checkpoint(vec_model_cpnt_bpo, config=vec_model_config_bpo)
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for param in self.model_aspect_bpo.parameters():
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param.requires_grad = False
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################## GO CCO model
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vec_model_cpnt_cco = os.path.join(os.path.dirname(__file__), 'aspect_vec_go_cco.ckpt')
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vec_model_config_cco = os.path.join(os.path.dirname(__file__), 'aspect_vec_go_cco_params.json')
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#Load the model
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vec_model_config_cco = trans_basic_block_Config_single.from_json(vec_model_config_cco)
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self.model_aspect_cco = trans_basic_block_single.load_from_checkpoint(vec_model_cpnt_cco, config=vec_model_config_cco)
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for param in self.model_aspect_cco.parameters():
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param.requires_grad = False
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def forward(self, x_i, src_key_padding_mask):
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#embedding
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src_key_padding_mask = src_key_padding_mask.to(x_i)
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enc_out = self.encoder(x_i, mask=None, src_key_padding_mask=src_key_padding_mask)
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lens = torch.logical_not(src_key_padding_mask).sum(dim=1).float()
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enc_out = enc_out.sum(dim=1) / lens.unsqueeze(1)
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out = self.mlp_1(enc_out)
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out = self.mlp_2(out)
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return out
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def distance_marginal_triplet(self, out_seq1, out_seq2, out_seq3, margin):
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d1 = self.pdist(out_seq1, out_seq2)
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d2 = self.pdist(out_seq1, out_seq3)
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zeros = torch.zeros(d1.shape).to(out_seq1)
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margin = margin.to(out_seq1)
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loss = torch.mean(torch.max(d1 - d2 + margin, zeros))
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return(loss)
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def triplet_margin_loss(self, output_seq1, output_seq2, output_seq3):
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loss = self.trip_margin_loss(output_seq1, output_seq2, output_seq3)
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return loss
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def distance_loss_tm_positive(self, output_seq1, output_seq2, tm_score):
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dist_seq = self.cos(output_seq1, output_seq2)
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dist_tm = self.l1_loss(dist_seq.unsqueeze(0), tm_score.unsqueeze(0))
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return dist_tm
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def distance_loss_tm_difference(self, output_seq1, output_seq2, output_seq3, tm_score):
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dist_seq1 = self.cos(output_seq1, output_seq2)
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dist_seq2 = self.cos(output_seq1, output_seq3)
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difference = dist_seq2 - dist_seq1
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dist_tm = self.l1_loss(difference.unsqueeze(0), tm_score.unsqueeze(0))
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return dist_tm
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def make_matrix(self, sequence, pad_mask):
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pad_mask = pad_mask.to(sequence)
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aspect1 = self.model_aspect_tmvec(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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aspect2 = self.model_aspect_pfam(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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aspect3 = self.model_aspect_gene3D(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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aspect4 = self.model_aspect_ec(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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aspect5 = self.model_aspect_mfo(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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aspect6 = self.model_aspect_bpo(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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aspect7 = self.model_aspect_cco(sequence, src_mask=None, src_key_padding_mask=pad_mask)[:,None,:]
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combine_aspects = torch.cat([aspect1, aspect2, aspect3, aspect4, aspect5, aspect6, aspect7], dim=1)
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return combine_aspects
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def training_step(self, train_batch, batch_idx):
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lookup_dict = {
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'nothing': 1, 'ENZYME': 2, 'PFAM':3, 'MFO':4, 'BPO':5, 'CCO':6,
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'TM':7,'GENE3D':8
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}
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all_cols = np.array(['TM', 'PFAM', 'GENE3D', 'ENZYME', 'MFO', 'BPO', 'CCO'])
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sampled_keys = train_batch['key']
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margins = train_batch['margin']
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tm_type = train_batch['tm']
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tm_scores = train_batch['tm_scores']
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subset_sampled_keys = [sampled_keys[j].split(",") for j in range(len(sampled_keys))]
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masks = []
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for i in range(len(subset_sampled_keys)):
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mask = [all_cols[k] in subset_sampled_keys[i] for k in range(len(all_cols))]
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masks.append(mask)
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masks = torch.logical_not(torch.tensor(masks, dtype=torch.bool))
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#Get the ID embeddings
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sequence_1 = train_batch['id']
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pad_mask_1 = train_batch['id_padding']
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sequence_2 = train_batch['positive']
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pad_mask_2 = train_batch['positive_padding']
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sequence_3 = train_batch['negative']
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pad_mask_3 = train_batch['negative_padding']
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#Make Aspect matrices
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out_seq1 = self.make_matrix(sequence_1, pad_mask_1)
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out_seq2 = self.make_matrix(sequence_2, pad_mask_2)
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out_seq3 = self.make_matrix(sequence_3, pad_mask_3)
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#Forward pass
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out_seq1 = self.forward(out_seq1, masks)
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out_seq2 = self.forward(out_seq2, masks)
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out_seq3 = self.forward(out_seq3, masks)
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#Triplet loss
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loss_trip = self.distance_marginal_triplet(out_seq1, out_seq2, out_seq3, margins)
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#Positive TM loss
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loss_tm_positive = self.distance_loss_tm_positive(out_seq1, out_seq2, tm_scores)
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loss_positive_mask = torch.tensor([tm_type[i] == 'Positive' for i in range(len(tm_type))]).to(loss_tm_positive).to(bool)
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loss_tm_positive_fin = loss_tm_positive.masked_fill(loss_positive_mask, 0.0)
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#TM difference loss
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loss_tm_difference = self.distance_loss_tm_difference(out_seq1, out_seq2, out_seq3, tm_scores)
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loss_difference_mask = torch.tensor([tm_type[i] == 'Difference' for i in range(len(tm_type))]).to(loss_tm_difference).to(bool)
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loss_tm_difference_fin = loss_tm_difference.masked_fill(loss_difference_mask, 0.0)
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#Combined TM loss
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loss_part_2 = (loss_tm_positive_fin + loss_tm_difference_fin).mean()
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#complete loss
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loss = loss_trip + loss_part_2
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self.log('train_loss', loss)
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return loss
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def validation_step(self, val_batch, batch_idx):
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all_cols = np.array(['TM', 'PFAM', 'GENE3D', 'ENZYME', 'MFO', 'BPO', 'CCO'])
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sampled_keys = val_batch['key']
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margins = val_batch['margin']
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tm_type = val_batch['tm']
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tm_scores = val_batch['tm_scores']
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subset_sampled_keys = [sampled_keys[j].split(",") for j in range(len(sampled_keys))]
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masks = []
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for i in range(len(subset_sampled_keys)):
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mask = [all_cols[k] in subset_sampled_keys[i] for k in range(len(all_cols))]
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masks.append(mask)
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masks = torch.logical_not(torch.tensor(masks, dtype=torch.bool))
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#Get the ID embeddings
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sequence_1 = val_batch['id']
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pad_mask_1 = val_batch['id_padding']
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sequence_2 = val_batch['positive']
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pad_mask_2 = val_batch['positive_padding']
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sequence_3 = val_batch['negative']
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pad_mask_3 = val_batch['negative_padding']
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out_seq1 = self.make_matrix(sequence_1, pad_mask_1)
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out_seq2 = self.make_matrix(sequence_2, pad_mask_2)
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out_seq3 = self.make_matrix(sequence_3, pad_mask_3)
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out_seq1 = self.forward(out_seq1, masks)
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out_seq2 = self.forward(out_seq2, masks)
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out_seq3 = self.forward(out_seq3, masks)
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#triplet loss
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loss_trip = self.distance_marginal_triplet(out_seq1, out_seq2, out_seq3, margins)
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#positive tm loss
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loss_tm_positive = self.distance_loss_tm_positive(out_seq1, out_seq2, tm_scores)
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loss_positive_mask = torch.tensor([tm_type[i] == 'Positive' for i in range(len(tm_type))]).to(loss_tm_positive).to(bool)
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loss_tm_positive_fin = loss_tm_positive.masked_fill(loss_positive_mask, 0.0)
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#difference tm loss
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loss_tm_difference = self.distance_loss_tm_difference(out_seq1, out_seq2, out_seq3, tm_scores)
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loss_difference_mask = torch.tensor([tm_type[i] == 'Difference' for i in range(len(tm_type))]).to(loss_tm_difference).to(bool)
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loss_tm_difference_fin = loss_tm_difference.masked_fill(loss_difference_mask, 0.0)
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#complete loss
|
| 310 |
-
loss_part_2 = (loss_tm_positive_fin + loss_tm_difference_fin).mean()
|
| 311 |
-
#complete loss
|
| 312 |
-
loss = loss_trip + loss_part_2
|
| 313 |
-
|
| 314 |
-
self.log('val_loss', loss)
|
| 315 |
-
|
| 316 |
-
def configure_optimizers(self):
|
| 317 |
-
optimizer = torch.optim.Adam(self.parameters(), lr=self.config.lr0)
|
| 318 |
-
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=5000)
|
| 319 |
-
return [optimizer], [lr_scheduler]
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8572714eb6e3ac24a705e9780cda73d6800ae0d365435ccb28d632246a9ab953
|
| 3 |
+
size 14801
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|
|
protein_vec_models/model_protein_vec_single_variable.py
CHANGED
|
@@ -1,169 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
from dataclasses import dataclass, asdict
|
| 5 |
-
|
| 6 |
-
import torch
|
| 7 |
-
from torch import nn
|
| 8 |
-
import torch.nn.functional as F
|
| 9 |
-
import pytorch_lightning as pl
|
| 10 |
-
import numpy as np
|
| 11 |
-
import random
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
@dataclass
|
| 16 |
-
class Config:
|
| 17 |
-
def isolate(self, config):
|
| 18 |
-
specifics = inspect.signature(config).parameters
|
| 19 |
-
my_specifics = {k: v for k, v in asdict(self).items() if k in specifics}
|
| 20 |
-
return config(**my_specifics)
|
| 21 |
-
|
| 22 |
-
def to_json(self, filename):
|
| 23 |
-
config = json.dumps(asdict(self), indent=2)
|
| 24 |
-
with open(filename, 'w') as f:
|
| 25 |
-
f.write(config)
|
| 26 |
-
|
| 27 |
-
@classmethod
|
| 28 |
-
def from_json(cls, filename):
|
| 29 |
-
with open(filename, 'r') as f:
|
| 30 |
-
js = json.loads(f.read())
|
| 31 |
-
config = cls(**js)
|
| 32 |
-
return config
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
@dataclass
|
| 36 |
-
class trans_basic_block_Config_single(Config):
|
| 37 |
-
d_model: int = 1024
|
| 38 |
-
nhead: int = 4
|
| 39 |
-
num_layers: int = 2
|
| 40 |
-
dim_feedforward: int = 2048
|
| 41 |
-
out_dim: int = 512
|
| 42 |
-
dropout: float = 0.1
|
| 43 |
-
activation: str = 'relu'
|
| 44 |
-
num_variables: int = 10 #9
|
| 45 |
-
vocab: int = 20
|
| 46 |
-
# data params
|
| 47 |
-
lr0: float = 0.0001
|
| 48 |
-
warmup_steps: int = 300
|
| 49 |
-
p_bernoulli: float = .5
|
| 50 |
-
|
| 51 |
-
def build(self):
|
| 52 |
-
return trans_basic_block_single(self)
|
| 53 |
-
|
| 54 |
-
class trans_basic_block_single(pl.LightningModule):
|
| 55 |
-
"""
|
| 56 |
-
TransformerEncoderLayer with preset parameters followed by global pooling and dropout
|
| 57 |
-
"""
|
| 58 |
-
def __init__(self, config: trans_basic_block_Config_single):
|
| 59 |
-
super().__init__()
|
| 60 |
-
self.config = config
|
| 61 |
-
|
| 62 |
-
#Encoding
|
| 63 |
-
encoder_args = {k: v for k, v in asdict(config).items() if k in inspect.signature(nn.TransformerEncoderLayer).parameters}
|
| 64 |
-
num_layers = config.num_layers
|
| 65 |
-
encoder_layer = nn.TransformerEncoderLayer(batch_first=True, **encoder_args)
|
| 66 |
-
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
|
| 67 |
-
#Linear and dropout
|
| 68 |
-
self.dropout = nn.Dropout(self.config.dropout)
|
| 69 |
-
|
| 70 |
-
#2 layer approach:
|
| 71 |
-
hidden_dim = self.config.d_model
|
| 72 |
-
self.mlp_1 = nn.Linear(hidden_dim, self.config.out_dim)
|
| 73 |
-
self.mlp_2 = nn.Linear(self.config.out_dim, self.config.out_dim)
|
| 74 |
-
|
| 75 |
-
#Loss functions
|
| 76 |
-
self.trip_margin_loss = nn.TripletMarginLoss(margin=1.0, reduction='mean')#p=2)
|
| 77 |
-
|
| 78 |
-
self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)
|
| 79 |
-
self.l1_loss = nn.L1Loss(reduction='mean')
|
| 80 |
-
self.pdist = nn.PairwiseDistance(p=2)
|
| 81 |
-
|
| 82 |
-
def forward(self, x_i, src_mask, src_key_padding_mask):
|
| 83 |
-
enc_out = self.encoder(x_i, mask=src_mask, src_key_padding_mask=src_key_padding_mask)
|
| 84 |
-
lens = torch.logical_not(src_key_padding_mask).sum(dim=1).float()
|
| 85 |
-
out = enc_out.sum(dim=1) / lens.unsqueeze(1)
|
| 86 |
-
|
| 87 |
-
out = self.mlp_1(out)
|
| 88 |
-
out = self.dropout(out)
|
| 89 |
-
out = self.mlp_2(out)
|
| 90 |
-
return out
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
def triplet_margin_loss(self, output_seq1, output_seq2, output_seq3):
|
| 94 |
-
loss = self.trip_margin_loss(output_seq1, output_seq2, output_seq3)
|
| 95 |
-
return loss
|
| 96 |
-
|
| 97 |
-
def distance_marginal_triplet(self, out_seq1, out_seq2, out_seq3, margin):
|
| 98 |
-
d1 = self.pdist(out_seq1, out_seq2)
|
| 99 |
-
d2 = self.pdist(out_seq1, out_seq3)
|
| 100 |
-
zeros = torch.zeros(d1.shape).to(out_seq1)
|
| 101 |
-
margin = margin.to(out_seq1)
|
| 102 |
-
loss = torch.mean(torch.max(d1 - d2 + margin, zeros))
|
| 103 |
-
|
| 104 |
-
return(loss)
|
| 105 |
-
|
| 106 |
-
def distance_loss(self, output_seq1, output_seq2, output_seq3, margin):
|
| 107 |
-
dist_seq1 = self.cos(output_seq1, output_seq2)
|
| 108 |
-
dist_seq2 = self.cos(output_seq1, output_seq3)
|
| 109 |
-
margin = margin.to(output_seq1)
|
| 110 |
-
diff = dist_seq2 - dist_seq1
|
| 111 |
-
dist_margin = self.l1_loss(diff.unsqueeze(0), margin.float().unsqueeze(0))
|
| 112 |
-
|
| 113 |
-
return dist_margin
|
| 114 |
-
|
| 115 |
-
def distance_loss2(self, output_seq1, output_seq2, output_seq3, margin):
|
| 116 |
-
dist_seq1 = self.cos(output_seq1, output_seq2)
|
| 117 |
-
dist_seq2 = self.cos(output_seq1, output_seq3)
|
| 118 |
-
margin = margin.to(output_seq1)
|
| 119 |
-
zeros = torch.zeros(dist_seq1.shape).to(output_seq1)
|
| 120 |
-
loss = torch.mean(torch.max(dist_seq1 - dist_seq2 + margin, zeros))
|
| 121 |
-
return loss
|
| 122 |
-
|
| 123 |
-
def training_step(self, train_batch, batch_idx):
|
| 124 |
-
#key_vars = train_batch['key']
|
| 125 |
-
margins = torch.FloatTensor(train_batch['key'])
|
| 126 |
-
|
| 127 |
-
#Get the ID embeddings
|
| 128 |
-
sequence_1 = train_batch['id']
|
| 129 |
-
pad_mask_1 = train_batch['id_padding']
|
| 130 |
-
sequence_2 = train_batch['positive']
|
| 131 |
-
pad_mask_2 = train_batch['positive_padding']
|
| 132 |
-
sequence_3 = train_batch['negative']
|
| 133 |
-
pad_mask_3 = train_batch['negative_padding']
|
| 134 |
-
|
| 135 |
-
out_seq1 = self.forward(sequence_1, src_mask=None, src_key_padding_mask=pad_mask_1)
|
| 136 |
-
out_seq2 = self.forward(sequence_2, src_mask=None, src_key_padding_mask=pad_mask_2)
|
| 137 |
-
out_seq3 = self.forward(sequence_3, src_mask=None, src_key_padding_mask=pad_mask_3)
|
| 138 |
-
|
| 139 |
-
loss = self.distance_marginal_triplet(out_seq1, out_seq2, out_seq3, margins)
|
| 140 |
-
|
| 141 |
-
self.log('train_loss', loss, sync_dist=True)
|
| 142 |
-
|
| 143 |
-
return loss
|
| 144 |
-
|
| 145 |
-
def validation_step(self, val_batch, batch_idx):
|
| 146 |
-
#key_vars = val_batch['key']
|
| 147 |
-
margins = torch.FloatTensor(val_batch['key'])
|
| 148 |
-
|
| 149 |
-
#Get the ID embeddings
|
| 150 |
-
sequence_1 = val_batch['id']
|
| 151 |
-
pad_mask_1 = val_batch['id_padding']
|
| 152 |
-
sequence_2 = val_batch['positive']
|
| 153 |
-
pad_mask_2 = val_batch['positive_padding']
|
| 154 |
-
sequence_3 = val_batch['negative']
|
| 155 |
-
pad_mask_3 = val_batch['negative_padding']
|
| 156 |
-
|
| 157 |
-
out_seq1 = self.forward(sequence_1, src_mask=None, src_key_padding_mask=pad_mask_1)
|
| 158 |
-
out_seq2 = self.forward(sequence_2, src_mask=None, src_key_padding_mask=pad_mask_2)
|
| 159 |
-
out_seq3 = self.forward(sequence_3, src_mask=None, src_key_padding_mask=pad_mask_3)
|
| 160 |
-
|
| 161 |
-
loss = self.distance_marginal_triplet(out_seq1, out_seq2, out_seq3, margins)
|
| 162 |
-
|
| 163 |
-
self.log('val_loss', loss, sync_dist=True)
|
| 164 |
-
|
| 165 |
-
|
| 166 |
-
def configure_optimizers(self):
|
| 167 |
-
optimizer = torch.optim.Adam(self.parameters(), lr=self.config.lr0)
|
| 168 |
-
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=5)
|
| 169 |
-
return [optimizer], [lr_scheduler]
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ffa417bc43f35260fa8651ac2d211186fd4dd4a957b7af9e21d9f6eadec53bb0
|
| 3 |
+
size 6509
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
protein_vec_models/pl_worker.bash
CHANGED
|
@@ -1,27 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
NGPUS=$(nvidia-smi --query-gpu=name --format=csv,noheader | wc -l)
|
| 6 |
-
#Source python virtual environment
|
| 7 |
-
source /mnt/home/thamamsy/projects/protein_vec/lib/environment/protein_vec_env/bin/activate
|
| 8 |
-
|
| 9 |
-
export NCCL_DEBUG=INFO
|
| 10 |
-
|
| 11 |
-
python train_protein_vec.py \
|
| 12 |
-
--nodes ${METAG_NNODES} \
|
| 13 |
-
--gpus ${NGPUS} \
|
| 14 |
-
--session ${METAG_SESSION} \
|
| 15 |
-
--data ${METAG_DATA} \
|
| 16 |
-
--embeddings ${METAG_EMBEDDING} \
|
| 17 |
-
--lr0 ${METAG_LR} \
|
| 18 |
-
--max-epochs ${EPOCHS} \
|
| 19 |
-
--batch-size ${METAG_BSIZE} \
|
| 20 |
-
--d_model ${METAG_DMODEL} \
|
| 21 |
-
--num_layers ${METAG_NLAYER} \
|
| 22 |
-
--dim_feedforward ${METAG_IN_DIM} \
|
| 23 |
-
--nhead ${METAG_NHEADS} \
|
| 24 |
-
--warmup_steps ${METAG_WARMUP_STEPS} \
|
| 25 |
-
--train-prop ${METAG_TRAIN_PROP} \
|
| 26 |
-
--val-prop ${METAG_VAL_PROP} \
|
| 27 |
-
--test-prop ${METAG_TEST_PROP}
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e8623e0d5e0ed68acf74164e3b809cbc0ad2a339c25dadcabcdc4d64ba86bb69
|
| 3 |
+
size 889
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
protein_vec_models/protein_vec_params.json
CHANGED
|
@@ -1,14 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
"num_layers": 2,
|
| 5 |
-
"dim_feedforward": 2048,
|
| 6 |
-
"out_dim": 512,
|
| 7 |
-
"dropout": 0.1,
|
| 8 |
-
"activation": "relu",
|
| 9 |
-
"num_variables": 10,
|
| 10 |
-
"vocab": 20,
|
| 11 |
-
"lr0": 0.0001,
|
| 12 |
-
"warmup_steps": 500,
|
| 13 |
-
"p_bernoulli": 0.5
|
| 14 |
-
}
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e54d4c6e8b562d8a71e9974618dfc661b822765e7b8a1af6a36563b4ea85c700
|
| 3 |
+
size 253
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
protein_vec_models/tm_vec_swiss_model_large.ckpt
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:59584bbfa81dd7756fef0b5f385fb7bccd78d7c30785ca1de6f26d199e6c8755
|
| 3 |
-
size 409544459
|
|
|
|
|
|
|
|
|
|
|
|
protein_vec_models/tm_vec_swiss_model_large_params.json
CHANGED
|
@@ -1,11 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
"num_layers": 4,
|
| 5 |
-
"dim_feedforward": 2048,
|
| 6 |
-
"out_dim": 512,
|
| 7 |
-
"dropout": 0.1,
|
| 8 |
-
"activation": "relu",
|
| 9 |
-
"lr0": 0.0001,
|
| 10 |
-
"warmup_steps": 300
|
| 11 |
-
}
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:234cc06da768a4c4c9cee390566c8f5a3482269877eb584628201bd78cce5a4b
|
| 3 |
+
size 191
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
protein_vec_models/train_protein_vec.py
CHANGED
|
@@ -1,156 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
from pathlib import Path
|
| 5 |
-
from dataclasses import fields, dataclass, field
|
| 6 |
-
import pickle
|
| 7 |
-
import numpy as np
|
| 8 |
-
import torch
|
| 9 |
-
from torch.utils.data import DataLoader
|
| 10 |
-
import pytorch_lightning as pl
|
| 11 |
-
from pytorch_lightning.strategies import DDPStrategy
|
| 12 |
-
from data_protein_vec import collate_fn, get_parquet, construct_datasets
|
| 13 |
-
from model_protein_moe import trans_basic_block, trans_basic_block_Config
|
| 14 |
-
from utils import SessionTree
|
| 15 |
-
|
| 16 |
-
#Comand line
|
| 17 |
-
def arguments():
|
| 18 |
-
parser = argparse.ArgumentParser(description="Train a structural embedding model")
|
| 19 |
-
|
| 20 |
-
parser.add_argument("--gpus",
|
| 21 |
-
type=int,
|
| 22 |
-
help="Num. gpus",
|
| 23 |
-
default=1
|
| 24 |
-
)
|
| 25 |
-
parser.add_argument("--nodes",
|
| 26 |
-
type=int,
|
| 27 |
-
help="Num. nodes",
|
| 28 |
-
default=1
|
| 29 |
-
)
|
| 30 |
-
|
| 31 |
-
parser.add_argument("--data",
|
| 32 |
-
type=Path,
|
| 33 |
-
required=True,
|
| 34 |
-
help="Data"
|
| 35 |
-
)
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
parser.add_argument("--embeddings",
|
| 39 |
-
type=Path,
|
| 40 |
-
required=True,
|
| 41 |
-
help="Embeddings path"
|
| 42 |
-
)
|
| 43 |
-
|
| 44 |
-
parser.add_argument("--session",
|
| 45 |
-
type=Path,
|
| 46 |
-
required=True,
|
| 47 |
-
help="Training session directory; models are saved here along with other important metadata"
|
| 48 |
-
)
|
| 49 |
-
|
| 50 |
-
parser.add_argument("--batch-size",
|
| 51 |
-
type=int,
|
| 52 |
-
help="Batch size",
|
| 53 |
-
default=1
|
| 54 |
-
)
|
| 55 |
-
|
| 56 |
-
parser.add_argument("--max-epochs",
|
| 57 |
-
type=int,
|
| 58 |
-
help="Epochs",
|
| 59 |
-
default=5
|
| 60 |
-
)
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
parser.add_argument("--seed",
|
| 64 |
-
type=int,
|
| 65 |
-
help="Random seed",
|
| 66 |
-
default=1230
|
| 67 |
-
)
|
| 68 |
-
parser.add_argument("--train-prop",
|
| 69 |
-
type=float,
|
| 70 |
-
default=0.9,
|
| 71 |
-
help="Proportion of dataset used to train"
|
| 72 |
-
)
|
| 73 |
-
parser.add_argument("--val-prop",
|
| 74 |
-
type=float,
|
| 75 |
-
default=0.05,
|
| 76 |
-
help="Proportion of data to use for validation"
|
| 77 |
-
)
|
| 78 |
-
|
| 79 |
-
parser.add_argument("--test-prop",
|
| 80 |
-
type=float,
|
| 81 |
-
default=0.05,
|
| 82 |
-
help="Proportion of data to use for test"
|
| 83 |
-
)
|
| 84 |
-
|
| 85 |
-
# Now add the transformer model arguments
|
| 86 |
-
for field in fields(trans_basic_block_Config):
|
| 87 |
-
parser.add_argument(
|
| 88 |
-
f"--{field.name}", default=field.default, type=field.type
|
| 89 |
-
)
|
| 90 |
-
|
| 91 |
-
return parser.parse_args()
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def collect_trans_block_arguments(args) -> trans_basic_block_Config:
|
| 95 |
-
trans_block_conf_args = inspect.signature(trans_basic_block_Config).parameters
|
| 96 |
-
return {k: v for k, v in args.items() if k in trans_block_conf_args}
|
| 97 |
-
|
| 98 |
-
if __name__ == '__main__':
|
| 99 |
-
|
| 100 |
-
#Construct datasets: Make train, test, and validation datasets
|
| 101 |
-
args = arguments()
|
| 102 |
-
config = collect_trans_block_arguments(vars(args))
|
| 103 |
-
config = trans_basic_block_Config(**config)
|
| 104 |
-
|
| 105 |
-
print(config, flush=True)
|
| 106 |
-
model = config.build()
|
| 107 |
-
tree = SessionTree(args.session)
|
| 108 |
-
config.to_json(tree.params)
|
| 109 |
-
|
| 110 |
-
train_ds, val_ds, test_ds = construct_datasets(args.data, args.embeddings, args.train_prop, args.val_prop, args.test_prop)
|
| 111 |
-
print("Constructed datasets")
|
| 112 |
-
#Build the data loaders: train data loader and validation data loader
|
| 113 |
-
train_dataloader = DataLoader(train_ds, batch_size=args.batch_size, collate_fn=collate_fn, shuffle=True, num_workers=4, pin_memory=False, persistent_workers=False)
|
| 114 |
-
val_dataloader = DataLoader(val_ds, batch_size=args.batch_size, collate_fn=collate_fn, num_workers=4, pin_memory=False, persistent_workers=False)
|
| 115 |
-
|
| 116 |
-
val_check_interval = 0.02
|
| 117 |
-
accumulate_grad_batches = 8
|
| 118 |
-
effective_batch_size = args.gpus * args.nodes * args.batch_size * accumulate_grad_batches
|
| 119 |
-
every_n_train_steps = int(len(train_ds) * val_check_interval / effective_batch_size)
|
| 120 |
-
print("Saving and validating every ", every_n_train_steps, " steps")
|
| 121 |
-
|
| 122 |
-
#Model checkpoints
|
| 123 |
-
ckpt = pl.callbacks.ModelCheckpoint(
|
| 124 |
-
dirpath=tree.checkpoints,
|
| 125 |
-
monitor="val_loss",
|
| 126 |
-
verbose=True,
|
| 127 |
-
filename="{epoch}-{step}-{val_loss:0.4f}",
|
| 128 |
-
every_n_train_steps=every_n_train_steps,
|
| 129 |
-
save_top_k=5,
|
| 130 |
-
save_weights_only=False,
|
| 131 |
-
save_last=True
|
| 132 |
-
)
|
| 133 |
-
|
| 134 |
-
#Logger
|
| 135 |
-
logger = pl.loggers.TensorBoardLogger(tree.logs)
|
| 136 |
-
|
| 137 |
-
checkpoint_path = str(tree.checkpoints) + '/last.ckpt'
|
| 138 |
-
#Trainer
|
| 139 |
-
trainer = pl.Trainer(
|
| 140 |
-
accelerator="cuda",
|
| 141 |
-
strategy=DDPStrategy(find_unused_parameters=False),
|
| 142 |
-
precision='32',
|
| 143 |
-
num_sanity_val_steps=0,
|
| 144 |
-
callbacks=[ckpt],
|
| 145 |
-
logger=logger,
|
| 146 |
-
gpus=args.gpus,
|
| 147 |
-
val_check_interval=val_check_interval,
|
| 148 |
-
num_nodes=args.nodes,
|
| 149 |
-
gradient_clip_val=0.5,
|
| 150 |
-
gradient_clip_algorithm="norm",
|
| 151 |
-
max_epochs=args.max_epochs,
|
| 152 |
-
accumulate_grad_batches=accumulate_grad_batches
|
| 153 |
-
)
|
| 154 |
-
#Setup model and fit
|
| 155 |
-
print("Training...")
|
| 156 |
-
trainer.fit(model, train_dataloader, val_dataloader)
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6584bf33318b39b245f154ecd49ec582f5d9a8f09b1eec43380d1c72606b980b
|
| 3 |
+
size 5307
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
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|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
protein_vec_models/utils_search.py
CHANGED
|
@@ -1,72 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
from transformers import T5EncoderModel, T5Tokenizer
|
| 5 |
-
import re
|
| 6 |
-
import gc
|
| 7 |
-
import h5py
|
| 8 |
-
import torch
|
| 9 |
-
from collections import defaultdict
|
| 10 |
-
from torch import nn
|
| 11 |
-
from torch.utils.data import DataLoader
|
| 12 |
-
import faiss
|
| 13 |
-
import os
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def load_database(lookup_database):
|
| 18 |
-
#Build an indexed database
|
| 19 |
-
d = lookup_database.shape[1]
|
| 20 |
-
index = faiss.IndexFlatIP(d)
|
| 21 |
-
faiss.normalize_L2(lookup_database)
|
| 22 |
-
index.add(lookup_database)
|
| 23 |
-
|
| 24 |
-
return(index)
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def query(index, queries, k=10):
|
| 28 |
-
faiss.normalize_L2(queries)
|
| 29 |
-
D, I = index.search(queries, k)
|
| 30 |
-
|
| 31 |
-
return(D, I)
|
| 32 |
-
|
| 33 |
-
def featurize_prottrans(sequences, model, tokenizer, device):
|
| 34 |
-
|
| 35 |
-
sequences = [(" ".join(sequences[i])) for i in range(len(sequences))]
|
| 36 |
-
sequences = [re.sub(r"[UZOB]", "X", sequence) for sequence in sequences]
|
| 37 |
-
ids = tokenizer.batch_encode_plus(sequences, add_special_tokens=True, padding=True)
|
| 38 |
-
input_ids = torch.tensor(ids['input_ids']).to(device)
|
| 39 |
-
attention_mask = torch.tensor(ids['attention_mask']).to(device)
|
| 40 |
-
|
| 41 |
-
with torch.no_grad():
|
| 42 |
-
embedding = model(input_ids=input_ids, attention_mask=attention_mask)
|
| 43 |
-
|
| 44 |
-
embedding = embedding.last_hidden_state.cpu().numpy()
|
| 45 |
-
|
| 46 |
-
features = []
|
| 47 |
-
for seq_num in range(len(embedding)):
|
| 48 |
-
seq_len = (attention_mask[seq_num] == 1).sum()
|
| 49 |
-
seq_emd = embedding[seq_num][:seq_len-1]
|
| 50 |
-
features.append(seq_emd)
|
| 51 |
-
|
| 52 |
-
prottrans_embedding = torch.tensor(features[0])
|
| 53 |
-
prottrans_embedding = torch.unsqueeze(prottrans_embedding, 0).to(device)
|
| 54 |
-
|
| 55 |
-
return(prottrans_embedding)
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
def embed_vec(prottrans_embedding, model_deep, masks, device):
|
| 59 |
-
padding = torch.zeros(prottrans_embedding.shape[0:2]).type(torch.BoolTensor).to(device)
|
| 60 |
-
out_seq = model_deep.make_matrix(prottrans_embedding, padding)
|
| 61 |
-
vec_embedding = model_deep(out_seq, masks)
|
| 62 |
-
return(vec_embedding.cpu().detach().numpy())
|
| 63 |
-
|
| 64 |
-
def encode(sequences, model_deep, model, tokenizer, masks, device):
|
| 65 |
-
i = 0
|
| 66 |
-
embed_all_sequences=[]
|
| 67 |
-
while i < len(sequences):
|
| 68 |
-
protrans_sequence = featurize_prottrans(sequences[i:i+1], model, tokenizer, device)
|
| 69 |
-
embedded_sequence = embed_vec(protrans_sequence, model_deep, masks, device)
|
| 70 |
-
embed_all_sequences.append(embedded_sequence)
|
| 71 |
-
i = i + 1
|
| 72 |
-
return np.concatenate(embed_all_sequences, axis=0)
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:357a56840c2d261fbe4dbdf6d82485ce00a16a473cbbb78b6cb6e7fedea5fe5d
|
| 3 |
+
size 2414
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|
|
results/calibration_probs.csv
ADDED
|
@@ -0,0 +1,1001 @@
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|
| 1 |
+
similarity,prob_exact_p0,prob_exact_p1,prob_partial_p0,prob_partial_p1
|
| 2 |
+
0.9996839761734009,6.738517229714705e-07,0.0,2.078392820399841e-06,0.0
|
| 3 |
+
0.9996842927522249,6.740783831825533e-07,0.0,2.08340278009267e-06,0.0
|
| 4 |
+
0.9996846093310489,6.743493034983218e-07,0.0,2.088175289786526e-06,0.0
|
| 5 |
+
0.9996849259098729,6.745544567812959e-07,0.0,2.0921552546571376e-06,0.0
|
| 6 |
+
0.9996852424886968,6.748517182062171e-07,0.0,2.0973066388144344e-06,0.0
|
| 7 |
+
0.9996855590675209,6.751177911766155e-07,0.0,2.102319910020708e-06,0.0
|
| 8 |
+
0.9996858756463449,6.753183957406318e-07,0.0,2.10607455082695e-06,0.0
|
| 9 |
+
0.9996861922251689,6.756090277580725e-07,0.0,2.1110541126500694e-06,0.0
|
| 10 |
+
0.9996865088039929,6.759122449614122e-07,0.0,2.1155200900365352e-06,0.0
|
| 11 |
+
0.999686825382817,6.76130236206098e-07,0.0,2.11960058246624e-06,0.0
|
| 12 |
+
0.9996871419616409,6.764110441689648e-07,0.0,2.125091378929294e-06,0.0
|
| 13 |
+
0.9996874585404649,6.766604910119187e-07,0.0,2.129526308168011e-06,0.0
|
| 14 |
+
0.9996877751192889,6.768730091472618e-07,0.0,2.133128552992246e-06,0.0
|
| 15 |
+
0.9996880916981129,6.771842408392209e-07,0.0,2.1383010342962105e-06,0.0
|
| 16 |
+
0.999688408276937,6.774438957901604e-07,0.0,2.1427913285520515e-06,0.0
|
| 17 |
+
0.9996887248557609,6.776454260966674e-07,0.0,2.1466321488217135e-06,0.0
|
| 18 |
+
0.9996890414345849,6.778967034561208e-07,0.0,2.152671788590409e-06,0.0
|
| 19 |
+
0.999689358013409,6.781569051631476e-07,0.0,2.161335013411084e-06,0.0
|
| 20 |
+
0.9996896745922329,6.783703644409109e-07,0.0,2.167716202594756e-06,0.0
|
| 21 |
+
0.999689991171057,6.786102062975028e-07,0.0,2.1756725003698645e-06,0.0
|
| 22 |
+
0.9996903077498809,6.788746430816564e-07,0.0,2.1844888186939816e-06,0.0
|
| 23 |
+
0.9996906243287049,6.790987002050878e-07,0.0,2.191108481780933e-06,0.0
|
| 24 |
+
0.999690940907529,6.793367499442944e-07,0.0,2.1992812748793696e-06,0.0
|
| 25 |
+
0.9996912574863529,6.798415425332664e-07,0.0,2.2076955852711382e-06,0.0
|
| 26 |
+
0.999691574065177,6.802684067025485e-07,0.0,2.21401560438198e-06,0.0
|
| 27 |
+
0.9996918906440009,6.808204703652466e-07,0.0,2.221664337621886e-06,0.0
|
| 28 |
+
0.9996922072228249,6.814481864278691e-07,0.0,2.2303456366633136e-06,0.0
|
| 29 |
+
0.999692523801649,6.818663773734917e-07,0.0,2.237632048261248e-06,0.0
|
| 30 |
+
0.9996928403804729,6.824294214431608e-07,0.0,2.2458132426623668e-06,0.0
|
| 31 |
+
0.999693156959297,6.830923042137915e-07,0.0,2.254725340632631e-06,0.0
|
| 32 |
+
0.999693473538121,6.835802653111726e-07,0.0,2.2613792604385265e-06,0.0
|
| 33 |
+
0.9996937901169449,6.840993421700726e-07,0.0,2.26951384743874e-06,0.0
|
| 34 |
+
0.999694106695769,6.845465666224678e-07,0.0,2.2760896779333107e-06,0.0
|
| 35 |
+
0.9996944232745929,6.851140475099187e-07,0.0,2.284977869989329e-06,0.0
|
| 36 |
+
0.999694739853417,6.856895225269579e-07,0.0,2.2930625685052444e-06,0.0
|
| 37 |
+
0.999695056432241,6.861769650392837e-07,0.0,2.3008922860285218e-06,0.0
|
| 38 |
+
0.9996953730110649,6.867796968691774e-07,0.0,2.3157475464654747e-06,0.0
|
| 39 |
+
0.999695689589889,6.873310454373934e-07,0.0,2.3288851626726285e-06,0.0
|
| 40 |
+
0.999696006168713,6.877796684076663e-07,0.0,2.3400297183774235e-06,0.0
|
| 41 |
+
0.999696322747537,6.883828510064157e-07,0.0,2.354558896936248e-06,0.0
|
| 42 |
+
0.999696639326361,6.889177928176187e-07,0.0,2.3685625430782314e-06,0.0
|
| 43 |
+
0.9996969559051849,6.893955793197872e-07,0.0,2.3794737080052632e-06,0.0
|
| 44 |
+
0.999697272484009,6.900025530094461e-07,0.0,2.394653218294193e-06,0.0
|
| 45 |
+
0.999697589062833,6.905204659908313e-07,0.0,2.407857320006646e-06,0.0
|
| 46 |
+
0.999697905641657,6.909470050556592e-07,0.0,2.4191929088617457e-06,0.0
|
| 47 |
+
0.999698222220481,6.915318467703734e-07,0.0,2.433824316825514e-06,0.0
|
| 48 |
+
0.999698538799305,6.921013928540531e-07,0.0,2.4473214067203446e-06,0.0
|
| 49 |
+
0.999698855378129,6.925207756232687e-07,0.0,2.45939538223923e-06,0.0
|
| 50 |
+
0.999699171956953,6.930621704489379e-07,0.0,2.4726646918317995e-06,0.0
|
| 51 |
+
0.999699488535777,6.936732147105891e-07,0.0,2.48741368674507e-06,0.0
|
| 52 |
+
0.999699805114601,6.943692211121572e-07,0.0,2.498488414509222e-06,0.0
|
| 53 |
+
0.999700121693425,6.952309244277033e-07,0.0,2.5127206482819273e-06,0.0
|
| 54 |
+
0.999700438272249,6.962425876273515e-07,0.0,2.5284641855690435e-06,0.0
|
| 55 |
+
0.999700754851073,6.969695068871042e-07,0.0,2.539585793557071e-06,0.0
|
| 56 |
+
0.999701071429897,6.979175536035577e-07,0.0,2.5538478826047204e-06,0.0
|
| 57 |
+
0.999701388008721,6.988882086376991e-07,0.0,2.569452295548681e-06,0.0
|
| 58 |
+
0.999701704587545,6.996098975211423e-07,0.0,2.580219008989483e-06,0.0
|
| 59 |
+
0.999702021166369,7.005116537118712e-07,0.0,2.5944509882263814e-06,0.0
|
| 60 |
+
0.999702337745193,7.019233401443295e-07,0.0,2.6097463587513928e-06,0.0
|
| 61 |
+
0.9997026543240171,7.030519485084753e-07,0.0,2.6219673669941503e-06,0.0
|
| 62 |
+
0.999702970902841,7.044833319243667e-07,0.0,2.6368039826287353e-06,0.0
|
| 63 |
+
0.999703287481665,7.056206921856742e-07,0.0,2.647849284418731e-06,0.0
|
| 64 |
+
0.999703604060489,7.070800633826568e-07,0.0,2.6637330087130708e-06,0.0
|
| 65 |
+
0.999703920639313,7.084104615223316e-07,0.0,2.678738849749538e-06,0.0
|
| 66 |
+
0.9997042372181371,7.095388990510627e-07,0.0,2.6906529407491315e-06,0.0
|
| 67 |
+
0.999704553796961,7.110150450783538e-07,0.0,2.7067775001827073e-06,0.0
|
| 68 |
+
0.999704870375785,7.124003975194218e-07,0.0,2.721688317697234e-06,0.0
|
| 69 |
+
0.9997051869546091,7.13450976643042e-07,0.0,2.7332336614124804e-06,0.0
|
| 70 |
+
0.999705503533433,7.148105894899969e-07,0.0,2.750577621300473e-06,0.0
|
| 71 |
+
0.9997058201122571,7.162800425756858e-07,0.0,2.766259381077126e-06,0.0
|
| 72 |
+
0.999706136691081,7.173415894568003e-07,0.0,2.7784029184344256e-06,0.0
|
| 73 |
+
0.999706453269905,7.187925435336704e-07,0.0,2.7955997260312267e-06,0.0
|
| 74 |
+
0.9997067698487291,7.200803033554302e-07,0.0,2.812401565945192e-06,0.0
|
| 75 |
+
0.999707086427553,7.211536727995979e-07,0.0,2.825313680451372e-06,0.0
|
| 76 |
+
0.9997074030063771,7.22583032016209e-07,0.0,2.84175677403771e-06,0.0
|
| 77 |
+
0.999707719585201,7.238597941487517e-07,0.0,2.8567184304046258e-06,0.0
|
| 78 |
+
0.999708036164025,7.249838872331063e-07,0.0,2.868814835215276e-06,0.0
|
| 79 |
+
0.9997083527428491,7.262290700782003e-07,0.0,2.8866610280554585e-06,0.0
|
| 80 |
+
0.999708669321673,7.276802282005196e-07,0.0,2.9136934852727363e-06,0.0
|
| 81 |
+
0.9997089859004971,7.287933878033511e-07,0.0,2.9339709830269777e-06,0.0
|
| 82 |
+
0.9997093024793211,7.302014991036776e-07,0.0,2.959017606154757e-06,0.0
|
| 83 |
+
0.999709619058145,7.316000752084877e-07,0.0,2.986688330112688e-06,0.0
|
| 84 |
+
0.9997099356369691,7.330787106611637e-07,0.0,3.0078263641996475e-06,0.0
|
| 85 |
+
0.999710252215793,7.349309752828014e-07,0.0,3.0333639703094334e-06,0.0
|
| 86 |
+
0.9997105687946171,7.369223910183899e-07,0.0,3.0611276582067302e-06,0.0
|
| 87 |
+
0.9997108853734411,7.385360737945245e-07,0.0,3.082338508579689e-06,0.0
|
| 88 |
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0.999711201952265,7.40277381935011e-07,0.0,3.1082542800661437e-06,0.0
|
| 89 |
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0.9997115185310891,7.423227129222795e-07,0.0,3.136871096556029e-06,0.0
|
| 90 |
+
0.9997118351099131,7.438644202952993e-07,0.0,3.15859960327989e-06,0.0
|
| 91 |
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0.9997121516887371,7.457149355515867e-07,0.0,3.184581530064042e-06,0.0
|
| 92 |
+
0.9997124682675611,7.476406330721824e-07,0.0,3.2128926958097453e-06,0.0
|
| 93 |
+
0.999712784846385,7.491057550049628e-07,0.0,3.2359317865579395e-06,0.0
|
| 94 |
+
0.9997131014252091,7.508916838746011e-07,0.0,3.2644427106626493e-06,0.0
|
| 95 |
+
0.9997134180040331,7.523973259799034e-07,0.0,3.288402789880927e-06,0.0
|
| 96 |
+
0.9997137345828571,7.544709951170637e-07,0.0,3.3196343090845112e-06,0.0
|
| 97 |
+
0.9997140511616811,7.562391621475074e-07,0.0,3.3506449991623385e-06,0.0
|
| 98 |
+
0.999714367740505,7.577749605388689e-07,0.0,3.3737167225016784e-06,0.0
|
| 99 |
+
0.9997146843193291,7.598755323877949e-07,0.0,3.404834865509023e-06,0.0
|
| 100 |
+
0.9997150008981531,7.618124737650829e-07,0.0,3.433948010027128e-06,0.0
|
| 101 |
+
0.9997153174769771,7.634654306670321e-07,0.0,3.4611896801168497e-06,0.0
|
| 102 |
+
0.9997156340558011,7.657894293490331e-07,0.0,3.4945607162451646e-06,0.0
|
| 103 |
+
0.9997159506346252,7.680096339128478e-07,0.0,3.5278595065230123e-06,0.0
|
| 104 |
+
0.9997162672134491,7.697257621054771e-07,0.0,3.5617482485102986e-06,0.0
|
| 105 |
+
0.9997165837922731,7.725503586951315e-07,0.0,3.6173429892275525e-06,0.0
|
| 106 |
+
0.9997169003710971,7.751841837620619e-07,0.0,3.6681901883248844e-06,0.0
|
| 107 |
+
0.9997172169499211,7.773740615151643e-07,0.0,3.712862096875998e-06,0.0
|
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0.9999992886821071,0.9914129586260734,0.990632318501171,1.0,0.9969742813918305
|
| 999 |
+
0.9999996052609311,0.9914129586260734,0.990632318501171,1.0,0.9970059880239521
|
| 1000 |
+
0.9999999218397551,1.0,0.9906687402799378,1.0,0.9970193740685543
|
| 1001 |
+
1.000000238418579,1.0,0.9907478797224364,1.0,0.9970457902511078
|
results/fdr_thresholds.csv
ADDED
|
@@ -0,0 +1,101 @@
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
alpha,lambda_threshold,exact_fdr,partial_fdr
|
| 2 |
+
0.01,0.9999949240263061,0.010185374953958805,0.012462918749228976
|
| 3 |
+
0.01191919191919192,0.9999936128264727,0.01359888340116152,0.013194054039467442
|
| 4 |
+
0.013838383838383839,0.9999937781661449,0.013336001540005167,0.015864244854067594
|
| 5 |
+
0.01575757575757576,0.9999930586598137,0.014784229738454809,0.015261559503773012
|
| 6 |
+
0.017676767676767676,0.999992980192406,0.015816785948359446,0.01796092767120976
|
| 7 |
+
0.019595959595959597,0.9999925253728422,0.01706158089803683,0.01767688380335364
|
| 8 |
+
0.021515151515151515,0.9999911076312116,0.021745861832097764,0.02250375853975188
|
| 9 |
+
0.023434343434343433,0.9999906110823755,0.023029831481470454,0.022533344782351358
|
| 10 |
+
0.025353535353535354,0.9999903944164816,0.023850379100668923,0.02495888356093521
|
| 11 |
+
0.027272727272727275,0.9999904412934275,0.024426502694654448,0.026975099246940585
|
| 12 |
+
0.029191919191919193,0.9999895336230595,0.028489706960071998,0.0304610264933836
|
| 13 |
+
0.03111111111111111,0.9999893959002061,0.02862530927848274,0.028237152533635127
|
| 14 |
+
0.03303030303030303,0.9999887306401223,0.03090901968701153,0.0327075062906193
|
| 15 |
+
0.03494949494949495,0.9999887376301217,0.03148591817763334,0.03301773357597203
|
| 16 |
+
0.03686868686868687,0.9999881089757185,0.03467247979473478,0.035683667441678384
|
| 17 |
+
0.03878787878787879,0.9999877510708992,0.03644334991921146,0.03866124086392765
|
| 18 |
+
0.040707070707070706,0.9999876388817123,0.037467874762772545,0.036849223906450186
|
| 19 |
+
0.04262626262626263,0.9999875548752871,0.037166994162225596,0.04091738217210246
|
| 20 |
+
0.04454545454545455,0.9999871700339849,0.040186400301408944,0.04357543182331895
|
| 21 |
+
0.046464646464646465,0.9999864587278079,0.04383542747584454,0.04598145484791631
|
| 22 |
+
0.04838383838383838,0.999986603247999,0.04260303745669757,0.04485210440140548
|
| 23 |
+
0.05030303030303031,0.999986624356472,0.043846202140285506,0.04648652953324028
|
| 24 |
+
0.052222222222222225,0.9999864888913703,0.04356698558085525,0.048833717330181964
|
| 25 |
+
0.05414141414141414,0.9999852841851687,0.05172593990422003,0.050572890980001325
|
| 26 |
+
0.05606060606060606,0.999985629645261,0.04879510092634534,0.05529522607763347
|
| 27 |
+
0.05797979797979798,0.9999852683929482,0.05160171094189089,0.05502291605188713
|
| 28 |
+
0.0598989898989899,0.9999850855150608,0.052718750137337705,0.05596329022315511
|
| 29 |
+
0.06181818181818182,0.9999843878577453,0.05795776784204275,0.05672573145980941
|
| 30 |
+
0.06373737373737373,0.9999842671854328,0.058174385269688,0.05979100541259345
|
| 31 |
+
0.06565656565656565,0.9999842784501084,0.05824603973556712,0.06393678535978718
|
| 32 |
+
0.06757575757575758,0.9999840076824631,0.06001966885061119,0.06210542227498189
|
| 33 |
+
0.0694949494949495,0.9999833802743392,0.06451303543422683,0.06650476621836858
|
| 34 |
+
0.07141414141414142,0.9999830201057474,0.06715053584230286,0.0681516380010545
|
| 35 |
+
0.07333333333333333,0.9999829925130111,0.06690858523980986,0.07063437846743635
|
| 36 |
+
0.07525252525252525,0.9999824937726511,0.07037361164456929,0.07000500117411773
|
| 37 |
+
0.07717171717171717,0.9999828484744738,0.06778579951126153,0.07320280203733241
|
| 38 |
+
0.07909090909090909,0.9999820711275546,0.07367272189457924,0.07954426700623937
|
| 39 |
+
0.081010101010101,0.9999817350356266,0.0763326709026035,0.07504278140170244
|
| 40 |
+
0.08292929292929292,0.9999816389999003,0.07685427916557867,0.07826117378728953
|
| 41 |
+
0.08484848484848484,0.9999815708760061,0.07726848357627737,0.07696555022252728
|
| 42 |
+
0.08676767676767676,0.9999812461089606,0.07974956621819239,0.08073604884230451
|
| 43 |
+
0.08868686868686868,0.9999814809873849,0.07751839474284367,0.08340588576767079
|
| 44 |
+
0.0906060606060606,0.9999803162162955,0.08617938280554417,0.08649804516382732
|
| 45 |
+
0.09252525252525252,0.9999806379127983,0.08378534620861243,0.08557828824778521
|
| 46 |
+
0.09444444444444444,0.9999803229413851,0.08601777125074161,0.09089167815178838
|
| 47 |
+
0.09636363636363636,0.9999799169735476,0.08954818731680803,0.08765844014570354
|
| 48 |
+
0.09828282828282828,0.999979118283349,0.09557528312833181,0.0894011643188325
|
| 49 |
+
0.1002020202020202,0.9999791005103276,0.0955518983223871,0.09550248321022452
|
| 50 |
+
0.10212121212121211,0.9999792466139553,0.09446083203724585,0.09624287664706835
|
| 51 |
+
0.10404040404040403,0.9999786618863695,0.09888456397319195,0.09858883898684782
|
| 52 |
+
0.10595959595959595,0.9999784119081017,0.10124077643493588,0.10187943281995546
|
| 53 |
+
0.10787878787878788,0.9999784073685156,0.10081770410713858,0.10689549139477561
|
| 54 |
+
0.1097979797979798,0.9999773867262733,0.10886578223085484,0.1024777466395523
|
| 55 |
+
0.11171717171717171,0.9999781995650493,0.1020918464844146,0.10217634449289079
|
| 56 |
+
0.11363636363636363,0.9999774829185369,0.10773046002348394,0.10661732584029503
|
| 57 |
+
0.11555555555555555,0.9999766455334848,0.114802366606891,0.11344956757586405
|
| 58 |
+
0.11747474747474747,0.999977281304321,0.10946826451192643,0.10699480882493338
|
| 59 |
+
0.11939393939393939,0.9999769649662152,0.1120868211874098,0.11313326905315614
|
| 60 |
+
0.1213131313131313,0.9999770278400844,0.11156377844771653,0.11471482933158571
|
| 61 |
+
0.12323232323232322,0.9999764390184421,0.1162996217068444,0.11400494343353461
|
| 62 |
+
0.12515151515151515,0.9999763431693569,0.11693979446028559,0.11778829402102094
|
| 63 |
+
0.12707070707070708,0.9999755758950204,0.12376381975850684,0.11648892420960806
|
| 64 |
+
0.128989898989899,0.9999760055903232,0.11969755176746474,0.11919426293739198
|
| 65 |
+
0.13090909090909092,0.9999756938339485,0.12222932166600574,0.12298918877771436
|
| 66 |
+
0.13282828282828282,0.9999753064398814,0.12567172901977824,0.12729358170468394
|
| 67 |
+
0.13474747474747475,0.9999746603014492,0.1310369408659496,0.13568257214967583
|
| 68 |
+
0.1366666666666667,0.9999749449166386,0.12883822771191963,0.12716191850994837
|
| 69 |
+
0.1385858585858586,0.9999751230261542,0.12691992923409465,0.12953942394563106
|
| 70 |
+
0.14050505050505052,0.9999747985541219,0.12938836978599488,0.1289417157491834
|
| 71 |
+
0.14242424242424243,0.9999743226802711,0.1336882623077459,0.13422210455027136
|
| 72 |
+
0.14434343434343436,0.9999742698127575,0.13405060992807366,0.13619812873391562
|
| 73 |
+
0.14626262626262626,0.9999740697518743,0.135432203802313,0.13875948196260027
|
| 74 |
+
0.1481818181818182,0.999973125120606,0.14414094851303244,0.14495648937641792
|
| 75 |
+
0.1501010101010101,0.9999728947637059,0.1459318325142054,0.1419717383328548
|
| 76 |
+
0.15202020202020203,0.9999732503325047,0.14268953378369953,0.14340356409130162
|
| 77 |
+
0.15393939393939396,0.999972721579099,0.14762731795949302,0.1443318258533784
|
| 78 |
+
0.15585858585858586,0.9999730234194284,0.14451045645484606,0.1469565780510405
|
| 79 |
+
0.1577777777777778,0.9999724640087649,0.1497022869258199,0.14763253305564106
|
| 80 |
+
0.1596969696969697,0.9999728219677704,0.14657499251510175,0.1505308176767557
|
| 81 |
+
0.16161616161616163,0.9999719656838312,0.15386995936700648,0.15109691003581505
|
| 82 |
+
0.16353535353535353,0.9999719234428021,0.1541934018031644,0.15565925561770272
|
| 83 |
+
0.16545454545454547,0.9999719741549156,0.15359000956129112,0.15478002156501133
|
| 84 |
+
0.16737373737373737,0.9999714928805227,0.15810594067233544,0.15290376725069374
|
| 85 |
+
0.1692929292929293,0.9999716300434537,0.15679040340718145,0.16271459028662183
|
| 86 |
+
0.17121212121212123,0.999971284866333,0.1598816581966213,0.15974932647830634
|
| 87 |
+
0.17313131313131314,0.9999709986496452,0.16273169813559193,0.1676515989624808
|
| 88 |
+
0.17505050505050507,0.9999705844334881,0.1663373228164135,0.16456892549447194
|
| 89 |
+
0.17696969696969697,0.9999706692466833,0.1654611200791199,0.1696725948847155
|
| 90 |
+
0.1788888888888889,0.9999699419435829,0.17240443752831447,0.17087952417168434
|
| 91 |
+
0.1808080808080808,0.9999698400617851,0.17311912262106527,0.16962366468215032
|
| 92 |
+
0.18272727272727274,0.9999697641110178,0.17387030342418328,0.17798671829770107
|
| 93 |
+
0.18464646464646464,0.9999693981625818,0.177188459742756,0.1732440393394975
|
| 94 |
+
0.18656565656565657,0.9999688904815249,0.18197740657109346,0.17595567257781167
|
| 95 |
+
0.1884848484848485,0.9999690811200576,0.18007582711497339,0.1781765421242088
|
| 96 |
+
0.1904040404040404,0.9999687765282813,0.1830614264042353,0.18196927666324808
|
| 97 |
+
0.19232323232323234,0.9999688645384529,0.182109346731833,0.1841558676104032
|
| 98 |
+
0.19424242424242424,0.999968309342259,0.18783444170731106,0.18425359479630368
|
| 99 |
+
0.19616161616161618,0.9999683889836976,0.1866941461107739,0.18350464473877412
|
| 100 |
+
0.19808080808080808,0.9999682745367591,0.18770722679691343,0.184747928297409
|
| 101 |
+
0.2,0.9999688140129802,0.18259724527349797,0.18993231828516052
|
results/fnr_thresholds.csv
ADDED
|
@@ -0,0 +1,101 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
alpha,lambda_threshold,exact_fnr,partial_fnr
|
| 2 |
+
0.01,0.9998742938041687,0.020635235359495976,0.01409944781383667
|
| 3 |
+
0.01191919191919192,0.9998710751533508,0.01744483623642699,0.014052502774959695
|
| 4 |
+
0.013838383838383839,0.9998725652694702,0.01917279311065824,0.017106673622876288
|
| 5 |
+
0.01575757575757576,0.9998771548271179,0.024662593105246593,0.017977477016279414
|
| 6 |
+
0.017676767676767676,0.9998763203620911,0.023135192780957093,0.020164174612590475
|
| 7 |
+
0.019595959595959597,0.9998796582221985,0.02704331755234262,0.022542406894361586
|
| 8 |
+
0.021515151515151515,0.9998812079429626,0.031016232318985808,0.025611221043186933
|
| 9 |
+
0.023434343434343433,0.9998826384544373,0.03240799445880963,0.02732065072628734
|
| 10 |
+
0.025353535353535354,0.9998836517333984,0.0336944991843362,0.027989665507256064
|
| 11 |
+
0.027272727272727275,0.9998853802680969,0.03635156142057201,0.03017265231240791
|
| 12 |
+
0.029191919191919193,0.9998857378959656,0.03817083908223782,0.031669190767258076
|
| 13 |
+
0.03111111111111111,0.999886691570282,0.03895682067441097,0.03641722262737586
|
| 14 |
+
0.03303030303030303,0.9998877644538879,0.040819717117805465,0.03585499142942621
|
| 15 |
+
0.03494949494949495,0.9998883605003357,0.04157202551095365,0.042290055436117815
|
| 16 |
+
0.03686868686868687,0.9998902678489685,0.045986257984200814,0.04198464755962938
|
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