Generation des resultats sur la meme machine et création/test du script de fine tunning multi-label

models/FineTuning/facebook-bart-large-mnli.py

@@ -47,7 +47,7 @@ print(len(data))
 
 articles = [article for article in data if article["PMID"] not in dataset_pmids]
 
-articles = articles[:1000]
+# articles = articles[:1000]
 
 print(len(articles))
 
@@ -126,25 +126,4 @@ model_to_save = model.module if isinstance(model, torch.nn.DataParallel) else mo
 
 # Save the fine-tuned model
 model_to_save.save_pretrained(DATA_DIR)
-tokenizer.save_pretrained(DATA_DIR) 
-
+tokenizer.save_pretrained(DATA_DIR) 
\ No newline at end of file
-title = "Consumption of Sodium and Its Ratio to Potassium in Relation to All-Cause, Cause-Specific, and Premature Noncommunicable Disease Mortality in Middle-Aged Japanese Adults: A Prospective Cohort Study."
-abstract = "Reducing premature noncommunicable disease (NCD) mortality is a global challenge. Sodium is thought to increase risk of NCDs via an effect of salt per se or high-salt foods on hypertension-induced cardiovascular disease (CVD) and gastrointestinal cancer. Further, relative risk of CVD is reportedly more closely associated with sodium-to-potassium ratio than that with sodium alone. However, few studies have investigated the effect of consumption of sodium or its ratio to consumption of potassium on risk of premature NCD death.We examined associations between intake of sodium and sodium-to-potassium ratio and risk of all-cause and cause-specific death, including premature NCD, in a Japanese prospective cohort study.During 1995-1998, a validated food frequency questionnaire was administered in 11 areas to 83,048 men and women aged 45-74 y. During 1,587,901 person-years of follow-up until the end of 2018, 17,727 all-cause deaths and 3555 premature NCD deaths were identified.Higher sodium intake was significantly associated with increased risk of all-cause and premature NCD mortality, but not all NCD mortality, among men: multivariate hazards ratios for the highest compared with lowest quintiles (HR) were 1.11 (95% CI: 1.03, 1.20; P-trend < 0.01) for all-cause and 1.25 (95% CI: 1.06, 1.47; P-trend < 0.01) for premature NCD mortality. When intakes were expressed as ratio to potassium intake, these associations (HR of all-cause: 1.19, 95% CI: 1.11-1.27; P-trend < 0.01; HR of premature NCD: 1.27, 95% CI: 1.10, 1.46; P-trend < 0.01), including associations with cancers (HR: 1.18, 95% CI: 1.07, 1.31; P-trend = 0.02), were strengthened in men.This prospective cohort study showed that both sodium intake and sodium-to-potassium ratio are associated with increased risk of all-cause and early NCD mortality in middle-aged men."
-sequence = title + abstract
-
-inputs = tokenizer(sequence, return_tensors="pt").to(device)
-
-outputs = model(**inputs)
-
-# Apply softmax to get probabilities for all the labels
-logits = outputs.logits
-probs = F.softmax(logits, dim=-1)  # Softmax over the last dimension (the logits for each class)
-
-# Print out the probabilities for each label
-probs = probs.squeeze()  # Remove the extra batch dimension
-for i, prob in enumerate(probs):
-    print(f"Label {i}: {prob.item():.4f}")
-
-# Get the predicted label id
-predicted_label_id = probs.argmax(-1).item()
-print(f"Predicted category: {predicted_label_id}")
\ No newline at end of file

models/FineTuning/finetunning-multi-label.py

+# https://medium.com/@lidores98/finetuning-huggingface-facebook-bart-model-2c758472e340
+
+import pandas as pd
+import torch
+from datasets import Dataset
+import random
+from transformers import BartTokenizerFast
+from transformers import BartForSequenceClassification, Trainer, TrainingArguments, EvalPrediction
+import numpy as np
+from transformers import pipeline
+import os
+import sys
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")))
+
+from testModel.utils import get_dataset_filename, get_article_data
+from parsers.jsonParser import parseJsonFile
+from variables.pubmed import NCDS, NCDS_MESH_TERM
+
+DATASET_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../testModel/dataset"))
+TMP_DATA_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../dataSources/PubMed/tmp"))
+DATA_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "./data"))
+LOGS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "./logs"))
+
+# Get PMIDs for all the articles from my testing dataset
+dataset_pmids = []
+
+for ncd in NCDS:
+    try:
+        filename = get_dataset_filename(ncd)
+        articles = parseJsonFile(f"{DATASET_DIR}/{filename}.json")
+    except Exception as e:
+        print(f"Error: {e}")
+
+    dataset_pmids += [article["PMID"] for article in articles]
+
+# Remove article of the testing dataset from the training data
+try:
+  data = parseJsonFile(f"{TMP_DATA_DIR}/save_3_years.json")
+except Exception as e:
+  print(f"Error: {e}")
+
+articles = [article for article in data if article["PMID"] not in dataset_pmids]
+
+# Creating the train dataset
+dataset = []
+
+for article in articles:
+    title, abstract = get_article_data(article)
+    text = title + abstract
+
+    articles_mesh_terms = [mesh.lower() for mesh in article["MeshTerms"]]
+
+    labels = [1.0 if NCDS_MESH_TERM[ncd].lower() in articles_mesh_terms else 0.0 for ncd in NCDS]
+
+    dataset.append({
+        "text": text,
+        "labels": labels
+    })
+
+train_dataset = Dataset.from_dict({
+    "text": [item["text"] for item in dataset],
+    "labels": [item["labels"] for item in dataset]
+})
+
+tokenizer = BartTokenizerFast.from_pretrained('facebook/bart-large-mnli')
+
+def preprocess_function(examples):
+    return tokenizer(examples["text"], truncation=True, padding="max_length")
+
+tokenized_dataset = train_dataset.map(preprocess_function, batched=True)
+tokenized_dataset = tokenized_dataset.remove_columns(["text"])
+tokenized_dataset.set_format("torch")
+
+training_args = TrainingArguments(
+    output_dir = DATA_DIR,
+    num_train_epochs = 32,
+    per_device_train_batch_size = 12,
+    warmup_steps = 500,
+    weight_decay = 0.01,
+    logging_dir = LOGS_DIR,
+    logging_steps=10,
+    fp16=True,
+    remove_unused_columns=False,
+)
+
+model = BartForSequenceClassification.from_pretrained(
+    "facebook/bart-large-mnli", 
+    num_labels = len(NCDS), 
+    problem_type="multi_label_classification",
+    ignore_mismatched_sizes=True
+)
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+print(f"Using device: {device}")
+
+if torch.cuda.device_count() > 1:
+    print(f"Using {torch.cuda.device_count()} GPUs!")
+    model = torch.nn.DataParallel(model)
+
+model.to(device)
+
+trainer = Trainer(
+  model = model,                     # The instantiated model to be trained
+  args = training_args,              # Training arguments, defined above
+  train_dataset = tokenized_dataset,     # Training dataset
+  tokenizer = tokenizer              # The tokenizer that was used
+)
+
+trainer.train()
+
+# If the model is wrapped in DataParallel, access the underlying model using `model.module`
+model_to_save = model.module if isinstance(model, torch.nn.DataParallel) else model
+
+# Save the fine-tuned model
+model_to_save.save_pretrained(DATA_DIR)
+tokenizer.save_pretrained(DATA_DIR) 
\ No newline at end of file

models/FineTuning/job_launch.sbatch

@@ -4,7 +4,7 @@
 #SBATCH --job-name=finetunning
 #SBATCH --time=12:00:00
 #SBATCH --partition=shared-gpu
-#SBATCH --gres=gpu:1,VramPerGpu:25G
+#SBATCH --gres=gpu:2,VramPerGpu:25G
 #SBATCH --mem=32G
 #SBATCH --ntasks=1                  # One task, multiple GPUs
 #SBATCH --nodes=1                   # Use a single node

models/FineTuning/test_multi_label.py

+from transformers import pipeline
+import os
+import sys
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")))
+
+from variables.pubmed import NCDS
+
+DATA_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "./data"))
+
+classifier = pipeline("text-classification", model=DATA_DIR, tokenizer=DATA_DIR, return_all_scores=True)
+
+title = "Consumption of Sodium and Its Ratio to Potassium in Relation to All-Cause, Cause-Specific, and Premature Noncommunicable Disease Mortality in Middle-Aged Japanese Adults: A Prospective Cohort Study."
+abstract = "Reducing premature noncommunicable disease (NCD) mortality is a global challenge. Sodium is thought to increase risk of NCDs via an effect of salt per se or high-salt foods on hypertension-induced cardiovascular disease (CVD) and gastrointestinal cancer. Further, relative risk of CVD is reportedly more closely associated with sodium-to-potassium ratio than that with sodium alone. However, few studies have investigated the effect of consumption of sodium or its ratio to consumption of potassium on risk of premature NCD death.We examined associations between intake of sodium and sodium-to-potassium ratio and risk of all-cause and cause-specific death, including premature NCD, in a Japanese prospective cohort study.During 1995-1998, a validated food frequency questionnaire was administered in 11 areas to 83,048 men and women aged 45-74 y. During 1,587,901 person-years of follow-up until the end of 2018, 17,727 all-cause deaths and 3555 premature NCD deaths were identified.Higher sodium intake was significantly associated with increased risk of all-cause and premature NCD mortality, but not all NCD mortality, among men: multivariate hazards ratios for the highest compared with lowest quintiles (HR) were 1.11 (95% CI: 1.03, 1.20; P-trend < 0.01) for all-cause and 1.25 (95% CI: 1.06, 1.47; P-trend < 0.01) for premature NCD mortality. When intakes were expressed as ratio to potassium intake, these associations (HR of all-cause: 1.19, 95% CI: 1.11-1.27; P-trend < 0.01; HR of premature NCD: 1.27, 95% CI: 1.10, 1.46; P-trend < 0.01), including associations with cancers (HR: 1.18, 95% CI: 1.07, 1.31; P-trend = 0.02), were strengthened in men.This prospective cohort study showed that both sodium intake and sodium-to-potassium ratio are associated with increased risk of all-cause and early NCD mortality in middle-aged men."
+sequence = title + abstract
+
+predictions = classifier(sequence)
+
+print(predictions)
+
+threshold = 0.7
+labels = [NCDS[i] for i, score in enumerate(predictions[0]) if score["score"] > threshold]
+
+print("Predicted Labels:", labels)
\ No newline at end of file

scripts/get_results_hpc.py

@@ -2,43 +2,41 @@ import argparse
 import os
 import sys
 
-CLUSTERS = {
-    "bamboo": "hpc-cluster-bamboo",
-    "baobab": "hpc-cluster-baobab",
-    "yggdrasil": "hpc-cluster-yggdrasil"
+REMOTES = {
+    "bamboo": { "remote": "hpc-cluster-bamboo", "destination":"/home/users/p/pavlovii/NCD-Project"},
+    "baobab": { "remote": "hpc-cluster-baobab", "destination":"/home/users/p/pavlovii/NCD-Project"},
+    "yggdrasil": { "remote": "hpc-cluster-yggdrasil", "destination":"/home/users/p/pavlovii/NCD-Project"},
+    "phenix": { "remote": "phenix-service", "destination": "/home/HES/ivan.pavlovic/NCD-Project"},
+    "laptop": { "remote": "anthoine-laptop", "destination": "/home/guest/Documents/NCD-Project"},
+    "tower": { "remote": "anthoine", "destination": "/home/guest/Documents/NCD-Project"}
 }
 
-parser = argparse.ArgumentParser(description="Sync project files to an HPC cluster.")
-parser.add_argument("--bamboo", action="store_true", help="Use hpc-cluster-bamboo")
-parser.add_argument("--baobab", action="store_true", help="Use hpc-cluster-baobab")
-parser.add_argument("--yggdrasil", action="store_true", help="Use hpc-cluster-yggdrasil")
+parser = argparse.ArgumentParser(description="Sync project files to a remote.")
 
-if len(sys.argv) == 1:
-    parser.print_help()
-    exit(1)
+parser.add_argument(
+    "--remote", 
+    type=str, 
+    default="bamboo",
+    help="Specify the remote to use (default: bamboo)"
+)
 
 args = parser.parse_args()
 
-selected_cluster = None
-for cluster_name, cluster_host in CLUSTERS.items():
-    if getattr(args, cluster_name):
-        selected_cluster = cluster_host
-        break
+selected_remote = REMOTES[args.remote]
 
-if not selected_cluster:
-    print("Error: No cluster selected. Use --bamboo, --baobab, or --yggdrasil.")
+if selected_remote is None:
+    print(f"Error: No cluster selected. Choises: {[remote for remote in REMOTES.keys()]}")
     parser.print_help()
     exit(1)
 
 PROJECT_PWD = os.path.abspath(os.path.join(os.path.dirname(__file__), "../"))
-DEST_PATH = f"/home/users/p/pavlovii/NCD-Project"
 
 commands = [
-    f"rclone copy {selected_cluster}:{DEST_PATH}/testModel/results {PROJECT_PWD}/testModel/results -P",
+    f"rclone copy {selected_remote["remote"]}:{selected_remote["destination"]}/testModel/results {PROJECT_PWD}/testModel/results -P",
 ]
 
 for cmd in commands:
     print(f"-> Running: {cmd}")
     os.system(cmd)
 
-print(f"Results successfully copied from {selected_cluster}:{DEST_PATH}")
+print(f"Files successfully copied from {selected_remote["remote"]}:{selected_remote["destination"]}")
\ No newline at end of file