version control

.github/workflows/ci.yml

@@ -19,26 +19,30 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v2
       with:
-        python-version: '3.9'  # Updated to 3.9
+        python-version: '3.9'
 
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements.txt
         pip install -r dev-requirements.txt
-        pip install "black[jupyter]"  # Install Jupyter support for Black
+        pip install "black[jupyter]"
 
-    - name: Run Black
+    - name: Run Isort to Auto-Fix Imports
+      run: |
+        isort . --skip-glob "*.ipynb"  # Automatically fix import order for all files except Jupyter notebooks
+
+    - name: Run Isort Check
       run: |
-        black . --exclude "\.ipynb$"  # Skip Jupyter notebook files
+        isort --check-only . --skip-glob "*.ipynb"  # Verify that all imports are correctly sorted
 
-    - name: Run isort
+    - name: Run Black
       run: |
-        isort --check-only . --skip-glob "*.ipynb"  # Skip Jupyter notebook files
+        black . --exclude "\.ipynb$"
 
     - name: Run Pylint
       run: |
-        pylint src tests --ignore-patterns=".*\.ipynb$"  # Skip Jupyter notebook files
+        pylint src tests --ignore-patterns=".*\.ipynb$"
 
     - name: Run tests with coverage
       run: |

src/data/data_augmentation.py

@@ -1,10 +1,11 @@
-import requests
+import logging
 import random
-import spacy
+
+import backoff
 import nltk
+import requests
+import spacy
 from nltk.corpus import wordnet
-import backoff
-import logging
 
 # Load Spacy model for NER and tokenization
 nlp = spacy.load("en_core_web_sm")

src/evaluation/contextual_metrics.py

@@ -1,5 +1,6 @@
 from bert_score import score
 
+
 class ContextualMetrics:
     @staticmethod
     def bleu(reference, candidate):

src/preprocessing/augmentation.py

@@ -1,7 +1,8 @@
 import random
 import re
-import spacy
+
 import nltk
+import spacy
 from nltk.corpus import stopwords
 from spellchecker import SpellChecker
 

src/preprocessing/cleaning.py

@@ -1,6 +1,8 @@
-import pandas as pd
 import re
 
+import pandas as pd
+
+
 class DataCleaning:
     def __init__(self, df):
         self.df = df

src/preprocessing/curriculum_learning.py

@@ -1,5 +1,6 @@
 import pandas as pd
 
+
 class CurriculumLearning:
     def __init__(self, df):
         self.df = df

src/reinforcement/multi_objective_rl.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class MultiObjectiveRL:
     def __init__(self, reward_weights):
         self.reward_weights = reward_weights

src/training/distributed_rl.py

@@ -1,8 +1,9 @@
 import torch
+import torch.distributed as dist
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
-import torch.distributed as dist
+
 
 class DistributedTraining:
     def __init__(self, model, dataset):

src/training/ppo_hyperparameter_tuning.py

@@ -2,6 +2,7 @@
 from ray import tune
 from ray.rllib.agents.ppo import PPOTrainer
 
+
 def ppo_training(config):
     trainer = PPOTrainer(config=config)
     for i in range(50):