added fixes for model generation

main.py

@@ -11,43 +11,72 @@
 from Bio import SeqIO
 from Bio.Seq import Seq
 from features import extract_features
-from model import load_model, predict_new_data
+from model import load_model, predict_new_data, train_model_cv, save_model
+from data_pipeline import load_and_preprocess_data, augment_data
 import numpy as np
 
+def generate_model(positive_file: str, negative_file: str):
+    """Generate and save the protein interaction model."""
+    print("Loading and preprocessing data...")
+    X, y, feature_names = load_and_preprocess_data(positive_file, negative_file)
+
+    print("Augmenting data...")
+    X_augmented, y_augmented = augment_data(X, y)
+
+    print("Training model...")
+    model, scaler = train_model_cv(X_augmented, y_augmented)
+
+    print("Saving model...")
+    save_model(model, scaler, "protein_interaction_model.joblib")
+
+    print("Model generation complete. Saved as 'protein_interaction_model.joblib'")
+
 def main():
-    parser = argparse.ArgumentParser(description="Predict protein interactions.")
-    parser.add_argument("sequence_file", help="Path to the protein sequence file (FASTA format)")
+    parser = argparse.ArgumentParser(description="Predict protein interactions or generate model.")
+    parser.add_argument("--generate", action="store_true", help="Generate new model")
+    parser.add_argument("--positive", help="Path to positive interaction FASTA file (for model generation)")
+    parser.add_argument("--negative", help="Path to negative interaction FASTA file (for model generation)")
+    parser.add_argument("sequence_file", nargs="?", help="Path to the protein sequence file (FASTA format) for prediction")
     args = parser.parse_args()
 
-    try:
-        # Load the sequence
-        with open(args.sequence_file, "r") as handle:
-            record = next(SeqIO.parse(handle, "fasta"))
-            sequence = record.seq
-    except FileNotFoundError:
-        print(f"Error: File '{args.sequence_file}' not found.")
-        sys.exit(1)
-    except StopIteration:
-        print(f"Error: No sequences found in '{args.sequence_file}'.")
-        sys.exit(1)
+    if args.generate:
+        if not args.positive or not args.negative:
+            print("Error: Both --positive and --negative files are required for model generation.")
+            sys.exit(1)
+        generate_model(args.positive, args.negative)
+    elif args.sequence_file:
+        try:
+            # Load the sequence
+            with open(args.sequence_file, "r") as handle:
+                record = next(SeqIO.parse(handle, "fasta"))
+                sequence = record.seq
+        except FileNotFoundError:
+            print(f"Error: File '{args.sequence_file}' not found.")
+            sys.exit(1)
+        except StopIteration:
+            print(f"Error: No sequences found in '{args.sequence_file}'.")
+            sys.exit(1)
 
-    # Extract features
-    features = extract_features(sequence)
-    X_new = np.array([list(features.values())])
+        # Extract features
+        features = extract_features(sequence)
+        X_new = np.array([list(features.values())])
 
-    # Load the pre-trained model
-    try:
-        model, scaler = load_model("protein_interaction_model.joblib")
-    except FileNotFoundError:
-        print("Error: Pre-trained model not found. Please ensure 'protein_interaction_model.joblib' is in the current directory.")
-        sys.exit(1)
+        # Load the pre-trained model
+        try:
+            model, scaler = load_model("protein_interaction_model.joblib")
+        except FileNotFoundError:
+            print("Error: Pre-trained model not found. Please ensure 'protein_interaction_model.joblib' is in the current directory.")
+            sys.exit(1)
 
-    # Make prediction
-    prediction = predict_new_data(model, scaler, X_new)
+        # Make prediction
+        prediction = predict_new_data(model, scaler, X_new)
 
-    # Print result
-    result = "likely to interact" if prediction[0] == 1 else "unlikely to interact"
-    print(f"The protein sequence in '{args.sequence_file}' is {result}.")
+        # Print result
+        result = "likely to interact" if prediction[0] == 1 else "unlikely to interact"
+        print(f"The protein sequence in '{args.sequence_file}' is {result}.")
+    else:
+        print("Error: Please provide either --generate with --positive and --negative files, or a sequence file for prediction.")
+        sys.exit(1)
 
 if __name__ == "__main__":
     main()

model.py

@@ -1,6 +1,6 @@
 import numpy as np
-from typing import Tuple, Dict
-from sklearn.model_selection import StratifiedKFold, GridSearchCV
+from typing import Tuple, Dict, Any
+from sklearn.model_selection import StratifiedKFold, cross_val_score
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
 from xgboost import XGBClassifier

setup.py

@@ -10,6 +10,11 @@
         "scikit-learn",
         "xgboost",
         "joblib",
+        "pandas",
+        "matplotlib",
+        "seaborn",
+        "imbalanced-learn",
+        "optuna",
     ],
     entry_points={
         "console_scripts": [