Update README.md: Clarify application description and improve example input/output formatting

Enhance my_utils.py: Add docstrings for randomSVM and randomSearch functions to improve code documentation

Update notebook files: Modify versioning information for 01_EDA_Psort.ipynb and 04_Training.ipynb

README.md

@@ -22,13 +22,12 @@ base_model:
 
     * [GUI Mode](#gui-mode)
   * [Example Input & Output](#example-input--output)
-  * [Model Details](#model-details)
   * [Project Structure](#project-structure)
   * [Contributing](#contributing)
 
 ## Protein Location Predictor
 
-A comprehensive GUI application for predicting protein subcellular localization using state-of-the-art machine learning models including PROST-T5 and ESM-C embeddings.
+A comprehensive GUI application for predicting protein subcellular localization using SVM and Random Forest classifiers using state-of-the-art protein language models including PROST-T5 and ESM-C embeddings as training data.
 
 ### Features
 
@@ -139,7 +138,7 @@ conda env create -f environment.yml
 
 ## Example Input & Output
 
-**Input FASTA (********`example/input.fasta`****\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*):**
+**Input FASTA (`example/input.fasta`):**
 
 ```
 >protein_1
@@ -148,7 +147,7 @@ MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG
 MKTIIALSYIFCLVFAHATAKASEQTDNLQWDLAAIDNSGGHNAVDIKQNLQFQCQNNLHGCF
 ```
 
-**Output CSV (********`example/output.csv`****\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*):**
+**Output CSV (`example/output.csv`):**
 
 ```csv
 Sequence_ID,Prediction 1,Prediction 2,Prediction 3,Prediction 4,Prediction 5,Prediction 6
@@ -156,14 +155,6 @@ protein_1,Cytoplasmic (0.9860),CytoplasmicMembrane (0.0081),Periplasmic (0.0029)
 protein_2,SignalPeptide (0.7523),Extracellular (0.1234),CytoplasmicMembrane (0.0645),Cellwall (0.0345),Periplasmic (0.0201),OuterMembrane (0.0052)
 ```
 
-## Model Details
-
-| Model        | Embedding Dim. | Classifier | GPU VRAM | RAM Usage |
-| ------------ | -------------- | ---------- | -------- | --------- |
-| PROST-T5     | 1024           | SVM        | \~4 GB   | \~8 GB    |
-| ESM-C (300M) | 960            | SVM        | \~2 GB   | \~6 GB    |
-| ESM-C (600M) | 1280           | SVM        | \~4 GB   | \~10 GB   |
-
 ## Project Structure
 
 ```

notebooks/01_EDA_Psort.ipynb

@@ -1,3 +1,3 @@
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+size 10363061

notebooks/04_Training.ipynb

@@ -1,3 +1,3 @@
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+size 699779

src/my_utils.py

@@ -375,6 +375,19 @@ def train_svm(title: str, x: np.ndarray, y: np.ndarray, params: dict) -> tuple[P
 
 
 def randomSVM(x: np.ndarray, y: np.ndarray) -> dict:
+    """
+    Performs randomized hyperparameter search for an SVM classifier using a pipeline with feature scaling.
+
+    Args:
+        x (np.ndarray): Feature matrix of shape (n_samples, n_features).
+        y (np.ndarray): Target labels of shape (n_samples,).
+
+    Returns:
+        dict: The best hyperparameters found during randomized search.
+
+    The function encodes the target labels, splits the data for training, constructs a pipeline with a StandardScaler and SVM,
+    and performs RandomizedSearchCV over a predefined hyperparameter space using weighted F1 score as the evaluation metric.
+    """
 
     le = LabelEncoder()
     y_encoded = le.fit_transform(y)
@@ -418,6 +431,19 @@ def randomSVM(x: np.ndarray, y: np.ndarray) -> dict:
     return random_search.best_params_
 
 def randomSearch(x: np.ndarray, y: np.ndarray) -> dict: #type: ignore
+    
+    """
+    Performs a randomized hyperparameter search for a RandomForestClassifier using the provided feature matrix and labels.
+    Args:
+        x (np.ndarray): Feature matrix of shape (n_samples, n_features).
+        y (np.ndarray): Target labels of shape (n_samples,).
+    Returns:
+        dict: The best hyperparameters found during the randomized search.
+    Notes:
+        - The function encodes the labels, splits the data for training, and uses RandomizedSearchCV to optimize hyperparameters.
+        - The search is performed using weighted F1 score and 3-fold cross-validation.
+        - Prints the best parameters found during the search.
+    """
 
     le = LabelEncoder()
     y_encoded = le.fit_transform(y)