Update model to detect class imbalance and retrain it

Author: mostafa

sqli_model/3/fingerprint.pb

@@ -1 +1 @@
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sqli_model/3/saved_model.pb

@@ -1,4 +1,5 @@
 import sys
+import os
 import pandas as pd
 import tensorflow as tf
 from tensorflow.keras.preprocessing.text import Tokenizer
@@ -17,6 +18,7 @@
 from tensorflow.keras.callbacks import EarlyStopping
 from sklearn.model_selection import KFold
 from sklearn.metrics import accuracy_score, precision_score, recall_score
+from sklearn.utils.class_weight import compute_class_weight
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -54,11 +56,7 @@ def build_model(input_dim, output_dim=128):
     model.compile(
         loss="binary_crossentropy",
         optimizer="adam",
-        metrics=[
-            "accuracy",
-            tf.keras.metrics.Precision(name="precision"),
-            tf.keras.metrics.Recall(name="recall"),
-        ],
+        metrics=["accuracy", tf.keras.metrics.Precision(), tf.keras.metrics.Recall()],
     )
     return model
 
@@ -75,31 +73,43 @@ def calculate_f1_f2(precision, recall, beta=1):
 
 def plot_history(history):
     """Plot the training and validation loss, accuracy, precision, and recall."""
+    available_metrics = history.history.keys()  # Check which metrics are available
     plt.figure(figsize=(12, 8))
-    for i, metric in enumerate(["loss", "accuracy", "precision", "recall"], start=1):
-        plt.subplot(2, 2, i)
-        plt.plot(history.history[metric], label=f"Training {metric.capitalize()}")
-        plt.plot(
-            history.history[f"val_{metric}"], label=f"Validation {metric.capitalize()}"
-        )
-        plt.title(metric.capitalize())
-        plt.xlabel("Epochs")
-        plt.ylabel(metric.capitalize())
-        plt.legend()
+
+    # Define metrics to plot
+    metrics_to_plot = ["loss", "accuracy", "precision", "recall"]
+    for i, metric in enumerate(metrics_to_plot, start=1):
+        if metric in available_metrics:
+            plt.subplot(2, 2, i)
+            plt.plot(history.history[metric], label=f"Training {metric.capitalize()}")
+            plt.plot(
+                history.history[f"val_{metric}"],
+                label=f"Validation {metric.capitalize()}",
+            )
+            plt.title(metric.capitalize())
+            plt.xlabel("Epochs")
+            plt.ylabel(metric.capitalize())
+            plt.legend()
+
     plt.tight_layout()
     plt.savefig("training_history.png")
 
 
-# Main function
 if __name__ == "__main__":
     if len(sys.argv) != 3:
         print("Usage: python train.py <input_file> <output_dir>")
         sys.exit(1)
 
+    # Constants
+    MAX_WORDS = 10000
+    MAX_LEN = 100
+    EPOCHS = 50
+    BATCH_SIZE = 32
+
     # Load and preprocess data
     data = load_data(sys.argv[1])
     X, tokenizer = preprocess_text(data)
-    y = data["Label"]
+    y = data["Label"].values  # Convert to NumPy array to avoid KeyError in KFold
 
     # Initialize cross-validation
     k_folds = 5
@@ -111,7 +121,13 @@ def plot_history(history):
 
         # Split the data
         X_train, X_val = X[train_idx], X[val_idx]
-        y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
+        y_train, y_val = y[train_idx], y[val_idx]
+
+        # Compute class weights to handle imbalance
+        class_weights = compute_class_weight(
+            "balanced", classes=np.unique(y_train), y=y_train
+        )
+        class_weight_dict = {i: class_weights[i] for i in range(len(class_weights))}
 
         # Build and train the model
         model = build_model(input_dim=len(tokenizer.word_index) + 1)
@@ -121,15 +137,16 @@ def plot_history(history):
         history = model.fit(
             X_train,
             y_train,
-            epochs=50,
-            batch_size=32,
+            epochs=EPOCHS,
+            batch_size=BATCH_SIZE,
             validation_data=(X_val, y_val),
+            class_weight=class_weight_dict,
             callbacks=[early_stopping],
             verbose=1,
         )
 
-        # Make predictions to manually calculate metrics
-        y_val_pred = (model.predict(X_val) > 0.5).astype(int)
+        # Make predictions to calculate metrics
+        y_val_pred = (model.predict(X_val) > 0.8).astype(int)
         accuracy = accuracy_score(y_val, y_val_pred)
         precision = precision_score(y_val, y_val_pred)
         recall = recall_score(y_val, y_val_pred)
@@ -143,12 +160,17 @@ def plot_history(history):
         fold_metrics["f1"].append(f1_score)
         fold_metrics["f2"].append(f2_score)
 
-    # Calculate average metrics across folds
+    # Calculate and display average metrics across folds
     avg_metrics = {metric: np.mean(scores) for metric, scores in fold_metrics.items()}
     print("\nCross-validation results:")
     for metric, value in avg_metrics.items():
         print(f"{metric.capitalize()}: {value:.2f}")
 
     # Save the final model trained on the last fold
-    model.export(sys.argv[2])
+    output_dir = sys.argv[2]
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    model.export(output_dir)
+
+    # Plot training history of the last fold
     plot_history(history)