import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, precision_recall_curve
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_auc_score
from sklearn.model_selection import RandomizedSearchCV


# Load dataset
data = pd.read_csv("Phishing_Legitimate_full.csv")

# Data Exploration
data.head()  # Display first five rows
data.info()  # Information about dataset
data.describe()  # Statistical summary
data.max(axis=0)  # Maximum values in each column
data.drop('id', axis=1, inplace=True)  # Drop 'id' column as it's not useful for prediction
data['CLASS_LABEL'].hist()  # Histogram of the class label

# Splitting data into features and labels
X = data.drop("CLASS_LABEL", axis=1)  # Features
y = data["CLASS_LABEL"]  # Labels
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)  # Split data

# Model Training with Random Forest Classifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Model Prediction
pred = model.predict(X_test)

# Model Evaluation
accuracy = accuracy_score(y_test, pred)
print("Accuracy:", accuracy)
precision = precision_score(y_test, pred)
print("Precision:", precision)
recall = recall_score(y_test, pred)
print("Recall:", recall)

# ROC Curve
fpr, tpr, _ = roc_curve(y_test, pred)
roc_auc = auc(fpr, tpr)
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC Curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC)')
plt.legend(loc="lower right")
plt.show()

# Precision-Recall Curve
precision, recall, _ = precision_recall_curve(y_test, pred)
plt.figure(figsize=(8, 6))
plt.plot(recall, precision, color='blue', lw=2, label='Precision-Recall')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.legend(loc="lower left")
plt.show()

# Hyperparameter Optimization
param_dist = {
    'n_estimators': [100, 200, 300, 400, 500],
    'max_features': ['sqrt'],
    'max_depth': [10, 20, 30, 40, 50],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
    'bootstrap': [True]
}

# Hyperparameter Tuning using Randomized Search CV
rfs_random = RandomizedSearchCV(estimator=model, param_distributions=param_dist, 
                                verbose=1, n_iter=10, cv=5, random_state=42, n_jobs=-1)
rfs_random.fit(X_train, y_train)
print("Best Parameters:", rfs_random.best_params_)

# Best Estimator Evaluation
best_rfs = rfs_random.best_estimator_
rfs_best_pred = best_rfs.predict(X_test)
print("Improved Accuracy:", accuracy_score(y_test, rfs_best_pred))
print("Improved ROC-AUC Score:", roc_auc_score(y_test, rfs_best_pred))
print("Improved Classification Report:\n", classification_report(y_test, rfs_best_pred))
print("Improved Confusion Matrix:\n", confusion_matrix(y_test, rfs_best_pred))