import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix

# -------------------------------
# Load the feature-engineered dataset
# -------------------------------
df = pd.read_csv('feature_engineered_transactions.csv')

# -------------------------------
# Split into features and labels
# -------------------------------
X = df.drop(columns=['is_anomalous'])
y = df['is_anomalous']

# -------------------------------
# Train-test split
# -------------------------------
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# -------------------------------
# Train Random Forest Classifier
# -------------------------------
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# -------------------------------
# Make predictions
# -------------------------------
y_pred = clf.predict(X_test)

# -------------------------------
# Evaluation Report
# -------------------------------
print("\n✅ Classification Report:\n")
print(classification_report(y_test, y_pred, digits=4))

# -------------------------------
# Create one page with subplots
# -------------------------------
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
plt.suptitle("Anomaly Detection Results", fontsize=16, fontweight='bold')

# --- A. Confusion Matrix ---
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(
    cm,
    annot=True,
    fmt="d",
    cmap="Blues",
    xticklabels=["Normal", "Suspicious"],
    yticklabels=["Normal", "Suspicious"],
    ax=axes[0]
)
axes[0].set_title("Confusion Matrix")
axes[0].set_xlabel("Predicted")
axes[0].set_ylabel("Actual")

# --- B. Feature Importance ---
importances = pd.Series(clf.feature_importances_, index=X.columns).sort_values(ascending=False)
sns.barplot(
    x=importances.values[:10],
    y=importances.index[:10],
    color='skyblue',
    ax=axes[1]
)
axes[1].set_title("Top 10 Feature Importances")
axes[1].set_xlabel("Importance")
axes[1].set_ylabel("Feature")

# --- Layout ---
plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adjust to fit title
plt.show()

import joblib

# Save model
joblib.dump(clf, 'anomaly_detector_rf_model.pkl')

# Save feature order for later use
joblib.dump(list(X.columns), 'feature_order.pkl')

print("✅ Model and feature list saved!")