train_classifier.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
    classification_report,
    confusion_matrix,
    ConfusionMatrixDisplay,
    roc_curve,
    auc
)

# Load the dataset (adjust path if needed)
df = pd.read_csv("edc_sanitized_with_fingerprints.csv")

# Convert fingerprint column (stored as strings) back into lists
df["fingerprint"] = df["fingerprint"].apply(eval)

# Features and labels
X = np.array(df["fingerprint"].tolist())
y = df["label"].values

# Split into training and testing sets (80/20 split with stratification)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Train the classifier
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train, y_train)

# Predictions and probabilities
y_pred = clf.predict(X_test)
y_proba = clf.predict_proba(X_test)[:, 1]  # Probabilities for class 1

# Print classification report
print(classification_report(y_test, y_pred))

# --- Confusion Matrix ---
cm = confusion_matrix(y_test, y_pred)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)
disp.plot(cmap="Blues")
plt.title("Confusion Matrix")
plt.savefig("confusion_matrix.png")
plt.show()

# --- ROC Curve ---
fpr, tpr, _ = roc_curve(y_test, y_proba)
roc_auc = auc(fpr, tpr)

plt.figure()
plt.plot(fpr, tpr, color="darkorange", lw=2, label=f"ROC curve (AUC = {roc_auc:.2f})")
plt.plot([0, 1], [0, 1], color="navy", lw=2, linestyle="--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver Operating Characteristic (ROC) Curve")
plt.legend(loc="lower right")
plt.savefig("roc_curve.png")
plt.show()