import json
import numpy as np

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split

def eval():
	y_pred_full, y_test_full = [], []

	# Re-train 10 times in order to reduce effects of randomness
	for i in range(10):
		### TODO: Exercise 2-4
		### 1. Load data from traces file
		### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
		### 3. Train classifier with X_train and y_train
		### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred

		# Do not modify the next two lines
		y_test_full.extend(y_test)
		y_pred_full.extend(y_pred)

	### TODO: Exercise 2-4 (continued)
	### 5. Print classification report using y_test_full and y_pred_full

if __name__ == "__main__":
	eval()