def main(argv):
""" entry point to the program """
if len(argv) != 3:
sys.exit(f"Usage python3 {argv[0]} <testing_file> <tree_json_dir>")
_, test_y, test_x = parse_data.read_data(argv[1],
skip_header=False,
delimiter=",")
json_files = glob.glob(f"{argv[2]}/*.json")
forest = RandomForest()
for filename in json_files:
with open(filename, "r") as tree_file:
tree = DecisionTree.from_json(tree_file.read())
forest.add_tree(tree)
total_right = 0
for i, point in enumerate(test_x):
expected = forest.predict(point)
if test_y[i] == expected:
total_right += 1
accuracy = total_right / len(test_y)
print(f"Accuracy: {accuracy}")
def main(argv):
if len(argv) != 3:
sys.exit(f"Usage python3 {argv[0]} <testing_file> <tree_json_dir>")
rna_ids, _, test_x = parse_data.read_data(argv[1],
skip_header=False,
delimiter=",")
json_files = glob.glob(f"{argv[2]}/*.json")
forest = RandomForest()
weights_filename = f"{argv[2]}/tree_weights.json"
json_files.remove(weights_filename)
weights = {}
with open(weights_filename, "r") as weights_file:
weights = json.loads(weights_file.read())
for filename in json_files:
with open(filename, "r") as tree_file:
tree = DecisionTree.from_json(tree_file.read())
forest.add_tree(tree)
forest.weights.append(weights[filename])
for i, x in enumerate(test_x):
prediction, confidence = forest.predict_with_confidence(x)
if prediction == 0.0:
confidence = 1 - confidence
print(f"{rna_ids[i]},{confidence}")
