def analyze_forest(data, rate):
file = open("_final_bigger_forest_{0}.csv".format(rate), "w")
start_trees = 5
num_of_trees = start_trees
percentage = []
data_learn, data_test = DataOperation.separate_data(DataOperation.bin_data(data, 5), 2)
true_values = list(data_test.iloc[:, len(data_test.columns)-1])
classifier = RandomForest.RandomForest(start_trees, data_learn, rate)
for _ in range(30):
results = classifier.estimate_class(data_test)
good, bad = 0, 0
for i in range(len(true_values)):
if results[i] == true_values[i]:
good += 1
else:
bad += 1
percentage.append(good/(good+bad)*100)
file.write("{0};{1}%\n".format(num_of_trees, good / (good + bad) * 100))
for _ in range(5):
classifier.add_new_tree()
num_of_trees += 1
file.close()
