0f0ae62cd5
📝 Uncommented the single tree training in Forest testing 📝 Silenced the single tree training output in Forest testing
105 lines
2.9 KiB
Python
105 lines
2.9 KiB
Python
import os
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import random
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from timeit import default_timer as timer
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from star_reader import read_stars
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from tree_bootstrapped import Tree
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from forest import Forest
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OUTPUT_FOLDER = "output/forest"
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def log(s, open_file):
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print(s)
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open_file.write(str(s) + '\n')
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if __name__ == '__main__':
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if not os.path.exists(OUTPUT_FOLDER):
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os.mkdir(OUTPUT_FOLDER)
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if not os.path.exists(OUTPUT_FOLDER + "/testing.txt"):
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output = open(OUTPUT_FOLDER + "/testing.txt", 'w',
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encoding="utf-8")
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else:
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output = open(OUTPUT_FOLDER + "/testing.txt", 'a', encoding="utf-8")
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dataset, fields = read_stars()
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random.shuffle(dataset)
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cutoff = 0.25
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forest_size = 10
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split = int(len(dataset) * cutoff)
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training, testing = dataset[:split], dataset[split + 1:]
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log("\n----------\n", output)
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log("\n-- TREE TRAINING --\n", output)
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log("Training Tree...", output)
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t_start = timer()
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log("Dataset split: Training with {}% of the set".format(cutoff*100), output)
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log("Training set: {} entries.".format(len(training)), output)
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log("Testing set: {} entries.".format(len(testing)), output)
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tree = Tree(fields, training, [i for i in range(len(training))])
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t_end = timer()
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log("Training complete.\nElapsed time: {:.3f}\n".format(t_end - t_start), output)
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log("\n-- TREE TEST --\n", output)
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total_success = 0
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for entry in testing:
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success, predict = tree.predict(entry)
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# print("Actual: {}\tPredicted: {}.\tSuccess: {}".format(entry.label, predict, success))
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total_success += success
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tested = len(testing)
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s_rate = float(total_success)*100/float(tested)
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log("\nTested {} entries.".format(tested), output)
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log("Accuracy: {:.2f}%\nError: {:.2f}%".format(s_rate, 100-s_rate), output)
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log("\n-- FOREST TRAINING --\n", output)
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log("Training Forest...", output)
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t_start = timer()
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log("Dataset split: Training with {}% of the set".format(cutoff*100), output)
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log("Training set: {} entries.".format(len(training)), output)
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log("Testing set: {} entries.".format(len(testing)), output)
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forest = Forest(fields, training, forest_size)
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t_end = timer()
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log("Training complete.\nElapsed time: {:.3f}\n".format(t_end - t_start), output)
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log("\n-- FOREST TEST --\n", output)
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total_success = 0
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for entry in testing:
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label = entry.label
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majority = forest.predict(entry)
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if majority in label:
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# print("Actual: {}\tPredicted: {}".format(label, predict))
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total_success += 1
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tested = len(testing)
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s_rate = float(total_success)*100/float(tested)
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log("\nTested {} entries.".format(tested), output)
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log("Accuracy: {:.2f}%\nError: {:.2f}%".format(s_rate, 100-s_rate), output)
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error = forest.error_oob()
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log("\nError Out-of-Bag: {:.2f}%".format(error*100), output)
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output.close()
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