9946ca10f9
🚧 Created new tree to enable bootstrapping with indices (to avoid making a whole new bootstrapped database per tree)
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
import os
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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 import Tree
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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"):
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os.mkdir("output")
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if not os.path.exists("output/tree_testing.txt"):
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output = open("output/tree_testing.txt", 'w', encoding="utf-8")
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else:
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output = open("output/tree_testing.txt", 'a', encoding="utf-8")
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dataset, fields = read_stars()
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log("\n----------\n", output)
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log("Training Tree...", output)
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t_start = timer()
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split = int(len(dataset) * 0.65)
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training, testing = dataset[:split], dataset[split + 1:]
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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)
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t_end = timer()
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timestamp = "Training complete.\nElapsed time: {:.3f}\n"
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log(timestamp.format(t_end - t_start), output)
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log(tree, output)
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log("\n-- TEST --\n", output)
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failures = 0
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for entry in testing:
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label = entry.label
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predict = tree.predict(entry)
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if predict not in label:
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print("Actual: {}\tPredicted: {}".format(label, predict))
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failures += 1
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tested = len(testing)
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success = tested - failures
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s_rate = float(success)*100/float(tested)
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log("\nSuccessfully predicted {} out of {} entries."
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.format(success, tested), output)
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log("Accuracy: {:.2f}%\nError: {:.2f}%".format(s_rate, 100-s_rate), output)
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output.close()
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