0f0ae62cd5
📝 Uncommented the single tree training in Forest testing 📝 Silenced the single tree training output in Forest testing
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import random
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import operator
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from tree_bootstrapped import Tree
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class Forest(object):
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def __init__(self, fields, dataset, size, tree_out=False, out=True):
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self.fields = fields
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self.dataset = dataset
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self.size = size
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self.trees = []
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for i in range(size):
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n = len(dataset)
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bootstrap = [random.randrange(n) for j in range(n)]
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tree = Tree(self.fields, self.dataset, bootstrap, (tree_out and out))
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self.trees.append(tree)
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if out:
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print("\nPlanted tree {}".format(i))
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def error_oob(self):
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oob = []
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for tree in self.trees:
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oob.extend(tree.oob)
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oob = set(oob)
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successes = 0
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for i in oob:
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entry = self.dataset[i]
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votes = {}
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for tree in self.trees:
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if i not in tree.indices:
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predict = tree.classify(entry).predictions
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for key, value in predict.items():
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if key not in votes:
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votes[key] = predict[key]
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else:
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votes[key] += predict[key]
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majority = max(votes.items(), key=operator.itemgetter(1))[0]
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if majority in entry.label:
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successes += 1
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return 1-(float(successes)/float(len(oob)))
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def predict(self, entry):
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votes = {}
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for tree in self.trees:
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predict = tree.classify(entry).predictions
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for key, value in predict.items():
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if key not in votes:
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votes[key] = predict[key]
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else:
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votes[key] += predict[key]
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majority = max(votes.items(), key=operator.itemgetter(1))[0]
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return majority
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