85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
from __future__ import print_function
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import numpy
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from sklearn.metrics import f1_score,accuracy_score
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from sklearn.multiclass import OneVsRestClassifier
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from sklearn.preprocessing import MultiLabelBinarizer
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class TopKRanker(OneVsRestClassifier):
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def predict(self, X, top_k_list):
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probs = numpy.asarray(super(TopKRanker, self).predict_proba(X))
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all_labels = []
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for i, k in enumerate(top_k_list):
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probs_ = probs[i, :]
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labels = self.classes_[probs_.argsort()[-k:]].tolist()
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probs_[:] = 0
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probs_[labels] = 1
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all_labels.append(probs_)
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return numpy.asarray(all_labels)
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class Classifier(object):
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def __init__(self, embeddings, clf):
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self.embeddings = embeddings
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self.clf = TopKRanker(clf)
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self.binarizer = MultiLabelBinarizer(sparse_output=True)
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def train(self, X, Y, Y_all):
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self.binarizer.fit(Y_all)
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X_train = [self.embeddings[x] for x in X]
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Y = self.binarizer.transform(Y)
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self.clf.fit(X_train, Y)
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def evaluate(self, X, Y):
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top_k_list = [len(l) for l in Y]
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Y_ = self.predict(X, top_k_list)
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Y = self.binarizer.transform(Y)
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averages = ["micro", "macro","samples", "weighted" ]#
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results = {}
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for average in averages:
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results[average] = f1_score(Y, Y_, average=average)
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#results['acc'] = accuracy_score(Y,Y_)
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print('-------------------')
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print(results)
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return results
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print('-------------------')
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def predict(self, X, top_k_list):
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X_ = numpy.asarray([self.embeddings[x] for x in X])
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Y = self.clf.predict(X_, top_k_list=top_k_list)
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return Y
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def split_train_evaluate(self, X, Y, train_precent, seed=0):
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state = numpy.random.get_state()
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training_size = int(train_precent * len(X))
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numpy.random.seed(seed)
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shuffle_indices = numpy.random.permutation(numpy.arange(len(X)))
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X_train = [X[shuffle_indices[i]] for i in range(training_size)]
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Y_train = [Y[shuffle_indices[i]] for i in range(training_size)]
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X_test = [X[shuffle_indices[i]] for i in range(training_size, len(X))]
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Y_test = [Y[shuffle_indices[i]] for i in range(training_size, len(X))]
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self.train(X_train, Y_train, Y)
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numpy.random.set_state(state)
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return self.evaluate(X_test, Y_test)
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def read_node_label(filename, skip_head=False):
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fin = open(filename, 'r')
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X = []
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Y = []
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while 1:
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if skip_head:
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fin.readline()
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l = fin.readline()
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if l == '':
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break
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vec = l.strip().split(' ')
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X.append(vec[0])
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Y.append(vec[1:])
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fin.close()
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return X, Y
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