| 20 | |
| 21 | |
| 22 | class Classifier(object): |
| 23 | |
| 24 | def __init__(self, embeddings, clf): |
| 25 | self.embeddings = embeddings |
| 26 | self.clf = TopKRanker(clf) |
| 27 | self.binarizer = MultiLabelBinarizer(sparse_output=True) |
| 28 | |
| 29 | def train(self, X, Y, Y_all): |
| 30 | self.binarizer.fit(Y_all) |
| 31 | X_train = [self.embeddings[x] for x in X] |
| 32 | Y = self.binarizer.transform(Y) |
| 33 | self.clf.fit(X_train, Y) |
| 34 | |
| 35 | def evaluate(self, X, Y): |
| 36 | top_k_list = [len(l) for l in Y] |
| 37 | Y_ = self.predict(X, top_k_list) |
| 38 | Y = self.binarizer.transform(Y) |
| 39 | averages = ["micro", "macro", "samples", "weighted"] |
| 40 | results = {} |
| 41 | for average in averages: |
| 42 | results[average] = f1_score(Y, Y_, average=average) |
| 43 | results['acc'] = accuracy_score(Y, Y_) |
| 44 | print('-------------------') |
| 45 | print(results) |
| 46 | return results |
| 47 | |
| 48 | def predict(self, X, top_k_list): |
| 49 | X_ = numpy.asarray([self.embeddings[x] for x in X]) |
| 50 | Y = self.clf.predict(X_, top_k_list=top_k_list) |
| 51 | return Y |
| 52 | |
| 53 | def split_train_evaluate(self, X, Y, train_precent, seed=0): |
| 54 | state = numpy.random.get_state() |
| 55 | |
| 56 | training_size = int(train_precent * len(X)) |
| 57 | numpy.random.seed(seed) |
| 58 | shuffle_indices = numpy.random.permutation(numpy.arange(len(X))) |
| 59 | X_train = [X[shuffle_indices[i]] for i in range(training_size)] |
| 60 | Y_train = [Y[shuffle_indices[i]] for i in range(training_size)] |
| 61 | X_test = [X[shuffle_indices[i]] for i in range(training_size, len(X))] |
| 62 | Y_test = [Y[shuffle_indices[i]] for i in range(training_size, len(X))] |
| 63 | |
| 64 | self.train(X_train, Y_train, Y) |
| 65 | numpy.random.set_state(state) |
| 66 | return self.evaluate(X_test, Y_test) |
| 67 | |
| 68 | |
| 69 | def read_node_label(filename, skip_head=False): |
no outgoing calls
no test coverage detected