def __init__(self, alpha=0, decay=1, ignore_leaves=True, smoothed=True, vector='word2vec', w2vdim=300, lowercase=True, tree='tree', kernel_path=KERNEL_PATH): Semeval.__init__(self, vector=vector, stop=False, lowercase=lowercase, punctuation=False, w2vdim=w2vdim) self.path = kernel_path self.tree = tree self.memoization = {} self.svm = Model() self.flat_traindata() self.treekernel = TreeKernel(alpha=alpha, decay=decay, ignore_leaves=ignore_leaves, smoothed=smoothed, lowercase=lowercase) self.train() del self.additional
def __init__(self, model='svm', features='bm25,', comment_features='bm25,', stop=True, vector='word2vec', lowercase=True, punctuation=True, proctrain=True, path=FEATURES_PATH, alpha=0.1, sigma=0.9, gridsearch='random'): Semeval.__init__(self, stop=stop, vector=vector, lowercase=lowercase, punctuation=punctuation) self.path = path self.features = features.split(',') self.comment_features = comment_features.split(',') self.gridsearch = gridsearch self.svm = Model() self.model = model self.bm25 = SemevalBM25( stop=stop, lowercase=lowercase, punctuation=punctuation, proctrain=proctrain ) if 'bm25' in self.features + self.comment_features else None self.cosine = SemevalCosine( stop=stop, lowercase=lowercase, punctuation=punctuation, proctrain=proctrain ) if 'cosine' in self.features + self.comment_features else None self.softcosine = SemevalSoftCosine( stop=stop, lowercase=lowercase, punctuation=punctuation, proctrain=proctrain, vector=vector ) if 'softcosine' in self.features + self.comment_features else None self.translation = SemevalTranslation( alpha=alpha, sigma=sigma, stop=stop, lowercase=lowercase, punctuation=punctuation, proctrain=proctrain, vector=self.vector ) if 'translation' in self.features + self.comment_features else None self.train()
def __init__(self, model='svm', features='bm25,', comment_features='bm25,', stop=True, vector='word2vec', path=FEATURES_PATH, alpha=0.1, sigma=0.9, gridsearch='random'): Quora.__init__(self, stop=stop, vector=vector) self.path = path self.features = features.split(',') self.comment_features = comment_features.split(',') self.gridsearch = gridsearch self.svm = Model() self.model = model self.bm25 = QuoraBM25(stop=stop) if 'bm25' in self.features+self.comment_features else None self.cosine = QuoraCosine(stop=stop) if 'cosine' in self.features+self.comment_features else None self.softcosine = QuoraSoftCosine(stop=stop, vector=vector) if 'softcosine' in self.features+self.comment_features else None self.translation = QuoraTranslations(alpha=alpha, sigma=sigma, stop=stop, vector=self.vector) if 'translation' in self.features+self.comment_features else None self.train()
def __init__(self, stop={}, lowercase={}, punctuation={}, vector={}, scale=True, alpha=0.9, sigma=0.1): self.stop = stop self.lowercase = lowercase self.punctuation = punctuation self.scale = scale self.vector = vector self.alpha = alpha self.sigma = sigma self.questions, self.ranking = self.load() self.ensemble = Model() self.train() ranking = self.test() p.dump(ranking, open(os.path.join(SEMI_PATH, 'reranking'), 'wb'))
def __init__(self, stop={}, lowercase={}, punctuation={}, vector={}, scale=True, w2vdim=300, kernel_path='', alpha=0.8, sigma=0.2): self.stop = stop self.lowercase = lowercase self.punctuation = punctuation self.scale = scale self.vector = vector self.alpha = alpha self.sigma = sigma self.kernel_path = kernel_path self.w2vdim = w2vdim self.theta = 0.9 self.ensemble = Model() self.train()