def setup(self, known_graphs=None, candidate_graphs=None): """Setup.""" # compute the nearest neighbors for the 'proposal_graphs' w.r.t. the # known graphs in the list 'known_graphs' parameters_priors = dict(n_neighbors=self.n_neighbors) parameters_priors.update(dict(vectorizer__complexity=self.complexity, vectorize__n_jobs=-1, vectorize__fit_flag=False, vectorize__n_blocks=5, vectorize__block_size=100)) fit_wrapped_knn_predictor_known = \ model(known_graphs, program=KNNWrapper(program=NearestNeighbors()), parameters_priors=parameters_priors) # compute distances of candidate_graphs to known_graphs knn_candidate_graphs = predict(candidate_graphs, program=fit_wrapped_knn_predictor_known) knn_candidate_graphs = list(knn_candidate_graphs) self.distances_to_known_graphs = [] for knn_candidate_graph in knn_candidate_graphs: distances = knn_candidate_graph.graph['distances'] self.distances_to_known_graphs.append(distances) # compute candidate_graphs encodings self.candidate_graphs_data_matrix = \ vectorize(candidate_graphs, vectorizer=Vectorizer(complexity=self.complexity), block_size=400, n_jobs=-1)
def setup(self, known_graphs=None, candidate_graphs=None): """Setup.""" # compute the nearest neighbors for the 'proposal_graphs' w.r.t. the # known graphs in the list 'known_graphs' parameters_priors = dict(n_neighbors=self.n_neighbors) parameters_priors.update(dict(vectorizer__complexity=self.complexity, vectorizer__discrete=True)) fit_wrapped_knn_predictor_known = \ model(known_graphs, program=KNNWrapper(program=NearestNeighbors()), parameters_priors=parameters_priors) # compute distances of candidate_graphs to known_graphs knn_candidate_graphs = predict(candidate_graphs, program=fit_wrapped_knn_predictor_known) knn_candidate_graphs = list(knn_candidate_graphs) self.distances_to_known_graphs = [] for knn_candidate_graph in knn_candidate_graphs: distances = knn_candidate_graph.graph['distances'] self.distances_to_known_graphs.append(distances) # compute candidate_graphs encodings vec = Vectorizer(complexity=self.complexity) self.candidate_graphs_data_matrix = vec.transform(candidate_graphs)
def setup(self, known_graphs=None, candidate_graphs=None): """Setup.""" # compute the nearest neighbors for the 'proposal_graphs' w.r.t. the # known graphs in the list 'known_graphs' parameters_priors = dict(n_neighbors=self.n_neighbors) parameters_priors.update( dict(vectorizer__complexity=self.complexity, vectorizer__discrete=True)) fit_wrapped_knn_predictor_known = \ model(known_graphs, program=KNNWrapper(program=NearestNeighbors()), parameters_priors=parameters_priors) # compute distances of candidate_graphs to known_graphs knn_candidate_graphs = predict(candidate_graphs, program=fit_wrapped_knn_predictor_known) knn_candidate_graphs = list(knn_candidate_graphs) self.distances_to_known_graphs = [] for knn_candidate_graph in knn_candidate_graphs: distances = knn_candidate_graph.graph['distances'] self.distances_to_known_graphs.append(distances) # compute candidate_graphs encodings vec = Vectorizer(complexity=self.complexity) self.candidate_graphs_data_matrix = vec.transform(candidate_graphs)