def getgraphs(aid): if aid == 'bursi': return list(gspan.gspan_to_eden("bursi.pos.gspan")), list( gspan.gspan_to_eden("bursi.neg.gspan")) download_active = curry(download)(active=True, stepsize=50) download_inactive = curry(download)(active=False, stepsize=50) active = pipe(aid, download_active, sdf_to_nx, list) inactive = pipe(aid, download_inactive, sdf_to_nx, list) return active, inactive
def bursi_get_extremes(num=200): po, ne = list(gspan.gspan_to_eden("bursi.pos.gspan")), list( gspan.gspan_to_eden("bursi.neg.gspan")) X, y = graphs_to_Xy(po, ne) esti = SGDClassifier(average=True, class_weight='balanced', shuffle=True, n_jobs=4, loss='log') esti.fit(X, y) res = [(score, idd) for idd, score in enumerate(esti.decision_function(X))] # list res.sort() graphs = po + ne # returns pos/neg return [graphs[idd] for (score, idd) in res[0 - num:] ], [graphs[idd] for (score, idd) in res[:num]]
exit() print "*raw args" print "*" * 80 print args # verbosity from eden.util import configure_logging import logging configure_logging(logging.getLogger(),verbosity=args['verbose']) args.pop('verbose') # graphs from eden.io.gspan import gspan_to_eden from itertools import islice args['graph_iter'] = islice(gspan_to_eden(args.pop('start_graphs')),args.pop('num_graphs')) #output OUTFILE=args.pop('out') MODEL=args.pop('model') # CREATE SAMPLER from graphlearn01.graphlearn import Sampler s=Sampler() s.load(MODEL) results=s.transform(**args) import graphlearn01.utils.draw_openbabel as ob
exit() print "*raw args" print "*" * 80 print args # verbosity from eden.util import configure_logging import logging configure_logging(logging.getLogger(), verbosity=args['verbose']) args.pop('verbose') # graphs from eden.io.gspan import gspan_to_eden from itertools import islice args['graph_iter'] = islice(gspan_to_eden(args.pop('start_graphs')), args.pop('num_graphs')) #output OUTFILE = args.pop('out') MODEL = args.pop('model') # CREATE SAMPLER from graphlearn01.graphlearn import Sampler s = Sampler() s.load(MODEL) results = s.transform(**args) import graphlearn01.utils.draw_openbabel as ob for i, samplepath in enumerate(results):
# estimator, if the user is providing a negative graph set, we use # the twoclass esti OO import graphlearn01.estimate as estimate if args['negative_input']==None: args['estimator']=estimate.OneClassEstimator(nu=.5, cv=2, n_jobs=-1) else: args['estimator']=estimate.TwoClassEstimator( cv=2, n_jobs=-1) #args for fitting: from eden.io.gspan import gspan_to_eden from itertools import islice fitargs={ k:args.pop(k) for k in ['lsgg_include_negatives','grammar_n_jobs','grammar_batch_size']} if args['negative_input']!=None: fitargs['negative_input'] = islice(gspan_to_eden(args.pop('negative_input')),args.pop('num_graphs_neg')) else: args.pop('negative_input') args.pop('num_graphs_neg') fitargs['input'] = islice(gspan_to_eden(args.pop('input')),args.pop('num_graphs')) #output OUTFILE=args.pop('output') print "*Sampler init" print "*"*80 print args # CREATE SAMPLER, dumping the rest of the parsed args :) from graphlearn01.graphlearn import Sampler
def get_graphs(dataset_fname='../../toolsdata/bursi.pos.gspan', size=100): return list(islice(gspan_to_eden(dataset_fname), size))