def tst_prod_rules_level1_individual(in_path): # files = glob("ProdRules/moreno_lesmis_lesmis.*_iprules.tsv") mdf = pd.DataFrame() for f in sorted(files, reverse=True): df = pd.read_csv(f, header=None, sep="\t") mdf = pd.concat([mdf, df]) # print f, mdf.shape # print mdf.head() g = pcfg.Grammar('S') from td_isom_jaccard_sim import listify_rhs for (id, lhs, rhs, prob) in df.values: rhs = listify_rhs(rhs) # print (id), (lhs), (rhs), (prob) g.add_rule(pcfg.Rule(id, lhs, rhs, float(prob))) num_nodes = 16 # G.number_of_nodes() print "Starting max size", 'n=', num_nodes g.set_max_size(num_nodes) print "Done with max size" Hstars = [] print '-' * 40 try: rule_list = g.sample(num_nodes) except Exception, e: print str(e) continue hstar = phrg.grow(rule_list, g)[0] Hstars.append(hstar) print '+' * 40
def grow_exact_size_hrg_graphs_from_prod_rules(prod_rules, gname, n, runs=1): """ Args: rules: production rules (model) gname: graph name n: target graph order (number of nodes) runs: how many graphs to generate Returns: list of synthetic graphs """ DBG = True if n <= 0: sys.exit(1) g = pcfg.Grammar('S') for (id, lhs, rhs, prob) in prod_rules: g.add_rule(pcfg.Rule(id, lhs, rhs, prob)) print print "Added rules HRG (pr", len(prod_rules), ", n,", n, ")" num_nodes = n if DBG: print "Starting max size ..." t_start = time.time() g.set_max_size(num_nodes) print "Done with max size, took %s seconds" % (time.time() - t_start) hstars_lst = [] print " ", i = 0 max_tries = 10000 tries = 0 failed = False while i != runs: tries += 1 if tries > max_tries: failed = True break print '>', rule_list = g.sample(num_nodes) hstar = phrg.grow(rule_list, g)[0] if n * 0.99 <= hstar.order() <= n * 1.01: hstars_lst.append(hstar) i += 1 if len(hstars_lst) != runs or failed: print('HRG failed') return None return hstars_lst
def Hstar_Graphs_Control(G, graph_name, axs=None): # Derive the prod rules in a naive way, where prod_rules = phrg.probabilistic_hrg_learning(G) pp.pprint(prod_rules) g = pcfg.Grammar('S') for (id, lhs, rhs, prob) in prod_rules: g.add_rule(pcfg.Rule(id, lhs, rhs, prob)) num_nodes = G.number_of_nodes() print "Starting max size", 'n=', num_nodes g.set_max_size(num_nodes) print "Done with max size" Hstars = [] num_samples = 20 print '*' * 40 for i in range(0, num_samples): rule_list = g.sample(num_nodes) hstar = phrg.grow(rule_list, g)[0] Hstars.append(hstar) # if 0: # g = nx.from_pandas_dataframe(df, 'src', 'trg', edge_attr=['ts']) # draw_degree_whole_graph(g,axs) # draw_degree(Hstars, axs=axs, col='r') # #axs.set_title('Rules derived by ignoring time') # axs.set_ylabel('Frequency') # axs.set_xlabel('degree') if 0: # metricx = [ 'degree','hops', 'clust', 'assort', 'kcore','eigen','gcd'] metricx = ['clust'] # g = nx.from_pandas_dataframe(df, 'src', 'trg',edge_attr=['ts']) # graph_name = os.path.basename(f_path).rstrip('.tel') if DBG: print ">", graph_name metrics.network_properties([G], metricx, Hstars, name=graph_name, out_tsv=True)
def grow_hrg_graphs_with_infinity( prod_rules, gname, n, runs=1, rnbr=1, ): """ Args: rules: production rules (model) gname: graph name n: target graph order (number of nodes) runs: how many graphs to generate Returns: list of synthetic graphs """ DBG = True if n <= 0: sys.exit(1) g = pcfg.Grammar('S') for (id, lhs, rhs, prob) in prod_rules: g.add_rule(pcfg.Rule(id, lhs, rhs, prob)) print print "Added rules HRG (pr", len(prod_rules), ", n,", n, ")" num_nodes = n if DBG: print "Starting max size ..." t_start = time.time() g.set_max_size(num_nodes) print "Done with max size, took %s seconds" % (time.time() - t_start) hstars_lst = [] print " ", for i in range(0, runs): rule_list = g.sample(num_nodes) hstar = phrg.grow(rule_list, g)[0] hstars_lst.append(hstar) return hstars_lst
def main_inf_mirr(gFname, gName, rnbr=1): retVal = None G = xt.load_edgelist(gFname) # rnbr = 1 # for j in range(1, rnbr + 1): # prs = get_hrg_production_rules_given(G) Hstars = [ ] # synthetic (stochastically generate) graphs using the graph grammars ProdRulesKth = [] # Note that therem might not be convergence, b/c the graph may degenerate early for j in range(0, 10): # nbr of times to do Inf. Mirr. tst for k in range(0, 1): # nbr of times to feedback the resulting graph # print ("\tGraph #:",k+1) prdrls = {} prod_rules = phrg.probabilistic_hrg_deriving_prod_rules(G) # print len(prod_rules) # initialize the Grammar g g = pcfg.Grammar('S') for (id, lhs, rhs, prob) in prod_rules: g.add_rule(pcfg.Rule(id, lhs, rhs, prob)) num_nodes = G.number_of_nodes() g.set_max_size(num_nodes) print "Done initializing the grammar data-structure" # Generate a synthetic graph using HRGs try: rule_list = g.sample(num_nodes) except Exception, e: print str(e) rule_list = g.sample(num_nodes) break hstar = phrg.grow(rule_list, g)[0] G = hstar # feed back the newly created graph # store the last synth graph & restart Hstars.append(hstar) #
import core.PHRG as phrg import core.probabilistic_cfg as pcfg G = graph Hstars = [ ] # synthetic (stochastically generate) graphs using the graph grammars ProdRulesKth = [] # Note that therem might not be convergence, b/c the graph may degenerate early for j in range(0, 10): # nbr of times to do Inf. Mirr. tst for k in range(1, rnbr + 1): # nbr of times to feedback the resulting graph prdrls = {} prod_rules = phrg.probabilistic_hrg_deriving_prod_rules(G) # print len(prod_rules) # initialize the Grammar g g = pcfg.Grammar('S') for (id, lhs, rhs, prob) in prod_rules: g.add_rule(pcfg.Rule(id, lhs, rhs, prob)) num_nodes = G.number_of_nodes() g.set_max_size(num_nodes) print "Done initializing the grammar data-structure" # Generate a synthetic graph using HRGs try: rule_list = g.sample(num_nodes) except Exception, e: print str(e) rule_list = g.sample(num_nodes) break
def tst_prod_rules_isom_intrxn(fname, origfname): """ Test the isomorphic subset of rules :param fname: isom intersection rules file :param origfname: reference input network (dataset) edgelist file :return: """ # Get the original file fdf = Pandas_DataFrame_From_Edgelist([origfname]) origG = nx.from_pandas_dataframe(fdf[0], 'src', 'trg') origG.name = graph_name(origfname) print origG.name, "+" * 80 # Read the subset of prod rules df = pd.read_csv(fname, header=None, sep="\t", dtype={ 0: str, 1: list, 2: list, 3: float }) g = pcfg.Grammar('S') if not willFire_check(df): print "-" * 10, fname, "contains production rules that WillNotFire" return None else: print "+" * 40 # Process dataframe from td_isom_jaccard_sim import listify_rhs for (id, lhs, rhs, prob) in df.values: rhs = listify_rhs(rhs) g.add_rule(pcfg.Rule(id, lhs, rhs, float(prob))) print "\n", "." * 40 #print 'Added the rules to the datastructure' num_nodes = origG.number_of_nodes() # print "Starting max size", 'n=', num_nodes g.set_max_size(num_nodes) # print "Done with max size" Hstars = [] ofname = "FakeGraphs/" + origG.name + "_isom_ntrxn.shl" database = shelve.open(ofname) num_samples = 20 # print '~' * 40 for i in range(0, num_samples): rule_list = g.sample(num_nodes) hstar = phrg.grow(rule_list, g)[0] Hstars.append(hstar) print hstar.number_of_nodes(), hstar.number_of_edges() print '-' * 40 database['hstars'] = Hstars database.close()