def generate_pws(euler_asp_rules, num_solutions=0):
euler_asp_soln, meta_data = run_clingo(clingo_rules=euler_asp_rules,
num_solutions=num_solutions)
# TODO Add meta data, such as column names
# TODO Add missing dataframes eg. if po_2 doesn't exist, then add an empty df
pw_rel_dfs, rel_schemas, pw_objs = load_worlds(
asp_output=euler_asp_soln, meta_data=meta_data, reasoner='clingo')
return pw_rel_dfs, rel_schemas, pw_objs
def are_equivalent_patterns(pw1, pw2, eq_check_encoding):
pw1_edge_facts = get_edge_facts(pw1, 1)
pw2_edge_facts = get_edge_facts(pw2, 2)
pw1_head_facts = get_query_head_facts(pw1, idx=1)
pw2_head_facts = get_query_head_facts(pw2, idx=2)
#print(pw1_edge_facts, pw2_edge_facts, pw1_head_facts, pw2_head_facts)
asp_out, _ = run_clingo(eq_check_encoding.split('\n')+pw1_edge_facts+pw2_edge_facts+pw1_head_facts+pw2_head_facts, num_solutions=1)
#print(asp_out)
_,_,eq_check_pws = load_worlds(asp_out, silent=True)
return len(eq_check_pws) >= 1
def get_unexplored(self):
map_soln, _ = run_clingo(self.encoding, num_solutions=1)
pw_rel_dfs, rel_schemas, pws = load_worlds(map_soln, silent=True)
if len(pws) == 0:
return None
if 'comp_1' not in pw_rel_dfs:
return {}
comp_1_dfs = pw_rel_dfs['comp_1']
comp_1_dfs = comp_1_dfs[comp_1_dfs['pw'] == 1]
comps = set(list(comp_1_dfs['x1']))
return comps
def get_unexplored_max(self):
map_soln, _ = run_clingo(self.encoding + '\n' + self.COMP_COUNT_RULE +
'\n' + self.COMP_MAXIMIZE_RULE,
num_solutions=0)
pw_rel_dfs, rel_schemas, pws = load_worlds(map_soln, silent=True)
if len(pws) == 0:
return None
if 'comp_1' not in pw_rel_dfs:
return {}
max_pw = max(pws, key=lambda pw: -pw.pw_soln)
max_pw_id = max_pw.pw_id
comp_1_dfs = pw_rel_dfs['comp_1']
comp_1_dfs = comp_1_dfs[comp_1_dfs['pw'] == max_pw_id]
comps = set(list(comp_1_dfs['x1']))
return comps
def remove_redundant_edges(orig_pw_obj, edges_rel_name: str='pp_2'):
_,_, sliced_orig_pw_obj = rel_slicer(None, None, [orig_pw_obj], [edges_rel_name])
orig_pp_facts = PWEExport.export_as_asp_facts(sliced_orig_pw_obj, include_pw_ids=False)
rel_name = edges_rel_name.rsplit('_', maxsplit=1)[0] # Without arity
output_rel_name = 'useful_e'
redundancy_removal_rules = ['% define child(CHILD, PARENT)',
'child(X,Y) :- {}(X,Y).'.format(rel_name),
'child(X,Y) :- {}(X,Z), child(Z,Y).'.format(rel_name),
'redundant_child(X,Y) :- child(X,Y), child(X,Z), child(Z,Y), X!=Y, Y!=Z, X!=Y.',
'{}(X,Y) :- child(X,Y), not redundant_child(X,Y).'.format(output_rel_name),
'#show {}/2.'.format(output_rel_name),
]
useful_pp_clingo_soln, _ = run_clingo(orig_pp_facts+redundancy_removal_rules)
useful_pp_dfs, _, _ = load_worlds(useful_pp_clingo_soln, silent=True, reasoner='clingo')
useful_pp_dfs, _ = pw_slicer(useful_pp_dfs, None, [1])
return useful_pp_dfs['{}_2'.format(output_rel_name)]
def visualize_figure_from_asp_rules(db_asp_rules,
db_rel_name='propDB',
db_rel_arity=3,
property_name_column='property_name',
property_value_column='property_value',
figID_column='figure_ID',
figIDs_to_visualize=None):
asp_output, md = run_clingo(db_asp_rules)
pws_rels_dfs, rel_schemas, pw_objects = load_worlds(asp_output,
meta_data=md,
silent=True)
propDB_df_rel_name = "{}_{}".format(db_rel_name, str(db_rel_arity))
propDB_df = pws_rels_dfs[propDB_df_rel_name]
# Assumes that running the reasoner will only produce one PW (or that atleast all of them have the same DB)
propDB_df = propDB_df[propDB_df['pw'] == 1]
return WODBVizLib.visualize_figures(
propDB_df,
property_name_column=property_name_column,
property_value_column=property_value_column,
figID_column=figID_column,
figIDs_to_visualize=figIDs_to_visualize)