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288 lines
9.9 KiB
288 lines
9.9 KiB
from . import script
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from . import queries
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import dimod
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import random
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import numpy as np
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from bson.objectid import ObjectId
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import pandas as pd
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from tqdm import tqdm
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def extract_wmis_qpu_results():
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edb = script.connect_to_experimetns_db()
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edb_cursor = edb.cursor()
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idb = script.connect_to_instance_pool()
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scope = input("scope: ")
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result_collection = input("result collection: ")
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table_name = input("table name: ")
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q = queries.WMIS_result_scope_query_raw(idb)
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q.query(scope, result_collection)
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insert_row = ("INSERT INTO {} "
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"(result_id, "
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" run, "
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" instance_id, "
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" number_of_found_assignments, "
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" chain_break_fraction, "
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" num_occurrences, "
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" energy, "
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" satisfiable, "
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" anneal_time, "
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" energy_reach, "
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" sample_size) "
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"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) ").format(table_name)
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run = int(input("run: "))
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for result in tqdm(q):
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if result["run"] == run:
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sample_set = queries.read_raw_wmis_sample_set(result["data"])
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data = script.analyze_wmis_sample(sample_set.first)
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sat = queries.get_instance_by_id(idb["instances"], result["instance"])
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model = script.majority_vote_sample(sample_set.first.sample)
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isSatisfiable = sat.checkAssignment(model)
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anneal_time = result["data"]["info"]["timing"]["qpu_anneal_time_per_sample"]
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energy_reach = abs(sample_set.record["energy"].max() -
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sample_set.record["energy"].min())
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sample_size = np.sum(sample_set.record["num_occurrences"])
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edb_cursor.execute(insert_row, (str(result["_id"]),
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int(result["run"]),
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str(result["instance"]),
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int(data["number_of_assignments"]),
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float(data["chain_break_fraction"]),
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int(data["num_occurrences"]),
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int(data["energy"]),
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isSatisfiable,
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int(anneal_time),
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int(energy_reach),
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int(sample_size)))
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edb.commit()
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edb_cursor.close()
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edb.close()
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def extract_wmis_2_qpu_results():
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edb = script.connect_to_experimetns_db()
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edb_cursor = edb.cursor()
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idb = script.connect_to_instance_pool()
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scope = input("scope: ")
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result_collection = input("result collection: ")
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table_name = input("table name: ")
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q = queries.WMIS_result_scope_query_raw(idb)
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q.query(scope, result_collection)
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insert_row = ("INSERT INTO {} "
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"(result_id, "
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" run, "
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" instance_id, "
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" chain_break_fraction, "
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" num_occurrences, "
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" energy, "
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" satisfiable, "
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" anneal_time, "
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" energy_reach, "
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" sample_size) "
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"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s) ").format(table_name)
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run = int(input("run: "))
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for result in tqdm(q):
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if result["run"] == run:
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sample_set = queries.read_raw_primitive_ising_sample_set(result["data"])
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data = script.analyze_wmis_sample(sample_set.first)
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sat = queries.get_instance_by_id(idb["instances"], result["instance"])
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post_process_results = __evaluate_wmis_2_sample(sat,
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sample_set.first.sample)
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anneal_time = result["data"]["info"]["timing"]["qpu_anneal_time_per_sample"]
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energy_reach = abs(sample_set.record["energy"].max() -
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sample_set.record["energy"].min())
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sample_size = np.sum(sample_set.record["num_occurrences"])
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edb_cursor.execute(insert_row, (str(result["_id"]),
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int(result["run"]),
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str(result["instance"]),
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float(data["chain_break_fraction"]),
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int(data["num_occurrences"]),
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int(data["energy"]),
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bool(post_process_results["satisfiable"]),
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int(anneal_time),
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int(energy_reach),
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int(sample_size)))
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edb.commit()
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edb_cursor.close()
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edb.close()
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def __evaluate_wmis_2_sample(sat, sample):
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post_process_results = {}
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assignments = {}
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vars = set()
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for node, energy in sample.items():
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if node[0] == "x":
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lit = int(node[1:])
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vars.add(abs(lit))
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assignments[lit] = energy
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model = [True for i in range(len(vars))]
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for var in vars:
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if var in assignments:
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model[var - 1] = True if assignments[var] == 1 else False
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elif -var in assignments:
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model[var - 1] = True if assignments[-var] == 0 else False
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post_process_results["satisfiable"] = sat.checkAssignment(model)
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return post_process_results
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def extract_minisat_results():
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edb = script.connect_to_experimetns_db()
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edb_cursor = edb.cursor();
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idb = script.connect_to_instance_pool()
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scope = input("scope: ")
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table_name = input("table name: ")
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runs = queries.Minisat_run_scope_query_raw(idb)
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runs.query(scope, "minisat_runs")
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insert_row = ("INSERT INTO {} "
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"(run_id, "
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" instance_id, "
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" satisfiable) "
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"VALUES (%s, %s, %s) ").format(table_name)
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for run in tqdm(runs):
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data = script.analyde_minisat_run(run)
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edb_cursor.execute(insert_row, (str(run["_id"]),
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str(run["instance"]),
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int(data["satisfiable"])))
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edb.commit()
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edb_cursor.close()
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edb.close()
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def extract_instance_parameters():
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edb = script.connect_to_experimetns_db()
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edb_cursor = edb.cursor()
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idb = script.connect_to_instance_pool()
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scope = input("scope: ")
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table_name = input("table name: ")
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instances = queries.Instance_scope_query(idb)
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instances.query(scope)
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insert_row = ("INSERT INTO {} "
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"(instance_id, "
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" number_of_clauses, "
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" number_of_variables) "
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"VALUES (%s, %s, %s)").format(table_name)
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for instance, instance_id in tqdm(instances):
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edb_cursor.execute(insert_row, (str(instance_id),
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int(instance.getNumberOfClauses()),
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int(instance.getNumberOfVariables())))
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edb.commit()
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edb_cursor.close()
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edb.close()
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def extract_embedding_data():
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edb = script.connect_to_experimetns_db()
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edb_cursor = edb.cursor()
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idb = script.connect_to_instance_pool()
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scope = input("scope: ")
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table_name = input("table name: ")
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qubo_collection = input("qubo collection: ")
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solver_graph_id = ObjectId(input("solver graph id: "))
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solver_input = queries.WMIS_solver_input_scope_query(idb, qubo_collection)
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solver_input.query(scope, solver_graph_id)
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results = pd.DataFrame(columns=["instance_id",
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"embedding_list_id",
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"emb_list_index",
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"num_qubits",
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"avgr_chain_length",
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"median_chain_length"]);
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for soin in solver_input:
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emb_index = 0;
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for emb in soin["embeddings"]:
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chain_lengths = []
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qubits = set()
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for node, chain in emb.items():
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chain_lengths.append(len(chain))
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qubits = qubits.union(set(chain))
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results = results.append({"instance_id": soin["instance_id"],
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"embedding_list_id": soin["embeddings_id"],
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"emb_list_index": emb_index,
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"num_qubits": len(qubits),
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"avgr_chain_length": np.mean(chain_lengths),
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"median_chain_length": np.median(chain_lengths)},
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ignore_index=True)
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insert_row = ('''INSERT INTO {}
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(instance_id,
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embedding_list_id,
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emb_list_index,
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num_qubits,
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avgr_chain_length,
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median_chain_length)
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VALUES(%s, %s, %s, %s, %s, %s)''').format(table_name)
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for index, row in results.iterrows():
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edb_cursor.execute(insert_row, (str(row["instance_id"]),
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str(row["embedding_list_id"]),
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int(row["emb_list_index"]),
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int(row["num_qubits"]),
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float(row["avgr_chain_length"]),
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float(row["median_chain_length"])))
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edb.commit()
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edb_cursor.close()
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edb.close()
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