diff --git a/Qommunity/iterative_searcher/iterative_hierarchical_searcher.py b/Qommunity/iterative_searcher/iterative_hierarchical_searcher.py index 50d0ab09e..e0a591ca8 100644 --- a/Qommunity/iterative_searcher/iterative_hierarchical_searcher.py +++ b/Qommunity/iterative_searcher/iterative_hierarchical_searcher.py @@ -9,6 +9,12 @@ from tqdm import tqdm import numpy as np import warnings +import pickle + +from Qommunity.samplers.hierarchical.advantage_sampler import AdvantageSampler +from Qommunity.searchers.utils import HierarchicalRunMetadata + +METADATA_KEYARG = "return_metadata" class MethodArgsWarning(Warning): @@ -53,6 +59,11 @@ def _verify_kwargs(self, kwargs) -> dict: return kwargs + def _check_sampler_and_it_searcher_metadata_flags_compatibility( + self, return_metadata_flag: bool + ) -> bool: + return self.sampler.return_metadata and return_metadata_flag + def run( self, num_runs: int, @@ -60,10 +71,17 @@ def run( saving_path: str | None = None, elapse_times: bool = True, iterative_verbosity: int = 0, + return_metadata: bool = False, **kwargs, ): kwargs = self._verify_kwargs(kwargs) + if return_metadata and not self.sampler.return_metadata: + raise MethodArgsWarning( + f"Set Advantage sampler's {METADATA_KEYARG} flag to True before running." + + f" HierarchicalIterativeSearcher with {METADATA_KEYARG}." + ) + if iterative_verbosity >= 1: print("Starting community detection iterations") @@ -74,11 +92,19 @@ def run( communities = np.empty((num_runs), dtype=object) times = np.zeros((num_runs)) + # List instead of samplesets_data = np.empty((num_runs), dtype=object) + # To prevent jupyter notebook kernel crashes + # as handling big objects is not efficient with numpy dtype=object arrs + samplesets_data = [] + for iter in tqdm(range(num_runs)): elapsed = time() result = self.searcher.hierarchical_community_search(**kwargs) times[iter] = time() - elapsed + if METADATA_KEYARG in kwargs: + result, sampleset_metadata = result + try: modularity_score = nx.community.modularity( self.searcher.sampler.G, @@ -91,18 +117,28 @@ def run( communities[iter] = result modularities[iter] = modularity_score + if return_metadata: + samplesets_data.append(sampleset_metadata) if save_results: np.save(f"{saving_path}_modularities", modularities) np.save(f"{saving_path}_communities", communities) if elapse_times: np.save(f"{saving_path}_times", times) + if return_metadata: + # Pickle saving tends to be safer for big objects + with open(f"{saving_path}_samplesets_data.pkl", "wb") as f: + pickle.dump(samplesets_data, f) if iterative_verbosity >= 1: print(f"Iteration {iter} completed") + if elapse_times and return_metadata and sampleset_metadata: + return communities, modularities, times, sampleset_metadata if elapse_times: return communities, modularities, times + if return_metadata: + return communities, modularities, samplesets_data return communities, modularities def run_with_sampleset_info( @@ -111,9 +147,16 @@ def run_with_sampleset_info( save_results: bool = True, saving_path: str | None = None, iterative_verbosity: int = 0, + return_metadata: bool = True, **kwargs, ): + if return_metadata and hasattr(self.sampler, "return_metadata") and not self.sampler.return_metadata: + raise MethodArgsWarning( + f"Set Advantage sampler's {METADATA_KEYARG} flag to True before running." + + f" HierarchicalIterativeSearcher with {METADATA_KEYARG}." + ) + if iterative_verbosity >= 1: print("Starting community detection iterations") @@ -125,21 +168,50 @@ def run_with_sampleset_info( times = np.zeros((num_runs)) division_modularities = np.empty((num_runs), dtype=object) division_trees = np.empty((num_runs), dtype=object) + samplesets_data = np.empty((num_runs), dtype=object) + + if return_metadata and isinstance(self.sampler, AdvantageSampler): + kwargs[METADATA_KEYARG] = True + else: + return_metadata = False + kwargs[METADATA_KEYARG] = False for iter in tqdm(range(num_runs)): + run_label = f"iter_{iter}" + elapsed = time() - ( - communities_result, - div_tree, - div_modularities, - ) = self.searcher.hierarchical_community_search( + result = self.searcher.hierarchical_community_search( return_modularities=True, division_tree=True, + saving_path=saving_path, + label=run_label, **kwargs, ) + + # Currently only AdvantageSampler among the hierarchical solvers + # provides sampleset metadata. + if ( + isinstance(self.sampler, AdvantageSampler) + and self.sampler.return_metadata + and return_metadata + ): + ( + communities_result, + div_tree, + div_modularities, + sampleset_data, + ) = result + else: + ( + communities_result, + div_tree, + div_modularities, + ) = result times[iter] = time() - elapsed division_trees[iter] = div_tree division_modularities[iter] = div_modularities + if return_metadata: + samplesets_data[iter] = sampleset_data try: modularity_score = nx.community.modularity( @@ -163,25 +235,38 @@ def run_with_sampleset_info( f"{saving_path}_division_modularities", division_modularities, ) + # Pickle saving tends to be safer for big objects + if return_metadata: + try: + sampleset_data.save_to_files(base_filename=f"{saving_path}_{run_label}") + except Exception as e: + print(f"Error while saving HierarchicalRunMetadata (sampleset_data) from iteration: {iter}", e) + if iterative_verbosity >= 1: print(f"Iteration {iter} completed") dtypes = [ ("communities", object), - ("modularity", np.float_), - ("time", np.float_), + ("modularity", np.float64), + ("time", np.float64), ("division_tree", object), ("division_modularities", object), ] + sampleset_components = [ + communities, + modularities, + times, + division_trees, + division_modularities, + ] + + if return_metadata: + dtypes.append(("samplesets_data", object)) + sampleset_components.append(samplesets_data) + sampleset = np.rec.fromarrays( - [ - communities, - modularities, - times, - division_trees, - division_modularities, - ], + sampleset_components, dtype=dtypes, ) diff --git a/Qommunity/samplers/hierarchical/advantage_sampler/advantage_sampler.py b/Qommunity/samplers/hierarchical/advantage_sampler/advantage_sampler.py index d5cfacc30..c7d921538 100644 --- a/Qommunity/samplers/hierarchical/advantage_sampler/advantage_sampler.py +++ b/Qommunity/samplers/hierarchical/advantage_sampler/advantage_sampler.py @@ -11,11 +11,13 @@ def __init__( resolution: float = 1, community: list | None = None, use_weights: bool = True, - version: str = "Advantage_system5.4", - region: str = "eu-central-1", + version: str | None = None, + region: str | None = None, num_reads: int = 100, chain_strength: float | None = None, use_clique_embedding: bool = False, + elapse_times: bool = False, + return_metadata: bool = True, ) -> None: if not community: community = [*range(G.number_of_nodes())] @@ -28,9 +30,22 @@ def __init__( self.chain_strength = chain_strength self.use_clique_embedding = use_clique_embedding self._use_weights = use_weights + self.elapse_times = elapse_times + self.return_metadata = return_metadata weight = "weight" if use_weights else None - network = Network(G, resolution=resolution, weight=weight, community=community) + + if not hasattr(self, "_full_modularity_matrix"): + self._full_modularity_matrix = Network( + G, resolution=resolution, weight=weight, community=community + ).calculate_full_modularity_matrix() + network = Network( + G, + resolution=resolution, + weight=weight, + community=community, + full_modularity_matrix=self._full_modularity_matrix, + ) problem = CommunityDetectionProblem( network, communities=2, one_hot_encoding=False ) @@ -41,10 +56,18 @@ def __init__( num_reads=num_reads, chain_strength=chain_strength, use_clique_embedding=use_clique_embedding, + elapse_times=elapse_times, ) - def sample_qubo_to_dict(self) -> dict: - sample = self.advantage.solve() + def sample_qubo_to_dict(self, return_metadata: bool | None = None, label: str | None = None, saving_path: str | None = None) -> dict: + if return_metadata: + sample = self.advantage.solve( + return_metadata=self.return_metadata, + label = label, + saving_path=saving_path + ) + else: + sample = self.advantage.solve() variables = sorted( [col for col in sample.probabilities.dtype.names if col.startswith("x")], @@ -52,7 +75,11 @@ def sample_qubo_to_dict(self) -> dict: ) community = sample.probabilities[variables][0] - return dict(zip(variables, community)) + result = dict(zip(variables, community)) + + if return_metadata: + return result, sample.sampleset_info + return result def update_community(self, community: list) -> None: self.__init__( @@ -65,4 +92,9 @@ def update_community(self, community: list) -> None: self.num_reads, self.chain_strength, self.use_clique_embedding, + self.elapse_times, + self.return_metadata, ) + + def __str__(self): + return self.advantage.version + " " + self.advantage.region diff --git a/Qommunity/samplers/regular/dqm_sampler/dqm_sampler.py b/Qommunity/samplers/regular/dqm_sampler/dqm_sampler.py index 1a834bce3..0e584fa01 100644 --- a/Qommunity/samplers/regular/dqm_sampler/dqm_sampler.py +++ b/Qommunity/samplers/regular/dqm_sampler/dqm_sampler.py @@ -1,4 +1,4 @@ -from QHyper.solvers.quantum_annealing.dqm import DQM +from QHyper.solvers.quantum_annealing.dwave.dqm import DQM from QHyper.problems.community_detection import Network, CommunityDetectionProblem import networkx as nx from ..regular_sampler import RegularSampler diff --git a/Qommunity/searchers/hierarchical_searcher/hierarchical_searcher.py b/Qommunity/searchers/hierarchical_searcher/hierarchical_searcher.py index 348a3cd53..b1278d117 100644 --- a/Qommunity/searchers/hierarchical_searcher/hierarchical_searcher.py +++ b/Qommunity/searchers/hierarchical_searcher/hierarchical_searcher.py @@ -1,6 +1,9 @@ from Qommunity.samplers.hierarchical.hierarchical_sampler import HierarchicalSampler +from Qommunity.samplers.hierarchical.advantage_sampler import AdvantageSampler import networkx as nx +from searchers.utils import HierarchicalRunMetadata + class HierarchicalSearcher: def __init__(self, sampler: HierarchicalSampler) -> None: @@ -43,21 +46,31 @@ def hierarchical_community_search( max_depth: int | None = None, division_tree: bool = False, return_modularities: bool = False, + return_metadata: bool = False, + saving_path: str | None = None, + label: str | None = None, + # kwargs** ) -> list: if verbosity >= 1: print("Starting community detection") - if max_depth == None: + if max_depth is None or max_depth > 1: if division_tree == False: division_tree = None else: division_tree = [] + samplesets_metadata = [] + result = self._hierarchical_search_recursion( verbosity=verbosity, level=1, max_depth=max_depth, division_tree=division_tree, + samplesets_metadata=samplesets_metadata, + return_metadata=return_metadata, + saving_path=saving_path, + label=label ) if division_tree: @@ -112,12 +125,19 @@ def list_of_lists_sorted(list_of_lists: list[list]) -> list[list]: for division in division_tree: print(division) + if len(samplesets_metadata) > 0 and return_metadata: + samplesets_metadata = HierarchicalRunMetadata(samplesets_metadata) + + if division_tree and return_modularities and return_metadata: + return result, division_tree, division_modularities, samplesets_metadata if division_tree and return_modularities: return result, division_tree, division_modularities if division_tree: return result, division_tree if return_modularities: return result, division_modularities + if return_metadata: + return result, samplesets_metadata else: return result elif max_depth < 1: @@ -133,11 +153,15 @@ def _hierarchical_search_recursion( level: int, community: list | None = None, division_tree: list | None = None, + samplesets_metadata: list | None = None, + return_metadata: bool = False, + saving_path: str | None = None, + label: str | None = None ): if not community: community = [*range(self.sampler.G.number_of_nodes())] - if len(community) == 1: + if len(community) < 2: return [community] if level == 1 and division_tree == []: @@ -153,11 +177,33 @@ def _hierarchical_search_recursion( ) print("===========================================") - self.sampler.update_community(community) - sample = self.sampler.sample_qubo_to_dict() - - c0, c1 = self._split_dict_to_lists(sample, community) + try: + self.sampler.update_community(community) + + # Currently only AdvantageSampler among the hierarchical solvers + # provides sampleset metadata. + if ( + isinstance(self.sampler, AdvantageSampler) + and self.sampler.return_metadata + and return_metadata + ): + sample, sampleset_full = self.sampler.sample_qubo_to_dict( + return_metadata=return_metadata, + saving_path=saving_path, + label = label + ) + samplesets_metadata.append(sampleset_full) + else: + sample = self.sampler.sample_qubo_to_dict() + c0, c1 = self._split_dict_to_lists(sample, community) + except Exception as e: + if verbosity >= 1: + print( + f"[WARNING] Skipping community {community} at level {level} due to error: {e}" + ) + return [community] + if verbosity >= 2: print("Base community:", community, sep="\n") print("Community division:", c0, c1, sep="\n") @@ -189,12 +235,20 @@ def _hierarchical_search_recursion( level=level + 1, community=c0, division_tree=division_tree, + samplesets_metadata=samplesets_metadata, + return_metadata=return_metadata, + saving_path=saving_path, + label=label ) + self._hierarchical_search_recursion( verbosity, max_depth, level=level + 1, community=c1, division_tree=division_tree, + samplesets_metadata=samplesets_metadata, + return_metadata=return_metadata, + saving_path=saving_path, + label=label ) elif c0: return [c0] diff --git a/Qommunity/searchers/utils.py b/Qommunity/searchers/utils.py new file mode 100644 index 000000000..495e62ed1 --- /dev/null +++ b/Qommunity/searchers/utils.py @@ -0,0 +1,264 @@ +from enum import Enum +from QHyper.solvers.base import SamplesetData +from dataclasses import dataclass, field +import numpy as np +from typing import Union, List, Dict, Any +import pickle +import os +from dimod.sampleset import SampleSet +import json + + +class MetadataFieldName(Enum): + DwaveSamplesetMetadata = "dwave_sampleset_metadata" + TimeMeasurements = "time_measurements" + DWaveSampleset = "dwave_sampleset" + Timing = "timing" + ProblemID = "problem_id" + CommunityHash = "community_hash" + ChainStrength = "chain_strength" + ChainBreakFraction = "chain_break_fraction" + ChainBreakMethod = "chain_break_method" + Embedding = "embedding" + Warnings = "warnings" + Community = "community" + + +@dataclass +class ChainsData: + chain_strength: float = field(default=None) + chain_break_fraction: float = field(default=None) + + +@dataclass +class HierarchicalRunMetadata: + dwave_sampleset_metadata: np.recarray = field(init=False) + time_measurements: np.recarray = field(init=False) + dwave_sampleset: List[Dict[str, Any]] = field(init=False) + timing: List[Dict[str, Any]] = field(init=False) + problem_id: List[Union[str, int, float]] = field(init=False) + community_hash: List[Union[str, int]] = field(init=False) + chain_strength: List[float] = field(init=False) + chain_break_fraction: List[float] = field(init=False) + chain_break_method: List[str] = field(init=False) + embedding: List[Dict[str, Any]] = field(init=False) + warnings: List[Dict[str, Any]] = field(init=False) + community: List[List[int]] = field(init=False) + + def __init__(self, sampleset: List[SamplesetData]): + self.dwave_sampleset_metadata = self._process_samples( + sampleset, MetadataFieldName.DwaveSamplesetMetadata.value + ) + self.time_measurements = self._process_samples( + sampleset, MetadataFieldName.TimeMeasurements.value + ) + self.dwave_sampleset = self._process_samples( + sampleset, MetadataFieldName.DWaveSampleset.value + ) + self.timing = self._process_samples( + sampleset, MetadataFieldName.Timing.value + ) + self.problem_id = self._process_samples( + sampleset, MetadataFieldName.ProblemID.value + ) + self.community_hash = self._process_samples( + sampleset, MetadataFieldName.CommunityHash.value + ) + self.chain_strength = self._process_samples( + sampleset, MetadataFieldName.ChainStrength.value + ) + self.chain_break_fraction = self._process_samples( + sampleset, MetadataFieldName.ChainBreakFraction.value + ) + self.chain_break_method = self._process_samples( + sampleset, MetadataFieldName.ChainBreakMethod.value + ) + self.embedding = self._process_samples( + sampleset, MetadataFieldName.Embedding.value + ) + self.warnings = self._process_samples( + sampleset, MetadataFieldName.Warnings.value + ) + self.community = self._process_samples( + sampleset, MetadataFieldName.Community.value + ) + + def __iter__(self): + self.idx = 0 + return self + + def __next__(self): + if self.idx < self.__len__(): + result = SamplesetData( + dwave_sampleset_metadata=self.dwave_sampleset_metadata[self.idx], + time_measurements=self.time_measurements[self.idx], + dwave_sampleset=self.dwave_sampleset[self.idx], + timing=self.timing[self.idx], + problem_id=self.problem_id[self.idx], + community_hash=self.community_hash[self.idx], + chain_strength=self.chain_strength[self.idx], + chain_break_fraction=self.chain_break_fraction[self.idx], + chain_break_method=self.chain_break_method[self.idx], + embedding=self.embedding[self.idx], + warnings=self.warnings[self.idx], + community=self.community[self.idx], + ) + self.idx += 1 + return result + else: + raise StopIteration + + def __len__(self): + return len(self.community_hash) + + + # def __getitem__(self, index): + # if index < 0 or index >= self.__len__(): + # raise IndexError("Index out of range") + # return SamplesetData( + # dwave_sampleset_metadata=self.dwave_sampleset_metadata[index], + # time_measurements=self.time_measurements[index], + # dwave_sampleset=self.dwave_sampleset[index], + # timing=self.timing[index], + # problem_id=self.problem_id[index], + # community_hash=self.community_hash[index], + # chain_strength=self.chain_strength[index], + # chain_break_fraction=self.chain_break_fraction[index], + # chain_break_method=self.chain_break_method[index], + # embedding=self.embedding[index], + # warnings=self.warnings[index], + # ) + + def __getitem__(self, community_hash: str | int): + self_hashes = self.community_hash + if community_hash not in self_hashes: + raise KeyError(f"Index must be community_hash in this method. Community hash '{community_hash}' not found") + index = self_hashes.index(community_hash) + if index < 0 or index >= self.__len__(): + raise IndexError("Index is a community_hash. Index out of range") + return SamplesetData( + dwave_sampleset_metadata=self.dwave_sampleset_metadata[index], + time_measurements=self.time_measurements[index], + dwave_sampleset=self.dwave_sampleset[index], + timing=self.timing[index], + problem_id=self.problem_id[index], + community_hash=self.community_hash[index], + chain_strength=self.chain_strength[index], + chain_break_fraction=self.chain_break_fraction[index], + chain_break_method=self.chain_break_method[index], + embedding=self.embedding[index], + warnings=self.warnings[index], + community=self.community[index], + ) + + def get_with_hash_id(self, community_hash: str | int) -> SamplesetData: + self_hashes = self.community_hash + if community_hash not in self_hashes: + raise KeyError(f"Index must be community_hash in this method. Community hash '{community_hash}' not found") + index = self_hashes.index(community_hash) + if index < 0 or index >= self.__len__(): + raise IndexError("Index is a community_hash. Index out of range") + return SamplesetData( + dwave_sampleset_metadata=self.dwave_sampleset_metadata[index], + time_measurements=self.time_measurements[index], + dwave_sampleset=self.dwave_sampleset[index], + timing=self.timing[index], + problem_id=self.problem_id[index], + community_hash=self.community_hash[index], + chain_strength=self.chain_strength[index], + chain_break_fraction=self.chain_break_fraction[index], + chain_break_method=self.chain_break_method[index], + embedding=self.embedding[index], + warnings=self.warnings[index], + community=self.community[index], + ) + + + def _process_samples( + self, sampleset: List[SamplesetData], field_name: str + ) -> Any: + """ + Process a list of SamplesetData and extract a given field. + - Returns np.recarray if the field is a recarray + - Returns a list of values (dict, int, float, str, etc.) otherwise + """ + first_value = getattr(sampleset[0], field_name) + + if isinstance(first_value, np.recarray): + dtype = first_value.dtype.descr + concatenated = np.concatenate( + [ + np.array([getattr(division, field_name)], dtype=dtype) + for division in sampleset + ] + ).view(np.recarray) + return concatenated + else: + # Works for dicts, numbers, strings, lists, etc. + return [getattr(division, field_name) for division in sampleset] + + + def save_to_files(self, base_filename: str) -> None: + """ + Save each metadata field to a separate .npy file. + """ + # Safety check to avoid overwriting existing files + headers_file = f"{base_filename}_headers.pkl" + if os.path.exists(headers_file): + raise FileExistsError(f"File '{headers_file}' already exists. The saving action is not recommended.") + for field in MetadataFieldName: + data_file = f"{base_filename}_{field.value}.pkl" + if os.path.exists(data_file): + raise FileExistsError(f"File '{data_file}' already exists. The saving action is not recommended.") + + headers = [field.value for field in MetadataFieldName] + with open(f"{base_filename}_headers.pkl", "wb") as f: + pickle.dump(headers, f) + for field in MetadataFieldName: + data = getattr(self, field.value) + with open(f"{base_filename}_{field.value}.pkl", "wb") as f: + pickle.dump(data, f) + + def save_dwave_samplesets_serializables(path: str): + dwave_samplesets_serializables = [ds.to_serializable() for ds in self.dwave_sampleset] + with open(path, 'wb') as file: + pickle.dump(dwave_samplesets_serializables, file, protocol=pickle.HIGHEST_PROTOCOL) + + def save_embeddings_dicts(path: str): + with open(path, 'w') as f: + json.dump(self.embedding, f, indent=4) + + save_dwave_samplesets_serializables(f"{base_filename}_{MetadataFieldName.DWaveSampleset.value}.pickle") + save_embeddings_dicts(f"{base_filename}_{MetadataFieldName.Embedding.value}_dict.json") + + @staticmethod + def load_from_files(base_filename: str) -> None: + with open(f"{base_filename}_headers.pkl", "rb") as f: + headers = pickle.load(f) + hierarchical_metadata_new = HierarchicalRunMetadata.__new__(HierarchicalRunMetadata) + for field in MetadataFieldName: + try: + with open(f"{base_filename}_{field.value}.pkl", "rb") as f: + data = pickle.load(f) + setattr(hierarchical_metadata_new, field.value, data) + except Exception as e: + print(f"Could not load {field.value}: {e}") + setattr(hierarchical_metadata_new, field.value, None) + assert sorted(headers) == sorted([field.value for field in MetadataFieldName]) + + with open(f"{base_filename}_{MetadataFieldName.DWaveSampleset.value}.pickle", "rb") as file: + data = pickle.load(file) + data = [SampleSet.from_serializable(ds) for ds in data] + setattr(hierarchical_metadata_new, MetadataFieldName.DWaveSampleset.value, data) + print(f"DWave sampleset info loaded.") + + + # try: + with open(f"{base_filename}_{MetadataFieldName.Embedding.value}_dict.json", "rb") as file: + data = json.load(file) + # except Exception as e: + # data = [] + setattr(hierarchical_metadata_new, MetadataFieldName.Embedding.value, data) + print(f"Embeddings info loaded") + + return hierarchical_metadata_new diff --git a/demo.ipynb b/demo.ipynb index 26ceb8362..333c15239 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -36,17 +36,17 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from Qommunity.samplers.hierarchical.advantage_sampler import AdvantageSampler\n", - "from Qommunity.searchers.hierarchical_community_searcher import (\n", - " HierarchicalCommunitySearcher,\n", + "from Qommunity.searchers.hierarchical_searcher.hierarchical_searcher import (\n", + " HierarchicalSearcher,\n", ")\n", "\n", "advantage = AdvantageSampler(G)\n", - "searcher = HierarchicalCommunitySearcher(advantage)" + "searcher = HierarchicalSearcher(advantage)" ] }, { @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -85,8 +85,8 @@ "Base community:\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "Community division:\n", - "[14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 21]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21]\n", + "[8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "===========================================\n", "\n", "Stopping community detection\n" @@ -95,11 +95,11 @@ { "data": { "text/plain": [ - "[[14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33],\n", - " [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 21]]" + "[[0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21],\n", + " [8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]]" ] }, - "execution_count": 25, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -133,85 +133,78 @@ "Base community:\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "Community division:\n", - "[0, 1, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21]\n", - "[2, 8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21]\n", + "[8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "===========================================\n", - "\n", "===========================================\n", - "Calculations for graph with 15 nodes, level of recursion: 2\n", + "Calculations for graph with 16 nodes, level of recursion: 2\n", "===========================================\n", "Base community:\n", - "[0, 1, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21]\n", "Community division:\n", + "[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21]\n", "[4, 5, 6, 10, 16]\n", - "[0, 1, 3, 7, 11, 12, 13, 17, 19, 21]\n", "===========================================\n", - "\n", "===========================================\n", - "Calculations for graph with 5 nodes, level of recursion: 3\n", + "Calculations for graph with 11 nodes, level of recursion: 3\n", "===========================================\n", "Base community:\n", - "[4, 5, 6, 10, 16]\n", + "[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21]\n", "Community division:\n", - "[4, 5, 6, 10, 16]\n", "[]\n", + "[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21]\n", "===========================================\n", - "\n", "===========================================\n", - "Calculations for graph with 10 nodes, level of recursion: 3\n", + "Calculations for graph with 5 nodes, level of recursion: 3\n", "===========================================\n", "Base community:\n", - "[0, 1, 3, 7, 11, 12, 13, 17, 19, 21]\n", + "[4, 5, 6, 10, 16]\n", "Community division:\n", "[]\n", - "[0, 1, 3, 7, 11, 12, 13, 17, 19, 21]\n", + "[4, 5, 6, 10, 16]\n", "===========================================\n", - "\n", "===========================================\n", - "Calculations for graph with 19 nodes, level of recursion: 2\n", + "Calculations for graph with 18 nodes, level of recursion: 2\n", "===========================================\n", "Base community:\n", - "[2, 8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", + "[8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "Community division:\n", - "[2, 9, 24, 25, 27, 28, 31]\n", - "[8, 14, 15, 18, 20, 22, 23, 26, 29, 30, 32, 33]\n", + "[8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33]\n", + "[23, 24, 25, 27, 28, 31]\n", "===========================================\n", - "\n", "===========================================\n", - "Calculations for graph with 7 nodes, level of recursion: 3\n", + "Calculations for graph with 12 nodes, level of recursion: 3\n", "===========================================\n", "Base community:\n", - "[2, 9, 24, 25, 27, 28, 31]\n", + "[8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33]\n", "Community division:\n", - "[2, 9, 24, 25, 27, 28, 31]\n", + "[8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33]\n", "[]\n", "===========================================\n", - "\n", "===========================================\n", - "Calculations for graph with 12 nodes, level of recursion: 3\n", + "Calculations for graph with 6 nodes, level of recursion: 3\n", "===========================================\n", "Base community:\n", - "[8, 14, 15, 18, 20, 22, 23, 26, 29, 30, 32, 33]\n", + "[23, 24, 25, 27, 28, 31]\n", "Community division:\n", + "[23, 24, 25, 27, 28, 31]\n", "[]\n", - "[8, 14, 15, 18, 20, 22, 23, 26, 29, 30, 32, 33]\n", "===========================================\n", - "\n", "Stopping community detection\n", "Result: \n", - "[[4, 5, 6, 10, 16], [0, 1, 3, 7, 11, 12, 13, 17, 19, 21], [2, 9, 24, 25, 27, 28, 31], [8, 14, 15, 18, 20, 22, 23, 26, 29, 30, 32, 33]]\n" + "[[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21], [4, 5, 6, 10, 16], [8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33], [23, 24, 25, 27, 28, 31]]\n" ] }, { "data": { "text/plain": [ - "[[4, 5, 6, 10, 16],\n", - " [0, 1, 3, 7, 11, 12, 13, 17, 19, 21],\n", - " [2, 9, 24, 25, 27, 28, 31],\n", - " [8, 14, 15, 18, 20, 22, 23, 26, 29, 30, 32, 33]]" + "[[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21],\n", + " [4, 5, 6, 10, 16],\n", + " [8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33],\n", + " [23, 24, 25, 27, 28, 31]]" ] }, - "execution_count": 30, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -236,24 +229,24 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from Qommunity.samplers.regular.dqm_sampler import DQMSampler\n", "from Qommunity.samplers.regular.louvain_sampler import LouvainSampler\n", - "from Qommunity.searchers.community_searcher import CommunitySearcher\n", + "from Qommunity.searchers.regular_searcher.regular_searcher import RegularSearcher\n", "\n", "dqm = DQMSampler(G, 5, 4)\n", "louvain = LouvainSampler(G, 1)\n", "\n", - "dqm_searcher = CommunitySearcher(dqm)\n", - "louvain_searcher = CommunitySearcher(louvain)" + "dqm_searcher = RegularSearcher(dqm)\n", + "louvain_searcher = RegularSearcher(louvain)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -268,8 +261,8 @@ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "Community division:\n", "[23, 24, 25, 27, 28, 31]\n", - "[4, 5, 6, 10, 16]\n", "[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21]\n", + "[4, 5, 6, 10, 16]\n", "[8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33]\n", "===========================================\n", "Stopping community detection\n" @@ -279,12 +272,12 @@ "data": { "text/plain": [ "[[23, 24, 25, 27, 28, 31],\n", - " [4, 5, 6, 10, 16],\n", " [0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21],\n", + " [4, 5, 6, 10, 16],\n", " [8, 9, 14, 15, 18, 20, 22, 26, 29, 30, 32, 33]]" ] }, - "execution_count": 19, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -295,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -309,10 +302,10 @@ "Base community:\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "Community division:\n", - "['x0', 'x1', 'x2', 'x3', 'x7', 'x11', 'x12', 'x13', 'x17', 'x19', 'x21']\n", - "['x4', 'x5', 'x6', 'x10', 'x16']\n", - "['x23', 'x24', 'x25', 'x27', 'x28', 'x31']\n", - "['x8', 'x9', 'x14', 'x15', 'x18', 'x20', 'x22', 'x26', 'x29', 'x30', 'x32', 'x33']\n", + "['s0', 's1', 's2', 's3', 's7', 's11', 's12', 's13', 's17', 's19', 's21']\n", + "['s4', 's5', 's6', 's10', 's16']\n", + "['s23', 's24', 's25', 's27', 's28', 's31']\n", + "['s8', 's9', 's14', 's15', 's18', 's20', 's22', 's26', 's29', 's30', 's32', 's33']\n", "===========================================\n", "Stopping community detection\n" ] @@ -320,43 +313,43 @@ { "data": { "text/plain": [ - "{'x0': 0,\n", - " 'x1': 0,\n", - " 'x2': 0,\n", - " 'x3': 0,\n", - " 'x4': 1,\n", - " 'x5': 1,\n", - " 'x6': 1,\n", - " 'x7': 0,\n", - " 'x8': 3,\n", - " 'x9': 3,\n", - " 'x10': 1,\n", - " 'x11': 0,\n", - " 'x12': 0,\n", - " 'x13': 0,\n", - " 'x14': 3,\n", - " 'x15': 3,\n", - " 'x16': 1,\n", - " 'x17': 0,\n", - " 'x18': 3,\n", - " 'x19': 0,\n", - " 'x20': 3,\n", - " 'x21': 0,\n", - " 'x22': 3,\n", - " 'x23': 2,\n", - " 'x24': 2,\n", - " 'x25': 2,\n", - " 'x26': 3,\n", - " 'x27': 2,\n", - " 'x28': 2,\n", - " 'x29': 3,\n", - " 'x30': 3,\n", - " 'x31': 2,\n", - " 'x32': 3,\n", - " 'x33': 3}" + "{'s0': 0,\n", + " 's1': 0,\n", + " 's2': 0,\n", + " 's3': 0,\n", + " 's4': 1,\n", + " 's5': 1,\n", + " 's6': 1,\n", + " 's7': 0,\n", + " 's8': 3,\n", + " 's9': 3,\n", + " 's10': 1,\n", + " 's11': 0,\n", + " 's12': 0,\n", + " 's13': 0,\n", + " 's14': 3,\n", + " 's15': 3,\n", + " 's16': 1,\n", + " 's17': 0,\n", + " 's18': 3,\n", + " 's19': 0,\n", + " 's20': 3,\n", + " 's21': 0,\n", + " 's22': 3,\n", + " 's23': 2,\n", + " 's24': 2,\n", + " 's25': 2,\n", + " 's26': 3,\n", + " 's27': 2,\n", + " 's28': 2,\n", + " 's29': 3,\n", + " 's30': 3,\n", + " 's31': 2,\n", + " 's32': 3,\n", + " 's33': 3}" ] }, - "execution_count": 20, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -367,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -383,8 +376,7 @@ "Community division:\n", "[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21]\n", "[16, 4, 5, 6, 10]\n", - "[32, 33, 8, 9, 14, 15, 18, 20, 22, 23, 26, 27, 29, 30]\n", - "[24, 25, 28, 31]\n", + "[8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "===========================================\n", "Stopping community detection\n" ] @@ -394,11 +386,10 @@ "text/plain": [ "[[0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21],\n", " [16, 4, 5, 6, 10],\n", - " [32, 33, 8, 9, 14, 15, 18, 20, 22, 23, 26, 27, 29, 30],\n", - " [24, 25, 28, 31]]" + " [8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]]" ] }, - "execution_count": 23, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -409,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -423,10 +414,10 @@ "Base community:\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]\n", "Community division:\n", - "['x0', 'x4', 'x5', 'x6', 'x10', 'x11', 'x16', 'x17', 'x19', 'x21']\n", - "['x1', 'x2', 'x3', 'x7', 'x12', 'x13']\n", - "['x23', 'x24', 'x25', 'x27', 'x28', 'x31']\n", - "['x32', 'x33', 'x8', 'x9', 'x14', 'x15', 'x18', 'x20', 'x22', 'x26', 'x29', 'x30']\n", + "['x0', 'x1', 'x2', 'x3', 'x7', 'x11', 'x12', 'x13', 'x17', 'x19', 'x21']\n", + "['x16', 'x4', 'x5', 'x6', 'x10']\n", + "['x24', 'x25', 'x28', 'x31']\n", + "['x32', 'x33', 'x8', 'x9', 'x14', 'x15', 'x18', 'x20', 'x22', 'x23', 'x26', 'x27', 'x29', 'x30']\n", "===========================================\n", "Stopping community detection\n" ] @@ -435,25 +426,23 @@ "data": { "text/plain": [ "{'x0': 0,\n", - " 'x4': 0,\n", - " 'x5': 0,\n", - " 'x6': 0,\n", - " 'x10': 0,\n", + " 'x1': 0,\n", + " 'x2': 0,\n", + " 'x3': 0,\n", + " 'x7': 0,\n", " 'x11': 0,\n", - " 'x16': 0,\n", + " 'x12': 0,\n", + " 'x13': 0,\n", " 'x17': 0,\n", " 'x19': 0,\n", " 'x21': 0,\n", - " 'x1': 1,\n", - " 'x2': 1,\n", - " 'x3': 1,\n", - " 'x7': 1,\n", - " 'x12': 1,\n", - " 'x13': 1,\n", - " 'x23': 2,\n", + " 'x16': 1,\n", + " 'x4': 1,\n", + " 'x5': 1,\n", + " 'x6': 1,\n", + " 'x10': 1,\n", " 'x24': 2,\n", " 'x25': 2,\n", - " 'x27': 2,\n", " 'x28': 2,\n", " 'x31': 2,\n", " 'x32': 3,\n", @@ -465,12 +454,14 @@ " 'x18': 3,\n", " 'x20': 3,\n", " 'x22': 3,\n", + " 'x23': 3,\n", " 'x26': 3,\n", + " 'x27': 3,\n", " 'x29': 3,\n", " 'x30': 3}" ] }, - "execution_count": 31, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -496,7 +487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.11.1" } }, "nbformat": 4, diff --git a/demo/demo_check_simple_graphs.ipynb b/demo/demo_check_simple_graphs.ipynb index 036feadbe..767ec712b 100644 --- a/demo/demo_check_simple_graphs.ipynb +++ b/demo/demo_check_simple_graphs.ipynb @@ -549,7 +549,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "qomm_env", "language": "python", "name": "python3" }, @@ -563,7 +563,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/demo/sampleset_times_extraction/iterative_searcher_sampleset_times_extraction.ipynb b/demo/sampleset_times_extraction/iterative_searcher_sampleset_times_extraction.ipynb new file mode 100644 index 000000000..d9c49ed1c --- /dev/null +++ b/demo/sampleset_times_extraction/iterative_searcher_sampleset_times_extraction.ipynb @@ -0,0 +1,1455 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6faf1a22", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from Qommunity.samplers.hierarchical.advantage_sampler import AdvantageSampler\n", + "from Qommunity.searchers.hierarchical_searcher import HierarchicalSearcher\n", + "from Qommunity.iterative_searcher import IterativeSearcher" + ] + }, + { + "cell_type": "markdown", + "id": "654446d8", + "metadata": {}, + "source": [ + "## Please use it with QHyper basiav/sampleset_info_extraction branch\n", + "https://github.com/qc-lab/QHyper/tree/basiav/sampleset_info_extraction" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6f6a54f6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "\n", + "test_dir = \"test\"\n", + "os.makedirs(test_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "355ce839", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Graph loaded from test/graph_powerlaw_n=100_m=1_p=0.1.npy.\n" + ] + } + ], + "source": [ + "n = 100\n", + "m = 1\n", + "p=0.1\n", + "\n", + "graph_powerlaw_100_path = f\"{test_dir}/graph_powerlaw_n={n}_m={m}_p={p}.npy\"\n", + "try:\n", + " G = np.load(graph_powerlaw_100_path, allow_pickle=True)[0]\n", + " print(f\"Graph loaded from {graph_powerlaw_100_path}.\")\n", + "except Exception as e:\n", + " print(f\"Could not load graph from {graph_powerlaw_100_path}. Generating a new one.\")\n", + " G = nx.powerlaw_cluster_graph(n=n, m=m, p=p)\n", + " G_arr = np.empty((1,), dtype=object)\n", + " G_arr[0] = G\n", + " np.save(graph_powerlaw_100_path, G_arr)\n", + " print(f\"Graph saved to {graph_powerlaw_100_path}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "22f9156a", + "metadata": {}, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "markdown", + "id": "4c5b6c56", + "metadata": {}, + "source": [ + "#### 1. For the `AdvantageSampler`, set `return_metadata=True` and `elapse times=True`" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "5b0a7d5d", + "metadata": {}, + "outputs": [], + "source": [ + "num_reads = 100\n", + "version = \"\"\n", + "region = \"na-west-1\"\n", + "\n", + "advantage = AdvantageSampler(\n", + " G, num_reads=num_reads, version=version, region=region, use_clique_embedding=True, elapse_times=True, return_metadata=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "ce0a356b", + "metadata": {}, + "outputs": [], + "source": [ + "iterative_searcher = IterativeSearcher(sampler=advantage)" + ] + }, + { + "cell_type": "markdown", + "id": "14bc4f03", + "metadata": {}, + "source": [ + "Quick reminder of IterativeSearcher's usage we have practiced so far for full hierarchical runs info:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "e3cea177", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting community detection iterations\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [02:32<00:00, 152.20s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "res_like_before = iterative_searcher.run_with_sampleset_info(\n", + " num_runs=1,\n", + " save_results=False,\n", + " iterative_verbosity=1,\n", + " return_metadata=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "be76f93d", + "metadata": {}, + "outputs": [], + "source": [ + "res = res_like_before\n", + "communities, modularities, times, division_trees, division_modularities = res.communities, res.modularity, res.time, res.division_tree, res.division_modularities" + ] + }, + { + "cell_type": "markdown", + "id": "3f1fef5f", + "metadata": {}, + "source": [ + "### 2. Set `return_metadata=True` to `ItearativeSearcher`'s run method" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "b2d79efe", + "metadata": {}, + "outputs": [], + "source": [ + "dir_adv_clique = f\"{test_dir}/advantage_clique\"\n", + "os.makedirs(dir_adv_clique, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "5fa48d56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting community detection iterations\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|██ | 1/5 [04:09<16:39, 249.93s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|████ | 2/5 [08:39<13:04, 261.50s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 3/5 [11:38<07:27, 223.93s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 2 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 80%|████████ | 4/5 [16:48<04:17, 257.97s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 3 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [19:01<00:00, 228.33s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 4 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "res = iterative_searcher.run_with_sampleset_info(\n", + " num_runs=5,\n", + " save_results=True,\n", + " saving_path=dir_adv_clique,\n", + " iterative_verbosity=1,\n", + " return_metadata=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "d5cddf69", + "metadata": {}, + "outputs": [], + "source": [ + "communities, modularities, times, division_trees, division_modularities, sampleset_datas = res.communities, res.modularity, res.time, res.division_tree, res.division_modularities, res.samplesets_data" + ] + }, + { + "cell_type": "markdown", + "id": "7e407be7", + "metadata": {}, + "source": [ + "## Sampleset data interpretation" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "165f5ffd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data from 5 iteartive runs\n", + "Number of Problem (QUBO) forumulations in the first run: 21\n", + "Number of Problem (QUBO) forumulations in the second run: 22\n" + ] + } + ], + "source": [ + "# Shape interpretation\n", + "print(f\"Data from {len(sampleset_datas)} iteartive runs\")\n", + "print(f\"Number of Problem (QUBO) forumulations in the first run: {len(sampleset_datas[0].dwave_sampleset_metadata)}\")\n", + "print(f\"Number of Problem (QUBO) forumulations in the second run: {len(sampleset_datas[1].dwave_sampleset_metadata)}\")\n", + "assert len(sampleset_datas[0].dwave_sampleset_metadata) == len(sampleset_datas[0].time_measurements)\n", + "assert len(sampleset_datas[1].dwave_sampleset_metadata) == len(sampleset_datas[1].time_measurements)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "15504907", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([HierarchicalRunMetadata(dwave_sampleset_metadata=rec.array([(24068., 20., 200.1 , 39831.96, 2004.04, 15763.96, 20.58, 44., 44.),\n", + " (13716., 20., 96.58, 29478.36, 1773.64, 15762.36, 20.58, 1., 1.),\n", + " (20584., 20., 165.26, 36345.96, 1762.04, 15761.96, 20.58, 1., 1.),\n", + " (12676., 20., 86.18, 28438.36, 1283.64, 15762.36, 20.58, 19., 19.),\n", + " (12368., 20., 83.1 , 28131.16, 1612.84, 15763.16, 20.58, 1., 1.),\n", + " (12412., 20., 83.54, 28174.76, 1118.24, 15762.76, 20.58, 1., 1.),\n", + " (20410., 20., 163.52, 36172.36, 1432.64, 15762.36, 20.58, 12., 12.),\n", + " ( 8254., 20., 41.96, 24016.36, 1284.64, 15762.36, 20.58, 20., 20.),\n", + " (19996., 20., 159.38, 35758.36, 1059.64, 15762.36, 20.58, 33., 33.),\n", + " (20020., 20., 159.62, 35782.36, 973.64, 15762.36, 20.58, 31., 31.),\n", + " (12500., 20., 84.42, 28262.76, 1047.24, 15762.76, 20.58, 1., 1.),\n", + " (17248., 20., 131.9 , 33010.36, 1133.64, 15762.36, 20.58, 1., 1.),\n", + " (13604., 20., 95.46, 29366.76, 1879.24, 15762.76, 20.58, 26., 26.),\n", + " (20884., 20., 168.26, 36646.76, 2018.24, 15762.76, 20.58, 25., 25.),\n", + " (12660., 20., 86.02, 28422.76, 1173.24, 15762.76, 20.58, 1., 1.),\n", + " (12368., 20., 83.1 , 28130.76, 966.24, 15762.76, 20.58, 1., 1.),\n", + " (12368., 20., 83.1 , 28130.76, 1331.24, 15762.76, 20.58, 23., 23.),\n", + " (19996., 20., 159.38, 35758.36, 1661.64, 15762.36, 20.58, 1., 1.),\n", + " (20020., 20., 159.62, 35782.76, 1662.24, 15762.76, 20.58, 1., 1.),\n", + " (12676., 20., 86.18, 28438.36, 1141.64, 15762.36, 20.58, 1., 1.),\n", + " (12568., 20., 85.1 , 28330.76, 877.24, 15762.76, 20.58, 1., 1.)],\n", + " dtype=[('qpu_sampling_time_us', '" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 3))\n", + "ax.scatter(no_communities_in_runs, qpu_access_times_per_hierarachical_run);\n", + "ax.set_xlabel(\"Number of communities detected\");\n", + "ax.set_ylabel(\"QPU access time (microseconds)\");\n", + "plt.title(\"More communities detected, higher summary qpu_access_time\", fontsize=10);" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "0997660e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[39831.96, 39831.96, 39831.96, 39831.96, 39831.96]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpu_access_times_first_divisions" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "0aa2fcac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qpu_access_times_first_divisions = [run.dwave_sampleset_metadata.qpu_access_time_us[0] for run in sampleset_datas]\n", + "qpu_access_times_first_divisions\n", + "\n", + "plt.plot(qpu_access_times_first_divisions, marker=\"o\")\n", + "plt.title(\"QPU access time of the first division (whole network division into two) in each run \\n looks stable (as expected) but the last one\", fontsize=10);" + ] + }, + { + "cell_type": "markdown", + "id": "68a97e74", + "metadata": {}, + "source": [ + "### Heuristic" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "955cba29", + "metadata": {}, + "outputs": [], + "source": [ + "advantage = AdvantageSampler(\n", + " G, num_reads=num_reads, version=version, region=region, use_clique_embedding=False, elapse_times=True, return_metadata=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "79d4ea41", + "metadata": {}, + "outputs": [], + "source": [ + "iterative_searcher = IterativeSearcher(advantage)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "529fcb0f", + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(f\"{test_dir}/heuristic\", exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68524cfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting community detection iterations\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 20%|██ | 1/5 [09:08<36:32, 548.02s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|████ | 2/5 [14:58<21:35, 431.79s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1 completed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 60%|██████ | 3/5 [25:06<17:04, 512.27s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 2 completed\n" + ] + } + ], + "source": [ + "res_heu = iterative_searcher.run_with_sampleset_info(\n", + " num_runs=5,\n", + " save_results=True,\n", + " saving_path=f\"{test_dir}/heuristic/adv_heuristic\",\n", + " iterative_verbosity=1,\n", + " return_metadata=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "8e8b34b1", + "metadata": {}, + "outputs": [], + "source": [ + "communities_heu, modularities_heu, times_heu, division_trees_heu, division_modularities_heu, sampleset_datas_heu = res_heu.communities, res_heu.modularity, res_heu.time, res_heu.division_tree, res_heu.division_modularities, res_heu.samplesets_data" + ] + }, + { + "cell_type": "markdown", + "id": "f45f8a80", + "metadata": {}, + "source": [ + "`find_embedding` - heuristic embedding time" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "679e1092", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([180.3938653, 18.6414687, 2.0937769, 1.3365376, 0.212603 ,\n", + " 0.8925314, 0.2529952, 1.0206537, 0.260631 , 0.224771 ,\n", + " 0.2351557, 0.3418358, 11.4603253, 1.939379 , 0.3559219,\n", + " 0.3140257, 0.3786017, 1.4273488, 0.2729576, 0.2385284,\n", + " 0.366778 , 0.5525911])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# find_embedding times for subdivisions in the first hierarchical run\n", + "first_run = sampleset_datas_heu[0]\n", + "first_run.time_measurements.find_heuristic_embedding_time_s" + ] + }, + { + "cell_type": "markdown", + "id": "1309fa72", + "metadata": {}, + "source": [ + "`sample` function - communication with the solver" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "12742aff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.1636481, 0.0431659, 0.0289183, 0.0279749, 0.0173655, 0.0258077,\n", + " 0.0220367, 0.027769 , 0.0193952, 0.0207995, 0.0238114, 0.0202728,\n", + " 0.0563187, 0.0270137, 0.0230981, 0.0201787, 0.0190755, 0.0271363,\n", + " 0.023578 , 0.0182567, 0.0402263, 0.0308277])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_run = sampleset_datas_heu[0]\n", + "first_run.time_measurements.sample_func_time_s" + ] + }, + { + "cell_type": "markdown", + "id": "396015bd", + "metadata": {}, + "source": [ + "Results heuristic embedding qpu_access_time" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "1f89040f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([38002.76, 32587.16, 27372.76, 25192.36, 24209.96, 24630.76,\n", + " 26503.56, 27692.36, 30662.36, 25495.96, 26767.96, 25691.96,\n", + " 27411.96, 27394.76, 29082.76, 34311.96, 24251.96, 23491.96,\n", + " 26910.36, 31259.96, 25163.96, 29028.36, 23139.96, 26447.96])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "it_searcher_iteration_to_consider = 2\n", + "\n", + "res_heu.samplesets_data[it_searcher_iteration_to_consider].dwave_sampleset_metadata.qpu_access_time_us" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "42ceafba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total QPU access time for the iteration: 2: 662705.84 microseconds == 0.6627 seconds\n" + ] + } + ], + "source": [ + "total_qpu_time_iteration_i = res_heu.samplesets_data[it_searcher_iteration_to_consider].dwave_sampleset_metadata.qpu_access_time_us.sum()\n", + "print(f\"Total QPU access time for the iteration: {it_searcher_iteration_to_consider}: {total_qpu_time_iteration_i:.2f} microseconds == {total_qpu_time_iteration_i / 1e6:.4f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "e3d66616", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[612521.12, 661713.04, 662705.84, 535441.24, 620261.9199999999]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpu_access_times_from_5_hierarchical_runs_heu_emb = [run.samplesets_data.dwave_sampleset_metadata.qpu_access_time_us.sum() for run in res_heu]\n", + "qpu_access_times_from_5_hierarchical_runs_heu_emb" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f91d10b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[612521.12, 661713.04, 662705.84, 535441.24, 620261.9199999999]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpu_access_times_from_5_hierarchical_runs_heu_emb" + ] + }, + { + "cell_type": "markdown", + "id": "4156ded7", + "metadata": {}, + "source": [ + "Let's compare them against clique embedding times" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "2701bc87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[798828.9600000001,\n", + " 709913.8799999999,\n", + " 713414.2800000003,\n", + " 714186.2800000003,\n", + " 689758.72]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# previous runs with clique embedding from top of this notebook\n", + "samplesets_metadata_cl_emb_5_runs = np.load(f\"{test_dir}/advantage_clique_samplesets_data.pkl\", allow_pickle=True)\n", + "qpu_access_times_from_5_hierarchical_runs_cl_emb = [run.dwave_sampleset_metadata.qpu_access_time_us.sum() for run in samplesets_metadata_cl_emb_5_runs]\n", + "qpu_access_times_from_5_hierarchical_runs_cl_emb" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "2352ee07", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(qpu_access_times_from_5_hierarchical_runs_heu_emb, marker=\"o\", label=\"Heuristic embedding\")\n", + "plt.plot(qpu_access_times_from_5_hierarchical_runs_cl_emb, marker=\"o\", label=\"Clique embedding\")\n", + "plt.legend()\n", + "plt.xlabel(\"Hierarchical search number (iteration)\");\n", + "plt.ylabel(\"QPU access time (microseconds) of a given run\");\n", + "plt.title(\"QPU access times in 5 runs of community search with Advantage heuristic embedding vs. clique embedding\");" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qomm_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/demo/sampleset_times_extraction/test/advantage_clique_communities.npy b/demo/sampleset_times_extraction/test/advantage_clique_communities.npy new file mode 100644 index 000000000..9213f348d Binary files /dev/null and b/demo/sampleset_times_extraction/test/advantage_clique_communities.npy differ diff --git a/demo/sampleset_times_extraction/test/advantage_clique_division_modularities.npy b/demo/sampleset_times_extraction/test/advantage_clique_division_modularities.npy new file mode 100644 index 000000000..10f5cf6fe Binary files /dev/null and b/demo/sampleset_times_extraction/test/advantage_clique_division_modularities.npy differ diff --git a/demo/sampleset_times_extraction/test/advantage_clique_division_trees.npy b/demo/sampleset_times_extraction/test/advantage_clique_division_trees.npy new file mode 100644 index 000000000..23421c2c6 Binary files /dev/null and b/demo/sampleset_times_extraction/test/advantage_clique_division_trees.npy differ diff --git 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differ diff --git a/demo/sampleset_times_extraction/test/heuristic/adv_heuristic_times.npy b/demo/sampleset_times_extraction/test/heuristic/adv_heuristic_times.npy new file mode 100644 index 000000000..daf1d1b38 Binary files /dev/null and b/demo/sampleset_times_extraction/test/heuristic/adv_heuristic_times.npy differ diff --git a/requirements.txt b/requirements.txt index 01586f949..ec2d76732 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,5 @@ +# Requires Python >=3.9,<=3.11 + bayanpy==0.7.7 dimod==0.12.16 dwave_cloud_client==0.12.0 @@ -9,5 +11,5 @@ leidenalg==0.10.2 networkx==3.3 pytest==8.2.2 python_igraph==0.11.6 -QHyper==0.2.2 +QHyper==0.3.3 powerlaw \ No newline at end of file diff --git a/sampleset_data/erdos-renyi_100_2_runs_CBF_modularity_analysis.ipynb b/sampleset_data/erdos-renyi_100_2_runs_CBF_modularity_analysis.ipynb new file mode 100644 index 000000000..0efea6731 --- /dev/null +++ b/sampleset_data/erdos-renyi_100_2_runs_CBF_modularity_analysis.ipynb @@ -0,0 +1,691 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import networkx as nx\n", + "\n", + "import os\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "results_dir = \"erdos_renyi_n=100_p=0.3_num_runs_2\"\n", + "os.mkdir(results_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 42\n", + "G = nx.erdos_renyi_graph(n=100, p=0.3, seed=seed)\n", + "\n", + "graph_arr = np.empty(1, dtype=object)\n", + "graph_arr[0] = G\n", + "np.save(f\"{results_dir}/erdos_renyi_n=100_p=0.3.npy\", graph_arr)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "from Qommunity.samplers.hierarchical.advantage_sampler import AdvantageSampler\n", + "from Qommunity.iterative_searcher import IterativeSearcher" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "adv_sampler = AdvantageSampler(G, num_reads=100, version=\"Advantage_system6.4\", region=\"na-west-1\", use_clique_embedding=True, elapse_times=True, return_metadata=True)\n", + "it_searcher = IterativeSearcher(adv_sampler)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:32<00:00, 16.15s/it]\n" + ] + } + ], + "source": [ + "results = it_searcher.run_with_sampleset_info(num_runs=2, saving_path=f\"{results_dir}/\", return_metadata=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to remember what the returned data is:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['communities',\n", + " 'modularity',\n", + " 'time',\n", + " 'division_tree',\n", + " 'division_modularities',\n", + " 'samplesets_data']" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[info_field for info_field in results.dtype.names]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save it somewhere easier" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "communities = results.communities\n", + "modularities = results.modularity\n", + "division_modularities = results.division_modularities\n", + "division_trees = results.division_tree\n", + "times = results.time\n", + "samplesets_datas = results.samplesets_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here every entry in the array is for the subsequent run:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of runs: 2\n" + ] + } + ], + "source": [ + "print(f\"Number of runs: {len(results)}\")\n", + "first_run_data = results[0]\n", + "second_run_data= results[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The structure of results:" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The first hierarchical run took: 14.88 seconds\n", + "This value can also be accessed like this: 14.88 seconds\n", + "The final modularity from the second run is: 0.1289 == 0.1289\n", + "Dendrogram can be made: \n" + ] + } + ], + "source": [ + "print(f\"The first hierarchical run took: {first_run_data.time:.2f} seconds\")\n", + "print(f\"This value can also be accessed like this: {times[0]:.2f} seconds\")\n", + "\n", + "print(f\"The final modularity from the second run is: {second_run_data.modularity:.4f} == {modularities[1]:.4f}\")\n", + "\n", + "from dendro import Dendrogram\n", + "\n", + "print(f\"Dendrogram can be made: {Dendrogram(G, communities[0], division_modularities[0], division_tree=[0])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's focus on samplesets data:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A class with lists\n" + ] + }, + { + "data": { + "text/plain": [ + "(Field(name='dwave_sampleset_metadata',type=,default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='time_measurements',type=,default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='dwave_sampleset',type=typing.List[typing.Dict[str, typing.Any]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='timing',type=typing.List[typing.Dict[str, typing.Any]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='problem_id',type=typing.List[typing.Union[str, int, float]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='community_hash',type=typing.List[typing.Union[str, int]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='chain_strength',type=typing.List[float],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='chain_break_fraction',type=typing.List[float],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='chain_break_method',type=typing.List[str],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='embedding',type=typing.List[typing.Dict[str, typing.Any]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='warnings',type=typing.List[typing.Dict[str, typing.Any]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD),\n", + " Field(name='community',type=typing.List[typing.List[int]],default=,default_factory=,init=False,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD))" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from Qommunity.searchers.utils import HierarchicalRunMetadata\n", + "from dataclasses import fields\n", + "\n", + "print(\"A class with lists\")\n", + "fields(HierarchicalRunMetadata)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There were 2 hierarchical runs\n", + "\n", + "Iter: 0: there were 7 subproblems submitted to DWave\n", + "0 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 16486.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 124.28, 'qpu_access_time': 32416.36, 'qpu_access_overhead_time': 1099.64, 'qpu_programming_time': 15930.36, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 42.0, 'total_post_processing_time': 42.0}\n", + "1 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 11080.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 70.22, 'qpu_access_time': 27010.76, 'qpu_access_overhead_time': 2059.24, 'qpu_programming_time': 15930.76, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 1.0, 'total_post_processing_time': 1.0}\n", + "2 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 8368.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 43.1, 'qpu_access_time': 24297.56, 'qpu_access_overhead_time': 668.44, 'qpu_programming_time': 15929.56, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 23.0, 'total_post_processing_time': 23.0}\n", + "3 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 8368.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 43.1, 'qpu_access_time': 24297.16, 'qpu_access_overhead_time': 753.84, 'qpu_programming_time': 15929.16, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 1.0, 'total_post_processing_time': 1.0}\n", + "4 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 10680.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 66.22, 'qpu_access_time': 26609.96, 'qpu_access_overhead_time': 1254.04, 'qpu_programming_time': 15929.96, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 43.0, 'total_post_processing_time': 43.0}\n", + "5 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 7988.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 39.3, 'qpu_access_time': 23917.16, 'qpu_access_overhead_time': 671.84, 'qpu_programming_time': 15929.16, 'qpu_delay_time_per_sample': 20.58, 'total_post_processing_time': 1.0, 'post_processing_overhead_time': 1.0}\n", + "6 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 8368.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 43.1, 'qpu_access_time': 24297.96, 'qpu_access_overhead_time': 631.04, 'qpu_programming_time': 15929.96, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 1.0, 'total_post_processing_time': 1.0}\n", + "CBF: 0.02 in suproblem 0\n", + "CBF: 0.0 in suproblem 1\n", + "CBF: 0.0 in suproblem 2\n", + "CBF: 0.0 in suproblem 3\n", + "CBF: 0.0 in suproblem 4\n", + "CBF: 0.0 in suproblem 5\n", + "CBF: 0.0 in suproblem 6\n", + "Iter: 1: there were 7 subproblems submitted to DWave\n", + "0 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 16486.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 124.28, 'qpu_access_time': 32416.36, 'qpu_access_overhead_time': 670.64, 'qpu_programming_time': 15930.36, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 11.0, 'total_post_processing_time': 11.0}\n", + "1 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 10680.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 66.22, 'qpu_access_time': 26610.76, 'qpu_access_overhead_time': 765.24, 'qpu_programming_time': 15930.76, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 38.0, 'total_post_processing_time': 38.0}\n", + "2 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 7988.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 39.3, 'qpu_access_time': 23917.16, 'qpu_access_overhead_time': 889.84, 'qpu_programming_time': 15929.16, 'qpu_delay_time_per_sample': 20.58, 'total_post_processing_time': 1.0, 'post_processing_overhead_time': 1.0}\n", + "3 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 8368.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 43.1, 'qpu_access_time': 24297.56, 'qpu_access_overhead_time': 800.44, 'qpu_programming_time': 15929.56, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 1.0, 'total_post_processing_time': 1.0}\n", + "4 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 11080.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 70.22, 'qpu_access_time': 27010.36, 'qpu_access_overhead_time': 812.64, 'qpu_programming_time': 15930.36, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 13.0, 'total_post_processing_time': 13.0}\n", + "5 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 8436.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 43.78, 'qpu_access_time': 24364.76, 'qpu_access_overhead_time': 861.24, 'qpu_programming_time': 15928.76, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 56.0, 'total_post_processing_time': 56.0}\n", + "6 suproblem (no order of division yet): Quantum timings {'qpu_sampling_time': 8334.0, 'qpu_anneal_time_per_sample': 20.0, 'qpu_readout_time_per_sample': 42.76, 'qpu_access_time': 24262.76, 'qpu_access_overhead_time': 790.24, 'qpu_programming_time': 15928.76, 'qpu_delay_time_per_sample': 20.58, 'post_processing_overhead_time': 1.0, 'total_post_processing_time': 1.0}\n", + "CBF: 0.05 in suproblem 0\n", + "CBF: 0.0 in suproblem 1\n", + "CBF: 0.0 in suproblem 2\n", + "CBF: 0.0 in suproblem 3\n", + "CBF: 0.0 in suproblem 4\n", + "CBF: 0.0 in suproblem 5\n", + "CBF: 0.0 in suproblem 6\n" + ] + } + ], + "source": [ + "print(f\"There were {len(samplesets_datas)} hierarchical runs\\n\")\n", + "for run, hierarchical_data in enumerate(samplesets_datas):\n", + " print(f\"Iter: {run}: there were {len(hierarchical_data)} subproblems submitted to DWave\")\n", + " # All of the above are lists\n", + " # THESE TWO WERE IN PREVIOUS EXPS\n", + " dwave_original_samplesets = hierarchical_data.dwave_sampleset_metadata # QPU access times in here, and the rest of quantum times - WERE IN PREVIOUS EXPS\n", + " custom_timing_measurements = hierarchical_data.time_measurements # Perf_counter times of embeddings and .sample function - WERE IN PREVIOUS EXPS\n", + "\n", + " # THESE ARE NEW (some redundant - timings)\n", + " dwave_sampleset_metadatas= hierarchical_data.dwave_sampleset # Whole DWave SampleSet class with 100 (#num_reads) elements for each entry in this list\n", + " timings = hierarchical_data.timing\n", + " problem_ids = hierarchical_data.problem_id\n", + " community_hashes = hierarchical_data.community_hash # To identify communities with ordinal numbers - for tree structure identifying\n", + " communities = hierarchical_data.community # For tree structure identifying\n", + " chain_strenghts = hierarchical_data.chain_strength\n", + " chain_break_fractions = hierarchical_data.chain_break_fraction\n", + " embeddings = hierarchical_data.embedding\n", + " warning_msgs = hierarchical_data.warnings\n", + "\n", + " for i, timing in enumerate(timings):\n", + " print(f\"{i} suproblem (no order of division yet): Quantum timings {timing}\")\n", + "\n", + " for i, cbs in enumerate(chain_break_fractions):\n", + " print(f\"CBF: {cbs} in suproblem {i}\") \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having fresh (not recovered from saved files) is cool, because it has some dwave properties (full \"SampleSet\" DWave class properties) and can be played with dwave.inspector" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "Serving Inspector on http://127.0.0.1:18000/?problemId=0119af05-2ff3-4741-8e8d-28cb52d6804b" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'http://127.0.0.1:18000/?problemId=0119af05-2ff3-4741-8e8d-28cb52d6804b'" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import dwave.inspector\n", + "\n", + "dwave.inspector.show(results[0].samplesets_data.dwave_sampleset[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How to reload data from dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n" + ] + } + ], + "source": [ + "from searchers.utils import HierarchicalRunMetadata\n", + "from dataclasses import fields\n", + "\n", + "\n", + "field_names = [f.name for f in fields(HierarchicalRunMetadata)]\n", + "sampleset_metadatas = []\n", + "\n", + "num_runs = 2\n", + "\n", + "for run in range(num_runs):\n", + " hierarchical_run_metadata = HierarchicalRunMetadata.load_from_files(f\"{results_dir}/_iter_{run}\")\n", + " sampleset_metadatas.append(hierarchical_run_metadata)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "communities = np.load(f\"{results_dir}/_communities.npy\", allow_pickle=True)\n", + "modularities = np.load(f\"{results_dir}/_modularities.npy\", allow_pickle=True)\n", + "division_modularities = np.load(f\"{results_dir}/_division_modularities.npy\", allow_pickle=True)\n", + "times = np.load(f\"{results_dir}/_times.npy\", allow_pickle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's stick to fresh data" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "communities = results.communities\n", + "modularities = results.modularity\n", + "division_modularities = results.division_modularities\n", + "division_trees = results.division_tree\n", + "times = results.time\n", + "samplesets_datas = results.samplesets_data" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['majority_vote',\n", + " 'majority_vote',\n", + " 'majority_vote',\n", + " 'majority_vote',\n", + " 'majority_vote',\n", + " 'majority_vote',\n", + " 'majority_vote']" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First iterative run (#num_runs)\n", + "samplesets_datas[0].dwave_sampleset_metadata # Quantum times of 7 subproblems\n", + "samplesets_datas[0].dwave_sampleset # DWave original SampleSet clases (#num_reads)\n", + "samplesets_datas[0].problem_id # DWave Leap problem id for futherh identification\n", + "samplesets_datas[0].chain_strength # Chain_strenghts used in subproblems\n", + "samplesets_datas[0].chain_break_fraction # Chain break fractions in each subproblem\n", + "samplesets_datas[0].chain_break_method # Chain break method" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "mods = []\n", + "cbf_max = []\n", + "cbf_avg = []\n", + "non_zero_cbfs = []\n", + "chain_strengths = []\n", + "comms_lens = []\n", + "energies = []\n", + "non_zero_cbf_indices = [] # Hierarchical division in which the CBF was non-zero\n", + "\n", + "for idx, sm in enumerate(sampleset_metadatas):\n", + " cbfs = np.array(sm.chain_break_fraction)\n", + " non_zero_cbfs_idxs = np.where(cbfs > 0)\n", + " cbf_max.append(cbfs.max())\n", + " cbf_avg.append(cbfs.mean())\n", + " non_zero_cbfs.append(cbfs[non_zero_cbfs_idxs])\n", + " non_zero_cbf_indices.append(non_zero_cbfs_idxs)\n", + "\n", + " chain_strengths.append(np.nanmean(np.array(sm.chain_strength, dtype=float)))\n", + " mods.append(modularities[idx])\n", + " comms_lens.append(communities[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import FormatStrFormatter\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", + "fig.suptitle(\"Erdos-Renyi 100 nodes\\n 2 hierarchical runs\")\n", + "\n", + "ax[0].scatter(cbf_max, mods)\n", + "ax[0].set_ylabel(\"Modularity (Q)\")\n", + "ax[0].set_xlabel(\"Max. CBF\")\n", + "ax[0].set_title(\"Max. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[1].scatter(cbf_avg, mods)\n", + "ax[1].set_ylabel(\"Modularity (Q)\")\n", + "ax[1].set_xlabel(\"Mean. CBF\")\n", + "ax[1].set_title(\"Mean. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[0].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import FormatStrFormatter\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "fig.suptitle(\"Erdos-Renyi 100 nodes\\n 2 hierarchical runs\")\n", + "\n", + "hb_0 = ax[0].hexbin(cbf_max, mods, cmap=\"Blues\", gridsize=20)\n", + "cb = fig.colorbar(hb_0, ax=ax[0])\n", + "cb.set_label(\"No. of points in hexbin\")\n", + "ax[0].set_ylabel(\"Modularity (Q)\")\n", + "ax[0].set_xlabel(\"Max. CBF\")\n", + "ax[0].set_title(\"Max. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "hb_1 = ax[1].hexbin(cbf_avg, mods, cmap=\"Blues\", gridsize=20)\n", + "cb = fig.colorbar(hb_1, ax=ax[1])\n", + "cb.set_label(\"No. of points in hexbin\")\n", + "ax[1].set_ylabel(\"Modularity (Q)\")\n", + "ax[1].set_xlabel(\"Mean. CBF\")\n", + "ax[1].set_title(\"Mean. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[0].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n", + "# ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Before your Python kernel cache (as long as you do not restart the kernel), you can access some DWave data from your cache" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [], + "source": [ + "from dwave.inspector.storage import get_problem, problemdata_bag, problemdata" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "problem_ids = {}\n", + "energies = {}\n", + "qbits_involved = {}\n", + "mean_energies = {}\n", + "stds = {}\n", + "min_energies = {}\n", + "max_energies = {}\n", + "for run, sm in enumerate(sampleset_metadatas):\n", + " problem_ids[run] = sm.problem_id\n", + " en = []\n", + " qbits = []\n", + " me = []\n", + " std = []\n", + " for problem_id in sm.problem_id:\n", + " pd = get_problem(problem_id=problem_id)\n", + " en.append(pd.response.energies[0])\n", + " qbits.append(len(pd.response.sampleset.variables))\n", + " me.append(pd.response.sampleset.record.energy.mean())\n", + " std.append(pd.response.sampleset.record.energy.std())\n", + " energies[run] = en\n", + " qbits_involved[run] = qbits\n", + " mean_energies[run] = me\n", + " stds[run] = std" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "plt.scatter(cbf_max, [v[0] for v in mean_energies.values()], s=7)\n", + "# plt.errorbar(cbf_max, [v[0] for v in mean_energies.values()], yerr=[v[0] for v in stds.values()])\n", + "# plt.yscale(\"log\")\n", + "plt.ylabel(\"Sample energy of suproblem where chain was broken\")\n", + "plt.xlabel(\"Max. CBF\")\n", + "plt.title(\"Max. CBF in the hierarchical tree vs. Energy\");" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "from utils import recover_ordering, plot_tree, get_extended_nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "nodes, root = recover_ordering(G, division_trees[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qomm_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/sampleset_data/erdos-renyi_100_2_runs_hierarchial_tree_CBF_CS_plot.ipynb b/sampleset_data/erdos-renyi_100_2_runs_hierarchial_tree_CBF_CS_plot.ipynb new file mode 100644 index 000000000..a6040e9e7 --- /dev/null +++ b/sampleset_data/erdos-renyi_100_2_runs_hierarchial_tree_CBF_CS_plot.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import networkx as nx\n", + "\n", + "import os\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "results_dir = \"erdos_renyi_n=100_p=0.3_num_runs_2\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "G = np.load(f\"{results_dir}/erdos_renyi_n=100_p=0.3.npy\", allow_pickle=True)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to load HierarchicalRunMetadata results from a results dir" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n" + ] + } + ], + "source": [ + "from Qommunity.searchers.utils import HierarchicalRunMetadata\n", + "\n", + "\n", + "num_runs = 2\n", + "\n", + "hierarchical_metadatas = []\n", + "\n", + "\n", + "for iter in range(num_runs):\n", + " base_filename = f\"{results_dir}/_iter_{iter}\" # Added this _iter_ prefix to identify\n", + " hm = HierarchicalRunMetadata.load_from_files(base_filename)\n", + " hierarchical_metadatas.append(hm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading iterative searcher other data:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "communities = np.load(f\"{results_dir}/_communities.npy\", allow_pickle=True)\n", + "division_modularities = np.load(f\"{results_dir}/_division_modularities.npy\", allow_pickle=True)\n", + "division_trees = np.load(f\"{results_dir}/_division_trees.npy\", allow_pickle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from utils import recover_ordering, recover_info_ordering, plot_tree_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recover the binary tree structure of the hierarchical process\n", + "division_tree is needed\n", + "\n", + "## Plot the info (chain_strenght and CBF) tree" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\basia\\anaconda3\\envs\\qomm_env\\Lib\\site-packages\\pygraphviz\\agraph.py:1403: RuntimeWarning: Warning: Could not load \"C:\\Users\\basia\\anaconda3\\envs\\qomm_env\\Library\\bin\\gvplugin_pango.dll\" - It was found, so perhaps one of its dependents was not. Try ldd.\n", + "\n", + " warnings.warn(b\"\".join(errors).decode(self.encoding), RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nodes, root = recover_ordering(G=G, division_tree=division_trees[0])\n", + "nodes_info, root_info = recover_info_ordering(root=root, nodes=nodes, sampleset_metadata=hierarchical_metadatas[0])\n", + "\n", + "from matplotlib import colormaps\n", + "\n", + "\n", + "cmap = colormaps.get_cmap(\"PuBu\")\n", + "plot_tree_info(root_info, division_modularities[0], cmap=cmap)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qomm_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/sampleset_data/erdos_renyi_169.ipynb b/sampleset_data/erdos_renyi_169.ipynb new file mode 100644 index 000000000..a349a8e79 --- /dev/null +++ b/sampleset_data/erdos_renyi_169.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import networkx as nx\n", + "\n", + "import os\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "results_dir = \"erdos_renyi_n=169_m=1_p=0.1_num_runs_10\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n" + ] + } + ], + "source": [ + "from Qommunity.searchers.utils import HierarchicalRunMetadata\n", + "\n", + "\n", + "num_runs = 10\n", + "\n", + "hierarchical_metadatas = []\n", + "\n", + "\n", + "for iter in range(7): # Kernel crashed by 6th run (out of 10)\n", + " base_filename = f\"{results_dir}/_{iter}\" # I saved it with such prefix\n", + " hm = HierarchicalRunMetadata.load_from_files(base_filename)\n", + " hierarchical_metadatas.append(hm)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "communities = np.load(f\"{results_dir}/_communities.npy\", allow_pickle=True)\n", + "division_modularities = np.load(f\"{results_dir}/_division_modularities.npy\", allow_pickle=True)\n", + "division_trees = np.load(f\"{results_dir}/_division_trees.npy\", allow_pickle=True)\n", + "modularities = np.load(f\"{results_dir}/_modularities.npy\", allow_pickle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mods = []\n", + "cbf_max = []\n", + "cbf_avg = []\n", + "non_zero_cbfs = []\n", + "chain_strengths = []\n", + "comms_lens = []\n", + "energies = []\n", + "indices = []\n", + "\n", + "for idx, sm in enumerate(hierarchical_metadatas):\n", + " cbfs = np.array(sm.chain_break_fraction)\n", + " non_zero_cbfs_idxs = np.where(cbfs > 0)\n", + " cbf_max.append(cbfs.max())\n", + " cbf_avg.append(cbfs.mean())\n", + " non_zero_cbfs.append(cbfs[non_zero_cbfs_idxs])\n", + " indices.append(non_zero_cbfs_idxs)\n", + "\n", + " chain_strengths.append(np.nanmean(np.array(sm.chain_strength, dtype=float)))\n", + " mods.append(modularities[idx])\n", + " comms_lens.append(communities[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CBF: [0.07100592] at idx: (array([0]),) of the hierarchical tree in run: 0 with chain_strenghts: [0.9805]\n", + "CBF: [0.08284024] at idx: (array([0]),) of the hierarchical tree in run: 1 with chain_strenghts: [0.9805]\n", + "CBF: [0.0887574] at idx: (array([0]),) of the hierarchical tree in run: 2 with chain_strenghts: [0.9805]\n", + "CBF: [0.03550296] at idx: (array([0]),) of the hierarchical tree in run: 3 with chain_strenghts: [0.9805]\n", + "CBF: [0.09467456] at idx: (array([0]),) of the hierarchical tree in run: 4 with chain_strenghts: [0.9805]\n", + "CBF: [0.07100592 0.02222222] at idx: (array([ 0, 39]),) of the hierarchical tree in run: 5 with chain_strenghts: [0.9805 0.8649]\n", + "CBF: [0.08284024 0.01265823] at idx: (array([0, 1]),) of the hierarchical tree in run: 6 with chain_strenghts: [0.9805 0.8778]\n" + ] + } + ], + "source": [ + "for i, (indices, nonzero) in enumerate(zip(indices, non_zero_cbfs)):\n", + " chain_strengths = np.round(np.array(hierarchical_metadatas[i].chain_strength, dtype=float), 4)\n", + " print(f\"CBF: {nonzero} at idx: {indices} of the hierarchical tree in run: {i} with chain_strenghts: {chain_strengths[indices]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import FormatStrFormatter\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n", + "fig.suptitle(\"Power-law of 169 nodes\\n 7 hierarchical runs\")\n", + "\n", + "ax[0].scatter(cbf_max, mods)\n", + "ax[0].set_ylabel(\"Modularity (Q)\")\n", + "ax[0].set_xlabel(\"Max. CBF\")\n", + "ax[0].set_title(\"Max. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[1].scatter(cbf_avg, mods)\n", + "ax[1].set_ylabel(\"Modularity (Q)\")\n", + "ax[1].set_xlabel(\"Mean. CBF\")\n", + "ax[1].set_title(\"Max. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[0].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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p7oCmX4gaDAoyZMgQs049m3XTTTeZMPnAAw+YA9hbb71lbvPyyy/LM888I8uWLcvusqXBPj96QNTXQ4OJfknrmVkNtaeddpo5S6mhQOmZzM8//9y8Vhpu9OD31FNPmS9xDVl6ULK6kukBVrfpsssukxNOOMHs6+zZs82Zz8qVK2c/tnZn0MfToKDvg4ceesi81rpfwdBAp10p9DlYsWKF2d+qVauagFnQ66uh+48//pArrrjCvMa6XyNHjjRd5DQ8BtLnQl9bPUha45VpwNHH0h9aQ4cONd3stOuahg593nN3GclrHd999515nvR11+v186HdON59910T1CPdz2Beh1D3I1gaYvSsr76u+oMpnDPA+uNE3+f6GR44cKD50amhW9+nGiTzos+lnlXX7wMNHoGP+/bbb5tt0fevNXbUddddZx5Dz87r66Ovib738vuRHAy97xlnnJGjS46GVH0c3b7cPv74Y/P509CtgfHff/81YVlbD+hrre8Lpd9pul79AaO30x+o+nnV7p7RpGGxdOnS5vtU/9XvB/3hou8VnVwkkBYZdNv1OdUWTHlty/HHH2/Onus69H2u70nrO0m71urftIXEsGHDDitWXHDBBTlaSuWmn1sNuC1atMhxvX6GlNW9NTf9ntXgr9+lubtO6ftLQ6nub9myZYN+3gAkP3JsTuRYciw5Nn/kWHIsORa24IetPP/883592b766iv/xIkT/WXKlPHv37/f/K1Xr17+Tp06mf+uXbu2v3v37jnua93Okp6e7m/cuLH/tNNOy3H9li1b/BUrVvSffvrp/oMHD/pbtGjhP+qoo/y7d+8OeXu//fZbs73XX3990PfR2+e19OzZ03/gwIEctx04cGCet+3QoUOBj7Fy5UpzuyFDhuS4/uabbzbXL1iwIMdjlCpVqtDt9vl8/mOOOcbftWtX89+Bz3vdunXN8xl4XW5Lly41j/3SSy9lXzd69Ghz3axZs/J8PLVw4UJzm+OPP968XpbHH3/cXP/9998XuN1jxowxt7vssstyXH/eeef5K1WqlOM6fV/p82G55557zHOzdu3aHLcbMWKE3+Px+Ddu3Ggur1+/3jxG2bJl/du3b89x24yMjBzbrf766y9/tWrVcmxTQes49dRTzWfht99+y/M5inQ/g3kdgt0PpevS7SmI9brWq1fvsPeLtS/5fT/ocxW4L3rdJ598kn2dPn9paWn+m266qcBt+Oijj8x933333RzXn3XWWWa7LOeee66/UaNG/mixvr/0Oa1evbp5n6kff/zRbM/ixYtzfBdamjdv7q9atar/zz//zPEd5Ha7/QMGDMi+Tr9LihcvnuP9ouvW92zg82q95/Sxcsv9Gub13Of1Ob/iiiv8JUuWzPFdpt9Xet8pU6Ycdnv9W+D3me5vftvUtm1bf5s2bXJcp+9Zvb2+nwrSv3//wz4H1nNarly5Au87fvx48xizZ8/Ocf20adPM9V9++WWB9wfgHORYcmzg4ylyLDnWQo4lx5JjYWcMC2BjeuZSz2jpGXs9u6j/FnR2TbuYWHTcET0rrGeM9ExY7m5LkyZNMl219O86m5+eIQznjI2e6VF5daMqiJ5B0sfXRc8Y6RlknTVV9y/zePAfPYtl3dZaHn300QLXb3UL0zNxgfTMv8pr5sDC6PP0888/m23Us3d6RlgXbbGgXZt0nBeri1Hga6HdVvT22g1JuyUEvh7alUEnNNAzr7nl7k6jrTUCz8paZwPXrVsX1PbnHqNG76/bZb2GedHx0vR22r3F2l+rS4Z2GdF9DqRnHfUsa+4uGdZ26/Oza9cucwZWu+Pkfm/mtQ7tPqKPo2fic7cqyavLUTj7GczrEOp+BEvP0Ae+X8Khs15a7welz592NyvsvaEtVbQ1g55FDvzu0M9Y7969s6/T9622fNCWLNGkz6l+z2nLJqVdMrUVUuC+WLSFiX4GtSWDtgKxNG3a1LResj7z+r786KOPpGfPnjneL3o2Pb8uWuEKfN30O1o/G7rt2lJGu/8F0tYreXVdC4WelddWFnrm3WI9Z9oypyD6GdDPcW663YV9f1t/19sGstaXSGMnAkgc5NhM5NhM5FhybH7IseRYcizsgGEBbEwPLHrw1+4F+iWnX7bazSA/Glrvvfde88Wdezym3LRJ/yuvvGLCmTbb11AVDivI5v6yKox2MQocM0W7gel4TtqlRPejR48eOQ5cucdXKYyOFaVdjzQI5g7keoDVv4dKA6kVIvKjPwT0i1p/TGh3Hu0apF2RAoO23saiBxcNYcHIHcisA4KGiEjvn98PEt1n7TaTO2gGTqIQSLsx5UW7YugPCT1Qa0gv6Pa5r7OClY71E6v9DPZ1CGU/ghXJfS15dWXU/S7svaFdF3W/9TtGvzM0OGn3Kt23wFCqs2xqVybtLqifKe2qpD/OtBtTpHQ92j1UZ1jW7dDvpry+s6zPrIbt3DRwahDVH4j6XaSfP53pOTe9bzjj8eXnhx9+MOOMaTeq3D96Aj/nSscii3TQf31NdNw4DaLa5UofQ78vdYy1vJ6z3HL/4LcCZ2Gh0vp+126Jea0vmMcG4Dzk2Ezk2EzkWHJsfsix5FhyLOyA4qrN6Re2jouzdetWM86JBqq86DhJGux0zKjJkyfLEUccYcZ60VCU12DdevZHZz9VOnaSnsEMZ2ZHPUDpgU3HhomUFYz17G5gKI1ENL8srbP5OgZNfuMS6Xg16tprrzXPvR5AdMwxHe9Ft0UPuIED6IdCw3mwB5po3V+3Vc+m3nrrrXn+XWfLDZTXmWv98aNnafUM7C233GIObLotGtoDz1wWtI6ifJ7yE+p+BCuv/c3vfZt7gPlo7LO+J3WsKh0LT/ft9ddflwYNGpgWEIGhb82aNSYAacscbSGh3zMajHScu0jo4PM6/pF+VtavXx/R2FehCvV5zj1xhJ5l1x86OoaU7oO2TtLWHxric3/OI31fWz80dNIHK5TqGFX6YyKYWaj1R39eP1K0tYgWMjZu3JjveIP6w1TpGGGBrPUFjqkHAIHIsZEhx0Z2f3Lsf8ix5NhA5FhyLEJHcdXmtHuHDsD+xRdf5OjykJseJPQLUc966Vk7iwajvFxzzTXmLI4eULUrkw7onrvrUTB0QHvtkqFnvDZt2mSa9YdLu6ZYg7JHqnbt2uagoGes9YBq2bZtmzmY6N9DZQ2ArQehwlog6MFCWwYEdvvSwdP1sXOvU2c4TFS6ffp6hNriIvdzoQczPZMcGAJyz9qYH+tAGMvnKZjXIdL9CIXVSkHfL4E/RMNpqVIY/SGrP2L1+0UHm9fPcl6z1+qspnrGWRcdfF5nSNVJGPT7o6AB6IOhkytoayX9rOb3g8/6zGo4zk1bYGgw0m3UbdEAaLXQCZT7voHPc6BgnmedEVV/3Ov7QZ9DiwbrWP6Q1i5V2h1Vu7ZpONWB/fOb7CGQ/tDQ22srgcBJVLQAoIULnZVXWy/kpi0ZtMurTo6RO5TqvmoxI/ePUwCwkGPDQ46NDnLsf8ix5NhA5FhyLELHmKs2p2eQdXZOnSmwoLPgesZPv8wCz1TpzJ46W2JeB1c9AOmsnSNGjDBn/PTLaO3atTlup2cxgzmTqQdlPbN4ySWX5Bkoly9fbrqhFMaa7S/wTGO4rNlic88COn78ePOvNetsKHRGQQ0vjzzySJ77qWMqBb4euc+26myQuc8kalcW7UZizfoazTPU0aDjCC1dutT82MlND+TWD4mCWGejA/dHx9vR9QZDu3LpQV/HU9OzkrF4joJ5HSLdj3B+AAWOBaZdhYL5HIVKQ4V209TPn846rK9pYFcqpeErkHYL0jPF+lxY3cqs8ZnCGbdIZ0TW75GCxqDT4KyBVZ+DwBCpPybmzp2b/ZnX10nHpNLvvsD3i86gm/t9rD8wNczmHnNNWzMUJq/3g4b1YO5bEA3WeQVli7b80m3WWYMXL14c1Nl+pS2PdFv1+zj3e19DrR4PrFZgFv1hf9VVV5kz+3n9UNF16X0Lm/EagHORY8NDjo0Ociw5VpFjD0eOJccidLRcTQIFjY1k0ZClgatbt26mO4KOIaSD/Wt3J6spvNLr9UumU6dOMmzYMHPdxIkTZeHChaaryJIlS7K7VVndmzTcFqRdu3bmsa6++mpzVknDqY4Toy0K9KzY7Nmzzdm8QBqAtXuKdTDTFg16sNHt1ftHSoOtPm/PPPNMdreHZcuWmcfQLiO6/6HS5+V///ufOSjoF7EO6q3jz+hYVPr86QHOCtba5UEP8PplrQdvDS461o92aQik3XL0R0KvXr3MQPcafHWAeX3OpkyZEpWAHgndPt0W3R99f+j2aTjS7nO63freKKwrhd5Xz4pq6xV9n+pZQt03fV6Cbd2hYxnp2Wg966hjq+n4TvrYOtaadgWJxn4W9jpEYz+CpWNBadeWwYMHm23TAKShXAN67mAeDRpC9UeTBsMmTZrkaCVjbY+O86ZjU1WrVs0EPP3e0OfBGiReP1/6udJ16I/oUOjZ/GDuo10Z9fOnAUufGx2TSrdbP2eB99cuXtrtSwfl1+8lDdp6O/3cBn4fWoFYA5n+q5M6aEDN/QM9v+89bTGg3zPXXXedKQroZz7SH0r6g0Rbeeh7S59bDana5cwa00y7yWohQZ9/fV9oa4lg6OdHv3/0e0hbaVl0fdpiTK/T2+j3mj4P+r2pLQG0e9jtt99uWngE0h8jGor1+QWAgpBjQ0eOjQ5yLDnW2h5ybE7kWHIswuCHrTz//PP6jeb/6quvCrxd7dq1/d27d89x3dSpU/3HHHOMPy0tzd+gQQOzrjFjxpj1Wc4//3x/mTJl/Bs2bMhx33feecfcbty4cTkeQ5dgLV++3H/xxRf7jzzySH+xYsX8FSpU8Hfu3Nn/4osv+r1eb/bt9HECF4/H469Zs6b/8ssv92/bti3HOgcOHOgvVaqUPxyHDh3y33XXXf66deua7alVq5Z/5MiR/gMHDkT0GN988415HitVqmSea32OLrroIv/8+fOzb/PXX3/5Bw0a5K9cubK/dOnS/q5du/pXr15tbquPF+jPP//0Dxs2zF+jRg1/amqqeS70Njt37jR/X7hwoXmeZs6cmeN+69evN9fr61wQ6z2wY8eOPN9ruh5LXtu3d+9e87zVr1/fbJ/uU7t27fyPPPKIPz09Pce2PPzww4c9vs/n899///1m3fp8tWjRwv/ee++Zxwl8fxW0DrVq1Sr/eeed5y9fvry/ePHi/uOOO84/atSoqO1nYa9DsPuh9PF0ewqS3+sa+Hlq06aN2ZajjjrKP378+Hz3Jfd3gerQoYNZgqH7pp8PXfe999572N+ffvpp/6mnnpr9nj/66KP9t9xyi3/37t2H7U9h+13QNgfzXfjxxx/7Tz75ZH+JEiX8ZcuW9ffo0cP/448/Hnb/xYsX+1u2bGmev3r16vmnTJly2Peh2r9/v3/w4MH+cuXKme9G/Sxv3779sH3J67n/7LPP/CeddJLZFv3eu/XWW/0fffSRuZ0+HxZ9HRo1apTnfub1Oun3ccOGDf0pKSl5fsaXLVtmrj/jjDP8objuuuvM5zgv+rm56aabsj/n1vezHlfy8uGHH5q///zzzyFtA4DkRo4lx5JjybGKHEuOJcci2bj0/8IpygIAgMSjXf+0a5mOLxVKCymdsVhbZemkD4XNrK2terTFhI4/qC3BcneZ0pZT2sohry6IAAAAQF7IsbAriqsAACQR7QqrXUN19m1rbKtgaXfaX375RebNm1fobbW7lI77pd3XdJwvHaNMaXc67XanXRkbN24c9n4AAADAWcixsCuKqwAAJAEdC+/HH3+UUaNGmWBqTWwCAAAAJDJyLOyO4ioAAEmgTp06sm3bNnMWXicdsCZhAAAAABIZORZ2lzldJgAAsDWdWVhnln377bcJpA6iM//26NFDjjzySDM+mL7+hdEZznVW6rS0NDN7+QsvvFAk2woAAJAXcqxzfZIkWZbiKgAAgE3t27dPmjVrJpMmTQrq9uvXr5fu3btLp06dzHhiN9xwgwwZMsSMNwYAAAAUpX1JkmUZFgAAACAJWDPb6iy3+bntttvk/fffl1WrVmVf16dPH/n7779lzpw5RbSlAAAAQPJk2ZS4PbLN+Xw+2bx5s2myrm8AAAAQf3rOeO/evaZrkdtd9B10Dhw4IOnp6RHvQ+5sod2edInU0qVLpUuXLjmu0/HN9Kw/nIUsCwBA4olnlo1GjnVqlqW4GiYNo7Vq1Yr3ZgAAgDxs2rRJatasWeSBtESZSiIZ+yNaT+nSpeWff/7Jcd2YMWNk7NixEW6hyNatW6VatWo5rtPLe/bsMWOdlShRIuLHgD2QZQEASFxFnWWjlWOdmmUprobJGmRZ3/Bly5aN9+YAAAARE6y0YBSPyRDMmf6M/ZLWcKCIJzW8lXjT5Z8fXzwsX0TjTD8QiCwLAEDiiVeWjUqOdXCWpbgaJquJs75ZCKQAACSWuHZzTikurjBDqd/ljmm+qF69umzbti3HdXpZH4tWq85ClgUAIHHFLctGkGOdnGUprgIAAESTZuFwA3GMc3Tbtm3lgw8+yHHdvHnzzPUAAABwuEhyrIOzbNHP9AAAAJDM9Ix9JEsIdDyrlStXmkWtX7/e/PfGjRvN5ZEjR8qAAQOyb3/llVfKunXr5NZbb5XVq1fL5MmT5fXXX5cbb7wxyk8CAAAAHJdjXc7MshRXE4zfL+Lzx269+q+T16titV5eu9iuV/Ha2XO9KpbrjcVrt2PXXvnx1y1RX6/dXjs7viec5uuvv5YWLVqYRQ0fPtz89+jRo83lLVu2ZIdTVbduXXn//ffNGf5mzZrJo48+Kv/73//MLKtANHCstud6Fa+dPddrx5zFeyK261V8nmO9Xr98t+Z3+WtP5JM/Od3XSZJlGRYgQegHPsP33xeVtqROcYu4XZG1yNb1eX0i3oD1etwingjXq19O3qxttqREcb26zdb3n67Tk/VcRLJe6zm21qvrS4nCenV9GV4R66mww2uXEbDeWL92um5Xgr521nPsCzjb5InRaxeN90SsX7vAz7NTXzsNSROnLZLpH3wlhzK8ckLDo+T6S06T8zq3kGLFPI5+7aL5XZz7tUvxRN4DKaHojoQ9LEBo9+vYsaMJ+Pl54YUX8rzPN998E9bmAXkhZ+XcZnIWOSv3eslZsf3NmwyvXaLnrKJ67WJZr4jGa3fg4CGZ+dFymfDSfNMQIy01RQae21au7ttBjqtbXcTpOdbBWZbiahzl9aWa/TcROeQL78CQ15dq4Hr18TLC/HLJ/aUayFpvOAf1vA6IFhMkveEd1PP6Ug3cl3RveAeGYF67cA4Mha031q9dOAf13AE3v9cu1IN6LF+73D9OsterS4xeu3A/z3F/7az3WhK/dl6vTz74ZJU8/vJ8+eybXyXF45YMfRARWbl6kwwc+YLcUvFNuebijnLZ+SdL5Qqlg95e63Mbz9cu1O/i/F47a72xeO2s90Q0wnRCCKNLVI77AjZBzvoPOSvneslZ5Kyi+M1Lzoptzor5axdw4ixwvYn42m3ZsVuenfmpTJnxiWmt6s6688H0DJk66zN5Zuan0vmkBnJd/9OkS9sG4na7nZljHZxlKa7GQUFfqnkJ9suloC/VvAT75VLQl2pegj2oF3ZAlAgO6gV9qUoEB4ZQXju/DV+7YA/qBR0Q8xJ4UC8sLMTytcsIYb3B/sAK9bUL9iREuK9dYSchCjr5kpdQQnpBP04S8bXb88+/8uI7X8iTryyQTVv/Ek9WCLIKq2afsnZm+669ctek9+S+pz+Qi89uLcMu7iSN6h/p6NcumO/iWL12Ca8IW64C8UDO+m+95Kz/tpmclXjH6kR47UL9zctrF/+cFct6RbivXUEnkGJVr1ArftwoT766QGZ+tMK0rrR+G1j/mu3M+u2w6Ku1Mv+L1XJ0rSqmyNqvR2spVSJNbKeIW64mC4qrRSiUL9VQDgyhfKmG8uUS6pdqsAeGUL/8gj0whHpADOXAEM3XLvDAYMfXLpSAm996cwe9SN8TRfLa5WpZEMqPk6J87fI7CcFr9996f/ltu0yevlheeOtzOZB+6L/b6xNXAJ/fL+mHvPLK7C/lhbeWyqmtjjHB6cxTGpmz00577fIrbIRaFMhLJC0LAMQGOcseGdnux2pyln1fu8D18trZM2dF+trldwIp4u/ifE4gxeq1y8jwyjsLv5UnXl4gy77fYHq1WQXUgli3Wff7DrnhgRlyx+Nvy9Bep8gVF50qtY+sGMYWwk4orhYB/ZI65A3/gJjfgUHpF0y01hv45RLt9VoHhmiuN/cXeCzWq6K5buvAEO312vG1K4r3hETztcsKeorXzp6v3epft8jYJ9+SuZ//aIqhwYSkvFgtWz9b8at88vXP0uy4mvLc/ZdJvaOqRmV7ee3y/oGlQ97ap8gaSXcqZ3alQmLTr71IijuHrY+clb1evu8zkbP+w2uXidfuP7x2sV1v4Gv37IxFMv6FubJ15x7xZAXPwF5tQW1n1ob9s/+gPPHyfDM+6zmdmsr9N/SUerWqSOKLcFgAcWaWdeZeF7FohtHcWG9s1xvLdbPe2K/XjtvMemP32j0zc7HMW/pT5lnuMAurgayWrs0b1o5aYTU3Xrv/1qsnKW3XnSrcBUgw+mPWjt9HrNd+3/fWulkvrx3rzXu9dtxmu6xXi6G3PvKGKawqb7jNugPoOnQ4gXcXfSfPv/W5OCLHupyZZWm5CgBwBB0bye1yiS/Kccys05+5bsBgQisAAABbKWjG+kjp3A76e8EWmNAqLM7cawAAAAAAAACIEC1XAQAAoimSLlG0gAYAAEC8RNq13+XMLJsQLVcnTZokderUkeLFi0ubNm1k2bJl+d62Y8eO4nK5Dlu6d+9u/n7o0CG57bbbpEmTJlKqVCk58sgjZcCAAbJ58+bsdWzYsEEGDx4sdevWlRIlSsjRRx8tY8aMkfT09CLZXwAAkMSs7lThLrAVciwAAEgakeZYlzOzbNz3esaMGTJ8+HATClesWCHNmjWTrl27yvbt2/O8/axZs2TLli3Zy6pVq8Tj8UivXr3M3/fv32/WM2rUKPOv3n7NmjVyzjnnZK9j9erV4vP55Omnn5YffvhBHnvsMZkyZYrcfvvtRbbfAAAAsDdyLAAAAOI+LMD48eNl6NChMmjQIHNZw+H7778vzz33nIwYMeKw21esWDHH5enTp0vJkiWzQ2m5cuVk3rx5OW4zceJEad26tWzcuFGOOuoo6datm1ks9erVM8H1qaeekkceeSRGewoAAByBYQEcgxwLAACSCsMC2K/lqnZfWr58uXTp0uW/DXK7zeWlS5cGtY6pU6dKnz59TNep/Ozevdt0uSpfvnyBt8kdeAMdPHhQ9uzZk2MBAAA4DF2pHMFOOVaRZQEAQKEYFiAscd3rnTt3itfrlWrVquW4Xi9v3bq10PvrmFbanWrIkCH53ubAgQNm7Kq+fftK2bJl87zNL7/8Ik8++aRcccUV+a7ngQceMK0JrKVWrVqFbh8AAHDqGf9wA6kzz/bbkZ1yrCLLAgCA2OZYt2OzrK1Lynq2Xwf8165SedFJAS666CLx+/2mq1Re/vjjD9O1Srtjabeu/IwcOdK0CrCWTZs2RW0/AACx16tbK3n23kvl5BOOido6j6hSTs48tYm4HRoiANgjxyqyLAAA+UtJccvMx6+Wmwd3k8oVSkd13V6fL6rrQ+KJ65irlStXNoP4b9u2Lcf1erl69eoF3nffvn1mnKq77767wED622+/yYIFC/I8268zr3bq1EnatWsnzzzzTIGPl5aWZpZweFwiPn9YdwUAREnbFvXNJDA9u5wgq9dtkSdfmS9vfvS1HEzPCHldLRvVlqv6dpJzu5wgWlbV4od220XspNjpdLDblbmEe1/Ygp1ybKRZVj9/GfwuBAAksRJpqXLaScdLpzYN5JbB3eT1D7+Sp6cvklU//xH2OlM8bsnw+qRhvSPk/C4tJOlzrIOzbFx/qqSmpkrLli1l/vz52dfpD1+93LZt2wLvO3PmTDN2VP/+/fMNpD///LN8/PHHUqlSpTzP9Hfs2NE8/vPPP2/GyIoVj1skzSNSTFtIx+xRAAAF0dalKR6P+e9j61STSaP7y08f3ie3X9ldqlXKu7tt7nB0/uktZf6Lt8jHL9wi53ZuYa7zeNwUVmNEn9ViWcdQPZbaBuNUOYJTcqxVXNXPof7Ltx0AIFm53S6T7VOLpUifs1rLp9NGygfP3iBndWhq/hYsjzvz90H3Dk1k3tQbZNnrI03jDFtgzFX7tVxVw4cPl4EDB0qrVq1Mt6gJEyaYs/nWrKsDBgyQGjVqmHGicnel6tmz52GBUwPphRdeKCtWrJD33nvPjIVljXulA/1rELYCae3atc2sqjt27Mi+f2EtDcKlv7u1Bav+ONRWrHr2n9asABAfViGiQtlSMvzSrnLjwDNk1rwV8tRrC2XlTxtz3LZCuVJy6Xkny5V9OkrVSmXF681svpWSklmoRfRpdtUijm1PfEcyyyqFeltxSo613popWXlWM6x+FdKYFQCQrKys37pJPXn1kfry+9a/zG+FV975XPbsO5DncdLvFyldMk2GXNherux9qtQ+8vATpEmdYx2cZeNeXO3du7cJhaNHjzbhsXnz5jJnzpzsyQE2btx42Nn4NWvWyJIlS2Tu3LmHrU8D5+zZs81/67oCLVy40ITRefPmmcH/dalZs2aO22jXzljTH4upnswPnjer0AoAiA89O63OP/0EuejME+XrVRtk0qvz5efftsvQi06Vvme1EU+KO3tcVev2iD4t2piWcc7MZLAhJ+bY3A0GtMiqeRYAgGRkZf8a1crLPdefJ3dcdba88s5SeXrGIlm3aYf5uza+qFujslx3yWnS7+w2psAKZ3H5iyKFJaE9e/aYmVZ1QoD8Zm8Nlr4CVmtWXgwAiC8dcF678qiMDC8tVGNM66iaWbVYE42iajSPz+E+dlqHMeJKKR7WOvwZB+Tg4rvisv1wlmhnWRoMAACcIsPrNcONXXzT03LwYLpcf0ln6dK2QVSG6YlXlo1GjnVylo17y1X81wJAW7QeYrgAAIgrq7Cq5x4prMaWHveKJWNLVYYFgMMEDhlw0BvvrQEAILa0sKq/FSbe2VeOrJJkBUSGBQgLfRsT7T0c740AABhMUlU0eJqB5EGWBQA46beCzscAKFquAgAARFMkM6U6dIZVAAAA2DzHOjjLUlwFAACIJoYFAAAAgB0xLEBYKK4CAABEEy1XAQAAYEe0XA2LM/caAAAAAAAAACJEy1UAAIBoYlgAAAAA2BHDAoSF4ioAAEBURdKdik5FAAAAiJcIhwUQZ2ZZiqsJxh/vDQAAAJGh5SocjCwLAHAKfzIe9Gi5GhZnlpQT8APp84ukZ2T+CwBAKDIyvOZfr9cndmId+3SzkzKcAg6hn1/9HB/MiPeWAABQdLyaZb3UcUDL1fgH0awwymcRABAqLaZ6fT6Z8cEyeefjb6RD6+Nk0AXtpWTxNBGXiNsGZ461HOzzmc0Vj1vEE+HJ8sQ54x/m+Wvb7zycmGUz7HVeBwCA6DYW8GZm2RS35m+bx7lIcqyy9c6Hj+JqnIKohlANowAAhEKLqR63W3bs2itPT18kL7z1mfz59z/mb/O/+EnGPfuB9OneRob16yx1alaWDK9PUrRqmeD0kKjHRm34pgVWDae2zWYaSMMurib+awXoD0n9vNJSBwCATHpIPJR1sjHFzg0GIsmxDs6yFFeLEEEUABBJ1/+UFI+sWvuHTHxlvrwz/xs5lDUcQKB9/6bL1Dc+lefeXCKd2x4vV198mnRq0yD7/nZgenV4M8/8myKr3U6CM+YqkngYK82yRFkAAPIX2GBA2zhoprUNxlwNC8XVIqBB9JCXIAoACJ7f7xeXy2VanmpEeW/RtzJ52kL56vv1Qd//489/NMuxdarJFb07Sr9zTpJiKR6zXmX9a5duVjZogAskJR3CiqIqAABhNhjQLOuxWZEVIaG4WgQIowCAUG3/c4/s3XdA3l34rUx94xP5Y9vfYa9r7YZtctO4GXLPU+/KpeedLGOGnWuKr3ZhDRlgm+IqwwIgyVjdHAEAQOh8WScq3XboRMawAGGhuAoAQAL6fdtf0uXSR6K6zr/37DdDCmhxNdFbrdoawwIAAADAjhgWICzOLCkDAAAAAAAAQIRouQoAABBNDAsAAAAAO2JYgLBQXAUAAIgmhgUAAACAHTEsQFgorgIAAESRjmcb9pi2Dg2kAAAAsHmOdXCWdWZ7XQAAAAAAAACIEC1XAQAAooiWqwAAALAjWq6Gh+IqAABANGmmDDdXOjOPAgAAwO451sFZluIqAAAJqFqlsvLe09fLB4u/l1fe+Vz27DsQ8TrTUlPkom4nRmX7kD9argIAgGjx+Xwy7/Mf5fk3l0iVimXk8t4dpMmxNeO9WUhStFwND8XVIuBxifj9Iv54bwgAwDZqVKtglrbNj5Y7rzpbXn5nqTw9Y5Gs27QjrELtZReeIkN7nSoVypUSvx6UssKTXaQwSjwQ189fhi/eWwEAzrJ33wF57f0vZfKrC+S3zX+Kx51Z9Hpl9lJp2+JoGdavs3Rt31g8HkJSotPEzcuU3CiuFgH9ELldIj5/ZjClyAoAKIxV+NR/SxRPlcsuaG9aKsz77AeZPG2hLFq2utB1ND/+KLmyT0e54IyWZj1W+LZLUdWVVdTRY6hNNtmg5SqSjX4OtbGA1y/iJcsCQEz99sdOefb1T+T5t5bIvwfSs6/3akEh6xt42bfrpd83z0jN6hXk6r6nSb9zTpKypUvEcauRF82wVpa1C1quhofiahHR95eGUv1d68sKphpQAQAIRkqKx/zb6aQGcvrJjeTnDdtk0rQF8voHy+Tfg4eyb6cF1LM7NpNr+p0mJzapKxkZ3uz72oUdg2ggiqtIRvrWTMnKs1aWpTErAESH9ir6fMUvMvm1hfLhJ9+L2+USry//b1nrb39s+0vumPCm3DN5tgzoebI5EV+vVpUi3HLkxZOVZe0Y6yiuhofiahzoj0W3RyRFg2lWa1YAAIKR4skslB59VFV5bGQfufu6njL1jU/l9Q+XyRknN5ar+naS6lXKiVcrHwFFWTu1jnNoJgNsgQYDABA9Bw4ekllzl8vEV+fLT79uMSfJtdDqzRrCqTDWzfRE+//e+ESembHInIS/+uLT5NQTj7VNb6Vk6vpPlnUmiqsJ1AKAIQMAAMFyZzXr1C5g117SRW4YeLoJ41aItsv4W3bt+l8QWq7CKWgwAACR6TLoEfnh5z+yc511cjwc1n3nf/GTdO/YjMJqEXEHDAWZDE85LVfDkxC/vCZNmiR16tSR4sWLS5s2bWTZsmX53rZjx47ZL3bg0r17d/P3Q4cOyW233SZNmjSRUqVKyZFHHikDBgyQzZs351jPrl27pF+/flK2bFkpX768DB48WP755x+JWwsAt0hain27QAIA4ifF4zbHQrc781+70GNeqifzGGijzS6cK8IFtuL0HJvdYECzrH0aygNAQti05U/zr8+MpxodWmStfWTFqK0P+dOGcqkpSZZlI82xLnGkuBdXZ8yYIcOHD5cxY8bIihUrpFmzZtK1a1fZvn17nrefNWuWbNmyJXtZtWqVeDwe6dWrl/n7/v37zXpGjRpl/tXbr1mzRs4555wc69FA+sMPP8i8efPkvffek08++UQuv/xyiTeHvg8BAA6VNEE0QF7Fs1AW2Ac5Nid9+/IOBoBEwLdxUUjG2BZpjnUl45MSBJdf+xDGkZ7hP/HEE2XixInmss/nk1q1asm1114rI0aMKPT+EyZMkNGjR5uAqmf48/LVV19J69at5bfffpOjjjpKfvrpJ2nYsKG5vlWrVuY2c+bMkbPOOkt+//1300qgMHv27JFy5crJ7t27TauBaDnkZdwqAIAzWC1XoylWx+dQHrtsr2fEVSy8GXv9h/6VPTMvj8v2wzk5NpaflYMZDHMFAMGq3elm2fPPgaiv9+1J10qH1sdFfb3ISXtt6JIMWTYaOdbJWTauLVfT09Nl+fLl0qVLl/82yO02l5cuXRrUOqZOnSp9+vTJN5AqfVG1eq7dppSuW//bCqRKH1Mf+8svv8xzHQcPHjRvtsAFAAAgz9Z7YZ/tj/fWIxlzrCLLAgCA2OZYl2OzbFyLqzt37hSv1yvVqlXLcb1e3rp1a6H31zGttDvVkCFD8r3NgQMHzNhVffv2za6a67qrVq2a43YpKSlSsWLFfB/3gQceMFV8a9FWCQAAALm5JIJASjc+27BTjlVkWQAAENMc63Julo37mKuR0LP9OuC/dpXKi04KcNFFF5nZk5966qmIHmvkyJGm5YC1bNq0KaL1AQCA5MQ4VUi0HKvIsgAAoDCMuRqeFImjypUrm0H8t23bluN6vVy9evUC77tv3z6ZPn263H333QUGUh2fasGCBTnGetB1555oICMjw8y8mt/jpqWlmQUAAACwU45VZFkAAIAkbLmampoqLVu2lPnz52dfpxMB6OW2bdsWeN+ZM2easaP69++fbyD9+eef5eOPP5ZKlSrl+Luu+++//zbjZFk0uOpj68QEAAAAYXNFuMAWyLEAACDpRJpjXeJIcW25qoYPHy4DBw40g/JrtyidNVXP5g8aNMj8fcCAAVKjRg0zTlTurlQ9e/Y8LHBqIL3wwgtlxYoV8t5775mxsKzxp3QsKg3Cxx9/vHTr1k2GDh0qU6ZMMfcZNmyYmVAg2BlWY4XZVQEAsLkIukT5HdqVyq7IsTn5/WRZAIBz6HEv6UTYtd/v0Cwb9+Jq7969ZceOHTJ69GgTHps3by5z5szJnhxg48aNZvbTQGvWrJElS5bI3LlzD1vfH3/8IbNnzzb/resKtHDhQunYsaP571dffdUE0c6dO5v1X3DBBfLEE09IvD6QPr+I1yfii8sWAABQ9PTYdzBDJMUt4jYzk0pSiGS8KaeOU2VX5NicWTaDIAsAIalasazs3XcgqoU6j9slW3fujs7KUCCvnlT0/pdlk0Gk46a6HJplXX4dJR8h27Nnj5lpVScECBwHKxT6zHuziqq8CAAAJ9MY5nGLeCIsskbj+BzpY1e8+Dlxp5YMax2+9P2ya9plcdl+OEu0sqwWVDXPAgBCt+eff2Xau1/I5GkLZdPWXaYw6tWzVWFI8bglw+uTNs3qybB+p8mZpzYVj4YrFAmNr9FoMBCvLBuNHOvkLMsnLQ40iB7yihz0ZgZS8igAwOn0WKjHRD026jEyzN8VCSEeM6xOmjRJ6tSpI8WLFzfjbi5btqzA22v39eOOO05KlCghtWrVkhtvvFEOHMhsOQMURj+f6VlZlsIqAISvbOkScmXfTvLN22Pl1Ucul5OaH51dKA2W2+0yt7+wWytZ/MptMud/w+XsTs0prBYxPRwe8gXUefzOzLEuh2bZuA8L4BTWGFT6IbPzD0YAAGLN9OrwZp4B9thxyIBIBvMP434zZswwY3/q+JsaRjVsdu3a1XQ/r1q16mG3nzZtmowYMUKee+45adeunaxdu1YuvfRSE4bHjx8f5oYj2QV2/SfKAkB0aSH0rA5NzfLDL3/IlNcWyYwPlonX6xNfHlU6t8tlrq9YrpRc0bujXHr+yVK1knNaCSY6PVZm6Ovq+i/L2kakk1K5nJllOZVRhGf3daGwCgBAcHxZLQD0+KlD6CBvGiJ1ciOdRKlhw4YmmJYsWdIEzrx8/vnncvLJJ8vFF19sWgicccYZ0rdv30JbCMC5sluVU1gFgJhrVL+GPDmqn/z04X1y+1VnS5UKZXK0UFXH1z9SnrprgPz4wb1y69AzKawmcIMB09Mjg1pQsmdZiqtFgDP8AACEz+r5YRfR6Eql414FLgcPHszzsdLT02X58uXSpUuX7Ot0giO9vHTp0jzvo2f49T5WAF23bp188MEHctZZZ8Xk+YD92enzBwDJolL50nLToK7ywwf3yrP3XionNqlrxlH94Nkb5dNXR0ifs1pLWmqxeG8mgsyydmkoEK1hAfY4LMsyLAAAAEAURTLelHU/HTsq0JgxY2Ts2LGH3X7nzp3i9XqzZ6e36OXVq1fn+Rh6ll/v1759e9F5TTMyMuTKK6+U22+/PaxtBgAAsVMsxSMXdm1lFiCRc6yTsyzFVQAAgAQrrm7atCnHDKtpaWlR275FixbJ/fffL5MnTzbjWv3yyy9y/fXXyz333COjRo2K2uMAAADAmcXVTQ7LshRXAQAAEoyG0cBAmp/KlSuLx+ORbdu25bheL1evXj3P+2jovOSSS2TIkCHmcpMmTWTfvn1y+eWXyx133GG6YgEAAADhKuuwLEt6BgAAiKJojFMVrNTUVGnZsqXMnz8/+zqfz2cut23bNs/77N+//7DQqaFWadcqAAAAOFO0xlx1Wpal5SoAAEA0aaYMtzdVGPcbPny4DBw4UFq1aiWtW7eWCRMmmLP3OuOqGjBggNSoUUMeeOABc7lHjx5mVtYWLVpkd6XSFgB6vRVMAQAA4ECR5FgHZ1mKqwAAAAk25mooevfuLTt27JDRo0fL1q1bpXnz5jJnzpzsiQE2btyY4+z+nXfeaR5H//3jjz+kSpUqJozed999YW0zAAAAkkO0xlx1WpZ1+en/FZY9e/ZIuXLlZPfu3YWOI5HuFfHxLAMAEDaNaWkp0T0+R5v12NUve0XcqSXDWocvfb9sfa5/XLYfzhLKZ+VARpFtFgAAScnjEinmSdwsG40c6+QsS8tVAAAAG7dcBQAAAOzacjUZUFwFAACIIoqrAAAAsCOKq+HJOb0WYtb825lvLwAAIqfH0BQSCxA3fP4AAIgsy3o4liY1Wq4WAf0QuV2Z4656fSK+eG8QAAA2KqrqMdRWJ8EjmWXVTvsJx9DPoScry2b4RJhKAACAwmmGtbKsI3KsstO+RhHF1SKiPwo1lGqh1SqyekmmAAAcxjpe2iqIBmBYACRzltXPpUZYLbIyYSsAAPmflLRjrGNYgPBQXI0DDaVuj0iKFlmzWgAAAOB0dg6igSiuIpnpW1TfpakeEX9WjqXBAADA6Wzb4yoXiqvhobgaR/qeS8lqBUA3KwCAEyVLEAWcSD+zxQIaDGjPLLIsAMBJdCjVFE9Wb3qyrGNRXE2wIQPSvXSxAgA4gxZUtfVbsnFJBC1XnTpQFZKmwcBBb7y3BgCAouHJOsmYTCLJsU7OshRXE4wz34YAACQPhgWA04cMoJ0AAMAJkjG2MSxAeCiuAgAAJMosq87MowAAALB7jnVwltXhIQAAAAAAAAAAIaLlKgAAQBQxLAAAAADsiGEBwkNxFQAAIIoorgIAAMCOKK6Gh2EBAAAAAAAAACAMtFwFAACI9ozpYZ60d+jJfgAAANg8xzo5y1JcTTD+eG8AAACIQigNd1iAqG8OUGT8frIsAMBZx71kE0mOdXKWpbiaIB9In18kw0cgBQA4hx77DmaIpLhF3BGeJU8okexLsjwHcFyW9erii/eWAABQdPTY5/dmZlmNcEmRZSPN5C5xJIqrCRJEKaoCAJxIj3+HsgoyGkw9yVRkBRyQZbVxgOZZAACc2lgg3ZtZU0y6BgOwz4RWkyZNkjp16kjx4sWlTZs2smzZsnxv27Fjx+yZywKX7t27Z99m1qxZcsYZZ0ilSpXM31auXHnYerZu3SqXXHKJVK9eXUqVKiUnnHCCvPnmm1KUH75DXpGDXlqrAgBg0WOiHhv1GKnHSrvKK6uEssBenJpl9Yekfl4prAIA8F+Dgew6j9+ZOdbl0Cwb1+LqjBkzZPjw4TJmzBhZsWKFNGvWTLp27Srbt2/P8/YaNrds2ZK9rFq1Sjwej/Tq1Sv7Nvv27ZP27dvLuHHj8n3cAQMGyJo1a2T27Nny/fffy/nnny8XXXSRfPPNNxLTVqo+kfSMzDBKEAUAIG96jNRjpR4zTe8Ovz0nAgh3gX04McvqUB7pNj8BAgBAUTQYsOPxMtIc63Jolo1rcXX8+PEydOhQGTRokDRs2FCmTJkiJUuWlOeeey7P21esWNGcobeWefPmmdsHBlI9iz969Gjp0qVLvo/7+eefy7XXXiutW7eWevXqyZ133inly5eX5cuXx2Q/TVFVW+L4RBiKCgCA4OgxU4+d5qSkjQ6gbrcrogX24ZQsm92qnB5XAACE3tMjwz5F1khzrNuhWTZuxdX09HQTAAODo9vtNpeXLl0a1DqmTp0qffr0Md2hQtGuXTvT0mDXrl3i8/lk+vTpcuDAAdNVKz8HDx6UPXv25FhCGuQ4pC0EAAAWf1ZxB0gkTsqyfP4AAIgsy9qpoQBsNKHVzp07xev1SrVq1XJcr5dXr15d6P11PCvtSqWhNFSvv/669O7d24xllZKSYloMvPXWW1K/fv187/PAAw/IXXfdFfJjAQAAZ4mkS1Sid6XSLusPPvigzJ8/33R918JeoHXr1olTkGUBAECyibRrv8uhWTZuxdVIaRBt0qSJ6Q4VqlGjRsnff/8tH3/8sVSuXFnefvttM07Vp59+ataZl5EjR5oxtSx6tr9WrVoR7QMAAEg+kQzmn+iTAAwZMkQWL15suq4fccQRCb+9iYwsCwAAEk2kk1K5HJpl41Zc1SCoA/hv27Ytx/V6WcegKqzSrN2f7r777pAf99dff5WJEyealgKNGjUy1+nkAxpGdbZXHSsrL2lpaWYBAABwasvVDz/8UN5//305+eSTxenIsgAAINkke8vVD2OUZeM25mpqaqq0bNnSNMW1aHNcvdy2bdsC7ztz5kwzblT//v1Dftz9+/dnj4kVSMNx7ubAAAAA+E+FChXMpEwgywIAANhNhRhl2bgVV5V2TXr22WflxRdflJ9++kmuuuoqcyZfZ1xVAwYMMF2Y8upG1bNnTzPOVG46sP/KlSvlxx9/NJfXrFljLm/dutVcbtCggRmP6oorrjBjXenZ/0cffdTM1qrrBAAAiEZ3qnCXRHbPPfeYmeytAp/TkWUBAEAyiTTHuhyaZeM65qoOxL9jxw6zYxoYmzdvLnPmzMmeGGDjxo2HnZXXgLlkyRKZO3dunuucPXt2dqBVOgOrGjNmjIwdO1aKFSsmH3zwgYwYMUJ69Ogh//zzjwmoGorPOuusmO4vAABIfsk85qoW8bSYp1mtTp06JlcFWrFihTgJWRYAACSTZB9z9dEYZVmX3+/3R2kbHUUnAShXrpzs3r1bypYtW+Bt070iPp5lAADCpjEtLSW6x+dosx670W3viCetVFjr8B7cJz+MOzcu2x+Mwmab1wIg7CGUz8qBjCLbLAAAkpLHJVLMk7hZNho51slZNq4tVwEAAJJNMk9oRfEUAAAgeSX7hFZjYpRlKa4CAICIHMrwyuwF38jUNz6VqhXLypV9O0qbpvUSvltQrLgkgmEBTBtdAAAA+/rhlz9kymuL5MdfNssl57aVi85qLSWLp8Z7sxDjHOvkLEtxtYiaf+vgC4wMAABIJrv+/kdeeOszeXr6Itm+a6+43Zlh7J3530jjY2vIsH6d5bzTT5DUYpHFDY1oKXGdgtPZLVd1RtW1a9dK5cqVzQyrBQVunYwJyUc/fxm+eG8FACCReb0++WjJKpn06gL5/JtfJMXjFq/PJyt+/E3GPPG2XHbhKTKk16lSo1oFcRpNTh6bZNlkbLlasQiyLMXVIqAfIrcWWCUzmDL+KgDAzrQVwpTpC2X6B8vEm+ETX9bw7T5zgMv87x9/3ixXjnlJ7njsTbmid0e59Pz2UqVimZAexx1wDE3EoOYUjz32mJQpk/naTZgwId6bgzgVV7WxgH7ENcsSZQEAlj3//Cuvzv5CJr+2QH7f+pd4NLhp7cP731m5PfsOyJMvfyxPvPyx9OjUXK6+uJOc2KRu0vdy0mOnlWWR3FmWCa3CFMkgw/6sYOrlmQcA2ITPp60RfpDJ0xbIkuU/i0dbIwSE5oJoi1aP2y29zjxRruzTUZocWzNmQTQRJrRqdvu74ike5oRWB/bJt/f3SNhJAJA8IvmsWEVWGgwAgHOt27RDnpmxWF5653M5cDDdXBdMdUlbtGrhtelxNWVY/85ybucWEfdyStSTkuHUjuM9oVUkOdbJWTa53sE24cqaJS7Fn1lg1d+mZFMAQCLau++ATHv3C1NU3bhllymSqmALq1aLVp/PKzM+WGbWdVLzo2VYv9Ok2ylNTJE2GkE0kSTbsAC5eb1eeeutt+Snn34ylxs2bCjnnnuupKQQK51CT3ykemgwAABOo23zPvlqrcmF8z77QdwhnGy3WC1aV/38h1w+6kW5fXxmL6fBF54iFcqFX9SLN2sYK7v3uErGYQGKIsuSguNI33Qprv+6Wel3DMNZAQASSeeBD8kvG7dnX9axs8Jlhe+vvlsv/Vc+a4L0uJsvTIogGki7uIU9oVWCPwk//PCDnHPOObJ161Y57rjjzHXjxo2TKlWqyLvvviuNGzeO9yaiCNFgAACcRSepuv2xN83Jcf2+D7WwGihzOCmRnX/9I598tUZuHtzNFG8TPQvlphnWyrJOz7HKqVnWJkPqJjd972nDndSU5PlAAgCSw7Y/92ROyhjFiokWaDV47dy1R9JSMo+BCZ7DkGXIkCHSqFEj+f3332XFihVm2bRpkzRt2lQuv/zyeG8e4tlgQLOsJ95bAgCIdS4sluKJqKial8pZ4/InemEuN20op8c+6jj2EassS8vVBMNnEgDgGEl60EvmYQFWrlwpX3/9tZlp1aL/fd9998mJJ54Y121Dgrz3s6e1AwAguSV6bgtHsg8LsDJGWZaWqwAAADHoThXuksiOPfZY2bZt22HXb9++XerXrx+XbQIAAEBi5FiXQ7MsxVUAAAAUOHustTzwwANy3XXXyRtvvGG6U+mi/33DDTeY8aoAAAAAp2VZhgUAAACIpki6UyXgyf7y5cvnaIWgk01cdNFF2dfpZdWjRw8z+yoAAABsKtJJZl3iyCxLcRUAACCKIukSlYhdqRYuXBjvTQAAAEARiLRrv8uhWZbiKgAAQBQl24RWHTp0iPcmAAAAoAgk44RWHYogyzLmKgAAAIIyduxY8fl8h12/e/du6du3b1y2CQAAAIhnlqW4mmAyR3oAACDZ+ZP2oJfMM6xOnTpV2rdvL+vWrcu+btGiRdKkSRP59ddf47ptiD9/8n6sAQBZrPEpkXncSzaR5liXQ7MsxdUE+UB6fSIHM0R8SfjhBADYV41qFcy/7igGJY/HbY59R1Qtb459egxMpnBqdacKd0lk3333ndSsWVOaN28uzz77rNxyyy1yxhlnyCWXXCKff/55vDcPcaKf3wyfSDrzmQFAUqtRrbxkeH0my0WLZp8tO/4Wn89vu8Kt1y+SnmRZNtIc63JolmXM1XgXVbPCKAAAiWjBi7fKGx99LRNfmS9r1m81YdqrCTIM1n07tWkg11x8mnRofZxp5XYoa3UpbhGPDUKZ0ya0ClShQgV5/fXX5fbbb5crrrhCUlJS5MMPP5TOnTvHe9MQB9ooQL8ONM8CAJLf0Is6SIuGteWp1xbKOx+vMNd5w2wh5nG7zH31RP45nVrIvn8PSplSxcVuNMZqL3NNcJ4kyLLJOKFVUWRZWq7GgX73HPKKHPRSWAUAJLbiacWk/zltZemMO2T2U9fJ6e0amsAYSosFDVm6nsEXniJfvzlaZj5+tXRs0+Cw8KXHRD02aus3enIkrieffFIef/xxMy5VvXr15LrrrpNvv/023puFImwc4MtqqaOfVQqrAOAsrRrXkan3DZLv3r1Hrh94upQtXSKkopqVIds0O1pefeRyWfn2XXLVxZ1sWVgN5A/IslrvSZaWrMnoyRhkWVquFnEQ1bP71FMBAHajgfmUVseaZcPvO+WZ1xfLC299JgcOppu/5w6QHrdbvD6fHFm1vFzT7zTpd05bKZcVvgtjCjfezBYA2prVbbMWAMnccrVbt27y9ddfy4svvigXXnih/PvvvzJ8+HA56aST5K677pJbb7013puIGPe4Ml0f470xAIC404w36upz5ObLumX3clq7YVu+vZzcbpfJhxedeaJc2beTND6mhiQrc7z0ZmZYK8vaRbK3XO0WoyxLcbUI6PeKnsEgiAIAkkGdmpXl/uEXyMgrustr730hE19dIJu27DKBWfOUjsV1YtO6MqzfadLtlCZhj8sVOGRAMe1mZZP+NpGMN5XgeVS8Xq8Zq+rII480l0uUKCFPPfWUnH322TJkyBCKq0lKcyy9rQAAeSlRPFUuObed6en06ddrZdK0BTJvyQ/i1jH2fX7x+f1SuUJpuaJPR7n0vJOlcoUy4hSBDQaKeexRZI103FSXQ7NsSMXVn376SaZPny6ffvqp/Pbbb7J//36pUqWKtGjRQrp27SoXXHCBpKWlhbUhyUzPWlBYBQAkG+2+dXnvjjKk16ky97MfZOobn0rVSmXlyt4dpclxNaP6WFrYsU9xNXlbrs6bNy/P67t37y7ff/+9JDqybHgorAIAgskwp554nFnWbdohz8xYLD+t22yKrud2biGpxZzbts+f1ejO7ZGEl+wtV+fFKMsG9TNlxYoV0qVLFxM8lyxZIm3atJEbbrhB7rnnHunfv7+Z0e2OO+4wld9x48bJwYMHw94gAABgL26327RQ1bFUJ43uH/XCKhKLFiY1/7Vt21b++OMPc93LL78sq1evlkRFlgUAoOjUq1VFHrz5Qnln8nXSq9uJji6swhlZNqh3uJ7Fv+WWW+SNN96Q8uXL53u7pUuXmkFhH330UTPzFgAAgNMk87AAb775plxyySXSr18/+eabb7KLkLt375b7779fPvjgA0lEZFkAAIDCJfuwAG/GKMsGVVxdu3atFCtWrNDbadVXl0OHDoW1MQAAAHaXzMMC3HvvvTJlyhQZMGCA6V5vOfnkk83fEhVZFgAAoHDJPizAvTHKskENCxBMGI3k9gAAAEh8a9askVNPPfWw68uVKyd///23JCqyLAAAANbEKMuGNDVERkaGPPzww3LCCSdI6dKlpWLFinLSSSfJ008/bcaqAgAAcDpXQJeqkBdJbNWrV5dffvnlsOt1HNN69epJoiPLAgAAxCjHupybZYMurv7777/SsWNHGTFihJlVdciQIaYZrVZ3r776aunRo4f4fD759ddf5YUXXgh7gwAAAOzM7XJFtCSyoUOHyvXXXy9ffvml6fa1efNmefXVV+Xmm2+Wq666ShIZWRYAACC2Odbt0Cwb9JRtDz74oGzatMkM+Nq0adMcf/v222/lnHPOkRtvvNEMDnvbbbeFvUEAAAB2lswTWmlhUguQnTt3lv3795tuVWlpaSaQXnvttZLIyLIAAADOntBqRIyybNAtV3Wg1/Hjxx8WRlWzZs3kkUcekSeffFK6du0a0gZNmjRJ6tSpI8WLF5c2bdrIsmXL8r2ttjawBtcNXLp37559m1mzZskZZ5whlSpVMn9buXJlvrPBnnbaaVKqVCkpW7aseUK1RQMAAADyptnqjjvukF27dsmqVavkiy++kB07dsg999wjiY4sCwAA4GyuGGXZoIurv/32m7Ru3Trfv+t4VbqRU6dODfrBZ8yYIcOHD5cxY8bIihUrTLDVQLt9+/Y8b69hc8uWLdmLPhEej0d69eqVfZt9+/ZJ+/btZdy4cfk+robRbt26meCqAfirr76SYcOGidsd0hC0ABAVP/zyh1x7z6vSrs99MvGV+bJ77/54bxKACORVPAtlCUcoBT6lA/Zfc801csQRR5iz9ccee6x88MEHQT9eamqqNGzY0GRDHbvUDsiyAAAUjZU/bZQrRr8op1z8gDzz+mLZu+9AvDcJRZRjXQ7NskEPC6BnxDUo1qpVK8+/b9261UwKEAptPaDjHQwaNMhcnjJlirz//vvy3HPPmaa6ueVev7ZAKFmyZI5Aeskll5h/N2zYkO/japev6667LsdjHHfccRIrKW6RQ14RpkkAYNGuCB8t+UEmvTpfPlvxi3g8bvF6fTL6ibflvinvSf9z2soVvTtI/drV4r2pQNy5so6lduF2ZS7h3jdUVoFPc5SG0QkTJpgCn86GWrVq1cNun56eLqeffrr52xtvvCE1atQwhcfy5csX+lha+NPu9fPnzze5UL/LAq1bt04SFVk2fMU0y+Z8qQEAyCEjwyvvL/7ONBb5etUGScn6fTPikZly18R3ZNB57WXoRadK7RqVxWk0xnrcyZ9jnZxlgy6udurUSe6//34zDlVedOP0NsHSJ2P58uUycuTI7Ov0bHuXLl3M2fhgaMuCPn36mO5QwdInTweu7devn7Rr185MWtCgQQO57777TCuB/Bw8eNAslj179oT05kpLEfH5RTJ8mf8CcKY9//wrr733pUyatkA2bdklnqyjjwYPpbNVHzh4SJ6ftUT+N/MTOe2k4+WafqdJpzYNwj4LCNiVZtAUz3+zltqGGasq3EFXQ79LqAU+vV67Qn3++edSrFgxc522FAiGTgK1ePFiUwDUlgJ2+l4iy4afZfUHoR6urCxLlAUAWP7avU9eeudzmfLaItm6c3f275uMrN83etDY/2+6TJm+SCa/tlC6ndJYrrn4NGl3Qn1b5YhweFz/HUMdkWMdnGWDLq5qdyetIGuXKa0oa4jTIsBPP/0kjz32mPz4449mrIJg7dy5U7xer1SrlrNVll5evXp1offXJsLalSqUrluBVeixY8easbWaN28uL730khnMVtd3zDHH5Hm/Bx54QO666y6JhH6gUj1aPMkMpl6SKeAY63/fIc/MWCwvvv25HDiYnn29N5+zLVaxdfFXa2TBFz/J0bWqyNX9TpPeZ7WWUiXSimy7gXgFUW2pmuR5u0C5C1/a3UmXaBT4Zs+eLW3btjVdqd555x2pUqWKXHzxxWYSJ+2iXpAPP/zQhN2TTz5Z7IYsG1mW1c+j9SORBgMAgDXrt8rTMxbJtHe/kPRDXnNMLfD3TVYLwXmf/SAffvK9NKh3hAzrd5pc0LWVFE/LLJAlC82xeswkyzonywbdMFnHIpg3b57s3bvXnGFv0aKFnHDCCWYH9LqPPvpIGjVqJEVFg2iTJk0KHDsrL1aT3yuuuMJUxXU/NFBrVyqtfudHX+jdu3dnLzrbbLj0A1bMI5LmyfrxGPaaACQyDRiffr1W+tw4RVqed5c8O/MT+fdAujnBkpU9CmUVWdf9vkNuenCGHH/mHTLmybfNWWEgGbv+67FRj5F2DqPWLKvhLkq7rpcrVy570cJYqAU+7eaeX3FOu1Dp/XRsqlGjRsmjjz4q9957b6H7VqFChZC7zicKsmz0sqzVYMDKsgAAZ9BjkBZHz7vmSTnponvlpbc/l4PpGdmF1WBYLVrXbtgqw+55VY4/6w65f8p7svOvvWJnrqxhdLLrPC5n5liXQ7Ns0C1XlZ7p/+GHH8yspWvXrjXX6dlxDXWhqly5sqkob9u2Lcf1erl69eqFjpGgY1TdfffdIT+uNvu1Anag448/XjZu3Jjv/fKrskdC33QpWa0A9OSOfscwnBWQPF6ZvVSuu3eaGW/IH1AoDYeVV3Qw+M++/lnuuranCTHJ3pUGzimqarEmWd7Orqz/hXtfpYUvHSPUEs0Moj+MdIyqZ555xmSxli1byh9//CEPP/ywad1ZEJ1JdfTo0fLiiy+asULthiwb2yzLkAEAkNwmT1soox5/y8wXIRH+vvFltXD9e89+Wfbdern9yrNt+ftGM6yVZZ2eY52cZUMqrlq0+5EukdCZufQJ0EFke/bsmf0E6WWd7bQgM2fONGNG9e/fP+TH1XEYjjzySDMwbiAN2GeeeabEQ2A3q3QvXayAZLHzr39M8MgebyhKypXNPAjYLXgA+bV+w+E0jAYG0mgW+LQ4p+NTBXab0sKctg7Qrlma0QJp4THw++aXX34xrQk0U1njXFlWrFghdkCWjU2W1c/0QW9cNgEAUES/b4qleORQRnS/7CuWL2XL3zd67NMeVzic07JsUMVVHeD/+uuvlxIlShR6Wx1gX5v1du/evdDb6nhXAwcOlFatWpkuUTojmJ7JtwaxHTBggJn1K3fzYe1GpSG2UqVKh61TB7XVs/abN282l63gqS+KLvqE3nLLLaaa3axZMxOstWKtY2Nps+J4s9dXCYDC8JkGnCeSWVZDvV84BT4dY2ratGnmdjqmlVWY06CaO4wqa712RpYtGqY7YObcJQAAJD2b1YJjnmOdnGWDKq7qAP9HHXWU9OrVS3r06GECpA4YqzIyMszflyxZIq+88ooJgjqofjB69+4tO3bsME1ytcKs4XDOnDnZYy1osLSeKIsGTH2suXPn5juwrRVolY6ppTSA6sD/6oYbbpADBw7IjTfeaAKsBlMdg+voo48OarsBAADyo8WvcFtehHO/UAt8V111lUycONEUG6+99lr5+eef5f7775frrrsuz/UX1r3KDsiyAAAAsc2xTs6yQRVXNWB+++23ZuN10H+d9Uub3+qYCfv3789uZjtkyBC59NJLpXjx4kFvgFai86tGL1q06LDrdLD+ggZL1sfXpTAjRowwCwAAQDQFDuYfzn1DFWqBTycY0MmbtDDXtGlTE1Y1nOoMq8mKLAsAABDbHOvkLBv0mKt6RvzZZ5+Vp59+Wr777jv57bff5N9//zXjI+iO678AAAAoeqEW+Nq2bStffPGFOAlZFgAAIDENs3mWDXlCK60WR2MSAAAAgGTkdrnMEu59EVtkWQAAgOjnWCdn2ZCLqwAAAEicYQEAAAAAuw4LkAworgIAANh4QisAAADArhNaJQOKqwkm/+kNANhRAXOWAMj6jDg0g9mS1+uVF154QebPny/bt28Xn8+X4+8LFiyI27YhMT7PHPYAILkVNCmj0/BU2I83RlmW4mqCfCB9fpEMH4EUSCZHHVlRvD6fpHjckuHN+aUdLi1Cbd25Ww4d8kpKituxZwaRHPTYl+4V8bhFPBF2QUokyTwsgM7EqoG0e/fu0rhxY76DkJ1lvVlZFgCQvGrXqGR+10T7980f2/4Sr9cnbndkrSaLmh77/BmZWdadJFk22YcFuD5GWTbk4urzzz8vvXv3lpIlS0ZlA5yMIAoktwvOaCWN6teQp2cskmnvfikZGV7xhXl60+N2m0JtjWoVpH+PtvJverqULVYi6tsMFDX9ROhxMEPf5y6RFHfihzInT2g1ffp0ef311+Wss84SuyLLRvcEif621jwLAEh+g85vL60a1ZGnXlsoM+d8ZX7b+PRgEMHvm7o1qshFZ7aWg4cOScniaWI3Ws7Rxo+a4JKhwUCyT2g1PUZZ1h3qHUaMGCHVq1eXwYMHy+effx7VjXFaS52DXgqrQLJrUO8IeWxkX1n94X0y6ppzpGqlsuZ6j57aDIKeFVZtmtWTVx+5XFa+fZdcdXEnKVuKwiqSjxZo9Niox8gwczpiLDU1VerXry92RpaNQuMAn0h6RuZnlcIqADhLk+NqyuSxl8gP798rtw05SyqWKxVSUc2T9fvm1BOPlZmPXy1fvTlKBl94ii0Lq3k1GNAse8jLkAFOy7IhF1f/+OMPefHFF2Xnzp3SsWNHadCggYwbN062bt0a9Y1LxiB6MCuI8qMRcJYK5UrJDQNPl1Xv3SPP3X+ZND/+qBzF09y0S0yxFI/0Pqu1fDptpLz/zA1yVoem2WEEcMRJyIzMY6fdwqkrwiWR3XTTTfL444/berw1smx49CXXH4362Tzky2ypAwBwLm00cuvQM+XHD+6Vp+4aIMfXP7LA3zfa/TotNUUG9jxZvpx5p8yaOEy6tGsobrc76RsM2Ck2RZpjXeLMLOvyR7DGbdu2ySuvvGIC6urVq6Vbt26mFUCPHj2S8gMSaM+ePVKuXDnZvXu3lC2b2RItP/rDkPFUAeS24offTJeat+Ytz/x+MOMv+6VyhdJyRe+Ocun5J0vlCmXivZlAQiim3azc0T0+R5v12BdM+VSKlSgd1joO/fuPvHnlKXHZ/mCcd955snDhQqlYsaI0atRIihUrluPvs2bNEjshywb3WTFDd1BNBQAUQEtLX3y7Tp6atlDeW/Stacnql8xhA6pXLmd63w04t52UL+u8YXm04FjMkzkua6Jm2WjkWCdn2YgmtKpWrZq0b99e1q5da5bvv/9eBg4cKBUqVDDjWWlrAGQNchzvjQCQcE5oVFuevfdSufv6nvLcG0tk+Y8bpM9ZbaRnlxaSWoz5BoFAWtixS8NtDc5BjvyR530TWfny5U0oTRZk2eBQWAUAFEZbprZtfrRZNm7ZJc++vlhWr9si/c9pK907NJWUFI84lT+r0Z3bk9w51slZNiXcs/wvv/yyCZ3r1q2Tnj17ynvvvSddunSRffv2yd13322C6W+//Rb1DQaAZHNElfJyx1Vnx3szAKBQmv2SAVkWAIDYOeqIinLP9clzMhbJ4/kYZdmQi6vaTeqjjz6SY489VoYOHSoDBgwwzWktpUqVMmMYPPzww9HeVgAAAFu03NAl3PsitsiyAAAA0c+xTs6yIRdXq1atKosXL5a2bdvme5sqVarI+vXrI902AAAAW0qmXHnCCSfI/PnzTVf5Fi1aFBiaV6xYIYmOLAsAAOCMHFtUWTbk4mqHDh3MhuWWnp4u06dPN2f/dUNr164d1gYBAADYWbK1XD333HMlLS3N/Ld2n7c7siwAAIBzWq6eWwRZ1uXXKd1C4PF4ZMuWLeasf6A///zTXOf1esUJQpnBLd2rM4AX2aYBAJB0NKalpSTuDKuBj9372c8ktWR4s6ym7/9HZgw9OWFnWE0GZNnQPysHMopsswAASEoel0gxT+Jm2WjkWCdn2ZBbrmotNq9K9O+//25eCAAAACeLZJbVRJ9hNRmQZQEAAKKfY52cZYMurlrjEujSuXNnSUn57656hl/HperWrVusthMAAMAWkm1YgGRBlgUAAHDesAAJVVy1xiVYuXKldO3aVUqX/q+ZcGpqqtSpU0cuuOCC2GwlAACATWikDDdWOjOOFg2yLAAAQOxyrJOzbNDF1TFjxph/NXj27t1bihcvHsvtAgAAAKKGLAsAAIBYcId6h4EDBxJGQ5Tidm71HgCASLmyjqV24Xa5IlrsRLvTa0vQv/76S+yCLBu6YmRZAADCpjHW43ZGjnU7NMsG9fJWrFhRdu7caf67QoUK5nJ+C/Ie0FdnOE71OHdwXwAAQqXHTD126jHULoFUaaaMZElkN9xwg0ydOjU7jHbo0EFOOOEEqVWrlixatEgSFVk2Mvr5088iRVYAAILnycqyqSn2qQVFmmNdDs2yQQ0L8Nhjj0mZMmXMf0+YMCHsB3M660ei3y/i9Ytk+OK9RQAAJGYQNb0+EjycOdEbb7wh/fv3N//97rvvmkmgVq9eLS+//LLccccd8tlnn0kiIstGTj+P+tnUQqtPs6wvM88CAID/aHzVY6UeM8myzsmyKcF2n1IZGRlm5i+dBKBatWphPSAyP2ApWQHVl1VkJZsCAJwsmYJoJLOsJvoMq9r6s3r16ua/P/jgA+nVq5cce+yxctlll8njjz8uiYosG/0GA26PSAoNBgAAyDGMlR4jEzzOxSzHOjnLhtTJLiUlRa688ko5cOBA2A+IXC0AsrpZMWQAAMCJNIhoV2M9DiZLa9Vk7kqlBckff/zRdKOaM2eOnH766eb6/fv3i8fjkURHlo1BgwG3SBpDBgAAHN713xrGKtGznNOHBagWoywbVMvVQK1bt5ZvvvlGateuHfaDIifzBpTMD2S6N7M1KwAAThkuJ9lEMph/ok8CMGjQILnooovkiCOOMC0TunTpYq7/8ssvpUGDBmIHZNnYDhlwICPeWwMAQNHQY1+xJMuykU5K5XZolg25uHr11VfLTTfdJL///ru0bNlSSpUqlePvTZs2DXtjwBl/AACQuMaOHSuNGzeWTZs2mW5UaWlp5no90z9ixAixA7Js7LMs7QQAAE6Q4HVEFGGWDbm42qdPH/Pvddddl32dVnv9fr/5V5vWAgAAOFUkXaISPaS/9NJL0rt37+wgaunbt69Mnz5d7IAsCwAAkLdIu/a7HJplQy6u6kxaAAAAcN6EVtqVqlu3blK1atUc1+/du9f8bcCAAZLoyLIAAADOnNBqUIyybEgTWikdn6qgJRyTJk2SOnXqSPHixaVNmzaybNmyfG/bsWPH7Bc7cOnevXv2bWbNmiVnnHGGVKpUyfxt5cqV+a5PWymceeaZ5nZvv/12WNsPAAAQGK4iWRKZ1bozN+1iX65cObGDaGdZciwAAEgWkeZYtzgzy4bcctWis2tt3LhR0tPTc1x/zjnnhLSeGTNmyPDhw2XKlCkmkE6YMEG6du0qa9asOaySbAXOwMf8888/pVmzZmasBMu+ffukffv2ZpDaoUOHFvj4+niJXlkHAACIpxYtWmQXAjt37iwpKf9FSO1Gr61BtRWAnUQjy5JjAQAAEl+ss2zIxdV169bJeeedJ99//332+FTKCnahjlM1fvx4Exy1+a3ScPr+++/Lc889l+dgshUrVsxxWcdEKFmyZI5Qeskll5h/N2zYUOBja0uARx99VL7++mszUxgAAECkknFYgJ49e2ZnJy0eli5dOvtvqamppuXmBRdcIHYQzSxLjgUAAMkkWYcF6BnjLBtycfX666+XunXryvz5882/2vVJz7rrrKuPPPJISOvSM/fLly+XkSNHZl/ndrulS5cusnTp0qDWMXXqVDMxQe6ZXguzf/9+ufjii01XrurVq4d0XwAAgPxopnQn2YRWY8aMMf9q8NRJALQLvF1FK8uSYwEAQLKJJMc6OcuGXFzVsLhgwQKpXLmyCZC6aNelBx54wMy6+s033wS9rp07d5rWAdWqVctxvV5evXp1offXMLxq1SoTTEN14403Srt27eTcc88N6vYHDx40i2XPnj0SbdpwIrPtBAAgUcfjQZSPe/7EDWE43MCBA7MLi9u3bxefz5fj70cddZQkumhlWTvlWEWWBQAgurI6v8BGYpVlQy6uaogsU6aM+W8NpZs3b5bjjjvOTACg40sVJQ2jTZo0kdatW4d0v9mzZ5tQHUohWAP3XXfdJbH6QHp18RFIASDevF6feDxuU1jN8HolxeOJ9yYlLT3mpXtFPG4Rjyt5iqzuCM74R9JSoCj8/PPPctlll8nnn3+e58mIUIeHiodEybJFmWOLKstm5Px9AgBAUtNjny9DJMWdmeGSIctGkmOdnGVDLq42btxYvv32W9ONSgfuf+ihh8z4BM8884zUq1cvpHVpoPV4PLJt27Yc1+vlwro46WD/Ok7V3XffHeoumED666+/Svny5XNcr+MrnHLKKbJo0aLD7qNdvnTCgsCz/bVq1ZJIg6iGUP1AAgDiKyPDKykpHln2/TqZ+MoC+XXjdhl60anSr0dbSS3miXj8IeRND4F6LMyQzAKrhlO7P83JOOaq5dJLLzUTALz33ntmnM9E395YZlk75dhYZVlfVpbVfwEAcCI9BB7KOrmYkgQNBpJ1zNVYZ9mQi6t33nmnCYRKA+HZZ59tglylSpXMjKmh0CDbsmVLM+aVNbisNsnVy8OGDSvwvjNnzjRdm/r37x/qLpgJBoYMGZLjOm058Nhjj0mPHj3yvE9aWppZooEgCgCJ1VLV5/fL6x9+JVOmL5RVa//I/tvN416Xeya/K5ec206uvriTHFGlfHbLVkSf6cXhzTzjbYqsCTxuk1NbruokADrOaIMGDcSuopVl7ZRjo5lltXGAlWWJsgAA/CewwYD+XEj0XOfElqsrY5RlQy6u6qxalvr165sxpXbt2iUVKlQIq+KrZ9B1zINWrVqZblETJkwwgdeadXXAgAFSo0YN05Upd1cqDbIahHPT7dm4caPp5qWsLl7aiiBwyU3HVtBWDLFAEAWAxKEFEB1ncedf/8gzry+SF2Z9Jjt27c3ztrv3/isTX5kvT722UM46takM63+atG5aL7ulK6JPj5c6XICmimTqZpUMGjZsaMYatbNoZlmn5FjFMFYAAITYYEALrWRZR2TZkIurealYsWLY99VZunbs2CGjR4+WrVu3SvPmzWXOnDnZkwNouNQfwIE0ZC5ZskTmzp2b71hUVqhVOgurNTvY2LFjpahpCLWaiQMA4kNbp/q8PlMQ/eGXzTLplfny1sffSPohPb9cOG2x+u7ClWZpelxNubJvJ+nVtZW43C5xM2RAzLtZFdNuVjZpMKxvhXDfDon+Nho3bpzceuutcv/995vWksWKFcvx97Jly4odhZtlnZBjs1vikGUBAAiJHjp1viSNd8U8id+qM9Ic6+Qs6/LrqK2FOP/884Ne4axZs8QJdJyqcuXKye7duwt98rX1DUMAAEB8ffr1Wtm6c7dMnfmpfPnduqiss2qlMvLEHf2k6ymNo7I+5E9zWlpKdI/P0WY99g0zl0taydJhrePg/n9kQq+Wcdn+YFiFwtwnExJ9QiuybGSflQPBnYMCAAD50KECtMCaqFk2GjnWyVk2qJar+gQDAGBnb85dLq/OXioZ2p0gSrb/uVc+WrJKTm/fyLReBZRGtnAb2SZ649yFCxeKHZFlAQAAYptjnZxlgyquPv/88zF5cAAAANhHhw4dxI7IsgAAAOgQoywblTFXAQAAkJxjrn733XfSuHFj041K/7sgTZs2LbLtAgAAQHQl45ir3xVBlg25uKqzkBY0ace6ddEZxw4AAMCO3JI5yVm49000OkmTTtZUtWpV89+aA/Masj+Rx1wNRJYFAACIfo51cpYNubh6ww035Lh86NAh+eabb8zMqLfccktYGwEAAJAskq3l6vr166VKlSrZ/213ZFkAAADntFxdXwRZNuTi6vXXX5/n9ZMmTZKvv/46GtsEAACABFG7du08/9uuyLIAAADOUbsIsmzUJvI688wz5c0334zW6gAAAGzJ7YpsSXS//vqrXHvttdKlSxezXHfddeY6uyPLAgAAp4s0x7odmmWjVlx94403pGLFitFaHQAAgC1pdygdqyqcJRG7UgX66KOPpGHDhrJs2TIz4L8uX375pTRq1EjmzZsndkaWBQAAThdJjnU7OMuGPCxAixYtckwCoIPA6sCwO3bskMmTJ4e9IQAAxNJ1l3SRK3t3lGdeXywzPlgm+w+kR7zOts2PlkvPPzmiQd8BOxkxYoTceOON8uCDDx52/W233Sann366JDqyLAAA9vTjL5vl6RmLZN5nP8i5nVvI5b07SN2amWNpAvHMsiEXV3v27JnjstvtNgPDduzYURo0aBDWRiS7FLfIIa/I4XORAQCKSt2alUUnhXx0RG8Ze+258vybS+R/Mz+R37f9FdJ6UoulyHmnnyDX9u8sjY6pIRk2mB3d7rQMVswjtpFsE1oF+umnn+T1118/7PrLLrtMJkyYIHZAlg1dMbdIho8sCwAoej6fT+Z+9oNMenWBLFn+s3g8bvF6ffLszE/k6emL5Iz2jeWafqdJ+5bH5Dh5mki0q7wnav3GYysZJ7QqiiwbcnF1zJgxYT+YU+kHKS1FxOcX8fpEvCRTAChyGrasg33Z0iXkmv6dZVj/zvLeom/lqWkL5cvv1hV4/yoVy8ig89ubM+SVypc2QU+leGxU9bPh8VNPUNph7KZAkYw3lej7qkXIlStXyjHHHJPjer2uatWqYgdk2dDpD0J9b1pZNvPbDwCA2Nm774C89t4Xpqi6ccsu8bgzq5NaWA38d/7SH+WjJavkuLrVTba/sGsrKZ5WTBKBJyvLJnrBMVCk46a6HZplgyqu7tmzJ+gVli1bNuyNSXbmTeoRSdFg6s9sAQAAiI+UrNPH3Ts0Nd2KvluzSSa+Ml/e/vgbOZTxX2vUpsfVlCv7djJBTbv/69lyq7UbYkNDqIZROwXRQK6s/4V730Q2dOhQufzyy2XdunXSrl07c91nn30m48aNk+HDh0uiIstGTj+P+rnUr0AaDAAAYmXD7zvl2ZmL5YW3PpN/A4bx8mY1bMgtI6vI+vOGbXLtPa/KnRNmydBeHeSyC9vLEVXKS1HTJOexcZaNJMc6OcsGVVwtX7580M2rvXSPLJQ+lSlZAVXDKd2sACB+UlIyW55qF/9n7rlU7r/xAnlm5mL5fetfMrDnydKmWT3JyPBm3w6xoSnDaqVqxyDqFKNGjZIyZcrIo48+KiNHjjTXHXnkkTJ27Fgz02qiIstGFw0GAADR9tnyn2XStAUy59PvTSMGq2VqsHw6/peI7N77r4x/4SN57IWP5LwzWsrwS8+Q448+UmLNHdDTgyzrvCwbVHF14cKF2f+9YcMGM9DrpZdeKm3btjXXLV26VF588UV54IEHwt4QJ8rdAkCDqf4LACh6VlejyhXLyIihZ+UIdRRWY9/1XzNosgTRZB4WQAuUOgmALnv37jXXaUBNdGTZ2KDBAAAgGrRr/4XXTTY9y7RGGmphNTdfVmFl6Ypf5NixA8zklbEaj9Wq6SR6hgtWsg8L4IpRlg2quNqhQ4fs/7777rtl/Pjx0rdv3+zrzjnnHGnSpIk888wzMnDgwIg3yon0DZjqEUn3UmAFgHjTwqqGMGsIAMT22Jdskrm4atm+fbusWbPG/LdOAqXjVyUysmzRNRg4kBHvrQEA2I22Ng3s4h8taWnFYprn9dhnp0lXg5HsxdVYZdmQ32V6Zr9Vq1aHXa/XLVu2LKKNQWbLHQBA/CXqbKOwy+Rp4S+JTM/wX3LJJab7lBYsddH/7t+/v+zevVvsgCwbW4n9DgYAIHoSPLbFJce6HJplQy6u1qpVS5599tnDrv/f//5n/gYAAIDkNGTIEPnyyy/l/fffl7///tss7733nnz99ddyxRVXiB2QZQEAAJxpSIyybFDDAgR67LHH5IILLpAPP/xQ2rRpY67Ts/w///yzvPnmm2FvCAAAQDJI5mEBNHx+9NFH0r59++zrunbtaoqV3bp1EzsgywIAADhzWID3YpRlQ265etZZZ5nw2aNHD9m1a5dZ9L/Xrl1r/gYAAOBk2hsqkiWRVapUScqVK3fY9XpdhQoVxA7IsgAAALHJsS6HZtmQW66qmjVryv333x/2gwIAAMB+7rzzThk+fLi8/PLLUr16dXPd1q1b5ZZbbpFRo0aJXZBlAQAAnOfOGGXZsKZN0zEJHn30UTNWgS7avcoukxgAAADEktvlimgJx6RJk6ROnTpSvHhx09U92ImZpk+fbiYe6NmzZ1C3f+qpp+SLL76Qo446SurXr28W/e/PP/9cnn76aTnhhBOyl0RGlgUAAIh+jnU7NMuG3HJVB3nV8QhKlCghrVu3NteNHz9e7rvvPpk7d27Ch2kAAIBkGnN1xowZ5gz8lClTTBidMGGCyWpr1qyRqlWr5nu/DRs2yM033yynnHJK0I8VbHBNZGRZAACAxBlzdUYSZFmX3+/3h3IH3Wit7OpgrykpmbXZjIwMc9Z/3bp18sknn4gT7Nmzx4zJoK0cypYtG5V16itxyCvii8raAABIbJq9Uj3RHZspFsfnUB973EffSolSZcJax7/79sptXZuFtP0aQk888USZOHGiuezz+cys99dee62MGDEiz/t4vV459dRT5bLLLpNPP/3UtOR8++23xQnIsrHNsge9UVkVAMBBZs1dLoPveD7q661Xq4osnzVGYsXjEinmie4645Vlo5FjnZxl3eGc7b/tttuyw6jS/7711lvN3xBeEM3wiaRTWAUAOIie3dVjnx4DQzvVm/w04AYuBw8ezPN26enpsnz5cunSpUv2dW6321xeunRpvuu/++67TUuAwYMHi9OQZaPPaiBAYRUAEI7aNSpJsRSPuKM81fxfe/bLX7v3SYhtCoPm1ZOKGSJesqw4PcuGXFzVyvPGjRsPu37Tpk1Spkz41W2nB1HzwzLeGwQAQBHTY58eA/VYaHpvJMHB0C2uiBalZ+u19YC1PPDAA3k+1s6dO82Z+2rVquW4Xi/r4Px5WbJkiUydOtW03HQismz06OdVT5Do51d/YAIAEI6WjerIjx/cKyMv7y6Vypcy14U7dqfHnVnmqlGtvNx8WVdxe9xmTM5Y0cPfoawsmwwNBiLNsW6HZtmQx1zt3bu3qQw/8sgj0q5dO3PdZ599ZmbW6tu3byy2ManoB836IZkMPyABAIgWLc54vZlnfj3uzDGbYpiFY8YVwXZb99NCX2BXqrS0tKhs2969e+WSSy4xYbRy5criRGTZyLOsZlgaBgAAoqlyhTJy8+Buct2ALvL2x9/IxFfmy/drf5cUj1sytGloIazbtW5aV67pd5p0O6WJeDRQFiE9NmZkDRdgZVkn5VgnZ9mQi6saRLXqP2DAADM+lSpWrJhcddVV8uCDD8ZiG5MCQRQAgOBofPb5Msdk1WDqsVmRNRoTWmkYDWacKg2VHo9Htm3bluN6vVy9evXDbv/rr7+awf979OiRfZ2Oa2V1jdeJA44++ugc99GuXEU9fm0skWXDz7LerCwLAECspBZLkYvOPFF6dWsly75bL0+9tlDeXbhSXOISb1ZmCaRDCWgr14vOai1X9u4oTY6rKYnSYEBjXYrNGgxEa0Krsg7LsiGX8VNTU+Xxxx+Xv/76S1auXGmWXbt2yWOPPRa1SnSy8VrdHSmsAgAQ1pABFHTyz2UtW7aU+fPn5wiYerlt27aH3b5Bgwby/fffZ2c4Xc455xzp1KmT+W/twpVbhQoVZPv27ea/TzvtNDNhgJ2RZUMXOIwVAABFQU+EtmlWT154cLB8+87dMqx/ZylTqrj5mzU2qw4hMGLoWfLjB/fJpNH9E6KwmteQAWZ+HYpBSZ1lQ265ailZsqQ0adIkuluTpBiDCgCAyE9U6pl/O9DWE+GOExbO/YYPHy4DBw6UVq1aSevWrWXChAmyb98+GTRokPm7ttCsUaOGGeuqePHi0rhx4xz3L1++vPk39/WW0qVLy59//mkmDVi0aJEcOnRIkgFZNnhkWQBAPNWsXkHGXnuu3DKkm7z+4VeyYOlPcnanZnLe6SeYlq6Jzp+VZd0eSeoc6+QsG/S78LLLLgvqds8991zIGzFp0iR5+OGHzWC1zZo1kyeffNI8oXnp2LGjLF68+LDrzzrrLHn//ffNf8+aNUumTJliZhzTlgjffPONNG/ePPu2et2YMWNk7ty5ZkKDKlWqSM+ePeWee+4xA+0CAADEc8zVUMcQ3bFjh4wePdpkKc08c+bMyZ4YQLOOzroaLp2tVVsDHH/88ebyeeedZ1oZ5GXBggWSqGKVZcmxAAAUjVIl0mTQ+e3NgsQec9VpWTbo4uoLL7wgtWvXlhYtWog/itOfzZgxw1SpNUS2adPGVKi7du1qxknQqnJuGjjT09OzL2v1WYNsr169sq/TCnf79u3loosukqFDhx62js2bN5tFx9xq2LCh/Pbbb3LllVea6954442o7RsAAEBRGDZsmFnyomfoC8t4BXnllVfkxRdfNGNcaWGwUaNGptWn3cQiy5JjAQAAImf3LOvyB5kur7nmGnnttddMKNWmuf3795eKFStGvAEaRE888USZOHFi9tgKOkbCtddeKyNGjCj0/hpitbq9ZcsWKVWqVI6/6SC3devWPeyMf15mzpxp9kkDrQ6CWxgdEFdbB+zevbvQgXEZXwMAgMjoSfC0IE4Jh3J8jjbrsZ+cv0pKlC4T1jr+/WevXNu5cVy2Pxh61v+tt97K7n5lJ7HIsnbNsaF+Vg5kzvsFAADCpBO0FvMkbpaNRo51cpZ1h9LlSYPfrbfeKu+++64JjnpG/aOPPgr77L+eudcuT9pEN3uD3G5zeenSpUGtY+rUqdKnT5/DAmmorBc+2EAKAABQUHeqcJdEtnDhwuwwqvkvmr2ZYi3aWZYcCwAAkk2kOdbl0Cwb0qAFOoNq3759Zd68efLjjz+aprRXX3211KlTR/7555+QH3znzp3i9Xqzx1Gw6GUdZ6Ewy5Ytk1WrVsmQIUNCfuzc26HjVF1++eX53ubgwYOmkh+4AAAA5BWuIlkS3UsvvWQmgipRooRZmjZtKi+//LLYQTSzrJ1yrCLLAgCAWOdYtzgzy4a933pm3uVymSqvBst40LP9+oTkN2lAMDRYdu/e3YxZNXbs2Hxvp7OSaRNpa9HWDgAAAE4yfvx4ueqqq8wETK+//rpZunXrZsb8fOyxx8RO4p1lizLHKrIsAABwuvExyrIhFVf1jLeOVXX66afLscceK99//70ZY0pn7ipdunTID165cmXxeDyybdu2HNfr5erVqxd4Xx1Tavr06TJ48GAJ1969e82TWKZMGTPmQrFixfK97ciRI02XK2vZtGlT2I8LAACSlxbsIlkS2ZNPPilPPfWUjBs3Ts455xyzPPTQQzJ58mR54oknJNFFM8vaKccqsiwAAIh1jnU5NMsGPTCTdpnSEKhnuS+77DITTDVURiI1NVVatmwp8+fPl549e2ZPBKCX85slLHDgfg3IOnh/uGf6dTZX7R42e/ZsKV68eIG319vpAgAAUBCNlOHGysSOo2LGLG3Xrt1h1+t1+rdEFu0sa6ccq8iyAAAgljnWyVk26OLqlClT5KijjpJ69erJ4sWLzZKXWbNmhbQBw4cPl4EDB0qrVq1MtyidNVXP5ussrmrAgAFSo0YN05Upd1cqDbKVKlU6bJ27du0yLRA2b95sLq9Zs8b8q60IdNFAesYZZ8j+/fvllVdeyTHuVJUqVUwrBAAAgHC4XS6zhHvfRFa/fn3Tfer222/Pcf2MGTPkmGOOkUQWiyxLjgUAAMkkkhzr5CwbdHFVw2Esmvf27t1bduzYIaNHjzaD/zdv3lzmzJmTPTmAhksdEyuQhswlS5bI3Llz81ynnsG3Qq3SWVjVmDFjzHhUK1askC+//DL7iQ20fv16M6lBNKW4RTK8Ir6orhUAAGfQ9FGMelFCuOuuu0x2++STT+Tkk08213322WemtaYG1UQWiyzrhByrimmW9YlEZz5dAACcxe3KrAshebOsy6+j+CNk2kJAJwPQMavKli0b1H18fhGvT8TLMw4AQKE8LhGPOzOQxvL4HC3WYz+z6EcpWbpMWOvY/89eubxjw7hsf7CWL19uBvz/6aefzOXjjz9ebrrpJmnRokW8Nw0Su8+K/mLQCKtFVs20AACgYFpQ1TzrskGWjUaOdXKWDbrlKiKnPw7dHpEULbL6M8MpAACIPIgmElcE226HfdZxRrU7OpzFvK91rFlPZqFVcywNBgAAyMmVlWW1/mOHXBfNHKucmmUprsaBvtlStDWOK/PMP92sAABOZ/cgGiiSmVITfYZVQOnbtFhAgwHtmUWWBQA4mfb6T/FkTQjlcmaOdXKWpbgaR/qes7o8WkVWulkBAJw4BlUoXf8BJGaDAS2y0jELAOAknqws69CaIrJQXE0Q+qNSu1ml68RXFFgBAA469iVjy4Vw5yxgrgPYvcHAwQxasQIAnEGPfck26WokOdbJWZbiaoLhZAcAAPbGsAAAAADJLxljG8MChMepRWUAAAAAAAAAiAjFVQAAgChyRbjY0eTJk+Xuu++O92YAAAAgjjnWJc7MshRXAQAAYtCdKtzFjt5880154YUX4r0ZAAAAiGOOdTk0yzLmKgAAQBQ5cUKr+fPnx3sTAAAAECGnTmg1P8Isa9f9BgAAQBz5/X6zAAAAAE7OshRXAQAAoijZu1K99NJL0qRJEylRooRZmjZtKi+//HK8NwsAAAARcsKwAC/FIMsyLEAC8fl8smnr31K9SgVxuxP/DQkAQCT0RLEuNshgIYlkMP9EfyrGjx8vo0aNkmHDhsnJJ59srluyZIlceeWVsnPnTrnxxhvjvYmIo38PpMs/B71SplSJeG8KAAAxl4xZNtJJqVzizCxLcTUB7N13QF6e/YU8+epCOb1dI3no1oskI8MrKSmeeG8aAAAxo51wDnpFUtwiHlfyBFNXBPuS6M/Bk08+KU899ZQMGDAg+7pzzjlHGjVqJGPHjqW46lC/b/tLnnn9U7M8OqK3XNC1lWR4vZLiIcsCAJKX1y/iy8qy2j4u0XNcrHOscmqWpbgaRxv+2CmTX1ssz836TPYfSDfXPTvzE1nw5Wq5/KIOcsm5bSUtNcU2TasBAAhHhk8kQzILrJ6scIrEtGXLFmnXrt1h1+t1+jc4y5ffrZeJry6Utz7+xrRV8fp8MuTOF2TqG5/KVX07SfeOTcXn91NkBQAkdWOBQ77M/062BgPJaEuMsixjrhYxHSz3k6/XSq8bnpaGPe6SydMXy75/07Obk6tfN26X2x6ZKQ3OvENGPf62bN7+t7ne6836xAIAkKRn/9O9IukZInrIs+tcSW5xRbQksvr168vrr79+2PUzZsyQY445Ji7bhKJ16JBXZnz4tZzc7yHpOPBReXv+SvH6/Kawalm68lcZcNv/pHnPsfLUtIXyz/4DJgNroRUAgGRuMKC9sjTP+hyaY90OzbK0XC0iBw4ektfnfC2Pv7xAfvx1i6R43CZkevWXZD72/POvTJ62QKZMXyhnntpEhvXrLCc1P5ohAwAASU1LNFqn0WjmsWELgGQeFuCuu+6S3r17yyeffJI9TtVnn30m8+fPzzOoInns/Osf09tq4rRFsmPX3uz5ATIKOPm/acsuGf3E2zLu2Q+kT/c2ck2/06RuzSrmPpqFAQBIRlpY1QKrHintNmRAsg8LcFeMsizF1SKgZ/dvfPB1+WvPfnG7Cg+iufl8fnl/0XdmaXxsDbmyTye56MwTTajV9TFkAAAgGfkDhgzQYKoL4uuCCy6QL7/8Uh577DF5++23zXXHH3+8LFu2TFq0aBHvzUOMjHriHXnilQUmv2ouVda/wdBeWjpUwHNvLpHTTmog1/TrLJ3aNDCtXcmyAAAnDBmQ6mHoq2TOshRXi8DMOV+bwqqKtDvUqrV/yLC7X5G7Jr4jU+8bJCefcEzCnxkAACBSek7SLsVVV9b/wr1vomvZsqW88sor8d4MFKFn31gi6Ye8Ea9He23NX/qTWY6tU03efPIaqVm9YlS2EQCARM+ybk9y51gnZ1mb/ExBbtod67MVvzAOKwAACdqdKtwFSDgxGDdu7YZt8uvGHdFfMQAAiFuOdTk0y9JyFQAAIIpcEQzmn6hn+91ud6Fdt/XvGRk6iAMAAACclmOdnGUprgIAAKBAb731Vr5/W7p0qTzxxBPiC5gtHgAAAHBKlqW4CgAAEEWRdIlK1K5U55577mHXrVmzRkaMGCHvvvuu9OvXT+6+++64bBsAAACiI9Ku/S6HZlnGXAUAAIiiZB+navPmzTJ06FBp0qSJ6Tq1cuVKefHFF6V27drx3jQAAABEwAljrm6OQZaluAoAABCDWVbD/V+i2r17t9x2221Sv359+eGHH2T+/PnmTH/jxo3jvWkAAABIgBzrcmiWZVgAAAAAFOihhx6ScePGSfXq1eW1117Ls2sVAAAA4MQsS3EVAAAgityuzCXc+yYiHY+qRIkS5ky/dpvSJS+zZs0q8m0DAABA/HOsk7MsxdUi4Pa4xe1yic/vj8r6PB63nN2xmVxybltJSWFkBwAAEkkkXaIStSvVgAEDxGWHQbQQE5o3o5llS5dMk4t7nCQnNq0rfr+f9xYAAAki0q79LodmWYqrRWDM1WdLisctb8//1lTxvb7wgmn5siVlwLnt5KqLO0n1yuXE6/MRRgEASU9PI6Z4xDYiGcw/UQ/rL7zwQrw3AXE09Z4Bcv8zH8qy7zeYTJvh9YW1nto1KsnlF3WQS887WYqnpSbs+x0AgGjyuETs0i4u0kmpXA7NshRXi0Cj+kfKtIeHyMYtu+Tp1z+RZ17/VPbtP6glfQmmAcCxdarJFb07Sr9zTpJiKZ7sgqrHbZNPJwAAYQZRjztxuxcBTtG1fSOzLP/hN5k4baHM/GiFaXHqC7LBQPuWx8jVF58mXds3Fp/PJyl2OlsCAECYtKCqeTZRC46InoSozk2aNEnq1KkjxYsXlzZt2siyZcvyvW3Hjh1NcTH30r179xxjJJxxxhlSqVIl87eVK1cetp4DBw7INddcY25TunRpueCCC2Tbtm0SS0cdUVHuu76nbPj4fpkwsrfUq1klu5t/brrdXdo1lFkTh8mXM0fJgJ7tJC21mLjdblqrAgCSPoimeUSKeexZWNVNTr75VeH0HKtaNqotz993qfz84T1y2+CuUqFsSXO9DhmQW1pqivTrcZIsnXGHvDvlejn95IbidrsorAIAkpoeEYtlZVnNtHYr30SWY12OzbJxL67OmDFDhg8fLmPGjJEVK1ZIs2bNpGvXrrJ9+/Y8b6+Bc8uWLdnLqlWrxOPxSK9evbJvs2/fPmnfvr2ZCSw/N954o7z77rsyc+ZMWbx4sWzevFnOP/98KQqlSqTJ5RedIt+9PUremXi1dDzxWHO9drMqkVZMBl94iiyfNVpmPn61nNLqmMy/EUQBAEnM7kE0r4kAwl1gH07MseqIKuVk9NVny7q598nTY/tJg3rVs7Ns1Upl5PYru8tPH94nT47qZ3pgZf6NLAsASF6a4VI9ImkpmT2v7JplI82xbpvud6Rcfu3TE0d6hv/EE0+UiRMnmsvaVahWrVpy7bXXmtm8CjNhwgQZPXq0CailSpXK8bcNGzZI3bp15ZtvvpHmzZtnX797926pUqWKTJs2TS688EJz3erVq+X444+XpUuXykknnVTo4+7Zs0fKlStn1lW2bFmJ1Jr1W2Xya4ulbq2qcnmfjiJ+MWf3AQBIZnqo02JqtA550T4+h/PYHyxfL6VKh/fY+/7ZI2e1rBuX7Ydzcmy0Pyv6c+LT5T/Lk68slBsHdZNmDWrl2TMLAIBkHU/VZfMsG40c6+QsG9fUk56eLsuXL5cuXbr8t0Fut7ms4TAYU6dOlT59+hwWSAuij3no0KEcj9ugQQM56qijgn7caDuubnV5/PbeMrTXKab1DoVVAIBTzvAn2yEvsq5USfZkJDFy7H90+IJTWx0rMydcIc0a1KSwCgBwTGFVh7GyayvVWORYl0OzbFwntNq5c6d4vV6pVi2zu5BFL+sZ+MLomFbanUqDaSi2bt0qqampUr58+cMeV/+Wl4MHD5olsKofC6nFUsQb17bEAAAgXrOsJlM4T3Z2yrFFmWW1+z9RFgDgBMmY2yLJscn6nATD1qeVNYw2adJEWrduHfPHeuCBB0wTaWvRLl8AAAB5TwQQ/gJnKMocq8iyAAAg1jnWJc4U1+Jq5cqVzSD+uWc31cvVq2cOjJ8fHex/+vTpMnjw4JAfV9etXbn+/vvvoB935MiRZswIa9m0aVPIjwsAAIDkYKccq8iyAAAASVhc1S5NLVu2lPnz52dfpxMB6OW2bdsWeF+dHVW7NvXv3z/kx9XHLFasWI7HXbNmjWzcuDHfx01LSzOD8QYuAAAAubnFJW5XmItjz/fbj51yrCLLAgCAmOZYl3OzbFzHXFXDhw+XgQMHSqtWrUy3KJ01Vc/mDxo0yPx9wIABUqNGDdOVKXdXqp49e0qlSpUOW+euXbtMwNy8eXN24FR6Nl8X7QqlLQX0sStWrGjCpc7qqoE02BlWAQAA8hJJlyhnxlH7IscCAIBkEmnXfpc4U9yLq71795YdO3bI6NGjzSD8zZs3lzlz5mRPDqDhUmdeDaQhc8mSJTJ37tw81zl79uzsUKt0FlY1ZswYGTt2rPnvxx57zKz3ggsuMC0HunbtKpMnT47hngIAAEeguuoY5FgAAJBUqK6GxeX3+5nQMww6w6q2HNAxq6LZreqQV8TLKwIAcAC3SyTVY4/jcyiP/fE3v0mpMuE99r69e6RLi9px2X44S6w+KwczRIiyAAAnSHFnLsmQZaORY52cZePechX/0TI3pW4AgNOOe64kO8PtyvpfuPcFbP2ZjvdGAABQRHxJmGUjybFOzrIUVxOAfhi1tWqGL95bAgBA0dEizEFv5hl/jyuJgmkk+5IszwEc9+PS66P3FQDAece/dK+IJ5mybKT74RJHorgaRwRRAAAyTy5mZA0ToIVW/ReAPVqpZnhFaB8AAHAqf0CW9WRl2aQosiIkFFfjEEStoipBFACAw8/+ax61iqx2DKfMZwUn9LjSLEv7AAAA/mOOj157NxhgPqvwUFwtIgRRAACCo8fJQ1lnIG05ZADVVSRpltWWOfS4AgAgiRsMUF0NC8XVIqAFVetHIgAACJ7VzSoWs7HGChNaIdkc8lJUBQAgkgYDxbTBgA2yLBNahccGL639EUYBAIj8RCWA+CDLAgAQeWtWJC9argIAAESRK4KuX7bpMgYAAICkE0mOdXKWpbgKAAAQRQy5CgAAADtiyNXwUFwFAACIJqqrAAAAsCOqq2FhzFUAAACbmzRpktSpU0eKFy8ubdq0kWXLluV722effVZOOeUUqVChglm6dOlS4O0BAACAWJpk8yxLcRUAACAGs6yG+79QzZgxQ4YPHy5jxoyRFStWSLNmzaRr166yffv2PG+/aNEi6du3ryxcuFCWLl0qtWrVkjPOOEP++OOPKOw9AAAAnJpjXQ7Nsi6/38+cZWHYs2ePlCtXTnbv3i1ly5Yt8LbpXmaGAwAgEhrT0lKie3yONuuxP131u5QuE95j/7N3j5zSuGZI269n90888USZOHGiuezz+UzIvPbaa2XEiBGF3t/r9Zqz/nr/AQMGhLXdsJ9QPisHMopsswAASEoel0gxT+Jm2WjkWCdnWVquAgAA2FR6erosX77cdIeyuN1uc1nP5Adj//79cujQIalYsWIMtxQAAABIzizLhFYAAABRFI35rLT1QKC0tDSz5LZz505ztr5atWo5rtfLq1evDuoxb7vtNjnyyCNzhFoAAAA4T7Tms9rjsCxLy1UAAIBYpNJwFxHTFUq7ZlnLAw88EJNNffDBB2X69Ony1ltvmQkEAAAA4GCR5liXM7MsLVeLQIpbJMPHuKsAAIR7JjgliDGqEkW4g/lb91WbNm3KMU5VXmf6VeXKlcXj8ci2bdtyXK+Xq1evXuBjPfLIIyaQfvzxx9K0adOwthfOUCwryxJlAQAIb7xVrQsle451cpa1yctrb26XSKpHJM2T+aECAACF02OmHjtTUzKPpU6iYTRwyS+QpqamSsuWLWX+/PnZ1+kkAHq5bdu2+a7/oYceknvuuUfmzJkjrVq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CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[0].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n", + "# ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qomm_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/sampleset_data/erdos_renyi_n=100_p=0.2_num_runs_1/_0_chain_break_fraction.pkl b/sampleset_data/erdos_renyi_n=100_p=0.2_num_runs_1/_0_chain_break_fraction.pkl new file mode 100644 index 000000000..a1bef6770 Binary files /dev/null and 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b/sampleset_data/powerlaw_100.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import networkx as nx\n", + "\n", + "import os\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "Graphs = np.load(\"networks/powerlaw_m=1_p=0.2/graphs.npy\", allow_pickle=True)\n", + "G = Graphs[9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load results" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "results_dir = \"hierarchical_metadata_20\"" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n", + "Could not load dwave_sampleset: EmbeddedStructure is immutable\n", + "Could not load embedding: EmbeddedStructure is immutable\n", + "DWave sampleset info loaded.\n", + "Embeddings info loaded\n" + ] + } + ], + "source": [ + "from Qommunity.searchers.utils import HierarchicalRunMetadata\n", + "\n", + "\n", + "num_runs = 20\n", + "\n", + "hierarchical_metadatas = []\n", + "\n", + "\n", + "for iter in range(num_runs):\n", + " base_filename = f\"{results_dir}/_{iter}\" # I saved it with such prefix\n", + " hm = HierarchicalRunMetadata.load_from_files(base_filename)\n", + " hierarchical_metadatas.append(hm)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "communities = np.load(f\"{results_dir}/_communities.npy\", allow_pickle=True)\n", + "division_modularities = np.load(f\"{results_dir}/_division_modularities.npy\", allow_pickle=True)\n", + "division_trees = np.load(f\"{results_dir}/_division_trees.npy\", allow_pickle=True)\n", + "modularities = np.load(f\"{results_dir}/_modularities.npy\", allow_pickle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "mods = []\n", + "cbf_max = []\n", + "cbf_avg = []\n", + "max_cbf_div_mod = []\n", + "chain_strengths = []\n", + "comms_lens = []\n", + "energies = []\n", + "indices = []\n", + "\n", + "for idx, sm in enumerate(hierarchical_metadatas):\n", + " cbf_max.append(np.array(sm.chain_break_fraction).max())\n", + " cbf_avg.append(np.array(sm.chain_break_fraction).mean())\n", + " i = np.array(sm.chain_break_fraction).argmax()\n", + " indices.append(i)\n", + "\n", + " chain_strengths.append(np.nanmean(np.array(sm.chain_strength, dtype=float)))\n", + " mods.append(modularities[idx])\n", + " max_cbf_div_mod.append(division_modularities[idx][i+1])\n", + " comms_lens.append(communities[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "fig.suptitle(\"Powerlaw-cluster 100 nodes \\n 20 hierarchical runs\")\n", + "\n", + "ax[0].scatter(cbf_max, mods)\n", + "ax[0].set_ylabel(\"Modularity (Q)\")\n", + "ax[0].set_xlabel(\"Max. CBF\")\n", + "ax[0].set_title(\"Max. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "ax[1].scatter(cbf_avg, mods)\n", + "ax[1].set_ylabel(\"Modularity (Q)\")\n", + "ax[1].set_xlabel(\"Mean. CBF\")\n", + "ax[1].set_title(\"Mean. CBF of each hierarchical run vs. Modularity (Q)\");\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qomm_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/sampleset_data/using_with_hierarchical_searcher.ipynb b/sampleset_data/using_with_hierarchical_searcher.ipynb new file mode 100644 index 000000000..d2aa781aa --- /dev/null +++ b/sampleset_data/using_with_hierarchical_searcher.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3ba51de4", + "metadata": {}, + "outputs": [], + "source": [ + "from Qommunity.samplers.hierarchical.advantage_sampler import AdvantageSampler\n", + "from Qommunity.samplers.hierarchical.gurobi_sampler import GurobiSampler\n", + "from Qommunity.searchers.hierarchical_searcher import HierarchicalSearcher\n", + "from Qommunity.iterative_searcher import IterativeSearcher\n", + "\n", + "import numpy as np\n", + "import networkx as nx" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1bb195f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G = np.load(\"networks/powerlaw_m=1_p=0.2/graphs.npy\", allow_pickle=True)[0]\n", + "G" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b33ee3c3", + "metadata": {}, + "outputs": [], + "source": [ + "adv = AdvantageSampler(G, use_clique_embedding=True, num_reads=100, version=\"Advantage_system6.4\", region=\"na-west-1\", elapse_times=True, return_metadata=True)\n", + "searcher = HierarchicalSearcher(adv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d1fa84d", + "metadata": {}, + "outputs": [], + "source": [ + "res = searcher.hierarchical_community_search(return_modularities=True, division_tree=True,return_metadata=True,)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "308de072", + "metadata": {}, + "outputs": [], + "source": [ + "comms, div_tree, div_mods, sampleset_metadata = res" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qomm_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/sampleset_data/utils.py b/sampleset_data/utils.py new file mode 100644 index 000000000..e18955964 --- /dev/null +++ b/sampleset_data/utils.py @@ -0,0 +1,511 @@ +import numpy as np +import networkx as nx +import matplotlib.pyplot as plt + + +class Node: + def __init__(self, id, values, left=None, right=None, parent=None): + self.id = id + self.values = values + self.left = left + self.right = right + self.parent = parent + + def __str__(self): + return f"Node(id={self.id}, values={self.values})" + + def __repr__(self): + return f"Node(values={self.values}, left={self.left}, right={self.right})" + + def __eq__(self, node): + if not isinstance(node, Node): + return False + return self.id == node.id and sorted(self.values) == sorted(node.values) + + +class Tree: + def __init__(self, root): + self.root = root + self.nodes = [root] + + +def recover_ordering(G, division_tree): + root = Node(id=hash(tuple(G.nodes)), values=list(G.nodes), left=None, right=None, parent=None) + nodes = {} + + for level in reversed(range(len(division_tree))): + level_clustering = division_tree[level] + + if level == len(division_tree) - 1: + for cluster in level_clustering: + leaf_node = Node(id=hash(tuple(cluster)), values=cluster, left=None, right=None, parent=None) + nodes[leaf_node.id] = leaf_node + else: + subsequent_clusters = division_tree[level + 1] + subclusters = {} + + for subsequent_cluster in subsequent_clusters: # children clusters + for cluster in level_clustering: # parent clusters + if set(subsequent_cluster).issubset(set(cluster)): # if subsequent cluster is child of the parent + key_clus = hash(tuple(cluster)) + + if key_clus not in subclusters.keys(): + subclusters[key_clus] = subsequent_cluster + else: + subclusters[key_clus] = ( + subclusters.get(key_clus), + subsequent_cluster, + ) + + for cluster in level_clustering: + key_clus = hash(tuple(cluster)) + + if type(subclusters[key_clus]) == tuple: + c0, c1 = subclusters[key_clus] + key1, key2 = hash(tuple(c0)), hash(tuple(c1)) + + child_node_1 = nodes[key1] + child_node_2 = nodes[key2] + + parent_node = Node(id=key_clus, values=cluster, left=child_node_1, right=child_node_2, parent=None) + nodes[parent_node.id] = parent_node + + child_node_1.parent = parent_node + child_node_2.parent = parent_node + + # Sanity checks + assert child_node_1.parent == parent_node + assert child_node_2.parent == parent_node + + + # only one subcommunity - list + elif type(subclusters[key_clus]) == list and level != 0: + pass # Nothing to do in here + + root = nodes[root.id] + return nodes, root + + +import networkx as nx +import matplotlib.pyplot as plt +from matplotlib.patches import Patch + +def build_graph(G, node, division_modularities, level=0): + """Recursively traverse the binary tree and add edges.""" + if node is None: + return + + def divide_label(values, len_per_line=6) -> str: + lines = [] + for i in range(0, len(values), len_per_line): + line = values[i:i+len_per_line] + lines.append(", ".join(map(str, line))) + return "\n".join(lines) + + label = divide_label(node.values) + mod_value = division_modularities[level] + color = "#4c72b0" if node.left or node.right else "#d35d6e" # blue for internal, red for leaves + values = node.values + + # pr_id = comm_hash_to_pr_id[hash(tuple(values))] + # chain_strength = pr_id_to_chain_strength[pr_id] + # chain_strength = comm_hash_to_ch_str[hash(tuple(values))] + chain_strength = 0.93 + + G.add_node(node.id, label=label, mod_value=mod_value, color=color, values=values, level=level, chain_strength=chain_strength) + + if node.left: + G.add_edge(node.id, node.left.id) + build_graph(G, node.left, division_modularities, level + 1) + if node.right: + G.add_edge(node.id, node.right.id) + build_graph(G, node.right, division_modularities, level + 1) + + +def center_parents(G, pos): + """Center parents above children.""" + for n, data in sorted(G.nodes(data=True), key=lambda x: x[1]['level'], reverse=True): + children = list(G.successors(n)) + if len(children) == 2: + x1, _ = pos[children[0]] + x2, _ = pos[children[1]] + _, y = pos[n] + pos[n] = ((x1 + x2)/2, y) + return pos + + +def print_tree(node, level=0): + if node is not None: + print_tree(node.right, level + 1) + print(' ' * 8 * level + '->', node) + print_tree(node.left, level + 1) + + +from QHyper.solvers.base import SamplesetData + + +class DivisionNode: + def __init__(self, community_hash: str | int, node: Node, sampleset_data: SamplesetData | None): + self.community_hash: int | str = community_hash + self.node: Node = node + self.sampleset_data: SamplesetData | None = sampleset_data + + @property + def id(self): + return self.node.id + + @property + def values(self): + return self.node.values + + @property + def left(self): + return self.node.left + + @property + def right(self): + return self.node.right + + @property + def parent(self): + return self.node.parent + + @property + def problem_id(self): + return self.sampleset_data.problem_id if self.sampleset_data else None + + @property + def chain_strength(self): + return self.sampleset_data.chain_strength if self.sampleset_data else None + + @property + def chain_break_fraction(self): + return self.sampleset_data.chain_break_fraction if self.sampleset_data else None + + @property + def chain_break_method(self): + return self.sampleset_data.chain_break_method if self.sampleset_data else None + + def __str__(self): + return f"ExtendedNode(id={self.id}, community={self.values}, left={self.left}, right={self.right}, community_hash={self.community_hash}, problem_id={self.problem_id}, chain_strength={self.chain_strength}, chain_break_fraction={self.chain_break_fraction}, chain_break_method={self.chain_break_method})" + + def __repr__(self): + return f"ExtendedNode(id={self.id}, community={self.values}, left={self.left}, right={self.right}, community_hash={self.community_hash}, problem_id={self.problem_id}, chain_strength={self.chain_strength}, chain_break_fraction={self.chain_break_fraction}, chain_break_method={self.chain_break_method})" + + def __eq__(self, node): + if not isinstance(node, DivisionNode): + return False + return self.id == node.id and sorted(self.values) == sorted(node.values) + + +def recover_info_ordering(root, nodes, sampleset_metadata): + extended_nodes = {nodes_k: DivisionNode(community_hash=node.id, node=node, sampleset_data=sampleset_metadata[nodes_k]) if nodes_k in sampleset_metadata.community_hash else DivisionNode(community_hash=node.id, node=node, sampleset_data=None) for nodes_k, node in nodes.items()} + for _, v in extended_nodes.items(): + if v.left: + v.node.left = extended_nodes[v.left.id] + if v.right: + v.node.right = extended_nodes[v.right.id] + # if v.parent: + # v.node.parent = extended_nodes[v.parent.id] + + root_extended = DivisionNode(community_hash=root.id, node=root, sampleset_data=sampleset_metadata[root.id]) + return extended_nodes, root_extended + + +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors + + + +def build_graph_extended(G, division_modularities, node, level=0): + """Recursively traverse the binary tree and add edges.""" + + if node is None: + return + + def divide_label(values, len_per_line=6) -> str: + lines = [] + for i in range(0, len(values), len_per_line): + line = values[i:i+len_per_line] + lines.append(", ".join(map(str, line))) + return "\n".join(lines) + + community_hash = node.community_hash + community = node.values + problem_id = node.problem_id + chain_strength = node.chain_strength + chain_break_fraction = node.chain_break_fraction + + label = divide_label(node.values) + mod_value = division_modularities[level] + # color = "#4c72b0" if node.left or node.right else "#d35d6e" # blue for internal, red for leaves + + + G.add_node(node.id, label=label, mod_value=mod_value, values=community, level=level, chain_strength=chain_strength, chain_break_fraction=chain_break_fraction, community_hash=community_hash, problem_id=problem_id) + + if node.left: + G.add_edge(node.id, node.left.id) + build_graph_extended(G, division_modularities, node.left, level + 1) + if node.right: + G.add_edge(node.id, node.right.id) + build_graph_extended(G, division_modularities, node.right, level + 1) + + +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors +import matplotlib as mpl +from matplotlib.colors import ListedColormap, BoundaryNorm + + +ROUND_DIGITS = 4 + + +def center_parents(G, pos): + """Center parents above children.""" + for n, data in sorted(G.nodes(data=True), key=lambda x: x[1]['level'], reverse=True): + children = list(G.successors(n)) + if len(children) == 2: + x1, _ = pos[children[0]] + x2, _ = pos[children[1]] + _, y = pos[n] + pos[n] = ((x1 + x2)/2, y) + return pos + + +def plot_tree_info(root, division_modularities, figsize=(14,8), cmap=plt.cm.viridis, value=None): + """Create and visualize the binary tree graph with a modern aesthetic.""" + G = nx.DiGraph() + build_graph_extended(G, division_modularities, root) + pos = nx.nx_agraph.graphviz_layout(G, prog="dot") + + center_parents(G, pos) + + # Node classification + leaves = [n for n in G.nodes() if G.out_degree(n) == 0] + internals = [n for n in G.nodes() if G.out_degree(n) > 0] + labels = nx.get_node_attributes(G, 'label') + + # Modern color scheme + internal_color = "#b8d3e9" + leaf_color = "#f2a7a7" + + # plt.figure(figsize=figsize) + fig, ax = plt.subplots(figsize=figsize) + plt.style.use("seaborn-v0_8-white") + + # Draw nodes + nx.draw_networkx_nodes(G, pos, + nodelist=internals, + node_color=internal_color, + node_size=2800, + ax=ax, + edgecolors="#2b3a42", + linewidths=0.8) + + nx.draw_networkx_nodes(G, pos, + nodelist=leaves, + node_color=leaf_color, + node_size=2800, + ax=ax, + edgecolors="#2b3a42", + linewidths=0.8) + + # Draw edges — subtle and transparent + nx.draw_networkx_edges(G, pos, + arrows=False, + edge_color="#7d8ca3", + width=1.0, + ax=ax, + alpha=0.6) + + + + cmap = cmap + values = [G.nodes[n]['chain_strength'] for n in G.nodes()] + valid_values = [v for v in values if v is not None] + norm = mcolors.Normalize(vmin=min(valid_values), vmax=max(valid_values)) + # none_color = "#d3d3d3" + none_color = "#CE6060" + colors = {v: mcolors.to_hex(cmap(norm(v))) if v is not None else none_color for v in values} + + import math + + def is_bright(rgb, threshold=240): + r, g, b = rgb + lum = 0.2126*r + 0.7152*g + 0.0722*b + return lum >= threshold + + def is_close_to_white(rgb, threshold=30): + """ + rgb: (R, G, B) tuple, 0–255 + threshold: max distance from white to be considered 'close' + """ + r, g, b = rgb + # Euclidean distance to white + dist = math.sqrt((255 - r)**2 + (255 - g)**2 + (255 - b)**2) + return dist < threshold + + def is_close_to_gray(color_hex, sat_threshold=0.15): + """ + Returns True if color is low saturation (grayish/whitish) + color_hex: "#rrggbb" + sat_threshold: maximum saturation allowed to still be considered 'gray' + """ + rgb = mcolors.to_rgb(color_hex) + h, s, v = mcolors.rgb_to_hsv(rgb) + return s < sat_threshold + + # Draw labels with clean, modern typography + for node, (x, y) in pos.items(): + node_data = G.nodes[node] + mod_value = node_data['mod_value'] + + color = colors[node_data['chain_strength']] + color_rgb = mcolors.to_rgb(color) + rgb_255 = tuple(int(255 * x) for x in color_rgb) + + # if is_close_to_white(rgb_255, 30): + # if is_bright(rgb_255, 200): + if is_close_to_gray(color, sat_threshold=0.4): + text_color = "#1e2a36" # dark text for bright backgrounds + else: + text_color = "white" # light text for dark backgrounds + + + values = node_data['values'] + chain_strength = node_data['chain_strength'] + chain_break_fraction = node_data['chain_break_fraction'] + + # label = f"{len(values)}\n{mod_value:.2f}" + # text_color = "white" if node in leaves else "#1e2a36" + # text_color = "white" + font_weight = "bold" if node in leaves else "semibold" + # label1 = str(len(values)) + # label2 = f"{mod_value:.2f}" + label1 = f"Pr.size: {len(values)}" + ch_str_label = f"{chain_strength:.3f}" if chain_strength is not None else chain_strength + label2 = f"Ch.str.: {ch_str_label}" + cbf_label = f"{chain_break_fraction:.3f}" if chain_break_fraction is not None else chain_break_fraction + label3 = f"CBF: {cbf_label}" + # label = label1 + "\n" + label2 + label = label1 + "\n" + label2 + "\n" + label3 + + ax.text(x, y, + label, + ha="center", va="center", + # ha="center", + fontsize=9, + color=text_color, + fontweight=font_weight, + wrap=True, + bbox=dict(facecolor=color, + boxstyle="round,pad=0.4", + edgecolor="#e6e6e6", + lw=0.8, + alpha=0.85) + ) + + levels = sorted(set(nx.get_node_attributes(G, 'level').values())) + modularities = [division_modularities[l] for l in levels] + + # Annotate modularity per level at the right of the plot + xmax = max(x for x, y in pos.values()) + pos_values_sorted = sorted(pos.values(), key=lambda x: (x[1], x[0])) + last_1, last_2 = pos_values_sorted[-1], pos_values_sorted[-2] + dx = abs(last_2[0] - last_1[0]) + for lvl, m in zip(levels, modularities): + # Find nodes at this level + nodes_at_level = [n for n, d in G.nodes(data=True) if d['level'] == lvl] + if nodes_at_level: + # Use mean X position of nodes at this level for placement + plt.text(xmax + dx/2, np.mean([pos[n][1] for n in nodes_at_level]), f"{m:.4f}", + ha="left", va="center", fontsize=9, color="#444444", fontweight="semibold") + + plt.hlines(y=np.mean([pos[n][1] for n in nodes_at_level]), + xmin=(xmax+dx) - (xmax+dx), xmax=xmax+dx/2, + colors="#bbbbbb", linestyles="dashed", lw=0.8, alpha=0.6) + + legend_elements = [ + Patch(facecolor=internal_color, edgecolor="#2b3a42", label="Internal nodes"), + Patch(facecolor=leaf_color, edgecolor="#2b3a42", label="Leaf nodes"), + ] + + plt.legend(handles=legend_elements, + # loc="upper left", # or "lower right" depending on your layout + frameon=False, + fontsize=10) + + + + # extended_colors = [none_color] + [cmap(i) for i in np.linspace(0, 1, 256)] + # extended_cmap = ListedColormap(extended_colors) + + # sm = plt.cm.ScalarMappable(cmap=extended_cmap, norm=norm) + # sm.set_array([]) + # cbar = plt.colorbar(sm, ax=ax, shrink=0.75, pad=0.02) + # ticks = ["None"] + sorted(set(valid_values)) + # cbar.set_ticks(ticks) + # cbar.set_ticklabels([f"{t:.4f}" if t is not None else "None" for t in ticks]) + # cbar.set_label("Chain Strength", fontsize=10) + + # color_array = [cmap(i) for i in np.linspace(0, 1, 256)] + # extended_colors = [mcolors.to_rgba(none_color)] + color_array + # extended_cmap = ListedColormap(extended_colors) + + # # Normalization: 0 = None, numeric values from 0.0001 upwards + # vmin, vmax = 0, max(valid_values) + # norm = mcolors.Normalize(vmin=vmin, vmax=vmax) + + # # Create ScalarMappable + # sm = plt.cm.ScalarMappable(cmap=extended_cmap, norm=norm) + # sm.set_array([]) + + # # Plot colorbar + # cbar_ax = fig.add_axes([0.15, 0.05, 0.7, 0.03]) # [left, bottom, width, height] + # cbar = plt.colorbar(sm, ax=ax, cax=cbar_ax, orientation="horizontal") + + # # Numeric ticks only! Map None to 0 + # ticks = [0] + list(np.linspace(min(valid_values), max(valid_values), 5)) + # cbar.set_ticks(ticks) + # cbar.set_ticklabels(["None"] + [f"{t:.3f}" for t in ticks[1:]]) + # cbar.set_label("Chain strength", fontsize=10) + + valid_values_sorted = sorted(valid_values) + + # Create colormap + cmap_vals = plt.cm.PuBu(np.linspace(0, 1, len(valid_values_sorted))) + colors = np.vstack([mcolors.to_rgba(none_color), cmap_vals]) + discrete_cmap = ListedColormap(colors) + + # Create boundaries: first bin = None + bounds = [-0.01] + valid_values_sorted + norm = BoundaryNorm(bounds, discrete_cmap.N) + + # ScalarMappable + sm = plt.cm.ScalarMappable(cmap=discrete_cmap, norm=norm) + sm.set_array([]) + + # Colorbar + # fig, ax = plt.subplots(figsize=(6,1)) + cbar = plt.colorbar(sm, ax=ax, orientation="vertical") + + # Ticks + ticks = [0] + valid_values_sorted + cbar.set_ticks(ticks) + cbar.set_ticklabels(["None"] + [f"{t:.3f}" for t in valid_values_sorted]) + cbar.set_label("Chain Strength", fontsize=10, labelpad=20) + + plt.title("Chain strength & problem size in the hierarchical division tree", fontsize=16, fontweight="bold", color="#2b3a42", pad=20) + # plt.axis("off") + ax.spines['top'].set_visible(False) + ax.spines['left'].set_visible(False) + ax.spines['bottom'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.set_ylabel("Modularity") + ax.yaxis.set_label_position("right") + plt.tight_layout() + # plt.show() + +from matplotlib import colormaps +cmap = colormaps.get_cmap("PuBu") +cmap \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 000000000..fcfcd22a7 --- /dev/null +++ b/setup.py @@ -0,0 +1,30 @@ +from setuptools import setup, find_packages + +setup( + name="Qommunity", + version="0.1.0", + description="Library for community detection where you can use variety of solvers, including quantum solvers", + packages=find_packages(), + python_requires=">=3.9,<=3.11", + install_requires=[ + "bayanpy==0.7.7", + "dimod==0.12.16", + "dwave_cloud_client==0.12.0", + "dwave_optimization==0.1.0", + "dwave_preprocessing==0.6.6", + "dwave_samplers==1.3.0", + "dwave_system==1.24.0", + "leidenalg==0.10.2", + "networkx==3.3", + "pytest==8.2.2", + "python_igraph==0.11.6", + "powerlaw", + "QHyper==0.3.3", + ], + classifiers=[ + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Operating System :: OS Independent", + ], +) \ No newline at end of file diff --git a/stats/stats_template.ipynb b/stats/stats_template.ipynb index 382ec11bc..03381e685 100644 --- a/stats/stats_template.ipynb +++ b/stats/stats_template.ipynb @@ -863,7 +863,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "qomm_env", "language": "python", "name": "python3" },