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94 lines
3.7 KiB
94 lines
3.7 KiB
1 year ago
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import pandas as pd
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import json
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from time import *
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from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer, Float
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from ConfigSpace.read_and_write import json as csj
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from smac import HyperparameterOptimizationFacade, Scenario
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from settings import *
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from ml_er.ml_entity_resolver import er_process
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from settings import ltable_path, ltable_id
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class Classifier:
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@property
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def configspace(self) -> ConfigurationSpace:
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cs = ConfigurationSpace(seed=0)
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ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1', sep='|')
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selected_attrs = ltable.columns.values.tolist()
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block_attr_items = selected_attrs[:]
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block_attr_items.remove(ltable_id)
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jed_blocker = Categorical("jed_blocker",
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["Standard", "QGrams", "ExtendedQG", "SuffixArrays", "ExtendedSA"])
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block_attr = Categorical("block_attr", block_attr_items)
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# filter_ratio = Float("filter_ratio", (0.0, 1.0), default=0.8)
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meta_blocker = Categorical("meta_blocker",
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["WEP", "WNP", "CEP", "CNP", "BLAST", "RCNP", "RWNP", "CP"])
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weighting_scheme = Categorical("weighting_scheme",
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['CN-CBS', 'CBS', 'SN-CBS', 'CNC', 'SNC', 'SND', 'CND', 'CNJ', 'SNJ',
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'COSINE', 'DICE', 'ECBS', 'JS', 'EJS', 'X2'])
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# todo other hyperparameters
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matching_metric = Categorical("matching_metric",
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['cosine', 'euclidean'])
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matching_tokenizer = Categorical("matching_tokenizer",
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['char_tokenizer', 'word_tokenizer', 'white_space_tokenizer'])
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matching_vectorizer = Categorical("matching_vectorizer",
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['tfidf', 'tf', 'boolean'])
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clusteror = Categorical("clusteror_name",
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["CCC", "UMC", "EC", "CenterC", "BMC", "MCC", "CC", "CTC", "MCL", "KMAC", "RSRC"])
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cs.add_hyperparameters([jed_blocker, block_attr, meta_blocker, weighting_scheme, matching_metric,
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matching_tokenizer, matching_vectorizer, clusteror])
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return cs
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def train(self, config: Configuration, seed: int = 0) -> float:
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indicators = er_process(config)
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return 1-indicators['performance']
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def ml_er_hpo():
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classifier = Classifier()
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cs = classifier.configspace
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str_configspace = csj.write(cs)
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dict_configspace = json.loads(str_configspace)
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with open(hpo_output_dir + r"\configspace.json", "w") as f:
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json.dump(dict_configspace, f, indent=4)
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scenario = Scenario(
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cs,
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deterministic=True,
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n_trials=50, # We want to run max 50 trials (combination of config and seed)
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n_workers=1
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)
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initial_design = HyperparameterOptimizationFacade.get_initial_design(scenario, n_configs=5)
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smac = HyperparameterOptimizationFacade(
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scenario,
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classifier.train,
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initial_design=initial_design,
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overwrite=True, # If the run exists, we overwrite it; alternatively, we can continue from last state
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)
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incumbent = smac.optimize()
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incumbent_cost = smac.validate(incumbent)
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default = cs.get_default_configuration()
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default_cost = smac.validate(default)
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print(f"Default Cost: {default_cost}")
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print(f"Incumbent Cost: {incumbent_cost}")
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if incumbent_cost > default_cost:
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incumbent = default
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print(f"Updated Incumbent Cost: {default_cost}")
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print(f"Optimized Configuration:{incumbent.values()}")
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with open(hpo_output_dir + r"\incumbent.json", "w") as f:
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json.dump(dict(incumbent), f, indent=4)
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return incumbent
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if __name__ == '__main__':
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ml_er_hpo()
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