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84 lines
3.1 KiB
84 lines
3.1 KiB
import json
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from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Integer, Float
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from ConfigSpace.conditions import InCondition
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from ConfigSpace.read_and_write import json as csj
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import py_entitymatching.catalog.catalog_manager as cm
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import pandas as pd
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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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class Classifier:
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@property
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def configspace(self) -> ConfigurationSpace:
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# Build Configuration Space which defines all parameters and their ranges
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cs = ConfigurationSpace(seed=0)
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ltable = pd.read_csv(ltable_path, encoding='ISO-8859-1')
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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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block_attr = Categorical("block_attr", block_attr_items)
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ml_matcher = Categorical("ml_matcher", ["dt", "svm", "rf", "lg", "ln", "nb"], default="rf")
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ml_blocker = Categorical("ml_blocker", ["over_lap", "attr_equiv"], default="over_lap")
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similarity_thresh = Float("similarity_thresh", (0, 0.2), default=0.2)
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support_thresh = Integer("support_thresh", (1, 5), default=1)
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confidence_thresh = Float("confidence_thresh", (0.3, 0.7), default=0.4)
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cs.add_hyperparameters([block_attr, ml_matcher, ml_blocker, similarity_thresh,
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support_thresh, confidence_thresh])
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return cs
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# train 就是整个函数 只需将返回结果由预测变成预测结果的评估
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def train(self, config: Configuration, seed: int = 0) -> float:
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cm.del_catalog()
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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 + "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 + "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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