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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,168 @@ | ||
| import json | ||
| import logging | ||
| from os.path import dirname, join | ||
|
|
||
| import numpy as np | ||
| from ConfigSpace import ( | ||
| Categorical, | ||
| Configuration, | ||
| ConfigurationSpace, | ||
| Constant, | ||
| Float, | ||
| Integer, | ||
| ) | ||
| from sklearn.model_selection import cross_val_score | ||
| from sklearn.pipeline import make_pipeline | ||
| from smac import HyperparameterOptimizationFacade, Scenario | ||
|
|
||
| from alpha_automl.scorer import make_scorer, make_splitter | ||
| from alpha_automl.utils import create_object | ||
| from alpha_automl.primitive_loader import PRIMITIVE_TYPES | ||
|
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| logger = logging.getLogger(__name__) | ||
| SMAC_PARAMETERS_PATH = join(dirname(__file__), 'smac_parameters.json') | ||
|
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| def load_smac_parameters(): | ||
| with open(SMAC_PARAMETERS_PATH) as fin: | ||
| primitives = json.load(fin) | ||
| logger.info('[SMAC] smac_parameters loaded') | ||
|
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| return primitives | ||
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| SMAC_DICT = load_smac_parameters() | ||
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| def gen_pipeline(config, pipeline): | ||
| new_pipeline = make_pipeline() | ||
| for step_name, step_obj in pipeline.steps: | ||
| step_type = PRIMITIVE_TYPES[step_name] | ||
|
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||
| if step_type == 'COLUMN_TRANSFORMER': | ||
| transformers = [] | ||
| for trans_name, _, trans_index in step_obj.__dict__['transformers']: | ||
| trans_prim_name = trans_name.split('-')[0] | ||
| trans_obj = create_object(trans_prim_name, get_primitive_params(config, trans_prim_name)) | ||
| transformers.append((trans_name, trans_obj, trans_index)) | ||
| step_obj.__dict__['transformers'] = transformers | ||
| new_pipeline.steps.append([step_name, create_object(step_name, step_obj.__dict__)]) | ||
| else: | ||
| new_pipeline.steps.append([step_name, create_object(step_name, get_primitive_params(config, step_name))]) | ||
|
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| return new_pipeline | ||
|
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|
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| def get_primitive_params(config, step_name): | ||
| params = list(SMAC_DICT[step_name].keys()) | ||
| class_params = {} | ||
| for param in params: | ||
| class_params[param] = config[param] | ||
| logger.critical(f'[SMAC] {step_name}: {class_params}') | ||
| return class_params | ||
|
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|
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| def gen_configspace(pipeline): | ||
| # (from build_configspace) Build Configuration Space which defines all parameters and their ranges | ||
| configspace = ConfigurationSpace(seed=0) | ||
| for primitive, prim_obj in pipeline.steps: | ||
| step_type = PRIMITIVE_TYPES[primitive] | ||
| try: | ||
| params = SMAC_DICT[primitive] | ||
| configspace.add_hyperparameters(cast_primitive(params)) | ||
| if step_type == 'COLUMN_TRANSFORMER': | ||
| for trans_name, _, _ in prim_obj.__dict__['transformers']: | ||
| trans_prim_name = trans_name.split('-')[0] | ||
| params = SMAC_DICT[trans_prim_name] | ||
| configspace.add_hyperparameters(cast_primitive(params)) | ||
| except Exception as e: | ||
| logger.critical(f'[SMAC] {str(e)}') | ||
| return configspace | ||
|
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||
|
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| def cast_primitive(params): | ||
| new_hyperparameters = [] | ||
| for name, conf in params.items(): | ||
| config_space = cast_hyperparameter(name, conf) | ||
| if config_space is not None: | ||
| new_hyperparameters.append(config_space) | ||
|
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| return new_hyperparameters | ||
|
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|
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| def cast_hyperparameter(param_name, param_conf): | ||
| param_type, param_value, param_default = '', '', '' | ||
| config_space = None | ||
| try: | ||
| param_type = param_conf['type'] | ||
| param_value = param_conf['value'] | ||
| param_default = param_conf['default'] | ||
| except Exception as e: | ||
| logger.critical(f'[SMAC] {str(e)}') | ||
| return | ||
| if param_type == 'Categorical': | ||
| config_space = Categorical(param_name, param_value, default=param_default) | ||
| elif param_type == 'Integer': | ||
| min_value = int(param_value[0]) | ||
| max_value = int(param_value[1]) | ||
| config_space = Integer( | ||
| param_name, (min_value, max_value), default=param_default | ||
| ) | ||
| elif param_type == 'Float': | ||
| min_value = float(param_value[0]) | ||
| max_value = float(param_value[1]) | ||
| config_space = Float(param_name, (min_value, max_value), default=param_default) | ||
| elif param_type == 'Constant': | ||
| config_space = Constant(param_name, param_value) | ||
| else: | ||
| logger.error(f'Unknown param_type {param_type}') | ||
|
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| return config_space | ||
|
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|
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| class SmacOptimizer: | ||
| def __init__( | ||
| self, | ||
| X=None, | ||
| y=None, | ||
| n_trials=50, | ||
| splitter=make_splitter('holdout'), | ||
| scorer=make_scorer('accuracy_score'), | ||
| ): | ||
| self.pipeline = None | ||
| self.X = X | ||
| self.y = y | ||
| self.n_trials = n_trials | ||
| self.splitter = splitter | ||
| self.scorer = scorer | ||
| return | ||
|
|
||
| def train(self, config: Configuration, seed: int = 0) -> float: | ||
| pipeline = gen_pipeline(config, self.pipeline) | ||
| scores = cross_val_score( | ||
| pipeline, | ||
| self.X, | ||
| self.y, | ||
| cv=self.splitter, | ||
| scoring=self.scorer, | ||
| error_score='raise', | ||
| ) | ||
| return 1 - np.mean(scores) | ||
|
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| def optimize_pipeline(self, pipeline): | ||
| self.pipeline = pipeline | ||
| if self.pipeline is None: | ||
| logger.critical('[SMAC] get_pipeline return None value!') | ||
| return | ||
| optimized_conf = self._optimize_pipeline(self.pipeline) | ||
| optimized_pipeline = gen_pipeline(optimized_conf, self.pipeline) | ||
| logger.debug(f'[SMAC] {pipeline} successfully optimized!') | ||
| return optimized_pipeline | ||
|
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| def _optimize_pipeline(self, pipeline): | ||
| scenario = Scenario( | ||
| gen_configspace(pipeline), deterministic=True, n_trials=self.n_trials | ||
| ) | ||
|
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| smac = HyperparameterOptimizationFacade(scenario, self.train) | ||
| return smac.optimize() |
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