-
Notifications
You must be signed in to change notification settings - Fork 3
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
6276390
commit dcdd44c
Showing
4 changed files
with
515 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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 | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
| SMAC_PARAMETERS_PATH = join(dirname(__file__), 'smac_parameters.json') | ||
|
|
||
|
|
||
| def load_smac_parameters(): | ||
| with open(SMAC_PARAMETERS_PATH) as fin: | ||
| primitives = json.load(fin) | ||
| logger.info('[SMAC] smac_parameters loaded') | ||
|
|
||
| return primitives | ||
|
|
||
|
|
||
| SMAC_DICT = load_smac_parameters() | ||
|
|
||
|
|
||
| def gen_pipeline(config, pipeline): | ||
| new_pipeline = make_pipeline() | ||
| for step_name, step_obj in pipeline.steps: | ||
| step_type = PRIMITIVE_TYPES[step_name] | ||
|
|
||
| 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))]) | ||
|
|
||
| return new_pipeline | ||
|
|
||
|
|
||
| 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.debug(f'[SMAC] {step_name}: {class_params}') | ||
| return class_params | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
|
|
||
| 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) | ||
|
|
||
| return new_hyperparameters | ||
|
|
||
|
|
||
| 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}') | ||
|
|
||
| return config_space | ||
|
|
||
|
|
||
| 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) | ||
|
|
||
| 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.info(f'\n[SMAC] {pipeline} successfully optimized! \nOptimized hyperparams: {optimized_conf}\n') | ||
| return optimized_pipeline | ||
|
|
||
| def _optimize_pipeline(self, pipeline): | ||
| scenario = Scenario( | ||
| gen_configspace(pipeline), deterministic=True, n_trials=self.n_trials | ||
| ) | ||
|
|
||
| smac = HyperparameterOptimizationFacade(scenario, self.train) | ||
| return smac.optimize() |
Oops, something went wrong.