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tabular_benchmark.py
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tabular_benchmark.py
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"""
Changelog:
==========
0.0.1:
* First implementation of the Tabular Benchmark.
"""
from pathlib import Path
from typing import Union, List, Dict
import ConfigSpace as CS
import numpy as np
from ConfigSpace.read_and_write import json as json_cs
from hpobench.abstract_benchmark import AbstractBenchmark
from hpobench.dependencies.ml.ml_benchmark_template import metrics
from hpobench.util.data_manager import TabularDataManager
__version__ = '0.0.1'
class TabularBenchmark(AbstractBenchmark):
def __init__(self,
model: str, task_id: int,
data_dir: Union[Path, str, None] = None,
rng: Union[int, np.random.RandomState, None] = None, **kwargs):
models = ['lr', 'svm', 'xgb', 'rf', 'nn']
assert model in models, f'Parameter `model` has to be one of {models} but was {model}'
self.task_id = task_id
self.model = model
self.dm = TabularDataManager(model, task_id, data_dir)
self.table, self.metadata = self.dm.load()
self.exp_args = self.metadata["exp_args"]
self.config_spaces = self.metadata["config_spaces"]
self.global_minimums = self.metadata["global_min"]
self.original_cs = json_cs.read(self.config_spaces['x'])
self.original_fs = json_cs.read(self.config_spaces['z'])
super(TabularBenchmark, self).__init__(rng=rng, **kwargs)
# pylint: disable=arguments-differ
@AbstractBenchmark.check_parameters
def objective_function(self,
configuration: Union[CS.Configuration, Dict],
fidelity: Union[Dict, CS.Configuration, None] = None,
rng: Union[np.random.RandomState, int, None] = None,
seed: Union[int, None] = None,
metric: Union[str, None] = 'acc',
**kwargs) -> Dict:
result = self._objective(configuration, fidelity, seed, metric, evaluation="val")
return result
# pylint: disable=arguments-differ
@AbstractBenchmark.check_parameters
def objective_function_test(self,
configuration: Union[CS.Configuration, Dict],
fidelity: Union[Dict, CS.Configuration, None] = None,
rng: Union[np.random.RandomState, int, None] = None,
seed: Union[int, None] = None,
metric: Union[str, None] = 'acc',
**kwargs) -> Dict:
result = self._objective(configuration, fidelity, seed, metric, evaluation="test")
return result
# pylint: disable=arguments-differ
def get_configuration_space(self, seed: Union[int, None] = None) -> CS.ConfigurationSpace:
cs = json_cs.read(self.config_spaces['x_discrete'])
# cs = self._preprocess_configspace(cs)
cs.seed(seed)
return cs
# pylint: disable=arguments-differ
def get_fidelity_space(self, seed: Union[int, None] = None) -> CS.ConfigurationSpace:
cs = json_cs.read(self.config_spaces['z_discrete'])
cs.seed(seed=seed)
return cs
# pylint: disable=arguments-differ
def get_meta_information(self) -> Dict:
""" Returns the meta information for the benchmark """
return {'name': 'TabularBenchmark',
'references': [],
'task_id': self.task_id,
'model': self.model,
'original_configuration_space': self.original_cs,
'original_fidelity_space': self.original_fs,
}
def _preprocess_configspace(self, config_space: CS.ConfigurationSpace) -> CS.ConfigurationSpace:
""" Converts floats to np.float32 """
for hp in config_space.get_hyperparameters():
hp.sequence = tuple(np.array(hp.sequence).astype(np.float32))
hp.default_value = np.float32(hp.default_value)
return config_space
def _total_number_of_configurations(self, space: str = "hyperparameters") -> int:
""" Returns the number of unique configurations in the parameter/fidelity space
"""
count = 1
cs = self.configuration_space if space == "hyperparameters" else self.fidelity_space
for hp in cs.get_hyperparameters():
count *= len(hp.sequence)
return count
def _seeds_used(self) -> List:
return self.table.seed.unique().tolist()
def sample_hyperparameter(self, n: int = 1) -> Union[CS.Configuration, List]:
return self.configuration_space.sample_configuration(n)
def sample_fidelity(self, n: int = 1) -> Union[CS.Configuration, List]:
return self.fidelity_space.sample_configuration(n)
def get_global_min(self, metric: str = "acc"):
""" Retrieves the minimum (1 - metric) for train, validation and test splits
"""
assert metric in self.global_minimums.keys(), \
"Not a valid metric: {}".format(list(self.global_minimums.keys()))
return self.global_minimums[metric]
def get_max_fidelity(self) -> Dict:
max_fidelity = dict()
for hp in self.fidelity_space.get_hyperparameters():
max_fidelity[hp.name] = np.sort(hp.sequence)[-1]
return max_fidelity
def get_fidelity_range(self) -> List:
fidelities = []
for hp in self.fidelity_space.get_hyperparameters():
if not isinstance(hp, CS.Constant) and len(hp.sequence) > 1:
fidelities.append((hp.name, hp.sequence[0], hp.sequence[-1]))
return fidelities
def _search_dataframe(self, row_dict, df):
# https://stackoverflow.com/a/46165056/8363967
mask = np.array([True] * df.shape[0])
for i, param in enumerate(df.drop("result", axis=1).columns):
mask *= df[param].values == row_dict[param]
idx = np.where(mask)
assert len(idx) == 1, 'The query has resulted into mulitple matches. This should not happen. ' \
f'The Query was {row_dict}'
idx = idx[0][0]
result = df.iloc[idx]["result"]
return result
def _objective(
self,
config: Dict,
fidelity: Dict,
seed: Union[int, None] = None,
metric: Union[str, None] = "acc",
evaluation: Union[str, None] = ""
) -> Dict:
metric_str = ', '.join(list(metrics.keys()))
assert metric in list(metrics.keys()), f"metric not found among: {metric_str}"
score_key = f"{evaluation}_scores"
cost_key = f"{evaluation}_scores"
key_path = dict()
for name in self.configuration_space.get_hyperparameter_names():
key_path[str(name)] = config[str(name)]
for name in self.fidelity_space.get_hyperparameter_names():
key_path[str(name)] = fidelity[str(name)]
if seed is not None:
assert seed in self._seeds_used()
seeds = [seed]
else:
seeds = self._seeds_used()
loss = []
costs = 0.0
info = dict()
for seed in seeds:
key_path["seed"] = seed
res = self._search_dataframe(key_path, self.table)
loss.append(1 - res["info"][score_key][metric])
costs += res["info"]["model_cost"] + res["info"][cost_key][metric]
info[seed] = res["info"]
key_path.pop("seed")
loss = np.mean(loss)
result = dict(function_value=float(loss), cost=costs, info=info)
return result
__all__ = ['TabularBenchmark']