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from typing import Any, Dict, List, Union
import numpy as np
import logging
import os
# Create a custom logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class RandomSearch:
@staticmethod
def random_choice(args: List[Any], n: int = 1):
"""
pick a random element from a set.
Example:
>> sampler = RandomSearch.random_choice(1,2,3)
>> sampler()
2
"""
choices = []
for arg in args:
choices.append(arg)
if n == 1:
return lambda: np.random.choice(choices, replace=False)
else:
return lambda: np.random.choice(choices, n, replace=False)
@staticmethod
def random_integer(low: Union[int, float], high: Union[int, float]):
"""
pick a random integer between two bounds
Example:
>> sampler = RandomSearch.random_integer(1, 10)
>> sampler()
9
"""
return lambda: int(np.random.randint(low, high))
@staticmethod
def random_loguniform(low: Union[float, int], high: Union[float, int]):
"""
pick a random float between two bounds, using loguniform distribution
Example:
>> sampler = RandomSearch.random_loguniform(1e-5, 1e-2)
>> sampler()
0.0004
"""
return lambda: np.exp(np.random.uniform(np.log(low), np.log(high)))
@staticmethod
def random_uniform(low: Union[float, int], high: Union[float, int]):
"""
pick a random float between two bounds, using uniform distribution
Example:
>> sampler = RandomSearch.random_uniform(0, 1)
>> sampler()
0.01
"""
return lambda: np.random.uniform(low, high)
class HyperparameterSearch:
def __init__(self, **kwargs):
self.search_space = {}
self.lambda_ = lambda: 0
for key, val in kwargs.items():
self.search_space[key] = val
def parse(self, val: Any):
if isinstance(val, (int, np.int)):
return int(val)
elif isinstance(val, (float, np.float)):
return val
elif isinstance(val, (np.ndarray, list)):
return " ".join(val)
elif val is None:
return None
if isinstance(val, str):
return val
else:
val = val()
if isinstance(val, (int, np.int)):
return int(val)
elif isinstance(val, (np.ndarray, list)):
return " ".join(val)
else:
return val
def sample(self) -> Dict:
res = {}
for key, val in self.search_space.items():
try:
res[key] = self.parse(val)
except (TypeError, ValueError) as error:
logger.error(f"Could not parse key {key} with value {val}. {error}")
return res
def update_environment(self, sample) -> None:
for key, val in sample.items():
os.environ[key] = str(val)
SEARCH_SPACE = {
"penalty": RandomSearch.random_choice(["l1", "l2"]),
"C": RandomSearch.random_uniform(0, 1),
"solver": "liblinear",
"multi_class": "auto",
"tol": RandomSearch.random_loguniform(10e-5, 10e-3),
"stopwords": RandomSearch.random_choice([0, 1]),
"weight": RandomSearch.random_choice(["hash"]),
"ngram_range": RandomSearch.random_choice(["1 2", "2 3", "1 3"]),
"random_state": RandomSearch.random_integer(0, 100000)
}
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