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1a3143c 0a5fbf6 1a3143c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import inspect
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.base import ClassifierMixin
from sklearn.datasets import *
import pkgutil
import importlib
import warnings
import ast
import pandas as pd
def safe_import_module(name):
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return importlib.import_module(name)
except Exception:
return None # or raise/log if desired
def get_sklearn_classifiers():
classifiers = {}
#for modname in dir(sklearn):
for _, modname, _ in pkgutil.walk_packages(sklearn.__path__, prefix="sklearn."):
if '._' in modname: # exclude hidden modules
continue
if modname.count('.') > 1: # exclude modules more than two levels deep
continue
#print(modname)
try:
#with warnings.catch_warnings():
#warnings.simplefilter("ignore")
module = importlib.import_module(modname)
for cls_name, cls in inspect.getmembers(module, inspect.isclass):
if '._' not in cls_name and ('ClassifierMixin' not in cls_name):
if issubclass(cls, ClassifierMixin) and cls.__module__.startswith("sklearn"):
classifiers[cls_name] = cls
#classifiers.append(f"{cls.__module__}.{cls_name}")
except:
continue
return classifiers
def get_sklearn_dataloaders():
dataloaders = {}
# these datasets either don't work or are too big and slow to load
# dataloaders['20newsgroup'] = fetch_20newsgroups
# dataloaders['20newsgroup_vectorized'] = fetch_20newsgroups_vectorized
# dataloaders['covtype'] = fetch_covtype
# dataloaders['kddcup99'] = fetch_kddcup99
dataloaders['iris'] = load_iris
return dataloaders
def parse_param_string(param_str):
param_str = param_str.replace("*,", "") # Remove '*' if present
params = {}
for item in param_str.split(','):
if not item.strip():
continue
if '=' not in item:
continue
key, value = item.split('=', 1)
key = key.strip()
try:
value = ast.literal_eval(value.strip())
except Exception:
value = value.strip() # fallback: treat as string
params[key] = value
return params
def read(filename):
if filename.endswith(".csv"):
return pd.read_csv(filename)
elif filename.endswith(".xlsx") or filename.endswith(".xls"):
return pd.read_excel(filename)
elif filename.endswith(".parquet"):
return pd.read_parquet(filename)
elif filename.endswith(".feather"):
return pd.read_feather(filename)
elif filename.endswith(".json"):
return pd.read_json(filename)
else:
raise ValueError("Unsupported file format.")
if __name__ == '__main__':
#print(classifier_list)
s = "penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1"
parsed = parse_param_string(s)
print(parsed)
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