Installation
git clone git@github.com:farazmah/hyperparameter.git cd hyperparamter/hyperparameter python ../setup.py install Usage example for lightGBM:
import pandas as pd import numpy as np from sklearn.model_selection import StratifiedKFold df = pd.read_csv("hyperparameter/test/titanic_train.csv") df = df.replace("", np.nan) df = df.drop(['Cabin', 'Name', 'PassengerId', 'Ticket'], axis=1) df = pd.get_dummies(columns=['Embarked', 'Sex'],data=df) y_train = df.Survived.values X_train = df.drop(['Survived'], axis=1).values skf = StratifiedKFold(n_splits=3) from hyperparameter.lgbm import LightgbmHyper hpopt = LightgbmHyper(is_classifier=True) hpopt.tune_model(ds_x=X_train, ds_y=y_train, folds=skf, eval_rounds = 20)Out[1]: {'colsample_bytree': 0.9, 'learning_rate': 0.17500000000000002, 'max_depth': 19, 'min_child_samples': 68, 'min_sum_hessian_in_leaf': 0.256, 'n_estimators': 505, 'num_leaves': 186, 'reg_alpha': 1.84, 'reg_lambda': 0.35000000000000003, 'subsample': 0.7000000000000001}Usage example for xgboost (last three lines from example above changes to):
from hyperparameter.xgb import XgboostHyper hpopt = XgboostHyper(is_classifier=True) hpopt.tune_model(ds_x=X_train, ds_y=y_train, folds=skf, eval_rounds = 20)Out[2]: {'colsample_bylevel': 0.65, 'colsample_bytree': 0.5, 'gamma': 0.75, 'learning_rate': 0.24, 'max_depth': 4, 'min_child_weight': 6, 'n_estimators': 1290, 'reg_alpha': 2.0, 'reg_lambda': 1.48, 'subsample': 0.7000000000000001}