Jung-Pu-Chang / UsefulML

ML useful package

UsefulML in python 3.8

ML Useful package.
Author: Jung-Pu-Chang、容噗玩Data

Directory

.
├── README.md
├── LICENSE
├── requirements.txt
└── LGB.py

Example

from ucimlrepo import fetch_ucirepo
from LGB import LightGBM

default_of_credit_card_clients = fetch_ucirepo(id=350)
train_X = default_of_credit_card_clients.data.features
train_Y = default_of_credit_card_clients.data.targets
train_X.columns = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0',
                   'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',
                   'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',
                   'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']

params = {
          'boosting_type': 'dart', 
          'n_estimators' : 1000,
          'learning_rate': 0.05,
          'n_jobs' : -1, 
          'random_state' : 7,
          'verbose' : 0
          }

scoring = {
           'accuracy' : make_scorer(accuracy_score), 
           'precision' : make_scorer(precision_score),
           'recall' : make_scorer(recall_score), 
           'f1_score' : make_scorer(f1_score),
           }

param_grid = {
              'learning_rate': [0.1, 0.05, 0.01],
              'max_depth': [3, 5, 7],
              'num_leaves': [15, 31, 63],
              'n_estimators': [1000, 1500, 2000],
              'boosting_type' : ['gbdt','dart'],
              'random_state' : [7], 
              }   

train_X_fs, feature_name = LightGBM.permutation_selection(train_X, train_Y, 
                                                          params = params,
                                                          imp = 0.005)
model, cv, cv_idx = LightGBM.build_model(train_X_fs, train_Y, 
                                         params = params, scoring = scoring, 
                                         fold_time = 5)

model_grid_tune = LightGBM.grid_tune(train_X_fs, train_Y, 
                                     fold_time = 3, param_grid = param_grid)
model_random_tune = LightGBM.random_tune(train_X_fs, train_Y, 
                                         fold_time = 3, param_grid = param_grid)

Module Description : LGB.py

Def Contents

def	purpose
permutation_selection	Feature Selection
build_model	Model Training
grid_tune	Model Tuning
random_tune	Model Tuning