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train_model.py
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train_model.py
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#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
'''
@File : train_model.py
@Time : 2019/12/05 13:49:58
@Author : Yan Yang
@Contact : yanyangbupt@gmail.com
@Desc : None
'''
# .::::.
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# '::::::::::::'
# .::::::::::
# '::::::::::::::..
# ..::::::::::::.
# ``::::::::::::::::
# ::::``:::::::::' .:::.
# ::::' ':::::' .::::::::.
# .::::' :::: .:::::::'::::.
# .:::' ::::: .:::::::::' ':::::.
# .::' :::::.:::::::::' ':::::.
# .::' ::::::::::::::' ``::::.
# ...::: ::::::::::::' ``::.
# ```` ':. ':::::::::' ::::..
# '.:::::' ':'````..
# 美女保佑 永无BUG
from channel2_v2 import *
import os
from xgboost import XGBClassifier
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
add_text_feature_for_train()
create_feature()
models = [
{ # 0.85926333738039 original best
'model_path': os.path.join(STACK_MODEL_DIR_v2, 'sm-191125-nosetinfo-extend3-sample11.pkl'),
'ss_path': os.path.join(STACK_MODEL_DIR_v2, 'standardscaler-last1year-nosetinfo-extend3-sample11.pkl'),
'cols': BASE_COLS,
'score': 0.85926333738039,
'name': 'sm-191125-nosetinfo-extend3-sample11.pkl',
'model': [
[
CatBoostClassifier(
iterations=180, learning_rate=0.1, depth=7, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
CatBoostClassifier(
iterations=500, learning_rate=0.1, depth=4, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
XGBClassifier(
max_depth=7, learning_rate=0.05, n_estimators=180, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
XGBClassifier(
max_depth=4, learning_rate=0.05, n_estimators=350, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=7, learning_rate=0.01, n_estimators=800, objective='binary',
subsample=0.8, n_jobs=23, num_leaves=82, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=4, learning_rate=0.01, n_estimators=2000, objective='binary',
subsample=0.8, n_jobs=23, num_leaves=12, random_state=RANDOM_SEED
),
RandomForestClassifier(
n_estimators=1000, max_depth=35, n_jobs=-1, verbose=0, random_state=RANDOM_SEED
),
],
[
CatBoostClassifier(
iterations=150, learning_rate=0.1, depth=2, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
],
],
'model_param': [
[
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{},
],
[
{'verbose': False},
],
],
},
{ # 0.858031834386063 with set info
'model_path': os.path.join(STACK_MODEL_DIR_v2, 'test-2-sm-191127-withsetinfo-sample11.pkl'),
'ss_path': os.path.join(STACK_MODEL_DIR_v2, 'standardscaler-last1year-withsetinfo-sample11.pkl'),
'cols': BASE_COLS + SET_INFO_COLS,
'score': 0.858031834386063,
'name': 'test-2-sm-191127-withsetinfo-sample11.pkl',
'model': [
[
CatBoostClassifier(
iterations=400, learning_rate=0.05, depth=7, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
CatBoostClassifier(
iterations=1000, learning_rate=0.05, depth=4, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
XGBClassifier(
max_depth=7, learning_rate=0.05, n_estimators=180, subsample=0.8,
n_jobs=-1, min_child_weight=4, random_state=RANDOM_SEED
),
XGBClassifier(
max_depth=4, learning_rate=0.03, n_estimators=500, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=7, learning_rate=0.01, n_estimators=1000, objective='binary',
subsample=0.8, n_jobs=23, num_leaves=35, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=4, learning_rate=0.01, n_estimators=3500, objective='binary',
subsample=0.8, n_jobs=23, num_leaves=5, random_state=RANDOM_SEED
),
RandomForestClassifier(
n_estimators=1000, max_depth=35, n_jobs=-1, verbose=0, random_state=RANDOM_SEED
),
],
[
CatBoostClassifier(
iterations=800, learning_rate=0.01, depth=3, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
],
],
'model_param': [
[
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{},
],
[
{'verbose': False},
],
],
},
{ # 0.856180351089599 with set info
'model_path': os.path.join(STACK_MODEL_DIR_v2, 'sm-191127-withsetinfo-11.pkl'),
'ss_path': os.path.join(STACK_MODEL_DIR_v2, 'standardscaler-last1year-withsetinfo-11.pkl'),
'cols': BASE_COLS + SET_INFO_COLS,
'score': 0.856180351089599,
'name': 'sm-191127-withsetinfo-11.pkl',
'model': [
[
CatBoostClassifier(
iterations=400, learning_rate=0.05, depth=7, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
CatBoostClassifier(
iterations=1000, learning_rate=0.05, depth=4, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
XGBClassifier(
max_depth=7, learning_rate=0.05, n_estimators=180, subsample=0.8,
n_jobs=-1, min_child_weight=4, random_state=RANDOM_SEED
),
XGBClassifier(
max_depth=4, learning_rate=0.03, n_estimators=500, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=7, learning_rate=0.01, n_estimators=1000, objective='binary',
subsample=0.8, n_jobs=23, num_leaves=35, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=4, learning_rate=0.01, n_estimators=3500, objective='binary',
subsample=0.8, n_jobs=23, num_leaves=5, random_state=RANDOM_SEED
),
RandomForestClassifier(
n_estimators=1000, max_depth=35, n_jobs=-1, verbose=0, random_state=RANDOM_SEED
),
],
[
CatBoostClassifier(
iterations=800, learning_rate=0.01, depth=3, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
],
],
'model_param': [
[
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{},
],
[
{'verbose': False},
],
],
},
{ # 0.855763586778158 with set info and title info
'model_path': os.path.join(STACK_MODEL_DIR_v2, 'sm-191128-withsetinfo-title-11-norf.pkl'),
'ss_path': os.path.join(STACK_MODEL_DIR_v2, 'standardscaler-last1year-withsetinfo-title-11.pkl'),
'cols': BASE_COLS + SET_INFO_COLS + TITLE_COLS,
'score': 0.855763586778158,
'name': 'sm-191128-withsetinfo-title-11-norf.pkl',
'model': [
[
CatBoostClassifier(
iterations=320, learning_rate=0.05, depth=7, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
CatBoostClassifier(
iterations=900, learning_rate=0.05, depth=4, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
XGBClassifier(
max_depth=7, learning_rate=0.05, n_estimators=180, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
XGBClassifier(
max_depth=4, learning_rate=0.03, n_estimators=500, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=7, learning_rate=0.01, n_estimators=1000, objective='binary',
subsample=0.8, n_jobs=-1, num_leaves=82, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=4, learning_rate=0.01, n_estimators=3500, objective='binary',
subsample=0.8, n_jobs=-1, num_leaves=5, random_state=RANDOM_SEED
),
],
[
CatBoostClassifier(
iterations=1200, learning_rate=0.01, depth=2, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
],
],
'model_param': [
[
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
],
[
{'verbose': False},
],
],
},
{ # 0.85364791527539
'model_path': os.path.join(STACK_MODEL_DIR_v2, 'sm-191126-withsetinfo-sample11.pkl'),
'ss_path': os.path.join(STACK_MODEL_DIR_v2, 'standardscaler-last1year-withsetinfo-sample11.pkl'),
'cols': BASE_COLS + SET_INFO_COLS,
'score': 0.85364791527539,
'name': 'sm-191126-withsetinfo-sample11.pkl',
'model': [
[
CatBoostClassifier(
iterations=180, learning_rate=0.1, depth=7, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
CatBoostClassifier(
iterations=500, learning_rate=0.1, depth=4, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
XGBClassifier(
max_depth=7, learning_rate=0.05, n_estimators=180, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
XGBClassifier(
max_depth=4, learning_rate=0.05, n_estimators=350, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=7, learning_rate=0.01, n_estimators=800, objective='binary',
subsample=0.8, n_jobs=-1, num_leaves=82, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=4, learning_rate=0.01, n_estimators=2000, objective='binary',
subsample=0.8, n_jobs=-1, num_leaves=12, random_state=RANDOM_SEED
),
RandomForestClassifier(
n_estimators=1000, max_depth=35, n_jobs=-1, verbose=0, random_state=RANDOM_SEED
),
],
[
CatBoostClassifier(
iterations=150, learning_rate=0.1, depth=2, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
],
],
'model_param': [
[
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{},
],
[
{'verbose': False},
],
],
},
{ # 0.855538436984147
'model_path': os.path.join(STACK_MODEL_DIR_v2, 'sm-191128-withsetinfo-title-11.pkl'),
'ss_path': os.path.join(STACK_MODEL_DIR_v2, 'standardscaler-last1year-withsetinfo-title-11.pkl'),
'cols': BASE_COLS + SET_INFO_COLS + TITLE_COLS,
'score': 0.855538436984147,
'name': 'sm-191128-withsetinfo-title-11.pkl',
'model': [
[
CatBoostClassifier(
iterations=320, learning_rate=0.05, depth=7, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
CatBoostClassifier(
iterations=900, learning_rate=0.05, depth=4, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
XGBClassifier(
max_depth=7, learning_rate=0.05, n_estimators=180, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
XGBClassifier(
max_depth=4, learning_rate=0.03, n_estimators=500, subsample=0.8,
n_jobs=-1, min_child_weight=6, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=7, learning_rate=0.01, n_estimators=1000, objective='binary',
subsample=0.8, n_jobs=-1, num_leaves=82, random_state=RANDOM_SEED
),
LGBMClassifier(
max_depth=4, learning_rate=0.01, n_estimators=3500, objective='binary',
subsample=0.8, n_jobs=-1, num_leaves=5, random_state=RANDOM_SEED
),
RandomForestClassifier(
n_estimators=1000, max_depth=60, n_jobs=-1, verbose=0, random_state=RANDOM_SEED
),
],
[
CatBoostClassifier(
iterations=1200, learning_rate=0.01, depth=2, loss_function='Logloss',
eval_metric='Logloss', task_type='GPU', random_seed=RANDOM_SEED
),
],
],
'model_param': [
[
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{'verbose': False},
{},
],
[
{'verbose': False},
],
],
},
]
for model_info in models:
print('--'*50)
train(model_info)