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model1_lgb_gbdt.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 26 17:59:02 2017
@author: ldong
"""
import numpy as np
import cPickle as pk
import pandas as pd
import lightgbm as lgb
import matplotlib.pyplot as plt
import datetime as dt
from data_alloc import *
plt.rcParams['figure.figsize'] = 7, 15
def data_categ(x):
categ = list(x.filter(regex='categ'))
# categ.extend(['date','age'])
categ.extend(list(x.filter(regex='num')))
categ.extend(list(x.filter(regex='flag')))
for i in xrange(len(categ)):
x.loc[:,categ[i]] = x.loc[:,categ[i]].astype('category')
return x
def model_run(tr_x, tr_y, tr_x17, tr_y17, quarter, params):
tr_x, tr_y, valid_x, valid_y, pid = data_quarter(tr_x, tr_y, tr_x17, tr_y17, quarter)
# tr_x = data_categ(tr_x)
# valid_x = data_categ(valid_x)
tr_y = tr_y.as_matrix()
valid_y = valid_y.as_matrix()
d_train = lgb.Dataset(tr_x, label=np.squeeze(tr_y))
d_valid = lgb.Dataset(valid_x, label=np.squeeze(valid_y))
param = params[0]
iter_max = params[1]
verbose_iter = params[2]
early_stop = params[3]
bst = lgb.train(param, d_train, iter_max, [d_train,d_valid], ['train','valid'], early_stopping_rounds=early_stop, verbose_eval=verbose_iter)
# lgb.plot_importance(bst)
pred = bst.predict(valid_x)
pid = pid.to_frame().assign(f_lgbdt=pred)
return pid
def model_pred(tr_x, tr_y, te_x, tr_x17, tr_y17, te_x17, quarter, params):
tr_x, tr_y, _, _, pid = data_quarter(tr_x, tr_y, tr_x17, tr_y17, quarter, False)
param = params[0]
iter_max = params[1]
verbose_iter = params[2]
if quarter == 3:
te_x = te_x.rename(columns={'parcelid':'date'})
te_x1 = te_x
te_x1.loc[:,'date'] = 10
te_x2 = te_x
te_x2.loc[:,'date'] = 11
te_x3 = te_x
te_x3.loc[:,'date'] = 12
# tr_x = data_categ(tr_x)
# te_x1 = data_categ(te_x1)
# te_x2 = data_categ(te_x2)
# te_x3 = data_categ(te_x3)
d_train = lgb.Dataset(tr_x, label=np.squeeze(tr_y))
bst = lgb.train(param, d_train, iter_max, [d_train], ['train'], verbose_eval=verbose_iter)
pred1 = bst.predict(te_x1)
pred2 = bst.predict(te_x2)
pred3 = bst.predict(te_x3)
elif quarter == 7:
te_x17 = te_x17.rename(columns={'parcelid':'date'})
te_x1 = te_x17
te_x1.loc[:,'date'] = 22
te_x2 = te_x17
te_x2.loc[:,'date'] = 23
te_x3 = te_x17
te_x3.loc[:,'date'] = 24
# tr_x = data_categ(tr_x)
# te_x1 = data_categ(te_x1)
# te_x2 = data_categ(te_x2)
# te_x3 = data_categ(te_x3)
d_train = lgb.Dataset(tr_x, label=np.squeeze(tr_y))
# np.random.seed(0)
bst = lgb.train(param, d_train, iter_max, [d_train], ['train'], verbose_eval=verbose_iter)
pred1 = bst.predict(te_x1)
pred2 = bst.predict(te_x2)
pred3 = bst.predict(te_x3)
pid1 = pid.to_frame().assign(f_lgbdt=pred1)
pid2 = pid.to_frame().assign(f_lgbdt=pred2)
pid3 = pid.to_frame().assign(f_lgbdt=pred3)
return pid1, pid2, pid3
booster = 'dt'
# year 2016
#%%
param = {}
param['learning_rate'] = 0.002 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 100 # num_leaf
param['min_data_in_leaf'] = 500 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 1
iter_max = 558
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
featQ2 = model_run(train_x, train_y, train_x_17, train_y_17, 1, params)
metaQ2 = featQ2.join(train_y.logerror)
#%%
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 1
iter_max = 768
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
featQ3 = model_run(train_x, train_y, train_x_17, train_y_17, 2, params)
metaQ3 = featQ3.join(train_y.logerror)
#%%
with open('featQ23_lgb'+booster+'.pkl','wb') as f:
pk.dump([metaQ2,metaQ3], f, protocol=pk.HIGHEST_PROTOCOL)
# %%
# year 2016
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 1
iter_max = 800
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
feat_10, feat_11, feat_12 = model_pred(train_x, train_y, all_x, train_x_17, train_y_17, all_x_17, 3, params)
#%%
with open('featAll16_lgb'+booster+'.pkl','wb') as f:
pk.dump([feat_10, feat_11, feat_12], f, protocol=pk.HIGHEST_PROTOCOL)
#%%
write_sub([feat_10.f_lgbdt.as_matrix(),feat_11.f_lgbdt.as_matrix(),feat_12.f_lgbdt.as_matrix(),
feat_10.f_lgbdt.as_matrix(),feat_11.f_lgbdt.as_matrix(),feat_12.f_lgbdt.as_matrix()])
#%%
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 1
iter_max = 584
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
featQ4 = model_run(train_x, train_y, train_x_17, train_y_17, 3, params)
metaQ4 = featQ4.join(train_y.logerror)
# year 2017
#%%
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 1
iter_max = 249
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
featQ5 = model_run(train_x, train_y, train_x_17, train_y_17, 4, params) # train on 2016Q4, valid on 2017Q1
metaQ5 = featQ5.join(train_y_17.logerror)
#%%
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 1
iter_max = 478
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
featQ6 = model_run(train_x, train_y, train_x_17, train_y_17, 5, params)
metaQ6 = featQ6.join(train_y_17.logerror)
#%%
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 8
iter_max = 557
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
featQ7 = model_run(train_x, train_y, train_x_17, train_y_17, 6, params)
metaQ7 = featQ7.join(train_y_17.logerror)
with open('featQ4567_lgb'+booster+'.pkl','wb') as f:
pk.dump([metaQ4,metaQ5,metaQ6,metaQ7], f, protocol=pk.HIGHEST_PROTOCOL)
#%%
# year 2017
param = {}
param['learning_rate'] = 0.0015 # shrinkage_rate
param['boosting_type'] = 'gbdt'
param['objective'] = 'regression'
param['metric'] = 'l1' # or 'mae'
param['max_depth'] = -1
param['feature_fraction'] = 0.65
param['bagging_fraction'] = 0.75 # sub_row
param['bagging_freq'] = 60
param['num_leaves'] = 500 # num_leaf
param['min_data_in_leaf'] = 100 # min_data_in_leaf
param['verbose'] = 1
param['feature_fraction_seed'] = 2
param['bagging_seed'] = 1
param['tree_learner'] = 'data'
param['nthread'] = 8
iter_max = 557
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
feat_22, feat_23, feat_24 = model_pred(train_x, train_y, all_x, train_x_17, train_y_17, all_x_17, 7, params)
with open('featAll17_lgb'+booster+'.pkl','wb') as f:
pk.dump([feat_22, feat_23, feat_24], f, protocol=pk.HIGHEST_PROTOCOL)
#%%
write_sub([feat_10.f_lgbdt.as_matrix(),feat_11.f_lgbdt.as_matrix(),feat_12.f_lgbdt.as_matrix(),
feat_22.f_lgbdt.as_matrix(),feat_23.f_lgbdt.as_matrix(),feat_24.f_lgbdt.as_matrix()])