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model1_xgb_gbtree.py
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model1_xgb_gbtree.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 xgboost as xgb
import matplotlib.pyplot as plt
import datetime as dt
from data_alloc import *
plt.rcParams['figure.figsize'] = 7, 15
def model_run(tr_x, tr_y, tr_x17, tr_y17, quarter, params, pred17=False):
tr_x, tr_y, valid_x, valid_y, pid = data_quarter(tr_x, tr_y, tr_x17, tr_y17, quarter)
tr_y = tr_y.as_matrix()
valid_y = valid_y.as_matrix()
xg_train = xgb.DMatrix(tr_x, tr_y)
xg_test = xgb.DMatrix(valid_x, valid_y)
watchlist = [(xg_train,'train'), (xg_test,'valid')]
param = params[0]
param['base_score'] = np.mean(tr_y)
iter_max = params[1]
verbose_iter = params[2]
early_stop = params[3]
bst = xgb.train(params=param, dtrain=xg_train, num_boost_round=iter_max, evals=watchlist, early_stopping_rounds=early_stop, verbose_eval=verbose_iter)
# xgb.plot_importance(bst)
pred = bst.predict(xgb.DMatrix(valid_x))
if pred17:
te_x17 = all_x_17
te_x17 = te_x17.rename(columns={'parcelid':'date'})
te_x1 = te_x17
te_x1.loc[:,'date'] = 19
te_x2 = te_x17
te_x2.loc[:,'date'] = 20
te_x3 = te_x17
te_x3.loc[:,'date'] = 21
pred1 = model.predict(te_x1, thread_count=8)
pred2 = model.predict(te_x2, thread_count=8)
pred3 = model.predict(te_x3, thread_count=8)
return pred1, pred2, pred3
pid = pid.to_frame().assign(f_xgbtree=pred)
return pid
def model_pred(tr_x, tr_y, te_x, tr_x17, tr_y17, te_x17, quarter, params):
tr_x, tr_y, valid_x, valid_y, pid = data_quarter(tr_x, tr_y, tr_x17, tr_y17, quarter, False)
tr_y = tr_y.as_matrix()
param = params[0]
param['base_score'] = np.mean(tr_y)
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
xg_train = xgb.DMatrix(tr_x, tr_y)
watchlist = [(xg_train,'train')]
bst = xgb.train(params=param, dtrain=xg_train, num_boost_round=iter_max, evals=watchlist, verbose_eval=verbose_iter)
# xgb.plot_importance(bst)
pred1 = bst.predict(xgb.DMatrix(te_x1))
pred2 = bst.predict(xgb.DMatrix(te_x2))
pred3 = bst.predict(xgb.DMatrix(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
xg_train = xgb.DMatrix(tr_x, tr_y)
watchlist = [(xg_train,'train')]
bst = xgb.train(params=param, dtrain=xg_train, num_boost_round=iter_max, evals=watchlist, verbose_eval=verbose_iter)
# xgb.plot_importance(bst)
pred1 = bst.predict(xgb.DMatrix(te_x1))
pred2 = bst.predict(xgb.DMatrix(te_x2))
pred3 = bst.predict(xgb.DMatrix(te_x3))
pid1 = pid.to_frame().assign(f_xgbtree=pred1)
pid2 = pid.to_frame().assign(f_xgbtree=pred2)
pid3 = pid.to_frame().assign(f_xgbtree=pred3)
# feat = pd.DataFrame({'parcelid':all_x['parcelid'], 'f_xgb':pred})
#feat.to_csv('feat_xgb.csv', index=False, float_format='%.8f')
return pid1, pid2, pid3
booster = 'tree'
# year 2016
#%%
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.02
param['gamma'] = 0
param['max_depth'] = 6
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 0.8
param['colsample_bytree'] = 0.5
param['colsample_bylevel'] = 1
param['min_child_weight'] = 4
param['alpha'] = 10
param['lambda'] = 1
iter_max = 184
early_stop = 100
verbose_iter = 1
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['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.02
param['gamma'] = 0
param['max_depth'] = 6
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 0.8
param['colsample_bytree'] = 0.5
param['colsample_bylevel'] = 1
param['min_child_weight'] = 4
param['alpha'] = 10
param['lambda'] = 5
iter_max = 289
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_xgb'+booster+'.pkl','wb') as f:
pk.dump([metaQ2,metaQ3], f, protocol=pk.HIGHEST_PROTOCOL)
# %%
# year 2016 pred
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.02
param['gamma'] = 0
param['max_depth'] = 6
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 0.8
param['colsample_bytree'] = 0.5
param['colsample_bylevel'] = 1
param['min_child_weight'] = 4
param['alpha'] = 10
param['lambda'] = 5
iter_max = 289
verbose_iter = 100
params = [param, iter_max, verbose_iter]
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_xgb'+booster+'.pkl','wb') as f:
pk.dump([feat_10, feat_11, feat_12], f, protocol=pk.HIGHEST_PROTOCOL)
#%%
write_sub([feat_10.f_xgbtree.as_matrix(),feat_11.f_xgbtree.as_matrix(),feat_12.f_xgbtree.as_matrix(),
feat_10.f_xgbtree.as_matrix(),feat_11.f_xgbtree.as_matrix(),feat_12.f_xgbtree.as_matrix()])
#%%
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.02
param['gamma'] = 0
param['max_depth'] = 10
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 1
param['colsample_bytree'] = 0.5
param['colsample_bylevel'] = 1
param['min_child_weight'] = 16
param['alpha'] = 1
param['lambda'] = 5
iter_max = 83
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['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.02
param['gamma'] = 0
param['max_depth'] = 12
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 0.8
param['colsample_bytree'] = 0.5
param['colsample_bylevel'] = 1
param['min_child_weight'] = 4
param['alpha'] = 10
param['lambda'] = 5
iter_max = 29
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)
metaQ5 = featQ5.join(train_y_17.logerror)
#%%
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.02
param['gamma'] = 0
param['max_depth'] = 16
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 0.8
param['colsample_bytree'] = 0.4
param['colsample_bylevel'] = 0.6
param['min_child_weight'] = 4
param['alpha'] = 1
param['lambda'] = 1
iter_max = 48
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['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.005
param['gamma'] = 0
param['max_depth'] = 20
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 1
param['colsample_bytree'] = 0.4
param['colsample_bylevel'] = 1
param['min_child_weight'] = 16
param['alpha'] = 1
param['lambda'] = 30
iter_max = 261
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_xgb'+booster+'.pkl','wb') as f:
pk.dump([metaQ4,metaQ5,metaQ6,metaQ7], f, protocol=pk.HIGHEST_PROTOCOL)
#%% use model trained without 2017 Q3 to predict, thus params same as last model
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.005
param['gamma'] = 0
param['max_depth'] = 20
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 1
param['colsample_bytree'] = 0.4
param['colsample_bylevel'] = 1
param['min_child_weight'] = 16
param['alpha'] = 1
param['lambda'] = 30
iter_max = 261
early_stop = 100
verbose_iter = 100
params = [param, iter_max, verbose_iter, early_stop]
pred_19, pred_20, pred_21 = model_run(train_x, train_y, train_x_17, train_y_17, 6, params, True)
with open('xgb_pred17_789.pkl','wb') as f:
pk.dump([pred_19, pred_20, pred_21], f, protocol=pk.HIGHEST_PROTOCOL)
#%%
## year 2017
param = {}
param['booster'] = 'gbtree'
param['objective'] = 'reg:linear'
param['eval_metric'] = 'mae'
param['eta'] = 0.005
param['gamma'] = 0
param['max_depth'] = 20
param['silent'] = 1
param['nthread'] = 16
param['subsample'] = 1
param['colsample_bytree'] = 0.4
param['colsample_bylevel'] = 1
param['min_child_weight'] = 16
param['alpha'] = 1
param['lambda'] = 30
iter_max = 280
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_xgb'+booster+'.pkl','wb') as f:
pk.dump([feat_22, feat_23, feat_24], f, protocol=pk.HIGHEST_PROTOCOL)
#%%
write_sub([feat_10.f_xgbtree.as_matrix(),feat_11.f_xgbtree.as_matrix(),feat_12.f_xgbtree.as_matrix(),
feat_22.f_xgbtree.as_matrix(),feat_23.f_xgbtree.as_matrix(),feat_24.f_xgbtree.as_matrix()])