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data_alloc.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 6 11:16:31 2017
@author: ldong
"""
import numpy as np
import cPickle as pk
import pandas as pd
from datetime import datetime
from sklearn.metrics import mean_absolute_error
with open('bundle_woImpute.pkl','rb') as f:
train_x, train_y, all_x, flag_all_nan, na_pct_all, train_x_17, train_y_17, all_x_17, flag_all_nan_17 = pk.load(f)
train_x.loc[:,'date'] = train_x.loc[:,'date']+12
train_x_17.loc[:,'date'] = train_x_17.loc[:,'date']-12
tmp = train_x
train_x = train_x_17
train_x_17 = tmp
tmp = train_y
train_y = train_y_17
train_y_17 = train_y
tmp = all_x
all_x = all_x_17
all_x_17 = tmp
tmp = flag_all_nan
flag_all_nan = flag_all_nan_17
flag_all_nan_17 = flag_all_nan
def real_mae(pred):
y17 = pd.read_csv('/workspace/ldong/ml/StackNet/input/train_2017.csv', low_memory=False)#.sort_values('parcelid')
y16 = pd.read_csv('/workspace/ldong/ml/StackNet/input/train_2016_v2.csv', low_memory=False)#.sort_values('parcelid')
y = pd.concat([y16,y17], axis=0)
pred = pred.merge(y,on='parcelid',how='left')
print 'Raw MAE: ', mean_absolute_error(pred.filter(regex='f_'),pred.logerror)
def write_sub(sub,flag_clip=False):
sample_sub = pd.read_csv('../../../input/sample_submission.csv',low_memory=False)
sub[0][flag_all_nan] = np.mean(sub[0])
sub[1][flag_all_nan] = np.mean(sub[1])
sub[2][flag_all_nan] = np.mean(sub[2])
sub[3][flag_all_nan_17] = np.mean(sub[3])
sub[4][flag_all_nan_17] = np.mean(sub[4])
sub[5][flag_all_nan_17] = np.mean(sub[5])
print 'mean logerrors:\n 16_10:%.4f, 16_11:%.4f, 16_12:%.4f \n 17_10:%.4f, 17_11:%.4f, 17_12:%.4f' % \
(np.mean(sub[0]),np.mean(sub[1]),np.mean(sub[2]),np.mean(sub[3]),np.mean(sub[4]),np.mean(sub[5]))
if flag_clip:
output = pd.DataFrame({'ParcelId': sample_sub['ParcelId'].astype(np.int32),
'201610': np.clip(sub[0],-0.08,0.08),
'201611': np.clip(sub[1],-0.08,0.08),
'201612': np.clip(sub[2],-0.08,0.08),
'201710': np.clip(sub[3],-0.08,0.08),
'201711': np.clip(sub[4],-0.08,0.08),
'201712': np.clip(sub[5],-0.08,0.08)})
else:
output = pd.DataFrame({'ParcelId': sample_sub['ParcelId'].astype(np.int32),
'201610': sub[0],
'201611': sub[1],
'201612': sub[2],
'201710': sub[3],
'201711': sub[4],
'201712': sub[5]})
cols = output.columns.tolist()
cols = cols[-1:] + cols[:-1]
output = output[cols]
output.to_csv('sub{}.csv'.format(datetime.now().strftime('%Y%m%d_%H%M%S')), index=False, float_format='%.4f')
def vw_format(tr_x, tr_y, flag_all=False):
tr_vw = []
if flag_all:
for i in range(tr_x.shape[0]):
tmp = np.array2string(tr_x[i], separator='', max_line_width=np.inf).replace('[','').replace(']','')
tr_vw.append(" | {x}".format(x=tmp))
else:
tr_y = tr_y.reset_index(drop=True)
for i in range(tr_x.shape[0]):
tmp = np.array2string(tr_x[i], separator='', max_line_width=np.inf).replace('[','').replace(']','')
tr_vw.append("{label} | {x}".format(label=tr_y.loc[i,'logerror'], x=tmp))
return tr_vw
def data_vw(tr_x, tr_y, a_x, tr_x17, tr_y17, a_x17):
pid_all = a_x.loc[:,'parcelid']
pid_all17 = a_x17.loc[:,'parcelid']
a_x['parcelid'] = 9
a_x17['parcelid'] = 21
a_x = vw_format(a_x.as_matrix(), [], True)
a_x17 =vw_format(a_x17.as_matrix(), [], True)
tr_x_q1, tr_y_q1, tr_x_q2, tr_y_q2, pid_q2 = data_quarter(tr_x, tr_y, tr_x17, tr_y17, 1, acc=False)
tr_q1 = vw_format(tr_x_q1.as_matrix(), tr_y_q1)
tr_q2 = vw_format(tr_x_q2.as_matrix(), tr_y_q2)
_, _, _, _, pid_q3 = data_quarter(tr_x, tr_y, tr_x17, tr_y17, 2, acc=False)
tr_x_q3, tr_y_q3, tr_x_q4, tr_y_q4, pid_q4 = data_quarter(tr_x, tr_y, tr_x17, tr_y17, 3, acc=False)
tr_q3 = vw_format(tr_x_q3.as_matrix(), tr_y_q3)
tr_q4 = vw_format(tr_x_q4.as_matrix(), tr_y_q4)
_, _, _, _, pid_q5 = data_quarter(tr_x, tr_y, tr_x17, tr_y17, 4, acc=False)
tr_x_q5, tr_y_q5, tr_x_q6, tr_y_q6, pid_q6 = data_quarter(tr_x, tr_y, tr_x17, tr_y17, 5, acc=False)
tr_q5 = vw_format(tr_x_q5.as_matrix(), tr_y_q5)
tr_q6 = vw_format(tr_x_q6.as_matrix(), tr_y_q6)
_, _, tr_x_q7, tr_y_q7, pid_q7 = data_quarter(tr_x, tr_y, tr_x17, tr_y17, 6, acc=False)
tr_q7 = vw_format(tr_x_q7.as_matrix(), tr_y_q7)
with open('bundleVW_woOutlier.pkl','wb') as f:
pk.dump([tr_q1, tr_y_q1,
tr_q2, tr_y_q2, pid_q2,
tr_q3, tr_y_q3, pid_q3,
tr_q4, tr_y_q4, pid_q4,
tr_q5, tr_y_q5, pid_q5,
tr_q6, tr_y_q6, pid_q6,
tr_q7, tr_y_q7, pid_q7,
a_x, pid_all,
a_x17, pid_all17], f, protocol=pk.HIGHEST_PROTOCOL)
def data_quarter(tr_x, tr_y, tr_x17, tr_y17, quarter, nextQ=True, acc=True):
tr_y = tr_y.drop('parcelid', axis=1)
tr_y17 = tr_y17.drop('parcelid', axis=1)
if quarter == 1:
pid = tr_x.loc[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3),'parcelid']
tr_x = tr_x.drop('parcelid', axis=1)
x_train = tr_x[tr_x.date <= quarter*3]
y_train = tr_y[tr_x.date <= quarter*3]
x_valid = tr_x[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3)]
y_valid = tr_y[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3)]
elif quarter == 2:
pid = tr_x.loc[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3),'parcelid']
tr_x = tr_x.drop('parcelid', axis=1)
if acc:
x_train = tr_x[(tr_x.date <= quarter*3)] #tr_x[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
y_train = tr_y[(tr_x.date <= quarter*3)] #tr_y[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
else:
x_train = tr_x[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
y_train = tr_y[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
x_valid = tr_x[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3)]
y_valid = tr_y[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3)]
elif quarter == 3:
if nextQ:
pid = tr_x.loc[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3),'parcelid']
tr_x = tr_x.drop('parcelid', axis=1)
if acc:
x_train = tr_x[(tr_x.date <= quarter*3)] #tr_x[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
y_train = tr_y[(tr_x.date <= quarter*3)] #tr_y[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
else:
x_train = tr_x[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
y_train = tr_y[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
x_valid = tr_x[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3)]
y_valid = tr_y[(tr_x.date >= quarter*3+1) & (tr_x.date <= (quarter+1)*3)]
else:
pid = all_x.loc[:,'parcelid'] # all_x is global
tr_x = tr_x.drop('parcelid', axis=1)
x_train = tr_x[tr_x.date <= quarter*3]
y_train = tr_y[tr_x.date <= quarter*3]
x_valid = []
y_valid = []
elif quarter == 4:
pid = tr_x17.loc[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3),'parcelid']
if (tr_x17.shape[1]-tr_x.shape[1]) == 4:
tr_x17 = tr_x17.drop(['val_tax_16','val_total_16','val_building_16','val_land_16'], axis=1)
tr_x17 = tr_x17.drop(['parcelid'], axis=1)
tr_x = tr_x.drop('parcelid', axis=1)
if acc:
x_train = tr_x[(tr_x.date <= quarter*3)] #tr_x[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
y_train = tr_y[(tr_x.date <= quarter*3)] #tr_y[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
else:
x_train = tr_x[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
y_train = tr_y[(tr_x.date >= quarter*3-2) & (tr_x.date <= quarter*3)]
x_valid = tr_x17[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3)]
y_valid = tr_y17[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3)]
# 2017 starts here
elif quarter == 5:
pid = tr_x17.loc[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3),'parcelid']
tr_x17 = tr_x17.drop('parcelid', axis=1)
if acc:
x_train = pd.concat([tr_x.drop('parcelid',axis=1),tr_x17[(tr_x17.date <= quarter*3)]],axis=0) #tr_x17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
y_train = pd.concat([tr_y,tr_y17[(tr_x17.date <= quarter*3)]],axis=0) #tr_y17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
else:
x_train = tr_x17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
y_train = tr_y17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
x_valid = tr_x17[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3)]
y_valid = tr_y17[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3)]
elif quarter == 6:
pid = tr_x17.loc[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3),'parcelid']
tr_x17 = tr_x17.drop('parcelid', axis=1)
if acc:
x_train = pd.concat([tr_x.drop('parcelid',axis=1),tr_x17[(tr_x17.date <= quarter*3)]],axis=0) #tr_x17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
y_train = pd.concat([tr_y,tr_y17[(tr_x17.date <= quarter*3)]],axis=0) #tr_y17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
else:
x_train = tr_x17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
y_train = tr_y17[(tr_x17.date >= quarter*3-2) & (tr_x17.date <= quarter*3)]
x_valid = tr_x17[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3)]
y_valid = tr_y17[(tr_x17.date >= quarter*3+1) & (tr_x17.date <= (quarter+1)*3)]
elif quarter == 7:
pid = all_x_17.loc[:,'parcelid'] # all_x is global
x_train = pd.concat([tr_x.drop('parcelid',axis=1),tr_x17.drop('parcelid', axis=1)],axis=0)
y_train = pd.concat([tr_y,tr_y17],axis=0)
x_valid = []
y_valid = []
return x_train, y_train, x_valid, y_valid, pid