import sys
import os
sys.path.append(os.path.abspath("../preprocessing/"))
import features_preprocessing as fp
import submission_preprocessing as sp
import submission_postprocess as pp
import pandas as pd
postprocess = pp.submission_postprocess()
data = pd.read_table('predictions.txt')
print(data)
postprocess.premier_submit(pred=data)
xa = x['ASS_ID']
xt = x['TIME']
yd = x['YEAR_DAY']
xy = x['YEAR']
#if xa == 35:
d = data.query('WEEK_DAY == @xwd and ASS_ID ==@xa and TIME == @xt and YEAR == @xy')
#else:
# d = data.query('WEEK_DAY == @xwd and ASS_ID ==@xa and TIME == @xt')
calls = d['CSPL_CALLS']
if calls.empty:
prediction.append(0)
else:
mean = calls.mean()
prediction.append(mean)
if i%1000 == 0:
print(i)
print(prediction)
post = postpro.submission_postprocess()
post.premier_submit(prediction, name = "result_submission_axa3.txt")
t2 = time.time()
print(t2 - t1)
#clf = svm.SVR(kernel='poly', degree=3)
#scores = cross_validation.cross_val_score(clf, X_train, Y_train, cv=2)
#print(scores)
xa = x['ASS_ID']
xt = x['TIME']
yd = x['YEAR_DAY']
xy = x['YEAR']
#if xa == 35:
d = data.query(
'WEEK_DAY == @xwd and ASS_ID ==@xa and TIME == @xt and YEAR == @xy')
#else:
# d = data.query('WEEK_DAY == @xwd and ASS_ID ==@xa and TIME == @xt')
calls = d['CSPL_CALLS']
if calls.empty:
prediction.append(0)
else:
mean = calls.mean()
prediction.append(mean)
if i % 1000 == 0:
print(i)
print(prediction)
post = postpro.submission_postprocess()
post.premier_submit(prediction, name="result_submission_axa3.txt")
t2 = time.time()
print(t2 - t1)
#clf = svm.SVR(kernel='poly', degree=3)
#scores = cross_validation.cross_val_score(clf, X_train, Y_train, cv=2)
#print(scores)
