def cv(method, theme, kind):
no_invest = gbs.get_no_invest_set()
hdd_pair = get_pair('HDD')
print hdd_pair[0]
hdd_pair = [x for x in hdd_pair if x[0] in no_invest]
print len(hdd_pair)
n_par_temp = 2
lines = []
if theme == 'eui_elec':
base_temp = '55F'
else:
base_temp = '65F'
lines.append('Building Number,CVRMSE_{0}'.format(method))
for p in hdd_pair:
b = p[0]
s = p[1]
df = pd.read_csv(weatherdir + 'dd_temp_eng/{0}_{1}_{2}.csv'.format(kind, b, s))
if len(df) < 36:
continue
df = df.tail(n=36)
df_pairs = get_partitions(3, df)
errs = []
for x in df_pairs:
df_train = x[0]
if df_train[theme].sum() == 0:
continue
df_test = x[1]
y = np.array(df_test[theme])
if method == 'temperature':
x = np.array(df_test[s])
d = ltm.piecewise_reg_one(b, s, n_par_temp, theme, None, df_train)
y_hat = d['fun'](x, *(d['regression_par']))
elif method == 'dd':
par_list = ld.opt_lireg(b, s, df_train, kind, theme, None)
x = df_test[par_list[-1]]
fun = lambda x: par_list[0] * x + par_list[1]
y_hat = x.map(fun)
elif method == 'vv':
par_list = vv.lean(b, s, df_train, theme, base_temp)
df_temp = df_test.copy()
df_temp = df_temp[['month', base_temp]]
df_perdd = par_list[0]
df_perdd.reset_index(inplace=True)
df_mg = pd.merge(df_test, df_perdd, left_on='month', right_index=True)
y = df_mg[theme]
y_hat = df_mg[base_temp] * df_mg['y_per_dd'] + par_list[-1]
cvrmse = util.CVRMSE(y, y_hat, n_par_temp)
print cvrmse
errs.append(cvrmse)
line = '{0},{1}'.format(b, (np.array(errs)).mean())
print line
lines.append(line)
with open(weatherdir + 'cv_{0}_{1}.csv'.format(method, theme), 'w+') as wt:
wt.write('\n'.join(lines))
return
def write_co_sets():
(cap_only, op_only, cap_and_op, cap_or_op) = gbs.get_invest_set()
no_invest = gbs.get_no_invest_set()
df1 = pd.DataFrame({'Building_Number': list(cap_only)})
df1['status'] = 'Capital Only'
df2 = pd.DataFrame({'Building_Number': list(op_only)})
df2['status'] = 'Operational Only'
df3 = pd.DataFrame({'Building_Number': list(cap_and_op)})
df3['status'] = 'Capital and Operational'
df4 = pd.DataFrame({'Building_Number': list(no_invest)})
df4['status'] = 'No Known Investment'
df5 = pd.DataFrame({'Building_Number': list(cap_or_op)})
df5['status'] = 'With Investment'
df = pd.concat([df1, df2, df3, df4, df5], ignore_index=True)
df.to_csv(r_input + 'cap_op_cnt.csv', index=False)
print 'end'
def write_co_sets():
(cap_only, op_only, cap_and_op, cap_or_op) = gbs.get_invest_set()
no_invest = gbs.get_no_invest_set()
df1 = pd.DataFrame({'Building_Number': list(cap_only)})
df1['status'] = 'Capital Only'
df2 = pd.DataFrame({'Building_Number': list(op_only)})
df2['status'] = 'Operational Only'
df3 = pd.DataFrame({'Building_Number': list(cap_and_op)})
df3['status'] = 'Capital and Operational'
df4 = pd.DataFrame({'Building_Number': list(no_invest)})
df4['status'] = 'No Known Investment'
df5 = pd.DataFrame({'Building_Number': list(cap_or_op)})
df5['status'] = 'With Investment'
df = pd.concat([df1, df2, df3, df4, df5], ignore_index=True)
df.to_csv(r_input + 'cap_op_cnt.csv', index=False)
print 'end'
