def get_year_ticket(yyyy, cutoff_date, ts_name):
start, end = xl_ut.iso_dates(yyyy)
cutoff_date = pd.to_datetime(cutoff_date)
ts_cfg, _ = dp.ts_setup(ts_name, cutoff_date, pd.to_datetime('2016-01-01'), 'W')
a_df = dp.ts_actuals(ts_name, ts_cfg, ['language', 'business_unit', 'channel', 'service_tier'], drop_cols=False)
a_df['ts_name'] = ts_name
f_df = ap.hive.query('select * from sup.cx_weekly_forecasts where ts_name = \'' + ts_name + '\' and cutoff =\'' + str(cutoff_date.date()) + '\';')
f_df.columns = [c.split('.')[1] for c in f_df.columns]
a_df['ds'] = pd.to_datetime(a_df['ds'].values)
a_df = a_df[(a_df['ds'] >= start) & (a_df['ds'] <= cutoff_date)].copy()
f_df['ds'] = pd.to_datetime(f_df['ds'].values)
f_df = f_df[f_df['ds'] <= end].copy()
f_df.rename(columns={'yhat': ts_name}, inplace=True)
return pd.concat([a_df, f_df], axis=0)
def basic_perf(ts_name, cutoff_date, upr, lwr, init_date='2016-01-01', time_scale='W'):
ts_cfg, cols = dp.ts_setup(ts_name, cutoff_date, init_date, time_scale)
# actuals
actuals_df = dp.ts_actuals(ts_name, ts_cfg, cols)
actuals_df.rename(columns={ts_cfg['ycol']: 'y'}, inplace=True)
actuals_df.drop_duplicates(inplace=True) # not sure why there are dups. Table problem?
# forecasts
f_df = get_lang_fcast(ts_cfg, cutoff_date, eq=True)
f_df.drop_duplicates(inplace=True) # not sure why there are dups. Table problem?
f_idx = fcast_idx(f_df)
f_df = f_df.merge(f_idx, on='dim_cfg', how='left')
f_df.drop('dim_cfg', axis=1, inplace=True)
f_df.rename(columns={'index': 'dim_cfg'}, inplace=True)
df = f_df.merge(actuals_df, on=['ds', 'language'], how='left')
df.drop('cutoff', axis=1, inplace=True)
df.drop_duplicates(inplace=True) # not sure why there are dups
p_ut.save_df(df, '~/my_tmp/df_all')
# perf
cu_sun = cutoff_date - pd.to_timedelta(6, unit='D')
sf = df[['ds', 'language', 'y']].drop_duplicates()
sf.dropna(inplace=True)
f_shift = sf.groupby('language').apply(get_shift, cu_sun=cu_sun, upr=upr, lwr=lwr).reset_index() # find best shift
nf = df.merge(f_shift, on=['ds', 'language', 'y'], how='left')
nf.set_index(['language', 'dim_cfg'], inplace=True) # avoids drop of nuisance cols
nf.dropna(inplace=True)
nf.drop_duplicates(inplace=True)
zperf = nf.groupby(['language', 'dim_cfg']).apply(cfg_perf, upr=upr, lwr=lwr, cu_sun=cu_sun).reset_index(drop=True) # do not groupby df.index
zperf.dropna(inplace=True)
p_ut.save_df(zperf, '~/my_tmp/zperf_' + ts_name + '_' + str(cutoff_date.date()))
nf.reset_index(inplace=True)
p_ut.save_df(nf, '~/my_tmp/nf')
fout = nf.merge(zperf, on=list(nf.columns), how='left')
return actuals_df, fout
def main(argv):
# ###########################
# parameters
# ###########################
time_scale = 'W' # forecasting time scale reset for daily ticket data
init_date = pd.to_datetime('2016-01-01')
upr = 12
lwr = 8
evals = 50
by_lang = False
# ###########################
# ###########################
# ###########################
print(argv)
if len(argv[1:]) == 1:
ts_name = argv[-1]
cutoff_date = pd.to_datetime('today')
to_table = False
elif len(argv[1:]) == 2:
ts_name, cutoff_date = argv[1:]
try:
cutoff_date = pd.to_datetime(cutoff_date)
to_table = False
except ValueError:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' +
str(argv))
sys.exit()
elif len(argv[1:]) == 3:
ts_name, cutoff_date, to_table = argv[1:]
try:
cutoff_date = pd.to_datetime(cutoff_date)
to_table = bool(int(to_table))
except ValueError:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' +
str(argv))
sys.exit()
else:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' +
str(argv))
sys.exit()
ts_cfg, cols = dp.ts_setup(ts_name, cutoff_date, init_date, time_scale)
# actuals
actuals_df = dp.ts_actuals(ts_name, ts_cfg, cols)
actuals_df.rename(columns={ts_cfg['ycol']: 'y'}, inplace=True)
# forecasts
f_df = fp.get_lang_fcast(ts_cfg, cutoff_date)
fcast_date = cutoff_date + pd.to_timedelta(upr, unit=time_scale)
perf_list = list()
for xens in [
'XGBRegressor', 'AdaBoostRegressor', 'BaggingRegressor',
'GradientBoostingRegressor', 'RandomForestRegressor',
'ExtraTreesRegressor', 'lasso'
]:
fcast_df = ep.make_fcast(ts_name,
f_df,
actuals_df,
cutoff_date,
fcast_date,
xens,
evals,
by_lang, (lwr, upr),
lwr=lwr,
upr=upr)
perf_df = perf.fcast_perf(fcast_df, actuals_df, cutoff_date, lwr, upr,
time_scale, xens)
if perf_df is None:
s_ut.my_print('pid: ' + str(os.getpid()) +
' WARNING: forecast performance detail failed for ' +
ts_name + ' ,cutoff date ' +
str(cutoff_date.date()) + ' and ensemble: ' +
str(xens))
else:
perf_df['ts_name'] = ts_name
perf_list.append(perf_df)
if len(perf_list) > 0:
pf = pd.concat(perf_list, axis=0)
p_ut.save_df(
pf, '~/my_tmp/perf/fcast_perf_detail_' + ts_name + '_' +
str(cutoff_date.date()))
if to_table is True:
tab_cols = ['language', 'y', 'yhat', 'err', 'lwr', 'upr', 'ens']
partition = {'cutoff': str(cutoff_date.date()), 'ts_name': ts_name}
ret = hql.to_tble(pf, tab_cols,
'sup.cx_language_forecast_performance_detail',
partition)
if ret != 0:
s_ut.my_print(
'pid: ' + str(os.getpid()) +
' ERROR: forecast performance detail failed for ' +
ts_name + ' and cutoff date ' + str(cutoff_date.date()))
sys.exit()
print('DONE')
else:
s_ut.my_print(
'pid: ' + str(os.getpid()) +
' ERROR: no data for forecast performance detail failed for ' +
ts_name + ' and cutoff date ' + str(cutoff_date.date()))
sys.exit()
cutoff_date = pd.to_datetime(cutoff_date)
else:
print('invalid args: ' + str(sys.argv))
sys.exit()
# #########################
actuals_weeks = 12
target_year = 2020
init_date = pd.to_datetime('2016-01-01')
time_scale = 'W'
ts_name = 'ticket_count'
# #########################
# get actuals
ts_cfg, _ = dp.ts_setup(ts_name, cutoff_date, init_date, time_scale)
adf = dp.ts_actuals(ts_name, ts_cfg, ['language', 'business_unit', 'channel', 'service_tier'], drop_cols=False, use_cache=False)
start, end = tm_ut.iso_dates(target_year - 1)
adf = adf[adf['ds'] >= start] # use for YoY comparison
adf.rename(columns={'ticket_count': 'y'}, inplace=True)
p_ut.save_df(adf, '~/my_tmp/a_df')
# current forecast
s_ut.my_print('current forecast')
cf_df = pd.concat([get_fcast(ts_name, ts_cfg, cutoff_date, bu, use_cache=False) for bu in ts_cfg['business_units']], axis=0)
cf_df = cf_df[cf_df['ds'] > cutoff_date].copy()
p_ut.save_df(cf_df, '~/my_tmp/cf_df')
# target year summary
xl_ut.year_summary(adf, cf_df, target_year, cutoff_date)
# get 90 day old forecast (last Sat of a month)
def main(argv):
# ###########################
# parameters
# ###########################
time_scale = 'W' # forecasting time scale reset for daily ticket data
init_date = pd.to_datetime('2016-01-01')
froot = '~/my_tmp/fbp/'
# ###########################
# ###########################
print(argv)
if len(argv) == 2:
ts_name = argv[-1]
to_table = False
run_date = pd.to_datetime('today')
elif len(argv) == 3:
ts_name, run_date = argv[-2:]
try:
run_date = pd.to_datetime(run_date)
to_table = False
except ValueError:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, run_date, to_table): ' +
str(argv))
sys.exit()
elif len(argv) == 4:
ts_name, run_date, to_table = argv[1:]
try:
run_date = pd.to_datetime(run_date)
to_table = bool(int(to_table))
except ValueError:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, run_date, to_table): ' +
str(argv))
sys.exit()
else:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, run_date, to_table): ' +
str(argv))
sys.exit()
# data cfg
cutoff_date = tm_ut.get_last_sat(
run_date
) # set to last saturday before run_date or the run_date if a saturday
ts_cfg, cols = dp.ts_setup(ts_name, cutoff_date, init_date, time_scale)
FCAST_DICT['outlier_coef'] = ts_cfg.get('outlier_coef', [3.0])
fcast_days = ts_cfg.get('fcast_days', None)
if fcast_days is None:
s_ut.my_print('pid: ' + str(os.getpid()) +
' ERROR" fcast_days must be specified in data_cfg')
sys.exit()
else:
fcast_date = cutoff_date + pd.to_timedelta(fcast_days, unit='D')
if time_scale == 'W':
fcast_date = fcast_date - pd.to_timedelta(
1 + fcast_date.weekday(), unit='D') # set to week starting Sunday
cu = cutoff_date - pd.to_timedelta(
1 + cutoff_date.weekday(), unit='D') # set to week starting Sunday
fcast_days = (fcast_date - cu).days # multiple of 7
upr_horizon = int(fcast_days / 7) # in time scale units
elif time_scale == 'D':
upr_horizon = int(fcast_days) # in time scale units
else:
s_ut.my_print('pid: ' + str(os.getpid()) + ' invalid time scale: ' +
str(time_scale))
sys.exit()
s_ut.my_print('pid: ' + str(os.getpid()) +
' ------------------------ start language forecast for ' +
str(ts_name) + ' from cutoff date ' +
str(cutoff_date.date()) + ' (excluded) to forecast date ' +
str(fcast_date.date()) +
' (included) -----------------------')
# get actuals
actuals_df = dp.ts_actuals(
ts_name, ts_cfg,
cols) # may have data past cutoff for accuracy checking
if actuals_df['ds'].max() < cutoff_date:
s_ut.my_print(
'ERROR: no actuals available for forecast from cutoff date: ' +
str(cutoff_date.date()))
sys.exit()
f_actuals_df = actuals_df[actuals_df['ds'] <= cutoff_date].copy(
) # actuals for forecast: only use up to cutoff date
# adjust FCAST_DICT
if len(FCAST_DICT['do_res']) == 2: # True, False
FCAST_DICT['do_res'] = [
True
] # MUST overwrite: the False care is always included and otherwise we double count.
if len(ts_cfg.get('regressors', list())) == 0:
FCAST_DICT['r_mode'] = [None]
reg_dict = dict()
else:
reg_dict = regs.ens_fcast(
ts_name, ts_cfg['regressors'], cutoff_date, time_scale, fcast_days,
init_date,
f_actuals_df) # stored by cutoff date on last Sat of the month
# update init_date
init_date = max([f_actuals_df['ds'].min()] +
[f['ds'].min() for f in reg_dict.values()])
f_actuals_df = f_actuals_df[f_actuals_df['ds'] >= init_date].copy()
reg_dict = {
lx: f[f['ds'] >= init_date].copy()
for lx, f in reg_dict.items()
}
ts_cfg['init_date'] = init_date
# set the list of fcast cfgs
tlist = get_f_cfg(FCAST_DICT, cutoff_date, init_date,
time_scale) # list of fcast cfg's
fix_pars = [
f_actuals_df, ts_name, reg_dict, fcast_date, cutoff_date, ts_cfg,
time_scale, upr_horizon
]
arg_list = [
fix_pars + [tlist[ix]] for ix in range(len(tlist))
] # 2 fcasts are done per input cfg (do_res = true and do_res = false)
n_fcfg = 2 * len(arg_list)
s_ut.my_print('pid: ' + str(os.getpid()) + ' ++++++++ there are ' +
str(n_fcfg) + ' fcast configs per language **********')
# ###############################################################################
# ###############################################################################
# ###############################################################################
if is_test:
df_list_ = s_ut.do_mp(fcast_lang,
arg_list,
is_mp=False,
cpus=None,
do_sigkill=True)
else:
df_list_ = s_ut.do_mp(fcast_lang,
arg_list,
is_mp=True,
cpus=None,
do_sigkill=True)
# ###############################################################################
# ###############################################################################
# ###############################################################################
# join all the fcasted data into a flat list
df_list = [f for f in df_list_ if f is not None]
if len(df_list) > 0:
ylist, alist = list(), list()
for fl in df_list:
if fl is not None:
fl = set_cfg(fl.copy(), CFG_COLS)
ylist.append(fl[[
'ds', 'language', 'yhat', 'ts_name', 'cutoff', 'dim_cfg',
'fcast_date'
]].copy())
alist.append(fl)
# save basic fcast data
fcast_df = pd.concat(
ylist, axis=0) # now all the list elements have the same columns
fcast_df.reset_index(inplace=True, drop=True)
ok_cfg = fcast_df['dim_cfg'].unique()
s_ut.my_print('pid: ' + str(os.getpid()) + str(len(ok_cfg)) +
' forecasts cfgs available for ' + str(ts_name) +
' from cutoff date ' + str(cutoff_date.date()) +
' (excluded) to forecast date ' +
str(fcast_date.date()) +
' (included) -----------------------')
# fcast_df = fcast_df[fcast_df['dim_cfg'].isin(ok_cfg)].copy()
fname = froot + 'lang_fcast_'
p_ut.save_df(fcast_df, fname + ts_name + '_' + str(cutoff_date.date()))
if to_table is True:
tab_cols = ['ds', 'language', 'dim_cfg', 'yhat']
partition = {
'cutoff': str(cutoff_date.date()),
'ts_name': ts_cfg['ts_key']
}
ret = hql.to_tble(fcast_df, tab_cols, 'sup.cx_language_forecast',
partition)
if ret != 0:
s_ut.my_print('pid: ' + str(os.getpid()) +
' ERROR: no forecasts loaded to table for ' +
str(ts_cfg['ts_key']) + ' and cutoff date ' +
str(cutoff_date.date()))
sys.exit()
# save all fcast data (y_upr, y_lwr, ...)
all_df = pd.concat(
alist, axis=0) # now all the list elements have the same columns
all_cols = list(set([c for c in all_df.columns if c not in CFG_COLS]))
all_df.reset_index(inplace=True, drop=True)
all_df = all_df[all_cols].copy()
all_df = all_df[all_df['dim_cfg'].isin(ok_cfg)].copy()
fname = froot + 'fcast_all_'
p_ut.save_df(all_df, fname + ts_name + '_' + str(cutoff_date.date()))
if to_table is True:
all_df.drop(['cutoff', 'ts_name'], axis=1, inplace=True)
mf = pd.melt(all_df,
id_vars=['ds', 'language', 'dim_cfg'],
var_name='key',
value_name='value')
mf.dropna(subset=['value'], inplace=True)
mf = mf[mf['value'] != 0.0].copy()
partition = {
'cutoff': str(cutoff_date.date()),
'ts_name': ts_cfg['ts_key']
}
ret = hql.to_tble(mf, list(mf.columns),
'sup.cx_language_forecast_detail', partition)
if ret != 0:
s_ut.my_print('pid: ' + str(os.getpid()) +
' ERROR: no forecasts loaded to table for ' +
str(ts_cfg['ts_key']) + ' and cutoff date ' +
str(cutoff_date.date()))
sys.exit()
print('DONE')
else:
s_ut.my_print('pid: ' + str(os.getpid()) +
' ERROR: no forecasts available for ' +
str(ts_cfg['ts_key']) + ' from cutoff date ' +
str(cutoff_date.date()) +
' (excluded) to forecast date ' +
str(fcast_date.date()) +
' (included) -----------------------')
def main(argv):
print(argv)
if len(argv) == 2:
ts_name = argv[-1]
to_table = False
elif len(argv) == 3:
ts_name, to_table = argv[1:]
try:
to_table = bool(int(to_table))
except ValueError:
s_ut.my_print('ERROR: invalid arguments (ts_name, to_table): ' +
str(argv))
sys.exit()
else:
s_ut.my_print('ERROR: invalid arguments (ts_name, to_table): ' +
str(argv))
sys.exit()
if any([bu in ts_name for bu in ['Homes', 'Experiences', 'China']]):
s_ut.my_print('ERROR: time series cannot be a BU time series: ' +
str(ts_name))
sys.exit()
data_cfg = os.path.expanduser(
'~/my_repos/capacity_planning/forecast/config/ratio_forecast_cfg.json')
if os.path.isfile(data_cfg):
with open(data_cfg, 'r') as fptr:
rf_dict = json.load(fptr)
else:
s_ut.my_print('ERROR: ' + data_cfg + ' file not found')
sys.exit()
d_date = rf_dict.get('data_date', None)
if d_date is None:
s_ut.my_print('ERROR: data_date cannot be null')
sys.exit()
data_date = pd.to_datetime(
d_date) # this is the cutoff date we get data from tables
a_date = rf_dict.get(
'adjust_date',
None) # if None, nothing to adjust and adj_date = data_date
adjust_date = data_date if a_date is None else pd.to_datetime(
a_date) # this is the actual cutoff date
window = rf_dict.get('ratio_windows', dict())
if len(window) == 0: # not set
window = {
'default': {
'start': adjust_date - pd.to_timedelta(6, unit='W'),
'end': adjust_date
}
}
else:
for k, v in window.items():
for kk, vv in v.items():
v[kk] = pd.to_datetime(vv)
s_ut.my_print('************************* read table date: ' +
str(data_date.date()) +
' ********************************************')
s_ut.my_print('************************* write table date: ' +
str(adjust_date.date()) +
' *******************************************')
# ###############################
# ###############################
time_scale = 'W'
init_date = pd.to_datetime('2016-01-01')
# ###############################
# ###############################
df_tilde, bottom_ts = hts.main(
ts_name, data_date, do_cov=True
) # coherent forecasts at language level + language level adjustments
f_df = adj.main(
df_tilde, 'language', bottom_ts, ts_name, adjust_date
) # must adjust at language level before service level ratios
fr_list, fr_cols = list(), list()
a_list = list()
ts_list = bottom_ts # ratios only on bottom_ts then aggregate to top TS
for ts in ts_list:
s_ut.my_print('============= starting ' + str(ts))
ts_cfg, _ = dp.ts_setup(ts, data_date, init_date, time_scale)
a_df = dp.ts_actuals(
ts,
ts_cfg, ['language', 'business_unit', 'channel', 'service_tier'],
drop_cols=False)
b_df = filter_actuals(a_df, window)
fr = ts_ratio(ts, b_df.copy(), f_df[['ds', 'language',
ts + '_tilde']].copy(), window,
data_date)
fr_list.append(fr)
fr_cols.append(fr.columns)
a_list.append(b_df)
check_ratios(ts, b_df, fr, True, 'service_tier')
check_ratios(ts, b_df, fr, False, 'service_tier')
check_ratios(ts, b_df, fr, True, 'channel')
check_ratios(ts, b_df, fr, False, 'channel')
# must adjust together to ensure coherence
fr = reduce(
lambda x, y: x.merge(
y, on=['ds', 'language', 'channel', 'service_tier'], how='outer'),
fr_list) if len(fr_list) > 0 else None
fr.fillna(0, inplace=True)
for k_col in ['channel',
'service_tier']: # language adj must be done before ratios
fr = adj.main(fr, k_col, bottom_ts, ts_name, adjust_date)
# save data
f_list = list()
for idx in range(len(ts_list)):
ts = ts_list[idx]
fx = fr[fr_cols[idx]].copy()
fx.rename(columns={ts + '_tilde': 'yhat'}, inplace=True)
fx['yhat'] = np.round(
fx['yhat'].values,
0) # this makes input totals and output totals to be a bit off
fx = fx[fx['yhat'] > 0]
fx['ts_name'] = ts
fx['cutoff'] = adjust_date
f_list.append(fx)
# get the aggregate series
fall = pd.concat(f_list, axis=0)
gall = fall.groupby(['ds', 'language', 'channel',
'service_tier']).sum(numeric_only=True).reset_index()
gall['cutoff'] = adjust_date
gall['ts_name'] = ts_name
# align cols (ap gets confused otherwise?)
tcols = [
'ds', 'language', 'channel', 'service_tier', 'yhat', 'ts_name',
'cutoff'
]
fall = fall[tcols].copy()
gall = gall[tcols].copy()
# final DF to save
fout = pd.concat([gall, fall], axis=0)
p_ut.save_df(
fout,
'~/my_tmp/fbp/ratios_fcast_' + ts_name + '_' + str(adjust_date.date()))
ts_cfg, _ = dp.ts_setup('ticket_count', data_date, init_date, time_scale)
a_df = dp.ts_actuals(
'ticket_count',
ts_cfg, ['language', 'business_unit', 'channel', 'service_tier'],
drop_cols=False)
p_ut.save_df(a_df, '~/my_tmp/a_df_ticket_count_' + str(adjust_date.date()))
# data summary
s_ut.my_print('**************** Data Summary *******************')
for c in ['language', 'channel', 'service_tier']:
s_ut.my_print('unique ' + c + ': ' + str(fout[c].unique()))
# save to DB
if to_table is True:
tcols.remove('cutoff')
tcols.remove('ts_name')
tcols.insert(1, 'ds_week_starting')
tcols.insert(2, 'fcst_date_inv_ending') # ds_week_ending
fout = fout[fout['ds'] > adjust_date.date()].copy(
) # only save forecasted values
fout['ds_week_starting'] = fout['ds']
fout['fcst_date_inv_ending'] = fout['ds'] + pd.to_timedelta(6,
unit='D')
for ts in fout['ts_name'].unique():
partition = {'cutoff': str(adjust_date.date()), 'ts_name': ts}
tb_df = fout[fout['ts_name'] == ts].copy()
ret = hql.to_tble(tb_df, tcols, 'sup.cx_weekly_forecasts',
partition)
if ret != 0:
s_ut.my_print('pid: ' + str(os.getpid()) +
' ERROR: no forecasts loaded to table for ' +
ts_name + ' and cutoff date ' +
str(adjust_date.date()))
sys.exit()
else:
s_ut.my_print(
'>>>>>>>>>>>>>>> SUCCESS: data saved to table <<<<<<<<<<<<<<<<<<<'
)
else:
s_ut.my_print(
'>>>>>>>>>>>>>>> WARNING: no data saved to table <<<<<<<<<<<<<<<<<<<'
)
def main(argv):
# ###########################
# parameters
# ###########################
time_scale = 'W' # forecasting time scale reset for daily ticket data
init_date = pd.to_datetime('2016-01-01')
froot = os.path.expanduser('~/my_tmp/fbp/')
evals = 250
by_lang = False
lwr, upr = 9, 12
# ###########################
# ###########################
print(argv)
if len(argv) == 3:
ts_name, cutoff_date = argv[-2:]
try:
cutoff_date = pd.to_datetime(cutoff_date)
to_table = False
except ValueError:
s_ut.my_print('ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' + str(argv))
sys.exit()
elif len(argv) == 4:
ts_name, cutoff_date, to_table = argv[1:]
try:
cutoff_date = pd.to_datetime(cutoff_date)
to_table = bool(int(to_table))
except ValueError:
s_ut.my_print('ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' + str(argv))
sys.exit()
else:
s_ut.my_print('ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' + str(argv))
sys.exit()
# data cfg
ts_cfg, cols = dp.ts_setup(ts_name, cutoff_date, init_date, time_scale)
fcast_days = ts_cfg.get('fcast_days', None)
if fcast_days is None:
s_ut.my_print('pid: ' + str(os.getpid()) + ' ERROR" fcast_days must be specified in data_cfg')
sys.exit()
else:
fcast_date = cutoff_date + pd.to_timedelta(fcast_days, unit='D')
if time_scale == 'W' and fcast_date.weekday() != 6: # set fcast date to week starting Sunday unless it is a Sunday already
fcast_date = fcast_date - pd.to_timedelta(1 + fcast_date.weekday(), unit='D')
s_ut.my_print('pid: ' + str(os.getpid()) + ' ------------------------ start ens forecast for ' + str(ts_name) + ' from cutoff date '
+ str(cutoff_date.date()) + ' (excluded) to forecast date ' + str(fcast_date.date()) + ' (included) -----------------------')
a_df = dp.ts_actuals(ts_name, ts_cfg, cols) # get actuals
a_df.rename(columns={ts_cfg['ycol']: 'y'}, inplace=True)
fcast_df = fp.get_lang_fcast(ts_cfg, cutoff_date) # get fcasts
if fcast_df is not None and a_df is not None:
s_ut.my_print(ts_name + ': combining ' + str(fcast_df['dim_cfg'].nunique()) + ' forecast configs')
xens_ = get_ens(ts_name, cutoff_date) # ts_cfg['ens'].get(str(cutoff_date.month), ens_dict['default'])
s_ut.my_print('aggregation for ' + ts_name + ' done with ' + xens_)
ts_fcast = ep.make_fcast(ts_name, fcast_df, a_df, cutoff_date, fcast_date, xens_, evals, by_lang, (lwr, upr), lwr=lwr, upr=upr)
ts_fcast['fcast_date'] = fcast_date
p_ut.save_df(ts_fcast, froot + 'ens_fcast_' + ts_name + '_' + str(cutoff_date.date()))
if to_table is True:
cols = ['ds', 'language', 'ens', 'yhat']
partition = {'cutoff': str(cutoff_date.date()), 'ts_name': ts_cfg['ts_key']}
ret = hql.to_tble(ts_fcast, cols, 'sup.cx_ens_forecast', partition)
if ret != 0:
s_ut.my_print('ERROR: DB write for ' + ts_name + ' ens forecast ' + ' at ' + str(cutoff_date.date()) + ' failed')
sys.exit()
print('DONE')
else:
s_ut.my_print('ERROR: no actuals or no data for errors of ' + ts_name + ' at ' + str(cutoff_date.date()))
def main(argv):
# ###########################
# parameters
# ###########################
time_scale = 'W' # forecasting time scale reset for daily ticket data
init_date = pd.to_datetime('2016-01-01')
upr = 12
lwr = 8
# ###########################
# ###########################
# ###########################
print(argv)
if len(argv) == 2:
ts_name = argv[-2:]
cutoff_date = pd.to_datetime('today')
to_table = False
elif len(argv) == 3:
ts_name, cutoff_date = argv[-2:]
try:
cutoff_date = pd.to_datetime(cutoff_date)
to_table = False
except ValueError:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' +
str(argv))
sys.exit()
elif len(argv) == 4:
ts_name, cutoff_date, to_table = argv[1:]
try:
cutoff_date = pd.to_datetime(cutoff_date)
to_table = bool(int(to_table))
except ValueError:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' +
str(argv))
sys.exit()
else:
s_ut.my_print(
'ERROR: invalid arguments (ts_name, cutoff_date, to_table): ' +
str(argv))
sys.exit()
ts_cfg, cols = dp.ts_setup(ts_name, cutoff_date, init_date, time_scale)
actuals_df = dp.ts_actuals(ts_name, ts_cfg, cols)
actuals_df.rename(columns={ts_cfg['ycol']: 'y'}, inplace=True)
fcast_df = fp.get_ens_fcast(ts_name, ts_cfg, cutoff_date)
ens = fcast_df.loc[fcast_df.index[0], 'ens']
f_df = ep.fcast_filter(fcast_df, actuals_df, ts_name,
cutoff_date + pd.to_timedelta(upr, unit=time_scale),
cutoff_date, time_scale)
pf = fcast_perf(f_df, actuals_df, cutoff_date, lwr, upr, time_scale, ens)
if pf is None:
return
else:
pf['ts_name'] = ts_name
p_ut.save_df(
pf, '~/my_tmp/perf/fcast_perf_' + ts_name + '_' +
str(cutoff_date.date()))
if to_table is True:
tab_cols = ['language', 'y', 'yhat', 'err', 'lwr', 'upr', 'ens']
partition = {'cutoff': str(cutoff_date.date()), 'ts_name': ts_name}
ret = hql.to_tble(pf, tab_cols,
'sup.cx_language_forecast_performance',
partition)
if ret != 0:
s_ut.my_print('pid: ' + str(os.getpid()) +
' ERROR: forecast performance failed for ' +
ts_name + ' and cutoff date ' +
str(cutoff_date.date()))
sys.exit()
print('DONE')