def main(RUNID='run001',
START_DT_STR=None,
MODELFILENAME='sm',
PICKLE_DATA=False,
DO_TESTS=False,
PROFILE=False,
verbose=False,
debug=False,
RELOAD=False,
n_cpus=1,
PICKLE_NAMES=['Xdf.pkl', 'ydf.pkl', 'used_column_headers.pkl']):
'''
Runs our series model or triggered series model job based on the runtime
conditions and run parameters.
IN:
RUNID - str - str name for the folder where output will be stored and the
name of the json (without extension) containing run parameters
for seriesmodel or triggeredseriesmodel
START_DT_STR - str - timestamp as a string to append to the logfile.
Set in the header global params of capstone
MODELFILENAME - str - filename of model (for pickling)
PICKLE_DATA - bool - if the raw data should be pickled after loading into
a data frame
DO_TESTS - bool - if unittests should be run (True), or a job run (False)
PROFILE - bool - if memory profiling should be performed (True)
verbose - bool - when set to true, verbose output
debug - bool - whether a full dataset should be used (False), or a smaller
set of time points (True)
RELOAD - bool - whether data should be loaded from pickle (False), or
reloaded from raw data (True). Set to true only for first run on a
new instance, then set to False for future runs to save load time.
n_cpus - int - number of cpus to use for multiprocessing jobs.
PICKLE_NAMES - list of str - list of the X (features) dataframe, y (labels)
data and spots_used file names. When RELOAD is set to True, this is
the filenames where this data will be saved. When RELOAD is set to
False, this is where the data will be loaded from.
OUT:
None
'''
RUNID = command_line_process(RUNID)
# prepare to run job
LOGFILENAME = 'log_%s_%s.txt' % (RUNID, START_DT_STR)
LOGFILE = create_logfile(RUNID, LOGFILENAME)
# get the run conditions for the runid from the json
# NOTE excludes verbose and debug flags - those are fit parameters
# and exludes runid since that is set up above
with open((RUNID + '.json')) as f:
run_params = json.load(f, object_hook=ascii_encode_dict)
# to see if more ram is used for more cpus
n_jobs = run_params['detection_model_arguments']['n_jobs']
### Unittests ###
if DO_TESTS:
start = time.time()
ptf('\n>> Unpickling data ...\n', LOGFILE)
X = my_unpickle(PICKLE_NAMES[0])
y = my_unpickle(PICKLE_NAMES[1])
used_column_headers = my_unpickle(PICKLE_NAMES[2])
end = time.time()
ptf(
'Data unpickled in %d seconds (%d total trials)' %
((end - start), len(X)), LOGFILE)
tsm_unit = run_tsm_unittests(X,
y,
used_column_headers.values,
verbose=verbose,
logfile=LOGFILE)
# sm_unit = run_unittests(X_test, y_test, verbose=False)
else:
# ouptput run conditions to screen and logfile
bigstart = time.time()
# start memory profiling
if PROFILE:
tr, tr_sm = start_memory_profiling
if RUNTYPE == 'trigger':
ptf('*** %s - TRIGGERED SERIES MODEL - ***' % RUNID)
elif RUNTYPE == 'series':
ptf('*** %s - SERIES MODEL - ***' % RUNID)
print_job_info(run_params,
n_jobs,
n_cpus,
RUNID,
START_DT_STR,
LOGFILE=LOGFILE,
debug=debug,
profile=PROFILE,
verbose=verbose,
start=True)
if RELOAD:
X, y, used_column_headers, df, df_raw = reload_data(
LOGFILE, PICKLE_DATA)
else:
start = time.time()
ptf('\n>> Unpickling data ...\n', LOGFILE)
X = my_unpickle(PICKLE_NAMES[0])
y = my_unpickle(PICKLE_NAMES[1])
used_column_headers = my_unpickle(PICKLE_NAMES[2])
end = time.time()
ptf(
'Data unpickled in %d seconds (%d total trials)' %
((end - start), len(X)), LOGFILE)
run_params['logfile'] = LOGFILE
run_params['runid'] = RUNID
# create model
if RUNTYPE == 'trigger':
sm = TriggeredSeriesModel(used_column_headers.values, **run_params)
elif RUNTYPE == 'series':
sm = SeriesModel(**run_params)
# Altogether now
print('** DOING THE FIT **')
sm.fit(X, y, verbose=verbose, debug=debug)
bigend = time.time()
ptf(
'====> %d seconds (%0.1f mins)' % ((bigend - bigstart),
(bigend - bigstart) / 60.0),
LOGFILE)
print_job_info(run_params,
n_jobs,
n_cpus,
RUNID,
START_DT_STR,
LOGFILE=LOGFILE,
debug=debug,
profile=PROFILE,
verbose=verbose,
start=False)
print_run_details(X, sm, LOGFILE)
save_model(sm, RUNID, MODELFILENAME, LOGFILE=LOGFILE)
## VIEW RESULTS
if RUNTYPE == 'trigger':
make_trigger_plots(sm, y, RUNID, debug=debug)
elif RUNTYPE == 'series':
make_series_plots(sm)
if PROFILE:
print_memory_profiles(sm, tr, tr_sm, LOGFILE=None)
LOGFILE.close()
# create model
if RUNTYPE == 'trigger':
sm = TriggeredSeriesModel(used_column_headers.values, **run_params)
elif RUNTYPE == 'series':
sm = SeriesModel(**run_params)
# Altogether now
print ('** DOING THE FIT **')
sm.fit(X, y, verbose=verbose, debug=debug)
bigend = time.time()
ptf('====> %d seconds (%0.1f mins)' % ((bigend-bigstart), (bigend-bigstart)/60.0), LOGFILE)
print_job_info(run_params, n_jobs, n_cpus, RUNID, START_DT_STR, LOGFILE=LOGFILE,
debug=debug, profile=PROFILE, verbose=verbose, start=False)
print_run_details(X, sm, LOGFILE)
save_model(sm, RUNID, MODELFILENAME, LOGFILE=LOGFILE)
## VIEW RESULTS
if RUNTYPE == 'trigger':
make_trigger_plots(sm, y, RUNID, debug=debug)
elif RUNTYPE == 'series':
make_series_plots(sm)
if PROFILE:
print_memory_profiles(sm, tr, tr_sm, LOGFILE = None)
LOGFILE.close()
def main(RUNID='run001', START_DT_STR=None, MODELFILENAME='sm', PICKLE_DATA=False,
DO_TESTS=False, PROFILE=False, verbose=False, debug=False,
RELOAD=False, n_cpus=1,
PICKLE_NAMES=['Xdf.pkl', 'ydf.pkl', 'used_column_headers.pkl']):
'''
Runs our series model or triggered series model job based on the runtime
conditions and run parameters.
IN:
RUNID - str - str name for the folder where output will be stored and the
name of the json (without extension) containing run parameters
for seriesmodel or triggeredseriesmodel
START_DT_STR - str - timestamp as a string to append to the logfile.
Set in the header global params of capstone
MODELFILENAME - str - filename of model (for pickling)
PICKLE_DATA - bool - if the raw data should be pickled after loading into
a data frame
DO_TESTS - bool - if unittests should be run (True), or a job run (False)
PROFILE - bool - if memory profiling should be performed (True)
verbose - bool - when set to true, verbose output
debug - bool - whether a full dataset should be used (False), or a smaller
set of time points (True)
RELOAD - bool - whether data should be loaded from pickle (False), or
reloaded from raw data (True). Set to true only for first run on a
new instance, then set to False for future runs to save load time.
n_cpus - int - number of cpus to use for multiprocessing jobs.
PICKLE_NAMES - list of str - list of the X (features) dataframe, y (labels)
data and spots_used file names. When RELOAD is set to True, this is
the filenames where this data will be saved. When RELOAD is set to
False, this is where the data will be loaded from.
OUT:
None
'''
RUNID = command_line_process(RUNID)
# prepare to run job
LOGFILENAME = 'log_%s_%s.txt' % (RUNID, START_DT_STR)
LOGFILE = create_logfile(RUNID, LOGFILENAME)
# get the run conditions for the runid from the json
# NOTE excludes verbose and debug flags - those are fit parameters
# and exludes runid since that is set up above
with open((RUNID + '.json')) as f:
run_params = json.load(f, object_hook=ascii_encode_dict)
# to see if more ram is used for more cpus
n_jobs = run_params['detection_model_arguments']['n_jobs']
### Unittests ###
if DO_TESTS:
start = time.time()
ptf( '\n>> Unpickling data ...\n', LOGFILE)
X = my_unpickle(PICKLE_NAMES[0])
y = my_unpickle(PICKLE_NAMES[1])
used_column_headers = my_unpickle(PICKLE_NAMES[2])
end = time.time()
ptf( 'Data unpickled in %d seconds (%d total trials)' % ((end-start),
len(X)), LOGFILE)
tsm_unit = run_tsm_unittests(X, y, used_column_headers.values,
verbose=verbose, logfile=LOGFILE)
# sm_unit = run_unittests(X_test, y_test, verbose=False)
else:
# ouptput run conditions to screen and logfile
bigstart = time.time()
# start memory profiling
if PROFILE:
tr, tr_sm = start_memory_profiling
if RUNTYPE == 'trigger':
ptf('*** %s - TRIGGERED SERIES MODEL - ***' % RUNID)
elif RUNTYPE == 'series':
ptf('*** %s - SERIES MODEL - ***' % RUNID)
print_job_info(run_params, n_jobs, n_cpus, RUNID, START_DT_STR, LOGFILE=LOGFILE,
debug=debug, profile=PROFILE, verbose=verbose, start=True)
if RELOAD:
X, y, used_column_headers, df, df_raw = reload_data(LOGFILE, PICKLE_DATA)
else:
start = time.time()
ptf( '\n>> Unpickling data ...\n', LOGFILE)
X = my_unpickle(PICKLE_NAMES[0])
y = my_unpickle(PICKLE_NAMES[1])
used_column_headers = my_unpickle(PICKLE_NAMES[2])
end = time.time()
ptf( 'Data unpickled in %d seconds (%d total trials)' % ((end-start), len(X)), LOGFILE)
run_params['logfile'] = LOGFILE
run_params['runid'] = RUNID
# create model
if RUNTYPE == 'trigger':
sm = TriggeredSeriesModel(used_column_headers.values, **run_params)
elif RUNTYPE == 'series':
sm = SeriesModel(**run_params)
# Altogether now
print ('** DOING THE FIT **')
sm.fit(X, y, verbose=verbose, debug=debug)
bigend = time.time()
ptf('====> %d seconds (%0.1f mins)' % ((bigend-bigstart), (bigend-bigstart)/60.0), LOGFILE)
print_job_info(run_params, n_jobs, n_cpus, RUNID, START_DT_STR, LOGFILE=LOGFILE,
debug=debug, profile=PROFILE, verbose=verbose, start=False)
print_run_details(X, sm, LOGFILE)
save_model(sm, RUNID, MODELFILENAME, LOGFILE=LOGFILE)
## VIEW RESULTS
if RUNTYPE == 'trigger':
make_trigger_plots(sm, y, RUNID, debug=debug)
elif RUNTYPE == 'series':
make_series_plots(sm)
if PROFILE:
print_memory_profiles(sm, tr, tr_sm, LOGFILE = None)
LOGFILE.close()
