def run_tsm_unittests(X,y,column_headers,verbose=True,logfile=None):
tsm = TriggeredSeriesModel(column_headers, logfile=logfile, verbose=verbose,
on_disk=False, debug=True)
# ptf('Try set-up', logfile)
# tsm.setup(X,y)
# print X.head()
# print X.iloc[0][0:15,0:4]
# print X.iloc[0].shape
# tsm._check_trial_integrity()
#
# print 'Preprocess'
# Xpp = tsm.preprocess(X)
# # print Xpp.iloc[0][0:15,0:4]
# print Xpp.iloc[0].shape
# print 'Prune'
# Xp = tsm.prune_spots(Xpp, ['26B', '11R', '45B', '36B', '30R', '11B'], column_headers)
# # print Xp.iloc[0][0:15]
# print Xp.iloc[0].shape
# print tsm.trigger_indexes, tsm.trigger_spots
#
# print 'Featurize'
# Xtrig = tsm.featurize_triggers(Xp,30)
# print Xtrig.shape
# print tsm.trigger_feature_times
# print tsm.trigger_features
# print tsm.trigger_featurizers
# print 'Now try doing all of these steps with fit'
# tsm = TriggeredSeriesModel(column_headers, logfile=logfile, verbose=verbose,
# on_disk=False, debug=True, detection_featurizer_arguments = {'order':2, 'dx':20.0, 'maxmin':True},
# detection_reducer_arguments={'n_components':3})
# Xf = tsm.fit(X,y, verbose=verbose, debug=True)
print 'Now with a stacked yp and ypp classifier'
tsm = TriggeredSeriesModel(column_headers, logfile=logfile, verbose=verbose,
on_disk=False, debug=True, color_vector_type='DI',
detection_featurizer_arguments = {'order':2, 'dx':20.0, 'maxmin':True, 'gauss':True, 'stacked':True, 'sigma':1},
detection_reducer_arguments={'n_components':3},
resample_method='over')
Xf = tsm.fit(X,y, verbose=verbose, debug=True)
return tsm
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)
