def GaurdWrapper(*args, **kwargs):
if util.get_flag(flag, default):
indent_lbl = flag.replace('--', '').replace('print-', '')
with util.Indenter2('[%s]' % indent_lbl):
return func(*args, **kwargs)
else:
if not __QUIET__:
print('\n~~~ %s ~~~\n' % flag)
def main(defaultdb='NAUTS', usedbcache=False, default_load_all=True):
import matplotlib
matplotlib.use('Qt4Agg')
imports()
from hscom import argparse2
from hscom import helpers
from hotspotter import HotSpotterAPI as api
args = argparse2.parse_arguments(defaultdb=defaultdb)
# Parse arguments
args = argparse2.fix_args_with_cache(args)
if usedbcache:
load_all, cids = preload_args_process(args)
else:
args = argparse2.fix_args_shortnames(args)
load_all = helpers.get_flag('--load-all', default_load_all)
# Preload process args
if args.delete_global:
from hscom import fileio as io
io.delete_global_cache()
# --- Build HotSpotter API ---
hs = api.HotSpotter(args)
setcfg = args.setcfg
if setcfg is not None:
import experiment_harness
print('[main] setting cfg to %r' % setcfg)
varried_list = experiment_harness.get_varried_params_list([setcfg])
cfg_dict = varried_list[0]
#print(cfg_dict)
hs.prefs.query_cfg.update_cfg(**cfg_dict)
hs.prefs.save()
#hs.prefs.printme()
# load default preferences
hs.default_preferences()
# Load all data if needed now, otherwise be lazy
try:
hs.load(load_all=load_all)
from hscom import fileio as io
#imported from wrong module
#from hotspotter import fileio as io
db_dir = hs.dirs.db_dir
io.global_cache_write('db_dir', db_dir)
except ValueError as ex:
print('[main] ValueError = %r' % (ex, ))
if hs.args.strict:
raise
return hs
def generate_detector_training_data(hs, uniform_size=(512, 256)):
print('')
print('===================')
print('Generating training data')
lazy = helpers.get_flag('--lazy', True)
output_dir = get_training_output_dir(hs)
batch_extract_kwargs = {
'lazy': lazy,
'num_procs': hs.args.num_procs,
'force_gray': False,
'uniform_size': uniform_size,
}
extract_detector_positives(hs, output_dir, batch_extract_kwargs)
extract_detector_negatives(hs, output_dir, batch_extract_kwargs)
def main_loop(app, is_root, back, runqtmain=True):
from hscom import helpers as util
from hsgui import guitools
import sys
hs = back.hs # NOQA
# Allow for a IPython connection by passing the --cmd flag
embedded = util.inIPython()
if not embedded and util.get_flag('--cmd'):
print('Embedding')
util.embed()
sys.exit(1)
if not embedded and runqtmain:
print('Running main loop')
# If not in IPython run the QT main loop
guitools.run_main_loop(app, is_root, back, frequency=100)
signal_reset()
print('hotspotter will exit')
def main(defaultdb='NAUTS', usedbcache=False, default_load_all=True):
import matplotlib
matplotlib.use('Qt4Agg')
imports()
from hscom import argparse2
from hscom import helpers
from hotspotter import HotSpotterAPI as api
args = argparse2.parse_arguments(defaultdb=defaultdb)
# Parse arguments
args = argparse2.fix_args_with_cache(args)
if usedbcache:
load_all, cids = preload_args_process(args)
else:
args = argparse2.fix_args_shortnames(args)
load_all = helpers.get_flag('--load-all', default_load_all)
# Preload process args
if args.delete_global:
from hscom import fileio as io
io.delete_global_cache()
# --- Build HotSpotter API ---
hs = api.HotSpotter(args)
setcfg = args.setcfg
if setcfg is not None:
import experiment_harness
print('[main] setting cfg to %r' % setcfg)
varried_list = experiment_harness.get_varried_params_list([setcfg])
cfg_dict = varried_list[0]
#print(cfg_dict)
hs.prefs.query_cfg.update_cfg(**cfg_dict)
hs.prefs.save()
#hs.prefs.printme()
#hs.default_preferences()
# Load all data if needed now, otherwise be lazy
try:
hs.load(load_all=load_all)
from hotspotter import fileio as io
db_dir = hs.dirs.db_dir
io.global_cache_write('db_dir', db_dir)
except ValueError as ex:
print('[main] ValueError = %r' % (ex,))
if hs.args.strict:
raise
return hs
def test_configurations(hs, qcx_list, test_cfg_name_list, fnum=1):
print('\n*********************\n')
print('[harn]================')
print('[harn]test_scoring(hs)')
varied_params_list = get_varried_params_list(test_cfg_name_list)
#vary_dicts = vary_dicts[0]
# query_cxs, other_cxs, notes
cfg_list = [Config.QueryConfig(hs, **_dict) for _dict in varied_params_list]
qdat = ds.QueryData()
# Preallocate test result aggregation structures
print('')
print('[harn] Testing %d different parameters' % len(cfg_list))
print('[harn] %d different chips' % len(qcx_list))
nCfg = len(cfg_list)
nQuery = len(qcx_list)
rc2_res = np.empty((nQuery, nCfg), dtype=list)
mat_list = []
c = hs.get_arg('cols', [])
for cfgx, query_cfg in enumerate(cfg_list):
print(textwrap.dedent('''
[harn]---------------')
[harn] TEST_CFG %d/%d'
[harn]---------------''' % (cfgx + 1, nCfg)))
qdat.set_cfg(query_cfg)
force_load = cfgx in c
(mat_vals, ), qx2_reslist = get_test_results(hs, qcx_list, qdat,
cfgx, nCfg, force_load)
mat_list.append(mat_vals)
for qx, reslist in enumerate(qx2_reslist):
assert len(reslist) == 1
qcx2_res = reslist[0]
assert len(qcx2_res) == 1
res = qcx2_res.values()[0]
rc2_res[qx, cfgx] = res
# Keep the best results
print('[harn] Finished testing parameters')
print('')
print('---------------------------------')
#--------------------
# Print Best Results
rank_mat = np.hstack(mat_list)
# Label the rank matrix:
_colxs = np.arange(nCfg)
lbld_mat = np.vstack([_colxs, rank_mat])
_rowxs = np.arange(nQuery + 1).reshape(nQuery + 1, 1) - 1
lbld_mat = np.hstack([_rowxs, lbld_mat])
# Build row labels
qx2_lbl = []
for qx in xrange(nQuery):
qcx = qcx_list[qx]
label = 'qx %d) q%s ' % (qx, hs.cidstr(qcx, notes=True))
qx2_lbl.append(label)
qx2_lbl = np.array(qx2_lbl)
# Build col labels
cfgx2_lbl = []
for cfgx in xrange(nCfg):
test_uid = mc3.simplify_test_uid(cfg_list[cfgx].get_uid())
test_uid = mc3.simplify_test_uid(cfg_list[cfgx].get_uid())
cfg_label = 'cfgx %3d) %s' % (cfgx, test_uid)
cfgx2_lbl.append(cfg_label)
cfgx2_lbl = np.array(cfgx2_lbl)
#------------
indent = helpers.indent
print('')
print('[harn]-------------')
print('[harn] queries:\n%s' % '\n'.join(qx2_lbl))
#------------
print('')
print('[harn]-------------')
print('[harn] configs:\n%s' % '\n'.join(cfgx2_lbl))
#------------
PRINT_ROW_SCORES = helpers.get_flag('--printrow', False)
if PRINT_ROW_SCORES:
print('')
print('[harn]-------------')
print('[harn] Scores per query')
print('[harn]-------------')
qx2_min_rank = []
qx2_argmin_rank = []
indent = helpers.indent
new_hard_qx_list = []
for qx in xrange(nQuery):
ranks = rank_mat[qx]
min_rank = ranks.min()
bestCFG_X = np.where(ranks == min_rank)[0]
qx2_min_rank.append(min_rank)
qx2_argmin_rank.append(bestCFG_X)
print('[row_score] %3d) %s' % (qx, qx2_lbl[qx]))
print('[row_score] best_rank = %d ' % min_rank)
print('[row_score] minimizing_configs = %s ' %
indent('\n'.join(cfgx2_lbl[bestCFG_X]), ' '))
if ranks.max() > 0:
new_hard_qx_list += [qx]
print('--- hard qcx_list (w.r.t these configs) ---')
new_hard_qcx_list = []
for qx in new_hard_qx_list:
# New list is in cid format instead of cx format
# because you should be copying and pasting it
notes = ' ranks = ' + str(rank_mat[qx])
qcx = qcx_list[qx]
qcid = hs.tables.cx2_cid[qcx]
new_hard_qcx_list += [(qcid, notes)]
print('\n'.join(map(repr, new_hard_qcx_list)))
else:
print('~~~~~~~~ --printrow')
#------------
def rankscore_str(thresh, nLess, total):
#helper to print rank scores of configs
percent = 100 * nLess / total
return '#ranks < %d = %d/%d = (%.1f%%) (err=%d)' % (thresh, nLess, total, percent, (total - nLess))
print('')
print('[harn]-------------')
print('[harn] Scores per config')
print('[harn]-------------')
X_list = [1, 5]
# Build a dictionary mapping X (as in #ranks < X) to a list of cfg scores
nLessX_dict = {int(X): np.zeros(nCfg) for X in iter(X_list)}
for cfgx in xrange(nCfg):
ranks = rank_mat[:, cfgx]
print('[col_score] %d) %s' % (cfgx, cfgx2_lbl[cfgx]))
for X in iter(X_list):
#nLessX_ = sum(np.bitwise_and(ranks < X, ranks >= 0))
nLessX_ = sum(np.logical_and(ranks < X, ranks >= 0))
print('[col_score] ' + rankscore_str(X, nLessX_, nQuery))
nLessX_dict[int(X)][cfgx] = nLessX_
LATEX_SUMMARY = True
if LATEX_SUMMARY:
print('--- LaTeX ---')
# Create configuration latex table
criteria_lbls = ['#ranks < %d' % X for X in X_list]
db_name = hs.get_db_name(True)
cfg_score_title = db_name + ' rank scores'
cfgscores = np.array([nLessX_dict[int(X)] for X in X_list]).T
from hscom import latex_formater as latex
replace_rowlbl = [(' *cfgx *', ' ')]
tabular_kwargs = dict(title=cfg_score_title, out_of=nQuery,
bold_best=True, replace_rowlbl=replace_rowlbl,
flip=True)
tabular_str = latex.make_score_tabular(cfgx2_lbl, criteria_lbls,
cfgscores, **tabular_kwargs)
print(tabular_str)
print('--- /LaTeX ---')
#------------
print('')
print('[harn]---------------')
print('[harn] Best configurations')
print('[harn]---------------')
best_rankscore_summary = []
for X, cfgx2_nLessX in nLessX_dict.iteritems():
max_LessX = cfgx2_nLessX.max()
bestCFG_X = np.where(cfgx2_nLessX == max_LessX)[0]
best_rankscore = '[best_cfg] %d config(s) scored ' % len(bestCFG_X)
best_rankscore += rankscore_str(X, max_LessX, nQuery)
best_rankcfg = indent('\n'.join(cfgx2_lbl[bestCFG_X]), ' ')
print(best_rankscore)
print(best_rankcfg)
best_rankscore_summary += [best_rankscore]
#------------
PRINT_MAT = True and (not '--noprintmat' in sys.argv)
if PRINT_MAT:
print('')
print('[harn]-------------')
print('[harn] labled rank matrix: rows=queries, cols=cfgs:\n%s' % lbld_mat)
print('[harn]-------------')
#------------
print('[col_score] --- summary ---')
print('\n'.join(best_rankscore_summary))
# Draw results
rciter = itertools.product(hs.get_arg('rows', []),
hs.get_arg('cols', []))
for r, c in rciter:
#print('viewing (r,c)=(%r,%r)' % (r,c))
res = rc2_res[r, c]
#res.printme()
res.show_topN(hs, fnum=fnum)
fnum += 1
print('--remember you have -r and -c available to you')
from hscom import helpers
print('main.py')
# Listen for ctrl+c
signal_set()
# Run qt app
app, is_root = guitools.init_qtapp()
# Create main window only after data is loaded
back = guiback.make_main_window(hs, app)
# --- Run Startup Commands ---
res = postload_args_process(hs, back)
# Connect database to the back gui
#app.setActiveWindow(back.front)
# Allow for a IPython connection by passing the --cmd flag
embedded = False
if helpers.get_flag('--cmd'):
import scripts
import generate_training
import sys
def do_encounters(seconds=None):
if not 'seconds' in vars() or seconds is None:
seconds = 5
scripts.rrr()
do_enc_loc = scripts.compute_encounters(hs, back, seconds)
return do_enc_loc
def do_extract_encounter(eid=None):
#if not 'eid' in vars() or eid is None:
#eid = 'ex=269_nGxs=21'
eid = 'ex=61_nGxs=18'
from hscom import helpers
print('main.py')
# Listen for ctrl+c
signal_set()
# Run qt app
app, is_root = guitools.init_qtapp()
# Create main window only after data is loaded
back = guiback.make_main_window(hs, app)
# --- Run Startup Commands ---
res = postload_args_process(hs, back)
# Connect database to the back gui
#app.setActiveWindow(back.front)
# Allow for a IPython connection by passing the --cmd flag
embedded = False
if helpers.get_flag('--cmd'):
import scripts
import generate_training
import sys
def do_encounters(seconds=None):
if not 'seconds' in vars() or seconds is None:
seconds = 5
scripts.rrr()
do_enc_loc = scripts.compute_encounters(hs, back, seconds)
return do_enc_loc
def do_extract_encounter(eid=None):
#if not 'eid' in vars() or eid is None:
#eid = 'ex=269_nGxs=21'
eid = 'ex=61_nGxs=18'
from hscom import helpers
from hscom import helpers as util
from hsviz import viz
import multiprocessing
import numpy as np # NOQA
if __name__ == '__main__':
multiprocessing.freeze_support()
# Debugging vars
chip_cfg = None
#l')=103.7900s
cx_list = None
kwargs = {}
# --- LOAD TABLES --- #
args = argparse2.parse_arguments(defaultdb='NAUTS')
hs = api.HotSpotter(args)
hs.load_tables()
hs.update_samples()
# --- LOAD CHIPS --- #
force_compute = helpers.get_flag('--force', default=False)
cc2.load_chips(hs, force_compute=force_compute)
cx = helpers.get_arg('--cx', type_=int)
if not cx is None:
#tau = np.pi * 2
#hs.change_theta(cx, tau / 8)
viz.show_chip(hs, cx, draw_kpts=False, fnum=1)
viz.show_image(hs, hs.cx2_gx(cx), fnum=2)
else:
print('usage: feature_compute.py --cx [cx]')
exec(viz.df2.present())
#!/usr/env python
from __future__ import division, print_function
from hotspotter import HotSpotterAPI as api
from hotspotter import feature_compute2 as fc2
from hscom import helpers
from hscom import helpers as util
from hsviz import viz
from hscom import argparse2
import multiprocessing
if __name__ == '__main__':
multiprocessing.freeze_support()
print('[fc2] __main__ = feature_compute2.py')
# Read Args
cx = helpers.get_arg('--cx', type_=int)
delete_features = helpers.get_flag('--delete-features', default=False)
nRandKpts = helpers.get_arg('--nRandKpts', type_=int)
# Debugging vars
feat_cfg = None
cx_list = None
kwargs = {}
# --- LOAD TABLES --- #
args = argparse2.parse_arguments(db='NAUTS')
hs = api.HotSpotter(args)
hs.load_tables()
# --- LOAD CHIPS --- #
hs.update_samples()
hs.load_chips()
# Delete features if needed
if delete_features:
fc2.clear_feature_cache(hs)
def test_configurations(hs, qcx_list, test_cfg_name_list, fnum=1):
if __QUIET__:
mc3.print_off()
from hotspotter import HotSpotterAPI as api
api.print_off()
# Test Each configuration
if not __QUIET__:
print(
textwrap.dedent("""
[harn]================
[harn] experiment_harness.test_configurations()""").strip())
hs.update_samples()
# Grab list of algorithm configurations to test
cfg_list = get_cfg_list(hs, test_cfg_name_list)
if not __QUIET__:
print('[harn] Testing %d different parameters' % len(cfg_list))
print('[harn] %d different chips' % len(qcx_list))
# Preallocate test result aggregation structures
sel_cols = params.args.sel_cols # FIXME
sel_rows = params.args.sel_rows # FIXME
sel_cols = [] if sel_cols is None else sel_cols
sel_rows = [] if sel_rows is None else sel_rows
nCfg = len(cfg_list)
nQuery = len(qcx_list)
#rc2_res = np.empty((nQuery, nCfg), dtype=list) # row/col -> result
mat_list = []
qreq = ds.QueryRequest()
# TODO Add to argparse2
nocache_testres = util.get_flag('--nocache-testres', False)
test_results_verbosity = 2 - (2 * __QUIET__)
test_cfg_verbosity = 2
dbname = hs.get_db_name()
testnameid = dbname + ' ' + str(test_cfg_name_list)
msg = textwrap.dedent('''
---------------------
[harn] TEST_CFG %d/%d: ''' + testnameid + '''
---------------------''')
mark_progress = util.simple_progres_func(test_cfg_verbosity, msg, '+')
nomemory = params.args.nomemory
# Run each test configuration
# Query Config / Col Loop
dcxs = hs.get_indexed_sample()
for cfgx, query_cfg in enumerate(cfg_list):
if not __QUIET__:
mark_progress(cfgx + 1, nCfg)
# Set data to the current config
qreq = mc3.prep_query_request(qreq=qreq,
qcxs=qcx_list,
dcxs=dcxs,
query_cfg=query_cfg)
# Run the test / read cache
with util.Indenter2('[%s cfg %d/%d]' % (dbname, cfgx + 1, nCfg)):
qx2_bestranks = get_test_results2(hs, qcx_list, qreq, cfgx, nCfg,
nocache_testres,
test_results_verbosity)
if not nomemory:
mat_list.append(qx2_bestranks)
# Store the results
if not __QUIET__:
print('[harn] Finished testing parameters')
if nomemory:
print('ran tests in memory savings mode. exiting')
return
#--------------------
# Print Best Results
rank_mat = np.hstack(
mat_list) # concatenate each query rank across configs
# Label the rank matrix:
_colxs = np.arange(nCfg)
lbld_mat = util.debug_vstack([_colxs, rank_mat])
_rowxs = np.arange(nQuery + 1).reshape(nQuery + 1, 1) - 1
lbld_mat = np.hstack([_rowxs, lbld_mat])
#------------
# Build row labels
qx2_lbl = []
for qx in xrange(nQuery):
qcx = qcx_list[qx]
label = 'qx=%d) q%s ' % (qx, hs.cidstr(qcx, notes=True))
qx2_lbl.append(label)
qx2_lbl = np.array(qx2_lbl)
#------------
# Build col labels
cfgx2_lbl = []
for cfgx in xrange(nCfg):
test_uid = cfg_list[cfgx].get_uid()
test_uid = cfg_list[cfgx].get_uid()
cfg_label = 'cfgx=(%3d) %s' % (cfgx, test_uid)
cfgx2_lbl.append(cfg_label)
cfgx2_lbl = np.array(cfgx2_lbl)
#------------
indent = util.indent
@ArgGaurdFalse
def print_rowlbl():
print('=====================')
print('[harn] Row/Query Labels: %s' % testnameid)
print('=====================')
print('[harn] queries:\n%s' % '\n'.join(qx2_lbl))
print('--- /Row/Query Labels ---')
print_rowlbl()
#------------
@ArgGaurdFalse
def print_collbl():
print('')
print('=====================')
print('[harn] Col/Config Labels: %s' % testnameid)
print('=====================')
print('[harn] configs:\n%s' % '\n'.join(cfgx2_lbl))
print('--- /Col/Config Labels ---')
print_collbl()
#------------
# Build Colscore
qx2_min_rank = []
qx2_argmin_rank = []
new_hard_qx_list = []
new_qcid_list = []
new_hardtup_list = []
for qx in xrange(nQuery):
ranks = rank_mat[qx]
min_rank = ranks.min()
bestCFG_X = np.where(ranks == min_rank)[0]
qx2_min_rank.append(min_rank)
qx2_argmin_rank.append(bestCFG_X)
# Mark examples as hard
if ranks.max() > 0:
new_hard_qx_list += [qx]
for qx in new_hard_qx_list:
# New list is in cid format instead of cx format
# because you should be copying and pasting it
notes = ' ranks = ' + str(rank_mat[qx])
qcx = qcx_list[qx]
qcid = hs.tables.cx2_cid[qcx]
new_hardtup_list += [(qcid, notes)]
new_qcid_list += [qcid]
@ArgGaurdFalse
def print_rowscore():
print('')
print('=======================')
print('[harn] Scores per Query: %s' % testnameid)
print('=======================')
for qx in xrange(nQuery):
bestCFG_X = qx2_argmin_rank[qx]
min_rank = qx2_min_rank[qx]
minimizing_cfg_str = indent('\n'.join(cfgx2_lbl[bestCFG_X]),
' ')
#minimizing_cfg_str = str(bestCFG_X)
print('-------')
print(qx2_lbl[qx])
print(' best_rank = %d ' % min_rank)
if len(cfgx2_lbl) != 1:
print(' minimizing_cfg_x\'s = %s ' % minimizing_cfg_str)
print_rowscore()
#------------
@ArgGaurdFalse
def print_hardcase():
print('===')
print('--- hard new_hardtup_list (w.r.t these configs): %s' %
testnameid)
print('\n'.join(map(repr, new_hardtup_list)))
print('There are %d hard cases ' % len(new_hardtup_list))
print(sorted([x[0] for x in new_hardtup_list]))
print('--- /Print Hardcase ---')
print_hardcase()
@ArgGaurdFalse
def echo_hardcase():
print('====')
print('--- hardcase commandline: %s' % testnameid)
hardcids_str = ' '.join(map(str, [' ', '--qcid'] + new_qcid_list))
print(hardcids_str)
print('--- /Echo Hardcase ---')
echo_hardcase()
#------------
# Build Colscore
X_list = [1, 5]
# Build a dictionary mapping X (as in #ranks < X) to a list of cfg scores
nLessX_dict = {int(X): np.zeros(nCfg) for X in iter(X_list)}
for cfgx in xrange(nCfg):
ranks = rank_mat[:, cfgx]
for X in iter(X_list):
#nLessX_ = sum(np.bitwise_and(ranks < X, ranks >= 0))
nLessX_ = sum(np.logical_and(ranks < X, ranks >= 0))
nLessX_dict[int(X)][cfgx] = nLessX_
@ArgGaurdFalse
def print_colscore():
print('')
print('==================')
print('[harn] Scores per Config: %s' % testnameid)
print('==================')
for cfgx in xrange(nCfg):
print('[score] %s' % (cfgx2_lbl[cfgx]))
for X in iter(X_list):
nLessX_ = nLessX_dict[int(X)][cfgx]
print(' ' + rankscore_str(X, nLessX_, nQuery))
print('--- /Scores per Config ---')
print_colscore()
#------------
@ArgGaurdFalse
def print_latexsum():
print('')
print('==========================')
print('[harn] LaTeX: %s' % testnameid)
print('==========================')
# Create configuration latex table
criteria_lbls = ['#ranks < %d' % X for X in X_list]
db_name = hs.get_db_name(True)
cfg_score_title = db_name + ' rank scores'
cfgscores = np.array([nLessX_dict[int(X)] for X in X_list]).T
replace_rowlbl = [(' *cfgx *', ' ')]
tabular_kwargs = dict(title=cfg_score_title,
out_of=nQuery,
bold_best=True,
replace_rowlbl=replace_rowlbl,
flip=True)
tabular_str = latex_formater.make_score_tabular(
cfgx2_lbl, criteria_lbls, cfgscores, **tabular_kwargs)
#latex_formater.render(tabular_str)
print(tabular_str)
print('--- /LaTeX ---')
print_latexsum()
#------------
best_rankscore_summary = []
to_intersect_list = []
# print each configs scores less than X=thresh
for X, cfgx2_nLessX in nLessX_dict.iteritems():
max_LessX = cfgx2_nLessX.max()
bestCFG_X = np.where(cfgx2_nLessX == max_LessX)[0]
best_rankscore = '[cfg*] %d cfg(s) scored ' % len(bestCFG_X)
best_rankscore += rankscore_str(X, max_LessX, nQuery)
best_rankscore_summary += [best_rankscore]
to_intersect_list += [cfgx2_lbl[bestCFG_X]]
intersected = to_intersect_list[0] if len(to_intersect_list) > 0 else []
for ix in xrange(1, len(to_intersect_list)):
intersected = np.intersect1d(intersected, to_intersect_list[ix])
@ArgGaurdFalse
def print_bestcfg():
print('')
print('==========================')
print('[harn] Best Configurations: %s' % testnameid)
print('==========================')
# print each configs scores less than X=thresh
for X, cfgx2_nLessX in nLessX_dict.iteritems():
max_LessX = cfgx2_nLessX.max()
bestCFG_X = np.where(cfgx2_nLessX == max_LessX)[0]
best_rankscore = '[cfg*] %d cfg(s) scored ' % len(bestCFG_X)
best_rankscore += rankscore_str(X, max_LessX, nQuery)
uid_list = cfgx2_lbl[bestCFG_X]
#best_rankcfg = ''.join(map(wrap_uid, uid_list))
best_rankcfg = format_uid_list(uid_list)
#indent('\n'.join(uid_list), ' ')
print(best_rankscore)
print(best_rankcfg)
print('[cfg*] %d cfg(s) are the best of %d total cfgs' %
(len(intersected), nCfg))
print(format_uid_list(intersected))
print('--- /Best Configurations ---')
print_bestcfg()
#------------
@ArgGaurdFalse
def print_rankmat():
print('')
print('-------------')
print('RankMat: %s' % testnameid)
print(' nRows=%r, nCols=%r' % lbld_mat.shape)
print(' labled rank matrix: rows=queries, cols=cfgs:')
#np.set_printoptions(threshold=5000, linewidth=5000, precision=5)
with util.NpPrintOpts(threshold=5000, linewidth=5000, precision=5):
print(lbld_mat)
print('[harn]-------------')
print_rankmat()
#------------
sumstrs = []
sumstrs.append('')
sumstrs.append('||===========================')
sumstrs.append('|| [cfg*] SUMMARY: %s' % testnameid)
sumstrs.append('||---------------------------')
sumstrs.append(util.joins('\n|| ', best_rankscore_summary))
sumstrs.append('||===========================')
print('\n' + '\n'.join(sumstrs) + '\n')
#print('--- /SUMMARY ---')
# Draw results
if not __QUIET__:
print('remember to inspect with --sel-rows (-r) and --sel-cols (-c) ')
if len(sel_rows) > 0 and len(sel_cols) == 0:
sel_cols = range(len(cfg_list))
if len(sel_cols) > 0 and len(sel_rows) == 0:
sel_rows = range(len(qcx_list))
if params.args.view_all:
sel_rows = range(len(qcx_list))
sel_cols = range(len(cfg_list))
sel_cols = list(sel_cols)
sel_rows = list(sel_rows)
total = len(sel_cols) * len(sel_rows)
rciter = itertools.product(sel_rows, sel_cols)
prev_cfg = None
skip_to = util.get_arg('--skip-to', default=None)
dev_mode = util.get_arg('--devmode', default=False)
skip_list = []
if dev_mode:
hs.prefs.display_cfg.N = 3
df2.FONTS.axtitle = df2.FONTS.smaller
df2.FONTS.xlabel = df2.FONTS.smaller
df2.FONTS.figtitle = df2.FONTS.smaller
df2.SAFE_POS['top'] = .8
df2.SAFE_POS['bottom'] = .01
for count, (r, c) in enumerate(rciter):
if skip_to is not None:
if count < skip_to:
continue
if count in skip_list:
continue
# Get row and column index
qcx = qcx_list[r]
query_cfg = cfg_list[c]
print('\n\n___________________________________')
print(' --- VIEW %d / %d --- ' % (count + 1, total))
print('--------------------------------------')
print('viewing (r, c) = (%r, %r)' % (r, c))
# Load / Execute the query
qreq = mc3.prep_query_request(qreq=qreq,
qcxs=[qcx],
dcxs=dcxs,
query_cfg=query_cfg)
qcx2_res = mc3.process_query_request(hs, qreq, safe=True)
res = qcx2_res[qcx]
# Print Query UID
print(res.uid)
# Draw Result
#res.show_top(hs, fnum=fnum)
if prev_cfg != query_cfg:
# This is way too aggro. Needs to be a bit lazier
hs.refresh_features()
prev_cfg = query_cfg
fnum = count
title_uid = res.uid
title_uid = title_uid.replace('_FEAT', '\n_FEAT')
res.show_analysis(hs,
fnum=fnum,
aug='\n' + title_uid,
annote=1,
show_name=False,
show_gname=False,
time_appart=False)
df2.adjust_subplots_safe()
if params.args.save_figures:
from hsviz import allres_viz
allres_viz.dump(hs, 'analysis', quality=True, overwrite=False)
if not __QUIET__:
print('[harn] EXIT EXPERIMENT HARNESS')
