def read_hog_kwargs(modeldir):
cfg = parse_configfile(modeldir)
hog_params = {k.split('hog__')[1]: ast.literal_eval(v) for \
k, v in cfg['param_grid'].iteritems() \
if k.startswith('hog') }
return hog_params
def read_hog_kwargs(modeldir) :
cfg = parse_configfile(modeldir)
hog_params = {k.split('hog__')[1]: ast.literal_eval(v) for \
k, v in cfg['param_grid'].iteritems() \
if k.startswith('hog') }
return hog_params
import sys
from sklearn.cross_validation import train_test_split
from StrongCNN.IO.config_parser import parse_configfile
from StrongCNN.IO.load_images import load_data
from StrongCNN.IO.augment_data import augment_methods, augment_data
from _tools import build_parameter_grid, grid_search
from StrongCNN.pipeline.build_pipeline import build_pipeline
import ast, time
cfg = parse_configfile(sys.argv[1])
start_time = time.time()
# Collect training and testing data
X, y = load_data(cfg['filenames']['non_lens_glob'],
cfg['filenames']['lens_glob'])
if 'augment_train_data' in cfg.keys() :
X, y = augment_data( X, y,
cfg['augment_train_data']['method_label'],
**ast.literal_eval(cfg['augment_train_data']['method_kwargs']))
print "len(X) = ", len(X)
print "len(y) = ", len(y)
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2 )
print "len(X_train) =", len(X_train)
print "len(y_train) =", len(y_train)
print "len(X_test) =", len(X_test)
print "len(y_test) =", len(y_test)
print
Get the score and false ids of the model on either the training set or the test set
'''
parser = argparse.ArgumentParser()
parser.add_argument('cfgdir')
parser.add_argument('set_name')
parser.add_argument('-p', '--roc_plot_filename', required = False)
parser.add_argument('-c', '--model_coeff_plot_filename', required = False)
parser.add_argument('-r', '--roc_data_filename', required = False)
parser.add_argument('-t', '--tpr_filename', required = False)
args = vars(parser.parse_args())
cfgdir = args['cfgdir']
set_name = args['set_name']
cfg = parse_configfile(cfgdir)
start_time = time.time()
assert(set_name in ['test','train'])
# Collect testing data
X_test, y_test = load_data(cfg[set_name+'_filenames']['non_lens_glob'],
cfg[set_name+'_filenames']['lens_glob'])
if 'augment_'+set_name+'_data' in cfg.keys() :
X_test, y_test = augment_data( X_test, y_test,
cfg['augment_'+set_name+'_data']['method_label'],
**ast.literal_eval(cfg['augment_'+set_name+'_data']['method_kwargs']))
parser = argparse.ArgumentParser()
parser.add_argument('cfgdir')
parser.add_argument('set_name')
parser.add_argument('-p', '--roc_plot_filename', required = False)
parser.add_argument('-c', '--model_coeff_plot_filename', required = False)
parser.add_argument('-r', '--roc_data_filename', required = False)
parser.add_argument('-t', '--tpr_filename', required = False)
parser.add_argument('-s', '--filenames_scores', required = False )
parser.add_argument('-T', '--time', required = False )
args = vars(parser.parse_args())
cfgdir = args['cfgdir']
set_name = args['set_name']
cfg = parse_configfile(cfgdir)
if args['time'] is not None : start_time = time.time()
else :
print "Time is not on!"
sys.exit()
assert(set_name in ['test','train'])
# Collect testing data
X_test, y_test, filenames = load_data(cfg[set_name+'_filenames']['non_lens_glob'],
cfg[set_name+'_filenames']['lens_glob'])
if 'augment_'+set_name+'_data' in cfg.keys() :
X_test, y_test = augment_data( X_test, y_test,
cfg['augment_'+set_name+'_data']['method_label'],
import sys
from sklearn.cross_validation import train_test_split
from StrongCNN.IO.config_parser import parse_configfile
from StrongCNN.IO.load_images import load_data
from StrongCNN.IO.augment_data import augment_methods, augment_data
from _tools import build_parameter_grid, grid_search
from StrongCNN.pipeline.build_pipeline import build_pipeline
import ast, time
cfg = parse_configfile(sys.argv[1])
start_time = time.time()
# Collect training and testing data
X, y, _ = load_data(cfg['filenames']['non_lens_glob'],
cfg['filenames']['lens_glob'])
if 'augment_train_data' in cfg.keys():
X, y = augment_data(
X, y, cfg['augment_train_data']['method_label'],
**ast.literal_eval(cfg['augment_train_data']['method_kwargs']))
print "len(X) = ", len(X)
print "len(y) = ", len(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print "len(X_train) =", len(X_train)
print "len(y_train) =", len(y_train)
print "len(X_test) =", len(X_test)
print "len(y_test) =", len(y_test)
print
