def selectBestModel(project_file, results_model_file):
f = open(results_model_file + '.results.html', 'w')
project = yaml.load(open(project_file, 'r'))
className = project['className']
results_dir = project['resultsDirectory']
if os.path.exists(results_dir):
classifierType = None # all types
cr = ClassificationResults()
print 'Loading all results...'
cr.readResults(results_dir)
accuracy, filename, params = cr.best(1, classifierType)[0]
print "RESULT " + project_file + '\t' + str(accuracy) + '\t' + filename
f.write('<h1>%s (%s)</h1>\nAccuracy: %s\n' % (className, project_file, accuracy))
cm = ConfusionMatrix()
cm.load(filename)
f.write(cm.toHtml())
filename = filename.replace('.result', '.param')
trainSVMHistory(project_file, filename, results_model_file, className)
shutil.copyfile(filename, results_model_file + '.param')
else:
print "RESULT " + "No results found for ", project_file, ": cannot build a model"
f.write('<h1>%s (%s) </h1>\nResults not found\n' % (collection, project_file))
def selectBestModel(project_file, results_model_file):
f = open(results_model_file + '.results.html', 'w')
project = yaml.load(open(project_file, 'r'))
className = project['className']
results_dir = project['resultsDirectory']
if os.path.exists(results_dir):
classifierType = None # all types
cr = ClassificationResults()
print('Loading all results...')
cr.readResults(results_dir)
accuracy, filename, params = cr.best(1, classifierType)[0]
print("RESULT " + project_file + '\t' + str(accuracy) + '\t' +
filename)
f.write('<h1>%s (%s)</h1>\nAccuracy: %s\n' %
(className, project_file, accuracy))
cm = ConfusionMatrix()
cm.load(filename)
f.write(cm.toHtml())
filename = filename.replace('.result', '.param')
trainSVMHistory(project_file, filename, results_model_file, className)
shutil.copyfile(filename, results_model_file + '.param')
else:
print("RESULT " + "No results found for ", project_file,
": cannot build a model")
f.write('<h1>%s (%s) </h1>\nResults not found\n' %
(collection, project_file))
def selectBestModel():
parser = OptionParser(
usage='%prog [options] project_file results_model_file')
options, args = parser.parse_args()
try:
project_file = args[0]
results_model_file = args[1]
except:
parser.print_help()
sys.exit(1)
f = open(results_model_file + '.results.html', 'w')
project = yaml.load(open(project_file, 'r'))
className = project['className']
results_dir = project['resultsDirectory']
if os.path.exists(results_dir):
classifierType = None # all types
cr = ClassificationResults()
print 'Loading all results...'
cr.readResults(results_dir)
accuracy, filename, params = cr.best(1, classifierType)[0]
print "RESULT " + project_file + '\t' + str(accuracy) + '\t' + filename
f.write('<h1>%s (%s)</h1>\nAccuracy: %s\n' %
(className, project_file, accuracy))
cm = ConfusionMatrix()
cm.load(filename)
f.write(cm.toHtml())
filename = filename.replace('.result', '.param')
trainSVMHistory(project_file, filename, results_model_file, className)
shutil.copyfile(filename, results_model_file + '.param')
else:
print "RESULT " + "No results found for ", project_file, ": cannot build a model"
f.write('<h1>%s (%s) </h1>\nResults not found\n' %
(collection, project_file))
# the terms of the GNU Affero General Public License as published by the Free
# Software Foundation (FSF), either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the Affero GNU General Public License
# version 3 along with this program. If not, see http://www.gnu.org/licenses/
from __future__ import print_function
import sys
from gaia2.classification import ConfusionMatrix
try:
results = sys.argv[1]
output_html = sys.argv[2]
except:
print('Usage: %s <results_file> <confusion_matrix_html_file>' % sys.argv[0])
exit(1)
cm = ConfusionMatrix()
cm.load(results)
open(output_html, 'w').write(cm.toHtml())
# Gaia is free software: you can redistribute it and/or modify it under
# the terms of the GNU Affero General Public License as published by the Free
# Software Foundation (FSF), either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the Affero GNU General Public License
# version 3 along with this program. If not, see http://www.gnu.org/licenses/
import sys
from gaia2.classification import ConfusionMatrix
try:
results = sys.argv[1]
output_html = sys.argv[2]
except:
print 'Usage: %s <results_file> <confusion_matrix_html_file>' % sys.argv[0]
exit(1)
cm = ConfusionMatrix()
cm.load(results)
open(output_html, 'w').write(cm.toHtml())
