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())