def create_features_labels_df_test(): cr = svcomp15.read_category('static/results-xml-raw', 'mixed-examples') sourcefiles = {i for k in cr.keys() for i in cr[k].index} features_df = f.create_feature_df(sourcefiles) # RPC dataset ranking_df = ranking.create_ranking_df(cr, svcomp15.compare_results) features_ranking_df = f.create_features_labels_df(features_df, ranking_df) # Learning via Utility Functions datasets score_dfdict = classification.create_benchmark_score_dfdict(cr, svcomp15.score) features_score_dfdict = {b: f.create_features_labels_df(features_df, score_dfdict[b]) for b in score_dfdict.keys()} cputime_dfdict = regression.create_benchmark_cputime_dfdict(cr) features_cputime_dfdict = {b: f.create_features_labels_df(features_df, cputime_dfdict[b]) for b in cputime_dfdict.keys()}
def extract_ranking_df(xml_dir, category, max_size): results = svcomp15.read_category(xml_dir, category, max_size) return ranking.create_ranking_df(results, svcomp15.compare_results)
def create_benchmark_ranking_df_test(): category_results = svcomp15.read_category('static/results-xml-raw', 'mixed-examples') df = ranking.create_ranking_df(category_results, svcomp15.compare_results) df.to_csv('ranking_df.csv')