diff options
| -rw-r--r-- | main.py | 19 |
1 files changed, 13 insertions, 6 deletions
@@ -33,6 +33,7 @@ def get_programs(path: str, names: str, do_canonicalize: bool = False): continue # canonicalize + original = code if do_canonicalize: code = canonicalize.canonicalize(code, given_names=names) @@ -43,7 +44,7 @@ def get_programs(path: str, names: str, do_canonicalize: bool = False): seq, total, passed = submission.split('-') if code not in programs: - programs[code] = {'users': set(), 'correct': total == passed} + programs[code] = {'users': set(), 'correct': total == passed, 'original': original} programs[code]['users'].add(user) return programs @@ -64,21 +65,27 @@ if __name__ == '__main__': attrs = collections.OrderedDict() attrs.update(regex.get_attributes(programs)) attrs.update(dynamic.get_attributes(programs, args.exec, args.inputs)) - print('Attributes:', attrs.keys()) + + print('Attributes:') + for attr in attrs: + print(attr, attrs[attr]['desc'].to_string(inline=True)) for program in programs: for attr in attrs: programs[program][attr] = program in attrs[attr]['programs'] data = pandas.DataFrame.from_dict(programs, orient='index') - y = data['correct'] - X = data.drop(['users', 'correct'], axis='columns') + + train = data.sample(frac=0.7, random_state=0) + Y = train['correct'] + X = train.drop(['users', 'correct', 'original'], axis='columns') + X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X, Y, test_size=0.33, random_state=0) learners = collections.OrderedDict([ ('major', sklearn.dummy.DummyClassifier()), ('tree', sklearn.tree.DecisionTreeClassifier()), - ('rf', sklearn.ensemble.RandomForestClassifier()), + ('rf', sklearn.ensemble.RandomForestClassifier(n_estimators=100)), ]) for name, learner in learners.items(): - scores = sklearn.model_selection.cross_val_score(learner, X, y, cv=10) + scores = sklearn.model_selection.cross_val_score(learner, X, Y, cv=10) print('{}:\t{}'.format(name, scores.mean())) |