#from sklearn.naive_bayes import BernoulliNB, MultinomialNB
#from sklearn.neighbors import NearestCentroid
from sklearn.grid_search import GridSearchCV
from bagging import AsymBaggingRFCs, get_asym_task
import classes
from features import all_features
from models import Repo
import utils
NGRAM_MIN = 1
NGRAM_MAX = 2 # not inclusive
sorted_stdlib_names = sorted(list(utils.stdlib_module_names()))
def ngrams(mods):
iters = []
for i in range(NGRAM_MIN, NGRAM_MAX):
iters.append(combinations(mods, i))
return chain(*iters)
def RandomForest():
return RandomForestClassifier(
n_estimators=200,
max_depth=None,
min_samples_split=1,
def get_classifier(X, y):
return RandomForestClassifier(
n_estimators=100, max_depth=None, min_samples_split=1,
random_state=0, # random seed is static for comparison
compute_importances=True,
)
if __name__ == '__main__':
repos = Repo.load_sample()
class_to_id, id_to_class = utils.create_bimap(classes.classes)
dict_repos = []
for r in repos:
d = {mod: False for mod in utils.stdlib_module_names()}
for mod in r.imported_stdlib_modules:
d[mod] = True
dict_repos.append(d)
vectorizer = DictVectorizer(sparse=False)
y = np.array([class_to_id[classes.classify(r)] for r in repos])
X = vectorizer.fit_transform(dict_repos)
clf = get_classifier(X, y)
clf.fit(X, y)
#function to use when evaluating results
score_func = functools.partial(metrics.classification_report,
def imported_stdlib_modules(repo):
"""Like imported_modules, but only keeps stdlib modules."""
return tuple(mod for mod in repo._calc('imported_modules')
if mod in utils.stdlib_module_names())
