def run_strategy(cfg, sample_file):
rec = Recommender(cfg)
repo_size = rec.items_repository.get_doccount()
results = ExperimentResults(repo_size)
label = get_label(cfg)
population_sample = []
sample_str = sample_file.split('/')[-1]
with open(sample_file, 'r') as f:
for line in f.readlines():
user_id = line.strip('\n')
population_sample.append(
os.path.join(cfg.popcon_dir, user_id[:2], user_id))
sample_dir = ("results/roc-sample/%s" % sample_str)
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
log_file = os.path.join(sample_dir, label["values"])
# n iterations per population user
for submission_file in population_sample:
user = PopconSystem(submission_file)
user.filter_pkg_profile(cfg.pkgs_filter)
user.maximal_pkg_profile()
for n in range(iterations):
# Fill sample profile
profile_len = len(user.pkg_profile)
item_score = {}
for pkg in user.pkg_profile:
item_score[pkg] = user.item_score[pkg]
sample = {}
sample_size = int(profile_len * 0.9)
for i in range(sample_size):
key = random.choice(item_score.keys())
sample[key] = item_score.pop(key)
iteration_user = User(item_score)
recommendation = rec.get_recommendation(iteration_user, repo_size)
if hasattr(recommendation, "ranking"):
results.add_result(recommendation.ranking, sample)
plot_roc(results, log_file)
plot_roc(results, log_file, 1)
with open(log_file + "-roc.jpg.comment", 'w') as f:
f.write("# %s\n# %s\n\n" %
(label["description"], label["values"]))
f.write("# roc AUC\n%.4f\n\n" % results.get_auc())
f.write(
"# threshold\tmean_fpr\tdev_fpr\t\tmean_tpr\tdev_tpr\t\tcoverage\n") # noqa
for size in results.thresholds:
f.write("%4d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\n" %
(size, numpy.mean(results.fpr[size]),
numpy.std(results.fpr[size]),
numpy.mean(results.recall[size]),
numpy.std(results.recall[size]),
numpy.mean(results.coverage(size))))
option_str)
for size in sizes:
c_10[size] = set()
c_100[size] = set()
p_10_summary[size] = []
f05_100_summary[size] = []
with open(log_file + "-%s%.3d" % (option_str, size), 'w') as f:
f.write("# sample %s\n" % sample_str)
f.write("# strategy %s-%s%.3d\n\n" %
(cfg.strategy, option_str, size))
f.write("# p_10\tf05_100\n\n")
# main loop per user
for submission_file in population_sample:
user = PopconSystem(submission_file)
user.filter_pkg_profile(cfg.pkgs_filter)
user.maximal_pkg_profile()
for size in sizes:
cfg.profile_size = size
cfg.k_neighbors = size
rec = Recommender(cfg)
repo_size = rec.items_repository.get_doccount()
p_10 = []
f05_100 = []
for n in range(iterations):
# Fill sample profile
profile_len = len(user.pkg_profile)
item_score = {}
for pkg in user.pkg_profile:
item_score[pkg] = user.item_score[pkg]
from apprecommender.evaluation import (CrossValidation, Precision, Recall,
F_score, FPR, Accuracy)
from apprecommender.recommender import Recommender
from apprecommender.user import PopconSystem
if __name__ == '__main__':
cfg = Config()
rec = Recommender()
# user = LocalSystem()
# user = RandomPopcon(cfg.popcon_dir)
# user = RandomPopcon(cfg.popcon_dir,os.path.join(cfg.filters_dir,
# "desktopapps"))
popcon_entries = "~/.app-recommender/popcon-entries/" \
"00/0001166d0737c6dffb083071e5ee69f5"
user = PopconSystem(os.path.expanduser(popcon_entries))
user.filter_pkg_profile(os.path.join(cfg.filters_dir, "desktopapps"))
user.maximal_pkg_profile()
begin_time = datetime.datetime.now()
metrics = []
metrics.append(Precision())
metrics.append(Recall())
metrics.append(F_score(0.5))
metrics.append(Accuracy())
metrics.append(FPR())
validation = CrossValidation(0.9, 20, rec, metrics, 0.005)
validation.run(user)
print validation
end_time = datetime.datetime.now()
