def test_mean_predictor_recommender(self):
db_dir = os.path.join(BASE_DIR, 'data', 'recommenders')
init_recommender_params = {
'neighbourhood_size': 2, 'predictor_name': 'mean'
}
evaluator = RecommenderEvaluator(
db_dir, recommender_class=PredictorRecommender,
init_recommender_params=init_recommender_params)
evaluator.run()
assert evaluator.f1 > 0
assert evaluator.precision > 0
assert evaluator.recall > 0
init_recommender_params = {
'neighbourhood_size': 15, 'predictor_name': 'mean'
}
evaluator2 = RecommenderEvaluator(
db_dir, recommender_class=PredictorRecommender,
init_recommender_params=init_recommender_params)
evaluator2.run()
assert evaluator2.f1 > 0
assert evaluator2.precision > 0
assert evaluator2.recall > 0
def test_frequent_item_recommender(self):
db_dir = os.path.join(BASE_DIR, 'data', 'recommenders')
init_recommender_params = {'neighbourhood_size': 3}
evaluator = RecommenderEvaluator(
db_dir, recommender_class=FrequentItemRecommender,
init_recommender_params=init_recommender_params)
evaluator.run()
assert evaluator.f1 > 0
assert evaluator.precision > 0
assert evaluator.recall > 0
init_recommender_params = {'neighbourhood_size': 11}
evaluator2 = RecommenderEvaluator(
db_dir, recommender_class=FrequentItemRecommender,
init_recommender_params=init_recommender_params)
evaluator2.run()
assert evaluator2.f1 > 0
assert evaluator2.precision > 0
assert evaluator2.recall > 0
def __init__(self,
kn=None,
neighbourhood_size=10,
similarity_metric='msd'):
db_dir = os.path.join(app.config['DATA_DIR'], 'db', 'ml-latest-small')
init_recommender_params = {
'neighbourhood_size': neighbourhood_size,
'similarity_metric': similarity_metric
}
app.logger.info('Starting FrequentItemRecommender Evaluator')
self.evaluator = RecommenderEvaluator(
db_dir,
recommender_class=FrequentItemRecommender,
init_recommender_params=init_recommender_params,
n_splits=kn)
self.evaluator.run()
self.total_execution_time = self.evaluator.total_execution_time