recommender.py
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#!/usr/bin/env python
"""
recommender - python module for classes related to recommenders.
"""
__author__ = "Tassia Camoes Araujo <tassia@gmail.com>"
__copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo"
__license__ = """
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import logging
import os
import xapian
import operator
import data
import strategy
import apt
class RecommendationResult:
"""
Class designed to describe a recommendation result: items and scores.
"""
def __init__(self,item_score,ranking=0):
"""
Set initial parameters.
"""
self.item_score = item_score
self.size = len(item_score)
if ranking:
self.ranking = ranking
def __str__(self):
"""
String representation of the object.
"""
#[FIXME] try alternative way to get pkgs summarys (efficiency)
#cache = apt.Cache()
result = self.get_prediction()
str = "\n"
for i in range(len((list(result)))):
#summary = cache[result[i][0]].candidate.summary
#str += "%2d: %s\t\t- %s\n" % (i,result[i][0],summary)
str += "%2d: %s\n" % (i,result[i][0])
return str
def get_prediction(self,limit=0):
"""
Return prediction based on recommendation size (number of items).
"""
sorted_result = sorted(self.item_score.items(),
key=operator.itemgetter(1))
if not limit or limit > self.size:
limit = self.size
return list(reversed(sorted_result[-limit:]))
class Recommender:
"""
Class designed to play the role of recommender.
"""
def __init__(self,cfg):
"""
Set initial parameters.
"""
self.cfg = cfg
# Load xapian indexes
#self.axi_programs = xapian.Database(cfg.axi_programs)
self.axi_desktopapps = xapian.Database(cfg.axi_desktopapps)
if cfg.popcon:
#self.popcon_programs = xapian.Database(cfg.popcon_programs)
self.popcon_desktopapps = xapian.Database(cfg.popcon_desktopapps)
# Load valid programs, desktopapps and tags
# format: one package or tag name per line
#self.valid_programs = []
self.valid_desktopapps = []
self.valid_tags = []
logging.info("Loading recommender filters")
#with open(os.path.join(cfg.filters_dir,"programs")) as pkgs:
# self.valid_programs = [line.strip() for line in pkgs
# if not line.startswith("#")]
with open(os.path.join(cfg.filters_dir,"desktopapps")) as pkgs:
self.valid_desktopapps = [line.strip() for line in pkgs
if not line.startswith("#")]
with open(os.path.join(cfg.filters_dir,"debtags")) as tags:
self.valid_tags = [line.strip() for line in tags
if not line.startswith("#")]
# Set xapian index weighting scheme
if cfg.weight == "bm25":
self.weight = xapian.BM25Weight(cfg.bm25_k1, cfg.bm25_k2,
cfg.bm25_k3, cfg.bm25_b,
cfg.bm25_nl)
else:
self.weight = xapian.TradWeight()
self.set_strategy(cfg.strategy)
def set_strategy(self,strategy_str,k=0,n=0):
"""
Set the recommendation strategy.
"""
if k:
k_neighbors = k
else:
k_neighbors = self.cfg.k_neighbors
if n:
profile_size = n
else:
profile_size = self.cfg.profile_size
logging.info("Setting recommender strategy to \'%s\'" % strategy_str)
# Check if collaborative strategies can be instanciated
if "knn" in strategy_str:
if not self.cfg.popcon:
logging.info("Cannot perform collaborative strategy")
return 1
#if self.cfg.pkgs_filter.split("/")[-1] == "desktopapps":
self.items_repository = self.axi_desktopapps
self.valid_pkgs = self.valid_desktopapps
if "knn" in strategy_str:
self.users_repository = self.popcon_desktopapps
#else:
# self.items_repository = self.axi_programs
# self.valid_pkgs = self.valid_programs
# if "knn" in strategy_str:
# self.users_repository = self.popcon_programs
# Set strategy based on strategy_str
if strategy_str == "cb":
self.strategy = strategy.ContentBased("mix",profile_size)
elif strategy_str == "cbt":
self.strategy = strategy.ContentBased("tag",profile_size)
elif strategy_str == "cbd":
self.strategy = strategy.ContentBased("desc",profile_size)
elif strategy_str == "cbh":
self.strategy = strategy.ContentBased("half",profile_size)
if strategy_str == "cb_eset":
self.strategy = strategy.ContentBased("mix_eset",profile_size)
elif strategy_str == "cbt_eset":
self.strategy = strategy.ContentBased("tag_eset",profile_size)
elif strategy_str == "cbd_eset":
self.strategy = strategy.ContentBased("desc_eset",profile_size)
elif strategy_str == "cbh_eset":
self.strategy = strategy.ContentBased("half_eset",profile_size)
elif strategy_str == "knn":
self.strategy = strategy.Knn(k_neighbors)
elif strategy_str == "knn_plus":
self.strategy = strategy.KnnPlus(k_neighbors)
elif strategy_str == "knn_eset":
self.strategy = strategy.KnnEset(k_neighbors)
elif strategy_str == "knnco":
self.strategy = strategy.KnnContent(k_neighbors)
elif strategy_str == "knnco_eset":
self.strategy = strategy.KnnContentEset(k_neighbors)
# [FIXME: fix repository instanciation]
#elif strategy_str.startswith("demo"):
# self.strategy = strategy.Demographic(strategy_str)
else:
logging.info("Strategy not defined.")
return
def get_recommendation(self,user,result_size=100):
"""
Produces recommendation using previously loaded strategy.
"""
return self.strategy.run(self,user,result_size)