k-suite.py
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#!/usr/bin/env python
"""
k-suite - experiment different neighborhood sizes
"""
__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 sys
sys.path.insert(0,'../')
from config import Config
from data import PopconXapianIndex, PopconSubmission
from recommender import Recommender
from user import LocalSystem, User
from evaluation import *
import logging
import random
import Gnuplot
import numpy
def plot_roc(k,roc_points,log_file):
g = Gnuplot.Gnuplot()
g('set style data points')
g.xlabel('False Positive Rate')
g.ylabel('True Positive Rate')
g('set xrange [0:1.0]')
g('set yrange [0:1.0]')
g.title("Setup: %s-k%d" % (log_file.split("/")[-1],k))
g.plot(Gnuplot.Data([[0,0],[1,1]],with_="lines lt 7"),
Gnuplot.Data(roc_points))
g.hardcopy(log_file+("-k%.3d.png"%k),terminal="png")
g.hardcopy(log_file+("-k%.3d.ps"%k),terminal="postscript",enhanced=1,color=1)
def plot_summary(precision,f05,mcc,log_file):
g = Gnuplot.Gnuplot()
g('set style data lines')
g.xlabel('Neighborhood (k)')
g.title("Setup: %s-size20" % (log_file.split("/")[-1]))
g.plot(Gnuplot.Data([[k,sum(precision[k])/len(precision[k])] for k in precision.keys()],title="P"),
Gnuplot.Data([[k,sum(f05[k])/len(f05[k])] for k in f05.keys()],title="F05"),
Gnuplot.Data([[k,sum(mcc[k])/len(mcc[k])] for k in mcc.keys()],title="MCC"))
g.hardcopy(log_file+(".png"),terminal="png")
g.hardcopy(log_file+(".ps"),terminal="postscript",enhanced=1,color=1)
class ExperimentResults:
def __init__(self,repo_size):
self.repository_size = repo_size
self.precision = []
self.recall = []
self.fpr = []
self.f05 = []
self.mcc = []
def add_result(self,ranking,sample):
predicted = RecommendationResult(dict.fromkeys(ranking,1))
real = RecommendationResult(sample)
evaluation = Evaluation(predicted,real,self.repository_size)
self.precision.append(evaluation.run(Precision()))
self.recall.append(evaluation.run(Recall()))
self.fpr.append(evaluation.run(FPR()))
self.f05.append(evaluation.run(F_score(0.5)))
self.mcc.append(evaluation.run(MCC()))
def get_roc_point(self):
tpr = self.recall
fpr = self.fpr
if not tpr or not fpr:
return [0,0]
return [sum(fpr)/len(fpr),sum(tpr)/len(tpr)]
def get_precision_summary(self):
if not self.precision: return 0
return sum(self.precision)/len(self.precision)
def get_f05_summary(self):
if not self.f05: return 0
return sum(self.f05)/len(self.f05)
def get_mcc_summary(self):
if not self.mcc: return 0
return sum(self.mcc)/len(self.mcc)
if __name__ == '__main__':
if len(sys.argv)<3:
print "Usage: k-suite strategy_str sample_file"
exit(1)
threshold = 20
iterations = 30
neighbors = [3,5,10,50,100,150,200,300,400,500]
cfg = Config()
cfg.strategy = sys.argv[1]
sample_file = sys.argv[2]
population_sample = []
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))
# setup dictionaries and files
roc_summary = {}
recommended = {}
precision_summary = {}
f05_summary = {}
mcc_summary = {}
sample_dir = ("results/k-suite/%s" % sample_file.split('/')[-1])
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
log_file = os.path.join(sample_dir,cfg.strategy)
with open(log_file,'w') as f:
f.write("# %s\n\n" % sample_file.split('/')[-1])
f.write("# strategy %s recommendation_size %d iterations %d\n\n" %
(cfg.strategy,threshold,iterations))
f.write("# k coverage \tprecision \tf05 \tmcc\n\n")
for k in neighbors:
roc_summary[k] = []
recommended[k] = set()
precision_summary[k] = []
f05_summary[k] = []
mcc_summary[k] = []
with open(log_file+"-k%.3d"%k,'w') as f:
f.write("# %s\n\n" % sample_file.split('/')[-1])
f.write("# strategy-k %s-k%.3d\n\n" % (cfg.strategy,k))
f.write("# roc_point \tprecision \tf05 \tmcc\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 k in neighbors:
cfg.k_neighbors = k
rec = Recommender(cfg)
repo_size = rec.items_repository.get_doccount()
results = ExperimentResults(repo_size)
# n iterations for same recommender and user
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,threshold)
if hasattr(recommendation,"ranking"):
results.add_result(recommendation.ranking,sample)
recommended[k] = recommended[k].union(recommendation.ranking)
# save summary
roc_point = results.get_roc_point()
roc_summary[k].append(roc_point)
precision = results.get_precision_summary()
precision_summary[k].append(precision)
f05 = results.get_f05_summary()
f05_summary[k].append(f05)
mcc = results.get_mcc_summary()
mcc_summary[k].append(mcc)
with open(log_file+"-k%.3d"%k,'a') as f:
f.write("[%.2f,%.2f] \t%.4f \t%.4f \t%.4f\n" %
(roc_point[0],roc_point[1],precision,f05,mcc))
# back to main flow
with open(log_file,'a') as f:
plot_summary(precision_summary,f05_summary,mcc_summary,log_file)
for k in neighbors:
coverage = len(recommended[size])/float(repo_size)
f.write("%3d \t%.2f \t%.4f \t%.4f \t%.4f\n" %
(k,coverage,float(sum(precision_summary[k]))/len(precision_summary[k]),
float(sum(f05_summary[k]))/len(f05_summary[k]),
float(sum(mcc_summary[k]))/len(mcc_summary[k])))
plot_roc(k,roc_summary[k],log_file)