def evaluate(self, ident, pct):
for i in range(1):
results = {}
(train_corpus, predict_corpus) = self.corpus.load_corpus(ident, float(pct), True, True)
savepickle(paths['svm.svm_data_tmp']+'wordids.pkl', self.corpus.wordids)
self.generate_libsvm_input(train_corpus, 'train')
self.generate_libsvm_input(predict_corpus, 'predict')
print "Done generating SVM input."
results = self.libsvm_train(True)
print "Iteration", i, ", pct", pct
print results
def recallprecision(articles):
"""
recall and precision for simularity method, make all pairs from urllist
and compute the similarity.
"""
urlids = articles.keys()
truepos = {}
falsepos = {}
# cutoff range [0.01, 1.0]
cutoff_begin = 0
cutoff_end = 100
for cutoff in range(cutoff_begin, cutoff_end):
truepos[cutoff]=0
falsepos[cutoff]=0
total = 0
pos = 0
N = len(urlids)
print "Number of articles: %d" % N
progress_step = N*(N-1)/200.0
simvals = {}
for i in range(0, N-1):
if (i % (N/10)) == 0:
print "Progress: %.0f%%" % (float(total)/progress_step)
for j in range(i+1, N):
key = (urlids[i], urlids[j])
val = corpus.cos_sim(articles[urlids[i]]['tfidf'],
articles[urlids[j]]['tfidf'])
total += 1
simvals[key] = val
if key in checklist:
flag = True
pos += 1
else:
flag = False
if articles[urlids[i]]['pubdate'] == None or \
articles[urlids[j]]['pubdate'] == None:
datedelta = 0.0
else:
datedelta = math.fabs((articles[urlids[i]]['pubdate'] - \
articles[urlids[j]]['pubdate']).days)
cutoff = cutoff_begin*0.01
for x in range(cutoff_begin,cutoff_end):
if datedelta < 14 and val >= cutoff:
if flag:
truepos[x] += 1
else:
#if cutoff >= 0.1 and cutoff < 0.11:
# print "false pos:", key, cutoff
falsepos[x] += 1
cutoff += 0.01
print "pos:",pos
best_cutoff = 0
best_f1 = 0
best_p = 0
best_r = 0
for cutoff in range(cutoff_begin, cutoff_end):
if truepos[cutoff]==0 and falsepos[cutoff]==0:
precision = 0
else:
precision = float(truepos[cutoff])/(truepos[cutoff]+falsepos[cutoff])
if pos == 0:
recall = 0
else:
recall = float(truepos[cutoff])/pos
if precision == 0 and recall == 0:
f1 = 0
else:
f1 = 2.0*(precision*recall)/(precision+recall)
if f1 > best_f1:
best_f1 = f1
best_cutoff = cutoff
best_p = precision
best_r = recall
print "cutoff:",best_cutoff*.01, "True Pos:", \
truepos[best_cutoff], "False Pos:", falsepos[best_cutoff],\
"precision:", best_p, "recall:", best_r, "f1:",best_f1
print
done = False
for i in range(0, N-1):
if done: break;
for j in range(i+1, N):
if done: break;
key = (urlids[i], urlids[j])
if key in notduplist_stored: continue
val = corpus.cos_sim(articles[urlids[i]]['tfidf'],
articles[urlids[j]]['tfidf'])
if key not in checklist and val >= (best_cutoff*0.01):
if articles[urlids[i]]['pubdate'] == None or \
articles[urlids[j]]['pubdate'] == None:
datedelta = 0.0
else:
datedelta = math.fabs((articles[urlids[i]]['pubdate'] - \
articles[urlids[j]]['pubdate']).days)
if datedelta > 14: continue
print "-- %s (%s)\n\n%s\n\n-- %s (%s)\n\n%s\n\n" % \
(articles[urlids[i]]['title'], str(articles[urlids[i]]['pubdate']),
summarizer.summarize_article(corpus, articles[urlids[i]], 4),
articles[urlids[j]]['title'], str(articles[urlids[j]]['pubdate']),
summarizer.summarize_article(corpus, articles[urlids[j]], 4))
answer = raw_input("Duplicates? (y/n/q): ")
if answer == "y" or answer == "Y":
duplist_stored.append(([key[0], key[1]], "duplist_stored"))
if answer == "n" or answer == "N":
notduplist_stored.add(key)
elif answer == "q" or answer == "Q":
done = True
print "\n\n----------------\n\n"
savepickle(paths['corpus.notduplist'], notduplist_stored)
savepickle(paths['corpus.duplist'], duplist_stored)
