def evaluate_config(ext_algo, reg_algo, sum_algo, red_algo, tradeoff, word_len, max_sent, f):
features = []
for i in f.keys():
if f[i][1] == True:
features.append(f[i][0])
features = "-".join(features)
idx = []
for x in f.keys():
if f[x][1] == True:
idx.append(x)
idx = sorted(idx)
d_name = gen_name(ext_algo, reg_algo, red_algo, sum_algo)
w2v_model = None
if red_algo == "w2v" or red_algo == "cnn":
print "\nLoading w2v model..."
w2v_path = "../sentiment-mining-for-movie-reviews/Data/GoogleNews-vectors-negative300.bin"
w2v_model = Word2Vec.load_word2vec_format(w2v_path, binary=True) # C binary format
print "\nLearn..."
cp = load(read)
(X, y, t) = cp.export_data(f)
w = learn_relevance(X, y, reg_algo)
print "\nInfo..."
print "train shape: ", X.shape
print "number of test collections: ", len(t)
print "\nGenerate..."
# sample_lead_1 = multi_lead(cp.collections['d301i'+'2005'], word_len, max_sent)
# sample_lead_2 = multi_lead(cp.collections['D0601A'+'2006'], word_len, max_sent)
# sample_regr = rel_summarize(cp.collections['d301i'+'2005'], w, word_len, max_sent, idx)
# sample_mmr = mmr_summarize(cp.collections['d301i'+'2005'], w, ext_algo, red_algo, word_len, max_sent, tradeoff, idx)
print "\nPrinting sample lead / regression..."
# plot_summary(sample_lead_1)
# plot_summary(sample_lead_2)
# plot_summary(sample_regr)
# plot_summary(sample_mmr)
if store_test:
print "\nGenerating summaries for test collections"
os.mkdir(d_name)
out_file = open(d_name + "/configurations", "w")
out_file.write("features: " + features + "\next_algo: " + ext_algo + "\nsum_algo: " + sum_algo)
if sum_algo != "lead":
out_file.write("\nregression_algo: " + reg_algo)
if sum_algo == "mmr":
out_file.write("\nred_algo: " + red_algo + "\ntradeoff: " + str(tradeoff))
start = time.time()
for c in t:
if sum_algo == "lead":
summ = multi_lead(c, word_len, max_sent)
out_file = open(d_name + "/" + c.code.lower() + "_" + sum_algo, "w")
elif sum_algo == "rel":
summ = rel_summarize(c, w, word_len, max_sent, idx)
out_file = open(d_name + "/" + c.code.lower() + "_" + reg_algo + "-" + sum_algo, "w")
elif sum_algo == "mmr":
summ = mmr_summarize(c, w, ext_algo, red_algo, word_len, max_sent, tradeoff, idx, w2v_model)
out_file = open(d_name + "/" + c.code.lower() + "_" + reg_algo + "-" + sum_algo + "-" + red_algo, "w")
else:
raise Exception("sum_algo: Invalid algorithm")
if human_inspect:
out_file.write("TOPIC\n")
out_file.write(c.code + "; " + c.topic_title)
out_file.write("\n\nDESCRIPTION\n")
out_file.write(c.topic_descr)
out_file.write("\n\nSUMMARY\n")
for s in summ:
out_file.write(s + "\n")
out_file.close()
print "summarize and store, test collections: %f seconds" % (time.time() - start)
human_inspect = False
store_test = True
# algorithms
ext_algo = "greedy"
reg_algo = "rf-R-25" # rf-R, linear-R, decisionT
sum_algo = "rel" # mmr, lead, rel
red_algo = "simpleRed" # simpleRed, uniCosRed, groupRnnEmbedding, cnn
# hyperparams
tradeoff = 0.2
word_len = 250
max_sent = 45
# execution
exec_list = ["rf-R-25", "rf-R-24", "rf-R-23", "rf-R-10", "rf-R-9"]
for a in exec_list:
evaluate_config(ext_algo, a, sum_algo, red_algo, tradeoff, word_len, max_sent, f)
if False:
pdb.set_trace()
exit()
feat_select(f)
print "\nEvaluate on true feed..."
c = Collection()
c.read_test_collections("grexit")
c.process_collection(False)
summ = rel_summarize(c, w, word_len, max_sent)
plot_summary(summ)
