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exp_batchrun.py
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exp_batchrun.py
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import sys
import os
import fio
import numpy
import ILP_MC
from ILP_baseline import getRouges
def run_Baseline():
from config import ConfigFile
config = ConfigFile()
Header = ['method', 'L'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_length = config.get_length_limit()
for L in [20, 25, 30, 35, 40]:
config.set_length_limit(L)
config.save()
row = ['baelinse']
row.append(L)
ilpdir = "../../data/ILP1_Sentence/"
rougename = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit())+ ".txt"
if not fio.IsExist(rougename):
os.system('python ILP_baseline.py')
os.system('python ILP_GetRouge.py ' + ilpdir)
scores = getRouges(rougename)
row = row + scores
body.append(row)
config.set_length_limit(old_length)
config.save()
newname = ilpdir + "rouge.sentence.txt"
fio.WriteMatrix(newname, body, Header)
def run_UnsupervisedMC():
from config import ConfigFile
config = ConfigFile()
Header = ['method', 'L', 'lambda', 'sparse'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_length = config.get_length_limit()
old_sparse = config.get_sparse_threshold()
old_softimpute_lambda = config.get_softImpute_lambda()
#for L in [20, 25, 30, 35, 40]:
for L in [30]:
config.set_length_limit(L)
for softimpute_lambda in numpy.arange(0.5, 8.0, 0.5):
#for softimpute_lambda in [2.0]:
#for sparse in [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0]:
for sparse in [0]:
config.set_sparse_threshold(sparse)
config.set_softImpute_lambda(softimpute_lambda)
config.save()
row = ['baseline+MC']
row.append(L)
row.append(softimpute_lambda)
row.append(sparse)
ilpdir = "../../data/ILP1_Sentence_MC/"
rougename = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit()) + '.l' + str(softimpute_lambda) +'.s'+ str(sparse) + ".txt"
if not fio.IsExist(rougename):
os.system('python ILP_MC.py')
os.system('python ILP_GetRouge.py ' + ilpdir)
rougefile = ilpdir + "rouge.sentence.L"+str(config.get_length_limit())+".txt"
os.system('mv ' + rougefile + ' ' + rougename)
scores = getRouges(rougename)
row = row + scores
body.append(row)
config.set_length_limit(old_length)
config.set_sparse_threshold(old_sparse)
config.set_softImpute_lambda(old_softimpute_lambda)
config.save()
newname = ilpdir + "rouge.sentence.txt"
fio.WriteMatrix(newname, body, Header)
def run_SupervisedMC():
from config import ConfigFile
config = ConfigFile()
Header = ['method'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_rank = config.get_rank_max()
old_sparse = config.get_sparse_threshold()
ilpdir = "../../data/ILP_Sentence_Supervised_FeatureWeightingAveragePerceptronMC/"
for sparse in [0, 0.9, 0.8, 0.1]:
for rank in [20, 50, 100, 200, 500]:
os.system('rm ' + ilpdir + '*.json')
config.set_rank_max(rank)
config.set_sparse_threshold(sparse)
config.save()
row = ['featureweight+MC']
row.append(rank)
row.append(sparse)
rougename = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit())+ '.w' + str(config.get_weight_normalization()) + config.get_prefixA() +'.s'+ str(sparse) + ".txt"
if not fio.IsExist(rougename):
os.system('python ILP_Supervised_FeatureWeight_MC_AveragePerceptron.py')
os.system('python ILP_GetRouge.py ' + ilpdir)
rougefile = ilpdir + "rouge.sentence.L"+str(config.get_length_limit())+".txt"
os.system('mv ' + rougefile + ' ' + rougename)
scores = getRouges(rougename)
row = row + scores
body.append(row)
config.set_rank_max(old_rank)
config.set_sparse_threshold(old_sparse)
config.save()
newname = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit())+ '.w' + str(config.get_weight_normalization()) + '.txt'
fio.WriteMatrix(newname, body, Header)
def run_SupervisedMC2():
from config import ConfigFile
config = ConfigFile()
Header = ['method'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_rank = config.get_rank_max()
old_sparse = config.get_sparse_threshold()
ilpdir = "../../data/ILP_Sentence_Supervised_FeatureWeightingAveragePerceptronMC/"
old_length = config.get_length_limit()
for L in [20, 25, 30, 35, 40]:
config.set_length_limit(L)
for sparse in [0, 0.9]:
for rank in [1000]:
os.system('rm ' + ilpdir + '*.json')
config.set_rank_max(rank)
config.set_sparse_threshold(sparse)
config.save()
row = ['featureweight+MC']
row.append(rank)
row.append(sparse)
rougename = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit())+ '.w' + str(config.get_weight_normalization()) + config.get_prefixA() +'.s'+ str(sparse) + ".txt"
if not fio.IsExist(rougename):
os.system('python ILP_Supervised_FeatureWeight_MC_AveragePerceptron.py')
os.system('python ILP_GetRouge.py ' + ilpdir)
rougefile = ilpdir + "rouge.sentence.L"+str(config.get_length_limit())+".txt"
os.system('mv ' + rougefile + ' ' + rougename)
scores = getRouges(rougename)
row = row + scores
body.append(row)
config.set_length_limit(old_length)
config.set_rank_max(old_rank)
config.set_sparse_threshold(old_sparse)
config.save()
newname = ilpdir + 'rouge.sentence' + '.w' + str(config.get_weight_normalization()) + '.txt'
fio.WriteMatrix(newname, body, Header)
def run_SupervisedMC3(iter=1):
from config import ConfigFile
config = ConfigFile()
Header = ['method', 'L', 'lambda', 'sparse'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_softimpute_lambda = config.get_softImpute_lambda()
old_sparse = config.get_sparse_threshold()
old_length = config.get_length_limit()
ilpdir = "../../data/ILP_Sentence_Supervised_FeatureWeightingAveragePerceptronMC/"
if fio.IsExist(ilpdir+"rouge.sentence.txt"):
path = ilpdir + "R" + str(iter) + '/'
if not fio.IsExistPath(path):
fio.NewPath(path)
cmd = 'mv ' + ilpdir + '*.txt ' + path
os.system(cmd)
for L in [20, 25, 30, 35, 40]:
config.set_length_limit(L)
for sparse in [0]:
#for softimpute_lambda in numpy.arange(0.1, 4.1, 0.1):
#for softimpute_lambda in numpy.arange(0.5, 4.1, 0.5):
for softimpute_lambda in [2.0]:
os.system('rm ' + ilpdir + '*.json')
config.set_softImpute_lambda(softimpute_lambda)
config.set_sparse_threshold(sparse)
config.save()
row = ['featureweight+MC']
row.append(L)
row.append(softimpute_lambda)
row.append(sparse)
rougename = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit()) + '.l' + str(softimpute_lambda) +'.s'+ str(sparse) + ".txt"
if not fio.IsExist(rougename):
os.system('python ILP_Supervised_FeatureWeight_MC_AveragePerceptron.py')
os.system('python ILP_GetRouge.py ' + ilpdir)
rougefile = ilpdir + "rouge.sentence.L"+str(config.get_length_limit())+".txt"
os.system('mv ' + rougefile + ' ' + rougename)
scores = getRouges(rougename)
row = row + scores
body.append(row)
config.set_length_limit(old_length)
config.set_softImpute_lambda(old_softimpute_lambda)
config.set_sparse_threshold(old_sparse)
config.save()
newname = ilpdir + "rouge.sentence.txt"
fio.WriteMatrix(newname, body, Header)
def run_SupervisedMC_weighting():
from config import ConfigFile
config = ConfigFile()
Header = ['method'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_rank = config.get_rank_max()
old_sparse = config.get_sparse_threshold()
old_threshold = config.get_perceptron_threshold()
for threshold in [-500, -400, -300, -200, -150, -100, -80, -50, 0]:
config.set_perceptron_threshold(threshold)
config.save()
row = ['featureweight+MC']
row.append(threshold)
rougename = '../../data/ILP_Sentence_Supervised_FeatureWeightingMC/rouge.sentence.L' +str(config.get_length_limit())+ '.' + str(threshold) + ".txt"
if not fio.IsExist(rougename):
os.system('python ILP_Supervised_FeatureWeight_MC.py')
os.system('python ILP_GetRouge.py "../../data/ILP_Sentence_Supervised_FeatureWeightingMC/"')
rougefile = "../../data/ILP_Sentence_Supervised_FeatureWeightingMC/" + "rouge.sentence.L"+str(config.get_length_limit())+".txt"
os.system('mv ' + rougefile + ' ' + rougename)
scores = getRouges(rougename)
row = row + scores
body.append(row)
config.set_rank_max(old_rank)
config.set_sparse_threshold(old_sparse)
config.save()
newname = "../../data/featureweighting_MC_perceptron_threshold.txt"
fio.WriteMatrix(newname, body, Header)
def run_CWLearning(iter = 1):
from config import ConfigFile
config = ConfigFile()
Header = ['method', 'L'] + ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']*3
body = []
old_length = config.get_length_limit()
old_softimpute_lambda = config.get_softImpute_lambda()
ilpdir = "../../data/ILP_Sentence_Supervised_FeatureWeightingAveragePerceptron/"
if iter != None:
if fio.IsExist(ilpdir+"rouge.sentence.L30.txt"):
path = ilpdir + "R" + str(iter) + '/'
if not fio.IsExistPath(path):
fio.NewPath(path)
cmd = 'mv ' + ilpdir + '*.txt ' + path
os.system(cmd)
for L in [20, 25, 30, 35, 40]:
config.set_length_limit(L)
config.save()
#assert(config.get_softImpute_lambda() == 2.0)
for softimpute_lambda in numpy.arange(0.5, 4.1, 0.5):
row = ['baelinse + CW']
row.append(L)
row.append(softimpute_lambda)
rougename = ilpdir + 'rouge.sentence.L' +str(config.get_length_limit()) + '.l' + str(softimpute_lambda) + ".txt"
if not fio.IsExist(rougename):
os.system('rm ' + ilpdir + '*.json')
os.system('python ILP_Supervised_FeatureWeight_AveragePerceptron.py')
os.system('python ILP_GetRouge.py ' + ilpdir)
rougefile = ilpdir + "rouge.sentence.L"+str(config.get_length_limit())+".txt"
os.system('mv ' + rougefile + ' ' + rougename)
scores = getRouges(rougename)
row = row + scores
body.append(row)
newname = ilpdir + "rouge.sentence.txt"
fio.WriteMatrix(newname, body, Header)
config.set_softImpute_lambda(old_softimpute_lambda)
config.set_length_limit(old_length)
config.save()
def getA(rougefile):
head, body = fio.ReadMatrix(rougefile, hasHead=True)
RougeHead = ['R1-R', 'R1-P', 'R1-F', 'R2-R', 'R2-P', 'R2-F', 'RSU4-R', 'RSU4-P', 'RSU4-F']
A = []
for i, row in enumerate(body):
if i == len(body) - 1: continue
N = len(row)
score_q3 = [float(x) for x in row[N - len(RougeHead):]]
score_q2 = [float(x) for x in row[N - len(RougeHead)*2:N - len(RougeHead)]]
score_q1 = [float(x) for x in row[N - len(RougeHead)*3:N - len(RougeHead)*2]]
for i, score in enumerate([score_q1, score_q2, score_q3]):
A.append(score)
return A
def get_average_CWLearning():
ilpdir = "../../data/ILP_Sentence_Supervised_FeatureWeightingAveragePerceptronMC/"
import numpy as np
rougefile = '../../data/ILP1_Sentence_MC/rouge.sentence.L30.l2.0.s0.txt'
A = getA(rougefile)
fio.WriteMatrix(rougefile + '.matrix', A, header=None)
SumA = []
for r in ['R1', 'R2', 'R3', 'R4', 'R5']:
rougefile = ilpdir + r + "/rouge.sentence.L30.l2.0.s0.txt"
A = getA(rougefile)
fio.WriteMatrix(rougefile + '.matrix', A, header=None)
A = np.array(A)
if SumA == []:
SumA = A
else:
SumA += A
fio.WriteMatrix(ilpdir + "/rouge.sentence.L30.average.txt", A, header=None)
if __name__ == '__main__':
#get_average_CWLearning()
#run_Baseline()
run_UnsupervisedMC()
# for iter in [5]:
# run_CWLearning(iter)
#get_Sparse()
# for iter in [2, 3, 4, 5]:
# run_SupervisedMC3(iter)
#run_SupervisedMC2()