def main():
scriptdir = os.path.dirname(os.path.realpath(__file__))
data = scriptdir+'/../data/cwi_training/cwi_training.txt.lbl.conll'
testdata = scriptdir+'/../data/cwi_testing/cwi_testing.gold.txt.lbl.conll'
pickled_data = scriptdir+'/../data.pickle'
parser = argparse.ArgumentParser()
parser.add_argument('--threshold', '-t', type=float, help='Threshold for predicting 0/1. If not specified, the optimal threshold will first be computed as the median of all CV splits. May take a while.')
parser.add_argument('--iterations', '-i', type=int, default=50, help='Training iterations.')
parser.add_argument('--hidden-layers', '-l', dest='layers', required=True, type=int, nargs='+', help='List of layer sizes')
parser.add_argument('--cv-splits', '-c', dest='splits', type=int, help='No. of crossvalidation splits. If not specified, no CV will be performed.')
parser.add_argument('--data', '-d', default=data, help='Features and labels')
parser.add_argument('--testdata', '-y', default=testdata, help='Test data (not needed for crossval).')
parser.add_argument('--verbose', '-v', dest='verbose', action='store_true', help='Print average loss at every training iteration.')
parser.add_argument('--output', '-o', help="Output file")
parser.add_argument('--features', '-f', dest='features', default=[], type=str, nargs='+', help='List of feature types')
args = parser.parse_args()
# X, y = load_pickled(args.data)
combined_data = 'X_y_all.txt'
cutoff = combine_data(args.data, args.testdata, combined_data)
X, y, _ = feats_and_classify.collect_features(combined_data, True, args.features)
X_tr = X[:cutoff]
y_tr = y[:cutoff]
X_te = X[cutoff:]
y_te = y[cutoff:]
conf = NeuralNetConfig(X=X, y=y, layers=args.layers, iterations=args.iterations, verbose=args.verbose)
if args.splits:
if args.threshold:
crossval(X_tr,y_tr,args.splits, conf, t=args.threshold)
else:
# compute optimal threshold for each CV split
print '### Computing optimal threshold... '
ts = crossval(X_tr,y_tr,args.splits, conf)
avg = np.average(ts)
med = np.median(ts)
print '\nThresholds for crossval splits:', ts
print 'Mean threshold', avg
print 'Median threshold', med
print 'Threshold st.dev.', np.std(ts)
# Run CV with fixed avg/median threshold
print '\n\n### Running with avg. threshold... '
crossval(X_tr,y_tr,args.splits, conf, t=avg)
print '\n\n### Running with med. threshold... '
crossval(X_tr,y_tr,args.splits, conf, t=med)
else:
nn = NN(conf)
nn.train(X_tr,y_tr,args.iterations)
if args.testdata:
# X_test, y_test = load_pickled(args.testdata)
pred = nn.get_output(X_te)
if args.output:
with open(args.output, 'w') as of:
for p in pred:
of.write('%f\n'%p)
t, res = nn.test(X_te,y_te,args.threshold)
resout = "G: %f, R: %f, A: %f, P: %f\n"%res
sys.stderr.write('%s %f\n'%(' '.join(args.features), t))
sys.stderr.write(resout)
def predictTestSet():
#generate training features and labels
trainfile='/home/natschluter/GroupAlgorithms/cwi2016/data/cwi_training/cwi_training_cat.lbl.conll'
trainfeatures, trainlabels, vec = feats_and_classify_py2.collect_features(trainfile)
#generate training+test features
bothfiles='/home/natschluter/GroupAlgorithms/cwi2016/data/train_and_test1.conll'
bothfeatures, bothlabels, bothvec = feats_and_classify_py2.collect_features(bothfiles)
thresholds_med=np.median(np.array([ 0.145, 0.85, 0.12, 0.657, 0.71, 0.824, 0.506, 0.461, 0.662, 0.888]))
TrainX=bothfeatures[np.array(range(len(trainfeatures)))]
TrainY=bothlabels[np.array(range(len(trainlabels)))]
TestX=bothfeatures[np.array(range(len(trainfeatures),len(bothfeatures)))]
maxent = LogisticRegression(penalty='l2')
print('training...')
maxent.fit(TrainX,TrainY)
print('predicting...')
ypred_probs=maxent.predict_proba(TestX)
def main():
scriptdir = os.path.dirname(os.path.realpath(__file__))
data = scriptdir + '/../data/cwi_training/cwi_training.txt.lbl.conll'
testdata = scriptdir + '/../data/cwi_testing/cwi_testing.gold.txt.lbl.conll'
pickled_data = scriptdir + '/../data.pickle'
parser = argparse.ArgumentParser()
parser.add_argument(
'--threshold',
'-t',
type=float,
help=
'Threshold for predicting 0/1. If not specified, the optimal threshold will first be computed as the median of all CV splits. May take a while.'
)
parser.add_argument('--iterations',
'-i',
type=int,
default=50,
help='Training iterations.')
parser.add_argument('--hidden-layers',
'-l',
dest='layers',
required=True,
type=int,
nargs='+',
help='List of layer sizes')
parser.add_argument(
'--cv-splits',
'-c',
dest='splits',
type=int,
help=
'No. of crossvalidation splits. If not specified, no CV will be performed.'
)
parser.add_argument('--data',
'-d',
default=data,
help='Features and labels')
parser.add_argument('--testdata',
'-y',
default=testdata,
help='Test data (not needed for crossval).')
parser.add_argument('--verbose',
'-v',
dest='verbose',
action='store_true',
help='Print average loss at every training iteration.')
parser.add_argument('--output', '-o', help="Output file")
parser.add_argument('--features',
'-f',
dest='features',
default=[],
type=str,
nargs='+',
help='List of feature types')
args = parser.parse_args()
# X, y = load_pickled(args.data)
combined_data = 'X_y_all.txt'
cutoff = combine_data(args.data, args.testdata, combined_data)
X, y, _ = feats_and_classify.collect_features(combined_data, True,
args.features)
X_tr = X[:cutoff]
y_tr = y[:cutoff]
X_te = X[cutoff:]
y_te = y[cutoff:]
conf = NeuralNetConfig(X=X,
y=y,
layers=args.layers,
iterations=args.iterations,
verbose=args.verbose)
if args.splits:
if args.threshold:
crossval(X_tr, y_tr, args.splits, conf, t=args.threshold)
else:
# compute optimal threshold for each CV split
print '### Computing optimal threshold... '
ts = crossval(X_tr, y_tr, args.splits, conf)
avg = np.average(ts)
med = np.median(ts)
print '\nThresholds for crossval splits:', ts
print 'Mean threshold', avg
print 'Median threshold', med
print 'Threshold st.dev.', np.std(ts)
# Run CV with fixed avg/median threshold
print '\n\n### Running with avg. threshold... '
crossval(X_tr, y_tr, args.splits, conf, t=avg)
print '\n\n### Running with med. threshold... '
crossval(X_tr, y_tr, args.splits, conf, t=med)
else:
nn = NN(conf)
nn.train(X_tr, y_tr, args.iterations)
if args.testdata:
# X_test, y_test = load_pickled(args.testdata)
pred = nn.get_output(X_te)
if args.output:
with open(args.output, 'w') as of:
for p in pred:
of.write('%f\n' % p)
t, res = nn.test(X_te, y_te, args.threshold)
resout = "G: %f, R: %f, A: %f, P: %f\n" % res
sys.stderr.write('%s %f\n' % (' '.join(args.features), t))
sys.stderr.write(resout)
