regressor = LinearRegression()
# Used with default setting here. However, in the real DKPro system, its setting
# were probably optmized by a CV gridsearch on the training data
# TODO: this approach is brain dead, because it keeps reading features from files
print "{:64s}\t".format("Features:"),
print "\t".join(["{:>16s}".format(p[1]) for p in id_pairs])
for feat in feats:
print "{:64s}\t".format(feat),
for train_id, test_id in id_pairs:
train_feat, train_scores = read_train_data(train_id, [feat])
regressor.fit(train_feat, train_scores)
test_feat, test_scores = read_test_data(test_id, [feat])
sys_scores = regressor.predict(test_feat)
sys_input = read_system_input(test_input_fnames[test_id])
postprocess(sys_input, sys_scores)
if isinstance(train_id, tuple):
train_id = "+".join(train_id)
print "{:16.2f}\t".format(correlation(sys_scores, test_scores)),
print
from sts.score import correlation
train = np.load("_npz_data/_STS2012.train.MSRpar.npz")
clf = SVR(kernel='rbf', C=50, epsilon=.2, gamma=.02)
print clf
clf.fit(train["X"], train["y"])
#print clf.score(train["X"], train["y"])
test = np.load("_npz_data/_STS2012.test.MSRpar.npz")
#print clf.score(test["X"], test["y"])
sys_scores = clf.predict(test["X"])
# postprocess
sys_inp = read_system_input("../../data/STS2012-test/STS.input.MSRpar.txt")
sys_scores[sys_inp["s1"] == sys_inp["s2"]] = 5.0
sys_scores[sys_scores > 5.0] = 5.0
sys_scores[sys_scores < 0.0] = 0.0
# compute correlation score
gold_scores = read_gold_standard("../../data/STS2012-test/STS.gs.MSRpar.txt")["gold"]
print correlation(gold_scores, sys_scores)
#from sklearn.cross_validation import KFold
#from sklearn.grid_search import GridSearchCV
#C_range = 10.0 ** np.arange(-2, 9)
#gamma_range = 10.0 ** np.arange(-5, 4)
#param_grid = dict(gamma=gamma_range, C=C_range)
test_ids,
"FNWN"),
(train_ids,
test_ids,
"OnWN") ]
feats = takelab_feats + takelab_lsa_feats + subsem_best_feats
scores = []
X_sts12_train, y_sts12_train = read_train_data(train_ids, feats)
X_sts12_test, y_sts12_test = read_test_data(test_ids, feats)
X_train = vstack([X_sts12_train, X_sts12_test])
y_train = hstack([y_sts12_train, y_sts12_test])
test_input = [read_system_input(test_input_fnames[sts13_test_id]) for sts13_test_id in sts13.test_ids]
test_input = concatenate(test_input)
X_sts13, y_sts13 = sts13.read_test_data(sts13.test_ids, feats)
X_sts13_val = X_sts13[0:X_sts13.shape[0]/2, :]
X_sts13_held = X_sts13[X_sts13.shape[0]/2:, :]
y_sts_val = y_sts13[0:len(y_sts13)/2]
y_sts_held = y_sts13[len(y_sts13)/2:]
test_input_val = test_input[0:len(test_input)/2]
test_input_held = test_input[len(test_input)/2:]
n_train = len(y_train)
n_test = len(y_sts_val)
filenames = []
for sts12_train_id, sts12_test_id, sts13_test_id, sts14_test_id in id_pairs:
# combine 2012, 2013 training and test data
X_sts12_train, y_sts12_train = ntnu_sts12.read_train_data(sts12_train_id, feats)
X_sts12_test, y_sts12_test = ntnu_sts12.read_test_data(sts12_test_id, feats)
X_sts13_test, y_sts13_test = sts13.read_test_data(sts13_test_id, feats)
X_train = np.vstack([X_sts12_train, X_sts12_test, X_sts13_test])
y_train = np.hstack([y_sts12_train, y_sts12_test, y_sts13_test])
regressor.fit(X_train, y_train)
X_test = read_blind_test_data(sts14_test_id, feats)
y_test = regressor.predict(X_test)
test_input = read_system_input(test_input_fnames[sts14_test_id])
postprocess(test_input, y_test)
fname = "{}/STS-en.output.{}.txt".format(out_dir, sts14_test_id)
write_scores(fname, y_test)
filenames.append(fname)
descr_fname = "{}/STS-en-{}-{}.description.txt".format(out_dir, GROUP, APPROACH)
open(descr_fname, "w").write(DESCRIPTION)
filenames.append(descr_fname)
filenames = " ".join(filenames)
zipfile = "STS-en-{}-{}.zip".format(GROUP, APPROACH)
call("zip -rv {} {}".format(zipfile, filenames),
from sts.io import read_system_input, read_gold_standard
from sts.score import correlation
train = np.load("_npz_data/_STS2012.train.MSRpar.npz")
clf = SVR(kernel='rbf', C=50, epsilon=.2, gamma=.02)
print clf
clf.fit(train["X"], train["y"])
#print clf.score(train["X"], train["y"])
test = np.load("_npz_data/_STS2012.test.MSRpar.npz")
#print clf.score(test["X"], test["y"])
sys_scores = clf.predict(test["X"])
# postprocess
sys_inp = read_system_input("../../data/STS2012-test/STS.input.MSRpar.txt")
sys_scores[sys_inp["s1"] == sys_inp["s2"]] = 5.0
sys_scores[sys_scores > 5.0] = 5.0
sys_scores[sys_scores < 0.0] = 0.0
# compute correlation score
gold_scores = read_gold_standard(
"../../data/STS2012-test/STS.gs.MSRpar.txt")["gold"]
print correlation(gold_scores, sys_scores)
#from sklearn.cross_validation import KFold
#from sklearn.grid_search import GridSearchCV
#C_range = 10.0 ** np.arange(-2, 9)
#gamma_range = 10.0 ** np.arange(-5, 4)
#param_grid = dict(gamma=gamma_range, C=C_range)
filenames = []
for sts12_train_id, sts12_test_id, sts13_test_id in id_pairs:
# combine 2012 training and test data
X_sts12_train, y_sts12_train = read_train_data(sts12_train_id, feats)
X_sts12_test, y_sts12_test = read_test_data(sts12_test_id, feats)
X_train = np.vstack([X_sts12_train, X_sts12_test])
y_train = np.hstack([y_sts12_train, y_sts12_test])
regressor.fit(X_train, y_train)
X_test = read_blind_test_data(sts13_test_id, feats)
y_test = regressor.predict(X_test)
test_input = read_system_input(test_input_fnames[sts13_test_id])
postprocess(test_input, y_test)
fname = "{}/STScore.output.{}.txt".format(out_dir, sts13_test_id)
write_scores(fname, y_test)
filenames.append(fname)
descr_fname = "{}/STScore-{}-{}.description.txt".format(
out_dir, GROUP, APPROACH)
open(descr_fname, "w").write(DESCRIPTION)
filenames.append(descr_fname)
filenames = " ".join(filenames)
zipfile = "STScore-{}-{}.zip".format(GROUP, APPROACH)
