def __init__(self, feat_path, feature_maker, label_path=None, fold_in_cv=None):
""" Construct a dat set.
@param feat_path: path to raw data file
@param feature_maker: instance of FeatureTransformer to convert raw data to features
@param label_path: (optional) path to label file
@param fold_in_cv: (optional) tuple (x,y). Split data to y folds, and take
only x-th fold if x>0, or all except x-th fold if x<0
"""
self.instances = []
feat_lines = util.open_csv(feat_path, fold_in_cv)
if label_path:
label_lines = util.open_csv(label_path, fold_in_cv)
else:
label_lines = itertools.repeat(None) # endless None
for (feat_line, label_line) in itertools.izip(feat_lines, label_lines):
self.instances.append(self._make_feature(feat_line, label_line))
def main():
global feature_maker
info('start')
start = datetime.now()
args = parse_args()
feature_maker = feature_transformer.get_maker(args.D, args.transform)
if args.cmd == 'test': # train on training data and predict on testing data
feature_maker.init_per_train(util.open_csv(args.train))
data = TrainData(args.train, feature_maker, args.train_label)
model = new_model(feature_maker.dim)
for r in xrange(args.R):
if r > 0: data.rewind()
model = train_one(data, model)
with open(args.prediction, 'w') as outfile:
outfile.write('id_label,pred\n')
for ID, x in TestData(args.test, feature_maker):
pred = model.predict(x)
for (k,p) in zip(K,pred):
outfile.write('%s_y%d,%.16f\n' % (ID,k+1,p))
if k == 12:
outfile.write('%s_y14,0.0\n' % ID)
else: # do cross validation
nfold = args.nfold
cnt_ins = [0]*args.R
cnt_loss = [0.]*args.R
for fold in xrange(1,nfold+1):
feature_maker.init_per_train(util.open_csv(args.train, (-fold,nfold)))
train_data = TrainData(args.train, feature_maker, args.train_label, (-fold,nfold))
model = new_model(feature_maker.dim)
valid_data = TestData(args.train, feature_maker, args.train_label, (fold,nfold))
for r in xrange(args.R):
if r > 0: train_data.rewind()
model = train_one(train_data, model)
if r > 0: valid_data.rewind()
f_ins, f_loss = evaluate(valid_data, model)
info("round validation: %f" % (f_loss/f_ins))
cnt_ins[r] += f_ins
cnt_loss[r] += f_loss
del train_data
del valid_data
del model
for r in xrange(args.R):
print "%d round CV result: %f"%(r, cnt_loss[r]/cnt_ins[r])
info('Done, elapsed time: %s' % str(datetime.now() - start))
def rewind(self):
self.feat_lines = util.open_csv(self.feat_path, self.fold_in_cv)
if self.label_path:
self.label_lines = util.open_csv(self.label_path, self.fold_in_cv)
else:
self.label_lines = itertools.repeat(None) # endless None
