def train(traindat, tag, hps):
valdat = Dataset('validation', hps, mode=Mode.eval)
# TODO: try set_base_margin (https://github.com/dmlc/xgboost/blob/master/demo/guide-python/boost_from_prediction.py)
with time_me('Made training dmatrix', mode='stderr'):
dtrain = traindat.as_dmatrix()
def quick_fscore(preds, _notused_dtrain):
global counter
counter += 1
if 0 and counter % 5 != 0:
return 'fscore', 0.0
with time_me('calculated validation fscore', mode='print'):
user_counts = defaultdict(lambda : dict(tpos=0, fpos=0, fneg=0))
uids = valdat.uids
labels = dval.get_label()
for i, prob in enumerate(preds):
uid = uids[i]
pred = prob >= THRESH
label = labels[i]
if pred and label:
user_counts[uid]['tpos'] += 1
elif pred and not label:
user_counts[uid]['fpos'] += 1
elif label and not pred:
user_counts[uid]['fneg'] += 1
fscore_sum = 0
for uid, res in user_counts.iteritems():
numerator = 2 * res['tpos']
denom = numerator + res['fpos'] + res['fneg']
if denom == 0:
fscore = 1
else:
fscore = numerator / denom
fscore_sum += fscore
return 'fscore', fscore_sum / len(user_counts)
dval = valdat.as_dmatrix()
# If you pass in more than one value to evals, early stopping uses the
# last one. Because why not.
watchlist = [(dtrain, 'train'), (dval, 'validation'),]
#watchlist = [(dval, 'validation'),]
xgb_params = hypers.xgb_params_from_hps(hps)
evals_result = {}
t0 = time.time()
model = xgb.train(xgb_params, dtrain, hps.rounds, evals=watchlist,
early_stopping_rounds=hps.early_stopping_rounds, evals_result=evals_result) #, feval=quick_fscore, maximize=True)
t1 = time.time()
model_path = common.resolve_xgboostmodel_path(tag)
model.save_model(model_path)
preds = model.predict(dval)
_, fscore = quick_fscore(preds, None)
logging.info('Final validation (quick) fscore = {}'.format(fscore))
resultsdict = dict(fscore=fscore, evals=evals_result, duration=t1-t0)
res_path = os.path.join(common.XGBOOST_DIR, 'results', tag+'.pickle')
with open(res_path, 'w') as f:
pickle.dump(resultsdict, f)
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