def predict(self, booster: xgb.Booster, **kwargs):
"""
Run local XGBoost prediction.
Parameters
----------
booster : xgboost.Booster
A trained booster.
**kwargs : dict
Other parameters for `xgboost.Booster.predict`.
Returns
-------
tuple
Pair of IP address of caller and pandas.DataFrame
with partial prediction result.
"""
local_dpredict = self._dpredict
booster.set_param({"nthread": self._nthreads})
s = time.time()
predictions = pandas.DataFrame(
booster.predict(local_dpredict["dmatrix"], **kwargs),
index=local_dpredict["index"],
)
LOGGER.info(f"Local prediction time: {time.time() - s} s")
return get_node_ip_address(), predictions
def predict(self, booster: xgb.Booster, *args, **kwargs):
local_dpredict = self._dpredict
booster.set_param({"nthread": self._nthreads})
s = time.time()
predictions = [
booster.predict(X, *args, **kwargs) for X in local_dpredict
]
LOGGER.info(f"Local prediction time: {time.time() - s} s")
return np.concatenate(predictions)
def predict(self, booster: xgb.Booster, **kwargs):
local_dpredict = self._dpredict
booster.set_param({"nthread": self._nthreads})
s = time.time()
predictions = [
pandas.DataFrame(booster.predict(X, **kwargs))
for X in local_dpredict
]
LOGGER.info(f"Local prediction time: {time.time() - s} s")
return predictions if len(predictions) > 1 else predictions[0]
def predict(self, booster: xgb.Booster, **kwargs):
local_dpredict = self._dpredict
booster.set_param({"nthread": self._nthreads})
s = time.time()
predictions = pandas.DataFrame(
booster.predict(local_dpredict["dmatrix"], **kwargs),
index=local_dpredict["index"],
)
LOGGER.info(f"Local prediction time: {time.time() - s} s")
return get_node_ip_address(), predictions
def my_train_xgboost(params, dtrain, num_boost_round=10, evals=(), obj=None,
feval=None, early_stopping_rounds=None, seed=0,
rt_eta=1.0006, rt_ssp=1.0006, rt_clb=1.0006,
rt_dpt=1.0001):
"""
Train a booster with given parameters.
Parameters
----------
params : dict
Booster params.
dtrain : DMatrix
Data to be trained.
num_boost_round: int
Number of boosting iterations.
watchlist : list of pairs (DMatrix, string)
List of items to be evaluated during training, this allows user to watch
performance on the validation set.
obj : function
Customized objective function.
feval : function
Customized evaluation function.
early_stopping_rounds: int
Activates early stopping. Validation error needs to decrease at least
every <early_stopping_rounds> round(s) to continue training.
Requires at least one item in evals.
If there's more than one, will use the last.
Returns the model from the last iteration (not the best one).
If early stopping occurs, the model will have two additional fields:
bst.best_score and bst.best_iteration.
Returns
-------
booster : a trained booster model
"""
eta = params['eta']
ssp = params['subsample']
clb = params['colsample_bytree']
# rt_eta=np.random.random()
rt_ssp=np.random.uniform(0.1,0.9)
rt_clb=np.random.uniform(0.1,0.9)
evals = list(evals)
bst = Booster(params, [dtrain] + [d[0] for d in evals], seed=seed)
if not early_stopping_rounds:
for i in range(num_boost_round):
bst.set_param({'eta': eta})
bst.set_param({'subsample': ssp})
bst.set_param({'colsample_bytree': clb})
eta = eta * rt_eta
# ssp = ssp * rt_ssp
# clb = clb * rt_clb
ssp = rt_ssp
clb = rt_clb
bst.update(dtrain, i, obj)
if len(evals) != 0:
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, string_types):
sys.stderr.write(bst_eval_set + '\n')
else:
sys.stderr.write(bst_eval_set.decode() + '\n')
return bst
else:
# early stopping
if len(evals) < 1:
raise ValueError('For early stopping you need at least on set in evals.')
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(evals[-1][1], early_stopping_rounds))
# is params a list of tuples? are we using multiple eval metrics?
if type(params) == list:
if len(params) != len(dict(params).items()):
raise ValueError('Check your params. Early stopping works with single eval metric only.')
params = dict(params)
# either minimize loss or maximize AUC/MAP/NDCG
maximize_score = False
if 'eval_metric' in params:
maximize_metrics = ('auc', 'map', 'ndcg')
if filter(lambda x: params['eval_metric'].startswith(x), maximize_metrics):
maximize_score = True
if maximize_score:
best_score = 0.0
else:
best_score = float('inf')
best_msg = ''
best_score_i = 0
for i in range(num_boost_round):
bst.set_param({'eta': eta})
bst.set_param({'subsample': ssp})
bst.set_param({'colsample_bytree': clb})
eta = eta * rt_eta
# ssp = ssp * rt_ssp
# clb = clb * rt_clb
ssp = rt_ssp
clb = rt_clb
bst.update(dtrain, i, obj)
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, string_types):
msg = bst_eval_set
else:
msg = bst_eval_set.decode()
sys.stderr.write(msg + '\n')
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
best_score = score
best_score_i = i
best_msg = msg
elif i - best_score_i >= early_stopping_rounds:
sys.stderr.write("Stopping. Best iteration:\n{}\n\n".format(best_msg))
bst.best_score = best_score
bst.best_iteration = best_score_i
return bst
return bst
