def callback(env):
"""internal function"""
score_train = env.evaluation_result_list[0][1]
score = env.evaluation_result_list[1][1]
if not state:
init(env)
best_score = state['best_score']
best_iteration = state['best_iteration']
maximize_score = state['maximize_score']
if (maximize_score and score > best_score and ((not symetric_overfit and score_train - score <= max_overfit) or
(symetric_overfit and abs(score_train - score) <= max_overfit))) or \
(not maximize_score and score < best_score and ((not symetric_overfit and score - score_train <= max_overfit) or
(symetric_overfit and abs(score - score_train) <= max_overfit))):
msg = '[%d]\t%s' % (env.iteration, '\t'.join(
[_fmt_metric(x) for x in env.evaluation_result_list]))
state['best_msg'] = msg
state['best_score'] = score
state['best_score_train'] = score_train
state['best_iteration'] = env.iteration
# save the property to attributes, so they will occur in checkpoint.
if env.model is not None:
env.model.set_attr(best_score=str(state['best_score']),
best_score_train=str(
state['best_score_train']),
best_iteration=str(state['best_iteration']),
best_msg=state['best_msg'])
elif env.iteration - best_iteration >= stopping_rounds:
best_msg = state['best_msg']
if verbose and env.rank == 0:
msg = "Stopping. Best iteration:\n{}\n\n"
rabit.tracker_print(msg.format(best_msg))
raise EarlyStopException(best_iteration)
def callback(env):
"""internal function"""
if env.iteration < start_round:
return
score = env.evaluation_result_list[eval_idx][1]
if len(state) == 0:
init(env)
best_score = state['best_score']
best_iteration = state['best_iteration']
maximize_score = state['maximize_score']
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
msg = '[%d]\t%s' % (env.iteration, '\t'.join(
[_fmt_metric(x) for x in env.evaluation_result_list]))
state['best_msg'] = msg
state['best_score'] = score
state['best_iteration'] = env.iteration
# save the property to attributes, so they will occur in checkpoint.
if env.model is not None:
env.model.set_attr(best_score=str(state['best_score']),
best_iteration=str(state['best_iteration']),
best_msg=state['best_msg'])
elif env.iteration - best_iteration >= stopping_rounds:
best_msg = state['best_msg']
if verbose and env.rank == 0:
msg = "Stopping. Best iteration:\n{}\n\n"
rabit.tracker_print(msg.format(best_msg))
raise xgb.core.EarlyStopException(best_iteration)
def callback(env):
"""internal function"""
if env.rank != 0 or (not env.evaluation_result_list) or period is False or period == 0:
return
i = env.iteration
if i % period == 0 or i + 1 == env.begin_iteration or i + 1 == env.end_iteration:
msg = '\t'.join([format_metric(x, show_stdv) for x in env.evaluation_result_list])
rabit.tracker_print('[%d]\t%s\n' % (i + start_iteration, msg))
def init(env):
"""internal function"""
bst = env.model
if not env.evaluation_result_list:
raise ValueError(
'For early stopping you need at least one set in evals.')
if len(env.evaluation_result_list) > 1 and verbose:
msg = ("Multiple eval metrics have been passed: "
"'{0}' will be used for early stopping.\n\n")
rabit.tracker_print(msg.format(env.evaluation_result_list[-1][0]))
maximize_metrics = ('auc', 'aucpr', 'map', 'ndcg')
maximize_at_n_metrics = ('auc@', 'aucpr@', 'map@', 'ndcg@')
maximize_score = maximize
metric_label = env.evaluation_result_list[-1][0]
metric = metric_label.split('-', 1)[-1]
if any(metric.startswith(x) for x in maximize_at_n_metrics):
maximize_score = True
if any(metric.split(":")[0] == x for x in maximize_metrics):
maximize_score = True
if verbose and env.rank == 0:
msg = "Will train until {} hasn't improved in {} rounds.\n"
rabit.tracker_print(msg.format(metric_label, stopping_rounds))
state['maximize_score'] = maximize_score
state['best_iteration'] = 0
state['best_msg'] = ''
if maximize_score:
state['best_score'] = float('-inf')
state['best_score_train'] = float('-inf')
else:
state['best_score'] = float('inf')
state['best_score_train'] = float('inf')
if bst is not None:
if bst.attr('best_score') is not None:
state['best_score'] = float(bst.attr('best_score'))
state['best_iteration'] = int(bst.attr('best_iteration'))
state['best_msg'] = bst.attr('best_msg')
else:
bst.set_attr(best_iteration=str(state['best_iteration']))
bst.set_attr(best_score=str(state['best_score']))
bst.set_attr(best_score_train=str(state['best_score_train']))
else:
assert env.cvfolds is not None
def init(env):
"""internal function"""
bst = env.model
if len(env.evaluation_result_list) == 0:
raise ValueError(
"For early stopping you need at least one set in evals.")
if len(env.evaluation_result_list) > 1 and verbose:
msg = "Multiple eval metrics have been passed: " "'{0}' will be used for early stopping.\n\n"
rabit.tracker_print(
msg.format(env.evaluation_result_list[eval_idx][0]))
maximize_metrics = ("auc", "map", "ndcg")
maximize_at_n_metrics = ("auc@", "map@", "ndcg@")
maximize_score = maximize
metric_label = env.evaluation_result_list[eval_idx][0]
metric = metric_label.split("-", 1)[-1]
if any(metric.startswith(x) for x in maximize_at_n_metrics):
maximize_score = True
if any(metric.split(":")[0] == x for x in maximize_metrics):
maximize_score = True
if verbose and env.rank == 0:
msg = "Will train until {} hasn't improved in {} rounds.\n"
rabit.tracker_print(msg.format(metric_label, stopping_rounds))
state["maximize_score"] = maximize_score
state["best_iteration"] = 0
if maximize_score:
state["best_score"] = float("-inf")
else:
state["best_score"] = float("inf")
if bst is not None:
if bst.attr("best_score") is not None:
state["best_score"] = float(bst.attr("best_score"))
state["best_iteration"] = int(bst.attr("best_iteration"))
state["best_msg"] = bst.attr("best_msg")
else:
bst.set_attr(best_iteration=str(state["best_iteration"]))
bst.set_attr(best_score=str(state["best_score"]))
else:
assert env.cvfolds is not None
