clearml/clearml/binding/frameworks/catboost_bind.py

134 lines
5.3 KiB
Python

import sys
from pathlib2 import Path
import six
from ..frameworks import WeightsFileHandler, _Empty, _patched_call
from ..frameworks.base_bind import PatchBaseModelIO
from ..import_bind import PostImportHookPatching
from ...model import Framework
class PatchCatBoostModelIO(PatchBaseModelIO):
_current_task = None
__patched = None
__callback_cls = None
@staticmethod
def update_current_task(task, **kwargs):
PatchCatBoostModelIO._current_task = task
if not task:
return
PatchCatBoostModelIO._patch_model_io()
PostImportHookPatching.add_on_import("catboost", PatchCatBoostModelIO._patch_model_io)
@staticmethod
def _patch_model_io():
if PatchCatBoostModelIO.__patched:
return
if "catboost" not in sys.modules:
return
PatchCatBoostModelIO.__patched = True
# noinspection PyBroadException
try:
from catboost import CatBoost, CatBoostClassifier, CatBoostRegressor, CatBoostRanker
CatBoost.save_model = _patched_call(CatBoost.save_model, PatchCatBoostModelIO._save)
CatBoost.load_model = _patched_call(CatBoost.load_model, PatchCatBoostModelIO._load)
PatchCatBoostModelIO.__callback_cls = PatchCatBoostModelIO._generate_training_callback_class()
CatBoost.fit = _patched_call(CatBoost.fit, PatchCatBoostModelIO._fit)
CatBoostClassifier.fit = _patched_call(CatBoostClassifier.fit, PatchCatBoostModelIO._fit)
CatBoostRegressor.fit = _patched_call(CatBoostRegressor.fit, PatchCatBoostModelIO._fit)
CatBoostRanker.fit = _patched_call(CatBoostRanker.fit, PatchCatBoostModelIO._fit)
except Exception as e:
logger = PatchCatBoostModelIO._current_task.get_logger()
logger.report_text("Failed patching Catboost. Exception is: '" + str(e) + "'")
@staticmethod
def _save(original_fn, obj, f, *args, **kwargs):
# see https://catboost.ai/en/docs/concepts/python-reference_catboost_save_model
ret = original_fn(obj, f, *args, **kwargs)
if not PatchCatBoostModelIO._current_task:
return ret
if isinstance(f, six.string_types):
filename = f
else:
filename = None
# give the model a descriptive name based on the file name
# noinspection PyBroadException
try:
model_name = Path(filename).stem
except Exception:
model_name = None
WeightsFileHandler.create_output_model(
obj, filename, Framework.catboost, PatchCatBoostModelIO._current_task, singlefile=True, model_name=model_name
)
return ret
@staticmethod
def _load(original_fn, f, *args, **kwargs):
# see https://catboost.ai/en/docs/concepts/python-reference_catboost_load_model
if not PatchCatBoostModelIO._current_task:
return original_fn(f, *args, **kwargs)
if isinstance(f, six.string_types):
filename = f
elif len(args) >= 1 and isinstance(args[0], six.string_types):
filename = args[0]
else:
filename = None
# register input model
empty = _Empty()
model = original_fn(f, *args, **kwargs)
WeightsFileHandler.restore_weights_file(empty, filename, Framework.catboost, PatchCatBoostModelIO._current_task)
if empty.trains_in_model:
# noinspection PyBroadException
try:
model.trains_in_model = empty.trains_in_model
except Exception:
pass
return model
@staticmethod
def _fit(original_fn, obj, *args, **kwargs):
if not PatchCatBoostModelIO._current_task:
return original_fn(obj, *args, **kwargs)
callbacks = kwargs.get("callbacks") or []
kwargs["callbacks"] = callbacks + [PatchCatBoostModelIO.__callback_cls(task=PatchCatBoostModelIO._current_task)]
# noinspection PyBroadException
try:
return original_fn(obj, *args, **kwargs)
except Exception:
logger = PatchCatBoostModelIO._current_task.get_logger()
logger.report_text(
"Catboost metrics logging is not supported for GPU. "
"See https://github.com/catboost/catboost/issues/1792"
)
del kwargs["callbacks"]
return original_fn(obj, *args, **kwargs)
@staticmethod
def _generate_training_callback_class():
class ClearMLCallback:
_scalar_index_counter = 0
def __init__(self, task):
self._logger = task.get_logger()
self._scalar_index = ClearMLCallback._scalar_index_counter
ClearMLCallback._scalar_index_counter += 1
def after_iteration(self, info):
info = vars(info)
iteration = info.get("iteration")
for title, metric in (info.get("metrics") or {}).items():
if self._scalar_index != 0:
title = "{} - {}".format(title, self._scalar_index)
for series, log in metric.items():
value = log[-1]
self._logger.report_scalar(title=title, series=series, value=value, iteration=iteration)
return True
return ClearMLCallback