зеркало из https://github.com/microsoft/LightGBM.git
[python-package] fix some warnings from mypy (#3891)
* minor mypy type errors fixed * fix some warnings from mypy * fix 3 mypy warnings * selectively ignored some mypy errors * minor mypy type errors fixed * minor mypy type errors fixed * minor mypy type errors fixed * added import * Update python-package/lightgbm/callback.py * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: James Lamb <jaylamb20@gmail.com> Co-authored-by: James Lamb <jaylamb20@gmail.com>
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@ -9,7 +9,7 @@ It is based on dask-lightgbm, which was based on dask-xgboost.
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import socket
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from collections import defaultdict
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from copy import deepcopy
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from typing import Any, Callable, Dict, Iterable, List, Optional, Type, Union
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from typing import Any, Callable, Dict, Iterable, List, Optional, Type, Union, Set
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from urllib.parse import urlparse
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import numpy as np
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@ -77,7 +77,6 @@ def _find_open_port(worker_ip: str, local_listen_port: int, ports_to_skip: Itera
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A free port on the machine referenced by ``worker_ip``.
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"""
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max_tries = 1000
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out_port = None
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found_port = False
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for i in range(max_tries):
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out_port = local_listen_port + i
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@ -117,7 +116,7 @@ def _find_ports_for_workers(client: Client, worker_addresses: Iterable[str], loc
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result : Dict[str, int]
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Dictionary where keys are worker addresses and values are an open port for LightGBM to use.
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"""
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lightgbm_ports = set()
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lightgbm_ports: Set[int] = set()
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worker_ip_to_port = {}
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for worker_address in worker_addresses:
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port = client.submit(
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@ -306,11 +305,11 @@ def _train(
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wait(parts)
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for part in parts:
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if part.status == 'error':
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if part.status == 'error': # type: ignore
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return part # trigger error locally
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# Find locations of all parts and map them to particular Dask workers
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key_to_part_dict = {part.key: part for part in parts}
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key_to_part_dict = {part.key: part for part in parts} # type: ignore
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who_has = client.who_has(parts)
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worker_map = defaultdict(list)
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for key, workers in who_has.items():
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@ -824,8 +824,8 @@ class LGBMRegressor(LGBMModel, _LGBMRegressorBase):
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return self
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_base_doc = LGBMModel.fit.__doc__
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_base_doc = (_base_doc[:_base_doc.find('group :')]
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+ _base_doc[_base_doc.find('eval_set :'):])
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_base_doc = (_base_doc[:_base_doc.find('group :')] # type: ignore
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+ _base_doc[_base_doc.find('eval_set :'):]) # type: ignore
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_base_doc = (_base_doc[:_base_doc.find('eval_class_weight :')]
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+ _base_doc[_base_doc.find('eval_init_score :'):])
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fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
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@ -897,8 +897,8 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
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return self
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_base_doc = LGBMModel.fit.__doc__
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_base_doc = (_base_doc[:_base_doc.find('group :')]
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+ _base_doc[_base_doc.find('eval_set :'):])
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_base_doc = (_base_doc[:_base_doc.find('group :')] # type: ignore
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+ _base_doc[_base_doc.find('eval_set :'):]) # type: ignore
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fit.__doc__ = (_base_doc[:_base_doc.find('eval_group :')]
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+ _base_doc[_base_doc.find('eval_metric :'):])
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@ -989,8 +989,8 @@ class LGBMRanker(LGBMModel):
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return self
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_base_doc = LGBMModel.fit.__doc__
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fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')]
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+ _base_doc[_base_doc.find('eval_init_score :'):])
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fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')] # type: ignore
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+ _base_doc[_base_doc.find('eval_init_score :'):]) # type: ignore
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_base_doc = fit.__doc__
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_before_early_stop, _early_stop, _after_early_stop = _base_doc.partition('early_stopping_rounds :')
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fit.__doc__ = (_before_early_stop
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@ -322,7 +322,7 @@ if __name__ == "__main__":
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if os.path.isfile(os.path.join(CURRENT_DIR, os.path.pardir, 'VERSION.txt')):
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copy_file(os.path.join(CURRENT_DIR, os.path.pardir, 'VERSION.txt'),
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os.path.join(CURRENT_DIR, 'lightgbm', 'VERSION.txt'),
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verbose=0)
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verbose=0) # type:ignore
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version = open(os.path.join(CURRENT_DIR, 'lightgbm', 'VERSION.txt'), encoding='utf-8').read().strip()
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readme = open(os.path.join(CURRENT_DIR, 'README.rst'), encoding='utf-8').read()
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