86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
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import distutils.util
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import numpy as np
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from tqdm import tqdm
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from mvector.utils.logger import setup_logger
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logger = setup_logger(__name__)
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def print_arguments(args=None, configs=None):
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if args:
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logger.info("----------- 额外配置参数 -----------")
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for arg, value in sorted(vars(args).items()):
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logger.info("%s: %s" % (arg, value))
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logger.info("------------------------------------------------")
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if configs:
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logger.info("----------- 配置文件参数 -----------")
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for arg, value in sorted(configs.items()):
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if isinstance(value, dict):
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logger.info(f"{arg}:")
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for a, v in sorted(value.items()):
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if isinstance(v, dict):
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logger.info(f"\t{a}:")
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for a1, v1 in sorted(v.items()):
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logger.info("\t\t%s: %s" % (a1, v1))
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else:
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logger.info("\t%s: %s" % (a, v))
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else:
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logger.info("%s: %s" % (arg, value))
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logger.info("------------------------------------------------")
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def add_arguments(argname, type, default, help, argparser, **kwargs):
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type = distutils.util.strtobool if type == bool else type
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argparser.add_argument("--" + argname,
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default=default,
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type=type,
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help=help + ' 默认: %(default)s.',
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**kwargs)
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class Dict(dict):
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__setattr__ = dict.__setitem__
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__getattr__ = dict.__getitem__
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def dict_to_object(dict_obj):
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if not isinstance(dict_obj, dict):
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return dict_obj
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inst = Dict()
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for k, v in dict_obj.items():
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inst[k] = dict_to_object(v)
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return inst
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# 根据对角余弦值计算准确率和最优的阈值
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def cal_accuracy_threshold(y_score, y_true):
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y_score = np.asarray(y_score)
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y_true = np.asarray(y_true)
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best_accuracy = 0
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best_threshold = 0
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for i in tqdm(range(0, 100)):
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threshold = i * 0.01
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y_test = (y_score >= threshold)
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acc = np.mean((y_test == y_true).astype(int))
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if acc > best_accuracy:
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best_accuracy = acc
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best_threshold = threshold
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return best_accuracy, best_threshold
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# 根据对角余弦值计算准确率
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def cal_accuracy(y_score, y_true, threshold=0.5):
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y_score = np.asarray(y_score)
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y_true = np.asarray(y_true)
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y_test = (y_score >= threshold)
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accuracy = np.mean((y_test == y_true).astype(int))
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return accuracy
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# 计算对角余弦值
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def cosin_metric(x1, x2):
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return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
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