泡菜和numpy版本

发布于 2025-01-19 16:44:06 字数 373 浏览 2 评论 0原文

我有一些我无法重新训练的旧型号。他们很久以前就被腌制了。我可以使用Python 3.6和Numpy 1.14打开它们。但是，当我尝试使用numpy 1.18移动到Python 3.8时，我会在加载它们时得到一个segfault。

我尝试用Python 3.6的协议4倾倒它们，这无济于事。

储蓄：

with open('model.pkl', 'wb') as fid:
    pickle.dump(model, fid, protocol=4)

加载：

model = pickle.load(open('model.pkl', "rb"))

在这种情况下我可以做什么？

原文

I have some old sklearn models which I can't retrain. They were pickled long time ago with unclear versions. I can open them with Python 3.6 and Numpy 1.14. But when I try to move to Python 3.8 with Numpy 1.18, I get a segfault on loading them.

I tried dumping them with protocol 4 from Python 3.6, it didn't help.

Saving:

with open('model.pkl', 'wb') as fid:
    pickle.dump(model, fid, protocol=4)

Loading:

model = pickle.load(open('model.pkl', "rb"))

Is there anything I can do in such situation?

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戈亓 2025-01-26 16:44:06

对我有用的是什么（特定于任务，但也许会帮助某人）：

旧依赖性：

import joblib
model = pickle.load(open('model.pkl', "rb"), encoding="latin1")
joblib.dump(model.tree_.get_arrays()[0], "training_data.pkl")

较新的依赖性：

import joblib
from sklearn.neighbors import KernelDensity

data = joblib.load("training_data.pkl")
kde = KernelDensity(
      algorithm="auto",
      atol=0,
      bandwidth=0.5,
      breadth_first=True,
      kernel="gaussian",
      leaf_size=40,
      metric="euclidean",
      metric_params=None,
      rtol=0
).fit(data)

with open("new_model.pkl", "wb") as f:
    pickle.dump(kde, f)

What worked for me (very task-specific but maybe will help someone):

Old dependencies:

import joblib
model = pickle.load(open('model.pkl', "rb"), encoding="latin1")
joblib.dump(model.tree_.get_arrays()[0], "training_data.pkl")

Newer dependencies:

import joblib
from sklearn.neighbors import KernelDensity

data = joblib.load("training_data.pkl")
kde = KernelDensity(
      algorithm="auto",
      atol=0,
      bandwidth=0.5,
      breadth_first=True,
      kernel="gaussian",
      leaf_size=40,
      metric="euclidean",
      metric_params=None,
      rtol=0
).fit(data)

with open("new_model.pkl", "wb") as f:
    pickle.dump(kde, f)

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