kandi has reviewed pickle and discovered the below as its top functions. This is intended to give you an instant insight into pickle implemented functionality, and help decide if they suit your requirements.
PHP Extension installer
Grab the latest phar at https://github.com/FriendsOfPHP/pickle/releases/latest ```sh wget https://github.com/FriendsOfPHP/pickle/releases/latest/download/pickle.phar ``` and run using ```sh $ php pickle.phar ``` or add the execute flag ```sh $ chmod +x pickle.phar ``` then run as: ```sh $ pickle.phar info apcu ``` You can also rename the phar to "pickle" ```sh $ mv pickle.phar pickle ``` so it can be called using pickle only. And finally you can add it to your path or copy it in /usr/local/bin or your favorite binary directory. On windows, use ```sh $ php pickle.phar ``` or create a .bat containing: ``` @echo OFF setlocal DISABLEDELAYEDEXPANSION c:\path\to\php.exe "c:\path\to\pickle.phar" %* ``` If someone would be kind enough to write an installer script, we would be eternally thankful :) Introduction
Pickle and Numpy versionsAsked 2022-Apr-08 at 08:24
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.
with open('model.pkl', 'wb') as fid: pickle.dump(model, fid, protocol=4)
model = pickle.load(open('model.pkl', "rb"))
Is there anything I can do in such situation?
ANSWERAnswered 2022-Apr-08 at 08:24
What worked for me (very task-specific but maybe will help someone):
import joblib model = pickle.load(open('model.pkl', "rb"), encoding="latin1") joblib.dump(model.tree_.get_arrays(), "training_data.pkl")
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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