Open ghost opened 5 years ago
One way was to give None
to py_model.model
but I wont be able to use predict
on it again.
I could wrap copy options and handling cases in Julia or Python but maybe there is clean to do this with c++ or python copy procedures ?
Hi, @gdestouet,
There are two methods to save the best model during training.
If the model is fully trained on your training data, you can use save_to_file and load_from_file to save and load the model.
You can save the best configuration and train the model again.
Hello everyone,
I am actually using thundersvm with Python through Julia. During training, I would like to save the best C-SVC model encountered.
Let's say that I find such a
best
model during training, I would then like to copy this python object along with the pointer on the C++ SVM object. I am actually doing this for now :But If I fit again the
py_model
object I would free the pointer on the c++ object since : https://github.com/Xtra-Computing/thundersvm/blob/6e28da802e483fc741056a1768c825737c840cca/python/thundersvm/thundersvm.py#L96-L99And consequently the pointer I have in
best.model
.Is there a way to avoid freeing this pointer if the original
py_model
has been copied or safely copy this c++ pointer into a fitted clone ofpy_model
?Thank you.