I was having the same issue. I use joblib and work was done. In case if someone wants to know other possibilities. save the model to disk from sklearn.externals import joblib filename=”finalized_model.sav” joblib.dump(model, filename) some time later… load the model from disk loaded_model = joblib.load(filename) result = loaded_model.score(X_test, Y_test) print(result)
I actually think you’d be best off with kryo (I’m not aware of alternatives that offer less schema defining other than non-binary protocols). You mention that pickle is not susceptible to the slowdowns and bloat that kryo gets without registering classes, but kryo is still faster and less bloated than pickle even without registering classes. … Read more
When the Pickler encounters an object of a type it knows nothing about, it looks for a reduce method. Defining this method when you build your custom class using type should solve the problem of pickling. If you provide initial args then in addition you might need to define a getnewargs method
Change: # Read from file f_myfile = open(‘myfile.pickle’, ‘wb’) to: f_myfile = open(‘myfile.pickle’, ‘rb’) and you can see the dict obj you’ve pickled.
You can combine your cPickle dump call with a zipfile: import cPickle import gzip def save_zipped_pickle(obj, filename, protocol=-1): with gzip.open(filename, ‘wb’) as f: cPickle.dump(obj, f, protocol) And to re-load a zipped pickled object: def load_zipped_pickle(filename): with gzip.open(filename, ‘rb’) as f: loaded_object = cPickle.load(f) return loaded_object
I had this error when I created a pkl file with python 2.7 and was trying to read it with python 3.6 I did: pd.read_pickle(‘foo.pkl’) and it worked
In Python 2.x and 3.0, 3.1 and 3.2, multiprocessing.Pool() objects are not context managers. You cannot use them in a with statement. Only in Python 3.3 and up can you use them as such. From the Python 3 multiprocessing.Pool() documentation: New in version 3.3: Pool objects now support the context management protocol – see Context … Read more
It’s a trivial mistake: pickle.dump(obj,file) takes a file object, not a file name. What I need is something like: with open(“a-file.pickle”,’wb’) as f: pickle.dump({“a dict”:True},f)
It is because you are setting Test.A as a class attribute instead of an instance attribute. Really what is happening is that with the test1.py, the object being read back from the pickle file is the same as test2.py, but its using the class in memory where you had originally assigned x.A. When your data … Read more
The second argument to cPickle.dump() must be a file object. You passed in a string containing a filename instead. You need to use the open() function to open a file object for that filename, then pass the file object to cPickle: with open(outfile, ‘wb’) as pickle_file: cPickle.dump(all, pickle_file) See the Reading and Writing Files section … Read more