pickle

问题 First off I'm not expecting a solution, just hoping for some pointers on how to start. I've got a C program with an embedded Python interpreter. The Python scripts the program uses as input obviously refer to the C-defined objects and functions. I'd now like to make some of these objects pickleable. The pickle docs describe how extension types can be made picklable using __reduce__ . But this is a Python method - how would I define this in the underlying PyObject? Fairly sure I'm mis

问题 This is something that has been biting me for quite sometime now. I have the following pickle file (named rawFile.raw) that I generated by serializing some Python Dict objects : content of rawFile.raw (truncated for legibility) : (dp0 S'request_body # 1' p1 S'' p2 sS'port # 1' p3 I80 sS'query_params # 1' p4 ccopy_reg _reconstructor p5 (cnetlib.odict ODict p6 c__builtin__ object p7 Ntp8 Rp9 (dp10 S'lst' p11 (lp12 (S'layoutId' p13 S'-1123196643' p14 tp15 asbsS'headers # 1' p16 g5 (cnetlib.odict

问题 This question already has answers here : Saving and loading multiple objects in pickle file? (6 answers) Closed last year . import pickle ListNames = [["Name1","City1","Email1"],["Name2","City2","Number2"]] ListNumbers = [1,2,3,4,5,6,7,8] with open ("TestPickle.pickle","wb") as fileSaver: pickle.dump(ListNames,fileSaver) pickle.dump(ListNumbers,fileSaver) with open ("TestPickle.pickle","rb") as fileOpener: print(pickle.load(fileOpener)) The output is: [['Name1', 'City1', 'Email1'], ['Name2',

问题 As far as I understand, while using the Spark Scala interface we have to be careful not to unnecessarily serialize a full object when only one or two attributes are needed: (http://erikerlandson.github.io/blog/2015/03/31/hygienic-closures-for-scala-function-serialization/) How does this work when using PySpark? If I have a class as follows: class C0(object): def func0(arg): ... def func1(rdd): result = rdd.map(lambda x: self.func0(x)) Does this result to pickling the full C0 instances? if yes

问题 I wanted to use this classifier in other computer without had to train it again. I used to save some classifiers from scikit with cPickle. Doing the same with LIBSVM it gives me a " ValueError: ctypes objects containing pointers cannot be pickled ". I'm using LibSVM 3.1 and Python 2.7.3. Thanks from libsvm.svm import * from libsvm.svmutil import * import cPickle x = [[1, 0, 1], [-1, 0, -1]] y = [1, -1] prob = svm_problem(y, x) param = svm_parameter() param.kernel_type = LINEAR param.C = 10 m

问题 How can I modify the classes below to make them pickeable? This question: How to make a class which has __getattr__ properly pickable? is similar but refer to wrong exception in the use of getattr . This other question seems to provide meaningful insight Why does pickle.dumps call __getattr__?, however it fails to provide an example, and I honestly cannot understand what I am suppose to implement. import pickle class Foo(object): def __init__(self, dct): for key in dct: setattr(self, key, dct

问题 I'm trying to train a NLTK classifier for sentiment analysis and then save the classifier using pickle. The freshly trained classifier works fine. However, if I load a saved classifier the classifier will either output 'positive', or 'negative' for ALL examples. I'm saving the classifier using classifier = nltk.NaiveBayesClassifier.train(training_set) classifier.classify(words_in_tweet) f = open('classifier.pickle', 'wb') pickle.dump(classifier, f) f.close() and loading the classifier using f

问题 I am trying to serialize a Spark RDD by pickling it, and read the pickled file directly into Python. a = sc.parallelize(['1','2','3','4','5']) a.saveAsPickleFile('test_pkl') I then copy the test_pkl files to my local. How can I read them directly into Python? When I try the normal pickle package, it fails when I attempt to read the first pickle part of 'test_pkl': pickle.load(open('part-00000','rb')) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib64

问题 I have some pickle files of deep learning models built on gpu. I'm trying to use them in production. But when i try to unpickle them on the server, i'm getting the following error. Traceback (most recent call last): File "score.py", line 30, in model = (cPickle.load(file)) File "/usr/local/python2.7/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/sandbox/cuda/type.py", line 485, in CudaNdarray_unpickler return cuda.CudaNdarray(npa) AttributeError: ("'NoneType' object has no

问题 I'm using an old thread to post new code which attempts to solve the same problem. What constitutes a secure pickle? this? sock.py from socket import socket from socket import AF_INET from socket import SOCK_STREAM from socket import gethostbyname from socket import gethostname class SocketServer: def __init__(self, port): self.sock = socket(AF_INET, SOCK_STREAM) self.port = port def listen(self, data): self.sock.bind(("127.0.0.1", self.port)) self.sock.listen(len(data)) while data: s = self