Python--pickle Serialization (persistence)

The machine learning process is divided into the training process and the prediction process. The training process gets a model and the prediction process gets the predicted results. To save time, it's a good idea to call the already constructed model every time the prediction is executed, rather than having to retrain the model before each prediction.

Taking the decision tree as an example, it is a good idea to call a decision tree that has already been constructed each time the classification is performed. You can serialize an object using the Pickle module in Python. The serialized object can save the object pickle.dump () on disk and read it out pickle.load () when needed. any object can perform serialization operations, and the Dictionary object is no exception!!!

Code:

1 ImportPickle2 #Storing the decision Tree Model Inputtree (Dictionary object) in the file filename3 defStoretree (inputtree,filename):4Fw=open (filename,'W')5 pickle.dump (INPUTTREE,FW)6 fw.close ()7 #to read the decision tree in file filename8 defgrabtree (filename):9Fr=open (filename)Ten     returnPickle.load (FR)

To open the DOS client test:

In [all]: Store a decision tree in a dictionary,mytree={' no surfacing ': {0: ' No ', 1:{' flippers ': {0: ' No ', 1: ' Yes ' }}}

In []: Get the file "ClassifierStorage.txt", open the file to see what it looks like after serialization:

In []: Read the decision tree in "ClassifierStorage.txt"

Out []: output "Mytree"

Python--pickle Serialization (persistence)