How to Save a Machine Learning Model

by Online Tutorials Library July 14, 2022

How to Save a Machine Learning Model

While using the scikit learn library for machine learning, it is necessary to save and restore the models to use them again to compare with other models or test the model against new data. The process of saving data is referred to as serialization, while the process of restoring data is referred to as Deserialization. We also handle different types and sizes of data. While some datasets can be trained quickly (e.g. they take less time), but the large datasets (more than 1GB) may take a lot of time to train, even on a local computer with GPU. To avoid losing time and avoid wastage, save the trained model from being used in future projects.

Two Ways to Save a Model from scikit-learn:

1. Pickle string: The pickle module implements an efficient yet fundamental algorithm for serializing or deserializing Python object structures.

The pickle model offers the following functions:

dump: For serializing an object hierarchy, we can use dump() function.
load: For deserializing a data stream, we can use the loads() function.

Example: Let’s use K Nearest Neighbor to the iris dataset, then save the model.

Code:

  import numpy as nmp  from sklearn.model_selection import train_test_split as tts     # Loading the dataset  from sklearn.datasets import load_iris as li  iris_1 = li()     A = iris_1.data  b = iris_1.target     # here, we are Spliting the dataset into train and test  A_train, A_test, b_train, b_test =       tts(A, b, test_size = 0.5,                          random_state = 2020)     # now, we are importing the KNeighborsClassifier model  from sklearn.neighbors import KNeighborsClassifier as KNNC  knn_1 = KNNC(n_neighbors = 4)     # training model  knn_1.fit(A_train, b_train)  

Output:

How to Save a Machine Learning Model

Now, we will save the above model to string using pickle –

Code:

  import pickle as pkl     # now, we are saving the trained model as a pickle string.  saved_model1 = pkl.dumps(knn_1)     # here, we are Loading the pickled model  knn_from_pkl = pkl.loads(saved_model1)     # at last we will use the loaded pickled model for making predictions  knn_from_pkl.predict(A_test)  

Output:

How to Save a Machine Learning Model

2. Pickled Model as File using joblib: Joblib replaces pickle because it is faster on objects with large numpy arrays. These functions only accepts file-like object instead of filename.

The pickled model as file using joblib offers the following functions:

dump: This is used for serializing object hierarchy.
load: This is used for deserializing a data stream.

Use joblib to save to pickled file

Example:

  import joblib as jbl     # Now, we are saving the model as a pickle in a file  jbl.dump(knn_1, ‘jtp.pkl’)     # Here, we are loading the model from the file  knn_from_joblib1 = jbl.load(‘jtp.pkl’)     # at last we will use the loaded pickled model for making predictions  knn_from_joblib1.predict(A_test)  

Output:

How to Save a Machine Learning Model

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