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Save and Load Auto Learning Models in Python with scikit-learn

Last Updated on August 28, 2022

Finding an accurate motorcar learning model is not the end of the project.

In this post you lot will discover how to save and load your automobile learning model in Python using scikit-acquire.

This allows you to salve your model to file and load information technology later in guild to brand predictions.

Kick-first your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.

Let'southward get started.

• Update January/2017: Updated to reflect changes to the scikit-acquire API in version 0.xviii.
• Update Mar/2018: Added alternate link to download the dataset as the original appears to accept been taken downwardly.
• Update October/2019: Stock-still typo in comment.
• Update Feb/2020: Updated joblib API.

Salvage and Load Motorcar Learning Models in Python with scikit-larn
Photo past Christine, some rights reserved.

Tutorial Overview

This tutorial is divided into iii parts, they are:

1. Save Your Model with pickle
2. Save Your Model with joblib
3. Tips for Saving Your Model

Relieve Your Model with pickle

Pickle is the standard fashion of serializing objects in Python.

You can utilise the pickle operation to serialize your automobile learning algorithms and salvage the serialized format to a file.

Later you can load this file to deserialize your model and use it to brand new predictions.

The example beneath demonstrates how you can train a logistic regression model on the Pima Indians onset of diabetes dataset, save the model to file and load it to brand predictions on the unseen test set (download from here).

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# Salve Model Using Pickle import pandas from sklearn import model_selection from sklearn . linear_model import LogisticRegression import pickle url = "https://raw.githubusercontent.com/jbrownlee/Datasets/chief/pima-indians-diabetes.data.csv" names = [ 'preg' , 'plas' , 'pres' , 'skin' , 'test' , 'mass' , 'pedi' , 'age' , 'class' ] dataframe = pandas . read_csv ( url , names = names ) assortment = dataframe . values 10 = assortment [ : , 0 : 8 ] Y = assortment [ : , 8 ] test_size = 0.33 seed = vii X_train , X_test , Y_train , Y_test = model_selection . train_test_split ( X , Y , test_size = test_size , random_state = seed ) # Fit the model on training set up model = LogisticRegression ( ) model . fit ( X_train , Y_train ) # salvage the model to disk filename = 'finalized_model.sav' pickle . dump ( model , open ( filename , 'wb' ) ) # some time later... # load the model from disk loaded_model = pickle . load ( open ( filename , 'rb' ) ) result = loaded_model . score ( X_test , Y_test ) print ( result )

Running the instance saves the model to finalized_model.sav in your local working directory.

Notation: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average issue.

Load the saved model and evaluating information technology provides an guess of accuracy of the model on unseen data.

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Save Your Model with joblib

Joblib is function of the SciPy ecosystem and provides utilities for pipelining Python jobs.

It provides utilities for saving and loading Python objects that make use of NumPy information structures, efficiently.

This can be useful for some automobile learning algorithms that require a lot of parameters or store the entire dataset (similar K-Nearest Neighbors).

The case beneath demonstrates how you tin can train a logistic regression model on the Pima Indians onset of diabetes dataset, saves the model to file using joblib and load it to make predictions on the unseen test set.

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# Save Model Using joblib import pandas from sklearn import model_selection from sklearn . linear_model import LogisticRegression import joblib url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" names = [ 'preg' , 'plas' , 'pres' , 'skin' , 'test' , 'mass' , 'pedi' , 'age' , 'form' ] dataframe = pandas . read_csv ( url , names = names ) array = dataframe . values X = assortment [ : , 0 : 8 ] Y = assortment [ : , 8 ] test_size = 0.33 seed = 7 X_train , X_test , Y_train , Y_test = model_selection . train_test_split ( 10 , Y , test_size = test_size , random_state = seed ) # Fit the model on training set model = LogisticRegression ( ) model . fit ( X_train , Y_train ) # salve the model to disk filename = 'finalized_model.sav' joblib . dump ( model , filename ) # some time later... # load the model from deejay loaded_model = joblib . load ( filename ) outcome = loaded_model . score ( X_test , Y_test ) print ( outcome )

Running the example saves the model to file as finalized_model.sav and too creates one file for each NumPy array in the model (iv boosted files).

Notation: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Later on the model is loaded an estimate of the accuracy of the model on unseen data is reported.

Tips for Saving Your Model

This section lists some important considerations when finalizing your machine learning models.

• Python Version. Have note of the python version. Yous well-nigh certainly require the same major (and maybe minor) version of Python used to serialize the model when y'all later load information technology and deserialize it.
• Library Versions. The version of all major libraries used in your machine learning projection about certainly demand to be the aforementioned when deserializing a saved model. This is not limited to the version of NumPy and the version of scikit-learn.
• Manual Serialization. You lot might like to manually output the parameters of your learned model so that you lot tin use them directly in scikit-larn or some other platform in the future. Often the algorithms used past machine learning algorithms to make predictions are a lot simpler than those used to learn the parameters can may be piece of cake to implement in custom code that yous have control over.

Take note of the version then that you tin can re-create the surround if for some reason you cannot reload your model on another motorcar or another platform at a later fourth dimension.

Summary

In this mail service you discovered how to persist your machine learning algorithms in Python with scikit-learn.

You learned two techniques that you can utilise:

• The pickle API for serializing standard Python objects.
• The joblib API for efficiently serializing Python objects with NumPy arrays.

Practise you have whatever questions about saving and loading your model?
Ask your questions in the comments and I will do my best to reply them.

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Source: https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/

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