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How to Fix FutureWarning Messages in scikit-learn

Last Updated on August 21, 2019

Upcoming changes to the scikit-learn library for machine learning are reported through the utilise of FutureWarning letters when the code is run.

Warning messages can exist confusing to beginners as it looks like in that location is a problem with the lawmaking or that they accept washed something wrong. Warning messages are also not adept for operational lawmaking as they can obscure errors and programme output.

There are many ways to handle a warning message, including ignoring the bulletin, suppressing warnings, and fixing the lawmaking.

In this tutorial, you will discover FutureWarning messages in the scikit-learn API and how to handle them in your ain machine learning projects.

Later completing this tutorial, you will know:

• FutureWarning messages are designed to inform you about upcoming changes to default values for arguments in the scikit-learn API.
• FutureWarning messages can be ignored or suppressed every bit they do not halt the execution of your program.
• Examples of FutureWarning letters and how to interpret the bulletin and change your code to address the upcoming alter.

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How to Fix FutureWarning Messages in scikit-learn
Photo past a.dombrowski, some rights reserved.

Tutorial Overview

This tutorial is divided into four parts; they are:

1. Problem of FutureWarnings
2. How to Suppress FutureWarnings
3. How to Fix FutureWarnings
4. FutureWarning Recommendations

Problem of FutureWarnings

The scikit-larn library is an open-source library that offers tools for information preparation and car learning algorithms.

It is a widely used and constantly updated library.

Like many actively maintained software libraries, the APIs often change over fourth dimension. This may be because better practices are discovered or preferred usage patterns change.

Most functions available in the scikit-learn API have one or more arguments that allow you customize the beliefs of the function. Many arguments take sensible defaults so that you don't accept to specify a value for the arguments. This is particularly helpful when you are starting out with auto learning or with scikit-larn and you don't know what bear on each of the arguments has.

Change to the scikit-acquire API over fourth dimension oftentimes comes in the form of changes to the sensible defaults to arguments to functions. Changes of this type are often not performed immediately; instead, they are planned.

For example, if your code was written for a prior version of the scikit-learn library and relies on a default value for a office argument and a subsequent version of the API plans to modify this default value, then the API will alert you lot to the upcoming change.

This alert comes in the form of a warning message each time your lawmaking is run. Specifically, a "FutureWarning" is reported on standard error (eastward.1000. on the command line).

This is a useful characteristic of the API and the project, designed for your benefit. It allows you to change your code ready for the next major release of the library to either retain the quondam behavior (specify a value for the argument) or adopt the new behavior (no change to your code).

A Python script that reports warnings when it runs can be frustrating.

• For a beginner, information technology may feel like the code is non working correctly, that perhaps you have done something incorrect.
• For a professional, it is a sign of a program that requires updating.

In either case, warning messages may obscure real error messages or output from the program.

How to Suppress FutureWarnings

Alarm messages are not fault messages.

As such, a warning message reported by your program, such as a FutureWarning, volition not halt the execution of your plan. The warning message will be reported and the program will conduct on executing.

You tin, therefore, ignore the alert each time your code is executed, if y'all wish.

It is also possible to programmatically ignore the alert messages. This can be done by suppressing warning letters when your program is run.

This can exist accomplished by explicitly configuring the Python warning organization to ignore alarm messages of a specific type, such as ignore all FutureWarnings, or more than generally, to ignore all warnings.

This can exist achieved past calculation the following block around your lawmaking that you know will generate warnings:

# run block of code and catch warnings with warnings . catch_warnings ( ) : # ignore all caught warnings warnings . filterwarnings ( "ignore" ) # execute lawmaking that will generate warnings . . .

Or, if y'all have a very unproblematic flat script (no functions or blocks), you can suppress all FutureWarnings past adding two lines to the meridian of your file:

# import warnings filter from warnings import simplefilter # ignore all future warnings simplefilter ( activity = 'ignore' , category = FutureWarning )

To learn more about suppressing in Python, see:

• Python Warning command API

How to Fix FutureWarnings

Alternately, you lot can modify your code to address the reported change to the scikit-learn API.

Typically, the warning message itself will instruct you on the nature of the change and how to alter your code to accost the warning.

Nevertheless, permit'southward look at a few recent examples of FutureWarnings that you may meet and be struggling with.

The examples in this section were developed with scikit-learn version 0.20.two. You can check your scikit-learn version by running the following lawmaking:

# check scikit-learn version import sklearn print ( 'sklearn: %south' % sklearn . __version__ )

You will see output like the post-obit:

Every bit new versions of scikit-learn are released over time, the nature of the alert messages reported will modify and new defaults will be adopted.

Every bit such, although the examples below are specific to a version of scikit-learn, the approach to diagnosing and addressing the nature of each API modify and provide good examples for handling future changes.

FutureWarning for LogisticRegression

The LogisticRegression algorithm has 2 recent changes to the default argument values that result in FutureWarning messages.

The offset has to practise with the solver for finding coefficients and the 2d has to exercise with how the model should exist used to make multi-class classifications. Permit'south wait at each with code examples.

Changes to the Solver

The example below will generate a FutureWarning about the solver argument used by LogisticRegression.

# example of LogisticRegression that generates a FutureWarning from sklearn . datasets import make_blobs from sklearn . linear_model import LogisticRegression # prepare dataset X , y = make_blobs ( n_samples = 100 , centers = 2 , n_features = 2 ) # create and configure model model = LogisticRegression ( ) # fit model model . fit ( X , y )

Running the example results in the following warning message:

FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this alarm.

This issue involves a change from the 'solver' argument that used to default to 'liblinear' and will modify to default to 'lbfgs' in a hereafter version. You must now specify the 'solver' statement.

To maintain the onetime behavior, you can specify the argument as follows:

# create and configure model model = LogisticRegression ( solver = 'liblinear' )

To support the new behavior (recommended), you tin can specify the argument as follows:

# create and configure model model = LogisticRegression ( solver = 'lbfgs' )

Changes to the Multi-Form

The instance below will generate a FutureWarning nearly the 'multi_class' statement used past LogisticRegression.

# example of LogisticRegression that generates a FutureWarning from sklearn . datasets import make_blobs from sklearn . linear_model import LogisticRegression # prepare dataset X , y = make_blobs ( n_samples = 100 , centers = 3 , n_features = two ) # create and configure model model = LogisticRegression ( solver = 'lbfgs' ) # fit model model . fit ( X , y )

Running the instance results in the following warning message:

FutureWarning: Default multi_class volition exist changed to 'car' in 0.22. Specify the multi_class option to silence this warning.

This alert bulletin only affects the utilize of logistic regression for multi-grade classification problems, instead of the binary classification problems for which the method was designed.

The default of the 'multi_class' argument is irresolute from 'ovr' to 'machine'.

To maintain the old beliefs, you can specify the argument as follows:

# create and configure model model = LogisticRegression ( solver = 'lbfgs' , multi_class = 'ovr' )

To support the new behavior (recommended), you can specify the argument equally follows:

# create and configure model model = LogisticRegression ( solver = 'lbfgs' , multi_class = 'auto' )

FutureWarning for SVM

The support vector machine implementation has had a recent change to the 'gamma' statement that results in a warning message, specifically the SVC and SVR classes.

The case below will generate a FutureWarning almost the 'gamma' statement used by SVC, just but every bit as applies to SVR.

# example of SVC that generates a FutureWarning from sklearn . datasets import make_blobs from sklearn . svm import SVC # ready dataset X , y = make_blobs ( n_samples = 100 , centers = 2 , n_features = 2 ) # create and configure model model = SVC ( ) # fit model model . fit ( X , y )

Running this example volition generate the following warning bulletin:

FutureWarning: The default value of gamma volition change from 'auto' to 'scale' in version 0.22 to business relationship better for unscaled features. Gear up gamma explicitly to 'automobile' or 'scale' to avert this alert.

This warning message reports that the default for the 'gamma' argument is irresolute from the current value of 'auto' to a new default value of 'scale'.

The gamma argument but impacts SVM models that use the RBF, Polynomial, or Sigmoid kernel.

The parameter controls the value of the 'gamma' coefficient used in the algorithm and if you do not specify a value, a heuristic is used to specify the value. The warning is nearly a change in the way that the default volition be calculated.

To maintain the old behavior, you tin can specify the argument equally follows:

# create and configure model model = SVC ( gamma = 'auto' )

To back up the new behavior (recommended), yous can specify the statement as follows:

# create and configure model model = SVC ( gamma = 'scale' )

FutureWarning for Determination Tree Ensemble Algorithms

The conclusion-tree based ensemble algorithms will change the number of sub-models or trees used in the ensemble controlled by the 'n_estimators' statement.

This affects models' random forest and extra trees for classification and regression, specifically the classes: RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor, and RandomTreesEmbedding.

The example below will generate a FutureWarning virtually the 'n_estimators' argument used by RandomForestClassifier, but just as equally applies to RandomForestRegressor and the extra trees classes.

# example of RandomForestClassifier that generates a FutureWarning from sklearn . datasets import make_blobs from sklearn . ensemble import RandomForestClassifier # prepare dataset Ten , y = make_blobs ( n_samples = 100 , centers = 2 , n_features = 2 ) # create and configure model model = RandomForestClassifier ( ) # fit model model . fit ( X , y )

Running this case will generate the following warning message:

FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.

This warning bulletin reports that the number of submodels is increasing from 10 to 100, likely because computers are getting faster and 10 is very small-scale, even 100 is small.

To maintain the old behavior, y'all tin specify the statement as follows:

# create and configure model model = RandomForestClassifier ( n_estimators = 10 )

To support the new beliefs (recommended), you can specify the argument as follows:

# create and configure model model = RandomForestClassifier ( n_estimators = 100 )

More than Future Warnings?

Are you lot struggling with a FutureWarning that is not covered?

Let me know in the comments below and I will do my all-time to aid.

FutureWarning Recommendations

Generally, I exercise not recommend ignoring or suppressing warning messages.

Ignoring warning messages means that the message may obscure real errors or plan output and that API future changes may negatively impact your plan unless yous have considered them.

Suppressing warnings might be a quick ready for R&D piece of work, but should not be used in a product system. Worse than simply ignoring the letters, suppressing the warnings may also suppress letters from other APIs.

Instead, I recommend that you fix the alert messages in your software.

How should you change your code?

In general, I recommend almost always adopting the new behavior of the API, e.grand. the new default, unless you explicitly rely on the prior behavior of the function.

For long-lived operational or production code, it might be a proficient idea to explicitly specify all function arguments and not employ defaults, equally they might be bailiwick to change in the hereafter.

I as well recommend that y'all keep your scikit-learn library upward to date, and keep rails of the changes to the API in each new release.

The easiest way to do this is to review the release notes for each release, available here:

• scikit-acquire Release History

Further Reading

This section provides more resources on the topic if you are looking to get deeper.

• Python Warning control API
• sklearn.linear_model.LogisticRegression API
• sklearn.svm.SVC API
• sklearn.svm.SVR API
• scikit-learn Release History

Summary

In this tutorial, y'all discovered FutureWarning messages in the scikit-learn API and how to handle them in your own machine learning projects.

Specifically, you learned:

• FutureWarning letters are designed to inform you about upcoming changes to default values for arguments in the scikit-larn API.
• FutureWarning messages can be ignored or suppressed as they do non halt the execution of your program.
• Examples of FutureWarning messages and how to interpret the message and change your code to address the upcoming change.

Do you have whatever questions?
Ask your questions in the comments beneath and I will do my all-time to answer.

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