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How to Fix FutureWarning Letters in scikit-learn

Last Updated on August 21, 2019

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

Warning messages tin be confusing to beginners every bit it looks like there is a problem with the code or that they have done something wrong. Warning messages are besides not good for operational lawmaking as they tin can obscure errors and program output.

In that location are many ways to handle a warning message, including ignoring the message, suppressing warnings, and fixing the code.

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

Subsequently completing this tutorial, yous will know:

FutureWarning letters are designed to inform you about upcoming changes to default values for arguments in the scikit-acquire API.
FutureWarning messages can exist ignored or suppressed as they do not 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.

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

Let'south get started.

How to Set up FutureWarning Letters in scikit-learn
Photo past a.dombrowski, some rights reserved.

Tutorial Overview

This tutorial is divided into four parts; they are:

Problem of FutureWarnings
How to Suppress FutureWarnings
How to Ready FutureWarnings
FutureWarning Recommendations

Problem of FutureWarnings

The scikit-learn library is an open-source library that offers tools for data preparation and machine learning algorithms.

Information technology is a widely used and constantly updated library.

Like many actively maintained software libraries, the APIs often modify over time. This may exist because improve practices are discovered or preferred usage patterns alter.

Most functions available in the scikit-learn API have 1 or more than arguments that permit you customize the beliefs of the role. Many arguments accept sensible defaults so that you lot don't have to specify a value for the arguments. This is particularly helpful when you are starting out with machine learning or with scikit-larn and you don't know what bear on each of the arguments has.

Change to the scikit-learn API over time often comes in the form of changes to the sensible defaults to arguments to functions. Changes of this type are oftentimes 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 function argument and a subsequent version of the API plans to change this default value, then the API volition alarm yous to the upcoming change.

This warning comes in the course of a alert message each time your code is run. Specifically, a "FutureWarning" is reported on standard fault (e.one thousand. on the command line).

This is a useful characteristic of the API and the project, designed for your do good. Information technology allows you to alter your lawmaking ready for the next major release of the library to either retain the old beliefs (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, it may feel like the code is not working correctly, that perhaps yous have done something wrong.
For a professional, it is a sign of a program that requires updating.

In either instance, alarm messages may obscure existent error messages or output from the program.

How to Suppress FutureWarnings

Warning letters are not error messages.

As such, a warning message reported by your plan, such as a FutureWarning, will not halt the execution of your program. The warning bulletin will exist reported and the programme will carry on executing.

You lot can, therefore, ignore the alarm each fourth dimension your code is executed, if you wish.

It is as well possible to programmatically ignore the warning messages. This can exist washed by suppressing warning messages when your program is run.

This can be achieved by explicitly configuring the Python warning system to ignore warning letters of a specific type, such equally ignore all FutureWarnings, or more by and large, to ignore all warnings.

This tin can be achieved past adding the following block around your code that y'all know volition generate warnings:

# run cake of lawmaking and grab warnings

with warnings . catch_warnings ( ) :

# ignore all caught warnings

warnings . filterwarnings ( "ignore" )

# execute code that will generate warnings

. . .

Or, if you have a very unproblematic apartment script (no functions or blocks), you lot can suppress all FutureWarnings by adding two lines to the elevation of your file:

# import warnings filter

from warnings import simplefilter

# ignore all future warnings

simplefilter ( action = 'ignore' , category = FutureWarning )

To acquire more than about suppressing in Python, see:

Python Warning control API

How to Fix FutureWarnings

Alternately, you tin can alter your lawmaking to address the reported modify to the scikit-learn API.

Typically, the alarm bulletin itself volition instruct you lot on the nature of the change and how to modify your lawmaking to accost the warning.

Nevertheless, let's wait at a few recent examples of FutureWarnings that yous may see and exist struggling with.

The examples in this section were developed with scikit-learn version 0.20.two. Yous tin check your scikit-learn version past running the following code:

# bank check scikit-learn version

import sklearn

print ( 'sklearn: %due south' % sklearn . __version__ )

You lot will see output like the following:

As new versions of scikit-learn are released over time, the nature of the warning messages reported will alter and new defaults will exist adopted.

As 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 two contempo changes to the default argument values that event in FutureWarning messages.

The outset has to do with the solver for finding coefficients and the 2d has to practice with how the model should be used to make multi-course classifications. Allow's look at each with code examples.

Changes to the Solver

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

# instance 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 post-obit warning message:

FutureWarning: Default solver volition be inverse to 'lbfgs' in 0.22. Specify a solver to silence this warning.

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

To maintain the sometime beliefs, yous can specify the statement every bit follows:

# create and configure model

model = LogisticRegression ( solver = 'liblinear' )

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

# create and configure model

model = LogisticRegression ( solver = 'lbfgs' )

Changes to the Multi-Class

The example below will generate a FutureWarning about the 'multi_class' argument used past LogisticRegression.

# case 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 = 2 )

# create and configure model

model = LogisticRegression ( solver = 'lbfgs' )

# fit model

model . fit ( X , y )

Running the case results in the post-obit warning message:

FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class pick to silence this warning.

This warning bulletin only affects the use of logistic regression for multi-form classification problems, instead of the binary classification bug for which the method was designed.

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

To maintain the sometime beliefs, you can specify the statement equally follows:

# create and configure model

model = LogisticRegression ( solver = 'lbfgs' , multi_class = 'ovr' )

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

# create and configure model

model = LogisticRegression ( solver = 'lbfgs' , multi_class = 'auto' )

FutureWarning for SVM

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

The instance beneath will generate a FutureWarning about the 'gamma' statement used by SVC, but only as every bit applies to SVR.

# example of SVC that generates a FutureWarning

from sklearn . datasets import make_blobs

from sklearn . svm import SVC

# set dataset

X , y = make_blobs ( n_samples = 100 , centers = 2 , n_features = 2 )

# create and configure model

model = SVC ( )

# fit model

model . fit ( 10 , y )

Running this example will generate the following warning bulletin:

FutureWarning: The default value of gamma will modify from 'auto' to 'scale' in version 0.22 to business relationship better for unscaled features. Prepare gamma explicitly to 'auto' or 'scale' to avert this warning.

This warning message reports that the default for the 'gamma' statement is irresolute from the electric 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 about a change in the way that the default volition exist calculated.

To maintain the former behavior, yous can specify the statement every bit follows:

# create and configure model

model = SVC ( gamma = 'auto' )

To back up the new beliefs (recommended), you can specify the argument equally follows:

# create and configure model

model = SVC ( gamma = 'scale' )

FutureWarning for Decision Tree Ensemble Algorithms

The decision-tree based ensemble algorithms volition change the number of sub-models or copse used in the ensemble controlled by the 'n_estimators' statement.

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

The example beneath will generate a FutureWarning well-nigh the 'n_estimators' statement used by RandomForestClassifier, but just as equally applies to RandomForestRegressor and the extra copse classes.

# instance 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 = ii , n_features = ii )

# create and configure model

model = RandomForestClassifier ( )

# fit model

model . fit ( X , y )

Running this example will generate the following alarm message:

FutureWarning: The default value of n_estimators will change from 10 in version 0.twenty to 100 in 0.22.

This alarm message reports that the number of submodels is increasing from 10 to 100, likely because computers are getting faster and 10 is very minor, even 100 is small.

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

# create and configure model

model = RandomForestClassifier ( n_estimators = 10 )

To support the new behavior (recommended), y'all can specify the argument as follows:

# create and configure model

model = RandomForestClassifier ( n_estimators = 100 )

More Futurity Warnings?

Are you struggling with a FutureWarning that is not covered?

Allow me know in the comments below and I will do my best to aid.

FutureWarning Recommendations

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

Ignoring warning messages ways that the bulletin may obscure real errors or program output and that API future changes may negatively impact your program unless you lot have considered them.

Suppressing warnings might be a quick prepare for R&D work, simply should not exist used in a product system. Worse than but ignoring the messages, suppressing the warnings may besides suppress messages from other APIs.

Instead, I recommend that y'all set up the warning messages in your software.

How should you modify your code?

In general, I recommend most e'er adopting the new behavior of the API, eastward.thousand. the new default, unless y'all explicitly rely on the prior behavior of the function.

For long-lived operational or production code, it might be a good idea to explicitly specify all office arguments and not use defaults, as they might be field of study to modify in the future.

I as well recommend that you keep your scikit-learn library upward to appointment, and proceed rail of the changes to the API in each new release.

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

scikit-learn Release History

Summary

In this tutorial, you discovered FutureWarning messages in the scikit-learn API and how to handle them in your own auto learning projects.

Specifically, you learned:

FutureWarning messages are designed to inform yous about upcoming changes to default values for arguments in the scikit-acquire API.
FutureWarning messages can be ignored or suppressed every bit they do not halt the execution of your plan.
Examples of FutureWarning messages and how to interpret the message and modify your code to address the upcoming change.

Do you accept any questions?
Ask your questions in the comments below and I will exercise my best to respond.

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Source: https://machinelearningmastery.com/how-to-fix-futurewarning-messages-in-scikit-learn/

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How To Replace Register 2-3" Compound Meter

How to Fix FutureWarning Letters in scikit-learn

Tutorial Overview

Problem of FutureWarnings

How to Suppress FutureWarnings