AI News, BOOK REVIEW: TPOT: A Python tool for automating data science

TPOT: A Python tool for automating data science

Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish.

After that, we’re going to step through a demo for a tool that intelligently automates the process of machine learning pipeline design, so we can spend our time working on the more interesting aspects of data science.

If we fit a random forest classifier with only 10 trees (scikit-learn’s default): The random forest achieves an average of 94.7% cross-validation accuracy on MNIST.

This small improvement in accuracy can translate into millions of additional digits classified correctly if we’re applying this model on the scale of, say, processing addresses for the U.S. Postal Service.

random forest to the problem: Then we’re going to find that the random forest isn’t well-suited for signal processing tasks like this one when it achieves a disappointing average of 61.8% cross-validation accuracy.

However, if we preprocess the features—denoising them via Principal Component Analysis (PCA), for example: We’ll find that the random forest now achieves an average of 94% cross-validation accuracy by applying a simple feature preprocessing step.

To summarize what we’ve learned so far about effective machine learning system design, we should: We must also consider the following: This is why it can be so tedious to design effective machine learning systems.

classification problem: (Before running the code below, make sure to install TPOT first.) Depending on the machine you’re running it on, 10 TPOT generations should take about 5 minutes to complete.

If we want to see what pipeline TPOT created, TPOT can export the corresponding scikit-learn code for us with the export() command: which will look something like: and shows us that a tuned gradient tree boosting classifier is probably the best model for this problem once the data has been normalized.

Crash/freeze issue with n_jobs > 1 under OSX or Linux

Automated machine learning (AutoML) takes a higher-level approach to machine learning than most practitioners are used to, so

You can tell TPOT to optimize a pipeline based on a data set with the fit function: The fit function initializes the genetic programming algorithm to find the highest-scoring pipeline based on average k-fold cross-validation Then,

You can then proceed to evaluate the final pipeline on the testing set with the score function: Finally, you can tell TPOT to export the corresponding Python code for the optimized pipeline to a text file with the export function: Once this code finishes running, tpot_exported_pipeline.py will contain the Python code for the optimized pipeline.

To use TPOT via the command line, enter the following command with a path to the data file: An example command-line call to TPOT may look like: TPOT offers several arguments that can be provided at the command line.

There are two ways to make use of scoring functions with TPOT: TPOT comes with a handful of default operators and parameter configurations that we believe work well for optimizing machine learning pipelines.

The custom TPOT configuration must be in nested dictionary format, where the first level key is the path and name of the operator (e.g., sklearn.naive_bayes.MultinomialNB) and the second level key is the corresponding parameter name for that operator (e.g., fit_prior).

For example: Command-line users must create a separate .py file with the custom configuration and provide the path to the file to the tpot call.

For example, if the simple example configuration above is saved in tpot_classifier_config.py, that configuration could be used on the command line with the command: When using the command-line interface, the configuration file specified in the -config parameter must name its custom TPOT configuration tpot_config.

This feature is used to avoid repeated computation by transformers within a pipeline if the parameters and input data are identical to another fitted pipeline during optimization process.

TPOT allows users to specify a custom directory path or sklearn.external.joblib.Memory in case they want to re-use the memory cache in future TPOT runs (or a warm_start run).

There are three methods for enabling memory caching in TPOT: Note: TPOT does NOT clean up memory caches if users set a custom directory path or Memory object.

One solution is to configure Python's multiprocessing module to use the forkserver start method (instead of the default fork) to manage the process pools.

You can enable the forkserver mode globally for your program by putting the following codes into your main script: More information about these start methods can be found in the multiprocessing documentation.

EpistasisLab/tpot

TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

An example Machine Learning pipeline Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.

TPOT is still under active development and we encourage you to check back on this repository regularly for updates.

Running this code should discover a pipeline that achieves about 98% testing accuracy, and the corresponding Python code should be exported to the tpot_mnist_pipeline.py file and look similar to the following: Similarly, TPOT can optimize pipelines for regression problems.

which should result in a pipeline that achieves about 12.77 mean squared error (MSE), and the Python code in tpot_boston_pipeline.py should look similar to: Check the documentation for more examples and tutorials.

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