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Random Forest Baseline

Let's try evaluating the RandomForest baseline, which uses scikit-learn's random forest:

Running the Benchmark


python randomforest 


python randomforest 


As noted above, we need to install the AutoML frameworks (and baselines) in a container. Add -m docker to the command as shown:

python randomforest -m docker


Future example usages will only show invocations without -m docker mode, but Windows users will need to run in some non-local mode.


After running the command, there will be a lot of output to the screen that reports on what is currently happening. After a few minutes final results are shown and should look similar to this:

Summing up scores for current run:
               id        task  fold    framework constraint     result      metric  duration      seed         kc2     0 RandomForest       test   0.865801         auc      11.1 851722466         kc2     1 RandomForest       test   0.857143         auc       9.1 851722467        iris     0 RandomForest       test  -0.120755 neg_logloss       8.7 851722466        iris     1 RandomForest       test  -0.027781 neg_logloss       8.5 851722467 cholesterol     0 RandomForest       test -44.220800    neg_rmse       8.7 851722466 cholesterol     1 RandomForest       test -55.216500    neg_rmse       8.7 851722467

The result denotes the performance of the framework on the test data as measured by the metric listed in the metric column. The result column always denotes performance in a way where higher is better (metrics which normally observe "lower is better" are converted, which can be observed from the neg_ prefix).

While running the command, the AutoML benchmark performed the following steps:

  1. Create a new virtual environment for the Random Forest experiment. This environment can be found in frameworks/randomforest/venv and will be re-used when you perform other experiments with RandomForest.
  2. It downloaded datasets from OpenML complete with a "task definition" which specifies cross-validation folds.
  3. It evaluated RandomForest on each (task, fold)-combination in a separate subprocess, where:
    1. The framework (RandomForest) is initialized.
    2. The training data is passed to the framework for training.
    3. The test data is passed to the framework to make predictions on.
    4. It passes the predictions back to the main process
  4. The predictions are evaluated and reported on. They are printed to the console and are stored in the results directory. There you will find:
    1. results/results.csv: a file with all results from all benchmarks conducted on your machine.
    2. results/randomforest.test.test.local.TIMESTAMP: a directory with more information about the run, such as logs, predictions, and possibly other artifacts.

Docker Mode

When using docker mode (with -m docker) a docker image will be made that contains the virtual environment. Otherwise, it functions much the same way.

Important Parameters

As you can see from the results above, the default behavior is to execute a short test benchmark. However, we can specify a different benchmark, provide different constraints, and even run the experiment in a container or on AWS. There are many parameters for the script, but the most important ones are:

Framework (required)

  • The AutoML framework or baseline to evaluate and is not case-sensitive. See integrated frameworks for a list of supported frameworks. In the above example, this benchmarked framework randomforest.

Benchmark (optional, default='test')

  • The benchmark suite is the dataset or set of datasets to evaluate the framework on. These can be defined as on OpenML as a study or task (formatted as openml/s/X or openml/t/Y respectively) or in a local file. The default is a short evaluation on two folds of iris, kc2, and cholesterol.

Constraints (optional, default='test')

  • The constraints applied to the benchmark as defined by default in constraints.yaml. These include time constraints, memory constrains, the number of available cpu cores, and more. Default constraint is test (2 folds for 10 min each).

    Constraints are not enforced!

    These constraints are forwarded to the AutoML framework if possible but, except for runtime constraints, are generally not enforced. It is advised when benchmarking to use an environment that mimics the given constraints.

    Constraints can be overriden by benchmark

    A benchmark definition can override constraints on a task level. This is useful if you want to define a benchmark which has different constraints for different tasks. The default "test" benchmark does this to limit runtime to 60 seconds instead of 600 seconds, which is useful to get quick results for its small datasets. For more information, see defining a benchmark.

Mode (optional, default='local')

  • The benchmark can be run in four modes:

    • local: install a local virtual environment and run the benchmark on your machine.
    • docker: create a docker image with the virtual environment and run the benchmark in a container on your machine. If a local or remote image already exists, that will be used instead. Requires Docker.
    • singularity: create a singularity image with the virtual environment and run the benchmark in a container on your machine. Requires Singularity.
    • aws: run the benchmark on AWS EC2 instances. It is possible to run directly on the instance or have the EC2 instance run in docker mode. Requires valid AWS credentials to be configured, for more information see Running on AWS.

For a full list of parameters available, run:

python --help