Benchmarking - comparing estimator performance

The benchmarking modules allows you to easily orchestrate benchmarking experiments in which you want to compare the performance of one or more algorithms over one or more datasets and benchmark configurations. This module is still in development.

Benchmarking as an endevour in general is very easy to get wrong, giving false conclusions about estimator performance - see this 2022 research from Princeton for numerous examples of such mistakes in peer reviewed academic papers as evidence of this.

aeon’s benchmarking module is designed to provide benchmarking functionality while enforcing best practices and structure to help users avoid making mistakes (such as data leakage, etc.) which invalidate their results. The benchmarking module is designed for easy usage in mind, as such it interfaces directly with aeon objects and classes. Previously developed estimator should be usable as they are without alterations.

We also include tools for comparing your results to published work and for testing and visualising relative performance of algorithms. See

• Loading results from timeseriesclassification.com

This notebook demonstrates usage of the benchmarking module.

import warnings

warnings.filterwarnings("ignore")

from aeon.benchmarking.forecasting import ForecastingBenchmark
from aeon.datasets import load_airline
from aeon.forecasting.model_selection import ExpandingWindowSplitter
from aeon.forecasting.naive import NaiveForecaster
from aeon.performance_metrics.forecasting import MeanSquaredPercentageError

Instantiate an instance of a benchmark class

In this example we are comparing forecasting estimators.

benchmark = ForecastingBenchmark()

Add competing estimators

We add different competing estimators to the benchmark instance. All added estimators will be automatically ran through each added benchmark tasks, and their results compiled.

benchmark.add_estimator(
    estimator=NaiveForecaster(strategy="mean", sp=12),
    estimator_id="NaiveForecaster-mean-v1",
)
benchmark.add_estimator(
    estimator=NaiveForecaster(strategy="last", sp=12),
    estimator_id="NaiveForecaster-last-v1",
)

Add benchmarking tasks

These are the prediction/validation tasks over which every estimator will be tested and their results compiled.

The exact arguments for a benchmarking task depend on the whether the objective is forecasting, classification, etc., but generally they are similar. The following are the required arguments for defining a forecasting benchmark task.

Specify cross-validation split regime(s)

Define cross-validation split regimes, using standard aeon objects.

cv_splitter = ExpandingWindowSplitter(
    initial_window=24,
    step_length=12,
    fh=12,
)

Specify performance metric(s)

Define performance metrics on which to compare estimators, using standard aeon objects.

scorers = [MeanSquaredPercentageError()]

Specify dataset loaders

Define dataset loaders, which are callables (functions) which should return a dataset. Generally this is a callable which returns a dataframe containing the entire dataset. One can use the aeon defined datasets, or define their own. Something as simple as the following example will suffice:

def my_dataset_loader():
    return pd.read_csv("path/to/data.csv")

The datasets will be loaded when running the benchmarking tasks, ran through the cross-validation regime(s) and subsequently the estimators will be tested over the dataset splits.

dataset_loaders = [load_airline]

Add tasks to the benchmark instance

Use the previously defined objects to add tasks to the benchmark instance. Optionally use loops etc. to easily setup multiple benchmark tasks reusing arguments.

for dataset_loader in dataset_loaders:
    benchmark.add_task(
        dataset_loader,
        cv_splitter,
        scorers,
    )

Run all task-estimator combinations and store results

Note that run won’t rerun tasks it already has results for, so adding a new estimator and running run again will only run tasks for that new estimator.

results_df = benchmark.run("./forecasting_results.csv")
results_df.T

	0	1
validation_id	[dataset=load_airline]_[cv_splitter=ExpandingW...	[dataset=load_airline]_[cv_splitter=ExpandingW...
model_id	NaiveForecaster-last-v1	NaiveForecaster-mean-v1
runtime_secs	0.092438	0.108086
MeanSquaredPercentageError_fold_0_test	0.024532	0.049681
MeanSquaredPercentageError_fold_1_test	0.020831	0.0737
MeanSquaredPercentageError_fold_2_test	0.001213	0.05352
MeanSquaredPercentageError_fold_3_test	0.01495	0.081063
MeanSquaredPercentageError_fold_4_test	0.031067	0.138163
MeanSquaredPercentageError_fold_5_test	0.008373	0.145125
MeanSquaredPercentageError_fold_6_test	0.007972	0.154337
MeanSquaredPercentageError_fold_7_test	0.000009	0.123298
MeanSquaredPercentageError_fold_8_test	0.028191	0.185644
MeanSquaredPercentageError_fold_9_test	0.003906	0.184654
MeanSquaredPercentageError_mean	0.014104	0.118918
MeanSquaredPercentageError_std	0.011451	0.051265

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Generated using nbsphinx. The Jupyter notebook can be found here.