Benchmarks

We compare computation time for a few algorithms implemented in the major machine learning toolkits accessible in Python.

Last Update: July-2011

We report the execution time for various machine learning libraries exposed in Python: MLPy, PyBrain, PyMVPA, MDP, Shogun and MiLK. Code for running the benchmarks can be retrieved its github repository.

We also print the score on a validation dataset for all algorithms. For classification algorithms, it’s the fraction of correctly classified samples, for regression algorithms it’s the mean squared error and for k-means it’s the inertia criterion.

Software used

We used the latest released version as of June 2011:

• scikit-learn 0.8
• MDP 3.1
• MLPy 2.2.2
• PyMVPA 0.6.0~rc3
• Shogun 0.10.0
• Milk 0.3.9

I ran it on an Intel Core2 CPU @ 1.86GHz.

Datasets used

We use the Madelong and Arcene data set. The Madelon data set, 4400 instances and 500 attributes, is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. The difficulty is that the problem is multivariate and highly non-linear.

The arcene data set task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge. All details about the preparation of the data are found in the technical report: Design of experiments for the NIPS 2003 variable selection benchmark, Isabelle Guyon, July 2003.

Support Vector Machines

We used several Support Vector Machine (RBF kernel) implementations. Numbers represent the time in seconds (lower is better) it took to train the dataset and perform prediction on a test dataset. In the plot, results are normalized to have the fastest method at 1.0.

Timing results in seconds

Dataset	PyMVPA	Shogun	MDP	Pybrain	MLPy	scikit-learn	Milk
Madelon	11.52	5.63	40.48	17.5	9.47	5.20	5.76
Arcene	1.30	0.39	4.87	–	1.61	0.38	0.33

The score by these classfifiers in in a test dataset is.

Classification score - smaller is better

Dataset	PyMVPA	Shogun	MDP	Pybrain	MLPy	scikit-learn	milk
Madelon	0.65	0.65	–	–	0.65	0.65	0.50
Arcene	0.73	0.73	0.73	–	0.73	0.73	0.73

Warning

This is just meant as a sanity check, should not be taken at face value since parameters are not cross-validated, etc.

Nearest neighbors

Timing results in seconds

Dataset	PyMVPA	Shogun	MDP	MLPy	scikit-learn	milk
Madelon	0.56	1.36	0.58	1.41	0.57	8.24
Arcene	0.10	0.22	0.10	0.21	0.09	1.33

Classification score - larger is better

Dataset	PyMVPA	Shogun	MDP	MLPy	scikit-learn	milk
Madelon	0.73	0.73	0.73	0.73	0.73	0.73
Arcene	0.73	0.73	0.73	0.73	0.73	0.73

K-means

We run the k-means algorithm on both Madelon and Arcene dataset. To make sure the methods are converging, we show in the second table the inertia of all methods, which are mostly equivalent.

Note: The shogun is failling ..

Timing results in seconds

Dataset	MDP	Shogun	Pybrain	MLPy	scikit-learn	milk
Madelon	35.75	0.68	NC	0.79	1.34	0.67
Arcene	2.07	0.19	20.50	0.33	0.51	0.23

NC = Not Converging after one hour iteration.

The following table shows the inertia, criterion that the k-means algorithm minimizes.

Inertia - smaller is better

Dataset	MDP	Shogun	Pybrain	MLPy	scikit-learn	Milk
Madelon	7.4x10^8	7.3x10^8	–	7.3x10^8	7.4x10^8	7.3x10^8
Arcene	1.4x10^9		oo	1.4x10^9	1.4x10^9	1.4x10^9

Elastic Net

We solve the elastic net using a coordinate descent algorithm on both Madelon and Arcene dataset.

Timing results in seconds

Dataset	PyMVPA	MLPy	scikit-learn
Madelon	1.44	73.7	0.52
Arcene	2.31	65.48	1.90

Mean squared error - smaller is better

Dataset	PyMVPA	MLPy	scikit-learn
Madelon	699.1	3759.8	597.1
Arcene	84.92	151.28	65.39

Lasso (LARS algorithm)

We solve the Lasso model by Least Angle Regression (LARS) algorithm. MLPy and scikit-learn use a pure Python implementation, while PyMVPA uses bindings to R code.

We also show the Mean Squared error as a sanity check for the model. Note that some NaN arise due to collinearity in the data.

Timing results in seconds

Dataset	PyMVPA	MLPy	scikit-learn
Madelon	37.35	105.3	1.17
Arcene	11.53	3.82	2.95

Mean Squared Error on a test dataset - smaller is better

Dataset	PyMVPA	MLPy	scikit-learn
Madelon	567.0	682.32	680.91
Arcene	87.5	NaN	65.39

Principal Component Analysis

We run principal component analysis on the madelon datasets. In the libraries that support it (scikit-learn, MDP, PyMVPA), we number of components in the projection to 9. For the arcene dataset, most libraries could not handle the memory requirements.

Timing results in seconds

Dataset	PyMVPA	MDP	Pybrain	scikit-learn	milk
Madelon	0.48	0.47	8.93	0.18	3.07

Explained variance - larger is better

Dataset	PyMVPA	MDP	Pybrain	scikit-learn	milk
Madelon	136705.5	136705.5	113545.8	135788.2	139158.7

Misc

Author : Fabian Pedregosa

License : Simplified BSD