With the popularity of machine learning and deep learning, several organisations and academia have started developing efficient tools and libraries. For instance, tech giants like Google, Microsoft, and Facebook have been heavily investing in building dynamic and robust deep learning models. When it comes to building deep learning models, Python is considered as one of the most suitable languages due to its plethora of tools and libraries available for performing machine learning tasks.
In this article, we compared the two popular Python machine learning libraries, scikit-learn and Pylearn2. Before delving deep into the libraries, let’s get through the basic definition first.
Built on top of NumPy, SciPy, and Matplotlib, scikit-learn is a popular machine learning library in Python language. The library supports supervised and unsupervised learning and provides various tools for model fitting, data preprocessing, model selection and evaluation, among many other utilities. The primary functions of scikit-learn are divided into classification, regression, clustering, dimensionality reduction, model selection and data preprocessing.
Pylearn2 is a popular machine learning research library developed by LISA at Montreal University. Most of the functionality in this library is built on top of Theano. This library aims to facilitate machine learning research by focussing on flexibility and extensibility and make sure that any research idea is feasible to implement in the library. Pylearn2 achieves flexibility and extensibility by decomposing into reusable parts. The three critical components used to implement most features are the dataset, model, and TrainingAlgorithm classes.
Comparing scikit-learn and Pylearn2
Both scikit-learn and Pylearn2 are ideal for tasks like data mining and data analysis in machine learning. The main differences between the two popular libraries are as below:
According to the official blog post, Pylearn2 differs from scikit-learn in that Pylearn2 aims to provide great flexibility and make it possible for a researcher to do almost anything. While the latter aims to work as a “black box” that can produce excellent results even if the user does not understand the implementation.
Also, while using Pylearn2, sometimes the user is required to be an expert practitioner who must understand how the algorithm works to accomplish basic data analysis tasks. On the other hand, while using scikit-learn library, one may not necessarily understand how the underlying algorithm works.
Some of the features of scikit-learn are:
- scikit-learn is simple and efficient for predictive data analysis
- The library is accessible to everybody and reusable in various contexts
- It is open-source in nature
- Scikit-learn provides popular models including dimensionality reduction, cross-validation ensemble methods, manifold learning, parameter tuning and much more
Some of the features of Pylearn2 are:
- Pylearn2 is a machine learning research library, which means there are no restrictions while imposing any machine learning tasks
- Pylearn2 is built from reusable parts that can be used in many combinations or independently
- Pylearn2 provides easy reuse of sub-component
- It supports cross-platform serialisation of learned models
- The library provides a domain-specific language that provides a compact way of specifying all hyperparameters for an experiment
Some of the advantages of scikit-learn are:
- This library covers almost all the mainstream algorithms for machine learning tasks
- It provides all the modular implementation of the ML algorithm
- scikit-learn regression algorithm covers nearly all the needs as each algorithm provides a simple and useful use case reference
- The library is easy to use as it relies on a smaller number of Python packages
Some of the advantages of Pylearn2 are:
- Pylearn2 includes interchangeable parts and most deep learning techniques are decomposed into cost, model and TrainingAlgorithm
- This library is designed to be extended
Some of the disadvantages of scikit-learn are:
- This library is not suitable for large-scale problems
- scikit-learn only performs machine learning expansions
- It has low flexibility
- scikit-learn never uses unaudited algorithms
- scikit-learn is generally not ideal for deep learning
- The library does not support GPU acceleration
Some of the disadvantages of this library are:
- The library does not have any current developer, and hence there will be no further development in this library
- The initial cost of learning to use Pylearn2 is high compared to scikit-learn
In the current scenario, the Pylearn project has discontinued being maintained as no developers are bringing about the updates. However, they will continue to review, pull requests and merge them when appropriate. Also, it is officially advised to use other popular machine learning libraries such as Keras, Blocks, Lasagne, among others. On the hand, scikit-learn is currently being maintained by the community members and a good option to be preferred over Pylearn-2.
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