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https://github.com/lisitsyn/tapkee

A flexible and efficient С++ template library for dimension reduction
https://github.com/lisitsyn/tapkee

dimension-reduction machine-learning

Last synced: 11 months ago
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A flexible and efficient С++ template library for dimension reduction

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README 

          
Tapkee is a C++ template library for dimensionality reduction with some bias on

spectral methods. The Tapkee origins from the code developed during

[GSoC 2011](http://www.google-melange.com/gsoc/homepage/google/gsoc2011) as

the part of the [Shogun machine learning toolbox](https://github.com/shogun-toolbox/shogun).

The project aim is to provide efficient and flexible standalone library for

dimensionality reduction which can be easily integrated to existing codebases.

Tapkee leverages capabilities of effective [Eigen3 linear algebra library](http://eigen.tuxfamily.org) and

optionally makes use of the [ARPACK eigensolver](http://www.caam.rice.edu/software/ARPACK/).

The library uses CoverTree and VP-tree data structures to compute nearest neighbors. To achieve

greater flexibility we provide a callback interface which decouples dimension reduction algorithms from

the data representation and storage schemes.



The library is distributed under permissive

[BSD 3-clause license](https://github.com/lisitsyn/tapkee/blob/master/include/LICENSE)

(except a few rather optional parts that are distributed under other

open sources licenses, see Licensing section of this document). If you use

this software in any publication we would be happy if you cite the following paper:



	Sergey Lisitsyn and Christian Widmer and Fernando J. Iglesias Garcia. Tapkee: An Efficient Dimension Reduction Library. Journal of Machine Learning Research, 14: 2355-2359, 2013.



To get started with dimension reduction you may try the

[go.py script](https://github.com/lisitsyn/tapkee/blob/master/examples/go.py)

that embeds common datasets (swissroll, helix, scurve) using

the Tapkee library and outputs it with the help of

Matplotlib library. To use the script build the

sample application (see the Application section for more details)

and call go.py with the following command:



	./examples/go.py [swissroll|helix|scurve|...] [lle|isomap|...]



You may also try out an minimal example using `make minimal` (examples/minimal)

and the RNA example using `make rna` (examples/rna). There are also a few graphical

examples. To run MNIST digits embedding example use `make mnist` (examples/mnist),

to run promoters embedding example use `make promoters` (examples/promoters)

and to run embedding for faces dataset use `make faces` (examples/faces).

All graphical examples require Matplotlib which can be usually

installed with a package manager. The promoters example also

has non-trivial dependency on Shogun machine learning toolbox

(minimal version is 2.1.0). We also provide

some examples of usage Tapkee in Shogun as

`make langs` (examples/langs) example.



API

---



We provide an interface based on the method chaining technique. The chain starts with the call

of the `with(const ParametersSet&)` method, which is used to provide parameters like the method

to use and its settings. The provided argument is formed with the following syntax:



	(keyword1=value1, keyword2=value2)



Such syntax is possible due to comma operator overloading which groups all assigned keywords

in the comma separated list.



Keywords are defined in the `tapkee` namespace. Currently, the following keywords

are defined: `method`, `eigen_method`, `neighbors_method`, `num_neighbors`, `target_dimension`,

`diffusion_map_timesteps`, `gaussian_kernel_width`, `max_iteration`, `spe_global_strategy`,

`spe_num_updates`, `spe_tolerance`, `landmark_ratio`, `nullspace_shift`, `klle_shift`,

`check_connectivity`, `fa_epsilon`, `progress_function`, `cancel_function`, `sne_perplexity`,

`sne_theta`. See the documentation for their detailed meaning.



As an example of parameters setting, if you want to use the Isomap

algorithm with the number of neighbors set to 15:



	tapkee::with((method=Isomap,num_neighbors=15))



Please note that the inner parentheses are necessary as it uses the

comma operator which appears to be ambiguous in this case.



Next, you may either embed the provided matrix with:



	tapkee::with((method=Isomap,num_neighbors=15)).embedUsing(matrix);



Or provide callbacks (kernel, distance and features) using any combination

of the `withKernel(KernelCallback)`, `withDistance(DistanceCallback)` and

`withFeatures(FeaturesCallback)` member functions:



	tapkee::with((method=Isomap,num_neighbors=15))

	       .withKernel(kernel_callback)

	       .withDistance(distance_callback)

	       .withFeatures(features_callback)



Once callbacks are initialized you may either embed data using an

STL-compatible sequence of indices or objects (that supports the

`begin()` and `end()` methods to obtain the corresponding iterators)

with the `embedUsing(Sequence)` member function

or embed the data using a sequence range with the

`embedRange(RandomAccessIterator, RandomAccessIterator)`

member function.



As a summary - a few examples:



	TapkeeOutput output = with((method=Isomap,num_neighbors=15))

	    .embedUsing(matrix);



	TapkeeOutput output = with((method=Isomap,num_neighbors=15))

	    .withDistance(distance_callback)

	    .embedUsing(indices);



	TapkeeOutput output = with((method=Isomap,num_neighbors=15))

	    .withDistance(distance_callback)

	    .embedRange(indices.begin(),indices.end());



Minimal example

---------------



A minimal working example of a program that uses the library is:



	#include 

	#include 



	using namespace std;

	using namespace tapkee;



	struct MyDistanceCallback

	{

		ScalarType distance(IndexType l, IndexType r) { return abs(l-r); }

	};



	int main(int argc, const char** argv)

	{

		const int N = 100;

		vector indices(N);

		for (int i=0; i`



Please note that if you don't include Eigen3 in your project there is no need to define that variable -

in this case Eigen3 will be included by Tapkee. This issue comes from the need of including the Eigen3 library

only once when using some specific parameters (like debug and extensions).



If you are able to use less restrictive licenses (such as LGPLv3) you may define

the following variable:



- `TAPKEE_USE_LGPL_COVERTREE` to use Covertree code by John Langford.



When compiling your software that includes Tapkee be sure Eigen3 headers are in include path and your code

is linked against ARPACK library (-larpack key for g++ and clang++).



For an example of integration you may check

[Tapkee adapter in Shogun](https://github.com/shogun-toolbox/shogun/blob/master/src/shogun/lib/tapkee/tapkee_shogun.cpp).



When working with installed headers you may check which version of the library

do you have with checking the values of `TAPKEE_WORLD_VERSION`, `TAPKEE_MAJOR_VERSION`

and `TAPKEE_MINOR_VERSION` defines.



We welcome any integration so please contact authors if you have got any questions. If you have

successfully used the library please also let authors know about that - mentions of any

applications are very appreciated.



Customization

-------------



Tapkee is designed to be highly customizable with preprocessor definitions.



If you want to use float as internal numeric type (default is double) you may do

that with definition of `#define TAPKEE_CUSTOM_NUMTYPE float`

before including [defines header](https://github.com/lisitsyn/tapkee/blob/master/include/tapkee_defines.hpp).



If you use some non-standard STL-compatible realization of vector, map and pair you may redefine them

with `TAPKEE_INTERNAL_VECTOR`, `TAPKEE_INTERNAL_PAIR`, `TAPKEE_INTERNAL_MAP`

(they are set to std::vector, std::pair and std::map by default otherwise).



You may define `TAPKEE_USE_FIBONACCI_HEAP` or `TAPKEE_USE_PRIORITY_QUEUE` to select which

data structure should be used in the shortest paths computing algorithm. By default

a priority queue is used.



Other properties can be loaded from some provided header file using `#define TAPKEE_CUSTOM_PROPERTIES`. Currently

such file should define only one variable - `COVERTREE_BASE` which defines the base of the CoverTree (default is 1.3).



Command line application

-----------



Tapkee comes with a sample application which can be used to construct

low-dimensional representations of dense feature matrices. For more information on

its usage please run:



`./bin/tapkee -h`



The application takes plain ASCII file containing dense matrix (each vector is a column and each

line contains values of some feature). The output of the application is stored into the provided

file in the same format (each line is feature).



To compile the application please use [CMake](http://cmake.org/). The workflow of compilation

Tapkee with CMake is usual. When using Unix-based

systems you may use the following command to compile the Tapkee application:



`mkdir build && cd build && cmake [definitions] .. && make`



There are a few cases when you'd want to put some definitions:



- To enable unit-tests compilation add to `-DBUILD_TESTS=1` to `[definitions]` when building. Please note that

  building unit-tests require googletest. If you are running Ubuntu you may install `libgtest-dev` package for that.

  Otherwise, if you have gtest sources around you may provide them as `-DGTEST_SOURCE_DIR` and `-DGTEST_INCLUDES_DIR`.

  You may also download gtest with the following command:



`wget https://github.com/google/googletest/archive/release-1.8.0.tar.gz && tar xfv release-1.8.0.tar.gz`



  Downloaded sources will be used by Tapkee.

  To run tests use `make test` command (or better 'ctest -VV').



- To let make script store test coverage information using GCOV and

  add a target for output test coverage in HTML with LCOV add the `-DUSE_GCOV=1` flag to `[definitions]`.



- To enable precomputation of kernel/distance matrices which can speed-up algorithms (but requires much more memory) add

  `-DPRECOMPUTED=1` to `[definitions]` when building.



- To build application without parts licensed by LGPLv3 use `-DGPL_FREE=1` definition.



The library requires Eigen3 to be available in your path. The ARPACK library is also highly

recommended to achieve best performance. On Debian/Ubuntu these packages can be installed with



	sudo apt-get install libeigen3-dev libarpack2-dev



If you are using Mac OS X and Macports you can install these packages with



	sudo port install eigen3 && sudo port install arpack`



In case you want to use some non-default

compiler use `CC=your-C-compiler CXX=your-C++-compiler cmake [definitions] ..` when running cmake.



Directory contents

------------------



The repository of Tapkee contains the following directories:



- `src/` that contains simple command-line application (`src/cli`)

  and CMake module finders (`src/cmake`).

- `includes/` that contains the library itself in the `includes/tapkee`

  subdirectory.

- `test/` that contains unit-tests in the `test/unit` subdirectory and

  a few helper scripts.

- `examples/` that contains a few examples including already mentioned

  (these examples are supposed to be called through `make` as described

   above, e.g. `make minimal`).

- `data/` a git submodule that contains data required for

  examples. To initialize this submodule use `git submodule update --init`.

- `doc/` that contains Doxygen interface file which is used to

  generate HTML documentation of the library. Calling

  `doxygen doc/Doxyfile` will generate it in this folder.



Once built, the root will also contain the following directories:

- `bin` that contains binaries (`tapkee` that is command line application

  and various tests with common naming `test_*`)

- `lib` that contains gtest shared libraries.



Need help?

----------



If you need any help or advice don't hesitate to send [an email](mailto://lisitsyn@hey.com) or

fire [an issue at github](https://github.com/lisitsyn/tapkee/issues/new).



Supported platforms

-------------------



Tapkee is tested to be fully functional on Linux (ICC, GCC, Clang compilers)

and Mac OS X (GCC and Clang compilers). It also compiles under Windows natively

(MSVS 2012 compiler) with a few known issues. In general, Tapkee uses no platform

specific code and should work on other systems as well. Please

[let us know](mailto://lisitsyn@hey.com) if you have successfully compiled

or have got any issues on any other system not listed above.



Supported dimension reduction methods

-------------------------------------



Tapkee provides implementations of the following dimension reduction methods (urls to descriptions provided):



* Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE)

* Neighborhood Preserving Embedding (NPE)

* Local Tangent Space Alignment (LTSA)

* Linear Local Tangent Space Alignment (LLTSA)

* Hessian Locally Linear Embedding (HLLE)

* Laplacian eigenmaps

* Locality Preserving Projections

* Diffusion map

* Isomap and landmark Isomap

* Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS)

* Stochastic Proximity Embedding (SPE)

* Principal Component Analysis (PCA)

* Kernel Principal Component Analysis (PCA)

* Random projection

* Factor analysis

* t-SNE

* Barnes-Hut-SNE



Licensing

---------



The library is distributed under the [BSD 3-clause](https://github.com/lisitsyn/tapkee/blob/master/LICENSE) license.



Exceptions are:



- [Barnes-Hut-SNE code](https://github.com/lisitsyn/tapkee/blob/master/include/external/barnes_hut_sne/) by Laurens van der Maaten which

  is distributed under the BSD 4-clause license.



- [Covertree code](https://github.com/lisitsyn/tapkee/blob/master/include/neighbors/covertree.hpp) by John Langford and Dinoj Surendran

  which is distributed under the [LGPLv3 license](https://github.com/lisitsyn/tapkee/blob/master/LGPL-LICENSE).