FItSNE wrapper

fitsne_wrapper.py

class FItSNE_wrapper:
    """
    Wrapper class for FItSNE `fast_tsne.fast_tsne` v1.1.0.
    It is currently designed to work in a Conda environment with Python 3.7 and FFTW 3.
    
    Install and compile as follow:
    
      $ conda create -n YOUR-ENV python=3.7 fftw=3
      $ source activate YOUR-ENV
      $ cd $CONDA_PREFIX/lib/python3.7/
      $ git clone https://github.com/KlugerLab/FIt-SNE.git FIt-SNE
      $ cd FIt-SNE
      $ git checkout tags/v1.1.0
      $ g++ -std=c++11 -O3 -pthread -I${CONDA_PREFIX}/include -o nbodyfft.o -c src/nbodyfft.cpp
      $ g++ -std=c++11 -O3 -pthread -I${CONDA_PREFIX}/include -o sptree.o -c src/sptree.cpp
      $ g++ -std=c++11 -O3 -pthread -I${CONDA_PREFIX}/include -o tsne.o -c src/tsne.cpp
      $ g++ -o bin/fast_tsne nbodyfft.o sptree.o tsne.o -L${CONDA_PREFIX}/lib -Wl,-rpath ${CONDA_PREFIX}/lib -lm -lfftw3 -lpthread
      $ file bin/fast_tsne  # should be an ELF 64-bit LSB executable

    References:
      - https://github.com/KlugerLab/FIt-SNE
      - https://arxiv.org/abs/1712.09005    
    """

    def __init__(self, **kwargs):
        self.__dict__ = {  # default values
            "theta": 0.5,
            "perplexity": 30,
            "map_dims": 2,
            "max_iter": 1000,
            "stop_early_exag_iter": 250,
            "K": -1,
            "sigma": -1,
            "nbody_algo": "FFT",
            "knn_algo": "annoy",
            "mom_switch_iter": 250,
            "momentum": 0.5,
            "final_momentum": 0.8,
            "learning_rate": 200,
            "early_exag_coeff": 12,
            "no_momentum_during_exag": False,
            "n_trees": 50,
            "search_k": None,
            "start_late_exag_iter": -1,
            "late_exag_coeff": -1,
            "nterms": 3,
            "intervals_per_integer": 1,
            "min_num_intervals": 50,
            "seed": -1,
            "initialization": None,
            "load_affinities": None,
            "perplexity_list": None,
            "df": 1,
            "return_loss": False,
            "nthreads": None,
        }
        self.__dict__ = {**self.__dict__, **kwargs}  # user inputs overwrite default params

    def fit_transform(self, X, fitsne_path=None):
        """

        """
        import os, sys

        if fitsne_path is None:
            fitsne_path = os.getenv("CONDA_PREFIX") + "/lib/python3.7/FIt-SNE"

        if fitsne_path not in sys.path:
            sys.path.append(fitsne_path)

        from fast_tsne import fast_tsne as _fast_tsne

        return _fast_tsne(X, **self.__dict__)