Source code for immuneML.ml_methods.dim_reduction.TSNE

import logging
from typing import List

import numpy as np
from sklearn.manifold import TSNE as SklearnTSNE

from immuneML.data_model.datasets.Dataset import Dataset
from immuneML.ml_methods.dim_reduction.DimRedMethod import DimRedMethod




class TSNE(DimRedMethod):
    """
    t-distributed Stochastic Neighbor Embedding (t-SNE) method which wraps scikit-learn's TSNE. It can be useful for
    visualizing high-dimensional data. Input arguments for the method are the same as supported by scikit-learn (see
    `TSNE scikit-learn documentation
    <https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE>`_ for
    details), plus one additional immuneML argument:

    - components (list): which two components (1-indexed) to use for visualization in the
      :ref:`DimensionalityReduction` report. Default: [1, 2].

    **YAML specification:**

    .. indent with spaces
    .. code-block:: yaml

        definitions:
            ml_methods:
                my_tsne:
                    TSNE:
                        n_components: 2
                        init: pca
                        components: [1, 2]

    """

    def __init__(self, name: str = None, **kwargs):
        super().__init__(name)
        self.components = kwargs.pop('components', None)
        self.method_kwargs = kwargs
        self.method = SklearnTSNE(**self.method_kwargs)
        self._validate_components(self.method.n_components)



    def transform(self, dataset: Dataset = None, design_matrix: np.ndarray = None):
        logging.warning(f"{TSNE.__name__}: calling transform method of TSNE, but it only supports fit_transform. "
                        f"Fitting the model and returning the transformed data...")
        data = dataset.encoded_data.get_examples_as_np_matrix() if dataset is not None else design_matrix
        return self.method.fit_transform(data)




    def get_dimension_names(self) -> List[str]:
        return [f"tSNE_{i+1}" for i in range(self.method.n_components)]