immuneML.ml_methods.dim_reduction package

Submodules

immuneML.ml_methods.dim_reduction.DimRedMethod module

class immuneML.ml_methods.dim_reduction.DimRedMethod.DimRedMethod(name: str = None)

Bases: object

Dimensionality reduction methods are algorithms which can be used to reduce the dimensionality of encoded datasets, in order to uncover and analyze patterns present in the data.

These methods can be used in the ExploratoryAnalysis and Clustering instructions.

DOCS_TITLE = 'Dimensionality reduction methods'

abstract fit(dataset: Dataset)

abstract fit_transform(dataset: Dataset)

classmethod get_title()

abstract transform(dataset: Dataset)

immuneML.ml_methods.dim_reduction.PCA module

class immuneML.ml_methods.dim_reduction.PCA.PCA(name: str = None, **kwargs)

Bases: DimRedMethod

Principal component analysis (PCA) method which wraps scikit-learn’s PCA. Input arguments for the method are the same as supported by scikit-learn (see PCA scikit-learn documentation for details).

YAML specification:

definitions:
    ml_methods:
        my_pca:
            PCA:
                # arguments as defined by scikit-learn
                n_components: 2

fit(dataset: Dataset)

fit_transform(dataset: Dataset)

transform(dataset: Dataset)

immuneML.ml_methods.dim_reduction.TSNE module

class immuneML.ml_methods.dim_reduction.TSNE.TSNE(name: str = None, **kwargs)

Bases: 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 for details).

YAML specification:

definitions:
    ml_methods:
        my_tsne:
            TSNE:
                # arguments as defined by scikit-learn
                n_components: 2
                init: pca

fit(dataset: Dataset)

fit_transform(dataset: Dataset)

transform(dataset: Dataset)

immuneML.ml_methods.dim_reduction.UMAP module

class immuneML.ml_methods.dim_reduction.UMAP.UMAP(name: str = None, **kwargs)

Bases: DimRedMethod

Uniform manifold approximation and projection (UMAP) method which wraps umap-learn’s UMAP. Input arguments for the method are the same as supported by umap-learn (see UMAP in the umap-learn documentation for details).

Note that when providing the arguments for UMAP in the immuneML’s specification, it is not possible to set functions as input values (e.g., for the metric parameter, it has to be one of the predefined metrics available in umap-learn).

YAML specification:

definitions:
    ml_methods:
        my_umap:
            UMAP:
                # arguments as defined by scikit-learn
                n_components: 2
                n_neighbors: 15
                metric: euclidean

fit(dataset: Dataset)

fit_transform(dataset: Dataset)

transform(dataset: Dataset)

Module contents