evomap.mapping ============== .. py:module:: evomap.mapping Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/evomap/mapping/_cmds/index /autoapi/evomap/mapping/_mds/index /autoapi/evomap/mapping/_optim/index /autoapi/evomap/mapping/_regression/index /autoapi/evomap/mapping/_sammon/index /autoapi/evomap/mapping/_tsne/index /autoapi/evomap/mapping/evomap/index Classes ------- .. autoapisummary:: evomap.mapping.CMDS evomap.mapping.EvoMDS evomap.mapping.EvoSammon evomap.mapping.EvoTSNE evomap.mapping.Sammon evomap.mapping.TSNE evomap.mapping.MDS Package Contents ---------------- .. py:class:: CMDS(n_dims=2) .. py:attribute:: n_dims :value: 2 .. py:method:: __str__() Create a string representation of the CMDS instance. .. py:method:: _cmdscale(D, n_dims, eps=1e-10) :staticmethod: Perform classical multidimensional scaling (CMDS) on the input distance matrix. CMDS reduces the dimensionality of a distance matrix while preserving the pairwise distances as well as possible using eigenvalue decomposition. :param D: Symmetric distance matrix to be scaled. :type D: np.array of shape (n, n) :param n_dims: Number of dimensions to which the data should be reduced. :type n_dims: int :param eps: Tolerance for numerical precision in rounding the resulting coordinates. :type eps: float, optional, default=1e-16 :returns: * **Y** (*np.array of shape (n, n_dims)*) -- Configuration matrix with the reduced dimensionality representation of the points. * **e** (*np.array of shape (n,)*) -- The eigenvalues corresponding to the dimensions. :raises ValueError: If the input matrix `D` is not square or symmetric. .. py:method:: fit(X) Fit the CMDS model to the provided distance matrix. :param X: Symmetric distance matrix to be scaled. :type X: np.array of shape (n, n) :returns: **self** -- Returns the instance itself with the configuration matrix `Y_` stored as an attribute. :rtype: object .. py:method:: fit_transform(X) Fit the CMDS model to the distance matrix and return the transformed coordinates. :param X: Symmetric distance matrix to be scaled. :type X: np.array of shape (n, n) :returns: The transformed coordinates (configuration matrix) in the reduced dimensional space. :rtype: np.array of shape (n, n_dims) .. py:class:: EvoMDS(alpha=0, p=1, n_dims=2, n_iter=2000, n_iter_check=50, init=None, mds_type='absolute', verbose=0, input_type='distance', max_halves=5, tol=0.001, n_inits=1, step_size=1, max_tries=5) Bases: :py:obj:`evomap.mapping.evomap._core.EvoMap` EvoMap Interface. Implements default functions shared by all implementation in its child classes. .. py:attribute:: n_dims :value: 2 .. py:attribute:: n_iter :value: 2000 .. py:attribute:: n_iter_check :value: 50 .. py:attribute:: init :value: None .. py:attribute:: verbose :value: 0 .. py:attribute:: mds_type :value: 'absolute' .. py:attribute:: input_type :value: 'distance' .. py:attribute:: max_halves :value: 5 .. py:attribute:: tol :value: 0.001 .. py:attribute:: n_inits :value: 1 .. py:attribute:: step_size :value: 1 .. py:attribute:: max_tries :value: 5 .. py:attribute:: method_str :value: 'EvoMDS' .. py:method:: __str__() Return a string representation of the EvoMDS instance with key parameters and user-modified values. .. py:method:: fit(Xs, inclusions=None) Fit the EvoMDS model to the input data over multiple periods. :param Xs: A list of input matrices, where each matrix corresponds to one period. The input matrices can either be feature vectors or distance matrices, depending on the `input_type`. :type Xs: list of np.ndarray :param inclusions: Binary array indicating which points are included in the calculation, by default None. :type inclusions: np.ndarray, optional :returns: **self** -- Returns the instance of the EvoMDS class with the configuration matrix `Ys_` stored as an attribute. :rtype: object .. py:method:: fit_transform(Xs, inclusions=None) Fit the EvoMDS model and return the transformed coordinates. :param Xs: A list of input matrices, where each matrix corresponds to one period. The input matrices can either be feature vectors or distance matrices, depending on the `input_type`. :type Xs: list of np.ndarray :param inclusions: Binary array indicating which points are included in the calculation, by default None. :type inclusions: np.ndarray, optional :returns: The transformed coordinates for each period, stored as a list of matrices. :rtype: list of np.ndarray :raises ValueError: If the `input_type` is not 'distance' or 'vector'. .. py:class:: EvoSammon(alpha=0, p=1, n_dims=2, n_iter=2000, n_iter_check=50, init=None, verbose=0, input_type='distance', max_halves=5, tol=0.001, n_inits=1, step_size=1, max_tries=5) Bases: :py:obj:`evomap.mapping.evomap._core.EvoMap` EvoMap Interface. Implements default functions shared by all implementation in its child classes. .. py:attribute:: n_dims :value: 2 .. py:attribute:: n_iter :value: 2000 .. py:attribute:: n_iter_check :value: 50 .. py:attribute:: init :value: None .. py:attribute:: verbose :value: 0 .. py:attribute:: input_type :value: 'distance' .. py:attribute:: max_halves :value: 5 .. py:attribute:: tol :value: 0.001 .. py:attribute:: n_inits :value: 1 .. py:attribute:: step_size :value: 1 .. py:attribute:: max_tries :value: 5 .. py:attribute:: method_str :value: 'EvoSammon' .. py:method:: __str__() Return a string representation of the EvoSammon instance with alpha, p, and user-modified parameters. .. py:method:: fit(Xs) Fit EvoSammon to the input data over multiple periods. :param Xs: A list of input matrices, where each matrix corresponds to one period. The input matrices can either be feature vectors or distance matrices, depending on the `input_type`. :type Xs: list of np.ndarray :returns: **self** -- Returns the instance of the EvoSammon class with the configuration matrix `Ys_` stored as an attribute. :rtype: object .. py:method:: fit_transform(Xs, inclusions=None) Fit the EvoSammon model and return the transformed coordinates. :param Xs: A list of input matrices, where each matrix corresponds to one period. The input matrices can either be feature vectors or distance matrices, depending on the `input_type`. :type Xs: list of np.ndarray :param inclusions: Binary array indicating which points are included in the calculation, by default None. :type inclusions: np.ndarray, optional :returns: The transformed coordinates for each period, stored as a list of matrices. :rtype: list of np.ndarray :raises ValueError: If the `input_type` is not 'distance' or 'vector'. .. py:class:: EvoTSNE(alpha=0, p=1, n_dims=2, perplexity=15, stop_lying_iter=250, early_exaggeration=4, eta='auto', initial_momentum=0.5, final_momentum=0.8, n_iter=2000, n_iter_check=50, init=None, verbose=0, input_type='distance', max_halves=5, tol=0.001, n_inits=1, max_tries=5) Bases: :py:obj:`evomap.mapping.evomap._core.EvoMap` EvoMap Interface. Implements default functions shared by all implementation in its child classes. .. py:attribute:: n_dims :value: 2 .. py:attribute:: perplexity :value: 15 .. py:attribute:: stop_lying_iter :value: 250 .. py:attribute:: early_exaggeration :value: 4 .. py:attribute:: eta :value: 'auto' .. py:attribute:: initial_momentum :value: 0.5 .. py:attribute:: final_momentum :value: 0.8 .. py:attribute:: n_iter :value: 2000 .. py:attribute:: n_iter_check :value: 50 .. py:attribute:: init :value: None .. py:attribute:: verbose :value: 0 .. py:attribute:: input_type :value: 'distance' .. py:attribute:: max_halves :value: 5 .. py:attribute:: tol :value: 0.001 .. py:attribute:: n_inits :value: 1 .. py:attribute:: max_tries :value: 5 .. py:attribute:: method_str :value: 'EvoTSNE' .. py:method:: __str__() Return a string representation of the EvoTSNE instance with alpha, p, perplexity, and user-modified parameters. .. py:method:: fit(Xs) Fit EvoTSNE to the input data over multiple periods. :param Xs: A list of input matrices, where each matrix corresponds to one period. The input matrices can either be feature vectors or distance matrices, depending on the `input_type`. :type Xs: list of np.ndarray :returns: **self** -- Returns the instance of the EvoTSNE class with the configuration matrix `Ys_` stored as an attribute. :rtype: object .. py:method:: fit_transform(Xs, inclusions=None) Fit EvoTSNE and return the transformed coordinates. :param Xs: A list of input matrices, where each matrix corresponds to one period. The input matrices can either be feature vectors or distance matrices, depending on the `input_type`. :type Xs: list of np.ndarray :param inclusions: Binary array indicating which points are included in the calculation, by default None. :type inclusions: np.ndarray, optional :returns: The transformed coordinates for each period, stored as a list of matrices. :rtype: list of np.ndarray :raises ValueError: If the `input_type` is not 'distance' or 'vector'. .. py:class:: Sammon(n_dims=2, n_iter=2000, n_iter_check=50, init=None, verbose=0, input_type='distance', max_halves=5, tol=0.001, n_inits=1, step_size=1) .. py:attribute:: n_dims :value: 2 .. py:attribute:: n_iter :value: 2000 .. py:attribute:: n_iter_check :value: 50 .. py:attribute:: init :value: None .. py:attribute:: verbose :value: 0 .. py:attribute:: input_type :value: 'distance' .. py:attribute:: max_halves :value: 5 .. py:attribute:: tol :value: 0.001 .. py:attribute:: n_inits :value: 1 .. py:attribute:: step_size :value: 1 .. py:attribute:: method_str :value: 'SAMMON' .. py:method:: __str__() Return a string representation of the Sammon instance with input_type and user-modified parameters. .. py:method:: fit(X) Fit the Sammon model to the input data. :param X: The input data. If `input_type` is 'vector', `X` should be the feature vectors of the samples. If `input_type` is 'distance', `X` should be the pairwise distance matrix. :type X: np.array of shape (n_samples, n_features) or (n_samples, n_samples) :returns: **self** -- Returns the instance of the Sammon class with the configuration matrix `Y_` stored as an attribute. :rtype: object .. py:method:: fit_transform(X) Fit the Sammon mapping model and return the transformed coordinates. :param X: The input data. If `input_type` is 'vector', `X` should be the feature vectors of the samples. If `input_type` is 'distance', `X` should be the pairwise distance matrix. :type X: np.array of shape (n_samples, n_features) or (n_samples, n_samples) :returns: The transformed coordinates of the samples in the reduced-dimensional space. :rtype: np.array of shape (n_samples, n_dims) :raises ValueError: If the `input_type` is not 'distance' or 'vector'. .. py:class:: TSNE(n_dims=2, perplexity=15, n_iter=2000, stop_lying_iter=250, early_exaggeration=4, initial_momentum=0.5, final_momentum=0.8, eta='auto', n_iter_check=50, init=None, verbose=0, input_type='distance', max_halves=5, tol=0.001, n_inits=1, step_size=1) .. py:attribute:: n_dims :value: 2 .. py:attribute:: perplexity :value: 15 .. py:attribute:: n_iter :value: 2000 .. py:attribute:: stop_lying_iter :value: 250 .. py:attribute:: early_exaggeration :value: 4 .. py:attribute:: initial_momentum :value: 0.5 .. py:attribute:: final_momentum :value: 0.8 .. py:attribute:: eta :value: 'auto' .. py:attribute:: n_iter_check :value: 50 .. py:attribute:: init :value: None .. py:attribute:: verbose :value: 0 .. py:attribute:: input_type :value: 'distance' .. py:attribute:: max_halves :value: 5 .. py:attribute:: tol :value: 0.001 .. py:attribute:: n_inits :value: 1 .. py:attribute:: step_size :value: 1 .. py:attribute:: method_str :value: 'TSNE' .. py:method:: __str__() Return a string representation of the TSNE instance with key parameters and user-modified values. .. py:method:: fit(X) Fit the TSNE model to the input data, without returning the transformed coordinates. :param X: The input data. If `input_type` is 'vector', `X` should be the feature vectors of the samples. If `input_type` is 'distance', `X` should be the pairwise distance matrix. :type X: np.array of shape (n_samples, n_features) or (n_samples, n_samples) :returns: **self** -- Returns the instance of the TSNE class with the configuration matrix `Y_` stored as an attribute. :rtype: object .. py:method:: fit_transform(X) Fit the TSNE model and return the transformed coordinates. :param X: The input data. If `input_type` is 'vector', `X` should be the feature vectors of the samples. If `input_type` is 'distance', `X` should be the pairwise distance matrix. :type X: np.array of shape (n_samples, n_features) or (n_samples, n_samples) :returns: The transformed coordinates in the reduced-dimensional space. :rtype: np.array of shape (n_samples, n_dims) :raises ValueError: If the `input_type` is not 'distance' or 'vector'. .. py:class:: MDS(n_dims=2, mds_type=None, n_iter=2000, n_iter_check=50, init=None, verbose=0, input_type='distance', max_halves=5, tol=0.001, n_inits=1, step_size=1) .. py:attribute:: n_dims :value: 2 .. py:attribute:: mds_type :value: None .. py:attribute:: n_iter :value: 2000 .. py:attribute:: n_iter_check :value: 50 .. py:attribute:: init :value: None .. py:attribute:: verbose :value: 0 .. py:attribute:: input_type :value: 'distance' .. py:attribute:: max_halves :value: 5 .. py:attribute:: tol :value: 0.001 .. py:attribute:: n_inits :value: 1 .. py:attribute:: step_size :value: 1 .. py:attribute:: method_str :value: 'MDS' .. py:method:: __str__() Create a string representation of the MDS instance. Displays the key attributes and all parameters modified by the user. :returns: A summary of the key attributes of this MDS object, including modified parameters. :rtype: str .. py:method:: fit(X) Fit the MDS model to the input data, without returning the transformed positions. :param X: The input data. If `input_type` is 'vector', X should be the feature vectors of the samples. If `input_type` is 'distance', X should be a pairwise distance matrix. :type X: np.array of shape (n_samples, n_features) or (n_samples, n_samples) :returns: **self** -- The instance of the MDS class, after fitting the model to the input data. :rtype: object .. py:method:: fit_transform(X) Fit the MDS model to the input data and return transformed positions. Dependning on 'input_type', the input data is either interpreted as a distance matrix or feature vectors. The method uses gradient descent to optimize the lower-dimensional positions such that a Stress function, measuring the discrepancy between the input distances and resulting configuration, is minimized. :param X: The input data. If `input_type` is 'vector', X should be the feature vectors of the samples. If `input_type` is 'distance', X should be a pairwise distance matrix. :type X: np.array of shape (n_samples, n_features) or (n_samples, n_samples) :returns: The transformed positions in the lower-dimensional space. :rtype: np.array of shape (n_samples, n_dims) :raises ValueError: If `input_type` is neither 'distance' nor 'vector', a ValueError is raised.