evomap.mapping

evomap.mapping#

Submodules#

Classes#

`CMDS`
`EvoMDS`	EvoMap Interface. Implements default functions shared by all
`EvoSammon`	EvoMap Interface. Implements default functions shared by all
`EvoTSNE`	EvoMap Interface. Implements default functions shared by all
`Sammon`
`TSNE`
`MDS`

Package Contents#

class evomap.mapping.CMDS(n_dims=2)[source]#

n_dims = 2#

__str__()[source]#: Create a string representation of the CMDS instance.

static _cmdscale(D, n_dims, eps=1e-10)[source]#

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.

Parameters:

D (np.array of shape (n, n)) – Symmetric distance matrix to be scaled.
n_dims (int) – Number of dimensions to which the data should be reduced.
eps (float, optional, default=1e-16) – Tolerance for numerical precision in rounding the resulting coordinates.

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.

fit(X)[source]#

Fit the CMDS model to the provided distance matrix.

Parameters:: X (np.array of shape (n, n)) – Symmetric distance matrix to be scaled.
Returns:: self – Returns the instance itself with the configuration matrix Y_ stored as an attribute.
Return type:: object

fit_transform(X)[source]#

Fit the CMDS model to the distance matrix and return the transformed coordinates.

Parameters:: X (np.array of shape (n, n)) – Symmetric distance matrix to be scaled.
Returns:: The transformed coordinates (configuration matrix) in the reduced dimensional space.
Return type:: np.array of shape (n, n_dims)

class evomap.mapping.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)[source]#

Bases: evomap.mapping.evomap._core.EvoMap

EvoMap Interface. Implements default functions shared by all implementation in its child classes.

n_dims = 2#

n_iter = 2000#

n_iter_check = 50#

init = None#

verbose = 0#

mds_type = 'absolute'#

input_type = 'distance'#

max_halves = 5#

tol = 0.001#

n_inits = 1#

step_size = 1#

max_tries = 5#

method_str = 'EvoMDS'#

__str__()[source]#: Return a string representation of the EvoMDS instance with key parameters and user-modified values.

fit(Xs, inclusions=None)[source]#

Fit the EvoMDS model to the input data over multiple periods.

Parameters:

Xs (list of np.ndarray) – 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.
inclusions (np.ndarray, optional) – Binary array indicating which points are included in the calculation, by default None.

Returns:

self – Returns the instance of the EvoMDS class with the configuration matrix Ys_ stored as an attribute.

Return type:

object

fit_transform(Xs, inclusions=None)[source]#

Fit the EvoMDS model and return the transformed coordinates.

Parameters:

Xs (list of np.ndarray) – 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.
inclusions (np.ndarray, optional) – Binary array indicating which points are included in the calculation, by default None.

Returns:

The transformed coordinates for each period, stored as a list of matrices.

Return type:

list of np.ndarray

Raises:

ValueError – If the input_type is not ‘distance’ or ‘vector’.

class evomap.mapping.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)[source]#

Bases: evomap.mapping.evomap._core.EvoMap

EvoMap Interface. Implements default functions shared by all implementation in its child classes.

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#

method_str = 'EvoSammon'#

__str__()[source]#: Return a string representation of the EvoSammon instance with alpha, p, and user-modified parameters.

fit(Xs)[source]#

Fit EvoSammon to the input data over multiple periods.

Parameters:: Xs (list of np.ndarray) – 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.
Returns:: self – Returns the instance of the EvoSammon class with the configuration matrix Ys_ stored as an attribute.
Return type:: object

fit_transform(Xs, inclusions=None)[source]#

Fit the EvoSammon model and return the transformed coordinates.

Parameters:

Xs (list of np.ndarray) – 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.
inclusions (np.ndarray, optional) – Binary array indicating which points are included in the calculation, by default None.

Returns:

The transformed coordinates for each period, stored as a list of matrices.

Return type:

list of np.ndarray

Raises:

ValueError – If the input_type is not ‘distance’ or ‘vector’.

class evomap.mapping.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)[source]#

Bases: evomap.mapping.evomap._core.EvoMap

EvoMap Interface. Implements default functions shared by all implementation in its child classes.

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#

method_str = 'EvoTSNE'#

__str__()[source]#: Return a string representation of the EvoTSNE instance with alpha, p, perplexity, and user-modified parameters.

fit(Xs)[source]#

Fit EvoTSNE to the input data over multiple periods.

Parameters:: Xs (list of np.ndarray) – 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.
Returns:: self – Returns the instance of the EvoTSNE class with the configuration matrix Ys_ stored as an attribute.
Return type:: object

fit_transform(Xs, inclusions=None)[source]#

Fit EvoTSNE and return the transformed coordinates.

Parameters:

Xs (list of np.ndarray) – 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.
inclusions (np.ndarray, optional) – Binary array indicating which points are included in the calculation, by default None.

Returns:

The transformed coordinates for each period, stored as a list of matrices.

Return type:

list of np.ndarray

Raises:

ValueError – If the input_type is not ‘distance’ or ‘vector’.

class evomap.mapping.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)[source]#

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#

method_str = 'SAMMON'#

__str__()[source]#: Return a string representation of the Sammon instance with input_type and user-modified parameters.

fit(X)[source]#

Fit the Sammon model to the input data.

Parameters:: X (np.array of shape (n_samples, n_features) or (n_samples, n_samples)) – 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.
Returns:: self – Returns the instance of the Sammon class with the configuration matrix Y_ stored as an attribute.
Return type:: object

fit_transform(X)[source]#

Fit the Sammon mapping model and return the transformed coordinates.

Parameters:: X (np.array of shape (n_samples, n_features) or (n_samples, n_samples)) – 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.
Returns:: The transformed coordinates of the samples in the reduced-dimensional space.
Return type:: np.array of shape (n_samples, n_dims)
Raises:: ValueError – If the input_type is not ‘distance’ or ‘vector’.

class evomap.mapping.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)[source]#

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#

method_str = 'TSNE'#

__str__()[source]#: Return a string representation of the TSNE instance with key parameters and user-modified values.

fit(X)[source]#

Fit the TSNE model to the input data, without returning the transformed coordinates.

Parameters:: X (np.array of shape (n_samples, n_features) or (n_samples, n_samples)) – 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.
Returns:: self – Returns the instance of the TSNE class with the configuration matrix Y_ stored as an attribute.
Return type:: object

fit_transform(X)[source]#

Fit the TSNE model and return the transformed coordinates.

Parameters:: X (np.array of shape (n_samples, n_features) or (n_samples, n_samples)) – 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.
Returns:: The transformed coordinates in the reduced-dimensional space.
Return type:: np.array of shape (n_samples, n_dims)
Raises:: ValueError – If the input_type is not ‘distance’ or ‘vector’.

class evomap.mapping.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)[source]#

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#

method_str = 'MDS'#

__str__()[source]#

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.
Return type:: str

fit(X)[source]#

Fit the MDS model to the input data, without returning the transformed positions.

Parameters:: X (np.array of shape (n_samples, n_features) or (n_samples, n_samples)) – 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.
Returns:: self – The instance of the MDS class, after fitting the model to the input data.
Return type:: object

fit_transform(X)[source]#

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.

Parameters:: X (np.array of shape (n_samples, n_features) or (n_samples, n_samples)) – 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.
Returns:: The transformed positions in the lower-dimensional space.
Return type:: np.array of shape (n_samples, n_dims)
Raises:: ValueError – If input_type is neither ‘distance’ nor ‘vector’, a ValueError is raised.