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spectrochempy.SIMPLISMA

class SIMPLISMA(*args, log_level='WARNING', warm_start=False, interactive=False, n_components=2, noise=3, tol=0.1)[source]

SIMPLe to use Interactive Self-modeling Mixture Analysis (SIMPLISMA).

This class performs a SIMPLISMA analysis of a 2D NDDataset . The algorithm is adapted from Windig [1997].

Parameters
  • log_level (any of ["INFO", "DEBUG", "WARNING", "ERROR"], optional, default: "WARNING") – The log level at startup. It can be changed later on using the set_log_level method or by changing the log_level attribute.

  • warm_start (bool, optional, default: False) – When fitting repeatedly on the same dataset, but for multiple parameter values (such as to find the value maximizing performance), it may be possible to reuse previous model learned from the previous parameter value, saving time.

    When warm_start is True, the existing fitted model attributes is used to initialize the new model in a subsequent call to fit.

  • interactive (bool, optional, default: False) – If True, the determination of purest variables is carried out interactively.

  • n_components (int, optional, default: 2) – The maximum number of pure compounds. Used only for non interactiveanalysis.

  • noise (float, optional, default: 3) – A correction factor (%) for low intensity variables (0 - no offset, 15 - large offset.

  • tol (float, optional, default: 0.1) – The convergence criterion on the percent of unexplained variance.

See also

EFA

Perform an Evolving Factor Analysis (forward and reverse).

FastICA

Perform Independent Component Analysis with a fast algorithm.

IRIS

Integral inversion solver for spectroscopic data.

MCRALS

Perform MCR-ALS of a dataset knowing the initial \(C\) or \(S^T\) matrix.

NMF

Non-Negative Matrix Factorization.

PCA

Perform Principal Components Analysis.

SVD

Perform a Singular Value Decomposition.

Attributes Summary

C

Intensities ('concentrations') of pure compounds in spectra.

Pt

Purity spectra.

St

Spectra of pure compounds.

X

Return the X input dataset (eventually modified by the model).

Y

The Y input.

components

NDDataset with components in feature space (n_components, n_features).

config

traitlets.config.Config object.

interactive

If True, the determination of purest variables is carried out interactively.

log

Return log output.

n_components

The maximum number of pure compounds.

name

Object name

noise

A correction factor (%) for low intensity variables (0 - no offset, 15 - large offset.

s

Standard deviation spectra.

tol

The convergence criterion on the percent of unexplained variance.

Methods Summary

fit(X)

Fit the SIMPLISMA model on X.

fit_transform(X[, Y])

Fit the model with X and apply the dimensionality reduction on X.

get_components([n_components])

Return the component's dataset: (selected n_components, n_features).

inverse_transform([X_transform])

Transform data back to its original space.

parameters([replace, removed, default])

Alias for params method.

params([default])

Current or default configuration values.

plotmerit([X, X_hat])

Plot the input (X), reconstructed (X_hat) and residuals.

reconstruct([X_transform])

Transform data back to its original space.

reduce([X])

Apply dimensionality reduction to X.

reset()

Reset configuration parameters to their default values

to_dict()

Return config value in a dict form.

transform([X])

Apply dimensionality reduction to X.

Attributes Documentation

C

Intensities (‘concentrations’) of pure compounds in spectra.

Pt

Purity spectra.

St

Spectra of pure compounds.

X

Return the X input dataset (eventually modified by the model).

Y

The Y input.

components

NDDataset with components in feature space (n_components, n_features).

See also

get_components

Retrieve only the specified number of components.

config

traitlets.config.Config object.

interactive

If True, the determination of purest variables is carried out interactively.

log

Return log output.

n_components

The maximum number of pure compounds. Used only for non interactiveanalysis.

name

Object name

noise

A correction factor (%) for low intensity variables (0 - no offset, 15 - large offset.

s

Standard deviation spectra.

tol

The convergence criterion on the percent of unexplained variance.

Methods Documentation

fit(X)[source]

Fit the SIMPLISMA model on X.

Parameters

X (NDDataset or array-like of shape (n_observations, n_features)) – Training data.

Returns

self – The fitted instance itself.

See also

fit_transform

Fit the model with an input dataset X and apply the dimensionality reduction on X.

fit_reduce

Alias of fit_transform (Deprecated).

fit_transform(X, Y=None, **kwargs)[source]

Fit the model with X and apply the dimensionality reduction on X.

Parameters
Returns

NDDataset – Dataset with shape (n_observations, n_components).

Other Parameters

n_components (int, optional) – The number of components to use for the reduction. If not given the number of components is eventually the one specified or determined in the fit process.

get_components(n_components=None)

Return the component’s dataset: (selected n_components, n_features).

Parameters

n_components (int, optional, default: None) – The number of components to keep in the output dataset. If None, all calculated components are returned.

Returns

NDDataset – Dataset with shape (n_components, n_features)

inverse_transform(X_transform=None, **kwargs)

Transform data back to its original space.

In other words, return an input X_original whose reduce/transform would be X_transform.

Parameters
  • X_transform (array-like of shape (n_observations, n_components), optional) – Reduced X data, where n_observations is the number of observations and n_components is the number of components. If X_transform is not provided, a transform of X provided in fit is performed first.

  • **kwargs (keyword parameters, optional) – See Other Parameters.

Returns

NDDataset – Dataset with shape (n_observations, n_features).

Other Parameters

n_components (int, optional) – The number of components to use for the reduction. If not given the number of components is eventually the one specified or determined in the fit process.

See also

reconstruct

Alias of inverse_transform (Deprecated).

parameters(replace="params", removed="0.7.1") def parameters(self, default=False)[source]

Alias for params method.

params(default=False)[source]

Current or default configuration values.

Parameters

default (bool, optional, default: False) – If default is True, the default parameters are returned, else the current values.

Returns

dict – Current or default configuration values.

plotmerit(X=None, X_hat=None, **kwargs)[source]

Plot the input (X), reconstructed (X_hat) and residuals.

\(X\) and \(\hat{X}\) can be passed as arguments. If not, the X attribute is used for \(X\)and \(\hat{X}\)is computed by the inverse_transform method

Parameters
  • X (NDDataset, optional) – Original dataset. If is not provided (default), the X attribute is used and X_hat is computed using inverse_transform.

  • X_hat (NDDataset, optional) – Inverse transformed dataset. if X is provided, X_hat must also be provided as compuyed externally.

  • **kwargs (keyword parameters, optional) – See Other Parameters.

Returns

Axes – Matplotlib subplot axe.

Other Parameters
  • colors (tuple or ndarray of 3 colors, optional) – Colors for X , X_hat and residuals E . in the case of 2D, The default colormap is used for X . By default, the three colors are NBlue , NGreen and NRed (which are colorblind friendly).

  • offset (float, optional, default: None) – Specify the separation (in percent) between the \(X\) , \(X_hat\) and \(E\).

  • nb_traces (int or 'all', optional) – Number of lines to display. Default is 'all'.

  • **others (Other keywords parameters) – Parameters passed to the internal plot method of the X dataset.

reconstruct(X_transform=None, **kwargs)[source]

Transform data back to its original space.

In other words, return an input X_original whose reduce/transform would be X_transform.

Parameters
  • X_transform (array-like of shape (n_observations, n_components), optional) – Reduced X data, where n_observations is the number of observations and n_components is the number of components. If X_transform is not provided, a transform of X provided in fit is performed first.

  • **kwargs (keyword parameters, optional) – See Other Parameters.

Returns

NDDataset – Dataset with shape (n_observations, n_features).

Other Parameters

n_components (int, optional) – The number of components to use for the reduction. If not given the number of components is eventually the one specified or determined in the fit process.

See also

reconstruct

Alias of inverse_transform (Deprecated).

Notes

Deprecated in version 0.6.

reduce(X=None, **kwargs)[source]

Apply dimensionality reduction to X.

Parameters
Returns

NDDataset – Dataset with shape (n_observations, n_components).

Other Parameters

n_components (int, optional) – The number of components to use for the reduction. If not given the number of components is eventually the one specified or determined in the fit process.

Notes

Deprecated in version 0.6.

reset()[source]

Reset configuration parameters to their default values

to_dict()[source]

Return config value in a dict form.

Returns

dict – A regular dictionary.

transform(X=None, **kwargs)

Apply dimensionality reduction to X.

Parameters
Returns

NDDataset – Dataset with shape (n_observations, n_components).

Other Parameters

n_components (int, optional) – The number of components to use for the reduction. If not given the number of components is eventually the one specified or determined in the fit process.

Examples using spectrochempy.SIMPLISMA

SIMPLISMA example

SIMPLISMA example