spectrochempy.Optimize

class Optimize(*, log_level='WARNING', warm_start=False, amplitude_mode='height', autoampl=False, autobase=False, callback_every=10, constraints=None, dry=False, max_fun_calls=0, max_iter=500, method='least_squares', script='')[source]

Non-linear curve fitting driven by the SpectroChemPy fitting DSL.

Optimize combines:

script-based model definition through script;
pre-fit validation through validate_script;
multiple public optimization-method families through method;
estimator-level access to raw solver artifacts such as jacobian;
grouped scientific outputs and diagnostics through result.

The current public method values are:

"least_squares"
"leastsq"
"simplex"
"basinhopping"

In current SpectroChemPy releases, "least_squares" is the clearest recommended entrypoint for ordinary local least-squares fitting. "leastsq" is still accepted as a public compatibility alias, but it currently uses the same least-squares backend family rather than defining a separate maintained fitting strategy of its own.

Least-squares-backed methods are the only ones that currently expose the retained Jacobian and the resulting uncertainty path on FitResult (covariance, standard errors, correlation, and confidence intervals).

Method selection

In current SpectroChemPy releases, the practical guidance is:

use "least_squares" as the default choice for ordinary local least-squares fitting;
treat "leastsq" mainly as a compatibility alias rather than as a separate maintained strategy;
use "simplex" when you explicitly want a derivative-free local search and can accept losing the least-squares uncertainty path;
reserve "basinhopping" for harder landscapes where a slower exploratory global-style search is justified.

The low-level SciPy least-squares backend variant (currently lm or trf) is still selected internally; users choose the high-level SpectroChemPy method, not the backend variant directly.

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) – Preserve the current estimator configuration instead of forcing a full reset to default configuration values during estimator reinitialization. This is a general estimator-state option shared with other SpectroChemPy analysis classes; it should not be interpreted as a dedicated Optimize solver-backend feature.
amplitude_mode (any value of ['area', 'height'], optional, default: 'height') – How line-shape amplitudes are interpreted during initialisation.
autoampl (bool, optional, default: False) – Whether to estimate initial amplitudes automatically during setup.
autobase (bool, optional, default: False) – Whether to estimate and apply a linear baseline correction automatically.
callback_every (int, optional, default: 10) – Number of iteration between each callback report. Used for printing or display intermediate results.
constraints (any value, optional, default: None) – Constraints.
dry (bool, optional, default: False) – If True, assemble the starting model without running the optimizer.
max_fun_calls (int, optional, default: 0) – Maximum number of function calls at each iteration.
max_iter (int, optional, default: 500) – Maximum number of fitting iteration.
method (any value of ['least_squares', 'leastsq', 'simplex', 'basinhopping'], optional, default: 'least_squares') – High-level optimization-method selector. ‘least_squares’ is the preferred local least-squares entrypoint; ‘leastsq’ is a compatibility alias using the same backend family; ‘simplex’ uses a derivative-free local search; ‘basinhopping’ uses a global-style exploratory search.
script (str, optional, default: '') – Script defining models and parameters for fitting.

Notes

The current implementation supports only 1D fitting. Multi-dimensional datasets are not yet handled by fit.

Initialize the Optimize class with configuration parameters.

Parameters:

log_level (str, optional) – Logging level, by default “WARNING”.
warm_start (bool, optional) – Preserve the current estimator configuration rather than resetting it to defaults during estimator reinitialization. This does not add any dedicated Optimize backend-specific warm-start strategy.
**kwargs (dict) – Additional keyword arguments.

Attributes Summary

`X`	Return the X input dataset (eventually modified by the model).
`Y`	The `Y` input.
`amplitude_mode`	How line-shape amplitudes are interpreted during initialisation.
`autoampl`	Whether to estimate initial amplitudes automatically during setup.
`autobase`	Whether to estimate and apply a linear baseline correction automatically.
`callback_every`	Number of iteration between each callback report.
`components`	`NDDataset` with components in feature space (n_components, n_features).
`config`	`traitlets.config.Config` object.
`constraints`	Constraints.
`dry`	If True, assemble the starting model without running the optimizer.
`fp`	A trait whose value must be an instance of a specified class.
`jacobian`	Return the retained raw solver Jacobian when the backend provides one.
`log`	Return `log` output.
`max_fun_calls`	Maximum number of function calls at each iteration.
`max_iter`	Maximum number of fitting iteration.
`method`	High-level optimization-method selector.
`modeldata`	An instance of a Python list.
`n_components`	Number of components that were fitted.
`name`	Object name
`result`	Return the Optimize fit result object.
`script`	Script defining models and parameters for fitting.
`usermodels`	User defined models.

Methods Summary

`fit`(X)	Perform a non-linear optimization of the `X` dataset.
`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).
`get_params`([deep])	Get the configuration parameters of this estimator.
`inverse_transform`([X_transform])	Transform data back to its original space.
`params`([default])	Return current or default configuration values.
`plot_merit`([X, X_hat])	Plot the input (`X`), reconstructed (`X_hat`) and residuals.
`plotmerit`([X, X_hat])	Plot the input (`X`), reconstructed (`X_hat`) and residuals.
`predict`()	Return the fitted model.
`reset`()	Reset configuration parameters to their default values.
`set_params`(**params)	Set configuration parameters on this estimator.
`to_dict`()	Return config value in a dict form.
`transform`([X])	Apply dimensionality reduction to `X`.
`validate_constraints`([constraints, script])	Validate lightweight constraint specifications without running a fit.
`validate_script`([script])	Validate a fitting script without running an optimisation.

Attributes Documentation

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

Y: The Y input.

amplitude_mode: How line-shape amplitudes are interpreted during initialisation.

autoampl: Whether to estimate initial amplitudes automatically during setup.

autobase: Whether to estimate and apply a linear baseline correction automatically.

callback_every: Number of iteration between each callback report. Used for printing or display intermediate results.

components

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

See also

reconstruct: Alias of inverse_transform (Deprecated).

params(default=False)[source]

Return 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.

plot_merit(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 \(X`and :math:\)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.

Returns:

Axes – Matplotlib subplot axe.

Other Parameters:

exp_c (color, colormap, or list of colors, optional) – Color(s) for experimental spectra. - None: use unified semantic resolver (auto-detect categorical/sequential) - Single color: use for all experimental spectra - Colormap name/object: sample colors from colormap - List/tuple: use as explicit color cycle
calc_c (color, colormap, or list of colors, optional) – Color(s) for calculated spectra. - None: use default blue “#2a6fbb” - Single color: use for all calculated spectra - Colormap name/object: sample colors from colormap - List/tuple: use as explicit color cycle
resid_c (color, colormap, or list of colors, optional) – Color(s) for residual spectra. - None: use default grey “0.4” - Single color: use for all residual spectra - Colormap name/object: sample colors from colormap - List/tuple: use as explicit color cycle
exp_linestyle (str, optional) – Line style for experimental spectra. Default: “-“.
calc_linestyle (str, optional) – Line style for calculated spectra. Default: “–“.
resid_linestyle (str, optional) – Line style for residual spectra. Default: “-“.
exp_linewidth (float, optional) – Line width for experimental spectra. Default: 1.2.
calc_linewidth (float, optional) – Line width for calculated spectra. Default: 1.0.
resid_linewidth (float, optional) – Line width for residual spectra. Default: 1.0.
min_contrast (float, optional) – Minimum contrast ratio for sequential colormaps. Default: 1.5.
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.

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 \(X`and :math:\)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.

Returns:

Axes – Matplotlib subplot axe.

Other Parameters:

exp_c (color, colormap, or list of colors, optional) – Color(s) for experimental spectra. - None: use unified semantic resolver (auto-detect categorical/sequential) - Single color: use for all experimental spectra - Colormap name/object: sample colors from colormap - List/tuple: use as explicit color cycle
calc_c (color, colormap, or list of colors, optional) – Color(s) for calculated spectra. - None: use default blue “#2a6fbb” - Single color: use for all calculated spectra - Colormap name/object: sample colors from colormap - List/tuple: use as explicit color cycle
resid_c (color, colormap, or list of colors, optional) – Color(s) for residual spectra. - None: use default grey “0.4” - Single color: use for all residual spectra - Colormap name/object: sample colors from colormap - List/tuple: use as explicit color cycle
exp_linestyle (str, optional) – Line style for experimental spectra. Default: “-“.
calc_linestyle (str, optional) – Line style for calculated spectra. Default: “–“.
resid_linestyle (str, optional) – Line style for residual spectra. Default: “-“.
exp_linewidth (float, optional) – Line width for experimental spectra. Default: 1.2.
calc_linewidth (float, optional) – Line width for calculated spectra. Default: 1.0.
resid_linewidth (float, optional) – Line width for residual spectra. Default: 1.0.
min_contrast (float, optional) – Minimum contrast ratio for sequential colormaps. Default: 1.5.
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.

predict()[source]

Return the fitted model.

Returns:: NDDataset – The fitted model.

reset()[source]: Reset configuration parameters to their default values.

set_params(**params)[source]

Set configuration parameters on this estimator.

Returns self so that calls can be chained.

Parameters:: **params – Parameter names and values to update.
Returns:: self – The estimator instance.
Raises:: SpectroChemPyError – If a parameter name does not correspond to a configurable trait.

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:

X (NDDataset or array-like of shape (n_observations, n_features), optional) – New data, where n_observations is the number of observations and n_features is the number of features. if not provided, the input dataset of the fit method will be used.
**kwargs (keyword parameters, optional) – See Other 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.

validate_constraints(constraints=None, script=None)[source]

Validate lightweight constraint specifications without running a fit.

This method currently validates:

the overall constraints container shape;
recognized constraint keys / types;
references to parameter names defined by the fitting script.

It does not guarantee that every accepted constraint is fully enforced by the current optimization backend. The present goal is to provide a stable validation surface before broader constraint execution semantics are expanded.

Parameters:

constraints (dict or sequence of dict, optional) – Constraint specification(s) to validate. If None (default), validates constraints.
script (str, optional) – Script used to resolve parameter names. If None (default), uses the currently assigned script.

Returns:

list of ConstraintError – Empty list when the constraint specification is valid.

validate_script(script=None)[source]

Validate a fitting script without running an optimisation.

Returns a list of ScriptError objects describing every problem found in the script. An empty list means the script is valid.

Parameters:: script (str, optional) – The script to validate. If None (default), validates the currently assigned script.
Returns:: list of ScriptError – Empty list when the script is valid.

Examples

>>> opt = scp.Optimize()
>>> errors = opt.validate_script(script)
>>> if errors:
...     for err in errors:
...         print(err)

Examples using spectrochempy.Optimize

Fitting 1D dataset

Analysis CP NMR spectra

Processing NMR spectra (slicing, baseline correction, peak picking, peak fitting)

Processing Relaxation measurement

Analysis CP NMR spectra

Processing NMR spectra (slicing, baseline correction, peak picking, peak fitting)

Processing Relaxation measurement