sbmlsim.fit.options
¶
Main options for the parameter fitting.
In the optimization the cost is minimized for Nk curves with every curve having Nki data points.
sum(Nk)( w{k}^2 * sum(NKi) (w{i,k}^2 * res{i,k}))
Module Contents¶
Classes¶
Type of optimization. 

Handling of the residuals. 

Determines the loss function. 

Weighting w_{k} of the curves k. 

Weighting w_{i,k} of the data points i within a single fit mapping k. 
 class sbmlsim.fit.options.OptimizationAlgorithmType[source]¶
Bases:
enum.Enum
Type of optimization.
least square : Least square is a local optimization method and works well in combination with many start values, i.e., many repeats of the optimization problem. See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html for more information.
differential evolution : Differential evolution is a global optimization method and normally is run with a limited number of repeats. See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html#scipy.optimize.differential_evolution for more information.
 class sbmlsim.fit.options.ResidualType[source]¶
Bases:
enum.Enum
Handling of the residuals.
How are the residuals calculated? Are the absolute residuals used, or are the residuals normalized based on the data points, i.e., relative residuals.
absolute (default) : uses the absolute data values for calculation of the residuals:
r(i,k) = y(i,k)  f(xi,k)
normalized : normalizing residuals of curve with 1/mean of the timecourse:
r(i,k) = (y(i,k)  f(xi,k))/mean(y(k))
This allows to use time courses with very different absolute values in a single optimization problem.
absolute_to_baseline (experimental) : uses the absolute changes to baseline with baseline being the first data point. This requires appropriate presimulations for the model to reach a baseline. Data and fits have to be checked carefully. Residuals are calculated as:
r(i,k) = (y(i,k)  ybase(k))  (f(xi,k)  fbase(k))
normalized changes baseline (experimental): Uses the normalized changes to baseline with baseline being the first data point. This requires appropriate presimulations for the model to reach a baseline. Data and fits have to be checked carefully. The residuals are calculated as:
r(i,k) = (y(i,k)  ybase(k))  (f(xi,k)  fbase(k))/mean(y(k))
 class sbmlsim.fit.options.LossFunctionType[source]¶
Bases:
enum.Enum
Determines the loss function.
minimize F(x) = 0.5 * sum(rho(residuals_weighted(x)**2)
The following loss functions are supported are allowed:
‘linear’ (default) : rho(z) = z. Gives a standard leastsquares problem.
‘soft_l1’ : rho(z) = 2 * ((1 + z)**0.5  1). The smooth approximation of l1 (absolute value) loss. Usually a good choice for robust least squares.
‘cauchy’ : rho(z) = ln(1 + z). Severely weakens outliers influence, but may cause difficulties in optimization process.
‘arctan’ : rho(z) = arctan(z). Limits a maximum loss on a single residual, has properties similar to ‘cauchy’.
 class sbmlsim.fit.options.WeightingCurvesType[source]¶
Bases:
enum.Enum
Weighting w_{k} of the curves k.
Users can provide set of weightings for the individual curves. By default no weightings are applied, i.e. all curves are weighted equally if no weighting option is provided:
w_{k} = 1.0
mapping : curves k are weighted with the provided user weights in the fit mappings, e.g., counts:
w_{k} = wu_{k}
points : weighting with the number of data points. Often time courses contain different number of data points. The residuals should contribute equally per data point:
w_{k} = 1.0/count{k}
The various options can be combined, e.g. mapping and points results in:
w_{k} = wu_{k}/count{k}/mean{y(k)}
 class sbmlsim.fit.options.WeightingPointsType[source]¶
Bases:
enum.Enum
Weighting w_{i,k} of the data points i within a single fit mapping k.
This decides how the data points within a single fit mapping are weighted.
no weighting (default) : all data points are weighted equally:
w_{i,k} = 1.0
error_weighting: data points are weighted as ~1/error
# FIXME: update documentation, These must probably be normalized also. if yerr{i,k}:
w_{i,k} = 1.0/yerr{i,k}
 else:
w_{i,k} = 1.0/yerr{i,k}