plot_functions.py contains a set of functions that can be called to evaluate the performance of a model, as well as comparing multiple models in a taylor diagram. See examples.ipynb for a minimal implementation.
accuracy_diagonal_plot(x, y, predictions, labels, path) takes two-dimensional np.array x (# of samples, # of features), two-dimensional np.array y (# of samples, # of output variables), two-dimensional np.array predictions (# of samples, # of output variables), list of output variable names of length (# of output variables), path as string defining the location where to save the figures to.
mean_correction_factor_plot(y, predictions, labels, path) takes two-dimensional np.array y (# of samples, # of output variables), two-dimensional np.array predictions (# of samples, # of output variables), list of output variable names of length (# of output variables), path as string defining the location where to save the figures to.
qq_plot(y, predictions, labels, path) takes two-dimensional np.array y (# of samples, # of output variables), two-dimensional np.array predictions (# of samples, # of output variables), list of output variable names of length (# of output variables), path as string defining the location where to save the figures to.
ratio_plot(y, predictions, labels, path) takes two-dimensional np.array y (# of samples, # of output variables), two-dimensional np.array predictions (# of samples, # of output variables), list of output variable names of length (# of output variables), path as string defining the location where to save the figures to.
taylor_plot(model_names, y, predictions, labels, path) takes list of model names, two-dimensional np.array y (# of samples, # of output variables), list of two-dimensional np.array predictions (# of samples, # of output variables) of same length as model_names, list of output variable names of length (# of output variables), path as string defining the location where to save the figures to.
