Getting started with GEM-SA Marc Kennedy Central Science Laboratory, York Tony OHagan, Jeremy Oakley University of Sheffield Part 1: Getting started
Starting GEM-SA program Creating input and output files Explanation of the menus, toolbars, etc. Description of the project window Starting GEM-SA Double-click the GEM-SA icon to start The main window appears, with Menu
Toolbar Sensitivity analysis output grid Tab windows for other types of output Log window menu toolbar Sensitivity analysis
output grid Log window Toolbar icons New project Open project Save project Print output report Edit project Generate input design
points Rescale an input Standardise design Copy input design to clipboard Convert input to integer Run the analysis Help Sensitivity analysis output grid
This will report the sensitivity results after the analysis is complete One line for each input parameter One line for each pair of inputs, if joint effects are selected Log Window output Tells us Which training data are being loaded/saved Transformations applied to the data Fitted Gaussian process parameters
Summary of uncertainty analysis results Creating a GEM project To build the emulator we first need 3 files: Data file of code inputs Data file of code outputs GEM-SA project file Restrictions on input/output data Single output Multiple outputs must be treated individually
GEM can read multiple outputs file, but a single column is specified within a project Max 30 input parameters Max 400 training points The data files should be plain text files One line for each point Input file can be space or tab delimited Generating a new input design Designs can be generated using the toolbar icon
or the menu: Input Generate The design dialog appears Generating a new input design Click OK and fill in the required range for each input Click OK again Editing input designs If you select a column, you can rescale values of that
input or round values to be integers Designs can be loaded into or saved from this window using the Inputs menu. Use to copy the points to the clipboard for use in other programs Types of design GEM-SA can generate 2 types of design LP- Maximin Latin Hypercube designs
Both have good space-filling properties Ensure all regions of the input space are well represented LP- quick to generate, good for increasing input design sequentially MmLH can be better in high dimensions Creating output data from these inputs Each row from the input design must be used to generate a single output, e.g. using Spreadsheet
Simple, but requires functional form Script Only need executable code Loop through inputs, modify code input file Modify code to loop through the points Can be difficult, need source code Example: using a spreadsheet Copy the input design to
the clipboard using Open Excel and paste inputs Create formula in final column Copy formula for all rows of the design Cut and paste special (values) in a new sheet Save as text file
Example: using a script Read base input file (read by executable code) Loop through lines of input design file Replace selected inputs in base input file Run executable code with new input file Calculate single output and add to training output file The project window Appears whenever you Load a project
Edit a project Create new project This window has 3 tabs Files Options Simulations Names for the input files
Names for the output files How many inputs? Which column from output file?
What are the input names? What should be calculated, and how? Which joint effects
should be calculated? Are the inputs uncertain? What prior mean for the output?
What kind of prediction? What kind of cross validation? MCMC control parameters How many realisations of predictions, main and joint effects to
generate How many points used to calculate main effects, joint effects Part 2 Uncertainty Analysis Using GEM-SA Part 2: Outline Setting up the project
Running a simple analysis More complex analyses Setting up the project Create a new project Select Project -> New, or click toolbar icon Project dialog appears Well specify the data
files first Files Using Browse buttons, select input and output files The Inputs file contains one column for each parameter and one row for each model training run (the design) The Outputs file contains the outputs from those runs (one column, in this examle)
Our example Well use the example model1 in the GEM-SA DEMO DATA directory This example is based on a vegetation model with 7 inputs RESAEREO, DEFLECT, FACTOR, MO, COVER, TREEHT, LAI The model has 16 outputs, but for the present we will consider output 4 June monthly GPP
Number of inputs Click on Options tab Select number of inputs using or click From Inputs File Define input names Click on Names
The Input parameter names dialog opens Enter parameter names Click OK Complete the project We will leave all other settings at their default
values for now Click OK The Input Parameter Ranges window appears Close and save project Click Defaults from input ranges button Click OK Select Project -> Save
Or click toolbar icon Choose a name and click Save Running a simple analysis Build the emulator Click to build the emulator A lot of things now start to happen! The log window at the bottom starts to
record various bits of information A little window appears showing progress of minimisation of the roughness parameter estimation criterion A new window appears in the Main Effects tab and several graphs appear Progress bar at the bottom Focus on the log window Ignore the outputs in the Main Effects and Sensitivity Analysis windows for now
These will be explained later Focus on the log window This reports two key things Diagnostics of the emulator build The basic uncertainty analysis results These also appear in the Output Summary window and can be printed using Emulation diagnostics Note where the log window reports Estimating emulator parameters by maximising probability distribution...
maximised posterior for emulator parameters: sigma-squared = 0.342826, roughness = 0.217456 0.0699709 0.191557 16.9933 0.599439 0.459675 1.01559 The first line says roughness parameters have been estimated by the simplest method The values of these indicate how non-linear the effect of each input parameter is Note the high value for input 4 (MO) Uncertainty analysis mean
Below this, the log reports Estimate of mean output is 24.145, with variance 0.00388252 So the best estimate of the output (June GPP) is 24.1 (mol C/m2) This is averaged over the uncertainty in the 7 inputs Better than just fixing inputs at best estimates There is an emulation standard error of 0.062 in this figure
Uncertainty analysis variance The final line of the log is Estimate of total output variance = 73.9033 This shows the uncertainty in the model output that is induced by input uncertainties The variance is 73.9 Equal to a standard deviation of 8.6 So although the best estimate of the output is 24.3, the uncertainty in inputs means it
could easily be as low as 16 or as high as 33 More complex analyses Input distributions Default is to assume the uncertainty in each input is represented by a uniform distribution Range determined by the range of values found in the input file, or input manually A normal (gaussian) distribution
is generally a more realistic representation of uncertainty Range unbounded More probability in the middle Changing input distributions In Project dialog, Options tab, click the button for All unknown, product
normal Then OK A new dialog opens to specify means and variances Model 1 example Uniform distributions from input ranges
Normal distributions to match Range is 4 std devs Except for MO Narrower distribution Uniform
Normal Parameter Lower Upper Mean Variance
RESAEREO 80 200 140 900
DEFLECT 0.6 1 0.8 0.01 FACTOR
0.1 0.5 0.3 0.01 MO
30 100 60 100 COVER 0.6
0.99 0.8 0.01 TREEHT 10
40 25 100 3.75 9 6.5
1 LAI Effect on UA After running the revised model, we see: It runs faster, with no need to rebuild the emulator The emulator fit is unchanged
The mean is changed a little and variance is halved Estimate of mean output is 26.2698, with variance 0.00784475 Estimate of total output variance = 38.1319 Reducing the MO uncertainty further If we reduce the variance of MO even more, to 49: UA mean changes a little more and variance reduces again Estimate of mean output is 26.3899, with variance 0.0108792
Estimate of total output variance = 27.1335 Notice also how the emulation uncertainty has increased (0.004 for uniform) This is because the design points cover the new ranges less thoroughly Cross-validation In the Project dialog, look at the bottom menu box, labelled Cross-validation There are 3 options
None Leave-one-out Leave final 20% out CV is a way of checking the emulator fit Default is None because CV takes time Leave-one-out CV After estimating roughness and other parameters, GEM predicts each training run point using only the remaining n-1 points Results appear in log window
Close to 1 Cross Validation Root Mean-Squared Error = 0.907869 Cross Validation Root Mean-Squared Relative Error = 4.34773 percent Cross Validation Root Mean-Squared Standardised Error = 1.15273 Largest standardised error is 4.32425 for data point 61 Cross Validation variances range from 0.18814 to 3.92191 Written cross-validation means to file cvpredmeans.txt Written cross-validation variances to file cvpredvars.txt Leave out final 20% CV This is an even better check, because it tests
the emulator on data that have not been used in any way to predict it Emulator is built on first 80% of data and used to predict last 20% Cross Validation Root Mean-Squared Error = 1.46954 Cross Validation Root Mean-Squared Relative Error = 7.4922 percent Cross Validation Root Mean-Squared Standardised Error = 1.73675 Largest standardised error is 5.05527 for data point 22 Cross Validation variances range from 0.277304 to 4.88653 Other options
There are various other options associated with the emulator building that we have not dealt with But weve done the main things that should be considered in practice And its enough to be going on with! When it all goes wrong How do we know when the emulator is not working? Large roughness parameters
Especially ones hitting the limit of 99 Large emulation variance on UA mean Poor CV standardised prediction error Especially when some are extremely large In such cases, see if a larger training set helps Other ideas like transforming output scale Part 3 Sensitivity Analysis in GEM-SA
Example Again we use the ForestETP vegetation model 7 input parameters 120 model runs Objective: conduct a variance-based sensitivity analysis to identify which uncertain inputs are driving the output uncertainty. Exploratory scatter plots
Sensitivity Analysis Walkthrough 1. Project New 2. Click Browse for the Inputs File From the GEM-SA Demo Data/Model1/ folder, select emulator7x120inputs.txt 3. Click Browse for the Outputs File From the GEM-SA Demo Data/Model1/ folder, select out11.txt 4. Select the Options tab Sensitivity Analysis Walkthrough
5. Change the Number of Inputs to 7. 6. Leave the other options unchanged Input uncertainty options: All unknown, uniform Prior mean options: Linear term for each input Generate predictions as: function realisations (correlated points)
Sensitivity Analysis Walkthrough Sensitivity Analysis Walkthrough 7. Click OK 8. Select Default from input ranges then OK 9. Project Run or use Main effect plots
Main effect plots Fixing X6 = 18, this point shows the expected value of the output (obtained by averaging over all other inputs). X6 Simply fixing all the other inputs at their central values and comparing X6=10 with X6=40 would underestimate the influence of this input (The thickness of the band shows emulator uncertainty) Variance of main effects
Main effects for each input. Input 6 has the greatest individual contribution to the variance Main effects sum to 66.8% of the total variance Interactions and total effects Main effects explain 2/3 of the variance Model must contain interactions Any input can have small main effect, but large
interaction effect, so overall this input is still important Can ask GEM-SA to compute all pair-wise interaction effects 435 in total for a 30 input model can take some time! Useful to know what to look for Interactions and total effects For each input Xi Total effect of Xi = main effect for Xi + all
interactions involving Xi Total effect >> main effect implies interactions in the model So for any input with large total effect relative to the main effect investigate possible interactions involving that input Interactions and total effects Total effects for inputs 4
and 7 much larger than its main effect. Implies presence of interactions Interaction effects 10. Project Edit or 11. In Options tab, tick calculate joint effects 12. De-select all inputs under Inputs to include in joint effects, then select X4, X5, X6, X7 Interaction effects
13. Click OK, then OK again 14. Project Run or Interaction effects Note interactions involving inputs 4 and 7 Main effects and selected interactions now sum to almost 92%
of the total variance Exercise 1. Set up a new project using SAex1_inputs.txt for the inputs and SAex1_outputs.txt for the output 8 input parameters (uniform on [0,1]) 100 model runs 2. Estimate the main effects only for this model and identify the influential input variables 3. By comparing main effects with total effects, can you spot any interactions?
4. Estimate any suspected interactions to test your intuition!
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