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VisruthSKVisruthSK
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Swapped more print tests to snaps
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12 files changed

+327
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‎tests/testthat/_snaps/compare.md‎

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# loo_compare returns expected results (2 models)
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WAoAAAACAAQFAAACAwAAAAMOAAAAEAAAAAAAAAAAwBA6U1+cRe4AAAAAAAAAAD+2ake0LxMB
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Code
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print(comp1)
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Output
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elpd_diff se_diff
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model1 0.0 0.0
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model2 0.0 0.0
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---
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Code
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print(comp2)
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Output
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elpd_diff se_diff
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model1 0.0 0.0
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model2 -4.1 0.1
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---
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WAoAAAACAAQFAgACAwAAAAMOAAAAEAAAAAAAAAAAwBA6U1+cRe4AAAAAAAAAAD+2ake0LxMB
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wFTh8N3JQljAVeWWE8MGuUARCD2zEXBfQBEalRIN2T9ACijAYdW5U0AmZ5XrANCKP/H9Zexy
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814/8ZtgnG1nx0Bk4fDdyUJYQGXllhPDBrlAIQg9sxFwX0AhGpUSDdk/AAAEAgAAAAEABAAJ
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AAAAA2RpbQAAAA0AAAACAAAAAgAAAAgAAAQCAAAAAQAEAAkAAAAIZGltbmFtZXMAAAATAAAA
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# loo_compare returns expected result (3 models)
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WAoAAAACAAQFAAACAwAAAAMOAAAAGAAAAAAAAAAAwBA6U1+cRe7AMA3KkbYEGAAAAAAAAAAA
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P7ZqR7QvEwE/y6/t4TTtXsBU4fDdyUJYwFXllhPDBrnAWOVjgjbDYkARCD2zEXBfQBEalRIN
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Code
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print(comp1)
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Output
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elpd_diff se_diff
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model1 0.0 0.0
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model2 -4.1 0.1
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model3 -16.1 0.2
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---
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WAoAAAACAAQFAgACAwAAAAMOAAAAGAAAAAAAAAAAwBA6U1+cRe7AMA3KkbYEGAAAAAAAAAAA
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P7ZqR7QvEwE/y6/t4TTtYMBU4fDdyUJYwFXllhPDBrnAWOVjgjbDYkARCD2zEXBfQBEalRIN
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2T9AEPIF3GigE0AKKMBh1blTQCZnlesA0IpAQcjYUhrdCj/x/WXscvNeP/GbYJxtZ8c/8YDQ
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kmfJX0Bk4fDdyUJYQGXllhPDBrlAaOVjgjbDYkAhCD2zEXBfQCEalRIN2T9AIPIF3GigEwAA
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BAIAAAABAAQACQAAAANkaW0AAAANAAAAAgAAAAMAAAAIAAAEAgAAAAEABAAJAAAACGRpbW5h

‎tests/testthat/_snaps/kfold_helpers.md‎

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# print_dims.kfold works
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Code
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print_dims(xx)
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Output
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Based on 17-fold cross-validation.
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‎tests/testthat/_snaps/loo_subsampling_cases.md‎

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# Test the vignette
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Code
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print(looss_1)
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Output
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Computed from 4000 by 100 subsampled log-likelihood
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values from 3020 total observations.
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Estimate SE subsampling SE
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elpd_loo -1968.5 15.6 0.3
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p_loo 3.1 0.1 0.4
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looic 3936.9 31.2 0.6
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------
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MCSE of elpd_loo is 0.0.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.7).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(looss_1b)
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Output
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Computed from 4000 by 200 subsampled log-likelihood
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values from 3020 total observations.
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Estimate SE subsampling SE
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elpd_loo -1968.3 15.6 0.2
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p_loo 3.2 0.1 0.4
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looic 3936.7 31.2 0.5
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------
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MCSE of elpd_loo is 0.0.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.7).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(aploo_1)
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Output
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Computed from 2000 by 3020 log-likelihood matrix.
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Estimate SE
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elpd_loo -1968.4 15.6
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p_loo 3.2 0.2
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looic 3936.8 31.2
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------
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Posterior approximation correction used.
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MCSE of elpd_loo is 0.0.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.7).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(looapss_1)
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Output
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Computed from 2000 by 100 subsampled log-likelihood
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values from 3020 total observations.
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Estimate SE subsampling SE
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elpd_loo -1968.2 15.6 0.4
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p_loo 2.9 0.1 0.5
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looic 3936.4 31.1 0.8
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------
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Posterior approximation correction used.
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MCSE of elpd_loo is 0.0.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.7).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(looss_2)
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Output
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Computed from 4000 by 100 subsampled log-likelihood
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values from 3020 total observations.
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Estimate SE subsampling SE
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elpd_loo -1952.0 16.2 0.2
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p_loo 2.6 0.1 0.3
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looic 3903.9 32.4 0.4
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------
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MCSE of elpd_loo is 0.0.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.7).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(comp)
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Output
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elpd_diff se_diff subsampling_se_diff
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model2 0.0 0.0 0.0
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model1 16.5 22.5 0.4
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---
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Code
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print(comp)
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Output
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elpd_diff se_diff subsampling_se_diff
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model2 0.0 0.0 0.0
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model1 16.1 4.4 0.1
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---
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Code
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print(comp2)
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Output
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elpd_diff se_diff subsampling_se_diff
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model2 0.0 0.0 0.0
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model1 16.3 4.4 0.1
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---
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Code
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print(comp3)
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Output
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elpd_diff se_diff subsampling_se_diff
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model2 0.0 0.0 0.0
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model1 16.5 4.4 0.3
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‎tests/testthat/_snaps/model_weighting.md‎

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# loo_model_weights (stacking and pseudo-BMA) gives expected result
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WAoAAAACAAQFAAACAwAAAAAOAAAAAz/KEXngFjO6P+l7oXTIUDU+YzIi3AAAAA==
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WAoAAAACAAQFAgACAwAAAAAOAAAAAz/KEXnf1DM9P+l7oXTYyi4+YzIpAwAAAA==
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---
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WAoAAAACAAQFAAACAwAAAAAOAAAAAz+xA6UGtqDFP+3eFS5zKzY/J2MLYAsc4w==
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Code
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print(w1)
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Output
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Method: stacking
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------
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weight
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model1 0.204
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model2 0.796
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model3 0.000
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---
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WAoAAAACAAQFAgACAwAAAAAOAAAAAz+xA6UGtqDDP+3eFS5zKzY/J2MLYAsc4A==
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---
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Code
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print(w2)
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Output
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Method: pseudo-BMA+ with Bayesian bootstrap
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------
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weight
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model1 0.066
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model2 0.933
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model3 0.000
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---
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Code
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print(w3)
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Output
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Method: pseudo-BMA
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------
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weight
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model1 0.000
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model2 1.000
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model3 0.000
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‎tests/testthat/_snaps/print_plot.md‎

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# print.waic output is ok
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Code
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print(waic1)
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Output
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Computed from 1000 by 32 log-likelihood matrix.
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Estimate SE
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elpd_waic -83.5 4.3
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p_waic 3.3 1.1
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waic 167.1 8.5
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3 (9.4%) p_waic estimates greater than 0.4. We recommend trying loo instead.
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# print.psis_loo and print.psis output ok
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Code
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print(psis1)
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Output
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Computed from 1000 by 32 log-weights matrix.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.67).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(loo1)
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Output
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Computed from 1000 by 32 log-likelihood matrix.
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Estimate SE
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elpd_loo -83.6 4.3
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p_loo 3.3 1.2
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looic 167.2 8.6
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------
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MCSE of elpd_loo is 0.1.
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MCSE and ESS estimates assume independent draws (r_eff=1).
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All Pareto k estimates are good (k < 0.67).
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See help('pareto-k-diagnostic') for details.
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---
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Code
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print(loo1_r_eff)
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Output
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Computed from 1000 by 32 log-likelihood matrix.
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Estimate SE
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elpd_loo -83.6 4.3
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p_loo 3.3 1.2
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looic 167.2 8.6
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------
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MCSE of elpd_loo is 0.1.
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MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.0]).
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All Pareto k estimates are good (k < 0.67).
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See help('pareto-k-diagnostic') for details.
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# mcse_loo extractor gives correct value
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WAoAAAACAAQFAAACAwAAAAAOAAAAAT+2J8YDcP5s

‎tests/testthat/_snaps/tisis.md‎

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All Pareto k estimates are good (k < 0.67).
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See help('pareto-k-diagnostic') for details.
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# tis_loo and sis_loo are returned
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Code
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print(loo_tis)
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Output
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Computed from 1000 by 32 log-likelihood matrix using tis_loo .
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Estimate SE
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elpd_loo -83.6 4.3
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p_loo 3.3 1.2
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looic 167.2 8.6
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------
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---
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Code
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print(loo_sis)
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Output
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Computed from 1000 by 32 log-likelihood matrix using sis_loo .
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Estimate SE
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elpd_loo -83.6 4.3
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p_loo 3.3 1.2
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looic 167.2 8.6
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------
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‎tests/testthat/test_compare.R‎

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Original file line numberDiff line numberDiff line change
@@ -89,13 +89,14 @@ test_that("loo_compare returns expected results (2 models)", {
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expect_s3_class(comp1, "compare.loo")
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expect_equal(colnames(comp1), comp_colnames)
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expect_equal(rownames(comp1), c("model1", "model2"))
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expect_output(print(comp1), "elpd_diff")
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expect_snapshot(print(comp1))
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expect_equal(comp1[1:2, 1], c(0, 0), ignore_attr = TRUE)
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expect_equal(comp1[1:2, 2], c(0, 0), ignore_attr = TRUE)
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comp2 <- loo_compare(w1, w2)
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expect_s3_class(comp2, "compare.loo")
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expect_equal(colnames(comp2), comp_colnames)
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expect_snapshot(print(comp2))
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expect_snapshot_value(comp2, style = "serialize")
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expect_equal(comp1[1, 1], 0)
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expect_s3_class(comp1, "compare.loo")
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expect_s3_class(comp1, "matrix")
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expect_snapshot(print(comp1))
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expect_snapshot_value(comp1, style = "serialize")
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‎tests/testthat/test_kfold_helpers.R‎

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test_that("print_dims.kfold works", {
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xx <- structure(list(), K = 17, class = c("kfold", "loo"))
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expect_output(print_dims(xx), "Based on 17-fold cross-validation")
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expect_snapshot(print_dims(xx))
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attr(xx, "K") <- NULL
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expect_silent(print_dims(xx))

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