R Under development (unstable) (2025-01-09 r87552 ucrt) -- "Unsuffered Consequences"
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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(bnns)
> 
> test_check("bnns")

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.38 seconds.
Chain 1: Adjust your expectations accordingly!
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Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9.9e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 6e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 7.8e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 7.8e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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Chain 1: WARNING: There aren't enough warmup iterations to fit the
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Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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Setting levels: control = 0, case = 1
Setting direction: controls < cases

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 7.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
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Chain 1: WARNING: There aren't enough warmup iterations to fit the
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Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000176 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.76 seconds.
Chain 1: Adjust your expectations accordingly!
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Chain 1: WARNING: There aren't enough warmup iterations to fit the
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Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
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Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1:          three stages of adaptation as currently configured.
Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.92 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
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Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1:          three stages of adaptation as currently configured.
Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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Call:

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 7e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.7 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1:          three stages of adaptation as currently configured.
Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
Chain 1:          the given number of warmup iterations:
Chain 1:            init_buffer = 7
Chain 1:            adapt_window = 38
Chain 1:            term_buffer = 5
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Chain 1: 
bnns.default(formula = y ~ -1 + x1 + x2, data = df, out_act_fn = 1, 
    iter = 100, warmup = 50, chains = 1)

Data Summary:
Number of observations: 10 
Number of features: 2 

Network Architecture:
Number of hidden layers: 1 
Nodes per layer: 2 
Activation functions: 2 
Output activation function: 1 

Posterior Summary (Key Parameters):
               mean    se_mean        sd       2.5%        25%        50%
w_out[1] -0.1713880 0.20961021 1.0156894 -1.6017942 -0.8651957 -0.3340310
w_out[2]  0.2283575 0.24329415 1.2285682 -1.9720321 -0.6920939  0.2754171
b_out     0.1395451 0.12572656 0.7139783 -1.2160169 -0.3129168  0.3041852
sigma     0.9248895 0.02656309 0.2172957  0.6090796  0.7702805  0.8845311
               75%    97.5%    n_eff      Rhat
w_out[1] 0.5253143 1.658340 23.47994 0.9862492
w_out[2] 1.0014267 2.859433 25.49971 1.1658971
b_out    0.6286221 1.346381 32.24897 1.0286079
sigma    1.1292343 1.325658 66.91836 1.0071005

Model Fit Information:
Iterations: 100 
Warmup: 50 
Thinning: 1 
Chains: 1 

Predictive Performance:
RMSE (training): 0.8223943 
MAE (training): 0.6339049 

Notes:
Check convergence diagnostics for parameters with high R-hat values.
Call:

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 7.4e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1:          three stages of adaptation as currently configured.
Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
Chain 1:          the given number of warmup iterations:
Chain 1:            init_buffer = 7
Chain 1:            adapt_window = 38
Chain 1:            term_buffer = 5
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Chain 1: 
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Chain 1: 
bnns.default(formula = y ~ -1 + x1 + x2, data = df, out_act_fn = 2, 
    iter = 100, warmup = 50, chains = 1)

Data Summary:
Number of observations: 10 
Number of features: 2 

Network Architecture:
Number of hidden layers: 1 
Nodes per layer: 2 
Activation functions: 2 
Output activation function: 2 

Posterior Summary (Key Parameters):
               mean    se_mean        sd      2.5%        25%        50%
w_out[1] -0.3649714 0.12085092 1.1138528 -2.104871 -0.9789200 -0.5133814
w_out[2] -0.1090883 0.09449403 0.8709280 -1.975452 -0.7353303 -0.1154986
b_out    -0.1403489 0.08924433 0.8225427 -1.623265 -0.6809250 -0.1027336
               75%    97.5%   n_eff      Rhat
w_out[1] 0.4720538 1.768876 84.9485 0.9872463
w_out[2] 0.5918031 1.429393 84.9485 0.9940373
b_out    0.4805673 1.364131 84.9485 0.9942880

Model Fit Information:
Iterations: 100 
Warmup: 50 
Thinning: 1 
Chains: 1 

Predictive Performance:
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Confusion matrix (training with 0.5 cutoff): 6 4 
Accuracy (training with 0.5 cutoff): 0.6 
AUC (training): 0.7083333 

Notes:
Check convergence diagnostics for parameters with high R-hat values.
Call:

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000138 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.38 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: There aren't enough warmup iterations to fit the
Chain 1:          three stages of adaptation as currently configured.
Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
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Chain 1:  Elapsed Time: 0.011 seconds (Warm-up)
Chain 1:                0.012 seconds (Sampling)
Chain 1:                0.023 seconds (Total)
Chain 1: 
bnns.default(formula = y ~ -1 + x1 + x2, data = df, out_act_fn = 3, 
    iter = 100, warmup = 50, chains = 1)

Data Summary:
Number of observations: 10 
Number of features: 2 

Network Architecture:
Number of hidden layers: 1 
Nodes per layer: 2 
Activation functions: 2 
Output activation function: 3 

Posterior Summary (Key Parameters):
                  mean    se_mean        sd      2.5%        25%          50%
w_out[1,1]  0.30337538 0.10655680 0.9045982 -1.147652 -0.2284013  0.375598131
w_out[1,2]  0.02070732 0.06979079 0.6432443 -1.055532 -0.4253862  0.005817218
w_out[1,3] -0.18923666 0.10348181 0.9537661 -1.747508 -0.8680601 -0.349467326
w_out[2,1]  0.05681854 0.13947342 1.0817701 -1.834346 -0.8420540  0.071166439
w_out[2,2] -0.04651837 0.09281668 0.8554682 -1.699958 -0.5654883 -0.127103660
w_out[2,3] -0.32611204 0.12839527 1.1833872 -2.381491 -1.2328260 -0.133899579
b_out[1]    0.31342622 0.09612829 0.8449873 -1.122507 -0.3906410  0.438005502
b_out[2]   -0.03543838 0.12785563 0.8679998 -1.532539 -0.7148858 -0.074961582
b_out[3]   -0.12810052 0.09201885 0.8481148 -1.691989 -0.5834308 -0.136298917
                 75%    97.5%    n_eff      Rhat
w_out[1,1] 0.8628406 1.814468 72.06910 0.9853562
w_out[1,2] 0.4790057 1.200000 84.94850 1.0207728
w_out[1,3] 0.4798715 1.400807 84.94850 0.9798348
w_out[2,1] 0.9390178 1.719748 60.15712 0.9838417
w_out[2,2] 0.5064584 1.394016 84.94850 0.9917639
w_out[2,3] 0.6006447 1.310496 84.94850 1.0051237
b_out[1]   0.7884494 2.194408 77.26770 1.0291487
b_out[2]   0.6155991 1.387425 46.08923 0.9911828
b_out[3]   0.4660517 1.286419 84.94850 0.9801153

Model Fit Information:
Iterations: 100 
Warmup: 50 
Thinning: 1 
Chains: 1 

Predictive Performance:
Log-loss (training): 0.9933839 
AUC (training): 0.7611111 

Notes:
Check convergence diagnostics for parameters with high R-hat values.
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 107 ]
> 
> proc.time()
   user  system elapsed 
  25.67    1.46  825.59