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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 8.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.88 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: No variance estimation is
Chain 1:          performed for num_warmup < 20
Chain 1: 
Chain 1: Iteration: 1 / 10 [ 10%]  (Warmup)
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Chain 1: 
Chain 1:  Elapsed Time: 0 seconds (Warm-up)
Chain 1:                0 seconds (Sampling)
Chain 1:                0 seconds (Total)
Chain 1: 
Call:
bnns.default(formula = y ~ -1 + x1 + x2, data = df, L = 1, nodes = 2, 
    act_fn = 5, out_act_fn = 1, iter = 10, warmup = 5, chains = 1, 
    normalize = FALSE)

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

Network Architecture:
Number of hidden layers: 1 
Nodes per layer: 2 
Activation functions: 5 
Output activation function: 1 

Posterior Summary (Key Parameters):
               mean    se_mean        sd        2.5%         25%         50%
w_out[1] -1.0359014 0.05729695 0.1071139 -1.15068141 -1.10050597 -1.02561473
w_out[2]  0.1227901 0.08070459 0.1508733 -0.04143046 -0.01264148  0.12676218
b_out     0.1520709 0.24501526 0.4580442 -0.10366284 -0.10366284 -0.04376424
sigma     0.7587657 0.04215803 0.0788124  0.63758814  0.74926020  0.79893127
                 75%      97.5%   n_eff      Rhat
w_out[1] -1.02561473 -0.8869252 3.49485 0.9037670
w_out[2]  0.27222963  0.2722296 3.49485 0.8015336
b_out     0.04750355  0.8722971 3.49485 0.9052667
sigma     0.81022854  0.8102285 3.49485 1.5081484

Model Fit Information:
Iterations: 10 
Warmup: 5 
Thinning: 1 
Chains: 1 

Predictive Performance:
RMSE (training): 0.7591902 
MAE (training): 0.680774 

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

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 7.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.75 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: No variance estimation is
Chain 1:          performed for num_warmup < 20
Chain 1: 
Chain 1: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%]  (Warmup)
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Chain 1: 
Chain 1:  Elapsed Time: 0 seconds (Warm-up)
Chain 1:                0 seconds (Sampling)
Chain 1:                0 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9.1e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: No variance estimation is
Chain 1:          performed for num_warmup < 20
Chain 1: 
Chain 1: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%]  (Warmup)
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Chain 1: Iteration: 10 / 10 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0 seconds (Warm-up)
Chain 1:                0 seconds (Sampling)
Chain 1:                0 seconds (Total)
Chain 1: 
Call:
bnns.default(formula = y ~ -1 + x1 + x2, data = df, L = 1, nodes = 2, 
    act_fn = 5, out_act_fn = 2, iter = 10, warmup = 5, 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: 5 
Output activation function: 2 

Posterior Summary (Key Parameters):
               mean    se_mean        sd       2.5%        25%        50%
w_out[1] -0.1014488 0.09208671 0.1721517 -0.3414034 -0.1392813 -0.1115307
w_out[2] -0.5491061 0.41717333 0.7798854 -1.2210193 -1.1364886 -0.9775488
b_out    -0.2200450 0.14687893 0.2745831 -0.6266570 -0.2451801 -0.1257945
                 75%      97.5%   n_eff      Rhat
w_out[1]  0.05371478 0.05371478 3.49485 0.7141784
w_out[2]  0.29945935 0.29945935 3.49485 0.8920884
b_out    -0.12579455 0.04644922 3.49485 0.7408003

Model Fit Information:
Iterations: 10 
Warmup: 5 
Thinning: 1 
Chains: 1 

Predictive Performance:
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Confusion matrix (training with 0.5 cutoff): 4 4 2 0 
Accuracy (training with 0.5 cutoff): 0.4 
AUC (training): 0.5833333 

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

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9.8e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.98 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: WARNING: No variance estimation is
Chain 1:          performed for num_warmup < 20
Chain 1: 
Chain 1: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%]  (Warmup)
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Chain 1: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 1: Iteration: 10 / 10 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0.001 seconds (Warm-up)
Chain 1:                0 seconds (Sampling)
Chain 1:                0.001 seconds (Total)
Chain 1: 
Call:
bnns.default(formula = y ~ -1 + x1 + x2, data = df, L = 1, nodes = 2, 
    act_fn = 5, out_act_fn = 3, iter = 10, warmup = 5, 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: 5 
Output activation function: 3 

Posterior Summary (Key Parameters):
                 mean   se_mean        sd       2.5%        25%        50%
w_out[1,1] -0.1158985 0.1396727 0.2611114 -0.3474307 -0.2812102 -0.2354677
w_out[1,2]  0.2454100 0.1279610 0.2392170  0.0065396  0.1110885  0.2087334
w_out[1,3] -0.1994521 0.2343021 0.4380165 -0.8026494 -0.2674540 -0.2671008
w_out[2,1]  0.3994595 0.5382460 1.0062249 -1.1636857  0.3954920  0.8907506
w_out[2,2]  0.8834425 0.9162438 1.7128736 -1.3991712 -0.3361378  1.8584841
w_out[2,3]  0.3543370 0.4926794 0.9210404 -0.5534430 -0.3204092  0.2911492
b_out[1]    0.4750640 0.2122317 0.3967569 -0.1253712  0.4192757  0.6493778
b_out[2]    1.1119310 0.3147135 0.5883417  0.2282603  1.0433232  1.2641093
b_out[3]   -0.3925753 0.1732960 0.3239685 -0.8247176 -0.5541784 -0.3516500
                   75%       97.5%   n_eff       Rhat
w_out[1,1]  0.01023817  0.25458596 3.49485  0.7806791
w_out[1,2]  0.28594182  0.59232082 3.49485  0.9786576
w_out[1,3]  0.18457894  0.21180579 3.49485 21.8403037
w_out[2,1]  0.94198149  1.08959911 3.49485  0.8957378
w_out[2,2]  2.07295215  2.31257528 3.49485  3.4800464
w_out[2,3]  0.61346549  1.65148005 3.49485  3.1659896
b_out[1]    0.66818468  0.80875070 3.49485  1.5615389
b_out[2]    1.48327696  1.61645084 3.49485  0.8426515
b_out[3]   -0.16642896 -0.04890031 3.49485  0.7131206

Model Fit Information:
Iterations: 10 
Warmup: 5 
Thinning: 1 
Chains: 1 

Predictive Performance:
Log-loss (training): 0.9307171 
AUC (training): 0.75 

Notes:
Check convergence diagnostics for parameters with high R-hat values.
Setting levels: control = 0, case = 1
Setting direction: controls < cases
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 94 ]
> 
> proc.time()
   user  system elapsed 
   8.81    0.82  233.00