Summary of the object

The AUTO_VI$..str..() method provides a string representation of the object. If a check is performed, the string will contain some simple statistics of the check result. This method does this same thing as AUTO_VI$..str..(), but it returns an AUTO_VI_SUMMARY object which stores those statistics, such as sample quantiles of the distribution of null visual signal strength, in the object.

Usage

AUTO_VI$summary()

Value

An AUTO_VI_SUMMARY object.

Examples

keras_model <- try(get_keras_model("vss_phn_32"))
if (!inherits(keras_model, "try-error")) {
  myvi <- auto_vi(lm(dist ~ speed, data = cars), keras_model)

  myvi$check()
  myvi_summary <- myvi$summary()
  print(myvi_summary)
  names(myvi_summary)
}
#> ✔ Generate null data.
#> ✔ Generate null plots.
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■                   46% | ETA:  1s
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Compute auxilary inputs.
#> Saving images ■■■■■■■■■■■■■■                    44% | ETA:  1s
#> Saving images ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Predict visual signal strength for 100 images.
#> ✔ Generate bootstrapped data.
#> ✔ Generate bootstrapped plots.
#> Warning: Problem in area computation (Returns NA)
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■■                  49% | ETA:  1s
#> Warning: Problem in area computation (Returns NA)
#> Warning: triangle collapsed!
#> Warning: three points coincide or are collinear!
#> Computing auxiliary inputs ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Compute auxilary inputs.
#> Saving images ■■■■■■■■■■■■■■■■■■■■■■■■■         81% | ETA:  0s
#> Saving images ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> ✔ Predict visual signal strength for 100 images.
#> ✔ Predict visual signal strength for 1 image.
#> 
#> ── <AUTO_VI_SUMMARY object>
#> Status:
#>  - Fitted model: lm
#>  - Keras model: (None, 32, 32, 3) + (None, 5) -> (None, 1)
#>     - Output node index: 1
#>  - Result:
#>     - Observed visual signal strength: 3.16 (p-value = 0.0396)
#>     - Null visual signal strength: [100 draws]
#>        - Mean: 1.36
#>        - Quantiles: 
#>           ╔═════════════════════════════════════════════════╗
#>           ║   25%    50%    75%    80%    90%    95%    99% ║
#>           ║0.8376 1.2136 1.7898 1.8466 2.0764 2.6935 3.5908 ║
#>           ╚═════════════════════════════════════════════════╝
#>     - Bootstrapped visual signal strength: [100 draws]
#>        - Mean: 2.759 (p-value = 0.05941)
#>        - Quantiles: 
#>           ╔══════════════════════════════════════════╗
#>           ║  25%   50%   75%   80%   90%   95%   99% ║
#>           ║2.459 2.898 3.180 3.262 3.453 3.519 3.617 ║
#>           ╚══════════════════════════════════════════╝
#>     - Likelihood ratio: 0.7172 (boot) / 0.04044 (null) = 17.74 
#>  [1] "..methods.."      "has_attr"         "del_attr"         "..bases.."       
#>  [5] "..mro.."          "..init.."         "null_likelihood"  "null_quantiles"  
#>  [9] "boot_likelihood"  "p_value"          "observed_vss"     "boot_p_value"    
#> [13] "null_mean"        "..type.."         "boot_quantiles"   "..class.."       
#> [17] "summary_string"   "..dir.."          "boot_mean"        "..new.."         
#> [21] "..class_tree.."   "..repr.."         "set_attr"         "null_draws"      
#> [25] "..method_env.."   "..instantiated.." "likelihood_ratio" "..str.."         
#> [29] "..init_call.."    "get_attr"         "..len.."          "boot_draws"