misvm_orova.md
In mildsvm: Multiple-Instance Learning with Support Vector Machines

misvm_orova() has reasonable performance

Code
  print(mzoe)
Output
  [1] 0.24
Code
  print(mae)
Output
  [1] 0.24
Code
  print(table(bag_resp, bag_pred))
Output
          bag_pred
  bag_resp  1  2  3  4  5
         1  2  8  0  0  0
         2  0 34  0  0  0
         3  0  3 11  9  0
         4  0  0  2 17  1
         5  0  0  0  1 12

Code
  print(mzoe)
Output
  [1] 0.36
Code
  print(mae)
Output
  [1] 0.36
Code
  print(table(bag_resp, bag_pred))
Output
          bag_pred
  bag_resp  1  2  3  4  5
         1  2 15  0  0  0
         2  0 29  2  0  0
         3  0  2 11 12  0
         4  0  0  2 11  1
         5  0  0  0  2 11

`misvm_orova()` value returns make sense

Code
  models <- list(heur = misvm_orova(x = df2[, 3:7], y = df2$bag_label, bags = df2$
    bag_name, method = "heuristic"), qp = misvm_orova(x = df2[, 3:7], y = df2$
    bag_label, bags = df2$bag_name, method = "qp-heuristic"), mip = misvm_orova(
    x = df2[, 3:7], y = df2$bag_label, bags = df2$bag_name, method = "mip"),
  formula = misvm_orova(mi(bag_label, bag_name) ~ V1 + V2, method = "qp-heuristic",
  data = df2), mi_df = misvm_orova(as_mi_df(df2, instance_label = NULL))) %>%
    suppressWarnings() %>% suppressMessages()
  print(lapply(models, names))
Output
  $heur
  [1] "fits"      "call_type" "levels"    "features"  "kernel"

  $qp
  [1] "fits"      "call_type" "levels"    "features"  "kernel"

  $mip
  [1] "fits"      "call_type" "levels"    "features"  "kernel"

  $formula
  [1] "fits"      "call_type" "levels"    "features"  "kernel"    "formula"  
  [7] "bag_name"

  $mi_df
  [1] "fits"      "call_type" "levels"    "features"  "kernel"    "bag_name"

Code
  print(models)
Output
  $heur
  An misvm_orova object called with misvm_orova.default

  Parameters: 
    method: heuristic 
    kernel: linear  
    cost: 1 
    scale: TRUE 
    weights: TRUE

  Model info: 
    Number of models: 5 
    Levels of `y`: chr [1:5] "1" "2" "3" "4" "5"
    Features: chr [1:5] "V1" "V2" "V3" "V4" "V5"
    Number of iterations: 3 2 3 2 3


  $qp
  An misvm_orova object called with misvm_orova.default

  Parameters: 
    method: qp-heuristic 
    kernel: linear  
    cost: 1 
    scale: TRUE 
    weights: TRUE

  Model info: 
    Number of models: 5 
    Levels of `y`: chr [1:5] "1" "2" "3" "4" "5"
    Features: chr [1:5] "V1" "V2" "V3" "V4" "V5"
    Number of iterations: 3 2 2 1 0


  $mip
  An misvm_orova object called with misvm_orova.default

  Parameters: 
    method: mip 
    kernel: linear  
    cost: 1 
    scale: TRUE 
    weights: TRUE

  Model info: 
    Number of models: 5 
    Levels of `y`: chr [1:5] "1" "2" "3" "4" "5"
    Features: chr [1:5] "V1" "V2" "V3" "V4" "V5"
    Gap to optimality: 0 0 0 0 0


  $formula
  An misvm_orova object called with misvm_orova.formula

  Parameters: 
    method: qp-heuristic 
    kernel: linear  
    cost: 1 
    scale: TRUE 
    weights: TRUE

  Model info: 
    Number of models: 5 
    Levels of `y`: chr [1:5] "1" "2" "3" "4" "5"
    Features: chr [1:2] "V1" "V2"
    Number of iterations: 2 2 2 1 1


  $mi_df
  An misvm_orova object called with misvm_orova.mi_df

  Parameters: 
    method: heuristic 
    kernel: linear  
    cost: 1 
    scale: TRUE 
    weights: TRUE

  Model info: 
    Number of models: 5 
    Levels of `y`: chr [1:5] "1" "2" "3" "4" "5"
    Features: chr [1:5] "V1" "V2" "V3" "V4" "V5"
    Number of iterations: 3 2 3 2 3