Ceteris Paribus Profiles aka Individual Variable Profiles

Source: R/ceteris_paribus.R

This explainer works for individual observations. For each observation it calculates Ceteris Paribus Profiles for selected variables. Such profiles can be used to hypothesize about model results if selected variable is changed. For this reason it is also called 'What-If Profiles'.

ceteris_paribus(x, ...)

# S3 method for explainer
ceteris_paribus(
  x,
  new_observation,
  y = NULL,
  variables = NULL,
  variable_splits = NULL,
  grid_points = 101,
  variable_splits_type = "quantiles",
  ...
)

# S3 method for default
ceteris_paribus(
  x,
  data,
  predict_function = predict,
  new_observation,
  y = NULL,
  variables = NULL,
  variable_splits = NULL,
  grid_points = 101,
  variable_splits_type = "quantiles",
  variable_splits_with_obs = FALSE,
  label = class(x)[1],
  ...
)

Arguments

x

an explainer created with the DALEX::explain() function, or a model to be explained.
...

other parameters
new_observation

a new observation with columns that corresponds to variables used in the model
y

true labels for new_observation. If specified then will be added to ceteris paribus plots. NOTE: It is best when target variable is not present in the new_observation
variables

names of variables for which profiles shall be calculated. Will be passed to calculate_variable_split. If NULL then all variables from the validation data will be used.
variable_splits

named list of splits for variables, in most cases created with calculate_variable_split. If NULL then it will be calculated based on validation data available in the explainer.
grid_points

maximum number of points for profile calculations. Note that the finaln number of points may be lower than grid_points, eg. if there is not enough unique values for a given variable. Will be passed to calculate_variable_split.
variable_splits_type

how variable grids shall be calculated? Use "quantiles" (default) for percentiles or "uniform" to get uniform grid of points
data

validation dataset. It will be extracted from x if it's an explainer NOTE: It is best when target variable is not present in the data
predict_function

predict function. It will be extracted from x if it's an explainer
variable_splits_with_obs

if TRUE then all values in new_observation will be included in variable_splits
label

name of the model. By default it's extracted from the class attribute of the model

Value

an object of the class ceteris_paribus_explainer.

Details

Find more details in Ceteris Paribus Chapter.

References

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://pbiecek.github.io/ema

Examples

library("DALEX")
# smaller data, quicker example
titanic_small <- select_sample(titanic_imputed, n = 500, seed = 1313)

# build a model
model_titanic_glm <- glm(survived ~ gender + age + fare,
                         data = titanic_small,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_small[,-8],
                               y = titanic_small[,8],
                               verbose = FALSE)

cp_rf <- ceteris_paribus(explain_titanic_glm, titanic_small[1,])
cp_rf
#> Top profiles    : 
#>         gender   age class    embarked fare sibsp parch    _yhat_ _vname_ _ids_
#> 515     female 45.00   2nd Southampton 10.1     0     0 0.5595687  gender   515
#> 515.1     male 45.00   2nd Southampton 10.1     0     0 0.1448038  gender   515
#> 5151      male  0.75   2nd Southampton 10.1     0     0 0.3135247     age   515
#> 515.110   male  2.99   2nd Southampton 10.1     0     0 0.3028164     age   515
#> 515.2     male  4.98   2nd Southampton 10.1     0     0 0.2934793     age   515
#> 515.3     male  7.00   2nd Southampton 10.1     0     0 0.2841757     age   515
#>         _label_
#> 515          lm
#> 515.1        lm
#> 5151         lm
#> 515.110      lm
#> 515.2        lm
#> 515.3        lm
#> 
#> 
#> Top observations:
#>     gender age class    embarked fare sibsp parch    _yhat_ _label_ _ids_
#> 515   male  45   2nd Southampton 10.1     0     0 0.1448038      lm     1

plot(cp_rf, variables = "age")

# \donttest{
library("randomForest")
model_titanic_rf <- randomForest(survived ~.,  data = titanic_imputed)
#> Warning: The response has five or fewer unique values.  Are you sure you want to do regression?


explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed[,8],
                              label = "Random Forest v7",
                              verbose = FALSE)

# select few passangers
selected_passangers <- select_sample(titanic_imputed, n = 20)
cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
cp_rf
#> Top profiles    : 
#>        gender age            class    embarked    fare sibsp parch     _yhat_
#> 515    female  45              2nd Southampton 10.1000     0     0 0.85053814
#> 515.1    male  45              2nd Southampton 10.1000     0     0 0.09490975
#> 604    female  17              3rd Southampton  7.1701     1     0 0.47411576
#> 604.1    male  17              3rd Southampton  7.1701     1     0 0.11321362
#> 1430   female  25 engineering crew Southampton  0.0000     0     0 0.69052740
#> 1430.1   male  25 engineering crew Southampton  0.0000     0     0 0.24005121
#>        _vname_ _ids_          _label_
#> 515     gender   515 Random Forest v7
#> 515.1   gender   515 Random Forest v7
#> 604     gender   604 Random Forest v7
#> 604.1   gender   604 Random Forest v7
#> 1430    gender  1430 Random Forest v7
#> 1430.1  gender  1430 Random Forest v7
#> 
#> 
#> Top observations:
#>      gender age            class    embarked    fare sibsp parch     _yhat_
#> 515    male  45              2nd Southampton 10.1000     0     0 0.09490975
#> 604    male  17              3rd Southampton  7.1701     1     0 0.11321362
#> 1430   male  25 engineering crew Southampton  0.0000     0     0 0.24005121
#> 865    male  20              3rd   Cherbourg  7.0406     0     0 0.11517589
#> 452  female  17              3rd  Queenstown  7.1408     0     0 0.64019375
#> 1534   male  38 victualling crew Southampton  0.0000     0     0 0.15301516
#>               _label_ _ids_
#> 515  Random Forest v7     1
#> 604  Random Forest v7     2
#> 1430 Random Forest v7     3
#> 865  Random Forest v7     4
#> 452  Random Forest v7     5
#> 1534 Random Forest v7     6

plot(cp_rf, variables = "age") +
  show_observations(cp_rf, variables = "age") +
  show_rugs(cp_rf, variables = "age", color = "red")

# }