Oscillations are proxies for local feature importance at the instance level. Find more details in Ceteris Paribus Oscillations Chapter.

calculate_oscillations(x, sort = TRUE, ...)

## Arguments

x a ceteris paribus explainer produced with the ceteris_paribus() function a logical value. If TRUE then rows are sorted along the oscillations other arguments

## Value

an object of the class ceteris_paribus_oscillations

## References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

## Examples

library("DALEX")
library("ingredients")

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])
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  500  rows  7  cols
#>   -> target variable   :  500  values
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task classification (  default  )
#>   -> predicted values  :  numerical, min =  0.0795294 , mean =  0.302 , max =  0.9859411
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.8204691 , mean =  8.796651e-12 , max =  0.8567173
#>   A new explainer has been created!
cp_rf <- ceteris_paribus(explain_titanic_glm, titanic_small[1,])

calculate_oscillations(cp_rf)
#>    _vname_ _ids_ oscillations
#> 1   gender   515   0.20738245
#> 2      age   515   0.05371374
#> 5     fare   515   0.04197095
#> 3    class   515   0.00000000
#> 4 embarked   515   0.00000000
#> 6    sibsp   515   0.00000000
#> 7    parch   515   0.00000000
# \donttest{
library("ranger")

apartments_rf_model <- ranger(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)

explainer_rf <- explain(apartments_rf_model,
data = apartments_test[,-1],
y = apartments_test\$m2.price,
label = "ranger forest",
verbose = FALSE)

apartment <- apartments_test[1,]

cp_rf <- ceteris_paribus(explainer_rf, apartment)

calculate_oscillations(cp_rf)
#>             _vname_ _ids_ oscillations
#> 5          district  1001   1161.14035
#> 2           surface  1001    383.71890
#> 3             floor  1001    327.63667
#> 4          no.rooms  1001     75.01989
#> 1 construction.year  1001     51.28965# }