local_attributions fails for classification: incorrect number of subscripts on matrix #69

agilebean · 2020-02-03T11:00:35Z

For classification, local_attributions() returns the error:

Error in contribution[nrow(contribution), ] <- cummulative[nrow(contribution),  : 
  incorrect number of subscripts on matrix

One hint for the root cause might be the warning message thrown by the explainer - it tries to calculate numeric residuals which of course it cannot do:

      DALEX.explainer <- DALEX::explain(
        model = model_object,
        data = features,
        y = training.set$.outcome == TARGET.VALUE,
        label = paste(model_object$method, " model"),
        colorize = TRUE
      )
  
  A new explainer has been created!  
Warning message:
In mean.default(residuals) :
  argument is not numeric or logical: returning NA

Reproducible example:

random.case <- structure(list(anger = 0.166666666666667, anticipation = 0, disgust = 0.166666666666667, 
    fear = 0.166666666666667, joy = 0, negative = 0.25, positive = 0.0833333333333333, 
    sadness = 0.0833333333333333, surprise = 0.0833333333333333, 
    trust = 0), class = "data.frame", row.names = c(NA, -1L))

training.set <- structure(list(.outcome = structure(c(3L, 4L, 5L, 4L, 4L, 5L, 
5L, 4L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 1L, 4L, 3L, 4L, 5L, 3L, 2L, 
5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), 
    anger = c(0, 0.0434782608695652, 0, 0, 0, 0.1, 0, 0.037037037037037, 
    0.0192307692307692, 0, 0, 0, 0, 0.0673076923076923, 0.181818181818182, 
    0.0408163265306122, 0, 0, 0, 0.0285714285714286, 0.0526315789473684, 
    0.0952380952380952, 0, 0.0441176470588235, 0), anticipation = c(0.333333333333333, 
    0.217391304347826, 0.125, 0.15, 0.2, 0.2, 0.217391304347826, 
    0.111111111111111, 0.173076923076923, 0.166666666666667, 
    0.111111111111111, 0.157894736842105, 0.214285714285714, 
    0.115384615384615, 0.0909090909090909, 0.0408163265306122, 
    0, 0.166666666666667, 0, 0.114285714285714, 0.184210526315789, 
    0.0476190476190476, 0.133333333333333, 0.102941176470588, 
    0.176470588235294), disgust = c(0, 0, 0, 0, 0, 0, 0, 0.0185185185185185, 
    0.0192307692307692, 0.0833333333333333, 0.0740740740740741, 
    0, 0, 0.0288461538461538, 0, 0.0204081632653061, 0, 0, 0.111111111111111, 
    0, 0, 0.0952380952380952, 0, 0.0294117647058824, 0), fear = c(0, 
    0.0434782608695652, 0, 0.05, 0, 0, 0, 0.0185185185185185, 
    0, 0, 0, 0, 0, 0.0673076923076923, 0, 0.0408163265306122, 
    0, 0.0833333333333333, 0.111111111111111, 0, 0.0263157894736842, 
    0.0952380952380952, 0, 0.0294117647058824, 0), joy = c(0, 
    0.130434782608696, 0.166666666666667, 0.15, 0.233333333333333, 
    0.2, 0.173913043478261, 0.166666666666667, 0.0961538461538462, 
    0.166666666666667, 0.037037037037037, 0.210526315789474, 
    0.214285714285714, 0.0961538461538462, 0.181818181818182, 
    0.0204081632653061, 0.333333333333333, 0.0833333333333333, 
    0.222222222222222, 0.2, 0.105263157894737, 0.0952380952380952, 
    0.2, 0.147058823529412, 0.176470588235294), negative = c(0, 
    0.0869565217391304, 0.0833333333333333, 0.1, 0, 0, 0, 0.0555555555555556, 
    0.0769230769230769, 0.166666666666667, 0.0740740740740741, 
    0.0526315789473684, 0.0714285714285714, 0.105769230769231, 
    0.181818181818182, 0.204081632653061, 0, 0.166666666666667, 
    0.222222222222222, 0.0285714285714286, 0.105263157894737, 
    0.19047619047619, 0, 0.102941176470588, 0.0294117647058824
    ), positive = c(0.333333333333333, 0.217391304347826, 0.291666666666667, 
    0.4, 0.3, 0.3, 0.347826086956522, 0.333333333333333, 0.326923076923077, 
    0.25, 0.259259259259259, 0.315789473684211, 0.285714285714286, 
    0.240384615384615, 0.181818181818182, 0.244897959183673, 
    0.333333333333333, 0.25, 0.222222222222222, 0.4, 0.342105263157895, 
    0.238095238095238, 0.4, 0.235294117647059, 0.352941176470588
    ), sadness = c(0.333333333333333, 0.0434782608695652, 0.0416666666666667, 
    0, 0, 0, 0, 0.0185185185185185, 0.0576923076923077, 0, 0.0740740740740741, 
    0, 0, 0.0480769230769231, 0.0909090909090909, 0.142857142857143, 
    0, 0, 0.111111111111111, 0, 0.0526315789473684, 0.0952380952380952, 
    0, 0.0441176470588235, 0.0294117647058824), surprise = c(0, 
    0.0434782608695652, 0.0833333333333333, 0.05, 0.0666666666666667, 
    0, 0.0434782608695652, 0.037037037037037, 0.0192307692307692, 
    0, 0.111111111111111, 0.0526315789473684, 0, 0.0865384615384615, 
    0, 0.0408163265306122, 0, 0, 0, 0.0285714285714286, 0.0526315789473684, 
    0, 0.0666666666666667, 0.0735294117647059, 0.0294117647058824
    ), trust = c(0, 0.173913043478261, 0.208333333333333, 0.1, 
    0.2, 0.2, 0.217391304347826, 0.203703703703704, 0.211538461538462, 
    0.166666666666667, 0.259259259259259, 0.210526315789474, 
    0.214285714285714, 0.144230769230769, 0.0909090909090909, 
    0.204081632653061, 0.333333333333333, 0.25, 0, 0.2, 0.0789473684210526, 
    0.0476190476190476, 0.2, 0.191176470588235, 0.205882352941176
    )), row.names = c(NA, 25L), class = "data.frame")

model.rf <- caret::train(
  form = .outcome ~ .,
  data = training.set,
  method = "rf", 
  trControl = trainControl(
    method = "repeatedcv", number = 5, repeats = 5)
)

target <- training.set$.outcome
features <- training.set %>% select(-.outcome)

TARGET.VALUE <- "1"

DALEX.explainer <- DALEX::explain(
        model = model.rf,
        data = features,
        y = target == TARGET.VALUE,
        label = paste(model_object$method, " model"),
        colorize = TRUE
  )

DALEX.attribution <- DALEX.explainer %>%
        iBreakDown::local_attributions(random.case)

The text was updated successfully, but these errors were encountered:

hbaniecki · 2020-02-03T11:13:42Z

Hi @agilebean! Can you provide the model please? It might be the problem with predict_function.
Your code works for me DALEX v0.9.4 && iBreakDown v0.9.9 :

library(dplyr)
random.case <- structure(list(anger = 0.166666666666667, anticipation = 0, disgust = 0.166666666666667, 
                              fear = 0.166666666666667, joy = 0, negative = 0.25, positive = 0.0833333333333333, 
                              sadness = 0.0833333333333333, surprise = 0.0833333333333333, 
                              trust = 0), class = "data.frame", row.names = c(NA, -1L))

training.set <- structure(list(.outcome = structure(c(3L, 4L, 5L, 4L, 4L, 5L, 
                                                      5L, 4L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 1L, 4L, 3L, 4L, 5L, 3L, 2L, 
                                                      5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), 
                               anger = c(0, 0.0434782608695652, 0, 0, 0, 0.1, 0, 0.037037037037037, 
                                         0.0192307692307692, 0, 0, 0, 0, 0.0673076923076923, 0.181818181818182, 
                                         0.0408163265306122, 0, 0, 0, 0.0285714285714286, 0.0526315789473684, 
                                         0.0952380952380952, 0, 0.0441176470588235, 0), anticipation = c(0.333333333333333, 
                                                                                                         0.217391304347826, 0.125, 0.15, 0.2, 0.2, 0.217391304347826, 
                                                                                                         0.111111111111111, 0.173076923076923, 0.166666666666667, 
                                                                                                         0.111111111111111, 0.157894736842105, 0.214285714285714, 
                                                                                                         0.115384615384615, 0.0909090909090909, 0.0408163265306122, 
                                                                                                         0, 0.166666666666667, 0, 0.114285714285714, 0.184210526315789, 
                                                                                                         0.0476190476190476, 0.133333333333333, 0.102941176470588, 
                                                                                                         0.176470588235294), disgust = c(0, 0, 0, 0, 0, 0, 0, 0.0185185185185185, 
                                                                                                                                         0.0192307692307692, 0.0833333333333333, 0.0740740740740741, 
                                                                                                                                         0, 0, 0.0288461538461538, 0, 0.0204081632653061, 0, 0, 0.111111111111111, 
                                                                                                                                         0, 0, 0.0952380952380952, 0, 0.0294117647058824, 0), fear = c(0, 
                                                                                                                                                                                                       0.0434782608695652, 0, 0.05, 0, 0, 0, 0.0185185185185185, 
                                                                                                                                                                                                       0, 0, 0, 0, 0, 0.0673076923076923, 0, 0.0408163265306122, 
                                                                                                                                                                                                       0, 0.0833333333333333, 0.111111111111111, 0, 0.0263157894736842, 
                                                                                                                                                                                                       0.0952380952380952, 0, 0.0294117647058824, 0), joy = c(0, 
                                                                                                                                                                                                                                                              0.130434782608696, 0.166666666666667, 0.15, 0.233333333333333, 
                                                                                                                                                                                                                                                              0.2, 0.173913043478261, 0.166666666666667, 0.0961538461538462, 
                                                                                                                                                                                                                                                              0.166666666666667, 0.037037037037037, 0.210526315789474, 
                                                                                                                                                                                                                                                              0.214285714285714, 0.0961538461538462, 0.181818181818182, 
                                                                                                                                                                                                                                                              0.0204081632653061, 0.333333333333333, 0.0833333333333333, 
                                                                                                                                                                                                                                                       0.222222222222222, 0.2, 0.105263157894737, 0.0952380952380952, 
                                                                                                                                                                                                                                                              0.2, 0.147058823529412, 0.176470588235294), negative = c(0, 
                                                                                                                                                                                                                                                                                                                       0.0869565217391304, 0.0833333333333333, 0.1, 0, 0, 0, 0.0555555555555556, 
                                                                                                                                                                                                                                                                                                                       0.0769230769230769, 0.166666666666667, 0.0740740740740741, 
                                                                                                                                                                                                                                                                                                                       0.0526315789473684, 0.0714285714285714, 0.105769230769231, 
                                                                                                                                                                                                                                                                                                                       0.181818181818182, 0.204081632653061, 0, 0.166666666666667, 
                                                                                                                                                                                                                                                                                                                       0.222222222222222, 0.0285714285714286, 0.105263157894737, 
                                                                                                                                                                                                                                                                                                                       0.19047619047619, 0, 0.102941176470588, 0.0294117647058824
                                                                                                                                                                                                                                                              ), positive = c(0.333333333333333, 0.217391304347826, 0.291666666666667, 
                                                                                                                                                                                                                                                                              0.4, 0.3, 0.3, 0.347826086956522, 0.333333333333333, 0.326923076923077, 
                                                                                                                                                                                                                                                                              0.25, 0.259259259259259, 0.315789473684211, 0.285714285714286, 
                                                                                                                                                                                                                                                                              0.240384615384615, 0.181818181818182, 0.244897959183673, 
                                                                                                                                                                                                                                                                              0.333333333333333, 0.25, 0.222222222222222, 0.4, 0.342105263157895, 
                                                                                                                                                                                                                                                                              0.238095238095238, 0.4, 0.235294117647059, 0.352941176470588
                                                                                                                                                                                                                                                              ), sadness = c(0.333333333333333, 0.0434782608695652, 0.0416666666666667, 
                                                                                                                                                                                                                                                                             0, 0, 0, 0, 0.0185185185185185, 0.0576923076923077, 0, 0.0740740740740741, 
                                                                                                                                                                                                                                                                             0, 0, 0.0480769230769231, 0.0909090909090909, 0.142857142857143, 
                                                                                                                                                                                                                                                                             0, 0, 0.111111111111111, 0, 0.0526315789473684, 0.0952380952380952, 
                                                                                                                                                                                                                                                                             0, 0.0441176470588235, 0.0294117647058824), surprise = c(0, 
                                                                                                                                                                                                                                                                                                                                      0.0434782608695652, 0.0833333333333333, 0.05, 0.0666666666666667, 
                                                                                                                                                                                                                                                                                                                                      0, 0.0434782608695652, 0.037037037037037, 0.0192307692307692, 
                                                                                                                                                                                                                                                                                                                                      0, 0.111111111111111, 0.0526315789473684, 0, 0.0865384615384615, 
                                                                                                                                                                                                                                                                                                                                      0, 0.0408163265306122, 0, 0, 0, 0.0285714285714286, 0.0526315789473684, 
                                                                                                                                                                                                                                                                                                                                      0, 0.0666666666666667, 0.0735294117647059, 0.0294117647058824
                                                                                                                                                                                                                                                                             ), trust = c(0, 0.173913043478261, 0.208333333333333, 0.1, 
                                                                                                                                                                                                                                                                                          0.2, 0.2, 0.217391304347826, 0.203703703703704, 0.211538461538462, 
                                                                                                                                                                                                                                                                                          0.166666666666667, 0.259259259259259, 0.210526315789474, 
                                                                                                                                                                                                                                                                                          0.214285714285714, 0.144230769230769, 0.0909090909090909, 
                                                                                                                                                                                                                                                                                          0.204081632653061, 0.333333333333333, 0.25, 0, 0.2, 0.0789473684210526, 
                                                                                                                                                                                                                                                                                          0.0476190476190476, 0.2, 0.191176470588235, 0.205882352941176
                                                                                                                                                                                                                                                                             )), row.names = c(NA, 25L), class = "data.frame")
target <- training.set$.outcome
features <- training.set %>% select(-.outcome)

TARGET.VALUE <- "1"

colnames(training.set)[1] <- "outcome"
model_object <- lm(outcome==TARGET.VALUE~., data = training.set)

DALEX.explainer <- DALEX::explain(
  model = model_object,
  data = features,
  y = training.set$outcome == TARGET.VALUE,
  label = paste(model_object$method, " model"),
  colorize = TRUE
)

DALEX.attribution <- DALEX.explainer %>% iBreakDown::local_attributions(random.case) 
DALEX.attribution

agilebean · 2020-02-03T11:14:30Z

Ha, crossing thoughts - I just included the model in the description!

agilebean · 2020-02-03T11:16:53Z

I just verified I had iBreakDown_0.9.9 and only 1 subrelease number below for DALEX, i.e. DALEX_0.9.3

agilebean · 2020-02-03T11:30:52Z

I just ran it again with the same error. However, when I run the same analysis - but with the model trained as regression instead of classification, it WORKS! Double checked just now.

agilebean · 2020-02-03T14:03:32Z

@hbaniecki Did you try it with the model I specified in the description above?

hbaniecki · 2020-02-03T14:28:01Z

Yes, it is a weird problem with data.frame/matrix behavior. I believe that this part

iBreakDown/R/local_attributions.R

Lines 252 to 255 in 43b6e0b

    
           cummulative <- do.call(rbind, c(list(baseline_yhat), yhats_mean, list(target_yhat))) 
        
           contribution <- rbind(0,apply(cummulative, 2, diff)) 
        
           contribution[1,] <- cummulative[1,] 
        
           contribution[nrow(contribution),] <- cummulative[nrow(contribution),]

can be handled better (to fix).

While running your example, there is a red warning (in the explainer output) saying that predict_function returns probabilities for multiple classes. For now, if you want to use local_attributions for one class (e.g. target = "1"), you can use a custom predict_function and pass it to the explainer.

custom_predict_caret_oneclass <- function(model, data, target = "1") {
  return(predict(model, data, type = "prob")[, target])
}

DALEX.explainer <- DALEX::explain(
  model = model.rf,
  data = features,
  y = target == TARGET.VALUE,
  predict_function = custom_predict_caret_oneclass,
  label = paste(model.rf$method, " model"),
  colorize = TRUE
)

DALEX.attribution <- DALEX.explainer %>%
  iBreakDown::local_attributions(random.case)

DALEX.attribution

agilebean · 2020-02-04T09:03:11Z

Great analysis.
Thanks for the oneclass predict_function code!
But that's a bummer, I need it for a publication.
Speaking of which, this issue on numbers on plots is extremely important for publications.

pbiecek · 2020-02-15T13:59:11Z

Thanks, there was a problem in the predict returns data.frame instead of matrix.
It is solved in the latest DALEX in the ema branch (will be on master on the beginning of the week and on CRAN in a week).

In the meantime you can use user defined predict_function

DALEX.explainer <- DALEX::explain(
  model = model.rf,
  data = features,
  y = target == TARGET.VALUE,
  predict_function = function(m,x) as.matrix(predict(m, newdata = x, type = "prob")),
  label = paste(model_object$method, " model"),
  colorize = TRUE
)

pbiecek · 2020-02-17T21:40:29Z

this is now fixed with the latest DALEX starting with 0.9.8 as in
https://github.com/ModelOriented/DALEX/tree/DALEX_1.0_ema_version

agilebean · 2020-02-21T03:05:54Z

I can confirm it works now - just ran local_attributions() on a classification dataset.
Wonderful.
Returns this plot:

agilebean changed the title ~~local_attributions fails for classification~~ local_attributions fails for classification: incorrect number of subscripts on matrix Feb 3, 2020

hbaniecki added the bug 💣 Bug to fix label Feb 3, 2020

pbiecek mentioned this issue Feb 15, 2020

yhat shall return numeric vector or marix ModelOriented/DALEX#150

Closed

pbiecek added a commit to ModelOriented/DALEX that referenced this issue Feb 15, 2020

candidate fix for ModelOriented/iBreakDown#69

d5119f7

pbiecek closed this as completed Feb 17, 2020

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

local_attributions fails for classification: incorrect number of subscripts on matrix #69

local_attributions fails for classification: incorrect number of subscripts on matrix #69

agilebean commented Feb 3, 2020 •

edited

Loading

hbaniecki commented Feb 3, 2020

agilebean commented Feb 3, 2020 •

edited

Loading

agilebean commented Feb 3, 2020

agilebean commented Feb 3, 2020

agilebean commented Feb 3, 2020

hbaniecki commented Feb 3, 2020

agilebean commented Feb 4, 2020

pbiecek commented Feb 15, 2020

pbiecek commented Feb 17, 2020

agilebean commented Feb 21, 2020

local_attributions fails for classification: incorrect number of subscripts on matrix #69

local_attributions fails for classification: incorrect number of subscripts on matrix #69

Comments

agilebean commented Feb 3, 2020 • edited Loading

Reproducible example:

hbaniecki commented Feb 3, 2020

agilebean commented Feb 3, 2020 • edited Loading

agilebean commented Feb 3, 2020

agilebean commented Feb 3, 2020

agilebean commented Feb 3, 2020

hbaniecki commented Feb 3, 2020

agilebean commented Feb 4, 2020

pbiecek commented Feb 15, 2020

pbiecek commented Feb 17, 2020

agilebean commented Feb 21, 2020

agilebean commented Feb 3, 2020 •

edited

Loading

agilebean commented Feb 3, 2020 •

edited

Loading