Comments (10)
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$method, " model"),
colorize = TRUE
)
DALEX.attribution <- DALEX.explainer %>% iBreakDown::local_attributions(random.case)
DALEX.attribution
from ibreakdown.
Ha, crossing thoughts - I just included the model in the description!
from ibreakdown.
I just verified I had iBreakDown_0.9.9
and only 1 subrelease number below for DALEX, i.e. DALEX_0.9.3
from ibreakdown.
I just ran it again with the same error. However, when I run the same analysis - but with the model trained as regression instead of classification, it WORKS! Double checked just now.
from ibreakdown.
@hbaniecki Did you try it with the model I specified in the description above?
from ibreakdown.
Yes, it is a weird problem with data.frame
/matrix
behavior. I believe that this part
iBreakDown/R/local_attributions.R
Lines 252 to 255 in 43b6e0b
While running your example, there is a red warning (in the explainer output) saying that predict_function
returns probabilities for multiple classes. For now, if you want to use local_attributions
for one class (e.g. target = "1"), you can use a custom predict_function
and pass it to the explainer.
custom_predict_caret_oneclass <- function(model, data, target = "1") {
return(predict(model, data, type = "prob")[, target])
}
DALEX.explainer <- DALEX::explain(
model = model.rf,
data = features,
y = target == TARGET.VALUE,
predict_function = custom_predict_caret_oneclass,
label = paste(model.rf$method, " model"),
colorize = TRUE
)
DALEX.attribution <- DALEX.explainer %>%
iBreakDown::local_attributions(random.case)
DALEX.attribution
from ibreakdown.
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.
from ibreakdown.
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
)
from ibreakdown.
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
from ibreakdown.
I can confirm it works now - just ran local_attributions()
on a classification dataset.
Wonderful.
Returns this plot:
from ibreakdown.
Related Issues (20)
- Ordered water fall plots (like shapviz)
- Deprecated function as of ggplot2 3.3.0
- Differentely calculated diffs in local_attributions and local_interactions HOT 2
- Word cummulative does not exist. Change it to cumulative. HOT 1
- which LICENSE? HOT 1
- [shap] NA in variable_value column
- plot() incompatible with old objects HOT 1
- Wrong values in shap sign column
- move DLAEX to suggests in the CRAN version v1.2
- Hidden last contribution label in the break_down plot HOT 5
- add max_vars as an alias to max_features in plots
- plot() gives BD profiles for all target classes HOT 2
- plotD3.break_down plots within tabs apart from active tab do not load/size correctly HOT 5
- bug in plotting distributions on R3.6
- Aggregate local_interactions to estimate shap with interactions HOT 5
- Error in shap on model with one variable
- More examples, and spellcheck HOT 1
- How can we apply model oriented ibreakdown to complete data set HOT 2
- Factor Variables Converted To Numeric Makes Results Less User Friendly HOT 3
- remove test dependency of `randomForest`, which requires R v4.1 to work
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ibreakdown.