modeloriented / eix Goto Github PK

In social and business sciences, the interactions are defined as between groups as opposed to intra-group interactions of features

For example
grpA = c("A", "B", "C", "D".........."Z")
grpB = c("p1", "p2", ......."p500")

I wanted to see the most important inter-group interaction of grpA and grpB.
e.g. A:B interaction is useless to know, they could be Christianity:SouthernBaptist so not very interesting.
Similarly p1:p2 interaction is useless to know, they could be Hispanic:African so not interesting

inter-group interaction such as A:p1 is interesting to know and could be Christianity:Hispanic

Any way I can achieve pairwise inter-group interaction in EIX?

Hi,
I tried to run your function EIX::interactions on a data set of 1(qf2020_div)+8 numeric columns with about 80,000 rows together with a tuned lightgbm model. However, an error is reported as follows:
"Error in rbindlist(treeList) :
Item 97 has 13 columns, inconsistent with item 1 which has 19 columns. To fill missing columns use fill=TRUE."
This does not happen when running the default lightgbm model parameter settings.

When I try to do with a small subset (100 rows) I encouter the same Error of "uneven columns" when num_leaves to min_gain_to_split are turned off. Otherwise
"Error: comparison (1) is possible only for atomic and list types"happens, or that error happens: "" Any idea why this occurs?

I used this code:

mmmf_df_100 = structure(list(qf2020_div = c(-0.683344740108416, -0.62200251820213,
-0.660933392581695, -0.931454042941375, -0.678846234812683, -0.678709195184706,
-0.62200251820213, -0.619032040654088, -0.741462927558781, -0.882350949746443,
-0.747540455479868, -0.743496834435778, 0, -0.63301735032532,
-0.850596218655163, -0.860808275916884, -0.62200251820213, -0.669529409627363,
-0.675469611757471, 0, 0, 0, -0.388044330254854, -0.850634478054759,
0, 0, -0.617546396858118, -0.8891822325675, -0.703075765512668,
-0.886130787928763, -0.681806828303268, 0, -0.88604646624308,
-0.926167021298114, -0.692090760819216, -0.660933392581695, -0.83931706735653,
-0.881578476738358, -0.684460497124147, -0.705416304923849, -0.685713271747449,
-0.686152296703342, -0.88723658127604, -0.846382748304772, -0.62200251820213,
-0.720211468617393, -0.684998539883293, -0.675830994910749, -0.61719971562315,
-0.908777071672487, 0, 0, 0, -0.813671235655738, 0, -0.886130787928763,
0, -0.388179591352467, -0.889236363195927, -0.883763006684634,
0, -0.681806828303268, -0.692090760819216, -0.670785617377905,
-0.675573715067695, 0, -0.746739480916366, -0.684460497124147,
-0.738360299567337, 0, -0.692090760819216, 0, -0.640423140555064,
-0.695504563944157, 0, -0.613657933810985, -0.74923545834839,
-0.660933392581695, -0.821653413397282, -0.738971403646119, -0.61719971562315,
-0.678846234812683, -0.819372375152443, -0.720211468617393, -0.886130787928763,
-0.629409957539496, -0.680296374263876, 0, -0.844873743332596,
-0.619032040654088, 0, -0.670286891070436, -0.678278455996463,
-0.739735765831987, -0.602360477184269, 0, -0.692034388476076,
-0.675469611757471, -0.886130787928763, -0.684998539883293),
watershed = c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3), compactnes = c(0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553,
0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553, 0.78553
), mmm_fsize = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), curve_numbe = c(65, 70, 70,
87, 65, 65, 70, 70, 65, 84, 65, 65, 56, 70, 77, 77, 70, 65,
65, 50, 50, 56, 56, 77, 56, 50, 70, 77, 65, 79, 70, 50, 77,
87, 65, 70, 77, 84, 70, 65, 70, 70, 79, 77, 70, 65, 70, 70,
70, 79, 56, 50, 50, 83, 56, 79, 50, 56, 84, 79, 56, 70, 65,
65, 65, 56, 65, 70, 65, 50, 65, 50, 70, 65, 50, 70, 65, 70,
83, 65, 70, 65, 83, 65, 79, 70, 70, 50, 77, 70, 56, 65, 65,
65, 70, 50, 65, 65, 79, 70), hsg = c(0, 2, 2, 0, 0, 0, 2,
2, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0,
2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2,
0, 2, 2, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0,
0, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 2, 0, 2, 0, 2,
0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2), aspect = c(86.1312789916992,
16.4371280670166, 5.29735994338989, 201.93327331543, 322.773468017578,
89.2040252685547, 219.443618774414, 314.053527832031, 180.033554077148,
312.130004882812, 193.037216186523, 9.60710716247559, 50.0658378601074,
279.368316650391, 63.079231262207, 296.165985107422, 249.407730102539,
264.652557373047, 263.472015380859, 113.461738586426, 352.356231689453,
73.4116973876953, 325.854156494141, 153.332122802734, 111.455612182617,
213.973266601562, 235.915802001953, 44.4550132751465, 266.452331542969,
130.592666625977, 278.087646484375, 12.3049230575562, 194.30876159668,
269.795562744141, 273.022857666016, 181.224151611328, 27.4992580413818,
8.57164478302002, 198.986557006836, 33.6422309875488, 38.4557991027832,
178.001922607422, 200.945281982422, 359.575592041016, 348.970916748047,
145.922546386719, 303.911651611328, 272.455993652344, 337.481353759766,
83.7395782470703, 3.79256057739258, 16.1068820953369, 342.483032226562,
250.845794677734, 202.625839233398, 115.868446350098, 125.002998352051,
54.4174537658691, 136.00732421875, 238.046249389648, 203.473831176758,
288.734497070312, 106.890609741211, 128.663162231445, 12.6799297332764,
71.0660247802734, 281.640441894531, 154.839492797852, 312.834503173828,
275.901824951172, 39.8677558898926, 90.3907852172852, 194.46012878418,
302.63037109375, 19.0449523925781, 12.9935855865479, 132.882751464844,
97.9574356079102, 336.990753173828, 59.7389221191406, 157.052444458008,
329.076629638672, 41.2350616455078, 49.0847129821777, 39.1097717285156,
44.3229064941406, 351.448303222656, 231.318969726562, 291.198272705078,
225.222579956055, 224.549331665039, 244.679962158203, 263.660064697266,
191.419311523438, 205.053527832031, 77.8348999023438, 320.839141845703,
270.162658691406, 147.001251220703, 101.014167785645), number_rain = c(4.11478662490845,
4.11478662490845, 4.69904613494873, 4.22641563415527, 4.11478662490845,
4.11478662490845, 4.11478662490845, 4.11478662490845, 5.06366062164307,
4.97407245635986, 4.97407245635986, 5.06366062164307, 4.22641563415527,
4.22641563415527, 4.11478662490845, 4.22641563415527, 4.11478662490845,
4.11478662490845, 4.11478662490845, 4.11478662490845, 4.11478662490845,
4.11478662490845, 4.11478662490845, 4.11478662490845, 4.4235634803772,
4.22641563415527, 4.11478662490845, 5.12175559997559, 4.22641563415527,
4.22641563415527, 5.06366062164307, 5.12175559997559, 5.06366062164307,
4.22641563415527, 4.22641563415527, 4.69904613494873, 4.11478662490845,
5.06366062164307, 5.06366062164307, 4.22641563415527, 5.06366062164307,
4.97407245635986, 4.22641563415527, 4.11478662490845, 4.11478662490845,
4.69904613494873, 5.06366062164307, 5.06366062164307, 4.11478662490845,
4.97407245635986, 4.22641563415527, 4.11478662490845, 5.12175559997559,
4.11478662490845, 4.22641563415527, 4.22641563415527, 4.22641563415527,
4.11478662490845, 4.11478662490845, 4.22641563415527, 4.11478662490845,
5.06366062164307, 4.22641563415527, 4.11478662490845, 4.11478662490845,
5.12175559997559, 5.06366062164307, 5.06366062164307, 5.06366062164307,
4.11478662490845, 4.22641563415527, 4.11478662490845, 4.22641563415527,
4.22641563415527, 4.22641563415527, 4.11478662490845, 4.97407245635986,
4.69904613494873, 4.11478662490845, 5.06366062164307, 4.11478662490845,
4.11478662490845, 4.11478662490845, 4.69904613494873, 4.22641563415527,
4.22641563415527, 5.12175559997559, 5.06366062164307, 4.11478662490845,
4.11478662490845, 4.22641563415527, 4.11478662490845, 4.11478662490845,
5.06366062164307, 4.11478662490845, 4.97407245635986, 4.22641563415527,
4.11478662490845, 4.22641563415527, 5.06366062164307), precipitat = c(105.631990780906,
105.631990780906, 113.525026987469, 95.7414173890674, 108.81347918132,
111.585622257657, 105.631990780906, 107.371452626728, 108.427198425172,
104.19571208197, 106.32300672077, 108.237226047213, 108.663529055459,
105.526280009557, 105.631990780906, 103.14488716731, 105.631990780906,
117.181404984187, 111.968032095167, 105.631990780906, 105.631990780906,
117.875289985112, 114.660130235884, 105.89557216281, 117.985597080655,
105.526280009557, 110.971759538802, 113.277256125496, 103.226915200551,
95.7414173890674, 107.448963218265, 113.277256125496, 115.204860694825,
102.406323243701, 108.663529055459, 113.525026987469, 116.909620224483,
106.32300672077, 104.19571208197, 100.017380517627, 105.95153774534,
105.95153774534, 96.1293804077875, 111.585622257657, 105.631990780906,
117.757825435154, 105.14771898966, 111.93966234298, 108.81347918132,
106.32300672077, 103.226915200551, 114.660130235884, 111.252964557163,
116.909620224483, 105.89557216281, 95.7414173890674, 96.1293804077875,
111.968032095167, 118.493426474314, 99.5515276136853, 117.875289985112,
107.448963218265, 108.663529055459, 117.875289985112, 112.081975460053,
107.530949713692, 105.14771898966, 104.19571208197, 113.237426341526,
118.493426474314, 108.663529055459, 105.631990780906, 103.226915200551,
105.526280009557, 102.406323243701, 112.081975460053, 105.95153774534,
113.525026987469, 111.585622257657, 111.93966234298, 108.81347918132,
108.81347918132, 112.081975460053, 117.757825435154, 95.7414173890674,
108.663529055459, 111.608108346424, 113.237426341526, 111.900469795106,
107.371452626728, 108.146229834784, 116.909620224483, 108.206884308467,
112.154623977722, 119.449564721849, 106.32300672077, 105.89557216281,
111.968032095167, 95.7414173890674, 105.14771898966)), row.names = c(NA,
-100L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001df3a7b1ef0>)

#preprocess dataframe
mmmf_df_rules= lgb.convert_with_rules(
data=mmmf_df)

#extracted prepared dataframe
mmmf_df_prep = mmmf_df_rules$data

#remove dependent variables
mmmf_df_prep_indie_vars <- as.matrix(mmmf_df_prep[, 2:8, with = FALSE])

#create correct dataset for lightgbm model "training"
mmmf_lgb_ds<- lgb.Dataset(data = mmmf_df_prep_indie_vars
,
label = mmmf_df_prep $qf2020_div
)

#or define specific data.matrix
mmmf_df_prep_indie_vars_2 <- Matrix::sparse.model.matrix(
qf2020_div ~ .,data = mmmf_df_prep, with = FALSE)
mmmf_lgb_ds_2 <- lightgbm::lgb.Dataset(mmmf_df_prep_indie_vars_2)

#define parameter space from tuned lightgbm model
params <- list(objective = "regression"
,
num_leaves = 100,
num_iterations = 1863,
learning_rate = 0.2556561,
max_depth = 12,
min_data_in_leaf = 34,
min_gain_to_split = 0.001104944,
num_threads = 1,
boosting = "goss",
tree_learner = "data",
extra_trees = T,
monotone_constraints_method = "advanced",
feature_pre_filter = F,
pre_partition = T,
two_round = F,
force_row_wise = T,
force_col_wise = F,
device_type = "cpu",
verbosity = -1
)
#train model on data and settings
lgb_model_intax <- lightgbm::lgb.train(params, mmmf_lgb_ds)

#check if trees were created
treedt = lightgbm::lgb.model.dt.tree(lgb_model_intax)

#extract the interactions for plotting
inter <- EIX::interactions(lgb_model_intax, mmmf_lgb_ds, option = "interactions");plot(inter)

Of course I could also create and provide a reprex.

Happy for advises :)

Felix

Line 106 of waterfall.R is prefixes = unique(gsub("\\.+[1-9]", "", colnames(df[, grepl("\\:", colnames(df))]))). My understanding is that this is supposed to identify the columns of df that are interactions.

If I have the interaction foo:bar, then gsub("\\.+[1-9]", "", "foo:bar") returns foo:bar.

However, if the interaction is foo.1:bar.2, thengsub("\\.+[1-9]", "", "foo.1:bar.2") returns foo1:bar1. This throws an error because . has been dropped from the names of the variables, which prevents startsWith from finding any matches on line 107.

Lots of warnings here: https://ekarbowiak.github.io/EIX/reference/lollipop.html

With short overview of key functions in EIX
see for example
https://pbiecek.github.io/DALEX/articles/vignette_titanic.html

👋 Hello! I'm James, one of the maintainers of LightGBM.

We were excited to see {EIX} show up as a "reverse suggests" on https://cran.r-project.org/web/packages/lightgbm/index.html. Thanks so much for enhancing what users can do in R with LightGBM models!

First, I wanted to let you know that the next release of LightGBM (the entire project, including the R package) will be a major version release with significant breaking changes. See this discussion of v4.0.0: microsoft/LightGBM#5153. We don't have a planned date for that release yet, as we have been struggling from a lack of maintainer attention / activity. But I expect it will be months not weeks from now.

Please open issues at https://github.com/microsoft/LightGBM/issues if there's anything we could do to make {lightgbm} easier to use with {EIX}.

Second, I wanted to share how you can test this project against {lightgbm}.

REPO_DIR="${HOME}/repos"

# install {EIX}'s Suggests dependencies
cd "${REPO_DIR}/EIX"
Rscript \
    --vanilla \
    -e "devtools::install_dev_deps()"

# build {lightgbm} R package from source and install it
git clone \
    --recursive \
    [email protected]:microsoft/LightGBM.git \
    "${REPO_DIR}/lgb-dev"

cd "${REPO_DIR}/lgb-dev"
sh build-cran-package.sh \
    --no-build-vignettes

R CMD INSTALL \
    --with-keep.source \
    ./lightgbm_*.tar.gz

# run R CMD check
cd "${REPO_DIR}/EIX"
R CMD build .
R CMD check --as-cran ./EIX_*.tar.gz

I'm happy to report that as of the most recent development version of {lightgbm} (microsoft/LightGBM@44fe591), R CMD check doesn't show any issues for this package! That probably means that the v4.0.0 release of {lightgbm} won't lead to check failures for this project on CRAN.

However, since this project does not have any unit tests, it's still possible that parts of {EIX} are incompatible with {lightgbm} or may become incompatible as additional changes are merged in the development version. I recommend setting up some automated tests that test the package against a development version of {lightgbm}, or at a minimum occasionally manually running something like the commands I provided.