fit the mems

2025-02-07 13:11:29 -08:00 · 2025-02-07 13:11:29 -08:00 · e56eac2f76
commit e56eac2f76
parent 8e57295a21
10 changed files with 4984 additions and 22 deletions
--- a/.RData
+++ b/.RData
--- a/mg-govdoc-cr_24074016.out
+++ b/mg-govdoc-cr_24074016.out
@ -0,0 +1,17 @@
 1. SSH tunnel from your workstation using the following command:
   ssh -N -L 8787:n3434:49157 mjilg@klone.hyak.uw.edu
   and point your web browser to http://localhost:8787
 2. log in to RStudio Server using the following credentials:
   user: mjilg
   password: shDpem/m/RHo7HO1CuWG
 When done using RStudio Server, terminate the job by:
 1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
 2. Issue the following command on the login node:
      scancel -f 24074016
--- a/mlm/contributing_did_model_fit.R
+++ b/mlm/contributing_did_model_fit.R
@ -2,12 +2,12 @@ library(dplyr)
 library(lubridate)
 library(rdd)
-contributing_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
+contributing_df_filepath <-  "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
 contributing_df = read.csv(contributing_df_filepath, header = TRUE) 
 window_num <- 5
 contributing_df <- contributing_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(scaled_age = scale(age)) |>
  mutate(scaled_age_at_commit = scale(age_at_commit))|>
  mutate(log1p_count = log1p(commit_count))
@ -16,13 +16,16 @@ library(lme4)
 library(optimx)
 library(lattice)
-#all_gmodel <- glmer.nb(log1p_count ~ before_after * week_index + scaled_age + (before_after * week_index | project_id),
+all_gmodel <- glmer.nb(log1p_count ~ before_after * relative_week + scaled_age + (before_after * relative_week | project_id),
-#                       control=glmerControl(optimizer="bobyqa",
+                       control=glmerControl(optimizer="bobyqa",
-#                                            optCtrl=list(maxfun=2e5)), nAGQ=0,
+                                            optCtrl=list(maxfun=2e5)), nAGQ=0,
-#                       data=contributing_df)
+                       data=contributing_df)
-all_gmodel <- readRDS("mlm/models/020125_contributing_model.rda")                 
+#all_gmodel <- readRDS("mlm/models/020125_contributing_model.rda")                 
 summary(all_gmodel)
-#saveRDS(all_gmodel, "020125_contributing_model.rda")
+
 saveRDS(all_gmodel, "020725_contributing_model.rda")
 model_residuals <- residuals(all_gmodel)
 acf(model_residuals)
 vif(all_gmodel)
@ -33,7 +36,7 @@ variance_components <- as.data.frame(VarCorr(all_gmodel))
 library(broom.mixed)
 library(ggplot2)
 condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
-glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:week_index"),]
+glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:relative_week"),]
 has_zero <- function(estimate, low, high){
  return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
 }
@ -46,4 +49,4 @@ g <- glmer_ranef_Dweek |>
  theme_bw()
 g
-write.csv(glmer_ranef_Dweek, "0201_contributing_dweek_ranefs.csv")
+write.csv(glmer_ranef_Dweek, "0207_contributing_dweek_ranefs.csv")
--- a/mlm/data/0207_contributing_dweek_ranefs.csv
+++ b/mlm/data/0207_contributing_dweek_ranefs.csv
@ -0,0 +1,715 @@
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 "659","ran_vals","project_id","Tux_Text-CSV_XS.git","before_after:relative_week",-0.105373638895695,0.124984944548458,-0.350339628820408,0.139592351029017,1,250
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 "661","ran_vals","project_id","twilio_twilio-python","before_after:relative_week",0.0781050352660303,0.0979521161428594,-0.113877584583459,0.270087655115519,1,405
 "662","ran_vals","project_id","twisted_pydoctor","before_after:relative_week",0.382477661413536,0.0646491576312097,0.255767640825512,0.509187682001559,2,659
 "663","ran_vals","project_id","ua-parser_uap-core.git","before_after:relative_week",0.0789759484613647,0.0967946513776876,-0.110738082135013,0.268689979057743,1,407
 "664","ran_vals","project_id","ukui_peony","before_after:relative_week",0.358204933102257,0.0666097871400071,0.227652149289964,0.48875771691455,2,638
 "665","ran_vals","project_id","Ultimaker_Cura","before_after:relative_week",0.388934174692847,0.0637647449298367,0.263957571146984,0.513910778238709,2,664
 "666","ran_vals","project_id","un33k_django-ipware","before_after:relative_week",-0.604917236744101,0.205100518555437,-1.00690686632325,-0.202927607164955,0,4
 "667","ran_vals","project_id","unknown-horizons_unknown-horizons.git","before_after:relative_week",0.467488512066609,0.0573897306365393,0.355006706936537,0.579970317196681,2,704
 "668","ran_vals","project_id","untitaker_python-atomicwrites","before_after:relative_week",-0.226403002277996,0.143965020960915,-0.508569258394944,0.0557632538389519,1,178
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 "674","ran_vals","project_id","virt-manager_virt-manager","before_after:relative_week",0.30301333905224,0.0722790628986063,0.161348978934666,0.444677699169813,2,585
 "675","ran_vals","project_id","voxpupuli_beaker","before_after:relative_week",0.372268501119901,0.0656773038290836,0.243543351013202,0.500993651226599,2,656
 "676","ran_vals","project_id","voxpupuli_librarian-puppet.git","before_after:relative_week",-0.492751055628007,0.186964012307536,-0.85919378615588,-0.126308325100134,0,47
 "677","ran_vals","project_id","voxpupuli_pypuppetdb","before_after:relative_week",-0.17214679367186,0.131752355222176,-0.430376664785653,0.0860830774419324,1,209
 "678","ran_vals","project_id","watson-developer-cloud_python-sdk.git","before_after:relative_week",0.197549163341503,0.127845854128935,-0.0530241063239714,0.448122433006978,1,492
 "679","ran_vals","project_id","webcamoid_webcamoid.git","before_after:relative_week",-0.064741623024538,0.117578411359495,-0.295191074648584,0.165707828599508,1,275
 "680","ran_vals","project_id","webrtchacks_adapter","before_after:relative_week",0.31774632231124,0.0709365654630771,0.178713208816641,0.456779435805839,2,606
 "681","ran_vals","project_id","websocket-client_websocket-client","before_after:relative_week",0.237019853137571,0.0796921921709049,0.0808260266335531,0.39321367964159,2,525
 "682","ran_vals","project_id","WoLpH_numpy-stl","before_after:relative_week",-0.113741091562897,0.12662490310735,-0.361921341199178,0.134439158073384,1,243
 "683","ran_vals","project_id","wtforms_flask-wtf.git","before_after:relative_week",-0.0186371018668255,0.110934792901426,-0.23606530058603,0.198791096852379,1,314
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 "685","ran_vals","project_id","Xastir_Xastir.git","before_after:relative_week",-0.00283980965345577,0.112393547499824,-0.223127114847803,0.217447495540892,1,332
 "686","ran_vals","project_id","Xaviju_inkscape-open-symbols","before_after:relative_week",-0.118378270501201,0.12243720691614,-0.358350786424515,0.121594245422112,1,240
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 "688","ran_vals","project_id","xtaran_unburden-home-dir","before_after:relative_week",-0.1494292017047,0.130897210974728,-0.405983020891908,0.107124617482508,1,222
 "689","ran_vals","project_id","ycm-core_ycmd","before_after:relative_week",0.153255147069415,0.126891109500249,-0.0954468575094006,0.40195715164823,1,457
 "690","ran_vals","project_id","ycm-core_YouCompleteMe","before_after:relative_week",0.340935251851471,0.068475318455112,0.206726093849541,0.475144409853401,2,624
 "691","ran_vals","project_id","yrro_command-t","before_after:relative_week",-0.351743619492421,0.162132906819184,-0.669518277566811,-0.033968961418032,0,115
 "692","ran_vals","project_id","ytdl-org_youtube-dl.git","before_after:relative_week",0.49486122893847,0.0551066216624639,0.386854235170366,0.602868222706574,2,709
 "693","ran_vals","project_id","zaach_jison","before_after:relative_week",-0.38470043881586,0.168398142942306,-0.714754734046207,-0.0546461435855138,0,96
 "694","ran_vals","project_id","zeroc-ice_ice-builder-gradle-debian-packaging.git","before_after:relative_week",-0.278428751372853,0.198658011044262,-0.667791298259966,0.11093379551426,1,147
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 "697","ran_vals","project_id","zeromq_pyzmq.git","before_after:relative_week",0.249656783993108,0.0782432139341652,0.0963029026474813,0.403010665338734,2,534
 "698","ran_vals","project_id","zkat_ssri.git","before_after:relative_week",0.0324813019143855,0.149722498417445,-0.260969402659161,0.325932006487932,1,360
 "699","ran_vals","project_id","zloirock_core-js.git","before_after:relative_week",-0.0295868024788037,0.11168558247737,-0.248486521726826,0.189312916769218,1,303
 "700","ran_vals","project_id","zmartzone_cjose.git","before_after:relative_week",-0.414127979755117,0.173691465819507,-0.754556997183321,-0.0736989623269135,0,78
 "701","ran_vals","project_id","zopefoundation_BTrees.git","before_after:relative_week",-0.577017546908904,0.200549209368341,-0.970086774398835,-0.183948319418974,0,10
 "702","ran_vals","project_id","zopefoundation_roman","before_after:relative_week",-0.566417248354125,0.199236546020181,-0.956913702957836,-0.175920793750413,0,17
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--- a/mlm/data/0207_readme_dweek_ranefs.csv
+++ b/mlm/data/0207_readme_dweek_ranefs.csv
--- a/mlm/gam_plot.R
+++ b/mlm/gam_plot.R
@ -1,17 +1,17 @@
 library(dplyr)
-contributing_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
+contributing_df_filepath <-"/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
 contributing_df = read.csv(contributing_df_filepath, header = TRUE) 
-readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
+readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_README_weekly_count_data.csv"
 readme_df = read.csv(readme_df_filepath, header = TRUE)
 window_num <- 5
 contributing_df <- contributing_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(doc_type = "CONTRIBUTING")
 readme_df <- readme_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(doc_type = "README")
 main_df <- rbind(contributing_df, readme_df)
@ -31,7 +31,7 @@ doctypeColors <-
            , c("CONTRIBUTING", "README"))
 time_plot <- main_df |>
-  ggplot(aes(x=week_index, y=commit_count, color=factor(doc_type))) +
+  ggplot(aes(x=relative_week, y=commit_count, color=factor(doc_type))) +
  scale_y_continuous(trans = 'log1p', labels = scales::comma) +
  labs(x="Weekly Offset", y="Commit Count", color="Document Type: ") +
  scale_color_manual(values = doctypeColors) + 
@ -41,4 +41,4 @@ time_plot <- main_df |>
  theme(legend.position = "top")
 time_plot
-ggsave(filename = "plots/cr-020325-gam-introduction.png", plot = time_plot, width = 9, height = 9, dpi = 800)
+ggsave(filename = "plots/cr-020725-gam-introduction.png", plot = time_plot, width = 9, height = 9, dpi = 800)
--- a/mlm/models/020725_contributing_model.rda
+++ b/mlm/models/020725_contributing_model.rda
--- a/mlm/models/020725_readme_model.rda
+++ b/mlm/models/020725_readme_model.rda
--- a/mlm/readme_did_model_fit.R
+++ b/mlm/readme_did_model_fit.R
@ -2,12 +2,12 @@ library(dplyr)
 library(lubridate)
 library(rdd)
-readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
+readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_README_weekly_count_data.csv"
 readme_df = read.csv(readme_df_filepath, header = TRUE)
 window_num <- 5
 readme_df <- readme_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
  mutate(scaled_age = scale(age)) |>
  mutate(scaled_age_at_commit = scale(age_at_commit))|>
  mutate(log1p_count = log1p(commit_count))
@ -16,13 +16,13 @@ library(lme4)
 library(optimx)
 library(lattice)
-all_gmodel <- glmer.nb(log1p_count ~ before_after * week_index + scaled_age + (before_after * week_index | project_id),
+all_gmodel <- glmer.nb(log1p_count ~ before_after * relative_week + scaled_age + (before_after * relative_week | project_id),
                       control=glmerControl(optimizer="bobyqa",
                                            optCtrl=list(maxfun=2e5)), nAGQ=0,
                       data=readme_df)
 summary(all_gmodel)
-#saveRDS(all_gmodel, "020325_readme_model.rda")
+saveRDS(all_gmodel, "020725_readme_model.rda")
 model_residuals <- residuals(all_gmodel)
 acf(model_residuals)
@ -35,7 +35,7 @@ variance_components <- as.data.frame(VarCorr(all_gmodel))
 library(broom.mixed)
 library(ggplot2)
 condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
-glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:week_index"),]
+glmer_ranef_Dweek <- condvals [which(condvals $term == "before_after:relative_week"),]
 has_zero <- function(estimate, low, high){
  return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
 }
@ -48,4 +48,4 @@ g <- glmer_ranef_Dweek |>
  theme_bw()
 g
-write.csv(glmer_ranef_Dweek, "0203_readme_dweek_ranefs.csv")
+write.csv(glmer_ranef_Dweek, "0207_readme_dweek_ranefs.csv")
--- a/plots/cr-020725-gam-introduction.png
+++ b/plots/cr-020725-gam-introduction.png