From 257c506411154d687386944b8304435874976030 Mon Sep 17 00:00:00 2001 From: naeemkh Date: Tue, 23 Jan 2024 11:25:39 -0500 Subject: [PATCH 1/4] update dockerfile --- docker_singularity/Dockerfile | 4 ++-- docker_singularity/README.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docker_singularity/Dockerfile b/docker_singularity/Dockerfile index c968974..8c71ffd 100644 --- a/docker_singularity/Dockerfile +++ b/docker_singularity/Dockerfile @@ -30,9 +30,9 @@ RUN R -e "install.packages(c( \ 'rlang', \ 'Rfast', \ 'SuperLearner', \ + 'ranger', \ 'wCorr'), repos='https://cloud.r-project.org')" -ENV RENV_VERSION 0.15.1 RUN R -e "install.packages('remotes', repos = c(CRAN = 'https://cloud.r-project.org'))" -RUN R -e "remotes::install_github('rstudio/renv@${RENV_VERSION}')" + diff --git a/docker_singularity/README.md b/docker_singularity/README.md index 6d1fbf3..83ee836 100644 --- a/docker_singularity/README.md +++ b/docker_singularity/README.md @@ -37,9 +37,9 @@ Run the following code to download and spin up the image. ```s docker run -it --rm \ - -p 8230:8787 \ + -p 8231:8787 \ -e USER=rstudio \ -e PASSWORD=pass \ - -v "/path/to/your/folder/on/host:/home/rstudio/Project" nsaphsoftware/gpcerf_dev + -v $PWD:/home/rstudio/Project nsaphsoftware/gpcerf_dev ``` From 5dd9cc2633a8f0274f91229e3497f7b3a5b61003 Mon Sep 17 00:00:00 2001 From: naeemkh Date: Tue, 23 Jan 2024 11:27:26 -0500 Subject: [PATCH 2/4] update figure 1 and figure 2 --- README.md | 20 ++++++++++---------- man/figures/png/readme_gp.png | Bin 269218 -> 130240 bytes man/figures/png/readme_nngp.png | Bin 306701 -> 151047 bytes paper/figures/readme_gp.png | Bin 269218 -> 130240 bytes paper/figures/readme_nngp.png | Bin 296568 -> 151047 bytes paper/paper.md | 28 +++++++++++++++++----------- 6 files changed, 27 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index fc70597..e96591a 100644 --- a/README.md +++ b/README.md @@ -73,12 +73,12 @@ Optimal hyper parameters(#trial: 300): alpha = 12.9154966501488 beta = 12.9154966501488 g_sigma = 0.1 Optimal covariate balance: - cf1 = 0.072 + cf1 = 0.069 cf2 = 0.082 - cf3 = 0.062 - cf4 = 0.068 + cf3 = 0.063 + cf4 = 0.066 cf5 = 0.056 - cf6 = 0.082 + cf6 = 0.081 Original covariate balance: cf1 = 0.222 @@ -87,7 +87,7 @@ Original covariate balance: cf4 = 0.318 cf5 = 0.198 cf6 = 0.257 - ----***---- + ----***---- ```

@@ -148,10 +148,10 @@ Optimal hyper parameters(#trial: 300): alpha = 0.0278255940220712 beta = 0.215443469003188 g_sigma = 0.1 Optimal covariate balance: - cf1 = 0.058 - cf2 = 0.071 - cf3 = 0.087 - cf4 = 0.066 + cf1 = 0.062 + cf2 = 0.070 + cf3 = 0.091 + cf4 = 0.062 cf5 = 0.076 cf6 = 0.088 @@ -162,7 +162,7 @@ Original covariate balance: cf4 = 0.296 cf5 = 0.208 cf6 = 0.225 - ----***---- + ----***---- ```

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zp?yEC?({(bt*^2>K(kP|{ZQWS@BNL@2S2WcVrwVlc3JnNh}dpdxE6p_hlFf1uXUF? zSRg_KDkL&Ge_2zl9Rl8O;w!oR_g5)sogYfurG}D=r)KHxJ;iKmM;=#l2kM^ZXFL`Zs6%-)ufz z6(VplH*;<3FP8l8_xVq+{S|~|S|j}e{?$AG&z3$`1nXOK-(nH{ixdCH+g=IS;(Ff8 z-vmYf`|a}DB!^0JRW5DHU;OW1kMp0l=Fgvkav7eKF8N;$^FK2KWHW<@5$tqzIce#C z-}Z0d5x+FN`L2`tkC*h5xh!Zwp=YzwBkc75_BzPZ(uKq9uF>@lu{*$?)a5+ax&-?!XZ#Pcl diff --git a/paper/paper.md b/paper/paper.md index 6edf8a9..97cde95 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -104,6 +104,9 @@ cerf_gp_obj <- estimate_cerf_gp(sim_data, w_all, gps_m, params = params_lst, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), nthread = 12) ``` @@ -120,12 +123,12 @@ Optimal hyper parameters(#trial: 300): alpha = 12.9154966501488 beta = 12.9154966501488 g_sigma = 0.1 Optimal covariate balance: - cf1 = 0.072 + cf1 = 0.069 cf2 = 0.082 - cf3 = 0.062 - cf4 = 0.068 + cf3 = 0.063 + cf4 = 0.066 cf5 = 0.056 - cf6 = 0.082 + cf6 = 0.081 Original covariate balance: cf1 = 0.222 @@ -134,7 +137,7 @@ Original covariate balance: cf4 = 0.318 cf5 = 0.198 cf6 = 0.257 - ----***---- + ----***---- ``` As one can see, as part of the grid search, 300 different combination of hyper parameters have been tried. \autoref{fig:gp} shows the causal exposure response function and achieved covariate balance in this simulated example. @@ -191,6 +194,9 @@ cerf_nngp_obj <- estimate_cerf_nngp(sim_data, w_all, gps_m, params = params_lst, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), nthread = 12) ``` @@ -203,16 +209,16 @@ The customized summary function provides the following: summary(cerf_nngp_obj) ``` ``` -GPCERF nearest neighbor Gaussian process exposure response function object summary +GPCERF nearest neighbore Gaussian process exposure response function object summary Optimal hyper parameters(#trial: 300): alpha = 0.0278255940220712 beta = 0.215443469003188 g_sigma = 0.1 Optimal covariate balance: - cf1 = 0.058 - cf2 = 0.071 - cf3 = 0.087 - cf4 = 0.066 + cf1 = 0.062 + cf2 = 0.070 + cf3 = 0.091 + cf4 = 0.062 cf5 = 0.076 cf6 = 0.088 @@ -223,7 +229,7 @@ Original covariate balance: cf4 = 0.296 cf5 = 0.208 cf6 = 0.225 - ----***---- + ----***---- ``` \autoref{fig:nngp} shows the result of `plot(cerf_nngp_obj)` function. From ab7d88326679261a23ef394225459e3c9c2715ba Mon Sep 17 00:00:00 2001 From: naeemkh Date: Tue, 23 Jan 2024 11:57:45 -0500 Subject: [PATCH 3/4] update SON --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 97cde95..7b54849 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -33,7 +33,7 @@ We present the GPCERF R package, which employs a novel Bayesian approach based o # Statement of need -In the GPCERF R package we have introduced a novel Bayesian approach. This method utilizes Gaussian Processes (GPs) as a prior for counterfactual outcome surfaces, offering a flexible way to estimate the CERF with automatic uncertainty quantification. Additionally, it can incorporate prior information about the level of smoothness of the underlying causal ERF through specifically designed covariance functions. Popular R packages for estimating causal ERF, such as CausalGPS [@CausalGPS_R], ipw [@ipw_paper], npcausal [@Kennedy2017npcausal] and CBPS [@CBPS_R], are primarily built on frequentist frameworks. To the best of the authors’ knowledge, however, Bayesian nonparametric alternatives are relatively scarce. causaldrf [@causaldrf_R] uses Bayesian Additive Regression Trees (BART) for flexible causal ERF estimation. The resulting CERF is usually not smooth due to the use of regression trees. Yet, it can achieve high accuracy when the underlying ERF is discontinuous or resembles step functions. BCEE [@bcee_R] applies a Bayesian model averaging approach for causal ERF estimation. bkmr [@bkmr_R] employs a kernel-based Bayesian model, which is equivalent to a GP prior, to estimate the effect of multivariate exposure on the outcome of interest. However, since it does not explicitly address confounding in the observational data, the resulting estimate does not have causal interpretation. +In the GPCERF R package we have introduced a novel Bayesian approach. This method utilizes Gaussian Processes (GPs) as a prior for counterfactual outcome surfaces, offering a flexible way to estimate the CERF with automatic uncertainty quantification. Additionally, it can incorporate prior information about the level of smoothness of the underlying causal ERF through specifically designed covariance functions. Popular R packages for estimating causal ERF, such as CausalGPS [@CausalGPS_R], ipw [@ipw_paper], npcausal [@Kennedy2017npcausal] and CBPS [@CBPS_R], are primarily built on frequentist frameworks. To the best of the authors’ knowledge, however, Bayesian nonparametric alternatives are relatively scarce. causaldrf [@causaldrf_R] uses Bayesian Additive Regression Trees (BART) for flexible causal ERF estimation and it is particularly useful when the underlying ERF is discontinuous or resembles step functions. The resulting CERF is usually not smooth due to the use of regression trees. Yet, it can achieve high accuracy when the underlying ERF is discontinuous or resembles step functions. BCEE [@bcee_R] applies a Bayesian model averaging approach for causal ERF estimation. bkmr [@bkmr_R] employs a kernel-based Bayesian model, which is equivalent to a GP prior, to estimate the effect of multivariate exposure on the outcome of interest. However, since it does not explicitly address confounding in the observational data, the resulting estimate does not have causal interpretation. While various R packages, like GauPro [@GauPro_2023], mlegp [@mlegp_2022], and GPfit [@GPfit_2019], offer Gaussian process regression capabilities, we chose not to use them. The primary reason is that these packages rely on traditional techniques for hyper parameter tuning, such as sampling from the hyper-parameters’ posterior distributions or maximizing the marginal likelihood function. Our approach, in contrast, aims to achieve optimal covariate balancing. By utilizing the posterior distributions of model parameters, we can automatically assess the uncertainty in our CERF estimates [for further details, see @Ren_2021_bayesian]. Since standard GPs are infamous for their scalability issues—particularly due to operations involving the inversion of covariance matrices—we adopt a nearest-neighbor GP (nnGP) prior to ensure computationally efficient inference of the CERF in large-scale datasets. Refer to \autoref{fig:performance} and \autoref{fig:performance_nn} for comparisons of the wall clock time between standard GP and nnGP. From 343b6b498db5f77db3ffa35c1de28b9e4a49ce06 Mon Sep 17 00:00:00 2001 From: naeemkh Date: Tue, 23 Jan 2024 11:58:07 -0500 Subject: [PATCH 4/4] Add paper run scripts --- functional_tests/README.md | 3 - functional_tests/ft_compute_deriv_nn.R | 14 - .../ft_compute_derive_weights_gp.R | 11 - functional_tests/ft_compute_inverse.R | 35 -- functional_tests/ft_compute_m_sigma.R | 24 -- functional_tests/ft_compute_posterior_m_nn.R | 51 --- functional_tests/ft_compute_posterior_sd_nn.R | 41 --- functional_tests/ft_compute_sd_gp.R | 34 -- functional_tests/ft_compute_w_corr.R | 17 - functional_tests/ft_compute_weight_gp.R | 31 -- functional_tests/ft_estimate_mean_sd_nn.R | 30 -- functional_tests/ft_estimate_noise_nn.R | 32 -- functional_tests/ft_find_optimal_nn.R | 36 -- .../ft_find_optimal_nn_non_parallel.R | 91 ----- functional_tests/ft_generate_synthetic_data.R | 5 - functional_tests/ft_gps_spec.Rmd | 52 --- functional_tests/ft_scalability.R | 51 --- functional_tests/ft_scaling_gp_test1.Rmd | 49 --- functional_tests/paper_runs/README.md | 40 +++ .../figure_1.R} | 16 +- .../figure_2.R} | 7 +- ...figure_3_data_compute_runtime_gp_vs_nngp.R | 63 ++++ .../figure_3_plot_runtime_gp_vs_nngp.R | 29 ++ ...ure_4_data_performance_variable_neighbor.R | 69 ++++ ...ure_4_plot_performance_variable_neighbor.R | 39 +++ .../paper_runs/runtime_data_gp_vs_nngp.txt | 320 ++++++++++++++++++ .../paper_runs/runtime_data_var_nn_2.txt | 60 ++++ 27 files changed, 627 insertions(+), 623 deletions(-) delete mode 100644 functional_tests/README.md delete mode 100644 functional_tests/ft_compute_deriv_nn.R delete mode 100644 functional_tests/ft_compute_derive_weights_gp.R delete mode 100644 functional_tests/ft_compute_inverse.R delete mode 100644 functional_tests/ft_compute_m_sigma.R delete mode 100644 functional_tests/ft_compute_posterior_m_nn.R delete mode 100644 functional_tests/ft_compute_posterior_sd_nn.R delete mode 100644 functional_tests/ft_compute_sd_gp.R delete mode 100644 functional_tests/ft_compute_w_corr.R delete mode 100644 functional_tests/ft_compute_weight_gp.R delete mode 100644 functional_tests/ft_estimate_mean_sd_nn.R delete mode 100644 functional_tests/ft_estimate_noise_nn.R delete mode 100644 functional_tests/ft_find_optimal_nn.R delete mode 100644 functional_tests/ft_find_optimal_nn_non_parallel.R delete mode 100644 functional_tests/ft_generate_synthetic_data.R delete mode 100644 functional_tests/ft_gps_spec.Rmd delete mode 100644 functional_tests/ft_scalability.R delete mode 100644 functional_tests/ft_scaling_gp_test1.Rmd create mode 100644 functional_tests/paper_runs/README.md rename functional_tests/{ft_estimate_cerf_gp.R => paper_runs/figure_1.R} (82%) rename functional_tests/{ft_estimate_cerf_nngp.R => paper_runs/figure_2.R} (89%) create mode 100644 functional_tests/paper_runs/figure_3_data_compute_runtime_gp_vs_nngp.R create mode 100644 functional_tests/paper_runs/figure_3_plot_runtime_gp_vs_nngp.R create mode 100644 functional_tests/paper_runs/figure_4_data_performance_variable_neighbor.R create mode 100644 functional_tests/paper_runs/figure_4_plot_performance_variable_neighbor.R create mode 100644 functional_tests/paper_runs/runtime_data_gp_vs_nngp.txt create mode 100644 functional_tests/paper_runs/runtime_data_var_nn_2.txt diff --git a/functional_tests/README.md b/functional_tests/README.md deleted file mode 100644 index d054e9a..0000000 --- a/functional_tests/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# GPCERF Functional Tests - -Unit tests are addressed under the tests folder. diff --git a/functional_tests/ft_compute_deriv_nn.R b/functional_tests/ft_compute_deriv_nn.R deleted file mode 100644 index f04d487..0000000 --- a/functional_tests/ft_compute_deriv_nn.R +++ /dev/null @@ -1,14 +0,0 @@ - -set.seed(365) -data <- generate_synthetic_data(sample_size = 200) -GPS_m <- train_GPS(cov_mt = as.matrix(data[,-(1:2)]), - w_all = as.matrix(data$treat)) -wi <- 4.8 -deriv_val <- GPCERF:::compute_deriv_nn(w = wi, - w_obs = data$treat, - GPS_m = GPS_m, - y_obs = data$Y, - hyperparam = c(0.1,0.2,1), - n_neighbor = 20, - expand = 1, - block_size = 1000) diff --git a/functional_tests/ft_compute_derive_weights_gp.R b/functional_tests/ft_compute_derive_weights_gp.R deleted file mode 100644 index e10105a..0000000 --- a/functional_tests/ft_compute_derive_weights_gp.R +++ /dev/null @@ -1,11 +0,0 @@ -set.seed(915) -data <- generate_synthetic_data(sample_size = 150) -GPS_m <- train_gps(cov_mt = data[,-(1:2)], - w_all = data$treat, - sl_lib = c("SL.xgboost"), - dnorm_log = FALSE) -wi <- 4.8 -weights <- compute_deriv_weights_gp(w = wi, - w_obs = data$treat, - GPS_m = GPS_m, - hyperparam = c(1,1,2)) diff --git a/functional_tests/ft_compute_inverse.R b/functional_tests/ft_compute_inverse.R deleted file mode 100644 index 756c352..0000000 --- a/functional_tests/ft_compute_inverse.R +++ /dev/null @@ -1,35 +0,0 @@ - -kernel_fn = function(x) exp(-x^2) - -df <- data.frame(matrix(ncol = 2, nrow = 0)) -colnames(df) <- c('size', 'wc') - -for (i in seq(1,10)){ - -n <- 300 * i - -set.seed(324) -A <- sample(seq(100,10000000), size = n, replace = TRUE) -set.seed(987) -B <- sample(seq(1000,100000000), size = n, replace = TRUE) - -C = cbind(A, B) -D = kernel_fn(as.matrix(dist(C))) - -t_1 <- proc.time() - -inv_sigma_obs <- GPCERF:::compute_inverse(D) - -t_2 <- proc.time() - -df <- rbind(df, data.frame(size=n, wc=t_2[[3]] - t_1[[3]])) - -} -#print(inv_sigma_obs) - -print(df) - -library(ggplot2) - -ggplot(data = df) + geom_line(aes(size, wc), color="red") + - geom_point(aes(size, wc), color="blue") diff --git a/functional_tests/ft_compute_m_sigma.R b/functional_tests/ft_compute_m_sigma.R deleted file mode 100644 index 23051b2..0000000 --- a/functional_tests/ft_compute_m_sigma.R +++ /dev/null @@ -1,24 +0,0 @@ - -t_1 <- proc.time() -set.seed(912) -data <- generate_synthetic_data(sample_size = 200, gps_spec = 3) - -w_all = seq(0,20,1) - - -#Estimate GPS function -GPS_m <- train_gps(cov_mt = data[,-(1:2)], - w_all = data$treat, - sl_lib = c("SL.xgboost"), - dnorm_log = FALSE) - -tune_res <- compute_m_sigma(hyperparam = c(0.09, 0.09, 10), - data = data, - w = w_all, - GPS_m = GPS_m, - tuning = FALSE) - -gp_cerf <- tune_res$est - -t_2 <- proc.time() -print(paste("Wall clock time: ", t_2[[3]] - t_1[[3]], "s.")) diff --git a/functional_tests/ft_compute_posterior_m_nn.R b/functional_tests/ft_compute_posterior_m_nn.R deleted file mode 100644 index c016dd1..0000000 --- a/functional_tests/ft_compute_posterior_m_nn.R +++ /dev/null @@ -1,51 +0,0 @@ -set.seed(1029) -data <- generate_synthetic_data(sample_size = 1000, gps_spec = 3) -# Estimate GPS function -GPS_m <- train_gps(cov_mt = data[,-(1:2)], - w_all = data$treat, - sl_lib = c("SL.xgboost"), - dnorm_log = FALSE) -# Hyperparameter -hyperparam <- c(0.1, 0.2, 1) -n_neighbor <- 10 -block_size <- 100 -# Exposure level -wi <- 0.4 -# Estimate GPS for the exposure level -GPS_w <- dnorm(wi, - mean = GPS_m$e_gps_pred, - sd = GPS_m$e_gps_std, log = TRUE) - -# Order data for easy selection -coord_obs = cbind(data$treat, GPS_m$GPS) -y_use <- data$Y -obs_ord <- coord_obs[order(coord_obs[,1]),] -y_use_ord <- y_use[order(coord_obs[,1])] - - -val1 <- compute_posterior_m_nn(hyperparam = hyperparam, - w = wi, - GPS_w = GPS_w, - obs_ord = obs_ord, - y_obs_ord = y_use_ord, - n_neighbor = n_neighbor, - block_size = 100) - - -val2 <- compute_posterior_m_nn(hyperparam = hyperparam, - w = wi, - GPS_w = GPS_w, - obs_ord = obs_ord, - y_obs_ord = y_use_ord, - n_neighbor = n_neighbor, - block_size = 200) - -val3 <- compute_posterior_m_nn(hyperparam = hyperparam, - w = wi, - GPS_w = GPS_w, - obs_ord = obs_ord, - y_obs_ord = y_use_ord, - n_neighbor = n_neighbor, - block_size = 350) - - diff --git a/functional_tests/ft_compute_posterior_sd_nn.R b/functional_tests/ft_compute_posterior_sd_nn.R deleted file mode 100644 index c38adde..0000000 --- a/functional_tests/ft_compute_posterior_sd_nn.R +++ /dev/null @@ -1,41 +0,0 @@ -set.seed(3089) -data <- generate_synthetic_data(sample_size = 150, gps_spec = 3) -# Estimate GPS function -GPS_m <- train_gps(cov_mt = data[,-(1:2)], - w_all = data$treat, - sl_lib = c("SL.xgboost"), - dnorm_log = FALSE) -# Hyperparameter -hyperparam <- c(0.1, 0.2, 1) -n_neighbor <- 10 -expand <- 1 -block_size <- 10000 -# Exposure level -wi <- 0.4 - -# Estimate GPS for the exposure level -GPS_w = dnorm(wi, - mean = GPS_m$e_gps_pred, - sd = GPS_m$e_gps_std, log = TRUE) -# Order data for easy selection -coord_obs = cbind(data$treat, GPS_m$GPS) -y_use <- data$Y - -obs_ord <- coord_obs[order(coord_obs[,1]),] -y_use_ord <- y_use[order(coord_obs[,1])] - -# compute noise -noise <- estimate_noise_nn(hyperparam = hyperparam, - w_obs = data$treat, - GPS_obs = GPS_m$GPS, - y_obs = y_use_ord, - n_neighbor = n_neighbor) - -# compute posterior standard deviation -pst_sd <- compute_posterior_sd_nn(hyperparam = hyperparam, - w = wi, - GPS_w = GPS_w, - obs_ord = obs_ord, - sigma2 = noise, - n_neighbor = 20, - expand = 1) diff --git a/functional_tests/ft_compute_sd_gp.R b/functional_tests/ft_compute_sd_gp.R deleted file mode 100644 index 094d287..0000000 --- a/functional_tests/ft_compute_sd_gp.R +++ /dev/null @@ -1,34 +0,0 @@ - set.seed(284) - #Generate synthetic data - data <- generate_synthetic_data(sample_size = 200, gps_spec = 3) - w_obs <- obs_exposure <- data$treat - - # Choose an exposure level to compute CERF - w = 1.2 - - # Define kernel function - kernel_fn <- function(x) exp(-x^2) - - # Estimate GPS function - GPS_m <- train_GPS(cov_mt = as.matrix(data[,-(1:2)]), - w_all = as.matrix(data$treat)) - - GPS <- GPS_m$GPS - - # set hyperparameters - hyperparam <- c(0.1, 0.4, 1) - alpha <- hyperparam[[1]] - beta <- hyperparam[[2]] - g_sigma <- hyperparam[[3]] - - # Compute scaled observation data and inverse of covariate matrix. - scaled_obs <- cbind(obs_exposure*sqrt(1/alpha), GPS*sqrt(1/beta)) - - tentative_sigma <- 0.1 - - post_sd <- GPCERF:::compute_sd_gp(w = w, - scaled_obs = scaled_obs, - hyperparam = hyperparam, - sigma = tentative_sigma, - GPS_m = GPS_m, - kernel_fn = kernel_fn) diff --git a/functional_tests/ft_compute_w_corr.R b/functional_tests/ft_compute_w_corr.R deleted file mode 100644 index c327f92..0000000 --- a/functional_tests/ft_compute_w_corr.R +++ /dev/null @@ -1,17 +0,0 @@ -set.seed(124) -data1 <- generate_synthetic_data(sample_size = 200) -data1[,"cf5"] <- as.factor(data1[, "cf5"]) - -set.seed(987) -weights1 <- runif(nrow(data1)) -val1 <- compute_w_corr(w = data1$treat, - confounders = data1[, 3:ncol(data1)], - weights = weights1) - - - - - -val2 <- compute_w_corr_2(w = data$treat, - covariate = data1[, 3:ncol(data1)], - weight = weights1) diff --git a/functional_tests/ft_compute_weight_gp.R b/functional_tests/ft_compute_weight_gp.R deleted file mode 100644 index ff79602..0000000 --- a/functional_tests/ft_compute_weight_gp.R +++ /dev/null @@ -1,31 +0,0 @@ - -set.seed(814) -#Generate synthetic data -data <- generate_synthetic_data(sample_size = 200, gps_spec = 3) -w_obs <- obs_exposure <- data$treat -# Choose an exposure level to compute CERF -w = 1.8 -# Define kernel function -kernel_fn <- function(x) exp(-x^2) -# Estimate GPS function -GPS_m <- train_gps(cov_mt = data[,-(1:2)], - w_all = data$treat, - sl_lib = c("SL.xgboost"), - dnorm_log = FALSE) -GPS <- GPS_m$GPS -# set hyperparameters -hyperparam <- c(0.1, 0.4, 1) -alpha <- hyperparam[1] -beta <- hyperparam[2] -g_sigma <- hyperparam[3] -# Compute scaled observation data and inverse of covariate matrix. -scaled_obs <- cbind(obs_exposure*sqrt(1/alpha), GPS*sqrt(1/beta)) -sigma_obs <- g_sigma*kernel_fn(as.matrix(dist(scaled_obs))) + diag(nrow(scaled_obs)) -inv_sigma_obs <- compute_inverse(sigma_obs) -weight <- compute_weight_gp(w = w, - w_obs = w_obs, - scaled_obs = scaled_obs, - hyperparam = hyperparam, - inv_sigma_obs = inv_sigma_obs, - GPS_m = GPS_m, - kernel_fn = kernel_fn) diff --git a/functional_tests/ft_estimate_mean_sd_nn.R b/functional_tests/ft_estimate_mean_sd_nn.R deleted file mode 100644 index eb1de67..0000000 --- a/functional_tests/ft_estimate_mean_sd_nn.R +++ /dev/null @@ -1,30 +0,0 @@ -set.seed(86) -data <- generate_synthetic_data(sample_size = 200, gps_spec = 3) -# Estimate GPS function -GPS_m <- train_gps(cov_mt = data[,-(1:2)], - w_all = data$treat, - sl_lib = c("SL.xgboost"), - dnorm_log = FALSE) -# Hyperparameter -hyperparam <- c(0.1, 0.2, 1) -n_neighbor <- 15 -expand <- 1 -block_size <- 10000 -# compute noise -noise <- estimate_noise_nn(hyperparam = hyperparam, - w_obs = data$treat, - GPS_obs = GPS_m$GPS, - y_obs = data$Y, - n_neighbor = n_neighbor) -# compute posterior mean and standard deviation for vector of w. -w <- seq(0,20,1) -val <- estimate_mean_sd_nn(hyperparam = hyperparam, - sigma2 = noise, - w_obs = data$treat, - w = w, - y_obs = data$Y, - GPS_m = GPS_m, - n_neighbor = n_neighbor, - expand = expand, - block_size = block_size, - nthread = 1) diff --git a/functional_tests/ft_estimate_noise_nn.R b/functional_tests/ft_estimate_noise_nn.R deleted file mode 100644 index 9d76337..0000000 --- a/functional_tests/ft_estimate_noise_nn.R +++ /dev/null @@ -1,32 +0,0 @@ -set.seed(425) -data <- generate_synthetic_data(sample_size = 200, gps_spec = 3) - -# Estimate GPS function -GPS_m <- train_GPS(cov_mt = data[,-(1:2)], w_all = data$treat) - -# Hyperparameter -hyperparam <- c(0.1, 0.2, 1) -n_neighbor <- 10 -expand <- 1 -block_size <- 10000 - -# Exposure level -wi <- 0.4 - -# Estimate GPS for the exposure level -GPS_w = dnorm(wi, - mean = GPS_m$e_gps_pred, - sd = GPS_m$e_gps_std, log = TRUE) - -# Order data for easy selection -coord_obs = cbind(data$treat, GPS_m$GPS) -y_use <- data$Y - -obs_ord <- coord_obs[order(coord_obs[,1]),] -y_use_ord <- y_use[order(coord_obs[,1])] - -noise <- estimate_noise_nn(hyperparam = hyperparam, - w_obs = data$treat, - GPS_obs = GPS_m$GPS, - y_obs = y_use_ord, - n_neighbor = n_neighbor) diff --git a/functional_tests/ft_find_optimal_nn.R b/functional_tests/ft_find_optimal_nn.R deleted file mode 100644 index bf0267a..0000000 --- a/functional_tests/ft_find_optimal_nn.R +++ /dev/null @@ -1,36 +0,0 @@ - -t_1 <- proc.time() -set.seed(89) -data <- generate_synthetic_data(sample_size = 200, gps_spec = 3) - - # Estimate GPS function -GPS_m <- train_gps(cov_mt = as.matrix(data[,-(1:2)]), - w_all = as.matrix(data$treat), - sl_lib = "SL.xgboost", - dnorm_log = FALSE) - -# Hyperparameter -hyperparam <- c(0.1, 0.2, 1) -n_neighbor <- 10 -block_size <- 10000 - -# compute posterior mean and standard deviation for vector of w. -w <- seq(0,20,2) -design_mt <- model.matrix(~.-1, data = data[, 3:ncol(data)]) -design_mt <- as.data.frame(design_mt) - -hyperparam_grid <- expand.grid(seq(0.5,1.0,1), - seq(0.4,0.6,0.2), - seq(0.5)) - -optimal_cb <- GPCERF:::find_optimal_nn(w_obs = data$treat, - w = w, - y_obs = data$Y, - GPS_m = GPS_m, - design_mt = design_mt, - hyperparams = hyperparam_grid, - n_neighbor = 50, - block_size = 2e3, - nthread = 1) -t_2 <- proc.time() -print(paste("Wall clock time: ", t_2[[3]] - t_1[[3]], "s.")) diff --git a/functional_tests/ft_find_optimal_nn_non_parallel.R b/functional_tests/ft_find_optimal_nn_non_parallel.R deleted file mode 100644 index 6d3975c..0000000 --- a/functional_tests/ft_find_optimal_nn_non_parallel.R +++ /dev/null @@ -1,91 +0,0 @@ - -t_1 <- proc.time() -set.seed(89) -data <- generate_synthetic_data(sample_size = 2000, gps_spec = 3) - - # Estimate GPS function -GPS_m <- train_gps(cov_mt = as.matrix(data[,-(1:2)]), - w_all = as.matrix(data$treat), - sl_lib = "SL.xgboost", - dnorm_log = TRUE) - - -# compute posterior mean and standard deviation for vector of w. -w <- seq(-10,20,0.4) -design_mt <- model.matrix(~.-1, data = data[, 3:ncol(data)]) -design_mt <- as.data.frame(design_mt) - -hyperparam_grid <- expand.grid(c(0.1, 0.2, 0.3, 0.4), - c(0.001, 0.004, 0.01, 0.04), - c(0.4, 0.5, 0.6)) - - -w_obs <- data$treat -w <- w -y_obs <- data$Y -GPS_m <- GPS_m -design_mt <- design_mt -hyperparams <- hyperparam_grid -n_neighbor <- 10 -expand <- 1 -block_size <- 75 -nthread <- 1 -kernel_fn = function(x) exp(-x ^ 2) - -coord_obs <- cbind(w_obs, GPS_m$GPS) - -# remove missing values -coord_obs <- coord_obs[!is.na(y_obs), ] -y_use <- y_obs[!is.na(y_obs)] -design_use <- design_mt[!is.na(y_obs), ] - -# order data based on exposure level -coord_obs_ord <- coord_obs[order(coord_obs[, 1]), ] -y_use_ord <- y_use[order(coord_obs[, 1])] -design_use_ord <- design_use[order(coord_obs[, 1]), ] - - -all_res <- apply(hyperparams, 1, function(hyperparam){ - - all_res_list <- lapply(w, function(wi){ - - # Compute generalized propensity score for all data. - GPS_w <- dnorm(wi, - mean = GPS_m$e_gps_pred, - sd = GPS_m$e_gps_std, - log = TRUE) - - # estimate posterior mean - res <- GPCERF:::compute_posterior_m_nn(hyperparam = hyperparam, - w = wi, - GPS_w = GPS_w, - obs_ord = coord_obs_ord, - y_obs_ord = y_use_ord, - n_neighbor = n_neighbor, - kernel_fn = kernel_fn, - expand = expand, - block_size = block_size) - - idx <- res[-nrow(res), 1] - weights <- res[-nrow(res), 2] - cb <- GPCERF:::compute_w_corr(w = coord_obs_ord[idx, 1], - confounders = design_use_ord[idx, ], weights) - list(cb = cb, est = res[nrow(res), 2]) - }) - - all_cb_tmp <- do.call(cbind, lapply(all_res_list, '[[', 'cb')) - all_est_tmp <- sapply(all_res_list, '[[', 'est') - - #covariate specific balance, averaged over w - list( cb = rowMeans(all_cb_tmp, na.rm = TRUE), - est = all_est_tmp ) - -}) - -# Extract the optimum hyperparameters -optimal_cb_res <- all_res -all_cb_res <- sapply(optimal_cb_res, '[[', 'cb') -opt_idx_nn <- order(colMeans(abs(all_cb_res)))[1] -posterior_mean <- optimal_cb_res[[opt_idx_nn]]$est -nn_opt_param <- unlist(hyperparam_grid[opt_idx_nn, ]) - diff --git a/functional_tests/ft_generate_synthetic_data.R b/functional_tests/ft_generate_synthetic_data.R deleted file mode 100644 index bfc2e44..0000000 --- a/functional_tests/ft_generate_synthetic_data.R +++ /dev/null @@ -1,5 +0,0 @@ - - -set.seed(21) -synthetic_data <- GPCERF::generate_synthetic_data(sample_size = 1000, - gps_spec = 6) diff --git a/functional_tests/ft_gps_spec.Rmd b/functional_tests/ft_gps_spec.Rmd deleted file mode 100644 index 81e1039..0000000 --- a/functional_tests/ft_gps_spec.Rmd +++ /dev/null @@ -1,52 +0,0 @@ ---- -title: "Studying GPS Spect" -author: "Naeem Khoshnevis" -date: "6/13/2022" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -In this document we focus on effect of different parameters in generating synthetic data. - -## Load libraries - -```{r} -library(ggplot2) -library(GPCERF) -``` - -## Overview - -The synthetic data is generated using `generate_synthetic_data.R` function. The function accepts the following input values. - -**`sample_size`**: Number of data samples. -**`outcome_sd`**: Standard deviation used to generate the outcome in the synthetic data set. -**`gps_spec`**: A numeric value (1-6) that indicates the GPS model used to generate the continuous exposure. -**`cova_spec`**: A numeric value (1-2) to modify the covariates. - -## Example - -Let's say we want to generate a synthetic dataset with 1000 data samples. - -```{r, cache=TRUE} -data_1 <- generate_synthetic_data(sample_size = 1000, - outcome_sd = 10, - gps_spec = 1, - cova_spec = 1) - -summary(data_1) -``` - -Let's take a look at the overal data plots. - -```{r, cache=TRUE} -plot(data_1) -``` - -cf1 ~ cf4 is generated using multivariate normal distribution. - - - diff --git a/functional_tests/ft_scalability.R b/functional_tests/ft_scalability.R deleted file mode 100644 index bb6e1a1..0000000 --- a/functional_tests/ft_scalability.R +++ /dev/null @@ -1,51 +0,0 @@ -library(GPCERF) -library(ggplot2) - -set.seed(781) -sim_data <- generate_synthetic_data(sample_size = 500, gps_spec = 1) - -m_xgboost <- function(nthread = 12, ...) { - SuperLearner::SL.xgboost(nthread = nthread, ...) -} - -m_ranger <- function(num.threads = 12, ...){ - SuperLearner::SL.ranger(num.threads = num.threads, ...) -} - -# Estimate GPS function -gps_m <- estimate_gps(cov_mt = sim_data[,-(1:2)], - w_all = sim_data$treat, - sl_lib = c("m_xgboost", "m_ranger"), - dnorm_log = TRUE) - -# exposure values -q1 <- stats::quantile(sim_data$treat, 0.05) -q2 <- stats::quantile(sim_data$treat, 0.95) - -w_all <- seq(q1, q2, 1) - -params_lst <- list(alpha = 10 ^ seq(-2, 2, length.out = 10), - beta = 10 ^ seq(-2, 2, length.out = 10), - g_sigma = c(0.1, 1, 10), - tune_app = "all") - - -time_df <- data.frame(core = numeric(0), wc = numeric(0)) - -for (i in c(1, 2, 4, 8)){ -t1 <- proc.time() -cerf_gp_obj <- estimate_cerf_gp(sim_data, - w_all, - gps_m, - params = params_lst, - nthread = i) -t2 <- proc.time() -time_df <- rbind(time_df, data.frame(core = i, wc = (t2 - t1)[[3]])) -print(paste("Wall clock time: ", (t2 - t1)[[3]], - " seconds.")) -} - -# summary(cerf_gp_obj) -# plot(cerf_gp_obj) - -ggplot(time_df) + geom_line(aes(log2(core), log2(wc))) + geom_point(aes(log2(core), log2(wc))) diff --git a/functional_tests/ft_scaling_gp_test1.Rmd b/functional_tests/ft_scaling_gp_test1.Rmd deleted file mode 100644 index 76cfe25..0000000 --- a/functional_tests/ft_scaling_gp_test1.Rmd +++ /dev/null @@ -1,49 +0,0 @@ ---- -title: "Scaling Up test for GP approach | Test 1" -author: "Naeem Khoshnevis" -date: "6/8/2022" -output: html_document ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -## Load Libraries - -```{r} -library(GPCERF) -``` - -### Example of run - -```{r} - -set.seed(129) -sim.data <- generate_synthetic_data(sample_size = 400, gps_spec = 3) - - -# Estimate GPS function -GPS_m <- train_GPS(cov.mt = as.matrix(sim.data[,-(1:2)]), - w.all = as.matrix(sim.data$treat)) - -# exposure values -w.all = seq(0,20,1) - -data.table::setDT(sim.data) - -cerf_gp_obj <- estimate_cerf_gp(sim.data, - w.all, - GPS_m, - params = list(alpha = 10^seq(-2,0,length.out = 5), - beta = 10^seq(-2,0,length.out = 5), - g_sigma = c(0.1,1,10), - tune_app = "all"), - nthread = 12) - - - -plot(cerf_gp_obj) -``` - -### Scaling test diff --git a/functional_tests/paper_runs/README.md b/functional_tests/paper_runs/README.md new file mode 100644 index 0000000..93b229f --- /dev/null +++ b/functional_tests/paper_runs/README.md @@ -0,0 +1,40 @@ +# Reproducing Software Paper Examples + +Follow these steps to reproduce the software paper examples: + +* Step 1: Clone the repo and navigate to the folder. + +``` +git clone -b JOSS https://github.com/NSAPH-Software/GPCERF.git +cd GPCERF +``` + + +* Step 2: Run docker container. In the terminal, run the following: + +``` +docker run -it --rm \ + -p 8230:8787 \ + -e USER=rstudio \ + -e PASSWORD=pass \ + -v "/path/to/your/folder/on/host:/home/rstudio/Project" nsaphsoftware/gpcerf_dev:3 +``` + +* Step 3: Open your browser and go to the following link: + +``` +http://localhost:8230/ +``` + +* Step 4: Log in with the following credentials: + +- Username: `rstudio` +- Password: `pass` + +* Step 5: Load the project and run the examples: + +``` +devtools::load_all() +devtools::install() +# and run the examples. +``` diff --git a/functional_tests/ft_estimate_cerf_gp.R b/functional_tests/paper_runs/figure_1.R similarity index 82% rename from functional_tests/ft_estimate_cerf_gp.R rename to functional_tests/paper_runs/figure_1.R index fc4e47b..648082e 100644 --- a/functional_tests/ft_estimate_cerf_gp.R +++ b/functional_tests/paper_runs/figure_1.R @@ -2,13 +2,14 @@ library(GPCERF) set.seed(781) # Generate synthetic data with 500 data samples. -sim_data <- generate_synthetic_data(sample_size = 500, gps_spec = 1) +sim_data <- generate_synthetic_data(sample_size = 500, + gps_spec = 1) + +# SuperLearner internal libraries' wrapper. (Optional) -# SuperLearner internal libraries' wrapper. m_xgboost <- function(nthread = 12, ...) { SuperLearner::SL.xgboost(nthread = nthread, ...) } - m_ranger <- function(num.threads = 12, ...){ SuperLearner::SL.ranger(num.threads = num.threads, ...) } @@ -39,14 +40,9 @@ cerf_gp_obj <- estimate_cerf_gp(sim_data, params = params_lst, outcome_col = "Y", treatment_col = "treat", - covariates_col = c("cf1"), # paste0("cf", seq(1,6)), + covariates_col = paste0("cf", seq(1,6)), nthread = 12) + summary(cerf_gp_obj) plot(cerf_gp_obj) - -# png("readme_gp.png", width = 12, height = 4, units = "in", res = 300) -# plot(cerf_gp_obj) -# dev.off() - - diff --git a/functional_tests/ft_estimate_cerf_nngp.R b/functional_tests/paper_runs/figure_2.R similarity index 89% rename from functional_tests/ft_estimate_cerf_nngp.R rename to functional_tests/paper_runs/figure_2.R index 359068d..13b8770 100644 --- a/functional_tests/ft_estimate_cerf_nngp.R +++ b/functional_tests/paper_runs/figure_2.R @@ -40,14 +40,9 @@ cerf_nngp_obj <- estimate_cerf_nngp(sim_data, params = params_lst, outcome_col = "Y", treatment_col = "treat", - covariates_col = "cf1", #paste0("cf", seq(1,6)), + covariates_col = paste0("cf", seq(1,6)), nthread = 12) summary(cerf_nngp_obj) plot(cerf_nngp_obj) - -# png("readme_nngp.png", width = 12, height = 4, units = "in", res = 300) -# plot(cerf_nngp_obj) -# dev.off() - diff --git a/functional_tests/paper_runs/figure_3_data_compute_runtime_gp_vs_nngp.R b/functional_tests/paper_runs/figure_3_data_compute_runtime_gp_vs_nngp.R new file mode 100644 index 0000000..d3f9f5d --- /dev/null +++ b/functional_tests/paper_runs/figure_3_data_compute_runtime_gp_vs_nngp.R @@ -0,0 +1,63 @@ +library(GPCERF) +library(SuperLearner) +library(ggplot2) + +# Function definitions +m_xgboost <- function(nthread = 4, ...) { + SuperLearner::SL.xgboost(nthread = nthread, ...) +} + +m_ranger <- function(num.threads = 4, ...){ + SuperLearner::SL.ranger(num.threads = num.threads, ...) +} + +# Parameters +w_all = c(10) +params_lst <- list(alpha = 1, + beta = 1, + g_sigma = 1, + tune_app = "all", + n_neighbor = 50, + block_size = 1e3) + +# File to store runtime data +runtime_file <- "runtime_data_3.txt" +file.create(runtime_file) + +# Generate a vector of 20 random seed values +# set.seed(568) +# seed_values <- sample(10000:20000, 20, replace = FALSE) +seed_values <- c(19259, 12216, 17973, 10706, 14989, 18401, 16808, + 16002, 16605, 12673, 13626, 16085, 13846, 18507, + 10682, 18505, 14516, 19595, 13994, 17511) + +for (seed in seed_values) { + set.seed(seed) + lapply(seq(3000, 10000, 1000), function(n){ + print(paste("Processing sample size:", n, "with seed:", seed)) + sim_data <- generate_synthetic_data(sample_size = n, gps_spec = 1) + gps_m <- estimate_gps(cov_mt = sim_data[,-(1:2)], w_all = sim_data$treat, + sl_lib = c("m_xgboost", "m_ranger"), dnorm_log = TRUE) + + t1 <- system.time(estimate_cerf_gp(sim_data, w_all, gps_m, + params = params_lst, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), + nthread = 1)) + t2 <- system.time(estimate_cerf_nngp(sim_data, + w_all, + gps_m, + params = params_lst, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), + nthread = 1)) + + # Write to file + cat(paste(seed, n, "GP", t1[3]), file = runtime_file, + append = TRUE, sep = "\n") + cat(paste(seed, n, "nnGP", t2[3]), file = runtime_file, + append = TRUE, sep = "\n") + }) +} diff --git a/functional_tests/paper_runs/figure_3_plot_runtime_gp_vs_nngp.R b/functional_tests/paper_runs/figure_3_plot_runtime_gp_vs_nngp.R new file mode 100644 index 0000000..5acbd0f --- /dev/null +++ b/functional_tests/paper_runs/figure_3_plot_runtime_gp_vs_nngp.R @@ -0,0 +1,29 @@ +# Read the data from the file +library(ggplot2) +runtime_file <- "runtime_data_gp_vs_nngp.txt" +runtime_data <- read.table(runtime_file, col.names = c("seed", + "n", + "Method", + "runtime")) + +# Calculate slope +slope_res <- coef(lm(log10(runtime) ~ log10(n) * Method, data = runtime_data)) + +g <- ggplot(runtime_data, aes(x = n, y = runtime, color = Method)) + + geom_smooth(se = F, method = "lm") + + geom_jitter(width = 0.005, height = 0.005, size = 1, alpha = 0.4) + + scale_color_manual(values = c("GP" = "#4876B0", "nnGP" = "#B52A2B")) + + scale_y_continuous(trans = "log10") + + scale_x_continuous(trans = "log10") + + ylab("Wall Clock Time (s)") + + xlab("Sample size") + + theme_bw() + + annotate(geom = "text", x = 5000, y = 200, + label = sprintf("Slope = %.2f", slope_res[2]), + color = "#4876B0") + + annotate(geom = "text", x = 5000, y = 20, + label = sprintf("Slope = %.2f", slope_res[2] + slope_res[4]), + color = "#B52A2B") + + +plot(g) diff --git a/functional_tests/paper_runs/figure_4_data_performance_variable_neighbor.R b/functional_tests/paper_runs/figure_4_data_performance_variable_neighbor.R new file mode 100644 index 0000000..08cf98c --- /dev/null +++ b/functional_tests/paper_runs/figure_4_data_performance_variable_neighbor.R @@ -0,0 +1,69 @@ +library(GPCERF) +library(SuperLearner) +library(ggplot2) + +set.seed(781) +m_xgboost <- function(nthread = 12, ...) { + SuperLearner::SL.xgboost(nthread = nthread, ...) +} + +m_ranger <- function(num.threads = 12, ...){ + SuperLearner::SL.ranger(num.threads = num.threads, ...) +} + +w_all = c(10) +params_lst_50 <- list(alpha = 1, beta = 1, g_sigma = 1, + tune_app = "all", n_neighbor = 50, block_size = 1e3) +params_lst_100 <- list(alpha = 1, beta = 1, g_sigma = 1, + tune_app = "all", n_neighbor = 100, block_size = 1e3) +params_lst_200 <- list(alpha = 1, beta = 1, g_sigma = 1, + tune_app = "all", n_neighbor = 200, block_size = 1e3) + +# File to store runtime data +runtime_file <- "runtime_data_var_nn_2.txt" +file.create(runtime_file) + +lapply(seq(5000, 100000, 5000), function(n){ + print(n) + sim_data <- generate_synthetic_data(sample_size = n, gps_spec = 1) + gps_m <- estimate_gps(cov_mt = sim_data[,-(1:2)], + w_all = sim_data$treat, + sl_lib = c("m_xgboost", "m_ranger"), + dnorm_log = TRUE) + + t1 <- system.time(cerf_nngp_obj <- estimate_cerf_nngp( + sim_data, + w_all, + gps_m, + params = params_lst_50, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), + nthread = 1)) + t2 <- system.time(cerf_nngp_obj <- estimate_cerf_nngp( + sim_data, + w_all, + gps_m, + params = params_lst_100, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), + nthread = 1)) + t3 <- system.time(cerf_nngp_obj <- estimate_cerf_nngp( + sim_data, + w_all, + gps_m, + params = params_lst_200, + outcome_col = "Y", + treatment_col = "treat", + covariates_col = paste0("cf", seq(1,6)), + nthread = 1)) + + # Write to file + cat(paste(n, "nnGP", 50, t1[3]), file = runtime_file, + append = TRUE, sep = "\n") + cat(paste(n, "nnGP", 100, t2[3]), file = runtime_file, + append = TRUE, sep = "\n") + cat(paste(n, "nnGP", 200, t3[3]), file = runtime_file, + append = TRUE, sep = "\n") +}) diff --git a/functional_tests/paper_runs/figure_4_plot_performance_variable_neighbor.R b/functional_tests/paper_runs/figure_4_plot_performance_variable_neighbor.R new file mode 100644 index 0000000..3602a60 --- /dev/null +++ b/functional_tests/paper_runs/figure_4_plot_performance_variable_neighbor.R @@ -0,0 +1,39 @@ +library(ggplot2) +runtime_file <- "runtime_data_var_nn_2.txt" +runtime_data <- NULL +runtime_data <- read.table(runtime_file, col.names = c("n", + "Method", + "nn_size", + "runtime")) +runtime_data$nn_size <- as.factor(runtime_data$nn_size) + +# Define colors for each nn_size category +colors <- c("#B52A2B", "#609E3A", "#4876B0") + +# Calculate slope for each nn_size +slopes <- by(runtime_data, runtime_data$nn_size, function(subset) { + coef(lm(log10(runtime) ~ log10(n), data = subset))[2] +}) + +# Plot +g <- ggplot(runtime_data, aes(x = n, y = runtime, color = nn_size)) + + geom_smooth(se = F, method = "lm") + + geom_point(size = 1, alpha = 0.4) + + scale_y_continuous(trans = "log10") + + scale_x_continuous(trans = "log10") + + ylab("Wall clock time (s)") + + xlab("Sample size") + + theme_bw() + + scale_color_manual(values = colors) # Apply manual colors + +# Add slope annotations +for (i in seq_along(slopes)) { + slope <- slopes[[i]] + nn_size <- names(slopes)[i] + g <- g + + annotate(geom = "text", x = 8500, y = 10^i, + label = sprintf("Slope (%s) = %.2f", nn_size, slope), + color = colors[i]) +} + +plot(g) diff --git a/functional_tests/paper_runs/runtime_data_gp_vs_nngp.txt b/functional_tests/paper_runs/runtime_data_gp_vs_nngp.txt new file mode 100644 index 0000000..d8c529a --- /dev/null +++ b/functional_tests/paper_runs/runtime_data_gp_vs_nngp.txt @@ -0,0 +1,320 @@ +19259 3000 GP 17.663 +19259 3000 nnGP 10.439 +19259 4000 GP 43.173 +19259 4000 nnGP 9.94499999999994 +19259 5000 GP 82.191 +19259 5000 nnGP 10.455 +19259 6000 GP 140.769 +19259 6000 nnGP 11.8299999999999 +19259 7000 GP 228.443 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nnGP 15.4220000000005 +13626 10000 GP 1130.437 +13626 10000 nnGP 17.5450000000001 +16085 3000 GP 17.2939999999999 +16085 3000 nnGP 8.81999999999971 +16085 4000 GP 41.2690000000002 +16085 4000 nnGP 10.0110000000004 +16085 5000 GP 83.393 +16085 5000 nnGP 11.4629999999997 +16085 6000 GP 137.342999999999 +16085 6000 nnGP 10.9890000000014 +16085 7000 GP 219.236000000001 +16085 7000 nnGP 12.9130000000005 +16085 8000 GP 318.902 +16085 8000 nnGP 13.5920000000006 +16085 9000 GP 503.573999999999 +16085 9000 nnGP 15.2829999999994 +16085 10000 GP 648.258999999998 +16085 10000 nnGP 15.1229999999996 +13846 3000 GP 21.2669999999998 +13846 3000 nnGP 11.3470000000016 +13846 4000 GP 47.6229999999996 +13846 4000 nnGP 10.1870000000017 +13846 5000 GP 100.343000000001 +13846 5000 nnGP 13.0109999999986 +13846 6000 GP 149.013999999999 +13846 6000 nnGP 11.9180000000015 +13846 7000 GP 231.888999999999 +13846 7000 nnGP 15.5970000000016 +13846 8000 GP 356.151000000002 +13846 8000 nnGP 14.5420000000013 +13846 9000 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