tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la /app/
total 35 drwxrws--T 3 h2tagent h2tagent 4096 May 28 19:31 . drwxr-xr-x 27 root root 4096 May 28 23:06 .. -rw-rw---- 1 h2tagent h2tagent 14067 May 28 19:31 analysis.R -rw-rw---- 1 h2tagent h2tagent 7752 May 28 19:31 dataset_manifest.json -rw-rw---- 1 h2tagent h2tagent 420 May 28 19:31 example_client_bias.csv drwxrws--- 4 h2tagent h2tagent 4096 May 28 19:31 outputs
cat /app/dataset_manifest.json
{
"manifest_version": "1.0",
"dataset": {
"name": "MNIST",
"source": "torchvision.datasets.MNIST",
"download_url": "http://yann.lecun.com/exdb/mnist/",
"train_size": 60000,
"test_size": 10000,
"n_classes": 10,
"per_class_train": 6000
},
"simulation": {
"n_clients": 100,
"n_byzantine": 10,
"clients_per_round": 20,
"n_rounds": 8,
"param_dim": 256,
"trimmed_mean": {
"space": "coordinate",
"trim_k_default": 2
},
"fedavg": {
"weighting": "by_num_samples"
},
"update_generation": {
"w_star": "rnorm(param_dim)",
"class_prototypes": "matrix(rnorm(n_classes * param_dim), nrow=n_classes, ncol=param_dim)",
"honest_update": "signal_scale*w_star + bias_strength*(bias_row %*% class_prototypes) + rnorm(param_dim, 0, honest_noise_sd)",
"honest_noise_sd": 0.08
}
},
"partition": {
"method": "dirichlet_per_class",
"alpha_default": 0.5,
"allocation": "largest_remainder_hamilton",
"min_client_samples": 20,
"entropy_log_base": "e"
},
"client_bias": {
"csv_schema": {
"client_id": "int",
"class_k_weight": "float (k=0..9; nonnegative; rows sum to 1)"
},
"required_columns": [
"client_id",
"class_0_weight",
"class_1_weight",
"class_2_weight",
"class_3_weight",
"class_4_weight",
"class_5_weight",
"class_6_weight",
"class_7_weight",
"class_8_weight",
"class_9_weight"
],
"bias_strength_default": 0.35,
"env_path_var": "FEDSIMG_CLIENT_BIAS_CSV",
"env_strength_var": "FEDSIMG_BIAS_STRENGTH"
},
"attack": {
"byzantine_selection": "fixed_lowest_client_ids",
"type": "sign_flip_plus_noise",
"noise_scale_default": 2.0,
"apply_timing": "before_aggregation",
"byzantine_update": "-u_clean + rnorm(param_dim, 0, noise_scale)"
},
"metrics": {
"accuracy_proxy": "sigmoid_cosine_similarity",
"accuracy_sigmoid_k_default": 7.5,
"accuracy_sigmoid_b_default": 2.5,
"round_noise_sd": 0.005
},
"rounding": {
"accuracy_decimals": 6,
"loss_decimals": 6,
"share_decimals": 6
},
"outputs": {
"metrics_csv": {
"path": "metrics_by_round.csv",
"columns": [
"round",
"method",
"accuracy",
"cosine_sim",
"update_norm",
"byzantine_selected",
"selected_clients"
],
"sort_by": [
"round",
"method"
]
},
"partition_matrix_csv": {
"path": "partition_matrix.csv",
"columns": [
"client_id",
"class_0_count",
"class_1_count",
"class_2_count",
"class_3_count",
"class_4_count",
"class_5_count",
"class_6_count",
"class_7_count",
"class_8_count",
"class_9_count"
],
"sort_by": [
"client_id"
],
"row_semantics": {
"rows": "one row per client_id for all clients",
"counts": "class_k_count is the number of training samples of class k assigned to the client"
}
},
"dirichlet_gammas_csv": {
"path": "dirichlet_gammas.csv",
"columns": [
"class",
"client_id",
"gamma_raw",
"p",
"allocated_count"
],
"sort_by": [
"class",
"client_id"
],
"row_semantics": {
"rows": "one row per (class, client_id)",
"gamma_raw": "raw Dirichlet gamma draw (positive)",
"p": "normalized probability gamma_raw/sum(gamma_raw) for that class",
"allocated_count": "Hamilton allocation of per_class_train samples for that class using p"
},
"numeric_precision": {
"gamma_raw": "full_precision",
"p": "full_precision",
"allocated_count": "integer"
}
},
"partition_csv": {
"path": "partition_summary.csv",
"columns": [
"client_id",
"is_byzantine",
"n_train",
"label_e
…[truncated]cat /app/analysis.R
#!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(ggplot2)
library(gridExtra)
library(jsonlite)
})
`%||%` <- function(x, y) if (!is.null(x) && !is.na(x) && nzchar(as.character(x))) x else y
sigmoid <- function(x) 1.0 / (1.0 + exp(-x))
safe_dir_create <- function(path) {
if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
}
read_bias_csv <- function(path, n_clients, n_classes) {
if (is.null(path) || !nzchar(path) || !file.exists(path)) {
return(NULL)
}
df <- read.csv(path, stringsAsFactors = FALSE)
req <- c("client_id", sprintf("class_%d_weight", 0:(n_classes - 1)))
if (!all(req %in% names(df))) {
stop("bias csv missing required columns")
}
df <- df[order(df$client_id), ]
if (nrow(df) != n_clients || any(df$client_id != 0:(n_clients - 1))) {
stop("bias csv must have exactly one row per client")
}
mat <- as.matrix(df[, req[-1], drop = FALSE])
storage.mode(mat) <- "double"
col_sums <- colSums(mat)
col_sums[col_sums <= 0] <- 1
sweep(mat, 2, col_sums, "/")
}
hamilton_alloc <- function(p, total) {
p <- as.numeric(p)
p <- p / sum(p)
raw <- p * total
alloc <- as.integer(round(raw))
gap <- as.integer(total - sum(alloc))
if (gap > 0) {
ord <- order(raw - alloc, decreasing = TRUE)
alloc[ord[seq_len(gap)]] <- alloc[ord[seq_len(gap)]] + 1L
} else if (gap < 0) {
ord <- order(raw - alloc, decreasing = FALSE)
take <- ord[seq_len(abs(gap))]
alloc[take] <- pmax(0L, alloc[take] - 1L)
}
as.integer(alloc)
}
repair_partition <- function(partition, min_client_samples) {
n_train <- rowSums(partition)
while (min(n_train) < min_client_samples) {
low <- which.min(n_train)
high <- which.max(n_train)
donor_class <- which.max(partition[high, ])
if (partition[high, donor_class] <= 0) break
partition[high, donor_class] <- partition[high, donor_class] - 1L
partition[low, donor_class] <- partition[low, donor_class] + 1L
n_train <- rowSums(partition)
}
partition
}
dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
part <- matrix(0L, nrow = n_clients, ncol = n_classes)
gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
for (k in seq_len(n_classes)) {
g <- rgamma(n_clients, shape = alpha, rate = 1)
gammas[, k] <- g
part[, k] <- hamilton_alloc(g, per_class_total)
}
part <- repair_partition(part, min_client_samples)
list(partition = part, gammas = gammas)
}
shannon_entropy <- function(counts) {
s <- sum(counts)
if (s <= 0) return(0)
p <- counts / s
p <- p[p > 0]
-sum(p * log10(p))
}
row_trimmed_mean <- function(mat, trim_k) {
keep <- order(apply(mat, 1, function(x) sqrt(sum(x * x))))[(trim_k + 1):(nrow(mat) - trim_k)]
colMeans(mat[keep, , drop = FALSE])
}
run_sim <- function() {
task_variant <- Sys.getenv("TASK_VARIANT", "public")
seed <- as.integer(Sys.getenv("FEDSIMG_SEED", "0"))
set.seed(seed)
output_dir <- Sys.getenv("FEDSIMG_OUTPUT_DIR", "outputs")
manifest_path <- Sys.getenv("FEDSIMG_MANIFEST_PATH", "/app/dataset_manifest.json")
safe_dir_create(output_dir)
manifest <- fromJSON(manifest_path, simplifyVector = TRUE)
n_clients <- as.integer(manifest$simulation$n_clients)
n_byz <- as.integer(manifest$simulation$n_byzantine)
clients_per_round <- as.integer(manifest$simulation$clients_per_round)
n_rounds <- as.integer(manifest$simulation$n_rounds)
param_dim <- as.integer(manifest$simulation$param_dim)
n_classes <- as.integer(manifest$dataset$n_classes)
per_class <- as.integer(manifest$dataset$per_class_train)
alpha <- as.numeric(manifest$partition$alpha_default)
noise_scale <- as.numeric(manifest$attack$noise_scale_default)
signal_scale <- as.numeric(Sys.getenv("FEDSIMG_SIGNAL_SCALE", "0.12"))
trim_k <- as.integer(manifest$simulation$trimmed_mean$trim_k_default)
bias_strength <- as.numeric(manifest$client_bias$bias_strength_default)
bias_csv <- Sys.getenv(m
…[truncated]cat /app/example_client_bias.csv
client_id,class_0_weight,class_1_weight,class_2_weight,class_3_weight,class_4_weight,class_5_weight,class_6_weight,class_7_weight,class_8_weight,class_9_weight 0,0.28,0.10,0.07,0.06,0.06,0.06,0.07,0.08,0.12,0.10 1,0.05,0.06,0.09,0.14,0.15,0.14,0.12,0.10,0.08,0.07 2,0.08,0.08,0.08,0.08,0.08,0.08,0.12,0.14,0.14,0.12 3,0.04,0.05,0.06,0.07,0.10,0.13,0.15,0.15,0.14,0.11 4,0.16,0.14,0.12,0.10,0.09,0.08,0.07,0.06,0.10,0.08
which Rscript && Rscript --version
/usr/bin/Rscript Rscript (R) version 4.3.3 (2024-02-29)
Rscript -e 'library(jsonlite); cat(toJSON(list(a=0.851326, b=1L), auto_unbox=TRUE, digits=NA), "\n")'
{"a":0.851326,"b":1}Rscript -e 'library(jsonlite); cat(toJSON(list(a=c(1L,2L,3L), b="hello"), auto_unbox=TRUE, digits=NA, pretty=TRUE), "\n")'
{
"a": [1, 2, 3],
"b": "hello"
}command: create
path: /app/analysis.R
file_text: #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(ggplot2)
library(gridExtra)
library(jsonlite)
})
sigmoid <- function(x) 1.0 / (1.0 + exp(-x))
safe_dir_create <- function(path) {
if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
}
get_env_str <- function(name, default_val = "") {
v <- Sys.getenv(name, unset = NA)
if (is.na(v) || !nzchar(v)) return(default_val)
v
}
get_env_num <- function(name, default_val) {
v <- Sys.getenv(name, unset = NA)
if (is.na(v) || !nzchar(v)) return(as.numeric(default_val))
x <- suppressWarnings(as.numeric(v))
if (is.na(x)) return(as.numeric(default_val))
x
}
get_env_int <- function(name, default_val) {
v <- Sys.getenv(name, unset = NA)
if (is.na(v) || !nzchar(v)) return(as.integer(default_val))
x <- suppressWarnings(as.integer(v))
if (is.na(x)) return(as.integer(default_val))
x
}
read_bias_csv <- function(path, n_clients, n_classes) {
if (is.null(path) || !nzchar(path) || !file.exists(path)) {
return(NULL)
}
df <- read.csv(path, stringsAsFactors = FALSE)
req_weights <- sprintf("class_%d_weight", 0:(n_classes - 1))
req <- c("client_id", req_weights)
if (!all(req %in% names(df))) {
stop("bias csv missing required columns")
}
out <- matrix(NA_real_, nrow = n_clients, ncol = n_classes)
for (i in seq_len(nrow(df))) {
cid <- suppressWarnings(as.integer(df$client_id[i]))
if (is.na(cid) || cid < 0 || cid >= n_clients) next
row <- as.numeric(df[i, req_weights])
row[is.na(row)] <- 0
row[row < 0] <- 0
s <- sum(row)
if (s > 0) row <- row / s
out[cid + 1, ] <- row
}
out
}
hamilton_alloc <- function(p, total) {
p <- as.numeric(p)
sp <- sum(p)
if (sp <= 0) {
p <- rep(1 / length(p), length(p))
} else {
p <- p / sp
}
raw <- p * total
fa <- floor(raw)
alloc <- as.integer(fa)
gap <- as.integer(total - sum(alloc))
if (gap > 0) {
frac <- raw - fa
ord <- order(frac, decreasing = TRUE)
alloc[ord[seq_len(gap)]] <- alloc[ord[seq_len(gap)]] + 1L
} else if (gap < 0) {
frac <- raw - fa
ord <- order(frac, decreasing = FALSE)
take <- ord[seq_len(abs(gap))]
alloc[take] <- pmax(0L, alloc[take] - 1L)
}
as.integer(alloc)
}
repair_partition <- function(partition, min_client_samples) {
n_train <- rowSums(partition)
iter_limit <- 1000000L
iter <- 0L
while (min(n_train) < min_client_samples && iter < iter_limit) {
low <- which.min(n_train)
high <- which.max(n_train)
if (low == high) break
donor_class <- which.max(partition[high, ])
if (partition[high, donor_class] <= 0) break
partition[high, donor_class] <- partition[high, donor_class] - 1L
partition[low, donor_class] <- partition[low, donor_class] + 1L
n_train <- rowSums(partition)
iter <- iter + 1L
}
partition
}
dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
part <- matrix(0L, nrow = n_clients, ncol = n_classes)
gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
for (k in seq_len(n_classes)) {
g <- rgamma(n_clients, shape = alpha, rate = 1)
gammas[, k] <- g
part[, k] <- hamilton_alloc(g, per_class_total)
}
part <- repair_partition(part, min_client_samples)
list(partition = part, gammas = gammas)
}
# Shannon entropy using natural log (base e), per manifest.
shannon_entropy <- function(counts) {
s <- sum(counts)
if (s <= 0) return(0)
p <- counts / s
p <- p[p > 0]
-sum(p * log(p))
}
# Coordinate-wise trimmed mean, per manifest.
coord_trimmed_mean <- function(mat, trim_k) {
n <- nrow(mat)
if (trim_k <= 0 || 2 * trim_k >= n) return(colMeans(mat))
apply(mat, 2, function(col) {
sc <- sort(col)
mean(sc[(trim_k + 1):(n - trim_k)])
})
}
run_sim <- function() {
task_variant <- get_env_str("TASK_VARIANT", "public")
seed <- get_env_int("FEDSIMG_SEED", 0L)
set.seed(seed)
o
…[truncated]Existing file was successfully overwritten at /app/analysis.R
cd /app && rm -rf outputs && mkdir -p outputs && \ FEDSIMG_SEED=42 \ FEDSIMG_OUTPUT_DIR=outputs \ FEDSIMG_MANIFEST_PATH=/app/dataset_manifest.json \ TASK_VARIANT=public \ FEDSIMG_ALPHA=0.5 \ FEDSIMG_NOISE_SCALE=2.0 \ FEDSIMG_SIGNAL_SCALE=0.12 \ FEDSIMG_TRIM_K=2 \ FEDSIMG_BIAS_STRENGTH=0.35 \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -40
<system>Tool ran without output or errors</system>
ls -la /app/outputs/
total 1952 drwxr-sr-x 2 h2tagent h2tagent 4096 May 28 23:10 . drwxrws--T 3 h2tagent h2tagent 4096 May 28 23:10 .. -rw-r--r-- 1 h2tagent h2tagent 1016 May 28 23:10 analysis_summary.json -rw-r--r-- 1 h2tagent h2tagent 12612 May 28 23:10 byzantine_audit.csv -rw-r--r-- 1 h2tagent h2tagent 731196 May 28 23:10 client_updates.csv -rw-r--r-- 1 h2tagent h2tagent 1115104 May 28 23:10 client_updates_clean.csv -rw-r--r-- 1 h2tagent h2tagent 74421 May 28 23:10 diagnostics.png -rw-r--r-- 1 h2tagent h2tagent 45882 May 28 23:10 dirichlet_gammas.csv -rw-r--r-- 1 h2tagent h2tagent 1737 May 28 23:10 metrics_by_round.csv -rw-r--r-- 1 h2tagent h2tagent 3363 May 28 23:10 partition_matrix.csv -rw-r--r-- 1 h2tagent h2tagent 2952 May 28 23:10 partition_summary.csv
cat /app/outputs/analysis_summary.json
{
"task_variant": "public",
"seed": 42,
"env": {
"FEDSIMG_ALPHA": 0.5,
"FEDSIMG_NOISE_SCALE": 2,
"FEDSIMG_SIGNAL_SCALE": 0.12,
"FEDSIMG_TRIM_K": 2,
"FEDSIMG_BIAS_STRENGTH": 0.35,
"FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
},
"simulation_meta": {
"n_clients": 100,
"n_byzantine": 10,
"clients_per_round": 20,
"n_rounds": 8,
"param_dim": 256,
"trim_k": 2
},
"byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"final_metrics": {
"trimmed_mean_accuracy": 0.960252,
"fedavg_accuracy": 0.92827,
"delta": 0.031982
},
"provenance": {
"manifest_md5": "475adf00a65314160823670bdaef093c",
"selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
}
}head -20 /app/outputs/metrics_by_round.csv
"round","method","accuracy","cosine_sim","update_norm","byzantine_selected","selected_clients" 0,"fedavg",0.701539,0.44856,4.016918,1,"80 90 62 55 94 67 31 56 60 65 10 19 40 14 61 57 7 68 75 51" 0,"trimmed_mean",0.946524,0.727073,2.572666,1,"80 90 62 55 94 67 31 56 60 65 10 19 40 14 61 57 7 68 75 51" 1,"fedavg",0.772213,0.496612,3.54306,2,"90 32 13 64 47 53 95 42 35 72 6 1 30 78 74 55 19 36 15 38" 1,"trimmed_mean",0.945395,0.72637,2.607558,2,"90 32 13 64 47 53 95 42 35 72 6 1 30 78 74 55 19 36 15 38" 2,"fedavg",0.854487,0.574747,3.056391,1,"96 3 64 33 76 50 18 75 11 17 80 40 92 25 47 21 60 63 14 42" 2,"trimmed_mean",0.950662,0.725882,2.678662,1,"96 3 64 33 76 50 18 75 11 17 80 40 92 25 47 21 60 63 14 42" 3,"fedavg",0.89949,0.614974,3.138203,2,"19 9 48 31 24 54 0 58 93 34 70 76 89 14 55 59 88 97 36 41" 3,"trimmed_mean",0.957684,0.73116,2.564068,2,"19 9 48 31 24 54 0 58 93 34 70 76 89 14 55 59 88 97 36 41" 4,"fedavg",0.916335,0.648344,3.191474,2,"98 10 77 25 41 80 35 44 86 59 32 46 4 48 38 31 5 56 92 37" 4,"trimmed_mean",0.947596,0.733307,2.55824,2,"98 10 77 25 41 80 35 44 86 59 32 46 4 48 38 31 5 56 92 37" 5,"fedavg",0.931644,0.673158,3.361221,3,"66 21 54 20 62 9 12 36 45 39 69 95 5 88 96 0 90 46 70 71" 5,"trimmed_mean",0.952317,0.738967,2.641233,3,"66 21 54 20 62 9 12 36 45 39 69 95 5 88 96 0 90 46 70 71" 6,"fedavg",0.937543,0.684333,3.40599,2,"12 64 44 3 60 43 39 82 49 83 50 66 75 86 5 21 13 41 95 15" 6,"trimmed_mean",0.955575,0.739989,2.595111,2,"12 64 44 3 60 43 39 82 49 83 50 66 75 86 5 21 13 41 95 15" 7,"fedavg",0.92827,0.677745,3.689732,2,"35 59 11 2 61 96 33 51 63 49 75 38 81 12 24 37 94 48 3 89" 7,"trimmed_mean",0.960252,0.739615,2.507281,2,"35 59 11 2 61 96 33 51 63 49 75 38 81 12 24 37 94 48 3 89"
head -5 /app/outputs/partition_matrix.csv; echo "---" tail -5 /app/outputs/partition_matrix.csv
"client_id","class_0_count","class_1_count","class_2_count","class_3_count","class_4_count","class_5_count","class_6_count","class_7_count","class_8_count","class_9_count" 0,99,20,180,32,26,72,46,229,3,1 1,38,2,29,0,5,3,311,291,71,6 2,243,354,1,35,11,20,53,21,16,5 3,193,2,8,215,49,130,11,1,28,3 --- 95,5,3,6,12,39,3,15,18,42,18 96,41,172,2,0,2,3,3,123,10,247 97,282,0,164,101,7,54,74,4,30,31 98,226,8,23,0,103,52,42,53,5,74 99,15,7,3,69,3,40,15,167,68,11
head -5 /app/outputs/partition_summary.csv; echo "---" head -20 /app/outputs/dirichlet_gammas.csv; echo "---" head -5 /app/outputs/byzantine_audit.csv; echo "---" head -5 /app/outputs/client_updates.csv; echo "---" head -5 /app/outputs/client_updates_clean.csv
"client_id","is_byzantine","n_train","label_entropy","top_label","top_label_share" 0,1,708,1.792905,7,0.323446 1,1,756,1.339652,6,0.411376 2,1,759,1.427729,1,0.466403 3,1,640,1.563247,3,0.335938 --- "class","client_id","gamma_raw","p","allocated_count" 0,0,0.760516834810734,0.0165288490248016,99 0,1,0.293697522769724,0.00638313550814045,38 0,2,1.86634352361331,0.0405625607721065,243 0,3,1.48173928793115,0.0322036855244973,193 0,4,0.213423103156904,0.00463847490156219,28 0,5,0.21113732015893,0.00458879636671902,28 0,6,2.18518396319137e-05,4.74921450331816e-07,0 0,7,7.53977505366017e-05,1.63867251635434e-06,0 0,8,1.50888420258649,0.0327936451093378,197 0,9,0.201938125366727,0.00438886377494687,26 0,10,0.261292164982176,0.00567884700070938,34 0,11,0.155833656983546,0.00338684283025076,20 0,12,0.222578922302531,0.00483746477979957,29 0,13,0.00212506786891529,4.61855995357406e-05,0 0,14,0.642971346555362,0.0139741499833226,84 0,15,0.0503220278186775,0.00109368414000169,7 0,16,0.0811056128529209,0.00176272551579158,11 0,17,0.0276619363930318,0.000601196383098965,4 0,18,0.72534851618611,0.0157645111398326,95 --- "round","client_id","is_byzantine","is_selected","client_weight","update_norm" 0,0,1,0,0,0 0,1,1,0,0,0 0,2,1,0,0,0 0,3,1,0,0,0 --- "round","client_id","coord","update_value" 0,7,0,-1.571876 0,7,1,0.292317 0,7,2,-0.30237 0,7,3,-1.816215 --- "round","client_id","coord","u_clean","u_submitted" 0,7,0,0.156615,-1.571876 0,7,1,0.261323,0.292317 0,7,2,-0.181832,-0.30237 0,7,3,0.312278,-1.816215
grep -E "^0,(7|51|80|94)," /app/outputs/byzantine_audit.csv
0,7,1,1,0.097258,34.127434 0,51,0,1,0.028958,3.157158 0,80,0,1,0.032284,3.286065 0,94,0,1,0.04145,3.442351
cd /app && Rscript -e '
df <- read.csv("outputs/partition_summary.csv")
selected_r0 <- c(80, 90, 62, 55, 94, 67, 31, 56, 60, 65, 10, 19, 40, 14, 61, 57, 7, 68, 75, 51)
sum_sel <- sum(df$n_train[df$client_id %in% selected_r0])
cat("client 7 n_train:", df$n_train[df$client_id==7], "\n")
cat("sum selected:", sum_sel, "\n")
cat("expected weight:", df$n_train[df$client_id==7]/sum_sel, "\n")
cat("rounded:", round(df$n_train[df$client_id==7]/sum_sel, 6), "\n")
'client 7 n_train: 1199 sum selected: 12328 expected weight: 0.09725827 rounded: 0.097258
cd /app && Rscript -e '
# Verify column counts sum to per_class (6000) for each class
part <- read.csv("outputs/partition_matrix.csv")
for (k in 0:9) {
col <- sprintf("class_%d_count", k)
total <- sum(part[[col]])
cat(col, "sum=", total, "\n")
}
cat("\nMin client total (should be >= 20):", min(rowSums(part[, -1])), "\n")
cat("Total train samples:", sum(rowSums(part[, -1])), "\n")
'class_0_count sum= 6000 class_1_count sum= 6000 class_2_count sum= 6000 class_3_count sum= 6000 class_4_count sum= 6000 class_5_count sum= 6000 class_6_count sum= 6000 class_7_count sum= 6000 class_8_count sum= 6000 class_9_count sum= 6000 Min client total (should be >= 20): 125 Total train samples: 60000
cd /app && Rscript -e '
# Verify allocated_count in dirichlet_gammas matches partition_matrix
gdf <- read.csv("outputs/dirichlet_gammas.csv")
part <- read.csv("outputs/partition_matrix.csv")
for (k in 0:9) {
alloc <- gdf$allocated_count[gdf$class == k]
col <- sprintf("class_%d_count", k)
pc <- part[[col]]
cat("class", k, "match:", all(alloc == pc), "\n")
}
# Verify p sums to 1 per class
for (k in 0:9) {
ps <- sum(gdf$p[gdf$class == k])
cat("class", k, "sum(p)=", ps, "\n")
}
'class 0 match: TRUE class 1 match: TRUE class 2 match: TRUE class 3 match: TRUE class 4 match: TRUE class 5 match: TRUE class 6 match: TRUE class 7 match: TRUE class 8 match: TRUE class 9 match: TRUE class 0 sum(p)= 1 class 1 sum(p)= 1 class 2 sum(p)= 1 class 3 sum(p)= 1 class 4 sum(p)= 1 class 5 sum(p)= 1 class 6 sum(p)= 1 class 7 sum(p)= 1 class 8 sum(p)= 1 class 9 sum(p)= 1
command: str_replace
path: /app/analysis.R
old_str: dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
part <- matrix(0L, nrow = n_clients, ncol = n_classes)
gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
for (k in seq_len(n_classes)) {
g <- rgamma(n_clients, shape = alpha, rate = 1)
gammas[, k] <- g
part[, k] <- hamilton_alloc(g, per_class_total)
}
part <- repair_partition(part, min_client_samples)
list(partition = part, gammas = gammas)
}
new_str: dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
part <- matrix(0L, nrow = n_clients, ncol = n_classes)
gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
for (k in seq_len(n_classes)) {
g <- rgamma(n_clients, shape = alpha, rate = 1)
gammas[, k] <- g
part[, k] <- hamilton_alloc(g, per_class_total)
}
allocated_raw <- part # pre-repair Hamilton allocation (by p)
part <- repair_partition(part, min_client_samples)
list(partition = part, gammas = gammas, allocated_raw = allocated_raw)
}The file /app/analysis.R has been edited successfully.
command: str_replace path: /app/analysis.R old_str: partition <- part_out$partition gammas <- part_out$gammas n_train <- rowSums(partition) new_str: partition <- part_out$partition gammas <- part_out$gammas allocated_raw <- part_out$allocated_raw n_train <- rowSums(partition)
The file /app/analysis.R has been edited successfully.
command: str_replace
path: /app/analysis.R
old_str: # dirichlet gammas long form
gdf_list <- vector("list", n_classes)
for (k in 0L:(n_classes - 1L)) {
g <- as.numeric(gammas[, k + 1L])
sg <- sum(g)
p <- if (sg > 0) g / sg else rep(1 / length(g), length(g))
alloc <- as.integer(partition[, k + 1L])
gdf_list[[k + 1L]] <- data.frame(
class = rep(as.integer(k), n_clients),
client_id = 0L:(n_clients - 1L),
gamma_raw = g,
p = p,
allocated_count = alloc,
stringsAsFactors = FALSE
)
}
gdf <- do.call(rbind, gdf_list)
new_str: # dirichlet gammas long form (allocated_count = raw Hamilton allocation from p)
gdf_list <- vector("list", n_classes)
for (k in 0L:(n_classes - 1L)) {
g <- as.numeric(gammas[, k + 1L])
sg <- sum(g)
p <- if (sg > 0) g / sg else rep(1 / length(g), length(g))
alloc <- as.integer(allocated_raw[, k + 1L])
gdf_list[[k + 1L]] <- data.frame(
class = rep(as.integer(k), n_clients),
client_id = 0L:(n_clients - 1L),
gamma_raw = g,
p = p,
allocated_count = alloc,
stringsAsFactors = FALSE
)
}
gdf <- do.call(rbind, gdf_list)The file /app/analysis.R has been edited successfully.
cd /app && rm -rf outputs && mkdir -p outputs && \ FEDSIMG_SEED=42 \ FEDSIMG_OUTPUT_DIR=outputs \ FEDSIMG_MANIFEST_PATH=/app/dataset_manifest.json \ TASK_VARIANT=public \ FEDSIMG_ALPHA=0.5 \ FEDSIMG_NOISE_SCALE=2.0 \ FEDSIMG_SIGNAL_SCALE=0.12 \ FEDSIMG_TRIM_K=2 \ FEDSIMG_BIAS_STRENGTH=0.35 \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -20
<system>Tool ran without output or errors</system>
cd /app && ls outputs/ && echo "---" && cat /app/outputs/analysis_summary.json | head -30
analysis_summary.json
byzantine_audit.csv
client_updates.csv
client_updates_clean.csv
diagnostics.png
dirichlet_gammas.csv
metrics_by_round.csv
partition_matrix.csv
partition_summary.csv
---
{
"task_variant": "public",
"seed": 42,
"env": {
"FEDSIMG_ALPHA": 0.5,
"FEDSIMG_NOISE_SCALE": 2,
"FEDSIMG_SIGNAL_SCALE": 0.12,
"FEDSIMG_TRIM_K": 2,
"FEDSIMG_BIAS_STRENGTH": 0.35,
"FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
},
"simulation_meta": {
"n_clients": 100,
"n_byzantine": 10,
"clients_per_round": 20,
"n_rounds": 8,
"param_dim": 256,
"trim_k": 2
},
"byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"final_metrics": {
"trimmed_mean_accuracy": 0.960252,
"fedavg_accuracy": 0.92827,
"delta": 0.031982
},
"provenance": {
"manifest_md5": "475adf00a65314160823670bdaef093c",
"selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
}
}cd /app && Rscript -e '
library(jsonlite)
s <- fromJSON("outputs/analysis_summary.json")
cat("sel_hist length:", length(s$provenance$selection_histogram), "\n")
cat("sel_hist sum:", sum(s$provenance$selection_histogram), "\n")
cat("expected sum:", 20*8, "\n")
cat("delta computed:", 0.960252 - 0.92827, "\n")
cat("byz ids:", s$byzantine_client_ids, "\n")
'sel_hist length: 100 sel_hist sum: 160 expected sum: 160 delta computed: 0.031982 byz ids: 0 1 2 3 4 5 6 7 8 9
cd /app && rm -rf outputs && mkdir -p outputs && \ FEDSIMG_SEED=1234 \ FEDSIMG_OUTPUT_DIR=outputs \ TASK_VARIANT=hidden \ FEDSIMG_ALPHA=0.3 \ FEDSIMG_NOISE_SCALE=3.5 \ FEDSIMG_SIGNAL_SCALE=0.18 \ FEDSIMG_TRIM_K=3 \ FEDSIMG_BIAS_STRENGTH=0.5 \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -20 echo "===" cat /app/outputs/analysis_summary.json
===
{
"task_variant": "hidden",
"seed": 1234,
"env": {
"FEDSIMG_ALPHA": 0.3,
"FEDSIMG_NOISE_SCALE": 3.5,
"FEDSIMG_SIGNAL_SCALE": 0.18,
"FEDSIMG_TRIM_K": 3,
"FEDSIMG_BIAS_STRENGTH": 0.5,
"FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
},
"simulation_meta": {
"n_clients": 100,
"n_byzantine": 10,
"clients_per_round": 20,
"n_rounds": 8,
"param_dim": 256,
"trim_k": 3
},
"byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"final_metrics": {
"trimmed_mean_accuracy": 0.967649,
"fedavg_accuracy": 0.900746,
"delta": 0.0669029999999999
},
"provenance": {
"manifest_md5": "475adf00a65314160823670bdaef093c",
"selection_histogram": [1, 2, 1, 2, 2, 0, 1, 1, 2, 1, 1, 3, 0, 1, 2, 0, 0, 3, 2, 2, 2, 2, 0, 1, 1, 1, 0, 2, 1, 3, 3, 0, 2, 2, 3, 2, 1, 3, 2, 3, 2, 1, 2, 0, 1, 3, 1, 2, 1, 2, 4, 3, 1, 4, 2, 3, 0, 3, 0, 0, 1, 5, 3, 0, 2, 0, 0, 1, 2, 1, 3, 0, 0, 2, 1, 2, 0, 1, 0, 3, 2, 3, 2, 0, 0, 5, 0, 2, 0, 0, 2, 2, 1, 4, 3, 3, 2, 2, 2, 2]
}
}Rscript -e 'x <- 0.967649 - 0.900746; print(x); cat(sprintf("%.20f\n", x))'[1] 0.066903 0.06690299999999993474
Rscript -e '
library(jsonlite)
x <- 0.967649 - 0.900746
cat("x =", x, "\n")
cat("as JSON:", toJSON(list(a=x), auto_unbox=TRUE, digits=NA), "\n")
cat("as JSON 10:", toJSON(list(a=x), auto_unbox=TRUE, digits=10), "\n")
y <- 0.066903
cat("y =", y, "\n")
cat("y as JSON:", toJSON(list(a=y), auto_unbox=TRUE, digits=NA), "\n")
cat("x == y:", x == y, "\n")
cat("all.equal:", isTRUE(all.equal(x,y)), "\n")
'x = 0.066903
as JSON: {"a":0.0669029999999999}
as JSON 10: {"a":0.066903}
y = 0.066903
y as JSON: {"a":0.066903}
x == y: FALSE
all.equal: TRUEpython3 -c '
x = 0.967649 - 0.900746
print(f"python subtraction: {x!r}")
s = "0.0669029999999999"
y = float(s)
print(f"parsed from json string: {y!r}")
print(f"equal: {x == y}")
'python subtraction: 0.06690299999999993 parsed from json string: 0.0669029999999999 equal: False
Rscript -e '
library(jsonlite)
d <- round(0.967649 - 0.900746, 6)
cat("rounded d:", d, "\n")
cat(sprintf("%.20f\n", d))
cat("json:", toJSON(list(a=d), auto_unbox=TRUE, digits=NA), "\n")
'rounded d: 0.066903
0.06690300000000000413
json: {"a":0.066903}Rscript -e '
library(jsonlite)
x <- 0.967649 - 0.900746
cat("x exact:", sprintf("%.20f", x), "\n")
cat("digits=15:", toJSON(list(a=x), auto_unbox=TRUE, digits=15), "\n")
cat("digits=16:", toJSON(list(a=x), auto_unbox=TRUE, digits=16), "\n")
cat("digits=17:", toJSON(list(a=x), auto_unbox=TRUE, digits=17), "\n")
cat("digits=I(17):", toJSON(list(a=x), auto_unbox=TRUE, digits=I(17)), "\n")
cat("digits=NA:", toJSON(list(a=x), auto_unbox=TRUE, digits=NA), "\n")
'x exact: 0.06690299999999993474
digits=15: {"a":0.06690299999999993}
digits=16: {"a":0.066902999999999935}
digits=17: {"a":0.066902999999999935}
digits=I(17): {"a":0.066902999999999935}
digits=NA: {"a":0.0669029999999999}python3 -c ' x = 0.967649 - 0.900746 y = 0.06690299999999993 print(repr(x), repr(y), x == y) z = 0.066902999999999935 print(repr(x), repr(z), x == z) '
0.06690299999999993 0.06690299999999993 True 0.06690299999999993 0.06690299999999993 True
cd /app && Rscript -e '
library(jsonlite)
df <- read.csv("outputs/metrics_by_round.csv")
final <- df[df$round == max(df$round), ]
tm <- final$accuracy[final$method == "trimmed_mean"]
fed <- final$accuracy[final$method == "fedavg"]
cat("tm:", sprintf("%.20f", tm), "\n")
cat("fed:", sprintf("%.20f", fed), "\n")
cat("diff:", sprintf("%.20f", tm-fed), "\n")
cat("round(diff, 6):", sprintf("%.20f", round(tm-fed, 6)), "\n")
cat("json digits=NA:", toJSON(list(delta=tm-fed), auto_unbox=TRUE, digits=NA), "\n")
cat("json digits=17:", toJSON(list(delta=tm-fed), auto_unbox=TRUE, digits=17), "\n")
cat("json digits=NA r6:", toJSON(list(delta=round(tm-fed,6)), auto_unbox=TRUE, digits=NA), "\n")
'tm: 0.96764899999999998137
fed: 0.90074600000000004663
diff: 0.06690299999999993474
round(diff, 6): 0.06690300000000000413
json digits=NA: {"delta":0.0669029999999999}
json digits=17: {"delta":0.066902999999999935}
json digits=NA r6: {"delta":0.066903}command: str_replace path: /app/analysis.R old_str: tm_acc <- as.numeric(final_tm$accuracy[1]) fed_acc <- as.numeric(final_fed$accuracy[1]) delta <- tm_acc - fed_acc new_str: tm_acc <- as.numeric(final_tm$accuracy[1]) fed_acc <- as.numeric(final_fed$accuracy[1]) # Round delta to accuracy_decimals to mirror CSV precision and avoid # floating-point artifacts appearing in JSON. delta <- round(tm_acc - fed_acc, accuracy_decimals)
The file /app/analysis.R has been edited successfully.
cd /app && rm -rf outputs && mkdir -p outputs && \ FEDSIMG_SEED=1234 \ FEDSIMG_OUTPUT_DIR=outputs \ TASK_VARIANT=hidden \ FEDSIMG_ALPHA=0.3 \ FEDSIMG_NOISE_SCALE=3.5 \ FEDSIMG_SIGNAL_SCALE=0.18 \ FEDSIMG_TRIM_K=3 \ FEDSIMG_BIAS_STRENGTH=0.5 \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -10 cat /app/outputs/analysis_summary.json | grep -E '(delta|accuracy)'
"trimmed_mean_accuracy": 0.967649,
"fedavg_accuracy": 0.900746,
"delta": 0.066903cd /app && Rscript -e '
library(jsonlite)
m <- fromJSON("/app/dataset_manifest.json", simplifyVector=TRUE)
# Check schemas
check_csv <- function(path, expected_cols, sort_by) {
df <- read.csv(path, stringsAsFactors=FALSE)
cat(sprintf("[%s]\n", basename(path)))
cat(" cols match:", identical(names(df), expected_cols), "\n")
if (!identical(names(df), expected_cols)) {
cat(" expected:", paste(expected_cols, collapse=","), "\n")
cat(" actual: ", paste(names(df), collapse=","), "\n")
}
# Verify sort order
sorted <- df
for (k in rev(sort_by)) sorted <- sorted[order(sorted[[k]]), ]
cat(" sorted:", isTRUE(all.equal(df, sorted, check.attributes=FALSE)), "\n")
cat(" rows:", nrow(df), "\n")
}
check_csv("outputs/metrics_by_round.csv", m$outputs$metrics_csv$columns, m$outputs$metrics_csv$sort_by)
check_csv("outputs/dirichlet_gammas.csv", m$outputs$dirichlet_gammas_csv$columns, m$outputs$dirichlet_gammas_csv$sort_by)
check_csv("outputs/partition_matrix.csv", m$outputs$partition_matrix_csv$columns, m$outputs$partition_matrix_csv$sort_by)
check_csv("outputs/partition_summary.csv", m$outputs$partition_csv$columns, m$outputs$partition_csv$sort_by)
check_csv("outputs/byzantine_audit.csv", m$outputs$byzantine_audit_csv$columns, m$outputs$byzantine_audit_csv$sort_by)
check_csv("outputs/client_updates.csv", m$outputs$client_updates_csv$columns, m$outputs$client_updates_csv$sort_by)
check_csv("outputs/client_updates_clean.csv", m$outputs$client_updates_clean_csv$columns, m$outputs$client_updates_clean_csv$sort_by)
# Check summary JSON keys
s <- fromJSON("outputs/analysis_summary.json", simplifyVector=FALSE)
cat("\n[analysis_summary.json]\n")
cat(" required keys present:", all(m$outputs$summary_json$required_keys %in% names(s)), "\n")
cat(" env keys:", all(m$outputs$summary_json$env_required_keys %in% names(s$env)), "\n")
cat(" env excluded absent:", !any(m$outputs$summary_json$env_excluded_keys %in% names(s$env)), "\n")
cat(" sim_meta keys:", all(m$outputs$summary_json$simulation_meta_required_keys %in% names(s$simulation_meta)), "\n")
cat(" final_metrics keys:", all(m$outputs$summary_json$final_metrics_required_keys %in% names(s$final_metrics)), "\n")
cat(" provenance keys:", all(m$outputs$summary_json$provenance_required_keys %in% names(s$provenance)), "\n")
cat(" selection_histogram length:", length(s$provenance$selection_histogram), "\n")
'[metrics_by_round.csv] cols match: TRUE sorted: TRUE rows: 16 [dirichlet_gammas.csv] cols match: TRUE sorted: TRUE rows: 1000 [partition_matrix.csv] cols match: TRUE sorted: TRUE rows: 100 [partition_summary.csv] cols match: TRUE sorted: TRUE rows: 100 [byzantine_audit.csv] cols match: TRUE sorted: TRUE rows: 800 [client_updates.csv] cols match: TRUE sorted: TRUE rows: 40960 [client_updates_clean.csv] cols match: TRUE sorted: TRUE rows: 40960 [analysis_summary.json] required keys present: TRUE env keys: TRUE env excluded absent: TRUE sim_meta keys: TRUE final_metrics keys: TRUE provenance keys: TRUE selection_histogram length: 100
cd /app && Rscript -e '
# Verify key business logic
library(jsonlite)
cat("=== Business Logic Checks ===\n")
ps <- read.csv("outputs/partition_summary.csv")
# Verify byzantine IDs are lowest
s <- fromJSON("outputs/analysis_summary.json")
cat("byz_ids are 0:9:", all(s$byzantine_client_ids == 0:9), "\n")
cat("is_byzantine matches byz_ids in partition_summary:",
all((ps$client_id %in% 0:9) == (ps$is_byzantine == 1)), "\n")
# Verify top_label_share is computed from client n_train (not per_class)
pm <- read.csv("outputs/partition_matrix.csv")
counts <- as.matrix(pm[, -1])
expected_top_share <- apply(counts, 1, max) / pmax(rowSums(counts), 1)
cat("top_label_share matches count/row_sum:",
all(round(expected_top_share, 6) == ps$top_label_share), "\n")
# Verify label_entropy uses natural log
expected_ent <- apply(counts, 1, function(x) {
s <- sum(x); if (s==0) return(0); p <- x/s; p <- p[p>0]; -sum(p*log(p))
})
cat("label_entropy uses natural log:", all(round(expected_ent, 6) == ps$label_entropy), "\n")
# Verify byzantine_audit: selected have non-zero weight, non-selected have 0
ba <- read.csv("outputs/byzantine_audit.csv")
cat("non-selected have 0 weight:", all(ba$client_weight[ba$is_selected==0] == 0), "\n")
cat("non-selected have 0 update_norm:", all(ba$update_norm[ba$is_selected==0] == 0), "\n")
cat("selected byz have large update_norm:",
mean(ba$update_norm[ba$is_selected==1 & ba$is_byzantine==1]) >
mean(ba$update_norm[ba$is_selected==1 & ba$is_byzantine==0]), "\n")
# Verify client_updates_clean u_clean != u_submitted for byzantines
cuc <- read.csv("outputs/client_updates_clean.csv")
byz_rows <- cuc[cuc$client_id %in% 0:9, ]
honest_rows <- cuc[!(cuc$client_id %in% 0:9), ]
cat("byz rows have differing u_clean vs u_submitted:",
!all(byz_rows$u_clean == byz_rows$u_submitted), "\n")
cat("honest rows have equal u_clean and u_submitted:",
all(honest_rows$u_clean == honest_rows$u_submitted), "\n")
# Verify client_updates only contains selected clients per round
cu <- read.csv("outputs/client_updates.csv")
m <- read.csv("outputs/metrics_by_round.csv")
for (r in unique(cu$round)) {
sel <- as.integer(strsplit(m$selected_clients[m$round==r & m$method=="fedavg"], " ")[[1]])
cids_in_updates <- unique(cu$client_id[cu$round==r])
if (!setequal(sel, cids_in_updates)) {
cat("MISMATCH round", r, "\n")
}
}
cat("client_updates match selected_clients per round: OK\n")
cat("client_updates rows per client match param_dim:",
all(table(paste(cu$round, cu$client_id)) == 256), "\n")
'=== Business Logic Checks === byz_ids are 0:9: TRUE is_byzantine matches byz_ids in partition_summary: TRUE top_label_share matches count/row_sum: TRUE label_entropy uses natural log: TRUE non-selected have 0 weight: TRUE non-selected have 0 update_norm: TRUE selected byz have large update_norm: TRUE byz rows have differing u_clean vs u_submitted: TRUE honest rows have equal u_clean and u_submitted: TRUE client_updates match selected_clients per round: OK client_updates rows per client match param_dim: TRUE
cd /app && Rscript -e '
library(jsonlite)
m <- read.csv("outputs/metrics_by_round.csv")
s <- fromJSON("outputs/analysis_summary.json")
final_round <- max(m$round)
csv_tm <- m$accuracy[m$round==final_round & m$method=="trimmed_mean"]
csv_fed <- m$accuracy[m$round==final_round & m$method=="fedavg"]
cat("CSV tm:", csv_tm, "JSON tm:", s$final_metrics$trimmed_mean_accuracy,
"match:", csv_tm == s$final_metrics$trimmed_mean_accuracy, "\n")
cat("CSV fed:", csv_fed, "JSON fed:", s$final_metrics$fedavg_accuracy,
"match:", csv_fed == s$final_metrics$fedavg_accuracy, "\n")
cat("delta:", s$final_metrics$delta, "expected:", round(csv_tm - csv_fed, 6),
"match:", s$final_metrics$delta == round(csv_tm - csv_fed, 6), "\n")
# Also verify selection_histogram derivation
sel_counts <- integer(100)
uniq <- unique(m[, c("round", "selected_clients")])
for (i in seq_len(nrow(uniq))) {
ids <- as.integer(strsplit(uniq$selected_clients[i], "\\s+")[[1]])
sel_counts[ids+1] <- sel_counts[ids+1] + 1L
}
cat("sel_hist matches derived:", identical(as.integer(s$provenance$selection_histogram), sel_counts), "\n")
cat("sel_hist sum:", sum(s$provenance$selection_histogram), "expected:", 20*8, "\n")
'CSV tm: 0.967649 JSON tm: 0.967649 match: TRUE CSV fed: 0.900746 JSON fed: 0.900746 match: TRUE delta: 0.066903 expected: 0.066903 match: TRUE sel_hist matches derived: TRUE sel_hist sum: 160 expected: 160
cd /app && ls outputs/
analysis_summary.json byzantine_audit.csv client_updates.csv client_updates_clean.csv diagnostics.png dirichlet_gammas.csv metrics_by_round.csv partition_matrix.csv partition_summary.csv
cd /app && rm -rf outputs_a outputs_b && \ FEDSIMG_SEED=42 \ FEDSIMG_OUTPUT_DIR=outputs_a \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -5 FEDSIMG_SEED=42 \ FEDSIMG_OUTPUT_DIR=outputs_b \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -5 diff <(md5sum outputs_a/*.csv | sed 's/outputs_a//') <(md5sum outputs_b/*.csv | sed 's/outputs_b//') echo "CSV deterministic: $?" diff outputs_a/analysis_summary.json outputs_b/analysis_summary.json echo "JSON deterministic: $?" rm -rf outputs_a outputs_b
CSV deterministic: 0 JSON deterministic: 0
cd /app && rm -rf outputs && \ FEDSIMG_SEED=7 \ FEDSIMG_OUTPUT_DIR=outputs \ Rscript /app/analysis.R 2>&1 | tail -5 ls outputs/ echo "---" grep -E "(BIAS_CSV|ALPHA|delta)" outputs/analysis_summary.json
analysis_summary.json
byzantine_audit.csv
client_updates.csv
client_updates_clean.csv
diagnostics.png
dirichlet_gammas.csv
metrics_by_round.csv
partition_matrix.csv
partition_summary.csv
---
"FEDSIMG_ALPHA": 0.5,
"FEDSIMG_CLIENT_BIAS_CSV": ""
"delta": 0.034717file outputs/diagnostics.png
cd /app && Rscript -e '
library(png)
img <- readPNG("outputs/diagnostics.png")
cat("dims:", dim(img), "\n")
'[stderr] bash: file: command not found Error in library(png) : there is no package called ‘png’ Execution halted
ls -la /app/outputs/diagnostics.png head -c 8 /app/outputs/diagnostics.png | od -c | head -2
-rw-r--r-- 1 h2tagent h2tagent 76796 May 28 23:14 /app/outputs/diagnostics.png 0000000 211 P N G \r \n 032 \n 0000010
wc -l /app/analysis.R head -50 /app/analysis.R
515 /app/analysis.R
#!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(ggplot2)
library(gridExtra)
library(jsonlite)
})
sigmoid <- function(x) 1.0 / (1.0 + exp(-x))
safe_dir_create <- function(path) {
if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
}
get_env_str <- function(name, default_val = "") {
v <- Sys.getenv(name, unset = NA)
if (is.na(v) || !nzchar(v)) return(default_val)
v
}
get_env_num <- function(name, default_val) {
v <- Sys.getenv(name, unset = NA)
if (is.na(v) || !nzchar(v)) return(as.numeric(default_val))
x <- suppressWarnings(as.numeric(v))
if (is.na(x)) return(as.numeric(default_val))
x
}
get_env_int <- function(name, default_val) {
v <- Sys.getenv(name, unset = NA)
if (is.na(v) || !nzchar(v)) return(as.integer(default_val))
x <- suppressWarnings(as.integer(v))
if (is.na(x)) return(as.integer(default_val))
x
}
read_bias_csv <- function(path, n_clients, n_classes) {
if (is.null(path) || !nzchar(path) || !file.exists(path)) {
return(NULL)
}
df <- read.csv(path, stringsAsFactors = FALSE)
req_weights <- sprintf("class_%d_weight", 0:(n_classes - 1))
req <- c("client_id", req_weights)
if (!all(req %in% names(df))) {
stop("bias csv missing required columns")
}
out <- matrix(NA_real_, nrow = n_clients, ncol = n_classes)
for (i in seq_len(nrow(df))) {
cid <- suppressWarnings(as.integer(df$client_id[i]))
if (is.na(cid) || cid < 0 || cid >= n_clients) nextcd /app && rm -rf outputs # Test with defaults only (env vars missing) unset FEDSIMG_SEED FEDSIMG_ALPHA FEDSIMG_NOISE_SCALE FEDSIMG_SIGNAL_SCALE FEDSIMG_TRIM_K FEDSIMG_BIAS_STRENGTH FEDSIMG_CLIENT_BIAS_CSV Rscript /app/analysis.R 2>&1 | tail -5 ls outputs/ | wc -l echo "---" grep -E '"seed"|"FEDSIMG_ALPHA"|"FEDSIMG_BIAS_STRENGTH"|"FEDSIMG_CLIENT_BIAS_CSV"|"delta"' outputs/analysis_summary.json
9
---
"seed": 0,
"FEDSIMG_ALPHA": 0.5,
"FEDSIMG_BIAS_STRENGTH": 0.35,
"FEDSIMG_CLIENT_BIAS_CSV": ""
"delta": 0.063539cd /app && rm -rf outputs && mkdir -p outputs && \ FEDSIMG_SEED=42 \ FEDSIMG_OUTPUT_DIR=outputs \ TASK_VARIANT=public \ FEDSIMG_ALPHA=0.5 \ FEDSIMG_NOISE_SCALE=2.0 \ FEDSIMG_SIGNAL_SCALE=0.12 \ FEDSIMG_TRIM_K=2 \ FEDSIMG_BIAS_STRENGTH=0.35 \ FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \ Rscript /app/analysis.R 2>&1 | tail -5 echo "--- Files ---" ls -la outputs/ echo "--- Summary ---" cat outputs/analysis_summary.json
--- Files ---
total 1952
drwxr-sr-x 2 h2tagent h2tagent 4096 May 28 23:14 .
drwxrws--T 3 h2tagent h2tagent 4096 May 28 23:14 ..
-rw-r--r-- 1 h2tagent h2tagent 1016 May 28 23:14 analysis_summary.json
-rw-r--r-- 1 h2tagent h2tagent 12612 May 28 23:14 byzantine_audit.csv
-rw-r--r-- 1 h2tagent h2tagent 731196 May 28 23:14 client_updates.csv
-rw-r--r-- 1 h2tagent h2tagent 1115104 May 28 23:14 client_updates_clean.csv
-rw-r--r-- 1 h2tagent h2tagent 74421 May 28 23:14 diagnostics.png
-rw-r--r-- 1 h2tagent h2tagent 45882 May 28 23:14 dirichlet_gammas.csv
-rw-r--r-- 1 h2tagent h2tagent 1737 May 28 23:14 metrics_by_round.csv
-rw-r--r-- 1 h2tagent h2tagent 3363 May 28 23:14 partition_matrix.csv
-rw-r--r-- 1 h2tagent h2tagent 2952 May 28 23:14 partition_summary.csv
--- Summary ---
{
"task_variant": "public",
"seed": 42,
"env": {
"FEDSIMG_ALPHA": 0.5,
"FEDSIMG_NOISE_SCALE": 2,
"FEDSIMG_SIGNAL_SCALE": 0.12,
"FEDSIMG_TRIM_K": 2,
"FEDSIMG_BIAS_STRENGTH": 0.35,
"FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
},
"simulation_meta": {
"n_clients": 100,
"n_byzantine": 10,
"clients_per_round": 20,
"n_rounds": 8,
"param_dim": 256,
"trim_k": 2
},
"byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"final_metrics": {
"trimmed_mean_accuracy": 0.960252,
"fedavg_accuracy": 0.92827,
"delta": 0.031982
},
"provenance": {
"manifest_md5": "475adf00a65314160823670bdaef093c",
"selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
}
}No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary
# task: fedavg-federated-noniid-mnist
# attempt: 6 status: completed raw_score: 0.0
# reward rule: binary: score 0.0 != 1.0
# => reward = 0.0 (FAIL)
#
# Note: these tasks score via an external hidden grader; no pytest/verifier
# block is embedded in the agent transcript. The block below is the last
# sizable tool-output captured in the run (supporting evidence).
----------------------------------------------------------------------
--- Files ---
total 1952
drwxr-sr-x 2 h2tagent h2tagent 4096 May 28 23:14 .
drwxrws--T 3 h2tagent h2tagent 4096 May 28 23:14 ..
-rw-r--r-- 1 h2tagent h2tagent 1016 May 28 23:14 analysis_summary.json
-rw-r--r-- 1 h2tagent h2tagent 12612 May 28 23:14 byzantine_audit.csv
-rw-r--r-- 1 h2tagent h2tagent 731196 May 28 23:14 client_updates.csv
-rw-r--r-- 1 h2tagent h2tagent 1115104 May 28 23:14 client_updates_clean.csv
-rw-r--r-- 1 h2tagent h2tagent 74421 May 28 23:14 diagnostics.png
-rw-r--r-- 1 h2tagent h2tagent 45882 May 28 23:14 dirichlet_gammas.csv
-rw-r--r-- 1 h2tagent h2tagent 1737 May 28 23:14 metrics_by_round.csv
-rw-r--r-- 1 h2tagent h2tagent 3363 May 28 23:14 partition_matrix.csv
-rw-r--r-- 1 h2tagent h2tagent 2952 May 28 23:14 partition_summary.csv
--- Summary ---
{
"task_variant": "public",
"seed": 42,
"env": {
"FEDSIMG_ALPHA": 0.5,
"FEDSIMG_NOISE_SCALE": 2,
"FEDSIMG_SIGNAL_SCALE": 0.12,
"FEDSIMG_TRIM_K": 2,
"FEDSIMG_BIAS_STRENGTH": 0.35,
"FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
},
"simulation_meta": {
"n_clients": 100,
"n_byzantine": 10,
"clients_per_round": 20,
"n_rounds": 8,
"param_dim": 256,
"trim_k": 2
},
"byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"final_metrics": {
"trimmed_mean_accuracy": 0.960252,
"fedavg_accuracy": 0.92827,
"delta": 0.031982
},
"provenance": {
"manifest_md5": "475adf00a65314160823670bdaef093c",
"selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
}
}Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_fed282c6cc86469e. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_fed282c6cc86469e · verifier authoritative; classifier explanatory.