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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# stransevalr
<!-- badges: start -->
<!-- badges: end -->
The goal of stransevalr is to use `reticulate` to evaluate question-answer pairs with sentence transformers in R.
## Installation and Usage
I've been using these modules on O2:
```
module load gcc/9.2.0 R/4.4.0 cmake/3.14.1 cuda/12.1 python/3.10.11
```
This is a basic example which shows you how to create the virtual environment with reticulate if needed, then analyze an input question-answer-response file. This code block will create the virtual environment for you if it doesn't exist or set it to be used if it does. I tried to cut it down as much as possible but these four python packages have ~50 dependencies which take forever to install :(
```{r eval=FALSE}
library(reticulate)
# change the virtual environment directory as you like
env_dir = file.path(Sys.getenv("HOME"),
".virtualenvs/sntenv")
if (dir.exists(env_dir)) {
use_virtualenv(env_dir)
} else {
strt = Sys.time()
virtualenv_create(env_dir,
python = "/n/app/python/3.10.11.conda/bin/python")
# ^ If this fails, you probably don't have the python 3.10.11 module loaded
virtualenv_install(env_dir,
packages = c("numpy==1.26.4", "cuda-python==12.1.0", "torch==2.2.2"),
pip_options = c("--upgrade", "--force-reinstall"))
virtualenv_install(env_dir,
packages = c("pandas", "sentence_transformers"))
fin = Sys.time()
print(fin - strt)
}
```
Exit R, activate the venv, then start R and you can install the package from [GitHub](https://github.com/) with:
``` r
# install.packages("remotes")
remotes::install_github("ccb-hms/stransevalr")
```
`stransevalr` in and of itself is pretty light on the R front outside of reticulate:
The first column must be named `question`, the second must be `answer`, and the remaining columns should have names indicating the model they came from e.g. `llama_70b_rag`:
```{r example, eval=FALSE}
library(data.table)
library(stransevalr)
options(datatable.print.trunc.cols = TRUE, digits = 3)
# show input file
system.file("extdata", "correct_fmt.tsv", package = "stransevalr") |>
fread() |>
tibble::as_tibble()
```
```
# A tibble: 10 × 4
question answer Response_Azure_Bioc_…¹ Response_Azure_GPT4_…²
<chr> <chr> <chr> <chr>
1 "I am a bit confused ab… "The … "The False Discovery … "FDR, FDR adjusted p-…
2 "I am working on RNA-Se… "Just… "It seems like you're… "In DESeq2, adding th…
3 "I am new in this kind … "Ther… "Yes, you're correct … "You're correct that …
4 "I am testing salmon an… "To a… "The `tximport` funct… "1. ScaledTPM and len…
5 "In all RNA-seq analysi… "The … "The dispersion param… "In RNA-seq analysis,…
6 "I know findOverlaps() … "From… "Based on your questi… "It seems like you're…
7 "I have just downloaded… "I wr… "To map the coordinat… "Mapping genomic coor…
8 "How can I filter out t… "If y… "Yes, you are on the … "Yes, you are on the …
9 "I am analysing my RNA-… "You … "The issue you're fac… "It seems like you ar…
10 "How do I merge a list … "Merg… "You can merge a list… "To merge a list of G…
# ℹ abbreviated names: ¹Response_Azure_Bioc_RAG, ²Response_Azure_GPT4_Temp0
```
To run the evaluation you hand the input file to `stransevalr()`:
```{r eval=FALSE}
input = system.file("extdata", "correct_fmt.tsv", package = "stransevalr")
res = stransevalr(input)
res |> tibble::as_tibble()
```
```
# A tibble: 7 × 3
m res cosine_sims
<chr> <list> <list>
1 Response_Azure_Bioc_RAG <dbl [10 × 384]> <dbl [10]>
2 Response_Azure_GPT4_Temp0 <dbl [10 × 384]> <dbl [10]>
3 scrambled_answer <dbl [10 × 384]> <dbl [10]>
4 scrambled_combined_answers <dbl [10 × 384]> <dbl [10]>
5 scrabble_match_nword <dbl [10 × 384]> <dbl [10]>
6 scrabble_match_nchar <dbl [10 × 384]> <dbl [10]>
7 reembed_ground_truth <dbl [10 × 384]> <dbl [10]>
```
The embeddings and cosine similarities are in nested list columns. You can unnest the similarities and add an index with a bit of data.table:
```{r eval=FALSE}
res[,.(m, cosine_sims)][,.(cos_sim = unlist(cosine_sims)), by = m][, q_i := 1:.N , by = m] |> head()
```
```
m cos_sim q_i
<char> <num> <int>
1: Response_Azure_Bioc_RAG 0.8001171 1
2: Response_Azure_Bioc_RAG 0.7180428 2
3: Response_Azure_Bioc_RAG 0.4934007 3
4: Response_Azure_Bioc_RAG 0.7712072 4
5: Response_Azure_Bioc_RAG 0.7607350 5
6: Response_Azure_Bioc_RAG 0.6629975 6
```
There are functions for creating the bar and dot/boxplots as well:
```{r eval=FALSE}
library(ggplot2)
p1 = plot_cos_sim_bars(res)
p2 = plot_cos_sim_boxes(res)
ggsave(p1, filename = "~/p1.png", w = 12, h = 7)
ggsave(p2, filename = "~/p2.png", w = 12, h = 7)
```
You can provide your own similarity function with the `sim_fun` argument:
```{r eval=FALSE}
euc_dist = \(x,y) sqrt(sum((x - y)^2))
res2 = stransevalr(input, sim_fun = euc_dist)
```