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get_markdown.py
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get_markdown.py
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import argparse
import json
import os
import matplotlib.pyplot as plt
import numpy as np
def get_extra_embedding(text: str) -> np.ndarray:
import google.generativeai as genai
genai.configure(transport="rest")
embedding_dict = genai.embed_content(
model="models/embedding-001",
content=text,
task_type="clustering",
)
embedding = embedding_dict["embedding"]
return np.array(embedding)
def build_paper_section(paper_dict: dict) -> str:
title = paper_dict["title"]
abstract = paper_dict.get("abstract", "No absctract")
openreview_link = paper_dict["link"]
pdf_link = paper_dict["pdf_link"]
result = ""
result += f"## {title}"
result += "\n\n"
result += f"\[[openreview]({openreview_link})\] \[[pdf]({pdf_link})\]"
result += "\n\n"
result += f"**Abstract** {abstract}"
return result
def dump_data_cdf(data: np.ndarray):
data_sorted = np.sort(data)
cdf = np.arange(1, len(data_sorted) + 1) / len(data_sorted)
plt.plot(data_sorted, cdf, marker='.', linestyle='none')
plt.xlabel('favor score')
plt.ylabel('CDF')
plt.title('CDF of scores for those paper')
plt.savefig("score_cdf.png", bbox_inches='tight')
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--crawl_result_dir", type=str, default="outputs")
parser.add_argument("--score_threshold", type=float)
parser.add_argument("--num_threshold", type=int)
parser.add_argument("--likes", nargs='+')
parser.add_argument("--dislikes", nargs='+')
parser.add_argument("--like_dislike_config", type=str)
parser.add_argument("--embedding_from", type=str, choices=["title", "title_abs"], default="title")
args = parser.parse_args()
assert not(args.score_threshold is not None and args.num_threshold is not None), "`score threshold` and `num threshld` cannot be both set"
assert not((args.likes is not None or args.dislikes is not None) and args.like_dislike_config is not None), "command line options passing likes, dislikes is conflicting with passing config file"
embeddings = np.load(os.path.join(args.crawl_result_dir, "embeddings.npy"))
crawl_results = [
os.path.join(args.crawl_result_dir, p)
for p in os.listdir(args.crawl_result_dir) if p.endswith(".json")
]
crawl_results.sort(key=lambda x: int(x.split('result')[1].split('.json')[0]))
paper_list = [] # type: list[dict[str, Any]]
for crawl_result in crawl_results:
with open(crawl_result, "r") as f:
paper_list.extend(json.load(f))
embedding_index_key = f"{args.embedding_from}_embedding_index"
title_to_embedding_index_lut = {
paper_dict["title"]: paper_dict[embedding_index_key]
for paper_dict in paper_list
}
## get projection weight by like and dislike
score_projection_weight = np.zeros(embeddings.shape[1])
if args.like_dislike_config is not None:
with open(args.like_dislike_config, "r") as f:
like_dislike_config = json.load(f)
likes = like_dislike_config["likes"]
dislikes = like_dislike_config["dislikes"]
elif args.likes is not None:
likes = args.likes
dislikes = args.dislikes
if likes is not None and len(likes) > 0:
like_embeddings = np.array([
embeddings[title_to_embedding_index_lut[title], :]
if title in title_to_embedding_index_lut
else get_extra_embedding(title)
for title in likes
])
score_projection_weight += np.mean(like_embeddings, axis=0)
if dislikes is not None and len(dislikes) > 0:
dislike_embeddings = np.array([
embeddings[title_to_embedding_index_lut[title], :]
if title in title_to_embedding_index_lut
else get_extra_embedding(title)
for title in dislikes
])
score_projection_weight -= np.mean(dislike_embeddings, axis=0)
scores = [
score_projection_weight @ embeddings[d[embedding_index_key], :]
for d in paper_list
]
scores = np.array(scores)
favor_indices = np.argsort(scores)[::-1]
if args.score_threshold is not None:
favor_indices = favor_indices[scores[favor_indices] > args.score_threshold]
if args.num_threshold is not None:
favor_indices = favor_indices[:args.num_threshold]
favor_papers = [paper_list[i] for i in favor_indices]
favor_scores = scores[favor_indices]
## output the markdown
header = "# Your ICLR Recommendation list"
header += "\n\n"
header += f"There are {len(favor_papers)} papers for you in ICLR 2025"
header += "\n\n"
dump_data_cdf(favor_scores)
header += "![score_cdf](score_cdf.png)"
paper_section = "\n\n".join([build_paper_section(d) for d in favor_papers])
markdown_str = header + "\n\n" + paper_section
with open("output.md", "wt") as f:
f.write(markdown_str)
if __name__ == "__main__":
main()