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"""
Usage:
python3 topic_clustering.py --in arena.json --english-only --min-length 32
python3 topic_clustering.py --in clean_conv_20230809_100k.json --english-only --min-length 32 --max-length 1536
"""
import argparse
import json
import pickle
import string
import time
import numpy as np
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
from sklearn.cluster import KMeans, AgglomerativeClustering
import torch
from tqdm import tqdm
from openai import OpenAI
from fastchat.utils import detect_language
def remove_punctuation(input_string):
# Make a translator object to remove all punctuation
translator = str.maketrans("", "", string.punctuation)
# Use the translator object to remove the punctuation
no_punct = input_string.translate(translator)
return no_punct
def read_texts(input_file, min_length, max_length, english_only):
visited = set()
texts = []
lines = json.load(open(input_file, "r"))
for l in tqdm(lines):
if "text" in l:
line_texts = [l["text"]]
elif "conversation_a" in l:
line_texts = [
x["content"] for x in l["conversation_a"] if x["role"] == "user"
]
elif "conversation" in l:
line_texts = [
x["content"] for x in l["conversation"] if x["role"] == "user"
]
elif "turns" in l:
line_texts = l["turns"]
for text in line_texts:
text = text.strip()
# Filter language
if english_only:
lang = detect_language(text)
if lang != "English":
continue
# Filter short or long prompts
if min_length:
if len(text) < min_length:
continue
if max_length:
if len(text) > max_length:
continue
# De-duplication
words = sorted([x.lower() for x in remove_punctuation(text).split(" ")])
words = "".join(words)
if words in visited:
continue
visited.add(words)
texts.append(text)
return np.array(texts)
def get_embeddings(texts, model_name, batch_size):
if model_name == "text-embedding-ada-002":
client = OpenAI()
texts = texts.tolist()
embeddings = []
for i in tqdm(range(0, len(texts), batch_size)):
text = texts[i : i + batch_size]
responses = client.embeddings.create(input=text, model=model_name).data
embeddings.extend([data.embedding for data in responses])
embeddings = torch.tensor(embeddings)
else:
model = SentenceTransformer(model_name)
embeddings = model.encode(
texts,
batch_size=batch_size,
show_progress_bar=True,
device="cuda",
convert_to_tensor=True,
)
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
return embeddings.cpu()
def run_k_means(embeddings, num_clusters):
np.random.seed(42)
clustering_model = KMeans(n_clusters=num_clusters, n_init="auto")
clustering_model.fit(embeddings.numpy())
centers = torch.from_numpy(clustering_model.cluster_centers_)
labels = torch.from_numpy(clustering_model.labels_)
# Sort labels
classes, counts = np.unique(labels, return_counts=True)
indices = np.argsort(counts)[::-1]
classes = [classes[i] for i in indices]
new_labels = torch.empty_like(labels)
new_centers = torch.empty_like(centers)
for i, c in enumerate(classes):
new_labels[labels == c] = i
new_centers[i] = centers[c]
return new_centers, new_labels
def run_agg_cluster(embeddings, num_clusters):
np.random.seed(42)
clustering_model = AgglomerativeClustering(n_clusters=num_clusters)
clustering_model.fit(embeddings)
labels = torch.from_numpy(clustering_model.labels_)
# Sort labels
classes, counts = np.unique(labels, return_counts=True)
indices = np.argsort(counts)[::-1]
classes = [classes[i] for i in indices]
new_labels = torch.empty_like(labels)
for i, c in enumerate(classes):
new_labels[labels == c] = i
# Compute centers
centers = []
for i in range(len(classes)):
centers.append(embeddings[new_labels == i].mean(axis=0, keepdim=True))
centers = torch.cat(centers)
return centers, new_labels
def run_hdbscan_cluster(embeddings):
import hdbscan
np.random.seed(42)
clusterer = hdbscan.HDBSCAN(min_cluster_size=10)
labels = torch.from_numpy(clusterer.fit_predict(embeddings))
# Sort labels
classes, counts = np.unique(labels, return_counts=True)
indices = np.argsort(counts)[::-1]
classes = [classes[i] for i in indices]
new_labels = torch.empty_like(labels)
for i, c in enumerate(classes):
new_labels[labels == c] = i
# Compute centers
centers = []
for i in range(len(classes)):
centers.append(embeddings[new_labels == i].mean(axis=0, keepdim=True))
centers = torch.cat(centers)
return centers, new_labels
def get_topk_indices(centers, labels, embeddings, topk):
indices = []
arange = torch.arange(len(labels))
counts = torch.unique(labels, return_counts=True)[1]
topk = min(topk, counts.min().item())
for i in range(len(centers)):
tmp_indices = labels == i
tmp_arange = arange[tmp_indices]
tmp_embeddings = embeddings[tmp_indices]
scores = cos_sim(centers[i].unsqueeze(0), tmp_embeddings)[0]
sorted_indices = torch.flip(torch.argsort(scores), dims=[0])
indices.append(tmp_arange[sorted_indices[:topk]].unsqueeze(0))
return torch.cat(indices)
def print_topk(texts, labels, topk_indices, show_cut_off):
ret = ""
for k in range(len(topk_indices)):
num_samples = torch.sum(labels == k).item()
ret += "=" * 20 + f" cluster {k}, #samples: {num_samples} " + "=" * 20 + "\n"
for idx in topk_indices[k]:
ret += "PROMPT: " + texts[idx][:show_cut_off] + "\n"
ret += "=" * 40 + "\n\n"
return ret
def get_cluster_info(texts, labels, topk_indices):
np.random.seed(42)
cluster_info = []
for k in range(len(topk_indices)):
num_samples = torch.sum(labels == k).item()
topk_prompts = []
for idx in topk_indices[k]:
topk_prompts.append(texts[idx])
random_prompts = []
for idx in range(len(topk_indices)):
random_prompts.append(np.random.choice(texts))
cluster_info.append((num_samples, topk_prompts, random_prompts))
return cluster_info
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input-file", type=str, required=True)
parser.add_argument("--model", type=str, default="all-mpnet-base-v2")
# default="all-MiniLM-L12-v2")
# default="multi-qa-distilbert-cos-v1")
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--min-length", type=int)
parser.add_argument("--max-length", type=int)
parser.add_argument("--english-only", action="store_true")
parser.add_argument("--num-clusters", type=int, default=20)
parser.add_argument(
"--cluster-alg",
type=str,
choices=["kmeans", "aggcls", "HDBSCAN"],
default="kmeans",
)
parser.add_argument("--show-top-k", type=int, default=200)
parser.add_argument("--show-cut-off", type=int, default=512)
parser.add_argument("--save-embeddings", action="store_true")
parser.add_argument("--embeddings-file", type=str, default=None)
args = parser.parse_args()
num_clusters = args.num_clusters
show_top_k = args.show_top_k
show_cut_off = args.show_cut_off
texts = read_texts(
args.input_file, args.min_length, args.max_length, args.english_only
)
print(f"#text: {len(texts)}")
if args.embeddings_file is None:
embeddings = get_embeddings(texts, args.model, args.batch_size)
if args.save_embeddings:
# allow saving embedding to save time and money
torch.save(embeddings, "embeddings.pt")
else:
embeddings = torch.load(args.embeddings_file)
print(f"embeddings shape: {embeddings.shape}")
if args.cluster_alg == "kmeans":
centers, labels = run_k_means(embeddings, num_clusters)
elif args.cluster_alg == "aggcls":
centers, labels = run_agg_cluster(embeddings, num_clusters)
elif args.cluster_alg == "HDBSCAN":
centers, labels = run_hdbscan_cluster(embeddings)
else:
raise ValueError(f"Invalid clustering algorithm: {args.cluster_alg}")
topk_indices = get_topk_indices(centers, labels, embeddings, args.show_top_k)
topk_str = print_topk(texts, labels, topk_indices, args.show_cut_off)
num_clusters = len(centers)
# Dump results
filename_prefix = f"results_c{num_clusters}_{args.cluster_alg}"
print(topk_str)
with open(filename_prefix + "_topk.txt", "w") as fout:
fout.write(topk_str)
with open(filename_prefix + "_all.jsonl", "w") as fout:
for i in range(len(centers)):
tmp_indices = labels == i
tmp_embeddings = embeddings[tmp_indices]
tmp_texts = texts[tmp_indices]
scores = cos_sim(centers[i].unsqueeze(0), tmp_embeddings)[0]
sorted_indices = torch.flip(torch.argsort(scores), dims=[0])
for text, score in zip(tmp_texts[sorted_indices], scores[sorted_indices]):
obj = {"cluster": i, "text": text, "sim": score.item()}
fout.write(json.dumps(obj, ensure_ascii=False) + "\n")
cluster_info = get_cluster_info(texts, labels, topk_indices)
with open(filename_prefix + "_cluster.pkl", "wb") as fout:
pickle.dump(cluster_info, fout)
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