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Author SHA1 Message Date
lphatnguyen
bfdaa142c2 [Add] Adaptive clustering - add files 2025-07-10 20:36:23 +00:00
trungkienbkhn
8e1d0568a2 [Add] Adaptive clustering 2025-07-10 20:35:46 +00:00
6 changed files with 909 additions and 85 deletions
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10
clustering_example.ipynb
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@ -111,7 +111,15 @@
"execution_count": null, "execution_count": null,
"id": "27fea4f3", "id": "27fea4f3",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 100% |███████████████| 1091/1091 [174.1ms elapsed, 0s remaining, 6.3K samples/s] \n"
]
}
],
"source": [ "source": [
"import fiftyone as fo\n", "import fiftyone as fo\n",
"import fiftyone.brain as fob\n", "import fiftyone.brain as fob\n",

210
clustering_layoutlm.ipynb
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@ -124,66 +124,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
" 100% |███████████████| 1090/1090 [179.3ms elapsed, 0s remaining, 6.1K samples/s] \n", " 100% |███████████████| 1090/1090 [182.4ms elapsed, 0s remaining, 6.0K samples/s] \n"
"Generating visualization...\n",
"[t-SNE] Computing 91 nearest neighbors...\n",
"[t-SNE] Indexed 1090 samples in 0.000s...\n",
"[t-SNE] Computed neighbors for 1090 samples in 0.116s...\n",
"[t-SNE] Computed conditional probabilities for sample 1000 / 1090\n",
"[t-SNE] Computed conditional probabilities for sample 1090 / 1090\n",
"[t-SNE] Mean sigma: 1.117175\n",
"[t-SNE] Computed conditional probabilities in 0.018s\n",
"[t-SNE] Iteration 50: error = 63.3665466, gradient norm = 0.0725846 (50 iterations in 0.175s)\n",
"[t-SNE] Iteration 100: error = 61.4698219, gradient norm = 0.0620725 (50 iterations in 0.064s)\n",
"[t-SNE] Iteration 150: error = 60.9316177, gradient norm = 0.0641517 (50 iterations in 0.061s)\n",
"[t-SNE] Iteration 200: error = 60.9066200, gradient norm = 0.0622800 (50 iterations in 0.063s)\n",
"[t-SNE] Iteration 250: error = 60.9928894, gradient norm = 0.0575472 (50 iterations in 0.062s)\n",
"[t-SNE] KL divergence after 250 iterations with early exaggeration: 60.992889\n",
"[t-SNE] Iteration 300: error = 0.9203525, gradient norm = 0.0060658 (50 iterations in 0.060s)\n",
"[t-SNE] Iteration 350: error = 0.8058189, gradient norm = 0.0041889 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 400: error = 0.7736985, gradient norm = 0.0027541 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 450: error = 0.7591555, gradient norm = 0.0019742 (50 iterations in 0.064s)\n",
"[t-SNE] Iteration 500: error = 0.7508169, gradient norm = 0.0015802 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 550: error = 0.7453735, gradient norm = 0.0013677 (50 iterations in 0.056s)\n",
"[t-SNE] Iteration 600: error = 0.7403219, gradient norm = 0.0012937 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 650: error = 0.7358239, gradient norm = 0.0008007 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 700: error = 0.7335896, gradient norm = 0.0008103 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 750: error = 0.7321761, gradient norm = 0.0006007 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 800: error = 0.7311318, gradient norm = 0.0006798 (50 iterations in 0.056s)\n",
"[t-SNE] Iteration 850: error = 0.7299591, gradient norm = 0.0006054 (50 iterations in 0.057s)\n",
"[t-SNE] Iteration 900: error = 0.7292019, gradient norm = 0.0004605 (50 iterations in 0.056s)\n",
"[t-SNE] Iteration 950: error = 0.7283084, gradient norm = 0.0005973 (50 iterations in 0.056s)\n",
"[t-SNE] Iteration 1000: error = 0.7276300, gradient norm = 0.0005537 (50 iterations in 0.057s)\n",
"[t-SNE] KL divergence after 1000 iterations: 0.727630\n",
"UMAP visualization computed. Launch the app to see the plot.\n"
]
},
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"800\"\n",
" src=\"http://localhost:5151/?notebook=True&subscription=9f7aad1e-72b8-41ab-873d-b776dbb9e9d5\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" \n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x798114e78160>"
]
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"metadata": {},
"output_type": "display_data"
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"output_type": "stream",
"text": [
"Notebook sessions cannot wait\n"
] ]
} }
], ],
@ -229,32 +170,135 @@
" sample[\"gmm_cluster_50_gaussians\"] = int(label)\n", " sample[\"gmm_cluster_50_gaussians\"] = int(label)\n",
" sample.save()\n", " sample.save()\n",
"\n", "\n",
"n_gaussians = 100\n", "# n_gaussians = 100\n",
"gmm = GaussianMixture(n_components=n_gaussians, random_state=42)\n", "# gmm = GaussianMixture(n_components=n_gaussians, random_state=42)\n",
"gmm.fit(embeddings)\n", "# gmm.fit(embeddings)\n",
"cluster_labels = gmm.predict(embeddings)\n", "# cluster_labels = gmm.predict(embeddings)\n",
"\n", "\n",
"# Adding labeled embeddings to visulization\n", "# # Adding labeled embeddings to visulization\n",
"dataset.add_sample_field(\"gmm_cluster\", fo.IntField)\n", "# dataset.add_sample_field(\"gmm_cluster\", fo.IntField)\n",
"for sample, label in zip(dataset, cluster_labels):\n", "# for sample, label in zip(dataset, cluster_labels):\n",
" sample[\"gmm_cluster_200_gaussians\"] = int(label)\n", "# sample[\"gmm_cluster_200_gaussians\"] = int(label)\n",
" sample.save()\n", "# sample.save()\n",
"\n", "\n",
"# --- Visualize the Embeddings with UMAP ---\n", "# # --- Visualize the Embeddings with UMAP ---\n",
"# This will compute a 2D representation of your embeddings\n", "# # This will compute a 2D representation of your embeddings\n",
"# for visualization.\n", "# # for visualization.\n",
"res = fob.compute_visualization(\n", "# res = fob.compute_visualization(\n",
" dataset,\n", "# dataset,\n",
" embeddings=embeddings,\n", "# embeddings=embeddings,\n",
" brain_key=\"qwen_vision_viz\",\n", "# brain_key=\"qwen_vision_viz\",\n",
" method=\"tsne\",\n", "# method=\"tsne\",\n",
" verbose=True\n", "# verbose=True\n",
")\n", "# )\n",
"dataset.set_values(\"qwen_umap\", res.current_points)\n", "# dataset.set_values(\"qwen_umap\", res.current_points)\n",
"\n", "\n",
"print(\"UMAP visualization computed. Launch the app to see the plot.\")\n", "# print(\"UMAP visualization computed. Launch the app to see the plot.\")\n",
"session = fo.launch_app(dataset)\n", "# session = fo.launch_app(dataset)\n",
"session.wait()" "# session.wait()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8c23ca3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 2.22716806e+06 -1.55841522e+05 1.20176465e+04 ... 7.78593400e+03\n",
" 1.90060269e+03 -1.81817591e+02]\n",
" [-1.55841522e+05 2.45425677e+06 9.82699914e+04 ... -5.37744672e+04\n",
" -1.14976214e+05 1.95498686e+05]\n",
" [ 1.20176465e+04 9.82699914e+04 2.05845769e+06 ... -6.42761657e+04\n",
" -1.57428613e+03 1.90527654e+05]\n",
" ...\n",
" [ 7.78593400e+03 -5.37744672e+04 -6.42761657e+04 ... 2.19897397e+06\n",
" 9.79439501e+04 2.63393791e+05]\n",
" [ 1.90060269e+03 -1.14976214e+05 -1.57428613e+03 ... 9.79439501e+04\n",
" 2.09478614e+06 1.35758244e+05]\n",
" [-1.81817591e+02 1.95498686e+05 1.90527654e+05 ... 2.63393791e+05\n",
" 1.35758244e+05 2.12691079e+06]]\n",
"\n",
" [[ 2.83658946e+06 -1.34780282e+05 6.49580505e+04 ... 8.54367930e+04\n",
" 2.68631019e+04 -2.00238083e+03]\n",
" [-1.34780282e+05 3.16043262e+06 1.74730403e+05 ... -1.52450289e+04\n",
" -1.33867983e+05 2.33287605e+05]\n",
" [ 6.49580505e+04 1.74730403e+05 2.62079599e+06 ... -1.34705133e+05\n",
" -3.42729631e+03 2.52694121e+05]\n",
" ...\n",
" [ 8.54367930e+04 -1.52450289e+04 -1.34705133e+05 ... 2.83179172e+06\n",
" 1.20704061e+05 3.58186648e+05]\n",
" [ 2.68631019e+04 -1.33867983e+05 -3.42729631e+03 ... 1.20704061e+05\n",
" 2.61548713e+06 2.01096011e+05]\n",
" [-2.00238083e+03 2.33287605e+05 2.52694121e+05 ... 3.58186648e+05\n",
" 2.01096011e+05 2.68561973e+06]]\n",
"\n",
" [[ 2.46084057e+06 -1.61990924e+05 2.05209921e+04 ... 5.80037219e+04\n",
" -2.11338740e+03 -1.79719159e+04]\n",
" [-1.61990924e+05 2.77033377e+06 1.21300499e+05 ... -2.88489495e+04\n",
" -1.56314630e+05 1.81241062e+05]\n",
" [ 2.05209921e+04 1.21300499e+05 2.26999115e+06 ... -1.22265990e+05\n",
" -1.16935046e+04 1.98900231e+05]\n",
" ...\n",
" [ 5.80037219e+04 -2.88489495e+04 -1.22265990e+05 ... 2.44690178e+06\n",
" 9.92107868e+04 2.87651396e+05]\n",
" [-2.11338740e+03 -1.56314630e+05 -1.16935046e+04 ... 9.92107868e+04\n",
" 2.24557593e+06 1.34111998e+05]\n",
" [-1.79719159e+04 1.81241062e+05 1.98900231e+05 ... 2.87651396e+05\n",
" 1.34111998e+05 2.34916522e+06]]\n",
"\n",
" ...\n",
"\n",
" [[ 1.92829727e+06 -1.59315776e+05 1.58992088e+03 ... 2.85003295e+04\n",
" 4.22064899e+03 -3.58290465e+04]\n",
" [-1.59315776e+05 2.20558282e+06 8.03460647e+04 ... -4.04459318e+04\n",
" -1.07734468e+05 1.56172361e+05]\n",
" [ 1.58992088e+03 8.03460647e+04 1.83371422e+06 ... -1.11957845e+05\n",
" 2.80346243e+03 1.49548841e+05]\n",
" ...\n",
" [ 2.85003295e+04 -4.04459318e+04 -1.11957845e+05 ... 1.91110989e+06\n",
" 9.80180683e+04 1.85174267e+05]\n",
" [ 4.22064899e+03 -1.07734468e+05 2.80346243e+03 ... 9.80180683e+04\n",
" 1.77703462e+06 8.43241833e+04]\n",
" [-3.58290465e+04 1.56172361e+05 1.49548841e+05 ... 1.85174267e+05\n",
" 8.43241833e+04 1.89600377e+06]]\n",
"\n",
" [[ 3.47995050e+06 -2.33949522e+04 2.34412434e+05 ... 1.74855689e+05\n",
" 1.42301226e+05 1.03626216e+05]\n",
" [-2.33949522e+04 3.91344835e+06 3.68391546e+05 ... 1.48516088e+05\n",
" -5.32260301e+04 4.19584565e+05]\n",
" [ 2.34412434e+05 3.68391546e+05 3.16329032e+06 ... -7.83149391e+04\n",
" 1.04717741e+05 4.01535013e+05]\n",
" ...\n",
" [ 1.74855689e+05 1.48516088e+05 -7.83149391e+04 ... 3.45461426e+06\n",
" 1.69208897e+05 5.00179297e+05]\n",
" [ 1.42301226e+05 -5.32260301e+04 1.04717741e+05 ... 1.69208897e+05\n",
" 3.23120264e+06 3.85596283e+05]\n",
" [ 1.03626216e+05 4.19584565e+05 4.01535013e+05 ... 5.00179297e+05\n",
" 3.85596283e+05 3.23650330e+06]]\n",
"\n",
" [[ 2.87968337e+06 -1.54796543e+05 6.48388780e+04 ... 6.24845766e+04\n",
" 4.24673639e+04 -9.91376939e+03]\n",
" [-1.54796543e+05 3.29781697e+06 1.82272830e+05 ... 9.13276300e+03\n",
" -1.04968382e+05 2.54428804e+05]\n",
" [ 6.48388780e+04 1.82272830e+05 2.68323883e+06 ... -1.43183305e+05\n",
" 1.35349522e+04 2.71469510e+05]\n",
" ...\n",
" [ 6.24845766e+04 9.13276300e+03 -1.43183305e+05 ... 2.94868837e+06\n",
" 1.25676092e+05 3.72271111e+05]\n",
" [ 4.24673639e+04 -1.04968382e+05 1.35349522e+04 ... 1.25676092e+05\n",
" 2.69905870e+06 2.40775248e+05]\n",
" [-9.91376939e+03 2.54428804e+05 2.71469510e+05 ... 3.72271111e+05\n",
" 2.40775248e+05 2.76807176e+06]]]\n"
]
}
],
"source": [
"from sklearn.metrics import silhouette_samples\n",
"print(silhouette_samples(np.array(embeddings), labels=cluster_labels))"
] ]
} }
], ],

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output_file.txt Normal file
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119
src/data_image_selection.py Normal file
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@ -0,0 +1,119 @@
import json
import numpy as np
from typing import List, Tuple
from sklearn.mixture import GaussianMixture
from sklearn.metrics import silhouette_samples
from tqdm import tqdm
import argparse
def load_embeddings_from_json(json_path: str) -> Tuple[List[str], np.ndarray]:
"""Loads file paths and embeddings from a JSON file."""
print(f"Loading embeddings from {json_path}...")
with open(json_path, "r") as f:
embedding_data = json.load(f)
file_paths = [record["filepath"] for record in embedding_data]
embeddings = np.array([record["embedding"] for record in embedding_data], dtype=np.float32)
print(f"Loaded {len(file_paths)} samples with embedding dimension {embeddings.shape[1]}.")
return file_paths, embeddings
def create_gmm_and_bic_score(embeddings: np.ndarray, n_clusters: int) -> Tuple[GaussianMixture, float]:
"""Fits a GMM and adds cluster labels to the dataset."""
field_name = f"gmm_cluster_{n_clusters}"
print(f"Running GMM with {n_clusters} components. Results will be in field '{field_name}'...")
gmm = GaussianMixture(n_components=n_clusters, random_state=42)
try:
gmm.fit(embeddings)
print("GMM clustering complete.")
except:
print("Passing this, error")
return None, np.inf
print("BIC score:", gmm.bic(embeddings))
return gmm, gmm.bic(embeddings)
def analyze_and_select_gmm(
embeddings: np.ndarray,
min_num_clusters: int,
max_num_clusters: int,
step_size: int = 10
):
n_clusters_list = list(range(min_num_clusters, max_num_clusters + 1, step_size))
bic_list = []
gmm_list = []
for n_clusters in tqdm(n_clusters_list, desc = "GMM analysis based on number of clusters"):
gmm, bic_score = create_gmm_and_bic_score(embeddings=embeddings, n_clusters=n_clusters)
gmm_list.append(gmm)
bic_list.append(bic_score)
bic_min_index = np.argmin(bic_list)
selected_gmm = gmm_list[bic_min_index]
return selected_gmm
def select_samples(
embeddings: np.ndarray,
gaussian_mixtures: GaussianMixture,
filepaths: List[str]
):
percentage_to_label:float = 0.5
percentage_representative: float = 0.33
total_number_images = len(filepaths)
total_images_to_label = int(total_number_images * percentage_to_label)
num_high_confidence = int(total_images_to_label * percentage_representative)
num_low_confidence = total_images_to_label - num_high_confidence
print(f"Total images: {total_number_images}")
print(f"Total to label ({percentage_to_label*100}%): {total_images_to_label}")
print(f" - Highly representative samples: {num_high_confidence}")
print(f" - Least representative samples: {num_low_confidence}")
labels = gaussian_mixtures.predict(embeddings)
individual_scores = silhouette_samples(embeddings, labels)
sorted_indices = np.argsort(individual_scores)
# 3. Identify the indices for low and high confidence samples
low_confidence_indices = sorted_indices[:num_low_confidence]
high_confidence_indices = sorted_indices[-num_high_confidence:]
# 4. Combine them into a final list of indices to be labeled
final_indices_to_label = np.concatenate([low_confidence_indices, high_confidence_indices])
# 5. Get the corresponding image file paths
labeling_queue = [filepaths[i] for i in final_indices_to_label]
return labeling_queue
def main():
"""Main function to run the analysis pipeline."""
parser = argparse.ArgumentParser(description="Cluster and visualize document embeddings with FiftyOne.")
parser.add_argument("json_path", type=str, help="Path to the JSON file containing embeddings.")
# parser.add_argument("json_label_path", type=str, help="Path to the JSON label file.")
# parser.add_argument("--bill-type", type = str, help="Health invoice document type, eg. Ostéopathie, lentille, etc.")
parser.add_argument("--min_num_clusters", type=int, default=10, help="Minimum numbers of clusters for GMM.")
parser.add_argument("--max_num_clusters", type=int, default=100, help="Maximum numbers of clusters for GMM.")
parser.add_argument("--step_size_clusters", type=int, default=10, help="Maximum numbers of clusters for GMM.")
args = parser.parse_args()
# 1. Load data
filepaths, embeddings = load_embeddings_from_json(args.json_path)
gaussian_mixture = analyze_and_select_gmm(
embeddings=embeddings,
min_num_clusters=args.min_num_clusters,
max_num_clusters=args.max_num_clusters,
step_size=args.step_size_clusters
)
selected_filepaths = select_samples(
embeddings=embeddings, gaussian_mixtures=gaussian_mixture, filepaths=filepaths
)
with open('./output_files.json', "w") as file:
file.write(json.dumps(selected_filepaths, indent=4))
if __name__ == "__main__":
main()

106
src/display_embeddings_and_images.py Normal file
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@ -0,0 +1,106 @@
import fiftyone as fo
import fiftyone.brain as fob
import numpy as np
from sklearn.mixture import GaussianMixture
import json
import argparse
from typing import List, Tuple
def load_embeddings_from_json(json_path: str) -> Tuple[List[str], np.ndarray]:
"""Loads file paths and embeddings from a JSON file."""
print(f"Loading embeddings from {json_path}...")
with open(json_path, "r") as f:
embedding_data = json.load(f)
file_paths = [record["filepath"] for record in embedding_data]
embeddings = np.array([record["embedding"] for record in embedding_data], dtype=np.float32)
print(f"Loaded {len(file_paths)} samples with embedding dimension {embeddings.shape[1]}.")
return file_paths, embeddings
def create_fiftyone_dataset(dataset_name: str, file_paths: List[str]) -> fo.Dataset:
"""Creates or overwrites a FiftyOne dataset from a list of file paths."""
if dataset_name in fo.list_datasets():
print(f"Dataset '{dataset_name}' already exists. Deleting it.")
fo.delete_dataset(dataset_name)
print(f"Creating new dataset '{dataset_name}'...")
dataset = fo.Dataset(dataset_name)
samples = [fo.Sample(filepath=p) for p in file_paths]
dataset.add_samples(samples)
print("Dataset created successfully.")
return dataset
def add_gmm_clusters_to_dataset(dataset: fo.Dataset, embeddings: np.ndarray, n_clusters: int):
"""Fits a GMM and adds cluster labels to the dataset."""
field_name = f"gmm_cluster_{n_clusters}"
print(f"Running GMM with {n_clusters} components. Results will be in field '{field_name}'...")
gmm = GaussianMixture(n_components=n_clusters, random_state=42)
cluster_labels = gmm.fit_predict(embeddings)
dataset.add_sample_field(f"{field_name}", fo.IntField)
for sample, label in zip(dataset, cluster_labels):
sample[field_name] = int(label)
sample.save()
print("GMM clustering complete.")
def compute_embedding_visualization(
dataset: fo.Dataset,
embeddings: np.ndarray,
brain_key: str,
method: str = "tsne"
):
"""Computes a 2D visualization of embeddings and adds it to the dataset."""
vis_field_name = f"{brain_key}_{method}"
print(f"Computing {method.upper()} visualization with brain key '{brain_key}'...")
results = fob.compute_visualization(
dataset,
embeddings=embeddings,
brain_key=brain_key,
method=method,
verbose=True
)
dataset.set_values(vis_field_name, results.current_points)
print(f"Visualization complete. Points stored in field '{vis_field_name}'.")
def main():
"""Main function to run the analysis pipeline."""
parser = argparse.ArgumentParser(description="Cluster and visualize document embeddings with FiftyOne.")
parser.add_argument("json_path", type=str, help="Path to the JSON file containing embeddings.")
parser.add_argument("--dataset_name", type=str, default="embedding_analysis_dataset", help="Name for the FiftyOne dataset.")
parser.add_argument("--n_clusters", type=int, nargs='+', default=[50, 100], help="A list of numbers of clusters for GMM.")
parser.add_argument("--vis_method", type=str, default="tsne", choices=["tsne", "umap"], help="Visualization method.")
parser.add_argument("--brain_key", type=str, default="doc_embeddings_viz", help="FiftyOne brain key for the visualization.")
parser.add_argument("--no_launch", action="store_true", help="Do not launch the FiftyOne App after processing.")
args = parser.parse_args()
# 1. Load data
file_paths, embeddings = load_embeddings_from_json(args.json_path)
# 2. Create FiftyOne Dataset
dataset = create_fiftyone_dataset(args.dataset_name, file_paths)
# 3. Run GMM clustering for each specified number of clusters
for n in args.n_clusters:
add_gmm_clusters_to_dataset(dataset, embeddings, n)
# 4. Compute visualization
compute_embedding_visualization(dataset, embeddings, args.brain_key, args.vis_method)
# 5. Launch the app
if not args.no_launch:
print("Launching the FiftyOne App...")
session = fo.launch_app(dataset)
session.wait()
else:
print("Processing complete. To view the results, launch the app manually with:")
print(f">>> import fiftyone as fo")
print(f">>> dataset = fo.load_dataset('{args.dataset_name}')")
print(f">>> session = fo.launch_app(dataset)")
if __name__ == "__main__":
main()

2
src/embedding_extraction.py
View File

@ -57,7 +57,7 @@ def get_layoutlm_image_embeddings(
return embeddings.cpu().numpy(), valid_paths return embeddings.cpu().numpy(), valid_paths
def get_image_embeddings( def get_qwenvl_image_embeddings(
model: AutoModel, model: AutoModel,
processor: AutoProcessor, processor: AutoProcessor,
image_paths: List[str], image_paths: List[str],