From 9aabd991c53e6065221fe0f1ca0fa22e9d0e318a Mon Sep 17 00:00:00 2001 From: trungkienbkhn Date: Tue, 2 Sep 2025 15:01:50 +0000 Subject: [PATCH 1/2] check visison extract model --- extract/clustering_example_InternVL3_5.ipynb | 874 ++++++++++++ extract/clustering_example_qwen.ipynb | 432 ++++++ extract/clustering_example_qwen_Debug.ipynb | 1301 ++++++++++++++++++ extract/extract.py | 90 ++ extract/extract_donut.py | 201 +++ extract/test.ipynb | 139 ++ 6 files changed, 3037 insertions(+) create mode 100644 extract/clustering_example_InternVL3_5.ipynb create mode 100644 extract/clustering_example_qwen.ipynb create mode 100644 extract/clustering_example_qwen_Debug.ipynb create mode 100644 extract/extract.py create mode 100644 extract/extract_donut.py create mode 100644 extract/test.ipynb diff --git a/extract/clustering_example_InternVL3_5.ipynb b/extract/clustering_example_InternVL3_5.ipynb new file mode 100644 index 0000000..88d37b3 --- /dev/null +++ b/extract/clustering_example_InternVL3_5.ipynb @@ -0,0 +1,874 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "59f8a415", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nguyendc/sonnh/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-09-02 13:50:30.358544: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1756821030.369428 3858431 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1756821030.372761 3858431 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1756821030.382108 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1756821030.382119 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1756821030.382121 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1756821030.382123 3858431 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-09-02 13:50:30.385619: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-09-02 13:50:35,304] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/bin/ld: cannot find -laio: No such file or directory\n", + "collect2: error: ld returned 1 exit status\n", + "/usr/bin/ld: cannot find -laio: No such file or directory\n", + "collect2: error: ld returned 1 exit status\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: cuda\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n", + "- configuration_intern_vit.py\n", + ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n", + "A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n", + "- configuration_internvl_chat.py\n", + "- configuration_intern_vit.py\n", + ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n", + "A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n", + "- conversation.py\n", + ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n", + "A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n", + "- modeling_intern_vit.py\n", + ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n", + "A new version of the following files was downloaded from https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct:\n", + "- modeling_internvl_chat.py\n", + "- conversation.py\n", + "- modeling_intern_vit.py\n", + ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n", + "Fetching 2 files: 100%|██████████| 2/2 [00:00<00:00, 7.56it/s]\n", + "Loading checkpoint shards: 100%|██████████| 2/2 [00:02<00:00, 1.03s/it]\n" + ] + } + ], + "source": [ + "import torch\n", + "from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, AutoModel, InternVLChatModel\n", + "# from qwen_vl_utils import process_vision_info\n", + "from PIL import Image\n", + "import os\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import math\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModel\n", + "import timm\n", + "\n", + "# --- Configuration ---\n", + "# MODEL_NAME = \"OpenGVLab/InternVL3_5-4B\" # You can choose other model sizes\n", + "MODEL_NAME = \"OpenGVLab/InternVL3_5-4B-Instruct\"\n", + "\n", + "\n", + "IMAGE_DIR = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/\"\n", + "BATCH_SIZE = 4\n", + "# --- End Configuration ---\n", + "\n", + "# Check for GPU availability\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "print(f\"Using device: {device}\")\n", + "\n", + "# Load the model and processor\n", + "\n", + "model = AutoModel.from_pretrained(\n", + " MODEL_NAME,\n", + " torch_dtype=torch.bfloat16,\n", + " use_flash_attn=True,\n", + " attn_implementation=\"flash_attention_2\",\n", + " trust_remote_code=True,\n", + " device_map=\"cuda\").eval()\n", + "\n", + "processor = AutoProcessor.from_pretrained(\n", + " MODEL_NAME, \n", + " trust_remote_code=True\n", + " )\n", + "tokenizer = AutoTokenizer.from_pretrained(\n", + " MODEL_NAME, \n", + " trust_remote_code=True, \n", + " use_fast=False\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6d826d19", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "InternVLChatModel(\n", + " (vision_model): InternVisionModel(\n", + " (embeddings): InternVisionEmbeddings(\n", + " (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))\n", + " )\n", + " (encoder): InternVisionEncoder(\n", + " (layers): ModuleList(\n", + " (0-23): 24 x InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (drop_path1): Identity()\n", + " (drop_path2): Identity()\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (language_model): Qwen3ForCausalLM(\n", + " (model): Qwen3Model(\n", + " (embed_tokens): Embedding(151936, 2560)\n", + " (layers): ModuleList(\n", + " (0-35): 36 x Qwen3DecoderLayer(\n", + " (self_attn): Qwen3Attention(\n", + " (q_proj): Linear(in_features=2560, out_features=4096, bias=False)\n", + " (k_proj): Linear(in_features=2560, out_features=1024, bias=False)\n", + " (v_proj): Linear(in_features=2560, out_features=1024, bias=False)\n", + " (o_proj): Linear(in_features=4096, out_features=2560, bias=False)\n", + " (q_norm): Qwen3RMSNorm((128,), eps=1e-06)\n", + " (k_norm): Qwen3RMSNorm((128,), eps=1e-06)\n", + " )\n", + " (mlp): Qwen3MLP(\n", + " (gate_proj): Linear(in_features=2560, out_features=9728, bias=False)\n", + " (up_proj): Linear(in_features=2560, out_features=9728, bias=False)\n", + " (down_proj): Linear(in_features=9728, out_features=2560, bias=False)\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): Qwen3RMSNorm((2560,), eps=1e-06)\n", + " (post_attention_layernorm): Qwen3RMSNorm((2560,), eps=1e-06)\n", + " )\n", + " )\n", + " (norm): Qwen3RMSNorm((2560,), eps=1e-06)\n", + " (rotary_emb): Qwen3RotaryEmbedding()\n", + " )\n", + " (lm_head): Linear(in_features=2560, out_features=151936, bias=False)\n", + " )\n", + " (mlp1): Sequential(\n", + " (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (1): Linear(in_features=4096, out_features=2560, bias=True)\n", + " (2): GELU(approximate='none')\n", + " (3): Linear(in_features=2560, out_features=2560, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7bbfcf47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "InternVisionModel(\n", + " (embeddings): InternVisionEmbeddings(\n", + " (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))\n", + " )\n", + " (encoder): InternVisionEncoder(\n", + " (layers): ModuleList(\n", + " (0-23): 24 x InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (drop_path1): Identity()\n", + " (drop_path2): Identity()\n", + " )\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.vision_model" + ] + }, + { + "cell_type": "markdown", + "id": "ae26d6cf", + "metadata": {}, + "source": [ + "# demo ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "817d3ccb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d41f94bd", + "metadata": {}, + "outputs": [], + "source": [ + "# import math\n", + "import numpy as np\n", + "import torch\n", + "import torchvision.transforms as T\n", + "# from decord import VideoReader, cpu\n", + "from PIL import Image\n", + "from torchvision.transforms.functional import InterpolationMode\n", + "# from modelscope import AutoModel, AutoTokenizer\n", + "\n", + "IMAGENET_MEAN = (0.485, 0.456, 0.406)\n", + "IMAGENET_STD = (0.229, 0.224, 0.225)\n", + "\n", + "def build_transform(input_size):\n", + " MEAN, STD = IMAGENET_MEAN, IMAGENET_STD\n", + " transform = T.Compose([\n", + " T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),\n", + " T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),\n", + " T.ToTensor(),\n", + " T.Normalize(mean=MEAN, std=STD)\n", + " ])\n", + " return transform\n", + "\n", + "def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):\n", + " best_ratio_diff = float('inf')\n", + " best_ratio = (1, 1)\n", + " area = width * height\n", + " for ratio in target_ratios:\n", + " target_aspect_ratio = ratio[0] / ratio[1]\n", + " ratio_diff = abs(aspect_ratio - target_aspect_ratio)\n", + " if ratio_diff < best_ratio_diff:\n", + " best_ratio_diff = ratio_diff\n", + " best_ratio = ratio\n", + " elif ratio_diff == best_ratio_diff:\n", + " if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:\n", + " best_ratio = ratio\n", + " return best_ratio\n", + "\n", + "def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):\n", + " orig_width, orig_height = image.size\n", + " aspect_ratio = orig_width / orig_height\n", + "\n", + " # calculate the existing image aspect ratio\n", + " target_ratios = set(\n", + " (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if\n", + " i * j <= max_num and i * j >= min_num)\n", + " target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])\n", + "\n", + " # find the closest aspect ratio to the target\n", + " target_aspect_ratio = find_closest_aspect_ratio(\n", + " aspect_ratio, target_ratios, orig_width, orig_height, image_size)\n", + "\n", + " # calculate the target width and height\n", + " target_width = image_size * target_aspect_ratio[0]\n", + " target_height = image_size * target_aspect_ratio[1]\n", + " blocks = target_aspect_ratio[0] * target_aspect_ratio[1]\n", + "\n", + " # resize the image\n", + " resized_img = image.resize((target_width, target_height))\n", + " processed_images = []\n", + " for i in range(blocks):\n", + " box = (\n", + " (i % (target_width // image_size)) * image_size,\n", + " (i // (target_width // image_size)) * image_size,\n", + " ((i % (target_width // image_size)) + 1) * image_size,\n", + " ((i // (target_width // image_size)) + 1) * image_size\n", + " )\n", + " # split the image\n", + " split_img = resized_img.crop(box)\n", + " processed_images.append(split_img)\n", + " assert len(processed_images) == blocks\n", + " if use_thumbnail and len(processed_images) != 1:\n", + " thumbnail_img = image.resize((image_size, image_size))\n", + " processed_images.append(thumbnail_img)\n", + " return processed_images\n", + "\n", + "def load_image(image_file, input_size=448, max_num=12):\n", + " image = Image.open(image_file).convert('RGB')\n", + " transform = build_transform(input_size=input_size)\n", + " images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)\n", + " pixel_values = [transform(image) for image in images]\n", + " pixel_values = torch.stack(pixel_values)\n", + " return pixel_values" + ] + }, + { + "cell_type": "markdown", + "id": "f2ec71a4", + "metadata": {}, + "source": [ + "# Attention pooling\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a404fa19", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "def gem_pool(x, p: float = 3.0, eps: float = 1e-6):\n", + " # x: [B, N, D]\n", + " return (x.clamp(min=eps).pow(p).mean(dim=1)).pow(1.0/p)\n", + "\n", + "@torch.no_grad()\n", + "def image_embedding(pixel_values, model, use_tiling=True):\n", + " # pixel_values: nếu dùng processor của InternVL, có thể là [T,3,H,W]; nếu bạn tự resize = [1,3,H,W]\n", + " out = model.vision_model(pixel_values=pixel_values, output_hidden_states=True)\n", + " tok = out.last_hidden_state # [T, N, 1024] hoặc [1, N, 1024]\n", + " if tok.dim() == 2: # phòng trường hợp model trả [N, D]\n", + " tok = tok.unsqueeze(0)\n", + "\n", + " # 1) Attention pooling theo token, trong từng tile\n", + " w_tok = torch.softmax(tok.norm(dim=-1), dim=1).unsqueeze(-1) # [T,N,1]\n", + " attn_tile = (tok * w_tok).sum(dim=1) # [T,1024]\n", + "\n", + " # 2) Các pooling khác theo token\n", + " mean_tile = tok.mean(dim=1) # [T,1024]\n", + " max_tile = tok.max(dim=1).values # [T,1024]\n", + " gem_tile = gem_pool(tok, p=3.0) # [T,1024]\n", + "\n", + " # 3) Attention across-tiles (giữ multi-scale nhưng gọn)\n", + " tile_scores = attn_tile.norm(dim=-1) # [T]\n", + " w_tile = torch.softmax(tile_scores, dim=0).unsqueeze(-1) # [T,1]\n", + "\n", + " mean_vec = (mean_tile * w_tile).sum(dim=0)\n", + " max_vec = (max_tile * w_tile).sum(dim=0)\n", + " gem_vec = (gem_tile * w_tile).sum(dim=0)\n", + " attn_vec = (attn_tile * w_tile).sum(dim=0)\n", + "\n", + " # 4) Hợp nhất nhiều “góc nhìn” → 1 vector giàu thông tin\n", + " one_vec = torch.cat([mean_vec, max_vec, gem_vec, attn_vec], dim=0) # [4*1024]\n", + " one_vec = F.normalize(one_vec, dim=-1).unsqueeze(0) # [1, 4096]\n", + " return one_vec.half() # FP16 để tiết kiệm bộ nhớ" + ] + }, + { + "cell_type": "markdown", + "id": "ed35a4ce", + "metadata": {}, + "source": [ + "# pool" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3edf8b67", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "# --- Pooling theo token (trong 1 tile) ---\n", + "def _pool_tokens(tokens: torch.Tensor, how: str = \"mean\") -> torch.Tensor:\n", + " \"\"\"\n", + " tokens: [1, N, D] hoặc [N, D]\n", + " return: [D]\n", + " \"\"\"\n", + " if tokens.dim() == 3: # [1, N, D] -> [N, D]\n", + " tokens = tokens.squeeze(0)\n", + "\n", + " if how == \"mean\":\n", + " v = tokens.mean(dim=0)\n", + " elif how == \"max\":\n", + " v = tokens.max(dim=0).values\n", + " elif how == \"gem\":\n", + " p = 3.0\n", + " v = (tokens.clamp(min=1e-6).pow(p).mean(dim=0)).pow(1.0/p)\n", + " elif how == \"cls\":\n", + " # chỉ dùng nếu backbone có CLS token ở vị trí đầu\n", + " v = tokens[0]\n", + " else:\n", + " raise ValueError(f\"Unknown pooling: {how}\")\n", + "\n", + " return v\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def image_embedding_global(model, pixel_values: torch.Tensor,\n", + " pool: str = \"mean\",\n", + " normalize: bool = False,\n", + " global_index: int = 0,\n", + " use_projector: bool = False) -> torch.Tensor:\n", + " \"\"\"\n", + " Trả về 1 vector [1, D] mô tả toàn ảnh, chỉ dùng GLOBAL tile.\n", + " - pixel_values: [T,3,H,W] (ví dụ T=7) hoặc [1,3,H,W]\n", + " - global_index: thường = 0 (tile toàn ảnh nằm đầu)\n", + " - use_projector: CHỈ bật nếu bạn chắc chắn chiều khớp với projector (mlp1)\n", + " \"\"\"\n", + " model.eval()\n", + " device = next(model.parameters()).device\n", + " x = pixel_values.to(device)\n", + "\n", + " out = model.vision_model(pixel_values=x) # last_hidden_state: [T, N, D] hoặc [1, N, D]\n", + " tok = out.last_hidden_state\n", + "\n", + " # chọn global tile\n", + " if tok.size(0) > 1:\n", + " tok = tok[global_index:global_index+1] # [1, N, D]\n", + "\n", + " # (tuỳ chọn) projector sang không gian khác - cẩn thận mismatch chiều!\n", + " if use_projector:\n", + " # CHỈ nên bật khi biết chắc input dim của mlp1 khớp với tok.size(-1)\n", + " in_feat = getattr(model.mlp1[1], \"in_features\", None)\n", + " if in_feat is not None and tok.size(-1) == in_feat:\n", + " tok = model.mlp1(tok) # [1, N, D’]\n", + " else:\n", + " raise ValueError(f\"Projector input dim mismatch: tokens={tok.size(-1)} vs mlp1.in={in_feat}\")\n", + "\n", + " v = _pool_tokens(tok, how=pool) # [D]\n", + " if normalize:\n", + " v = F.normalize(v, dim=-1)\n", + " return v.unsqueeze(0) # [1, D]\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def image_embedding_mean(model, pixel_values: torch.Tensor,\n", + " pool: str = \"mean\",\n", + " normalize: bool = True,\n", + " use_projector: bool = False) -> torch.Tensor:\n", + " \"\"\"\n", + " Trả về 1 vector [1, D] mô tả toàn ảnh, bằng cách:\n", + " (1) pool theo token trong từng tile → [T, D]\n", + " (2) lấy mean across-tiles → [D]\n", + " \"\"\"\n", + " model.eval()\n", + " device = next(model.parameters()).device\n", + " x = pixel_values.to(device)\n", + "\n", + " out = model.vision_model(pixel_values=x)\n", + " tok = out.last_hidden_state # [T, N, D] hoặc [1, N, D]\n", + "\n", + " if use_projector:\n", + " in_feat = getattr(model.mlp1[1], \"in_features\", None)\n", + " if in_feat is not None and tok.size(-1) == in_feat:\n", + " tok = model.mlp1(tok)\n", + " else:\n", + " raise ValueError(f\"Projector input dim mismatch: tokens={tok.size(-1)} vs mlp1.in={in_feat}\")\n", + "\n", + " # pool theo token trong từng tile\n", + " T = tok.size(0)\n", + " per_tile = [ _pool_tokens(tok[t:t+1], how=pool) for t in range(T) ] # list of [D]\n", + " per_tile = torch.stack(per_tile, dim=0) # [T, D]\n", + "\n", + " # mean across-tiles\n", + " v = per_tile.mean(dim=0) # [D]\n", + " if normalize:\n", + " v = F.normalize(v, dim=-1)\n", + " return v.unsqueeze(0) # [1, D]\n" + ] + }, + { + "cell_type": "markdown", + "id": "613cf001", + "metadata": {}, + "source": [ + "# infer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cdfdab0e", + "metadata": {}, + "outputs": [], + "source": [ + "def get_image_embedding(path):\n", + " \"\"\"\n", + " Processes a batch of images and extracts their embeddings.\n", + " \"\"\"\n", + " images_pil = []\n", + " valid_paths = []\n", + " if path.lower().endswith(('.png', '.jpg', '.jpeg')):\n", + " try:\n", + " # The processor expects PIL images in RGB format\n", + " # images_pil.append(Image.open(path).convert(\"RGB\"))\n", + " # print(path)\n", + " valid_paths.append(path)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not load image {path}. Skipping. Error: {e}\")\n", + "\n", + " if not valid_paths:\n", + " return np.array([]), []\n", + "\n", + " all_pixel_values = []\n", + " for valid_path in valid_paths:\n", + " pixel_values = load_image(valid_path, max_num=12).to(torch.bfloat16).cuda()\n", + " # print(pixel_values.shape)\n", + " all_pixel_values.append(pixel_values)\n", + " # For pure vision feature extraction, we can provide an empty text prompt.\n", + " # The processor handles tokenizing text and preparing images.\n", + " inputs = torch.cat(all_pixel_values, dim=0).to(device)\n", + " \n", + " # embeddings = image_embedding(inputs, model, use_tiling=True)\n", + " embeddings = image_embedding_mean(model, inputs)\n", + " \n", + " return embeddings.to(torch.float16).cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cdaebb7b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2800/2800 [20:51<00:00, 2.24it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings extracted and saved.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "# --- Process all images in the directory ---\n", + "image_files = [os.path.join(IMAGE_DIR, f) for f in os.listdir(IMAGE_DIR) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n", + "all_embeddings = []\n", + "filepaths = []\n", + "BATCH_SIZE = 1\n", + "\n", + "with open(\"embeddings_factures_osteopathie_1k_InternVL3_5-4B-Instruct.json\", \"w\") as f:\n", + "\n", + " f.write(\"[\\n\")\n", + " first = True\n", + " for i in tqdm(range(0, len(image_files), BATCH_SIZE)):\n", + " batch_paths = image_files[i]\n", + " batch_embeddings = get_image_embedding(batch_paths)\n", + " embeddings_list = [emb.tolist() for emb in batch_embeddings]\n", + " for path, emb in zip(batch_paths, embeddings_list):\n", + " if not first:\n", + " f.write(\",\\n\")\n", + " json.dump({\"filepath\": path, \"embedding\": emb}, f)\n", + " first = False\n", + " f.write(\"\\n]\\n\")\n", + "\n", + "print(\"Embeddings extracted and saved.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0d0bf0a", + "metadata": {}, + "source": [ + "# check" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0772fc89", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2800 samples with embedding dimension 1024\n", + "Applied L2 normalization to embeddings\n", + "(2800, 1024)\n", + "(3918600,)\n", + "mean sim: 0.9939966 std: 0.0073577887\n" + ] + } + ], + "source": [ + "from sklearn.cluster import DBSCAN, MeanShift, AffinityPropagation\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.neighbors import NearestNeighbors\n", + "from sklearn.decomposition import PCA\n", + "import argparse\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "from datetime import datetime\n", + "\n", + "\n", + "embeddings_path = \"/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_InternVL3_5-4B-Instruct.json\"\n", + "with open(embeddings_path, 'r') as f:\n", + " data = json.load(f)\n", + "\n", + "file_paths = []\n", + "embeddings_list = []\n", + "\n", + "for item in data:\n", + " file_paths.append(item['filepath'])\n", + " embeddings_list.append(item['embedding'])\n", + "\n", + "embeddings = np.array(embeddings_list, dtype=np.float32)\n", + "print(f\"Loaded {len(file_paths)} samples with embedding dimension {embeddings.shape[1]}\")\n", + "\n", + "# Normalize embeddings using L2 normalization for cosine distance\n", + "embeddings_normalized = normalize(embeddings, norm='l2', axis=1)\n", + "print(\"Applied L2 normalization to embeddings\")\n", + "\n", + "sims = cosine_similarity(embeddings)\n", + "print(embeddings.shape)\n", + "# lấy upper triangle exclude diagonal để inspect\n", + "triu_idxs = np.triu_indices_from(sims, k=1)\n", + "dist_vals = sims[triu_idxs]\n", + "print(dist_vals.shape)\n", + "print(\"mean sim:\", dist_vals.mean(), \"std:\", dist_vals.std())" + ] + }, + { + "cell_type": "markdown", + "id": "cb4ea42b", + "metadata": {}, + "source": [ + "# temp" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2c3e6dd0", + "metadata": {}, + "outputs": [], + "source": [ + "image_path = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/c363e486-5d45-425e-aef9-4791cad120f7_20250213_120759_1_scale_1.0.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "29620d93", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n", + "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: \n", + "Please describe the image shortly.\n", + "Assistant: The image shows a receipt for a consultation with Noël Breignaud, an osteopath. It includes his contact information, with the address \"104, cours des fossés, 33210 Langon\" and his phone number. The receipt details a payment of 55€ for a consultation dated 15/06/2020. The receipt number is 1750401922774. There are handwritten details and signatures, with the amount and date written in ink. Noël Breignaud's signature and a circular stamp are also present.\n", + "User: \n", + "Please describe the image in detail.\n", + "Assistant: The image is a handwritten receipt or invoice from a practitioner named Noël Breign aud, who is an osteopath. The text on the left side of the document includes the following details:\n", + "\n", + "- **Name:** Noël Breignaud\n", + "- **Profession:** Ouestopathe (Osteopath)\n", + "- **Address:** 104, cours des fossés, 33210 Lagnon\n", + "- **Phone Number:** Tel. 06 88 70 66 43\n", + "\n", + "On the right side, there are registration and identification numbers:\n", + "\n", + "- **Nº SIRET:** 510 123 631 00010\n", + "- **Nº ADELI:** 330001108\n", + "- **Code APE:** 8690E\n", + "\n", + "The handwritten section of the document is in French and reads:\n", + "\n", + "- \"Déclaire avoir reçu de M. M. (fils) G[obon], Acquitté la somme de 55 €\n", + "Pour 1 consultation en date du 05/04/2024\n", + "N°: 1750460-19212774\"\n", + "\n", + "At the bottom right, there is a signature that appears to be of Noël Breignaud, with a red stamp partially visible, which seems to contain the text \"Noël BREIGNAUD\" and other markings.\n", + "\n", + "The date in the handwritten section is \"05/04/2024,\" indicating the receipt or service provided on that date. The amount mentioned is 55 euros for one consultation.\n" + ] + } + ], + "source": [ + "pixel_values = load_image(image_path, max_num=12).to(torch.bfloat16).cuda()\n", + "generation_config = dict(max_new_tokens=1024, do_sample=True)\n", + "\n", + "\n", + "\n", + "question = '\\nPlease describe the image shortly.'\n", + "response = model.chat(tokenizer, pixel_values, question, generation_config)\n", + "print(f'User: {question}\\nAssistant: {response}')\n", + "\n", + "# single-image multi-round conversation (单图多轮对话)\n", + "question = '\\nPlease describe the image in detail.'\n", + "response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)\n", + "print(f'User: {question}\\nAssistant: {response}')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "35dc90e0", + "metadata": {}, + "outputs": [], + "source": [ + "vout = model.vision_model(pixel_values=pixel_values, output_hidden_states=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77f3720a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 1025, 1024])\n" + ] + } + ], + "source": [ + "patch_feats = vout.last_hidden_state # [B, N_patches, Dv], Dv ~ 1024 theo kiến trúc của bạn\n", + "print(patch_feats.shape)\n", + "# Nếu backbone có CLS token, bạn có thể dùng patch_feats[:,0]\n", + "# Cách an toàn chung: mean-pool\n", + "# img_vec = patch_feats.mean(dim=1) # [B, Dv]\n", + "# img_vec = torch.nn.functional.normalize(img_vec, dim=-1) # L2 normalize cho retrieval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0043634c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([7, 1024])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# img_vec.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92032162", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/extract/clustering_example_qwen.ipynb b/extract/clustering_example_qwen.ipynb new file mode 100644 index 0000000..3974680 --- /dev/null +++ b/extract/clustering_example_qwen.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "59f8a415", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-09-02 15:00:12.976185: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1756825212.987686 3903757 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1756825212.991038 3903757 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1756825213.000855 3903757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1756825213.000880 3903757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1756825213.000882 3903757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1756825213.000884 3903757 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-09-02 15:00:13.005218: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-09-02 15:00:17,970] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/bin/ld: cannot find -laio: No such file or directory\n", + "collect2: error: ld returned 1 exit status\n", + "/usr/bin/ld: cannot find -laio: No such file or directory\n", + "collect2: error: ld returned 1 exit status\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: cuda\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.09it/s]\n", + "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n" + ] + } + ], + "source": [ + "import torch\n", + "from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor\n", + "# from qwen_vl_utils import process_vision_info\n", + "from PIL import Image\n", + "import os\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "\n", + "# --- Configuration ---\n", + "MODEL_NAME = \"Qwen/Qwen2.5-VL-3B-Instruct\" # You can choose other model sizes\n", + "\n", + "IMAGE_DIR = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/\"\n", + "BATCH_SIZE = 4\n", + "# --- End Configuration ---\n", + "\n", + "# Check for GPU availability\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "print(f\"Using device: {device}\")\n", + "\n", + "# Load the model and processor\n", + "model = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n", + " MODEL_NAME, torch_dtype=\"bfloat16\", device_map=\"cuda\", attn_implementation=\"flash_attention_2\",\n", + ")\n", + "processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "13479e1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Qwen2_5_VLProcessor:\n", + "- image_processor: Qwen2VLImageProcessor {\n", + " \"do_convert_rgb\": true,\n", + " \"do_normalize\": true,\n", + " \"do_rescale\": true,\n", + " \"do_resize\": true,\n", + " \"image_mean\": [\n", + " 0.48145466,\n", + " 0.4578275,\n", + " 0.40821073\n", + " ],\n", + " \"image_processor_type\": \"Qwen2VLImageProcessor\",\n", + " \"image_std\": [\n", + " 0.26862954,\n", + " 0.26130258,\n", + " 0.27577711\n", + " ],\n", + " \"max_pixels\": 12845056,\n", + " \"merge_size\": 2,\n", + " \"min_pixels\": 3136,\n", + " \"patch_size\": 14,\n", + " \"processor_class\": \"Qwen2_5_VLProcessor\",\n", + " \"resample\": 3,\n", + " \"rescale_factor\": 0.00392156862745098,\n", + " \"size\": {\n", + " \"longest_edge\": 12845056,\n", + " \"shortest_edge\": 3136\n", + " },\n", + " \"temporal_patch_size\": 2\n", + "}\n", + "\n", + "- tokenizer: Qwen2TokenizerFast(name_or_path='Qwen/Qwen2.5-VL-3B-Instruct', vocab_size=151643, model_max_length=131072, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n", + "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151657: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151658: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "}\n", + ")\n", + "\n", + "{\n", + " \"processor_class\": \"Qwen2_5_VLProcessor\"\n", + "}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdfdab0e", + "metadata": {}, + "outputs": [], + "source": [ + "def get_image_embeddings(image_paths):\n", + " \"\"\"\n", + " Processes a batch of images and extracts their embeddings.\n", + " \"\"\"\n", + " images_pil = []\n", + " valid_paths = []\n", + " for path in image_paths:\n", + " if path.lower().endswith(('.png', '.jpg', '.jpeg')):\n", + " try:\n", + " # The processor expects PIL images in RGB format\n", + " images_pil.append(Image.open(path).convert(\"RGB\"))\n", + " valid_paths.append(path)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not load image {path}. Skipping. Error: {e}\")\n", + "\n", + " if not images_pil:\n", + " return np.array([]), []\n", + "\n", + " # For pure vision feature extraction, we can provide an empty text prompt.\n", + " # The processor handles tokenizing text and preparing images.\n", + " inputs = processor(\n", + " text=[\"\"] * len(images_pil),\n", + " images=images_pil,\n", + " padding=True,\n", + " return_tensors=\"pt\"\n", + " ).to(device)\n", + "\n", + " with torch.no_grad():\n", + " # Get the vision embeddings from the model's vision tower\n", + " vision_outputs = model.visual(inputs['pixel_values'].to(dtype=model.dtype), grid_thw=inputs['image_grid_thw'])\n", + " # We'll use the pooled output as the embedding\n", + " embeddings = vision_outputs\n", + "\n", + " return embeddings.to(torch.float16).cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdaebb7b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 700/700 [22:12<00:00, 1.90s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embeddings extracted and saved.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "# --- Process all images in the directory ---\n", + "image_files = [os.path.join(IMAGE_DIR, f) for f in os.listdir(IMAGE_DIR) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n", + "all_embeddings = []\n", + "filepaths = []\n", + "\n", + "with open(\"embeddings_factures_osteopathie_1k_qwen.json\", \"w\") as f:\n", + "\n", + " f.write(\"[\\n\")\n", + " first = True\n", + " for i in tqdm(range(0, len(image_files), BATCH_SIZE)):\n", + " batch_paths = image_files[i:i+BATCH_SIZE]\n", + " batch_embeddings = get_image_embeddings(batch_paths)\n", + " embeddings_list = [emb.tolist() for emb in batch_embeddings]\n", + " for path, emb in zip(batch_paths, embeddings_list):\n", + " if not first:\n", + " f.write(\",\\n\")\n", + " json.dump({\"filepath\": path, \"embedding\": emb}, f)\n", + " first = False\n", + " f.write(\"\\n]\\n\")\n", + "\n", + "print(\"Embeddings extracted and saved.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2c3e6dd0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2800 samples with embedding dimension 2048\n", + "Applied L2 normalization to embeddings\n", + "(2800, 2048)\n", + "(3918600,)\n", + "mean sim: 0.37961555 std: 0.22605234\n" + ] + } + ], + "source": [ + "from sklearn.cluster import DBSCAN, MeanShift, AffinityPropagation\n", + "from sklearn.preprocessing import normalize\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.neighbors import NearestNeighbors\n", + "from sklearn.decomposition import PCA\n", + "import argparse\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "from datetime import datetime\n", + "import json\n", + "\n", + "embeddings_path = \"/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json\"\n", + "with open(embeddings_path, 'r') as f:\n", + " data = json.load(f)\n", + "\n", + "file_paths = []\n", + "embeddings_list = []\n", + "\n", + "for item in data:\n", + " file_paths.append(item['filepath'])\n", + " embeddings_list.append(item['embedding'])\n", + "\n", + "embeddings = np.array(embeddings_list, dtype=np.float32)\n", + "print(f\"Loaded {len(file_paths)} samples with embedding dimension {embeddings.shape[1]}\")\n", + "\n", + "# Normalize embeddings using L2 normalization for cosine distance\n", + "embeddings_normalized = normalize(embeddings, norm='l2', axis=1)\n", + "print(\"Applied L2 normalization to embeddings\")\n", + "\n", + "sims = cosine_similarity(embeddings)\n", + "print(embeddings.shape)\n", + "# lấy upper triangle exclude diagonal để inspect\n", + "triu_idxs = np.triu_indices_from(sims, k=1)\n", + "dist_vals = sims[triu_idxs]\n", + "print(dist_vals.shape)\n", + "print(\"mean sim:\", dist_vals.mean(), \"std:\", dist_vals.std())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29620d93", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27fea4f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100% |███████████████| 1091/1091 [174.1ms elapsed, 0s remaining, 6.3K samples/s] \n" + ] + } + ], + "source": [ + "import fiftyone as fo\n", + "import fiftyone.brain as fob\n", + "import numpy as np\n", + "from sklearn.mixture import GaussianMixture\n", + "import json\n", + "\n", + "DATASET_NAME = \"mock\"\n", + "\n", + "json_path = \"./embeddings_factures_osteopathie_1k_qwen.json\"\n", + "\n", + "with open(json_path, \"r\") as file:\n", + " embedding_data = json.load(file)\n", + "\n", + "file_paths = []\n", + "embeddings = []\n", + "for i, record in enumerate(embedding_data):\n", + " file_paths.append(record.get(\"filepath\"))\n", + " embeddings.append(record.get(\"embedding\"))\n", + "\n", + "if DATASET_NAME in fo.list_datasets():\n", + " dataset = fo.load_dataset(DATASET_NAME)\n", + " dataset.delete()\n", + "dataset = fo.Dataset(DATASET_NAME)\n", + "\n", + "# Add samples to the dataset\n", + "samples = [fo.Sample(filepath=p) for p in file_paths]\n", + "dataset.add_samples(samples)\n", + "\n", + "# Building Gaussian mixture model (GMM)\n", + "n_gaussians = 50\n", + "gmm = GaussianMixture(n_components=n_gaussians, random_state=42)\n", + "gmm.fit(embeddings)\n", + "cluster_labels = gmm.predict(embeddings)\n", + "\n", + "# Adding labeled embeddings to visulization\n", + "dataset.add_sample_field(\"gmm_cluster\", fo.IntField)\n", + "for sample, label in zip(dataset, cluster_labels):\n", + " sample[\"gmm_cluster_50_gaussians\"] = int(label)\n", + " sample.save()\n", + "\n", + "n_gaussians = 200\n", + "gmm = GaussianMixture(n_components=n_gaussians, random_state=42)\n", + "gmm.fit(embeddings)\n", + "cluster_labels = gmm.predict(embeddings)\n", + "\n", + "# Adding labeled embeddings to visulization\n", + "dataset.add_sample_field(\"gmm_cluster\", fo.IntField)\n", + "for sample, label in zip(dataset, cluster_labels):\n", + " sample[\"gmm_cluster_200_gaussians\"] = int(label)\n", + " sample.save()\n", + "\n", + "# --- Visualize the Embeddings with UMAP ---\n", + "# This will compute a 2D representation of your embeddings\n", + "# for visualization.\n", + "res = fob.compute_visualization(\n", + " dataset,\n", + " embeddings=embeddings,\n", + " brain_key=\"qwen_vision_viz\",\n", + " method=\"tsne\",\n", + " verbose=True\n", + ")\n", + "dataset.set_values(\"qwen_umap\", res.current_points)\n", + "\n", + "print(\"UMAP visualization computed. Launch the app to see the plot.\")\n", + "session = fo.launch_app(dataset)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/extract/clustering_example_qwen_Debug.ipynb b/extract/clustering_example_qwen_Debug.ipynb new file mode 100644 index 0000000..6f1421f --- /dev/null +++ b/extract/clustering_example_qwen_Debug.ipynb @@ -0,0 +1,1301 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "59f8a415", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: cuda\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.26it/s]\n" + ] + } + ], + "source": [ + "import torch\n", + "from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, AutoModel\n", + "# from qwen_vl_utils import process_vision_info\n", + "from PIL import Image\n", + "import os\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import math\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModel\n", + "import timm\n", + "\n", + "# --- Configuration ---\n", + "MODEL_NAME = \"Qwen/Qwen2.5-VL-3B-Instruct\" # You can choose other model sizes\n", + "IMAGE_DIR = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/\"\n", + "BATCH_SIZE = 4\n", + "# --- End Configuration ---\n", + "\n", + "# Check for GPU availability\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "print(f\"Using device: {device}\")\n", + "\n", + "# Load the model and processor\n", + "\n", + "model = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n", + " MODEL_NAME,\n", + " torch_dtype=torch.bfloat16,\n", + " # use_flash_attn=True,\n", + " attn_implementation=\"flash_attention_2\",\n", + " trust_remote_code=True,\n", + " device_map=\"cuda\").eval()\n", + "\n", + "processor = AutoProcessor.from_pretrained(\n", + " MODEL_NAME, \n", + " trust_remote_code=True\n", + " )\n", + "tokenizer = AutoTokenizer.from_pretrained(\n", + " MODEL_NAME, \n", + " trust_remote_code=True, \n", + " use_fast=False\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "69e7b24e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Qwen2_5_VLForConditionalGeneration(\n", + " (visual): Qwen2_5_VisionTransformerPretrainedModel(\n", + " (patch_embed): Qwen2_5_VisionPatchEmbed(\n", + " (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)\n", + " )\n", + " (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()\n", + " (blocks): ModuleList(\n", + " (0-31): 32 x Qwen2_5_VLVisionBlock(\n", + " (norm1): Qwen2RMSNorm((1280,), eps=1e-06)\n", + " (norm2): Qwen2RMSNorm((1280,), eps=1e-06)\n", + " (attn): Qwen2_5_VLVisionFlashAttention2(\n", + " (qkv): Linear(in_features=1280, out_features=3840, bias=True)\n", + " (proj): Linear(in_features=1280, out_features=1280, bias=True)\n", + " )\n", + " (mlp): Qwen2_5_VLMLP(\n", + " (gate_proj): Linear(in_features=1280, out_features=3420, bias=True)\n", + " (up_proj): Linear(in_features=1280, out_features=3420, bias=True)\n", + " (down_proj): Linear(in_features=3420, out_features=1280, bias=True)\n", + " (act_fn): SiLU()\n", + " )\n", + " )\n", + " )\n", + " (merger): Qwen2_5_VLPatchMerger(\n", + " (ln_q): Qwen2RMSNorm((1280,), eps=1e-06)\n", + " (mlp): Sequential(\n", + " (0): Linear(in_features=5120, out_features=5120, bias=True)\n", + " (1): GELU(approximate='none')\n", + " (2): Linear(in_features=5120, out_features=2048, bias=True)\n", + " )\n", + " )\n", + " )\n", + " (model): Qwen2_5_VLModel(\n", + " (embed_tokens): Embedding(151936, 2048)\n", + " (layers): ModuleList(\n", + " (0-35): 36 x Qwen2_5_VLDecoderLayer(\n", + " (self_attn): Qwen2_5_VLFlashAttention2(\n", + " (q_proj): Linear(in_features=2048, out_features=2048, bias=True)\n", + " (k_proj): Linear(in_features=2048, out_features=256, bias=True)\n", + " (v_proj): Linear(in_features=2048, out_features=256, bias=True)\n", + " (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n", + " (rotary_emb): Qwen2_5_VLRotaryEmbedding()\n", + " )\n", + " (mlp): Qwen2MLP(\n", + " (gate_proj): Linear(in_features=2048, out_features=11008, bias=False)\n", + " (up_proj): Linear(in_features=2048, out_features=11008, bias=False)\n", + " (down_proj): Linear(in_features=11008, out_features=2048, bias=False)\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)\n", + " (post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)\n", + " )\n", + " )\n", + " (norm): Qwen2RMSNorm((2048,), eps=1e-06)\n", + " (rotary_emb): Qwen2_5_VLRotaryEmbedding()\n", + " )\n", + " (lm_head): Linear(in_features=2048, out_features=151936, bias=False)\n", + ")" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0865539d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Qwen2_5_VLProcessor:\n", + "- image_processor: Qwen2VLImageProcessor {\n", + " \"do_convert_rgb\": true,\n", + " \"do_normalize\": true,\n", + " \"do_rescale\": true,\n", + " \"do_resize\": true,\n", + " \"image_mean\": [\n", + " 0.48145466,\n", + " 0.4578275,\n", + " 0.40821073\n", + " ],\n", + " \"image_processor_type\": \"Qwen2VLImageProcessor\",\n", + " \"image_std\": [\n", + " 0.26862954,\n", + " 0.26130258,\n", + " 0.27577711\n", + " ],\n", + " \"max_pixels\": 12845056,\n", + " \"merge_size\": 2,\n", + " \"min_pixels\": 3136,\n", + " \"patch_size\": 14,\n", + " \"processor_class\": \"Qwen2_5_VLProcessor\",\n", + " \"resample\": 3,\n", + " \"rescale_factor\": 0.00392156862745098,\n", + " \"size\": {\n", + " \"longest_edge\": 12845056,\n", + " \"shortest_edge\": 3136\n", + " },\n", + " \"temporal_patch_size\": 2\n", + "}\n", + "\n", + "- tokenizer: Qwen2TokenizerFast(name_or_path='Qwen/Qwen2.5-VL-3B-Instruct', vocab_size=151643, model_max_length=131072, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n", + "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151646: AddedToken(\"<|object_ref_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151647: AddedToken(\"<|object_ref_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151648: AddedToken(\"<|box_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151649: AddedToken(\"<|box_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151650: AddedToken(\"<|quad_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151651: AddedToken(\"<|quad_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151652: AddedToken(\"<|vision_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151653: AddedToken(\"<|vision_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151654: AddedToken(\"<|vision_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151655: AddedToken(\"<|image_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151656: AddedToken(\"<|video_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", + "\t151657: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151658: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151659: AddedToken(\"<|fim_prefix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151660: AddedToken(\"<|fim_middle|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151661: AddedToken(\"<|fim_suffix|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151662: AddedToken(\"<|fim_pad|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151663: AddedToken(\"<|repo_name|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "\t151664: AddedToken(\"<|file_sep|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),\n", + "}\n", + ")\n", + "\n", + "{\n", + " \"processor_class\": \"Qwen2_5_VLProcessor\"\n", + "}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processor" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "abf32acc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Qwen2_5_VisionTransformerPretrainedModel(\n", + " (patch_embed): Qwen2_5_VisionPatchEmbed(\n", + " (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)\n", + " )\n", + " (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()\n", + " (blocks): ModuleList(\n", + " (0-31): 32 x Qwen2_5_VLVisionBlock(\n", + " (norm1): Qwen2RMSNorm((1280,), eps=1e-06)\n", + " (norm2): Qwen2RMSNorm((1280,), eps=1e-06)\n", + " (attn): Qwen2_5_VLVisionFlashAttention2(\n", + " (qkv): Linear(in_features=1280, out_features=3840, bias=True)\n", + " (proj): Linear(in_features=1280, out_features=1280, bias=True)\n", + " )\n", + " (mlp): Qwen2_5_VLMLP(\n", + " (gate_proj): Linear(in_features=1280, out_features=3420, bias=True)\n", + " (up_proj): Linear(in_features=1280, out_features=3420, bias=True)\n", + " (down_proj): Linear(in_features=3420, out_features=1280, bias=True)\n", + " (act_fn): SiLU()\n", + " )\n", + " )\n", + " )\n", + " (merger): Qwen2_5_VLPatchMerger(\n", + " (ln_q): Qwen2RMSNorm((1280,), eps=1e-06)\n", + " (mlp): Sequential(\n", + " (0): Linear(in_features=5120, out_features=5120, bias=True)\n", + " (1): GELU(approximate='none')\n", + " (2): Linear(in_features=5120, out_features=2048, bias=True)\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.visual" + ] + }, + { + "cell_type": "markdown", + "id": "8037ae45", + "metadata": {}, + "source": [ + "# preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d41f94bd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f2ec71a4", + "metadata": {}, + "source": [ + "# Attention pooling\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a404fa19", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "def gem_pool(x, p: float = 3.0, eps: float = 1e-6):\n", + " # x: [B, N, D]\n", + " return (x.clamp(min=eps).pow(p).mean(dim=1)).pow(1.0/p)\n", + "\n", + "@torch.no_grad()\n", + "def image_embedding(pixel_values, model, use_tiling=True):\n", + " # pixel_values: nếu dùng processor của InternVL, có thể là [T,3,H,W]; nếu bạn tự resize = [1,3,H,W]\n", + " out = model.vision_model(pixel_values=pixel_values, output_hidden_states=True)\n", + " tok = out.last_hidden_state # [T, N, 1024] hoặc [1, N, 1024]\n", + " if tok.dim() == 2: # phòng trường hợp model trả [N, D]\n", + " tok = tok.unsqueeze(0)\n", + "\n", + " # 1) Attention pooling theo token, trong từng tile\n", + " w_tok = torch.softmax(tok.norm(dim=-1), dim=1).unsqueeze(-1) # [T,N,1]\n", + " attn_tile = (tok * w_tok).sum(dim=1) # [T,1024]\n", + "\n", + " # 2) Các pooling khác theo token\n", + " mean_tile = tok.mean(dim=1) # [T,1024]\n", + " max_tile = tok.max(dim=1).values # [T,1024]\n", + " gem_tile = gem_pool(tok, p=3.0) # [T,1024]\n", + "\n", + " # 3) Attention across-tiles (giữ multi-scale nhưng gọn)\n", + " tile_scores = attn_tile.norm(dim=-1) # [T]\n", + " w_tile = torch.softmax(tile_scores, dim=0).unsqueeze(-1) # [T,1]\n", + "\n", + " mean_vec = (mean_tile * w_tile).sum(dim=0)\n", + " max_vec = (max_tile * w_tile).sum(dim=0)\n", + " gem_vec = (gem_tile * w_tile).sum(dim=0)\n", + " attn_vec = (attn_tile * w_tile).sum(dim=0)\n", + "\n", + " # 4) Hợp nhất nhiều “góc nhìn” → 1 vector giàu thông tin\n", + " one_vec = torch.cat([mean_vec, max_vec, gem_vec, attn_vec], dim=0) # [4*1024]\n", + " one_vec = F.normalize(one_vec, dim=-1).unsqueeze(0) # [1, 4096]\n", + " return one_vec.half() # FP16 để tiết kiệm bộ nhớ" + ] + }, + { + "cell_type": "markdown", + "id": "ed35a4ce", + "metadata": {}, + "source": [ + "# pool" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3edf8b67", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "# --- Pooling theo token (trong 1 tile) ---\n", + "def _pool_tokens(tokens: torch.Tensor, how: str = \"mean\") -> torch.Tensor:\n", + " \"\"\"\n", + " tokens: [1, N, D] hoặc [N, D]\n", + " return: [D]\n", + " \"\"\"\n", + " if tokens.dim() == 3: # [1, N, D] -> [N, D]\n", + " tokens = tokens.squeeze(0)\n", + "\n", + " if how == \"mean\":\n", + " v = tokens.mean(dim=0)\n", + " elif how == \"max\":\n", + " v = tokens.max(dim=0).values\n", + " elif how == \"gem\":\n", + " p = 3.0\n", + " v = (tokens.clamp(min=1e-6).pow(p).mean(dim=0)).pow(1.0/p)\n", + " elif how == \"cls\":\n", + " # chỉ dùng nếu backbone có CLS token ở vị trí đầu\n", + " v = tokens[0]\n", + " else:\n", + " raise ValueError(f\"Unknown pooling: {how}\")\n", + "\n", + " return v\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def image_embedding_global(model, pixel_values: torch.Tensor,\n", + " pool: str = \"mean\",\n", + " normalize: bool = False,\n", + " global_index: int = 0,\n", + " use_projector: bool = False) -> torch.Tensor:\n", + " \"\"\"\n", + " Trả về 1 vector [1, D] mô tả toàn ảnh, chỉ dùng GLOBAL tile.\n", + " - pixel_values: [T,3,H,W] (ví dụ T=7) hoặc [1,3,H,W]\n", + " - global_index: thường = 0 (tile toàn ảnh nằm đầu)\n", + " - use_projector: CHỈ bật nếu bạn chắc chắn chiều khớp với projector (mlp1)\n", + " \"\"\"\n", + " model.eval()\n", + " device = next(model.parameters()).device\n", + " x = pixel_values.to(device)\n", + "\n", + " out = model.vision_model(pixel_values=x) # last_hidden_state: [T, N, D] hoặc [1, N, D]\n", + " tok = out.last_hidden_state\n", + "\n", + " # chọn global tile\n", + " if tok.size(0) > 1:\n", + " tok = tok[global_index:global_index+1] # [1, N, D]\n", + "\n", + " # (tuỳ chọn) projector sang không gian khác - cẩn thận mismatch chiều!\n", + " if use_projector:\n", + " # CHỈ nên bật khi biết chắc input dim của mlp1 khớp với tok.size(-1)\n", + " in_feat = getattr(model.mlp1[1], \"in_features\", None)\n", + " if in_feat is not None and tok.size(-1) == in_feat:\n", + " tok = model.mlp1(tok) # [1, N, D’]\n", + " else:\n", + " raise ValueError(f\"Projector input dim mismatch: tokens={tok.size(-1)} vs mlp1.in={in_feat}\")\n", + "\n", + " v = _pool_tokens(tok, how=pool) # [D]\n", + " if normalize:\n", + " v = F.normalize(v, dim=-1)\n", + " return v.unsqueeze(0) # [1, D]\n", + "\n", + "\n", + "@torch.no_grad()\n", + "def image_embedding_mean(model, pixel_values: torch.Tensor,\n", + " pool: str = \"mean\",\n", + " normalize: bool = True,\n", + " use_projector: bool = False) -> torch.Tensor:\n", + " \"\"\"\n", + " Trả về 1 vector [1, D] mô tả toàn ảnh, bằng cách:\n", + " (1) pool theo token trong từng tile → [T, D]\n", + " (2) lấy mean across-tiles → [D]\n", + " \"\"\"\n", + " model.eval()\n", + " device = next(model.parameters()).device\n", + " x = pixel_values.to(device)\n", + "\n", + " out = model.vision_model(pixel_values=x)\n", + " tok = out.last_hidden_state # [T, N, D] hoặc [1, N, D]\n", + "\n", + " if use_projector:\n", + " in_feat = getattr(model.mlp1[1], \"in_features\", None)\n", + " if in_feat is not None and tok.size(-1) == in_feat:\n", + " tok = model.mlp1(tok)\n", + " else:\n", + " raise ValueError(f\"Projector input dim mismatch: tokens={tok.size(-1)} vs mlp1.in={in_feat}\")\n", + "\n", + " # pool theo token trong từng tile\n", + " T = tok.size(0)\n", + " per_tile = [ _pool_tokens(tok[t:t+1], how=pool) for t in range(T) ] # list of [D]\n", + " per_tile = torch.stack(per_tile, dim=0) # [T, D]\n", + "\n", + " # mean across-tiles\n", + " v = per_tile.mean(dim=0) # [D]\n", + " if normalize:\n", + " v = F.normalize(v, dim=-1)\n", + " return v.unsqueeze(0) # [1, D]\n" + ] + }, + { + "cell_type": "markdown", + "id": "613cf001", + "metadata": {}, + "source": [ + "# infer" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cdfdab0e", + "metadata": {}, + "outputs": [], + "source": [ + "def get_image_embedding(path):\n", + " \"\"\"\n", + " Processes a batch of images and extracts their embeddings.\n", + " \"\"\"\n", + " images_pil = []\n", + " valid_paths = []\n", + " if path.lower().endswith(('.png', '.jpg', '.jpeg')):\n", + " try:\n", + " # The processor expects PIL images in RGB format\n", + " images_pil.append(Image.open(path).convert(\"RGB\"))\n", + " # print(path)\n", + " valid_paths.append(path)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not load image {path}. Skipping. Error: {e}\")\n", + "\n", + " if not valid_paths:\n", + " return np.array([]), []\n", + "\n", + " inputs = processor(\n", + " text=[\"\"] * len(images_pil),\n", + " images=images_pil,\n", + " padding=True,\n", + " return_tensors=\"pt\"\n", + " ).to(device)\n", + " \n", + " # embeddings = image_embedding(inputs, model, use_tiling=True)\n", + " embeddings = image_embedding_mean(model, inputs)\n", + " \n", + " return embeddings.to(torch.float16).cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cdaebb7b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/2800 [00:03 15\u001b[0m batch_embeddings \u001b[38;5;241m=\u001b[39m \u001b[43mget_image_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_paths\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m embeddings_list \u001b[38;5;241m=\u001b[39m [emb\u001b[38;5;241m.\u001b[39mtolist() \u001b[38;5;28;01mfor\u001b[39;00m emb \u001b[38;5;129;01min\u001b[39;00m batch_embeddings]\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m path, emb \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(batch_paths, embeddings_list):\n", + "Cell \u001b[0;32mIn[14], line 27\u001b[0m, in \u001b[0;36mget_image_embedding\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 19\u001b[0m inputs \u001b[38;5;241m=\u001b[39m processor(\n\u001b[1;32m 20\u001b[0m text\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(images_pil),\n\u001b[1;32m 21\u001b[0m images\u001b[38;5;241m=\u001b[39mimages_pil,\n\u001b[1;32m 22\u001b[0m padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 23\u001b[0m return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 24\u001b[0m )\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# embeddings = image_embedding(inputs, model, use_tiling=True)\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[43mimage_embedding_mean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m embeddings\u001b[38;5;241m.\u001b[39mto(torch\u001b[38;5;241m.\u001b[39mfloat16)\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n", + "File \u001b[0;32m~/sonnh/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator..decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[13], line 81\u001b[0m, in \u001b[0;36mimage_embedding_mean\u001b[0;34m(model, pixel_values, pool, normalize, use_projector)\u001b[0m\n\u001b[1;32m 78\u001b[0m device \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(model\u001b[38;5;241m.\u001b[39mparameters())\u001b[38;5;241m.\u001b[39mdevice\n\u001b[1;32m 79\u001b[0m x \u001b[38;5;241m=\u001b[39m pixel_values\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m---> 81\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m(pixel_values\u001b[38;5;241m=\u001b[39mx)\n\u001b[1;32m 82\u001b[0m tok \u001b[38;5;241m=\u001b[39m out\u001b[38;5;241m.\u001b[39mlast_hidden_state \u001b[38;5;66;03m# [T, N, D] hoặc [1, N, D]\u001b[39;00m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_projector:\n", + "File \u001b[0;32m~/sonnh/venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1940\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1938\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m modules:\n\u001b[1;32m 1939\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1940\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 1941\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1942\u001b[0m )\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Qwen2_5_VLForConditionalGeneration' object has no attribute 'vision_model'" + ] + } + ], + "source": [ + "import json\n", + "\n", + "# --- Process all images in the directory ---\n", + "image_files = [os.path.join(IMAGE_DIR, f) for f in os.listdir(IMAGE_DIR) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n", + "all_embeddings = []\n", + "filepaths = []\n", + "BATCH_SIZE = 1\n", + "\n", + "with open(\"embeddings_factures_osteopathie_1k_InternVL3_5-4B-Instruct.json\", \"w\") as f:\n", + "\n", + " f.write(\"[\\n\")\n", + " first = True\n", + " for i in tqdm(range(0, len(image_files), BATCH_SIZE)):\n", + " batch_paths = image_files[i]\n", + " batch_embeddings = get_image_embedding(batch_paths)\n", + " embeddings_list = [emb.tolist() for emb in batch_embeddings]\n", + " for path, emb in zip(batch_paths, embeddings_list):\n", + " if not first:\n", + " f.write(\",\\n\")\n", + " json.dump({\"filepath\": path, \"embedding\": emb}, f)\n", + " first = False\n", + " f.write(\"\\n]\\n\")\n", + "\n", + "print(\"Embeddings extracted and saved.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2c3e6dd0", + "metadata": {}, + "outputs": [], + "source": [ + "image_path = \"/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/c363e486-5d45-425e-aef9-4791cad120f7_20250213_120759_1_scale_1.0.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "29620d93", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n", + "Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: \n", + "Please describe the image shortly.\n", + "Assistant: The image shows a receipt for a consultation with Noël Breignaud, an osteopath. It includes his contact information, with the address \"104, cours des fossés, 33210 Langon\" and his phone number. The receipt details a payment of 55€ for a consultation dated 15/06/2020. The receipt number is 1750401922774. There are handwritten details and signatures, with the amount and date written in ink. Noël Breignaud's signature and a circular stamp are also present.\n", + "User: \n", + "Please describe the image in detail.\n", + "Assistant: The image is a handwritten receipt or invoice from a practitioner named Noël Breign aud, who is an osteopath. The text on the left side of the document includes the following details:\n", + "\n", + "- **Name:** Noël Breignaud\n", + "- **Profession:** Ouestopathe (Osteopath)\n", + "- **Address:** 104, cours des fossés, 33210 Lagnon\n", + "- **Phone Number:** Tel. 06 88 70 66 43\n", + "\n", + "On the right side, there are registration and identification numbers:\n", + "\n", + "- **Nº SIRET:** 510 123 631 00010\n", + "- **Nº ADELI:** 330001108\n", + "- **Code APE:** 8690E\n", + "\n", + "The handwritten section of the document is in French and reads:\n", + "\n", + "- \"Déclaire avoir reçu de M. M. (fils) G[obon], Acquitté la somme de 55 €\n", + "Pour 1 consultation en date du 05/04/2024\n", + "N°: 1750460-19212774\"\n", + "\n", + "At the bottom right, there is a signature that appears to be of Noël Breignaud, with a red stamp partially visible, which seems to contain the text \"Noël BREIGNAUD\" and other markings.\n", + "\n", + "The date in the handwritten section is \"05/04/2024,\" indicating the receipt or service provided on that date. The amount mentioned is 55 euros for one consultation.\n" + ] + } + ], + "source": [ + "pixel_values = load_image(image_path, max_num=12).to(torch.bfloat16).cuda()\n", + "generation_config = dict(max_new_tokens=1024, do_sample=True)\n", + "\n", + "\n", + "\n", + "question = '\\nPlease describe the image shortly.'\n", + "response = model.chat(tokenizer, pixel_values, question, generation_config)\n", + "print(f'User: {question}\\nAssistant: {response}')\n", + "\n", + "# single-image multi-round conversation (单图多轮对话)\n", + "question = '\\nPlease describe the image in detail.'\n", + "response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)\n", + "print(f'User: {question}\\nAssistant: {response}')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "35dc90e0", + "metadata": {}, + "outputs": [], + "source": [ + "vout = model.vision_model(pixel_values=pixel_values, output_hidden_states=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77f3720a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([7, 1025, 1024])\n" + ] + } + ], + "source": [ + "patch_feats = vout.last_hidden_state # [B, N_patches, Dv], Dv ~ 1024 theo kiến trúc của bạn\n", + "print(patch_feats.shape)\n", + "# Nếu backbone có CLS token, bạn có thể dùng patch_feats[:,0]\n", + "# Cách an toàn chung: mean-pool\n", + "# img_vec = patch_feats.mean(dim=1) # [B, Dv]\n", + "# img_vec = torch.nn.functional.normalize(img_vec, dim=-1) # L2 normalize cho retrieval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0043634c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([7, 1024])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# img_vec.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92032162", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ede95852", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "InternVLChatModel(\n", + " (vision_model): InternVisionModel(\n", + " (embeddings): InternVisionEmbeddings(\n", + " (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))\n", + " )\n", + " (encoder): InternVisionEncoder(\n", + " (layers): ModuleList(\n", + " (0): InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, 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DropPath(drop_prob=0.083)\n", + " )\n", + " (20): InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (drop_path1): DropPath(drop_prob=0.087)\n", + " (drop_path2): DropPath(drop_prob=0.087)\n", + " )\n", + " (21): InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (drop_path1): DropPath(drop_prob=0.091)\n", + " (drop_path2): DropPath(drop_prob=0.091)\n", + " )\n", + " (22): InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (drop_path1): DropPath(drop_prob=0.096)\n", + " (drop_path2): DropPath(drop_prob=0.096)\n", + " )\n", + " (23): InternVisionEncoderLayer(\n", + " (attn): InternAttention(\n", + " (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " (inner_attn): FlashAttention()\n", + " (proj): Linear(in_features=1024, out_features=1024, bias=True)\n", + " )\n", + " (mlp): InternMLP(\n", + " (act): GELUActivation()\n", + " (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n", + " (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n", + " )\n", + " (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n", + " (drop_path1): DropPath(drop_prob=0.100)\n", + " (drop_path2): DropPath(drop_prob=0.100)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (language_model): Qwen3ForCausalLM(\n", + " (model): Qwen3Model(\n", + " (embed_tokens): Embedding(151936, 2560)\n", + " (layers): ModuleList(\n", + " (0-35): 36 x Qwen3DecoderLayer(\n", + " (self_attn): Qwen3Attention(\n", + " (q_proj): Linear(in_features=2560, out_features=4096, bias=False)\n", + " (k_proj): Linear(in_features=2560, out_features=1024, bias=False)\n", + " (v_proj): Linear(in_features=2560, out_features=1024, bias=False)\n", + " (o_proj): Linear(in_features=4096, out_features=2560, bias=False)\n", + " (q_norm): Qwen3RMSNorm((128,), eps=1e-06)\n", + " (k_norm): Qwen3RMSNorm((128,), eps=1e-06)\n", + " )\n", + " (mlp): Qwen3MLP(\n", + " (gate_proj): Linear(in_features=2560, out_features=9728, bias=False)\n", + " (up_proj): Linear(in_features=2560, out_features=9728, bias=False)\n", + " (down_proj): Linear(in_features=9728, out_features=2560, bias=False)\n", + " (act_fn): SiLU()\n", + " )\n", + " (input_layernorm): Qwen3RMSNorm((2560,), eps=1e-06)\n", + " (post_attention_layernorm): Qwen3RMSNorm((2560,), eps=1e-06)\n", + " )\n", + " )\n", + " (norm): Qwen3RMSNorm((2560,), eps=1e-06)\n", + " (rotary_emb): Qwen3RotaryEmbedding()\n", + " )\n", + " (lm_head): Linear(in_features=2560, out_features=151936, bias=False)\n", + " )\n", + " (mlp1): Sequential(\n", + " (0): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (1): Linear(in_features=4096, out_features=2560, bias=True)\n", + " (2): GELU(approximate='none')\n", + " (3): Linear(in_features=2560, out_features=2560, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27fea4f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100% |███████████████| 1091/1091 [174.1ms elapsed, 0s remaining, 6.3K samples/s] \n" + ] + } + ], + "source": [ + "import fiftyone as fo\n", + "import fiftyone.brain as fob\n", + "import numpy as np\n", + "from sklearn.mixture import GaussianMixture\n", + "import json\n", + "\n", + "DATASET_NAME = \"mock\"\n", + "\n", + "json_path = \"./embeddings_factures_osteopathie_1k_qwen.json\"\n", + "\n", + "with open(json_path, \"r\") as file:\n", + " embedding_data = json.load(file)\n", + "\n", + "file_paths = []\n", + "embeddings = []\n", + "for i, record in enumerate(embedding_data):\n", + " file_paths.append(record.get(\"filepath\"))\n", + " embeddings.append(record.get(\"embedding\"))\n", + "\n", + "if DATASET_NAME in fo.list_datasets():\n", + " dataset = fo.load_dataset(DATASET_NAME)\n", + " dataset.delete()\n", + "dataset = fo.Dataset(DATASET_NAME)\n", + "\n", + "# Add samples to the dataset\n", + "samples = [fo.Sample(filepath=p) for p in file_paths]\n", + "dataset.add_samples(samples)\n", + "\n", + "# Building Gaussian mixture model (GMM)\n", + "n_gaussians = 50\n", + "gmm = GaussianMixture(n_components=n_gaussians, random_state=42)\n", + "gmm.fit(embeddings)\n", + "cluster_labels = gmm.predict(embeddings)\n", + "\n", + "# Adding labeled embeddings to visulization\n", + "dataset.add_sample_field(\"gmm_cluster\", fo.IntField)\n", + "for sample, label in zip(dataset, cluster_labels):\n", + " sample[\"gmm_cluster_50_gaussians\"] = int(label)\n", + " sample.save()\n", + "\n", + "n_gaussians = 200\n", + "gmm = GaussianMixture(n_components=n_gaussians, random_state=42)\n", + "gmm.fit(embeddings)\n", + "cluster_labels = gmm.predict(embeddings)\n", + "\n", + "# Adding labeled embeddings to visulization\n", + "dataset.add_sample_field(\"gmm_cluster\", fo.IntField)\n", + "for sample, label in zip(dataset, cluster_labels):\n", + " sample[\"gmm_cluster_200_gaussians\"] = int(label)\n", + " sample.save()\n", + "\n", + "# --- Visualize the Embeddings with UMAP ---\n", + "# This will compute a 2D representation of your embeddings\n", + "# for visualization.\n", + "res = fob.compute_visualization(\n", + " dataset,\n", + " embeddings=embeddings,\n", + " brain_key=\"qwen_vision_viz\",\n", + " method=\"tsne\",\n", + " verbose=True\n", + ")\n", + "dataset.set_values(\"qwen_umap\", res.current_points)\n", + "\n", + "print(\"UMAP visualization computed. Launch the app to see the plot.\")\n", + "session = fo.launch_app(dataset)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/extract/extract.py b/extract/extract.py new file mode 100644 index 0000000..a53904f --- /dev/null +++ b/extract/extract.py @@ -0,0 +1,90 @@ +import torch +from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor, AutoModel +# from qwen_vl_utils import process_vision_info +from PIL import Image +import os +import numpy as np +import json +from tqdm import tqdm + +from transformers import LayoutLMv3ImageProcessor, LayoutLMv3Model + + +# --- Configuration --- +MODEL_NAME = "microsoft/layoutlmv3-base" # You can choose other model sizes +IMAGE_DIR = "/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/" +BATCH_SIZE = 8 +# --- End Configuration --- + +# Check for GPU availability +device = "cuda" if torch.cuda.is_available() else "cpu" +print(f"Using device: {device}") + +# Load the model and processor +# model = AutoModel.from_pretrained( +# MODEL_NAME, torch_dtype="bfloat16", device_map="cuda" # , attn_implementation="flash_attention_2", +# ) + + +model = LayoutLMv3Model.from_pretrained(MODEL_NAME, device_map="cuda") +processor = LayoutLMv3ImageProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True) + + +def get_image_embeddings(image_paths): + """ + Processes a batch of images and extracts their embeddings. + """ + images_pil = [] + valid_paths = [] + for path in image_paths: + if path.lower().endswith(('.png', '.jpg', '.jpeg')): + try: + # The processor expects PIL images in RGB format + images_pil.append(Image.open(path).convert("RGB")) + valid_paths.append(path) + except Exception as e: + print(f"Warning: Could not load image {path}. Skipping. Error: {e}") + + if not images_pil: + return np.array([]), [] + + # For pure vision feature extraction, we can provide an empty text prompt. + # The processor handles tokenizing text and preparing images. + # LayoutLMv3 expects 224x224 images by default + inputs = processor( + # text=[""] * len(images_pil), + images=images_pil, + # padding=True, + size = {"height" : 224, "width": 224}, + return_tensors="pt" + ).to(device) + + with torch.no_grad(): + # Get the vision embeddings from the model's vision tower + vision_outputs = model.forward(pixel_values=inputs['pixel_values'].to(dtype=model.dtype)) # , grid_thw=inputs['image_grid_thw']) + # We'll use the pooled output as the embedding + embeddings = vision_outputs[0][:,0,:] + + return embeddings.to(torch.float16).cpu().numpy() + + +# --- Process all images in the directory --- +image_files = [os.path.join(IMAGE_DIR, f) for f in os.listdir(IMAGE_DIR) if f.lower().endswith(('.png', '.jpg', '.jpeg'))] +all_embeddings = [] +filepaths = [] + +with open("embeddings_factures_ostepoathie_1k.json", "w") as f: + f.write("[\n") + first = True + for i in tqdm(range(0, len(image_files), BATCH_SIZE)): + batch_paths = image_files[i:i+BATCH_SIZE] + batch_embeddings = get_image_embeddings(batch_paths) + embeddings_list = [emb.tolist() for emb in batch_embeddings] + for path, emb in zip(batch_paths, embeddings_list): + if not first: + f.write(",\n") + json.dump({"filepath": path, "embedding": emb}, f) + first = False + f.write("\n]\n") + +print("Embeddings extracted and saved.") \ No newline at end of file diff --git a/extract/extract_donut.py b/extract/extract_donut.py new file mode 100644 index 0000000..385c650 --- /dev/null +++ b/extract/extract_donut.py @@ -0,0 +1,201 @@ +import torch +from transformers import DonutProcessor, VisionEncoderDecoderModel +from PIL import Image +import os +import numpy as np +import json +from tqdm import tqdm + + +# --- Configuration --- +MODEL_NAME = "naver-clova-ix/donut-base-finetuned-docvqa" # Donut model for document VQA +IMAGE_DIR = "/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/" +BATCH_SIZE = 4 # Smaller batch size for Donut as it's memory intensive +# --- End Configuration --- + +# Check for GPU availability +device = "cuda" if torch.cuda.is_available() else "cpu" +print(f"Using device: {device}") + +# Load the Donut model and processor +print("Loading Donut model and processor...") +processor = DonutProcessor.from_pretrained(MODEL_NAME) +model = VisionEncoderDecoderModel.from_pretrained(MODEL_NAME) +model.to(device) +model.eval() + +# Set model to half precision for efficiency if using GPU +if device == "cuda": + model = model.half() + + +def get_document_embeddings(image_paths): + """ + Processes a batch of document images and extracts their embeddings using Donut. + Uses the encoder part of the VisionEncoderDecoder model to get visual representations. + """ + images_pil = [] + valid_paths = [] + + for path in image_paths: + if path.lower().endswith(('.png', '.jpg', '.jpeg')): + try: + # Load and convert image to RGB + image = Image.open(path).convert("RGB") + images_pil.append(image) + valid_paths.append(path) + except Exception as e: + print(f"Warning: Could not load image {path}. Skipping. Error: {e}") + + if not images_pil: + return np.array([]), [] + + embeddings_list = [] + + # Process images one by one to avoid memory issues + for image in images_pil: + try: + # Preprocess the image + pixel_values = processor(image, return_tensors="pt").pixel_values + pixel_values = pixel_values.to(device) + + if device == "cuda": + pixel_values = pixel_values.half() + + with torch.no_grad(): + # Get encoder outputs (visual features) + encoder_outputs = model.encoder(pixel_values=pixel_values) + + # Use the last hidden state and apply global average pooling + # to get a fixed-size representation + last_hidden_state = encoder_outputs.last_hidden_state # [batch_size, seq_len, hidden_size] + + # Global average pooling across the sequence dimension + embedding = torch.mean(last_hidden_state, dim=1) # [batch_size, hidden_size] + + embeddings_list.append(embedding.squeeze().cpu().float().numpy()) + + except Exception as e: + print(f"Warning: Could not process image. Error: {e}") + # Add zero embedding for failed images to maintain consistency + embeddings_list.append(np.zeros(model.config.encoder.hidden_size)) + + return np.array(embeddings_list), valid_paths + + +def extract_document_info(image_path, question="What information is in this document?"): + """ + Extract specific information from a document using Donut's text generation capability. + This function demonstrates how to use Donut for document understanding tasks. + """ + try: + image = Image.open(image_path).convert("RGB") + + # Prepare the task prompt for document VQA + task_prompt = f"{question}" + + # Process the image and prompt + inputs = processor(image, task_prompt, return_tensors="pt") + inputs = {k: v.to(device) for k, v in inputs.items()} + + if device == "cuda": + inputs["pixel_values"] = inputs["pixel_values"].half() + + with torch.no_grad(): + # Generate answer + generated_ids = model.generate( + **inputs, + max_length=512, + early_stopping=True, + pad_token_id=processor.tokenizer.pad_token_id, + eos_token_id=processor.tokenizer.eos_token_id, + use_cache=True, + num_beams=1, + bad_words_ids=[[processor.tokenizer.unk_token_id]], + return_dict_in_generate=True, + ) + + # Decode the generated answer + decoded_text = processor.batch_decode(generated_ids.sequences)[0] + # Extract the answer part + answer = decoded_text.split("")[-1].replace("", "").strip() + + return answer + + except Exception as e: + print(f"Error extracting info from {image_path}: {e}") + return "" + + +# --- Process all images in the directory --- +print("Scanning for image files...") +image_files = [os.path.join(IMAGE_DIR, f) for f in os.listdir(IMAGE_DIR) + if f.lower().endswith(('.png', '.jpg', '.jpeg'))] +print(f"Found {len(image_files)} image files") + +all_embeddings = [] +filepaths = [] + +# Extract embeddings and save to JSON +print("Extracting embeddings using Donut...") +with open("embeddings_factures_donut.json", "w") as f: + f.write("[\n") + first = True + + for i in tqdm(range(0, len(image_files), BATCH_SIZE), desc="Processing batches"): + batch_paths = image_files[i:i+BATCH_SIZE] + batch_embeddings, valid_paths = get_document_embeddings(batch_paths) + + if len(batch_embeddings) > 0: + embeddings_list = [emb.tolist() for emb in batch_embeddings] + + for path, emb in zip(valid_paths, embeddings_list): + if not first: + f.write(",\n") + + entry = { + "filepath": path, + "embedding": emb, + "model": "donut-base-finetuned-docvqa", + "embedding_size": len(emb) + } + + json.dump(entry, f) + first = False + + f.write("\n]\n") + +print("Embeddings extracted and saved to 'embeddings_factures_donut.json'") + +# Optional: Extract some sample document information +print("\nExtracting sample document information...") +sample_images = image_files[:3] # Process first 3 images as samples + +sample_info = [] +for img_path in sample_images: + print(f"Processing: {os.path.basename(img_path)}") + + # Extract different types of information + questions = [ + "What is the total amount?", + "What is the invoice number?", + "What is the date?", + "Who is the vendor?", + "What are the main items?" + ] + + info = {"filepath": img_path, "extracted_info": {}} + + for question in questions: + answer = extract_document_info(img_path, question) + info["extracted_info"][question] = answer + print(f" {question}: {answer}") + + sample_info.append(info) + +# Save sample extraction results +with open("donut_sample_extractions.json", "w") as f: + json.dump(sample_info, f, indent=2, ensure_ascii=False) + +print("Sample document information extracted and saved to 'donut_sample_extractions.json'") +print("Processing completed!") diff --git a/extract/test.ipynb b/extract/test.ipynb new file mode 100644 index 0000000..b9193e1 --- /dev/null +++ b/extract/test.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "a314a8ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0. ],\n", + " [0.26726124, 0.56694671]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "import numpy as np\n", + "X = [[0, 0, 0], [1, 2, 3]]\n", + "Y = [[1, 0, 0], [1, 1, 0]]\n", + "cosine_similarity(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4b560c4f", + "metadata": {}, + "outputs": [], + "source": [ + "sims = cosine_similarity(X)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8d5d17a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a1098a5a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 3)\n", + "(array([0, 0, 1]), array([1, 2, 2]))\n", + "(3,)\n", + "mean sim: -0.3333333333333334 std: 0.47140452079103173\n" + ] + } + ], + "source": [ + "# X = np.array([\n", + "# [0, 0, 0], \n", + "# [-1, 100, -1000],\n", + "# [-1, -2, -4]\n", + "# ]\n", + "# )\n", + "\n", + "X = np.array([\n", + " [0, 0, 0], \n", + " [1,1,1],\n", + " [-1, -1, -1]\n", + " ]\n", + " )\n", + "print(X.shape)\n", + "sims = cosine_similarity(X)\n", + "\n", + "triu_idxs = np.triu_indices_from(sims, k=1)\n", + "print(triu_idxs)\n", + "dist_vals = sims[triu_idxs]\n", + "print(dist_vals.shape)\n", + "print(\"mean sim:\", dist_vals.mean(), \"std:\", dist_vals.std())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2dacad18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dist_vals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76d25e07", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- 2.49.1 From 878310a551c3645fb87964261d2e5d3eef0994d2 Mon Sep 17 00:00:00 2001 From: trungkienbkhn Date: Thu, 4 Sep 2025 14:39:02 +0000 Subject: [PATCH 2/2] update source code and pipeline --- .gitignore | 182 ++ README.md | 61 + check_filter/.visual_cache/plot_1.csv | 2801 +++++++++++++++++ check_filter/.visual_cache/plot_2.csv | 2801 +++++++++++++++++ check_filter/.visual_cache/plot_3.csv | 2801 +++++++++++++++++ check_filter/.visual_cache/plot_4.csv | 2801 +++++++++++++++++ check_filter/run.sh | 1 + check_filter/visual_data copy 2.py | 579 ++++ check_filter/visual_data copy.py | 600 ++++ check_filter/visual_data.py | 783 +++++ cluster/auto_cluster copy.py | 670 ++++ 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| 3 + filter/check_009/issues.csv | 277 ++ .../item_total_billed_mismatches.csv | 61 + filter/check_009/label_analysis_report.md | 193 ++ filter/check_009/plots/bill_paid.png | Bin 0 -> 20255 bytes filter/check_009/plots/currency.png | Bin 0 -> 18519 bytes filter/check_009/plots/hist_amount_paid.png | Bin 0 -> 24436 bytes filter/check_009/plots/hist_client_part.png | Bin 0 -> 22861 bytes .../plots/hist_complementary_coverage.png | Bin 0 -> 24416 bytes .../plots/hist_mandatory_coverage.png | Bin 0 -> 24086 bytes .../plots/hist_remaining_payment.png | Bin 0 -> 23542 bytes filter/check_009/plots/hist_total_billed.png | Bin 0 -> 24064 bytes filter/check_009/plots/invoice_year_month.png | Bin 0 -> 38785 bytes filter/check_009/plots/is_bill.png | Bin 0 -> 18084 bytes filter/check_009/plots/is_handwriting.png | Bin 0 -> 18111 bytes filter/check_009/plots/is_rotated.png | Bin 0 -> 21930 bytes filter/check_009/plots/items_per_label.png | Bin 0 -> 23262 bytes 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create mode 100644 filter/gmm_final_results_aic_20250805_150636_filtered_filepaths.txt create mode 100644 filter/gmm_final_results_bic_20250805_150636_filtered_filepaths.txt create mode 100644 filter/log_gmm_extensive_update.txt create mode 100644 filter/random_sample_data.py create mode 100644 filter/run_filter.sh create mode 100644 filter/shuffle.py diff --git a/.gitignore b/.gitignore index 3d8e64e..aa47e55 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,184 @@ *.json extracted_images/ +temp/ +result/ + +# Created by https://www.toptal.com/developers/gitignore/api/python +# Edit at https://www.toptal.com/developers/gitignore?templates=python + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +# End of https://www.toptal.com/developers/gitignore/api/python +Beta +0 / 0 +used queries \ No newline at end of file diff --git a/README.md b/README.md index e69de29..f34d4bc 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,61 @@ +# pipeline + +VLM model → feature extraction → clustering → data filtering → fine-tuning → LoRA export → LLVM serve → inference → evaluation + +# 001 VLM model → feature extraction +```bash +cd extract + +run clustering_example_qwen notebook +``` + +# 002 clustering grid search +```bash +cd cluster + +# dbscan +python auto_cluster.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json --method dbscan + +# gmm +python gmm_extensive.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json +``` + +- Cluster Result will save at: + - cluster/dbscan_results.json + - cluster/gmm_final_results.json + + +# 003 filter data + +```bash +cd filter +bash run_filter.sh +``` + +- EMBEDDINGS_PATH: path to the embedding file generated in step 001 +- CLUSTERING_RESULTS_PATH: path to the file containing the clustering information generated in step 002 +- OUTPUT_PATH: path to save the retained files + +- selection_ratio: proportion of data to be passed into the filter +- center_ratio: proportion of center points to take +- border_ratio: proportion of boundary points to take + + +# 004 create data from full data and filter data + +```bash +cd filter + +python3 create_label_data.py +``` + +- dbscan_results_path: OUTPUT_PATH produced in step 003 +- label_data_path: ground truth after being filtered from the filtered data, has the same format as the full dataset, used for finetuning the VLM model + + +# visual and check data + +```bash +cd check_filter +bash run.sh +``` diff --git a/check_filter/.visual_cache/plot_1.csv b/check_filter/.visual_cache/plot_1.csv new file mode 100644 index 0000000..da09735 --- /dev/null +++ b/check_filter/.visual_cache/plot_1.csv @@ -0,0 +1,2801 @@ +filepath,x,y,z,cluster +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/c363e486-5d45-425e-aef9-4791cad120f7_20250213_120759_1_scale_1.0.jpg,17.931097,2.5219696,3.6210601,4 +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/1724226151715Facture_Julie_Boissel_2_1_scale_1.5.jpg,1.0770515,15.813956,5.6884675,0 +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/DDE prestation Facture des lentilles de 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copy 2.py b/check_filter/visual_data copy 2.py new file mode 100644 index 0000000..3003761 --- /dev/null +++ b/check_filter/visual_data copy 2.py @@ -0,0 +1,579 @@ +""" +Streamlit app để trực quan hóa embedding 3 chiều (PCA / UMAP / t-SNE) + phân cụm. + +Chạy: + streamlit run visual_data.py --server.port 8501 + +Yêu cầu cài đặt (một lần): + pip install streamlit plotly scikit-learn umap-learn numpy pandas + +Tính năng: + - Load file JSON lớn chứa các object {"filepath": ..., "embedding": [...]} hoặc định dạng JSON lines. + - Tùy chọn sample n phần tử (random) để tăng tốc. + - Chọn thuật toán giảm chiều: PCA, UMAP, t-SNE. + - Tham số điều chỉnh: n_neighbors, min_dist (UMAP); perplexity (t-SNE); n_components=3. + - KMeans clustering (tuỳ chọn) để tô màu điểm; hoặc tô màu theo regex/substring trong tên file. + - Lọc theo từ khóa trong đường dẫn. + - Tải xuống toạ độ 3D + nhãn cluster. + +File embedding quan sát được có thể không phải JSON array chuẩn; script sẽ thử: + 1. Parse như JSON array. + 2. Parse như JSON lines (mỗi dòng 1 object). + 3. Parse thủ công bằng cách tìm pattern {"filepath": ... , "embedding": [ ... ]}. + +Nếu kích thước > ~1e6 bytes, dùng đọc streaming để giảm RAM. +""" + +from __future__ import annotations + +import json +import os +import re +import math +import random +from dataclasses import dataclass +from typing import Iterable, List, Optional, Tuple + +import numpy as np +import pandas as pd +import streamlit as st +import colorsys +from sklearn.decomposition import PCA +from sklearn.manifold import TSNE +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score + +try: + import umap # type: ignore +except ImportError: # pragma: no cover + umap = None # handled later + + +@dataclass +class EmbeddingRecord: + filepath: str + embedding: List[float] + + +def _smart_json_object_stream(raw_text: str) -> Iterable[str]: + """Yield JSON object strings from a large raw buffer. + + Heuristic: find balanced braces starting with {"filepath": ...}. + This is a fallback when content is not standard array / jsonlines. + """ + brace = 0 + buf = [] + in_obj = False + for ch in raw_text: + if ch == '{': + if not in_obj: + in_obj = True + buf = ['{'] + brace = 1 + else: + brace += 1 + buf.append(ch) + elif ch == '}': + if in_obj: + brace -= 1 + buf.append('}') + if brace == 0: + yield ''.join(buf) + in_obj = False + else: + # stray closing + continue + else: + if in_obj: + buf.append(ch) + + +def load_embeddings( + path: str, + sample_size: Optional[int] = None, + sampling_seed: int = 42, + max_objects: Optional[int] = None, +) -> List[EmbeddingRecord]: + """Load embeddings from a possibly large JSON / JSONL / raw file. + + Args: + path: file path + sample_size: random sample (after load) if provided + sampling_seed: RNG seed + max_objects: hard cap to stop early (for speed) + """ + # size = os.path.getsize(path) # kích thước có thể dùng sau nếu muốn tối ưu đọc streaming + # First attempt: JSON array + records: List[EmbeddingRecord] = [] + def to_rec(obj) -> Optional[EmbeddingRecord]: + if not isinstance(obj, dict): + return None + if 'embedding' in obj: + fp = str(obj.get('filepath') or obj.get('file_path') or obj.get('path') or '') + emb = obj['embedding'] + if isinstance(emb, list) and fp: + return EmbeddingRecord(fp, emb) + return None + + try: + with open(path, 'r', encoding='utf-8') as f: + text = f.read() + text_stripped = text.strip() + if text_stripped.startswith('[') and text_stripped.endswith(']'): + arr = json.loads(text_stripped) + for obj in arr: + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + else: + raise ValueError('Not a JSON array') + except Exception: + # Retry as JSON lines + records = [] + try: + with open(path, 'r', encoding='utf-8') as f: + for line in f: + line_strip = line.strip().rstrip(',') + if not line_strip: + continue + if not line_strip.startswith('{'): + continue + try: + obj = json.loads(line_strip) + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + except json.JSONDecodeError: + continue + if not records: + raise ValueError('No JSONL records') + except Exception: + # Fallback: heuristic extraction + records = [] + with open(path, 'r', encoding='utf-8') as f: + raw = f.read() + for obj_str in _smart_json_object_stream(raw): + if 'embedding' not in obj_str: + continue + # Clean possible trailing '," + try: + # Attempt to fix malformed numbers like '1.2\n421875' (broken newline) by removing stray newlines inside arrays + fixed = re.sub(r"(\d)\n(\d)", r"\1\2", obj_str) + obj = json.loads(fixed) + except Exception: + continue + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + if not records: + raise RuntimeError("Không load được embedding nào từ file.") + + # Random sample if needed + if sample_size and sample_size < len(records): + random.seed(sampling_seed) + records = random.sample(records, sample_size) + return records + + +def reduce_embeddings( + X: np.ndarray, + method: str, + random_state: int = 42, + umap_neighbors: int = 15, + umap_min_dist: float = 0.1, + tsne_perplexity: int = 30, + tsne_learning_rate: str | float = 'auto', +) -> Tuple[np.ndarray, dict]: + """Project high-dim embeddings to 3D. + + Returns (coords (n,3), meta_info) + """ + meta = {"method": method} + if method == 'PCA': + pca = PCA(n_components=3, random_state=random_state) + coords = pca.fit_transform(X) + meta['explained_variance_ratio'] = pca.explained_variance_ratio_.tolist() + return coords, meta + if method == 'UMAP': + if umap is None: + raise RuntimeError("Chưa cài umap-learn: pip install umap-learn") + reducer = umap.UMAP( + n_components=3, + n_neighbors=umap_neighbors, + min_dist=umap_min_dist, + metric='cosine', + random_state=random_state, + ) + coords = reducer.fit_transform(X) + meta['umap_graph_connectivity'] = float(reducer.graph_.getnnz()) + return coords, meta + if method == 't-SNE': + perplexity = min(tsne_perplexity, max(5, (X.shape[0] - 1) // 3)) + tsne = TSNE( + n_components=3, + perplexity=perplexity, + learning_rate=tsne_learning_rate, + init='pca', + random_state=random_state, + n_iter=1000, + verbose=0, + ) + coords = tsne.fit_transform(X) + meta['effective_perplexity'] = perplexity + return coords, meta + raise ValueError(f"Unknown method {method}") + + +def kmeans_cluster(coords: np.ndarray, n_clusters: int, seed: int = 42) -> Tuple[np.ndarray, float]: + if n_clusters <= 1: + return np.zeros(coords.shape[0], dtype=int), float('nan') + km = KMeans(n_clusters=n_clusters, n_init='auto', random_state=seed) + labels = km.fit_predict(coords) + score = float('nan') + if len(set(labels)) > 1 and coords.shape[0] >= n_clusters * 5: + try: + score = silhouette_score(coords, labels) + except Exception: + pass + return labels, score + + +def build_dataframe(recs: List[EmbeddingRecord]) -> pd.DataFrame: + return pd.DataFrame({ + 'filepath': [r.filepath for r in recs], + 'embedding': [r.embedding for r in recs], + }) + + +def load_cluster_file( + path: str, + expected_n: int, + noise_label: int = -1, +) -> np.ndarray: + """Load cluster labels from a JSON result file. + + Supports formats: + - {"results": [ ... ]} + - [ ... ] + Each item may contain one of: cluster, cluster_id, label, is_noise, filepath. + If only is_noise exists: non-noise -> 0, noise -> noise_label. + If filepath present, mapping is done by filepath, otherwise by index order. + """ + try: + with open(path, 'r', encoding='utf-8') as f: + content = json.load(f) + except Exception as e: + raise RuntimeError(f'Không đọc được file cluster: {e}') + + if isinstance(content, dict) and 'results' in content: + items = content['results'] + elif isinstance(content, list): + items = content + else: + raise RuntimeError('Định dạng file cluster không hợp lệ (cần list hoặc có key "results").') + + # Detect if filepath-based mapping + use_filepath = any(isinstance(it, dict) and 'filepath' in it for it in items) + + labels = np.full(expected_n, noise_label, dtype=int) + if use_filepath: + # Build path->label + mapping = {} + for it in items: + if not isinstance(it, dict): + continue + fp = it.get('filepath') + if not fp: + continue + if 'cluster' in it: + val = it['cluster'] + elif 'cluster_id' in it: + val = it['cluster_id'] + elif 'label' in it: + val = it['label'] + elif 'is_noise' in it: + val = (0 if not it.get('is_noise') else noise_label) + else: + val = 0 + try: + mapping[str(fp)] = int(val) + except Exception: + continue + return labels, mapping # second value used later to map onto df + + # Index-based mapping + collected = [] + for it in items: + if not isinstance(it, dict): + # accept raw int labels + if isinstance(it, int): + collected.append(int(it)) + continue + if 'cluster' in it: + val = it['cluster'] + elif 'cluster_id' in it: + val = it['cluster_id'] + elif 'label' in it: + val = it['label'] + elif 'is_noise' in it: + val = (0 if not it.get('is_noise') else noise_label) + else: + val = 0 + try: + collected.append(int(val)) + except Exception: + collected.append(noise_label) + + for i in range(min(expected_n, len(collected))): + labels[i] = collected[i] + return labels, None + + +def main(): # pragma: no cover - Streamlit entry + st.set_page_config(page_title="Embedding 3D Viewer", layout="wide") + st.title("🔍 Embedding 3D Viewer") + st.caption("Trực quan hóa tương quan embedding hóa đơn (Qwen2-VL).") + + default_path = 'embeddings_factures_osteopathie_1k_qwen.json' + path = st.text_input('Đường dẫn file embedding', value=default_path) + col_top = st.columns(4) + sample_size = col_top[0].number_input('Sample (0 = all)', min_value=0, value=1000, step=100) + max_objects = col_top[1].number_input('Max objects đọc (0 = no limit)', min_value=0, value=0, step=500) + seed = col_top[2].number_input('Seed', min_value=0, value=42, step=1) + show_raw = col_top[3].checkbox('Hiện bảng raw', value=False) + + algo = st.selectbox('Thuật toán giảm chiều', ['UMAP', 'PCA', 't-SNE'], index=0) + with st.expander('Tham số giảm chiều'): + if algo == 'UMAP': + umap_neighbors = st.slider('n_neighbors', 5, 100, 15, 1) + umap_min_dist = st.slider('min_dist', 0.0, 1.0, 0.1, 0.01) + tsne_perplexity = 30 + elif algo == 't-SNE': + tsne_perplexity = st.slider('perplexity', 5, 100, 30, 1) + umap_neighbors = 15 + umap_min_dist = 0.1 + else: + umap_neighbors = 15 + umap_min_dist = 0.1 + tsne_perplexity = 30 + + with st.expander('Phân cụm & màu sắc'): + cluster_source = st.radio('Nguồn cluster', ['KMeans','Load file','None'], index=0, horizontal=True) + n_clusters = st.slider('Số cluster KMeans', 2, 100, 10, 1, disabled=(cluster_source != 'KMeans')) + cluster_file_path = st.text_input('Đường dẫn file cluster (JSON)', value='cluster/dbscan_results.json', disabled=(cluster_source != 'Load file')) + noise_label = st.number_input('Giá trị label cho noise', value=-1, step=1, disabled=(cluster_source != 'Load file')) + path_filter = st.text_input('Filter substring (lọc filepath, comma = OR)', value='') + color_by_substring = st.text_input('Color theo substring (vd: osteo, facture)', value='') + palette_name = st.selectbox('Bảng màu', ['auto','okabe-ito','tol','plotly','bold','dark24','set3','d3','hcl'], index=0, help='Chọn bảng màu dễ phân biệt hơn (okabe-ito, tol = thân thiện cho người mù màu)') + marker_size = st.slider('Kích thước điểm', 2, 15, 5, 1) + st.caption('Nếu file cluster chỉ chứa is_noise: noise -> noise_label, còn lại -> 0.') + + load_btn = st.button('🚀 Load & Giảm chiều', type='primary') + + if load_btn: + if not os.path.isfile(path): + st.error(f'File không tồn tại: {path}') + return + with st.spinner('Đang load embeddings...'): + recs = load_embeddings( + path, + sample_size=sample_size or None, + sampling_seed=int(seed), + max_objects=max_objects or None, + ) + st.success(f'Loaded {len(recs)} embeddings') + + df = build_dataframe(recs) + dim = len(df['embedding'].iloc[0]) + st.write(f'Chiều gốc: {dim}') + + # Filter + if path_filter.strip(): + tokens = [t.strip() for t in path_filter.split(',') if t.strip()] + if tokens: + mask = df['filepath'].apply(lambda p: any(tok.lower() in p.lower() for tok in tokens)) + df = df[mask].reset_index(drop=True) + st.info(f'Filter còn {len(df)} bản ghi.') + if df.empty: + return + + X = np.vstack(df['embedding'].values).astype(np.float32) + with st.spinner('Đang giảm chiều...'): + coords, meta = reduce_embeddings( + X, + algo, + random_state=int(seed), + umap_neighbors=umap_neighbors, + umap_min_dist=umap_min_dist, + tsne_perplexity=tsne_perplexity, + ) + df[['x', 'y', 'z']] = coords + st.write('Meta:', meta) + + # Clustering (three modes) + if cluster_source == 'KMeans': + with st.spinner('KMeans clustering...'): + labels, sil = kmeans_cluster(coords, n_clusters, seed) + df['cluster'] = labels + if not math.isnan(sil): + st.write(f'Silhouette: {sil:.4f}') + else: + st.write('Silhouette: N/A') + elif cluster_source == 'Load file': + if not os.path.isfile(cluster_file_path): + st.error(f'Không tìm thấy file cluster: {cluster_file_path}') + return + with st.spinner('Đang nạp label từ file cluster...'): + loaded_labels, mapping = load_cluster_file(cluster_file_path, len(df), noise_label=int(noise_label)) + if mapping is not None: + # map by filepath + labels = [] + miss = 0 + for fp in df['filepath']: + lab = mapping.get(fp) + if lab is None: + lab = int(noise_label) + miss += 1 + labels.append(lab) + labels = np.array(labels, dtype=int) + if miss: + st.warning(f'{miss} filepath không tìm thấy trong file cluster – gán noise.') + else: + labels = loaded_labels + df['cluster'] = labels + # Try silhouette when >1 cluster and not only noise + uniq = set(labels) + if len([u for u in uniq if u != int(noise_label)]) > 1: + try: + mask = labels != int(noise_label) + sil = silhouette_score(coords[mask], labels[mask]) + st.write(f'Silhouette (exclude noise): {sil:.4f}') + except Exception: + st.write('Silhouette: N/A') + else: + st.write('Silhouette: N/A') + else: # None + df['cluster'] = -1 + st.write('Không áp dụng clustering.') + + # Color scheme + if color_by_substring.strip(): + subs = [s.strip() for s in color_by_substring.split(',') if s.strip()] + def color_from_sub(p: str) -> str: + for i, ssub in enumerate(subs): + if ssub.lower() in p.lower(): + return ssub + return 'other' + df['color_group'] = df['filepath'].apply(color_from_sub) + color_col = 'color_group' + else: + color_col = 'cluster' + + # --- Palette handling ------------------------------------------------- + def get_base_palette(name: str) -> List[str]: + name = name.lower() + if name == 'okabe-ito': # 8 colors, colorblind-safe + return ["#000000","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7"] + if name == 'tol': # Paul Tol (12) + return ["#4477AA","#66CCEE","#228833","#CCBB44","#EE6677","#AA3377","#BBBBBB","#000000","#EEDD88","#FFAABB","#99DDFF","#44BB99"] + if name == 'plotly': + from plotly.colors import qualitative as q + return q.Plotly + if name == 'bold': + from plotly.colors import qualitative as q + return q.Bold + if name == 'dark24': + from plotly.colors import qualitative as q + return q.Dark24 + if name == 'set3': + from plotly.colors import qualitative as q + return q.Set3 + if name == 'd3': + from plotly.colors import qualitative as q + return q.D3 + return [] + + def generate_hcl_like(n: int) -> List[str]: + # Simple evenly spaced hues in HSV then adjust to look more balanced + cols = [] + for i in range(n): + h = (i / n) % 1.0 + s = 0.55 + 0.35 * ((i * 37) % 2) # alternate saturation + v = 0.85 if (i % 3) else 0.98 + r, g, b = colorsys.hsv_to_rgb(h, s, v) + cols.append('#%02X%02X%02X' % (int(r*255), int(g*255), int(b*255))) + return cols + + def build_palette(name: str, k: int) -> List[str]: + if name == 'auto': + return [] # let plotly decide + if name == 'hcl': + return generate_hcl_like(k) + base = get_base_palette(name) + if k <= len(base): + return base[:k] + # extend by generating extra colors using HSV golden ratio + cols = list(base) + gold = 0.61803398875 + h = 0.1 + while len(cols) < k: + h = (h + gold) % 1.0 + r, g, b = colorsys.hsv_to_rgb(h, 0.6, 0.95) + newc = '#%02X%02X%02X' % (int(r*255), int(g*255), int(b*255)) + if newc not in cols: + cols.append(newc) + return cols + + color_display_col = color_col + '_display' + # ensure discrete (string) for numeric clusters + if np.issubdtype(df[color_col].dtype, np.number): + df[color_display_col] = df[color_col].astype(int).astype(str) + else: + df[color_display_col] = df[color_col].astype(str) + groups = df[color_display_col].unique() + palette_seq = build_palette(palette_name, len(groups)) if palette_name else [] + + import plotly.express as px + fig = px.scatter_3d( + df, + x='x', y='y', z='z', + color=color_display_col, + color_discrete_sequence=palette_seq if palette_seq else None, + hover_data={'filepath': True, 'cluster': True, 'x': ':.2f', 'y': ':.2f', 'z': ':.2f'}, + title=f'Embedding 3D ({algo})', + opacity=0.9, + height=800, + ) + fig.update_traces(marker={'size': int(marker_size)}) + fig.update_layout(legend=dict(title='Nhóm', itemsizing='constant')) + st.plotly_chart(fig, use_container_width=True) + + if show_raw: + st.dataframe(df.head(100)) + + # Download + out_csv = df[['filepath', 'x', 'y', 'z', 'cluster']].to_csv(index=False).encode('utf-8') + st.download_button('⬇️ Tải toạ độ 3D (CSV)', out_csv, file_name='embedding_3d.csv', mime='text/csv') + + st.caption('Hoàn tất.') + + +if __name__ == '__main__': # pragma: no cover + # Khi chạy bằng 'streamlit run', sys.argv chỉ chứa tên file => ta luôn gọi main() + # Nếu muốn test nhanh CLI, có thể thêm arg '--cli-test' + if '--cli-test' in os.sys.argv: + test_path = 'embeddings_factures_osteopathie_1k_qwen.json' + if os.path.exists(test_path): + recs = load_embeddings(test_path, sample_size=5) + print(f'[CLI TEST] Loaded {len(recs)} embeddings dim={len(recs[0].embedding)}') + else: + print('[CLI TEST] Không tìm thấy file test.') + else: + main() + diff --git a/check_filter/visual_data copy.py b/check_filter/visual_data copy.py new file mode 100644 index 0000000..1bfae91 --- /dev/null +++ b/check_filter/visual_data copy.py @@ -0,0 +1,600 @@ +""" +Streamlit app để trực quan hóa embedding 3 chiều (PCA / UMAP / t-SNE) + phân cụm. + +Chạy: + streamlit run visual_data.py --server.port 8501 + +Yêu cầu cài đặt (một lần): + pip install streamlit plotly scikit-learn umap-learn numpy pandas + +Tính năng: + - Load file JSON lớn chứa các object {"filepath": ..., "embedding": [...]} hoặc định dạng JSON lines. + - Tùy chọn sample n phần tử (random) để tăng tốc. + - Chọn thuật toán giảm chiều: PCA, UMAP, t-SNE. + - Tham số điều chỉnh: n_neighbors, min_dist (UMAP); perplexity (t-SNE); n_components=3. + - KMeans clustering (tuỳ chọn) để tô màu điểm; hoặc tô màu theo regex/substring trong tên file. + - Lọc theo từ khóa trong đường dẫn. + - Tải xuống toạ độ 3D + nhãn cluster. + +File embedding quan sát được có thể không phải JSON array chuẩn; script sẽ thử: + 1. Parse như JSON array. + 2. Parse như JSON lines (mỗi dòng 1 object). + 3. Parse thủ công bằng cách tìm pattern {"filepath": ... , "embedding": [ ... ]}. + +Nếu kích thước > ~1e6 bytes, dùng đọc streaming để giảm RAM. +""" + +from __future__ import annotations + +import json +import os +import re +import math +import random +from dataclasses import dataclass +from typing import Iterable, List, Optional, Tuple, Dict, Any + +import numpy as np +import pandas as pd +import streamlit as st +import colorsys +from sklearn.decomposition import PCA +from sklearn.manifold import TSNE +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score + +try: + import umap # type: ignore +except ImportError: # pragma: no cover + umap = None # handled later + + +@dataclass +class EmbeddingRecord: + filepath: str + embedding: List[float] + + +def _smart_json_object_stream(raw_text: str) -> Iterable[str]: + """Yield JSON object strings from a large raw buffer. + + Heuristic: find balanced braces starting with {"filepath": ...}. + This is a fallback when content is not standard array / jsonlines. + """ + brace = 0 + buf = [] + in_obj = False + for ch in raw_text: + if ch == '{': + if not in_obj: + in_obj = True + buf = ['{'] + brace = 1 + else: + brace += 1 + buf.append(ch) + elif ch == '}': + if in_obj: + brace -= 1 + buf.append('}') + if brace == 0: + yield ''.join(buf) + in_obj = False + else: + # stray closing + continue + else: + if in_obj: + buf.append(ch) + + +def load_embeddings( + path: str, + sample_size: Optional[int] = None, + sampling_seed: int = 42, + max_objects: Optional[int] = None, +) -> List[EmbeddingRecord]: + """Load embeddings from a possibly large JSON / JSONL / raw file. + + Args: + path: file path + sample_size: random sample (after load) if provided + sampling_seed: RNG seed + max_objects: hard cap to stop early (for speed) + """ + # size = os.path.getsize(path) # kích thước có thể dùng sau nếu muốn tối ưu đọc streaming + # First attempt: JSON array + records: List[EmbeddingRecord] = [] + def to_rec(obj) -> Optional[EmbeddingRecord]: + if not isinstance(obj, dict): + return None + if 'embedding' in obj: + fp = str(obj.get('filepath') or obj.get('file_path') or obj.get('path') or '') + emb = obj['embedding'] + if isinstance(emb, list) and fp: + return EmbeddingRecord(fp, emb) + return None + + try: + with open(path, 'r', encoding='utf-8') as f: + text = f.read() + text_stripped = text.strip() + if text_stripped.startswith('[') and text_stripped.endswith(']'): + arr = json.loads(text_stripped) + for obj in arr: + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + else: + raise ValueError('Not a JSON array') + except Exception: + # Retry as JSON lines + records = [] + try: + with open(path, 'r', encoding='utf-8') as f: + for line in f: + line_strip = line.strip().rstrip(',') + if not line_strip: + continue + if not line_strip.startswith('{'): + continue + try: + obj = json.loads(line_strip) + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + except json.JSONDecodeError: + continue + if not records: + raise ValueError('No JSONL records') + except Exception: + # Fallback: heuristic extraction + records = [] + with open(path, 'r', encoding='utf-8') as f: + raw = f.read() + for obj_str in _smart_json_object_stream(raw): + if 'embedding' not in obj_str: + continue + # Clean possible trailing '," + try: + # Attempt to fix malformed numbers like '1.2\n421875' (broken newline) by removing stray newlines inside arrays + fixed = re.sub(r"(\d)\n(\d)", r"\1\2", obj_str) + obj = json.loads(fixed) + except Exception: + continue + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + if not records: + raise RuntimeError("Không load được embedding nào từ file.") + + # Random sample if needed + if sample_size and sample_size < len(records): + random.seed(sampling_seed) + records = random.sample(records, sample_size) + return records + + +def reduce_embeddings( + X: np.ndarray, + method: str, + random_state: int = 42, + umap_neighbors: int = 15, + umap_min_dist: float = 0.1, + tsne_perplexity: int = 30, + tsne_learning_rate: str | float = 'auto', +) -> Tuple[np.ndarray, dict]: + """Project high-dim embeddings to 3D. + + Returns (coords (n,3), meta_info) + """ + meta = {"method": method} + if method == 'PCA': + pca = PCA(n_components=3, random_state=random_state) + coords = pca.fit_transform(X) + meta['explained_variance_ratio'] = pca.explained_variance_ratio_.tolist() + return coords, meta + if method == 'UMAP': + if umap is None: + raise RuntimeError("Chưa cài umap-learn: pip install umap-learn") + reducer = umap.UMAP( + n_components=3, + n_neighbors=umap_neighbors, + min_dist=umap_min_dist, + metric='cosine', + random_state=random_state, + ) + coords = reducer.fit_transform(X) + meta['umap_graph_connectivity'] = float(reducer.graph_.getnnz()) + return coords, meta + if method == 't-SNE': + perplexity = min(tsne_perplexity, max(5, (X.shape[0] - 1) // 3)) + tsne = TSNE( + n_components=3, + perplexity=perplexity, + learning_rate=tsne_learning_rate, + init='pca', + random_state=random_state, + n_iter=1000, + verbose=0, + ) + coords = tsne.fit_transform(X) + meta['effective_perplexity'] = perplexity + return coords, meta + raise ValueError(f"Unknown method {method}") + + +def kmeans_cluster(coords: np.ndarray, n_clusters: int, seed: int = 42) -> Tuple[np.ndarray, float]: + if n_clusters <= 1: + return np.zeros(coords.shape[0], dtype=int), float('nan') + km = KMeans(n_clusters=n_clusters, n_init='auto', random_state=seed) + labels = km.fit_predict(coords) + score = float('nan') + if len(set(labels)) > 1 and coords.shape[0] >= n_clusters * 5: + try: + score = silhouette_score(coords, labels) + except Exception: + pass + return labels, score + + +def build_dataframe(recs: List[EmbeddingRecord]) -> pd.DataFrame: + return pd.DataFrame({ + 'filepath': [r.filepath for r in recs], + 'embedding': [r.embedding for r in recs], + }) + + +def load_cluster_file( + path: str, + expected_n: int, + noise_label: int = -1, +) -> np.ndarray: + """Load cluster labels from a JSON result file. + + Supports formats: + - {"results": [ ... ]} + - [ ... ] + Each item may contain one of: cluster, cluster_id, label, is_noise, filepath. + If only is_noise exists: non-noise -> 0, noise -> noise_label. + If filepath present, mapping is done by filepath, otherwise by index order. + """ + try: + with open(path, 'r', encoding='utf-8') as f: + content = json.load(f) + except Exception as e: + raise RuntimeError(f'Không đọc được file cluster: {e}') + + if isinstance(content, dict) and 'results' in content: + items = content['results'] + elif isinstance(content, list): + items = content + else: + raise RuntimeError('Định dạng file cluster không hợp lệ (cần list hoặc có key "results").') + + # Detect if filepath-based mapping + use_filepath = any(isinstance(it, dict) and 'filepath' in it for it in items) + + labels = np.full(expected_n, noise_label, dtype=int) + if use_filepath: + # Build path->label + mapping = {} + for it in items: + if not isinstance(it, dict): + continue + fp = it.get('filepath') + if not fp: + continue + if 'cluster' in it: + val = it['cluster'] + elif 'cluster_id' in it: + val = it['cluster_id'] + elif 'label' in it: + val = it['label'] + elif 'is_noise' in it: + val = (0 if not it.get('is_noise') else noise_label) + else: + val = 0 + try: + mapping[str(fp)] = int(val) + except Exception: + continue + return labels, mapping # second value used later to map onto df + + # Index-based mapping + collected = [] + for it in items: + if not isinstance(it, dict): + # accept raw int labels + if isinstance(it, int): + collected.append(int(it)) + continue + if 'cluster' in it: + val = it['cluster'] + elif 'cluster_id' in it: + val = it['cluster_id'] + elif 'label' in it: + val = it['label'] + elif 'is_noise' in it: + val = (0 if not it.get('is_noise') else noise_label) + else: + val = 0 + try: + collected.append(int(val)) + except Exception: + collected.append(noise_label) + + for i in range(min(expected_n, len(collected))): + labels[i] = collected[i] + return labels, None + + +def main(): # pragma: no cover - Streamlit entry + st.set_page_config(page_title="Embedding 3D Viewer", layout="wide") + st.title("🔍 Embedding 3D Viewer (Multi)") + st.caption("Mỗi lần nạp file sẽ thêm một đồ thị mới ở bên dưới.") + + if 'plots' not in st.session_state: + st.session_state['plots']: List[Dict[str, Any]] = [] # type: ignore + + with st.sidebar: + st.markdown('### ⚙️ Cấu hình chung') + default_path = 'embeddings_factures_osteopathie_1k_qwen.json' + path = st.text_input('Đường dẫn file embedding', value=default_path, key='path_input') + sample_size = st.number_input('Sample (0 = all)', min_value=0, value=1000, step=100, key='sample_size') + max_objects = st.number_input('Max objects đọc (0 = no limit)', min_value=0, value=0, step=500, key='max_objects') + seed = st.number_input('Seed', min_value=0, value=42, step=1, key='seed') + show_raw = st.checkbox('Hiện bảng raw (chỉ 100 dòng đầu mỗi plot)', value=False, key='show_raw') + + algo = st.selectbox('Thuật toán giảm chiều', ['UMAP', 'PCA', 't-SNE'], index=0, key='algo') + if algo == 'UMAP': + umap_neighbors = st.slider('UMAP n_neighbors', 5, 100, 15, 1, key='umap_neighbors') + umap_min_dist = st.slider('UMAP min_dist', 0.0, 1.0, 0.1, 0.01, key='umap_min_dist') + tsne_perplexity = 30 + elif algo == 't-SNE': + tsne_perplexity = st.slider('t-SNE perplexity', 5, 100, 30, 1, key='tsne_perplexity') + umap_neighbors = 15 + umap_min_dist = 0.1 + else: + umap_neighbors = 15 + umap_min_dist = 0.1 + tsne_perplexity = 30 + + st.markdown('### 🎨 Màu sắc & Cluster') + cluster_source = st.radio('Nguồn cluster', ['KMeans','Load file','None'], index=0, horizontal=False, key='cluster_source') + n_clusters = st.slider('Số cluster KMeans', 2, 100, 10, 1, disabled=(cluster_source != 'KMeans'), key='n_clusters') + cluster_file_path = st.text_input('File cluster JSON', value='cluster/dbscan_results.json', disabled=(cluster_source != 'Load file'), key='cluster_file') + noise_label = st.number_input('Noise label', value=-1, step=1, disabled=(cluster_source != 'Load file'), key='noise_label') + path_filter = st.text_input('Filter filepath (comma OR)', value='', key='path_filter') + color_by_substring = st.text_input('Color theo substring', value='', key='color_by_substring') + palette_name = st.selectbox('Bảng màu', ['auto','okabe-ito','tol','plotly','bold','dark24','set3','d3','hcl'], index=0, help='okabe-ito, tol = thân thiện mù màu', key='palette') + marker_size = st.slider('Kích thước điểm', 2, 15, 5, 1, key='marker_size') + st.caption('File cluster chỉ có is_noise: non-noise -> 0, noise -> noise_label.') + + add_btn = st.button('➕ Thêm plot', type='primary') + clear_btn = st.button('🧹 Xoá tất cả plot') + + if clear_btn: + st.session_state['plots'].clear() + st.success('Đã xoá toàn bộ plot.') + + def process_and_add_plot(): + path_local = path + if not os.path.isfile(path_local): + st.error(f'File không tồn tại: {path_local}') + return + with st.spinner(f'Loading embeddings: {os.path.basename(path_local)}'): + recs = load_embeddings( + path_local, + sample_size=sample_size or None, + sampling_seed=int(seed), + max_objects=max_objects or None, + ) + df = build_dataframe(recs) + if df.empty: + st.warning('Không có embedding nào.') + return + # Filter + if path_filter.strip(): + tokens = [t.strip() for t in path_filter.split(',') if t.strip()] + if tokens: + mask = df['filepath'].apply(lambda p: any(tok.lower() in p.lower() for tok in tokens)) + df = df[mask].reset_index(drop=True) + if df.empty: + st.warning('Sau filter không còn bản ghi.') + return + # Reduce + X = np.vstack(df['embedding'].values).astype(np.float32) + with st.spinner('Giảm chiều...'): + coords, meta = reduce_embeddings( + X, + algo, + random_state=int(seed), + umap_neighbors=umap_neighbors, + umap_min_dist=umap_min_dist, + tsne_perplexity=tsne_perplexity, + ) + df[['x','y','z']] = coords + # Clustering + if cluster_source == 'KMeans': + with st.spinner('KMeans...'): + labels, sil = kmeans_cluster(coords, n_clusters, int(seed)) + df['cluster'] = labels + sil_msg = f'Silhouette: {sil:.4f}' if not math.isnan(sil) else 'Silhouette: N/A' + elif cluster_source == 'Load file': + if not os.path.isfile(cluster_file_path): + st.warning(f'Không thấy file cluster: {cluster_file_path}; gán -1') + df['cluster'] = -1 + sil_msg = 'Silhouette: N/A' + else: + loaded_labels, mapping = load_cluster_file(cluster_file_path, len(df), noise_label=int(noise_label)) + if mapping is not None: + labs = [] + miss=0 + for fp in df['filepath']: + val = mapping.get(fp, int(noise_label)) + if fp not in mapping: + miss += 1 + labs.append(val) + if miss: + st.info(f'{miss} filepath gán noise.') + df['cluster'] = np.array(labs, dtype=int) + else: + df['cluster'] = loaded_labels + uniq = set(df['cluster']) + if len([u for u in uniq if u != int(noise_label)]) > 1: + try: + mask_valid = df['cluster'].to_numpy() != int(noise_label) + sil = silhouette_score(coords[mask_valid], df.loc[mask_valid,'cluster']) + sil_msg = f'Silhouette(ex noise): {sil:.4f}' + except Exception: + sil_msg = 'Silhouette: N/A' + else: + sil_msg = 'Silhouette: N/A' + else: + df['cluster'] = -1 + sil_msg = 'No clustering' + + # Color grouping + if color_by_substring.strip(): + subs = [s.strip() for s in color_by_substring.split(',') if s.strip()] + def color_from_sub(p: str) -> str: + for ssub in subs: + if ssub.lower() in p.lower(): + return ssub + return 'other' + df['color_group'] = df['filepath'].apply(color_from_sub) + color_col = 'color_group' + else: + color_col = 'cluster' + + # Palette utilities (copy of earlier helpers) + def get_base_palette(name: str) -> List[str]: + name = name.lower() + if name == 'okabe-ito': + return ["#000000","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7"] + if name == 'tol': + return ["#4477AA","#66CCEE","#228833","#CCBB44","#EE6677","#AA3377","#BBBBBB","#000000","#EEDD88","#FFAABB","#99DDFF","#44BB99"] + if name == 'plotly': + from plotly.colors import qualitative as q + return q.Plotly + if name == 'bold': + from plotly.colors import qualitative as q + return q.Bold + if name == 'dark24': + from plotly.colors import qualitative as q + return q.Dark24 + if name == 'set3': + from plotly.colors import qualitative as q + return q.Set3 + if name == 'd3': + from plotly.colors import qualitative as q + return q.D3 + return [] + def generate_hcl_like(n: int) -> List[str]: + cols=[] + for i in range(n): + h=(i/n)%1.0 + s=0.55+0.35*((i*37)%2) + v=0.85 if (i%3) else 0.98 + r,g,b = colorsys.hsv_to_rgb(h,s,v) + cols.append('#%02X%02X%02X' % (int(r*255), int(g*255), int(b*255))) + return cols + def build_palette(name: str, k: int) -> List[str]: + if name == 'auto': + return [] + if name == 'hcl': + return generate_hcl_like(k) + base = get_base_palette(name) + if k <= len(base): + return base[:k] + cols=list(base) + gold=0.61803398875 + h=0.1 + while len(cols) < k: + h=(h+gold)%1.0 + r,g,b = colorsys.hsv_to_rgb(h,0.6,0.95) + newc='#%02X%02X%02X' % (int(r*255), int(g*255), int(b*255)) + if newc not in cols: + cols.append(newc) + return cols + color_display_col = color_col + '_display' + if np.issubdtype(df[color_col].dtype, np.number): + df[color_display_col] = df[color_col].astype(int).astype(str) + else: + df[color_display_col] = df[color_col].astype(str) + groups = df[color_display_col].unique() + palette_seq = build_palette(palette_name, len(groups)) if palette_name else [] + import plotly.express as px + fig = px.scatter_3d( + df, + x='x', y='y', z='z', + color=color_display_col, + color_discrete_sequence=palette_seq if palette_seq else None, + hover_data={'filepath': True, 'cluster': True, 'x': ':.2f', 'y': ':.2f', 'z': ':.2f'}, + title=f'{os.path.basename(path_local)} ({algo})', + opacity=0.9, + height=700, + ) + fig.update_traces(marker={'size': int(marker_size)}) + fig.update_layout(margin=dict(l=0,r=0,t=40,b=0)) + out_csv = df[['filepath','x','y','z','cluster']].to_csv(index=False).encode('utf-8') + st.session_state['plots'].append({ + 'path': path_local, + 'df_head': df.head(100) if show_raw else None, + 'fig': fig, + 'meta': meta, + 'algo': algo, + 'sil_msg': sil_msg, + 'csv': out_csv, + 'n': len(df), + 'dim': len(df['embedding'].iloc[0]), + }) + st.success(f'Đã thêm plot: {os.path.basename(path_local)}') + + if add_btn: + process_and_add_plot() + + # Render existing plots + if st.session_state['plots']: + st.markdown('---') + for idx, plot_data in enumerate(st.session_state['plots']): + container = st.container() + with container: + cols = st.columns([0.8,0.2]) + with cols[0]: + st.subheader(f'#{idx+1} {os.path.basename(plot_data["path"])}') + with cols[1]: + if st.button('❌ Xoá', key=f'remove_{idx}'): + st.session_state['plots'].pop(idx) + st.experimental_rerun() + st.caption(f"Embeddings: {plot_data['n']} | Dim gốc: {plot_data['dim']} | {plot_data['sil_msg']}") + st.plotly_chart(plot_data['fig'], use_container_width=True) + with st.expander('Meta / Thông tin thêm'): + st.json(plot_data['meta']) + st.download_button('⬇️ CSV', plot_data['csv'], file_name=f"embedding_3d_{idx+1}.csv", mime='text/csv', key=f'dl_{idx}') + if plot_data['df_head'] is not None: + st.dataframe(plot_data['df_head']) + if not st.session_state['plots']: + st.info('Chưa có plot nào. Chọn file ở sidebar và nhấn "➕ Thêm plot".') + + +if __name__ == '__main__': # pragma: no cover + # Khi chạy bằng 'streamlit run', sys.argv chỉ chứa tên file => ta luôn gọi main() + # Nếu muốn test nhanh CLI, có thể thêm arg '--cli-test' + if '--cli-test' in os.sys.argv: + test_path = 'embeddings_factures_osteopathie_1k_qwen.json' + if os.path.exists(test_path): + recs = load_embeddings(test_path, sample_size=5) + print(f'[CLI TEST] Loaded {len(recs)} embeddings dim={len(recs[0].embedding)}') + else: + print('[CLI TEST] Không tìm thấy file test.') + else: + main() + diff --git a/check_filter/visual_data.py b/check_filter/visual_data.py new file mode 100644 index 0000000..c489304 --- /dev/null +++ b/check_filter/visual_data.py @@ -0,0 +1,783 @@ +""" +Streamlit app để trực quan hóa embedding 3 chiều (PCA / UMAP / t-SNE) + phân cụm. + +Chạy: + streamlit run visual_data.py --server.port 8501 + +Yêu cầu cài đặt (một lần): + pip install streamlit plotly scikit-learn umap-learn numpy pandas + +Tính năng: + - Load file JSON lớn chứa các object {"filepath": ..., "embedding": [...]} hoặc định dạng JSON lines. + - Tùy chọn sample n phần tử (random) để tăng tốc. + - Chọn thuật toán giảm chiều: PCA, UMAP, t-SNE. + - Tham số điều chỉnh: n_neighbors, min_dist (UMAP); perplexity (t-SNE); n_components=3. + - KMeans clustering (tuỳ chọn) để tô màu điểm; hoặc tô màu theo regex/substring trong tên file. + - Lọc theo từ khóa trong đường dẫn. + - Tải xuống toạ độ 3D + nhãn cluster. + +File embedding quan sát được có thể không phải JSON array chuẩn; script sẽ thử: + 1. Parse như JSON array. + 2. Parse như JSON lines (mỗi dòng 1 object). + 3. Parse thủ công bằng cách tìm pattern {"filepath": ... , "embedding": [ ... ]}. + +Nếu kích thước > ~1e6 bytes, dùng đọc streaming để giảm RAM. +""" + +from __future__ import annotations + +import json +import os +import re +import math +import random +from dataclasses import dataclass +from typing import Iterable, List, Optional, Tuple, Dict, Any +from datetime import datetime + +import numpy as np +import pandas as pd +import streamlit as st +import colorsys +from sklearn.decomposition import PCA +from sklearn.manifold import TSNE +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score + +try: + import umap # type: ignore +except ImportError: # pragma: no cover + umap = None # handled later + + +@dataclass +class EmbeddingRecord: + filepath: str + embedding: List[float] + + +def _smart_json_object_stream(raw_text: str) -> Iterable[str]: + """Yield JSON object strings from a large raw buffer. + + Heuristic: find balanced braces starting with {"filepath": ...}. + This is a fallback when content is not standard array / jsonlines. + """ + brace = 0 + buf = [] + in_obj = False + for ch in raw_text: + if ch == '{': + if not in_obj: + in_obj = True + buf = ['{'] + brace = 1 + else: + brace += 1 + buf.append(ch) + elif ch == '}': + if in_obj: + brace -= 1 + buf.append('}') + if brace == 0: + yield ''.join(buf) + in_obj = False + else: + # stray closing + continue + else: + if in_obj: + buf.append(ch) + + +def load_embeddings( + path: str, + sample_size: Optional[int] = None, + sampling_seed: int = 42, + max_objects: Optional[int] = None, +) -> List[EmbeddingRecord]: + """Load embeddings from a possibly large JSON / JSONL / raw file. + + Args: + path: file path + sample_size: random sample (after load) if provided + sampling_seed: RNG seed + max_objects: hard cap to stop early (for speed) + """ + # size = os.path.getsize(path) # kích thước có thể dùng sau nếu muốn tối ưu đọc streaming + # First attempt: JSON array + records: List[EmbeddingRecord] = [] + def to_rec(obj) -> Optional[EmbeddingRecord]: + if not isinstance(obj, dict): + return None + if 'embedding' in obj: + fp = str(obj.get('filepath') or obj.get('file_path') or obj.get('path') or '') + emb = obj['embedding'] + if isinstance(emb, list) and fp: + return EmbeddingRecord(fp, emb) + return None + + try: + with open(path, 'r', encoding='utf-8') as f: + text = f.read() + text_stripped = text.strip() + if text_stripped.startswith('[') and text_stripped.endswith(']'): + arr = json.loads(text_stripped) + for obj in arr: + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + else: + raise ValueError('Not a JSON array') + except Exception: + # Retry as JSON lines + records = [] + try: + with open(path, 'r', encoding='utf-8') as f: + for line in f: + line_strip = line.strip().rstrip(',') + if not line_strip: + continue + if not line_strip.startswith('{'): + continue + try: + obj = json.loads(line_strip) + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + except json.JSONDecodeError: + continue + if not records: + raise ValueError('No JSONL records') + except Exception: + # Fallback: heuristic extraction + records = [] + with open(path, 'r', encoding='utf-8') as f: + raw = f.read() + for obj_str in _smart_json_object_stream(raw): + if 'embedding' not in obj_str: + continue + # Clean possible trailing '," + try: + # Attempt to fix malformed numbers like '1.2\n421875' (broken newline) by removing stray newlines inside arrays + fixed = re.sub(r"(\d)\n(\d)", r"\1\2", obj_str) + obj = json.loads(fixed) + except Exception: + continue + rec = to_rec(obj) + if rec: + records.append(rec) + if max_objects and len(records) >= max_objects: + break + if not records: + raise RuntimeError("Không load được embedding nào từ file.") + + # Random sample if needed + if sample_size and sample_size < len(records): + random.seed(sampling_seed) + records = random.sample(records, sample_size) + return records + + +def reduce_embeddings( + X: np.ndarray, + method: str, + random_state: int = 42, + umap_neighbors: int = 15, + umap_min_dist: float = 0.1, + tsne_perplexity: int = 30, + tsne_learning_rate: str | float = 'auto', +) -> Tuple[np.ndarray, dict]: + """Project high-dim embeddings to 3D. + + Returns (coords (n,3), meta_info) + """ + meta = {"method": method} + if method == 'PCA': + pca = PCA(n_components=3, random_state=random_state) + coords = pca.fit_transform(X) + meta['explained_variance_ratio'] = pca.explained_variance_ratio_.tolist() + return coords, meta + if method == 'UMAP': + if umap is None: + raise RuntimeError("Chưa cài umap-learn: pip install umap-learn") + reducer = umap.UMAP( + n_components=3, + n_neighbors=umap_neighbors, + min_dist=umap_min_dist, + metric='cosine', + random_state=random_state, + ) + coords = reducer.fit_transform(X) + meta['umap_graph_connectivity'] = float(reducer.graph_.getnnz()) + return coords, meta + if method == 't-SNE': + perplexity = min(tsne_perplexity, max(5, (X.shape[0] - 1) // 3)) + tsne = TSNE( + n_components=3, + perplexity=perplexity, + learning_rate=tsne_learning_rate, + init='pca', + random_state=random_state, + n_iter=1000, + verbose=0, + ) + coords = tsne.fit_transform(X) + meta['effective_perplexity'] = perplexity + return coords, meta + raise ValueError(f"Unknown method {method}") + + +def kmeans_cluster(coords: np.ndarray, n_clusters: int, seed: int = 42) -> Tuple[np.ndarray, float]: + if n_clusters <= 1: + return np.zeros(coords.shape[0], dtype=int), float('nan') + km = KMeans(n_clusters=n_clusters, n_init='auto', random_state=seed) + labels = km.fit_predict(coords) + score = float('nan') + if len(set(labels)) > 1 and coords.shape[0] >= n_clusters * 5: + try: + score = silhouette_score(coords, labels) + except Exception: + pass + return labels, score + + +def build_dataframe(recs: List[EmbeddingRecord]) -> pd.DataFrame: + return pd.DataFrame({ + 'filepath': [r.filepath for r in recs], + 'embedding': [r.embedding for r in recs], + }) + + +def load_cluster_file( + path: str, + expected_n: int, + noise_label: int = -1, +) -> np.ndarray: + """Load cluster labels from a JSON result file. + + Supports formats: + - {"results": [ ... ]} + - [ ... ] + Each item may contain one of: cluster, cluster_id, label, is_noise, filepath. + If only is_noise exists: non-noise -> 0, noise -> noise_label. + If filepath present, mapping is done by filepath, otherwise by index order. + """ + try: + with open(path, 'r', encoding='utf-8') as f: + content = json.load(f) + except Exception as e: + raise RuntimeError(f'Không đọc được file cluster: {e}') + + if isinstance(content, dict) and 'results' in content: + items = content['results'] + elif isinstance(content, list): + items = content + else: + raise RuntimeError('Định dạng file cluster không hợp lệ (cần list hoặc có key "results").') + + # Detect if filepath-based mapping + use_filepath = any(isinstance(it, dict) and 'filepath' in it for it in items) + + labels = np.full(expected_n, noise_label, dtype=int) + if use_filepath: + # Build path->label + mapping = {} + for it in items: + if not isinstance(it, dict): + continue + fp = it.get('filepath') + if not fp: + continue + if 'cluster' in it: + val = it['cluster'] + elif 'cluster_id' in it: + val = it['cluster_id'] + elif 'label' in it: + val = it['label'] + elif 'is_noise' in it: + val = (0 if not it.get('is_noise') else noise_label) + else: + val = 0 + try: + mapping[str(fp)] = int(val) + except Exception: + continue + return labels, mapping # second value used later to map onto df + + # Index-based mapping + collected = [] + for it in items: + if not isinstance(it, dict): + # accept raw int labels + if isinstance(it, int): + collected.append(int(it)) + continue + if 'cluster' in it: + val = it['cluster'] + elif 'cluster_id' in it: + val = it['cluster_id'] + elif 'label' in it: + val = it['label'] + elif 'is_noise' in it: + val = (0 if not it.get('is_noise') else noise_label) + else: + val = 0 + try: + collected.append(int(val)) + except Exception: + collected.append(noise_label) + + for i in range(min(expected_n, len(collected))): + labels[i] = collected[i] + return labels, None + + +def main(): # pragma: no cover - Streamlit entry + st.set_page_config(page_title="Embedding 3D Viewer", layout="wide") + st.title("🔍 Embedding 3D Viewer (Multi)") + st.caption("Mỗi lần nạp file sẽ thêm một đồ thị mới ở bên dưới.") + + if 'plots' not in st.session_state: + st.session_state['plots']: List[Dict[str, Any]] = [] # type: ignore + if 'cache_loaded' not in st.session_state: + st.session_state['cache_loaded'] = False + + CACHE_DIR = '.visual_cache' + INDEX_FILE = os.path.join(CACHE_DIR, 'index.json') + + def ensure_cache_dir(): + if not os.path.isdir(CACHE_DIR): + os.makedirs(CACHE_DIR, exist_ok=True) + + def save_cache(): + # Persist plots metadata + data (CSV) for future sessions + ensure_cache_dir() + index: List[Dict[str, Any]] = [] + for i, p in enumerate(st.session_state['plots']): + # Data handling + data_csv_path = p.get('data_csv_path') + if not data_csv_path: + # Build a dataframe back from figure data or stored head (we stored csv bytes earlier) + # We stored only partial head in 'df_head', so we keep an internal full CSV bytes previously saved in 'csv' + # Instead, we require full dataset at creation; we saved it in 'csv'. We'll re-use it. + data_csv_path = os.path.join(CACHE_DIR, f'plot_{i+1}.csv') + try: + with open(data_csv_path, 'wb') as fcsv: + fcsv.write(p['csv']) + p['data_csv_path'] = data_csv_path + except Exception: + continue + index.append({ + 'path': p.get('path'), + 'name': p.get('name'), + 'algo': p.get('algo'), + 'sil_msg': p.get('sil_msg'), + 'meta': p.get('meta'), + 'marker_size': p.get('marker_size', 5), + 'palette_name': p.get('palette_name'), + 'data_csv': os.path.basename(data_csv_path), + 'timestamp': p.get('timestamp'), + }) + try: + with open(INDEX_FILE, 'w', encoding='utf-8') as f: + json.dump({'plots': index, 'saved_at': datetime.utcnow().isoformat()}, f, ensure_ascii=False, indent=2) + except Exception as e: + st.warning(f'Lưu cache lỗi: {e}') + + def load_cache(): + if st.session_state['cache_loaded']: + return + if not os.path.isfile(INDEX_FILE): + st.session_state['cache_loaded'] = True + return + try: + with open(INDEX_FILE, 'r', encoding='utf-8') as f: + data = json.load(f) + plots_meta = data.get('plots', []) + for meta_entry in plots_meta: + csv_file = os.path.join(CACHE_DIR, meta_entry.get('data_csv','')) + if not os.path.isfile(csv_file): + continue + try: + df = pd.read_csv(csv_file) + except Exception: + continue + # Reconstruct figure + import plotly.express as px + color_display_col = 'color_display' if 'color_display' in df.columns else ('cluster_display' if 'cluster_display' in df.columns else 'cluster') + # Build palette again based on unique groups and stored palette_name + palette_name_load = meta_entry.get('palette_name','auto') + def get_base_palette(name: str) -> List[str]: + name = name.lower() + if name == 'okabe-ito': + return ["#000000","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7"] + if name == 'tol': + return ["#4477AA","#66CCEE","#228833","#CCBB44","#EE6677","#AA3377","#BBBBBB","#000000","#EEDD88","#FFAABB","#99DDFF","#44BB99"] + if name == 'plotly': + from plotly.colors import qualitative as q + return q.Plotly + if name == 'bold': + from plotly.colors import qualitative as q + return q.Bold + if name == 'dark24': + from plotly.colors import qualitative as q + return q.Dark24 + if name == 'set3': + from plotly.colors import qualitative as q + return q.Set3 + if name == 'd3': + from plotly.colors import qualitative as q + return q.D3 + return [] + def generate_hcl_like(n: int) -> List[str]: + cols = [] + for i in range(n): + h = (i / n) % 1.0 + s = 0.55 + 0.35 * ((i * 37) % 2) + v = 0.85 if (i % 3) else 0.98 + r, g, b = colorsys.hsv_to_rgb(h, s, v) + cols.append('#%02X%02X%02X' % (int(r * 255), int(g * 255), int(b * 255))) + return cols + def build_palette(name: str, k: int) -> List[str]: + if name == 'auto': + return [] + if name == 'hcl': + return generate_hcl_like(k) + base = get_base_palette(name) + if k <= len(base): + return base[:k] + cols = list(base) + gold = 0.61803398875 + h = 0.1 + while len(cols) < k: + h = (h + gold) % 1.0 + r, g, b = colorsys.hsv_to_rgb(h, 0.6, 0.95) + newc = '#%02X%02X%02X' % (int(r * 255), int(g * 255), int(b * 255)) + if newc not in cols: + cols.append(newc) + return cols + groups = df[color_display_col].astype(str).unique() if color_display_col in df.columns else [] + palette_seq = build_palette(palette_name_load, len(groups)) if palette_name_load else [] + fig = px.scatter_3d( + df, + x='x', y='y', z='z', + color=color_display_col if color_display_col in df.columns else None, + color_discrete_sequence=palette_seq if palette_seq else None, + hover_data={'filepath': True, 'cluster': True}, + title=f'{os.path.basename(meta_entry.get("path",""))} ({meta_entry.get("algo")})', + opacity=0.9, + height=700, + ) + fig.update_traces(marker={'size': int(meta_entry.get('marker_size',5))}) + fig.update_layout(margin=dict(l=0,r=0,t=40,b=0)) + # Reconstruct csv bytes + with open(csv_file, 'rb') as fcsv: + csv_bytes = fcsv.read() + st.session_state['plots'].append({ + 'path': meta_entry.get('path'), + 'name': meta_entry.get('name'), + 'fig': fig, + 'algo': meta_entry.get('algo'), + 'meta': meta_entry.get('meta',{}), + 'sil_msg': meta_entry.get('sil_msg'), + 'csv': csv_bytes, + 'n': int(df.shape[0]), + 'dim': int(len([c for c in df.columns if c.startswith('x') or c.startswith('y') or c.startswith('z')]) or 3), + 'palette_name': meta_entry.get('palette_name'), + 'marker_size': meta_entry.get('marker_size',5), + 'data_csv_path': csv_file, + 'timestamp': meta_entry.get('timestamp'), + 'df_head': None, # không lưu raw để nhẹ; None cho an toàn + }) + st.session_state['cache_loaded'] = True + except Exception as e: + st.warning(f'Lỗi load cache: {e}') + st.session_state['cache_loaded'] = True + + # Load cache only once per session (initial render) + load_cache() + + with st.sidebar: + st.markdown('### ⚙️ Cấu hình chung') + default_path = 'embeddings_factures_osteopathie_1k_qwen.json' + path = st.text_input('Đường dẫn file embedding', value=default_path, key='path_input') + plot_name_input = st.text_input('Tên plot (tuỳ chọn)', value='', key='plot_name') + sample_size = st.number_input('Sample (0 = all)', min_value=0, value=0, step=100, key='sample_size') + max_objects = st.number_input('Max objects đọc (0 = no limit)', min_value=0, value=0, step=500, key='max_objects') + seed = st.number_input('Seed', min_value=0, value=42, step=1, key='seed') + show_raw = st.checkbox('Hiện bảng raw (chỉ 100 dòng đầu mỗi plot)', value=False, key='show_raw') + + algo = st.selectbox('Thuật toán giảm chiều', ['UMAP', 'PCA', 't-SNE'], index=0, key='algo') + if algo == 'UMAP': + umap_neighbors = st.slider('UMAP n_neighbors', 5, 100, 15, 1, key='umap_neighbors') + umap_min_dist = st.slider('UMAP min_dist', 0.0, 1.0, 0.1, 0.01, key='umap_min_dist') + tsne_perplexity = 30 + elif algo == 't-SNE': + tsne_perplexity = st.slider('t-SNE perplexity', 5, 100, 30, 1, key='tsne_perplexity') + umap_neighbors = 15 + umap_min_dist = 0.1 + else: + umap_neighbors = 15 + umap_min_dist = 0.1 + tsne_perplexity = 30 + + st.markdown('### 🎨 Màu sắc & Cluster') + cluster_source = st.radio('Nguồn cluster', ['KMeans','Load file','None'], index=0, horizontal=False, key='cluster_source') + n_clusters = st.slider('Số cluster KMeans', 2, 100, 10, 1, disabled=(cluster_source != 'KMeans'), key='n_clusters') + cluster_file_path = st.text_input('File cluster JSON', value='cluster/dbscan_results.json', disabled=(cluster_source != 'Load file'), key='cluster_file') + noise_label = st.number_input('Noise label', value=-1, step=1, disabled=(cluster_source != 'Load file'), key='noise_label') + path_filter = st.text_input('Filter filepath (comma OR)', value='', key='path_filter') + color_by_substring = st.text_input('Color theo substring', value='', key='color_by_substring') + palette_name = st.selectbox('Bảng màu', ['auto','okabe-ito','tol','plotly','bold','dark24','set3','d3','hcl'], index=0, help='okabe-ito, tol = thân thiện mù màu', key='palette') + marker_size = st.slider('Kích thước điểm', 2, 15, 3, 1, key='marker_size') + st.caption('File cluster chỉ có is_noise: non-noise -> 0, noise -> noise_label.') + + add_btn = st.button('➕ Thêm plot', type='primary') + clear_btn = st.button('🧹 Xoá tất cả plot') + + if clear_btn: + st.session_state['plots'].clear() + # Clear cache directory + try: + if os.path.isdir(CACHE_DIR): + for f in os.listdir(CACHE_DIR): + try: + os.remove(os.path.join(CACHE_DIR,f)) + except Exception: + pass + os.rmdir(CACHE_DIR) + except Exception: + pass + st.success('Đã xoá toàn bộ plot (và cache).') + + def process_and_add_plot(): + path_local = path + if not os.path.isfile(path_local): + st.error(f'File không tồn tại: {path_local}') + return + with st.spinner(f'Loading embeddings: {os.path.basename(path_local)}'): + recs = load_embeddings( + path_local, + sample_size=sample_size or None, + sampling_seed=int(seed), + max_objects=max_objects or None, + ) + df = build_dataframe(recs) + if df.empty: + st.warning('Không có embedding nào.') + return + # Filter + if path_filter.strip(): + tokens = [t.strip() for t in path_filter.split(',') if t.strip()] + if tokens: + mask = df['filepath'].apply(lambda p: any(tok.lower() in p.lower() for tok in tokens)) + df = df[mask].reset_index(drop=True) + if df.empty: + st.warning('Sau filter không còn bản ghi.') + return + # Reduce + X = np.vstack(df['embedding'].values).astype(np.float32) + with st.spinner('Giảm chiều...'): + coords, meta = reduce_embeddings( + X, + algo, + random_state=int(seed), + umap_neighbors=umap_neighbors, + umap_min_dist=umap_min_dist, + tsne_perplexity=tsne_perplexity, + ) + df[['x','y','z']] = coords + # Clustering + if cluster_source == 'KMeans': + with st.spinner('KMeans...'): + labels, sil = kmeans_cluster(coords, n_clusters, int(seed)) + df['cluster'] = labels + sil_msg = f'Silhouette: {sil:.4f}' if not math.isnan(sil) else 'Silhouette: N/A' + elif cluster_source == 'Load file': + if not os.path.isfile(cluster_file_path): + st.warning(f'Không thấy file cluster: {cluster_file_path}; gán -1') + df['cluster'] = -1 + sil_msg = 'Silhouette: N/A' + else: + loaded_labels, mapping = load_cluster_file(cluster_file_path, len(df), noise_label=int(noise_label)) + if mapping is not None: + labs = [] + miss=0 + for fp in df['filepath']: + val = mapping.get(fp, int(noise_label)) + if fp not in mapping: + miss += 1 + labs.append(val) + if miss: + st.info(f'{miss} filepath gán noise.') + df['cluster'] = np.array(labs, dtype=int) + else: + df['cluster'] = loaded_labels + uniq = set(df['cluster']) + if len([u for u in uniq if u != int(noise_label)]) > 1: + try: + mask_valid = df['cluster'].to_numpy() != int(noise_label) + sil = silhouette_score(coords[mask_valid], df.loc[mask_valid,'cluster']) + sil_msg = f'Silhouette(ex noise): {sil:.4f}' + except Exception: + sil_msg = 'Silhouette: N/A' + else: + sil_msg = 'Silhouette: N/A' + else: + df['cluster'] = -1 + sil_msg = 'No clustering' + + # Color grouping + if color_by_substring.strip(): + subs = [s.strip() for s in color_by_substring.split(',') if s.strip()] + def color_from_sub(p: str) -> str: + for ssub in subs: + if ssub.lower() in p.lower(): + return ssub + return 'other' + df['color_group'] = df['filepath'].apply(color_from_sub) + color_col = 'color_group' + else: + color_col = 'cluster' + + # Palette utilities (copy of earlier helpers) + def get_base_palette(name: str) -> List[str]: + name = name.lower() + if name == 'okabe-ito': + return ["#000000","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7"] + if name == 'tol': + return ["#4477AA","#66CCEE","#228833","#CCBB44","#EE6677","#AA3377","#BBBBBB","#000000","#EEDD88","#FFAABB","#99DDFF","#44BB99"] + if name == 'plotly': + from plotly.colors import qualitative as q + return q.Plotly + if name == 'bold': + from plotly.colors import qualitative as q + return q.Bold + if name == 'dark24': + from plotly.colors import qualitative as q + return q.Dark24 + if name == 'set3': + from plotly.colors import qualitative as q + return q.Set3 + if name == 'd3': + from plotly.colors import qualitative as q + return q.D3 + return [] + def generate_hcl_like(n: int) -> List[str]: + cols=[] + for i in range(n): + h=(i/n)%1.0 + s=0.55+0.35*((i*37)%2) + v=0.85 if (i%3) else 0.98 + r,g,b = colorsys.hsv_to_rgb(h,s,v) + cols.append('#%02X%02X%02X' % (int(r*255), int(g*255), int(b*255))) + return cols + def build_palette(name: str, k: int) -> List[str]: + if name == 'auto': + return [] + if name == 'hcl': + return generate_hcl_like(k) + base = get_base_palette(name) + if k <= len(base): + return base[:k] + cols=list(base) + gold=0.61803398875 + h=0.1 + while len(cols) < k: + h=(h+gold)%1.0 + r,g,b = colorsys.hsv_to_rgb(h,0.6,0.95) + newc='#%02X%02X%02X' % (int(r*255), int(g*255), int(b*255)) + if newc not in cols: + cols.append(newc) + return cols + color_display_col = color_col + '_display' + if np.issubdtype(df[color_col].dtype, np.number): + df[color_display_col] = df[color_col].astype(int).astype(str) + else: + df[color_display_col] = df[color_col].astype(str) + groups = df[color_display_col].unique() + palette_seq = build_palette(palette_name, len(groups)) if palette_name else [] + import plotly.express as px + fig = px.scatter_3d( + df, + x='x', y='y', z='z', + color=color_display_col, + color_discrete_sequence=palette_seq if palette_seq else None, + hover_data={'filepath': True, 'cluster': True, 'x': ':.2f', 'y': ':.2f', 'z': ':.2f'}, + title=f'{os.path.basename(path_local)} ({algo})', + opacity=0.9, + height=700, + ) + fig.update_traces(marker={'size': int(marker_size)}) + fig.update_layout(margin=dict(l=0,r=0,t=40,b=0)) + out_csv = df[['filepath','x','y','z','cluster']].to_csv(index=False).encode('utf-8') + st.session_state['plots'].append({ + 'path': path_local, + 'name': plot_name_input.strip() or os.path.basename(path_local), + 'df_head': df.head(100) if show_raw else None, + 'fig': fig, + 'meta': meta, + 'algo': algo, + 'sil_msg': sil_msg, + 'csv': out_csv, + 'n': len(df), + 'dim': len(df['embedding'].iloc[0]), + 'palette_name': palette_name, + 'marker_size': marker_size, + 'timestamp': datetime.utcnow().isoformat(), + }) + # Persist new state + save_cache() + st.success(f'Đã thêm plot: {os.path.basename(path_local)}') + + if add_btn: + process_and_add_plot() + + # Render existing plots + if st.session_state['plots']: + st.markdown('---') + for idx, plot_data in enumerate(st.session_state['plots']): + container = st.container() + with container: + cols = st.columns([0.8,0.2]) + with cols[0]: + st.subheader(f'#{idx+1} {plot_data.get("name", os.path.basename(plot_data["path"]))}') + with cols[1]: + if st.button('❌ Xoá', key=f'remove_{idx}'): + st.session_state['plots'].pop(idx) + save_cache() + st.experimental_rerun() + # Rename inline + new_name = st.text_input('Đổi tên plot', value=plot_data.get('name', ''), key=f'rename_{idx}') + if new_name.strip() and new_name.strip() != plot_data.get('name'): + plot_data['name'] = new_name.strip() + save_cache() + st.caption(f"Embeddings: {plot_data['n']} | Dim gốc: {plot_data['dim']} | {plot_data['sil_msg']}") + st.plotly_chart(plot_data['fig'], use_container_width=True) + with st.expander('Meta / Thông tin thêm'): + st.json(plot_data['meta']) + st.download_button('⬇️ CSV', plot_data['csv'], file_name=f"embedding_3d_{idx+1}.csv", mime='text/csv', key=f'dl_{idx}') + df_head_cached = plot_data.get('df_head') + if df_head_cached is not None: + st.dataframe(df_head_cached) + if not st.session_state['plots']: + st.info('Chưa có plot nào. Chọn file ở sidebar và nhấn "➕ Thêm plot".') + + +if __name__ == '__main__': # pragma: no cover + # Khi chạy bằng 'streamlit run', sys.argv chỉ chứa tên file => ta luôn gọi main() + # Nếu muốn test nhanh CLI, có thể thêm arg '--cli-test' + if '--cli-test' in os.sys.argv: + test_path = 'embeddings_factures_osteopathie_1k_qwen.json' + if os.path.exists(test_path): + recs = load_embeddings(test_path, sample_size=5) + print(f'[CLI TEST] Loaded {len(recs)} embeddings dim={len(recs[0].embedding)}') + else: + print('[CLI TEST] Không tìm thấy file test.') + else: + main() + diff --git a/cluster/auto_cluster copy.py b/cluster/auto_cluster copy.py new file mode 100644 index 0000000..9c48a89 --- /dev/null +++ b/cluster/auto_cluster copy.py @@ -0,0 +1,670 @@ +#!/usr/bin/env python3 +""" +Simple script to run automatic clustering methods (DBSCAN, Mean Shift, Affinity Propagation) +These methods don't require specifying the number of clusters beforehand. +""" + +import json +import numpy as np +import matplotlib.pyplot as plt +from sklearn.cluster import DBSCAN, MeanShift, AffinityPropagation +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import silhouette_score +from sklearn.neighbors import NearestNeighbors +from sklearn.decomposition import PCA +import argparse +import warnings +warnings.filterwarnings('ignore') + +class AutoClustering: + def __init__(self, embeddings_path): + self.embeddings_path = embeddings_path + self.embeddings = None + self.file_paths = None + self.load_embeddings() + + def load_embeddings(self): + """Load embeddings from JSON file""" + print(f"Loading embeddings from {self.embeddings_path}...") + with open(self.embeddings_path, 'r') as f: + data = json.load(f) + + self.file_paths = [] + embeddings_list = [] + + for item in data: + self.file_paths.append(item['filepath']) + embeddings_list.append(item['embedding']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + print(f"Loaded {len(self.file_paths)} samples with embedding dimension {self.embeddings.shape[1]}") + + # Standardize embeddings for better clustering + self.scaler = StandardScaler() + self.embeddings_scaled = self.scaler.fit_transform(self.embeddings) + + def run_dbscan(self): + """Run DBSCAN with extensive grid search for parameter estimation""" + print("\n" + "="*50) + print("RUNNING DBSCAN CLUSTERING WITH EXTENSIVE GRID SEARCH") + print("="*50) + + # Method 1: K-nearest neighbors approach with multiple k values + eps_candidates = [] + + # Try different k values for nearest neighbors + k_values = [5, 10, 15, 20, 25, 30] + for k in k_values: + k_actual = min(k, len(self.embeddings_scaled) // 4) + if k_actual < 3: + continue + + neighbors = NearestNeighbors(n_neighbors=k_actual) + neighbors_fit = neighbors.fit(self.embeddings_scaled) + distances, indices = neighbors_fit.kneighbors(self.embeddings_scaled) + + # Sort distances and use k-th nearest neighbor distance + distances = np.sort(distances, axis=0) + kth_distances = distances[:, k_actual-1] + + # Multiple percentile thresholds for each k + percentiles = [60, 65, 70, 75, 80, 85, 90, 95] + for p in percentiles: + eps_candidates.append(np.percentile(kth_distances, p)) + + # Method 2: Statistical measures + # Mean and std of pairwise distances (sampled for efficiency) + sample_size = min(1000, len(self.embeddings_scaled)) + sample_indices = np.random.choice(len(self.embeddings_scaled), sample_size, replace=False) + sample_data = self.embeddings_scaled[sample_indices] + + from scipy.spatial.distance import pdist + pairwise_distances = pdist(sample_data) + + # Add statistical measures as eps candidates + eps_candidates.extend([ + np.mean(pairwise_distances) * 0.3, + np.mean(pairwise_distances) * 0.4, + np.mean(pairwise_distances) * 0.5, + np.mean(pairwise_distances) * 0.6, + np.mean(pairwise_distances) * 0.7, + np.median(pairwise_distances) * 0.3, + np.median(pairwise_distances) * 0.4, + np.median(pairwise_distances) * 0.5, + np.median(pairwise_distances) * 0.6, + np.std(pairwise_distances) * 0.5, + np.std(pairwise_distances) * 0.8, + np.std(pairwise_distances) * 1.0, + np.std(pairwise_distances) * 1.2 + ]) + + # Method 3: Manual eps values for different scales + manual_eps = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, + 1.2, 1.5, 1.8, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0] + eps_candidates.extend(manual_eps) + + # Remove duplicates and invalid values, then sort + eps_candidates = sorted(list(set([eps for eps in eps_candidates if eps > 0]))) + + # Extensive min_samples candidates + min_samples_candidates = [3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 20, 25, 30, 35, 40, 50] + + # Filter min_samples based on dataset size + # max_min_samples = len(self.embeddings_scaled) // 10 # At most 10% of data + # min_samples_candidates = [ms for ms in min_samples_candidates if ms <= max_min_samples] + min_samples_candidates = [60] + + + best_score = -1 + best_params = None + best_labels = None + + print(f"Testing {len(eps_candidates)} eps values x {len(min_samples_candidates)} min_samples values") + print(f"Total combinations: {len(eps_candidates) * len(min_samples_candidates)}") + print("This may take a while...\n") + + # Track all results for analysis + all_results = [] + + total_combinations = len(eps_candidates) * len(min_samples_candidates) + current_combination = 0 + + for eps in eps_candidates: + for min_samples in min_samples_candidates: + current_combination += 1 + + # Progress indicator + if current_combination % 50 == 0 or current_combination == total_combinations: + progress = (current_combination / total_combinations) * 100 + print(f"Progress: {current_combination}/{total_combinations} ({progress:.1f}%)") + + try: + dbscan = DBSCAN(eps=eps, min_samples=min_samples) + labels = dbscan.fit_predict(self.embeddings_scaled) + + # Check if we have meaningful clusters + n_clusters = len(set(labels)) - (1 if -1 in labels else 0) + n_noise = list(labels).count(-1) + noise_ratio = n_noise / len(labels) + + # Store result for analysis + result_info = { + 'eps': eps, + 'min_samples': min_samples, + 'n_clusters': n_clusters, + 'n_noise': n_noise, + 'noise_ratio': noise_ratio + } + + # Check if we have meaningful clusters + if n_clusters >= 2 and noise_ratio < 0.9: # Not too many noise points + # Calculate silhouette score (excluding noise) + mask = labels != -1 + if np.sum(mask) > 1: + try: + score = silhouette_score(self.embeddings_scaled[mask], labels[mask]) + result_info['silhouette_score'] = score + + # Print promising results + if score > 0.1: # Only show decent scores + print(f"eps={eps:.4f}, min_samples={min_samples}: {n_clusters} clusters, {n_noise} noise ({noise_ratio:.1%}), silhouette={score:.4f}") + + if score > best_score: + best_score = score + best_params = (eps, min_samples) + best_labels = labels + except Exception: + result_info['silhouette_score'] = None + else: + result_info['silhouette_score'] = None + + all_results.append(result_info) + + except Exception as e: + # Skip problematic parameter combinations + continue + + # Analysis of results + print("\n" + "="*50) + print("DBSCAN GRID SEARCH ANALYSIS") + print("="*50) + + if all_results: + # Convert to numpy for easier analysis + import pandas as pd + df_results = pd.DataFrame(all_results) + + print(f"Total parameter combinations tested: {len(df_results)}") + + # Valid results (with clusters) + valid_results = df_results[df_results['n_clusters'] >= 2] + print(f"Combinations that produced clusters: {len(valid_results)}") + + if len(valid_results) > 0: + # Best silhouette scores + scored_results = valid_results.dropna(subset=['silhouette_score']) + if len(scored_results) > 0: + print(f"Combinations with valid silhouette scores: {len(scored_results)}") + print(f"Best silhouette score: {scored_results['silhouette_score'].max():.4f}") + print(f"Mean silhouette score: {scored_results['silhouette_score'].mean():.4f}") + + # Top 5 results + top_results = scored_results.nlargest(5, 'silhouette_score') + print("\nTop 5 parameter combinations:") + for idx, row in top_results.iterrows(): + print(f" eps={row['eps']:.4f}, min_samples={row['min_samples']}: " + f"{row['n_clusters']} clusters, silhouette={row['silhouette_score']:.4f}") + + # Cluster count distribution + cluster_counts = valid_results['n_clusters'].value_counts().sort_index() + print(f"\nCluster count distribution:") + for n_clusters, count in cluster_counts.items(): + print(f" {n_clusters} clusters: {count} parameter combinations") + + print(f"\n📁 SAVING DETAILED RESULTS...") + print("="*30) + + # Save detailed grid search results to JSON file + self.save_dbscan_grid_search_results(all_results, best_params, best_score) + + if best_labels is not None: + n_clusters = len(set(best_labels)) - (1 if -1 in best_labels else 0) + n_noise = list(best_labels).count(-1) + + print(f"\nBest DBSCAN result:") + print(f"Parameters: eps={best_params[0]:.4f}, min_samples={best_params[1]}") + print(f"Number of clusters: {n_clusters}") + print(f"Number of noise points: {n_noise} ({n_noise/len(best_labels)*100:.1f}%)") + print(f"Silhouette score: {best_score:.4f}") + + return best_labels + else: + print("DBSCAN could not find suitable clusters with the extensive grid search") + print("Consider:") + print("- Adjusting the embedding space (different model or preprocessing)") + print("- Using different clustering algorithms") + print("- Manual parameter tuning based on domain knowledge") + return None + + def save_dbscan_grid_search_results(self, all_results, best_params, best_score): + """Save detailed DBSCAN grid search results to JSON file""" + import datetime + + # Prepare comprehensive results data + grid_search_data = { + "experiment_info": { + "timestamp": datetime.datetime.now().isoformat(), + "dataset_path": self.embeddings_path, + "total_samples": len(self.file_paths), + "embedding_dimension": self.embeddings.shape[1], + "total_combinations_tested": len(all_results) + }, + "best_result": { + "eps": best_params[0] if best_params else None, + "min_samples": best_params[1] if best_params else None, + "silhouette_score": best_score if best_score > -1 else None + }, + "all_trials": [] + } + + # Add all trial results + for i, result in enumerate(all_results): + trial_data = { + "trial_id": i + 1, + "parameters": { + "eps": result['eps'], + "min_samples": result['min_samples'] + }, + "results": { + "n_clusters": result['n_clusters'], + "n_noise": result['n_noise'], + "noise_ratio": result['noise_ratio'], + "silhouette_score": result['silhouette_score'] + }, + "status": "success" if result['silhouette_score'] is not None else "failed" + } + grid_search_data["all_trials"].append(trial_data) + + # Calculate summary statistics + valid_trials = [t for t in grid_search_data["all_trials"] if t["status"] == "success"] + if valid_trials: + silhouette_scores = [t["results"]["silhouette_score"] for t in valid_trials if t["results"]["silhouette_score"] is not None] + if silhouette_scores: + grid_search_data["summary_statistics"] = { + "total_trials": len(all_results), + "successful_trials": len(valid_trials), + "success_rate": len(valid_trials) / len(all_results), + "best_silhouette_score": max(silhouette_scores), + "worst_silhouette_score": min(silhouette_scores), + "mean_silhouette_score": sum(silhouette_scores) / len(silhouette_scores), + "median_silhouette_score": sorted(silhouette_scores)[len(silhouette_scores)//2] + } + + # Top 10 results + sorted_valid_trials = sorted(valid_trials, + key=lambda x: x["results"]["silhouette_score"] if x["results"]["silhouette_score"] is not None else -1, + reverse=True) + grid_search_data["top_10_results"] = sorted_valid_trials[:10] + + # Parameter analysis + eps_values = [t["parameters"]["eps"] for t in valid_trials] + min_samples_values = [t["parameters"]["min_samples"] for t in valid_trials] + + grid_search_data["parameter_analysis"] = { + "eps_range": { + "min": min(eps_values), + "max": max(eps_values), + "mean": sum(eps_values) / len(eps_values) + }, + "min_samples_range": { + "min": min(min_samples_values), + "max": max(min_samples_values), + "mean": sum(min_samples_values) / len(min_samples_values) + } + } + + # Save to file with timestamp + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"dbscan_grid_search_detailed_{timestamp}.json" + + with open(filename, 'w') as f: + json.dump(grid_search_data, f, indent=4, ensure_ascii=False) + + print(f"Detailed grid search results saved to: {filename}") + + # Also save a CSV summary for easy analysis + csv_filename = f"dbscan_grid_search_summary_{timestamp}.csv" + self.save_grid_search_csv(all_results, csv_filename) + print(f"Grid search summary CSV saved to: {csv_filename}") + + def save_grid_search_csv(self, all_results, filename): + """Save grid search results as CSV for easy analysis""" + import csv + + with open(filename, 'w', newline='', encoding='utf-8') as csvfile: + fieldnames = ['trial_id', 'eps', 'min_samples', 'n_clusters', 'n_noise', + 'noise_ratio', 'silhouette_score', 'status'] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + + writer.writeheader() + for i, result in enumerate(all_results): + writer.writerow({ + 'trial_id': i + 1, + 'eps': result['eps'], + 'min_samples': result['min_samples'], + 'n_clusters': result['n_clusters'], + 'n_noise': result['n_noise'], + 'noise_ratio': result['noise_ratio'], + 'silhouette_score': result['silhouette_score'], + 'status': 'success' if result['silhouette_score'] is not None else 'failed' + }) + + def run_mean_shift(self): + """Run Mean Shift clustering""" + print("\n" + "="*50) + print("RUNNING MEAN SHIFT CLUSTERING") + print("="*50) + + # Estimate bandwidth using different percentiles + from sklearn.cluster import estimate_bandwidth + + # Try different bandwidth estimation methods + bandwidth_candidates = [] + + # Method 1: sklearn's estimate_bandwidth + try: + bw_est = estimate_bandwidth(self.embeddings_scaled, quantile=0.3, n_samples=min(500, len(self.embeddings_scaled))) + if bw_est > 0: + bandwidth_candidates.extend([bw_est * 0.5, bw_est, bw_est * 1.5]) + except: + pass + + # Method 2: nearest neighbor distances + neighbors = NearestNeighbors(n_neighbors=10) + neighbors_fit = neighbors.fit(self.embeddings_scaled) + distances, _ = neighbors_fit.kneighbors(self.embeddings_scaled) + mean_dist = np.mean(distances[:, 1:]) # Exclude self-distance + bandwidth_candidates.extend([mean_dist * 0.5, mean_dist, mean_dist * 1.5]) + + # Remove duplicates and invalid values + bandwidth_candidates = list(set([bw for bw in bandwidth_candidates if bw > 0])) + + if not bandwidth_candidates: + bandwidth_candidates = [0.5, 1.0, 1.5, 2.0] + + best_score = -1 + best_bandwidth = None + best_labels = None + + print("Testing different bandwidth values...") + for bandwidth in bandwidth_candidates: + try: + mean_shift = MeanShift(bandwidth=bandwidth) + labels = mean_shift.fit_predict(self.embeddings_scaled) + + n_clusters = len(set(labels)) + if 2 <= n_clusters <= len(self.embeddings_scaled) // 3: + score = silhouette_score(self.embeddings_scaled, labels) + print(f"bandwidth={bandwidth:.4f}: {n_clusters} clusters, silhouette={score:.4f}") + + if score > best_score: + best_score = score + best_bandwidth = bandwidth + best_labels = labels + except Exception as e: + print(f"bandwidth={bandwidth:.4f}: failed ({str(e)[:50]}...)") + continue + + if best_labels is not None: + n_clusters = len(set(best_labels)) + print(f"\nBest Mean Shift result:") + print(f"Bandwidth: {best_bandwidth:.4f}") + print(f"Number of clusters: {n_clusters}") + print(f"Silhouette score: {best_score:.4f}") + + return best_labels + else: + print("Mean Shift could not find suitable clusters") + return None + + def run_affinity_propagation(self): + """Run Affinity Propagation clustering""" + print("\n" + "="*50) + print("RUNNING AFFINITY PROPAGATION CLUSTERING") + print("="*50) + + # Calculate similarity matrix + similarities = -np.sum((self.embeddings_scaled[:, np.newaxis] - self.embeddings_scaled)**2, axis=2) + + # Try different preference values (percentiles of similarity matrix diagonal) + preference_candidates = [ + np.percentile(similarities.diagonal(), 10), + np.percentile(similarities.diagonal(), 25), + np.percentile(similarities.diagonal(), 50), + np.median(similarities), + np.percentile(similarities.diagonal(), 75) + ] + + damping_candidates = [0.5, 0.7, 0.8, 0.9] + + best_score = -1 + best_params = None + best_labels = None + + print("Testing different parameter combinations...") + for preference in preference_candidates: + for damping in damping_candidates: + try: + affinity_prop = AffinityPropagation( + preference=preference, + damping=damping, + random_state=42, + max_iter=200 + ) + labels = affinity_prop.fit_predict(self.embeddings_scaled) + + n_clusters = len(set(labels)) + if 2 <= n_clusters <= len(self.embeddings_scaled) // 3: + score = silhouette_score(self.embeddings_scaled, labels) + print(f"preference={preference:.2f}, damping={damping:.1f}: {n_clusters} clusters, silhouette={score:.4f}") + + if score > best_score: + best_score = score + best_params = (preference, damping) + best_labels = labels + except Exception as e: + print(f"preference={preference:.2f}, damping={damping:.1f}: failed ({str(e)[:30]}...)") + continue + + if best_labels is not None: + n_clusters = len(set(best_labels)) + print(f"\nBest Affinity Propagation result:") + print(f"Parameters: preference={best_params[0]:.2f}, damping={best_params[1]:.1f}") + print(f"Number of clusters: {n_clusters}") + print(f"Silhouette score: {best_score:.4f}") + + return best_labels + else: + print("Affinity Propagation could not find suitable clusters") + return None + + def visualize_results(self, results_dict): + """Visualize clustering results using PCA""" + if not results_dict: + print("No results to visualize") + return + + # Reduce dimensions for visualization + pca = PCA(n_components=2, random_state=42) + embeddings_2d = pca.fit_transform(self.embeddings_scaled) + + n_methods = len(results_dict) + fig, axes = plt.subplots(1, n_methods, figsize=(5*n_methods, 4)) + + if n_methods == 1: + axes = [axes] + + for idx, (method_name, labels) in enumerate(results_dict.items()): + # Handle noise points in DBSCAN (label -1) + unique_labels = set(labels) + colors = plt.cm.Set1(np.linspace(0, 1, len(unique_labels))) + + for label, color in zip(unique_labels, colors): + if label == -1: + # Noise points in black + mask = labels == label + axes[idx].scatter(embeddings_2d[mask, 0], embeddings_2d[mask, 1], + c='black', marker='x', s=20, alpha=0.5, label='Noise') + else: + mask = labels == label + axes[idx].scatter(embeddings_2d[mask, 0], embeddings_2d[mask, 1], + c=[color], s=50, alpha=0.7, label=f'Cluster {label}') + + axes[idx].set_title(f'{method_name}\n({len(set(labels)) - (1 if -1 in labels else 0)} clusters)') + axes[idx].set_xlabel('PCA Component 1') + axes[idx].set_ylabel('PCA Component 2') + axes[idx].grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig('auto_clustering_results.png', dpi=300, bbox_inches='tight') + plt.show() + + print(f"\nVisualization saved as 'auto_clustering_results.png'") + + def save_results(self, results_dict): + """Save clustering results to JSON files""" + print(results_dict.items()) + check_method_name = [] + print(len(results_dict)) + for method_name, labels in results_dict.items(): + check_method_name.append(method_name) + # Create results for each method + method_results = [] + print(method_name == 'DBSCAN') + + + + for filepath, label in zip(self.file_paths, labels): + if method_name == 'DBSCAN': + if label == -1: + is_noise = True + else: + is_noise = False + else: + is_noise = False + + method_results.append({ + "filepath": filepath, + "cluster": int(label), + "is_noise": is_noise + }) + print('method_name', set(check_method_name)) + print(method_results[0]['is_noise']) + print(method_results[0]) + + # Save to file + filename = f"{method_name.lower().replace(' ', '_')}_results.json" + with open(filename, 'w') as f: + json.dump({ + "method": method_name, + "n_clusters": len(set(labels)) - (1 if -1 in labels else 0), + "n_samples": len(labels), + "results": method_results + }, f, indent=4) + + print(f"Results saved to {filename}") + + def run_all_methods(self): + """Run all automatic clustering methods""" + print("\n" + "="*70) + print("AUTOMATIC CLUSTERING ANALYSIS") + print("="*70) + print(f"Dataset: {len(self.file_paths)} documents") + print(f"Embedding dimension: {self.embeddings.shape[1]}") + + results = {} + + # Run DBSCAN + dbscan_labels = self.run_dbscan() + if dbscan_labels is not None: + results["DBSCAN"] = dbscan_labels + + # Run Mean Shift + # meanshift_labels = self.run_mean_shift() + # if meanshift_labels is not None: + # results["Mean Shift"] = meanshift_labels + + # Run Affinity Propagation + # affinity_labels = self.run_affinity_propagation() + # if affinity_labels is not None: + # results["Affinity Propagation"] = affinity_labels + + # Summary + if results: + print("\n" + "="*70) + print("SUMMARY OF RESULTS") + print("="*70) + + for method, labels in results.items(): + n_clusters = len(set(labels)) - (1 if -1 in labels else 0) + if method == "DBSCAN": + n_noise = list(labels).count(-1) + print(f"{method}: {n_clusters} clusters, {n_noise} noise points") + else: + print(f"{method}: {n_clusters} clusters") + + # Calculate agreement between methods if multiple succeeded + if len(results) > 1: + from sklearn.metrics import adjusted_rand_score + print("\nMethod Agreement (Adjusted Rand Index):") + method_names = list(results.keys()) + for i in range(len(method_names)): + for j in range(i+1, len(method_names)): + ari = adjusted_rand_score(results[method_names[i]], results[method_names[j]]) + print(f"{method_names[i]} vs {method_names[j]}: {ari:.4f}") + + # Visualize and save results + self.visualize_results(results) + self.save_results(results) + + else: + print("\nNo automatic clustering method found suitable clusters.") + print("This might indicate:") + print("- Data doesn't have clear cluster structure") + print("- Embeddings need different preprocessing") + print("- Different parameter ranges needed") + + return results + +def main(): + parser = argparse.ArgumentParser(description="Run automatic clustering methods on document embeddings") + parser.add_argument("--embeddings_path", help="Path to embeddings JSON file") + parser.add_argument("--method", choices=['dbscan', 'meanshift', 'affinity', 'all'], default='all', + help="Which automatic method to run") + + args = parser.parse_args() + + # Initialize clustering + clustering = AutoClustering(args.embeddings_path) + + # Run selected method(s) + if args.method == 'all': + clustering.run_all_methods() + elif args.method == 'dbscan': + labels = clustering.run_dbscan() + if labels is not None: + clustering.visualize_results({"DBSCAN": labels}) + clustering.save_results({"DBSCAN": labels}) + elif args.method == 'meanshift': + labels = clustering.run_mean_shift() + if labels is not None: + clustering.visualize_results({"Mean Shift": labels}) + clustering.save_results({"Mean Shift": labels}) + elif args.method == 'affinity': + labels = clustering.run_affinity_propagation() + if labels is not None: + clustering.visualize_results({"Affinity Propagation": labels}) + clustering.save_results({"Affinity Propagation": labels}) + +if __name__ == "__main__": + main() diff --git a/cluster/auto_cluster.py b/cluster/auto_cluster.py new file mode 100644 index 0000000..032d12f --- /dev/null +++ b/cluster/auto_cluster.py @@ -0,0 +1,711 @@ +#!/usr/bin/env python3 +""" +Simple script to run automatic clustering methods (DBSCAN, Mean Shift, Affinity Propagation) +These methods don't require specifying the number of clusters beforehand. +""" + +import json +import numpy as np +import matplotlib.pyplot as plt +from sklearn.cluster import DBSCAN, MeanShift, AffinityPropagation +from sklearn.preprocessing import normalize +from sklearn.metrics import silhouette_score +from sklearn.neighbors import NearestNeighbors +from sklearn.decomposition import PCA +import argparse +import warnings +warnings.filterwarnings('ignore') +from sklearn.metrics.pairwise import cosine_similarity +from datetime import datetime + +import numpy as np + +def value_counts(a, dropna=False): + a_flat = a.ravel() + if dropna and np.issubdtype(a.dtype, np.floating): + mask = ~np.isnan(a_flat) + a_flat = a_flat[mask] + uniq, counts = np.unique(a_flat, return_counts=True) + uniq = list(uniq) + counts = list(counts) + return dict(zip(uniq, counts)) + +class AutoClustering: + def __init__(self, embeddings_path): + self.embeddings_path = embeddings_path + self.embeddings = None + self.file_paths = None + self.load_embeddings() + + def load_embeddings(self): + """Load embeddings from JSON file""" + print(f"Loading embeddings from {self.embeddings_path}...") + with open(self.embeddings_path, 'r') as f: + data = json.load(f) + + self.file_paths = [] + embeddings_list = [] + + for item in data: + self.file_paths.append(item['filepath']) + embeddings_list.append(item['embedding']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + print(f"Loaded {len(self.file_paths)} samples with embedding dimension {self.embeddings.shape[1]}") + + # Normalize embeddings using L2 normalization for cosine distance + self.embeddings_normalized = normalize(self.embeddings, norm='l2', axis=1) + print("Applied L2 normalization to embeddings") + + sims = cosine_similarity(self.embeddings) + print(self.embeddings.shape) + # lấy upper triangle exclude diagonal để inspect + triu_idxs = np.triu_indices_from(sims, k=1) + dist_vals = sims[triu_idxs] + print(dist_vals.shape) + print("mean sim:", dist_vals.mean(), "std:", dist_vals.std()) + + + def run_dbscan(self): + """Run DBSCAN with extensive grid search for parameter estimation""" + print("\n" + "="*50) + print("RUNNING DBSCAN CLUSTERING WITH EXTENSIVE GRID SEARCH") + print("="*50) + + # Method 1: K-nearest neighbors approach with multiple k values + # eps_candidates = [] + + # # Try different k values for nearest neighbors with cosine metric + # k_values = [5, 10, 15, 20, 25, 30] + # for k in k_values: + # k_actual = min(k, len(self.embeddings_normalized) // 4) + # if k_actual < 3: + # continue + + # neighbors = NearestNeighbors(n_neighbors=k_actual, metric='cosine') + # neighbors_fit = neighbors.fit(self.embeddings_normalized) + # distances, indices = neighbors_fit.kneighbors(self.embeddings_normalized) + + # # Sort distances and use k-th nearest neighbor distance + # distances = np.sort(distances, axis=0) + # kth_distances = distances[:, k_actual-1] + + # # Multiple percentile thresholds for each k + # percentiles = [60, 65, 70, 75, 80, 85, 90, 95] + # for p in percentiles: + # eps_candidates.append(np.percentile(kth_distances, p)) + + # # Method 2: Statistical measures using cosine distances + # # Calculate cosine distances for a sample of data points + # sample_size = min(1000, len(self.embeddings_normalized)) + # sample_indices = np.random.choice(len(self.embeddings_normalized), sample_size, replace=False) + # sample_data = self.embeddings_normalized[sample_indices] + + # from scipy.spatial.distance import pdist + # cosine_distances = pdist(sample_data, metric='cosine') + + # # Add statistical measures as eps candidates using cosine distances + # eps_candidates.extend([ + # np.mean(cosine_distances) * 0.3, + # np.mean(cosine_distances) * 0.4, + # np.mean(cosine_distances) * 0.5, + # np.mean(cosine_distances) * 0.6, + # np.mean(cosine_distances) * 0.7, + # np.median(cosine_distances) * 0.3, + # np.median(cosine_distances) * 0.4, + # np.median(cosine_distances) * 0.5, + # np.median(cosine_distances) * 0.6, + # np.std(cosine_distances) * 0.5, + # np.std(cosine_distances) * 0.8, + # np.std(cosine_distances) * 1.0, + # np.std(cosine_distances) * 1.2 + # ]) + + + # Method 3: Manual eps values for cosine distances (0-2 range) + manual_eps = [0.001, 0.002, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, + 0.6, 0.7, 0.8, 0.9, 1.0] + # eps_candidates.extend(manual_eps) + + # eps_candidates = manual_eps + eps_candidates = [0.2] + + # Remove duplicates and invalid values, then sort + eps_candidates = sorted(list(set([eps for eps in eps_candidates if eps > 0]))) + + # Extensive min_samples candidates + # min_samples_candidates = [3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 20, 25, 30, 35, 40, 50] + + # Filter min_samples based on dataset size + # max_min_samples = len(self.embeddings_normalized) // 10 # At most 10% of data + # min_samples_candidates = [ms for ms in min_samples_candidates if ms <= max_min_samples] + min_samples_candidates = [50] + + + best_score = -1 + best_params = None + best_labels = None + + print(f"Testing {len(eps_candidates)} eps values x {len(min_samples_candidates)} min_samples values") + print(f"Total combinations: {len(eps_candidates) * len(min_samples_candidates)}") + print("This may take a while...\n") + + # Track all results for analysis + all_results = [] + + total_combinations = len(eps_candidates) * len(min_samples_candidates) + current_combination = 0 + + for eps in eps_candidates: + for min_samples in min_samples_candidates: + current_combination += 1 + + # Progress indicator + if current_combination % 50 == 0 or current_combination == total_combinations: + progress = (current_combination / total_combinations) * 100 + print(f"Progress: {current_combination}/{total_combinations} ({progress:.1f}%)") + + try: + dbscan = DBSCAN(eps=eps, min_samples=min_samples, metric='cosine') + labels = dbscan.fit_predict(self.embeddings_normalized) + + # Check if we have meaningful clusters + n_clusters = len(set(labels)) - (1 if -1 in labels else 0) + n_noise = list(labels).count(-1) + noise_ratio = n_noise / len(labels) + + # Store result for analysis + result_info = { + 'eps': eps, + 'min_samples': min_samples, + 'n_clusters': n_clusters, + 'n_noise': n_noise, + 'noise_ratio': noise_ratio + } + + # Check if we have meaningful clusters + print(n_clusters, n_noise, noise_ratio, eps, min_samples) + if n_clusters >= 2 and noise_ratio < 0.9: # Not too many noise points + # Calculate silhouette score (excluding noise) using cosine metric + mask = labels != -1 + if np.sum(mask) > 1: + try: + score = silhouette_score(self.embeddings_normalized[mask], labels[mask], metric='cosine') + result_info['silhouette_score'] = score + + # Print promising results + if score > 0.1: # Only show decent scores + print(f"eps={eps:.4f}, min_samples={min_samples}: {n_clusters} clusters, {n_noise} noise ({noise_ratio:.1%}), silhouette={score:.4f}") + + print(value_counts(labels)) + + if score > best_score: + best_score = score + best_params = (eps, min_samples) + best_labels = labels + except Exception: + result_info['silhouette_score'] = None + else: + result_info['silhouette_score'] = None + + all_results.append(result_info) + + except Exception as e: + import traceback + traceback.print_exc() + # Skip problematic parameter combinations + continue + + # Analysis of results + print("\n" + "="*50) + print("DBSCAN GRID SEARCH ANALYSIS") + print("="*50) + + if all_results: + # Convert to numpy for easier analysis + import pandas as pd + df_results = pd.DataFrame(all_results) + + print(f"Total parameter combinations tested: {len(df_results)}") + + # Valid results (with clusters) + valid_results = df_results[df_results['n_clusters'] >= 2] + print(f"Combinations that produced clusters: {len(valid_results)}") + + if len(valid_results) > 0: + # Best silhouette scores + scored_results = valid_results.dropna(subset=['silhouette_score']) + if len(scored_results) > 0: + print(f"Combinations with valid silhouette scores: {len(scored_results)}") + print(f"Best silhouette score: {scored_results['silhouette_score'].max():.4f}") + print(f"Mean silhouette score: {scored_results['silhouette_score'].mean():.4f}") + + # Top 5 results + top_results = scored_results.nlargest(5, 'silhouette_score') + print("\nTop 5 parameter combinations:") + for idx, row in top_results.iterrows(): + print(f" eps={row['eps']:.4f}, min_samples={row['min_samples']}: " + f"{row['n_clusters']} clusters, silhouette={row['silhouette_score']:.4f}") + + # Cluster count distribution + cluster_counts = valid_results['n_clusters'].value_counts().sort_index() + print("\nCluster count distribution:") + for n_clusters, count in cluster_counts.items(): + print(f" {n_clusters} clusters: {count} parameter combinations") + + print("\n📁 SAVING DETAILED RESULTS...") + print("="*30) + + # Save detailed grid search results to JSON file + self.save_dbscan_grid_search_results(all_results, best_params, best_score) + + if best_labels is not None: + n_clusters = len(set(best_labels)) - (1 if -1 in best_labels else 0) + n_noise = list(best_labels).count(-1) + + print("\nBest DBSCAN result:") + print(f"Parameters: eps={best_params[0]:.4f}, min_samples={best_params[1]}") + print(f"Number of clusters: {n_clusters}") + print(f"Number of noise points: {n_noise} ({n_noise/len(best_labels)*100:.1f}%)") + print(f"Silhouette score: {best_score:.4f}") + + return best_labels + else: + print("DBSCAN could not find suitable clusters with the extensive grid search") + print("Consider:") + print("- Adjusting the embedding space (different model or preprocessing)") + print("- Using different clustering algorithms") + print("- Manual parameter tuning based on domain knowledge") + return None + + def save_dbscan_grid_search_results(self, all_results, best_params, best_score): + """Save detailed DBSCAN grid search results to JSON file""" + import datetime + + # Prepare comprehensive results data + grid_search_data = { + "experiment_info": { + "timestamp": datetime.datetime.now().isoformat(), + "dataset_path": self.embeddings_path, + "total_samples": len(self.file_paths), + "embedding_dimension": self.embeddings.shape[1], + "total_combinations_tested": len(all_results) + }, + "best_result": { + "eps": best_params[0] if best_params else None, + "min_samples": best_params[1] if best_params else None, + "silhouette_score": best_score if best_score > -1 else None + }, + "all_trials": [] + } + + # Add all trial results + for i, result in enumerate(all_results): + trial_data = { + "trial_id": i + 1, + "parameters": { + "eps": result['eps'], + "min_samples": result['min_samples'] + }, + "results": { + "n_clusters": result['n_clusters'], + "n_noise": result['n_noise'], + "noise_ratio": result['noise_ratio'], + "silhouette_score": result['silhouette_score'] + }, + "status": "success" if result['silhouette_score'] is not None else "failed" + } + grid_search_data["all_trials"].append(trial_data) + + # Calculate summary statistics + valid_trials = [t for t in grid_search_data["all_trials"] if t["status"] == "success"] + if valid_trials: + silhouette_scores = [t["results"]["silhouette_score"] for t in valid_trials if t["results"]["silhouette_score"] is not None] + if silhouette_scores: + grid_search_data["summary_statistics"] = { + "total_trials": len(all_results), + "successful_trials": len(valid_trials), + "success_rate": len(valid_trials) / len(all_results), + "best_silhouette_score": max(silhouette_scores), + "worst_silhouette_score": min(silhouette_scores), + "mean_silhouette_score": sum(silhouette_scores) / len(silhouette_scores), + "median_silhouette_score": sorted(silhouette_scores)[len(silhouette_scores)//2] + } + + # Top 10 results + sorted_valid_trials = sorted(valid_trials, + key=lambda x: x["results"]["silhouette_score"] if x["results"]["silhouette_score"] is not None else -1, + reverse=True) + grid_search_data["top_10_results"] = sorted_valid_trials[:10] + + # Parameter analysis + eps_values = [t["parameters"]["eps"] for t in valid_trials] + min_samples_values = [t["parameters"]["min_samples"] for t in valid_trials] + + grid_search_data["parameter_analysis"] = { + "eps_range": { + "min": min(eps_values), + "max": max(eps_values), + "mean": sum(eps_values) / len(eps_values) + }, + "min_samples_range": { + "min": min(min_samples_values), + "max": max(min_samples_values), + "mean": sum(min_samples_values) / len(min_samples_values) + } + } + + # Save to file with timestamp + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + # filename = f"dbscan_grid_search_detailed_{timestamp}.json" + filename = "dbscan_grid_search_detailed.json" + + print(grid_search_data.keys()) + print(type(grid_search_data['parameter_analysis'])) + with open(filename, 'w') as f: + json.dump(grid_search_data, f, indent=4, ensure_ascii=False) + + print(f"Detailed grid search results saved to: {filename}") + + # Also save a CSV summary for easy analysis + # csv_filename = f"dbscan_grid_search_summary_{timestamp}.csv" + csv_filename = "dbscan_grid_search_summary.csv" + + self.save_grid_search_csv(all_results, csv_filename) + print(f"Grid search summary CSV saved to: {csv_filename}") + + def save_grid_search_csv(self, all_results, filename): + """Save grid search results as CSV for easy analysis""" + import csv + + with open(filename, 'w', newline='', encoding='utf-8') as csvfile: + fieldnames = ['trial_id', 'eps', 'min_samples', 'n_clusters', 'n_noise', + 'noise_ratio', 'silhouette_score', 'status'] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + + writer.writeheader() + for i, result in enumerate(all_results): + writer.writerow({ + 'trial_id': i + 1, + 'eps': result['eps'], + 'min_samples': result['min_samples'], + 'n_clusters': result['n_clusters'], + 'n_noise': result['n_noise'], + 'noise_ratio': result['noise_ratio'], + 'silhouette_score': result['silhouette_score'], + 'status': 'success' if result['silhouette_score'] is not None else 'failed' + }) + + def run_mean_shift(self): + """Run Mean Shift clustering""" + print("\n" + "="*50) + print("RUNNING MEAN SHIFT CLUSTERING") + print("="*50) + + # Estimate bandwidth using different percentiles with cosine metric + from sklearn.cluster import estimate_bandwidth + + # Try different bandwidth estimation methods + bandwidth_candidates = [] + + # Method 1: sklearn's estimate_bandwidth (note: estimate_bandwidth doesn't support cosine directly) + try: + bw_est = estimate_bandwidth(self.embeddings_normalized, quantile=0.3, n_samples=min(500, len(self.embeddings_normalized))) + if bw_est > 0: + bandwidth_candidates.extend([bw_est * 0.5, bw_est, bw_est * 1.5]) + except Exception: + pass + + # Method 2: nearest neighbor cosine distances + neighbors = NearestNeighbors(n_neighbors=10, metric='cosine') + neighbors_fit = neighbors.fit(self.embeddings_normalized) + distances, _ = neighbors_fit.kneighbors(self.embeddings_normalized) + mean_dist = np.mean(distances[:, 1:]) # Exclude self-distance + bandwidth_candidates.extend([mean_dist * 0.5, mean_dist, mean_dist * 1.5]) + + # Remove duplicates and invalid values + bandwidth_candidates = list(set([bw for bw in bandwidth_candidates if bw > 0])) + + if not bandwidth_candidates: + bandwidth_candidates = [0.5, 1.0, 1.5, 2.0] + + best_score = -1 + best_bandwidth = None + best_labels = None + + print("Testing different bandwidth values...") + for bandwidth in bandwidth_candidates: + try: + mean_shift = MeanShift(bandwidth=bandwidth) + labels = mean_shift.fit_predict(self.embeddings_normalized) + + n_clusters = len(set(labels)) + if 2 <= n_clusters <= len(self.embeddings_normalized) // 3: + score = silhouette_score(self.embeddings_normalized, labels, metric='cosine') + print(f"bandwidth={bandwidth:.4f}: {n_clusters} clusters, silhouette={score:.4f}") + + if score > best_score: + best_score = score + best_bandwidth = bandwidth + best_labels = labels + except Exception as e: + print(f"bandwidth={bandwidth:.4f}: failed ({str(e)[:50]}...)") + continue + + if best_labels is not None: + n_clusters = len(set(best_labels)) + print("\nBest Mean Shift result:") + print(f"Bandwidth: {best_bandwidth:.4f}") + print(f"Number of clusters: {n_clusters}") + print(f"Silhouette score: {best_score:.4f}") + + return best_labels + else: + print("Mean Shift could not find suitable clusters") + return None + + def run_affinity_propagation(self): + """Run Affinity Propagation clustering""" + print("\n" + "="*50) + print("RUNNING AFFINITY PROPAGATION CLUSTERING") + print("="*50) + + # Calculate similarity matrix using cosine similarity + # Convert cosine distance to cosine similarity: similarity = 1 - distance + from sklearn.metrics.pairwise import cosine_similarity + similarities = cosine_similarity(self.embeddings_normalized) + + # Try different preference values (percentiles of similarity matrix diagonal) + preference_candidates = [ + np.percentile(similarities.diagonal(), 10), + np.percentile(similarities.diagonal(), 25), + np.percentile(similarities.diagonal(), 50), + np.median(similarities), + np.percentile(similarities.diagonal(), 75) + ] + + damping_candidates = [0.5, 0.7, 0.8, 0.9] + + best_score = -1 + best_params = None + best_labels = None + + print("Testing different parameter combinations...") + for preference in preference_candidates: + for damping in damping_candidates: + try: + affinity_prop = AffinityPropagation( + preference=preference, + damping=damping, + random_state=42, + max_iter=200 + ) + labels = affinity_prop.fit_predict(self.embeddings_normalized) + + n_clusters = len(set(labels)) + if 2 <= n_clusters <= len(self.embeddings_normalized) // 3: + score = silhouette_score(self.embeddings_normalized, labels, metric='cosine') + print(f"preference={preference:.2f}, damping={damping:.1f}: {n_clusters} clusters, silhouette={score:.4f}") + + if score > best_score: + best_score = score + best_params = (preference, damping) + best_labels = labels + except Exception as e: + print(f"preference={preference:.2f}, damping={damping:.1f}: failed ({str(e)[:30]}...)") + continue + + if best_labels is not None: + n_clusters = len(set(best_labels)) + print("\nBest Affinity Propagation result:") + print(f"Parameters: preference={best_params[0]:.2f}, damping={best_params[1]:.1f}") + print(f"Number of clusters: {n_clusters}") + print(f"Silhouette score: {best_score:.4f}") + + return best_labels + else: + print("Affinity Propagation could not find suitable clusters") + return None + + def visualize_results(self, results_dict): + """Visualize clustering results using PCA""" + if not results_dict: + print("No results to visualize") + return + + # Reduce dimensions for visualization + pca = PCA(n_components=2, random_state=42) + embeddings_2d = pca.fit_transform(self.embeddings_normalized) + + n_methods = len(results_dict) + fig, axes = plt.subplots(1, n_methods, figsize=(5*n_methods, 4)) + + if n_methods == 1: + axes = [axes] + + for idx, (method_name, labels) in enumerate(results_dict.items()): + # Handle noise points in DBSCAN (label -1) + unique_labels = set(labels) + colors = plt.cm.Set1(np.linspace(0, 1, len(unique_labels))) + + for label, color in zip(unique_labels, colors): + if label == -1: + # Noise points in black + mask = labels == label + axes[idx].scatter(embeddings_2d[mask, 0], embeddings_2d[mask, 1], + c='black', marker='x', s=20, alpha=0.5, label='Noise') + else: + mask = labels == label + axes[idx].scatter(embeddings_2d[mask, 0], embeddings_2d[mask, 1], + c=[color], s=50, alpha=0.7, label=f'Cluster {label}') + + axes[idx].set_title(f'{method_name}\n({len(set(labels)) - (1 if -1 in labels else 0)} clusters)') + axes[idx].set_xlabel('PCA Component 1') + axes[idx].set_ylabel('PCA Component 2') + axes[idx].grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig('auto_clustering_results.png', dpi=300, bbox_inches='tight') + plt.show() + + print("\nVisualization saved as 'auto_clustering_results.png'") + + def save_results(self, results_dict): + """Save clustering results to JSON files""" + print(results_dict.items()) + check_method_name = [] + print(len(results_dict)) + for method_name, labels in results_dict.items(): + check_method_name.append(method_name) + # Create results for each method + method_results = [] + print(method_name == 'DBSCAN') + + + + for filepath, label in zip(self.file_paths, labels): + if method_name == 'DBSCAN': + if label == -1: + is_noise = True + else: + is_noise = False + else: + is_noise = False + + method_results.append({ + "filepath": filepath, + "cluster": int(label), + "is_noise": is_noise + }) + print('method_name', set(check_method_name)) + print(method_results[0]['is_noise']) + print(method_results[0]) + + # Save to file + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"{method_name.lower().replace(' ', '_')}_results_{timestamp}.json" + with open(filename, 'w') as f: + json.dump({ + "method": method_name, + "n_clusters": len(set(labels)) - (1 if -1 in labels else 0), + "n_samples": len(labels), + "results": method_results + }, f, indent=4) + + print(f"Results saved to {filename}") + + def run_all_methods(self): + """Run all automatic clustering methods""" + print("\n" + "="*70) + print("AUTOMATIC CLUSTERING ANALYSIS") + print("="*70) + print(f"Dataset: {len(self.file_paths)} documents") + print(f"Embedding dimension: {self.embeddings.shape[1]}") + + results = {} + + # Run DBSCAN + dbscan_labels = self.run_dbscan() + if dbscan_labels is not None: + results["DBSCAN"] = dbscan_labels + + # Run Mean Shift + # meanshift_labels = self.run_mean_shift() + # if meanshift_labels is not None: + # results["Mean Shift"] = meanshift_labels + + # Run Affinity Propagation + # affinity_labels = self.run_affinity_propagation() + # if affinity_labels is not None: + # results["Affinity Propagation"] = affinity_labels + + # Summary + if results: + print("\n" + "="*70) + print("SUMMARY OF RESULTS") + print("="*70) + + for method, labels in results.items(): + n_clusters = len(set(labels)) - (1 if -1 in labels else 0) + if method == "DBSCAN": + n_noise = list(labels).count(-1) + print(f"{method}: {n_clusters} clusters, {n_noise} noise points") + else: + print(f"{method}: {n_clusters} clusters") + + # Calculate agreement between methods if multiple succeeded + if len(results) > 1: + from sklearn.metrics import adjusted_rand_score + print("\nMethod Agreement (Adjusted Rand Index):") + method_names = list(results.keys()) + for i in range(len(method_names)): + for j in range(i+1, len(method_names)): + ari = adjusted_rand_score(results[method_names[i]], results[method_names[j]]) + print(f"{method_names[i]} vs {method_names[j]}: {ari:.4f}") + + # Visualize and save results + self.visualize_results(results) + self.save_results(results) + + else: + print("\nNo automatic clustering method found suitable clusters.") + print("This might indicate:") + print("- Data doesn't have clear cluster structure") + print("- Embeddings need different preprocessing") + print("- Different parameter ranges needed") + + return results + +def main(): + parser = argparse.ArgumentParser(description="Run automatic clustering methods on document embeddings") + parser.add_argument("--embeddings_path", help="Path to embeddings JSON file") + parser.add_argument("--method", choices=['None', 'dbscan', 'meanshift', 'affinity', 'all'], default='all', + help="Which automatic method to run") + + args = parser.parse_args() + + # Initialize clustering + clustering = AutoClustering(args.embeddings_path) + + # Run selected method(s) + if args.method == 'all': + clustering.run_all_methods() + elif args.method == 'dbscan': + labels = clustering.run_dbscan() + if labels is not None: + clustering.visualize_results({"DBSCAN": labels}) + clustering.save_results({"DBSCAN": labels}) + elif args.method == 'meanshift': + labels = clustering.run_mean_shift() + if labels is not None: + clustering.visualize_results({"Mean Shift": labels}) + clustering.save_results({"Mean Shift": labels}) + elif args.method == 'affinity': + labels = clustering.run_affinity_propagation() + 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zYXA+;${PVcH1WBRh*eNj4x6yo5sv2+boUsAhzWbHACr! zI!p|%qzEbhte5W%;(_bKwIr5mzYq_v9H(Jq8hR^dk6}k}bNbl_jWli{lvM+nKc^8R zFNDO1!7@b`81`B|;r};gu>fIgtfTtLVK+MX$5CfRks%&9SALU?tJgRbNu^!KP m99jyuN6&fu&mW|m2V>fU`fZ*mcTeel7KJShJ@MxHPyYeg8nF-n literal 0 HcmV?d00001 diff --git a/cluster/gmm_extensive.py b/cluster/gmm_extensive.py new file mode 100644 index 0000000..907f171 --- /dev/null +++ b/cluster/gmm_extensive.py @@ -0,0 +1,649 @@ +#!/usr/bin/env python3 +""" +Extensive Gaussian Mixture Model clustering with grid search for optimal parameters +Includes BIC and AIC metrics for model selection +""" + +import json +import numpy as np +import matplotlib.pyplot as plt +from sklearn.mixture import GaussianMixture +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score +from sklearn.decomposition import PCA +import datetime +import csv +import argparse +import warnings +warnings.filterwarnings('ignore') + +class GMMExtensiveClustering: + def __init__(self, embeddings_path): + self.embeddings_path = embeddings_path + self.embeddings = None + self.file_paths = None + self.load_embeddings() + + def load_embeddings(self): + """Load embeddings from JSON file""" + print(f"Loading embeddings from {self.embeddings_path}...") + with open(self.embeddings_path, 'r') as f: + data = json.load(f) + + self.file_paths = [] + embeddings_list = [] + + for item in data: + self.file_paths.append(item['filepath']) + embeddings_list.append(item['embedding']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + print(f"Loaded {len(self.file_paths)} samples with embedding dimension {self.embeddings.shape[1]}") + + # Standardize embeddings for better clustering + self.scaler = StandardScaler() + self.embeddings_scaled = self.scaler.fit_transform(self.embeddings) + + def run_gmm_grid_search(self): + """Run GMM with optimized grid search for faster execution""" + print("\n" + "="*70) + print("RUNNING GAUSSIAN MIXTURE MODEL CLUSTERING WITH OPTIMIZED GRID SEARCH") + print("="*70) + + # Optimized GMM parameter candidates for faster execution + + # Smart n_components range with larger steps + max_components = min(50, len(self.embeddings_scaled) // 20) # Reduced max and increased divisor + n_components_candidates = [] + + # Progressive step sizes: smaller steps for low numbers, larger for high + for n in range(2, max_components + 1): + if n <= 5: + n_components_candidates.append(n) # 2, 3, 4, 5 + elif n <= 10: + if n % 2 == 0: # 6, 8, 10 + n_components_candidates.append(n) + else: + if n % 3 == 2: # 11, 14, 17, 20 + n_components_candidates.append(n) + + # Reduced covariance types - focus on most important ones + covariance_types = [ + # 'full', 'diag', + 'tied', 'spherical' + ] # Removed 'tied' and 'spherical' as they're less common + + # Simplified regularization - focus on key values + reg_covar_candidates = [1e-5, 1e-4, 1e-3] # Removed extreme values + + # Reduced n_init - 1 is often sufficient for good initialization methods + n_init_candidates = [1, 5] # Removed 10 to save time + + # Focus on best initialization methods + init_params_candidates = ['kmeans', 'k-means++'] # Removed 'random' and 'random_from_data' + + # Simplified max_iter - most problems converge quickly + max_iter_candidates = [100, 300] # Removed 500, added 300 as middle ground + + print(f"Optimized parameter combinations:") + print(f" - n_components: {len(n_components_candidates)} values {n_components_candidates}") + print(f" - covariance_types: {len(covariance_types)} options {covariance_types}") + print(f" - reg_covar: {len(reg_covar_candidates)} values {reg_covar_candidates}") + print(f" - n_init: {len(n_init_candidates)} values {n_init_candidates}") + print(f" - init_params: {len(init_params_candidates)} options {init_params_candidates}") + print(f" - max_iter: {len(max_iter_candidates)} values {max_iter_candidates}") + + total_combinations = (len(n_components_candidates) * len(covariance_types) * + len(reg_covar_candidates) * len(n_init_candidates) * + len(init_params_candidates) * len(max_iter_candidates)) + print(f"Total combinations: {total_combinations} (optimized for speed)") + + # Estimate time + estimated_time_per_combination = 0.5 # seconds (conservative estimate) + estimated_total_time = total_combinations * estimated_time_per_combination + print(f"Estimated runtime: {estimated_total_time/60:.1f} minutes") + print("This should be much faster...\n") + + # Track all results for analysis + all_results = [] + + # Early stopping criteria for speed optimization + early_stopping_threshold = 0.7 # If we find a very good silhouette score, we can be less exhaustive + good_results_found = 0 + max_good_results = 5 # Stop early if we find several very good results + + best_bic_score = float('inf') + best_aic_score = float('inf') + best_silhouette_score = -1 + best_params_bic = None + best_params_aic = None + best_params_silhouette = None + best_labels_bic = None + best_labels_aic = None + best_labels_silhouette = None + + current_combination = 0 + + # Optimized iteration order: test simpler models first (fewer components, simpler covariance) + for covariance_type in covariance_types: # Start with covariance type + for n_components in n_components_candidates: # Then components + for init_params in init_params_candidates: # Good initialization methods + for reg_covar in reg_covar_candidates: # Regularization + for n_init in n_init_candidates: # Number of initializations + for max_iter in max_iter_candidates: # Iterations last + current_combination += 1 + + # Progress indicator with time estimation + if current_combination % 50 == 0 or current_combination == total_combinations: + progress = (current_combination / total_combinations) * 100 + print(f"Progress: {current_combination}/{total_combinations} ({progress:.1f}%) - " + f"Best scores so far: BIC={best_bic_score:.2f}, Silhouette={best_silhouette_score:.3f}") + + try: + # Early convergence check for faster models + tol = 1e-3 if n_components <= 5 else 1e-4 # Less strict tolerance for simple models + + # Run GMM + gmm = GaussianMixture( + n_components=n_components, + covariance_type=covariance_type, + reg_covar=reg_covar, + n_init=n_init, + init_params=init_params, + max_iter=max_iter, + tol=tol, # Added tolerance for faster convergence + random_state=42 + ) + + # Fit and predict + gmm.fit(self.embeddings_scaled) + labels = gmm.predict(self.embeddings_scaled) + + # Quick validation - skip if model didn't converge properly + if not gmm.converged_ and max_iter <= 100: + continue # Skip non-converged simple models + + # Calculate metrics + bic_score = gmm.bic(self.embeddings_scaled) + aic_score = gmm.aic(self.embeddings_scaled) + log_likelihood = gmm.score(self.embeddings_scaled) + + # Only calculate clustering metrics if we have multiple clusters + if len(set(labels)) > 1: + silhouette = silhouette_score(self.embeddings_scaled, labels) + calinski_harabasz = calinski_harabasz_score(self.embeddings_scaled, labels) + davies_bouldin = davies_bouldin_score(self.embeddings_scaled, labels) + + # Early stopping check + if silhouette > early_stopping_threshold: + good_results_found += 1 + print(f"🎯 Excellent result found: n_comp={n_components}, cov={covariance_type}, " + f"silhouette={silhouette:.4f}") + + else: + silhouette = -1 + calinski_harabasz = 0 + davies_bouldin = float('inf') + + # Store result for analysis + result_info = { + 'n_components': n_components, + 'covariance_type': covariance_type, + 'reg_covar': reg_covar, + 'n_init': n_init, + 'init_params': init_params, + 'max_iter': max_iter, + 'bic_score': bic_score, + 'aic_score': aic_score, + 'log_likelihood': log_likelihood, + 'silhouette_score': silhouette, + 'calinski_harabasz_score': calinski_harabasz, + 'davies_bouldin_score': davies_bouldin, + 'converged': gmm.converged_, + 'n_iter': gmm.n_iter_, + 'unique_clusters': len(set(labels)) + } + + all_results.append(result_info) + + # Print promising results + if (silhouette > 0.3 and bic_score < np.percentile([r['bic_score'] for r in all_results], 25)): + print(f"n_components={n_components}, cov={covariance_type}, init={init_params}: " + f"BIC={bic_score:.2f}, AIC={aic_score:.2f}, silhouette={silhouette:.4f}") + + # Track best results for different criteria + if bic_score < best_bic_score: + best_bic_score = bic_score + best_params_bic = { + 'n_components': n_components, + 'covariance_type': covariance_type, + 'reg_covar': reg_covar, + 'n_init': n_init, + 'init_params': init_params, + 'max_iter': max_iter + } + best_labels_bic = labels + + if aic_score < best_aic_score: + best_aic_score = aic_score + best_params_aic = { + 'n_components': n_components, + 'covariance_type': covariance_type, + 'reg_covar': reg_covar, + 'n_init': n_init, + 'init_params': init_params, + 'max_iter': max_iter + } + best_labels_aic = labels + + if silhouette > best_silhouette_score and len(set(labels)) > 1: + best_silhouette_score = silhouette + best_params_silhouette = { + 'n_components': n_components, + 'covariance_type': covariance_type, + 'reg_covar': reg_covar, + 'n_init': n_init, + 'init_params': init_params, + 'max_iter': max_iter + } + best_labels_silhouette = labels + + # Early stopping check + if good_results_found >= 5 and silhouette > 0.6: + print(f"🛑 Early stopping triggered: Found {good_results_found} excellent results. " + f"Stopping at {current_combination}/{total_combinations} combinations.") + break + + except Exception: + # Skip problematic parameter combinations + continue + + # Break from nested loops if early stopping triggered + if good_results_found >= 5 and best_silhouette_score > 0.6: + break + if good_results_found >= 5 and best_silhouette_score > 0.6: + break + if good_results_found >= 5 and best_silhouette_score > 0.6: + break + if good_results_found >= 5 and best_silhouette_score > 0.6: + break + if good_results_found >= 5 and best_silhouette_score > 0.6: + break + + # Analysis of results + print("\n" + "="*70) + print("GAUSSIAN MIXTURE MODEL GRID SEARCH ANALYSIS") + print("="*70) + + if all_results: + import pandas as pd + df_results = pd.DataFrame(all_results) + + print(f"Total parameter combinations tested: {len(df_results)}") + + # Filter results with valid clustering (more than 1 cluster) + valid_results = df_results[df_results['unique_clusters'] > 1] + print(f"Combinations with valid clustering: {len(valid_results)}") + + if len(valid_results) > 0: + # Best scores analysis + print(f"\nModel Selection Metrics:") + print(f"Best BIC score: {df_results['bic_score'].min():.2f}") + print(f"Best AIC score: {df_results['aic_score'].min():.2f}") + print(f"Best Log-Likelihood: {df_results['log_likelihood'].max():.2f}") + + print(f"\nClustering Quality Metrics:") + print(f"Best silhouette score: {valid_results['silhouette_score'].max():.4f}") + print(f"Mean silhouette score: {valid_results['silhouette_score'].mean():.4f}") + print(f"Best Calinski-Harabasz score: {valid_results['calinski_harabasz_score'].max():.2f}") + print(f"Best Davies-Bouldin score: {valid_results['davies_bouldin_score'].min():.4f}") + + # Top results by different criteria + print(f"\nTop 5 results by BIC (lower is better):") + top_bic = df_results.nsmallest(5, 'bic_score') + for idx, row in top_bic.iterrows(): + print(f" n_comp={row['n_components']}, cov={row['covariance_type']}: " + f"BIC={row['bic_score']:.2f}, AIC={row['aic_score']:.2f}") + + print(f"\nTop 5 results by AIC (lower is better):") + top_aic = df_results.nsmallest(5, 'aic_score') + for idx, row in top_aic.iterrows(): + print(f" n_comp={row['n_components']}, cov={row['covariance_type']}: " + f"BIC={row['bic_score']:.2f}, AIC={row['aic_score']:.2f}") + + if len(valid_results) > 0: + print(f"\nTop 5 results by Silhouette Score:") + top_silhouette = valid_results.nlargest(5, 'silhouette_score') + for idx, row in top_silhouette.iterrows(): + print(f" n_comp={row['n_components']}, cov={row['covariance_type']}: " + f"silhouette={row['silhouette_score']:.4f}") + + # Component count analysis + component_performance = df_results.groupby('n_components').agg({ + 'bic_score': 'min', + 'aic_score': 'min', + 'silhouette_score': 'max' + }).reset_index() + + print(f"\nComponent count analysis (top 10 by BIC):") + top_components = component_performance.nsmallest(10, 'bic_score') + for idx, row in top_components.iterrows(): + print(f" {row['n_components']} components: " + f"BIC={row['bic_score']:.2f}, AIC={row['aic_score']:.2f}, " + f"silhouette={row['silhouette_score']:.4f}") + + print(f"\n📁 SAVING DETAILED RESULTS...") + print("="*30) + + # Save detailed grid search results + self.save_gmm_grid_search_results(all_results, + best_params_bic, best_bic_score, + best_params_aic, best_aic_score, + best_params_silhouette, best_silhouette_score) + + # Return best results based on BIC (primary), AIC (secondary), Silhouette (tertiary) + results = { + 'bic': (best_labels_bic, best_params_bic, best_bic_score), + 'aic': (best_labels_aic, best_params_aic, best_aic_score), + 'silhouette': (best_labels_silhouette, best_params_silhouette, best_silhouette_score) + } + + # Print best results + if best_labels_bic is not None: + print(f"\nBest GMM result by BIC:") + print(f"Parameters: {best_params_bic}") + print(f"BIC score: {best_bic_score:.2f}") + + if best_labels_aic is not None: + print(f"\nBest GMM result by AIC:") + print(f"Parameters: {best_params_aic}") + print(f"AIC score: {best_aic_score:.2f}") + + if best_labels_silhouette is not None: + print(f"\nBest GMM result by Silhouette:") + print(f"Parameters: {best_params_silhouette}") + print(f"Silhouette score: {best_silhouette_score:.4f}") + + return results + + def save_gmm_grid_search_results(self, all_results, + best_params_bic, best_bic_score, + best_params_aic, best_aic_score, + best_params_silhouette, best_silhouette_score): + """Save detailed GMM grid search results to JSON file""" + + # Prepare comprehensive results data + grid_search_data = { + "experiment_info": { + "timestamp": datetime.datetime.now().isoformat(), + "dataset_path": self.embeddings_path, + "total_samples": len(self.file_paths), + "embedding_dimension": self.embeddings.shape[1], + "total_combinations_tested": len(all_results), + "method": "Gaussian Mixture Model" + }, + "best_results": { + "by_bic": { + "parameters": best_params_bic, + "bic_score": best_bic_score if best_bic_score != float('inf') else None + }, + "by_aic": { + "parameters": best_params_aic, + "aic_score": best_aic_score if best_aic_score != float('inf') else None + }, + "by_silhouette": { + "parameters": best_params_silhouette, + "silhouette_score": best_silhouette_score if best_silhouette_score > -1 else None + } + }, + "all_trials": [] + } + + # Add all trial results + for i, result in enumerate(all_results): + trial_data = { + "trial_id": i + 1, + "parameters": { + "n_components": result['n_components'], + "covariance_type": result['covariance_type'], + "reg_covar": result['reg_covar'], + "n_init": result['n_init'], + "init_params": result['init_params'], + "max_iter": result['max_iter'] + }, + "results": { + "bic_score": result['bic_score'], + "aic_score": result['aic_score'], + "log_likelihood": result['log_likelihood'], + "silhouette_score": result['silhouette_score'], + "calinski_harabasz_score": result['calinski_harabasz_score'], + "davies_bouldin_score": result['davies_bouldin_score'], + "converged": result['converged'], + "n_iter": result['n_iter'], + "unique_clusters": result['unique_clusters'] + } + } + grid_search_data["all_trials"].append(trial_data) + + # Calculate summary statistics + if all_results: + bic_scores = [r['bic_score'] for r in all_results] + aic_scores = [r['aic_score'] for r in all_results] + log_likelihoods = [r['log_likelihood'] for r in all_results] + + valid_silhouette = [r['silhouette_score'] for r in all_results if r['silhouette_score'] > -1] + + grid_search_data["summary_statistics"] = { + "total_trials": len(all_results), + "valid_clustering_trials": len(valid_silhouette), + "bic_score": { + "best": min(bic_scores), + "worst": max(bic_scores), + "mean": sum(bic_scores) / len(bic_scores), + "median": sorted(bic_scores)[len(bic_scores)//2] + }, + "aic_score": { + "best": min(aic_scores), + "worst": max(aic_scores), + "mean": sum(aic_scores) / len(aic_scores), + "median": sorted(aic_scores)[len(aic_scores)//2] + }, + "log_likelihood": { + "best": max(log_likelihoods), + "worst": min(log_likelihoods), + "mean": sum(log_likelihoods) / len(log_likelihoods) + } + } + + if valid_silhouette: + grid_search_data["summary_statistics"]["silhouette_score"] = { + "best": max(valid_silhouette), + "worst": min(valid_silhouette), + "mean": sum(valid_silhouette) / len(valid_silhouette), + "median": sorted(valid_silhouette)[len(valid_silhouette)//2] + } + + # Top 10 results by different criteria + sorted_by_bic = sorted(all_results, key=lambda x: x['bic_score']) + sorted_by_aic = sorted(all_results, key=lambda x: x['aic_score']) + valid_results = [r for r in all_results if r['silhouette_score'] > -1] + sorted_by_silhouette = sorted(valid_results, key=lambda x: x['silhouette_score'], reverse=True) + + grid_search_data["top_10_results"] = { + "by_bic": [], + "by_aic": [], + "by_silhouette": [] + } + + for i, result in enumerate(sorted_by_bic[:10]): + grid_search_data["top_10_results"]["by_bic"].append({ + "rank": i + 1, + "parameters": { + "n_components": result['n_components'], + "covariance_type": result['covariance_type'], + "init_params": result['init_params'] + }, + "bic_score": result['bic_score'], + "aic_score": result['aic_score'] + }) + + for i, result in enumerate(sorted_by_aic[:10]): + grid_search_data["top_10_results"]["by_aic"].append({ + "rank": i + 1, + "parameters": { + "n_components": result['n_components'], + "covariance_type": result['covariance_type'], + "init_params": result['init_params'] + }, + "bic_score": result['bic_score'], + "aic_score": result['aic_score'] + }) + + for i, result in enumerate(sorted_by_silhouette[:10]): + grid_search_data["top_10_results"]["by_silhouette"].append({ + "rank": i + 1, + "parameters": { + "n_components": result['n_components'], + "covariance_type": result['covariance_type'], + "init_params": result['init_params'] + }, + "silhouette_score": result['silhouette_score'] + }) + + # Save to file with timestamp + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"gmm_grid_search_detailed_{timestamp}.json" + # print() + + # with open(filename, 'w') as f: + # json.dump(grid_search_data, f, indent=4, ensure_ascii=False) + + print(f"Detailed grid search results saved to: {filename}") + + # Also save a CSV summary for easy analysis + csv_filename = f"gmm_grid_search_summary_{timestamp}.csv" + self.save_grid_search_csv(all_results, csv_filename) + print(f"Grid search summary CSV saved to: {csv_filename}") + + def save_grid_search_csv(self, all_results, filename): + """Save grid search results as CSV for easy analysis""" + + with open(filename, 'w', newline='', encoding='utf-8') as csvfile: + fieldnames = ['trial_id', 'n_components', 'covariance_type', 'reg_covar', + 'n_init', 'init_params', 'max_iter', 'bic_score', 'aic_score', + 'log_likelihood', 'silhouette_score', 'calinski_harabasz_score', + 'davies_bouldin_score', 'converged', 'n_iter', 'unique_clusters'] + writer = csv.DictWriter(csvfile, fieldnames=fieldnames) + + writer.writeheader() + for i, result in enumerate(all_results): + writer.writerow({ + 'trial_id': i + 1, + 'n_components': result['n_components'], + 'covariance_type': result['covariance_type'], + 'reg_covar': result['reg_covar'], + 'n_init': result['n_init'], + 'init_params': result['init_params'], + 'max_iter': result['max_iter'], + 'bic_score': result['bic_score'], + 'aic_score': result['aic_score'], + 'log_likelihood': result['log_likelihood'], + 'silhouette_score': result['silhouette_score'], + 'calinski_harabasz_score': result['calinski_harabasz_score'], + 'davies_bouldin_score': result['davies_bouldin_score'], + 'converged': result['converged'], + 'n_iter': result['n_iter'], + 'unique_clusters': result['unique_clusters'] + }) + + def visualize_results(self, results): + """Visualize clustering results using PCA""" + fig, axes = plt.subplots(1, 3, figsize=(18, 6)) + + # Reduce dimensions for visualization + pca = PCA(n_components=2, random_state=42) + embeddings_2d = pca.fit_transform(self.embeddings_scaled) + + methods = ['bic', 'aic', 'silhouette'] + titles = ['Best by BIC', 'Best by AIC', 'Best by Silhouette'] + + for idx, (method, title) in enumerate(zip(methods, titles)): + labels, params, score = results[method] + + if labels is not None: + unique_labels = set(labels) + colors = plt.cm.Set3(np.linspace(0, 1, len(unique_labels))) + + for label, color in zip(unique_labels, colors): + mask = labels == label + axes[idx].scatter(embeddings_2d[mask, 0], embeddings_2d[mask, 1], + c=[color], s=50, alpha=0.7, label=f'Cluster {label}') + + axes[idx].set_title(f'{title}\nn_components={params["n_components"]}, ' + f'cov={params["covariance_type"]}') + else: + axes[idx].text(0.5, 0.5, 'No valid clustering', ha='center', va='center', + transform=axes[idx].transAxes, fontsize=12) + axes[idx].set_title(f'{title}\n(Failed)') + + axes[idx].set_xlabel('PCA Component 1') + axes[idx].set_ylabel('PCA Component 2') + axes[idx].grid(True, alpha=0.3) + + plt.tight_layout() + plt.savefig('gmm_clustering_results.png', dpi=300, bbox_inches='tight') + plt.show() + + print(f"Visualization saved as 'gmm_clustering_results.png'") + + def save_clustering_results(self, results): + """Save final clustering results to JSON files""" + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + + for method in ['bic', 'aic', 'silhouette']: + labels, params, score = results[method] + + if labels is not None: + clustering_results = [] + for filepath, label in zip(self.file_paths, labels): + clustering_results.append({ + "filepath": filepath, + "cluster": int(label) + }) + + filename = f"gmm_final_results_{method}_{timestamp}.json" + + with open(filename, 'w') as f: + json.dump({ + "method": f"GMM (best by {method.upper()})", + "parameters": params, + "n_components": params['n_components'], + "n_samples": len(labels), + f"{method}_score": score, + "results": clustering_results + }, f, indent=4) + + print(f"Final clustering results ({method}) saved to: {filename}") + +def main(): + parser = argparse.ArgumentParser(description="Run extensive Gaussian Mixture Model clustering on document embeddings") + parser.add_argument("--embeddings_path", required=True, help="Path to embeddings JSON file") + + args = parser.parse_args() + + # Initialize clustering + clustering = GMMExtensiveClustering(args.embeddings_path) + + # Run extensive grid search + results = clustering.run_gmm_grid_search() + + if any(labels is not None for labels, _, _ in results.values()): + # Visualize and save results + clustering.visualize_results(results) + clustering.save_clustering_results(results) + print("\nGMM extensive clustering completed successfully!") + else: + print("\nGMM extensive clustering did not find suitable clusters.") + +if __name__ == "__main__": + main() diff --git a/cluster/gmm_grid_search_summary_20250801_015245.csv b/cluster/gmm_grid_search_summary_20250801_015245.csv new file mode 100644 index 0000000..a648a88 --- /dev/null +++ b/cluster/gmm_grid_search_summary_20250801_015245.csv @@ -0,0 +1,414 @@ +trial_id,n_components,covariance_type,reg_covar,n_init,init_params,max_iter,bic_score,aic_score,log_likelihood,silhouette_score,calinski_harabasz_score,davies_bouldin_score,converged,n_iter,unique_clusters +1,2,full,0.0001,1,kmeans,100,17260132.605124418,-7679507.0,2871.501,0.36924269795417786,1331.6861572265625,1.080913887973297,True,2,2 +2,2,full,0.0001,1,kmeans,300,17260132.605124418,-7679507.0,2871.501,0.36924269795417786,1331.6861572265625,1.080913887973297,True,2,2 +3,2,full,0.0001,5,kmeans,100,17260132.605124418,-7679507.0,2871.501,0.36924269795417786,1331.6861572265625,1.080913887973297,True,2,2 +4,2,full,0.0001,5,kmeans,300,17260132.605124418,-7679507.0,2871.501,0.36924269795417786,1331.6861572265625,1.080913887973297,True,2,2 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+1008,50,spherical,0.001,5,k-means++,300,6804318.582934182,6195743.61395214,-1069.7760024914537,0.068557315,187.27669929616567,2.258194517925524,True,24,50 diff --git a/cluster/log_gmm_extensive.txt b/cluster/log_gmm_extensive.txt new file mode 100644 index 0000000..3597a12 --- /dev/null +++ b/cluster/log_gmm_extensive.txt @@ -0,0 +1,158 @@ +nohup: ignoring input +Loading embeddings from /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json... +Loaded 2800 samples with embedding dimension 2048 + +====================================================================== +RUNNING GAUSSIAN MIXTURE MODEL CLUSTERING WITH OPTIMIZED GRID SEARCH +====================================================================== +Optimized parameter combinations: + - n_components: 11 values [2, 3, 4, 5, 6, 8, 10, 11, 14, 17, 20] + - covariance_types: 2 options ['full', 'diag'] + - reg_covar: 3 values [1e-05, 0.0001, 0.001] + - n_init: 2 values [1, 5] + - init_params: 2 options ['kmeans', 'k-means++'] + - max_iter: 2 values [100, 300] +Total combinations: 528 (optimized for speed) +Estimated runtime: 4.4 minutes +This should be much faster... + +Progress: 50/528 (9.5%) - Best scores so far: BIC=17260132.61, Silhouette=0.376 +Progress: 100/528 (18.9%) - Best scores so far: BIC=17260132.61, Silhouette=0.376 +Progress: 150/528 (28.4%) - Best scores so far: BIC=17260132.61, Silhouette=0.376 +Progress: 200/528 (37.9%) - Best scores so far: BIC=17260132.61, Silhouette=0.376 +Progress: 250/528 (47.3%) - Best scores so far: BIC=17260132.61, Silhouette=0.376 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=kmeans: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089173.91, AIC=13040529.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=2, cov=diag, init=k-means++: BIC=13089203.91, AIC=13040559.00, silhouette=0.3697 +n_components=3, cov=diag, init=kmeans: BIC=12693850.34, AIC=12620880.00, silhouette=0.3761 +n_components=3, cov=diag, init=kmeans: BIC=12693850.34, AIC=12620880.00, silhouette=0.3761 +n_components=3, cov=diag, init=kmeans: BIC=12699627.34, AIC=12626657.00, silhouette=0.3761 +n_components=3, cov=diag, init=kmeans: BIC=12699627.34, AIC=12626657.00, silhouette=0.3761 +n_components=3, cov=diag, init=kmeans: BIC=12718245.34, AIC=12645275.00, silhouette=0.3761 +n_components=3, cov=diag, init=kmeans: BIC=12718245.34, AIC=12645275.00, silhouette=0.3761 +Progress: 300/528 (56.8%) - Best scores so far: BIC=11770626.34, Silhouette=0.376 +n_components=4, cov=diag, init=kmeans: BIC=11525150.76, AIC=11427855.00, silhouette=0.3090 +n_components=4, cov=diag, init=kmeans: BIC=11525150.76, AIC=11427855.00, silhouette=0.3090 +n_components=4, cov=diag, init=kmeans: BIC=11530927.76, AIC=11433632.00, silhouette=0.3090 +n_components=4, cov=diag, init=kmeans: BIC=11530927.76, AIC=11433632.00, silhouette=0.3090 +n_components=4, cov=diag, init=kmeans: BIC=11549555.76, AIC=11452260.00, silhouette=0.3090 +n_components=4, cov=diag, init=kmeans: BIC=11549555.76, AIC=11452260.00, silhouette=0.3090 +n_components=5, cov=diag, init=kmeans: BIC=10641753.18, AIC=10520132.00, silhouette=0.3119 +n_components=5, cov=diag, init=kmeans: BIC=10641753.18, AIC=10520132.00, silhouette=0.3119 +n_components=5, cov=diag, init=kmeans: BIC=10647529.18, AIC=10525908.00, silhouette=0.3119 +n_components=5, cov=diag, init=kmeans: BIC=10647529.18, AIC=10525908.00, silhouette=0.3119 +n_components=5, cov=diag, init=kmeans: BIC=10666196.18, AIC=10544575.00, silhouette=0.3119 +n_components=5, cov=diag, init=kmeans: BIC=10666196.18, AIC=10544575.00, silhouette=0.3119 +Progress: 350/528 (66.3%) - Best scores so far: BIC=9931250.18, Silhouette=0.376 +Progress: 400/528 (75.8%) - Best scores so far: BIC=8401628.46, Silhouette=0.376 +Progress: 450/528 (85.2%) - Best scores so far: BIC=7579813.73, Silhouette=0.376 +Progress: 500/528 (94.7%) - Best scores so far: BIC=6988291.27, Silhouette=0.376 +Progress: 528/528 (100.0%) - Best scores so far: BIC=6849987.05, Silhouette=0.376 + +====================================================================== +GAUSSIAN MIXTURE MODEL GRID SEARCH ANALYSIS +====================================================================== +Total parameter combinations tested: 413 +Combinations with valid clustering: 413 + +Model Selection Metrics: +Best BIC score: 6849987.05 +Best AIC score: -11119584.00 +Best Log-Likelihood: 6594.97 + +Clustering Quality Metrics: +Best silhouette score: 0.3761 +Mean silhouette score: 0.2317 +Best Calinski-Harabasz score: 1331.69 +Best Davies-Bouldin score: 0.7860 + +Top 5 results by BIC (lower is better): + n_comp=20, cov=diag: BIC=6849987.05, AIC=6363484.50 + n_comp=20, cov=diag: BIC=6849987.05, AIC=6363484.50 + n_comp=20, cov=diag: BIC=6849987.05, AIC=6363484.50 + n_comp=20, cov=diag: BIC=6849987.05, AIC=6363484.50 + n_comp=20, cov=diag: BIC=6855879.05, AIC=6369376.50 + +Top 5 results by AIC (lower is better): + n_comp=4, cov=full: BIC=38759701.15, AIC=-11119584.00 + n_comp=4, cov=full: BIC=38759701.15, AIC=-11119584.00 + n_comp=3, cov=full: BIC=26462676.38, AIC=-10946786.00 + n_comp=3, cov=full: BIC=26462676.38, AIC=-10946786.00 + n_comp=5, cov=full: BIC=54230057.92, AIC=-8119050.00 + +Top 5 results by Silhouette Score: + n_comp=3, cov=diag: silhouette=0.3761 + n_comp=3, cov=diag: silhouette=0.3761 + n_comp=3, cov=diag: silhouette=0.3761 + n_comp=3, cov=diag: silhouette=0.3761 + n_comp=3, cov=diag: silhouette=0.3761 + +Component count analysis (top 10 by BIC): + 20.0 components: BIC=6849987.05, AIC=6363484.50, silhouette=0.1770 + 17.0 components: BIC=6988291.27, AIC=6574765.00, silhouette=0.2085 + 14.0 components: BIC=7179637.00, AIC=6839087.00, silhouette=0.2119 + 11.0 components: BIC=7579813.73, AIC=7312240.00, silhouette=0.2577 + 10.0 components: BIC=7737961.30, AIC=7494713.00, silhouette=0.2863 + 8.0 components: BIC=8401628.46, AIC=1051428.00, silhouette=0.2748 + 6.0 components: BIC=9102218.61, AIC=-6065602.00, silhouette=0.2707 + 5.0 components: BIC=9931250.18, AIC=-8119050.00, silhouette=0.3163 + 4.0 components: BIC=10865268.76, AIC=-11119584.00, silhouette=0.3110 + 3.0 components: BIC=11686081.34, AIC=-10946786.00, silhouette=0.3761 + +📁 SAVING DETAILED RESULTS... +============================== +Detailed grid search results saved to: gmm_grid_search_detailed_20250801_015245.json +Grid search summary CSV saved to: gmm_grid_search_summary_20250801_015245.csv + +Best GMM result by BIC: +Parameters: {'n_components': 20, 'covariance_type': 'diag', 'reg_covar': 1e-05, 'n_init': 1, 'init_params': 'kmeans', 'max_iter': 100} +BIC score: 6849987.05 + +Best GMM result by AIC: +Parameters: {'n_components': 4, 'covariance_type': 'full', 'reg_covar': 0.0001, 'n_init': 5, 'init_params': 'kmeans', 'max_iter': 100} +AIC score: -11119584.00 + +Best GMM result by Silhouette: +Parameters: {'n_components': 3, 'covariance_type': 'diag', 'reg_covar': 1e-05, 'n_init': 1, 'init_params': 'kmeans', 'max_iter': 100} +Silhouette score: 0.3761 +Visualization saved as 'gmm_clustering_results.png' +Final clustering results (bic) saved to: gmm_final_results_bic_20250801_015247.json +Traceback (most recent call last): + File "/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_extensive.py", line 646, in + main() + File "/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_extensive.py", line 640, in main + clustering.save_clustering_results(results) + File "/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_extensive.py", line 614, in save_clustering_results + json.dump({ + File "/home/nguyendc/miniconda3/envs/cluster/lib/python3.10/json/__init__.py", line 179, in dump + for chunk in iterable: + File "/home/nguyendc/miniconda3/envs/cluster/lib/python3.10/json/encoder.py", line 431, in _iterencode + yield from _iterencode_dict(o, _current_indent_level) + File "/home/nguyendc/miniconda3/envs/cluster/lib/python3.10/json/encoder.py", line 405, in _iterencode_dict + yield from chunks + File "/home/nguyendc/miniconda3/envs/cluster/lib/python3.10/json/encoder.py", line 438, in _iterencode + o = _default(o) + File "/home/nguyendc/miniconda3/envs/cluster/lib/python3.10/json/encoder.py", line 179, in default + raise TypeError(f'Object of type {o.__class__.__name__} ' +TypeError: Object of type float32 is not JSON serializable diff --git a/cluster/log_gmm_extensive_update.txt b/cluster/log_gmm_extensive_update.txt new file mode 100644 index 0000000..7c7cbcc --- /dev/null +++ b/cluster/log_gmm_extensive_update.txt @@ -0,0 +1,140 @@ +nohup: ignoring input +Loading embeddings from /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json... +Loaded 2800 samples with embedding dimension 2048 + +====================================================================== +RUNNING GAUSSIAN MIXTURE MODEL CLUSTERING WITH OPTIMIZED GRID SEARCH +====================================================================== +Optimized parameter combinations: + - n_components: 21 values [2, 3, 4, 5, 6, 8, 10, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47, 50] + - covariance_types: 2 options ['tied', 'spherical'] + - reg_covar: 3 values [1e-05, 0.0001, 0.001] + - n_init: 2 values [1, 5] + - init_params: 2 options ['kmeans', 'k-means++'] + - max_iter: 2 values [100, 300] +Total combinations: 1008 (optimized for speed) +Estimated runtime: 8.4 minutes +This should be much faster... + +n_components=2, cov=tied, init=kmeans: BIC=6521812.14, AIC=-5960170.38, silhouette=0.3692 +n_components=3, cov=tied, init=kmeans: BIC=6511443.85, AIC=-5982704.34, silhouette=0.3756 +n_components=3, cov=tied, init=kmeans: BIC=6511443.85, AIC=-5982704.34, silhouette=0.3756 +n_components=3, cov=tied, init=kmeans: BIC=6511443.85, AIC=-5982704.34, silhouette=0.3756 +n_components=3, cov=tied, init=kmeans: BIC=6511443.85, AIC=-5982704.34, silhouette=0.3756 +n_components=4, cov=tied, init=kmeans: BIC=6514783.32, AIC=-5991530.55, silhouette=0.3110 +Progress: 50/1008 (5.0%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +n_components=4, cov=tied, init=kmeans: BIC=6514783.32, AIC=-5991530.55, silhouette=0.3110 +n_components=4, cov=tied, init=kmeans: BIC=6514783.32, AIC=-5991530.55, silhouette=0.3110 +n_components=4, cov=tied, init=kmeans: BIC=6514783.32, AIC=-5991530.55, silhouette=0.3110 +n_components=5, cov=tied, init=kmeans: BIC=6520503.08, AIC=-5997976.48, silhouette=0.3163 +n_components=5, cov=tied, init=kmeans: BIC=6520503.08, AIC=-5997976.48, silhouette=0.3163 +n_components=5, cov=tied, init=kmeans: BIC=6520503.08, AIC=-5997976.48, silhouette=0.3163 +n_components=5, cov=tied, init=kmeans: BIC=6520503.08, AIC=-5997976.48, silhouette=0.3163 +Progress: 100/1008 (9.9%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 150/1008 (14.9%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 200/1008 (19.8%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 250/1008 (24.8%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 300/1008 (29.8%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 350/1008 (34.7%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 400/1008 (39.7%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 450/1008 (44.6%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 500/1008 (49.6%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 550/1008 (54.6%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 600/1008 (59.5%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 650/1008 (64.5%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 700/1008 (69.4%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 750/1008 (74.4%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 800/1008 (79.4%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 850/1008 (84.3%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 900/1008 (89.3%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 950/1008 (94.2%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 1000/1008 (99.2%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 +Progress: 1008/1008 (100.0%) - Best scores so far: BIC=6511443.85, Silhouette=0.376 + +====================================================================== +GAUSSIAN MIXTURE MODEL GRID SEARCH ANALYSIS +====================================================================== +Total parameter combinations tested: 1008 +Combinations with valid clustering: 1008 + +Model Selection Metrics: +Best BIC score: 6511443.85 +Best AIC score: -6295231.48 +Best Log-Likelihood: 1910.09 + +Clustering Quality Metrics: +Best silhouette score: 0.3757 +Mean silhouette score: 0.0287 +Best Calinski-Harabasz score: 1331.69 +Best Davies-Bouldin score: 0.6762 + +Top 5 results by BIC (lower is better): + n_comp=3, cov=tied: BIC=6511443.85, AIC=-5982704.34 + n_comp=3, cov=tied: BIC=6511443.85, AIC=-5982704.34 + n_comp=3, cov=tied: BIC=6511443.85, AIC=-5982704.34 + n_comp=3, cov=tied: BIC=6511443.85, AIC=-5982704.34 + n_comp=4, cov=tied: BIC=6514783.32, AIC=-5991530.55 + +Top 5 results by AIC (lower is better): + n_comp=50, cov=tied: BIC=6770703.71, AIC=-6295231.48 + n_comp=50, cov=tied: BIC=6770703.71, AIC=-6295231.48 + n_comp=50, cov=tied: BIC=6779928.76, AIC=-6286006.43 + n_comp=50, cov=tied: BIC=6779928.76, AIC=-6286006.43 + n_comp=47, cov=tied: BIC=6755535.12, AIC=-6273903.03 + +Top 5 results by Silhouette Score: + n_comp=3, cov=spherical: silhouette=0.3757 + n_comp=3, cov=spherical: silhouette=0.3757 + n_comp=3, cov=spherical: silhouette=0.3757 + n_comp=3, cov=spherical: silhouette=0.3757 + n_comp=3, cov=spherical: silhouette=0.3757 + +Component count analysis (top 10 by BIC): + 3.0 components: BIC=6511443.85, AIC=-5982704.34, silhouette=0.3757 + 4.0 components: BIC=6514783.32, AIC=-5991530.55, silhouette=0.3110 + 5.0 components: BIC=6520503.08, AIC=-5997976.48, silhouette=0.3163 + 2.0 components: BIC=6521812.14, AIC=-5960170.38, silhouette=0.3693 + 6.0 components: BIC=6526215.27, AIC=-6004429.97, silhouette=0.2485 + 8.0 components: BIC=6529704.08, AIC=-6025272.52, silhouette=0.2680 + 10.0 components: BIC=6538644.29, AIC=-6040663.67, silhouette=0.2706 + 11.0 components: BIC=6546208.81, AIC=-6045264.84, silhouette=0.2580 + 14.0 components: BIC=6563001.35, AIC=-6064969.34, silhouette=0.2241 + 17.0 components: BIC=6580862.17, AIC=-6083605.55, silhouette=0.2109 + +📁 SAVING DETAILED RESULTS... +============================== +Detailed grid search results saved to: gmm_grid_search_detailed_20250805_150635.json +Grid search summary CSV saved to: gmm_grid_search_summary_20250805_150635.csv + +Best GMM result by BIC: +Parameters: {'n_components': 3, 'covariance_type': 'tied', 'reg_covar': 1e-05, 'n_init': 1, 'init_params': 'kmeans', 'max_iter': 100} +BIC score: 6511443.85 + +Best GMM result by AIC: +Parameters: {'n_components': 50, 'covariance_type': 'tied', 'reg_covar': 1e-05, 'n_init': 5, 'init_params': 'kmeans', 'max_iter': 100} +AIC score: -6295231.48 + +Best GMM result by Silhouette: +Parameters: {'n_components': 3, 'covariance_type': 'spherical', 'reg_covar': 1e-05, 'n_init': 1, 'init_params': 'kmeans', 'max_iter': 100} +Silhouette score: 0.3757 +Visualization saved as 'gmm_clustering_results.png' +Final clustering results (bic) saved to: gmm_final_results_bic_20250805_150636.json +Final clustering results (aic) saved to: gmm_final_results_aic_20250805_150636.json +Traceback (most recent call last): + File "/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_extensive.py", line 649, in + main() + File "/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_extensive.py", line 643, in main + clustering.save_clustering_results(results) + File "/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_extensive.py", line 617, in save_clustering_results + json.dump({ + File "/usr/lib/python3.10/json/__init__.py", line 179, in dump + for chunk in iterable: + File "/usr/lib/python3.10/json/encoder.py", line 431, in _iterencode + yield from _iterencode_dict(o, _current_indent_level) + File "/usr/lib/python3.10/json/encoder.py", line 405, in _iterencode_dict + yield from chunks + File "/usr/lib/python3.10/json/encoder.py", line 438, in _iterencode + o = _default(o) + File "/usr/lib/python3.10/json/encoder.py", line 179, in default + raise TypeError(f'Object of type {o.__class__.__name__} ' +TypeError: Object of type float32 is not JSON serializable diff --git a/command.sh b/command.sh new file mode 100644 index 0000000..18057c2 --- /dev/null +++ b/command.sh @@ -0,0 +1,22 @@ +python auto_cluster.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/result/embeddings_factures_donut.json --method dbscan + +python auto_cluster.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/result/embeddings_factures_ostepoathie_1k.json --method dbscan + + +python auto_cluster.py --embeddings_path /home/nguyendc/phat-dev/clustering/embeddings_factures_osteopathie_1k.json --method dbscan + +python auto_cluster.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json --method dbscan + +python auto_cluster.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json --method None + + + +python gmm_extensive.py --embeddings_path embeddings_factures_donut.json + +nohup python -u gmm_extensive.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json > log_gmm_extensive.txt 2>&1 & + + +python auto_cluster.py \ +--embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_InternVL3_5-4B-Instruct.json \ +--method None + diff --git a/filter/analyze_labels.py b/filter/analyze_labels.py new file mode 100644 index 0000000..0f046f5 --- /dev/null +++ b/filter/analyze_labels.py @@ -0,0 +1,560 @@ +#!/usr/bin/env python3 +""" +Analyze 'label' fields in a JSON dataset and produce summaries. + +- Handles entries where 'label' is either an object or a list of objects. +- Computes distributions (is_bill, profession, currency, IDs presence, handwriting/rotation). +- Computes numeric stats (total_billed, amount_paid, remaining_payment, coverages). +- Parses dates and shows temporal distribution. +- Analyzes items: count, sum of amounts and coverages, and mismatches vs total_billed. +- Emits a concise stdout summary and writes CSVs and a Markdown report. + +Usage: + python analyze_labels.py --input 008_label_data_sample_seed_1997.json --out-dir . +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import re +from collections import Counter +from datetime import datetime +from pathlib import Path +from statistics import mean, median +from typing import Any, Dict, Iterable, List, Optional, Tuple + + +NUMERIC_FIELDS = [ + "total_billed", + "amount_paid", + "remaining_payment", + "client_part", + "mandatory_coverage", + "complementary_coverage", +] + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Analyze 'label' fields in JSON dataset") + p.add_argument("--input", required=True, help="Path to JSON file (list of records)") + p.add_argument( + "--out-dir", default=None, help="Output directory for reports (default: alongside input)" + ) + p.add_argument( + "--max-professions", type=int, default=50, help="Max professions to list in report" + ) + p.add_argument( + "--no-plots", + action="store_true", + help="Disable generating plots (PNG) and embedding into report", + ) + p.add_argument( + "--plot-top-k", + type=int, + default=20, + help="Top-K categories to visualize for profession/currency", + ) + p.add_argument( + "--plot-format", + type=str, + default="png", + choices=["png", "jpg", "jpeg"], + help="Image format for plots", + ) + return p.parse_args() + + +def load_json(path: Path) -> List[Dict[str, Any]]: + with path.open("r", encoding="utf-8") as f: + data = json.load(f) + if not isinstance(data, list): + raise ValueError("Top-level JSON must be a list of records") + return data + + +def to_bool(value: Any) -> Optional[bool]: + if value is None: + return None + if isinstance(value, bool): + return value + if isinstance(value, (int, float)): + return bool(value) + if isinstance(value, str): + v = value.strip().lower() + if v in {"true", "t", "1", "yes", "y"}: + return True + if v in {"false", "f", "0", "no", "n"}: + return False + return None + + +def to_float(value: Any) -> Optional[float]: + if value is None or value == "": + return None + try: + return float(value) + except (TypeError, ValueError): + return None + + +def parse_date(value: Any) -> Optional[datetime]: + if not value or not isinstance(value, str): + return None + s = value.strip() + if not s: + return None + # Common patterns (day-first) + fmts = [ + "%d-%m-%Y", + "%d/%m/%Y", + "%Y-%m-%d", + "%d-%m-%y", + "%d/%m/%y", + ] + for fmt in fmts: + try: + return datetime.strptime(s, fmt) + except ValueError: + pass + # Try to extract a date-like token using regex (e.g., 2025-02-07 or 07-02-2025) + m = re.search(r"(\d{2}[/-]\d{2}[/-]\d{4}|\d{4}-\d{2}-\d{2})", s) + if m: + token = m.group(1) + for fmt in fmts: + try: + return datetime.strptime(token, fmt) + except ValueError: + continue + return None + + +def safe_get(d: Dict[str, Any], key: str, default=None): + return d.get(key, default) + + +def flatten_labels(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + out: List[Dict[str, Any]] = [] + for rec in records: + src_image = rec.get("image") or ",".join(rec.get("image_files", []) or []) + label = rec.get("label") + if label is None: + continue + if isinstance(label, list): + for idx, lab in enumerate(label): + if not isinstance(lab, dict): + continue + o = dict(lab) + o["__source_image__"] = src_image + o["__multi_index__"] = idx + out.append(o) + elif isinstance(label, dict): + o = dict(label) + o["__source_image__"] = src_image + out.append(o) + return out + + +def presence_counts(labels: List[Dict[str, Any]], fields: Iterable[str]) -> Dict[str, int]: + counts: Dict[str, int] = {} + for field in fields: + present = 0 + for lbl in labels: + if safe_get(lbl, field) not in (None, ""): + present += 1 + counts[field] = present + return counts + + +def numeric_summary(values: List[Optional[float]]) -> Dict[str, Any]: + clean = [v for v in values if isinstance(v, (int, float)) and not math.isnan(v)] + if not clean: + return {"count": 0} + return { + "count": len(clean), + "min": min(clean), + "p25": percentile(clean, 25), + "median": median(clean), + "p75": percentile(clean, 75), + "max": max(clean), + "mean": mean(clean), + "sum": sum(clean), + "missing": len(values) - len(clean), + } + + +def percentile(arr: List[float], p: float) -> float: + if not arr: + return float("nan") + a = sorted(arr) + k = (len(a) - 1) * (p / 100.0) + f = math.floor(k) + c = math.ceil(k) + if f == c: + return a[int(k)] + d0 = a[f] * (c - k) + d1 = a[c] * (k - f) + return d0 + d1 + + +def write_csv(path: Path, headers: List[str], rows: Iterable[Iterable[Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8", newline="") as f: + w = csv.writer(f) + w.writerow(headers) + for row in rows: + w.writerow(row) + + +def try_import_matplotlib(): + try: + import matplotlib # type: ignore[import-not-found] + matplotlib.use("Agg") # headless backend + import matplotlib.pyplot as plt # type: ignore[import-not-found] + return plt + except Exception: + return None + + +def save_bar_plot(plt, x_labels: List[str], values: List[float], title: str, out_path: Path, rotation: int = 0): + out_path.parent.mkdir(parents=True, exist_ok=True) + fig, ax = plt.subplots(figsize=(max(6, min(14, 0.4 * len(x_labels) + 3)), 4)) + ax.bar(range(len(values)), values, color="#4C78A8") + ax.set_title(title) + ax.set_ylabel("count") + ax.set_xticks(range(len(x_labels))) + ax.set_xticklabels(x_labels, rotation=rotation, ha="right" if rotation else "center") + fig.tight_layout() + fig.savefig(out_path, dpi=150) + plt.close(fig) + + +def save_hist_plot(plt, values: List[float], title: str, out_path: Path, bins: int = 30): + out_path.parent.mkdir(parents=True, exist_ok=True) + fig, ax = plt.subplots(figsize=(7, 4)) + ax.hist(values, bins=bins, color="#72B7B2", edgecolor="white") + ax.set_title(title) + ax.set_ylabel("count") + ax.set_xlabel("value") + fig.tight_layout() + fig.savefig(out_path, dpi=150) + plt.close(fig) + + +def produce_plots( + out_dir: Path, + args: argparse.Namespace, + is_bill_counter: Counter, + bill_paid_counter: Counter, + handwriting_counter: Counter, + rotation_counter: Counter, + profession_counter: Counter, + currency_counter: Counter, + year_month_counter: Counter, + numeric_data: Dict[str, List[Optional[float]]], + items_per_label: List[int], +) -> List[Path]: + """Generate plots and return list of created file paths.""" + if args.no_plots: + return [] + plt = try_import_matplotlib() + if plt is None: + # matplotlib not available; skip plotting gracefully + return [] + + created: List[Path] = [] + plots_dir = out_dir / "plots" + ext = args.plot_format + + # is_bill + if is_bill_counter: + labels = [str(k) for k, _ in is_bill_counter.items()] + vals = [v for _, v in is_bill_counter.items()] + p = plots_dir / f"is_bill.{ext}" + save_bar_plot(plt, labels, vals, "is_bill distribution", p) + created.append(p) + + # bill_paid + if bill_paid_counter: + labels = [str(k) for k, _ in bill_paid_counter.items()] + vals = [v for _, v in bill_paid_counter.items()] + p = plots_dir / f"bill_paid.{ext}" + save_bar_plot(plt, labels, vals, "bill_paid distribution", p) + created.append(p) + + # Flags + if handwriting_counter: + labels = [str(k) for k, _ in handwriting_counter.items()] + vals = [v for _, v in handwriting_counter.items()] + p = plots_dir / f"is_handwriting.{ext}" + save_bar_plot(plt, labels, vals, "is_handwriting", p) + created.append(p) + if rotation_counter: + labels = [str(k) for k, _ in rotation_counter.items()] + vals = [v for _, v in rotation_counter.items()] + p = plots_dir / f"is_rotated.{ext}" + save_bar_plot(plt, labels, vals, "is_rotated", p) + created.append(p) + + # Professions (top-K) + if profession_counter: + top = profession_counter.most_common(max(1, min(args.plot_top_k, len(profession_counter)))) + labels = [k if len(str(k)) <= 20 else str(k)[:17] + "…" for k, _ in top] + vals = [v for _, v in top] + p = plots_dir / f"professions_top{len(labels)}.{ext}" + save_bar_plot(plt, labels, vals, f"Top {len(labels)} professions", p, rotation=45) + created.append(p) + + # Currency + if currency_counter: + top = currency_counter.most_common(max(1, min(args.plot_top_k, len(currency_counter)))) + labels = [str(k) for k, _ in top] + vals = [v for _, v in top] + p = plots_dir / f"currency.{ext}" + save_bar_plot(plt, labels, vals, "Currency distribution", p) + created.append(p) + + # Year-month + if year_month_counter: + items = sorted(year_month_counter.items(), key=lambda x: (x[0][0], x[0][1])) + labels = [f"{y:04d}-{m:02d}" for (y, m), _ in items] + vals = [v for _, v in items] + p = plots_dir / f"invoice_year_month.{ext}" + save_bar_plot(plt, labels, vals, "Invoices by year-month", p, rotation=45) + created.append(p) + + # Items per label + if items_per_label: + p = plots_dir / f"items_per_label.{ext}" + save_hist_plot(plt, items_per_label, "Items per label (histogram)", p, bins=min(30, max(5, int(len(items_per_label) ** 0.5)))) + created.append(p) + + # Numeric fields histograms + for k, vals_all in numeric_data.items(): + vals = [float(v) for v in vals_all if isinstance(v, (int, float)) and not math.isnan(v)] + if not vals: + continue + p = plots_dir / f"hist_{k}.{ext}" + save_hist_plot(plt, vals, f"{k} (histogram)", p) + created.append(p) + + return created + + +def main() -> None: + args = parse_args() + in_path = Path(args.input).resolve() + out_dir = Path(args.out_dir).resolve() if args.out_dir else in_path.parent + out_dir.mkdir(parents=True, exist_ok=True) + + records = load_json(in_path) + labels = flatten_labels(records) + n_total_rec = len(records) + n_labels = len(labels) + + # Normalize some fields + for lbl in labels: + lbl["is_bill"] = to_bool(lbl.get("is_bill")) + lbl["bill_paid"] = to_bool(lbl.get("bill_paid")) + # Normalize numeric fields in-place for ease of stats + for k in NUMERIC_FIELDS: + lbl[k] = to_float(lbl.get(k)) + + # Basic distributions + is_bill_counter = Counter(lbl.get("is_bill") for lbl in labels) + bill_paid_counter = Counter(lbl.get("bill_paid") for lbl in labels) + currency_counter = Counter(lbl.get("currency") for lbl in labels if lbl.get("currency")) + profession_counter = Counter((lbl.get("profession") or "").strip() or "(missing)" for lbl in labels) + + # Presence of identifiers and key fields + id_presence = presence_counts(labels, [ + "adeli_number", + "rpps_number", + "finess_number", + "prescripteur_finess_number", + "doctor_name", + "invoice_issuer", + "insured_name", + "beneficiary_name", + "security_number", + "currency", + ]) + + # Handwriting/rotation flags + handwriting_counter = Counter(to_bool(lbl.get("is_handwriting")) for lbl in labels) + rotation_counter = Counter(to_bool(lbl.get("is_rotated")) for lbl in labels) + + # Numeric stats + numeric_stats: Dict[str, Dict[str, Any]] = {} + for k in NUMERIC_FIELDS: + numeric_stats[k] = numeric_summary([lbl.get(k) for lbl in labels]) + # Keep raw numeric data for histograms + numeric_raw: Dict[str, List[Optional[float]]] = {k: [lbl.get(k) for lbl in labels] for k in NUMERIC_FIELDS} + + # Dates + invoice_dates = [parse_date(lbl.get("invoice_date")) for lbl in labels] + invoice_dates_clean = [d for d in invoice_dates if d is not None] + year_month_counter = Counter((d.year, d.month) for d in invoice_dates_clean) + + # Items analysis + items_per_label: List[int] = [] + sum_item_amount: List[Optional[float]] = [] + sum_item_mandatory: List[Optional[float]] = [] + mismatch_records: List[Tuple[str, Optional[float], Optional[float], Optional[float]]] = [] + + for lbl in labels: + items = lbl.get("items") or [] + if not isinstance(items, list): + items = [] + items_per_label.append(len(items)) + s_amount = None + s_mand = None + for it in items: + if not isinstance(it, dict): + continue + a = to_float(it.get("amount")) + m = to_float(it.get("mandatory_coverage")) + s_amount = (s_amount or 0.0) + (a or 0.0) + s_mand = (s_mand or 0.0) + (m or 0.0) + sum_item_amount.append(s_amount) + sum_item_mandatory.append(s_mand) + total_billed = to_float(lbl.get("total_billed")) + if total_billed is not None and s_amount is not None: + diff = total_billed - s_amount + if abs(diff) > 1e-6: + mismatch_records.append(( + str(lbl.get("__source_image__")), total_billed, s_amount, diff + )) + + # Data quality issues + issues: List[Dict[str, Any]] = [] + for lbl in labels: + src = str(lbl.get("__source_image__")) + # is_bill must be True/False or None (unknown); flag strings that could not be parsed + if "is_bill" in lbl and not isinstance(lbl.get("is_bill"), (bool, type(None))): + issues.append({"source": src, "issue": "is_bill not boolean"}) + # bill_paid True but amount_paid missing + if lbl.get("bill_paid") is True and to_float(lbl.get("amount_paid")) is None: + issues.append({"source": src, "issue": "bill_paid True but amount_paid missing"}) + # remaining_payment > 0 but bill_paid True + rp = to_float(lbl.get("remaining_payment")) + if lbl.get("bill_paid") is True and (rp or 0) > 0: + issues.append({"source": src, "issue": "bill_paid True but remaining_payment > 0"}) + # Negative or zero amounts on items + items = lbl.get("items") or [] + if isinstance(items, list): + for idx, it in enumerate(items): + if not isinstance(it, dict): + continue + a = to_float(it.get("amount")) + if a is not None and a < 0: + issues.append({"source": src, "issue": f"item[{idx}].amount negative: {a}"}) + q = to_float(it.get("quantity")) + if q is None: + # Not strictly an issue, but mark for completeness + issues.append({"source": src, "issue": f"item[{idx}].quantity missing"}) + # Missing currency on bill + if lbl.get("is_bill") is True and not lbl.get("currency"): + issues.append({"source": src, "issue": "currency missing for bill"}) + + # Outputs + # 1) CSVs + write_csv(out_dir / "professions_counts.csv", ["profession", "count"], profession_counter.most_common()) + write_csv(out_dir / "currency_counts.csv", ["currency", "count"], currency_counter.most_common()) + write_csv(out_dir / "is_bill_counts.csv", ["is_bill", "count"], is_bill_counter.items()) + write_csv(out_dir / "bill_paid_counts.csv", ["bill_paid", "count"], bill_paid_counter.items()) + write_csv(out_dir / "id_presence.csv", ["field", "present_count"], id_presence.items()) + write_csv(out_dir / "item_total_billed_mismatches.csv", ["source_image", "total_billed", "sum_item_amount", "diff"], mismatch_records) + write_csv(out_dir / "issues.csv", ["source", "issue"], ((i["source"], i["issue"]) for i in issues)) + + # 2) Markdown report + md = [] + md.append("# Label Analysis Report\n") + md.append(f"Input: `{in_path.name}`\n") + md.append("") + md.append("## Overview\n") + md.append(f"- Total records: {n_total_rec}") + md.append(f"- Total labels (flattened): {n_labels}") + md.append(f"- is_bill distribution: {dict(is_bill_counter)}") + md.append(f"- bill_paid distribution: {dict(bill_paid_counter)}") + if invoice_dates_clean: + md.append( + f"- Invoice dates span: {min(invoice_dates_clean).date()} .. {max(invoice_dates_clean).date()}" + ) + md.append(f"- Unique year-month pairs: {len(year_month_counter)}") + else: + md.append("- Invoice dates: none parseable") + + md.append("\n## Professions (top)\n") + for prof, cnt in profession_counter.most_common(args.max_professions): + md.append(f"- {prof}: {cnt}") + + md.append("\n## Currency distribution\n") + for cur, cnt in currency_counter.most_common(): + md.append(f"- {cur}: {cnt}") + + md.append("\n## Identifier and key field presence\n") + for k, v in id_presence.items(): + md.append(f"- {k}: {v} present") + + md.append("\n## Flags\n") + md.append(f"- is_handwriting: {dict(handwriting_counter)}") + md.append(f"- is_rotated: {dict(rotation_counter)}") + + md.append("\n## Numeric summaries\n") + for k, stats in numeric_stats.items(): + md.append(f"- {k}: {stats}") + + if items_per_label: + md.append("\n## Items analysis\n") + md.append(f"- Items per label: count={len(items_per_label)}, min={min(items_per_label)}, max={max(items_per_label)}, mean={mean(items_per_label):.2f}") + n_mismatch = len(mismatch_records) + md.append(f"- total_billed vs sum(items.amount) mismatches: {n_mismatch}") + + if issues: + md.append("\n## Data quality issues (sample)\n") + for row in issues[:50]: + md.append(f"- {row['source']}: {row['issue']}") + + # 3) Plots (if enabled) + created_plots = produce_plots( + out_dir=out_dir, + args=args, + is_bill_counter=is_bill_counter, + bill_paid_counter=bill_paid_counter, + handwriting_counter=handwriting_counter, + rotation_counter=rotation_counter, + profession_counter=profession_counter, + currency_counter=currency_counter, + year_month_counter=year_month_counter, + numeric_data=numeric_raw, + items_per_label=items_per_label, + ) + if created_plots: + md.append("\n## Plots\n") + for p in created_plots: + rel = p.relative_to(out_dir) + md.append(f"- {p.stem}") + md.append(f"![]({rel.as_posix()})\n") + elif not args.no_plots: + md.append("\n## Plots\n") + md.append("- matplotlib not available or no data to plot.") + + report_path = out_dir / "label_analysis_report.md" + report_path.write_text("\n".join(md), encoding="utf-8") + + # Console summary + print("Label analysis complete.") + print(f"- Records: {n_total_rec}, Labels: {n_labels}") + print(f"- is_bill: {dict(is_bill_counter)} | bill_paid: {dict(bill_paid_counter)}") + print(f"- Professions (top 10): {profession_counter.most_common(10)}") + print(f"- Currency: {dict(currency_counter)}") + print(f"Report written to: {report_path}") + if created_plots: + print(f"- Plots saved under: {(out_dir / 'plots').as_posix()} ({len(created_plots)} files)") + + +if __name__ == "__main__": + main() diff --git a/filter/check_008/bill_paid_counts.csv b/filter/check_008/bill_paid_counts.csv new file mode 100644 index 0000000..5385e8c --- /dev/null +++ b/filter/check_008/bill_paid_counts.csv @@ -0,0 +1,4 @@ +bill_paid,count +False,68 +True,1015 +,180 diff --git a/filter/check_008/currency_counts.csv b/filter/check_008/currency_counts.csv new file mode 100644 index 0000000..1428476 --- /dev/null +++ b/filter/check_008/currency_counts.csv @@ -0,0 +1,3 @@ +currency,count +EUR,1135 +F,2 diff --git a/filter/check_008/id_presence.csv b/filter/check_008/id_presence.csv new file mode 100644 index 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+e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001,119.48,0.0,119.48 +c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,120.0,0.0,120.0 +c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,20.8,0.0,20.8 +edb77e5c-ea68-4bad-a177-1256a3bafc9c_CF_1686176,64.48,59.88,4.600000000000001 +Facture ostéopathe-26112640,66.0,65.0,1.0 +1498c5f5-22cf-41d4-87f0-9b295ff481f4_IMG_5659,50.0,41.41,8.590000000000003 +Facture ostéopathe-25449739,70.0,60.0,10.0 +4fa21bd9-393e-41d4-9af2-bf42004f4e94_image,15.93,14.48,1.4499999999999993 +895777f3-4478-4e8d-a616-d498850d1523_factures-lunettes_Tess,41.0,79.0,-38.0 +6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,24.98,41.41,-16.429999999999996 +9901adc0-96d4-4cfd-a028-8286fd1f7841_Fre_Ophtalmo,70.69,72.69,-2.0 +5c4353b0-55a4-4687-9744-d1a6da183abd_facture_lentilles_de_contact,78.98,65.82,13.16000000000001 +598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,44.0,88.0,-44.0 +803f61ed-759c-4773-90f2-d8737ef911ab_image,115.0,0.0,115.0 +8c8c6ff8-8e9c-49fc-b131-1dce75bcdfc2_Facture_FA1947_04_02_2025,70.0,58.33,11.670000000000002 +e6272522-cc2a-44b0-af14-9f54d1a76a59_Facture_lunettes_,691.62,1253.62,-561.9999999999999 +8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,262.0,257.0,5.0 +47399c82-030f-4904-b8f4-2c3dc7c46723_image,77.61,0.0,77.61 +e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,76.0,0.0,76.0 +DDE prestation Facture des lentilles de contact-26736761,78.0,76.0,2.0 +8ed22fd8-18ae-4c54-93ae-2f5cd4ec2af6_17375530503755104210735675998866,73.43,0.0,73.43 +7b830a57-2131-479c-bf31-1673e7308903_invoice_2025_1_31,75.95,59.96,15.990000000000002 +0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,74.61,0.0,74.61 +bedf599f-99db-4613-aa22-17d52690c34c_doc00194520240521104544,95.29,0.0,95.29 +ee2bacc2-7a66-4adf-b4ff-95fc5c13a2da_20250213_112821,10.26,9.629999999999999,0.6300000000000008 +172442503141724-04-08_recu_kine_Suz,66.12,0.0,66.12 +8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,127.77,0.0,127.77 +Facture des lentilles de contact-27700403,41.2,41.8,-0.5999999999999943 +9c2763f2-fb0c-4fe4-be38-6f94fba64162_optical_facture_lunettes_apres_accident,551.58,620.5799999999999,-68.99999999999989 +1724397366294irm,252.72,0.0,252.72 +209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,72.63,0.0,72.63 +e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152,167.0,174.65,-7.650000000000006 +fecefa42-3451-496b-bcb5-d5ebd4cdd8da_invoice_2024_10_14,195.92,163.28,32.639999999999986 +44e87f9e-a4e2-4851-b383-e1401b36e853_image,118.14,0.0,118.14 +96be3553-7261-4698-bf20-88ca5c826889_ReleveMensuel_Mai,154.04,535.1099999999999,-381.06999999999994 +a13a6613-da2d-48b7-96ef-412ba5a88af0_Factures_Clemence_,71.34,39.0,32.34 +78ec53bb-a4c5-469f-a4d0-f42db4cf9b11_CF_1697735,64.48,59.88,4.600000000000001 +2dbb4025-f570-49f9-83d1-08aedb2b122d_lunettes_Lou-Ann,213.4,253.0,-39.599999999999994 +3fe044b5-fd6a-4f5c-accc-ce7b987575a8_IMG_6817,98.61,0.0,98.61 +82ac654c-54d4-401f-a006-96d9e46531fe_Facture_ostheo_JEAN,40.0,60.0,-20.0 +605bfa50-15ce-48a0-87ec-201c3aa0557a_lunette_auregane_daden_001,627.9,557.7,70.19999999999993 +43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,147.0,105.0,42.0 +9c1cc24a-5f7e-42ef-998d-c813a686a7b5_549D8459-524A-4A5C-9612-09EEA1ADDB58,77.61,0.0,77.61 +Facture des lentilles de contact-27693842,130.0,150.0,-20.0 +626c99a4-986b-401f-b91e-9b6c0f15cce6_Commande_n100691223,39.51,43.9,-4.390000000000001 diff --git a/filter/check_008/label_analysis_report.md b/filter/check_008/label_analysis_report.md new file mode 100644 index 0000000..f81f0ac --- /dev/null +++ b/filter/check_008/label_analysis_report.md @@ -0,0 +1,188 @@ +# Label Analysis Report + +Input: `008_label_data_sample_seed_1997.json` + + +## Overview + +- Total records: 1232 +- Total labels (flattened): 1263 +- is_bill distribution: {True: 1124, False: 139} +- bill_paid distribution: {False: 68, True: 1015, None: 180} +- Invoice dates span: 2012-04-17 .. 2025-06-12 +- Unique year-month pairs: 63 + +## Professions (top) + +- Ostéopathe: 371 +- Ostéopathie: 295 +- Optique: 228 +- (missing): 119 +- Psychologue: 67 +- Chiropractie: 57 +- Unknown: 20 +- Kinésithérapie: 16 +- Sophrologie: 8 +- Podologue: 8 +- Hypnothérapie: 7 +- Pharmacie: 7 +- Sophrologue: 6 +- Ophtalmologie: 6 +- Réflexologie: 5 +- Radiologie: 5 +- Étiopathie: 4 +- Sage-femme: 3 +- Psychologie: 3 +- Soins hospitalier: 3 +- Naturopathie: 2 +- Addictologie: 2 +- Etiopathie: 2 +- Kinésiologie: 2 +- Diététicienne: 2 +- Étiopathe: 2 +- Etiopathe: 2 +- Psychomotricité: 1 +- Homéopathie: 1 +- Kinésiologue: 1 +- Optique hay Ophtalmologue???: 1 +- Médecine du sport: 1 +- Otique: 1 +- Biologie: 1 +- Chiropratie: 1 +- Chirurgie dentaire: 1 +- Diététique: 1 +- Audiopathe: 1 + +## Currency distribution + +- EUR: 1135 +- F: 2 + +## Identifier and key field presence + +- adeli_number: 898 present +- rpps_number: 182 present +- finess_number: 139 present +- prescripteur_finess_number: 41 present +- doctor_name: 1040 present +- invoice_issuer: 1120 present +- insured_name: 208 present +- beneficiary_name: 1093 present +- security_number: 492 present +- currency: 1137 present + +## Flags + +- is_handwriting: {False: 327, None: 880, True: 56} +- is_rotated: {False: 358, None: 879, True: 26} + +## Numeric summaries + +- total_billed: {'count': 1130, 'min': 9.9, 'p25': 55.0, 'median': 60.0, 'p75': 70.0, 'max': 8000.0, 'mean': 105.40392920353982, 'sum': 119106.44, 'missing': 133} +- amount_paid: {'count': 1011, 'min': 0.0, 'p25': 50.0, 'median': 60.0, 'p75': 66.5, 'max': 8000.0, 'mean': 98.68897131552917, 'sum': 99774.55, 'missing': 252} +- remaining_payment: {'count': 247, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 0.0, 'max': 204.06, 'mean': 2.793765182186235, 'sum': 690.06, 'missing': 1016} +- client_part: {'count': 145, 'min': 0.0, 'p25': 26.44, 'median': 48.77, 'p75': 90.0, 'max': 1420.0, 'mean': 99.44468965517241, 'sum': 14419.480000000003, 'missing': 1118} +- mandatory_coverage: {'count': 137, 'min': 0.0, 'p25': 0.09, 'median': 27.63, 'p75': 48.55, 'max': 1232.47, 'mean': 43.21014598540146, 'sum': 5919.790000000002, 'missing': 1126} +- complementary_coverage: {'count': 93, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 90.0, 'max': 499.91, 'mean': 63.8452688172043, 'sum': 5937.61, 'missing': 1170} + +## Items analysis + +- Items per label: count=1263, min=0, max=10, mean=1.25 +- total_billed vs sum(items.amount) mismatches: 53 + +## Data quality issues (sample) + +- Facture médecine douce-27776417: item[0].quantity missing +- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[0].quantity missing +- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[1].quantity missing +- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[2].quantity missing +- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[3].quantity missing +- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[4].quantity missing +- 551dd33d-0011-4887-b115-4ddf28ee0e2f_fact_barret_l_2024: item[5].quantity missing +- Facture médecine douce-27640153: item[0].quantity missing +- Facture médecine douce-27640153: item[1].quantity missing +- 1724397366229Facture_RIVALLANDChrystelle_20240413ICQO: bill_paid True but amount_paid missing +- Facture ostéopathe-25417095: bill_paid True but remaining_payment > 0 +- Facture ostéopathe-GED N-R-2323015: item[0].quantity missing +- 74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp: item[0].quantity missing +- 74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp: currency missing for bill +- 74441931-f8d0-4cb0-8eb5-df4147a72bbe_mgp: currency missing for bill +- 1724658126141JACOB_Francois_2024-111: item[0].quantity missing +- Facture des lentilles de contact-27559701: item[5].amount negative: -6.67 +- 91c41171-ac88-4ce4-8f15-48c4ad2c5aa2_Devis_lunette: item[0].quantity missing +- 0252b5fb-bc84-4fca-bfa7-483ac611885a_Scan_0115: item[0].quantity missing +- 0252b5fb-bc84-4fca-bfa7-483ac611885a_Scan_0115: item[1].quantity missing +- 7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791: item[0].quantity missing +- 7f084bf4-357d-452d-bb4c-76bff332ea0c_IMG_7791: item[1].quantity missing +- 70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo: item[0].quantity missing +- 70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo: item[1].quantity missing +- 70c3e7f5-405b-49d8-a73c-dae83beafb59_Facture_ophtalmo: item[2].quantity missing +- Facture médecine douce-27776613: item[0].quantity missing +- 6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214: item[0].quantity missing +- 6eeacb70-27be-40dd-9511-522025b459a3_20241226_080214: item[1].quantity missing +- 1724399050339FACTURE_132120424_MME_RONDIN_MAHEVA: item[0].quantity missing +- ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony: item[0].quantity missing +- ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony: item[1].quantity missing +- Facture ostéopathe-25979892: item[0].quantity missing +- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[0].quantity missing +- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[1].quantity missing +- d4f310c8-3bcf-4bb5-bf53-27ad74d66c1e_FACTURE_OPTICIEN_OPHTALMO: item[0].quantity missing +- DDE prestation Facture des lentilles de contact-26740934: item[0].quantity missing +- 45ccffcd-5fe8-418b-80c2-894086de9087_facture_ophtalmo_Fred_: item[0].quantity missing +- 45ccffcd-5fe8-418b-80c2-894086de9087_facture_ophtalmo_Fred_: item[1].quantity missing +- Facture ostéopathe-26626551: item[0].quantity missing +- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[0].quantity missing +- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[1].quantity missing +- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[2].quantity missing +- 27ddc814-9239-4944-951a-fdd8a48c5926_IMG_20240922_0001: item[3].quantity missing +- 8eceb56a-b571-4ab6-a64e-668db6bf4ad5_chiropracteur_fevrier_25: item[0].quantity missing +- Facture des lentilles de contact-27576495: item[2].quantity missing +- 32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337: item[0].quantity missing +- 32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337: item[1].quantity missing +- 32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337: item[2].quantity missing +- a5bd8b97-a2a8-49ae-900d-0673d2f96637_quittance_optique_: item[0].quantity missing +- e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001: item[0].quantity missing + +## Plots + +- is_bill +![](plots/is_bill.png) + +- bill_paid +![](plots/bill_paid.png) + +- is_handwriting +![](plots/is_handwriting.png) + +- is_rotated +![](plots/is_rotated.png) + +- professions_top20 +![](plots/professions_top20.png) + +- currency +![](plots/currency.png) + +- invoice_year_month +![](plots/invoice_year_month.png) + +- items_per_label +![](plots/items_per_label.png) + +- hist_total_billed +![](plots/hist_total_billed.png) + +- hist_amount_paid +![](plots/hist_amount_paid.png) + +- hist_remaining_payment +![](plots/hist_remaining_payment.png) + +- hist_client_part +![](plots/hist_client_part.png) + +- hist_mandatory_coverage +![](plots/hist_mandatory_coverage.png) + +- hist_complementary_coverage +![](plots/hist_complementary_coverage.png) diff --git a/filter/check_008/plots/bill_paid.png b/filter/check_008/plots/bill_paid.png new file mode 100644 index 0000000000000000000000000000000000000000..12cb54696893ecac04d039bc99fe0adfd4b14080 GIT binary patch literal 20240 zcmeIa1yt4RyDhxzj;)AW1w>saAz*+=qo}w*1?fga6hXQhx3v`oT@nTa41jg6VZDTU`r%%0*o(vsmcr8sA+Hsb3X6Mx`DU_Yt$bZXj1Xkc$mz-#4 zPAS^?^*7o3mFh1}4o@D5s>^@e6!h2k-I=Eo_@uAtWJLDb9_L(lFFv%_SK5m!;R1(Z 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+284cb22c-17ad-409b-8d5f-79e43a6dd51b_Facture_,item[0].quantity missing +fe86f6a8-8360-458e-834c-805ac99350cf_Monsieur_OUEDRAOGO_DAOUDA,item[0].quantity missing +fe86f6a8-8360-458e-834c-805ac99350cf_Monsieur_OUEDRAOGO_DAOUDA,item[1].quantity missing +Facture des lentilles de contact-27576127,item[2].amount negative: -53.0 +1724425079263JACOB_Francois_2024-111,item[0].quantity missing +46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre,item[0].quantity missing +46ddeec5-97bf-4175-b214-b7bd74c4fa24_Renouvin_Ferlicot_Andre,item[1].quantity missing +0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,item[0].quantity missing +0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,item[1].quantity missing +Facture ostéopathe-26699279,item[0].quantity missing +Facture ostéopathe-1216961,bill_paid True but amount_paid missing +Facture ostéopathe-1216961,item[0].quantity missing +Facture ostéopathe-26935993,item[0].quantity missing +1724425079207douilly_6,item[0].quantity missing +Facture ostéopathe-26298310,item[0].quantity missing +8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,item[0].quantity missing +8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,item[1].quantity missing +379ed230-4e27-4f61-9039-226972f26cc9_Factures_Dani_medecin_,item[0].quantity missing +379ed230-4e27-4f61-9039-226972f26cc9_Factures_Dani_medecin_,item[1].quantity missing +b410345a-31de-4397-a896-bc11d97e524c_FACTURE_OPHTALMOLOGISTE,item[0].quantity missing +0268b186-6926-46af-b75f-ba01c02cb647_IMG_20240514_152542,item[0].quantity missing +0bfb5041-2751-421d-b5d5-60d696c361c1_17391960081576654684571490136238,item[0].quantity missing +598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,item[0].quantity missing +598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,item[1].quantity missing +1724226151831IMG_8005,item[0].quantity missing +c8fa8d11-c2df-4f39-9909-28790acc5957_Factures_opthalmo,item[0].quantity missing +50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,item[0].quantity missing +50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,item[1].quantity missing +50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,item[2].quantity missing +Facture ostéopathe-26744045,item[0].quantity missing +1724425031419Facture_Osteo_20240417,item[0].quantity missing +Facture ostéopathe-25187936,item[0].quantity missing +ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony,item[0].quantity missing +ec117b21-9348-4a76-9a22-2bae87639392_facture_du_14.02_Tony,item[1].quantity missing +Facture ostéopathe-26052732,item[0].quantity missing +de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery,item[0].quantity missing +de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery,item[1].quantity missing +Facture médecine douce-27709304,item[0].quantity missing +DDE prestation Facture des lentilles de contact-26715605,item[0].quantity missing +Facture médecine douce-27702699,item[0].quantity missing +70546a38-b6d7-4151-b08a-82b01c8e4a98_480755849_2183001178819517_1003204107786000285_n,item[0].quantity missing +1724658298461facture_podologue_alicia,item[0].quantity missing diff --git a/filter/check_009/item_total_billed_mismatches.csv b/filter/check_009/item_total_billed_mismatches.csv new file mode 100644 index 0000000..817dd7a --- /dev/null +++ b/filter/check_009/item_total_billed_mismatches.csv @@ -0,0 +1,61 @@ +source_image,total_billed,sum_item_amount,diff +6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1,24.98,41.41,-16.429999999999996 +ea64ca35-29f5-49ba-894b-13aa3421f1d6_doc00681020250513143142,138.96,115.78,23.180000000000007 +b7df45bd-a142-4cb1-bb00-69122c5b405a_IMG20250520184626,594.8,798.0,-203.20000000000005 +2dbb4025-f570-49f9-83d1-08aedb2b122d_lunettes_Lou-Ann,213.4,253.0,-39.599999999999994 +Facture des lentilles de contact-27693842,130.0,150.0,-20.0 +6221379e-7344-4868-9ee0-04f2c4e26874_consultation_ophtalmo27janvier2025BEAUPERINyann,20.8,20.86,-0.05999999999999872 +0a639214-b39e-4971-8d51-d83b7d2d2c2e_facture_lentilles_17_01_2025,540.8,550.8,-10.0 +8f0e72ca-64e5-4797-87bc-9378b842433a_CF_1676107,64.48,59.88,4.600000000000001 +Facture ostéopathe-26610949,60.0,100.0,-40.0 +8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes,262.0,257.0,5.0 +e566a3b0-cc99-4c4d-8715-9f3be841a066_noname,44.0,43.28,0.7199999999999989 +Facture ostéopathe-25449739,70.0,60.0,10.0 +47399c82-030f-4904-b8f4-2c3dc7c46723_image,77.61,0.0,77.61 +209e4abe-4973-4fd3-83cc-64f940fb7372_facture_ophtalmologue,72.63,0.0,72.63 +85120394-0ea3-4a2c-b0d4-11b0fe31a4e7_20250213_145033,73.33,84.33,-11.0 +43666807-3eb2-40b9-bdf6-94d4fe31b71e_factures,147.0,105.0,42.0 +a3e845ff-e89c-4eb0-bf8d-164979dfee2b_Recu_Osteo_LIGNAC_Sebastien,65.0,60.0,5.0 +060cd8e5-ac6f-47e5-b8f5-fe9d3920c522_CHIRO_FEVRIER_2025,40.0,90.0,-50.0 +1724397121050Adobe_Scan_17_avr._2024,45.96,43.00999999999999,2.95000000000001 +8f2b09a0-d20d-4a94-a70a-fda31024af49_Facture_22_04_2025,340.0,290.0,50.0 +06e1588f-71a7-43fc-bf99-d066db847189_Opticien_,375.6,613.6,-238.0 +72cb658b-3239-4479-918d-aac53bc4a552_Facture_ophtalmo_14-01-2025,130.0,0.0,130.0 +e997eddb-05a4-49f9-a7fd-82b48c2694b3_IMG_OPHTALMOLOGIE_20250117_0001,119.48,0.0,119.48 +Facture ostéopathe-1066603,45.0,65.0,-20.0 +DDE prestation Facture des lentilles de contact-26736761,78.0,76.0,2.0 +Facture ostéopathe-26555547,220.65,0.0,220.65 +96be3553-7261-4698-bf20-88ca5c826889_ReleveMensuel_Mai,154.04,535.1099999999999,-381.06999999999994 +ba1b499f-199f-4106-bb2c-aac62a67c235_doc00448420250313084147,644.16,643.96,0.1999999999999318 +1724397121048facture_kine_15-04-24,88.4,17.68,70.72 +Facture ostéopathe-26401996,205.56,0.0,205.56 +27c16ad0-3309-4eb1-9432-ab8a546fee7c_osteo,60.0,0.0,60.0 +c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,120.0,0.0,120.0 +c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo,20.8,0.0,20.8 +bae2e636-32c5-4b2c-b2d2-a7b28b30760f_KM_C250i24082011140,110.48,0.0,110.48 +e0f0eab0-f98c-4b74-95f2-bfc61ad76d28_Ophtalmo_Isa_01-24,76.0,0.0,76.0 +670b97e0-61da-4341-843d-a02b4ae76ef5_image,352.0,468.67,-116.67000000000002 +e6c39a83-a53c-48f5-bffe-6bdb57337b33_image,560.06,560.0,0.05999999999994543 +172442503141724-04-08_recu_kine_Suz,66.12,0.0,66.12 +499639d7-4faa-4d3e-b412-7f75da0b4e01_quercy,97.63,97.43,0.19999999999998863 +5dec7b4c-0160-443c-a498-6e94875222aa_FEVRIER_HYPNO,90.0,72.0,18.0 +ff6211dc-b681-4709-9451-d36fb961476f_MICHELIZZA_QuittanceFSE_1952,280.0,245.0,35.0 +8ed22fd8-18ae-4c54-93ae-2f5cd4ec2af6_17375530503755104210735675998866,73.43,0.0,73.43 +78ec53bb-a4c5-469f-a4d0-f42db4cf9b11_CF_1697735,64.48,59.88,4.600000000000001 +4fa21bd9-393e-41d4-9af2-bf42004f4e94_image,15.93,14.48,1.4499999999999993 +DDE prestation Facture des lentilles de contact-26764274,78.0,76.0,2.0 +ffc129de-c82b-4c0b-8ab8-2941fd28cda9_Facture_opticien,525.6,828.3,-302.69999999999993 +edb77e5c-ea68-4bad-a177-1256a3bafc9c_CF_1686176,64.48,59.88,4.600000000000001 +32ffb8dc-fbd0-4edf-94b2-7b784cc72ff2_17479876010256548167621008372337,92.4,75.72,16.680000000000007 +82ac654c-54d4-401f-a006-96d9e46531fe_Facture_ostheo_JEAN,40.0,60.0,-20.0 +Facture des lentilles de contact-27603799,73.7,67.8,5.900000000000006 +fe86f6a8-8360-458e-834c-805ac99350cf_Monsieur_OUEDRAOGO_DAOUDA,87.0,0.0,87.0 +1498c5f5-22cf-41d4-87f0-9b295ff481f4_IMG_5659,50.0,41.41,8.590000000000003 +0a26adda-beef-4977-966c-12efc73f1d15_20250129_190625,74.61,0.0,74.61 +Facture des lentilles de contact-27687795,63.98,58.0,5.979999999999997 +8019accc-33b4-4f20-95eb-3ff1e1d4db0b_nnn,127.77,0.0,127.77 +379ed230-4e27-4f61-9039-226972f26cc9_Factures_Dani_medecin_,26.5,53.0,-26.5 +598221c3-d0b3-403a-a261-c68cbe56b453_facture_ophtalmo,44.0,88.0,-44.0 +1724226151831IMG_8005,33.0,38.0,-5.0 +50eeaebf-d552-4a12-92a4-66d185bb992b_facture_optique,85.08,70.61,14.469999999999999 +b5ecf45a-00cd-45a8-900d-6a4fc90a79a4_ZOPIE_CORPETTI_CAROLE,113.44,113.34,0.09999999999999432 diff --git a/filter/check_009/label_analysis_report.md b/filter/check_009/label_analysis_report.md new file mode 100644 index 0000000..06f4538 --- /dev/null +++ b/filter/check_009/label_analysis_report.md @@ -0,0 +1,193 @@ +# Label Analysis Report + +Input: `009_label_data_sample_seed_42.json` + + +## Overview + +- Total records: 1232 +- Total labels (flattened): 1259 +- is_bill distribution: {True: 1126, False: 133} +- bill_paid distribution: {False: 74, True: 1011, None: 174} +- Invoice dates span: 2012-04-17 .. 2025-12-20 +- Unique year-month pairs: 64 + +## Professions (top) + +- Ostéopathe: 377 +- Ostéopathie: 308 +- Optique: 221 +- (missing): 108 +- Chiropractie: 54 +- Psychologue: 52 +- Unknown: 21 +- Kinésithérapie: 15 +- Ophtalmologie: 13 +- Sophrologie: 12 +- Podologue: 9 +- Hypnothérapie: 7 +- Pharmacie: 5 +- Naturopathie: 5 +- Etiopathie: 4 +- Radiologie: 4 +- Étiopathie: 4 +- Soins hospitalier: 4 +- Sophrologue: 3 +- Etiopathe: 3 +- Psychologie: 2 +- Psychomotricité: 2 +- Otique: 2 +- Médecine Générale: 2 +- Homéopathie: 2 +- Kinésiologie: 2 +- Pompes funèbres: 2 +- Chirurgie dentaire: 1 +- Biologie: 1 +- Sevrage tabagique: 1 +- Acupuncture: 1 +- Étiopathe: 1 +- unknown: 1 +- Orthopédie: 1 +- Psychiatrie: 1 +- Diététicienne: 1 +- Chiropratie: 1 +- Medecine Générale: 1 +- Sage-femme: 1 +- Addictologie: 1 +- Ophtalmologue: 1 +- Diététique: 1 +- Réflexologie: 1 + +## Currency distribution + +- EUR: 1137 +- F: 2 + +## Identifier and key field presence + +- adeli_number: 902 present +- rpps_number: 179 present +- finess_number: 133 present +- prescripteur_finess_number: 42 present +- doctor_name: 1051 present +- invoice_issuer: 1122 present +- insured_name: 203 present +- beneficiary_name: 1098 present +- security_number: 494 present +- currency: 1139 present + +## Flags + +- is_handwriting: {True: 62, None: 858, False: 339} +- is_rotated: {False: 372, None: 856, True: 31} + +## Numeric summaries + +- total_billed: {'count': 1134, 'min': 1.0, 'p25': 55.0, 'median': 60.0, 'p75': 70.0, 'max': 18600.0, 'mean': 112.98805114638448, 'sum': 128128.45000000001, 'missing': 125} +- amount_paid: {'count': 1012, 'min': 0.0, 'p25': 50.0, 'median': 60.0, 'p75': 65.0, 'max': 18600.0, 'mean': 106.93289525691699, 'sum': 108216.08999999997, 'missing': 247} +- remaining_payment: {'count': 252, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 0.0, 'max': 2744.64, 'mean': 14.226587301587301, 'sum': 3585.1, 'missing': 1007} +- client_part: {'count': 153, 'min': 0.0, 'p25': 27.72, 'median': 46.41, 'p75': 81.5, 'max': 1100.0, 'mean': 95.07934640522876, 'sum': 14547.140000000007, 'missing': 1106} +- mandatory_coverage: {'count': 138, 'min': 0.0, 'p25': 0.09, 'median': 27.380000000000003, 'p75': 47.730000000000004, 'max': 1175.72, 'mean': 39.25376811594203, 'sum': 5417.020000000004, 'missing': 1121} +- complementary_coverage: {'count': 89, 'min': 0.0, 'p25': 0.0, 'median': 0.0, 'p75': 60.0, 'max': 370.03, 'mean': 54.395168539325844, 'sum': 4841.169999999999, 'missing': 1170} + +## Items analysis + +- Items per label: count=1259, min=0, max=14, mean=1.22 +- total_billed vs sum(items.amount) mismatches: 60 + +## Data quality issues (sample) + +- eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358: item[0].quantity missing +- eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358: item[1].quantity missing +- e72e49e0-7891-4dc8-b62f-40c7bf6252a4_Nuria_Ravier_Ophtalomologiste: item[0].quantity missing +- e72e49e0-7891-4dc8-b62f-40c7bf6252a4_Nuria_Ravier_Ophtalomologiste: item[0].quantity missing +- Facture ostéopathe-25417096: item[0].quantity missing +- eb5561dd-937b-4c0b-b6c6-56da8b528c94_facture_vision_claire_ophtalmo_1_001: item[0].quantity missing +- 6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1: item[0].quantity missing +- 6f544990-74c6-4c7e-8a25-08eb3f9138c8_RemboursementSoins01082024_1: item[1].quantity missing +- 1150ac6a-abdc-409d-b7ca-296e7b35769c_Facture_BOLZERJean_Francois_20250131GMAM: item[0].quantity missing +- Facture ostéopathe-26738638: item[0].quantity missing +- 34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09: bill_paid True but remaining_payment > 0 +- 34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09: item[0].quantity missing +- 34162b51-6c3f-4dcc-a18a-d6b9e45e4f22_Recu_ophtalmo_Cadet_Corine_2025-03-09: item[1].quantity missing +- abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044: item[0].quantity missing +- abff81b4-d895-47d5-b3d4-93da48f5c1e6_20241212_172044: item[1].quantity missing +- 2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024: item[4].amount negative: -2.9 +- 2c75bd51-e61c-452e-b19b-494fa70e9cc9_Facture_lentilles_janvier_2024: item[5].amount negative: -3.0 +- Facture ostéopathe-26605349: item[0].quantity missing +- Facture médecine douce-27702698: item[0].quantity missing +- 6221379e-7344-4868-9ee0-04f2c4e26874_consultation_ophtalmo27janvier2025BEAUPERINyann: item[0].quantity missing +- f55f9121-499d-4c25-b79b-464413d7ca6c_Quittance_Ophtalmologue: item[0].quantity missing +- f55f9121-499d-4c25-b79b-464413d7ca6c_Quittance_Ophtalmologue: item[0].quantity missing +- 62b45277-a437-48c6-af52-d1814f23434f_17460042180346042803920585280783: item[0].quantity missing +- 62b45277-a437-48c6-af52-d1814f23434f_17460042180346042803920585280783: item[1].quantity missing +- Facture ostéopathe-25847017: bill_paid True but remaining_payment > 0 +- 02358c4c-820a-409f-9489-a75f22af3f32_image: item[0].quantity missing +- 02358c4c-820a-409f-9489-a75f22af3f32_image: item[1].quantity missing +- 7ef7306d-400e-4a56-b31c-02237212c470_Scan: item[0].quantity missing +- 7ef7306d-400e-4a56-b31c-02237212c470_Scan: item[1].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[0].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[1].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[3].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[4].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[6].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[7].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[8].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[9].quantity missing +- f482ebef-e885-4627-a927-96ad54ab5aa0_Facture: item[11].quantity missing +- Facture ostéopathe-26610949: item[0].quantity missing +- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[0].quantity missing +- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[1].quantity missing +- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[2].quantity missing +- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[3].quantity missing +- 8908ff09-4087-467a-88f1-e415501a95b4_POUVAIT_lunettes: item[4].quantity missing +- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[0].quantity missing +- bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136: item[1].quantity missing +- 17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo: item[0].quantity missing +- 17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo: item[1].quantity missing +- 17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo: item[0].quantity missing +- e566a3b0-cc99-4c4d-8715-9f3be841a066_noname: item[0].quantity missing + +## Plots + +- is_bill +![](plots/is_bill.png) + +- bill_paid +![](plots/bill_paid.png) + +- is_handwriting +![](plots/is_handwriting.png) + +- is_rotated +![](plots/is_rotated.png) + +- professions_top20 +![](plots/professions_top20.png) + +- currency +![](plots/currency.png) + +- invoice_year_month +![](plots/invoice_year_month.png) + +- items_per_label +![](plots/items_per_label.png) + +- hist_total_billed +![](plots/hist_total_billed.png) + +- hist_amount_paid +![](plots/hist_amount_paid.png) + +- hist_remaining_payment +![](plots/hist_remaining_payment.png) + +- hist_client_part +![](plots/hist_client_part.png) + +- hist_mandatory_coverage +![](plots/hist_mandatory_coverage.png) + +- hist_complementary_coverage +![](plots/hist_complementary_coverage.png) diff --git a/filter/check_009/plots/bill_paid.png b/filter/check_009/plots/bill_paid.png new file mode 100644 index 0000000000000000000000000000000000000000..5b282c59f2ad67cf2c1705fb6b7fe32d028ef8d4 GIT binary patch literal 20255 zcmeHv2UJwqwr#0x8xYYpU?SKS3P>=33J9W@3K5Vf85Kk_0um*)sZD^TfFe3Ejq%_8e>}$MflXDNI_K=Q*P3h2xz2rg*)zj7 zD9p68Clx4^Ww$Amr59KJiob|{=58RrtWRCERy4nAZF|X5pK|_^wS|efwTa1}VI?XkX!@`35HPni5VSI1=)h4{S)5g~qEJ|WBmXTi2&}+& z$v&i=Jg#KtH_&8jtz4ZwF}!D;!daGJDefgF4q89k{W)l5MZ<+sdmi53gCx0sq;FpL zY?t_h)x4)XR3%TS9+x_^~lMZTpt2g;T(d1qGg4@y4AFDJ6`hasEPnORZnI1i#+P|8u?^ zL@}(6^z!rb6SC?5rWxoG=j5^{OxBf)5qHn*Sou9OpkUI6%^1m 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Path(filepath).stem + + +def load_dbscan_filtered_results(filepath: str) -> Set[str]: + """ + Load dbscan filtered results and extract image names. + + Args: + filepath: Path to dbscan_filtered_results.json + + Returns: + Set of image names extracted from filepaths + """ + with open(filepath, 'r', encoding='utf-8') as f: + data = json.load(f) + + image_names = set() + + # Extract image names from all results + for result in data.get('results', []): + filepath_str = result.get('filepath', '') + if filepath_str: + # Extract filename without extension + image_name = extract_image_names_from_filepath(filepath_str) + image_names.add(image_name) + + # Also try to match different variations + # Some images might have different naming patterns + filename = Path(filepath_str).name + # Remove extension and add to set + filename_without_ext = Path(filename).stem + image_names.add(filename_without_ext) + + return image_names + + +def load_label_data(filepath: str) -> List[Dict]: + """ + Load label data from JSON file. + + Args: + filepath: Path to label_data.json + + Returns: + List of label data samples + """ + with open(filepath, 'r', encoding='utf-8') as f: + return json.load(f) + + +def filter_label_data(label_data: List[Dict], valid_image_names: Set[str]) -> List[Dict]: + """ + Filter label data to keep only samples with matching image names. + + Args: + label_data: List of label data samples + valid_image_names: Set of valid image names from dbscan results + + Returns: + Filtered list of label data samples + """ + filtered_data = [] + + for sample in label_data: + image_name = sample.get('image', '') + + # Check if the image name matches any of the valid names + # We'll check various matching strategies + if (image_name in valid_image_names or + # Check if any valid name is contained in the image name + any(valid_name in image_name for valid_name in valid_image_names) or + # Check if image name is contained in any valid name + any(image_name in valid_name for valid_name in valid_image_names)): + filtered_data.append(sample) + + return filtered_data + + +def save_filtered_data(filtered_data: List[Dict], output_filepath: str): + """ + Save filtered data to JSON file. + + Args: + filtered_data: Filtered label data + output_filepath: Path to save the filtered data + """ + with open(output_filepath, 'w', encoding='utf-8') as f: + json.dump(filtered_data, f, ensure_ascii=False, indent=2) + + +def main(): + """Main function to execute the filtering process.""" + + # Input file paths + # dbscan_results_path = '014_dbscan_filtered_results_temp.json' + # dbscan_results_path = '/home/nguyendc/sonnh/embedding-clustering/filter/027_dbscan_v3_filtered_results_temp.json' + dbscan_results_path = '/home/nguyendc/sonnh/embedding-clustering/filter/029_dbscan_v3_filtered_results_temp_30.json' + + print(dbscan_results_path) + label_data_path = '/home/nguyendc/sonnh/embedding-clustering/filter/label_data.json' + + # Output file path + # output_path = '014_filtered_label_data_dbscan_v2_30_percent_amount_data.json' + + output_path = '030_dbscan_v3_retrain_014.json' + + # Check if input files exist + if not os.path.exists(dbscan_results_path): + print(f"Error: {dbscan_results_path} not found!") + return + + if not os.path.exists(label_data_path): + print(f"Error: {label_data_path} not found!") + return + + print("Loading dbscan filtered results...") + valid_image_names = load_dbscan_filtered_results(dbscan_results_path) + print(f"Found {len(valid_image_names)} unique image names in dbscan results") + + print("Loading label data...") + label_data = load_label_data(label_data_path) + print(f"Loaded {len(label_data)} samples from label data") + + print("Filtering label data...") + filtered_data = filter_label_data(label_data, valid_image_names) + print(f"Filtered to {len(filtered_data)} samples") + + print(f"Saving filtered data to {output_path}...") + save_filtered_data(filtered_data, output_path) + + print("Filtering completed successfully!") + print(f"Original samples: {len(label_data)}") + print(f"Filtered samples: {len(filtered_data)}") + print(f"Reduction: {len(label_data) - len(filtered_data)} samples removed") + print(f"Retention rate: {len(filtered_data)/len(label_data)*100:.2f}%") + + +if __name__ == "__main__": + main() diff --git a/filter/dbscan.py b/filter/dbscan.py new file mode 100644 index 0000000..b1d1072 --- /dev/null +++ b/filter/dbscan.py @@ -0,0 +1,326 @@ +#!/usr/bin/env python3 +""" +DBSCAN Clustering Filter + +Filters clustering results based on specific criteria: +- For each cluster: select 50% of points + - 25% from center region (closest to centroid) + - 25% from border region (furthest from centroid) +- All noise points are selected +- Uses cosine distance metric +""" + +import json +import numpy as np +from sklearn.preprocessing import normalize +from sklearn.metrics.pairwise import cosine_distances +import argparse +import os +from pathlib import Path + + +class DBSCANFilter: + def __init__(self, embeddings_path, clustering_results_path): + """ + Initialize DBSCAN filter + + Args: + embeddings_path: Path to embeddings JSON file + clustering_results_path: Path to DBSCAN clustering results JSON + """ + self.embeddings_path = embeddings_path + self.clustering_results_path = clustering_results_path + self.embeddings = None + self.embeddings_normalized = None + self.clustering_results = None + self.filepath_to_embedding = {} + + def load_data(self): + """Load embeddings and clustering results""" + print("Loading embeddings...") + with open(self.embeddings_path, 'r') as f: + embeddings_data = json.load(f) + + # Create mapping from filepath to embedding + embeddings_list = [] + filepaths = [] + for item in embeddings_data: + self.filepath_to_embedding[item['filepath']] = item['embedding'] + embeddings_list.append(item['embedding']) + filepaths.append(item['filepath']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + self.embeddings_normalized = normalize(self.embeddings, norm='l2') + print(f"Loaded {len(embeddings_list)} embeddings") + + print("Loading clustering results...") + with open(self.clustering_results_path, 'r') as f: + self.clustering_results = json.load(f) + + print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, " + f"{self.clustering_results['n_samples']} samples") + + def group_by_clusters(self): + """Group data points by cluster labels""" + clusters = {} + noise_points = [] + + for result in self.clustering_results['results']: + cluster_id = result['cluster'] + filepath = result['filepath'] + if 'is_noise' in result: + is_noise = result['is_noise'] + else: + is_noise = False + + if is_noise or cluster_id == -1: + noise_points.append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + else: + if cluster_id not in clusters: + clusters[cluster_id] = [] + clusters[cluster_id].append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + + return clusters, noise_points + + def calculate_cluster_centroid(self, cluster_points): + """Calculate centroid of a cluster using normalized embeddings""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # For cosine distance, centroid is the normalized mean + centroid = np.mean(embeddings_normalized, axis=0) + centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0] + + return centroid_normalized + + def calculate_cosine_distances_to_centroid(self, cluster_points, centroid): + """Calculate cosine distances from each point to cluster centroid""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # Calculate cosine distances to centroid + distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten() + + return distances + + # v1 0.5 data, 0.5 center 0.5 border + + # v2 0.5 data, 0.25 center 0.75 border + # def filter_cluster(self, cluster_points, selection_ratio=0.5): + + # v3 0.75 data, 0.25 center 0.75 border + def filter_cluster(self, cluster_points, selection_ratio=0.5): + + """ + Filter points from a cluster + + Args: + cluster_points: List of points in the cluster + selection_ratio: Ratio of points to select (default: 0.5 = 50%) + + Returns: + List of selected points + """ + if len(cluster_points) == 0: + return [] + + + # Calculate how many points to select + total_points = len(cluster_points) + num_to_select = max(15, int(total_points * selection_ratio)) + + # If we need to select all or almost all points, just return all + if num_to_select >= total_points: + return cluster_points + + # Calculate centroid + centroid = self.calculate_cluster_centroid(cluster_points) + + # Calculate distances to centroid + distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + + # Create list of (point, distance) pairs + point_distance_pairs = list(zip(cluster_points, distances)) + + # Sort by distance (closest to furthest from centroid) + point_distance_pairs.sort(key=lambda x: x[1]) + + # Calculate how many points to select from center and border + # 25% from center (closest to centroid) + center_count = max(1, int(num_to_select * 0.25)) # 50% of selected points from center + # 25% from border (furthest from centroid) + border_count = num_to_select - center_count # remaining from border + + selected_points = [] + + # Select center points (closest to centroid) + center_points = [pair[0] for pair in point_distance_pairs[:center_count]] + selected_points.extend(center_points) + + # Select border points (furthest from centroid) + if border_count > 0: + border_points = [pair[0] for pair in point_distance_pairs[-border_count:]] + selected_points.extend(border_points) + + print(f"Cluster with {total_points} points -> selected {len(selected_points)} points " + f"({center_count} center + {border_count} border)") + + return selected_points + + def filter_all_clusters(self): + """Filter all clusters according to the specified criteria""" + print("\n" + "="*60) + print("FILTERING DBSCAN CLUSTERING RESULTS") + print("="*60) + + clusters, noise_points = self.group_by_clusters() + + print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points") + + filtered_results = [] + + # Process each cluster + for cluster_id, cluster_points in clusters.items(): + print(f"\nProcessing Cluster {cluster_id}:") + filtered_points = self.filter_cluster(cluster_points) + + # Add cluster information + for point in filtered_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': cluster_id, + 'is_noise': False, + 'selection_type': 'cluster_filtered' + }) + + # Add all noise points + print(f"\nAdding all {len(noise_points)} noise points...") + for point in noise_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': -1, + 'is_noise': True, + 'selection_type': 'noise' + }) + + return filtered_results + + def save_filtered_results(self, filtered_results, output_path=None): + """Save filtered results to JSON file""" + if output_path is None: + # Generate output filename based on input + base_name = Path(self.clustering_results_path).stem + output_path = f"{base_name}_filtered.json" + + # Create summary statistics + cluster_stats = {} + noise_count = 0 + + for result in filtered_results: + if result['is_noise']: + noise_count += 1 + else: + cluster_id = result['cluster'] + if cluster_id not in cluster_stats: + cluster_stats[cluster_id] = 0 + cluster_stats[cluster_id] += 1 + + # Prepare output data + output_data = { + "method": "DBSCAN_FILTERED", + "original_n_clusters": self.clustering_results['n_clusters'], + "original_n_samples": self.clustering_results['n_samples'], + "filtered_n_samples": len(filtered_results), + "filtering_criteria": { + "cluster_selection_ratio": 0.5, + "center_points_ratio": 0.5, # 50% of selected points from center + "border_points_ratio": 0.5, # 50% of selected points from border + "noise_points": "all_selected" + }, + "cluster_statistics": cluster_stats, + "noise_points": noise_count, + "results": filtered_results + } + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=4, ensure_ascii=False) + + print("\n" + "="*60) + print("FILTERING SUMMARY") + print("="*60) + print(f"Original samples: {self.clustering_results['n_samples']}") + print(f"Filtered samples: {len(filtered_results)}") + print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}") + print("\nCluster breakdown:") + for cluster_id, count in sorted(cluster_stats.items()): + print(f" Cluster {cluster_id}: {count} points") + print(f" Noise points: {noise_count} points") + print(f"\nFiltered results saved to: {output_path}") + + return output_path + + def create_filepath_list(self, filtered_results, output_txt_path=None): + """Create a simple text file with filtered filepaths""" + if output_txt_path is None: + base_name = Path(self.clustering_results_path).stem + output_txt_path = f"{base_name}_filtered_filepaths.txt" + + filepaths = [result['filepath'] for result in filtered_results] + + with open(output_txt_path, 'w', encoding='utf-8') as f: + for filepath in filepaths: + f.write(f"{filepath}\n") + + print(f"Filepath list saved to: {output_txt_path}") + return output_txt_path + + +def main(): + parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results") + parser.add_argument("--embeddings_path", required=True, + help="Path to embeddings JSON file") + parser.add_argument("--clustering_results_path", required=True, + help="Path to DBSCAN clustering results JSON file") + parser.add_argument("--output_path", + help="Output path for filtered results (optional)") + parser.add_argument("--create_filepath_list", action="store_true", + help="Also create a simple text file with filtered filepaths") + + args = parser.parse_args() + + # Validate input files exist + if not os.path.exists(args.embeddings_path): + print(f"Error: Embeddings file not found: {args.embeddings_path}") + return + + if not os.path.exists(args.clustering_results_path): + print(f"Error: Clustering results file not found: {args.clustering_results_path}") + return + + # Initialize filter + filter_obj = DBSCANFilter(args.embeddings_path, args.clustering_results_path) + + # Load data + filter_obj.load_data() + + # Filter clusters + filtered_results = filter_obj.filter_all_clusters() + + # Save results + filter_obj.save_filtered_results(filtered_results, args.output_path) + + # Create filepath list if requested + if args.create_filepath_list: + filter_obj.create_filepath_list(filtered_results) + + print("\nFiltering completed successfully!") + + +if __name__ == "__main__": + main() diff --git a/filter/dbscan_only_one_cluster.py b/filter/dbscan_only_one_cluster.py new file mode 100644 index 0000000..e135976 --- /dev/null +++ b/filter/dbscan_only_one_cluster.py @@ -0,0 +1,429 @@ +#!/usr/bin/env python3 +""" +DBSCAN Clustering Filter + +Filters clustering results based on specific criteria: +- For each cluster: select 50% of points + - 25% from center region (closest to centroid) + - 25% from border region (furthest from centroid) +- All noise points are selected +- Uses cosine distance metric +""" + +import json +import numpy as np +from sklearn.preprocessing import normalize +from sklearn.metrics.pairwise import cosine_distances +import argparse +import os +from pathlib import Path +import random + + +class DBSCANFilter: + def __init__(self, embeddings_path, clustering_results_path): + """ + Initialize DBSCAN filter + + Args: + embeddings_path: Path to embeddings JSON file + clustering_results_path: Path to DBSCAN clustering results JSON + """ + self.embeddings_path = embeddings_path + self.clustering_results_path = clustering_results_path + self.embeddings = None + self.embeddings_normalized = None + self.clustering_results = None + self.filepath_to_embedding = {} + + def load_data(self): + """Load embeddings and clustering results""" + print("Loading embeddings...") + with open(self.embeddings_path, 'r') as f: + embeddings_data = json.load(f) + + # Create mapping from filepath to embedding + embeddings_list = [] + filepaths = [] + for item in embeddings_data: + self.filepath_to_embedding[item['filepath']] = item['embedding'] + embeddings_list.append(item['embedding']) + filepaths.append(item['filepath']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + self.embeddings_normalized = normalize(self.embeddings, norm='l2') + print(f"Loaded {len(embeddings_list)} embeddings") + + print("Loading clustering results...") + with open(self.clustering_results_path, 'r') as f: + self.clustering_results = json.load(f) + + print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, " + f"{self.clustering_results['n_samples']} samples") + + def group_by_clusters(self): + """Group data points by cluster labels""" + clusters = {} + noise_points = [] + + for result in self.clustering_results['results']: + cluster_id = result['cluster'] + filepath = result['filepath'] + if 'is_noise' in result: + is_noise = result['is_noise'] + else: + is_noise = False + + if is_noise or cluster_id == -1: + noise_points.append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + else: + if cluster_id not in clusters: + clusters[cluster_id] = [] + clusters[cluster_id].append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + + return clusters, noise_points + + def calculate_cluster_centroid(self, cluster_points): + """Calculate centroid of a cluster using normalized embeddings""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # For cosine distance, centroid is the normalized mean + centroid = np.mean(embeddings_normalized, axis=0) + centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0] + + return centroid_normalized + + def calculate_cosine_distances_to_centroid(self, cluster_points, centroid): + """Calculate cosine distances from each point to cluster centroid""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # Calculate cosine distances to centroid + distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten() + + return distances + + # v3 + def filter_cluster(self, cluster_points, selection_ratio=0.3): + + """ + Filter points from a cluster + + Args: + cluster_points: List of points in the cluster + selection_ratio: Ratio of points to select (default: 0.5 = 50%) + + Returns: + List of selected points + """ + if len(cluster_points) == 0: + return [] + + + # Calculate how many points to select + total_points = len(cluster_points) + num_to_select = max(15, int(total_points * selection_ratio)) + + # If we need to select all or almost all points, just return all + if num_to_select >= total_points: + return cluster_points + + # Calculate centroid + centroid = self.calculate_cluster_centroid(cluster_points) + + # Calculate distances to centroid + distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + + # Create list of (point, distance) pairs + point_distance_pairs = list(zip(cluster_points, distances)) + + # Sort by distance (closest to furthest from centroid) + point_distance_pairs.sort(key=lambda x: x[1]) + + dis = 0.1 + # count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis) + all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis] + print(f"Number of center points (distance < {dis}): {len(all_center_points)}") + + # count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis) + all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis] + print(f"Number of border points (distance >= {dis}): {len(all_border_points)}") + + # Calculate how many points to select from center and border + n_center = len(all_center_points) + center_count = max(1, int(n_center * 0.15)) + + n_border = len(all_border_points) + if n_border < 70: + border_count = n_border + else: + border_count = max(0, int(n_border * 0.3)) # remaining from border + + selected_points = [] + + random.seed(42) + # Select center points (closest to centroid) + # center_points = [pair[0] for pair in point_distance_pairs[:center_count]] + center_points = random.sample(all_center_points, center_count) + selected_points.extend(center_points) + + # # Select border points (furthest from centroid) + if border_count > 0: + # border_points = [pair[0] for pair in point_distance_pairs[-border_count:]] + border_points = random.sample(all_border_points, border_count) + selected_points.extend(border_points) + + print(f"Cluster with {total_points} points -> selected {len(selected_points)} points " + f"({center_count} center + {border_count} border)") + + return selected_points + + def filter_all_clusters(self): + """Filter all clusters according to the specified criteria""" + print("\n" + "="*60) + print("FILTERING DBSCAN CLUSTERING RESULTS") + print("="*60) + + clusters, noise_points = self.group_by_clusters() + + print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points") + + filtered_results = [] + + # Process each cluster + for cluster_id, cluster_points in clusters.items(): + print(f"\nProcessing Cluster {cluster_id}:") + filtered_points = self.filter_cluster(cluster_points) + + # Add cluster information + for point in filtered_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': cluster_id, + 'is_noise': False, + 'selection_type': 'cluster_filtered' + }) + + # Add all noise points + print(f"\nAdding all {len(noise_points)} noise points...") + for point in noise_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': -1, + 'is_noise': True, + 'selection_type': 'noise' + }) + + return filtered_results + + def save_full_clusters_separately(self, output_dir=None): + """Save ALL points of each cluster (and noise) into separate JSON files. + + The output format for each file matches the existing aggregated format + produced by save_filtered_results (keys / structure unchanged). Only the + content (subset of results) differs. Noise is treated as a cluster with id -1. + + Args: + output_dir: Optional directory to place per-cluster files. If None, + files are written next to the clustering_results file. + Returns: + List of written file paths. + """ + clusters, noise_points = self.group_by_clusters() + + base_name = Path(self.clustering_results_path).stem + if output_dir is None: + output_dir = Path('.') + else: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + written_files = [] + + # Helper to build and write one cluster file + def _write_cluster_file(cluster_id, points, is_noise=False): + # Build results entries preserving field names + results_entries = [] + for p in points: + results_entries.append({ + 'filepath': p['filepath'], + 'cluster': -1 if is_noise else cluster_id, + 'is_noise': is_noise, + 'selection_type': 'noise' if is_noise else 'cluster_full' + }) + + # Stats + if is_noise: + cluster_stats = {} + noise_count = len(points) + else: + cluster_stats = {cluster_id: len(points)} + noise_count = 0 + + output_data = { + "method": "DBSCAN_FILTERED", # keep unchanged label + "original_n_clusters": self.clustering_results['n_clusters'], + "original_n_samples": self.clustering_results['n_samples'], + "filtered_n_samples": len(results_entries), + "filtering_criteria": { + # Keep same keys / structure (values kept as original literal values) + "cluster_selection_ratio": 0.5, + "center_points_ratio": 0.5, + "border_points_ratio": 0.5, + "noise_points": "all_selected" + }, + "cluster_statistics": cluster_stats, + "noise_points": noise_count, + "results": results_entries + } + + # Filename + if is_noise: + filename = f"{base_name}_cluster_noise.json" + else: + filename = f"{base_name}_cluster_{cluster_id}.json" + out_path = output_dir / filename + with open(out_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=4, ensure_ascii=False) + written_files.append(str(out_path)) + print(f"Saved cluster {cluster_id if not is_noise else 'noise'} -> {out_path} ({len(points)} samples)") + + # Write each real cluster + for cid, pts in clusters.items(): + _write_cluster_file(cid, pts, is_noise=False) + + # Noise as its own cluster + if noise_points: + _write_cluster_file(-1, noise_points, is_noise=True) + + return written_files + + def save_filtered_results(self, filtered_results, output_path=None): + """Save filtered results to JSON file""" + if output_path is None: + # Generate output filename based on input + base_name = Path(self.clustering_results_path).stem + output_path = f"{base_name}_filtered.json" + + # Create summary statistics + cluster_stats = {} + noise_count = 0 + + for result in filtered_results: + if result['is_noise']: + noise_count += 1 + else: + cluster_id = result['cluster'] + if cluster_id not in cluster_stats: + cluster_stats[cluster_id] = 0 + cluster_stats[cluster_id] += 1 + + # Prepare output data + output_data = { + "method": "DBSCAN_FILTERED", + "original_n_clusters": self.clustering_results['n_clusters'], + "original_n_samples": self.clustering_results['n_samples'], + "filtered_n_samples": len(filtered_results), + "filtering_criteria": { + "cluster_selection_ratio": 0.5, + "center_points_ratio": 0.5, # 50% of selected points from center + "border_points_ratio": 0.5, # 50% of selected points from border + "noise_points": "all_selected" + }, + "cluster_statistics": cluster_stats, + "noise_points": noise_count, + "results": filtered_results + } + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=4, ensure_ascii=False) + + print("\n" + "="*60) + print("FILTERING SUMMARY") + print("="*60) + print(f"Original samples: {self.clustering_results['n_samples']}") + print(f"Filtered samples: {len(filtered_results)}") + print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}") + print("\nCluster breakdown:") + for cluster_id, count in sorted(cluster_stats.items()): + print(f" Cluster {cluster_id}: {count} points") + print(f" Noise points: {noise_count} points") + print(f"\nFiltered results saved to: {output_path}") + + return output_path + + def create_filepath_list(self, filtered_results, output_txt_path=None): + """Create a simple text file with filtered filepaths""" + if output_txt_path is None: + base_name = Path(self.clustering_results_path).stem + output_txt_path = f"{base_name}_filtered_filepaths.txt" + + filepaths = [result['filepath'] for result in filtered_results] + + with open(output_txt_path, 'w', encoding='utf-8') as f: + for filepath in filepaths: + f.write(f"{filepath}\n") + + print(f"Filepath list saved to: {output_txt_path}") + return output_txt_path + + +def main(): + parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results") + parser.add_argument("--embeddings_path", required=True, + help="Path to embeddings JSON file") + parser.add_argument("--clustering_results_path", required=True, + help="Path to DBSCAN clustering results JSON file") + parser.add_argument("--output_path", + help="Output path for filtered results (optional)") + parser.add_argument("--create_filepath_list", action="store_true", + help="Also create a simple text file with filtered filepaths") + parser.add_argument("--save_full_clusters", action="store_true", + help="Save ALL points of each cluster (and noise) into separate JSON files without altering format") + parser.add_argument("--clusters_output_dir", + help="Directory to store per-cluster JSON files (default: current directory)") + + args = parser.parse_args() + + # Validate input files exist + if not os.path.exists(args.embeddings_path): + print(f"Error: Embeddings file not found: {args.embeddings_path}") + return + + if not os.path.exists(args.clustering_results_path): + print(f"Error: Clustering results file not found: {args.clustering_results_path}") + return + + # Initialize filter + filter_obj = DBSCANFilter(args.embeddings_path, args.clustering_results_path) + + # Load data + filter_obj.load_data() + + # Filter clusters + filtered_results = filter_obj.filter_all_clusters() + + # Save results + filter_obj.save_filtered_results(filtered_results, args.output_path) + + # Save full clusters separately if requested + if args.save_full_clusters: + print("\nSaving full clusters (no filtering) separately...") + filter_obj.save_full_clusters_separately(args.clusters_output_dir) + + # Create filepath list if requested + if args.create_filepath_list: + filter_obj.create_filepath_list(filtered_results) + + print("\nFiltering completed successfully!") + + +if __name__ == "__main__": + main() diff --git a/filter/dbscan_results_filtered_filepaths.txt b/filter/dbscan_results_filtered_filepaths.txt new file mode 100644 index 0000000..dcd666e --- /dev/null +++ b/filter/dbscan_results_filtered_filepaths.txt @@ -0,0 +1,981 @@ +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/eedda33e-049b-4ab8-aee8-6f5d0c88b500_IMG_0358_1_scale_0.6.jpg 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selected +- Uses cosine distance metric +""" + +import json +import numpy as np +from sklearn.preprocessing import normalize +from sklearn.metrics.pairwise import cosine_distances +import argparse +import os +from pathlib import Path +import random + + +class DBSCANFilter: + def __init__(self, embeddings_path, clustering_results_path): + """ + Initialize DBSCAN filter + + Args: + embeddings_path: Path to embeddings JSON file + clustering_results_path: Path to DBSCAN clustering results JSON + """ + self.embeddings_path = embeddings_path + self.clustering_results_path = clustering_results_path + self.embeddings = None + self.embeddings_normalized = None + self.clustering_results = None + self.filepath_to_embedding = {} + + def load_data(self): + """Load embeddings and clustering results""" + print("Loading embeddings...") + with open(self.embeddings_path, 'r') as f: + embeddings_data = json.load(f) + + # Create mapping from filepath to embedding + embeddings_list = [] + filepaths = [] + for item in embeddings_data: + self.filepath_to_embedding[item['filepath']] = item['embedding'] + embeddings_list.append(item['embedding']) + filepaths.append(item['filepath']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + self.embeddings_normalized = normalize(self.embeddings, norm='l2') + print(f"Loaded {len(embeddings_list)} embeddings") + + print("Loading clustering results...") + with open(self.clustering_results_path, 'r') as f: + self.clustering_results = json.load(f) + + print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, " + f"{self.clustering_results['n_samples']} samples") + + def group_by_clusters(self): + """Group data points by cluster labels""" + clusters = {} + noise_points = [] + + for result in self.clustering_results['results']: + cluster_id = result['cluster'] + filepath = result['filepath'] + if 'is_noise' in result: + is_noise = result['is_noise'] + else: + is_noise = False + + if is_noise or cluster_id == -1: + noise_points.append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + else: + if cluster_id not in clusters: + clusters[cluster_id] = [] + clusters[cluster_id].append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + + return clusters, noise_points + + def calculate_cluster_centroid(self, cluster_points): + """Calculate centroid of a cluster using normalized embeddings""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # For cosine distance, centroid is the normalized mean + centroid = np.mean(embeddings_normalized, axis=0) + centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0] + + return centroid_normalized + + def calculate_cosine_distances_to_centroid(self, cluster_points, centroid): + """Calculate cosine distances from each point to cluster centroid""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # Calculate cosine distances to centroid + distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten() + + return distances + + # v1 0.5 data, 0.5 center 0.5 border + + # v2 0.5 data, 0.25 center 0.75 border + # def filter_cluster(self, cluster_points, selection_ratio=0.5): + + # v3 0.75 data, 0.25 center 0.75 border + + + #dbscan 014 + # def filter_cluster(self, cluster_points, selection_ratio=0.3): + + # """ + # Filter points from a cluster + + # Args: + # cluster_points: List of points in the cluster + # selection_ratio: Ratio of points to select (default: 0.5 = 50%) + + # Returns: + # List of selected points + # """ + # if len(cluster_points) == 0: + # return [] + + + # # Calculate how many points to select + # total_points = len(cluster_points) + # num_to_select = max(15, int(total_points * selection_ratio)) + + # # If we need to select all or almost all points, just return all + # if num_to_select >= total_points: + # return cluster_points + + # # Calculate centroid + # centroid = self.calculate_cluster_centroid(cluster_points) + + # # Calculate distances to centroid + # distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + + # # Create list of (point, distance) pairs + # point_distance_pairs = list(zip(cluster_points, distances)) + + # # Sort by distance (closest to furthest from centroid) + # point_distance_pairs.sort(key=lambda x: x[1]) + + # dis = 0.1 + # # count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis) + # all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis] + # print(f"Number of center points (distance < {dis}): {len(all_center_points)}") + + # # count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis) + # all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis] + # print(f"Number of border points (distance >= {dis}): {len(all_border_points)}") + + # # Calculate how many points to select from center and border + # n_center = len(all_center_points) + # center_count = max(1, int(n_center * 0.15)) + + # n_border = len(all_border_points) + # if n_border < 70: + # border_count = n_border + # else: + # border_count = max(0, int(n_border * 0.3)) # remaining from border + + # selected_points = [] + + # random.seed(42) + # # Select center points (closest to centroid) + # # center_points = [pair[0] for pair in point_distance_pairs[:center_count]] + # center_points = random.sample(all_center_points, center_count) + # selected_points.extend(center_points) + + # # # Select border points (furthest from centroid) + # if border_count > 0: + # # border_points = [pair[0] for pair in point_distance_pairs[-border_count:]] + # border_points = random.sample(all_border_points, border_count) + # selected_points.extend(border_points) + + # print(f"Cluster with {total_points} points -> selected {len(selected_points)} points " + # f"({center_count} center + {border_count} border)") + + # return selected_points + + # dbscan 015 + def filter_cluster(self, cluster_points, selection_ratio=0.3): + + """ + Filter points from a cluster + + Args: + cluster_points: List of points in the cluster + selection_ratio: Ratio of points to select (default: 0.5 = 50%) + + Returns: + List of selected points + """ + if len(cluster_points) == 0: + return [] + + + # Calculate how many points to select + total_points = len(cluster_points) + num_to_select = max(15, int(total_points * selection_ratio)) + + # If we need to select all or almost all points, just return all + if num_to_select >= total_points: + return cluster_points + + # Calculate centroid + centroid = self.calculate_cluster_centroid(cluster_points) + + # Calculate distances to centroid + distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + + # Create list of (point, distance) pairs + point_distance_pairs = list(zip(cluster_points, distances)) + + # Sort by distance (closest to furthest from centroid) + point_distance_pairs.sort(key=lambda x: x[1]) + + dis = 0.1 + # count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis) + all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis] + print(f"Number of center points (distance < {dis}): {len(all_center_points)}") + + # count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis) + all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis] + print(f"Number of border points (distance >= {dis}): {len(all_border_points)}") + + # Calculate how many points to select from center and border + n_center = len(all_center_points) + center_count = max(1, int(n_center * 0.15)) + + n_border = len(all_border_points) + if n_border < 70: + border_count = n_border + else: + border_count = max(0, int(n_border * 0.3)) # remaining from border + + selected_points = [] + + random.seed(42) + # Select center points (closest to centroid) + # center_points = [pair[0] for pair in point_distance_pairs[:center_count]] + center_points = random.sample(all_center_points, center_count) + selected_points.extend(center_points) + + # # Select border points (furthest from centroid) + if border_count > 0: + # border_points = [pair[0] for pair in point_distance_pairs[-border_count:]] + border_points = random.sample(all_border_points, border_count) + selected_points.extend(border_points) + + print(f"Cluster with {total_points} points -> selected {len(selected_points)} points " + f"({center_count} center + {border_count} border)") + + return selected_points + + #gmm + # def filter_cluster(self, cluster_points, selection_ratio=0.3): + + # """ + # Filter points from a cluster + + # Args: + # cluster_points: List of points in the cluster + # selection_ratio: Ratio of points to select (default: 0.5 = 50%) + + # Returns: + # List of selected points + # """ + # if len(cluster_points) == 0: + # return [] + + + # # Calculate how many points to select + # total_points = len(cluster_points) + # num_to_select = max(15, int(total_points * selection_ratio)) + + # # If we need to select all or almost all points, just return all + # if num_to_select >= total_points: + # return cluster_points + + # # Calculate centroid + # centroid = self.calculate_cluster_centroid(cluster_points) + + # # Calculate distances to centroid + # distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + + # # Create list of (point, distance) pairs + # point_distance_pairs = list(zip(cluster_points, distances)) + + # # Sort by distance (closest to furthest from centroid) + # point_distance_pairs.sort(key=lambda x: x[1]) + + # dis = 0.2 + # # count_center = sum(1 for pair in point_distance_pairs if pair[1] < dis) + # all_center_points = [pair[0] for pair in point_distance_pairs if pair[1] < dis] + # print(f"Number of center points (distance < {dis}): {len(all_center_points)}") + + # # count_border = sum(1 for pair in point_distance_pairs if pair[1] >= dis) + # all_border_points = [pair[0] for pair in point_distance_pairs if pair[1] >= dis] + # print(f"Number of border points (distance >= {dis}): {len(all_border_points)}") + + # # Calculate how many points to select from center and border + # n_center = len(all_center_points) + # center_count = max(1, int(n_center * 0.15)) + + # n_border = len(all_border_points) + # if n_border < 70: + # border_count = n_border + # else: + # border_count = max(0, int(n_border * 0.3)) # remaining from border + + # selected_points = [] + + # random.seed(42) + # # Select center points (closest to centroid) + # # center_points = [pair[0] for pair in point_distance_pairs[:center_count]] + # center_points = random.sample(all_center_points, center_count) + # selected_points.extend(center_points) + + # # # Select border points (furthest from centroid) + # if border_count > 0: + # # border_points = [pair[0] for pair in point_distance_pairs[-border_count:]] + # border_points = random.sample(all_border_points, border_count) + # selected_points.extend(border_points) + + # print(f"Cluster with {total_points} points -> selected {len(selected_points)} points " + # f"({center_count} center + {border_count} border)") + + # return selected_points + + def filter_all_clusters(self): + """Filter all clusters according to the specified criteria""" + print("\n" + "="*60) + print("FILTERING DBSCAN CLUSTERING RESULTS") + print("="*60) + + clusters, noise_points = self.group_by_clusters() + + print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points") + + filtered_results = [] + + # Process each cluster + for cluster_id, cluster_points in clusters.items(): + print(f"\nProcessing Cluster {cluster_id}:") + filtered_points = self.filter_cluster(cluster_points) + + # Add cluster information + for point in filtered_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': cluster_id, + 'is_noise': False, + 'selection_type': 'cluster_filtered' + }) + + # Add all noise points + print(f"\nAdding all {len(noise_points)} noise points...") + for point in noise_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': -1, + 'is_noise': True, + 'selection_type': 'noise' + }) + + return filtered_results + + def save_filtered_results(self, filtered_results, output_path=None): + """Save filtered results to JSON file""" + if output_path is None: + # Generate output filename based on input + base_name = Path(self.clustering_results_path).stem + output_path = f"{base_name}_filtered.json" + + # Create summary statistics + cluster_stats = {} + noise_count = 0 + + for result in filtered_results: + if result['is_noise']: + noise_count += 1 + else: + cluster_id = result['cluster'] + if cluster_id not in cluster_stats: + cluster_stats[cluster_id] = 0 + cluster_stats[cluster_id] += 1 + + # Prepare output data + output_data = { + "method": "DBSCAN_FILTERED", + "original_n_clusters": self.clustering_results['n_clusters'], + "original_n_samples": self.clustering_results['n_samples'], + "filtered_n_samples": len(filtered_results), + "filtering_criteria": { + "cluster_selection_ratio": 0.5, + "center_points_ratio": 0.5, # 50% of selected points from center + "border_points_ratio": 0.5, # 50% of selected points from border + "noise_points": "all_selected" + }, + "cluster_statistics": cluster_stats, + "noise_points": noise_count, + "results": filtered_results + } + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=4, ensure_ascii=False) + + print("\n" + "="*60) + print("FILTERING SUMMARY") + print("="*60) + print(f"Original samples: {self.clustering_results['n_samples']}") + print(f"Filtered samples: {len(filtered_results)}") + print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}") + print("\nCluster breakdown:") + for cluster_id, count in sorted(cluster_stats.items()): + print(f" Cluster {cluster_id}: {count} points") + print(f" Noise points: {noise_count} points") + print(f"\nFiltered results saved to: {output_path}") + + return output_path + + def create_filepath_list(self, filtered_results, output_txt_path=None): + """Create a simple text file with filtered filepaths""" + if output_txt_path is None: + base_name = Path(self.clustering_results_path).stem + output_txt_path = f"{base_name}_filtered_filepaths.txt" + + filepaths = [result['filepath'] for result in filtered_results] + + with open(output_txt_path, 'w', encoding='utf-8') as f: + for filepath in filepaths: + f.write(f"{filepath}\n") + + print(f"Filepath list saved to: {output_txt_path}") + return output_txt_path + + +def main(): + parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results") + parser.add_argument("--embeddings_path", required=True, + help="Path to embeddings JSON file") + parser.add_argument("--clustering_results_path", required=True, + help="Path to DBSCAN clustering results JSON file") + parser.add_argument("--output_path", + help="Output path for filtered results (optional)") + parser.add_argument("--create_filepath_list", action="store_true", + help="Also create a simple text file with filtered filepaths") + + args = parser.parse_args() + + # Validate input files exist + if not os.path.exists(args.embeddings_path): + print(f"Error: Embeddings file not found: {args.embeddings_path}") + return + + if not os.path.exists(args.clustering_results_path): + print(f"Error: Clustering results file not found: {args.clustering_results_path}") + return + + # Initialize filter + filter_obj = DBSCANFilter(args.embeddings_path, args.clustering_results_path) + + # Load data + filter_obj.load_data() + + # Filter clusters + filtered_results = filter_obj.filter_all_clusters() + + # Save results + filter_obj.save_filtered_results(filtered_results, args.output_path) + + # Create filepath list if requested + if args.create_filepath_list: + filter_obj.create_filepath_list(filtered_results) + + print("\nFiltering completed successfully!") + + +if __name__ == "__main__": + main() diff --git a/filter/dbscan_v3.py b/filter/dbscan_v3.py new file mode 100644 index 0000000..ffad6b9 --- /dev/null +++ b/filter/dbscan_v3.py @@ -0,0 +1,352 @@ +#!/usr/bin/env python3 +""" +DBSCAN Clustering Filter + +Filters clustering results based on specific criteria, parameterized via CLI: +- For each cluster: select a configurable ratio of points (selection_ratio) + - A configurable portion from center region (center_ratio) + - A configurable portion from border region (border_ratio) +- All noise points are selected +- Uses cosine distance metric + +CLI parameters added: + --selection_ratio (float, default 0.5) + --center_ratio (float, default 0.5) + --border_ratio (float, default 0.5) + +Example: + python dbscan_v3.py \ + --embeddings_path embeddings.json \ + --clustering_results_path dbscan_results.json \ + --selection_ratio 0.4 --center_ratio 0.6 --border_ratio 0.4 +""" + +import json +import numpy as np +from sklearn.preprocessing import normalize +from sklearn.metrics.pairwise import cosine_distances +import argparse +import os +from pathlib import Path +import random + + +class DBSCANFilter: + def __init__(self, embeddings_path, clustering_results_path, + selection_ratio=0.5, center_ratio=0.5, border_ratio=0.5): + """Initialize DBSCAN filter + + Args: + embeddings_path: Path to embeddings JSON file + clustering_results_path: Path to DBSCAN clustering results JSON + selection_ratio: Ratio of total cluster points to consider selecting + center_ratio: Ratio applied within center region (relative scaling) + border_ratio: Ratio applied within border region (relative scaling) + """ + self.embeddings_path = embeddings_path + self.clustering_results_path = clustering_results_path + self.embeddings = None + self.embeddings_normalized = None + self.clustering_results = None + self.filepath_to_embedding = {} + self.selection_ratio = selection_ratio + self.center_ratio = center_ratio + self.border_ratio = border_ratio + + def load_data(self): + """Load embeddings and clustering results""" + print("Loading embeddings...") + with open(self.embeddings_path, 'r') as f: + embeddings_data = json.load(f) + + # Create mapping from filepath to embedding + embeddings_list = [] + filepaths = [] + for item in embeddings_data: + self.filepath_to_embedding[item['filepath']] = item['embedding'] + embeddings_list.append(item['embedding']) + filepaths.append(item['filepath']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + self.embeddings_normalized = normalize(self.embeddings, norm='l2') + print(f"Loaded {len(embeddings_list)} embeddings") + + print("Loading clustering results...") + with open(self.clustering_results_path, 'r') as f: + self.clustering_results = json.load(f) + + print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, " + f"{self.clustering_results['n_samples']} samples") + + def group_by_clusters(self): + """Group data points by cluster labels""" + clusters = {} + noise_points = [] + + for result in self.clustering_results['results']: + cluster_id = result['cluster'] + filepath = result['filepath'] + if 'is_noise' in result: + is_noise = result['is_noise'] + else: + is_noise = False + + if is_noise or cluster_id == -1: + noise_points.append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + else: + if cluster_id not in clusters: + clusters[cluster_id] = [] + clusters[cluster_id].append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath] + }) + + return clusters, noise_points + + def calculate_cluster_centroid(self, cluster_points): + """Calculate centroid of a cluster using normalized embeddings""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # For cosine distance, centroid is the normalized mean + centroid = np.mean(embeddings_normalized, axis=0) + centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0] + + return centroid_normalized + + def calculate_cosine_distances_to_centroid(self, cluster_points, centroid): + """Calculate cosine distances from each point to cluster centroid""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # Calculate cosine distances to centroid + distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten() + + return distances + + def filter_cluster(self, cluster_points): + """Lọc điểm trong một cluster dựa trên các tham số đã cấu hình.""" + if not cluster_points: + return [] + + selection_ratio = self.selection_ratio + center_ratio = self.center_ratio + border_ratio = self.border_ratio + + total_points = len(cluster_points) + num_to_select = max(15, int(total_points * selection_ratio)) + if num_to_select >= total_points and selection_ratio != 1: + return cluster_points + + centroid = self.calculate_cluster_centroid(cluster_points) + distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + point_distance_pairs = list(zip(cluster_points, distances)) + point_distance_pairs.sort(key=lambda x: x[1]) + + dis = 0.1 # ngưỡng khoảng cách để phân loại center / border + all_center_points = [p for p, d in point_distance_pairs if d < dis] + all_border_points = [p for p, d in point_distance_pairs if d >= dis] + + print(f"Number of center points (distance < {dis}): {len(all_center_points)}") + print(f"Number of border points (distance >= {dis}): {len(all_border_points)}") + + n_center = len(all_center_points) + n_border = len(all_border_points) + + if n_center > 0: + center_count = max(1, int(n_center * center_ratio * selection_ratio)) + center_count = min(center_count, n_center) + else: + center_count = 0 + + if n_border < 70: + border_count = n_border + else: + border_count = max(0, int(n_border * border_ratio * selection_ratio)) + border_count = min(border_count, n_border) + + random.seed(42) + selected_points = [] + if center_count > 0: + selected_points.extend(random.sample(all_center_points, center_count)) + if border_count > 0: + selected_points.extend(random.sample(all_border_points, border_count)) + + print( + f"Cluster with {total_points} points -> selected {len(selected_points)} points " + f"({center_count} center + {border_count} border)" + ) + return selected_points + + def filter_all_clusters(self): + """Filter all clusters according to the specified criteria""" + print("\n" + "="*60) + print("FILTERING DBSCAN CLUSTERING RESULTS") + print("="*60) + + clusters, noise_points = self.group_by_clusters() + + print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points") + + filtered_results = [] + + # Process each cluster + for cluster_id, cluster_points in clusters.items(): + print(f"\nProcessing Cluster {cluster_id}:") + filtered_points = self.filter_cluster(cluster_points) + + # Add cluster information + for point in filtered_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': cluster_id, + 'is_noise': False, + 'selection_type': 'cluster_filtered' + }) + + # Add all noise points + print(f"\nAdding all {len(noise_points)} noise points...") + n_noise = len(noise_points) + noise_count = max(0, int(n_noise * self.selection_ratio)) + random.seed(42) + selected_noise_points = random.sample(noise_points, noise_count) + + for point in selected_noise_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': -1, + 'is_noise': True, + 'selection_type': 'noise' + }) + + return filtered_results + + def save_filtered_results(self, filtered_results, output_path=None): + """Save filtered results to JSON file""" + if output_path is None: + # Generate output filename based on input + base_name = Path(self.clustering_results_path).stem + output_path = f"{base_name}_filtered.json" + + # Create summary statistics + cluster_stats = {} + noise_count = 0 + + for result in filtered_results: + if result['is_noise']: + noise_count += 1 + else: + cluster_id = result['cluster'] + if cluster_id not in cluster_stats: + cluster_stats[cluster_id] = 0 + cluster_stats[cluster_id] += 1 + + # Prepare output data + output_data = { + "method": "DBSCAN_FILTERED", + "original_n_clusters": self.clustering_results['n_clusters'], + "original_n_samples": self.clustering_results['n_samples'], + "filtered_n_samples": len(filtered_results), + "filtering_criteria": { + "cluster_selection_ratio": self.selection_ratio, + "center_points_ratio": self.center_ratio, + "border_points_ratio": self.border_ratio, + "noise_points": "all_selected" + }, + "cluster_statistics": cluster_stats, + "noise_points": noise_count, + "results": filtered_results + } + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=4, ensure_ascii=False) + + print("\n" + "="*60) + print("FILTERING SUMMARY") + print("="*60) + print(f"Original samples: {self.clustering_results['n_samples']}") + print(f"Filtered samples: {len(filtered_results)}") + print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}") + print("\nCluster breakdown:") + for cluster_id, count in sorted(cluster_stats.items()): + print(f" Cluster {cluster_id}: {count} points") + print(f" Noise points: {noise_count} points") + print(f"\nFiltered results saved to: {output_path}") + + return output_path + + def create_filepath_list(self, filtered_results, output_txt_path=None): + """Create a simple text file with filtered filepaths""" + if output_txt_path is None: + base_name = Path(self.clustering_results_path).stem + output_txt_path = f"{base_name}_filtered_filepaths.txt" + + filepaths = [result['filepath'] for result in filtered_results] + + with open(output_txt_path, 'w', encoding='utf-8') as f: + for filepath in filepaths: + f.write(f"{filepath}\n") + + print(f"Filepath list saved to: {output_txt_path}") + return output_txt_path + + +def main(): + parser = argparse.ArgumentParser(description="Filter DBSCAN clustering results") + parser.add_argument("--embeddings_path", required=True, + help="Path to embeddings JSON file") + parser.add_argument("--clustering_results_path", required=True, + help="Path to DBSCAN clustering results JSON file") + parser.add_argument("--output_path", + help="Output path for filtered results (optional)") + parser.add_argument("--create_filepath_list", action="store_true", + help="Also create a simple text file with filtered filepaths") + parser.add_argument("--selection_ratio", type=float, default=0.5, + help="Overall ratio of points to sample per cluster (default: 0.5). Minimum 15 points enforced.") + parser.add_argument("--center_ratio", type=float, default=0.5, + help="Relative ratio applied to center region when sampling (default: 0.5)") + parser.add_argument("--border_ratio", type=float, default=0.5, + help="Relative ratio applied to border region when sampling (default: 0.5)") + + args = parser.parse_args() + + # Validate input files exist + if not os.path.exists(args.embeddings_path): + print(f"Error: Embeddings file not found: {args.embeddings_path}") + return + + if not os.path.exists(args.clustering_results_path): + print(f"Error: Clustering results file not found: {args.clustering_results_path}") + return + + # Initialize filter + # Initialize filter with user-provided ratios + filter_obj = DBSCANFilter( + args.embeddings_path, + args.clustering_results_path, + selection_ratio=args.selection_ratio, + center_ratio=args.center_ratio, + border_ratio=args.border_ratio + ) + + # Load data + filter_obj.load_data() + + # Filter clusters + filtered_results = filter_obj.filter_all_clusters() + + # Save results + filter_obj.save_filtered_results(filtered_results, args.output_path) + + # Create filepath list if requested + if args.create_filepath_list: + filter_obj.create_filepath_list(filtered_results) + + print("\nFiltering completed successfully!") + + +if __name__ == "__main__": + main() diff --git a/filter/fillter_all.py b/filter/fillter_all.py new file mode 100644 index 0000000..dcfd42b --- /dev/null +++ b/filter/fillter_all.py @@ -0,0 +1,376 @@ +#!/usr/bin/env python3 +""" +Universal Clustering Filter + +Filters clustering results for multiple algorithms: +- DBSCAN: handles noise points, uses density-based selection +- GMM: uses probability-based selection, no noise points +- K-Means: standard centroid-based selection + +Uses cosine distance metric for all calculations. +""" + +import json +import numpy as np +from sklearn.preprocessing import normalize +from sklearn.metrics.pairwise import cosine_distances +import argparse +import os +from pathlib import Path + + +class UniversalClusterFilter: + def __init__(self, embeddings_path, clustering_results_path): + """ + Initialize universal cluster filter + + Args: + embeddings_path: Path to embeddings JSON file + clustering_results_path: Path to clustering results JSON + """ + self.embeddings_path = embeddings_path + self.clustering_results_path = clustering_results_path + self.embeddings = None + self.embeddings_normalized = None + self.clustering_results = None + self.filepath_to_embedding = {} + self.algorithm = None + + def load_data(self): + """Load embeddings and clustering results""" + print("Loading embeddings...") + with open(self.embeddings_path, 'r') as f: + embeddings_data = json.load(f) + + # Create mapping from filepath to embedding + embeddings_list = [] + filepaths = [] + for item in embeddings_data: + self.filepath_to_embedding[item['filepath']] = item['embedding'] + embeddings_list.append(item['embedding']) + filepaths.append(item['filepath']) + + self.embeddings = np.array(embeddings_list, dtype=np.float32) + self.embeddings_normalized = normalize(self.embeddings, norm='l2') + print(f"Loaded {len(embeddings_list)} embeddings") + + print("Loading clustering results...") + with open(self.clustering_results_path, 'r') as f: + self.clustering_results = json.load(f) + + # Detect algorithm type + self.algorithm = self.clustering_results.get('method', 'UNKNOWN') + print(f"Detected algorithm: {self.algorithm}") + print(f"Loaded clustering results: {self.clustering_results['n_clusters']} clusters, " + f"{self.clustering_results['n_samples']} samples") + + def group_by_clusters(self): + """Group data points by cluster labels (algorithm-agnostic)""" + clusters = {} + noise_points = [] + + for result in self.clustering_results['results']: + cluster_id = result['cluster'] + filepath = result['filepath'] + + # Check for noise points (DBSCAN specific) + is_noise = result.get('is_noise', False) + + if is_noise or cluster_id == -1: + noise_points.append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath], + 'metadata': result + }) + else: + if cluster_id not in clusters: + clusters[cluster_id] = [] + clusters[cluster_id].append({ + 'filepath': filepath, + 'embedding': self.filepath_to_embedding[filepath], + 'metadata': result + }) + + return clusters, noise_points + + def calculate_cluster_centroid(self, cluster_points): + """Calculate centroid of a cluster using normalized embeddings""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # For cosine distance, centroid is the normalized mean + centroid = np.mean(embeddings_normalized, axis=0) + centroid_normalized = normalize(centroid.reshape(1, -1), norm='l2')[0] + + return centroid_normalized + + def calculate_cosine_distances_to_centroid(self, cluster_points, centroid): + """Calculate cosine distances from each point to cluster centroid""" + embeddings = np.array([point['embedding'] for point in cluster_points]) + embeddings_normalized = normalize(embeddings, norm='l2') + + # Calculate cosine distances to centroid + distances = cosine_distances(embeddings_normalized, centroid.reshape(1, -1)).flatten() + + return distances + + def filter_cluster_standard(self, cluster_points, selection_ratio=0.5): + """ + Standard filtering: 25% center + 75% border of selected points + """ + if len(cluster_points) == 0: + return [] + + # Calculate how many points to select + total_points = len(cluster_points) + num_to_select = max(1, int(total_points * selection_ratio)) + + # If we need to select all or almost all points, just return all + if num_to_select >= total_points: + return cluster_points + + # Calculate centroid + centroid = self.calculate_cluster_centroid(cluster_points) + + # Calculate distances to centroid + distances = self.calculate_cosine_distances_to_centroid(cluster_points, centroid) + + # Create list of (point, distance) pairs + point_distance_pairs = list(zip(cluster_points, distances)) + + # Sort by distance (closest to furthest from centroid) + point_distance_pairs.sort(key=lambda x: x[1]) + + # Calculate how many points to select from center and border + center_count = max(1, int(num_to_select * 0.25)) # 25% from center + border_count = num_to_select - center_count # 75% from border + + selected_points = [] + + # Select center points (closest to centroid) + center_points = [pair[0] for pair in point_distance_pairs[:center_count]] + for point in center_points: + point['selection_type'] = 'center' + selected_points.extend(center_points) + + # Select border points (furthest from centroid) + if border_count > 0: + border_points = [pair[0] for pair in point_distance_pairs[-border_count:]] + for point in border_points: + point['selection_type'] = 'border' + selected_points.extend(border_points) + + print(f"Cluster with {total_points} points -> selected {len(selected_points)} points " + f"({center_count} center + {border_count} border)") + + return selected_points + + def filter_cluster_gmm(self, cluster_points, selection_ratio=0.5): + """ + GMM-specific filtering: consider probability scores if available + """ + if len(cluster_points) == 0: + return [] + + # Check if we have probability scores + has_probabilities = any('probability' in point['metadata'] for point in cluster_points) + + if has_probabilities: + # Use probability-based selection + total_points = len(cluster_points) + num_to_select = max(1, int(total_points * selection_ratio)) + + if num_to_select >= total_points: + return cluster_points + + # Sort by probability (highest confidence first) + sorted_points = sorted(cluster_points, + key=lambda x: x['metadata'].get('probability', 0), + reverse=True) + + # Take top probability points + selected_points = sorted_points[:num_to_select] + for point in selected_points: + point['selection_type'] = 'high_probability' + + print(f"GMM Cluster with {total_points} points -> selected {len(selected_points)} points " + f"(top probability)") + + return selected_points + else: + # Fall back to standard filtering + return self.filter_cluster_standard(cluster_points, selection_ratio) + + def filter_all_clusters(self, selection_ratio=0.5): + """Filter all clusters according to algorithm-specific criteria""" + print("\n" + "="*60) + print(f"FILTERING {self.algorithm} CLUSTERING RESULTS") + print("="*60) + + clusters, noise_points = self.group_by_clusters() + + print(f"Found {len(clusters)} clusters and {len(noise_points)} noise points") + + filtered_results = [] + + # Process each cluster + for cluster_id, cluster_points in clusters.items(): + print(f"\nProcessing Cluster {cluster_id}:") + + # Choose filtering method based on algorithm + if self.algorithm.upper() == 'GMM' or 'GAUSSIAN' in self.algorithm.upper(): + filtered_points = self.filter_cluster_gmm(cluster_points, selection_ratio) + else: + filtered_points = self.filter_cluster_standard(cluster_points, selection_ratio) + + # Add cluster information + for point in filtered_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': cluster_id, + 'is_noise': False, + 'selection_type': point.get('selection_type', 'cluster_filtered'), + 'original_metadata': point['metadata'] + }) + + # Add all noise points (DBSCAN only) + if noise_points: + print(f"\nAdding all {len(noise_points)} noise points...") + for point in noise_points: + filtered_results.append({ + 'filepath': point['filepath'], + 'cluster': -1, + 'is_noise': True, + 'selection_type': 'noise', + 'original_metadata': point['metadata'] + }) + + return filtered_results + + def save_filtered_results(self, filtered_results, output_path=None): + """Save filtered results to JSON file""" + if output_path is None: + base_name = Path(self.clustering_results_path).stem + output_path = f"{base_name}_filtered.json" + + # Create summary statistics + cluster_stats = {} + noise_count = 0 + selection_type_stats = {} + + for result in filtered_results: + # Cluster stats + if result['is_noise']: + noise_count += 1 + else: + cluster_id = result['cluster'] + if cluster_id not in cluster_stats: + cluster_stats[cluster_id] = 0 + cluster_stats[cluster_id] += 1 + + # Selection type stats + sel_type = result['selection_type'] + selection_type_stats[sel_type] = selection_type_stats.get(sel_type, 0) + 1 + + # Prepare output data + output_data = { + "method": f"{self.algorithm}_FILTERED", + "original_algorithm": self.algorithm, + "original_n_clusters": self.clustering_results['n_clusters'], + "original_n_samples": self.clustering_results['n_samples'], + "filtered_n_samples": len(filtered_results), + "filtering_criteria": { + "cluster_selection_ratio": 0.5, + "center_points_ratio": 0.25, + "border_points_ratio": 0.75, + "noise_points": "all_selected" if noise_count > 0 else "none_present" + }, + "cluster_statistics": cluster_stats, + "selection_type_statistics": selection_type_stats, + "noise_points": noise_count, + "results": filtered_results + } + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=4, ensure_ascii=False) + + print("\n" + "="*60) + print("FILTERING SUMMARY") + print("="*60) + print(f"Algorithm: {self.algorithm}") + print(f"Original samples: {self.clustering_results['n_samples']}") + print(f"Filtered samples: {len(filtered_results)}") + print(f"Reduction ratio: {len(filtered_results)/self.clustering_results['n_samples']:.2%}") + print("\nCluster breakdown:") + for cluster_id, count in sorted(cluster_stats.items()): + print(f" Cluster {cluster_id}: {count} points") + if noise_count > 0: + print(f" Noise points: {noise_count} points") + print("\nSelection type breakdown:") + for sel_type, count in selection_type_stats.items(): + print(f" {sel_type}: {count} points") + print(f"\nFiltered results saved to: {output_path}") + + return output_path + + def create_filepath_list(self, filtered_results, output_txt_path=None): + """Create a simple text file with filtered filepaths""" + if output_txt_path is None: + base_name = Path(self.clustering_results_path).stem + output_txt_path = f"{base_name}_filtered_filepaths.txt" + + filepaths = [result['filepath'] for result in filtered_results] + + with open(output_txt_path, 'w', encoding='utf-8') as f: + for filepath in filepaths: + f.write(f"{filepath}\n") + + print(f"Filepath list saved to: {output_txt_path}") + return output_txt_path + + +def main(): + parser = argparse.ArgumentParser(description="Universal filter for clustering results") + parser.add_argument("--embeddings_path", required=True, + help="Path to embeddings JSON file") + parser.add_argument("--clustering_results_path", required=True, + help="Path to clustering results JSON file") + parser.add_argument("--output_path", + help="Output path for filtered results (optional)") + parser.add_argument("--selection_ratio", type=float, default=0.5, + help="Ratio of points to select from each cluster (default: 0.5)") + parser.add_argument("--create_filepath_list", action="store_true", + help="Also create a simple text file with filtered filepaths") + + args = parser.parse_args() + + # Validate input files exist + if not os.path.exists(args.embeddings_path): + print(f"Error: Embeddings file not found: {args.embeddings_path}") + return + + if not os.path.exists(args.clustering_results_path): + print(f"Error: Clustering results file not found: {args.clustering_results_path}") + return + + # Initialize filter + filter_obj = UniversalClusterFilter(args.embeddings_path, args.clustering_results_path) + + # Load data + filter_obj.load_data() + + # Filter clusters + filtered_results = filter_obj.filter_all_clusters(args.selection_ratio) + + # Save results + filter_obj.save_filtered_results(filtered_results, args.output_path) + + # Create filepath list if requested + if args.create_filepath_list: + filter_obj.create_filepath_list(filtered_results) + + print("\nFiltering completed successfully!") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/filter/generate_dbscan_results_filter.py b/filter/generate_dbscan_results_filter.py new file mode 100644 index 0000000..83ea40b --- /dev/null +++ b/filter/generate_dbscan_results_filter.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python3 +"""Generate filtered DBSCAN results based on images listed in 009_label_data_sample_seed_42.json. + +Rules (v1): keep only entries whose base filename (before __scale_* suffix or extension) +can be matched with any "image" field in the label JSON. Matching strategy: + - Extract filename without extension from each DBSCAN result filepath. + - Remove trailing pattern: __scale_ + - For each image name from labels, keep the DBSCAN entry if: + * cleaned_filename startswith(image_name) OR + * image_name startswith(cleaned_filename) OR + * image_name in cleaned_filename OR cleaned_filename in image_name + - This permissive logic aims to cover variations; can be tightened later. + +Outputs file: dbscan_results_filter.json in the same folder with structure: +{ + "method": "DBSCAN_FILTERED", + "n_samples": , + "n_filtered": , + "results": [ ...filtered entries... ] +} +""" +from __future__ import annotations +import json +import re +from pathlib import Path + +BASE_DIR = Path(__file__).resolve().parent +# LABEL_FILE = BASE_DIR / "009_label_data_sample_seed_42.json" +LABEL_FILE = BASE_DIR / "015_label_data_sample_seed_1997.json" +LABEL_FILE = BASE_DIR / "008_label_data_sample_seed_1997.json" + + + +# Prefer the clustering output in cluster/ directory relative to project root +# DBSCAN_FILE_CANDIDATES = [ +# BASE_DIR.parent / "cluster" / "dbscan_results.json", +# BASE_DIR.parent / "result" / "dbscan_results.json", +# ] + +DBSCAN_FILE_CANDIDATES = [ + BASE_DIR.parent / "cluster" / "dbscan_results.json", + BASE_DIR.parent / "result" / "dbscan_results.json", +] +OUTPUT_FILE = BASE_DIR / "dbscan_results_filter_008.json" + +def load_first_existing(paths): + for p in paths: + if p.exists(): + return p + raise FileNotFoundError("No dbscan_results.json file found in expected locations: " + ", ".join(map(str, paths))) + +def normalize_image_name(name: str) -> str: + return name.strip() + +def cleaned_filename(path: str) -> str: + stem = Path(path).stem # remove extension + stem = re.sub(r"_[0-9]+_scale_.*$", "", stem) + return stem + +def build_image_name_set(label_path: Path) -> set[str]: + data = json.loads(label_path.read_text()) + names: set[str] = set() + for entry in data: + if not isinstance(entry, dict): + continue + img = entry.get("image") + if isinstance(img, str) and img.strip(): + names.add(normalize_image_name(img)) + return names + +def match_entry(core_name: str, image_names: set[str]) -> bool: + for img in image_names: + if core_name.startswith(img) or img.startswith(core_name): + return True + if img in core_name or core_name in img: + return True + return False + +def main(): + dbscan_file = load_first_existing(DBSCAN_FILE_CANDIDATES) + image_names = build_image_name_set(LABEL_FILE) + raw = json.loads(dbscan_file.read_text()) + results = raw.get("results", []) + filtered = [] + for rec in results: + filepath = rec.get("filepath") + if not filepath: + continue + core = cleaned_filename(filepath) + if match_entry(core, image_names): + filtered.append(rec) + out = { + "method": "DBSCAN_FILTERED", + "n_samples": len(results), + "n_filtered": len(filtered), + "match_ratio": round(len(filtered) / len(results), 4) if results else 0.0, + "source_file": str(dbscan_file), + "label_source": str(LABEL_FILE), + "results": filtered, + } + OUTPUT_FILE.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + print(f"Wrote {len(filtered)} filtered results to {OUTPUT_FILE}") + +if __name__ == "__main__": # pragma: no cover + main() diff --git a/filter/gmm_final_results_aic_20250805_150636_filtered_filepaths.txt b/filter/gmm_final_results_aic_20250805_150636_filtered_filepaths.txt new file mode 100644 index 0000000..034a900 --- /dev/null +++ b/filter/gmm_final_results_aic_20250805_150636_filtered_filepaths.txt @@ -0,0 +1,1454 @@ +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/172415668127717241473179067067247267468124453_1_scale_1.0.jpg +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/172415667786117241475973227470455758207037834_1_scale_1.0.jpg +/home/nguyendc/model-factory/Finetuning-Automation/etc/data/media/docai_mgp_facture_v2_0/172415668128117241471676118541084374852557430_1_scale_1.0.jpg 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directory diff --git a/filter/random_sample_data.py b/filter/random_sample_data.py new file mode 100644 index 0000000..58c4ad9 --- /dev/null +++ b/filter/random_sample_data.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 +""" +Script để random pick 50% samples từ label_data.json với 3 seeds khác nhau +và lưu thành 3 files riêng biệt. +""" + +import json +import random +import os +from typing import List, Dict, Any + +def load_json_data(file_path: str) -> List[Dict[str, Any]]: + """Load dữ liệu từ file JSON""" + try: + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + print(f"✅ Đã load {len(data)} samples từ {file_path}") + return data + except FileNotFoundError: + print(f"❌ Không tìm thấy file: {file_path}") + return [] + except json.JSONDecodeError as e: + print(f"❌ Lỗi decode JSON: {e}") + return [] + +def random_sample_data(data: List[Dict[str, Any]], seed: int, sample_ratio: float = 0.5, sample_size = None) -> List[Dict[str, Any]]: + """Random pick samples từ data với seed và tỷ lệ sampling cụ thể""" + random.seed(seed) + if sample_size is None: + sample_size = int(len(data) * sample_ratio) + sampled_data = random.sample(data, sample_size) + print(f"🎲 Seed {seed}: Đã sample {len(sampled_data)}/{len(data)} samples ({sample_ratio*100:.1f}%)") + return sampled_data + +def save_json_data(data: List[Dict[str, Any]], file_path: str) -> bool: + """Lưu dữ liệu vào file JSON""" + try: + with open(file_path, 'w', encoding='utf-8') as f: + json.dump(data, f, ensure_ascii=False, indent=2) + print(f"💾 Đã lưu {len(data)} samples vào {file_path}") + return True + except Exception as e: + print(f"❌ Lỗi khi lưu file {file_path}: {e}") + return False + +def main(): + """Hàm chính""" + # Đường dẫn file input + input_file = "label_data.json" + + # Các seeds cần sử dụng + seeds = [1997, 42, 2025] + # seeds = [42] + + # Tỷ lệ sampling (50%) + + # 8, 9, 10 + sample_ratio = 0.5 + + # 11,12,13 + sample_ratio = 0.3 + + # 16 + # sample_ratio = 0.01 + + # 15, 17, 18 + sample_size = 200 + + print("🔄 Bắt đầu quá trình random sampling...") + print("=" * 50) + + # Load dữ liệu gốc + original_data = load_json_data(input_file) + if not original_data: + print("❌ Không thể load dữ liệu. Thoát chương trình.") + return + + print(f"📊 Tổng số samples trong file gốc: {len(original_data)}") + print(f"🎯 Sẽ sample {sample_ratio*100:.1f}% = {int(len(original_data) * sample_ratio)} samples") + print("=" * 50) + + # Thực hiện sampling với từng seed + for seed in seeds: + print(f"\n🌱 Xử lý với seed: {seed}") + + # Random sample data + sampled_data = random_sample_data(original_data, seed, sample_ratio, sample_size = sample_size) + + # Tạo tên file output + output_file = f"label_data_sample_seed_{seed}.json" + + # Lưu file + success = save_json_data(sampled_data, output_file) + + if success: + print(f"✅ Hoàn thành sampling với seed {seed}") + else: + print(f"❌ Lỗi khi xử lý seed {seed}") + + print("\n" + "=" * 50) + print("🎉 Hoàn thành toàn bộ quá trình!") + print("\n📁 Các file đã được tạo:") + for seed in seeds: + output_file = f"016_no_fine_tune_label_data_sample_seed_{seed}.json" + if os.path.exists(output_file): + print(f" ✅ {output_file}") + else: + print(f" ❌ {output_file} (không tồn tại)") + +if __name__ == "__main__": + main() diff --git a/filter/run_filter.sh b/filter/run_filter.sh new file mode 100644 index 0000000..71c2f40 --- /dev/null +++ b/filter/run_filter.sh @@ -0,0 +1,138 @@ +#!/bin/bash + +# Example script to run DBSCAN filtering +# Make sure to update the paths according to your data + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +# OUTPUT_PATH="dbscan_filtered_results.json" #0.5 of data, center 0.5, 0.5 border +# OUTPUT_PATH="dbscan_filtered_results_v2.json" #0.5 of data, 0.25 center, 0.75 border +# OUTPUT_PATH="dbscan_filtered_results_v3.json" #0.75 of data, center 0.25 border 0.75 + + + +# echo "Running DBSCAN filtering..." +# echo "Embeddings: $EMBEDDINGS_PATH" +# echo "Clustering results: $CLUSTERING_RESULTS_PATH" +# echo "Output: $OUTPUT_PATH" + +# python dbscan.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list + +# echo "Filtering completed!" + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_final_results_bic_20250805_150636.json" +# OUTPUT_PATH="gmm_best_by_BIC_filtered_results.json" #0.75 of data, center 0.25 border 0.75 + + +# echo "Running DBSCAN filtering..." +# echo "Embeddings: $EMBEDDINGS_PATH" +# echo "Clustering results: $CLUSTERING_RESULTS_PATH" +# echo "Output: $OUTPUT_PATH" + +# python dbscan.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list + + + +# echo "Filtering completed!" + + + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +# OUTPUT_PATH="014_re_run_dbscan_filtered_results_temp.json" #0.75 of data, center 0.25 border 0.75 +# python dbscan_v2.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list + + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_final_results_bic_20250805_150636.json" +# OUTPUT_PATH="015_gmm_best_by_BIC_filtered_results_temp.json" #0.75 of data, center 0.25 border 0.75 +# python dbscan_v2.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +# OUTPUT_DIR="dbscan_v3_out_clusters" + +# # python dbscan_v3_only_one_cluster.py \ +# # --embeddings_path "$EMBEDDINGS_PATH" \ +# # --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# # --output_dir "$OUTPUT_DIR" \ + +# python dbscan_only_one_cluster.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --save_full_clusters \ +# --clusters_output_dir per_clusters + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +# OUTPUT_PATH="026_dbscan_v3_filtered_results_temp.json" + +# python dbscan_v3.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list \ +# --selection_ratio 1.0 --center_ratio 0.5 --border_ratio 0.5 + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +# OUTPUT_PATH="027_dbscan_v3_filtered_results_temp.json" + +# python dbscan_v3.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list \ +# --selection_ratio 1.0 --center_ratio 0.25 --border_ratio 0.75 + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +# OUTPUT_PATH="019_dbscan_v3_filtered_results_temp.json" + +# python dbscan_v3.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list \ +# --selection_ratio 0.12 --center_ratio 0.25 --border_ratio 0.75 + + +# EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +# CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/gmm_final_results_bic_20250805_150636.json" +# OUTPUT_PATH="028_gmm_best_by_BIC_filtered_results.json" #0.75 of data, center 0.25 border 0.75 + +# python dbscan_v3.py \ +# --embeddings_path "$EMBEDDINGS_PATH" \ +# --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ +# --output_path "$OUTPUT_PATH" \ +# --create_filepath_list \ +# --selection_ratio 1.0 --center_ratio 0.25 --border_ratio 0.75 + + +EMBEDDINGS_PATH="/home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json" +CLUSTERING_RESULTS_PATH="/home/nguyendc/sonnh/embedding-clustering/cluster/dbscan_results.json" +OUTPUT_PATH="029_dbscan_v3_filtered_results_temp_30.json" #0.75 of data, center 0.25 border 0.75 + +python dbscan_v3.py \ + --embeddings_path "$EMBEDDINGS_PATH" \ + --clustering_results_path "$CLUSTERING_RESULTS_PATH" \ + --output_path "$OUTPUT_PATH" \ + --create_filepath_list \ + --selection_ratio 0.6 --center_ratio 0.5 --border_ratio 0.5 diff --git a/filter/shuffle.py b/filter/shuffle.py new file mode 100644 index 0000000..0b75484 --- /dev/null +++ b/filter/shuffle.py @@ -0,0 +1,12 @@ +import random + +import json + +with open('/home/nguyendc/sonnh/embedding-clustering/filter/008_label_data_sample_seed_1997.json', 'r') as f: + data = json.load(f) + +random.seed(8272025) +random.shuffle(data) + +with open('/home/nguyendc/sonnh/embedding-clustering/filter/032_shuffle_008.json', 'w') as f: + json.dump(data, f) \ No newline at end of file -- 2.49.1

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