139 lines
3.5 KiB
Plaintext
139 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "499abdf6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# 设置 Hugging Face 镜像\n",
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"os.environ[\"HF_ENDPOINT\"] = \"https://hf-mirror.com\"\n",
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"\n",
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"# 如果是完全离线模式\n",
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"# os.environ[\"HF_HUB_OFFLINE\"] = \"1\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "08edf2a5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "183e4df62da84c9389f36d6e011f6be0",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"EmbeddingAtlasWidget()"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from embedding_atlas.widget import EmbeddingAtlasWidget \n",
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"import pandas as pd \n",
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" \n",
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"df = pd.read_csv('data/drugbank_pre_filtered_mordred_qed_id_selfies.csv') \n",
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" \n",
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"# 计算投影 \n",
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"from embedding_atlas.projection import compute_text_projection \n",
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"compute_text_projection(df, text=\"smiles\", \n",
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" x=\"projection_x\", y=\"projection_y\", neighbors=\"neighbors\" \n",
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") \n",
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" \n",
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"# 创建并显示 widget \n",
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"widget = EmbeddingAtlasWidget(df, text=\"smiles\", \n",
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" x=\"projection_x\", y=\"projection_y\", neighbors=\"neighbors\" \n",
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") \n",
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" \n",
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"# 确保在单独的单元格中显示 \n",
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"widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "be723d90",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b67dc99218614138bf6657716f3739f8",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"EmbeddingAtlasWidget()"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd \n",
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"from embedding_atlas.projection import compute_text_projection \n",
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"from embedding_atlas.widget import EmbeddingAtlasWidget \n",
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" \n",
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"# 读取您的数据 \n",
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"df = pd.read_csv('data/drugbank_pre_filtered_mordred_qed_id_selfies.csv') \n",
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" \n",
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"# 计算 SMILES 的文本嵌入和 2D 投影 \n",
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"compute_text_projection( \n",
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" df, \n",
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" text=\"smiles\", # 使用 SMILES 列作为文本输入 \n",
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" x=\"projection_x\", # X 坐标列名 \n",
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" y=\"projection_y\", # Y 坐标列名 \n",
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" neighbors=\"neighbors\" # 最近邻列名 \n",
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") \n",
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"\n",
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"# 在 Jupyter 中显示可视化 \n",
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"EmbeddingAtlasWidget(df, text=\"smiles\", \n",
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" x=\"projection_x\", y=\"projection_y\", \n",
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" neighbors=\"neighbors\")\n",
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e3bb412",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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