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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 数据聚合：「输入药物 SMILES，输出基因表达 + 细胞形态」的多模态虚拟细胞模型，配对不齐，训练集不均衡。\n",
"\n",
"## 策略有3种：\n",
"\n",
"### 1 是按 SMILES 聚合，对每个 SMILES 在每个模态上 取平均或中位数（或更复杂的 embedding 聚合）。\n",
"\n",
"### 2 是多实例学习（Multiple Instance Learning, MIL）：思路：把一个 SMILES 的所有 replicate 当作一个 bag，模型学习 bag-level 对齐。\n",
"\n",
"### 3 是不对齐 replicate，随机采样：思路：训练时，每个 batch 从两个模态中随机采样该 SMILES 的一个 replicate，逐步逼近两个分布。"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import os\n",
"import h5py\n",
"\n",
"def load_from_HDF(fname):\n",
" \"\"\"Load data from a HDF5 file to a dictionary.\"\"\"\n",
" data = dict()\n",
" with h5py.File(fname, 'r') as f:\n",
" for key in f:\n",
" data[key] = np.asarray(f[key])\n",
" if isinstance(data[key][0], np.bytes_):\n",
" data[key] = data[key].astype(str)\n",
" return data\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"CP_path = './Processed_Paired_CP_KeepRep.h5'\n",
"GE_path = './Processed_Paired_GE_KeepRep.h5'\n",
"\n",
"CP_CSV = load_from_HDF(CP_path)\n",
"GE_CSV = load_from_HDF(GE_path)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'canonical_smiles': array(['CCCOc1cc(N)ccc1C(=O)OCCN(CC)CC',\n",
" 'C=C(C)[C@@H]1CCC(C)=C[C@H]1c1c(OC)cc(C)cc1OC',\n",
" 'COc1cc(O)cc(/C=C/c2ccccc2)c1', ...,\n",
" 'C[C@@H]1CN([C@@H](C)CO)C(=O)CCCn2cc(nn2)CO[C@H]1CN(C)C(=O)c1ccncc1',\n",
" 'C[C@H](CO)N1C[C@H](C)[C@@H](CN(C)C(=O)c2ccncc2)OCc2cn(nn2)CCCC1=O',\n",
" 'C[C@@H]1CN([C@@H](C)CO)C(=O)CCCn2nncc2CO[C@H]1CN(C)Cc1ccc2c(c1)OCCN2C'],\n",
" dtype='<U227'),\n",
" 'control': array([[-0.15425342, 0.20473444, 0.04847282, ..., 0.22247523,\n",
" 0.41372228, 0.09528223],\n",
" [-0.15425342, 0.20473444, 0.04847282, ..., 0.22247523,\n",
" 0.41372228, 0.09528223],\n",
" [-0.15425342, 0.20473444, 0.04847282, ..., 0.22247523,\n",
" 0.41372228, 0.09528223],\n",
" ...,\n",
" [ 0.08794986, 0.04630309, 0.0316207 , ..., -0.18811361,\n",
" -0.08507317, -0.19418037],\n",
" [ 0.08794986, 0.04630309, 0.0316207 , ..., -0.18811361,\n",
" -0.08507317, -0.19418037],\n",
" [ 0.08794986, 0.04630309, 0.0316207 , ..., -0.18811361,\n",
" -0.08507317, -0.19418037]], dtype=float32),\n",
" 'dose': array([ 6.05, 10. , 10. , ..., 10.41, 10.14, 10.22], dtype=float32),\n",
" 'target': array([[ 1.7987275 , -1.0678272 , -1.3908694 , ..., -2.2844946 ,\n",
" -1.4380481 , -0.4035441 ],\n",
" [ 0.3030874 , 0.57465357, -0.18462732, ..., -1.4371198 ,\n",
" -0.8579998 , 0.20614216],\n",
" [-0.7811053 , 0.5069796 , 0.9922175 , ..., 0.3974406 ,\n",
" -0.13609461, 0.42841423],\n",
" ...,\n",
" [ 1.466404 , -1.2910391 , -0.19340979, ..., -2.0167367 ,\n",
" -1.2818346 , -1.4399147 ],\n",
" [ 1.0264087 , -0.42999583, -1.681699 , ..., -1.4709337 ,\n",
" -0.9747103 , -1.4030336 ],\n",
" [ 1.4138446 , -0.93786097, -1.3868744 , ..., -1.2337868 ,\n",
" -0.58811694, -0.94757 ]], dtype=float32)}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CP_CSV"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'canonical_smiles': array(['CC(C)[C@H](CO)Nc1nc(Nc2cc(N)cc(Cl)c2)c2ncn(C(C)C)c2n1',\n",
" 'Ic1cccc(CSc2nnc(-c3ccncc3)o2)c1',\n",
" 'O=C(Nc1ccncc1)Nc1c(Cl)cc(Cl)cc1Cl', ...,\n",
" 'O=C(O)c1ccc(-c2ccc(/C=c3\\\\sc4nc5ccccc5n4c3=O)o2)cc1',\n",
" 'O=S(=O)(c1cccc2cncc(C(F)F)c12)N1CCCNCC1',\n",
" 'COc1ccc(S(=O)(=O)N(C)C[C@@H]2Oc3c(NC(=O)Nc4cccc5ccccc45)cccc3C(=O)N([C@@H](C)CO)C[C@H]2C)cc1'],\n",
" dtype='<U227'),\n",
" 'control': array([[ 0.305876 , -0.59514517, -0.21795662, ..., 0.20644613,\n",
" -0.07786681, 0.14617148],\n",
" [ 0.305876 , -0.59514517, -0.21795662, ..., 0.20644613,\n",
" -0.07786681, 0.14617148],\n",
" [ 0.305876 , -0.59514517, -0.21795662, ..., 0.20644613,\n",
" -0.07786681, 0.14617148],\n",
" ...,\n",
" [ 0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [ 0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [ 0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ]], dtype=float32),\n",
" 'dose': array([50. , 12.7, 50. , ..., 20. , 40. , 20. ], dtype=float32),\n",
" 'target': array([[-0.7184085 , -1.4126618 , -1.7777987 , ..., 1.1767539 ,\n",
" 2.0003324 , 2.4972866 ],\n",
" [ 0.25343022, -1.2717106 , -0.0366732 , ..., 1.8126812 ,\n",
" -0.6341776 , -1.2102923 ],\n",
" [-0.43184918, 1.0067521 , -0.00558909, ..., -0.26218557,\n",
" -0.8643179 , 1.4752281 ],\n",
" ...,\n",
" [-0.67632276, 0.7587029 , 0.3990235 , ..., 2.8850307 ,\n",
" -0.92035365, -1.8870968 ],\n",
" [-2.1307466 , 2.1573339 , -1.6117429 , ..., 0.566304 ,\n",
" -1.1584423 , -2.332463 ],\n",
" [ 2.0216608 , 1.4105401 , -1.0605472 , ..., -0.1974064 ,\n",
" -0.30219036, -1.2475965 ]], dtype=float32)}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GE_CSV"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((86844, 741), (57835, 977))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CP_CSV['target'].shape, GE_CSV['target'].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 按照第一种方式直接 mean"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ 聚合后的数据已保存到 ./Aggregated_CP_GE.h5\n"
]
}
],
"source": [
"import h5py\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"def aggregate_modalities(CP, GE, out_path):\n",
" # 转成 DataFrame 方便聚合\n",
" CP_df = pd.DataFrame({\n",
" \"SMILES\": CP[\"canonical_smiles\"],\n",
" \"control\": list(CP[\"control\"]),\n",
" \"target\": list(CP[\"target\"])\n",
" })\n",
" GE_df = pd.DataFrame({\n",
" \"SMILES\": GE[\"canonical_smiles\"],\n",
" \"control\": list(GE[\"control\"]),\n",
" \"target\": list(GE[\"target\"])\n",
" })\n",
"\n",
" # groupby + mean pooling\n",
" CP_grouped = CP_df.groupby(\"SMILES\").agg(lambda x: np.nanmean(np.stack(x), axis=0)).reset_index()\n",
" GE_grouped = GE_df.groupby(\"SMILES\").agg(lambda x: np.nanmean(np.stack(x), axis=0)).reset_index()\n",
"\n",
" # merge 保证对齐\n",
" merged = pd.merge(CP_grouped, GE_grouped, on=\"SMILES\", how=\"inner\", suffixes=(\"_CP\", \"_GE\"))\n",
"\n",
" # 转 numpy array 并替换剩余 NaN\n",
" def clean_array(arr_list):\n",
" arr = np.stack(arr_list.to_numpy())\n",
" arr = np.nan_to_num(arr, nan=0.0, posinf=0.0, neginf=0.0) # 替换 NaN/Inf\n",
" return arr.astype(np.float32)\n",
"\n",
" smiles_array = merged[\"SMILES\"].to_numpy(dtype=\"S\")\n",
" control_CP_array = clean_array(merged[\"control_CP\"])\n",
" target_CP_array = clean_array(merged[\"target_CP\"])\n",
" control_GE_array = clean_array(merged[\"control_GE\"])\n",
" target_GE_array = clean_array(merged[\"target_GE\"])\n",
"\n",
" # 存 HDF5\n",
" with h5py.File(out_path, \"w\") as f:\n",
" f.create_dataset(\"canonical_smiles\", data=smiles_array)\n",
" f.create_dataset(\"control_CP\", data=control_CP_array)\n",
" f.create_dataset(\"target_CP\", data=target_CP_array)\n",
" f.create_dataset(\"control_GE\", data=control_GE_array)\n",
" f.create_dataset(\"target_GE\", data=target_GE_array)\n",
"\n",
" print(f\"✅ 聚合后的数据已保存到 {out_path}\")\n",
"\n",
"# 用法\n",
"# root = '/home/bob/boom/VCBench/data/MVC/CDRP-BBBC047-Bray/'\n",
"CP_path = './Processed_Paired_CP.h5'\n",
"GE_path = './Processed_Paired_GE.h5'\n",
"\n",
"CP_CSV = load_from_HDF(CP_path)\n",
"GE_CSV = load_from_HDF(GE_path)\n",
"\n",
"out_path = './Aggregated_CP_GE.h5'\n",
"aggregate_modalities(CP_CSV, GE_CSV, out_path)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'canonical_smiles': array(['Brc1c(CSc2nc3ccccc3s2)nc2ncccn12', 'Brc1c(NC2=NCCN2)ccc2nccnc12',\n",
" 'Brc1ccc(CSc2nnc(-c3ccccn3)n2Cc2ccco2)cc1', ...,\n",
" 'c1nc(CC2CCNCC2)c[nH]1', 'c1nc(CCCC2CCNCC2)c[nH]1',\n",
" 'c1ncn(CCOCCOc2ccc3c(c2)CCC3)n1'], dtype='<U227'),\n",
" 'control_CP': array([[-0.15487705, 0.11688805, -0.06308393, ..., 0.24665973,\n",
" 0.18521263, 0.24594025],\n",
" [-0.1634329 , 0.15811297, -0.03295023, ..., 0.2232287 ,\n",
" 0.18645084, 0.20041683],\n",
" [-0.13265091, 0.11912386, -0.04087081, ..., 0.23031679,\n",
" 0.15957166, 0.2795726 ],\n",
" ...,\n",
" [-0.16390067, 0.1292826 , -0.02731108, ..., 0.2356141 ,\n",
" 0.20089747, 0.22413647],\n",
" [-0.1634329 , 0.15811297, -0.03295023, ..., 0.2232287 ,\n",
" 0.18645084, 0.20041683],\n",
" [-0.1903376 , 0.16364928, -0.02507655, ..., 0.2462861 ,\n",
" 0.21280092, 0.2474828 ]], dtype=float32),\n",
" 'control_GE': array([[-0.11589041, 0.09791137, -0.10001642, ..., -0.04151818,\n",
" 0.00727702, -0.21572095],\n",
" [ 0.01338525, -0.02699645, 0.0147262 , ..., -0.01713243,\n",
" -0.02412532, 0.08308113],\n",
" [ 0.02007259, 0.03755556, -0.02430556, ..., -0.18552697,\n",
" -0.0794616 , -0.020855 ],\n",
" ...,\n",
" [ 0.02062 , -0.00183125, -0.13683268, ..., -0.17978694,\n",
" 0.21799538, 0.17114288],\n",
" [ 0.01338525, -0.02699645, 0.0147262 , ..., -0.01713243,\n",
" -0.02412532, 0.08308113],\n",
" [-0.06893609, -0.02563992, 0.06738283, ..., 0.07980002,\n",
" -0.11235818, -0.03411045]], dtype=float32),\n",
" 'target_CP': array([[ 0.38674092, -0.62070274, -0.62418985, ..., 0.44180262,\n",
" 0.42434335, -1.2392346 ],\n",
" [ 0.31655684, -0.23943731, 0.18836854, ..., -0.05182697,\n",
" 0.2953927 , 1.5328658 ],\n",
" [-1.4637322 , 1.873155 , 2.345407 , ..., 2.3001804 ,\n",
" 2.5974057 , -0.5258522 ],\n",
" ...,\n",
" [-0.22405519, 0.21074149, -0.0310891 , ..., -0.20554903,\n",
" -0.28407168, -0.17878559],\n",
" [ 0.36180133, -0.36012647, 0.18524444, ..., -0.5896245 ,\n",
" -0.28982347, -0.09377167],\n",
" [-0.11151703, 0.40477425, 0.04721215, ..., 1.3899186 ,\n",
" 0.70356005, 0.23341325]], dtype=float32),\n",
" 'target_GE': array([[ 0.09065 , 0.0786 , -0.06155001, ..., -0.1333 ,\n",
" 0.097875 , 0.15165001],\n",
" [-0.07302775, 0.0460825 , -0.02222175, ..., 0.17655626,\n",
" -0.154201 , 0.033125 ],\n",
" [-0.03446667, -0.01266666, 0.04358333, ..., 0.27253333,\n",
" -0.06081 , 0.28026667],\n",
" ...,\n",
" [ 0.22472501, 0.20904249, -0.2872 , ..., -0.22035149,\n",
" 0.5861085 , 0.278331 ],\n",
" [ 0.01292225, 0.07139751, -0.13613375, ..., -0.16344374,\n",
" 0.053449 , -0.200645 ],\n",
" [ 0.14866668, -0.054 , 0.04613333, ..., 0.22286667,\n",
" -0.09675 , -0.01868333]], dtype=float32)}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out_path = './Aggregated_CP_GE.h5'\n",
"\n",
"agg_data = load_from_HDF(out_path)\n",
"agg_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((20341, 775), (20341, 775), (20341, 977), (20341, 977))"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_data['control_CP'].shape, agg_data['target_CP'].shape, agg_data['control_GE'].shape, agg_data['target_GE'].shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'O=C(C[C@@H]1CC[C@@H]2[C@H](COC[C@H](O)CN2Cc2cc(F)cc(F)c2)O1)Nc1nccs1' in agg_data['canonical_smiles']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 复杂点，样本级别的配对"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"加载源数据...\n",
"开始创建多模态配对数据集...\n",
"Cell Painting 唯一化合物数: 20341\n",
"Gene Expression 唯一化合物数: 20341\n",
"多模态共有化合物数 (Intersection): 20341\n",
"生成配对样本总数: 223805\n",
"正在组装最终矩阵...\n",
"✅ 最终数据集已保存至: ./Paired_CP_GE_Dataset.h5\n",
"------------------------------\n",
"Dataset Shapes:\n",
"canonical_smiles: (223805,)\n",
"control_CP: (223805, 741)\n",
"target_CP: (223805, 741)\n",
"control_GE: (223805, 977)\n",
"target_GE: (223805, 977)\n",
"CP_dose: (223805,)\n",
"GE_dose: (223805,)\n"
]
}
],
"source": [
"import h5py\n",
"import numpy as np\n",
"import pandas as pd\n",
"from collections import defaultdict\n",
"\n",
"# ==========================================\n",
"# 0. 辅助函数：读取 HDF5\n",
"# ==========================================\n",
"def load_from_HDF(fname):\n",
" data = dict()\n",
" with h5py.File(fname, 'r') as f:\n",
" for key in f:\n",
" data[key] = np.asarray(f[key])\n",
" # 处理字符串解码\n",
" if data[key].dtype.kind == 'S':\n",
" data[key] = data[key].astype(str)\n",
" return data\n",
"\n",
"# ==========================================\n",
"# 1. 核心逻辑：创建稳健配对数据集\n",
"# ==========================================\n",
"def create_robust_paired_dataset(CP_data, GE_data, out_path, max_samples_per_smiles=5, seed=42):\n",
" \"\"\"\n",
" CP_data, GE_data: 包含 'canonical_smiles', 'control', 'target', 'dose' 的字典\n",
" max_samples_per_smiles: 每个模态最多采样的重复数\n",
" seed: 随机种子，保证配对结果可复现\n",
" \"\"\"\n",
" print(\"开始创建多模态配对数据集...\")\n",
" \n",
" # [新增] 检查输入数据是否包含 dose\n",
" if 'dose' not in CP_data or 'dose' not in GE_data:\n",
" raise ValueError(\"输入数据缺少 'dose' 字段，请检查上一步是否正确保存了剂量信息。\")\n",
"\n",
" # [新增] 设置随机种子\n",
" np.random.seed(seed)\n",
" \n",
" # --- A. 构建索引映射 ---\n",
" cp_smiles_to_indices = defaultdict(list)\n",
" ge_smiles_to_indices = defaultdict(list)\n",
" \n",
" for idx, smiles in enumerate(CP_data['canonical_smiles']):\n",
" cp_smiles_to_indices[smiles].append(idx)\n",
" \n",
" for idx, smiles in enumerate(GE_data['canonical_smiles']):\n",
" ge_smiles_to_indices[smiles].append(idx)\n",
" \n",
" # --- B. 找交集 ---\n",
" common_smiles = set(cp_smiles_to_indices.keys()) & set(ge_smiles_to_indices.keys())\n",
" print(f\"Cell Painting 唯一化合物数: {len(cp_smiles_to_indices)}\")\n",
" print(f\"Gene Expression 唯一化合物数: {len(ge_smiles_to_indices)}\")\n",
" print(f\"多模态共有化合物数 (Intersection): {len(common_smiles)}\")\n",
" \n",
" # --- C. 生成配对索引 ---\n",
" paired_indices = []\n",
" \n",
" for i, smiles in enumerate(common_smiles):\n",
" # 获取该 SMILES 的所有索引\n",
" cp_indices = cp_smiles_to_indices[smiles]\n",
" ge_indices = ge_smiles_to_indices[smiles]\n",
" \n",
" # 限制采样数量\n",
" n_cp = min(max_samples_per_smiles, len(cp_indices))\n",
" n_ge = min(max_samples_per_smiles, len(ge_indices))\n",
" \n",
" # 无放回随机采样\n",
" sampled_cp_indices = np.random.choice(cp_indices, n_cp, replace=False)\n",
" sampled_ge_indices = np.random.choice(ge_indices, n_ge, replace=False)\n",
" \n",
" # 笛卡尔积配对\n",
" for cp_idx in sampled_cp_indices:\n",
" for ge_idx in sampled_ge_indices:\n",
" paired_indices.append({\n",
" 'smiles': smiles,\n",
" 'cp_idx': cp_idx,\n",
" 'ge_idx': ge_idx\n",
" })\n",
" \n",
" print(f\"生成配对样本总数: {len(paired_indices)}\")\n",
"\n",
" # --- D. 组装数据 ---\n",
" n_samples = len(paired_indices)\n",
" \n",
" # 获取特征维度\n",
" cp_feat_dim = CP_data['target'].shape[1]\n",
" ge_feat_dim = GE_data['target'].shape[1]\n",
" \n",
" # 预分配内存\n",
" final_data = {\n",
" 'canonical_smiles': np.empty(n_samples, dtype='S200'),\n",
" 'control_CP': np.empty((n_samples, cp_feat_dim), dtype=np.float32),\n",
" 'target_CP': np.empty((n_samples, cp_feat_dim), dtype=np.float32),\n",
" 'control_GE': np.empty((n_samples, ge_feat_dim), dtype=np.float32),\n",
" 'target_GE': np.empty((n_samples, ge_feat_dim), dtype=np.float32),\n",
" # [新增] 两个模态的剂量\n",
" 'CP_dose': np.empty(n_samples, dtype=np.float32), \n",
" 'GE_dose': np.empty(n_samples, dtype=np.float32)\n",
" }\n",
" \n",
" print(\"正在组装最终矩阵...\")\n",
" for i, item in enumerate(paired_indices):\n",
" cp_idx = item['cp_idx']\n",
" ge_idx = item['ge_idx']\n",
" \n",
" final_data['canonical_smiles'][i] = item['smiles'].encode('utf-8')\n",
" \n",
" # 填入 CP 数据\n",
" final_data['control_CP'][i] = CP_data['control'][cp_idx]\n",
" final_data['target_CP'][i] = CP_data['target'][cp_idx]\n",
" final_data['CP_dose'][i] = CP_data['dose'][cp_idx] # [新增]\n",
" \n",
" # 填入 GE 数据\n",
" final_data['control_GE'][i] = GE_data['control'][ge_idx]\n",
" final_data['target_GE'][i] = GE_data['target'][ge_idx]\n",
" final_data['GE_dose'][i] = GE_data['dose'][ge_idx] # [新增]\n",
" \n",
" # --- E. 数据清洗与保存 ---\n",
" # 检查并处理 NaN/Inf (Dose 通常不需要处理，但如果有 NaN 也顺便处理一下安全)\n",
" check_cols = ['control_CP', 'target_CP', 'control_GE', 'target_GE', 'CP_dose', 'GE_dose']\n",
" \n",
" for key in check_cols:\n",
" if np.isnan(final_data[key]).any() or np.isinf(final_data[key]).any():\n",
" print(f\"警告: {key} 包含 NaN 或 Inf，正在填充 0...\")\n",
" final_data[key] = np.nan_to_num(final_data[key], nan=0.0, posinf=0.0, neginf=0.0)\n",
"\n",
" # 保存\n",
" with h5py.File(out_path, 'w') as f:\n",
" for key, val in final_data.items():\n",
" f.create_dataset(key, data=val)\n",
" \n",
" print(f\"✅ 最终数据集已保存至: {out_path}\")\n",
" print(\"-\" * 30)\n",
" print(f\"Dataset Shapes:\")\n",
" for k, v in final_data.items():\n",
" print(f\"{k}: {v.shape}\")\n",
"\n",
"# ==========================================\n",
"# 2. 执行流程\n",
"# ==========================================\n",
"# 定义输入输出路径\n",
"cp_input = './Processed_Paired_CP_KeepRep.h5'\n",
"ge_input = './Processed_Paired_GE_KeepRep.h5'\n",
"final_output = './Paired_CP_GE_Dataset.h5'\n",
"\n",
"# 加载数据\n",
"print(\"加载源数据...\")\n",
"cp_data = load_from_HDF(cp_input)\n",
"ge_data = load_from_HDF(ge_input)\n",
"\n",
"# 执行配对\n",
"create_robust_paired_dataset(cp_data, ge_data, final_output, max_samples_per_smiles=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'CP_dose': array([10.65, 10.65, 10.65, ..., 10. , 10. , 10. ], dtype=float32),\n",
" 'GE_dose': array([10.6501, 10.6501, 10.6501, ..., 10. , 10. , 10. ],\n",
" dtype=float32),\n",
" 'canonical_smiles': array(['C[C@H](CO)N1C[C@H](C)[C@H](CN(C)Cc2ccncc2)Oc2ccc(NS(C)(=O)=O)cc2CC1=O',\n",
" 'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)Cc2ccncc2)Oc2ccc(NS(C)(=O)=O)cc2CC1=O',\n",
" 'C[C@H](CO)N1C[C@H](C)[C@H](CN(C)Cc2ccncc2)Oc2ccc(NS(C)(=O)=O)cc2CC1=O',\n",
" ..., 'CN(C)C(=O)n1nnnc1Cc1ccc(-c2ccccc2)cc1',\n",
" 'CN(C)C(=O)n1nnnc1Cc1ccc(-c2ccccc2)cc1',\n",
" 'CN(C)C(=O)n1nnnc1Cc1ccc(-c2ccccc2)cc1'], dtype='<U200'),\n",
" 'control_CP': array([[-0.13953187, 0.06501261, -0.00263713, ..., 0.08332227,\n",
" 0.12313318, -0.02047802],\n",
" [-0.13953187, 0.06501261, -0.00263713, ..., 0.08332227,\n",
" 0.12313318, -0.02047802],\n",
" [-0.13953187, 0.06501261, -0.00263713, ..., 0.08332227,\n",
" 0.12313318, -0.02047802],\n",
" ...,\n",
" [ 0.07532343, 0.01590352, 0.11149302, ..., 0.09762042,\n",
" -0.0291416 , 0.18638068],\n",
" [ 0.06642057, -0.06762539, 0.07449123, ..., -0.2643804 ,\n",
" -0.08335885, -0.47207668],\n",
" [ 0.06642057, -0.06762539, 0.07449123, ..., -0.2643804 ,\n",
" -0.08335885, -0.47207668]], dtype=float32),\n",
" 'control_GE': array([[ 0.24454208, 0.60980904, 0.60357696, ..., 0.18017063,\n",
" -0.88657326, 0.01677261],\n",
" [-0.56268126, 0.15758337, -0.13244417, ..., -0.6394515 ,\n",
" 1.1245222 , 1.1233364 ],\n",
" [ 0.44537392, 0.6973298 , -1.5441636 , ..., -0.425022 ,\n",
" -0.23347344, 0.57313305],\n",
" ...,\n",
" [ 0.65565985, 0.11522385, 0.9597033 , ..., 0.43092778,\n",
" -0.15957022, 0.03049997],\n",
" [ 0.7688196 , 0.9452282 , -0.80648696, ..., -0.3607209 ,\n",
" 0.51043606, -1.139512 ],\n",
" [ 0.65565985, 0.11522385, 0.9597033 , ..., 0.43092778,\n",
" -0.15957022, 0.03049997]], dtype=float32),\n",
" 'target_CP': array([[-0.7322785 , -0.1324034 , -0.92490584, ..., 0.0871982 ,\n",
" 0.19238813, -0.2730067 ],\n",
" [-0.7322785 , -0.1324034 , -0.92490584, ..., 0.0871982 ,\n",
" 0.19238813, -0.2730067 ],\n",
" [-0.7322785 , -0.1324034 , -0.92490584, ..., 0.0871982 ,\n",
" 0.19238813, -0.2730067 ],\n",
" ...,\n",
" [-6.900763 , 10. , 10. , ..., 8.523304 ,\n",
" 8.528054 , 9.481978 ],\n",
" [-6.2097163 , 9.116677 , 10. , ..., 5.122239 ,\n",
" 5.808592 , 1.9759494 ],\n",
" [-6.2097163 , 9.116677 , 10. , ..., 5.122239 ,\n",
" 5.808592 , 1.9759494 ]], dtype=float32),\n",
" 'target_GE': array([[-0.13318162, -2.3996215 , -0.60478145, ..., -1.7717572 ,\n",
" -0.7603032 , 1.6145478 ],\n",
" [ 0.71842533, -2.600954 , 0.4446499 , ..., -0.6659924 ,\n",
" -1.0708426 , -0.40832376],\n",
" [-1.034582 , 1.0713423 , -4.108202 , ..., 0.01961743,\n",
" 1.2580668 , 0.00507223],\n",
" ...,\n",
" [ 0.11101682, 1.4468411 , 0.9668232 , ..., 0.43233392,\n",
" -0.3482708 , -1.8351849 ],\n",
" [-0.41256198, -0.05275529, -3.0842478 , ..., -0.2453646 ,\n",
" 1.7232248 , 3.0262802 ],\n",
" [ 0.11101682, 1.4468411 , 0.9668232 , ..., 0.43233392,\n",
" -0.3482708 , -1.8351849 ]], dtype=float32)}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。\n",
"\u001b[1;31m请查看单元格中的代码，以确定故障的可能原因。\n",
"\u001b[1;31m单击<a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>了解详细信息。\n",
"\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
]
}
],
"source": [
"out_path = './Paired_CP_GE_Dataset.h5'\n",
"\n",
"agg_data = load_from_HDF(out_path)\n",
"agg_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"共有化合物: 20341\n",
"总配对样本数: 242192\n",
"数据集形状:\n",
" control_CP: (242192, 775)\n",
" target_CP: (242192, 775)\n",
" control_GE: (242192, 977)\n",
" target_GE: (242192, 977)\n",
"✅ 配对数据集已保存到: ./Paired_CP_GE_Dataset.h5\n"
]
}
],
"source": [
"# import h5py\n",
"# import numpy as np\n",
"# import pandas as pd\n",
"# from collections import defaultdict\n",
"\n",
"# def create_paired_dataset(CP_data, GE_data, out_path):\n",
"# \"\"\"\n",
"# 创建样本级配对的多模态数据集\n",
"# 保持原始样本的完整性，只配对真正对应的样本\n",
"# \"\"\"\n",
" \n",
"# # 构建SMILES到索引列表的映射\n",
"# cp_smiles_to_indices = defaultdict(list)\n",
"# ge_smiles_to_indices = defaultdict(list)\n",
" \n",
"# for idx, smiles in enumerate(CP_data['canonical_smiles']):\n",
"# cp_smiles_to_indices[smiles].append(idx)\n",
" \n",
"# for idx, smiles in enumerate(GE_data['canonical_smiles']):\n",
"# ge_smiles_to_indices[smiles].append(idx)\n",
" \n",
"# # 找到共有的SMILES\n",
"# common_smiles = set(cp_smiles_to_indices.keys()) & set(ge_smiles_to_indices.keys())\n",
"# print(f\"共有化合物: {len(common_smiles)}\")\n",
" \n",
"# # 收集所有有效配对\n",
"# paired_samples = []\n",
" \n",
"# for smiles in common_smiles:\n",
"# cp_indices = cp_smiles_to_indices[smiles]\n",
"# ge_indices = ge_smiles_to_indices[smiles]\n",
" \n",
"# # 为每个SMILES创建所有可能的CP-GE配对\n",
"# for cp_idx in cp_indices:\n",
"# for ge_idx in ge_indices:\n",
"# paired_samples.append({\n",
"# 'canonical_smiles': smiles,\n",
"# 'cp_index': cp_idx,\n",
"# 'ge_index': ge_idx\n",
"# })\n",
" \n",
"# print(f\"总配对样本数: {len(paired_samples)}\")\n",
" \n",
"# # 创建最终的字典形数据集\n",
"# dataset_dict = {\n",
"# 'canonical_smiles': [],\n",
"# 'control_CP': [],\n",
"# 'target_CP': [], \n",
"# 'control_GE': [],\n",
"# 'target_GE': []\n",
"# }\n",
" \n",
"# for sample in paired_samples:\n",
"# dataset_dict['canonical_smiles'].append(sample['canonical_smiles'])\n",
"# dataset_dict['control_CP'].append(CP_data['control'][sample['cp_index']])\n",
"# dataset_dict['target_CP'].append(CP_data['target'][sample['cp_index']])\n",
"# dataset_dict['control_GE'].append(GE_data['control'][sample['ge_index']]) \n",
"# dataset_dict['target_GE'].append(GE_data['target'][sample['ge_index']])\n",
" \n",
"# # 转换为numpy数组\n",
"# final_dataset = {}\n",
"# final_dataset['canonical_smiles'] = np.array(dataset_dict['canonical_smiles'], dtype='S')\n",
"# final_dataset['control_CP'] = np.array(dataset_dict['control_CP'], dtype=np.float32)\n",
"# final_dataset['target_CP'] = np.array(dataset_dict['target_CP'], dtype=np.float32)\n",
"# final_dataset['control_GE'] = np.array(dataset_dict['control_GE'], dtype=np.float32)\n",
"# final_dataset['target_GE'] = np.array(dataset_dict['target_GE'], dtype=np.float32)\n",
" \n",
"# print(f\"数据集形状:\")\n",
"# print(f\" control_CP: {final_dataset['control_CP'].shape}\")\n",
"# print(f\" target_CP: {final_dataset['target_CP'].shape}\")\n",
"# print(f\" control_GE: {final_dataset['control_GE'].shape}\")\n",
"# print(f\" target_GE: {final_dataset['target_GE'].shape}\")\n",
" \n",
"# # 保存为HDF5\n",
"# with h5py.File(out_path, 'w') as f:\n",
"# for key, value in final_dataset.items():\n",
"# f.create_dataset(key, data=value)\n",
" \n",
"# print(f\"✅ 配对数据集已保存到: {out_path}\")\n",
"# return final_dataset\n",
"\n",
"# # 使用方案\n",
"# out_path = './Paired_CP_GE_Dataset.h5'\n",
"# paired_dataset = create_paired_dataset(CP_CSV, GE_CSV, out_path)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'canonical_smiles': array(['C[C@@H]1CCCCO[C@@H](CN(C)C(=O)c2ccccc2)[C@@H](C)CN([C@H](C)CO)C(=O)c2cc(N(C)C)ccc2O1',\n",
" 'C[C@@H]1CCCCO[C@@H](CN(C)C(=O)c2ccccc2)[C@@H](C)CN([C@H](C)CO)C(=O)c2cc(N(C)C)ccc2O1',\n",
" 'C[C@@H]1CCCCO[C@@H](CN(C)C(=O)c2ccccc2)[C@@H](C)CN([C@H](C)CO)C(=O)c2cc(N(C)C)ccc2O1',\n",
" ...,\n",
" 'C/C=C/c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1.C/C=C\\\\c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1',\n",
" 'C/C=C/c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1.C/C=C\\\\c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1',\n",
" 'C/C=C/c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1.C/C=C\\\\c1ccc2n(c1=O)C[C@H]1[C@H](CO)[C@@H](C(=O)N3CCCCC3)[C@@H]2N1C(=O)C1CCC1'],\n",
" dtype='<U227'),\n",
" 'control_CP': array([[-0.08486467, 0.1714288 , -0.05182531, ..., 0.12218629,\n",
" 0.21159805, 0.24014652],\n",
" [-0.08486467, 0.1714288 , -0.05182531, ..., 0.12218629,\n",
" 0.21159805, 0.24014652],\n",
" [-0.08486467, 0.1714288 , -0.05182531, ..., 0.12218629,\n",
" 0.21159805, 0.24014652],\n",
" ...,\n",
" [-0.2378668 , 0.23262355, -0.0475704 , ..., 0.21625724,\n",
" 0.23215774, 0.23364826],\n",
" [-0.2378668 , 0.23262355, -0.0475704 , ..., 0.21625724,\n",
" 0.23215774, 0.23364826],\n",
" [-0.2378668 , 0.23262355, -0.0475704 , ..., 0.21625724,\n",
" 0.23215774, 0.23364826]], dtype=float32),\n",
" 'control_GE': array([[-0.0330701 , 0.01753275, -0.05136025, ..., 0.06250588,\n",
" -0.0405625 , -0.19137537],\n",
" [ 0.01287525, 0.03997513, 0.035275 , ..., -0.04473713,\n",
" -0.1068325 , 0.03364687],\n",
" [ 0.04092 , -0.0524448 , -0.0302184 , ..., -0.045687 ,\n",
" 0.018924 , -0.026421 ],\n",
" ...,\n",
" [ 0.1941795 , -0.00547 , -0.2204975 , ..., -0.05459375,\n",
" 0.10817 , 0.011832 ],\n",
" [ 0.03131568, 0.1014732 , -0.214663 , ..., 0.111306 ,\n",
" -0.0012838 , 0.143236 ],\n",
" [-0.0118007 , -0.124132 , 0.111037 , ..., 0.1051161 ,\n",
" -0.275516 , -0.05782 ]], dtype=float32),\n",
" 'target_CP': array([[ 1.1871912 , -0.0034563 , 0.52810115, ..., -1.6746186 ,\n",
" -0.9638308 , 0.73771006],\n",
" [ 1.1871912 , -0.0034563 , 0.52810115, ..., -1.6746186 ,\n",
" -0.9638308 , 0.73771006],\n",
" [ 1.1871912 , -0.0034563 , 0.52810115, ..., -1.6746186 ,\n",
" -0.9638308 , 0.73771006],\n",
" ...,\n",
" [-0.45244193, -0.05774116, -0.9494869 , ..., -0.27629495,\n",
" -0.4013643 , -0.337052 ],\n",
" [-0.45244193, -0.05774116, -0.9494869 , ..., -0.27629495,\n",
" -0.4013643 , -0.337052 ],\n",
" [-0.45244193, -0.05774116, -0.9494869 , ..., -0.27629495,\n",
" -0.4013643 , -0.337052 ]], dtype=float32),\n",
" 'target_GE': array([[ 0.10045115, 0.0189385 , -0.136369 , ..., -0.115892 ,\n",
" 0.021835 , -0.240325 ],\n",
" [ 0.105046 , -0.199388 , 0.0151 , ..., -0.36268 ,\n",
" 0.01984 , 0.060748 ],\n",
" [ 0.17609 , 0.012886 , -0.05032 , ..., 0.045372 ,\n",
" 0.37968 , 0.180597 ],\n",
" ...,\n",
" [-0.120883 , 0.42693624, -0.11864 , ..., 0.14749 ,\n",
" -0.361165 , -0.1950255 ],\n",
" [ 0.013835 , -0.42966 , 0.396749 , ..., 0.25792 ,\n",
" -0.1543 , 0.03015 ],\n",
" [ 0.1609315 , -0.40984 , 0.028058 , ..., 0.31154 ,\n",
" -0.104255 , -0.16908 ]], dtype=float32)}"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out_path = './Paired_CP_GE_Dataset.h5'\n",
"\n",
"agg_data = load_from_HDF(out_path)\n",
"agg_data"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((223689, 775), (223689, 775), (223689, 977), (223689, 977))"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_data['control_CP'].shape, agg_data['target_CP'].shape, agg_data['control_GE'].shape, agg_data['target_GE'].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 限制配对的数量，更好收敛。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"开始创建稳健配对数据集...\n",
"共有化合物: 20341\n",
"处理进度: 0/20341\n",
"处理进度: 1000/20341\n",
"处理进度: 2000/20341\n",
"处理进度: 3000/20341\n",
"处理进度: 4000/20341\n",
"处理进度: 5000/20341\n",
"处理进度: 6000/20341\n",
"处理进度: 7000/20341\n",
"处理进度: 8000/20341\n",
"处理进度: 9000/20341\n",
"处理进度: 10000/20341\n",
"处理进度: 11000/20341\n",
"处理进度: 12000/20341\n",
"处理进度: 13000/20341\n",
"处理进度: 14000/20341\n",
"处理进度: 15000/20341\n",
"处理进度: 16000/20341\n",
"处理进度: 17000/20341\n",
"处理进度: 18000/20341\n",
"处理进度: 19000/20341\n",
"处理进度: 20000/20341\n",
"总配对样本数: 169287\n",
"数据收集进度: 0/169287\n",
"数据收集进度: 10000/169287\n",
"数据收集进度: 20000/169287\n",
"数据收集进度: 30000/169287\n",
"数据收集进度: 40000/169287\n",
"数据收集进度: 50000/169287\n",
"数据收集进度: 60000/169287\n",
"数据收集进度: 70000/169287\n",
"数据收集进度: 80000/169287\n",
"数据收集进度: 90000/169287\n",
"数据收集进度: 100000/169287\n",
"数据收集进度: 110000/169287\n",
"数据收集进度: 120000/169287\n",
"数据收集进度: 130000/169287\n",
"数据收集进度: 140000/169287\n",
"数据收集进度: 150000/169287\n",
"数据收集进度: 160000/169287\n",
"control_CP: 均值=0.0175, 标准差=0.3551, 形状=(169287, 775)\n",
"target_CP: 均值=-0.0107, 标准差=1.1325, 形状=(169287, 775)\n",
"control_GE: 均值=-0.0003, 标准差=0.1331, 形状=(169287, 977)\n",
"target_GE: 均值=-0.0007, 标准差=0.2798, 形状=(169287, 977)\n",
"✅ 稳健配对数据集已保存到: ./Paired_CP_GE_Dataset.h5\n"
]
}
],
"source": [
"import h5py\n",
"import numpy as np\n",
"import pandas as pd\n",
"from collections import defaultdict\n",
"\n",
"def create_robust_paired_dataset(CP_data, GE_data, out_path, max_pairs_per_smiles=20):\n",
" \"\"\"\n",
" 创建稳健的样本级配对数据集\n",
" 添加样本限制和数据处理\n",
" \"\"\"\n",
" \n",
" print(\"开始创建稳健配对数据集...\")\n",
" \n",
" # 构建SMILES到索引列表的映射\n",
" cp_smiles_to_indices = defaultdict(list)\n",
" ge_smiles_to_indices = defaultdict(list)\n",
" \n",
" for idx, smiles in enumerate(CP_data['canonical_smiles']):\n",
" cp_smiles_to_indices[smiles].append(idx)\n",
" \n",
" for idx, smiles in enumerate(GE_data['canonical_smiles']):\n",
" ge_smiles_to_indices[smiles].append(idx)\n",
" \n",
" # 找到共有的SMILES\n",
" common_smiles = set(cp_smiles_to_indices.keys()) & set(ge_smiles_to_indices.keys())\n",
" print(f\"共有化合物: {len(common_smiles)}\")\n",
" \n",
" # 收集所有有效配对（带采样限制）\n",
" paired_samples = []\n",
" \n",
" for i, smiles in enumerate(common_smiles):\n",
" # if i % 1000 == 0:\n",
" # print(f\"处理进度: {i}/{len(common_smiles)}\")\n",
" \n",
" cp_indices = cp_smiles_to_indices[smiles]\n",
" ge_indices = ge_smiles_to_indices[smiles]\n",
" \n",
" # 限制每个SMILES的最大配对数，避免样本爆炸\n",
" max_cp_samples = min(3, len(cp_indices))\n",
" max_ge_samples = min(3, len(ge_indices))\n",
" \n",
" # 随机采样\n",
" sampled_cp = np.random.choice(cp_indices, max_cp_samples, replace=False)\n",
" sampled_ge = np.random.choice(ge_indices, max_ge_samples, replace=False)\n",
" \n",
" # 创建配对\n",
" for cp_idx in sampled_cp:\n",
" for ge_idx in sampled_ge:\n",
" paired_samples.append({\n",
" 'canonical_smiles': smiles,\n",
" 'cp_index': cp_idx,\n",
" 'ge_index': ge_idx\n",
" })\n",
" \n",
" print(f\"总配对样本数: {len(paired_samples)}\")\n",
" \n",
" # 创建最终的字典形数据集\n",
" dataset_dict = {\n",
" 'canonical_smiles': [],\n",
" 'control_CP': [],\n",
" 'target_CP': [], \n",
" 'control_GE': [],\n",
" 'target_GE': []\n",
" }\n",
" \n",
" for i, sample in enumerate(paired_samples):\n",
" if i % 10000 == 0:\n",
" print(f\"数据收集进度: {i}/{len(paired_samples)}\")\n",
" \n",
" dataset_dict['canonical_smiles'].append(sample['canonical_smiles'])\n",
" dataset_dict['control_CP'].append(CP_data['control'][sample['cp_index']])\n",
" dataset_dict['target_CP'].append(CP_data['target'][sample['cp_index']])\n",
" dataset_dict['control_GE'].append(GE_data['control'][sample['ge_index']]) \n",
" dataset_dict['target_GE'].append(GE_data['target'][sample['ge_index']])\n",
" \n",
" # 转换为numpy数组并进行数据清洗\n",
" final_dataset = {}\n",
" final_dataset['canonical_smiles'] = np.array(dataset_dict['canonical_smiles'], dtype='S')\n",
" \n",
" # 数据清洗函数\n",
" def clean_and_validate_data(data_list, name):\n",
" data_array = np.array(data_list, dtype=np.float32)\n",
" \n",
" # 检查NaN和Inf\n",
" nan_count = np.isnan(data_array).sum()\n",
" inf_count = np.isinf(data_array).sum()\n",
" \n",
" if nan_count > 0 or inf_count > 0:\n",
" print(f\"警告: {name} 包含 {nan_count} NaN 和 {inf_count} Inf\")\n",
" data_array = np.nan_to_num(data_array, nan=0.0, posinf=1.0, neginf=-1.0)\n",
" \n",
" # 数据标准化\n",
" mean = data_array.mean()\n",
" std = data_array.std()\n",
" \n",
" print(f\"{name}: 均值={mean:.4f}, 标准差={std:.4f}, 形状={data_array.shape}\")\n",
" \n",
" return data_array\n",
" \n",
" final_dataset['control_CP'] = clean_and_validate_data(dataset_dict['control_CP'], 'control_CP')\n",
" final_dataset['target_CP'] = clean_and_validate_data(dataset_dict['target_CP'], 'target_CP')\n",
" final_dataset['control_GE'] = clean_and_validate_data(dataset_dict['control_GE'], 'control_GE')\n",
" final_dataset['target_GE'] = clean_and_validate_data(dataset_dict['target_GE'], 'target_GE')\n",
" \n",
" # 保存为HDF5\n",
" with h5py.File(out_path, 'w') as f:\n",
" for key, value in final_dataset.items():\n",
" f.create_dataset(key, data=value)\n",
" \n",
" print(f\"✅ 稳健配对数据集已保存到: {out_path}\")\n",
" return final_dataset\n",
"\n",
"\n",
"out_path = './Paired_CP_GE_Dataset.h5'\n",
"paired_dataset = create_robust_paired_dataset(CP_CSV, GE_CSV, out_path)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"out_path = './Paired_CP_GE_Dataset.h5'\n",
"\n",
"agg_data = load_from_HDF(out_path)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((169287, 775), (169287, 775), (169287, 977), (169287, 977))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg_data['control_CP'].shape, agg_data['target_CP'].shape, agg_data['control_GE'].shape, agg_data['target_GE'].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "boom",
"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": "3.11.10"
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"nbformat": 4,
"nbformat_minor": 2
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|