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"cells": [
{
"cell_type": "markdown",
"id": "389cd136",
"metadata": {},
"source": [
"# 处理异常数据\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "917fa110",
"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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cddb976e",
"metadata": {},
"outputs": [],
"source": [
"path47 = 'Aggregated_CP_GE.h5'\n",
"\n",
"# data36 = load_from_HDF(path36)\n",
"data36 = load_from_HDF(path47)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "92260f6e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1916, 602)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data36['control_CP'].shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "54e51b05",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"整个数组的数据范围：[-0.15427954494953156, 32876.36328125]\n",
"------------------------------\n",
"整个数组的数据范围：[-8.007139205932617, 370889.125]\n",
"------------------------------\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# 计算整个数组的最小值和最大值\n",
"data_min = data36['control_CP'].min()\n",
"data_max = data36['control_CP'].max()\n",
"\n",
"# 计算每列（即每个特征）的最小值和最大值\n",
"# axis=0 表示对每一列进行操作\n",
"min_per_column = np.min(data36['control_CP'], axis=0)\n",
"max_per_column = np.max(data36['control_CP'], axis=0)\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)\n",
"\n",
"# 计算整个数组的最小值和最大值\n",
"data_min = data36['target_CP'].min()\n",
"data_max = data36['target_CP'].max()\n",
"\n",
"# 计算每列（即每个特征）的最小值和最大值\n",
"# axis=0 表示对每一列进行操作\n",
"min_per_column = np.min(data36['target_CP'], axis=0)\n",
"max_per_column = np.max(data36['target_CP'], axis=0)\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bde80b4c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"整个数组的数据范围：[-0.059715718030929565, 0.10131344199180603]\n",
"------------------------------\n",
"整个数组的数据范围：[-4.208789825439453, 6.420098304748535]\n",
"------------------------------\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# 计算整个数组的最小值和最大值\n",
"data_min = data36['control_GE'].min()\n",
"data_max = data36['control_GE'].max()\n",
"\n",
"# 计算每列（即每个特征）的最小值和最大值\n",
"# axis=0 表示对每一列进行操作\n",
"min_per_column = np.min(data36['control_GE'], axis=0)\n",
"max_per_column = np.max(data36['control_GE'], axis=0)\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)\n",
"\n",
"# 计算整个数组的最小值和最大值\n",
"data_min = data36['target_GE'].min()\n",
"data_max = data36['target_GE'].max()\n",
"\n",
"# 计算每列（即每个特征）的最小值和最大值\n",
"# axis=0 表示对每一列进行操作\n",
"min_per_column = np.min(data36['target_GE'], axis=0)\n",
"max_per_column = np.max(data36['target_GE'], axis=0)\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)"
]
},
{
"cell_type": "markdown",
"id": "053cfaa7",
"metadata": {},
"source": [
"## 看看 cpg0016 的数值范围"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c81440ab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"整个数组的数据范围：[-1.2491892576217651, 1.3490736484527588]\n",
"------------------------------\n",
"整个数组的数据范围：[-6.057624816894531, 6.188350677490234]\n",
"------------------------------\n"
]
}
],
"source": [
"# cpg0016_data = '/home/bob/boom/VCBench/data/Image/cpg0016_data.h5'\n",
"\n",
"# # data36 = load_from_HDF(path36)\n",
"# cpg0016_data = load_from_HDF(cpg0016_data)\n",
"\n",
"# import numpy as np\n",
"\n",
"# # 计算整个数组的最小值和最大值\n",
"# data_min = cpg0016_data['control'].min()\n",
"# data_max = cpg0016_data['control'].max()\n",
"\n",
"# # 计算每列（即每个特征）的最小值和最大值\n",
"# # axis=0 表示对每一列进行操作\n",
"# min_per_column = np.min(cpg0016_data['control'], axis=0)\n",
"# max_per_column = np.max(cpg0016_data['control'], axis=0)\n",
"\n",
"# print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"# print(\"-\" * 30)\n",
"\n",
"# # 计算整个数组的最小值和最大值\n",
"# data_min = cpg0016_data['target'].min()\n",
"# data_max = cpg0016_data['target'].max()\n",
"\n",
"# # 计算每列（即每个特征）的最小值和最大值\n",
"# # axis=0 表示对每一列进行操作\n",
"# min_per_column = np.min(cpg0016_data['target'], axis=0)\n",
"# max_per_column = np.max(cpg0016_data['target'], axis=0)\n",
"\n",
"# print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"# print(\"-\" * 30)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7005461",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2b2f331e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"标准化后的整个数组的数据范围：[-19.69989776611328, 31.564712524414062]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# 假设 data36['target_CP'] 是你的原始数据, target_CP, control_CP\n",
"data_with_outlier = data36['target_CP']\n",
"\n",
"# 1. 异常值处理 - 使用截断法 (Clipping)\n",
"# 设定一个更保守的上限，例如基于分位数\n",
"q99 = np.percentile(data_with_outlier, 99)\n",
"clip_max = q99 * 2 # 或者直接用一个固定值，比如 5\n",
"data_clipped = np.clip(data_with_outlier, a_min=None, a_max=clip_max)\n",
"# 2. 数据标准化 - Z-score Normalization\n",
"# 计算每列（特征）的均值和标准差\n",
"mu = data_clipped.mean(axis=0)\n",
"sigma = data_clipped.std(axis=0)\n",
"\n",
"# 初始化一个与数据形状相同的零数组\n",
"data_normalized = np.zeros_like(data_clipped, dtype=float)\n",
"\n",
"# 找到标准差不为0的列\n",
"# 为了避免浮点数误差，使用一个非常小的阈值来检查\n",
"epsilon = 1e-8\n",
"nonzero_sigma_cols = sigma > epsilon\n",
"\n",
"# 对这些列进行零均值化\n",
"data_normalized[:, nonzero_sigma_cols] = (data_clipped[:, nonzero_sigma_cols] - mu[nonzero_sigma_cols]) / sigma[nonzero_sigma_cols]\n",
"\n",
"# 检查结果\n",
"data_min = data_normalized.min()\n",
"data_max = data_normalized.max()\n",
"\n",
"print(f\"\\n标准化后的整个数组的数据范围：[{data_min}, {data_max}]\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9ccda958",
"metadata": {},
"outputs": [],
"source": [
"# control_CP = data_normalized.copy()\n",
"# print(control_CP.shape)\n",
"target_CP = data_normalized.copy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6e515034",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"整个数组的数据范围：[-43.761436462402344, 6.348904132843018]\n",
"------------------------------\n",
"整个数组的数据范围：[-19.69989776611328, 31.564712524414062]\n",
"------------------------------\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"data_min = control_CP.min()\n",
"data_max = control_CP.max()\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)\n",
"# 计算整个数组的最小值和最大值\n",
"data_min = target_CP.min()\n",
"data_max = target_CP.max()\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "481b57e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float64 float64\n",
"float32 float32\n"
]
}
],
"source": [
"print(control_CP.dtype, target_CP.dtype)\n",
"\n",
"control_CP_float32 = control_CP.astype(np.float32)\n",
"target_CP_float32 = target_CP.astype(np.float32)\n",
"print(control_CP_float32.dtype, target_CP_float32.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6e2d6030",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已删除旧的 'control_CP' 数据集。\n",
"已删除旧的 'target_CP' 数据集。\n",
"已成功写入新的 'control_CP' 和 'target_CP' 数据集。\n",
"操作完成。\n"
]
}
],
"source": [
"file_path = 'Aggregated_CP_GE.h5'\n",
"# 使用 'a' (append) 模式打开文件\n",
"with h5py.File(file_path, 'a') as f:\n",
" \n",
" # 1. 删除旧的数据集（如果存在）\n",
" if \"control_CP\" in f:\n",
" del f[\"control_CP\"]\n",
" print(\"已删除旧的 'control_CP' 数据集。\")\n",
" \n",
" if \"target_CP\" in f:\n",
" del f[\"target_CP\"]\n",
" print(\"已删除旧的 'target_CP' 数据集。\")\n",
" \n",
" # 2. 创建并写入新的数据集\n",
" f.create_dataset(\"control_CP\", data=control_CP_float32)\n",
" f.create_dataset(\"target_CP\", data=target_CP_float32)\n",
" \n",
" print(\"已成功写入新的 'control_CP' 和 'target_CP' 数据集。\")\n",
"\n",
"print(\"操作完成。\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b131c13e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "5a82372f",
"metadata": {},
"source": [
"# 看基因数据是否异常 -- 没有"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a81ddc86",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"整个数组的数据范围：[-43.761436462402344, 6.348904132843018]\n",
"------------------------------\n",
"每列数据的最小值（前10个）：\n",
"[-1.6301312 -1.4819461 -1.9675876 -2.272238 -3.393242 -1.1765975\n",
" -2.6176555 -1.2396002 -2.462905 -2.8029377]\n",
"------------------------------\n",
"每列数据的最大值（前10个）：\n",
"[3.0697236 1.985853 1.09526 1.1315886 2.1168044 2.9211075 2.1078165\n",
" 2.3139157 2.1509798 1.5131917]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"data36 = load_from_HDF( 'Aggregated_CP_GE.h5')\n",
"# 计算整个数组的最小值和最大值\n",
"data_min = data36['control_CP'].min()\n",
"data_max = data36['control_CP'].max()\n",
"\n",
"# 计算每列（即每个特征）的最小值和最大值\n",
"# axis=0 表示对每一列进行操作\n",
"min_per_column = np.min(data36['control_CP'], axis=0)\n",
"max_per_column = np.max(data36['control_CP'], axis=0)\n",
"\n",
"print(f\"整个数组的数据范围：[{data_min}, {data_max}]\")\n",
"print(\"-\" * 30)\n",
"print(\"每列数据的最小值（前10个）：\")\n",
"print(min_per_column[:10])\n",
"print(\"-\" * 30)\n",
"print(\"每列数据的最大值（前10个）：\")\n",
"print(max_per_column[:10])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "770f3727",
"metadata": {},
"outputs": [],
"source": [
"# 看看方差\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10693804",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c072eea5",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|