{ "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 }