Create AutoAI Tools.ipynb

竞赛优胜技巧/.ipynb_checkpoints/AutoAI Tools-checkpoint.ipynb

@@ -12,7 +12,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c153ba08",
+   "id": "b2143f17",
    "metadata": {},
    "outputs": [],
    "source": []

竞赛优胜技巧/AutoAI Tools.ipynb

@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c32f110a",
+   "metadata": {},
+   "source": [
+    "# 自动机器学习工具\n",
+    "该notebook将比较市面上的多个AutoML工具，分别采用两组数据集进行比较，分别是波士顿房价（回归）和森林植被类型（多分类）"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba41b787",
+   "metadata": {},
+   "source": [
+    "# optuna一种超参数优化框架\n",
+    "https://github.com/optuna/optuna"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4d7f73a",
+   "metadata": {},
+   "source": [
+    "### 波士顿房价预测任务（回归）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d010375",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import time\n",
+    "import gc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6d01294d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_boston\n",
+    "# 预处理\n",
+    "X, y = data['data'], data['target']\n",
+    "# 由于模型标签需要从0开始，所以数字需要全部减1\n",
+    "print('七分类任务，处理前：',np.unique(y))\n",
+    "print(y)\n",
+    "ord = OrdinalEncoder()\n",
+    "y = ord.fit_transform(y.reshape(-1, 1))\n",
+    "y = y.reshape(-1, )\n",
+    "print('七分类任务，处理后：',np.unique(y))\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "80a90475",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
+       "        4.9800e+00],\n",
+       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
+       "        9.1400e+00],\n",
+       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
+       "        4.0300e+00],\n",
+       "       ...,\n",
+       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
+       "        5.6400e+00],\n",
+       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
+       "        6.4800e+00],\n",
+       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
+       "        7.8800e+00]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9977a37c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9193c33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 分类"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82687da3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_covtype\n",
+    "data = fetch_covtype()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

竞赛优胜技巧/AutoML Tools.ipynb

@@ -1,42 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c32f110a",
-   "metadata": {},
-   "source": [
-    "# 自动机器学习工具\n",
-    "该notebook将比较市面上的多个AutoML工具，分别采用两组数据集进行比较，分别是波士顿房价（回归）和森林植被类型（多分类）"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c153ba08",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}