From a6518e9e03544bc9d4a387b5190127cc5e9cde87 Mon Sep 17 00:00:00 2001
From: NLP-LOVE <52538954+NLP-LOVE@users.noreply.github.com>
Date: Tue, 9 Jul 2019 20:32:09 +0800
Subject: [PATCH] Add files via upload

---
 Machine Learning/3.2 GBDT/GBDT_demo.ipynb | 413 ++++++++++++++++++++++
 Machine Learning/3.2 GBDT/test_feat.txt   |   5 +
 Machine Learning/3.2 GBDT/train_feat.txt  |   5 +
 3 files changed, 423 insertions(+)
 create mode 100644 Machine Learning/3.2 GBDT/GBDT_demo.ipynb
 create mode 100644 Machine Learning/3.2 GBDT/test_feat.txt
 create mode 100644 Machine Learning/3.2 GBDT/train_feat.txt

diff --git a/Machine Learning/3.2 GBDT/GBDT_demo.ipynb b/Machine Learning/3.2 GBDT/GBDT_demo.ipynb
new file mode 100644
index 0000000..c7c9c8f
--- /dev/null
+++ b/Machine Learning/3.2 GBDT/GBDT_demo.ipynb	
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.ensemble import GradientBoostingRegressor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 获取训练数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.005988</td>\n",
+       "      <td>0.569231</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>0.951220</td>\n",
+       "      <td>-0.225434</td>\n",
+       "      <td>0.837989</td>\n",
+       "      <td>0.357258</td>\n",
+       "      <td>-0.003058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.161677</td>\n",
+       "      <td>0.743195</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>0.960976</td>\n",
+       "      <td>-0.086705</td>\n",
+       "      <td>0.780527</td>\n",
+       "      <td>0.282945</td>\n",
+       "      <td>0.149847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.113772</td>\n",
+       "      <td>0.744379</td>\n",
+       "      <td>0.541176</td>\n",
+       "      <td>0.990244</td>\n",
+       "      <td>-0.005780</td>\n",
+       "      <td>0.721468</td>\n",
+       "      <td>0.434110</td>\n",
+       "      <td>-0.318043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.053892</td>\n",
+       "      <td>0.608284</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.951220</td>\n",
+       "      <td>-0.248555</td>\n",
+       "      <td>0.821229</td>\n",
+       "      <td>0.848604</td>\n",
+       "      <td>-0.003058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.173653</td>\n",
+       "      <td>0.866272</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>0.951220</td>\n",
+       "      <td>0.017341</td>\n",
+       "      <td>0.704709</td>\n",
+       "      <td>-0.021002</td>\n",
+       "      <td>-0.195719</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4         5         6  \\\n",
+       "0  0.005988  0.569231  0.647059  0.951220 -0.225434  0.837989  0.357258   \n",
+       "1  0.161677  0.743195  0.682353  0.960976 -0.086705  0.780527  0.282945   \n",
+       "2  0.113772  0.744379  0.541176  0.990244 -0.005780  0.721468  0.434110   \n",
+       "3  0.053892  0.608284  0.764706  0.951220 -0.248555  0.821229  0.848604   \n",
+       "4  0.173653  0.866272  0.682353  0.951220  0.017341  0.704709 -0.021002   \n",
+       "\n",
+       "          7  \n",
+       "0 -0.003058  \n",
+       "1  0.149847  \n",
+       "2 -0.318043  \n",
+       "3 -0.003058  \n",
+       "4 -0.195719  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_feature = np.genfromtxt(\"train_feat.txt\",dtype=np.float32)\n",
+    "num_feature = len(train_feature[0])\n",
+    "train_feature = pd.DataFrame(train_feature)\n",
+    "\n",
+    "train_label = train_feature.iloc[:, num_feature - 1]\n",
+    "train_feature = train_feature.iloc[:, 0:num_feature - 2]\n",
+    "train_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    320.0\n",
+       "1    361.0\n",
+       "2    364.0\n",
+       "3    336.0\n",
+       "4    358.0\n",
+       "Name: 9, dtype: float32"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 获取测试数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.005988</td>\n",
+       "      <td>0.569231</td>\n",
+       "      <td>0.647059</td>\n",
+       "      <td>0.951220</td>\n",
+       "      <td>-0.225434</td>\n",
+       "      <td>0.837989</td>\n",
+       "      <td>0.357258</td>\n",
+       "      <td>-0.003058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.161677</td>\n",
+       "      <td>0.743195</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>0.960976</td>\n",
+       "      <td>-0.086705</td>\n",
+       "      <td>0.780527</td>\n",
+       "      <td>0.282945</td>\n",
+       "      <td>0.149847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.113772</td>\n",
+       "      <td>0.744379</td>\n",
+       "      <td>0.541176</td>\n",
+       "      <td>0.990244</td>\n",
+       "      <td>-0.005780</td>\n",
+       "      <td>0.721468</td>\n",
+       "      <td>0.434110</td>\n",
+       "      <td>-0.318043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.053892</td>\n",
+       "      <td>0.608284</td>\n",
+       "      <td>0.764706</td>\n",
+       "      <td>0.951220</td>\n",
+       "      <td>-0.248555</td>\n",
+       "      <td>0.821229</td>\n",
+       "      <td>0.848604</td>\n",
+       "      <td>-0.003058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.173653</td>\n",
+       "      <td>0.866272</td>\n",
+       "      <td>0.682353</td>\n",
+       "      <td>0.951220</td>\n",
+       "      <td>0.017341</td>\n",
+       "      <td>0.704709</td>\n",
+       "      <td>-0.021002</td>\n",
+       "      <td>-0.195719</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1         2         3         4         5         6  \\\n",
+       "0  0.005988  0.569231  0.647059  0.951220 -0.225434  0.837989  0.357258   \n",
+       "1  0.161677  0.743195  0.682353  0.960976 -0.086705  0.780527  0.282945   \n",
+       "2  0.113772  0.744379  0.541176  0.990244 -0.005780  0.721468  0.434110   \n",
+       "3  0.053892  0.608284  0.764706  0.951220 -0.248555  0.821229  0.848604   \n",
+       "4  0.173653  0.866272  0.682353  0.951220  0.017341  0.704709 -0.021002   \n",
+       "\n",
+       "          7  \n",
+       "0 -0.003058  \n",
+       "1  0.149847  \n",
+       "2 -0.318043  \n",
+       "3 -0.003058  \n",
+       "4 -0.195719  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_feature = np.genfromtxt(\"test_feat.txt\",dtype=np.float32)\n",
+    "num_feature = len(test_feature[0])\n",
+    "test_feature = pd.DataFrame(test_feature)\n",
+    "\n",
+    "test_label = test_feature.iloc[:, num_feature - 1]\n",
+    "test_feature = test_feature.iloc[:, 0:num_feature - 2]\n",
+    "test_feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    320.0\n",
+       "1    361.0\n",
+       "2    364.0\n",
+       "3    336.0\n",
+       "4    358.0\n",
+       "Name: 9, dtype: float32"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GBDT模型建立"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pred: 320.0008173984891  label: 320.0\n",
+      "pred: 360.99965033119537  label: 361.0\n",
+      "pred: 363.99928183902097  label: 364.0\n",
+      "pred: 336.0002344322584  label: 336.0\n",
+      "pred: 358.0000159974151  label: 358.0\n",
+      "均方误差: 0.0005218003748239915\n"
+     ]
+    }
+   ],
+   "source": [
+    "gbdt = GradientBoostingRegressor(\n",
+    "  loss = 'ls'\n",
+    ", learning_rate = 0.1\n",
+    ", n_estimators = 100\n",
+    ", subsample = 1\n",
+    ", min_samples_split = 2\n",
+    ", min_samples_leaf = 1\n",
+    ", max_depth = 3\n",
+    ", init = None\n",
+    ", random_state = None\n",
+    ", max_features = None\n",
+    ", alpha = 0.9\n",
+    ", verbose = 0\n",
+    ", max_leaf_nodes = None\n",
+    ", warm_start = False\n",
+    ")\n",
+    "\n",
+    "gbdt.fit(train_feature, train_label)\n",
+    "pred = gbdt.predict(test_feature)\n",
+    "total_err = 0\n",
+    "\n",
+    "for i in range(pred.shape[0]):\n",
+    "    print('pred:', pred[i], ' label:', test_label[i])\n",
+    "print('均方误差:', np.sqrt(((pred - test_label) ** 2).mean()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Machine Learning/3.2 GBDT/test_feat.txt b/Machine Learning/3.2 GBDT/test_feat.txt
new file mode 100644
index 0000000..7d5cd20
--- /dev/null
+++ b/Machine Learning/3.2 GBDT/test_feat.txt	
@@ -0,0 +1,5 @@
+0.00598802 0.569231 0.647059 0.95122 -0.225434 0.837989 0.357258 -0.0030581 -0.383475 320
+0.161677 0.743195 0.682353 0.960976 -0.0867052 0.780527 0.282945 0.149847 -0.0529661 361
+0.113772 0.744379 0.541176 0.990244 -0.00578035 0.721468 0.43411 -0.318043 0.288136 364
+0.0538922 0.608284 0.764706 0.95122 -0.248555 0.821229 0.848604 -0.0030581 0.239407 336
+0.173653 0.866272 0.682353 0.95122 0.017341 0.704709 -0.0210016 -0.195719 0.150424 358
\ No newline at end of file
diff --git a/Machine Learning/3.2 GBDT/train_feat.txt b/Machine Learning/3.2 GBDT/train_feat.txt
new file mode 100644
index 0000000..7d5cd20
--- /dev/null
+++ b/Machine Learning/3.2 GBDT/train_feat.txt	
@@ -0,0 +1,5 @@
+0.00598802 0.569231 0.647059 0.95122 -0.225434 0.837989 0.357258 -0.0030581 -0.383475 320
+0.161677 0.743195 0.682353 0.960976 -0.0867052 0.780527 0.282945 0.149847 -0.0529661 361
+0.113772 0.744379 0.541176 0.990244 -0.00578035 0.721468 0.43411 -0.318043 0.288136 364
+0.0538922 0.608284 0.764706 0.95122 -0.248555 0.821229 0.848604 -0.0030581 0.239407 336
+0.173653 0.866272 0.682353 0.95122 0.017341 0.704709 -0.0210016 -0.195719 0.150424 358
\ No newline at end of file
-- 
GitLab