{ "cells": [ { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 读取数据\n", "# 1.获取数据 read_csv\n", "data = pd.read_csv('./data/FBlocation/train.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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row_idxyaccuracytimeplace_id
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" ], "text/plain": [ " row_id x y accuracy time place_id\n", "0 0 0.7941 9.0809 54 470702 8523065625\n", "1 1 5.9567 4.7968 13 186555 1757726713\n", "2 2 8.3078 7.0407 74 322648 1137537235\n", "3 3 7.3665 2.5165 65 704587 6567393236\n", "4 4 4.0961 1.1307 31 472130 7440663949" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 1.获取数据 read_csv\n", "\n", "# 2.数据基本处理 数据集划分，把时间戳提出成明显的时间特征（周几，几号，几点）， 剔除签到人数少的place_id\n", "\n", "# 3.特征工程 标准化\n", "\n", "# 4.机器学习\n", "# 4.1 创建模型\n", "# 4.2 训练模型\n", "\n", "# 5.模型评估 分类 准确率" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# 2.数据基本处理 \n", "# 取一小部分数据做演示\n", "data = data.query('x>2 & x<2.5 & y>2 & y<2.5')\n", "\n", "# 把时间戳提出成明显的时间特征（周几，几号，几点） 特征提取 用户地址 -> 经纬度\n", "time = pd.to_datetime(data['time'], unit='s')\n", "time = pd.DatetimeIndex(time)\n", "data['weekday'] = time.weekday\n", "data['day'] = time.day\n", "data['hour'] = time.hour\n", "\n", "\n", "# 剔除签到人数少的place_id\n", "# 统计place_id出现的次数\n", "temp = data.groupby('place_id')['row_id'].count()\n", "res = temp[temp > 3].index\n", "data = data[data['place_id'].isin(res)]\n", "\n", "\n", "# 数据集划分\n", "x = data[['x', 'y', 'accuracy', 'weekday', 'day', 'hour']]\n", "y = data['place_id']\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.partial_fit(X, y)\n", "/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.fit(X, **fit_params).transform(X)\n", "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " after removing the cwd from sys.path.\n" ] } ], "source": [ "# 3.特征工程 标准化\n", "transfer = StandardScaler()\n", "x_train = transfer.fit_transform(x_train)\n", "x_test = transfer.transform(x_test)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n", " % (min_groups, self.n_splits)), Warning)\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=3, error_score='raise-deprecating',\n", " estimator=KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n", " weights='uniform'),\n", " fit_params=None, iid='warn', n_jobs=None,\n", " param_grid={'n_neighbors': [1, 3, 5]}, pre_dispatch='2*n_jobs',\n", " refit=True, return_train_score='warn', scoring=None, verbose=0)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4.机器学习\n", "# 4.1 创建模型\n", "estimator = KNeighborsClassifier(algorithm='kd_tree')\n", " # 构建参数字典\n", "param_dict = {'n_neighbors':[1,3,5]}\n", " # 初始化GSCV估计器\n", "estimator_gscv = GridSearchCV(estimator, param_grid=param_dict, cv=3)\n", "\n", "# 4.2 训练模型\n", "estimator_gscv.fit(x_train, y_train)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3602829711975745" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 5.模型评估 分类 准确率\n", "estimator_gscv.score(x_test, y_test)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='kd_tree', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n", " weights='uniform')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 获取最优模型\n", "estimator_gscv.best_estimator_" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'n_neighbors': 1}" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 获取最优超参数\n", "estimator_gscv.best_params_" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }