Skip to content

Instantly share code, notes, and snippets.

@enakai00

enakai00/chap7-compare_with_ml.ipynb

Last active December 19, 2022 05:23
Show Gist options
  • Save enakai00/95c2c2f394510fc72f0bfb698068c044 to your computer and use it in GitHub Desktop.
Save enakai00/95c2c2f394510fc72f0bfb698068c044 to your computer and use it in GitHub Desktop.
Download ZIP
chap7-compare_with_ML.ipynb
Display the source blob
Display the rendered blob
Raw
chap7-compare_with_ml.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "chap7-compare_with_ML.ipynb",
"provenance": [],
"collapsed_sections": [
"evzu_3wZZnS_",
"J0GhOWBMc3ax",
"osY-cbQ0wDWt",
"MchIhb3NWcpB"
],
"authorship_tag": "ABX9TyM7x5QtzdTxe46z6uShkmFo",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/enakai00/95c2c2f394510fc72f0bfb698068c044/chap7-compare_with_ml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"## 環境準備"
],
"metadata": {
"id": "evzu_3wZZnS_"
}
},
{
"cell_type": "code",
"source": [
"!git clone https://github.com/ohmsha/PyOptBook.git"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SWM3JCiv2HHK",
"outputId": "c252da39-c745-49b2-fdea-8221e43a2444"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'PyOptBook'...\n",
"remote: Enumerating objects: 187, done.\u001b[K\n",
"remote: Counting objects: 100% (111/111), done.\u001b[K\n",
"remote: Compressing objects: 100% (70/70), done.\u001b[K\n",
"remote: Total 187 (delta 71), reused 61 (delta 41), pack-reused 76\u001b[K\n",
"Receiving objects: 100% (187/187), 2.25 MiB | 8.83 MiB/s, done.\n",
"Resolving deltas: 100% (88/88), done.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install pulp"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZGH7xhSbZhD4",
"outputId": "8c8805ee-a509-45f7-cf21-a5438866bc6b"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting pulp\n",
" Downloading PuLP-2.7.0-py3-none-any.whl (14.3 MB)\n",
"\u001b[K |████████████████████████████████| 14.3 MB 7.9 MB/s \n",
"\u001b[?25hInstalling collected packages: pulp\n",
"Successfully installed pulp-2.7.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from pandas import DataFrame, Series\n",
"\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.pipeline import Pipeline"
],
"metadata": {
"id": "Szy_mcQuJa4C"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## データ準備"
],
"metadata": {
"id": "J0GhOWBMc3ax"
}
},
{
"cell_type": "code",
"source": [
"!ls PyOptBook/7.recommendation/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lvErYOc_dB6u",
"outputId": "27b465f5-bc35-41e7-96b5-c987dffe73f6"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"access_log.csv\trecommendation.ipynb\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"log_df = pd.read_csv('PyOptBook/7.recommendation/access_log.csv', parse_dates=['date'])"
],
"metadata": {
"id": "nwkofoafdJV6"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"log_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "S81I7infdTig",
"outputId": "89c88c36-917e-441b-ccda-79c85ed250b2"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id date\n",
"0 4 205587 2015-07-04\n",
"1 4 748683 2015-07-04\n",
"2 4 790055 2015-07-04\n",
"3 4 790055 2015-07-04\n",
"4 4 764638 2015-07-04"
],
"text/html": [
"\n",
" <div id=\"df-e311181c-0d16-4d89-b0fc-b9e79df5b4dd\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>205587</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>748683</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>764638</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e311181c-0d16-4d89-b0fc-b9e79df5b4dd')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-e311181c-0d16-4d89-b0fc-b9e79df5b4dd button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-e311181c-0d16-4d89-b0fc-b9e79df5b4dd');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"import datetime\n",
"start_date = datetime.datetime(2015, 7, 1)\n",
"end_date = datetime.datetime(2015, 7, 7)\n",
"target_date = datetime.datetime(2015, 7, 8)"
],
"metadata": {
"id": "xaARGWpo6V1J"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x_df = log_df[(start_date <= log_df['date'])&\n",
" (log_df['date'] <= end_date)]\n",
"x_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rYriR4Tq6rcC",
"outputId": "4d7d7426-d29f-41dc-cdf4-34778223676b"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id date\n",
"0 4 205587 2015-07-04\n",
"1 4 748683 2015-07-04\n",
"2 4 790055 2015-07-04\n",
"3 4 790055 2015-07-04\n",
"4 4 764638 2015-07-04"
],
"text/html": [
"\n",
" <div id=\"df-db52721d-b830-42ea-8ab4-6d48773e0080\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>205587</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>748683</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>764638</td>\n",
" <td>2015-07-04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db52721d-b830-42ea-8ab4-6d48773e0080')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-db52721d-b830-42ea-8ab4-6d48773e0080 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-db52721d-b830-42ea-8ab4-6d48773e0080');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"y_df = log_df[log_df['date'] == target_date]\n",
"y_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "ro4BbIAX68xw",
"outputId": "d90069cc-97a0-45b1-94c2-653ed3508220"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id date\n",
"103 94 603852 2015-07-08\n",
"104 94 28600 2015-07-08\n",
"105 94 987320 2015-07-08\n",
"106 94 109924 2015-07-08\n",
"107 94 886214 2015-07-08"
],
"text/html": [
"\n",
" <div id=\"df-eb725ea0-d27e-457e-9cad-89a0a566b732\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>94</td>\n",
" <td>603852</td>\n",
" <td>2015-07-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>94</td>\n",
" <td>28600</td>\n",
" <td>2015-07-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>94</td>\n",
" <td>987320</td>\n",
" <td>2015-07-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>94</td>\n",
" <td>109924</td>\n",
" <td>2015-07-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>94</td>\n",
" <td>886214</td>\n",
" <td>2015-07-08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb725ea0-d27e-457e-9cad-89a0a566b732')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-eb725ea0-d27e-457e-9cad-89a0a566b732 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-eb725ea0-d27e-457e-9cad-89a0a566b732');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"U2I2Rcens = {}\n",
"for row in x_df.itertuples():\n",
" rcen = (target_date - row.date).days\n",
" U2I2Rcens.setdefault(row.user_id, {})\n",
" U2I2Rcens[row.user_id].setdefault(row.item_id, [])\n",
" U2I2Rcens[row.user_id][row.item_id].append(rcen)"
],
"metadata": {
"id": "7w3E2oZA7bmU"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"Rows1 = []\n",
"for user_id, I2Rcens in U2I2Rcens.items():\n",
" for item_id, Rcens in I2Rcens.items():\n",
" freq = len(Rcens)\n",
" rcen = min(Rcens)\n",
" Rows1.append((user_id, item_id, rcen, freq))\n",
"UI2RF_df = pd.DataFrame(Rows1, columns=['user_id', 'item_id', 'rcen', 'freq'])\n",
"print(len(UI2RF_df))\n",
"UI2RF_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "IE3NgJzy8Ts9",
"outputId": "e8a9e7ec-f951-47dd-88c2-b7103ef9c42a"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"204661\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id rcen freq\n",
"0 4 205587 4 1\n",
"1 4 748683 4 1\n",
"2 4 790055 4 3\n",
"3 4 764638 4 2\n",
"4 4 492434 4 1"
],
"text/html": [
"\n",
" <div id=\"df-622abd45-8c01-4d50-87d9-151b12c14193\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>rcen</th>\n",
" <th>freq</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>205587</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>748683</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>764638</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>492434</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-622abd45-8c01-4d50-87d9-151b12c14193')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-622abd45-8c01-4d50-87d9-151b12c14193 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-622abd45-8c01-4d50-87d9-151b12c14193');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# 閲覧フラグ(pv_flag)の作成\n",
"y_df = y_df.drop_duplicates()\n",
"print(len(y_df))\n",
"y_df = y_df.copy()\n",
"y_df['pv_flag'] = 1\n",
"y_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "HKb7688H-BKd",
"outputId": "4cd671a3-1729-4f5c-e8fe-883da9d19279"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"29651\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id date pv_flag\n",
"103 94 603852 2015-07-08 1\n",
"104 94 28600 2015-07-08 1\n",
"105 94 987320 2015-07-08 1\n",
"106 94 109924 2015-07-08 1\n",
"107 94 886214 2015-07-08 1"
],
"text/html": [
"\n",
" <div id=\"df-2172b8d6-66f6-411e-998c-d1f42a37a9d5\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>date</th>\n",
" <th>pv_flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>94</td>\n",
" <td>603852</td>\n",
" <td>2015-07-08</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>94</td>\n",
" <td>28600</td>\n",
" <td>2015-07-08</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>94</td>\n",
" <td>987320</td>\n",
" <td>2015-07-08</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>94</td>\n",
" <td>109924</td>\n",
" <td>2015-07-08</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>94</td>\n",
" <td>886214</td>\n",
" <td>2015-07-08</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2172b8d6-66f6-411e-998c-d1f42a37a9d5')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-2172b8d6-66f6-411e-998c-d1f42a37a9d5 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-2172b8d6-66f6-411e-998c-d1f42a37a9d5');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"# 閲覧フラグ(pv_flag)の追加\n",
"UI2RFP_df = pd.merge(UI2RF_df, y_df[['user_id', 'item_id', 'pv_flag']], how='left', on=['user_id', 'item_id'])\n",
"UI2RFP_df['pv_flag'].fillna(0, inplace=True)\n",
"print(len(UI2RFP_df))\n",
"UI2RFP_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "UENhl36f-YyT",
"outputId": "1e163214-a893-447b-e6c4-958fa30db3da"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"204661\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id rcen freq pv_flag\n",
"0 4 205587 4 1 0.0\n",
"1 4 748683 4 1 0.0\n",
"2 4 790055 4 3 0.0\n",
"3 4 764638 4 2 0.0\n",
"4 4 492434 4 1 0.0"
],
"text/html": [
"\n",
" <div id=\"df-adbe340e-cfae-415a-b3cc-6191eaaa4ee3\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>rcen</th>\n",
" <th>freq</th>\n",
" <th>pv_flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>205587</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>748683</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>764638</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>492434</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adbe340e-cfae-415a-b3cc-6191eaaa4ee3')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-adbe340e-cfae-415a-b3cc-6191eaaa4ee3 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-adbe340e-cfae-415a-b3cc-6191eaaa4ee3');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"# 頻度(freq)が7以下になるようにデータを抽出(本書の都合でrcenと規模感を合わせるため)\n",
"tar_df = UI2RFP_df[UI2RFP_df['freq'] <= 7]\n",
"print(len(tar_df))\n",
"tar_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "wMpltdiw-cvT",
"outputId": "fe9247b3-d6cd-4a28-acad-f7eaa9bdcdfd"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"203456\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" user_id item_id rcen freq pv_flag\n",
"0 4 205587 4 1 0.0\n",
"1 4 748683 4 1 0.0\n",
"2 4 790055 4 3 0.0\n",
"3 4 764638 4 2 0.0\n",
"4 4 492434 4 1 0.0"
],
"text/html": [
"\n",
" <div id=\"df-18dd06f3-a4bd-45a9-85f3-af7fa2d152c6\">\n",
" <div class=\"colab-df-container\">\n",
" <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>user_id</th>\n",
" <th>item_id</th>\n",
" <th>rcen</th>\n",
" <th>freq</th>\n",
" <th>pv_flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>205587</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>748683</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>790055</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>764638</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>492434</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18dd06f3-a4bd-45a9-85f3-af7fa2d152c6')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-18dd06f3-a4bd-45a9-85f3-af7fa2d152c6 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-18dd06f3-a4bd-45a9-85f3-af7fa2d152c6');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対する総件数と再閲覧件数の算出\n",
"RF2N = {}\n",
"RF2PV = {}\n",
"for row in tar_df.itertuples():\n",
" RF2N.setdefault((row.rcen, row.freq), 0)\n",
" RF2PV.setdefault((row.rcen, row.freq), 0)\n",
" RF2N[row.rcen, row.freq] += 1\n",
" if row.pv_flag == 1:\n",
" RF2PV[row.rcen, row.freq] += 1"
],
"metadata": {
"id": "tUMyJK_1D5fk"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対する再閲覧確率の算出\n",
"RF2Prob = {}\n",
"for rf, N in RF2N.items():\n",
" RF2Prob[rf] = RF2PV[rf] / N"
],
"metadata": {
"id": "8Cv8wDwXD6RA"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対して総件数、再閲覧件数、再閲覧確率を対応付けるデータの作成\n",
"Rows3 = []\n",
"for rf, N in sorted(RF2N.items()):\n",
" pv = RF2PV[rf]\n",
" prob = RF2Prob[rf]\n",
" row = (rf[0], rf[1], N, pv, prob)\n",
" Rows3.append(row)\n",
"rf_df = pd.DataFrame(Rows3, columns = ['rcen', 'freq', 'N', 'pv', 'prob'])\n",
"print(len(rf_df))\n",
"rf_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "7kfo1VH7D8zw",
"outputId": "8e95ad6d-57f7-4cb4-fff5-2db92fa53a1c"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"49\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" rcen freq N pv prob\n",
"0 1 1 19602 245 0.012499\n",
"1 1 2 3323 132 0.039723\n",
"2 1 3 1120 81 0.072321\n",
"3 1 4 539 36 0.066790\n",
"4 1 5 285 36 0.126316"
],
"text/html": [
"\n",
" <div id=\"df-6b2eb5d5-c123-433f-913f-d56d46aacbd5\">\n",
" <div class=\"colab-df-container\">\n",
" <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>rcen</th>\n",
" <th>freq</th>\n",
" <th>N</th>\n",
" <th>pv</th>\n",
" <th>prob</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>19602</td>\n",
" <td>245</td>\n",
" <td>0.012499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3323</td>\n",
" <td>132</td>\n",
" <td>0.039723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1120</td>\n",
" <td>81</td>\n",
" <td>0.072321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>539</td>\n",
" <td>36</td>\n",
" <td>0.066790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>285</td>\n",
" <td>36</td>\n",
" <td>0.126316</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b2eb5d5-c123-433f-913f-d56d46aacbd5')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-6b2eb5d5-c123-433f-913f-d56d46aacbd5 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-6b2eb5d5-c123-433f-913f-d56d46aacbd5');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"source": [
"## 凸二次計画による予測\n",
"\n",
"* Recency に関する単調性\n",
"* Frequency に関する単調性\n",
"* Recency に関する凸性\n",
"* Frequency に関する凹性\n",
"\n",
"という制約条件を明示的にモデルに取り込むことができる。"
],
"metadata": {
"id": "osY-cbQ0wDWt"
}
},
{
"cell_type": "code",
"source": [
"import cvxopt\n",
"\n",
"R = sorted(tar_df['rcen'].unique().tolist())\n",
"F = sorted(tar_df['freq'].unique().tolist())\n",
"\n",
"Idx = []\n",
"RF2Idx = {}\n",
"idx = 0\n",
"for r in R:\n",
" for f in F:\n",
" Idx.append(idx)\n",
" RF2Idx[r, f] = idx\n",
" idx += 1\n",
"\n",
"G_list = []\n",
"h_list = []\n",
"var_vec = [0.0] * len(Idx)\n",
"\n",
"# - pred_prob[r,f] <= 0 のモデリング\n",
"for r in R:\n",
" for f in F:\n",
" idx = RF2Idx[r,f]\n",
" G_row = var_vec[:]\n",
" G_row[idx] = -1 # pred_prob[r,f]の係数は-1\n",
" G_list.append(G_row)\n",
" h_list.append(0) # 右辺は定数項0\n",
"\n",
"# pred_prob[r,f] <= 1 のモデリング\n",
"for r in R:\n",
" for f in F:\n",
" idx = RF2Idx[r,f]\n",
" G_row = var_vec[:]\n",
" G_row[idx] = 1\n",
" G_list.append(G_row)\n",
" h_list.append(1)\n",
"\n",
"# - pred_prob[r,f] + pred_prob[r+1,f] <= 0 のモデリング\n",
"for r in R[:-1]:\n",
" for f in F:\n",
" idx1 = RF2Idx[r,f]\n",
" idx2 = RF2Idx[r+1,f]\n",
" G_row = var_vec[:]\n",
" G_row[idx1] = -1 # pred_prob[r,f]の係数は-1\n",
" G_row[idx2] = 1 # pred_prob[r+1,f]の係数は1\n",
" G_list.append(G_row)\n",
" h_list.append(0) # 右辺は定数項0\n",
"\n",
"# pred_prob[r,f] - pred_prob[r,f+1] <= 0 のモデリング\n",
"for r in R:\n",
" for f in F[:-1]:\n",
" idx1 = RF2Idx[r,f]\n",
" idx2 = RF2Idx[r,f+1]\n",
" G_row = var_vec[:]\n",
" G_row[idx1] = 1 # pred_prob[r,f]の係数は1\n",
" G_row[idx2] = -1 # pred_prob[r,f+1]の係数は-1\n",
" G_list.append(G_row)\n",
" h_list.append(0) # 右辺は定数項0\n",
"\n",
"# - pred_prob[r,f] + 2 * pred_prob[r+1,f] - pred_prob[r+2,f] <= 0 のモデリング\n",
"for r in R[:-2]:\n",
" for f in F:\n",
" idx1 = RF2Idx[r,f]\n",
" idx2 = RF2Idx[r+1,f]\n",
" idx3 = RF2Idx[r+2,f]\n",
" G_row = var_vec[:]\n",
" G_row[idx1] = -1 # pred_prob[r,f]の係数は-1\n",
" G_row[idx2] = 2 # pred_prob[r+1,f]の係数は2\n",
" G_row[idx3] = -1 # pred_prob[r+2,f]の係数は-1\n",
" G_list.append(G_row)\n",
" h_list.append(0) # 右辺は定数項0\n",
"\n",
"# pred_prob[r,f] - 2 * pred_prob[r,f+1] + pred_prob[r,f+2] <= 0 のモデリング\n",
"for r in R:\n",
" for f in F[:-2]:\n",
" idx1 = RF2Idx[r,f]\n",
" idx2 = RF2Idx[r,f+1]\n",
" idx3 = RF2Idx[r,f+2]\n",
" G_row = var_vec[:]\n",
" G_row[idx1] = 1 # pred_prob[r,f]の係数は-1\n",
" G_row[idx2] = -2 # pred_prob[r,f+1]の係数は2\n",
" G_row[idx3] = 1 # pred_prob[r,f+2]の係数は-1\n",
" G_list.append(G_row)\n",
" h_list.append(0) # 右辺は定数項0\n",
"\n",
"P_list = []\n",
"q_list = []\n",
"\n",
"# N[r,f] * pred_prob[r,f]^2 - 2 * N[r,f] * prob[r,f] のモデリング\n",
"for r in R:\n",
" for f in F:\n",
" idx = RF2Idx[r,f]\n",
" N = RF2N[r,f]\n",
" prob = RF2Prob[r,f]\n",
" P_row = var_vec[:]\n",
" P_row[idx] = 2 * N # (1/2)を打ち消すために2を掛ける\n",
" P_list.append(P_row)\n",
" q_list.append( - 2 * N * prob)\n",
"\n",
"G = cvxopt.matrix(np.array(G_list), tc='d')\n",
"h = cvxopt.matrix(np.array(h_list), tc='d')\n",
"P = cvxopt.matrix(np.array(P_list), tc='d')\n",
"q = cvxopt.matrix(np.array(q_list), tc='d')\n",
"\n",
"sol = cvxopt.solvers.qp(P, q, G, h)\n",
"status = sol['status']\n",
"print(status)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eOGPxSdFwTlj",
"outputId": "18884f40-4b4c-4169-c49e-7903f149bbe6"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" pcost dcost gap pres dres\n",
" 0: -5.2387e+01 -1.0684e+02 5e+02 2e+00 2e-02\n",
" 1: -5.2162e+01 -7.3611e+01 5e+01 2e-01 1e-03\n",
" 2: -5.1452e+01 -6.2961e+01 2e+01 4e-02 4e-04\n",
" 3: -5.1298e+01 -5.5766e+01 6e+00 1e-02 8e-05\n",
" 4: -5.1190e+01 -5.2365e+01 1e+00 1e-03 1e-05\n",
" 5: -5.1182e+01 -5.1589e+01 4e-01 1e-04 1e-06\n",
" 6: -5.1206e+01 -5.1382e+01 2e-01 5e-05 4e-07\n",
" 7: -5.1216e+01 -5.1298e+01 8e-02 6e-06 5e-08\n",
" 8: -5.1223e+01 -5.1241e+01 2e-02 1e-06 1e-08\n",
" 9: -5.1225e+01 -5.1228e+01 2e-03 2e-16 2e-16\n",
"10: -5.1226e+01 -5.1226e+01 7e-05 1e-16 8e-16\n",
"11: -5.1226e+01 -5.1226e+01 1e-06 2e-16 2e-15\n",
"Optimal solution found.\n",
"optimal\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対応する推定した再閲覧確率の辞書を作成\n",
"RF2PredProb2 = {}\n",
"X = sol['x']\n",
"for r in R:\n",
" for f in F:\n",
" idx = RF2Idx[r,f]\n",
" pred_prob = X[idx]\n",
" RF2PredProb2[r,f] = pred_prob\n",
"rf_df['pred_prob2'] = rf_df.apply(lambda x:RF2PredProb2[x['rcen'], x['freq']], axis=1)\n",
"rf_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "olLTEWRawpym",
"outputId": "972f3865-affe-4374-87d9-0b2f138202df"
},
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" rcen freq N pv prob pred_prob2\n",
"0 1 1 19602 245 0.012499 0.012499\n",
"1 1 2 3323 132 0.039723 0.039723\n",
"2 1 3 1120 81 0.072321 0.066240\n",
"3 1 4 539 36 0.066790 0.087729\n",
"4 1 5 285 36 0.126316 0.109218"
],
"text/html": [
"\n",
" <div id=\"df-f399c8f0-5695-4f7e-9ecb-0b20f8656f16\">\n",
" <div class=\"colab-df-container\">\n",
" <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>rcen</th>\n",
" <th>freq</th>\n",
" <th>N</th>\n",
" <th>pv</th>\n",
" <th>prob</th>\n",
" <th>pred_prob2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>19602</td>\n",
" <td>245</td>\n",
" <td>0.012499</td>\n",
" <td>0.012499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3323</td>\n",
" <td>132</td>\n",
" <td>0.039723</td>\n",
" <td>0.039723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1120</td>\n",
" <td>81</td>\n",
" <td>0.072321</td>\n",
" <td>0.066240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>539</td>\n",
" <td>36</td>\n",
" <td>0.066790</td>\n",
" <td>0.087729</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>285</td>\n",
" <td>36</td>\n",
" <td>0.126316</td>\n",
" <td>0.109218</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f399c8f0-5695-4f7e-9ecb-0b20f8656f16')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-f399c8f0-5695-4f7e-9ecb-0b20f8656f16 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-f399c8f0-5695-4f7e-9ecb-0b20f8656f16');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対して推定した再閲覧確率を対応付ける3Dグラフの作成\n",
"Freq = rf_df['freq'].unique().tolist()\n",
"Rcen = rf_df['rcen'].unique().tolist()\n",
"Z = [rf_df[(rf_df['freq']==freq) & (rf_df['rcen']==rcen)]['pred_prob2'].iloc[0] for freq in Freq for rcen in Rcen]\n",
"Z = np.array(Z).reshape((len(Freq), len(Rcen)))\n",
"X, Y = np.meshgrid(Rcen, Freq)\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111\n",
" , projection=\"3d\"\n",
" , xlabel='rcen'\n",
" , ylabel='freq'\n",
" , zlabel='pred_prob2'\n",
" )\n",
"ax.plot_wireframe(X, Y, Z)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "BfKc8RBRTD2E",
"outputId": "06c7cdf1-a90f-4b8d-891d-bed002b63807"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7fc0db5e7d30>"
]
},
"metadata": {},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"平均二乗誤差を計算"
],
"metadata": {
"id": "WTtdt2YtYwnx"
}
},
{
"cell_type": "code",
"source": [
"y = rf_df['prob'].tolist()\n",
"pred2 = rf_df['pred_prob2'].tolist()\n",
"\n",
"print ('Mean Squared Error: {:.10f}'.format(mean_squared_error(y, pred2)))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8gQavTwJxfdu",
"outputId": "634946a5-e919-4864-f995-1def0c1fb300"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mean Squared Error: 0.0000773011\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## 機械学習による予測\n",
"\n",
"制約条件によっては、明示的にモデルに取り込むことが難しくなる"
],
"metadata": {
"id": "vyg_Z8cGv8TI"
}
},
{
"cell_type": "code",
"source": [
"features = rf_df[['rcen', 'freq']]\n",
"labels = rf_df['prob']"
],
"metadata": {
"id": "vmPn81PS_Ghj"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Recency と Frequency の線形予測モデル\n",
"\n",
"線形モデルなので単調増加という制約は自動的に満たされるが、モデルが単純すぎて平均二乗誤差が大きくなる"
],
"metadata": {
"id": "MchIhb3NWcpB"
}
},
{
"cell_type": "code",
"source": [
"model = LinearRegression()\n",
"model.fit(features, labels)\n",
"\n",
"pred = model.predict(features)\n",
"print ('Mean Squared Error: {:.10f}'.format(mean_squared_error(labels, pred)))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ACljuqSJ_ZR1",
"outputId": "53f01e49-b708-426a-8585-ce762c67dbb1"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mean Squared Error: 0.0004456157\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対して推定した再閲覧確率を対応付ける3Dグラフの作成\n",
"X, Y = np.meshgrid(Rcen, Freq)\n",
"Z = np.array(\n",
" [[model.predict(DataFrame({'rcen': rcen, 'freq': freq}, index=[1]))[0]\n",
" for rcen in Rcen]for freq in Freq]\n",
" )\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111\n",
" , projection=\"3d\"\n",
" , xlabel='rcen'\n",
" , ylabel='freq'\n",
" , zlabel='pred_prob2'\n",
" )\n",
"ax.plot_wireframe(X, Y, Z)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "yXoUyQ74TOH_",
"outputId": "9417493e-c43e-4485-a0cb-bc06343dd372"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7fc0d70e5f70>"
]
},
"metadata": {},
"execution_count": 24
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"### Recency と Frequency の 2 次までの項を用いた線形予測モデル\n",
"\n",
"よりデータにフィットした自然な結果が得られるが、単調増加という制約は満たされなくなってしまう。"
],
"metadata": {
"id": "CGKqDElUWoCW"
}
},
{
"cell_type": "code",
"source": [
"pf = PolynomialFeatures(degree=2, interaction_only=False, include_bias=True)\n",
"model = LinearRegression()\n",
"pipeline = Pipeline([('PF', pf), ('MODEL', model)])\n",
"pipeline.fit(features, labels)\n",
"\n",
"pred2 = pipeline.predict(features)\n",
"print ('Mean Squared Error: {:.10f}'.format(mean_squared_error(labels, pred2)))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oCYs0n-4CFzn",
"outputId": "e4b5732e-9799-49c3-b8e1-b29c52a27bed"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mean Squared Error: 0.0001606279\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対して推定した再閲覧確率を対応付ける3Dグラフの作成\n",
"X, Y = np.meshgrid(Rcen, Freq)\n",
"Z = np.array(\n",
" [[pipeline.predict(DataFrame({'rcen': rcen, 'freq': freq}, index=[1]))[0]\n",
" for rcen in Rcen]for freq in Freq]\n",
" )\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111\n",
" , projection=\"3d\"\n",
" , xlabel='rcen'\n",
" , ylabel='freq'\n",
" , zlabel='pred_prob2'\n",
" )\n",
"ax.plot_wireframe(X, Y, Z)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
},
"id": "_qV7e0d1A2nT",
"outputId": "9420b110-7680-482f-f174-e4d6ead52a2d"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7fc0d7061c40>"
]
},
"metadata": {},
"execution_count": 26
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"### ロジスティック回帰\n",
"\n",
"確率値を線形モデルで予測するのは筋が悪いので、ロジットを線形モデルで予測してみる。"
],
"metadata": {
"id": "WDkkBOLs0Rve"
}
},
{
"cell_type": "code",
"source": [
"features = rf_df[['rcen', 'freq']]\n",
"labels = rf_df['prob']"
],
"metadata": {
"id": "W78MXre40SKo"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"logit_labels = labels.apply(lambda p: np.log(p/(1-p)))"
],
"metadata": {
"id": "XCNRywZ5s7ba"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = LinearRegression()\n",
"model.fit(features[:48], logit_labels[:48])\n",
"\n",
"pred = model.predict(features[:48])\n",
"print ('Mean Squared Error: {:.10f}'.format(mean_squared_error(labels[:48], 1 / (1+np.exp(-pred)))))"
],
"metadata": {
"id": "1MwaS3Fcs5uk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "bec1e01d-e08b-4360-a796-4bc2bdaf6bd2"
},
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mean Squared Error: 0.0002905992\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# 最新度(rcen)と頻度(freq)に対して推定した再閲覧確率を対応付ける3Dグラフの作成\n",
"X, Y = np.meshgrid(Rcen, Freq)\n",
"Z = np.array(\n",
" [[1 / ( 1+ np.exp(-model.predict(DataFrame({'rcen': rcen, 'freq': freq}, index=[1]))[0]))\n",
" for rcen in Rcen]for freq in Freq]\n",
" )\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111\n",
" , projection=\"3d\"\n",
" , xlabel='rcen'\n",
" , ylabel='freq'\n",
" , zlabel='pred_prob2'\n",
" )\n",
"ax.plot_wireframe(X, Y, Z)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
},
"id": "b6jAvKh6t8cF",
"outputId": "4df51d1a-2d21-404d-8ce4-05bb6e4fe9ad"
},
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7fc0d6d5f940>"
]
},
"metadata": {},
"execution_count": 32
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment