{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "associationrules.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "oo1FeOZvQ2d2" }, "source": [ "# アソシエーション分析の例\n", "- 全体の流れ\n", " - 少し偏りのあるデータセットを作成。\n", " - orange3-associate パッケージによりアソシエーション分析。\n", "- 参考\n", " - [Python でアソシエーション分析 - Orange3-Associate](https://fits.hatenablog.com/entry/2018/01/09/211900)" ] }, { "cell_type": "markdown", "metadata": { "id": "A71_siYIRTAM" }, "source": [ "## 環境構築" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ecy6rFx4KIaC", "outputId": "35a97f10-f93c-4640-ff74-c19126637280" }, "source": [ "!pip install orange3\n", "!pip install orange3-associate" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: orange3 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cryptography>=2.0 in /usr/local/lib/python3.7/dist-packages (from SecretStorage>=3.2; sys_platform == \"linux\"->keyring->Orange3>=3.25.0->orange3-associate) (3.4.7)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=3.6->keyring->Orange3>=3.25.0->orange3-associate) (3.4.1)\n", "Requirement already satisfied: msgpack>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from cachecontrol[filecache]->orange-canvas-core<0.2a,>=0.1.21->Orange3>=3.25.0->orange3-associate) (1.0.2)\n", "Requirement already satisfied: lockfile>=0.9; extra == \"filecache\" in /usr/local/lib/python3.7/dist-packages (from cachecontrol[filecache]->orange-canvas-core<0.2a,>=0.1.21->Orange3>=3.25.0->orange3-associate) (0.12.2)\n", "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography>=2.0->SecretStorage>=3.2; sys_platform == \"linux\"->keyring->Orange3>=3.25.0->orange3-associate) (1.14.5)\n", "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=2.0->SecretStorage>=3.2; sys_platform == \"linux\"->keyring->Orange3>=3.25.0->orange3-associate) (2.20)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "WHxgYiFTRVkM" }, "source": [ "## データセット作成" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9FtaywVGIWvl", "outputId": "1ef75f51-e41c-4b2f-ca10-cd1d8ba8edc0" }, "source": [ "import random\n", "\n", "def make_dataset(num_sample=100, num_item=15):\n", " \"\"\"データセット構築。\n", " 一度に購入した商品軍をリストとして作成。\n", " 偏り（傾向）を持たせるため、特定条件に合致する場合は別商品も購入するように設定。\n", "\n", " num_sample (int): サンプル数。\n", " num_item (int) : 商品種類数。\n", " \"\"\"\n", " dataset = []\n", " for i in range(num_sample):\n", " temp = []\n", " size = random.randint(1, 3)\n", " for j in range(size):\n", " item = random.randint(1, num_item)\n", " temp.append(item)\n", " if item == 1: # 特別条件1\n", " temp.append(10) # 　商品1を購入した場合は、商品10も購入する。\n", " elif item == 2: # 特別条件2(商品2を購入した場合は、70%の確率で商品3,4も購入する。)\n", " if random.random() <= 0.7:\n", " temp.append(3)\n", " temp.append(4)\n", " dataset.append(temp[:])\n", " return dataset\n", "\n", "dataset = make_dataset(num_sample=100)\n", "print(dataset[0:5])" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "[[5, 9, 2, 3, 4], [8], [8, 4], [3], [9, 14]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Gcs0rvjKR1xy" }, "source": [ "## データセットをCSVファイルとして保存" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H0Bu4TpUOGmK", "outputId": "4240ed50-aef8-4c8c-cd38-6bd101767d7e" }, "source": [ "def save_dataset(dataset, filename=\"data.basket\"):\n", " with open(filename, \"w\") as f:\n", " for items in dataset:\n", " line = \"\"\n", " for index, value in enumerate(items):\n", " if index == 0:\n", " line += str(value)\n", " else:\n", " line += \",\" + str(value)\n", " line += \"\\n\"\n", " f.write(line)\n", "\n", "filename = \"data.basket\"\n", "save_dataset(dataset, filename)\n", "!head data.basket" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "5,9,2,3,4\n", "8\n", "8,4\n", "3\n", "9,14\n", "15\n", "15,6\n", "14\n", "11\n", "11,2,3,4\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "YHRuyIkDR6Sv" }, "source": [ "## アソシエーション分析\n", "- モジュール読み込み後、Orange.data.Table()によりCSVファイルを読み込む。各商品を OneHoe.encode によりone-hotエンコードする。\n", "- frequent_itemsets() によりカウント。第2引数 min_support は、抽出件数最小値を設定。（比率でも設定できる模様）\n", "- association_rules() によりアソシエーションルールの抽出。第2引数 min_conficende は、抽出する confidence の最小値を指定。" ] }, { "cell_type": "code", "metadata": { "id": "8SrcLryvNutY" }, "source": [ "import Orange\n", "from orangecontrib.associate.fpgrowth import *" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3BkxZ5KAPaff", "outputId": "b4381076-040e-46c3-b0d2-b967a938eaa0" }, "source": [ "# データファイル読み込み\n", "tbl = Orange.data.Table(filename)\n", "\n", "X, mapping = OneHot.encode(tbl)\n", "\n", "itemsets = dict(frequent_itemsets(X, 5))\n", "print(X[:5])\n", "print(itemsets)" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "[[0, 1, 2, 3, 4], [5], [4, 5], [3], [1, 6]]\n", "{frozenset({0}): 13, frozenset({1}): 14, frozenset({2}): 11, frozenset({3}): 21, frozenset({2, 3}): 8, frozenset({4}): 22, frozenset({1, 4}): 5, frozenset({2, 4}): 9, frozenset({3, 4}): 8, frozenset({2, 3, 4}): 8, frozenset({5}): 10, frozenset({6}): 19, frozenset({7}): 16, frozenset({8}): 16, frozenset({9}): 13, frozenset({9, 6}): 5, frozenset({10}): 10, frozenset({11}): 14, frozenset({12}): 11, frozenset({13}): 12, frozenset({14}): 24, frozenset({3, 14}): 5, frozenset({4, 14}): 6, frozenset({13, 14}): 12}\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: .\n", " \"\"\")\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "PAs5da3KPlJW" }, "source": [ "# アソシエーションルールの抽出\n", "# 第2引数: min_confidence は確信度の最小値\n", "rules = association_rules(itemsets, 0.2)" ], "execution_count": 6, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jaP8SOILPrmR", "outputId": "e647fdab-262e-42d9-854b-61df18742991" }, "source": [ "def decode_onehot(d):\n", " items = OneHot.decode(d, tbl, mapping)\n", " # ContinuousVariable の name 値を取得\n", " return list(map(lambda v: v[1].name, items))\n", "\n", "for P, Q, support, confidence in rules:\n", " lhs = decode_onehot(P)\n", " rhs = decode_onehot(Q)\n", " print(f\"lhs = {lhs}, rhs = {rhs}, support = {support}, confidence = {confidence}\")\n" ], "execution_count": 7, "outputs": [ { "output_type": "stream", "text": [ "lhs = ['3', '4'], rhs = ['2'], support = 8, confidence = 1.0\n", "lhs = ['2', '4'], rhs = ['3'], support = 8, confidence = 0.8888888888888888\n", "lhs = ['4'], rhs = ['2', '3'], support = 8, confidence = 0.36363636363636365\n", "lhs = ['2', '3'], rhs = ['4'], support = 8, confidence = 1.0\n", "lhs = ['3'], rhs = ['2', '4'], support = 8, confidence = 0.38095238095238093\n", "lhs = ['2'], rhs = ['3', '4'], support = 8, confidence = 0.7272727272727273\n", "lhs = ['3'], rhs = ['2'], support = 8, confidence = 0.38095238095238093\n", "lhs = ['2'], rhs = ['3'], support = 8, confidence = 0.7272727272727273\n", "lhs = ['4'], rhs = ['9'], support = 5, confidence = 0.22727272727272727\n", "lhs = ['9'], rhs = ['4'], support = 5, confidence = 0.35714285714285715\n", "lhs = ['4'], rhs = ['2'], support = 9, confidence = 0.4090909090909091\n", "lhs = ['2'], rhs = ['4'], support = 9, confidence = 0.8181818181818182\n", "lhs = ['4'], rhs = ['3'], support = 8, confidence = 0.36363636363636365\n", "lhs = ['3'], rhs = ['4'], support = 8, confidence = 0.38095238095238093\n", "lhs = ['14'], rhs = ['11'], support = 5, confidence = 0.2631578947368421\n", "lhs = ['11'], rhs = ['14'], support = 5, confidence = 0.38461538461538464\n", "lhs = ['10'], rhs = ['3'], support = 5, confidence = 0.20833333333333334\n", "lhs = ['3'], rhs = ['10'], support = 5, confidence = 0.23809523809523808\n", "lhs = ['10'], rhs = ['4'], support = 6, confidence = 0.25\n", "lhs = ['4'], rhs = ['10'], support = 6, confidence = 0.2727272727272727\n", "lhs = ['10'], rhs = ['1'], support = 12, confidence = 0.5\n", "lhs = ['1'], rhs = ['10'], support = 12, confidence = 1.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "JJAKtrhSPyIL" }, "source": [ "" ], "execution_count": 7, "outputs": [] } ] }