{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5aa81727",
   "metadata": {},
   "source": [
    "## バスケット分析（アソシエーション分析）とは？\n",
    "\n",
    "ある事象と\"同時に発生する事象\"が何かを分析する手法。\n",
    "\n",
    "ex.)消費者が「ある商品」を購入した際に、よく一緒に買われている商品は何か？\n",
    "\n",
    "\n",
    "## よく使用される指標\n",
    "\n",
    "### ・Confidence（信頼度）\n",
    "「リンゴを買った人のうち、どれくらいの人がミカンも買ったか」という確率\n",
    "\n",
    "### ・Support（支持度）\n",
    "「そもそもリンゴとミカンを一緒に買った人がどのくらいいるのか」という指標\n",
    "\n",
    "### ・Lift（リフト）\n",
    "「そもそもミカン自体がどれだけ売れているのか」という指標\n",
    "\n",
    "\n",
    "## Aprioriアルゴリズム\n",
    "実際は1対1だけでなく、「リンゴとミカンを買った人がモモをどれだけ買うか」、のような多対多の関係性もある。\n",
    "\n",
    "・・・が、商品数が多くなってくるとそこまですべて計算するのはほぼ無理なので、この計算を高速で行うためのアルゴリズム。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49793bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#データの読み込み＆整形\n",
    "import pandas as pd\n",
    "\n",
    "df_sales = pd.read_csv(\"9-8_baslet.csv\",index_col=\"名前\")\n",
    "df_sales = df_sales.fillna(False).replace(\"○\",True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26645d61",
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>(みかん)</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>0.073964</td>\n",
       "      <td>1.641026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(みかん)</td>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>0.073964</td>\n",
       "      <td>1.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>(すいか)</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>0.059172</td>\n",
       "      <td>1.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(すいか)</td>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>0.059172</td>\n",
       "      <td>1.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(もも, りんご)</td>\n",
       "      <td>(みかん)</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.041420</td>\n",
       "      <td>1.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(りんご, みかん)</td>\n",
       "      <td>(もも)</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>0.048817</td>\n",
       "      <td>1.634615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(もも)</td>\n",
       "      <td>(りんご, みかん)</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>0.048817</td>\n",
       "      <td>1.211538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(みかん)</td>\n",
       "      <td>(もも, りんご)</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.041420</td>\n",
       "      <td>1.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(バナナ, りんご)</td>\n",
       "      <td>(みかん)</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>2.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(りんご, みかん)</td>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>(りんご, みかん)</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.292308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(みかん)</td>\n",
       "      <td>(バナナ, りんご)</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.208791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(なし, りんご)</td>\n",
       "      <td>(すいか)</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(りんご, すいか)</td>\n",
       "      <td>(なし)</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>0.062130</td>\n",
       "      <td>2.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(なし)</td>\n",
       "      <td>(りんご, すいか)</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>0.062130</td>\n",
       "      <td>1.269231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(すいか)</td>\n",
       "      <td>(なし, りんご)</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.292308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(バナナ, もも)</td>\n",
       "      <td>(みかん)</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>2.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(バナナ, みかん)</td>\n",
       "      <td>(もも)</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>0.048817</td>\n",
       "      <td>1.634615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(もも, みかん)</td>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(バナナ)</td>\n",
       "      <td>(もも, みかん)</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.292308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(もも)</td>\n",
       "      <td>(バナナ, みかん)</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>0.048817</td>\n",
       "      <td>1.211538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(みかん)</td>\n",
       "      <td>(バナナ, もも)</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>0.056213</td>\n",
       "      <td>1.208791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   antecedents consequents  antecedent support  consequent support   support  \\\n",
       "0        (バナナ)       (みかん)            0.307692            0.384615  0.192308   \n",
       "1        (みかん)       (バナナ)            0.384615            0.307692  0.192308   \n",
       "2        (バナナ)       (すいか)            0.307692            0.307692  0.153846   \n",
       "3        (すいか)       (バナナ)            0.307692            0.307692  0.153846   \n",
       "4    (もも, りんご)       (みかん)            0.192308            0.384615  0.115385   \n",
       "5   (りんご, みかん)        (もも)            0.192308            0.346154  0.115385   \n",
       "6         (もも)  (りんご, みかん)            0.346154            0.192308  0.115385   \n",
       "7        (みかん)   (もも, りんご)            0.384615            0.192308  0.115385   \n",
       "8   (バナナ, りんご)       (みかん)            0.153846            0.384615  0.115385   \n",
       "9   (りんご, みかん)       (バナナ)            0.192308            0.307692  0.115385   \n",
       "10       (バナナ)  (りんご, みかん)            0.307692            0.192308  0.115385   \n",
       "11       (みかん)  (バナナ, りんご)            0.384615            0.153846  0.115385   \n",
       "12   (なし, りんご)       (すいか)            0.192308            0.307692  0.115385   \n",
       "13  (りんご, すいか)        (なし)            0.153846            0.346154  0.115385   \n",
       "14        (なし)  (りんご, すいか)            0.346154            0.153846  0.115385   \n",
       "15       (すいか)   (なし, りんご)            0.307692            0.192308  0.115385   \n",
       "16   (バナナ, もも)       (みかん)            0.153846            0.384615  0.115385   \n",
       "17  (バナナ, みかん)        (もも)            0.192308            0.346154  0.115385   \n",
       "18   (もも, みかん)       (バナナ)            0.192308            0.307692  0.115385   \n",
       "19       (バナナ)   (もも, みかん)            0.307692            0.192308  0.115385   \n",
       "20        (もも)  (バナナ, みかん)            0.346154            0.192308  0.115385   \n",
       "21       (みかん)   (バナナ, もも)            0.384615            0.153846  0.115385   \n",
       "\n",
       "    confidence      lift  leverage  conviction  \n",
       "0     0.625000  1.625000  0.073964    1.641026  \n",
       "1     0.500000  1.625000  0.073964    1.384615  \n",
       "2     0.500000  1.625000  0.059172    1.384615  \n",
       "3     0.500000  1.625000  0.059172    1.384615  \n",
       "4     0.600000  1.560000  0.041420    1.538462  \n",
       "5     0.600000  1.733333  0.048817    1.634615  \n",
       "6     0.333333  1.733333  0.048817    1.211538  \n",
       "7     0.300000  1.560000  0.041420    1.153846  \n",
       "8     0.750000  1.950000  0.056213    2.461538  \n",
       "9     0.600000  1.950000  0.056213    1.730769  \n",
       "10    0.375000  1.950000  0.056213    1.292308  \n",
       "11    0.300000  1.950000  0.056213    1.208791  \n",
       "12    0.600000  1.950000  0.056213    1.730769  \n",
       "13    0.750000  2.166667  0.062130    2.615385  \n",
       "14    0.333333  2.166667  0.062130    1.269231  \n",
       "15    0.375000  1.950000  0.056213    1.292308  \n",
       "16    0.750000  1.950000  0.056213    2.461538  \n",
       "17    0.600000  1.733333  0.048817    1.634615  \n",
       "18    0.600000  1.950000  0.056213    1.730769  \n",
       "19    0.375000  1.950000  0.056213    1.292308  \n",
       "20    0.333333  1.733333  0.048817    1.211538  \n",
       "21    0.300000  1.950000  0.056213    1.208791  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.frequent_patterns import apriori, association_rules, fpgrowth\n",
    "\n",
    "#aprioriでまずsupportが高い単品or組み合わせを選出\n",
    "freq_items = apriori(df_sales, min_support=0.1, use_colnames=True)\n",
    "\n",
    "#上で選ばれた組み合わせの中でliftが高い組み合わせを選出\n",
    "freq_items_top = association_rules(freq_items, metric = \"lift\",min_threshold = 1.5)\n",
    "\n",
    "freq_items_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a900cbb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('バナナ', 'もも'),\n",
       " ('バナナ',),\n",
       " ('もも', 'りんご'),\n",
       " ('バナナ', 'りんご'),\n",
       " ('バナナ', 'みかん'),\n",
       " ('もも', 'みかん'),\n",
       " ('すいか',),\n",
       " ('りんご', 'すいか'),\n",
       " ('みかん',),\n",
       " ('りんご', 'みかん'),\n",
       " ('もも',),\n",
       " ('なし', 'りんご'),\n",
       " ('なし',)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#図にして表示\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 親ノードの抽出\n",
    "ant = freq_items_top['antecedents'].values\n",
    "ant = [tuple(x) for x in ant]\n",
    "\n",
    "# 子ノードの抽出\n",
    "con = freq_items_top['consequents'].values\n",
    "con = [tuple(x) for x in con]\n",
    "\n",
    "# 全ノードのリストアップ\n",
    "both = list(set(ant + con))\n",
    "\n",
    "both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fda4919",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 関係グラフの初期化\n",
    "G = nx.DiGraph()\n",
    "\n",
    "# ノードの追加\n",
    "for n in both:\n",
    "    G.add_node(n)\n",
    "\n",
    "# エッジの追加\n",
    "for i in range(len(freq_items_top)):\n",
    "    item = freq_items_top.loc[i]\n",
    "    ant = tuple(item['antecedents'])\n",
    "    con = tuple(item['consequents'])\n",
    "    G.add_edge(ant, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7943571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#グラフの描画\n",
    "pos = nx.spring_layout(G,seed=1)\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "nx.draw_networkx_nodes(G, pos)\n",
    "nx.draw_networkx_edges(G, pos)\n",
    "nx.draw_networkx_labels(G, pos,\n",
    "    horizontalalignment='left', \n",
    "    verticalalignment='center',font_family='Hiragino Maru Gothic Pro')\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00c917c1",
   "metadata": {},
   "outputs": [],
   "source": []
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