apriori-algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84143d3f-63c9-424b-ad91-b4a50d2a143b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "from IPython.display import display_html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e423e118-146e-4c89-9881-51681245e4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toy_dataset():\n",
    "    data= [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
    "    ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],\n",
    "    ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],\n",
    "    ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],\n",
    "    ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]\n",
    "    print(\"Do you want to view the raw data?\")\n",
    "    choice =input()\n",
    "    if choice=='yes':\n",
    "        print(\"Raw Data:\")\n",
    "        print(data)\n",
    "    te = TransactionEncoder()\n",
    "    te_ary = te.fit(data).transform(data)\n",
    "    df = pd.DataFrame(te_ary, columns=te.columns_)\n",
    "    print(\"Do you want to view the Encoded data?\")\n",
    "    choice = input()\n",
    "    if choice=='yes':\n",
    "        print(\"Encoded Data:\")\n",
    "        display_html(df)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4000ac1-0acf-4b12-9deb-4a17c8cb62be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def frequent_itemset(data):\n",
    "    print(\"Enter the value of minimum support threshold:\")\n",
    "    support=float(input())\n",
    "    frequent_itemsets = apriori(data, min_support=support, use_colnames=True)\n",
    "    print(\"Do you want to view frequent itemsets generated by Apriori?\")\n",
    "    choice =input()\n",
    "    if choice=='yes':\n",
    "        print(\"Frequent itemset:\")\n",
    "        display_html(frequent_itemsets)\n",
    "    return frequent_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11fc05d3-c0ff-439c-9a65-382af0bd7c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def association_rule(frequent_itemsets):\n",
    "    print(\"Enter your metric of interest confidence or lift:\")\n",
    "    choice=input()\n",
    "    if choice=='confidence':\n",
    "        print(\"Enter minimum confidence threshold value:\")\n",
    "        min_confidence=float(input())\n",
    "        rule=association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=min_confidence)\n",
    "    elif choice=='lift':\n",
    "        print(\"Enter minimum lift threshold value:\")\n",
    "        min_lift=float(input())\n",
    "        rule=association_rules(frequent_itemsets, metric=\"lift\", min_threshold=min_lift)\n",
    "    print(\"Do you want to view the learned association rules?\")\n",
    "    choice=input()\n",
    "    if choice=='yes':\n",
    "        display_html(rule.drop(['leverage','conviction'],axis=1))\n",
    "    else:\n",
    "        quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6699bdf-c385-47a0-8ca5-e6d6437c5ef4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to view the raw data?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " yes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Data:\n",
      "[['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'], ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'], ['Milk', 'Apple', 'Kidney Beans', 'Eggs'], ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'], ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]\n",
      "Do you want to view the Encoded data?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " yes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Data:\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Apple</th>\n",
       "      <th>Corn</th>\n",
       "      <th>Dill</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Ice cream</th>\n",
       "      <th>Kidney Beans</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Nutmeg</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Unicorn</th>\n",
       "      <th>Yogurt</th>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the value of minimum support threshold:\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " 0.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to view frequent itemsets generated by Apriori?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " yes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequent itemset:\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(Kidney Beans)</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk)</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion)</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt)</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Kidney Beans, Eggs)</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion, Eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Milk)</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Yogurt)</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Kidney Beans, Onion, Eggs)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your metric of interest confidence or lift:\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " confidence\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter minimum confidence threshold value:\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " 0.7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to view the learned association rules?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " yes\n"
     ]
    },
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       "    <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",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Onion)</td>\n",
       "      <td>(Eggs)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
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       "      <th>3</th>\n",
       "      <td>(Eggs)</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Milk)</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Onion)</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Yogurt)</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
       "      <td>(Eggs)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Kidney Beans, Eggs)</td>\n",
       "      <td>(Onion)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Onion, Eggs)</td>\n",
       "      <td>(Kidney Beans)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Onion)</td>\n",
       "      <td>(Kidney Beans, Eggs)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Eggs)</td>\n",
       "      <td>(Kidney Beans, Onion)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def main():\n",
    "    data=toy_dataset()\n",
    "    frequent_itemsets=frequent_itemset(data)\n",
    "    association_rule(frequent_itemsets)\n",
    "main()"
   ]
  }
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