recommendation_model_build.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the path to the CSV file\n",
    "csv_file = 'hobby_dataset_rated.csv'\n",
    "\n",
    "# Check if the CSV file exists\n",
    "if os.path.isfile(csv_file):\n",
    "    # Load the dataset into a pandas dataframe\n",
    "    df = pd.read_csv(csv_file)\n",
    "\n",
    "    # Check if the base_rating column already exists in the dataframe\n",
    "    if 'base_rating' not in df.columns:\n",
    "        # Assign a base rating of 15 to each hobby in the dataframe\n",
    "        df['base_rating'] = 15\n",
    "\n",
    "        # Save the updated dataset to a CSV file\n",
    "        df.to_csv('hobby_dataset_rated.csv', index=False)\n",
    "else:\n",
    "    # Log missing rated file and create one using original dataset\n",
    "    df = pd.read_csv('hobby_dataset_original.csv')\n",
    "    df['base_rating'] = 15\n",
    "    df.to_csv('hobby_dataset_rated.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Hobby</th>\n",
       "      <th>Type of Hobby</th>\n",
       "      <th>base_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drawing</td>\n",
       "      <td>Creative</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Writing fiction</td>\n",
       "      <td>Creative</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Hobby Type of Hobby  base_rating\n",
       "0          Drawing      Creative           15\n",
       "1  Writing fiction      Creative           15"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get all hobbies of a given type (Private Method)\n",
    "def get_hobbies_by_type(hobby_type):\n",
    "    return df[df['Type of Hobby'] == hobby_type]['Hobby'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to randomly suggest a hobby of a given type\n",
    "def suggest_hobby(hobby_type):\n",
    "        hobbies = get_hobbies_by_type(hobby_type)\n",
    "        return np.random.choice(hobbies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to randomly suggest a hobby\n",
    "def suggest_random_hobby():\n",
    "    hobbies = df['Hobby'].dropna().values\n",
    "    return np.random.choice(hobbies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to update the base rating of a hobby based on user feedback\n",
    "def update_rating(hobby, liked):\n",
    "    if liked:\n",
    "        df.loc[df['Hobby'] == hobby, 'base_rating'] += 1\n",
    "        print(df.loc[df['Hobby'] == hobby]['base_rating'])\n",
    "    else:\n",
    "        df.loc[df['Hobby'] == hobby, 'base_rating'] -= 1\n",
    "        print(df.loc[df['Hobby'] == hobby]['base_rating'])\n",
    "        if df.loc[df['Hobby'] == hobby, 'base_rating'].values[0] < 0:\n",
    "            df.drop(df[df['Hobby'] == hobby].index, inplace=True)\n",
    "\n",
    "    # Save the updated dataset to a CSV file\n",
    "    df.to_csv('hobby_dataset_rated.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get recommended hobbies\n",
    "def get_recommendations(n=5):\n",
    "    # Calculate the weighted rating for each hobby based on its base rating\n",
    "    weighted_ratings = []\n",
    "    for hobby in df['Hobby'].unique():\n",
    "        if hobby in df['Hobby'].values:\n",
    "            weighted_rating = df.loc[df['Hobby'] == hobby, 'base_rating'].values[0]\n",
    "            weighted_ratings.append((hobby, weighted_rating))\n",
    "\n",
    "    # Sort the hobbies by weighted rating in descending order\n",
    "    sorted_ratings = sorted(weighted_ratings, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # Return the top n hobbies with the highest weighted rating\n",
    "    recommendations = []\n",
    "    for hobby, rating in sorted_ratings:\n",
    "        recommendations.append(hobby)\n",
    "        if len(recommendations) == n:\n",
    "            break\n",
    "    print(recommendations)\n",
    "    recommended_hobby = random.choice(recommendations)\n",
    "    return recommended_hobby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drawing\n",
      "Reading\n"
     ]
    }
   ],
   "source": [
    "# Testing functions - random hobby\n",
    "print(suggest_hobby('Creative'))\n",
    "print(suggest_random_hobby())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4    14\n",
      "Name: base_rating, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Testing functions - update hobby liking\n",
    "update_rating('Pottery', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Drawing', 'Writing fiction', 'Playing music', 'Photography', 'Baking']\n",
      "Playing music\n"
     ]
    }
   ],
   "source": [
    "# Testing functions - get recommendation\n",
    "rec_hobby = get_recommendations()\n",
    "print(rec_hobby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset all hobbies to base rating\n",
    "def reset_rating():\n",
    "    df['base_rating'] = 15\n",
    "    df.to_csv('hobby_dataset_rated.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saves the functions as a dictionary in a pickle file because pickle only takes one parameter\n",
    "functions = {'get_recommendations': get_recommendations,\n",
    "             'update_rating': update_rating,\n",
    "             'suggest_hobby': suggest_hobby,\n",
    "             'reset_rating': reset_rating}\n",
    "\n",
    "with open('recommendation_model.pkl', 'wb') as f:\n",
    "    pickle.dump(functions, f)"
   ]
  }
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