final_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18e99bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /Users/wenxiaowang/anaconda3/lib/python3.10/site-packages (1.5.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/wenxiaowang/anaconda3/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/wenxiaowang/anaconda3/lib/python3.10/site-packages (from pandas) (2022.7)\n",
      "Requirement already satisfied: numpy>=1.21.0 in /Users/wenxiaowang/anaconda3/lib/python3.10/site-packages (from pandas) (1.25.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/wenxiaowang/anaconda3/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f41283f",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89b95ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter, defaultdict\n",
    "import statistics\n",
    "import re\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78edcd2",
   "metadata": {},
   "source": [
    "# Data cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db8cfdcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_cleaning(raw_address):\n",
    "    data = pd.ExcelFile(raw_address)\n",
    "    sponsor_id = [5419, 5479, 5550]\n",
    "    Posting_Des = pd.read_excel(data, 'Posting Descriptions')\n",
    "    d_likes = pd.read_excel(data, 'Posting Likes')\n",
    "    d_clicks = pd.read_excel(data, 'Posting Clicks')\n",
    "    \n",
    "    df_posting_goals = adding_kid_safety(data)\n",
    "    \n",
    "    \n",
    "    merged_data = likes_by_sponsor(sponsor_id, d_likes, Posting_Des)\n",
    "    merged_data = clicks_by_sponsor(sponsor_id, d_clicks, merged_data)\n",
    "    \n",
    "    \n",
    "    df_posting_des = description_with_goals(df_posting_goals, Posting_Des)\n",
    "    \n",
    "    columns_drop = ['module_code','description']\n",
    "    df_posting_des = df_posting_des.drop(columns = columns_drop)\n",
    "    \n",
    "    for s in sponsor_id:\n",
    "        clean_data = cleaned_data(merged_data, df_posting_des, s)\n",
    "        clean_data.to_excel(str(s)+\" Cleaned Data.xlsx\", index=False)  \n",
    "\n",
    "\n",
    "def cleaned_data(merged_data, d_postingDes, s):\n",
    "\n",
    "    result = pd.DataFrame(merged_data['postable_id'])\n",
    "    \n",
    "    result['like']=merged_data[\"likes_\" + str(s)]\n",
    "    result['click']=merged_data[\"clicks_\" + str(s)]\n",
    "    result = result.merge(d_postingDes, how='left', on='postable_id')\n",
    "    return result\n",
    "\n",
    "\n",
    "def description_with_goals(df_posting_goals, Posting_Des):\n",
    "    df_posting_goals[\"postable_id\"] = df_posting_goals[\"postable_id\"].apply(remove_xx)\n",
    "    goals_dict = goals_to_dict(df_posting_goals)\n",
    "    \n",
    "    original_columns = Posting_Des.columns.tolist()\n",
    "    original_columns.append('goal')\n",
    "    original_columns.append('business')\n",
    "    original_columns.append('not-if')\n",
    "    original_columns.append('situation')\n",
    "    updated_df_posting_des = pd.DataFrame()\n",
    "    for index, row in Posting_Des.iterrows():\n",
    "        new_row = add_columns_from_dict(row, goals_dict)\n",
    "        updated_df_posting_des = pd.concat([updated_df_posting_des, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    df_posting_des = updated_df_posting_des.fillna(0)\n",
    "    df_posting_des = df_posting_des[original_columns]\n",
    "    return df_posting_des\n",
    "    \n",
    "\n",
    "def clicks_by_sponsor(sponsor_id, clicks_data, merged_data):\n",
    "    columns_drop=['sponsor_id', 'timestamp', 'postable_type']\n",
    "    for s in sponsor_id:\n",
    "        result = clicks_data.loc[clicks_data['sponsor_id'] == s]\n",
    "        result = result.groupby(['postable_id']).count()\n",
    "        result = result.reset_index()\n",
    "        result = result.drop(columns = columns_drop)\n",
    "        new_col_name = \"clicks_\" + str(s)\n",
    "        result = result.rename(columns={\"id\": new_col_name})\n",
    "        merged_data = merged_data.merge(result, how='left', on='postable_id')\n",
    "    merged_data = merged_data.fillna(0)\n",
    "    return merged_data\n",
    "\n",
    "\n",
    "def likes_by_sponsor(sponsor_id, likes_data, Posting_Des):\n",
    "    likes_data=likes_data.drop(labels=[\"id\",\"postable_type\",\"post_create_time\"],axis=1)\n",
    "    grouped = likes_data.groupby(['postable_id','sponsor_id'])\n",
    "    combined_likes = grouped['like_count'].sum().reset_index()\n",
    "    for s in sponsor_id:\n",
    "        like=combined_likes[combined_likes[\"sponsor_id\"]==s].drop(labels=\"sponsor_id\",axis=1)\n",
    "        new_col_name = \"likes_\" + str(s)\n",
    "        like.columns=[\"postable_id\", new_col_name]\n",
    "        Posting_Des = Posting_Des.merge(like, on='postable_id', how='left')\n",
    "    merged = Posting_Des.fillna(0)\n",
    "    return merged  \n",
    "\n",
    "\n",
    "def adding_kid_safety(raw_data):\n",
    "    df=pd.read_excel(raw_data,'Posting Descriptions')\n",
    "    pattern = r'(?i)(Child\\s*\\w*)'\n",
    "    mask = (df[\"title\"].str.extract(pattern, flags=re.IGNORECASE).notna().any(axis=1)) | (df[\"postable_id\"] == 575)\n",
    "    kids_related=df[mask]\n",
    "    kids_related.loc[:,\"module_code\"]=\"auto\"\n",
    "    kids_related.loc[:,\"gs_type\"]=\"goal\"\n",
    "    kids_related.loc[:,\"gs_id\"]=\"kids_safety\"\n",
    "    kids_related=kids_related.drop(labels=[\"title\",\"description\"],axis=1)\n",
    "    df_main=pd.read_excel(raw_data,'Posting Goals (Benefits)')\n",
    "    kids_related[\"postable_id\"]=kids_related[\"postable_id\"].astype(str)\n",
    "    df_main = pd.concat([df_main,kids_related],ignore_index=True)\n",
    "    df_main[\"postable_id\"]=df_main[\"postable_id\"].astype(str)\n",
    "    df_main.sort_values(\"postable_id\", inplace = True)\n",
    "    return df_main\n",
    "        \n",
    "\n",
    "def remove_xx(s):\n",
    "    s = str(s)\n",
    "    if '-xx' in s:\n",
    "        return int(s.replace('-xx', ''))\n",
    "    else:\n",
    "        return int(s)\n",
    "\n",
    "def goals_to_dict(goals_data):\n",
    "    result_dict = {}\n",
    "    for index, row in goals_data.iterrows():\n",
    "        id_value = row['postable_id']\n",
    "        gs_type_value = row['gs_type']\n",
    "        gs_id_value = row['gs_id']\n",
    "\n",
    "        if id_value not in result_dict:\n",
    "            result_dict[id_value] = defaultdict(list)\n",
    "\n",
    "        result_dict[id_value][gs_type_value].append(gs_id_value)\n",
    "    return result_dict\n",
    "\n",
    "def add_columns_from_dict(row, result_dict):\n",
    "    id_value = row['postable_id']\n",
    "    if id_value in result_dict:\n",
    "        values_dict = result_dict[id_value]\n",
    "        for col_name, col_values in values_dict.items():\n",
    "            row[col_name] = \", \".join(col_values)\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b16c930",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n9/zvs6108d0xz5wwfs5rwf07v40000gn/T/ipykernel_31666/1128820430.py:85: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  kids_related.loc[:,\"module_code\"]=\"auto\"\n",
      "/var/folders/n9/zvs6108d0xz5wwfs5rwf07v40000gn/T/ipykernel_31666/1128820430.py:86: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  kids_related.loc[:,\"gs_type\"]=\"goal\"\n",
      "/var/folders/n9/zvs6108d0xz5wwfs5rwf07v40000gn/T/ipykernel_31666/1128820430.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  kids_related.loc[:,\"gs_id\"]=\"kids_safety\"\n"
     ]
    }
   ],
   "source": [
    "data_cleaning(\"dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c301a93d",
   "metadata": {},
   "source": [
    "# Encode the data and run the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c762e71f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encoding(df):\n",
    "    all_goals = set()\n",
    "    for index, row in df.iterrows():\n",
    "        if row['goal'] != 0:\n",
    "            value = row['goal']\n",
    "            value_list = [v.strip() for v in value.split(\",\")]\n",
    "            all_goals.update(value_list)\n",
    "    columns_to_drop = ['like', 'click', 'title', 'business', 'not-if', 'situation']\n",
    "    df = df.drop(columns=columns_to_drop)\n",
    "    for goal in all_goals:\n",
    "        df[goal] = 0\n",
    "        for index, row in df.iterrows():\n",
    "            if row['goal'] != 0:\n",
    "                value = row['goal']\n",
    "                value_list = [v.strip() for v in value.split(\",\")]\n",
    "                if goal in value_list:\n",
    "                    df.loc[index, goal] = 1\n",
    "    return df\n",
    "\n",
    "encoding_5419 = encoding(pd.read_excel('5419 Cleaned Data.xlsx'))\n",
    "encoding_5479 = encoding(pd.read_excel('5479 Cleaned Data.xlsx'))\n",
    "encoding_5550 = encoding(pd.read_excel('5550 Cleaned Data.xlsx'))\n",
    "l_5419 = pd.read_excel('5419 Cleaned Data.xlsx').drop(columns=['title', 'goal', 'business', 'not-if', 'situation'])\n",
    "l_5479 = pd.read_excel('5479 Cleaned Data.xlsx').drop(columns=['title', 'goal', 'business', 'not-if', 'situation'])\n",
    "l_5550 = pd.read_excel('5550 Cleaned Data.xlsx').drop(columns=['title', 'goal', 'business', 'not-if', 'situation'])\n",
    "\n",
    "def combination(ghat):\n",
    "    ghat = np.zeros((math.comb(15,3) + math.comb(15,2) + math.comb(15,1), 15))\n",
    "    row = 0\n",
    "    for i in range(16):\n",
    "        for j in range(i+1, 16):\n",
    "            for q in range(j+1, 16):\n",
    "                if i != 15:\n",
    "                    ghat[row][i] = 1\n",
    "                if j != 15:\n",
    "                    ghat[row][j] = 1\n",
    "                if q != 15:\n",
    "                    ghat[row][q] = 1\n",
    "                row+=1\n",
    "        if i != 15:\n",
    "            ghat[row][i] = 1\n",
    "            row+=1\n",
    "    return ghat\n",
    "\n",
    "def model_sum(encoding_goals_file, like_clicks_file):\n",
    "    g = encoding_goals_file.drop(columns = 'goal')\n",
    "#     l = normalize(like_clicks_file)\n",
    "    g_matrix = g.drop(columns=\"postable_id\").values\n",
    "    l_matrix = like_clicks_file.drop(columns=\"postable_id\").values\n",
    "    g_transpose = np.transpose(g_matrix)\n",
    "    g_trans_inverse = np.linalg.inv(g_transpose @ g_matrix)\n",
    "    q_matrix = g_trans_inverse @ g_transpose @ l_matrix\n",
    "    \n",
    "    ghat = np.zeros((math.comb(15,3) + math.comb(15,2) + math.comb(15,1), 15))\n",
    "    ghat = combination(ghat)\n",
    "    goal_ratings = ghat @ q_matrix\n",
    "    \n",
    "    res_rank = []\n",
    "    goal_rankings = []\n",
    "    goal_rankings_solo = []\n",
    "    gl = g.drop(columns=\"postable_id\").columns.tolist()\n",
    "    def f(x,y):\n",
    "        return x + y\n",
    "\n",
    "    e = ''\n",
    "    c = ','\n",
    "    row = 0\n",
    "    for i in range(16):\n",
    "        for j in range(i+1, 16):\n",
    "            for q in range(j+1, 16):\n",
    "                goal_rankings.append((f'{gl[i]+c if i != 15 else e}{gl[j]+c if j != 15 else e}{gl[q] if q != 15 else e}', f(*goal_ratings[row])))\n",
    "                row+=1\n",
    "        if i != 15:\n",
    "            goal_rankings.append((f'SOLO:{gl[i]+c if i != 15 else e}{gl[j]+c if j != 15 else e}{gl[q] if q != 15 else e}', f(*goal_ratings[row])))\n",
    "            goal_rankings_solo.append((f'SOLO:{gl[i]+c if i != 15 else e}{gl[j]+c if j != 15 else e}{gl[q] if q != 15 else e}', f(*goal_ratings[row])))\n",
    "            ghat[row][i] = 1\n",
    "            row+=1\n",
    "    goal_rankings.sort(key=lambda x: x[1], reverse=True)\n",
    "    goal_rankings_solo.sort(key=lambda x: x[1], reverse=True)\n",
    "    res_rank.append(goal_rankings)\n",
    "    res_rank.append(goal_rankings_solo)\n",
    "    return res_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e207181a",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_5419 = model_sum(encoding_5419, l_5419)\n",
    "res_5479 = model_sum(encoding_5479, l_5479)\n",
    "res_5550 = model_sum(encoding_5550, l_5550)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a6648b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_rank_like(encoding_goals_file, like_clicks_file):\n",
    "    g = encoding_goals_file.drop(columns = 'goal')\n",
    "#     l = normalize(like_clicks_file)\n",
    "    g_matrix = g.drop(columns=\"postable_id\").values\n",
    "    l_matrix = like_clicks_file.drop(columns=\"postable_id\").values\n",
    "    g_transpose = np.transpose(g_matrix)\n",
    "    g_trans_inverse = np.linalg.inv(g_transpose @ g_matrix)\n",
    "    q_matrix = g_trans_inverse @ g_transpose @ l_matrix\n",
    "    \n",
    "    ghat = np.zeros((math.comb(15,3) + math.comb(15,2) + math.comb(15,1), 15))\n",
    "    ghat = combination(ghat)\n",
    "    goal_ratings = ghat @ q_matrix\n",
    "    \n",
    "    res_rank = []\n",
    "    goal_rankings = []\n",
    "    goal_rankings_solo = []\n",
    "    gl = g.drop(columns=\"postable_id\").columns.tolist()\n",
    "    def f(x,y):\n",
    "        if x + y == 0:\n",
    "            return 0\n",
    "        else:\n",
    "            return x / (x + y)\n",
    "\n",
    "    e = ''\n",
    "    c = ','\n",
    "    row = 0\n",
    "    for i in range(16):\n",
    "        for j in range(i+1, 16):\n",
    "            for q in range(j+1, 16):\n",
    "                goal_rankings.append((f'{gl[i]+c if i != 15 else e}{gl[j]+c if j != 15 else e}{gl[q] if q != 15 else e}', f(*goal_ratings[row])))\n",
    "                row+=1\n",
    "        if i != 15:\n",
    "            goal_rankings.append((f'SOLO:{gl[i]+c if i != 15 else e}{gl[j]+c if j != 15 else e}{gl[q] if q != 15 else e}', f(*goal_ratings[row])))\n",
    "            goal_rankings_solo.append((f'SOLO:{gl[i]+c if i != 15 else e}{gl[j]+c if j != 15 else e}{gl[q] if q != 15 else e}', f(*goal_ratings[row])))\n",
    "            ghat[row][i] = 1\n",
    "            row+=1\n",
    "    goal_rankings.sort(key=lambda x: x[1], reverse=True)\n",
    "    goal_rankings_solo.sort(key=lambda x: x[1], reverse=True)\n",
    "    res_rank.append(goal_rankings)\n",
    "    res_rank.append(goal_rankings_solo)\n",
    "    return res_rank\n",
    "\n",
    "res_5419_1 = model_rank_like(encoding_5419, l_5419)\n",
    "res_5479_1 = model_rank_like(encoding_5479, l_5479)\n",
    "res_5550_1 = model_rank_like(encoding_5550, l_5550)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2716cd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_average_score(df, df1):\n",
    "    category_data = {}\n",
    "    # Iterate over the first list\n",
    "    for category, value in df[1]:\n",
    "        if category in category_data:\n",
    "            category_data[category][0] += value\n",
    "            category_data[category][1] += 1\n",
    "        else:\n",
    "            category_data[category] = [value, 1]\n",
    "\n",
    "    # Iterate over the second list\n",
    "    for category, value in df1[1]:\n",
    "        if category in category_data:\n",
    "            category_data[category][0] += value\n",
    "            category_data[category][1] += 1\n",
    "        else:\n",
    "            category_data[category] = [value, 1]\n",
    "\n",
    "    # Calculate the average for each category\n",
    "    averages = {category: category_data[category][0] / category_data[category][1] for category in category_data}\n",
    "\n",
    "    sorted_averages = sorted(averages.items(), key=lambda item: item[1], reverse=True)\n",
    "\n",
    "    # Print the averages\n",
    "    for category, average in sorted_averages:\n",
    "        print(f\"{category}: {average}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8079c9ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for Sponsor_5419:\n",
      "SOLO:pet_safety,: 1.0\n",
      "SOLO:acc_tires,: 0.7313319950289883\n",
      "SOLO:rust,: 0.5624568010537305\n",
      "SOLO:safe,: 0.5219083025532811\n",
      "SOLO:prev_maint,: 0.4880550991197363\n",
      "SOLO:money,: 0.45753007723568995\n",
      "SOLO:value,: 0.41371936658833425\n",
      "SOLO:comf_conv,: 0.3334339317276326\n",
      "SOLO:fuel_econ,: 0.30358385612992594\n",
      "SOLO:locks_security_car,: 0.29352269720418067\n",
      "SOLO:teen_driver,: 0.23535560051540244\n",
      "SOLO:kids_safety,: 0.22892587814213705\n",
      "SOLO:locks_security_home,: 0.1926935947462961\n",
      "SOLO:allergy,: -0.3736381849631916\n",
      "SOLO:appear,: -0.39691748000728744\n"
     ]
    }
   ],
   "source": [
    "print(\"Score for Sponsor_5419:\")\n",
    "calculate_average_score(res_5419, res_5419_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f627570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for Sponsor_5479:\n",
      "SOLO:acc_tires,: 2.2998248031173767\n",
      "SOLO:prev_maint,: 1.2964516408713025\n",
      "SOLO:rust,: 0.9269181548326433\n",
      "SOLO:money,: 0.7310107927368911\n",
      "SOLO:fuel_econ,: 0.5190358864803964\n",
      "SOLO:value,: 0.4374411770906197\n",
      "SOLO:locks_security_car,: 0.408733257304196\n",
      "SOLO:appear,: 0.36644797026823306\n",
      "SOLO:safe,: 0.19634176753976929\n",
      "SOLO:comf_conv,: 0.018902153056941595\n",
      "SOLO:pet_safety,: 0.0\n",
      "SOLO:kids_safety,: -0.009333665558778126\n",
      "SOLO:teen_driver,: -0.014860985768155102\n",
      "SOLO:allergy,: -0.9671900293506996\n",
      "SOLO:locks_security_home,: -1.8007368871684786\n"
     ]
    }
   ],
   "source": [
    "print(\"Score for Sponsor_5479:\")\n",
    "calculate_average_score(res_5479, res_5479_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "897e6b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for Sponsor_5550:\n",
      "SOLO:prev_maint,: 2.642039185570717\n",
      "SOLO:comf_conv,: 2.0331120353198493\n",
      "SOLO:money,: 1.1213273874529694\n",
      "SOLO:pet_safety,: 0.75\n",
      "SOLO:appear,: 0.5842505733635819\n",
      "SOLO:fuel_econ,: 0.4518507204373865\n",
      "SOLO:kids_safety,: 0.3271263477047172\n",
      "SOLO:teen_driver,: 0.27185678842717786\n",
      "SOLO:value,: -0.11149206558015794\n",
      "SOLO:locks_security_car,: -0.2479272106596654\n",
      "SOLO:safe,: -0.3880849979584582\n",
      "SOLO:rust,: -0.4749697277896042\n",
      "SOLO:acc_tires,: -0.5893287325442723\n",
      "SOLO:allergy,: -0.6274627276085413\n",
      "SOLO:locks_security_home,: -1.768417591909437\n"
     ]
    }
   ],
   "source": [
    "print(\"Score for Sponsor_5550:\")\n",
    "calculate_average_score(res_5550, res_5550_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85ab0f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SOLO:prev_maint,', 4.79934945974464),\n",
       " ('SOLO:comf_conv,', 3.9645710371749794),\n",
       " ('SOLO:money,', 1.5926090081550113),\n",
       " ('SOLO:kids_safety,', 0.69614251680167),\n",
       " ('SOLO:teen_driver,', 0.659941786385636),\n",
       " ('SOLO:fuel_econ,', 0.5465257744875532),\n",
       " ('SOLO:pet_safety,', 0.5),\n",
       " ('SOLO:appear,', -0.14480292166413405),\n",
       " ('SOLO:value,', -0.2599984746379659),\n",
       " ('SOLO:safe,', -0.659941786385636),\n",
       " ('SOLO:locks_security_car,', -0.6701224157530031),\n",
       " ('SOLO:allergy,', -1.0576477416626566),\n",
       " ('SOLO:rust,', -1.4768880941462128),\n",
       " ('SOLO:acc_tires,', -1.553744649458146),\n",
       " ('SOLO:locks_security_home,', -3.7607822323066635)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_5550[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "71759591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26899625210701605"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_comb_5479 = 0\n",
    "for i in range(6):\n",
    "    avg_comb_5479 += res_5479[1][i][1]\n",
    "avg_comb_5479 = avg_comb_5479/8\n",
    "avg_comb_5479"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfe9720f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SOLO:prev_maint,', 4.79934945974464),\n",
       " ('SOLO:comf_conv,', 3.9645710371749794),\n",
       " ('SOLO:money,', 1.5926090081550113),\n",
       " ('SOLO:kids_safety,', 0.69614251680167),\n",
       " ('SOLO:teen_driver,', 0.659941786385636),\n",
       " ('SOLO:fuel_econ,', 0.5465257744875532),\n",
       " ('SOLO:pet_safety,', 0.5),\n",
       " ('SOLO:appear,', -0.14480292166413405),\n",
       " ('SOLO:value,', -0.2599984746379659),\n",
       " ('SOLO:safe,', -0.659941786385636),\n",
       " ('SOLO:locks_security_car,', -0.6701224157530031),\n",
       " ('SOLO:allergy,', -1.0576477416626566),\n",
       " ('SOLO:rust,', -1.4768880941462128),\n",
       " ('SOLO:acc_tires,', -1.553744649458146),\n",
       " ('SOLO:locks_security_home,', -3.7607822323066635)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_5550[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973f0f40",
   "metadata": {},
   "source": [
    "# Print out the selected goals as an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e0da6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_goals = {\n",
    "    \"Sponsor_5419\": [1, 1, 1, 0, 0, 0, 1, 0, 1, 2, 0, 2, 2, 0, 0],\n",
    "    \"Sponsor_5479\": [0, 1, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0],\n",
    "    \"Sponsor_5550\": [1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 2, 1, 1, 2, 1]\n",
    "}\n",
    "\n",
    "# Create a DataFrame from the dictionary\n",
    "df = pd.DataFrame(selected_goals, index=[\n",
    "    \"pet_safety\", \"rust\", \"safe\", \"locks_security_car\", \"locks_security_home\",\n",
    "    \"allergy\", \"prev_maint\", \"acc_tires\", \"money\", \"value\", \"appear\",\n",
    "    \"fuel_econ\", \"comf_conv\", \"teen_driver\", \"kid_safety\"\n",
    "])\n",
    "\n",
    "# Define the Excel file path\n",
    "file_path = \"selected_goals.xlsx\"\n",
    "\n",
    "# Save the DataFrame to an Excel file\n",
    "df.to_excel(file_path, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "224c3954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the data\n",
    "numbers = [4.799, 10.356]\n",
    "labels = ['prev_maint', 'prev_maint + money + comf_conv']\n",
    "\n",
    "# Create a line chart\n",
    "plt.plot(labels, numbers, marker='o', linestyle='-', color='b')\n",
    "plt.xlabel('Goals')\n",
    "plt.ylabel('Values')\n",
    "plt.title('Single Score vs Combination Score')\n",
    "\n",
    "for i in range(len(numbers)):\n",
    "    plt.text(labels[i], numbers[i], f'{numbers[i]:.3f}', ha='center', va='bottom')\n",
    "\n",
    "# Show the plot\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6135b75a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "line": {
          "color": "blue"
         },
         "marker": {
          "size": 10,
          "symbol": "circle"
         },
         "mode": "lines+markers",
         "type": "scatter",
         "x": [
          "prev_maint",
          "prev_maint + money + comf_conv"
         ],
         "y": [
          4.799,
          10.356
         ]
        }
       ],
       "layout": {
        "annotations": [
         {
          "showarrow": true,
          "text": "4.799",
          "x": "prev_maint",
          "y": 4.799
         },
         {
          "showarrow": true,
          "text": "10.356",
          "x": "prev_maint + money + comf_conv",
          "y": 10.356
         }
        ],
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Single Score vs Combination Score"
        },
        "xaxis": {
         "title": {
          "text": "Goals"
         }
        },
        "yaxis": {
         "title": {
          "text": "Values"
         }
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"b875a13d-9c11-4049-b1da-a8cf38130f3f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"b875a13d-9c11-4049-b1da-a8cf38130f3f\")) {                    Plotly.newPlot(                        \"b875a13d-9c11-4049-b1da-a8cf38130f3f\",                        [{\"line\":{\"color\":\"blue\"},\"marker\":{\"size\":10,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"x\":[\"prev_maint\",\"prev_maint + money + comf_conv\"],\"y\":[4.799,10.356],\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"title\":{\"text\":\"Single Score vs Combination Score\"},\"xaxis\":{\"title\":{\"text\":\"Goals\"}},\"yaxis\":{\"title\":{\"text\":\"Values\"}},\"annotations\":[{\"showarrow\":true,\"text\":\"4.799\",\"x\":\"prev_maint\",\"y\":4.799},{\"showarrow\":true,\"text\":\"10.356\",\"x\":\"prev_maint + money + comf_conv\",\"y\":10.356}]},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('b875a13d-9c11-4049-b1da-a8cf38130f3f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# Define the data\n",
    "numbers = [4.799, 10.356]\n",
    "labels = ['prev_maint', 'prev_maint + money + comf_conv']\n",
    "\n",
    "# Create a line chart\n",
    "fig = go.Figure(data=go.Scatter(x=labels, y=numbers, mode='lines+markers', line=dict(color='blue'), marker=dict(symbol='circle', size=10)))\n",
    "fig.update_layout(title='Single Score vs Combination Score',\n",
    "                  xaxis_title='Goals',\n",
    "                  yaxis_title='Values')\n",
    "\n",
    "# Annotate the data points with their values\n",
    "for label, number in zip(labels, numbers):\n",
    "    fig.add_annotation(x=label, y=number, text=f'{number:.3f}', showarrow=True)\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "430baec0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41614c81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}