feat: add rentals

processed/rentals/final.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb1ebabaee1148e8960e20e063dd5aebe6dddfdd4013911c4e3cbdd5002328cb
+size 20448052

processors/rentals.ipynb

@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_DIR = \"../data\"\n",
+    "PROCESSED_DIR = \"../processed/\"\n",
+    "FACET_DIR = \"rentals/\"\n",
+    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
+    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
+      "       'Home Type', 'Date', 'Rent (Smoothed)'],\n",
+      "      dtype='object')\n",
+      "['Rent (Smoothed) (Seasonally Adjusted)', 'RegionID', 'Home Type', 'Date']\n",
+      "Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
+      "       'Home Type', 'Date', 'Rent (Smoothed)',\n",
+      "       'Rent (Smoothed) (Seasonally Adjusted)'],\n",
+      "      dtype='object')\n",
+      "['RegionID', 'Home Type', 'Date']\n",
+      "Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',\n",
+      "       'Home Type', 'Date', 'Rent (Smoothed)',\n",
+      "       'Rent (Smoothed) (Seasonally Adjusted)'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "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>RegionID</th>\n",
+       "      <th>SizeRank</th>\n",
+       "      <th>RegionName</th>\n",
+       "      <th>RegionType</th>\n",
+       "      <th>StateName</th>\n",
+       "      <th>Home Type</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Rent (Smoothed)</th>\n",
+       "      <th>Rent (Smoothed) (Seasonally Adjusted)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2015-01-31</td>\n",
+       "      <td>1251.119548</td>\n",
+       "      <td>1253.380721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2015-02-28</td>\n",
+       "      <td>1257.678915</td>\n",
+       "      <td>1258.745304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2015-03-31</td>\n",
+       "      <td>1266.242657</td>\n",
+       "      <td>1263.914519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2015-04-30</td>\n",
+       "      <td>1276.548397</td>\n",
+       "      <td>1269.232278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>102001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>country</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SFR</td>\n",
+       "      <td>2015-05-31</td>\n",
+       "      <td>1286.191645</td>\n",
+       "      <td>1273.346695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96007</th>\n",
+       "      <td>845167</td>\n",
+       "      <td>296</td>\n",
+       "      <td>Ottawa, IL</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>multifamily</td>\n",
+       "      <td>2023-08-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96008</th>\n",
+       "      <td>845167</td>\n",
+       "      <td>296</td>\n",
+       "      <td>Ottawa, IL</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>multifamily</td>\n",
+       "      <td>2023-09-30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96009</th>\n",
+       "      <td>845167</td>\n",
+       "      <td>296</td>\n",
+       "      <td>Ottawa, IL</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>multifamily</td>\n",
+       "      <td>2023-10-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96010</th>\n",
+       "      <td>845167</td>\n",
+       "      <td>296</td>\n",
+       "      <td>Ottawa, IL</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>multifamily</td>\n",
+       "      <td>2023-11-30</td>\n",
+       "      <td>804.147562</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96011</th>\n",
+       "      <td>845167</td>\n",
+       "      <td>296</td>\n",
+       "      <td>Ottawa, IL</td>\n",
+       "      <td>msa</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>multifamily</td>\n",
+       "      <td>2023-12-31</td>\n",
+       "      <td>800.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>96012 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       RegionID  SizeRank     RegionName RegionType StateName    Home Type  \\\n",
+       "0        102001         0  United States    country       NaN          SFR   \n",
+       "1        102001         0  United States    country       NaN          SFR   \n",
+       "2        102001         0  United States    country       NaN          SFR   \n",
+       "3        102001         0  United States    country       NaN          SFR   \n",
+       "4        102001         0  United States    country       NaN          SFR   \n",
+       "...         ...       ...            ...        ...       ...          ...   \n",
+       "96007    845167       296     Ottawa, IL        msa        IL  multifamily   \n",
+       "96008    845167       296     Ottawa, IL        msa        IL  multifamily   \n",
+       "96009    845167       296     Ottawa, IL        msa        IL  multifamily   \n",
+       "96010    845167       296     Ottawa, IL        msa        IL  multifamily   \n",
+       "96011    845167       296     Ottawa, IL        msa        IL  multifamily   \n",
+       "\n",
+       "             Date  Rent (Smoothed)  Rent (Smoothed) (Seasonally Adjusted)  \n",
+       "0      2015-01-31      1251.119548                            1253.380721  \n",
+       "1      2015-02-28      1257.678915                            1258.745304  \n",
+       "2      2015-03-31      1266.242657                            1263.914519  \n",
+       "3      2015-04-30      1276.548397                            1269.232278  \n",
+       "4      2015-05-31      1286.191645                            1273.346695  \n",
+       "...           ...              ...                                    ...  \n",
+       "96007  2023-08-31              NaN                                    NaN  \n",
+       "96008  2023-09-30              NaN                                    NaN  \n",
+       "96009  2023-10-31              NaN                                    NaN  \n",
+       "96010  2023-11-30       804.147562                                    NaN  \n",
+       "96011  2023-12-31       800.000000                                    NaN  \n",
+       "\n",
+       "[96012 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
+    "\n",
+    "exclude_columns = [\n",
+    "    \"RegionID\",\n",
+    "    \"SizeRank\",\n",
+    "    \"RegionName\",\n",
+    "    \"RegionType\",\n",
+    "    \"StateName\",\n",
+    "    \"Home Type\",\n",
+    "]\n",
+    "\n",
+    "batches = {\"rent\": []}\n",
+    "\n",
+    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "    if filename.endswith(\".csv\"):\n",
+    "        # print(\"processing \" + filename)\n",
+    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "\n",
+    "        if \"_sfrcondomfr_\" in filename:\n",
+    "            cur_df[\"Home Type\"] = \"all homes plus multifamily\"\n",
+    "            # skip for now\n",
+    "            continue\n",
+    "        elif \"_sfr_\" in filename:\n",
+    "            cur_df[\"Home Type\"] = \"SFR\"\n",
+    "        elif \"_mfr_\" in filename:\n",
+    "            cur_df[\"Home Type\"] = \"multifamily\"\n",
+    "\n",
+    "        # Identify columns to pivot\n",
+    "        columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
+    "\n",
+    "        smoothed = \"_sm_\" in filename\n",
+    "        seasonally_adjusted = \"_sa_\" in filename\n",
+    "\n",
+    "        # if \"_mlp_\" in filename:\n",
+    "        col_name = \"Rent\"\n",
+    "        if smoothed:\n",
+    "            col_name += \" (Smoothed)\"\n",
+    "        if seasonally_adjusted:\n",
+    "            col_name += \" (Seasonally Adjusted)\"\n",
+    "        cur_df = pd.melt(\n",
+    "            cur_df,\n",
+    "            id_vars=exclude_columns,\n",
+    "            value_vars=columns_to_pivot,\n",
+    "            var_name=\"Date\",\n",
+    "            value_name=col_name,\n",
+    "        )\n",
+    "        batches[\"rent\"].append(cur_df)\n",
+    "        # print(filename)\n",
+    "\n",
+    "\n",
+    "def get_combined_df(data_frames):\n",
+    "    combined_df = None\n",
+    "    if len(data_frames) > 1:\n",
+    "        # iterate over dataframes and merge or concat\n",
+    "        combined_df = data_frames[0]\n",
+    "        for i in range(1, len(data_frames)):\n",
+    "            cur_df = data_frames[i]\n",
+    "            if combined_df.columns.equals(cur_df.columns):\n",
+    "                combined_df = pd.concat([combined_df, cur_df])\n",
+    "            else:\n",
+    "                cols_to_use = list(cur_df.columns.difference(combined_df.columns))\n",
+    "                on = [\"RegionID\", \"Home Type\", \"Date\"]\n",
+    "                for col in on:\n",
+    "                    if col not in cols_to_use:\n",
+    "                        cols_to_use.append(col)\n",
+    "                print(cols_to_use)\n",
+    "\n",
+    "                combined_df = pd.merge(\n",
+    "                    combined_df,\n",
+    "                    cur_df[cols_to_use],\n",
+    "                    on=on,\n",
+    "                    how=\"outer\",\n",
+    "                )\n",
+    "\n",
+    "            print(combined_df.columns)\n",
+    "    elif len(data_frames) == 1:\n",
+    "        combined_df = data_frames[0]\n",
+    "\n",
+    "    return combined_df\n",
+    "\n",
+    "\n",
+    "combined_df = get_combined_df(batches[\"rent\"])\n",
+    "combined_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df = combined_df\n",
+    "final_df = final_df.rename(\n",
+    "    columns={\n",
+    "        \"RegionID\": \"Region ID\",\n",
+    "        \"SizeRank\": \"Size Rank\",\n",
+    "        \"RegionName\": \"Region\",\n",
+    "        \"RegionType\": \"Region Type\",\n",
+    "        \"StateName\": \"State\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# sort by region id and date\n",
+    "# final_df.sort_values(by=[\"Region ID\", \"Date\", \"Home Type\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
+    "    os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
+    "\n",
+    "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

tester.ipynb

@@ -26,25 +26,21 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "BuilderConfig 'for_sale_listings' not found. Available: ['home_value_forecasts', 'new_constructions']",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m configs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhome_value_forecasts\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnew_constructions\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor_sale_listings\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m----> 3\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmisikoff/zillow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfor_sale_listings\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/load.py:2548\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2543\u001b[0m verification_mode \u001b[38;5;241m=\u001b[39m VerificationMode(\n\u001b[1;32m   2544\u001b[0m     (verification_mode \u001b[38;5;129;01mor\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mBASIC_CHECKS) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m save_infos \u001b[38;5;28;01melse\u001b[39;00m VerificationMode\u001b[38;5;241m.\u001b[39mALL_CHECKS\n\u001b[1;32m   2545\u001b[0m )\n\u001b[1;32m   2547\u001b[0m \u001b[38;5;66;03m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 2548\u001b[0m builder_instance \u001b[38;5;241m=\u001b[39m \u001b[43mload_dataset_builder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2549\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2550\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2551\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2552\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2553\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2554\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2555\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2556\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2557\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2558\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2559\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2560\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrust_remote_code\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2561\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_require_default_config_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   2562\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2563\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2565\u001b[0m \u001b[38;5;66;03m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   2566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m streaming:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/load.py:2257\u001b[0m, in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\u001b[0m\n\u001b[1;32m   2255\u001b[0m builder_cls \u001b[38;5;241m=\u001b[39m get_dataset_builder_class(dataset_module, dataset_name\u001b[38;5;241m=\u001b[39mdataset_name)\n\u001b[1;32m   2256\u001b[0m \u001b[38;5;66;03m# Instantiate the dataset builder\u001b[39;00m\n\u001b[0;32m-> 2257\u001b[0m builder_instance: DatasetBuilder \u001b[38;5;241m=\u001b[39m \u001b[43mbuilder_cls\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2258\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2259\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2260\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2262\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_files\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_files\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2263\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mhash\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2264\u001b[0m \u001b[43m    \u001b[49m\u001b[43minfo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2268\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbuilder_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2269\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2270\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2271\u001b[0m builder_instance\u001b[38;5;241m.\u001b[39m_use_legacy_cache_dir_if_possible(dataset_module)\n\u001b[1;32m   2273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m builder_instance\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:371\u001b[0m, in \u001b[0;36mDatasetBuilder.__init__\u001b[0;34m(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, use_auth_token, repo_id, data_files, data_dir, storage_options, writer_batch_size, name, **config_kwargs)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_dir \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    370\u001b[0m     config_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_dir\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m data_dir\n\u001b[0;32m--> 371\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_builder_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcustom_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;66;03m# prepare info: DatasetInfo are a standardized dataclass across all datasets\u001b[39;00m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;66;03m# Prefill datasetinfo\u001b[39;00m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m info \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# TODO FOR PACKAGED MODULES IT IMPORTS DATA FROM src/packaged_modules which doesn't make sense\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:592\u001b[0m, in \u001b[0;36mDatasetBuilder._create_builder_config\u001b[0;34m(self, config_name, custom_features, **config_kwargs)\u001b[0m\n\u001b[1;32m    590\u001b[0m     builder_config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuilder_configs\u001b[38;5;241m.\u001b[39mget(config_name)\n\u001b[1;32m    591\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m builder_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mBUILDER_CONFIGS:\n\u001b[0;32m--> 592\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    593\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBuilderConfig \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m not found. Available: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuilder_configs\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    594\u001b[0m         )\n\u001b[1;32m    596\u001b[0m \u001b[38;5;66;03m# if not using an existing config, then create a new config on the fly\u001b[39;00m\n\u001b[1;32m    597\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m builder_config:\n",
-      "\u001b[0;31mValueError\u001b[0m: BuilderConfig 'for_sale_listings' not found. Available: ['home_value_forecasts', 'new_constructions']"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading builder script: 100%|██████████| 16.4k/16.4k [00:00<00:00, 13.8MB/s]\n",
+      "Downloading data: 100%|██████████| 739M/739M [00:17<00:00, 42.9MB/s] \n",
+      "Generating train split: 2398149 examples [01:07, 35317.36 examples/s]\n",
+      "Generating validation split: 2398149 examples [01:08, 35184.06 examples/s]\n",
+      "Generating test split: 2398149 examples [01:09, 34579.04 examples/s]\n"
      ]
     }
    ],
    "source": [
-    "configs = [\"home_value_forecasts\", \"new_constructions\", \"for_sale_listings\"]\n",
+    "configs = [\"home_value_forecasts\", \"new_constructions\", \"for_sale_listings\", \"rentals\"]\n",
     "\n",
-    "dataset = load_dataset(\"misikoff/zillow\", \"for_sale_listings\", trust_remote_code=True)"
+    "dataset = load_dataset(\"misikoff/zillow\", \"rentals\", trust_remote_code=True)"
    ]
   },
   {

zillow.py

@@ -86,6 +86,11 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             version=VERSION,
             description="This part of my dataset covers a second domain",
         ),
+        datasets.BuilderConfig(
+            name="rentals",
+            version=VERSION,
+            description="This part of my dataset covers a second domain",
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = "home_value_forecasts"  # It's not mandatory to have a default configuration. Just use one if it make sense.
@@ -172,6 +177,25 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                     # These are the features of your dataset like images, labels ...
                 }
             )
+        elif self.config.name == "rentals":
+            features = datasets.Features(
+                {
+                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
+                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
+                    "Region": datasets.Value(dtype="string", id="Region"),
+                    "Region Type": datasets.Value(dtype="string", id="Region Type"),
+                    "State": datasets.Value(dtype="string", id="State"),
+                    "Home Type": datasets.Value(dtype="string", id="Home Type"),
+                    "Date": datasets.Value(dtype="string", id="Date"),
+                    "Rent (Smoothed)": datasets.Value(
+                        dtype="float32", id="Rent (Smoothed)"
+                    ),
+                    "Rent (Smoothed) (Seasonally Adjusted)": datasets.Value(
+                        dtype="float32", id="Rent (Smoothed) (Seasonally Adjusted)"
+                    ),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
         # else:  # This is an example to show how to have different features for "home_value_forecasts" and "second_domain"
         #     features = datasets.Features(
         #         {
@@ -312,6 +336,22 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                         "New Pending": data["New Pending"],
                         # "answer": "" if split == "test" else data["answer"],
                     }
+                elif self.config.name == "rentals":
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "Region ID": data["Region ID"],
+                        "Size Rank": data["Size Rank"],
+                        "Region": data["Region"],
+                        "Region Type": data["Region Type"],
+                        "State": data["State"],
+                        "Home Type": data["Home Type"],
+                        "Date": data["Date"],
+                        "Rent (Smoothed)": data["Rent (Smoothed)"],
+                        "Rent (Smoothed) (Seasonally Adjusted)": data[
+                            "Rent (Smoothed) (Seasonally Adjusted)"
+                        ],
+                        # "answer": "" if split == "test" else data["answer"],
+                    }
                 # else:
                 #     yield key, {
                 #         "sentence": data["sentence"],