fix

process_home_value_forecasts.ipynb

@@ -415,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -446,7 +446,7 @@
        "      <th>State</th>\n",
        "      <th>City</th>\n",
        "      <th>Metro</th>\n",
-       "      <th>CountyName</th>\n",
+       "      <th>County</th>\n",
        "      <th>BaseDate</th>\n",
        "      <th>Month Over Month % (Smoothed)</th>\n",
        "      <th>Quarter Over Quarter % (Smoothed)</th>\n",
@@ -483,7 +483,7 @@
        "      <td>1</td>\n",
        "      <td>NY</td>\n",
        "      <td>New York</td>\n",
-       "      <td>New York, NY</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2023-12-31</td>\n",
        "      <td>0.2</td>\n",
@@ -501,7 +501,7 @@
        "      <td>2</td>\n",
        "      <td>CA</td>\n",
        "      <td>Los Angeles</td>\n",
-       "      <td>Los Angeles, CA</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2023-12-31</td>\n",
        "      <td>-0.1</td>\n",
@@ -519,7 +519,7 @@
        "      <td>3</td>\n",
        "      <td>IL</td>\n",
        "      <td>Chicago</td>\n",
-       "      <td>Chicago, IL</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2023-12-31</td>\n",
        "      <td>0.1</td>\n",
@@ -537,7 +537,7 @@
        "      <td>4</td>\n",
        "      <td>TX</td>\n",
        "      <td>Dallas</td>\n",
-       "      <td>Dallas, TX</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2023-12-31</td>\n",
        "      <td>-0.1</td>\n",
@@ -674,12 +674,12 @@
        "20165     92811            79078        zip     39992    TX          NaN   \n",
        "20166     98183            95419        zip     39992    CA  Camp Meeker   \n",
        "\n",
-       "                                      Metro         CountyName    BaseDate  \\\n",
+       "                                      Metro             County    BaseDate  \\\n",
        "0                                       NaN                NaN  2023-12-31   \n",
-       "1                              New York, NY                NaN  2023-12-31   \n",
-       "2                           Los Angeles, CA                NaN  2023-12-31   \n",
-       "3                               Chicago, IL                NaN  2023-12-31   \n",
-       "4                                Dallas, TX                NaN  2023-12-31   \n",
+       "1                                       NaN                NaN  2023-12-31   \n",
+       "2                                       NaN                NaN  2023-12-31   \n",
+       "3                                       NaN                NaN  2023-12-31   \n",
+       "4                                       NaN                NaN  2023-12-31   \n",
        "...                                     ...                ...         ...   \n",
        "20162              Faribault-Northfield, MN        Rice County  2023-12-31   \n",
        "20163                      St. Louis, MO-IL    Macoupin County  2023-12-31   \n",
@@ -729,7 +729,7 @@
        "[21062 rows x 15 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,13 +743,13 @@
     "\n",
     "all_cols = ['RegionID', 'RegionName', 'RegionType', 'SizeRank', 'StateName', 'State', 'City', 'Metro', 'CountyName',\n",
     "       'BaseDate'] + result_cols\n",
-    "all_cols\n",
     "\n",
     "if not os.path.exists(FULL_PROCESSED_DIR_PATH):\n",
     "    os.makedirs(FULL_PROCESSED_DIR_PATH)\n",
     "\n",
     "final_df = combined_df[all_cols]\n",
     "final_df = final_df.drop('StateName', axis=1)\n",
+    "final_df = final_df.rename(columns={\"CountyName\": \"County\"})\n",
     "\n",
     "# iterate over rows of final_df and populate State and City columns if the regionType is msa\n",
     "for index, row in final_df.iterrows():\n",
@@ -768,11 +768,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "final_df.to_csv(FULL_PROCESSED_DIR_PATH + 'final.csv', index=False)"
+    "# final_df.to_csv(FULL_PROCESSED_DIR_PATH + 'final.csv', index=False)\n",
+    "final_df.to_json(FULL_PROCESSED_DIR_PATH + 'final.jsonl', orient='records', lines=True)"
    ]
   }
  ],

zillow.py

@@ -47,10 +47,10 @@ _LICENSE = ""
 # TODO: Add link to the official dataset URLs here
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "first_domain": "https://files.zillowstatic.com/research/public_csvs/zhvf_growth/Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv",
-    # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-}
+# _URLS = {
+#     "first_domain": "https://files.zillowstatic.com/research/public_csvs/zhvf_growth/Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv",
+#     # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
 
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
@@ -92,9 +92,22 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
             features = datasets.Features(
                 {
-                    # "RegionID": datasets.Value("RegionID"),
+                    "RegionID": datasets.Value(dtype='string', id="RegionID"),
                     "SizeRank": datasets.Value(dtype='int32', id="SizeRank"),
-                    # "RegionName": datasets.Value("RegionName"),
+                    "RegionName": datasets.Value(dtype='string', id="RegionName"),
+                    "RegionType": datasets.Value(dtype='string', id="RegionType"),
+                    'State': datasets.Value(dtype='string', id="State"),
+                    'City': datasets.Value(dtype='string', id="City"),
+                    'Metro': datasets.Value(dtype='string', id="Metro"),
+                    'County': datasets.Value(dtype='string', id="County"),
+                    'BaseDate': datasets.Value(dtype='string', id="BaseDate"),
+                    'Month Over Month % (Smoothed)': datasets.Value(dtype='float32', id="Month Over Month % (Smoothed)"),
+                    'Quarter Over Quarter % (Smoothed)': datasets.Value(dtype='float32', id="Month Over Month % (Smoothed)"),
+                    'Year Over Year % (Smoothed)'	: datasets.Value(dtype='float32', id="Month Over Month % (Smoothed)"),
+                    'Month Over Month % (Raw)': datasets.Value(dtype='float32', id="Month Over Month % (Smoothed)"),
+                    'Quarter Over Quarter % (Raw)': datasets.Value(dtype='float32', id="Month Over Month % (Smoothed)"),
+                    'Year Over Year % (Raw)': datasets.Value(dtype='float32', id="Month Over Month % (Smoothed)"),
+
                     # These are the features of your dataset like images, labels ...
                 }
             )
@@ -130,12 +143,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+
         # urls = _URLS[self.config.name]
         # data_dir = dl_manager.download_and_extract(urls)
         # file_train = dl_manager.download(os.path.join('./data/home_value_forecasts', "Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_month.csv"))
-        file_path = os.path.join('processed', "test.jsonl")
-        print('*********************')
-        print(file_path)
+        file_path = os.path.join('processed/home_value_forecasts', "final.jsonl")
+        # print('*********************')
+        # print(file_path)
+
         file_train = dl_manager.download(file_path)
         # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
         # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))
@@ -176,9 +191,21 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 if self.config.name == "first_domain":
                     # Yields examples as (key, example) tuples
                     yield key, {
-                        # "RegionID": data["RegionID"],
+                        "RegionID": data["RegionID"],
                         "SizeRank": data["SizeRank"],
-                        # "RegionName": data["RegionName"],
+                        "RegionName": data["RegionName"],
+                        "RegionType": data["RegionType"],
+                        'State': data["State"],
+                        'City': data["City"],
+                        'Metro': data["Metro"],
+                        'County': data["County"],
+                        'BaseDate': data["BaseDate"],
+                        'Month Over Month % (Smoothed)': data["Month Over Month % (Smoothed)"],
+                        'Quarter Over Quarter % (Smoothed)': data["Quarter Over Quarter % (Smoothed)"],
+                        'Year Over Year % (Smoothed)'	: data["Year Over Year % (Smoothed)"],
+                        'Month Over Month % (Raw)': data["Month Over Month % (Raw)"],
+                        'Quarter Over Quarter % (Raw)': data["Quarter Over Quarter % (Raw)"],
+                        'Year Over Year % (Raw)': data["Year Over Year % (Raw)"],
                         # "answer": "" if split == "test" else data["answer"],
                     }
                 # else: