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: