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misikoff
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zillow

Tasks:
Tabular Regression
Time Series Forecasting
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misikoff commited on
Commit
2150692
•
1 Parent(s): 864476a

fix: update zillow and add tester

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Files changed (2) hide show
  1. tester.ipynb +87 -0
  2. zillow.py +0 -2
tester.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "/Users/misikoff/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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+ " from .autonotebook import tqdm as notebook_tqdm\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "# !pip install datasets\n",
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+ "\n",
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+ "from datasets import load_dataset"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Downloading builder script: 100%|██████████| 13.7k/13.7k [00:00<00:00, 11.1MB/s]\n",
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+ "Downloading readme: 100%|██████████| 215/215 [00:00<00:00, 747kB/s]\n",
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+ "Downloading data: 100%|██████████| 9.87M/9.87M [00:00<00:00, 16.5MB/s]\n",
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+ "Generating train split: 0 examples [00:00, ? examples/s]\n"
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+ ]
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+ },
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+ {
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+ "ename": "DatasetGenerationError",
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+ "evalue": "An error occurred while generating the dataset",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:1726\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1725\u001b[0m _time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m-> 1726\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 1727\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmax_shard_size\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_bytes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmax_shard_size\u001b[49m\u001b[43m:\u001b[49m\n",
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+ "File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/misikoff--zillow/c8c1c5afe126b95501e8be6a19b2de8d85dc9cee2bc9ba878e9bf4c48cfc02a9/zillow.py:257\u001b[0m, in \u001b[0;36mNewDataset._generate_examples\u001b[0;34m(self, filepath, split)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnew_constructions\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 250\u001b[0m \u001b[38;5;66;03m# Yields examples as (key, example) tuples\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m key, {\n\u001b[1;32m 252\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRegion ID\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRegion ID\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 253\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSize Rank\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSize Rank\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 254\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRegion\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRegion\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 255\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRegion Type\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRegion Type\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 256\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mState\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mState\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m--> 257\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValue Type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mValue Type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m,\n\u001b[1;32m 258\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHome Type\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHome Type\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 259\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 260\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSale Price\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSale Price\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 261\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSale Price per Sqft\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSale Price per Sqft\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 262\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCount\u001b[39m\u001b[38;5;124m\"\u001b[39m: data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCount\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 263\u001b[0m \u001b[38;5;66;03m# \"answer\": \"\" if split == \"test\" else data[\"answer\"],\u001b[39;00m\n\u001b[1;32m 264\u001b[0m }\n",
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+ "\u001b[0;31mKeyError\u001b[0m: 'Value Type'",
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+ "\nThe above exception was the direct cause of the following exception:\n",
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+ "\u001b[0;31mDatasetGenerationError\u001b[0m Traceback (most recent call last)",
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+ "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\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;43mnew_constructions\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",
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+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/load.py:2574\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 2571\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 2573\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 2574\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2575\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 2576\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 2577\u001b[0m \u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2578\u001b[0m \u001b[43m \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2579\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2580\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 2581\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2583\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 2584\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 2585\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m 2586\u001b[0m )\n",
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+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:1005\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 1003\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \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 1004\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m-> 1005\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1006\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1007\u001b[0m \u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1008\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1009\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1010\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1011\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m 1012\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
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+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:1767\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1766\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verification_mode, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1767\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1768\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1769\u001b[0m \u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1770\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBASIC_CHECKS\u001b[49m\n\u001b[1;32m 1771\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mALL_CHECKS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1773\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
54
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:1100\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 1096\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m-> 1100\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1102\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find data file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1104\u001b[0m \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_download_instructions \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1105\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mOriginal error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m 1107\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
55
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:1605\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split\u001b[0;34m(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m 1603\u001b[0m job_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pbar:\n\u001b[0;32m-> 1605\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mjob_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split_single\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1606\u001b[0m \u001b[43m \u001b[49m\u001b[43mgen_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgen_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjob_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjob_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_prepare_split_args\u001b[49m\n\u001b[1;32m 1607\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 1608\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 1609\u001b[0m \u001b[43m \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\n",
56
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/datasets/builder.py:1762\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, SchemaInferenceError) \u001b[38;5;129;01mand\u001b[39;00m e\u001b[38;5;241m.\u001b[39m__context__ \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 1761\u001b[0m e \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__context__\n\u001b[0;32m-> 1762\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DatasetGenerationError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while generating the dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1764\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m job_id, \u001b[38;5;28;01mTrue\u001b[39;00m, (total_num_examples, total_num_bytes, writer\u001b[38;5;241m.\u001b[39m_features, num_shards, shard_lengths)\n",
57
+ "\u001b[0;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset"
58
+ ]
59
+ }
60
+ ],
61
+ "source": [
62
+ "dataset = load_dataset(\"misikoff/zillow\", \"new_constructions\", trust_remote_code=True)"
63
+ ]
64
+ }
65
+ ],
66
+ "metadata": {
67
+ "kernelspec": {
68
+ "display_name": "sta663",
69
+ "language": "python",
70
+ "name": "python3"
71
+ },
72
+ "language_info": {
73
+ "codemirror_mode": {
74
+ "name": "ipython",
75
+ "version": 3
76
+ },
77
+ "file_extension": ".py",
78
+ "mimetype": "text/x-python",
79
+ "name": "python",
80
+ "nbconvert_exporter": "python",
81
+ "pygments_lexer": "ipython3",
82
+ "version": "3.12.2"
83
+ }
84
+ },
85
+ "nbformat": 4,
86
+ "nbformat_minor": 2
87
+ }
zillow.py CHANGED
@@ -130,7 +130,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
130
  "Region": datasets.Value(dtype="string", id="Region"),
131
  "Region Type": datasets.Value(dtype="string", id="Region Type"),
132
  "State": datasets.Value(dtype="string", id="State"),
133
- "Value Type": datasets.Value(dtype="string", id="Value Type"),
134
  "Home Type": datasets.Value(dtype="string", id="Home Type"),
135
  "Date": datasets.Value(dtype="string", id="Date"),
136
  "Sale Price": datasets.Value(dtype="float32", id="Sale Price"),
@@ -254,7 +253,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
254
  "Region": data["Region"],
255
  "Region Type": data["Region Type"],
256
  "State": data["State"],
257
- "Value Type": data["Value Type"],
258
  "Home Type": data["Home Type"],
259
  "Date": data["Date"],
260
  "Sale Price": data["Sale Price"],
 
130
  "Region": datasets.Value(dtype="string", id="Region"),
131
  "Region Type": datasets.Value(dtype="string", id="Region Type"),
132
  "State": datasets.Value(dtype="string", id="State"),
 
133
  "Home Type": datasets.Value(dtype="string", id="Home Type"),
134
  "Date": datasets.Value(dtype="string", id="Date"),
135
  "Sale Price": datasets.Value(dtype="float32", id="Sale Price"),
 
253
  "Region": data["Region"],
254
  "Region Type": data["Region Type"],
255
  "State": data["State"],
 
256
  "Home Type": data["Home Type"],
257
  "Date": data["Date"],
258
  "Sale Price": data["Sale Price"],