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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'timestamp', 'config'}) and 6 missing columns ({'config_hash', 'text', 'file_path', 'header_level', 'header', 'embedding'}).
This happened while the json dataset builder was generating data using
hf://datasets/linroger023/vault/VectorDB.vector_embeddings_metadata.json (at revision b2bf9941e7ec4cdc67d742e329af2f0a89455301)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
_id: string
config: struct<batch_token_limit: int64, export_n_frequent_words: int64, hdbscan_min_cluster_size: int64, hdbscan_min_samples: int64, max_batch_items: int64, max_chunk_tokens: int64, min_file_size_kb: int64, tfidf_n_keywords: int64, umap_metric: string, umap_min_dist: double, umap_n_neighbors: int64, vault_path: string, voyage_model: string>
child 0, batch_token_limit: int64
child 1, export_n_frequent_words: int64
child 2, hdbscan_min_cluster_size: int64
child 3, hdbscan_min_samples: int64
child 4, max_batch_items: int64
child 5, max_chunk_tokens: int64
child 6, min_file_size_kb: int64
child 7, tfidf_n_keywords: int64
child 8, umap_metric: string
child 9, umap_min_dist: double
child 10, umap_n_neighbors: int64
child 11, vault_path: string
child 12, voyage_model: string
timestamp: timestamp[ns, tz=UTC]
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 446
to
{'_id': {'$oid': Value(dtype='string', id=None)}, 'file_path': Value(dtype='string', id=None), 'header': Value(dtype='string', id=None), 'header_level': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'config_hash': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'timestamp', 'config'}) and 6 missing columns ({'config_hash', 'text', 'file_path', 'header_level', 'header', 'embedding'}).
This happened while the json dataset builder was generating data using
hf://datasets/linroger023/vault/VectorDB.vector_embeddings_metadata.json (at revision b2bf9941e7ec4cdc67d742e329af2f0a89455301)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
_id dict | file_path string | header string | header_level int64 | text string | config_hash string | embedding sequence |
|---|---|---|---|---|---|---|
{
"$oid": "67f7ab88ba831ffd61af117d"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | What it is: Deeper Dive | 3 | The Ornstein-Uhlenbeck (OU) process stands as a fundamental concept in the study of **stochastic processes**, which are mathematical models for systems evolving randomly over time. Its defining characteristic is **mean reversion**, a property that makes it particularly suitable for modeling real-world phenomena where v... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
-0.029104869812726003,
0.01624321937561,
0.091209031641483,
0.033781856298446,
-0.010975033976137001,
0.019704774022102002,
0.035040877759456,
-0.06587853282690001,
-0.069987602531909,
-0.012876478955149002,
-0.05169729515910101,
-0.007717220112681,
-0.030501620844006004,
0.005242640152573... |
{
"$oid": "67f7ab88ba831ffd61af117e"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Analytical Derivation (Solving the SDE) Explained | 3 | Solving the OU SDE provides an explicit formula for `X(t)`, revealing its structure. We employ the integrating factor method, a standard technique for linear differential equations, adapted for the stochastic context using the rules of Itô calculus.
1. Rearrange the SDE: Group terms involving X(t) on one side:
... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
-0.026653554290533003,
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0.065542764961719,
0.02497586607933,
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0.003326941980049,
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0.012005980126559,
... |
{
"$oid": "67f7ab88ba831ffd61af117f"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Properties Explored | 3 | The analytical solution and the SDE reveal several key properties:
1. **Mean Reversion:** As already emphasized, the drift term `θ(μ - X(t))` inherently drives the process towards `μ`. This property is crucial for modeling phenomena that don't exhibit unbounded growth or decay but rather fluctuate around a stable aver... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
0.0006793922511860001,
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0.01689858... |
{
"$oid": "67f7ab88ba831ffd61af1180"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Parameters Recap and Impact | 3 | The three parameters `μ`, `θ`, and `σ` fully define the univariate OU process and control its behavior:
- `μ` (Long-term mean): Determines the central level around which the process fluctuates. Changing `μ` shifts the entire process vertically without altering the nature of the fluctuations.
- `θ` (Mean reversion... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
-0.017152018845081,
0.030878778547048003,
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0.017248759046196,
0.006432458292692,
0.024551544338464,
0.003183014690876,
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0.019928138703107,
0.005616830196231001,
0.008750923909246,
0.0... |
{
"$oid": "67f7ab88ba831ffd61af1181"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Behavior Summary | 3 | An OU process path exhibits random fluctuations around its mean `μ`. When the process deviates far from `μ`, the deterministic drift term dominates, pulling it back strongly. When close to `μ`, the random diffusion term becomes more influential, causing local jitter. While random shocks can cause temporary overshooting... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
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0.0037658938672390004,
-0.002137154107913,
-0.01163234... |
{
"$oid": "67f7ab88ba831ffd61af1182"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Multivariate OU Process Insights | 3 | The generalization to `n` dimensions allows modeling complex systems where multiple variables interact:
`d**X**(t) = **Θ**( **μ** - **X**(t) ) dt + **Σ** d**W**(t)`
- **X**(t), **μ**, **W**(t): Now n-dimensional vectors.
- **Θ** (Mean reversion matrix): An `n x n` matrix. Its diagonal elements `Θᵢᵢ` relate to th... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
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-0.0018091646488750002,
0.017374204471707... |
{
"$oid": "67f7ab88ba831ffd61af1183"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | --- Example Usage: Univariate --- | 1 | print("--- Univariate OU Simulation ---")
x0_uni = 5.0 # Initial value
T_uni = 100.0 # Total time
dt_uni = 0.1 # Time step
mu_uni = 10.0 # Long-term mean
theta_uni = 0.5 # Speed of reversion
sigma_uni = 2.0 # Volatility
t_uni, X_uni = simulate_ou_univariate(x0_uni, T_uni, dt_uni, mu_uni, theta_un... | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
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0.029638778418302,
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0.008954538963735001,
0.001222485676407,
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0.000740558898542,
0.018108520656824,
... |
{
"$oid": "67f7ab88ba831ffd61af1184"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Plotting Univariate | 1 | plt.figure(figsize=(10, 6))
plt.plot(t_uni, X_uni, label='Simulated OU Process')
plt.axhline(mu_uni, color='r', linestyle='--', label=f'Long-term Mean (μ={mu_uni})')
plt.title('Univariate Ornstein-Uhlenbeck Process (Exact Simulation)')
plt.xlabel('Time')
plt.ylabel('X(t)')
plt.legend()
plt.grid(True)
plt.show() | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [
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0.006931302137672001,
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0.051154643... |
{
"$oid": "67f7ab88ba831ffd61af1185"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | Calculate theoretical vs. sample stats | 1 | "theoretical_stationary_var = sigma_uni**2 / (2 * theta_uni)\nsample_mean_latter_half = np.mean(X_un(...TRUNCATED) | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [0.023013859987258002,-0.007919199764728001,-0.006980841979384001,0.016545364633202,0.05350409820675(...TRUNCATED) |
{
"$oid": "67f7ab88ba831ffd61af1186"
} | /Users/rogerlin/Documents/Wealth_of_Nations/The Ornstein-Uhlenbeck (OU) Process.md | --- Example Usage: Multivariate (2D) --- | 1 | "print(\"\\n--- Multivariate OU Simulation ---\")\nn_dim = 2\nx0_multi = np.array([1.0, -1.0]) #(...TRUNCATED) | 76d0b1757cfb585e82d66083c46ae1e62ecd5e072a34d562a41323b1225ca975 | [0.0062542846426360005,0.028607387095689003,0.042031291872262004,-0.049392577260732005,0.05601408332(...TRUNCATED) |
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