HKUDS/Vibe-Trading · error · ValueError

label_end_times must be 1-D, got shape {span_ends.shape}

Error message

label_end_times must be 1-D, got shape {span_ends.shape}

What it means

When label_end_times is not a pandas Series, _as_label_spans converts it with np.asarray to a 1-D float array of span end positions. Passing a 2-D array (or any higher-dimensional array) breaks the positional correspondence between labels and observations, so it is rejected with the offending shape in the message.

Source

Thrown at agent/src/quantlib/crossvalidation.py:145

    Raises:
        ValueError: If the input is empty, not 1-D, holds a non-finite value, or
            declares a label ending before it starts.
    """
    if isinstance(label_end_times, pd.Series):
        if label_end_times.empty:
            raise ValueError("label_end_times is empty")
        starts = label_end_times.index
        ends = label_end_times.to_numpy()
        # searchsorted on the start index converts label end *times* into label
        # end *positions*; the right insertion point minus one keeps a label
        # that ends between two observations attached to the earlier one.
        positions = np.searchsorted(starts, ends, side="right") - 1
        positions = np.clip(positions, np.arange(len(starts)), len(starts) - 1)
        span_ends = positions.astype(int)
    else:
        span_ends = np.asarray(label_end_times, dtype=float)
        if span_ends.ndim != 1:
            raise ValueError(f"label_end_times must be 1-D, got shape {span_ends.shape}")
        if span_ends.size == 0:
            raise ValueError("label_end_times is empty")
        if not np.isfinite(span_ends).all():
            raise ValueError("label_end_times holds a non-finite value")
        span_ends = span_ends.astype(int)
        if (span_ends < np.arange(span_ends.size)).any():
            raise ValueError(
                "a label cannot end before the observation it belongs to starts"
            )

    if n_samples is not None and span_ends.size != n_samples:
        raise ValueError(
            f"label_end_times has {span_ends.size} entries but the sample has {n_samples}"
        )
    return span_ends


def _apply_purge_and_embargo(

View on GitHub (pinned to 80ffdda44c)

Solutions

1. Flatten to 1-D: label_end_times = arr.reshape(-1) or arr.ravel()
2. Select a single column from DataFrames: df['label_end'].values
3. Verify span_ends.ndim == 1 before calling

Example fix

# before
folds = purged_kfold_splits(ends.reshape(-1, 1), n_splits=5)

# after
folds = purged_kfold_splits(ends.reshape(-1), n_splits=5)

ends = np.asarray(label_end_times)
if ends.ndim != 1:
    ends = ends.reshape(-1)
folds = purged_kfold_splits(ends, n_splits=5)

def is_1d_array_like(x) -> bool:
    a = np.asarray(x)
    return a.ndim == 1

try:
    folds = purged_kfold_splits(ends, n_splits=5)
except ValueError as e:
    if '1-D' in str(e):
        folds = purged_kfold_splits(np.asarray(ends).reshape(-1), n_splits=5)
    else:
        raise

When it happens

Trigger: Calling the purged CV entry points with a numpy array of shape (n, 1), (1, n), or a DataFrame (which converts to 2-D) instead of a 1-D array or Series.

Common situations: Column vectors from shape (n,1) produced by .reshape(-1,1) during preprocessing; passing a one-column DataFrame where a Series is expected; batched arrays with a leading batch dimension.

Related errors

• label_end_times holds a non-finite value
• survival_prob must be in (0.0, 1.0], got {survival_prob}
• tenor_years must be strictly positive, got {tenor_years}
• spread_bps must be non-negative, got {spread_bps}
• recovery_rate must be in [0.0, 1.0), got {recovery_rate}

AI-assisted analysis of HKUDS/Vibe-Trading@80ffdda44c (2026-08-28). Data as JSON: /api/errors/2d138309edee41fe. Report an issue: GitHub.