jax-ml/jax · error · ValueError

`dataset` input should have multiple elements.

Error message

`dataset` input should have multiple elements.

What it means

gaussian_kde requires the dataset to contain more than one element (dataset.size > 1) because a covariance/bandwidth estimate is undefined for a single point. Note atleast_2d is applied first, so even a scalar becomes a 1-element matrix and still fails.

Source

Thrown at jax/_src/scipy/stats/kde.py:60

    dataset: arraylike, real-valued. Data from which to estimate the distribution.
      If 1D, shape is (n_data,). If 2D, shape is (n_dimensions, n_data).
    bw_method: string, scalar, or callable. Either "scott", "silverman", a scalar
      value, or a callable function which takes ``self`` as a parameter.
    weights: arraylike, optional. Weights of the same shape as the dataset.
  """
  neff: Any
  dataset: Any
  weights: Any
  covariance: Any
  inv_cov: Any

  def __init__(self, dataset, bw_method: BwMethod = None, weights=None):
    check_arraylike("gaussian_kde", dataset)
    dataset = jnp.atleast_2d(dataset)
    if dtypes.issubdtype(lax.dtype(dataset), np.complexfloating):
      raise NotImplementedError("gaussian_kde does not support complex data")
    if not dataset.size > 1:
      raise ValueError("`dataset` input should have multiple elements.")

    d, n = dataset.shape
    if weights is not None:
      check_arraylike("gaussian_kde", weights)
      dataset, weights = promote_dtypes_inexact(dataset, weights)
      weights = jnp.atleast_1d(weights)
      weights /= jnp.sum(weights)
      if weights.ndim != 1:
        raise ValueError("`weights` input should be one-dimensional.")
      if len(weights) != n:
        raise ValueError("`weights` input should be of length n")
    else:
      dataset, = promote_dtypes_inexact(dataset)
      weights = jnp.full(n, 1.0 / n, dtype=dataset.dtype)

    self._setattr("dataset", dataset)
    self._setattr("weights", weights)
    neff = self._setattr("neff", 1 / jnp.sum(weights**2))

View on GitHub (pinned to 1e1c6a8fc0)

Solutions

1. Ensure at least 2 data points per KDE fit
2. Guard group-wise fits: only fit a KDE when len(group) >= 2, else fall back to a parametric density or skip
3. Check for empty/single-element slices before constructing the object

Example fix

// before
kde = gaussian_kde(group)  # group may have 1 element
// after
kde = gaussian_kde(group) if group.size > 1 else None

if jnp.asarray(dataset).size < 2:
    raise ValueError('need >= 2 points for KDE')

def has_kde_enough_data(dataset) -> bool:
    return jnp.asarray(dataset).size > 1

When it happens

Trigger: Calling gaussian_kde(jnp.array([1.0])) or gaussian_kde(3.0); passing an empty array also fails the size check.

Common situations: Running KDE in a loop/over groups where some group has one sample; feeding a placeholder or dummy scalar during prototyping.

Related errors

• n must be a non-negative integer.
• gaussian_kde does not support complex data
• `weights` input should be one-dimensional.
• `weights` input should be of length n
• `bw_method` should be 'scott', 'silverman', a scalar, or a c

AI-assisted analysis of jax-ml/jax@1e1c6a8fc0 (2026-08-27). Data as JSON: /api/errors/af86becb6280c89d. Report an issue: GitHub.