apache/beam · error · ValueError

"RobustZScore.score_one expected univariate input, but got

Error message

"RobustZScore.score_one expected univariate input, but got %s", str(x)

What it means

RobustZScore.score_one computes the robust z-score of a single point and requires a univariate beam.Row with exactly one field. Rows with a different field count raise ValueError. The score math (x - median) / MAD only applies to one value per point.

Solutions

  1. Project the input to a single numeric field before scoring.
  2. Ensure the score-stage Row shape matches the learn-stage Row shape.
  3. Add a pre-scoring validation step that rejects or logs multi-field rows.

Example fix

// before
enriched_rows | beam.Map(detector.score_one)
// after
enriched_rows | beam.Map(lambda r: beam.Row(value=r.metric)) | beam.Map(detector.score_one)
Defensive patterns

Strategy: validation

Validate before calling

if len(row.__dict__) != 1:
    raise ValueError(f"RobustZScore.score_one requires exactly one field, got: {list(row.__dict__)}")

Type guard

def is_univariate(row): return len(getattr(row, '__dict__', {})) == 1

Try / catch

try:
    score = detector.score_one(row)
except ValueError as e:
    score = None
    logger.warning("unscorable row %r: %s", row, e)

Prevention

When it happens

Trigger: Scoring beam.Row objects with multiple attributes or empty rows through RobustZScore.score_one, often when the scored PCollection still contains metadata columns.

Common situations: Joining/enriching rows before scoring so they gain extra fields; passing raw source rows; inconsistent shaping between the learn and score stages of the pipeline.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/3fb8b443eeb38d82. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/ml/anomaly/detectors/robust_zscore.py:98

    if len(x.__dict__) != 1:
      raise ValueError(
          "RobustZScore.learn_one expected univariate input, but got %s",
          str(x))

    v = next(iter(x))
    self._mad_tracker.push(v)

  def score_one(self, x: beam.Row) -> Optional[float]:
    """Scores a data point using the Robust Z-Score.

    Args:
      x: A `beam.Row` containing a single numerical value.

    Returns:
      float | None: The Robust Z-Score.
    """
    if len(x.__dict__) != 1:
      raise ValueError(
          "RobustZScore.score_one expected univariate input, but got %s",
          str(x))

    v = next(iter(x))
    if v is None or math.isnan(v):
      return None

    median = self._mad_tracker.get_median()
    mad = self._mad_tracker.get()

    # not enough data points to compute median or median absolute deviation
    if math.isnan(mad) or math.isnan(median):
      return float('NaN')

    if abs(mad) < EPSILON:
      return 0.0

    return abs(RobustZScore.SCALE_FACTOR * (v - median) / mad)

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