TheAlgorithms/Python · error · ValueError

The length of the two arrays should be the same.

Error message

The length of the two arrays should be the same.

What it means

Thrown by mean_absolute_percentage_error when y_true and y_pred have different lengths. MAPE averages |(y_true - y_pred)/y_true| per element, so both arrays must be aligned; zero y_true entries are replaced by epsilon to avoid division by zero.

Source

Thrown at machine_learning/loss_functions.py:476

    Examples:
    >>> y_true = np.array([10, 20, 30, 40])
    >>> y_pred = np.array([12, 18, 33, 45])
    >>> float(mean_absolute_percentage_error(y_true, y_pred))
    0.13125

    >>> y_true = np.array([1, 2, 3, 4])
    >>> y_pred = np.array([2, 3, 4, 5])
    >>> float(mean_absolute_percentage_error(y_true, y_pred))
    0.5208333333333333

    >>> y_true = np.array([34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24])
    >>> y_pred = np.array([37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23])
    >>> float(mean_absolute_percentage_error(y_true, y_pred))
    0.064671076436071
    """
    if len(y_true) != len(y_pred):
        raise ValueError("The length of the two arrays should be the same.")

    y_true = np.where(y_true == 0, epsilon, y_true)
    absolute_percentage_diff = np.abs((y_true - y_pred) / y_true)

    return np.mean(absolute_percentage_diff)


def perplexity_loss(
    y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-7
) -> float:
    """
    Calculate the perplexity for the y_true and y_pred.

    Compute the Perplexity which useful in predicting language model
    accuracy in Natural Language Processing (NLP.)
    Perplexity is measure of how certain the model in its predictions.

    Perplexity Loss = exp(-1/N (Σ ln(p(x)))

View on GitHub (pinned to f5988cc097)

Solutions

1. Align both arrays to the same evaluation window before computing MAPE.
2. If using pandas, drop NaN rows jointly: df = df.dropna(subset=['y_true','y_pred']).
3. Add an assert len(y_true) == len(y_pred) guard in the eval script.

Example fix

# before
y_true = np.array([1, 2, 3])
y_pred = np.array([2, 3, 4, 5])
mean_absolute_percentage_error(y_true, y_pred)

# after
y_true = np.array([1, 2, 3, 4])
mean_absolute_percentage_error(y_true, y_pred)

assert len(y_true) == len(y_pred)
mape = mean_absolute_percentage_error(y_true, y_pred)

def same_length(y_true: np.ndarray, y_pred: np.ndarray) -> bool:
    return len(y_true) == len(y_pred)

When it happens

Trigger: Calling mean_absolute_percentage_error with len(y_true) != len(y_pred), e.g. 4 truth values vs 4 predictions is fine but 4 vs 5 raises.

Common situations: Time-series evaluation where predicted horizon length differs from the label window; dataframes aligned by index with NaNs dropped in one column only.

Related errors

• Input arrays must have the same length.
• Input arrays must have the same shape.
• y_true must be one-hot encoded.
• Predicted probabilities must sum to approximately 1.
• Shape of y_true and y_pred must be the same.

AI-assisted analysis of TheAlgorithms/Python@f5988cc097 (2026-08-14). Data as JSON: /api/errors/5f008444776167f5. Report an issue: GitHub.