Function _num_features

sklearn/utils/validation.py:312–362

Return the number of features in an array-like X. This helper function tries hard to avoid to materialize an array version of X unless necessary. For instance, if X is a list of lists, this function will return the length of the first element, assuming that subsequent elements are a

(X)

def _num_features(X):
    """Return the number of features in an array-like X.

    This helper function tries hard to avoid to materialize an array version
    of X unless necessary. For instance, if X is a list of lists,
    this function will return the length of the first element, assuming
    that subsequent elements are all lists of the same length without
    checking.
    Parameters
    ----------
    X : array-like
        array-like to get the number of features.

    Returns
    -------
    features : int
        Number of features
    """
    type_ = type(X)
    if type_.__module__ == "builtins":
        type_name = type_.__qualname__
    else:
        type_name = f"{type_.__module__}.{type_.__qualname__}"
    message = f"Unable to find the number of features from X of type {type_name}"
    if not hasattr(X, "__len__") and not hasattr(X, "shape"):
        if not hasattr(X, "__array__"):
            raise TypeError(message)
        # Only convert X to a numpy array if there is no cheaper, heuristic
        # option.
        X = np.asarray(X)

    if hasattr(X, "shape"):
        if not hasattr(X.shape, "__len__") or len(X.shape) <= 1:
            message += f" with shape {X.shape}"
            raise TypeError(message)
        return X.shape[1]

    first_sample = X[0]

    # Do not consider an array-like of strings or dicts to be a 2D array
    if isinstance(first_sample, (str, bytes, dict)):
        message += f" where the samples are of type {type(first_sample).__qualname__}"
        raise TypeError(message)

    try:
        # If X is a list of lists, for instance, we assume that all nested
        # lists have the same length without checking or converting to
        # a numpy array to keep this function call as cheap as possible.
        return len(first_sample)
    except Exception as err:
        raise TypeError(message) from err


def _num_samples(x):