Repository: scikit-learn/scikit-learn
File: sklearn/ensemble/_bagging.py (Python)

Code before:
    if max_samples is None:
        return n_samples
    elif isinstance(max_samples, Integral):
        return max_samples
    elif not isinstance(max_samples, Real):
        raise ValueError(
            f"max_samples must be None, int or float got {type(max_samples)}"
        )

    if sample_weight is None:
        weighted_n_samples = n_samples
        weighted_n_samples_msg = f"the number of samples is {weighted_n_samples} "
    else:
        weighted_n_samples = sample_weight.sum()
        weighted_n_samples_msg = (
            f"the total sum of sample weights is {weighted_n_samples} "
        )

    # max_samples Real fractional value relative to weighted_n_samples
    n_samples_bootstrap = max(int(max_samples * weighted_n_samples), 1)
    # raise warning if n_samples_bootstrap small (< 10)
    if n_samples_bootstrap < 10:
        warn(
            f"Using the fractional value {max_samples=} when {weighted_n_samples_msg}"
            f"results in a low number ({n_samples_bootstrap}) of bootstrap samples. "
            "We recommend passing `max_samples` as an integer."
        )
    return n_samples_bootstrap


def _generate_indices(random_state, bootstrap, n_population, n_samples):
    """Draw randomly sampled indices."""
    # Draw sample indices
    if bootstrap:
        indices = random_state.randint(0, n_population, n_samples)
    else:
        indices = sample_without_replacement(
            n_population, n_samples, random_state=random_state
        )

    return indices


def _generate_bagging_indices(
    random_state,
    bootstrap_features,
    bootstrap_samples,
    n_features,
    n_samples,
    max_features,
    max_samples,

Review comment (suggestion):
```suggestion
            "We recommend passing `max_samples` as an integer instead."
```

Code after:
    -------
    n_samples_bootstrap : int
        The total number of samples to draw for the bootstrap sample.
    """
    if max_samples is None:
        return n_samples
    elif isinstance(max_samples, Integral):
        return max_samples

    if sample_weight is None:
        weighted_n_samples = n_samples
        weighted_n_samples_msg = f"the number of samples is {weighted_n_samples} "
    else:
        weighted_n_samples = sample_weight.sum()
        weighted_n_samples_msg = (
            f"the total sum of sample weights is {weighted_n_samples} "
        )

    # max_samples Real fractional value relative to weighted_n_samples
    n_samples_bootstrap = max(int(max_samples * weighted_n_samples), 1)
    # Warn when number of bootstrap samples is suspiciously small
    # This heuristic for "suspiciously small" might be adapted if found
    # unsuitable in practice
    if n_samples_bootstrap < max(10, n_samples ** (1 / 3)):
        warn(
            f"Using the fractional value {max_samples=} when {weighted_n_samples_msg}"
            f"results in a low number ({n_samples_bootstrap}) of bootstrap samples. "
            "We recommend passing `max_samples` as an integer instead."
        )
    return n_samples_bootstrap


def _generate_indices(random_state, bootstrap, n_population, n_samples):
    """Draw randomly sampled indices."""
    # Draw sample indices
    if bootstrap:
        indices = random_state.randint(0, n_population, n_samples)
    else:
        indices = sample_without_replacement(
            n_population, n_samples, random_state=random_state
        )

    return indices


def _generate_bagging_indices(
    random_state,
    bootstrap_features,
    bootstrap_samples,
    n_features,
    n_samples,