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

Code before:
        The last bin is reserved for missing values, whether missing values
        are present in the data or not.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The data to bin.
        y: None
            Ignored.

        Returns
        -------
        self : object
        """
        if not (3 <= self.n_bins <= 256):
            # min is 3: at least 2 distinct bins and a missing values bin
            raise ValueError(
                "n_bins={} should be no smaller than 3 and no larger than 256.".format(
                    self.n_bins
                )
            )

        X = check_array(X, dtype=[X_DTYPE], force_all_finite=False)
        max_bins = self.n_bins - 1

        sample_weight_bin = np.ones(X.shape[0])
        if sample_weight is not None:
            sample_weight_bin = sample_weight / np.sum(sample_weight)

        rng = check_random_state(self.random_state)
        if self.subsample is not None and X.shape[0] > self.subsample:
            subset = rng.choice(
                X.shape[0], self.subsample, p=sample_weight_bin, replace=False
            )
            X = X.take(subset, axis=0)

        if self.is_categorical is None:
            self.is_categorical_ = np.zeros(X.shape[1], dtype=np.uint8)
        else:
            self.is_categorical_ = np.asarray(self.is_categorical, dtype=np.uint8)

        n_features = X.shape[1]
        known_categories = self.known_categories
        if known_categories is None:
            known_categories = [None] * n_features

        # validate is_categorical and known_categories parameters
        for f_idx in range(n_features):
            is_categorical = self.is_categorical_[f_idx]
            known_cats = known_categories[f_idx]
            if is_categorical and known_cats is None:

Review comment (suggestion):
I would rename this to `subsampling_probabilities` to make it explicit that those weights are only used when `subsample` is large enough to trigger resampling. Let's move that below the `if self.subsample is not None and X.shape[0] > self.subsample` condition.

Code after:
        For each feature, gives the number of bins actually used for
        non-missing values. For features with a lot of unique values, this is
        equal to ``n_bins - 1``.
    is_categorical_ : ndarray of shape (n_features,), dtype=np.uint8
        Indicator for categorical features.
    missing_values_bin_idx_ : np.uint8
        The index of the bin where missing values are mapped. This is a
        constant across all features. This corresponds to the last bin, and
        it is always equal to ``n_bins - 1``. Note that if ``n_bins_non_missing_``
        is less than ``n_bins - 1`` for a given feature, then there are
        empty (and unused) bins.
    """

    def __init__(
        self,
        n_bins=256,
        subsample=int(2e5),
        is_categorical=None,
        known_categories=None,
        random_state=None,
        n_threads=None,
    ):
        self.n_bins = n_bins
        self.subsample = subsample
        self.is_categorical = is_categorical
        self.known_categories = known_categories
        self.random_state = random_state
        self.n_threads = n_threads

    def fit(self, X, y=None, sample_weight=None):
        """Fit data X by computing the binning thresholds.

        The last bin is reserved for missing values, whether missing values
        are present in the data or not.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The data to bin.
        y: None
            Ignored.

        Returns
        -------
        self : object
        """
        if not (3 <= self.n_bins <= 256):
            # min is 3: at least 2 distinct bins and a missing values bin
            raise ValueError(
                "n_bins={} should be no smaller than 3 and no larger than 256.".format(
                    self.n_bins