Method quantize

catboost/python-package/catboost/core.py:1239–1323

Quantize this pool Parameters ---------- pool : catboost.Pool Dataset to quantize. ignored_features : list, [default=None] Indices or names of features that should be excluded when training. per_float_feature_quantization :

(self, ignored_features=None, per_float_feature_quantization=None, border_count=None,
                 max_bin=None, feature_border_type=None, sparse_features_conflict_fraction=None,
                 nan_mode=None, input_borders=None, task_type=None, used_ram_limit=None, random_seed=None, **kwargs)

        self._save(fname)

    def quantize(self, ignored_features=None, per_float_feature_quantization=None, border_count=None,
                 max_bin=None, feature_border_type=None, sparse_features_conflict_fraction=None,
                 nan_mode=None, input_borders=None, task_type=None, used_ram_limit=None, random_seed=None, **kwargs):
        """
        Quantize this pool

        Parameters
        ----------
        pool : catboost.Pool
            Dataset to quantize.

        ignored_features : list, [default=None]
            Indices or names of features that should be excluded when training.

        per_float_feature_quantization : list of strings, [default=None]
            List of float binarization descriptions.
            Format : described in documentation on catboost.ai
            Example 1: ['0:1024'] means that feature 0 will have 1024 borders.
            Example 2: ['0:border_count=1024', '1:border_count=1024', ...] means that two first features have 1024 borders.
            Example 3: ['0:nan_mode=Forbidden,border_count=32,border_type=GreedyLogSum',
                        '1:nan_mode=Forbidden,border_count=32,border_type=GreedyLogSum'] - defines more quantization properties for first two features.

        border_count : int, [default = 254 for training on CPU or 128 for training on GPU]
            The number of partitions in numeric features binarization. Used in the preliminary calculation.
            range: [1,65535] on CPU, [1,255] on GPU

        max_bin : float, synonym for border_count.

        feature_border_type : string, [default='GreedyLogSum']
            The binarization mode in numeric features binarization. Used in the preliminary calculation.
            Possible values:
                - 'Median'
                - 'Uniform'
                - 'UniformAndQuantiles'
                - 'GreedyLogSum'
                - 'MaxLogSum'
                - 'MinEntropy'

        sparse_features_conflict_fraction : float, [default=0.0]
            CPU only. Maximum allowed fraction of conflicting non-default values for features in exclusive features bundle.
            Should be a real value in [0, 1) interval.

        nan_mode : string, [default=None]
            Way to process missing values for numeric features.
            Possible values:
                - 'Forbidden' - raises an exception if there is a missing value for a numeric feature in a dataset.
                - 'Min' - each missing value will be processed as the minimum numerical value.
                - 'Max' - each missing value will be processed as the maximum numerical value.
            If None, then nan_mode=Min.

        input_borders : string or os.PathLike, [default=None]
            input file with borders used in numeric features binarization.

        task_type : string, [default=None]
            The calcer type that will be used to train the model after quantization.
            Possible values:
                - 'CPU'
                - 'GPU'