Method fit

catboost/python-package/catboost/core.py:6476–6590

Fit the CatBoostRanker model. Parameters ---------- X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset. y : list or n

(self, X, y=None, group_id=None, cat_features=None, text_features=None,
            embedding_features=None, pairs=None, graph=None, sample_weight=None, group_weight=None,
            subgroup_id=None, pairs_weight=None, baseline=None, use_best_model=None,
            eval_set=None, verbose=None, logging_level=None, plot=False, plot_file=None, column_description=None,
            verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
            save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
            log_cout=None, log_cerr=None)

        super(CatBoostRanker, self).__init__(params)

    def fit(self, X, y=None, group_id=None, cat_features=None, text_features=None,
            embedding_features=None, pairs=None, graph=None, sample_weight=None, group_weight=None,
            subgroup_id=None, pairs_weight=None, baseline=None, use_best_model=None,
            eval_set=None, verbose=None, logging_level=None, plot=False, plot_file=None, column_description=None,
            verbose_eval=None, metric_period=None, silent=None, early_stopping_rounds=None,
            save_snapshot=None, snapshot_file=None, snapshot_interval=None, init_model=None, callbacks=None,
            log_cout=None, log_cerr=None):
        """
        Fit the CatBoostRanker model.
        Parameters
        ----------
        X : catboost.Pool or list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame
            If not catboost.Pool, 2 dimensional Feature matrix or string - file with dataset.
        y : list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame or polars.Series, optional (default=None)
            Labels of the training data.
            If not None, can be a single-dimensional array with numerical values.
            Use only if X is not catboost.Pool and does not point to a file.
        group_id : numpy.ndarray or pandas.DataFrame or pandas.Series or polars.Series, optional (default=None)
            Ranking groups, 1 dimensional array like.
            Use only if X is not catboost.Pool.
        cat_features : list or numpy.ndarray, optional (default=None)
            If not None, giving the list of Categ columns indices.
            Use only if X is not catboost.Pool.
        text_features : list or numpy.ndarray, optional (default=None)
            If not None, giving the list of Text columns indices.
            Use only if X is not catboost.Pool.
        embedding_features : list or numpy.ndarray, optional (default=None)
            If not None, giving the list of Embedding columns indices.
            Use only if X is not catboost.Pool.
        pairs : list or numpy.ndarray or pandas.DataFrame or polars.DataFrame, optional (default=None)
            The pairs description in the form of a two-dimensional matrix of shape N by 2:
            N is the number of pairs.
            The first element of the pair is the zero-based index of the winner object from the input dataset for pairwise comparison.
            The second element of the pair is the zero-based index of the loser object from the input dataset for pairwise comparison.
        graph : list or numpy.ndarray or pandas.DataFrame or polars.DataFrame
            The graph edges list description.
            If list or numpy.ndarrays or pandas.DataFrame, giving 2 dimensional.
        sample_weight : list or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.Series, optional (default=None)
            Instance weights, 1 dimensional array like.
        group_weight : list or numpy.ndarray or polars.Series (default=None)
            The weights of all objects within the defined groups from the input data in the form of one-dimensional array-like data.
            Used for calculating the final values of trees. By default, it is set to 1 for all objects in all groups.
            Only a weight or group_weight parameter can be used at a time
        subgroup_id : list or numpy.ndarray or polars.Series (default=None)
            Subgroup identifiers for all input objects. Supported identifier types are:
            int
            string types (string or unicode for Python 2 and bytes or string for Python 3).
        pairs_weight : list or numpy.ndarray or polars.Series (default=None)
            The weight of each input pair of objects in the form of one-dimensional array-like pairs.
            The number of given values must match the number of specified pairs.
            This information is used for calculation and optimization of Pairwise metrics .
            By default, it is set to 1 for all pairs.
        baseline : list or numpy.ndarray or polars.DataFrame or polars.Series, optional (default=None)
            If not None, giving 2 dimensional array like data.
            Use only if X is not catboost.Pool.
        use_best_model : bool, optional (default=None)
            Flag to use best model
        eval_set : catboost.Pool or list of catboost.Pool or tuple (X, y) or list [(X, y)], optional (default=None)