[python-package] support customizing Dataset creation in Booster.refit() (fixes #3038)

python-package/lightgbm/basic.py

@@ -3503,7 +3503,21 @@ def predict(self, data, start_iteration=0, num_iteration=None,
                                  raw_score, pred_leaf, pred_contrib,
                                  data_has_header, is_reshape)
 
-    def refit(self, data, label, decay_rate=0.9, **kwargs):
+    def refit(
+        self,
+        data,
+        label,
+        decay_rate=0.9,
+        reference=None,
+        weight=None,
+        group=None,
+        init_score=None,
+        feature_name='auto',
+        categorical_feature='auto',
+        dataset_params=None,
+        free_raw_data=True,
+        **kwargs
+    ):
         """Refit the existing Booster by new data.
 
         Parameters
@@ -3516,6 +3530,35 @@ def refit(self, data, label, decay_rate=0.9, **kwargs):
         decay_rate : float, optional (default=0.9)
             Decay rate of refit,
             will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees.
+        reference : Dataset or None, optional (default=None)
+            Reference for ``data``.
+        weight : list, numpy 1-D array, pandas Series or None, optional (default=None)
+            Weight for each ``data`` instance. Weight should be non-negative values because the Hessian
+            value multiplied by weight is supposed to be non-negative.
+        group : list, numpy 1-D array, pandas Series or None, optional (default=None)
+            Group/query size for ``data``.
+            Only used in the learning-to-rank task.
+            sum(group) = n_samples.
+            For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
+            where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
+        init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
+            Init score for ``data``.
+        feature_name : list of str, or 'auto', optional (default="auto")
+            Feature names for ``data``.
+            If 'auto' and data is pandas DataFrame, data columns names are used.
+        categorical_feature : list of str or int, or 'auto', optional (default="auto")
+            Categorical features for ``data``.
+            If list of int, interpreted as indices.
+            If list of str, interpreted as feature names (need to specify ``feature_name`` as well).
+            If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.
+            All values in categorical features should be less than int32 max value (2147483647).
+            Large values could be memory consuming. Consider using consecutive integers starting from zero.
+            All negative values in categorical features will be treated as missing values.
+            The output cannot be monotonically constrained with respect to a categorical feature.
+        dataset_params : dict or None, optional (default=None)
+            Other parameters for Dataset ``data``.
+        free_raw_data : bool, optional (default=True)
+            If True, raw data is freed after constructing inner Dataset for ``data``.
         **kwargs
             Other parameters for refit.
             These parameters will be passed to ``predict`` method.
@@ -3527,6 +3570,8 @@ def refit(self, data, label, decay_rate=0.9, **kwargs):
         """
         if self.__set_objective_to_none:
             raise LightGBMError('Cannot refit due to null objective function.')
+        if dataset_params is None:
+            dataset_params = {}
         predictor = self._to_predictor(deepcopy(kwargs))
         leaf_preds = predictor.predict(data, -1, pred_leaf=True)
         nrow, ncol = leaf_preds.shape
@@ -3540,7 +3585,19 @@ def refit(self, data, label, decay_rate=0.9, **kwargs):
             default_value=None
         )
         new_params["linear_tree"] = bool(out_is_linear.value)
-        train_set = Dataset(data, label, params=new_params)
+        new_params.update(dataset_params)
+        train_set = Dataset(
+            data=data,
+            label=label,
+            reference=reference,
+            weight=weight,
+            group=group,
+            init_score=init_score,
+            feature_name=feature_name,
+            categorical_feature=categorical_feature,
+            params=new_params,
+            free_raw_data=free_raw_data,
+        )
         new_params['refit_decay_rate'] = decay_rate
         new_booster = Booster(new_params, train_set)
         # Copy models

tests/python_package_test/test_engine.py

@@ -1545,6 +1545,40 @@ def test_refit():
     assert err_pred > new_err_pred
 
 
+def test_refit_dataset_params():
+    # check refit accepts dataset_params
+    X, y = load_breast_cancer(return_X_y=True)
+    lgb_train = lgb.Dataset(X, y, init_score=np.zeros(y.size))
+    train_params = {
+        'objective': 'binary',
+        'verbose': -1,
+        'seed': 123
+    }
+    gbm = lgb.train(train_params, lgb_train, num_boost_round=10)
+    non_weight_err_pred = log_loss(y, gbm.predict(X))
+    refit_weight = np.random.rand(y.shape[0])
+    dataset_params = {
+        'max_bin': 260,
+        'min_data_in_bin': 5,
+        'data_random_seed': 123,
+    }
+    new_gbm = gbm.refit(
+        data=X,
+        label=y,
+        weight=refit_weight,
+        dataset_params=dataset_params,
+        decay_rate=0.0,
+    )
+    weight_err_pred = log_loss(y, new_gbm.predict(X))
+    train_set_params = new_gbm.train_set.get_params()
+    stored_weights = new_gbm.train_set.get_weight()
+    assert weight_err_pred != non_weight_err_pred
+    assert train_set_params["max_bin"] == 260
+    assert train_set_params["min_data_in_bin"] == 5
+    assert train_set_params["data_random_seed"] == 123
+    np.testing.assert_allclose(stored_weights, refit_weight)
+
+
 def test_mape_rf():
     X, y = load_boston(return_X_y=True)
     params = {