[python-package] fix retrain on sequence dataset

python-package/lightgbm/basic.py

@@ -1192,15 +1192,21 @@ def predict(
                 predict_type=predict_type,
             )
         elif isinstance(data, list):
-            try:
-                data = np.array(data)
-            except BaseException as err:
-                raise ValueError("Cannot convert data list to numpy array.") from err
+            if isinstance(data[0], Sequence):
+                try:
+                    data = np.concatenate([i[:] for i in data])
+                except BaseException as err:
+                    raise ValueError('Cannot convert Sequence list to numpy array.') from err
+            else:
+                try:
+                    data = np.array(data)
+                except BaseException as err:
+                    raise ValueError('Cannot convert data list to numpy array.') from err
             preds, nrow = self.__pred_for_np2d(
                 mat=data,
                 start_iteration=start_iteration,
                 num_iteration=num_iteration,
-                predict_type=predict_type,
+                predict_type=predict_type
             )
         elif isinstance(data, dt_DataTable):
             preds, nrow = self.__pred_for_np2d(

tests/python_package_test/test_sequence.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# Author: mithril
+# Created Date: 2024-07-14 14:18:46
+# Last Modified: 2024-07-14 14:44:50
+
+
+import numpy as np
+import pytest
+import sklearn.datasets
+import lightgbm as lgb
+
+
+class PartitionSequence(lgb.Sequence):
+    def __init__(self, data:np.ndarray, batch_size=4096):
+        self.data = data
+        self.batch_size = batch_size
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+    def __len__(self):
+        return len(self.data)
+
+
+def test_list_of_sequence():
+    X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
+    X_seq = list()
+    y_seq = list()
+    for i in range(2):
+        X_seq.append(PartitionSequence(X, 200))
+        y_seq.append(y)
+
+    y = np.concatenate(y_seq)
+
+    dataset = lgb.Dataset(X_seq, label=y, free_raw_data=False)
+
+    params = {
+        "objective": "binary",
+        "metric": "auc",
+        "min_data": 10,
+        "num_leaves": 10,
+        "verbose": -1,
+        "num_threads": 1,
+        "max_bin": 255,
+        "gpu_use_dp": True,
+    }
+
+    model1 = lgb.train(
+        params,
+        dataset,
+        keep_training_booster=True,
+    )
+
+    model2 = lgb.train(
+        params,
+        dataset,
+        init_model=model1,
+    )