merge latest, Jan 12 2024

demo/guide-python/callbacks.py

@@ -7,6 +7,7 @@ Demo for using and defining callback functions
 import argparse
 import os
 import tempfile
+from typing import Dict
 
 import numpy as np
 from matplotlib import pyplot as plt
@@ -17,24 +18,26 @@ import xgboost as xgb
 
 
 class Plotting(xgb.callback.TrainingCallback):
-    """Plot evaluation result during training.  Only for demonstration purpose as it's quite
-    slow to draw.
+    """Plot evaluation result during training.  Only for demonstration purpose as it's
+    quite slow to draw using matplotlib.
 
     """
 
-    def __init__(self, rounds):
+    def __init__(self, rounds: int) -> None:
         self.fig = plt.figure()
         self.ax = self.fig.add_subplot(111)
         self.rounds = rounds
-        self.lines = {}
+        self.lines: Dict[str, plt.Line2D] = {}
         self.fig.show()
         self.x = np.linspace(0, self.rounds, self.rounds)
         plt.ion()
 
-    def _get_key(self, data, metric):
+    def _get_key(self, data: str, metric: str) -> str:
         return f"{data}-{metric}"
 
-    def after_iteration(self, model, epoch, evals_log):
+    def after_iteration(
+        self, model: xgb.Booster, epoch: int, evals_log: Dict[str, dict]
+    ) -> bool:
         """Update the plot."""
         if not self.lines:
             for data, metric in evals_log.items():
@@ -55,7 +58,7 @@ class Plotting(xgb.callback.TrainingCallback):
         return False
 
 
-def custom_callback():
+def custom_callback() -> None:
     """Demo for defining a custom callback function that plots evaluation result during
     training."""
     X, y = load_breast_cancer(return_X_y=True)
@@ -82,19 +85,27 @@ def custom_callback():
     )
 
 
-def check_point_callback():
-    # only for demo, set a larger value (like 100) in practice as checkpointing is quite
+def check_point_callback() -> None:
+    """Demo for using the checkpoint callback. Custom logic for handling output is
+    usually required and users are encouraged to define their own callback for
+    checkpointing operations. The builtin one can be used as a starting point.
+
+    """
+    # Only for demo, set a larger value (like 100) in practice as checkpointing is quite
     # slow.
     rounds = 2
 
-    def check(as_pickle):
+    def check(as_pickle: bool) -> None:
         for i in range(0, 10, rounds):
             if i == 0:
                 continue
             if as_pickle:
                 path = os.path.join(tmpdir, "model_" + str(i) + ".pkl")
             else:
-                path = os.path.join(tmpdir, "model_" + str(i) + ".json")
+                path = os.path.join(
+                    tmpdir,
+                    f"model_{i}.{xgb.callback.TrainingCheckPoint.default_format}",
+                )
             assert os.path.exists(path)
 
     X, y = load_breast_cancer(return_X_y=True)

demo/guide-python/cat_in_the_dat.py

@@ -78,6 +78,10 @@ def categorical_model(X: pd.DataFrame, y: pd.Series, output_dir: str) -> None:
     X_train, X_test, y_train, y_test = train_test_split(
         X, y, random_state=1994, test_size=0.2
     )
+    # Be aware that the encoding for X_train and X_test are the same here. In practice,
+    # we should try to use an encoder like (sklearn OrdinalEncoder) to obtain the
+    # categorical values.
+
     # Specify `enable_categorical` to True.
     clf = xgb.XGBClassifier(
         **params,

demo/guide-python/individual_trees.py

@@ -58,7 +58,7 @@ def individual_tree() -> None:
 
 
 def model_slices() -> None:
-    """Inference with each individual using model slices."""
+    """Inference with each individual tree using model slices."""
     X_train, y_train = load_svmlight_file(train)
     X_test, y_test = load_svmlight_file(test)
     Xy_train = xgb.QuantileDMatrix(X_train, y_train)

demo/guide-python/quantile_regression.py

@@ -9,7 +9,7 @@ https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_qu
 
 .. note::
 
-    The feature is only supported using the Python package. In addition, quantile
+    The feature is only supported using the Python, R, and C packages. In addition, quantile
     crossing can happen due to limitation in the algorithm.
 
 """