Fix callback in AFT viz demo. (#9333)

* Fix callback in AFT viz demo.

- Update the callback function.
- Add lint check.

demo/aft_survival/aft_survival_viz_demo.py

@@ -11,33 +11,43 @@ import numpy as np
 
 import xgboost as xgb
 
-plt.rcParams.update({'font.size': 13})
+plt.rcParams.update({"font.size": 13})
+
 
 # Function to visualize censored labels
-def plot_censored_labels(X, y_lower, y_upper):
+def plot_censored_labels(
-    def replace_inf(x, target_value):
+    X: np.ndarray, y_lower: np.ndarray, y_upper: np.ndarray
+) -> None:
+    def replace_inf(x: np.ndarray, target_value: float) -> np.ndarray:
         x[np.isinf(x)] = target_value
         return x
-    plt.plot(X, y_lower, 'o', label='y_lower', color='blue')
+
-    plt.plot(X, y_upper, 'o', label='y_upper', color='fuchsia')
+    plt.plot(X, y_lower, "o", label="y_lower", color="blue")
-    plt.vlines(X, ymin=replace_inf(y_lower, 0.01), ymax=replace_inf(y_upper, 1000),
+    plt.plot(X, y_upper, "o", label="y_upper", color="fuchsia")
-               label='Range for y', color='gray')
+    plt.vlines(
+        X,
+        ymin=replace_inf(y_lower, 0.01),
+        ymax=replace_inf(y_upper, 1000.0),
+        label="Range for y",
+        color="gray",
+    )
+
 
 # Toy data
 X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
 INF = np.inf
-y_lower = np.array([ 10,  15, -INF, 30, 100])
+y_lower = np.array([10, 15, -INF, 30, 100])
-y_upper = np.array([INF, INF,   20, 50, INF])
+y_upper = np.array([INF, INF, 20, 50, INF])
 
 # Visualize toy data
 plt.figure(figsize=(5, 4))
 plot_censored_labels(X, y_lower, y_upper)
 plt.ylim((6, 200))
-plt.legend(loc='lower right')
+plt.legend(loc="lower right")
-plt.title('Toy data')
+plt.title("Toy data")
-plt.xlabel('Input feature')
+plt.xlabel("Input feature")
-plt.ylabel('Label')
+plt.ylabel("Label")
-plt.yscale('log')
+plt.yscale("log")
 plt.tight_layout()
 plt.show(block=True)
 
@@ -46,54 +56,83 @@ grid_pts = np.linspace(0.8, 5.2, 1000).reshape((-1, 1))
 
 # Train AFT model using XGBoost
 dmat = xgb.DMatrix(X)
-dmat.set_float_info('label_lower_bound', y_lower)
+dmat.set_float_info("label_lower_bound", y_lower)
-dmat.set_float_info('label_upper_bound', y_upper)
+dmat.set_float_info("label_upper_bound", y_upper)
-params = {'max_depth': 3, 'objective':'survival:aft', 'min_child_weight': 0}
+params = {"max_depth": 3, "objective": "survival:aft", "min_child_weight": 0}
 
 accuracy_history = []
-def plot_intermediate_model_callback(env):
-    """Custom callback to plot intermediate models"""
-    # Compute y_pred = prediction using the intermediate model, at current boosting iteration
-    y_pred = env.model.predict(dmat)
-    # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
-    #              the corresponding predicted label (y_pred)
-    acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper)/len(X) * 100)
-    accuracy_history.append(acc)
 
-    # Plot ranged labels as well as predictions by the model
-    plt.subplot(5, 3, env.iteration + 1)
-    plot_censored_labels(X, y_lower, y_upper)
-    y_pred_grid_pts = env.model.predict(xgb.DMatrix(grid_pts))
-    plt.plot(grid_pts, y_pred_grid_pts, 'r-', label='XGBoost AFT model', linewidth=4)
-    plt.title('Iteration {}'.format(env.iteration), x=0.5, y=0.8)
-    plt.xlim((0.8, 5.2))
-    plt.ylim((1 if np.min(y_pred) < 6 else 6, 200))
-    plt.yscale('log')
 
-res = {}
+class PlotIntermediateModel(xgb.callback.TrainingCallback):
-plt.figure(figsize=(12,13))
+    """Custom callback to plot intermediate models."""
-bst = xgb.train(params, dmat, 15, [(dmat, 'train')], evals_result=res,
+
-                callbacks=[plot_intermediate_model_callback])
+    def __init__(self) -> None:
+        super().__init__()
+
+    def after_iteration(
+        self,
+        model: xgb.Booster,
+        epoch: int,
+        evals_log: xgb.callback.TrainingCallback.EvalsLog,
+    ) -> bool:
+        """Run after training is finished."""
+        # Compute y_pred = prediction using the intermediate model, at current boosting
+        # iteration
+        y_pred = model.predict(dmat)
+        # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper)
+        #              includes the corresponding predicted label (y_pred)
+        acc = np.sum(
+            np.logical_and(y_pred >= y_lower, y_pred <= y_upper) / len(X) * 100
+        )
+        accuracy_history.append(acc)
+
+        # Plot ranged labels as well as predictions by the model
+        plt.subplot(5, 3, epoch + 1)
+        plot_censored_labels(X, y_lower, y_upper)
+        y_pred_grid_pts = model.predict(xgb.DMatrix(grid_pts))
+        plt.plot(
+            grid_pts, y_pred_grid_pts, "r-", label="XGBoost AFT model", linewidth=4
+        )
+        plt.title("Iteration {}".format(epoch), x=0.5, y=0.8)
+        plt.xlim((0.8, 5.2))
+        plt.ylim((1 if np.min(y_pred) < 6 else 6, 200))
+        plt.yscale("log")
+        return False
+
+
+res: xgb.callback.TrainingCallback.EvalsLog = {}
+plt.figure(figsize=(12, 13))
+bst = xgb.train(
+    params,
+    dmat,
+    15,
+    [(dmat, "train")],
+    evals_result=res,
+    callbacks=[PlotIntermediateModel()],
+)
 plt.tight_layout()
-plt.legend(loc='lower center', ncol=4,
+plt.legend(
-           bbox_to_anchor=(0.5, 0),
+    loc="lower center",
-           bbox_transform=plt.gcf().transFigure)
+    ncol=4,
+    bbox_to_anchor=(0.5, 0),
+    bbox_transform=plt.gcf().transFigure,
+)
 plt.tight_layout()
 
 # Plot negative log likelihood over boosting iterations
-plt.figure(figsize=(8,3))
+plt.figure(figsize=(8, 3))
 plt.subplot(1, 2, 1)
-plt.plot(res['train']['aft-nloglik'], 'b-o', label='aft-nloglik')
+plt.plot(res["train"]["aft-nloglik"], "b-o", label="aft-nloglik")
-plt.xlabel('# Boosting Iterations')
+plt.xlabel("# Boosting Iterations")
-plt.legend(loc='best')
+plt.legend(loc="best")
 
 # Plot "accuracy" over boosting iterations
 # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
 #              the corresponding predicted label (y_pred)
 plt.subplot(1, 2, 2)
-plt.plot(accuracy_history, 'r-o', label='Accuracy (%)')
+plt.plot(accuracy_history, "r-o", label="Accuracy (%)")
-plt.xlabel('# Boosting Iterations')
+plt.xlabel("# Boosting Iterations")
-plt.legend(loc='best')
+plt.legend(loc="best")
 plt.tight_layout()
 
 plt.show()

tests/ci_build/lint_python.py

@@ -37,6 +37,7 @@ class LintersPaths:
         "demo/guide-python/quantile_regression.py",
         "demo/guide-python/multioutput_regression.py",
         "demo/guide-python/learning_to_rank.py",
+        "demo/aft_survival/aft_survival_viz_demo.py",
         # CI
         "tests/ci_build/lint_python.py",
         "tests/ci_build/test_r_package.py",
@@ -78,6 +79,7 @@ class LintersPaths:
         "demo/guide-python/quantile_regression.py",
         "demo/guide-python/multioutput_regression.py",
         "demo/guide-python/learning_to_rank.py",
+        "demo/aft_survival/aft_survival_viz_demo.py",
         # CI
         "tests/ci_build/lint_python.py",
         "tests/ci_build/test_r_package.py",
@@ -114,7 +116,13 @@ def run_black(rel_path: str, fix: bool) -> bool:
 @cd(PY_PACKAGE)
 def run_isort(rel_path: str, fix: bool) -> bool:
     # Isort gets confused when trying to find the config file, so specified explicitly.
-    cmd = ["isort", "--settings-path", PY_PACKAGE, os.path.join(ROOT, rel_path)]
+    cmd = [
+        "isort",
+        "--settings-path",
+        PY_PACKAGE,
+        f"--src={PY_PACKAGE}",
+        os.path.join(ROOT, rel_path),
+    ]
     if not fix:
         cmd += ["--check"]