851cba931e
* Define `best_iteration` only if early stopping is used. This is the behavior specified by the document but not honored in the actual code. - Don't set the attributes if there's no early stopping. - Clean up the code for callbacks, and replace assertions with proper exceptions. - Assign the attributes when early stopping `save_best` is used. - Turn the attributes into Python properties. --------- Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
"""
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Demo for using and defining callback functions
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==============================================
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.. versionadded:: 1.3.0
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"""
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import argparse
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import os
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import tempfile
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import numpy as np
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from matplotlib import pyplot as plt
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from sklearn.datasets import load_breast_cancer
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from sklearn.model_selection import train_test_split
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import xgboost as xgb
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class Plotting(xgb.callback.TrainingCallback):
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"""Plot evaluation result during training. Only for demonstration purpose as it's quite
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slow to draw.
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"""
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def __init__(self, rounds):
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self.fig = plt.figure()
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self.ax = self.fig.add_subplot(111)
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self.rounds = rounds
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self.lines = {}
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self.fig.show()
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self.x = np.linspace(0, self.rounds, self.rounds)
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plt.ion()
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def _get_key(self, data, metric):
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return f"{data}-{metric}"
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def after_iteration(self, model, epoch, evals_log):
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"""Update the plot."""
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if not self.lines:
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for data, metric in evals_log.items():
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for metric_name, log in metric.items():
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key = self._get_key(data, metric_name)
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expanded = log + [0] * (self.rounds - len(log))
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(self.lines[key],) = self.ax.plot(self.x, expanded, label=key)
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self.ax.legend()
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else:
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# https://pythonspot.com/matplotlib-update-plot/
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for data, metric in evals_log.items():
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for metric_name, log in metric.items():
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key = self._get_key(data, metric_name)
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expanded = log + [0] * (self.rounds - len(log))
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self.lines[key].set_ydata(expanded)
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self.fig.canvas.draw()
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# False to indicate training should not stop.
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return False
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def custom_callback():
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"""Demo for defining a custom callback function that plots evaluation result during
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training."""
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X, y = load_breast_cancer(return_X_y=True)
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X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)
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D_train = xgb.DMatrix(X_train, y_train)
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D_valid = xgb.DMatrix(X_valid, y_valid)
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num_boost_round = 100
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plotting = Plotting(num_boost_round)
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# Pass it to the `callbacks` parameter as a list.
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xgb.train(
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{
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"objective": "binary:logistic",
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"eval_metric": ["error", "rmse"],
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"tree_method": "hist",
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"device": "cuda",
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},
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D_train,
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evals=[(D_train, "Train"), (D_valid, "Valid")],
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num_boost_round=num_boost_round,
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callbacks=[plotting],
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)
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def check_point_callback():
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# only for demo, set a larger value (like 100) in practice as checkpointing is quite
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# slow.
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rounds = 2
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def check(as_pickle):
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for i in range(0, 10, rounds):
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if i == 0:
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continue
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if as_pickle:
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path = os.path.join(tmpdir, "model_" + str(i) + ".pkl")
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else:
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path = os.path.join(tmpdir, "model_" + str(i) + ".json")
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assert os.path.exists(path)
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X, y = load_breast_cancer(return_X_y=True)
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m = xgb.DMatrix(X, y)
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# Check point to a temporary directory for demo
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with tempfile.TemporaryDirectory() as tmpdir:
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# Use callback class from xgboost.callback
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# Feel free to subclass/customize it to suit your need.
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check_point = xgb.callback.TrainingCheckPoint(
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directory=tmpdir, iterations=rounds, name="model"
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)
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xgb.train(
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{"objective": "binary:logistic"},
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m,
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num_boost_round=10,
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verbose_eval=False,
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callbacks=[check_point],
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)
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check(False)
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# This version of checkpoint saves everything including parameters and
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# model. See: doc/tutorials/saving_model.rst
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check_point = xgb.callback.TrainingCheckPoint(
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directory=tmpdir, iterations=rounds, as_pickle=True, name="model"
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)
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xgb.train(
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{"objective": "binary:logistic"},
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m,
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num_boost_round=10,
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verbose_eval=False,
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callbacks=[check_point],
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)
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check(True)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--plot", default=1, type=int)
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args = parser.parse_args()
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check_point_callback()
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if args.plot:
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custom_callback()
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