Demo for creating customized multi-class objective function

This demo is only applicable after (excluding) XGBoost 1.0.0, as before this version XGBoost returns transformed prediction for multi-class objective function. More details in comments.

See Custom Objective and Evaluation Metric for detailed tutorial and notes.

import argparse

import numpy as np
from matplotlib import pyplot as plt

import xgboost as xgb


kRows = 100
kCols = 10
kClasses = 4                    # number of classes

kRounds = 10                    # number of boosting rounds.

# Generate some random data for demo.
X = np.random.randn(kRows, kCols)
y = np.random.randint(0, 4, size=kRows)

m = xgb.DMatrix(X, y)

def softmax(x):
    '''Softmax function with x as input vector.'''
    e = np.exp(x)
    return e / np.sum(e)

def softprob_obj(predt: np.ndarray, data: xgb.DMatrix):
    '''Loss function.  Computing the gradient and approximated hessian (diagonal).
    Reimplements the `multi:softprob` inside XGBoost.

    labels = data.get_label()
    if data.get_weight().size == 0:
        # Use 1 as weight if we don't have custom weight.
        weights = np.ones((kRows, 1), dtype=float)
        weights = data.get_weight()

    # The prediction is of shape (rows, classes), each element in a row
    # represents a raw prediction (leaf weight, hasn't gone through softmax
    # yet).  In XGBoost 1.0.0, the prediction is transformed by a softmax
    # function, fixed in later versions.
    assert predt.shape == (kRows, kClasses)

    grad = np.zeros((kRows, kClasses), dtype=float)
    hess = np.zeros((kRows, kClasses), dtype=float)

    eps = 1e-6

    # compute the gradient and hessian, slow iterations in Python, only
    # suitable for demo.  Also the one in native XGBoost core is more robust to
    # numeric overflow as we don't do anything to mitigate the `exp` in
    # `softmax` here.
    for r in range(predt.shape[0]):
        target = labels[r]
        p = softmax(predt[r, :])
        for c in range(predt.shape[1]):
            assert target >= 0 or target <= kClasses
            g = p[c] - 1.0 if c == target else p[c]
            g = g * weights[r]
            h = max((2.0 * p[c] * (1.0 - p[c]) * weights[r]).item(), eps)
            grad[r, c] = g
            hess[r, c] = h

    # After 2.1.0, pass the gradient as it is.
    return grad, hess

def predict(booster: xgb.Booster, X):
    '''A customized prediction function that converts raw prediction to
    target class.

    # Output margin means we want to obtain the raw prediction obtained from
    # tree leaf weight.
    predt = booster.predict(X, output_margin=True)
    out = np.zeros(kRows)
    for r in range(predt.shape[0]):
        # the class with maximum prob (not strictly prob as it haven't gone
        # through softmax yet so it doesn't sum to 1, but result is the same
        # for argmax).
        i = np.argmax(predt[r])
        out[r] = i
    return out

def merror(predt: np.ndarray, dtrain: xgb.DMatrix):
    y = dtrain.get_label()
    # Like custom objective, the predt is untransformed leaf weight when custom objective
    # is provided.

    # With the use of `custom_metric` parameter in train function, custom metric receives
    # raw input only when custom objective is also being used.  Otherwise custom metric
    # will receive transformed prediction.
    assert predt.shape == (kRows, kClasses)
    out = np.zeros(kRows)
    for r in range(predt.shape[0]):
        i = np.argmax(predt[r])
        out[r] = i

    assert y.shape == out.shape

    errors = np.zeros(kRows)
    errors[y != out] = 1.0
    return 'PyMError', np.sum(errors) / kRows

def plot_history(custom_results, native_results):
    fig, axs = plt.subplots(2, 1)
    ax0 = axs[0]
    ax1 = axs[1]

    pymerror = custom_results['train']['PyMError']
    merror = native_results['train']['merror']

    x = np.arange(0, kRounds, 1)
    ax0.plot(x, pymerror, label='Custom objective')
    ax1.plot(x, merror, label='multi:softmax')

def main(args):
    custom_results = {}
    # Use our custom objective function
    booster_custom = xgb.train({'num_class': kClasses,
                                'disable_default_eval_metric': True},
                               evals=[(m, 'train')])

    predt_custom = predict(booster_custom, m)

    native_results = {}
    # Use the same objective function defined in XGBoost.
    booster_native = xgb.train({'num_class': kClasses,
                                "objective": "multi:softmax",
                                'eval_metric': 'merror'},
                               evals=[(m, 'train')])
    predt_native = booster_native.predict(m)

    # We are reimplementing the loss function in XGBoost, so it should
    # be the same for normal cases.
    assert np.all(predt_custom == predt_native)

    if args.plot != 0:
        plot_history(custom_results, native_results)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='Arguments for custom softmax objective function demo.')
        help='Set to 0 to disable plotting the evaluation history.')
    args = parser.parse_args()

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