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taking address of temporary array

See original GitHub issue

XGBoost Classifier generated code for objective = ‘binary:logistic’, does not compile in c++

The last line that copies the prediction to what i am assuming to be a 0 or 1 sigmoid output value.

memcpy(output, (const double[]){(1.0) - (var1000), var1000}, 2 * sizeof(double));

gives this error:

error: taking address of temporary array
     memcpy(output, (double[]){(1.0) - (var1000), var1000}, 2 * sizeof(double));

it highlights this part:

{(1.0) - (var1000), var1000}

does anyone know how I can fix this error? And what if I just returned the raw values from var1000 could that work also?


g++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0



Issue Analytics

  • State:open
  • Created 2 years ago
  • Comments:5 (2 by maintainers)

github_iconTop GitHub Comments

jdubz93commented, Sep 8, 2021
import xgboost as xgb

When trying on random data I get:

RecursionError: maximum recursion depth exceeded while calling a Python object

When I use:

import sys


the error goes away

Model Used + Random Data:

from sklearn.datasets import load_breast_cancer
from xgboost import XGBClassifier

import m2cgen as m2c

X, y = load_breast_cancer(return_X_y=True)
xgb_model_random = XGBClassifier(
        objective = 'reg:logistic',
        colsample_bytree = 1,
        eta = 0.01,
        grow_policy = 'lossguide',
        max_depth = 8,
        min_child_weight = 0.15,
        n_estimators = 1000,
        subsample = 0.75), y, verbose=False)

And the model used with the random data has the same exact output / issue:

#include <math.h>
#include <string.h>
void score(double * input, double * output) {
    double var0;
# top of file ^
# near end of file
    double var1000;
    var1000 = (1.0) / ((1.0) + (exp((0.0) - ((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((var0) + (var1)) + (var2)) + (var3)) .... etc
    memcpy(output, (double[]){(1.0) - (var1000), var1000}, 2 * sizeof(double));

Here is my entire conda environment: conda_environment.txt

Here is the random model (which is the same as the other model minus the data, but same issue. random_model_test.txt

G++ Compiler used:
StrikerRUScommented, Sep 9, 2021

OK, thanks for sharing the code with random data to reproduce the error! I’ll try it on my end.

Read more comments on GitHub >

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