Predictions from R and Python don't match

[hafen]

I am trying to build a prediction function in R for a model that was trained in Python and the results don't match. See below for an example of how to reproduce.

I am fairly new to xgboost, particularly using it across languages, so I may be missing something obvious.

Environment info

Operating System: macOS 10.12

Compiler: gcc

Package used (python/R/jvm/C++): Python, R

xgboost version used: 0.6

If you are using python package, please provide

1. The python version and distribution: Python 2.7.12 :: Anaconda 2.1.0 (x86_64)
2. The command to install xgboost if you are not installing from source: Installed from source

If you are using R package, please provide

1. The R sessionInfo()

   > sessionInfo()
   R version 3.3.1 (2016-06-21)
   Platform: x86_64-apple-darwin13.4.0 (64-bit)
   Running under: OS X 10.12 (Sierra)
   
   locale:
   [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
   
   attached base packages:
   [1] stats     graphics  grDevices utils     datasets  methods   base     
   
   other attached packages:
   [1] xgboost_0.6  colorout_1.1-0
   
   loaded via a namespace (and not attached):
   [1] magrittr_1.5     Matrix_1.2-6     tools_3.3.1      stringi_1.1.1   
   [5] grid_3.3.1       data.table_1.9.6 stringr_1.1.0    chron_2.3-47    
   [9] lattice_0.20-33 

2. The command to install xgboost if you are not installing from source: Installed from source

   Steps to reproduce

3. Download this model file: http://ml.stat.purdue.edu/hafen/WTKG.model

4. Run this script in R:

   library(xgboost)
   mod <- xgb.load("WTKG.model")
   x <- c(91, 9, 9, NA, NA, 273, 20, 170, NA, NA, 14, 14, 0,
    2, 0.94289404091, 0.94289404091, 0.93087973569, 0.0120143052199997, 0.95490834613,
    0.95490834613, 1, 90, 0.95490834613, 1, 90,
    0.93087973569, 357, -266, 0.93087973569, 357, -266,
    0.95490834613, NA, 0.93087973569, NA, NA, NA)
   d <- xgb.DMatrix(matrix(x, nrow = 1), missing = NA)
   predict(mod, d)
   # [1] 0.6483372

5. Run this script in Python:

   import numpy as np
   import xgboost as xgb
   
   bst = xgb.Booster({'nthread': 4})
   bst.load_model('WTKG.model')
   x = [91, 9, 9, np.nan, np.nan, 273, 20, 170, np.nan, np.nan, 14, 14, 0,
    2, 0.94289404091, 0.94289404091, 0.93087973569, 0.0120143052199997, 0.95490834613,
    0.95490834613, 1, 90, 0.95490834613, 1, 90,
    0.93087973569, 357, -266, 0.93087973569, 357, -266,
    0.95490834613, np.nan, 0.93087973569, np.nan, np.nan, np.nan]
   d = xgb.DMatrix(data=[x], missing=np.nan)
   bst.predict(d)[0]
   #1.3775804

[hafen]

Never mind - it looks like the issue was on the Python end - should wrap [x] in np.array([x])