XGBoost and sparse matrix
<p>I am trying to use xgboost to run -using python - on a classification problem, where I have the data in a <strong>numpy matrix X</strong> (rows = observations &amp; columns = features) and the labels in a <strong>numpy array y</strong>. 
Because my data are sparse, I would like to make it run using a sparse version of X, but it seems I am missing something as an error occurs. </p>

<p>Here is what I do : </p>

<pre><code># Library import

import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from scipy.sparse import csr_matrix

# Converting to sparse data and running xgboost

X_csr = csr_matrix(X)
xgb1 = XGBClassifier()
xgtrain = xgb.DMatrix(X_csr, label = y )      #to work with the xgb format
xgtest = xgb.DMatrix(Xtest_csr)
xgb1.fit(xgtrain, y, eval_metric='auc')
dtrain_predictions = xgb1.predict(xgtest)   
</code></pre>

<p>etc...</p>

<p>Now I get an error when trying to fit the classifier : </p>

<pre><code>File ".../xgboost/python-package/xgboost/sklearn.py", line 432, in fit
self._features_count = X.shape[1]

AttributeError: 'DMatrix' object has no attribute 'shape'
</code></pre>

<p>Now, I looked for a while on where it could come from, and believe it has to do with the sparse format I wish to use. But what it is, and how I could fix it, I have no clue. </p>

<p>I would welcome any help or comments ! 
Thank you very much</p>