GPU Plugin: Add bosch demo, update build instructions (#1872)

demo/gpu_acceleration/bosch.py

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+import numpy as np
+import pandas as pd
+import xgboost as xgb
+import time
+import random
+from sklearn.cross_validation import StratifiedKFold
+
+#For sub sampling rows from input file
+random_seed = 9
+subsample = 0.4
+
+n_rows = 1183747;
+train_rows = int(n_rows * subsample)
+random.seed(random_seed)
+skip = sorted(random.sample(xrange(1,n_rows + 1),n_rows-train_rows))
+data = pd.read_csv("../data/train_numeric.csv", index_col=0, dtype=np.float32, skiprows=skip)
+y = data['Response'].values
+del data['Response']
+X = data.values
+
+param = {}
+param['objective'] = 'binary:logistic'
+param['eval_metric'] = 'auc'
+param['max_depth'] = 5
+param['eta'] = 0.3
+param['silent'] = 0
+param['updater'] = 'grow_gpu'
+#param['updater'] = 'grow_colmaker'
+
+num_round = 20
+
+cv = StratifiedKFold(y, n_folds=5)
+
+for i, (train, test) in enumerate(cv):
+    dtrain = xgb.DMatrix(X[train], label=y[train])
+    tmp = time.time()
+    bst = xgb.train(param, dtrain, num_round)
+    boost_time = time.time() - tmp
+    res = bst.eval(xgb.DMatrix(X[test], label=y[test]))
+    print("Fold {}: {}, Boost Time {}".format(i, res, str(boost_time)))
+    del bst
+