[Breaking] Set output margin to True for custom objective. (#5564)

* Set output margin to True for custom objective in Python and R.

* Add a demo for writing multi-class custom objective function.

* Run tests on selected demos.

demo/guide-python/basic_walkthrough.py

@@ -1,16 +1,22 @@
-#!/usr/bin/python
+#!/usr/bin/env python
 import numpy as np
 import scipy.sparse
 import pickle
 import xgboost as xgb
+import os
 
-### simple example
+# Make sure the demo knows where to load the data.
+CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+XGBOOST_ROOT_DIR = os.path.dirname(os.path.dirname(CURRENT_DIR))
+DEMO_DIR = os.path.join(XGBOOST_ROOT_DIR, 'demo')
+
+# simple example
 # load file from text file, also binary buffer generated by xgboost
-dtrain = xgb.DMatrix('../data/agaricus.txt.train')
-dtest = xgb.DMatrix('../data/agaricus.txt.test')
+dtrain = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train'))
+dtest = xgb.DMatrix(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.test'))
 
 # specify parameters via map, definition are same as c++ version
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
+param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
 
 # specify validations set to watch performance
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]
@@ -20,12 +26,14 @@ bst = xgb.train(param, dtrain, num_round, watchlist)
 # this is prediction
 preds = bst.predict(dtest)
 labels = dtest.get_label()
-print('error=%f' % (sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))))
+print('error=%f' %
+      (sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) /
+       float(len(preds))))
 bst.save_model('0001.model')
 # dump model
 bst.dump_model('dump.raw.txt')
 # dump model with feature map
-bst.dump_model('dump.nice.txt', '../data/featmap.txt')
+bst.dump_model('dump.nice.txt', os.path.join(DEMO_DIR, 'data/featmap.txt'))
 
 # save dmatrix into binary buffer
 dtest.save_binary('dtest.buffer')
@@ -50,14 +58,18 @@ assert np.sum(np.abs(preds3 - preds)) == 0
 # build dmatrix from scipy.sparse
 print('start running example of build DMatrix from scipy.sparse CSR Matrix')
 labels = []
-row = []; col = []; dat = []
+row = []
+col = []
+dat = []
 i = 0
-for l in open('../data/agaricus.txt.train'):
+for l in open(os.path.join(DEMO_DIR, 'data', 'agaricus.txt.train')):
     arr = l.split()
     labels.append(int(arr[0]))
     for it in arr[1:]:
-        k,v = it.split(':')
-        row.append(i); col.append(int(k)); dat.append(float(v))
+        k, v = it.split(':')
+        row.append(i)
+        col.append(int(k))
+        dat.append(float(v))
     i += 1
 csr = scipy.sparse.csr_matrix((dat, (row, col)))
 dtrain = xgb.DMatrix(csr, label=labels)
@@ -72,8 +84,8 @@ watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 bst = xgb.train(param, dtrain, num_round, watchlist)
 
 print('start running example of build DMatrix from numpy array')
-# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix in internal implementation
-# then convert to DMatrix
+# NOTE: npymat is numpy array, we will convert it into scipy.sparse.csr_matrix
+# in internal implementation then convert to DMatrix
 npymat = csr.todense()
 dtrain = xgb.DMatrix(npymat, label=labels)
 watchlist = [(dtest, 'eval'), (dtrain, 'train')]