Refactor linear modelling and add new coordinate descent updater (#3103)

* Refactor linear modelling and add new coordinate descent updater

* Allow unsorted column iterator

* Add prediction cacheing to gblinear

tests/benchmark/benchmark_linear.py

@@ -0,0 +1,69 @@
+#pylint: skip-file
+import sys, argparse
+import xgboost as xgb
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+import time
+import ast
+
+rng = np.random.RandomState(1994)
+
+
+def run_benchmark(args):
+
+    try:
+        dtest = xgb.DMatrix('dtest.dm')
+        dtrain = xgb.DMatrix('dtrain.dm')
+
+        if not (dtest.num_col() == args.columns \
+                and dtrain.num_col() == args.columns):
+            raise ValueError("Wrong cols")
+        if not (dtest.num_row() == args.rows * args.test_size \
+                and dtrain.num_row() == args.rows * (1-args.test_size)):
+            raise ValueError("Wrong rows")
+    except:
+
+        print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
+        print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
+        tmp = time.time()
+        X, y = make_classification(args.rows, n_features=args.columns, n_redundant=0, n_informative=args.columns, n_repeated=0, random_state=7)
+        if args.sparsity < 1.0:
+           X = np.array([[np.nan if rng.uniform(0, 1) < args.sparsity else x for x in x_row] for x_row in X])
+
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
+        print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
+        tmp = time.time()
+        print ("DMatrix Start")
+        dtrain = xgb.DMatrix(X_train, y_train)
+        dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
+        print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
+
+        dtest.save_binary('dtest.dm')
+        dtrain.save_binary('dtrain.dm')
+
+    param = {'objective': 'binary:logistic','booster':'gblinear'}
+    if args.params is not '':
+        param.update(ast.literal_eval(args.params))
+
+    param['updater'] = args.updater
+    print("Training with '%s'" % param['updater'])
+    tmp = time.time()
+    xgb.train(param, dtrain, args.iterations, evals=[(dtrain,"train")], early_stopping_rounds = args.columns)
+    print ("Train Time: %s seconds" % (str(time.time() - tmp)))
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--updater', default='coord_descent')
+parser.add_argument('--sparsity', type=float, default=0.0)
+parser.add_argument('--lambda', type=float, default=1.0)
+parser.add_argument('--tol', type=float, default=1e-5)
+parser.add_argument('--alpha', type=float, default=1.0)
+parser.add_argument('--rows', type=int, default=1000000)
+parser.add_argument('--iterations', type=int, default=10000)
+parser.add_argument('--columns', type=int, default=50)
+parser.add_argument('--test_size', type=float, default=0.25)
+parser.add_argument('--standardise', type=bool, default=False)
+parser.add_argument('--params', default='', help='Provide additional parameters as a Python dict string, e.g. --params \"{\'max_depth\':2}\"')
+args = parser.parse_args()
+
+run_benchmark(args)