text: Title: Warn if x=0 when prior='log-uniform' / Space instance should check that bounds are valid
  Body: I got the exception below while running forest_minimize().  I checked and the objective function never returned a NaN/inf.  The objective function was using precision@6 as a metric.  It had just run 11 iterations.  The 11th iteration returned -0.0920692682266 as its score.  So I'm guessing the exception is due to some number not being convertable to float32??
  
  Any ideas what is wrong and how to work around it?
  
  2017-01-19` 19:31:12,321 INFO __main__: Finished iteration 11, NOT-BEST, metrics:
  {'epochs': 41, 'precision@6': 0.092069268, 'learning_rate': 0.0029863437810542567, 'alpha': 1.0590354793387869e-06, 'no_components': 41}
  Traceback (most recent call last):
    File "./lightfm_optimize.py", line 86, in <module>
      res_fm = forest_minimize(objective, space, x0=initial_x, n_calls=args.num_iterations, random_state=random_state, verbose=verbose)
    File "/usr/local/lib/python2.7/site-packages/skopt/optimizer/forest.py", line 167, in forest_minimize
      callback=callback, acq_optimizer="sampling")
    File "/usr/local/lib/python2.7/site-packages/skopt/optimizer/base.py", line 262, in base_minimize
      gp.fit(space.transform(Xi), yi)
    File "/usr/local/lib64/python2.7/site-packages/sklearn/ensemble/forest.py", line 247, in fit
      X = check_array(X, accept_sparse="csc", dtype=DTYPE)
    File "/usr/local/lib64/python2.7/site-packages/sklearn/utils/validation.py", line 407, in check_array
      _assert_all_finite(array)
    File "/usr/local/lib64/python2.7/site-packages/sklearn/utils/validation.py", line 58, in _assert_all_finite
      " or a value too large for %r." % X.dtype)
  ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
  