Make arguments consistent

skopt/__init__.py

@@ -78,4 +78,8 @@ def f(x):
            "learning",
            "optimizer",
            "plots",
-           "space")
+           "space",
+           "gp_minimize",
+           "dummy_minimize",
+           "forest_minimize",
+           "gbrt_minimize")

skopt/optimizer/forest_opt.py

@@ -2,15 +2,16 @@
 
 from sklearn.utils import check_random_state
 
-from .base_opt import base_minimize
+from .base import base_minimize
 from ..learning import ExtraTreesRegressor
 from ..learning import RandomForestRegressor
 
 
-def forest_minimize(func, dimensions, base_estimator='et', n_calls=100,
-                    n_points=1000, n_random_starts=10, x0=None, y0=None,
-                    n_jobs=1, random_state=None, acq_func="EI",
-                    xi=0.01, kappa=1.96, verbose=False, callback=None):
+def forest_minimize(func, dimensions, base_estimator=None,
+                    n_calls=100, n_random_starts=10,
+                    acq_func="EI", acq_optimizer="auto",
+                    x0=None, y0=None, random_state=None, verbose=False,
+                    callback=None, n_points=1000, xi=0.01, kappa=1.96, n_jobs=1):
     """Sequential optimisation using decision trees.
 
     A tree based regression model is used to model the expensive to evaluate

skopt/optimizer/gbrt_opt.py

@@ -2,14 +2,15 @@
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.utils import check_random_state
 
-from .base_opt import base_minimize
+from .base import base_minimize
 from ..learning import GradientBoostingQuantileRegressor
 
 
-def gbrt_minimize(func, dimensions, base_estimator=None, n_calls=100,
-                  n_points=1000, n_random_starts=10, x0=None, y0=None,
-                  n_jobs=1, random_state=None, acq_func="EI",
-                  xi=0.01, kappa=1.96, verbose=False, callback=None):
+def gbrt_minimize(func, dimensions, base_estimator=None,
+                  n_calls=100, n_random_starts=10,
+                  acq_func="EI", acq_optimizer="auto",
+                  x0=None, y0=None, random_state=None, verbose=False,
+                  callback=None, n_points=1000, xi=0.01, kappa=1.96, n_jobs=1):
     """Sequential optimization using gradient boosted trees.
 
     Gradient boosted regression trees are used to model the (very)

skopt/optimizer/gp_opt.py

@@ -9,16 +9,15 @@
 from sklearn.gaussian_process.kernels import WhiteKernel
 from sklearn.utils import check_random_state
 
-from .base_opt import base_minimize
+from .base import base_minimize
 from ..space import Space
 
 
 def gp_minimize(func, dimensions, base_estimator=None,
-                acq_func="EI", xi=0.01, kappa=1.96,
-                acq_optimizer="auto",
-                n_calls=100, n_points=500, n_random_starts=10,
-                n_restarts_optimizer=5, x0=None, y0=None,
-                random_state=None, verbose=False, callback=None):
+                n_calls=100, n_random_starts=10,
+                acq_func="EI", acq_optimizer="auto", x0=None, y0=None,
+                random_state=None, verbose=False, callback=None,
+                n_points=10000, n_restarts_optimizer=5, xi=0.01, kappa=1.96):
     """Bayesian optimization using Gaussian Processes.
 
     If every function evaluation is expensive, for instance