Profile: improve profiling code

pypesto/profile/options.py

@@ -32,9 +32,9 @@ class ProfileOptions(dict):
     reg_order:
         Maximum degree of regression polynomial used in regression based
         adaptive profile points proposal.
-    magic_factor_obj_value:
-        There is this magic factor in the old profiling code which slows down
-        profiling at small ratios (must be >= 0 and < 1).
+    adaptive_target_scaling_factor:
+        The scaling factor of the next_obj_target in next guess generation.
+        Larger values result in larger next_guess step size (must be > 1).
     whole_path:
         Whether to profile the whole bounds or only till we get below the
         ratio.
@@ -44,13 +44,13 @@ def __init__(
         self,
         default_step_size: float = 0.01,
         min_step_size: float = 0.001,
-        max_step_size: float = 1.0,
+        max_step_size: float = 0.1,
         step_size_factor: float = 1.25,
         delta_ratio_max: float = 0.1,
         ratio_min: float = 0.145,
         reg_points: int = 10,
         reg_order: int = 4,
-        magic_factor_obj_value: float = 0.5,
+        adaptive_target_scaling_factor: float = 1.5,
         whole_path: bool = False,
     ):
         super().__init__()
@@ -63,7 +63,7 @@ def __init__(
         self.delta_ratio_max = delta_ratio_max
         self.reg_points = reg_points
         self.reg_order = reg_order
-        self.magic_factor_obj_value = magic_factor_obj_value
+        self.adaptive_target_scaling_factor = adaptive_target_scaling_factor
         self.whole_path = whole_path
 
         self.validate()
@@ -112,5 +112,5 @@ def validate(self):
         if self.default_step_size < self.min_step_size:
             raise ValueError("default_step_size must be >= min_step_size.")
 
-        if self.magic_factor_obj_value < 0 or self.magic_factor_obj_value >= 1:
-            raise ValueError("magic_factor_obj_value must be >= 0 and < 1.")
+        if self.adaptive_target_scaling_factor < 1:
+            raise ValueError("adaptive_target_scaling_factor must be > 1.")

pypesto/profile/profile.py

@@ -24,7 +24,7 @@ def parameter_profile(
     profile_index: Iterable[int] = None,
     profile_list: int = None,
     result_index: int = 0,
-    next_guess_method: Union[Callable, str] = "adaptive_step_regression",
+    next_guess_method: Union[Callable, str] = "adaptive_step_order_1",
     profile_options: ProfileOptions = None,
     progress_bar: bool = None,
     filename: Union[str, Callable, None] = None,
@@ -93,7 +93,9 @@ def parameter_profile(
     profile_options = ProfileOptions.create_instance(profile_options)
     profile_options.validate()
 
-    # create a function handle that will be called later to get the next point
+    # Create a function handle that will be called later to get the next point.
+    # This function will be used to generate the initial points of optimization
+    # steps in profiling in `walk_along_profile.py`
     if isinstance(next_guess_method, str):
 
         def create_next_guess(
@@ -104,6 +106,8 @@ def create_next_guess(
             current_profile_,
             problem_,
             global_opt_,
+            min_step_increase_factor_,
+            max_step_reduce_factor_,
         ):
             return next_guess(
                 x,
@@ -114,6 +118,8 @@ def create_next_guess(
                 current_profile_,
                 problem_,
                 global_opt_,
+                min_step_increase_factor_,
+                max_step_reduce_factor_,
             )
 
     elif callable(next_guess_method):