Implement new parameter setting config style (#393)

Summary:

To support more granular parameter settings in the future, we need to change the config style to be able to separately handle settings for each parameter.

Currently the only setting for each parameter are the lower and upper bounds, but we also implement parameter types here (which currently does nothing).

This is technically not a breaking change. Even though we have changed all documentation to match the new style, it is possible to define parameter bounds the old way. This will throw a warning however as this causes all parameter-specific bounds to be ignored.

Old tests in test_config.py are also modified to match the new style unless specifically testing for these changes. Other tests not in test_config.py are left unchanged (this was the case for the previous major config change where experiment was a section).

Reviewed By: crasanders

Differential Revision: D63679916

aepsych/config.py

@@ -22,7 +22,6 @@
 
 _T = TypeVar("_T")
 
-
 class Config(configparser.ConfigParser):
 
     # names in these packages can be referred to by string name
@@ -157,6 +156,26 @@ def update(
         if config_str is not None:
             self.read_string(config_str)
 
+        # Warn if ub/lb is defined in common section
+        if "ub" in self["common"] and "lb" in self["common"]:
+            warnings.warn(
+                "ub and lb have been defined in common section, ignoring parameter specific blocks, be very careful!"
+            )
+        elif "parnames" in self["common"]: # it's possible to pass no parnames
+            par_names = self.getlist("common", "parnames", element_type=str, fallback = [])
+            lb = [None] * len(par_names)
+            ub = [None] * len(par_names)
+            for i, par_name in enumerate(par_names):
+                # Validate the parameter-specific block
+                self._check_param_settings(par_name)
+
+                lb[i] = self[par_name]["lower_bound"]
+                ub[i] = self[par_name]["upper_bound"]
+
+            self["common"]["lb"] = f"[{', '.join(lb)}]"
+            self["common"]["ub"] = f"[{', '.join(ub)}]"
+
+
         # Deprecation warning for "experiment" section
         if "experiment" in self:
             for i in self["experiment"]:
@@ -191,6 +210,33 @@ def _str_to_obj(self, v: str, fallback_type: _T = str, warn: bool = True) -> obj
                 warnings.warn(f'No known object "{v}"!')
             return fallback_type(v)
 
+    def _check_param_settings(self, param_name: str) -> None:
+        """Check parameter-specific blocks have the correct settings, raises a ValueError if not.
+
+        Args:
+            param_name (str): Parameter block to check.
+        """
+        # Check if the config block exists at all
+        if param_name not in self:
+            raise ValueError(f"Parameter {param_name} is missing its own config block.")
+
+        param_block = self[param_name]
+
+        # Checking if param_type is set
+        if "par_type" not in param_block:
+            raise ValueError(f"Parameter {param_name} is missing the param_type setting.")
+
+        # Each parameter type has a different set of required settings
+        if param_block['par_type'] == "continuous":
+            # Check if bounds exist
+            if "lower_bound" not in param_block:
+                raise ValueError(f"Parameter {param_name} is missing the lower_bound setting.")
+            if "upper_bound" not in param_block:
+                raise ValueError(f"Parameter {param_name} is missing the upper_bound setting.")
+        else:
+            raise ValueError(f"Parameter {param_name} has an unsupported parameter type {param_block['par_type']}.")
+
+
     def __repr__(self):
         return f"Config at {hex(id(self))}: \n {str(self)}"
 

configs/nonmonotonic_optimization_example.ini

@@ -11,12 +11,21 @@
 ## reused in multiple other classes
 [common]
 parnames = [par1, par2] # names of the parameters
-lb = [0, 0] # lower bounds of the parameters, in the same order as above
-ub = [1, 1] # upper bounds of parameter, in the same order as above
 stimuli_per_trial = 1 # the number of stimuli shown in each trial; 1 for single, or 2 for pairwise experiments
 outcome_types = [binary] # the type of response given by the participant; can be [binary] or [continuous] for single
 strategy_names = [init_strat, opt_strat] # The strategies that will be used, corresponding to the named sections below
 
+# Parameter settings, blocks based on parameter names in [common]
+[par1]
+par_type = continuous
+lower_bound = 0 # lower bound
+upper_bound = 1 # upper bound
+
+[par2]
+par_type = continuous
+lower_bound = 0
+upper_bound = 1
+
 # Configuration for the initialization strategy, which we use to gather initial points
 # before we start doing model-based acquisition
 [init_strat]

configs/ordinal_exploration_example.ini

@@ -10,12 +10,20 @@
 ## reused in multiple other classes
 [common]
 parnames = [par1, par2] # names of the parameters
-lb = [-1, -1] # lower bounds of the parameters, in the same order as above
-ub = [1, 1] # upper bounds of parameter, in the same order as above
 stimuli_per_trial = 1 # the number of stimuli shown in each trial; 1 for single, or 2 for pairwise experiments
 outcome_types = [ordinal]
 strategy_names = [init_strat] # The strategies that will be used, corresponding to the named sections below
 
+[par1]
+par_type = continuous
+lower_bound = -1
+upper_bound = 1
+
+[par2]
+par_type = continuous
+lower_bound = -1
+upper_bound = 1
+
 # Configuration for the initialization strategy, which we use to gather initial points
 # before we start doing model-based acquisition
 [init_strat]

configs/pairwise_al_example.ini

@@ -9,12 +9,21 @@
 ## reused in multiple other classes
 [common]
 parnames = [par1, par2] # names of the parameters
-lb = [0, 0] # lower bounds of the parameters, in the same order as above
-ub = [1, 1] # upper bounds of parameter, in the same order as above
 stimuli_per_trial = 2 # the number of stimuli shown in each trial; 1 for single, or 2 for pairwise experiments
 outcome_types = [binary] # the type of response given by the participant; can only be [binary] for pairwise for now
 strategy_names = [init_strat, opt_strat] # The strategies that will be used, corresponding to the named sections below
 
+# Parameter settings, blocks based on parameter names in [common]
+[par1]
+par_type = continuous
+lower_bound = 0 # lower bound
+upper_bound = 1 # upper bound
+
+[par2]
+par_type = continuous
+lower_bound = 0
+upper_bound = 1
+
 # Configuration for the initialization strategy, which we use to gather initial points
 # before we start doing model-based acquisition
 [init_strat]

configs/pairwise_opt_example.ini

@@ -9,12 +9,21 @@
 ## reused in multiple other classes
 [common]
 parnames = [par1, par2] # names of the parameters
-lb = [0, 0] # lower bounds of the parameters, in the same order as above
-ub = [1, 1] # upper bounds of parameter, in the same order as above
 stimuli_per_trial = 2 # the number of stimuli shown in each trial; 1 for single, or 2 for pairwise experiments
 outcome_types = [binary] # the type of response given by the participant; can only be [binary] for pairwise for now
 strategy_names = [init_strat, opt_strat] # The strategies that will be used, corresponding to the named sections below
 
+# Parameter settings, blocks based on parameter names in [common]
+[par1]
+par_type = continuous
+lower_bound = 0 # lower bound
+upper_bound = 1 # upper bound
+
+[par2]
+par_type = continuous
+lower_bound = 0
+upper_bound = 1
+
 # Configuration for the initialization strategy, which we use to gather initial points
 # before we start doing model-based acquisition
 [init_strat]