Implement new parameter setting config style

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]