Only Modified Base.py

autoprof/fit/base.py

@@ -19,6 +19,29 @@ class BaseOptimizer(object):
 
     """
     def __init__(self, model, initial_state = None, relative_tolerance = 1e-3, **kwargs):
+        """
+    Initializes a new instance of the class.
+
+    Args:
+        model (object): An object representing the model.
+        initial_state (Union[None, Tensor]): The initial state of the model. If `None`, the model's default
+                                             initial state will be used.
+        relative_tolerance (float): The relative tolerance for the optimization.
+        **kwargs (dict): Additional keyword arguments.
+
+    Attributes:
+        model (object): An object representing the model.
+        verbose (int): The verbosity level.
+        current_state (Tensor): The current state of the model.
+        max_iter (int): The maximum number of iterations.
+        iteration (int): The current iteration number.
+        save_steps (Union[None, int]): The frequency at which to save intermediate results.
+        relative_tolerance (float): The relative tolerance for the optimization.
+        lambda_history (List[ndarray]): A list of the optimization steps.
+        loss_history (List[float]): A list of the optimization losses.
+        message (str): An informational message.
+    """
+
         self.model = model
         self.verbose = kwargs.get("verbose", 0)
 
@@ -44,19 +67,54 @@ def __init__(self, model, initial_state = None, relative_tolerance = 1e-3, **kwa
         self.message = ""
 
     def fit(self):
+        """ 
+        Raises:
+            NotImplementedError: Error is raised if this method is not implemented in a subclass of BaseOptimizer.
+        """
         raise NotImplementedError("Please use a subclass of BaseOptimizer for optimization")
     def step(self, current_state = None):
+        """ Args:
+                current_state (torch.Tensor, optional): Current state of the model parameters. Defaults to None.
+
+            Raises:
+                NotImplementedError: Error is raised if this method is not implemented in a subclass of BaseOptimizer.
+        """
         raise NotImplementedError("Please use a subclass of BaseOptimizer for optimization")
 
     def chi2min(self):
+        """
+            Returns the minimum value of chi^2 loss in the loss history.
+
+            Returns:
+                    float: Minimum value of chi^2 loss.
+        """
         return np.nanmin(self.loss_history)
     def res(self):
+        """ Returns the value of lambda (regularization strength) at which minimum chi^2 loss was achieved.
+
+            Returns:
+            ndarray: Value of lambda at which minimum chi^2 loss was achieved.
+        """
         N = np.isfinite(self.loss_history)
         return np.array(self.lambda_history)[N][np.argmin(np.array(self.loss_history)[N])]
 
     def chi2contour(self, n_params, confidence = 0.682689492137):
-
+        """
+        Calculates the chi^2 contour for the given number of parameters.
+
+        Args:
+            n_params (int): The number of parameters.
+            confidence (float, optional): The confidence interval (default is 0.682689492137).
+
+        Returns:
+            float: The calculated chi^2 contour value.
+
+        Raises:
+            RuntimeError: If unable to compute the Chi^2 contour for the given number of parameters.
+
+        """
         def _f(x, nu):
+            """Helper function for calculating chi^2 contour."""
             return (gammainc(nu/2, x/2) - confidence)**2
 
         for method in ["L-BFGS-B", "Powell", "Nelder-Mead"]: