Fix name for GradientDescent and more info in epf run

docs/tutorials/popt/tutorial_popt.ipynb

@@ -404,7 +404,7 @@
       "                    - restart: restart optimization from a restart file (default false)\n",
       "                    - restartsave: save a restart file after each successful iteration (defalut false)\n",
       "                    - tol: convergence tolerance for the objective function (default 1e-6)\n",
-      "                    - alpha: step size for the steepest decent method (default 0.1)\n",
+      "                    - alpha: step size for the steepest descent method (default 0.1)\n",
       "                    - beta: momentum coefficient for running accelerated optimization (default 0.0)\n",
       "                    - alpha_maxiter: maximum number of backtracing trials (default 5)\n",
       "                    - resample: number indicating how many times resampling is tried if no improvement is found\n",

pipt/loop/ensemble.py

@@ -174,7 +174,7 @@ def check_assimindex_simultaneous(self):
     def _org_obs_data(self):
         """
         Organize the input true observed data. The obs_data will be a list of length equal length of "TRUEDATAINDEX",
-        and each entery in the list will be a dictionary with keys equal to the "DATATYPE".
+        and each entry in the list will be a dictionary with keys equal to the "DATATYPE".
         Also, the pred_data variable (predicted data or forward simulation) will be initialized here with the same
         structure as the obs_data variable.
 

popt/loop/optimize.py

@@ -205,6 +205,10 @@ def run_loop(self):
                     self.ftol *= self.epf['tol_factor']  # decrease tolerance
                     self.obj_func_values = self.fun(self.xk, epf = self.epf)
                     self.iteration = 0
+                    info_str = '       {:<10} {:<10} {:<15} {:<15} {:<15} '.format('iter', 'alpha_iter',
+                                                                                   'obj_func', 'step-size', 'cov[0,0]')
+                    self.logger.info(info_str)
+                    self.logger.info('       {:<21} {:<15.4e}'.format(self.iteration, np.mean(self.obj_func_values)))
                     self.epf_iteration += 1
                     optimize_result = ot.get_optimize_result(self)
                     ot.save_optimize_results(optimize_result)

popt/update_schemes/enopt.py

@@ -63,7 +63,7 @@ def __init__(self, fun, x, args, jac, hess, bounds=None, **options):
             - restart: restart optimization from a restart file (default false)
             - restartsave: save a restart file after each successful iteration (defalut false)
             - tol: convergence tolerance for the objective function (default 1e-6)
-            - alpha: step size for the steepest decent method (default 0.1)
+            - alpha: step size for the steepest descent method (default 0.1)
             - beta: momentum coefficient for running accelerated optimization (default 0.0)
             - alpha_maxiter: maximum number of backtracing trials (default 5)
             - resample: number indicating how many times resampling is tried if no improvement is found
@@ -130,7 +130,7 @@ def __set__variable(var_name=None, defalut=None):
         # Initialize optimizer
         optimizer = __set__variable('optimizer', 'GA')
         if optimizer == 'GA':
-            self.optimizer = opt.GradientAscent(self.alpha, self.beta)
+            self.optimizer = opt.GradientDescent(self.alpha, self.beta)
         elif optimizer == 'Adam':
             self.optimizer = opt.Adam(self.alpha, self.beta)
         elif optimizer == 'AdaMax':

popt/update_schemes/genopt.py

@@ -103,7 +103,7 @@ def __set__variable(var_name=None, defalut=None):
         # Initialize optimizer
         optimizer = __set__variable('optimizer', 'GA')
         if optimizer == 'GA':
-            self.optimizer = opt.GradientAscent(self.alpha, self.beta)
+            self.optimizer = opt.GradientDescent(self.alpha, self.beta)
         elif optimizer == 'Adam':
             self.optimizer = opt.Adam(self.alpha, self.beta)
 

popt/update_schemes/smcopt.py

@@ -93,7 +93,7 @@ def __set__variable(var_name=None, defalut=None):
                 self.logger.info(info_str)
                 self.logger.info('       {:<21} {:<15.4e}'.format(self.iteration, np.mean(self.obj_func_values)))
 
-        self.optimizer = opt.GradientAscent(self.alpha, 0)
+        self.optimizer = opt.GradientDescent(self.alpha, 0)
 
         # The SmcOpt class self-ignites
         self.run_loop()  # run_loop resides in the Optimization class (super)

popt/update_schemes/subroutines/optimizers.py

@@ -1,12 +1,12 @@
 """Gradient acceleration."""
 import numpy as np
 
-__all__ = ['GradientAscent', 'Adam', 'AdaMax', 'Steihaug', ]
+__all__ = ['GradientDescent', 'Adam', 'AdaMax', 'Steihaug', ]
 
 
-class GradientAscent:
+class GradientDescent:
     r"""
-    A class for performing gradient ascent optimization with momentum and backtracking.
+    A class for performing gradient descent optimization with momentum and backtracking.
     The gradient descent update equation with momentum is given by:
 
     $$ \begin{align}
@@ -52,7 +52,7 @@ def __init__(self, step_size, momentum):
         Parameters
         ----------
         step_size : float
-            The step size (learning rate) for the gradient ascent.
+            The step size (learning rate) for the gradient descent.
 
         momentum : float
             The momentum factor to apply during updates.
@@ -72,7 +72,7 @@ def apply_update(self, control, gradient, **kwargs):
         Apply a gradient update to the control parameter.
 
         !!! note
-            This is the steepest decent update: x_new = x_old - x_step.
+            This is the steepest descent update: x_new = x_old - x_step.
 
         Parameters
         -------------------------------------------------------------------------------------
@@ -240,7 +240,7 @@ def apply_update(self, control, gradient, **kwargs):
         Apply a gradient update to the control parameter.
 
         !!! note
-            This is the steepest decent update: x_new = x_old - x_step.
+            This is the steepest descent update: x_new = x_old - x_step.
 
         Parameters
         -------------------------------------------------------------------------------------
@@ -269,7 +269,7 @@ def apply_update(self, control, gradient, **kwargs):
         vel2_hat  = self.temp_vel2/(1-beta2**iter)
 
         step = alpha*vel1_hat/(np.sqrt(vel2_hat)+self.eps)
-        new_control = control - step  # steepest decent
+        new_control = control - step  # steepest descent
         return new_control, step
 
     def apply_backtracking(self):