Remove deprecated methods/parameters from algorithms and opflow (Qisk…

…it#5777) * Remove deprecated methods/parameters from algorithms and opflow * Revert grover change as it is done in another PR Co-authored-by: Julien Gacon <[email protected]>
ElePT · Feb 3, 2021 · 7cbee4d · 7cbee4d
1 parent 84814b5
commit 7cbee4d
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Showing 3 changed files with 4 additions and 37 deletions.
diff --git a/qiskit/algorithms/optimizers/aqgd.py b/qiskit/algorithms/optimizers/aqgd.py
@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2019, 2020.
+# (C) Copyright IBM 2019, 2021.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -13,7 +13,6 @@
 """Analytical Quantum Gradient Descent (AQGD) optimizer."""
 
 import logging
-import warnings
 from typing import Callable, Tuple, List, Dict, Union
 
 import numpy as np
@@ -50,7 +49,6 @@ def __init__(self,
                  maxiter: Union[int, List[int]] = 1000,
                  eta: Union[float, List[float]] = 1.0,
                  tol: float = 1e-6,  # this is tol
-                 disp: bool = False,
                  momentum: Union[float, List[float]] = 0.25,
                  param_tol: float = 1e-6,
                  averaging: int = 10) -> None:
@@ -64,7 +62,6 @@ def __init__(self,
             tol: Tolerance for change in windowed average of objective values.
                 Convergence occurs when either objective tolerance is met OR parameter
                 tolerance is met.
-            disp: Set to True to display convergence messages.
             momentum: Bias towards the previous gradient momentum in current
                 update. Must be within the bounds: [0,1)
             param_tol: Tolerance for change in norm of parameters.
@@ -93,12 +90,6 @@ def __init__(self,
         self._param_tol = param_tol
         self._tol = tol
         self._averaging = averaging
-        if disp:
-            warnings.warn('The disp parameter is deprecated as of '
-                          '0.8.0 and will be removed no sooner than 3 months after the release. '
-                          'The information is now available if you enable INFO level logging.',
-                          DeprecationWarning, stacklevel=2)
-        self._disp = disp
 
         # state
         self._avg_objval = None
@@ -299,9 +290,6 @@ def optimize(self,
         converged = False
         for (eta, mom_coeff) in zip(self._eta, self._momenta_coeff):
             logger.info("Epoch: %4d | Stepsize: %6.4f | Momentum: %6.4f", epoch, eta, mom_coeff)
-            if self._disp:
-                print("Epoch: {:4d} | Stepsize: {:6.4f} | Momentum: {:6.4f}"
-                      .format(epoch, eta, mom_coeff))
 
             sum_max_iters = sum(self._maxiter[0:epoch + 1])
             while iter_count < sum_max_iters:
@@ -323,9 +311,6 @@ def optimize(self,
 
                 logger.info(" Iter: %4d | Obj: %11.6f | Grad Norm: %f",
                             iter_count, objval, np.linalg.norm(gradient, ord=np.inf))
-                if self._disp:
-                    print(" Iter: {:4d} | Obj: {:11.6f} | Grad Norm: {:f}"
-                          .format(iter_count, objval, np.linalg.norm(gradient, ord=np.inf)))
 
                 # Check for objective convergence
                 converged = self._converged_objective(objval, self._tol, self._averaging)

diff --git a/qiskit/algorithms/optimizers/gsls.py b/qiskit/algorithms/optimizers/gsls.py
@@ -12,7 +12,6 @@
 
 """Line search with Gaussian-smoothed samples on a sphere."""
 
-import warnings
 from typing import Dict, Optional, Tuple, List, Callable
 import logging
 import numpy as np
@@ -48,8 +47,7 @@ def __init__(self,
                  step_size_multiplier: float = 0.4,
                  armijo_parameter: float = 1.0e-1,
                  min_gradient_norm: float = 1e-8,
-                 max_failed_rejection_sampling: int = 50,
-                 max_iter: Optional[int] = None) -> None:
+                 max_failed_rejection_sampling: int = 50) -> None:
         """
         Args:
             maxiter: Maximum number of iterations.
@@ -67,15 +65,8 @@ def __init__(self,
             min_gradient_norm: If the gradient norm is below this threshold, the algorithm stops.
             max_failed_rejection_sampling: Maximum number of attempts to sample points within
                 bounds.
-            max_iter: Deprecated, use maxiter.
         """
         super().__init__()
-        if max_iter is not None:
-            warnings.warn('The max_iter parameter is deprecated as of '
-                          '0.8.0 and will be removed no sooner than 3 months after the release. '
-                          'You should use maxiter instead.',
-                          DeprecationWarning)
-            maxiter = max_iter
         for k, v in list(locals().items()):
             if k in self._OPTIONS:
                 self._options[k] = v

diff --git a/qiskit/algorithms/optimizers/spsa.py b/qiskit/algorithms/optimizers/spsa.py
@@ -12,8 +12,7 @@
 
 """Simultaneous Perturbation Stochastic Approximation optimizer."""
 
-import warnings
-from typing import Optional, List, Callable
+from typing import List, Callable
 import logging
 
 import numpy as np
@@ -70,8 +69,7 @@ def __init__(self,
                  c2: float = 0.602,
                  c3: float = 0.101,
                  c4: float = 0,
-                 skip_calibration: bool = False,
-                 max_trials: Optional[int] = None) -> None:
+                 skip_calibration: bool = False) -> None:
         """
         Args:
             maxiter: Maximum number of iterations to perform.
@@ -84,17 +82,10 @@ def __init__(self,
             c3: The gamma in the paper, and it is used to adjust c (c1) at each iteration.
             c4: The parameter used to control a as well.
             skip_calibration: Skip calibration and use provided c(s) as is.
-            max_trials: Deprecated, use maxiter.
         """
         validate_min('save_steps', save_steps, 1)
         validate_min('last_avg', last_avg, 1)
         super().__init__()
-        if max_trials is not None:
-            warnings.warn('The max_trials parameter is deprecated as of '
-                          '0.8.0 and will be removed no sooner than 3 months after the release. '
-                          'You should use maxiter instead.',
-                          DeprecationWarning)
-            maxiter = max_trials
         for k, v in list(locals().items()):
             if k in self._OPTIONS:
                 self._options[k] = v