Cleanup

.envs/testenv-linux.yml

@@ -12,8 +12,6 @@ dependencies:
   - pytest-cov  # tests
   - pytest-xdist  # dev, tests
   - statsmodels  # dev, tests
-  - bokeh<=2.4.3  # run, tests
-  - click  # run, tests
   - cloudpickle  # run, tests
   - joblib  # run, tests
   - numpy<2.0  # run, tests
@@ -30,7 +28,6 @@ dependencies:
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests
       - kaleido  # dev, tests
-      - simoptlib==1.0.1  # dev, tests
       - pandas-stubs  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests

.envs/testenv-others.yml

@@ -11,8 +11,6 @@ dependencies:
   - pytest-cov  # tests
   - pytest-xdist  # dev, tests
   - statsmodels  # dev, tests
-  - bokeh<=2.4.3  # run, tests
-  - click  # run, tests
   - cloudpickle  # run, tests
   - joblib  # run, tests
   - numpy<2.0  # run, tests
@@ -29,7 +27,6 @@ dependencies:
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests
       - kaleido  # dev, tests
-      - simoptlib==1.0.1  # dev, tests
       - pandas-stubs  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests

.envs/testenv-pandas.yml

@@ -11,8 +11,6 @@ dependencies:
   - pytest-cov  # tests
   - pytest-xdist  # dev, tests
   - statsmodels  # dev, tests
-  - bokeh<=2.4.3  # run, tests
-  - click  # run, tests
   - cloudpickle  # run, tests
   - joblib  # run, tests
   - numpy<2.0  # run, tests
@@ -28,7 +26,6 @@ dependencies:
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests
       - kaleido  # dev, tests
-      - simoptlib==1.0.1  # dev, tests
       - types-cffi  # dev, tests
       - types-openpyxl  # dev, tests
       - -e ../

CHANGES.md

@@ -5,7 +5,23 @@ chronological order. We follow [semantic versioning](https://semver.org/) and al
 releases are available on [Anaconda.org](https://anaconda.org/OpenSourceEconomics/estimagic).
 
 Following the [scientific python guidelines](https://scientific-python.org/specs/spec-0000/)
-we drop the official support for Python 3.8.
+we drop the official support for Python 3.9.
+
+
+## 0.5.0
+
+This is a major release with several breaking changes and deprecations. On a high level,
+the major changes are:
+
+- Implement EP-02: Static typing
+- Implement EP-03: Alignment with SciPy
+- Rename the package from `estimagic` to `optimagic` (while keeping the `estimagic`
+  namespace for the estimation capabilities).
+
+### Breaking changes
+
+- {gh}`500` removes the dashboard, the support for simopt optimizers and the
+  `derivative_plot` ({ghuser}`janosg`)
 
 
 ## 0.4.6

README.md

@@ -28,8 +28,8 @@ provides functionality to perform statistical inference on estimated parameters.
 - The complete history of parameters and function evaluations can be saved in a database
   for maximum reproducibility.
 - Painless and efficient multistart optimization.
-- The progress of the optimization is displayed in real time via an interactive
-  dashboard.
+- The progress of the optimization can be displayed in in `criterion_plot` and
+  `params_plot` while the optimization is still running.
 
 ### Estimation and Inference
 

docs/source/algorithms.md

@@ -2076,7 +2076,7 @@ To use ipopt, you need to have
       - `num_linear_variables`: since estimagic may reparametrize your problem
         and this changes the parameter problem, we do not support this option.
       - derivative checks
-      - print options. Use estimagic's dashboard to monitor your optimization.
+      - print options.
 
 
     - **convergence.relative_criterion_tolerance** (float): The algorithm
@@ -3894,134 +3894,6 @@ addition to estimagic when using an NLOPT algorithm. To install nlopt run
       10 * (number of parameters + 1).
 ```
 
-## The SimOpt Optimizers (simopt)
-
-estimagic supports the following [SimOpt](https://github.com/simopt-admin/simopt)
-algorithms. Please add the
-[appropriate citations](https://github.com/simopt-admin/simopt) in addition to estimagic
-when using a SimOpt algorithm. To install simopt run `pip install simoptlib==1.0.1`.
-
-```{eval-rst}
-.. dropdown:: simopt_adam
-
-    .. code-block::
-
-        "simopt_adam"
-
-    Minimize a scalar function using the ADAM algorithm from SimOpt.
-
-    - **stopping_max_iterations** (int): If the maximum number of iterations is reached,
-      the optimization stops, but we do not count this as convergence.
-    - **crn_across_solns** (bool): Use CRN across solutions? Default True.
-    - **r** (int): Number of replications taken at each solution. Default 1.
-    - **beta_1** (float): Exponential decay of the rate for the first moment estimates.
-      Default 0.9.
-    - **beta_2** (float): Exponential decay rate for the second-moment estimates.
-      Default 0.999.
-    - **alpha** (float): Step size. Default 1.0.
-    - **epsilon** (float): A small value to prevent zero-division. Default 10e-8.
-    - **sensitivity** (float): Shrinking scale for variable bounds. Default 10e-7.
-```
-
-```{eval-rst}
-.. dropdown:: simopt_astrodf
-
-    .. code-block::
-
-        "simopt_astrodf"
-
-    Minimize a scalar function using the ASTRODF algorithm from SimOpt.
-
-    - **stopping_max_iterations** (int): If the maximum number of iterations is reached,
-      the optimization stops, but we do not count this as convergence.
-    - **bounds_padding** (float): Subtract (add) this value of the bounds which will be
-      used by ASTRODF internally. Default 1e-8.
-    - **crn_across_solns** (bool): Use CRN across solutions? Default True.
-    - **delta_max** (float): Maximum value of the trust-region radius. Default 50.0
-    - **eta_1** (float): Threshhold for a successful iteration. Default 0.1.
-    - **eta_2** (float): Threshhold for a very successful iteration. Default 0.5.
-    - **gamma_1** (float): Very successful step trust-region radius increase. Default
-      2.0.
-    - **gamma_2** (float): Unsuccessful step trust-region radius decrease. Default 0.5.
-    - **w** (float): Trust-region radius rate of shrinkage in contracation loop. Default
-      0.85.
-    - **mu** (int): Trust-region radius ratio upper bound in contraction loop. Default
-      1000.
-    - **beta** (int): Trust-region radius ratio lower bound in contraction loop. Default
-      10.
-    - **lambda_min** (int): Minimum sample size value. Default 8.
-    - **simple_solve** (bool): Solve subproblem with Cauchy point (rough approximate)?
-      Default False.
-    - **criticality_select** (bool): Skip contraction loop if not near critical
-      region? Default True.
-    - **criticality_threshold** (float): Threshold on gradient norm indicating
-      near-critical region. Default 0.1.
-
-    .. note::
-        To get more accurate results in the case of bounds we revert the subtraction of
-        a large value from the bounds that is done internally in simopt.
-        Since the algorithm is numerically instable in the case of binding bounds
-        without this substraction, we subtract a (small) value defined by
-        ``bounds_padding``. See the ASTRODF `source code
-        <https://tinyurl.com/5fxcvw2k>`_ for details.
-
-```
-
-```{eval-rst}
-.. dropdown:: simopt_spsa
-
-    .. code-block::
-
-        "simopt_spsa"
-
-    Minimize a scalar function using the SPSA algorithm from SimOpt.
-
-    - **stopping_max_iterations** (int): If the maximum number of iterations is reached,
-      the optimization stops, but we do not count this as convergence.
-    - **crn_across_solns** (bool): Use CRN across solutions? Default True.
-    - **alpha** (float): Non-negative coefficient in the SPSA gain sequecence ak.
-      Default 0.602.
-    - **gamma** (float): Non-negative coefficient in the SPSA gain sequence ck. Default
-      0.101.
-    - **step** (float): Initial desired magnitude of change in the theta elements.
-      Default 0.5.
-    - **gavg** (int): Averaged SP gradients used per iteration. Default 1.
-    - **n_reps** (int): Number of replications takes at each solution. Default 2.
-    - **n_loss** (int): Number of loss function evaluations used in this gain
-      calculation. Default 2.
-    - **eval_pct** (float): Percentage of the expected number of loss evaluations per
-      run. Default 2/3.
-    - **iter_pct** (float): Percentage of the maximum expected number of iterations.
-      Default 0.1.
-```
-
-```{eval-rst}
-.. dropdown:: simopt_strong
-
-    .. code-block::
-
-        "simopt_strong"
-
-    Minimize a scalar function using the STRONG algorithm from SimOpt.
-
-    - **stopping_max_iterations** (int): If the maximum number of iterations is reached,
-      the optimization stops, but we do not count this as convergence.
-    - **crn_across_solns** (bool): Use CRN across solutions? Default True.
-    - **n0** (int): Initial sample size Default 10.
-    - **n_r** (int): Number of replications taken at each solution. Default 1.
-    - **sensitivity** (float): Shrinking scale for VarBds. Default 10e-7.
-    - **delta_threshold** (float): Maximum value of the radius. Default 1.2.
-    - **delta_T** (float): Initial size of trust region. Default 2.0.
-    - **eta_0** (float): Constant for accepting. Default 0.01.
-    - **eta_1** (float): Constant for more confident accepting. Default 0.3.
-    - **gamma_1** (float): Constant for shrinking the trust region. Default 0.9.
-    - **gamma_2** (float): Constant for expanding the trust region. Default 1.11.
-    - **lambda** (int): Magnifying factor for n_r inside the finite difference function.
-      Default 2.
-    - **lambda_2** (float): Magnifying factor for n_r in stage I and stage II. Default
-      1.01.
-```
-
 ## References
 
 ```{eval-rst}

docs/source/development/styleguide.md

@@ -45,13 +45,12 @@ Your contribution should fulfill the criteria provided below.
   are in doubt. Example:
 
   ```python
-  def ordered_logit(formula, data, dashboard=False):
+  def ordered_logit(formula, data):
       """Estimate an ordered probit model with maximum likelihood.
 
       Args:
           formula (str): A patsy formula.
           data (str): A pandas DataFrame.
-          dashboard (bool): Switch on the dashboard.
 
       Returns:
           res: optimization result.

docs/source/explanations/optimization/internal_optimizers.md

@@ -11,7 +11,6 @@ The advantages of using the algorithm with estimagic over using it directly are:
 
 - estimagic turns an unconstrained optimizer into constrained ones.
 - You can use logging.
-- You get a real time dashboard to monitor your optimization.
 - You get great error handling for exceptions in the criterion function or gradient.
 - You get a parallelized and customizable numerical gradient if the user did not provide
   a closed form gradient.

docs/source/getting_started/first_optimization_with_estimagic.ipynb

@@ -640,7 +640,7 @@
    "source": [
     "For more information on what you can do with the log file and LogReader object, check out [the logging tutorial](../how_to_guides/optimization/how_to_use_logging.ipynb)\n",
     "\n",
-    "The persistent log file is always instantly synchronized when the optimizer tries a new parameter vector. This is very handy if an optimization has to be aborted and you want to extract the current status. It is also used by the  [estimagic dashboard](../how_to_guides/optimization/how_to_use_the_dashboard.md). "
+    "The persistent log file is always instantly synchronized when the optimizer tries a new parameter vector. This is very handy if an optimization has to be aborted and you want to extract the current status. It can be displayed in `criterion_plot` and `params_plot`, even while the optimization is running. "
    ]
   },
   {

docs/source/how_to_guides/differentiation/how_to_calculate_first_derivatives.ipynb

@@ -217,7 +217,7 @@
     "> For an explaination of the argument ``n_steps`` and the Richardson method, please see the API Reference and the Richardson Extrapolation explanation in the documentation.\n",
     "\n",
     "\n",
-    "The objects returned when ``return_info`` is ``True`` are rarely of any use directly and can be safely ignored. However, they are necessary data when using the plotting function ``derivative_plot`` as explained below. For better understanding, we print each of these additional objects once:"
+    "The objects returned when ``return_info`` is ``True`` are rarely of any use directly and can be safely ignored. "
    ]
   },
   {

docs/source/how_to_guides/inference/how_to_do_bootstrap_inference.ipynb

@@ -536,8 +536,6 @@
     "\n",
     "It is often the case that, for speed reasons, you set the number of bootstrap draws quite low, so you can look at the results earlier and later decide that you need more draws. \n",
     "\n",
-    "In the long run, we will offer a Dashboard integration for this. For now, you can do it manually.\n",
-    "\n",
     "As an example, we will take an initial sample of 500 draws. We then extend it with another 1500 draws. \n",
     "\n",
     "*Note*: It is very important to use a different random seed when you calculate the additional outcomes!!!"

docs/source/how_to_guides/optimization/how_to_benchmark_optimization_algorithms.ipynb

@@ -184,7 +184,7 @@
    "source": [
     "## 4b. Convergence plots\n",
     "\n",
-    "**Convergence Plots** look at particular problems and show the convergence of each optimizer on each problem. They look similar to what you've seen from estimagic's dashboard."
+    "**Convergence Plots** look at particular problems and show the convergence of each optimizer on each problem. "
    ]
   },
   {

docs/source/how_to_guides/optimization/how_to_use_logging.ipynb

@@ -7,9 +7,7 @@
     "# How to use logging\n",
     "\n",
     "\n",
-    "Estimagic can keep a persistent log of the parameter and criterion values tried out by an optimizer in a sqlite database. \n",
-    "\n",
-    "The sqlite database is also used to exchange data between the optimization and the dashboard."
+    "Estimagic can keep a persistent log of the parameter and criterion values tried out by an optimizer in a sqlite database. \n"
    ]
   },
   {