Optimization Parameter scatter plot (#1015)

* Small update to parameter historgram

* Added parameter scatter plot for optimization.

* Renamed to optimization_scatter, added two tests.

* Added to an example doc. Fixed test

* Added id to optimization.

* Added correct id to optimization. Increased possible runtime

* small change

* fixed typo

* More informative doc

---------

Co-authored-by: Polina Lakrisenko <p.lakrisenko@gmail.com>

.github/workflows/ci.yml

@@ -38,7 +38,7 @@ jobs:
       run: .github/workflows/install_deps.sh amici
 
     - name: Run tests
-      timeout-minutes: 15
+      timeout-minutes: 25
       run: tox -e base
 
     - name: Coverage

pypesto/visualize/__init__.py

@@ -26,7 +26,12 @@
 )
 from .optimizer_convergence import optimizer_convergence
 from .optimizer_history import optimizer_history, optimizer_history_lowlevel
-from .parameters import parameter_hist, parameters, parameters_lowlevel
+from .parameters import (
+    optimization_scatter,
+    parameter_hist,
+    parameters,
+    parameters_lowlevel,
+)
 from .profile_cis import profile_cis
 from .profiles import profile_lowlevel, profiles, profiles_lowlevel
 from .reference_points import ReferencePoint, create_references

pypesto/visualize/parameters.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Callable, Iterable, List, Optional, Sequence, Tuple, Union
 
 import matplotlib.axes
@@ -19,6 +20,8 @@
 )
 from .reference_points import ReferencePoint, create_references
 
+logger = logging.getLogger(__name__)
+
 
 def parameters(
     results: Union[Result, Sequence[Result]],
@@ -207,10 +210,10 @@ def parameter_hist(
     parameter_index = result.problem.x_names.index(parameter_name)
     parameter_values = [x[parameter_index] for x in xs]
 
-    ax.hist(parameter_values, color=color, bins=bins)
+    ax.hist(parameter_values, color=color, bins=bins, label=parameter_name)
     ax.set_xlabel(parameter_name)
     ax.set_ylabel("counts")
-    ax.set_title(f"Parameter {parameter_name}")
+    ax.set_title(f"{parameter_name}")
 
     return ax
 
@@ -471,3 +474,92 @@ def parameters_correlation_matrix(
     if return_table:
         return ax, df
     return ax
+
+
+def optimization_scatter(
+    result: Result,
+    parameter_indices: Union[str, Sequence[int]] = 'free_only',
+    start_indices: Optional[Union[int, Iterable[int]]] = None,
+    diag_kind: str = "kde",
+    suptitle: str = None,
+    size: Tuple[float, float] = None,
+    show_bounds: bool = False,
+):
+    """
+    Plot a scatter plot of all pairs of parameters for the given starts.
+
+    Parameters
+    ----------
+    result:
+        Optimization result obtained by 'optimize.py'.
+    parameter_indices:
+        List of integers specifying the parameters to be considered.
+    start_indices:
+        List of integers specifying the multistarts to be plotted or
+        int specifying up to which start index should be plotted.
+    diag_kind:
+        Visualization mode for marginal densities {‘auto’, ‘hist’, ‘kde’,
+        None}.
+    suptitle:
+        Title of the plot.
+    size:
+        Size of the plot.
+    show_bounds:
+        Whether to show the parameter bounds.
+
+    Returns
+    -------
+    ax:
+        The plot axis.
+    """
+    import seaborn as sns
+
+    start_indices = process_start_indices(
+        start_indices=start_indices, result=result
+    )
+    parameter_indices = process_parameter_indices(
+        parameter_indices=parameter_indices, result=result
+    )
+    # remove all start indices, that encounter an inf value at the start
+    # resulting in optimize_result[start]["x"] being None
+    start_indices_finite = start_indices[
+        [
+            result.optimize_result[i_start]['x'] is not None
+            for i_start in start_indices
+        ]
+    ]
+    # compare start_indices with start_indices_finite and log a warning
+    if not np.all(start_indices == start_indices_finite):
+        logger.warning(
+            'Some start indices were removed due to inf values at the start.'
+        )
+    # put all parameters into a dataframe, where columns are parameters
+    parameters = [
+        result.optimize_result[i_start]['x'][parameter_indices]
+        for i_start in start_indices_finite
+    ]
+    x_labels = [
+        result.problem.x_names[parameter_index]
+        for parameter_index in parameter_indices
+    ]
+    df = pd.DataFrame(parameters, columns=x_labels)
+
+    sns.set(style="ticks")
+
+    ax = sns.pairplot(
+        df,
+        diag_kind=diag_kind,
+    )
+
+    if size is not None:
+        ax.fig.set_size_inches(size)
+    if suptitle:
+        ax.fig.suptitle(suptitle)
+    if show_bounds:
+        # set bounds of plot to parameter bounds. Only use diagonal as
+        # sns.PairGrid has sharex,sharey = True by default.
+        for i_axis, axis in enumerate(np.diag(ax.axes)):
+            axis.set_xlim(result.problem.lb[i_axis], result.problem.ub[i_axis])
+            axis.set_ylim(result.problem.lb[i_axis], result.problem.ub[i_axis])
+
+    return ax

test/visualize/test_visualize.py

@@ -453,6 +453,22 @@ def test_parameters_hist():
     visualize.parameter_hist(result_1, 'x1', start_indices=list(range(10)))
 
 
+@close_fig
+def test_optimization_scatter():
+    result = create_optimization_result()
+    visualize.optimization_scatter(result)
+
+
+@close_fig
+def test_optimization_scatter_with_x_None():
+    result = create_optimization_result()
+    # create an optimizerResult with x=None
+    optimizer_result = pypesto.OptimizerResult(x=None, fval=np.inf, id="inf")
+    result.optimize_result.append(optimize_result=optimizer_result)
+
+    visualize.optimization_scatter(result)
+
+
 # @close_fig
 def _test_ensemble_dimension_reduction():
     # creates a test problem