Repository: OpenSourceEconomics/estimagic

Issue:
Generalize plotting code 
### Problem

`slice_plot` and its 3d version (see #274) still assume that params are a DataFrame. 

### Solution

Use `get_tree_converter` to make these plots more flexible. The rough idea is:
- get the tree_converter (with `primary_key="value"`) and `flat_params`
- calculate the evaluation points in terms of flattened params 
- Convert them to tree params 
- Evaluate the function on all evaluation points
- Convert the function outputs to scalar values (with `tree_converter.func_to_internal`)

Most of the plotting code will stay as is. Labels can come from `flat_params.names`




Discussion:
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 See visual diffs & provide feedback on Jupyter Notebooks. 

---

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```diff
@@            Coverage Diff             @@
##             main     #344      +/-   ##
==========================================
+ Coverage   91.21%   91.32%   +0.11%     
==========================================
  Files         184      184              
  Lines       13598    13589       -9     
==========================================
+ Hits        12403    12410       +7     
+ Misses       1195     1179      -16     
```


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------

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Patch:
diff --git a/docs/source/how_to_guides/optimization/how_to_visualize_an_optimization_problem.ipynb b/docs/source/how_to_guides/optimization/how_to_visualize_an_optimization_problem.ipynb
index 15452cc..15446cd 100644
--- a/docs/source/how_to_guides/optimization/how_to_visualize_an_optimization_problem.ipynb
+++ b/docs/source/how_to_guides/optimization/how_to_visualize_an_optimization_problem.ipynb
@@ -18,18 +18,22 @@
    "source": [
     "# How to visualize an optimization problem\n",
     "\n",
-    "In order to choose the right optimization algorithm, it is important to know as much as possible about the problem one wants to solve. If the criterion function has only one or two parameters, plotting it over the sample space can be helpful. However, such low dimensional problems are rare. \n",
+    "Plotting the criterion function of an optimization problem can answer important questions\n",
+    "- Is the function smooth?\n",
+    "- Is the function flat in some directions?\n",
+    "- Should the optimization problem be scaled?\n",
+    "- Is a candidate optimum a global one?\n",
     "\n",
-    "In this notebook we show how higher dimensional functions can be visualized using estimagic and which properties of the criterion function can be learned from them. \n",
-    "\n",
-    "We start with the sphere function and then progress to more interesting examples. "
+    "Below we show how to do a slice plot of the criterion function"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The simple sphere function"
+    "## The simple sphere function\n",
+    "\n",
+    "Let's look at the simple sphere function again. This time, we specify params as dictionary, but of course, any other param format would work as well. "
    ]
   },
   {
@@ -39,44 +43,42 @@
    "outputs": [],
    "source": [
     "def sphere(params):\n",
-    "    return (params[\"value\"] ** 2).sum()\n",
+    "    x = np.array(list(params.values()))\n",
+    "    return x @ x\n",
     "\n",
     "\n",
-    "params = pd.DataFrame(\n",
-    "    data=np.zeros(5),\n",
-    "    columns=[\"value\"],\n",
-    "    index=[f\"x_{i}\" for i in range(5)],\n",
-    ")\n",
-    "\n",
-    "params[\"lower_bound\"] = -5\n",
-    "params[\"upper_bound\"] = 5"
+    "params = {\"alpha\": 0, \"beta\": 0, \"gamma\": 0, \"delta\": 0}\n",
+    "lower_bounds = {name: -5 for name in params}\n",
+    "upper_bounds = {name: i + 2 for i, name in enumerate(params)}"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "fig = slice_plot(criterion=sphere, params=params)"
+    "## Creating a simple slice plot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
+    "fig = slice_plot(\n",
+    "    func=sphere,\n",
+    "    params=params,\n",
+    "    lower_bounds=lower_bounds,\n",
+    "    upper_bounds=upper_bounds,\n",
+    ")\n",
     "fig.show(renderer=\"png\")"
    ]
   },
@@ -84,6 +86,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Interpreting the plot\n",
+    "\n",
     "The plot gives us the following insights:\n",
     " \n",
     "- There is no sign of local optima. \n",
@@ -93,6 +97,43 @@
     "-> We would expect almost any derivative based optimizer to work well here (which we know to be correct in that case)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using advanced options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = slice_plot(\n",
+    "    func=sphere,\n",
+    "    params=params,\n",
+    "    lower_bounds=lower_bounds,\n",
+    "    upper_bounds=upper_bounds,\n",
+    "    # selecting a subset of params\n",
+    "    selector=lambda x: [x[\"alpha\"], x[\"beta\"]],\n",
+    "    # evaluate func in parallel\n",
+    "    n_cores=4,\n",
+    "    # rename the parameters\n",
+    "    param_names={\"alpha\": \"Alpha\", \"beta\": \"Beta\"},\n",
+    "    title=\"Amazing Plot\",\n",
+    ")\n",
+    "fig.show(renderer=\"png\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -117,7 +158,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,
diff --git a/src/estimagic/visualization/plotting_utilities.py b/src/estimagic/visualization/plotting_utilities.py
index cab2250..570eb45 100644
--- a/src/estimagic/visualization/plotting_utilities.py
+++ b/src/estimagic/visualization/plotting_utilities.py
@@ -101,8 +101,8 @@ def combine_plots(
         ub = []
         for f in plots:
             for d in f.data:
-                lb.append(d["y"].min())
-                ub.append(d["y"].max())
+                lb.append(np.min(d["y"]))
+                ub.append(np.max(d["y"]))
         ub = np.max(ub)
         lb = np.min(lb)
         y_range = ub - lb
@@ -114,8 +114,8 @@ def combine_plots(
         ub = []
         for f in plots:
             for d in f.data:
-                lb.append(d["x"].min())
-                ub.append(d["x"].max())
+                lb.append(np.min(d["x"]))
+                ub.append(np.max(d["x"]))
         x_upper = np.max(ub)
         x_lower = np.min(lb)
         fig.update_xaxes(range=[x_lower, x_upper])
diff --git a/src/estimagic/visualization/slice_plot.py b/src/estimagic/visualization/slice_plot.py
index cf4b286..0eeec51 100644
--- a/src/estimagic/visualization/slice_plot.py
+++ b/src/estimagic/visualization/slice_plot.py
@@ -1,87 +1,84 @@
+from functools import partial
+
 import numpy as np
 import pandas as pd
 import plotly.express as px
 from estimagic.batch_evaluators import process_batch_evaluator
 from estimagic.config import DEFAULT_N_CORES
-from estimagic.config import PLOTLY_PALETTE
 from estimagic.config import PLOTLY_TEMPLATE
+from estimagic.parameters.conversion import get_converter
+from estimagic.parameters.tree_registry import get_registry
 from estimagic.visualization.plotting_utilities import combine_plots
 from estimagic.visualization.plotting_utilities import get_layout_kwargs
+from plotly import graph_objects as go
+from pybaum import tree_just_flatten
 
 
 def slice_plot(
-    criterion,
+    func,
     params,
-    batch_evaluator=None,
+    lower_bounds=None,
+    upper_bounds=None,
+    func_kwargs=None,
+    selector=None,
     n_cores=DEFAULT_N_CORES,
-    param_name_mapping=None,
-    n_gridpoints=21,
-    n_random_values=0,
-    seed=5471,
-    share_yrange_all=True,
+    n_gridpoints=20,
+    plots_per_row=2,
+    param_names=None,
+    share_y=True,
     expand_yrange=0.02,
-    share_xrange_all=False,
-    colorscale=PLOTLY_PALETTE,
+    share_x=False,
+    color="#497ea7",
     template=PLOTLY_TEMPLATE,
-    showlegend=True,
-    layout_kwargs=None,
-    legend_kwargs=None,
-    title_kwargs=None,
+    title=None,
     return_dict=False,
-    plots_per_row=2,
-    sharex=False,
-    sharey=True,
     make_subplot_kwargs=None,
-    clean_legend=True,
+    batch_evaluator="joblib",
 ):
     """Plot criterion along coordinates at given and random values.
 
-    Generates individual plots for each parameter, combines them into a figure with
-    subplots.
+    Generates plots for each parameter and optionally combines them into a figure
+    with subplots.
 
     Args:
-        criterion (callable): criterion function. Takes a DataFrame and returns a
+        criterion (callable): criterion function that takes params and returns a
             scalar value or dictionary with the entry "value".
-        params (pandas.DataFrame): See :ref:`params`. Must contain finite lower and
-            upper bounds for all parameters.
-        batch_evaluator (str or callable): See :ref:`batch_evaluators`.
+        params (pytree): A pytree with parameters.
+        lower_bounds (pytree): A pytree with same structure as params. Must be
+            specified and finite for all parameters unless params is a DataFrame
+            containing with "lower_bound" column.
+        upper_bounds (pytree): A pytree with same structure as params. Must be
+            specified and finite for all parameters unless params is a DataFrame
+            containing with "lower_bound" column.
+        selector (callable): Function that takes params and returns a subset
+            of params for which we actually want to generate the plot.
         n_cores (int): Number of cores.
-        param_name_mapping (dict or NoneType): Dictionary mapping old parameter names
-            to new ones.
         n_gridpoins (int): Number of gridpoints on which the criterion function is
             evaluated. This is the number per plotted line.
-        n_random_values (int): Number of random parameter vectors that are used as
-            center of the plots.
-        seed (int): Numpy randoms seed used when generating the random values.
-        share_yrange_all (bool): If True, the individual plots share the scale on the
-            yaxis.
-        share_xrange_all (bool): If True, set the same range of x axis for all plots.
+        plots_per_row (int): Number of plots per row.
+        param_names (dict or NoneType): Dictionary mapping old parameter names
+            to new ones.
+        share_y (bool): If True, the individual plots share the scale on the
+            yaxis and plots in one row actually share the y axis.
+        share_x (bool): If True, set the same range of x axis for all plots and share
+            the x axis for all plots in one column.
         expand_y (float): The ration by which to expand the range of the (shared) y
             axis, such that the axis is not cropped at exactly max of Criterion Value.
-        colorscale: The coloring palette for traces. Default is "qualitative.Set2".
+        color: The line color.
         template (str): The template for the figure. Default is "plotly_white".
-        showlegend (bool): If True, show legend.
         layout_kwargs (dict or NoneType): Dictionary of key word arguments used to
             update layout of plotly Figure object. If None, the default kwargs defined
             in the function will be used.
-        legend_kwargs (dict or NoneType): Dictionary of key word arguments used to
-            update position, orientation and title of figure legend. If None, default
-            position and orientation will be used with no title.
-        title_kwargs (dict or NoneType): Dictionary of key word arguments used to
-            update properties of the figure title. Use {'text': '<desired title>'}
-            to set figure title.
+        title (str): The figure title.
         return_dict (bool): If True, return dictionary with individual plots of each
             parameter, else, ombine individual plots into a figure with subplots.
-        plots_per_row (int): Number of plots per row.
-        sharex (bool): Whether to share the properties of x-axis across subplots. In
-            the sam column
-        sharey (bool): If True, share the properties of y-axis across subplots in the
         make_subplot_kwargs (dict or NoneType): Dictionary of keyword arguments used
             to instantiate plotly Figure with multiple subplots. Is used to define
             properties such as, for example, the spacing between subplots (governed by
             'horizontal_spacing' and 'vertical_spacing'). If None, default arguments
             defined in the function are used.
-        clean_legend (bool): If True, then cleans the legend from duplicates.
+        batch_evaluator (str or callable): See :ref:`batch_evaluators`.
+
 
     Returns:
         out (dict or plotly.Figure): Returns either dictionary with individual slice
@@ -90,86 +87,127 @@ def slice_plot(
 
     """
 
-    params = params.copy(deep=True)
-
-    np.random.seed(seed)
-    if (
-        "lower_bound" not in params.columns
-        or not np.isfinite(params["lower_bound"]).all()
-    ):
-        raise ValueError("All parameters need a finite lower bound.")
-    if (
-        "upper_bound" not in params.columns
-        or not np.isfinite(params["upper_bound"]).all()
-    ):
-        raise ValueError("All parameters need a finite upper bound.")
-
-    if "name" not in params.columns:
-        names = [_index_element_to_string(tup) for tup in params.index]
-        params["name"] = names
-
-    plot_data = _get_plot_data(
+    layout_kwargs = None
+    if title is not None:
+        title_kwargs = {"text": title}
+    else:
+        title_kwargs = None
+
+    if func_kwargs is not None:
+        func = partial(func, **func_kwargs)
+
+    func_eval = func(params)
+
+    converter, flat_params = get_converter(
+        func=func,
         params=params,
-        use_random_value=False,
-        value_identifier="start values",
-        n_gridpoints=n_gridpoints,
+        constraints=None,
+        lower_bounds=lower_bounds,
+        upper_bounds=upper_bounds,
+        func_eval=func_eval,
+        primary_key="value",
+        scaling=False,
+        scaling_options=None,
     )
-    to_concat = [plot_data]
-
-    for i in range(n_random_values):
-        to_concat.append(
-            _get_plot_data(
-                params=params,
-                use_random_value=True,
-                value_identifier=f"random value {i}",
-                n_gridpoints=n_gridpoints,
-            )
+
+    n_params = len(flat_params.values)
+
+    selected = np.arange(n_params, dtype=int)
+    if selector is not None:
+        helper = converter.params_from_internal(selected)
+        registry = get_registry(extended=True)
+        selected = np.array(
+            tree_just_flatten(selector(helper), registry=registry), dtype=int
         )
 
-    plot_data = pd.concat(to_concat).reset_index()
-    param_names = plot_data["name"].unique()
-    param_name_mapping = _process_names_mapping(param_name_mapping, param_names)
-    arguments = []
-    for _, row in plot_data.iterrows():
-        p = params.copy(deep=True)
-        p["value"] = row[params.index].astype(float)
-        arguments.append(p)
+    if not np.isfinite(flat_params.lower_bounds[selected]).all():
+        raise ValueError("All selected parameters must have finite lower bounds.")
+
+    if not np.isfinite(flat_params.upper_bounds[selected]).all():
+        raise ValueError("All selected parameters must have finite upper bounds.")
+
+    evaluation_points, metadata = [], []
+    for pos in selected:
+        lb = flat_params.lower_bounds[pos]
+        ub = flat_params.upper_bounds[pos]
+        grid = np.linspace(lb, ub, n_gridpoints)
+        name = flat_params.names[pos]
+        for param_value in grid:
+            if param_value != flat_params.values[pos]:
+                meta = {
+                    "name": name,
+                    "Parameter Value": param_value,
+                }
+
+                x = flat_params.values.copy()
+                x[pos] = param_value
+                point = converter.params_from_internal(x)
+                evaluation_points.append(point)
+                metadata.append(meta)
+
     batch_evaluator = process_batch_evaluator(batch_evaluator)
 
-    function_values = batch_evaluator(
-        criterion,
-        arguments=arguments,
-        unpack_symbol=None,
-        error_handling="raise",
+    func_values = batch_evaluator(
+        func=func,
+        arguments=evaluation_points,
+        error_handling="continue",
         n_cores=n_cores,
     )
-    if isinstance(function_values[0], dict):
-        function_values = [val["value"] for val in function_values]
 
-    plot_data["Criterion Value"] = function_values
-    lb = plot_data["Criterion Value"].min()
-    ub = plot_data["Criterion Value"].max()
+    # add NaNs where an evaluation failed
+    func_values = [val if not isinstance(val, str) else np.nan for val in func_values]
+
+    func_values += [converter.func_to_internal(func_eval)] * len(selected)
+    for pos in selected:
+        meta = {
+            "name": flat_params.names[pos],
+            "Parameter Value": flat_params.values[pos],
+        }
+        metadata.append(meta)
+
+    plot_data = pd.DataFrame(metadata)
+    plot_data["Function Value"] = func_values
+
+    if param_names is not None:
+        plot_data["name"] = plot_data["name"].replace(param_names)
+
+    lb = plot_data["Function Value"].min()
+    ub = plot_data["Function Value"].max()
     y_range = ub - lb
     yaxis_ub = ub + y_range * expand_yrange
     yaxis_lb = lb - y_range * expand_yrange
     layout_kwargs = get_layout_kwargs(
-        layout_kwargs, legend_kwargs, title_kwargs, template, showlegend
+        layout_kwargs,
+        None,
+        title_kwargs,
+        template,
+        False,
     )
 
     plots_dict = {}
-    for par_name in plot_data["name"].unique():
-        df = plot_data[plot_data["name"] == par_name]
+    for pos in selected:
+        par_name = flat_params.names[pos]
+        if param_names is not None and par_name in param_names:
+            par_name = param_names[par_name]
+
+        df = plot_data[plot_data["name"] == par_name].sort_values("Parameter Value")
         subfig = px.line(
             df,
-            y="Criterion Value",
+            y="Function Value",
             x="Parameter Value",
-            color="value_identifier",
-            color_discrete_sequence=colorscale,
+            color_discrete_sequence=[color],
+        )
+        subfig.add_trace(
+            go.Scatter(
+                x=[flat_params.values[pos]],
+                y=[converter.func_to_internal(func_eval)],
+                marker={"color": color},
+            )
         )
         subfig.update_layout(**layout_kwargs)
-        subfig.update_xaxes(title={"text": param_name_mapping[par_name]})
-        subfig.update_yaxes(title={"text": "Criterion Value"})
-        if share_yrange_all is True:
+        subfig.update_xaxes(title={"text": par_name})
+        subfig.update_yaxes(title={"text": "Function Value"})
+        if share_y is True:
             subfig.update_yaxes(range=[yaxis_lb, yaxis_ub])
         plots_dict[par_name] = subfig
     if return_dict:
@@ -179,65 +217,17 @@ def slice_plot(
         out = combine_plots(
             plots=plots,
             plots_per_row=plots_per_row,
-            sharex=sharex,
-            sharey=sharey,
-            share_yrange_all=share_yrange_all,
-            share_xrange_all=share_xrange_all,
+            sharex=share_x,
+            sharey=share_y,
+            share_yrange_all=share_y,
+            share_xrange_all=share_x,
             expand_yrange=expand_yrange,
             make_subplot_kwargs=make_subplot_kwargs,
-            showlegend=showlegend,
+            showlegend=False,
             template=template,
-            clean_legend=clean_legend,
+            clean_legend=True,
             layout_kwargs=layout_kwargs,
-            legend_kwargs=legend_kwargs,
+            legend_kwargs={},
             title_kwargs=title_kwargs,
         )
     return out
-
-
-def _get_plot_data(params, use_random_value, value_identifier, n_gridpoints):
-    params = params.copy(deep=True)
-    if use_random_value:
-        params = params.copy()
-        params["value"] = np.random.uniform(
-            params["lower_bound"], params["upper_bound"]
-        )
-
-    to_concat = []
-    for loc in params.index:
-        param_name = params.loc[loc, "name"]
-        lb = params.loc[loc, "lower_bound"]
-        ub = params.loc[loc, "upper_bound"]
-        df = pd.DataFrame(
-            data=[params["value"].to_numpy()] * n_gridpoints,
-            columns=params.index,
-        )
-        grid = np.linspace(lb, ub, n_gridpoints)
-        df[loc] = grid
-        df["Parameter Value"] = grid
-        df["name"] = param_name
-        df["value_identifier"] = value_identifier
-        to_concat.append(df)
-
-    plot_data = pd.concat(to_concat).reset_index()
-    return plot_data
-
-
-def _index_element_to_string(element, separator="_"):
-    if isinstance(element, (tuple, list)):
-        as_strings = [str(entry).replace("-", "_") for entry in element]
-        res_string = separator.join(as_strings)
-    else:
-        res_string = str(element)
-    return res_string
-
-
-def _process_names_mapping(params_mapping, old_names):
-    """Get dictionary mappping old parameter names to new ones."""
-    if params_mapping is None:
-        params_mapping = {par: par for par in old_names}
-    else:
-        for par in old_names:
-            if par not in params_mapping:
-                params_mapping[par] = par
-    return params_mapping


Test patch:
diff --git a/tests/visualization/test_slice_plot.py b/tests/visualization/test_slice_plot.py
index ee082b4..55434c5 100644
--- a/tests/visualization/test_slice_plot.py
+++ b/tests/visualization/test_slice_plot.py
@@ -1,39 +1,42 @@
-import itertools
-
 import numpy as np
-import pandas as pd
 import pytest
 from estimagic.visualization.slice_plot import slice_plot
 
 
 @pytest.fixture
-def problem():
-    params = pd.DataFrame(
-        np.zeros(4),
-        columns=["value"],
-    )
+def fixed_inputs():
+    def sphere(params):
+        x = np.array(list(params.values()))
+        return x @ x
 
-    params["lower_bound"] = -5
-    params["upper_bound"] = 5
+    params = {"alpha": 0, "beta": 0, "gamma": 0, "delta": 0}
+    lower_bounds = {name: -5 for name in params}
+    upper_bounds = {name: i + 2 for i, name in enumerate(params)}
 
     out = {
-        "criterion": lambda params: (params["value"] ** 2).sum(),  # sphere
+        "func": sphere,
         "params": params,
+        "lower_bounds": lower_bounds,
+        "upper_bounds": upper_bounds,
     }
     return out
 
 
-@pytest.mark.parametrize(
-    "n_gridpoints, n_random_values, plots_per_row, return_dict",
-    itertools.product([21, 41], [2, 3], [1, 2], [False, True]),
-)
-def test_slice_plot(problem, n_gridpoints, n_random_values, plots_per_row, return_dict):
+KWARGS = [
+    {},
+    {"plots_per_row": 4},
+    {"selector": lambda x: [x["alpha"], x["beta"]]},
+    {"param_names": {"alpha": "Alpha", "beta": "Beta"}},
+    {"share_x": True},
+    {"share_y": False},
+    {"return_dict": True},
+]
+
+
+@pytest.mark.parametrize("kwargs", KWARGS)
+def test_slice_plot(fixed_inputs, kwargs):
 
     slice_plot(
-        criterion=problem["criterion"],
-        params=problem["params"],
-        n_gridpoints=n_gridpoints,
-        n_random_values=n_random_values,
-        return_dict=return_dict,
-        plots_per_row=plots_per_row,
+        **fixed_inputs,
+        **kwargs,
     )