Support constraints plot_rank (optuna#4899)

* refactor: Passing color as normalized rgb ndarray.

* feat: Support optimization with constraints in plot_rank.

* fix: Make color ditribution same between tick and points.

* refactor: Passing color with 0~255 RGB not 0~1.

* test: Test color converter by assert_array_equal.

* docs: Add constraints to rank_plot example.

* fix: Delete debug message in test.

Co-authored-by: Shinichi Hemmi <50256998+Alnusjaponica@users.noreply.github.com>

---------

Co-authored-by: Shinichi Hemmi <50256998+Alnusjaponica@users.noreply.github.com>

optuna/visualization/_rank.py

@@ -10,6 +10,7 @@
 
 from optuna._experimental import experimental_func
 from optuna.logging import get_logger
+from optuna.samplers._base import _CONSTRAINTS_KEY
 from optuna.study import Study
 from optuna.trial import FrozenTrial
 from optuna.trial import TrialState
@@ -45,15 +46,15 @@ class _RankSubplotInfo(NamedTuple):
     ys: list[Any]
     trials: list[FrozenTrial]
     zs: np.ndarray
-    color_idxs: np.ndarray
+    colors: np.ndarray
 
 
 class _RankPlotInfo(NamedTuple):
     params: list[str]
     sub_plot_infos: list[list[_RankSubplotInfo]]
     target_name: str
     zs: np.ndarray
-    color_idxs: np.ndarray
+    colors: np.ndarray
     has_custom_target: bool
 
 
@@ -81,10 +82,18 @@ def plot_rank(
             def objective(trial):
                 x = trial.suggest_float("x", -100, 100)
                 y = trial.suggest_categorical("y", [-1, 0, 1])
+
+                c0 = 400 - (x + y)**2
+                trial.set_user_attr("constraint", [c0])
+
                 return x ** 2 + y
 
 
-            sampler = optuna.samplers.TPESampler(seed=10)
+            def constraints(trial):
+                return trial.user_attrs["constraint"]
+
+
+            sampler = optuna.samplers.TPESampler(seed=10, constraints_func=constraints)
             study = optuna.create_study(sampler=sampler)
             study.optimize(objective, n_trials=30)
 
@@ -160,16 +169,18 @@ def target(trial: FrozenTrial) -> float:
     target_values = np.array([target(trial) for trial in trials])
     raw_ranks = _get_order_with_same_order_averaging(target_values)
     color_idxs = raw_ranks / (len(trials) - 1) if len(trials) >= 2 else np.array([0.5])
+    colors = _convert_color_idxs_to_scaled_rgb_colors(color_idxs)
+
     sub_plot_infos: list[list[_RankSubplotInfo]]
     if len(params) == 2:
         x_param = params[0]
         y_param = params[1]
-        sub_plot_info = _get_rank_subplot_info(trials, target_values, color_idxs, x_param, y_param)
+        sub_plot_info = _get_rank_subplot_info(trials, target_values, colors, x_param, y_param)
         sub_plot_infos = [[sub_plot_info]]
     else:
         sub_plot_infos = [
             [
-                _get_rank_subplot_info(trials, target_values, color_idxs, x_param, y_param)
+                _get_rank_subplot_info(trials, target_values, colors, x_param, y_param)
                 for x_param in params
             ]
             for y_param in params
@@ -180,21 +191,29 @@ def target(trial: FrozenTrial) -> float:
         sub_plot_infos=sub_plot_infos,
         target_name=target_name,
         zs=target_values,
-        color_idxs=color_idxs,
+        colors=colors,
         has_custom_target=has_custom_target,
     )
 
 
 def _get_rank_subplot_info(
     trials: list[FrozenTrial],
     target_values: np.ndarray,
-    color_idxs: np.ndarray,
+    colors: np.ndarray,
     x_param: str,
     y_param: str,
 ) -> _RankSubplotInfo:
     xaxis = _get_axis_info(trials, x_param)
     yaxis = _get_axis_info(trials, y_param)
 
+    infeasible_trial_ids = []
+    for i in range(len(trials)):
+        constraints = trials[i].system_attrs.get(_CONSTRAINTS_KEY)
+        if constraints is not None and any([x > 0.0 for x in constraints]):
+            infeasible_trial_ids.append(i)
+
+    colors[infeasible_trial_ids] = plotly.colors.hex_to_rgb("#cccccc")
+
     filtered_ids = [
         i
         for i in range(len(trials))
@@ -204,15 +223,15 @@ def _get_rank_subplot_info(
     xs = [trial.params[x_param] for trial in filtered_trials]
     ys = [trial.params[y_param] for trial in filtered_trials]
     zs = target_values[filtered_ids]
-    color_idxs = color_idxs[filtered_ids]
+    colors = colors[filtered_ids]
     return _RankSubplotInfo(
         xaxis=xaxis,
         yaxis=yaxis,
         xs=xs,
         ys=ys,
         trials=filtered_trials,
         zs=np.array(zs),
-        color_idxs=np.array(color_idxs),
+        colors=colors,
     )
 
 
@@ -269,10 +288,6 @@ def _get_axis_info(trials: list[FrozenTrial], param_name: str) -> _AxisInfo:
 def _get_rank_subplot(
     info: _RankSubplotInfo, target_name: str, print_raw_objectives: bool
 ) -> "Scatter":
-    colormap = "RdYlBu_r"
-    # sample_colorscale requires plotly >= 5.0.0.
-    colors = plotly.colors.sample_colorscale(colormap, info.color_idxs)
-
     def get_hover_text(trial: FrozenTrial, target_value: float) -> str:
         lines = [f"Trial #{trial.number}"]
         lines += [f"{k}: {v}" for k, v in trial.params.items()]
@@ -285,7 +300,7 @@ def get_hover_text(trial: FrozenTrial, target_value: float) -> str:
         x=info.xs,
         y=info.ys,
         marker={
-            "color": colors,
+            "color": list(map(plotly.colors.label_rgb, info.colors)),
             "line": {"width": 0.5, "color": "Grey"},
         },
         mode="markers",
@@ -403,3 +418,11 @@ def _get_rank_plot(
     )
     figure.add_trace(colorbar_trace)
     return figure
+
+
+def _convert_color_idxs_to_scaled_rgb_colors(color_idxs: np.ndarray) -> np.ndarray:
+    colormap = "RdYlBu_r"
+    # sample_colorscale requires plotly >= 5.0.0.
+    labeled_colors = plotly.colors.sample_colorscale(colormap, color_idxs)
+    scaled_rgb_colors = np.array([plotly.colors.unlabel_rgb(cl) for cl in labeled_colors])
+    return scaled_rgb_colors

optuna/visualization/matplotlib/_rank.py

@@ -54,10 +54,18 @@ def plot_rank(
             def objective(trial):
                 x = trial.suggest_float("x", -100, 100)
                 y = trial.suggest_categorical("y", [-1, 0, 1])
+
+                c0 = 400 - (x + y)**2
+                trial.set_user_attr("constraint", [c0])
+
                 return x ** 2 + y
 
 
-            sampler = optuna.samplers.TPESampler(seed=10)
+            def constraints(trial):
+                return trial.user_attrs["constraint"]
+
+
+            sampler = optuna.samplers.TPESampler(seed=10, constraints_func=constraints)
             study = optuna.create_study(sampler=sampler)
             study.optimize(objective, n_trials=30)
 
@@ -126,7 +134,8 @@ def _get_rank_plot(
 
     tick_info = _get_tick_info(info.zs)
 
-    cbar = fig.colorbar(pc, ax=axs, ticks=tick_info.coloridxs, cmap=plt.get_cmap("RdYlBu_r"))
+    pc.set_cmap(plt.get_cmap("RdYlBu_r"))
+    cbar = fig.colorbar(pc, ax=axs, ticks=tick_info.coloridxs)
     cbar.ax.set_yticklabels(tick_info.text)
     cbar.outline.set_edgecolor("gray")
     return axs
@@ -151,6 +160,4 @@ def _add_rank_subplot(
     if info.yaxis.is_log:
         ax.set_yscale("log")
 
-    return ax.scatter(
-        x=info.xs, y=info.ys, c=info.color_idxs, cmap=plt.get_cmap("RdYlBu_r"), edgecolors="grey"
-    )
+    return ax.scatter(x=info.xs, y=info.ys, c=info.colors / 255, edgecolors="grey")