Remove various args from InteractionPlot.__init__

Summary:
Simplifying InteractionPlot by removing some arguments from its initializer. If this simplifications seem appropriate then I will continue to simplify the Plot.

Removals:
* top_k: Prefer to show sobol indices for all components on bar chart, slice/sufrace for top 6 always
* data: Let's always use the data on the experiment
* most_important: Always sort most important to least important, never least to most
* display_components: Always display components
* decompose_components: Never decompose components
* plots_share_range: Always share range
* num_mc_samples: Always use 10k samples
* [RFC] model_fit_seed: Do not bother with seed setting -- we dont do this for any other plots so its probably not worth the complexity here

The following diffs will restructure the code here to take advantage of the simplified options

Differential Revision: D65148289

ax/analysis/plotly/interaction.py

@@ -5,7 +5,6 @@
 
 # pyre-strict
 
-# pyre-unsafe
 
 import math
 from typing import Any
@@ -19,11 +18,9 @@
 from ax.analysis.analysis import AnalysisCardLevel
 
 from ax.analysis.plotly.plotly_analysis import PlotlyAnalysis, PlotlyAnalysisCard
-from ax.core.data import Data
 from ax.core.experiment import Experiment
 from ax.core.generation_strategy_interface import GenerationStrategyInterface
 from ax.core.observation import ObservationFeatures
-from ax.exceptions.core import UserInputError
 from ax.modelbridge.registry import Models
 from ax.modelbridge.torch import TorchModelBridge
 from ax.models.torch.botorch_modular.surrogate import Surrogate
@@ -40,7 +37,6 @@
 from plotly import graph_objects as go, io as pio
 from plotly.subplots import make_subplots
 from pyre_extensions import none_throws
-from torch import Tensor
 
 
 TOP_K_TOO_LARGE_ERROR = (
@@ -142,57 +138,31 @@ class InteractionPlot(PlotlyAnalysis):
     def __init__(
         self,
         metric_name: str,
-        top_k: int = 6,
-        data: Data | None = None,
-        most_important: bool = True,
         fit_interactions: bool = True,
-        display_components: bool = False,
-        decompose_components: bool = False,
-        plots_share_range: bool = True,
-        num_mc_samples: int = 10_000,
-        model_fit_seed: int = 0,
+        most_important: bool = True,
+        seed: int = 0,
         torch_device: torch.device | None = None,
     ) -> None:
         """Constructor for InteractionAnalysis.
 
         Args:
             metric_name: The metric to analyze.
-            top_k: The 'k' most imortant interactions according to Sobol indices.
-                Supports up to 6 components visualized at once.
-            data: The data to analyze. Defaults to None, in which case the data is taken
-                from the experiment.
-            most_important: Whether to plot the most or least important interactions.
             fit_interactions: Whether to fit interaction effects in addition to main
                 effects.
-            display_components: Display individual components instead of the summarized
-                plot of sobol index values.
-            decompose_components: Whether to visualize surfaces as the total effect of
-                x1 & x2 (False) or only the interaction term (True). Setting
-                decompose_components = True thus plots f(x1, x2) - f(x1) - f(x2).
-            plots_share_range: Whether to have all plots share the same output range in
-                the final visualization.
-            num_mc_samples: The number of Monte Carlo samples to use for the Sobol
-                index calculations.
-            model_fit_seed: The seed with which to fit the model. Defaults to 0. Used
+            most_important: Whether to sort by most or least important features in the
+                bar subplot. Also controls whether the six most or least important
+                features are plotted in the surface subplots.
+            seed: The seed with which to fit the model. Defaults to 0. Used
                 to ensure that the model fit is identical across the generation of
                 various plots.
             torch_device: The torch device to use for the model.
         """
 
-        super().__init__()
-        if top_k > 6 and display_components:
-            raise UserInputError(TOP_K_TOO_LARGE_ERROR.format(str(top_k)))
-        self.metric_name: str = metric_name
-        self.top_k: int = top_k
-        self.data: Data | None = data
-        self.most_important: bool = most_important
-        self.fit_interactions: bool = fit_interactions
-        self.display_components: bool = display_components
-        self.decompose_components: bool = decompose_components
-        self.num_mc_samples: int = num_mc_samples
-        self.model_fit_seed: int = model_fit_seed
-        self.torch_device: torch.device | None = torch_device
-        self.plots_share_range: bool = plots_share_range
+        self.metric_name = metric_name
+        self.fit_interactions = fit_interactions
+        self.most_important = most_important
+        self.seed = seed
+        self.torch_device = torch_device
 
     def get_model(
         self, experiment: Experiment, metric_names: list[str] | None = None
@@ -207,19 +177,16 @@ def get_model(
             num_parameters=len(experiment.search_space.tunable_parameters),
             torch_device=self.torch_device,
         )
-        data = experiment.lookup_data() if self.data is None else self.data
+        data = experiment.lookup_data()
         if metric_names:
             data = data.filter(metric_names=metric_names)
-        with torch.random.fork_rng():
-            # fixing the seed to ensure that the model is fit identically across
-            # different analyses of the same experiment
-            torch.torch.manual_seed(self.model_fit_seed)
-            model_bridge = Models.BOTORCH_MODULAR(
-                search_space=experiment.search_space,
-                experiment=experiment,
-                data=data,
-                surrogate=Surrogate(**covar_module_kwargs),
-            )
+
+        model_bridge = Models.BOTORCH_MODULAR(
+            search_space=experiment.search_space,
+            experiment=experiment,
+            data=data,
+            surrogate=Surrogate(**covar_module_kwargs),
+        )
         return model_bridge  # pyre-ignore[7] Return type is always a TorchModelBridge
 
     # pyre-ignore[14] Must pass in an Experiment (not Experiment | None)
@@ -241,19 +208,29 @@ def compute(
         """
         experiment = none_throws(experiment)
         model_bridge = self.get_model(experiment, [self.metric_name])
-        with torch.random.fork_rng():
-            # fixing the seed to ensure that the model is fit identically across
-            # different analyses of the same experiment
-            torch.torch.manual_seed(self.model_fit_seed)
-            sens = ax_parameter_sens(
-                model_bridge=model_bridge,
-                metrics=[self.metric_name],
-                order="second" if self.fit_interactions else "first",
-                signed=not self.fit_interactions,
-                num_mc_samples=self.num_mc_samples,
-            )
+        sens = ax_parameter_sens(
+            model_bridge=model_bridge,
+            metrics=[self.metric_name],
+            order="second" if self.fit_interactions else "first",
+            signed=not self.fit_interactions,
+        )
         sens = sort_and_filter_top_k_components(
-            indices=sens, k=self.top_k, most_important=self.most_important
+            indices=sens,
+            k=6,
+        )
+        # reformat the keys from tuple to a proper "x1 & x2" string
+        interaction_name = "Interaction" if self.fit_interactions else "Main Effect"
+        return PlotlyAnalysisCard(
+            name="Interaction Analysis",
+            title="Feature Importance Analysis",
+            subtitle=f"{interaction_name} Analysis for {self.metric_name}",
+            level=AnalysisCardLevel.MID,
+            df=pd.DataFrame(sens),
+            blob=pio.to_json(
+                plot_feature_importance_by_feature_plotly(
+                    sensitivity_values=sens,  # pyre-ignore[6]
+                )
+            ),
         )
         if not self.display_components:
             return PlotlyAnalysisCard(