diff --git a/ax/analysis/plotly/interaction.py b/ax/analysis/plotly/interaction.py index b38bac2cfd9..d4748fe2307 100644 --- a/ax/analysis/plotly/interaction.py +++ b/ax/analysis/plotly/interaction.py @@ -29,6 +29,7 @@ from ax.generation_strategy.generation_strategy import GenerationStrategy from ax.modelbridge.registry import Generators from ax.modelbridge.torch import TorchAdapter +from ax.modelbridge.transforms.one_hot import OH_PARAM_INFIX from ax.models.torch.botorch_modular.surrogate import Surrogate from ax.utils.common.logger import get_logger from ax.utils.sensitivity.sobol_measures import ax_parameter_sens @@ -39,10 +40,12 @@ from gpytorch.priors import LogNormalPrior from plotly import express as px, graph_objects as go from plotly.subplots import make_subplots -from pyre_extensions import assert_is_instance +from pyre_extensions import assert_is_instance, none_throws logger: Logger = get_logger(__name__) +DISPLAY_SAMPLED_THRESHOLD: int = 50 + class InteractionPlot(PlotlyAnalysis): """ @@ -63,6 +66,7 @@ def __init__( metric_name: str | None = None, fit_interactions: bool = True, most_important: bool = True, + use_oak_model: bool = False, seed: int = 0, torch_device: torch.device | None = None, ) -> None: @@ -74,6 +78,8 @@ def __init__( 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. + use_oak_model: Whether to use an OAK model for the analysis. If False, use + Adapter from the current GenerationNode. 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. @@ -83,6 +89,7 @@ def __init__( self.metric_name = metric_name self.fit_interactions = fit_interactions self.most_important = most_important + self.use_oak_model = use_oak_model self.seed = seed self.torch_device = torch_device @@ -103,6 +110,12 @@ def compute( if experiment is None: raise UserInputError("InteractionPlot requires an Experiment") + if generation_strategy is None and not self.use_oak_model: + raise UserInputError( + "InteractionPlot requires a GenerationStrategy when use_oak_model is " + "False" + ) + metric_name = self.metric_name or select_metric(experiment=experiment) # Fix the seed to ensure that the model is fit identically across different @@ -110,19 +123,42 @@ def compute( with torch.random.fork_rng(): torch.torch.manual_seed(self.seed) - # Fit the OAK model. - oak_model = self._get_oak_model( - experiment=experiment, metric_name=metric_name - ) + if self.use_oak_model: + adapter = self._get_oak_model( + experiment=experiment, metric_name=metric_name + ) + else: + gs = none_throws(generation_strategy) + if gs.model is None: + gs._fit_current_model(None) - # Calculate first- or second-order Sobol indices. - sens = ax_parameter_sens( - model_bridge=oak_model, - metrics=[metric_name], - order="second" if self.fit_interactions else "first", - signed=not self.fit_interactions, - )[metric_name] + adapter = assert_is_instance(gs.model, TorchAdapter) + try: + # Calculate first- or second-order Sobol indices. + sens = ax_parameter_sens( + model_bridge=adapter, + metrics=[metric_name], + order="second" if self.fit_interactions else "first", + signed=not self.fit_interactions, + )[metric_name] + except Exception as e: + logger.exception( + f"Failed to compute sensitivity analysis with {e}. Falling back " + "on the surrogate model's feature importances." + ) + + sens = { + metric_name: adapter.feature_importances(metric_name) + for metric_name in adapter.metric_names + } + # Filter out an parameters that have been added to the search space via one-hot + # encoding -- these make the sensitivity analysis less interpretable and break + # the surface plots. + # TODO: Do something more principled here. + sens = {k: v for k, v in sens.items() if OH_PARAM_INFIX not in k} + + # Create a DataFrame with the sensitivity analysis. sensitivity_df = pd.DataFrame( [*sens.items()], columns=["feature", "sensitivity"] ).sort_values(by="sensitivity", key=abs, ascending=self.most_important) @@ -138,13 +174,16 @@ def compute( by="sensitivity", ascending=self.most_important, inplace=True ) + plotly_blue = px.colors.qualitative.Plotly[0] + plotly_orange = px.colors.qualitative.Plotly[4] + sensitivity_fig = px.bar( plotting_df, x="sensitivity", y="feature", color="direction", # Increase gets blue, decrease gets orange. - color_discrete_sequence=["orange", "blue"], + color_discrete_sequence=[plotly_blue, plotly_orange], orientation="h", ) @@ -158,7 +197,7 @@ def compute( surface_figs.append( _prepare_surface_plot( experiment=experiment, - model=oak_model, + model=adapter, feature_name=feature_name, metric_name=metric_name, ) @@ -245,16 +284,18 @@ def compute( width=1000, ) - subtitle_substring = ( - "one- or two-dimensional" if self.fit_interactions else "one-dimensional" - ) + subtitle_substring = ", or pairs of parameters" if self.fit_interactions else "" return self._create_plotly_analysis_card( title=f"Interaction Analysis for {metric_name}", subtitle=( - f"Understand an Experiment's data as {subtitle_substring} additive " - "components with sparsity. Important components are visualized through " - "slice or contour plots" + f"Understand how changes to your parameters affect {metric_name}. " + f"Parameters{subtitle_substring} which rank higher here explain more " + f"of the observed variation in {metric_name}. The direction of the " + "effect is indicated by the color of the bar plot. Additionally, the " + "six most important parameters are visualized through surface plots " + f"which show the predicted outcomes for {metric_name} as a function " + "of the plotted parameters with the other parameters held fixed." ), level=AnalysisCardLevel.MID, df=sensitivity_df, @@ -333,7 +374,7 @@ def _prepare_surface_plot( log_y=is_axis_log_scale( parameter=experiment.search_space.parameters[y_parameter_name] ), - display_sampled=True, + display_sampled=df["sampled"].sum() <= DISPLAY_SAMPLED_THRESHOLD, ) # If the feature is a first-order component, plot a slice plot. @@ -351,5 +392,5 @@ def _prepare_surface_plot( log_x=is_axis_log_scale( parameter=experiment.search_space.parameters[feature_name] ), - display_sampled=True, + display_sampled=df["sampled"].sum() <= DISPLAY_SAMPLED_THRESHOLD, )