Source code for evalml.automl.pipeline_search_plots

"""Plots displayed during pipeline search."""
from evalml.utils import import_or_raise, jupyter_check


[docs]class SearchIterationPlot: """Search iteration plot. Args: results (dict): Dictionary of current results. objective (ObjectiveBase): Objective that AutoML is optimizing for. """ def __init__(self, results, objective): self._go = import_or_raise( "plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects", ) if jupyter_check(): import_or_raise("ipywidgets", warning=True) self.best_score_by_iter_fig = None self.curr_iteration_scores = list() self.best_iteration_scores = list() title = "Pipeline Search: Iteration vs. {}<br><sub>Gray marker indicates the score at current iteration</sub>".format( objective.name ) data = [ self._go.Scatter(x=[], y=[], mode="lines+markers", name="Best Score"), self._go.Scatter( x=[], y=[], mode="markers", name="Iter score", marker={"color": "gray"} ), ] layout = { "title": title, "xaxis": {"title": "Iteration", "rangemode": "tozero"}, "yaxis": {"title": "Score"}, } self.best_score_by_iter_fig = self._go.FigureWidget(data, layout) self.best_score_by_iter_fig.update_layout(showlegend=False) self.update(results, objective) self._go = None
[docs] def update(self, results, objective): """Update the search plot.""" if len(results["search_order"]) > 0 and len(results["pipeline_results"]) > 0: iter_idx = results["search_order"] pipeline_res = results["pipeline_results"] iter_scores = [pipeline_res[i]["mean_cv_score"] for i in iter_idx] iter_score_pairs = zip(iter_idx, iter_scores) iter_score_pairs = sorted(iter_score_pairs, key=lambda value: value[0]) sorted_iter_idx, sorted_iter_scores = zip(*iter_score_pairs) # Create best score data best_iteration_scores = list() curr_best = None for score in sorted_iter_scores: if curr_best is None: best_iteration_scores.append(score) curr_best = score else: if ( objective.greater_is_better and score > curr_best or not objective.greater_is_better and score < curr_best ): best_iteration_scores.append(score) curr_best = score else: best_iteration_scores.append(curr_best) # Update entire line plot best_score_trace = self.best_score_by_iter_fig.data[0] best_score_trace.x = sorted_iter_idx best_score_trace.y = best_iteration_scores curr_score_trace = self.best_score_by_iter_fig.data[1] curr_score_trace.x = sorted_iter_idx curr_score_trace.y = sorted_iter_scores
[docs]class PipelineSearchPlots: """Plots for the AutoMLSearch class during search. Args: results (dict): Dictionary of current results. objective (ObjectiveBase): Objective that AutoML is optimizing for. """ def __init__(self, results, objective): self._go = import_or_raise( "plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects", ) self.results = results self.objective = objective
[docs] def search_iteration_plot(self, interactive_plot=False): """Shows a plot of the best score at each iteration using data gathered during training. Args: interactive_plot (bool): Whether or not to show an interactive plot. Defaults to False. Returns: plot Raises: ValueError: If engine_str is not a valid engine. """ if not interactive_plot: plot_obj = SearchIterationPlot(self.results, self.objective) return self._go.Figure(plot_obj.best_score_by_iter_fig) try: ipython_display = import_or_raise( "IPython.display", error_msg="Cannot find dependency IPython.display" ) plot_obj = SearchIterationPlot(self.results, self.objective) ipython_display.display(plot_obj.best_score_by_iter_fig) return plot_obj except ImportError: return self.search_iteration_plot(interactive_plot=False)