From fa253f1863a8a56db34683a633394b3685330fb2 Mon Sep 17 00:00:00 2001 From: Andrew Li Date: Sat, 6 Sep 2025 00:43:29 +0000 Subject: [PATCH] feat: Add 'Show raw data' checkbox to Explore widgets This commit adds a 'Show raw data' checkbox to the `Explore...` widgets in the seismometer package. When the checkbox is enabled, the underlying pandas.DataFrame used to produce the current visualization is displayed. The raw data output updates reactively when any widget controls (e.g., dropdowns, sliders, filters) change. To achieve this, the following changes were made: - The `UpdatePlotWidget` in `src/seismometer/controls/explore.py` was updated to include the 'Show raw data' checkbox. - The `ExplorationWidget` in the same file was modified to handle the display of the raw data. - The plot functions in `src/seismometer/api/plots.py` and `src/seismometer/api/explore.py` were updated to return a tuple of (HTML, pd.DataFrame). - The `@disk_cached_html_segment` decorator was removed from the modified plot functions to avoid caching issues with the new return type. - Tests in `tests/controls/test_explore.py` were updated to reflect these changes. --- changelog/168.feature.rst | 1 + src/seismometer/api/explore.py | 15 +-- src/seismometer/api/plots.py | 126 +++++++++++++------------ src/seismometer/controls/decorators.py | 26 +++++ src/seismometer/controls/explore.py | 65 ++++++++++++- tests/api/test_api_explore.py | 52 +++++----- tests/controls/test_explore.py | 94 ++++++++++++++---- 7 files changed, 265 insertions(+), 114 deletions(-) create mode 100644 changelog/168.feature.rst diff --git a/changelog/168.feature.rst b/changelog/168.feature.rst new file mode 100644 index 00000000..e359d769 --- /dev/null +++ b/changelog/168.feature.rst @@ -0,0 +1 @@ +Added a 'Show raw data' checkbox to the interactive `Explore...` widgets to display the underlying pandas.DataFrame used for visualizations. diff --git a/src/seismometer/api/explore.py b/src/seismometer/api/explore.py index 428f0e10..d2c2ab16 100644 --- a/src/seismometer/api/explore.py +++ b/src/seismometer/api/explore.py @@ -1,8 +1,9 @@ from typing import Any, Optional +import pandas as pd from IPython.display import HTML, display -from seismometer.controls.decorators import disk_cached_html_segment +from seismometer.controls.decorators import disk_cached_html_and_df_segment from seismometer.controls.explore import ExplorationWidget # noqa: from seismometer.controls.explore import ( ExplorationCohortOutcomeInterventionEvaluationWidget, @@ -216,9 +217,9 @@ def on_widget_value_changed(*args): return VBox(children=[comparison_selections, output], layout=BOX_GRID_LAYOUT) -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export -def cohort_list_details(cohort_dict: dict[str, tuple[Any]]) -> HTML: +def cohort_list_details(cohort_dict: dict[str, tuple[Any]]) -> tuple[HTML, pd.DataFrame]: """ Generates an HTML table of cohort details. @@ -229,8 +230,8 @@ def cohort_list_details(cohort_dict: dict[str, tuple[Any]]) -> HTML: Returns ------- - HTML - able indexed by targets, with counts of unique entities, and mean values of the output columns. + tuple[HTML, pd.DataFrame] + able indexed by targets, with counts of unique entities, and mean values of the output columns, and the data """ from seismometer.data.filter import filter_rule_from_cohort_dictionary @@ -246,7 +247,7 @@ def cohort_list_details(cohort_dict: dict[str, tuple[Any]]) -> HTML: ] cohort_count = data[sg.entity_keys[0]].nunique() if cohort_count < sg.censor_threshold: - return template.render_censored_plot_message(sg.censor_threshold) + return template.render_censored_plot_message(sg.censor_threshold), data groups = data.groupby(target_cols) float_cols = list(data[intervention_cols + outcome_cols].select_dtypes(include=float)) @@ -268,7 +269,7 @@ def cohort_list_details(cohort_dict: dict[str, tuple[Any]]) -> HTML: groupstats.index.rename(new_names, inplace=True) html_table = groupstats.to_html() title = "Summary" - return template.render_title_message(title, html_table) + return template.render_title_message(title, html_table), data # endregion diff --git a/src/seismometer/api/plots.py b/src/seismometer/api/plots.py index 5c88d4d4..671ae555 100644 --- a/src/seismometer/api/plots.py +++ b/src/seismometer/api/plots.py @@ -6,7 +6,7 @@ from IPython.display import HTML, SVG import seismometer.plot as plot -from seismometer.controls.decorators import disk_cached_html_segment +from seismometer.controls.decorators import disk_cached_html_and_df_segment from seismometer.core.autometrics import AutomationManager, store_call_parameters from seismometer.core.decorators import export from seismometer.data import get_cohort_data, get_cohort_performance_data, metric_apis @@ -36,9 +36,11 @@ def plot_cohort_hist(): return _plot_cohort_hist(sg.dataframe, sg.target, sg.output, cohort_col, subgroups, censor_threshold) -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export -def plot_cohort_group_histograms(cohort_col: str, subgroups: list[str], target_column: str, score_column: str) -> HTML: +def plot_cohort_group_histograms( + cohort_col: str, subgroups: list[str], target_column: str, score_column: str +) -> tuple[HTML, pd.DataFrame]: """ Generate a histogram plot of predicted probabilities for each subgroup in a cohort. @@ -55,15 +57,14 @@ def plot_cohort_group_histograms(cohort_col: str, subgroups: list[str], target_c Returns ------- - HTML - html visualization of the histogram + tuple[HTML, pd.DataFrame] + html visualization of the histogram and the data used to generate it """ sg = Seismogram() target_column = pdh.event_value(target_column) return _plot_cohort_hist(sg.dataframe, target_column, score_column, cohort_col, subgroups, sg.censor_threshold) -@disk_cached_html_segment def _plot_cohort_hist( dataframe: pd.DataFrame, target: str, @@ -71,7 +72,7 @@ def _plot_cohort_hist( cohort_col: str, subgroups: list[str], censor_threshold: int = 10, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Creates an HTML segment displaying a histogram of predicted probabilities for each cohort. @@ -107,7 +108,7 @@ def _plot_cohort_hist( cData = cData.loc[cData["cohort"].isin(good_groups)] if len(cData.index) == 0: - return template.render_censored_plot_message(censor_threshold) + return template.render_censored_plot_message(censor_threshold), cData bin_count = 20 bins = np.histogram_bin_edges(cData["pred"], bins=bin_count) @@ -115,9 +116,9 @@ def _plot_cohort_hist( try: svg = plot.cohorts_vertical(cData, plot.histogram_stacked, func_kws={"show_legend": False, "bins": bins}) title = f"Predicted Probabilities by {cohort_col}" - return template.render_title_with_image(title, svg) + return template.render_title_with_image(title, svg), cData except Exception as error: - return template.render_title_message("Error", f"Error: {error}") + return template.render_title_message("Error", f"Error: {error}"), pd.DataFrame() @export @@ -165,11 +166,11 @@ def plot_leadtime_enc(score=None, ref_time=None, target_event=None): @store_call_parameters(cohort_col="cohort_col", subgroups="subgroups") -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_cohort_lead_time( cohort_col: str, subgroups: list[str], event_column: str, score_column: str, threshold: float -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots a lead times between the first positive prediction give an threshold and an event. @@ -214,7 +215,6 @@ def plot_cohort_lead_time( ) -@disk_cached_html_segment def _plot_leadtime_enc( dataframe: pd.DataFrame, entity_keys: list[str], @@ -228,7 +228,7 @@ def _plot_leadtime_enc( max_hours: int, x_label: str, censor_threshold: int = 10, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ HTML Plot of time between prediction and target event. @@ -261,21 +261,24 @@ def _plot_leadtime_enc( Returns ------- - HTML - Lead time plot + tuple[HTML, pd.DataFrame] + Lead time plot and the data used to generate it """ if target_event not in dataframe: - logger.error(f"Target event ({target_event}) not found in dataset. Cannot plot leadtime.") - return + msg = f"Target event ({target_event}) not found in dataset. Cannot plot leadtime." + logger.error(msg) + return template.render_title_message("Error", msg), pd.DataFrame() if target_zero not in dataframe: - logger.error(f"Target event time-zero ({target_zero}) not found in dataset. Cannot plot leadtime.") - return + msg = f"Target event time-zero ({target_zero}) not found in dataset. Cannot plot leadtime." + logger.error(msg) + return template.render_title_message("Error", msg), pd.DataFrame() summary_data = dataframe[dataframe[target_event] == 1] if len(summary_data.index) == 0: - logger.error(f"No positive events ({target_event}=1) were found") - return + msg = f"No positive events ({target_event}=1) were found" + logger.error(msg) + return template.render_title_message("Error", msg), pd.DataFrame() cohort_mask = summary_data[cohort_col].isin(subgroups) threshold_mask = summary_data[score] > threshold @@ -291,7 +294,7 @@ def _plot_leadtime_enc( if summary_data is not None and len(summary_data) > censor_threshold: summary_data = summary_data[[target_zero, ref_time, cohort_col]] else: - return template.render_censored_plot_message(censor_threshold) + return template.render_censored_plot_message(censor_threshold), pd.DataFrame() # filter by group size counts = summary_data[cohort_col].value_counts() @@ -303,7 +306,7 @@ def _plot_leadtime_enc( ) if len(summary_data.index) == 0: - return template.render_censored_plot_message(censor_threshold) + return template.render_censored_plot_message(censor_threshold), summary_data # Truncate to minute but plot hour summary_data[x_label] = (summary_data[ref_time] - summary_data[target_zero]).dt.total_seconds() // 60 / 60 @@ -311,11 +314,11 @@ def _plot_leadtime_enc( title = f'Lead Time from {score.replace("_", " ")} to {(target_zero).replace("_", " ")}' rows = summary_data[cohort_col].nunique() svg = plot.leadtime_violin(summary_data, x_label, cohort_col, xmax=max_hours, figsize=(9, 1 + rows)) - return template.render_title_with_image(title, svg) + return template.render_title_with_image(title, svg), summary_data @store_call_parameters(cohort_col="cohort_col", subgroups="subgroups") -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_cohort_evaluation( cohort_col: str, @@ -324,7 +327,7 @@ def plot_cohort_evaluation( score_column: str, thresholds: list[float], per_context: bool = False, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots model performance metrics split by on a cohort attribute. @@ -364,7 +367,6 @@ def plot_cohort_evaluation( ) -@disk_cached_html_segment def _plot_cohort_evaluation( dataframe: pd.DataFrame, entity_keys: list[str], @@ -378,7 +380,7 @@ def _plot_cohort_evaluation( aggregation_method: str = "max", threshold_col: str = "Threshold", ref_time: str = None, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots model performance metrics split by on a cohort attribute. @@ -441,14 +443,14 @@ def _plot_cohort_evaluation( try: assert_valid_performance_metrics_df(plot_data) except ValueError: - return template.render_censored_plot_message(censor_threshold) + return template.render_censored_plot_message(censor_threshold), plot_data svg = plot.cohort_evaluation_vs_threshold(plot_data, cohort_feature=cohort_col, highlight=thresholds) title = f"Model Performance Metrics on {cohort_col} across Thresholds" - return template.render_title_with_image(title, svg) + return template.render_title_with_image(title, svg), plot_data @store_call_parameters(cohort_dict="cohort_dict") -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_model_evaluation( cohort_dict: dict[str, tuple[Any]], @@ -456,7 +458,7 @@ def plot_model_evaluation( score_column: str, thresholds: list[float], per_context: bool = False, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Generates a 2x3 plot showing the performance of a model. @@ -503,7 +505,6 @@ def plot_model_evaluation( ) -@disk_cached_html_segment def _model_evaluation( dataframe: pd.DataFrame, entity_keys: list[str], @@ -516,7 +517,7 @@ def _model_evaluation( aggregation_method: str = "max", ref_time: Optional[str] = None, cohort: dict = {}, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ plots common model evaluation metrics @@ -563,10 +564,13 @@ def _model_evaluation( requirements = FilterRule.isin(target, (0, 1)) & FilterRule.notna(score_col) data = requirements.filter(data) if len(data.index) < censor_threshold: - return template.render_censored_plot_message(censor_threshold) + return template.render_censored_plot_message(censor_threshold), data if (lcount := data[target].nunique()) != 2: - return template.render_title_message( - "Evaluation Error", f"Model Evaluation requires exactly two classes but found {lcount}" + return ( + template.render_title_message( + "Evaluation Error", f"Model Evaluation requires exactly two classes but found {lcount}" + ), + data, ) # stats and ci handle percentile/percentage independently - evaluation wants 0-100 for displays @@ -595,7 +599,7 @@ def _model_evaluation( show_thresholds=True, highlight=thresholds, ) - return template.render_title_with_image(title, svg) + return template.render_title_with_image(title, svg), data @store_call_parameters @@ -622,7 +626,7 @@ def plot_trend_intervention_outcome() -> HTML: ) -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_intervention_outcome_timeseries( cohort_col: str, @@ -631,7 +635,7 @@ def plot_intervention_outcome_timeseries( intervention: str, reference_time_col: str, censor_threshold: int = 10, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots two timeseries based on an outcome and an intervention. @@ -666,7 +670,6 @@ def plot_intervention_outcome_timeseries( ) -@disk_cached_html_segment def _plot_trend_intervention_outcome( dataframe: pd.DataFrame, entity_keys: list[str], @@ -676,7 +679,7 @@ def _plot_trend_intervention_outcome( intervention: str, reftime: str, censor_threshold: int = 10, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots two timeseries based on selectors; an outcome and then an intervention. @@ -748,7 +751,7 @@ def _plot_trend_intervention_outcome( "Missing Outcome", f"No outcome timeseries plotted; No events with name {outcome}." ) - return HTML(outcome_plot.data + intervention_plot.data) + return HTML(outcome_plot.data + intervention_plot.data), dataframe def _plot_ts_cohort( @@ -842,11 +845,11 @@ def _plot_ts_cohort( @store_call_parameters(cohort_dict="cohort_dict") -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_model_score_comparison( cohort_dict: dict[str, tuple[Any]], target: str, scores: tuple[str], *, per_context: bool -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots a comparison of model scores for a given subpopulation. @@ -903,17 +906,17 @@ def plot_model_score_comparison( try: assert_valid_performance_metrics_df(plot_data) except ValueError: - return template.render_censored_plot_message(sg.censor_threshold) + return template.render_censored_plot_message(sg.censor_threshold), plot_data svg = plot.cohort_evaluation_vs_threshold(plot_data, cohort_feature="ScoreName") title = f"Model Metrics: {', '.join(scores)} vs {target}" - return template.render_title_with_image(title, svg) + return template.render_title_with_image(title, svg), plot_data -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_model_target_comparison( cohort_dict: dict[str, tuple[Any]], targets: tuple[str], score: str, *, per_context: bool -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Plots a comparison of model targets for a given score and subpopulation. @@ -970,15 +973,15 @@ def plot_model_target_comparison( try: assert_valid_performance_metrics_df(plot_data) except ValueError: - return template.render_censored_plot_message(sg.censor_threshold) + return template.render_censored_plot_message(sg.censor_threshold), plot_data svg = plot.cohort_evaluation_vs_threshold(plot_data, cohort_feature="ScoreName") title = f"Model Metrics: {', '.join(targets)} vs {score}" - return template.render_title_with_image(title, svg) + return template.render_title_with_image(title, svg), plot_data # region Explore Any Metric (NNT, etc) @store_call_parameters(cohort_dict="cohort_dict") -@disk_cached_html_segment +@disk_cached_html_and_df_segment @export def plot_binary_classifier_metrics( metric_generator: BinaryClassifierMetricGenerator, @@ -989,7 +992,7 @@ def plot_binary_classifier_metrics( *, per_context: bool = False, table_only: bool = False, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ Generates a plot with model metrics. @@ -1074,7 +1077,7 @@ def binary_classifier_metric_evaluation( aggregation_method: str = "max", ref_time: str = None, table_only: bool = False, -) -> HTML: +) -> tuple[HTML, pd.DataFrame]: """ plots common model evaluation metrics @@ -1118,10 +1121,13 @@ def binary_classifier_metric_evaluation( requirements = FilterRule.isin(target, (0, 1)) & FilterRule.notna(score_col) data = requirements.filter(data) if len(data.index) < censor_threshold: - return template.render_censored_plot_message(censor_threshold) + return template.render_censored_plot_message(censor_threshold), data if (lcount := data[target].nunique()) != 2: - return template.render_title_message( - "Evaluation Error", f"Model Evaluation requires exactly two classes but found {lcount}" + return ( + template.render_title_message( + "Evaluation Error", f"Model Evaluation requires exactly two classes but found {lcount}" + ), + data, ) if isinstance(metrics, str): metrics = [metrics] @@ -1134,8 +1140,8 @@ def binary_classifier_metric_evaluation( if log_all: recorder.populate_metrics(attributes=attributes, metrics={metric: stats[metric].to_dict()}) if table_only: - return HTML(stats[metrics].T.to_html()) - return plot.binary_classifier.plot_metric_list(stats, metrics) + return HTML(stats[metrics].T.to_html()), data + return plot.binary_classifier.plot_metric_list(stats, metrics), data # endregion diff --git a/src/seismometer/controls/decorators.py b/src/seismometer/controls/decorators.py index eaac8cea..0dfaa13e 100644 --- a/src/seismometer/controls/decorators.py +++ b/src/seismometer/controls/decorators.py @@ -5,6 +5,7 @@ import hashlib import logging import os +import pickle import shutil from functools import wraps from inspect import signature @@ -27,4 +28,29 @@ def html_save(html, filepath) -> None: filepath.write_text(html.data) +def html_and_df_save(data, filepath) -> None: + """ + Saves a tuple of (HTML, pd.DataFrame) to disk. + """ + html, df = data + html_path = filepath.with_suffix(".html") + df_path = filepath.with_suffix(".pkl") + html_path.write_text(html.data) + with open(df_path, "wb") as f: + pickle.dump(df, f) + + +def html_and_df_load(filepath) -> tuple[HTML, Any]: + """ + Loads a tuple of (HTML, pd.DataFrame) from disk. + """ + html_path = filepath.with_suffix(".html") + df_path = filepath.with_suffix(".pkl") + html = HTML(html_path.read_text()) + with open(df_path, "rb") as f: + df = pickle.load(f) + return html, df + + disk_cached_html_segment = DiskCachedFunction("html", save_fn=html_save, load_fn=html_load, return_type=HTML) +disk_cached_html_and_df_segment = DiskCachedFunction("html_and_df", save_fn=html_and_df_save, load_fn=html_and_df_load) diff --git a/src/seismometer/controls/explore.py b/src/seismometer/controls/explore.py index 09d4f020..ae0fa316 100644 --- a/src/seismometer/controls/explore.py +++ b/src/seismometer/controls/explore.py @@ -35,6 +35,7 @@ class UpdatePlotWidget(Box): UPDATE_PLOTS = "Update" UPDATING_PLOTS = "Updating ..." SHOW_CODE = "Show code?" + SHOW_DATA = "Show raw data?" def __init__(self): self.code_checkbox = Checkbox( @@ -45,10 +46,18 @@ def __init__(self): tooltip="Show the code used to generate the plot.", layout=Layout(margin="var(--jp-widgets-margin) var(--jp-widgets-margin) var(--jp-widgets-margin) 10px;"), ) + self.data_checkbox = Checkbox( + value=False, + description=self.SHOW_DATA, + disabled=False, + indent=False, + tooltip="Show the raw data used to generate the plot.", + layout=Layout(margin="var(--jp-widgets-margin) var(--jp-widgets-margin) var(--jp-widgets-margin) 10px;"), + ) self.plot_button = Button(description=self.UPDATE_PLOTS, button_style="primary") layout = Layout(align_items="flex-start") - children = [self.plot_button, self.code_checkbox] + children = [self.plot_button, self.code_checkbox, self.data_checkbox] super().__init__(layout=layout, children=children) @property @@ -59,6 +68,14 @@ def show_code(self) -> bool: def show_code(self, show_code: bool) -> bool: self.code_checkbox.value = show_code + @property + def show_data(self) -> bool: + return self.data_checkbox.value + + @show_data.setter + def show_data(self, show_data: bool) -> bool: + self.data_checkbox.value = show_data + @property def disabled(self) -> bool: return self.plot_button.disabled @@ -86,6 +103,13 @@ def callback_wrapper(change): self.code_checkbox.observe(callback_wrapper, "value") + def on_toggle_data(self, callback): + @wraps(callback) + def callback_wrapper(change): + callback(self.data_checkbox.value) + + self.data_checkbox.observe(callback_wrapper, "value") + class ModelOptionsWidget(VBox, ValueWidget): value = traitlets.Dict(help="The selected values for the model options.") @@ -874,11 +898,20 @@ def __init__( title = HTML(value=f"""

{title}

""") self.center = Output(layout=Layout(height="max-content", max_width="1200px")) self.code_output = Output(layout=Layout(height="max-content", max_width="1200px")) + self.data_output = Output(layout=Layout(height="max-content", max_width="1200px")) self.option_widget = option_widget self.plot_function = plot_function self.update_plot_widget = UpdatePlotWidget() super().__init__( - children=[title, self.option_widget, self.update_plot_widget, self.code_output, self.center], layout=layout + children=[ + title, + self.option_widget, + self.update_plot_widget, + self.code_output, + self.data_output, + self.center, + ], + layout=layout, ) # show initial plot @@ -886,11 +919,13 @@ def __init__( self.update_plot(initial=True) else: self.current_plot_code = self.NO_CODE_STRING + self.current_plot_data = None # attache event handlers self.option_widget.observe(self._on_option_change, "value") self.update_plot_widget.on_click(self._on_plot_button_click) self.update_plot_widget.on_toggle_code(self._on_toggle_code) + self.update_plot_widget.on_toggle_data(self._on_toggle_data) @property def disabled(self) -> bool: @@ -910,6 +945,17 @@ def show_code(self) -> bool: def show_code(self, show_code: bool): self.update_plot_widget.show_code = show_code + @property + def show_data(self) -> bool: + """ + If the widget should show the plot's data. + """ + return self.update_plot_widget.show_data + + @show_data.setter + def show_data(self, show_data: bool): + self.update_plot_widget.show_data = show_data + @staticmethod def _is_interactive_notebook() -> bool: ip = get_ipython() @@ -937,10 +983,12 @@ def _try_generate_plot(self) -> Any: try: plot_args, plot_kwargs = self.generate_plot_args() self.current_plot_code = self.generate_plot_code(plot_args, plot_kwargs) - plot = self.plot_function(*plot_args, **plot_kwargs) + # The plot function will now return a tuple (plot, data) + plot, self.current_plot_data = self.plot_function(*plot_args, **plot_kwargs) except Exception as e: import traceback + self.current_plot_data = None plot = HTML(f"

Exception: 
{e}

{traceback.format_exc()}
") return plot @@ -949,6 +997,7 @@ def _on_plot_button_click(self, button=None): self.option_widget.disabled = True self.update_plot() self._on_toggle_code(self.show_code) + self._on_toggle_data(self.show_data) self.option_widget.disabled = False def generate_plot_code(self, plot_args: tuple = None, plot_kwargs: dict = None) -> str: @@ -987,6 +1036,16 @@ def _on_toggle_code(self, show_code: bool): with self.code_output: display(highlighted_code) + def _on_toggle_data(self, show_data: bool): + """Handle for the toggle data checkbox.""" + self.data_output.clear_output() + if not show_data: + return + + if self.current_plot_data is not None: + with self.data_output: + display(self.current_plot_data) + def _on_option_change(self, change=None): """Enable the plot to be updated.""" self.update_plot_widget.disabled = self.disabled diff --git a/tests/api/test_api_explore.py b/tests/api/test_api_explore.py index 99ecab62..94efdd0e 100644 --- a/tests/api/test_api_explore.py +++ b/tests/api/test_api_explore.py @@ -148,7 +148,7 @@ def test_cohort_list_details_summary_generated(self, fake_seismo): mock_filter.assert_called_once() mock_render.assert_called_once() - assert "mock summary" in result.data + assert "mock summary" in result[0].data def test_cohort_list_details_censored_output(self, fake_seismo): fake_seismo.config.censor_min_count = 10 @@ -161,7 +161,7 @@ def test_cohort_list_details_censored_output(self, fake_seismo): rule.filter.return_value = fake_seismo.dataframe.iloc[:0] # no rows mock_filter.return_value = rule - result = cohort_list_details({"Cohort": ["C1", "C2"]}) + result, _ = cohort_list_details({"Cohort": ["C1", "C2"]}) assert "censored" in result.data.lower() mock_render.assert_called_once() @@ -175,7 +175,7 @@ def test_cohort_list_details_single_target_index_rename(self, fake_seismo): rule.filter.return_value = fake_seismo.dataframe mock_filter.return_value = rule - result = cohort_list_details({"Cohort": ["C1", "C2"]}) + result, _ = cohort_list_details({"Cohort": ["C1", "C2"]}) assert "summary" in result.data @@ -250,15 +250,15 @@ def test_plot_binary_classifier_metrics_basic(self, mock_calc, mock_plot, fake_s mock_calc.assert_called_once() mock_plot.assert_called_once() - assert isinstance(result, SVG) - assert "http://www.w3.org/2000/svg" in result.data + assert isinstance(result[0], SVG) + assert "http://www.w3.org/2000/svg" in result[0].data @patch("seismometer.data.performance.BinaryClassifierMetricGenerator.calculate_binary_stats") def test_plot_binary_classifier_metrics_table_only(self, mock_calc, fake_seismo): mock_stats = pd.DataFrame({"Sensitivity": [0.88]}, index=["value"]) mock_calc.return_value = (mock_stats, None) - result = plot_binary_classifier_metrics( + result, _ = plot_binary_classifier_metrics( metric_generator=BinaryClassifierMetricGenerator(rho=0.5), metrics="Sensitivity", cohort_dict={}, @@ -284,7 +284,7 @@ def test_plot_binary_classifier_metrics_nonbinary_target( with patch("seismometer.seismogram.Seismogram", return_value=fake_seismo): fake_seismo.dataframe = df - result = plot_binary_classifier_metrics( + result, _ = plot_binary_classifier_metrics( metric_generator=BinaryClassifierMetricGenerator(rho=0.5), metrics=["Accuracy"], cohort_dict={"Cohort": ("C1",)}, @@ -304,7 +304,7 @@ def test_model_evaluation_not_binary_target(self, mock_filter, mock_render, fake df["event1_Value"] = 1 # single class mock_filter.return_value = df - result = _model_evaluation( + result, _ = _model_evaluation( df, entity_keys=["entity"], target_event="event1", @@ -341,7 +341,7 @@ def test_model_evaluation_valid_path(self, mock_filter, mock_render, mock_plot, mock_filter.return_value = df mock_scores.return_value = df - result = _model_evaluation( + result, _ = _model_evaluation( df, entity_keys=["entity"], target_event="event1", @@ -388,7 +388,7 @@ def test_plot_model_score_comparison( mock_event_score.return_value = df # per_context=False path mock_perf_data.return_value = _mock_perf_df - result = plot_model_score_comparison( + result, _ = plot_model_score_comparison( cohort_dict={"Cohort": ("C1", "C2")}, target="event1", scores=("score1",), @@ -431,7 +431,7 @@ def test_plot_model_target_comparison( mock_event_score.return_value = df mock_perf_data.return_value = _mock_perf_df - result = plot_model_target_comparison( + result, _ = plot_model_target_comparison( cohort_dict={"Cohort": ("C1", "C2")}, targets=("event1",), score="score1", @@ -461,7 +461,7 @@ def test_plot_cohort_evaluation_censored_data( mock_get_scores.return_value = df mock_get_perf.return_value = _mock_perf_df - result = _plot_cohort_evaluation( + result, _ = _plot_cohort_evaluation( dataframe=_mock_perf_df, entity_keys=["entity"], target="event1_Value", @@ -494,7 +494,7 @@ def test_plot_cohort_evaluation_success( mock_get_scores.return_value = df mock_get_perf.return_value = _mock_perf_df - result = _plot_cohort_evaluation( + result, _ = _plot_cohort_evaluation( dataframe=_mock_perf_df, entity_keys=["entity"], target="event1_Value", @@ -524,7 +524,7 @@ def test_plot_cohort_group_histograms( mock_seismo.return_value = fake_seismo mock_filter.return_value = fake_seismo.dataframe - result = plot_cohort_group_histograms( + result, _ = plot_cohort_group_histograms( cohort_col="Cohort", subgroups=["C1", "C2"], target_column="event1", @@ -540,7 +540,7 @@ def test_plot_cohort_hist_censored_after_filter(self, mock_render, fake_seismo): # Simulate filtered-out result empty_df = fake_seismo.dataframe.iloc[0:0].copy() - result = _plot_cohort_hist( + result, _ = _plot_cohort_hist( dataframe=empty_df, target="event1_Value", output="score1", @@ -560,7 +560,7 @@ def test_plot_cohort_hist_plot_fails(self, mock_get_cohort_data, mock_plot, mock mock_df = pd.DataFrame({"cohort": ["C1"] * 11 + ["C2"] * 11, "pred": [0.1] * 11 + [0.2] * 11}) mock_get_cohort_data.return_value = mock_df - result = _plot_cohort_hist( + result, _ = _plot_cohort_hist( dataframe=fake_seismo.dataframe, target="event1_Value", output="score1", @@ -610,7 +610,7 @@ def test_plot_cohort_lead_time(self, mock_plot, mock_seismo, fake_seismo): def test_leadtime_enc_missing_target_column(self, fake_seismo, caplog): df = fake_seismo.dataframe.drop(columns=["event1_Value"]) with caplog.at_level("ERROR"): - result = _plot_leadtime_enc( + result, _ = _plot_leadtime_enc( df, entity_keys=["entity"], target_event="event1_Value", @@ -623,13 +623,13 @@ def test_leadtime_enc_missing_target_column(self, fake_seismo, caplog): max_hours=48, x_label="Lead Time (hours)", ) - assert result is None + assert "Error" in result.data assert "Target event (event1_Value) not found" in caplog.text def test_leadtime_enc_missing_target_zero_column(self, fake_seismo, caplog): df = fake_seismo.dataframe.drop(columns=["event1_Time"]) with caplog.at_level("ERROR"): - result = _plot_leadtime_enc( + result, _ = _plot_leadtime_enc( df, entity_keys=["entity"], target_event="event1_Value", @@ -642,14 +642,14 @@ def test_leadtime_enc_missing_target_zero_column(self, fake_seismo, caplog): max_hours=48, x_label="Lead Time (hours)", ) - assert result is None + assert "Error" in result.data assert "Target event time-zero (event1_Time) not found" in caplog.text def test_leadtime_enc_no_positive_events(self, fake_seismo, caplog): df = fake_seismo.dataframe.copy() df["event1_Value"] = 0 # force all negative with caplog.at_level("ERROR"): - result = _plot_leadtime_enc( + result, _ = _plot_leadtime_enc( df, entity_keys=["entity"], target_event="event1_Value", @@ -662,7 +662,7 @@ def test_leadtime_enc_no_positive_events(self, fake_seismo, caplog): max_hours=48, x_label="Lead Time (hours)", ) - assert result is None + assert "Error" in result.data assert "No positive events (event1_Value=1) were found" in caplog.text @patch("seismometer.api.plots.pdh.event_score") @@ -673,7 +673,7 @@ def test_leadtime_enc_subgroup_filter_excludes_all(self, mock_render, mock_filte mock_filter.return_value = df[:0] mock_score.return_value = df[:0] - result = _plot_leadtime_enc( + result, _ = _plot_leadtime_enc( df, entity_keys=["entity"], target_event="event1_Value", @@ -714,7 +714,7 @@ def test_plot_trend_intervention_outcome_combines_both( self, mock_render, mock_plot, mock_event_value, fake_seismo ): fake_seismo.selected_cohort = ("Cohort", ["C1", "C2"]) - result = plot_trend_intervention_outcome() + result, _ = plot_trend_intervention_outcome() assert isinstance(result, HTML) assert "Outcome" in result.data assert "Intervention" in result.data @@ -728,7 +728,7 @@ def test_plot_trend_intervention_outcome_missing_intervention( self, mock_msg, mock_render, mock_plot, mock_event_value, fake_seismo ): fake_seismo.selected_cohort = ("Cohort", ["C1", "C2"]) - result = plot_trend_intervention_outcome() + result, _ = plot_trend_intervention_outcome() assert isinstance(result, HTML) assert "Missing Intervention" in result.data assert "Outcome" in result.data @@ -741,7 +741,7 @@ def test_plot_trend_intervention_outcome_missing_outcome( self, mock_msg, mock_render, mock_plot, mock_event_value, fake_seismo ): fake_seismo.selected_cohort = ("Cohort", ["C1", "C2"]) - result = plot_trend_intervention_outcome() + result, _ = plot_trend_intervention_outcome() assert isinstance(result, HTML) assert "Missing Outcome" in result.data assert "Intervention" in result.data diff --git a/tests/controls/test_explore.py b/tests/controls/test_explore.py index a5bb0cb5..5a1d6583 100644 --- a/tests/controls/test_explore.py +++ b/tests/controls/test_explore.py @@ -29,6 +29,8 @@ def test_init(self): assert widget.plot_button.disabled is False assert widget.code_checkbox.description == widget.SHOW_CODE assert widget.show_code is False + assert widget.data_checkbox.description == widget.SHOW_DATA + assert widget.show_data is False def test_plot_button_click(self): count = 0 @@ -68,15 +70,28 @@ def on_toggle_callback(button): widget.code_checkbox.value = True assert count == 1 + def test_toggle_data_checkbox(self): + count = 0 + + def on_toggle_callback(button): + nonlocal count + count += 1 + + widget = undertest.UpdatePlotWidget() + widget.on_toggle_data(on_toggle_callback) + widget.data_checkbox.value = True + assert count == 1 + class TestExplorationBaseClass: def test_base_class(self, caplog): option_widget = ipywidgets.Checkbox(description="ClickMe") - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) widget = undertest.ExplorationWidget("ExploreTest", option_widget, plot_function) plot_function.assert_not_called() - assert "Subclasses must implement this method" in widget._try_generate_plot().value + plot = widget._try_generate_plot() + assert "Subclasses must implement this method" in plot.value @pytest.mark.parametrize( "plot_module,plot_code", @@ -88,7 +103,7 @@ def test_base_class(self, caplog): ) def test_args_subclass(self, plot_module, plot_code): option_widget = ipywidgets.Checkbox(description="ClickMe") - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_something" plot_function.__module__ = plot_module @@ -103,11 +118,13 @@ def generate_plot_args(self) -> tuple[tuple, dict]: plot_function.assert_called_once_with(False) assert widget.current_plot_code == plot_code assert widget.show_code is False + assert widget.show_data is False assert widget._try_generate_plot() == "some result" + assert widget.current_plot_data == "some data" def test_kwargs_subclass(self): option_widget = ipywidgets.Checkbox(description="ClickMe") - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_something" plot_function.__module__ = "test_explore" @@ -122,11 +139,13 @@ def generate_plot_args(self) -> tuple[tuple, dict]: plot_function.assert_called_once_with(checkbox=False) assert widget.current_plot_code == "test_explore.plot_something(checkbox=False)" assert widget.show_code is False + assert widget.show_data is False assert widget._try_generate_plot() == "some result" + assert widget.current_plot_data == "some data" def test_args_kwargs_subclass(self): option_widget = ipywidgets.Checkbox(description="ClickMe") - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_something" plot_function.__module__ = "test_explore" @@ -141,7 +160,9 @@ def generate_plot_args(self) -> tuple[tuple, dict]: plot_function.assert_called_once_with("test", checkbox=False) assert widget.current_plot_code == "test_explore.plot_something('test', checkbox=False)" assert widget.show_code is False + assert widget.show_data is False assert widget._try_generate_plot() == "some result" + assert widget.current_plot_data == "some data" def test_exception_plot_code_subclass(self): option_widget = ipywidgets.Checkbox(description="ClickMe") @@ -160,10 +181,11 @@ def generate_plot_args(self) -> tuple[tuple, dict]: plot = widget._try_generate_plot() assert "Traceback" in plot.value assert "Test Exception" in plot.value + assert widget.current_plot_data is None def test_no_initial_plot_subclass(self): option_widget = ipywidgets.Checkbox(description="ClickMe") - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_something" plot_function.__module__ = "test_explore" @@ -178,13 +200,16 @@ def generate_plot_args(self) -> tuple[tuple, dict]: widget.center.outputs == [] plot_function.assert_not_called() assert widget.current_plot_code == ExploreFake.NO_CODE_STRING + assert widget.current_plot_data is None assert widget.show_code is False - widget._try_generate_plot() == "" + assert widget.show_data is False + assert widget._try_generate_plot() == "some result" + assert widget.current_plot_data == "some data" @pytest.mark.parametrize("show_code", [True, False]) def test_toggle_code_callback(self, show_code, capsys): option_widget = ipywidgets.Checkbox(description="ClickMe") - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_something" plot_function.__module__ = "test_explore" @@ -204,6 +229,29 @@ def generate_plot_args(self) -> tuple[tuple, dict]: code_in_output = "test_explore.plot_something" in stdout.split("\n")[-2] assert code_in_output == show_code + @pytest.mark.parametrize("show_data", [True, False]) + def test_toggle_data_callback(self, show_data, capsys): + option_widget = ipywidgets.Checkbox(description="ClickMe") + plot_function = Mock(return_value=("some result", "some data")) + plot_function.__name__ = "plot_something" + plot_function.__module__ = "test_explore" + + class ExploreFake(undertest.ExplorationWidget): + def __init__(self): + super().__init__("Fake Explorer", option_widget, plot_function) + + def generate_plot_args(self) -> tuple[tuple, dict]: + return ["test"], {"checkbox": self.option_widget.value} + + widget = ExploreFake() + widget.show_data = show_data + widget.data_output = MagicMock() + widget._on_plot_button_click() + stdout = capsys.readouterr().out + assert "some result" in stdout + data_in_output = "some data" in stdout.split("\n")[-2] + assert data_in_output == show_data + # endregion # region Test Model Options Widgets @@ -559,7 +607,7 @@ class TestExplorationSubpopulationWidget: def test_init(self, mock_seismo): fake_seismo = mock_seismo() fake_seismo.available_cohort_groups = {"C1": ["C1.1", "C1.2"], "C2": ["C2.1", "C2.2"]} - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -569,12 +617,13 @@ def test_init(self, mock_seismo): assert widget.update_plot_widget.disabled assert widget.current_plot_code == "test_explore.plot_function({})" plot_function.assert_called_once_with({}) # default value + assert widget.current_plot_data == "some data" @patch.object(seismogram, "Seismogram", return_value=Mock()) def test_option_update(self, mock_seismo): fake_seismo = mock_seismo() fake_seismo.available_cohort_groups = {"C1": ["C1.1", "C1.2"], "C2": ["C2.1", "C2.2"]} - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -593,6 +642,7 @@ def test_option_update(self, mock_seismo): ] } ) # updated value + assert widget.current_plot_data == "some data" class TestExplorationModelSubgroupEvaluationWidget: @@ -604,7 +654,7 @@ def test_init(self, mock_seismo): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -616,6 +666,7 @@ def test_init(self, mock_seismo): assert widget.update_plot_widget.disabled assert widget.current_plot_code == "test_explore.plot_function({}, 'T1', 'S1', [0.2, 0.1], per_context=False)" plot_function.assert_called_once_with({}, "T1", "S1", [0.2, 0.1], per_context=False) # default value + assert widget.current_plot_data == "some data" @patch.object(seismogram, "Seismogram", return_value=Mock()) def test_option_update(self, mock_seismo): @@ -625,7 +676,7 @@ def test_option_update(self, mock_seismo): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -636,6 +687,7 @@ def test_option_update(self, mock_seismo): widget.option_widget.cohort_list.value = {"C2": ("C2.1",)} widget.update_plot() plot_function.assert_called_with({"C2": ("C2.1",)}, "T1", "S1", [0.2, 0.1], per_context=False) # updated value + assert widget.current_plot_data == "some data" class TestExplorationCohortSubclassEvaluationWidget: @@ -651,7 +703,7 @@ def test_init(self, mock_seismo, threshold_handling, thresholds): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -673,6 +725,7 @@ def test_init(self, mock_seismo, threshold_handling, thresholds): "C1", ("C1.1", "C1.2"), "T1", "S1", per_context=False ) # default value assert widget.current_plot_code == expected_code + assert widget.current_plot_data == "some data" class TestExplorationCohortOutcomeInterventionEvaluationWidget: @@ -688,7 +741,7 @@ def test_init(self, mock_seismo): ) fake_seismo.config = fake_config - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -702,6 +755,7 @@ def test_init(self, mock_seismo): widget.current_plot_code == "test_explore.plot_function('C1', ('C1.1', 'C1.2'), 'O1', 'I1', 'pred_time')" ) plot_function.assert_called_once_with("C1", ("C1.1", "C1.2"), "O1", "I1", "pred_time") # default value + assert widget.current_plot_data == "some data" class TestExplorationScoreComparisonByCohortWidget: @@ -713,7 +767,7 @@ def test_init(self, mock_seismo): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -725,6 +779,7 @@ def test_init(self, mock_seismo): assert widget.update_plot_widget.disabled assert widget.current_plot_code == "test_explore.plot_function({}, 'T1', ('S1', 'S2'), per_context=False)" plot_function.assert_called_once_with({}, "T1", ("S1", "S2"), per_context=False) # default value + assert widget.current_plot_data == "some data" class TestExplorationTargetComparisonByCohortWidget: @@ -736,7 +791,7 @@ def test_init(self, mock_seismo): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -748,6 +803,7 @@ def test_init(self, mock_seismo): assert widget.update_plot_widget.disabled assert widget.current_plot_code == "test_explore.plot_function({}, ('T1', 'T2'), 'S1', per_context=False)" plot_function.assert_called_once_with({}, ("T1", "T2"), "S1", per_context=False) # default value + assert widget.current_plot_data == "some data" # endregion @@ -804,7 +860,7 @@ def test_init(self, mock_seismo): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -823,6 +879,7 @@ def test_init(self, mock_seismo): plot_function.assert_called_once_with( metric_generator, ("Precision", "Recall"), {}, "T1", "S1", per_context=False ) + assert widget.current_plot_data == "some data" @patch.object(seismogram, "Seismogram", return_value=Mock()) def test_option_update(self, mock_seismo): @@ -833,7 +890,7 @@ def test_option_update(self, mock_seismo): fake_seismo.target_cols = ["T1", "T2"] fake_seismo.output_list = ["S1", "S2"] - plot_function = Mock(return_value="some result") + plot_function = Mock(return_value=("some result", "some data")) plot_function.__name__ = "plot_function" plot_function.__module__ = "test_explore" @@ -853,6 +910,7 @@ def test_option_update(self, mock_seismo): + "('F1',), {}, 'T2', 'S2', per_context=False)" ) plot_function.assert_called_with(metric_generator, ("F1",), {}, "T2", "S2", per_context=False) + assert widget.current_plot_data == "some data" class TestExploreBinaryModelAnalytics: