Fix/MetricsApp display (#169)

- Fixed `MetricsApp` `display` method
- Added `nbformat` to dependencies

Author: Gooogr

CHANGELOG.md

@@ -10,6 +10,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - `Debias` mechanism for classification, ranking and auc metrics. New parameter `is_debiased` to `calc_from_confusion_df`, `calc_per_user_from_confusion_df` methods of classification metrics, `calc_from_fitted`, `calc_per_user_from_fitted` methods of auc and rankning (`MAP`) metrics, `calc_from_merged`, `calc_per_user_from_merged` methods of ranking (`NDCG`, `MRR`) metrics. ([#152](https://github.com/MobileTeleSystems/RecTools/pull/152))
+- `nbformat >= 4.2.0` dependency to `[visuals]` extra ([#169](https://github.com/MobileTeleSystems/RecTools/pull/169))
+
+### Fixed
+- `display()` method in `MetricsApp` ([#169](https://github.com/MobileTeleSystems/RecTools/pull/169))
 
 ## [0.7.0] - 29.07.2024
 

CONTRIBUTING.rst

@@ -33,7 +33,7 @@ Pull Request Process
 #. Create a virtual environment and install dependencies including all 
    extras and development dependencies.
 
-   #. Make sure you have ``python3`` and ``poetry==1.4.0`` installed
+   #. Make sure you have ``python>=3.8`` and ``poetry>=1.5.0`` installed
    #. Deactivate any active virtual environments. Deactivate conda ``base``
       environment if applicable
    #. Run ``make install`` command which will create a virtual env and

README.md

@@ -125,7 +125,7 @@ See [recommender baselines extended tutorial](https://github.com/MobileTeleSyste
 [Contributing guide](CONTRIBUTING.rst)
 
 To install all requirements
-- you must have `python3` and `poetry==1.4.0` installed
+- you must have `python>=3.8` and `poetry>=1.5.0` installed
 - make sure you have no active virtual environments (deactivate conda `base` if applicable)
 - run
 ```

poetry.lock

@@ -84,18 +84,19 @@ pytorch-lightning = {version = ">=1.6.0, <3.0.0", optional = true}
 
 ipywidgets = {version = ">=7.7,<8.2", optional = true}
 plotly = {version="^5.22.0", optional = true}
+nbformat = {version = ">=4.2.0", optional = true}
 
 
 [tool.poetry.extras]
 lightfm = ["rectools-lightfm"]
 nmslib = ["nmslib"]
 torch = ["torch", "pytorch-lightning"]
-visuals = ["ipywidgets", "plotly"]
+visuals = ["ipywidgets", "plotly", "nbformat"]
 all = [
     "rectools-lightfm",
     "nmslib", 
     "torch", "pytorch-lightning",
-    "ipywidgets", "plotly",
+    "ipywidgets", "plotly", "nbformat",
 ]
 
 

pyproject.toml

@@ -337,14 +337,10 @@ def display(self) -> None:
             options=self.meta_names,
         )
 
-        # Initialize go.FigureWidget initial chart state
-        if not self.fig.data:
-            chart_data = self._create_chart_data(use_avg, fold_i)
-            legend_title = f"{meta_feature.value}, {DEFAULT_LEGEND_TITLE}" if use_meta.value else DEFAULT_LEGEND_TITLE
-            self.fig = self._create_chart_figure(
-                chart_data, metric_x.value, metric_y.value, Columns.Model, legend_title
-            )
-            fig_widget = go.FigureWidget(data=self.fig.data, layout=self.fig.layout)
+        chart_data = self._create_chart_data(use_avg, fold_i)
+        legend_title = f"{meta_feature.value}, {DEFAULT_LEGEND_TITLE}" if use_meta.value else DEFAULT_LEGEND_TITLE
+        self.fig = self._create_chart_figure(chart_data, metric_x.value, metric_y.value, Columns.Model, legend_title)
+        fig_widget = go.FigureWidget(data=self.fig.data, layout=self.fig.layout)
 
         def update(event: tp.Callable[..., tp.Any]) -> None:  # pragma: no cover
             self._update_figure_widget(fig_widget, metric_x, metric_y, use_avg, fold_i, meta_feature, use_meta)

rectools/visuals/metrics_app.py

@@ -13,7 +13,6 @@
 #  limitations under the License.
 
 import typing as tp
-from unittest.mock import MagicMock, patch
 
 import pandas as pd
 import pytest
@@ -97,27 +96,13 @@ def test_happy_path(
         show_legend: bool,
         auto_display: bool,
         scatter_kwargs: tp.Optional[tp.Dict[str, tp.Any]],
-    ) -> None:
-        with patch("rectools.visuals.metrics_app.MetricsApp.display", MagicMock()):
-            app = MetricsApp.construct(
-                models_metrics=models_metrics,
-                models_metadata=model_metadata,
-                show_legend=show_legend,
-                auto_display=auto_display,
-                scatter_kwargs=scatter_kwargs,
-            )
-            _ = app.fig
-
-    @pytest.mark.parametrize("model_metadata", (None, DF_METAINFO))
-    def test_display(
-        self,
-        model_metadata: tp.Optional[pd.DataFrame],
     ) -> None:
         app = MetricsApp.construct(
-            models_metrics=DF_METRICS,
+            models_metrics=models_metrics,
             models_metadata=model_metadata,
-            show_legend=True,
-            auto_display=False,
+            show_legend=show_legend,
+            auto_display=auto_display,
+            scatter_kwargs=scatter_kwargs,
         )
         app.display()