plot slices w/ one pint

Author: a-kore

cyclops/report/templates/cyclops_generic_template.jinja

@@ -270,15 +270,15 @@
 
 {% macro render_overview(name, comp) %}
 {{ render_perf(name, comp) }}
-{% set val = {'history': false} %}
+{# {% set val = {'history': false} %}
 {% for metric_card in comp.metric_cards.collection%}
     {% if metric_card.history|length > 1 %}
       {% set _ = val.update({'history':true}) %}
     {% endif %}
 {% endfor %}
-{% if val['history'] %}
-  {{ render_perf_over_time(name, comp) }}
-{% endif %}
+{% if val['history'] %} #}
+{{ render_perf_over_time(name, comp) }}
+{# {% endif %} #}
 {% endmacro %}
 
 {% macro render_objects(section)%}

docs/source/tutorials/kaggle/heart_failure_prediction.ipynb

@@ -1126,17 +1126,6 @@
     "fairness_plot.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Performance over time\n",
-    "\n",
-    "We can monitor the change of performance metrics over time by leveraging historical reports. This involves using the `get_metrics_trends` function to gather prior metrics and merge them with recent results. We can specify which metrics and slices we wish to observe. Once the data is collected, we generate the plot using the `metrics_trends` method from the plotter, which can then be integrated into the report.\n",
-    "\n",
-    "Please note, for the purpose of this tutorial, we will create three dummy reports to demonstrate the process of plotting these metric trends."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},