add plots back to quant-analysis section (#512)

* add plots back to quant-analysis section * add check to see if graphics is not none
VectorInstitute · Nov 24, 2023 · b916535 · b916535
1 parent b19bd76
commit b916535
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diff --git a/cyclops/report/templates/cyclops_generic_template.jinja b/cyclops/report/templates/cyclops_generic_template.jinja
@@ -337,6 +337,14 @@
           {{ render_metric_card(metric_card) }}
         {% endif %}
       {% endfor %}
+      {% if section is hasattr "graphics"%}
+        {% if section.graphics is not none%}
+          <div class="subcard" style="min-width:1400px;">
+            <h3>{{ "Graphics" }}</h3>
+            {{ render_if_exist_list(section.graphics) }}
+          </div>
+        {% endif %}
+      {% endif %}
     {% else %}
       {{ render_objects(section) }}
     {% endif %}

diff --git a/docs/source/tutorials/kaggle/heart_failure_prediction.ipynb b/docs/source/tutorials/kaggle/heart_failure_prediction.ipynb
@@ -1232,49 +1232,11 @@
     "shutil.copy(f\"{report_path}\", \".\")\n",
     "for i in range(4):\n",
     "    report._model_card.overview = None\n",
-    "    report._model_card.quantitative_analysis = None\n",
-    "    results_flat = flatten_results_dict(\n",
-    "        results=results,\n",
-    "        remove_metrics=[\"BinaryROCCurve\", \"BinaryPrecisionRecallCurve\"],\n",
-    "        model_name=model_name,\n",
-    "    )\n",
-    "\n",
-    "    for name, metric in results_female_flat.items():\n",
-    "        split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "        descriptions = {\n",
-    "            \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "            \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "            \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "            \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "            \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "        }\n",
-    "        report.log_quantitative_analysis(\n",
-    "            \"performance\",\n",
-    "            name=name,\n",
-    "            value=np.clip(metric + np.random.normal(0, 0.1), 0, 1),\n",
-    "            description=descriptions[name],\n",
-    "            metric_slice=split,\n",
-    "            pass_fail_thresholds=0.7,\n",
-    "            pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
-    "        )\n",
-    "\n",
-    "    for name, metric in results_flat.items():\n",
-    "        split, name = name.split(\"/\")  # noqa: PLW2901\n",
-    "        descriptions = {\n",
-    "            \"BinaryPrecision\": \"The proportion of predicted positive instances that are correctly predicted.\",\n",
-    "            \"BinaryRecall\": \"The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.\",\n",
-    "            \"BinaryAccuracy\": \"The proportion of all instances that are correctly predicted.\",\n",
-    "            \"BinaryAUROC\": \"The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.\",\n",
-    "            \"BinaryF1Score\": \"The harmonic mean of precision and recall.\",\n",
-    "        }\n",
-    "        report.log_quantitative_analysis(\n",
-    "            \"performance\",\n",
-    "            name=name,\n",
-    "            value=np.clip(metric + np.random.normal(0, 0.1), 0, 1),\n",
-    "            description=descriptions[name],\n",
-    "            metric_slice=split,\n",
-    "            pass_fail_thresholds=0.7,\n",
-    "            pass_fail_threshold_fns=lambda x, threshold: bool(x >= threshold),\n",
+    "    for metric in report._model_card.quantitative_analysis.performance_metrics:\n",
+    "        metric.value = np.clip(\n",
+    "            metric.value + np.random.normal(0, 0.1),\n",
+    "            0,\n",
+    "            1,\n",
     "        )\n",
     "    report_path = report.export(\n",
     "        output_filename=\"heart_failure_report_periodic.html\",\n",