Pushing the docs to dev/ for branch: main, commit b7f3fd6887c56138a21a9151105694945a5f5377

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -102,3 +102,27 @@
 print(f"Confusion matrix:\n{disp.confusion_matrix}")
 
 plt.show()
+
+###############################################################################
+# If the results from evaluating a classifier are stored in the form of a
+# :ref:`confusion matrix <confusion_matrix>` and not in terms of `y_true` and
+# `y_pred`, one can still build a :func:`~sklearn.metrics.classification_report`
+# as follows:
+
+
+# The ground truth and predicted lists
+y_true = []
+y_pred = []
+cm = disp.confusion_matrix
+
+# For each cell in the confusion matrix, add the corresponding ground truths
+# and predictions to the lists
+for gt in range(len(cm)):
+    for pred in range(len(cm)):
+        y_true += [gt] * cm[gt][pred]
+        y_pred += [pred] * cm[gt][pred]
+
+print(
+    "Classification report rebuilt from confusion matrix:\n"
+    f"{metrics.classification_report(y_true, y_pred)}\n"
+)

dev/_downloads/1a55101a8e49ab5d3213dadb31332045/plot_digits_classification.py

@@ -118,6 +118,24 @@
       "source": [
         "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\ndisp.figure_.suptitle(\"Confusion Matrix\")\nprint(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n\nplt.show()"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If the results from evaluating a classifier are stored in the form of a\n`confusion matrix <confusion_matrix>` and not in terms of `y_true` and\n`y_pred`, one can still build a :func:`~sklearn.metrics.classification_report`\nas follows:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# The ground truth and predicted lists\ny_true = []\ny_pred = []\ncm = disp.confusion_matrix\n\n# For each cell in the confusion matrix, add the corresponding ground truths\n# and predictions to the lists\nfor gt in range(len(cm)):\n    for pred in range(len(cm)):\n        y_true += [gt] * cm[gt][pred]\n        y_pred += [pred] * cm[gt][pred]\n\nprint(\n    \"Classification report rebuilt from confusion matrix:\\n\"\n    f\"{metrics.classification_report(y_true, y_pred)}\\n\"\n)"
+      ]
     }
   ],
   "metadata": {