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Author: brainless-bot[bot]

panoptica/example_aggregation.ipynb

@@ -122,37 +122,45 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "reference_array = np.array([\n",
-    "    [0,1,0,0,0],\n",
-    "    [1,1,0,0,0],\n",
-    "    [0,0,0,2,0],\n",
-    "    [0,0,2,2,2],\n",
-    "    [0,0,0,2,0],\n",
-    "])\n",
+    "reference_array = np.array(\n",
+    "    [\n",
+    "        [0, 1, 0, 0, 0],\n",
+    "        [1, 1, 0, 0, 0],\n",
+    "        [0, 0, 0, 2, 0],\n",
+    "        [0, 0, 2, 2, 2],\n",
+    "        [0, 0, 0, 2, 0],\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "# Lets give them names, in practice this should be sample/subject names for easy recognition\n",
     "predictions = {\n",
-    "    \"subject_perfect\": np.array([\n",
-    "        [0,1,0,0,0],\n",
-    "        [1,1,0,0,0],\n",
-    "        [0,0,0,2,0],\n",
-    "        [0,0,2,2,2],\n",
-    "        [0,0,0,2,0],\n",
-    "    ]),\n",
-    "    \"subject_horrible\": np.array([\n",
-    "        [0,0,0,0,0],\n",
-    "        [1,0,0,0,0],\n",
-    "        [0,0,0,0,2],\n",
-    "        [0,0,0,0,2],\n",
-    "        [0,0,0,0,2],\n",
-    "    ]),\n",
-    "    \"subject_overprediction\": np.array([\n",
-    "        [0,1,0,0,0],\n",
-    "        [1,1,1,0,0],\n",
-    "        [0,1,2,2,2],\n",
-    "        [0,0,2,2,2],\n",
-    "        [0,0,0,2,2],\n",
-    "    ]),\n",
+    "    \"subject_perfect\": np.array(\n",
+    "        [\n",
+    "            [0, 1, 0, 0, 0],\n",
+    "            [1, 1, 0, 0, 0],\n",
+    "            [0, 0, 0, 2, 0],\n",
+    "            [0, 0, 2, 2, 2],\n",
+    "            [0, 0, 0, 2, 0],\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"subject_horrible\": np.array(\n",
+    "        [\n",
+    "            [0, 0, 0, 0, 0],\n",
+    "            [1, 0, 0, 0, 0],\n",
+    "            [0, 0, 0, 0, 2],\n",
+    "            [0, 0, 0, 0, 2],\n",
+    "            [0, 0, 0, 0, 2],\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"subject_overprediction\": np.array(\n",
+    "        [\n",
+    "            [0, 1, 0, 0, 0],\n",
+    "            [1, 1, 1, 0, 0],\n",
+    "            [0, 1, 2, 2, 2],\n",
+    "            [0, 0, 2, 2, 2],\n",
+    "            [0, 0, 0, 2, 2],\n",
+    "        ]\n",
+    "    ),\n",
     "}"
    ]
   },
@@ -182,10 +190,13 @@
    "source": [
     "# let's calculate one sample as usual\n",
     "\n",
-    "# Let's define that label 1 and 2 (see arrays above) should be treated as different groups \n",
+    "# Let's define that label 1 and 2 (see arrays above) should be treated as different groups\n",
     "from panoptica.utils import SegmentationClassGroups, LabelGroup\n",
+    "\n",
     "# (in practice, this could be different classes of labels, instead of multiple instances of the same class)\n",
-    "segmentation_class_groups = SegmentationClassGroups({\"Structure1\": LabelGroup(1), \"Structure2\": LabelGroup(2)})\n",
+    "segmentation_class_groups = SegmentationClassGroups(\n",
+    "    {\"Structure1\": LabelGroup(1), \"Structure2\": LabelGroup(2)}\n",
+    ")\n",
     "\n",
     "evaluator = Panoptica_Evaluator(\n",
     "    expected_input=InputType.MATCHED_INSTANCE,\n",
@@ -291,7 +302,11 @@
     "import os\n",
     "\n",
     "output_file = str(Path(os.path.abspath(\"\")).parent.joinpath(\"example_aggregation.tsv\"))\n",
-    "aggregator = Panoptica_Aggregator(panoptica_evaluator=evaluator, output_file=output_file, log_times=True,)\n",
+    "aggregator = Panoptica_Aggregator(\n",
+    "    panoptica_evaluator=evaluator,\n",
+    "    output_file=output_file,\n",
+    "    log_times=True,\n",
+    ")\n",
     "print(aggregator.evaluation_metrics)"
    ]
   },
@@ -486,7 +501,9 @@
    ],
    "source": [
     "# get a summary dictionary across groups and metrics\n",
-    "statistics_obj.get_summary_dict(include_across_group=False) # we set this to false because we only have on group, otherwise this would yield also the averages across all groups"
+    "statistics_obj.get_summary_dict(\n",
+    "    include_across_group=False\n",
+    ")  # we set this to false because we only have on group, otherwise this would yield also the averages across all groups"
    ]
   },
   {
@@ -2425,13 +2442,14 @@
    "source": [
     "# We can also make plots over multiple statistics objects (usually reflecting different algorithms/predictors)\n",
     "from panoptica.panoptica_statistics import make_curve_over_setups\n",
+    "\n",
     "# we simulate this by multiplying our statistics object\n",
     "make_curve_over_setups(\n",
-    "    statistics_dict = {\n",
+    "    statistics_dict={\n",
     "        \"algorithm1\": statistics_obj,\n",
     "        \"algorithm2\": statistics_obj,\n",
     "        \"algorithm3\": statistics_obj,\n",
-    "        },\n",
+    "    },\n",
     "    metric=\"global_bin_dsc\",\n",
     ")\n",
     "# of course, as we use the same statistic object multiple times, each pair of bars is identical"