evaluate_eigd-h.ipynb

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   "source": [
    "import pandas as pd\n",
    "\n",
    "from utils.scenes import get_annotator_handball_scenes\n",
    "from utils.validation import perform_full_validation\n",
    "from utils.eval import (\n",
    "    format_nnm_output,\n",
    "    format_scm_output,\n",
    "    result_records2df,\n",
    "    stack_df_nnm_scm,\n",
    ")\n",
    "\n",
    "# read data\n",
    "df = pd.read_json(\"data/eigd/handball/event_annotations.jsonl\", lines=True, orient=\"records\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# extract annotator sequences\n",
    "sequences_a = get_annotator_handball_scenes(df, \"A\")\n",
    "sequences_b = get_annotator_handball_scenes(df, \"B\")\n",
    "sequences_c = get_annotator_handball_scenes(df, \"C\")\n",
    "\n",
    "# define predictions and references\n",
    "# in this case FP=FN -> precision == recall as predictions == references\n",
    "predictions = [sequences_a, sequences_b, sequences_c]\n",
    "references = [sequences_a, sequences_b, sequences_c]\n",
    "\n",
    "\n",
    "# performance for annotator A vs mean expert (A excluded)\n",
    "# predictions = [sequences_a]  \n",
    "# references = [sequences_b, sequences_c]\n",
    "\n",
    "# validate\n",
    "df_results = result_records2df(perform_full_validation(predictions, references))\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_nnm = format_nnm_output(df_results)\n",
    "df_results_scm = format_scm_output(df_results)\n",
    "df_nnm_scm = stack_df_nnm_scm(df_results_nnm, df_results_scm)\n",
    "df_nnm_scm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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    "df_nnm_scm[\n",
    "    [\n",
    "        (\"NNM\", \"precision\"),\n",
    "        (\"NNM\", \"recall\"),\n",
    "        (\"SCM\", \"precision\"),\n",
    "        (\"SCM\", \"recall\"),\n",
    "        (\"SCM\", \"SQC_frac\"),\n",
    "    ]\n",
    "]"
   ]
  },
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