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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Manual CPE matching evaluation\n",
"\n",
"This notebook assists the manual matching evaluation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sec_certs.dataset import CCDataset\n",
"import pandas as pd\n",
"import json\n",
"import tempfile\n",
"from sec_certs.utils.label_studio_utils import to_label_studio_json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare the input data for label studio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading CC Dataset: 100%|██████████| 139M/139M [00:15<00:00, 9.61MB/s] \n"
]
}
],
"source": [
"dset = CCDataset.from_web()\n",
"df = dset.to_pandas()\n",
"\n",
"eval_digests = pd.read_csv(\"./../../data/cpe_eval/random.csv\", sep=\";\").set_index(\"dgst\").index\n",
"eval_certs = df.loc[(df.index.isin(eval_digests)) & (df.cpe_matches.notnull())].copy()\n",
"\n",
"# It may be handy to display max number of assigned cpe matches here\n",
"eval_certs[\"n_cpes\"] = eval_certs.cpe_matches.map(len)\n",
"max_n_cpes = eval_certs.n_cpes.max()\n",
"print(f\"Max CPE matches: {max_n_cpes}\")\n",
"\n",
"# Now you may want to adjust the key `cpe_n_max_matches` config in sec_certs/config/settings.yml according to max_n_cpes\n",
"# This helps to avoid clutter in label studio interface\n",
"with tempfile.TemporaryDirectory() as tmp_dir:\n",
" dset.root_dir = tmp_dir\n",
" dset.certs = {x.dgst: x for x in dset if x.dgst in eval_certs.index.tolist()}\n",
" to_label_studio_json(dset, \"./label_studio_input_data.json\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Now you import this data to label studio and label it`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data from label studio and show the results"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluated 607 CPE matches in 100 certificates\n",
"In total, 546 (89.95%) are correct (precision of the positive class).\n",
"Also, 81 (81.00%) certificates have perfect matches.\n",
"\\newcommand{\\evalCcPrecision}{$89.95\\%$}\n",
"\\newcommand{\\evalCcRatioErrorFree}{$81\\%$}\n"
]
}
],
"source": [
"with open(\"./../../data/cpe_eval/manual_cpe_labels.json\", \"r\") as handle:\n",
" data = json.load(handle)\n",
"\n",
"results = []\n",
"for sample in data:\n",
" option_keys = [key for key in sample.keys() if \"option_\" in key]\n",
" n_cpe_matches = len([sample[key] for key in option_keys if sample[key] != \"No good match\"])\n",
"\n",
" if not \"verified_cpe_match\" in sample.keys():\n",
" n_wrong = 0\n",
" elif isinstance(sample[\"verified_cpe_match\"], str):\n",
" n_wrong = 1\n",
" else:\n",
" n_wrong = len(sample[\"verified_cpe_match\"][\"choices\"])\n",
"\n",
" results.append((n_cpe_matches, n_wrong))\n",
"\n",
"correct = [x[0] - x[1] for x in results]\n",
"wrong = [x[1] for x in results]\n",
"n_candidates = [x[0] for x in results]\n",
"completely_right = [x == 0 for x in wrong]\n",
"\n",
"precision = 100 * sum(correct) / sum(n_candidates)\n",
"completely_right_ratio = 100 * sum(completely_right) / len(n_candidates)\n",
"\n",
"print(f\"Evaluated {sum(n_candidates)} CPE matches in {len(results)} certificates\")\n",
"print(f\"In total, {sum(correct)} ({precision:.2f}%) are correct (precision of the positive class).\")\n",
"print(f\"Also, {sum(completely_right)} ({completely_right_ratio:.2f}%) certificates have perfect matches.\")\n",
"\n",
"print(f\"\\\\newcommand{{\\\\evalCcPrecision}}{{${precision:.2f}\\%$}}\")\n",
"print(f\"\\\\newcommand{{\\\\evalCcRatioErrorFree}}{{${completely_right_ratio:.0f}\\%$}}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.13 ('venv': venv)",
"language": "python",
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},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "a5b8c5b127d2cfe5bc3a1c933e197485eb9eba25154c3661362401503b4ef9d4"
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}
|
