diff options
| -rw-r--r-- | notebooks/cc/references.ipynb | 4072 | ||||
| -rw-r--r-- | notebooks/fixed_sankey_plot.py | 62 | ||||
| -rw-r--r-- | pyproject.toml | 1 | ||||
| -rw-r--r-- | src/sec_certs/configuration.py | 3 | ||||
| -rw-r--r-- | src/sec_certs/model/references_nlp/annotator_trainer.py | 3 | ||||
| -rw-r--r-- | src/sec_certs/model/references_nlp/segment_extractor.py | 3 |
6 files changed, 3859 insertions, 285 deletions
diff --git a/notebooks/cc/references.ipynb b/notebooks/cc/references.ipynb index b031af06..fc145799 100644 --- a/notebooks/cc/references.ipynb +++ b/notebooks/cc/references.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" @@ -25,93 +25,59 @@ }, "outputs": [], "source": [ - "from __future__ import annotations\n", + "import warnings\n", + "from pathlib import Path\n", + "from typing import Iterable\n", + "\n", + "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "import networkx.algorithms.community as nx_comm\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.ticker as mtick\n", - "from sec_certs.dataset.cc import CCDataset\n", - "from sec_certs.model.references.reference_finder import ReferenceFinder\n", - "from sec_certs.sample.cc_certificate_id import CertificateId\n", + "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", - "import numpy as np\n", - "from tqdm import tqdm\n", - "from pathlib import Path\n", - "from typing import Literal\n", - "import warnings\n", "from notebooks.fixed_sankey_plot import sankey\n", - "from typing import Iterable\n", + "from tqdm import tqdm\n", + "\n", + "from sec_certs.dataset.cc import CCDataset\n", + "\n", "# Surpress user warnings\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)\n", "\n", "%matplotlib inline\n", "\n", "# matplotlib.use(\"pgf\")\n", - "sns.set_theme(style='white')\n", + "sns.set_theme(style=\"white\")\n", "plt.rcParams[\"axes.linewidth\"] = 0.5\n", "plt.rcParams[\"legend.fontsize\"] = 6.5\n", "plt.rcParams[\"xtick.labelsize\"] = 8\n", "plt.rcParams[\"ytick.labelsize\"] = 8\n", "plt.rcParams[\"ytick.left\"] = True\n", - "plt.rcParams['ytick.major.size'] = 5\n", - "plt.rcParams['ytick.major.width'] = 0.5\n", - "plt.rcParams['ytick.major.pad'] = 0\n", + "plt.rcParams[\"ytick.major.size\"] = 5\n", + "plt.rcParams[\"ytick.major.width\"] = 0.5\n", + "plt.rcParams[\"ytick.major.pad\"] = 0\n", "plt.rcParams[\"xtick.bottom\"] = True\n", - "plt.rcParams['xtick.major.size'] = 5\n", - "plt.rcParams['xtick.major.width'] = 0.5\n", - "plt.rcParams['xtick.major.pad'] = 0\n", + "plt.rcParams[\"xtick.major.size\"] = 5\n", + "plt.rcParams[\"xtick.major.width\"] = 0.5\n", + "plt.rcParams[\"xtick.major.pad\"] = 0\n", "# plt.rcParams[\"pgf.texsystem\"] = \"pdflatex\"\n", "# plt.rcParams[\"font.family\"] = \"serif\"\n", "# plt.rcParams[\"text.usetex\"] = True\n", "# plt.rcParams[\"pgf.rcfonts\"] = False\n", "plt.rcParams[\"axes.titlesize\"] = 8\n", "plt.rcParams[\"legend.handletextpad\"] = 0.3\n", - "plt.rcParams['lines.markersize'] = 4\n", - "plt.rcParams['savefig.pad_inches'] = 0.01\n", + "plt.rcParams[\"lines.markersize\"] = 4\n", + "plt.rcParams[\"savefig.pad_inches\"] = 0.01\n", "sns.set_palette(\"deep\")\n", "\n", - "#plt.style.use(\"seaborn-whitegrid\")\n", - "#sns.set_palette(\"deep\")\n", - "#sns.set_context(\"notebook\") # Set to \"paper\" for use in paper :)\n", + "# plt.style.use(\"seaborn-whitegrid\")\n", + "# sns.set_palette(\"deep\")\n", + "# sns.set_context(\"notebook\") # Set to \"paper\" for use in paper :)\n", "\n", - "#plt.rcParams['figure.figsize'] = (10, 6)\n", + "# plt.rcParams['figure.figsize'] = (10, 6)\n", "\n", "RESULTS_DIR = Path(\"./results/references\")\n", "RESULTS_DIR.mkdir(exist_ok=True, parents=True)\n", - "\n", - "# The snippet below stores references to unknown certificate IDs for further analysis if required.\n", - "\n", - "# def ref_lookup(kw_attr):\n", - "# def func(cert):\n", - "# kws = getattr(cert.pdf_data, kw_attr)\n", - "# if not kws:\n", - "# return set()\n", - "# res = set()\n", - "# for scheme, matches in kws[\"cc_cert_id\"].items():\n", - "# for match in matches:\n", - "# try:\n", - "# canonical = CertificateId(scheme, match).canonical\n", - "# res.add(canonical)\n", - "# except Exception:\n", - "# res.add(match)\n", - "# return res\n", - "\n", - "# return func\n", - "\n", - "# report_finder = ReferenceFinder()\n", - "# report_finder.fit(dset.certs, lambda cert: cert.heuristics.cert_id, ref_lookup(\"report_keywords\"))\n", - "# report_unknown_references = report_finder.unknown_references\n", - "\n", - "# st_finder = ReferenceFinder()\n", - "# st_finder.fit(dset.certs, lambda cert: cert.heuristics.cert_id, ref_lookup(\"st_keywords\"))\n", - "# st_unknown_references = st_finder.unknown_references\n", - "\n", - "# # References are stored in `report_unknown_references` and `st_unknown_references` for further analysis\n", - "# Resolve all TODOs in this notebook\n", - "\n", - "SMARTCARD_CATEGORY = \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"" + "SMARTCARD_CATEGORY = \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"\n" ] }, { @@ -123,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -135,20 +101,17 @@ " \"\"\"\n", " Creates new columns with number of references for each certificate.\n", " \"\"\"\n", - " return (\n", - " df__.copy().assign(\n", - " n_refs=lambda df_: df_.directly_referencing.map(len_if_exists),\n", - " n_trans_refs=lambda df_: df_.indirectly_referencing.map(len_if_exists),\n", - " n_in_refs=lambda df_: df_.directly_referenced_by.map(len_if_exists),\n", - " n_in_trans_refs=lambda df_: df_.indirectly_referenced_by.map(len_if_exists),\n", - " ),\n", + " return df__.copy().assign(\n", + " n_refs=lambda df_: df_.refs.map(len_if_exists),\n", + " n_trans_refs=lambda df_: df_.trans_refs.map(len_if_exists),\n", + " n_in_refs=lambda df_: df_.in_refs.map(len_if_exists),\n", + " n_in_trans_refs=lambda df_: df_.in_trans_refs.map(len_if_exists),\n", " )\n", "\n", "\n", "def preprocess_cc_df(cc_df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Pre-processing run on the CC dataset for the sake of this notebook.\n", - " # TODO: Move this to .to_pandas() function\n", " \"\"\"\n", " return (\n", " cc_df.loc[cc_df.cert_id.notnull()]\n", @@ -171,41 +134,31 @@ " )\n", "\n", "\n", - "def compute_references(cc_df: pd.DataFrame, annotated_refs: pd.DataFrame, label: str | Iterable[str]) -> pd.DataFrame:\n", + "def compute_references(cc_df: pd.DataFrame, graph: nx.DiGraph, label: str | Iterable[str]) -> pd.DataFrame:\n", " \"\"\"\n", " Limits the columns with references to a given label.\n", " \"\"\"\n", - " sub_edges = [(u, v) for u, v, d in graph.edges(data=True) if d.get(\"reference_label\") == label]\n", + " label = label if isinstance(label, Iterable) else [label]\n", + " sub_edges = [(u, v) for u, v, d in graph.edges(data=True) if d.get(\"reference_label\") in label]\n", " subgraph = graph.edge_subgraph(sub_edges)\n", "\n", - " df = (\n", - " cc_df.copy()\n", - " .assign(\n", - " refs=lambda df_: df_.apply(\n", - " lambda row: set(subgraph.successors(row.cert_id)) if row.cert_id in subgraph else np.nan,\n", - " axis=1,\n", - " ),\n", - " trans_refs=lambda df_: df_.apply(\n", - " lambda row: set(nx.descendants(subgraph, row.cert_id)) if row.cert_id in subgraph else np.nan, axis=1\n", - " ),\n", - " in_refs=lambda df_: df_.apply(\n", - " lambda row: set(subgraph.predecessors(row.cert_id)) if row.cert_id in subgraph else np.nan,\n", - " axis=1,\n", - " ),\n", - " in_trans_refs=lambda df_: df_.apply(\n", - " lambda row: set(nx.ancestors(subgraph, row.cert_id)) if row.cert_id in subgraph else np.nan, axis=1\n", - " ),\n", - " )\n", - " .assign(\n", - " refs=lambda df_: df_.refs.map(lambda x: x if x else np.nan),\n", - " trans_refs=lambda df_: df_.trans_refs.map(lambda x: x if x else np.nan),\n", - " in_refs=lambda df_: df_.in_refs.map(lambda x: x if x else np.nan),\n", - " in_trans_refs=lambda df_: df_.in_trans_refs.map(lambda x: x if x else np.nan),\n", - " )\n", + " return cc_df.assign(\n", + " refs=lambda df_: df_.apply(\n", + " lambda row: set(subgraph.successors(row.cert_id)) if row.cert_id in subgraph else np.nan,\n", + " axis=1,\n", + " ),\n", + " trans_refs=lambda df_: df_.apply(\n", + " lambda row: set(nx.descendants(subgraph, row.cert_id)) if row.cert_id in subgraph else np.nan, axis=1\n", + " ),\n", + " in_refs=lambda df_: df_.apply(\n", + " lambda row: set(subgraph.predecessors(row.cert_id)) if row.cert_id in subgraph else np.nan,\n", + " axis=1,\n", + " ),\n", + " in_trans_refs=lambda df_: df_.apply(\n", + " lambda row: set(nx.ancestors(subgraph, row.cert_id)) if row.cert_id in subgraph else np.nan, axis=1\n", + " ),\n", " )\n", "\n", - " return df\n", - "\n", "\n", "def preprocess_refs_df(csv_path: str | Path, cc_df: pd.DataFrame) -> pd.DataFrame:\n", " return (\n", @@ -214,6 +167,7 @@ " .assign(cert_id=lambda df_: df_.dgst.map(cc_df.cert_id.to_dict()))\n", " )\n", "\n", + "\n", "def get_reference_graph_from_refs_df(refs_df: pd.DataFrame) -> nx.DiGraph:\n", " return nx.from_pandas_edgelist(\n", " refs_df,\n", @@ -221,49 +175,37 @@ " target=\"reference\",\n", " create_using=nx.DiGraph,\n", " edge_attr=[\"reference_label\"],\n", - " )" + " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load data" + "## Load data and compute reference graph" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "dset = CCDataset.from_json(\"/var/tmp/xjanovsk/certs/sec-certs/dataset/cc_final_run_may_23/dataset.json\")\n", + "dset = CCDataset.from_json(\"/Users/adam/phd/projects/certificates/sec-certs/dataset/cc_final_run_may_23/dataset.json\")\n", "cc_df = preprocess_cc_df(dset.to_pandas())\n", - "refs_df = preprocess_refs_df(\"/var/tmp/xjanovsk/certs/sec-certs/dataset/reference_prediction/predictions.csv\", cc_df)\n", - "\n", - "\n", + "refs_df = preprocess_refs_df(\"/Users/adam/Downloads/predictions.csv\", cc_df)\n", + "unique_labels = refs_df.reference_label.unique().tolist()\n", "\n", "# Load labeled reference graph as networkx directed graph\n", "graph = nx.from_pandas_edgelist(\n", - " annotated_references,\n", + " refs_df,\n", " source=\"cert_id\",\n", " target=\"canonical_reference_keyword\",\n", " edge_attr=\"reference_label\",\n", " create_using=nx.DiGraph,\n", ")\n", - "comp_edges = [(u, v) for u, v, d in graph.edges(data=True) if d.get(\"reference_label\") == \"COMPONENT_USED\"]\n", - "comp_subgraph = graph.edge_subgraph(comp_edges)\n", - "\n", - "# TODO: Replace references in df_id_rich by those obtained from the graph. They are more precise. Those from\n", - "# df_id_rich contain only report_references\n", - "\n", - "# all_nodes = comp_subgraph.nodes()\n", "\n", - "# for x in tqdm(all_nodes):\n", - "# desc = nx.descendants(comp_subgraph, x)\n", - "\n", - "# TODO: Examine cycles in component subgraph\n", - "# nx.simple_cycles(comp_subgraph))" + "cc_df = compute_reference_numbers(compute_references(cc_df, graph, unique_labels))\n" ] }, { @@ -275,26 +217,6 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "test = preprocess_cc_df(dset.to_pandas())" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "metadata": {}, - "outputs": [], - "source": [ - "test = limit_references_to_label(df_id_rich, graph, \"COMPONENT_USED\")\n", - "# test[\"n_refs\"] = test.refs.map(len_if_exists)\n", - "# test[\"n_trans_refs\"] = test.trans_refs.map(len_if_exists)" - ] - }, - { - "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], @@ -304,7 +226,7 @@ "# - The number of those references is computed in the function itself\n", "# - Each analytical method should have some tests at the end\n", "# - If some LaTeX output accompanies the computaiton, the function should return it as a string\n", - "# - Those are stored in a dictionary that keeps expanding" + "# - Those are stored in a dictionary that keeps expanding\n" ] }, { @@ -317,21 +239,1958 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of smartcard certificates that reference some other certificate: 1219 (71.33%)\n", - "Number of non-smartcard certificates that reference some other certificate: 334 (9.36%)\n", - "Total number of referencing certificates: 1553 (29.42%)\n" + "Number of smartcard certificates that reference some other certificate: 1205 (72.11%)\n", + "Number of non-smartcard certificates that reference some other certificate: 333 (9.43%)\n", + "Total number of referencing certificates: 1538 (29.57%)\n" ] }, { "data": { - "image/png": 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graph: nx.DiGraph) -> dict[str, str]:\n", - " df = df__.copy().assign(\n", - " has_refs = lambda df_: df_.directly_referencing.notnull()\n", - " ).pipe(compute_reference_numbers)\n", + " df = df__.copy().assign(has_refs=lambda df_: df_.refs.notnull()).pipe(compute_reference_numbers)\n", "\n", " n_ref_smartcards = df.loc[(df.has_refs) & (df.category == SMARTCARD_CATEGORY)].shape[0]\n", " n_ref_others = df.loc[(df.has_refs) & (df.category != SMARTCARD_CATEGORY)].shape[0]\n", - " \n", - " print(f\"Number of smartcard certificates that reference some other certificate: {n_ref_smartcards} ({100 * n_ref_smartcards / df.loc[df.category == SMARTCARD_CATEGORY].shape[0]:.2f}%)\")\n", - " print(f\"Number of non-smartcard certificates that reference some other certificate: {n_ref_others} ({100 * n_ref_others / df.loc[df.category != SMARTCARD_CATEGORY].shape[0]:.2f}%)\")\n", - " print(f\"Total number of referencing certificates: {n_ref_smartcards + n_ref_others} ({100 * (n_ref_smartcards + n_ref_others) / df.shape[0]:.2f}%)\")\n", + "\n", + " print(\n", + " f\"Number of smartcard certificates that reference some other certificate: {n_ref_smartcards} ({100 * n_ref_smartcards / df.loc[df.category == SMARTCARD_CATEGORY].shape[0]:.2f}%)\"\n", + " )\n", + " print(\n", + " f\"Number of non-smartcard certificates that reference some other certificate: {n_ref_others} ({100 * n_ref_others / df.loc[df.category != SMARTCARD_CATEGORY].shape[0]:.2f}%)\"\n", + " )\n", + " print(\n", + " f\"Total number of referencing certificates: {n_ref_smartcards + n_ref_others} ({100 * (n_ref_smartcards + n_ref_others) / df.shape[0]:.2f}%)\"\n", + " )\n", "\n", " df_melted = df[[\"n_refs\", \"n_trans_refs\", \"n_in_refs\", \"n_in_trans_refs\"]].melt()\n", - " df_melted[\"incoming\"] = df_melted.variable.map(lambda x: True if x.endswith(\"by\") else False)\n", + " df_melted[\"incoming\"] = df_melted.variable.map(lambda x: bool(x.endswith(\"by\")))\n", " sns.catplot(data=df_melted, kind=\"boxen\", x=\"variable\", y=\"value\", col=\"variable\", sharex=False, sharey=False)\n", " plt.savefig(RESULTS_DIR / \"boxen_plot_references.pdf\", bbox_inches=\"tight\")\n", "\n", @@ -372,7 +2235,8 @@ "\n", " return {}\n", "\n", - "compute_basic_reference_graph_stats(df_id_rich, graph)" + "\n", + "compute_basic_reference_graph_stats(cc_df, graph)\n" ] }, { @@ -385,96 +2249,1775 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_certs_top_reach(df__: pd.DataFrame, graph: nx.DiGraph) -> dict:\n", - " return {}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 89, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "dgst\n", - "9be76c10474e0c80 178\n", - "13c393fa406a72cb 120\n", - "2d835bff00514238 101\n", - "3984580c5700b85f 88\n", - "d12cc4580cb099f8 85\n", - "a4d0e44f4527180f 84\n", - "ab88698a0c6ee1bb 83\n", - "5e06be752f7bf01a 83\n", - "5cdef03a3004a6ff 83\n", - "8aca11dd8e33c964 80\n", - "Name: n_trans_referenced_by, dtype: int64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# TODO: Display cert_id, not digest\n", - "top_10 = df_id_rich.n_trans_referenced_by.sort_values(ascending=False).head(10)\n", - "top_10_cert_ids = df_id_rich.loc[top_10.index].cert_id\n", - "top_10" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + " cert_id n_in_trans_refs\n", + "dgst \n", + "2d835bff00514238 BSI-DSZ-CC-0891-V2-2016 9\n", + "9664c0f0ec6401b9 BSI-DSZ-CC-0891-V3-2018 5\n", + "b5b8de155e4e40e6 ANSSI-CC-2016/44 4\n", + "9dab3f1341f54c42 BSI-DSZ-CC-0555-2009 4\n", + "f041e5b526e79ef4 BSI-DSZ-CC-0410-2007 4\n", + "dd000f356b48904d BSI-DSZ-CC-0813-2012 3\n", + "5e06be752f7bf01a BSI-DSZ-CC-0404-2007 3\n", + "77acb3a4a3f58e0b ANSSI-CC-2015/80 3\n", + "dfc0e5c6e48f7c5a BSI-DSZ-CC-0837-V2-2014 3\n", + "981f7ea91d7bdcb6 ANSSI-CC-2014/86 3\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10/10 [01:29<00:00, 8.97s/it]\n" + "\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + " 0%| | 0/10 [09:40<?, ?it/s]\n", + " 0%| | 0/10 [08:46<?, ?it/s]\n", + " 0%| | 0/10 [08:30<?, ?it/s]\n", + " 0%| | 0/10 [05:03<?, ?it/s]\n", + " 0%| | 0/10 [06:37<?, ?it/s]\n", + "\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "\u001b[A\u001b[A\u001b[A\n", + "\n", + "\n", + "100%|██████████| 10/10 [01:18<00:00, 7.88s/it]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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& (date <= df_subset.not_valid_after)].shape[0]\n", - " return pd.Series(dct, name=cert_id)\n", + "# TODO: Check that it actually works, the data on small subset was fairly weird\n", + "# TODO: Work only on sub-component references?\n", + "def compute_certs_top_reach(df__: pd.DataFrame) -> dict:\n", + " def find_reach_over_time(df_: pd.DataFrame, cert_id: str, date_range: pd.DatetimeIndex) -> pd.Series:\n", + " df = df_.copy().loc[lambda df_: df_.in_trans_refs.apply(lambda x: pd.notnull(x) and cert_id in x)]\n", + " dct = {\n", + " date: df.loc[(date >= df.not_valid_before) & (date <= df.not_valid_after)].shape[0] for date in date_range\n", + " }\n", + " return pd.Series(dct, name=cert_id)\n", "\n", - "certs_with_top_reach = top_10_cert_ids.tolist()\n", - "date_range = pd.date_range(df_id_rich.not_valid_before.min(), df_id_rich.not_valid_before.max())\n", - "data = [find_reach_over_time(df_id_rich, x, date_range) for x in tqdm(certs_with_top_reach)]\n", - "df_reach_evolution = pd.concat(data, axis=1)\n", + " df = df__.copy()\n", + " top_10_certs = df.sort_values(by=\"n_in_trans_refs\", ascending=False).head(10)\n", + " print(top_10_certs[[\"cert_id\", \"n_in_trans_refs\"]])\n", + "\n", + " date_range = pd.date_range(df.not_valid_before.min(), df.not_valid_before.max())\n", + " data = [find_reach_over_time(df, x, date_range) for x in tqdm(top_10_certs.cert_id.tolist())]\n", + " df_reach_evolution_melted = (\n", + " pd.concat(data, axis=1)\n", + " .rename_axis(\"date\")\n", + " .reset_index()\n", + " .melt(id_vars=\"date\", var_name=\"certificate\", value_name=\"reach\")\n", + " )\n", "\n", - "df_reach_evolution.index.name = \"date\"\n", - "df_reach_evolution = df_reach_evolution.reset_index()\n", + " g = sns.lineplot(data=df_reach_evolution_melted, x=\"date\", y=\"reach\", hue=\"certificate\")\n", + " g.set(title=\"Reach of top-10 certificates in time\", xlabel=\"Time\", ylabel=\"Certificate reach\")\n", + " plt.savefig(RESULTS_DIR / \"lineplot_top_certificate_reach.pdf\", bbox_inches=\"tight\")\n", + " plt.show()\n", "\n", + " return {}\n", "\n", - "df_reach_evolution_melted = df_reach_evolution.melt(id_vars=\"date\", var_name=\"certificate\", value_name=\"reach\")\n", "\n", - "g = sns.lineplot(data=df_reach_evolution_melted, x=\"date\", y=\"reach\", hue=\"certificate\")\n", - "g.set(title=\"Reach of top-10 certificates in time\", xlabel=\"Time\", ylabel=\"Certificate reach\")\n", - "plt.savefig(RESULTS_DIR / \"lineplot_top_certificate_reach.pdf\", bbox_inches=\"tight\")\n", - "plt.show()" + "compute_certs_top_reach(cc_df)\n" ] }, { @@ -537,7 +4080,8 @@ " ][\"target_variable\"].mean()\n", " return dct\n", "\n", - "date_range = pd.date_range(df_id_rich.not_valid_before.min(), df_id_rich.not_valid_before.max())\n", + "\n", + "date_range = pd.date_range(df_id_rich.not_valid_before.min(), df_id_rich.not_valid_before.max())\n", "\n", "refs_smartcards = compute_avg_references(df_id_rich, True, \"directly_referencing\", date_range)\n", "trans_refs_smartcards = compute_avg_references(df_id_rich, True, \"indirectly_referencing\", date_range)\n", @@ -641,7 +4185,9 @@ "for date in tqdm(date_range):\n", " active_certs = df_id_rich.loc[(date >= df_id_rich.not_valid_before) & (date <= df_id_rich.not_valid_after)]\n", " dct_active_all[date] = active_certs.shape[0]\n", - " dct_isolated_all[date] = active_certs.loc[(active_certs.n_references == 0) & (active_certs.n_referenced_by == 0)].shape[0]\n", + " dct_isolated_all[date] = active_certs.loc[\n", + " (active_certs.n_references == 0) & (active_certs.n_referenced_by == 0)\n", + " ].shape[0]\n", " dct_active_others[date] = active_certs.loc[\n", " active_certs.category != \"ICs, Smart Cards and Smart Card-Related Devices and Systems\"\n", " ].shape[0]\n", @@ -691,11 +4237,14 @@ " pd.Series(dct_reference_rich_all, name=\"ref. rich certificates\"),\n", " pd.Series(dct_referenced_all, name=\"referenced certificates\"),\n", " pd.Series(dct_isolated_all, name=\"isolated certificates\"),\n", - " ], axis=1\n", + " ],\n", + " axis=1,\n", ")\n", "df_summary_references.index.name = \"date\"\n", "df_summary_references = df_summary_references.reset_index()\n", - "df_summary_references_melted = df_summary_references.melt(id_vars=[\"date\"], var_name=\"category\", value_name=\"number of certificates\")\n", + "df_summary_references_melted = df_summary_references.melt(\n", + " id_vars=[\"date\"], var_name=\"category\", value_name=\"number of certificates\"\n", + ")\n", "\n", "g = sns.lineplot(data=df_summary_references_melted, x=\"date\", y=\"number of certificates\", hue=\"category\", errorbar=None)\n", "g.set(\n", @@ -738,7 +4287,7 @@ " ylabel=\"Number of certificates\",\n", ")\n", "plt.savefig(RESULTS_DIR / \"lineplot_reference_ratio.pdf\", bbox_inches=\"tight\")\n", - "plt.show()" + "plt.show()\n" ] }, { @@ -951,7 +4500,7 @@ " title=f\"Countplot of {' '.join(col.split('_'))}\",\n", " )\n", " countplot.tick_params(axis=\"x\", rotation=90)\n", - " countplot.legend(title=' '.join(col.split('_')), bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n", + " countplot.legend(title=\" \".join(col.split(\"_\")), bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n", "\n", "plt.savefig(str(RESULTS_DIR / \"references_countplot.pdf\"), bbox_inches=\"tight\")\n", "plt.close(figure)\n" @@ -1027,7 +4576,9 @@ "cert_id_to_category_mapping[np.NaN] = \"No references\"\n", "\n", "exploded = df_id_rich.loc[:, [\"category\", \"directly_referencing\"]].explode(\"directly_referencing\")\n", - "exploded[\"ref_category\"] = exploded.directly_referencing.map(lambda x: cert_id_to_category_mapping[x] if pd.notnull(x) else np.nan)\n", + "exploded[\"ref_category\"] = exploded.directly_referencing.map(\n", + " lambda x: cert_id_to_category_mapping[x] if pd.notnull(x) else np.nan\n", + ")\n", "exploded = exploded.loc[exploded.ref_category.notnull()]\n", "\n", "exploded_with_refs = exploded.loc[exploded.ref_category != \"No references\"]\n", @@ -1040,11 +4591,19 @@ "figure.set_size_inches(24, 10)\n", "figure.set_tight_layout(True)\n", "\n", - "sankey(exploded.category, exploded.ref_category, colorDict=color_dict, leftLabels=list(exploded.category.unique()), rightLabels=list(exploded.ref_category.unique()), fontsize=12, ax=axes)\n", + "sankey(\n", + " exploded.category,\n", + " exploded.ref_category,\n", + " colorDict=color_dict,\n", + " leftLabels=list(exploded.category.unique()),\n", + " rightLabels=list(exploded.ref_category.unique()),\n", + " fontsize=12,\n", + " ax=axes,\n", + ")\n", "\n", "figure.savefig(str(RESULTS_DIR / \"category_references.pdf\"), bbox_inches=\"tight\")\n", "figure.savefig(str(RESULTS_DIR / \"category_references.pgf\"), bbox_inches=\"tight\")\n", - "plt.close(figure)" + "plt.close(figure)\n" ] }, { @@ -1079,10 +4638,10 @@ " title=f\"Countplot of {' '.join(col.split('_'))}\",\n", " )\n", " countplot.tick_params(axis=\"x\", rotation=90)\n", - " countplot.legend(title=' '.join(col.split('_')), bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n", + " countplot.legend(title=\" \".join(col.split(\"_\")), bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n", "\n", "plt.savefig(str(RESULTS_DIR / \"references_per_scheme_countplot.pdf\"), bbox_inches=\"tight\")\n", - "plt.close(figure)" + "plt.close(figure)\n" ] }, { @@ -1127,11 +4686,14 @@ "\n", " return any([x in cert_ids] for x in references)\n", "\n", + "\n", "cert_ids = set(df.loc[((df.cert_id.notnull()) & (df.status == \"archived\")), \"cert_id\"].tolist())\n", "df[\"references_archived_cert\"] = df.directly_referenced_by.map(references_archived_cert)\n", "\n", "# TODO: We should limit on the number of certificates that referenced an archived certificate at some point where they were active as well.\n", - "print(f\"Number of certificates that reference some archived certificate: {df.loc[df.references_archived_cert].shape[0]}\")\n", + "print(\n", + " f\"Number of certificates that reference some archived certificate: {df.loc[df.references_archived_cert].shape[0]}\"\n", + ")\n", "\n", "col_to_depict = [\"category\", \"scheme\"]\n", "\n", @@ -1147,7 +4709,7 @@ " title=\"Countplot of certificates that reference some archived certificate\",\n", " )\n", " countplot.tick_params(axis=\"x\", rotation=90)\n", - " countplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)" + " countplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)\n" ] }, { @@ -1232,12 +4794,20 @@ "figure.set_size_inches(4, 4)\n", "figure.set_tight_layout(True)\n", "\n", - "sankey(exploded.scheme, exploded.ref_scheme, colorDict=color_dict, leftLabels=list(exploded.scheme.unique()), rightLabels=list(exploded.ref_scheme.unique()), fontsize=7, ax=axes)\n", + "sankey(\n", + " exploded.scheme,\n", + " exploded.ref_scheme,\n", + " colorDict=color_dict,\n", + " leftLabels=list(exploded.scheme.unique()),\n", + " rightLabels=list(exploded.ref_scheme.unique()),\n", + " fontsize=7,\n", + " ax=axes,\n", + ")\n", "\n", "figure.savefig(str(RESULTS_DIR / \"scheme_references.pdf\"), bbox_inches=\"tight\")\n", "figure.savefig(str(RESULTS_DIR / \"scheme_references.pgf\"), bbox_inches=\"tight\")\n", "plt.show()\n", - "plt.close(figure)" + "plt.close(figure)\n" ] }, { @@ -1270,7 +4840,7 @@ "certs_with_ids = {cert.heuristics.cert_id: cert for cert in dset if cert.heuristics.cert_id}\n", "\n", "print(f\"Certificates in dataset: {len(dset)}\")\n", - "print(f\"Certificates with extracted IDs: {len(certs_with_ids)}\")" + "print(f\"Certificates with extracted IDs: {len(certs_with_ids)}\")\n" ] }, { @@ -1311,7 +4881,7 @@ " for ref_id in cr_refs:\n", " if ref_id in certs_with_ids:\n", " refs_cr.add_edge(cert_id, ref_id, type=(\"cr\",))\n", - "print(f\"References in certificate reports: {len(refs_cr.edges)}\")" + "print(f\"References in certificate reports: {len(refs_cr.edges)}\")\n" ] }, { @@ -1352,7 +4922,7 @@ " for ref_id in st_refs:\n", " if ref_id in certs_with_ids:\n", " refs_st.add_edge(cert_id, ref_id, type=(\"st\",))\n", - "print(f\"References in security targets: {len(refs_st.edges)}\")" + "print(f\"References in security targets: {len(refs_st.edges)}\")\n" ] }, { @@ -1400,12 +4970,12 @@ " if ref not in certs_with_ids:\n", " continue\n", " if ref in cr_refs and ref not in st_refs:\n", - " refs.add_edge(cert_id, ref, type=(\"cr\", ))\n", + " refs.add_edge(cert_id, ref, type=(\"cr\",))\n", " elif ref in st_refs and ref not in cr_refs:\n", - " refs.add_edge(cert_id, ref, type=(\"st\", ))\n", + " refs.add_edge(cert_id, ref, type=(\"st\",))\n", " else:\n", " refs.add_edge(cert_id, ref, type=(\"cr\", \"st\"))\n", - "print(f\"Combined references (not double counted): {len(refs.edges)}\")" + "print(f\"Combined references (not double counted): {len(refs.edges)}\")\n" ] }, { @@ -1423,7 +4993,7 @@ "metadata": {}, "outputs": [], "source": [ - "cert_id = \"ANSSI-CC-2019/02\"" + "cert_id = \"ANSSI-CC-2019/02\"\n" ] }, { @@ -1459,7 +5029,7 @@ "print(f\" - is in a component with {len(view.nodes)} certificates and {len(view.edges)} references.\")\n", "print(f\" - references {list(view[cert_id].keys())}\")\n", "print(f\" - is referenced by {list(view.predecessors(cert_id))}\")\n", - "print(f\" - its page is at https://seccerts.org/cc/{cert.dgst}/\")" + "print(f\" - its page is at https://seccerts.org/cc/{cert.dgst}/\")\n" ] }, { @@ -1479,7 +5049,7 @@ } ], "source": [ - "nx.draw(view, pos=nx.planar_layout(view), with_labels=True)" + "nx.draw(view, pos=nx.planar_layout(view), with_labels=True)\n" ] }, { @@ -1513,7 +5083,7 @@ ], "source": [ "print(f\"Density = {nx.density(refs)}\")\n", - "print(f\"Transitivity = {nx.transitivity(refs)}\")" + "print(f\"Transitivity = {nx.transitivity(refs)}\")\n" ] }, { @@ -1558,7 +5128,7 @@ "degree_centrality_vals = [(node, val) for node, val in nx.degree_centrality(refs).items()]\n", "degree_centrality_vals.sort(key=lambda pair: pair[1], reverse=True)\n", "for pair in degree_centrality_vals[:20]:\n", - " print(f\"\\t{pair[0]} = {pair[1]}\")" + " print(f\"\\t{pair[0]} = {pair[1]}\")\n" ] }, { @@ -1603,7 +5173,7 @@ "eigenvector_centrality_vals = [(node, val) for node, val in nx.eigenvector_centrality(refs).items()]\n", "eigenvector_centrality_vals.sort(key=lambda pair: pair[1], reverse=True)\n", "for pair in eigenvector_centrality_vals[:20]:\n", - " print(f\"\\t{pair[0]} = {pair[1]}\")" + " print(f\"\\t{pair[0]} = {pair[1]}\")\n" ] }, { @@ -1648,7 +5218,7 @@ "closeness_centrality_vals = [(node, val) for node, val in nx.closeness_centrality(refs).items()]\n", "closeness_centrality_vals.sort(key=lambda pair: pair[1], reverse=True)\n", "for pair in closeness_centrality_vals[:20]:\n", - " print(f\"\\t{pair[0]} = {pair[1]}\")" + " print(f\"\\t{pair[0]} = {pair[1]}\")\n" ] }, { @@ -1693,7 +5263,7 @@ "betweenness_centrality_vals = [(node, val) for node, val in nx.betweenness_centrality(refs).items()]\n", "betweenness_centrality_vals.sort(key=lambda pair: pair[1], reverse=True)\n", "for pair in betweenness_centrality_vals[:20]:\n", - " print(f\"\\t{pair[0]} = {pair[1]}\")" + " print(f\"\\t{pair[0]} = {pair[1]}\")\n" ] }, { @@ -1717,9 +5287,9 @@ "source": [ "component_lengths = list(filter(lambda comp_len: comp_len > 1, map(len, nx.weakly_connected_components(refs))))\n", "component_lengths.sort(reverse=True)\n", - "#print(component_lengths)\n", + "# print(component_lengths)\n", "print(f\"Number of weakly connected subgraphs: {len(component_lengths)}\")\n", - "print(f\"Size of the largest weakly connected subgraphs: {component_lengths[:10]}\")" + "print(f\"Size of the largest weakly connected subgraphs: {component_lengths[:10]}\")\n" ] }, { @@ -1742,7 +5312,7 @@ "source": [ "big_boy = refs.subgraph(max(nx.weakly_connected_components(refs), key=len))\n", "communities = list(nx_comm.greedy_modularity_communities(big_boy))\n", - "print(len(communities))" + "print(len(communities))\n" ] }, { @@ -2783,7 +6353,7 @@ "source": [ "for com in communities:\n", " for i in sorted(com):\n", - " print(f\"\\t{i}\")" + " print(f\"\\t{i}\")\n" ] }, { @@ -2836,7 +6406,7 @@ "\n", "# print(\"\")\n", "# print(f\"\\\\newcommand{{\\\\numCCUSReferencing}}{{{len(df_id_rich.loc[(df_id_rich.scheme == 'US') & (df_id_rich.directly_referencing.notnull())])}}}\")\n", - "# print(f\"\\\\newcommand{{\\\\numCCUS}}{{{len(df_id_rich.loc[(df_id_rich.scheme == 'US')])}}}\")" + "# print(f\"\\\\newcommand{{\\\\numCCUS}}{{{len(df_id_rich.loc[(df_id_rich.scheme == 'US')])}}}\")\n" ] } ], diff --git a/notebooks/fixed_sankey_plot.py b/notebooks/fixed_sankey_plot.py index 71c1683a..b8d062f9 100644 --- a/notebooks/fixed_sankey_plot.py +++ b/notebooks/fixed_sankey_plot.py @@ -1,5 +1,5 @@ # type: ignore - +# ruff: noqa: UP007 """ This is a fork of https://github.com/anazalea/pySankey/blob/master/pysankey/sankey.py. We've had some problems with the plot, mostly related to resizing (likely, I don't remember now). @@ -9,7 +9,7 @@ This code should fix the problems and should be used to produce figures in the r import logging import warnings from collections import defaultdict -from typing import Any, Dict, List, Optional, Set, Tuple, Union +from typing import Any, Optional, Union import matplotlib.pyplot as plt import numpy as np @@ -35,7 +35,7 @@ class LabelMismatch(PySankeyException): LOGGER = logging.getLogger(__name__) -def check_data_matches_labels(labels: Union[List[str], Set[str]], data: Series, side: str) -> None: +def check_data_matches_labels(labels: Union[list[str], set[str]], data: Series, side: str) -> None: """Check whether data matches labels. Raise a LabelMismatch Exception if not.""" if len(labels) > 0: @@ -55,19 +55,19 @@ def check_data_matches_labels(labels: Union[List[str], Set[str]], data: Series, def sankey( - left: Union[List, ndarray, Series], + left: Union[list, ndarray, Series], right: Union[ndarray, Series], leftWeight: Optional[ndarray] = None, rightWeight: Optional[ndarray] = None, - colorDict: Optional[Dict[str, str]] = None, - leftLabels: Optional[List[str]] = None, - rightLabels: Optional[List[str]] = None, + colorDict: Optional[dict[str, str]] = None, + leftLabels: Optional[list[str]] = None, + rightLabels: Optional[list[str]] = None, aspect: int = 4, rightColor: bool = False, fontsize: int = 14, figureName: Optional[str] = None, closePlot: bool = False, - figSize: Optional[Tuple[int, int]] = None, + figSize: Optional[tuple[int, int]] = None, ax: Optional[Any] = None, ) -> Any: """ @@ -158,7 +158,7 @@ def save_image(figureName: Optional[str]) -> None: LOGGER.info("Sankey diagram generated in '%s'", file_name) -def identify_labels(dataFrame: DataFrame, leftLabels: List[str], rightLabels: List[str]) -> Tuple[ndarray, ndarray]: +def identify_labels(dataFrame: DataFrame, leftLabels: list[str], rightLabels: list[str]) -> tuple[ndarray, ndarray]: # Identify left labels if len(leftLabels) == 0: leftLabels = pd.Series(dataFrame.left.unique()).unique() @@ -175,14 +175,14 @@ def identify_labels(dataFrame: DataFrame, leftLabels: List[str], rightLabels: Li def init_values( ax: Optional[Any], closePlot: bool, - figSize: Optional[Tuple[int, int]], + figSize: Optional[tuple[int, int]], figureName: Optional[str], - left: Union[List, ndarray, Series], - leftLabels: Optional[List[str]], + left: Union[list, ndarray, Series], + leftLabels: Optional[list[str]], leftWeight: Optional[ndarray], - rightLabels: Optional[List[str]], + rightLabels: Optional[list[str]], rightWeight: Optional[ndarray], -) -> Tuple[Any, List[str], ndarray, List[str], ndarray]: +) -> tuple[Any, list[str], ndarray, list[str], ndarray]: deprecation_warnings(closePlot, figSize, figureName) if ax is None: ax = plt.gca() @@ -202,7 +202,7 @@ def init_values( return ax, leftLabels, leftWeight, rightLabels, rightWeight -def deprecation_warnings(closePlot: bool, figSize: Optional[Tuple[int, int]], figureName: Optional[str]) -> None: +def deprecation_warnings(closePlot: bool, figSize: Optional[tuple[int, int]], figureName: Optional[str]) -> None: warn = [] if figureName is not None: msg = "use of figureName in sankey() is deprecated" @@ -223,10 +223,10 @@ def deprecation_warnings(closePlot: bool, figSize: Optional[Tuple[int, int]], fi ) -def determine_widths(dataFrame: DataFrame, leftLabels: ndarray, rightLabels: ndarray) -> Tuple[Dict, Dict]: +def determine_widths(dataFrame: DataFrame, leftLabels: ndarray, rightLabels: ndarray) -> tuple[dict, dict]: # Determine widths of individual strips - ns_l: Dict = defaultdict() - ns_r: Dict = defaultdict() + ns_l: dict = defaultdict() + ns_r: dict = defaultdict() for leftLabel in leftLabels: left_dict = {} right_dict = {} @@ -244,12 +244,12 @@ def determine_widths(dataFrame: DataFrame, leftLabels: ndarray, rightLabels: nda def draw_vertical_bars( ax: Any, - colorDict: Union[Dict[str, Tuple[float, float, float]], Dict[str, str]], + colorDict: Union[dict[str, tuple[float, float, float]], dict[str, str]], fontsize: int, leftLabels: ndarray, - leftWidths: Dict, + leftWidths: dict, rightLabels: ndarray, - rightWidths: Dict, + rightWidths: dict, xMax: float64, ) -> None: # Draw vertical bars on left and right of each label's section & print label @@ -286,8 +286,8 @@ def draw_vertical_bars( def create_colors( - allLabels: ndarray, colorDict: Optional[Dict[str, str]] -) -> Union[Dict[str, Tuple[float, float, float]], Dict[str, str]]: + allLabels: ndarray, colorDict: Optional[dict[str, str]] +) -> Union[dict[str, tuple[float, float, float]], dict[str, str]]: # If no colorDict given, make one if colorDict is None: colorDict = {} @@ -306,7 +306,7 @@ def create_colors( def _create_dataframe( - left: Union[List, ndarray, Series], + left: Union[list, ndarray, Series], leftWeight: Union[ndarray, Series], right: Union[ndarray, Series], rightWeight: Union[ndarray, Series], @@ -336,15 +336,15 @@ def _create_dataframe( def plot_strips( ax: Any, - colorDict: Union[Dict[str, Tuple[float, float, float]], Dict[str, str]], + colorDict: Union[dict[str, tuple[float, float, float]], dict[str, str]], dataFrame: DataFrame, leftLabels: ndarray, - leftWidths: Dict, - ns_l: Dict, - ns_r: Dict, + leftWidths: dict, + ns_l: dict, + ns_r: dict, rightColor: bool, rightLabels: ndarray, - rightWidths: Dict, + rightWidths: dict, xMax: float64, ) -> None: # Plot strips @@ -380,9 +380,9 @@ def plot_strips( ax.axis("off") -def _get_positions_and_total_widths(df: DataFrame, labels: ndarray, side: str) -> Tuple[Dict, float64]: +def _get_positions_and_total_widths(df: DataFrame, labels: ndarray, side: str) -> tuple[dict, float64]: """Determine positions of label patches and total widths""" - widths: Dict = defaultdict() + widths: dict = defaultdict() for i, label in enumerate(labels): label_widths = {} label_widths[side] = df[df[side] == label][side + "Weight"].sum() diff --git a/pyproject.toml b/pyproject.toml index d67c7888..19b34bdf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,7 @@ "scipy>=1.9.0", "networkx", "pydantic", + "pydantic-settings", "psutil", "pytesseract", ] diff --git a/src/sec_certs/configuration.py b/src/sec_certs/configuration.py index d7fb734a..3ebb22bd 100644 --- a/src/sec_certs/configuration.py +++ b/src/sec_certs/configuration.py @@ -5,7 +5,8 @@ from pathlib import Path from typing import Literal, Optional import yaml -from pydantic import AnyHttpUrl, BaseSettings, Field +from pydantic import AnyHttpUrl, Field +from pydantic_settings import BaseSettings class Configuration(BaseSettings): diff --git a/src/sec_certs/model/references_nlp/annotator_trainer.py b/src/sec_certs/model/references_nlp/annotator_trainer.py index 1f973842..0f506456 100644 --- a/src/sec_certs/model/references_nlp/annotator_trainer.py +++ b/src/sec_certs/model/references_nlp/annotator_trainer.py @@ -1,8 +1,9 @@ from __future__ import annotations import logging +from collections.abc import Callable from functools import partial -from typing import Callable, Final, Literal +from typing import Final, Literal import pandas as pd from datasets import ClassLabel, Dataset, Features, NamedSplit, Value diff --git a/src/sec_certs/model/references_nlp/segment_extractor.py b/src/sec_certs/model/references_nlp/segment_extractor.py index ce144b93..d5d727a1 100644 --- a/src/sec_certs/model/references_nlp/segment_extractor.py +++ b/src/sec_certs/model/references_nlp/segment_extractor.py @@ -4,10 +4,11 @@ import itertools import json import logging import re +from collections.abc import Iterable from dataclasses import dataclass from importlib.resources import files from pathlib import Path -from typing import Any, Iterable, Literal +from typing import Any, Literal # import langdetect import numpy as np |