Merge pull request #404 from crocs-muni/chain-of-trust-artifacts

Artifacts for chain-of-trust paper

notebooks/cc/chain_of_trust_plots.ipynb

@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.dates as mdates\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from matplotlib import lines\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "# LaTeX plotting\n",
+    "matplotlib.use(\"pgf\")\n",
+    "sns.set_palette(\"Set2\")\n",
+    "sns.set_context(\"paper\")\n",
+    "\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",
+    "\n",
+    "plt.rcParams[\"axes.linewidth\"] = 0.5\n",
+    "plt.rcParams[\"axes.labelsize\"] = 14\n",
+    "\n",
+    "plt.rcParams[\"xtick.labelsize\"] = 12\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\"] = 0.1\n",
+    "\n",
+    "plt.rcParams[\"ytick.labelsize\"] = 12\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\"] = 0.1\n",
+    "\n",
+    "plt.rcParams[\"legend.title_fontsize\"] = 12\n",
+    "plt.rcParams[\"legend.fontsize\"] = 12\n",
+    "plt.rcParams[\"legend.handletextpad\"] = 0.3\n",
+    "plt.rcParams[\"lines.markersize\"] = 0.5\n",
+    "plt.rcParams[\"savefig.pad_inches\"] = 0.01\n",
+    "\n",
+    "INPUT_DIR = Path(\"./paper_artifacts/chain_of_trust/data/plots/\")\n",
+    "OUTPUT_DIR = Path(\"./results/figures/\")\n",
+    "INPUT_DIR.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Average number of transitive references over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_to_plot = pd.read_csv(INPUT_DIR / \"avg_refs_over_time.csv\", parse_dates=[\"date\"])\n",
+    "df_to_plot[\"category\"] = df_to_plot[\"category\"].map(lambda x: \"others\" if x == \"others categories\" else x)\n",
+    "\n",
+    "plt.figure()\n",
+    "g = sns.lineplot(data=df_to_plot, x=\"date\", y=\"n_references\", hue=\"category\", errorbar=None)\n",
+    "plt.legend(frameon=True, handlelength=2, title=\"Product category\")\n",
+    "g.set_xlabel(\"\")\n",
+    "g.set_ylabel(\"Avg. \\# transitive refs.\")\n",
+    "\n",
+    "dtFmt = mdates.DateFormatter(\"%Y\")\n",
+    "g.xaxis.set_major_formatter(dtFmt)\n",
+    "g.set_xticks(\n",
+    "    [\n",
+    "        pd.to_datetime(\"1998-01-01\"),\n",
+    "        pd.to_datetime(\"2003-01-01\"),\n",
+    "        pd.to_datetime(\"2008-01-01\"),\n",
+    "        pd.to_datetime(\"2013-01-01\"),\n",
+    "        pd.to_datetime(\"2018-01-01\"),\n",
+    "        pd.to_datetime(\"2023-01-01\"),\n",
+    "    ]\n",
+    ")\n",
+    "g.figure.set_size_inches(3.9, 3)\n",
+    "plt.tight_layout(pad=0.1)\n",
+    "g.figure.savefig(OUTPUT_DIR / \"lineplot_avg_refs.pdf\")\n",
+    "g.figure.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Average reach over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_to_plot = pd.read_csv(INPUT_DIR / \"avg_reach_over_time.csv\", parse_dates=[\"date\"])\n",
+    "\n",
+    "plt.figure()\n",
+    "g = sns.lineplot(data=df_to_plot, x=\"date\", y=\"n_references\", hue=\"category\", errorbar=None)\n",
+    "plt.legend(frameon=True, handlelength=2, title=\"Product category\")\n",
+    "g.set_xlabel(\"\")\n",
+    "g.set_ylabel(\"Average certificate reach\")\n",
+    "dtFmt = mdates.DateFormatter(\"%Y\")\n",
+    "g.xaxis.set_major_formatter(dtFmt)\n",
+    "g.set_xticks(\n",
+    "    [\n",
+    "        pd.to_datetime(\"1998-01-01\"),\n",
+    "        pd.to_datetime(\"2003-01-01\"),\n",
+    "        pd.to_datetime(\"2008-01-01\"),\n",
+    "        pd.to_datetime(\"2013-01-01\"),\n",
+    "        pd.to_datetime(\"2018-01-01\"),\n",
+    "        pd.to_datetime(\"2023-01-01\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "g.figure.set_size_inches(3.9, 3)\n",
+    "plt.tight_layout(pad=0.1)\n",
+    "g.figure.savefig(OUTPUT_DIR / \"lineplot_avg_reach.pdf\")\n",
+    "g.figure.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Area under curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(2.8, 1.8))\n",
+    "sns.set_palette(\"Set2\")\n",
+    "colors = plt.cm.Dark2(np.linspace(0, 1, 8))\n",
+    "\n",
+    "df_sent = pd.read_csv(INPUT_DIR / \"df_pred_sentence_transformers.csv\")\n",
+    "df_tf_idf = pd.read_csv(INPUT_DIR / \"df_pred_tf_idf.csv\")\n",
+    "df_baseline = pd.read_csv(INPUT_DIR / \"df_pred_baseline.csv\")\n",
+    "\n",
+    "fpr, tpr, thresholds = metrics.roc_curve(df_sent.y_true, df_sent.y_pred)\n",
+    "auc = metrics.roc_auc_score(df_sent.y_true, df_sent.y_pred)\n",
+    "plt.plot(fpr, tpr, label=f\"Sent. trans. (AUC={auc:.2f})\", color=colors[0])\n",
+    "\n",
+    "fpr, tpr, thresholds = metrics.roc_curve(df_tf_idf.y_true, df_tf_idf.y_pred)\n",
+    "auc = metrics.roc_auc_score(df_tf_idf.y_true, df_tf_idf.y_pred)\n",
+    "plt.plot(fpr, tpr, label=f\"TF-IDF (AUC={auc:.2f})\", color=colors[1])\n",
+    "\n",
+    "fpr, tpr, thresholds = metrics.roc_curve(df_baseline.y_true, df_baseline.y_pred)\n",
+    "auc = metrics.roc_auc_score(df_baseline.y_true, df_baseline.y_pred)\n",
+    "with plt.rc_context({\"legend.fontsize\": 8}):\n",
+    "    plt.plot(fpr, tpr, label=f\"Random guess (AUC={auc:.2f})\", color=colors[2])\n",
+    "\n",
+    "    plt.legend(loc=\"lower right\")\n",
+    "    plt.savefig(OUTPUT_DIR / \"roc_auc.pdf\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stack-bar plot of annotations in categories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(INPUT_DIR / \"ref_categories_stackplot.csv\")\n",
+    "\n",
+    "ax = df.plot.barh(stacked=True, rot=0, width=0.95)\n",
+    "ax.set_ylim(-0.6, 2.6)\n",
+    "ax.set_xlabel(\"\\# references\", fontsize=12)\n",
+    "ax.set_yticklabels([\"Others\", \"Smartcard-related\", \"Smartcards\"])\n",
+    "ax.legend(title=\"Reference context\", loc=\"lower right\", frameon=True)\n",
+    "\n",
+    "plt.text(0.4, 0.8, df.iloc[2][\"Component reuse\"], transform=ax.transAxes, color=\"white\", fontsize=14)\n",
+    "plt.text(0.81, 0.8, df.iloc[2][\"Predecessor\"], transform=ax.transAxes, color=\"white\", fontsize=14)\n",
+    "\n",
+    "plt.axhline(y=1.21, xmin=0.05, xmax=0.18, color=\"black\", linewidth=0.75)\n",
+    "plt.axhline(y=0.9, xmin=0.12, xmax=0.18, color=\"black\", linewidth=0.75)\n",
+    "plt.text(0.2, 0.55, df.iloc[1][\"Component reuse\"], transform=ax.transAxes, color=\"black\", fontsize=14)\n",
+    "plt.text(0.2, 0.45, df.iloc[1][\"Predecessor\"], transform=ax.transAxes, color=\"black\", fontsize=14)\n",
+    "\n",
+    "plt.axhline(y=0.17, xmin=0.02, xmax=0.1, color=\"black\", linewidth=0.75)\n",
+    "plt.axhline(y=-0.1, xmin=0.05, xmax=0.1, color=\"black\", linewidth=0.75)\n",
+    "plt.text(0.12, 0.22, df.iloc[0][\"Component reuse\"], transform=ax.transAxes, color=\"black\", fontsize=14)\n",
+    "plt.text(0.12, 0.13, df.iloc[0][\"Predecessor\"], transform=ax.transAxes, color=\"black\", fontsize=14)\n",
+    "\n",
+    "ax.figure.set_size_inches(4, 3)\n",
+    "plt.tight_layout(pad=0.1)\n",
+    "plt.savefig(OUTPUT_DIR / \"stacked_barplot.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Archived certificate half-life"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "\n",
+    "df = pd.read_csv(INPUT_DIR / \"archived_half_life.csv\")\n",
+    "\n",
+    "with plt.rc_context({\"legend.fontsize\": 10, \"legend.title_fontsize\": 10}):\n",
+    "    g = sns.ecdfplot(data=df.n_days, complementary=True)\n",
+    "\n",
+    "    plt.axvline(x=365, color=\"r\", linestyle=\"--\", linewidth=0.75)\n",
+    "    vertical_line = lines.Line2D(\n",
+    "        [], [], color=\"r\", marker=\"\", linestyle=\"--\", markersize=10, markeredgewidth=1.5, label=\"One year\"\n",
+    "    )\n",
+    "    plt.legend(handles=[vertical_line])\n",
+    "\n",
+    "    g.figure.set_size_inches(3, 2)\n",
+    "    g.set_xlim(0, 2000)\n",
+    "\n",
+    "    g.set_xlabel(\"Number of days\")\n",
+    "    g.set_ylabel(\"Proportion\")\n",
+    "\n",
+    "    g.yaxis.set_major_formatter(matplotlib.ticker.PercentFormatter(xmax=1))\n",
+    "    g.set_yticks([0, 0.25, 0.5, 0.75, 1])\n",
+    "\n",
+    "    plt.tight_layout(pad=0.05)\n",
+    "    g.figure.savefig(OUTPUT_DIR / \"cdf_half_life.pdf\")\n",
+    "    g.figure.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Age of referenced certificate in composite-evaluation products"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "\n",
+    "df = pd.read_csv(INPUT_DIR / \"ecdf_archival_data.csv\")\n",
+    "df = df.loc[df.scheme.isin({\"FR\", \"DE\", \"NL\"})]\n",
+    "\n",
+    "with plt.rc_context({\"legend.fontsize\": 10, \"legend.title_fontsize\": 10}):\n",
+    "    g = sns.ecdfplot(data=df, x=\"date_diff\", hue=\"scheme\", legend=True)\n",
+    "    plt.axvline(x=540, color=\"r\", linestyle=\"--\", linewidth=0.75)\n",
+    "\n",
+    "    vertical_line = lines.Line2D([], [], color=\"r\", linestyle=\"--\", markersize=10, label=\"18 months\")\n",
+    "    unique_hues = df[\"scheme\"].unique()\n",
+    "    handles = [\n",
+    "        plt.Line2D([], [], color=g.lines[color_idx].get_color(), label=label)\n",
+    "        for color_idx, label in enumerate(unique_hues)\n",
+    "    ]\n",
+    "\n",
+    "    handles.append(vertical_line)\n",
+    "    labels = list(unique_hues) + [\"18 months\"]\n",
+    "\n",
+    "    g.legend(handles=handles, labels=labels)\n",
+    "\n",
+    "    g.figure.set_size_inches(3, 2)\n",
+    "    g.yaxis.set_major_formatter(matplotlib.ticker.PercentFormatter(xmax=1))\n",
+    "    g.set_yticks([0, 0.25, 0.5, 0.75, 1])\n",
+    "    g.set_xlim(0, 2000)\n",
+    "    g.set_xlabel(\"Number of days\")\n",
+    "    g.set_ylabel(\"Proportion\")\n",
+    "    plt.tight_layout(pad=0.05)\n",
+    "    g.figure.savefig(OUTPUT_DIR / \"ref_comp_age.pdf\")\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}