results/summary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# utils\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sincfold.metrics import f1_strict\n",
    "from sincfold.utils import dot2bp\n",
    "import json"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ablation study "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 966x483 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dotbracket_format = [\"IPknot\", \"ProbKnot\", \"RNAstructure\", \"LinearFold\", \"REDfold\", \n",
    "                          \"LinearFold-C\", \"LinearFold-V\", \"LinearPartition-C\", \"LinearPartition-V\", \"MXfold2\"]\n",
    "methods = [\"C1D\", \"R1D\", \"C1D+C2D\", \"C1D+R2D\", \"R1D+R2D\"]\n",
    "summary = pd.read_csv(f\"ablation.csv\", index_col=\"id\")\n",
    "\n",
    "dataset = pd.read_csv(f\"../data/ablation.csv\", index_col=\"id\")\n",
    "splits = pd.read_csv(f\"../data/ablation_splits.csv\")\n",
    "\n",
    "# add references and compute F1 per sequence\n",
    "summary[\"ref\"] = dataset.loc[summary.index][\"base_pairs\"].apply(lambda x: json.loads(x))        \n",
    "\n",
    "\n",
    "def get_f1(x):\n",
    "    if x.method in dotbracket_format:\n",
    "        _, _, f1 = f1_strict(x[\"ref\"], dot2bp(x[\"folding\"]))\n",
    "    else:\n",
    "        _, _, f1 = f1_strict(x[\"ref\"], json.loads(x[\"folding\"]))\n",
    "    \n",
    "    return f1\n",
    "\n",
    "summary[\"F1\"] = summary.apply(get_f1, axis=1)\n",
    "\n",
    "# add fold id\n",
    "for fold_number in splits.fold_number.unique():\n",
    "    test_ids = splits[(splits.partition==\"test\") & (splits.fold_number==fold_number)].id\n",
    "    summary.loc[summary.index.isin(test_ids), \"fold_number\"] = fold_number\n",
    "\n",
    "# average over folds\n",
    "summary_avg = summary.groupby([\"fold_number\", \"method\"]).F1.mean().unstack().T\n",
    "\n",
    "plt.figure(figsize=(3.22, 1.61), dpi=300)\n",
    "\n",
    "ax = sns.boxplot(data=summary_avg.T,  orient=\"h\", palette=\"viridis\" , order=methods, linewidth=0.5, fliersize=0)\n",
    "ax.set_xlabel(\"$F_1$ score\", fontsize=6)\n",
    "ax.set_ylabel(\"\")\n",
    "plt.xticks(fontsize=6)\n",
    "plt.yticks(fontsize=6)\n",
    "ax.spines['left'].set_linewidth(0.5)\n",
    "ax.spines['bottom'].set_linewidth(0.5)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.tick_params(width=0.5)\n",
    "\n",
    "plt.savefig(\"ablation.pdf\", \n",
    "               bbox_inches='tight', \n",
    "               transparent=False,\n",
    "               dpi=300,\n",
    "               pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interconnection distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(0, 400, 40+1)\n",
    "histn = {}\n",
    "for method in [\"R1D\", \"R1D+R2D\"]:\n",
    "    method_summary = summary[summary.method==method]\n",
    "    tps = [] # true positives\n",
    "\n",
    "    for k in range(len(method_summary)):\n",
    "        for p in method_summary.iloc[k].ref:\n",
    "            tp = 1\n",
    "            if p not in json.loads(method_summary.iloc[k].folding): # strict error\n",
    "                tp = 0\n",
    "            tps.append([p[1]-p[0], tp])\n",
    "    tps = pd.DataFrame(tps, columns=[\"dist\", \"tp\"])\n",
    " \n",
    "    h = []\n",
    "    for i in range(len(bins)-1):\n",
    "        nn = tps[(tps.dist>bins[i]) & (tps.dist<=bins[i+1])].tp.count()\n",
    "        tp = tps[(tps.dist>bins[i]) & (tps.dist<=bins[i+1]) &  (tps.tp==1)].tp.count()\n",
    "        if nn>0: h.append(tp/nn)\n",
    "        else: h.append(0)\n",
    "    histn[method] = h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2025x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6.75, 2), dpi=300)\n",
    "\n",
    "strbins = []\n",
    "for i in range(len(bins)-1):\n",
    "    strbins.append(str(int(bins[i])) + \"-\" + str(int(bins[i+1])))\n",
    "\n",
    "df = pd.DataFrame(histn)\n",
    "df[\"bins\"] = strbins\n",
    "df = df.iloc[:-2]\n",
    "df = pd.melt(df, id_vars=\"bins\", value_vars=[\"R1D\", \"R1D+R2D\"], var_name=\"method\", value_name=\"F1\") \n",
    "\n",
    "sns.barplot(data=df, x=\"bins\", y=\"F1\", hue=\"method\", palette=\"viridis\")\n",
    "plt.xticks(rotation = 45, fontsize=6)\n",
    "plt.yticks(fontsize=6)\n",
    "plt.ylabel(\"True positive rate\", fontsize=6)\n",
    "plt.xlabel(\"Interconnection distance\", fontsize=6)\n",
    "plt.legend(fontsize=6)\n",
    "\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.spines['left'].set_linewidth(0.5)\n",
    "ax.spines['bottom'].set_linewidth(0.5)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.tick_params(width=0.5)\n",
    "\n",
    "plt.savefig(\"distR.pdf\", \n",
    "               bbox_inches='tight', \n",
    "               transparent=False,\n",
    "               dpi=300,\n",
    "               pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNAstralign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 966x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dotbracket_format = [\"IPknot\", \"ProbKnot\", \"RNAstructure\", \"LinearFold\", \"REDfold\", \n",
    "                          \"LinearFold-C\", \"LinearFold-V\", \"LinearPartition-C\", \"LinearPartition-V\", \"MXfold2\"]\n",
    "\n",
    "methods = ['RNAfold', 'RNAstructure', 'ProbKnot', 'IPknot', 'LinearPartition-V', 'LinearFold-V', \n",
    "           'LinearPartition-C', 'LinearFold-C',   \n",
    "           'MXfold2', 'UFold', 'REDfold', 'sincFold']\n",
    "\n",
    "summary = pd.read_csv(f\"RNAstralign.csv\", index_col=\"id\")\n",
    "\n",
    "dataset = pd.read_csv(f\"../data/RNAstralign.csv\", index_col=\"id\")\n",
    "splits = pd.read_csv(f\"../data/RNAstralign_splits.csv\")\n",
    "\n",
    "# add references and compute F1 per sequence\n",
    "summary[\"ref\"] = dataset.loc[summary.index][\"base_pairs\"].apply(lambda x: json.loads(x))        \n",
    "\n",
    "summary[\"F1\"] = summary.apply(get_f1, axis=1)\n",
    "\n",
    "# add fold id\n",
    "for fold_number in splits.fold_number.unique():\n",
    "    test_ids = splits[(splits.partition==\"test\") & (splits.fold_number==fold_number)].id\n",
    "    summary.loc[summary.index.isin(test_ids), \"fold_number\"] = fold_number\n",
    "\n",
    "# add average over folds\n",
    "summary = summary.groupby([\"fold_number\", \"method\"]).F1.mean().unstack().T\n",
    "\n",
    "for method in methods:\n",
    "    if method not in summary.index:\n",
    "        summary = pd.concat((summary, pd.Series(index=[method])))\n",
    "\n",
    "base = sns.color_palette(\"RdBu\", 25)\n",
    "palette = base[:6] + base[9:11] + base[13:14] + base[15:16] + base[18:19] + base[-2:-1]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(3.22, 2), dpi=300)\n",
    "sns.boxplot(data=summary.T,  orient=\"h\", palette=palette, order=methods, hue_order=methods, ax=ax[0], linewidth=0.5, fliersize=1)\n",
    "sns.boxplot(data=summary.T,  orient=\"h\", palette=palette, order=methods, hue_order=methods, ax=ax[1], linewidth=0.5, fliersize=1)\n",
    "ax[0].set_xlim(0.5, .75)\n",
    "ax[1].set_xlim(.9, 1)\n",
    "ax[1].set_ylabel(\"\")\n",
    "# remove ticks from ax[1]\n",
    "ax[1].set_yticks([])\n",
    "ax[0].set_title(\"RNAstralign\", x=1.05, fontsize=6)\n",
    "\n",
    "ax[0].spines['right'].set_visible(False)\n",
    "ax[1].spines['left'].set_visible(False)\n",
    "ax[0].spines['top'].set_visible(False)\n",
    "ax[1].spines['top'].set_visible(False)\n",
    "ax[1].spines['right'].set_visible(False)\n",
    "ax[0].tick_params(labelright=False)  \n",
    "\n",
    "for axi in ax:\n",
    "    axi.spines['left'].set_linewidth(0.5)\n",
    "    axi.spines['bottom'].set_linewidth(0.5)\n",
    "    axi.tick_params(labelsize=6)\n",
    "    axi.tick_params(width=0.5)\n",
    "\n",
    "d = .020 \n",
    "kwargs = dict(transform=ax[0].transAxes, color='k', clip_on=False)\n",
    "ax[0].plot((1 - d, 1 + d), (-d, +d), lw=0.5, **kwargs)  \n",
    "\n",
    "kwargs.update(transform=ax[1].transAxes)  \n",
    "ax[1].plot((0 - d, 0 + d), (-d, +d), lw=0.5, **kwargs)  \n",
    "\n",
    "ax[0].set_xlabel(\"$F_1$ score\", x=1.05, fontsize=6)\n",
    "ax[0].set_ylabel(\"\")\n",
    "\n",
    "plt.savefig(\"rnastralign.pdf\", \n",
    "               bbox_inches='tight', \n",
    "               transparent=False,\n",
    "               dpi=300,\n",
    "               pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ArchiveII"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 966x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dotbracket_format = [\"IPknot\",  \"RNAstructure\", \"REDfold\", \n",
    "                          \"LinearFold-C\", \"LinearFold-V\", \"LinearPartition-C\", \"LinearPartition-V\", \"MXfold2\"]\n",
    "\n",
    "methods = ['RNAfold', 'RNAstructure', 'ProbKnot', 'IPknot', 'LinearPartition-V', 'LinearFold-V', \n",
    "           'LinearPartition-C', 'LinearFold-C',   \n",
    "           'MXfold2', 'UFold', 'REDfold', 'sincFold']\n",
    "\n",
    "summary = pd.read_csv(f\"ArchiveII.csv\", index_col=\"id\")\n",
    "\n",
    "dataset = pd.read_csv(f\"../data/ArchiveII.csv\", index_col=\"id\")\n",
    "splits = pd.read_csv(f\"../data/ArchiveII_splits.csv\")\n",
    "\n",
    "# add references and compute F1 per sequence\n",
    "summary[\"ref\"] = dataset.loc[summary.index][\"base_pairs\"].apply(lambda x: json.loads(x))        \n",
    "\n",
    "summary[\"F1\"] = summary.apply(get_f1, axis=1)\n",
    "\n",
    "# add fold id\n",
    "for fold_number in splits.fold_number.unique():\n",
    "    test_ids = splits[(splits.partition==\"test\") & (splits.fold_number==fold_number)].id\n",
    "    summary.loc[summary.index.isin(test_ids), \"fold_number\"] = fold_number\n",
    "\n",
    "# add average over folds\n",
    "summary_avg = summary.groupby([\"fold_number\", \"method\"]).F1.mean().unstack().T\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(3.22, 2), dpi=300)\n",
    "ax = sns.boxplot(data=summary_avg.T,  orient=\"h\", palette=palette, order=methods, hue_order=methods, linewidth=0.5, fliersize=1)\n",
    "ax.set_xlabel(\"$F_1$ score\", fontsize=6)\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_title(\"ArchiveII\", fontsize=6)\n",
    "\n",
    "#ax.set(yticklabels=[])\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.tick_params(labelright=False)  \n",
    "\n",
    "ax.spines['left'].set_linewidth(0.5)\n",
    "ax.spines['bottom'].set_linewidth(0.5)\n",
    "ax.tick_params(labelsize=6)\n",
    "ax.tick_params(width=0.5)\n",
    "\n",
    "plt.savefig(\"archiveII.pdf\", \n",
    "               bbox_inches='tight', \n",
    "               transparent=False,\n",
    "               dpi=300,\n",
    "               pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance according to test-train structural distance on random partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2025x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = splits[splits.partition==\"test\"].set_index(\"id\")\n",
    "\n",
    "\n",
    "pal = palette[:1] + palette[5:6] + palette[7:8] + palette[9:10] + palette[10:11] + palette[-1:]\n",
    "methods = ['RNAfold', 'LinearPartition-C', 'MXfold2', 'UFold', 'REDfold', 'sincFold']\n",
    "\n",
    "summary[\"min_train_dist\"] = dist.loc[summary.index, \"min_train_dist\"]\n",
    "summary[\"dist_bin\"] = pd.cut(summary[\"min_train_dist\"], include_lowest=True,  bins=[0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.55])\n",
    "summary['dist_bin'] = summary.dist_bin.apply(lambda x: f'{x.right:.2f}-{x.left:.2f}' if x.right != 0.05 else f'{x.right:.2f}-{0:.2f}')\n",
    "\n",
    "dist_count = summary.groupby(\"dist_bin\").ref.count()\n",
    "dist_count /= dist_count.sum()\n",
    "\n",
    "# get average per fold per method and dist\n",
    "summary_avg = summary.groupby([\"fold_number\", \"method\", \"dist_bin\"]).F1.mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(6.75, 2), dpi=300)\n",
    "bins = ['0.55-0.45', '0.45-0.40', '0.40-0.35', '0.35-0.30', '0.30-0.25', '0.25-0.20', '0.20-0.15', '0.15-0.10', '0.10-0.05', '0.05-0.00']\n",
    "bp = sns.barplot(x=dist_count.index.to_numpy(), y=dist_count.values, \n",
    "                 facecolor=(0.0,0.0,1.0,0.1), linewidth=.5, \n",
    "                 edgecolor=(0.0,0.0,1.0,0.3), zorder=-1, order=bins)\n",
    "vp = sns.boxplot(data=summary_avg, x=\"dist_bin\", y=\"F1\", hue=\"method\", linewidth=.5, fliersize=0.5,\n",
    "                 zorder=1, hue_order=methods, order=bins, palette=pal)\n",
    "vp.set_ylabel(\"$F_1$ score \\n Proportion of sequences in each bin\", fontsize=6);\n",
    "vp.legend(bbox_to_anchor=(0.05,.65), ncol=1, fontsize=4.5, frameon=False)\n",
    "plt.xlabel(\"Minimum structural distance to the closest training sequence\", fontsize=6);\n",
    "vp.spines[['right', 'top']].set_visible(False);\n",
    "vp.spines['left'].set_linewidth(0.5)\n",
    "vp.spines['bottom'].set_linewidth(0.5)\n",
    "\n",
    "plt.xticks(fontsize=6)\n",
    "plt.yticks(fontsize=6);\n",
    "\n",
    "\n",
    "plt.savefig(\"structural_diff.pdf\", \n",
    "               bbox_inches='tight', \n",
    "               transparent=False,\n",
    "               dpi=300,\n",
    "               pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 966x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary[\"Length\"] = dataset.loc[summary.index, \"len\"]\n",
    "summary[\"len_bin\"] = pd.cut(summary[\"Length\"], include_lowest=True,  bins=[0, 100, 200, 300, 400, 512])\n",
    "summary['len_bin'] = summary.len_bin.apply(lambda x: f'{int(x.left)}-{int(x.right)}')\n",
    "\n",
    "bin_count = summary.groupby(\"len_bin\").ref.count()\n",
    "bin_count /= bin_count.sum()\n",
    "\n",
    "summary_avg = summary.groupby([\"fold_number\", \"method\", \"len_bin\"]).F1.mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(3.22, 2), dpi=300)\n",
    "bp = sns.barplot(x=bin_count.index, y=bin_count, \n",
    "                 facecolor=(0.0,0.0,1.0,0.1), linewidth=.5, \n",
    "                 edgecolor=(0.0,0.0,1.0,0.3), zorder=-1)\n",
    "vp = sns.boxplot(data=summary_avg, x=\"len_bin\", y=\"F1\", hue=\"method\", linewidth=.5, fliersize=.5, \n",
    "                 palette=pal, zorder=1, hue_order=methods, order=bin_count.index)\n",
    "vp.set_ylabel(\"$F_1$ score \\n Proportion of sequences in each bin\", fontsize=6);\n",
    "#vp.legend(bbox_to_anchor=(.95,.85), frameon=False, fontsize=4.5, ncol=1);\n",
    "vp.legend(bbox_to_anchor=(.32,.24), frameon=False, fontsize=4.5, ncol=1);\n",
    "plt.xlabel(\"Sequence length\", fontsize=6);\n",
    "vp.spines[['right', 'top']].set_visible(False);\n",
    "vp.spines['left'].set_linewidth(0.5)\n",
    "vp.spines['bottom'].set_linewidth(0.5)\n",
    "\n",
    "# increase y ticks \n",
    "plt.yticks(np.arange(0, 1.1, .1), fontsize=6)\n",
    "\n",
    "plt.xticks(fontsize=6)\n",
    "plt.yticks(fontsize=6);\n",
    "\n",
    "plt.savefig(\"seqlen.pdf\", \n",
    "               bbox_inches='tight', \n",
    "               transparent=False,\n",
    "               dpi=300,\n",
    "               pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homology-aware validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TR0-TS0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>WL</th>\n",
       "      <th>INF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RNAfold</th>\n",
       "      <td>0.508013</td>\n",
       "      <td>0.680951</td>\n",
       "      <td>0.519687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearPartition-C</th>\n",
       "      <td>0.547173</td>\n",
       "      <td>0.737353</td>\n",
       "      <td>0.555723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MXfold2</th>\n",
       "      <td>0.502317</td>\n",
       "      <td>0.735587</td>\n",
       "      <td>0.508717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.579879</td>\n",
       "      <td>0.742644</td>\n",
       "      <td>0.589669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.620637</td>\n",
       "      <td>0.799825</td>\n",
       "      <td>0.625906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.631124</td>\n",
       "      <td>0.827189</td>\n",
       "      <td>0.641295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         F1        WL       INF\n",
       "method                                         \n",
       "RNAfold            0.508013  0.680951  0.519687\n",
       "LinearPartition-C  0.547173  0.737353  0.555723\n",
       "MXfold2            0.502317  0.735587  0.508717\n",
       "UFold              0.579879  0.742644  0.589669\n",
       "REDfold            0.620637  0.799825  0.625906\n",
       "sincFold           0.631124  0.827189  0.641295"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import json \n",
    "from sincfold.metrics import WL, f1_strict, mcc\n",
    "from sincfold.utils import  dot2bp, bp2matrix\n",
    "\n",
    "def get_metrics(x):\n",
    "    try:\n",
    "        if x.method in dotbracket_format:\n",
    "            pred = dot2bp(x[\"folding\"])\n",
    "        else:\n",
    "            pred = json.loads(x[\"folding\"])\n",
    "            \n",
    "        _, _, f1 = f1_strict(x[\"ref\"], pred)\n",
    "        pred_mat = bp2matrix(x[\"L\"], pred).numpy()\n",
    "        ref_mat = bp2matrix(x[\"L\"], x[\"ref\"]).numpy()\n",
    "        wl = WL(pred_mat, ref_mat, x[\"sequence\"])\n",
    "        inf = mcc(pred_mat, ref_mat)\n",
    "    except:\n",
    "        print(x.name, x.method)\n",
    "    \n",
    "    return f1, wl, inf\n",
    "\n",
    "dotbracket_format = [\"RNAstructure\", \"LinearFold\", \"REDfold\", \"RNAfold\",\n",
    "                          \"LinearFold-C\", \"LinearFold-V\", \"LinearPartition-C\", \"LinearPartition-V\", \"MXfold2\"]\n",
    "\n",
    "summary = pd.read_csv(f\"TR0-TS0.csv\", index_col=\"id\")\n",
    "\n",
    "dataset = pd.read_csv(f\"../data/TR0-TS0.csv\", index_col=\"id\")\n",
    "splits = pd.read_csv(f\"../data/TR0-TS0_splits.csv\", index_col=\"id\")\n",
    "dataset = dataset.loc[splits[splits.partition==\"TS0\"].index]\n",
    "\n",
    "# add references and compute F1 per sequence\n",
    "summary[\"ref\"] = dataset.loc[summary.index][\"base_pairs\"].apply(lambda x: json.loads(x))    \n",
    "summary[\"L\"] = dataset.loc[summary.index].sequence.str.len()    \n",
    "summary[\"sequence\"] = dataset.loc[summary.index].sequence    \n",
    "# get metrics\n",
    "summary[\"F1\"], summary[\"WL\"], summary[\"INF\"] = zip(*summary.apply(get_metrics, axis=1))\n",
    "summary.groupby([\"method\"])[[\"F1\", \"WL\", \"INF\"]].mean().loc[methods]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inter-family performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>WL</th>\n",
       "      <th>INF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RNAfold</th>\n",
       "      <td>0.580646</td>\n",
       "      <td>0.699369</td>\n",
       "      <td>0.580843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearPartition-C</th>\n",
       "      <td>0.618335</td>\n",
       "      <td>0.738068</td>\n",
       "      <td>0.619280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MXfold2</th>\n",
       "      <td>0.523421</td>\n",
       "      <td>0.669426</td>\n",
       "      <td>0.522947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.351484</td>\n",
       "      <td>0.602634</td>\n",
       "      <td>0.370008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.348574</td>\n",
       "      <td>0.609427</td>\n",
       "      <td>0.366430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.413938</td>\n",
       "      <td>0.631354</td>\n",
       "      <td>0.432180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         F1        WL       INF\n",
       "method                                         \n",
       "RNAfold            0.580646  0.699369  0.580843\n",
       "LinearPartition-C  0.618335  0.738068  0.619280\n",
       "MXfold2            0.523421  0.669426  0.522947\n",
       "UFold              0.351484  0.602634  0.370008\n",
       "REDfold            0.348574  0.609427  0.366430\n",
       "sincFold           0.413938  0.631354  0.432180"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import json \n",
    "from sincfold.metrics import WL, f1_strict, mcc\n",
    "from sincfold.utils import  dot2bp, bp2matrix\n",
    "\n",
    "def get_metrics(x):\n",
    "    try:\n",
    "        if x.method in dotbracket_format:\n",
    "            pred = dot2bp(x[\"folding\"])\n",
    "        else:\n",
    "            pred = json.loads(x[\"folding\"])\n",
    "            \n",
    "        _, _, f1 = f1_strict(x[\"ref\"], pred)\n",
    "        pred_mat = bp2matrix(x[\"L\"], pred).numpy()\n",
    "        ref_mat = bp2matrix(x[\"L\"], x[\"ref\"]).numpy()\n",
    "        wl = WL(pred_mat, ref_mat, x[\"sequence\"])\n",
    "        inf = mcc(pred_mat, ref_mat)\n",
    "    except:\n",
    "        print(x.name, x.method)\n",
    "    \n",
    "    return f1, wl, inf\n",
    "\n",
    "dotbracket_format = [\"RNAstructure\", \"LinearFold\", \"REDfold\", \n",
    "                          \"LinearFold-C\", \"LinearFold-V\", \"LinearPartition-C\", \"LinearPartition-V\", \"MXfold2\"]\n",
    "\n",
    "summary = pd.read_csv(f\"ArchiveII_famfold.csv\", index_col=\"id\")\n",
    "\n",
    "dataset = pd.read_csv(f\"../data/ArchiveII.csv\", index_col=\"id\")\n",
    "\n",
    "# add references and compute F1 per sequence\n",
    "summary[\"ref\"] = dataset.loc[summary.index][\"base_pairs\"].apply(lambda x: json.loads(x))    \n",
    "summary[\"L\"] = dataset.loc[summary.index].sequence.str.len()    \n",
    "summary[\"sequence\"] = dataset.loc[summary.index].sequence    \n",
    "# get metrics\n",
    "summary[\"F1\"], summary[\"WL\"], summary[\"INF\"] = zip(*summary.apply(get_metrics, axis=1))\n",
    "summary.groupby([\"method\"])[[\"F1\", \"WL\", \"INF\"]].mean().loc[methods]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family</th>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">16s</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.366000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.332391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.391664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">23s</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.401774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.393633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.402430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">5s</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.472025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.377045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.438967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RNaseP</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.357543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.421389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.442618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">grp1</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.310811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.428962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.349969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">srp</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.178760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.199409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.249175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">tRNA</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.414961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.491916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.685143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">telomerase</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.112168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.177278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.154173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">tmRNA</th>\n",
       "      <th>REDfold</th>\n",
       "      <td>0.274052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UFold</th>\n",
       "      <td>0.346815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sincFold</th>\n",
       "      <td>0.350146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           F1\n",
       "family     method            \n",
       "16s        REDfold   0.366000\n",
       "           UFold     0.332391\n",
       "           sincFold  0.391664\n",
       "23s        REDfold   0.401774\n",
       "           UFold     0.393633\n",
       "           sincFold  0.402430\n",
       "5s         REDfold   0.472025\n",
       "           UFold     0.377045\n",
       "           sincFold  0.438967\n",
       "RNaseP     REDfold   0.357543\n",
       "           UFold     0.421389\n",
       "           sincFold  0.442618\n",
       "grp1       REDfold   0.310811\n",
       "           UFold     0.428962\n",
       "           sincFold  0.349969\n",
       "srp        REDfold   0.178760\n",
       "           UFold     0.199409\n",
       "           sincFold  0.249175\n",
       "tRNA       REDfold   0.414961\n",
       "           UFold     0.491916\n",
       "           sincFold  0.685143\n",
       "telomerase REDfold   0.112168\n",
       "           UFold     0.177278\n",
       "           sincFold  0.154173\n",
       "tmRNA      REDfold   0.274052\n",
       "           UFold     0.346815\n",
       "           sincFold  0.350146"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary[summary.method.isin([\"sincFold\", \"UFold\", \"REDfold\"])].groupby([\"family\", \"method\"])[[\"F1\"]].mean()"
   ]
  }
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