run_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import json\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from scripts.dataset.dataloader import SemEvalTask8, collate_fn\n",
    "from scripts.models.lstmer import LSTMER\n",
    "\n",
    "from scripts.utils.test_utils import get_predictions, decode_vocab\n",
    "from scripts.utils.test_utils import write_results, calculate_scores\n",
    "from scripts.utils.utils import seed_everything, plot_bar, plot_hist\n",
    "from scripts.utils.gpu_cuda_helper import select_device\n",
    "\n",
    "import warnings\n",
    "from dgl.base import DGLWarning\n",
    "\n",
    "warnings.simplefilter(\"ignore\", DGLWarning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 9001\n",
    "seed_everything(seed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_dir = \"dataset/semeval_task8/processed/\"\n",
    "tree_type = [\"shortest_path\", \"full_tree\"]\n",
    "config_path = \"scripts/config.json\"\n",
    "checkpoints_dir = Path(\"/srv/data/zarzouram/lt2011/checkpoints/\")\n",
    "answer_dir = Path(\"dataset/semeval_task8/processed/\").resolve().expanduser()\n",
    "answer_keys_path = Path(\n",
    "    \"dataset/semeval_task8/processed/answer_keys.txt\").resolve().expanduser()\n",
    "scorer_path = Path(\n",
    "    \"dataset/semeval_task8/SemEval2010_task8_scorer-v1.2/semeval2010_task8_scorer-v1.2.pl\"  # noqa: E501\n",
    ").resolve().expanduser()  # noqa: E501\n",
    "\n",
    "checkpoints = {\n",
    "    tree_type[0]: str(checkpoints_dir / \"1005.0200/checkpoint_best.pth.tar\"),\n",
    "    tree_type[1]: str(checkpoints_dir / \"1005.0359/checkpoint_best.pth.tar\")\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = torch.load(Path(ds_dir) / \"raw_train.pt\")\n",
    "val_data = torch.load(Path(ds_dir) / \"raw_val.pt\")\n",
    "test_data = torch.load(Path(ds_dir) / \"raw_test.pt\")\n",
    "\n",
    "_, _, ents_trn, _, dep_trn, _, _, _, _, rel_trn, dir_trn = train_data\n",
    "_, _, ents_val, _, dep_val, _, _, _, _, rel_val, dir_val = val_data\n",
    "_, _, ents_tst, _, dep_tst, _, _, _, _, rel_tst, dir_tst = test_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1105.92x898.56 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_bar(rel=[rel_trn, rel_val, rel_tst], dir=[dir_trn, dir_val, dir_tst])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabs = torch.load(Path(ds_dir) / \"vocabs.pt\")\n",
    "pad_values = {\n",
    "    \"token_pad_value\": vocabs[\"tokens\"][\"<pad>\"],\n",
    "    \"pos_pad_value\": vocabs[\"pos\"][\"<pad>\"],\n",
    "    \"dep_pad_value\": vocabs[\"dep\"][\"<pad>\"],\n",
    "    \"entl_pad_value\": vocabs[\"ents\"][\"<pad>\"]\n",
    "}\n",
    "vocab_sizes = {\n",
    "    \"token_vocab_sz\": len(vocabs[\"tokens\"]),\n",
    "    \"pos_vocab_sz\": len(vocabs[\"pos\"]),\n",
    "    \"dep_vocab_sz\": len(vocabs[\"dep\"]),\n",
    "    \"entl_vocab_sz\": len(vocabs[\"ents\"]),\n",
    "    \"rel_num\": len(vocabs[\"rels\"])\n",
    "}\n",
    "\n",
    "train_ds = SemEvalTask8(Path(ds_dir) / \"train.pt\")\n",
    "val_ds = SemEvalTask8(Path(ds_dir) / \"val.pt\")\n",
    "test_ds = SemEvalTask8(Path(ds_dir) / \"test.pt\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " cuda id   Memory usage  \n",
      "    0          0.02%     \n",
      "    1          0.02%     \n",
      "    2          0.02%     \n",
      "    3          0.02%     \n",
      "selected device is cuda:3.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "device = select_device(\"gpu\")\n",
    "print(f\"selected device is {device}.\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load confuguration file\n",
    "with open(config_path, \"r\") as f:\n",
    "    config = json.load(f)\n",
    "e1_tag = config[\"dataset\"][\"tags\"][\"ent\"][\"e1\"]\n",
    "e2_tag = config[\"dataset\"][\"tags\"][\"ent\"][\"e2\"]\n",
    "es_tag = [vocabs[\"ents\"][e1_tag], vocabs[\"ents\"][e2_tag]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1105.92x449.28 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_loader = DataLoader(train_ds,\n",
    "                          batch_size=len(train_ds),\n",
    "                          shuffle=False,\n",
    "                          collate_fn=collate_fn(list(pad_values.values()),\n",
    "                                                \"shortest_path\"))\n",
    "test_loader = DataLoader(test_ds,\n",
    "                         batch_size=len(test_ds),\n",
    "                         shuffle=False,\n",
    "                         collate_fn=collate_fn(list(pad_values.values()),\n",
    "                                               \"shortest_path\"))\n",
    "\n",
    "num_nodes = []\n",
    "dep_tags = []\n",
    "rels = []\n",
    "ent_dep = []\n",
    "for data_loader in [train_loader, test_loader]:\n",
    "    count = 0\n",
    "    for batch in data_loader:\n",
    "        num_nodes.append(batch[6]._batch_num_nodes[\"_N\"])\n",
    "        dep_tags.append(batch[4])\n",
    "        rels.append(batch[-2])\n",
    "        idx0 = torch.repeat_interleave(torch.arange(num_nodes[-1].size(0)),\n",
    "                                       num_nodes[-1])\n",
    "        tokens_idx = batch[6].ndata[\"ID\"]\n",
    "        ent_dep.append(\n",
    "            torch.split(dep_tags[-1][idx0, tokens_idx],\n",
    "                        num_nodes[-1].tolist()))\n",
    "\n",
    "title = \"Histogram of number of nodes of the shortest path in train and test splits \"\n",
    "label_x = \"number of nodes\"\n",
    "label_y = \"frequency\"\n",
    "label_hist = [\"train split\", \"test split\"]\n",
    "fig_data = {\n",
    "    \"label_h\": label_hist,\n",
    "    \"xlabel\": label_x,\n",
    "    \"ylabel\": label_y,\n",
    "    \"title\": title\n",
    "}\n",
    "\n",
    "figs, axes = plot_hist(data=[nn.tolist() for nn in num_nodes],\n",
    "                       fig_data=fig_data,\n",
    "                       bins=[\n",
    "                           list(range(2, max(num_nodes[0]))),\n",
    "                           list(range(2, max(num_nodes[1])))\n",
    "                       ],\n",
    "                       count=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, clear_output, HTML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "idx = torch.nonzero(num_nodes[0] == 2)\n",
    "dep = torch.stack([ent_dep[0][i] for i in idx])\n",
    "rel = rels[0][num_nodes[0] == 2]\n",
    "dep0, dep1 = dep[:, 0], dep[:, 1]\n",
    "\n",
    "dep0 = vocabs[\"dep\"].lookup_tokens(dep0.tolist())\n",
    "dep1 = vocabs[\"dep\"].lookup_tokens(dep1.tolist())\n",
    "rel = vocabs[\"rels\"].lookup_tokens(rel.tolist())\n",
    "\n",
    "rel_labels = sorted(vocabs[\"rels\"].get_itos())\n",
    "dep_label = np.unique(np.array(dep0))\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    \"rel\": rel,\n",
    "    \"dep_0\": dep0,\n",
    "    \"dep_1\": dep1,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_reldep = df.groupby([\"rel\", \"dep_0\"])[\"dep_0\"].agg(\"count\")\n",
    "df_reldep = df_reldep.to_frame(name=\"count\")\n",
    "df_reldep[\"percent\"] = df_reldep.groupby(\n",
    "    level=0).transform(lambda x: x / sum(x) * 100).round(2)\n",
    "df_reldep = df_reldep.reset_index()\n",
    "df_rest_ids = df_reldep[df_reldep[\"percent\"] < 5.].index\n",
    "df_reldep.loc[df_rest_ids, \"dep_0\"] = \"rest\"\n",
    "\n",
    "df_reldep = df_reldep.groupby([\"rel\", \"dep_0\"]).sum()\n",
    "\n",
    "fig = plt.figure(figsize=(12, 10), constrained_layout=True, facecolor=\"w\")\n",
    "spec = fig.add_gridspec(ncols=len(rel_labels), nrows=1)\n",
    "fig.suptitle(\n",
    "    \"Dependencies frequency of occurrence\\ncalculated per relation label\\nin the shortest-path graphs with two nodes.\",\n",
    "    fontsize=14,\n",
    "    y=1.1)\n",
    "\n",
    "df_reldep = df_reldep.reset_index().sort_values(by=[\"percent\", \"dep_0\"])\n",
    "dep_sorted_map = dict.fromkeys(df_reldep[\"dep_0\"].astype(str).values.tolist())\n",
    "dep_sorted_map.update((k, i) for i, k in enumerate(dep_sorted_map))\n",
    "\n",
    "cmap = matplotlib.cm.get_cmap(\"jet\")\n",
    "\n",
    "axes = []\n",
    "for c, r in enumerate(rel_labels):\n",
    "    ax = fig.add_subplot(spec[0, c])\n",
    "    axes.append(ax)\n",
    "    df_r = df_reldep[df_reldep[\"rel\"] == r]\n",
    "    dep_tags = df_r[\"dep_0\"].astype(str).values.tolist()\n",
    "    values = df_r[\"percent\"].astype(float).values.tolist()\n",
    "\n",
    "    values_sorted = [0.] * len(dep_sorted_map)\n",
    "    for d, v in zip(dep_tags, values):\n",
    "        idx = dep_sorted_map[d]\n",
    "        values_sorted[idx] = v\n",
    "\n",
    "    y_ticks = list(dep_sorted_map.values())\n",
    "    norm = matplotlib.colors.Normalize(vmin=min(values), vmax=max(values))\n",
    "\n",
    "    bar = axes[-1].barh(y_ticks,\n",
    "                        values_sorted,\n",
    "                        color=[cmap(norm(v)) for v in values_sorted])\n",
    "    _ = axes[-1].spines[\"top\"].set_color(\"#DDDDDD\")\n",
    "    _ = axes[-1].spines[\"right\"].set_visible(False)\n",
    "    _ = axes[-1].spines[\"left\"].set_color(\"#DDDDDD\")\n",
    "    _ = axes[-1].spines[\"bottom\"].set_color(\"#DDDDDD\")\n",
    "    _ = axes[-1].yaxis.grid(False)\n",
    "    _ = axes[-1].xaxis.grid(False)\n",
    "    _ = axes[-1].set_xlim(0, 100)\n",
    "    _ = axes[-1].xaxis.set_tick_params(labeltop=True)\n",
    "\n",
    "    xlabel = r if r == \"Other\" else \"\\n\".join(r.split(\"-\"))\n",
    "    _ = axes[-1].set_xlabel(xlabel, labelpad=8, fontsize=8)\n",
    "\n",
    "    if c == 0:\n",
    "        _ = axes[0].set_yticks(y_ticks)\n",
    "        _ = axes[0].set_yticklabels(list(dep_sorted_map.keys()))\n",
    "        _ = axes[0].set_ylabel(\"Dependency\", labelpad=12, fontsize=10)\n",
    "        _ = axes[0].tick_params(bottom=True, left=False, top=True)\n",
    "    else:\n",
    "        _ = axes[-1].tick_params(bottom=True, left=False, top=True)\n",
    "        _ = axes[-1].set_yticklabels([])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_rel = df.groupby([\"rel\", \"dep_0\", \"dep_1\"])[\"dep_1\"].agg(\"count\")\n",
    "# df_rel = df_rel.to_frame(name=\"count\")\n",
    "# df_rel[\"percent\"] = df_rel.groupby(\n",
    "#     level=0).transform(lambda x: x / sum(x) * 100).round(2)\n",
    "# df_rel = df_rel.reset_index()\n",
    "# df_rest_ids = df_rel[df_rel[\"percent\"] < 5.].index\n",
    "# df_rel.loc[df_rest_ids, [\"dep_0\", \"dep_1\"]] = \"rest\"\n",
    "\n",
    "# df_rel = df_rel.groupby([\"rel\", \"dep_0\", \"dep_1\"]).sum()\n",
    "\n",
    "# df_dep = df.groupby([\"dep_0\", \"rel\", \"dep_1\"])[\"dep_1\"].agg(\"count\")\n",
    "# df_dep = df_dep.to_frame(name=\"count\")\n",
    "# df_dep[\"percent\"] = df_dep.groupby(\n",
    "#     level=0).transform(lambda x: x / sum(x) * 100).round(2)\n",
    "# df_dep = df_dep.reset_index()\n",
    "# df_rest_ids = df_dep[df_dep[\"percent\"] < 5.].index\n",
    "# df_dep.loc[df_rest_ids, [\"rel\", \"dep_1\"]] = \"rest\"\n",
    "\n",
    "# df_dep = df_dep.groupby([\"dep_0\", \"rel\", \"dep_1\"]).sum()\n",
    "\n",
    "# display(HTML(df[\"percent\"].loc[:, \"nmod\"].to_frame().to_html()))\n",
    "# df_dep[\"percent\"].loc[\"nsubj\"].unstack().plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import ipywidgets as widgets\n",
    "# import numpy as np\n",
    "\n",
    "# dep0_label = np.unique(np.array(dep0))\n",
    "\n",
    "# out_plot = widgets.Output()\n",
    "# out_df = widgets.Output(\n",
    "#     layout=widgets.Layout(height=\"100%\", width=\"30%\", border=\"solid\"))\n",
    "\n",
    "# def f(*args):\n",
    "#     out_plot.clear_output()\n",
    "#     with out_plot:\n",
    "#         clear_output()\n",
    "#         fig, ax = plt.subplots(figsize=(6, 4))\n",
    "#         fig.show()\n",
    "\n",
    "# filter_dropdown = widgets.Dropdown(\n",
    "#     value=\"\",\n",
    "#     options=[\"\", \"Relation Labels\", \"Node 0 Dependency\"],\n",
    "#     description=\"Filter by\")\n",
    "\n",
    "# focus_dropdown = widgets.Dropdown(disabled=True,\n",
    "#                                   options=[],\n",
    "#                                   description=\"Select an item\")\n",
    "\n",
    "# def filter_selected(change):\n",
    "#     if change.new != \"\":\n",
    "#         focus_dropdown.disabled = False\n",
    "#         if change.new == \"Relation Labels\":\n",
    "#             focus_dropdown.options = rel_labels\n",
    "#         else:\n",
    "#             focus_dropdown.options = dep0_label\n",
    "\n",
    "#     else:\n",
    "#         focus_dropdown.options = []\n",
    "#         focus_dropdown.disabled = True\n",
    "\n",
    "# filter_dropdown.observe(filter_selected, \"value\")\n",
    "\n",
    "# user_interact_form = widgets.VBox([filter_dropdown, focus_dropdown, out_plot],\n",
    "#                                   layout=widgets.Layout(height=\"100%\",\n",
    "#                                                         width=\"70%\",\n",
    "#                                                         border=\"solid\"))\n",
    "# form = widgets.HBox([user_interact_form, out_df])\n",
    "# display(form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3be685b4894a48fdb14298f3e25b476a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2716 [00:00<?, ?step/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08e1af08ddb64143861679237fddcf07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2716 [00:00<?, ?step/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = {}\n",
    "neg_rel = vocabs[\"rels\"][\"Other\"]\n",
    "dir_ids = [\n",
    "    vocabs[\"rel_dir\"][\"ndir\"], vocabs[\"rel_dir\"][\"(e1,e2)\"],\n",
    "    vocabs[\"rel_dir\"][\"(e2,e1)\"]\n",
    "]\n",
    "for t_type, path in checkpoints.items():\n",
    "\n",
    "    data_loader = DataLoader(test_ds,\n",
    "                             batch_size=1,\n",
    "                             shuffle=False,\n",
    "                             num_workers=4,\n",
    "                             collate_fn=collate_fn(list(pad_values.values()),\n",
    "                                                   t_type))\n",
    "\n",
    "    model_hyp = config[\"model_hyperp\"]\n",
    "    model_hyp[\"dep_tree\"][\"tree_type\"] = t_type\n",
    "    model = LSTMER(**vocab_sizes,\n",
    "                   **pad_values,\n",
    "                   **model_hyp[\"embeddings\"],\n",
    "                   **model_hyp[\"entity_detect\"],\n",
    "                   **model_hyp[\"dep_tree\"],\n",
    "                   **model_hyp[\"rel_extract\"],\n",
    "                   **model_hyp[\"modes\"],\n",
    "                   dropouts=model_hyp[\"dropouts\"])\n",
    "\n",
    "    # load checkpoint\n",
    "    state = torch.load(path, map_location=torch.device(\"cpu\"))\n",
    "    model.load_state_dict(state[\"model\"])\n",
    "    model = model.to(device)\n",
    "\n",
    "    model.eval()\n",
    "    pb = tqdm(data_loader, total=len(data_loader), unit=\"step\")\n",
    "    ids = []\n",
    "    rel_allpreds, dir_allpreds = [], []\n",
    "    rel_alltargets, dir_alltargets = [], []\n",
    "    for step, batch in enumerate(pb):\n",
    "        # set progress bar description and metrics\n",
    "        pb.set_description(f\"Tree Type: {t_type}\")\n",
    "        with torch.no_grad():\n",
    "            # move data to device\n",
    "            batch = [b.to(device) for b in batch]\n",
    "            logits_12, logits_21, *_ = model(batch, es_tag)\n",
    "            rel_preds, dir_preds = get_predictions(logits_12, logits_21,\n",
    "                                                   neg_rel, dir_ids)\n",
    "            rel_targets, dir_targets = batch[-2:]\n",
    "            ids.append(batch[0])\n",
    "            rel_allpreds.append(rel_preds)\n",
    "            dir_allpreds.append(dir_preds)\n",
    "            rel_alltargets.append(rel_targets)\n",
    "            dir_alltargets.append(dir_targets)\n",
    "\n",
    "    ids = torch.cat(ids).tolist()\n",
    "    rel_allpreds = torch.cat(rel_allpreds).tolist()\n",
    "    dir_allpreds = torch.cat(dir_allpreds).tolist()\n",
    "\n",
    "    rel_preds_str = decode_vocab(vocabs[\"rels\"], rel_allpreds)\n",
    "    dir_preds_str = decode_vocab(vocabs[\"rel_dir\"], dir_allpreds)\n",
    "\n",
    "    answer_path = str(answer_dir / f\"{t_type}_answers.txt\")\n",
    "    write_results(ids, rel_preds_str, dir_preds_str, answer_path)\n",
    "    p, r, f1, acc, scores, result = calculate_scores(str(scorer_path),\n",
    "                                                     answer_path,\n",
    "                                                     str(answer_keys_path))\n",
    "\n",
    "    results[t_type] = {\n",
    "        \"f1\": f1,\n",
    "        \"accuracy\": acc,\n",
    "        \"precision\": p,\n",
    "        \"recall\": r,\n",
    "        \"scores\": scores,\n",
    "        \"result\": result\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shortest_path  : f1_score: 76.30%, Precision: 76.52%, Recall: 76.35%\n",
      "full_tree      : f1_score: 75.26%, Precision: 75.79%, Recall: 75.07%\n"
     ]
    }
   ],
   "source": [
    "for t_type in results:\n",
    "    print(\n",
    "        f\"{t_type:15s}: f1_score: {results[t_type]['f1']:.2f}%, Precision: {results[t_type]['precision']}%, Recall: {results[t_type]['recall']}%\"\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">p</th>\n",
       "      <th colspan=\"2\" halign=\"left\">r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>full_tree</th>\n",
       "      <th>shortest_path</th>\n",
       "      <th>full_tree</th>\n",
       "      <th>shortest_path</th>\n",
       "      <th>full_tree</th>\n",
       "      <th>shortest_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cause-Effect</th>\n",
       "      <td>84.24</td>\n",
       "      <td>87.38</td>\n",
       "      <td>86.26</td>\n",
       "      <td>90.52</td>\n",
       "      <td>82.32</td>\n",
       "      <td>84.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component-Whole</th>\n",
       "      <td>71.36</td>\n",
       "      <td>68.50</td>\n",
       "      <td>71.02</td>\n",
       "      <td>68.83</td>\n",
       "      <td>71.70</td>\n",
       "      <td>68.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Content-Container</th>\n",
       "      <td>80.58</td>\n",
       "      <td>83.13</td>\n",
       "      <td>75.45</td>\n",
       "      <td>78.34</td>\n",
       "      <td>86.46</td>\n",
       "      <td>88.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entity-Destination</th>\n",
       "      <td>85.23</td>\n",
       "      <td>86.45</td>\n",
       "      <td>83.55</td>\n",
       "      <td>81.71</td>\n",
       "      <td>86.99</td>\n",
       "      <td>91.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entity-Origin</th>\n",
       "      <td>82.40</td>\n",
       "      <td>78.75</td>\n",
       "      <td>79.71</td>\n",
       "      <td>79.22</td>\n",
       "      <td>85.27</td>\n",
       "      <td>78.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instrument-Agency</th>\n",
       "      <td>56.34</td>\n",
       "      <td>63.95</td>\n",
       "      <td>62.50</td>\n",
       "      <td>68.12</td>\n",
       "      <td>51.28</td>\n",
       "      <td>60.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Member-Collection</th>\n",
       "      <td>77.61</td>\n",
       "      <td>79.75</td>\n",
       "      <td>77.12</td>\n",
       "      <td>77.64</td>\n",
       "      <td>78.11</td>\n",
       "      <td>81.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Message-Topic</th>\n",
       "      <td>74.07</td>\n",
       "      <td>73.60</td>\n",
       "      <td>80.00</td>\n",
       "      <td>76.99</td>\n",
       "      <td>68.97</td>\n",
       "      <td>70.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product-Producer</th>\n",
       "      <td>65.49</td>\n",
       "      <td>65.18</td>\n",
       "      <td>66.52</td>\n",
       "      <td>67.28</td>\n",
       "      <td>64.50</td>\n",
       "      <td>63.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_Other</th>\n",
       "      <td>39.57</td>\n",
       "      <td>36.68</td>\n",
       "      <td>38.66</td>\n",
       "      <td>36.36</td>\n",
       "      <td>40.53</td>\n",
       "      <td>37.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          f1                       p                       r  \\\n",
       "                   full_tree shortest_path full_tree shortest_path full_tree   \n",
       "Cause-Effect           84.24         87.38     86.26         90.52     82.32   \n",
       "Component-Whole        71.36         68.50     71.02         68.83     71.70   \n",
       "Content-Container      80.58         83.13     75.45         78.34     86.46   \n",
       "Entity-Destination     85.23         86.45     83.55         81.71     86.99   \n",
       "Entity-Origin          82.40         78.75     79.71         79.22     85.27   \n",
       "Instrument-Agency      56.34         63.95     62.50         68.12     51.28   \n",
       "Member-Collection      77.61         79.75     77.12         77.64     78.11   \n",
       "Message-Topic          74.07         73.60     80.00         76.99     68.97   \n",
       "Product-Producer       65.49         65.18     66.52         67.28     64.50   \n",
       "_Other                 39.57         36.68     38.66         36.36     40.53   \n",
       "\n",
       "                                  \n",
       "                   shortest_path  \n",
       "Cause-Effect               84.45  \n",
       "Component-Whole            68.17  \n",
       "Content-Container          88.54  \n",
       "Entity-Destination         91.78  \n",
       "Entity-Origin              78.29  \n",
       "Instrument-Agency          60.26  \n",
       "Member-Collection          81.97  \n",
       "Message-Topic              70.50  \n",
       "Product-Producer           63.20  \n",
       "_Other                     37.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = results[\"full_tree\"][\"scores\"].keys()\n",
    "scores = {\n",
    "    label: {\n",
    "        (\"p\", t): 0,\n",
    "        (\"r\", t): 0,\n",
    "        (\"f1\", t): 0\n",
    "    }\n",
    "    for label in labels for t in results.keys()\n",
    "}\n",
    "for t_type, scores_dict in results.items():\n",
    "    for label, (p, r, f) in scores_dict[\"scores\"].items():\n",
    "        scores[label][(\"p\", t_type)] = p\n",
    "        scores[label][(\"r\", t_type)] = r\n",
    "        scores[label][(\"f1\", t_type)] = f\n",
    "\n",
    "scores_new = {}\n",
    "for label, scores_dict in scores.items():\n",
    "    keys_sorted = sorted(list(scores_dict.keys()), key=lambda x: x[0])\n",
    "    scores_dict_sorted = {k: scores_dict[k] for k in keys_sorted}\n",
    "    scores_new[label] = scores_dict_sorted\n",
    "\n",
    "df = pd.DataFrame.from_dict(scores_new, orient=\"index\")\n",
    "display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "import seaborn as sns\n",
    "\n",
    "confusion_matrix_lstr = results[\"full_tree\"][\"result\"].strip().split(\"\\n\")[:15]\n",
    "cm_str = list(map(lambda x: re.findall(r\"\\d+\", x),\n",
    "                  confusion_matrix_lstr[3:-2]))\n",
    "cm = [[int(c) for c in cm[:10]] for cm in cm_str]\n",
    "labels = confusion_matrix_lstr[1].strip().split()[:10]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10), facecolor=\"w\")\n",
    "sns.heatmap(cm,\n",
    "            annot=True,\n",
    "            fmt=\".2f\",\n",
    "            xticklabels=labels,\n",
    "            yticklabels=labels)\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "_ = fig.suptitle(\"Confusion Matrix - FullTree Model\", fontsize=14)\n",
    "_ = plt.tight_layout(w_pad=1)\n",
    "# print(\"\\n\".join(confusion_matrix_lstr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_lstr = results[\"shortest_path\"][\"result\"].strip().split(\"\\n\")[:15]\n",
    "cm_str = list(map(lambda x: re.findall(r\"\\d+\", x),\n",
    "                  confusion_matrix_lstr[3:-2]))\n",
    "cm = [[int(c) for c in cm[:10]] for cm in cm_str]\n",
    "labels = confusion_matrix_lstr[1].strip().split()[:10]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(cm,\n",
    "            annot=True,\n",
    "            fmt=\".2f\",\n",
    "            xticklabels=labels,\n",
    "            yticklabels=labels)\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "_ = fig.suptitle(\"Confusion Matrix - Shortest Path Model\", fontsize=14)\n",
    "_ = plt.tight_layout(w_pad=1)\n",
    "# print(\"\\n\".join(results[\"shortest_path\"][\"result\"].strip().split(\"\\n\")[:15]))"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "cf37ef9cc0c57077adfd2b3961027ab0306b020050581ebc62ad0a1918cd2e27"
  },
  "kernelspec": {
   "display_name": "Python 3.8.12 ('restlstm')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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