malidup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Malidup Benchmark</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/Bio/SubsMat/__init__.py:126: BiopythonDeprecationWarning: Bio.SubsMat has been deprecated, and we intend to remove it in a future release of Biopython. As an alternative, please consider using Bio.Align.substitution_matrices as a replacement, and contact the Biopython developers if you still need the Bio.SubsMat module.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy.stats import sem\n",
    "from Bio import pairwise2\n",
    "from Bio.SubsMat import MatrixInfo as matlist\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "from plmalign.dataset.score import alignment_score, score_global_alignment, score_local_alignment\n",
    "from plmalign.dataset.parse_mali import read_mali, read_mali_mammoth\n",
    "\n",
    "from plmalign.dataset.utils import revstate_f, state_f\n",
    "matrix = matlist.blosum62\n",
    "def nw_f(x, y):\n",
    "    alignments = pairwise2.align.globaldx(x, y, matrix)\n",
    "    states = list(map(state_f, zip(list(alignments[0].seqA), list(alignments[0].seqB))))\n",
    "    states = ''.join(list(map(revstate_f, states)))\n",
    "    return states\n",
    "\n",
    "def sw_f(x, y):\n",
    "    alignments = pairwise2.align.localdx(x, y, matrix)\n",
    "    states = list(map(state_f, zip(list(alignments[0][0]), list(alignments[0][1]))))\n",
    "    states = ''.join(list(map(revstate_f, states)))\n",
    "    return states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "malidup_root = './data/alignment_benchmark/malidup'\n",
    "\n",
    "mammoth_dup_root = './data/alignment_benchmark/mammoth/malidup'\n",
    "\n",
    "mali_root = malidup_root\n",
    "mali_mammoth = mammoth_dup_root\n",
    "benchmark = 'malidup'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Load manual alignment</h3>\n",
    "<h3>Smith-Waterman and Needleman-Wunsch</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load manual alignment\n",
    "mammoth = read_mali_mammoth(mali_mammoth, report_ids=True)\n",
    "manual = read_mali(mali_root, tool='manual', report_ids=True)\n",
    "res = pd.merge(manual, mammoth, left_on='dir', right_on='pdb')\n",
    "res = res[['0_x', '1_x', '2_x', '2_y', 'dir']]\n",
    "res = res.rename(columns={'0_x' : 0, '1_x' : 1, '2_x' : 'manual', '2_y': 'mammoth'})\n",
    "res = res[~res.set_index([0, 1]).index.duplicated(keep='first')]\n",
    "res = res.set_index([0, 1])\n",
    "coded = pd.read_csv('./data/alignment_benchmark/pdblistdup.csv', index_col=0)\n",
    "res = pd.merge(res, coded, left_on='dir', right_index=True)\n",
    "\n",
    "#Smith-Waterman and Needleman-Wunsch\n",
    "sw     = res.reset_index().apply(lambda x: sw_f(x[0], x[1]), axis=1)\n",
    "res['smith-waterman'] = sw.values\n",
    "\n",
    "nw     = res.reset_index().apply(lambda x: nw_f(x[0], x[1]), axis=1)\n",
    "res['needleman-wunsch'] = nw.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Gnenrate ESM-1b & ProtT5 embedding</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "448\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda:0\n",
      "##########################\n",
      "Loading cached model from: data/model/Rostlab/prot_t5_xl_uniref50/\n",
      "##########################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at data/model/Rostlab/prot_t5_xl_uniref50/ were not used when initializing T5EncoderModel: ['decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.20.layer.2.DenseReluDense.wo.weight', 'decoder.block.18.layer.1.EncDecAttention.o.weight', 'decoder.block.17.layer.1.EncDecAttention.v.weight', 'decoder.block.11.layer.1.layer_norm.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.20.layer.0.layer_norm.weight', 'decoder.block.23.layer.1.EncDecAttention.o.weight', 'decoder.block.21.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.1.EncDecAttention.v.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'decoder.block.20.layer.1.EncDecAttention.k.weight', 'decoder.block.20.layer.2.layer_norm.weight', 'decoder.block.13.layer.0.SelfAttention.q.weight', 'decoder.block.7.layer.2.DenseReluDense.wo.weight', 'decoder.block.11.layer.0.layer_norm.weight', 'decoder.block.12.layer.0.SelfAttention.k.weight', 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'decoder.block.23.layer.1.EncDecAttention.v.weight', 'decoder.block.20.layer.1.layer_norm.weight', 'decoder.block.3.layer.0.SelfAttention.q.weight', 'decoder.block.7.layer.1.EncDecAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'decoder.block.4.layer.2.DenseReluDense.wo.weight']\n",
      "- This IS expected if you are initializing T5EncoderModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing T5EncoderModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "########################################\n",
      "Total number of sequences: 448\n",
      "Average sequence length: 115.40625\n",
      "Number of sequences >1022: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 13/448 [00:02<01:11,  6.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedded protein DLEAVKSDLEELMTSSQDWWPADYGHYGPLFIRMAWHSAGTYRTADGRGGAAGGRQRFAPINSWPDNANLDKARRLLLPIKQKYGQKISWADLMILAGNVAIESMGFKTFGYAGGREDAFEEDKAVNWGPEDEFETQERFDEPGEIQEGLGASVMGLIYVNPEGPDGNPDPEASAKNIRQTFDRMAMNDKETAALIAGGHTFGKVHGADDPEENLGPEPEAAPIEQQGLGWQNKNGNSKGGEMITSGIEGPWTQSPTEWDMGYINNLLDYEWEPEKGPGGAWQWAPKSEELKNSVPDAHDPDEKQTPMMLTTDIALKRDPDYREVMETFQENPMEFGMNFAKAWYKLTHRDMGPPERFLGPEVPDEEMIWQDPL with length 374 to emb. of shape: torch.Size([374, 1024])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 448/448 [00:17<00:00, 24.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "############# STATS #############\n",
      "Total number of embeddings: 448\n"
     ]
    }
   ],
   "source": [
    "#ProtT5\n",
    "sequences = set()\n",
    "sw     = res.reset_index().apply(lambda x: sequences.add(x[0]), axis=1)\n",
    "sw     = res.reset_index().apply(lambda x: sequences.add(x[1]), axis=1)\n",
    "print(len(sequences))\n",
    "\n",
    "fasta_file = 'data/alignment_benchmark/malidup/protein.fasta'\n",
    "with open(fasta_file, 'w') as handle:\n",
    "    for protein in sequences:\n",
    "        handle.write(f\">{protein}\\n\")\n",
    "        handle.write(f\"{protein}\\n\")\n",
    "\n",
    "from plmalign.embedding_generate import prottrans_embedding_generate\n",
    "import torch\n",
    "\n",
    "embeddings = prottrans_embedding_generate(fasta_file)\n",
    "\n",
    "final_embeddings = {}\n",
    "\n",
    "output_file = 'data/alignment_benchmark/malidup/t5_embedding.pt'\n",
    "\n",
    "torch.save(\n",
    "    embeddings,\n",
    "    output_file,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "448\n",
      "Transferred model to GPU\n",
      "Read data/alignment_benchmark/malidup/protein.fasta with 448 sequences\n",
      "Processing 1 of 5 batches (177 sequences)\n",
      "Processing 2 of 5 batches (125 sequences)\n",
      "Processing 3 of 5 batches (88 sequences)\n",
      "Processing 4 of 5 batches (54 sequences)\n",
      "Processing 5 of 5 batches (4 sequences)\n"
     ]
    }
   ],
   "source": [
    "#ESM-1b\n",
    "sequences = set()\n",
    "sw     = res.reset_index().apply(lambda x: sequences.add(x[0]), axis=1)\n",
    "sw     = res.reset_index().apply(lambda x: sequences.add(x[1]), axis=1)\n",
    "print(len(sequences))\n",
    "\n",
    "fasta_file = 'data/alignment_benchmark/malidup/protein.fasta'\n",
    "with open(fasta_file, 'w') as handle:\n",
    "    for protein in sequences:\n",
    "        handle.write(f\">{protein}\\n\")\n",
    "        handle.write(f\"{protein}\\n\")\n",
    "\n",
    "from plmalign.embedding_generate import esm_embedding_generate\n",
    "import torch\n",
    "\n",
    "embeddings = esm_embedding_generate(fasta_file)\n",
    "\n",
    "final_embeddings = {}\n",
    "\n",
    "output_file = 'data/alignment_benchmark/malidup/esm_embedding.pt'\n",
    "\n",
    "torch.save(\n",
    "    embeddings,\n",
    "    output_file,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>PLMAlign (local and global) and pLM-BLAST (local and global)</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/data/lw/plmalign/plmalign/plmalign_util/numeric.py:138: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  @numba.jit(fastmath=True, cache=True)\n",
      "/mnt/data/lw/plmalign/plmalign/plmblast_util/numeric.py:172: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
      "  @numba.jit(fastmath=True, cache=True)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from plmalign.plmalign import pairwise_align\n",
    "\n",
    "embeddings = torch.load('data/alignment_benchmark/malidup/t5_embedding.pt')\n",
    "#embeddings = torch.load('data/alignment_benchmark/malidup/esm_embedding.pt')\n",
    "\n",
    "def plmblast_sw_f(x, y):\n",
    "    x_e = embeddings[x]\n",
    "    y_e = embeddings[y]\n",
    "    _, alignments = pairwise_align(x_e, y_e, x, y, mode='local', method='plmblast')\n",
    "    first_newline_index = alignments.find('\\n')\n",
    "    x_seq = alignments[:first_newline_index]\n",
    "    second_newline_index = alignments.find('\\n', first_newline_index + 1)\n",
    "    y_seq = alignments[second_newline_index + 1:]\n",
    "    states = list(map(state_f, zip(list(x_seq), list(y_seq))))\n",
    "    states = ''.join(list(map(revstate_f, states)))\n",
    "    return states\n",
    "\n",
    "def plmblast_nw_f(x, y):\n",
    "    x_e = embeddings[x]\n",
    "    y_e = embeddings[y]\n",
    "    _, alignments = pairwise_align(x_e, y_e, x, y, mode='global', method='plmblast')\n",
    "    first_newline_index = alignments.find('\\n')\n",
    "    x_seq = alignments[:first_newline_index]\n",
    "    second_newline_index = alignments.find('\\n', first_newline_index + 1)\n",
    "    y_seq = alignments[second_newline_index + 1:]\n",
    "    states = list(map(state_f, zip(list(x_seq), list(y_seq))))\n",
    "    states = ''.join(list(map(revstate_f, states)))\n",
    "    return states\n",
    "\n",
    "def plmalign_sw_f(x, y):\n",
    "    x_e = embeddings[x]\n",
    "    y_e = embeddings[y]\n",
    "    _, alignments = pairwise_align(x_e, y_e, x, y, mode='local', method='plmalign')\n",
    "    first_newline_index = alignments.find('\\n')\n",
    "    x_seq = alignments[:first_newline_index]\n",
    "    second_newline_index = alignments.find('\\n', first_newline_index + 1)\n",
    "    y_seq = alignments[second_newline_index + 1:]\n",
    "    states = list(map(state_f, zip(list(x_seq), list(y_seq))))\n",
    "    states = ''.join(list(map(revstate_f, states)))\n",
    "    return states\n",
    "\n",
    "def plmalign_nw_f(x, y):\n",
    "    x_e = embeddings[x]\n",
    "    y_e = embeddings[y]\n",
    "    _, alignments = pairwise_align(x_e, y_e, x, y, mode='global', method='plmalign')\n",
    "    first_newline_index = alignments.find('\\n')\n",
    "    x_seq = alignments[:first_newline_index]\n",
    "    second_newline_index = alignments.find('\\n', first_newline_index + 1)\n",
    "    y_seq = alignments[second_newline_index + 1:]\n",
    "    states = list(map(state_f, zip(list(x_seq), list(y_seq))))\n",
    "    states = ''.join(list(map(revstate_f, states)))\n",
    "    return states\n",
    "\n",
    "# plm-blast and plmalign\n",
    "plmalign     = res.reset_index().apply(lambda x: plmalign_sw_f(x[0], x[1]), axis=1)\n",
    "res['plmalign'] = plmalign.values\n",
    "\n",
    "plmalign_global     = res.reset_index().apply(lambda x: plmalign_nw_f(x[0], x[1]), axis=1)\n",
    "res['plmalign_global'] = plmalign_global.values\n",
    "\n",
    "plmblast     = res.reset_index().apply(lambda x: plmblast_sw_f(x[0], x[1]), axis=1)\n",
    "res['plmblast'] = plmblast.values\n",
    "\n",
    "plmblast_global     = res.reset_index().apply(lambda x: plmblast_nw_f(x[0], x[1]), axis=1)\n",
    "res['plmblast_global'] = plmblast_global.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Parse BLAST and HMMER</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Blast and HMMER\n",
    "from plmalign.dataset.parse_hmmer import get_hmmer_alignments\n",
    "from plmalign.dataset.parse_blast import get_blast_alignments\n",
    "\n",
    "hmmer_path = f'./data/alignment_benchmark/hmmer/{benchmark}-hmm.out'\n",
    "blast_path = f'./data/alignment_benchmark/blast/{benchmark}_blast_alignments.xml'\n",
    "\n",
    "hmmer_df = get_hmmer_alignments(hmmer_path, mali_root)\n",
    "blast_df = get_blast_alignments(blast_path, mali_root)\n",
    "\n",
    "ref = read_mali(mali_root, tool='manual', report_ids=True)\n",
    "ref = ref.set_index(['query_id', 'hit_id'])\n",
    "\n",
    "ref_hmmer = pd.merge(ref, hmmer_df.set_index(['query_id', 'hit_id']), \n",
    "                     left_index=True, right_index=True, how='left')\n",
    "ref_hmmer = ref_hmmer.rename(columns={2: 'manual'})\n",
    "ref_blast = pd.merge(ref, blast_df.set_index(['query_id', 'hit_id']), \n",
    "                     left_index=True, right_index=True, how='left')\n",
    "ref_blast = ref_blast.rename(columns={2: 'manual'})\n",
    "\n",
    "# clean up types\n",
    "ref_blast = ref_blast.fillna(-1)\n",
    "ref_blast['query_start'] = ref_blast['query_start'].astype(np.int64)\n",
    "ref_blast['hit_start'] = ref_blast['hit_start'].astype(np.int64)\n",
    "\n",
    "ref_hmmer = ref_hmmer.fillna(-1)\n",
    "ref_hmmer['query_start'] = ref_hmmer['query_start'].astype(np.int64)\n",
    "ref_hmmer['hit_start'] = ref_hmmer['hit_start'].astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are some shit mammoth alignments, filter those out\n",
    "idx = res['mammoth'].apply(len) > 0\n",
    "res = res.loc[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Define scoring functions with predefined kernels, and parallelize with Dask</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/home/lw/anaconda3/envs/plmblast/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "k = [1, 3, 5, 10]\n",
    "\n",
    "blast_stats = score_local_alignment(ref_blast.reset_index(), k, n_cores=30)\n",
    "hmmer_stats = score_local_alignment(ref_hmmer.reset_index(), k, n_cores=30)\n",
    "\n",
    "nw_stats = score_global_alignment(res.reset_index(), 'needleman-wunsch', k, n_cores=30)\n",
    "sw_stats = score_global_alignment(res.reset_index(), 'smith-waterman', k, n_cores=30)\n",
    "plmblast_stats = score_global_alignment(res.reset_index(), 'plmblast', k, n_cores=30)\n",
    "plmblast_global_stats = score_global_alignment(res.reset_index(), 'plmblast_global', k, n_cores=30)\n",
    "plmalign_stats = score_global_alignment(res.reset_index(), 'plmalign', k, n_cores=30)\n",
    "plmalign_global_stats = score_global_alignment(res.reset_index(), 'plmalign_global', k, n_cores=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Perform some dataset massaging to plot</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "blast_stats['tool'] = 'blast'\n",
    "hmmer_stats['tool'] = 'hmmer'\n",
    "nw_stats['tool'] = 'nw'\n",
    "sw_stats['tool'] = 'sw'\n",
    "plmblast_stats['tool'] = 'plmblast'\n",
    "plmblast_global_stats['tool'] = 'plmblast_global'\n",
    "plmalign_stats['tool'] = 'plmalign'\n",
    "plmalign_global_stats['tool'] = 'plmalign_global'\n",
    "\n",
    "# add additional metadata regarding the pdb files\n",
    "manual = read_mali(mali_root, tool='manual', report_ids=True)\n",
    "blast_stats['pdb'] = manual['pdb']\n",
    "hmmer_stats['pdb'] = manual['pdb']\n",
    "nw_stats['pdb'] = manual['pdb']\n",
    "sw_stats['pdb'] = manual['pdb']\n",
    "plmblast_stats['pdb'] = manual['pdb']\n",
    "plmblast_global_stats['pdb'] = manual['pdb']\n",
    "plmalign_stats['pdb'] = manual['pdb']\n",
    "plmalign_global_stats['pdb'] = manual['pdb']\n",
    "\n",
    "# combine stats\n",
    "data = pd.concat((blast_stats, hmmer_stats,\n",
    "                  nw_stats, sw_stats,\n",
    "                  plmblast_stats, plmblast_global_stats,\n",
    "                  plmalign_stats, plmalign_global_stats\n",
    "                  ))\n",
    "\n",
    "# save file locally for later\n",
    "data.to_csv(f'./data/alignment_benchmark/result/{benchmark}_perc_id.csv')\n",
    "\n",
    "# melt dataframe to make it easier to plot\n",
    "data = pd.melt(data, id_vars=['tool', 'pdb'], var_name='kernel_width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Statistics</h3>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Run if need to regenerate figures from existing data\n",
    "benchmark = 'malidup'\n",
    "data = pd.read_csv(f'./data/alignment_benchmark/result/{benchmark}_perc_id.csv', index_col=0)\n",
    "data = pd.melt(data, id_vars=['tool', 'pdb'], var_name='kernel_width')\n",
    "\n",
    "# Filter out blast and hmmer. Comment if desired\n",
    "idx = data.tool.apply(lambda x: x not in ['blast', 'hmmer'])\n",
    "data = data.loc[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>tool</th>\n",
       "      <th>pdb</th>\n",
       "      <th>kernel_width</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast</td>\n",
       "      <td>1sr9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blast</td>\n",
       "      <td>1d5y</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blast</td>\n",
       "      <td>1bml_C1C2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>blast</td>\n",
       "      <td>1h9m</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blast</td>\n",
       "      <td>1kt1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>plmalign_global</td>\n",
       "      <td>2bn2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7708</th>\n",
       "      <td>plmalign_global</td>\n",
       "      <td>1f52</td>\n",
       "      <td>10</td>\n",
       "      <td>0.336634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7709</th>\n",
       "      <td>plmalign_global</td>\n",
       "      <td>1nu9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7710</th>\n",
       "      <td>plmalign_global</td>\n",
       "      <td>1dlp</td>\n",
       "      <td>10</td>\n",
       "      <td>0.201835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7711</th>\n",
       "      <td>plmalign_global</td>\n",
       "      <td>1pw4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.385366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7712 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tool        pdb kernel_width     value\n",
       "0               blast       1sr9            1  0.000000\n",
       "1               blast       1d5y            1  0.000000\n",
       "2               blast  1bml_C1C2            1  0.000000\n",
       "3               blast       1h9m            1  0.000000\n",
       "4               blast       1kt1            1  0.000000\n",
       "...               ...        ...          ...       ...\n",
       "7707  plmalign_global       2bn2           10  0.235294\n",
       "7708  plmalign_global       1f52           10  0.336634\n",
       "7709  plmalign_global       1nu9           10  0.000000\n",
       "7710  plmalign_global       1dlp           10  0.201835\n",
       "7711  plmalign_global       1pw4           10  0.385366\n",
       "\n",
       "[7712 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tool\n",
       "blast              964\n",
       "hmmer              964\n",
       "nw                 964\n",
       "sw                 964\n",
       "plmblast           964\n",
       "plmblast_global    964\n",
       "plmalign           964\n",
       "plmalign_global    964\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['tool'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup = {\n",
    " 'blast' : 'BLAST',\n",
    " 'hmmer' : 'HMMER',\n",
    " 'sw' : 'Smith-Waterman',\n",
    " 'nw' : 'Needleman-Wunsch',\n",
    " 'plmblast' : 'pLM-BLAST',\n",
    " 'plmblast_global' : 'pLM-BLAST-global',\n",
    " 'plmalign' : 'PLMAlign', \n",
    " 'plmalign_global' : 'PLMAlign-global', \n",
    "}\n",
    "\n",
    "data['Method'] = data.apply(lambda x: lookup[x['tool']], axis=1)\n",
    "\n",
    "palette = {\n",
    "    'Smith-Waterman': \"green\", \n",
    "    'Needleman-Wunsch': \"orange\",\n",
    "    'pLM-BLAST': \"cyan\",\n",
    "    'pLM-BLAST-global': \"blue\",\n",
    "    'PLMAlign': \"red\",\n",
    "    'PLMAlign-global': \"purple\"\n",
    "}\n",
    "\n",
    "def get_matched_res(aln_text):\n",
    "    i,j,pos=0,0,[]\n",
    "    for x in aln_text:\n",
    "        if x == ':':\n",
    "            pos, i, j = pos + [(i,j)], i+1, j+1\n",
    "        elif x == '1':\n",
    "            i += 1\n",
    "        elif x == '2':\n",
    "            j += 1\n",
    "        elif x == '.':\n",
    "            i, j = i+1, j+1\n",
    "        else:\n",
    "            raise ValueError\n",
    "    return pos\n",
    "\n",
    "def f1score(target, prediction):\n",
    "    truth, pred= set(get_matched_res(target)),set(get_matched_res(prediction))\n",
    "    # p_, r_ =(x:=len(t_&p_))/(len(p_)+ 1e-10), x/(len(t_)+ 1e-10)\n",
    "    # return p_, r_, 2 * p_ * r_ / (p_ + r_ +1e-10)\n",
    "    tp = len(truth & pred)\n",
    "    fp = len(pred - truth)\n",
    "    fn = len(truth - pred)\n",
    "    perc_id = tp / len(target)\n",
    "    ppv = tp / (tp + fp)\n",
    "    fnr = fn / (fn + tp)\n",
    "    fdr = fp / (fp + tp)\n",
    "    return tp, fp, fn, perc_id, ppv, fnr, fdr\n",
    "\n",
    "def local_alignment_score(x):\n",
    "    if x['aln'] == -1:\n",
    "        n_matches = np.sum(np.array(list(x['manual'])) == ':')\n",
    "        return 0, np.nan, n_matches, 0, 0, 1, 0\n",
    "    else:\n",
    "        return f1score(x['manual'], x['aln'])\n",
    "\n",
    "columns = [\n",
    "        'val_tp', 'val_fp', 'val_fn', 'val_perc_id',\n",
    "        'val_ppv', 'val_fnr', 'val_fdr'\n",
    "]\n",
    "blast_stats = pd.DataFrame(list(ref_blast.apply(local_alignment_score, axis=1).values), \n",
    "                           columns=columns)\n",
    "hmmer_stats = pd.DataFrame(list(ref_hmmer.apply(local_alignment_score, axis=1).values), \n",
    "                           columns=columns)\n",
    "sw_stats = pd.DataFrame(list(res.apply(lambda x: f1score(x['manual'], x['smith-waterman']), axis=1).values), \n",
    "                          columns=columns)\n",
    "nw_stats = pd.DataFrame(list(res.apply(lambda x: f1score(x['manual'], x['needleman-wunsch']), axis=1).values), \n",
    "                          columns=columns)\n",
    "plmblast_stats = pd.DataFrame(list(res.apply(lambda x: f1score(x['manual'], x['plmblast']), axis=1).values), \n",
    "                          columns=columns)\n",
    "plmblast_global_stats = pd.DataFrame(list(res.apply(lambda x: f1score(x['manual'], x['plmblast_global']), axis=1).values), \n",
    "                          columns=columns)\n",
    "plmalign_stats = pd.DataFrame(list(res.apply(lambda x: f1score(x['manual'], x['plmalign']), axis=1).values), \n",
    "                          columns=columns)\n",
    "plmalign_global_stats = pd.DataFrame(list(res.apply(lambda x: f1score(x['manual'], x['plmalign_global']), axis=1).values), \n",
    "                          columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "blast_stats['tool'] = 'BLAST'\n",
    "hmmer_stats['tool'] = 'HMMER'\n",
    "sw_stats['tool'] = 'Smith-Waterman'\n",
    "nw_stats['tool'] = 'Needleman-Wunsch'\n",
    "plmblast_stats['tool'] = 'pLM-BLAST'\n",
    "plmblast_global_stats['tool'] = 'pLM-BLAST-global'\n",
    "plmalign_stats['tool'] = 'PLMAlign'\n",
    "plmalign_global_stats['tool'] = 'PLMAlign-global'\n",
    "\n",
    "\n",
    "data = pd.concat((blast_stats, hmmer_stats,\n",
    "                  sw_stats, nw_stats,\n",
    "                  plmblast_stats, plmblast_global_stats, \n",
    "                  plmalign_stats, plmalign_global_stats))\n",
    "\n",
    "data.to_csv(f'./data/alignment_benchmark/result/{benchmark}_allstats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in locally to save time\n",
    "data = pd.read_csv(f'./data/alignment_benchmark/result/{benchmark}_allstats.csv', index_col=0)\n",
    "\n",
    "data = data.dropna()\n",
    "data['precision'] = data.apply(lambda x: x['val_tp'] / (x['val_tp'] + x['val_fp']), axis=1)\n",
    "data['recall'] = data.apply(lambda x: x['val_tp'] / (x['val_tp'] + x['val_fn']), axis=1)\n",
    "data['f1'] = data.apply(lambda x: 2 / ((1 / (x['precision'] + 1e-6)) + (1 / (x['recall'] + 1e-6))), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tool\n",
       "Smith-Waterman      241\n",
       "Needleman-Wunsch    241\n",
       "pLM-BLAST           241\n",
       "pLM-BLAST-global    241\n",
       "PLMAlign            241\n",
       "PLMAlign-global     241\n",
       "HMMER                 8\n",
       "BLAST                 5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['tool'].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     "metadata": {},
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   ],
   "source": [
    "sns.boxplot(data=data.query(\"tool != 'HMMER' & tool != 'BLAST'\"), \n",
    "            x='f1', y='tool', orient='h', palette=palette)\n",
    "locs, labels = plt.xticks()\n",
    "_ = plt.setp(labels, fontsize=16)\n",
    "\n",
    "locs, labels = plt.yticks()\n",
    "_ = plt.setp(labels, fontsize=16)\n",
    "\n",
    "plt.xlabel('F1 score', fontsize=18)\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'./data/alignment_benchmark/result/{benchmark}_f1_score.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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   "source": [
    "sns.boxplot(data=data.query(\"tool != 'HMMER' & tool != 'BLAST'\"), \n",
    "            x='recall', y='tool', orient='h', palette=palette)\n",
    "locs, labels = plt.xticks()\n",
    "_ = plt.setp(labels, fontsize=16)\n",
    "\n",
    "locs, labels = plt.yticks()\n",
    "_ = plt.setp(labels, fontsize=16)\n",
    "\n",
    "plt.xlabel('Recall', fontsize=18)\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'./data/alignment_benchmark/result/{benchmark}_recall.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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    "sns.boxplot(data=data.query(\"tool != 'HMMER' & tool != 'BLAST'\"), \n",
    "            x='precision', y='tool', orient='h', palette=palette)\n",
    "locs, labels = plt.xticks()\n",
    "_ = plt.setp(labels, fontsize=16)\n",
    "\n",
    "locs, labels = plt.yticks()\n",
    "_ = plt.setp(labels, fontsize=16)\n",
    "\n",
    "plt.xlabel('Precision', fontsize=18)\n",
    "plt.ylabel('')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'./data/alignment_benchmark/result/{benchmark}_precision.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tool\n",
      "BLAST               0.013335\n",
      "HMMER               0.024391\n",
      "Needleman-Wunsch    0.149796\n",
      "PLMAlign            0.517844\n",
      "PLMAlign-global     0.560915\n",
      "Smith-Waterman      0.151368\n",
      "pLM-BLAST           0.182006\n",
      "pLM-BLAST-global    0.523628\n",
      "Name: f1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby(['tool']).mean()['f1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tool\n",
      "BLAST               0.013334\n",
      "HMMER               0.024390\n",
      "Needleman-Wunsch    0.011335\n",
      "PLMAlign            0.020966\n",
      "PLMAlign-global     0.018145\n",
      "Smith-Waterman      0.011409\n",
      "pLM-BLAST           0.017333\n",
      "pLM-BLAST-global    0.018821\n",
      "Name: f1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby(['tool']).agg(sem)['f1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tool\n",
      "BLAST               0.006897\n",
      "HMMER               0.013514\n",
      "Needleman-Wunsch    0.121419\n",
      "PLMAlign            0.499765\n",
      "PLMAlign-global     0.542082\n",
      "Smith-Waterman      0.123675\n",
      "pLM-BLAST           0.157958\n",
      "pLM-BLAST-global    0.479681\n",
      "Name: recall, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby(['tool']).mean()['recall'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tool\n",
      "BLAST               0.006897\n",
      "HMMER               0.013514\n",
      "Needleman-Wunsch    0.009497\n",
      "PLMAlign            0.020598\n",
      "PLMAlign-global     0.017959\n",
      "Smith-Waterman      0.009632\n",
      "pLM-BLAST           0.016257\n",
      "pLM-BLAST-global    0.018357\n",
      "Name: recall, dtype: float64\n"
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    }
   ],
   "source": [
    "print(data.groupby(['tool']).agg(sem)['recall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tool\n",
      "BLAST               0.200000\n",
      "HMMER               0.125000\n",
      "Needleman-Wunsch    0.197376\n",
      "PLMAlign            0.538650\n",
      "PLMAlign-global     0.582471\n",
      "Smith-Waterman      0.196751\n",
      "pLM-BLAST           0.244556\n",
      "pLM-BLAST-global    0.581760\n",
      "Name: precision, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby(['tool']).mean()['precision'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tool\n",
      "BLAST               0.200000\n",
      "HMMER               0.125000\n",
      "Needleman-Wunsch    0.014302\n",
      "PLMAlign            0.021414\n",
      "PLMAlign-global     0.018387\n",
      "Smith-Waterman      0.014210\n",
      "pLM-BLAST           0.020952\n",
      "pLM-BLAST-global    0.019380\n",
      "Name: precision, dtype: float64\n"
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    }
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   "source": [
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