main2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "import re\n",
    "from collections import defaultdict\n",
    "\n",
    "class train_processor():\n",
    "\n",
    "    def __init__(self,path):\n",
    "        self.path=path\n",
    "\n",
    "    def loader(self):\n",
    "        with open(self.path,'r') as train_file:\n",
    "            out=[]\n",
    "            for line in tqdm(train_file):\n",
    "                if line.startswith('>'):\n",
    "                    out.append([])\n",
    "                out[-1].append(line.strip())\n",
    "        out=list(map(lambda x:''.join(x),out))\n",
    "        out=[out[i].split('}') for i in range(len(out))]\n",
    "        \n",
    "        dic_out=defaultdict(list)\n",
    "        for item in out:\n",
    "            tmp=re.search(r'[a-z]\\.[0-9]+\\.[0-9]+\\.[0-9]+',item[0]).group().split('.')\n",
    "            label='.'.join(tmp[:2])\n",
    "            sequence=item[1]\n",
    "            \n",
    "            dic_out['label'].append(label)\n",
    "            dic_out['sequence'].append(sequence)\n",
    "        \n",
    "        df=pd.DataFrame(dic_out)\n",
    "        return df\n",
    "\n",
    "class test_processor():\n",
    "\n",
    "    def __init__(self,path):\n",
    "        self.path=path\n",
    "    \n",
    "    def loader(self):\n",
    "        test_dict=defaultdict(list)\n",
    "        \n",
    "        with open(self.path,'r') as test_file:\n",
    "            tmp=[]\n",
    "            for line in test_file:\n",
    "                if line.startswith('>'):\n",
    "                    tmp.append([])\n",
    "                tmp[-1].append(line.strip())\n",
    "            \n",
    "            for item in tmp:\n",
    "                test_dict['sample_id'].append(item[0][1:])\n",
    "                test_dict['sequence'].append(''.join(item[1:]))\n",
    "\n",
    "        df=pd.DataFrame(test_dict)   \n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_processor=train_processor(path='./data/astral_train.fa')\n",
    "te_processor=test_processor(path='./data/astral_test.fa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "43028it [00:00, 86687.61it/s]\n"
     ]
    }
   ],
   "source": [
    "train_df=tr_processor.loader()\n",
    "test_df=te_processor.loader()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seq length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore whether the sequence length matters in classification of different groups\n",
    "train_df['length']=train_df.sequence.apply(lambda x:len(x))\n",
    "test_df['length']=test_df.sequence.apply(lambda x:len(x))\n",
    "\n",
    "avg_len={}\n",
    "for label in train_df.label.unique():\n",
    "    sub_train_df=train_df[train_df.label==label]\n",
    "    avg_len[label]=sub_train_df.length.mean()  \n",
    "\n",
    "avg_len_sorted=sorted(avg_len.items(),key=lambda x:x[1])\n",
    "\n",
    "labels,avglen=[i[0] for i in avg_len_sorted],[i[1] for i in avg_len_sorted]\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.bar(labels,avglen)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Avg length for different groups of proteins')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### proportion of each amino acid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of tokens:23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_df['tokens']=train_df.sequence.apply(lambda x:[x[i] for i in range(len(x))])\n",
    "test_df['tokens']=test_df.sequence.apply(lambda x:[x[i] for i in range(len(x))])\n",
    "\n",
    "tokens_set=set()\n",
    "for i in tqdm(range(len(train_df))):\n",
    "    tokens_set.update(train_df.tokens[i])\n",
    "\n",
    "for i in tqdm(range(len(test_df))):\n",
    "    tokens_set.update(test_df.tokens[i])\n",
    "\n",
    "print(f'size of tokens:{len(tokens_set)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 groups of amino acids with different physical properties\n",
    "amino_acids={1:['a','v','l','i','p','m'],\n",
    "             2:['w','f'],\n",
    "             3:['q','s','t','c','n','y','g'],\n",
    "             4:['d','e'],\n",
    "             5:['k','r','h']}\n",
    "\n",
    "amino_acids_li=amino_acids[1]+amino_acids[2]+amino_acids[3]+amino_acids[4]+amino_acids[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for 'X','x','z' which are quite rare, I just use dummy to represent whether they exist or not\n",
    "train_df['hasX']=train_df.tokens.apply(lambda x:'X' in x).astype(int)\n",
    "train_df['hasx']=train_df.tokens.apply(lambda x:'x' in x).astype(int)\n",
    "train_df['hasz']=train_df.tokens.apply(lambda x:'z' in x).astype(int)\n",
    "\n",
    "test_df['hasX']=test_df.tokens.apply(lambda x:'X' in x).astype(int)\n",
    "test_df['hasx']=test_df.tokens.apply(lambda x:'x' in x).astype(int)\n",
    "test_df['hasz']=test_df.tokens.apply(lambda x:'z' in x).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the proportion of each group's amino acid in protein sequence\n",
    "def cal_group_prop(df):\n",
    "    for group_idx in range(1,6):\n",
    "        df[f'group{group_idx}']=0.0\n",
    "        for i in tqdm(range(df.shape[0])):\n",
    "            count=0\n",
    "            for token in df.tokens[i]:\n",
    "                if token in amino_acids[group_idx]:\n",
    "                    count+=1\n",
    "            df[f'group{group_idx}'][i]=count/df.length[i]\n",
    "    return df    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/9472 [00:00<?, ?it/s]/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
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     ]
    }
   ],
   "source": [
    "train_df=cal_group_prop(train_df)\n",
    "test_df=cal_group_prop(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calulate each amino acid's proportion in the protein sequence\n",
    "def cal_aa_proportion(df,start,end): \n",
    "        \n",
    "    df[f'tokens{start}_{end}']=df.tokens.apply(lambda x:x[int(start*len(x)):int(end*len(x))])\n",
    "    for token in sorted(amino_acids_li):\n",
    "        df[f'{token}{start}_{end}']=0.0\n",
    "        for i in tqdm(range(df.shape[0])):\n",
    "            df[f'{token}{start}_{end}'][i]=df[f'tokens{start}_{end}'][i].count(token)/len(df[f'tokens{start}_{end}'][i])\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
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      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
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     ]
    }
   ],
   "source": [
    "# split the sequence into 10 sub-parts\n",
    "split_props=[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]\n",
    "\n",
    "# for each sub-part of the sequence,calculate each amino acid's proportion\n",
    "for i in range(len(split_props)-1):\n",
    "    train_df=cal_aa_proportion(train_df,split_props[i],split_props[i+1])\n",
    "    test_df=cal_aa_proportion(test_df,split_props[i],split_props[i+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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   ],
   "source": [
    "# calculate the proportion of each amino acid in other different parts of protein sequence\n",
    "split_props2=[0,0.2,0.4,0.6,0.8,1]\n",
    "for i in range(len(split_props2)-1):\n",
    "    train_df=cal_aa_proportion(train_df,split_props2[i],split_props2[i+1])\n",
    "    test_df=cal_aa_proportion(test_df,split_props2[i],split_props2[i+1])\n",
    "\n",
    "split_props2_2=[0.1,0.3,0.5,0.7,0.9]\n",
    "for i in range(len(split_props2_2)-1):\n",
    "    train_df=cal_aa_proportion(train_df,split_props2_2[i],split_props2_2[i+1])\n",
    "    test_df=cal_aa_proportion(test_df,split_props2_2[i],split_props2_2[i+1])\n",
    "\n",
    "split_props3=[0,0.33,0.67,1]\n",
    "for i in range(len(split_props3)-1):\n",
    "    train_df=cal_aa_proportion(train_df,split_props3[i],split_props3[i+1])\n",
    "    test_df=cal_aa_proportion(test_df,split_props3[i],split_props3[i+1])\n",
    "\n",
    "\n",
    "split_props4=[0,0.5,1]\n",
    "for i in range(len(split_props4)-1):\n",
    "    train_df=cal_aa_proportion(train_df,split_props4[i],split_props4[i+1])\n",
    "    test_df=cal_aa_proportion(test_df,split_props4[i],split_props4[i+1])\n",
    "\n",
    "\n",
    "train_df=cal_aa_proportion(train_df,0,1)\n",
    "test_df=cal_aa_proportion(test_df,0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### num of unique amino acids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['variety']=train_df.tokens.apply(lambda x:len(set(x)))\n",
    "test_df['variety']=test_df.tokens.apply(lambda x:len(set(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### length of max consecutive repetition subsequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_len_max_crs(token_li):\n",
    "    max_len=0\n",
    "    max_start=0\n",
    "    \n",
    "    start,end=0,1\n",
    "    \n",
    "    while end<len(token_li):\n",
    "        if token_li[start]==token_li[end]:\n",
    "            tmp=token_li[start]\n",
    "            tmp_len=1\n",
    "            while token_li[end]==tmp and end<len(token_li):\n",
    "                tmp_len+=1\n",
    "                end+=1\n",
    "                if end==len(token_li):\n",
    "                    break\n",
    "                \n",
    "            if tmp_len>max_len:\n",
    "                max_len=tmp_len\n",
    "                max_start=start\n",
    "            \n",
    "            start=end\n",
    "            end=start+1\n",
    "        else:\n",
    "            start+=1\n",
    "            end+=1\n",
    "    return max_len           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['max_crs']=train_df.tokens.apply(cal_len_max_crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['max_crs']=train_df.tokens.apply(cal_len_max_crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### info of dipeptides & tripeptides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**I also try to explore the proportion of all kinds of dipeptides and tripeptides and use them as features for modeling, But unfortunately, it is not useful !**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # dipeptide (exclude X,x,z)\n",
    "# dipeptides=[]\n",
    "# for i in amino_acids_li:\n",
    "#     for j in amino_acids_li:\n",
    "#         dipeptides.append(i+j)\n",
    "\n",
    "# train_df['tokens2']=train_df.tokens.apply(lambda x:[x[i]+x[i+1] for i in range(len(x)-1)])\n",
    "# test_df['tokens2']=test_df.tokens.apply(lambda x:[x[i]+x[i+1] for i in range(len(x)-1)])\n",
    "\n",
    "# # build dipeptide combination\n",
    "# for dipeptide in dipeptides:\n",
    "#     train_df[dipeptide]=0.0\n",
    "#     test_df[dipeptide]=0.0\n",
    "\n",
    "# for i in tqdm(range(train_df.shape[0])):\n",
    "#     for dipeptide in dipeptides:\n",
    "#         train_df[dipeptide][i]=train_df.tokens2[i].count(dipeptide)/(len(train_df.tokens2[i]))\n",
    "\n",
    "# for i in tqdm(range(test_df.shape[0])):\n",
    "#     for dipeptide in dipeptides:\n",
    "#         test_df[dipeptide][i]=test_df.tokens2[i].count(dipeptide)/(len(test_df.tokens2[i]))\n",
    "\n",
    "\n",
    "\n",
    "# # tripeptide\n",
    "# tripeptides=[]\n",
    "# for i in amino_acids_li:\n",
    "#     for j in amino_acids_li:\n",
    "#         for k in amino_acids_li:\n",
    "#             tripeptides.append(i+j+k)\n",
    "\n",
    "# for tripeptide in tripeptides:\n",
    "#     train_df[tripeptide]=0.0\n",
    "#     test_df[tripeptide]=0.0\n",
    "\n",
    "# train_df['tokens3']=train_df.tokens.apply(lambda x:[x[i]+x[i+1]+x[i+2] for i in range(len(x)-2)])\n",
    "# test_df['tokens3']=test_df.tokens.apply(lambda x:[x[i]+x[i+1]+x[i+2] for i in range(len(x)-2)])\n",
    "\n",
    "# for i in tqdm(range(train_df.shape[0])):\n",
    "#     for tripeptide in tripeptides:\n",
    "#         train_df[tripeptide][i]=train_df.tokens3[i].count(tripeptide)/(len(train_df.tokens3[i]))\n",
    "\n",
    "# for i in tqdm(range(test_df.shape[0])):\n",
    "#     for tripeptide in tripeptides:\n",
    "#         test_df[tripeptide][i]=test_df.tokens3[i].count(tripeptide)/(len(test_df.tokens3[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_feat=train_df.drop(['label',\n",
    "                          'sequence',\n",
    "                          'tokens',\n",
    "                          'tokens0_0.1',\n",
    "                          'tokens0.1_0.2',\n",
    "                          'tokens0.2_0.3',\n",
    "                          'tokens0.3_0.4',\n",
    "                          'tokens0.4_0.5',\n",
    "                          'tokens0.5_0.6',\n",
    "                          'tokens0.6_0.7',\n",
    "                          'tokens0.7_0.8',\n",
    "                          'tokens0.8_0.9',\n",
    "                          'tokens0.9_1',\n",
    "                          'tokens0_0.2',\n",
    "                          'tokens0.2_0.4',\n",
    "                          'tokens0.4_0.6',\n",
    "                          'tokens0.6_0.8',\n",
    "                          'tokens0.8_1',\n",
    "                          'tokens0.1_0.3',\n",
    "                          'tokens0.3_0.5',\n",
    "                          'tokens0.5_0.7',\n",
    "                          'tokens0.7_0.9',\n",
    "                          'tokens0_0.33',\n",
    "                          'tokens0.33_0.67',\n",
    "                          'tokens0.67_1',\n",
    "                          'tokens0_0.5',\n",
    "                          'tokens0.5_1',\n",
    "                          'tokens0_1'],axis=1)\n",
    "test_feat=test_df.drop(['sample_id',\n",
    "                        'sequence',\n",
    "                        'tokens',\n",
    "                        'tokens0_0.1',\n",
    "                        'tokens0.1_0.2',\n",
    "                        'tokens0.2_0.3',\n",
    "                        'tokens0.3_0.4',\n",
    "                        'tokens0.4_0.5',\n",
    "                        'tokens0.5_0.6',\n",
    "                        'tokens0.6_0.7',\n",
    "                        'tokens0.7_0.8',\n",
    "                        'tokens0.8_0.9',\n",
    "                        'tokens0.9_1',\n",
    "                        'tokens0_0.2',\n",
    "                        'tokens0.2_0.4',\n",
    "                        'tokens0.4_0.6',\n",
    "                        'tokens0.6_0.8',\n",
    "                        'tokens0.8_1',\n",
    "                        'tokens0.1_0.3',\n",
    "                        'tokens0.3_0.5',\n",
    "                        'tokens0.5_0.7',\n",
    "                        'tokens0.7_0.9',\n",
    "                        'tokens0_0.33',\n",
    "                        'tokens0.33_0.67',\n",
    "                        'tokens0.67_1',\n",
    "                        'tokens0_0.5',\n",
    "                        'tokens0.5_1',\n",
    "                        'tokens0_1'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y=train_df.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# svd(dim reduction)\n",
    "# from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "# svd=TruncatedSVD(n_components=100)\n",
    "# train_x=svd.fit_transform(train_feat)\n",
    "# test_x=svd.transform(test_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do the normalization\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "\n",
    "train_x_norm=scaler.fit_transform(train_feat)\n",
    "test_x_norm=scaler.transform(test_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(class_weight='balanced'),\n",
       "             param_grid={'C': [0.1, 0.5, 1, 2, 10]})"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters={'C':[0.1,0.5,1,2,10]}\n",
    "svm=SVC(class_weight='balanced')\n",
    "\n",
    "clf=GridSearchCV(svm,parameters)\n",
    "clf.fit(train_x_norm,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, class_weight='balanced')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_svm=SVC(C=clf.best_params_['C'],class_weight='balanced')\n",
    "best_svm.fit(train_x_norm,train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=best_svm.predict(test_x_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['category_id']=prediction\n",
    "output=test_df[['sample_id','category_id']]\n",
    "output.to_csv('./tmp/submission.csv',header=True,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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