Large_Dataset_Independent_Test_Set_Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c27c603",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12598, 81) (12598,)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from Bio import SeqIO\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "os.chdir(\"/home/t326h379/Prot_T5/After_CD_HIT_deepUBI_protein\")\n",
    "# Independent Testing\n",
    "indep_test_x_81 = []\n",
    "indep_test_y_81 = []\n",
    "posit_1 = 1;\n",
    "negat_0 = 0;\n",
    "\n",
    "aminoacids='ARNDCQEGHILKMFPSTWYV-'\n",
    "\n",
    "def binary_encode(Sequence_one_hot_encoding): \n",
    "    aa2v={x:y for x,y in zip(aminoacids,np.eye(21,21).tolist())}\n",
    "    aa2v['X']=np.zeros(21)\n",
    "    encoding=[]\n",
    "    for i in range(len(Sequence_one_hot_encoding)):\n",
    "        s=Sequence_one_hot_encoding[i]\n",
    "        encoding.append([aa2v[x] for x in s])\n",
    "    encoding=np.array(encoding)\n",
    "    return encoding\n",
    "\n",
    "aaindex=pd.read_table('aaindex31.txt',sep='\\s+',header=None)\n",
    "aaindex=aaindex.subtract(aaindex.min(axis=1),axis=0).divide((aaindex.max(axis=1)-aaindex.min(axis=1)),axis=0)\n",
    "aa=[x for x in 'ARNDCQEGHILKMFPSTWYV']\n",
    "aaindex=aaindex.to_numpy().T\n",
    "index={x:y for x,y in zip(aa,aaindex.tolist())}\n",
    "index['-']=np.zeros(31).tolist()\n",
    "index['X']=np.zeros(31).tolist()\n",
    "\n",
    "def index_encode(Sequence_one_hot_encoding):\n",
    "    encoding=[]\n",
    "    for i in range(len(Sequence_one_hot_encoding)):\n",
    "        s=Sequence_one_hot_encoding[i]\n",
    "        encoding.append([index[x] for x in (s)])\n",
    "    encoding=np.array(encoding)\n",
    "    return encoding\n",
    "\n",
    "alphabet = 'ARNDCQEGHILKMFPSTWYV-'\n",
    "\n",
    "\n",
    "char_to_int = dict((c, i) for i, c in enumerate(alphabet))\n",
    "int_to_char = dict((i, c) for i, c in enumerate(alphabet))\n",
    "\n",
    "def inner1():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "\n",
    "    for char in data:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in data]\n",
    "    indep_test_x_81.append(integer_encoded)\n",
    "    indep_test_y_81.append(posit_1)\n",
    "for seq_record in SeqIO.parse(\"Positive_10_percent_independent_test_set_DeepUBI.fasta\", \"fasta\"):\n",
    "    inner1()\n",
    "\n",
    "def inner2():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "\n",
    "    for char in data:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in data]\n",
    "    indep_test_x_81.append(integer_encoded)\n",
    "    indep_test_y_81.append(negat_0)\n",
    "for seq_record in SeqIO.parse(\"Negative_10_percent_independent_test_set_DeepUBI.fasta\", \"fasta\"):\n",
    "    inner2()\n",
    "\n",
    "# Changing to array (matrix)    \n",
    "indep_test_x_81 = np.array(indep_test_x_81)\n",
    "indep_test_y_81 = np.array(indep_test_y_81)\n",
    "\n",
    "print(indep_test_x_81.shape,indep_test_y_81.shape)\n",
    "\n",
    "AAindex_Test_AAAAA_81_Sequence = []\n",
    "for i in range(len(indep_test_x_81)):\n",
    "    integer_of_string = str(indep_test_x_81[i])\n",
    "    integer_of_string = integer_of_string.strip(\"[\").strip(\"]\").strip(\" \")\n",
    "    keras_job = integer_of_string.split()\n",
    "    individual_AA_peptide = []\n",
    "    for i in range(len(keras_job)):\n",
    "        index_value = int(keras_job[i])\n",
    "        individual_AA_peptide.append(int_to_char.get(index_value))\n",
    "    individual_AA_peptide = \"\".join(individual_AA_peptide)\n",
    "    AAindex_Test_AAAAA_81_Sequence.append(individual_AA_peptide) \n",
    "\n",
    "yval = indep_test_y_81\n",
    "\n",
    "x_ind_49 = indep_test_x_81[:,[i for i in range(16,65)]]\n",
    "\n",
    "AAindex_indi_AA_Sequence = []\n",
    "for i in range(len(x_ind_49)):\n",
    "    integer_of_string = str(x_ind_49[i])\n",
    "    integer_of_string = integer_of_string.strip(\"[\").strip(\"]\").strip(\" \")\n",
    "    keras_job = integer_of_string.split()\n",
    "    individual_AA_peptide = []\n",
    "    for i in range(len(keras_job)):\n",
    "        index_value = int(keras_job[i])\n",
    "        individual_AA_peptide.append(int_to_char.get(index_value))\n",
    "    individual_AA_peptide = \"\".join(individual_AA_peptide)\n",
    "    AAindex_indi_AA_Sequence.append(individual_AA_peptide) \n",
    "\n",
    "binaryCoding_Independent_Test = binary_encode(AAindex_indi_AA_Sequence)\n",
    "\n",
    "AAindexCoding_Independent_Test = index_encode(AAindex_indi_AA_Sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a99e8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12598, 49, 21)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binaryCoding_Independent_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14465844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12598, 49, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAindexCoding_Independent_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b398f456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12598, 49)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ind_49.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b25654",
   "metadata": {},
   "source": [
    "# Load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b503d672",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "model = keras.models.load_model(\"DeepUBI_AAindex_One_Hot_Emb_drop_out2423.h5\")\n",
    "\n",
    "Y_pred = model.predict([AAindexCoding_Independent_Test,binaryCoding_Independent_Test,x_ind_49])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e247d49e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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z84uKigoMDFR3LtANvL29eTxedHS0sjGVlZW7d+92dXU1NjYWCAS2trZBQUHtzsrpmNyVOSYmRunn0GN6c26gemq5CyhCxXcK3AUAAKBzUCdqX2BgIMMwbm5u6k5ERVpaWnx9fVeuXBkSEvLw4cOsrCwbGxt3d/dvvvlGqRhCiEwmc3V1TUxMjI2NLS0tzczM1NfX9/b2zsnJUTyf2traX375hRDi4+PDMExTU9Pt27c/+eST27dvOzo6zp8//48//uiu5w790vjx4zMzM7///vuampqOI0tKSvLz88+fP29ubt6VM3ZXP93oyy+/3LBhw4YNG9SdSK9QW1tra2vLnXTTt+zfv5+uU96JmIiIiGXLlvn4+Ny6dau8vDwhISErK8vBweHEiRNK5SB3ZV6xYoVSD+9RvTk36Gnvvfcej8fT19fnNqr+LqAIFd8p2r0LREZG0nJqS0uLCnIAAIA+SW0jmVSlK/PO3NzcdHR0ujcfFRAKhS+//LJSD0lMTCSELFu2jG1pbW0dNWqUkZFRZWWl4jEMw7z33nsGBgbFxcVsS21trY6Ozq+//qpUStzf+Lno8pPe3t7sKgN9hbLviypHem/dupV7WVi9erVqzqsUzCFVAdJ/553V1NQMGzZs+vTpKkipXZ24MrMKCwuNjIzoeijsPtyKx7z99tvvvPMOt4Wua2tra6tsJk+6MqvSk17J3pBbV6jsp69PXPAx16n3wHsBAPAMwngiIISQ48ePE86W24QQHo/n4+NTWVnJLqaoSExJScmePXuCgoLMzMzYMKFQWF9f310bsn766adOTk6nTp2SG8cEXbFixQrudaHjiS0AfZFIJMrLy/vuu+/UnUhnLFy40N/fv+PtijqIiY+P//LLL7ktEolEIBDk5eUx6lhmGNQLF3wAAADoGOpEQAghJSUlhBC5/W4tLCwIIampqYrHnDp1qqWlZdKkST2XKo/HW7p0KSHk888/77mzAAD0EgkJCTk5OR0vuKNIDJdMJqurqxszZgyPx+uOHAEAAACg/0Cd6H9u377t6+srFouFQqGLiwtb+6Do7rPUnTt35syZY2JiQr8tKysjhJSXl3/wwQfDhw/n8/lGRkbTp0+/ePEifWxMTAyNtLa2zsjIcHNzE4lEenp6U6dOvXLlCvcsHXQSHR1NO2GrMKdPn6YtAwYM4J5IJpNduXKFHtLS0lLkudMeaCWI9ejRI0LI/fv3FY/5+eefCSFGRkbLly8fNGgQn88fMmRIaGhoRUWFImkoiL4C6enpTU1N/ft9AYBuwb1Q1NfXy7Xcv38/ICDA0NDQxMTEy8srLy+PPkqRS0RPXwEKCgqWL1+ekJAgEom6EiPn6NGjhJDVq1crGN+BvvJKUs3NzUeOHPnHP/5hbm4uEAjGjh0bFxfX2tpKCKmqquJx0FE2zc3NbMvs2bNpJ48ePQoNDbWxseHz+QMHDvTz86Pz+IgCvyoAAAAA9AEqneWmDgquaXL37l1DQ0MrK6sffvjh8ePHN27ccHd3t7GxkVufyMfHhxAyefLkixcvymSy9PR0TU3NR48eSaXSoUOHmpmZJScnV1dX37lzx8/Pj8fj7d27l32sRCIRCoXOzs4//fRTbW1tRkbGuHHj+Hz+pUuXaIAinbRdl8HBwcHExITb0olVMHbs2EH+vvYQ7ZkQ4ujoqHgMfX3Mzc2DgoLy8vIqKyv37dsnFArt7OyqqqrYR02dOtXY2DgtLa2DlDpYaaKuro5+eouKirjn7WfvC1YEkIP1iVSA9N/1iZi/LhR1dXVyLT4+PvTH/+zZswKBYOLEidxHPfUSwfTYlZlhGA8Pj8WLF9Ov6RbgbdcnUiSGq7i42MzMrO223F25MveGV1KR9YnoOt8bN26sqKh49OjR9u3bNTQ0uPOwPDw8NDQ07t27x32Us7PzoUOH6NdFRUVDhgwxMzP79ttvHz9+fPPmzcmTJ+vq6v70009yr0bbW1IHiTF986ev5+AO2HvgvQAAeAb1/+u+gn9b+vv7E0KOHTvGthQWFuro6LRbJ/ruu+/kHv7WW28RQr7++mu2pb6+3tLSUiAQsCs6SyQSQsgvv/zCxty4cYMQIpFIFO+kh/4aqaurc3Bw0NbW3rlzZ1lZ2e+//75kyRK6GYeLi4viMR4eHoSQoUOHNjU1sZ3T/8quXbuWbZk8ebKRkRH3t+q2OviNn93sTK5O1M/eF/xmJgd1IhXoi3+pdr1OlJyczLbQMSPcP+mfeolgeuzKvGfPnmHDhtXW1tJv260BKRLDVVZWZm9vHxAQ0NzcLHeoK1fm3vBKKlgnmjJlCrclODhYW1u7urqafnvmzBlCCFt3YxgmNTXVysqqsbGRfvvmm28SQtiyEcMwUqlUR0fHwcFB7tVoe0vqWF/86es5uAP2HngvAACeQZj88qfTp08TQmiZg7K0tLSzs8vNzW0b/MILL8i10DWeZ8yYwbbo6Oi4ubkdOHDgzJkz8+bNo41CodDe3p6NGTt2rKWlZXZ2tlQqtbCwULCTnqCrq3vx4sVPPvkkJibm/fffNzEx8fPzO3r0qIuLC7t1qyIxQqGQEDJt2jTuXICZM2euWbPmzJkzn3zyCW25dOlSV7KVSqWEEG1tbXYyAtX/3hfy1/QQIIQUFBQQvCDQAyZOnMh+PWjQIEJIUVER9/LS8SWih7J68OBBRETEyZMn6XW10zFcMpnMw8Nj9OjR+/fv19TUlDvaxSsz6a2vJJeXl5eXlxe3RSKRHDx4MCcnx9nZmRDi7u4+duzYxMTETz75xMTEhBCydevWZcuWaWtr0/gTJ05oaGhwOzE3N3/++eevX79eUFBgbW3Ntre9JT1Veno6DytGEUIISU9PJ7jg9w70vQAAgGcK6kSEENLQ0PD48WNdXV19fX1uu6mpabt1IrnfyBsaGqqrq3V1deXWhqB7fhUXF7MthoaGcl2ZmpoWFRWVlpYaGxsr2EkPEYlEW7du5W6XS/+tOmHCBMVjbGxsCCH0d2sWXfqarmTULejSUc7Ozuwv7lS/fF/mzJnT06foW9LS0tSdAvQ3YrGY/ZrP5xNC6II1rA4uET1X3aATXadMmSLXvnbt2rVr1xJC7t69e+bMmafGjBgxgjY2Nzf7+/tbWVnt27evbZGoW/TOV5Krurr6s88+O378eEFBQVVVFdvODlMlhISHh7/99tuff/752rVrc3NzL1y48O9//5seorcV8vdnyrp79y63TqRg8Y4rNjY2NjZW2Uf1Y7gDAgAAqAXWsSaEEB0dHZFIVF9fX1tby21XcPVlHR0dsVhcX1//+PFjbjtd8pkda0MIKS8vZ/6+CXFpaSkhxNTUVMFONDQ0GhsbuQHc33Sp7vpvJC3H+Pn5KR5D1x+l431Y9DnSwkrXtba27tq1ixCyZMmSjiP7x/ui7iGHvQjmnalAJz6iz4IOLhH02564AixZskTu3ZGbUzZixAhFYtgOFy1a1NDQkJSUxI73HDFihIpHCqjlleSaOXPm+vXrFy5cmJub29rayjAMrctwswoKCjIzM9u5c2dDQ8Nnn3325ptvGhkZ0UM6OjqGhoZaWlrcudWsqVOndjoxCvPOWJjr1HvQ9wIAAJ4pqBP9afr06eSv2WdUWVnZnTt3FHz4rFmzCCHffvst29LQ0HD+/HmBQMCdy1ZfX5+RkcF+++uvvxYVFUkkEvp/VEU6sbCwKCwsZAOKi4sfPHggl4yenh77e/bIkSP37Nnz1PzLyso0NDSKiorYlpqamvj4+MDAQDs7O8VjXn31VSsrq9OnT9MdhSi6bqivr+9T01BEVFTUtWvXZs2aRZeU6lhff18AoDfo+BJB+sIV4OOPP87JyTl58qSOjo4qzytHXa+klpbW7du3W1parly5Ym5uHhoaOnDgQFpvYjdGYOno6CxevLi0tPSzzz47dOhQWFgY96ifn19zc7PcnpibN28ePHhwc3PzUzMBAAAA6P1QJ/rTxo0bjY2Nw8PDz549W1tbe+vWreDgYLlpaB3YtGnT0KFDw8PDU1JSHj9+nJub+8Ybb0il0ri4OO44GrFYvGrVqrS0NJlMlpmZGRwczOfz4+LiFO/E3d29qKho586dtbW1eXl5YWFh7L9hWRMmTMjNzX348GFaWlp+fr6Li4siT4FhmPnz59+7d6+hoeHatWuenp5mZmZ05I7iMTo6OvHx8eXl5YGBgXfv3q2qqjpw4MCmTZucnJxCQ0PZMFdXVxMTE8X/j93a2lpaWnry5Ek3N7ctW7YsWLDg0KFDivxLuR+8LwCgdh1fIkivvwIkJiauW7fu6tWrIpGIu/U7u289peyVuRPU+0pqampOmTKluLh469atZWVldXV1Fy9e3L17d9vIxYsXCwSCNWvWTJs2jTsmixCyadOm4cOHL1iw4Pvvv6+urq6oqPjyyy/pyn3chfkAAAAA+jB1j2btcYrPVblz546vr6+BgQHdzTclJcXNzY2+Sm+//XbbVVHkHl5WVhYeHj506FBtbW2xWOzh4XH+/HlugEQisbKyunXrloeHh0gkEggEkydPTk1NVaqTqqqqkJAQCwsLgUAwadKkjIwMujM9IeSf//wnjbl9+7aLi4tQKBw0aNCuXbsUfKHOnj3r7e1tbm4uEAjGjBmzfv36P/74oxMxDMP89NNPHh4eYrGYz+ePGjXq/7N353FNXHv/wE9YEmMIEWlZBFpX5GW1QbFXqfCo4AW9trhccSmo1UJ5rAvWpQWrfbTopa32ok9LRUUudasCr1e1WrW3tVZfICrqhVo3FLXKLrtQCdv8/jhP5zdNIExC9nzef5EzJycnE/L9zpycObNx40alaoGBgervqqO0rINAIJDJZCNGjFiyZMnVq1e5NS34c8GseyW47swAiBle+cLnm0JXo2dFREQohY4PPviA+fP1UFOnTqXP5RMi9BeZqZiYGKVAFxoayr8Odxl+Jbm5uWwLmkbmrVu3MipB2Fh7stvFgG7dusUwzJMnT2JiYry8vOzt7V1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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model,show_shapes = True, to_file='Large_Ubiquitination.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39226dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity =  0.8067729083665338  Sensitivity =  0.7498020585906572\n",
      "Model: \"functional_221\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_444 (InputLayer)          [(None, 49)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_110 (Embedding)       (None, 49, 21)       483         input_444[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_331 (Conv1D)             (None, 49, 256)      5632        embedding_110[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_331 (MaxPooling1D (None, 24, 256)      0           conv1d_331[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_995 (Dropout)           (None, 24, 256)      0           max_pooling1d_331[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_332 (Conv1D)             (None, 18, 256)      459008      dropout_995[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_332 (MaxPooling1D (None, 9, 256)       0           conv1d_332[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "input_442 (InputLayer)          [(None, 49, 21)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_996 (Dropout)           (None, 9, 256)       0           max_pooling1d_332[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_330 (Conv1D)             (None, 49, 16)       1024        input_442[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "input_441 (InputLayer)          [(None, 49, 31)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_332 (Flatten)           (None, 2304)         0           dropout_996[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_330 (MaxPooling1D (None, 24, 16)       0           conv1d_330[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lstm_110 (LSTM)                 (None, 49, 16)       3072        input_441[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_1438 (Dense)              (None, 768)          1770240     flatten_332[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_331 (Flatten)           (None, 384)          0           max_pooling1d_330[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "flatten_330 (Flatten)           (None, 784)          0           lstm_110[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_997 (Dropout)           (None, 768)          0           dense_1438[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1432 (Dense)              (None, 512)          197120      flatten_331[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1430 (Dense)              (None, 512)          401920      flatten_330[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1439 (Dense)              (None, 256)          196864      dropout_997[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dropout_991 (Dropout)           (None, 512)          0           dense_1432[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_990 (Dropout)           (None, 512)          0           dense_1430[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_998 (Dropout)           (None, 256)          0           dense_1439[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1433 (Dense)              (None, 2)            1026        dropout_991[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1431 (Dense)              (None, 2)            1026        dropout_990[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1440 (Dense)              (None, 2)            514         dropout_998[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_110 (Concatenate)   (None, 6)            0           dense_1433[0][0]                 \n",
      "                                                                 dense_1431[0][0]                 \n",
      "                                                                 dense_1440[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1441 (Dense)              (None, 6)            42          concatenate_110[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_220 (Activation)     (None, 6)            0           dense_1441[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1442 (Dense)              (None, 2)            14          activation_220[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_221 (Activation)     (None, 2)            0           dense_1442[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 3,037,985\n",
      "Trainable params: 3,037,985\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "testx (12598,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import confusion_matrix\n",
    "Y_pred = model.predict([AAindexCoding_Independent_Test,binaryCoding_Independent_Test,x_ind_49])\n",
    "\n",
    "t_pred2 = Y_pred[:,1]\n",
    "\n",
    "Y_pred = (Y_pred > 0.5)\n",
    "y_pred1 = [np.argmax(y, axis=None, out=None) for y in Y_pred]\n",
    "y_pred1 = np.array(y_pred1)\n",
    "\n",
    "\n",
    "mcc_test = matthews_corrcoef(yval, y_pred1)\n",
    "# For sensitivity and specificity\n",
    "sp_1, sn_1 = confusion_matrix(yval, y_pred1)\n",
    "sp_2_test = sp_1[0]/(sp_1[0]+sp_1[1])\n",
    "sn_2_test = sn_1[1]/(sn_1[0]+sn_1[1])\n",
    "# ROC\n",
    "\n",
    "\n",
    "print(\"Specificity = \",sp_2_test, \" Sensitivity = \",sn_2_test)\n",
    "print(model.summary())\n",
    "print(\"testx\",yval.shape)\n",
    "\n",
    "y_pred = y_pred1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e31fe2d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matthews Correlation :  0.5458386140298045\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[4050  970]\n",
      " [1896 5682]]\n",
      "\n",
      "\n",
      "Accuracy on test set:    0.7725035719955549\n",
      "\n",
      "\n",
      "Sensitivity:    0.7498020585906572 \t Specificity:    0.8067729083665338\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.81      0.74      5020\n",
      "           1       0.85      0.75      0.80      7578\n",
      "\n",
      "    accuracy                           0.77     12598\n",
      "   macro avg       0.77      0.78      0.77     12598\n",
      "weighted avg       0.79      0.77      0.77     12598\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve, auc, classification_report\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score,matthews_corrcoef\n",
    "Accuracyyyyy = accuracy_score(yval, y_pred)\n",
    "cm = confusion_matrix(yval, y_pred)\n",
    "mcc = matthews_corrcoef(yval, y_pred)\n",
    "\n",
    "print(\"Matthews Correlation : \",mcc)\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix : \\n\",cm)\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy on test set:   \",Accuracyyyyy)\n",
    "print(\"\\n\")\n",
    "\n",
    "TP = cm[1][1]\n",
    "TN = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "\n",
    "Sensitivity = TP/(TP+FN)\n",
    "\n",
    "Specificity = TN/(TN+FP)\n",
    "\n",
    "\n",
    "print(\"Sensitivity:   \",Sensitivity,\"\\t\",\"Specificity:   \",Specificity)\n",
    "\n",
    "print(classification_report(yval, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0843d3a7",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a7d199c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a3c5757",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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