Y_Independent_Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e003cb32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17723, 51) (17723,)\n",
      "Load the Prot_T5 Index datasets\n",
      "Predict and Test the independent datasets\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from numpy import sort\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import *\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, classification_report,auc\n",
    "%matplotlib inline\n",
    "import pickle\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.keras.layers import Dense, Bidirectional,Dense, LSTM, Activation, Dropout, Flatten\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from sklearn.metrics import accuracy_score\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras import regularizers\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "import os \n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score,matthews_corrcoef\n",
    "from tensorflow.keras.callbacks import Callback,EarlyStopping\n",
    "import random\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras import backend as K\n",
    "import os\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, recall_score, matthews_corrcoef, roc_curve, auc\n",
    "from tensorflow.keras import backend as K\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "import tensorflow\n",
    "import tensorflow as tf\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Reshape\n",
    "from tensorflow.keras.layers import Conv1D,Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras import utils\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.optimizers import Adam,SGD\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import copy\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from Bio import SeqIO\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Reshape, Lambda, LSTM\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D\n",
    "from tensorflow.keras.layers import Bidirectional, TimeDistributed\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,ReduceLROnPlateau\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.utils import shuffle\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.backend import expand_dims\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.regularizers import l1, l2\n",
    "from sklearn.metrics import roc_curve, auc, classification_report\n",
    "from tensorflow.keras.models import load_model\n",
    "import pandas as pd\n",
    "from tensorflow.keras.layers import Conv1D,Conv2D, MaxPooling2D,GlobalMaxPooling1D\n",
    "from Bio import SeqIO\n",
    "import random\n",
    "import functools\n",
    "import itertools\n",
    "import os\n",
    "import random\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n",
    "from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Reshape, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv1D, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import copy    \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "cut_off = int(15)\n",
    "alphabet = 'ARNDCQEGHILKMFPSTWYV-'\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",
    "test_x_51 = []\n",
    "test_y_51 = []\n",
    "posit_1 = 1;\n",
    "negat_0 = 0;\n",
    "\n",
    "allpos = []\n",
    "allneg = []\n",
    "\n",
    "# TRAIN DATASET -------------------------------------------------------------\n",
    "#for positive sequence\n",
    "def inner_1():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "    data = data[cut_off:-cut_off]\n",
    "    allpos.append(data)\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",
    "    test_x_51.append(integer_encoded)\n",
    "    test_y_51.append(posit_1)\n",
    "for seq_record in SeqIO.parse(\"data/test_positive_Y_81_window_3248.fasta\", \"fasta\"):\n",
    "    inner_1()\n",
    "# for negative sequence\n",
    "def inner_2():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "    data = data[cut_off:-cut_off]\n",
    "    allneg.append(data)\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",
    "    test_x_51.append(integer_encoded)\n",
    "    test_y_51.append(negat_0)\n",
    "for seq_record in SeqIO.parse(\"data/test_negative_Y_81_window_14503.fasta\", \"fasta\"):\n",
    "    inner_2()\n",
    "x_test_51 = array(test_x_51)\n",
    "y_test_51 = array(test_y_51)\n",
    "\n",
    "print(x_test_51.shape,y_test_51.shape)\n",
    "\n",
    "x_test_41 = x_test_51[:,[i for i in range(5,46)]]\n",
    "x_test_33 = x_test_41[:,[i for i in range(4,37)]]\n",
    "x_test_21 = x_test_33[:,[i for i in range(6,27)]]\n",
    "\n",
    "AAindex_Test_AA_Sequence = []\n",
    "for i in range(len(x_test_51)):\n",
    "    integer_of_string = str(x_test_51[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_AA_Sequence.append(individual_AA_peptide)\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "print(\"Load the Prot_T5 Index datasets\")\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",
    "Header_name = [\"PID\",\"Position\",\"Sequence\",\"Middle_Amino_Acid\"]\n",
    "\n",
    "col_of_feature = [i for i in range(1,1025)]\n",
    "\n",
    "Target = [\"Target\"]\n",
    "\n",
    "Header_name = Header_name + col_of_feature + Target\n",
    "\n",
    "df_Test_Negative = pd.read_csv(\"data/14503_Index_DeepPSP_33_window_Y___Test_Negative.txt\",header=None)\n",
    "df_Test_Negative.columns = Header_name\n",
    "\n",
    "df_Test_Positive = pd.read_csv(\"data/3248_Index_DeepPSP_33_window_Y___Test_Positive.txt\",header=None)\n",
    "df_Test_Positive.columns = Header_name\n",
    "\n",
    "frames = [df_Test_Positive, df_Test_Negative]\n",
    "\n",
    "total_mean_deeppsp_testing = pd.concat(frames,ignore_index = True)\n",
    "\n",
    "df_Test_array = total_mean_deeppsp_testing.drop([\"PID\",\"Position\",\"Sequence\",\"Middle_Amino_Acid\",\"Target\"],axis=1)\n",
    "df_Test_array = np.array(df_Test_array)\n",
    "\n",
    "df_Test_Sequence = np.array(total_mean_deeppsp_testing[\"Sequence\"])\n",
    "\n",
    "dictionary_Test_Set = dict(zip(df_Test_Sequence,df_Test_array))\n",
    "\n",
    "Test_array = []\n",
    "for i in range(len(AAindex_Test_AA_Sequence)):\n",
    "    AAindex_Test_AA = AAindex_Test_AA_Sequence[i][9:-9]\n",
    "    sequences_of_test_sequence = AAindex_Test_AA.strip(\"-\")\n",
    "    value_of_the_sequence =dictionary_Test_Set.get(sequences_of_test_sequence)\n",
    "    Test_array.append(value_of_the_sequence)\n",
    "\n",
    "Prot_T5_Test_array = np.array(Test_array)\n",
    "\n",
    "binaryCoding_Independent_Test = binary_encode(AAindex_Test_AA_Sequence)\n",
    "\n",
    "y_test = y_test_51\n",
    "print(\"Predict and Test the independent datasets\")\n",
    "\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d81bbe96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "Em_Prot_model = keras.models.load_model(\"models/Y_______Again_job_was_performed_stacked_generilization_model_\"+str(2899160)+\".h5\")\n",
    "\n",
    "Y_pred1_EM_Prot = Em_Prot_model.predict([x_test_51, Prot_T5_Test_array])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "675d9809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3875067 , 0.61249334],\n",
       "       [0.29724708, 0.70275295],\n",
       "       [0.5206364 , 0.4793636 ],\n",
       "       ...,\n",
       "       [0.8444547 , 0.15554531],\n",
       "       [0.3241983 , 0.67580175],\n",
       "       [0.5302691 , 0.46973085]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred1_EM_Prot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd1c677f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matthews Correlation :  0.2984138958329886\n",
      "Confusion Matrix : \n",
      " [[10735  3748]\n",
      " [ 1230  2010]]\n",
      "Test accuracy: 0.7191220448005416\n",
      "Specificity =  0.741213836912242  Sensitivity =  0.6203703703703703\n"
     ]
    }
   ],
   "source": [
    "yhat = argmax(Y_pred1_EM_Prot, axis=1)\n",
    "r_test_y = y_test\n",
    "print(\"Matthews Correlation : \",matthews_corrcoef(r_test_y, yhat))\n",
    "print(\"Confusion Matrix : \\n\",confusion_matrix(r_test_y, yhat))\n",
    "print('Test accuracy:', accuracy_score( r_test_y, yhat))\n",
    "sp_1, sn_1 = confusion_matrix(r_test_y, yhat)\n",
    "sp_2 = sp_1[0]/(sp_1[0]+sp_1[1])\n",
    "sn_2 = sn_1[1]/(sn_1[0]+sn_1[1])\n",
    "print(\"Specificity = \",sp_2, \" Sensitivity = \",sn_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a416c4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(Em_Prot_model,show_shapes = True, to_file='Embdding+ProtT5_Y.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d5de442",
   "metadata": {},
   "outputs": [],
   "source": [
    "CM = confusion_matrix(r_test_y, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44a9942b",
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = CM[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1eb7b8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "TN = CM[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6fd830b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "FP = CM[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8b800a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "FN = CM[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72da3c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "Recall=  (TP/(TP+FN))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7555d3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.03703703703704"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "93bf1353",
   "metadata": {},
   "outputs": [],
   "source": [
    "Precision=  TP/(TP+FP)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "44a38d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.90795415074679"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "457c4b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "FSCORE = 2*((Precision*Recall)/(Precision+Recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b9ee3296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.67659479884419"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FSCORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6bc68fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_21\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "embedding_layer_input (InputLay [(None, 51)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_embedding_layer (Emb (None, 51, 21)       483         embedding_layer_input[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_Conv1D_1 (Conv1D)    (None, 49, 128)      8192        ensemble_1_embedding_layer[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_drop_out_1 (Dropout) (None, 49, 128)      0           ensemble_1_Conv1D_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_max_pooling1d_3434 ( (None, 24, 128)      0           ensemble_1_drop_out_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_Conv1D_3 (Conv1D)    (None, 22, 64)       24640       ensemble_1_max_pooling1d_3434[0][\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_drop_out_3 (Dropout) (None, 22, 64)       0           ensemble_1_Conv1D_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "input_172 (InputLayer)          [(None, 1024)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_max_pooling1d_3435 ( (None, 11, 64)       0           ensemble_1_drop_out_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_Dense_1 (Dense)      (None, 128)          131200      input_172[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_flatten_1717 (Flatte (None, 704)          0           ensemble_1_max_pooling1d_3435[0][\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_dropout_342 (Dropout (None, 128)          0           ensemble_2_Dense_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_dense_penultimate_la (None, 256)          180480      ensemble_1_flatten_1717[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_Dense_2 (Dense)      (None, 32)           4128        ensemble_2_dropout_342[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_drop_out_bilstm_3 (D (None, 256)          0           ensemble_1_dense_penultimate_laye\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_dropout_343 (Dropout (None, 32)           0           ensemble_2_Dense_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "ensemble_1_classification_layer (None, 1)            257         ensemble_1_drop_out_bilstm_3[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ensemble_2_dense_171 (Dense)    (None, 1)            33          ensemble_2_dropout_343[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 2)            0           ensemble_1_classification_layer_l\n",
      "                                                                 ensemble_2_dense_171[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                (None, 2)            6           concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_21 (Dense)                (None, 2)            6           dense_20[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 349,425\n",
      "Trainable params: 12\n",
      "Non-trainable params: 349,413\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "Em_Prot_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0534f1",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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