CNN_RNAP_Seq_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Neural Network that predicts whether RNAP will bind to an arbitrary sequence\n",
    "\n",
    "(Trained on the Ecoli genome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "input_data_raw =  pd.read_csv('rnap_scanned_over_ecoli_genome.csv')\n",
    "sequences = input_data_raw['seq'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw Data\n",
    "The seq column below contains positive training examples. Sequences that RNAP will bind to. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>val</th>\n",
       "      <th>seq</th>\n",
       "      <th>left</th>\n",
       "      <th>right</th>\n",
       "      <th>ori</th>\n",
       "      <th>contig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.9083</td>\n",
       "      <td>CTATTACCTGCCCACGGTAAAGGCGTTCTGGTTGCCCTACG</td>\n",
       "      <td>3695629</td>\n",
       "      <td>3695669</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.5831</td>\n",
       "      <td>TTTCTGCCCGCGGCCTGGCAATTGATACCGCCTTGCCATCG</td>\n",
       "      <td>1109059</td>\n",
       "      <td>1109099</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.4205</td>\n",
       "      <td>TTTTTACACGCCTTGCGTAAAGCGGCACGGCGCGGGGTGCG</td>\n",
       "      <td>822216</td>\n",
       "      <td>822256</td>\n",
       "      <td>-</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.4098</td>\n",
       "      <td>TTGCTGCCTGGCCGATGGTCTATGCCGCTGCGTTCTCCGGC</td>\n",
       "      <td>772478</td>\n",
       "      <td>772518</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.3640</td>\n",
       "      <td>CTTTTACACGCAAAATGCCGTCACGTTCACGGGTCTGGGCT</td>\n",
       "      <td>2748531</td>\n",
       "      <td>2748571</td>\n",
       "      <td>+</td>\n",
       "      <td>MG1655.fa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       val                                        seq     left    right ori  \\\n",
       "0  23.9083  CTATTACCTGCCCACGGTAAAGGCGTTCTGGTTGCCCTACG  3695629  3695669   +   \n",
       "1  23.5831  TTTCTGCCCGCGGCCTGGCAATTGATACCGCCTTGCCATCG  1109059  1109099   +   \n",
       "2  23.4205  TTTTTACACGCCTTGCGTAAAGCGGCACGGCGCGGGGTGCG   822216   822256   -   \n",
       "3  23.4098  TTGCTGCCTGGCCGATGGTCTATGCCGCTGCGTTCTCCGGC   772478   772518   +   \n",
       "4  23.3640  CTTTTACACGCAAAATGCCGTCACGTTCACGGGTCTGGGCT  2748531  2748571   +   \n",
       "\n",
       "      contig  \n",
       "0  MG1655.fa  \n",
       "1  MG1655.fa  \n",
       "2  MG1655.fa  \n",
       "3  MG1655.fa  \n",
       "4  MG1655.fa  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up lists that will contain positive sequences and positive test sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#np.array([np.random.choice(['A', 'C', 'G', 'T'], 1, p=np.array([1,1,1,1])/4.0) for i in range(seq_length)]).transpose()\n",
    "np.random.seed(0)\n",
    "random_seq_indices = np.random.randint(9000, size = (1000))\n",
    "test_random_seq_indices = np.random.randint(1000, size = (500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos = []\n",
    "test_pos = []\n",
    "\n",
    "num_sample = 9000\n",
    "test_size = 1000\n",
    "\n",
    "seq_length = 41\n",
    "\n",
    "for index in range(len(sequences[0:num_sample])):\n",
    "    #if(index in random_seq_indices):\n",
    "    #    pos.append(np.array([np.random.choice(['A', 'C', 'G', 'T'], p=np.array([1,1,1,1])/4.0) for i in range(41)]).transpose())\n",
    "    #else:    \n",
    "        pos.append(list(sequences[index]))\n",
    "    \n",
    "for test_index in range(len(sequences[num_sample:num_sample+test_size])):\n",
    "    #if(test_index in test_random_seq_indices):\n",
    "    #    test_pos.append(np.array([np.random.choice(['A', 'C', 'G', 'T'], p=np.array([1,1,1,1])/4.0) for i in range(41)]).transpose())\n",
    "    #else:\n",
    "        test_pos.append(list(sequences[test_index]))    \n",
    "\n",
    "pos = np.array(pos)\n",
    "test_pos = np.array(test_pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and negative training and test sequences "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "_FyVOov3NoFy",
    "outputId": "77e43c57-5fa1-43f4-b21f-eeb17e06e785"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TCGTGGAGAAGCCCTAGATGAGCGTCACATGGAGCCATCGC',\n",
       " 'CAAGCAATGGAAAAGTATTTCGGGGCCTCCTCTCTTGAACC',\n",
       " 'TCTATGTAGGTCGCTGCCCGACTCGGGGTTGGCGCCTCAAC',\n",
       " 'ACCCGCGACGCCGAGAGCACGCGGTTGTTCATTTTGGGCCA',\n",
       " 'GTAAAATCTTTGGTTCATCTTAGGTTATCCTCGGAGAATCT',\n",
       " 'TGGTACTAGAGGGCGTTGAGACCTCCGTGCGCAAATATGGC',\n",
       " 'AGAAAAGTTAAAAGAAATCGCTAAGGTGATACTCAAGACCC',\n",
       " 'CTACCTTCGTAATCGTGAGTTTGGAGCGTGTCCAGATCTCT',\n",
       " 'TAGGACTCCGATTGGTTCACAGACTAATTCCTCTGTTCCAA']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg = np.array([np.random.choice(['A', 'C', 'G', 'T'], num_sample, p=np.array([1,1,1,1])/4.0)\n",
    "                for i in range(seq_length)]).transpose()\n",
    "\n",
    "test_neg = np.array([np.random.choice(['A', 'C', 'G', 'T'], test_size, p=np.array([1,1,1,1])/4.0)\n",
    "                for i in range(seq_length)]).transpose()\n",
    "\n",
    "[''.join(x) for x in neg[1:10,:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9000, 41)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change training examples to one-hot encoded tensors\n",
    "\n",
    "### and put labels in tensors called 'y' and 'y_test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "riQRzV6CNp33",
    "outputId": "a27104ff-7917-4987-dccc-7780792beac2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive sample matrix shape: (9000, 41)\n",
      "Positive sample tensor shape: (9000, 41, 4)\n",
      "Training set shape: (18000, 41, 4)\n",
      "Training set label shape: (18000,)\n",
      "\n",
      "One-hot encoding looks like:\n",
      " [[0. 1. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 0. 1.]\n",
      " [1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "pos_tensor = np.zeros(list(pos.shape) + [4])\n",
    "neg_tensor = np.zeros(list(neg.shape) + [4])\n",
    "\n",
    "test_pos_tensor = np.zeros(list(test_pos.shape) + [4])\n",
    "test_neg_tensor = np.zeros(list(test_neg.shape) + [4])\n",
    "\n",
    "base_dict = {'A': 0, 'C': 1, 'G': 2, 'T': 3}\n",
    "\n",
    "#naive one-hot encoding\n",
    "for row in range(num_sample):\n",
    "    for col in range(seq_length):\n",
    "        pos_tensor[row,col,base_dict[pos[row,col]]] = 1\n",
    "        neg_tensor[row,col,base_dict[neg[row,col]]] = 1\n",
    "        if(row<test_size):\n",
    "            test_pos_tensor[row,col,base_dict[test_pos[row,col]]] = 1\n",
    "            test_neg_tensor[row,col,base_dict[test_neg[row,col]]] = 1        \n",
    "\n",
    "print('Positive sample matrix shape: {}'.format(pos.shape))\n",
    "# this should be a 3D tensor with shape: (samples, steps, input_dim)\n",
    "print('Positive sample tensor shape: {}'.format(pos_tensor.shape))\n",
    "\n",
    "X = np.vstack((pos_tensor, neg_tensor))\n",
    "y = np.concatenate((np.ones(num_sample), np.zeros(num_sample)))\n",
    "#y = np.concatenate((np.zeros(num_sample), np.ones(num_sample)))\n",
    "\n",
    "y_test = np.concatenate((np.ones(test_size), np.zeros(test_size)))\n",
    "#y_test = np.concatenate((np.zeros(test_size), np.ones(test_size)))\n",
    "\n",
    "print('Training set shape: {}'.format(X.shape))\n",
    "print('Training set label shape: {}'.format(y.shape))\n",
    "print('\\nOne-hot encoding looks like:\\n {}'.format(X[0,0:10,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 41, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Neural Network using Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "bD1LpHUKNtVh",
    "outputId": "ec377700-10d7-44a3-c80b-45c2a7133a11"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the output of first layer: (1, 41, 1)\n"
     ]
    }
   ],
   "source": [
    "#here comes the deep learning part\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D, Dense, Flatten, Dropout\n",
    "from keras.activations import relu\n",
    "from keras.layers.pooling import MaxPooling1D\n",
    "from keras.optimizers import SGD, Adam\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(1, 41, padding='same', input_shape=(seq_length, 4), activation='relu'))\n",
    "\n",
    "\n",
    "#sanity check for dimensions\n",
    "print('Shape of the output of first layer: {}'.format(model.predict_on_batch(pos_tensor[0:1,:,:]).shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "2CkOlW_EN2gn",
    "outputId": "fbdf111b-fb35-4569-c9e3-34ead250e689"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 41, 1)             165       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 10, 1)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 10, 10)            210       \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 5, 10)             0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 20)                1020      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 1,416\n",
      "Trainable params: 1,416\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 12600 samples, validate on 5400 samples\n",
      "Epoch 1/25\n",
      "12600/12600 [==============================] - 2s 142us/step - loss: 0.4389 - acc: 0.7921 - val_loss: 0.3250 - val_acc: 0.8761\n",
      "Epoch 2/25\n",
      "12600/12600 [==============================] - 1s 108us/step - loss: 0.0911 - acc: 0.9748 - val_loss: 0.1133 - val_acc: 0.9598\n",
      "Epoch 3/25\n",
      "12600/12600 [==============================] - 1s 105us/step - loss: 0.0267 - acc: 0.9944 - val_loss: 0.0733 - val_acc: 0.9770\n",
      "Epoch 4/25\n",
      "12600/12600 [==============================] - 1s 102us/step - loss: 0.0160 - acc: 0.9971 - val_loss: 0.0526 - val_acc: 0.9846\n",
      "Epoch 5/25\n",
      "12600/12600 [==============================] - 1s 99us/step - loss: 0.0110 - acc: 0.9979 - val_loss: 0.0684 - val_acc: 0.9806\n",
      "Epoch 6/25\n",
      "12600/12600 [==============================] - 1s 100us/step - loss: 0.0090 - acc: 0.9979 - val_loss: 0.0639 - val_acc: 0.9835\n",
      "Epoch 7/25\n",
      "12600/12600 [==============================] - 1s 101us/step - loss: 0.0072 - acc: 0.9984 - val_loss: 0.0487 - val_acc: 0.9880\n",
      "Epoch 8/25\n",
      "12600/12600 [==============================] - 2s 131us/step - loss: 0.0066 - acc: 0.9987 - val_loss: 0.0697 - val_acc: 0.9846\n",
      "Epoch 9/25\n",
      "12600/12600 [==============================] - 3s 203us/step - loss: 0.0057 - acc: 0.9986 - val_loss: 0.0728 - val_acc: 0.9841\n",
      "Epoch 10/25\n",
      "12600/12600 [==============================] - 2s 152us/step - loss: 0.0050 - acc: 0.9994 - val_loss: 0.0490 - val_acc: 0.9896\n",
      "Epoch 11/25\n",
      "12600/12600 [==============================] - 2s 158us/step - loss: 0.0035 - acc: 0.9996 - val_loss: 0.0538 - val_acc: 0.9885\n",
      "Epoch 12/25\n",
      "12600/12600 [==============================] - 2s 156us/step - loss: 0.0045 - acc: 0.9991 - val_loss: 0.0839 - val_acc: 0.9846\n",
      "Epoch 13/25\n",
      "12600/12600 [==============================] - 2s 185us/step - loss: 0.0047 - acc: 0.9990 - val_loss: 0.0622 - val_acc: 0.9869\n",
      "Epoch 14/25\n",
      "12600/12600 [==============================] - 2s 161us/step - loss: 0.0049 - acc: 0.9988 - val_loss: 0.0520 - val_acc: 0.9907\n",
      "Epoch 15/25\n",
      "12600/12600 [==============================] - 2s 170us/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0362 - val_acc: 0.9943\n",
      "Epoch 16/25\n",
      "12600/12600 [==============================] - 2s 177us/step - loss: 0.0062 - acc: 0.9987 - val_loss: 0.0386 - val_acc: 0.9931\n",
      "Epoch 17/25\n",
      "12600/12600 [==============================] - 2s 166us/step - loss: 0.0032 - acc: 0.9994 - val_loss: 0.0525 - val_acc: 0.9906\n",
      "Epoch 18/25\n",
      "12600/12600 [==============================] - 2s 163us/step - loss: 0.0030 - acc: 0.9994 - val_loss: 0.0573 - val_acc: 0.9906\n",
      "Epoch 19/25\n",
      "12600/12600 [==============================] - 2s 190us/step - loss: 0.0040 - acc: 0.9992 - val_loss: 0.0618 - val_acc: 0.9902\n",
      "Epoch 20/25\n",
      "12600/12600 [==============================] - 2s 175us/step - loss: 0.0031 - acc: 0.9997 - val_loss: 0.0655 - val_acc: 0.9893\n",
      "Epoch 21/25\n",
      "12600/12600 [==============================] - 4s 316us/step - loss: 0.0035 - acc: 0.9995 - val_loss: 0.0600 - val_acc: 0.9894\n",
      "Epoch 22/25\n",
      "12600/12600 [==============================] - 3s 206us/step - loss: 0.0050 - acc: 0.9988 - val_loss: 0.0705 - val_acc: 0.9887\n",
      "Epoch 23/25\n",
      "12600/12600 [==============================] - 2s 172us/step - loss: 0.0029 - acc: 0.9994 - val_loss: 0.0734 - val_acc: 0.9874\n",
      "Epoch 24/25\n",
      "12600/12600 [==============================] - 2s 166us/step - loss: 0.0035 - acc: 0.9993 - val_loss: 0.0525 - val_acc: 0.9915\n",
      "Epoch 25/25\n",
      "12600/12600 [==============================] - 2s 156us/step - loss: 0.0035 - acc: 0.9994 - val_loss: 0.0744 - val_acc: 0.9878\n"
     ]
    }
   ],
   "source": [
    "model.add(MaxPooling1D(pool_size=4))\n",
    "model.add(Conv1D(10, 20, padding='same', input_shape=(seq_length, 4), activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(20, activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#model.compile(optimizer=sgd,loss='binary_crossentropy',metrics=['accuracy'])\n",
    "#model.compile(optimizer=Adam(lr=0.001),loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.compile(optimizer=Adam(lr=0.001),loss='binary_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "history = model.fit(X, y, validation_split=0.3, epochs=25)  # starts training\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfrom keras.models import Sequential\\nfrom keras.layers import Dense, Dropout\\nfrom keras.layers import Embedding\\nfrom keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D\\n\\nseq_length = 41\\n\\nmodel = Sequential()\\nmodel.add(Conv1D(64, 3, activation='relu', input_shape=(seq_length, 4)))\\nmodel.add(Conv1D(64, 3, activation='relu'))\\nmodel.add(MaxPooling1D(3))\\nmodel.add(Conv1D(128, 3, activation='relu'))\\nmodel.add(Conv1D(128, 3, activation='relu'))\\nmodel.add(GlobalAveragePooling1D())\\nmodel.add(Dropout(0.5))\\nmodel.add(Dense(1, activation='sigmoid'))\\n\\nmodel.compile(loss='binary_crossentropy',\\n              optimizer='rmsprop',\\n              metrics=['accuracy'])\\n\\nhistory = model.fit(X, y, batch_size=16, epochs=10)\\n#score = model.evaluate(x_test, y_test, batch_size=16)\\n\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the following also works\n",
    "\n",
    "'''\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D\n",
    "\n",
    "seq_length = 41\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(64, 3, activation='relu', input_shape=(seq_length, 4)))\n",
    "model.add(Conv1D(64, 3, activation='relu'))\n",
    "model.add(MaxPooling1D(3))\n",
    "model.add(Conv1D(128, 3, activation='relu'))\n",
    "model.add(Conv1D(128, 3, activation='relu'))\n",
    "model.add(GlobalAveragePooling1D())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X, y, batch_size=16, epochs=10)\n",
    "#score = model.evaluate(x_test, y_test, batch_size=16)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nconvlayer = model.layers[0]\\nweights = convlayer.get_weights()[0].squeeze()\\nprint(\\'Convolution parameter shape: {}\\'.format(weights.shape))\\n\\nnum2seq = [\\'A\\',\\'C\\',\\'G\\',\\'T\\']\\n\\ntest_pred = \\'\\'.join([num2seq[np.argmax(weights[i,:])] for i in range(weights.shape[0])])\\n\\n\\n# characters stacked at the top of the RNAP PWM logo\\ntop_logo_chars = [\\'C\\',\\'T\\',\\'A\\',\\'C\\',\\'T\\',\\'A\\',\\'C\\',\\'A\\',\\'C\\',\\'G\\',\\n                  \\'C\\',\\'C\\',\\'C\\',\\'C\\',\\'A\\',\\'T\\',\\'A\\',\\'T\\',\\'T\\',\\'G\\',\\n                  \\'A\\',\\'G\\',\\'C\\',\\'G\\',\\'A\\',\\'T\\',\\'C\\',\\'C\\',\\'C\\',\\'G\\',\\n                  \\'G\\',\\'G\\',\\'T\\',\\'T\\',\\'C\\',\\'C\\',\\'C\\',\\'C\\',\\'C\\',\\'C\\',\\n                  \\'G\\']\\n\\ntest_pred_array = np.array(list(test_pred))\\ntest_pred_array\\n\\ntotal = 0\\nfor base_index in range(len(np.array(top_logo_chars))):\\n    if(top_logo_chars[base_index]==test_pred_array[base_index]):\\n        total+=1\\nprint(\"PWM top base prediction: \",total,\"out of 41\")  \\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I want to see if I can train the weights of the first conv layer to recover the \n",
    "# RNAP position weight matrix. I decided on using 1 filter with kernel size equal to \n",
    "# the sequences length (41). I will have to play around it with it more, right now the predictions\n",
    "# for the PWM don't look better than random ,on average, although sometimes I can get more than 60%\n",
    "# accuracy, though that might just be chance.\n",
    "\n",
    "\"\"\"\n",
    "convlayer = model.layers[0]\n",
    "weights = convlayer.get_weights()[0].squeeze()\n",
    "print('Convolution parameter shape: {}'.format(weights.shape))\n",
    "\n",
    "num2seq = ['A','C','G','T']\n",
    "\n",
    "test_pred = ''.join([num2seq[np.argmax(weights[i,:])] for i in range(weights.shape[0])])\n",
    "\n",
    "\n",
    "# characters stacked at the top of the RNAP PWM logo\n",
    "top_logo_chars = ['C','T','A','C','T','A','C','A','C','G',\n",
    "                  'C','C','C','C','A','T','A','T','T','G',\n",
    "                  'A','G','C','G','A','T','C','C','C','G',\n",
    "                  'G','G','T','T','C','C','C','C','C','C',\n",
    "                  'G']\n",
    "\n",
    "test_pred_array = np.array(list(test_pred))\n",
    "test_pred_array\n",
    "\n",
    "total = 0\n",
    "for base_index in range(len(np.array(top_logo_chars))):\n",
    "    if(top_logo_chars[base_index]==test_pred_array[base_index]):\n",
    "        total+=1\n",
    "print(\"PWM top base prediction: \",total,\"out of 41\")  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Loss functions and model Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "-VpzZqooOAPx",
    "outputId": "30d34bfd-b21e-47c7-9c64-d1cf407b23c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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alNvrgKO7LmRmVwFfAHKBD3b3RGY2D5gHMGHChP5XlD8E8oe+5wC2qvoWivLS+VGIiOwf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\nREQk6f8D5eR+WUncaJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "VeIdsrBEO49U",
    "outputId": "04003ed6-30d1-494a-da95-3672e19fb46d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hEC6pPeQIiIUk0Rhv0xVSIiJkkTDcPQWcNoBtbwQmZYxPjKZlbnuTu1/i7rOB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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_pos = model.predict(test_pos_tensor)\n",
    "y_pred_neg = model.predict(test_neg_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_pred_pos,bins=100,label='positive_test')\n",
    "plt.hist(y_pred_neg,bins=100,label='negative_test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wrong_neg_predictions = 0\n",
    "for neg_index in range(len(y_test[1000:2000].reshape(1000,1))):\n",
    "    if(abs(y_pred_neg[neg_index]-y_test[neg_index+1000])>0.1):\n",
    "        #print(neg_index,y_pred_neg[neg_index])\n",
    "        wrong_neg_predictions+=1\n",
    "\n",
    "wrong_pos_predictions = 0\n",
    "for pos_index in range(len(y_test[0:1000].reshape(1000,1))):\n",
    "    if(abs(y_pred_pos[pos_index]-y_test[pos_index])>0.1):\n",
    "        #print(pos_index,y_pred_pos[pos_index])\n",
    "        wrong_pos_predictions+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrong predictions on negative test: 15 out of  1000  predictions\n",
      "wrong predictions on positive test: 0  out of  1000  predictions\n"
     ]
    }
   ],
   "source": [
    "print(\"wrong predictions on negative test:\",wrong_neg_predictions, \"out of \",len(y_pred_pos),\" predictions\")\n",
    "print(\"wrong predictions on positive test:\",wrong_pos_predictions,\" out of \",len(y_pred_neg),\" predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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