Tutorial_CPP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n",
    "\n",
    "from utils.utils_cpp import cpp_predictor\n",
    "from utils.utils_cpp import cpp_generator\n",
    "from utils.utils_cpp import cpp_optimizer\n",
    "from utils.utils_common.activator import Activation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filepaths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*_DATA_PATH : For datasets, to be used when training and sampling <br>\n",
    "*_MODEL_PATH : For models, to be used when training to save the model, otherwise to load pre-trained models <br>\n",
    "PREDICTOR_STATS_PATH : To save/load statistics for predictor training dataset <br>\n",
    "\n",
    "SEQ_MAX : Maximum sequence length for predictor <br>\n",
    "SEED_SEQ_LENGTH : Seed sequence length for generator <br>\n",
    "\n",
    "SMILES_PATH : SMILES for monomers <br>\n",
    "FP_RADIUS : Radius of topological exploration for fingerprint <br>\n",
    "FP_BITS : Size of fingerprint bit-vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "GENERATOR_DATA_PATH = './dataset/data_cpp/cpp_generator_dataset.txt'\n",
    "GENERATOR_MODEL_PATH = './model/model_cpp/cpp_generator.hdf5'\n",
    "SEED_SEQ_LENGTH = 10\n",
    "\n",
    "PREDICTOR_DATA_PATH = './dataset/data_cpp/cpp_predictor_dataset.csv'\n",
    "PREDICTOR_MODEL_PATH = './model/model_cpp/cpp_predictor.hdf5'\n",
    "PREDICTOR_STATS_PATH = './dataset/data_cpp/cpp_predictor_dataset_stats.json'\n",
    "\n",
    "SMILES_PATH = './dataset/data_cpp/cpp_smiles.json'\n",
    "FP_RADIUS = 3\n",
    "FP_BITS = 1024\n",
    "SEQ_MAX = 108"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an instance for Generator class with the dataset. <br>\n",
    "Training and saving the generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Data for Training of Generator\n",
      "Pre-Processing Data for Generator\n",
      "Featurizing Data for Generator\n",
      "Starting Training of Generator\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "Train on 5987 samples, validate on 2567 samples\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/2\n",
      "5987/5987 [==============================] - 5s 776us/sample - loss: 2.9019 - acc: 0.1527 - val_loss: 2.7053 - val_acc: 0.1944\n",
      "Epoch 2/2\n",
      "5987/5987 [==============================] - 4s 588us/sample - loss: 2.6929 - acc: 0.1884 - val_loss: 2.6680 - val_acc: 0.1944\n"
     ]
    }
   ],
   "source": [
    "generator = cpp_generator.Generator(data_path = GENERATOR_DATA_PATH, seq_length = SEED_SEQ_LENGTH)\n",
    "\n",
    "generator.train_model(\n",
    "    model_params = {\n",
    "        'save_checkpoint': True,\n",
    "        'checkpoint_filepath': './model/'\n",
    "        }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an instance for Predictor class with the dataset and other parameters. <br>\n",
    "Training and saving the predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Data for Training of Predictor\n",
      "Featurizing Data for Predictor\n",
      "Creating Model for Predictor\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 409 samples, validate on 103 samples\n",
      "Epoch 1/2\n",
      "409/409 [==============================] - 3s 6ms/sample - loss: 1.2173 - val_loss: 0.4443\n",
      "Epoch 2/2\n",
      "409/409 [==============================] - 1s 1ms/sample - loss: 0.4253 - val_loss: 0.5574\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictor = cpp_predictor.Predictor(\n",
    "    data_path = PREDICTOR_DATA_PATH,\n",
    "    smiles_path = SMILES_PATH,\n",
    "    fp_radius = FP_RADIUS,\n",
    "    fp_bits = FP_BITS,\n",
    "    seq_max = SEQ_MAX\n",
    ")\n",
    "\n",
    "predictor.train_model(\n",
    "    model_params = {\n",
    "        'save_checkpoint': True,\n",
    "        'checkpoint_filepath': './model/'\n",
    "        }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an instance for Optimizer class with the pre-trained models and data files. <br>\n",
    "Sampling sequences using a pre-trained generator to seed the genetic algorithm. <br>\n",
    "Optimizing the seed sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = cpp_optimizer.Optimizer(\n",
    "    model_path = PREDICTOR_MODEL_PATH,\n",
    "    data_path = PREDICTOR_DATA_PATH,\n",
    "    smiles_path = SMILES_PATH,\n",
    "    stats_path = PREDICTOR_STATS_PATH,\n",
    "    fp_radius = FP_RADIUS,\n",
    "    fp_bits = FP_BITS,\n",
    "    seq_max = SEQ_MAX\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = cpp_generator.Generator(\n",
    "    model_path = GENERATOR_MODEL_PATH,\n",
    "    data_path = GENERATOR_DATA_PATH,\n",
    "    seq_length = SEED_SEQ_LENGTH\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating Seeds for Optimizer\n",
      "Generating Seed  1\n",
      "Generating Seed  2\n"
     ]
    }
   ],
   "source": [
    "list_seeds = generator.generate_seed(n_seeds = 2, seed_length = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up Optimizer\n",
      "Optimizing Seed  1\n",
      "Optimizing Seed  2\n",
      "Post-Processing Optimized Sequences\n"
     ]
    },
    {
     "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>sequences</th>\n",
       "      <th>intensity</th>\n",
       "      <th>length</th>\n",
       "      <th>relative_Arg</th>\n",
       "      <th>relative_charge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>LXXRHFPPKCHHFCLKKPPLKKNCTHTXXLGBNXGBCG</td>\n",
       "      <td>11.7136</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0263158</td>\n",
       "      <td>0.237084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>FXXCHFPPKCHHFCLKKPPXKKNCTHTXXLGBNXGBNGK</td>\n",
       "      <td>11.4768</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.230962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  sequences intensity length relative_Arg  \\\n",
       "73   LXXRHFPPKCHHFCLKKPPLKKNCTHTXXLGBNXGBCG   11.7136     38    0.0263158   \n",
       "79  FXXCHFPPKCHHFCLKKPPXKKNCTHTXXLGBNXGBNGK   11.4768     39            0   \n",
       "\n",
       "   relative_charge  \n",
       "73        0.237084  \n",
       "79        0.230962  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = optimizer.optimize(list_seeds)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Activation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the gradient activation of peptide sequence (ex. penetratin) based on pre-trained predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From /home/somesh/anaconda3/envs/keras/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Positive activation for feature map\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive activation averaged over residues\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive activation averaged over fingerprints\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "activator = Activation(\n",
    "    mode = 'cpp',\n",
    "    model_path = PREDICTOR_MODEL_PATH,\n",
    "    smiles_path = SMILES_PATH,\n",
    "    stats_path = PREDICTOR_STATS_PATH,\n",
    "    fp_radius = FP_RADIUS,\n",
    "    fp_bits = FP_BITS,\n",
    "    seq_max = SEQ_MAX\n",
    ")\n",
    "\n",
    "activator.analyze('RQIKIWFQNRRMKWKK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:keras] *",
   "language": "python",
   "name": "conda-env-keras-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}