{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7529b0be-6ee0-40b5-b38f-c873ec1e29d2",
   "metadata": {},
   "source": [
    "# RESP pipeline example\n",
    "\n",
    "This notebook illustrates how to use resp_protein_toolkit to train a model using data collected against\n",
    "a single target or antigen of interest then run an in silico search for novel binders using the resulting\n",
    "model. It takes as input two gzipped csv files included with the package under the \"example_dataset\" folder.\n",
    "To run this script, obtain the files \"mHER_H3_AgNeg.csv\" and \"mHER_H3_AgPos.csv\" from the repo https://github.com/dahjan/DMS_opt\n",
    "and move them to the same directory as this notebook.\n",
    "\n",
    "**IMPORTANT NOTE:** We have only conducted a very minimal set of hyperparameter tuning experiments for the model used in this notebook. It is very likely possible to achieve better performance with more extensive hyperparameter tuning for this model. Also note that for some hyperparameter settings the model can perform quite poorly. We do not suggest that you use the hyperparameters shown here for your dataset without testing them to ensure they achieve satisfactory performance. In other words: use this notebook as a guide to how to set up and run the pipeline, not as a guide to which hyperparameters to use for your deep learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6fd6f0c-be49-491c-bb87-65b823c99e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import matthews_corrcoef as MCC, accuracy_score, average_precision_score\n",
    "from sklearn.calibration import calibration_curve, CalibrationDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import resp_protein_toolkit \n",
    "from resp_protein_toolkit import OneHotProteinEncoder\n",
    "from resp_protein_toolkit import ByteNetSingleSeq\n",
    "from resp_protein_toolkit import InSilicoDirectedEvolution as ISDE\n",
    "\n",
    "negative_examples = pd.read_csv(\"mHER_H3_AgNeg.csv\")\n",
    "positive_examples = pd.read_csv(\"mHER_H3_AgPos.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "286daf4a-b27e-4ca8-9c3e-eccc90190089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>Count</th>\n",
       "      <th>Fraction</th>\n",
       "      <th>NucSeq</th>\n",
       "      <th>AASeq</th>\n",
       "      <th>AgClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>TGTAGCAGGTACACTATCTGCAGTTTCTACAAGCTCCAGTATTGG</td>\n",
       "      <td>YTICSFYKLQ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>TGTAGCAGGTGGTTCCTCTGCGGCTTCTACCAGAACATGTATTGG</td>\n",
       "      <td>WFLCGFYQNM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>TGTAGCAGGTTCGGCAACATCAGCTCCTTCGCGATCGCGTATTGG</td>\n",
       "      <td>FGNISSFAIA</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>TGTAGCAGGTTCAAGGTCAACGGTCTGTTCCCGCACCTCTATTGG</td>\n",
       "      <td>FKVNGLFPHL</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>TGTAGCAGGTACACTATCTGCAGTATGTACGAGTTCGATTATTGG</td>\n",
       "      <td>YTICSMYEFD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27534</th>\n",
       "      <td>27534</td>\n",
       "      <td>79</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>TGTAGCAGGTGGGACGAGGGCGACCCCTACCCCTACCAGTATTGG</td>\n",
       "      <td>WDEGDPYPYQ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27535</th>\n",
       "      <td>27535</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>TGTAGCAGGTGGCATGAGGACGGCATGTACCAGAACGAGTATTGG</td>\n",
       "      <td>WHEDGMYQNE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27536</th>\n",
       "      <td>27536</td>\n",
       "      <td>115</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>TGTAGCAGGTACCGCGACTCCCACTCCTTCACGTTCGTCTATTGG</td>\n",
       "      <td>YRDSHSFTFV</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27537</th>\n",
       "      <td>27537</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>TGTAGCAGGTGGGACGTCCTCAACTACTTCGTGTTCATCTATTGG</td>\n",
       "      <td>WDVLNYFVFI</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27538</th>\n",
       "      <td>27538</td>\n",
       "      <td>46</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>TGTAGCAGATGGGTGCTCGTCGGTATGTACATGTTCGGGTATTGG</td>\n",
       "      <td>WVLVGMYMFG</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27539 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Count  Fraction  \\\n",
       "0               0      7  0.000007   \n",
       "1               1     95  0.000041   \n",
       "2               2      3  0.000001   \n",
       "3               3     10  0.000005   \n",
       "4               4     16  0.000016   \n",
       "...           ...    ...       ...   \n",
       "27534       27534     79  0.000034   \n",
       "27535       27535      3  0.000003   \n",
       "27536       27536    115  0.000049   \n",
       "27537       27537     14  0.000006   \n",
       "27538       27538     46  0.000021   \n",
       "\n",
       "                                              NucSeq       AASeq  AgClass  \n",
       "0      TGTAGCAGGTACACTATCTGCAGTTTCTACAAGCTCCAGTATTGG  YTICSFYKLQ        0  \n",
       "1      TGTAGCAGGTGGTTCCTCTGCGGCTTCTACCAGAACATGTATTGG  WFLCGFYQNM        0  \n",
       "2      TGTAGCAGGTTCGGCAACATCAGCTCCTTCGCGATCGCGTATTGG  FGNISSFAIA        0  \n",
       "3      TGTAGCAGGTTCAAGGTCAACGGTCTGTTCCCGCACCTCTATTGG  FKVNGLFPHL        0  \n",
       "4      TGTAGCAGGTACACTATCTGCAGTATGTACGAGTTCGATTATTGG  YTICSMYEFD        0  \n",
       "...                                              ...         ...      ...  \n",
       "27534  TGTAGCAGGTGGGACGAGGGCGACCCCTACCCCTACCAGTATTGG  WDEGDPYPYQ        0  \n",
       "27535  TGTAGCAGGTGGCATGAGGACGGCATGTACCAGAACGAGTATTGG  WHEDGMYQNE        0  \n",
       "27536  TGTAGCAGGTACCGCGACTCCCACTCCTTCACGTTCGTCTATTGG  YRDSHSFTFV        0  \n",
       "27537  TGTAGCAGGTGGGACGTCCTCAACTACTTCGTGTTCATCTATTGG  WDVLNYFVFI        0  \n",
       "27538  TGTAGCAGATGGGTGCTCGTCGGTATGTACATGTTCGGGTATTGG  WVLVGMYMFG        0  \n",
       "\n",
       "[27539 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f465caed-f3ce-4ef6-a444-c52e0dd39242",
   "metadata": {},
   "source": [
    "The input data contains two columns, one of which is a sequence and the other of which is the assigned category.\n",
    "This is a binary classification task. These are very short sequences (10 AAs only) so we'll use a very simple\n",
    "neural network for this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a398758c-7b8d-4a85-b975-e07911c35ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_sequences = negative_examples.AASeq.tolist() + positive_examples.AASeq.tolist()\n",
    "all_labels = negative_examples.AgClass.tolist() + positive_examples.AgClass.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe58a3c3-86b7-4079-b5ff-985bd466fc0e",
   "metadata": {},
   "source": [
    "We'll write a short training function and evaluation function for checking performance on the validation set. We'll also split the data into train and validation. This dataset is sufficiently small that if we are using one-hot encoding we can save the encodings in memory. For larger datasets, it may be necessary to encode each minibatch immediately before feeding it to the model -- this is fast if using one-hot encoding. If the sequence representations are very expensive to generate, it may be better to encode the data\n",
    "and save it to disk (although this can require a large amount of disk space for high-dimensional embeddings). Either way, a PyTorch\n",
    "dataloader can be helpful here.\n",
    "\n",
    "Finally, we'll construct a cnn model using the ByteNet variant that is part of this package with a last-layer\n",
    "Gaussian process so that we can estimate uncertainty on our predictions. There are of course a number of hyperparameters --\n",
    "minibatch size, learning rate, the number of layers, dropout, the dilution factor, the hidden dim size and so on -- that can be altered to try to improve the performance of this model, and in real applications it may be useful to do some preliminary experiments and estimate\n",
    "performance on a validation set. For the sake of simplicity, in this example we've done only a couple rounds of hyperparameter tuning\n",
    "and found some settings that worked well enough for this demo -- in a real world application, you may want to experiment with other architectures or tune more extensively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad925c5e-f91f-4cce-bf47-8cdbca0ccfd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use alphabet = \"gapped\" here if sequences contain gaps. In this case they do not. Notice that if you\n",
    "# want InSilicoDirectedEvolution to introduce gaps into your starting sequence when optimizing it,\n",
    "# use the \"gapped\" alphabet here.\n",
    "one_hot_encoder = OneHotProteinEncoder(alphabet=\"standard\")\n",
    "xvalues = one_hot_encoder.encode(all_sequences)\n",
    "yvalues = np.array(all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e13f26-01ba-42de-86bb-5848de803897",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "423aa187-5d29-4b2d-a035-7329ba16a76d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76b4c792-9afc-4744-87d3-e123ef41ac12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ac642de-4d4b-4b19-a2b1-0b9bea547967",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_train(model, xdata, ydata, epochs=2,\n",
    "                    minibatch=500, random_seed=123,\n",
    "                    optimizer=None, scheduler=None):\n",
    "    \"\"\"Simple model train function, easily adapted for more complex\n",
    "    problems.\"\"\"\n",
    "    model = model.train()\n",
    "    model = model.cuda()\n",
    "\n",
    "    # The loss function here is BCELoss. You may need to change this if you\n",
    "    # are for example doing regression or want to use a different loss function.\n",
    "    loss_fn = torch.nn.BCELoss()\n",
    "\n",
    "    for epoch in range(0, epochs):\n",
    "\n",
    "        for j in range(0, xdata.shape[0], minibatch):\n",
    "            xmini, ymini = xdata[j:j+minibatch,...], ydata[j:j+minibatch]\n",
    "            # Important to convert to float because the onehot data is\n",
    "            # a uint8 array (to save on memory).\n",
    "            xmini = torch.from_numpy(xmini).float().cuda()\n",
    "            ymini = torch.from_numpy(ymini).float().cuda()\n",
    "\n",
    "            # EXTREMELY important to use update_precision on the training loop --\n",
    "            # otherwise the covariance estimate will not be updated during\n",
    "            # training!\n",
    "            y_pred = model(xmini, update_precision=True)\n",
    "            loss = loss_fn(y_pred, ymini)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "            print(scheduler.get_last_lr())\n",
    "        print(\"Epoch complete\")\n",
    "\n",
    "    # The ByteNet model will automatically reset to eval when we call its\n",
    "    # \"predict\" method, but this may be necessary for other models.\n",
    "    model = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6e597ea-d602-4397-b447-c3529cf6b3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xval, ytrain, yval = train_test_split(xvalues, yvalues,\n",
    "                            test_size=0.2, random_state=123, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5708d4fb-1890-4e60-93e9-1cc45ae714a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9853a84b-4ba9-4607-b79f-823b4e22897d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "893b1966-cfe6-4d64-9c59-764b07c61159",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_model(model, xdata, ydata, get_full_predictions=True, batch_size=500):\n",
    "    # Important to feed the data to the model in batches, since trying to generate\n",
    "    # predictions on a large dataset all at once will lead to out of memory.\n",
    "    predicted_y, variance = [], []\n",
    "\n",
    "    for j in range(0, xdata.shape[0], batch_size):\n",
    "        xbatch = xdata[j:j+batch_size,...]\n",
    "        # The predict method of the model will convert the numpy input to\n",
    "        # a pytorch float array, send it to the appropriate device, then\n",
    "        # return us numpy arrays on CPU.\n",
    "        if get_full_predictions:\n",
    "            preds, var = model.predict(xbatch, get_var = True)\n",
    "            predicted_y.append(preds)\n",
    "            variance.append(var)\n",
    "        else:\n",
    "            predicted_y.append(model.predict(xbatch))\n",
    "\n",
    "    predicted_y = np.concatenate(predicted_y)\n",
    "    predicted_categories = np.rint(predicted_y)\n",
    "    mcc = MCC(ydata, predicted_categories)\n",
    "    avprc = average_precision_score(ydata, predicted_y)\n",
    "    accuracy = accuracy_score(ydata, predicted_categories)\n",
    "\n",
    "    if get_full_predictions:\n",
    "        return mcc, avprc, accuracy, predicted_y, np.concatenate(variance)\n",
    "    return mcc, avprc, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "129a83ff-16ca-47c7-acbb-277b9f39d371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch complete\n",
      "Epoch complete\n",
      "2 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "4 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "6 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "8 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "10 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "12 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "14 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "16 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "18 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "20 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "22 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "24 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "26 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "28 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "30 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "32 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "34 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "36 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "38 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "40 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "42 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "44 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "46 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "48 epochs\n",
      "Epoch complete\n",
      "Epoch complete\n",
      "50 epochs\n"
     ]
    }
   ],
   "source": [
    "train_mcc, train_auc_prc, train_accuracy = [], [], []\n",
    "val_mcc, val_auc_prc, val_accuracy = [], [], []\n",
    "\n",
    "nepochs = 50\n",
    "\n",
    "# See the \"Built-in models for protein sequence data\" section of the docs\n",
    "# for more on what these arguments do. input_dim is 20 because we used the\n",
    "# standard alphabet with one-hot encoding (if there were gaps, we would\n",
    "# specify 21 instead). Many other arguments are hyperparameters we can\n",
    "# tune. The \"llgp\" argument is True if we want an uncertainty-aware model,\n",
    "# False otherwise. \"objective\" is \"binary_classifier\" because we have a classification\n",
    "# task; otherwise, it might be \"regression\" or \"multiclass\". If \"multiclass\",\n",
    "# we would have to supply another argument, \"num_predicted_categories\"\n",
    "# as well.\n",
    "\n",
    "base_model = ByteNetSingleSeq(input_dim=20, hidden_dim=21, n_layers=2,\n",
    "                              kernel_size=5, dil_factor=1, rep_dim=100,\n",
    "                              pool_type = \"max\",\n",
    "                              dropout=0.2, slim=False, llgp=True,\n",
    "                              objective=\"binary_classifier\")\n",
    "\n",
    "# The weight decay and learning rate may also need to be optimized. In a real\n",
    "# project, you may want to set up a config file with parameters like these\n",
    "# and experiment with them on a validation set.\n",
    "\n",
    "optimizer = torch.optim.Adam(base_model.parameters(), lr = 0.0005,\n",
    "                                 weight_decay = 1e-6)\n",
    "\n",
    "for i in range(int(nepochs / 2)):\n",
    "    _ = model_train(base_model, xtrain, ytrain, epochs=2,\n",
    "                     minibatch=200, random_seed=123,\n",
    "                    optimizer = optimizer)\n",
    "    print(f\"{i * 2 + 2} epochs\", flush=True)\n",
    "\n",
    "    mcc, aucprc, accuracy = eval_model(base_model, xtrain, ytrain, False)\n",
    "    train_mcc.append(mcc)\n",
    "    train_auc_prc.append(aucprc)\n",
    "    train_accuracy.append(accuracy)\n",
    "\n",
    "    mcc, aucprc, accuracy = eval_model(base_model, xval, yval, False)\n",
    "    val_mcc.append(mcc)\n",
    "    val_auc_prc.append(aucprc)\n",
    "    val_accuracy.append(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61d68cd-34b1-4849-a6cd-a39dfd590a9b",
   "metadata": {},
   "source": [
    "It's very important to check that the validation set performance is acceptable before you proceed. If not,\n",
    "consider changing hyperparameters, or using a different model architecture. In this case, since this notebook\n",
    "is purely for demonstration purposes, we proceed with the model as-is rather than trying to refine it further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e488ce7e-3b0b-47a4-8b8a-b0f409391b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"bmh\")\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(12,4))\n",
    "\n",
    "ax1.plot(np.arange(0, nepochs, 2), train_mcc, color=\"green\", label=\"train\")\n",
    "ax1.plot(np.arange(0, nepochs, 2), val_mcc, color=\"red\", label=\"validation\")\n",
    "\n",
    "ax2.plot(np.arange(0, nepochs, 2), train_accuracy, color=\"green\", label=\"train\")\n",
    "ax2.plot(np.arange(0, nepochs, 2), val_accuracy, color=\"red\", label=\"validation\")\n",
    "\n",
    "ax3.plot(train_auc_prc, color=\"green\", label=\"train\")\n",
    "ax3.plot(val_auc_prc, color=\"red\", label=\"validation\")\n",
    "\n",
    "ax1.set_title(\"Matthews\\ncorrelation coefficient\")\n",
    "ax2.set_title(\"Accuracy\")\n",
    "ax3.set_title(\"AUC-PRC\")\n",
    "\n",
    "ax1.set_ylabel(\"Matthews\\ncorrelation coefficient\")\n",
    "ax2.set_ylabel(\"Accuracy\")\n",
    "ax3.set_ylabel(\"AUC-PRC\")\n",
    "\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "\n",
    "ax1.set_xlabel(\"Epochs\")\n",
    "ax2.set_xlabel(\"Epochs\")\n",
    "ax3.set_xlabel(\"Epochs\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f322b7-0095-4189-8b23-899ce7d079c7",
   "metadata": {},
   "source": [
    "It's also important to see what the uncertainty calibration looks like. Here's an easy way to\n",
    "do this with scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa4d17e6-59ad-49c5-8463-d70be07b603c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.calibration.CalibrationDisplay at 0x72c693dfae30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _, _, pred_y, variance = eval_model(base_model, xtrain, ytrain, True)\n",
    "\n",
    "prob_true, prob_pred = calibration_curve(ytrain, pred_y, n_bins=20)\n",
    "disp = CalibrationDisplay(prob_true, prob_pred, pred_y)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04b9cd44-b4e4-470f-a1f2-7ef9b899ac10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ab17750a-6ab5-4db1-97ab-b60160dd1660",
   "metadata": {},
   "source": [
    "If we are happy with the results we will proceed. We could probably achieve further improvement by tweaking\n",
    "the hyperparameters further, but for this demonstration notebook we'll simply proceed.\n",
    "\n",
    "Next let's look at the variance and use the 95th percentile on the training set as a threshold for the maximum uncertainty we're willing to allow. The LLGP configuration should also ensure that p(y=1) should go to 0.5 as we go further from the training set which will provide us with another safeguard as well. (It's a good idea to test this assumption, but for this demo notebook we'll skip this step.)\n",
    "\n",
    "Notice that for some applications, especially regression, we can set a variance threshold by taking into account what kind of value\n",
    "we are trying to predict and setting a threshold for how wide of a window we can tolerate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ede27b29-cf33-4c62-b637-3b1453a35c8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "715c7a84-19b1-4d55-af7a-2d42f3e52a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95ee5360-5abe-4a36-893d-5a3bcf9c6cc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.020829717\n"
     ]
    }
   ],
   "source": [
    "print(np.percentile(variance, 95))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d73464c-670b-4804-ba1d-5002767497be",
   "metadata": {},
   "source": [
    "Now we'll set up an object that houses our model. When provided with a sequence, this object will 1) encode the sequence, 2) make\n",
    "a prediction and 3) return the predicted score ( p(y=1) ) and the variance. Once we have this object we can feed it to the InSilicoDirectedEvolution class, which will run an in silico search for improved sequences.\n",
    "\n",
    "Notice that it's important for the object to return two 1d numpy arrays each of which has the same shape[0]\n",
    "as there are number of antigens. In this case there is one antigen so it should return two numpy arrays of shape[0] = 1. When\n",
    "setting up the ISDE, we can also enter a probability distribution that indicates the probability of each AA at each\n",
    "position -- this should be an array of shape (sequence_length, 21). In this case, we do not want to introduce any gaps\n",
    "but all other AAs are ok, so we'll set a probability distribution where all elements are 1/20 except the last column\n",
    "which will be zero. Because we set our sequence encoder to use a non-gapped alphabet, trying to encode a proposed sequence\n",
    "that contained gaps in this case would actually cause an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8208557-97ca-44de-9608-00d1e4d50434",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SequenceAssessor:\n",
    "\n",
    "    def __init__(self, model, encoder):\n",
    "        self.encoder = encoder\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, sequence):\n",
    "        encoded_seq = self.encoder.encode([sequence])\n",
    "        pred, var = self.model.predict(encoded_seq, get_var=True)\n",
    "        return pred, var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7b7d9192-71c8-4d74-aa71-f3aa19a189c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_distro = np.ones((10,21))\n",
    "prob_distro[:,-1] = 0\n",
    "prob_distro /= prob_distro.sum(axis=1)[:,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74f700f9-7226-41d2-a88a-3f147aa86487",
   "metadata": {},
   "outputs": [],
   "source": [
    "isde = ISDE(SequenceAssessor(base_model, one_hot_encoder),\n",
    "            uncertainty_threshold = np.percentile(variance, 95), prob_distro = prob_distro, seed=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72590106-49df-40cf-a61c-0ea80f4f6210",
   "metadata": {},
   "source": [
    "Usually we start with the wild type as our starting sequence; for this demo, we'll just use a known nonbinder as a starting point\n",
    "then optimize this sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f07b8efa-70bd-4646-8c86-0d1b4ec60f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature: 2.026462904054532\n",
      "Temperature: 0.16426207606036508\n",
      "Temperature: 0.013314840147172557\n"
     ]
    }
   ],
   "source": [
    "starting_sequence = \"YTICSFYKLQ\"\n",
    "\n",
    "isde.run_chain(starting_sequence, max_iterations = 3000, cooldown = 0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7492bdab-8804-4392-9b39-b4966b6e1180",
   "metadata": {},
   "source": [
    "Now that the in silico directed evolution experiment has run, we can retrieve the results. Usually you'll need\n",
    "to run multiple experiments with different starting random seeds and/or starting sequences to thoroughly explore\n",
    "the region of sequence space that you're interested in. For this demo, we'll content ourselves with a single ISDE\n",
    "chain. First, let's see how the sequence scores changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d759b25f-12c8-436e-8668-c423c9c68074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Iteration number')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(isde.get_scores())\n",
    "plt.title(\"Scores $p(y=1)$ vs iteration number for\\nin silico directed evolution\")\n",
    "plt.ylabel(\"Score $p(y=1)$\")\n",
    "plt.xlabel(\"Iteration number\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def5e7a9-5a41-4cf4-981b-aa317bb44fbc",
   "metadata": {},
   "source": [
    "In this case, the chain spent a long time exploring before it converged on some sequences that had high probability of being binders. If we're unahppy with this result, we could re-run the chain changing the cooldown rate (lower numbers cause the chain to converge faster but may mean more limited exploration) or other settings.\n",
    "\n",
    "For now, let's look at the accepted sequences. Notice that we have to filter the accepted sequences, because many of them are from the early stages of exploration and have low scores. We want sequences that have high probability of being binders, so we'll filter the accepted sequences for anything associated with a score above 0.7. 0.7 is arbitrary and can be set variously depending on how many sequences you can afford to test experimentally and on how confident you want to be about the sequences that are selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2c4c5f3-171a-46cb-8cf2-3389e885a11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109\n"
     ]
    }
   ],
   "source": [
    "accepted_seqs, accepted_scores = isde.get_accepted_seqs(), isde.get_scores().flatten().tolist()\n",
    "\n",
    "filtered_seqs = [f for (f,s) in zip(accepted_seqs, accepted_scores) if s > 0.7]\n",
    "print(len(filtered_seqs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1019e3c0-66b7-4883-998a-927b0f4bbe6a",
   "metadata": {},
   "source": [
    "Our exploration has yielded 109 sequences that we can consider for further optimization, depending on our criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ad0055-9726-4fea-a76a-03180ebf9593",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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