examples/MolecularGNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KKVdX_O7ZvY0"
   },
   "source": [
    "# Introduction: GNN for molecules\n",
    "\n",
    "This example will showcase how to define and evaluate a GNN-like model in PyNeuraLogic on a sample molecule classification task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8CEE1Vy1Z5pQ"
   },
   "source": [
    "Install PyNeuraLogic from PyPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SujsflJQZ6Nj"
   },
   "outputs": [],
   "source": [
    "! pip install neuralogic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "flQw3rmscE7K"
   },
   "source": [
    "## A Relational Dataset\n",
    "\n",
    "We are going to use one of the predefined datasets called [Mutagenesis](https://www.doc.ic.ac.uk/~shm/mutagenesis.html). This popular dataset contains information about molecules that we are going to classify for mutagenicity on Salmonella typhimurium.\n",
    "\n",
    "Predefined datasets are located in `neuralogic.utils.data`, and by calling `Mutagenesis`, we retrieve a tuple containing the input data (dataset) as well as a default template (rules). For this example, we are going to ignore the template as we will practice defining our own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "mglV4jndaL2B"
   },
   "outputs": [],
   "source": [
    "from neuralogic.utils.data import Mutagenesis\n",
    "\n",
    "_, dataset = Mutagenesis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gk8AIcCFasFV"
   },
   "source": [
    "Predefined examples are loaded from a file. We can check the first sample to get an idea how the data look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6VaRsqkIasaZ",
    "outputId": "e0c391ff-139e-4ead-8a5a-fe458f508580"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bond(d59_23, d59_5, 0), h(d59_23), c(d59_5), b_1(0), bond(d59_5, d59_23, 0), bond(d59_20, d59_19, 1), c(d59_20), c(d59_19), b_7(1), bond(d59_19, d59_20, 1), bond(d59_10, d59_3, 2), c(d59_10), c(d59_3), b_7(2), bond(d59_3, d59_10, 2), bond(d59_14, d59_10, 3), c(d59_14), b_7(3), bond(d59_10, d59_14, 3), bond(d59_4, d59_3, 4), c(d59_4), b_7(4), bond(d59_3, d59_4, 4), bond(d59_10, d59_9, 5), c(d59_9), b_7(5), bond(d59_9, d59_10, 5), bond(d59_15, d59_28, 6), c(d59_15), h(d59_28), b_1(6), bond(d59_28, d59_15, 6), bond(d59_12, d59_13, 7), c(d59_12), c(d59_13), b_7(7), bond(d59_13, d59_12, 7), bond(d59_8, d59_9, 8), c(d59_8), b_7(8), bond(d59_9, d59_8, 8), bond(d59_7, d59_25, 9), c(d59_7), h(d59_25), b_1(9), bond(d59_25, d59_7, 9), bond(d59_30, d59_17, 10), h(d59_30), c(d59_17), b_1(10), bond(d59_17, d59_30, 10), bond(d59_15, d59_12, 11), b_7(11), bond(d59_12, d59_15, 11), bond(d59_2, d59_1, 12), c(d59_2), c(d59_1), b_7(12), bond(d59_1, d59_2, 12), bond(d59_17, d59_16, 13), c(d59_16), b_7(13), bond(d59_16, d59_17, 13), bond(d59_31, d59_19, 14), h(d59_31), b_1(14), bond(d59_19, d59_31, 14), bond(d59_9, d59_11, 15), c(d59_11), b_7(15), bond(d59_11, d59_9, 15), bond(d59_3, d59_2, 16), b_7(16), bond(d59_2, d59_3, 16), bond(d59_27, d59_11, 17), h(d59_27), b_1(17), bond(d59_11, d59_27, 17), bond(d59_22, d59_4, 18), h(d59_22), b_1(18), bond(d59_4, d59_22, 18), bond(d59_7, d59_8, 19), b_7(19), bond(d59_8, d59_7, 19), bond(d59_24, d59_6, 20), h(d59_24), c(d59_6), b_1(20), bond(d59_6, d59_24, 20), bond(d59_15, d59_16, 21), b_7(21), bond(d59_16, d59_15, 21), bond(d59_7, d59_2, 22), b_7(22), bond(d59_2, d59_7, 22), bond(d59_18, d59_20, 23), c(d59_18), b_7(23), bond(d59_20, d59_18, 23), bond(d59_18, d59_17, 24), b_7(24), bond(d59_17, d59_18, 24), bond(d59_18, d59_13, 25), b_7(25), bond(d59_13, d59_18, 25), bond(d59_32, d59_33, 26), n(d59_32), o(d59_33), b_2(26), bond(d59_33, d59_32, 26), bond(d59_5, d59_6, 27), b_7(27), bond(d59_6, d59_5, 27), bond(d59_6, d59_1, 28), b_7(28), bond(d59_1, d59_6, 28), bond(d59_19, d59_14, 29), b_7(29), bond(d59_14, d59_19, 29), bond(d59_29, d59_16, 30), h(d59_29), b_1(30), bond(d59_16, d59_29, 30), bond(d59_11, d59_12, 31), b_7(31), bond(d59_12, d59_11, 31), bond(d59_14, d59_13, 32), b_7(32), bond(d59_13, d59_14, 32), bond(d59_26, d59_8, 33), h(d59_26), b_1(33), bond(d59_8, d59_26, 33), bond(d59_32, d59_34, 34), o(d59_34), b_2(34), bond(d59_34, d59_32, 34), bond(d59_32, d59_20, 35), b_1(35), bond(d59_20, d59_32, 35), bond(d59_1, d59_21, 36), h(d59_21), b_1(36), bond(d59_21, d59_1, 36), bond(d59_5, d59_4, 37), b_7(37), bond(d59_4, d59_5, 37).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(dataset.examples_file) as fp:\n",
    "    print(fp.readline())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TLfOk69ublG9"
   },
   "source": [
    "As can be seen, the molecules are essentially encoded as heterogeneous graphs with edges called `bonds`, where the first two terms are the chemical atom ids, and the third term is the bond id. The atoms' ids are then associated with specific atom types, such as `h` for hydrogen. Bond ids are then associated with bond types, such as `b_1` for a single bond, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\* Note that this textual storage format is just a bit more compact version of the way we would encode the data directly in the PyNeuraLogic language (Python), where we would instead write:\n",
    "\n",
    "```\n",
    "Relation.bond(Term.d59_23, Term.d59_5, 0), Relation.h(Term.d59_23), ...\n",
    "```\n",
    "\n",
    "Next we take a look at the associated *queries* which are the target labels associated with the examples in supervised learning scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 predict.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(dataset.queries_file) as fp:\n",
    "    print(fp.readline())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "we can see that each example molecule is associated with just a simple binary label called 'predict' here.\n",
    "\n",
    "<sup>\n",
    "* Note that in general you can associate each example with multiple different queries (e.g. joint classification), and that these can also be structured.\n",
    "<sup>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KOZev_cScMQy"
   },
   "source": [
    "## A Relational Template\n",
    "\n",
    "The specification of the learning template is now up to the user. This is where you can get creative, but any valid template has to somehow connect the input example representations with the output queries (i.e. the 'predict' label).\n",
    "In this example, we are going to first turn the atom and bond types into embeddings, and then propagate these jointly through the molecular graphs in a GNN-like fashion.\n",
    "\n",
    "So let's start by embedding each chemical atom type into an `atom_embed` relation with a unique learnable parameter vector of shape `[3]` (a 3-dimensional embedding).\n",
    "Doing this for each type can be simplified with list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "vf-ryNvOcOYX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3} atom_embed(A) :- c(A).\n",
      "{3} atom_embed(A) :- o(A).\n",
      "{3} atom_embed(A) :- br(A).\n",
      "{3} atom_embed(A) :- i(A).\n",
      "{3} atom_embed(A) :- f(A).\n",
      "{3} atom_embed(A) :- h(A).\n",
      "{3} atom_embed(A) :- n(A).\n",
      "{3} atom_embed(A) :- cl(A).\n"
     ]
    }
   ],
   "source": [
    "from neuralogic.core import Template, R, V\n",
    "\n",
    "template = Template()\n",
    "\n",
    "template.add_rules([\n",
    "    (R.atom_embed(V.A)[3,] <= R.get(atom)(V.A)) for atom in [\"c\", \"o\", \"br\", \"i\", \"f\", \"h\", \"n\", \"cl\"]\n",
    "])\n",
    "\n",
    "print(template)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qV58Qp-JcVlu"
   },
   "source": [
    "In the same way, we can encode the embeddings for the bonds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "wuliEkxzcWZJ"
   },
   "outputs": [],
   "source": [
    "template.add_rules([\n",
    "    (R.bond_embed(V.B)[3,] <= R.get(bond)(V.B)) for bond in [\"b_1\", \"b_2\", \"b_3\", \"b_4\", \"b_5\", \"b_7\"]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yMI5Icgrccum"
   },
   "source": [
    "Next we add the GNN-like graph propagation rule itself.\n",
    "Particularly, we will want to project representations of all the nodes (`atom_embed`) through a learnable matrix of shape `[3, 3]`.\n",
    "We will do this separately for the \"central\" node `X` and its neighbors `Y` (nodes connected by the `bond` relation) to accommodate for their different roles here.\n",
    "Since we also have different bond types here, we will combine these atom projections together with the bond embedding.\n",
    "For each central node `X`, we will then aggregate these representations from all the corresponding neighbors `Y`, to serve as the new representation for the central node `X`.\n",
    "Let's call this resulting representation `layer_1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "YAGhZIy9cdcg"
   },
   "outputs": [],
   "source": [
    "template +=  R.layer_1(V.X) <= (R.atom_embed(V.X)[3, 3], R.atom_embed(V.Y)[3, 3], R.bond(V.X, V.Y, V.B), R.bond_embed(V.B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GA5ehjCNcnlu"
   },
   "source": [
    "In the same way, we can now add some more \"layers\" that will be 'connected' by utilizing the previously defined representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "df_vNmbwcoIF"
   },
   "outputs": [],
   "source": [
    "template +=  R.layer_2(V.X) <= (R.layer_1(V.X)[3, 3], R.layer_1(V.Y)[3, 3], R.bond(V.X, V.Y, V.B), R.bond_embed(V.B))\n",
    "template +=  R.layer_3(V.X) <= (R.layer_2(V.X)[3, 3], R.layer_2(V.Y)[3, 3], R.bond(V.X, V.Y, V.B), R.bond_embed(V.B))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4ZhNNrfrcuw7"
   },
   "source": [
    "Finally, to obatin a single label for the whole graph, we then aggregate all the nodes' (atoms') representations from the last layer (`layer_3`), and project them through another learnable vector of shape `[1, 3]` into a scalar value.\n",
    "We will call the resulting representation `predict` to correspond to the learning target queries given in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "K29q8NvLcvq4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3} atom_embed(A) :- c(A).\n",
      "{3} atom_embed(A) :- o(A).\n",
      "{3} atom_embed(A) :- br(A).\n",
      "{3} atom_embed(A) :- i(A).\n",
      "{3} atom_embed(A) :- f(A).\n",
      "{3} atom_embed(A) :- h(A).\n",
      "{3} atom_embed(A) :- n(A).\n",
      "{3} atom_embed(A) :- cl(A).\n",
      "{3} bond_embed(B) :- b_1(B).\n",
      "{3} bond_embed(B) :- b_2(B).\n",
      "{3} bond_embed(B) :- b_3(B).\n",
      "{3} bond_embed(B) :- b_4(B).\n",
      "{3} bond_embed(B) :- b_5(B).\n",
      "{3} bond_embed(B) :- b_7(B).\n",
      "layer_1(X) :- {3, 3} atom_embed(X), {3, 3} atom_embed(Y), bond(X, Y, B), bond_embed(B).\n",
      "layer_2(X) :- {3, 3} layer_1(X), {3, 3} layer_1(Y), bond(X, Y, B), bond_embed(B).\n",
      "layer_3(X) :- {3, 3} layer_2(X), {3, 3} layer_2(Y), bond(X, Y, B), bond_embed(B).\n",
      "{1, 3} predict :- layer_3(X).\n"
     ]
    }
   ],
   "source": [
    "template += R.predict[1, 3] <= R.layer_3(V.X)\n",
    "\n",
    "print(template)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take a look at the template visually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uju8LDYwcy2m"
   },
   "source": [
    "## Training\n",
    "\n",
    "When we have our dataset and template ready, it's time to build (\"ground\") the template over the dataset and start training.\n",
    "We can do the training manually and write our own custom training loop, but we can also use predefined helpers - *evaluators*,\n",
    "that handle model and dataset building, training, and more. Evaluators can be customized via `Settings`.\n",
    "\n",
    "<sup>Note that building the dataset (=grounding the logic rules and translating into neural networks) may take a while, depending on the complexity of your template.\n",
    "But this is only done once before the training itself, which takes up most of the time anyway.\n",
    "<sup>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "OvixrLnZc2Ao"
   },
   "outputs": [],
   "source": [
    "from neuralogic.core import Settings\n",
    "from neuralogic.nn.loss import MSE, CrossEntropy\n",
    "from neuralogic.nn import get_evaluator\n",
    "from neuralogic.optim import Adam\n",
    "\n",
    "settings = Settings(optimizer=Adam(lr=0.001), epochs=100, error_function=MSE())\n",
    "evaluator = get_evaluator(template, settings)\n",
    "\n",
    "built_dataset = evaluator.build_dataset(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t-AH9fwFddSn"
   },
   "source": [
    "Finally, we iterate through the iterator encapsulated in the `train` method of the evaluator, which yields a total loss of the epoch and the number of samples of the current epoch.\n",
    "We then get access to the results from the training loop that we can further visualize, inspect, log, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "sSVqIDvoddzV",
    "outputId": "9b4ed322-e895-4005-8f03-5e6486b159c7"
   },
   "outputs": [
    {
     "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": [
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "average_losses = []\n",
    "\n",
    "for current_total_loss, number_of_samples in evaluator.train(built_dataset):\n",
    "    clear_output(wait=True)\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "\n",
    "    plt.xlim(0, settings.epochs)\n",
    "\n",
    "    average_losses.append(current_total_loss / number_of_samples)\n",
    "    \n",
    "    plt.plot(average_losses, label=\"Average loss\")\n",
    "\n",
    "    plt.legend()\n",
    "    plt.pause(0.001)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jl4xOLjP0vOG"
   },
   "source": [
    "We can then check the trained model predictions (for the same sample set here) by utilizing the `test` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OrRL7-wk0t_L",
    "outputId": "9f62a6ce-b29d-4d1a-947f-eb16f5d93683",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: 1, Predicted: 1 (0.7026368974269119)\n",
      "Target: 1, Predicted: 1 (0.6904064061867354)\n",
      "Target: 1, Predicted: 1 (0.5504211771855159)\n",
      "Target: 1, Predicted: 1 (0.7359609708676949)\n",
      "Target: 1, Predicted: 1 (0.6902418986517329)\n",
      "Target: 1, Predicted: 1 (0.7008111017944308)\n",
      "Target: 1, Predicted: 1 (0.6937441205376326)\n",
      "Target: 1, Predicted: 1 (0.7226091356957475)\n",
      "Target: 1, Predicted: 1 (0.7077460911344604)\n",
      "Target: 1, Predicted: 0 (0.2986499260543944)\n",
      "Target: 1, Predicted: 1 (0.5790265044439502)\n",
      "Target: 1, Predicted: 1 (0.7795784063312476)\n",
      "Target: 1, Predicted: 0 (0.3013618848153191)\n",
      "Target: 1, Predicted: 0 (0.2932146369964413)\n",
      "Target: 1, Predicted: 1 (0.6348146041870414)\n",
      "Target: 1, Predicted: 0 (0.4004008314100911)\n",
      "Target: 1, Predicted: 1 (0.7496072819413515)\n",
      "Target: 1, Predicted: 1 (0.7395275341023883)\n",
      "Target: 1, Predicted: 1 (0.7989766052318185)\n",
      "Target: 1, Predicted: 1 (0.7032556694781277)\n",
      "Target: 1, Predicted: 1 (0.7176233664419791)\n",
      "Target: 1, Predicted: 1 (0.7027728746745395)\n",
      "Target: 1, Predicted: 1 (0.5797525231278106)\n",
      "Target: 1, Predicted: 1 (0.7005018176506311)\n",
      "Target: 1, Predicted: 1 (0.7496072819413515)\n",
      "Target: 1, Predicted: 1 (0.6219234126622631)\n",
      "Target: 1, Predicted: 1 (0.7612964934973568)\n",
      "Target: 1, Predicted: 0 (0.2573626752025805)\n",
      "Target: 1, Predicted: 1 (0.6096165238172607)\n",
      "Target: 1, Predicted: 1 (0.6914615576433846)\n",
      "Target: 1, Predicted: 1 (0.619899206620835)\n",
      "Target: 1, Predicted: 1 (0.7143057469046757)\n",
      "Target: 1, Predicted: 1 (0.5239545631271084)\n",
      "Target: 1, Predicted: 1 (0.7949716139249732)\n",
      "Target: 1, Predicted: 1 (0.5863747785585229)\n",
      "Target: 1, Predicted: 1 (0.6262296680754531)\n",
      "Target: 1, Predicted: 1 (0.7944153228119154)\n",
      "Target: 1, Predicted: 1 (0.7505228926186903)\n",
      "Target: 1, Predicted: 1 (0.787595697282139)\n",
      "Target: 1, Predicted: 1 (0.8165583749824841)\n",
      "Target: 1, Predicted: 1 (0.6035482860017612)\n",
      "Target: 1, Predicted: 1 (0.6650777917661508)\n",
      "Target: 1, Predicted: 1 (0.7944592427000694)\n",
      "Target: 1, Predicted: 1 (0.685571506329729)\n",
      "Target: 1, Predicted: 1 (0.678016718394248)\n",
      "Target: 1, Predicted: 1 (0.6823619770221327)\n",
      "Target: 1, Predicted: 1 (0.6251284210716886)\n",
      "Target: 1, Predicted: 1 (0.7364444914636102)\n",
      "Target: 1, Predicted: 1 (0.6987523945529631)\n",
      "Target: 1, Predicted: 1 (0.6743616734728992)\n",
      "Target: 1, Predicted: 1 (0.7917815557613503)\n",
      "Target: 1, Predicted: 0 (0.2489211890909235)\n",
      "Target: 1, Predicted: 1 (0.6738763357313804)\n",
      "Target: 1, Predicted: 1 (0.757602997229552)\n",
      "Target: 1, Predicted: 1 (0.7244116616368611)\n",
      "Target: 1, Predicted: 0 (0.3199883249213296)\n",
      "Target: 1, Predicted: 0 (-0.0350002175033594)\n",
      "Target: 1, Predicted: 0 (0.4770196747911321)\n",
      "Target: 1, Predicted: 1 (0.6700221258275278)\n",
      "Target: 1, Predicted: 0 (0.3941119390426405)\n",
      "Target: 1, Predicted: 1 (0.8412684065884467)\n",
      "Target: 1, Predicted: 1 (0.7801526151759206)\n",
      "Target: 1, Predicted: 1 (0.6653007054438275)\n",
      "Target: 1, Predicted: 1 (0.6317100897421696)\n",
      "Target: 1, Predicted: 1 (0.5418037322907534)\n",
      "Target: 1, Predicted: 1 (0.8060912221296683)\n",
      "Target: 1, Predicted: 1 (0.8215681963130859)\n",
      "Target: 1, Predicted: 1 (0.6190533270795747)\n",
      "Target: 1, Predicted: 1 (0.6304253557635253)\n",
      "Target: 1, Predicted: 1 (0.7329699914958111)\n",
      "Target: 1, Predicted: 1 (0.7539918035398223)\n",
      "Target: 1, Predicted: 1 (0.8742588197069522)\n",
      "Target: 1, Predicted: 0 (0.3588091356896146)\n",
      "Target: 1, Predicted: 1 (0.7250082082468052)\n",
      "Target: 1, Predicted: 1 (0.833966915160538)\n",
      "Target: 1, Predicted: 1 (0.5979509058880185)\n",
      "Target: 1, Predicted: 1 (0.7045550331025819)\n",
      "Target: 1, Predicted: 1 (0.6194476036582949)\n",
      "Target: 1, Predicted: 1 (0.767671557691695)\n",
      "Target: 1, Predicted: 1 (0.8845926184672759)\n",
      "Target: 1, Predicted: 1 (0.5986217063938915)\n",
      "Target: 1, Predicted: 1 (0.7831798645339698)\n",
      "Target: 1, Predicted: 1 (0.8068610265146567)\n",
      "Target: 1, Predicted: 1 (0.6022906756814678)\n",
      "Target: 1, Predicted: 1 (0.7176233664419791)\n",
      "Target: 1, Predicted: 1 (0.5855458100765066)\n",
      "Target: 1, Predicted: 1 (0.6648560337310697)\n",
      "Target: 1, Predicted: 1 (0.7072481559322175)\n",
      "Target: 1, Predicted: 1 (0.6700707643993694)\n",
      "Target: 1, Predicted: 1 (0.6219442563692812)\n",
      "Target: 1, Predicted: 1 (0.6533004813701336)\n",
      "Target: 1, Predicted: 1 (0.5286427775470153)\n",
      "Target: 1, Predicted: 1 (0.7478344288413458)\n",
      "Target: 1, Predicted: 0 (0.3941119390426405)\n",
      "Target: 1, Predicted: 1 (0.7336921195130874)\n",
      "Target: 1, Predicted: 1 (0.716963671807175)\n",
      "Target: 1, Predicted: 1 (0.6735771457904617)\n",
      "Target: 1, Predicted: 1 (0.5146628074158598)\n",
      "Target: 1, Predicted: 1 (0.6817687820775752)\n",
      "Target: 1, Predicted: 1 (0.6904064061867353)\n",
      "Target: 1, Predicted: 1 (0.7091336452734822)\n",
      "Target: 1, Predicted: 1 (0.6816805900873435)\n",
      "Target: 1, Predicted: 1 (0.6514051484704003)\n",
      "Target: 1, Predicted: 1 (0.5872420057890291)\n",
      "Target: 1, Predicted: 1 (0.73101136721284)\n",
      "Target: 1, Predicted: 1 (0.8055110893478385)\n",
      "Target: 1, Predicted: 1 (0.66485603373107)\n",
      "Target: 1, Predicted: 1 (0.59632771099188)\n",
      "Target: 1, Predicted: 1 (0.5946204835757387)\n",
      "Target: 1, Predicted: 1 (0.5288214729156085)\n",
      "Target: 1, Predicted: 1 (0.5504211771855159)\n",
      "Target: 1, Predicted: 1 (0.6292763290917608)\n",
      "Target: 1, Predicted: 1 (0.8210562814041938)\n",
      "Target: 1, Predicted: 1 (0.7243642657044721)\n",
      "Target: 1, Predicted: 1 (0.7944592427000695)\n",
      "Target: 1, Predicted: 1 (0.7605657801512321)\n",
      "Target: 1, Predicted: 1 (0.7298163386317261)\n",
      "Target: 1, Predicted: 1 (0.5515764654364143)\n",
      "Target: 1, Predicted: 1 (0.7232976839723096)\n",
      "Target: 1, Predicted: 1 (0.7931092765316698)\n",
      "Target: 1, Predicted: 1 (0.723514448190856)\n",
      "Target: 1, Predicted: 1 (0.7765012600227507)\n",
      "Target: 1, Predicted: 0 (0.4607683791649742)\n",
      "Target: 1, Predicted: 1 (0.5132243758902051)\n",
      "Target: 1, Predicted: 0 (0.2922118015470833)\n",
      "Target: 0, Predicted: 0 (-0.0394300413317778)\n",
      "Target: 0, Predicted: 0 (0.1148120510109717)\n",
      "Target: 0, Predicted: 0 (0.1143279366830088)\n",
      "Target: 0, Predicted: 0 (0.3398657995273492)\n",
      "Target: 0, Predicted: 1 (0.6896601030882002)\n",
      "Target: 0, Predicted: 0 (0.1143395087742605)\n",
      "Target: 0, Predicted: 0 (0.3860924280104016)\n",
      "Target: 0, Predicted: 1 (0.674107617498229)\n",
      "Target: 0, Predicted: 0 (0.2702855525441364)\n",
      "Target: 0, Predicted: 1 (0.7691746498609768)\n",
      "Target: 0, Predicted: 0 (0.117914565844908)\n",
      "Target: 0, Predicted: 0 (0.3509278054927216)\n",
      "Target: 0, Predicted: 0 (0.1878182966154081)\n",
      "Target: 0, Predicted: -1 (-0.7566105059652202)\n",
      "Target: 0, Predicted: 0 (0.2401830755959291)\n",
      "Target: 0, Predicted: 0 (0.0769507201331569)\n",
      "Target: 0, Predicted: 0 (0.1158034647115982)\n",
      "Target: 0, Predicted: 0 (0.2019271111258744)\n",
      "Target: 0, Predicted: 0 (0.4706215578542266)\n",
      "Target: 0, Predicted: 0 (0.0346164016098891)\n",
      "Target: 0, Predicted: 0 (0.1388792033745835)\n",
      "Target: 0, Predicted: 0 (-0.3514609513743888)\n",
      "Target: 0, Predicted: 0 (0.3539149923036192)\n",
      "Target: 0, Predicted: 1 (0.6890260709249717)\n",
      "Target: 0, Predicted: 1 (0.6189085808515762)\n",
      "Target: 0, Predicted: 0 (0.2796435783693391)\n",
      "Target: 0, Predicted: 0 (0.0614634732429842)\n",
      "Target: 0, Predicted: 0 (0.2030613104753487)\n",
      "Target: 0, Predicted: 0 (0.0757770616202641)\n",
      "Target: 0, Predicted: 0 (0.2619814262112929)\n",
      "Target: 0, Predicted: 0 (0.2916526076353683)\n",
      "Target: 0, Predicted: 0 (0.2663687152920109)\n",
      "Target: 0, Predicted: 1 (0.676513214417772)\n",
      "Target: 0, Predicted: 0 (0.1591659211997141)\n",
      "Target: 0, Predicted: 0 (0.271806487712706)\n",
      "Target: 0, Predicted: 0 (0.4560209547401755)\n",
      "Target: 0, Predicted: 0 (-0.2947240731745945)\n",
      "Target: 0, Predicted: 1 (0.7625356762097797)\n",
      "Target: 0, Predicted: 1 (0.6423922423477477)\n",
      "Target: 0, Predicted: 0 (0.3481039532280162)\n",
      "Target: 0, Predicted: 0 (-0.128835331728237)\n",
      "Target: 0, Predicted: 0 (0.1674139775201678)\n",
      "Target: 0, Predicted: 0 (-0.3033048555868405)\n",
      "Target: 0, Predicted: 0 (0.4392389938200967)\n",
      "Target: 0, Predicted: 0 (-0.3191720388335538)\n",
      "Target: 0, Predicted: 0 (-0.1701318577781864)\n",
      "Target: 0, Predicted: 0 (0.2023317856009427)\n",
      "Target: 0, Predicted: 0 (-0.0918852215533877)\n",
      "Target: 0, Predicted: 0 (0.0025984213346176)\n",
      "Target: 0, Predicted: 0 (-0.0193068894036323)\n",
      "Target: 0, Predicted: 1 (0.5492928864958802)\n",
      "Target: 0, Predicted: 0 (-0.2775029069177272)\n",
      "Target: 0, Predicted: 0 (0.0884302294683838)\n",
      "Target: 0, Predicted: 0 (0.4962025062533703)\n",
      "Target: 0, Predicted: 0 (-0.1063545356624672)\n",
      "Target: 0, Predicted: 0 (0.4164728106219625)\n",
      "Target: 0, Predicted: 1 (0.5095554329831252)\n",
      "Target: 0, Predicted: 0 (0.157342984962016)\n",
      "Target: 0, Predicted: 0 (0.1708977697231347)\n",
      "Target: 0, Predicted: 1 (0.5416824006858901)\n",
      "Target: 0, Predicted: 1 (0.6894797584628718)\n",
      "Target: 0, Predicted: 0 (0.3686907487402431)\n",
      "Target: 0, Predicted: 0 (0.122156500184662)\n"
     ]
    }
   ],
   "source": [
    "for sample, y_hat in zip(built_dataset.samples, evaluator.test(built_dataset)):\n",
    "    print(f\"Target: {sample.java_sample.target}, Predicted: {round(y_hat)} ({y_hat})\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Mutagenesis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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 },
 "nbformat": 4,
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}