code/ML-evaluating-final-models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KAIxgm30Ci5H"
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SYI2SN8QCsQ7"
   },
   "source": [
    "### Colab Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "DfCk3CVTCrGu",
    "outputId": "575970c2-5538-48e4-ec59-410f6c912639"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "--2019-08-28 14:35:22--  https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-model-binary.pickle\n",
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      "--2019-08-28 14:35:30--  https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-random-padding-model-binary.pickle\n",
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      "--2019-08-28 14:35:39--  https://github.com/dj311/uob-summer-project/raw/master/data/dense-graph2vec-y-test\n",
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      "--2019-08-28 14:35:40--  https://github.com/dj311/uob-summer-project/raw/master/data/dense-graph2vec-y-predict\n",
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      "--2019-08-28 14:35:41--  https://github.com/dj311/uob-summer-project/raw/master/data/cnn-graph2vec-y-test\n",
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      "--2019-08-28 14:35:45--  https://github.com/dj311/uob-summer-project/raw/master/data/stack-model-y-test\n",
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      "\n",
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      "\n",
      "--2019-08-28 14:35:53--  https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-y-test\n",
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      "--2019-08-28 14:35:54--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/cnn-adj-feat-y-test\n",
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      "\n",
      "--2019-08-28 14:35:55--  https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-y-predict\n",
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      "--2019-08-28 14:35:55--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/cnn-adj-feat-y-predict\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "\n",
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      "\n",
      "--2019-08-28 14:35:56--  https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-rand-pad-y-test\n",
      "Resolving github.com (github.com)... 140.82.113.3\n",
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      "\n",
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      "\n",
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      "\n",
      "--2019-08-28 14:35:58--  https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-rand-pad-y-predict\n",
      "Resolving github.com (github.com)... 140.82.113.3\n",
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      "--2019-08-28 14:35:58--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/cnn-adj-feat-rand-pad-y-predict\n",
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      "\n",
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      "\n",
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      "\n",
      "--2019-08-28 14:35:59--  https://github.com/dj311/uob-summer-project/raw/master/data/dense-model-history\n",
      "Resolving github.com (github.com)... 140.82.113.3\n",
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      "--2019-08-28 14:36:00--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/dense-model-history\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "Length: 4839255 (4.6M) [application/octet-stream]\n",
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      "\n",
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      "\n",
      "2019-08-28 14:36:01 (72.0 MB/s) - ‘dense-model-history’ saved [4839255/4839255]\n",
      "\n",
      "--2019-08-28 14:36:01--  https://github.com/dj311/uob-summer-project/raw/master/data/binary-convolutional-history\n",
      "Resolving github.com (github.com)... 140.82.113.3\n",
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      "--2019-08-28 14:36:02--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/binary-convolutional-history\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "Length: 5042039 (4.8M) [application/octet-stream]\n",
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      "\n",
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      "\n",
      "2019-08-28 14:36:03 (12.6 MB/s) - ‘binary-convolutional-history’ saved [5042039/5042039]\n",
      "\n",
      "--2019-08-28 14:36:04--  https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-matrix-model-binary-training-history.pickle\n",
      "Resolving github.com (github.com)... 192.30.255.113\n",
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      "--2019-08-28 14:36:04--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/adjacency-matrix-model-binary-training-history.pickle\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "Length: 71125323 (68M) [application/octet-stream]\n",
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      "\n",
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      "\n",
      "2019-08-28 14:36:06 (207 MB/s) - ‘adjacency-matrix-history.pickle’ saved [71125323/71125323]\n",
      "\n",
      "--2019-08-28 14:36:07--  https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-matrix-model-random-training-history.pickle\n",
      "Resolving github.com (github.com)... 192.30.255.113\n",
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      "--2019-08-28 14:36:07--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/adjacency-matrix-model-random-training-history.pickle\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 71125329 (68M) [application/octet-stream]\n",
      "Saving to: ‘adjacency-matrix-random-padding-history.pickle’\n",
      "\n",
      "adjacency-matrix-ra 100%[===================>]  67.83M   234MB/s    in 0.3s    \n",
      "\n",
      "2019-08-28 14:36:09 (234 MB/s) - ‘adjacency-matrix-random-padding-history.pickle’ saved [71125329/71125329]\n",
      "\n",
      "--2019-08-28 14:36:10--  https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-history.pickle\n",
      "Resolving github.com (github.com)... 192.30.255.113\n",
      "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
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      "Location: https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/adjacency-feature-matrix-history.pickle [following]\n",
      "--2019-08-28 14:36:10--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/adjacency-feature-matrix-history.pickle\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 323109856 (308M) [application/octet-stream]\n",
      "Saving to: ‘adjacency-feature-matrix-history.pickle’\n",
      "\n",
      "adjacency-feature-m 100%[===================>] 308.14M   245MB/s    in 1.3s    \n",
      "\n",
      "2019-08-28 14:36:17 (245 MB/s) - ‘adjacency-feature-matrix-history.pickle’ saved [323109856/323109856]\n",
      "\n",
      "--2019-08-28 14:36:18--  https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-random-padding-history.pickle\n",
      "Resolving github.com (github.com)... 192.30.255.113\n",
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      "Location: https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/adjacency-feature-matrix-random-padding-history.pickle [following]\n",
      "--2019-08-28 14:36:18--  https://media.githubusercontent.com/media/dj311/uob-summer-project/master/data/adjacency-feature-matrix-random-padding-history.pickle\n",
      "Resolving media.githubusercontent.com (media.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
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      "Length: 323110202 (308M) [application/octet-stream]\n",
      "Saving to: ‘adjacency-feature-matrix-random-padding-history.pickle’\n",
      "\n",
      "adjacency-feature-m 100%[===================>] 308.14M   236MB/s    in 1.3s    \n",
      "\n",
      "2019-08-28 14:36:26 (236 MB/s) - ‘adjacency-feature-matrix-random-padding-history.pickle’ saved [323110202/323110202]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! mkdir -p /project/data && cd /project/data && wget -O stack-model.pkl https://github.com/dj311/uob-summer-project/raw/master/data/stack-model.pkl\n",
    "! mkdir -p /project/data && cd /project/data && wget -O dense-model-binary https://github.com/dj311/uob-summer-project/raw/master/data/dense-model-binary\n",
    "! mkdir -p /project/data && cd /project/data && wget -O binary-convolutional https://github.com/dj311/uob-summer-project/raw/master/data/binary-convolutional\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-matrix-model-binary.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-matrix-model-binary.pickle\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-matrix-model-random-padding.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-matrix-model-random-padding.pickle\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-feature-matrix-model-binary.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-model-binary.pickle\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-feature-matrix-random-padding-model-binary.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-random-padding-model-binary.pickle\n",
    "\n",
    "! mkdir -p /project/data && cd /project/data && wget -O dense-graph2vec-y-test https://github.com/dj311/uob-summer-project/raw/master/data/dense-graph2vec-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O dense-graph2vec-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/dense-graph2vec-y-predict\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-graph2vec-y-test https://github.com/dj311/uob-summer-project/raw/master/data/cnn-graph2vec-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-graph2vec-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/cnn-graph2vec-y-predict\n",
    "! mkdir -p /project/data && cd /project/data && wget -O stack-model-y-test https://github.com/dj311/uob-summer-project/raw/master/data/stack-model-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O stack-model-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/stack-model-y-predict\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-y-test https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-y-predict\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-rand-pad-y-test https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-rand-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-rand-pad-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-rand-y-predict\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-feat-y-test https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-feat-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-y-predict\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-feat-rand-pad-y-test https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-rand-pad-y-test\n",
    "! mkdir -p /project/data && cd /project/data && wget -O cnn-adj-feat-rand-pad-y-predict https://github.com/dj311/uob-summer-project/raw/master/data/cnn-adj-feat-rand-pad-y-predict\n",
    "\n",
    "! mkdir -p /project/data && cd /project/data && wget -O dense-model-history https://github.com/dj311/uob-summer-project/raw/master/data/dense-model-history\n",
    "! mkdir -p /project/data && cd /project/data && wget -O binary-convolutional-history https://github.com/dj311/uob-summer-project/raw/master/data/binary-convolutional-history\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-matrix-history.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-matrix-model-binary-training-history.pickle\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-matrix-random-padding-history.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-matrix-model-random-training-history.pickle\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-feature-matrix-history.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-history.pickle\n",
    "! mkdir -p /project/data && cd /project/data && wget -O adjacency-feature-matrix-random-padding-history.pickle https://github.com/dj311/uob-summer-project/raw/master/data/adjacency-feature-matrix-random-padding-history.pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 46
    },
    "colab_type": "code",
    "id": "QpJwwvU3Dgpc",
    "outputId": "712105d6-21fb-4116-a4d8-1cf02610d9aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/project/code\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p /project/code\n",
    "!mkdir -p /project/images\n",
    "%cd /project/code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BCkJ8RreCi5L"
   },
   "source": [
    "### Load in Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zGuctBtRCi5O"
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 531
    },
    "colab_type": "code",
    "id": "6xJZqVXPCi5Z",
    "outputId": "32481185-e68e-455c-fd19-82136319fd81"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xihajun/anaconda3/lib/python3.7/site-packages/sklearn/base.py:251: UserWarning: Trying to unpickle estimator SVC from version 0.21.3 when using version 0.20.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/xihajun/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: 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 /Users/xihajun/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: 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 /Users/xihajun/anaconda3/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"
     ]
    }
   ],
   "source": [
    "with open('../data/stack-model.pkl', 'rb') as f:\n",
    "    stack_model = pickle.load(f)\n",
    "    \n",
    "with open('../data/dense-model-binary', 'rb') as f:\n",
    "    dense_graph2vec = pickle.load(f)\n",
    "    \n",
    "with open('../data/binary-convolutional', 'rb') as f:\n",
    "    conv_graph2vec = pickle.load(f)\n",
    "\n",
    "with open('../data/adjacency-matrix-model-binary.pickle', 'rb') as f:\n",
    "    adj_original = pickle.load(f)\n",
    "\n",
    "with open('../data/adjacency-matrix-model-random-padding.pickle', 'rb') as f:\n",
    "    adj_rand_pad_final = pickle.load(f)\n",
    "    \n",
    "with open('../data/adjacency-feature-matrix-model-binary.pickle', 'rb') as f:\n",
    "    adj_feat_original = pickle.load(f)\n",
    "    \n",
    "with open('../data/adjacency-feature-matrix-random-padding-model-binary.pickle', 'rb') as f:\n",
    "    adj_feat_rand_pad_final = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M0s-rumj9ONJ"
   },
   "outputs": [],
   "source": [
    "with open('../data/dense-graph2vec-y-test', 'rb') as f:\n",
    "    dense_graph2vec_y_test = pickle.load(f)\n",
    "with open('../data/dense-graph2vec-y-predict', 'rb') as f:\n",
    "    dense_graph2vec_y_predict = pickle.load(f)\n",
    "\n",
    "with open('../data/cnn-graph2vec-y-test', 'rb') as f:\n",
    "    cnn_graph2vec_y_test = pickle.load(f)\n",
    "with open('../data/cnn-graph2vec-y-predict', 'rb') as f:\n",
    "    cnn_graph2vec_y_predict = pickle.load(f)\n",
    "\n",
    "with open('../data/stack-model-y-test', 'rb') as f:\n",
    "    stack_model_y_test = pickle.load(f)\n",
    "with open('../data/stack-model-y-predict', 'rb') as f:\n",
    "    stack_model_y_predict = pickle.load(f)\n",
    "\n",
    "with open('../data/cnn-adj-y-test', 'rb') as f:\n",
    "    cnn_adj_y_test = pickle.load(f)\n",
    "with open('../data/cnn-adj-y-predict', 'rb') as f:\n",
    "    cnn_adj_y_predict = pickle.load(f)\n",
    "\n",
    "with open('../data/cnn-adj-rand-y-test', 'rb') as f:\n",
    "    cnn_adj_rand_pad_y_test = pickle.load(f)\n",
    "with open('../data/cnn-adj-rand-y-predict', 'rb') as f:\n",
    "    cnn_adj_rand_pad_y_predict = pickle.load(f)\n",
    "\n",
    "with open('../data/cnn-adj-feat-y-test', 'rb') as f:\n",
    "    cnn_adj_feat_y_test = pickle.load(f)\n",
    "with open('../data/cnn-adj-feat-y-predict', 'rb') as f:\n",
    "    cnn_adj_feat_y_predict = pickle.load(f)\n",
    "\n",
    "with open('../data/cnn-adj-feat-rand-pad-y-test', 'rb') as f:\n",
    "    cnn_adj_feat_rand_pad_y_test = pickle.load(f)\n",
    "with open('../data/cnn-adj-feat-rand-pad-y-predict', 'rb') as f:\n",
    "    cnn_adj_feat_rand_pad_y_predict = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tFayIT3TCi5e"
   },
   "source": [
    "## Metrics Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QDuxt66SCi5g"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, precision_recall_curve, matthews_corrcoef, f1_score, roc_auc_score, auc, confusion_matrix, precision_score, recall_score\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def rates(y_test, y_predict):\n",
    "    tn, fp, fn, tp = confusion_matrix(\n",
    "    y_test.astype(int), \n",
    "    np.rint(y_predict)\n",
    "    ).flatten().tolist()\n",
    "\n",
    "    fpr = fp/(fp+tp)\n",
    "    fnr = fn/(fn+tn)\n",
    "    return fpr, fnr\n",
    "\n",
    "def get_metrics(y_test, y_predict):\n",
    "    fpr_plot, tpr_plot, thresholds_roc = roc_curve(y_test, y_predict)\n",
    "    precision_plot, recall_plot, thresholds_pr = precision_recall_curve(y_test, y_predict)\n",
    "    roc_auc = roc_auc_score(y_test, y_predict)\n",
    "    pr_auc = auc(recall_plot, precision_plot)\n",
    "    f1 = f1_score(y_test, np.rint(y_predict))\n",
    "    mcc = matthews_corrcoef(y_test, np.rint(y_predict))\n",
    "    precision = precision_score(y_test, np.rint(y_predict))\n",
    "    recall = recall_score(y_test, np.rint(y_predict))\n",
    "    fpr, fnr = rates(y_test, y_predict)\n",
    "    metric_values = {\n",
    "        'fpr_plot' : fpr_plot,\n",
    "        'tpr_plot' : tpr_plot,\n",
    "        'precision_plot' : precision_plot,\n",
    "        'recall_plot' : recall_plot,\n",
    "        'thresholds_pr' : thresholds_pr,\n",
    "        'ROC AUC' : roc_auc,\n",
    "        'Precision Recall AUC' : pr_auc,\n",
    "        'F1 Score' : f1,\n",
    "        'Matthews Correlation Coefficient' : mcc,\n",
    "        'Precision' : precision,\n",
    "        'Recall' : recall,\n",
    "        'FPR' : fpr,\n",
    "        'FNR' : fnr\n",
    "    }\n",
    "    return metric_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-72JN_vw_wB9"
   },
   "outputs": [],
   "source": [
    "metrics_dense_graph2vec = get_metrics(dense_graph2vec_y_test, dense_graph2vec_y_predict)\n",
    "metrics_cnn_graph2vec = get_metrics(cnn_graph2vec_y_test, cnn_graph2vec_y_predict)\n",
    "metrics_stack_model = get_metrics(stack_model_y_test, stack_model_y_predict)\n",
    "metrics_cnn_adj = get_metrics(cnn_adj_y_test, cnn_adj_y_predict)\n",
    "metrics_cnn_adj_rand_pad = get_metrics(cnn_adj_rand_pad_y_test, cnn_adj_rand_pad_y_predict)\n",
    "metrics_cnn_adj_feat = get_metrics(cnn_adj_feat_y_test, cnn_adj_feat_y_predict)\n",
    "metrics_cnn_adj_feat_rand_pad = get_metrics(cnn_adj_feat_rand_pad_y_test, cnn_adj_feat_rand_pad_y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "oeLUvSFUpngi",
    "outputId": "dc4b4b46-0375-4162-c9e4-e4370c666b03"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.08948339, 1.        ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_stack_model['fpr_plot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eKllpteCGnds"
   },
   "outputs": [],
   "source": [
    "model_names = pd.DataFrame({\n",
    "  'Model': [\n",
    "    'Dense NN on graph2vec (Baseline)', \n",
    "    'CNN on graph2vec', \n",
    "    'Stacked Model', \n",
    "    'CNN on Adjacency Matrices (Original Padding)', \n",
    "    'CNN on Adjacency Matrices (Randomly Padded)', \n",
    "    'CNN on Adjacency and Feature Matrices (Original Padding)',\n",
    "    'CNN on Adjacency and Feature Matrices (Randomly Padded)'\n",
    "  ]\n",
    "})\n",
    "\n",
    "model_accuracies = pd.DataFrame({\n",
    "  'Accuracy' : [\n",
    "      0.954,\n",
    "      0.950,\n",
    "      0.920,\n",
    "      0.988,\n",
    "      0.936,\n",
    "      0.976,\n",
    "      0.854              \n",
    "  ]\n",
    "})\n",
    "\n",
    "all_metrics = pd.DataFrame(\n",
    "    [metrics_dense_graph2vec, metrics_cnn_graph2vec, metrics_stack_model, metrics_cnn_adj, metrics_cnn_adj_rand_pad, metrics_cnn_adj_feat, metrics_cnn_adj_feat_rand_pad] #add in cnn adj and cnn adj/feat random padded\n",
    ")\n",
    "all_metrics = model_names.join(model_accuracies).join(all_metrics)\n",
    "\n",
    "key_metrics = all_metrics.filter(['Model', 'Accuracy', 'Precision', 'Recall', 'FPR', 'FNR'])\n",
    "\n",
    "deep_learning_metrics = all_metrics.filter(['Model', 'ROC AUC', 'Precision Recall AUC', 'F1 Score', 'Matthews Correlation Coefficient'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "1M7N4tjpNCrh",
    "outputId": "07615df4-ee94-4436-fe6f-5e5406b6a5c7"
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>FPR</th>\n",
       "      <th>FNR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dense NN on graph2vec (Baseline)</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CNN on graph2vec</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stacked Model</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CNN on Adjacency Matrices (Original Padding)</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CNN on Adjacency Matrices (Randomly Padded)</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CNN on Adjacency and Feature Matrices (Origina...</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CNN on Adjacency and Feature Matrices (Randoml...</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Model  Accuracy  Precision  \\\n",
       "0                   Dense NN on graph2vec (Baseline)     0.954      0.969   \n",
       "1                                   CNN on graph2vec     0.950      0.940   \n",
       "2                                      Stacked Model     0.920      0.911   \n",
       "3       CNN on Adjacency Matrices (Original Padding)     0.988      0.999   \n",
       "4        CNN on Adjacency Matrices (Randomly Padded)     0.936      0.963   \n",
       "5  CNN on Adjacency and Feature Matrices (Origina...     0.976      0.961   \n",
       "6  CNN on Adjacency and Feature Matrices (Randoml...     0.854      0.886   \n",
       "\n",
       "   Recall    FPR    FNR  \n",
       "0   0.938  0.031  0.059  \n",
       "1   0.962  0.060  0.039  \n",
       "2   0.931  0.089  0.070  \n",
       "3   0.977  0.001  0.024  \n",
       "4   0.911  0.037  0.088  \n",
       "5   0.992  0.039  0.008  \n",
       "6   0.814  0.114  0.177  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_metrics.round(decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "colab_type": "code",
    "id": "luHP0dg9NDB7",
    "outputId": "e412d5bb-58f5-46ba-b78f-74a84ec68d2a"
   },
   "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>Model</th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>Precision Recall AUC</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Matthews Correlation Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dense NN on graph2vec (Baseline)</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CNN on graph2vec</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stacked Model</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CNN on Adjacency Matrices (Original Padding)</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CNN on Adjacency Matrices (Randomly Padded)</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CNN on Adjacency and Feature Matrices (Origina...</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CNN on Adjacency and Feature Matrices (Randoml...</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Model  ROC AUC  \\\n",
       "0                   Dense NN on graph2vec (Baseline)    0.994   \n",
       "1                                   CNN on graph2vec    0.994   \n",
       "2                                      Stacked Model    0.921   \n",
       "3       CNN on Adjacency Matrices (Original Padding)    0.999   \n",
       "4        CNN on Adjacency Matrices (Randomly Padded)    0.981   \n",
       "5  CNN on Adjacency and Feature Matrices (Origina...    0.995   \n",
       "6  CNN on Adjacency and Feature Matrices (Randoml...    0.868   \n",
       "\n",
       "   Precision Recall AUC  F1 Score  Matthews Correlation Coefficient  \n",
       "0                 0.994     0.953                             0.909  \n",
       "1                 0.994     0.950                             0.901  \n",
       "2                 0.938     0.921                             0.841  \n",
       "3                 0.999     0.988                             0.975  \n",
       "4                 0.985     0.936                             0.874  \n",
       "5                 0.993     0.976                             0.951  \n",
       "6                 0.845     0.848                             0.707  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_learning_metrics.round(decimals = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latex table generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "                                             Model &   ROC AUC &  Precision Recall AUC &  F1 Score &  Matthews Correlation Coefficient \\\\\n",
      "\\midrule\n",
      "                  Dense NN on graph2vec (Baseline) &  0.994241 &              0.994366 &  0.953289 &                          0.909310 \\\\\n",
      "                                  CNN on graph2vec &  0.994428 &              0.994494 &  0.950417 &                          0.900744 \\\\\n",
      "                                     Stacked Model &  0.920565 &              0.937997 &  0.920686 &                          0.841177 \\\\\n",
      "      CNN on Adjacency Matrices (Original Padding) &  0.998910 &              0.999220 &  0.987707 &                          0.975446 \\\\\n",
      "       CNN on Adjacency Matrices (Randomly Padded) &  0.981010 &              0.984869 &  0.935958 &                          0.874327 \\\\\n",
      " CNN on Adjacency and Feature Matrices (Origina... &  0.995083 &              0.993405 &  0.976283 &                          0.951499 \\\\\n",
      " CNN on Adjacency and Feature Matrices (Randoml... &  0.867791 &              0.844833 &  0.848331 &                          0.706996 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(deep_learning_metrics.to_latex(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lawglj6SCi5k"
   },
   "source": [
    "## ROC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1nT-r8qvezwx"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 913
    },
    "colab_type": "code",
    "id": "JW-tGj9DCi5l",
    "outputId": "5d81f225-659e-4238-effc-b42dd185994c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BAHCSTVn13L9fwy+cGnd+wZc3EewAzCmEvSKZuADL9Ze2pufmSelhm5L0vrsK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\nKunomONjI+fGWiFphTHmKWPMs8aYa7M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5BfBxAwAAAOcLf6cDwPf8gv3lF+QvT71Ap6MA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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for index, row in all_metrics.iterrows():\n",
    "  if row['Model'] == 'Stacked Model':\n",
    "    plt.plot(row['fpr_plot'][1], row['tpr_plot'][1], 'x', markersize = 20, label='{} (area = {:.3f})'.format(row['Model'], row['ROC AUC']))\n",
    "  else:\n",
    "    plt.plot(row['fpr_plot'], row['tpr_plot'], label='{} (area = {:.3f})'.format(row['Model'], row['ROC AUC']))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('../images/roc_curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "izt8LEGnCi5r"
   },
   "source": [
    "## Precision-Recall Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 913
    },
    "colab_type": "code",
    "id": "v6szbNIyCi5v",
    "outputId": "0ee38a9f-8687-4e87-8906-044eeafb6759"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Bi665LiMjfFs+93lGXfN5yi7LnfvuGXuYXWs2b948u3DhwmyX0U/bk5to+/dG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\nQ4dKkq6++up6oyIXX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DhQklRZWank5GQtXbpUw4YN82Vs165dg/e3a9cuJSUl+a4XFxdr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\nHMjY/IBDEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for index, row in all_metrics.iterrows():\n",
    "  if row['Model'] == 'Stacked Model':\n",
    "    plt.plot(row['recall_plot'][1], row['precision_plot'][1], 'x', markersize = 20, label='{} (area = {:.3f})'.format(row['Model'], row['Precision Recall AUC']))\n",
    "  else:\n",
    "    plt.plot(row['recall_plot'], row['precision_plot'], label='{} (area = {:.3f})'.format(row['Model'], row['Precision Recall AUC']))\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision Recall Curve')\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('../images/pr_curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "xKQDyQOtN7fS",
    "outputId": "039411cf-eacf-497f-ba6e-337d09d5acb2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b9razs7mn70y/6Ib6MgeGykYYTm1pLgWhAAsr8/tOyKQzElxGXuacklxqSnLJ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\n7pJKygsG1WcoXzSxDUom9Hc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GyTOV+yMjae2OcdE3HqErkuAtp8zhH16/lHKbLcIYY0aX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8zflH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0vUtn6rLUn5BlxvRyR6zLvLvSpJTST7ozsF6oM9/LmcDMiPhdRNwbEa/ptuh6l87U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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x1728 with 8 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_prec_recall_vs_tresh(precisions, recalls, thresholds, index, model):\n",
    "    i = index%4\n",
    "    j = index//4\n",
    "    axs[i, j].plot(thresholds, precisions[:-1], label='precision')\n",
    "    axs[i, j].plot(thresholds, recalls[:-1], label = 'recall')\n",
    "    axs[i, j].set_title(model)\n",
    "    axs[i, j].set_xlabel('Threshold')\n",
    "    axs[i, j].legend(loc='lower left')\n",
    "    axs[i, j].set_ylim([0,1])\n",
    "\n",
    "fig, axs = plt.subplots(4, 2, figsize=(18, 24), sharey=True)\n",
    "for index, row in all_metrics.iterrows():\n",
    "  plot_prec_recall_vs_tresh(row['precision_plot'], row['recall_plot'], row['thresholds_pr'], index, row['Model'])\n",
    "fig.savefig('../images/precision-against-recall.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lRwkRlUmva3P"
   },
   "source": [
    "## Train vs Val Loss Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 254
    },
    "colab_type": "code",
    "id": "OPAEuXuxhVga",
    "outputId": "c4cbc8be-580d-4a2e-a931-23186c96ac8b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0827 13:09:31.005782 140459794061184 nn_ops.py:4224] Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n"
     ]
    }
   ],
   "source": [
    "with open('../data/dense-model-history', 'rb') as f:\n",
    "    dense_model_history = pickle.load(f)\n",
    "with open('../data/binary-convolutional-history', 'rb') as f:\n",
    "    binary_convolutional_history = pickle.load(f)\n",
    "with open('../data/adjacency-matrix-history.pickle', 'rb') as f:\n",
    "    adjacency_matrix_history = pickle.load(f)\n",
    "with open('../data/adjacency-matrix-random-padding-history.pickle', 'rb') as f:\n",
    "    adjacency_matrix_random_padding_history = pickle.load(f)\n",
    "with open('../data/adjacency-feature-matrix-history.pickle', 'rb') as f:\n",
    "    adjacency_feature_matrix_history = pickle.load(f)\n",
    "with open('../data/adjacency-feature-matrix-random-padding-history.pickle', 'rb') as f:\n",
    "    adjacency_feature_matrix_random_padding_history = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "Mc3ea7GMvZoZ",
    "outputId": "b4d4e940-2889-41f1-8ad4-b27b29208103"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V0xn6T4Tv34CqCu6aNJDi8iqe/sJq5Ywx/tPYpJCoqv9W1Sr39QrQSvubaEGG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\nzH6WFIwxxuxnScEYY8x+/w+drEwHhRkppQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dense_model_history.history['loss'])\n",
    "plt.plot(dense_model_history.history['val_loss'])\n",
    "plt.title('Dense NN on graph2vec Model Losses')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.savefig('../images/dense-g2v-losses.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "xnES6_yq3I5E",
    "outputId": "8488f982-053f-4d78-dee6-6ae5061c5e83"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pp5+msbGRT37yk9x9993U1tZy9dVXU1xcTEtLC9///vcpKSlhz549nHvuuWRlZbFo0aKQ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\nrSwpGGOMafX/AWUFf2L4ZB1+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(binary_convolutional_history.history['loss'])\n",
    "plt.plot(binary_convolutional_history.history['val_loss'])\n",
    "plt.title('CNN on graph2vec Model Losses')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.savefig('../images/cnn-g2v-losses.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "colab_type": "code",
    "id": "u8E8FNAU3Tx4",
    "outputId": "6fa0b2e5-8d10-4bdf-a9a6-782332a3c65e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RkqRWGxgyHt66Aj66zXfbOO0xqJhe7G9lZjTMrETDzEqc0aEeAJu37+KHZeuY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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(18, 6), sharey=True)\n",
    "axs[0].plot(adjacency_matrix_history.history['loss'])\n",
    "axs[0].plot(adjacency_matrix_history.history['val_loss'])\n",
    "axs[0].set_title('CNN on Adjacency Matrices (Original Padding) Model Losses')\n",
    "axs[0].set_ylabel('Loss')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].legend(['train', 'test'], loc='upper right')\n",
    "axs[1].plot(adjacency_matrix_random_padding_history.history['loss'])\n",
    "axs[1].plot(adjacency_matrix_random_padding_history.history['val_loss'])\n",
    "axs[1].set_title('CNN on Adjacency Matrices (Random Padding) Model Losses')\n",
    "axs[1].set_ylabel('Loss')\n",
    "axs[1].set_xlabel('Epoch')\n",
    "axs[1].legend(['train', 'test'], loc='upper right')\n",
    "fig.show()\n",
    "fig.savefig('../images/cnn-adj-losses.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 504
    },
    "colab_type": "code",
    "id": "CEqzvrX74bmX",
    "outputId": "25673267-e79a-46fa-e831-72d7e3a9f68b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bN/9668X2zfza7kNdSG4YtPuQUOOKD4WpGCEiIiIiIiJSEZzzVzv80ujkRl+Q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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(18, 6), sharey=True)\n",
    "axs[0].plot(adjacency_feature_matrix_history.history['loss'])\n",
    "axs[0].plot(adjacency_feature_matrix_history.history['val_loss'])\n",
    "axs[0].set_title('CNN on Adjacency and Feature Matrices (Original Padding) Model Losses')\n",
    "axs[0].set_ylabel('Loss')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].legend(['train', 'test'], loc='upper right')\n",
    "axs[1].plot(adjacency_feature_matrix_random_padding_history.history['loss'])\n",
    "axs[1].plot(adjacency_feature_matrix_random_padding_history.history['val_loss'])\n",
    "axs[1].set_title('CNN on Adjacency and Feature Matrices (Random Padding) Model Losses')\n",
    "axs[1].set_ylabel('Loss')\n",
    "axs[1].set_xlabel('Epoch')\n",
    "axs[1].legend(['train', 'test'], loc='upper right')\n",
    "fig.show()\n",
    "fig.savefig('../images/cnn-adj-feat-losses.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_kfKTnhJ6jrG"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "ML-evaluating-final-models.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
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  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
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 "nbformat": 4,
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}