OpenVaccine_COVID_19_mRNA_Vaccine_Degradation_Prediction.ipynb

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    "colab": {
      "name": "OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyNX29c3vuv71QJdVO/g2eNj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pea-sys/Kaggle_Public/blob/master/OpenVaccine_COVID_19_mRNA_Vaccine_Degradation_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_JNcZudNec_"
      },
      "source": [
        "\n",
        "\n",
        "<center><img src=\"https://daslab.stanford.edu/site_data/news_img/openvaccine_lores.png\" width=\"400\" height=\"400\"></center>\n",
        "\n",
        "順位:63位/1636チーム  \n",
        "\n",
        "##【概要】(公式DeepL)\n",
        "mRNAワクチンはCOVID-19の最速ワクチン候補として先頭に立ってきたが、現在のところ、重要な潜在的な限界に直面している。現在の最大の課題の一つは、超安定なメッセンジャーRNA分子（mRNA）をどのように設計するかということである。従来のワクチン（季節性インフルエンザの予防接種のようなもの）は、使い捨ての注射器に包装され、世界中で冷蔵下で出荷されていますが、mRNAワクチンでは現在のところそれができません。\n",
        "\n",
        "研究者たちは、RNA分子が自然に分解される傾向があることを観察しています。これは重大な制限であり、1回の切断でmRNAワクチンが使えなくなる可能性があります。現在のところ、あるRNAのバックボーンのどの部分が最も影響を受けやすいかについての詳細はほとんど知られていません。この知識がなければ、COVID-19に対する現在のmRNAワクチンは準備され、強烈な冷蔵下で出荷されなければならず、安定化されない限り、地球上のごく一部の人間にしか届かない可能性は低い。\n",
        "スタンフォード大学医学部の計算生物化学者であるRhiju Das教授が率いるEternaコミュニティでは、科学者とゲーマーが一緒になってパズルを解いたり、薬を発明したりしています。Eternaは、mRNAデザインなどの科学的な問題をパズルで解くことにプレイヤーを挑戦させるオンライン・ビデオゲーム・プラットフォームです。解答は、RNA分子に関する新たな洞察を得るために、研究者がスタンフォード大学で合成し、実験的にテストしています。Eternaコミュニティは、これまでにも新しい科学的原理を解き明かし、致命的な病気に対する新しい診断法を開発し、世界で最も強力な知的資源を利用して公衆のために貢献してきました。Eternaコミュニティは、RNAバイオテクノロジーの進歩を含む20以上の出版物への貢献を通じてバイオテクノロジーを発展させてきました。\n",
        "\n",
        "このコンペティションでは、Kaggleコミュニティのデータサイエンスの専門知識を活用して、RNA分解のためのモデルと設計ルールを開発したいと考えています。あなたのモデルは、3000以上のRNA分子（多様な配列と構造を持つ）からなるEternaデータセットのサブセットと各位置での分解率を用いて、RNA分子の各塩基における分解率の可能性を予測します。次に、COVID-19 mRNAワクチンのためにEternaのプレイヤーが考案した第二世代のRNA配列を用いて、モデルをスコア化します。これらの最終的なテスト配列は現在スタンフォード大学で合成され、実験的に特徴づけられており、あなたのモデリング作業と並行して行われています。\n",
        "\n",
        "mRNAワクチンの安定性を向上させることは、パンデミックの前から検討されていた問題ですが、解決には何年もかかると予想されていました。今、私たちは、mRNAワクチンの研究を加速し、COVID-19の背後にあるウイルスであるSARS-CoV-2に対する冷蔵庫で安定なワクチンを提供するために、数週間ではなく数ヶ月でこの深い科学的課題を解決しなければなりません。私たちが解決しようとしている問題は、学術研究室、産業界の研究開発グループ、スーパーコンピューターでは解決できませんでした。この壊滅的なパンデミックとの戦いの鍵を解き明かすために、Eternaのビデオゲームプレイヤー、科学者、開発者のチームに参加することができます。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CoiF0HEXU9gU"
      },
      "source": [
        "##評価指標\n",
        "MCRMSE（平均列和ルート平均二乗誤差）\n",
        "\n",
        "##データセット\n",
        "[こちら](https://www.kaggle.com/c/stanford-covid-vaccine/data)\n",
        "\n",
        "#スケジュール\n",
        "2020年9月一杯\n",
        "※通常はKaggleコンペティションは三か月に及ぶが、今回は短期開催\n",
        "\n",
        "#●やったことメモ\n",
        "* 1週目：データ探索とLGBMモデルの作成\n",
        "* 2週目：ニューラルネットワークの方が適したデータセットであることが分かり、公開ノートブックの読み込み。\n",
        "* 3週目：複数の優れた公開ノートブックを少しずつ改良\n",
        "* 4週目：3週目で改良したノートブックをブレンド\n",
        "\n",
        "#ソリューション\n",
        "  * 3種のモデルをfold数やseed、dropoutを変えたものをアンサンブル  \n",
        "  [GCN](https://www.kaggle.com/symyksr/openvaccine-deepergcn),  [GNN+CNN+Attention](https://www.kaggle.com/mrkmakr/covid-ae-pretrain-gnn-attn-cnn),  [GRU+LSTM](https://www.kaggle.com/its7171/gru-lstm-with-feature-engineering-and-augmentation)\n",
        "\n",
        "  * データ拡張  \n",
        "  [viennaエンジンでデータ拡張](https://www.kaggle.com/its7171/how-to-generate-augmentation-data)\n",
        "\n",
        "#●感想\n",
        "ニューラルネットワークアーキテクチャに精通していないため、独自にアーキテクチャを考案できなかったので無力感が残るコンペだった。\n",
        "短期のコンペティションのためか、他のコンペティションと比較して、競技者の動きがとても速く、\n",
        "優れたソリューションがどんどん公開されていき、ソリューション理解を進めるだけで精一杯だった。  \n",
        "パブリックテストデータとプライベートテストデータの長さが異なるため、そこを常に意識して作業を進めていったため、そこそこの順位で落着になった。  \n",
        "書籍を購入して、RNAについての知識を深めたりしたが、あまり役立てなくて残念ではあった。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2p8698NZceij"
      },
      "source": [
        "↓オリジナル公開ノートブックとほとんど同じです。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7uVqqF8E5n5",
        "outputId": "d4fa344c-565d-4e68-f82c-bb60b08496e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "try:\n",
        "    import torch_geometric\n",
        "except:\n",
        "    import subprocess\n",
        "    import torch\n",
        "nvcc_stdout = str(subprocess.check_output(['nvcc', '-V']))\n",
        "tmp = nvcc_stdout[nvcc_stdout.rfind('release') + len('release') + 1:]\n",
        "cuda_version = tmp[:tmp.find(',')]\n",
        "cuda = {\n",
        "            '9.2': 'cu92',\n",
        "            '10.1': 'cu101',\n",
        "            '10.2': 'cu102',\n",
        "            }\n",
        "\n",
        "CUDA = cuda[cuda_version]\n",
        "TORCH = torch.__version__.split('.')\n",
        "TORCH[-1] = '0'\n",
        "TORCH = '.'.join(TORCH)\n",
        "\n",
        "install1 = 'pip install torch-scatter==latest+' + CUDA + ' -f https://pytorch-geometric.com/whl/torch-' + TORCH + '.html'\n",
        "install2 = 'pip install torch-sparse==latest+' + CUDA + ' -f https://pytorch-geometric.com/whl/torch-' + TORCH + '.html'\n",
        "install3 = 'pip install torch-cluster==latest+' + CUDA + ' -f https://pytorch-geometric.com/whl/torch-' + TORCH + '.html'\n",
        "install4 = 'pip install torch-spline-conv==latest+' + CUDA + ' -f https://pytorch-geometric.com/whl/torch-' + TORCH + '.html'\n",
        "install5 = 'pip install torch-geometric'\n",
        "\n",
        "subprocess.run(install1.split())\n",
        "subprocess.run(install2.split())\n",
        "subprocess.run(install3.split())\n",
        "subprocess.run(install4.split())\n",
        "subprocess.run(install5.split())\n",
        "# ------------------ install torch_geometric end -----------------\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CompletedProcess(args=['pip', 'install', 'torch-geometric'], returncode=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfFH4YJeEsvB",
        "outputId": "483639e0-f612-4d69-e651-3ae871da1047",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PW_YkrsQEwVu",
        "outputId": "21ad4117-ea7a-4890-ccbb-86d18e6c1ba8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/drive/My\\ Drive/COVID-19 mRNA Vaccine Degradation Prediction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/COVID-19 mRNA Vaccine Degradation Prediction\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SW_jNl56E9yk",
        "outputId": "e5ea5665-ccfc-45c3-adc0-d317ca42a2f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import torch\n",
        "from torch.nn import Linear, LayerNorm, ReLU, Dropout\n",
        "from torch_geometric.nn import ChebConv, NNConv, DeepGCNLayer\n",
        "from torch_geometric.data import Data, DataLoader\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "import copy\n",
        "#import gc\n",
        "\n",
        "# settings\n",
        "seed = 777\n",
        "train_file = 'train.json'\n",
        "test_file = 'test.json'\n",
        "bpps_top = 'bpps'\n",
        "nb_fold = 10#5\n",
        "device = 'cuda'\n",
        "batch_size = 16\n",
        "epochs = 100\n",
        "lr = 0.001\n",
        "train_with_noisy_data = True\n",
        "add_edge_for_paired_nodes = True\n",
        "add_codon_nodes = True\n",
        "T = 5\n",
        "node_hidden_channels = 96\n",
        "edge_hidden_channels = 16\n",
        "hidden_channels3 = 32\n",
        "num_layers = 10\n",
        "dropout1 = 0.1\n",
        "dropout2 = 0.1\n",
        "dropout3 = 0.1\n",
        "bpps_nb_mean = 0.077522 # mean of bpps_nb across all training data\n",
        "bpps_nb_std = 0.08914   # std of bpps_nb across all training data\n",
        "error_mean_limit = 0.5\n",
        "\n",
        "def match_pair(structure):\n",
        "    pair = [-1] * len(structure)\n",
        "    pair_no = -1\n",
        "\n",
        "    pair_no_stack = []\n",
        "    for i, c in enumerate(structure):\n",
        "        if c == '(':\n",
        "            pair_no += 1\n",
        "            pair[i] = pair_no\n",
        "            pair_no_stack.append(pair_no)\n",
        "        elif c == ')':\n",
        "            pair[i] = pair_no_stack.pop()\n",
        "    return pair\n",
        "\n",
        "class MyData(Data):\n",
        "    def __init__(self, x=None, edge_index=None, edge_attr=None, y=None,\n",
        "                 pos=None, norm=None, face=None, weight=None, **kwargs):\n",
        "        super(MyData, self).__init__(x=x, edge_index=edge_index,\n",
        "                                     edge_attr=edge_attr, y=y, pos=pos,\n",
        "                                     norm=norm, face=face, **kwargs)\n",
        "        self.weight = weight\n",
        "\n",
        "def calc_error_mean(row):\n",
        "    reactivity_error = row['reactivity_error']\n",
        "    deg_error_Mg_pH10 = row['deg_error_Mg_pH10']\n",
        "    deg_error_Mg_50C = row['deg_error_Mg_50C']\n",
        "\n",
        "    return np.mean(np.abs(reactivity_error) +\n",
        "                   np.abs(deg_error_Mg_pH10) + \\\n",
        "                   np.abs(deg_error_Mg_50C)) / 3\n",
        "\n",
        "def calc_sample_weight(row):\n",
        "    if sample_is_clean(row):\n",
        "        return 1.\n",
        "    else:\n",
        "        error_mean = calc_error_mean(row)\n",
        "        if error_mean >= error_mean_limit:\n",
        "            return 0.\n",
        "\n",
        "        return 1. - error_mean / error_mean_limit\n",
        "\n",
        "# add directed edge for node1 -> node2 and for node2 -> node1\n",
        "def add_edges(edge_index, edge_features, node1, node2, feature1, feature2):\n",
        "    edge_index.append([node1, node2])\n",
        "    edge_features.append(feature1)\n",
        "    edge_index.append([node2, node1])\n",
        "    edge_features.append(feature2)\n",
        "\n",
        "def add_edges_between_base_nodes(edge_index, edge_features, node1, node2):\n",
        "    edge_feature1 = [\n",
        "        0, # is edge for paired nodes\n",
        "        0, # is edge between codon node and base node\n",
        "        0, # is edge between coden nodes\n",
        "        1, # forward edge: 1, backward edge: -1\n",
        "        1, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    edge_feature2 = [\n",
        "        0, # is edge for paired nodes\n",
        "        0, # is edge between codon node and base node\n",
        "        0, # is edge between coden nodes\n",
        "        -1, # forward edge: 1, backward edge: -1\n",
        "        1, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    add_edges(edge_index, edge_features, node1, node2,\n",
        "              edge_feature1, edge_feature2)\n",
        "\n",
        "def add_edges_between_paired_nodes(edge_index, edge_features, node1, node2,\n",
        "                                   bpps_value):\n",
        "    edge_feature1 = [\n",
        "        1, # is edge for paired nodes\n",
        "        0, # is edge between codon node and base node\n",
        "        0, # is edge between coden nodes\n",
        "        0, # forward edge: 1, backward edge: -1\n",
        "        bpps_value, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    edge_feature2 = [\n",
        "        1, # is edge for paired nodes\n",
        "        0, # is edge between codon node and base node\n",
        "        0, # is edge between coden nodes\n",
        "        0, # forward edge: 1, backward edge: -1\n",
        "        bpps_value, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    add_edges(edge_index, edge_features, node1, node2,\n",
        "              edge_feature1, edge_feature2)\n",
        "\n",
        "def add_edges_between_codon_nodes(edge_index, edge_features, node1, node2):\n",
        "    edge_feature1 = [\n",
        "        0, # is edge for paired nodes\n",
        "        0, # is edge between codon node and base node\n",
        "        1, # is edge between coden nodes\n",
        "        1, # forward edge: 1, backward edge: -1\n",
        "        0, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    edge_feature2 = [\n",
        "        0, # is edge for paired nodes\n",
        "        0, # is edge between codon node and base node\n",
        "        1, # is edge between coden nodes\n",
        "        -1, # forward edge: 1, backward edge: -1\n",
        "        0, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    add_edges(edge_index, edge_features, node1, node2,\n",
        "              edge_feature1, edge_feature2)\n",
        "\n",
        "def add_edges_between_codon_and_base_node(edge_index, edge_features,\n",
        "                                          node1, node2):\n",
        "    edge_feature1 = [\n",
        "        0, # is edge for paired nodes\n",
        "        1, # is edge between codon node and base node\n",
        "        0, # is edge between coden nodes\n",
        "        0, # forward edge: 1, backward edge: -1\n",
        "        0, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    edge_feature2 = [\n",
        "        0, # is edge for paired nodes\n",
        "        1, # is edge between codon node and base node\n",
        "        0, # is edge between coden nodes\n",
        "        0, # forward edge: 1, backward edge: -1\n",
        "        0, # bpps if edge is for paired nodes\n",
        "    ]\n",
        "    add_edges(edge_index, edge_features, node1, node2,\n",
        "              edge_feature1, edge_feature2)\n",
        "\n",
        "def add_node(node_features, feature):\n",
        "    node_features.append(feature)\n",
        "\n",
        "def add_base_node(node_features, sequence, predicted_loop_type,\n",
        "                  bpps_sum, bpps_nb):\n",
        "    feature = [\n",
        "        0, # is codon node\n",
        "        sequence == 'A',\n",
        "        sequence == 'C',\n",
        "        sequence == 'G',\n",
        "        sequence == 'U',\n",
        "        predicted_loop_type == 'S',\n",
        "        predicted_loop_type == 'M',\n",
        "        predicted_loop_type == 'I',\n",
        "        predicted_loop_type == 'B',\n",
        "        predicted_loop_type == 'H',\n",
        "        predicted_loop_type == 'E',\n",
        "        predicted_loop_type == 'X',\n",
        "        bpps_sum,\n",
        "        bpps_nb,\n",
        "    ]\n",
        "    add_node(node_features, feature)\n",
        "\n",
        "def add_codon_node(node_features):\n",
        "    feature = [\n",
        "        1, # is codon node\n",
        "        0, # sequence == 'A',\n",
        "        0, # sequence == 'C',\n",
        "        0, # sequence == 'G',\n",
        "        0, # sequence == 'U',\n",
        "        0, # predicted_loop_type == 'S',\n",
        "        0, # predicted_loop_type == 'M',\n",
        "        0, # predicted_loop_type == 'I',\n",
        "        0, # predicted_loop_type == 'B',\n",
        "        0, # predicted_loop_type == 'H',\n",
        "        0, # predicted_loop_type == 'E',\n",
        "        0, # predicted_loop_type == 'X',\n",
        "        0, # bpps_sum\n",
        "        0, # bpps_nb\n",
        "    ]\n",
        "    add_node(node_features, feature)\n",
        "\n",
        "def build_data(df, is_train):\n",
        "    data = []\n",
        "    for i in range(len(df)):\n",
        "        targets = []\n",
        "        node_features = []\n",
        "        edge_features = []\n",
        "        edge_index = []\n",
        "        train_mask = []\n",
        "        test_mask = []\n",
        "        weights = []\n",
        "\n",
        "        id = df.loc[i, 'id']\n",
        "        path = os.path.join(bpps_top, id + '.npy')\n",
        "        bpps = np.load(path)\n",
        "        bpps_sum = bpps.sum(axis=0)\n",
        "        sequence = df.loc[i, 'sequence']\n",
        "        structure = df.loc[i, 'structure']\n",
        "        pair_info = match_pair(structure)\n",
        "        predicted_loop_type = df.loc[i, 'predicted_loop_type']\n",
        "        seq_length = df.loc[i, 'seq_length']\n",
        "        seq_scored = df.loc[i, 'seq_scored']\n",
        "        bpps_nb = (bpps > 0).sum(axis=0) / seq_length\n",
        "        bpps_nb = (bpps_nb - bpps_nb_mean) / bpps_nb_std\n",
        "        if is_train:\n",
        "            sample_weight = calc_sample_weight(df.loc[i])\n",
        "\n",
        "            reactivity = df.loc[i, 'reactivity']\n",
        "            deg_Mg_pH10 = df.loc[i, 'deg_Mg_pH10']\n",
        "            deg_Mg_50C = df.loc[i, 'deg_Mg_50C']\n",
        "\n",
        "            for j in range(seq_length):\n",
        "                if j < seq_scored:\n",
        "                    targets.append([\n",
        "                        reactivity[j],\n",
        "                        deg_Mg_pH10[j],\n",
        "                        deg_Mg_50C[j],\n",
        "                        ])\n",
        "                else:\n",
        "                    targets.append([0, 0, 0])\n",
        "\n",
        "        paired_nodes = {}\n",
        "        for j in range(seq_length):\n",
        "            add_base_node(node_features, sequence[j], predicted_loop_type[j],\n",
        "                          bpps_sum[j], bpps_nb[j])\n",
        "\n",
        "            if j + 1 < seq_length: # edge between current node and next node\n",
        "                add_edges_between_base_nodes(edge_index, edge_features,\n",
        "                                             j, j + 1)\n",
        "\n",
        "            if pair_info[j] != -1:\n",
        "                if pair_info[j] not in paired_nodes:\n",
        "                    paired_nodes[pair_info[j]] = [j]\n",
        "                else:\n",
        "                    paired_nodes[pair_info[j]].append(j)\n",
        "\n",
        "            train_mask.append(j < seq_scored)\n",
        "            test_mask.append(True)\n",
        "            if is_train:\n",
        "                weights.append(sample_weight)\n",
        "\n",
        "        if add_edge_for_paired_nodes:\n",
        "            for pair in paired_nodes.values():\n",
        "                bpps_value = bpps[pair[0], pair[1]]\n",
        "                add_edges_between_paired_nodes(edge_index, edge_features,\n",
        "                                               pair[0], pair[1], bpps_value)\n",
        "\n",
        "        if add_codon_nodes:\n",
        "            codon_node_idx = seq_length - 1\n",
        "            for j in range(seq_length):\n",
        "                if j % 3 == 0:\n",
        "                    # add codon node\n",
        "                    add_codon_node(node_features)\n",
        "                    codon_node_idx += 1\n",
        "                    train_mask.append(False)\n",
        "                    test_mask.append(False)\n",
        "                    if is_train:\n",
        "                        weights.append(0)\n",
        "                        targets.append([0, 0, 0])\n",
        "\n",
        "                    if codon_node_idx > seq_length:\n",
        "                        # add edges between adjacent codon nodes\n",
        "                        add_edges_between_codon_nodes(edge_index, edge_features,\n",
        "                                                      codon_node_idx - 1,\n",
        "                                                      codon_node_idx)\n",
        "\n",
        "                # add edges between codon node and base node\n",
        "                add_edges_between_codon_and_base_node(edge_index, edge_features,\n",
        "                                                      j, codon_node_idx)\n",
        "\n",
        "        node_features = torch.tensor(node_features, dtype=torch.float)\n",
        "        edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()\n",
        "        edge_features = torch.tensor(edge_features, dtype=torch.float)\n",
        "\n",
        "        if is_train:\n",
        "            data.append(MyData(x=node_features, edge_index=edge_index,\n",
        "                               edge_attr=edge_features,\n",
        "                               train_mask=torch.tensor(train_mask),\n",
        "                               weight=torch.tensor(weights, dtype=torch.float),\n",
        "                               y=torch.tensor(targets, dtype=torch.float)))\n",
        "        else:\n",
        "            data.append(MyData(x=node_features, edge_index=edge_index,\n",
        "                               edge_attr=edge_features,\n",
        "                               test_mask=torch.tensor(test_mask)))\n",
        "\n",
        "    return data\n",
        "\n",
        "#\n",
        "# originally copied from\n",
        "# https://github.com/rusty1s/pytorch_geometric/blob/master/examples/ogbn_proteins_deepgcn.py\n",
        "# \n",
        "class MapE2NxN(torch.nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, hidden_channels):\n",
        "        super(MapE2NxN, self).__init__()\n",
        "        self.linear1 = Linear(in_channels, hidden_channels)\n",
        "        self.linear2 = Linear(hidden_channels, out_channels)\n",
        "        self.dropout = Dropout(dropout3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.linear1(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.linear2(x)\n",
        "        return x\n",
        "\n",
        "class MyDeeperGCN(torch.nn.Module):\n",
        "    def __init__(self, num_node_features, num_edge_features,\n",
        "                 node_hidden_channels,\n",
        "                 edge_hidden_channels,\n",
        "                 num_layers, num_classes):\n",
        "        super(MyDeeperGCN, self).__init__()\n",
        "\n",
        "        self.node_encoder = ChebConv(num_node_features, node_hidden_channels, T)\n",
        "        self.edge_encoder = Linear(num_edge_features, edge_hidden_channels)\n",
        "\n",
        "        self.layers = torch.nn.ModuleList()\n",
        "        for i in range(1, num_layers + 1):\n",
        "            conv = NNConv(node_hidden_channels, node_hidden_channels,\n",
        "                          MapE2NxN(edge_hidden_channels,\n",
        "                                   node_hidden_channels * node_hidden_channels,\n",
        "                                   hidden_channels3))\n",
        "            norm = LayerNorm(node_hidden_channels, elementwise_affine=True)\n",
        "            act = ReLU(inplace=True)\n",
        "\n",
        "            layer = DeepGCNLayer(conv, norm, act, block='res+',\n",
        "                                 dropout=dropout1, ckpt_grad=i % 3)\n",
        "            self.layers.append(layer)\n",
        "\n",
        "        self.lin = Linear(node_hidden_channels, num_classes)\n",
        "        self.dropout = Dropout(dropout2)\n",
        "\n",
        "    def forward(self, data):\n",
        "        x = data.x\n",
        "        edge_index = data.edge_index\n",
        "        edge_attr = data.edge_attr\n",
        "\n",
        "        # edge for paired nodes are excluded for encoding node\n",
        "        seq_edge_index = edge_index[:, edge_attr[:,0] == 0]\n",
        "        x = self.node_encoder(x, seq_edge_index)\n",
        "\n",
        "        edge_attr = self.edge_encoder(edge_attr)\n",
        "\n",
        "        x = self.layers[0].conv(x, edge_index, edge_attr)\n",
        "\n",
        "        for layer in self.layers[1:]:\n",
        "            x = layer(x, edge_index, edge_attr)\n",
        "\n",
        "        x = self.layers[0].act(self.layers[0].norm(x))\n",
        "        x = self.dropout(x)\n",
        "\n",
        "        return self.lin(x)\n",
        "\n",
        "def weighted_mse_loss(prds, tgts, weight):\n",
        "    return torch.mean(weight * (prds - tgts)**2)\n",
        "\n",
        "def criterion(prds, tgts, weight=None):\n",
        "    if weight is None:\n",
        "        return (torch.sqrt(torch.nn.MSELoss()(prds[:,0], tgts[:,0])) +\n",
        "                torch.sqrt(torch.nn.MSELoss()(prds[:,1], tgts[:,1])) +\n",
        "                torch.sqrt(torch.nn.MSELoss()(prds[:,2], tgts[:,2]))) / 3\n",
        "    else:\n",
        "        return (torch.sqrt(weighted_mse_loss(prds[:,0], tgts[:,0], weight)) +\n",
        "                torch.sqrt(weighted_mse_loss(prds[:,1], tgts[:,1], weight)) +\n",
        "                torch.sqrt(weighted_mse_loss(prds[:,2], tgts[:,2], weight))) / 3\n",
        "\n",
        "def build_id_seqpos(df):\n",
        "    id_seqpos = []\n",
        "    for i in range(len(df)):\n",
        "        id = df.loc[i, 'id']\n",
        "        seq_length = df.loc[i, 'seq_length']\n",
        "        for seqpos in range(seq_length):\n",
        "            id_seqpos.append(id + '_' + str(seqpos))\n",
        "    return id_seqpos\n",
        "\n",
        "def sample_is_clean(row):\n",
        "    return row['SN_filter'] == 1\n",
        "    #return row['signal_to_noise'] > 1 and \\\n",
        "    #       min((min(row['reactivity']),\n",
        "    #            min(row['deg_Mg_pH10']),\n",
        "    #            min(row['deg_pH10']),\n",
        "    #            min(row['deg_Mg_50C']),\n",
        "    #            min(row['deg_50C']))) > -0.5\n",
        "\n",
        "# categorical value for target (used for stratified kfold)\n",
        "def add_y_cat(df):\n",
        "    target_mean = df['reactivity'].apply(np.mean) + \\\n",
        "                  df['deg_Mg_pH10'].apply(np.mean) + \\\n",
        "                  df['deg_Mg_50C'].apply(np.mean)\n",
        "    df['y_cat'] = pd.qcut(np.array(target_mean), q=20).codes\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    torch.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    random.seed(seed)\n",
        "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
        "    # not enough ? reproducibility issue still remains\n",
        "\n",
        "    print('Reading', train_file)\n",
        "    df_tr = pd.read_json(train_file, lines=True)\n",
        "   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading train.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-SsPq7KFwJu",
        "outputId": "7e6a9196-8176-47ef-eb11-608000741608",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "aug_df = pd.concat([pd.read_csv('aug_data500.csv'),pd.read_csv('aug_data1000.csv'),pd.read_csv('aug_data1500.csv'),pd.read_csv('aug_data2000.csv'),pd.read_csv('aug_data2500.csv')])\n",
        "print(df_tr.shape)\n",
        "def aug_data(df):\n",
        "    target_df = df.copy()\n",
        "    new_df = aug_df[aug_df['id'].isin(target_df['id'])]\n",
        "                         \n",
        "    del target_df['structure']\n",
        "    del target_df['predicted_loop_type']\n",
        "    new_df = new_df.merge(target_df, on=['id','sequence'], how='left')\n",
        "\n",
        "    df['cnt'] = df['id'].map(new_df[['id','cnt']].set_index('id').to_dict()['cnt'])\n",
        "    df['log_gamma'] = 100\n",
        "    df['score'] = 1.0\n",
        "    df = df.append(new_df[df.columns])\n",
        "    return df\n",
        "df_tr = aug_data(df_tr)\n",
        "print(df_tr.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2400, 19)\n",
            "(4800, 22)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3jXOcjMFYNz",
        "outputId": "9e98a3d5-8dbd-4135-9509-bf38bb5f4369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "add_y_cat(df_tr)\n",
        "\n",
        "is_clean = df_tr.apply(sample_is_clean, axis=1)\n",
        "df_clean = df_tr[is_clean].reset_index(drop=True)\n",
        "df_noisy = df_tr[is_clean==False].reset_index(drop=True)\n",
        "del df_tr\n",
        "\n",
        "print('Training')\n",
        "all_ys = torch.zeros((0, 3)).to(device).detach()\n",
        "all_outs = torch.zeros((0, 3)).to(device).detach()\n",
        "best_model_states = []\n",
        "kf = StratifiedKFold(nb_fold, shuffle=True, random_state=seed)\n",
        "for fold, ((clean_train_idx, clean_valid_idx),\n",
        "           (noisy_train_idx, noisy_valid_idx)) \\\n",
        "               in enumerate(zip(kf.split(df_clean, df_clean['y_cat']),\n",
        "                                kf.split(df_noisy, df_noisy['y_cat']))):\n",
        "  print('Fold', fold)\n",
        "\n",
        "  # build train data\n",
        "  df_train = df_clean.loc[clean_train_idx]\n",
        "  if train_with_noisy_data:\n",
        "    df_train_noisy = df_noisy.loc[noisy_train_idx]\n",
        "    df_train_noisy = \\\n",
        "    df_train_noisy[df_train_noisy.apply(calc_error_mean, axis=1) <= \\\n",
        "      error_mean_limit]\n",
        "    df_train = df_train.append(df_train_noisy)\n",
        "    data_train = build_data(df_train.reset_index(drop=True), True)\n",
        "    del df_train\n",
        "    loader_train = DataLoader(data_train, batch_size=batch_size,\n",
        "                                  shuffle=True)\n",
        "\n",
        "    # build validation data\n",
        "    df_valid_clean = df_clean.loc[clean_valid_idx].reset_index(drop=True)\n",
        "    data_valid_clean = build_data(df_valid_clean, True)\n",
        "    del df_valid_clean\n",
        "    loader_valid_clean = DataLoader(data_valid_clean, batch_size=batch_size,\n",
        "                                        shuffle=False)\n",
        "\n",
        "    model = MyDeeperGCN(data_train[0].num_node_features,\n",
        "                        data_train[0].num_edge_features,\n",
        "                        node_hidden_channels=node_hidden_channels,\n",
        "                        edge_hidden_channels=edge_hidden_channels,\n",
        "                        num_layers=num_layers,\n",
        "                        num_classes=3).to(device)\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "    best_mcrmse = np.inf\n",
        "    for epoch in range(epochs):\n",
        "        print('Epoch', epoch)\n",
        "        model.train()\n",
        "        train_loss = 0.0\n",
        "        nb = 0\n",
        "        for data in tqdm(loader_train):\n",
        "            data = data.to(device)\n",
        "            mask = data.train_mask\n",
        "            weight = data.weight[mask]\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            out = model(data)[mask]\n",
        "            y = data.y[mask]\n",
        "            loss = criterion(out, y, weight)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_loss += loss.item() * y.size(0)\n",
        "            nb += y.size(0)\n",
        "\n",
        "            del data\n",
        "            del out\n",
        "            del y\n",
        "            del loss\n",
        "            #gc.collect()\n",
        "            #torch.cuda.empty_cache()\n",
        "        train_loss /= nb\n",
        "\n",
        "        model.eval()\n",
        "        valid_loss = 0.0\n",
        "        nb = 0\n",
        "        ys = torch.zeros((0, 3)).to(device).detach()\n",
        "        outs = torch.zeros((0, 3)).to(device).detach()\n",
        "        for data in tqdm(loader_valid_clean):\n",
        "            data = data.to(device)\n",
        "            mask = data.train_mask\n",
        "\n",
        "            out = model(data)[mask].detach()\n",
        "            y = data.y[mask].detach()\n",
        "            loss = criterion(out, y).detach()\n",
        "            valid_loss += loss.item() * y.size(0)\n",
        "            nb += y.size(0)\n",
        "\n",
        "            outs = torch.cat((outs, out), dim=0)\n",
        "            ys = torch.cat((ys, y), dim=0)\n",
        "\n",
        "            del data\n",
        "            del out\n",
        "            del y\n",
        "            del loss\n",
        "            #gc.collect()\n",
        "            #torch.cuda.empty_cache()\n",
        "        valid_loss /= nb\n",
        "\n",
        "        mcrmse = criterion(outs, ys).item()\n",
        "\n",
        "        print(\"T Loss: {:.4f} V Loss: {:.4f} V MCRMSE: {:.4f}\".\\\n",
        "                format(train_loss, valid_loss, mcrmse))\n",
        "\n",
        "        if mcrmse < best_mcrmse:\n",
        "            print('Best valid MCRMSE updated to', mcrmse)\n",
        "            best_mcrmse = mcrmse\n",
        "            best_model_state = copy.deepcopy(model.state_dict())\n",
        "\n",
        "    del data_train\n",
        "    del data_valid_clean\n",
        "    #gc.collect()\n",
        "    #torch.cuda.empty_cache()\n",
        "\n",
        "    best_model_states.append(best_model_state)\n",
        "\n",
        "    # predict for CV\n",
        "    model.load_state_dict(best_model_state)\n",
        "    model.eval()\n",
        "    for data in tqdm(loader_valid_clean):\n",
        "        data = data.to(device)\n",
        "        mask = data.train_mask\n",
        "\n",
        "        out = model(data)[mask].detach()\n",
        "        y = data.y[mask].detach()\n",
        "\n",
        "        all_ys = torch.cat((all_ys, y), dim=0)\n",
        "        all_outs = torch.cat((all_outs, out), dim=0)\n",
        "\n",
        "        del data\n",
        "        del out\n",
        "        del y\n",
        "        #gc.collect()\n",
        "        #torch.cuda.empty_cache()\n",
        "\n",
        "# calculate MCRMSE by all training data\n",
        "print('CV MCRMSE ', criterion(all_outs, all_ys).item())\n",
        "del all_outs\n",
        "del all_ys\n",
        "#gc.collect()\n",
        "#torch.cuda.empty_cache()\n",
        "\n",
        "# predict for test data\n",
        "print('Predicting test data')\n",
        "print('Reading', test_file)\n",
        "df_te = pd.read_json(test_file, lines=True)\n",
        "data_test = build_data(df_te, False)\n",
        "loader_test = DataLoader(data_test, batch_size=batch_size, shuffle=False)\n",
        "id_seqpos = build_id_seqpos(df_te)\n",
        "\n",
        "preds = torch.zeros((len(id_seqpos), 3)).to(device).detach()\n",
        "for best_model_state in best_model_states:\n",
        "    model.load_state_dict(best_model_state)\n",
        "    model.eval()\n",
        "\n",
        "    outs = torch.zeros((0, 3)).to(device).detach()\n",
        "    for data in tqdm(loader_test):\n",
        "        data = data.to(device)\n",
        "        mask = data.test_mask\n",
        "\n",
        "        out = model(data)[mask].detach()\n",
        "        outs = torch.cat((outs, out), dim=0)\n",
        "\n",
        "        del data\n",
        "        del out\n",
        "        #gc.collect()\n",
        "        #torch.cuda.empty_cache()\n",
        "    preds += outs\n",
        "preds /= len(best_model_states)\n",
        "preds = preds.cpu().numpy()\n",
        "\n",
        "df_sub = pd.DataFrame({'id_seqpos': id_seqpos,\n",
        "                       'reactivity': preds[:,0],\n",
        "                       'deg_Mg_pH10': preds[:,1],\n",
        "                       'deg_pH10': 0,\n",
        "                       'deg_Mg_50C': preds[:,2],\n",
        "                       'deg_50C': 0})\n",
        "print('Writing submission.csv')\n",
        "df_sub.to_csv('submission.csv', index=False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training\n",
            "Fold 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/243 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 243/243 [00:30<00:00,  8.06it/s]\n",
            "100%|██████████| 20/20 [00:00<00:00, 20.89it/s]\n",
            "  0%|          | 1/243 [00:00<00:29,  8.34it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "T Loss: 0.3561 V Loss: 0.2913 V MCRMSE: 0.2920\n",
            "Best valid MCRMSE updated to 0.29198020696640015\n",
            "Epoch 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 243/243 [00:29<00:00,  8.16it/s]\n",
            "100%|██████████| 20/20 [00:00<00:00, 20.84it/s]\n",
            "  0%|          | 1/243 [00:00<00:29,  8.07it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "T Loss: 0.2894 V Loss: 0.2661 V MCRMSE: 0.2671\n",
            "Best valid MCRMSE updated to 0.26705822348594666\n",
            "Epoch 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 243/243 [00:29<00:00,  8.16it/s]\n",
            "100%|██████████| 20/20 [00:00<00:00, 20.78it/s]\n",
            "  0%|          | 1/243 [00:00<00:29,  8.16it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "T Loss: 0.2702 V Loss: 0.2592 V MCRMSE: 0.2602\n",
            "Best valid MCRMSE updated to 0.2601886987686157\n",
            "Epoch 3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 243/243 [00:29<00:00,  8.16it/s]\n",
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            "  0%|          | 1/243 [00:00<00:29,  8.30it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
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            "CV MCRMSE  0.15893882513046265\n",
            "Predicting test data\n",
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          "text": [
            "Writing submission.csv\n"
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      ]
    }
  ]
}