ADAGE_Project_Model_implementation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ADAGE Project Model implementation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/qczhao92/ADAGE-on-L1000/blob/main/ADAGE_Project_Model_implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvFfTJ-Qvf-x",
        "outputId": "bfade7bd-7162-4f3a-8418-59d02a868295"
      },
      "source": [
        "colab = True\n",
        "# In case in Colab activate this cell by passing the corect path to the images\n",
        "if colab==True: \n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive')\n",
        "    work_dir = 'drive/MyDrive/AIforGenomics2021/ADAGE'\n",
        "else:\n",
        "    work_dir = ''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsKi3Q3viZ1A"
      },
      "source": [
        "import sys, os\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.autograd as autograd\n",
        "from torch.autograd import Variable\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# set the path of files\n",
        "l5data = \"/content/drive/MyDrive/AIforGenomics2021/ADAGE/GSE70138_Broad_LINCS_Level5_COMPZ_n118050x12328_2017-03-06.gctx\"\n",
        "gene_info_txt = \"/content/drive/MyDrive/AIforGenomics2021/ADAGE/GSE70138_Broad_LINCS_gene_info_2017-03-06.txt\"\n",
        "pert_info_txt = \"/content/drive/MyDrive/AIforGenomics2021/ADAGE/GSE70138_Broad_LINCS_pert_info.txt\"\n",
        "sig_info_txt = \"/content/drive/MyDrive/AIforGenomics2021/ADAGE/GSE70138_Broad_LINCS_sig_info_2017-03-06.txt\"\n",
        "cell_info_txt = \"/content/drive/MyDrive/AIforGenomics2021/ADAGE/GSE70138_Broad_LINCS_cell_info_2017-04-28.txt\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sfx7EK9de3V6"
      },
      "source": [
        "# 0. Download L1000 data\n",
        "download the 2 phase level 5 data to the working directory\n",
        "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70138"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yx4YzYbdiMYx"
      },
      "source": [
        "from tqdm import *\n",
        "import os\n",
        "\n",
        "if not os.path.exists(work_dir):\n",
        "    os.system(f'mkdir -p {work_dir}')\n",
        "\n",
        "if not os.path.isfile(gene_info_txt):\n",
        "    gene_info_link ='ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70138/suppl/GSE70138_Broad_LINCS_gene_info_2017-03-06.txt.gz'\n",
        "    os.system(f'wget -O {gene_info_txt}.gz {gene_info_link}')\n",
        "    os.system(f'gunzip {gene_info_txt}.gz')\n",
        "\n",
        "if not os.path.isfile(pert_info_txt):\n",
        "    pert_info_link ='ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70138/suppl/GSE70138_Broad_LINCS_pert_info.txt.gz'\n",
        "    os.system(f'wget -O {pert_info_txt}.gz {pert_info_link}')\n",
        "    os.system(f'gunzip {pert_info_txt}.gz')\n",
        "\n",
        "if not os.path.isfile(sig_info_txt):\n",
        "    sig_info_link ='ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70138/suppl/GSE70138_Broad_LINCS_sig_info_2017-03-06.txt.gz'\n",
        "    os.system(f'wget -O {sig_info_txt}.gz {sig_info_link}')\n",
        "    os.system(f'gunzip {sig_info_txt}.gz')\n",
        "\n",
        "if not os.path.isfile(cell_info_txt):\n",
        "    cell_info_link ='ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70138/suppl/GSE70138_Broad_LINCS_cell_info_2017-04-28.txt.gz'\n",
        "    os.system(f'wget -O {cell_info_txt}.gz {cell_info_link}')\n",
        "    os.system(f'gunzip {cell_info_txt}.gz')\n",
        "\n",
        "if not os.path.isfile(l5data):\n",
        "    if not os.path.isfile(f'{l5data}.gz'):\n",
        "        print('downloading L1000 level5 data... could take a while')\n",
        "        l5data_link ='ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70138/suppl/GSE70138_Broad_LINCS_Level5_COMPZ_n118050x12328_2017-03-06.gctx.gz'\n",
        "        os.system(f'wget -O {l5data} {l5data_link}')\n",
        "    else:\n",
        "        print('Zipped L1000 level5 data exits!', f'starting unzip {l5data}.gz')\n",
        "    os.system(f'gunzip {l5data}.gz')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxqzLriW4IN7",
        "outputId": "59ae0f96-e1cc-4800-d0f4-90d7cd99659d"
      },
      "source": [
        "!ls /content/drive/MyDrive/AIforGenomics2021/ADAGE -all"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 5999957\n",
            "-rw------- 1 root root  276119388 Mar 11 22:15  A549_full_gene_expression.csv\n",
            "-rw------- 1 root root   21735820 Mar  8 19:16  A549_landmark_expression.csv\n",
            "-rw------- 1 root root      11850 Mar  1 18:07  GSE70138_Broad_LINCS_cell_info_2017-04-28.txt\n",
            "-rw------- 1 root root     621887 Mar  1 18:07  GSE70138_Broad_LINCS_gene_info_2017-03-06.txt\n",
            "-rw------- 1 root root 5824120558 Mar  1 18:07  GSE70138_Broad_LINCS_Level5_COMPZ_n118050x12328_2017-03-06.gctx\n",
            "-rw------- 1 root root     219965 Mar  1 18:07  GSE70138_Broad_LINCS_pert_info.txt\n",
            "-rw------- 1 root root   18260895 Mar  1 18:07  GSE70138_Broad_LINCS_sig_info_2017-03-06.txt\n",
            "-rw------- 1 root root     392615 Mar 11 19:04  string_normal_image.png\n",
            "drwx------ 2 root root       4096 Mar  6 21:02  trained_model\n",
            "-rw------- 1 root root    2466424 Mar 11 22:21 'weight matrices after training'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpQqeTSGgLFf"
      },
      "source": [
        "## take a look of the information files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "ypRszBXftZr1",
        "outputId": "ff9a9ca9-e94f-4c81-96e8-ff167e0622c7"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "gene_info = pd.read_csv(gene_info_txt, sep = '\\t')\n",
        "print(gene_info.shape)\n",
        "gene_info.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12328, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pr_gene_id</th>\n",
              "      <th>pr_gene_symbol</th>\n",
              "      <th>pr_gene_title</th>\n",
              "      <th>pr_is_lm</th>\n",
              "      <th>pr_is_bing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>780</td>\n",
              "      <td>DDR1</td>\n",
              "      <td>discoidin domain receptor tyrosine kinase 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7849</td>\n",
              "      <td>PAX8</td>\n",
              "      <td>paired box 8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2978</td>\n",
              "      <td>GUCA1A</td>\n",
              "      <td>guanylate cyclase activator 1A</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2049</td>\n",
              "      <td>EPHB3</td>\n",
              "      <td>EPH receptor B3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2101</td>\n",
              "      <td>ESRRA</td>\n",
              "      <td>estrogen related receptor alpha</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pr_gene_id pr_gene_symbol  ... pr_is_lm  pr_is_bing\n",
              "0         780           DDR1  ...        1           1\n",
              "1        7849           PAX8  ...        1           1\n",
              "2        2978         GUCA1A  ...        0           0\n",
              "3        2049          EPHB3  ...        0           1\n",
              "4        2101          ESRRA  ...        0           1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "t3JgACdPgIFC",
        "outputId": "6ff1c923-c521-45cb-c1dc-fdb67efee681"
      },
      "source": [
        "pert_info = pd.read_csv(pert_info_txt, sep = '\\t')\n",
        "print(pert_info.shape)\n",
        "pert_info.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2170, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>pert_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>inchi_key</th>\n",
              "      <th>pert_iname</th>\n",
              "      <th>pert_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BRD-K70792160</td>\n",
              "      <td>CCN(CC)CCCCN1c2ccccc2Oc2ccc(Cl)cc12</td>\n",
              "      <td>GYBXAGDWMCJZJK-UHFFFAOYSA-N</td>\n",
              "      <td>10-DEBC</td>\n",
              "      <td>trt_cp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BRD-K68552125</td>\n",
              "      <td>CCCCCCCCCCCCCC(=O)O[C@@H]1[C@@H](C)[C@]2(O)[C@...</td>\n",
              "      <td>PHEDXBVPIONUQT-RGYGYFBISA-N</td>\n",
              "      <td>phorbol-myristate-acetate</td>\n",
              "      <td>trt_cp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BRD-K92301463</td>\n",
              "      <td>CCCCC(C)(C)[C@H](O)\\C=C\\[C@H]1[C@H](O)CC(=O)[C...</td>\n",
              "      <td>QAOBBBBDJSWHMU-WMBBNPMCSA-N</td>\n",
              "      <td>16,16-dimethylprostaglandin-e2</td>\n",
              "      <td>trt_cp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BRD-A29731977</td>\n",
              "      <td>CCCCCC(=O)O[C@@]1(CCC2C3CCC4=CC(=O)CC[C@]4(C)C...</td>\n",
              "      <td>DOMWKUIIPQCAJU-JKPPDDDBSA-N</td>\n",
              "      <td>17-hydroxyprogesterone-caproate</td>\n",
              "      <td>trt_cp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BRD-K07954936</td>\n",
              "      <td>OC(=O)CCCC[C@@H]1SC[C@@H]2NC(=N)N[C@H]12</td>\n",
              "      <td>WWVANQJRLPIHNS-ZKWXMUAHSA-N</td>\n",
              "      <td>2-iminobiotin</td>\n",
              "      <td>trt_cp</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         pert_id  ... pert_type\n",
              "0  BRD-K70792160  ...    trt_cp\n",
              "1  BRD-K68552125  ...    trt_cp\n",
              "2  BRD-K92301463  ...    trt_cp\n",
              "3  BRD-A29731977  ...    trt_cp\n",
              "4  BRD-K07954936  ...    trt_cp\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "3VN3UTKN51fI",
        "outputId": "dc21accf-3a77-4b43-cb67-ff7defbc01d0"
      },
      "source": [
        "sig_info = pd.read_csv(sig_info_txt, sep = '\\t')\n",
        "print(sig_info.shape)\n",
        "sig_info.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(118050, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>sig_id</th>\n",
              "      <th>pert_id</th>\n",
              "      <th>pert_iname</th>\n",
              "      <th>pert_type</th>\n",
              "      <th>cell_id</th>\n",
              "      <th>pert_idose</th>\n",
              "      <th>pert_itime</th>\n",
              "      <th>distil_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LJP005_A375_24H:A03</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>ctl_vehicle</td>\n",
              "      <td>A375</td>\n",
              "      <td>-666</td>\n",
              "      <td>24 h</td>\n",
              "      <td>LJP005_A375_24H_X1_B19:A03|LJP005_A375_24H_X2_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LJP005_A375_24H:A04</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>ctl_vehicle</td>\n",
              "      <td>A375</td>\n",
              "      <td>-666</td>\n",
              "      <td>24 h</td>\n",
              "      <td>LJP005_A375_24H_X1_B19:A04|LJP005_A375_24H_X2_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LJP005_A375_24H:A05</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>ctl_vehicle</td>\n",
              "      <td>A375</td>\n",
              "      <td>-666</td>\n",
              "      <td>24 h</td>\n",
              "      <td>LJP005_A375_24H_X1_B19:A05|LJP005_A375_24H_X2_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LJP005_A375_24H:A06</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>DMSO</td>\n",
              "      <td>ctl_vehicle</td>\n",
              "      <td>A375</td>\n",
              "      <td>-666</td>\n",
              "      <td>24 h</td>\n",
              "      <td>LJP005_A375_24H_X1_B19:A06|LJP005_A375_24H_X2_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LJP005_A375_24H:A07</td>\n",
              "      <td>BRD-K76908866</td>\n",
              "      <td>CP-724714</td>\n",
              "      <td>trt_cp</td>\n",
              "      <td>A375</td>\n",
              "      <td>10.0 um</td>\n",
              "      <td>24 h</td>\n",
              "      <td>LJP005_A375_24H_X1_B19:A07|LJP005_A375_24H_X2_...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                sig_id  ...                                          distil_id\n",
              "0  LJP005_A375_24H:A03  ...  LJP005_A375_24H_X1_B19:A03|LJP005_A375_24H_X2_...\n",
              "1  LJP005_A375_24H:A04  ...  LJP005_A375_24H_X1_B19:A04|LJP005_A375_24H_X2_...\n",
              "2  LJP005_A375_24H:A05  ...  LJP005_A375_24H_X1_B19:A05|LJP005_A375_24H_X2_...\n",
              "3  LJP005_A375_24H:A06  ...  LJP005_A375_24H_X1_B19:A06|LJP005_A375_24H_X2_...\n",
              "4  LJP005_A375_24H:A07  ...  LJP005_A375_24H_X1_B19:A07|LJP005_A375_24H_X2_...\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9z-FeX3dcCi"
      },
      "source": [
        "## load L1000 level 5 data (gctx)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "hzfnOjPgpMg1",
        "outputId": "f58cdbaf-b44d-4a75-8479-33dc5224c3cc"
      },
      "source": [
        "landmark_gene_list = gene_info[gene_info['pr_is_lm'] == 1].astype(str)\n",
        "print(landmark_gene_list.shape)\n",
        "landmark_gene_list.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(978, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>pr_gene_id</th>\n",
              "      <th>pr_gene_symbol</th>\n",
              "      <th>pr_gene_title</th>\n",
              "      <th>pr_is_lm</th>\n",
              "      <th>pr_is_bing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>780</td>\n",
              "      <td>DDR1</td>\n",
              "      <td>discoidin domain receptor tyrosine kinase 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7849</td>\n",
              "      <td>PAX8</td>\n",
              "      <td>paired box 8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>6193</td>\n",
              "      <td>RPS5</td>\n",
              "      <td>ribosomal protein S5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>23</td>\n",
              "      <td>ABCF1</td>\n",
              "      <td>ATP binding cassette subfamily F member 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>9552</td>\n",
              "      <td>SPAG7</td>\n",
              "      <td>sperm associated antigen 7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pr_gene_id pr_gene_symbol  ... pr_is_lm pr_is_bing\n",
              "0         780           DDR1  ...        1          1\n",
              "1        7849           PAX8  ...        1          1\n",
              "25       6193           RPS5  ...        1          1\n",
              "43         23          ABCF1  ...        1          1\n",
              "49       9552          SPAG7  ...        1          1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bx3z__YwPc3",
        "outputId": "fb14501c-1448-462d-e6dc-3a9f16d7c5b0"
      },
      "source": [
        "!pip install cmapPy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting cmapPy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/e1/9e90b0dc2051f4a3a2b9553b254889e93a2a3c7a3b925433c6298d99d596/cmapPy-4.0.1-py2.py3-none-any.whl (150kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 5.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.18 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cmapPy) (1.15.0)\n",
            "Requirement already satisfied: requests>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (2.23.0)\n",
            "Requirement already satisfied: h5py>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (2.10.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.18->cmapPy) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.18->cmapPy) (2.8.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (2.10)\n",
            "Installing collected packages: cmapPy\n",
            "Successfully installed cmapPy-4.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsRyQp_90sy7"
      },
      "source": [
        "## load the full gene expression data\n",
        "#from cmapPy.pandasGEXpress.parse import parse\n",
        "#gene_expr_matrix_full = parse(l5data).data_df.T\n",
        "\n",
        "## extract full gene data of A549:\n",
        "#A549_gene_expr = gene_expr_matrix_full.loc[[\"_A549_\" in row_name for row_name in gene_expr_matrix_full.index]]\n",
        "#print(A549_gene_expr.shape)\n",
        "## save the A549 data for future use\n",
        "#A549_gene_expr.to_csv(f\"{work_dir}/A549_full_gene_expression.csv\")\n",
        "\n",
        "#A549_gene_expr.head()\n",
        "\n",
        "# del gene_expr_matrix_full  ## release RAM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFEHJqP1dYhG"
      },
      "source": [
        "## only load landmark genes\n",
        "from cmapPy.pandasGEXpress.parse import parse\n",
        "\n",
        "## Load the matrix of differential gene expressions as a pandas dataframe\n",
        "gene_expr_matrix = parse(l5data, rid=landmark_gene_list[\"pr_gene_id\"]).data_df.T\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ezu-EM4C5Zjj",
        "outputId": "7dfe0f3b-ead9-47e2-815a-dc30b1fee9e6"
      },
      "source": [
        "## choose a cell line to start with\n",
        "# see what cell lines are available\n",
        "print(\"number of cell lines:\", len(sig_info.cell_id.unique()))\n",
        "sig_info.cell_id.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of cell lines: 41\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MCF7        13476\n",
              "A375        12740\n",
              "PC3         12719\n",
              "HT29        12529\n",
              "HA1E        12481\n",
              "YAPC        10621\n",
              "HELA        10617\n",
              "A549         2082\n",
              "HCC515       1900\n",
              "HEPG2        1899\n",
              "HME1         1895\n",
              "BT20         1520\n",
              "HS578T       1516\n",
              "MDAMB231     1515\n",
              "MCF10A       1514\n",
              "SKBR3        1514\n",
              "NPC.TAK      1283\n",
              "NPC          1198\n",
              "ASC          1136\n",
              "ASC.C        1135\n",
              "SKL.C        1132\n",
              "HUVEC        1129\n",
              "SKL          1128\n",
              "CD34         1121\n",
              "NEU          1113\n",
              "NPC.CAS9     1104\n",
              "LNCAP         759\n",
              "PC3.101       380\n",
              "PC3.311       380\n",
              "A375.311      380\n",
              "HELA.311      380\n",
              "MCF7.101      380\n",
              "HT29.311      380\n",
              "HA1E.311      380\n",
              "YAPC.311      380\n",
              "A549.311      380\n",
              "HA1E.101      380\n",
              "MCF7.311      379\n",
              "MNEU.E        377\n",
              "JURKAT        366\n",
              "HUES3         352\n",
              "Name: cell_id, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vq7_EJK4D_8D"
      },
      "source": [
        "# extract data of A549:\n",
        "#A549_gene_expr = gene_expr_matrix.loc[[\"_A549_\" in row_name for row_name in gene_expr_matrix.index]]\n",
        "#print(A549_gene_expr.shape)\n",
        "# save the A549 data for future use\n",
        "#A549_gene_expr.to_csv(f\"{work_dir}/A549_landmark_expression.csv\")\n",
        "\n",
        "#A549_gene_expr.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rK_HN1A832Nc"
      },
      "source": [
        "# 1. ADAGE model\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCfSBTJj3lMR"
      },
      "source": [
        "construct an ADAGE-based model:\n",
        "1. adding random noise to the input data\n",
        "2. training a neural network with hidden nodes that were able to remove added noise to reconstruct the initial data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQFqIYouFGH6"
      },
      "source": [
        "### 1.1 define the model\n",
        "\n",
        "for each sample $x$:\n",
        "- add random noise to a percentage of the genes \n",
        "- use noise-added sample $x'$ as input of denoising autoencoder\n",
        "\n",
        "Process input $x'$ with single-layer linear encoder:\n",
        "> $y = s(Wx'+b)$\n",
        "\n",
        "then reconstruct gene expression by one-layer or multi-layer decoder:\n",
        "> $z =  s(W'y + b') $\n",
        "\n",
        "> keep the last layer of decoder without activation function \n",
        "\n",
        "use Mean Squared Error as loss function\n",
        "> $L(x,z) = \\frac{1}{n}\\sum_{k=1}^n (x_k  -z_k)^2$\n",
        "\n",
        "\n",
        "After training, use the weight of encoder $W$ as gene weights.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Rv4TUB67Wn8"
      },
      "source": [
        "class Adage_linear(nn.Module):\n",
        "    \"\"\"Denoising Auto-Encoder: to reconstruct the input from a corrupted gene expression\"\"\"\n",
        "    \n",
        "    def __init__(self, gene_number, node_number = 50, decoder_layers = 1):\n",
        "        super(Adage_linear, self).__init__()\n",
        "\n",
        "        hidden_layers = []\n",
        "        current_node = node_number\n",
        "        if decoder_layers > 1:\n",
        "            delta = (gene_number - node_number) / decoder_layers\n",
        "            for i in range(decoder_layers-1):\n",
        "                n = int(current_node + delta)\n",
        "                hidden_layers.append(nn.Linear(current_node, n))\n",
        "                hidden_layers.append(nn.ReLU(inplace=True))\n",
        "                current_node = n\n",
        "\n",
        "\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Linear(gene_number, node_number),\n",
        "            nn.ReLU(inplace=True)\n",
        "        )\n",
        "        self.decoder = nn.Sequential(\n",
        "            *hidden_layers,\n",
        "            nn.Linear(current_node, gene_number)\n",
        "        )    \n",
        "      \n",
        "    def forward(self,x):\n",
        "        y = self.encoder(x)\n",
        "        y = self.decoder(y)\n",
        "        return y\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNDIOZo_CFiH"
      },
      "source": [
        "Some functions for training:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1Pq9n3fd2iz"
      },
      "source": [
        "## function to split the training and validation data\n",
        "def train_valid_split(dataframe, train_portion = 0.7):\n",
        "    \"\"\"take a pandas dataframe as input, output two numpy arrays according to train_portion\"\"\"\n",
        "    data = dataframe.to_numpy()\n",
        "    np.random.shuffle(data)\n",
        "    train_data, valid_data = data[:int(data.shape[0]*train_portion),:], data[int(data.shape[0]*train_portion):,:]\n",
        "\n",
        "    print('samples in training data:',train_data.shape[0],'\\nsamples in validation data:',valid_data.shape[0])\n",
        "    return train_data, valid_data\n",
        "\n",
        "## function to prepare input data\n",
        "def dataloader(data, batch_size, reuse = True):\n",
        "    \"\"\" prepare a batch of datasets for ADAGE model input \"\"\"\n",
        "    np.random.shuffle(data)\n",
        "    num_batches = len(data) // batch_size\n",
        "    while True:\n",
        "        for i in range(num_batches):\n",
        "            yield data[i * batch_size : (i + 1) * batch_size]\n",
        "        if not reuse and i == num_batches - 1:\n",
        "            yield None\n",
        "\n",
        "## function to save model\n",
        "def save_models(work_dir, model, epoch_number,model_name):\n",
        "    \"\"\"Function to save trained model\"\"\"\n",
        "    save_dir = os.path.join(work_dir, \"trained_model\",model_name)\n",
        "    os.makedirs(save_dir, exist_ok=True)\n",
        "    torch.save(model.state_dict(), '{}/epoch{}'.format(save_dir, epoch_number))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWaTaGxjICZZ"
      },
      "source": [
        "functions for adding noise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlprVTfDaysv"
      },
      "source": [
        "# define an random noise introducer function\n",
        "def random_noise(train_batch, random_rate = 0.05, std=0.1):\n",
        "    noise=np.random.normal(0,std,train_batch.shape)\n",
        "    rate=np.random.choice([0, 1], size=train_batch.shape, p=[1-random_rate, random_rate])\n",
        "    new_signal=train_batch+noise*rate\n",
        "    return new_signal\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGud6CysE1_I"
      },
      "source": [
        "### 1.2 train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-XE379CozU4"
      },
      "source": [
        "#### Define the training function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JIZqFI8oyrv"
      },
      "source": [
        "## training function\n",
        "def train_model(model, model_name , noise = False, valid_point_epoch = 30 , model_save_epoch = 200 , iteration_n = 15000, print_train = True):\n",
        "    \n",
        "    criterion = nn.MSELoss()\n",
        "    optimizer=optim.Adam(list(model.parameters()), lr=learning_rate)\n",
        "\n",
        "    start_time = time.time()\n",
        "    print('training start at %s '%(time.ctime(start_time)),'_'*50)\n",
        "    train_loss_record = []\n",
        "    valid_loss_record = []\n",
        "\n",
        "    for epoch in range(iteration_n): \n",
        "        inputs = next(train_loader)\n",
        "        if noise:\n",
        "            inputs_n = random_noise(inputs)\n",
        "        else:\n",
        "            inputs_n = inputs\n",
        "\n",
        "        inputs = torch.tensor(inputs).float()\n",
        "        inputs_n = torch.tensor(inputs_n).float()\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(model(inputs_n), inputs)\n",
        "        loss.backward()\n",
        "        optimizer.step()  # Update the parameters of the model\n",
        "\n",
        "        # print statistics\n",
        "        batch_loss = np.mean(loss.item())\n",
        "        #epochs.append(epoch+1)\n",
        "        #train_loss.append(batch_loss) \n",
        "        train_loss_record.append((epoch+1, batch_loss))\n",
        "\n",
        "        if print_train and epoch % 50 == 49:    # print every 50 epochs\n",
        "            print('epoch %d loss: %.3f' %\n",
        "                  (epoch + 1, np.mean(loss.item())))\n",
        "        if epoch % valid_point_epoch == valid_point_epoch-1:\n",
        "            valid_inputs = next(valid_loader)\n",
        "            valid_inputs = torch.tensor(valid_inputs).float()\n",
        "            valid_loss = criterion(model(valid_inputs), valid_inputs)\n",
        "            valid_loss_record.append((epoch+1, np.mean(valid_loss.item())))\n",
        "\n",
        "        # save check point models\n",
        "        if epoch % model_save_epoch == model_save_epoch-1:\n",
        "            save_models(work_dir,model,train_loss_record[-1][0],model_name)\n",
        "    print('_'*50,'\\nFinished Training at', time.ctime(time.time()), '\\ntotal training time: %.2f s' %(time.time()-start_time))\n",
        "\n",
        "    with open(f'{work_dir}/trained_model/{model_name}_train_loss_record.txt','w+') as ft:\n",
        "        ft.write('\\n'.join('%s\\t%s' % x for x in train_loss_record))\n",
        "    with open(f'{work_dir}/trained_model/{model_name}_valid_loss_record.txt','w+') as fv:\n",
        "        fv.write('\\n'.join('%s\\t%s' % x for x in valid_loss_record))\n",
        "    \n",
        "    %matplotlib inline\n",
        "    ## plot the training and validation curve\n",
        "    plt.close()\n",
        "    plt.plot(*zip(*train_loss_record),color='orange')\n",
        "    plt.plot(*zip(*valid_loss_record),color='green')\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.ylabel(\"MSE\")\n",
        "    plt.savefig(f'{work_dir}/trained_model/{model_name}.pdf')\n",
        "    \n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ekj7oqEICMqx"
      },
      "source": [
        "#### load the data for training and validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5olVZF8oNUh",
        "outputId": "8d4e37bb-75df-4d0f-ccd4-ac1b251ec854"
      },
      "source": [
        "!pip install cmapPy\n",
        "## only load landmark genes\n",
        "from cmapPy.pandasGEXpress.parse import parse\n",
        "import pandas as pd\n",
        "\n",
        "gene_info = pd.read_csv(gene_info_txt, sep = '\\t')\n",
        "\n",
        "landmark_gene_list = gene_info[gene_info['pr_is_lm'] == 1].astype(str)\n",
        "print(landmark_gene_list.shape)\n",
        "## Load the matrix of differential gene expressions as a pandas dataframe\n",
        "gene_expr_matrix = parse(l5data, rid=landmark_gene_list[\"pr_gene_id\"]).data_df.T\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting cmapPy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/e1/9e90b0dc2051f4a3a2b9553b254889e93a2a3c7a3b925433c6298d99d596/cmapPy-4.0.1-py2.py3-none-any.whl (150kB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas>=0.18 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (1.1.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cmapPy) (1.15.0)\n",
            "Requirement already satisfied: requests>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (1.19.5)\n",
            "Requirement already satisfied: h5py>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from cmapPy) (2.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.18->cmapPy) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.18->cmapPy) (2018.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->cmapPy) (3.0.4)\n",
            "Installing collected packages: cmapPy\n",
            "Successfully installed cmapPy-4.0.1\n",
            "(978, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-ro2IeigYF-"
      },
      "source": [
        "# define the dataframe to be used\n",
        "dataf = gene_expr_matrix\n",
        "# split datasets\n",
        "train_data, valid_data = train_valid_split(dataf)\n",
        "# initiate dataloader\n",
        "train_loader = dataloader(train_data, 80)\n",
        "valid_loader = dataloader(valid_data,80)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0x4NPR-pE_1"
      },
      "source": [
        "#### Set parameters and launch training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "tP0yPwnkE1Hr",
        "outputId": "2fdf9721-720d-40a3-92d0-5aa3f989850d"
      },
      "source": [
        "## set hyper-parameters\n",
        "node_n=50\n",
        "layers = 5\n",
        "\n",
        "learning_rate = 0.002\n",
        "iteration_n = 10000\n",
        "\n",
        "model_name=f\"{layers}layer_{node_n}nodes_lr{learning_rate}_allsample_landmark_gene\"\n",
        "valid_point_epoch = 50\n",
        "model_save_epoch = 500\n",
        "\n",
        "## instantiate the model\n",
        "DA = Adage_linear(train_data.shape[1], node_number=node_n, decoder_layers = layers)\n",
        "print(\"{}\\nADAGE Network Architecture:\\n{}\\n{}\\n\".format(\"=\"*31, \"~\"*31, DA))\n",
        "\n",
        "## train the model\n",
        "DA = train_model(DA, model_name, valid_point_epoch = valid_point_epoch, model_save_epoch = model_save_epoch, iteration_n = iteration_n, print_train = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Mar 26 20:30:50 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Mar 26 20:40:58 2021 \n",
            "total training time: 608.36 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fGyv6WoyTOMJ"
      },
      "source": [
        "# 2. extracting high-weight genes\n",
        "\n",
        "from the learned weights of each node, we extract genes corresponding to high weights, aka 2 or more stdev away from mean.\n",
        "\n",
        "$w_{gene} \\geq \\bar{w} \\pm 2 \\sigma $\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EzFqBT66b9m7",
        "outputId": "66a31186-c846-49f2-9685-51b51cb7b3bb"
      },
      "source": [
        "# load the trained model\n",
        "node_n=50\n",
        "layers = 5\n",
        "\n",
        "learning_rate = 0.002\n",
        "epoch_n = 10000\n",
        "\n",
        "model_name_epoch=f\"trial1/{layers}layers_{node_n}nodes_allsample_landmark_gene/epoch{epoch_n}\"\n",
        "DA = Adage_linear(train_data.shape[1], node_number=node_n, decoder_layers = layers)\n",
        "DA.load_state_dict(torch.load(f'{work_dir}/trained_model/{model_name_epoch}'))\n",
        "DA.eval()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adage_linear(\n",
              "  (encoder): Sequential(\n",
              "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "  )\n",
              "  (decoder): Sequential(\n",
              "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
              "    (3): ReLU(inplace=True)\n",
              "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "XQXMsU0Q81C9",
        "outputId": "6d576e67-cf10-4c04-a09c-4695048fd830"
      },
      "source": [
        "gene_info = pd.read_csv(gene_info_txt, sep = '\\t')\n",
        "print(gene_info.shape)\n",
        "#gene_info.head()\n",
        "landmark_gene = gene_info[gene_info['pr_is_lm'] == 1].astype(str)\n",
        "print(landmark_gene.shape)\n",
        "landmark_gene.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12328, 5)\n",
            "(978, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>pr_gene_id</th>\n",
              "      <th>pr_gene_symbol</th>\n",
              "      <th>pr_gene_title</th>\n",
              "      <th>pr_is_lm</th>\n",
              "      <th>pr_is_bing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>780</td>\n",
              "      <td>DDR1</td>\n",
              "      <td>discoidin domain receptor tyrosine kinase 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7849</td>\n",
              "      <td>PAX8</td>\n",
              "      <td>paired box 8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>6193</td>\n",
              "      <td>RPS5</td>\n",
              "      <td>ribosomal protein S5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>23</td>\n",
              "      <td>ABCF1</td>\n",
              "      <td>ATP binding cassette subfamily F member 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>9552</td>\n",
              "      <td>SPAG7</td>\n",
              "      <td>sperm associated antigen 7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pr_gene_id pr_gene_symbol  ... pr_is_lm pr_is_bing\n",
              "0         780           DDR1  ...        1          1\n",
              "1        7849           PAX8  ...        1          1\n",
              "25       6193           RPS5  ...        1          1\n",
              "43         23          ABCF1  ...        1          1\n",
              "49       9552          SPAG7  ...        1          1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aq0iXGDKTWwb"
      },
      "source": [
        "def extract_high_weight_gene(DAmodel, dataframe = dataf, gene_info_file = gene_info_txt, stdev_cutoff = 2, save = True, \n",
        "                             savepath = f'{work_dir}/trained_model/{model_name_epoch}_high_weight_genes.txt'):\n",
        "    '''\n",
        "    This function read weight matrix, extract the corresponding gene id from input, and out put a summary dataframe\n",
        "    '''\n",
        "    ## load gene info\n",
        "    gene_info = pd.read_csv(gene_info_file, sep = '\\t')\n",
        "    gene_info['pr_gene_id'] = gene_info['pr_gene_id'].astype(str)\n",
        "\n",
        "    # get the gene id in the order of training data\n",
        "    gene_id = dataframe.columns.to_list()\n",
        "    # subset gene info and organize in the order of gene_id\n",
        "    gene_df = gene_info.set_index('pr_gene_id').loc[gene_id].reset_index()\n",
        "\n",
        "    #get the weitht matrix between genes and nodes\n",
        "    t_weight = DAmodel.encoder[0].weight\n",
        "\n",
        "    #calculate mean and std at each node of matrix\n",
        "    w_mean = t_weight.mean(dim=1).reshape(t_weight.shape[0],1)\n",
        "    w_std = t_weight.std(dim=1).reshape(t_weight.shape[0],1)\n",
        "\n",
        "    #check for the weights which is more than two std from the mean of each nodes\n",
        "    t_torf_HW=abs(t_weight-w_mean)- stdev_cutoff*w_std >= 0\n",
        "   \n",
        "    #extract and save the genes\n",
        "    dfs = []\n",
        "    for i in range(t_torf_HW.shape[0]):\n",
        "        node_hw = t_torf_HW[i].tolist()\n",
        "        node_HWgene = gene_df[node_hw]\n",
        "        node_HWgene.loc[:,'gene_weight'] = t_weight[i][node_hw].tolist()\n",
        "        node_HWgene.loc[:,'node_id'] = i\n",
        "        dfs.append(node_HWgene)\n",
        "\n",
        "    HWgene = pd.concat(dfs)\n",
        "\n",
        "    if save:\n",
        "        HWgene.to_csv(savepath, sep='\\t',index=False)\n",
        "\n",
        "    return HWgene"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "N6aFMy89cNZh",
        "outputId": "72b8de65-ce1b-42d5-e090-9fc86a9f48c1"
      },
      "source": [
        "HWgene = extract_high_weight_gene(DA)\n",
        "print(HWgene.shape)\n",
        "\n",
        "HWgene.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[key] = _infer_fill_value(value)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1743: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(ilocs[0], value)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(2226, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>pr_gene_id</th>\n",
              "      <th>pr_gene_symbol</th>\n",
              "      <th>pr_gene_title</th>\n",
              "      <th>pr_is_lm</th>\n",
              "      <th>pr_is_bing</th>\n",
              "      <th>gene_weight</th>\n",
              "      <th>node_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>533</td>\n",
              "      <td>ATP6V0B</td>\n",
              "      <td>ATPase, H+ transporting, lysosomal 21kDa, V0 s...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.253299</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>823</td>\n",
              "      <td>CAPN1</td>\n",
              "      <td>calpain 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.193122</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>949</td>\n",
              "      <td>SCARB1</td>\n",
              "      <td>scavenger receptor class B member 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.297632</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>6603</td>\n",
              "      <td>SMARCD2</td>\n",
              "      <td>SWI/SNF related, matrix associated, actin depe...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.226271</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>5831</td>\n",
              "      <td>PYCR1</td>\n",
              "      <td>pyrroline-5-carboxylate reductase 1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.230741</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    pr_gene_id pr_gene_symbol  ... gene_weight  node_id\n",
              "8          533        ATP6V0B  ...   -0.253299        0\n",
              "23         823          CAPN1  ...   -0.193122        0\n",
              "151        949         SCARB1  ...   -0.297632        0\n",
              "153       6603        SMARCD2  ...   -0.226271        0\n",
              "194       5831          PYCR1  ...   -0.230741        0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTMU3t7X-ya8"
      },
      "source": [
        "# 3. Evaluate the learning results\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htLieG0B_hYU"
      },
      "source": [
        "### 3.1 Compare gene sets with protein interaction database (STRING database) \n",
        "\n",
        "download and prepare the interaction database "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YrDHj5L8_ljx",
        "outputId": "593fa6e1-19c9-4d5f-96b4-3821e6db992d"
      },
      "source": [
        "# download STRING database for human\n",
        "#stringdb_path = f'{work_dir}/9606.protein.links.full.v11.0.txt'\n",
        "stringdb_path = f'{work_dir}/9606.protein.links.v11.0.txt'\n",
        "if not os.path.isfile(stringdb_path):\n",
        "    #os.system(f'wget -O {stringdb_path}.gz https://stringdb-static.org/download/protein.links.full.v11.0/9606.protein.links.full.v11.0.txt.gz')\n",
        "    os.system(f'wget -O {stringdb_path}.gz https://stringdb-static.org/download/protein.links.v11.0/9606.protein.links.v11.0.txt.gz')\n",
        "    os.system(f'gunzip {stringdb_path}.gz')\n",
        "\n",
        "stringdb = pd.read_csv(stringdb_path, sep=' ') \n",
        "stringdb.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>protein1</th>\n",
              "      <th>protein2</th>\n",
              "      <th>combined_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000272298</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000253401</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000401445</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000418915</td>\n",
              "      <td>606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000327801</td>\n",
              "      <td>167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               protein1              protein2  combined_score\n",
              "0  9606.ENSP00000000233  9606.ENSP00000272298             490\n",
              "1  9606.ENSP00000000233  9606.ENSP00000253401             198\n",
              "2  9606.ENSP00000000233  9606.ENSP00000401445             159\n",
              "3  9606.ENSP00000000233  9606.ENSP00000418915             606\n",
              "4  9606.ENSP00000000233  9606.ENSP00000327801             167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFIDwROIrkUO"
      },
      "source": [
        "useful funtions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkH4GKgR-xTm"
      },
      "source": [
        "import requests \n",
        "\n",
        "def symbol_to_ID(list_of_HUGO_symbol, string_api_url = \"https://string-db.org/api\", output_format = \"tsv-no-header\", method = \"get_string_ids\", species = 9606):  \n",
        "    '''A function to match HUGO gene symbol (used in L1000) to protein ID (used in STRING)\n",
        "    return a list of tuples (symbol, STRING_ID)\n",
        "    (script adapted and modified from STRING API page: https://string-db.org/help/api/)\n",
        "    '''\n",
        "    \n",
        "    params = {\n",
        "        \"identifiers\" : \"\\r\".join(list_of_HUGO_symbol), # your protein list\n",
        "        \"species\" : species, # species NCBI identifier \n",
        "        \"limit\" : 1, # only one (best) identifier per input protein\n",
        "        \"echo_query\" : 1, # see your input identifiers in the output\n",
        "        \"caller_identity\" : \"ADAGE_project\" # your app name\n",
        "    }\n",
        "\n",
        "    ## Construct URL\n",
        "    request_url = \"/\".join([string_api_url, output_format, method])\n",
        "\n",
        "    ## Call STRING\n",
        "    results = requests.post(request_url, data=params)\n",
        "\n",
        "    ## Read and parse the results\n",
        "    IDmatch = []\n",
        "    for line in results.text.strip().split(\"\\n\"):\n",
        "        l = line.split(\"\\t\")\n",
        "        IDmatch.append((l[0], l[2]))\n",
        "        #input_identifier, string_identifier = l[0], l[2]\n",
        "        #print(\"Input:\", input_identifier, \"STRING:\", string_identifier, sep=\"\\t\")\n",
        "\n",
        "    return IDmatch\n",
        "\n",
        "def extract_STRINGinteractions(gene_df, save = False, save_path = f'{work_dir}/trained_model/stringdb_record.txt'):\n",
        "    '''the gene_df dataframe must have column \"node_id\" and \"STRING_ID\"'''\n",
        "    links = []\n",
        "    for i in gene_df['node_id'].unique():\n",
        "        s_ids = gene_df.loc[gene_df['node_id']==i]['STRING_ID'].tolist()\n",
        "\n",
        "        g_links = stringdb[stringdb['protein1'].isin(s_ids) & stringdb['protein2'].isin(s_ids)].copy()\n",
        "        #print(f'=== gene set #{i} ===\\n  {len(s_ids)} genes contain {len(g_links.index)} links')\n",
        "        \n",
        "        g_links.loc[:,'node_id'] = i\n",
        "        links.append(g_links)\n",
        "\n",
        "    total_interactions = pd.concat(links)\n",
        "    if save:\n",
        "        total_interactions.to_csv(save_path,sep='\\t',index=False)\n",
        "\n",
        "    return total_interactions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmjn7OqqrySC"
      },
      "source": [
        "# build a dictionary of gene symbol to StringID\n",
        "if os.path.isfile(f'{work_dir}/landmark_gene_STRING_ID.txt'):\n",
        "    STRINGID_dict = {}\n",
        "    with open(f'{work_dir}/landmark_gene_STRING_ID.txt') as f:\n",
        "        for line in f:\n",
        "            (key, val) = line[:-1].split('\\t')\n",
        "            STRINGID_dict[key] = val\n",
        "else:\n",
        "    landmark_gene_STRINGIDs = symbol_to_ID(landmark_gene_list['pr_gene_symbol'])\n",
        "    with open(f'{work_dir}/landmark_gene_STRING_ID.txt','w+') as f:\n",
        "        f.write('\\n'.join('%s\\t%s' % x for x in landmark_gene_STRINGIDs))\n",
        "    STRINGID_dict = dict(landmark_gene_STRINGIDs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OAV-oupESuGG"
      },
      "source": [
        "Look at how many interactions in each gene set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "cXDsbUrFSo02",
        "outputId": "1c57439c-dc27-4ec5-d6d8-94664dbd0d55"
      },
      "source": [
        "# get STRING IDs of the high weight genes \n",
        "HWgene['STRING_ID'] = [STRINGID_dict[s] for s in HWgene['pr_gene_symbol'].to_list()]\n",
        "# interactions in each gene set\n",
        "total_interactions = extract_STRINGinteractions(HWgene,  save=True, save_path= f'{work_dir}/trained_model/{model_name_epoch}_HWgene_stringdb_records.txt')\n",
        "print(total_interactions.shape)\n",
        "total_interactions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(13798, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>protein1</th>\n",
              "      <th>protein2</th>\n",
              "      <th>combined_score</th>\n",
              "      <th>node_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>302177</th>\n",
              "      <td>9606.ENSP00000216554</td>\n",
              "      <td>9606.ENSP00000432083</td>\n",
              "      <td>246</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302184</th>\n",
              "      <td>9606.ENSP00000216554</td>\n",
              "      <td>9606.ENSP00000421689</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302276</th>\n",
              "      <td>9606.ENSP00000216554</td>\n",
              "      <td>9606.ENSP00000318697</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>676318</th>\n",
              "      <td>9606.ENSP00000229314</td>\n",
              "      <td>9606.ENSP00000287878</td>\n",
              "      <td>270</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>676388</th>\n",
              "      <td>9606.ENSP00000229314</td>\n",
              "      <td>9606.ENSP00000349860</td>\n",
              "      <td>287</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    protein1              protein2  combined_score  node_id\n",
              "302177  9606.ENSP00000216554  9606.ENSP00000432083             246        0\n",
              "302184  9606.ENSP00000216554  9606.ENSP00000421689             176        0\n",
              "302276  9606.ENSP00000216554  9606.ENSP00000318697             173        0\n",
              "676318  9606.ENSP00000229314  9606.ENSP00000287878             270        0\n",
              "676388  9606.ENSP00000229314  9606.ENSP00000349860             287        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-i2HCmnYMhu"
      },
      "source": [
        "To understand the interaction-pair numbers, we estimate interaction-pair number with random genes in L1000:\n",
        "\n",
        "we generate same number of genesets with same number of random landmark genes in each set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8LrTrBi5YLp4",
        "outputId": "028dfd29-80e5-43f7-9ea8-67ef3e8a26e5"
      },
      "source": [
        "import random\n",
        "L1000gene = landmark_gene['pr_gene_symbol'].to_list()\n",
        "\n",
        "random_gene_list = []\n",
        "for i in HWgene['node_id'].unique():\n",
        "    gene_number = HWgene.node_id.value_counts()[i]\n",
        "    random_gene = random.sample(L1000gene, gene_number)\n",
        "    random_gene_stringID = [STRINGID_dict[s] for s in random_gene]\n",
        "    random_df = pd.DataFrame([(x[0],x[1],i) for x in zip(random_gene,random_gene_stringID)], columns=['pr_gene_symbol','STRING_ID','node_id'])\n",
        "    random_gene_list.append(random_df)\n",
        "\n",
        "random_genes = pd.concat(random_gene_list)\n",
        "random_genes.to_csv(f'{work_dir}/trained_model/{model_name_epoch}_random_landmark_genes.txt',sep='\\t',index=False)\n",
        "random_genes.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>pr_gene_symbol</th>\n",
              "      <th>STRING_ID</th>\n",
              "      <th>node_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SCP2</td>\n",
              "      <td>9606.ENSP00000360569</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ADGRG1</td>\n",
              "      <td>9606.ENSP00000455215</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HOXA10</td>\n",
              "      <td>9606.ENSP00000283921</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VAT1</td>\n",
              "      <td>9606.ENSP00000347872</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MIF</td>\n",
              "      <td>9606.ENSP00000215754</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  pr_gene_symbol             STRING_ID  node_id\n",
              "0           SCP2  9606.ENSP00000360569        0\n",
              "1         ADGRG1  9606.ENSP00000455215        0\n",
              "2         HOXA10  9606.ENSP00000283921        0\n",
              "3           VAT1  9606.ENSP00000347872        0\n",
              "4            MIF  9606.ENSP00000215754        0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y66b8XXzqQP6"
      },
      "source": [
        "Compare the interaction numbers between high-weight gene sets and random gene sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1z3XS3V2Dz_"
      },
      "source": [
        "# calculate interactions\n",
        "random_gene_interaction = extract_STRINGinteractions(random_genes,save=True,save_path=f'{work_dir}/trained_model/{model_name_epoch}_random_landmark_stringdb_records.txt')\n",
        "# load interactions \n",
        "random_gene_interaction = pd.read_csv(f'{work_dir}/trained_model/{model_name_epoch}_random_landmark_stringdb_records.txt',sep='\\t')\n",
        "total_interactions = pd.read_csv(f'{work_dir}/trained_model/{model_name_epoch}_HWgene_stringdb_records.txt',sep='\\t')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YO3aPhFZrO5q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "b32eecb9-db4e-4631-cf07-bc6fb324ecde"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy import stats\n",
        "\n",
        "# compare number of interactions\n",
        "rdata = random_gene_interaction.node_id.value_counts()\n",
        "hdata = total_interactions.node_id.value_counts()\n",
        "\n",
        "# perform t-test\n",
        "t, p = stats.ttest_ind(rdata, hdata)\n",
        "print('\\n\\nt-test p =', p)\n",
        "\n",
        "plt.close()\n",
        "data = [rdata, hdata]\n",
        "fig, ax = plt.subplots()\n",
        "ax.set_title('number of STRING DB interactions within one gene set')\n",
        "ax.set_xticklabels(['random gene set','high-weight gene set'])\n",
        "ax.boxplot(data)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "t-test p = 0.0008602219652915392\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "L5W2QWuMqOtu",
        "outputId": "a1c9bd2a-79f3-43f0-a13d-e64e625a1575"
      },
      "source": [
        "%matplotlib inline\n",
        "# compare combined_score of interactions\n",
        "#rdata = random_gene_interaction.combined_score   # all combined_score\n",
        "#hdata = total_interactions.combined_score  # all combined_score\n",
        "\n",
        "rdata = [random_gene_interaction.loc[random_gene_interaction.node_id == i].combined_score.mean() for i in range(50)]   # average combined_score for each node\n",
        "hdata = [total_interactions.loc[total_interactions.node_id == i].combined_score.mean() for i in range(50)]  # average combined_score for each node\n",
        "\n",
        "# perform t-test\n",
        "t, p = stats.ttest_ind(rdata, hdata)\n",
        "print('\\n\\nt-test p =', p)\n",
        "\n",
        "## plot the training and validation curve\n",
        "plt.close()\n",
        "\n",
        "data = [rdata, hdata]\n",
        "fig, ax = plt.subplots()\n",
        "ax.set_title('STRING DB score of interactions')\n",
        "ax.set_xticklabels(['random gene set','high-weight gene set'])\n",
        "ax.boxplot(data)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "t-test p = 0.0025092139788756803\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyNXDL0IqzAL"
      },
      "source": [
        "# Appendix: automatic tools\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5Jj8VS_TZyR"
      },
      "source": [
        "## trainings with different hyper parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HhmTMVyRqyap",
        "outputId": "61f7c78b-fdbf-4099-f3f2-fd34d739d797"
      },
      "source": [
        "# define the dataframe to be used\n",
        "dataf = gene_expr_matrix\n",
        "\n",
        "noise_add = False\n",
        "#noise_add = True\n",
        "iteration_n = 10000\n",
        "\n",
        "for trial in [1, 2, 3]: # repeat 3 experiments for each model structure\n",
        "    if noise_add:\n",
        "        trial_name = f\"noise_trial{trial}\"\n",
        "    else:\n",
        "        trial_name = f\"trial{trial}\"\n",
        "\n",
        "    os.makedirs(f\"{work_dir}/trained_model/{trial_name}\", exist_ok=True)\n",
        "    # split datasets\n",
        "    train_data, valid_data = train_valid_split(dataf)\n",
        "    np.save(f\"{work_dir}/trained_model/{trial_name}/train_data_allsample_landmark_gene.npy\",train_data)\n",
        "    np.save(f\"{work_dir}/trained_model/{trial_name}/valid_data_allsample_landmark_gene.npy\",valid_data)\n",
        "\n",
        "    # initiate dataloader\n",
        "    train_loader = dataloader(train_data, 80)\n",
        "    valid_loader = dataloader(valid_data,80)\n",
        "\n",
        "    ##### auto train with different hyper-parameters\n",
        "    iteration_n = 10000\n",
        "\n",
        "    for node_n in [50, 100]:\n",
        "        for layers in [1, 5, 10]:\n",
        "\n",
        "            valid_point_epoch = 50\n",
        "            model_save_epoch = 500\n",
        "            learning_rate = 0.002\n",
        "\n",
        "            ## instantiate the model\n",
        "            DA = Adage_linear(train_data.shape[1], node_number=node_n, decoder_layers = layers)\n",
        "            print(\"{}\\nADAGE Network Architecture:\\n{}\\n{}\\n\".format(\"=\"*31, \"~\"*31, DA))\n",
        "            ## train the model\n",
        "            model_name = f\"{trial_name}/{layers}layers_{node_n}nodes_allsample_landmark_gene\"\n",
        "            DA = train_model(DA, model_name,noise=noise_add, valid_point_epoch = valid_point_epoch, model_save_epoch = model_save_epoch, iteration_n = iteration_n, print_train = False)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "samples in training data: 82635 \n",
            "samples in validation data: 35415\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 03:21:36 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 03:22:07 2021 \n",
            "total training time: 31.40 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 03:22:07 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 03:31:21 2021 \n",
            "total training time: 553.94 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=142, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=142, out_features=234, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=234, out_features=326, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=326, out_features=418, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=418, out_features=510, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=510, out_features=602, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=602, out_features=694, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=694, out_features=786, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=786, out_features=878, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=878, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 03:31:22 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 03:51:06 2021 \n",
            "total training time: 1184.66 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 03:51:06 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 03:51:53 2021 \n",
            "total training time: 46.20 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=275, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=275, out_features=450, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=450, out_features=625, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=625, out_features=800, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=800, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 03:51:53 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 04:01:53 2021 \n",
            "total training time: 600.45 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=187, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=187, out_features=274, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=274, out_features=361, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=361, out_features=448, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=448, out_features=535, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=535, out_features=622, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=622, out_features=709, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=709, out_features=796, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=796, out_features=883, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=883, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 04:01:53 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 04:23:31 2021 \n",
            "total training time: 1297.13 s\n",
            "samples in training data: 82635 \n",
            "samples in validation data: 35415\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 04:23:35 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 04:24:03 2021 \n",
            "total training time: 28.67 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 04:24:03 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 04:33:20 2021 \n",
            "total training time: 556.64 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=142, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=142, out_features=234, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=234, out_features=326, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=326, out_features=418, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=418, out_features=510, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=510, out_features=602, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=602, out_features=694, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=694, out_features=786, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=786, out_features=878, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=878, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 04:33:20 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 04:53:22 2021 \n",
            "total training time: 1201.52 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 04:53:22 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 04:54:05 2021 \n",
            "total training time: 42.58 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=275, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=275, out_features=450, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=450, out_features=625, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=625, out_features=800, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=800, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 04:54:05 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 05:03:59 2021 \n",
            "total training time: 594.26 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=187, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=187, out_features=274, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=274, out_features=361, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=361, out_features=448, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=448, out_features=535, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=535, out_features=622, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=622, out_features=709, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=709, out_features=796, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=796, out_features=883, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=883, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 05:03:59 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 05:25:40 2021 \n",
            "total training time: 1300.69 s\n",
            "samples in training data: 82635 \n",
            "samples in validation data: 35415\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 05:25:44 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 05:26:18 2021 \n",
            "total training time: 34.41 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 05:26:18 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 05:35:36 2021 \n",
            "total training time: 558.27 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=142, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=142, out_features=234, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=234, out_features=326, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=326, out_features=418, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=418, out_features=510, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=510, out_features=602, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=602, out_features=694, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=694, out_features=786, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=786, out_features=878, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=878, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 05:35:37 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 05:55:24 2021 \n",
            "total training time: 1187.58 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 05:55:24 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 05:56:06 2021 \n",
            "total training time: 41.98 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=275, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=275, out_features=450, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=450, out_features=625, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=625, out_features=800, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=800, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 05:56:07 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 06:05:57 2021 \n",
            "total training time: 589.94 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=187, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=187, out_features=274, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=274, out_features=361, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=361, out_features=448, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=448, out_features=535, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=535, out_features=622, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=622, out_features=709, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=709, out_features=796, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=796, out_features=883, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=883, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 06:05:57 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 06:27:15 2021 \n",
            "total training time: 1277.54 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IsV4QydAyuqe",
        "outputId": "b9751a09-d23f-449e-804a-fc4757070abc"
      },
      "source": [
        "# define the dataframe to be used\n",
        "dataf = gene_expr_matrix\n",
        "noise_add = True\n",
        "iteration_n = 10000\n",
        "\n",
        "for trial in [1, 2, 3]: # repeat 3 experiments for each model structure\n",
        "    if noise_add:\n",
        "        trial_name = f\"noise_trial{trial}\"\n",
        "    else:\n",
        "        trial_name = f\"trial{trial}\"\n",
        "\n",
        "    os.makedirs(f\"{work_dir}/trained_model/{trial_name}\", exist_ok=True)\n",
        "    # split datasets\n",
        "    train_data, valid_data = train_valid_split(dataf)\n",
        "    np.save(f\"{work_dir}/trained_model/{trial_name}/train_data_allsample_landmark_gene.npy\",train_data)\n",
        "    np.save(f\"{work_dir}/trained_model/{trial_name}/valid_data_allsample_landmark_gene.npy\",valid_data)\n",
        "\n",
        "    # initiate dataloader\n",
        "    train_loader = dataloader(train_data, 80)\n",
        "    valid_loader = dataloader(valid_data,80)\n",
        "\n",
        "    ##### auto train with different hyper-parameters\n",
        "    iteration_n = 10000\n",
        "\n",
        "    for node_n in [50, 100]:\n",
        "        for layers in [1, 5, 10]:\n",
        "\n",
        "            valid_point_epoch = 50\n",
        "            model_save_epoch = 500\n",
        "            learning_rate = 0.002\n",
        "\n",
        "            ## instantiate the model\n",
        "            DA = Adage_linear(train_data.shape[1], node_number=node_n, decoder_layers = layers)\n",
        "            print(\"{}\\nADAGE Network Architecture:\\n{}\\n{}\\n\".format(\"=\"*31, \"~\"*31, DA))\n",
        "            ## train the model\n",
        "            model_name = f\"{trial_name}/{layers}layers_{node_n}nodes_allsample_landmark_gene\"\n",
        "            DA = train_model(DA, model_name,noise=noise_add, valid_point_epoch = valid_point_epoch, model_save_epoch = model_save_epoch, iteration_n = iteration_n, print_train = False)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "samples in training data: 82635 \n",
            "samples in validation data: 35415\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 06:27:20 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 06:28:54 2021 \n",
            "total training time: 94.03 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 06:28:54 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 06:39:19 2021 \n",
            "total training time: 625.13 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=142, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=142, out_features=234, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=234, out_features=326, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=326, out_features=418, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=418, out_features=510, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=510, out_features=602, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=602, out_features=694, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=694, out_features=786, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=786, out_features=878, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=878, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 06:39:20 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 07:00:40 2021 \n",
            "total training time: 1280.89 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 07:00:41 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 07:02:34 2021 \n",
            "total training time: 113.26 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=275, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=275, out_features=450, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=450, out_features=625, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=625, out_features=800, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=800, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 07:02:34 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 07:13:37 2021 \n",
            "total training time: 662.38 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=187, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=187, out_features=274, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=274, out_features=361, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=361, out_features=448, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=448, out_features=535, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=535, out_features=622, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=622, out_features=709, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=709, out_features=796, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=796, out_features=883, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=883, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 07:13:37 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 07:35:39 2021 \n",
            "total training time: 1322.25 s\n",
            "samples in training data: 82635 \n",
            "samples in validation data: 35415\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 07:35:43 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 07:37:17 2021 \n",
            "total training time: 94.54 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 07:37:17 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 07:47:44 2021 \n",
            "total training time: 626.64 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=142, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=142, out_features=234, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=234, out_features=326, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=326, out_features=418, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=418, out_features=510, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=510, out_features=602, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=602, out_features=694, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=694, out_features=786, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=786, out_features=878, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=878, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 07:47:44 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 08:08:44 2021 \n",
            "total training time: 1259.42 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 08:08:44 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 08:10:34 2021 \n",
            "total training time: 110.49 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=275, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=275, out_features=450, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=450, out_features=625, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=625, out_features=800, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=800, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 08:10:35 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 08:21:34 2021 \n",
            "total training time: 659.82 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=187, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=187, out_features=274, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=274, out_features=361, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=361, out_features=448, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=448, out_features=535, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=535, out_features=622, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=622, out_features=709, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=709, out_features=796, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=796, out_features=883, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=883, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 08:21:35 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 08:43:37 2021 \n",
            "total training time: 1322.66 s\n",
            "samples in training data: 82635 \n",
            "samples in validation data: 35415\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 08:43:41 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 08:45:18 2021 \n",
            "total training time: 97.00 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=235, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=235, out_features=420, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=420, out_features=605, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=605, out_features=790, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=790, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 08:45:18 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 08:55:31 2021 \n",
            "total training time: 612.42 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=50, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=50, out_features=142, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=142, out_features=234, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=234, out_features=326, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=326, out_features=418, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=418, out_features=510, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=510, out_features=602, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=602, out_features=694, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=694, out_features=786, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=786, out_features=878, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=878, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 08:55:31 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 09:16:24 2021 \n",
            "total training time: 1253.51 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 09:16:25 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 09:18:12 2021 \n",
            "total training time: 107.72 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=275, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=275, out_features=450, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=450, out_features=625, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=625, out_features=800, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=800, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 09:18:13 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 09:29:07 2021 \n",
            "total training time: 654.69 s\n",
            "===============================\n",
            "ADAGE Network Architecture:\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "Adage_linear(\n",
            "  (encoder): Sequential(\n",
            "    (0): Linear(in_features=978, out_features=100, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=100, out_features=187, bias=True)\n",
            "    (1): ReLU(inplace=True)\n",
            "    (2): Linear(in_features=187, out_features=274, bias=True)\n",
            "    (3): ReLU(inplace=True)\n",
            "    (4): Linear(in_features=274, out_features=361, bias=True)\n",
            "    (5): ReLU(inplace=True)\n",
            "    (6): Linear(in_features=361, out_features=448, bias=True)\n",
            "    (7): ReLU(inplace=True)\n",
            "    (8): Linear(in_features=448, out_features=535, bias=True)\n",
            "    (9): ReLU(inplace=True)\n",
            "    (10): Linear(in_features=535, out_features=622, bias=True)\n",
            "    (11): ReLU(inplace=True)\n",
            "    (12): Linear(in_features=622, out_features=709, bias=True)\n",
            "    (13): ReLU(inplace=True)\n",
            "    (14): Linear(in_features=709, out_features=796, bias=True)\n",
            "    (15): ReLU(inplace=True)\n",
            "    (16): Linear(in_features=796, out_features=883, bias=True)\n",
            "    (17): ReLU(inplace=True)\n",
            "    (18): Linear(in_features=883, out_features=978, bias=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "training start at Fri Apr 16 09:29:08 2021  __________________________________________________\n",
            "__________________________________________________ \n",
            "Finished Training at Fri Apr 16 09:51:11 2021 \n",
            "total training time: 1322.95 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lkEx4-iIKIk"
      },
      "source": [
        "## Auto run for evaluating STRING interactions within gene sets:\n",
        "\n",
        "1. function to compare STRING interactions with random gene interactions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CG_KXjn8T8_O"
      },
      "source": [
        "from scipy import stats\n",
        "def STRINGdb_compare(HWgene, random_genes, work_dir, model_name_epoch):\n",
        "    HWgene['STRING_ID'] = [STRINGID_dict[s] for s in HWgene['pr_gene_symbol'].to_list()]\n",
        "    total_interactions = extract_STRINGinteractions(HWgene,  save=True, save_path= f'{work_dir}/trained_model/{model_name_epoch}_HWgene_stringdb_records.txt')\n",
        "    random_gene_interaction = extract_STRINGinteractions(random_genes,save=True,save_path=f'{work_dir}/trained_model/{model_name_epoch}_random_landmark_stringdb_records.txt')\n",
        "\n",
        "    ## plot the number of interactions \n",
        "    rdata = random_gene_interaction.node_id.value_counts()\n",
        "    hdata = total_interactions.node_id.value_counts()\n",
        "    # perform t-test\n",
        "    t, p = stats.ttest_ind(rdata, hdata)\n",
        "    #print('\\n\\nt-test p =', p)\n",
        "\n",
        "    plt.close()\n",
        "    data = [rdata, hdata]\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.set_title(f'number of STRING DB interactions within one gene set\\nt-test p ={p}')\n",
        "    ax.set_xticklabels(['random gene set','high-weight gene set'])\n",
        "    ax.boxplot(data)\n",
        "    plt.savefig(f'{work_dir}/trained_model/{model_name_epoch}_stringdb_interaction_number.pdf')\n",
        "\n",
        "    ## plot combined_score of interactions\n",
        "\n",
        "    rdata2 = [random_gene_interaction.loc[random_gene_interaction.node_id == i].combined_score.mean() for i in range(50)]   # average combined_score for each node\n",
        "    hdata2 = [total_interactions.loc[total_interactions.node_id == i].combined_score.mean() for i in range(50)]  # average combined_score for each node\n",
        "    # perform t-test\n",
        "    t2, p2 = stats.ttest_ind(rdata2, hdata2)\n",
        "    \n",
        "    plt.close()\n",
        "    data2 = [rdata2, hdata2]\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.set_title(f'STRING DB score of interactions\\nt-test p ={p2}')\n",
        "    ax.set_xticklabels(['random gene set','high-weight gene set'])\n",
        "    ax.boxplot(data2)\n",
        "    plt.savefig(f'{work_dir}/trained_model/{model_name_epoch}_stringdb_interaction_score.pdf')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8UKbpfy9MUaI",
        "outputId": "ff29a038-71bd-4397-ce58-045d143e356f"
      },
      "source": [
        "## load stringdb ID dictionary\n",
        "with open(f'{work_dir}/landmark_gene_STRING_ID.txt') as f:\n",
        "    for line in f:\n",
        "        (key, val) = line[:-1].split('\\t')\n",
        "        STRINGID_dict[key] = val\n",
        "## load stringdb interactions\n",
        "stringdb = pd.read_csv(f'{work_dir}/9606.protein.links.v11.0.txt', sep=' ') \n",
        "stringdb.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>protein1</th>\n",
              "      <th>protein2</th>\n",
              "      <th>combined_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000272298</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000253401</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000401445</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000418915</td>\n",
              "      <td>606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9606.ENSP00000000233</td>\n",
              "      <td>9606.ENSP00000327801</td>\n",
              "      <td>167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               protein1              protein2  combined_score\n",
              "0  9606.ENSP00000000233  9606.ENSP00000272298             490\n",
              "1  9606.ENSP00000000233  9606.ENSP00000253401             198\n",
              "2  9606.ENSP00000000233  9606.ENSP00000401445             159\n",
              "3  9606.ENSP00000000233  9606.ENSP00000418915             606\n",
              "4  9606.ENSP00000000233  9606.ENSP00000327801             167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTxGD1PPTifD"
      },
      "source": [
        "2. loop to run different comparisons\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYrA5auhFuNC"
      },
      "source": [
        "## evaluate interactions in each gene set\n",
        "# load trained model\n",
        "epoch_n = 5000\n",
        "\n",
        "for trial in ['trial1','trial2','trial3','noise_trial1','noise_trial2','noise_trial3']:\n",
        "    for node_n in [50, 100]:\n",
        "        for layers in [1, 5, 10]:\n",
        "\n",
        "            model_name_epoch=f\"{trial}/{layers}layers_{node_n}nodes_allsample_landmark_gene/epoch{epoch_n}\"\n",
        "            DA = Adage_linear(train_data.shape[1], node_number=node_n, decoder_layers = layers)\n",
        "            DA.load_state_dict(torch.load(f'{work_dir}/trained_model/{model_name_epoch}'))\n",
        "\n",
        "            # extract high weight gene\n",
        "            HWgene = extract_high_weight_gene(DA)\n",
        "            HWgene['STRING_ID'] = [STRINGID_dict[s] for s in HWgene['pr_gene_symbol'].to_list()]\n",
        "\n",
        "            # matplotlib histogram\n",
        "            t_weight = DA.encoder[0].weight\n",
        "            node_num = t_weight.shape[0]\n",
        "\n",
        "            w_mean = t_weight.mean(dim=1).reshape(t_weight.shape[0],1)\n",
        "            w_std = t_weight.std(dim=1).reshape(t_weight.shape[0],1)\n",
        "\n",
        "            plt.figure(figsize=(32,node_num))\n",
        "            for i in range(node_num):\n",
        "                hwgene_num = len(HWgene.loc[HWgene.node_id == i].index)\n",
        "\n",
        "                ax = plt.subplot(node_num/5, 5, i + 1)\n",
        "                ax.hist(t_weight[i].tolist(), color = '#56B4E9', edgecolor = 'white',bins = int(80))\n",
        "                ax.vlines([(w_mean-2*w_std)[i].item(),(w_mean+2*w_std)[i].item()], ymin=0, ymax=50, colors= 'red')\n",
        "                ax.vlines(w_mean[i].item(), ymin=0, ymax=50, colors= 'red',linestyles='dashed')\n",
        "                # Add labels\n",
        "                ax.set_title(f'weight distribution from node #{i}\\n number of high-weight genes: {hwgene_num}')\n",
        "                ax.set_xlabel('gene weight')\n",
        "                ax.set_ylabel('genes')\n",
        "\n",
        "            plt.tight_layout()\n",
        "            plt.savefig(f\"{work_dir}/trained_model/{trial}/{layers}layers_{node_n}nodes_allsample_landmark_gene/epoch{epoch_n}_weight_distribution.pdf\")\n",
        "            plt.close()\n",
        "\n",
        "            # generate random gene set\n",
        "            import random\n",
        "            L1000gene = landmark_gene_list['pr_gene_symbol'].to_list()\n",
        "            random_gene_list = []\n",
        "            for i in HWgene['node_id'].unique():\n",
        "                gene_number = HWgene.node_id.value_counts()[i]\n",
        "                random_gene = random.sample(L1000gene, gene_number)\n",
        "                random_gene_stringID = [STRINGID_dict[s] for s in random_gene]\n",
        "                random_df = pd.DataFrame([(x[0],x[1],i) for x in zip(random_gene,random_gene_stringID)], columns=['pr_gene_symbol','STRING_ID','node_id'])\n",
        "                random_gene_list.append(random_df)\n",
        "            random_genes = pd.concat(random_gene_list)\n",
        "            random_genes.to_csv(f'{work_dir}/trained_model/{model_name_epoch}_random_landmark_genes.txt',sep='\\t',index=False)\n",
        "            random_genes.head()\n",
        "\n",
        "            # compare the interactions\n",
        "            STRINGdb_compare(HWgene, random_genes, work_dir, model_name_epoch)\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t42lElxXmDPJ"
      },
      "source": [
        "## Calculate $r^2$ score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dWuFTL1lSRJm",
        "outputId": "cefb713a-c940-49df-9716-539f0f5a8142"
      },
      "source": [
        "epoch_n = 5000\n",
        "\n",
        "r2_dict = {}\n",
        "\n",
        "for trial in ['trial1','trial2','trial3','noise_trial1','noise_trial2','noise_trial3']:\n",
        "    valid_data = np.load(f'drive/MyDrive/AIforGenomics2021/ADAGE/trained_model/{trial}/valid_data_allsample_landmark_gene.npy')\n",
        "    r2_dict[trial] = {}\n",
        "    for node_n in [50, 100]:\n",
        "        r2_dict[trial][node_n] = []\n",
        "        for layers in [1, 5, 10]:\n",
        "            # calculate r2 per gene to show relation between original gene expression and reconstruct gene expression\n",
        "            model_name_epoch=f\"{trial}/{layers}layers_{node_n}nodes_allsample_landmark_gene/epoch{epoch_n}\"\n",
        "            DA = Adage_linear(train_data.shape[1], node_number=node_n, decoder_layers = layers)\n",
        "            DA.load_state_dict(torch.load(f'{work_dir}/trained_model/{model_name_epoch}'))\n",
        "\n",
        "            valid_data_ = DA(torch.tensor(valid_data).float()).detach().numpy()\n",
        "\n",
        "            r2_score_record = []\n",
        "            for g in range(valid_data.shape[1]):\n",
        "                r2_score_record.append(r2_score(valid_data[:,g], valid_data_[:,g]))\n",
        "            r2_mean = sum(r2_score_record)/len(r2_score_record)\n",
        "            print('===',model_name_epoch)\n",
        "            print('average r2 score on valid dataset:',r2_mean)\n",
        "            r2_dict[trial][node_n].append(r2_mean)\n",
        "\n",
        "            %matplotlib inline\n",
        "            plt.figure(figsize=(5,5))\n",
        "            ax = plt.subplot(1, 1, 1)\n",
        "            ax.hist(r2_score_record, color = '#56B4E9', edgecolor = 'white',bins = int(80))\n",
        "            ax.vlines(sum(r2_score_record)/978, ymin=0, ymax=50, colors= 'red',linestyles='dashed')\n",
        "            # Add labels\n",
        "            ax.set_title(f'single gene r2 distribution')\n",
        "            ax.set_xlabel('r2_score')\n",
        "            ax.set_ylabel('genes')\n",
        "            plt.show()\n",
        "            plt.savefig(f'{work_dir}/trained_model/{model_name_epoch}_valid_r2_distribution.pdf')\n",
        "            plt.close()\n",
        "\n",
        "            with open(f'{work_dir}/trained_model/{model_name_epoch}.tsv','w+') as f:\n",
        "                f.write('gene\\tr2_score\\n')\n",
        "                for i in range(len(r2_score_record)):\n",
        "                    f.write(f'{landmark_gene_list.iloc[i].pr_gene_symbol}\\t{r2_score_record[i]}\\n')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "=== trial1/1layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.49272635308330837\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial1/5layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47049371842292237\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial1/10layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3681148543049696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial1/1layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.544227006884306\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial1/5layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47322029221943546\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial1/10layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.37896883352773164\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial2/1layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.4816234474319709\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial2/5layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.4652604828859224\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial2/10layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3330795839557285\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial2/1layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.5388968011509241\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial2/5layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47707525549874347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial2/10layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3755756724533088\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial3/1layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.4887399517295668\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial3/5layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.4786182214289981\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial3/10layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3701592248622593\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial3/1layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.5514416238570417\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial3/5layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.46940779324714443\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== trial3/10layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.34156932881356383\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial1/1layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.48743127293801336\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial1/5layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47053734410631304\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial1/10layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3749939934566708\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial1/1layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.5395252340472297\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial1/5layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.4713657698389191\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial1/10layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.30603452707684725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial2/1layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.4852907946932625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial2/5layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47528025277399466\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial2/10layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3573022610494219\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial2/1layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.5378463453326968\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HZ5wYrWP86EdlR2AVkDc5zpU0A/g5yf2oAYiIKwuJysysZHmT47uBJcBONfMCcHK09nPkkcnzueeWG4d1tLyX8hxUdCBmTTNvXtkRWAXkba3eSNJsSfenrz8s6cRiQzMzK0/e7oMXAscBr8MbN8+aXFRQ1r1eWxb9vq6i2n3shv3tFHnPOa4aEf8jqXbe0gLisS43dIiYNHPhG69nTFirxGhao3afu2F/O0Xe5PispPfz5sATE4GnC4vKrBEbbVR2BFYBeZPjF4BpwAckPQn8EfhMYVGZNWLatLIjsArImxz3BGYBN5Gcp3wZGC/pzohw06CZVU7eBpke4DBgdWA14FBgZ+BCSccUFJvZ4EydmjzMGpC35jgK2DwiXoI3bpswE/gEcCdJ32uruNeWBUOHaLnpRtcfzLb69fDDzduWda28yXEtaroNklzSs3ZE/E3Sq3XWsYpptFW13vpurbV2lDc5/hi4XdLV6evdgJ+kt0v4fSGRmZmVKG/3wVMlXc+bdxs8LCLmptNutTazyhnISOBzgbkrXNCsbOPGlR2BVUDu5Gjdo9DGklZo8Wg8eRqaOvJz7HJOjrYcN5AMjBuaqinvdY5mneOzn00eZg1wzdGqZ8GCsiOwCnDN0cwsg5OjlSLvuIV5xjrMOx6ix020gfBhtZUib2NFnuWauS2zXk6OVj1bbVV2BFYBTo5WPV/7WtkRWAX4nKOZWQYnRxuUMho3cpe5zz7Jw6wBhSVHSetJuknS7yU9IOlf0/lrSLpB0iPp8+pFxWDF6W3cmDRzYcu6xeUuc/Hi5GHWgCJrjkuBoyJiE2BL4AuSNgGOBWZHxIbA7PS1mVlbKSw5RsTTEXFXOv0i8CAwEtgDmJ4uNp3k/jRmZm2lJeccJY0BNgNuJxlBvPe2rn8G1m5FDGZmA1H4pTyS3gX8DDgyIl6Q3jxXFBEhKfPMuqSpwFSA0aNHFx2mdZh6Q4C9tiwYusMO/S7T971WDCfWt4xWD2Hm4dMGrtDkKOntJInxxxFxZTr7GUnrRMTTktYBFmatGxHTSO6VTU9Pj/t62Vv0O0zY5ocm8/tJAK3uLVNbXqvKrFe+ewflU2RrtYDvAw9GxLdq3roGmJJOTwGu7ruumVnZiqw5bg0cANwnaV4673jgTOAySQcDTwD7FRiDdaFjv7p/MjFhdrmBWEcrLDlGxG+Besc1OxRVrtnQV18pOwSrAPeQMTPL4ORoZpbBydHMLIOHLLPKuWuLHQHYtOQ4rLM5OVrlXLf354HkUgmzwfJhtZlZBtccKy5vt7F+u+N1WFezk47dK5mYcEtD2ym7y5+Vy8mx4hq9+VQ3dzsru8uflcuH1WZmGZwczcwyODmamWXwOccu0i1j+t32j7sD+a9zbObnknf9gZbTLd9dO3Fy7CLd0rjyqwkHAXBwzuWb+bk02gDWihgtHydHq5yhrywpOwSrACdHq5xjT/5MMrFPY9c5Wndzg4yZWQYnxzb02rLInB7scmVq17gGo10/73pxtVOMnciH1W2oqJP6ZeiEGPNq133pG1c7xtiJXHM0M8vgmqNVzm/GTwI8nqM1xsnRKuc34ycD8PmS47DO5sPqLlXlk/XD/rqYYX9dXHYYg+IGlfbhmmOXqvJwXP/2tUOSiU933nWO7dro041cczQzy+DkaGaWwcnRzCyDk6M1rG/DgRsSiuXPtzXcIGMNa7fGnRt2mQJU9zrHdvu8q8rJ0Srn1k/sCcCRJcdhnc3J0SpnzUVPplOuUdngOTla5Xzhm19MJg7svOscrX24QcbMLIOTYwW5NbMayv4eu70row+rK8hd0Kqh7O+x7PLL5pqjmVkG1xytcq7b6zCgutc5Wms4OVrl3PWxT5YdglWAk6M13WvLgqFDVFr56yx4NJ0a+HmyvLGXvY+tVru/3bLvTo7WdGWfyP/cd/49mTh04Nc5VunmZs3UbfsLbpAxM8vk5GhmlsHJ0cwsg5NjB+nGXgo2eP57aUxhDTKSfgDsCiyMiA+m89YAZgBjgPnAfhHxXFExVI3H8cvnqsnJYGXdfp1jNzaiNFORNceLgZ37zDsWmB0RGwKz09dmTXXfuG25b9y2ZYdhHa6w5BgRNwN/6TN7D2B6Oj0d2LOo8q17rf/4/az/+P1lh2EdrtXXOa4dEU+n038G1m5x+dYFpkz7SjJxxPblBmIdrbQGmYgIoO4ZY0lTJc2VNHfRokUtjKwz+eT78vyZFKNbhjJrdc3xGUnrRMTTktYBFtZbMCKmAdMAenp6qvsNNIlPvi/PDVjF6Ja/tVbXHK8BpqTTU4CrW1y+mVkuhSVHST8FbgXGSlog6WDgTGBHSY8A49PXZmZtp7DD6ojYv85bOxRVphnApVOOB+DUkuOwzuZReaxyHt74o2WH0DH6Dj/WLcOR5eHkaJWz0YN3JBMTJpQbSAdwo1V9To5WOZOnn5FMHO3kaIPngSfMzDI4OZqZZXBybBP1ehpUuQeCWTtzcmwTvSfGa0+O9zffzIrlBhmrnOlTkyscv15yHNbZnBytcp543wfLDsEqwIfVVjkfmvcbPjTvN2WHYR3ONUernL0uPTeZOGHfcgOxjuaaY4ncEm1l8N9dPk6OJXJLtJXBf3f5ODmamWVwcjQzy+AGGaucC7/4DQDOLTkO62xOjlY5T4/aoOwQOlYzx3Os3VYnjhPp5GiVs/ntv0wmJhxQbiAdqJk3z+r0G3E5OVrl7HrV95KJU5wcbfDcIGNmlsHJ0cwsg5NjAWp7IPTtjeDeCWadweccC9DfiehOP0lt1i2cHK1yzjvqOwCcX3Ic1tmcHK1yFo8YWXYIVgFOjlY5W93882RiwtRyA7GO5gaZJvENstrHjrOms+Os6WWH0RX6a3wczHL1linjd+SaY5PUa2ipnd/3PbNO1/fvvt7f+kAbItvhd+Oao5lZBidHM7MMTo5mZhl8zjGHekMvdeIwTN3gnOMuAuCikuOw5snzG2z279HJMYc8jS1uaGkfL75nzbJDsCYr4zfo5GiVs+2NlyYTE/6l3ECsozk5WuVse+OMdMrJ0QbPDTJmZhmcHM3MMjg5pgbavcnMBqfeb63dfnc+55hyy7NZa9TrcthuvzsnR6ucM0/+MQCXlByHdTYnR6uc11ZZtewQrAKcHK1ydpr5w2RiwpfLDcQ6WmWTY1HdjdxlsP1t+d/XpFNOjjZ4pbRWS9pZ0kOSHpV0bBFl9J70nTRz4XLTWcsMZrtmVl0tT46ShgDnAZ8CNgH2l7RJq+MwM+tPGTXHLYBHI+LxiHgNuBTYo4Q4zMzqKiM5jgT+VPN6QTrPzKxtKKK1V6VLmgjsHBGHpK8PAD4WEV/ss9xUoPf2cR8E7m9poMsbDjzr8l1+F8dQxfLXj4gRWW+U0Vr9JLBezetR6by3iIhpwDQASXMjoqc14WUrOwaX393lt0MM3VZ+GYfVdwAbSnqvpKHAZOCaFaxjZtZSLa85RsRSSV8EfgkMAX4QEQ+0Og4zs/6UchF4RMwCZg1glWlFxTIAZcfg8ru7fCg/hq4qv+UNMmZmncDjOZqZZWir5LiiboWSPiHpLklL00uCWl3+lyT9XtK9kmZLWr+EGA6TdJ+keZJ+2+zeRXm7dkraR1JIamrrYY79P1DSonT/50k6pJXlp8vsl/4dPCDpJ60sX9I5Nfv+sKTnm1l+zhhGS7pJ0t3pb2GXFpe/fvr7u1fSHEmjmln+GyKiLR4kjTOPAe8DhgL3AJv0WWYM8GHgv4CJJZT/T8Cq6fThwIwSYnh3zfTuwC9aWX663DDgZuA2oKfF+38g8J0S/wY3BO4GVk9fr9Xqz79m+SNIGjRb/RlMAw5PpzcB5re4/MuBKen09sAlRfw9tFPNcYXdCiNifkTcC/y9pPJviogl6cvbSK7RbHUML9S8fCfQzJPGebt2ngqcBbzSxLIHUn5R8pT/OeC8iHgOICKaOQLJQPd/f+CnTSw/bwwBvDudfg/wVIvL3wT4dTp9U8b7TdFOybHsboUDLf9g4PoyYpD0BUmPAV+nufcfXWH5kjYH1ouImU0sN3f5qX3SQ6orJK2X8X6R5W8EbCTpFkm3Sdq5xeUDyaEl8F7eTBKtjOFk4LOSFpBcdXJEi8u/B9g7nd4LGCZpzSbGALRXcuwYkj4L9ADfKKP8iDgvIt5PMmDhia0qV9LbgG8BR7WqzAzXAmMi4sPADcD0Fpe/Esmh9XYkNbcLJa3W4hgg6TxxRUQsK6Hs/YGLI2IUsAtwSfq30SpHA9tKuhvYlqSHXdM/h3ZKjrm6FZZdvqTxwAnA7hHxahkx1LgU2LOF5Q8j6ec+R9J8YEvgmiY2yqxw/yNicc3nfhHw/5pUdq7ySWoy10TE6xHxR+BhkmTZqvJ7Tab5h9R5YzgYuAwgIm4FViHp99yS8iPiqYjYOyI2I/ktEhFNb5hq+knMBk7ErgQ8TnKo0HsidtM6y15M8xtkVlg+sBnJyeINy/oMassGdgPmlvEdpMvPobkNMnn2f52a6b2A21pc/s7A9HR6OMkh4Jqt/PyBDwDzSa9TLuFv8HrgwHR6Y5Jzjk2JJWf5w4G3pdOnA6c0+3OIiPZJjumO7kLyn/gx4IR03ikktTSAj5L8534ZWAw80OLybwSeAealj2tK+Ay+DTyQln9Tf8mriPL7LNvU5Jhz/7+W7v896f5/oMXli+TUwu+B+4DJrf78Sc75ndnsv70BfAabALek38E8YKcWlz8ReCRd5iJg5SI+B/eQMTPL0E7nHM3M2oaTo5lZBidHM7MMTo5mZhmcHM3MMjg5mpllcHK0tiFpVUkzJf0hHQ7szLJjsu7l5GjtRMC3IuIDJL2Rtpb0qZYVLg1pVVnW/pwcrVSSxqQDm/4X8D/AowCRDFd1F/0MCydpX0n3S7pH0s3pvCGSzk7n3yvpiHT+DungrPdJ+oGkldP58yWdJekuYF9JO0m6NR1U+XJJ7yr4I7A25eRo7WBD4PyI2DQingBIR7rZDZjdz3onAZ+MiI+QDPwLMJVkUORxkYzc82NJq5D0x58UER8i6b97eM12FkfE5iTdQ08Exqev5wJfas4uWqdxcrR28ERE3Nb7QtJKJCPO/GdEPN7PercAF0v6HMkI0gDjgQsiYilARPwFGAv8MSIeTpeZDnyiZjsz0uctSfsNS5oHTAGafisM6wyl3JrVrI+X+7yeBjwSEef2t1JEHCbpY8AE4E5Jgx2+rLd8ATdExP6D3I5ViGuO1lYknUYy9P6ROZZ9f0TcHhEnAYtIxgG8ATg0rX0iaQ3gIWCMpA3SVQ8AfpOxydtIGoE2SNd9p6SNGt0n60xOjtY20rvInUByaHtXjrsLfiNtYLkf+B3JEFoXAf8L3CvpHuDTEfEKcBBwuaT7SO5B9L2+G4uIRSQ38PqppHuBW0nGTrQu5CHLzMwyuOZoZpbBDTLW9iSdAOzbZ/blEXF6GfFYd/BhtZlZBh9Wm5llcHI0M8vg5GhmlsHJ0cwsg5OjmVmG/wOADMXKj4IvIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial2/5layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47458709988960057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial2/10layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.36130326136206464\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial3/1layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.468424316205941\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial3/5layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47399751442903076\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial3/10layers_50nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.38563812450345764\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial3/1layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.5381147975703795\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial3/5layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.47964553746536753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "=== noise_trial3/10layers_100nodes_allsample_landmark_gene/epoch5000\n",
            "average r2 score on valid dataset: 0.3676184187809084\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "plDl-JHPWCkO",
        "outputId": "68fce29a-6eb6-4a05-e596-02a74d0d6ddc"
      },
      "source": [
        "from statistics import mean, stdev\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "for x in range(2):\n",
        "    node = [50, 100][x]\n",
        "    linestyle = ['solid','dashed'][x]\n",
        "    ave=[mean([r2_dict[f'trial{i}'][node][n] for i in [1,2,3]]) for n in range(3)]\n",
        "    stdev1=[stdev([r2_dict[f'trial{i}'][node][n] for i in range(1,4)]) for n in range(3)]\n",
        "    plt.errorbar([0, 1, 2], ave, yerr=stdev1, label=f'{node} nodes (- Noise)', color='blue',linestyle=linestyle, linewidth=1.5)\n",
        "\n",
        "    ave=[mean([r2_dict[f'noise_trial{i}'][node][n] for i in [1,2,3]]) for n in range(3)]\n",
        "    stdev2=[stdev([r2_dict[f'noise_trial{i}'][node][n] for i in range(1,4)]) for n in range(3)]\n",
        "    plt.errorbar([0, 1, 2], ave, yerr=stdev2, label=f'{node} nodes (+ Noise)', color='orange',linestyle=linestyle, linewidth=1.5)\n",
        "\n",
        "\n",
        "ticks = [0, 1, 2]\n",
        "labels = [\"1-layer\", \"5-layer\", \"10-layer\"]\n",
        "\n",
        "plt.xticks(ticks, labels)\n",
        "plt.title('Multi-layer decoder')\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.ylabel('r2 score')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'r2 score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGtSKIKjWDvV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}