cgae_alanine_dipeptide_colab_walkthrough.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/wwang2/Coarse-Graining-Auto-encoders/blob/master/cgae_alanine_dipeptide_colab_walkthrough.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q65Vp2TqOLJg"
      },
      "source": [
        "\n",
        "In this notbook you will use a Coarse-Grained Autoencoder applied on Alanine Dipeptide Trajectores. The code is SUPER simple. \n",
        "\n",
        "First you will need to install some packages. This might take a few miniutes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rwCALI0NlxF",
        "outputId": "69d8ece1-61aa-45c5-8744-278a445dfc27"
      },
      "source": [
        "! pip install nglview\n",
        "! pip install ase\n",
        "! pip install mdtraj\n",
        "! pip install mdshare"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6q45sKwPSUb",
        "outputId": "cbf1b81b-f7f1-4ce4-f881-9c1233025483",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "6af273ff3e7345ca89ce6df53c2605b8"
          ]
        }
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import numpy as np\n",
        "import mdshare \n",
        "import mdtraj as md \n",
        "import glob\n",
        "import torch.nn.functional as F\n",
        "from ase import Atoms\n",
        "from nglview import show_ase\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# this is needed is you want to use nglview to visualize reconstructed coordinates \n",
        "from google.colab import output\n",
        "output.enable_custom_widget_manager()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6af273ff3e7345ca89ce6df53c2605b8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              ""
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "FedZxqG1EIvf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGVGTD4dPaQG"
      },
      "source": [
        "# load alanine dipeptide trajectory \n",
        "mdshare.fetch('alanine-dipeptide-nowater.pdb', working_directory='data')\n",
        "mdshare.fetch('alanine-dipeptide-*-250ns-nowater.xtc', working_directory='data')\n",
        "\n",
        "xtc_files = glob.glob('./data/alanine-dipeptide-*-250ns-nowater.xtc')\n",
        "traj = [md.load_xtc(file, top='./data/alanine-dipeptide-nowater.pdb') for file in xtc_files]\n",
        "\n",
        "traintraj = md.join(traj[:-1])\n",
        "testtraj = traj[-1]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jZcr_nVYDYsB"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# retrieve bond info \n",
        "g = traintraj.topology.to_bondgraph()\n",
        "bond_idx = torch.LongTensor( [[pair[0].index, pair[1].index] for pair in  g.edges] )\n",
        "# todo: add two-hop bond list "
      ],
      "metadata": {
        "id": "vESmlG5f9oDr"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2ldy885QSIw"
      },
      "source": [
        "# build dataset and dataloader \n",
        "train_xyz = torch.Tensor( traintraj.xyz[::1000]) * 10.0 # take 500 data points \n",
        "test_xyz = torch.Tensor( testtraj.xyz[::1000])* 10.0\n",
        "\n",
        "trainset = torch.utils.data.TensorDataset(train_xyz)\n",
        "testset = torch.utils.data.TensorDataset(test_xyz)\n",
        "\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcQ6-k1AQnDQ"
      },
      "source": [
        "# define a CGAE\n",
        "\n",
        "class cgae(nn.Module):\n",
        "    \n",
        "    def __init__(self, n_atoms, n_cgs):\n",
        "        super().__init__()\n",
        "\n",
        "        assign_map = torch.randn(n_atoms, n_cgs)\n",
        "        decode = torch.randn(n_cgs, n_atoms)\n",
        "        \n",
        "        \n",
        "        self.n_atoms = n_atoms \n",
        "        self.assign_map = nn.Parameter(assign_map)\n",
        "        self.decode = nn.Parameter(decode)\n",
        "        \n",
        "    def forward(self, xyz, tau=1.0):\n",
        "        \n",
        "        # recenter coordinates \n",
        "        xyz = xyz.reshape(-1, self.n_atoms, 3)\n",
        "        shift = xyz.mean(1)\n",
        "        xyz = xyz - shift.unsqueeze(1)\n",
        "        \n",
        "        # get discrete assignment map\n",
        "        M = F.gumbel_softmax(self.assign_map, dim=-1)\n",
        "        M_norm = M / M.sum(-2).unsqueeze(-2)\n",
        "        \n",
        "        cg_xyz = torch.einsum('bij,in->bnj', xyz, M_norm)\n",
        "        xyz_recon = torch.einsum('bnj,ni->bij', cg_xyz, self.decode)\n",
        "        \n",
        "        return xyz, xyz_recon, M, cg_xyz"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQFK9YTGQosj"
      },
      "source": [
        "\n",
        "# Define the latent CG dimension, feel free to set your own, it only needs to be smaller than the number of total atoms \n",
        "\n",
        "device = \"cpu\" # device cardinal\n",
        "N_cg = 6 # play with this by yourself. \n",
        "ae = cgae(train_xyz.shape[1], N_cg).to(device)\n",
        "\n",
        "optimizer = torch.optim.Adam(list(ae.parameters()), lr=4e-3)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXvIkJWtQ626",
        "outputId": "962fbd5c-6f5d-41a1-d539-a4481958c230"
      },
      "source": [
        "# training loop\n",
        "tau = 1.0\n",
        "\n",
        "for epoch in range(600):\n",
        "    all_bond_loss = []\n",
        "    all_loss = []\n",
        "    all_reg = []\n",
        "    all_recon = [] \n",
        "    for i, xyz in enumerate(trainloader):\n",
        "        \n",
        "        # recenter xyz \n",
        "        xyz = xyz[0].to(device)\n",
        "\n",
        "        # encode and decode coordinates \n",
        "        xyz, xyz_recon, M, cg_xyz = ae(xyz, tau)\n",
        "        \n",
        "        # lift the cg_xyz back to the FG space \n",
        "        X_lift = torch.einsum('bij,ni->bnj', cg_xyz, M)\n",
        "        \n",
        "        # compute regularization to penalize atoms that are assigned too far away \n",
        "        loss_reg = (xyz - X_lift).pow(2).sum(-1).mean()\n",
        "        \n",
        "        # comput reconstruction \n",
        "        loss_recon = (xyz - xyz_recon).pow(2).mean() \n",
        "\n",
        "        # compute bond loss \n",
        "        bond_true = (xyz[:, bond_idx[:,0]] - xyz[:, bond_idx[:,1]] + 1e-9).pow(2).sum(-1).sqrt()\n",
        "        bond_prd = (xyz_recon[:, bond_idx[:,0]] - xyz_recon[:, bond_idx[:,1]]+ 1e-9).pow(2).sum(-1).sqrt()\n",
        "        loss_bond = (bond_true - bond_prd).pow(2).mean()\n",
        "\n",
        "        # total loss \n",
        "        loss = loss_recon + 0.5 * loss_reg + loss_bond\n",
        "        \n",
        "        all_bond_loss.append(loss_bond.item())\n",
        "        all_reg.append(loss_reg.item())\n",
        "        all_recon.append(loss_recon.item())\n",
        "        all_loss.append(loss.item())\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        all_loss.append(loss.item())\n",
        "\n",
        "    # temperature scheduler for gubmel softmax     \n",
        "    if tau >= 0.1:\n",
        "        tau -= 0.001\n",
        "    if epoch == 0:\n",
        "      print(\"epoch\", \"tau\", \"reconstruction\", \"regularization loss\", \"bond loss\")\n",
        "    print(epoch, tau, np.array(all_recon).mean(), np.array(all_reg).mean(), np.array(all_bond_loss).mean())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch tau reconstruction regularization loss bond loss\n",
            "0 0.999 0.18794269394129515 0.9494620151817799 0.07071357336826622\n",
            "1 0.998 0.18811730481684208 0.9817295782268047 0.026141098176594824\n",
            "2 0.997 0.18896980211138725 0.954344879835844 0.021235478518065065\n",
            "3 0.996 0.18892700597643852 0.9527863301336765 0.018947689910419285\n",
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            "588 0.4109999999999995 0.1847247388213873 0.9126867279410362 0.015762688068207353\n",
            "589 0.4099999999999995 0.19087291043251753 0.981050543487072 0.017381025128997862\n",
            "590 0.4089999999999995 0.18969771917909384 0.9512696042656898 0.01783174864249304\n",
            "591 0.4079999999999995 0.18937277980148792 0.9444962106645107 0.015151621541008353\n",
            "592 0.4069999999999995 0.18816056847572327 0.9351953119039536 0.014437723555602133\n",
            "593 0.4059999999999995 0.18632372748106718 0.9655054286122322 0.014334512234199792\n",
            "594 0.40499999999999947 0.1856518555432558 0.9608059823513031 0.012661578948609531\n",
            "595 0.40399999999999947 0.1847725072875619 0.9327945820987225 0.013308364199474454\n",
            "596 0.40299999999999947 0.1836976744234562 0.9269726872444153 0.012695978919509798\n",
            "597 0.40199999999999947 0.18498047161847353 0.9318039119243622 0.012150358990766108\n",
            "598 0.40099999999999947 0.18727784231305122 0.9339183941483498 0.015411746047902852\n",
            "599 0.39999999999999947 0.18802868854254484 0.9238518513739109 0.014755389041965827\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "QnAbOz1qQ9G9",
        "outputId": "3fc5e6f3-1b2f-40bd-81fb-e9b8f56e4e69"
      },
      "source": [
        "# visualize the learned CG assignment \n",
        "plt.figure(figsize=(12,20))\n",
        "plt.imshow(M.t().detach().cpu().numpy())\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
          "referenced_widgets": [
            "f71f5ecb80d54213b9e212ba7365a1c6",
            "073df332c63d4b7b9c83743ec7c4993b",
            "5307fb8871b94b48bb14d503d0863370",
            "f14404ea5b9b488b92b749246f5b1194",
            "4efb02001fe7432997e2e7b67daa3ad9",
            "4f3db072db604377be5d53408ab858ce",
            "2632bc37bdcd4e74800fe3309ba3bfeb",
            "3494d2f6f8ed470fb6660d0be1f29a80",
            "ae44f4d616bb43f7bfa8473bd4fb4443",
            "08bef93559e3453686566354c1d237a1",
            "23ddc08f69ac4c70a4becff90a10e157",
            "5bf56640027d4a698354ec13e4dded47",
            "a6e016ee3af54482913535059ef49a9b"
          ]
        },
        "id": "kYElo-J2RPhy",
        "outputId": "8c9ba4c7-8eb3-4ba0-8278-a3386c8aa79b"
      },
      "source": [
        "# visualze reconstructed alanine dipeptide \n",
        "atomic_nums = [atom.element.atomic_number for atom in traintraj.top.atoms] \n",
        "reconstructed_xyz = xyz_recon[0].detach().cpu().numpy()\n",
        "recon_alaine_dipep = Atoms(positions= reconstructed_xyz, numbers=atomic_nums)\n",
        "show_ase(recon_alaine_dipep)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f71f5ecb80d54213b9e212ba7365a1c6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "NGLWidget()"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualze reconstructed alanine dipeptide \n",
        "atomic_nums = [atom.element.atomic_number for atom in traintraj.top.atoms] \n",
        "reconstructed_xyz = xyz_recon[0].detach().cpu().numpy()\n",
        "recon_alaine_dipep = Atoms(positions= reconstructed_xyz, numbers=atomic_nums)\n",
        "show_ase(recon_alaine_dipep)"
      ],
      "metadata": {
        "id": "hI1ZHFuBBwKM",
        "outputId": "a4b237f8-e56d-4eec-80a2-3a2c268d50d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
          "referenced_widgets": [
            "85adca26ad624e94bbd61be35b8f9233",
            "e853895946f14465af7fab30a08aa3c7",
            "b5526aab07bc47a6a0c4399f5c1bd540",
            "a00bc93def234573adcb598725332c00",
            "50de5aba0b794aab96daebbbe3526fcf",
            "5df0d34e232c42f48522e69f79262560",
            "e6f4b3a2677c49f7a72d287eb5e6777e",
            "480f90a5b6bb4b2abee5af7f691d0b13",
            "ff98ae8ff67d4529b80f0aacb60ee076",
            "5081b65767374d19929d2ea97eddefb3",
            "bb5a3c7e1c76459d88adbb789711bac5",
            "1729db97d0474cfb81328aed20bca08c",
            "a886ba2d73d54f28bbdb75034c50376e"
          ]
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "85adca26ad624e94bbd61be35b8f9233",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "NGLWidget()"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualze reconstructed alanine dipeptide \n",
        "atomic_nums = [atom.element.atomic_number for atom in traintraj.top.atoms] \n",
        "reconstructed_xyz = xyz[0].detach().cpu().numpy()\n",
        "recon_alaine_dipep = Atoms(positions= reconstructed_xyz, numbers=atomic_nums)\n",
        "show_ase(recon_alaine_dipep)"
      ],
      "metadata": {
        "id": "SdbljGI3CEO8",
        "outputId": "dc13ee4e-51dc-4950-a6aa-155b08c55956",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
          "referenced_widgets": [
            "095c8e72ced64cb3a31f627c44cd2837",
            "fff3939f18bf4ac6ad6cbf0d5c62cbaf",
            "593118a07ccc4ff5ad8b76990221f091",
            "ca38f46ac050472a8192e290945ca4cf",
            "77940126f5894427b3fd8df5bbeb702a",
            "433316230b0e4428ba5d992fb558cae9",
            "efdc04754c8e4892bcbbe47125edb4f9",
            "9ee354c3ddd341c1a5c7d265ae25d3d7",
            "3dc9bd7f8b4246b8a02bf3a92c4b5899",
            "67d25719e3fe49dabc429e5ede25dd02",
            "cbb03f8997ed457b879e1d3f49c47316",
            "1c216857e758439f9d2934c1efa0f9af",
            "e61add1c582e4c9c88cfdfde652fe134"
          ]
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "095c8e72ced64cb3a31f627c44cd2837",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "NGLWidget()"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
          "referenced_widgets": [
            "b799008e9f7c487c80b0bb366fe7be15",
            "cacaf8e6830b4968948dfe6fb3492cae",
            "baa6f406a3524f69a0a9e4256c3a5043",
            "76951ee7fbcf46fb9b18df855e98a920",
            "624b14eca5fc460585e45c9269dbfa40",
            "7548cf4a92d049fdad804935ed10cc4f",
            "842daf92ce5b40a192f8123a54ba690e",
            "70fbffac2c764572832b534bc6a6c50a",
            "798c82741b7f4aef9715e4f1a10a4d1e",
            "6215658b34344002a345fd611633554d",
            "3243914a170342b4bd4ccd24b3fa7d7d",
            "bfa24f90de7d4f7086077cba51491e24",
            "dfa37f94503148c79c5a2d5bd05251e1"
          ]
        },
        "id": "MXvLiM8JWBLN",
        "outputId": "a4017296-9297-4491-9078-0a23df210aee"
      },
      "source": [
        "# visualize Coarse-Grained alanine dipeptide\n",
        "cg_nums = [1] * N_cg\n",
        "cg_alaine_dipep = Atoms(positions= cg_xyz[0].detach().cpu().numpy(), numbers=cg_nums)\n",
        "show_ase(cg_alaine_dipep)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b799008e9f7c487c80b0bb366fe7be15",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "NGLWidget()"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
                }
              }
            }
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      "metadata": {
        "id": "TVg6VvHIWBln"
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      "source": [
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}