iBio_SummerSchool_PLM.ipynb

{
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      "metadata": {
        "id": "kvqIQ7R_d39h"
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      "outputs": [],
      "source": [
        "from os import path\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import asarray\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import gc\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.utils import class_weight\n",
        "\n",
        "from tensorflow.keras.layers import Input, Layer, BatchNormalization, Add, Lambda, Dense, Concatenate, Flatten\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.constraints import max_norm\n",
        "\n",
        "import time\n",
        "from tqdm.keras import TqdmCallback\n",
        "import h5py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A00v-bTz0KW1"
      },
      "source": [
        "#### **Retrieve data: a set of protein sequences, each one associated with some label.** \n",
        "\n",
        "The labels indicate the [Pfam](https://pfam.xfam.org) families to which the sequences belong to. The sequences belonging to the same family share some sequence and structural similarity, indicative of a common ancestry and common function(s)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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        "id": "F0kFFsVr0FMO",
        "outputId": "14b7cbdd-9e4d-42bb-8de7-b3390c531309"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1ZRKQdrYu0tCT7kW-tmbpJ3N9ov9igbQG\n",
            "To: /content/input.fasta\n",
            "100% 42.0k/42.0k [00:00<00:00, 58.5MB/s]\n",
            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Me9zJth8PV7z4LCYttjVNQnEwBMD9K4W\n",
            "To: /content/input.csv\n",
            "100% 43.8k/43.8k [00:00<00:00, 59.0MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --id '1ZRKQdrYu0tCT7kW-tmbpJ3N9ov9igbQG'\n",
        "!gdown --id '1Me9zJth8PV7z4LCYttjVNQnEwBMD9K4W'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSExjfxLYzPz"
      },
      "source": [
        "Read the labels and store them in a data frame."
      ]
    },
    {
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            UniprotId                                                Seq  \\\n",
              "0    A0A671VDZ5/18-86  INLKVAGQDGSVVQFKIKRHTPLSKLMKAYCERQGLTLRQIRFRFD...   \n",
              "1    A0A2T4H7P2/23-88  IKVTDNNNEVFFKIKRTTKLEKLMGAFCERQGKATSSVRFLFDGTR...   \n",
              "2     A0A2A3EG26/7-35                      CPCCKELFVEPVLLPCWHALCLACAVNLQ   \n",
              "3  A0A384DIE7/467-542  VVLHRGSTGLGFNIVGGEDGEGIFISFILAGGPADLSGELRKGDRI...   \n",
              "4  A0A481BKW6/137-162                         WVENKTPDGKVYYYNARTRESAWTKP   \n",
              "\n",
              "     Class  \n",
              "0  PF11976  \n",
              "1  PF11976  \n",
              "2  PF00097  \n",
              "3  PF00595  \n",
              "4  PF00397  "
            ],
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          "metadata": {},
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      ],
      "source": [
        "df = pd.read_csv('/content/input.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxFUYxpWZfre"
      },
      "source": [
        "#### **Questions**\n",
        "\n",
        "- How many protein sequences do  we have?\n",
        "- What are the minimum and maximum lengths of the sequences?\n",
        "\n",
        "Write some code in the cell below to find the answers!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "P1zZhs0zZjq6",
        "outputId": "b77770b5-6074-4293-fbf1-a4d4f73ed9d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have : 320 sequences.\n",
            " \n",
            "The shortest sequence is GCWTCRIRHRRCDESVPNCN of length 20\n",
            "The longest sequence is VGLEANLPNAYCNAMLQVLYFIFPLRLAILTHRCSKEFCLSCELGFLFHMLDVGSSSSPCQASNFLRSFRTVPEAAALGLILSSTSANVNLISLIQNWNRFVLHQIHQELLEATKKCETMQFYEQQQQLADQDGVQKYQIVNLGNGRDHQQGHNSSNSGSSATTRDESEIGRLFGTQQQTTHRCNKCNTTRVKDNVLLVCTLLYPTATTNNVEGTFGTILRHSLGVEKTTPAWCETCNKFTPTNQKSRVTDLPAILSINCGLDNEKELEYLKRQMSRAAPQQQQAQQQQHQLQQQQQQQQQQQQQQQQQEQLKQQQQQQQTATGTGADPSTNSGAKMCRYGTNCSRVDCHFAHPRKSSPCVISSASSSTSAAAHGMQKSWFPLSFQMSLDEQKNLEISSPRTASNGEPVMPVIVPGASDQQEAPGQQEPPTTEDGTEEGNKANEQCPTQKITKTYNLSAVVCYINDGSQRNLVSLVHVPGSYHDLKQPPDGGTSSTASSASSSAGGWYIFNDFSI of length 515\n"
          ]
        }
      ],
      "source": [
        "print(f\"We have : {df.shape[0]} sequences.\\n \")\n",
        "max_index = df[\"Seq\"].str.len().idxmax()\n",
        "min_index = df[\"Seq\"].str.len().idxmin()\n",
        "print(f\"The shortest sequence is {df['Seq'][min_index]} of length {len(df['Seq'][min_index])}\\nThe longest sequence is {df['Seq'][max_index]} of length {len(df['Seq'][max_index])}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5t1oOkUTYaD"
      },
      "source": [
        "# The Protein Language Model ProSE ([Bepler and Berger 2019](https://arxiv.org/pdf/1902.08661.pdf))\n",
        "\n",
        "The neural network architecture is a bi-directional LSTM. It was pre-trained on millions of sequences with the task of predicting some masked amino acid, given the previous ones and the following ones (the full context).\n",
        "\n",
        "The first step here is to retrieve the pre-trained model, define by an architecture and the associated weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZliRwwcTYD1",
        "outputId": "66380c35-79c5-4f92-864d-ebbd6d473a41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'prose'...\n",
            "remote: Enumerating objects: 26, done.\u001b[K\n",
            "remote: Counting objects: 100% (26/26), done.\u001b[K\n",
            "remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
            "remote: Total 26 (delta 1), reused 23 (delta 1), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (26/26), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/tbepler/prose.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAcjbPV4UKXo",
        "outputId": "ed6e402d-946b-49c2-8628-b79aca848314"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-12-17 19:54:53--  http://bergerlab-downloads.csail.mit.edu/prose/saved_models.zip\n",
            "Resolving bergerlab-downloads.csail.mit.edu (bergerlab-downloads.csail.mit.edu)... 128.30.48.147\n",
            "Connecting to bergerlab-downloads.csail.mit.edu (bergerlab-downloads.csail.mit.edu)|128.30.48.147|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 581846360 (555M) [application/zip]\n",
            "Saving to: ‘prose/saved_models.zip’\n",
            "\n",
            "saved_models.zip    100%[===================>] 554.89M  11.1MB/s    in 48s     \n",
            "\n",
            "2022-12-17 19:55:41 (11.6 MB/s) - ‘prose/saved_models.zip’ saved [581846360/581846360]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -nc -P prose/ http://bergerlab-downloads.csail.mit.edu/prose/saved_models.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "wnuUS839UxA_"
      },
      "outputs": [],
      "source": [
        "!unzip -q -n prose/saved_models.zip -d prose/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xhuKi3clVjY4"
      },
      "outputs": [],
      "source": [
        "#!pip install -r prose/requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpaV390WI411"
      },
      "source": [
        "The second step is to use the pre-trained model to compute an \"embedding\" for each input protein sequence. The output of the model is a matrix of floats with dimensions L x D, where L is the length of the sequence (number of amino acids) and D is the encodding dimension. \n",
        "\n",
        "As a post-processing step, the full embedding matrix is \"reduced\" to a vector. This operation allows representing every sequences as a fixed-size vector, where the size  is the dimension D. How should we aggregate the per-residue embeddings into a single vector representing the protein? Here, you can try to compute the sum, the max, or the average."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "573nAzCAWBIP",
        "outputId": "96264747-c613-4557-e213-08e429b6964a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'prose/data/output.h5': No such file or directory\n",
            "Chosen Pooling method is avg\n",
            "# loading the pre-trained ProSE MT model\n",
            "# writing: prose/data/output.h5\n",
            "# embedding with pool=avg\n"
          ]
        }
      ],
      "source": [
        "#@title Compute the embeddings (pooling types: sum, max, avg)\n",
        "pooling_type = 'avg' #@param {type : \"string\"}\n",
        "!rm prose/data/output.h5\n",
        "!echo 'Chosen Pooling method is' {pooling_type}\n",
        "!python3 prose/embed_sequences.py --pool {pooling_type} -o prose/data/output.h5 /content/input.fasta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "F87RNlw43XU0"
      },
      "outputs": [],
      "source": [
        "hf_embeddings = h5py.File('prose/data/output.h5', 'r')\n",
        "embeddings = {x: np.array(hf_embeddings[x]) for x in df.UniprotId.to_list()}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can check that the identifiers of the sequences are indeed the same in the dictionary storing the embeddings (keys) and in the data frame storing the labels."
      ],
      "metadata": {
        "id": "VTtY9j_TrS4Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNvcaBZpehlm",
        "outputId": "5826818c-426f-4f44-cdf1-1e1ba4378ba2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A0A671VDZ5/18-86', 'A0A2T4H7P2/23-88', 'A0A2A3EG26/7-35', 'A0A384DIE7/467-542', 'A0A481BKW6/137-162', 'A0A3P9DKY7/475-591', 'A0A316YP75/8-46', 'A0A2R8PDT9/22-91', 'A0A6J2WFH4/12-52', 'A0A6J0SWF7/652-705']\n",
            "['A0A671VDZ5/18-86', 'A0A2T4H7P2/23-88', 'A0A2A3EG26/7-35', 'A0A384DIE7/467-542', 'A0A481BKW6/137-162', 'A0A3P9DKY7/475-591', 'A0A316YP75/8-46', 'A0A2R8PDT9/22-91', 'A0A6J2WFH4/12-52', 'A0A6J0SWF7/652-705']\n"
          ]
        }
      ],
      "source": [
        "print(list(embeddings.keys())[:10])\n",
        "print(list(df.UniprotId.to_list())[:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rh4PhdH-W56",
        "outputId": "f4a41223-146e-443b-cf8c-34d909567190"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.02898551  0.07246377  0.02898551 ...  0.04140409  0.08653378\n",
            " -0.00195555]\n"
          ]
        }
      ],
      "source": [
        "print(embeddings['A0A671VDZ5/18-86'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Questions**\n",
        "\n",
        "- What is the dimension D?  (write some code below)"
      ],
      "metadata": {
        "id": "Ezo0Nn9IriW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Shape of D is : {len(embeddings.keys())}\")"
      ],
      "metadata": {
        "id": "jt6W2N5NsDmB",
        "outputId": "2c6a94cc-0644-4c53-bf66-6329c50cba0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of D is : 320\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we are going to define X and y variables representing our processed inputs (the embeddings) and the labels (Pfam family). We encode the labels in one-hot vectors. This will be useful later on for performing supervised classification (see below)."
      ],
      "metadata": {
        "id": "zXyxc8RFtQld"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "MuOE4X_tbWvq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "382d87ca-b961-4ce5-cfb2-24e13dba8b99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(320, 6165)\n",
            "(320, 8)\n",
            "[0. 0. 0. 0. 0. 0. 1. 0.]\n",
            "{'PF11976', 'PF00097', 'PF00397', 'PF00172', 'PF00145', 'PF00595', 'PF00179', 'PF13423'}\n"
          ]
        }
      ],
      "source": [
        "X = np.vstack(list(embeddings.values()))\n",
        "\n",
        "classes = set(df.Class.to_list())\n",
        "labels = [[c] for c in classes]\n",
        "encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')\n",
        "onehot = encoder.fit(asarray(labels))\n",
        "y = df.Class.apply(lambda x: encoder.transform([[x]])[0]).to_list()\n",
        "y = np.vstack(y)\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "print(y[0])\n",
        "print(classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also define some weights that will determine the respective contributions of the label classes during the training phase. For this, we need to know how balanced our training set, in other words how many samples we have for each class. Here, the classes are balanced (same number of samples for each) so we will put the same weight (1) to every clases."
      ],
      "metadata": {
        "id": "TC0dS6h7uI-B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Finding weights for each class\n",
        "class_weights = class_weight.compute_class_weight(class_weight = 'balanced',\n",
        "                                                  classes = np.unique(sorted(df.Class.to_list())),\n",
        "                                                  y = sorted(df.Class.to_list()))\n",
        "d_class_weights = dict(enumerate(class_weights))\n",
        "d_class_weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuQUE7Y0ZXt2",
        "outputId": "da39de16-549c-48e3-bc07-c2f326c2ff28"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0, 6: 1.0, 7: 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9QVcHAPJBj_"
      },
      "source": [
        "#### **Visualise and interpret the embeddings** \n",
        "\n",
        "Here, we perform a [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) analysis to project the embeddings, which are of very high diemnsion, to only 2 dimensions, to be able to visualise them and see how they group together. t-SNE is a non-linear dimensionality reduction technique. It defines a measure of similarity for each pair of points in high dimension. The aim of the the aanalysis is to find a map in 2D that preserves these similarities. In practice, the difference between the similarity distribution in the low dimensional space (2D) and the similarity distribution in high dimensional space (the target distribution) is minimized using the Kullback-Leibler divergence. Once the points are plotted in 2D, we can color them according to the labels. This can be seeen as \"zero-shot prediction\" of the labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ajS30_DhbWvu",
        "outputId": "a8f3ea41-a364-4142-d151-22d554ee702a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f249fe3d1c0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "X_embedded = TSNE(n_components=2,random_state=42).fit_transform(X)\n",
        "\n",
        "df['E1'] = X_embedded[:,0]\n",
        "df['E2'] = X_embedded[:,1]\n",
        "\n",
        "#plt.rcParams.update({'font.size': 10})\n",
        "sns.jointplot(data=df, x='E1', y='E2', hue = 'Class', height=15)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Questions**\n",
        "\n",
        "- To what  extent do the projections of the embeddings allow to segregate the different Pfam families? Is this result expected?\n",
        "\n",
        "Try to change the aggregation operation for goiong from ressidue-based embeddings to protein-based embeddings to see how it affects the projections. You can also investigate the parts that are not well segregated."
      ],
      "metadata": {
        "id": "qK7FNZJ61ZDQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IpV6KoxTIIpk",
        "outputId": "38fa37e9-7c89-4730-a6d3-2c7f37f2a3e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             UniprotId                                                Seq  \\\n",
              "21  A0A7K1RDA1/424-497  NIRAASGGSSRSYVAQQWAVRRLLPQECERLQGFDDDYTNVPWRGK...   \n",
              "30    A0A673L3S7/15-45                    RQIRFRFDGQPINETDTPAQLEMEDEDTIDV   \n",
              "38     A0A3P9DT30/3-67  KTDQDHAAEQAEGASLYDLVATVPHILDAGAGGNLVAHIKVGETYH...   \n",
              "41      A8IN82/424-472  AGLENDLPNAYCNALFQALYFLPEFRGLVLAHVPEPDAEFCLTCELGFL   \n",
              "49  A0A2U1J6J9/466-533  FKIKIRSKLGKDIYMDIAKSVKISFVIEAYKKACELGNEVKVKLSF...   \n",
              "\n",
              "      Class        E1        E2  \n",
              "21  PF00145  0.221851  1.104965  \n",
              "30  PF11976  1.452280  0.528495  \n",
              "38  PF13423  0.464097  0.917415  \n",
              "41  PF13423  0.262274  1.874383  \n",
              "49  PF11976  1.493699  0.402538  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17b135e8-726e-4321-91c4-9fbeae00a579\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>UniprotId</th>\n",
              "      <th>Seq</th>\n",
              "      <th>Class</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>A0A7K1RDA1/424-497</td>\n",
              "      <td>NIRAASGGSSRSYVAQQWAVRRLLPQECERLQGFDDDYTNVPWRGK...</td>\n",
              "      <td>PF00145</td>\n",
              "      <td>0.221851</td>\n",
              "      <td>1.104965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>A0A673L3S7/15-45</td>\n",
              "      <td>RQIRFRFDGQPINETDTPAQLEMEDEDTIDV</td>\n",
              "      <td>PF11976</td>\n",
              "      <td>1.452280</td>\n",
              "      <td>0.528495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>A0A3P9DT30/3-67</td>\n",
              "      <td>KTDQDHAAEQAEGASLYDLVATVPHILDAGAGGNLVAHIKVGETYH...</td>\n",
              "      <td>PF13423</td>\n",
              "      <td>0.464097</td>\n",
              "      <td>0.917415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>A8IN82/424-472</td>\n",
              "      <td>AGLENDLPNAYCNALFQALYFLPEFRGLVLAHVPEPDAEFCLTCELGFL</td>\n",
              "      <td>PF13423</td>\n",
              "      <td>0.262274</td>\n",
              "      <td>1.874383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>A0A2U1J6J9/466-533</td>\n",
              "      <td>FKIKIRSKLGKDIYMDIAKSVKISFVIEAYKKACELGNEVKVKLSF...</td>\n",
              "      <td>PF11976</td>\n",
              "      <td>1.493699</td>\n",
              "      <td>0.402538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17b135e8-726e-4321-91c4-9fbeae00a579')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17b135e8-726e-4321-91c4-9fbeae00a579 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-17b135e8-726e-4321-91c4-9fbeae00a579');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df_new = df[(df.E1.between(0,10)) & (df.E2.between(0,10))]\n",
        "df_new.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Downstream task: classification**\n",
        "\n",
        "Here, we will perform supervised learning as a downstream task to improve the segregation of the Pfam families. For this, we will build a very simple architecture of 3 dense (fully connected) layers of neurons. The numbers of neurons are 1000, 500, and 100 respectively. The final layer is of the size of the output (number of classes) and equipped with a softmax to normalize the predictions. The outut is a probability vector, whose distance to the one-hot encoding of the label is minimized during training.\n",
        "\n",
        "We first split our set into training and test subsets. Then, we define the architecture. You may change the activation function (elu) and the loss (categorical cross-entopy) for your tests.\n",
        "\n",
        "You can specify the number of epochs (number of times the machine will see the input samples) and the batch size (number of samples considered at each update of the parameters)."
      ],
      "metadata": {
        "id": "i6Zu3CIgcBpo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, shuffle=True)"
      ],
      "metadata": {
        "id": "x6DTXwAJUxkr"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model(input_shape):\n",
        "    X_in = Input(shape=input_shape)\n",
        "    H = Dense(1000, use_bias=True, activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(1e-3), kernel_constraint=max_norm(4), bias_constraint=max_norm(4))(X_in)\n",
        "    H = Dense(500, use_bias=True, activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(1e-3), kernel_constraint=max_norm(4), bias_constraint=max_norm(4))(H)\n",
        "    H = Dense(100, use_bias=True, activation='elu', kernel_initializer='he_uniform', kernel_regularizer=l2(1e-3), kernel_constraint=max_norm(4), bias_constraint=max_norm(4))(H)\n",
        "    Y = Dense(len(classes), activation='softmax')(H)\n",
        "\n",
        "    _model = Model(inputs=[X_in], outputs=Y)\n",
        "    _model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001))\n",
        "    _model.summary()\n",
        "    \n",
        "    return _model"
      ],
      "metadata": {
        "id": "oEfvsv6QSrwE"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 50 #@param {type : \"number\"}\n",
        "batch_size = 50 #@param {type : \"number\"}"
      ],
      "metadata": {
        "id": "EdNhbPbEe6lN"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = get_model(6165)\n",
        "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=batch_size, epochs=num_epochs, verbose=0, class_weight=d_class_weights, callbacks=[TqdmCallback(verbose=2)])"
      ],
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        },
        "id": "Nz0RGsv0S3LM",
        "outputId": "407c9770-fcfb-40d7-f072-39570b45b457"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 6165)]            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1000)              6166000   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 500)               500500    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 100)               50100     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8)                 808       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,717,408\n",
            "Trainable params: 6,717,408\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
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    {
      "cell_type": "code",
      "source": [
        "def plot_train_val_loss(history):\n",
        "  plt.figure(figsize=(10,10))\n",
        "  plt.rcParams.update({'font.size': 22})\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('Model loss by epoch')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epoch')\n",
        "  plt.legend(['train', 'valid'], loc='right')\n",
        "  plt.savefig('tf_trainvalcurve_scr_classificaiton.svg')"
      ],
      "metadata": {
        "id": "T1ognFNQS4sE"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_train_val_loss(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "iD1ZzMeXTX70",
        "outputId": "4de87c7b-3a80-48c0-febe-38be8851a4ca"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
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        "Designed by **Yasser MOHSENI BEHBAHANI** and **Élodie LAINE**, LCQB, Sorbonne Université, 2022\n",
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