hackiwha_rnn.ipynb

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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"drive/MyDrive/Hck/sequence_esm1_t34_670M_UR100_cls_embeddings.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "pkOuDdaqfyKh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_main = pd.read_csv(\"drive/MyDrive/Hck/train.csv\")\n",
        "df_main.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Q6UrRZ7hf4AW",
        "outputId": "2fb134eb-994e-415d-a347-27b11c581691"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID           protein_name  \\\n",
              "0  train_0   sp|Q9J523|V212_FOWPN   \n",
              "1  train_1  sp|Q14990|ODFP1_HUMAN   \n",
              "2  train_2  sp|Q9C994|DTX14_ARATH   \n",
              "3  train_3   sp|Q58425|CARA_METJA   \n",
              "4  train_4   sp|P78814|SRP2_SCHPO   \n",
              "\n",
              "                                            sequence  biological_process  \\\n",
              "0  MPQNKVLSFPLPEGTLLEDITKNKWILGKQLGSGGFGLVYQVSCKS...                   0   \n",
              "1  MAALSCLLDSVRRDIKKVDRELRQLRCIDEFSTRCLCDLYMHPYCC...                   0   \n",
              "2  MDSAEKGLLVVSDREEVNKKDGFLRETKKLSYIAGPMIAVNSSMYV...                   0   \n",
              "3  MEAVLILEDGTILKGKGFGAEKEVFGELVFTTVMTGYVEVLTDPSY...                   0   \n",
              "4  MSETRLFVGRIPPQATREDMMDFFKGYGQILDCKLMNGFGFVEVED...                   0   \n",
              "\n",
              "   cellular_component  molecular_function  \n",
              "0                   0                   1  \n",
              "1                   0                   1  \n",
              "2                   0                   1  \n",
              "3                   1                   0  \n",
              "4                   0                   1  "
            ],
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              "      <td>MPQNKVLSFPLPEGTLLEDITKNKWILGKQLGSGGFGLVYQVSCKS...</td>\n",
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              "      <td>sp|Q14990|ODFP1_HUMAN</td>\n",
              "      <td>MAALSCLLDSVRRDIKKVDRELRQLRCIDEFSTRCLCDLYMHPYCC...</td>\n",
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              "      <td>sp|Q9C994|DTX14_ARATH</td>\n",
              "      <td>MDSAEKGLLVVSDREEVNKKDGFLRETKKLSYIAGPMIAVNSSMYV...</td>\n",
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              "      <th>3</th>\n",
              "      <td>train_3</td>\n",
              "      <td>sp|Q58425|CARA_METJA</td>\n",
              "      <td>MEAVLILEDGTILKGKGFGAEKEVFGELVFTTVMTGYVEVLTDPSY...</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>sp|P78814|SRP2_SCHPO</td>\n",
              "      <td>MSETRLFVGRIPPQATREDMMDFFKGYGQILDCKLMNGFGFVEVED...</td>\n",
              "      <td>0</td>\n",
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              "type": "dataframe",
              "variable_name": "df_main",
              "summary": "{\n  \"name\": \"df_main\",\n  \"rows\": 33622,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33622,\n        \"samples\": [\n          \"train_23754\",\n          \"train_16439\",\n          \"train_5211\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protein_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33622,\n        \"samples\": [\n          \"sp|Q27712|CP2L1_PANAR\",\n          \"sp|P13673|SKGR_XENLA\",\n          \"sp|Q9HKL4|ACTH_THEAC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sequence\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33622,\n        \"samples\": [\n          \"MLTGALLLLLLVVIVYLLDKKPSGLPPGIWGWPLVGRMPSRSKHLADQVKQLRKKYGDIITWRIGTRVNVFLCNFKLVKTALSKFECSDRPDFYTFKLFGEGNDVGVVFSNGVMWQTHRRFILRQLRDLGMGKSRLEAAIQHEAACLVQELKKHTDQPMPLPKSINLAVLNVIWKLVADHRYSLQDQEGQYFTQLLTTTTDNMQGFALNLFNYLPWLLMITPDFVKNWMGVRVLRDGVCELKDYMKTFIKEHQATLDPSNPKDLLDAYLIDLQERKEDPLSTMNIETVRAVIMDLFGAGTETTSTMIRWTILYLMKYPEVQAKIQREIDAAVPRGTLPSLEHKDKLAYFEATIHEVHRIVSLVPLGVSHYTNQDTELAGYRLPKGTVVMSHLECCHRDPSYWEKPNEFYPEHFLDDQGKFVKREHLVNFSVGRRVCVGESLARMELFVFLSAILQNFTFSAPKGEVLHTEKDPQQMLFSFPKPYQVIIRERE\",\n          \"METMYHRFLCIPFLLILGLAQGQSKGLQTVTTFRTGLKPIDVTAIRTGLQPIATFHTGQKPIDVTAIRTGLQPIATFHTGLQPVDVTAIRTGLQPIATFQTGVQRVSTFHGEPASTLQGSGSTVIKKIMVSSMNQPVSKNEGIVEVPPPSGGTHVITEEMNWHGGRNGHKMKKLGKKKHHKNRHGGKNHHKMKKIGKHHGGGRKFGKKHRHHK\",\n          \"MVVVGLDVGYGDTKVIGVDGKRIIFPSRWAVTETESWGIGGKIPVLSTDGGQTKFIYGKYASGNNIRVPQGDGRLASKEAFPLIAAALWESGIHNDGSPVDLVIGSGTPLGTFDLEVKAAKEALENKVLTVTGPEGEVRQFNITRLIMRPQGVGAALYLLNQGIIEQQPGYGVVIDVGSRTTDVLTINLMDMEPVVELSFSLQIGVGDAISALSRKIAKETGFVVPFDLAQEALSHPVMFRQKQVGGPEVSGPILEDLANRIIENIRLNLRGEVDRVTSLIPVGGGSNLIGDRFEEIAPGTLVKIKPEDLQFANALGYRDAAERSM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biological_process\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cellular_component\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"molecular_function\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 3
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "joined_df = pd.merge(df, df_main, on='ID')\n",
        "joined_df = joined_df.drop(columns=['protein_name', 'sequence'])\n",
        "joined_df.head()"
      ],
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        "id": "zQL7FBs0gJwx",
        "outputId": "4f13a96e-d13f-4050-ff5a-58aeb89cb7d1"
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      "execution_count": 7,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "           ID     emb_0     emb_1     emb_2     emb_3     emb_4     emb_5  \\\n",
              "0     train_0  0.081642 -1.496138  6.191256  0.679270 -1.894954  4.502281   \n",
              "1     train_1 -0.339906 -0.648377  5.818838 -0.628044 -3.921365  4.332178   \n",
              "2    train_10  1.769063 -1.486243  7.261370 -1.914409 -2.150486  4.021339   \n",
              "3   train_100 -0.875047 -0.755958  6.746736  0.169595 -3.256755  5.133801   \n",
              "4  train_1000  1.375432 -1.485533  6.716955 -0.113301 -3.485360  4.589830   \n",
              "\n",
              "      emb_6     emb_7     emb_8  ...  emb_1273  emb_1274  emb_1275  emb_1276  \\\n",
              "0 -0.514480  3.705019 -0.492262  ...  2.121812 -0.186634  2.419172  1.625475   \n",
              "1 -1.524251  4.316974 -0.746856  ...  3.244596 -0.824896  0.850447 -2.062417   \n",
              "2 -1.634238  4.790095 -0.374175  ...  1.596033  0.121016  1.480196 -0.077234   \n",
              "3 -2.103694  5.345797  0.062203  ...  2.900385  0.083600  1.125013 -1.735611   \n",
              "4  1.116525  4.952724  1.901705  ...  3.332405 -1.423786  2.461106 -0.964605   \n",
              "\n",
              "   emb_1277  emb_1278  emb_1279  biological_process  cellular_component  \\\n",
              "0 -1.370067  3.178513 -1.555542                   0                   0   \n",
              "1  1.170538 -2.074513 -2.184298                   0                   0   \n",
              "2 -1.287797  3.832566  1.213811                   0                   0   \n",
              "3  1.038142  0.507925 -1.397163                   1                   0   \n",
              "4 -0.446336  1.997515 -1.162120                   0                   0   \n",
              "\n",
              "   molecular_function  \n",
              "0                   1  \n",
              "1                   1  \n",
              "2                   1  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "joined_df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Assuming `joined_df` is your final DataFrame after preprocessing\n",
        "features = joined_df.drop(columns=['ID', 'biological_process', 'cellular_component', 'molecular_function']).values\n",
        "targets = joined_df[['biological_process', 'cellular_component', 'molecular_function']].values\n",
        "\n",
        "# Convert targets to categorical (one-hot encoding) if they're not already\n",
        "targets = to_categorical(targets)\n",
        "\n",
        "# Split the data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, targets, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "yeAVY02uidiF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import SimpleRNN, Dense\n",
        "\n",
        "# Define the RNN model\n",
        "model = Sequential()\n",
        "model.add(SimpleRNN(128, input_shape=(X_train.shape[1], 1), return_sequences=True))\n",
        "model.add(SimpleRNN(64))\n",
        "model.add(Dense(y_train.shape[1], activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "X_train_reshaped = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
        "X_test_reshaped = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
        "\n"
      ],
      "metadata": {
        "id": "wDrZlwjEiqZy"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_reshaped, y_train, epochs=10, validation_split=0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWO2vmyVkxjN",
        "outputId": "ef5e316f-db9b-4b39-dfa9-cba70ee0ac84"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "673/673 [==============================] - 581s 859ms/step - loss: 1.1035 - accuracy: 0.3629 - val_loss: 1.0930 - val_accuracy: 0.3716\n",
            "Epoch 2/10\n",
            "673/673 [==============================] - 573s 851ms/step - loss: 1.0967 - accuracy: 0.3708 - val_loss: 1.2346 - val_accuracy: 0.3429\n",
            "Epoch 3/10\n",
            "673/673 [==============================] - 566s 841ms/step - loss: 1.1045 - accuracy: 0.3355 - val_loss: 1.0997 - val_accuracy: 0.3470\n",
            "Epoch 4/10\n",
            "673/673 [==============================] - 567s 843ms/step - loss: 1.1009 - accuracy: 0.3388 - val_loss: 1.0995 - val_accuracy: 0.3470\n",
            "Epoch 5/10\n",
            "673/673 [==============================] - 567s 842ms/step - loss: 1.1013 - accuracy: 0.3420 - val_loss: 1.0996 - val_accuracy: 0.3470\n",
            "Epoch 6/10\n",
            "673/673 [==============================] - 561s 832ms/step - loss: 1.1019 - accuracy: 0.3404 - val_loss: 1.0991 - val_accuracy: 0.3396\n",
            "Epoch 7/10\n",
            "673/673 [==============================] - 561s 834ms/step - loss: 1.1016 - accuracy: 0.3434 - val_loss: 1.1013 - val_accuracy: 0.3335\n",
            "Epoch 8/10\n",
            "673/673 [==============================] - 578s 859ms/step - loss: 1.1013 - accuracy: 0.3426 - val_loss: 1.0962 - val_accuracy: 0.3673\n",
            "Epoch 9/10\n",
            "673/673 [==============================] - 590s 877ms/step - loss: 1.0986 - accuracy: 0.3565 - val_loss: 1.0939 - val_accuracy: 0.3522\n",
            "Epoch 10/10\n",
            "673/673 [==============================] - 568s 844ms/step - loss: 1.0975 - accuracy: 0.3609 - val_loss: 1.1122 - val_accuracy: 0.3429\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "plGt38Jgk3QR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}