openvaccine-simple-gru-model.ipynb

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   "source": "# Introduction",
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   "source": "Winning the fight against the COVID-19 pandemic will require an effective vaccine that can be equitably and widely distributed. Building upon decades of research has allowed scientists to accelerate the search for a vaccine against COVID-19, but every day that goes by without a vaccine has enormous costs for the world nonetheless. We need new, fresh ideas from all corners of the world. Could online gaming and crowdsourcing help solve a worldwide pandemic? Pairing scientific and crowdsourced intelligence could help computational biochemists make measurable progress.\n\n## mRNA Vaccine\n\nmRNA vaccines have taken the lead as the fastest vaccine candidates for COVID-19, but currently, they face key potential limitations. One of the biggest challenges right now is how to design super stable messenger RNA molecules (mRNA). Conventional vaccines (like your seasonal flu shots) are packaged in disposable syringes and shipped under refrigeration around the world, but that is not currently possible for mRNA vaccines.\n\n## mRNA Stability Issue\n\nResearchers have observed that RNA molecules have the tendency to spontaneously degrade. This is a serious limitation--a single cut can render the mRNA vaccine useless. Currently, little is known on the details of where in the backbone of a given RNA is most prone to being affected. Without this knowledge, current mRNA vaccines against COVID-19 must be prepared and shipped under intense refrigeration, and are unlikely to reach more than a tiny fraction of human beings on the planet unless they can be stabilized.",
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   "source": "## Building Predictive Model\nWe need to design the model that will predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position.\n\n## Improving Stability of mRNA\nImproving the stability of mRNA vaccines was a problem that was being explored before the pandemic but was expected to take many years to solve.Now, we must solve this deep scientific challenge in months, if not weeks, to accelerate mRNA vaccine research and deliver a refrigerator-stable vaccine against SARS-CoV-2, the virus behind COVID-19.",
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   "source": "![Picture title](Images/image-20210621-125546.png)",
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   "source": "# Getting Ready",
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   "source": "# Dataframe\nimport json\nimport pandas as pd\nimport numpy as np\n# Visualization\nimport plotly.express as px\n# Deeplearning\nimport tensorflow.keras.layers as L\nimport tensorflow as tf\n# Sklearn\nfrom sklearn.model_selection import train_test_split\n#Setting seeds\ntf.random.set_seed(2021)\nnp.random.seed(2021)",
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   },
   "source": "# This will tell us the columns we are predicting\ntarget_cols = ['reactivity', 'deg_Mg_pH10', 'deg_Mg_50C', 'deg_pH10', 'deg_50C']\nModel_Train = True # True if you want to Train model which take 1 hour to train.",
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   "source": "## Metric",
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  {
   "cell_type": "markdown",
   "source": "![Picture title](Images/image-20210621-122600.png)\n![Picture title](Images/image-20210621-122723.png)",
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   "source": "def MCRMSE(y_true, y_pred):## Monte Carlo root mean squared errors \n    colwise_mse = tf.reduce_mean(tf.square(y_true - y_pred), axis=1)\n    return tf.reduce_mean(tf.sqrt(colwise_mse), axis=1)",
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  {
   "cell_type": "markdown",
   "source": "# Loading Data",
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   "source": "[![stanford](https://img.shields.io/badge/Stanford_University-OpenVaccine-800080?logo=Kaggle&logoColor=white&style=for-the-badge)](https://www.kaggle.com/c/stanford-covid-vaccine/data)",
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   "source": "## Columns detail explaination \n- **id** - An arbitrary identifier for each sample.\n- **seq_scored** - (68 in Train and Public Test, 91 in Private Test) Integer value denoting the number of positions used in scoring with predicted values. This should match the length of reactivity, deg_* and *_error_* columns. Note that molecules used for the Private Test will be longer than those in the Train and Public Test data, so the size of this vector will be different.\n- **seq_length** - (107 in Train and Public Test, 130 in Private Test) Integer values, denotes the length of sequence. Note that molecules used for the Private Test will be longer than those in the Train and Public Test data, so the size of this vector will be different.\n- **sequence** - (1x107 string in Train and Public Test, 130 in Private Test) Describes the RNA sequence, a combination of A, G, U, and C for each sample. Should be 107 characters long, and the first 68 bases should correspond to the 68 positions specified in seq_scored (note: indexed starting at 0).\n- **structure** - (1x107 string in Train and Public Test, 130 in Private Test) An array of (, ), and . characters that describe whether a base is estimated to be paired or unpaired. Paired bases are denoted by opening and closing parentheses e.g. (....) means that base 0 is paired to base 5, and bases 1-4 are unpaired.\n- **reactivity** - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likely secondary structure of the RNA sample.\n- **deg_pH10** - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating without magnesium at high pH (pH 10).\n- **deg_Mg_pH10** - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating with magnesium in high pH (pH 10).\n- **deg_50C** - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating without magnesium at high temperature (50 degrees Celsius).\n- **deg_Mg_50C** - (1x68 vector in Train and Public Test, 1x91 in Private Test) An array of floating point numbers, should have the same length as seq_scored. These numbers are reactivity values for the first 68 bases as denoted in sequence, and used to determine the likelihood of degradation at the base/linkage after incubating with magnesium at high temperature (50 degrees Celsius).\n*_error_* - An array of floating point numbers, should have the same length as the corresponding reactivity or deg_* columns, calculated errors in experimental values obtained in reactivity and deg_* columns.\n- **predicted_loop_type** - (1x107 string) Describes the structural context (also referred to as 'loop type')of each character in sequence. Loop types assigned by bpRNA from Vienna RNAfold 2 structure. From the bpRNA_documentation: S: paired \"Stem\" M: Multiloop I: Internal loop B: Bulge H: Hairpin loop E: dangling End X: eXternal loop\nS/N filter Indicates if the sample passed filters described below in Additional Notes.",
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   "source": "## Observing Data",
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   "source": "data_dir = \"Data/\"\ntrain = pd.read_json(data_dir + \"train.json\", lines=True)\ntest = pd.read_json(data_dir + \"test.json\", lines=True)\nsample_df = pd.read_csv(data_dir + \"sample_submission.csv\")\n",
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   "cell_type": "markdown",
   "source": "we have sequence, structure and predicted loop type which are text formate and we have to convert them into token to train the model. Then we have arrays with in columns from reactivity_error to deg_50C.",
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     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00014-1e15ddfb-7bd6-4a15-a35b-2c9490062443",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "62e2b4a8",
    "execution_start": 1624282808712,
    "execution_millis": 11,
    "deepnote_cell_type": "code"
   },
   "source": "print('Train shapes: ', train.shape)\nprint('Test shapes: ', test.shape) ",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Train shapes:  (2400, 19)\nTest shapes:  (3634, 7)\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Test have sequence, structure, predicted_loop_type, seq_length, seq_scored and id  ",
   "metadata": {
    "tags": [],
    "cell_id": "00028-80e02cf1-bbc7-49bd-b5b6-5feabfeda967",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "## Signal to noise distribution",
   "metadata": {
    "tags": [],
    "cell_id": "00026-15eb1e3b-61ae-4e95-b9bb-fd2cfc88f940",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00017-dfa41b3c-941c-483c-b0d5-ef2b773a830d",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1ef9c982",
    "execution_start": 1624282808716,
    "execution_millis": 1865,
    "deepnote_cell_type": "code"
   },
   "source": "fig = px.histogram(\n    train, \n    \"signal_to_noise\", \n    nbins=25, \n    title='signal_to_noise distribution', \n    width=800,\n    height=400\n)\nfig.show()",
   "execution_count": null,
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Removing negetive values",
   "metadata": {
    "tags": [],
    "cell_id": "00028-789118f6-2bce-47c9-b19f-48c1a0c5d7d8",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:57:23.036105Z",
     "iopub.execute_input": "2021-06-19T12:57:23.036542Z",
     "iopub.status.idle": "2021-06-19T12:57:23.057929Z",
     "shell.execute_reply.started": "2021-06-19T12:57:23.036494Z",
     "shell.execute_reply": "2021-06-19T12:57:23.056757Z"
    },
    "trusted": true,
    "cell_id": "00014-ec742ec0-4467-43bb-9681-6f498f574d39",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c25631b3",
    "execution_start": 1624282810620,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "source": "train = train.query(\"signal_to_noise >= 1\")",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Sequence Test length\nWe need to divide our test dataset into two different data frames based on different sequence lengths. Splitting them into Public and private data frames.",
   "metadata": {
    "tags": [],
    "cell_id": "00023-d4616df3-54f2-4d82-ac23-4651da9c1d02",
    "deepnote_cell_type": "markdown"
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  {
   "cell_type": "code",
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    "tags": [],
    "cell_id": "00023-b8d4a8be-f714-46e5-a40d-4d2a58122c9a",
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    "source_hash": "b237e3fa",
    "execution_start": 1624282810620,
    "execution_millis": 19,
    "deepnote_cell_type": "code"
   },
   "source": "fig = px.histogram(\n    test, \n    \"seq_length\", \n    nbins=25, \n    title='sequence_length distribution', \n    width=800,\n    height=400\n)\nfig.show()",
   "execution_count": null,
   "outputs": [
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\"zerolinewidth\": 2}}}, \"title\": {\"text\": \"sequence_length distribution\"}, \"width\": 800, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"seq_length\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('f1ca465f-8e3d-40f1-8011-2d69f78411c7');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Spliting Test into Public and Private Dataframe\nSplitting test datasets into public and private data frames due to variation in sequence length. This has improved the overall result.",
   "metadata": {
    "tags": [],
    "cell_id": "00025-d1f20df5-fe5b-43ba-8093-6b34b59c5648",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00025-673fac35-ce45-4518-bfba-609121cad8e1",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "998c1853",
    "execution_start": 1624282810652,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "source": "public_df = test.query(\"seq_length == 107\")\nprivate_df = test.query(\"seq_length == 130\")",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# Pre-processing Data",
   "metadata": {
    "tags": [],
    "cell_id": "00028-9910b96e-6b4f-4468-b894-2219ca5da538",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "We will use this dictionary to map each character to an integer ",
   "metadata": {
    "tags": [],
    "cell_id": "00031-d4bd3a44-ae0c-481d-811f-6da46e20e930",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:57:23.062135Z",
     "iopub.execute_input": "2021-06-19T12:57:23.062453Z",
     "iopub.status.idle": "2021-06-19T12:57:23.336101Z",
     "shell.execute_reply.started": "2021-06-19T12:57:23.062422Z",
     "shell.execute_reply": "2021-06-19T12:57:23.334907Z"
    },
    "trusted": true,
    "cell_id": "00015-ef73936d-8c0c-45bc-98d3-2e19c1ab4133",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6e08b64c",
    "execution_start": 1624282810653,
    "execution_millis": 8,
    "deepnote_cell_type": "code"
   },
   "source": "token2int = {x: i for i, x in enumerate(\"().ACGUBEHIMSX\")}\n\ntoken2int\n",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "{'(': 0,\n ')': 1,\n '.': 2,\n 'A': 3,\n 'C': 4,\n 'G': 5,\n 'U': 6,\n 'B': 7,\n 'E': 8,\n 'H': 9,\n 'I': 10,\n 'M': 11,\n 'S': 12,\n 'X': 13}"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Converting DataFrame to 3D Array\nIt takes dataframe of shape (x, y), containing list of length `l` and returning numpy 3D array `(x,l,y)`.",
   "metadata": {
    "tags": [],
    "cell_id": "00014-1b16741c-4371-4467-bc29-1573a0245453",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00031-009f8d4f-06f1-429e-be70-22fc38a28580",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8dee68b4",
    "execution_start": 1624282810653,
    "execution_millis": 1,
    "deepnote_cell_type": "code"
   },
   "source": "def dataframe_to_array(df):\n   return np.transpose(np.array(df.values.tolist()), (0, 2, 1))",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Tokeniziation of Sequence\nIt takes dataframe then convert it into 3D numpy array and then tokenize it using Label Encoding.",
   "metadata": {
    "tags": [],
    "cell_id": "00032-f3e822da-deed-4a3c-a3a6-00bcd16aef8d",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00031-c0c7caf4-68b2-425f-ac8b-c3972fa8b0de",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7edf8964",
    "execution_start": 1624282810655,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "source": "def dataframe_label_encoding(\n    df, token2int, cols=[\"sequence\", \"structure\", \"predicted_loop_type\"]\n):\n    return dataframe_to_array(\n        df[cols].applymap(lambda seq: [token2int[x] for x in seq])\n    )  ## tokenization of Sequence, Structure, Predicted loop\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Preprocessing Features and Labels \nTrain input contains `[\"sequence\", \"structure\", \"predicted_loop_type\"]` which need to tokenize and before that converting dataframe into 3D numpy array. ",
   "metadata": {
    "tags": [],
    "cell_id": "00032-117b2dfc-2ea3-4cbb-aea7-b65ec5060d2d",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00031-e6eba83b-cb42-41e7-9fd5-8dd3da908905",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ef963d10",
    "execution_start": 1624282810658,
    "execution_millis": 130,
    "deepnote_cell_type": "code"
   },
   "source": "train_inputs = dataframe_label_encoding(train, token2int)  ## Label encoding\ntrain_labels = dataframe_to_array(train[target_cols])  ## dataframe to 3D array to\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Train & Validation split ",
   "metadata": {
    "tags": [],
    "cell_id": "00036-2247de54-90a4-41e7-a5b0-eafff687bfc6",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:57:23.338745Z",
     "iopub.execute_input": "2021-06-19T12:57:23.339087Z",
     "iopub.status.idle": "2021-06-19T12:57:23.354828Z",
     "shell.execute_reply.started": "2021-06-19T12:57:23.339058Z",
     "shell.execute_reply": "2021-06-19T12:57:23.353598Z"
    },
    "trusted": true,
    "cell_id": "00016-b816e87b-6cb4-47b0-8987-f3c223594010",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a5ca4828",
    "execution_start": 1624282810794,
    "execution_millis": 4,
    "deepnote_cell_type": "code"
   },
   "source": "x_train, x_val, y_train, y_val = train_test_split(\n    train_inputs, train_labels, test_size=0.1, random_state=34, stratify=train.SN_filter\n)\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Preprocessing Public and Private Dataframe  ",
   "metadata": {
    "cell_id": "00017-2593de2f-b940-451e-addf-5134146bbd68",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:57:23.357363Z",
     "iopub.execute_input": "2021-06-19T12:57:23.358051Z",
     "iopub.status.idle": "2021-06-19T12:57:23.734298Z",
     "shell.execute_reply.started": "2021-06-19T12:57:23.358001Z",
     "shell.execute_reply": "2021-06-19T12:57:23.733051Z"
    },
    "trusted": true,
    "cell_id": "00018-23a3bbec-4a70-4dbd-aa35-20013d753270",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "bea5ba79",
    "execution_start": 1624282810871,
    "execution_millis": 125,
    "deepnote_cell_type": "code"
   },
   "source": "public_inputs = dataframe_label_encoding(public_df, token2int)\nprivate_inputs = dataframe_label_encoding(private_df, token2int)\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# Training / Evaluating Model",
   "metadata": {
    "cell_id": "00019-cd079ff8-e692-4134-86be-0e0d0b8e6861",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "## Build Model\n\nI have experimented with multiple traditional models including Light GBM, Catboost, and BiLSTM, but the result was quite bad as compare to triple GRU layers. \n\nUsing simple 3 Bidirectional GRU layer with linear activation. This model is quite simple and drived from [xhlulu](https://www.kaggle.com/xhlulu/openvaccine-simple-gru-model) inital model. I am big fan of his work and its just blew my mind on how he produced lower MCRMSE score with no feature engineering or data augumentation.  \n\nTo learn more about RNNs, LSTM and GRU, please see [this blog post](https://colah.github.io/posts/2015-08-Understanding-LSTMs/).",
   "metadata": {
    "tags": [],
    "cell_id": "00038-c647b19c-5044-47b1-b161-71b4843fde13",
    "deepnote_cell_type": "markdown"
   }
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   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00037-14983c04-d182-4981-9202-691dbf1a2064",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1828cc7b",
    "execution_start": 1624282811042,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "source": "def build_model(\n    embed_size,  # Length of uniqe tokens\n    seq_len=107,  # public dataset seq_len\n    pred_len=68,  # pred_len for public data\n    dropout=0.5,  # trying best dropout (general)\n    sp_dropout=0.2,  # Spatial Droput\n    embed_dim=200,  # embeding dimention\n    hidden_dim=256,  # hidden layer units\n):\n    inputs = L.Input(shape=(seq_len, 3))\n    embed = L.Embedding(input_dim=embed_size, output_dim=embed_dim)(inputs)\n\n    reshaped = tf.reshape(\n        embed, shape=(-1, embed.shape[1], embed.shape[2] * embed.shape[3])\n    )\n    hidden = L.SpatialDropout1D(sp_dropout)(reshaped)\n\n    # 3X BiGRU layers\n    hidden = L.Bidirectional(\n        L.GRU(\n            hidden_dim,\n            dropout=dropout,\n            return_sequences=True,\n            kernel_initializer=\"orthogonal\",\n        )\n    )(hidden)\n\n    hidden = L.Bidirectional(\n        L.GRU(\n            hidden_dim,\n            dropout=dropout,\n            return_sequences=True,\n            kernel_initializer=\"orthogonal\",\n        )\n    )(hidden)\n\n    hidden = L.Bidirectional(\n        L.GRU(\n            hidden_dim,\n            dropout=dropout,\n            return_sequences=True,\n            kernel_initializer=\"orthogonal\",\n        )\n    )(hidden)\n\n    # Since we are only making predictions on the first part of each sequence,\n    # we have to truncate it\n    truncated = hidden[:, :pred_len]\n    out = L.Dense(5, activation=\"linear\")(truncated)\n\n    model = tf.keras.Model(inputs=inputs, outputs=out)\n    model.compile(optimizer=\"Adam\", loss=MCRMSE)  # loss function as of Eval Metric\n\n    return model\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Building model",
   "metadata": {
    "tags": [],
    "cell_id": "00043-c445a82a-8a3f-4a2f-8682-f75bff35c76d",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:57:23.736735Z",
     "iopub.execute_input": "2021-06-19T12:57:23.737545Z",
     "iopub.status.idle": "2021-06-19T12:57:26.614972Z",
     "shell.execute_reply.started": "2021-06-19T12:57:23.737494Z",
     "shell.execute_reply": "2021-06-19T12:57:26.613466Z"
    },
    "trusted": true,
    "cell_id": "00020-9b316885-ce82-493d-9796-16ce402734ea",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "45b62f48",
    "execution_start": 1624282811043,
    "execution_millis": 1489,
    "deepnote_cell_type": "code"
   },
   "source": "model = build_model(\n    embed_size=len(token2int) ## embed_size = 14\n)  ## uniquie token in sequence, structure, predicted_loop_type\nmodel.summary()\n",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Model: \"model\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ninput_1 (InputLayer)         [(None, 107, 3)]          0         \n_________________________________________________________________\nembedding (Embedding)        (None, 107, 3, 200)       2800      \n_________________________________________________________________\ntf.reshape (TFOpLambda)      (None, 107, 600)          0         \n_________________________________________________________________\nspatial_dropout1d (SpatialDr (None, 107, 600)          0         \n_________________________________________________________________\nbidirectional (Bidirectional (None, 107, 512)          1317888   \n_________________________________________________________________\nbidirectional_1 (Bidirection (None, 107, 512)          1182720   \n_________________________________________________________________\nbidirectional_2 (Bidirection (None, 107, 512)          1182720   \n_________________________________________________________________\ntf.__operators__.getitem (Sl (None, 68, 512)           0         \n_________________________________________________________________\ndense (Dense)                (None, 68, 5)             2565      \n=================================================================\nTotal params: 3,688,693\nTrainable params: 3,688,693\nNon-trainable params: 0\n_________________________________________________________________\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Training Model\nFitting our model on train dataset and saving best model checkpoint in Model folder.\n- batch size = 64\n- epochs = 40",
   "metadata": {
    "tags": [],
    "cell_id": "00045-cbf75e52-93e6-45e9-b70b-1393ebcb3dbd",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.status.busy": "2021-06-19T12:57:26.619053Z",
     "iopub.execute_input": "2021-06-19T12:57:26.619400Z",
     "iopub.status.idle": "2021-06-19T12:59:26.302943Z",
     "shell.execute_reply.started": "2021-06-19T12:57:26.619367Z",
     "shell.execute_reply": "2021-06-19T12:59:26.299454Z"
    },
    "trusted": true,
    "cell_id": "00021-863ca5c6-7ff6-4362-bd14-56e2fe688327",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fa9a1b6e",
    "execution_start": 1624282812530,
    "execution_millis": 2305274,
    "deepnote_cell_type": "code"
   },
   "source": "if Model_Train:\n    history = model.fit(\n        x_train,\n        y_train,\n        validation_data=(x_val, y_val),\n        batch_size=64,\n        epochs=40,\n        verbose=2,\n        callbacks=[\n            tf.keras.callbacks.ReduceLROnPlateau(patience=5),\n            tf.keras.callbacks.ModelCheckpoint(\"Model/model.h5\"),\n        ],\n    )\n",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/40\n30/30 - 69s - loss: 0.4536 - val_loss: 0.3796\nEpoch 2/40\n30/30 - 57s - loss: 0.3856 - val_loss: 0.3601\nEpoch 3/40\n30/30 - 57s - loss: 0.3637 - val_loss: 0.3410\nEpoch 4/40\n30/30 - 57s - loss: 0.3488 - val_loss: 0.3255\nEpoch 5/40\n30/30 - 57s - loss: 0.3357 - val_loss: 0.3188\nEpoch 6/40\n30/30 - 57s - loss: 0.3295 - val_loss: 0.3163\nEpoch 7/40\n30/30 - 57s - loss: 0.3200 - val_loss: 0.3098\nEpoch 8/40\n30/30 - 57s - loss: 0.3117 - val_loss: 0.2997\nEpoch 9/40\n30/30 - 57s - loss: 0.3046 - val_loss: 0.2899\nEpoch 10/40\n30/30 - 57s - loss: 0.2993 - val_loss: 0.2875\nEpoch 11/40\n30/30 - 57s - loss: 0.2919 - val_loss: 0.2786\nEpoch 12/40\n30/30 - 57s - loss: 0.2830 - val_loss: 0.2711\nEpoch 13/40\n30/30 - 57s - loss: 0.2777 - val_loss: 0.2710\nEpoch 14/40\n30/30 - 57s - loss: 0.2712 - val_loss: 0.2584\nEpoch 15/40\n30/30 - 57s - loss: 0.2640 - val_loss: 0.2580\nEpoch 16/40\n30/30 - 57s - loss: 0.2592 - val_loss: 0.2518\nEpoch 17/40\n30/30 - 57s - loss: 0.2540 - val_loss: 0.2512\nEpoch 18/40\n30/30 - 57s - loss: 0.2514 - val_loss: 0.2461\nEpoch 19/40\n30/30 - 57s - loss: 0.2485 - val_loss: 0.2492\nEpoch 20/40\n30/30 - 57s - loss: 0.2453 - val_loss: 0.2434\nEpoch 21/40\n30/30 - 57s - loss: 0.2424 - val_loss: 0.2411\nEpoch 22/40\n30/30 - 57s - loss: 0.2397 - val_loss: 0.2391\nEpoch 23/40\n30/30 - 57s - loss: 0.2380 - val_loss: 0.2412\nEpoch 24/40\n30/30 - 57s - loss: 0.2357 - val_loss: 0.2432\nEpoch 25/40\n30/30 - 57s - loss: 0.2330 - val_loss: 0.2384\nEpoch 26/40\n30/30 - 57s - loss: 0.2316 - val_loss: 0.2364\nEpoch 27/40\n30/30 - 57s - loss: 0.2306 - val_loss: 0.2397\nEpoch 28/40\n30/30 - 57s - loss: 0.2282 - val_loss: 0.2343\nEpoch 29/40\n30/30 - 57s - loss: 0.2242 - val_loss: 0.2392\nEpoch 30/40\n30/30 - 57s - loss: 0.2232 - val_loss: 0.2326\nEpoch 31/40\n30/30 - 57s - loss: 0.2207 - val_loss: 0.2318\nEpoch 32/40\n30/30 - 57s - loss: 0.2192 - val_loss: 0.2339\nEpoch 33/40\n30/30 - 57s - loss: 0.2175 - val_loss: 0.2287\nEpoch 34/40\n30/30 - 57s - loss: 0.2160 - val_loss: 0.2310\nEpoch 35/40\n30/30 - 57s - loss: 0.2137 - val_loss: 0.2299\nEpoch 36/40\n30/30 - 57s - loss: 0.2119 - val_loss: 0.2288\nEpoch 37/40\n30/30 - 57s - loss: 0.2101 - val_loss: 0.2271\nEpoch 38/40\n30/30 - 57s - loss: 0.2088 - val_loss: 0.2274\nEpoch 39/40\n30/30 - 57s - loss: 0.2082 - val_loss: 0.2265\nEpoch 40/40\n30/30 - 57s - loss: 0.2064 - val_loss: 0.2276\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Evaluate training history\nThe validation MCRMSE score became flat on 35 but training MCRMSE kept reducing so I have to limit my training to 40 ephchs at most",
   "metadata": {
    "cell_id": "00022-547e6f8e-3708-4eb7-b0b4-674446db6c73",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:59:26.304152Z",
     "iopub.status.idle": "2021-06-19T12:59:26.304712Z"
    },
    "trusted": true,
    "cell_id": "00023-c36cbb5b-74de-4b07-bcc0-f8954354fabc",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "30f6fa9b",
    "execution_start": 1624285117807,
    "execution_millis": 107,
    "deepnote_cell_type": "code"
   },
   "source": "if Model_Train:\n    fig = px.line(\n        history.history,\n        y=[\"loss\", \"val_loss\"],\n        labels={\"index\": \"epoch\", \"value\": \"MCRMSE\"},\n        title=\"History\",\n    )\n    fig.show()\n",
   "execution_count": null,
   "outputs": [
    {
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true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"History\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"epoch\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"MCRMSE\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('40e1767e-8ea1-4dbd-bee8-84c167061018');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "# Loading models and making predictions",
   "metadata": {
    "cell_id": "00024-a1f7bee8-3504-4360-85fc-4943f1ca6121",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "Public and private sets have different sequence lengths, so we will preprocess them separately and load models of different tensor shapes. This is possible because RNN models can accept sequences of varying lengths as inputs.",
   "metadata": {
    "cell_id": "00025-0b36e50d-0ee8-4e86-ab97-06af57ac1114",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "If the length of the sequence of an id is, e.g., 107, then you should make 107 predictions. Positions greater than the seq_scored value of a sample are not scored, but still need a value in the solution file.\n\n| id_seqpos      | reactivity | deg_Mg_pH10 | deg_pH10 | deg_Mg_50C | deg_50C |\n|----------------|------------|-------------|----------|------------|---------|\n| id_00073f8be_0 | 0.1        | 0.3         | 0.2      | 0.5        | 0.4     |\n| id_00073f8be_1 | 0.3        | 0.2         | 0.5      | 0.4        | 0.2     |\n| id_00073f8be_2 | 0.5        | 0.4         | 0.2      | 0.1        | 0.2     |\n",
   "metadata": {
    "tags": [],
    "cell_id": "00052-bea62083-2f5c-4b6e-9752-db0708ef3849",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "## Loading weights",
   "metadata": {
    "tags": [],
    "cell_id": "00052-7271f652-30d7-45f9-8010-88fdff17778e",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "Prediction formate required by competition organizers, `seq_length` is similar to `pred_len` so we are also creating two models with different `seq_len` and `pred_len`. After that loading our saved weights into both models.",
   "metadata": {
    "tags": [],
    "cell_id": "00051-21c62596-f6dd-4a37-97b6-920027570454",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:59:26.306110Z",
     "iopub.status.idle": "2021-06-19T12:59:26.306833Z"
    },
    "trusted": true,
    "cell_id": "00026-16ebd74f-b564-4646-a00d-59b4de278a05",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e64d477d",
    "execution_start": 1624285117917,
    "execution_millis": 2690,
    "deepnote_cell_type": "code"
   },
   "source": "model_public = build_model(seq_len=107, pred_len=107, embed_size=len(token2int))\nmodel_private = build_model(seq_len=130, pred_len=130, embed_size=len(token2int))\n\nmodel_public.load_weights(\"Model/model.h5\")\nmodel_private.load_weights(\"Model/model.h5\")\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Prediction",
   "metadata": {
    "tags": [],
    "cell_id": "00055-c6b3ce27-c768-4606-824d-57f7b73c7b1d",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:59:26.308171Z",
     "iopub.status.idle": "2021-06-19T12:59:26.308951Z"
    },
    "trusted": true,
    "cell_id": "00027-b8dd1ab4-1e9a-428d-a47c-bec8839aeb80",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1a7421be",
    "execution_start": 1624285120610,
    "execution_millis": 40457,
    "deepnote_cell_type": "code"
   },
   "source": "public_preds = model_public.predict(public_inputs)\nprivate_preds = model_private.predict(private_inputs)\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### Checking the output shape ",
   "metadata": {
    "tags": [],
    "cell_id": "00057-7d14661d-3c5c-4ff2-9832-baa62f614419",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00037-f9ee3b6d-b070-49d7-8205-7ed0b645d462",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b2e57d08",
    "execution_start": 1624285161112,
    "execution_millis": 7,
    "deepnote_cell_type": "code"
   },
   "source": "private_preds.shape",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "(3005, 130, 5)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "# Post-processing and submit",
   "metadata": {
    "cell_id": "00028-70ae740d-6b85-4a68-85be-045c277eac17",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "**Converting 3D numpy array into Dataframe:**\n- combining both **private(df,prediction )** and **public(df,prediction)** dataframe\n- Dividing unique id into 130 and 107 diferent samples `[id_00073f8be_0,id_00073f8be_1,id_00073f8be_2 ..]`\n- concating all of the data into 2D Dataframe and preparing for submission. \n",
   "metadata": {
    "cell_id": "00029-6defe67b-abec-424a-a5eb-40c10a542699",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:59:26.310688Z",
     "iopub.status.idle": "2021-06-19T12:59:26.311688Z"
    },
    "trusted": true,
    "cell_id": "00030-6cedbb54-719e-494e-b962-af922998addc",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "571c5418",
    "execution_start": 1624285161113,
    "execution_millis": 2589,
    "deepnote_cell_type": "code"
   },
   "source": "preds_ls = []\n\nfor df, preds in [(public_df, public_preds), (private_df, private_preds)]:\n    for i, uid in enumerate(df.id):\n\n        single_pred = preds[i]\n\n        single_df = pd.DataFrame(single_pred, columns=target_cols)\n        single_df[\"id_seqpos\"] = [f\"{uid}_{x}\" for x in range(single_df.shape[0])]\n\n        preds_ls.append(single_df)\n\npreds_df = pd.concat(preds_ls)\npreds_df.head()\n",
   "execution_count": null,
   "outputs": [
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     "output_type": "execute_result",
     "execution_count": 24,
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        {
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         "id_seqpos": "id_00073f8be_4",
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      "text/plain": "   reactivity  deg_Mg_pH10  deg_Mg_50C  deg_pH10   deg_50C       id_seqpos\n0    0.685760     0.703746    0.585288  1.857178  0.808561  id_00073f8be_0\n1    2.158555     3.243329    3.443042  4.394709  3.012130  id_00073f8be_1\n2    1.432280     0.674404    0.672512  0.662341  0.718279  id_00073f8be_2\n3    1.296234     1.306208    1.898748  1.324560  1.827133  id_00073f8be_3\n4    0.851104     0.670810    0.971952  0.573919  0.962205  id_00073f8be_4",
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     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Submission\nmergining our predicted dataframe on `id_seqpos` so that all the predicted values follows the same id. The Creating submission file",
   "metadata": {
    "tags": [],
    "cell_id": "00062-3099bde0-c8fd-4426-a5d4-f88c22fe4efb",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T12:59:26.313371Z",
     "iopub.status.idle": "2021-06-19T12:59:26.314310Z"
    },
    "trusted": true,
    "cell_id": "00031-2643e197-abb2-4554-9be9-928c89f07e7d",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "583cd46a",
    "execution_start": 1624285163693,
    "execution_millis": 2734,
    "deepnote_cell_type": "code"
   },
   "source": "submission = sample_df[[\"id_seqpos\"]].merge(preds_df, on=[\"id_seqpos\"])\nsubmission.to_csv(\"Submission/submission.csv\", index=False)\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### Observing submission file",
   "metadata": {
    "tags": [],
    "cell_id": "00064-5279b448-f133-4ef1-862f-7df70cc74392",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.status.busy": "2021-06-19T13:12:42.919147Z",
     "iopub.execute_input": "2021-06-19T13:12:42.919555Z",
     "iopub.status.idle": "2021-06-19T13:12:42.936704Z",
     "shell.execute_reply.started": "2021-06-19T13:12:42.919522Z",
     "shell.execute_reply": "2021-06-19T13:12:42.934848Z"
    },
    "trusted": true,
    "cell_id": "00032-56fe12ac-86b7-44d8-aaf4-4e94977836c5",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "defecc0b",
    "execution_start": 1624285166441,
    "execution_millis": 18,
    "deepnote_cell_type": "code"
   },
   "source": "submission.head()",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 26,
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      "text/plain": "        id_seqpos  reactivity  deg_Mg_pH10  deg_Mg_50C  deg_pH10   deg_50C\n0  id_00073f8be_0    0.685760     0.703746    0.585288  1.857178  0.808561\n1  id_00073f8be_1    2.158555     3.243329    3.443042  4.394709  3.012130\n2  id_00073f8be_2    1.432280     0.674404    0.672512  0.662341  0.718279\n3  id_00073f8be_3    1.296234     1.306208    1.898748  1.324560  1.827133\n4  id_00073f8be_4    0.851104     0.670810    0.971952  0.573919  0.962205",
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     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Private Score\nI got private score of 0.2723 which is quite better. This result was produced on **50** epochs. ",
   "metadata": {
    "tags": [],
    "cell_id": "00053-b715f625-5eb2-4e18-aa87-06150c545698",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "![Picture title](Images/image-20210621-190910.png)",
   "metadata": {
    "tags": [],
    "cell_id": "00067-ad98daee-5f9a-4c71-baaa-c46fc19e3e67",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "# Final Thoughts\nThis was a unique experience for me as I was dealing with JSON files with multiple arrays within single samples. Once I research it and figure out how to deal with the data, it became quite simple. I did a lot of EDA and Data Analysis in another notebook, also experimenting with multiple models but I wanted to keep this notebook clean and easy to understand with minimal layers used. My main goal was to produce a notebook with easy to understand coding format so that anyone even a beginner can understand and make some changes to produce an even better result than me. I would like to thank the Kaggle community for helping me figure out how to process the data and then used the best possible model which is also the simplest model. In the end, I just wanted to share how some time complex problems have simple solutions. ",
   "metadata": {
    "tags": [],
    "cell_id": "00068-0c87ab9a-016a-4d6d-92ad-9a6c67d43664",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "![](https://media.giphy.com/media/ybYTOunnNPGco/giphy.gif)",
   "metadata": {
    "tags": [],
    "cell_id": "00069-db71ef35-cecb-4bf4-a037-34eaa3597e29",
    "deepnote_cell_type": "markdown"
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  },
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   "source": "# Addidional Data\n\n**How RNA vaccines work, and their issues, featuring Dr. Rhiju Das**\nhttps://www.pbs.org/wgbh/nova/video/rna-coronavirus-vaccine/\n\n**Launch of the OpenVaccine challenge**\nhttps://scopeblog.stanford.edu/2020/05/20/stanford-biochemist-works-with-gamers-to-develop-covid-19-vaccine/\n\n**The impossibility of mass immunization with current RNA vaccines**\nhttps://www.wsj.com/articles/from-freezer-farms-to-jets-logistics-operators-prepare-for-a-covid-19-vaccine-11598639012\n\n**CDC prepares for frozen mRNA vaccines**\nhttps://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2020-08/COVID-08-Dooling.pdf\n\n**Eterna, the crowdsourcing platform for RNA design where OpenVaccine's RNA sequences are coming from**\n[https://eternagame.org](https://eternagame.org/)\n\n**How to build a better vaccine from the comfort of your own web browser**\nhttps://medium.com/eternaproject/how-to-build-a-better-vaccine-from-the-comfort-of-your-own-web-browser-233343e0210d",
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