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"}}},{"cell_type":"markdown","source":"This notebook attempts to understand the structure of the [CAFA 5 Protein Function Prediction](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/overview) Data and perform **Data Processing** to train a **Deep Neural Network** for the given task.","metadata":{}},{"cell_type":"markdown","source":"# Table of Contents\n\n- [Introduction](#Introduction)\n- [Understanding the Data](#Understanding-the-Data)\n- [Exploring the Data](#Exploring-the-Data)\n- [Preparing the Data](#Preparing-the-Data)\n- [Building and Training the DNN model](#Building-and-Training-the-DNN-model)\n- [Conclusion](#Conclusion)","metadata":{}},{"cell_type":"markdown","source":"<div style=\"border-radius: 10px; background-color: #0077B6\">\n    <h1 style=\"color: white; padding: 1rem\">Introduction</h1>\n</div>\n\n<br>\n\nBasically, [CAFA 5 Protein](https://www.kaggle.com/competitions/cafa-5-protein-function-prediction/overview) dataset is built for predicting/classifying the different *functions* of various **protein sequences**. Before we build a DNN model though, we need to understand the underlying data and it's structure then finally process it to make it ready to be used by the DNN models.\n\nLet us get a basic understanding of what the data means.","metadata":{}},{"cell_type":"markdown","source":"<div style=\"border-radius: 10px; background-color: #0077B6\">\n    <h1 style=\"color: white; padding: 1rem\">Understanding the Data</h1>\n</div>\n\n<br>\n\nEach protein in this dataset is essentially made of a very long chain of amino acids linked together. Biologically. For representing the protein sequence, the individual amino acid is given a one-letter or three-letter code. Thus a protein sequence, it is written down as a long string of characters.\n\nThe file `train_sequences.fasta` contains all the string representations of the proteins observed, along with their metadata.\n\n> The `train_sequences.fasta` file will indicate from which database the sequence originate. For example, `sp|P9WHI7|RECN_MYCT` in the FASTA header indicates the protein with UniProt ID `P9WHI7` and gene name `RECN_MYCT` was taken from Swiss-Prot (sp). Any sequences taken from `TrEMBL` will have tr in the header instead of sp. Swiss-Prot and TrEMBL are both parts of UniProtKB.\n\n## Protein functions\n\nA protein's *function* is defined by **Gene Ontology (GO)**. Gene Ontology describes the functions (biologically) in three domains:\n\n- **Biological Process (BPO)**\n- **Molecular Function (MF)**\n- **Cellular Component (CC)**\n\nThe file `train_terms.tsv` contains information about the *functions* (GO terms) of a particular protein. It has three columns:\n\n- `EntryID`: The unique protein id (UniProt ID)\n- `term`: The GO term that protein contains\n- `aspect`: The domain of the GO term\n\nSince each protein can have multiple GO terms associated with it, our task falls under **Multi-Label Classification**.\n\n**<span style=\"color: darkblue\">The labels for our task will be the GO terms.</span>**","metadata":{}},{"cell_type":"code","source":"import os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:50:41.208699Z","iopub.execute_input":"2023-06-27T09:50:41.209051Z","iopub.status.idle":"2023-06-27T09:50:41.252275Z","shell.execute_reply.started":"2023-06-27T09:50:41.209015Z","shell.execute_reply":"2023-06-27T09:50:41.251358Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Protein Embeddings\n\nIn order to train a machine learning model, it is not possible to directly use the protein sequences listed in `train_sequences.fasta` in their alphabetical form. They need to be transformed into a vector representation. We will utilize protein sequence embeddings to train the model. Protein embeddings can be compared to word embeddings that are employed in training NLP models. One approach involves training a customized model to acquire the protein embeddings for the protein sequences within the dataset used in this notebook. Since the dataset represents proteins using amino-acid sequences, which is a conventional method, we can utilize any publicly available pre-trained protein embedding models to generate the embeddings.\n\nEmbeddings to be used: [Sergei Fironov's T5 Embeds](https://www.kaggle.com/datasets/sergeifironov/t5embeds)","metadata":{}},{"cell_type":"markdown","source":"<div style=\"border-radius: 10px; background-color: #0077B6\">\n    <h1 style=\"color: white; padding: 1rem\">Exploring the Data</h1>\n</div>\n\nThe file `go-basic.obo` contains the information about the GO terms. It is in the form of a **Directed Acyclic Graph (DAG)**.","metadata":{}},{"cell_type":"code","source":"# basic imports\nimport numpy as np\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2023-06-27T14:40:37.477363Z","iopub.execute_input":"2023-06-27T14:40:37.477749Z","iopub.status.idle":"2023-06-27T14:40:37.482725Z","shell.execute_reply.started":"2023-06-27T14:40:37.477720Z","shell.execute_reply":"2023-06-27T14:40:37.481776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install obonet","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-06-27T09:50:41.261179Z","iopub.execute_input":"2023-06-27T09:50:41.261515Z","iopub.status.idle":"2023-06-27T09:50:56.222644Z","shell.execute_reply.started":"2023-06-27T09:50:41.261485Z","shell.execute_reply":"2023-06-27T09:50:56.221443Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We will use the Python library `obonet` to read the DAG file.","metadata":{}},{"cell_type":"code","source":"import obonet as obo","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:50:56.227168Z","iopub.execute_input":"2023-06-27T09:50:56.227515Z","iopub.status.idle":"2023-06-27T09:50:56.621790Z","shell.execute_reply.started":"2023-06-27T09:50:56.227484Z","shell.execute_reply":"2023-06-27T09:50:56.620848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"go_graph = obo.read_obo(\"/kaggle/input/cafa-5-protein-function-prediction/Train/go-basic.obo\")\ngo_graph","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:50:56.623175Z","iopub.execute_input":"2023-06-27T09:50:56.623539Z","iopub.status.idle":"2023-06-27T09:51:13.908466Z","shell.execute_reply.started":"2023-06-27T09:50:56.623495Z","shell.execute_reply":"2023-06-27T09:51:13.907356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Nodes in the GO graph: \", len(go_graph))\nprint(\"Edges in the GO graph: \", go_graph.number_of_edges())","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:13.910079Z","iopub.execute_input":"2023-06-27T09:51:13.910790Z","iopub.status.idle":"2023-06-27T09:51:14.042069Z","shell.execute_reply.started":"2023-06-27T09:51:13.910756Z","shell.execute_reply":"2023-06-27T09:51:14.041018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The **Ontology Graph Structure** has around $43,000$ nodes and $84,800$ edges.\n\n> GO is a directed acyclic graph. The nodes in this graph are functional descriptors (terms or classes) connected by relational ties between them (is_a, part_of, etc.). For example, terms 'protein binding activity' and 'binding activity' are related by an is_a relationship; however, the edge in the graph is often reversed to point from binding towards protein binding. This graph contains three subgraphs (subontologies): Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), defined by their root nodes. Biologically, each subgraph represent a different aspect of the protein's function: what it does on a molecular level (MF), which biological processes it participates in (BP) and where in the cell it is located (CC).","metadata":{}},{"cell_type":"code","source":"# retrieve node properties\ngo_graph.nodes['GO:0003301']","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:14.043471Z","iopub.execute_input":"2023-06-27T09:51:14.043897Z","iopub.status.idle":"2023-06-27T09:51:14.052474Z","shell.execute_reply.started":"2023-06-27T09:51:14.043865Z","shell.execute_reply":"2023-06-27T09:51:14.051346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The above node falls under `BPO` (biological process) with the corresponding relationship.","metadata":{}},{"cell_type":"markdown","source":"Now, our main dataset files are `train_terms.tsv`, `train_ids.npy`, and `train_embeds.npy`\n\n- `train_terms.tsv` contains **protein ID** (UnitProt ID, Entry ID, or train ID), the **GO term** associated to that protein ID, and the **aspect** of that term.\n- `train_ids.npy` is the numpy `ndarray` of **protein IDs**.\n- `train_embeds.npy` is the numpy `ndarray` of **protein sequence embeddings**.\n\nLet us load these files.","metadata":{}},{"cell_type":"code","source":"train_terms = pd.read_csv(\"/kaggle/input/cafa-5-protein-function-prediction/Train/train_terms.tsv\", sep=\"\\t\")","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:14.054002Z","iopub.execute_input":"2023-06-27T09:51:14.054521Z","iopub.status.idle":"2023-06-27T09:51:17.496081Z","shell.execute_reply.started":"2023-06-27T09:51:14.054486Z","shell.execute_reply":"2023-06-27T09:51:17.494906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_terms.head(10)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:17.501167Z","iopub.execute_input":"2023-06-27T09:51:17.502244Z","iopub.status.idle":"2023-06-27T09:51:17.519462Z","shell.execute_reply.started":"2023-06-27T09:51:17.502204Z","shell.execute_reply":"2023-06-27T09:51:17.518548Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let us also check how many GO terms are present in this `DataFrame` along with their counts.","metadata":{}},{"cell_type":"code","source":"term_vcs = train_terms['term'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:17.520832Z","iopub.execute_input":"2023-06-27T09:51:17.521185Z","iopub.status.idle":"2023-06-27T09:51:18.366420Z","shell.execute_reply.started":"2023-06-27T09:51:17.521154Z","shell.execute_reply":"2023-06-27T09:51:18.365443Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"term_vcs","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:18.368020Z","iopub.execute_input":"2023-06-27T09:51:18.368418Z","iopub.status.idle":"2023-06-27T09:51:18.380882Z","shell.execute_reply.started":"2023-06-27T09:51:18.368385Z","shell.execute_reply":"2023-06-27T09:51:18.379982Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"term_vcs.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:18.382281Z","iopub.execute_input":"2023-06-27T09:51:18.382924Z","iopub.status.idle":"2023-06-27T09:51:18.393763Z","shell.execute_reply.started":"2023-06-27T09:51:18.382887Z","shell.execute_reply":"2023-06-27T09:51:18.392686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Damn, there are $31466$ GO terms!\n\nNext, let us look at the pie chart of the distribution of the `aspect` column.","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:18.395282Z","iopub.execute_input":"2023-06-27T09:51:18.395696Z","iopub.status.idle":"2023-06-27T09:51:19.069264Z","shell.execute_reply.started":"2023-06-27T09:51:18.395658Z","shell.execute_reply":"2023-06-27T09:51:19.068307Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Setting some styles\nsns.set_style(\"darkgrid\")\nsns.set_palette(\"viridis\")","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:19.070523Z","iopub.execute_input":"2023-06-27T09:51:19.071172Z","iopub.status.idle":"2023-06-27T09:51:19.079333Z","shell.execute_reply.started":"2023-06-27T09:51:19.071124Z","shell.execute_reply":"2023-06-27T09:51:19.078420Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\n\ntrain_terms['aspect'].value_counts().plot.pie(autopct='%1.1f%%', ax=ax, explode=[0.1, 0.1, 0.1], shadow=True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:19.080825Z","iopub.execute_input":"2023-06-27T09:51:19.081855Z","iopub.status.idle":"2023-06-27T09:51:20.102370Z","shell.execute_reply.started":"2023-06-27T09:51:19.081820Z","shell.execute_reply":"2023-06-27T09:51:20.101470Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- **65.2%** GO terms fall under Biological Process (BPO)\n- **22.3%** GO terms fall under Cellular Component (CCO)\n- Only **12.5%** GO terms fall under Molecular Function (MFO)","metadata":{}},{"cell_type":"markdown","source":"Let us plot the **100 most frequent** GO terms.","metadata":{}},{"cell_type":"code","source":"import plotly.express as px","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:20.103997Z","iopub.execute_input":"2023-06-27T09:51:20.104610Z","iopub.status.idle":"2023-06-27T09:51:21.030724Z","shell.execute_reply.started":"2023-06-27T09:51:20.104574Z","shell.execute_reply":"2023-06-27T09:51:21.027769Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"term_vcs_100 = term_vcs[:100]\n\nfig = px.bar(term_vcs_100,\n             labels={\n                 \"index\": \"GO term\",\n                 \"value\": \"Count\",\n             },\n            title=\"100 most frequent GO terms\")\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:21.031996Z","iopub.execute_input":"2023-06-27T09:51:21.032364Z","iopub.status.idle":"2023-06-27T09:51:22.944163Z","shell.execute_reply.started":"2023-06-27T09:51:21.032328Z","shell.execute_reply":"2023-06-27T09:51:22.943004Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Since, there are $31466$ GO terms in total. To reduce the complexity and training time, we will select only the $2000$ most frequent GO terms from the dataset for the DNN model to be trained on.","metadata":{}},{"cell_type":"markdown","source":"# T5 protein embeds\n\n<br>\n\nLet us look at the **protein IDs** and their **embeddings**.","metadata":{}},{"cell_type":"code","source":"train_ids = np.load(\"/kaggle/input/t5embeds/train_ids.npy\")\ntrain_embeds = np.load(\"/kaggle/input/t5embeds/train_embeds.npy\")","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:22.945803Z","iopub.execute_input":"2023-06-27T09:51:22.946177Z","iopub.status.idle":"2023-06-27T09:51:33.006742Z","shell.execute_reply.started":"2023-06-27T09:51:22.946144Z","shell.execute_reply":"2023-06-27T09:51:33.005669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Train ids shape: \", train_ids.shape)\nprint(\"Train embeds shape: \", train_embeds.shape)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.008450Z","iopub.execute_input":"2023-06-27T09:51:33.008861Z","iopub.status.idle":"2023-06-27T09:51:33.015471Z","shell.execute_reply.started":"2023-06-27T09:51:33.008822Z","shell.execute_reply":"2023-06-27T09:51:33.014520Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Each protein is embedded into a vector of size $1024$ as represented by the number of columns of `train_embeds`.","metadata":{}},{"cell_type":"code","source":"train_ids","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.016803Z","iopub.execute_input":"2023-06-27T09:51:33.017686Z","iopub.status.idle":"2023-06-27T09:51:33.029063Z","shell.execute_reply.started":"2023-06-27T09:51:33.017653Z","shell.execute_reply":"2023-06-27T09:51:33.027788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_embeds","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.030547Z","iopub.execute_input":"2023-06-27T09:51:33.030979Z","iopub.status.idle":"2023-06-27T09:51:33.041544Z","shell.execute_reply.started":"2023-06-27T09:51:33.030945Z","shell.execute_reply":"2023-06-27T09:51:33.040143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now, let us check whether the protein IDs in `train_ids` and the protein IDs in `train_terms.tsv` are actually same or not.","metadata":{}},{"cell_type":"code","source":"entry_ids = train_terms['EntryID'].unique()\n\ntrain_ids.shape == entry_ids.shape","metadata":{"execution":{"iopub.status.busy":"2023-06-27T14:56:40.119217Z","iopub.execute_input":"2023-06-27T14:56:40.119689Z","iopub.status.idle":"2023-06-27T14:56:40.606160Z","shell.execute_reply.started":"2023-06-27T14:56:40.119659Z","shell.execute_reply":"2023-06-27T14:56:40.605234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_ids_sorted = np.sort(train_ids)\nentry_ids_sorted = np.sort(entry_ids)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.526663Z","iopub.execute_input":"2023-06-27T09:51:33.527302Z","iopub.status.idle":"2023-06-27T09:51:33.755898Z","shell.execute_reply.started":"2023-06-27T09:51:33.527248Z","shell.execute_reply":"2023-06-27T09:51:33.754924Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Sort for better comparison speed...","metadata":{}},{"cell_type":"code","source":"print(\"T5 ids and entry ids are equal?\", np.array_equal(train_ids_sorted, entry_ids_sorted))","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.762232Z","iopub.execute_input":"2023-06-27T09:51:33.762548Z","iopub.status.idle":"2023-06-27T09:51:33.789102Z","shell.execute_reply.started":"2023-06-27T09:51:33.762520Z","shell.execute_reply":"2023-06-27T09:51:33.788008Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let us look at each protein ID and its embedding individually...","metadata":{}},{"cell_type":"code","source":"for i, v in enumerate(train_ids[:10]):\n    print(\"Protein Entry id: \", v)\n    print(\"Embed: \", train_embeds[i])","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.790748Z","iopub.execute_input":"2023-06-27T09:51:33.791522Z","iopub.status.idle":"2023-06-27T09:51:33.803630Z","shell.execute_reply.started":"2023-06-27T09:51:33.791485Z","shell.execute_reply":"2023-06-27T09:51:33.802125Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"border-radius: 10px; background-color: #0077B6\">\n    <h1 style=\"color: white; padding: 1rem\">Preparing the Data</h1>\n</div>\n\n<br>\n\nAs mentioned above, we will select only the $2000$ most frequent GO terms as labels, to reduce training time and *dimensionality*.\n\nDiscarding the other rows from `train_terms.tsv`...","metadata":{}},{"cell_type":"code","source":"NUM_LABELS = 2000\n\nterms_2k = term_vcs.index[:NUM_LABELS].tolist()\nfor t in terms_2k[:10]:\n    print(t, end=\", \")","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:33.805102Z","iopub.execute_input":"2023-06-27T09:51:33.805714Z","iopub.status.idle":"2023-06-27T09:51:33.815433Z","shell.execute_reply.started":"2023-06-27T09:51:33.805681Z","shell.execute_reply":"2023-06-27T09:51:33.814254Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Retreive the appropriate `train_terms data` from the `terms_2k` list.","metadata":{}},{"cell_type":"code","source":"train_terms_2k = train_terms.loc[train_terms['term'].isin(terms_2k)]\ntrain_terms_2k","metadata":{"execution":{"iopub.status.busy":"2023-06-27T15:01:37.864222Z","iopub.execute_input":"2023-06-27T15:01:37.864641Z","iopub.status.idle":"2023-06-27T15:01:38.533887Z","shell.execute_reply.started":"2023-06-27T15:01:37.864609Z","shell.execute_reply":"2023-06-27T15:01:38.532766Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<h1 style=\"color: darkblue\">Processing steps</h1>\n\nAny protein (train id) can have multiple GO terms associated to it. So, this is a **Multi-label Classification** task.\n\nWe have a `DataFrame` of train/protein ids (essentially, their embeddings) of shape $(N, 1024)$ as the training data. Here, $N$ is the number of train/protein ids. The labels for this **supervised** task will be of shape $(N, 2000)$.\n\nThe columns stand for the most frequent $2000$ GO terms. A cell $(i,\\space j)$ in `labels` can have values either `0` or `1` representing whether the $j^{th}$ GO term is associated with the $i^{th}$ train/protein id or not.\n\nTo prepare the `labels` we need to perform the following steps...\n\n1. **Iterate through the $2000$ labels**\n2. **Find all the rows in `train_terms` containing that GO term**\n3. **Get the unique `EntryID`s of those rows in `train_terms` from step 2**\n4. **Replace the `ith` column of `labels` with $1.0$ if GO term belongs to that protein else $0.0$**","metadata":{}},{"cell_type":"code","source":"# initialize empty labels array\nlabels_shape = (train_ids.shape[0], NUM_LABELS)\nlabels = np.empty(labels_shape)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:34.482114Z","iopub.execute_input":"2023-06-27T09:51:34.482576Z","iopub.status.idle":"2023-06-27T09:51:34.487837Z","shell.execute_reply.started":"2023-06-27T09:51:34.482537Z","shell.execute_reply":"2023-06-27T09:51:34.486639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# progress bar\nfrom tqdm import tqdm","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:34.489655Z","iopub.execute_input":"2023-06-27T09:51:34.490429Z","iopub.status.idle":"2023-06-27T09:51:34.500421Z","shell.execute_reply.started":"2023-06-27T09:51:34.490391Z","shell.execute_reply":"2023-06-27T09:51:34.499219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> For large datasets, pandas `isin` works better than numpy's `isin`","metadata":{}},{"cell_type":"code","source":"series_train_ids = pd.Series(train_ids)\n\nwith tqdm(total=NUM_LABELS) as pbar:\n    # Step 1\n    for i in range(NUM_LABELS):\n        go_term = terms_2k[i]\n        \n        # Step 2 \n        curr_train_terms = train_terms_2k[train_terms_2k['term'] == go_term]\n        \n        # Step 3\n        uniq_entry_ids = curr_train_terms['EntryID'].unique()\n        \n        # Step 4\n        labels[:, i] = series_train_ids.isin(uniq_entry_ids).astype(float)\n        \n        pbar.update(1)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T09:51:34.502167Z","iopub.execute_input":"2023-06-27T09:51:34.502796Z","iopub.status.idle":"2023-06-27T10:15:46.150608Z","shell.execute_reply.started":"2023-06-27T09:51:34.502759Z","shell.execute_reply":"2023-06-27T10:15:46.149688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Got our labels, creating a `DataFrame`...","metadata":{}},{"cell_type":"code","source":"labels_df = pd.DataFrame(labels, columns=terms_2k)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T10:15:46.152265Z","iopub.execute_input":"2023-06-27T10:15:46.152986Z","iopub.status.idle":"2023-06-27T10:15:46.158876Z","shell.execute_reply.started":"2023-06-27T10:15:46.152947Z","shell.execute_reply":"2023-06-27T10:15:46.157866Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels_df","metadata":{"execution":{"iopub.status.busy":"2023-06-27T10:15:46.160272Z","iopub.execute_input":"2023-06-27T10:15:46.161057Z","iopub.status.idle":"2023-06-27T10:15:46.252335Z","shell.execute_reply.started":"2023-06-27T10:15:46.161024Z","shell.execute_reply":"2023-06-27T10:15:46.251227Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.DataFrame(train_embeds, columns=[f\"col{i+1}\" for i in range(train_embeds.shape[1])])","metadata":{"execution":{"iopub.status.busy":"2023-06-27T10:15:46.253644Z","iopub.execute_input":"2023-06-27T10:15:46.254495Z","iopub.status.idle":"2023-06-27T10:15:46.261420Z","shell.execute_reply.started":"2023-06-27T10:15:46.254459Z","shell.execute_reply":"2023-06-27T10:15:46.260360Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T10:15:46.262736Z","iopub.execute_input":"2023-06-27T10:15:46.263125Z","iopub.status.idle":"2023-06-27T10:15:46.292169Z","shell.execute_reply.started":"2023-06-27T10:15:46.263088Z","shell.execute_reply":"2023-06-27T10:15:46.291196Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There we have it:\n\n- `train_df`: Our DNN model ready training data. Shape: $(N, 1024)$\n- `labels_df`: Our DNN model ready labels for the multi-label classification task. Shape: $(N, 2000)$","metadata":{}},{"cell_type":"markdown","source":"<div style=\"border-radius: 10px; background-color: #0077B6\">\n    <h1 style=\"color: white; padding: 1rem\">Building and Training the DNN model</h1>\n</div>\n\nNow is the time to build our DNN model using `Tensorflow Keras`.","metadata":{}},{"cell_type":"markdown","source":"I will use the below model configuration:\n\n- **Input Layer, $1024$ neurons**\n- **Batch Normalization Layer**\n- **$10$ Hidden Layers, $300$ neurons each having:**\n    - Kernel Initialization: `He normal`\n    - Batch Normalization layer\n    - Leaky ReLU activation layer\n- **Output Layer, $2000$ neurons:**\n    - Sigmoid activation function\n\nAlong with this, I will use the loss function `binary_crossentropy` for multi-label classification, and `ADAM` optimizer.","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\ntf.config.list_physical_devices(\"GPU\")","metadata":{"execution":{"iopub.status.busy":"2023-06-27T15:23:11.623482Z","iopub.execute_input":"2023-06-27T15:23:11.623878Z","iopub.status.idle":"2023-06-27T15:23:11.630278Z","shell.execute_reply.started":"2023-06-27T15:23:11.623849Z","shell.execute_reply":"2023-06-27T15:23:11.629346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The input shape in this case will be $1024$.","metadata":{}},{"cell_type":"code","source":"INPUT_SHAPE = [train_df.shape[1]]","metadata":{"execution":{"iopub.status.busy":"2023-06-27T10:15:54.786024Z","iopub.execute_input":"2023-06-27T10:15:54.787233Z","iopub.status.idle":"2023-06-27T10:15:54.793183Z","shell.execute_reply.started":"2023-06-27T10:15:54.787199Z","shell.execute_reply":"2023-06-27T10:15:54.791835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In order to write error-prone code, I will use the `partial` function to define a thin wrapper for each of the **Hidden Layers**.","metadata":{}},{"cell_type":"code","source":"from functools import partial","metadata":{"execution":{"iopub.status.busy":"2023-06-27T10:15:59.076815Z","iopub.execute_input":"2023-06-27T10:15:59.077269Z","iopub.status.idle":"2023-06-27T10:15:59.082140Z","shell.execute_reply.started":"2023-06-27T10:15:59.077235Z","shell.execute_reply":"2023-06-27T10:15:59.081248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Next, writing a function to create and return a model with the above structure...","metadata":{}},{"cell_type":"code","source":"# LeakyReLU alpha\nalpha = 0.1\n\nNUM_HIDDEN_LAYERS = 10\n\n# Function to create a sequential keras DNN model\ndef create_model(inp_shape):\n    m = tf.keras.Sequential()\n    \n    # 1 and 2) Input layer and BatchNorm layer\n    m.add(tf.keras.layers.BatchNormalization(input_shape=inp_shape))\n    \n    # Wrapper for the hidden layers\n    DenseLayer = partial(\n        tf.keras.layers.Dense,\n        kernel_initializer=\"he_normal\",\n        use_bias=False\n    )\n    \n    # 3) 10 Hidden Layers\n    for _ in range(NUM_HIDDEN_LAYERS):\n        m.add(DenseLayer(300))\n        m.add(tf.keras.layers.BatchNormalization())\n        m.add(tf.keras.layers.LeakyReLU(alpha=alpha))\n        \n    # 4) output layer\n    m.add(tf.keras.layers.Dense(NUM_LABELS, activation=\"sigmoid\"))\n    \n    return m","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:50:07.924255Z","iopub.execute_input":"2023-06-27T12:50:07.924783Z","iopub.status.idle":"2023-06-27T12:50:07.933401Z","shell.execute_reply.started":"2023-06-27T12:50:07.924732Z","shell.execute_reply":"2023-06-27T12:50:07.932020Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To ensure an optimal model performance, we need a **Validation set** to evaluate the model on, after each epoch.","metadata":{}},{"cell_type":"code","source":"# train and validation set\nfrom sklearn.model_selection import train_test_split\n\nX = train_df.to_numpy()\ny = labels_df.to_numpy()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:46:22.480613Z","iopub.execute_input":"2023-06-27T12:46:22.480983Z","iopub.status.idle":"2023-06-27T12:46:22.487444Z","shell.execute_reply.started":"2023-06-27T12:46:22.480952Z","shell.execute_reply":"2023-06-27T12:46:22.485252Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(X.shape, y.shape)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:46:39.798113Z","iopub.execute_input":"2023-06-27T12:46:39.798601Z","iopub.status.idle":"2023-06-27T12:46:39.804823Z","shell.execute_reply.started":"2023-06-27T12:46:39.798553Z","shell.execute_reply":"2023-06-27T12:46:39.803858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We will keep $80$% of the dataset for training, and $20$% for validation...","metadata":{}},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)\n\nprint(X_train.shape, X_val.shape)\nprint(y_train.shape, y_val.shape)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:48:28.137882Z","iopub.execute_input":"2023-06-27T12:48:28.138242Z","iopub.status.idle":"2023-06-27T12:48:29.414249Z","shell.execute_reply.started":"2023-06-27T12:48:28.138212Z","shell.execute_reply":"2023-06-27T12:48:29.413046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create a new model with the appropriate input shape...","metadata":{}},{"cell_type":"code","source":"new_model = create_model(inp_shape=[X_train.shape[1]])","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:50:11.799786Z","iopub.execute_input":"2023-06-27T12:50:11.800147Z","iopub.status.idle":"2023-06-27T12:50:12.100074Z","shell.execute_reply.started":"2023-06-27T12:50:11.800118Z","shell.execute_reply":"2023-06-27T12:50:12.099137Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"new_model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:50:14.005109Z","iopub.execute_input":"2023-06-27T12:50:14.005693Z","iopub.status.idle":"2023-06-27T12:50:14.069786Z","shell.execute_reply.started":"2023-06-27T12:50:14.005661Z","shell.execute_reply":"2023-06-27T12:50:14.068789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"$1.7M$ parameters in total. Hmm, nice!\n\nCompiling the model to calculate `binary_accuracy` and `AUC (Area Under the Curve)` as evaluation metrics.","metadata":{}},{"cell_type":"code","source":"# compile model with ADAM\nnew_model.compile(\n    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n    loss='binary_crossentropy',\n    metrics=['binary_accuracy', tf.keras.metrics.AUC()],\n)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:51:16.993463Z","iopub.execute_input":"2023-06-27T12:51:16.993823Z","iopub.status.idle":"2023-06-27T12:51:17.014613Z","shell.execute_reply.started":"2023-06-27T12:51:16.993795Z","shell.execute_reply":"2023-06-27T12:51:17.013710Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"While training a DNN model, we can define some **callback functions** that are performed after each epoch (by default).\n\nI will use two callbacks:\n\n- **Early Stopping Callback**: This will monitor the `loss` of the model and will stop training if it doesn't change for $10$ epochs.\n- **Model Checkpoint Callback**: This will save the best model on the disk for persistence.","metadata":{}},{"cell_type":"code","source":"# callbacks\nearlystopping_cb = tf.keras.callbacks.EarlyStopping(monitor=\"loss\", patience=10)\ncheckpoint_cb = tf.keras.callbacks.ModelCheckpoint(\"/kaggle/working/best_model.h5\", save_best_only=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T15:25:08.536229Z","iopub.execute_input":"2023-06-27T15:25:08.536931Z","iopub.status.idle":"2023-06-27T15:25:08.542216Z","shell.execute_reply.started":"2023-06-27T15:25:08.536898Z","shell.execute_reply":"2023-06-27T15:25:08.541167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train new model\nhistory = new_model.fit(X_train, y_train, epochs=30,\n                       validation_data=(X_val, y_val),\n                       callbacks=[earlystopping_cb, checkpoint_cb])","metadata":{"execution":{"iopub.status.busy":"2023-06-27T12:53:04.188590Z","iopub.execute_input":"2023-06-27T12:53:04.188949Z","iopub.status.idle":"2023-06-27T13:22:33.294919Z","shell.execute_reply.started":"2023-06-27T12:53:04.188920Z","shell.execute_reply":"2023-06-27T13:22:33.293884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"A validation accuracy of $0.985$. Nice! \n\nThe `history.history` variable contains the information of each epoch.","metadata":{}},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\nhistory_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T13:41:30.106218Z","iopub.execute_input":"2023-06-27T13:41:30.106783Z","iopub.status.idle":"2023-06-27T13:41:30.131683Z","shell.execute_reply.started":"2023-06-27T13:41:30.106744Z","shell.execute_reply":"2023-06-27T13:41:30.130147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Plotting the training loss and accuracy along with validation loss and accuracy...","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12, 8))\n\nsns.lineplot(history_df.drop(columns=[\"auc_1\", \"val_auc_1\"]))\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-06-27T13:43:51.883636Z","iopub.execute_input":"2023-06-27T13:43:51.884243Z","iopub.status.idle":"2023-06-27T13:43:52.435135Z","shell.execute_reply.started":"2023-06-27T13:43:51.884210Z","shell.execute_reply":"2023-06-27T13:43:52.434221Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This seems to perform well!\n\n**Hyperparameter tuning** and be further performed on this model for even better results. Below is the full model architecture.","metadata":{}},{"cell_type":"code","source":"img_file = '/kaggle/working/model_arch.png'\n\ntf.keras.utils.plot_model(new_model, to_file=img_file, show_shapes=True, show_layer_names=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-27T15:47:06.554754Z","iopub.execute_input":"2023-06-27T15:47:06.555144Z","iopub.status.idle":"2023-06-27T15:47:07.067884Z","shell.execute_reply.started":"2023-06-27T15:47:06.555114Z","shell.execute_reply":"2023-06-27T15:47:07.066998Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"border-radius: 10px; background-color: #0077B6\">\n    <h1 style=\"color: white; padding: 1rem\">Conclusion</h1>\n</div>\n\n<br>\n\nThis was all about the **CAFA 5 Protein** dataset and how DNN models can be effectively used in the biological field to predict the *function* of various proteins, in order to understand the biological systems more thoroughly.\n\n**<span style=\"color: darkblue\">If this notebook helped you a slightest bit, do consider upvoting it and leaving a comment below. Feel free to extend this notebook with more knowledge. Thank you! ❤️</span>**","metadata":{}}]}