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helical/examples/notebooks/Hyena-DNA-Inference.ipynb at release · helicalAI/helical · GitHub
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\"cells\": ["," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"# Downstream prediction \\n\","," \"\\n\","," \"Run this notebook on google colab to use a free GPU! \\n\","," \"\\n\","," \"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Hyena-DNA-Inference.ipynb)\\n\","," \"\\n\","," \"In this notebook, a HyenaDNA model is used for various classifications tasks with a given sequence of nucleotides.\\n\","," \"\\n\","," \"A HyenaDNA model (2 layers and width 256) is used to create embeddings of nucleotides.\\n\","," \"\\n\","," \"A neural network is then trained, using the embeddings as inputs, to make a prediction.\\n\","," \"\\n\","," \"This notebook can be used for any of the 18 [nucleotide transformer downstream tasks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks).\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 16,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"Requirement already satisfied: tensorflow_addons in /Users/bputzeys/miniforge3/envs/head_example/lib/python3.11/site-packages (0.23.0)\\n\","," \"Requirement already satisfied: typeguard\u003c3.0.0,\u003e=2.7 in /Users/bputzeys/miniforge3/envs/head_example/lib/python3.11/site-packages (from tensorflow_addons) (2.13.3)\\n\","," \"Requirement already satisfied: packaging in /Users/bputzeys/miniforge3/envs/head_example/lib/python3.11/site-packages (from tensorflow_addons) (24.0)\\n\","," \" Running command git clone --filter=blob:none --quiet https://github.com/helicalAI/helical.git /private/var/folders/pm/76n9tjdj18z7cbc55rk10yfw0000gn/T/pip-req-build-oa9zv4bw\\n\","," \"Successfully installed helical-0.0.0\\n\""," ]"," }"," ],"," \"source\": ["," \"# !pip install helical\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 1,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"2024-09-30 17:44:04.360305: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\\n\","," \"2024-09-30 17:44:04.369540: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\\n\","," \"2024-09-30 17:44:04.378786: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\\n\","," \"2024-09-30 17:44:04.381519: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\\n\","," \"2024-09-30 17:44:04.389495: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\\n\","," \"To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\\n\""," ]"," },"," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"2024-09-30 17:44:04.844402: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\\n\""," ]"," }"," ],"," \"source\": ["," \"from tensorflow.keras.models import Sequential\\n\","," \"from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\\n\","," \"from tensorflow.keras.callbacks import TensorBoard\\n\","," \"from tensorflow.keras.optimizers import Adam\\n\","," \"from tensorflow.keras.utils import to_categorical\\n\","," \"from sklearn import svm\\n\","," \"from sklearn.metrics import accuracy_score\\n\","," \"from sklearn.model_selection import train_test_split\\n\","," \"from sklearn.preprocessing import LabelEncoder\\n\","," \"import numpy as np\\n\","," \"from datasets import DatasetDict\\n\","," \"import datetime\\n\","," \"import torch\\n\","," \"from tqdm import tqdm\\n\","," \"from typing import Tuple\\n\","," \"import logging, warnings\\n\","," \"\\n\","," \"logging.getLogger().setLevel(logging.ERROR)\\n\","," \"\\n\","," \"warnings.filterwarnings(\\\"ignore\\\")\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"### Use the Helical package to get the Hyena model\\n\","," \"We use a small HyenaDNA model with 2 layers and width 256.\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 2,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"/home/matthew/helical-dev/helical/helical/models/scgpt/model_dir/multiomic_model.py:19: UserWarning: flash_attn is not installed\\n\","," \" warnings.warn(\\\"flash_attn is not installed\\\")\\n\","," \"INFO:helical.services.downloader:File: '/home/matthew/.cache/helical/models/hyena_dna/hyenadna-tiny-1k-seqlen-d256.ckpt' exists already. File is not overwritten and nothing is downloaded.\\n\","," \"INFO:helical.services.downloader:File saved to: '/home/matthew/.cache/helical/models/hyena_dna/hyenadna-tiny-1k-seqlen-d256.ckpt'\\n\","," \"INFO:helical.models.hyena_dna.pretrained_model:Loaded pretrained weights ok!\\n\","," \"INFO:helical.models.hyena_dna.model:Model finished initializing.\\n\""," ]"," }"," ],"," \"source\": ["," \"from helical.models.hyena_dna.model import HyenaDNA\\n\","," \"from helical.models.hyena_dna.hyena_dna_config import HyenaDNAConfig \\n\","," \"device = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n\","," \"configurer = HyenaDNAConfig(model_name=\\\"hyenadna-tiny-1k-seqlen-d256\\\", device=device, batch_size=5)\\n\","," \"hyena_model = HyenaDNA(configurer=configurer)\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"### Download the dataset\\n\","," \"Several datasets are available from the [Nucleotide Transformer](https://arxiv.org/abs/2306.15794). Using the `get_dataset_config_names()` function, we get a list of the available the datasets for the downstream tasks.\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 3,"," \"metadata\": {},"," \"outputs\": ["," {"," \"data\": {"," \"text/plain\": ["," \"['H4ac',\\n\","," \" 'H3K36me3',\\n\","," \" 'splice_sites_donors',\\n\","," \" 'splice_sites_acceptors',\\n\","," \" 'H3',\\n\","," \" 'H4',\\n\","," \" 'H3K4me3',\\n\","," \" 'splice_sites_all',\\n\","," \" 'H3K4me1',\\n\","," \" 'H3K14ac',\\n\","," \" 'enhancers_types',\\n\","," \" 'promoter_no_tata',\\n\","," \" 'H3K79me3',\\n\","," \" 'H3K4me2',\\n\","," \" 'promoter_tata',\\n\","," \" 'enhancers',\\n\","," \" 'H3K9ac',\\n\","," \" 'promoter_all']\""," ]"," },"," \"execution_count\": 3,"," \"metadata\": {},"," \"output_type\": \"execute_result\""," }"," ],"," \"source\": ["," \"from datasets import get_dataset_config_names\\n\","," \"\\n\","," \"configs = get_dataset_config_names(\\\"InstaDeepAI/nucleotide_transformer_downstream_tasks\\\", trust_remote_code=True)\\n\","," \"configs\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"We can select any of the 18 downstream tasks. Let us take the `promoter_tata` as an example.\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 4,"," \"metadata\": {},"," \"outputs\": [],"," \"source\": ["," \"from datasets import load_dataset\\n\","," \"label = \\\"promoter_tata\\\"\\n\","," \"dataset = load_dataset(\\\"InstaDeepAI/nucleotide_transformer_downstream_tasks\\\", label, trust_remote_code=True)\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"To familiarize ourselves with the data, we can print the first seqence and see if it is a splice site acceptor or not:\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 5,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"Nucleotide sequence: CGCTCCCCCA ...\\n\","," \"Label name: promoter_tata and value: 0\\n\","," \"Number of classes: 2\\n\""," ]"," }"," ],"," \"source\": ["," \"print(\\\"Nucleotide sequence:\\\", dataset[\\\"train\\\"][\\\"sequence\\\"][0][:10], \\\"...\\\")\\n\","," \"print(\\\"Label name:\\\", dataset[\\\"train\\\"].config_name, \\\"and value:\\\", dataset[\\\"train\\\"][\\\"label\\\"][0])\\n\","," \"num_classes = len(set(dataset[\\\"train\\\"][\\\"label\\\"]))\\n\","," \"print(\\\"Number of classes:\\\", num_classes)\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"Define a function that gets the embeddings for each nucleotide sequence in the training dataset.\\n\","," \"\\n\","," \"According to the HyenaDNA [paper](https://arxiv.org/pdf/2306.15794): \\\"[they] average across the tokens to obtain a single classification token\\\".\\n\","," \"\\n\","," \"In our code below, the Hyena model returns a (302, 256) matrix. We average column wise resulting in a vector of shape (256, ) for each observation.\\n\","," \"\\n\","," \"During the training process, we also found that it is beneficial to normalize the data row-wise.\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 6,"," \"metadata\": {},"," \"outputs\": [],"," \"source\": ["," \"def get_model_inputs(dataset: DatasetDict) -\u003e Tuple[np.ndarray, np.ndarray]:\\n\","," \" \\\"\\\"\\\"\\n\","," \" Parameters\\n\","," \" ----------\\n\","," \" dataset : DatasetDict\\n\","," \" The dataset containing the sequences and labels.\\n\","," \"\\n\","," \" Returns\\n\","," \" -------\\n\","," \" Tuple[np.ndarray, np.ndarray]\\n\","," \" A tuple containing the input features and labels.\\n\","," \" \\\"\\\"\\\"\\n\","," \" processed_data = hyena_model.process_data(dataset[\\\"sequence\\\"])\\n\","," \"\\n\","," \" embeddings = hyena_model.get_embeddings(processed_data)\\n\","," \" embeddings = embeddings.mean(axis=1)\\n\","," \" means = np.mean(embeddings, axis=1, keepdims=True)\\n\","," \" stds = np.std(embeddings, axis=1, keepdims=True)\\n\","," \"\\n\","," \" x = (embeddings - means) / stds\\n\","," \"\\n\","," \" return x, dataset[\\\"label\\\"]\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"It may be beneficial to do this step once and save the output in a `.npy` file.\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 8,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"INFO:helical.models.hyena_dna.model:Processing data\\n\","," \"Processing sequences: 100%|██████████| 5509/5509 [00:00\u003c00:00, 10331.56it/s]\\n\","," \"INFO:helical.models.hyena_dna.model:Data processing finished.\\n\","," \"INFO:helical.models.hyena_dna.model:Inference started\\n\","," \"Getting embeddings: 100%|██████████| 1102/1102 [00:01\u003c00:00, 606.64it/s]\\n\""," ]"," }"," ],"," \"source\": ["," \"x, y = get_model_inputs(dataset[\\\"train\\\"])\\n\","," \"#np.save(f\\\"data/train/x_{label}_norm_256\\\", x)\\n\","," \"#np.save(f\\\"data/train/y_{label}_norm_256\\\", y)\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"Load the data and one-hot-encode the labels.\\n\","," \"\\n\","," \"We split the training set into actual training data and a test set. \\n\","," \"\\n\","," \"This is optional and the entire dataset can be used for training. We did this to avoid data leakage by not touching the test set during the training process.\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 9,"," \"metadata\": {},"," \"outputs\": [],"," \"source\": ["," \"#x = np.load(f\\\"data/train/x_{label}_norm_256.npy\\\")\\n\","," \"#y = np.load(f\\\"data/train/y_{label}_norm_256.npy\\\")\\n\","," \"\\n\","," \"# # One-hot encode the labels\\n\","," \"encoder = LabelEncoder()\\n\","," \"y_encoded = encoder.fit_transform(y)\\n\","," \"y_encoded = to_categorical(y_encoded, num_classes=num_classes)\\n\","," \"y_encoded\\n\","," \"X_train, X_test, y_train, y_test = train_test_split(x, y_encoded, test_size=0.1, random_state=42)\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"### Define and train the model\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 10,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"/home/matthew/miniconda3/envs/helical-dev/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\\n\","," \" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\\n\","," \"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\\n\","," \"I0000 00:00:1727711071.312412 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.314627 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.315966 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.319098 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.320464 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.321742 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.323065 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.324408 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"I0000 00:00:1727711071.325692 1848296 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\\n\","," \"2024-09-30 17:44:31.326935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 46294 MB memory: -\u003e device: 0, name: NVIDIA RTX 6000 Ada Generation, pci bus id: 0000:01:00.0, compute capability: 8.9\\n\""," ]"," },"," {"," \"data\": {"," \"text/html\": ["," \"\u003cpre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\"\u003e\u003cspan style=\\\"font-weight: bold\\\"\u003eModel: \\\"sequential\\\"\u003c/span\u003e\\n\","," \"\u003c/pre\u003e\\n\""," ],"," \"text/plain\": ["," \"\\u001b[1mModel: \\\"sequential\\\"\\u001b[0m\\n\""," ]"," },"," \"metadata\": {},"," \"output_type\": \"display_data\""," },"," {"," \"data\": {"," \"text/html\": ["," \"\u003cpre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\"\u003e┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\\n\","," \"┃\u003cspan style=\\\"font-weight: bold\\\"\u003e Layer (type) \u003c/span\u003e┃\u003cspan style=\\\"font-weight: bold\\\"\u003e Output Shape \u003c/span\u003e┃\u003cspan style=\\\"font-weight: bold\\\"\u003e Param # \u003c/span\u003e┃\\n\","," \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\\n\","," \"│ dense (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDense\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e256\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e65,792\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDropout\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e256\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e0\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_1 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDense\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e128\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e32,896\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout_1 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDropout\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e128\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e0\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_2 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDense\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e128\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e16,512\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout_2 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDropout\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e128\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e0\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_3 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDense\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e64\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e8,256\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout_3 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDropout\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e64\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e0\u003c/span\u003e │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_4 (\u003cspan style=\\\"color: #0087ff; text-decoration-color: #0087ff\\\"\u003eDense\u003c/span\u003e) │ (\u003cspan style=\\\"color: #00d7ff; text-decoration-color: #00d7ff\\\"\u003eNone\u003c/span\u003e, \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e2\u003c/span\u003e) │ \u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e130\u003c/span\u003e │\\n\","," \"└─────────────────────────────────┴────────────────────────┴───────────────┘\\n\","," \"\u003c/pre\u003e\\n\""," ],"," \"text/plain\": ["," \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\\n\","," \"┃\\u001b[1m \\u001b[0m\\u001b[1mLayer (type) \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1mOutput Shape \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m Param #\\u001b[0m\\u001b[1m \\u001b[0m┃\\n\","," \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\\n\","," \"│ dense (\\u001b[38;5;33mDense\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m256\\u001b[0m) │ \\u001b[38;5;34m65,792\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout (\\u001b[38;5;33mDropout\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m256\\u001b[0m) │ \\u001b[38;5;34m0\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_1 (\\u001b[38;5;33mDense\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m128\\u001b[0m) │ \\u001b[38;5;34m32,896\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout_1 (\\u001b[38;5;33mDropout\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m128\\u001b[0m) │ \\u001b[38;5;34m0\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_2 (\\u001b[38;5;33mDense\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m128\\u001b[0m) │ \\u001b[38;5;34m16,512\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout_2 (\\u001b[38;5;33mDropout\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m128\\u001b[0m) │ \\u001b[38;5;34m0\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_3 (\\u001b[38;5;33mDense\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m64\\u001b[0m) │ \\u001b[38;5;34m8,256\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dropout_3 (\\u001b[38;5;33mDropout\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m64\\u001b[0m) │ \\u001b[38;5;34m0\\u001b[0m │\\n\","," \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\","," \"│ dense_4 (\\u001b[38;5;33mDense\\u001b[0m) │ (\\u001b[38;5;45mNone\\u001b[0m, \\u001b[38;5;34m2\\u001b[0m) │ \\u001b[38;5;34m130\\u001b[0m │\\n\","," \"└─────────────────────────────────┴────────────────────────┴───────────────┘\\n\""," ]"," },"," \"metadata\": {},"," \"output_type\": \"display_data\""," },"," {"," \"data\": {"," \"text/html\": ["," \"\u003cpre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\"\u003e\u003cspan style=\\\"font-weight: bold\\\"\u003e Total params: \u003c/span\u003e\u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e123,586\u003c/span\u003e (482.76 KB)\\n\","," \"\u003c/pre\u003e\\n\""," ],"," \"text/plain\": ["," \"\\u001b[1m Total params: \\u001b[0m\\u001b[38;5;34m123,586\\u001b[0m (482.76 KB)\\n\""," ]"," },"," \"metadata\": {},"," \"output_type\": \"display_data\""," },"," {"," \"data\": {"," \"text/html\": ["," \"\u003cpre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\"\u003e\u003cspan style=\\\"font-weight: bold\\\"\u003e Trainable params: \u003c/span\u003e\u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e123,586\u003c/span\u003e (482.76 KB)\\n\","," \"\u003c/pre\u003e\\n\""," ],"," \"text/plain\": ["," \"\\u001b[1m Trainable params: \\u001b[0m\\u001b[38;5;34m123,586\\u001b[0m (482.76 KB)\\n\""," ]"," },"," \"metadata\": {},"," \"output_type\": \"display_data\""," },"," {"," \"data\": {"," \"text/html\": ["," \"\u003cpre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\"\u003e\u003cspan style=\\\"font-weight: bold\\\"\u003e Non-trainable params: \u003c/span\u003e\u003cspan style=\\\"color: #00af00; text-decoration-color: #00af00\\\"\u003e0\u003c/span\u003e (0.00 B)\\n\","," \"\u003c/pre\u003e\\n\""," ],"," \"text/plain\": ["," \"\\u001b[1m Non-trainable params: \\u001b[0m\\u001b[38;5;34m0\\u001b[0m (0.00 B)\\n\""," ]"," },"," \"metadata\": {},"," \"output_type\": \"display_data\""," },"," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"Epoch 1/25\\n\""," ]"," },"," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\\n\","," \"I0000 00:00:1727711073.129685 1848553 service.cc:146] XLA service 0x7b6504005ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\\n\","," \"I0000 00:00:1727711073.129707 1848553 service.cc:154] StreamExecutor device (0): NVIDIA RTX 6000 Ada Generation, Compute Capability 8.9\\n\","," \"2024-09-30 17:44:33.147607: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\\n\","," \"2024-09-30 17:44:33.233553: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8902\\n\""," ]"," },"," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"\\u001b[1m68/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m━━━\\u001b[0m \\u001b[1m0s\\u001b[0m 747us/step - loss: 0.7839\""," ]"," },"," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"I0000 00:00:1727711074.391854 1848553 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\\n\""," ]"," },"," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m5s\\u001b[0m 43ms/step - loss: 0.7765 - val_loss: 0.6899\\n\","," \"Epoch 2/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.6960 - val_loss: 0.6799\\n\","," \"Epoch 3/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.6770 - val_loss: 0.6013\\n\","," \"Epoch 4/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.5919 - val_loss: 0.4149\\n\","," \"Epoch 5/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.4671 - val_loss: 0.3438\\n\","," \"Epoch 6/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.4267 - val_loss: 0.3105\\n\","," \"Epoch 7/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3886 - val_loss: 0.3123\\n\","," \"Epoch 8/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.4019 - val_loss: 0.2902\\n\","," \"Epoch 9/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.4156 - val_loss: 0.2799\\n\","," \"Epoch 10/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.4017 - val_loss: 0.3112\\n\","," \"Epoch 11/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3943 - val_loss: 0.2919\\n\","," \"Epoch 12/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3970 - val_loss: 0.3130\\n\","," \"Epoch 13/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3900 - val_loss: 0.3074\\n\","," \"Epoch 14/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3863 - val_loss: 0.2567\\n\","," \"Epoch 15/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.4020 - val_loss: 0.2711\\n\","," \"Epoch 16/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3646 - val_loss: 0.2593\\n\","," \"Epoch 17/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3369 - val_loss: 0.2412\\n\","," \"Epoch 18/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3264 - val_loss: 0.2440\\n\","," \"Epoch 19/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3205 - val_loss: 0.2701\\n\","," \"Epoch 20/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 2ms/step - loss: 0.3531 - val_loss: 0.3047\\n\","," \"Epoch 21/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3563 - val_loss: 0.2383\\n\","," \"Epoch 22/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3208 - val_loss: 0.2551\\n\","," \"Epoch 23/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3257 - val_loss: 0.2952\\n\","," \"Epoch 24/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3272 - val_loss: 0.2438\\n\","," \"Epoch 25/25\\n\","," \"\\u001b[1m78/78\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 1ms/step - loss: 0.3195 - val_loss: 0.2562\\n\""," ]"," }"," ],"," \"source\": ["," \"input_shape = (configurer.config['d_model'],)\\n\","," \"\\n\","," \"# define the model\\n\","," \"head_model = Sequential()\\n\","," \"head_model.add(Dense(256, activation='relu', input_shape=input_shape))\\n\","," \"head_model.add(Dropout(0.4)) \\n\","," \"head_model.add(Dense(128, activation='relu'))\\n\","," \"head_model.add(Dropout(0.4))\\n\","," \"head_model.add(Dense(128, activation='relu'))\\n\","," \"head_model.add(Dropout(0.4)) \\n\","," \"head_model.add(Dense(64, activation='relu'))\\n\","," \"head_model.add(Dropout(0.4)) \\n\","," \"head_model.add(Dense(num_classes, activation='softmax'))\\n\","," \"\\n\","," \"# compile the model\\n\","," \"optimizer = Adam(learning_rate=0.001)\\n\","," \"\\n\","," \"head_model.compile(loss='categorical_crossentropy', optimizer=optimizer)\\n\","," \"head_model.summary()\\n\","," \"\\n\","," \"# Setup callbacks\\n\","," \"log_dir = \\\"logs/fit/\\\" + datetime.datetime.now().strftime(\\\"%Y%m%d-%H%M%S\\\")\\n\","," \"tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)\\n\","," \"history = head_model.fit(X_train, y_train, epochs=25, batch_size=64, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 12,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stderr\","," \"output_type\": \"stream\","," \"text\": ["," \"INFO:helical.models.hyena_dna.model:Processing data\\n\","," \"Processing sequences: 100%|██████████| 621/621 [00:00\u003c00:00, 10475.13it/s]\\n\","," \"INFO:helical.models.hyena_dna.model:Data processing finished.\\n\","," \"INFO:helical.models.hyena_dna.model:Inference started\\n\","," \"Getting embeddings: 100%|██████████| 125/125 [00:00\u003c00:00, 637.68it/s]\\n\""," ]"," }"," ],"," \"source\": ["," \"X_unseen, y_unseen = get_model_inputs(dataset[\\\"test\\\"])\\n\","," \"#np.save(f\\\"data/test/x_{label}_norm_256.npy\\\")\\n\","," \"#np.save(f\\\"data/test/y_{label}_norm_256.npy\\\")\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"### Evaluate the model on the test data\\n\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 13,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"\\u001b[1m20/20\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m1s\\u001b[0m 25ms/step \\n\","," \"Correct predictions: 90.18%\\n\""," ]"," }"," ],"," \"source\": ["," \"#X_unseen = np.load(f\\\"data/test/x_{label}_norm_256.npy\\\")\\n\","," \"#y_unseen = np.load(f\\\"data/test/y_{label}_norm_256.npy\\\")\\n\","," \"\\n\","," \"predictions_nn = head_model.predict(X_unseen)\\n\","," \"\\n\","," \"y_pred = np.argmax(predictions_nn, axis=1)\\n\","," \"print(\\\"Correct predictions: {:.2f}%\\\".format(sum(np.equal(y_pred, y_unseen))*100/len(y_unseen)))\\n\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"The [Hyena](https://arxiv.org/pdf/2306.15794) and the [Nucleotide transformer](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1.full.pdf) papers, report accuracies around 95% for this task. Our results underperform in comparison. This is probably due to the much larger models being used for the NT, while the Hyena model was re-trained from scratch for this task. In future work, we want to achieve these accuracies too with either approaches.\""," ]"," },"," {"," \"cell_type\": \"markdown\","," \"metadata\": {},"," \"source\": ["," \"## OPTIONAL\\n\","," \"For reference, we also trained an SVM model and obtained similar results (to our small NN).\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 14,"," \"metadata\": {},"," \"outputs\": ["," {"," \"data\": {"," \"text/html\": ["," \"\u003cstyle\u003e#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \\\"▸\\\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \\\"▾\\\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \\\"\\\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \\\"\\\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \\\"\\\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}\u003c/style\u003e\u003cdiv id=\\\"sk-container-id-1\\\" class=\\\"sk-top-container\\\"\u003e\u003cdiv class=\\\"sk-text-repr-fallback\\\"\u003e\u003cpre\u003eSVC(C=1)\u003c/pre\u003e\u003cb\u003eIn a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \u003cbr /\u003eOn GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\\\"sk-container\\\" hidden\u003e\u003cdiv class=\\\"sk-item\\\"\u003e\u003cdiv class=\\\"sk-estimator sk-toggleable\\\"\u003e\u003cinput class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-1\\\" type=\\\"checkbox\\\" checked\u003e\u003clabel for=\\\"sk-estimator-id-1\\\" class=\\\"sk-toggleable__label sk-toggleable__label-arrow\\\"\u003eSVC\u003c/label\u003e\u003cdiv class=\\\"sk-toggleable__content\\\"\u003e\u003cpre\u003eSVC(C=1)\u003c/pre\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\""," ],"," \"text/plain\": ["," \"SVC(C=1)\""," ]"," },"," \"execution_count\": 14,"," \"metadata\": {},"," \"output_type\": \"execute_result\""," }"," ],"," \"source\": ["," \"# Train the SVM model\\n\","," \"svm_model = svm.SVC(kernel='rbf', degree=3, C=1, decision_function_shape='ovr') # One-vs-rest strategy\\n\","," \"X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)\\n\","," \"svm_model.fit(X_train, y_train)\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 15,"," \"metadata\": {},"," \"outputs\": ["," {"," \"name\": \"stdout\","," \"output_type\": \"stream\","," \"text\": ["," \"Test accuracy: 89.7%\\n\""," ]"," }"," ],"," \"source\": ["," \"# Evaluate the model\\n\","," \"unseen_predictions_svm = svm_model.predict(X_unseen)\\n\","," \"\\n\","," \"accuracy = accuracy_score(y_unseen, unseen_predictions_svm)\\n\","," \"print(\\\"Test accuracy: {:.1f}%\\\".format(accuracy*100))\\n\""," ]"," },"," {"," \"cell_type\": \"code\","," \"execution_count\": 16,"," \"metadata\": {},"," \"outputs\": ["," {"," \"data\": {"," \"image/png\": 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\","," \"text/plain\": ["," \"\u003cFigure size 1000x700 with 2 Axes\u003e\""," ]"," },"," \"metadata\": {},"," \"output_type\": \"display_data\""," }"," ],"," \"source\": ["," \"import seaborn as sns\\n\","," \"import matplotlib.pyplot as plt\\n\","," \"from sklearn.metrics import confusion_matrix\\n\","," \"\\n\","," \"# NN\\n\","," \"# y_true = np.argmax(y_test, axis=1)\\n\","," \"# y_pred_classes = np.argmax(predictions_nn, axis=1)\\n\","," \"\\n\","," \"# SVM\\n\","," \"y_true = y_test\\n\","," \"y_pred_classes = svm_model.predict(X_test)\\n\","," \"\\n\","," \"# Compute the confusion matrix\\n\","," \"cm = confusion_matrix(y_true, y_pred_classes)\\n\","," \"\\n\","," \"# Create a heatmap\\n\","," \"plt.figure(figsize=(10,7))\\n\","," \"sns.heatmap(cm, annot=True, fmt='d')\\n\","," \"plt.xlabel('Predicted')\\n\","," \"plt.ylabel('Truth')\\n\","," \"plt.show()\""," ]"," }"," ],"," \"metadata\": {"," \"kernelspec\": {"," \"display_name\": \"helical-package\","," \"language\": 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class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>