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https://github.com/NicolasBohn/NexQuant.git
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you-n-g
c707a40073
* scen * scen2 * app * fix * Simplify workflow * We can share more code in new scenarios * rename model to rd loop * Optimize data path * Update rdagent/app/data_mining/model.py * Add TODO * Support GPU * gpu --------- Co-authored-by: SH-Src <suhan.c@outlook.com>
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
from pathlib import Path
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import pandas as pd
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import torch
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import torch.nn.functional as F
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from torchvision import transforms, datasets
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from torch.utils.data import DataLoader, Dataset
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import torch.nn as nn
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import sparse
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import random
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import os
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from model import model_cls
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from sklearn.metrics import accuracy_score, roc_auc_score
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import numpy as np
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# Set device for training
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# device = torch.device("cpu")
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class MyDataset(Dataset):
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def __init__(self, x, label, device):
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self.x1 = x
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self.label = label
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self.device = device
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def __len__(self):
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return len(self.label)
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def __getitem__(self, idx):
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if torch.is_tensor(idx):
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idx = idx.tolist()
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return torch.FloatTensor(self.x1[idx]).to(self.device), \
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torch.tensor(self.label[idx], dtype=torch.float).to(self.device)
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def collate_fn(batch):
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x, label = [], []
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for data in batch:
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x.append(data[0])
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label.append(data[1])
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return torch.stack(x, 0), torch.stack(label, 0)
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datapath = '/root/.data'
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# datapath = '/home/v-suhancui/RD-Agent/physionet.org/files/mimic-eicu-fiddle-feature/1.0.0/FIDDLE_mimic3'
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X = sparse.load_npz(datapath+'/features/ARF_12h/X.npz').todense()
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df_pop = pd.read_csv(datapath+'/population/ARF_12h.csv')['ARF_LABEL']
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X = X.transpose(0, 2, 1)
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indices = [i for i in range(len(df_pop))]
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random.shuffle(indices)
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split_point = int(0.7 * len(df_pop))
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X_train, y_train = X[indices[:split_point]], np.array(df_pop[indices[:split_point]])
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X_test, y_test = X[indices[split_point:]], np.array(df_pop[indices[split_point:]])
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train_dataloader = DataLoader(MyDataset(X_train, y_train, device), collate_fn=collate_fn, shuffle=True, drop_last=True, batch_size=64)
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test_dataloader = DataLoader(MyDataset(X_test, y_test, device), collate_fn=collate_fn, shuffle=False, drop_last=False, batch_size=64)
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num_features = 4816
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num_timesteps = 12
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# Define the optimizer and loss function
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model = model_cls(num_features=num_features, num_timesteps=num_timesteps).to(device)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
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criterion = nn.CrossEntropyLoss()
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# Train the model
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for i in range(10):
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for data in train_dataloader:
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x, y = data
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out = model(x)
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optimizer.zero_grad()
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loss = criterion(out.squeeze(), y)
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loss.backward()
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optimizer.step()
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y_pred = []
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for data in test_dataloader:
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x, y = data
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out = model(x)
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y_pred.append(out.cpu().detach().numpy())
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acc = roc_auc_score(y_test, np.concatenate(y_pred))
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print(acc)
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# Save the predictions to submission.csv
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with open('./submission.txt', 'w') as f:
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f.write(str(acc)) |