ium_444498/neutral_network.py

from ast import arg
import numpy as np
import pandas as pd
import torch
import argparse
from torch import nn
from torch.utils.data import DataLoader, Dataset
from sacred import Experiment
from sacred.observers import FileStorageObserver, MongoObserver

default_batch_size = 64
default_epochs = 4

device = "cuda" if torch.cuda.is_available() else "cpu"

class AtpDataset(Dataset):
    def __init__(self, file_name):
        df = pd.read_csv(file_name, usecols=["AvgL", "AvgW"])
        df = df.dropna()

        # Loser avg and Winner avg
        x = df.iloc[:, 1].values
        y = df.iloc[:, 0].values

        self.x_train = torch.from_numpy(x)
        self.y_train = torch.from_numpy(y)
        self.x_train.type(torch.LongTensor)

    def __len__(self):
        return len(self.y_train)

    def __getitem__(self, idx):
        return self.x_train[idx].float(), self.y_train[idx].float()


class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(1, 64),
            nn.ReLU(),
            nn.Linear(64, 32),
            nn.ReLU(),
            nn.Linear(32, 1),
        )

    def forward(self, x):
        x = x.view(x.size(0), -1)
        return self.layers(x)


def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)
        pred = model(X)
        loss = loss_fn(pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")


def test(dataloader, model, loss_fn):
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
    test_loss /= num_batches
    print(f"Avg loss (using {loss_fn}): {test_loss:>8f} \n")
    return test_loss


def setup_args():
    args_parser = argparse.ArgumentParser(prefix_chars="-")
    args_parser.add_argument("-b", "--batchSize", type=int, default=default_batch_size)
    args_parser.add_argument("-e", "--epochs", type=int, default=default_epochs)
    return args_parser.parse_args()

def main(batch_size, epochs):
    print(f"Using {device} device")

    plant_test = AtpDataset("atp_test.csv")
    plant_train = AtpDataset("atp_train.csv")

    train_dataloader = DataLoader(plant_train, batch_size=batch_size)
    test_dataloader = DataLoader(plant_test, batch_size=batch_size)

    for i, (data, labels) in enumerate(train_dataloader):
        print(data.shape, labels.shape)
        print(data, labels)
        break

    model = MLP()
    print(model)

    loss_fn = nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

    for t in range(epochs):
        print(f"Epoch {t + 1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer)
        test(test_dataloader, model, loss_fn)
    print("Finish!")

    torch.save(model.state_dict(), "./model.zip")
    print("Model saved in ./model.zip file.")

def setup_experiment():
    ex = Experiment('Simple Experiment')
    ex.observers.append(FileStorageObserver('sacred_runs'))
    ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
    return ex


ex = setup_experiment()

@ex.config
def experiment_config():
    batch_size = 64
    epochs = 5

@ex.automain
def run(batch_size, epochs):
    main(batch_size, epochs)
    ex.add_artifact('model.zip')