131 lines
4.0 KiB
Python
131 lines
4.0 KiB
Python
import torch
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import jovian
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import torchvision
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import matplotlib
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import torch.nn as nn
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import torch.nn.functional as F
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from torchvision.datasets.utils import download_url
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from torch.utils.data import DataLoader, TensorDataset, random_split
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import random
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import os
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import sys
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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from sacred.observers import MongoObserver
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ex = Experiment("IUM_478839", save_git_info=False)
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ex.observers.append(FileStorageObserver('IUM_478839'))
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 1000
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#load data
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dataframe = pd.read_csv("understat.csv")
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#choose columns
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input_cols=list(dataframe.columns)[4:11]
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output_cols = ['position']
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input_cols, output_cols
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def dataframe_to_arrays(dataframe):
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dataframe_loc = dataframe.copy(deep=True)
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inputs_array = dataframe_loc[input_cols].to_numpy()
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targets_array = dataframe_loc[output_cols].to_numpy()
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return inputs_array, targets_array
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inputs_array, targets_array = dataframe_to_arrays(dataframe)
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inputs = torch.from_numpy(inputs_array).type(torch.float)
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targets = torch.from_numpy(targets_array).type(torch.float)
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dataset = TensorDataset(inputs, targets)
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train_ds, val_ds = random_split(dataset, [548, 136])
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batch_size=50
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train_loader = DataLoader(train_ds, batch_size, shuffle=True)
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val_loader = DataLoader(val_ds, batch_size)
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class Model_xPosition(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(input_size,output_size)
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def forward(self, xb):
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out = self.linear(xb)
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return out
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def training_step(self, batch):
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inputs, targets = batch
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# Generate predictions
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out = self(inputs)
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# Calcuate loss
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loss = F.l1_loss(out,targets)
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return loss
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def validation_step(self, batch):
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inputs, targets = batch
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out = self(inputs)
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loss = F.l1_loss(out,targets)
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return {'val_loss': loss.detach()}
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def validation_epoch_end(self, outputs):
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batch_losses = [x['val_loss'] for x in outputs]
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epoch_loss = torch.stack(batch_losses).mean()
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return {'val_loss': epoch_loss.item()}
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def epoch_end(self, epoch, result, num_epochs):
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if (epoch+1) % 100 == 0 or epoch == num_epochs-1:
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print("Epoch {} loss: {:.4f}".format(epoch+1, result['val_loss']))
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def evaluate(model, val_loader):
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outputs = [model.validation_step(batch) for batch in val_loader]
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return model.validation_epoch_end(outputs)
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def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
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history = []
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optimizer = opt_func(model.parameters(), lr)
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for epoch in range(epochs):
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for batch in train_loader:
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loss = model.training_step(batch)
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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result = evaluate(model, val_loader)
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model.epoch_end(epoch, result, epochs)
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history.append(result)
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return history
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def predict_single(input, target, model):
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inputs = input.unsqueeze(0)
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predictions = model(inputs)
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prediction = predictions[0].detach()
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return "Target: "+str(target)+" Predicted: "+str(prediction)+"\n"
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input_size = len(input_cols)
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output_size = len(output_cols)
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model=Model_xPosition()
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lr = 1e-5
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@ex.automain
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def my_main(epochs):
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learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
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for i in random.sample(range(0, len(val_ds)), 10):
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input_, target = val_ds[i]
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print(predict_single(input_, target, model),end="")
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with open("result.txt", "w+") as file:
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for i in range(0, len(val_ds), 1):
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input_, target = val_ds[i]
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file.write(str(predict_single(input_, target, model)))
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torch.save(model, "Model_xPosition.pkl")
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ex.add_artifact("Model_xPosition.pkl") |