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ml_pytroch_sacred.py

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+import torch
+import jovian
+import torchvision
+import matplotlib
+import torch.nn as nn
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import torch.nn.functional as F
+from torchvision.datasets.utils import download_url
+from torch.utils.data import DataLoader, TensorDataset, random_split
+import random
+import os
+import sys
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sacred.observers import MongoObserver
+
+ex = Experiment("IUM_478839", interactive=True)
+ex.observers.append(FileStorageObserver('my_run'))
+
+@ex.config
+def my_config():
+    epochs = 100
+    
+#load data
+dataframe = pd.read_csv("understat.csv")
+
+#choose columns
+input_cols=list(dataframe.columns)[4:11]
+output_cols = ['position']
+input_cols, output_cols
+
+def dataframe_to_arrays(dataframe):
+    dataframe_loc = dataframe.copy(deep=True)
+    inputs_array = dataframe_loc[input_cols].to_numpy()
+    targets_array = dataframe_loc[output_cols].to_numpy()
+    return inputs_array, targets_array
+
+inputs_array, targets_array = dataframe_to_arrays(dataframe)
+
+inputs = torch.from_numpy(inputs_array).type(torch.float)
+targets = torch.from_numpy(targets_array).type(torch.float)
+
+dataset = TensorDataset(inputs, targets)
+
+train_ds, val_ds = random_split(dataset, [548, 136])
+batch_size=50
+train_loader = DataLoader(train_ds, batch_size, shuffle=True)
+val_loader = DataLoader(val_ds, batch_size)
+
+class Model_xPosition(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(input_size,output_size) 
+        
+    def forward(self, xb): 
+        out = self.linear(xb)
+        return out
+    
+    def training_step(self, batch):
+        inputs, targets = batch 
+        # Generate predictions
+        out = self(inputs)          
+        # Calcuate loss
+        loss = F.l1_loss(out,targets) 
+        return loss
+    
+    def validation_step(self, batch):
+        inputs, targets = batch
+        out = self(inputs)
+        loss = F.l1_loss(out,targets)   
+        return {'val_loss': loss.detach()}
+        
+    def validation_epoch_end(self, outputs):
+        batch_losses = [x['val_loss'] for x in outputs]
+        epoch_loss = torch.stack(batch_losses).mean() 
+        return {'val_loss': epoch_loss.item()}
+    
+    def epoch_end(self, epoch, result, num_epochs):
+        if (epoch+1) % 100 == 0 or epoch == num_epochs-1:
+            print("Epoch {} loss: {:.4f}".format(epoch+1, result['val_loss']))
+            
+            
+def evaluate(model, val_loader):
+    outputs = [model.validation_step(batch) for batch in val_loader]
+    return model.validation_epoch_end(outputs)
+
+def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
+    history = []
+    optimizer = opt_func(model.parameters(), lr)
+    for epoch in range(epochs):
+        for batch in train_loader:
+            loss = model.training_step(batch)
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+        result = evaluate(model, val_loader)
+        model.epoch_end(epoch, result, epochs)
+        history.append(result)
+    return history
+
+@ex.automain
+def my_main(epochs):
+    input_size = len(input_cols)
+    output_size = len(output_cols)
+    model=Model_xPosition()
+    epochs = 2000
+    lr = 1e-5
+    learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
+    def predict_single(input, target, model):
+        inputs = input.unsqueeze(0)
+        predictions = model(inputs)
+        prediction = predictions[0].detach()
+
+        return "Target: "+str(target)+"          Predicted: "+str(prediction)+"\n"
+    
+    for i in random.sample(range(0, len(val_ds)), 10):
+        input_, target = val_ds[i]
+        print(predict_single(input_, target, model),end="")
+    
+    with open("result.txt", "w+") as file:
+        for i in range(0, len(val_ds), 1):
+            input_, target = val_ds[i]
+            file.write(str(predict_single(input_, target, model)))