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ml_pytorch_results.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+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 sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+
+
+# In[2]:
+
+
+#load data
+dataframe = pd.read_csv("understat.csv")
+
+#choose columns
+input_cols=list(dataframe.columns)[4:11]
+output_cols = ['position']
+input_cols, output_cols
+
+
+# In[3]:
+
+
+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)
+
+
+# In[4]:
+
+
+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)
+
+
+# In[5]:
+
+
+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
+
+
+# In[6]:
+
+
+input_size = len(input_cols)
+output_size = len(output_cols)
+model=Model_xPosition()
+
+
+# In[7]:
+
+
+epochs = 1000
+lr = 1e-5
+learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
+
+
+# In[8]:
+
+
+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"
+
+
+# In[9]:
+
+
+def prediction(input, target, model):
+    inputs = input.unsqueeze(0)
+    predictions = model(inputs)
+    predicted = predictions[0].detach()
+    return predicted
+
+
+# In[10]:
+
+
+with open("result.txt", "a+") as file:
+    for i in range(0, len(val_ds), 1):
+        input_, target = val_ds[i]
+        file.write(str(predict_single(input_, target, model)))
+
+
+# In[11]:
+
+
+expected = []
+predicted = []
+for i in range(0, len(val_ds), 1):
+        input_, target = val_ds[i]
+        expected.append(float(target))
+        predicted.append(float(prediction(input_, target, model)))
+
+MSE = mean_squared_error(expected, predicted)
+MAE = mean_absolute_error(expected, predicted)
+
+with open("metrics.txt", "a+") as file:
+    file.write("Mean squared error: MSE = "+ str(MSE) + "\n")
+    file.write("Mean absolute error: MAE = "+ str(MAE)+ "\n")
+
+with open("MSE.txt", "a+") as file:
+    file.write(str(MSE) + "\n")
+
+
+# In[12]:
+
+
+with open('MSE.txt') as file:
+    y_MSE = [float(line) for line in file if line]
+    x_builds = list(range(1, len(y_MSE) + 1))
+
+
+# In[13]:
+
+
+plt.xlabel('Number of builds')
+plt.ylabel('MSE')
+plt.plot(x_builds, y_MSE, label='Mean squared error')
+plt.legend()
+plt.show()
+plt.savefig('RMSplot.png')
+
+
+# In[ ]:
+
+
+# get_ipython().system('jupyter nbconvert --to script ml_pytorch.ipynb')
+