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