208 lines
4.5 KiB
Python
208 lines
4.5 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf-8
|
|
|
|
# In[18]:
|
|
|
|
|
|
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
|
|
|
|
# In[2]:
|
|
ex = Experiment(save_git_info=False)
|
|
|
|
ex.observers.append(FileStorageObserver('my_runs'))
|
|
|
|
# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017', db_name='sacred'))
|
|
|
|
#try:
|
|
# numberOfEpochParam = int(sys.argv[1])
|
|
#except:
|
|
# dafault val
|
|
#numberOfEpochParam = 1500
|
|
|
|
@ex.config
|
|
def my_config():
|
|
global epochs
|
|
epochs = 1500
|
|
|
|
|
|
|
|
|
|
dataframe_raw = pd.read_csv("winequality-red.csv")
|
|
dataframe_raw.head()
|
|
|
|
|
|
# In[3]:
|
|
|
|
|
|
input_cols=list(dataframe_raw.columns)[:-1]
|
|
output_cols = ['quality']
|
|
input_cols,output_cols
|
|
|
|
|
|
# In[4]:
|
|
|
|
|
|
def dataframe_to_arrays(dataframe):
|
|
dataframe1 = dataframe_raw.copy(deep=True)
|
|
inputs_array = dataframe1[input_cols].to_numpy()
|
|
targets_array = dataframe1[output_cols].to_numpy()
|
|
return inputs_array, targets_array
|
|
|
|
inputs_array, targets_array = dataframe_to_arrays(dataframe_raw)
|
|
inputs_array, targets_array
|
|
|
|
|
|
# In[5]:
|
|
|
|
|
|
inputs = torch.from_numpy(inputs_array).type(torch.float)
|
|
targets = torch.from_numpy(targets_array).type(torch.float)
|
|
inputs,targets
|
|
|
|
|
|
# In[6]:
|
|
|
|
|
|
dataset = TensorDataset(inputs, targets)
|
|
dataset
|
|
|
|
|
|
# In[7]:
|
|
|
|
|
|
train_ds, val_ds = random_split(dataset, [1300, 299])
|
|
batch_size=50
|
|
train_loader = DataLoader(train_ds, batch_size, shuffle=True)
|
|
val_loader = DataLoader(val_ds, batch_size)
|
|
|
|
|
|
# In[8]:
|
|
|
|
|
|
class WineQuality(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
|
|
# Generate predictions
|
|
out = self(inputs)
|
|
# Calculate loss
|
|
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):
|
|
# Print result every 100th epoch
|
|
if (epoch+1) % 100 == 0 or epoch == num_epochs-1:
|
|
print("Epoch [{}], val_loss: {:.4f}".format(epoch+1, result['val_loss']))
|
|
|
|
|
|
# In[9]:
|
|
|
|
|
|
input_size = len(input_cols)
|
|
output_size = len(output_cols)
|
|
|
|
|
|
# In[10]:
|
|
|
|
|
|
model=WineQuality()
|
|
|
|
|
|
# In[11]:
|
|
|
|
|
|
def evaluate(model, val_loader):
|
|
outputs = [model.validation_step(batch) for batch in val_loader]
|
|
return model.validation_epoch_end(outputs)
|
|
|
|
@ex.capture
|
|
def fit(epochs, lr, model, train_loader, val_loader, _run, opt_func=torch.optim.SGD):
|
|
epochs=epochs
|
|
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)
|
|
ex.add_artifact("saved_model.pb")
|
|
return history
|
|
|
|
|
|
|
|
def predict_single(input, target, model):
|
|
inputs = input.unsqueeze(0)
|
|
predictions = model(inputs)
|
|
prediction = predictions[0].detach()
|
|
|
|
return "Target: "+str(target)+"----- Prediction: "+str(prediction)+"\n"
|
|
|
|
|
|
# In[32]:
|
|
|
|
|
|
#wylosuj 10 próbek predykcji
|
|
for i in random.sample(range(0, len(val_ds)), 10):
|
|
input_, target = val_ds[i]
|
|
print(predict_single(input_, target, model),end="")
|
|
|
|
|
|
|
|
# In[36]:
|
|
|
|
|
|
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)))
|
|
|
|
|
|
@ex.automain
|
|
def main(epochs, _run):
|
|
lr = 1e-6
|
|
my_config()
|
|
print("number of epochs is: ", epochs)
|
|
history5 = fit(epochs, lr, model, train_loader, val_loader)
|
|
|
|
#ex.run()
|