144 lines
3.9 KiB
Python
144 lines
3.9 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
|
|
import pathlib
|
|
|
|
import os
|
|
import sys
|
|
|
|
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
|
|
|
|
from NeuralNetwork import NeuralNetwork
|
|
|
|
from sacred import Experiment
|
|
from sacred.observers import FileStorageObserver, MongoObserver
|
|
|
|
# Create new sacred experiment
|
|
ex = Experiment("s464863")
|
|
|
|
# Setup observers
|
|
ex.observers.append(FileStorageObserver('my_runs'))
|
|
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017', db_name='sacred'))
|
|
|
|
@ex.config
|
|
def config():
|
|
# Default parameters
|
|
hidden_size = 128
|
|
|
|
# Default learning parameters
|
|
learning_rate = 0.001
|
|
weight_decay = 0.001
|
|
num_epochs = 1000
|
|
|
|
# Learning parameters from sys.argv
|
|
if len(sys.argv) > 1:
|
|
num_epochs = int(sys.argv[1])
|
|
learning_rate = float(sys.argv[2])
|
|
weight_decay = float(sys.argv[3])
|
|
|
|
@ex.automain
|
|
def experiment(hidden_size, learning_rate, weight_decay, num_epochs, _run):
|
|
# Seed for reproducibility
|
|
torch.manual_seed(1234)
|
|
|
|
# Load data with sacred
|
|
train_data = ex.open_resource('./datasets/train.csv', "r")
|
|
test_data = ex.open_resource('./datasets/test.csv', "r")
|
|
|
|
# Convert to pandas dataframe
|
|
train = pd.read_csv(train_data)
|
|
test = pd.read_csv(test_data)
|
|
|
|
# Split data
|
|
X_train = train.drop(columns=['id', 'diagnosis']).values
|
|
y_train = train['diagnosis'].values
|
|
|
|
X_test = test.drop(columns=['id', 'diagnosis']).values
|
|
y_test = test['diagnosis'].values
|
|
|
|
# Convert data to PyTorch tensors
|
|
X_train = torch.FloatTensor(X_train)
|
|
y_train = torch.FloatTensor(y_train).view(-1, 1)
|
|
|
|
X_test = torch.FloatTensor(X_test)
|
|
y_test = torch.FloatTensor(y_test).view(-1, 1)
|
|
|
|
# Parameters
|
|
input_size = X_train.shape[1]
|
|
|
|
# Model initialization
|
|
model = NeuralNetwork(input_size, hidden_size)
|
|
|
|
# Loss function and optimizer
|
|
criterion = nn.BCELoss()
|
|
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
|
|
|
|
# Training loop
|
|
model.train()
|
|
|
|
for epoch in range(num_epochs):
|
|
# Zero the gradients
|
|
optimizer.zero_grad()
|
|
|
|
# Forward pass
|
|
outputs = model(X_train)
|
|
|
|
# Compute loss
|
|
loss = criterion(outputs, y_train)
|
|
|
|
# Backward pass
|
|
loss.backward()
|
|
|
|
# Update weights
|
|
optimizer.step()
|
|
|
|
# Print loss
|
|
if (epoch + 1) % 100 == 0:
|
|
print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item()}')
|
|
|
|
# Test the model
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
|
|
# Make predictions
|
|
y_pred = model(X_test)
|
|
y_pred = np.where(y_pred > 0.5, 1, 0)
|
|
|
|
# Calculate metrics
|
|
accuracy = accuracy_score(y_test, y_pred)
|
|
precision = precision_score(y_test, y_pred)
|
|
recall = recall_score(y_test, y_pred)
|
|
f1 = f1_score(y_test, y_pred)
|
|
|
|
# Save metrics to sacred
|
|
_run.log_scalar("accuracy", accuracy)
|
|
_run.log_scalar("precision", precision)
|
|
_run.log_scalar("recall", recall)
|
|
_run.log_scalar("f1", f1)
|
|
|
|
# If directory models does not exist, create it
|
|
if not os.path.exists('./models'):
|
|
os.makedirs('./models')
|
|
|
|
# Save the model
|
|
torch.save(model, './models/model.pth')
|
|
|
|
# Add artifact to sacred experiment
|
|
ex.add_artifact('./models/model.pth', content_type="application/x-pythorch")
|
|
|
|
# Save id of the run
|
|
with open("experiment_id.txt", "w") as f:
|
|
f.write(str(_run._id))
|
|
|
|
# Save sources and resources paths
|
|
with open("sources.txt", "w") as f:
|
|
for source in _run.observers[1].run_entry["experiment"]["sources"]:
|
|
f.write(source[1] + "\n")
|
|
|
|
with open("resources.txt", "w") as f:
|
|
for resource in _run.observers[1].run_entry["resources"]:
|
|
f.write(resource[1] + "\n") |