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