from sklearn.linear_model import LinearRegression
import sys
sys.path.append('..')
from helpers import load_data_in_chunks, save_model
(Xs, Ys) = load_data_in_chunks('train', chunk_size=5)
regr = LinearRegression()
regr.fit(Xs.reshape(Xs.shape[0], -1), Ys)
save_model(regr, 'linear-multiple')
import sys
sys.path.append('..')
from helpers import load_data_in_chunks, load_model, report_results
(Xs, Ys) = load_data_in_chunks('test', chunk_size=5)
Xs = Xs.reshape(Xs.shape[0], -1)
regr = load_model('linear-multiple')
Ys_pred = regr.predict(Xs)
report_results(Xs, Ys, Ys_pred, 'linear', 'linear-multiple.svg')
from skorch import NeuralNet
from skorch.callbacks import EarlyStopping
from torch.nn import MSELoss
from torch.optim import SGD
import numpy as np
import sys
sys.path.append('..')
from helpers import load_data_in_chunks, save_model
from model import Net
from RelativeEntropyLoss import RelativeEntropyLoss
(Xs, Ys) = load_data_in_chunks('survival', 'train', chunk_size=5)
Xs = Xs.astype(np.float32)
Ys = Ys.astype(np.float32)
regr = NeuralNet(Net,
max_epochs=10000000000,
batch_size=100,
iterator_train__shuffle=True,
criterion=RelativeEntropyLoss,
optimizer=SGD,
optimizer__lr=1e-5,
optimizer__momentum=0.9,
optimizer__nesterov=True,
optimizer__dampening=0,
verbose=5,
callbacks=[('early_stop', EarlyStopping())])
regr.fit(Xs, Ys)