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)