BuildingMachineLearningSystemsWithPython/ch07/lr10k.py at master · mindis/BuildingMachineLearningSystemsWithPython

Repository navigation

# This code is supporting material for the book

# Building Machine Learning Systems with Python

# by Willi Richert and Luis Pedro Coelho

# published by PACKT Publishing

# It is made available under the MIT License

import numpy as np

from sklearn.datasets import load_svmlight_file

from sklearn.linear_model import ElasticNet, LinearRegression

data, target = load_svmlight_file('E2006.train')

lr = LinearRegression(fit_intercept=True)

from sklearn.cross_validation import KFold

kf = KFold(len(target), n_folds=10)

err = 0

for train, test in kf:

lr.fit(data[train], target[train])

p = map(lr.predict, data[test])

p = np.array(p).ravel()

e = p - target[test]

err += np.dot(e, e)

rmse_10cv = np.sqrt(err / len(target))

lr.fit(data, target)

p = np.array(map(lr.predict, data))

p = p.ravel()

e = p - target

total_error = np.dot(e, e)

rmse_train = np.sqrt(total_error / len(p))

print('RMSE on training: {}'.format(rmse_train))

print('RMSE on 10-fold CV: {}'.format(rmse_10cv))