RFCT Merge cv10_lr.py & boston_cv_penalized.py

luispedro · luispedro · commit 95d2f95ab224 · 2014-09-17T19:11:06.000+02:00
diff --git a/ch07/README.rst b/ch07/README.rst
@@ -16,8 +16,7 @@ boston1.py
 boston1numpy.py
 Version of above script using numpy operations for linear regression
 boston_cv_penalized.py
- Test different penalized regression schemes on the Boston dataset
-cv10_lr.py
+ Test different penalized (and OLS) regression schemes on the Boston dataset
 figure1_2.py
 Show the regression line for Boston data
 figure3.py
diff --git a/ch07/boston_cv_penalized.py b/ch07/boston_cv_penalized.py
@@ -10,17 +10,18 @@
 from __future__ import print_function
 import numpy as np
 from sklearn.cross_validation import KFold
-from sklearn.linear_model import ElasticNet, Lasso, Ridge
+from sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge
 from sklearn.metrics import r2_score
 from sklearn.datasets import load_boston
 boston = load_boston()
 x = boston.data
 y = boston.target
 
 for name, met in [
- ('elastic-net(.5)', ElasticNet(fit_intercept=True, alpha=0.5)),
- ('lasso(.5)', Lasso(fit_intercept=True, alpha=0.5)),
- ('ridge(.5)', Ridge(fit_intercept=True, alpha=0.5)),
+ ('linear regression', LinearRegression()),
+ ('elastic-net(.5)', ElasticNet(alpha=0.5)),
+ ('lasso(.5)', Lasso(alpha=0.5)),
+ ('ridge(.5)', Ridge(alpha=0.5)),
 ]:
 # Fit on the whole data:
 met.fit(x, y)
diff --git a/ch07/cv10_lr.py b/ch07/cv10_lr.py

-Original file line number
+Diff line change
 from __future__ importprint_function
 importnumpyasnp
 fromsklearn.cross_validationimportKFold
 -fromsklearn.linear_modelimportElasticNet, Lasso, Ridge
 +fromsklearn.linear_modelimportLinearRegression, ElasticNet, Lasso, Ridge
 fromsklearn.metricsimportr2_score
 fromsklearn.datasetsimportload_boston
 boston=load_boston()
 x=boston.data
 y=boston.target
 forname, metin [
 - ('elastic-net(.5)', ElasticNet(fit_intercept=True, alpha=0.5)),
 - ('lasso(.5)', Lasso(fit_intercept=True, alpha=0.5)),
 - ('ridge(.5)', Ridge(fit_intercept=True, alpha=0.5)),
 + ('linear regression', LinearRegression()),
 + ('elastic-net(.5)', ElasticNet(alpha=0.5)),
 + ('lasso(.5)', Lasso(alpha=0.5)),
 + ('ridge(.5)', Ridge(alpha=0.5)),
 ]:
 # Fit on the whole data:
 met.fit(x, y)