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--- code:ridge_regression [2013/09/18 14:38]
asa created
+++ code:ridge_regression [2016/08/09 10:25] (current)
@@ Line 1: / Line 1: @@
-=== Ridge Regression ===
+===== Ridge Regression =====
+Here's code for ridge regression:
 <file python ridge_regression.py>
@@ Line 81: / Line 83: @@
 </file>
+Now let's play with the code.
+<code python>
+from PyML import *
+import ridge_regression
+rr = ridge_regression.RidgeRegression(regression=True)
+</code>
+We are going to use ridge regression for regression, so we have to set the regression flag to True.
+Next we will read in some data taken from the UCI machine learning repository.  The task is to predict
+where in the body a CT scan is obtained from.  Here's a [[http://archive.ics.uci.edu/ml/datasets/Relative+location+of+CT+slices+on+axial+axis | link to the data]].
+<code python>
+data = vectorDatasets.PyVectorDataSet('../data/slice_localization_data.csv', labelsColumn = -1, numericLabels=True)
+</code>
+Note that we had to tell PyML to interpret the labels as numeric.
+Evaluating the classifier:
+<code python>
+results = rr.cv(data)
+</code>
+A couple of other things to do with the data:
+<code python>
+# how are the labels distributed?
+from matplotlib import pyplot as plt
+plt.hist(data.labels.Y, 50)
+# Looking at the weight vectors
+rr.train(data)
+plt.hist(rr.w, 25)
+</code>
+Using ridge regression as a classifier:
+<code python>
+from PyML import *
+data = vectorDatasets.PyVectorDataSet('../data/gisette_sample.data', labelsColumn = 0)
+import ridge_regression
+rr = ridge_regression.RidgeRegression()
+rr.train(data)
+from matplotlib import pyplot as plt
+plt.hist(rr.w, 100)
+import perceptron
+p = perceptron.Perceptron()
+p.train(data)
+plt.hist(p.w, 100)
+# compare accuracy of ridge regression and the perceptron
+perceptron_results = p.stratifiedCV(data)
+ridge_results = rr.stratifiedCV(data)
+</code>

CS545 fall 2016

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