Hi Andy,
Thanks for your help. Is there something in the scikit-learn documentation (or
any other resource) that explains why the kernel matrix at test time needs to
be the kernel between the test data and the training data? I am quite new to
machine learning. What is the reason as to why we do this and how do we obtain
a kernel matrix between the test and the training data?
I applied the MinMaxScaler to the gram matrix to scale the values in my matrix.
Right now I get entries in the gram matrix that range from 0.7 to 1 and I want
to scale this range of values from 0 to 1, so that a 0.7 is really a 0.
Thanks!
Date: Tue, 6 Jan 2015 12:45:06 -0500
From: t3k...@gmail.com
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] Cross validation with a pre-computed
kernel
The kernel matrix at test time needs to be the kernel between the
test data and the training data.
Which I guess is not what get_gram_matrix
does.
Why are you applying the MinMaxScaler to the gram matrix? I'm not
sure that makes sense...
Without the scaler you could just do
print(cross_val_score(SVC(kernel=precomputed), get_gram_matrix(X),
Y))
with the MinMaxScaler you can do
pipe = make_pipeline(MinMaxScaler(),
SVC(kernel='precomputed'))
print(cross_val_score(pipe,
get_gram_matrix(X), Y))
which is a bit shorter than your code and resolves the need to worry
about the gram matrix ;)
On 01/06/2015 12:27 PM, Morgan Hoffman
wrote:
Hi,
I am trying to do a k-fold cross validation with a
precomputed kernel. However, I end up with an error message
that looks like this:
Traceback (most recent call last):
File "kfold_simple_data.py", line 64, in <module>
score = clf.score(test_gram_matrix, test_labels)
File "/usr/local/lib/python2.7/
dist-packages/sklearn/base.py", line 291, in score
return accuracy_score(y, self.predict(X),
sample_weight=sample_weight)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py",
line 467, in predict
y = super(BaseSVC, self).predict(X)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py",
line 283, in predict
X = self._validate_for_predict(X)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/svm/base.py",
line 401, in _validate_for_predict
(X.shape[1], self.shape_fit_[0]))
ValueError: X.shape[1] = 2 should be equal to 6, the number
of samples at training time
This is what my code looks like:
def cross_validate(data, folds, is_scaled):
X = data["values"]
Y = data["labels"]
kf = KFold(len(Y), folds,
indices=False)
scores = []
for train, test in kf:
scaler =
preprocessing.MinMaxScaler()
X_train, X_test, y_train, y_test =
X[train], X[test], Y[train], Y[test]
training_data = OrderedDict()
for i in range(len(X_train)):
training_data[X_train[i]]
= y_train[i]
train_gram_matrix =
get_gram_matrix(training_data)
train_gram_matrix =
scaler.fit_transform(train_gram_matrix)
train_labels =
get_label_array(training_data)
test_data = OrderedDict()
for i in range(len(X_test)):
test_data[X_test[i]]
= y_test[i]
test_gram_matrix =
get_gram_matrix(test_data)
test_gram_matrix =
scaler.transform(test_gram_matrix)
test_labels =
get_label_array(test_data)
clf = svm.SVC(kernel='precomputed')
clf.fit(train_gram_matrix,
train_labels)
print "Score:"
score = clf.score(test_gram_matrix,
test_labels)
scores.append(score)
print score
Does anyone have an idea of what I may be doing wrong? Any
help is appreciated.
Thanks!
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sponsored by Intel and developed in partnership with Slashdot Media, is your
hub for all things parallel software development, from weekly thought
leadership blogs to news, videos, case studies, tutorials and more. Take a
look and join the conversation now. http://goparallel.sourceforge.net
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