Hi scikit-learn members,
0.13.1 documentation states that individual datasets can
be loaded in svmlight / libsvm format. So I have fed in
"iris.scale" libSVM dataset however some erroneous
behaviour happens. I am just trying to reproduce
"plot_iris_exercise.py" with
iris.scale(http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale).
The problem in CLI:
$python2.7 linsvm.py iris.scale
Accuracy 0.973333333333
Traceback (most recent call last):
File "linsvm.py", line 28, in <module>
n_sample = len(X)
File
"/usr/lib/python2.7/dist-packages/scipy/sparse/base.py",
line 175, in __len__
raise TypeError("sparse matrix length is ambiguous;
use getnnz()"
TypeError: sparse matrix length is ambiguous; use getnnz()
or shape[0]
The code is very simple below:
import numpy as np
import pylab as pl
from sklearn import datasets, svm
from sklearn.datasets import load_svmlight_file
import sys
#iris = datasets.load_iris()
#X = iris.data
#y = iris.target
##import iris.scale dataset in LibSVM
X_in, y_in=load_svmlight_file(sys.argv[1])
X_train=X_in
y_train=y_in
X_test=X_in
y_test=y_in
svc=svm.SVC(kernel='linear')
clf=svc.fit(X_train, y_train)
y_pred =clf.predict(X_test)
print "Accuracy", np.mean(y_pred == y_test)
X = X_in[y_in != 0, :2]
y = y_in[y_in != 0]
##The following code is from plot_iris_exercise.py
n_sample = len(X)
np.random.seed(0)
order = np.random.permutation(n_sample)
X = X[order]
y = y[order].astype(np.float)
X_train = X[:.9 * n_sample]
y_train = y[:.9 * n_sample]
X_test = X[.9 * n_sample:]
y_test = y[.9 * n_sample:]
# fit the model
for fig_num, kernel in enumerate(('linear', 'rbf',
'poly')):
clf = svm.SVC(kernel=kernel, gamma=10)
clf.fit(X_train, y_train)
pl.figure(fig_num)
pl.clf()
pl.scatter(X[:, 0], X[:, 1], c=y, zorder=10,
cmap=pl.cm.Paired)
# Circle out the test data
pl.scatter(X_test[:, 0], X_test[:, 1], s=80,
facecolors='none', zorder=10)
pl.axis('tight')
x_min = X[:, 0].min()
x_max = X[:, 0].max()
y_min = X[:, 1].min()
y_max = X[:, 1].max()
XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
Z = clf.decision_function(np.c_[XX.ravel(),
YY.ravel()])
# Put the result into a color plot
Z = Z.reshape(XX.shape)
pl.pcolormesh(XX, YY, Z > 0, cmap=pl.cm.Paired)
pl.contour(XX, YY, Z, colors=['k', 'k', 'k'],
linestyles=['--', '-', '--'],
levels=[-.5, 0, .5])
pl.title(kernel)
pl.show()
Many thanks,
Hakan
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