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POlibsvm prediction problem using a generated model
 

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  1. POlibsvm prediction problem using a generated model
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    <p>I have a question regarding libsvm predicting accuracy. I generated svm model file using easy.py. Now, when I am trying to predict the test vectors programatically in python, it shows wrongly predicted labels (all 1's) whereas, using easy.py I get an accuracy of 91%.</p> <p>Each line of my test and train data is in the following format:</p> <pre><code>1 1:255 2:246 3:218 4:198 5:186 6:168 7:177 8:218 9:255 10:255 11:255 12:255 13:255 14:255 15:255 16:255 17:255 18:255 19:255 20:255 21:255 22:255 23:255 24:255 25:255 26:219 27:185 28:162 29:145 30:144 31:255 32:253 33:228 34:197 </code></pre> <p>The code is as follows, am I doing anything wrong over here?</p> <pre><code>wimn_model = svm.svm_model("newtraindata.txt.model") #load model wimn_f_test=open('newtestdata.txt','r'); #load test data and train data wimn_f_train=open('newtraindata.txt','r'); ii=0 for eachline in wimn_f_test: vec=eachline v=vec.split() vector={} ii=ii+1 #print v[0] wimn_test_labels.append(int(v[0])) for i in range(1,len(v)): s=v[i].split(":") #print s[1] vector[i]=int(s[1]) wimn_test_vectors.append(vector) print "wimn test "+str(len(wimn_test_vectors)) # get the training and testing vectors and labels. ii=0 for eachline in wimn_f_train: vec=eachline v=vec.split() vector={} ii=ii+1 wimn_train_labels.append(int(v[0])) #print v[0] for i in range(1,len(v)): s=v[i].split(":") #print s[1] vector[i]=int(s[1]) wimn_train_vectors.append(vector) print "wimn train "+str( len(wimn_train_vectors)) s=len(wimn_train_labels) for i_s in range(0,s): #print i_s ww.append(wimn_model.predict(wimn_train_vectors[i_s])) # wrongly predicted labels are in ww. correct labels are in wimn_train_labels, wimn_test_labels. </code></pre>
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    1. COWhy do you give the data in a dict instead of list/tuple? Also, what is your libsvm version?
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    2. COby default libsvm maximizes accuracy, (TP+TN)/ALL, which in a binary problem with majority of samples coming from one class results in labelling all data with one label. Maximize F-score (2*precision*recall)/(precision+recall), instead.
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    3. COI figured out the solution. For some reason libsvm (libsvm 3.0 is the version I use) predicts wrong class labels when I try to predict it with original test_data it predicts all as 1's. But, when I try to predict it with scaled test_data, it predicts the right values (as expected from output of executing easy.py ) @highbandwidth: I use libsvm 3.0 (though 3.1 is out there). I am reading the test vectors as a list of dicts with each dict representing a feature vector. I found some tutorial on the web using the same, so I did the same.
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