CODEWITHSUNDEEP
pythonnlptext-classificationlibshorttext
Franck Dernoncourt
Franck Dernoncourt

Reputation: 83197

How can I know to which class each score corresponds to in LibShortText prediction output file?

I use LibShortText for short-text classification.

I trained a model and use it to get class predictions on my test set by running:

python text-train.py -L 0 -f ./demo/train_file
python text-predict.py ./demo/train_file train_file.model output

The output file contains the score of each class for each test sample. She is the beginning of the output file:

version: 1
analyzable: 1
text-src: ./demo/train_file
extra-files:    
model-id: 22d9e6defd38ed92e45662d576262915d10c3374

Tickets Tickets 1.045974012515694   -0.1533289000025808 -0.142460215262256  -0.1530588765291932 -0.1249182478102407 -0.1190708362082807 -0.06841237067728836    0.04587568197139553 -0.2283616562229066 -0.102238591774343
Stamps  Stamps  -0.1187719176481736 1.118188003417143   -0.08034439513604429    -0.1973997029054026 -0.06355109135595602    -0.1786639939826796 -0.1169254102259164 -0.01967861752032143    -0.06964465109882922    -0.2732082235438185
Music   Music   -0.1315596826953709 -0.2641082947449856 1.008713836384851   -0.04068831625284784    -0.1545790157496564 -0.1010212095804389 -0.02069378431571431    -0.02404317930606417    0.008960552873498827    -0.2809809066132714
Jewelry & Watches   Jewelry & Watches   -0.0749032450936907 -0.1369122108940684 -0.2159355702219642 0.9582440549577076  -0.141187218792264  -0.1290355317490395 -0.04287756450848382    -0.0919782002284954 -0.04312539181047169    -0.0822891216592294
Tickets Tickets 0.9291396425612148  -0.1597595507175184 -0.07086077554348413    -0.07087036006347401    -0.1111802245732816 -0.2329161314957608 -0.07080154336497513    -0.07093153970747144    -0.07096098431125453    -0.07085853278399512
Books   Books   -0.03482279197164031    -0.02622229736755784    -0.08576360644172253    -0.1209545478269265 0.9735039690597804  -0.02640896142537765    -0.1511226188239169 -0.1785299152500055 -0.1569282110333412 -0.1927510189192921
Tickets Tickets 1.165624491239117   -0.1643444003616841 -0.279795018266336  -0.05911033737681937    -0.1496733471948844 -0.1774767469424229 -0.1806900189575362 -0.05711408596057094    0.06427848575613292 -0.1616990219349959
Art Art -0.07563152438778584    -0.1926345255861422 -0.1379519287608234 -0.1728869014895525 -0.2081235484009353 0.9764371359082827  -0.06097998223834129    -0.06082239643658216    -0.0434090642865785 -0.0239972643215402
Art Art -0.21374038053991   0.0146962630542977  -0.02279914632208601    -0.001108284295731699   -0.2621058759589903 1.016592310148241   0.01436347343617804 -0.04476369315079338    -0.1246095742882179 -0.3765250920829869
Books   Books   -0.08063364674726788    -0.08053738921453879    -0.08032365427931695    -0.1496633152184083 0.9195583554164264  -0.08011940998873018    -0.08053175336913043    -0.16302082274963   -0.1105339242133948 -0.09419443963601073

How can I know to which class each score corresponds to?

I know I could infer it by looking at the predicted class and the maximum score for several test samples, but I'm hoping there exist some mmore direct way.

Upvotes: 0

Views: 58

Answers (1)

MassPikeMike
MassPikeMike

Reputation: 682

The labels member of the PredictionResult returned from predict_text() contains the ordering. So a small addition to classifier_impl.py will expose as column headers in the output file:

*** libshorttext-1.1/libshorttext/classifier/classifier_impl.py.orig
--- libshorttext-1.1/libshorttext/classifier/classifier_impl.py
***************
*** 113,118 ****
--- 113,125 ----

        fmt = '\t{{0:{0}}}'.format(fmt)
        for i in range(len(self.predicted_y)):
+                         if i == 0:
+                                 label_text  = 'Predicted' * 18
+                                 label_text += 'True class' * 18
+                                 for l in self.labels:
+                                         label_text += " {0: <18}".format(l)
+                                 fout.write(label_text + "\n")
+ 
            fout.write("{py}\t{y}".format(py = self.predicted_y[i], y = self.true_y[i]))
            for v in self.decvals[i]:
                fout.write(fmt.format(v))

Upvotes: 1

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