test_multilabel.py

#!/usr/bin/env python3
"""
Tests of ktrain text classification flows
"""
import testenv
import numpy as np
from unittest import TestCase, main, skip
import ktrain
from ktrain.imports import ACC_NAME, VAL_ACC_NAME
from ktrain import utils as U


Sequential = ktrain.imports.keras.models.Sequential
Dense = ktrain.imports.keras.layers.Dense
Embedding = ktrain.imports.keras.layers.Embedding
GlobalAveragePooling1D = ktrain.imports.keras.layers.GlobalAveragePooling1D

def synthetic_multilabel():



    # data
    X = [[1,0,0,0,0,0,0],
          [1,2,0,0,0,0,0],
          [3,0,0,0,0,0,0],
          [3,4,0,0,0,0,0],
          [2,0,0,0,0,0,0],
          [3,0,0,0,0,0,0],
          [4,0,0,0,0,0,0],
          [2,3,0,0,0,0,0],
          [1,2,3,0,0,0,0],
          [1,2,3,4,0,0,0],
          [0,0,0,0,0,0,0],
          [1,1,2,3,0,0,0],
          [2,3,3,4,0,0,0],
          [4,4,1,1,2,0,0],
          [1,2,3,3,3,3,3],
          [2,4,2,4,2,0,0],
          [1,3,3,3,0,0,0],
          [4,4,0,0,0,0,0],
          [3,3,0,0,0,0,0],
          [1,1,4,0,0,0,0]]
                                                                    
    Y = [[1,0,0,0],                                                     
        [1,1,0,0],                                                      
        [0,0,1,0],                                                      
        [0,0,1,1],
        [0,1,0,0],
        [0,0,1,0],
        [0,0,0,1],
        [0,1,1,0],
        [1,1,1,0],
        [1,1,1,1],
        [0,0,0,0],
        [1,1,1,0],
        [0,1,1,1],
        [1,1,0,1],
        [1,1,1,0],
        [0,1,0,0],
        [1,0,1,0],
        [0,0,0,1],
        [0,0,1,0],
        [1,0,0,1]]


    X = np.array(X)
    Y = np.array(Y)
    return (X, Y)


class TestMultilabel(TestCase):

    def test_multilabel(self):
        X, Y = synthetic_multilabel()
        self.assertTrue(U.is_multilabel( (X,Y)))
        MAXLEN = 7
        MAXFEATURES = 4
        NUM_CLASSES = 4
        model = Sequential()
        model.add(Embedding(MAXFEATURES+1,
                            50,
                            input_length=MAXLEN))
        model.add(GlobalAveragePooling1D())
        model.add(Dense(NUM_CLASSES, activation='sigmoid'))
        model.compile(loss='binary_crossentropy',
                      optimizer='adam',
                      metrics=['accuracy'])
        learner = ktrain.get_learner(model, 
                                     train_data=(X, Y),
                                     val_data=(X, Y),
                                     batch_size=1)
        learner.lr_find(max_epochs=5) # use max_epochs until TF 2.4


        # use loss instead of accuracy due to: https://github.com/tensorflow/tensorflow/issues/41114
        hist = learner.fit(0.001, 200)
        learner.view_top_losses(n=5)
        learner.validate()
        #final_acc = hist.history[VAL_ACC_NAME][-1]
        #print('final_accuracy:%s' % (final_acc))
        #self.assertGreater(final_acc, 0.97)

        final_loss = hist.history['val_loss'][-1]
        print('final_loss:%s' % (final_loss))
        self.assertLess(final_loss, 0.05)


if __name__ == "__main__":
    main()