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test_transformers.py
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test_transformers.py
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#!/usr/bin/env python3
"""
Tests of ktrain text classification flows
"""
from unittest import TestCase, main, skip
import IPython
import numpy as np
import testenv
import ktrain
from ktrain import text as txt
from ktrain.imports import ACC_NAME, VAL_ACC_NAME
TEST_DOC = "god christ jesus mother mary church sunday lord heaven amen"
EVAL_BS = 64
class TestTransformers(TestCase):
def setUp(self):
# fetch the dataset using scikit-learn
categories = [
"alt.atheism",
"soc.religion.christian",
"comp.graphics",
"sci.med",
]
from sklearn.datasets import fetch_20newsgroups
train_b = fetch_20newsgroups(
subset="train", categories=categories, shuffle=True, random_state=42
)
test_b = fetch_20newsgroups(
subset="test", categories=categories, shuffle=True, random_state=42
)
print("size of training set: %s" % (len(train_b["data"])))
print("size of validation set: %s" % (len(test_b["data"])))
print("classes: %s" % (train_b.target_names))
x_train = train_b.data
y_train = train_b.target
x_test = test_b.data
y_test = test_b.target
# convert to string labels
y_train = [train_b.target_names[y] for y in y_train]
y_test = [train_b.target_names[y] for y in y_test]
# setup
self.trn = (x_train, y_train)
self.val = (x_test, y_test)
# self.classes = train_b.target_names
self.classes = [] # discover from string labels
# @skip('temporarily disabled')
def test_transformers_api_1(self):
trn, val, preproc = txt.texts_from_array(
x_train=self.trn[0],
y_train=self.trn[1],
x_test=self.val[0],
y_test=self.val[1],
class_names=self.classes,
preprocess_mode="distilbert",
maxlen=500,
max_features=35000,
)
model = txt.text_classifier("distilbert", train_data=trn, preproc=preproc)
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=6, eval_batch_size=EVAL_BS
)
# test weight decay
# NOTE due to transformers and/or AdamW bug, # val_accuracy is missing in training history if setting weight decay prior to training
# self.assertEqual(learner.get_weight_decay(), None)
# learner.set_weight_decay(1e-2)
# self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# train
lr = 5e-5
hist = learner.fit_onecycle(lr, 1)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.9)
# test top losses
obs = learner.top_losses(n=1, val_data=None)
self.assertIn(obs[0][0], list(range(len(val.x))))
learner.view_top_losses(preproc=preproc, n=1, val_data=None)
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# test load and save model
tmp_folder = ktrain.imports.tempfile.mkdtemp()
learner.save_model(tmp_folder)
learner.load_model(tmp_folder)
# test validate
cm = learner.validate()
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc, batch_size=EVAL_BS)
self.assertEqual(p.predict([TEST_DOC])[0], "soc.religion.christian")
tmp_folder = ktrain.imports.tempfile.mkdtemp()
p.save(tmp_folder)
p = ktrain.load_predictor(tmp_folder, batch_size=EVAL_BS)
self.assertEqual(p.predict(TEST_DOC), "soc.religion.christian")
self.assertEqual(np.argmax(p.predict_proba([TEST_DOC])[0]), 3)
self.assertEqual(type(p.explain(TEST_DOC)), IPython.core.display.HTML)
# @skip('temporarily disabled')
def test_transformers_api_2(self):
MODEL_NAME = "distilbert-base-uncased"
preproc = txt.Transformer(MODEL_NAME, maxlen=500, classes=self.classes)
trn = preproc.preprocess_train(self.trn[0], self.trn[1])
val = preproc.preprocess_test(self.val[0], self.val[1])
model = preproc.get_classifier()
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=6, eval_batch_size=EVAL_BS
)
lr = 5e-5
hist = learner.fit_onecycle(lr, 1)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.9)
# test top losses
obs = learner.top_losses(n=1, val_data=None)
self.assertIn(obs[0][0], list(range(len(val.x))))
learner.view_top_losses(preproc=preproc, n=1, val_data=None)
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# test load and save model
tmp_folder = ktrain.imports.tempfile.mkdtemp()
learner.save_model(tmp_folder)
learner.load_model(tmp_folder)
# test validate
cm = learner.validate()
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc, batch_size=EVAL_BS)
self.assertEqual(p.predict([TEST_DOC])[0], "soc.religion.christian")
tmp_folder = ktrain.imports.tempfile.mkdtemp()
p.save(tmp_folder)
p = ktrain.load_predictor(tmp_folder, batch_size=EVAL_BS)
self.assertEqual(p.predict(TEST_DOC), "soc.religion.christian")
self.assertEqual(np.argmax(p.predict_proba([TEST_DOC])[0]), 3)
self.assertEqual(type(p.explain(TEST_DOC)), IPython.core.display.HTML)
if __name__ == "__main__":
main()