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test_textclassification.py
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test_textclassification.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 TestTextClassification(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
self.trn = (x_train, y_train)
self.val = (x_test, y_test)
self.classes = train_b.target_names
# @skip('temporarily disabled')
def test_fasttext(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="standard",
maxlen=350,
max_features=35000,
)
model = txt.text_classifier("fasttext", train_data=trn, preproc=preproc)
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=32, eval_batch_size=EVAL_BS
)
lr = 0.01
hist = learner.fit(lr, 10, cycle_len=1)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.8)
# test top losses
obs = learner.top_losses(n=1, val_data=None)
self.assertIn(obs[0][0], list(range(len(val[0]))))
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
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# 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")
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor", 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)
def test_nbsvm(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="standard",
maxlen=700,
max_features=35000,
ngram_range=3,
)
model = txt.text_classifier("nbsvm", train_data=trn, preproc=preproc)
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=32, eval_batch_size=EVAL_BS
)
lr = 0.01
hist = learner.fit_onecycle(lr, 10)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.92)
self.assertAlmostEqual(max(hist.history["momentum"]), 0.95)
self.assertAlmostEqual(min(hist.history["momentum"]), 0.85)
# test top losses
obs = learner.top_losses(n=1, val_data=None)
self.assertIn(obs[0][0], list(range(len(val[0]))))
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
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# 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")
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor", 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_logreg(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="standard",
maxlen=700,
max_features=35000,
ngram_range=3,
)
model = txt.text_classifier("logreg", train_data=trn, preproc=preproc)
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=32, eval_batch_size=EVAL_BS
)
lr = 0.01
hist = learner.autofit(lr)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.9)
self.assertAlmostEqual(max(hist.history["momentum"]), 0.95)
self.assertAlmostEqual(min(hist.history["momentum"]), 0.85)
# test top losses
obs = learner.top_losses(n=1, val_data=None)
self.assertIn(obs[0][0], list(range(len(val[0]))))
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
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# 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")
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor", 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_bigru(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="standard",
maxlen=350,
max_features=35000,
ngram_range=1,
)
model = txt.text_classifier("bigru", train_data=trn, preproc=preproc)
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=32, eval_batch_size=EVAL_BS
)
lr = 0.01
hist = learner.autofit(lr, 1)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.89)
self.assertAlmostEqual(max(hist.history["momentum"]), 0.95)
self.assertAlmostEqual(min(hist.history["momentum"]), 0.85)
# test top losses
obs = learner.top_losses(n=1, val_data=None)
self.assertIn(obs[0][0], list(range(len(val[0]))))
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
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# 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")
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor", batch_size=EVAL_BS)
self.assertEqual(p.predict([TEST_DOC])[0], "soc.religion.christian")
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_bert(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="bert",
maxlen=350,
max_features=35000,
)
model = txt.text_classifier("bert", train_data=trn, preproc=preproc)
learner = ktrain.get_learner(
model, train_data=trn, batch_size=6, eval_batch_size=EVAL_BS
)
lr = 2e-5
hist = learner.fit_onecycle(lr, 1)
# test training results
self.assertAlmostEqual(max(hist.history["lr"]), lr)
self.assertGreater(max(hist.history[ACC_NAME]), 0.7)
# test top losses
obs = learner.top_losses(n=1, val_data=val)
self.assertIn(obs[0][0], list(range(len(val[0][0]))))
learner.view_top_losses(preproc=preproc, n=1, val_data=val)
# 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
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# test validate
cm = learner.validate(val_data=val)
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")
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor", 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()