def test_text_classifier():
model_file = os.path.join(temp_path_generator(),
'bert_classifier/pytorch_model.bin')
if os.path.exists(model_file):
os.remove(model_file)
file_path1 = "examples/task_modules/text/train_data.tsv"
file_path2 = "examples/task_modules/text/test_data.tsv"
x_train, y_train = read_tsv_file(input_file=file_path1)
x_train, y_train = x_train[:1], y_train[:1]
x_test, y_test = read_tsv_file(input_file=file_path2)
x_test, y_test = x_test[:1], y_test[:1]
Constant.BERT_TRAINER_BATCH_SIZE = 1
Constant.BERT_TRAINER_EPOCHS = 1
clf = TextClassifier(verbose=True)
clf.fit(x=x_train, y=y_train, time_limit=12 * 60 * 60)
y_pred = clf.predict(x_test)
if len(y_pred) != len(y_test):
raise AssertionError()
import numpy as np
from autokeras.text.text_supervised import TextClassifier
from autokeras.utils import read_tsv_file
def convert_labels_to_one_hot(labels, num_labels):
one_hot = np.zeros((len(labels), num_labels))
one_hot[np.arange(len(labels)), labels] = 1
return one_hot
if __name__ == '__main__':
file_path1 = "train_data.tsv"
file_path2 = "test_data.tsv"
x_train, y_train = read_tsv_file(input_file=file_path1)
x_test, y_test = read_tsv_file(input_file=file_path2)
y_train = convert_labels_to_one_hot(y_train, num_labels=2)
y_test = convert_labels_to_one_hot(y_test, num_labels=2)
clf = TextClassifier(verbose=True)
clf.fit(x=x_train, y=y_train, time_limit=12 * 60 * 60)
print("Classification accuracy is : ", 100 * clf.evaluate(x_test, y_test),
"%")