fastNLP/test/core/test_callbacks.py

import unittest

import numpy as np

from fastNLP.core.callback import EchoCallback, EarlyStopCallback, GradientClipCallback
from fastNLP.core.dataset import DataSet
from fastNLP.core.instance import Instance
from fastNLP.core.losses import BCELoss
from fastNLP.core.metrics import AccuracyMetric
from fastNLP.core.optimizer import SGD
from fastNLP.core.trainer import Trainer
from fastNLP.models.base_model import NaiveClassifier


def prepare_env():
    def prepare_fake_dataset():
        mean = np.array([-3, -3])
        cov = np.array([[1, 0], [0, 1]])
        class_A = np.random.multivariate_normal(mean, cov, size=(1000,))

        mean = np.array([3, 3])
        cov = np.array([[1, 0], [0, 1]])
        class_B = np.random.multivariate_normal(mean, cov, size=(1000,))

        data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
                           [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
        return data_set

    data_set = prepare_fake_dataset()
    data_set.set_input("x")
    data_set.set_target("y")
    model = NaiveClassifier(2, 1)
    return data_set, model


class TestCallback(unittest.TestCase):
    def test_echo_callback(self):
        data_set, model = prepare_env()
        trainer = Trainer(data_set, model,
                          loss=BCELoss(pred="predict", target="y"),
                          n_epochs=2,
                          batch_size=32,
                          print_every=50,
                          optimizer=SGD(lr=0.1),
                          check_code_level=2,
                          use_tqdm=False,
                          callbacks=[EchoCallback()])
        trainer.train()

    def test_gradient_clip(self):
        data_set, model = prepare_env()
        trainer = Trainer(data_set, model,
                          loss=BCELoss(pred="predict", target="y"),
                          n_epochs=30,
                          batch_size=32,
                          print_every=50,
                          optimizer=SGD(lr=0.1),
                          check_code_level=2,
                          use_tqdm=False,
                          dev_data=data_set,
                          metrics=AccuracyMetric(pred="predict", target="y"),
                          callbacks=[GradientClipCallback(model.parameters(), clip_value=2)])
        trainer.train()

    def test_early_stop(self):
        data_set, model = prepare_env()
        trainer = Trainer(data_set, model,
                          loss=BCELoss(pred="predict", target="y"),
                          n_epochs=50,
                          batch_size=32,
                          print_every=50,
                          optimizer=SGD(lr=0.01),
                          check_code_level=2,
                          use_tqdm=False,
                          dev_data=data_set,
                          metrics=AccuracyMetric(pred="predict", target="y"),
                          callbacks=[EarlyStopCallback(5)])
        trainer.train()