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test_cv_utils.py
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test_cv_utils.py
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import unittest
from unittest.mock import patch, Mock
import torch
import torchbearer
from torchbearer.cv_utils import *
class TestCVUtils(unittest.TestCase):
def test_train_valid_splitter_sizes(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 0.1
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(x.size()[0] == 90)
self.assertTrue(y.size()[0] == 90)
self.assertTrue(x_val.size()[0] == 10)
self.assertTrue(y_val.size()[0] == 10)
def test_train_valid_splitter_sizes_2(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 0.4
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(x.size()[0] == 60)
self.assertTrue(y.size()[0] == 60)
self.assertTrue(x_val.size()[0] == 40)
self.assertTrue(y_val.size()[0] == 40)
def test_train_valid_splitter_sizes_2d(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
x = torch.stack([x, x], -1)
y = torch.stack([y, y], -1)
valid_split = 0.1
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(list(x.size()) == [90, 2])
self.assertTrue(list(y.size()) == [90, 2])
self.assertTrue(list(x_val.size()) == [10, 2])
self.assertTrue(list(y_val.size()) == [10, 2])
def test_train_valid_splitter_order(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 0.1
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(list(x.numpy()) == list(range(11, 101)))
self.assertTrue(list(y.numpy()) == list(range(11, 101)))
self.assertTrue(list(x_val.numpy()) == list(range(1, 11)))
self.assertTrue(list(y_val.numpy()) == list(range(1, 11)))
def test_train_valid_splitter_split_negative(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = -0.1
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(list(x.numpy()) == list(range(91, 101)))
self.assertTrue(list(y.numpy()) == list(range(91, 101)))
self.assertTrue(list(x_val.numpy()) == list(range(1, 91)))
self.assertTrue(list(y_val.numpy()) == list(range(1, 91)))
def test_train_valid_splitter_split_zero(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 0
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(list(x.numpy()) == list(range(1, 101)))
self.assertTrue(list(y.numpy()) == list(range(1, 101)))
self.assertTrue(list(x_val.numpy()) == list(range(0, 0)))
self.assertTrue(list(y_val.numpy()) == list(range(0, 0)))
def test_train_valid_splitter_split_too_big(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 1.8
shuffle = False
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(list(x.numpy()) == list(range(0, 0)))
self.assertTrue(list(y.numpy()) == list(range(0, 0)))
self.assertTrue(list(x_val.numpy()) == list(range(1, 101)))
self.assertTrue(list(y_val.numpy()) == list(range(1, 101)))
def test_train_valid_splitter_shuffle_size(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 0.1
shuffle = True
x, y, x_val, y_val = train_valid_splitter(x, y, valid_split, shuffle)
self.assertTrue(x.size()[0] == 90)
self.assertTrue(y.size()[0] == 90)
self.assertTrue(x_val.size()[0] == 10)
self.assertTrue(y_val.size()[0] == 10)
def test_get_train_valid_sets_splitter_args(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = 0.1
shuffle = True
torchbearer.cv_utils.train_valid_splitter = Mock(return_value=(x,y,x,y))
tvs = torchbearer.cv_utils.train_valid_splitter
trainset, valset = get_train_valid_sets(x, y, None, valid_split, shuffle)
tvs.assert_called_once()
self.assertTrue(tvs.call_args[0][-1] == valid_split)
self.assertTrue(list(tvs.call_args[0][0].numpy()) == list(x.numpy()))
self.assertTrue(list(tvs.call_args[0][1].numpy()) == list(y.numpy()))
self.assertTrue(tvs.call_args[1]['shuffle'] == shuffle)
def test_get_train_valid_sets_given_valid_data(self):
x = range(1, 101)
y = range(1, 101)
x_val = range(101, 121)
y_val = range(101, 121)
x = torch.Tensor(x)
y = torch.Tensor(y)
x_val = torch.Tensor(x_val)
y_val = torch.Tensor(y_val)
valid_split = 0.1
shuffle = False
trainset, valset = get_train_valid_sets(x, y, (x_val, y_val), valid_split, shuffle)
self.assertTrue(len(valset) == len(x_val))
def test_get_train_valid_sets_no_valid(self):
x = range(1, 101)
y = range(1, 101)
x = torch.Tensor(x)
y = torch.Tensor(y)
valid_split = None
shuffle = False
trainset, valset = get_train_valid_sets(x, y, None, valid_split, shuffle)
self.assertTrue(valset is None)
self.assertTrue(len(trainset) == len(x))