class TestLRManager(unittest.TestCase):
def setUp(self):
self.output = Raster('../../examples/multifact.tif')
#~ [1,1,3]
#~ [3,2,1]
#~ [0,3,1]
self.output.resetMask([0])
self.state = self.output
self.factors = [
Raster('../../examples/sites.tif'),
Raster('../../examples/sites.tif')
]
#~ [1,2,1],
#~ [1,2,1],
#~ [0,1,2]
self.output1 = Raster('../../examples/data.tif')
self.state1 = self.output1
self.factors1 = [Raster('../../examples/fact16.tif')]
def test_LR(self):
#~ data = [
#~ [3.0, 1.0, 3.0],
#~ [3.0, 1.0, 3.0],
#~ [0, 3.0, 1.0]
#~ ]
#~ result = np.ma.array(data = data, mask = (data==0))
lr = LR(ns=0) # 3-class problem
lr.setState(self.state)
lr.setFactors(self.factors)
lr.setOutput(self.output)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state, self.factors)
predict = predict.getBand(1)
assert_array_equal(predict, self.output.getBand(1))
lr = LR(ns=1) # Two-class problem (it's because of boundary effect)
lr.setState(self.state1)
lr.setFactors(self.factors1)
lr.setOutput(self.output1)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state1,
self.factors1,
calcTransitions=True)
predict = predict.getBand(1)
self.assertEquals(predict.dtype, np.uint8)
data = [
[0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]
result = np.ma.array(data=data, mask=(data == 0))
assert_array_equal(predict, result)
# Confidence is zero
confid = lr.getConfidence()
self.assertEquals(confid.getBand(1).dtype, np.uint8)
# Transition Potentials
potentials = lr.getTransitionPotentials()
cats = self.output.getBandGradation(1)
for cat in [1.0, 2.0]:
map = potentials[cat]
self.assertEquals(map.getBand(1).dtype, np.uint8)
class TestMlpManager (unittest.TestCase):
def setUp(self):
self.factors = [Raster('../../examples/multifact.tif')]
self.output = Raster('../../examples/sites.tif')
#~ sites.tif is 1-band 3x3 raster:
#~ [1,2,1],
#~ [1,2,1],
#~ [0,1,2]
self.factors2 = [Raster('../../examples/multifact.tif'), Raster('../../examples/multifact.tif')]
self.factors3 = [Raster('../../examples/two_band.tif')]
self.factors4 = [Raster('../../examples/two_band.tif'), Raster('../../examples/multifact.tif')]
self.output1 = Raster('../../examples/data.tif')
self.state1 = self.output1
self.factors1 = [Raster('../../examples/fact16.tif')]
def test_MlpManager(self):
mng = MlpManager(ns=1)
mng.createMlp(self.output, self.factors2, self.output, [10])
assert_array_equal(mng.getMlpTopology(), [2*9 + 2*9, 10, 3])
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
assert_array_equal(mng.getMlpTopology(), [2*1 + 1*1, 10, 3])
def test_setTrainingData(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
stat = self.factors[0].getBandStat(1) # mean & std
m,s = stat['mean'], stat['std']
mng.setTrainingData(self.output, self.factors, self.output, shuffle=False)
min, max = mng.sigmin, mng.sigmax
data = np.array(
[
((0,3), [0, 1], (1.0-m)/s, [min, max, min]),
((1,3), [0, 0], (1.0-m)/s, [min, min, max]),
((2,3), [0, 1], (3.0-m)/s, [min, max, min]),
((0,2), [0, 1], (3.0-m)/s, [min, max, min]),
((1,2), [0, 0], (2.0-m)/s, [min, min, max]),
((2,2), [0, 1], (1.0-m)/s, [min, max, min]),
((0,1), [1, 0], (0.0-m)/s, [max, min, min]),
((1,1), [0, 1], (3.0-m)/s, [min, max, min]),
((2,1), [0, 0], (1.0-m)/s, [min, min, max]),
],
dtype=[('coords', float, 2),('state', float, (2,)), ('factors', float, (1,)), ('output', float, 3)]
)
self.assertEqual(mng.data.shape, (9,))
for i in range(len(data)):
assert_array_equal(data[i]['coords'], mng.data[i]['coords'])
assert_array_equal(data[i]['factors'], mng.data[i]['factors'])
assert_array_equal(data[i]['output'], mng.data[i]['output'])
# two input rasters
mng = MlpManager(ns=1)
mng.createMlp(self.output, self.factors2, self.output, [10])
mng.setTrainingData(self.output, self.factors2, self.output)
data = [
{
'factors': (np.array([ 1., 1., 3., 3., 2., 1., 0., 3., 1.,
1., 1., 3., 3., 2., 1., 0., 3., 1.]) - stat['mean'])/stat['std'],
'output': np.array([min, min, max]),
'state': np.array([0,1, 0,0, 0,1, 0,1, 0,0, 0,1, 1,0, 0,1, 0,0])
}
]
self.assertEqual(mng.data.shape, (1,))
assert_array_equal(data[0]['factors'], mng.data[0]['factors'])
assert_array_equal(data[0]['output'], mng.data[0]['output'])
assert_array_equal(data[0]['state'], mng.data[0]['state'])
# Multiband input
mng = MlpManager(ns=1)
mng.createMlp(self.output, self.factors3, self.output, [10])
stat1 = self.factors3[0].getBandStat(1) # mean & std
m1,s1 = stat1['mean'], stat1['std']
stat2 = self.factors3[0].getBandStat(2) # mean & std
m2,s2 = stat2['mean'], stat2['std']
mng.setTrainingData(self.output, self.factors3, self.output)
data = [
{
'factors': np.array([ (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (0.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1,
(1.-m2)/s2, (1.-m2)/s2, (3.-m2)/s2, (3.-m2)/s2, (2.-m2)/s2, (1.-m2)/s2, (0.-m2)/s2, (3.-m2)/s2, (1.-m2)/s2]),
'output': np.array([min, min, max]),
'state': np.array([0,1, 0,0, 0,1, 0,1, 0,0, 0,1, 1,0, 0,1, 0,0])
}
]
self.assertEqual(mng.data.shape, (1,))
assert_array_equal(data[0]['factors'], mng.data[0]['factors'])
assert_array_equal(data[0]['output'], mng.data[0]['output'])
assert_array_equal(data[0]['state'], mng.data[0]['state'])
# Complex case:
mng = MlpManager(ns=1)
mng.createMlp(self.output, self.factors4, self.output, [10])
stat1 = self.factors4[0].getBandStat(1) # mean & std
m1,s1 = stat1['mean'], stat1['std']
stat2 = self.factors4[0].getBandStat(2) # mean & std
m2,s2 = stat2['mean'], stat2['std']
stat3 = self.factors4[1].getBandStat(1)
m3,s3 = stat3['mean'], stat2['std']
mng.setTrainingData(self.output, self.factors4, self.output)
data = [
{
'factors': np.array([ (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (0.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1,
(1.-m2)/s2, (1.-m2)/s2, (3.-m2)/s2, (3.-m2)/s2, (2.-m2)/s2, (1.-m2)/s2, (0.-m2)/s2, (3.-m2)/s2, (1.-m2)/s2,
(1.-m3)/s3, (1.-m3)/s3, (3.-m3)/s3, (3.-m3)/s3, (2.-m3)/s3, (1.-m3)/s3, (0.-m3)/s3, (3.-m3)/s3, (1.-m3)/s3]),
'output': np.array([min, min, max]),
'state': np.array([0,1, 0,0, 0,1, 0,1, 0,0, 0,1, 1,0, 0,1, 0,0])
}
]
self.assertEqual(mng.data.shape, (1,))
assert_array_equal(data[0]['factors'], mng.data[0]['factors'])
assert_array_equal(data[0]['output'], mng.data[0]['output'])
assert_array_equal(data[0]['state'], mng.data[0]['state'])
def test_train(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
mng.setTrainingData(self.output, self.factors, self.output)
mng.train(1, valPercent=50)
val = mng.getMinValError()
tr = mng.getTrainError()
mng.train(20, valPercent=50, continue_train=True)
self.assertGreaterEqual(val, mng.getMinValError())
mng = MlpManager(ns=1)
mng.createMlp(self.state1, self.factors1, self.output1, [10])
mng.setTrainingData(self.state1, self.factors1, self.output1)
mng.train(1, valPercent=20)
predict = mng.getPrediction(self.state1, self.factors1)
mask = predict.getBand(1).mask
self.assertTrue(not all(mask.flatten()))
def test_predict(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
weights = mng.copyWeights()
# Set weights=0
# then the output must be sigmoid(0)
layers = []
for layer in weights:
shape = layer.shape
layers.append(np.zeros(shape))
mng.setMlpWeights(layers)
mng._predict(self.output, self.factors)
# Prediction ( the output must be sigmoid(0) )
raster = mng.getPrediction(self.output, self.factors, calcTransitions=True)
assert_array_equal(raster.getBand(1), sigmoid(0)*np.ones((3,3)))
# Confidence is zero
confid = mng.getConfidence()
self.assertEquals(confid.getBand(1).dtype, np.uint8)
assert_array_equal(confid.getBand(1), np.zeros((3,3)))
# Transition Potentials (is (sigmoid(0) - sigmin)/sigrange )
potentials = mng.getTransitionPotentials()
cats = self.output.getBandGradation(1)
for cat in cats:
map = potentials[cat]
self.assertEquals(map.getBand(1).dtype, np.uint8)
assert_array_equal(map.getBand(1), 50*np.ones((3,3)))
def test_create(self):
raster = Raster()
raster.create([self.data1], geodata=self.r1.getGeodata())
self.assertTrue(raster.geoDataMatch(self.r1))
self.assertEqual(raster.getBandsCount(), 1)
self.assertEqual(set(raster.getBandGradation(1)), set([0, 1, 2, 3]))
class TestRaster(unittest.TestCase):
def setUp(self):
self.r1 = Raster('examples/multifact.tif')
self.r2 = Raster('examples/sites.tif')
self.r3 = Raster('examples/two_band.tif')
# r1
data1 = np.array([[1, 1, 3], [3, 2, 1], [0, 3, 1]])
# r2
data2 = np.array([[1, 2, 1], [1, 2, 1], [0, 1, 2]])
mask = [[False, False, False], [False, False, False],
[False, False, False]]
self.data1 = ma.array(data=data1, mask=mask)
self.data2 = ma.array(data=data2, mask=mask)
def test_RasterInit(self):
self.assertEqual(self.r1.getBandsCount(), 1)
band = self.r1.getBand(1)
shape = band.shape
x = self.r1.getXSize()
y = self.r1.getYSize()
self.assertEqual(shape, (x, y))
self.assertEqual(self.r2.getBandsCount(), 1)
band = self.r2.getBand(1)
assert_array_equal(band, self.data2)
self.assertTrue(self.r1.geoDataMatch(self.r2))
self.assertTrue(self.r1.isMetricProj())
def test_create(self):
raster = Raster()
raster.create([self.data1], geodata=self.r1.getGeodata())
self.assertTrue(raster.geoDataMatch(self.r1))
self.assertEqual(raster.getBandsCount(), 1)
self.assertEqual(set(raster.getBandGradation(1)), set([0, 1, 2, 3]))
def test_roundBands(self):
rast = Raster('examples/multifact.tif')
rast.bands = rast.bands * 0.1
rast.roundBands()
answer = [[[
0,
0,
0,
], [0, 0, 0], [0, 0, 0]]]
assert_array_equal(answer, rast.bands)
rast = Raster('examples/multifact.tif')
rast.bands = rast.bands * 1.1
rast.roundBands(decimals=1)
answer = np.array([[[1.1, 1.1, 3.3], [3.3, 2.2, 1.1], [0.0, 3.3,
1.1]]])
assert_array_equal(answer, rast.bands)
def test_isContinues(self):
rast = Raster('examples/multifact.tif')
self.assertFalse(rast.isCountinues(bandNo=1))
rast = Raster('examples/dist_roads.tif')
self.assertTrue(rast.isCountinues(bandNo=1))
def test_getBandStat(self):
stat = self.r1.getBandStat(1)
self.assertAlmostEqual(stat['mean'], 15.0 / 9)
self.assertAlmostEqual(stat['std'], np.sqrt(10.0 / 9))
def test_normalize(self):
multifact = [
[1, 1, 3],
[3, 2, 1],
[0, 3, 1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_getNeighbours(self):
neighbours = self.r2.getNeighbours(row=1, col=0, size=0)
self.assertEqual(neighbours, [[1]])
neighbours = self.r2.getNeighbours(row=1, col=1, size=1)
assert_array_equal(neighbours, [self.data2])
neighbours = self.r3.getNeighbours(row=1, col=1, size=1)
assert_array_equal(neighbours, [self.data2, self.data1])
# Check pixel on the raster bound and nonzero neighbour size
self.assertRaises(ProviderError,
self.r2.getNeighbours,
col=1,
row=0,
size=1)
self.assertRaises(ProviderError,
self.r2.getNeighbours,
col=1,
row=1,
size=2)
def test_geodata(self):
geodata = self.r1.getGeodata()
self.r1.setGeoData(geodata)
geodata['xSize'] = geodata['xSize'] + 10
self.assertRaises(ProviderError, self.r1.setGeoData, geodata=geodata)
self.assertTrue(self.r1.geoDataMatch(self.r1))
self.assertTrue(
self.r1.geoDataMatch(raster=None, geodata=self.r1.getGeodata()))
self.assertTrue(self.r1.geoTransformMatch(self.r1))
self.assertTrue(
self.r1.geoTransformMatch(raster=None,
geodata=self.r1.getGeodata()))
def test_save(self):
try:
filename = 'temp.tiff'
self.r1.save(filename)
r2 = Raster(filename)
self.assertEqual(r2.get_dtype(), self.r1.get_dtype())
self.assertEqual(r2.getBandsCount(), self.r1.getBandsCount())
for i in range(r2.getBandsCount()):
assert_array_equal(r2.getBand(i + 1), self.r1.getBand(i + 1))
finally:
os.remove(filename)
def test_getBandGradation(self):
self.assertEqual(set(self.r1.getBandGradation(1)), set([0, 1, 2, 3]))
class TestLRManager (unittest.TestCase):
def setUp(self):
self.output = Raster('../../examples/multifact.tif')
#~ [1,1,3]
#~ [3,2,1]
#~ [0,3,1]
self.output.resetMask([0])
self.state = self.output
self.factors = [Raster('../../examples/sites.tif'), Raster('../../examples/sites.tif')]
#~ [1,2,1],
#~ [1,2,1],
#~ [0,1,2]
self.output1 = Raster('../../examples/data.tif')
self.state1 = self.output1
self.factors1 = [Raster('../../examples/fact16.tif')]
def test_LR(self):
#~ data = [
#~ [3.0, 1.0, 3.0],
#~ [3.0, 1.0, 3.0],
#~ [0, 3.0, 1.0]
#~ ]
#~ result = np.ma.array(data = data, mask = (data==0))
lr = LR(ns=0) # 3-class problem
lr.setState(self.state)
lr.setFactors(self.factors)
lr.setOutput(self.output)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state, self.factors)
predict = predict.getBand(1)
assert_array_equal(predict, self.output.getBand(1))
lr = LR(ns=1) # Two-class problem (it's because of boundary effect)
lr.setState(self.state1)
lr.setFactors(self.factors1)
lr.setOutput(self.output1)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state1, self.factors1, calcTransitions=True)
predict = predict.getBand(1)
self.assertEquals(predict.dtype, np.uint8)
data = [
[0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]
result = np.ma.array(data = data, mask = (data==0))
assert_array_equal(predict, result)
# Confidence is zero
confid = lr.getConfidence()
self.assertEquals(confid.getBand(1).dtype, np.uint8)
# Transition Potentials
potentials = lr.getTransitionPotentials()
cats = self.output.getBandGradation(1)
for cat in [1.0, 2.0]:
map = potentials[cat]
self.assertEquals(map.getBand(1).dtype, np.uint8)
def test_create(self):
raster = Raster()
raster.create([self.data1], geodata=self.r1.getGeodata())
self.assertTrue(raster.geoDataMatch(self.r1))
self.assertEqual(raster.getBandsCount(), 1)
self.assertEqual(set(raster.getBandGradation(1)), set([0, 1, 2, 3]))
class TestRaster (unittest.TestCase):
def setUp(self):
self.r1 = Raster('examples/multifact.tif')
self.r2 = Raster('examples/sites.tif')
self.r3 = Raster('examples/two_band.tif')
# r1
data1 = np.array(
[
[1,1,3],
[3,2,1],
[0,3,1]
])
# r2
data2 = np.array(
[
[1,2,1],
[1,2,1],
[0,1,2]
])
mask = [
[False, False, False],
[False, False, False],
[False, False, False]
]
self.data1 = ma.array(data=data1, mask=mask)
self.data2 = ma.array(data=data2, mask=mask)
def test_RasterInit(self):
self.assertEqual(self.r1.getBandsCount(), 1)
band = self.r1.getBand(1)
shape = band.shape
x = self.r1.getXSize()
y = self.r1.getYSize()
self.assertEqual(shape, (x,y))
self.assertEqual(self.r2.getBandsCount(), 1)
band = self.r2.getBand(1)
assert_array_equal(band, self.data2)
self.assertTrue(self.r1.geoDataMatch(self.r2))
self.assertTrue(self.r1.isMetricProj())
def test_create(self):
raster = Raster()
raster.create([self.data1], geodata=self.r1.getGeodata())
self.assertTrue(raster.geoDataMatch(self.r1))
self.assertEqual(raster.getBandsCount(), 1)
self.assertEqual(set(raster.getBandGradation(1)), set([0, 1, 2, 3]))
def test_roundBands(self):
rast = Raster('examples/multifact.tif')
rast.bands = rast.bands*0.1
rast.roundBands()
answer = [[[ 0, 0, 0,],
[ 0, 0, 0],
[ 0, 0, 0]]]
assert_array_equal(answer, rast.bands)
rast = Raster('examples/multifact.tif')
rast.bands = rast.bands*1.1
rast.roundBands(decimals=1)
answer = np.array(
[[
[1.1,1.1,3.3],
[3.3,2.2,1.1],
[0.0,3.3,1.1]
]])
assert_array_equal(answer, rast.bands)
def test_isContinues(self):
rast = Raster('examples/multifact.tif')
self.assertFalse(rast.isCountinues(bandNo=1))
rast = Raster('examples/dist_roads.tif')
self.assertTrue(rast.isCountinues(bandNo=1))
def test_getBandStat(self):
stat = self.r1.getBandStat(1)
self.assertAlmostEqual(stat['mean'], 15.0/9)
self.assertAlmostEqual(stat['std'], np.sqrt(10.0/9))
def test_normalize(self):
multifact = [
[1,1,3],
[3,2,1],
[0,3,1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_getNeighbours(self):
neighbours = self.r2.getNeighbours(row=1,col=0, size=0)
self.assertEqual(neighbours, [[1]])
neighbours = self.r2.getNeighbours(row=1,col=1, size=1)
assert_array_equal(neighbours, [self.data2])
neighbours = self.r3.getNeighbours(row=1,col=1, size=1)
assert_array_equal(neighbours, [self.data2, self.data1])
# Check pixel on the raster bound and nonzero neighbour size
self.assertRaises(ProviderError, self.r2.getNeighbours, col=1, row=0, size=1)
self.assertRaises(ProviderError, self.r2.getNeighbours, col=1, row=1, size=2)
def test_geodata(self):
geodata = self.r1.getGeodata()
self.r1.setGeoData(geodata)
geodata['xSize'] = geodata['xSize'] + 10
self.assertRaises(ProviderError, self.r1.setGeoData, geodata=geodata)
self.assertTrue(self.r1.geoDataMatch(self.r1))
self.assertTrue(self.r1.geoDataMatch(raster=None, geodata=self.r1.getGeodata()))
self.assertTrue(self.r1.geoTransformMatch(self.r1))
self.assertTrue(self.r1.geoTransformMatch(raster=None, geodata=self.r1.getGeodata()))
def test_save(self):
try:
filename = 'temp.tiff'
self.r1.save(filename)
r2 = Raster(filename)
self.assertEqual(r2.get_dtype(), self.r1.get_dtype())
self.assertEqual(r2.getBandsCount(), self.r1.getBandsCount())
for i in range(r2.getBandsCount()):
assert_array_equal(r2.getBand(i+1), self.r1.getBand(i+1))
finally:
os.remove(filename)
def test_getBandGradation(self):
self.assertEqual(set(self.r1.getBandGradation(1)), set([0, 1, 2, 3]))
