def test_woe(self):
wPlus1 = np.math.log ( (2.0/3 + EPSILON)/(2.0/5 + EPSILON) )
wMinus1 = np.math.log ( (1.0/3 + EPSILON)/(3.0/5 + EPSILON) )
wPlus2 = np.math.log ( (1.0/3 + EPSILON)/(EPSILON) )
wMinus2 = np.math.log ( (2.0/3 + EPSILON)/(1.0 + EPSILON) )
wPlus3 = np.math.log ( (EPSILON)/(3.0/5 + EPSILON) )
wMinus3 = np.math.log ( (1.0 + EPSILON)/(2.0/5 + EPSILON) )
# Binary classes
ans = [
[wPlus1, wPlus1, wMinus1,],
[wMinus1, wMinus1, wPlus1, ],
[None, wMinus1, wPlus1, ]
]
ans = ma.array(data=ans, mask=self.mask)
np.testing.assert_equal(woe(self.factor, self.sites), ans)
# Multiclass
w1, w2, w3 = (wPlus1 + wMinus2+wMinus3), (wPlus2 + wMinus1 + wMinus3), (wPlus3 + wMinus1 + wMinus2)
ans = [
[w1, w1, w3,],
[w3, w2, w1,],
[ 0, w3, w1,]
]
ans = ma.array(data=ans, mask=self.mask)
weights = woe(self.multifact, self.sites)
np.testing.assert_equal(ans, weights)
def test_binary_woe(self):
wPlus = np.math.log((2.0 / 3 + EPSILON) / (2.0 / 5 + EPSILON))
wMinus = np.math.log((1.0 / 3 + EPSILON) / (3.0 / 5 + EPSILON))
self.assertEqual(_binary_woe(self.factor, self.sites), (wPlus, wMinus))
wPlus = np.math.log((5.0 / 7 + EPSILON) / (0.5 / 3.5 + EPSILON))
wMinus = np.math.log((2.0 / 7 + EPSILON) / (3.0 / 3.5 + EPSILON))
self.assertEqual(_binary_woe(self.bigfactor, self.bigsite, unitcell=2),
(wPlus, wMinus))
# if Sites=Factor:
wPlus = np.math.log((1 + EPSILON) / EPSILON)
wMinus = np.math.log(EPSILON / (1 + EPSILON))
self.assertEqual(_binary_woe(self.factor, self.factor),
(wPlus, wMinus))
# Check areas size
self.assertRaises(WoeError, _binary_woe, self.factor, self.zero)
self.assertRaises(WoeError, _binary_woe, self.zero, self.sites)
self.assertRaises(WoeError, _binary_woe, self.bigfactor, self.bigsite,
3)
# Non-binary sites
self.assertRaises(WoeError, woe, self.fact1, self.sites1)
# Assert does not raises
woe(self.multifact, self.sites2)
def test_woe(self):
wPlus1 = np.math.log((2.0 / 3 + EPSILON) / (2.0 / 5 + EPSILON))
wMinus1 = np.math.log((1.0 / 3 + EPSILON) / (3.0 / 5 + EPSILON))
wPlus2 = np.math.log((1.0 / 3 + EPSILON) / (EPSILON))
wMinus2 = np.math.log((2.0 / 3 + EPSILON) / (1.0 + EPSILON))
wPlus3 = np.math.log((EPSILON) / (3.0 / 5 + EPSILON))
wMinus3 = np.math.log((1.0 + EPSILON) / (2.0 / 5 + EPSILON))
# Binary categories
ans = [[
wPlus1,
wPlus1,
wMinus1,
], [
wMinus1,
wMinus1,
wPlus1,
], [
None,
wMinus1,
wPlus1,
]]
ans = ma.array(data=ans, mask=self.mask)
w = woe(self.factor, self.sites)
np.testing.assert_equal(w['map'], ans)
# Multiclass
w1, w2, w3 = (wPlus1 + wMinus2 +
wMinus3), (wPlus2 + wMinus1 +
wMinus3), (wPlus3 + wMinus1 + wMinus2)
ans = [[
w1,
w1,
w3,
], [
w3,
w2,
w1,
], [
0,
w3,
w1,
]]
ans = ma.array(data=ans, mask=self.mask)
weights = woe(self.multifact, self.sites)
np.testing.assert_equal(ans, weights['map'])
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
p = w1.getPrediction(self.sites).getBand(1)
assert_array_equal(p, self.sites.getBand(1))
initState = Raster("../../examples/data.tif")
finalState = Raster("../../examples/data1.tif")
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
p = w.getPrediction(initState).getBand(1)
# Calculate by hands:
# 1->1 transition raster:
r11 = [[1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 1->2 raster:
r12 = [[0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 1->3 raster:
r13 = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->1
r21 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->2
r22 = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->3
r23 = [[0, 0, 0, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]
# 3->1
r31 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]]
# 3->2
r32 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]
# 3->3
r33 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0]]
geodata = initState.getGeodata()
sites = {"11": r11, "12": r12, "13": r13, "21": r21, "22": r22, "23": r23, "31": r31, "32": r32, "33": r33}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k != "21": # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
# w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
# w2max = np.maximum(woeDict['22'], woeDict['23'])
# w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is index of finalClass that maximizes weights of transiotion initClass -> finalClass
answer = [[1, 1, 1, 1], [1, 1, 3, 3], [3, 3, 3, 3], [1, 1, 1, 1]]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins={0: [[2]]})
p = w.getPrediction(initState).getBand(1)
def test_binary_woe(self):
wPlus = np.math.log ( (2.0/3 + EPSILON)/(2.0/5 + EPSILON) )
wMinus = np.math.log ( (1.0/3 + EPSILON)/(3.0/5 + EPSILON) )
self.assertEqual(_binary_woe(self.factor, self.sites), (wPlus, wMinus))
wPlus = np.math.log ( (5.0/7 + EPSILON)/(0.5/3.5 + EPSILON) )
wMinus = np.math.log ( (2.0/7 + EPSILON)/(3.0/3.5 + EPSILON) )
self.assertEqual(_binary_woe(self.bigfactor, self.bigsite, unitcell=2), (wPlus, wMinus))
# if Sites=Factor:
wPlus = np.math.log ( (1 + EPSILON)/EPSILON )
wMinus = np.math.log ( EPSILON/(1 + EPSILON) )
self.assertEqual(_binary_woe(self.factor, self.factor), (wPlus, wMinus))
# Check areas size
self.assertRaises(WoeError, _binary_woe, self.factor, self.zero)
self.assertRaises(WoeError, _binary_woe, self.zero, self.sites)
self.assertRaises(WoeError, _binary_woe, self.bigfactor, self.bigsite, 3)
# Non-binary sites
self.assertRaises(WoeError, woe, self.fact1, self.sites1)
# Assert does not raises
woe(self.multifact, self.sites2)
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
w1.train()
p = w1.getPrediction(self.sites).getBand(1)
answer = [[0,3,0], [0,3,0], [9,0,3]]
answer = ma.array(data = answer, mask = self.mask)
assert_array_equal(p, answer)
initState = Raster('../../examples/data.tif')
#~ [1,1,1,1],
#~ [1,1,2,2],
#~ [2,2,2,2],
#~ [3,3,3,3]
finalState = Raster('../../examples/data1.tif')
#~ [1,1,2,3],
#~ [3,1,2,3],
#~ [3,3,3,3],
#~ [1,1,3,2]
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
w.train()
#print w.woe
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
# Calculate by hands:
#1->1 transition raster:
r11 = [
[1, 1, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
#1->2 raster:
r12 = [
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
#1->3 raster:
r13 = [
[0, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->1
r21 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->2
r22 = [
[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->3
r23 = [
[0, 0, 0, 0],
[0, 0, 0, 1],
[1, 1, 1, 1],
[0, 0, 0, 0]
]
# 3->1
r31 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 0, 0]
]
# 3->2
r32 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]
]
# 3->3
r33 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 1, 0]
]
geodata = initState.getGeodata()
sites = {'11': r11, '12': r12, '13': r13, '21': r21, '22': r22, '23': r23, '31': r31, '32': r32, '33': r33}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k !='21' : # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
#w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
#w2max = np.maximum(woeDict['22'], woeDict['23'])
#w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is a transition code with max weight
answer = [
[0, 0, 0, 0],
[0, 0, 5, 5],
[5, 5, 5, 5],
[6, 6, 6, 6]
]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins = {0: [[2], ],})
w.train()
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
c = w.getConfidence().getBand(1)
self.assertEquals(c.dtype, np.uint8)
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
w1.train()
p = w1.getPrediction(self.sites).getBand(1)
answer = [[0, 3, 0], [0, 3, 0], [9, 0, 3]]
answer = ma.array(data=answer, mask=self.mask)
assert_array_equal(p, answer)
initState = Raster('../../examples/data.tif')
#~ [1,1,1,1],
#~ [1,1,2,2],
#~ [2,2,2,2],
#~ [3,3,3,3]
finalState = Raster('../../examples/data1.tif')
#~ [1,1,2,3],
#~ [3,1,2,3],
#~ [3,3,3,3],
#~ [1,1,3,2]
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
w.train()
#print w.woe
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
# Calculate by hands:
#1->1 transition raster:
r11 = [[1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
#1->2 raster:
r12 = [[0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
#1->3 raster:
r13 = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->1
r21 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->2
r22 = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->3
r23 = [[0, 0, 0, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]
# 3->1
r31 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]]
# 3->2
r32 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]
# 3->3
r33 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0]]
geodata = initState.getGeodata()
sites = {
'11': r11,
'12': r12,
'13': r13,
'21': r21,
'22': r22,
'23': r23,
'31': r31,
'32': r32,
'33': r33
}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k != '21': # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
#w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
#w2max = np.maximum(woeDict['22'], woeDict['23'])
#w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is a transition code with max weight
answer = [[0, 0, 0, 0], [0, 0, 5, 5], [5, 5, 5, 5], [6, 6, 6, 6]]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins={
0: [
[2],
],
})
w.train()
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
c = w.getConfidence().getBand(1)
self.assertEquals(c.dtype, np.uint8)
