def test_anova(self):
"Tests anova"
(E, NOx, _, _, _, results) = self.d
gas = loess(E,NOx, span=2./3.)
gas.fit()
gas_null = loess(E, NOx, span=1.0)
gas_null.fit()
gas_anova = loess_anova(gas, gas_null)
gas_anova_theo = results[4]
assert_almost_equal(gas_anova.dfn, gas_anova_theo[0], 5)
assert_almost_equal(gas_anova.dfd, gas_anova_theo[1], 5)
assert_almost_equal(gas_anova.F_value, gas_anova_theo[2], 5)
assert_almost_equal(gas_anova.Pr_F, gas_anova_theo[3], 5)
def test_anova(self):
"Tests anova"
(E, NOx, _, _, _, results) = self.d
gas = loess(E,NOx, span=2./3.)
gas.fit()
gas_null = loess(E, NOx, span=1.0)
gas_null.fit()
gas_anova = loess_anova(gas, gas_null)
gas_anova_theo = results[4]
assert_almost_equal(gas_anova.dfn, gas_anova_theo[0], 5)
assert_almost_equal(gas_anova.dfd, gas_anova_theo[1], 5)
assert_almost_equal(gas_anova.F_value, gas_anova_theo[2], 5)
assert_almost_equal(gas_anova.Pr_F, gas_anova_theo[3], 5)
def __init__(self, *args, **kwds):
TestCase.__init__(self, *args, **kwds)
dfile = open(os.path.join('tests','madeup_data'), 'r')
dfile.readline()
x = fromiter((float(v) for v in dfile.readline().rstrip().split()),
float_).reshape(-1,2)
x = marray(x)
dfile.readline()
y = fromiter((float(v) for v in dfile.readline().rstrip().split()),
float_)
y = marray(y)
#
rfile = open(os.path.join('tests','madeup_result'), 'r')
results = []
for i in range(8):
rfile.readline()
z = fromiter((float(v) for v in rfile.readline().rstrip().split()),
float_)
results.append(z)
#
newdata1 = numpy.array([[-2.5, 0.0, 2.5], [0., 0., 0.]])
newdata2 = numpy.array([[-0.5, 0.5], [0., 0.]])
#
madeup = loess(x,y)
self.d = (x, y, results, newdata1, newdata2, madeup)
def test_mask(self):
NOx = marray([4.818, 2.849, 3.275, 4.691, 4.255, 5.064, 2.118, 4.602,
2.286, 0.970, 3.965, 5.344, 3.834, 1.990, 5.199, 5.283,
-9999, -9999, 3.752, 0.537, 1.640, 5.055, 4.937, 1.561])
NOx = maskedarray.masked_values(NOx, -9999)
E = marray([0.831, 1.045, 1.021, 0.970, 0.825, 0.891, 0.71, 0.801,
1.074, 1.148, 1.000, 0.928, 0.767, 0.701, 0.807, 0.902,
-9999, -9999, 0.997, 1.224, 1.089, 0.973, 0.980, 0.665])
gas_fit_E = numpy.array([0.665, 0.949, 1.224])
newdata = numpy.array([0.6650000, 0.7581667, 0.8513333, 0.9445000,
1.0376667, 1.1308333, 1.2240000])
coverage = 0.99
rfile = open(os.path.join('tests','gas_result'), 'r')
results = []
for i in range(8):
rfile.readline()
z = fromiter((float(v) for v in rfile.readline().rstrip().split()),
float_)
results.append(z)
#
gas = loess(E,NOx)
gas.model.span = 2./3.
gas.fit()
assert_almost_equal(gas.outputs.fitted_values.compressed(), results[0], 6)
assert_almost_equal(gas.outputs.enp, 5.5, 1)
assert_almost_equal(gas.outputs.s, 0.3404, 4)
def __init__(self, *args, **kwds):
TestCase.__init__(self, *args, **kwds)
dfile = open(os.path.join('tests','madeup_data'), 'r')
dfile.readline()
x = fromiter((float(v) for v in dfile.readline().rstrip().split()),
float_).reshape(-1,2)
x = marray(x)
dfile.readline()
y = fromiter((float(v) for v in dfile.readline().rstrip().split()),
float_)
y = marray(y)
#
rfile = open(os.path.join('tests','madeup_result'), 'r')
results = []
for i in range(8):
rfile.readline()
z = fromiter((float(v) for v in rfile.readline().rstrip().split()),
float_)
results.append(z)
#
newdata1 = numpy.array([[-2.5, 0.0, 2.5], [0., 0., 0.]])
newdata2 = numpy.array([[-0.5, 0.5], [0., 0.]])
#
madeup = loess(x,y)
self.d = (x, y, results, newdata1, newdata2, madeup)
def test_1dpredict(self):
"Basic test 1d - prediction"
(E, NOx, gas_fit_E, _, _, results) = self.d
gas = loess(E,NOx, span=2./3.)
gas.fit()
gas.predict(gas_fit_E, stderror=False)
assert_almost_equal(gas.predicted.values, results[2], 6)
def test_mask(self):
NOx = marray([4.818, 2.849, 3.275, 4.691, 4.255, 5.064, 2.118, 4.602,
2.286, 0.970, 3.965, 5.344, 3.834, 1.990, 5.199, 5.283,
-9999, -9999, 3.752, 0.537, 1.640, 5.055, 4.937, 1.561])
NOx = maskedarray.masked_values(NOx, -9999)
E = marray([0.831, 1.045, 1.021, 0.970, 0.825, 0.891, 0.71, 0.801,
1.074, 1.148, 1.000, 0.928, 0.767, 0.701, 0.807, 0.902,
-9999, -9999, 0.997, 1.224, 1.089, 0.973, 0.980, 0.665])
gas_fit_E = numpy.array([0.665, 0.949, 1.224])
newdata = numpy.array([0.6650000, 0.7581667, 0.8513333, 0.9445000,
1.0376667, 1.1308333, 1.2240000])
coverage = 0.99
rfile = open(os.path.join('tests','gas_result'), 'r')
results = []
for i in range(8):
rfile.readline()
z = fromiter((float(v) for v in rfile.readline().rstrip().split()),
float_)
results.append(z)
#
gas = loess(E,NOx)
gas.model.span = 2./3.
gas.fit()
assert_almost_equal(gas.outputs.fitted_values.compressed(), results[0], 6)
assert_almost_equal(gas.outputs.enp, 5.5, 1)
assert_almost_equal(gas.outputs.s, 0.3404, 4)
def test_1dpredict(self):
"Basic test 1d - prediction"
(E, NOx, gas_fit_E, _, _, results) = self.d
gas = loess(E,NOx, span=2./3.)
gas.fit()
gas.predict(gas_fit_E, stderror=False)
assert_almost_equal(gas.predicted.values, results[2], 6)
def test_1dbasic_alt(self):
"Basic test 1d - part #2"
(E, NOx, _, _, _, results) = self.d
gas_null = loess(E, NOx)
gas_null.model.span = 1.0
gas_null.fit()
assert_almost_equal(gas_null.outputs.fitted_values, results[1], 5)
assert_almost_equal(gas_null.outputs.enp, 3.5, 1)
assert_almost_equal(gas_null.outputs.s, 0.5197, 4)
def test_1dbasic(self):
"Basic test 1d"
(E, NOx, _, _, _, results) = self.d
gas = loess(E,NOx)
gas.model.span = 2./3.
gas.fit()
assert_almost_equal(gas.outputs.fitted_values, results[0], 6)
assert_almost_equal(gas.outputs.enp, 5.5, 1)
assert_almost_equal(gas.outputs.s, 0.3404, 4)
def test_1dbasic(self):
"Basic test 1d"
(E, NOx, _, _, _, results) = self.d
gas = loess(E,NOx)
gas.model.span = 2./3.
gas.fit()
assert_almost_equal(gas.outputs.fitted_values, results[0], 6)
assert_almost_equal(gas.outputs.enp, 5.5, 1)
assert_almost_equal(gas.outputs.s, 0.3404, 4)
def test_1dbasic_alt(self):
"Basic test 1d - part #2"
(E, NOx, _, _, _, results) = self.d
gas_null = loess(E, NOx)
gas_null.model.span = 1.0
gas_null.fit()
assert_almost_equal(gas_null.outputs.fitted_values, results[1], 5)
assert_almost_equal(gas_null.outputs.enp, 3.5, 1)
assert_almost_equal(gas_null.outputs.s, 0.5197, 4)
def test_2dbasic(self):
"2D standard"
(x, y, results, _, _, madeup) = self.d
madeup = loess(x,y)
madeup.model.span = 0.5
madeup.model.normalize = True
madeup.fit()
assert_almost_equal(madeup.outputs.fitted_values, results[0], 5)
assert_almost_equal(madeup.outputs.enp, 14.9, 1)
assert_almost_equal(madeup.outputs.s, 0.9693, 4)
def test_2dbasic(self):
"2D standard"
(x, y, results, _, _, madeup) = self.d
madeup = loess(x,y)
madeup.model.span = 0.5
madeup.model.normalize = True
madeup.fit()
assert_almost_equal(madeup.outputs.fitted_values, results[0], 5)
assert_almost_equal(madeup.outputs.enp, 14.9, 1)
assert_almost_equal(madeup.outputs.s, 0.9693, 4)
def test_2d_modflags_tot(self):
"2D - modification of model flags"
(x, y, results, _, _, madeup) = self.d
madeup = loess(x,y)
madeup.model.span = 0.8
madeup.model.drop_square_flags = [True, False]
madeup.model.parametric_flags = [True, False]
assert_equal(madeup.model.parametric_flags[:2],[1,0])
madeup.fit()
assert_almost_equal(madeup.outputs.fitted_values, results[1], 5)
assert_almost_equal(madeup.outputs.enp, 6.9, 1)
assert_almost_equal(madeup.outputs.s, 1.4804, 4)
def test_1dpredict_2(self):
"Basic test 1d - new predictions"
(E, NOx, _, newdata, _, results) = self.d
gas = loess(E,NOx, span=2./3.)
gas.predict(newdata, stderror=True)
gas.predicted.confidence(0.99)
assert_almost_equal(gas.predicted.confidence_intervals.lower,
results[3][0::3], 6)
assert_almost_equal(gas.predicted.confidence_intervals.fit,
results[3][1::3], 6)
assert_almost_equal(gas.predicted.confidence_intervals.upper,
results[3][2::3], 6)
def test_2d_modflags_tot(self):
"2D - modification of model flags"
(x, y, results, _, _, madeup) = self.d
madeup = loess(x,y)
madeup.model.span = 0.8
madeup.model.drop_square_flags = [True, False]
madeup.model.parametric_flags = [True, False]
assert_equal(madeup.model.parametric_flags[:2],[1,0])
madeup.fit()
assert_almost_equal(madeup.outputs.fitted_values, results[1], 5)
assert_almost_equal(madeup.outputs.enp, 6.9, 1)
assert_almost_equal(madeup.outputs.s, 1.4804, 4)
def test_1dpredict_2(self):
"Basic test 1d - new predictions"
(E, NOx, _, newdata, _, results) = self.d
gas = loess(E,NOx, span=2./3.)
gas.predict(newdata, stderror=True)
gas.predicted.confidence(0.99)
assert_almost_equal(gas.predicted.confidence_intervals.lower,
results[3][0::3], 6)
assert_almost_equal(gas.predicted.confidence_intervals.fit,
results[3][1::3], 6)
assert_almost_equal(gas.predicted.confidence_intervals.upper,
results[3][2::3], 6)
def test_failures(self):
"Tests failures"
(E, NOx, gas_fit_E, _, _, _) = self.d
gas = loess(E,NOx, span=2./3.)
# This one should fail (all parametric)
gas.model.parametric_flags = True
self.assertRaises(ValueError, gas.fit)
# This one also (all drop_square)
gas.model.drop_square_flags = True
self.assertRaises(ValueError, gas.fit)
gas.model.degree = 1
self.assertRaises(ValueError, gas.fit)
# This one should not (revert to std)
gas.model.parametric_flags = False
gas.model.drop_square_flags = False
gas.model.degree = 2
gas.fit()
# Now, for predict .................
gas.predict(gas_fit_E, stderror=False)
# This one should fail (extrapolation & blending)
self.assertRaises(ValueError,
gas.predict, gas.predicted.values, stderror=False)
# But this one should not ..........
gas.predict(gas_fit_E, stderror=False)
def test_failures(self):
"Tests failures"
(E, NOx, gas_fit_E, _, _, _) = self.d
gas = loess(E,NOx, span=2./3.)
# This one should fail (all parametric)
gas.model.parametric_flags = True
self.assertRaises(ValueError, gas.fit)
# This one also (all drop_square)
gas.model.drop_square_flags = True
self.assertRaises(ValueError, gas.fit)
gas.model.degree = 1
self.assertRaises(ValueError, gas.fit)
# This one should not (revert to std)
gas.model.parametric_flags = False
gas.model.drop_square_flags = False
gas.model.degree = 2
gas.fit()
# Now, for predict .................
gas.predict(gas_fit_E, stderror=False)
# This one should fail (extrapolation & blending)
self.assertRaises(ValueError,
gas.predict, gas.predicted.values, stderror=False)
# But this one should not ..........
gas.predict(gas_fit_E, stderror=False)