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_2d_pred_nostderr(self):
"2D prediction - no stderr"
(_, _, results, newdata1, _, madeup) = self.d
madeup.model.span = 0.5
madeup.model.normalize = True
madeup.predict(newdata1, stderror=False)
assert_almost_equal(madeup.predicted.values, results[4], 5)
#
madeup_pred = madeup.predict(newdata1, stderror=False)
assert_almost_equal(madeup_pred.values, results[4], 5)
def test_lowess_2(self):
"Tests lowess on typical data. part #2."
(X, Y) = self.data
YS = [13.659,12.347,11.034, 9.722,10.511,11.300,11.300,11.300,
11.300,11.300,11.300,11.300,11.300,11.300,11.300,13.000,
6.440, 5.596, 5.456,18.998]
Z = lowess(X, Y, span=0.25, nsteps=0, delta=3)
assert_almost_equal(Z.outputs.fitted_values, YS, decimal=3)
assert_almost_equal(Z.outputs.fitted_residuals+Z.outputs.fitted_values,
Z.inputs.y, decimal=3)
def test_lowess_3(self):
"Tests lowess on typical data. part #3."
(X, Y) = self.data
YS = [14.811,12.115, 8.984, 9.676,10.000,11.346,11.346,11.346,
11.346,11.346,11.346,11.346,11.346,11.346,11.346,13.000,
6.734, 5.744, 5.415,18.998 ]
Z = lowess(X, Y, span=0.25, nsteps=2, delta=0)
assert_almost_equal(Z.outputs.fitted_values, YS, decimal=3)
assert_almost_equal(Z.outputs.fitted_residuals+Z.outputs.fitted_values,
Z.inputs.y, decimal=3)
def test_lowess_4(self):
"Tests lowess on masked data."
X = masked_values([ 1, 2, 3, 4, 5,-999, 6, 6, 6, 6, -999,-999,
6, 6, 6, 6, 6, 6, 8,-999,10,12,14,50,-999],-999)
Y = marray([18, 2,15, 6,10,-999, 4, 16,11, 7, -999,-999,
3,14,17,20,12, 9,13,-999, 1, 8, 5,19,-999])
YS = [14.811,12.115, 8.984, 9.676,10.000,11.346,11.346,11.346,
11.346,11.346,11.346,11.346,11.346,11.346,11.346,13.000,
6.734, 5.744, 5.415,18.998 ]
Z = lowess(X, Y, span=0.25, nsteps=2, delta=0)
assert_almost_equal(Z.outputs.fitted_values.compressed(), YS, decimal=3)
assert_almost_equal(Z.outputs.fitted_residuals + Z.outputs.fitted_values,
Z.inputs.y, decimal=3)
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_stl_2(self):
"Tests a robust STL."
(co2_data, co2_results, parameters) = self.d
co2_fitted = stl(co2_data, robust=True, **parameters).outputs
assert_almost_equal(co2_fitted.seasonal, co2_results[4], 6)
assert_almost_equal(co2_fitted.trend, co2_results[5], 6)
assert_almost_equal(co2_fitted.weights, co2_results[6], 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_stl_1(self):
"Tests a classic STL."
(co2_data, co2_results, parameters) = self.d
co2_fitted = stl(co2_data, robust=False, **parameters).outputs
assert_almost_equal(co2_fitted.seasonal, co2_results[0], 6)
assert_almost_equal(co2_fitted.trend, co2_results[1], 6)
assert_almost_equal(co2_fitted.weights, co2_results[2], 6)
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_2d_modfamily(self):
"2D - family modification"
(_, _, results, _, _, madeup) = self.d
madeup.model.span = 0.8
madeup.model.drop_square_flags = [True, False]
madeup.model.parametric_flags = [True, False]
madeup.model.family = "symmetric"
madeup.fit()
assert_almost_equal(madeup.outputs.fitted_values, results[2], 5)
assert_almost_equal(madeup.outputs.enp, 6.9, 1)
assert_almost_equal(madeup.outputs.s, 1.0868, 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)
from maskedarray import masked_values
from numpy import fromiter
import os
if 1:
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_2d_pred_stderr(self):
"2D prediction - w/ stderr"
(_, _, results, _, newdata2, madeup) = self.d
madeup.model.span = 0.5
madeup.model.normalize = True
madeup_pred = madeup.predict(newdata2, stderror=True)
assert_almost_equal(madeup_pred.values, results[5], 5)
assert_almost_equal(madeup_pred.stderr, [0.276746, 0.278009], 5)
assert_almost_equal(madeup_pred.residual_scale, 0.969302, 6)
assert_almost_equal(madeup_pred.df, 81.2319, 4)
# Direct access
madeup.predict(newdata2, stderror=True)
assert_almost_equal(madeup.predicted.values, results[5], 5)
assert_almost_equal(madeup.predicted.stderr, [0.276746, 0.278009], 5)
assert_almost_equal(madeup.predicted.residual_scale, 0.969302, 6)
assert_almost_equal(madeup.predicted.df, 81.2319, 4)
def test_2d_pred_confinv(self):
"2D prediction - confidence"
(_, _, results, _, newdata2, madeup) = self.d
madeup.model.span = 0.5
madeup.model.normalize = True
madeup_pred = madeup.predict(newdata2, stderror=True)
madeup.predicted.confidence(coverage=0.99)
assert_almost_equal(madeup.predicted.confidence_intervals.lower,
results[6][::3], 5)
assert_almost_equal(madeup.predicted.confidence_intervals.fit,
results[6][1::3], 5)
assert_almost_equal(madeup.predicted.confidence_intervals.upper,
results[6][2::3], 5)
# Direct access
confinv = madeup.predicted.confidence(coverage=0.99)
assert_almost_equal(confinv.lower, results[6][::3], 5)
assert_almost_equal(confinv.fit, results[6][1::3], 5)
assert_almost_equal(confinv.upper, results[6][2::3], 5)
