def setUp(self):
# Generate 2 fake classification datasets, one with 2 classes and another with 3
self.twoclass_dataset = make_classification(40,
n_features=100,
n_informative=5,
n_redundant=5,
n_classes=2)
self.three_classdataset = make_classification(40,
n_features=100,
n_informative=5,
n_redundant=5,
n_classes=3)
y_scaler = ChemometricsScaler(with_mean=False, with_std=False)
self.plsreg = ChemometricsPLS(n_comps=3, yscaler=y_scaler)
self.plslog = ChemometricsPLS_Logistic(n_comps=3)
class test_plslogistic(unittest.TestCase):
"""
Test the functionality of the PLS - Logistic code. Most of the tests focus on the DA model specifics and classification metrics.
The underlying PLS regression functionality is tested in the 'test_plsobjconsistency' suite of tests.
"""
def setUp(self):
# Generate 2 fake classification datasets, one with 2 classes and another with 3
self.twoclass_dataset = make_classification(40,
n_features=100,
n_informative=5,
n_redundant=5,
n_classes=2)
self.three_classdataset = make_classification(40,
n_features=100,
n_informative=5,
n_redundant=5,
n_classes=3)
y_scaler = ChemometricsScaler(with_mean=False, with_std=False)
self.plsreg = ChemometricsPLS(n_comps=3, yscaler=y_scaler)
self.plslog = ChemometricsPLS_Logistic(n_comps=3)
def test_single_y(self):
self.plsreg.fit(self.twoclass_dataset[0], self.twoclass_dataset([1]))
self.plslog.fit(self.twoclass_dataset[0], self.twoclass_dataset([1]))
self.assertEqual(self.plsreg.rotations_cs, self.plslog.rotations_cs)
def test_multi_y(self):
self.plsreg.fit(self.threeclass_dataset[0],
self.threeclass_dataset([1]))
self.plslog.fit(self.threeclass_dataset[0],
self.threeclass_dataset([1]))
self.assertEqual(self.data.noFeatures, self.noFeat)
def test_(self):
self.assertEqual()
def setUp(self):
"""
:return:
"""
try:
multiclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_multiclass.csv'))
twoclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_twoclass.csv'))
except OSError as exp:
#os.system("python gen_synthetic_datasets.py")
import tests.gen_synthetic_datasets
multiclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_multiclass.csv'))
twoclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_twoclass.csv'))
finally:
# check this
self.da_mat = multiclass['Class_Vector'].values
self.da = twoclass['Class'].values
self.xmat_multi = multiclass.iloc[:, 5::].values
self.xmat = twoclass.iloc[:, 1::].values
# Set up the same scalers
y_scaler = ChemometricsScaler(0, with_std=False, with_mean=True)
self.plsreg = ChemometricsPLS(ncomps=3, yscaler=y_scaler)
self.plsda = ChemometricsPLSDA(ncomps=3)
# Generate the dummy matrix so we can run the pls regression objects in the same conditions as
# the discriminant ones
self.dummy_y = pds.get_dummies(self.da_mat).values
class TestPLSObjectConsistency(unittest.TestCase):
"""
Verify agreement of the PLS regression component between ChemometricsPLS and Discriminant analysis versions.
"""
def setUp(self):
"""
:return:
"""
try:
multiclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_multiclass.csv'))
twoclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_twoclass.csv'))
except OSError as exp:
#os.system("python gen_synthetic_datasets.py")
import tests.gen_synthetic_datasets
multiclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_multiclass.csv'))
twoclass = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/classification_twoclass.csv'))
finally:
# check this
self.da_mat = multiclass['Class_Vector'].values
self.da = twoclass['Class'].values
self.xmat_multi = multiclass.iloc[:, 5::].values
self.xmat = twoclass.iloc[:, 1::].values
# Set up the same scalers
y_scaler = ChemometricsScaler(0, with_std=False, with_mean=True)
self.plsreg = ChemometricsPLS(ncomps=3, yscaler=y_scaler)
self.plsda = ChemometricsPLSDA(ncomps=3)
# Generate the dummy matrix so we can run the pls regression objects in the same conditions as
# the discriminant ones
self.dummy_y = pds.get_dummies(self.da_mat).values
def test_single_y(self):
"""
:return:
"""
self.plsreg.fit(self.xmat, self.da)
self.plsda.fit(self.xmat, self.da)
assert_allclose(self.plsreg.scores_t, self.plsda.scores_t)
assert_allclose(self.plsreg.scores_u, self.plsda.scores_u)
assert_allclose(self.plsreg.rotations_cs, self.plsda.rotations_cs)
assert_allclose(self.plsreg.rotations_ws, self.plsda.rotations_ws)
assert_allclose(self.plsreg.weights_w, self.plsda.weights_w)
assert_allclose(self.plsreg.weights_c, self.plsda.weights_c)
assert_allclose(self.plsreg.loadings_p, self.plsda.loadings_p)
assert_allclose(self.plsreg.loadings_q, self.plsda.loadings_q)
assert_allclose(self.plsreg.beta_coeffs, self.plsda.beta_coeffs)
assert_allclose(self.plsreg.modelParameters['R2Y'],
self.plsda.modelParameters['PLS']['R2Y'])
assert_allclose(self.plsreg.modelParameters['R2X'],
self.plsda.modelParameters['PLS']['R2X'])
assert_allclose(self.plsreg.modelParameters['SSX'],
self.plsda.modelParameters['PLS']['SSX'])
assert_allclose(self.plsreg.modelParameters['SSY'],
self.plsda.modelParameters['PLS']['SSY'])
assert_allclose(self.plsreg.modelParameters['SSXcomp'],
self.plsda.modelParameters['PLS']['SSXcomp'])
assert_allclose(self.plsreg.modelParameters['SSYcomp'],
self.plsda.modelParameters['PLS']['SSYcomp'])
def test_multi_y(self):
"""
:return:
"""
self.plsreg.fit(self.xmat_multi, self.dummy_y)
self.plsda.fit(self.xmat_multi, self.da_mat)
assert_allclose(self.plsreg.scores_t, self.plsda.scores_t)
assert_allclose(self.plsreg.scores_u, self.plsda.scores_u)
assert_allclose(self.plsreg.rotations_cs, self.plsda.rotations_cs)
assert_allclose(self.plsreg.rotations_ws, self.plsda.rotations_ws)
assert_allclose(self.plsreg.weights_w, self.plsda.weights_w)
assert_allclose(self.plsreg.weights_c, self.plsda.weights_c)
assert_allclose(self.plsreg.loadings_p, self.plsda.loadings_p)
assert_allclose(self.plsreg.loadings_q, self.plsda.loadings_q)
assert_allclose(self.plsreg.beta_coeffs, self.plsda.beta_coeffs)
assert_allclose(self.plsreg.modelParameters['R2Y'],
self.plsda.modelParameters['PLS']['R2Y'])
assert_allclose(self.plsreg.modelParameters['R2X'],
self.plsda.modelParameters['PLS']['R2X'])
assert_allclose(self.plsreg.modelParameters['SSX'],
self.plsda.modelParameters['PLS']['SSX'])
assert_allclose(self.plsreg.modelParameters['SSY'],
self.plsda.modelParameters['PLS']['SSY'])
assert_allclose(self.plsreg.modelParameters['SSXcomp'],
self.plsda.modelParameters['PLS']['SSXcomp'])
assert_allclose(self.plsreg.modelParameters['SSYcomp'],
self.plsda.modelParameters['PLS']['SSYcomp'])
from pyChemometrics import ChemometricsScaler, ChemometricsPLS
import numpy as np
import pandas as pds
# Use the standard datasets
t_dset = pds.read_csv('./tests/test_data/regression.csv')
xmat = t_dset.iloc[:, 1::].values
y = t_dset.iloc[:, 0].values
x_scaler = ChemometricsScaler(1)
y_scaler = ChemometricsScaler(1)
plsmodel = ChemometricsPLS(ncomps=3, xscaler=x_scaler, yscaler=y_scaler)
plsmodel.fit(xmat, y)
np.random.seed(0)
plsmodel.cross_validation(xmat, y)
np.savetxt('./tests/test_data/pls_loadings_p.csv',
plsmodel.loadings_p,
fmt='%.18e',
delimiter=',',
newline='\n',
header='',
footer='',
comments='#')
np.savetxt('./tests/test_data/pls_scores_t.csv',
plsmodel.scores_t,
fmt='%.18e',
def setUp(self):
try:
regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression.csv'))
multiblock_regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression_multiblock.csv'))
except (IOError, OSError) as ioerr:
#os.system("python gen_synthetic_datasets.py")
import tests.gen_synthetic_datasets
regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression.csv'))
multiblock_regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression_multiblock.csv'))
finally:
# Load expected values for a PLS regression against a Y vector
self.expected_loadings_p = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_loadings_p.csv'),
delimiter=',')
self.expected_loadings_q = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_loadings_q.csv'),
delimiter=',')[np.newaxis, :]
self.expected_weights_w = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_weights_w.csv'),
delimiter=',')
self.expected_weights_c = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_weights_c.csv'),
delimiter=',')[np.newaxis, :]
self.expected_scores_t = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_t.csv'),
delimiter=',')
self.expected_scores_u = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_u.csv'),
delimiter=',')
self.expected_betacoefs = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_betas.csv'),
delimiter=',')[:, np.newaxis]
self.expected_vips = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_vip.csv'),
delimiter=',')
self.expected_dmodx = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_dmodx.csv'),
delimiter=',')
# Load expected values for a PLS regression model against a Y matrix
#self.expected_loadings_p_yblock = np.loadtxt('./test_data/pls_reg_yblock_loadings_p.csv', delimiter=',')
#self.expected_weights_w_yblock = np.loadtxt('./test_data/pls_reg_yblock_weights_w.csv', delimiter=',')
#self.expected_scores_t_yblock = np.loadtxt('./test_data/pls_reg_yblock_scores_t.csv', delimiter=',')
#self.expected_scores_u_yblock = np.loadtxt('./test_data/pls_reg_yblock_scores_u.csv', delimiter=',')
#self.expected_weights_c_yblock = np.loadtxt('./test_data/pls_reg_yblock_weights_c.csv', delimiter=',')
#self.expected_loadings_q_yblock = np.loadtxt('./test_data/pls_reg_yblock_loadings_q.csv', delimiter=',')
#self.expected_betacoefs_yblock = np.loadtxt('./test_data/pls_reg_yblock_betacoefs.csv', delimiter=',')
self.expected_modelParameters = {
'R2Y': 0.99442967438303576,
'R2X': 0.022903901163376705,
'SSYcomp': np.array([5.42418672, 1.20742786, 0.27851628]),
'SSXcomp':
np.array([9750.59475071, 9779.57249348, 9770.96098837])
}
self.expected_cvParameters = {
'Q2Y': 0.069284226071602006,
'Q2X': -0.12391667143436425,
'MeanR2X_Training': 0.025896665665079883,
'MeanR2Y_Training': 0.99636477396947942,
'StdevR2Y_Training': 0.00091660538957527582,
'StdevR2X_Training': 0.0010098198504153058,
'StdevR2X_Test': 0.02386260538832127,
'StdevR2Y_Test': 0.25034195769401973,
'MeanR2X_Test': -0.022542842216950101,
'MeanR2Y_Test': 0.096991536519031446
}
self.expected_t2 = np.array([7.00212848, 6.63400492, 5.6325462])
self.expected_outliers_t2 = np.array([5, 33])
self.expected_outliers_dmodx = np.array([])
self.expected_scores_t_par = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_t_par.csv'),
delimiter=',')
self.expected_betas_par = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_betas_par.csv'),
delimiter=',')[:, np.newaxis]
self.expected_scores_t_mc = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_t_mc.csv'),
delimiter=',')
self.expected_betas_mc = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_betas_mc.csv'),
delimiter=',')[:, np.newaxis]
self.expected_vip_mc = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_vip_mc.csv'),
delimiter=',')
self.expected_vip_par = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_vip_par.csv'),
delimiter=',')
# check this
self.y = regression_problem.iloc[:, 0].values
self.ymat = multiblock_regression_problem.values
self.xmat = regression_problem.iloc[:, 1::].values
self.xmat_multiy = multiblock_regression_problem.values
self.expected_permutation = {}
x_scaler = ChemometricsScaler(1)
y_scaler = ChemometricsScaler(1)
self.plsreg = ChemometricsPLS(ncomps=3,
xscaler=x_scaler,
yscaler=y_scaler)
self.plsreg_multiblock = ChemometricsPLS(ncomps=3,
xscaler=x_scaler,
yscaler=y_scaler)
def test_scalers(self):
"""
:return:
"""
x_scaler_par = ChemometricsScaler(1 / 2)
y_scaler_par = ChemometricsScaler(1 / 2)
x_scaler_mc = ChemometricsScaler(0)
y_scaler_mc = ChemometricsScaler(0)
pareto_model = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_par,
yscaler=y_scaler_par)
pareto_model_multiy = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_par,
yscaler=y_scaler_par)
mc_model = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_mc,
yscaler=y_scaler_mc)
mc_model_multiy = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_mc,
yscaler=y_scaler_mc)
pareto_model.fit(self.xmat, self.y)
pareto_model_multiy.fit(self.xmat_multiy, self.ymat)
mc_model.fit(self.xmat, self.y)
mc_model_multiy.fit(self.xmat_multiy, self.ymat)
assert_allclose(pareto_model.scores_t, self.expected_scores_t_par)
assert_allclose(pareto_model.beta_coeffs, self.expected_betas_par)
assert_allclose(pareto_model.VIP(), self.expected_vip_par)
#assert_allclose(pareto_model_multiy.scores_t, self.expected_scores_t_yblock_par)
#assert_allclose(pareto_model_multiy.beta_coeffs, self.expected_betacoefs_yblock_par)
assert_allclose(mc_model.scores_t, self.expected_scores_t_mc)
assert_allclose(mc_model.beta_coeffs, self.expected_betas_mc)
assert_allclose(mc_model.VIP(), self.expected_vip_mc)
class TestPLS(unittest.TestCase):
"""
Test the ChemometricsPLS object functionality, in single Y and multiple Y case.
"""
def setUp(self):
try:
regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression.csv'))
multiblock_regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression_multiblock.csv'))
except (IOError, OSError) as ioerr:
#os.system("python gen_synthetic_datasets.py")
import tests.gen_synthetic_datasets
regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression.csv'))
multiblock_regression_problem = pds.read_csv(
os.path.join(os.path.dirname(__file__),
'./test_data/regression_multiblock.csv'))
finally:
# Load expected values for a PLS regression against a Y vector
self.expected_loadings_p = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_loadings_p.csv'),
delimiter=',')
self.expected_loadings_q = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_loadings_q.csv'),
delimiter=',')[np.newaxis, :]
self.expected_weights_w = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_weights_w.csv'),
delimiter=',')
self.expected_weights_c = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_weights_c.csv'),
delimiter=',')[np.newaxis, :]
self.expected_scores_t = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_t.csv'),
delimiter=',')
self.expected_scores_u = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_u.csv'),
delimiter=',')
self.expected_betacoefs = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_betas.csv'),
delimiter=',')[:, np.newaxis]
self.expected_vips = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_vip.csv'),
delimiter=',')
self.expected_dmodx = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_dmodx.csv'),
delimiter=',')
# Load expected values for a PLS regression model against a Y matrix
#self.expected_loadings_p_yblock = np.loadtxt('./test_data/pls_reg_yblock_loadings_p.csv', delimiter=',')
#self.expected_weights_w_yblock = np.loadtxt('./test_data/pls_reg_yblock_weights_w.csv', delimiter=',')
#self.expected_scores_t_yblock = np.loadtxt('./test_data/pls_reg_yblock_scores_t.csv', delimiter=',')
#self.expected_scores_u_yblock = np.loadtxt('./test_data/pls_reg_yblock_scores_u.csv', delimiter=',')
#self.expected_weights_c_yblock = np.loadtxt('./test_data/pls_reg_yblock_weights_c.csv', delimiter=',')
#self.expected_loadings_q_yblock = np.loadtxt('./test_data/pls_reg_yblock_loadings_q.csv', delimiter=',')
#self.expected_betacoefs_yblock = np.loadtxt('./test_data/pls_reg_yblock_betacoefs.csv', delimiter=',')
self.expected_modelParameters = {
'R2Y': 0.99442967438303576,
'R2X': 0.022903901163376705,
'SSYcomp': np.array([5.42418672, 1.20742786, 0.27851628]),
'SSXcomp':
np.array([9750.59475071, 9779.57249348, 9770.96098837])
}
self.expected_cvParameters = {
'Q2Y': 0.069284226071602006,
'Q2X': -0.12391667143436425,
'MeanR2X_Training': 0.025896665665079883,
'MeanR2Y_Training': 0.99636477396947942,
'StdevR2Y_Training': 0.00091660538957527582,
'StdevR2X_Training': 0.0010098198504153058,
'StdevR2X_Test': 0.02386260538832127,
'StdevR2Y_Test': 0.25034195769401973,
'MeanR2X_Test': -0.022542842216950101,
'MeanR2Y_Test': 0.096991536519031446
}
self.expected_t2 = np.array([7.00212848, 6.63400492, 5.6325462])
self.expected_outliers_t2 = np.array([5, 33])
self.expected_outliers_dmodx = np.array([])
self.expected_scores_t_par = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_t_par.csv'),
delimiter=',')
self.expected_betas_par = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_betas_par.csv'),
delimiter=',')[:, np.newaxis]
self.expected_scores_t_mc = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_scores_t_mc.csv'),
delimiter=',')
self.expected_betas_mc = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_betas_mc.csv'),
delimiter=',')[:, np.newaxis]
self.expected_vip_mc = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_vip_mc.csv'),
delimiter=',')
self.expected_vip_par = np.loadtxt(os.path.join(
os.path.dirname(__file__), './test_data/pls_vip_par.csv'),
delimiter=',')
# check this
self.y = regression_problem.iloc[:, 0].values
self.ymat = multiblock_regression_problem.values
self.xmat = regression_problem.iloc[:, 1::].values
self.xmat_multiy = multiblock_regression_problem.values
self.expected_permutation = {}
x_scaler = ChemometricsScaler(1)
y_scaler = ChemometricsScaler(1)
self.plsreg = ChemometricsPLS(ncomps=3,
xscaler=x_scaler,
yscaler=y_scaler)
self.plsreg_multiblock = ChemometricsPLS(ncomps=3,
xscaler=x_scaler,
yscaler=y_scaler)
def test_single_y(self):
"""
:return:
"""
self.plsreg.fit(self.xmat, self.y)
# Test model coefficients , scores and goodness of fit
assert_allclose(self.plsreg.loadings_p, self.expected_loadings_p)
assert_allclose(self.plsreg.loadings_q, self.expected_loadings_q)
assert_allclose(self.plsreg.weights_w, self.expected_weights_w)
assert_allclose(self.plsreg.weights_c, self.expected_weights_c)
assert_allclose(self.plsreg.scores_t, self.expected_scores_t)
assert_allclose(self.plsreg.scores_u, self.expected_scores_u)
assert_allclose(self.plsreg.beta_coeffs, self.expected_betacoefs)
assert_allclose(self.plsreg.modelParameters['R2Y'],
self.expected_modelParameters['R2Y'])
assert_allclose(self.plsreg.modelParameters['R2X'],
self.expected_modelParameters['R2X'])
assert_allclose(self.plsreg.modelParameters['SSXcomp'],
self.expected_modelParameters['SSXcomp'])
assert_allclose(self.plsreg.modelParameters['SSYcomp'],
self.expected_modelParameters['SSYcomp'])
assert_allclose(self.plsreg.VIP(), self.expected_vips)
#def test_multi_y(self):
# self.plsreg_multiblock.fit(self.xmat_multiy, self.ymat)
# # Assert equality of main model parameters
# assert_allclose(self.plsreg_multiblock.loadings_p, self.expected_loadings_p_yblock)
# assert_allclose(self.plsreg_multiblock.loadings_q, self.expected_loadings_q_yblock)
# assert_allclose(self.plsreg_multiblock.weights_w, self.expected_weights_w_yblock)
# assert_allclose(self.plsreg_multiblock.weights_c, self.expected_weights_c_yblock)
# assert_allclose(self.plsreg_multiblock.scores_t, self.expected_scores_t_yblock)
# assert_allclose(self.plsreg_multiblock.scores_u, self.expected_scores_u_yblock)
# assert_allclose(self.plsreg_multiblock.beta_coeffs, self.expected_betacoefs_yblock)
# assert_allclose(self.plsreg_multiblock.VIP(), self.expected_vipsw_yblock)
# assert_allclose(self.plsreg.modelParameters, self.expected_modelParameters)
def test_scalers(self):
"""
:return:
"""
x_scaler_par = ChemometricsScaler(1 / 2)
y_scaler_par = ChemometricsScaler(1 / 2)
x_scaler_mc = ChemometricsScaler(0)
y_scaler_mc = ChemometricsScaler(0)
pareto_model = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_par,
yscaler=y_scaler_par)
pareto_model_multiy = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_par,
yscaler=y_scaler_par)
mc_model = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_mc,
yscaler=y_scaler_mc)
mc_model_multiy = ChemometricsPLS(ncomps=3,
xscaler=x_scaler_mc,
yscaler=y_scaler_mc)
pareto_model.fit(self.xmat, self.y)
pareto_model_multiy.fit(self.xmat_multiy, self.ymat)
mc_model.fit(self.xmat, self.y)
mc_model_multiy.fit(self.xmat_multiy, self.ymat)
assert_allclose(pareto_model.scores_t, self.expected_scores_t_par)
assert_allclose(pareto_model.beta_coeffs, self.expected_betas_par)
assert_allclose(pareto_model.VIP(), self.expected_vip_par)
#assert_allclose(pareto_model_multiy.scores_t, self.expected_scores_t_yblock_par)
#assert_allclose(pareto_model_multiy.beta_coeffs, self.expected_betacoefs_yblock_par)
assert_allclose(mc_model.scores_t, self.expected_scores_t_mc)
assert_allclose(mc_model.beta_coeffs, self.expected_betas_mc)
assert_allclose(mc_model.VIP(), self.expected_vip_mc)
#assert_allclose(mc_model_multiy.scores_t, self.expected_scores_t_yblock_mc)
#assert_allclose(mc_model_multiy.beta_coeffs, self.expected_betacoefs_yblock_mc)
def test_cv_single_y(self):
"""
:return:
"""
# Fix the seed for the permutation test and cross_validation
np.random.seed(0)
self.plsreg.cross_validation(self.xmat, self.y)
assert_allclose(self.plsreg.cvParameters['Q2Y'],
self.expected_cvParameters['Q2Y'])
assert_allclose(self.plsreg.cvParameters['Q2X'],
self.expected_cvParameters['Q2X'])
assert_allclose(self.plsreg.cvParameters['MeanR2X_Training'],
self.expected_cvParameters['MeanR2X_Training'])
assert_allclose(self.plsreg.cvParameters['MeanR2Y_Training'],
self.expected_cvParameters['MeanR2Y_Training'])
assert_allclose(self.plsreg.cvParameters['MeanR2X_Test'],
self.expected_cvParameters['MeanR2X_Test'])
assert_allclose(self.plsreg.cvParameters['MeanR2Y_Test'],
self.expected_cvParameters['MeanR2Y_Test'])
assert_allclose(self.plsreg.cvParameters['StdevR2X_Training'],
self.expected_cvParameters['StdevR2X_Training'])
assert_allclose(self.plsreg.cvParameters['StdevR2Y_Training'],
self.expected_cvParameters['StdevR2Y_Training'])
assert_allclose(self.plsreg.cvParameters['StdevR2X_Test'],
self.expected_cvParameters['StdevR2X_Test'])
assert_allclose(self.plsreg.cvParameters['StdevR2Y_Test'],
self.expected_cvParameters['StdevR2Y_Test'])
#def test_cv_multi_y(self):
# # Fix the seed for the permutation test and cross_validation
# np.random.seed(0)
# self.plsreg_multiy.cross_validation(self.xmat_multi, self.da_mat)
# assert_allclose(self.plsreg_multiblock.cvParameters, self.expected_cvParams_multi)
def test_permutation(self):
"""
:return:
"""
#self.plsreg.fit(self.xmat, self.y)
# Fix the seed for the permutation test and cross_validation
#np.random.seed(0)
#self.plsreg.cross_validation(self.xmat, self.y)
#permutation_results = self.plsreg.permutation_test(self.xmat, self.da, nperms=5)
#assert_allclose(permutation_results[0], self.permutation_results)
def test_hotellingt2(self):
"""
:return:
"""
self.plsreg.fit(self.xmat, self.y)
t2 = self.plsreg.hotelling_T2(comps=None)
assert_allclose(t2, self.expected_t2)
def test_dmodx(self):
"""
:return:
"""
self.plsreg.fit(self.xmat, self.y)
dmodx = self.plsreg.dmodx(self.xmat)
assert_allclose(dmodx, self.expected_dmodx)
def test_outliers(self):
"""
:return:
"""
self.plsreg.fit(self.xmat, self.y)
outliers_t2 = self.plsreg.outlier(self.xmat)
outliers_dmodx = self.plsreg.outlier(self.xmat, measure='DmodX')
assert_allclose(outliers_t2, self.expected_outliers_t2)
assert_allclose(outliers_dmodx, self.expected_outliers_dmodx)