def test_cv():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
df = stratified_folds(df, nfolds=3, sortby=('comp', 'SiO2'))
params = {'n_components': [1, 2, 3], 'scale': [False]}
paramgrid = list(ParameterGrid(params))
cv_obj = cv.cv(paramgrid)
df_out, output, models, modelkeys, predictkeys = cv_obj.do_cv(
df,
xcols='wvl',
ycol=[('comp', 'SiO2')],
method='PLS',
yrange=[0, 100],
calc_path=False,
alphas=None)
expected_predicts = [
56.55707481, 57.93716105, 59.34785052, 60.59708391, 55.83934129,
56.7456989
]
expected_output_rmsec = [18.6509206, 14.64015186, 13.80182457]
np.testing.assert_array_almost_equal(
expected_predicts, np.array(df_out['predict'].iloc[0, :]))
np.testing.assert_array_almost_equal(expected_output_rmsec,
np.array(output[('cv', 'RMSEC')]))
assert output.shape == (3, 8)
assert len(models) == 3
assert len(modelkeys) == 3
assert modelkeys[
0] == 'PLS - SiO2 - (0, 100) {\'n_components\': 1, \'scale\': False}'
assert len(predictkeys) == 6
assert predictkeys[
0] == '"PLS- CV -{\'n_components\': 1, \'scale\': False}"'
def run(self):
Modules.data_count += 1
self.train_ind = Modules.data_count
Modules.data_count += 1
self.test_ind = Modules.data_count
datakey = self.chooseDataToStratifyComboBox.currentText()
nfolds = self.nFoldsSpinBox.value()
try:
testfold = int(self.testFoldsSpinBox.value())
except:
testfold = 1
colname = ('comp', self.chooseVarComboBox.currentText())
self.data[datakey] = spectral_data(
stratified_folds(self.data[datakey].df,
nfolds=nfolds,
sortby=colname))
self.data[datakey + '-Train'] = spectral_data(
rows_match(self.data[datakey].df, ('meta', 'Folds'), [testfold],
invert=True))
self.data[datakey + '-Test'] = spectral_data(
rows_match(self.data[datakey].df, ('meta', 'Folds'), [testfold]))
self.list_amend(self.datakeys, self.train_ind, datakey + '-Train')
self.list_amend(self.datakeys, self.test_ind, datakey + '-Test')
print(self.datakeys)
print('Test set: ' +
str(self.data[datakey + '-Test'].df.index.shape[0]))
print('Training set: ' +
str(self.data[datakey + '-Train'].df.index.shape[0]))
def test_cv_calc_path():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
df = stratified_folds(df, nfolds=3, sortby=('comp', 'SiO2'))
params = {
'fit_intercept': [True, False],
'max_iter': [1000],
'tol': [1e-3],
'precompute': [True],
'copy_X': [True],
'positive': [True, False],
'selection': ['random'],
'random_state': [1]
}
alphas = np.logspace(np.log10(0.0000001), np.log10(0.01), num=10)
paramgrid = list(ParameterGrid(params))
cv_obj = cv.cv(paramgrid)
df_out, output, models, modelkeys, predictkeys = cv_obj.do_cv(
df,
xcols='wvl',
ycol=[('comp', 'SiO2')],
method='LASSO',
yrange=[0, 100],
calc_path=True,
alphas=alphas)
expected_predicts = [
57.87064, 57.868983, 57.868983, 57.868983, 57.868983, 59.315111,
59.315113, 59.315114, 59.315114, 59.315114
]
expected_output_rmsec = [
18.490365, 18.490365, 18.490365, 18.490365, 18.490365, 7.042796,
6.986007, 6.967643, 6.959045, 6.953588
]
np.testing.assert_array_almost_equal(
expected_predicts, np.array(df_out['predict'].iloc[0, 5:15]))
np.testing.assert_array_almost_equal(
expected_output_rmsec, np.array(output[('cv', 'RMSEC')].iloc[5:15]))
assert output.shape == (40, 15)
assert len(models) == 40
assert len(modelkeys) == 40
assert modelkeys[
0] == 'LASSO - SiO2 - (0, 100) Alpha: 0.01, {\'copy_X\': True, \'fit_intercept\': True, \'max_iter\': 1000, \'positive\': True, \'precompute\': True, \'random_state\': 1, \'selection\': \'random\', \'tol\': 0.001}'
assert len(predictkeys) == 80
assert predictkeys[
0] == '"LASSO - SiO2 - CV - Alpha:0.01 - {\'copy_X\': True, \'fit_intercept\': True, \'max_iter\': 1000, \'positive\': True, \'precompute\': True, \'random_state\': 1, \'selection\': \'random\', \'tol\': 0.001}"'
def run(self):
datakey = self.chooseDataToStratifyComboBox.currentText()
nfolds = self.nFoldsSpinBox.value()
try:
testfold = int(self.testFoldsSpinBox.value())
except:
testfold = 1
colname = ('comp', self.chooseVarComboBox.currentText())
self.data[datakey] = spectral_data(
stratified_folds(self.data[datakey].df,
nfolds=nfolds,
sortby=colname))
self.data[datakey + '-Train'] = spectral_data(
rows_match(self.data[datakey].df, ('meta', 'Folds'), [testfold],
invert=True))
self.data[datakey + '-Test'] = spectral_data(
rows_match(self.data[datakey].df, ('meta', 'Folds'), [testfold]))
self.datakeys.append(datakey + '-Train')
self.datakeys.append(datakey + '-Test')
print(self.data.keys())
print(self.data[datakey + '-Test'].df.index.shape)
print(self.data[datakey + '-Train'].df.index.shape)
#self.stratifiedfoldshist()
folds = self.data[datakey].df[('meta', 'Folds')]
folds_unique = folds.unique()[np.isfinite(folds.unique())]
for fold in folds_unique:
dat_col_folds = self.data[datakey].df[colname][folds == fold]
plt.hist(dat_col_folds, bins=20)
plt.xlabel(colname[1])
plt.ylabel('Frequency')
plt.title('Histogram of Fold ' + str(int(fold)))
#plt.axis([0, 100, 0, 100])
#plt.grid(True)
# plt.show()
plt.savefig(self.outpath + '//' + colname[1] + '_fold' +
str(int(fold)) + '_hist.png')
plt.clf()
def test_cv_local_regression():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
df = df.iloc[0:20, :] #make data set smaller so this test runs faster
df = stratified_folds(df, nfolds=3, sortby=('comp', 'SiO2'))
params = {
'n_neighbors': [5, 6],
'fit_intercept': [True],
'positive': [False],
'random_state': [1],
'tol': [1e-2]
}
paramgrid = list(ParameterGrid(params))
cv_obj = cv.cv(paramgrid)
df_out, output, models, modelkeys, predictkeys = cv_obj.do_cv(
df,
xcols='wvl',
ycol=[('comp', 'SiO2')],
method='Local Regression',
yrange=[0, 100],
calc_path=False,
alphas=None)
expected_predicts = [51.30212, 54.25293063, 48.54834655, 54.18676067]
expected_output_rmsec = [10.32151211, 10.89018268]
np.testing.assert_array_almost_equal(
expected_predicts, np.array(df_out['predict'].iloc[5, :]))
np.testing.assert_array_almost_equal(expected_output_rmsec,
np.array(output[('cv', 'RMSEC')]))
assert output.shape == (2, 11)
assert len(models) == 2
assert len(modelkeys) == 2
assert modelkeys[
0] == 'Local Regression - SiO2 - (0, 100) {\'fit_intercept\': True, \'positive\': False, \'random_state\': 1, \'tol\': 0.01} n_neighbors: 5'
assert len(predictkeys) == 4
assert predictkeys[
0] == '"Local Regression- CV -{\'fit_intercept\': True, \'positive\': False, \'random_state\': 1, \'tol\': 0.01} n_neighbors: 5"'
def stratified_folds(self):
self.df, self.df_baseline = folds.stratified_folds(self.df,
nfolds=5,
sortby=None)
