def run(self):
load_fit = False
col = 'wvl'
method = self.chooseMethodComboBox.currentText()
datakey = self.chooseDataComboBox.currentText()
if method == 'LDA' or method == 'LFDA':
params, modelkey, ycol = self.getMethodParams(self.chooseMethodComboBox.currentIndex())
df, dimred_obj = dim_red(self.data[datakey].df, col, method, [], params, load_fit, ycol=ycol)
else:
params, modelkey = self.getMethodParams(self.chooseMethodComboBox.currentIndex())
df, dimred_obj = dim_red(self.data[datakey].df, col, method, [], params, load_fit)
dimredkey = datakey+'-'+method
self.dimredkeys.append(dimredkey)
self.dimred[dimredkey] = dimred_obj
def dim_red(self, col, method, params, kws, load_fit):
self.df, self.dim_red = dim_red.dim_red(self.df,
col=col,
method=method,
params=params,
kws=kws,
load_fit=load_fit)
def test_dimred_LDA():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
kws = {
'n_clusters': 5,
'n_init': 10,
'max_iter': 100,
'tol': 0.01,
'n_jobs': 1,
'random_state': 1
}
cluster.cluster(df, 'wvl', 'K-Means', [], kws)
params = {'n_components': 3}
df, dimred_obj = dim_red.dim_red(df,
'wvl',
'LDA', [],
params,
ycol='K-Means')
expected_coefs = [
-0.02209121, -0.0016516, -0.01139357, -0.06448139, 0.07085655
]
expected_scores = [-11.89340048, 0.41598425, 0.22964169]
assert df['LDA'].shape == (103, 3)
np.testing.assert_array_almost_equal(expected_coefs, dimred_obj.coef_[:,
0])
np.testing.assert_array_almost_equal(expected_scores,
np.array(df['LDA'].iloc[0, :]))
def run(self):
method = self.chooseMethodComboBox.currentText()
datakey = self.chooseDataComboBox.currentText()
# xvars = [str(x.text()) for x in self.xVariableList.selectedItems()]
params, modelkey = self.getMethodParams(self.chooseMethodComboBox.currentIndex())
load_fit = False
col = 'wvl'
df, PCA_obj = dim_red(self.data[datakey].df, col, method, [], params, load_fit)
def test_dimred_PCA():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
params = {'n_components': 3}
df, dimred_obj = dim_red.dim_red(df, 'wvl', 'PCA', [], params)
expected_expl_var = [0.96051211, 0.01683739, 0.01471955]
expected_scores = [10092.96265442, -628.16699776, -359.06894452]
assert df['PCA'].shape == (103, 3)
np.testing.assert_array_almost_equal(expected_expl_var,
dimred_obj.explained_variance_ratio_)
np.testing.assert_array_almost_equal(expected_scores,
np.array(df['PCA'].iloc[0, :]))
def test_dimred_FastICA():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
params = {'n_components': 3, 'random_state': 1}
df, dimred_obj = dim_red.dim_red(df, 'wvl', 'FastICA', [], params)
expected_comps = [-2.190278e-05, 1.498101e-06, 9.082887e-07]
expected_scores = [0.03252833, -0.03749623, -0.11434307]
assert df['FastICA'].shape == (103, 3)
np.testing.assert_array_almost_equal(expected_comps,
dimred_obj.components_[:, 0])
np.testing.assert_array_almost_equal(expected_scores,
np.array(df['FastICA'].iloc[0, :]))
def test_dimred_NMF():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
df['wvl'] = df[
'wvl'] - 1000 #make some values negative to test adding a constant
dim_red.check_positive(df['wvl'])
params = {'n_components': 3, 'random_state': 0, 'add_constant': True}
df, dimred_obj = dim_red.dim_red(df, 'wvl', 'NMF', [], params)
expected_comps = [10.27191532, 34.62489686, 3.06822373]
expected_scores = [49.42458628, 3.9910722, 27.03100371]
assert df['NMF'].shape == (103, 3)
np.testing.assert_array_almost_equal(expected_comps,
dimred_obj.components_[:, 0])
np.testing.assert_array_almost_equal(expected_scores,
np.array(df['NMF'].iloc[0, :]))
def test_dimred_JADE():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
params = {'n_components': 3}
df, dimred_obj = dim_red.dim_red(df, 'wvl', 'JADE-ICA', [], params)
expected_loadings = [0.56247385, 0.19292341, 3.42289881]
expected_scores = [174708.34499912, 125682.55985134, 145155.40758151]
assert df['JADE-ICA'].shape == (103, 3)
np.testing.assert_almost_equal(
expected_loadings,
np.squeeze(np.array(dimred_obj.ica_jade_loadings[:, 0])))
np.testing.assert_array_almost_equal(expected_scores,
np.array(df['JADE-ICA'].iloc[0, :]))
def test_dimred_LLE():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
params = {'n_components': 3, 'n_neighbors': 10, 'reg': 1e-3}
df, dimred_obj = dim_red.dim_red(df, 'wvl', 'LLE', [], params)
expected_err = 2.0687806439705738e-05
expected_scores = [0.11088153, 0.01215013, -0.03551393]
assert df['LLE'].shape == (103, 3)
np.testing.assert_almost_equal(expected_err,
dimred_obj.reconstruction_error_)
np.testing.assert_array_almost_equal(np.abs(expected_scores),
np.abs(np.array(
df['LLE'].iloc[0, :])),
decimal=4)
def test_dimred_tSNE():
df = pd.read_csv(get_path('test_data.csv'), header=[0, 1])
params = {
'n_components': 2,
'learning_rate': 200.0,
'n_iter': 1000,
'n_iter_without_progress': 300,
'perplexity': 30,
'init': 'pca'
}
df, dimred_obj = dim_red.dim_red(df, 'wvl', 't-SNE', [], params)
expected_div = 0.38829776644706726
expected_scores = [9938.469727, -802.161682]
assert df['t-SNE'].shape == (103, 2)
np.testing.assert_almost_equal(expected_div, dimred_obj.kl_divergence_)
np.testing.assert_array_almost_equal(expected_scores,
np.array(df['t-SNE'].iloc[0, :]))