From 3b1b5aa359614d4810f162c3e9de0b502362ac99 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Fri, 13 Jan 2023 16:39:47 +0100
Subject: [PATCH] Added pre-PCA step before Nystroem to reduce the
dimensionality in advance.
---
pes_to_spec/model.py | 53 ++++++++++++++++++++++----------------------
1 file changed, 26 insertions(+), 27 deletions(-)
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 761e086..5200c68 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -502,31 +502,32 @@ class Model(TransformerMixin, BaseEstimator):
def __init__(self,
channels:List[str]=[f"channel_{j}_{k}"
for j, k in product(range(1, 5), ["A", "B", "C", "D"])],
- n_pca_lr: int=1000,
- n_pca_hr: int=40,
+ n_pca_lr: int=600,
+ n_pca_hr: int=20,
high_res_sigma: float=0.2,
tof_start: Optional[int]=None,
delta_tof: Optional[int]=300,
validation_size: float=0.05,
- n_nonlinear_kernel: int=10000):
+ n_nonlinear_kernel: int=5000):
# models
- self.x_model = Pipeline([
- ('select', SelectRelevantLowResolution(channels, tof_start, delta_tof)),
- ('pca', PCA(n_pca_lr, whiten=True)),
- ('unc', UncertaintyHolder()),
- ])
+ x_model_steps = list()
+ x_model_steps += [('select', SelectRelevantLowResolution(channels, tof_start, delta_tof))]
+ if n_nonlinear_kernel > 0:
+ x_model_steps += [('fex', Pipeline([('prepca', PCA(n_pca_lr, whiten=True)),
+ ('nystroem', Nystroem(n_components=n_nonlinear_kernel, kernel='rbf', gamma=None, n_jobs=-1)),
+ ]))]
+ x_model_steps += [
+ ('pca', PCA(n_pca_lr, whiten=True)),
+ ('unc', UncertaintyHolder()),
+ ]
+ self.x_model = Pipeline(x_model_steps)
self.y_model = Pipeline([
('smoothen', HighResolutionSmoother(high_res_sigma)),
('pca', PCA(n_pca_hr, whiten=True)),
('unc', UncertaintyHolder()),
])
- fit_steps = list()
- if n_nonlinear_kernel > 0:
- fit_steps += [('fex', Nystroem(n_components=n_nonlinear_kernel, kernel='rbf', gamma=None, n_jobs=-1))]
- #fit_steps += [('regression', FitModel())]
- fit_steps += [('regression', MultiOutputWithStd(ARDRegression(n_iter=30, verbose=True)))]
- #fit_steps += [('regression', MultiOutputWithStd(LinearSVR(verbose=10, max_iter=2000, tol=1e-5)))]
- self.fit_model = Pipeline(fit_steps)
+ #self.fit_model = FitModel()
+ self.fit_model = MultiOutputWithStd(ARDRegression(n_iter=30, verbose=True))
# size of the test subset
self.validation_size = validation_size
@@ -581,12 +582,11 @@ class Model(TransformerMixin, BaseEstimator):
self.y_model['unc'].set_uncertainty(high_pca_unc)
low_res = self.x_model['select'].transform(low_res_data)
- low_pca = self.x_model['pca'].transform(low_res)
- if isinstance(self.x_model['pca'], FeatureUnion):
- n = self.x_model['pca'].transformer_list[0][1].n_components
- low_pca_rec = self.x_model['pca'].transformer_list[0][1].inverse_transform(low_pca[:, :n])
- else:
- low_pca_rec = self.x_model['pca'].inverse_transform(low_pca)
+ pca_model = self.x_model['pca']
+ if 'fex' in self.x_model.named_steps:
+ pca_model = self.x_model['fex'].named_steps['prepca']
+ low_pca = pca_model.transform(low_res)
+ low_pca_rec = pca_model.inverse_transform(low_pca)
low_pca_unc = np.mean(np.sqrt(np.mean((low_res - low_pca_rec)**2, axis=1, keepdims=True)), axis=0, keepdims=True)
self.x_model['unc'].set_uncertainty(low_pca_unc)
@@ -603,12 +603,11 @@ class Model(TransformerMixin, BaseEstimator):
Returns: Ratio of root-mean-squared-error of the data reconstruction using the existing PCA model and the one from the original model.
"""
low_res = self.x_model['select'].transform(low_res_data)
- low_pca = self.x_model['pca'].transform(low_res)
- if isinstance(self.x_model['pca'], FeatureUnion):
- n = self.x_model['pca'].transformer_list[0][1].n_components
- low_pca_rec = self.x_model['pca'].transformer_list[0][1].inverse_transform(low_pca[:, :n])
- else:
- low_pca_rec = self.x_model['pca'].inverse_transform(low_pca)
+ pca_model = self.x_model['pca']
+ if 'fex' in self.x_model.named_steps:
+ pca_model = self.x_model['fex'].named_steps['prepca']
+ low_pca = pca_model.transform(low_res)
+ low_pca_rec = pca_model.inverse_transform(low_pca)
low_pca_unc = self.x_model['unc'].uncertainty()
#fig = plt.figure(figsize=(8, 16))
--
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