From 8ef4ee33df5615e302cec47db17164f8c429532c Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 19 Dec 2022 20:11:00 +0100
Subject: [PATCH] Many bug fixes when writing the PCA model.
---
pes_to_spec/model.py | 28 +++++++++++++++-------------
1 file changed, 15 insertions(+), 13 deletions(-)
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 2f73ec2..8e5d701 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -34,14 +34,15 @@ def save_pca(pca_obj: Union[IncrementalPCA, PCA], pca_group: h5py.Group):
"singular_values_",
"mean_"]
attrs = ["n_components_",
- "n_features_",
- "n_samples_",
+ #"n_features_",
+ #"n_samples_",
"noise_variance_",
- "n_features_in_"]
+ #"n_features_in_"
+ ]
for p in props:
- pca_group.create_dataset(p, getattr(pca_obj, p))
+ pca_group.create_dataset(p, data=getattr(pca_obj, p))
for a in attrs:
- pca_group.attrs[p] = getattr(pca_obj, a)
+ pca_group.attrs[a] = getattr(pca_obj, a)
def load_pca(pca_obj: Union[IncrementalPCA, PCA], pca_group: h5py.Group) -> Union[IncrementalPCA, PCA]:
"""
@@ -60,14 +61,15 @@ def load_pca(pca_obj: Union[IncrementalPCA, PCA], pca_group: h5py.Group) -> Unio
"singular_values_",
"mean_"]
attrs = ["n_components_",
- "n_features_",
- "n_samples_",
+ #"n_features_",
+ #"n_samples_",
"noise_variance_",
- "n_features_in_"]
+ #"n_features_in_"
+ ]
for p in props:
- setattr(pca_obj, p, pca_group[p])
+ setattr(pca_obj, p, pca_group[p][()])
for a in attrs:
- setattr(pca_obj, a, pca_group[a])
+ setattr(pca_obj, a, pca_group.attrs[a])
return pca_obj
class PromptNotFoundError(Exception):
@@ -323,7 +325,7 @@ class Model(object):
"""
with h5py.File(filename, 'r') as hf:
- d = {k: hf[k][()] for k in hf.keys()}
+ d = {k: hf[k][()] for k in hf.keys() if not isinstance(hf[k], h5py.Group)}
d.update({k: hf.attrs[k] for k in hf.attrs})
self.fit_model.from_dict(d)
for key in self.parameters().keys():
@@ -333,8 +335,8 @@ class Model(object):
# files
lr_pca = hf["/lr_pca/"]
hr_pca = hf["/hr_pca/"]
- self.lr_pca = IncrementalPCA(self.n_pca_lr)
- self.hr_pca = PCA(self.n_pca_hr)
+ self.lr_pca = IncrementalPCA(self.n_pca_lr, whiten=True)
+ self.hr_pca = PCA(self.n_pca_hr, whiten=True)
self.lr_pca = load_pca(self.lr_pca, lr_pca)
self.hr_pca = load_pca(self.hr_pca, hr_pca)
#self.lr_pca = joblib.load(lr_pca_filename)
--
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