Many bug fixes when writing the PCA model.

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)