Better use of multithreading.

pes_to_spec/__init__.py

@@ -2,4 +2,4 @@
 Estimate high-resolution photon spectrometer data from low-resolution non-invasive measurements.
 """
 
-VERSION = "0.1.5"
+VERSION = "0.1.6"

pes_to_spec/model.py

@@ -623,7 +623,7 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
 
         return self
 
-    def predict(self, X: np.ndarray, return_std: bool=False) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
+    def predict(self, X: np.ndarray, return_std: bool=False, n_jobs: Optional[int]=None) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
         """Predict multi-output variable using model for each target variable.
 
         Args:
@@ -634,7 +634,9 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
             Multi-output targets predicted across multiple predictors.
             Note: Separate models are generated for each predictor.
         """
-        y = Parallel(n_jobs=self.n_jobs)(
+        if n_jobs is None:
+            n_jobs = self.n_jobs
+        y = Parallel(n_jobs=n_jobs)(
             delayed(e.predict)(X, return_std) for e in self.estimators_
             #delayed(e.predict)(X) for e in self.estimators_
         )
@@ -698,7 +700,7 @@ class Model(TransformerMixin, BaseEstimator):
                                 ('pca', PCA(n_pca_hr, whiten=True)),
                                 ('unc', UncertaintyHolder()),
                                 ])
-        self.ood = {ch: IsolationForest()
+        self.ood = {ch: IsolationForest(n_jobs=-1)
                     for ch in channels+['full']}
         #self.fit_model = MultiOutputWithStd(ARDRegression(n_iter=300, tol=1e-8, verbose=True), n_jobs=8)
         self.fit_model = MultiOutputWithStd(BayesianRidge(n_iter=300, tol=1e-8, verbose=True), n_jobs=8)
@@ -990,7 +992,7 @@ class Model(TransformerMixin, BaseEstimator):
         """
         low_res_pre = self.x_select.transform(low_res_data)
         low_pca = self.x_model.transform(low_res_pre)
-        high_pca, high_pca_unc = self.fit_model.predict(low_pca, return_std=True)
+        high_pca, high_pca_unc = self.fit_model.predict(low_pca, return_std=True, n_jobs=-1)
         #high_pca = self.fit_model.predict(low_pca)
         #high_pca_unc = 0
         n_trains = high_pca.shape[0]