From b5931e52566ea25ec8f424371dd12b581f6add5c Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 6 Mar 2023 16:01:56 +0100
Subject: [PATCH] Clean up
---
pes_to_spec/__init__.py | 2 +-
pes_to_spec/model.py | 20 ++------------------
2 files changed, 3 insertions(+), 19 deletions(-)
diff --git a/pes_to_spec/__init__.py b/pes_to_spec/__init__.py
index 5f570b9..1f1123e 100644
--- a/pes_to_spec/__init__.py
+++ b/pes_to_spec/__init__.py
@@ -2,4 +2,4 @@
Estimate high-resolution photon spectrometer data from low-resolution non-invasive measurements.
"""
-VERSION = "0.2.2"
+VERSION = "0.2.3"
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index d04881b..695dc68 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -645,18 +645,6 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
y_std = np.sqrt(sigmas_squared_data + self.fast_inv_alpha)
return y, y_std
- #n_jobs = self.n_jobs
- #y = Parallel(n_jobs=n_jobs, prefer="threads")(
- # delayed(e.predict)(X, return_std) for e in self.estimators_
- # #delayed(e.predict)(X) for e in self.estimators_
- #)
- #if return_std:
- # y, unc = zip(*y)
- # return np.asarray(y).T, np.asarray(unc).T
-
- #return np.asarray(y).T
-
-
class UncorrelatedDeviation(OutlierMixin, BaseEstimator):
"""
Detect outliers from uncorrelated inputs.
@@ -1055,12 +1043,8 @@ class Model(TransformerMixin, BaseEstimator):
def is_inlier(in_data, ch: str) -> np.ndarray:
data_pca = self.channel_pca[ch].transform(in_data)
return self.ood[ch].predict(data_pca)
-
- #result = Parallel(n_jobs=-1)(
- # delayed(is_inlier)(low_res_selected[ch], ch) for ch in channels
- #)
- #result = dict(result)
- return {ch: is_inlier(low_res_selected[ch], ch) for ch in channels}
+ result = {ch: is_inlier(low_res_selected[ch], ch) for ch in channels}
+ return result
def check_compatibility(self, low_res_data: Dict[str, np.ndarray]) -> np.ndarray:
"""
--
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