Clean up.

pes_to_spec/model.py

@@ -23,8 +23,6 @@ from sklearn.model_selection import train_test_split
 from sklearn.base import clone, MetaEstimatorMixin
 from joblib import Parallel, delayed
 
-import matplotlib.pyplot as plt
-
 from typing import Any, Dict, List, Optional, Union, Tuple
 
 def matching_ids(a: np.ndarray, b: np.ndarray, c: np.ndarray) -> np.ndarray:
@@ -71,7 +69,6 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
 
         Returns: The object itself.
         """
-        print("Storing high resolution energy")
         self.energy = np.copy(fit_params["energy"])
         if len(self.energy.shape) == 2:
             self.energy = self.energy[0,:]
@@ -86,7 +83,6 @@ class HighResolutionSmoother(TransformerMixin, BaseEstimator):
 
         Returns: Smoothened out spectrum.
         """
-        print("Smoothing high-resolution spectrum")
         if self.high_res_sigma <= 0:
             return X
         # use a default energy axis is none is given
@@ -198,7 +194,6 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
 
         Returns: Concatenated and pre-processed low-resolution data of shape (train_id, features).
         """
-        print("Selecting area close to the peak")
         if self.tof_start is None:
             raise NotImplementedError("The low-resolution data cannot be transformed before the prompt has been identified. Call the fit function first.")
         items = [X[k] for k in self.channels]
@@ -244,7 +239,6 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
 
         Returns: The object itself.
         """
-        print("Estimating peak position")
         self.tof_start = self.estimate_prompt_peak(X)
         return self
 
@@ -259,6 +253,7 @@ class SelectRelevantLowResolution(TransformerMixin, BaseEstimator):
         """
         sum_low_res = - np.mean(sum(list(X.values())), axis=0)
         peak_idx = self.estimate_prompt_peak(X)
+        import matplotlib.pyplot as plt
         fig = plt.figure(figsize=(8, 16))
         ax = plt.gca()
         ax.plot(np.arange(peak_idx-100, peak_idx+300),
@@ -458,14 +453,12 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
                 "multi-output regression but has only one."
             )
 
-        print(f"Fitting multiple regressors with n_jobs={self.n_jobs}")
         self.estimators_ = Parallel(n_jobs=self.n_jobs)(
             delayed(_fit_estimator)(
                 self.estimator, X, y[:, i]
             )
             for i in range(y.shape[1])
         )
-        print("End of fit")
 
         return self
 
@@ -480,7 +473,6 @@ class MultiOutputWithStd(MetaEstimatorMixin, BaseEstimator):
             Multi-output targets predicted across multiple predictors.
             Note: Separate models are generated for each predictor.
         """
-        print("Inferring ...")
         y = Parallel(n_jobs=self.n_jobs)(
             delayed(e.predict)(X, return_std) for e in self.estimators_
             #delayed(e.predict)(X) for e in self.estimators_
@@ -525,11 +517,11 @@ class Model(TransformerMixin, BaseEstimator):
         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', IncrementalPCA(n_pca_lr, whiten=True, batch_size=n_pca_lr*2)),
+            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=8)),
                                                 ]))]
         x_model_steps += [
-                          ('pca', IncrementalPCA(n_pca_lr, whiten=True, batch_size=n_pca_lr*2)),
+                          ('pca', PCA(n_pca_lr, whiten=True)),
                           ('unc', UncertaintyHolder()),
                           ]
         self.x_model = Pipeline(x_model_steps)
@@ -593,12 +585,9 @@ class Model(TransformerMixin, BaseEstimator):
 
         Returns: Smoothened high resolution spectrum.
         """
-        print("Fitting x ...")
         x_t = self.x_model.fit_transform(low_res_data)
-        print("Fitting y ...")
         y_t = self.y_model.fit_transform(high_res_data, smoothen__energy=high_res_photon_energy)
         #self.fit_model.set_params(fex__gamma=1.0/float(x_t.shape[0]))
-        print("Fitting linear model ...")
         self.fit_model.fit(x_t, y_t)
 
         # calculate the effect of the PCA

pyproject.toml

@@ -27,7 +27,7 @@ dynamic = ["version", "readme"]
 dependencies = [
           "numpy>=1.21",
           "scipy>=1.6",
-          "scikit-learn",
+          "scikit-learn==1.0.2",
           "autograd",
           "h5py"
           ]