From cda5dbe8f0931445bb6357d44b30f175b536c27e Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 23 Jan 2023 18:09:10 +0100
Subject: [PATCH] Clean up.
---
pes_to_spec/model.py | 17 +++--------------
pyproject.toml | 2 +-
2 files changed, 4 insertions(+), 15 deletions(-)
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index 0df98b8..692c7ea 100644
--- a/pes_to_spec/model.py
+++ b/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
diff --git a/pyproject.toml b/pyproject.toml
index c56a623..5e730a1 100644
--- a/pyproject.toml
+++ b/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"
]
--
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