Train a neural network for dE/dx-based particle identification
Definition at line 42 of file train.py.
◆ create_dedx_bins()
| create_dedx_bins |
( |
| self, |
|
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| data ) |
Construct the dE/dx bins and then populate them with the data
Definition at line 52 of file train.py.
52 def create_dedx_bins(self, data):
53 """Construct the dE/dx bins and then populate them with the data"""
54 dedx_bins = np.linspace(
55 data[
56 self.dedx_column].min(), data[
57 self.dedx_column].max(), GroupedDEDXEstimationTrainer.number_of_bins_in_dedx)
58 dedx_cuts = pd.cut(data[self.dedx_column], dedx_bins)
59 return data.groupby(dedx_cuts), dedx_bins
60
◆ create_fit_data()
| create_fit_data |
( |
| self, |
|
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| dedx_bin ) |
Fit track-momentum values
Definition at line 74 of file train.py.
74 def create_fit_data(self, dedx_bin):
75 """Fit track-momentum values"""
76 p_binned_data, p_bins = self.create_p_bins(dedx_bin)
77
78 number_of_p_values = pd.Series(p_binned_data.count().p.values, name="number_of_p_values")
79 p_bin_centers = pd.Series(0.5 * (p_bins[:-1] + p_bins[1:]), name="p_bin_centers")
80
81 all_fit_data = pd.DataFrame([number_of_p_values, p_bin_centers]).T
82 fit_data = self.use_only_the_highest_values(all_fit_data, GroupedDEDXEstimationTrainer.number_of_head_values_used_to_fit)
83
84 return fit_data
85
◆ create_p_bins()
| create_p_bins |
( |
| self, |
|
|
| data ) |
Construct the momentum bins and then populate them with the data
Definition at line 61 of file train.py.
61 def create_p_bins(self, data):
62 """Construct the momentum bins and then populate them with the data"""
63 p_bins = np.linspace(data.p.min(), data.p.max(), GroupedDEDXEstimationTrainer.number_of_bins_in_p)
64 p_cuts = pd.cut(data.p, p_bins)
65 return data.groupby(p_cuts), p_bins
66
◆ fit_p_to_dedx_bin()
| fit_p_to_dedx_bin |
( |
| self, |
|
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| dedx_bin ) |
Fit the track-momentum values in the selected dE/dx bin, then train on the fitted values
Definition at line 86 of file train.py.
86 def fit_p_to_dedx_bin(self, dedx_bin):
87 """Fit the track-momentum values in the selected dE/dx bin, then train on the fitted values"""
88 fit_data = self.create_fit_data(dedx_bin)
89 return self.train_function(fit_data)
90
91
◆ test()
Get the trained neural-network output value for test data
Reimplemented in MVADEDXEstimationTrainer.
Definition at line 34 of file train.py.
34 def test(self, data):
35 """Get the trained neural-network output value for test data"""
36 if self.dedx_estimator_function is None:
37 raise ValueError("Train the estimator first!")
38
39 return self.dedx_estimator_function(data[self.dedx_column])
40
41
◆ train()
◆ use_only_the_highest_values()
| use_only_the_highest_values |
( |
| self, |
|
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| data, |
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| number_of_values = None ) |
Sort the data then select only the highest N values
Definition at line 67 of file train.py.
67 def use_only_the_highest_values(self, data, number_of_values=None):
68 """Sort the data then select only the highest N values"""
69 if number_of_values is None:
70 return data
71 else:
72 return data.sort("number_of_p_values", ascending=False).head(number_of_values).sort()
73
◆ dedx_column
the default data column is 'dedx'
Definition at line 27 of file train.py.
◆ dedx_estimator_function
| dedx_estimator_function = None |
|
inherited |
by default, the dE/dx-particle-identification trainer has not run yet
Definition at line 25 of file train.py.
◆ number_of_bins_in_dedx
| int number_of_bins_in_dedx = 20 |
|
static |
number of dE/dx bins
Definition at line 46 of file train.py.
◆ number_of_bins_in_p
| int number_of_bins_in_p = 29 |
|
static |
number of track-momentum bins
Definition at line 48 of file train.py.
◆ number_of_head_values_used_to_fit
| int number_of_head_values_used_to_fit = 20 |
|
static |
number of head values in fit
Definition at line 50 of file train.py.
The documentation for this class was generated from the following file:
- tracking/studies/low_momentum_vxd_estimation/train.py
1.13.2