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from symbolfit.symbolfit import *
from symbolfit.symbolfit import *
Detected IPython. Loading juliacall extension. See https://juliapy.github.io/PythonCall.jl/stable/compat/#IPython
Dataset¶
Five inputs are needed, which can be python lists or numpy arrays (more options will be added in future!):
x: independent variable (bin center location).y: dependent variable.y_up: upward uncertainty in y per bin.y_down: downward uncertainty in y per bin.bin_widths_1dbin widths in x.
- Elements in both y_up and y_down should be non-negative values.
- These values are the "delta" in y,
- y + y_up = y shifted up by one standard deviation.
- y - y_down = y shifted down by one standard deviation.
- If no uncertainty in the dataset, one can set both y_up and y_down to ones with the same shape as x.
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'''
CMS search for high-mass dijet resonances at sqrt(s) = 13 TeV
https://arxiv.org/abs/1911.03947
https://doi.org/10.1007/JHEP05(2020)033
Differential dijet spectrum (Figure 5), public data taken from HEPDATA
https://www.hepdata.net/record/ins1764471
'''
x=[1568.5, 1647.0, 1728.5, 1813.0, 1900.5, 1991.0, 2084.5, 2181.5, 2281.5, 2385.0, 2492.0, 2602.5,
#2717.0, 2835.0, 2957.0, 3083.0, 3213.0, 3347.5, 3487.0,
3631.0, 3779.0, 3932.0, 4090.5, 4254.0, 4423.0, 4597.5, 4777.5, 4963.5, 5155.5, 5354.0, 5559.0, 5770.0, 5988.0, 6213.5, 6446.0, 6686.0, 6934.0, 7190.0, 7454.5, 7727.5, 8009.0, 8452.0]
y=[149.27999877929688, 109.44000244140625, 80.0770034790039, 58.715999603271484, 43.082000732421875, 31.559999465942383, 23.219999313354492, 16.982999801635742, 12.36400032043457, 9.121100425720215, 6.679200172424316, 4.889999866485596,
#3.589400053024292, 2.5933001041412354, 1.902999997138977, 1.3653000593185425, 0.9902999997138977, 0.7092800140380859, 0.5142099857330322,
0.3630400002002716, 0.26298001408576965, 0.18937000632286072, 0.12946000695228577, 0.08928799629211426, 0.06131099909543991, 0.04499199986457825, 0.03179299831390381, 0.021355999633669853, 0.013650000095367432, 0.009144900366663933, 0.005454500205814838, 0.0038403000216931105, 0.0025553000159561634, 0.0015561999753117561, 0.0010168999433517456, 0.0005365100223571062, 0.00023088000307325274, 0.00022378000721801072, 0.00021629000548273325, 0.0, 7.628699677297845e-05, 1.2120999599574134e-05]
y_up=[0.11884000152349472, 0.09983699768781662, 0.08385500311851501, 0.07055199891328812, 0.0594169981777668, 0.05002899840474129, 0.042238999158144, 0.03539599850773811, 0.029911000281572342, 0.025085000321269035, 0.021276000887155533, 0.017805000767111778,
#0.015064000152051449, 0.012597999535501003, 0.010623999871313572, 0.008864900097250938, 0.007441999856382608, 0.006189499981701374, 0.005183400120586157,
0.004302599932998419, 0.003619600087404251, 0.0030181999318301678, 0.002463799901306629, 0.002022000029683113, 0.0016528000123798847, 0.001401199959218502, 0.001164300017990172, 0.0009455800172872841, 0.0007514799945056438, 0.0006105700158514082, 0.0004726200131699443, 0.0003963200142607093, 0.0003233299939893186, 0.0002555900136940181, 0.00020963999850209802, 0.00015859999984968454, 0.00011385999823687598, 0.00011036000068997964, 0.00010666000162018463, 4.726000042865053e-05, 7.420800102408975e-05, 2.787400080705993e-05]
y_down=[0.11874999850988388, 0.09974599629640579, 0.08376699686050415, 0.07046700268983841, 0.059335000813007355, 0.049949999898672104, 0.042162999510765076, 0.035321999341249466, 0.02983899973332882, 0.02501700073480606, 0.02120799943804741, 0.017741000279784203,
#0.015002000145614147, 0.012536999769508839, 0.010564999654889107, 0.008807900361716747, 0.0073866997845470905, 0.006136199925094843, 0.00513189984485507,
0.0042524999007582664, 0.0035707999486476183, 0.0029712000396102667, 0.002418200019747019, 0.0019777000416070223, 0.0016099000349640846, 0.001359499990940094, 0.0011238999431952834, 0.0009062800090759993, 0.0007132000173442066, 0.0005734399892389774, 0.0004362500039860606, 0.0003607299877330661, 0.00028870999813079834, 0.00022154999896883965, 0.00017612999363336712, 0.00012527000217232853, 7.989699952304363e-05, 7.743899914203212e-05, 7.484800153179094e-05, -0.0, 4.151900066062808e-05, 1.0026999916590285e-05]
bin_widths_1d=[77.0, 80.0, 83.0, 86.0, 89.0, 92.0, 95.0, 99.0, 101.0, 106.0, 108.0, 113.0,
#116., 120., 124., 128., 132., 137., 142.,
146.0, 150.0, 156.0, 161.0, 166.0, 172.0, 177.0, 183.0, 189.0, 195.0, 202.0, 208.0, 214.0, 222.0, 229.0, 236.0, 244.0, 252.0, 260.0, 269.0, 277.0, 286.0, 600.0]
'''
CMS search for high-mass dijet resonances at sqrt(s) = 13 TeV
https://arxiv.org/abs/1911.03947
https://doi.org/10.1007/JHEP05(2020)033
Differential dijet spectrum (Figure 5), public data taken from HEPDATA
https://www.hepdata.net/record/ins1764471
'''
x=[1568.5, 1647.0, 1728.5, 1813.0, 1900.5, 1991.0, 2084.5, 2181.5, 2281.5, 2385.0, 2492.0, 2602.5,
#2717.0, 2835.0, 2957.0, 3083.0, 3213.0, 3347.5, 3487.0,
3631.0, 3779.0, 3932.0, 4090.5, 4254.0, 4423.0, 4597.5, 4777.5, 4963.5, 5155.5, 5354.0, 5559.0, 5770.0, 5988.0, 6213.5, 6446.0, 6686.0, 6934.0, 7190.0, 7454.5, 7727.5, 8009.0, 8452.0]
y=[149.27999877929688, 109.44000244140625, 80.0770034790039, 58.715999603271484, 43.082000732421875, 31.559999465942383, 23.219999313354492, 16.982999801635742, 12.36400032043457, 9.121100425720215, 6.679200172424316, 4.889999866485596,
#3.589400053024292, 2.5933001041412354, 1.902999997138977, 1.3653000593185425, 0.9902999997138977, 0.7092800140380859, 0.5142099857330322,
0.3630400002002716, 0.26298001408576965, 0.18937000632286072, 0.12946000695228577, 0.08928799629211426, 0.06131099909543991, 0.04499199986457825, 0.03179299831390381, 0.021355999633669853, 0.013650000095367432, 0.009144900366663933, 0.005454500205814838, 0.0038403000216931105, 0.0025553000159561634, 0.0015561999753117561, 0.0010168999433517456, 0.0005365100223571062, 0.00023088000307325274, 0.00022378000721801072, 0.00021629000548273325, 0.0, 7.628699677297845e-05, 1.2120999599574134e-05]
y_up=[0.11884000152349472, 0.09983699768781662, 0.08385500311851501, 0.07055199891328812, 0.0594169981777668, 0.05002899840474129, 0.042238999158144, 0.03539599850773811, 0.029911000281572342, 0.025085000321269035, 0.021276000887155533, 0.017805000767111778,
#0.015064000152051449, 0.012597999535501003, 0.010623999871313572, 0.008864900097250938, 0.007441999856382608, 0.006189499981701374, 0.005183400120586157,
0.004302599932998419, 0.003619600087404251, 0.0030181999318301678, 0.002463799901306629, 0.002022000029683113, 0.0016528000123798847, 0.001401199959218502, 0.001164300017990172, 0.0009455800172872841, 0.0007514799945056438, 0.0006105700158514082, 0.0004726200131699443, 0.0003963200142607093, 0.0003233299939893186, 0.0002555900136940181, 0.00020963999850209802, 0.00015859999984968454, 0.00011385999823687598, 0.00011036000068997964, 0.00010666000162018463, 4.726000042865053e-05, 7.420800102408975e-05, 2.787400080705993e-05]
y_down=[0.11874999850988388, 0.09974599629640579, 0.08376699686050415, 0.07046700268983841, 0.059335000813007355, 0.049949999898672104, 0.042162999510765076, 0.035321999341249466, 0.02983899973332882, 0.02501700073480606, 0.02120799943804741, 0.017741000279784203,
#0.015002000145614147, 0.012536999769508839, 0.010564999654889107, 0.008807900361716747, 0.0073866997845470905, 0.006136199925094843, 0.00513189984485507,
0.0042524999007582664, 0.0035707999486476183, 0.0029712000396102667, 0.002418200019747019, 0.0019777000416070223, 0.0016099000349640846, 0.001359499990940094, 0.0011238999431952834, 0.0009062800090759993, 0.0007132000173442066, 0.0005734399892389774, 0.0004362500039860606, 0.0003607299877330661, 0.00028870999813079834, 0.00022154999896883965, 0.00017612999363336712, 0.00012527000217232853, 7.989699952304363e-05, 7.743899914203212e-05, 7.484800153179094e-05, -0.0, 4.151900066062808e-05, 1.0026999916590285e-05]
bin_widths_1d=[77.0, 80.0, 83.0, 86.0, 89.0, 92.0, 95.0, 99.0, 101.0, 106.0, 108.0, 113.0,
#116., 120., 124., 128., 132., 137., 142.,
146.0, 150.0, 156.0, 161.0, 166.0, 172.0, 177.0, 183.0, 189.0, 195.0, 202.0, 208.0, 214.0, 222.0, 229.0, 236.0, 244.0, 252.0, 260.0, 269.0, 277.0, 286.0, 600.0]
Plot the dataset to see what we will be fitting to:
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fig, axes = plt.subplots(figsize = (6, 4))
plt.errorbar(np.array(x).flatten(),
np.array(y).flatten(),
yerr = [np.array(y_down).flatten(), np.array(y_up).flatten()],
xerr = np.array(bin_widths_1d)/2,
fmt = '.', c = 'black', ecolor = 'grey', capsize = 0,
)
plt.savefig('img/dijet_blinded/dataset.png')
fig, axes = plt.subplots(figsize = (6, 4))
plt.errorbar(np.array(x).flatten(),
np.array(y).flatten(),
yerr = [np.array(y_down).flatten(), np.array(y_up).flatten()],
xerr = np.array(bin_widths_1d)/2,
fmt = '.', c = 'black', ecolor = 'grey', capsize = 0,
)
plt.savefig('img/dijet_blinded/dataset.png')
This is a steeply falling shape, let's plot in log scale:
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fig, axes = plt.subplots(figsize = (6, 4))
plt.errorbar(np.array(x).flatten(),
np.array(y).flatten(),
yerr = [np.array(y_down).flatten(), np.array(y_up).flatten()],
xerr = np.array(bin_widths_1d)/2,
fmt = '.', c = 'black', ecolor = 'grey', capsize = 0,
)
plt.xscale('log')
plt.yscale('log')
plt.savefig('img/dijet_blinded/dataset_log.png')
fig, axes = plt.subplots(figsize = (6, 4))
plt.errorbar(np.array(x).flatten(),
np.array(y).flatten(),
yerr = [np.array(y_down).flatten(), np.array(y_up).flatten()],
xerr = np.array(bin_widths_1d)/2,
fmt = '.', c = 'black', ecolor = 'grey', capsize = 0,
)
plt.xscale('log')
plt.yscale('log')
plt.savefig('img/dijet_blinded/dataset_log.png')
Configure the fit¶
Configure PySR to define the function space being searched for with symbolic regression:
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from pysr import PySRRegressor
pysr_config = PySRRegressor(
model_selection = 'accuracy',
niterations = 200,
maxsize = 80,
binary_operators = [
'+', '*', '/', '^'
],
unary_operators = [
'exp',
'tanh',
],
nested_constraints = {
'exp': {'exp': 0, 'tanh': 0, '*': 2, '/': 1, '^': 1},
'tanh': {'exp': 0, 'tanh': 0, '*': 2, '/': 1, '^': 1},
'*': {'exp': 1, 'tanh': 1, '*': 2, '/': 1, '^': 1},
'^': {'exp': 1, 'tanh': 1, '*': 2, '/': 1, '^': 0},
'/': {'exp': 1, 'tanh': 1, '*': 2, '/': 0, '^': 1},
},
loss='loss(y, y_pred, weights) = (y - y_pred)^2 * weights',
)
from pysr import PySRRegressor
pysr_config = PySRRegressor(
model_selection = 'accuracy',
niterations = 200,
maxsize = 80,
binary_operators = [
'+', '*', '/', '^'
],
unary_operators = [
'exp',
'tanh',
],
nested_constraints = {
'exp': {'exp': 0, 'tanh': 0, '*': 2, '/': 1, '^': 1},
'tanh': {'exp': 0, 'tanh': 0, '*': 2, '/': 1, '^': 1},
'*': {'exp': 1, 'tanh': 1, '*': 2, '/': 1, '^': 1},
'^': {'exp': 1, 'tanh': 1, '*': 2, '/': 1, '^': 0},
'/': {'exp': 1, 'tanh': 1, '*': 2, '/': 0, '^': 1},
},
loss='loss(y, y_pred, weights) = (y - y_pred)^2 * weights',
)
Here, we allow four binary operators (+, *, /, pow) and two unary operators (exp, tanh) when searching for functional forms. The custom-defined gauss in the previous example may not be needed here since it this dataset is not obvious with a peak.
Nested constraints are imposed to prohibit, e.g., exp(exp(x))...
Loss function is a weighted MSE, where the weight is the sqaured uncertainty by default in SymbolFit.
For PySR options, please see:
Configure SymbolFit with the PySR config and for the re-optimization process:
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model = SymbolFit(
# Dataset: x, y, y_up, y_down.
x = x,
y = y,
y_up = y_up,
y_down = y_down,
# PySR configuration of the function space.
pysr_config = pysr_config,
# Constrain the maximum function size and over-write maxsize in pysr_config.
# Set a higher value for more complex shape, or when the lower one does not fit well.
max_complexity = 80,
# Whether to scale input x to be within 0 and 1 for the fits for numerical stability,
# as large x could lead to overflow when there is e.g. exp(x) -> exp(10000).
# So set this to False when your x's are or close to O(1), otherwise recommended to set True.
# After the fits, the functions will be unscaled to relect the original dataset.
input_rescale = True,
# ^ scaling needed here since the input x is O(1000).
# Whether to scale y for the fits for numerical stability,
# options are (when input_rescale is True): None / 'mean' / 'max' / 'l2'.
# This is useful to stabilize fits when your y's are very large or very small.
# After the fits, the functions will be unscaled to relect the original dataset.
scale_y_by = None,
# ^ scaling may or may not be needed here since the input y is widely spreading and not too extreme.
# Set a maximum standard error (%) for all parameters to avoid bad fits during re-optimization.
# In the refit loop, when any of the parameters returns a standard error larger than max_stderr,
# the fit is considered failed, and the fit will retry itself for fewer or other combination of varying parameters,
# by freezing some of the parameters to their initial values and kept fixed during re-optimization.
# This is to avoid bad fits when the objective is too complex to minimize, which could cause some parameters
# to have unrealistically large standard errors.
# In most cases 10 < max_stderr < 100 suffices.
max_stderr = 20,
# Consider y_up and y_down to weight the MSE loss during SR search and re-optimization.
fit_y_unc = True,
# Set a random seed for returning the same batch of functional forms every time (single-threaded),
# otherwise set None to explore more functions every time (multi-threaded and faster).
# In most cases the function space is huge, one can retry the fits with the exact same fit configuration
# and get completely different sets of candidate functions, merely by using different random seeds.
# So if the candidate functions are not satisfactory this time, rerun it few times more with
# random_seed = None or a different seed each time.
random_seed = None,
# Custome loss weight to set "(y - y_pred)^2 * loss_weights", overwriting that with y_up and y_down.
loss_weights = None
)
model = SymbolFit(
# Dataset: x, y, y_up, y_down.
x = x,
y = y,
y_up = y_up,
y_down = y_down,
# PySR configuration of the function space.
pysr_config = pysr_config,
# Constrain the maximum function size and over-write maxsize in pysr_config.
# Set a higher value for more complex shape, or when the lower one does not fit well.
max_complexity = 80,
# Whether to scale input x to be within 0 and 1 for the fits for numerical stability,
# as large x could lead to overflow when there is e.g. exp(x) -> exp(10000).
# So set this to False when your x's are or close to O(1), otherwise recommended to set True.
# After the fits, the functions will be unscaled to relect the original dataset.
input_rescale = True,
# ^ scaling needed here since the input x is O(1000).
# Whether to scale y for the fits for numerical stability,
# options are (when input_rescale is True): None / 'mean' / 'max' / 'l2'.
# This is useful to stabilize fits when your y's are very large or very small.
# After the fits, the functions will be unscaled to relect the original dataset.
scale_y_by = None,
# ^ scaling may or may not be needed here since the input y is widely spreading and not too extreme.
# Set a maximum standard error (%) for all parameters to avoid bad fits during re-optimization.
# In the refit loop, when any of the parameters returns a standard error larger than max_stderr,
# the fit is considered failed, and the fit will retry itself for fewer or other combination of varying parameters,
# by freezing some of the parameters to their initial values and kept fixed during re-optimization.
# This is to avoid bad fits when the objective is too complex to minimize, which could cause some parameters
# to have unrealistically large standard errors.
# In most cases 10 < max_stderr < 100 suffices.
max_stderr = 20,
# Consider y_up and y_down to weight the MSE loss during SR search and re-optimization.
fit_y_unc = True,
# Set a random seed for returning the same batch of functional forms every time (single-threaded),
# otherwise set None to explore more functions every time (multi-threaded and faster).
# In most cases the function space is huge, one can retry the fits with the exact same fit configuration
# and get completely different sets of candidate functions, merely by using different random seeds.
# So if the candidate functions are not satisfactory this time, rerun it few times more with
# random_seed = None or a different seed each time.
random_seed = None,
# Custome loss weight to set "(y - y_pred)^2 * loss_weights", overwriting that with y_up and y_down.
loss_weights = None
)
Symbol Fit it!¶
Run the fits: SR fit for functional form searching -> parameterization -> re-optimization fit for improved best-fits and uncertainty estimation -> evaluation.
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model.fit()
model.fit()
Compiling Julia backend...
[ Info: Started!
Expressions evaluated per second: 6.970e+05
Head worker occupation: 11.5%
Progress: 1559 / 3000 total iterations (51.967%)
====================================================================================================
Hall of Fame:
---------------------------------------------------------------------------------------------------
Complexity Loss Score Equation
1 9.188e-03 1.594e+01 y = -0.079682
2 2.795e-03 1.190e+00 y = tanh(0.00027823)
3 2.708e-03 3.133e-02 y = 0.00051651 ^ x₀
5 1.853e-03 1.899e-01 y = 3.1328e-05 ^ (x₀ + -0.26968)
7 1.993e-05 2.266e+00 y = 0.00014414 ^ (-0.56768 + (x₀ / 0.35022))
8 1.323e-05 4.097e-01 y = 0.00014414 ^ ((x₀ / 0.3711) + tanh(-0.63691))
9 1.164e-05 1.280e-01 y = 0.00014414 ^ ((tanh(x₀) / 0.3711) + tanh(-0.63691))
10 1.092e-05 6.419e-02 y = (-0.033238 * -0.021517) ^ ((tanh(x₀) / 0.30777) + -0.68422...
)
11 2.237e-06 1.585e+00 y = 0.00014414 ^ (-0.56768 + ((x₀ / (0.48726 + x₀)) + x₀))
12 9.479e-07 8.588e-01 y = 0.00014414 ^ (tanh(-0.63691) + (x₀ + (x₀ / (0.49882 + x₀))...
))
13 8.195e-07 1.455e-01 y = 0.00014414 ^ (tanh(-0.63691) + (x₀ + (x₀ / (tanh(0.53853) ...
+ x₀))))
14 3.990e-07 7.197e-01 y = (-0.025128 * -0.025128) ^ (x₀ + ((x₀ / tanh(0.38277 + x₀))...
+ -0.67808))
15 3.972e-07 4.649e-03 y = (-0.025128 * tanh(-0.025128)) ^ (x₀ + ((x₀ / tanh(0.38277 ...
+ x₀)) + -0.67808))
16 1.345e-07 1.083e+00 y = (-0.025128 * -0.025128) ^ (x₀ + ((x₀ / tanh(0.38301 + (x₀ ...
* 0.93211))) + -0.67808))
17 1.306e-07 2.971e-02 y = (-0.025128 * tanh(-0.025128)) ^ (x₀ + ((x₀ / tanh((x₀ * 0....
93211) + 0.38301)) + -0.67808))
18 5.409e-08 8.812e-01 y = ((-0.025128 * -0.025128) ^ (x₀ + (-0.67808 + (x₀ / tanh(0....
38301 + (0.91588 * x₀)))))) * 1.0085
19 4.142e-08 2.670e-01 y = (-0.025128 * tanh(-0.025128 * 0.98769)) ^ (x₀ + ((x₀ / tan...
h((x₀ * 0.93211) + 0.38301)) + -0.67808))
20 3.950e-08 4.758e-02 y = (-0.025128 * (-0.025128 * 0.98769)) ^ (x₀ + ((x₀ / tanh((x...
₀ * (0.93211 + -0.0064717)) + 0.38301)) + -0.67808))
22 3.750e-08 2.596e-02 y = (tanh(-0.025128 * -0.025128) ^ (x₀ + (-0.67808 + (x₀ / tan...
h(0.38301 + x₀))))) * ((exp(-0.025128) + x₀) ^ -0.2704)
47 2.960e-08 9.458e-03 y = tanh(((1.2673 + 1.0353) * x₀) ^ (x₀ * (-0.924 * x₀))) * ((...
((-0.029311 / exp(0.87704 + -0.039602)) * (exp(1.2637) + (0.67...
826 + x₀))) * -0.035065) ^ ((x₀ / (tanh(0.38379) + tanh(x₀))) ...
+ (-0.79093 + ((1.7258 * -0.028759) + (x₀ / tanh(0.68257))))))
48 2.673e-08 1.021e-01 y = tanh(((1.2673 + 1.0353) * x₀) ^ (x₀ * (-0.924 * x₀))) * ((...
((-0.029311 / exp(0.87704 + -0.039602)) * (exp(1.2637) + (0.67...
826 + x₀))) * tanh(-0.035065)) ^ ((x₀ / (tanh(0.38379) + tanh(...
x₀))) + (-0.79093 + ((1.7258 * -0.028759) + (x₀ / tanh(0.68257...
))))))
---------------------------------------------------------------------------------------------------
====================================================================================================
Press 'q' and then <enter> to stop execution early.
Checking if pysr_model_temp.pkl exists...
Loading model from pysr_model_temp.pkl
Re-optimizing parameterized candidate function 1/21...
Re-optimizing parameterized candidate function 2/21...bad fits 2/2...
Re-optimizing parameterized candidate function 3/21...bad fits 2/2...
Re-optimizing parameterized candidate function 4/21...bad fits 2/2...
>>> loop of re-parameterization with less NDF for bad fits 2/4...
Re-optimizing parameterized candidate function 5/21...
>>> loop of re-parameterization with less NDF for bad fits 3/4...
Re-optimizing parameterized candidate function 6/21...
>>> loop of re-parameterization with less NDF for bad fits 2/8...
Re-optimizing parameterized candidate function 7/21...
>>> loop of re-parameterization with less NDF for bad fits 2/8...
Re-optimizing parameterized candidate function 8/21...
>>> loop of re-parameterization with less NDF for bad fits 1/8...
Re-optimizing parameterized candidate function 9/21...
>>> loop of re-parameterization with less NDF for bad fits 1/8...
Re-optimizing parameterized candidate function 10/21...
>>> loop of re-parameterization with less NDF for bad fits 4/16...
Re-optimizing parameterized candidate function 11/21...
>>> loop of re-parameterization with less NDF for bad fits 1/8...
Re-optimizing parameterized candidate function 12/21...
>>> loop of re-parameterization with less NDF for bad fits 1/8...
Re-optimizing parameterized candidate function 13/21...
>>> loop of re-parameterization with less NDF for bad fits 1/16...
Re-optimizing parameterized candidate function 14/21...
>>> loop of re-parameterization with less NDF for bad fits 1/16...
Re-optimizing parameterized candidate function 15/21...
>>> loop of re-parameterization with less NDF for bad fits 1/16...
Re-optimizing parameterized candidate function 16/21...
>>> loop of re-parameterization with less NDF for bad fits 1/16...
Re-optimizing parameterized candidate function 17/21...
>>> loop of re-parameterization with less NDF for bad fits 2/16...
Re-optimizing parameterized candidate function 18/21...
>>> loop of re-parameterization with less NDF for bad fits 2/16...
Re-optimizing parameterized candidate function 19/21...
>>> loop of re-parameterization with less NDF for bad fits 2/16...
Re-optimizing parameterized candidate function 20/21...
>>> loop of re-parameterization with less NDF for bad fits 2/16...
Re-optimizing parameterized candidate function 21/21...
>>> loop of re-parameterization with less NDF for bad fits 7/32...
Save results to output files¶
Save results to csv tables:
candidates.csv: saves all candidate functions and evaluations in a csv table.candidates_reduced.csv: saves a reduced version for essential information without intermediate results.
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model.save_to_csv(output_dir = 'output_dijet_blinded/')
model.save_to_csv(output_dir = 'output_dijet_blinded/')
Saving full results >>> output_dijet_blinded/candidates.csv Saving reduced results >>> output_dijet_blinded/candidates_reduced.csv
Plot results to pdf files:
candidates.pdf: plots all candidate functions with associated uncertainties one by one for fit quality evaluation.candidates_sampling.pdf: plots all candidate functions with total uncertainty coverage generated by sampling parameters.candidates_gof.pdf: plots the goodness-of-fit scores.candidates_correlation.pdf: plots the correlation matrices for the parameters of the candidate functions.
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model.plot_to_pdf(
output_dir = 'output_dijet_blinded/',
bin_widths_1d = bin_widths_1d,
#bin_edges_2d = bin_edges_2d,
plot_logy = True,
plot_logx = True,
sampling_95quantile = False
)
model.plot_to_pdf(
output_dir = 'output_dijet_blinded/',
bin_widths_1d = bin_widths_1d,
#bin_edges_2d = bin_edges_2d,
plot_logy = True,
plot_logx = True,
sampling_95quantile = False
)
Plotting candidate functions 21/21 >>> output_dijet_blinded/candidates.pdf Plotting candidate functions (sampling parameters) 21/21 >>> output_dijet_blinded/candidates_sampling.pdf Plotting correlation matrices 21/21 >>> output_dijet_blinded/candidates_correlation.pdf Plotting goodness-of-fit scores >>> output_dijet_blinded/candidates_gof.pdf