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Bootstrap export/import (#198)
* feat: export_bootstrap method added. * feat: import_bootstrap function added. * tests: first test for import/export bootstrap added. * feat: bootstrap feature cleaned up. * docs: boostrap docstrings improved.
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@ -3,6 +3,7 @@ import hashlib
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import pickle
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import numpy as np
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import autograd.numpy as anp # Thinly-wrapped numpy
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import scipy
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from autograd import jacobian
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import matplotlib.pyplot as plt
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from scipy.stats import skew, skewtest, kurtosis, kurtosistest
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@ -684,6 +685,49 @@ class Obs:
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tmp_jacks[1:] = (n * mean - full_data) / (n - 1)
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return tmp_jacks
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def export_bootstrap(self, samples=500, random_numbers=None, save_rng=None):
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"""Export bootstrap samples from the Obs
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Parameters
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----------
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samples : int
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Number of bootstrap samples to generate.
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random_numbers : np.ndarray
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Array of shape (samples, length) containing the random numbers to generate the bootstrap samples.
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If not provided the bootstrap samples are generated bashed on the md5 hash of the enesmble name.
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save_rng : str
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Save the random numbers to a file if a path is specified.
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Returns
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-------
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numpy.ndarray
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Returns a numpy array of length N + 1 where N is the number of samples
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for the given ensemble and replicum. The zeroth entry of the array contains
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the mean value of the Obs, entries 1 to N contain the N import_bootstrap samples
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derived from the Obs. The current implementation only works for observables
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defined on exactly one ensemble and replicum. The derived bootstrap samples
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should agree with samples from a full bootstrap analysis up to O(1/N).
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"""
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if len(self.names) != 1:
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raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
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name = self.names[0]
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length = self.N
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if random_numbers is None:
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seed = int(hashlib.md5(name.encode()).hexdigest(), 16) & 0xFFFFFFFF
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rng = np.random.default_rng(seed)
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random_numbers = rng.integers(0, length, size=(samples, length))
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if save_rng is not None:
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np.savetxt(save_rng, random_numbers, fmt='%i')
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proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
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ret = np.zeros(samples + 1)
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ret[0] = self.value
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ret[1:] = proj @ (self.deltas[name] + self.r_values[name])
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return ret
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def __float__(self):
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return float(self.value)
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@ -1550,6 +1594,36 @@ def import_jackknife(jacks, name, idl=None):
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return new_obs
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def import_bootstrap(boots, name, random_numbers):
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"""Imports bootstrap samples and returns an Obs
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Parameters
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----------
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boots : numpy.ndarray
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numpy array containing the mean value as zeroth entry and
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the N bootstrap samples as first to Nth entry.
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name : str
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name of the ensemble the samples are defined on.
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random_numbers : np.ndarray
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Array of shape (samples, length) containing the random numbers to generate the bootstrap samples,
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where samples is the number of bootstrap samples and length is the length of the original Monte Carlo
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chain to be reconstructed.
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"""
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samples, length = random_numbers.shape
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if samples != len(boots) - 1:
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raise ValueError("Random numbers do not have the correct shape.")
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if samples < length:
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raise ValueError("Obs can't be reconstructed if there are fewer bootstrap samples than Monte Carlo data points.")
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proj = np.vstack([np.bincount(o, minlength=length) for o in random_numbers]) / length
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samples = scipy.linalg.lstsq(proj, boots[1:])[0]
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ret = Obs([samples], [name])
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ret._value = boots[0]
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return ret
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def merge_obs(list_of_obs):
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"""Combine all observables in list_of_obs into one new observable
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@ -1094,6 +1094,20 @@ def test_import_jackknife():
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assert my_obs == reconstructed_obs
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def test_import_bootstrap():
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seed = 4321
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samples = 1234
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length = 820
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name = "test"
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rng = np.random.default_rng(seed)
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random_numbers = rng.integers(0, length, size=(samples, length))
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obs = pe.pseudo_Obs(2.447, 0.14, name, length)
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boots = obs.export_bootstrap(1234, random_numbers=random_numbers)
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re_obs = pe.import_bootstrap(boots, name, random_numbers=random_numbers)
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assert obs == re_obs
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def test_reduce_deltas():
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idx_old = range(1, 101)
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deltas = [float(i) for i in idx_old]
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