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nudges.py
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nudges.py
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import numpy as np
import dit, random, itertools
from derkjanistic_nudges import do_derkjanistic_nudge as dj_nudge
from nudge_utils import generate_log_nudge, generate_nudge, perform_nudge, perform_log_nudge
def individual_nudge(old_X: dit.Distribution, eps: float = 0.01, rvs_other=None) -> dit.Distribution:
mask = old_X._mask
base = old_X.get_base()
if old_X.outcome_length() == 1:
return global_nudge(old_X, eps)
outcomes = old_X.outcomes
rv_names = old_X.get_rv_names()
if rvs_other == None:
rvs = old_X.get_rv_names()
rvs_other = np.random.choice(rvs, len(rvs) - 1, replace=False)
X_other, Xi_given_Xother = old_X.condition_on(rvs_other)
nudge_size = len(Xi_given_Xother[0])
if base == 'linear':
nudge = generate_nudge(nudge_size, eps / len(Xi_given_Xother))
for Xi in Xi_given_Xother:
perform_nudge(Xi, nudge)
else:
nudge, sign = generate_log_nudge(nudge_size, eps)
for Xi in Xi_given_Xother:
perform_log_nudge(Xi, nudge, sign)
new_X = dit.joint_from_factors(X_other, Xi_given_Xother).copy(base)
#add back any missing outcomes
dct = {o: new_X[o] if o in new_X.outcomes else 0.0 for o in outcomes}
#print(outcomes, dct)
new_X = dit.Distribution(dct)
new_X.set_rv_names(rv_names)
new_X.make_dense()
new_X._mask = mask
return new_X
def local_nudge1(old_X: dit.Distribution, eps: float = 0.01) -> dit.Distribution:
mask = old_X._mask
base = old_X.get_base()
new_X = old_X.copy(base=base)
old_X.make_dense()
outcomes = old_X.outcomes
rvs = list(old_X.get_rv_names())
random.shuffle(rvs)
#print(rvs)
new_Xs = np.zeros((len(rvs), len(old_X)))
for i in range(len(rvs)):
rvs_other = rvs[:i] + rvs[i + 1:]
tmp = individual_nudge(old_X, eps, rvs_other=rvs_other)
#print("tmp",tmp)
tmp.make_dense()
old_X.make_dense()
if base == 'linear':
new_Xs[i, :] = tmp.pmf - old_X.pmf
else:
new_Xs[i] = tmp.copy(base='linear').pmf - old_X.copy(base='linear').pmf
#old_X.make_sparse()
nudge = new_Xs.sum(axis=0)
nudge = eps * nudge / (abs(nudge).sum())
if base == 'linear':
perform_nudge(new_X, nudge)
else:
perform_log_nudge(new_X, np.log(np.abs(nudge)), np.sign(nudge))
new_X = dit.Distribution({o: new_X[o] if o in new_X.outcomes else 0 for o in outcomes})
new_X.set_rv_names(rvs)
new_X._mask = mask
return new_X
def local_nudge2(old_X: dit.Distribution, eps: float = 0.01) -> dit.Distribution:
mask = old_X._mask
base = old_X.get_base()
new_X = old_X.copy(base=base)
old_X.make_dense()
rvs = list(old_X.get_rv_names())
random.shuffle(rvs)
new_Xs = np.zeros((len(rvs), len(old_X)))
for i in range(len(rvs)):
rvs_other = rvs[:i] + rvs[i + 1:]
#print(new_X.get_rv_names())
new_X = individual_nudge(new_X, eps / len(rvs), rvs_other=rvs_other)
return new_X
local_nudge = local_nudge2
def derkjanistic_nudge(old_X: dit.Distribution, eps: float = 0.01) -> dit.Distribution:
base = old_X.get_base()
outcomes = old_X.outcomes
new_X = old_X.copy(base='linear')
rvs = old_X.get_rv_names()
if len(rvs) < 2:
return global_nudge(old_X, eps)
delta = eps / len(old_X)
new_pmf = dj_nudge(new_X, delta)
# print(delta)
new_X.pmf = new_pmf
new_X = dit.Distribution({o: new_X[o] if o in new_X.outcomes else 0 for o in outcomes})
new_X.set_rv_names(rvs)
new_X.normalize()
new_X = new_X.copy(base=base)
return new_X
def synergistic_nudge(old_X: dit.Distribution, eps: float = 0.01) -> dit.Distribution:
base = old_X.get_base()
outcomes = old_X.outcomes
new_X = old_X.copy(base=base)
rvs = old_X.get_rv_names()
if len(rvs) < 3:
return global_nudge(old_X, eps)
synergy_vars = np.random.choice(len(rvs), 2, replace=False)
states = old_X.alphabet[0]
nudge_size = int(len(old_X) / (len(states) ** 2))
outcome_dict = {state: np.zeros(nudge_size, dtype=int) for state in list(itertools.product(states, repeat=2))}
for i, outcome in enumerate(old_X.outcomes):
cur_state = outcome[synergy_vars[0]], outcome[synergy_vars[1]]
outcome_dict[cur_state][np.argmax(outcome_dict[cur_state] == 0)] = i # Choose the first zero entry to fill
if base == 'linear':
nudge = generate_nudge(nudge_size, eps / len(outcome_dict))
perform_nudge(new_X, nudge, outcome_dict.values())
else:
nudge, sign = generate_log_nudge(nudge_size, eps / len(outcome_dict))
perform_log_nudge(new_X, nudge, sign, outcome_dict.values())
new_X = dit.Distribution({o: new_X[o] if o in new_X.outcomes else 0.0 for o in outcomes})
new_X.set_rv_names(rvs)
new_X.pmf[new_X.pmf == np.nan] = -np.inf
new_X.normalize()
return new_X
def global_nudge(old_X: dit.Distribution, eps: float = 0.01) -> dit.Distribution:
base = old_X.get_base()
new_X = old_X.copy(base=base)
old_X.make_dense()
outcomes = old_X.outcomes
nudge_size = len(old_X)
rvs = old_X.get_rv_names()
if base == 'linear':
nudge = generate_nudge(nudge_size, eps)
perform_nudge(new_X, nudge)
else:
nudge, sign = generate_log_nudge(nudge_size, eps)
perform_log_nudge(new_X, nudge, sign)
new_X = dit.Distribution({o: new_X[o] if o in new_X.outcomes else 0 for o in outcomes})
new_X.set_rv_names(rvs)
return new_X