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Louis Lacoste 2025-12-01 21:07:40 +01:00
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.gitignore vendored Normal file
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.venv
data
__pycache__

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load_data.R Normal file
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library(dplyr)
full_data <- read.csv("data/data_fourchoices.csv")
# mapping of button names to choice numbers
button_mapping <- c(
"antifragile" = 1,
"robuste" = 2,
"fragile" = 3,
"vulnerable" = 4
)
data <- full_data[, c("participant", "click_number", "button_name", "button_value")] %>%
rename(participant_id = participant, trial = click_number, choice = button_name, reward = button_value) %>%
mutate(
option = choice,
choice = button_mapping[choice]
) %>%
select(participant_id, trial, choice, reward, option) -> data
write.csv(data, file = "data/prepared_data.csv", row.names = FALSE)

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load_data.py Normal file
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# %%
import pandas as pd
full_data = pd.read_csv("data/data_fourchoices.csv")
# %%
all_participant_data = full_data[["participant", "button_name", "button_value"]]
# %%
dict_mapping_button_name_to_index = {
"antifragile": 0,
"fragile": 1,
"robuste": 2,
"vulnerable": 3
}
all_participant_data["choice"] = all_participant_data["button_name"].map(dict_mapping_button_name_to_index)
all_participant_data = all_participant_data.rename(columns={"button_value": "reward"})
all_participant_data["rescaled_reward"] = (all_participant_data["reward"]) / 3000 # rescale rewards to [-1,1]
all_participant_data["rescaled_reward"] = all_participant_data["rescaled_reward"].round(4)
unique_participants = all_participant_data["participant"].unique()
# %%

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model0.py Normal file
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# MLE_fit.py
# %%
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.special import logsumexp
# %%
def simulate_model(theta, choices=None, rewards=None, n_arms=4, T=None, seed=None):
"""Simple simulator to generate choices+rewards if not provided.
theta = (alpha, lam)
"""
alpha, f = theta
rng = np.random.RandomState(seed)
if T is None:
if choices is None:
raise ValueError("Provide T or choices")
T = len(choices)
Q = np.zeros(n_arms)
sim_choices = []
sim_rewards = []
# example reward distributions: Bernoulli with different p
ps = np.linspace(0.2, 0.8, n_arms)
for t in range(T):
V = Q
logits = V - logsumexp(V) # numerically stable
probs = np.exp(logits)
a = rng.choice(n_arms, p=probs)
r = rng.binomial(1, ps[a]) # example reward
# updates
Q[a] = Q[a] + alpha * (r - Q[a])
non_chosen = [i for i in range(n_arms) if i != a]
Q[non_chosen] = (1-f) * Q[non_chosen]
sim_choices.append(a)
sim_rewards.append(r)
return np.array(sim_choices), np.array(sim_rewards)
def neg_log_likelihood_raw(params, choices, rewards, n_arms=4):
"""Params are in unconstrained real space; we'll transform inside the function."""
# params vector: [logit_alpha, log_lambda, logit_f]
la, lb = params
# transforms
alpha = 1/(1+np.exp(-la)) # sigmoid -> (0,1)
f = 1/(1+np.exp(-lb)) # sigmoid -> (0,1)
T = len(choices)
Q = np.zeros(n_arms)
loglik = 0.0
for t in range(T):
V = Q
# compute stable log-softmax
logp = V - logsumexp(V)
a = choices[t]
loglik += logp[a]
r = rewards[t]
# update chosen
Q[a] = Q[a] + alpha * (r - Q[a])
# update non-chosen via forgetting
non_chosen = [i for i in range(n_arms) if i != a]
Q[non_chosen] = (1-f) * Q[non_chosen] # no forgetting in this model
return -loglik # negative log-likelihood for minimization
def fit_mle(choices, rewards, n_arms=4, n_starts=10):
best = None
for s in range(n_starts):
x0 = np.random.normal(size=2) # la, lb
res = minimize(neg_log_likelihood_raw, x0,
args=(choices, rewards, n_arms),
method='L-BFGS-B',
options={'maxiter':5000})
if not res.success:
continue
if best is None or res.fun < best['fun']:
best = res
if best is None:
raise RuntimeError("MLE failed")
la, lb = best.x
# transform back
alpha = 1 / (1+np.exp(-la))
f = 1 / (1 + np.exp(-lb))
return {'alpha':alpha, 'f':f, 'nll':best.fun, 'opt_result':best}
# %%
# --- Example usage with synthetic data ---
if __name__ == "__main__":
# generate synthetic participant
true_theta = (0.2, 0.05) # alpha, f
choices, rewards = simulate_model(true_theta, T=200, seed=123)
res = fit_mle(choices, rewards, n_starts=20)
print("True theta:", true_theta)
print("MLE estimate:", res)
# %%
# Loading participants data
from load_data import all_participant_data, unique_participants
# %%
import os
save_dir = "results/model0"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, "mle.csv")
for pid in unique_participants:
pdata = all_participant_data[all_participant_data["participant"] == pid]
choices = pdata["choice"].values
rewards = pdata["rescaled_reward"].values
res = fit_mle(choices, rewards, n_starts=20)
print(f"Participant {pid} MLE estimate:", res)
pd_res = pd.DataFrame([res])
pd_res["participant"] = pid
pd_res = pd_res.reindex(columns=["participant", "alpha", "f", "nll"])
# Save results
pd_res.to_csv(save_path, index=False, mode="a", header=not os.path.exists(save_path))
# %%

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# MLE_fit.py
# %%
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.special import logsumexp
# %%
def simulate_model(theta, choices=None, rewards=None, n_arms=4, T=None, seed=None):
"""Simple simulator to generate choices+rewards if not provided.
theta = (alpha, lam, f)
"""
alpha, lam, f = theta
rng = np.random.RandomState(seed)
if T is None:
if choices is None:
raise ValueError("Provide T or choices")
T = len(choices)
Q = np.zeros(n_arms)
sim_choices = []
sim_rewards = []
# example reward distributions: Bernoulli with different p
ps = np.linspace(0.2, 0.8, n_arms)
for t in range(T):
V = lam * Q
logits = V - logsumexp(V) # numerically stable
probs = np.exp(logits)
a = rng.choice(n_arms, p=probs)
r = rng.binomial(1, ps[a]) # example reward
# updates
Q[a] = Q[a] + alpha * (r - Q[a])
non_chosen = [i for i in range(n_arms) if i != a]
Q[non_chosen] = Q[non_chosen] * (1 - f)
sim_choices.append(a)
sim_rewards.append(r)
return np.array(sim_choices), np.array(sim_rewards)
def neg_log_likelihood_raw(params, choices, rewards, n_arms=4):
"""Params are in unconstrained real space; we'll transform inside the function."""
# params vector: [logit_alpha, log_lambda, logit_f]
la, lb, lc = params
# transforms
alpha = 1/(1+np.exp(-la)) # sigmoid -> (0,1)
lam = np.exp(lb) # positive
f = 1/(1+np.exp(-lc)) # sigmoid -> (0,1)
T = len(choices)
Q = np.zeros(n_arms)
loglik = 0.0
for t in range(T):
V = lam * Q
# compute stable log-softmax
logp = V - logsumexp(V)
a = choices[t]
loglik += logp[a]
r = rewards[t]
# update chosen
Q[a] = Q[a] + alpha * (r - Q[a])
# update non-chosen via forgetting
non_chosen = [i for i in range(n_arms) if i != a]
Q[non_chosen] = Q[non_chosen] * (1 - f)
return -loglik # negative log-likelihood for minimization
def fit_mle(choices, rewards, n_arms=4, n_starts=10):
best = None
for s in range(n_starts):
x0 = np.random.normal(size=3)
res = minimize(neg_log_likelihood_raw, x0,
args=(choices, rewards, n_arms),
method='L-BFGS-B',
options={'maxiter':5000})
if not res.success:
continue
if best is None or res.fun < best['fun']:
best = res
if best is None:
raise RuntimeError("MLE failed")
la, lb, lc = best.x
# transform back
alpha = 1/(1+np.exp(-la))
lam = np.exp(lb)
f = 1/(1+np.exp(-lc))
return {'alpha':alpha, 'lambda':lam, 'f':f, 'nll':best.fun, 'opt_result':best}
# %%
# --- Example usage with synthetic data ---
if __name__ == "__main__":
# generate synthetic participant
true_theta = (0.2, 2.0, 0.05) # alpha, lambda, f
choices, rewards = simulate_model(true_theta, T=200, seed=123)
res = fit_mle(choices, rewards, n_starts=20)
print("True theta:", true_theta)
print("MLE estimate:", res)
# %%
# Loading participants data
from load_data import all_participant_data, unique_participants
# %%
import os
save_dir = "results/model1"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, "mle.csv")
for pid in unique_participants:
pdata = all_participant_data[all_participant_data["participant"] == pid]
choices = pdata["choice"].values
rewards = pdata["rescaled_reward"].values
res = fit_mle(choices, rewards, n_starts=20)
print(f"Participant {pid} MLE estimate:", res)
pd_res = pd.DataFrame([res])
pd_res["participant"] = pid
pd_res = pd_res.reindex(columns=["participant", "alpha", "lambda", "f", "nll"])
# Save results
pd_res.to_csv(save_path, index=False, mode="a", header=not os.path.exists(save_path))
# %%

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# MLE_fit.py
# %%
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.special import logsumexp
# %%
N_PARAM = 2 # alpha_gain, alpha_loss
def simulate_model(theta, choices=None, rewards=None, n_arms=4, T=None, seed=None):
"""Simple simulator to generate choices+rewards if not provided.
theta = (alpha, lam)
"""
alpha_gain, alpha_loss = theta
rng = np.random.RandomState(seed)
if T is None:
if choices is None:
raise ValueError("Provide T or choices")
T = len(choices)
Q = np.zeros(n_arms)
sim_choices = []
sim_rewards = []
# example reward distributions: Bernoulli with different p
ps = np.linspace(0.2, 0.8, n_arms)
for t in range(T):
V = Q
logits = V - logsumexp(V) # numerically stable
probs = np.exp(logits)
a = rng.choice(n_arms, p=probs)
r = rng.binomial(1, ps[a]) * (1 if rng.rand() < 0.5 else -1) # example reward with gain/loss
# updates
if r >= 0:
Q[a] = Q[a] + alpha_gain * (r - Q[a])
else:
Q[a] = Q[a] + alpha_loss * (r - Q[a])
non_chosen = [i for i in range(n_arms) if i != a]
Q[non_chosen] = Q[non_chosen]
sim_choices.append(a)
sim_rewards.append(r)
return np.array(sim_choices), np.array(sim_rewards)
def neg_log_likelihood_raw(params, choices, rewards, n_arms=4):
"""Params are in unconstrained real space; we'll transform inside the function."""
# params vector: [logit_alpha_gain, logit_alpha_loss]
lag, lal = params
# transforms
alpha_gain = 1 / (1 + np.exp(-lag)) # sigmoid -> (0,1)
alpha_loss = 1 / (1 + np.exp(-lal)) # sigmoid -> (0,1)
T = len(choices)
Q = np.zeros(n_arms)
loglik = 0.0
for t in range(T):
V = Q
# compute stable log-softmax
logp = V - logsumexp(V)
a = choices[t]
loglik += logp[a]
r = rewards[t]
# update chosen
if r >= 0:
Q[a] = Q[a] + alpha_gain * (r - Q[a])
else:
Q[a] = Q[a] + alpha_loss * (r - Q[a])
# update non-chosen via forgetting
non_chosen = [i for i in range(n_arms) if i != a]
Q[non_chosen] = Q[non_chosen] # no forgetting in this model
return -loglik # negative log-likelihood for minimization
def fit_mle(choices, rewards, n_arms=4, x0=None, n_starts=10):
best = None
for s in range(n_starts):
if x0 is None:
x0 = np.random.normal(size=N_PARAM) # lag, lal
res = minimize(
neg_log_likelihood_raw,
x0,
args=(choices, rewards, n_arms),
method="L-BFGS-B",
options={"maxiter": 5000},
)
if not res.success:
continue
if best is None or res.fun < best["fun"]:
best = res
if best is None:
raise RuntimeError("MLE failed")
lag, lal = best.x
# transform back
alpha_gain = 1 / (1 + np.exp(-lag))
alpha_loss = 1 / (1 + np.exp(-lal))
return {"alpha_gain": alpha_gain, "alpha_loss": alpha_loss, "nll": best.fun, "opt_result": best}
# %%
# --- Example usage with synthetic data ---
if __name__ == "__main__":
np.random.seed(42)
# generate synthetic participant
true_theta = (0.2, 0.9) # alpha_gain, alpha_loss
choices, rewards = simulate_model(true_theta, T=200, seed=123)
res = fit_mle(choices, rewards, n_starts=20)
print("True theta:", true_theta)
print("MLE estimate:", res)
# %%
# Loading participants data
from load_data import all_participant_data, unique_participants
# %%
import os
save_dir = "results/model2"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir, "mle.csv")
for pid in unique_participants:
pdata = all_participant_data[all_participant_data["participant"] == pid]
choices = pdata["choice"].values
rewards = pdata["rescaled_reward"].values
res = fit_mle(choices, rewards, x0=np.array([1.0, 1.0]), n_starts=20)
print(f"Participant {pid} MLE estimate:", res)
pd_res = pd.DataFrame([res])
pd_res["participant"] = pid
pd_res = pd_res.reindex(columns=["participant", "alpha_gain", "alpha_loss", "nll"])
pd_res["model"] = "model2_loss_gain"
# Save results
pd_res.to_csv(
save_path, index=False, mode="a", header=not os.path.exists(save_path)
)
# %%

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# ============================================================================
# MODÈLES Q-LEARNING EMBOÎTÉS POUR DÉCISION AVEC ÉVÉNEMENTS RARES
# ============================================================================
library(tidyverse)
library(DEoptim)
library(numDeriv)
library(reticulate) # Pour interfacer avec PyVBMC
# Configuration optionnelle de l'environnement Python
# use_python("/usr/bin/python3") # Ajuster selon votre installation
# py_install("pyvbmc") # Installer PyVBMC si nécessaire
# ============================================================================
# FONCTION GÉNÉRIQUE DE Q-LEARNING
# ============================================================================
qlearning_generic <- function(params, data, model_config, return_negLL = TRUE) {
# Conversion des choix en indices numériques
if (is.factor(data$choice) || is.character(data$choice)) {
choice_levels <- c("antifragile", "fragile", "robuste", "vulnerable")
data$choice_idx <- match(as.character(data$choice), choice_levels)
} else {
data$choice_idx <- data$choice
}
n_arms <- 4
n_trials <- nrow(data)
# Extraction des paramètres selon la configuration du modèle
param_idx <- 1
# ALPHA(S)
if (model_config$n_alpha == 1) {
alpha_loss <- alpha_gain <- alpha_BS <- alpha_JP <- plogis(params[param_idx])
param_idx <- param_idx + 1
} else if (model_config$n_alpha == 2) {
alpha_loss <- plogis(params[param_idx])
alpha_gain <- plogis(params[param_idx + 1])
alpha_BS <- alpha_loss
alpha_JP <- alpha_gain
param_idx <- param_idx + 2
} else if (model_config$n_alpha == 4) {
alpha_loss <- plogis(params[param_idx])
alpha_gain <- plogis(params[param_idx + 1])
alpha_BS <- plogis(params[param_idx + 2])
alpha_JP <- plogis(params[param_idx + 3])
param_idx <- param_idx + 4
}
# FORGET(S)
if (model_config$n_forget == 1) {
forget <- rep(plogis(params[param_idx]), n_arms)
param_idx <- param_idx + 1
} else if (model_config$n_forget == 4) {
forget <- plogis(params[param_idx:(param_idx + 3)])
param_idx <- param_idx + 4
}
# LAMBDA(S)
if (model_config$n_lambda == 1) {
lambda <- rep(exp(params[param_idx]), n_arms)
param_idx <- param_idx + 1
} else if (model_config$n_lambda == 4) {
lambda <- exp(params[param_idx:(param_idx + 3)])
param_idx <- param_idx + 4
}
# RHO(S) - Biais pour événements rares
if (model_config$has_rho) {
rho_BS <- params[param_idx] # BS avoidance
rho_JP <- params[param_idx + 1] # JP seeking
param_idx <- param_idx + 2
} else {
rho_BS <- rho_JP <- 0
}
# Initialisation des Q-values
Q <- rep(0, n_arms)
log_lik <- 0
for (t in 1:n_trials) {
choice <- data$choice_idx[t]
reward <- data$reward[t]
# Calcul des valeurs subjectives V(t)
V <- lambda * Q
# Ajout des biais pour événements rares si le modèle le permet
if (model_config$has_rho) {
# Identification des options susceptibles de produire BS/JP
# antifragile (1) = JP possible, fragile (2) = BS possible
# vulnerable (4) = BS et JP possibles
V[1] <- V[1] + rho_JP # antifragile
V[2] <- V[2] + rho_BS # fragile
V[4] <- V[4] + rho_BS + rho_JP # vulnerable
}
# Softmax
V_max <- max(V)
exp_V <- exp(V - V_max)
probs <- exp_V / sum(exp_V)
probs <- pmax(probs, 1e-10)
probs <- probs / sum(probs)
# Log-likelihood
log_lik <- log_lik + log(probs[choice])
# Mise à jour Q-learning
Q_new <- Q
# Choix de l'alpha approprié
if (reward == -3000) {
alpha_used <- alpha_BS
} else if (reward == 3000) {
alpha_used <- alpha_JP
} else if (reward < 0) {
alpha_used <- alpha_loss
} else {
alpha_used <- alpha_gain
}
# Option choisie : Q(t+1) = Q(t) + alpha * (r(t) - Q(t))
Q_new[choice] <- Q[choice] + alpha_used * (reward - Q[choice])
# Options non choisies : Q(t+1) = Q(t) * (1 - f)
not_chosen <- setdiff(1:n_arms, choice)
Q_new[not_chosen] <- Q[not_chosen] * (1 - forget[not_chosen])
Q <- Q_new
}
if (return_negLL) {
return(-log_lik)
} else {
return(log_lik)
}
}
# ============================================================================
# CONFIGURATIONS DES MODÈLES EMBOÎTÉS
# ============================================================================
get_model_configs <- function() {
list(
HOMOGENEOUS = list(
name = "HOMOGENEOUS",
n_alpha = 1,
n_forget = 1,
n_lambda = 1,
has_rho = FALSE,
n_params = 3,
param_names = c("alpha", "forget", "lambda"),
lower = c(-5, -5, -3),
upper = c(5, 5, 3)
),
GAIN_LOSS = list(
name = "GAIN_LOSS",
n_alpha = 2,
n_forget = 1,
n_lambda = 1,
has_rho = FALSE,
n_params = 4,
param_names = c("alpha_loss", "alpha_gain", "forget", "lambda"),
lower = c(-5, -5, -5, -3),
upper = c(5, 5, 5, 3)
),
BIASED = list(
name = "BIASED",
n_alpha = 2,
n_forget = 4,
n_lambda = 4,
has_rho = FALSE,
n_params = 10,
param_names = c("alpha_loss", "alpha_gain",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4"),
lower = c(-5, -5, rep(-5, 4), rep(-3, 4)),
upper = c(5, 5, rep(5, 4), rep(3, 4))
),
REE_BIASED_SIMPLE = list(
name = "REE_BIASED_SIMPLE",
n_alpha = 2,
n_forget = 1,
n_lambda = 1,
has_rho = TRUE,
n_params = 6,
param_names = c("alpha_loss", "alpha_gain", "forget", "lambda",
"rho_BS", "rho_JP"),
lower = c(-5, -5, -5, -3, -10, -10),
upper = c(5, 5, 5, 3, 10, 10)
),
REE_BIASED_COMPLEX = list(
name = "REE_BIASED_COMPLEX",
n_alpha = 2,
n_forget = 4,
n_lambda = 4,
has_rho = TRUE,
n_params = 12,
param_names = c("alpha_loss", "alpha_gain",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4",
"rho_BS", "rho_JP"),
lower = c(-5, -5, rep(-5, 4), rep(-3, 4), -10, -10),
upper = c(5, 5, rep(5, 4), rep(3, 4), 10, 10)
),
REE_LEARNING_SIMPLE = list(
name = "REE_LEARNING_SIMPLE",
n_alpha = 4,
n_forget = 1,
n_lambda = 1,
has_rho = FALSE,
n_params = 6,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget", "lambda"),
lower = c(-5, -5, -5, -5, -5, -3),
upper = c(5, 5, 5, 5, 5, 3)
),
REE_LEARNING_COMPLEX = list(
name = "REE_LEARNING_COMPLEX",
n_alpha = 4,
n_forget = 4,
n_lambda = 4,
has_rho = FALSE,
n_params = 12,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4"),
lower = c(-5, -5, -5, -5, rep(-5, 4), rep(-3, 4)),
upper = c(5, 5, 5, 5, rep(5, 4), rep(3, 4))
),
REE_LEARNING_BIASED_SIMPLE = list(
name = "REE_LEARNING_BIASED_SIMPLE",
n_alpha = 4,
n_forget = 1,
n_lambda = 1,
has_rho = TRUE,
n_params = 8,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget", "lambda", "rho_BS", "rho_JP"),
lower = c(-5, -5, -5, -5, -5, -3, -10, -10),
upper = c(5, 5, 5, 5, 5, 3, 10, 10)
),
REE_LEARNING_BIASED_COMPLEX = list(
name = "REE_LEARNING_BIASED_COMPLEX",
n_alpha = 4,
n_forget = 4,
n_lambda = 4,
has_rho = TRUE,
n_params = 14,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4",
"rho_BS", "rho_JP"),
lower = c(-5, -5, -5, -5, rep(-5, 4), rep(-3, 4), -10, -10),
upper = c(5, 5, 5, 5, rep(5, 4), rep(3, 4), 10, 10)
)
)
}
# ============================================================================
# ESTIMATION AVEC VBMC (MÉTHODE BAYÉSIENNE)
# ============================================================================
fit_participant_vbmc <- function(participant_data, model_config, use_python_vbmc = TRUE) {
if (!use_python_vbmc) {
warning("VBMC nécessite Python. Utilisation de DEoptim à la place.")
return(fit_participant(participant_data, model_config))
}
# Import de PyVBMC
tryCatch({
pyvbmc <- import("pyvbmc")
}, error = function(e) {
stop("PyVBMC n'est pas installé. Installez-le avec: py_install('pyvbmc')")
})
# Wrapper pour la fonction log-posterior
log_posterior_wrapper <- function(params_array) {
# PyVBMC passe un array numpy, on le convertit en vecteur R
params_vec <- as.vector(params_array)
# Retourne la log-vraisemblance (négatif de negLL)
negLL <- qlearning_generic(params_vec, participant_data, model_config, return_negLL = TRUE)
return(-negLL) # VBMC maximise, donc on retourne -negLL
}
# Point de départ (milieu des bornes plausibles)
x0 <- (model_config$lower + model_config$upper) / 2
# Bornes plausibles (25%-75% de la plage)
plb <- model_config$lower + 0.25 * (model_config$upper - model_config$lower)
pub <- model_config$upper - 0.25 * (model_config$upper - model_config$lower)
# Initialisation de VBMC
vbmc_obj <- pyvbmc$VBMC(
log_density = log_posterior_wrapper,
x0 = x0,
lower_bounds = model_config$lower,
upper_bounds = model_config$upper,
plausible_lower_bounds = plb,
plausible_upper_bounds = pub
)
# Optimisation
vbmc_result <- vbmc_obj$optimize()
vp <- vbmc_result[[1]] # Variational posterior
results_dict <- vbmc_result[[2]] # Résultats
# Extraction des statistiques
posterior_stats <- vp$moments()
posterior_mean <- as.vector(posterior_stats[[1]])
posterior_sd <- sqrt(diag(posterior_stats[[2]]))
elbo <- results_dict$elbo
elbo_sd <- results_dict$elbo_sd
# Calcul du BIC avec la posterior mean
negLL <- qlearning_generic(posterior_mean, participant_data, model_config, return_negLL = TRUE)
# Création du tibble de résultats
result_df <- tibble(
model = model_config$name,
n_params = model_config$n_params,
negLL = negLL,
ELBO = elbo,
ELBO_SD = elbo_sd,
AIC = 2 * negLL + 2 * model_config$n_params,
BIC = 2 * negLL + model_config$n_params * log(nrow(participant_data)),
method = "VBMC",
n_iterations = results_dict$iterations,
converged = TRUE # VBMC a ses propres critères
)
# Ajout des paramètres (posterior mean et SD)
for (i in 1:model_config$n_params) {
result_df[[model_config$param_names[i]]] <- posterior_mean[i]
result_df[[paste0("sd_", model_config$param_names[i])]] <- posterior_sd[i]
}
# Stockage de la posterior complète pour visualisations
result_df$vp <- list(vp)
result_df$vbmc_results <- list(results_dict)
return(result_df)
}
# ============================================================================
# VISUALISATIONS DE CONVERGENCE AVANCÉES
# ============================================================================
plot_convergence_detailed <- function(fit_results, participant_id = NULL) {
require(ggplot2)
require(patchwork)
if (!is.null(participant_id)) {
fit_results <- fit_results %>% filter(participant_id == !!participant_id)
}
plots <- list()
# 1. Trace de convergence (negLL)
if ("convergence_range" %in% names(fit_results)) {
p1 <- fit_results %>%
mutate(participant_id = factor(participant_id)) %>%
ggplot(aes(x = participant_id, y = convergence_range, fill = converged)) +
geom_col() +
scale_y_log10() +
geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
coord_flip() +
theme_minimal() +
labs(title = "Range de convergence par participant",
subtitle = "Seuil = 1.0 (ligne rouge)",
y = "Range negLL (log scale)")
plots$convergence_range <- p1
}
# 2. Distribution des paramètres estimés
param_cols <- grep("^alpha|^forget|^lambda|^rho", names(fit_results), value = TRUE)
param_cols <- setdiff(param_cols, grep("^se_|^sd_", param_cols, value = TRUE))
if (length(param_cols) > 0) {
p2 <- fit_results %>%
select(participant_id, all_of(param_cols)) %>%
pivot_longer(-participant_id, names_to = "parameter", values_to = "value") %>%
ggplot(aes(x = value, fill = parameter)) +
geom_histogram(bins = 30, alpha = 0.7) +
facet_wrap(~parameter, scales = "free", ncol = 3) +
theme_minimal() +
theme(legend.position = "none") +
labs(title = "Distribution des paramètres estimés",
x = "Valeur", y = "Fréquence")
plots$param_distribution <- p2
}
# 3. Corrélation paramètres vs convergence
if ("convergence_range" %in% names(fit_results) && length(param_cols) > 0) {
# Sélectionner quelques paramètres clés
key_params <- param_cols[1:min(4, length(param_cols))]
p3 <- fit_results %>%
select(convergence_range, all_of(key_params)) %>%
pivot_longer(-convergence_range, names_to = "parameter", values_to = "value") %>%
ggplot(aes(x = value, y = convergence_range)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "loess", se = FALSE, color = "red") +
scale_y_log10() +
facet_wrap(~parameter, scales = "free_x") +
theme_minimal() +
labs(title = "Convergence vs valeur des paramètres",
y = "Range convergence (log scale)")
plots$param_vs_convergence <- p3
}
# 4. Heatmap Hessienne (si disponible)
if ("hessian_positive_definite" %in% names(fit_results)) {
p4 <- fit_results %>%
mutate(participant_id = factor(participant_id)) %>%
ggplot(aes(x = participant_id, y = 1, fill = hessian_positive_definite)) +
geom_tile() +
scale_fill_manual(values = c("FALSE" = "red", "TRUE" = "green"),
na.value = "grey") +
coord_flip() +
theme_minimal() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
labs(title = "Qualité de la Hessienne",
subtitle = "Vert = définie positive, Rouge = problème",
fill = "Hessienne OK", x = "Participant", y = "")
plots$hessian_quality <- p4
}
# 5. Incertitude des paramètres (erreurs standard)
se_cols <- grep("^se_|^sd_", names(fit_results), value = TRUE)
if (length(se_cols) > 0) {
p5 <- fit_results %>%
select(participant_id, all_of(se_cols)) %>%
pivot_longer(-participant_id, names_to = "parameter", values_to = "se") %>%
mutate(parameter = str_remove(parameter, "^se_|^sd_")) %>%
ggplot(aes(x = se, fill = parameter)) +
geom_histogram(bins = 30, alpha = 0.7) +
facet_wrap(~parameter, scales = "free", ncol = 3) +
theme_minimal() +
theme(legend.position = "none") +
labs(title = "Distribution des erreurs standard",
subtitle = "Plus petit = meilleure précision",
x = "Erreur standard", y = "Fréquence")
plots$se_distribution <- p5
}
return(plots)
}
# Visualisation spécifique VBMC (posteriors bayésiennes)
plot_vbmc_diagnostics <- function(vbmc_fit_results, participant_id) {
require(ggplot2)
require(patchwork)
participant_data <- vbmc_fit_results %>%
filter(participant_id == !!participant_id)
if (nrow(participant_data) == 0) {
stop("Participant non trouvé")
}
if (!"vp" %in% names(participant_data)) {
stop("Pas de résultats VBMC disponibles (vp manquant)")
}
vp <- participant_data$vp[[1]]
vbmc_results <- participant_data$vbmc_results[[1]]
# Échantillonnage de la posterior
n_samples <- 10000
samples <- vp$sample(n_samples)
# Conversion en dataframe
param_names <- vbmc_fit_results %>%
select(starts_with("alpha"), starts_with("forget"),
starts_with("lambda"), starts_with("rho")) %>%
select(-starts_with("se_"), -starts_with("sd_")) %>%
names()
samples_df <- as.data.frame(samples)
names(samples_df) <- param_names
plots <- list()
# 1. Marginal posteriors
p1 <- samples_df %>%
pivot_longer(everything(), names_to = "parameter", values_to = "value") %>%
ggplot(aes(x = value)) +
geom_histogram(aes(y = after_stat(density)), bins = 50,
fill = "steelblue", alpha = 0.7) +
geom_density(color = "red", linewidth = 1) +
facet_wrap(~parameter, scales = "free", ncol = 3) +
theme_minimal() +
labs(title = paste("Posterior distributions - Participant", participant_id),
subtitle = "Histogramme + densité estimée",
x = "Valeur", y = "Densité")
plots$marginal_posteriors <- p1
# 2. Pairwise correlations (pour les 4 premiers paramètres)
if (length(param_names) >= 2) {
n_plot <- min(4, length(param_names))
pairs_data <- samples_df[, 1:n_plot]
p2 <- GGally::ggpairs(pairs_data,
lower = list(continuous = "points"),
diag = list(continuous = "densityDiag"),
upper = list(continuous = "cor")) +
theme_minimal() +
labs(title = "Corrélations entre paramètres")
plots$pairwise_correlations <- p2
}
# 3. Trace de l'ELBO
if (!is.null(vbmc_results$elbo_trace)) {
elbo_trace <- vbmc_results$elbo_trace
p3 <- tibble(
iteration = seq_along(elbo_trace),
ELBO = elbo_trace
) %>%
ggplot(aes(x = iteration, y = ELBO)) +
geom_line(color = "darkblue", linewidth = 1) +
geom_point(size = 2, alpha = 0.5) +
theme_minimal() +
labs(title = "Convergence de l'ELBO",
subtitle = "Evidence Lower Bound",
x = "Itération", y = "ELBO")
plots$elbo_trace <- p3
}
# 4. Incertitude des paramètres (credible intervals)
posterior_mean <- colMeans(samples_df)
posterior_sd <- apply(samples_df, 2, sd)
ci_lower <- apply(samples_df, 2, quantile, probs = 0.025)
ci_upper <- apply(samples_df, 2, quantile, probs = 0.975)
p4 <- tibble(
parameter = param_names,
mean = posterior_mean,
sd = posterior_sd,
ci_lower = ci_lower,
ci_upper = ci_upper
) %>%
mutate(parameter = fct_reorder(parameter, mean)) %>%
ggplot(aes(x = parameter, y = mean)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), width = 0.2) +
coord_flip() +
theme_minimal() +
labs(title = "Estimations postérieures avec intervalles de crédibilité à 95%",
x = "Paramètre", y = "Valeur")
plots$credible_intervals <- p4
return(plots)
}
# Fonction pour comparer DEoptim vs VBMC
compare_optimization_methods <- function(data, participant_ids = NULL,
model_config, n_participants = 5) {
if (is.null(participant_ids)) {
participant_ids <- sample(unique(data$participant_id),
min(n_participants, length(unique(data$participant_id))))
}
comparison_results <- map_df(participant_ids, function(pid) {
cat("Participant", pid, "\n")
participant_data <- data %>%
filter(participant_id == pid) %>%
arrange(trial)
# Méthode 1: DEoptim
fit_deoptim <- fit_participant(participant_data, model_config, n_runs = 5)
# Méthode 2: VBMC
fit_vbmc <- tryCatch({
fit_participant_vbmc(participant_data, model_config, use_python_vbmc = TRUE)
}, error = function(e) {
cat(" VBMC échoué pour participant", pid, ":", e$message, "\n")
return(NULL)
})
if (!is.null(fit_vbmc)) {
bind_rows(
fit_deoptim %>% mutate(method = "DEoptim", participant_id = pid),
fit_vbmc %>% mutate(participant_id = pid)
)
} else {
fit_deoptim %>% mutate(method = "DEoptim", participant_id = pid)
}
})
# Visualisation de la comparaison
p <- comparison_results %>%
select(participant_id, method, negLL, BIC) %>%
pivot_longer(c(negLL, BIC), names_to = "metric", values_to = "value") %>%
ggplot(aes(x = method, y = value, fill = method)) +
geom_boxplot() +
facet_wrap(~metric, scales = "free_y") +
theme_minimal() +
labs(title = "Comparaison DEoptim vs VBMC",
subtitle = paste("N =", length(unique(comparison_results$participant_id)), "participants"),
y = "Valeur")
print(p)
return(comparison_results)
all_results <- vector("list", n_runs)
for (run in 1:n_runs) {
set.seed(1000 * as.numeric(factor(model_config$name)) + run)
result <- DEoptim(
fn = qlearning_generic,
lower = model_config$lower,
upper = model_config$upper,
data = participant_data,
model_config = model_config,
control = DEoptim.control(
itermax = 200,
trace = FALSE,
parallelType = 0,
NP = max(50, model_config$n_params * 10)
)
)
all_results[[run]] <- list(
params = result$optim$bestmem,
negLL = result$optim$bestval
)
}
# Sélection du meilleur run
all_negLL <- sapply(all_results, function(x) x$negLL)
best_run <- which.min(all_negLL)
negLL_sd <- sd(all_negLL)
negLL_range <- max(all_negLL) - min(all_negLL)
params <- all_results[[best_run]]$params
negLL <- all_results[[best_run]]$negLL
# Calcul de la Hessienne
hessian_result <- tryCatch({
numDeriv::hessian(qlearning_generic, params,
data = participant_data,
model_config = model_config)
}, error = function(e) NULL)
hessian_positive_definite <- FALSE
param_se <- rep(NA, model_config$n_params)
if (!is.null(hessian_result)) {
eigenvalues <- eigen(hessian_result, only.values = TRUE)$values
hessian_positive_definite <- all(eigenvalues > 0)
if (hessian_positive_definite) {
param_vcov <- tryCatch(solve(hessian_result), error = function(e) NULL)
if (!is.null(param_vcov)) {
param_se <- sqrt(diag(param_vcov))
}
}
}
# Création du tibble de résultats
result_df <- tibble(
model = model_config$name,
n_params = model_config$n_params,
negLL = negLL,
AIC = 2 * negLL + 2 * model_config$n_params,
BIC = 2 * negLL + model_config$n_params * log(nrow(participant_data)),
convergence_sd = negLL_sd,
convergence_range = negLL_range,
hessian_positive_definite = hessian_positive_definite,
converged = negLL_range < 1
)
# Ajout des paramètres estimés
for (i in 1:model_config$n_params) {
result_df[[model_config$param_names[i]]] <- params[i]
result_df[[paste0("se_", model_config$param_names[i])]] <- param_se[i]
}
return(result_df)
}
# ============================================================================
# ESTIMATION POUR TOUS LES PARTICIPANTS
# ============================================================================
fit_all_participants_all_models <- function(data, models_to_fit = NULL) {
model_configs <- get_model_configs()
if (!is.null(models_to_fit)) {
model_configs <- model_configs[models_to_fit]
}
participants <- unique(data$participant_id)
all_results <- list()
for (model_name in names(model_configs)) {
cat("\n=== Fitting model:", model_name, "===\n")
model_config <- model_configs[[model_name]]
model_results <- map_df(participants, function(pid) {
cat(" Participant", pid, "\n")
participant_data <- data %>%
filter(participant_id == pid) %>%
arrange(trial)
fit <- fit_participant(participant_data, model_config)
fit %>% mutate(participant_id = pid, .before = 1)
})
all_results[[model_name]] <- model_results
}
return(all_results)
}
# ============================================================================
# COMPARAISON DES MODÈLES EMBOÎTÉS
# ============================================================================
compare_nested_models <- function(all_results) {
# Comparaison globale
global_comparison <- map_df(names(all_results), function(model_name) {
results <- all_results[[model_name]]
tibble(
model = model_name,
n_params = unique(results$n_params),
n_converged = sum(results$converged),
mean_negLL = mean(results$negLL),
total_negLL = sum(results$negLL),
total_AIC = sum(results$AIC),
total_BIC = sum(results$BIC),
mean_convergence_range = mean(results$convergence_range)
)
}) %>%
arrange(total_BIC)
cat("\n=== COMPARAISON GLOBALE DES MODÈLES ===\n")
print(global_comparison)
# Meilleur modèle par participant (BIC)
best_models_per_participant <- map_df(names(all_results), function(model_name) {
all_results[[model_name]] %>%
select(participant_id, model, BIC, converged)
}) %>%
group_by(participant_id) %>%
slice_min(BIC, n = 1) %>%
ungroup()
cat("\n=== MEILLEUR MODÈLE PAR PARTICIPANT (BIC) ===\n")
print(table(best_models_per_participant$model))
# Comparaison par paires de modèles emboîtés
cat("\n=== TESTS LRT POUR MODÈLES EMBOÎTÉS ===\n")
# Exemples de paires emboîtées
nested_pairs <- list(
c("HOMOGENEOUS", "GAIN_LOSS"),
c("GAIN_LOSS", "BIASED"),
c("GAIN_LOSS", "REE_BIASED_SIMPLE"),
c("REE_BIASED_SIMPLE", "REE_BIASED_COMPLEX"),
c("GAIN_LOSS", "REE_LEARNING_SIMPLE"),
c("REE_LEARNING_SIMPLE", "REE_LEARNING_COMPLEX"),
c("REE_LEARNING_SIMPLE", "REE_LEARNING_BIASED_SIMPLE"),
c("REE_LEARNING_BIASED_SIMPLE", "REE_LEARNING_BIASED_COMPLEX")
)
lrt_results <- map_df(nested_pairs, function(pair) {
simple_model <- pair[1]
complex_model <- pair[2]
if (simple_model %in% names(all_results) && complex_model %in% names(all_results)) {
simple_res <- all_results[[simple_model]]
complex_res <- all_results[[complex_model]]
# LRT par participant
lrt_df <- simple_res %>%
select(participant_id, negLL_simple = negLL, converged_simple = converged) %>%
left_join(
complex_res %>% select(participant_id, negLL_complex = negLL, converged_complex = converged),
by = "participant_id"
) %>%
filter(converged_simple, converged_complex) %>%
mutate(
LR_stat = 2 * (negLL_simple - negLL_complex),
df_diff = unique(complex_res$n_params) - unique(simple_res$n_params),
p_value = pchisq(LR_stat, df = df_diff, lower.tail = FALSE),
significant = p_value < 0.05
)
tibble(
simple_model = simple_model,
complex_model = complex_model,
n_participants = nrow(lrt_df),
pct_significant = mean(lrt_df$significant) * 100,
mean_LR = mean(lrt_df$LR_stat),
median_p = median(lrt_df$p_value)
)
}
})
print(lrt_results)
list(
global_comparison = global_comparison,
best_models_per_participant = best_models_per_participant,
lrt_results = lrt_results,
all_results = all_results
)
}
# ============================================================================
# VISUALISATION
# ============================================================================
plot_model_comparison <- function(comparison_results) {
require(ggplot2)
require(patchwork)
# 1. Comparaison BIC globale
p1 <- comparison_results$global_comparison %>%
mutate(model = fct_reorder(model, total_BIC)) %>%
ggplot(aes(x = model, y = total_BIC, fill = model)) +
geom_col() +
coord_flip() +
theme_minimal() +
labs(title = "Comparaison globale des modèles (BIC)",
subtitle = "Plus bas = meilleur") +
theme(legend.position = "none")
# 2. Meilleur modèle par participant
p2 <- comparison_results$best_models_per_participant %>%
count(model) %>%
mutate(model = fct_reorder(model, n)) %>%
ggplot(aes(x = model, y = n, fill = model)) +
geom_col() +
coord_flip() +
theme_minimal() +
labs(title = "Meilleur modèle par participant",
y = "Nombre de participants") +
theme(legend.position = "none")
# 3. Tests LRT
if (nrow(comparison_results$lrt_results) > 0) {
p3 <- comparison_results$lrt_results %>%
mutate(comparison = paste(simple_model, "→", complex_model)) %>%
ggplot(aes(x = fct_reorder(comparison, pct_significant),
y = pct_significant)) +
geom_col(fill = "steelblue") +
geom_hline(yintercept = 50, linetype = "dashed", color = "red") +
coord_flip() +
theme_minimal() +
labs(title = "Tests LRT entre modèles emboîtés",
y = "% participants avec p < 0.05",
x = "Comparaison")
} else {
p3 <- ggplot() + theme_void()
}
# 4. Convergence par modèle
p4 <- map_df(names(comparison_results$all_results), function(model_name) {
comparison_results$all_results[[model_name]] %>%
select(model, convergence_range, converged)
}) %>%
ggplot(aes(x = model, y = convergence_range, fill = converged)) +
geom_boxplot() +
scale_y_log10() +
coord_flip() +
theme_minimal() +
labs(title = "Convergence par modèle",
y = "Range negLL (log scale)")
(p1 | p2) / (p3 | p4)
}
# ============================================================================
# EXEMPLE D'UTILISATION
# ============================================================================
# Charger vos données
# data <- read_csv("votre_fichier.csv")
# Colonnes requises: participant_id, trial, choice, reward
# Estimation de tous les modèles
# all_results <- fit_all_participants_all_models(data)
# Ou seulement certains modèles
# all_results <- fit_all_participants_all_models(
# data,
# models_to_fit = c("HOMOGENEOUS", "GAIN_LOSS", "REE_BIASED_SIMPLE",
# "REE_LEARNING_SIMPLE", "REE_LEARNING_BIASED_SIMPLE")
# )
# Comparaison
# comparison <- compare_nested_models(all_results)
# Visualisation
# plot_model_comparison(comparison)
# Sauvegarder les résultats
# for (model_name in names(all_results)) {
# write_csv(all_results[[model_name]],
# paste0("results_", model_name, ".csv"))
# }
# write_csv(comparison$global_comparison, "global_comparison.csv")
# write_csv(comparison$best_models_per_participant, "best_models.csv")

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# ============================================================================
# MODÈLES Q-LEARNING EMBOÎTÉS POUR DÉCISION AVEC ÉVÉNEMENTS RARES
# ============================================================================
library(tidyverse)
library(DEoptim)
library(numDeriv)
# ============================================================================
# FONCTION GÉNÉRIQUE DE Q-LEARNING
# ============================================================================
qlearning_generic <- function(params, data, model_config, return_negLL = TRUE) {
# Conversion des choix en indices numériques
if (is.factor(data$button_name) || is.character(data$button_name)) {
choice_levels <- c("antifragile", "fragile", "robuste", "vulnerable")
data$choice_idx <- match(as.character(data$button_name), choice_levels)
} else {
data$choice_idx <- data$button_name
}
n_arms <- 4
n_trials <- nrow(data)
# Extraction des paramètres selon la configuration du modèle
param_idx <- 1
# ALPHA(S)
if (model_config$n_alpha == 1) {
alpha_loss <- alpha_gain <- alpha_BS <- alpha_JP <- plogis(params[param_idx])
param_idx <- param_idx + 1
} else if (model_config$n_alpha == 2) {
alpha_loss <- plogis(params[param_idx])
alpha_gain <- plogis(params[param_idx + 1])
alpha_BS <- alpha_loss
alpha_JP <- alpha_gain
param_idx <- param_idx + 2
} else if (model_config$n_alpha == 4) {
alpha_loss <- plogis(params[param_idx])
alpha_gain <- plogis(params[param_idx + 1])
alpha_BS <- plogis(params[param_idx + 2])
alpha_JP <- plogis(params[param_idx + 3])
param_idx <- param_idx + 4
}
# FORGET(S)
if (model_config$n_forget == 1) {
forget <- rep(plogis(params[param_idx]), n_arms)
param_idx <- param_idx + 1
} else if (model_config$n_forget == 4) {
forget <- plogis(params[param_idx:(param_idx + 3)])
param_idx <- param_idx + 4
}
# LAMBDA(S)
if (model_config$n_lambda == 1) {
lambda <- rep(exp(params[param_idx]), n_arms)
param_idx <- param_idx + 1
} else if (model_config$n_lambda == 4) {
lambda <- exp(params[param_idx:(param_idx + 3)])
param_idx <- param_idx + 4
}
# RHO(S) - Biais pour événements rares
if (model_config$has_rho) {
rho_BS <- params[param_idx] # BS avoidance
rho_JP <- params[param_idx + 1] # JP seeking
param_idx <- param_idx + 2
} else {
rho_BS <- rho_JP <- 0
}
# Initialisation des Q-values
Q <- rep(0, n_arms)
log_lik <- 0
for (t in 1:n_trials) {
choice <- data$choice_idx[t]
reward <- data$button_value[t]
# Calcul des valeurs subjectives V(t)
V <- lambda * Q
# Ajout des biais pour événements rares si le modèle le permet
if (model_config$has_rho) {
# Identification des options susceptibles de produire BS/JP
# antifragile (1) = JP possible, fragile (2) = BS possible
# vulnerable (4) = BS et JP possibles
V[1] <- V[1] + rho_JP # antifragile
V[2] <- V[2] + rho_BS # fragile
V[4] <- V[4] + rho_BS + rho_JP # vulnerable
}
# Softmax
V_max <- max(V)
exp_V <- exp(V - V_max)
probs <- exp_V / sum(exp_V)
probs <- pmax(probs, 1e-10)
probs <- probs / sum(probs)
# Log-likelihood
log_lik <- log_lik + log(probs[choice])
# Mise à jour Q-learning
Q_new <- Q
# Choix de l'alpha approprié
if (reward == -3000) {
alpha_used <- alpha_BS
} else if (reward == 3000) {
alpha_used <- alpha_JP
} else if (reward < 0) {
alpha_used <- alpha_loss
} else {
alpha_used <- alpha_gain
}
# Option choisie : Q(t+1) = Q(t) + alpha * (r(t) - Q(t))
Q_new[choice] <- Q[choice] + alpha_used * (reward - Q[choice])
# Options non choisies : Q(t+1) = Q(t) * (1 - f)
not_chosen <- setdiff(1:n_arms, choice)
Q_new[not_chosen] <- Q[not_chosen] * (1 - forget[not_chosen])
Q <- Q_new
}
if (return_negLL) {
return(-log_lik)
} else {
return(log_lik)
}
}
# ============================================================================
# CONFIGURATIONS DES MODÈLES EMBOÎTÉS
# ============================================================================
get_model_configs <- function() {
list(
HOMOGENEOUS = list(
name = "HOMOGENEOUS",
n_alpha = 1,
n_forget = 1,
n_lambda = 1,
has_rho = FALSE,
n_params = 3,
param_names = c("alpha", "forget", "lambda"),
lower = c(-5, -5, -3),
upper = c(5, 5, 3)
),
GAIN_LOSS = list(
name = "GAIN_LOSS",
n_alpha = 2,
n_forget = 1,
n_lambda = 1,
has_rho = FALSE,
n_params = 4,
param_names = c("alpha_loss", "alpha_gain", "forget", "lambda"),
lower = c(-5, -5, -5, -3),
upper = c(5, 5, 5, 3)
),
BIASED = list(
name = "BIASED",
n_alpha = 2,
n_forget = 4,
n_lambda = 4,
has_rho = FALSE,
n_params = 10,
param_names = c("alpha_loss", "alpha_gain",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4"),
lower = c(-5, -5, rep(-5, 4), rep(-3, 4)),
upper = c(5, 5, rep(5, 4), rep(3, 4))
),
REE_BIASED_SIMPLE = list(
name = "REE_BIASED_SIMPLE",
n_alpha = 2,
n_forget = 1,
n_lambda = 1,
has_rho = TRUE,
n_params = 6,
param_names = c("alpha_loss", "alpha_gain", "forget", "lambda",
"rho_BS", "rho_JP"),
lower = c(-5, -5, -5, -3, -10, -10),
upper = c(5, 5, 5, 3, 10, 10)
),
REE_BIASED_COMPLEX = list(
name = "REE_BIASED_COMPLEX",
n_alpha = 2,
n_forget = 4,
n_lambda = 4,
has_rho = TRUE,
n_params = 12,
param_names = c("alpha_loss", "alpha_gain",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4",
"rho_BS", "rho_JP"),
lower = c(-5, -5, rep(-5, 4), rep(-3, 4), -10, -10),
upper = c(5, 5, rep(5, 4), rep(3, 4), 10, 10)
),
REE_LEARNING_SIMPLE = list(
name = "REE_LEARNING_SIMPLE",
n_alpha = 4,
n_forget = 1,
n_lambda = 1,
has_rho = FALSE,
n_params = 6,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget", "lambda"),
lower = c(-5, -5, -5, -5, -5, -3),
upper = c(5, 5, 5, 5, 5, 3)
),
REE_LEARNING_COMPLEX = list(
name = "REE_LEARNING_COMPLEX",
n_alpha = 4,
n_forget = 4,
n_lambda = 4,
has_rho = FALSE,
n_params = 12,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4"),
lower = c(-5, -5, -5, -5, rep(-5, 4), rep(-3, 4)),
upper = c(5, 5, 5, 5, rep(5, 4), rep(3, 4))
),
REE_LEARNING_BIASED_SIMPLE = list(
name = "REE_LEARNING_BIASED_SIMPLE",
n_alpha = 4,
n_forget = 1,
n_lambda = 1,
has_rho = TRUE,
n_params = 8,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget", "lambda", "rho_BS", "rho_JP"),
lower = c(-5, -5, -5, -5, -5, -3, -10, -10),
upper = c(5, 5, 5, 5, 5, 3, 10, 10)
),
REE_LEARNING_BIASED_COMPLEX = list(
name = "REE_LEARNING_BIASED_COMPLEX",
n_alpha = 4,
n_forget = 4,
n_lambda = 4,
has_rho = TRUE,
n_params = 14,
param_names = c("alpha_loss", "alpha_gain", "alpha_BS", "alpha_JP",
"forget_1", "forget_2", "forget_3", "forget_4",
"lambda_1", "lambda_2", "lambda_3", "lambda_4",
"rho_BS", "rho_JP"),
lower = c(-5, -5, -5, -5, rep(-5, 4), rep(-3, 4), -10, -10),
upper = c(5, 5, 5, 5, rep(5, 4), rep(3, 4), 10, 10)
)
)
}
# ============================================================================
# ESTIMATION POUR UN PARTICIPANT
# ============================================================================
fit_participant <- function(participant_data, model_config, n_runs = 5) {
all_results <- vector("list", n_runs)
for (run in 1:n_runs) {
set.seed(1000 * as.numeric(factor(model_config$name)) + run)
result <- DEoptim(
fn = qlearning_generic,
lower = model_config$lower,
upper = model_config$upper,
data = participant_data,
model_config = model_config,
control = DEoptim.control(
itermax = 200,
trace = FALSE,
parallelType = 0,
NP = max(50, model_config$n_params * 10)
)
)
all_results[[run]] <- list(
params = result$optim$bestmem,
negLL = result$optim$bestval
)
}
# Sélection du meilleur run
all_negLL <- sapply(all_results, function(x) x$negLL)
best_run <- which.min(all_negLL)
negLL_sd <- sd(all_negLL)
negLL_range <- max(all_negLL) - min(all_negLL)
params <- all_results[[best_run]]$params
negLL <- all_results[[best_run]]$negLL
# Calcul de la Hessienne
hessian_result <- tryCatch({
numDeriv::hessian(qlearning_generic, params,
data = participant_data,
model_config = model_config)
}, error = function(e) NULL)
hessian_positive_definite <- FALSE
param_se <- rep(NA, model_config$n_params)
if (!is.null(hessian_result)) {
eigenvalues <- eigen(hessian_result, only.values = TRUE)$values
hessian_positive_definite <- all(eigenvalues > 0)
if (hessian_positive_definite) {
param_vcov <- tryCatch(solve(hessian_result), error = function(e) NULL)
if (!is.null(param_vcov)) {
param_se <- sqrt(diag(param_vcov))
}
}
}
# Création du tibble de résultats
result_df <- tibble(
model = model_config$name,
n_params = model_config$n_params,
negLL = negLL,
AIC = 2 * negLL + 2 * model_config$n_params,
BIC = 2 * negLL + model_config$n_params * log(nrow(participant_data)),
convergence_sd = negLL_sd,
convergence_range = negLL_range,
hessian_positive_definite = hessian_positive_definite,
converged = negLL_range < 1
)
# Ajout des paramètres estimés
for (i in 1:model_config$n_params) {
result_df[[model_config$param_names[i]]] <- params[i]
result_df[[paste0("se_", model_config$param_names[i])]] <- param_se[i]
}
return(result_df)
}
# ============================================================================
# ESTIMATION POUR TOUS LES PARTICIPANTS
# ============================================================================
fit_all_participants_all_models <- function(data, models_to_fit = NULL) {
model_configs <- get_model_configs()
if (!is.null(models_to_fit)) {
model_configs <- model_configs[models_to_fit]
}
participants <- unique(data$participant_id)
all_results <- list()
for (model_name in names(model_configs)) {
cat("\n=== Fitting model:", model_name, "===\n")
model_config <- model_configs[[model_name]]
model_results <- map_df(participants, function(pid) {
cat(" Participant", pid, "\n")
participant_data <- data %>%
filter(participant_id == pid) %>%
arrange(trial)
fit <- fit_participant(participant_data, model_config)
fit %>% mutate(participant_id = pid, .before = 1)
})
all_results[[model_name]] <- model_results
}
return(all_results)
}
# ============================================================================
# COMPARAISON DES MODÈLES EMBOÎTÉS
# ============================================================================
compare_nested_models <- function(all_results) {
# Comparaison globale
global_comparison <- map_df(names(all_results), function(model_name) {
results <- all_results[[model_name]]
tibble(
model = model_name,
n_params = unique(results$n_params),
n_converged = sum(results$converged),
mean_negLL = mean(results$negLL),
total_negLL = sum(results$negLL),
total_AIC = sum(results$AIC),
total_BIC = sum(results$BIC),
mean_convergence_range = mean(results$convergence_range)
)
}) %>%
arrange(total_BIC)
cat("\n=== COMPARAISON GLOBALE DES MODÈLES ===\n")
print(global_comparison)
# Meilleur modèle par participant (BIC)
best_models_per_participant <- map_df(names(all_results), function(model_name) {
all_results[[model_name]] %>%
select(participant_id, model, BIC, converged)
}) %>%
group_by(participant_id) %>%
slice_min(BIC, n = 1) %>%
ungroup()
cat("\n=== MEILLEUR MODÈLE PAR PARTICIPANT (BIC) ===\n")
print(table(best_models_per_participant$model))
# Comparaison par paires de modèles emboîtés
cat("\n=== TESTS LRT POUR MODÈLES EMBOÎTÉS ===\n")
# Exemples de paires emboîtées
nested_pairs <- list(
c("HOMOGENEOUS", "GAIN_LOSS"),
c("GAIN_LOSS", "BIASED"),
c("GAIN_LOSS", "REE_BIASED_SIMPLE"),
c("REE_BIASED_SIMPLE", "REE_BIASED_COMPLEX"),
c("GAIN_LOSS", "REE_LEARNING_SIMPLE"),
c("REE_LEARNING_SIMPLE", "REE_LEARNING_COMPLEX"),
c("REE_LEARNING_SIMPLE", "REE_LEARNING_BIASED_SIMPLE"),
c("REE_LEARNING_BIASED_SIMPLE", "REE_LEARNING_BIASED_COMPLEX")
)
lrt_results <- map_df(nested_pairs, function(pair) {
simple_model <- pair[1]
complex_model <- pair[2]
if (simple_model %in% names(all_results) && complex_model %in% names(all_results)) {
simple_res <- all_results[[simple_model]]
complex_res <- all_results[[complex_model]]
# LRT par participant
lrt_df <- simple_res %>%
select(participant_id, negLL_simple = negLL, converged_simple = converged) %>%
left_join(
complex_res %>% select(participant_id, negLL_complex = negLL, converged_complex = converged),
by = "participant_id"
) %>%
filter(converged_simple, converged_complex) %>%
mutate(
LR_stat = 2 * (negLL_simple - negLL_complex),
df_diff = unique(complex_res$n_params) - unique(simple_res$n_params),
p_value = pchisq(LR_stat, df = df_diff, lower.tail = FALSE),
significant = p_value < 0.05
)
tibble(
simple_model = simple_model,
complex_model = complex_model,
n_participants = nrow(lrt_df),
pct_significant = mean(lrt_df$significant) * 100,
mean_LR = mean(lrt_df$LR_stat),
median_p = median(lrt_df$p_value)
)
}
})
print(lrt_results)
list(
global_comparison = global_comparison,
best_models_per_participant = best_models_per_participant,
lrt_results = lrt_results,
all_results = all_results
)
}
# ============================================================================
# VISUALISATION
# ============================================================================
plot_model_comparison <- function(comparison_results) {
require(ggplot2)
require(patchwork)
# 1. Comparaison BIC globale
p1 <- comparison_results$global_comparison %>%
mutate(model = fct_reorder(model, total_BIC)) %>%
ggplot(aes(x = model, y = total_BIC, fill = model)) +
geom_col() +
coord_flip() +
theme_minimal() +
labs(title = "Comparaison globale des modèles (BIC)",
subtitle = "Plus bas = meilleur") +
theme(legend.position = "none")
# 2. Meilleur modèle par participant
p2 <- comparison_results$best_models_per_participant %>%
count(model) %>%
mutate(model = fct_reorder(model, n)) %>%
ggplot(aes(x = model, y = n, fill = model)) +
geom_col() +
coord_flip() +
theme_minimal() +
labs(title = "Meilleur modèle par participant",
y = "Nombre de participants") +
theme(legend.position = "none")
# 3. Tests LRT
if (nrow(comparison_results$lrt_results) > 0) {
p3 <- comparison_results$lrt_results %>%
mutate(comparison = paste(simple_model, "→", complex_model)) %>%
ggplot(aes(x = fct_reorder(comparison, pct_significant),
y = pct_significant)) +
geom_col(fill = "steelblue") +
geom_hline(yintercept = 50, linetype = "dashed", color = "red") +
coord_flip() +
theme_minimal() +
labs(title = "Tests LRT entre modèles emboîtés",
y = "% participants avec p < 0.05",
x = "Comparaison")
} else {
p3 <- ggplot() + theme_void()
}
# 4. Convergence par modèle
p4 <- map_df(names(comparison_results$all_results), function(model_name) {
comparison_results$all_results[[model_name]] %>%
select(model, convergence_range, converged)
}) %>%
ggplot(aes(x = model, y = convergence_range, fill = converged)) +
geom_boxplot() +
scale_y_log10() +
coord_flip() +
theme_minimal() +
labs(title = "Convergence par modèle",
y = "Range negLL (log scale)")
(p1 | p2) / (p3 | p4)
}
# ============================================================================
# EXEMPLE D'UTILISATION
# ============================================================================
# Charger vos données
# data <- read_csv("votre_fichier.csv")
# Colonnes requises: participant_id, trial, choice, reward
# Estimation de tous les modèles
# all_results <- fit_all_participants_all_models(data)
# Ou seulement certains modèles
# all_results <- fit_all_participants_all_models(
# data,
# models_to_fit = c("HOMOGENEOUS", "GAIN_LOSS", "REE_BIASED_SIMPLE",
# "REE_LEARNING_SIMPLE", "REE_LEARNING_BIASED_SIMPLE")
# )
# Comparaison
# comparison <- compare_nested_models(all_results)
# Visualisation
# plot_model_comparison(comparison)
# Sauvegarder les résultats
# for (model_name in names(all_results)) {
# write_csv(all_results[[model_name]],
# paste0("results_", model_name, ".csv"))
# }
# write_csv(comparison$global_comparison, "global_comparison.csv")
# write_csv(comparison$best_models_per_participant, "best_models.csv")

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requirements.txt Normal file
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numpy
scipy
pandas

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results/model0/mle.csv Normal file
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@ -0,0 +1,61 @@
participant,alpha,f,nll
qfmtmjjy,0.582784971895418,0.999999066293681,552.9655592003581
l5hj4bqu,0.9999999999056042,5.46564082409206e-13,547.8314792895943
ax6yt76a,0.031045765133638766,0.999999771173566,553.9560713683154
n7zr7k22,0.06065557843979855,0.9999999663593895,553.8393776741615
n274eufg,2.3664079880887658e-08,0.9967507965525135,554.5177445759583
d3n1xm9e,0.04203999064289608,0.9999854195433653,554.2427744376569
e41xqg7i,0.007942222018805265,8.0242615476356e-11,554.5121903640234
qdl2kwne,1.0,6.826428647305449e-46,492.2241079443528
8y0d23i5,0.777177246966759,7.402298460381941e-15,534.5235366710108
1e1vfxfy,0.3330278516103173,0.0850735445577119,553.9630735526995
4vfjhtfy,0.1122311594415092,1.871749156748348e-26,552.2074993081454
kmbtnydb,0.13667778040270864,2.0884909923554726e-09,547.5307630064009
a3whtqnm,3.629020342740599e-07,0.00328190336364656,554.5177410374322
bqckq56i,0.03402038741621446,7.86918333059074e-08,552.9721272204469
wtmppyq1,0.06906140463867033,9.607058911881154e-08,553.7477997790903
gyboex2p,0.9999999999700879,6.2820427656554785e-12,545.1872533519312
6v2kv490,4.235265828311803e-09,0.2648614681419073,554.5177444590045
goe4ggy1,0.7254990519220672,1.9745911879265448e-08,548.0978286404306
wjhrr05w,0.09471712411279669,0.8213415076416049,554.5092420387667
97wdqw3s,0.9999999999995051,9.901870701857771e-13,500.4256381607084
u8hygigo,0.05070763094787091,0.8321426728828714,551.7461707772945
cf10378m,0.2095885843287755,1.6521145726363753e-08,553.3156063628437
v5r7mq91,0.5253114772877784,0.0005372516169283673,551.7146877045956
3x6nteue,0.2686405242216113,0.0003538486737042658,551.7640263917281
xecqf0dd,1.0,2.3760270339932486e-14,531.703008010426
m9i1kvu6,0.05114247893954163,1.3281881318099996e-12,553.2707089568645
gp3vvd4k,0.9999999999999882,1.1337302888068697e-34,516.2411532388203
2w7l1ve3,0.17190627716472015,5.340979032968921e-10,551.4238344441382
inpc0pfu,3.4061084339887766e-11,0.9992991841689133,554.5177444479662
x18jrbof,1.0,2.224445142889756e-43,509.74931877274776
nao85d8o,0.009160817690823834,5.1534149290441666e-14,554.4567848446477
owfwd7iz,0.05978783217663562,1.7357980024613605e-11,552.6129603387225
iiupv9e6,0.09814668824802089,1.6943395241865024e-09,547.4859266833736
rht8c10d,0.9478576700137822,0.9999999994412778,553.674648932509
uc5u8lob,0.0578416087666933,0.9999999989655655,553.1812054538608
idc0abhp,0.037067505840974964,1.928656015149807e-09,552.4401642516339
gfy4u3y9,3.0467043647872075e-11,0.967600368549238,554.5177444480236
vd2z262g,0.05823220877937151,3.868526723600325e-15,552.8274284304949
gaq9bw7l,0.014725646990138265,1.085129359971333e-12,554.2958912874782
92minocl,5.36098831541956e-09,0.37631916992506054,554.5177444683173
5kyg1fo7,0.048134854427945,1.7177194138139772e-09,551.9780048138557
a95e64ov,0.43712798452105045,2.5556448950394826e-09,545.509286092445
yasfsrkr,0.21329794299173746,2.059294640948317e-245,546.9216420033767
qfmtmjjy,1.0,6.331953602927268e-08,553.5640344190499
p6p2uc66,0.09705693757894857,0.6449707555360135,551.7811547180477
8mrt2jk8,0.9999999921780748,2.542845157662274e-11,527.4195785127077
74ubi75c,0.37099733903488924,0.9999999964659654,554.2958824030542
l5hj4bqu,0.42321676612719833,0.21940814540809023,546.7609390875771
k2mcerli,0.0462287559453904,0.9999757375629387,553.3444107444034
nrwlu1nf,2.5695476201442106e-08,0.005983916620578978,554.5177441485276
ax6yt76a,7.09619494188235e-08,0.9999926916808033,554.3761235503644
3t3sh2es,0.07493989361956131,2.9956578631889184e-11,549.3828475837245
n7zr7k22,2.444167462227958e-08,0.9999999539800323,502.1996879300243
7srzfhpq,0.3482551579565603,2.6477991358741416e-09,545.4042735754294
4hfaww7m,0.02315510868424729,1.956763314467002e-09,553.3006552174948
n274eufg,2.1898408386418047e-07,0.0873503997081469,552.8203462427547
yk2obfqv,0.01618059281802409,1.2650645235924713e-09,553.7560186105892
ng1u69iy,0.20445541057918212,1.868337206801065e-12,540.1518009334501
tkngvs6x,0.195823881743137,1.0911950272637065e-09,548.2683590155018
j1cshr0g,0.41032544288457223,1.8233454044592947e-40,553.1190812699057
1 participant alpha f nll
2 qfmtmjjy 0.582784971895418 0.999999066293681 552.9655592003581
3 l5hj4bqu 0.9999999999056042 5.46564082409206e-13 547.8314792895943
4 ax6yt76a 0.031045765133638766 0.999999771173566 553.9560713683154
5 n7zr7k22 0.06065557843979855 0.9999999663593895 553.8393776741615
6 n274eufg 2.3664079880887658e-08 0.9967507965525135 554.5177445759583
7 d3n1xm9e 0.04203999064289608 0.9999854195433653 554.2427744376569
8 e41xqg7i 0.007942222018805265 8.0242615476356e-11 554.5121903640234
9 qdl2kwne 1.0 6.826428647305449e-46 492.2241079443528
10 8y0d23i5 0.777177246966759 7.402298460381941e-15 534.5235366710108
11 1e1vfxfy 0.3330278516103173 0.0850735445577119 553.9630735526995
12 4vfjhtfy 0.1122311594415092 1.871749156748348e-26 552.2074993081454
13 kmbtnydb 0.13667778040270864 2.0884909923554726e-09 547.5307630064009
14 a3whtqnm 3.629020342740599e-07 0.00328190336364656 554.5177410374322
15 bqckq56i 0.03402038741621446 7.86918333059074e-08 552.9721272204469
16 wtmppyq1 0.06906140463867033 9.607058911881154e-08 553.7477997790903
17 gyboex2p 0.9999999999700879 6.2820427656554785e-12 545.1872533519312
18 6v2kv490 4.235265828311803e-09 0.2648614681419073 554.5177444590045
19 goe4ggy1 0.7254990519220672 1.9745911879265448e-08 548.0978286404306
20 wjhrr05w 0.09471712411279669 0.8213415076416049 554.5092420387667
21 97wdqw3s 0.9999999999995051 9.901870701857771e-13 500.4256381607084
22 u8hygigo 0.05070763094787091 0.8321426728828714 551.7461707772945
23 cf10378m 0.2095885843287755 1.6521145726363753e-08 553.3156063628437
24 v5r7mq91 0.5253114772877784 0.0005372516169283673 551.7146877045956
25 3x6nteue 0.2686405242216113 0.0003538486737042658 551.7640263917281
26 xecqf0dd 1.0 2.3760270339932486e-14 531.703008010426
27 m9i1kvu6 0.05114247893954163 1.3281881318099996e-12 553.2707089568645
28 gp3vvd4k 0.9999999999999882 1.1337302888068697e-34 516.2411532388203
29 2w7l1ve3 0.17190627716472015 5.340979032968921e-10 551.4238344441382
30 inpc0pfu 3.4061084339887766e-11 0.9992991841689133 554.5177444479662
31 x18jrbof 1.0 2.224445142889756e-43 509.74931877274776
32 nao85d8o 0.009160817690823834 5.1534149290441666e-14 554.4567848446477
33 owfwd7iz 0.05978783217663562 1.7357980024613605e-11 552.6129603387225
34 iiupv9e6 0.09814668824802089 1.6943395241865024e-09 547.4859266833736
35 rht8c10d 0.9478576700137822 0.9999999994412778 553.674648932509
36 uc5u8lob 0.0578416087666933 0.9999999989655655 553.1812054538608
37 idc0abhp 0.037067505840974964 1.928656015149807e-09 552.4401642516339
38 gfy4u3y9 3.0467043647872075e-11 0.967600368549238 554.5177444480236
39 vd2z262g 0.05823220877937151 3.868526723600325e-15 552.8274284304949
40 gaq9bw7l 0.014725646990138265 1.085129359971333e-12 554.2958912874782
41 92minocl 5.36098831541956e-09 0.37631916992506054 554.5177444683173
42 5kyg1fo7 0.048134854427945 1.7177194138139772e-09 551.9780048138557
43 a95e64ov 0.43712798452105045 2.5556448950394826e-09 545.509286092445
44 yasfsrkr 0.21329794299173746 2.059294640948317e-245 546.9216420033767
45 qfmtmjjy 1.0 6.331953602927268e-08 553.5640344190499
46 p6p2uc66 0.09705693757894857 0.6449707555360135 551.7811547180477
47 8mrt2jk8 0.9999999921780748 2.542845157662274e-11 527.4195785127077
48 74ubi75c 0.37099733903488924 0.9999999964659654 554.2958824030542
49 l5hj4bqu 0.42321676612719833 0.21940814540809023 546.7609390875771
50 k2mcerli 0.0462287559453904 0.9999757375629387 553.3444107444034
51 nrwlu1nf 2.5695476201442106e-08 0.005983916620578978 554.5177441485276
52 ax6yt76a 7.09619494188235e-08 0.9999926916808033 554.3761235503644
53 3t3sh2es 0.07493989361956131 2.9956578631889184e-11 549.3828475837245
54 n7zr7k22 2.444167462227958e-08 0.9999999539800323 502.1996879300243
55 7srzfhpq 0.3482551579565603 2.6477991358741416e-09 545.4042735754294
56 4hfaww7m 0.02315510868424729 1.956763314467002e-09 553.3006552174948
57 n274eufg 2.1898408386418047e-07 0.0873503997081469 552.8203462427547
58 yk2obfqv 0.01618059281802409 1.2650645235924713e-09 553.7560186105892
59 ng1u69iy 0.20445541057918212 1.868337206801065e-12 540.1518009334501
60 tkngvs6x 0.195823881743137 1.0911950272637065e-09 548.2683590155018
61 j1cshr0g 0.41032544288457223 1.8233454044592947e-40 553.1190812699057

211
results/model0/unrestrained-mle.csv Normal file
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@ -0,0 +1,211 @@
alpha,lambda,nll,opt_result,participant
0.0019909996639928752,0.0784037743181824,524.515296073272," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 524.515296073272
x: [-6.217e+00 -2.546e+00]
nit: 24
jac: [ 1.137e-05 -3.411e-05]
nfev: 96
njev: 32
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",qfmtmjjy
0.0041258076334695195,0.17916292668471764,232.26294177560447," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 232.26294177560447
x: [-5.486e+00 -1.719e+00]
nit: 16
jac: [ 3.411e-05 3.411e-05]
nfev: 51
njev: 17
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",l5hj4bqu
8.580076240145284e-10,42804.74116720703,551.346218644511," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 551.346218644511
x: [-2.088e+01 1.066e+01]
nit: 37
jac: [-1.819e-04 -1.933e-04]
nfev: 129
njev: 43
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",ax6yt76a
9.183501708224334e-11,2938700.1970509873,504.4244914945921," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 504.4244914945921
x: [-2.311e+01 1.489e+01]
nit: 34
jac: [ 3.490e-03 3.490e-03]
nfev: 126
njev: 42
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",n7zr7k22
1.3285404087992438e-12,150903313.1944923,526.8342605685835," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 526.8342605685835
x: [-2.735e+01 1.883e+01]
nit: 36
jac: [-6.821e-05 -6.821e-05]
nfev: 126
njev: 42
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",n274eufg
0.01649599700173123,0.01686233697780474,499.4082573033442," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 499.4082573033442
x: [-4.088e+00 -4.083e+00]
nit: 23
jac: [ 2.842e-05 -5.684e-05]
nfev: 81
njev: 27
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",d3n1xm9e
2.759963002574895e-06,0.975782284437965,554.5108100039844," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 554.5108100039844
x: [-1.280e+01 -2.452e-02]
nit: 37
jac: [-1.251e-04 -1.592e-04]
nfev: 147
njev: 49
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",e41xqg7i
0.8250744843164314,0.0010126000014369413,470.3492586229785," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 470.3492586229785
x: [ 1.551e+00 -6.895e+00]
nit: 22
jac: [-5.116e-05 -4.547e-05]
nfev: 69
njev: 23
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",qdl2kwne
0.0067738863236796905,0.03748214730335093,471.11474910271704," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 471.11474910271704
x: [-4.988e+00 -3.284e+00]
nit: 21
jac: [ 3.411e-05 2.274e-05]
nfev: 69
njev: 23
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",8y0d23i5
0.3152175910708728,7.705164135712456e-05,554.438430475421," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 554.438430475421
x: [-7.758e-01 -9.471e+00]
nit: 24
jac: [ 6.821e-05 2.274e-05]
nfev: 75
njev: 25
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",1e1vfxfy
3.0097559768213683e-09,48217.30222264478,537.6960374952457," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 537.6960374952457
x: [-1.962e+01 1.078e+01]
nit: 40
jac: [ 2.069e-03 2.069e-03]
nfev: 129
njev: 43
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",4vfjhtfy
0.017080076809904806,0.01834882372168268,475.4223519403967," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 475.4223519403967
x: [-4.053e+00 -3.998e+00]
nit: 17
jac: [-1.705e-05 2.842e-05]
nfev: 63
njev: 21
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",kmbtnydb
6.437703242339829e-08,1111.8568124210485,551.9888338131033," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 551.9888338131033
x: [-1.656e+01 7.014e+00]
nit: 44
jac: [-6.594e-04 -6.594e-04]
nfev: 150
njev: 50
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",a3whtqnm
3.698305553137226e-11,10937793.673686024,448.24507173971307," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 448.24507173971307
x: [-2.402e+01 1.621e+01]
nit: 36
jac: [-1.046e-03 -1.052e-03]
nfev: 144
njev: 48
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",bqckq56i
2.3474015194739323e-09,27880.14281193776,550.037229129203," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 550.037229129203
x: [-1.987e+01 1.024e+01]
nit: 37
jac: [ 2.274e-05 2.274e-05]
nfev: 117
njev: 39
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",wtmppyq1
0.006699227131566248,0.02396872654682817,498.04299030539954," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 498.04299030539954
x: [-4.999e+00 -3.731e+00]
nit: 21
jac: [-3.411e-05 -6.253e-05]
nfev: 84
njev: 28
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",gyboex2p
8.873267040119318e-08,2.6736837774273013e-06,554.5177445402869," message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
success: True
status: 0
fun: 554.5177445402869
x: [-1.624e+01 -1.283e+01]
nit: 32
jac: [ 0.000e+00 0.000e+00]
nfev: 105
njev: 35
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",6v2kv490
2.6887086013618105e-11,4106709.344670519,527.0250476183091," message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
success: True
status: 0
fun: 527.0250476183091
x: [-2.434e+01 1.523e+01]
nit: 38
jac: [ 0.000e+00 0.000e+00]
nfev: 123
njev: 41
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",goe4ggy1
5.703446820155234e-10,141678.15993334388,546.8526377885472," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 546.8526377885472
x: [-2.128e+01 1.186e+01]
nit: 35
jac: [ 1.251e-04 1.251e-04]
nfev: 117
njev: 39
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",wjhrr05w
6.942499761718846e-08,17717.856856409046,331.41259185330904," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 331.41259185330904
x: [-1.648e+01 9.782e+00]
nit: 29
jac: [-2.893e-03 -2.950e-03]
nfev: 93
njev: 31
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",97wdqw3s
1.37409628136384e-10,20060552.496022884,28.39754844462764," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 28.39754844462764
x: [-2.271e+01 1.681e+01]
nit: 35
jac: [-1.435e-04 -1.435e-04]
nfev: 129
njev: 43
hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>",u8hygigo
1 alpha lambda nll opt_result participant
2 0.0019909996639928752 0.0784037743181824 524.515296073272 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 524.515296073272 x: [-6.217e+00 -2.546e+00] nit: 24 jac: [ 1.137e-05 -3.411e-05] nfev: 96 njev: 32 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> qfmtmjjy
3 0.0041258076334695195 0.17916292668471764 232.26294177560447 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 232.26294177560447 x: [-5.486e+00 -1.719e+00] nit: 16 jac: [ 3.411e-05 3.411e-05] nfev: 51 njev: 17 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> l5hj4bqu
4 8.580076240145284e-10 42804.74116720703 551.346218644511 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 551.346218644511 x: [-2.088e+01 1.066e+01] nit: 37 jac: [-1.819e-04 -1.933e-04] nfev: 129 njev: 43 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> ax6yt76a
5 9.183501708224334e-11 2938700.1970509873 504.4244914945921 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 504.4244914945921 x: [-2.311e+01 1.489e+01] nit: 34 jac: [ 3.490e-03 3.490e-03] nfev: 126 njev: 42 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> n7zr7k22
6 1.3285404087992438e-12 150903313.1944923 526.8342605685835 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 526.8342605685835 x: [-2.735e+01 1.883e+01] nit: 36 jac: [-6.821e-05 -6.821e-05] nfev: 126 njev: 42 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> n274eufg
7 0.01649599700173123 0.01686233697780474 499.4082573033442 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 499.4082573033442 x: [-4.088e+00 -4.083e+00] nit: 23 jac: [ 2.842e-05 -5.684e-05] nfev: 81 njev: 27 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> d3n1xm9e
8 2.759963002574895e-06 0.975782284437965 554.5108100039844 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 554.5108100039844 x: [-1.280e+01 -2.452e-02] nit: 37 jac: [-1.251e-04 -1.592e-04] nfev: 147 njev: 49 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> e41xqg7i
9 0.8250744843164314 0.0010126000014369413 470.3492586229785 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 470.3492586229785 x: [ 1.551e+00 -6.895e+00] nit: 22 jac: [-5.116e-05 -4.547e-05] nfev: 69 njev: 23 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> qdl2kwne
10 0.0067738863236796905 0.03748214730335093 471.11474910271704 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 471.11474910271704 x: [-4.988e+00 -3.284e+00] nit: 21 jac: [ 3.411e-05 2.274e-05] nfev: 69 njev: 23 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> 8y0d23i5
11 0.3152175910708728 7.705164135712456e-05 554.438430475421 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 554.438430475421 x: [-7.758e-01 -9.471e+00] nit: 24 jac: [ 6.821e-05 2.274e-05] nfev: 75 njev: 25 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> 1e1vfxfy
12 3.0097559768213683e-09 48217.30222264478 537.6960374952457 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 537.6960374952457 x: [-1.962e+01 1.078e+01] nit: 40 jac: [ 2.069e-03 2.069e-03] nfev: 129 njev: 43 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> 4vfjhtfy
13 0.017080076809904806 0.01834882372168268 475.4223519403967 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 475.4223519403967 x: [-4.053e+00 -3.998e+00] nit: 17 jac: [-1.705e-05 2.842e-05] nfev: 63 njev: 21 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> kmbtnydb
14 6.437703242339829e-08 1111.8568124210485 551.9888338131033 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 551.9888338131033 x: [-1.656e+01 7.014e+00] nit: 44 jac: [-6.594e-04 -6.594e-04] nfev: 150 njev: 50 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> a3whtqnm
15 3.698305553137226e-11 10937793.673686024 448.24507173971307 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 448.24507173971307 x: [-2.402e+01 1.621e+01] nit: 36 jac: [-1.046e-03 -1.052e-03] nfev: 144 njev: 48 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> bqckq56i
16 2.3474015194739323e-09 27880.14281193776 550.037229129203 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 550.037229129203 x: [-1.987e+01 1.024e+01] nit: 37 jac: [ 2.274e-05 2.274e-05] nfev: 117 njev: 39 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> wtmppyq1
17 0.006699227131566248 0.02396872654682817 498.04299030539954 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 498.04299030539954 x: [-4.999e+00 -3.731e+00] nit: 21 jac: [-3.411e-05 -6.253e-05] nfev: 84 njev: 28 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> gyboex2p
18 8.873267040119318e-08 2.6736837774273013e-06 554.5177445402869 message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL success: True status: 0 fun: 554.5177445402869 x: [-1.624e+01 -1.283e+01] nit: 32 jac: [ 0.000e+00 0.000e+00] nfev: 105 njev: 35 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> 6v2kv490
19 2.6887086013618105e-11 4106709.344670519 527.0250476183091 message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL success: True status: 0 fun: 527.0250476183091 x: [-2.434e+01 1.523e+01] nit: 38 jac: [ 0.000e+00 0.000e+00] nfev: 123 njev: 41 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> goe4ggy1
20 5.703446820155234e-10 141678.15993334388 546.8526377885472 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 546.8526377885472 x: [-2.128e+01 1.186e+01] nit: 35 jac: [ 1.251e-04 1.251e-04] nfev: 117 njev: 39 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> wjhrr05w
21 6.942499761718846e-08 17717.856856409046 331.41259185330904 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 331.41259185330904 x: [-1.648e+01 9.782e+00] nit: 29 jac: [-2.893e-03 -2.950e-03] nfev: 93 njev: 31 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> 97wdqw3s
22 1.37409628136384e-10 20060552.496022884 28.39754844462764 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 28.39754844462764 x: [-2.271e+01 1.681e+01] nit: 35 jac: [-1.435e-04 -1.435e-04] nfev: 129 njev: 43 hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64> u8hygigo

4
results/model1/mle.csv Normal file
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@ -0,0 +1,4 @@
participant,alpha,lambda,f,nll
qfmtmjjy,1.1193176839005968e-16,3.4183388875710508e+16,1.0,469.4113688856362
l5hj4bqu,0.0030442296624999866,928.7290018408265,0.026793671988813828,225.6172699416345
ax6yt76a,9.376652676467416e-20,4.30661887267533e+19,1.0,441.8673631389228
1 participant alpha lambda f nll
2 qfmtmjjy 1.1193176839005968e-16 3.4183388875710508e+16 1.0 469.4113688856362
3 l5hj4bqu 0.0030442296624999866 928.7290018408265 0.026793671988813828 225.6172699416345
4 ax6yt76a 9.376652676467416e-20 4.30661887267533e+19 1.0 441.8673631389228

191
results/model1/unrestrained-mle.csv Normal file
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@ -0,0 +1,191 @@
alpha,lambda,f,nll,opt_result,participant
8.871704778471776e-11,14376474.143789845,0.9999999984752381,469.33695814123934," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 469.33695814123934
x: [-2.315e+01 1.648e+01 2.030e+01]
nit: 29
jac: [ 8.072e-04 8.072e-04 0.000e+00]
nfev: 148
njev: 37
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",qfmtmjjy
0.0030548027746643705,0.3085218170752524,0.02685309882404673,225.54778324822848," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 225.54778324822848
x: [-5.788e+00 -1.176e+00 -3.590e+00]
nit: 26
jac: [ 2.558e-05 2.842e-06 1.421e-05]
nfev: 132
njev: 33
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",l5hj4bqu
3.2198476500118582e-15,417862591134.8987,0.9999999999999423,441.6555549359772," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 441.6555549359772
x: [-3.337e+01 2.676e+01 3.048e+01]
nit: 21
jac: [ 7.958e-05 7.958e-05 0.000e+00]
nfev: 132
njev: 33
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",ax6yt76a
1.3010725964224816e-09,1225860.6537195262,0.9999999937812794,466.43975387712266," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 466.43975387712266
x: [-2.046e+01 1.402e+01 1.890e+01]
nit: 32
jac: [-5.116e-05 -5.116e-05 0.000e+00]
nfev: 140
njev: 35
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",n7zr7k22
1.160548917142861e-07,1.6863783919195292e-05,0.9016665769083677,554.5177444822273," message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
success: True
status: 0
fun: 554.5177444822273
x: [-1.597e+01 -1.099e+01 2.216e+00]
nit: 29
jac: [ 0.000e+00 0.000e+00 0.000e+00]
nfev: 120
njev: 30
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",n274eufg
9.579437532994249e-17,6131931277693.485,0.9999999998655666,539.3988636277782," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 539.3988636277782
x: [-3.688e+01 2.944e+01 2.273e+01]
nit: 24
jac: [-1.137e-05 0.000e+00 0.000e+00]
nfev: 136
njev: 34
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",d3n1xm9e
0.0002172028784807661,0.012459143465035454,5.585119121446817e-06,554.5108523711202," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 554.5108523711202
x: [-8.434e+00 -4.385e+00 -1.210e+01]
nit: 37
jac: [-3.411e-05 -1.137e-05 4.547e-05]
nfev: 248
njev: 62
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",e41xqg7i
0.8250800085276067,0.001012589017492184,1.2649395927827455e-16,470.3492586242423," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 470.3492586242423
x: [ 1.551e+00 -6.895e+00 -3.661e+01]
nit: 28
jac: [-2.274e-05 -3.752e-04 0.000e+00]
nfev: 148
njev: 37
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",qdl2kwne
1.2610421898523663e-18,494323555893264.56,0.04295594209878908,436.20220706531273," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 436.20220706531273
x: [-4.121e+01 3.383e+01 -3.104e+00]
nit: 38
jac: [-1.705e-04 -1.592e-04 -6.821e-05]
nfev: 232
njev: 58
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",8y0d23i5
1.116371444999944e-07,2524.9268841535963,0.21851369775877533,553.2746446547244," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 553.2746446547244
x: [-1.601e+01 7.834e+00 -1.274e+00]
nit: 40
jac: [ 1.592e-04 1.592e-04 1.592e-04]
nfev: 172
njev: 43
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",1e1vfxfy
6.565360147334132e-11,6632821.084165682,0.012003731763428675,513.6175539157593," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 513.6175539157593
x: [-2.345e+01 1.571e+01 -4.410e+00]
nit: 46
jac: [-4.968e-03 -4.957e-03 1.223e-02]
nfev: 340
njev: 85
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",4vfjhtfy
0.017079822973559262,0.018348802029635628,9.9230306968482e-10,475.42235386434925," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 475.42235386434925
x: [-4.053e+00 -3.998e+00 -2.073e+01]
nit: 36
jac: [-7.560e-04 -1.177e-03 0.000e+00]
nfev: 204
njev: 51
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",kmbtnydb
1.3990137307531058e-06,51.16930938606098,4.982987068878401e-11,551.9888438686838," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 551.9888438686838
x: [-1.348e+01 3.935e+00 -2.372e+01]
nit: 53
jac: [ 2.274e-05 2.274e-05 0.000e+00]
nfev: 388
njev: 97
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",a3whtqnm
2.0976159715180872e-12,425089944.21903425,0.3185943891474155,405.3794868584693," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 405.3794868584693
x: [-2.689e+01 1.987e+01 -7.602e-01]
nit: 24
jac: [ 1.137e-05 1.137e-05 1.705e-05]
nfev: 140
njev: 35
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",bqckq56i
4.453063202131555e-14,1469667428.4008932,4.095080332704109e-15,550.0372290171044," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 550.0372290171044
x: [-3.074e+01 2.111e+01 -3.313e+01]
nit: 46
jac: [-3.411e-05 -3.411e-05 0.000e+00]
nfev: 236
njev: 59
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",wtmppyq1
0.006699186509785594,0.02396884830138067,1.786102972042448e-13,498.042990305876," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 498.042990305876
x: [-4.999e+00 -3.731e+00 -2.935e+01]
nit: 45
jac: [-1.705e-05 5.116e-05 0.000e+00]
nfev: 324
njev: 81
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",gyboex2p
1.7876073951029804e-08,1.7329412122410143e-06,0.9719326721535358,554.5177444480751," message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
success: True
status: 0
fun: 554.5177444480751
x: [-1.784e+01 -1.327e+01 3.545e+00]
nit: 28
jac: [ 0.000e+00 0.000e+00 0.000e+00]
nfev: 124
njev: 31
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",6v2kv490
1.307936563746064e-06,6.0156094409861654e-05,0.9112155489671344,554.5177444919409," message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL
success: True
status: 0
fun: 554.5177444919409
x: [-1.355e+01 -9.719e+00 2.329e+00]
nit: 28
jac: [ 0.000e+00 0.000e+00 0.000e+00]
nfev: 124
njev: 31
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",goe4ggy1
4.070711600689151e-05,0.9698096110426262,0.8087323463467103,554.5067963631062," message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH
success: True
status: 0
fun: 554.5067963631062
x: [-1.011e+01 -3.066e-02 1.442e+00]
nit: 33
jac: [ 7.958e-05 9.095e-05 1.023e-04]
nfev: 144
njev: 36
hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64>",wjhrr05w
1 alpha lambda f nll opt_result participant
2 8.871704778471776e-11 14376474.143789845 0.9999999984752381 469.33695814123934 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 469.33695814123934 x: [-2.315e+01 1.648e+01 2.030e+01] nit: 29 jac: [ 8.072e-04 8.072e-04 0.000e+00] nfev: 148 njev: 37 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> qfmtmjjy
3 0.0030548027746643705 0.3085218170752524 0.02685309882404673 225.54778324822848 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 225.54778324822848 x: [-5.788e+00 -1.176e+00 -3.590e+00] nit: 26 jac: [ 2.558e-05 2.842e-06 1.421e-05] nfev: 132 njev: 33 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> l5hj4bqu
4 3.2198476500118582e-15 417862591134.8987 0.9999999999999423 441.6555549359772 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 441.6555549359772 x: [-3.337e+01 2.676e+01 3.048e+01] nit: 21 jac: [ 7.958e-05 7.958e-05 0.000e+00] nfev: 132 njev: 33 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> ax6yt76a
5 1.3010725964224816e-09 1225860.6537195262 0.9999999937812794 466.43975387712266 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 466.43975387712266 x: [-2.046e+01 1.402e+01 1.890e+01] nit: 32 jac: [-5.116e-05 -5.116e-05 0.000e+00] nfev: 140 njev: 35 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> n7zr7k22
6 1.160548917142861e-07 1.6863783919195292e-05 0.9016665769083677 554.5177444822273 message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL success: True status: 0 fun: 554.5177444822273 x: [-1.597e+01 -1.099e+01 2.216e+00] nit: 29 jac: [ 0.000e+00 0.000e+00 0.000e+00] nfev: 120 njev: 30 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> n274eufg
7 9.579437532994249e-17 6131931277693.485 0.9999999998655666 539.3988636277782 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 539.3988636277782 x: [-3.688e+01 2.944e+01 2.273e+01] nit: 24 jac: [-1.137e-05 0.000e+00 0.000e+00] nfev: 136 njev: 34 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> d3n1xm9e
8 0.0002172028784807661 0.012459143465035454 5.585119121446817e-06 554.5108523711202 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 554.5108523711202 x: [-8.434e+00 -4.385e+00 -1.210e+01] nit: 37 jac: [-3.411e-05 -1.137e-05 4.547e-05] nfev: 248 njev: 62 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> e41xqg7i
9 0.8250800085276067 0.001012589017492184 1.2649395927827455e-16 470.3492586242423 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 470.3492586242423 x: [ 1.551e+00 -6.895e+00 -3.661e+01] nit: 28 jac: [-2.274e-05 -3.752e-04 0.000e+00] nfev: 148 njev: 37 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> qdl2kwne
10 1.2610421898523663e-18 494323555893264.56 0.04295594209878908 436.20220706531273 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 436.20220706531273 x: [-4.121e+01 3.383e+01 -3.104e+00] nit: 38 jac: [-1.705e-04 -1.592e-04 -6.821e-05] nfev: 232 njev: 58 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> 8y0d23i5
11 1.116371444999944e-07 2524.9268841535963 0.21851369775877533 553.2746446547244 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 553.2746446547244 x: [-1.601e+01 7.834e+00 -1.274e+00] nit: 40 jac: [ 1.592e-04 1.592e-04 1.592e-04] nfev: 172 njev: 43 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> 1e1vfxfy
12 6.565360147334132e-11 6632821.084165682 0.012003731763428675 513.6175539157593 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 513.6175539157593 x: [-2.345e+01 1.571e+01 -4.410e+00] nit: 46 jac: [-4.968e-03 -4.957e-03 1.223e-02] nfev: 340 njev: 85 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> 4vfjhtfy
13 0.017079822973559262 0.018348802029635628 9.9230306968482e-10 475.42235386434925 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 475.42235386434925 x: [-4.053e+00 -3.998e+00 -2.073e+01] nit: 36 jac: [-7.560e-04 -1.177e-03 0.000e+00] nfev: 204 njev: 51 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> kmbtnydb
14 1.3990137307531058e-06 51.16930938606098 4.982987068878401e-11 551.9888438686838 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 551.9888438686838 x: [-1.348e+01 3.935e+00 -2.372e+01] nit: 53 jac: [ 2.274e-05 2.274e-05 0.000e+00] nfev: 388 njev: 97 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> a3whtqnm
15 2.0976159715180872e-12 425089944.21903425 0.3185943891474155 405.3794868584693 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 405.3794868584693 x: [-2.689e+01 1.987e+01 -7.602e-01] nit: 24 jac: [ 1.137e-05 1.137e-05 1.705e-05] nfev: 140 njev: 35 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> bqckq56i
16 4.453063202131555e-14 1469667428.4008932 4.095080332704109e-15 550.0372290171044 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 550.0372290171044 x: [-3.074e+01 2.111e+01 -3.313e+01] nit: 46 jac: [-3.411e-05 -3.411e-05 0.000e+00] nfev: 236 njev: 59 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> wtmppyq1
17 0.006699186509785594 0.02396884830138067 1.786102972042448e-13 498.042990305876 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 498.042990305876 x: [-4.999e+00 -3.731e+00 -2.935e+01] nit: 45 jac: [-1.705e-05 5.116e-05 0.000e+00] nfev: 324 njev: 81 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> gyboex2p
18 1.7876073951029804e-08 1.7329412122410143e-06 0.9719326721535358 554.5177444480751 message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL success: True status: 0 fun: 554.5177444480751 x: [-1.784e+01 -1.327e+01 3.545e+00] nit: 28 jac: [ 0.000e+00 0.000e+00 0.000e+00] nfev: 124 njev: 31 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> 6v2kv490
19 1.307936563746064e-06 6.0156094409861654e-05 0.9112155489671344 554.5177444919409 message: CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL success: True status: 0 fun: 554.5177444919409 x: [-1.355e+01 -9.719e+00 2.329e+00] nit: 28 jac: [ 0.000e+00 0.000e+00 0.000e+00] nfev: 124 njev: 31 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> goe4ggy1
20 4.070711600689151e-05 0.9698096110426262 0.8087323463467103 554.5067963631062 message: CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH success: True status: 0 fun: 554.5067963631062 x: [-1.011e+01 -3.066e-02 1.442e+00] nit: 33 jac: [ 7.958e-05 9.095e-05 1.023e-04] nfev: 144 njev: 36 hess_inv: <3x3 LbfgsInvHessProduct with dtype=float64> wjhrr05w

82
results/model2/mle.csv Normal file
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@ -0,0 +1,82 @@
participant,alpha_gain,alpha_loss,nll,model
qfmtmjjy,1.0,6.331953602927268e-08,553.5640344190499,model2_loss_gain
qfmtmjjy,1.0,6.331953602927268e-08,553.5640344190499,model2_loss_gain
l5hj4bqu,0.42321676612719833,0.21940814540809023,546.7609390875771,model2_loss_gain
ax6yt76a,7.09619494188235e-08,0.9999926916808033,554.3761235503644,model2_loss_gain
n7zr7k22,2.444167462227958e-08,0.9999999539800323,502.1996879300243,model2_loss_gain
n274eufg,2.1898408386418047e-07,0.0873503997081469,552.8203462427547,model2_loss_gain
d3n1xm9e,7.557448923824224e-10,0.49316262300646374,536.7090539494609,model2_loss_gain
e41xqg7i,3.620211097661501e-05,0.013882225967113861,554.5032044585822,model2_loss_gain
qdl2kwne,0.1488199986109225,0.999999915877716,490.03138247284346,model2_loss_gain
8y0d23i5,3.1353878914548706e-07,0.9999990782665764,508.52029649601076,model2_loss_gain
1e1vfxfy,2.6828870011444275e-06,0.8464278794999273,548.0820963166587,model2_loss_gain
4vfjhtfy,4.789637034959933e-07,0.9999996758398828,540.0816425215404,model2_loss_gain
kmbtnydb,2.5482845557987327e-07,0.2127432312542269,545.8392364682928,model2_loss_gain
a3whtqnm,5.853822199059074e-08,0.5990978930901857,532.5704878175919,model2_loss_gain
bqckq56i,0.45134623957080383,1.6739867943334115e-08,550.7972119494303,model2_loss_gain
wtmppyq1,4.506323507164776e-07,0.20132652284582794,551.7209953750038,model2_loss_gain
gyboex2p,0.7315951368264882,0.9999994624999908,544.9177728210519,model2_loss_gain
6v2kv490,7.176003008630198e-16,0.9996580030679997,554.8198842643062,model2_loss_gain
goe4ggy1,0.08928820972079568,0.923176664636646,545.2746923213128,model2_loss_gain
wjhrr05w,2.2875552027867153e-07,0.8334131430433657,551.3703828886879,model2_loss_gain
97wdqw3s,0.99999908618204,0.9999999293659182,500.42564088776396,model2_loss_gain
u8hygigo,0.061751261020003355,5.6914260715717614e-08,548.7283246258174,model2_loss_gain
cf10378m,0.053136759386351734,0.9999998878232516,551.7779144590984,model2_loss_gain
v5r7mq91,0.19107124279103968,4.14211806035215e-07,549.7314348984426,model2_loss_gain
3x6nteue,0.07757806051692856,1.1763894527174054e-07,549.2756329288749,model2_loss_gain
xecqf0dd,0.9996431490434584,0.9999999088133392,531.7030088087863,model2_loss_gain
m9i1kvu6,0.292942196627816,0.8319760919457827,553.0350995516853,model2_loss_gain
gp3vvd4k,0.6544092186694408,0.9999993531057779,515.8326659000236,model2_loss_gain
2w7l1ve3,0.049817764580400326,0.2096126310457826,551.2154842831172,model2_loss_gain
inpc0pfu,1.171384230140469e-06,0.14850086667882983,551.4114139367338,model2_loss_gain
x18jrbof,0.35420927180951134,0.9999998419223542,508.2621175941522,model2_loss_gain
nao85d8o,7.728752512680148e-08,0.9371913104055626,537.088205130901,model2_loss_gain
owfwd7iz,0.5467286785975776,3.957100499126383e-07,551.0093016982407,model2_loss_gain
iiupv9e6,6.305736445494951e-06,0.1553419191193692,546.2786469156417,model2_loss_gain
rht8c10d,0.999998604627353,6.331553332137362e-07,551.8406870812335,model2_loss_gain
uc5u8lob,8.413515847164314e-07,0.5090831419625563,542.2055749945935,model2_loss_gain
idc0abhp,0.0786066114237155,1.0542693009318418e-09,549.7369816839649,model2_loss_gain
gfy4u3y9,4.722094779504361e-08,0.999992910214223,555.7513263189353,model2_loss_gain
vd2z262g,6.551046215106814e-08,0.4171153617163101,542.3096018602956,model2_loss_gain
gaq9bw7l,1.1726121173821847e-05,0.4067721222705871,554.0727455527086,model2_loss_gain
92minocl,2.2726345955612876e-07,0.5290818745238947,545.3325320271144,model2_loss_gain
5kyg1fo7,1.2470648028542125e-07,0.13842924629322795,549.7303076122271,model2_loss_gain
a95e64ov,0.08742389212343478,0.5826743191349707,543.8881933459938,model2_loss_gain
yasfsrkr,0.052784864734305,0.3155278575042451,545.4196656737446,model2_loss_gain
p6p2uc66,0.9999999455603333,3.3458261911750984e-08,547.7628819441434,model2_loss_gain
8mrt2jk8,0.99999732297481,0.9999998785643968,527.4195825672078,model2_loss_gain
74ubi75c,1.5334485228066952e-07,0.9999997912052608,507.52748762945,model2_loss_gain
k2mcerli,4.08062572137162e-08,0.19025233105851136,548.0592429198056,model2_loss_gain
nrwlu1nf,3.0404252738935575e-08,0.31608280812005457,549.0292739069076,model2_loss_gain
3t3sh2es,0.8249784093592426,0.48173009573483305,550.8255484189891,model2_loss_gain
7srzfhpq,3.970915388175658e-07,0.5566323247988982,540.2455464988313,model2_loss_gain
4hfaww7m,1.1892101808930413e-06,0.06840288119107804,552.473445790118,model2_loss_gain
yk2obfqv,1.3807351068135839e-06,0.993205019802573,555.6478666383708,model2_loss_gain
ng1u69iy,1.1802499187075957e-08,0.35483082886603434,533.8987961562939,model2_loss_gain
tkngvs6x,2.593512222239559e-06,0.44609953046479717,545.4166798043648,model2_loss_gain
j1cshr0g,0.36040895629733516,0.5832026620438543,552.9572936835381,model2_loss_gain
o7om1jen,2.3329330146721812e-08,0.3028246183831722,549.653042653292,model2_loss_gain
5w1y4e6s,0.9999973367121154,0.9999999766450439,504.60432280755185,model2_loss_gain
8dxvwlpc,0.08995510228445398,1.7168811661739754e-07,550.8164507448334,model2_loss_gain
8ghzl9o9,0.6891432497828257,0.7859147951044387,549.908798836067,model2_loss_gain
mmh62k83,1.1634105601886457e-08,0.999999848031814,538.3616162789655,model2_loss_gain
167y1h5i,0.15854439946664312,0.9999997511544267,547.6961304214166,model2_loss_gain
rfqmmb1e,0.05873960866856111,1.1141297455053465e-08,551.9305982015765,model2_loss_gain
6fajoau3,8.843376612924671e-07,0.1990406633896888,550.4247946752638,model2_loss_gain
j84pqw2m,0.10134622377518297,0.9999999271154836,489.36197096912457,model2_loss_gain
pz3wg8u0,0.9999795583215213,0.9999999052839689,491.5285933344341,model2_loss_gain
kwvbd3su,4.032274791578176e-06,0.08023742791079048,553.452284290126,model2_loss_gain
uw0ebq3n,4.35018656332699e-07,0.9999772938158631,554.7977781728154,model2_loss_gain
3uodgqnr,0.9998263255919533,0.2905615093179284,546.9326572266009,model2_loss_gain
i0g5hocs,3.039034688388457e-06,0.2937809186233787,542.4067826459491,model2_loss_gain
vqptyp2x,0.1296311132473564,0.999999316589332,533.5635483449804,model2_loss_gain
jfir3t88,2.0940724443610376e-07,0.8842319795054664,556.2116376937038,model2_loss_gain
kp8bwxso,9.208005798129224e-09,0.9999996182780255,550.847509163659,model2_loss_gain
nm7b54pf,1.681812725386848e-07,0.24251287386386436,545.1105744763561,model2_loss_gain
rh8bt6c8,2.293740670538188e-07,0.9999999695126307,527.4634975679522,model2_loss_gain
yrvhkfwj,1.8022098166473515e-07,0.9999991347681113,507.4038877186516,model2_loss_gain
ahceacmk,7.868228047088838e-08,0.9999998157421053,513.7805503460337,model2_loss_gain
ga9ow9zr,0.3298128065195124,1.4256285650099293e-06,549.1969358882476,model2_loss_gain
p9hhh9iw,0.9999909345665606,1.0319072839939956e-07,550.3175663358292,model2_loss_gain
odu11wbc,0.13289158187773722,0.9999983760379403,554.5549374937649,model2_loss_gain
cjwflpfp,0.5597238185910073,0.9999986205440585,524.6505504348717,model2_loss_gain
1 participant alpha_gain alpha_loss nll model
2 qfmtmjjy 1.0 6.331953602927268e-08 553.5640344190499 model2_loss_gain
3 qfmtmjjy 1.0 6.331953602927268e-08 553.5640344190499 model2_loss_gain
4 l5hj4bqu 0.42321676612719833 0.21940814540809023 546.7609390875771 model2_loss_gain
5 ax6yt76a 7.09619494188235e-08 0.9999926916808033 554.3761235503644 model2_loss_gain
6 n7zr7k22 2.444167462227958e-08 0.9999999539800323 502.1996879300243 model2_loss_gain
7 n274eufg 2.1898408386418047e-07 0.0873503997081469 552.8203462427547 model2_loss_gain
8 d3n1xm9e 7.557448923824224e-10 0.49316262300646374 536.7090539494609 model2_loss_gain
9 e41xqg7i 3.620211097661501e-05 0.013882225967113861 554.5032044585822 model2_loss_gain
10 qdl2kwne 0.1488199986109225 0.999999915877716 490.03138247284346 model2_loss_gain
11 8y0d23i5 3.1353878914548706e-07 0.9999990782665764 508.52029649601076 model2_loss_gain
12 1e1vfxfy 2.6828870011444275e-06 0.8464278794999273 548.0820963166587 model2_loss_gain
13 4vfjhtfy 4.789637034959933e-07 0.9999996758398828 540.0816425215404 model2_loss_gain
14 kmbtnydb 2.5482845557987327e-07 0.2127432312542269 545.8392364682928 model2_loss_gain
15 a3whtqnm 5.853822199059074e-08 0.5990978930901857 532.5704878175919 model2_loss_gain
16 bqckq56i 0.45134623957080383 1.6739867943334115e-08 550.7972119494303 model2_loss_gain
17 wtmppyq1 4.506323507164776e-07 0.20132652284582794 551.7209953750038 model2_loss_gain
18 gyboex2p 0.7315951368264882 0.9999994624999908 544.9177728210519 model2_loss_gain
19 6v2kv490 7.176003008630198e-16 0.9996580030679997 554.8198842643062 model2_loss_gain
20 goe4ggy1 0.08928820972079568 0.923176664636646 545.2746923213128 model2_loss_gain
21 wjhrr05w 2.2875552027867153e-07 0.8334131430433657 551.3703828886879 model2_loss_gain
22 97wdqw3s 0.99999908618204 0.9999999293659182 500.42564088776396 model2_loss_gain
23 u8hygigo 0.061751261020003355 5.6914260715717614e-08 548.7283246258174 model2_loss_gain
24 cf10378m 0.053136759386351734 0.9999998878232516 551.7779144590984 model2_loss_gain
25 v5r7mq91 0.19107124279103968 4.14211806035215e-07 549.7314348984426 model2_loss_gain
26 3x6nteue 0.07757806051692856 1.1763894527174054e-07 549.2756329288749 model2_loss_gain
27 xecqf0dd 0.9996431490434584 0.9999999088133392 531.7030088087863 model2_loss_gain
28 m9i1kvu6 0.292942196627816 0.8319760919457827 553.0350995516853 model2_loss_gain
29 gp3vvd4k 0.6544092186694408 0.9999993531057779 515.8326659000236 model2_loss_gain
30 2w7l1ve3 0.049817764580400326 0.2096126310457826 551.2154842831172 model2_loss_gain
31 inpc0pfu 1.171384230140469e-06 0.14850086667882983 551.4114139367338 model2_loss_gain
32 x18jrbof 0.35420927180951134 0.9999998419223542 508.2621175941522 model2_loss_gain
33 nao85d8o 7.728752512680148e-08 0.9371913104055626 537.088205130901 model2_loss_gain
34 owfwd7iz 0.5467286785975776 3.957100499126383e-07 551.0093016982407 model2_loss_gain
35 iiupv9e6 6.305736445494951e-06 0.1553419191193692 546.2786469156417 model2_loss_gain
36 rht8c10d 0.999998604627353 6.331553332137362e-07 551.8406870812335 model2_loss_gain
37 uc5u8lob 8.413515847164314e-07 0.5090831419625563 542.2055749945935 model2_loss_gain
38 idc0abhp 0.0786066114237155 1.0542693009318418e-09 549.7369816839649 model2_loss_gain
39 gfy4u3y9 4.722094779504361e-08 0.999992910214223 555.7513263189353 model2_loss_gain
40 vd2z262g 6.551046215106814e-08 0.4171153617163101 542.3096018602956 model2_loss_gain
41 gaq9bw7l 1.1726121173821847e-05 0.4067721222705871 554.0727455527086 model2_loss_gain
42 92minocl 2.2726345955612876e-07 0.5290818745238947 545.3325320271144 model2_loss_gain
43 5kyg1fo7 1.2470648028542125e-07 0.13842924629322795 549.7303076122271 model2_loss_gain
44 a95e64ov 0.08742389212343478 0.5826743191349707 543.8881933459938 model2_loss_gain
45 yasfsrkr 0.052784864734305 0.3155278575042451 545.4196656737446 model2_loss_gain
46 p6p2uc66 0.9999999455603333 3.3458261911750984e-08 547.7628819441434 model2_loss_gain
47 8mrt2jk8 0.99999732297481 0.9999998785643968 527.4195825672078 model2_loss_gain
48 74ubi75c 1.5334485228066952e-07 0.9999997912052608 507.52748762945 model2_loss_gain
49 k2mcerli 4.08062572137162e-08 0.19025233105851136 548.0592429198056 model2_loss_gain
50 nrwlu1nf 3.0404252738935575e-08 0.31608280812005457 549.0292739069076 model2_loss_gain
51 3t3sh2es 0.8249784093592426 0.48173009573483305 550.8255484189891 model2_loss_gain
52 7srzfhpq 3.970915388175658e-07 0.5566323247988982 540.2455464988313 model2_loss_gain
53 4hfaww7m 1.1892101808930413e-06 0.06840288119107804 552.473445790118 model2_loss_gain
54 yk2obfqv 1.3807351068135839e-06 0.993205019802573 555.6478666383708 model2_loss_gain
55 ng1u69iy 1.1802499187075957e-08 0.35483082886603434 533.8987961562939 model2_loss_gain
56 tkngvs6x 2.593512222239559e-06 0.44609953046479717 545.4166798043648 model2_loss_gain
57 j1cshr0g 0.36040895629733516 0.5832026620438543 552.9572936835381 model2_loss_gain
58 o7om1jen 2.3329330146721812e-08 0.3028246183831722 549.653042653292 model2_loss_gain
59 5w1y4e6s 0.9999973367121154 0.9999999766450439 504.60432280755185 model2_loss_gain
60 8dxvwlpc 0.08995510228445398 1.7168811661739754e-07 550.8164507448334 model2_loss_gain
61 8ghzl9o9 0.6891432497828257 0.7859147951044387 549.908798836067 model2_loss_gain
62 mmh62k83 1.1634105601886457e-08 0.999999848031814 538.3616162789655 model2_loss_gain
63 167y1h5i 0.15854439946664312 0.9999997511544267 547.6961304214166 model2_loss_gain
64 rfqmmb1e 0.05873960866856111 1.1141297455053465e-08 551.9305982015765 model2_loss_gain
65 6fajoau3 8.843376612924671e-07 0.1990406633896888 550.4247946752638 model2_loss_gain
66 j84pqw2m 0.10134622377518297 0.9999999271154836 489.36197096912457 model2_loss_gain
67 pz3wg8u0 0.9999795583215213 0.9999999052839689 491.5285933344341 model2_loss_gain
68 kwvbd3su 4.032274791578176e-06 0.08023742791079048 553.452284290126 model2_loss_gain
69 uw0ebq3n 4.35018656332699e-07 0.9999772938158631 554.7977781728154 model2_loss_gain
70 3uodgqnr 0.9998263255919533 0.2905615093179284 546.9326572266009 model2_loss_gain
71 i0g5hocs 3.039034688388457e-06 0.2937809186233787 542.4067826459491 model2_loss_gain
72 vqptyp2x 0.1296311132473564 0.999999316589332 533.5635483449804 model2_loss_gain
73 jfir3t88 2.0940724443610376e-07 0.8842319795054664 556.2116376937038 model2_loss_gain
74 kp8bwxso 9.208005798129224e-09 0.9999996182780255 550.847509163659 model2_loss_gain
75 nm7b54pf 1.681812725386848e-07 0.24251287386386436 545.1105744763561 model2_loss_gain
76 rh8bt6c8 2.293740670538188e-07 0.9999999695126307 527.4634975679522 model2_loss_gain
77 yrvhkfwj 1.8022098166473515e-07 0.9999991347681113 507.4038877186516 model2_loss_gain
78 ahceacmk 7.868228047088838e-08 0.9999998157421053 513.7805503460337 model2_loss_gain
79 ga9ow9zr 0.3298128065195124 1.4256285650099293e-06 549.1969358882476 model2_loss_gain
80 p9hhh9iw 0.9999909345665606 1.0319072839939956e-07 550.3175663358292 model2_loss_gain
81 odu11wbc 0.13289158187773722 0.9999983760379403 554.5549374937649 model2_loss_gain
82 cjwflpfp 0.5597238185910073 0.9999986205440585 524.6505504348717 model2_loss_gain