From 51e9e930b90d879bba562bbfa1f5078f6e261d3b Mon Sep 17 00:00:00 2001
From: PoppyCollis <poppy.collis@gmail.com>
Date: Thu, 15 May 2025 13:52:01 +0100
Subject: [PATCH 1/5] added pauls scripts

---
 decision-maker.py => decision-maker-old.py |  41 ---
 decision_maker.py                          | 323 +++++++++++++++++++++
 plots for submission.py                    | 211 ++++++++++++++
 utils.py                                   |  16 +-
 4 files changed, 548 insertions(+), 43 deletions(-)
 rename decision-maker.py => decision-maker-old.py (87%)
 create mode 100644 decision_maker.py
 create mode 100644 plots for submission.py

diff --git a/decision-maker.py b/decision-maker-old.py
similarity index 87%
rename from decision-maker.py
rename to decision-maker-old.py
index 1811388..1344b2a 100644
--- a/decision-maker.py
+++ b/decision-maker-old.py
@@ -222,47 +222,6 @@ def _sample_from(self, posterior: Dict[str, float]) -> str:
     
     ch_det, conf_det = agent_det.choose(first_pair)
     
-    
-
-    
-    # ------------------------------------------------------------------
-    #  Produce plots
-    # ------------------------------------------------------------------
-    # fig, axs = plt.subplots(2, 2, figsize=(10, 7))
-    # plot_all_gaussians(
-    #     {k: (v.mu, v.sigma) for k, v in cats.items()},
-    #     highlight_pair=first_pair,
-    #     ax=axs[0, 0],
-    # )
-    # plot_pair_with_threshold(
-    #     {k: (cats[k].mu, cats[k].sigma) for k in first_pair},
-    #     threshold,
-    #     ax=axs[0, 1],
-    # )
-    # plot_success_bernoulli(
-    #     {k: (cats[k].mu, cats[k].sigma) for k in first_pair},
-    #     threshold,
-    #     ax=axs[1, 0],
-    # )
-    # plot_reward_bernoulli(
-    #     {
-    #         "wide_low":  (0.22, 0.06, 11),
-    #         "narrow_high":   (0.30, 0.06, 18),
-    #     },
-    #     threshold,
-    #     {
-    #         "wide_low":  (0.22, 0.06, 11),
-    #         "narrow_high":   (0.30, 0.06, 18),
-    #     },
-    #     threshold,
-    #     ax=axs[1, 1],
-    # )
-    # plt.tight_layout()
-    # plt.show()
-    #     ax=axs[1, 1],
-    # )
-    # plt.tight_layout()
-    # plt.show()
 
     # ------------------------------------------------------------------
     #  Run two example trials
diff --git a/decision_maker.py b/decision_maker.py
new file mode 100644
index 0000000..e9118b9
--- /dev/null
+++ b/decision_maker.py
@@ -0,0 +1,323 @@
+
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Dict, Tuple, Sequence
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import random
+from utils import *
+
+def gaussian_cdf(t: float, mu: float, sigma: float) -> float:
+    """Cumulative distribution Φ((t‑μ)/σ) using math.erf.
+    """
+    if sigma <= 0:
+        raise ValueError("sigma must be positive")
+    z = (t - mu) / (sigma * math.sqrt(2.0))
+    return 0.5 * (1.0 + math.erf(z))
+
+@dataclass(slots=True)
+class GaussianCategory:
+    """A univariate Gaussian with fixed parameters."""
+
+    mu: float
+    sigma: float
+
+    def success_prob(self, threshold: float) -> float:
+        """Return P[X < threshold], tells you how probable 
+        a draw from that category will end below the threshold."""
+        return gaussian_cdf(threshold, self.mu, self.sigma)
+
+
+class DecisionAgent:
+    
+    """Chooses between categories by maximising expected utility.
+
+    Parameters
+    ----------
+    categories
+        Mapping *id → GaussianCategory*.
+    rewards
+        Mapping *id → (reward_success, reward_failure)*.
+        If a category id is missing, defaults to (1.0, 0.0).
+    threshold
+        Global decision threshold T.
+    beta
+        Inverse ‘temperature’ for the exponential utility.
+        beta=1  -> risk‑sensitive expected exp‑utility
+        beta=0  -> risk‑neutral expected reward
+    soft
+        If True, draw action from soft‑max distribution instead of arg‑max.
+    rng
+        Optional random.Random instance used only when *soft* is True.
+    """
+
+    def __init__(
+        self,
+        categories: Dict[int, GaussianCategory],
+        rewards: Dict[int, Tuple[float, float]] | None = None,
+        model: str="entropy", 
+        threshold: float = 0.0,
+        beta: float = 1.0,
+        soft: bool = False,
+        rng: random.Random | None = None,
+        verbose: bool = False,
+    ):
+        
+        allowed_models = {"map", "diff", "entropy"}
+        if model not in allowed_models:
+            raise ValueError(f"Invalid model '{model}'. Must be one of {allowed_models}.")
+        self.categories = categories
+        self.rewards = rewards or {}
+        self.model=model
+        self.T = threshold
+        self.beta = beta
+        self.soft = soft
+        self.rng = rng or random.Random()
+        self.verbose = verbose
+
+
+    def choose(self, pair: Sequence[str]) -> Tuple[str, float]:
+        """Return (choice, confidence = –entropy(posterior))."""
+        post = self.posterior(pair)  # full posterior over the two actions
+        
+        if self.verbose:
+            self.visualize_pair(pair)
+
+
+        if self.soft:
+            choice = self._sample_from(post)
+        else:
+            choice = max(post, key=post.get)  # MAP
+
+        confidence = self._confidence(post)
+
+        return choice, confidence
+    
+    def posterior(self, pair: Sequence[str]) -> Dict[str, float]:
+        """Compute normalised posterior p(c|β,T,rewards) for the pair."""
+        if len(pair) != 2:
+            raise ValueError("pair must contain exactly two category ids")
+
+        util = {cid: self._expected_utility(cid) for cid in pair}
+        total = sum(util.values())
+        if total == 0.0:
+            # in pathological case, fall back to uniform
+            return {cid: 1.0 / len(pair) for cid in pair}
+
+        return {cid: u / total for cid, u in util.items()}
+    
+    def visualize_pair(self, pair: Sequence[str]) -> None:
+        """
+        1) Overplot all 4 gaussians in grey;
+        2) Highlight the two in `pair` (yellow / blue).
+        3) Shade their success‐regions under the curves.
+        4) Bar‐plot the renormalised P(success) Bernoulli.
+        """
+        # 1. Grab the full categories dict:
+        cat_params = {cid: (c.mu, c.sigma) for cid, c in self.categories.items()}
+        # 2. Posterior of raw success-prob (no reward):
+        post = self.posterior(pair)
+        # 3. Call your existing plotting functions—but pass them only
+        #    the raw CDF probabilities and colours:
+        fig, axes = plt.subplots(1,1, figsize=(5,5))
+    
+        # # A: all four in grey, highlight pair
+        # plot_all_gaussians(
+        #     cat_params,
+        #     highlight_pair=pair,
+        #     ax=axes[0])
+    
+        # # B: plot pair with shaded success region:
+        # pair_params = {cid: cat_params[cid] for cid in pair}
+        
+        # plot_pair_with_threshold(
+        #     pair_params,
+        #     threshold=self.T,
+        #     ax=axes[1])
+        
+        # # PK: bar‐plot of raw success-prob
+        # raw_prob = self._raw(pair)
+        # plot_bar_from_probs(
+        #     probs=raw_prob,
+        #     raw=True,
+        #     ax=axes[2])    
+    
+    
+        # C: bar‐plot Bernoulli of raw success-prob: ??? PK: NOT RAW - reward weighted
+        #    pass post directly—no recomputation!
+        
+        # calculate confidence and print in title of final plot
+        confidence  = self._confidence(post)
+
+        
+        plot_bar_from_probs(
+            probs=post,
+            c=confidence,
+            beta=self.beta)
+
+        plt.tight_layout()
+        plt.show()
+
+
+    def _expected_utility(self, cid: int) -> float:
+        cat = self.categories[cid]  
+        p_succ = cat.success_prob(self.T) # compute prob of success via cdf
+        w_succ, w_fail = self.rewards.get(cid, (1.0, 0.0)) # get the rewards
+        
+        if self.beta == 0.0:
+            # risk‑neutral: ordinary expected reward
+            return p_succ * w_succ + (1.0 - p_succ) * w_fail
+
+        # risk‑sensitive: expected exp‑utility
+
+        # return p_succ * math.exp(self.beta * w_succ) + (1.0 - p_succ) * math.exp(
+        #     self.beta * w_fail
+        # ) ### poppy original
+        
+        # return p_succ * math.exp(self.beta * np.log(w_succ)) + (1.0 - p_succ) * math.exp(
+        #     self.beta * np.log(w_fail)
+        # )
+    
+        return p_succ * (w_succ**self.beta) + (1.0 - p_succ) * (w_fail**self.beta)
+    
+    def _raw(self, pair: Sequence[str]) -> Dict[str, float]:
+        raw={}
+        for cid in pair: 
+            cat = self.categories[cid]  
+            p_succ = cat.success_prob(self.T) # compute prob of success via cdf
+            raw[cid]=p_succ
+        return raw
+
+        
+    
+    @staticmethod
+    def _neg_entropy(posterior: Dict[str, float]) -> float:
+        """Return –∑ p log p  (≥ 0 when some probabilities >1, ≤ 0 otherwise)."""
+        return sum(p * math.log(p) for p in posterior.values() if p > 0.0)
+    
+    def _confidence(self, posterior: Dict[str, float]) -> float:
+        """
+        Normalised confidence in [0,1]:
+        1 when the posterior is a delta (min entropy),
+        0 when it's uniform (max entropy).
+        """
+        if self.model=="entropy":
+            # 1) raw negative-entropy = sum p log p
+            ne = sum(p * math.log(p) for p in posterior.values() if p > 0.0)
+            # 2) maximum entropy (nats) for N equally-likely outcomes
+            H_max = math.log(len(posterior))
+            # 3) normalise: 1 + ne/H_max
+            return 1.0 + ne / H_max
+        elif self.model=="map":
+            my_max = max(posterior.values())
+            return (len(posterior)*my_max-1)/(len(posterior)-1)
+        elif self.model=="diff":
+            sorted_values=list(posterior.values())
+            sorted_values.sort(reverse=True)
+            diff = sorted_values[0] - sorted_values[1]
+            return diff
+
+    def _sample_from(self, posterior: Dict[str, float]) -> str:
+        """Draw key according to its probability weight."""
+        r = self.rng.random()
+        acc = 0.0
+        for k, p in posterior.items():
+            acc += p
+            if r <= acc:
+                return k
+        return k  # numerical precision fallback
+    
+    
+if __name__ == "__main__": 
+    
+    # ------------------------------------------------------------------
+    #  Model parameters
+    # ------------------------------------------------------------------
+    
+    cats = {
+        "narrow_low": GaussianCategory(mu=0.22, sigma=0.02),
+        "wide_low": GaussianCategory(mu=0.22, sigma=0.06),
+        "narrow_high": GaussianCategory(mu=0.30, sigma=0.02),
+        "wide_high": GaussianCategory(mu=0.30, sigma=0.06),
+    }
+    rewards = {
+        "narrow_low": (10.0, 1e-6),
+        "wide_low": (11.0, 1e-6),
+        "narrow_high": (14.0, 1e-6),
+        "wide_high": (18.0, 1e-6),
+    }
+
+
+    
+    threshold = 0.31
+    first_pair = ("wide_low", "narrow_high")
+
+    # ------------------------------------------------------------------
+    #  Agents
+    # ------------------------------------------------------------------
+
+    agent_det = DecisionAgent(cats, rewards, threshold=0.28, beta=10, soft=False, verbose=True)
+    # agent_soft = DecisionAgent(cats, rewards, threshold=0.31, beta=10, soft=True, verbose=True)
+    
+    ch_det, conf_det = agent_det.choose(first_pair)
+    
+    
+
+    
+    # ------------------------------------------------------------------
+    #  Produce plots
+    # ------------------------------------------------------------------
+    # fig, axs = plt.subplots(2, 2, figsize=(10, 7))
+    # plot_all_gaussians(
+    #     {k: (v.mu, v.sigma) for k, v in cats.items()},
+    #     highlight_pair=first_pair,
+    #     ax=axs[0, 0],
+    # )
+    # plot_pair_with_threshold(
+    #     {k: (cats[k].mu, cats[k].sigma) for k in first_pair},
+    #     threshold,
+    #     ax=axs[0, 1],
+    # )
+    # plot_success_bernoulli(
+    #     {k: (cats[k].mu, cats[k].sigma) for k in first_pair},
+    #     threshold,
+    #     ax=axs[1, 0],
+    # )
+    # plot_reward_bernoulli(
+    #     {
+    #         "wide_low":  (0.22, 0.06, 11),
+    #         "narrow_high":   (0.30, 0.06, 18),
+    #     },
+    #     threshold,
+    #     {
+    #         "wide_low":  (0.22, 0.06, 11),
+    #         "narrow_high":   (0.30, 0.06, 18),
+    #     },
+    #     threshold,
+    #     ax=axs[1, 1],
+    # )
+    # plt.tight_layout()
+    # plt.show()
+    #     ax=axs[1, 1],
+    # )
+    # plt.tight_layout()
+    # plt.show()
+
+    # ------------------------------------------------------------------
+    #  Run two example trials
+    # ------------------------------------------------------------------
+    # for pair in [first_pair, ("narrow_low", "narrow_high")]:
+    #     ch_det, conf_det = agent_det.choose(pair)
+    #     ch_soft, conf_soft = agent_soft.choose(pair)
+    #     # print(
+        #     f"{pair}: MAP→{ch_det} (conf={conf_det:+.3f})"
+        #     f"sample→{ch_soft} (conf={conf_soft:+.3f})"
+        # )
+
+
+    # for pair in [("wide_low", "wide_high"), ("narrow_low", "narrow_high")]:
+    #     ch_det, conf_det = agent_det.choose(pair)
+    #     ch_soft, conf_soft = agent_soft.choose(pair)
+    #     print(f"{pair}: MAP→{ch_det} (conf={conf_det:+.3f}), "
+    #           f"sample→{ch_soft} (conf={conf_soft:+.3f})")
\ No newline at end of file
diff --git a/plots for submission.py b/plots for submission.py
new file mode 100644
index 0000000..5cc4938
--- /dev/null
+++ b/plots for submission.py	
@@ -0,0 +1,211 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 14 09:02:21 2025
+
+@author: pfkin
+"""
+
+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+from decision_maker import DecisionAgent,GaussianCategory
+
+
+
+def plot_A():
+    # A: plot impact of beta on a pair
+    threshold = .28
+    first_pair = ("wide_low", "narrow_high")
+    
+    ch_det_history=[]
+    conf_det_history=[]
+    for beta in np.logspace(-2,1,50):
+    #for beta in [.1]:
+        agent_det = DecisionAgent(cats, rewards, model="entropy", threshold=threshold, beta=beta, soft=False, verbose=True)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history.append(ch_det)
+        conf_det_history.append(conf_det)
+    
+    fig, ax1 = plt.subplots()
+    ax2 = ax1.twinx()
+    ln1=ax1.plot(np.logspace(-2,1,50),ch_det_history, label="Choice (LH axis)", color='k')
+    ln2=ax2.plot(np.logspace(-2,1,50),conf_det_history, label="Confidence (RH axis)", color='r')
+    lns=ln1+ln2
+    labs = [l.get_label() for l in lns]
+    ax1.legend(lns, labs, loc="upper left")
+    ax1.set_xlabel("Beta")
+    ax1.set_xscale('log')
+    ax1.set_ylabel("Choice", color='k')
+    ax2.set_ylabel("Confidence", color='r')
+    ax1.tick_params(axis='y', colors='k')  
+    ax2.tick_params(axis='y', colors='r') 
+    plt.show()
+
+def plot_B():
+    # B: plot impact of beta on a pair for 3 confidence measure types
+    threshold = .28
+    first_pair = ("wide_low", "narrow_high")
+    
+    ch_det_history_ent=[]
+    conf_det_history_ent=[]
+    for beta in np.logspace(-2,1,50):
+    #for beta in [.1]:
+        agent_det = DecisionAgent(cats, rewards, model="entropy", threshold=threshold, beta=beta, soft=False, verbose=False)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history_ent.append(ch_det)
+        conf_det_history_ent.append(conf_det)
+    
+    ch_det_history_diff=[]
+    conf_det_history_diff=[]
+    for beta in np.logspace(-2,1,50):
+    #for beta in [.1]:
+        agent_det = DecisionAgent(cats, rewards, model="diff", threshold=threshold, beta=beta, soft=False, verbose=False)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history_diff.append(ch_det)
+        conf_det_history_diff.append(conf_det)
+    
+    ch_det_history_map=[]
+    conf_det_history_map=[]
+    for beta in np.logspace(-2,1,50):
+    #for beta in [.1]:
+        agent_det = DecisionAgent(cats, rewards, model="map", threshold=threshold, beta=beta, soft=False, verbose=False)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history_map.append(ch_det)
+        conf_det_history_map.append(conf_det)
+    
+    
+    fig, ax1 = plt.subplots()
+    ax2 = ax1.twinx()
+    ln1=ax1.plot(np.logspace(-2,1,50),ch_det_history_ent, label="Choice (LH axis)", color='k')
+    ln2=ax2.plot(np.logspace(-2,1,50),conf_det_history_ent, label="Entropy as Confidence (RH axis)", color='r')
+    ln3=ax2.plot(np.logspace(-2,1,50),conf_det_history_diff, label="Diff as Confidence (RH axis)", color='r', linestyle='dotted', marker="x")
+    ln4=ax2.plot(np.logspace(-2,1,50),conf_det_history_map, label="Max as Confidence (RH axis)", color='r', linestyle='dashed')
+    lns=ln1+ln2+ln3+ln4
+    labs = [l.get_label() for l in lns]
+    ax1.legend(lns, labs, loc="upper left")
+    ax1.set_xlabel("Beta")
+    ax1.set_xscale('log')
+    ax1.set_ylabel("Choice", color='k')
+    ax2.set_ylabel("Confidence", color='r')
+    ax1.tick_params(axis='y', colors='k')  
+    ax2.tick_params(axis='y', colors='r') 
+    plt.show()
+
+
+def plot_C():
+    # C: plot impact of threshold on a pair
+    beta=.1
+    
+    first_pair = ("wide_low", "narrow_high")
+    
+    ch_det_history=[]
+    conf_det_history=[]
+    for threshold in np.linspace(.1,.5,50):
+        agent_det = DecisionAgent(cats, rewards, threshold=threshold, beta=beta, soft=False, verbose=False)
+        # agent_soft = DecisionAgent(cats, rewards, threshold=0.31, beta=10, soft=True, verbose=True)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history.append(ch_det)
+        conf_det_history.append(conf_det)
+    
+    fig, ax1 = plt.subplots()
+    ax2 = ax1.twinx()
+    ln1=ax1.plot(ch_det_history, label="Choice (LH axis)", color='k')
+    ln2=ax2.plot(conf_det_history, label="Confidence (RH axis)", color='r')
+    lns=ln1+ln2
+    labs = [l.get_label() for l in lns]
+    ax1.legend(lns, labs, loc="center right")
+    ax1.set_xlabel("Threshold")
+    #ax1.set_xscale('log')
+    ax1.set_ylabel("Choice", color='k')
+    ax2.set_ylabel("Confidence", color='r')
+    ax1.tick_params(axis='y', colors='k')  
+    ax2.tick_params(axis='y', colors='r') 
+    plt.show()
+
+
+
+def plot_D():
+    # # D: plot impact of threshold on a pair for different confidence measures
+    beta=1
+    first_pair = ("wide_low", "narrow_high")
+    
+    ch_det_history_ent=[]
+    conf_det_history_ent=[]
+    for threshold in np.linspace(.1,.5,50):
+        agent_det = DecisionAgent(cats, rewards, model='entropy', threshold=threshold, beta=beta, soft=False, verbose=False)
+        # agent_soft = DecisionAgent(cats, rewards, threshold=0.31, beta=10, soft=True, verbose=True)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history_ent.append(ch_det)
+        conf_det_history_ent.append(conf_det)
+    
+    ch_det_history_diff=[]
+    conf_det_history_diff=[]
+    for threshold in np.linspace(.1,.5,50):
+        agent_det = DecisionAgent(cats, rewards, model='diff', threshold=threshold, beta=beta, soft=False, verbose=False)
+        # agent_soft = DecisionAgent(cats, rewards, threshold=0.31, beta=10, soft=True, verbose=True)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history_diff.append(ch_det)
+        conf_det_history_diff.append(conf_det)
+    
+    ch_det_history_map=[]
+    conf_det_history_map=[]
+    for threshold in np.linspace(.1,.5,50):
+        agent_det = DecisionAgent(cats, rewards, model='map', threshold=threshold, beta=beta, soft=False, verbose=False)
+        # agent_soft = DecisionAgent(cats, rewards, threshold=0.31, beta=10, soft=True, verbose=True)
+        
+        ch_det, conf_det = agent_det.choose(first_pair)
+        ch_det_history_map.append(ch_det)
+        conf_det_history_map.append(conf_det)    
+    
+    
+    fig, ax1 = plt.subplots()
+    ax2 = ax1.twinx()
+    ln1=ax1.plot(np.linspace(.1,.5,50),ch_det_history_ent, label="Choice (LH axis)", color='k')
+    ln2=ax2.plot(np.linspace(.1,.5,50),conf_det_history_ent, label="Entropy as Confidence (RH axis)", color='r')
+    ln3=ax2.plot(np.linspace(.1,.5,50),conf_det_history_diff, label="Diff as Confidence (RH axis)", color='r', linestyle='dotted', marker="x")
+    ln4=ax2.plot(np.linspace(.1,.5,50),conf_det_history_map, label="Map as Confidence (RH axis)", color='r', linestyle='dashed')
+    lns=ln1+ln2+ln3+ln4
+    labs = [l.get_label() for l in lns]
+    ax1.legend(lns, labs, loc="center right")
+    ax1.set_xlabel("Threshold")
+    #ax1.set_xscale('log')
+    ax1.set_ylabel("Choice", color='k')
+    ax2.set_ylabel("Confidence", color='r')
+    ax1.tick_params(axis='y', colors='k')  
+    ax2.tick_params(axis='y', colors='r') 
+    plt.show()
+    
+if __name__ == "__main__":    
+    cats = {
+        "narrow_low": GaussianCategory(mu=0.22, sigma=0.02),
+        "wide_low": GaussianCategory(mu=0.22, sigma=0.06),
+        "narrow_high": GaussianCategory(mu=0.30, sigma=0.02),
+        "wide_high": GaussianCategory(mu=0.30, sigma=0.06),
+    }
+    # rewards = {
+    #     "narrow_low": (10.0, 1e-6),
+    #     "wide_low": (11.0, 1e-6),
+    #     "narrow_high": (14.0, 1e-6),
+    #     "wide_high": (18.0, 1e-6),
+    # }
+    rewards = {
+        "narrow_low": (10.0, 1e-6),
+        "wide_low": (10.0, 1e-6),
+        "narrow_high": (10.0, 1e-6),
+        "wide_high": (18.0, 1e-6),
+    }
+
+    plot_A()
+    plot_B()
+    plot_C()
+    plot_D()
+    
+    
\ No newline at end of file
diff --git a/utils.py b/utils.py
index 10847ab..1cd6006 100644
--- a/utils.py
+++ b/utils.py
@@ -114,10 +114,12 @@ def plot_success_bernoulli(
 
 def plot_bar_from_probs(
     probs: Dict[str, float],
-    c: float,
+    c: Optional[float] = None,
+    beta: Optional[float] = None,
     ax: Optional[plt.Axes] = None,
     color_map: Optional[Dict[str, str]] = None,
     title: Optional[str] = None,
+    raw: bool = False, 
     alpha: float = 0.35,
     ) -> plt.Axes:
     """
@@ -155,7 +157,17 @@ def plot_bar_from_probs(
 
     ax.set_ylim(0, 1)
     ax.set_ylabel("Probability")
-    ax.set_title(title or f"Reward-weighted probabilities. Confidence={c:.2f}")
+    if raw:
+        default_title = "Raw success probabilities."
+    else:
+        if c is None or beta is None:
+            default_title = "Reward-weighted probabilities."
+        else:
+            default_title = (
+                f"Reward-weighted probabilities. "
+                f"Confidence={c:.2f}. Beta={beta:.2f}"
+            )
+    ax.set_title(title or default_title)
     ax.set_xticks(range(len(labels)))
     ax.set_xticklabels(labels, rotation=15)
 

From d2159839c816e4e599d9a61920d9f6647377638a Mon Sep 17 00:00:00 2001
From: PoppyCollis <poppy.collis@gmail.com>
Date: Thu, 15 May 2025 16:21:42 +0100
Subject: [PATCH 2/5] import csv data, clean and structure into matrices

---
 clean_data_old.py                             |   66 +
 ...ll_participants_action_selection_data.csv# |    1 +
 ...all_participants_action_selection_data.csv | 1077 +++++++++++++++++
 data/all_participants_training_data.csv       |  744 ++++++++++++
 import_data.py                                |   83 ++
 5 files changed, 1971 insertions(+)
 create mode 100644 clean_data_old.py
 create mode 100644 data/.~lock.all_participants_action_selection_data.csv#
 create mode 100644 data/all_participants_action_selection_data.csv
 create mode 100644 data/all_participants_training_data.csv
 create mode 100644 import_data.py

diff --git a/clean_data_old.py b/clean_data_old.py
new file mode 100644
index 0000000..e8facec
--- /dev/null
+++ b/clean_data_old.py
@@ -0,0 +1,66 @@
+import numpy as np
+import pandas as pd
+
+# Load
+data_path = 'data/all_participants_action_selection_data.csv'
+cols = ['participant_id', 'trial_number', 'first_option', 'second_option', 'chosen_type', 'action_selection_conf']
+df = pd.read_csv(data_path, usecols=cols)
+
+# Create a complete MultiIndex of (participant_id, trial_number)
+pids = df['participant_id'].unique()
+trials = np.arange(1, 109)       # 1 through 108
+full_idx = pd.MultiIndex.from_product([pids, trials],
+                                      names=['participant_id', 'trial_number'])
+
+# Re-index, so missing rows become NaN
+df = df.set_index(['participant_id', 'trial_number']).reindex(full_idx).reset_index()
+
+# (Optional) report which trials were missing per participant
+missing = (
+    df[df['first_option'].isna()]
+      .groupby('participant_id')['trial_number']
+      .apply(list)
+)
+print("Missing trials per participant:\n", missing)
+
+# Map your strings to ints (will leave NaN alone)
+dist_map = {
+    'narrow_low': 0,
+    'wide_low':   1,
+    'narrow_high':2,
+    'wide_high':3
+}
+for col in ['first_option', 'second_option', 'chosen_type']:
+    df[col] = df[col].map(dist_map)
+
+# Build the arrays
+for pid, group in df.groupby('participant_id'):
+    pid_str = str(pid)
+    globals()[f"{pid_str}_option1"] = group['first_option'].to_numpy()
+    globals()[f"{pid_str}_option2"] = group['second_option'].to_numpy()
+    globals()[f"{pid_str}_optionC"]  = group['chosen_type'].to_numpy()
+    globals()[f"{pid_str}_confA"]    = group['action_selection_conf'].to_numpy()
+
+
+# — assume `df` already has your mapped ints and full 108‐trial padding ——
+
+# 1) Create two new columns where the smaller code is always in “opt1”
+#    and the larger in “opt2”
+df[['opt1','opt2']] = pd.DataFrame(
+    np.sort(df[['first_option','second_option']].to_numpy(), axis=1),
+    index=df.index
+)
+
+# 2) Now for each participant, get a frequency table of (opt1, opt2)
+pair_counts = (
+    df
+    .groupby(['participant_id','opt1','opt2'])
+    .size()
+    .reset_index(name='count')
+)
+
+# 3) View the results
+#    e.g. all unique pairs & counts for participant 217:
+print(pair_counts[pair_counts['participant_id']==217])
+
+
diff --git a/data/.~lock.all_participants_action_selection_data.csv# b/data/.~lock.all_participants_action_selection_data.csv#
new file mode 100644
index 0000000..b3666eb
--- /dev/null
+++ b/data/.~lock.all_participants_action_selection_data.csv#
@@ -0,0 +1 @@
+,pzc,pzc-Blade-15-2022-RZ09-0421,15.05.2025 15:19,file:///home/pzc/.config/libreoffice/4;
\ No newline at end of file
diff --git a/data/all_participants_action_selection_data.csv b/data/all_participants_action_selection_data.csv
new file mode 100644
index 0000000..3325e98
--- /dev/null
+++ b/data/all_participants_action_selection_data.csv
@@ -0,0 +1,1077 @@
+participant_id,trial_number,distribution_type,first_option,second_option,chosen_type,ball_weight,action_selection_conf,success_prediction_conf,balls_in_box,trial_success,trial_reward,cumulative_reward,action_conf_selection_time,action_conf_confirmation_time,prediction_conf_selection_time,prediction_conf_confirmation_time
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diff --git a/data/all_participants_training_data.csv b/data/all_participants_training_data.csv
new file mode 100644
index 0000000..fa75fbb
--- /dev/null
+++ b/data/all_participants_training_data.csv
@@ -0,0 +1,744 @@
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diff --git a/import_data.py b/import_data.py
new file mode 100644
index 0000000..830e501
--- /dev/null
+++ b/import_data.py
@@ -0,0 +1,83 @@
+import numpy as np
+import pandas as pd
+
+# --- Load and preprocess the CSV ---
+data_path = 'data/all_participants_action_selection_data.csv'
+cols = ['participant_id', 'trial_number', 'first_option', 'second_option', 'chosen_type', 'action_selection_conf']
+df = pd.read_csv(data_path, usecols=cols)
+
+# --- (Optional) pad missing trials for participant 217 only ---
+out_dfs = []
+for pid, sub in df.groupby('participant_id'):
+    sub = sub.set_index('trial_number').sort_index()
+    if pid == 217:
+        sub = sub.reindex(range(1,109))    # insert NaNs for missing trials
+    out_dfs.append(sub.assign(participant_id=pid).reset_index())
+df = pd.concat(out_dfs, ignore_index=True)
+
+# --- Map distribution-type strings to integers ---
+dist_map = {
+    'narrow_low':  0,
+    'wide_low':    1,
+    'narrow_high': 2,
+    'wide_high':   3,
+}
+for col in ['first_option', 'second_option', 'chosen_type']:
+    df[col] = df[col].map(dist_map)
+
+# --- Standardize option pairs and compute aggregated metrics ---
+results = {}
+for pid, sub in df.groupby('participant_id'):
+    pid_str = str(pid)
+    # Ensure sorted pairs
+    sub = sub.assign(
+        opt_min = np.minimum(sub['first_option'], sub['second_option']),
+        opt_max = np.maximum(sub['first_option'], sub['second_option'])
+    )
+
+    # Prepare containers
+    pair_list = []
+    count_list = []
+    bern_list = []
+    conf_list = []
+
+    # Process each unique pair
+    grouped = sub.groupby(['opt_min', 'opt_max'])
+    for (a, b), grp in grouped:
+        total = len(grp)
+        # Bernoulli P(choice=min), P(choice=max)
+        count_min = np.sum(grp['chosen_type'] == a)
+        p_min = count_min / total if total > 0 else np.nan
+        p_max = 1 - p_min if total > 0 else np.nan
+        # Categorical distribution over confidence levels 1-6
+        conf_counts = [np.sum(grp['action_selection_conf'] == lvl) for lvl in range(1,7)]
+        conf_probs = np.array(conf_counts, dtype=float) / total if total > 0 else np.full(6, np.nan)
+
+        pair_list.append((a, b))
+        count_list.append(total)
+        bern_list.append((p_min, p_max))
+        conf_list.append(conf_probs)
+
+    # Sort by pair
+    sorted_idx = np.argsort(np.array(pair_list), axis=0)[:,0]  # sort by first then second
+    pair_array = np.array(pair_list)[sorted_idx]
+    counts = np.array(count_list)[sorted_idx]
+    bern = np.array(bern_list)[sorted_idx]
+    conf = np.vstack(conf_list)[sorted_idx]
+
+    # Unpack pair_array
+    opt1_pairs = pair_array[:,0]
+    opt2_pairs = pair_array[:,1]
+
+    # Assign to globals
+    globals()[f"{pid_str}_option1_pairs"] = opt1_pairs
+    globals()[f"{pid_str}_option2_pairs"] = opt2_pairs
+    globals()[f"{pid_str}_pair_counts"]   = counts
+    globals()[f"{pid_str}_optionC"]       = bern   # (n_pairs x 2) Bernoulli
+    globals()[f"{pid_str}_confA"]         = conf   # (n_pairs x 6) Categorical
+
+# After running this script, each participant has:
+#  - '<pid>_option1_pairs' & '<pid>_option2_pairs': arrays of unique sorted option codes
+#  - '<pid>_pair_counts': total presentations per pair
+#  - '<pid>_optionC': one row per pair, [P(pick opt_min), P(pick opt_max)]
+#  - '<pid>_confA': one row per pair, length-6 vector of P(confidence=1..6)

From 6b47afbd9836f7fe59e4b9f061a4cd1dfd74ea57 Mon Sep 17 00:00:00 2001
From: PoppyCollis <poppy.collis@gmail.com>
Date: Thu, 15 May 2025 16:59:33 +0100
Subject: [PATCH 3/5] we now have 6 x 2 conf x decision matrices per pair

---
 import_data.py     | 70 +++++++++++++-------------------------
 import_data_old.py | 83 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 106 insertions(+), 47 deletions(-)
 create mode 100644 import_data_old.py

diff --git a/import_data.py b/import_data.py
index 830e501..21779e8 100644
--- a/import_data.py
+++ b/import_data.py
@@ -25,59 +25,35 @@
 for col in ['first_option', 'second_option', 'chosen_type']:
     df[col] = df[col].map(dist_map)
 
-# --- Standardize option pairs and compute aggregated metrics ---
-results = {}
+# --- For each participant, create joint confidence-choice matrices per unique sorted pair ---
+# We'll name each matrix '{participant_id}_{a}_{b}' where (a,b) is the sorted option code pair.
 for pid, sub in df.groupby('participant_id'):
     pid_str = str(pid)
-    # Ensure sorted pairs
+    # Compute sorted pairs
     sub = sub.assign(
         opt_min = np.minimum(sub['first_option'], sub['second_option']),
         opt_max = np.maximum(sub['first_option'], sub['second_option'])
     )
-
-    # Prepare containers
-    pair_list = []
-    count_list = []
-    bern_list = []
-    conf_list = []
-
-    # Process each unique pair
     grouped = sub.groupby(['opt_min', 'opt_max'])
+
     for (a, b), grp in grouped:
         total = len(grp)
-        # Bernoulli P(choice=min), P(choice=max)
-        count_min = np.sum(grp['chosen_type'] == a)
-        p_min = count_min / total if total > 0 else np.nan
-        p_max = 1 - p_min if total > 0 else np.nan
-        # Categorical distribution over confidence levels 1-6
-        conf_counts = [np.sum(grp['action_selection_conf'] == lvl) for lvl in range(1,7)]
-        conf_probs = np.array(conf_counts, dtype=float) / total if total > 0 else np.full(6, np.nan)
-
-        pair_list.append((a, b))
-        count_list.append(total)
-        bern_list.append((p_min, p_max))
-        conf_list.append(conf_probs)
-
-    # Sort by pair
-    sorted_idx = np.argsort(np.array(pair_list), axis=0)[:,0]  # sort by first then second
-    pair_array = np.array(pair_list)[sorted_idx]
-    counts = np.array(count_list)[sorted_idx]
-    bern = np.array(bern_list)[sorted_idx]
-    conf = np.vstack(conf_list)[sorted_idx]
-
-    # Unpack pair_array
-    opt1_pairs = pair_array[:,0]
-    opt2_pairs = pair_array[:,1]
-
-    # Assign to globals
-    globals()[f"{pid_str}_option1_pairs"] = opt1_pairs
-    globals()[f"{pid_str}_option2_pairs"] = opt2_pairs
-    globals()[f"{pid_str}_pair_counts"]   = counts
-    globals()[f"{pid_str}_optionC"]       = bern   # (n_pairs x 2) Bernoulli
-    globals()[f"{pid_str}_confA"]         = conf   # (n_pairs x 6) Categorical
-
-# After running this script, each participant has:
-#  - '<pid>_option1_pairs' & '<pid>_option2_pairs': arrays of unique sorted option codes
-#  - '<pid>_pair_counts': total presentations per pair
-#  - '<pid>_optionC': one row per pair, [P(pick opt_min), P(pick opt_max)]
-#  - '<pid>_confA': one row per pair, length-6 vector of P(confidence=1..6)
+        # Initialize joint count matrix: rows=confidence levels 1..6, cols=[choice_min, choice_max]
+        joint_counts = np.zeros((6, 2), dtype=int)
+        for lvl in range(1, 7):
+            mask_lvl = grp['action_selection_conf'] == lvl
+            joint_counts[lvl-1, 0] = np.sum(grp[mask_lvl]['chosen_type'] == a)
+            joint_counts[lvl-1, 1] = np.sum(grp[mask_lvl]['chosen_type'] == b)
+        # Convert counts to probabilities (joint distribution)
+        joint_dist = joint_counts.astype(float) / total
+
+        # Assign to a named variable in globals
+        var_name = f"{pid_str}_{a}_{b}"
+        globals()[var_name] = joint_dist
+
+# After running this script, for each participant you'll have 6 new arrays:
+#  - '<pid>_0_1', '<pid>_0_2', '<pid>_0_3', '<pid>1_2', '<pid>1_3', '<pid>2_3'
+# each is a 6×2 matrix where:
+#   - row i (0-indexed) is confidence level (i+1)
+#   - col 0 = P(conf=i+1 AND choice=opt_min)
+#   - col 1 = P(conf=i+1 AND choice=opt_max)
diff --git a/import_data_old.py b/import_data_old.py
new file mode 100644
index 0000000..830e501
--- /dev/null
+++ b/import_data_old.py
@@ -0,0 +1,83 @@
+import numpy as np
+import pandas as pd
+
+# --- Load and preprocess the CSV ---
+data_path = 'data/all_participants_action_selection_data.csv'
+cols = ['participant_id', 'trial_number', 'first_option', 'second_option', 'chosen_type', 'action_selection_conf']
+df = pd.read_csv(data_path, usecols=cols)
+
+# --- (Optional) pad missing trials for participant 217 only ---
+out_dfs = []
+for pid, sub in df.groupby('participant_id'):
+    sub = sub.set_index('trial_number').sort_index()
+    if pid == 217:
+        sub = sub.reindex(range(1,109))    # insert NaNs for missing trials
+    out_dfs.append(sub.assign(participant_id=pid).reset_index())
+df = pd.concat(out_dfs, ignore_index=True)
+
+# --- Map distribution-type strings to integers ---
+dist_map = {
+    'narrow_low':  0,
+    'wide_low':    1,
+    'narrow_high': 2,
+    'wide_high':   3,
+}
+for col in ['first_option', 'second_option', 'chosen_type']:
+    df[col] = df[col].map(dist_map)
+
+# --- Standardize option pairs and compute aggregated metrics ---
+results = {}
+for pid, sub in df.groupby('participant_id'):
+    pid_str = str(pid)
+    # Ensure sorted pairs
+    sub = sub.assign(
+        opt_min = np.minimum(sub['first_option'], sub['second_option']),
+        opt_max = np.maximum(sub['first_option'], sub['second_option'])
+    )
+
+    # Prepare containers
+    pair_list = []
+    count_list = []
+    bern_list = []
+    conf_list = []
+
+    # Process each unique pair
+    grouped = sub.groupby(['opt_min', 'opt_max'])
+    for (a, b), grp in grouped:
+        total = len(grp)
+        # Bernoulli P(choice=min), P(choice=max)
+        count_min = np.sum(grp['chosen_type'] == a)
+        p_min = count_min / total if total > 0 else np.nan
+        p_max = 1 - p_min if total > 0 else np.nan
+        # Categorical distribution over confidence levels 1-6
+        conf_counts = [np.sum(grp['action_selection_conf'] == lvl) for lvl in range(1,7)]
+        conf_probs = np.array(conf_counts, dtype=float) / total if total > 0 else np.full(6, np.nan)
+
+        pair_list.append((a, b))
+        count_list.append(total)
+        bern_list.append((p_min, p_max))
+        conf_list.append(conf_probs)
+
+    # Sort by pair
+    sorted_idx = np.argsort(np.array(pair_list), axis=0)[:,0]  # sort by first then second
+    pair_array = np.array(pair_list)[sorted_idx]
+    counts = np.array(count_list)[sorted_idx]
+    bern = np.array(bern_list)[sorted_idx]
+    conf = np.vstack(conf_list)[sorted_idx]
+
+    # Unpack pair_array
+    opt1_pairs = pair_array[:,0]
+    opt2_pairs = pair_array[:,1]
+
+    # Assign to globals
+    globals()[f"{pid_str}_option1_pairs"] = opt1_pairs
+    globals()[f"{pid_str}_option2_pairs"] = opt2_pairs
+    globals()[f"{pid_str}_pair_counts"]   = counts
+    globals()[f"{pid_str}_optionC"]       = bern   # (n_pairs x 2) Bernoulli
+    globals()[f"{pid_str}_confA"]         = conf   # (n_pairs x 6) Categorical
+
+# After running this script, each participant has:
+#  - '<pid>_option1_pairs' & '<pid>_option2_pairs': arrays of unique sorted option codes
+#  - '<pid>_pair_counts': total presentations per pair
+#  - '<pid>_optionC': one row per pair, [P(pick opt_min), P(pick opt_max)]
+#  - '<pid>_confA': one row per pair, length-6 vector of P(confidence=1..6)

From 32491a40d5795d1f9e0ab68281c4c726abf8bc9f Mon Sep 17 00:00:00 2001
From: PoppyCollis <poppy.collis@gmail.com>
Date: Thu, 15 May 2025 17:02:25 +0100
Subject: [PATCH 4/5] tidy up old scripts

---
 clean_data_old.py => old_scripts/clean_data_old.py         | 0
 decision-maker-old.py => old_scripts/decision-maker-old.py | 0
 import_data_old.py => old_scripts/import_data_old.py       | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename clean_data_old.py => old_scripts/clean_data_old.py (100%)
 rename decision-maker-old.py => old_scripts/decision-maker-old.py (100%)
 rename import_data_old.py => old_scripts/import_data_old.py (100%)

diff --git a/clean_data_old.py b/old_scripts/clean_data_old.py
similarity index 100%
rename from clean_data_old.py
rename to old_scripts/clean_data_old.py
diff --git a/decision-maker-old.py b/old_scripts/decision-maker-old.py
similarity index 100%
rename from decision-maker-old.py
rename to old_scripts/decision-maker-old.py
diff --git a/import_data_old.py b/old_scripts/import_data_old.py
similarity index 100%
rename from import_data_old.py
rename to old_scripts/import_data_old.py

From f5459a68c9e9b16b2856ecf876659b7d21634011 Mon Sep 17 00:00:00 2001
From: PoppyCollis <poppy.collis@gmail.com>
Date: Thu, 15 May 2025 17:04:21 +0100
Subject: [PATCH 5/5] Ignore __pycache__ and byte-compiled files

---
 .gitignore                        |   6 ++++++
 __pycache__/utils.cpython-310.pyc | Bin 6452 -> 0 bytes
 2 files changed, 6 insertions(+)
 delete mode 100644 __pycache__/utils.cpython-310.pyc

diff --git a/.gitignore b/.gitignore
index 65fece9..3313022 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,9 @@
 
 # Egg Info
 *.egg-info/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
diff --git a/__pycache__/utils.cpython-310.pyc b/__pycache__/utils.cpython-310.pyc
deleted file mode 100644
index fe64c660c9a236892932d006d9873255d469b147..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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