Merge branch 'master' into 1920x1080

4 files changed, 313 insertions, 0 deletions

diff --git a/experiment/analysis/analysis.org b/experiment/analysis/analysis.org
new file mode 100644
index 0000000..e726046
--- /dev/null
+++ b/experiment/analysis/analysis.org
@@ -0,0 +1,103 @@
+#+title: Analysis
+#+PROPERTY: header-args:python+ :session *python* :exports both :tangle yes
+
+* Imports
+#+begin_src python :results none
+import pandas as pd
+from pathlib import Path
+from pprint import pprint
+
+import tools
+
+#+end_src
+
+* Constants
+#+begin_src python :results none
+data_path = Path("/home/niclas/repos/uni/master_thesis/experiment/data")
+
+procedures = ["1", "2", "3", "4", "5", "6", "overall"]
+#+end_src
+
+* Import Data
+** Conditions
+#+begin_src python
+conditions = [x.stem for x in data_path.iterdir() if x.is_dir()]
+conditions
+#+end_src
+
+#+RESULTS:
+| random | fixed | blocked |
+
+** Data
+#+begin_src python :results none
+data = {}
+for condition in conditions:
+    data[condition] = {}
+    for vp in (data_path / condition).iterdir():
+        data[condition][vp.stem] = tools.unpickle(vp / "vp.pkl")
+
+data_train, data_test = tools.train_test_split(data)
+#+end_src
+
+* Basic statistics
+** Total percent correct
+To find out how well VP solved the tasked, we calculate the accuracy for train
+and test phase.
+
+#+begin_src python
+condition = "random"
+df = pd.DataFrame([tools.total_accuracy(data[condition][vp], procedures) for vp in data[condition].keys()], index=data[condition].keys(), columns=["train", "test"])
+df
+#+end_src
+
+#+RESULTS:
+#+begin_example
+         train      test
+vp12  0.822222  0.820000
+vp19  0.966667  0.800000
+vp15  0.973333  0.980000
+vp17  0.911111  0.960000
+vp20  0.906667  0.980000
+vp10  0.924444  0.943333
+vp16  0.957778  0.926667
+vp13  0.857778  0.946667
+vp18  0.962222  0.970000
+vp14  0.982222  0.986667
+#+end_example
+
+Most subjects have an accuracy of over 95% in both training and test phase.
+Some however are notably lower, under 90% in either training or test phase, or
+both.
+This could be a systematic misunderstanding of specific equations, that are
+present in both, or only one of the two phases.
+To investigate, we look at the per procedure accuracy per subject.
+
+#+begin_src python
+condition = "random"
+proc_accs = [
+    tools.count_correct(data[condition][vp], data[condition][vp].keys(), procedures)
+    for vp in data[condition].keys()
+]
+for vp in proc_accs:
+    for proc in vp.keys():
+        vp[proc] /= len(next(iter(data[condition].values())).keys())
+df = pd.DataFrame(proc_accs, index=data[condition].keys())
+df
+#+end_src
+
+#+RESULTS:
+#+begin_example
+          1      2      3      4      5      6  overall
+vp12  0.992  0.592  0.392  0.976  0.960  1.000    0.016
+vp19  1.000  0.992  0.000  0.576  0.992  0.992    0.848
+vp15  0.992  0.992  0.960  0.392  0.592  1.000    0.928
+vp17  0.392  0.968  0.584  1.000  1.000  0.992    0.648
+vp20  0.992  0.376  0.952  0.976  0.976  0.560    0.784
+vp10  0.968  0.360  0.592  0.984  0.984  0.992    0.712
+vp16  0.976  0.600  0.376  0.976  0.992  1.000    0.752
+vp13  0.384  0.960  0.928  0.560  0.992  0.968    0.568
+vp18  0.976  0.976  0.960  0.392  0.600  0.984    0.904
+vp14  0.992  0.976  0.992  0.976  0.400  0.600    0.968
+#+end_example
+
+We can see that most vp have around 2 procedures with accuracy of around 50%
diff --git a/experiment/analysis/analysis.pdf b/experiment/analysis/analysis.pdf
new file mode 100644
index 0000000..751625d
--- /dev/null
+++ b/experiment/analysis/analysis.pdf
diff --git a/experiment/analysis/analysis.tex b/experiment/analysis/analysis.tex
new file mode 100644
index 0000000..5b56bc8
--- /dev/null
+++ b/experiment/analysis/analysis.tex
@@ -0,0 +1,150 @@
+% Created 2023-10-28 Sat 19:43
+% Intended LaTeX compiler: pdflatex
+\documentclass[11pt]{article}
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{graphicx}
+\usepackage{longtable}
+\usepackage{wrapfig}
+\usepackage{rotating}
+\usepackage[normalem]{ulem}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{capt-of}
+\usepackage{hyperref}
+\author{Niclas Dobbertin}
+\date{\today}
+\title{Analysis}
+\hypersetup{
+ pdfauthor={Niclas Dobbertin},
+ pdftitle={Analysis},
+ pdfkeywords={},
+ pdfsubject={},
+ pdfcreator={Emacs 29.1 (Org mode 9.7)}, 
+ pdflang={English}}
+\usepackage{biblatex}
+\addbibresource{/home/niclas/bib/references.bib}
+\begin{document}
+
+\maketitle
+\tableofcontents
+
+\section{Imports}
+\label{sec:orgbdc2c77}
+\begin{verbatim}
+import pandas as pd
+from pathlib import Path
+from pprint import pprint
+
+import tools
+
+\end{verbatim}
+\section{Constants}
+\label{sec:orgcb8c537}
+\begin{verbatim}
+data_path = Path("/home/niclas/repos/uni/master_thesis/experiment/data")
+
+procedures = ["1", "2", "3", "4", "5", "6", "overall"]
+\end{verbatim}
+\section{Import Data}
+\label{sec:org87e67b0}
+\subsection{Conditions}
+\label{sec:orga12f2b6}
+\begin{verbatim}
+conditions = [x.stem for x in data_path.iterdir() if x.is_dir()]
+conditions
+\end{verbatim}
+
+\begin{center}
+\begin{tabular}{lll}
+random & fixed & blocked\\[0pt]
+\end{tabular}
+\end{center}
+\subsection{Data}
+\label{sec:orgcac95cb}
+\begin{verbatim}
+data = {}
+for condition in conditions:
+    data[condition] = {}
+    for vp in (data_path / condition).iterdir():
+        data[condition][vp.stem] = tools.unpickle(vp / "vp.pkl")
+\end{verbatim}
+
+\begin{verbatim}
+None
+\end{verbatim}
+\subsection{Useful Subdata}
+\label{sec:org4384120}
+\begin{verbatim}
+# data_correct = {conditons[0]: {}, conditons[1]: {}, conditons[2]: {}}
+pass
+# for condition in conditions:
+#     data_correct[condition] = None
+\end{verbatim}
+
+\begin{verbatim}
+None
+\end{verbatim}
+\section{Basic statistics}
+\label{sec:org44d0851}
+\subsection{Total percent correct}
+\label{sec:org461b551}
+To find out how well VP solved the tasked, we calculate the accuracy for train
+and test phase.
+
+\begin{verbatim}
+condition = "random"
+df = pd.DataFrame([tools.total_accuracy(data[condition][vp], procedures) for vp in data[condition].keys()], index=data[condition].keys(), columns=["train", "test"])
+df
+\end{verbatim}
+
+\begin{verbatim}
+         train      test
+vp12  0.822222  0.820000
+vp19  0.966667  0.800000
+vp15  0.973333  0.980000
+vp17  0.911111  0.960000
+vp20  0.906667  0.980000
+vp10  0.924444  0.943333
+vp16  0.957778  0.926667
+vp13  0.857778  0.946667
+vp18  0.962222  0.970000
+vp14  0.982222  0.986667
+\end{verbatim}
+
+Most subjects have an accuracy of over 95\% in both training and test phase.
+Some however are notably lower, under 90\% in either training or test phase, or
+both.
+This could be a systematic misunderstanding of specific equations, that are
+present in both, or only one of the two phases.
+To investigate, we look at the per procedure accuracy per subject.
+
+\begin{verbatim}
+condition = "random"
+proc_accs = [
+    tools.count_correct(data[condition][vp], data[condition][vp].keys(), procedures)
+    for vp in data[condition].keys()
+]
+for vp in proc_accs:
+    for proc in vp.keys():
+        vp[proc] /= len(next(iter(data[condition].values())).keys())
+df = pd.DataFrame(proc_accs, index=data[condition].keys())
+df
+\end{verbatim}
+
+\begin{verbatim}
+          1      2      3      4      5      6  overall
+vp12  0.992  0.592  0.392  0.976  0.960  1.000    0.016
+vp19  1.000  0.992  0.000  0.576  0.992  0.992    0.848
+vp15  0.992  0.992  0.960  0.392  0.592  1.000    0.928
+vp17  0.392  0.968  0.584  1.000  1.000  0.992    0.648
+vp20  0.992  0.376  0.952  0.976  0.976  0.560    0.784
+vp10  0.968  0.360  0.592  0.984  0.984  0.992    0.712
+vp16  0.976  0.600  0.376  0.976  0.992  1.000    0.752
+vp13  0.384  0.960  0.928  0.560  0.992  0.968    0.568
+vp18  0.976  0.976  0.960  0.392  0.600  0.984    0.904
+vp14  0.992  0.976  0.992  0.976  0.400  0.600    0.968
+\end{verbatim}
+
+We can see that most vp have around 2 procedures with accuracy of around 50\%
+\end{document}
\ No newline at end of file
diff --git a/experiment/analysis/tools.py b/experiment/analysis/tools.py
new file mode 100644
index 0000000..d32ccd3
--- /dev/null
+++ b/experiment/analysis/tools.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+
+import pickle
+from copy import deepcopy
+
+def unpickle(pkl):
+    with open(pkl, "rb") as f:
+        data = pickle.load(f)
+    return data
+
+
+def count_correct(vp, trials, procedures):
+    trials_correct = {}
+    for proc in procedures:
+        trials_correct[proc] = 0
+    for sample in trials:
+        for proc in vp[sample]["procedure_order"]:
+            vp_ans = vp[sample][proc]["answer"]
+            for c in vp_ans:
+                if not c.isdigit():
+                    vp_ans = vp_ans.replace(c, "")
+            vp_ans = int(vp_ans)
+            if vp_ans == vp[sample]["water_sample"][proc][0]:
+                trials_correct[proc] += 1
+    return trials_correct
+
+
+def total_accuracy(vp, procedures):
+    train = [x for x in vp.keys() if "train" in x]
+    test = [x for x in vp.keys() if "test" in x]
+
+    train_total = len(train) * len(vp[train[0]]["procedure_order"])
+    test_total = len(test) * len(vp[test[0]]["procedure_order"])
+
+    acc_train = count_correct(vp, train, procedures)
+    acc_test = count_correct(vp, test, procedures)
+
+    acc_train = sum([acc_train[x] for x in acc_train.keys()]) / train_total
+    acc_test = sum([acc_test[x] for x in acc_test.keys()]) / test_total
+
+    return acc_train, acc_test
+
+
+def train_test_split(data):
+    def delete_trials(data, string):
+        new_dict = {}
+        for cond in data.keys():
+            new_dict[cond] = {}
+            for vp in data[cond].keys():
+                new_dict[cond][vp] = {}
+                for trial in data[cond][vp].keys():
+                    if string in trial:
+                        new_dict[cond][vp][trial] = data[cond][vp][trial]
+        return new_dict
+    data_train = delete_trials(data, "train")
+    data_test = delete_trials(data, "test")
+
+    return data_train, data_test
+
+print("imported tools")