summaryrefslogtreecommitdiff
path: root/experiment
diff options
context:
space:
mode:
Diffstat (limited to 'experiment')
-rw-r--r--experiment/analysis/analysis.org144
-rw-r--r--experiment/analysis/analysis.pdfbin0 -> 127259 bytes
-rw-r--r--experiment/analysis/analysis.tex148
3 files changed, 292 insertions, 0 deletions
diff --git a/experiment/analysis/analysis.org b/experiment/analysis/analysis.org
new file mode 100644
index 0000000..7f6a58d
--- /dev/null
+++ b/experiment/analysis/analysis.org
@@ -0,0 +1,144 @@
+#+title: Analysis
+#+PROPERTY: header-args:python+ :session *python* :exports both :tangle yes
+
+* Imports
+#+begin_src python :results none
+import pandas as pd
+import pickle
+from pathlib import Path
+from pprint import pprint
+
+#+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
+#+begin_src python :results none
+def unpickle(pkl):
+ with open(pkl, "rb") as f:
+ data = pickle.load(f)
+ return data
+#+end_src
+
+** 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
+data = {}
+for condition in conditions:
+ data[condition] = {}
+ for vp in (data_path / condition).iterdir():
+ data[condition][vp.stem] = unpickle(vp / "vp.pkl")
+#+end_src
+
+#+RESULTS:
+: None
+
+* 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 :results none
+def count_correct(vp, trials):
+ 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
+#+end_src
+
+#+begin_src python :results none
+def total_accuracy(vp):
+ 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)
+ acc_test = count_correct(vp, test)
+
+ 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
+#+end_src
+
+#+begin_src python
+train = [x for x in vp.keys() if "train" in x]
+test = [x for x in vp.keys() if "test" in x]
+
+condition = "random"
+df = pd.DataFrame([total_accuracy(data[condition][vp]) 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 = [
+ count_correct(data[condition][vp], data[condition][vp].keys())
+ 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..c12a0e2
--- /dev/null
+++ b/experiment/analysis/analysis.pdf
Binary files differ
diff --git a/experiment/analysis/analysis.tex b/experiment/analysis/analysis.tex
new file mode 100644
index 0000000..2896b4f
--- /dev/null
+++ b/experiment/analysis/analysis.tex
@@ -0,0 +1,148 @@
+% Created 2023-10-23 Mon 20:13
+% 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:orgf19bf7c}
+\begin{verbatim}
+import pandas as pd
+import pickle
+from pathlib import Path
+
+\end{verbatim}
+\section{Constants}
+\label{sec:orgb587203}
+\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:org3427b7b}
+\begin{verbatim}
+def unpickle(pkl):
+ with open(pkl, "rb") as f:
+ data = pickle.load(f)
+ return data
+\end{verbatim}
+\subsection{Conditions}
+\label{sec:org9e15909}
+\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:org65d4664}
+\begin{verbatim}
+data = {}
+for condition in conditions:
+ data[condition] = {}
+ for vp in (data_path / condition).iterdir():
+ data[condition][vp.stem] = unpickle(vp / "vp.pkl")
+\end{verbatim}
+
+\begin{verbatim}
+None
+\end{verbatim}
+\section{Basic statistics}
+\label{sec:orgea2a5f1}
+\subsection{Total percent correct}
+\label{sec:org2eef721}
+To find out how well VP solved the tasked, we calculate the accuracy for train
+and test phase.
+
+\begin{verbatim}
+def percent_correct(vp):
+ 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"])
+
+ train_correct = 0
+ test_correct = 0
+
+ def count_correct(trials):
+ trials_correct = 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 += 1
+ return trials_correct
+
+ return count_correct(train) / train_total, count_correct(test) / test_total
+\end{verbatim}
+
+\begin{verbatim}
+condition = "random"
+df = pd.DataFrame([percent_correct(data[condition][vp]) for vp in data[condition].keys()], columns=["train", "test"])
+df
+\end{verbatim}
+
+\begin{verbatim}
+ train test
+0 0.822222 0.820000
+1 0.966667 0.800000
+2 0.973333 0.980000
+3 0.911111 0.960000
+4 0.906667 0.980000
+5 0.924444 0.943333
+6 0.957778 0.926667
+7 0.857778 0.946667
+8 0.962222 0.970000
+9 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}
+pass
+\end{verbatim}
+
+\begin{verbatim}
+None
+\end{verbatim}
+\end{document} \ No newline at end of file
generated by cgit v1.2.3 (git 2.25.1) at 2024-12-23 17:38:45 +0000