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Diffstat (limited to 'experiment/analysis')
| -rw-r--r-- | experiment/analysis/analysis.org | 103 | ||||
| -rw-r--r-- | experiment/analysis/analysis.pdf | bin | 0 -> 128522 bytes | |||
| -rw-r--r-- | experiment/analysis/analysis.tex | 150 | ||||
| -rw-r--r-- | experiment/analysis/tools.py | 60 |
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 Binary files differnew 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") |