From 04fc4afdbdaec95241779b54aa3ba1f7fa3e5398 Mon Sep 17 00:00:00 2001 From: Niclas Dobbertin Date: Tue, 24 Oct 2023 22:41:55 +0200 Subject: start of experiment analysis --- experiment/analysis/analysis.org | 144 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 experiment/analysis/analysis.org (limited to 'experiment/analysis/analysis.org') 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% -- cgit v1.2.3 From 4c71eec3cd5f5f36c1cdc6d2284f6dd93facc193 Mon Sep 17 00:00:00 2001 From: Niclas Dobbertin Date: Wed, 1 Nov 2023 11:20:45 +0100 Subject: put analysis functions into own py file --- experiment/analysis/analysis.org | 57 ++++++---------------------------------- 1 file changed, 8 insertions(+), 49 deletions(-) (limited to 'experiment/analysis/analysis.org') diff --git a/experiment/analysis/analysis.org b/experiment/analysis/analysis.org index 7f6a58d..e726046 100644 --- a/experiment/analysis/analysis.org +++ b/experiment/analysis/analysis.org @@ -4,10 +4,11 @@ * Imports #+begin_src python :results none import pandas as pd -import pickle from pathlib import Path from pprint import pprint +import tools + #+end_src * Constants @@ -18,13 +19,6 @@ 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()] @@ -35,59 +29,24 @@ conditions | random | fixed | blocked | ** Data -#+begin_src python +#+begin_src python :results none data = {} for condition in conditions: data[condition] = {} for vp in (data_path / condition).iterdir(): - data[condition][vp.stem] = unpickle(vp / "vp.pkl") -#+end_src + data[condition][vp.stem] = tools.unpickle(vp / "vp.pkl") -#+RESULTS: -: None +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 :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 = 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 @@ -116,7 +75,7 @@ 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()) + tools.count_correct(data[condition][vp], data[condition][vp].keys(), procedures) for vp in data[condition].keys() ] for vp in proc_accs: -- cgit v1.2.3