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+#+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%