4 files changed, 139 insertions, 103 deletions
diff --git a/experiment/analysis/RT.png b/experiment/analysis/RT.png
index 20cd0b8..3d36478 100644
--- a/experiment/analysis/RT.png
+++ b/experiment/analysis/RT.png
diff --git a/experiment/analysis/analysis.ipynb b/experiment/analysis/analysis.ipynb
index b140e96..3da688b 100644
--- a/experiment/analysis/analysis.ipynb
+++ b/experiment/analysis/analysis.ipynb
@@ -21,7 +21,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "import tools\n"
+    "import tools\n",
+    "\n",
+    "plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.tab10.colors)"
    ]
   },
   {
@@ -31,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_path = Path(\"/home/niclas/repos/uni/thesis/experiment/data\")\n",
+    "data_path = Path(\"/home/niclas/repos/uni/master_thesis/experiment/data\")\n",
     "\n",
     "procedures = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"overall\"]\n"
    ]
@@ -45,7 +47,7 @@
     {
      "data": {
       "text/plain": [
-       "['random', 'blocked', 'fixed']"
+       "['random', 'fixed', 'blocked']"
       ]
      },
      "execution_count": 3,
@@ -81,7 +83,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "condition = \"blocked\""
+    "condition = \"blocked\"\n",
+    "#print(data_train[\"fixed\"])"
    ]
   },
   {
@@ -125,13 +128,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "id": "eb3f2e96-2246-4b08-a7d1-999161ab3fd3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1500x500 with 2 Axes>"
       ]
@@ -162,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "497bd4dc-943a-41f3-a694-3f4b8f049dee",
    "metadata": {},
    "outputs": [
@@ -193,54 +196,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>vp05</th>\n",
-       "      <td>0.755556</td>\n",
-       "      <td>0.836667</td>\n",
+       "      <th>vp12</th>\n",
+       "      <td>0.822222</td>\n",
+       "      <td>0.820000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp02</th>\n",
-       "      <td>0.842222</td>\n",
-       "      <td>0.983333</td>\n",
+       "      <th>vp19</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp09</th>\n",
-       "      <td>0.806667</td>\n",
-       "      <td>0.923333</td>\n",
+       "      <th>vp15</th>\n",
+       "      <td>0.973333</td>\n",
+       "      <td>0.980000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp11</th>\n",
-       "      <td>0.842222</td>\n",
-       "      <td>0.870000</td>\n",
+       "      <th>vp17</th>\n",
+       "      <td>0.911111</td>\n",
+       "      <td>0.960000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp07</th>\n",
-       "      <td>0.733333</td>\n",
-       "      <td>0.956667</td>\n",
+       "      <th>vp20</th>\n",
+       "      <td>0.906667</td>\n",
+       "      <td>0.980000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp08</th>\n",
-       "      <td>0.711111</td>\n",
-       "      <td>0.830000</td>\n",
+       "      <th>vp10</th>\n",
+       "      <td>0.924444</td>\n",
+       "      <td>0.943333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp21</th>\n",
-       "      <td>0.871111</td>\n",
-       "      <td>0.470000</td>\n",
+       "      <th>vp16</th>\n",
+       "      <td>0.957778</td>\n",
+       "      <td>0.926667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp06</th>\n",
-       "      <td>0.726667</td>\n",
-       "      <td>0.950000</td>\n",
+       "      <th>vp13</th>\n",
+       "      <td>0.857778</td>\n",
+       "      <td>0.946667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp03</th>\n",
-       "      <td>0.813333</td>\n",
-       "      <td>0.923333</td>\n",
+       "      <th>vp18</th>\n",
+       "      <td>0.962222</td>\n",
+       "      <td>0.970000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp04</th>\n",
-       "      <td>0.808889</td>\n",
-       "      <td>0.983333</td>\n",
+       "      <th>vp14</th>\n",
+       "      <td>0.982222</td>\n",
+       "      <td>0.986667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -248,25 +251,25 @@
       ],
       "text/plain": [
        "         train      test\n",
-       "vp05  0.755556  0.836667\n",
-       "vp02  0.842222  0.983333\n",
-       "vp09  0.806667  0.923333\n",
-       "vp11  0.842222  0.870000\n",
-       "vp07  0.733333  0.956667\n",
-       "vp08  0.711111  0.830000\n",
-       "vp21  0.871111  0.470000\n",
-       "vp06  0.726667  0.950000\n",
-       "vp03  0.813333  0.923333\n",
-       "vp04  0.808889  0.983333"
+       "vp12  0.822222  0.820000\n",
+       "vp19  0.966667  0.800000\n",
+       "vp15  0.973333  0.980000\n",
+       "vp17  0.911111  0.960000\n",
+       "vp20  0.906667  0.980000\n",
+       "vp10  0.924444  0.943333\n",
+       "vp16  0.957778  0.926667\n",
+       "vp13  0.857778  0.946667\n",
+       "vp18  0.962222  0.970000\n",
+       "vp14  0.982222  0.986667"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "condition = \"fixed\"\n",
+    "condition = \"random\"\n",
     "df = pd.DataFrame([tools.total_accuracy(data[condition][vp], procedures) for vp in data[condition].keys()], index=data[condition].keys(), columns=[\"train\", \"test\"])\n",
     "df\n"
    ]
@@ -309,24 +312,24 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>vp14</th>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.976</td>\n",
+       "      <th>vp12</th>\n",
        "      <td>0.992</td>\n",
+       "      <td>0.592</td>\n",
+       "      <td>0.392</td>\n",
        "      <td>0.976</td>\n",
-       "      <td>0.400</td>\n",
-       "      <td>0.600</td>\n",
-       "      <td>0.968</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp18</th>\n",
-       "      <td>0.976</td>\n",
-       "      <td>0.976</td>\n",
-       "      <td>0.960</td>\n",
-       "      <td>0.392</td>\n",
-       "      <td>0.600</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>0.904</td>\n",
+       "      <th>vp19</th>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.576</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>vp15</th>\n",
@@ -339,6 +342,16 @@
        "      <td>0.928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>vp17</th>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.968</td>\n",
+       "      <td>0.584</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>vp20</th>\n",
        "      <td>0.992</td>\n",
        "      <td>0.376</td>\n",
@@ -359,6 +372,16 @@
        "      <td>0.712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>vp16</th>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.376</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>vp13</th>\n",
        "      <td>0.384</td>\n",
        "      <td>0.960</td>\n",
@@ -369,44 +392,24 @@
        "      <td>0.568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp17</th>\n",
-       "      <td>0.392</td>\n",
-       "      <td>0.968</td>\n",
-       "      <td>0.584</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.648</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>vp12</th>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.592</td>\n",
-       "      <td>0.392</td>\n",
+       "      <th>vp18</th>\n",
+       "      <td>0.976</td>\n",
        "      <td>0.976</td>\n",
        "      <td>0.960</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.016</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>vp19</th>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.576</td>\n",
+       "      <th>vp14</th>\n",
        "      <td>0.992</td>\n",
+       "      <td>0.976</td>\n",
        "      <td>0.992</td>\n",
-       "      <td>0.848</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>vp16</th>\n",
        "      <td>0.976</td>\n",
+       "      <td>0.400</td>\n",
        "      <td>0.600</td>\n",
-       "      <td>0.376</td>\n",
-       "      <td>0.976</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.752</td>\n",
+       "      <td>0.968</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -414,16 +417,16 @@
       ],
       "text/plain": [
        "          1      2      3      4      5      6  overall\n",
-       "vp14  0.992  0.976  0.992  0.976  0.400  0.600    0.968\n",
-       "vp18  0.976  0.976  0.960  0.392  0.600  0.984    0.904\n",
+       "vp12  0.992  0.592  0.392  0.976  0.960  1.000    0.016\n",
+       "vp19  1.000  0.992  0.000  0.576  0.992  0.992    0.848\n",
        "vp15  0.992  0.992  0.960  0.392  0.592  1.000    0.928\n",
+       "vp17  0.392  0.968  0.584  1.000  1.000  0.992    0.648\n",
        "vp20  0.992  0.376  0.952  0.976  0.976  0.560    0.784\n",
        "vp10  0.968  0.360  0.592  0.984  0.984  0.992    0.712\n",
+       "vp16  0.976  0.600  0.376  0.976  0.992  1.000    0.752\n",
        "vp13  0.384  0.960  0.928  0.560  0.992  0.968    0.568\n",
-       "vp17  0.392  0.968  0.584  1.000  1.000  0.992    0.648\n",
-       "vp12  0.992  0.592  0.392  0.976  0.960  1.000    0.016\n",
-       "vp19  1.000  0.992  0.000  0.576  0.992  0.992    0.848\n",
-       "vp16  0.976  0.600  0.376  0.976  0.992  1.000    0.752"
+       "vp18  0.976  0.976  0.960  0.392  0.600  0.984    0.904\n",
+       "vp14  0.992  0.976  0.992  0.976  0.400  0.600    0.968"
       ]
      },
      "execution_count": 11,
@@ -469,7 +472,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.12.2"
   }
  },
  "nbformat": 4,
diff --git a/experiment/analysis/analysis.py b/experiment/analysis/analysis.py
new file mode 100644
index 0000000..bd2d2b9
--- /dev/null
+++ b/experiment/analysis/analysis.py
@@ -0,0 +1,33 @@
+import pandas as pd
+from pathlib import Path
+from pprint import pprint
+
+import tools
+
+data_path = Path("/home/niclas/repos/uni/master_thesis/experiment/data")
+
+procedures = ["1", "2", "3", "4", "5", "6", "overall"]
+
+conditions = [x.stem for x in data_path.iterdir() if x.is_dir()]
+
+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)
+print(data_train["random"]["vp12"])
+
+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"])
+
+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())
diff --git a/experiment/analysis/tools.py b/experiment/analysis/tools.py
index 1dffc9a..cde322f 100644
--- a/experiment/analysis/tools.py
+++ b/experiment/analysis/tools.py
@@ -44,8 +44,8 @@ def blocked_time(vp):
 
     result = {}
     sum_time = 0
-    block_i = 0
-    for trial in range(trial_count):
+    block_i = 1
+    for trial in range(1, trial_count):
         if trial % 5 == 0:
             sum_time = 0
             block_i += 1
@@ -119,7 +119,7 @@ def train_test_split(data):
             for vp in data[cond].keys():
                 new_dict[cond][vp] = {}
                 for trial in data[cond][vp].keys():
-                    if string in trial:
+                    if string in trial and trial != "train_0":
                         new_dict[cond][vp][trial] = data[cond][vp][trial]
         return new_dict