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|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d372d82b-0842-4c24-86d1-b75c6637d2a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"imported tools\n"
]
}
],
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"from pprint import pprint\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"import tools\n",
"\n",
"plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.tab10.colors)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3f7c451c-6afb-439d-8bfe-4e545c4f7992",
"metadata": {},
"outputs": [],
"source": [
"data_path = Path(\"/home/niclas/repos/uni/master_thesis/experiment/data\")\n",
"\n",
"procedures = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"overall\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cffed6cb-ed44-4f8d-92dc-760b752d4302",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['random', 'fixed', 'blocked']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conditions = [x.stem for x in data_path.iterdir() if x.is_dir()]\n",
"conditions\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "511c7800-8ca9-457b-90b0-d3c3302f6ef0",
"metadata": {},
"outputs": [],
"source": [
"data = {}\n",
"for condition in conditions:\n",
" data[condition] = {}\n",
" for vp in (data_path / condition).iterdir():\n",
" data[condition][vp.stem] = tools.fix_vp(tools.unpickle(vp / \"vp.pkl\"), procedures)\n",
"\n",
"data_train, data_test = tools.train_test_split(data)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "39503de8-fa01-4ce7-a0c9-90f337548945",
"metadata": {},
"outputs": [],
"source": [
"condition = \"blocked\"\n",
"#print(data_train[\"fixed\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8f79bdbb-ac99-4577-866a-d566eee527ed",
"metadata": {},
"outputs": [],
"source": [
"train_blocked_fixed = tools.block_vps(data_train, \"fixed\")\n",
"train_blocked_random = tools.block_vps(data_train, \"random\")\n",
"train_blocked_blocked = tools.block_vps(data_train, \"blocked\")\n",
"\n",
"test_blocked_fixed = tools.block_vps(data_test, \"fixed\")\n",
"test_blocked_random = tools.block_vps(data_test, \"random\")\n",
"test_blocked_blocked = tools.block_vps(data_test, \"blocked\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3ab5e63b-9c5a-4c53-9d80-983a27f833e1",
"metadata": {},
"outputs": [],
"source": [
"\n",
"#fig = tools.plot_vp(blocked_vps[list(blocked_vps.keys())[0]])\n",
"#plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e371b733-1b94-4ec9-8223-06b82dbac3df",
"metadata": {},
"outputs": [],
"source": [
"#tools.plot_average_vps(test_blocked_fixed)\n",
"#plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "eb3f2e96-2246-4b08-a7d1-999161ab3fd3",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams[\"figure.figsize\"] = [15,5]\n",
"fig, axes = plt.subplots(ncols=2, nrows=1)\n",
"tools.plot_average_vps(axes[0], \"fixed\", train_blocked_fixed)\n",
"tools.plot_average_vps(axes[0], \"random\", train_blocked_random)\n",
"tools.plot_average_vps(axes[0], \"blocked\", train_blocked_blocked)\n",
"\n",
"tools.plot_average_vps(axes[1], \"fixed\", test_blocked_fixed)\n",
"tools.plot_average_vps(axes[1], \"random\", test_blocked_random)\n",
"tools.plot_average_vps(axes[1], \"blocked\", test_blocked_blocked)\n",
"\n",
"axes[0].set_title(\"Train\")\n",
"axes[0].set_xlabel(\"Block\")\n",
"axes[0].set_ylabel(\"RTsum\")\n",
"axes[1].set_title(\"Transfer\")\n",
"#plt.xlabel(\"Block\")\n",
"axes[1].set_xlabel(\"Block\")\n",
"plt.ylabel(\"RTsum\")\n",
"plt.legend()\n",
"plt.savefig(\"RT.png\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "497bd4dc-943a-41f3-a694-3f4b8f049dee",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>train</th>\n",
" <th>test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>vp12</th>\n",
" <td>0.822222</td>\n",
" <td>0.820000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp19</th>\n",
" <td>0.966667</td>\n",
" <td>0.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp15</th>\n",
" <td>0.973333</td>\n",
" <td>0.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp17</th>\n",
" <td>0.911111</td>\n",
" <td>0.960000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp20</th>\n",
" <td>0.906667</td>\n",
" <td>0.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp10</th>\n",
" <td>0.924444</td>\n",
" <td>0.943333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp16</th>\n",
" <td>0.957778</td>\n",
" <td>0.926667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp13</th>\n",
" <td>0.857778</td>\n",
" <td>0.946667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp18</th>\n",
" <td>0.962222</td>\n",
" <td>0.970000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp14</th>\n",
" <td>0.982222</td>\n",
" <td>0.986667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" train test\n",
"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,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "143f7497-2c6c-492c-85ab-da3d2cf2a828",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>overall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\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>0.960</td>\n",
" <td>1.000</td>\n",
" <td>0.016</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",
" <td>0.992</td>\n",
" <td>0.992</td>\n",
" <td>0.848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp15</th>\n",
" <td>0.992</td>\n",
" <td>0.992</td>\n",
" <td>0.960</td>\n",
" <td>0.392</td>\n",
" <td>0.592</td>\n",
" <td>1.000</td>\n",
" <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",
" <td>0.952</td>\n",
" <td>0.976</td>\n",
" <td>0.976</td>\n",
" <td>0.560</td>\n",
" <td>0.784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vp10</th>\n",
" <td>0.968</td>\n",
" <td>0.360</td>\n",
" <td>0.592</td>\n",
" <td>0.984</td>\n",
" <td>0.984</td>\n",
" <td>0.992</td>\n",
" <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",
" <td>0.928</td>\n",
" <td>0.560</td>\n",
" <td>0.992</td>\n",
" <td>0.968</td>\n",
" <td>0.568</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",
" </tr>\n",
" <tr>\n",
" <th>vp14</th>\n",
" <td>0.992</td>\n",
" <td>0.976</td>\n",
" <td>0.992</td>\n",
" <td>0.976</td>\n",
" <td>0.400</td>\n",
" <td>0.600</td>\n",
" <td>0.968</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3 4 5 6 overall\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",
"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,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"condition = \"random\"\n",
"proc_accs = [\n",
" tools.count_correct(data[condition][vp], data[condition][vp].keys(), procedures)\n",
" for vp in data[condition].keys()\n",
"]\n",
"for vp in proc_accs:\n",
" for proc in vp.keys():\n",
" vp[proc] /= len(next(iter(data[condition].values())).keys())\n",
"df = pd.DataFrame(proc_accs, index=data[condition].keys())\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52d6e2e6-999d-47a2-a829-cee5042d5c68",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
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|
