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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": 9,
"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[1].set_title(\"Test\")\n",
"plt.xlabel(\"Block\")\n",
"plt.ylabel(\"RTsum\")\n",
"plt.legend()\n",
"plt.savefig(\"RT.png\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 13,
"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": {
"text/html": [
"<div>\n",
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"\n",
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"</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": []
}
],
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|
