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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/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', 'blocked', 'fixed']"
]
},
"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": 12,
"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",
"fig.tight_layout()\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>vp14</th>\n",
" <td>0.982222</td>\n",
" <td>0.986667</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>vp15</th>\n",
" <td>0.973333</td>\n",
" <td>0.980000</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>vp13</th>\n",
" <td>0.857778</td>\n",
" <td>0.946667</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>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>vp16</th>\n",
" <td>0.957778</td>\n",
" <td>0.926667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" train test\n",
"vp14 0.982222 0.986667\n",
"vp18 0.962222 0.970000\n",
"vp15 0.973333 0.980000\n",
"vp20 0.906667 0.980000\n",
"vp10 0.924444 0.943333\n",
"vp13 0.857778 0.946667\n",
"vp17 0.911111 0.960000\n",
"vp12 0.822222 0.820000\n",
"vp19 0.966667 0.800000\n",
"vp16 0.957778 0.926667"
]
},
"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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"\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>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",
" <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>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>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>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>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",
" <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>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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"vp15 0.992 0.992 0.960 0.392 0.592 1.000 0.928\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",
"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"
]
},
"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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|
