coolwarm
under
bad
over
",
"image/png": "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\n"
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"sns.color_palette(\"coolwarm\", as_cmap=True)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"ExecuteTime": {
"end_time": "2021-08-01T20:32:31.730981Z",
"start_time": "2021-08-01T20:32:31.724659Z"
}
},
"outputs": [
{
"data": {
"text/plain": "5.19933758270342"
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(mean_fd)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 55,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.02798282700643162\n"
]
},
{
"data": {
"text/plain": "0.0"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nulldist = []\n",
"unpermuted = _r2_cat_cont(y, mean_fd)\n",
"\n",
"print(unpermuted)\n",
"\n",
"for i in range(1000):\n",
" yperm = np.random.permutation(y)\n",
" nulldist.append(_r2_cat_cont(yperm, mean_fd))\n",
"(nulldist >= unpermuted).sum()/len(nulldist)\n",
"\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 56,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.019007371520746053\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tspisak/src/mlconfound/venv/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
" warnings.warn('Inverting hessian failed, no bse or cov_params '\n"
]
},
{
"data": {
"text/plain": "0.284"
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nulldist = []\n",
"unpermuted = _r2_cat_cat(site_int, y)\n",
"\n",
"print(unpermuted)\n",
"\n",
"for i in range(1000):\n",
" yperm = np.random.permutation(y)\n",
" nulldist.append(_r2_cat_cat(site_int, yperm))\n",
"(nulldist >= unpermuted).sum()/len(nulldist)\n",
"\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 56,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"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",
"version": "3.7.4"
}
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"nbformat": 4,
"nbformat_minor": 2
}