TODOs
This commit is contained in:
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@ -28,7 +28,21 @@
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{
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"cell_type": "code",
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"execution_count": 1131,
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"execution_count": null,
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"outputs": [],
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"source": [
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"# TODO: Poprzestawiać kolejność definicji funkcji?"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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"cell_type": "code",
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"execution_count": 252,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -48,7 +62,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1132,
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"execution_count": 253,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -65,7 +79,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1133,
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"execution_count": 254,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -75,6 +89,8 @@
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"outputs": [],
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"source": [
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"def t_stat_single(sample, population_mean=2):\n",
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" # TODO: Wywalić min, funkcja nie powinna działać dla pustej próbki\n",
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" # TODO: population mean nie powinien mieć defaultowego argumentu\n",
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" \"\"\"Funkcja oblicza wartość statystyki testowej dla jednej próbki\"\"\"\n",
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" sample = sample[0].values.tolist()\n",
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" sample_size = len(sample)\n",
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@ -84,7 +100,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1134,
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"execution_count": 255,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -103,7 +119,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1135,
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"execution_count": 256,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -114,6 +130,8 @@
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"source": [
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"def t_stat_dep(sample_1, sample_2):\n",
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" \"\"\"Funkcja oblicza wartość statystyki testowej dla dwóch próbek zależnych\"\"\"\n",
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" # TODO: Wywalić min\n",
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" # TODO: Przenieść mu jako opcjonalny argument?\n",
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" sample_1 = sample_1[0].values.tolist()\n",
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" sample_2 = sample_2[0].values.tolist()\n",
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" differences = [x_1 - x_2 for x_1, x_2 in zip(sample_1, sample_2)]\n",
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@ -124,12 +142,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1136,
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"execution_count": 257,
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"metadata": {},
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"outputs": [],
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"source": [
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"def df_dep(sample_1, sample_2):\n",
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" \"\"\"Funkcja oblicza stopnie swobody dla dwóch próbek zależnych\"\"\"\n",
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" # TODO: Assert działa chyba tylko w trybie debugowania\n",
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" l1, l2 = len(sample_1), len(sample_2)\n",
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" assert l1 == l2 \n",
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"\n",
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@ -138,7 +157,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1137,
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"execution_count": 258,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -149,7 +168,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1138,
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"execution_count": 259,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -161,7 +180,7 @@
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{
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"cell_type": "code",
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"execution_count": 1139,
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"execution_count": 260,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -172,7 +191,7 @@
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},
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"cell_type": "code",
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"execution_count": 1140,
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"execution_count": 261,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"execution_count": 1141,
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"execution_count": 262,
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"cell_type": "code",
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"execution_count": 1142,
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"execution_count": 263,
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"cell_type": "code",
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"execution_count": 1143,
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"execution_count": 264,
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"cell_type": "code",
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"execution_count": 1144,
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"execution_count": 265,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -266,7 +285,7 @@
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{
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"cell_type": "code",
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"execution_count": 1145,
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"execution_count": 266,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -301,7 +320,7 @@
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"cell_type": "code",
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"execution_count": 1146,
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"execution_count": 267,
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"metadata": {
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"collapsed": false,
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@ -310,8 +329,7 @@
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},
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"outputs": [],
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"source": [
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"def draw_distribution(stats): \n",
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" # To powinno być zdefiniowane przed make decision w sumie\n",
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"def draw_distribution(stats):\n",
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" \"\"\"\n",
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" Funkcja rysuje rozkład statystyki testowej\n",
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" stats: lista statystyk testowych\n",
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},
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{
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"cell_type": "code",
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"execution_count": 1147,
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"execution_count": 268,
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"metadata": {
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@ -334,13 +352,13 @@
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"outputs": [],
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"source": [
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"def make_decision(data, columns):\n",
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" # TODO\n",
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" # TODO: Potrzebna ta funkcja w ogóle? Decyzja jest zależna od wybranych hipotez chyba.\n",
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" pass"
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]
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"cell_type": "code",
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"execution_count": 1148,
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"execution_count": 269,
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"metadata": {
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@ -394,22 +412,28 @@
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"cell_type": "code",
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"execution_count": 1149,
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"execution_count": 270,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Statystyki dla jednej próby:\n",
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"t: 1.6371853975970775e-07, df: 5, cv: 2.015048372669157, p: 0.9999998757026942\n",
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"\n",
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"Statystyki dla dwóch prób zależnych:\n",
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"t: 2.721731710913334e-07, df: 5, cv: 2.015048372669157, p: 0.9999997933624869\n",
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"\n",
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"Statystyki dla dwóch prób niezależnych:\n",
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"t: 56.011644110212046, df: 8, cv: 1.8595480375228421, p: 1.145550321268729e-11\n",
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"\n"
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"Statystyki dla jednej próby:\n"
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]
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},
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{
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"ename": "TypeError",
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"evalue": "t_stat_single() missing 1 required positional argument: 'population_mean'",
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"output_type": "error",
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"traceback": [
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"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
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"Input \u001B[1;32mIn [270]\u001B[0m, in \u001B[0;36m<cell line: 7>\u001B[1;34m()\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mt_stat\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, df: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mdf\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, cv: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcv\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, p: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mp\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 6\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStatystyki dla jednej próby:\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m----> 7\u001B[0m t_stat, df, cv, p, _ \u001B[38;5;241m=\u001B[39m \u001B[43mbootstrap_one_sample\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdummy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 8\u001B[0m pretty_print_full_stats(t_stat, df, cv, p)\n\u001B[0;32m 10\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mStatystyki dla dwóch prób zależnych:\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
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"Input \u001B[1;32mIn [262]\u001B[0m, in \u001B[0;36mbootstrap_one_sample\u001B[1;34m(sample)\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mbootstrap_one_sample\u001B[39m(sample):\n\u001B[1;32m----> 2\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mt_test\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[43msample_1\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mdf_fn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf_single\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[43mt_stat_fn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mt_stat_single\u001B[49m\n\u001B[0;32m 6\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
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"Input \u001B[1;32mIn [266]\u001B[0m, in \u001B[0;36mt_test\u001B[1;34m(sample_1, sample_2, df_fn, t_stat_fn, alpha)\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mt_test\u001B[39m(sample_1, sample_2\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, df_fn\u001B[38;5;241m=\u001B[39mdf_ind, t_stat_fn\u001B[38;5;241m=\u001B[39mt_stat_ind, alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.05\u001B[39m):\n\u001B[0;32m 2\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m 3\u001B[0m \u001B[38;5;124;03m Funkcja przeprowadza test T-studenta dla dwóch zmiennych.\u001B[39;00m\n\u001B[0;32m 4\u001B[0m \u001B[38;5;124;03m liczba kolumn wynosi 1, test jest przeprowadzany dla jednej zmiennej.\u001B[39;00m\n\u001B[0;32m 5\u001B[0m \u001B[38;5;124;03m @param df_fn - funkcja obliczająca stopnie swobody\u001B[39;00m\n\u001B[0;32m 6\u001B[0m \u001B[38;5;124;03m @param t_stat_fn - funkcja obliczająca statystykę T\u001B[39;00m\n\u001B[0;32m 7\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m----> 8\u001B[0m t_stat_list \u001B[38;5;241m=\u001B[39m \u001B[43mget_t_stats\u001B[49m\u001B[43m(\u001B[49m\u001B[43msample_1\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msample_2\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mt_stat_fn\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 9\u001B[0m t_stat_sum \u001B[38;5;241m=\u001B[39m \u001B[38;5;28msum\u001B[39m(t_stat_list)\n\u001B[0;32m 11\u001B[0m data_size \u001B[38;5;241m=\u001B[39m sample_1\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]\n",
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"Input \u001B[1;32mIn [265]\u001B[0m, in \u001B[0;36mget_t_stats\u001B[1;34m(sample_1, sample_2, t_stat_fn)\u001B[0m\n\u001B[0;32m 6\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m sample_2 \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 7\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m bootstrap \u001B[38;5;129;01min\u001B[39;00m generate_bootstraps(sample_1):\n\u001B[1;32m----> 8\u001B[0m stat \u001B[38;5;241m=\u001B[39m \u001B[43mt_stat_fn\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbootstrap\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 9\u001B[0m t_stat_list\u001B[38;5;241m.\u001B[39mappend(stat)\n\u001B[0;32m 10\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m t_stat_list\n",
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"\u001B[1;31mTypeError\u001B[0m: t_stat_single() missing 1 required positional argument: 'population_mean'"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 1150,
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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