From 0dea9ae3fddd539f850a5173a2df2c2a81d84e65 Mon Sep 17 00:00:00 2001 From: s444501 Date: Tue, 17 May 2022 13:58:25 +0200 Subject: [PATCH] TODOs --- bootstrap-t.ipynb | 90 ++++++++++++++++++++++++++++++----------------- 1 file changed, 57 insertions(+), 33 deletions(-) diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb index ae6ee27..f89a173 100644 --- a/bootstrap-t.ipynb +++ b/bootstrap-t.ipynb @@ -28,7 +28,21 @@ }, { "cell_type": "code", - "execution_count": 1131, + "execution_count": null, + "outputs": [], + "source": [ + "# TODO: Poprzestawiać kolejność definicji funkcji?" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 252, "metadata": { "pycharm": { "name": "#%%\n" @@ -48,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1132, + "execution_count": 253, "metadata": { "pycharm": { "name": "#%%\n" @@ -65,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 1133, + "execution_count": 254, "metadata": { "collapsed": false, "pycharm": { @@ -75,6 +89,8 @@ "outputs": [], "source": [ "def t_stat_single(sample, population_mean=2):\n", + " # TODO: Wywalić min, funkcja nie powinna działać dla pustej próbki\n", + " # TODO: population mean nie powinien mieć defaultowego argumentu\n", " \"\"\"Funkcja oblicza wartość statystyki testowej dla jednej próbki\"\"\"\n", " sample = sample[0].values.tolist()\n", " sample_size = len(sample)\n", @@ -84,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 1134, + "execution_count": 255, "metadata": { "collapsed": false, "pycharm": { @@ -103,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 1135, + "execution_count": 256, "metadata": { "collapsed": false, "pycharm": { @@ -114,6 +130,8 @@ "source": [ "def t_stat_dep(sample_1, sample_2):\n", " \"\"\"Funkcja oblicza wartość statystyki testowej dla dwóch próbek zależnych\"\"\"\n", + " # TODO: Wywalić min\n", + " # TODO: Przenieść mu jako opcjonalny argument?\n", " sample_1 = sample_1[0].values.tolist()\n", " sample_2 = sample_2[0].values.tolist()\n", " differences = [x_1 - x_2 for x_1, x_2 in zip(sample_1, sample_2)]\n", @@ -124,12 +142,13 @@ }, { "cell_type": "code", - "execution_count": 1136, + "execution_count": 257, "metadata": {}, "outputs": [], "source": [ "def df_dep(sample_1, sample_2):\n", " \"\"\"Funkcja oblicza stopnie swobody dla dwóch próbek zależnych\"\"\"\n", + " # TODO: Assert działa chyba tylko w trybie debugowania\n", " l1, l2 = len(sample_1), len(sample_2)\n", " assert l1 == l2 \n", "\n", @@ -138,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 1137, + "execution_count": 258, "metadata": {}, "outputs": [], "source": [ @@ -149,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 1138, + "execution_count": 259, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 1139, + "execution_count": 260, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 1140, + "execution_count": 261, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 1141, + "execution_count": 262, "metadata": { "collapsed": false, "pycharm": { @@ -202,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 1142, + "execution_count": 263, "metadata": { "collapsed": false, "pycharm": { @@ -222,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 1143, + "execution_count": 264, "metadata": { "collapsed": false, "pycharm": { @@ -242,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 1144, + "execution_count": 265, "metadata": {}, "outputs": [], "source": [ @@ -266,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 1145, + "execution_count": 266, "metadata": { "pycharm": { "name": "#%%\n" @@ -301,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 1146, + "execution_count": 267, "metadata": { "collapsed": false, "pycharm": { @@ -310,8 +329,7 @@ }, "outputs": [], "source": [ - "def draw_distribution(stats): \n", - " # To powinno być zdefiniowane przed make decision w sumie\n", + "def draw_distribution(stats):\n", " \"\"\"\n", " Funkcja rysuje rozkład statystyki testowej\n", " stats: lista statystyk testowych\n", @@ -324,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 1147, + "execution_count": 268, "metadata": { "collapsed": false, "pycharm": { @@ -334,13 +352,13 @@ "outputs": [], "source": [ "def make_decision(data, columns):\n", - " # TODO\n", + " # TODO: Potrzebna ta funkcja w ogóle? Decyzja jest zależna od wybranych hipotez chyba.\n", " pass" ] }, { "cell_type": "code", - "execution_count": 1148, + "execution_count": 269, "metadata": { "collapsed": false, "pycharm": { @@ -394,22 +412,28 @@ }, { "cell_type": "code", - "execution_count": 1149, + "execution_count": 270, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Statystyki dla jednej próby:\n", - "t: 1.6371853975970775e-07, df: 5, cv: 2.015048372669157, p: 0.9999998757026942\n", - "\n", - "Statystyki dla dwóch prób zależnych:\n", - "t: 2.721731710913334e-07, df: 5, cv: 2.015048372669157, p: 0.9999997933624869\n", - "\n", - "Statystyki dla dwóch prób niezależnych:\n", - "t: 56.011644110212046, df: 8, cv: 1.8595480375228421, p: 1.145550321268729e-11\n", - "\n" + "Statystyki dla jednej próby:\n" + ] + }, + { + "ename": "TypeError", + "evalue": "t_stat_single() missing 1 required positional argument: 'population_mean'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)", + "Input \u001B[1;32mIn [270]\u001B[0m, in \u001B[0;36m\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", + "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", + "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", + "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", + "\u001B[1;31mTypeError\u001B[0m: t_stat_single() missing 1 required positional argument: 'population_mean'" ] } ], @@ -434,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 1150, + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { @@ -473,4 +497,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file