Add pretty prints
This commit is contained in:
parent
fdb95361cb
commit
39c9a260e1
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},
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{
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"cell_type": "code",
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"execution_count": 313,
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"execution_count": 61,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -47,7 +47,7 @@
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"cell_type": "code",
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"execution_count": 314,
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"execution_count": 62,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -56,7 +56,7 @@
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"cell_type": "code",
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"execution_count": 315,
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"execution_count": 63,
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"outputs": [],
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"source": [
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@ -67,7 +67,7 @@
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"cell_type": "code",
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"execution_count": 316,
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"execution_count": 64,
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"metadata": {},
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"outputs": [],
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@ -88,7 +88,7 @@
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"cell_type": "code",
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"execution_count": 317,
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"execution_count": 65,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -110,7 +110,7 @@
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"cell_type": "code",
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"execution_count": 318,
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -128,7 +128,7 @@
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{
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"cell_type": "code",
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"execution_count": 319,
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"execution_count": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -146,7 +146,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 320,
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"execution_count": 68,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -171,6 +171,36 @@
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" return t_stat_list"
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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": 69,
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"metadata": {},
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"outputs": [],
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"source": [
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"def pretty_print_test(p, t_stat_from_sample, t_stat_list, thesis, alternative, max_print=5):\n",
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" print('Wyniki bootstrapowej wersji testu T-studenta')\n",
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" print()\n",
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" print(f'Hipoteza: {thesis}')\n",
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" if alternative is Alternatives.LESS:\n",
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" print(f'Hipoteza alternatywna: średnia jest mniejsza')\n",
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" else:\n",
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" print(f'Hipoteza alternatywna: średnia jest większa')\n",
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" print()\n",
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" print(f'p: {p}')\n",
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" print(f'Wartość statystyki testowej z próby: {t_stat_from_sample}')\n",
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" print(f'Wartości statystyk z prób boostrapowych:')\n",
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"\n",
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" t_stat_list_len = len(t_stat_list)\n",
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" for i in range(min(max_print, t_stat_list_len)):\n",
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" print(f'{t_stat_list[i]}, ', end='')\n",
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" if max_print < t_stat_list_len:\n",
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" remaining = t_stat_list_len - max_print\n",
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" print(f'... (i {remaining} pozostałych)')\n",
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"\n",
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" print()\n",
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" print()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -193,7 +223,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 321,
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"execution_count": 70,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -221,7 +251,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 322,
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"execution_count": 71,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -231,11 +261,15 @@
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"outputs": [],
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"source": [
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"def bootstrap_one_sample(sample, population_mean, alternative=Alternatives.LESS):\n",
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" return t_test_1_samp(\n",
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" p, t, ts = t_test_1_samp(\n",
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" sample_1=sample,\n",
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" population_mean=population_mean,\n",
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" alternative=alternative,\n",
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" )"
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" )\n",
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" \n",
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" pretty_print_test(p, t, ts, f'średnia jest równa {population_mean}', alternative)\n",
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" print()\n",
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" return p, t, ts"
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]
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},
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{
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@ -247,7 +281,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 323,
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"execution_count": 72,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -258,7 +292,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 324,
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"execution_count": 73,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -270,15 +304,22 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"p: 0.73\n"
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"Wyniki bootstrapowej wersji testu T-studenta\n",
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"\n",
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"Hipoteza: średnia jest równa 165\n",
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"Hipoteza alternatywna: średnia jest mniejsza\n",
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"\n",
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"p: 0.58\n",
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"Wartość statystyki testowej z próby: [-229.1025971]\n",
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"Wartości statystyk z prób boostrapowych:\n",
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"[-316.92367438], [-406.5], [-201.5], [-243.92267776], [-330.27286699], ... (i 95 pozostałych)\n"
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]
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}
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],
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"source": [
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"#TODO: poprawić kod aby można było podawać kolumny\n",
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"\n",
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"p, _ = bootstrap_one_sample(dummy, 165)\n",
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"print(f'p: {p}')"
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"p, t, ts = bootstrap_one_sample(dummy, 165)"
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]
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},
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{
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@ -302,7 +343,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 325,
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"execution_count": 74,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -312,11 +353,14 @@
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"outputs": [],
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"source": [
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"def bootstrap_independent(sample_1, sample_2, alternative=Alternatives.LESS):\n",
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" return t_test_ind(\n",
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" p, t, ts = t_test_ind(\n",
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" sample_1=sample_1,\n",
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" sample_2=sample_2,\n",
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" alternative=alternative,\n",
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" )"
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" )\n",
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" \n",
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" pretty_print_test(p, t, ts, 'średnie są takie same', alternative)\n",
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" return p, t, ts"
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]
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},
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{
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@ -348,7 +392,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 326,
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"execution_count": 75,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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@ -358,11 +402,14 @@
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"outputs": [],
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"source": [
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"def bootstrap_dependent(sample_1, sample_2, alternative=Alternatives.LESS):\n",
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" return t_test_dep(\n",
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" p, t, ts = t_test_dep(\n",
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" sample_1=sample_1,\n",
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" sample_2=sample_2,\n",
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" alternative=alternative,\n",
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" )"
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" )\n",
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" \n",
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" pretty_print_test(p, t, ts, 'średnie są takie same', alternative)\n",
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" return p, t, ts"
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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": 327,
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"execution_count": 76,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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},
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{
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"cell_type": "code",
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"execution_count": 328,
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"execution_count": 77,
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"metadata": {},
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"outputs": [
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{
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@ -427,11 +474,35 @@
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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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"0.44\n",
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"Wyniki bootstrapowej wersji testu T-studenta\n",
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"\n",
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"Hipoteza: średnia jest równa 2\n",
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"Hipoteza alternatywna: średnia jest mniejsza\n",
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"\n",
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"p: 0.52\n",
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"Wartość statystyki testowej z próby: [1.41421356]\n",
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"Wartości statystyk z prób boostrapowych:\n",
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"[-0.53452248], [0.], [-1.63299316], [1.5], [2.3590713], ... (i 95 pozostałych)\n",
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"Statystyki dla dwóch prób zależnych:\n",
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"0.0\n",
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"Wyniki bootstrapowej wersji testu T-studenta\n",
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"\n",
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"Hipoteza: średnie są takie same\n",
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"Hipoteza alternatywna: średnia jest mniejsza\n",
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"\n",
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"p: 0.0\n",
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"Wartość statystyki testowej z próby: [10.61445555]\n",
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"Wartości statystyk z prób boostrapowych:\n",
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"[nan], [nan], [nan], [nan], [nan], ... (i 95 pozostałych)\n",
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"Statystyki dla dwóch prób niezależnych:\n",
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"1.0\n"
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"Wyniki bootstrapowej wersji testu T-studenta\n",
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"\n",
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"Hipoteza: średnie są takie same\n",
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"Hipoteza alternatywna: średnia jest mniejsza\n",
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"\n",
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"p: 1.0\n",
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"Wartość statystyki testowej z próby: [2.4140394]\n",
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"Wartości statystyk z prób boostrapowych:\n",
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"[0.], [0.], [0.], [0.], [0.], ... (i 95 pozostałych)\n"
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]
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}
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],
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"\n",
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"\n",
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"print('Statystyki dla jednej próby:')\n",
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"p, _ = bootstrap_one_sample(dummy, 2)\n",
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"print(f'p {p}')\n",
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"p, t, ts = bootstrap_one_sample(dummy, 2)\n",
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"\n",
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"print('Statystyki dla dwóch prób zależnych:')\n",
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"p, _ = bootstrap_dependent(dummy2, dummy3)\n",
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"print(f'p {p}')\n",
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"p, t, ts = bootstrap_dependent(dummy2, dummy3)\n",
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"\n",
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"print('Statystyki dla dwóch prób niezależnych:')\n",
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"p, _ = bootstrap_independent(dummy2, dummy3)\n",
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"print(f'p {p}')"
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"p, t, ts = bootstrap_independent(dummy2, dummy3)"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10-final"
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"version": "3.9.1"
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},
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"orig_nbformat": 4
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},
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