diff --git a/EDA/owncloud.ipynb b/EDA/owncloud.ipynb index 32aa864..68572b0 100644 --- a/EDA/owncloud.ipynb +++ b/EDA/owncloud.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "aab6b326-a583-47ad-8bb7-723c2fddcc63", "metadata": { "scrolled": true @@ -18,18 +18,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "4f42846c-27c3-4394-a40a-e22d73c2902e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "143.946026802063\n" - ] - } - ], + "outputs": [], "source": [ "start = time.time()\n", "\n", @@ -48,18 +40,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "3714dec2-85d0-4f76-af46-ea45ebec2fa3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.5121121406555176\n" - ] - } - ], + "outputs": [], "source": [ "start = time.time()\n", "df_performance = pd.read_hdf(\"tmp22.h5\", \"PERFORMANCE\")\n", @@ -69,312 +53,30 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "f50e97d0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "22\n" - ] - } - ], + "outputs": [], "source": [ "print(22)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c131c816", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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8n-backtest03NaNNaNNaNNaN0.6400000.8289120.7272730.571429NaNNaNNaNNaNNaN
9n-backtest041.0000001.3092861.0000001.0000001.0000000.9429161.0000001.000000NaNNaNNaNNaNNaN
10n-backtest050.7826091.3164840.8181820.7500000.8148151.1514050.9166670.733333NaNNaNNaNNaNNaN
11n-backtest060.3636361.7035830.5000000.2857140.4761901.5300540.7142860.357143NaNNaNNaNNaNNaN
3k-drivetest01NaNNaNNaNNaNNaNNaNNaNNaN1.0000001.0000000.4469141.000000NaN
4k-drivetest02NaNNaNNaNNaNNaNNaNNaNNaN0.9142860.9142860.7025710.9142860.454545
5k-drivetest03NaNNaNNaNNaNNaNNaNNaNNaN0.7863250.9387761.1757970.6764710.347826
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AU14_sum AU15_sum AU17_sum \\\n", - "7315 388.0 0.0 83.0 ... 191.0 697.0 584.0 \n", - "7316 407.0 0.0 86.0 ... 191.0 693.0 594.0 \n", - "7317 409.0 0.0 87.0 ... 187.0 703.0 597.0 \n", - "7318 417.0 0.0 94.0 ... 169.0 711.0 603.0 \n", - "7319 436.0 0.0 100.0 ... 178.0 720.0 621.0 \n", - "\n", - " AU20_sum AU23_sum AU24_sum AU25_sum AU26_sum AU28_sum AU43_sum \n", - "7315 15.0 81.0 319.0 421.0 247.0 88.0 35.0 \n", - "7316 14.0 73.0 312.0 414.0 242.0 83.0 40.0 \n", - "7317 14.0 64.0 314.0 411.0 248.0 98.0 38.0 \n", - "7318 15.0 63.0 327.0 398.0 245.0 100.0 35.0 \n", - "7319 17.0 65.0 337.0 377.0 246.0 101.0 31.0 \n", - "\n", - "[5 rows x 25 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.tail()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "69e53731", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 7320 entries, 0 to 7319\n", - "Data columns (total 25 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 subjectID 7320 non-null int64 \n", - " 1 start_time 7320 non-null int64 \n", - " 2 STUDY 7320 non-null object \n", - " 3 LEVEL 7320 non-null int8 \n", - " 4 PHASE 7320 non-null object \n", - " 5 AU01_sum 7320 non-null float64\n", - " 6 AU02_sum 7320 non-null float64\n", - " 7 AU04_sum 7320 non-null float64\n", - " 8 AU05_sum 7320 non-null float64\n", - " 9 AU06_sum 7320 non-null float64\n", - " 10 AU07_sum 7320 non-null float64\n", - " 11 AU09_sum 7320 non-null float64\n", - " 12 AU10_sum 7320 non-null float64\n", - " 13 AU11_sum 7320 non-null float64\n", - " 14 AU12_sum 7320 non-null float64\n", - " 15 AU14_sum 7320 non-null float64\n", - " 16 AU15_sum 7320 non-null float64\n", - " 17 AU17_sum 7320 non-null float64\n", - " 18 AU20_sum 7320 non-null float64\n", - " 19 AU23_sum 7320 non-null float64\n", - " 20 AU24_sum 7320 non-null float64\n", - " 21 AU25_sum 7320 non-null float64\n", - " 22 AU26_sum 7320 non-null float64\n", - " 23 AU28_sum 7320 non-null float64\n", - " 24 AU43_sum 7320 non-null float64\n", - "dtypes: float64(20), int64(2), int8(1), object(2)\n", - "memory usage: 1.3+ MB\n" - ] - } - ], + "outputs": [], "source": [ "df.info()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "3754c664", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "STUDY PHASE LEVEL\n", - "k-drive train 1 155\n", - " 3 156\n", - " 2 162\n", - " baseline 3 248\n", - "n-back baseline 2 252\n", - " test 5 255\n", - " 6 256\n", - " 1 258\n", - " 4 258\n", - " 2 260\n", - " 3 260\n", - "k-drive baseline 2 267\n", - " 1 896\n", - "n-back baseline 1 901\n", - "k-drive test 1 911\n", - " 2 912\n", - " 3 913\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Zeigt alle Kombinationen mit Häufigkeit\n", "df[['STUDY', 'PHASE', 'LEVEL']].value_counts(ascending=True)" @@ -541,21 +65,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "f83b595c", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1031, 25)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "high_nback = df[\n", " (df[\"STUDY\"]==\"n-back\") &\n", @@ -567,19 +80,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "c0940343", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3080, 25)\n", - "(3209, 25)\n" - ] - } - ], + "outputs": [], "source": [ "low_all = df[\n", " ((df[\"PHASE\"] == \"baseline\") |\n", @@ -594,20 +98,10 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "f7ce38d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "7320\n", - "7320\n" - ] - } - ], + "outputs": [], "source": [ "print((df.shape[0]==(high_kdrive.shape[0]+high_nback.shape[0]+low_all.shape[0])))\n", "print(df.shape[0])\n", @@ -616,21 +110,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "48ba0379", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4240, 25)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "high_all = pd.concat([high_nback, high_kdrive])\n", "high_all.shape" @@ -638,20 +121,10 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "77dda26c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gesamt: 7320==7320\n", - "Anzahl an low load Samples: 3080\n", - "Anzahl an high load Samples: 4240\n" - ] - } - ], + "outputs": [], "source": [ "print(f\"Gesamt: {df.shape[0]}=={low_all.shape[0]+high_all.shape[0]}\")\n", "print(f\"Anzahl an low load Samples: {low_all.shape[0]}\")\n",