{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ML Basics, warming up with small data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Automatically created module for IPython interactive environment\n" ] } ], "source": [ "print(__doc__)\n", "\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.decomposition import PCA\n", "from sklearn import metrics\n", "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read poll data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TimestampQ1_General background in data analysis?Q2_Hands-on experience in data analysis using Python?Q3_Experience in programming in general?Q4_General background in machine learning?Q5_Hands-on experience in running machine learning applications?Q6_Which one would you prefer on a Sunday afternoon?Q7_Hands-on experience in image analysis using satellite images?Q8_Level of interest in mathematics?Q9_Level of interest in reading?Q10_Level of stress about this class?Q11_Your overall motivation about this class?
02020/01/14 5:11:10 PM EST85467Running53573
12020/01/14 5:15:45 PM EST88556Reading77678
22020/01/14 10:10:14 PM EST66665Watching a movie77777
32020/01/15 10:02:48 AM EST53644Watching a movie388510
42020/01/15 10:03:20 AM EST66543Reading454108
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" ], "text/plain": [ " Timestamp Q1_General background in data analysis? \\\n", "0 2020/01/14 5:11:10 PM EST 8 \n", "1 2020/01/14 5:15:45 PM EST 8 \n", "2 2020/01/14 10:10:14 PM EST 6 \n", "3 2020/01/15 10:02:48 AM EST 5 \n", "4 2020/01/15 10:03:20 AM EST 6 \n", "\n", " Q2_Hands-on experience in data analysis using Python? \\\n", "0 5 \n", "1 8 \n", "2 6 \n", "3 3 \n", "4 6 \n", "\n", " Q3_Experience in programming in general? \\\n", "0 4 \n", "1 5 \n", "2 6 \n", "3 6 \n", "4 5 \n", "\n", " Q4_General background in machine learning? \\\n", "0 6 \n", "1 5 \n", "2 6 \n", "3 4 \n", "4 4 \n", "\n", " Q5_Hands-on experience in running machine learning applications? \\\n", "0 7 \n", "1 6 \n", "2 5 \n", "3 4 \n", "4 3 \n", "\n", " Q6_Which one would you prefer on a Sunday afternoon? \\\n", "0 Running \n", "1 Reading \n", "2 Watching a movie \n", "3 Watching a movie \n", "4 Reading \n", "\n", " Q7_Hands-on experience in image analysis using satellite images? \\\n", "0 5 \n", "1 7 \n", "2 7 \n", "3 3 \n", "4 4 \n", "\n", " Q8_Level of interest in mathematics? Q9_Level of interest in reading? \\\n", "0 3 5 \n", "1 7 6 \n", "2 7 7 \n", "3 8 8 \n", "4 5 4 \n", "\n", " Q10_Level of stress about this class? \\\n", "0 7 \n", "1 7 \n", "2 7 \n", "3 5 \n", "4 10 \n", "\n", " Q11_Your overall motivation about this class? \n", "0 3 \n", "1 8 \n", "2 7 \n", "3 10 \n", "4 8 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfInit = pd.read_csv(('./Data/MUSA-650WelcomePoll.csv'))\n", "dfInit.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate relative timestamp" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TimestamptsRel
02020-01-14 17:11:100.0
12020-01-14 17:15:45275.0
22020-01-14 22:10:1417944.0
32020-01-15 10:02:4860698.0
42020-01-15 10:03:2060730.0
52020-01-15 10:03:4360753.0
62020-01-15 10:03:5060760.0
72020-01-15 10:03:5360763.0
82020-01-15 10:03:5960769.0
92020-01-15 10:04:0360773.0
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" ], "text/plain": [ " Timestamp tsRel\n", "0 2020-01-14 17:11:10 0.0\n", "1 2020-01-14 17:15:45 275.0\n", "2 2020-01-14 22:10:14 17944.0\n", "3 2020-01-15 10:02:48 60698.0\n", "4 2020-01-15 10:03:20 60730.0\n", "5 2020-01-15 10:03:43 60753.0\n", "6 2020-01-15 10:03:50 60760.0\n", "7 2020-01-15 10:03:53 60763.0\n", "8 2020-01-15 10:03:59 60769.0\n", "9 2020-01-15 10:04:03 60773.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfInit.Timestamp = pd.to_datetime(dfInit.Timestamp, format='%Y/%m/%d %I:%M:%S %p EST')\n", "dfInit['tsRel'] = (dfInit.Timestamp - dfInit.Timestamp.min()).dt.total_seconds()\n", "dfInit[['Timestamp', 'tsRel']].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Column names" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Q1_General background in data analysis?',\n", " 'Q2_Hands-on experience in data analysis using Python?',\n", " 'Q3_Experience in programming in general?',\n", " 'Q4_General background in machine learning?',\n", " 'Q5_Hands-on experience in running machine learning applications?',\n", " 'Q6_Which one would you prefer on a Sunday afternoon?',\n", " 'Q7_Hands-on experience in image analysis using satellite images?',\n", " 'Q8_Level of interest in mathematics?',\n", " 'Q9_Level of interest in reading?',\n", " 'Q10_Level of stress about this class?',\n", " 'Q11_Your overall motivation about this class?',\n", " 'tsRel']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = dfInit[dfInit.columns[1:]]\n", "initCol = df.columns.tolist()\n", "initCol" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11tsRel
085467Running535730.0
188556Reading77678275.0
266665Watching a movie7777717944.0
353644Watching a movie38851060698.0
466543Reading45410860730.0
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 tsRel\n", "0 8 5 4 6 7 Running 5 3 5 7 3 0.0\n", "1 8 8 5 5 6 Reading 7 7 6 7 8 275.0\n", "2 6 6 6 6 5 Watching a movie 7 7 7 7 7 17944.0\n", "3 5 3 6 4 4 Watching a movie 3 8 8 5 10 60698.0\n", "4 6 6 5 4 3 Reading 4 5 4 10 8 60730.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns = df.columns.str.split('_', 1).str[0].tolist()\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize correlations" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#sns.pairplot(df, kind = 'reg')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q7Q8Q9Q10Q11tsRel
Q11.0000000.7667250.7138770.6240630.7388570.6761500.405270-0.244388-0.4418830.0422060.313668
Q20.7667251.0000000.6390080.4818360.5682440.6922750.399874-0.321784-0.3299910.0993480.383485
Q30.7138770.6390081.0000000.5648250.5440570.6164670.596732-0.083087-0.6316570.4005800.457261
Q40.6240630.4818360.5648251.0000000.9455410.4507520.426714-0.461877-0.1688680.0367390.440323
Q50.7388570.5682440.5440570.9455411.0000000.4449400.467669-0.463201-0.263556-0.0239300.407507
Q70.6761500.6922750.6164670.4507520.4449401.0000000.184545-0.198770-0.2844180.0452760.152171
Q80.4052700.3998740.5967320.4267140.4676690.1845451.000000-0.126656-0.2056980.5010680.036620
Q9-0.244388-0.321784-0.083087-0.461877-0.463201-0.198770-0.1266561.000000-0.2174100.228420-0.193531
Q10-0.441883-0.329991-0.631657-0.168868-0.263556-0.284418-0.205698-0.2174101.000000-0.133846-0.311850
Q110.0422060.0993480.4005800.036739-0.0239300.0452760.5010680.228420-0.1338461.0000000.430875
tsRel0.3136680.3834850.4572610.4403230.4075070.1521710.036620-0.193531-0.3118500.4308751.000000
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 \\\n", "Q1 1.000000 0.766725 0.713877 0.624063 0.738857 0.676150 0.405270 \n", "Q2 0.766725 1.000000 0.639008 0.481836 0.568244 0.692275 0.399874 \n", "Q3 0.713877 0.639008 1.000000 0.564825 0.544057 0.616467 0.596732 \n", "Q4 0.624063 0.481836 0.564825 1.000000 0.945541 0.450752 0.426714 \n", "Q5 0.738857 0.568244 0.544057 0.945541 1.000000 0.444940 0.467669 \n", "Q7 0.676150 0.692275 0.616467 0.450752 0.444940 1.000000 0.184545 \n", "Q8 0.405270 0.399874 0.596732 0.426714 0.467669 0.184545 1.000000 \n", "Q9 -0.244388 -0.321784 -0.083087 -0.461877 -0.463201 -0.198770 -0.126656 \n", "Q10 -0.441883 -0.329991 -0.631657 -0.168868 -0.263556 -0.284418 -0.205698 \n", "Q11 0.042206 0.099348 0.400580 0.036739 -0.023930 0.045276 0.501068 \n", "tsRel 0.313668 0.383485 0.457261 0.440323 0.407507 0.152171 0.036620 \n", "\n", " Q9 Q10 Q11 tsRel \n", "Q1 -0.244388 -0.441883 0.042206 0.313668 \n", "Q2 -0.321784 -0.329991 0.099348 0.383485 \n", "Q3 -0.083087 -0.631657 0.400580 0.457261 \n", "Q4 -0.461877 -0.168868 0.036739 0.440323 \n", "Q5 -0.463201 -0.263556 -0.023930 0.407507 \n", "Q7 -0.198770 -0.284418 0.045276 0.152171 \n", "Q8 -0.126656 -0.205698 0.501068 0.036620 \n", "Q9 1.000000 -0.217410 0.228420 -0.193531 \n", "Q10 -0.217410 1.000000 -0.133846 -0.311850 \n", "Q11 0.228420 -0.133846 1.000000 0.430875 \n", "tsRel -0.193531 -0.311850 0.430875 1.000000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "corr = df.corr()\n", "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Q1_General background in data analysis?',\n", " 'Q2_Hands-on experience in data analysis using Python?',\n", " 'Q3_Experience in programming in general?',\n", " 'Q4_General background in machine learning?',\n", " 'Q5_Hands-on experience in running machine learning applications?',\n", " 'Q6_Which one would you prefer on a Sunday afternoon?',\n", " 'Q7_Hands-on experience in image analysis using satellite images?',\n", " 'Q8_Level of interest in mathematics?',\n", " 'Q9_Level of interest in reading?',\n", " 'Q10_Level of stress about this class?',\n", " 'Q11_Your overall motivation about this class?',\n", " 'tsRel']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initCol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling categorical variables (visualization)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.catplot(x=\"Q6\", y=\"Q11\", data=df);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling categorical variables (Data analysis)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df2 = pd.get_dummies(df, columns=['Q6'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "12 0 1 \n", "13 0 1 \n", "14 1 0 \n", "15 0 1 \n", "16 0 0 \n", "17 0 1 \n", "18 0 1 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "dfTmp = df2[['Q8', 'Q9', 'Q10', 'Q11', 'Q6_Reading', 'Q6_Running', 'Q6_Watching a movie',]].copy()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#sns.pairplot(dfTmp, kind = 'reg')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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l2gYqHjTb7pX7VBo/tlQ/6jdp2jaVxq+6RNxBKz5TaXyA6Yf+Q6Xxq17V9YS3PFptA8CSb+xUeRvd8hi6mVlDuIduZtYQ7qGbmTXEgHvoZmbN0IAKdE7oZmYAg+6hm5k1QxOWdnVCNzOjGSdF66opuo2kyyTdJWm1pLNStmtm1q1BqfTWq2qpKUq2bO62EfFnwCHAeyTNStW2mVm3BjrYelUtNUWBW4HnS5pCluS3AH9I2LaZWVeaMMulrpqi9wCPAxuBB4B/johHWgUp1hS97P6NCQ/PzKy9QVR661UpE/pINUWnkv2msicwG/igpJYLnUTE4og4NCIOPXnWHgkPz8ysvehgK0PSPElrJK2VdGaL5yXpC/nzd0p6ebfvIWVCXwVsVQ+vUFP0r4HvR8TTEfEgcOPwfc3MxtOgym+jyUtwXggcD+wPvE3S/sN2O57sXOMcYCHwxW7fQ8qEfh2wvaQF8Mwb+jRwAfBz4Jj8f6Tnk1Uuuidh22ZmXRnsYCvhcGBtRKyLiC3AFcD8YfvMB74amZuBnSR1NSxRV03RC4FpwEpgGXBJRNyZqm0zs24NqPxWwgxgfeF+X/5Yp/t0pLaaomRTF83MelInFxZJWkg2TDJkcUQsLu7S4mXDh9/L7NMR1xQ1M6OzhJ4n78Uj7NIH7FW4PxPYMIZ9OpL0SlEzs4kqVH4rYRkwR9JsSVOBk4Alw/ZZAizIzy0eCTwaEV3N1fZaLmZmpF3LJSL6JZ0O/ACYDFwcEasknZo/vwhYCpwArAWeAN7Vbbs9ndDfd9uulcZ/eHBzpfFfs+LpSuNftnlTpfHr8Nrtd6w0ftX1PgHOvfVjlcZ/z6EfrjT+L79baXgAfjp1S+VtvKPL16e+pD8ilpIl7eJjiwq3AzgtZZs9ndDNzOrShEv/ndDNzGjG8rlO6GZmOKGbmTWGKxaZmTWEx9DNzBqilwtXlFVXCbqpki7JS9DdIenolO2amXVrkCi99aq6StD9D4C8BN1c4NOSfJWqmfWMxKstjouUSfVZJeiADwALgJeTLa9Lvh767/F66GbWQ1IXuBgPdZWgWwPMlzRF0myyQtF7PSuCmdk4aUIPPeVJ0ZFK0P0Q2IOsWPQvgZuA/pZBCstSHr7LwcyZNjvhIZqZtdavXu57l1NXCbpVEfGBiDg4IuYDOwH3tgpSrCnqZG5mdfGQy9ZGKkE3VHoOSXOB/oi4O2HbZmZdacKQS10l6HYDbpO0GjgDeGeqds3MUmjCtMU6S9C9NGVbZmYp9W6aLs8l6MzM6O2hlLJ86b+ZGTDQgD66E7qZGc3oofvyezMzIDr40w1Ju0i6Jl/z6hpJO7fYZy9J10taLWmVpPeXid3TPfQVT26oNP6Mqc/6HJM6Z+P1lcY/bc+jKo0P8Ot4stL4v42nKo1fx5KoVdf8/NKt51ca/x9rqLu6fvDRytvoVo099DOB6yLiPEln5vfPGLZPP/DBiLhN0guA5ZKuGW26t3voZmbUOm1xPnBZfvsy4E3Dd4iIjRFxW377MWA1MGO0wE7oZmZ0dqWopIWSbi1sCztoaveI2AhZ4ia7TqctSbOAlwG3jBa4p4dczMzq0t9BzzsiFgOL2z0v6VrgRS2eOruTY5I0Dfgm8Hf5YocjckI3M4OuT3ZuFSviuHbPSfqNpD0iYqOkPYAH2+y3DVky/3pEfKtMux5yMTOj1rVclgAn57dPBq4avkNeMOgiYHVEfKZsYCd0MzPqm7YInAfMzde8mpvfR9Kekpbm+7yKbM2rYyStyLcTRgs8piEXSTOBC4H9gcnAUuCDwDTgG8BhwKURcXrhNYcAl5KVpVsKvD9f0MvMbNzVNW0xIh4Gjm3x+AbghPz2T8lqSXSk4x76KLVDnwTOAT7U4qVfJCtcMfSaeZ22bWZWlYGI0luvGsuQy0i1Q5X/z7LV1Sj5wP8OEfGzvFf+VVrMvTQzGy9NWD53LAl9pNqh+7V5zQygr3C/jzaT5IvzOx/Z3PLkr5lZcjWOoVdmLAl9pNqhI71muJafSrEE3S7PG3G+vZlZMs/VikUj1Q5d0+Y1fcDMwv2ZQLULtZiZdeC5OuTStnZoRGxu9YL88tbHJB2Zn1RdQIu5l2Zm4+U5OeQySu1QJN0PfAY4RVKfpP3zl/5P4CvAWuA+4OruD9/MLI0mzHIZ0zz0kWqHRsSsNq+5FThwrAdqZlalXh5KKavrtVxcO9TMmqCXT3aW5cW5zMxIuzjXeHFCNzPDQy5mZo3RhKWlejqhnz1530rj77hloNL4lx/f7sLZNK5eVv2P79HJ0yqNv2t/tf+ITnhL9bUsf/ndauNXXfPzn279WKXxAR5/77srb6NbA+6hm5k1g4dczMwawkMuZmYN4R66mVlDeNqimVlD9PIl/WW5pqiZGfWttihpF0nXSLo3/3vnEfadLOl2SaXmUo0poUuaKemq/IDWSbpA0raSdpV0vaRNki4Y9pqPS1ovadNY2jQzq1KNy+eeCVyXl/C8Lr/fzvuB1WUD11lT9DvA4Z22Z2ZWh4govXVpPnBZfvsy2pTjlDQT+O9kq9SWUktN0Xy/m/N10c3Mek4nPfRiqcx8W9hBU7sP5cL873al2T4HfJgO1g0by0nRljVF83XQ9wNWjCHmM/IPZiHAu3c8nGO3r/ZqSzMz6GyWS0QsBha3e17StcCLWjx1dpn4kl4PPBgRyyUdXfa4xpLQx1JTtLTiB3X5nu+Y+KedzWxCGIh0C+hGxHHtnpP0G0l7RMRGSXsAD7bY7VXAGyWdAGwH7CDp3yLir0Zqt66aomZmPa3GMfQlwMn57ZNpUY4zIs6KiJl5waCTgB+OlsyhppqiZma9rsZZLucBc/MSnnPz+0jaU9LSbgLXVlNU0vmS+sj+M+iTdG43B25mllJdRaIj4uGIODYi5uR/P5I/viEiTmix/48i4vVlYtdZU/TDZGdszcx6zmADrhR1TVEzM7yWi5lZY6Sc5TJenNDNzPCQi5lZYzRhyEW9XKWj74hjKj24502vtqbolN22qTQ+FdfjBBh8otrPaOWN0yuN/8Ck7SqND/DTqVsqjb9+8PFK4//bq56oND7A8//losrb2Gb6Pl1d3Ljv9JeX/gd130O3JbmQMjX30M3MaEYP3QndzAwYiGp/G62DE7qZGS4SbWbWGC4SbWbWEO6hm5k1RBPmoY+4OJeknST97Sj73C/pLkl3SvqxpBGXAZB0yvB6o2Zm462uxbmqNNpqizsBIyb03Osi4s+BHwH/0O1BmZnVbSAGS2+9arSEfh6wr6QVkr4s6Yb89kpJR7XY/2fADABJL5T0TUnL8u1VqQ/ezCyVGgtcVGa0hH4mcF9EHAzcA/wgv30QrWuHzgOuzG9/HvhsRBwGnEjJytXF4qtff3BDmZeYmXVtMKL01qs6OSm6DLhY0jbAlRFRTOjXS9qdrDbe0JDLccD+0jNXyO4g6QWjNVKsKVr1pf9mZkN6ueddVumKRRFxA/Aa4FfA14ZK0OVeR7Ym+irgnwqxXxERB+fbjIh4LNFxm5klVVcJOkm7SLpG0r353zu32W8nSd+QdI+k1ZJeMVrs0RL6Y8AL8uAvBh6MiC8DFwEvL+6Y1xP9O2CBpF2A/wROLxzcwaMdjJnZeKlxDP1M4LqImENWo/nMNvt9Hvh+RPwJ2TD36tECjzjkEhEPS7pR0krg+cDjkp4GNgELWuy/UdLlwGnA+4ALJd2Zt3MDcOpoB2RmNh5qnL0yHzg6v30Z2ezAM4o7SNqBbETkFICI2AKMuqznqGPoEfH2UZ6fNez+ewt339pi/0uBS0dr18ysTjWe7Nw9IjbCM53g3Vrssw/wW+ASSQcBy4H3R8SIaymXHkM3M2uyToZcirPx8m1hMZaka/Pp3cO3+SUPZwrZsPYXI+JlwOO0H5rZ6kVmZs95nVwBWpyN1+b549o9J+k3kvbIe+d7kM0OHK4P6IuIW/L736BEQncP3cyMWk+KLgFOzm+fDFzV4lh+DayX9NL8oWOBu0cL7B66mRm1jqGfB/w/Se8GHgDeAiBpT+ArEXFCvt97ga9LmgqsA941auRO/lfq9Q1YOJHjN+E9+DMa//hNeA91fEZN3Jo25LJw9F16On4dbUz0+HW0MdHj19HGRI/fSE1L6GZmz1lO6GZmDdG0hN52GtEEiV9HGxM9fh1tTPT4dbQx0eM3kvITEGZmNsE1rYduZvac5YRuZtYQEzKhS5op6ap8PeF1ki6QtK2kXSVdL2lTt4WoR2hjrqTleWHs5ZKOSRz/8LzM3wpJd0h6c8r4hef3zj+nD40l/ijvYZakzYX3sShx/HcUYq+QNDiW5ZlHiL+NpMvyn/FqSWeN5fhHaWOqpEvyNu6QdHSCmG2//5IOydtaK+kL0h8rzySK/3FJ6yVtahPTBefrMN4T4cdwwYGA/wLeld+fTLY+++fJlvh9NdkyvRdU1MbLgD3zxw8EfpU4/vbAlPzxoXUepqSKX9jnm8B/AB+q4DOaBays6uc8bL8/A9YlPv63A1fkj28P3A/MStzGacAl+eO7ka2mN6mq73/+mlfkr78aOD5x/CPz7+umNnFH/U7kn/P0/PZHgS+Psv8pw4/jub6N+wF0fMDZmgY3DHtsB+B3wLQUP+gybeSPCXgY2Lai+LOB39B5Qh8xPvAm4FPAuYw9oY/UxoGj/eNN+Bl9Avh44vjvBL5DtjTGrsDPgV0St3ER8FeFx68DDk/xuQz//ueJ9p7C/bcBX0oVf9i+7RL6FcBmslrEXyarj7ACWAkcle9zP39M6POApfntF5J1QJbl26tGO47n6jYRh1wOIOvNPCMi/kD2Zdiv5jZOBG6PiKdSxpd0hKRVwF3AqRHRnzD+QWSL6X+0w5idtDEFmC3p9vxX56MSxy/+DN4KXJ44/j1ky5VuJFtr458j4pHEbawB5kuaImk2cAiwV5cx233/Z5Ct3jekL38sVfwyai84/1w0ERfnErRc57LtmGAVbUg6APgk8Bep40e2ZOYBkv4UuEzS1RHxZKL4HyX7x7FphGHUbtvYFtg7sopXhwBXSjogTwwp4mc3pCOAJyJiZQdxy8SfCgwAewI7Az+RdG1ErEvYxg/Jes63Ar8EbgLK/Mc9lu9/q+fazVeu499XLQXnn4smYg99FXBo8QFl5Zp2J+v1VN6GpJnAt4EFEXFf6vhDj0XEarKe4oEJ4+8InC/pfrIasP9b0unPitBdG3dGxMP5e1gO3Ae8JGH8oc/oJMbWOx8t/l+T1XJ8OiIeBG4cvm+CNlZFxAciK6A+H9gJuLfLmO2+/33AzML9mcCGhPE7Ei44X5mJmNCvA7Yf+hJImgx8mmwsbXPVbZD1Pr8HnBURN1YQ/0WSpuSPvxh4Kdmvu0niR8RhETErstKBnwM+ERFjmSkw0nuYlt9H0j7AHLLlP1O9h82SJpEtO3rFGI59tOP/OXCMMs8nO+F3T+I2hmIjaS7QHxGjrnc9Usx23//Iyp09JunIfHbLAlqswT3W+CW54HwdxnsQfywb2VjjErIeze8pnOAhS36PkBWy7gP2T9kG2a+Bj5ON+w1tuyWM/06y3skK4DbgTak/o8I+5zLGk6KjvIcT8/dwR/4e3lDBz/lo4OYqvkdkJ47/I38PdwP/q4I2ZpH1eFcD1wIvrvL7T9brXkn229IF5FeJJ4x/fn5/MP/73BZx/29+DL/I/74d+AkwuxB7emH/fwHOAaYD/w7cmf88FuXPn4JPim79GY/3AXT9BuCVZGOQh0zUNiZ6/Ca8h4n6GTXhc/GWbvNaLmZmDTERx9DNzKwFJ3Qzs4ZwQjczawgndDOzhnBCNzNrCCd0M7OG+P8fAUIPfuGowAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "corr = df.corr()\n", "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dealing with outliers (focusing on tsRel)\n", "\n", "### Visualize data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Q1_General background in data analysis?',\n", " 'Q2_Hands-on experience in data analysis using Python?',\n", " 'Q3_Experience in programming in general?',\n", " 'Q4_General background in machine learning?',\n", " 'Q5_Hands-on experience in running machine learning applications?',\n", " 'Q6_Which one would you prefer on a Sunday afternoon?',\n", " 'Q7_Hands-on experience in image analysis using satellite images?',\n", " 'Q8_Level of interest in mathematics?',\n", " 'Q9_Level of interest in reading?',\n", " 'Q10_Level of stress about this class?',\n", " 'Q11_Your overall motivation about this class?',\n", " 'tsRel']" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initCol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Q: Is there a correlation between how fast a student answered the poll and answers to questions?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tsRel 1.000000\n", "Q3 0.457261\n", "Q10 -0.311850\n", "Q11 0.430875\n", "Name: tsRel, dtype: float64\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dfTmp = df[['tsRel','Q3', 'Q10', 'Q11']].copy()\n", "corr = dfTmp.corr()\n", "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)\n", "print(corr['tsRel'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### WARNING: Outliers may lead to incorrect conclusions!" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.regplot(x='tsRel', y='Q3', data=dfTmp, color=\"g\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0RVOD8iKSCwqUhGn0RgrQJS8RyT0FSsJku+R1tOFoPg9JRLoJBUrCNHpj0yWvHgXRbcO65CUiuaBASRB3111eIpI3CpQEcaJfPU4HSnpQXoEiIrmgQEmQ9DO7WvZQNIYiIrmgQEmQloGSMvVQRCR3FCgJkg6UlvNQFCgikgsKlARx1xiKiORPrEAxs2lmtsnMys1sTpb1ZmZ3hfVrzez89tqa2UAze9rMNof3ARnr7gj1N5nZx0JZHzNbnfGqNrMfh3U3mFlVxrrPn8pJ6aqaLnm1mNh4tF5jKCLS+doNFDMrBO4BpgMTgOvMbEKLatOBceE1G7g3Rts5wAp3HwesCJ8J62cCZwPTgJ+aWaG7H3T3SekXsBV4KOMYFmes//mJnogkeNcYinooIpJDcXooFwLl7r7F3euARcCMFnVmAAs98iLQ38yGt9N2BrAgLC8ArskoX+TuR939TaA8bKeJmY0DhgJ/OoHvmngKFBHJpziBMhLYlvF5eyiLU6ettqXuvgsgvA89gf1dR9Qj8Yyya8PltqVmNjrbFzGz2WZWZmZlVVVV2ap0aRqUF5F8ihMolqXMY9aJ0/Zk9jcTeDDj82PAGHefCDzD8Z5P8424z3f3Ke4+ZciQIe0cRtfTcmKjnuUlIrkUJ1C2A5l/8Y8Cdsas01bbinBZjPBeGWd/ZnYekHL3Vekyd9/j7ul/Ne8DJsf4XonT2sRG9VBEJBfiBMpKYJyZjTWzIqLewbIWdZYB14e7vaYC+8NlrLbaLgNmheVZwKMZ5TPNrNjMxhIN9L+csa/raN47SQdS2tXAazG+V+K0DJQCK6BHQQ8FiojkRLu/Ke/u9WZ2K/AUUAjc7+4bzOzmsH4esBy4imgAvRa4sa22YdNzgSVmdhPwNvCp0GaDmS0BXgXqgVvcvSHjkD4d9pXpy2Z2dai/F7jhhM5CQrQMFICiwiIFiojkRLuBAuDuy4lCI7NsXsayA7fEbRvK9wCXtdLmu8B3W1n3vixldwB3tP4NuoeWg/IQBYrmoYhILmimfII0zZRHPRQRyT0FSoJku+RVnCqmrlGBIiKdT4GSIBpDEZF8UqAkSGuBojEUEckFBUqCNJJ9UF49FBHJBQVKgrR8fD0oUEQkdxQoCZJ1UL6wWIEiIjmhQEmQ1nooepaXiOSCAiVBdJeXiOSTAiVBWpspr0ARkVxQoCRIy58AhjCxUYEiIjmgQEkQ3TYsIvmkQEmQrIPyBZrYKCK5oUBJEA3Ki0g+KVASpNWHQypQRCQHFCgJoh6KiOSTAiVBmm4bpsUPbGlio4jkgAIlQVqbKd/ojTQ0NrTWTESkQyhQEqS1Z3kBuuwlIp1OgZIgrY2hgAJFRDpfrEAxs2lmtsnMys1sTpb1ZmZ3hfVrzez89tqa2UAze9rMNof3ARnr7gj1N5nZxzLK/xDKVofX0FBebGaLQ5uXzGzMyZ2Ori09sTFboGgcRUQ6W7uBYmaFwD3AdGACcJ2ZTWhRbTowLrxmA/fGaDsHWOHu44AV4TNh/UzgbGAa8NOwnbTPuPuk8KoMZTcB+9z9TOBHwPfjn4LkaO1ZXqAeioh0vjg9lAuBcnff4u51wCJgRos6M4CFHnkR6G9mw9tpOwNYEJYXANdklC9y96Pu/iZQHrbTlsxtLQUus8x/VbuJbIPyxSmNoYhIbsQJlJHAtozP20NZnDpttS11910A4X1ozP39MlzuujMjNJrauHs9sB8Y1PKLmNlsMyszs7KqqqrWv3EXpTEUEcmnOIGS7S99j1knTtsT2d9n3P1c4MPh9dkTOEbcfb67T3H3KUOGDGnnMLqetgJFz/MSkc4WJ1C2A6MzPo8Cdsas01bbinBZjPCeHg9ptY277wjvB4HfcPxSWFMbM0sB/YC9Mb5boqiHIiL5FCdQVgLjzGysmRURDZgva1FnGXB9uNtrKrA/XMZqq+0yYFZYngU8mlE+M9y5NZZooP9lM0uZ2WAAM+sBfBxYn2VbnwSe9fSAQjfS2kx5UKCISOdLtVfB3evN7FbgKaAQuN/dN5jZzWH9PGA5cBXRAHotcGNbbcOm5wJLzOwm4G3gU6HNBjNbArwK1AO3uHuDmfUCngphUgg8A9wXtvUL4AEzKyfqmcw8lZPSVWUdlNfERhHJkXYDBcDdlxOFRmbZvIxlB26J2zaU7wEua6XNd4HvtiirASa3Uv8IIZC6szbHUDQPRUQ6mWbKJ0gjjRimeSgikhcKlARp9MZmYQIKFBHJHQVKgjR6Y7PLXaCJjSKSOwqUBHH3dwWK5qGISK4oUBIkWw9Fl7xEJFcUKAnSVg9FgSIinU2BkiCN3thsUiNoHoqI5I4CJUEa0SUvEckfBUqCZBtD6VHYA9DERhHpfAqUBMk2hlJgBaQKUuqhiEinU6AkSLYeCkTjKAoUEelsCpQEyTZTHqJxFAWKiHQ2BUqCNHojBVn+kxYVFmlio4h0OgVKgrTZQ2lUD0VEOpcCJUGyDcqDLnmJSG4oUBKk1UH5lAblRaTzKVASJNvERtAYiojkhgIlQXSXl4jkkwIlQVq75KVAEZFcUKAkSGuD8prYKCK5ECtQzGyamW0ys3Izm5NlvZnZXWH9WjM7v722ZjbQzJ42s83hfUDGujtC/U1m9rFQdpqZPWFmG81sg5nNzah/g5lVmdnq8Pr8yZ6QrqytHoqe5SUina3dQDGzQuAeYDowAbjOzCa0qDYdGBdes4F7Y7SdA6xw93HAivCZsH4mcDYwDfhp2A7AD919PPBB4GIzm55xDIvdfVJ4/fwEzkFitDWxUT0UEalrqOO+VfexqXpTp2w/Tg/lQqDc3be4ex2wCJjRos4MYKFHXgT6m9nwdtrOABaE5QXANRnli9z9qLu/CZQDF7p7rbs/BxC29Qow6iS+c2JpUF5E2lJVU8Xsx2fz/NbnO2X7cQJlJLAt4/P2UBanTlttS919F0B4Hxp3f2bWH/gEUc8m7dpwuW2pmY3O9kXMbLaZlZlZWVVVVbYqXZrmoYhIW6pqo3/3Bp82uFO2HydQ3v0nL3jMOnHantD+zCwFPAjc5e5bQvFjwBh3nwg8w/GeT/ONuM939ynuPmXIkCHtHEbX0+pM+QLNQxERqK6tBvIbKNuBzL/4RwE7Y9Zpq21FuCxGeK+Mub/5wGZ3/3G6wN33uHv6X8z7gMkxvldObKrexA9e+EFO9tXWxEb1UETkvRAoK4FxZjbWzIqIBsyXtaizDLg+3O01FdgfLmO11XYZMCsszwIezSifaWbFZjaWaKD/ZQAz+w7QD/hq5s7TwRRcDbwW43vlxMI1C7n9mdvZd3hfp2z/5R0v87UVXwM0D0VE2pYOlCGndc4VmnYDxd3rgVuBp4j+oV7i7hvM7GYzuzlUWw5sIRpAvw/4UlttQ5u5wBVmthm4InwmrF8CvAr8DrjF3RvMbBTwdaK7xV5pcXvwl8OtxGuALwM3nOwJ6WgVNRXN3jvag+se5Ht//h5H64+2OiivMRQRgShQDGNAzwHtVz4JqTiV3H05UWhkls3LWHbglrhtQ/ke4LJW2nwX+G6Lsu1kH1/B3e8A7mjzS+RJU6AcqmD84PEdvv3dNbsBOHD0gJ42LCJtqq6tZkDPAaQKYv3Tf8I0U76TVRzq3B5KevsH6g60ecmrwRtoaGzolGMQka6hqraq08ZPQIHS6TJ7KJ1h96HQQzlyoM2JjYB6KSLdXHVttQKlq3L3Tu+hNAXK0QM4rV/yAgWKSHenQOnCDhw90PQMrcqaynZqn7ij9UfZdyS6e2z/0f2tD8oXFgMKFJHurrq2utPu8AIFSqfKDJHO6KFkbvPA0bbHUAA9IFKkG3N39VC6svQ/+KmCVKeMoWRuM06gqIci0n0drDtIXUOdAqWrSv+D/4HBH+iUHkp6/GRAyYB2bxsGBYpId9bZs+RBgdKp0iEysXQiFYcqiKbrdJx0oJw37LymHkprExtBgSLSnSlQuriKQxUYxtlDzuZw/WEO1R3q0O2nA2Xi0IlNg/JtjqHoAZEi3VZnP3YFFCidqqKmgkGnDWJk35FNnzvS7kO7GdhzIKf3P536xnqO1B/RJS8RySoXPZTOmX+fcPNXzc9aPnvy7GafK2oqKO1VSmmv0ujzoQrOHHhmhx1Hy+07romNIpKVLnl1cRWHKijtXUpp7xAop9hD2Xt4L3c8cwdH6o8AUQ9lWO9hDOs9rKlO1h/Y0jwUkW6vqqaKVEGKvsV9O20fCpROlK2HciqWvrqUuS/MZcWW6IcqswVKaz8BDJqHItKdpeegZPs3oqMoUE7CY5se47FNj7Vbr+JQFChDekWDYKfaQ1m5Y2X0vjN6b6+H8m+P/xugS14iAtWHj09q/NYfvtUp+1CgnITHNz/O45sfb7NOTV0NNcdqKO1d2vSo6FPtoZTtKoved5ZxqO4QNcdqGNZ7GAN6DqDQCoG2eygKFJHuK/OxK//5/H92yj4UKKfg7pfvZvOezU2fDx49yLef/za1x2qbeiPpy11w4j2Uv+36G8cajgFw+Nhh1lWsA6IeSvqW4dJepRRYAX2K+wCtjKFoHopIt9fZj10BBcopWVe5jue3Pt/0+YG1D/DNP3yTJRuWNPVG0gPycDxQXt/zOqt2rmpz2xurNzJ5/mR+8tJPAFhTsYYGb+Dy911OZU0lZTuj3kr6cld6oE3zUEQkbfeh3dTU1QDRoLwC5T1ufeV66hvrAXhk4yNN79l6KJv3bGb+qvlc8cAVXLrgUu5deS8QPbTtXxb/Cz/664+a6i59dSmOs2TDEuD4+MkXp3wRgMdej8Zw0oHSr7gfgG4bFhEg+v99/N3j+eiCjzKvbB57D+/l7f1vtzrtoSMoUE5Brx69OFx/mM17N1NTV8Nzbz1HSaqEp954ijf3vQk076EcOHqAHQd28Pb+tzlYd5D1lesBeGnHSzy88WH+68//1dSL+O1rvwWiy1tb39lK2a4yhvUexlXjriJVkOLJzU8CJ9ZDUaCIdB9PvP4E+4/u55Xdr1BVU4Xj9C7q3an7VKCcgk+8/xP0KOjB2oq1TT2VOy+5kyP1R1i4diEAQ3sNbap/tOEof9z6RwqsgF49evHi9hcB+Nmqn2EY1bXVPLLxEd7Y+ward6/m5sk3A/DQaw+xcsdKLhhxASWpEs4dei77juyjwAqaurDpQNGgvEj3tHnP5maX0u9ffT8lqRLqG+t59s1nARQo72WTR0xm/ODxrK1Yy+rdqxnWexi3/f1tDCgZwOrdqxnYc2DTP+ZpL2x7gXOGnsPUUVNZW7mWLfu2sHj9Ym764E2M7T+Wn636WVPv5PZ/uJ3zSs9jwZoFbKzeyJQRUwC4YMQFQPRMnsKC6O6uWGMomocikghH6482uyFo7+G9/OOv/pEP//LDbN6zmV0Hd7F883I+cvpHGN13NC9sewF4jwSKmU0zs01mVm5mc7KsNzO7K6xfa2bnt9fWzAaa2dNmtjm8D8hYd0eov8nMPpZRPtnM1oV1d1n4c9zMis1scSh/yczGnNzpODF9i/sysXQi1bXVrK5YzYyzZlBUWMQnzvoE0Hz8JO1Y4zEuGnURF426iPrGej655JMcrj/MFy/4Il84/ws899Zz/HTlT5k8fDJj+o/h2g9cy5qKNTjeFCTpYMmcf9I0hpIlUAqsgFRBqlkPZd/hffxm3W/YeXAnEI3j/OTFn3D6j09vmmPj7ixYvYAvPfEl9h7e29T2L9v+wl+2/aXZPt458o56QCIdoHxvOYePHW76vLF6I1/93VebAmT/kf1ctvAyzrr7LO7/2/0AfOmJL1FVW0VRYRHXP3I9v1z9Sxq9kb8f/fdcPPpijjVGd4t2dqC0+ywvMysE7gGuALYDK81smbu/mlFtOjAuvD4E3At8qJ22c4AV7j43BM0c4HYzmwDMBM4GRgDPmNn73b0hbHc28CKwHJgGPAncBOxz9zPNbCbwfeB/nMqJac2OAzuafZ5YOpH/XvffNHoj/zz+nwG45qxrWLhmYbPxk7TTepzGuUPPJVWQYmSfkfxt99+YPHwy5w8/nxF9RvDNP3yTrfu38m+To0mJ1064lm/+4ZvA8SDJFiht9VAg6qVU11bzp61/4uGNDzN/1XxqjtXQt7gv37vse6yrWMe8VfPoX7NTud8AAAu0SURBVNKfGYtm8L3LvseaijU8uP5BAJ7Y/AT3XHUPizcs5tdrfw3AZyd+lq986CvcW3Yvv1r9K0b0GcE3LvkGF426iHvL7uXhjQ9zyemX8JUPfYWeqZ4sXLOQsl1lXHXmVXxm4mfYcWAHD298mMqaSv5p3D9x+fsuZ13lOp5+42lSBSmuPONKzhl6Dit3ruQv2/7C8N7D+ciYjzD4tMG8vONl1leu5/2D3s/UUVNp9EZW7VrF9gPbOWfoOZxXeh57D+9lTcUaaupqOLf0XM4ceCbb9m/j1apX6VHYg7OHnM3QXkN585032bxnM4NOG8QHBn+AklQJb+x7g237tzGq7yjOGHgGDY0NvLHvDfbU7mFM/zGM7jeaQ3WH2LJvC3UNdYztP5ahvYZSVVvF1ne2UlRYxJj+Y+hT3IddB3ex4+AO+hX34+/6/R2pghQ7D+6ksqaS0t6ljOgzgvrGerYf2M7BowcZ0WcEQ3oNofZYLdv2b6PBGxjVdxT9ivux78g+dhzYQXGqmFF9R1GSKqGqpordh3bTr6QfI/qMoMAK2H1oN3tq9zC011CG9hrKscZj7Dy4k5q6Gob3Gc6gnoOoOVbD9gPbcXdG9h1Jn6I+7Duyj50Hd1KSKmFkn5EUp4qprKlk96HdDCgZwIg+IwDYdWgXew/vpbRXKUN7DaWuoY7tB7ZTe6yWkX1HMqjnIA4cPcDb+98G4O/6/R19i/tSVVvFtv3b6FXUi9F9R1OcKmbHgR3sOrSLwacNZnTf0TjO1ne2Ul1bzai+oxjZdyQ1dTVs2beF2mO1jB0wlmG9h1FZU0n53nIKrIBxA8cxsOdAtu7fSvnecvoV9+OswWdRkiphU/Um3nrnLUb1HcX4weM51niMdRXr2HlwJ+MHj2f84PFU1lTyyq5XOFR3iEnDJjFu0Dg2VW9i5c6VFBUWceHICxnZZyQv73iZl3a8xMg+I7nk9EsoSZXwzJZneGXXK0waNokrzriC6tpqHn7tYV7f+zpXvO8Kpp05jZU7VvLA2gfYf3Q/M8+eyUfHfpTfrPtN0/9zX/nQVzhn6Dnc+dyd/K78d4zsM5JvX/ptDtcf5rbf38bh+sP8/JWfM/fyuSxYs4DVu1czecRkblp2E89seYbFGxbznUu/wxkDz+C6315H2c4yLh59MaW9S+ld1Julry2lvrE+/4ECXAiUu/sWADNbBMwAMgNlBrDQox/8eNHM+pvZcGBMG21nAB8J7RcAfwBuD+WL3P0o8KaZlQMXmtlbQF93/2vY1kLgGqJAmQF8K2xrKXC3mZl39A+QAI+/3nxCY/+S/ozpN4bdNbu5dOylAFx5xpWUpEqa/YOfdsGIC+hR2AOAi0ZdxNLXljaFx7Dew5hx1gx++9pvuXbCtQBMGDKBDwz+ADXHappm3J8z9Jx3bb9fSdRDSU9wbKkkVcJ9r9zHfa/cR6EVMvOcmXx24mf54V9/yC3LbwHg9otv5xuXfIMbHrmBOSvmUGAFfOfS73D5+y7nXx/6Vz7xYDRm9PUPfx2AH7zwAx5Y+wBFhUV84fwvsLpiddPs/OLCYq4840p+/8bvm+5UKyosYvzg8Xzt2a/xtWe/BtD0bKEFaxY0HWuhFeI433r+W7H+m8RlGI6fcFm2OgVWQKM3nlRZy+1lq1NohTR4Q7OyVEGq6Y7Ctuq13F7cbcXZvhGN0WUef7btxy1reaydfa6ziVOnPdnO3aCeg5r++Ep/7lvclxsevaGp7NIxl1JZU8mNj94IRD+W97//8X/zZPmTfG7Z5wD42Bkf49uXfps5K+bw70/+O0WFRTz06Ye48owr+fTST/Pg+geZOmoqt//D7aQKUjy66VEWrV/EjZNupMEb6FXUi0nDJlG2s6zTA8Xa+zfXzD4JTHP3z4fPnwU+5O63ZtR5HJjr7n8On1cQhcOY1tqa2Tvu3j9jG/vcfYCZ3Q286O6/DuW/IAqNt8I+Lg/lHwZud/ePm9n6sJ/tYd0bYT/VLb7LbKIeDsBZwKYTO11NBgPV7dbqXnROmtP5eDedk+a66vk43d2z/qhKnB5KtieJtUyh1urEaRt3f21tK9Z+3H0+cMo3YZtZmbtPOdXtJInOSXM6H++mc9JcEs9HnEH57cDojM+jgJ0x67TVtiJcFiO8V8bY1qhWttXUxsxSQD9gLyIikjNxAmUlMM7MxppZEdGA+bIWdZYB14e7vaYC+919VzttlwGzwvIs4NGM8pnhzq2xRAP9L4ftHTSzqeHurutbtElv65PAs50xfiIiIq1r95KXu9eb2a3AU0AhcL+7bzCzm8P6eUR3XF0FlAO1wI1ttQ2bngssMbObgLeBT4U2G8xsCdHAfT1wS7jDC+CLwK+AnkTjKk+G8l8AD4QB/L1EwdWZOu/ZBV2XzklzOh/vpnPSXOLOR7uD8iIiInFopryIiHQIBYqIiHQIBcoJau8xNF2Nmd1vZpVhLk+6LCePxTGzWWEfm80sfVNFXpnZaDN7zsxeM7MNZvaVUN6dz0mJmb1sZmvCOfnPUN5tzwlETxExs79ZNA+v258PIHpek17xXkQ3FrwBvA8oAtYAE/J9XKf4nS4BzgfWZ5T9AJgTlucA3w/LE8J3LgbGhnNRGNa9DFxENCfoSWB6KP8SMC8szwQWh+WBwJbwPiAsD3gPnI/hwPlhuQ/wevje3fmcGNA7LPcAXgKmdudzEo7tfwK/AR7v7v/fNJ2TfB9AV3qF//BPZXy+A7gj38fVAd9rDM0DZRMwPCwPBzZl+75Ed+9dFOpszCi/DvhZZp2wnCKaGWyZdcK6nwHX5ftcZDk3jxI9i07nJDqm04BXiJ7Z123PCdE8uBXARzkeKN32fKRfuuR1YkYC2zI+bw9lSVPq0bwfwnv6R11a+/4jw3LL8mZt3L0e2A8MamNb7xnhMsMHif4i79bnJFzeWU00Aflpd+/u5+THwP8CMh8e1p3PB6AxlBN1Mo+SSZKOfCzOe/pcmllv4LfAV939QFtVs5Ql7py4e4O7TyL6y/xCMzunjeqJPidm9nGg0t1XtVs5NMlSlpjzkUmBcmLiPIYmCXLxWJz37Lk0sx5EYfLf7v5QKO7W5yTN3d8hejL4NLrvObkYuNqiJ6AvAj5qZr+m+56P4/J9za0rvYiuZW4hGlhLD8qfne/j6oDvNYbmYyj/l+aDiz8Iy2fTfHBxC8cHF1cSDdSmBxevCuW30HxwcUlYHgi8STSwOCAsD3wPnAsDFgI/blHenc/JEKB/WO4J/An4eHc+Jxnn5iMcH0PR+cj3AXS1F9EjZl4nulPj6/k+ng74Pg8Cu4BjRH/93ER0rXYFsDm8D8yo//Xw3TcR7kgJ5VOA9WHd3Rx/CkMJ8P+IHsvzMvC+jDafC+XlwI35PhfhmP6B6BLCWmB1eF3Vzc/JROBv4ZysB74ZyrvtOck4to9wPFC6/fnQo1dERKRDaAxFREQ6hAJFREQ6hAJFREQ6hAJFREQ6hAJFREQ6hAJFpJOZWX8z+1I7dd4KT51da2bPm9np7dS/wczu7tgjFTk1ChSRztef6Omx7bnU3ScSzUT/RqcekUgnUKCIdL65wBlmttrM7jOzP4bl9Wb24Sz1/0p44J+ZDTGz35rZyvC6OKdHLnICFCginW8O8IZHD1fcSPQTCJOA84hm4rc0DXgkLP8E+JG7XwBcC/w8B8crclJS+T4AkW5mJXB/eADlI+6eGSjPmVkp0UMF05e8LgcmhB/yA+hrZn1ydrQiJ0A9FJEccvc/Ev1K5g7gATO7PmP1pcDpwAbg/4SyAqIfWpoUXiPd/WBOD1okJgWKSOc7SPRzwoS7tyrd/T7gF0Q/v9zE3Q8DXwWuN7OBwO+BW9PrzWxSrg5a5ETpkpdIJ3P3PWb2gpmtB3oBNWZ2DDgEXJ+l/i4ze5DoEeZfBu4xs7VE/7/+Ebg5d0cvEp+eNiwiIh1Cl7xERKRDKFBERKRDKFBERKRDKFBERKRDKFBERKRDKFBERKRDKFBERKRD/H81oMAqkdqhSQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(dfTmp.tsRel, hist=True, rug=True, color=\"g\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is an outlier? Let's zoom into the data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 100000.0)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.distplot(dfTmp.tsRel, hist = True, color=\"g\")\n", "ax.set_xlim(0, 100000)\n", "#ax.set_ylim(0, 0.008)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "60773.00000000001" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfTmp.tsRel.median()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 -60773.0\n", "1 -60498.0\n", "2 -42829.0\n", "3 -75.0\n", "4 -43.0\n", "5 -20.0\n", "6 -13.0\n", "7 -10.0\n", "8 -4.0\n", "9 0.0\n", "10 10.0\n", "11 17.0\n", "12 27.0\n", "13 28.0\n", "14 39.0\n", "15 44.0\n", "16 50.0\n", "17 166.0\n", "18 383183.0\n", "Name: tsRel, dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfTmp.tsRel - dfTmp.tsRel.median()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2020-01-14 17:11:10\n", "1 2020-01-14 17:15:45\n", "2 2020-01-14 22:10:14\n", "3 2020-01-15 10:02:48\n", "4 2020-01-15 10:03:20\n", "5 2020-01-15 10:03:43\n", "6 2020-01-15 10:03:50\n", "7 2020-01-15 10:03:53\n", "8 2020-01-15 10:03:59\n", "9 2020-01-15 10:04:03\n", "10 2020-01-15 10:04:13\n", "11 2020-01-15 10:04:20\n", "12 2020-01-15 10:04:30\n", "13 2020-01-15 10:04:31\n", "14 2020-01-15 10:04:42\n", "15 2020-01-15 10:04:47\n", "16 2020-01-15 10:04:53\n", "17 2020-01-15 10:06:49\n", "18 2020-01-19 20:30:26\n", "Name: Timestamp, dtype: datetime64[ns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfInit.Timestamp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detect outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What about using standard scaling (z-score transformation) + thresholding\n", "#### Typical outlier threshold: more than +- 2 std. (z<-2 or z>2)\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 -0.783125\n", "1 -0.780147\n", "2 -0.588803\n", "3 -0.125805\n", "4 -0.125459\n", "5 -0.125209\n", "6 -0.125134\n", "7 -0.125101\n", "8 -0.125036\n", "9 -0.124993\n", "10 -0.124885\n", "11 -0.124809\n", "12 -0.124700\n", "13 -0.124690\n", "14 -0.124571\n", "15 -0.124516\n", "16 -0.124451\n", "17 -0.123195\n", "18 4.024628\n", "Name: tsRel, dtype: float64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tsRel_z = (dfTmp.tsRel - dfTmp.tsRel.mean()) / dfTmp.tsRel.std()\n", "tsRel_z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Outlier detection is a serious task!\n", "\n", "#### SciKit methods\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: a more advanced outlier detection" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tsRelQ3Q10Q11outScore
00.0473-971.093118
1275.0578-969.221765
217944.0677-848.985675
360698.06510-2.550890
460730.05108-1.646488
560753.0828-1.071656
660760.0188-0.988349
760763.0769-0.988349
860769.0557-1.012356
960773.0656-1.025628
1060783.0487-0.890923
1160790.0758-0.915438
1260800.0868-1.035467
1360801.0478-1.023812
1460812.07610-1.085007
1560817.0657-1.085007
1660823.0647-1.194836
1760939.0679-5.640073
18443956.09410-13811.188253
\n", "
" ], "text/plain": [ " tsRel Q3 Q10 Q11 outScore\n", "0 0.0 4 7 3 -971.093118\n", "1 275.0 5 7 8 -969.221765\n", "2 17944.0 6 7 7 -848.985675\n", "3 60698.0 6 5 10 -2.550890\n", "4 60730.0 5 10 8 -1.646488\n", "5 60753.0 8 2 8 -1.071656\n", "6 60760.0 1 8 8 -0.988349\n", "7 60763.0 7 6 9 -0.988349\n", "8 60769.0 5 5 7 -1.012356\n", "9 60773.0 6 5 6 -1.025628\n", "10 60783.0 4 8 7 -0.890923\n", "11 60790.0 7 5 8 -0.915438\n", "12 60800.0 8 6 8 -1.035467\n", "13 60801.0 4 7 8 -1.023812\n", "14 60812.0 7 6 10 -1.085007\n", "15 60817.0 6 5 7 -1.085007\n", "16 60823.0 6 4 7 -1.194836\n", "17 60939.0 6 7 9 -5.640073\n", "18 443956.0 9 4 10 -13811.188253" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.neighbors import LocalOutlierFactor\n", "\n", "# fit the model for outlier detection (default)\n", "X = np.array(dfTmp.tsRel).reshape(dfTmp.shape[0],1)\n", "X.shape\n", "clf = LocalOutlierFactor(n_neighbors=5, contamination=0.1)\n", "clf.fit_predict(X)\n", "dfTmp['outScore'] = clf.negative_outlier_factor_.tolist()\n", "dfTmp" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dfTmp.plot.scatter(x='tsRel', y='outScore', c='DarkBlue')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlations for filtered data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tsRelQ3Q10Q11outScore
360698.06510-2.550890
460730.05108-1.646488
560753.0828-1.071656
660760.0188-0.988349
760763.0769-0.988349
860769.0557-1.012356
960773.0656-1.025628
1060783.0487-0.890923
1160790.0758-0.915438
1260800.0868-1.035467
1360801.0478-1.023812
1460812.07610-1.085007
1560817.0657-1.085007
1660823.0647-1.194836
\n", "
" ], "text/plain": [ " tsRel Q3 Q10 Q11 outScore\n", "3 60698.0 6 5 10 -2.550890\n", "4 60730.0 5 10 8 -1.646488\n", "5 60753.0 8 2 8 -1.071656\n", "6 60760.0 1 8 8 -0.988349\n", "7 60763.0 7 6 9 -0.988349\n", "8 60769.0 5 5 7 -1.012356\n", "9 60773.0 6 5 6 -1.025628\n", "10 60783.0 4 8 7 -0.890923\n", "11 60790.0 7 5 8 -0.915438\n", "12 60800.0 8 6 8 -1.035467\n", "13 60801.0 4 7 8 -1.023812\n", "14 60812.0 7 6 10 -1.085007\n", "15 60817.0 6 5 7 -1.085007\n", "16 60823.0 6 4 7 -1.194836" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfTmpFil = dfTmp[np.logical_and(dfTmp.outScore>-5, dfTmp.outScore<5)]\n", "dfTmpFil" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.distplot(dfTmpFil.tsRel, hist = True, color=\"g\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tsRel 1.000000\n", "Q3 0.130838\n", "Q10 -0.163241\n", "Q11 -0.344576\n", "outScore 0.702471\n", "Name: tsRel, dtype: float64\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "corr = dfTmpFil.corr()\n", "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)\n", "print(corr['tsRel'])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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353644Watching a movie38851060698.0
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587833Running84102860753.0
643111Reading11108860760.0
773765Reading4686960763.0
855544Watching a movie4455760769.0
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1044453Watching a movie5278760783.0
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1288866Watching a movie8786860800.0
1344411Watching a movie1577860801.0
1487777Running710561060812.0
1577666Watching a movie6665760817.0
1676655Reading1774760823.0
1766655Watching a movie2997960939.0
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 tsRel\n", "0 8 5 4 6 7 Running 5 3 5 7 3 0.0\n", "1 8 8 5 5 6 Reading 7 7 6 7 8 275.0\n", "2 6 6 6 6 5 Watching a movie 7 7 7 7 7 17944.0\n", "3 5 3 6 4 4 Watching a movie 3 8 8 5 10 60698.0\n", "4 6 6 5 4 3 Reading 4 5 4 10 8 60730.0\n", "5 8 7 8 3 3 Running 8 4 10 2 8 60753.0\n", "6 4 3 1 1 1 Reading 1 1 10 8 8 60760.0\n", "7 7 3 7 6 5 Reading 4 6 8 6 9 60763.0\n", "8 5 5 5 4 4 Watching a movie 4 4 5 5 7 60769.0\n", "9 6 6 6 6 6 Watching a movie 4 6 6 5 6 60773.0\n", "10 4 4 4 5 3 Watching a movie 5 2 7 8 7 60783.0\n", "11 7 7 7 2 2 Running 7 6 7 5 8 60790.0\n", "12 8 8 8 6 6 Watching a movie 8 7 8 6 8 60800.0\n", "13 4 4 4 1 1 Watching a movie 1 5 7 7 8 60801.0\n", "14 8 7 7 7 7 Running 7 10 5 6 10 60812.0\n", "15 7 7 6 6 6 Watching a movie 6 6 6 5 7 60817.0\n", "16 7 6 6 5 5 Reading 1 7 7 4 7 60823.0\n", "17 6 6 6 5 5 Watching a movie 2 9 9 7 9 60939.0\n", "18 9 9 9 9 9 Watching a movie 7 6 5 4 10 443956.0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dimensionality reduction" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9.99999996e-01 1.81226177e-09 6.72112392e-10 5.91733094e-10\n", " 3.30434976e-10]\n", "0.9999999994658001\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mdlPCA = PCA(n_components=5)\n", "XPCA = mdlPCA.fit_transform(df2)\n", "\n", "print(mdlPCA.explained_variance_ratio_)\n", "print(np.sum(mdlPCA.explained_variance_ratio_))\n", "plt.plot(np.arange(0,mdlPCA.explained_variance_ratio_.shape[0]), mdlPCA.explained_variance_ratio_, '-ro')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.23403869e-06 7.40036105e-06 8.97709992e-06 9.61319140e-06\n", " 9.32793474e-06 3.94503126e-06 8.95009702e-07 -3.58017421e-06\n", " -6.06213801e-06 7.55257058e-06 1.00000000e+00 -7.70261980e-07\n", " -6.96515391e-07 1.46677737e-06]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(mdlPCA.components_[0,:])\n", "plt.plot(np.arange(0,mdlPCA.components_[0,:].shape[0]), mdlPCA.components_[0,:], '-ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question: What is \"wrong\" in the data?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q7Q8Q9Q10Q11tsRelQ6_ReadingQ6_RunningQ6_Watching a movie
count19.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.000000
mean6.4736845.7894745.7894744.7894744.6315794.7894745.7368426.8421056.0000007.78947472315.0526320.2631580.2105260.526316
std1.5408661.7819761.8128842.0160182.1137262.3939492.2568931.7082531.7950551.61860592341.6913300.4524140.4188540.512989
min4.0000003.0000001.0000001.0000001.0000001.0000001.0000004.0000002.0000003.0000000.0000000.0000000.0000000.000000
25%5.5000004.5000005.0000004.0000003.0000003.5000004.5000005.5000005.0000007.00000060741.5000000.0000000.0000000.000000
50%7.0000006.0000006.0000005.0000005.0000005.0000006.0000007.0000006.0000008.00000060773.0000000.0000000.0000001.000000
75%8.0000007.0000007.0000006.0000006.0000007.0000007.0000008.0000007.0000008.50000060806.5000000.5000000.0000001.000000
max9.0000009.0000009.0000009.0000009.0000008.00000010.00000010.00000010.00000010.000000443956.0000001.0000001.0000001.000000
\n", "
" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 \\\n", "count 19.000000 19.000000 19.000000 19.000000 19.000000 19.000000 \n", "mean 6.473684 5.789474 5.789474 4.789474 4.631579 4.789474 \n", "std 1.540866 1.781976 1.812884 2.016018 2.113726 2.393949 \n", "min 4.000000 3.000000 1.000000 1.000000 1.000000 1.000000 \n", "25% 5.500000 4.500000 5.000000 4.000000 3.000000 3.500000 \n", "50% 7.000000 6.000000 6.000000 5.000000 5.000000 5.000000 \n", "75% 8.000000 7.000000 7.000000 6.000000 6.000000 7.000000 \n", "max 9.000000 9.000000 9.000000 9.000000 9.000000 8.000000 \n", "\n", " Q8 Q9 Q10 Q11 tsRel Q6_Reading \\\n", "count 19.000000 19.000000 19.000000 19.000000 19.000000 19.000000 \n", "mean 5.736842 6.842105 6.000000 7.789474 72315.052632 0.263158 \n", "std 2.256893 1.708253 1.795055 1.618605 92341.691330 0.452414 \n", "min 1.000000 4.000000 2.000000 3.000000 0.000000 0.000000 \n", "25% 4.500000 5.500000 5.000000 7.000000 60741.500000 0.000000 \n", "50% 6.000000 7.000000 6.000000 8.000000 60773.000000 0.000000 \n", "75% 7.000000 8.000000 7.000000 8.500000 60806.500000 0.500000 \n", "max 10.000000 10.000000 10.000000 10.000000 443956.000000 1.000000 \n", "\n", " Q6_Running Q6_Watching a movie \n", "count 19.000000 19.000000 \n", "mean 0.210526 0.526316 \n", "std 0.418854 0.512989 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 1.000000 \n", "75% 0.000000 1.000000 \n", "max 1.000000 1.000000 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's normalize the data" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "df2_norm = (df2-df2.min())/(df2.max()-df2.min())" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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count19.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.00000019.000000
mean0.4947370.4649120.5986840.4736840.4539470.5413530.5263160.4736840.5000000.6842110.1628880.2631580.2105260.526316
std0.3081730.2969960.2266110.2520020.2642160.3419930.2507660.2847090.2243820.2312290.2079970.4524140.4188540.512989
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
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max1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 \\\n", "count 19.000000 19.000000 19.000000 19.000000 19.000000 19.000000 \n", "mean 0.494737 0.464912 0.598684 0.473684 0.453947 0.541353 \n", "std 0.308173 0.296996 0.226611 0.252002 0.264216 0.341993 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.300000 0.250000 0.500000 0.375000 0.250000 0.357143 \n", "50% 0.600000 0.500000 0.625000 0.500000 0.500000 0.571429 \n", "75% 0.800000 0.666667 0.750000 0.625000 0.625000 0.857143 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " Q8 Q9 Q10 Q11 tsRel Q6_Reading \\\n", "count 19.000000 19.000000 19.000000 19.000000 19.000000 19.000000 \n", "mean 0.526316 0.473684 0.500000 0.684211 0.162888 0.263158 \n", "std 0.250766 0.284709 0.224382 0.231229 0.207997 0.452414 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.388889 0.250000 0.375000 0.571429 0.136819 0.000000 \n", "50% 0.555556 0.500000 0.500000 0.714286 0.136890 0.000000 \n", "75% 0.666667 0.666667 0.625000 0.785714 0.136965 0.500000 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " Q6_Running Q6_Watching a movie \n", "count 19.000000 19.000000 \n", "mean 0.210526 0.526316 \n", "std 0.418854 0.512989 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 1.000000 \n", "75% 0.000000 1.000000 \n", "max 1.000000 1.000000 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2_norm.describe()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.31431827 0.29012089 0.15348486 0.07788586 0.04541328]\n", "0.8812231522564273\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mdlPCA = PCA(n_components=5)\n", "XPCA = mdlPCA.fit_transform(df2_norm)\n", "\n", "print(mdlPCA.explained_variance_ratio_)\n", "print(np.sum(mdlPCA.explained_variance_ratio_))\n", "\n", "plt.plot(np.arange(0,mdlPCA.explained_variance_ratio_.shape[0]), mdlPCA.explained_variance_ratio_, '-ro')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.28489905 -0.3141605 -0.26107415 -0.25405393 -0.27500032 -0.36871538\n", " -0.17301488 0.12875075 0.16485422 -0.03296608 -0.13459097 0.4957779\n", " -0.16340968 -0.33236822]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(mdlPCA.components_[0,:])\n", "plt.plot(np.arange(0,mdlPCA.components_[0,:].shape[0]), mdlPCA.components_[0,:], '-ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove tsRel column" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 Q9 Q10 \\\n", "0 0.8 0.333333 0.375 0.625 0.750 0.571429 0.222222 0.166667 0.625 \n", "1 0.8 0.833333 0.500 0.500 0.625 0.857143 0.666667 0.333333 0.625 \n", "2 0.4 0.500000 0.625 0.625 0.500 0.857143 0.666667 0.500000 0.625 \n", "3 0.2 0.000000 0.625 0.375 0.375 0.285714 0.777778 0.666667 0.375 \n", "4 0.4 0.500000 0.500 0.375 0.250 0.428571 0.444444 0.000000 1.000 \n", "\n", " Q11 Q6_Reading Q6_Running Q6_Watching a movie \n", "0 0.000000 0.0 1.0 0.0 \n", "1 0.714286 1.0 0.0 0.0 \n", "2 0.571429 0.0 0.0 1.0 \n", "3 1.000000 0.0 0.0 1.0 \n", "4 0.714286 1.0 0.0 0.0 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2_noTs = df2_norm[df2_norm.columns[df2_norm.columns.str.contains('tsRel')==False]]\n", "df2_noTs.head()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 Q9 Q10 \\\n", "0 0.8 0.333333 0.375 0.625 0.750 0.571429 0.222222 0.166667 0.625 \n", "1 0.8 0.833333 0.500 0.500 0.625 0.857143 0.666667 0.333333 0.625 \n", "2 0.4 0.500000 0.625 0.625 0.500 0.857143 0.666667 0.500000 0.625 \n", "3 0.2 0.000000 0.625 0.375 0.375 0.285714 0.777778 0.666667 0.375 \n", "4 0.4 0.500000 0.500 0.375 0.250 0.428571 0.444444 0.000000 1.000 \n", "\n", " Q11 Q6_Reading Q6_Running Q6_Watching a movie \n", "0 0.000000 0.0 1.0 0.0 \n", "1 0.714286 1.0 0.0 0.0 \n", "2 0.571429 0.0 0.0 1.0 \n", "3 1.000000 0.0 0.0 1.0 \n", "4 0.714286 1.0 0.0 0.0 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2_noTs = df2_norm.drop(columns=['tsRel'])\n", "df2_noTs.head()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.31894549 0.29885772 0.1547158 0.07966809 0.04678632]\n", "0.898973407637008\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mdlPCA = PCA(n_components=5)\n", "XPCA = mdlPCA.fit_transform(df2_noTs)\n", "\n", "print(mdlPCA.explained_variance_ratio_)\n", "print(np.sum(mdlPCA.explained_variance_ratio_))\n", "\n", "plt.plot(np.arange(0,mdlPCA.explained_variance_ratio_.shape[0]), mdlPCA.explained_variance_ratio_, '-ro')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.30244657 -0.32214438 -0.26410732 -0.25227651 -0.27659662 -0.3869891\n", " -0.17634825 0.12991123 0.1677439 -0.02453888 0.49300779 -0.20299601\n", " -0.29001178]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(mdlPCA.components_[0,:])\n", "plt.plot(np.arange(0,mdlPCA.components_[0,:].shape[0]), mdlPCA.components_[0,:], '-ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Alternative approach: Keep tsRel, but exclude outliers" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q7Q8Q9Q10Q11tsRelQ6_ReadingQ6_RunningQ6_Watching a movie
30.250.00.7142860.5000000.5000000.2857140.7777780.6666670.3751.000.0000000.00.01.0
40.500.60.5714290.5000000.3333330.4285710.4444440.0000001.0000.500.1327801.00.00.0
51.000.81.0000000.3333330.3333331.0000000.3333331.0000000.0000.500.2282160.01.00.0
60.000.00.0000000.0000000.0000000.0000000.0000001.0000000.7500.500.2572611.00.00.0
70.750.00.8571430.8333330.6666670.4285710.5555560.6666670.5000.750.2697101.00.00.0
\n", "
" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 Q9 \\\n", "3 0.25 0.0 0.714286 0.500000 0.500000 0.285714 0.777778 0.666667 \n", "4 0.50 0.6 0.571429 0.500000 0.333333 0.428571 0.444444 0.000000 \n", "5 1.00 0.8 1.000000 0.333333 0.333333 1.000000 0.333333 1.000000 \n", "6 0.00 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 \n", "7 0.75 0.0 0.857143 0.833333 0.666667 0.428571 0.555556 0.666667 \n", "\n", " Q10 Q11 tsRel Q6_Reading Q6_Running Q6_Watching a movie \n", "3 0.375 1.00 0.000000 0.0 0.0 1.0 \n", "4 1.000 0.50 0.132780 1.0 0.0 0.0 \n", "5 0.000 0.50 0.228216 0.0 1.0 0.0 \n", "6 0.750 0.50 0.257261 1.0 0.0 0.0 \n", "7 0.500 0.75 0.269710 1.0 0.0 0.0 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2Fil = df2[np.logical_and(df2.tsRel>60000, df2.tsRel<61000)]\n", "df2Fil_norm = (df2Fil-df2Fil.min())/(df2Fil.max()-df2Fil.min())\n", "df2Fil_norm.head()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.34631596 0.27017825 0.14469026 0.07712654 0.05616314]\n", "0.8944741480656595\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mdlPCA = PCA(n_components=5)\n", "XPCA = mdlPCA.fit_transform(df2Fil_norm)\n", "\n", "print(mdlPCA.explained_variance_ratio_)\n", "print(np.sum(mdlPCA.explained_variance_ratio_))\n", "\n", "plt.plot(np.arange(0,mdlPCA.explained_variance_ratio_.shape[0]), mdlPCA.explained_variance_ratio_, '-ro')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.43656834 -0.36520933 -0.30003171 -0.22317841 -0.26032745 -0.38821589\n", " -0.21312891 0.05576004 0.17168435 -0.07017853 -0.05979342 0.27181642\n", " -0.3812503 0.10943387]\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(mdlPCA.components_[0,:])\n", "plt.plot(np.arange(0,mdlPCA.components_[0,:].shape[0]), mdlPCA.components_[0,:], '-ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive models\n", "\n", "#### Can we predict motivation?\n", "#### Can we predict stress?" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q7Q8Q9Q10Q11Q6_ReadingQ6_RunningQ6_Watching a movie
00.80.3333330.3750.6250.7500.5714290.2222220.1666670.6250.0000000.01.00.0
10.80.8333330.5000.5000.6250.8571430.6666670.3333330.6250.7142861.00.00.0
20.40.5000000.6250.6250.5000.8571430.6666670.5000000.6250.5714290.00.01.0
30.20.0000000.6250.3750.3750.2857140.7777780.6666670.3751.0000000.00.01.0
40.40.5000000.5000.3750.2500.4285710.4444440.0000001.0000.7142861.00.00.0
\n", "
" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 Q9 Q10 \\\n", "0 0.8 0.333333 0.375 0.625 0.750 0.571429 0.222222 0.166667 0.625 \n", "1 0.8 0.833333 0.500 0.500 0.625 0.857143 0.666667 0.333333 0.625 \n", "2 0.4 0.500000 0.625 0.625 0.500 0.857143 0.666667 0.500000 0.625 \n", "3 0.2 0.000000 0.625 0.375 0.375 0.285714 0.777778 0.666667 0.375 \n", "4 0.4 0.500000 0.500 0.375 0.250 0.428571 0.444444 0.000000 1.000 \n", "\n", " Q11 Q6_Reading Q6_Running Q6_Watching a movie \n", "0 0.000000 0.0 1.0 0.0 \n", "1 0.714286 1.0 0.0 0.0 \n", "2 0.571429 0.0 0.0 1.0 \n", "3 1.000000 0.0 0.0 1.0 \n", "4 0.714286 1.0 0.0 0.0 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = df2.drop(columns=['tsRel'])\n", "df3_norm = (df3-df3.min())/(df3.max()-df3.min())\n", "df3_norm.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select X and y\n", "" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":1: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead\n", " colX = df3.columns[pd.np.r_[0:8,10:13]]\n" ] }, { "data": { "text/plain": [ "['Q1',\n", " 'Q2',\n", " 'Q3',\n", " 'Q4',\n", " 'Q5',\n", " 'Q7',\n", " 'Q8',\n", " 'Q9',\n", " 'Q6_Reading',\n", " 'Q6_Running',\n", " 'Q6_Watching a movie']" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colX = df3.columns[pd.np.r_[0:8,10:13]]\n", "colX.tolist()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Q11']" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colY = ['Q10'] # level of stress;\n", "colY = ['Q11'] # level of motivation;\n", "\n", "colY" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(19, 11)\n" ] }, { "data": { "text/plain": [ "array([[ 8, 5, 4, 6, 7, 5, 3, 5, 0, 1, 0],\n", " [ 8, 8, 5, 5, 6, 7, 7, 6, 1, 0, 0],\n", " [ 6, 6, 6, 6, 5, 7, 7, 7, 0, 0, 1],\n", " [ 5, 3, 6, 4, 4, 3, 8, 8, 0, 0, 1],\n", " [ 6, 6, 5, 4, 3, 4, 5, 4, 1, 0, 0],\n", " [ 8, 7, 8, 3, 3, 8, 4, 10, 0, 1, 0],\n", " [ 4, 3, 1, 1, 1, 1, 1, 10, 1, 0, 0],\n", " [ 7, 3, 7, 6, 5, 4, 6, 8, 1, 0, 0],\n", " [ 5, 5, 5, 4, 4, 4, 4, 5, 0, 0, 1],\n", " [ 6, 6, 6, 6, 6, 4, 6, 6, 0, 0, 1],\n", " [ 4, 4, 4, 5, 3, 5, 2, 7, 0, 0, 1],\n", " [ 7, 7, 7, 2, 2, 7, 6, 7, 0, 1, 0],\n", " [ 8, 8, 8, 6, 6, 8, 7, 8, 0, 0, 1],\n", " [ 4, 4, 4, 1, 1, 1, 5, 7, 0, 0, 1],\n", " [ 8, 7, 7, 7, 7, 7, 10, 5, 0, 1, 0],\n", " [ 7, 7, 6, 6, 6, 6, 6, 6, 0, 0, 1],\n", " [ 7, 6, 6, 5, 5, 1, 7, 7, 1, 0, 0],\n", " [ 6, 6, 6, 5, 5, 2, 9, 9, 0, 0, 1],\n", " [ 9, 9, 9, 9, 9, 7, 6, 5, 0, 0, 1]])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array(df3[colX])\n", "print(X.shape)\n", "X" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(19, 1)\n" ] }, { "data": { "text/plain": [ "array([[ 3],\n", " [ 8],\n", " [ 7],\n", " [10],\n", " [ 8],\n", " [ 8],\n", " [ 8],\n", " [ 9],\n", " [ 7],\n", " [ 6],\n", " [ 7],\n", " [ 8],\n", " [ 8],\n", " [ 8],\n", " [10],\n", " [ 7],\n", " [ 7],\n", " [ 9],\n", " [10]])" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = np.array(df3[colY])\n", "print(y.shape)\n", "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalize data" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "#scaler = StandardScaler()\n", "scaler = MinMaxScaler()\n", "\n", "scaler.fit(X)\n", "Xnorm = scaler.transform(X)\n", "Xnorm.max(axis=0)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.svm import SVR\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cross validation with leave one out" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAIN: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [0]\n", "TRAIN: [ 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [1]\n", "TRAIN: [ 0 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [2]\n", "TRAIN: [ 0 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [3]\n", "TRAIN: [ 0 1 2 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [4]\n", "TRAIN: [ 0 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [5]\n", "TRAIN: [ 0 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18] TEST: [6]\n", "TRAIN: [ 0 1 2 3 4 5 6 8 9 10 11 12 13 14 15 16 17 18] TEST: [7]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18] TEST: [8]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18] TEST: [9]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18] TEST: [10]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18] TEST: [11]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18] TEST: [12]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18] TEST: [13]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18] TEST: [14]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18] TEST: [15]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18] TEST: [16]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18] TEST: [17]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17] TEST: [18]\n" ] } ], "source": [ "from sklearn.model_selection import LeaveOneOut\n", "\n", "loo = LeaveOneOut()\n", "for train_index, test_index in loo.split(Xnorm):\n", " print('TRAIN: ' + str(train_index) + ' TEST: ' + str(test_index))\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Y : [[3]] , pred as: [6.71770473]\n", "Y : [[8]] , pred as: [8.44358673]\n", "Y : [[7]] , pred as: [8.57175498]\n", "Y : [[10]] , pred as: [7.62213136]\n", "Y : [[8]] , pred as: [6.70446542]\n", "Y : [[8]] , pred as: [8.9269527]\n", "Y : [[8]] , pred as: [5.14777884]\n", "Y : [[9]] , pred as: [8.02397316]\n", "Y : [[7]] , pred as: [6.22595715]\n", "Y : [[6]] , pred as: [7.94496256]\n", "Y : [[7]] , pred as: [6.21621235]\n", "Y : [[8]] , pred as: [7.00076291]\n", "Y : [[8]] , pred as: [9.45348764]\n", "Y : [[8]] , pred as: [6.27163448]\n", "Y : [[10]] , pred as: [6.519619]\n", "Y : [[7]] , pred as: [8.10618284]\n", "Y : [[7]] , pred as: [8.58400087]\n", "Y : [[9]] , pred as: [7.92971992]\n", "Y : [[10]] , pred as: [6.78948034]\n" ] } ], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "from sklearn.svm import LinearSVR\n", "\n", "loo = LeaveOneOut()\n", "\n", "predAll = np.zeros([y.shape[0],1])\n", "\n", "i=0\n", "for train_index, test_index in loo.split(Xnorm):\n", "\n", " X_train, X_test = Xnorm[train_index], Xnorm[test_index]\n", " y_train, y_test = y[train_index], y[test_index]\n", "\n", " regr = LinearSVR(random_state=0, tol=1e-5)\n", " \n", " regr.fit(X_train, y_train) # Train the model\n", "\n", " ypred = regr.predict(X_test) # Apply the model\n", " \n", " predAll[i] = ypred\n", " \n", " print('Y : ' + str(y_test) + ' , pred as: ' + str(ypred))\n", " i = i + 1 \n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 0.01086243],\n", " [0.01086243, 1. ]])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(y.T, predAll.T)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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