{ "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": 73, "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?\\tQ4_General background in machine learning?\\tQ5_Hands-on experience in running machine learning applications?\\tQ6_Which one would you prefer on a Sunday afternoon?\\tQ7_Hands-on experience in image analysis using satellite images?\\tQ8_Level of interest in mathematics?\\tQ9_Level of interest in reading?\\tQ10_Level of stress about this class?\\tQ11_Your overall motivation about this class?
52021/01/25 10:34:46 AM EST77577Reading661079
62021/01/25 10:34:50 AM EST77777Watching a movie4910810
72021/01/25 10:34:51 AM EST46533Watching a movie52456
82021/01/25 10:35:21 AM EST55533Reading37759
92021/01/25 10:35:38 AM EST32411Coding for the homework, project or just for fun12199
102021/01/25 10:37:42 AM EST98765Reading171049
112021/01/25 10:37:54 AM EST99989Watching a movie56668
122021/01/25 10:41:52 AM EST44444Coding for the homework, project or just for fun67489
132021/01/25 10:44:07 AM EST44222Reading1110810
142021/01/25 5:10:59 PM EST55621Running15957
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" ], "text/plain": [ " Timestamp Q1_General background in data analysis? \\\n", "5 2021/01/25 10:34:46 AM EST 7 \n", "6 2021/01/25 10:34:50 AM EST 7 \n", "7 2021/01/25 10:34:51 AM EST 4 \n", "8 2021/01/25 10:35:21 AM EST 5 \n", "9 2021/01/25 10:35:38 AM EST 3 \n", "10 2021/01/25 10:37:42 AM EST 9 \n", "11 2021/01/25 10:37:54 AM EST 9 \n", "12 2021/01/25 10:41:52 AM EST 4 \n", "13 2021/01/25 10:44:07 AM EST 4 \n", "14 2021/01/25 5:10:59 PM EST 5 \n", "\n", " Q2_Hands-on experience in data analysis using Python? \\\n", "5 7 \n", "6 7 \n", "7 6 \n", "8 5 \n", "9 2 \n", "10 8 \n", "11 9 \n", "12 4 \n", "13 4 \n", "14 5 \n", "\n", " Q3_Experience in programming in general?\\t \\\n", "5 5 \n", "6 7 \n", "7 5 \n", "8 5 \n", "9 4 \n", "10 7 \n", "11 9 \n", "12 4 \n", "13 2 \n", "14 6 \n", "\n", " Q4_General background in machine learning?\\t \\\n", "5 7 \n", "6 7 \n", "7 3 \n", "8 3 \n", "9 1 \n", "10 6 \n", "11 8 \n", "12 4 \n", "13 2 \n", "14 2 \n", "\n", " Q5_Hands-on experience in running machine learning applications?\\t \\\n", "5 7 \n", "6 7 \n", "7 3 \n", "8 3 \n", "9 1 \n", "10 5 \n", "11 9 \n", "12 4 \n", "13 2 \n", "14 1 \n", "\n", " Q6_Which one would you prefer on a Sunday afternoon?\\t \\\n", "5 Reading \n", "6 Watching a movie \n", "7 Watching a movie \n", "8 Reading \n", "9 Coding for the homework, project or just for fun \n", "10 Reading \n", "11 Watching a movie \n", "12 Coding for the homework, project or just for fun \n", "13 Reading \n", "14 Running \n", "\n", " Q7_Hands-on experience in image analysis using satellite images?\\t \\\n", "5 6 \n", "6 4 \n", "7 5 \n", "8 3 \n", "9 1 \n", "10 1 \n", "11 5 \n", "12 6 \n", "13 1 \n", "14 1 \n", "\n", " Q8_Level of interest in mathematics?\\t \\\n", "5 6 \n", "6 9 \n", "7 2 \n", "8 7 \n", "9 2 \n", "10 7 \n", "11 6 \n", "12 7 \n", "13 1 \n", "14 5 \n", "\n", " Q9_Level of interest in reading?\\t \\\n", "5 10 \n", "6 10 \n", "7 4 \n", "8 7 \n", "9 1 \n", "10 10 \n", "11 6 \n", "12 4 \n", "13 10 \n", "14 9 \n", "\n", " Q10_Level of stress about this class?\\t \\\n", "5 7 \n", "6 8 \n", "7 5 \n", "8 5 \n", "9 9 \n", "10 4 \n", "11 6 \n", "12 8 \n", "13 8 \n", "14 5 \n", "\n", " Q11_Your overall motivation about this class? \n", "5 9 \n", "6 10 \n", "7 6 \n", "8 9 \n", "9 9 \n", "10 9 \n", "11 8 \n", "12 9 \n", "13 10 \n", "14 7 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfInit = pd.read_csv(('./Data/MUSA650-Spring2021-WelcomePoll.csv'))\n", "dfInit.tail(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate relative timestamp" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TimestamptsRel
02021-01-25 08:17:440.0
12021-01-25 08:37:141170.0
22021-01-25 09:15:333469.0
32021-01-25 10:34:178193.0
42021-01-25 10:34:428218.0
52021-01-25 10:34:468222.0
62021-01-25 10:34:508226.0
72021-01-25 10:34:518227.0
82021-01-25 10:35:218257.0
92021-01-25 10:35:388274.0
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" ], "text/plain": [ " Timestamp tsRel\n", "0 2021-01-25 08:17:44 0.0\n", "1 2021-01-25 08:37:14 1170.0\n", "2 2021-01-25 09:15:33 3469.0\n", "3 2021-01-25 10:34:17 8193.0\n", "4 2021-01-25 10:34:42 8218.0\n", "5 2021-01-25 10:34:46 8222.0\n", "6 2021-01-25 10:34:50 8226.0\n", "7 2021-01-25 10:34:51 8227.0\n", "8 2021-01-25 10:35:21 8257.0\n", "9 2021-01-25 10:35:38 8274.0" ] }, "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?\\t',\n", " 'Q4_General background in machine learning?\\t',\n", " 'Q5_Hands-on experience in running machine learning applications?\\t',\n", " 'Q6_Which one would you prefer on a Sunday afternoon?\\t',\n", " 'Q7_Hands-on experience in image analysis using satellite images?\\t',\n", " 'Q8_Level of interest in mathematics?\\t',\n", " 'Q9_Level of interest in reading?\\t',\n", " 'Q10_Level of stress about this class?\\t',\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
075755Coding for the homework, project or just for fun7775100.0
177665Coding for the homework, project or just for fun747961170.0
234332Watching a movie526873469.0
353321Watching a movie355588193.0
457722Watching a movie888588218.0
\n", "
" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q6 Q7 \\\n", "0 7 5 7 5 5 Coding for the homework, project or just for fun 7 \n", "1 7 7 6 6 5 Coding for the homework, project or just for fun 7 \n", "2 3 4 3 3 2 Watching a movie 5 \n", "3 5 3 3 2 1 Watching a movie 3 \n", "4 5 7 7 2 2 Watching a movie 8 \n", "\n", " Q8 Q9 Q10 Q11 tsRel \n", "0 7 7 5 10 0.0 \n", "1 4 7 9 6 1170.0 \n", "2 2 6 8 7 3469.0 \n", "3 5 5 5 8 8193.0 \n", "4 8 8 5 8 8218.0 " ] }, "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": [ { "data": { "text/html": [ "
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Q20.8236031.0000000.8060480.7850480.7719820.3516660.4901940.536814-0.290010-0.126485-0.061205
Q30.7816310.8060481.0000000.6407840.6606870.3228910.6456720.254697-0.374491-0.0184870.037162
Q40.8498140.7850480.6407841.0000000.9733590.3505470.4828470.4026120.0465610.133677-0.293579
Q50.8052700.7719820.6606870.9733591.0000000.3745130.4814810.3298200.0566210.213745-0.308792
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 \\\n", "Q1 1.000000 0.823603 0.781631 0.849814 0.805270 0.153481 0.576032 \n", "Q2 0.823603 1.000000 0.806048 0.785048 0.771982 0.351666 0.490194 \n", "Q3 0.781631 0.806048 1.000000 0.640784 0.660687 0.322891 0.645672 \n", "Q4 0.849814 0.785048 0.640784 1.000000 0.973359 0.350547 0.482847 \n", "Q5 0.805270 0.771982 0.660687 0.973359 1.000000 0.374513 0.481481 \n", "Q7 0.153481 0.351666 0.322891 0.350547 0.374513 1.000000 0.336587 \n", "Q8 0.576032 0.490194 0.645672 0.482847 0.481481 0.336587 1.000000 \n", "Q9 0.511197 0.536814 0.254697 0.402612 0.329820 -0.052633 0.376937 \n", "Q10 -0.321381 -0.290010 -0.374491 0.046561 0.056621 0.061390 -0.373035 \n", "Q11 0.162826 -0.126485 -0.018487 0.133677 0.213745 -0.218872 0.384742 \n", "tsRel -0.122377 -0.061205 0.037162 -0.293579 -0.308792 -0.516008 0.011703 \n", "\n", " Q9 Q10 Q11 tsRel \n", "Q1 0.511197 -0.321381 0.162826 -0.122377 \n", "Q2 0.536814 -0.290010 -0.126485 -0.061205 \n", "Q3 0.254697 -0.374491 -0.018487 0.037162 \n", "Q4 0.402612 0.046561 0.133677 -0.293579 \n", "Q5 0.329820 0.056621 0.213745 -0.308792 \n", "Q7 -0.052633 0.061390 -0.218872 -0.516008 \n", "Q8 0.376937 -0.373035 0.384742 0.011703 \n", "Q9 1.000000 -0.223927 0.283455 0.199320 \n", "Q10 -0.223927 1.000000 0.051258 -0.273305 \n", "Q11 0.283455 0.051258 1.000000 -0.159292 \n", "tsRel 0.199320 -0.273305 -0.159292 1.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "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": [ "corr = df.corr()\n", "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)" ] }, { "cell_type": "code", "execution_count": 8, "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?\\t',\n", " 'Q4_General background in machine learning?\\t',\n", " 'Q5_Hands-on experience in running machine learning applications?\\t',\n", " 'Q6_Which one would you prefer on a Sunday afternoon?\\t',\n", " 'Q7_Hands-on experience in image analysis using satellite images?\\t',\n", " 'Q8_Level of interest in mathematics?\\t',\n", " 'Q9_Level of interest in reading?\\t',\n", " 'Q10_Level of stress about this class?\\t',\n", " 'Q11_Your overall motivation about this class?',\n", " 'tsRel']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initCol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling categorical variables (visualization)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2, 3]),\n", " [Text(0, 0, 'Coding for the homework, project or just for fun'),\n", " Text(1, 0, 'Watching a movie'),\n", " Text(2, 0, 'Reading'),\n", " Text(3, 0, 'Running')])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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czcwKOBzNzAo4HM3MCjgczcwKOBzNzAo4HM3MCjgczcwKOBzNzAo4HM3MCjgczcwKOBzNzAo4HM3MCjgczcwKOBzNzAo4HM3MCjgczcwKOBzNzArURThKWkfS8IqPiZKOb3XO9pImVJxzWknlmlkH0C67D7a1iHgB2AhAUldgDHBjwakPRsReVSzNzDqoumg5trIT8HJEjCy7EDPruOoxHA8Erp7LsS0lPSXpdknrV7MoM+tY6iocJXUH9gGuKzj8BLB6RHwGuAC4aS6vMVRSk6Sm5ubmdqvVzOpbXYUjsDvwRES81fpAREyMiPfz58OAbpJWKDjvkohojIjG3r17t3/FZlaX6i0cD2Iul9SS+khS/nwQ6b29U8XazKwDqYvRagBJPYFdgG9WPHcEQERcDAwBjpQ0E5gKHBgRUUatZlb/1Jnzo7GxMZqamsouw8zKo7kdqLfLajOzqnA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFXA4mpkVcDiamRVwOJqZFaiLcJS0jqThFR8TJR3f6hxJOl/SCElPS9qkpHLNrANoKLuABRERLwAbAUjqCowBbmx12u7AgPyxOXBR/tPKNGMqDPsuPHsTLN0Pdv8F9N+u7KrM5qsuWo6t7AS8HBEjWz2/L3BFJA8Dy0haufrl2Rwe/DU8eSVMfx+an4drvwLTJ5ddlVXR9FGjGHnwV3h+4AaMPPgrTB81quySFkg9huOBwNUFz68KVP6tj87PWZlef3jOxx9MgLefL6cWK8XYk3/AlKYmmDmTKU1NjD35B2WXtEDqKhwldQf2Aa4rOlzwXBS8xlBJTZKampub27pEa61v45yPuy8JvdcppxYrxZThw+f5uFbVVTiS+hWfiIi3Co6NBvpVPO4LvNH6pIi4JCIaI6Kxd+/e7VSmfWjbE2HgEOjSAMuuAZ+/HBZbsuyqrIp6brTRPB/XqnoLx4MovqQGuAU4JI9abwFMiIix1SvNCnXvBUMug1PHwXHDYcAuZVdkVbbyz39Gz8ZGaGigZ2MjK//8Z2WXtEAU8bErz5okqSepT7F/REzIzx0BEBEXSxJwITAYmAIcGhFN83rNxsbGaGqa5ylm1rEVdccBdTKVByAipgDLt3ru4orPAzi62nWZWcdUb5fVZmZV4XA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK+BwNDMr4HA0MyvgcDQzK1A34ShpGUnXS/qfpOclbdnq+PaSJkganj9OK6tWM6t/DWUX8AmcB9wREUMkdQd6FpzzYETsVeW6zKwDqotwlLQUsC3wNYCImA5ML7MmM+vY6uWyuj/QDFwu6UlJl0rqVXDelpKeknS7pPWrXKOZdSD1Eo4NwCbARRGxMTAZ+H6rc54AVo+IzwAXADcVvZCkoZKaJDU1Nze3Y8lmVs/qJRxHA6Mj4pH8+HpSWH4oIiZGxPv582FAN0krtH6hiLgkIhojorF3797tXbeZ1am6CMeIeBMYJWmd/NROwHOV50jqI0n580Gk9/ZOVQs1sw6jLgZksmOBq/JI9SvAoZKOAIiIi4EhwJGSZgJTgQMjIkqr1szqmjpzfjQ2NkZTU1PZZZhZeTS3A3VxWW1mVm0ORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswJ1E46SlpF0vaT/SXpe0patjkvS+ZJGSHpa0iZl1Wpm9a+h7AI+gfOAOyJiiKTuQM9Wx3cHBuSPzYGL8p817Z7n3+JXd77AhKkz+EJjP47feQCSyi6reh66EB75HXTrAdudBBsMKbsiM6BOwlHSUsC2wNcAImI6ML3VafsCV0REAA/nlubKETG2qsV+Am9N/IAjr3yC6bNmA3DePS/Rb7meDNm0b8mVVckLd8Bd//fR4xsOhz4bQu+1y6vJLKuXy+r+QDNwuaQnJV0qqVerc1YFRlU8Hp2fq1lNr43/MBhbPPTyuJKqKcGrD8z5OGbDaw+WU4tZK/USjg3AJsBFEbExMBn4fqtziq5Fo/UTkoZKapLU1Nzc3PaVfgIDV12K1lfQG6y6dDnFlGGVjRbsObMS1Es4jgZGR8Qj+fH1pLBsfU6/isd9gTdav1BEXBIRjRHR2Lt373YpdkGtvnwvfrrfQJZevBsNXcQBm/Tly5uvXmpNVTVwCAwaCl27Q/clYecfwqqbll2VGQBKXXS1T9KDwGER8YKkHwK9IuLEiuN7AscAe5AGYs6PiEHzes3GxsZoampqx6oXzKzZwYxZs+nRrWvZpZRj5jRQV+haF13g1rHMdfSznv43HgtclUeqXwEOlXQEQERcDAwjBeMIYApwaFmFflJdu4iuXTppMAI0LFZ2BWYfUzctx/ZQKy1HMyvNXFuO9dLnaGZWVQ5HM7MCDkczswIORzOzAg5HM7MCDkczswIORzOzAg5HM7MCDkczswKd+g4ZSc3AyBJLWAHoRGuUfUxnf//gv4Oy3/+4iBhcdKBTh2PZJDVFRGPZdZSls79/8N9BLb9/X1abmRVwOJqZFXA4luuSsgsoWWd//+C/g5p9/+5zNDMr4JajmVkBh6OZWQGHo5lZAYej1R2p9Ya2Zm3P4Wh1JyJC0o6STpK0paS5bvbdEqSStpa0p6Q9qldp25G0saQT539mx1Pxb9izmt/X4Wh1o+KHZDPgHGAgMBQ4TNLyBed3yUG6G/A7oBdwq6QvVLHshVbxfrck7aZ5qKRvlVtVdUlS/jccBJwnaUC1vrfD0epG/iFpBH4FfD0ivgLcDKwMfE3SCgCSlpfUEBGzc6vyKGB/0j28w4EHS3kDn1B+vzsAfwb+CVwDbC3p5FILq6L8d7ALcAKwJ3BJtQLS4Wg1r1UfYwOwKfB5gIi4CXgA6E9qQS4NfB1YNR+fQArE/YAfAwdFxFhJX85BW+vWAC6MiBtIreWLgN0lHVtuWdUhaR3S+/4xsBrwMvBTSWu29/d2OFrNy62Hz0r6ckQ8DOxBRUBExC3AP0ityMnAZcA0Sf+Xg7U78BPgcxHxgqSNge8BXct4P/NSMNg0E/impD4RMQl4GHgd2E7S/lUvsPqmACOA8RExMyIOA/qQWpB9oP0G6ByOVrMkVf7/XJrUYjgwIh4kXSofLOm7ABFxI/BCRMwEZgCNwNrAkRFxMnAT8GdJF5DC87SIeKR672bB5F8EW0s6XtJawF+AK4HfSOoLDAB6klpQq5VYaruo6GddTFJ3oBmYBAyqGHj7Nen/w9mQ/s7apRbfPmi1RtJqwKyIGCNpFVKrYaqkwaT+xjMj4ipJ2wIXAPsAo3If4zrAd4Ejgc8CXwKeiojfStoJWIy0ht+jLZ39ZbzH1vLg0WxJW5MunV8gdSH8FXgS+CKwFyDgYGCr/PFNYHatvI+2IGkfUtfIW8C5wPLAD4BHgfeBvUn/xj8AhkZEc7vU0YH+Tq0DkLQGMAz4GjAWOJp0WfXniPggB+RVwEkRcamkZSLivVav8SBwc0ScJWl3Ukf+GOCsiJhRvXczf5KWioiJ+fOBwHnAdyJiuKTDgY2BByLiGklLkMKxEbiY1E3wXFm1t4f8y+13pF8QfUhXCPsBAewKbABcCCxD6ovcIfcrtzlfVlvNyJdUmwO3Am8C++Y/1we+IGmJiLgDuAE4UdKKLcEoaUlJLX2IJwCrSuoB3EPqj+xHDV2GKulJmlrUJz/9KdL0pM8BRMTvgSZgL0kHAx8AiwPb0UGCUdKKkjbPnw8kXSr/MyL+GhHnka4UbgSWjYjzI+JwYCXSaj5fba9gBLccrcZIWgl4mnRJuVlEvCLpm8A6wGjgv8BXSCO4j+Z+ydVIfXM3As+QwvA24DcRcbOkxYCl2uvya2FI6pFbwsuRpiJtkFuH+5Lmbt4cEZfkcw8H/hMRz+THi0XEtNKKbyP5l9nXgPuBUaTW4WXAssARwJjcB3sE6RJ6o4h4N49Ud42IF9u1Poej1Yr8w9KFFGzrAd/NgdGd1Jr6LLAt8H/AsPyD0xARMyVtAnya9EN0Lmlqzw6kFtbY6r+buZO0DHAncEJE/DvP47sT+GJEXCdpb1Kf2z0RcWHF13WJiNmlFN1OJHUjXSKfClwNPE4KyPdIfctj8nl9I2J0Nf8OfFltNSMiZuU+wd2AnYAzJB0ZEdOBayPiW8AuEXEbQA6RqyT9A1g1Iq4iTfNZm9RftRnQrYz3Mi+5K+DPpBHoxoi4G9gFuEzSFyLi78AVwB6S+rWM4HakYKx4TzNIU60mkOaubggcBiwJ/CiP0BMRo/OfVfs7cMvRaoKkZSNifP68pTW4BfBH0uXxBflYy+1kjcClpA77/qTb666MiMtzX+MsYMOIeLyM9zM3krpGxCxJnyIF4KdJrdt/SdqZNDr9rTwav2JEvF1mve1J0qakf6dRpP7U40i/1P5E6h65HDgjIp4tpcCI8Ic/Sv0Adib1Ey7NR7+wu+Q/tyJtn9uv1dccAFxR8XhH0uTojQteX2W/x4L3+zgwGPgt8Abw2XxsMDCVFBI1VXcbvfeWf99t8vu+DvgDsDVpsOkk0mDLZmW/f19WW6kkrUuarnNiVIw8Rprz1yUiHiJdao3O57f8n30NaJDUX1K3iLgXuB5YqvX3iPzTWEMagTsj4o6IOAr4OXCLpG0ijcb3jYg3a7DuRRYRIWl7UtfJ7qSBl4dI81I3Jc1bfR2YXPb7dzhaafJUlj1J03f6w5xBFh/1L03MP1R7AKdL+naky+Vm4FhgH6UFGvYl3U1R68aQWsktgywXAM8Bv5e0FGkwosOtW1nxfg4nDZxNjIh3SINR95KmYA0CfhY1ME3JfY5WVRV9hi39ij2Bb5PC8Y+Rbg0s+ro1gLuBXwJfBV6KiK9J+jZpZLsvcEFEDKvOO1kwFe93C1JjZBKpm+AO4BbSCO3KwEHAVRHxaGnFtpOKv4MlIuL9/Nx1wJoRsUl+vBqpS+HRiBheXrUfcTha1VQE4t6k2/p6A2eROuR3IY0yXxsR/2z1desDy5F+mP6Yn3sU+F9EHJIfLxcR71brvXwSkvYCziAF4U6ke4MfA35Dug98E1K3Qk0Fe1vKdyodSupnPDnS7aDXAqtFxBb5nJqav+lwtHYnqT/wQUS8UTHKfDSwFmlC90WkNRaHku4SORF4L7c2tiWNWo4jtbq+HxFN+XX/S5ooPLhW5wAqLR7xB9Ivg8Gk9z2BNAJ/naQG0jSkkSWW2a6UFqo9h3S3y1BSy/nMiBgp6e+kwbaNWlqYZdZayeFo7UrS6sC/gC9FxIOSdgUOrmjx7Qr8njSCO500Sv1qPrY9qeP+KtKUj6+SlrD6e0Q8mc/ZKg/a1CSlhTOWIbV8LyTdJ7wvadrKuRFxfq2FQlvKU5bOBp6JiNNyN8rZwGzglxHxmqSNW/49a4kHZKy9rUOauzdTaQXrGUCXilHmu0i3/fWJiJEtwZh9k7T6yrsR8Typj25x0n3WmwLUWjC2DDpIWjdPYJ6eBxfWBC6NiNdIq83cAjwCNTma3ibyfNP3SDMNdpG0eURMAY4HlgBOybdR1lwwgsPR2t+TpNbfLcC/I+I+UgvxCGDvPMq8N2mR2jlExEGkFXpuzI8fIt1a2A2YWJXqP6HcFbA7cC3pvuFHlBaWmA0MlXQMaVXrq6MG15NsK3kAbTiwAmnu4jDgEEmbRcQHpMvrc/PnNcmX1dZu8pzELqQ7QdYmLRbxxzxd5ThgFVIf48dGmVsGb/Lnt5BWZdkmP14mWi1TViskrU16v18mTVH6PrBNREyQ9BXS9g1Pd+TBlxaSfki6J34/YDzpbqa1gYvq4ReDw9GqIofGxaT+wnMqpnd8eNtgwddUBuRdwHIR0VhLgy+5T3Fp4P2IGKW0C+JXSROZv0fqax2RF5f4d76s/HB6S2mFtxOl9RjfjbwCkqTvkwbdBpP6i48B/hZ5haFa5nC0NiOpe6RFIpC0ZKQ9TypXuW4kTWn5B/C7iJhYEZLbkFqSzflul5bXrAzITaOG7pXOd/dcRWoVvUm6/L+T1Je4ErBift9bkO6COSwiXi6r3vZQ8cthah5cuZm0OPGZFQF5Oen2wB2BsS3/nrXOfY7WJvIl9KGS9pL0GeACSb3gw1sBlafgnEpa7n/ZfKylj+73pNVZbpN0WMvr5nmRDfnzWgrG9UjzFr8LHAL8h7Te4PukaTsTSXfzHEWaqnRuBwzGdYG/k275O0PSdqS7X/oDx0paMZ96J2nPm0/VSzCCW47WhiStSrrn+R1SP9tLlZfAFS3Iyq0BViUtPvB10mrdF5AGXC6KiLPKeB8LQmmvlwciokt+vBZpHclTSUHQlXSL3NvA8Ii4uyNdSudfDleRbvl7gbQveJ+IOCXf7XIeqWthDGnq0uER8Vw9/R00lF2A1b+K//BvkqbttNwv/RJpz5PWPrz/OdImWl8gXYaeGRHrKi3ddZek8RFxWfu/g08u0hJje0h6JSL6k97vFqQW8CzSqjs35alKLV9TF6GwgJYDPpNnHyDpDuBcpW1vXyKtmnQ06RfemXk6U139HTgcbZFU9BnuSpq79g3SiGyTpOUj4jxJG5AGLF6Fj/+ARFrheT1SCwRSH95NFY9rUkTcIekYSe8DzwMrkkKjJ2mUelyZ9bWnBfjl8DTpiuDC/P+jblqMLRyOtkjyf/zBwPmkS6dpwCu5/+lf+Q6J3YBvSHqt8gdEeeHX/PBtYHruvB9E2nLz37X+QxURw5S2Er0idx+0BOJRJZZVFQvwy6G55d+ulv8N58Z9jrbQlPZ86Ulq5f06B8XOpO0zbyVN9v4qqW/un/lrtiJtp3lGflzZJ7kFaR7cmIi4p8pvZ5HkQaU/AevMbWpSRyVpR9Ivh75l19KWHI620CT1iojJkk4lLUjbjbSoQhdSq+GEHKCzcwtzEHAgad7bryPizPw63aLVftK13mIsorTe5JRotapQZ9ARfzk4HO0Ty9N2ViftqbwZ8D5pVefnIuKxfJl9AmkEc3IOxu1Jq/EcR+qkPxi4NyJOa3nNWpnYvajqMdjbQkf75eA+R1tgLQGWQ+xVSb8B/gnsFBF/yufsQFqa6vt5zl+LPsDvI+K23Jr8F/BXSZMi4lcdJRihPvvX2kLkWyI7yi8HTwK3+ZK0BHw4mXttSZvlx6eRJjg/KmlAPn1nUmf8MGmOZf4FfE1SzzwI8xxpM/f9JX2tSm/FqqAjBCO45WjzobQB/TGSLiFN7j4WmC1pdkQ8HhE/zyPST+f7ak+vuN1vhxyk90TE1ZI2BO6VdABpwYllgBtI2wSY1RSHo81PV9JK1t2BbYGTgR8Bn8+X2Y8B15BGqNckbXnQsvrzeaT5bgMlNQE/I82Bu5w05eMQ0vy4nfItgrM6SqvD6p8HZGy+ch/hYcAOpMvoJuAnQA/SSiufBY6LiEfzpfSmpI2wvhsRT0jan7RP8SukfVMC6EUazLkIOCDK2rjdbC7c52jzlfsIbyGtpnMIKdR+QNoNcAYpCE+VtEJu+S1GahEekL/+BlL/4kDSQg0NpBW9N8bBaDXKLUf7mIpbAudYVFZpRet9SMF3XaQN6FuOrQAsD6wXETfmUevzSAtIXJTP2R94PtKWB4XzG81qhfsc7WNyMO4E7CrpDGBSJG8qrcoNaZS5O2nx2oiIcfmWwd/nwZqblbYEOEtpy81zcwvyw/B1MFotczjax+RR5yGkeYlz7NVSEZABbBgRt+R1+yZFxN8kTSOt5aiIuElpJehzJd0AjM7zJH25YjXP4WgfygMvSwC/I90KOLeFSYO0Ef0Gkp4ibaAkST+IiFslBXBOvmy+TtJ2HeWWMus8HI5WeUfD7EgbQR1JWrh1R0kvR8TkynNJS96vATxI2hPkKNIS+CdIWjq3JhuACyU9QFpxx6yuOBw7uYrBl52AAyW9DtxLmrrzR2CGpCsi7weTz72P1LLcFfggIp4mTQJ/H/h6bjH+TdJDkfcRMas3nsrTyeWw24G0PUETaVWdC0jTbA4jraDzNUldKm4H7EZapuwuYFlJQ/NrXZqf+4akFR2MVs88lceQ9A1g8Yi4MD/einQXzP7A+qQMfSQf2x04h9RqfI+0Wda2wGORtzSQ1C8iRlX7fZi1JV9WG6StNfcDLsyPHyftB9MnIh5uOUnSRsBZpFW6X8/P3QbMBvZQ2kb1d8Do6pVu1j58Wd2J5dFpIuJs4B1JN0rqQVq4dn2gR6uVdXoAt0fEA5K65zCcANxO2n7zofx6vhyxuufL6k5CUk9g6YgYK2lgRDyTn2/ZLrUrcC1poYlVgV8A70TEfZL2JAXja6S+xv3zghNI2i2/7rVVf1Nm7cjh2ElIaiQNrrwOfAvYJSJezMc+XJw0L1HWDXgX+DHpVsF+wJERca+kI0gTxH9DWgH818APIuLW6r4js/blcOxEJJ1Dmpd4bERc3OrYhzsBVkzvWZu0UdbzwOdJk8J7AF8gbXMwDrgyT/zuEKs/m7VwOHZwFUG3JrAusGX+8xzSCPP0ynPhw+k9PSLig7yQ7cmklbzPiIiRFed3jYhZDkbriDwg08HloNuLNHdxVEScQprP+ANgbaWN2X/acm4+fzfgknynTBdSa7MB+J6kwyW9LGl10ii1B2CsQ3LLsYOT9Gngr8BhEfFoxfMnk0alNwTOBl6MiAcl7UK6dfBY0lzHN0kL0j5A2htmdeC2iLipim/DrOo8z7HjWxkY0RKMefmwaZH2flmRtNDEl4F18lYGu5H6FFcGliLdSngkqYH4U0ndI2K6L6Wto/NldQfT0m8oqVt+6hVguqQtc6BNk7S9pONJU3VeAe4g3emyfUR8l7SR1snA9sBPgfWAgyT1aemjdDBaR+eWYweT+wwHA9tIepcUfK8DuwNbShoO/BY4HfiupEsj4jFJpwC/kPQmMJI013Fx0g6BLwPnRsSbVX9DZiVxOHYQFZO5tyfNPfwqacJ2D9JgzA6ke6A3JO3jsgVwArBZ7n+8l9SfuE9E/EjSX0l7xACcHBHPVe/dmJXPAzJ1Lk+1mRkRoyV1IU3cvg+YTArFIRExMi8jNkPSkhExKd8RcyFpS9WHgJdIe0kvBVwcEc/me6knR8RL7mO0zsYtx/q3E2ktxbfzQMkI0kjzKnwUjF8BlpZ0PTBE0uMR8R9JlwDbkfaankFqXe4OfFrSLhExvOWbOBits/GATJ3Ly4SNBP6TW5FPkILxHGCUpM8A3wNGAD2BAcAZOTCfJ/UpToqIW0hTdR7goyk+Zp2WL6vrVOvLXEk/J21fcADpLpj9SdNxFgfOioib83mLkVqIvyf1TXYjLVd2RET8V9JywHIRMaKKb8es5jgc61DFLYEDSC2//+V+xJOAfYHPRcRbklYj/RuPLAjTdUmX328CBwHPAsdExFtVf0NmNcjhWKck7U1aePYFYFnSZfQ9pMDbCzg4IkbMayBF0vLAQOB40uTv9SLitfav3qz2ORzrkKQVgKtIU2yeyNscbAH8JiKGSzoV2IM0LWeB9nGRtFHlAIxZZ+cBmTojaTPS3MUPSH2KLYMyE0n7RxMRPyFN3F5jAV6vS/6a4fmx5vkFZp2Ew7GOSNoQOB9YCfgXsK6k9fLhm4BxSrsErggsD8y31RgRs1s99qWEGb6srhuS1iItPHteRFwkaXPSXTBLk0JwMPC9iLglt/56RMTU8io2q28OxzqRF5L4MzAI2CAiJkvqT9rCYD1geJ7Y7TtZzNqAw7FGVUzX2YA0qftRYDrp9sCNgf0iYmKZNZp1ZA7HGiZpZ9L9z6NIm1ndAdxMWjBiG9ImWe+XV6FZx+UBmRqVB1q+TdoGdRfgamAt0iX0/wFP58/NrB04HGuQpMWBXYHG/EHeF3oGaXL3jIj4ZuW2B2bWthyONSiPMl9Huvd5B0m75kN3A0tK6lVacWadhPsca1heZWc/4OukNRo3BM6OiL+XWJZZp+BwrHGSVgEOId0DfV++G8bM2pkXu61xEfFG3rJgErCzpJci4oGy6zLr6ByOJZPUCDwbEVMLlhXrGhGzIuJVSbcDs0jbGZhZO/OATJVJWknSdvnz7sDP+GgBicjPby+pZ0TMalkIIm+hemlEjC2pdLNOxeFYRZIagH2AIyTtmPeADmBmPkZeoHY/4GCYcyGIiJhZ9aLNOilfVldRRMyUdAvpl9IBknoCjwNvVARfM/BvvIeLWakcjlVS0Z84gLTk2DPAoaQVuBslvUNak7EH6c6YJcqq1cwcjlVTsYjEKcC3gPFAd2A2af+Wa0j/HstExLvA5fDxjbTMrDocjlWSJ3QfA0yNiBfzc7eQVtrZEugbEf+oOF+RlVGvWWfnAZl21GrLgfGkfaKXlPR5gIh4FbiL1O84x65/DkWzcvkOmXZSsR7j1kAfYHZE3CDpW0B/4J8RcVM+d3Gv2m1WW9xybCc5GHcBLiUNwvxU0ln58YvAHpIOyOc6GM1qjFuO7SBfTjeQtjX4e0RclSd8309aQOIU0oK1t0XE8+VVamZz45Zj+1ghImYAI1ueyBO+vwhsmHf8O8/BaFa7HI5tSMmKwI152s6/gKMkDcinrAosJWlpwHe7mNUwT+VpW0tGxNuSLift/bIbsA7wG0mjgE2A0yNiQplFmtn8uc+xDeQ+xo2AfwCnA3cCOwJExO/yyjuz8+MnPLHbrPY5HBdBwRJjtwLjSJfPz5MC8dSImFRSiWa2kNznuAjydJ0tJF0kaTHSLYDDgO+QAvIY4Hdl1mhmC8ctx4VQ2WKU1AO4kjR38T1gX+BEoAnYH3grIu4rqVQzW0gOx0+o4s6XnYE1gckRcaWkfYDVgeNIWxocFBH/q/ya8qo2s0/Ko9WfUA7GPYCfAj8ALpa0ekScIakLaYrOkYAqv6acas1sYbnP8ROS1BU4IH90B0YDV+TW4eyIuAjYzRO8zeqbW46fXAAfkNZk3BD4ekSMkjRE0qyIuBF4s9QKzWyRueU4Hy3LjklaX9JK+da/u0h9i7+IiBclbQH8hLzsmC+jzeqfW47zkfsYdwKuAP4t6VXgAuCrwCV5buPWwPci4qESSzWzNuTR6rmoGJVeinQJfRdpj5chwCrAD4GlgZ4AEfGUR6XNOg6H4zzkUemdgM2Bb0TEC5I2BPYmTeM5NyKeLrNGM2sf7nOci3w/9MnAw0BX4DSAHIa3Aa8Bs8qqz8zal1uOBSStBpwLvBARJ+f5i3eR9pc+JJ+zRES8X2KZZtaO3HIs1oV0O+B2knbII9S7AAMkXQPgYDTr2DxazRyDL58BegFvA7/If34hz198QNJWwKAyazWz6nDLkQ+n6+wG3Ah8GbgX2B64GxgBHCZp+7yN9CPlVWpm1dLpwzFvbbAsaVL34RFxNPBN4ChgJeAPwLOkdRrNrJPo9JfVeV7ieEljgGUkNUTE7ZJWAk6KiF0kXRARU0ou1cyqqNO1HFtuB8yfr5w3xAJ4CdgC6JsfPw80S+rqYDTrfDrVVB5JfYF1I+IfkvYEzgGeIo1MnwJcBCwBTAM2BX6UF5Iws06ms11WbwOcIGkFYHfgcOBV4M/ALyPiCEmbAusCl0TEI74l0Kxz6lQtRwBJhwBDgVHA0IiYlAdkbgBei4hDSy3QzGpCp+lzbOlrjIgrSKvqrA5sLal7RIwnLV47QNKGlf2SZtY5dZrL6jyXsUterfuvknqSdgkMSfdFxLt5LuPMsms1s/J1mnAEiIjZkrpFxIyIuFzSbOB0oIuk2x2MZtaiw4ZjxS2BW5Om57wL3B8R0ypakH+S1AC840EXM6vUoQdk8nappwJ/AfYB/hYRF+ZjXSPCS46ZWaEOOyCT+xQ/T1pNZwxpxe7rJHXNLUcHo5nNVYe5rC6YjyjSYrQ/AjYGvhQRb+UFJt4Bmkoo08zqRIdpObYEo6Q+kpaKiMnA/cC+pAneL0vaFjiftL2qmdlc1X2fY743ep+IuFTSrsCP+Whbg5HAbqSdAv8B7AmcGBG3llWvmdWHug7HPFl7L+BA4H/AVqS5i4OA/YA/Af8E1iH1OU6IiMd9S6CZzU/d9jm2TMeRNAzoAXwWaIiI54DnJE0jtRh7AddHxActX+tgNLP5qcs+R0mLAXtIWhz4FLAk8ADQVdJxABFxNfA30srey5ZUqpnVqXptOXYDlgYeJAXfdqTpOl2AHSQdExEXRsSfJf0jIsaWWKuZ1aG6bDnmnf/GAKuRVtd5P18q300aeNlY0nfy6W+VU6WZ1bO6G5CpHEyRtCVpUdodgVMi4jlJawObAU9ExPMllmpmdawew3GxiJhW8bgvaeBlM+AmYAfSCt6vlFOhmXUEdXVZnecxniWpe8X6jKOBS4F7gCNII9MORjNbJHXTcsyXy78kXT4/M5dzloqIiZ7HaGaLqi5ajpJ6A8eSpu1Myc8VrdY9CTyP0cwWXc2GY2X4RUQzcAXwLDBE0qpFAehQNLO2UpOX1ZIaImJmXkFne2Bx4EygP+lWwVeB63J/o5lZm6uplmNeUad7DsbtSH2M/yUtJHEn8AapBbkecFC+U8bMrM3VTDhKGkAKwPXyU5sAN0TEXyLiWOAa4BbgaeBG4LbKKT1mZm2pJm4flNQFGAxcBTRL2pc08NKv5ZyI+Lmk9YHlI2JYOZWaWWdREy3HiJgNPAGcADwEPAncBmwv6ShJfSVtQbobpld5lZpZZ1ETLcfsv6T7pVcCloqIZyR9g7TNwWbA+qSFakeUWKOZdRI1NVqdlyDbG/g5cFhE3CdpeWA6sFxEjPQEbzOrhpoIxzxCPb3i8WGkFb2Pjoh7y6vMzDqrUi+rJa0XEc9FxPQ8KBPZpZK6AZdJ2iQixpdZp5l1PlVvObZcFktaC3gM+FtEHJaPdQVmVyxJ1tcTvc2sDKVcVkvaC/gS8DJpubH7IuKr+diHAen+RTMrSxktx8WBW4HzI+JmSb2AR4CHImJoPqdLnt5jZlaKMuY5TgNeA94GiIjJwHHAFySdlZ9zMJpZqdo9HFtW15G0Zl5vsWXC95WSlsmnTQHOA7aStEN712RmNj/tOlpdMfiyK3AZ8ICkkcCppF0DH5R0F3AAsA9p9R33MZpZ6dq9z1HSIFLwDQNm5c+XIs1j3ADoTRqY6QNcABwQES+3a1FmZvPR5uEoaXWgEbiZdNn+AjAhIjbKxzcmtRRXAU6PiFGSPk3aB+aoiHiqTQsyM1sI7dHn2JO0GO0y+a6XbYAVJf0UICKeJO0S+CawdP6a0cDeDkYzqxVt1nKUtDJp/+hhwETgPuDmiPi1pH7Aw8BlEXFaPn/JiJjUJt/czKyNteWAzMbAF4GGiPiTpJOAH0uaHhEX5L7H5/J91N93MJpZLWuzcIyIYZLWBPaR1CsifivpZOBXkmblx+sD67TV9zQzay9tFo6SBpOWG2sGTsgtxkslnQj8VlK3iDgPGO3bAs2s1i1yOOZJ3ssC3wVOi4iHJA0h3fHSEBEXSzqaisEfB6OZ1bpFDsccdO9KaiZdMj8UEdfnQZifSJoZEZcu6vcxM6umhZrKU3FL4IqSPpWffhBYWdJm+fGdpBHq/yxqkWZm1bbQU3nyDoE/AAQ8QFqbcStgOVLobggc55W8zaweLXA4Vm5lIGkN0jaq3yBN4D4FmABcD/QgTet5MSLcajSzurRAfY6S1gG+I+l60mXybNLSYxMjYpKknwG3AFMi4lzg6Xaq18ysKubb5yhpPVKL8Fng6Tx5ezxpK9VtJK0UERNI90Z3bemPNDOrZ/O8rJa0FHAjcFVE/KHVsYOA7UityGeAE4GhEXF3+5VrZlYd87usngqMAf4Gc+7vEhFXS3ofWAFYH/iGB1/MrKOYXzj2Ig2ubA3cFhGzJHXJl85LAtMi4nLv+WJmHc08+xwj4j3yArSSNspPKwfh9sDBkpbEq3ebWQezIJPAbwTGAkdI2hGYLemzwM+BqyNikm8HNLOOZoHmOUpaCfgCcBRpc6w1gTMj4iYvImFmHdEnukMmh+RsYLGI8Oo6ZtZhtfsGW2Zm9ajd9602M6tHDkczswIORzOzAg5HM7MCDkczswIOR+sUJPWVdLOklyS9IulCSYvlYxtK+o+kZyX9V1KPsuu18jkcrcPLawHcANwUEQOAAcDiwC8lNQBXAkdExPqk22JnlFWr1Y4225rVrIbtCHwQEZcD5AVUvg2MBO4hrVP6VD72TnllWi1xy9E6g/WBxyufiIiJwGtAfyAk3SnpCUnfK6E+q0FuOVpnIIpXjhLpZ2BrYDNgCnCPpMcj4p4q1mc1yC1H6wyeBRorn8ir3K9EWsz5/ogYFxFTgGHAJtUv0WqNw9E6g3uAnpIOgQ9XtP81cCFwB7ChpJ55cGY74LnSKrWa4XC0Di+vHPU5YIikl4B3SNt9nBER44GzSfuuDweeiIjbSivWaoZX5bFOR9JWwNXA/hHx+PzOt87J4WhmVsCX1WZmBRyOZmYFHI5mZgUcjmZmBRyOZmYFHI5mZgUcjmZmBf4fQwb64QIHzKwAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.catplot(x=\"Q6\", y=\"Q11\", data=df);\n", "plt.xticks(rotation=45)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Handling categorical variables (Data analysis)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "df = pd.get_dummies(df, columns=['Q6'])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(dfTmp, kind = 'reg')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "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": [ "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": 15, "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?\\t',\n", " 'Q4_General background in machine learning?\\t',\n", " 'Q5_Hands-on experience in running machine learning applications?\\t',\n", " 'Q6_Which one would you prefer on a Sunday afternoon?\\t',\n", " 'Q7_Hands-on experience in image analysis using satellite images?\\t',\n", " 'Q8_Level of interest in mathematics?\\t',\n", " 'Q9_Level of interest in reading?\\t',\n", " 'Q10_Level of stress about this class?\\t',\n", " 'Q11_Your overall motivation about this class?',\n", " 'tsRel']" ] }, "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tsRel 1.000000\n", "Q3 0.037162\n", "Q4 -0.293579\n", "Q10 -0.273305\n", "Q11 -0.159292\n", "Name: tsRel, dtype: float64\n", " tsRel Q3 Q4 Q10 Q11\n", "tsRel 1.000000 0.037162 -0.293579 -0.273305 -0.159292\n", "Q3 0.037162 1.000000 0.640784 -0.374491 -0.018487\n", "Q4 -0.293579 0.640784 1.000000 0.046561 0.133677\n", "Q10 -0.273305 -0.374491 0.046561 1.000000 0.051258\n", "Q11 -0.159292 -0.018487 0.133677 0.051258 1.000000\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dfTmp = df[['tsRel','Q3', 'Q4', 'Q10', 'Q11']].copy()\n", "corr = dfTmp.corr()\n", "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)\n", "print(corr['tsRel'])\n", "print(corr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### WARNING: Outliers may lead to incorrect conclusions!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " tsRel Q4\n", "tsRel 1.000000 -0.293579\n", "Q4 -0.293579 1.000000\n" ] }, { "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='Q4', data=dfTmp, color=\"g\")\n", "print(df[['tsRel','Q4']].corr())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.displot(dfTmp.tsRel, kind=\"hist\", rug=True, bins=50, kde=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8227.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfTmp.tsRel.median()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 -8227.0\n", "1 -7057.0\n", "2 -4758.0\n", "3 -34.0\n", "4 -9.0\n", "5 -5.0\n", "6 -1.0\n", "7 0.0\n", "8 30.0\n", "9 47.0\n", "10 171.0\n", "11 183.0\n", "12 421.0\n", "13 556.0\n", "14 23768.0\n", "Name: tsRel, dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfTmp.tsRel - dfTmp.tsRel.median()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 2021-01-25 08:17:44\n", "1 2021-01-25 08:37:14\n", "2 2021-01-25 09:15:33\n", "3 2021-01-25 10:34:17\n", "4 2021-01-25 10:34:42\n", "5 2021-01-25 10:34:46\n", "6 2021-01-25 10:34:50\n", "7 2021-01-25 10:34:51\n", "8 2021-01-25 10:35:21\n", "9 2021-01-25 10:35:38\n", "10 2021-01-25 10:37:42\n", "11 2021-01-25 10:37:54\n", "12 2021-01-25 10:41:52\n", "13 2021-01-25 10:44:07\n", "14 2021-01-25 17:10:59\n", "Name: Timestamp, dtype: datetime64[ns]" ] }, "execution_count": 21, "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": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 -1.208080\n", "1 -1.043072\n", "2 -0.718840\n", "3 -0.052605\n", "4 -0.049079\n", "5 -0.048515\n", "6 -0.047951\n", "7 -0.047810\n", "8 -0.043579\n", "9 -0.041181\n", "10 -0.023693\n", "11 -0.022001\n", "12 0.011565\n", "13 0.030604\n", "14 3.304238\n", "Name: tsRel, dtype: float64" ] }, "execution_count": 22, "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": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tsRelQ3Q4Q10Q11outScore
00.075510-106.428199
11170.06696-99.580677
23469.03387-86.125590
38193.03258-0.993845
48218.07258-0.993845
58222.05779-1.017140
68226.077810-1.028787
78227.05356-1.022963
88257.05359-0.917871
98274.04199-1.173222
108398.07649-3.154907
118410.09868-3.307403
128648.04489-4.318204
138783.022810-5.346800
1431995.06257-179.072608
\n", "
" ], "text/plain": [ " tsRel Q3 Q4 Q10 Q11 outScore\n", "0 0.0 7 5 5 10 -106.428199\n", "1 1170.0 6 6 9 6 -99.580677\n", "2 3469.0 3 3 8 7 -86.125590\n", "3 8193.0 3 2 5 8 -0.993845\n", "4 8218.0 7 2 5 8 -0.993845\n", "5 8222.0 5 7 7 9 -1.017140\n", "6 8226.0 7 7 8 10 -1.028787\n", "7 8227.0 5 3 5 6 -1.022963\n", "8 8257.0 5 3 5 9 -0.917871\n", "9 8274.0 4 1 9 9 -1.173222\n", "10 8398.0 7 6 4 9 -3.154907\n", "11 8410.0 9 8 6 8 -3.307403\n", "12 8648.0 4 4 8 9 -4.318204\n", "13 8783.0 2 2 8 10 -5.346800\n", "14 31995.0 6 2 5 7 -179.072608" ] }, "execution_count": 23, "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": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "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": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tsRelQ3Q4Q10Q11outScore
38193.03258-0.993845
48218.07258-0.993845
58222.05779-1.017140
68226.077810-1.028787
78227.05356-1.022963
88257.05359-0.917871
98274.04199-1.173222
108398.07649-3.154907
118410.09868-3.307403
128648.04489-4.318204
138783.022810-5.346800
\n", "
" ], "text/plain": [ " tsRel Q3 Q4 Q10 Q11 outScore\n", "3 8193.0 3 2 5 8 -0.993845\n", "4 8218.0 7 2 5 8 -0.993845\n", "5 8222.0 5 7 7 9 -1.017140\n", "6 8226.0 7 7 8 10 -1.028787\n", "7 8227.0 5 3 5 6 -1.022963\n", "8 8257.0 5 3 5 9 -0.917871\n", "9 8274.0 4 1 9 9 -1.173222\n", "10 8398.0 7 6 4 9 -3.154907\n", "11 8410.0 9 8 6 8 -3.307403\n", "12 8648.0 4 4 8 9 -4.318204\n", "13 8783.0 2 2 8 10 -5.346800" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfTmpFil = dfTmp[np.logical_and(dfTmp.outScore>-10, dfTmp.outScore<5)]\n", "dfTmpFil" ] }, { "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.displot(dfTmpFil.tsRel, color=\"g\", bins=10)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tsRel 1.000000\n", "Q3 -0.343333\n", "Q4 -0.054589\n", "Q10 0.381243\n", "Q11 0.413380\n", "outScore -0.973143\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": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " tsRel Q4\n", "tsRel 1.000000 -0.293579\n", "Q4 -0.293579 1.000000\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.regplot(x='tsRel', y='Q4', data=dfTmpFil, color=\"g\")\n", "print(df[['tsRel','Q4']].corr())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 Q9 Q10 Q11 tsRel \\\n", "0 7 5 7 5 5 7 7 7 5 10 0.0 \n", "1 7 7 6 6 5 7 4 7 9 6 1170.0 \n", "2 3 4 3 3 2 5 2 6 8 7 3469.0 \n", "3 5 3 3 2 1 3 5 5 5 8 8193.0 \n", "4 5 7 7 2 2 8 8 8 5 8 8218.0 \n", "5 7 7 5 7 7 6 6 10 7 9 8222.0 \n", "6 7 7 7 7 7 4 9 10 8 10 8226.0 \n", "7 4 6 5 3 3 5 2 4 5 6 8227.0 \n", "8 5 5 5 3 3 3 7 7 5 9 8257.0 \n", "9 3 2 4 1 1 1 2 1 9 9 8274.0 \n", "10 9 8 7 6 5 1 7 10 4 9 8398.0 \n", "11 9 9 9 8 9 5 6 6 6 8 8410.0 \n", "12 4 4 4 4 4 6 7 4 8 9 8648.0 \n", "13 4 4 2 2 2 1 1 10 8 10 8783.0 \n", "14 5 5 6 2 1 1 5 9 5 7 31995.0 \n", "\n", " Q6_Coding for the homework, project or just for fun Q6_Reading \\\n", "0 1 0 \n", "1 1 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 1 \n", "6 0 0 \n", "7 0 0 \n", "8 0 1 \n", "9 1 0 \n", "10 0 1 \n", "11 0 0 \n", "12 1 0 \n", "13 0 1 \n", "14 0 0 \n", "\n", " Q6_Running Q6_Watching a movie \n", "0 0 0 \n", "1 0 0 \n", "2 0 1 \n", "3 0 1 \n", "4 0 1 \n", "5 0 0 \n", "6 0 1 \n", "7 0 1 \n", "8 0 0 \n", "9 0 0 \n", "10 0 0 \n", "11 0 1 \n", "12 0 0 \n", "13 0 0 \n", "14 1 0 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's normalize the data" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "df_norm = (df-df.min())/(df.max()-df.min())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q7Q8Q9Q10Q11tsRelQ6_Coding for the homework, project or just for funQ6_ReadingQ6_RunningQ6_Watching a movie
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std0.3261120.2798720.2735510.3215610.310530.3465780.310530.2983400.3453090.3362960.2216160.4577380.4577380.2581990.507093
min0.0000000.0000000.0000000.0000000.000000.0000000.000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%0.1666670.2857140.2857140.1428570.125000.1428570.250000.5000000.2000000.3750000.2564620.0000000.0000000.0000000.000000
50%0.3333330.4285710.4285710.2857140.250000.5714290.625000.6666670.4000000.7500000.2571340.0000000.0000000.0000000.000000
75%0.6666670.7142860.7142860.7142860.500000.7142860.750000.9444440.8000000.7500000.2626660.5000000.5000000.0000001.000000
max1.0000001.0000001.0000001.0000001.000001.0000001.000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 \\\n", "count 15.000000 15.000000 15.000000 15.000000 15.00000 15.000000 \n", "mean 0.433333 0.504762 0.476190 0.438095 0.35000 0.457143 \n", "std 0.326112 0.279872 0.273551 0.321561 0.31053 0.346578 \n", "min 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 \n", "25% 0.166667 0.285714 0.285714 0.142857 0.12500 0.142857 \n", "50% 0.333333 0.428571 0.428571 0.285714 0.25000 0.571429 \n", "75% 0.666667 0.714286 0.714286 0.714286 0.50000 0.714286 \n", "max 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000 \n", "\n", " Q8 Q9 Q10 Q11 tsRel \\\n", "count 15.00000 15.000000 15.000000 15.000000 15.000000 \n", "mean 0.52500 0.659259 0.493333 0.583333 0.267729 \n", "std 0.31053 0.298340 0.345309 0.336296 0.221616 \n", "min 0.00000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.25000 0.500000 0.200000 0.375000 0.256462 \n", "50% 0.62500 0.666667 0.400000 0.750000 0.257134 \n", "75% 0.75000 0.944444 0.800000 0.750000 0.262666 \n", "max 1.00000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " Q6_Coding for the homework, project or just for fun Q6_Reading \\\n", "count 15.000000 15.000000 \n", "mean 0.266667 0.266667 \n", "std 0.457738 0.457738 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 0.000000 \n", "75% 0.500000 0.500000 \n", "max 1.000000 1.000000 \n", "\n", " Q6_Running Q6_Watching a movie \n", "count 15.000000 15.000000 \n", "mean 0.066667 0.400000 \n", "std 0.258199 0.507093 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 0.000000 \n", "75% 0.000000 1.000000 \n", "max 1.000000 1.000000 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_norm.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive models\n", "\n", "#### Can we predict motivation?\n", "#### Can we predict stress?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Q1Q2Q3Q4Q5Q7Q8Q9Q10Q11Q6_Coding for the homework, project or just for funQ6_ReadingQ6_RunningQ6_Watching a movie
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10.6666670.7142860.5714290.7142860.5000.8571430.3750.6666671.00.001.00.00.00.0
20.0000000.2857140.1428570.2857140.1250.5714290.1250.5555560.80.250.00.00.01.0
30.3333330.1428570.1428570.1428570.0000.2857140.5000.4444440.20.500.00.00.01.0
40.3333330.7142860.7142860.1428570.1251.0000000.8750.7777780.20.500.00.00.01.0
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" ], "text/plain": [ " Q1 Q2 Q3 Q4 Q5 Q7 Q8 Q9 \\\n", "0 0.666667 0.428571 0.714286 0.571429 0.500 0.857143 0.750 0.666667 \n", "1 0.666667 0.714286 0.571429 0.714286 0.500 0.857143 0.375 0.666667 \n", "2 0.000000 0.285714 0.142857 0.285714 0.125 0.571429 0.125 0.555556 \n", "3 0.333333 0.142857 0.142857 0.142857 0.000 0.285714 0.500 0.444444 \n", "4 0.333333 0.714286 0.714286 0.142857 0.125 1.000000 0.875 0.777778 \n", "\n", " Q10 Q11 Q6_Coding for the homework, project or just for fun Q6_Reading \\\n", "0 0.2 1.00 1.0 0.0 \n", "1 1.0 0.00 1.0 0.0 \n", "2 0.8 0.25 0.0 0.0 \n", "3 0.2 0.50 0.0 0.0 \n", "4 0.2 0.50 0.0 0.0 \n", "\n", " Q6_Running Q6_Watching a movie \n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 1.0 \n", "3 0.0 1.0 \n", "4 0.0 1.0 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.drop(columns=['tsRel'])\n", "df_norm = (df-df.min())/(df.max()-df.min())\n", "df_norm.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select X and y\n", "" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Q1', 'Q2', 'Q3', 'Q4', 'Q5']" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colX = df.columns[np.r_[0:8,10:13]]\n", "colX = df.columns[np.r_[0:5]]\n", "colX.tolist()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Q10']" ] }, "execution_count": 83, "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": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15, 5)\n" ] }, { "data": { "text/plain": [ "array([[7, 5, 7, 5, 5],\n", " [7, 7, 6, 6, 5],\n", " [3, 4, 3, 3, 2],\n", " [5, 3, 3, 2, 1],\n", " [5, 7, 7, 2, 2],\n", " [7, 7, 5, 7, 7],\n", " [7, 7, 7, 7, 7],\n", " [4, 6, 5, 3, 3],\n", " [5, 5, 5, 3, 3],\n", " [3, 2, 4, 1, 1],\n", " [9, 8, 7, 6, 5],\n", " [9, 9, 9, 8, 9],\n", " [4, 4, 4, 4, 4],\n", " [4, 4, 2, 2, 2],\n", " [5, 5, 6, 2, 1]])" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = np.array(df[colX])\n", "print(X.shape)\n", "X" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15, 1)\n" ] }, { "data": { "text/plain": [ "array([[5],\n", " [9],\n", " [8],\n", " [5],\n", " [5],\n", " [7],\n", " [8],\n", " [5],\n", " [5],\n", " [9],\n", " [4],\n", " [6],\n", " [8],\n", " [8],\n", " [5]])" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = np.array(df[colY])\n", "print(y.shape)\n", "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Normalize data" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.79863132, 1.83162188, 1.98206242, 1.80876105, 2.16666667])" ] }, "execution_count": 86, "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": 87, "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": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAIN: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14] TEST: [0]\n", "TRAIN: [ 0 2 3 4 5 6 7 8 9 10 11 12 13 14] TEST: [1]\n", "TRAIN: [ 0 1 3 4 5 6 7 8 9 10 11 12 13 14] TEST: [2]\n", "TRAIN: [ 0 1 2 4 5 6 7 8 9 10 11 12 13 14] TEST: [3]\n", "TRAIN: [ 0 1 2 3 5 6 7 8 9 10 11 12 13 14] TEST: [4]\n", "TRAIN: [ 0 1 2 3 4 6 7 8 9 10 11 12 13 14] TEST: [5]\n", "TRAIN: [ 0 1 2 3 4 5 7 8 9 10 11 12 13 14] TEST: [6]\n", "TRAIN: [ 0 1 2 3 4 5 6 8 9 10 11 12 13 14] TEST: [7]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 9 10 11 12 13 14] TEST: [8]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 10 11 12 13 14] TEST: [9]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 11 12 13 14] TEST: [10]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 12 13 14] TEST: [11]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 13 14] TEST: [12]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 14] TEST: [13]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13] TEST: [14]\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": 89, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Y : [[5]] , pred as: [5.49637039]\n", "Y : [[9]] , pred as: [5.6460679]\n", "Y : [[8]] , pred as: [6.61766184]\n", "Y : [[5]] , pred as: [6.047231]\n", "Y : [[5]] , pred as: [3.0178263]\n", "Y : [[7]] , pred as: [5.90665549]\n", "Y : [[8]] , pred as: [6.17522537]\n", "Y : [[5]] , pred as: [6.81497724]\n", "Y : [[5]] , pred as: [5.82713838]\n", "Y : [[9]] , pred as: [5.43843141]\n", "Y : [[4]] , pred as: [4.82455428]\n", "Y : [[6]] , pred as: [4.87604068]\n", "Y : [[8]] , pred as: [6.84140789]\n", "Y : [[8]] , pred as: [6.06047721]\n", "Y : [[5]] , pred as: [3.69118754]\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": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 0.42609039],\n", " [0.42609039, 1. ]])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(y.T, predAll.T)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 91, "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(y.T, predAll.T)\n", "plt.plot([5,10],[5,10],'k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Testing on independent sample" ] }, { "cell_type": "code", "execution_count": 92, "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
\n", "
" ], "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": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df20Init = pd.read_csv(('./Data/MUSA650-Spring2020-WelcomePoll.csv'))\n", "df20Init.head()" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "df20 = df20Init.drop(columns=['Timestamp'])\n", "df20.columns = df20.columns.str.split('_', 1).str[0].tolist()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[8, 5, 4, 6, 7],\n", " [8, 8, 5, 5, 6],\n", " [6, 6, 6, 6, 5],\n", " [5, 3, 6, 4, 4],\n", " [6, 6, 5, 4, 3],\n", " [8, 7, 8, 3, 3],\n", " [4, 3, 1, 1, 1],\n", " [7, 3, 7, 6, 5],\n", " [5, 5, 5, 4, 4],\n", " [6, 6, 6, 6, 6],\n", " [4, 4, 4, 5, 3],\n", " [7, 7, 7, 2, 2],\n", " [8, 8, 8, 6, 6],\n", " [4, 4, 4, 1, 1],\n", " [8, 7, 7, 7, 7],\n", " [7, 7, 6, 6, 6],\n", " [7, 6, 6, 5, 5],\n", " [6, 6, 6, 5, 5],\n", " [9, 9, 9, 9, 9]])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X20 = np.array(df21[colX])\n", "X20" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 7],\n", " [ 7],\n", " [ 7],\n", " [ 5],\n", " [10],\n", " [ 2],\n", " [ 8],\n", " [ 6],\n", " [ 5],\n", " [ 5],\n", " [ 8],\n", " [ 5],\n", " [ 6],\n", " [ 7],\n", " [ 6],\n", " [ 5],\n", " [ 4],\n", " [ 7],\n", " [ 4]])" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y20 = np.array(df20[colY])\n", "y20" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.79863132, 1.83162188, 1.98206242, 2.26861556, 2.16666667])" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X20norm = scaler.transform(X20)\n", "X20norm.max(axis=0)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "from sklearn.svm import LinearSVR\n", "\n", "regr = LinearSVR(random_state=0, tol=1e-5)\n", " \n", "regr.fit(X, y) # Train the model\n", "\n", "ypred = regr.predict(X20) # Apply the model\n" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 0.01457753],\n", " [0.01457753, 1. ]])" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(y20.T, ypred.T)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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