diff --git a/Data-X HW0 Sp18.ipynb b/Data-X HW0 Sp18.ipynb new file mode 100644 index 0000000..cceab11 --- /dev/null +++ b/Data-X HW0 Sp18.ipynb @@ -0,0 +1,593 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 0\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this homework you will complete a couple of simple exercises in order to show your understanding with Python. If these exercises are challenging or new to you, you may want to reconsider taking the class and/or brush up on your Python skills. For the following exercises you are not allowed to use any Python packages (i.e. Numpy, Pandas, etc.)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an empty Python list called 'a' in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = []\n", + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Store all values between 1-100 (inclusive) with increments of 3 (i.e. 1, 4, 7...) in 'a'." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49, 52, 55, 58, 61, 64, 67, 70, 73, 76, 79, 82, 85, 88, 91, 94, 97, 100]\n" + ] + } + ], + "source": [ + "a = list(range(1,101,3))\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create another list called 'a2' with numbers from 2-46 (inclusive) with increments of 0.5 (i.e. 2, 2.5, 3...)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5, 24.0, 24.5, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5, 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5, 40.0, 40.5, 41.0, 41.5, 42.0, 42.5, 43.0, 43.5, 44.0, 44.5, 45.0, 45.5, 46.0]\n" + ] + } + ], + "source": [ + "a2 = list(range(4,93,1))\n", + "a2 = [x/2 for x in a2]\n", + "print(a2) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Double every even integer element from list 'a'. Store the results back in 'a'." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 8, 7, 20, 13, 32, 19, 44, 25, 56, 31, 68, 37, 80, 43, 92, 49, 104, 55, 116, 61, 128, 67, 140, 73, 152, 79, 164, 85, 176, 91, 188, 97, 200]\n" + ] + } + ], + "source": [ + "for i,x in enumerate(a):\n", + " if x%2 == 0:\n", + " a[i] = a[i] * 2\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add all numbers in 'a' except for the 2nd and 21st elements (the 2nd element here means the element at list index 1)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2532\n" + ] + } + ], + "source": [ + "asum=0\n", + "for i,x in enumerate(a):\n", + " if i!=1 and i!=20:\n", + " asum = asum + a[i]\n", + "print(asum) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the mean of 'a'." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2601\n" + ] + }, + { + "data": { + "text/plain": [ + "76.5" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total = 0\n", + "for i,x in enumerate(a):\n", + " total = total + x\n", + "print(total)\n", + "mean = total/len(a)\n", + "mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an empty list called 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "b = list()\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Store the words in the sentence below as elements into the list 'b'. \n", + "\n", + "'I am so excited about Data-X. It is important to be able to work with data.'" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['I', 'am', 'so', 'excited', 'about', 'Data-X.', 'It', 'is', 'important', 'to', 'be', 'able', 'to', 'work', 'with', 'data.']\n" + ] + } + ], + "source": [ + "complete_sentence = 'I am so excited about Data-X. It is important to be able to work with data.'\n", + "b = complete_sentence.split()\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Return the count of the occurences of the lower-case letter 'e' in the list 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I*am*so*excited*about*Data-X.*It*is*important*to*be*able*to*work*with*data.\n", + "4\n" + ] + } + ], + "source": [ + "b_full = ('*'.join(b))\n", + "print(b_full)\n", + "b_e = b_full.count('e')\n", + "print(b_e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace every lower- or upper-case letter 'i' in the list b with a '1'." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['I', '*', 'a', 'm', '*', 's', 'o', '*', 'e', 'x', 'c', 'i', 't', 'e', 'd', '*', 'a', 'b', 'o', 'u', 't', '*', 'D', 'a', 't', 'a', '-', 'X', '.', '*', 'I', 't', '*', 'i', 's', '*', 'i', 'm', 'p', 'o', 'r', 't', 'a', 'n', 't', '*', 't', 'o', '*', 'b', 'e', '*', 'a', 'b', 'l', 'e', '*', 't', 'o', '*', 'w', 'o', 'r', 'k', '*', 'w', 'i', 't', 'h', '*', 'd', 'a', 't', 'a', '.']\n", + "['I', 'am', 'so', 'excited', 'about', 'Data-X.', 'It', 'is', 'important', 'to', 'be', 'able', 'to', 'work', 'with', 'data.']\n", + "['1', 'am', 'so', 'exc1ted', 'about', 'Data-X.', '1t', '1s', '1mportant', 'to', 'be', 'able', 'to', 'work', 'w1th', 'data.']\n" + ] + } + ], + "source": [ + "b2 = list(b_full)\n", + "print(b2)\n", + "for i,x in enumerate(b2):\n", + " if x == 'i' or x== 'I':\n", + " b2[i] = '1'\n", + "\n", + " if x == '*':\n", + " b2[i] = ' '\n", + "print(b)\n", + "b_full = (''.join(b2))\n", + "b = b_full.split(' ')\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Append the string \"This is the end of the first HW.\" to the list 'b'." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['This', 'is', 'the', 'end', 'of', 'the', 'first', 'HW']\n", + "['1', 'am', 'so', 'exc1ted', 'about', 'Data-X.', '1t', '1s', '1mportant', 'to', 'be', 'able', 'to', 'work', 'w1th', 'data.', 'This', 'is', 'the', 'end', 'of', 'the', 'first', 'HW']\n" + ] + } + ], + "source": [ + "ns = \"This is the end of the first HW\"\n", + "ns1 = ns.split(' ')\n", + "print(ns1)\n", + "for x in ns1:\n", + " b.append(x)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print 'b' as ONE string backwards (starting with \"WH tsrif...\")." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 am so exc1ted about Data-X. 1t 1s 1mportant to be able to work w1th data. This is the end of the first HW\n", + "\n", + "WH tsrif eht fo dne eht si sihT .atad ht1w krow ot elba eb ot tnatropm1 s1 t1 .X-ataD tuoba det1cxe os ma 1\n" + ] + } + ], + "source": [ + "b_3 = (' '.join(b))\n", + "print(b_3)\n", + "b = b_3[::-1]\n", + "print (type(b))\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Put the following in a dictionary called 'codes':\n", + "\n", + "Keys: 1001, 1002, 1003, 1004, 1005 \n", + "Values: 'Alpha', Beta, 'Gamma, 'Delta', 'Tau'\n", + "\n", + "then traverse the dictionary by its keys and change every value to be all lower case." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1001: 'alpha', 1002: 'beta', 1003: 'gamma', 1004: 'delta', 1005: 'tau'}" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "codes=dict([(1001,'Alpha'),(1002,'Beta'),(1003,'Gamma'),(1004,'Delta'),(1005,'Tau')])\n", + "list(codes.keys())\n", + "codes1= {k:v.lower() for k, v in codes.items()}\n", + "codes1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete 'alpha' from the dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1002: 'beta', 1003: 'gamma', 1004: 'delta', 1005: 'tau'}\n" + ] + } + ], + "source": [ + "for k,v in codes1.items():\n", + " if v =='alpha':\n", + " del codes1[k]\n", + "print(codes1)\n", + "#del codes[1001]\n", + "#codes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a set called 'c' with the all the odd numbers less than 10." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 3, 5, 7, 9}\n" + ] + } + ], + "source": [ + "c = {1,3,5,7,9}\n", + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create another set called 'd' with elements 2, 5, 10, 30." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2, 10, 5, 30}\n" + ] + } + ], + "source": [ + "d = {2,5,10,30}\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the union between sets 'c' and 'd' and store this in a new set called 'e'." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 2, 3, 5, 7, 9, 10, 30}\n" + ] + } + ], + "source": [ + "e = c|d\n", + "print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the intersection between sets 'c' and 'd'." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{5}\n" + ] + } + ], + "source": [ + "print(c&d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Data-X HW1 Sp18.ipynb b/Data-X HW1 Sp18.ipynb new file mode 100644 index 0000000..ba23cb0 --- /dev/null +++ b/Data-X HW1 Sp18.ipynb @@ -0,0 +1,3950 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this homework, you will get a chance to do some exercises with Numpy, Pandas, and Matplotlib to show us your understanding with this libraries. \n", + "\n", + "If you have questions, Google! Additionally you can ask your peers questions on Piazza and/or go to Office Hours.\n", + "\n", + "This homework is due **Thursday Feb. 8th, 2018 at 11:59 PM**. Please upload your .ipynb to your private repo on Github. Additionally, submit a pdf on bCourses and in the comment section include a link to your private repo.\n", + "\n", + "This homework is long, please start early!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy Basics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create two numpy arrays (`a` and `b`). `a` should be all integers between 10-19 (inclusive), and `b` should be ten evenly spaced numbers between 1-7. Print the results below." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a: [10 11 12 13 14 15 16 17 18 19]\n", + "b: [ 1. 1.66666667 2.33333333 3. 3.66666667 4.33333333\n", + " 5. 5.66666667 6.33333333 7. ]\n" + ] + } + ], + "source": [ + "a = np.arange(10,20)\n", + "b = np.linspace(1,7,10)\n", + "\n", + "print(\"a: \", a)\n", + "print(\"b: \", b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For `a` and `b` above do the follow and print out the results.\n", + "\n", + "1. Square all the elements in both arrays (element-wise). \n", + "2. Add both the squared arrays (e.g. [1,2] + [3,4] = [4,6]). \n", + "3. Sum the elements with even indices of the added array. \n", + "4. Take the square root of the added array (element-wise square root)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. [100 121 144 169 196 225 256 289 324 361] [ 1. 2.77777778 5.44444444 9. 13.44444444\n", + " 18.77777778 25. 32.11111111 40.11111111 49. ]\n", + "2. [ 101. 123.77777778 149.44444444 178. 209.44444444\n", + " 243.77777778 281. 321.11111111 364.11111111 410. ]\n", + "3. 1105.0\n", + "4. [ 10.04987562 11.12554618 12.22474721 13.34166406 14.47219556\n", + " 15.61338457 16.76305461 17.91957341 19.08169571 20.24845673]\n" + ] + } + ], + "source": [ + "asq= a**2\n", + "bsq=b**2\n", + "#############################\n", + "#c=len(b)\n", + "#d=len(a)\n", + "#diff=c-d\n", + "#zero=np.zeros(diff)\n", + "#newa=np.append(a**2,zero)\n", + "#print(newa)\n", + "two = asq+bsq\n", + "#############################\n", + "evensum=0\n", + "for i in range(len(two)):\n", + " if i%2==0:\n", + " evensum=evensum+two[i]\n", + " three=evensum\n", + "############################\n", + "four = np.sqrt(two)\n", + "############################\n", + "print(\"1. \",asq,bsq)\n", + "print(\"2. \",two)\n", + "print(\"3. \",three)\n", + "print(\"4. \",four)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Append `b` to `a`. Reshape the appended array so that it is a 5x4, 2D-array and store the results in a variable called `m`. Print `m`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m: [[ 10. 11. 12. 13. ]\n", + " [ 14. 15. 16. 17. ]\n", + " [ 18. 19. 1. 1.66666667]\n", + " [ 2.33333333 3. 3.66666667 4.33333333]\n", + " [ 5. 5.66666667 6.33333333 7. ]]\n" + ] + } + ], + "source": [ + "m = np.append(a,b).reshape(5,4)\n", + "\n", + "print(\"m: \", m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the second and the third column of the matrix `m`. Store the resulting 5x2 matrix in a new variable called `m2`. Print `m2`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m2: [[ 11. 12. ]\n", + " [ 15. 16. ]\n", + " [ 19. 1. ]\n", + " [ 3. 3.66666667]\n", + " [ 5.66666667 6.33333333]]\n" + ] + } + ], + "source": [ + "m2 = m[:,1:3].reshape(5,2)\n", + "\n", + "print(\"m2: \", m2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take the dot product of `m2` and `m` store the results in a matrix called `m3`. Print `m3`.\n", + "Note that dot product of two matrices $A\\cdot B = A^T B$" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m3: [[ 697.33333333 748.11111111 437.88888889 482.33333333]\n", + " [ 402.22222222 437.88888889 454.55555556 489.88888889]]\n" + ] + } + ], + "source": [ + "m3 = np.dot(m2.T,m)\n", + "\n", + "print(\"m3: \", m3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Round the `m3` matrix to two decimal points. Store the result in place and print the new `m3`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m3: [[ 697.33 748.11 437.89 482.33]\n", + " [ 402.22 437.89 454.56 489.89]]\n" + ] + } + ], + "source": [ + "m3 = m3.round(decimals=2)\n", + "\n", + "print(\"m3: \", m3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sort the `m3` array so that the highest value is at the top left, the next highest value to the right of the highest, and the lowest value is at the bottom right. Print the sorted `m3` array." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sorted m3: [[ 748.11 697.33 489.89 482.33]\n", + " [ 454.56 437.89 437.89 402.22]]\n" + ] + } + ], + "source": [ + "sorted_m3 = np.sort(m3,axis=None)[::-1].reshape(m3.shape)\n", + "print(\"sorted m3: \", sorted_m3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy and Masks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an array called `f` where there are 100 equally-spaced values from 0 to pi, inclusive.\n", + "Take the sin of the array `f` (element-wise) and store that in place. Print `f`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "f: [ 0.00000000e+00 3.17279335e-02 6.34239197e-02 9.50560433e-02\n", + " 1.26592454e-01 1.58001396e-01 1.89251244e-01 2.20310533e-01\n", + " 2.51147987e-01 2.81732557e-01 3.12033446e-01 3.42020143e-01\n", + " 3.71662456e-01 4.00930535e-01 4.29794912e-01 4.58226522e-01\n", + " 4.86196736e-01 5.13677392e-01 5.40640817e-01 5.67059864e-01\n", + " 5.92907929e-01 6.18158986e-01 6.42787610e-01 6.66769001e-01\n", + " 6.90079011e-01 7.12694171e-01 7.34591709e-01 7.55749574e-01\n", + " 7.76146464e-01 7.95761841e-01 8.14575952e-01 8.32569855e-01\n", + " 8.49725430e-01 8.66025404e-01 8.81453363e-01 8.95993774e-01\n", + " 9.09631995e-01 9.22354294e-01 9.34147860e-01 9.45000819e-01\n", + " 9.54902241e-01 9.63842159e-01 9.71811568e-01 9.78802446e-01\n", + " 9.84807753e-01 9.89821442e-01 9.93838464e-01 9.96854776e-01\n", + " 9.98867339e-01 9.99874128e-01 9.99874128e-01 9.98867339e-01\n", + " 9.96854776e-01 9.93838464e-01 9.89821442e-01 9.84807753e-01\n", + " 9.78802446e-01 9.71811568e-01 9.63842159e-01 9.54902241e-01\n", + " 9.45000819e-01 9.34147860e-01 9.22354294e-01 9.09631995e-01\n", + " 8.95993774e-01 8.81453363e-01 8.66025404e-01 8.49725430e-01\n", + " 8.32569855e-01 8.14575952e-01 7.95761841e-01 7.76146464e-01\n", + " 7.55749574e-01 7.34591709e-01 7.12694171e-01 6.90079011e-01\n", + " 6.66769001e-01 6.42787610e-01 6.18158986e-01 5.92907929e-01\n", + " 5.67059864e-01 5.40640817e-01 5.13677392e-01 4.86196736e-01\n", + " 4.58226522e-01 4.29794912e-01 4.00930535e-01 3.71662456e-01\n", + " 3.42020143e-01 3.12033446e-01 2.81732557e-01 2.51147987e-01\n", + " 2.20310533e-01 1.89251244e-01 1.58001396e-01 1.26592454e-01\n", + " 9.50560433e-02 6.34239197e-02 3.17279335e-02 1.22464680e-16]\n" + ] + } + ], + "source": [ + "f = np.sin(np.linspace(0,np.pi,100))\n", + "\n", + "print(\"f: \", f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use a 'mask' and print an array that is True when `f` >= 1/2 and False when `f` < 1/2.\n", + "Print an array sequence that has only those values where `f` >= 1/2." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.51367739 0.54064082 0.56705986 0.59290793 0.61815899 0.64278761\n", + " 0.666769 0.69007901 0.71269417 0.73459171 0.75574957 0.77614646\n", + " 0.79576184 0.81457595 0.83256985 0.84972543 0.8660254 0.88145336\n", + " 0.89599377 0.909632 0.92235429 0.93414786 0.94500082 0.95490224\n", + " 0.96384216 0.97181157 0.97880245 0.98480775 0.98982144 0.99383846\n", + " 0.99685478 0.99886734 0.99987413 0.99987413 0.99886734 0.99685478\n", + " 0.99383846 0.98982144 0.98480775 0.97880245 0.97181157 0.96384216\n", + " 0.95490224 0.94500082 0.93414786 0.92235429 0.909632 0.89599377\n", + " 0.88145336 0.8660254 0.84972543 0.83256985 0.81457595 0.79576184\n", + " 0.77614646 0.75574957 0.73459171 0.71269417 0.69007901 0.666769\n", + " 0.64278761 0.61815899 0.59290793 0.56705986 0.54064082 0.51367739]\n" + ] + } + ], + "source": [ + "mask = f>=0.5\n", + "print(f[mask])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## NumPy and 2 Variable Prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let `x` be the number of miles a person drives per day and `y` be the dollars spent on buying car fuel per day.\n", + " \n", + "We have created 2 numpy arrays each of size 100 that represent `x` and `y`. \n", + "`x` (number of miles) ranges from 1 to 10 with a uniform noise of (0, 1/2). \n", + "`y` (money spent in dollars) will be from 1 to 20 with a uniform noise (0, 1). \n", + "\n", + "Run the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [ 1.34683976 1.12176759 1.51512398 1.55233174 1.40619168\n", + " 1.65075498 1.79399331 1.80243817 1.89844195 2.00100023\n", + " 2.3344038 2.22424872 2.24914511 2.36268477 2.49808849\n", + " 2.8212704 2.68452475 2.68229427 3.09511169 2.95703884\n", + " 3.09047742 3.2544361 3.41541904 3.40886375 3.50672677\n", + " 3.74960644 3.64861355 3.7721462 3.56368566 4.01092701\n", + " 4.15630694 4.06088549 4.02517179 4.25169402 4.15897504\n", + " 4.26835333 4.32520644 4.48563164 4.78490721 4.84614839\n", + " 4.96698768 5.18754259 5.29582013 5.32097781 5.0674106\n", + " 5.47601124 5.46852704 5.64537452 5.49642807 5.89755027\n", + " 5.68548923 5.76276141 5.94613234 6.18135713 5.96522091\n", + " 6.0275473 6.54290191 6.4991329 6.74003765 6.81809807\n", + " 6.50611821 6.91538752 7.01250925 6.89905417 7.31314433\n", + " 7.20472297 7.1043621 7.48199528 7.58957227 7.61744354\n", + " 7.6991707 7.85436822 8.03510784 7.80787781 8.22410224\n", + " 7.99366248 8.40581097 8.28913792 8.45971515 8.54227144\n", + " 8.6906456 8.61856507 8.83489887 8.66309658 8.94837987\n", + " 9.20890222 8.9614749 8.92608294 9.13231416 9.55889896\n", + " 9.61488451 9.54252979 9.42015491 9.90952569 10.00659591\n", + " 10.02504265 10.07330937 9.93489915 10.0892334 10.36509991]\n", + "y= [ 1.6635012 2.0214592 2.10816052 2.26016496 1.96287558\n", + " 2.9554635 3.02881887 3.33565296 2.75465779 3.4250107\n", + " 3.39670148 3.39377767 3.78503343 4.38293049 4.32963586\n", + " 4.03925039 4.73691868 4.30098399 4.8416329 4.78175957\n", + " 4.99765787 5.31746817 5.76844671 5.93723749 5.72811642\n", + " 6.70973615 6.68143367 6.57482731 7.17737603 7.54863252\n", + " 7.30221419 7.3202573 7.78023884 7.91133365 8.2765417\n", + " 8.69203281 8.78219865 8.45897546 8.89094715 8.81719921\n", + " 8.87106971 9.66192562 9.4020625 9.85990783 9.60359778\n", + " 10.07386266 10.6957995 10.66721916 11.18256285 10.57431836\n", + " 11.46744716 10.94398916 11.26445259 12.09754828 12.11988037\n", + " 12.121557 12.17613693 12.43750193 13.00912372 12.86407194\n", + " 13.24640866 12.76120085 13.11723062 14.07841099 14.19821707\n", + " 14.27289001 14.30624942 14.63060835 14.2770918 15.0744923\n", + " 14.45261619 15.11897313 15.2378667 15.27203124 15.32491892\n", + " 16.01095271 15.71250558 16.29488506 16.70618934 16.56555394\n", + " 16.42379457 17.18144744 17.13813976 17.69613625 17.37763019\n", + " 17.90942839 17.90343733 18.01951169 18.35727914 18.16841269\n", + " 18.61813748 18.66062754 18.81217983 19.44995194 19.7213867\n", + " 19.71966726 19.78961904 19.64385088 20.69719809 20.07974319]\n" + ] + } + ], + "source": [ + "# seed the random number generator with a fixed value\n", + "np.random.seed(500)\n", + "\n", + "x=np.linspace(1,10,100)+ np.random.uniform(low=0,high=.5,size=100) #purpose of this command????\n", + "y=np.linspace(1,20,100)+ np.random.uniform(low=0,high=1,size=100)\n", + "print ('x = ',x)\n", + "print ('y= ',y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the expected value of `x` and the expected value of `y`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5.7825325415879227, 11.012981683344968)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##### ignore\n", + "expx=np.mean(x)\n", + "expy=np.mean(y)\n", + "expx, expy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the variance for `x` and `y`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.03332752948\n", + "30.1139035755\n" + ] + } + ], + "source": [ + "##### ignore\n", + "varx=np.var(x)\n", + "vary=np.var(y)\n", + "print(varx)\n", + "print(vary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the co-variance of `x` and `y`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 7.10437124, 14.65774383],\n", + " [ 14.65774383, 30.41808442]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##### ignore\n", + "covar=np.cov(x,y)\n", + "covar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assume that the number of dollars spent on car fuel is only linearly dependent on the miles driven.\n", + "Write code that uses a linear predictor to calculate a predicted value of `y` for each `x`. \n", + "\n", + "i.e. $y_{predicted} = f(x) = mx + b.$" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m y_predicted =\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "y_predicted =" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predict `y` for each value in `x`, put the error into an array called $y_{error}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_error = np.array(y-y_predicted)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write code that calculates the root mean square error (RMSE)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rmse = " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading a File" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in a CSV file called 'data3.csv' into a dataframe called df.\n", + "\n", + "Data description\n", + "* Data source: http://www.fao.org/nr/water/aquastat/data/query/index.html\n", + "* Data, units\n", + " * GDP, current USD (CPI adjusted)\n", + " * NRI, mm/yr\n", + " * Population density, inhab/km^2\n", + " * Total area of the country, 1000 ha = 10km^2\n", + " * Total Population, unit 1000 inhabitants\n", + "\n", + "Display the first 10 lines of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaArea IdVariable NameVariable IdYearValueSymbolMd
0Argentina9.0Total area of the country4100.01962.0278040.0ENaN
1Argentina9.0Total area of the country4100.01967.0278040.0ENaN
2Argentina9.0Total area of the country4100.01972.0278040.0ENaN
3Argentina9.0Total area of the country4100.01977.0278040.0ENaN
4Argentina9.0Total area of the country4100.01982.0278040.0ENaN
5Argentina9.0Total area of the country4100.01987.0278040.0ENaN
6Argentina9.0Total area of the country4100.01992.0278040.0ENaN
7Argentina9.0Total area of the country4100.01997.0278040.0ENaN
8Argentina9.0Total area of the country4100.02002.0278040.0ENaN
9Argentina9.0Total area of the country4100.02007.0278040.0ENaN
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" + ], + "text/plain": [ + " Area Area Id Variable Name Variable Id Year \\\n", + "0 Argentina 9.0 Total area of the country 4100.0 1962.0 \n", + "1 Argentina 9.0 Total area of the country 4100.0 1967.0 \n", + "2 Argentina 9.0 Total area of the country 4100.0 1972.0 \n", + "3 Argentina 9.0 Total area of the country 4100.0 1977.0 \n", + "4 Argentina 9.0 Total area of the country 4100.0 1982.0 \n", + "5 Argentina 9.0 Total area of the country 4100.0 1987.0 \n", + "6 Argentina 9.0 Total area of the country 4100.0 1992.0 \n", + "7 Argentina 9.0 Total area of the country 4100.0 1997.0 \n", + "8 Argentina 9.0 Total area of the country 4100.0 2002.0 \n", + "9 Argentina 9.0 Total area of the country 4100.0 2007.0 \n", + "\n", + " Value Symbol Md \n", + "0 278040.0 E NaN \n", + "1 278040.0 E NaN \n", + "2 278040.0 E NaN \n", + "3 278040.0 E NaN \n", + "4 278040.0 E NaN \n", + "5 278040.0 E NaN \n", + "6 278040.0 E NaN \n", + "7 278040.0 E NaN \n", + "8 278040.0 E NaN \n", + "9 278040.0 E NaN " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"data3.csv\")\n", + "df.head(n=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display the column names." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Area', 'Area Id', 'Variable Name', 'Variable Id', 'Year', 'Value',\n", + " 'Symbol', 'Md'],\n", + " dtype='object')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a mask of NAN values (i.e. apply `.isnull` on the dataframe). Inspect the mask for 'True' values, they denote NANs. \n", + "*Hint: You will notice that the last 8 rows and the last column ('Other') have NAN values. You can also use df.tail() to see the last row.*\n", + "\n", + "Remove the bottom 8 rows from the dataframe because they contain NAN values. Also remove the column 'Other'." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaArea IdVariable NameVariable IdYearValueSymbol
385United States of America231.0National Rainfall Index (NRI)4472.01981.0949.2E
386United States of America231.0National Rainfall Index (NRI)4472.01984.0974.6E
387United States of America231.0National Rainfall Index (NRI)4472.01992.01020.0E
388United States of America231.0National Rainfall Index (NRI)4472.01996.01005.0E
389United States of America231.0National Rainfall Index (NRI)4472.02002.0938.7E
\n", + "
" + ], + "text/plain": [ + " Area Area Id Variable Name \\\n", + "385 United States of America 231.0 National Rainfall Index (NRI) \n", + "386 United States of America 231.0 National Rainfall Index (NRI) \n", + "387 United States of America 231.0 National Rainfall Index (NRI) \n", + "388 United States of America 231.0 National Rainfall Index (NRI) \n", + "389 United States of America 231.0 National Rainfall Index (NRI) \n", + "\n", + " Variable Id Year Value Symbol \n", + "385 4472.0 1981.0 949.2 E \n", + "386 4472.0 1984.0 974.6 E \n", + "387 4472.0 1992.0 1020.0 E \n", + "388 4472.0 1996.0 1005.0 E \n", + "389 4472.0 2002.0 938.7 E " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = df.isnull()\n", + "newdf=df.head(-8)\n", + "del newdf['Md']\n", + "newdf.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the columns in our dataframe are not required for analysis. Drop these columns: `Area Id`, `Variable Id`, and `Symbol` and save the new dataframe as `df1`. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaVariable NameYearValue
0ArgentinaTotal area of the country1962.02.780400e+05
1ArgentinaTotal area of the country1967.02.780400e+05
2ArgentinaTotal area of the country1972.02.780400e+05
3ArgentinaTotal area of the country1977.02.780400e+05
4ArgentinaTotal area of the country1982.02.780400e+05
5ArgentinaTotal area of the country1987.02.780400e+05
6ArgentinaTotal area of the country1992.02.780400e+05
7ArgentinaTotal area of the country1997.02.780400e+05
8ArgentinaTotal area of the country2002.02.780400e+05
9ArgentinaTotal area of the country2007.02.780400e+05
10ArgentinaTotal area of the country2012.02.780400e+05
11ArgentinaTotal area of the country2014.02.780400e+05
12ArgentinaTotal population1962.02.128800e+04
13ArgentinaTotal population1967.02.293200e+04
14ArgentinaTotal population1972.02.478300e+04
15ArgentinaTotal population1977.02.687900e+04
16ArgentinaTotal population1982.02.899400e+04
17ArgentinaTotal population1987.03.132600e+04
18ArgentinaTotal population1992.03.365500e+04
19ArgentinaTotal population1997.03.583400e+04
20ArgentinaTotal population2002.03.788900e+04
21ArgentinaTotal population2007.03.997000e+04
22ArgentinaTotal population2012.04.209500e+04
23ArgentinaTotal population2015.04.341700e+04
24ArgentinaPopulation density1962.07.656000e+00
25ArgentinaPopulation density1967.08.248000e+00
26ArgentinaPopulation density1972.08.913000e+00
27ArgentinaPopulation density1977.09.667000e+00
28ArgentinaPopulation density1982.01.043000e+01
29ArgentinaPopulation density1987.01.127000e+01
...............
360United States of AmericaPopulation density1972.02.214000e+01
361United States of AmericaPopulation density1977.02.317000e+01
362United States of AmericaPopulation density1982.02.430000e+01
363United States of AmericaPopulation density1987.02.549000e+01
364United States of AmericaPopulation density1992.02.678000e+01
365United States of AmericaPopulation density1997.02.834000e+01
366United States of AmericaPopulation density2002.02.995000e+01
367United States of AmericaPopulation density2007.03.132000e+01
368United States of AmericaPopulation density2012.03.202000e+01
369United States of AmericaPopulation density2015.03.273000e+01
370United States of AmericaGross Domestic Product (GDP)1962.06.050000e+11
371United States of AmericaGross Domestic Product (GDP)1967.08.620000e+11
372United States of AmericaGross Domestic Product (GDP)1972.01.280000e+12
373United States of AmericaGross Domestic Product (GDP)1977.02.090000e+12
374United States of AmericaGross Domestic Product (GDP)1982.03.340000e+12
375United States of AmericaGross Domestic Product (GDP)1987.04.870000e+12
376United States of AmericaGross Domestic Product (GDP)1992.06.540000e+12
377United States of AmericaGross Domestic Product (GDP)1997.08.610000e+12
378United States of AmericaGross Domestic Product (GDP)2002.01.100000e+13
379United States of AmericaGross Domestic Product (GDP)2007.01.450000e+13
380United States of AmericaGross Domestic Product (GDP)2012.01.620000e+13
381United States of AmericaGross Domestic Product (GDP)2015.01.790000e+13
382United States of AmericaNational Rainfall Index (NRI)1965.09.285000e+02
383United States of AmericaNational Rainfall Index (NRI)1969.09.522000e+02
384United States of AmericaNational Rainfall Index (NRI)1974.01.008000e+03
385United States of AmericaNational Rainfall Index (NRI)1981.09.492000e+02
386United States of AmericaNational Rainfall Index (NRI)1984.09.746000e+02
387United States of AmericaNational Rainfall Index (NRI)1992.01.020000e+03
388United States of AmericaNational Rainfall Index (NRI)1996.01.005000e+03
389United States of AmericaNational Rainfall Index (NRI)2002.09.387000e+02
\n", + "

390 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Area Variable Name Year \\\n", + "0 Argentina Total area of the country 1962.0 \n", + "1 Argentina Total area of the country 1967.0 \n", + "2 Argentina Total area of the country 1972.0 \n", + "3 Argentina Total area of the country 1977.0 \n", + "4 Argentina Total area of the country 1982.0 \n", + "5 Argentina Total area of the country 1987.0 \n", + "6 Argentina Total area of the country 1992.0 \n", + "7 Argentina Total area of the country 1997.0 \n", + "8 Argentina Total area of the country 2002.0 \n", + "9 Argentina Total area of the country 2007.0 \n", + "10 Argentina Total area of the country 2012.0 \n", + "11 Argentina Total area of the country 2014.0 \n", + "12 Argentina Total population 1962.0 \n", + "13 Argentina Total population 1967.0 \n", + "14 Argentina Total population 1972.0 \n", + "15 Argentina Total population 1977.0 \n", + "16 Argentina Total population 1982.0 \n", + "17 Argentina Total population 1987.0 \n", + "18 Argentina Total population 1992.0 \n", + "19 Argentina Total population 1997.0 \n", + "20 Argentina Total population 2002.0 \n", + "21 Argentina Total population 2007.0 \n", + "22 Argentina Total population 2012.0 \n", + "23 Argentina Total population 2015.0 \n", + "24 Argentina Population density 1962.0 \n", + "25 Argentina Population density 1967.0 \n", + "26 Argentina Population density 1972.0 \n", + "27 Argentina Population density 1977.0 \n", + "28 Argentina Population density 1982.0 \n", + "29 Argentina Population density 1987.0 \n", + ".. ... ... ... \n", + "360 United States of America Population density 1972.0 \n", + "361 United States of America Population density 1977.0 \n", + "362 United States of America Population density 1982.0 \n", + "363 United States of America Population density 1987.0 \n", + "364 United States of America Population density 1992.0 \n", + "365 United States of America Population density 1997.0 \n", + "366 United States of America Population density 2002.0 \n", + "367 United States of America Population density 2007.0 \n", + "368 United States of America Population density 2012.0 \n", + "369 United States of America Population density 2015.0 \n", + "370 United States of America Gross Domestic Product (GDP) 1962.0 \n", + "371 United States of America Gross Domestic Product (GDP) 1967.0 \n", + "372 United States of America Gross Domestic Product (GDP) 1972.0 \n", + "373 United States of America Gross Domestic Product (GDP) 1977.0 \n", + "374 United States of America Gross Domestic Product (GDP) 1982.0 \n", + "375 United States of America Gross Domestic Product (GDP) 1987.0 \n", + "376 United States of America Gross Domestic Product (GDP) 1992.0 \n", + "377 United States of America Gross Domestic Product (GDP) 1997.0 \n", + "378 United States of America Gross Domestic Product (GDP) 2002.0 \n", + "379 United States of America Gross Domestic Product (GDP) 2007.0 \n", + "380 United States of America Gross Domestic Product (GDP) 2012.0 \n", + "381 United States of America Gross Domestic Product (GDP) 2015.0 \n", + "382 United States of America National Rainfall Index (NRI) 1965.0 \n", + "383 United States of America National Rainfall Index (NRI) 1969.0 \n", + "384 United States of America National Rainfall Index (NRI) 1974.0 \n", + "385 United States of America National Rainfall Index (NRI) 1981.0 \n", + "386 United States of America National Rainfall Index (NRI) 1984.0 \n", + "387 United States of America National Rainfall Index (NRI) 1992.0 \n", + "388 United States of America National Rainfall Index (NRI) 1996.0 \n", + "389 United States of America National Rainfall Index (NRI) 2002.0 \n", + "\n", + " Value \n", + "0 2.780400e+05 \n", + "1 2.780400e+05 \n", + "2 2.780400e+05 \n", + "3 2.780400e+05 \n", + "4 2.780400e+05 \n", + "5 2.780400e+05 \n", + "6 2.780400e+05 \n", + "7 2.780400e+05 \n", + "8 2.780400e+05 \n", + "9 2.780400e+05 \n", + "10 2.780400e+05 \n", + "11 2.780400e+05 \n", + "12 2.128800e+04 \n", + "13 2.293200e+04 \n", + "14 2.478300e+04 \n", + "15 2.687900e+04 \n", + "16 2.899400e+04 \n", + "17 3.132600e+04 \n", + "18 3.365500e+04 \n", + "19 3.583400e+04 \n", + "20 3.788900e+04 \n", + "21 3.997000e+04 \n", + "22 4.209500e+04 \n", + "23 4.341700e+04 \n", + "24 7.656000e+00 \n", + "25 8.248000e+00 \n", + "26 8.913000e+00 \n", + "27 9.667000e+00 \n", + "28 1.043000e+01 \n", + "29 1.127000e+01 \n", + ".. ... \n", + "360 2.214000e+01 \n", + "361 2.317000e+01 \n", + "362 2.430000e+01 \n", + "363 2.549000e+01 \n", + "364 2.678000e+01 \n", + "365 2.834000e+01 \n", + "366 2.995000e+01 \n", + "367 3.132000e+01 \n", + "368 3.202000e+01 \n", + "369 3.273000e+01 \n", + "370 6.050000e+11 \n", + "371 8.620000e+11 \n", + "372 1.280000e+12 \n", + "373 2.090000e+12 \n", + "374 3.340000e+12 \n", + "375 4.870000e+12 \n", + "376 6.540000e+12 \n", + "377 8.610000e+12 \n", + "378 1.100000e+13 \n", + "379 1.450000e+13 \n", + "380 1.620000e+13 \n", + "381 1.790000e+13 \n", + "382 9.285000e+02 \n", + "383 9.522000e+02 \n", + "384 1.008000e+03 \n", + "385 9.492000e+02 \n", + "386 9.746000e+02 \n", + "387 1.020000e+03 \n", + "388 1.005000e+03 \n", + "389 9.387000e+02 \n", + "\n", + "[390 rows x 4 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = newdf.drop(['Area Id','Variable Id','Symbol'],axis=1)\n", + "\n", + "df1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display all the unique values in your new dataframe for these columns: `Area`, `Variable Name`, and `Year`. \n", + "\n", + "Note the Countries and the Metrics (ie.recorded variables) represented in your dataset.\n", + "*Hint: Use .unique( ) method.*" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Argentina' 'Australia' 'Germany' 'Iceland' 'Ireland' 'Sweden'\n", + " 'United States of America']\n", + "[ 1962. 1967. 1972. 1977. 1982. 1987. 1992. 1997. 2002. 2007.\n", + " 2012. 2014. 2015. 1963. 1970. 1974. 1978. 1984. 1990. 1964.\n", + " 1981. 1985. 1996. 2001. 1969. 1973. 1979. 1993. 1971. 1975.\n", + " 1986. 1991. 1998. 2000. 1965. 1983. 1988. 1995.]\n", + "['Total area of the country' 'Total population' 'Population density'\n", + " 'Gross Domestic Product (GDP)' 'National Rainfall Index (NRI)']\n" + ] + } + ], + "source": [ + "print(newdf['Area'].unique())\n", + "print(newdf['Year'].unique())\n", + "print(newdf['Variable Name'].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the `Year` column string values to pandas datetime objects, where only the year is specified. \n", + "*Hint: df1['Year'] = pd.to_datetime(pd.Series(df1['Year']).astype(int),format='%Y').dt.year*\n", + "\n", + "Run df1.tail() to see part of the result." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaVariable NameYearValue
385United States of AmericaNational Rainfall Index (NRI)1981949.2
386United States of AmericaNational Rainfall Index (NRI)1984974.6
387United States of AmericaNational Rainfall Index (NRI)19921020.0
388United States of AmericaNational Rainfall Index (NRI)19961005.0
389United States of AmericaNational Rainfall Index (NRI)2002938.7
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" + ], + "text/plain": [ + " Area Variable Name Year Value\n", + "385 United States of America National Rainfall Index (NRI) 1981 949.2\n", + "386 United States of America National Rainfall Index (NRI) 1984 974.6\n", + "387 United States of America National Rainfall Index (NRI) 1992 1020.0\n", + "388 United States of America National Rainfall Index (NRI) 1996 1005.0\n", + "389 United States of America National Rainfall Index (NRI) 2002 938.7" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1['Year'] = pd.to_datetime(pd.Series(df1['Year']).astype(int),format='%Y').dt.year\n", + "df1.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extracting Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a dataframe 'dftemp' to store rows where the `Area` is `Iceland`." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaVariable NameYearValue
166IcelandTotal area of the country196210300.0
167IcelandTotal area of the country196710300.0
168IcelandTotal area of the country197210300.0
169IcelandTotal area of the country197710300.0
170IcelandTotal area of the country198210300.0
171IcelandTotal area of the country198710300.0
172IcelandTotal area of the country199210300.0
173IcelandTotal area of the country199710300.0
174IcelandTotal area of the country200210300.0
175IcelandTotal area of the country200710300.0
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" + ], + "text/plain": [ + " Area Variable Name Year Value\n", + "166 Iceland Total area of the country 1962 10300.0\n", + "167 Iceland Total area of the country 1967 10300.0\n", + "168 Iceland Total area of the country 1972 10300.0\n", + "169 Iceland Total area of the country 1977 10300.0\n", + "170 Iceland Total area of the country 1982 10300.0\n", + "171 Iceland Total area of the country 1987 10300.0\n", + "172 Iceland Total area of the country 1992 10300.0\n", + "173 Iceland Total area of the country 1997 10300.0\n", + "174 Iceland Total area of the country 2002 10300.0\n", + "175 Iceland Total area of the country 2007 10300.0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dftemp = df1[(df1['Area'] == \"Iceland\")]\n", + "#dftemp = df1.where(df1['Area'] == 'Iceland') ...... where command keeps array size same so not used here\n", + "dftemp.head(10)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the years when the National Rainfall Index (NRI) was `> 950` or `< 900` in Iceland using the dataframe you created in the previous question." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaVariable NameYearValue
214IcelandNational Rainfall Index (NRI)1967816.0
215IcelandNational Rainfall Index (NRI)1971963.2
216IcelandNational Rainfall Index (NRI)19751010.0
218IcelandNational Rainfall Index (NRI)1986968.5
219IcelandNational Rainfall Index (NRI)19911095.0
220IcelandNational Rainfall Index (NRI)1997993.2
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" + ], + "text/plain": [ + " Area Variable Name Year Value\n", + "214 Iceland National Rainfall Index (NRI) 1967 816.0\n", + "215 Iceland National Rainfall Index (NRI) 1971 963.2\n", + "216 Iceland National Rainfall Index (NRI) 1975 1010.0\n", + "218 Iceland National Rainfall Index (NRI) 1986 968.5\n", + "219 Iceland National Rainfall Index (NRI) 1991 1095.0\n", + "220 Iceland National Rainfall Index (NRI) 1997 993.2" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nri = dftemp[(dftemp['Variable Name'] == \"National Rainfall Index (NRI)\")]\n", + "nri = nri[(nri.Value>950) |(nri.Value<900) ]\n", + "nri.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get all the rows of df1 (from the preprocessed data section of this notebook) where the `Area` is `United States of America` and store that into a new dataframe called `df_usa`. Set the indices of the this dataframe to be the `Year` column. \n", + "*Hint: Use .set_index()*" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AreaVariable NameValue
Year
1962United States of AmericaTotal area of the country962909.0
1967United States of AmericaTotal area of the country962909.0
1972United States of AmericaTotal area of the country962909.0
1977United States of AmericaTotal area of the country962909.0
1982United States of AmericaTotal area of the country962909.0
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" + ], + "text/plain": [ + " Area Variable Name Value\n", + "Year \n", + "1962 United States of America Total area of the country 962909.0\n", + "1967 United States of America Total area of the country 962909.0\n", + "1972 United States of America Total area of the country 962909.0\n", + "1977 United States of America Total area of the country 962909.0\n", + "1982 United States of America Total area of the country 962909.0" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_usa = df1[(df1['Area'] == \"United States of America\")].set_index('Year')\n", + "df_usa.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pivot the dataframe so that the unique `Variable Name` entries become the column entries. The dataframe values should be the ones in the `Value` column. Do this by running the lines of code below." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Variable NameGross Domestic Product (GDP)National Rainfall Index (NRI)Population densityTotal area of the countryTotal population
Year
19626.050000e+11NaN19.93962909.0191861.0
1965NaN928.5NaNNaNNaN
19678.620000e+11NaN21.16962909.0203713.0
1969NaN952.2NaNNaNNaN
19721.280000e+12NaN22.14962909.0213220.0
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" + ], + "text/plain": [ + "Variable Name Gross Domestic Product (GDP) National Rainfall Index (NRI) \\\n", + "Year \n", + "1962 6.050000e+11 NaN \n", + "1965 NaN 928.5 \n", + "1967 8.620000e+11 NaN \n", + "1969 NaN 952.2 \n", + "1972 1.280000e+12 NaN \n", + "\n", + "Variable Name Population density Total area of the country Total population \n", + "Year \n", + "1962 19.93 962909.0 191861.0 \n", + "1965 NaN NaN NaN \n", + "1967 21.16 962909.0 203713.0 \n", + "1969 NaN NaN NaN \n", + "1972 22.14 962909.0 213220.0 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_usa=df_usa.pivot(columns='Variable Name',values='Value')\n", + "df_usa.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rename the corresponding columns to ['GDP','NRI','PD','Area','Population']." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Variable NameGDPNRIPDAreaPopulation
Year
19626.050000e+11NaN19.93962909.0191861.0
1965NaN928.5NaNNaNNaN
19678.620000e+11NaN21.16962909.0203713.0
1969NaN952.2NaNNaNNaN
19721.280000e+12NaN22.14962909.0213220.0
1974NaN1008.0NaNNaNNaN
19772.090000e+12NaN23.17962909.0223091.0
1981NaN949.2NaNNaNNaN
19823.340000e+12NaN24.30962909.0233954.0
1984NaN974.6NaNNaNNaN
19874.870000e+12NaN25.49962909.0245425.0
19926.540000e+121020.026.78962909.0257908.0
1996NaN1005.0NaNNaNNaN
19978.610000e+12NaN28.34962909.0272883.0
20021.100000e+13938.729.95963203.0288471.0
20071.450000e+13NaN31.32963203.0301656.0
20121.620000e+13NaN32.02983151.0314799.0
2014NaNNaNNaN983151.0NaN
20151.790000e+13NaN32.73NaN321774.0
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" + ], + "text/plain": [ + "Variable Name GDP NRI PD Area Population\n", + "Year \n", + "1962 6.050000e+11 NaN 19.93 962909.0 191861.0\n", + "1965 NaN 928.5 NaN NaN NaN\n", + "1967 8.620000e+11 NaN 21.16 962909.0 203713.0\n", + "1969 NaN 952.2 NaN NaN NaN\n", + "1972 1.280000e+12 NaN 22.14 962909.0 213220.0\n", + "1974 NaN 1008.0 NaN NaN NaN\n", + "1977 2.090000e+12 NaN 23.17 962909.0 223091.0\n", + "1981 NaN 949.2 NaN NaN NaN\n", + "1982 3.340000e+12 NaN 24.30 962909.0 233954.0\n", + "1984 NaN 974.6 NaN NaN NaN\n", + "1987 4.870000e+12 NaN 25.49 962909.0 245425.0\n", + "1992 6.540000e+12 1020.0 26.78 962909.0 257908.0\n", + "1996 NaN 1005.0 NaN NaN NaN\n", + "1997 8.610000e+12 NaN 28.34 962909.0 272883.0\n", + "2002 1.100000e+13 938.7 29.95 963203.0 288471.0\n", + "2007 1.450000e+13 NaN 31.32 963203.0 301656.0\n", + "2012 1.620000e+13 NaN 32.02 983151.0 314799.0\n", + "2014 NaN NaN NaN 983151.0 NaN\n", + "2015 1.790000e+13 NaN 32.73 NaN 321774.0" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_usa=df_usa.rename(columns={'Gross Domestic Product (GDP)':'GDP'})\n", + "df_usa=df_usa.rename(columns={'National Rainfall Index (NRI)':'NRI'})\n", + "df_usa=df_usa.rename(columns={'Population density':'PD'})\n", + "df_usa=df_usa.rename(columns={'Total area of the country':'Area'})\n", + "df_usa=df_usa.rename(columns={'Total population':'Population'})\n", + "df_usa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the output of `df_usa.isnull().sum()`. This gives us the number of NAN values in each column. Replace the NAN values by 0, using `df_usa=df_usa.fillna(0)`. Print the output of `df_usa.isnull().sum()` again." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of NAN values before: Variable Name\n", + "GDP 7\n", + "NRI 11\n", + "PD 7\n", + "Area 7\n", + "Population 7\n", + "dtype: int64\n", + "Number of NAN values after: Variable Name\n", + "GDP 0\n", + "NRI 0\n", + "PD 0\n", + "Area 0\n", + "Population 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(\"Number of NAN values before: \", df_usa.isnull().sum())\n", + "df_usa=df_usa.fillna(0)\n", + "print(\"Number of NAN values after: \", df_usa.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate and print all the column averages and the column standard deviations." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---averages---\n", + "Variable Name\n", + "GDP 4.620895e+12\n", + "NRI 4.092737e+02\n", + "PD 1.670158e+01\n", + "Area 6.103147e+05\n", + "Population 1.615134e+05\n", + "dtype: float64\n", + "---SD---\n", + "Variable Name\n", + "GDP 6.088656e+12\n", + "NRI 4.935515e+02\n", + "PD 1.355462e+01\n", + "Area 4.789482e+05\n", + "Population 1.313805e+05\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "print('---averages---')\n", + "print(df_usa.mean())\n", + "print('---SD---')\n", + "print(df_usa.std())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `df_usa` dataframe, multiply the `Area` by 10 (so instead of 1000 ha, the unit becomes 100 ha = 1km^2). Store the result in place." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Variable NameGDPNRIPDAreaPopulation
Year
19626.050000e+110.019.939629090.0191861.0
19650.000000e+00928.50.000.00.0
19678.620000e+110.021.169629090.0203713.0
19690.000000e+00952.20.000.00.0
19721.280000e+120.022.149629090.0213220.0
19740.000000e+001008.00.000.00.0
19772.090000e+120.023.179629090.0223091.0
19810.000000e+00949.20.000.00.0
19823.340000e+120.024.309629090.0233954.0
19840.000000e+00974.60.000.00.0
19874.870000e+120.025.499629090.0245425.0
19926.540000e+121020.026.789629090.0257908.0
19960.000000e+001005.00.000.00.0
19978.610000e+120.028.349629090.0272883.0
20021.100000e+13938.729.959632030.0288471.0
20071.450000e+130.031.329632030.0301656.0
20121.620000e+130.032.029831510.0314799.0
20140.000000e+000.00.009831510.00.0
20151.790000e+130.032.730.0321774.0
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" + ], + "text/plain": [ + "Variable Name GDP NRI PD Area Population\n", + "Year \n", + "1962 6.050000e+11 0.0 19.93 9629090.0 191861.0\n", + "1965 0.000000e+00 928.5 0.00 0.0 0.0\n", + "1967 8.620000e+11 0.0 21.16 9629090.0 203713.0\n", + "1969 0.000000e+00 952.2 0.00 0.0 0.0\n", + "1972 1.280000e+12 0.0 22.14 9629090.0 213220.0\n", + "1974 0.000000e+00 1008.0 0.00 0.0 0.0\n", + "1977 2.090000e+12 0.0 23.17 9629090.0 223091.0\n", + "1981 0.000000e+00 949.2 0.00 0.0 0.0\n", + "1982 3.340000e+12 0.0 24.30 9629090.0 233954.0\n", + "1984 0.000000e+00 974.6 0.00 0.0 0.0\n", + "1987 4.870000e+12 0.0 25.49 9629090.0 245425.0\n", + "1992 6.540000e+12 1020.0 26.78 9629090.0 257908.0\n", + "1996 0.000000e+00 1005.0 0.00 0.0 0.0\n", + "1997 8.610000e+12 0.0 28.34 9629090.0 272883.0\n", + "2002 1.100000e+13 938.7 29.95 9632030.0 288471.0\n", + "2007 1.450000e+13 0.0 31.32 9632030.0 301656.0\n", + "2012 1.620000e+13 0.0 32.02 9831510.0 314799.0\n", + "2014 0.000000e+00 0.0 0.00 9831510.0 0.0\n", + "2015 1.790000e+13 0.0 32.73 0.0 321774.0" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_usa['Area'] = df_usa['Area']*10\n", + "df_usa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new column in `df_usa` called `GDP/capita` and populate it with the calculated GDP per capita. Round the results to two decimal points. Store the result in place." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in true_divide\n", + " app.launch_new_instance()\n" + ] + }, + { + "data": { + "text/html": [ + "
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Variable NameGDPNRIPDAreaPopulationGDP/capita
Year
19626.050000e+110.019.939629090.0191861.03153.32
19650.000000e+00928.50.000.00.0NaN
19678.620000e+110.021.169629090.0203713.04231.44
19690.000000e+00952.20.000.00.0NaN
19721.280000e+120.022.149629090.0213220.06003.19
19740.000000e+001008.00.000.00.0NaN
19772.090000e+120.023.179629090.0223091.09368.37
19810.000000e+00949.20.000.00.0NaN
19823.340000e+120.024.309629090.0233954.014276.31
19840.000000e+00974.60.000.00.0NaN
19874.870000e+120.025.499629090.0245425.019843.13
19926.540000e+121020.026.789629090.0257908.025357.88
19960.000000e+001005.00.000.00.0NaN
19978.610000e+120.028.349629090.0272883.031551.98
20021.100000e+13938.729.959632030.0288471.038132.08
20071.450000e+130.031.329632030.0301656.048068.00
20121.620000e+130.032.029831510.0314799.051461.41
20140.000000e+000.00.009831510.00.0NaN
20151.790000e+130.032.730.0321774.055629.11
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" + ], + "text/plain": [ + "Variable Name GDP NRI PD Area Population GDP/capita\n", + "Year \n", + "1962 6.050000e+11 0.0 19.93 9629090.0 191861.0 3153.32\n", + "1965 0.000000e+00 928.5 0.00 0.0 0.0 NaN\n", + "1967 8.620000e+11 0.0 21.16 9629090.0 203713.0 4231.44\n", + "1969 0.000000e+00 952.2 0.00 0.0 0.0 NaN\n", + "1972 1.280000e+12 0.0 22.14 9629090.0 213220.0 6003.19\n", + "1974 0.000000e+00 1008.0 0.00 0.0 0.0 NaN\n", + "1977 2.090000e+12 0.0 23.17 9629090.0 223091.0 9368.37\n", + "1981 0.000000e+00 949.2 0.00 0.0 0.0 NaN\n", + "1982 3.340000e+12 0.0 24.30 9629090.0 233954.0 14276.31\n", + "1984 0.000000e+00 974.6 0.00 0.0 0.0 NaN\n", + "1987 4.870000e+12 0.0 25.49 9629090.0 245425.0 19843.13\n", + "1992 6.540000e+12 1020.0 26.78 9629090.0 257908.0 25357.88\n", + "1996 0.000000e+00 1005.0 0.00 0.0 0.0 NaN\n", + "1997 8.610000e+12 0.0 28.34 9629090.0 272883.0 31551.98\n", + "2002 1.100000e+13 938.7 29.95 9632030.0 288471.0 38132.08\n", + "2007 1.450000e+13 0.0 31.32 9632030.0 301656.0 48068.00\n", + "2012 1.620000e+13 0.0 32.02 9831510.0 314799.0 51461.41\n", + "2014 0.000000e+00 0.0 0.00 9831510.0 0.0 NaN\n", + "2015 1.790000e+13 0.0 32.73 0.0 321774.0 55629.11" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''g = df_usa['GDP'].values\n", + "print(g)\n", + "p= df_usa['Population'].values\n", + "print(p)\n", + "for i,value in enumerate(p):\n", + " print(i,value,g[i])\n", + " if value!=0:\n", + " df_usa['GDP/capita']= (g[i]/value)\n", + " else:\n", + " df_usa['GDP/capita']=0\n", + "df_usa''' #want to know why this is not working\n", + "\n", + "\n", + "g = df_usa['GDP'].values\n", + "p= df_usa['Population'].values\n", + "df_usa['GDP/capita']= g/(p*1000)\n", + "df_usa.round(decimals=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new column in `df_usa` called `PD2` (i.e. population density 2). Calculate the population density. **Note: the units should be inhab/km^2**. Round the reults to two decimal point. Store the result in place." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n", + "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, + { + "data": { + "text/html": [ + "
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Variable NameGDPNRIPDAreaPopulationGDP/capitaPD2
Year
19626.050000e+110.019.939629090.0191861.03153.3219.930000
19650.000000e+00928.50.000.00.0NaNNaN
19678.620000e+110.021.169629090.0203713.04231.4421.160000
19690.000000e+00952.20.000.00.0NaNNaN
19721.280000e+120.022.149629090.0213220.06003.1922.140000
19740.000000e+001008.00.000.00.0NaNNaN
19772.090000e+120.023.179629090.0223091.09368.3723.170000
19810.000000e+00949.20.000.00.0NaNNaN
19823.340000e+120.024.309629090.0233954.014276.3124.300000
19840.000000e+00974.60.000.00.0NaNNaN
19874.870000e+120.025.499629090.0245425.019843.1325.490000
19926.540000e+121020.026.789629090.0257908.025357.8826.780000
19960.000000e+001005.00.000.00.0NaNNaN
19978.610000e+120.028.349629090.0272883.031551.9828.340000
20021.100000e+13938.729.959632030.0288471.038132.0829.950000
20071.450000e+130.031.329632030.0301656.048068.0031.320000
20121.620000e+130.032.029831510.0314799.051461.4132.020000
20140.000000e+000.00.009831510.00.0NaN0.000000
20151.790000e+130.032.730.0321774.055629.11inf
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" + ], + "text/plain": [ + "Variable Name GDP NRI PD Area Population GDP/capita \\\n", + "Year \n", + "1962 6.050000e+11 0.0 19.93 9629090.0 191861.0 3153.32 \n", + "1965 0.000000e+00 928.5 0.00 0.0 0.0 NaN \n", + "1967 8.620000e+11 0.0 21.16 9629090.0 203713.0 4231.44 \n", + "1969 0.000000e+00 952.2 0.00 0.0 0.0 NaN \n", + "1972 1.280000e+12 0.0 22.14 9629090.0 213220.0 6003.19 \n", + "1974 0.000000e+00 1008.0 0.00 0.0 0.0 NaN \n", + "1977 2.090000e+12 0.0 23.17 9629090.0 223091.0 9368.37 \n", + "1981 0.000000e+00 949.2 0.00 0.0 0.0 NaN \n", + "1982 3.340000e+12 0.0 24.30 9629090.0 233954.0 14276.31 \n", + "1984 0.000000e+00 974.6 0.00 0.0 0.0 NaN \n", + "1987 4.870000e+12 0.0 25.49 9629090.0 245425.0 19843.13 \n", + "1992 6.540000e+12 1020.0 26.78 9629090.0 257908.0 25357.88 \n", + "1996 0.000000e+00 1005.0 0.00 0.0 0.0 NaN \n", + "1997 8.610000e+12 0.0 28.34 9629090.0 272883.0 31551.98 \n", + "2002 1.100000e+13 938.7 29.95 9632030.0 288471.0 38132.08 \n", + "2007 1.450000e+13 0.0 31.32 9632030.0 301656.0 48068.00 \n", + "2012 1.620000e+13 0.0 32.02 9831510.0 314799.0 51461.41 \n", + "2014 0.000000e+00 0.0 0.00 9831510.0 0.0 NaN \n", + "2015 1.790000e+13 0.0 32.73 0.0 321774.0 55629.11 \n", + "\n", + "Variable Name PD2 \n", + "Year \n", + "1962 19.930000 \n", + "1965 NaN \n", + "1967 21.160000 \n", + "1969 NaN \n", + "1972 22.140000 \n", + "1974 NaN \n", + "1977 23.170000 \n", + "1981 NaN \n", + "1982 24.300000 \n", + "1984 NaN \n", + "1987 25.490000 \n", + "1992 26.780000 \n", + "1996 NaN \n", + "1997 28.340000 \n", + "2002 29.950000 \n", + "2007 31.320000 \n", + "2012 32.020000 \n", + "2014 0.000000 \n", + "2015 inf " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p=df_usa['Population'].values\n", + "a = df_usa['Area'].values\n", + "df_usa['PD2']=(p*1000)/a\n", + "df_usa.round(decimals=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum value and minimum value of the 'NRI' column in the USA (using pandas methods). What years do the min and max values occur in?" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max NRI: 1020.0 in year: [1992]\n", + "min NRI: 0.0 in year: [1962 1967 1972 1977 1982 1987 1997 2007 2012 2014 2015]\n" + ] + } + ], + "source": [ + "a=df_usa['NRI'].max()\n", + "b=df_usa.index[df_usa['NRI']==a].values\n", + "c=df_usa['NRI'].min()\n", + "d=df_usa.index[df_usa['NRI']==c].values\n", + "print('max NRI:',a,'in year:',b)\n", + "print('min NRI:',c,'in year:',d)\n", + "\n", + "#for value in df_usa['NRI'].values:\n", + "# if value !=0:\n", + "# c=value.min()\n", + "#print(c) ### for finding min value excluding zeros" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a dataframe called `icecream` that has column `Flavor` with entries `Strawberry`, `Vanilla`, and `Chocolate` and another column with `Price` with entries `3.50`, `3.00`, and `4.25`. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FlavorPrice
0Strawberry3.50
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2Chocolate4.25
\n", + "
" + ], + "text/plain": [ + " Flavor Price\n", + "0 Strawberry 3.50\n", + "1 Vanilla 3.00\n", + "2 Chocolate 4.25" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d={'Flavor':['Strawberry', 'Vanilla', 'Chocolate'],'Price':[3.50,3.00,4.25]}\n", + "icecream = pd.DataFrame(data=d)\n", + "icecream" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a bar chart representing the three flavors and their associated prices." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(icecream.Flavor, icecream.Price)\n", + "plt.ylabel('Price')\n", + "plt.xlabel('Flavor')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create 9 random plots. The top three should be scatter plots (one with green dots, one with purple crosses, and one with blue triangles. The middle three graphs should be a line graph, a horizontal bar chart, and a histogram. The bottom three graphs should be trignometric functions (one sin, one cosine, one tangent)." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Z2UOSaiSNN7MGM7s1qGsflUP/dP2FmjLmdH3n0Q16/tVdPmIAx6Vh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\nRPe0JCb/a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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, ax = plt.subplots(nrows=3,ncols=3,figsize=(20,10))\n", + "\n", + "a=np.random.randn(10)\n", + "b=np.random.randn(10)\n", + "\n", + "ax[0,0].scatter(a,b,color='green',marker='.')\n", + "ax[0,1].scatter(a,b,color='purple',marker='x')\n", + "ax[0,2].scatter(a,b,color='blue',marker='^')\n", + "#---------------------------\n", + "ax[1,0].plot(a)\n", + "ax[1,1].barh(a,b)\n", + "ax[1,2].hist(a)\n", + "#---------------------------\n", + "x=np.linspace(0,2*np.pi,100)\n", + "ax[2,0].plot(np.sin(x))\n", + "ax[2,1].plot(np.cos(x))\n", + "ax[2,2].plot(np.tan(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Extra Credit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the cell below to read in the data. See: https://www.quantshare.com/sa-43-10-ways-to-download-historical-stock-quotes-data-for-free" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateOpenHighLowCloseVolume
08-Feb-18160.29161.00155.03155.1554390516
17-Feb-18163.08163.40159.07159.5451608580
26-Feb-18154.83163.72154.00163.0368243838
35-Feb-18159.10163.88156.00156.4972738522
42-Feb-18166.00166.80160.10160.5086593825
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" + ], + "text/plain": [ + " Date Open High Low Close Volume\n", + "0 8-Feb-18 160.29 161.00 155.03 155.15 54390516\n", + "1 7-Feb-18 163.08 163.40 159.07 159.54 51608580\n", + "2 6-Feb-18 154.83 163.72 154.00 163.03 68243838\n", + "3 5-Feb-18 159.10 163.88 156.00 156.49 72738522\n", + "4 2-Feb-18 166.00 166.80 160.10 160.50 86593825" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_google = pd.read_csv('https://finance.google.com/finance/historical?output=csv&q=goog')\n", + "df_apple = pd.read_csv('https://finance.google.com/finance/historical?output=csv&q=aapl')\n", + "\n", + "df_disney = pd.read_csv('https://finance.google.com/finance/historical?output=csv&q=dis')\n", + "df_nike= pd.read_csv('https://finance.google.com/finance/historical?output=csv&q=nke')\n", + "\n", + "df_apple.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show a 3 x 3 correlation matrix for Nike, Apple, and Disney stock prices for the month of July, 2017. \n", + "\n", + "Hint: Convert `Date` to a pandas datetime object. \n", + "Change the indices of all the dataframes to `Date`. Use `Date` indices to filter rows.\n", + "Create a new dataframe that stores values of the `Close` column from each dataframe.\n", + "Use the `Close` column of each company's stock data to find the correlation using df.corr()." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "################# ignore\n", + "'''# google\n", + "df_google.set_index(df_google.Date,inplace=True)\n", + "df_google.loc['31-Jul-17':'3-Jul-17', ['Close']]\n", + "#close_google.columns = [\"Google closing price\"]\n", + "#print(\"Google stock close: \\n\",close_google)\n", + "#year = df_google.loc['31-Jul-17':'3-Jul-17'].index\n", + "#g=pd.DataFrame({'Google':[close_google]})\n", + "\n", + "\n", + "# apple\n", + "df_apple.set_index(df_apple.Date,inplace=True)\n", + "df_apple.loc['31-Jul-17':'3-Jul-17', ['Close']]\n", + "#close_apple.columns = [\"Apple closing price\"]\n", + "#print(\"\\n Apple stock close: \\n\", close_apple)\n", + "#a=pd.DataFrame({'Apple':[close_apple]})\n", + "\n", + "\n", + "# disney\n", + "df_disney.set_index(df_disney.Date,inplace=True)\n", + "df_disney.loc['31-Jul-17':'3-Jul-17', ['Close']]\n", + "#close_disney.columns = [\"Disney closing price\"]\n", + "#print(\"\\n Disney stock close: \\n\", close_disney)\n", + "#ds=pd.DataFrame({'Disney':[close_disney]})\n", + "\n", + "\n", + "# nike\n", + "df_nike.set_index(df_nike.Date,inplace=True)\n", + "df_nike.loc['31-Jul-17':'3-Jul-17', ['Close']]\n", + "#close_nike.columns = [\"Nike closing price\"]\n", + "#print(\"\\n Nike stock close: \\n\", close_nike)\n", + "#n=pd.DataFrame({'Nike':[close_google]})\n", + "\n", + "stock_table=pd.concat[df_apple,df_nike,df_disney]\n", + "print(stock_table)\n", + "\n", + "\n", + "# correlation matrix of three 2017 dateframes'''\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "df_nike=df_nike.set_index(pd.to_datetime(df_nike.Date))\n", + "df_nike.drop('Date',axis=1,inplace=True)\n", + "del df_nike.index.name\n", + "\n", + "df_apple=df_apple.set_index(pd.to_datetime(df_apple.Date))\n", + "df_apple.drop('Date',axis=1,inplace=True)\n", + "del df_apple.index.name\n", + "\n", + "df_disney=df_disney.set_index(pd.to_datetime(df_disney.Date))\n", + "df_disney.drop('Date',axis=1,inplace=True)\n", + "del df_disney.index.name" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NikeAppleDisney
2018-02-0862.49155.15101.35
2018-02-0765.63159.54104.76
2018-02-0665.22163.03106.17
2018-02-0564.39156.49104.70
2018-02-0267.22160.50108.70
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" + ], + "text/plain": [ + " Nike Apple Disney\n", + "2018-02-08 62.49 155.15 101.35\n", + "2018-02-07 65.63 159.54 104.76\n", + "2018-02-06 65.22 163.03 106.17\n", + "2018-02-05 64.39 156.49 104.70\n", + "2018-02-02 67.22 160.50 108.70" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "closing_values=pd.concat([df_nike.Close,df_apple.Close,df_disney.Close],axis=1)\n", + "closing_values.columns=['Nike','Apple','Disney']\n", + "closing_values.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "closing_values[\"2017-17\"].corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show the same correlation matrix but over different time periods.\n", + "1. the last 20 days \n", + "2. the last 80 days" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change the code so that it accepts a list of any stock symbols (i.e. ['NKE', 'APPL', 'DIS', ... ]) and creates a correlation matrix for the past 100 days." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Energy.csv b/Energy.csv new file mode 100644 index 0000000..a295089 --- /dev/null +++ b/Energy.csv 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0.9,563.5,318.5,122.5,7,5,0.4,5,35.24 0.86,588,294,147,7,2,0.4,5,32.31 0.86,588,294,147,7,3,0.4,5,31.81 0.86,588,294,147,7,4,0.4,5,31.12 0.86,588,294,147,7,5,0.4,5,32.06 0.82,612.5,318.5,147,7,2,0.4,5,30 0.82,612.5,318.5,147,7,3,0.4,5,29.5 0.82,612.5,318.5,147,7,4,0.4,5,29.06 0.82,612.5,318.5,147,7,5,0.4,5,29.92 0.79,637,343,147,7,2,0.4,5,42.11 0.79,637,343,147,7,3,0.4,5,41.96 0.79,637,343,147,7,4,0.4,5,41.09 0.79,637,343,147,7,5,0.4,5,40.79 0.76,661.5,416.5,122.5,7,2,0.4,5,38.82 0.76,661.5,416.5,122.5,7,3,0.4,5,39.72 0.76,661.5,416.5,122.5,7,4,0.4,5,39.31 0.76,661.5,416.5,122.5,7,5,0.4,5,39.86 0.74,686,245,220.5,3.5,2,0.4,5,14.41 0.74,686,245,220.5,3.5,3,0.4,5,14.19 0.74,686,245,220.5,3.5,4,0.4,5,14.17 0.74,686,245,220.5,3.5,5,0.4,5,14.39 0.71,710.5,269.5,220.5,3.5,2,0.4,5,12.43 0.71,710.5,269.5,220.5,3.5,3,0.4,5,12.63 0.71,710.5,269.5,220.5,3.5,4,0.4,5,12.76 0.71,710.5,269.5,220.5,3.5,5,0.4,5,12.42 0.69,735,294,220.5,3.5,2,0.4,5,14.12 0.69,735,294,220.5,3.5,3,0.4,5,14.28 0.69,735,294,220.5,3.5,4,0.4,5,14.37 0.69,735,294,220.5,3.5,5,0.4,5,14.21 0.66,759.5,318.5,220.5,3.5,2,0.4,5,14.96 0.66,759.5,318.5,220.5,3.5,3,0.4,5,14.92 0.66,759.5,318.5,220.5,3.5,4,0.4,5,14.92 0.66,759.5,318.5,220.5,3.5,5,0.4,5,15.16 0.64,784,343,220.5,3.5,2,0.4,5,17.69 0.64,784,343,220.5,3.5,3,0.4,5,18.19 0.64,784,343,220.5,3.5,4,0.4,5,18.16 0.64,784,343,220.5,3.5,5,0.4,5,17.88 0.62,808.5,367.5,220.5,3.5,2,0.4,5,16.54 0.62,808.5,367.5,220.5,3.5,3,0.4,5,16.44 0.62,808.5,367.5,220.5,3.5,4,0.4,5,16.48 0.62,808.5,367.5,220.5,3.5,5,0.4,5,16.64 \ No newline at end of file diff --git a/Eye_Color.csv b/Eye_Color.csv new file mode 100644 index 0000000..50c00f0 --- /dev/null +++ b/Eye_Color.csv @@ -0,0 +1,2069 @@ +gender,age,year,eyecolor,height,miles,brothers,sisters,computertime,exercise,exercisehours,musiccds,playgames,watchtv +female,18,first,hazel,68,195,0,1,20,Yes,3,75,6,18 +male,20,third,brown,70,120,3,0,24,No,0,50,0,3 +female,18,first,green,67,200,0,1,35,Yes,3,53,8,1 +male,23,fourth,hazel,74,140,1,1,5,Yes,25,50,0,7 +female,19,second,blue,62,60,0,1,5,Yes,4,30,2,5 +male,19,second,green,67,0,0,1,5,Yes,8,100,0,10 +male,37,third,brown,74,511,1,1,3,No,0,50,3,8 +male,22,third,brown,73,210,3,2,2,Yes,10,100,6,10 +female,26,fourth,brown,70,120,2,1,5,No,0,130,0,20 +male,21,third,blue,68,90,1,1,5,Yes,3,34,0.5,5 +male,25,fourth,blue,69,90,0,3,3,Yes,6,55,0,21 +female,20,second,blue,70,93,0,1,15,Yes,7,60,1,1 +male,29,other,brown,69,244,1,1,5,No,0,100,20,20 +female,19,second,green,65,40,1,0,6,Yes,4,175,0,12 +male,19,first,blue,70,40,0,0,5,No,0,150,3,2 +male,22,other,blue,73,90,0,1,15,Yes,10,35,20,4 +female,20,second,brown,64,100,2,1,9,No,0,30,10,8 +male,21,third,brown,70,124,1,2,4,Yes,6,5,1,5 +male,20,third,blue,70,400,0,0,30,Yes,4,50,5,1 +female,19,second,hazel,63,206,1,1,10,No,0,20,20,15 +male,20,third,hazel,70,184,2,0,20,No,0,40,10,20 +female,19,second,blue,69,240,1,1,14,No,0,15,1,10 +female,19,first,hazel,65,230,1,0,12.5,Yes,9,30,0,4.5 +female,19,second,blue,64,0,1,0,6,Yes,5,52,0,0 +male,18,first,green,58,175,1,0,10,No,0,40,5,5 +male,19,second,blue,67,60,4,1,5,Yes,2,50,10,12 +male,23,fourth,brown,74,160,2,1,20,No,0,60,15,40 +male,24,other,blue,72,180,2,2,2,Yes,7,100,7,21 +female,20,second,brown,64,0,0,2,3,Yes,3,50,0,10 +male,21,third,green,70,100,1,1,6,Yes,5,20,0,10 +female,22,fourth,brown,66,300,1,0,1,Yes,1,50,1,1 +male,22,fourth,blue,72,0,0,2,10,Yes,6,250,0,25 +female,19,second,brown,67,180,0,2,40,Yes,5,70,1,7 +female,21,third,blue,63,0,2,3,10,No,0,50,0.5,8 +male,21,third,green,70,80,0,2,10,No,0,50,10,30 +male,22,fourth,green,74,120,2,1,10,No,0,35,1,5 +male,20,third,other,71,90,1,0,15,Yes,4,10,0,1 +male,21,third,green,71,220,1,0,18,Yes,30,20,0,21 +male,21,third,brown,71,190,1,0,10,Yes,5,25,1,10 +female,19,second,blue,64,200,0,1,5,Yes,3,130,0,8 +male,18,first,green,71,130,2,1,2,No,0,15,2,25 +male,20,second,blue,72,120,0,1,4,Yes,10,56,12,8 +male,21,third,brown,70,157,0,1,15,No,0,50,5,28 +female,21,fourth,green,69,230,1,0,10,Yes,8,215,2,10 +male,19,first,brown,67,75,2,0,10,No,0,0,12,12 +male,22,fourth,hazel,70,200,0,2,5,No,0,5,5,20 +male,20,second,green,67,109,1,1,5,No,0,80,0,1 +female,20,second,blue,62,200,1,0,12,Yes,1,300,0,10 +male,21,third,brown,66,0,1,2,5,Yes,5,0,10,30 +male,20,third,hazel,69,50,2,0,8,Yes,2.5,250,0,5 +female,20,second,hazel,67,600,1,0,15,Yes,4,140,0,15 +female,19,second,brown,62,40,1,0,15,Yes,8,25,0,13 +female,22,fourth,brown,65,5,1,0,1,Yes,6,50,0,3 +male,20,third,hazel,72,80,1,1,2.5,Yes,5.5,10,5.5,14 +male,21,third,hazel,70,150,1,0,2,Yes,10,100,15,7 +female,19,second,brown,62,30,4,2,10,No,0,10,0,20 +male,21,fourth,blue,69,140,1,0,5,No,0,100,0,2 +female,21,fourth,blue,66,10,2,1,3,Yes,3,20,5,10 +female,21,fourth,green,65,305,2,3,14,Yes,2,110,2,12 +male,20,third,blue,79,79,0,2,28,Yes,6,40,2,15 +female,20,second,brown,62,220,0,0,24,Yes,5,25,0,7 +male,20,third,hazel,71,55,0,1,12,Yes,6,150,12,12 +male,19,first,brown,68,20,2,0,10,No,0,40,10,14 +male,18,first,blue,71,180,2,0,3,Yes,5,30,2,2 +male,18,first,blue,72,75,0,2,3,Yes,2,15,0,4 +female,20,second,blue,64,150,0,3,7,No,0,150,0,28 +male,21,third,hazel,75,210,0,0,7,No,0,26,10,10 +male,20,second,brown,70,11,3,1,7,Yes,10,5,14,40 +male,23,other,brown,80,1500,1,0,10,No,0,20,10,1 +female,22,fourth,green,64,154,1,0,5,No,0,100,0,10 +female,18,second,green,67,80,1,0,70,No,0,56,0,5 +female,19,second,hazel,67,360,1,2,10,Yes,4,30,3,6 +male,21,third,blue,75,170,1,1,5,Yes,2,6,1,5 +female,38,third,hazel,67,40,1,1,4,Yes,7,30,14,14 +female,20,third,brown,67,35,0,0,3,Yes,5,60,0,10 +male,23,fourth,blue,74,0,1,1,8,Yes,5,500,11,16 +female,19,second,hazel,65,35,0,2,5,Yes,4,30,0,5 +male,21,third,blue,71,95,1,0,10,Yes,8,30,5,10 +male,20,second,blue,75,190,2,0,10,No,0,55,10,1 +female,21,third,blue,68,101,1,3,2,No,0,0,1,10 +female,19,second,green,67,150,1,5,4,Yes,3,200,0,3 +male,21,third,blue,75,15,2,1,7,Yes,4,100,10,10 +male,20,third,blue,76,210,1,1,5,Yes,3.5,100,10,10 +male,18,first,green,71,215,0,2,4,Yes,5,60,0,1 +female,20,third,brown,62,45,1,1,60,No,0,100,0,2 +male,22,fourth,blue,73,13,1,1,4,Yes,6,50,4,8 +male,22,other,blue,68,0,2,0,10,No,0,150,2,15 +female,18,first,green,67,215,0,2,30,No,0,20,1,3 +male,24,first,brown,70,28,3,3,20,Yes,6,0,8,20 +male,19,first,blue,72,260,0,1,20,No,0,100,10,20 +female,23,other,other,65,30,1,1,20,No,0,20,5,15 +male,21,second,brown,69,8587.2,1,0,20,Yes,6,0,6,2 +female,20,third,brown,60,30,0,1,7,No,0,50,0,25 +female,20,second,blue,62,60,1,0,18,Yes,5,350,0,18 +female,21,third,brown,63,7000,0,1,10,No,0,300,0,4 +female,19,first,hazel,68,230,0,1,30,No,0,350,2,2 +female,20,third,other,70,22,3,0,10,No,0,15,1,3 +female,20,second,blue,62,130,2,1,25,No,0,10,0,8 +male,23,other,blue,71,180,0,1,16,Yes,4,58,10,15 +female,17,other,brown,67,18,2,1,10,No,0,50,0,5 +female,19,second,blue,61,50,0,1,14,No,0,150,0,20 +female,21,fourth,brown,63,120,1,0,5,No,0,500,30,15 +male,20,second,blue,71,30,0,1,15,Yes,4,200,2,8 +female,27,fourth,blue,63,2000,1,1,8,Yes,6,27,0,7 +female,21,third,brown,67,220,2,1,6,Yes,5,100,0,10 +male,19,second,hazel,78,300,0,1,24,Yes,16,100,2,18 +male,21,third,brown,69,180,3,0,15,No,0,25,1,10 +male,20,second,brown,73,82,2,2,10,No,0,500,1,3 +female,20,second,green,65,160,5,6,3,Yes,0,30,0.5,12 +female,24,other,blue,64,100,1,0,4,No,0,133,0,10 +male,18,first,green,72,360,1,0,5,Yes,5,100,5,10 +male,21,third,green,73,0,1,1,2,No,0,200,0.5,2 +male,20,third,brown,71,58,0,2,52.5,No,0,12,12.5,22.5 +male,21,third,brown,72,0,0,1,2,Yes,9,100,14,7 +female,18,first,green,67,65,0,2,8,Yes,10,50,0,8 +male,20,second,blue,71,150,0,1,4,Yes,10,100,10,20 +female,20,second,hazel,68,190,6,2,3,Yes,6,15,0,5 +female,20,second,green,65,288.5,0,2,25,No,0,47,1,5 +female,19,second,blue,65,300,0,3,5,No,0,75,0.5,7 +male,20,second,green,68,132,2,0,25,Yes,7,50,10,10 +male,19,second,brown,71,4000,2,0,45,Yes,6,60,5,35 +female,19,second,brown,69,70,1,0,15,Yes,2,40,5,10 +female,20,third,blue,70,150,1,1,14,Yes,6,25,1,3 +male,20,third,brown,75,300,0,2,3,Yes,20,45,18,5 +female,20,second,green,64,215,0,0,5,Yes,25,200,0.5,2 +female,20,third,green,67,30,1,1,10,Yes,5,50,0,2 +male,22,fourth,brown,75,40,1,0,7,Yes,8,200,13,35 +female,20,third,blue,68,150,0,1,3,Yes,3.5,50,0,5 +male,20,second,blue,73,190,0,1,4,Yes,2,40,3,6 +male,19,first,brown,70,375,0,1,15,No,0,300,5,5 +female,19,second,green,64,193,0,1,40,No,0,1000,0,20 +female,21,fourth,green,66,180,1,0,14,Yes,5,100,2,30 +male,23,other,brown,68,293,3,1,2,Yes,15,150,21,14 +female,19,first,blue,69,250,0,1,12,Yes,8,55,0,6 +male,20,second,hazel,68,45,0,2,1.5,No,0,80,2,9 +female,21,fourth,green,66,45,1,0,5,No,0,20,1,8 +female,21,third,brown,66,300,1,2,15,Yes,6,200,0,15 +female,22,third,blue,70,240,3,2,1,Yes,1,12,0.5,0.5 +male,19,second,green,72,1500,1,0,2,Yes,3,31,6,16 +female,19,second,blue,67,40,1,2,2,Yes,3,60,0,10 +female,20,second,other,63,13000,2,1,20,No,0,10,0,2 +female,20,third,blue,66,260,1,1,7,No,0,34,1,15 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matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#%matplotlib inline\n", + "matplotlib.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(171639, 43)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " q3 q4 q5 q6 q7 \\\n", + "count 24205.000000 24205.000000 24205.000000 24205.000000 24205.000000 \n", + "mean 6.673497 5.532121 4.990002 9.002644 1.507177 \n", + "std 7.468561 25.407165 22.812399 28.164931 0.889274 \n", + "min 1.000000 1.000000 1.000000 1.000000 0.000000 \n", + "25% 2.000000 1.000000 1.000000 2.000000 0.690000 \n", + "50% 5.000000 2.000000 2.000000 5.000000 1.610000 \n", + "75% 8.000000 4.000000 4.000000 9.000000 2.080000 \n", + "max 360.000000 2333.000000 2093.000000 2420.000000 5.890000 \n", + "\n", + " q8 q9 q10 q11 q12 \\\n", + "count 24205.000000 24205.000000 24205.000000 24205.000000 24205.000000 \n", + "mean 0.930915 0.863740 1.540158 26.765398 25.708965 \n", + "std 0.980001 0.951531 1.027086 10.166944 11.569436 \n", + "min 0.000000 0.000000 0.000000 2.780000 1.410000 \n", + "25% 0.000000 0.000000 0.690000 20.000000 16.670000 \n", + "50% 0.690000 0.690000 1.610000 25.930000 25.000000 \n", + "75% 1.390000 1.390000 2.200000 33.330000 33.330000 \n", + "max 7.750000 7.650000 7.790000 84.620000 89.290000 \n", + "\n", + " ... q16r q16u q16v q16w \\\n", + "count ... 24205.000000 24205.000000 24205.000000 24205.000000 \n", + "mean ... 3.633588 0.460916 22.550671 25.064697 \n", + "std ... 1.490942 0.509305 14.454279 28.650883 \n", + "min ... 1.000000 0.000000 1.000000 1.000000 \n", + "25% ... 3.000000 0.000000 10.000000 9.000000 \n", + "50% ... 4.000000 0.333333 21.000000 17.000000 \n", + "75% ... 5.000000 0.666667 34.000000 32.000000 \n", + "max ... 5.000000 7.000000 53.000000 1289.000000 \n", + "\n", + " q16x q16y q16z q16aa q16ab \\\n", + "count 24205.000000 24205.000000 24205.000000 24205.000000 24205.000000 \n", + "mean 0.003781 73.981978 0.642677 0.561124 1.141080 \n", + "std 0.006020 51.060694 1.427017 1.908011 3.234828 \n", + "min 0.000000 0.250000 0.000000 0.000000 0.000000 \n", + "25% 0.000477 39.125000 0.000000 0.000000 0.000000 \n", + "50% 0.001944 61.842593 0.000000 0.000000 0.500000 \n", + "75% 0.004666 95.375000 1.000000 0.000000 1.333333 \n", + "max 0.150618 549.333333 44.000000 106.000000 342.300000 \n", + "\n", + " q16ac \n", + "count 24205.000000 \n", + "mean 3.649456 \n", + "std 0.990191 \n", + "min 1.000000 \n", + "25% 3.200000 \n", + "50% 3.800000 \n", + "75% 4.333333 \n", + "max 5.000000 \n", + "\n", + "[8 rows x 40 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('yelp_reviewers.csv')\n", + "print(df.shape)\n", + "df.dropna().describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 1 ###\n", + "\n", + "Choose an implementation of k-means and specify it in the google sheet \n", + "\n", + "** Answer **: \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans # Scikit-learn\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 2 ###\n", + "What is the best choice of k according to the silhouette metric for clustering q4-q6 (# of cool, funny, useful votes). Only consider 2 <= k <= 8. \n", + "\n", + "NOTE: For features with high variance, empty clusters can occur. There are several ways of dealing with empty clusters. A common approach is to drop empty clusters, the prefered approach for this Lab is to treat the empty cluster as a “singleton” leaving it empty with a single point placeholder. This can be accomplished in MATLAB, for example, with the command: [IDX,C]=kmeans(d,4, 'Distance', 'sqeuclidean','EmptyAction','singleton')\n", + "\n", + "**Answer**: " + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best choice: k = 2 , silhouette score = 0.98939672069\n" + ] + } + ], + "source": [ + "current_max, current_index = float(\"-inf\"),-1\n", + "#new_df = df.dropna().sample(n = 10000) # Sampling 10000 data points randomly since the whole dataset is too large to load\n", + "#print(type(new_df)) # Dataframe\n", + "#b = [1,3,5]\n", + "#print(df.iloc[:,b])\n", + "\n", + "for k in range(2,9):\n", + " kmeans = KMeans(n_clusters = k).fit(df.iloc[:, 1:4])\n", + " s_score = silhouette_score(df.iloc[:,1:4], kmeans.labels_, sample_size = 10000)\n", + " if s_score > current_max:\n", + " current_max = s_score\n", + " current_index = k\n", + "\n", + "print(\"Best choice: k = \", current_index, \", silhouette score = \", current_max)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "\n", + "### Question 3 ###\n", + "Answer question 2 but using the log of the features (q7-q10)\n", + "\n", + "**Answer**: " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nfrom sklearn.preprocessing import Imputer\\n\\ncol_loc = new_df.columns.get_loc(\\'q7\\')\\n#new_df.iloc[:,col_loc:(col_loc+4)].head()\\n\\nlog_new_df = np.log(new_df.iloc[:,col_loc:(col_loc+4)])\\nlog_new_df[log_new_df == float(\\'-inf\\')] = float(\\'nan\\')\\n#print(log_new_df.head())\\nimputer = Imputer()\\nlog_new_df = imputer.fit_transform(log_new_df) # It is an array now\\n#print(log_new_df[:5])\\n\\n\\n\\ncurrent_max, current_index = float(\"-inf\"),-1\\nfor k in range(2,9):\\n kmeans = KMeans(n_clusters = k).fit(log_new_df)\\n s_score = silhouette_score(log_new_df, kmeans.labels_)\\n if s_score > current_max:\\n current_max = s_score\\n current_index = k\\nprint(\"Best choice: k = \", current_index)\\n\\n'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I had taken log of the features q7-q10 in this code.\n", + "\n", + "\"\"\"\n", + "\n", + "from sklearn.preprocessing import Imputer\n", + "\n", + "col_loc = new_df.columns.get_loc('q7')\n", + "#new_df.iloc[:,col_loc:(col_loc+4)].head()\n", + "\n", + "log_new_df = np.log(new_df.iloc[:,col_loc:(col_loc+4)])\n", + "log_new_df[log_new_df == float('-inf')] = float('nan')\n", + "#print(log_new_df.head())\n", + "imputer = Imputer()\n", + "log_new_df = imputer.fit_transform(log_new_df) # It is an array now\n", + "#print(log_new_df[:5])\n", + "\n", + "\n", + "\n", + "current_max, current_index = float(\"-inf\"),-1\n", + "for k in range(2,9):\n", + " kmeans = KMeans(n_clusters = k).fit(log_new_df)\n", + " s_score = silhouette_score(log_new_df, kmeans.labels_)\n", + " if s_score > current_max:\n", + " current_max = s_score\n", + " current_index = k\n", + "print(\"Best choice: k = \", current_index)\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best choice: k = 2 , silhouette score = 0.525805639106\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import Imputer\n", + "\n", + "col_loc = df.columns.get_loc('q7')\n", + "\n", + "\n", + "log_df = df.iloc[:,col_loc:(col_loc+4)]\n", + "log_df[log_df == float('-inf')] = float('nan')\n", + "#print(log_new_df.head())\n", + "imputer = Imputer()\n", + "log_df = imputer.fit_transform(log_df) # It is an array now\n", + "#print(log_new_df[:5])\n", + "\n", + "\n", + "\n", + "current_max, current_index = float(\"-inf\"),-1\n", + "for k in range(2,9):\n", + " kmeans = KMeans(n_clusters = k).fit(log_df)\n", + " s_score = silhouette_score(log_df, kmeans.labels_, sample_size = 10000)\n", + " if s_score > current_max:\n", + " current_max = s_score\n", + " current_index = k\n", + "print(\"Best choice: k = \", current_index, \", silhouette score = \", current_max)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#new_df.iloc[:,col_loc:(col_loc+4)].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "\n", + "### Question 4 ###\n", + "Answer question 2 but using the percentage of the features (q11-q13)\n", + "\n", + "**Answer**:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000,)\n", + "Best choice: k = 8\n" + ] + } + ], + "source": [ + "# Here again, I took the percentage of each column w.r.t the sum of all the 3 columns\n", + "\n", + "\"\"\"\n", + "col_loc = new_df.columns.get_loc('q11')\n", + "new_df_slice = new_df.iloc[:,col_loc:(col_loc+3)]\n", + "sum_across_cols = new_df_slice.sum(axis=1) # Axis of repeat isn't supported in pandas' implememtation of repeat\n", + "\n", + "\n", + "#print(type(sum_across_cols)) # Series\n", + "#print(np.repeat(sum_across_cols, 3, axis =1).shape)\n", + "#percent_new_df = np.divide(, np.repeat(sum_across_cols, 3, axis=1))*100\n", + "\n", + "for i in range(len(new_df_slice.iloc[0])): # new_df_slice[:1] wasn't giving the desired results, think why\n", + " new_df_slice.iloc[:,i] = np.divide(new_df_slice.iloc[:,i], sum_across_cols)*100\n", + "#new_df_slice.describe()\n", + "\n", + "current_max, current_index = float(\"-inf\"),-1\n", + "for k in range(2,9):\n", + " kmeans = KMeans(n_clusters = k).fit(new_df_slice)\n", + " s_score = silhouette_score(new_df_slice, kmeans.labels_)\n", + " if s_score > current_max:\n", + " current_max = s_score\n", + " current_index = k\n", + "print(\"Best choice: k = \", current_index)\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best choice: k = 8\n" + ] + } + ], + "source": [ + "col_loc = df.columns.get_loc('q11')\n", + "df_slice = df.iloc[:,col_loc:(col_loc+3)]\n", + "\n", + "imputer = Imputer()\n", + "df_slice = imputer.fit_transform(df_slice)\n", + "current_max, current_index = float(\"-inf\"),-1\n", + "for k in range(2,9):\n", + " kmeans = KMeans(n_clusters = k).fit(df_slice)\n", + " s_score = silhouette_score(df_slice, kmeans.labels_, sample_size = 10000)\n", + " if s_score > current_max:\n", + " current_max = s_score\n", + " current_index = k\n", + "print(\"Best choice: k = \", current_index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "\n", + "### Question 5 ###\n", + "Inspect the [best] clustering generated from question 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.a ####\n", + "**Question**: List the number of data points in each cluster (eg. C1: 2,000 C2: 4,200 etc)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C0 : 8677, C1 : 14865, C2 : 86641, C3 : 5805, C4 : 4026, C5 : 10188, C6 : 32922, C7 : 8515, " + ] + } + ], + "source": [ + "kmeans = KMeans(n_clusters = current_index).fit(df_slice)\n", + "\n", + "from collections import Counter\n", + "cnt = Counter()\n", + "for label in kmeans.labels_:\n", + " cnt[label]+=1\n", + "for item in sorted(cnt.items(), key = lambda x: x[0]): # item is (key,value)\n", + " print(\"C\"+str(item[0]),\":\",item[1], end = \", \")\n", + "#print(sorted(cnt, key = lambda x:)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.b ####\n", + "**Question**: Were there clusters that represented very funny but useless reviewers? \n", + "\n", + "**Answer**: " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 26.35629771 1.56193359 72.08165802]\n", + " [ 32.79166095 33.46908443 33.73584191]\n", + " [ 19.21344078 18.71325288 62.07314944]\n", + " [ 1.36427907 97.08347976 1.55237037]\n", + " [ 98.20565574 0.89977894 0.894615 ]\n", + " [ 49.12774441 2.46021692 48.41214861]\n", + " [ 0.17617186 0.40948758 99.41432112]\n", + " [ 2.01080446 46.42119789 51.56799883]]\n" + ] + } + ], + "source": [ + "print(kmeans.cluster_centers_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the coordinates of the cluster centers, we see that cluster 3 (in this case, the exact label could vary depending on initializations etc.) has only about 1.5% from useful votes and about 97% from funny votes. Instead of just looking at the cluster centers and saying this (which would be hard if there were many centers, we could also have a threshold, say 80%, for the difference between the percentages of funny and useful votes and return all clusters for which the threshold is exceeded." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.c ####\n", + "**Question**: How many reviewers were in the cluster that represented relatively equal strength in all voting categories (assuming such a cluster exists in your clustering)? \n", + "\n", + "**Answer**:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of reviewers in the cluster that represented relatively equal strength in all voting categories: 14865\n" + ] + } + ], + "source": [ + "latest_cluster = -1 # The latest cluster that satisfies the required criteria\n", + "for i,centers in enumerate(kmeans.cluster_centers_):\n", + " if max(centers)-min(centers)<10: # some heuristic threshold\n", + " latest_cluster = i\n", + "if latest_cluster == -1:\n", + " print(\"No such cluster found\")\n", + "else:\n", + " print(\"The number of reviewers in the cluster that represented relatively equal strength in all voting categories: \",cnt[latest_cluster])\n", + " \n", + "# We could also have eyeballed it in this case since we see that cluster 6 has almost equal coordinates, but my code is more general, ofcourse with the caveat of having to select the threshold " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "\n", + "### Question 6 ###\n", + "Cluster the dataset using $k = 5$ and using features q7-q13 (log and % type votes) and q14 (most active year feature) and the natural log of q15 (avg review chars)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "col_loc = df.columns.get_loc('q7')\n", + "q6_df = df.iloc[:,col_loc:(col_loc+8)] #q6 refers to question 6 here, not some feature\n", + "#new_df.iloc[:,new_df.columns.get_loc('q15')]\n", + "q6_df = q6_df.join(pd.DataFrame(np.log(df.iloc[:,df.columns.get_loc('q15')]))) \n", + "imputer = Imputer()\n", + "q6_df = imputer.fit_transform(q6_df)\n", + "\n", + "kmeans = KMeans(n_clusters = 5).fit(q6_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.a ####\n", + "**Question**: What is the silhouette metric for this clustering? \n", + "You may use the max, as you did in question 2. For a more in-depth understanding of cluster analysis with silhouette, look [here](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The silhouette metric for this clustering is: 0.648573840098\n" + ] + } + ], + "source": [ + "s_score = silhouette_score(q6_df, kmeans.labels_, sample_size = 10000)\n", + "print(\"The silhouette metric for this clustering is: \", s_score)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.b ####\n", + "**Question**: What was the average “number of reviews per reviewer (q3)” among the points in each of the clusters (eg. C1: 1.4 C2: 4.2 C3: 3.4 etc)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C0 : 5.13274199769, C1 : 2.87911339962, C2 : 2.67239955971, C3 : 2.85209182152, C4 : 2.60288629012, " + ] + } + ], + "source": [ + "dict1 = {}\n", + "#for i in range(5):\n", + "# dict1[i] = list()\n", + "#print(kmeans.labels_[9999])\n", + "#print([new_df.iloc[9999,new_df.columns.get_loc('q3')]])\n", + "#print()\n", + "\n", + "for i in range(len(df)):\n", + " if kmeans.labels_[i] in dict1:\n", + " dict1[kmeans.labels_[i]].append(df.iloc[i,df.columns.get_loc('q3')])\n", + " else:\n", + " dict1[kmeans.labels_[i]] = ([df.iloc[i,df.columns.get_loc('q3')]])\n", + "#print(dict1.keys())\n", + "for key in sorted(dict1.keys()):\n", + " print(\"C\"+str(key),\":\",np.mean(np.array(dict1[key])), end =\", \")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "\n", + "### Question 7 ###\n", + "Cluster the dataset using the features described in question 6 + every group’s question 16 features (you may drop features with high incidents of -Inf / blank / or NaN values). It is suggested that you perform some form of normalization on these question 16 features so as not to over bias the clustering towards the larger magnitude features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data Cleansing and Normalization ####\n", + "Check how many null values there are in each column." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'q7': 0, 'q8': 117853, 'q9': 122162, 'q10': 81153, 'q11': 71282, 'q12': 71282, 'q13': 71282, 'q14': 0, 'q15': 0, 'q16a': 0, 'q16b': 0, 'q16c': 0, 'q16d': 0, 'q16e': 0, 'q16f': 0, 'q16g': 0, 'q16h': 0, 'q16i': 0, 'q16j': 0, 'q16k': 0, 'q16l': 0, 'q16m': 0, 'q16n': 0, 'q16o': 0, 'q16p': 0, 'q16q': 0, 'q16r': 0, 'q16s': 0, 'q16t': 0, 'q16u': 0, 'q16v': 0, 'q16w': 0, 'q16x': 0, 'q16y': 0, 'q16z': 0, 'q16aa': 0, 'q16ab': 47594, 'q16ac': 0}\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "col_loc = df.columns.get_loc('q7')\n", + "q6_df = df.iloc[:,col_loc:(col_loc+8)] #q6 refers to question 6 here, not some feature\n", + "q6_df = q6_df.join(pd.DataFrame(np.log(df.iloc[:,df.columns.get_loc('q15')])))\n", + "q7_df = pd.DataFrame(q6_df).join(df.iloc[:,df.columns.get_loc('q16a'):])\n", + "#print(q7_df.shape)\n", + "#print(q7_df.head())\n", + "#print(q7_df['q8'].isnull().sum())\n", + "\n", + "d = {}\n", + "for col in q7_df.columns:\n", + " d[col] = np.sum(q7_df[col].isnull().sum())\n", + "print(d)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like q8 - q13 and q16ab have a lot of null values, especially q8 and q9. Let's see what the impact is of removing some of these columnsbefore removing any columns" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#print(q7_df.columns)\n", + "del q7_df['q8'] # Could also do df.drop\n", + "del q7_df['q9']\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By removing 2 features, we double the number of rows remaining. That's pretty good. \n", + "Preprocess categorical variables to dummy values." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{dtype('float64'), dtype('int64'), dtype('O')}\n", + " q7 q10 q11 q12 q13 q14 q15 q16a q16b q16c \\\n", + "1 1.10 0.00 0.0 0.0 100.0 9 5.030438 0 0.577350 0.002179 \n", + "3 1.10 0.00 0.0 0.0 100.0 10 6.219934 1 2.309401 0.000663 \n", + "5 1.10 1.10 0.0 0.0 100.0 9 5.950643 0 2.309401 0.002604 \n", + "6 2.08 1.79 25.0 0.0 75.0 9 6.013104 5 1.995531 0.002446 \n", + "9 0.69 0.00 0.0 50.0 50.0 9 5.898527 1 2.121320 0.002743 \n", + "\n", + " ... q16t q16u q16v q16w q16x q16y q16z q16aa \\\n", + "1 ... 1 1.0 19 14 0.000000 21.833333 1 0 \n", + "3 ... 1 0.0 8 11 0.002242 61.500000 0 0 \n", + "5 ... 1 0.0 27 11 0.005814 41.000000 0 0 \n", + "6 ... 0 0.5 1 27 0.006479 42.156250 0 0 \n", + "9 ... 1 0.5 31 7 0.000000 56.750000 0 0 \n", + "\n", + " q16ab q16ac \n", + "1 0.333333 4.666667 \n", + "3 0.333333 3.666667 \n", + "5 1.000000 3.666667 \n", + "6 1.000000 3.375000 \n", + "9 1.000000 3.500000 \n", + "\n", + "[5 rows x 36 columns]\n" + ] + } + ], + "source": [ + "q7_df = q7_df.dropna() # Till now I have been imputing and not dropping the NaN values, but siunce this question refers to dropping NaN, I have done so here\n", + "list_of_types = []\n", + "for col in q7_df.columns:\n", + " list_of_types.append(q7_df[col].dtype)\n", + "print(set(list_of_types))\n", + "\n", + "for col in q7_df.columns:\n", + " if q7_df[col].dtype == 'O':\n", + " cats = set(q7_df[col])\n", + " #print(cats)\n", + " count = 0\n", + " d = {}\n", + " for elt in cats:\n", + " d[elt] = count\n", + " count+=1\n", + " #print(d)\n", + " #break\n", + " new_q7 = []\n", + " for elt in q7_df[col]:\n", + " new_q7.append(d[elt])\n", + " q7_df[col]=new_q7\n", + " \n", + "print(q7_df.head()) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now normalize the remaining features." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " q7 q10 q11 q12 q13 q14 q15 q16a \\\n", + "0 0.186757 0.000000 0.000000 0.000000 1.000000 0.833333 0.511036 0.0 \n", + "1 0.186757 0.000000 0.000000 0.000000 1.000000 0.916667 0.677844 0.1 \n", + "2 0.186757 0.141207 0.000000 0.000000 1.000000 0.833333 0.640080 0.0 \n", + "3 0.353141 0.229782 0.267867 0.000000 0.738083 0.833333 0.648840 0.5 \n", + "4 0.117148 0.000000 0.000000 0.559973 0.476166 0.833333 0.632772 0.1 \n", + "\n", + " q16b q16c ... q16t q16u q16v q16w q16x \\\n", + "0 0.204124 0.000654 ... 1.0 0.111111 0.346154 0.010093 0.000000 \n", + "1 0.816497 0.000187 ... 1.0 0.000000 0.134615 0.007764 0.005809 \n", + "2 0.816497 0.000784 ... 1.0 0.000000 0.500000 0.007764 0.015063 \n", + "3 0.705527 0.000736 ... 0.0 0.055556 0.000000 0.020186 0.016786 \n", + "4 0.750000 0.000827 ... 1.0 0.055556 0.576923 0.004658 0.000000 \n", + "\n", + " q16y q16z q16aa q16ab q16ac \n", + "0 0.031555 0.022727 0.0 0.000974 0.916667 \n", + "1 0.089547 0.000000 0.0 0.000974 0.666667 \n", + "2 0.059576 0.000000 0.0 0.002921 0.666667 \n", + "3 0.061266 0.000000 0.0 0.002921 0.593750 \n", + "4 0.082602 0.000000 0.0 0.002921 0.625000 \n", + "\n", + "[5 rows x 36 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = q7_df.columns\n", + "scaler = MinMaxScaler()\n", + "scaler.fit(q7_df)\n", + "q7_df = pd.DataFrame(scaler.transform(q7_df), columns = cols)\n", + "#q7_df = pd.DataFrame(normalize(q7_df, axis = 0))\n", + "q7_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7.a ####\n", + "**Question**: Using the silhouette metric, what was the best k? \n", + "\n", + "**Answer**: " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best choice: k = 2 , silhouette score = 0.232197223377\n" + ] + } + ], + "source": [ + "current_max, current_index = float(\"-inf\"),-1\n", + "sse = []\n", + "for k in range(2,9):\n", + " kmeans = KMeans(n_clusters = k).fit(q7_df)\n", + " sse.append(kmeans.inertia_)\n", + " s_score = silhouette_score(q7_df, kmeans.labels_, sample_size = 10000)\n", + " if s_score > current_max:\n", + " current_max = s_score\n", + " current_index = k\n", + "\n", + "print(\"Best choice: k = \", current_index, \", silhouette score = \", current_max)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7.b ####\n", + "**Question**: Using the the sum of within cluster variance metric with the elbow method what was the best k? \n", + "**Answer**: " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bYa6fir30F9jXV2LfeRO7fg2md//A1qWtz/E6ooQolYNIPWD8LTBjb8KO/Dn2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\n9TpKlezYsYMNGzYwZcoUFix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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "k = range(2,9)\n", + "plt.plot(k,sse)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "By the elbow method, it is seen that maximum slope change occurs at k=3." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Question 8 ###\n", + "For this question please come up with your own question about this dataset and using a clustering technique as part of your method of answering it. This question answer should be submitted to a bCourses assignment in the form a pdf. This report is expected to be between 2 and 3 pages. It is meant to give you practice writing up your results. The report should have the following sections:\n", + "\n", + "\n", + "RESEARCH QUESTION: Describe what it is you want to ask of this dataset\n", + " (the question can not be the same as question 9)\n", + " \n", + " \n", + "DATASET: describe the yelp_reviews to yelp_reviewers transformation. have a subsection called FEATURES SELECTED: briefly describe the features from the dataset you choose (at least 1 features should be from a q16)\n", + "\n", + "\n", + "METHODS: Briefly describe the methods you used (such as k-means) and why you used them\n", + "\n", + "\n", + "RESULTS: Here you can tell the story of how you investigated the question (plots are always nice) and the conclusions you drew.\n", + "\n", + "\n", + "NOTE: If you are hypothesizing that clusters might be formed with respect to a particular attribute, consider withholding that attribute from clustering and then looking to see what the mean value or distribution of that attribute is among the formed clusters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#I have submitted the pdf and a separate Ipython notebook for this titled Lab2 Q8.ipynb." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus question (+15%) - Reviewer overlap:\n", + "Create a dataset with f reviewers as the rows and business_ids as the columns (there are a lot) where the feature value is is ‘1’ if the reviewer has written a review for that business and ‘0’ if not. Use the methods described in this assignment to answer the question of how much overlap of businesses reviewed exists among reviewers in this dataset. Append this answer to your question 8 document." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Attached after Q8\n", + "\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Lab3_Decision_Trees.ipynb b/Lab3_Decision_Trees.ipynb new file mode 100644 index 0000000..2480693 --- /dev/null +++ b/Lab3_Decision_Trees.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab 3 - Decision Trees" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This assignment uses a dataset obtained from the JSE Data Archive that aims at identifying whether there is a statistically significant dependence between gender and biological/activity traits in humans. \n", + "\n", + "FEATURE DESCRIPTIONS: \n", + "\n", + "
    \n", + "
  • Color (Blue, Brown, Green, Hazel, Other)\n", + "
  • Age (in years)\n", + "
  • YearinSchool (First, Second, Third, Fourth, Other)\n", + "
  • Height (in inches)\n", + "
  • Miles (distance from home town of student to Ames, IA) \n", + "
  • Brothers (number of brothers)\n", + "
  • Sisters (number of sisters)\n", + "
  • CompTime (number of hours spent on computer per week)\n", + "
  • Exercise (whether the student exercises Yes or No)\n", + "
  • ExerTime (number of hours spent exercising per week)\n", + "
  • MusicCDs (number of music CDs student owns)\n", + "
  • PlayGames (number of hours spent playing games per week)\n", + "
  • WatchTV (number of hours spent watching TV per week\n", + "
\n", + "\n", + "https://ww2.amstat.org/publications/jse/jse_data_archive.htm" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from collections import Counter, defaultdict\n", + "from itertools import combinations \n", + "import pandas as pd\n", + "import numpy as np\n", + "import operator" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 2068 entries, 0 to 2067\n", + "Data columns (total 14 columns):\n", + "gender 2068 non-null object\n", + "age 2068 non-null int64\n", + "year 2068 non-null object\n", + "eyecolor 2068 non-null object\n", + "height 2051 non-null float64\n", + "miles 2052 non-null float64\n", + "brothers 2068 non-null int64\n", + "sisters 2068 non-null int64\n", + "computertime 2061 non-null float64\n", + "exercise 2068 non-null object\n", + "exercisehours 2068 non-null float64\n", + "musiccds 2024 non-null float64\n", + "playgames 2067 non-null float64\n", + "watchtv 2067 non-null float64\n", + "dtypes: float64(7), int64(3), object(4)\n", + "memory usage: 226.3+ KB\n" + ] + } + ], + "source": [ + "df = pd.read_csv('Eye_Color.csv')\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1988 entries, 0 to 1987\n", + "Data columns (total 14 columns):\n", + "gender 1988 non-null object\n", + "age 1988 non-null int64\n", + "year 1988 non-null object\n", + "eyecolor 1988 non-null object\n", + "height 1988 non-null float64\n", + "miles 1988 non-null float64\n", + "brothers 1988 non-null int64\n", + "sisters 1988 non-null int64\n", + "computertime 1988 non-null float64\n", + "exercise 1988 non-null object\n", + "exercisehours 1988 non-null float64\n", + "musiccds 1988 non-null float64\n", + "playgames 1988 non-null float64\n", + "watchtv 1988 non-null float64\n", + "dtypes: float64(7), int64(3), object(4)\n", + "memory usage: 217.5+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# remove NA's and reset the index\n", + "df = df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\n", + "df = df.reset_index(drop=True)\n", + "df.info()\n", + "df.head()\n", + "len(df.height.unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculating Gini Index " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "**Question 1: How many rows are there in the dataset for males? For females? **\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "male: 910 female: 1078\n" + ] + } + ], + "source": [ + "grouped=df.groupby('gender').size()\n", + "grouped\n", + "print('male: ',grouped['male'],'female: ',grouped['female'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 2: What is the Gini Index of this dataset, using males and females as the target classes?**" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.496429279905\n" + ] + } + ], + "source": [ + "ratio=grouped['female']/(grouped['female']+grouped['male'])\n", + "\n", + "def gini(p):\n", + " return 1-p*p-(1-p)*(1-p)\n", + "print(gini(ratio))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Split of a Numeric Feature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 3: What is the best split point of the 'height' feature. **" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[44.0, 52.0, 54.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 82.0, 85.0]\n", + "[48.0, 53.0, 55.0, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 81.0, 83.5]\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'female'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 2482\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2483\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlibts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2484\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box (pandas\\_libs\\tslib.c:18843)\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box (pandas\\_libs\\tslib.c:18477)\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;31m# bsplitcount=bottomcount['male'] + bottomcount['female']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;31m#else:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mbsplitcount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbottomcount\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'male'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mbottomcount\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'female'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 28\u001b[0m \u001b[0mbottomsplitprob\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbottomcount\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'male'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mbsplitcount\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 600\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 603\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 2489\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2490\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2491\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2492\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2493\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 2475\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2476\u001b[0m return self._engine.get_value(s, k,\n\u001b[1;32m-> 2477\u001b[1;33m tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m 2478\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2479\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'female'" + ] + } + ], + "source": [ + "sorted_heights = sorted(df[\"height\"].unique())\n", + "print(sorted_heights)\n", + "psp=[] #potential split point\n", + "for i,value in enumerate(sorted_heights):\n", + " if i<29:\n", + " midpoint=((sorted_heights[i]+sorted_heights[i+1])/2)\n", + " psp=np.append(midpoint,psp)\n", + "print(sorted(psp))\n", + "\n", + "for x in psp:\n", + " top=df[df['height']<=x]\n", + " topcount=top.groupby('gender').size()\n", + " bottom=df[df['height']>x]\n", + " bottomcount=bottom.groupby('gender').size()\n", + " \n", + " #if len(topcount==1):\n", + " # topsplitprob=1\n", + " # tsplitcount=topcount['male'] + topcount['female']\n", + " #else:\n", + " tsplitcount=topcount['male'] + topcount['female']\n", + " topsplitprob=topcount['male']/tsplitcount\n", + " \n", + " #if len(bottomcount==1):\n", + " # bottomsplitprob=1\n", + " # bsplitcount=bottomcount['male'] + bottomcount['female']\n", + " #else:\n", + " bsplitcount=bottomcount['male'] + bottomcount['female']\n", + " bottomsplitprob=bottomcount['male']/bsplitcount \n", + " \n", + " # totalcount=bsplitcount+tsplitcount\n", + " # final_gini=gini(tsplitcount)*(topsplitprob/totalcount) +gini(bottomsplitprob)*(bsplitcount/totalcount)\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 4: What is the Gini Index of this best split?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 5: How much does this partitioning reduce the Gini Index over that of the overall dataset?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 6: How many 'female' rows are below your best split point? 'male' rows?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 7: How many 'female' rows are above your best split point? 'male' rows?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Recall that, to calculate the best split of this numeric field, you'll need to order your data by 'height', then consider the midpoint between each pair of consecutive heights as a potential split point, then calculate the Gini Index for that partitioning. You'll want to keep track of the best split point and its Gini Index (remember that you are trying to minimize the Gini Index). \n", + "\n", + "There are a lot of ways to do this. Some are very fast, others very slow. One tip to make this run quickly is, as you consecutively step through the data and calculate the Gini Index of each possible split point, keep a running total of the number of rows for each candidate that are located above and below the split point. \n", + "\n", + "Some Python tips: \n", + "\n", + "* Counter(), from the collections module, is a special dictionary for counting values of a key\n", + "* zip() lets you concatenate lists into a list of tuples (for example, if we have a list of the candidates and a list of transaction amounts, zip(candidate_list, transaction_amount) would give us a list of (candidate, transaction amount) pairs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Split of a Categorial Variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 8: How many possible splits are there of the eyecolor feature?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 9: Which split of eyecolor best splits the female and male rows, as measured by the Gini Index?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 10: What is the Gini Index of this best split?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 11: How much does this partitioning reduce the Gini Index over that of the overall data set?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 12: How many 'female' rows and 'male' rows are in your first partition? How many 'female' rows and 'male' rows are in your second partition?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python tip: the combinations function of the itertools module allows you to enumerate combinations of a list. You might want to Google 'power set'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training a decision tree" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 13: Using all of the features in the original dataframe read in at the top of this notebook, train a decision tree classifier that has a depth of three (including the root node and leaf nodes). What is the accuracy of this classifier on the training data?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-learn classifiers require class labels and features to be in numeric arrays. As such, you will need to turn your categorical features into numeric arrays using DictVectorizer. This is a helpful notebook for understanding how to do this: http://nbviewer.ipython.org/gist/sarguido/7423289. You can turn a pandas dataframe of features into a dictionary of the form needed by DictVectorizer by using df.to_dict('records'). Make sure you remove the class label first (in this case, gender). If you use the class label as a feature, your classifier will have a training accuracy of 100%! The example notebook link also shows how to turn your class labels into a numeric array using sklearn.preprocessing.LabelEncoder()." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sklearn\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.feature_extraction import DictVectorizer #to turn categorial variables into numeric arrays\n", + "from sklearn import preprocessing #to transform the feature labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 14: Using the following code snippet, export your decision tree to graphviz and visualize it. In your write-up, write down the interpretation of the rule at each node which is used to perform the splitting.**\n", + "\n", + "In order to install graphviz, you may need to download the tool from [this website](https://graphviz.gitlab.io), and then pip3/conda install the python libraries you do not have.\n", + "\n", + "Mac users can use ```brew install graphviz``` instead of following the link, and linux users can do the same using their favourite package manager (for example, Ubuntu users can use ```sudo apt-get install graphviz```, followed by the necessary pip3/conda installations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from IPython.display import Image \n", + "import pydotplus \n", + "import pydot\n", + "from sklearn.externals.six import StringIO\n", + "\n", + "#\n", + "# clf = your classifier\n", + "\n", + "dotfile = StringIO() \n", + "tree.export_graphviz(clf, out_file=dotfile,\n", + "# feature_names=df.columns, \n", + "# class_names=['Female', 'Male'], \n", + " filled=True, rounded=True, \n", + " special_characters=True)\n", + " \n", + "graph = pydotplus.graph_from_dot_data(dotfile.getvalue())\n", + "Image(graph.create_png())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question 15 (Extra Credit): For each of your leaf nodes, specify the percentage of 'female' rows in that node (out of the total number of rows at that node).**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See this notebook for the basics of training a decision tree in scikit-learn and exporting the outputs to view in graphviz: http://nbviewer.ipython.org/gist/tebarkley/b68c04d9b31e64ce6023" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "gist_id": "0945553f802dad1bfa88", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Pre-Lab 2 Tutorial.ipynb b/Pre-Lab 2 Tutorial.ipynb new file mode 100644 index 0000000..c142ec5 --- /dev/null +++ b/Pre-Lab 2 Tutorial.ipynb @@ -0,0 +1,469 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learning Goals:\n", + "\n", + "1. Jupyter functionality for markdown text\n", + "2. Plotting with matplotlib\n", + "3. Using scikit-learn for k-means\n", + "4. Calculate silhouette_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Markdown" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can do cool stuff with markdown. You can make numbered or bullet lists. You can even write cool equations like: $\\sum{x}=\\log(y)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Matplotlib\n", + "matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "x=np.array(range(10))\n", + "y=np.array([1,1.1,1.2,1.3,4,2.3,3.9,7,10,15])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nbueuQ09yhZ4QD71/NL9bzJP0/t+LBFW3aHyA4H8138+4E3b+x/Yfz8F9h0f8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\nGGNMof4fNeY6JvufmY0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x,y)\n", + "plt.plot(x,y,color='r',linewidth=5)\n", + "plt.plot(x,y,x,-y)\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Portfolio [$]')\n", + "plt.title('Stocks')\n", + "plt.legend(['good','bad'])\n", + "#plt.xlim([-1,5])\n", + "#plt.ylim([1,10])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/AoqAp0XEq6rXqupeEXmUwZOTAeCf3Rjh4vH5WTEz39ZuMWMyoUBXVS+wasQn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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.figure(1,figsize=(10,5))\n", + "plt.subplot(121)\n", + "plt.title('Plot 1')\n", + "plt.plot(x,y)\n", + "plt.subplot(122)\n", + "plt.title('Plot 2')\n", + "plt.plot(-x,-y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Scikit-learn K-means" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 2)\n" + ] + } + ], + "source": [ + "X = np.array([[1, 2], [1, 4], [1.5, 3], [4, 2], [4, 4], [3.5, 1.9],[3.7,3],[4.2,3.8]])\n", + "print(X.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:,0],X[:,1])\n", + "plt.xlim([0,5])\n", + "plt.ylim([0,5])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "kmeans = KMeans(n_clusters=2, random_state=0).fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 0, 0, 0, 0, 0])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans.predict([[0, 0], [4, 4]])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 3.88 , 2.94 ],\n", + " [ 1.16666667, 3. ]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans.cluster_centers_" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[:,0],X[:,1])\n", + "plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],color='r')\n", + "plt.legend(['Training Data','Cluster Center'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Scikit-learn Silhouette_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. The Silhouette Coefficient for a sample is $\\frac{(b - a)}{\\max(a, b)}$. To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of.\n", + "\n", + "http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.metrics import silhouette_score" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "score = silhouette_score(X, kmeans.labels_)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.546688066289\n" + ] + } + ], + "source": [ + "print(score)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X = np.array([[1.1, 1.1], [1, 1.1], [1.1, 1], [4, 2], [4, 2.1], [4.1, 1.9]])\n", + "plt.scatter(X[:,0],X[:,1])\n", + "plt.xlim([0,5])\n", + "plt.ylim([0,5])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "kmeans = KMeans(n_clusters=2, random_state=0).fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 0, 0, 0])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "score = silhouette_score(X, kmeans.labels_)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.956852497597\n" + ] + } + ], + "source": [ + "print(score)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/diabetesdata.csv b/diabetesdata.csv new file mode 100644 index 0000000..35d3b9f --- /dev/null +++ b/diabetesdata.csv @@ -0,0 +1 @@ +TimesPregnant,glucoseLevel,BP,insulin,BMI,Pedigree,Age,IsDiabetic 6,148,72,0,33.6,0.627,50,1 1,,66,0,26.6,0.351,31,0 8,183,64,0,23.3,0.672,,1 1,,66,94,28.1,0.167,21,0 0,137,40,168,43.1,2.288,33,1 5,116,74,0,25.6,0.201,30,0 3,78,50,88,31,0.248,26,1 10,115,0,0,35.3,0.134,29,0 2,197,70,543,30.5,0.158,53,1 8,,96,0,0,0.232,54,1 4,110,92,0,37.6,0.191,,0 10,168,74,0,38,0.537,34,1 10,139,80,0,27.1,1.441,57,0 1,,60,846,30.1,0.398,59,1 5,166,72,175,25.8,0.587,51,1 7,100,0,0,30,0.484,32,1 0,,84,230,45.8,0.551,31,1 7,107,74,0,29.6,0.254,31,1 1,103,30,83,43.3,0.183,33,0 1,115,70,96,34.6,0.529,32,1 3,126,88,235,39.3,0.704,27,0 8,99,84,0,35.4,0.388,50,0 7,196,90,0,39.8,0.451,41,1 9,119,80,0,29,0.263,29,1 11,143,94,146,36.6,0.254,51,1 10,125,70,115,31.1,0.205,41,1 7,147,76,0,39.4,0.257,43,1 1,97,66,140,23.2,0.487,,0 13,,82,110,22.2,0.245,57,0 5,117,92,0,34.1,0.337,38,0 5,109,75,0,36,0.546,60,0 3,158,76,245,31.6,0.851,28,1 3,88,58,54,24.8,0.267,22,0 6,,92,0,19.9,0.188,28,0 10,122,78,0,27.6,0.512,,0 4,103,60,192,24,0.966,33,0 11,138,76,0,33.2,0.42,35,0 9,102,76,0,32.9,0.665,46,1 2,,68,0,38.2,0.503,27,1 4,111,72,207,37.1,1.39,56,1 3,180,64,70,34,0.271,26,0 7,133,84,0,40.2,0.696,37,0 7,106,92,0,22.7,0.235,48,0 9,171,110,240,45.4,0.721,54,1 7,159,64,0,27.4,0.294,40,0 0,180,66,0,42,1.893,25,1 1,146,56,0,29.7,0.564,29,0 2,71,70,0,28,0.586,22,0 7,103,66,0,39.1,0.344,31,1 7,105,0,0,0,0.305,24,0 1,103,80,82,19.4,0.491,22,0 1,101,50,36,24.2,0.526,26,0 5,88,66,23,24.4,0.342,30,0 8,176,90,300,33.7,0.467,58,1 7,150,66,342,34.7,0.718,42,0 1,73,50,0,23,0.248,21,0 7,187,68,304,37.7,0.254,41,1 0,100,88,110,46.8,0.962,31,0 0,146,82,0,40.5,1.781,44,0 0,105,64,142,41.5,0.173,22,0 2,84,0,0,0,0.304,21,0 8,133,72,0,32.9,0.27,39,1 5,44,62,0,25,0.587,36,0 2,141,58,128,25.4,0.699,24,0 7,114,66,0,32.8,0.258,42,1 5,99,74,0,29,0.203,32,0 0,109,88,0,32.5,0.855,38,1 2,109,92,0,42.7,0.845,54,0 1,95,66,38,19.6,0.334,25,0 4,146,85,100,28.9,0.189,27,0 2,100,66,90,32.9,0.867,28,1 5,139,64,140,28.6,0.411,26,0 13,126,90,0,43.4,0.583,42,1 4,129,86,270,35.1,0.231,23,0 1,79,75,0,32,0.396,22,0 1,0,48,0,24.7,0.14,,0 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4,76,62,0,34,0.391,25,0 7,160,54,175,30.5,0.588,39,1 4,146,92,0,31.2,0.539,61,1 5,124,74,0,34,0.22,38,1 5,78,48,0,33.7,0.654,25,0 4,97,60,0,28.2,0.443,22,0 4,99,76,51,23.2,0.223,21,0 0,162,76,100,53.2,0.759,25,1 6,111,64,0,34.2,0.26,24,0 2,107,74,100,33.6,0.404,23,0 5,132,80,0,26.8,0.186,69,0 0,113,76,0,33.3,0.278,,1 1,88,30,99,55,0.496,26,1 3,120,70,135,42.9,0.452,30,0 1,118,58,94,33.3,0.261,23,0 1,117,88,145,34.5,0.403,,1 0,105,84,0,27.9,0.741,62,1 4,173,70,168,29.7,0.361,33,1 9,122,56,0,33.3,1.114,33,1 3,170,64,225,34.5,0.356,30,1 8,84,74,0,38.3,0.457,39,0 2,96,68,49,21.1,0.647,26,0 2,125,60,140,33.8,0.088,31,0 0,100,70,50,30.8,0.597,21,0 0,93,60,92,28.7,0.532,22,0 0,129,80,0,31.2,0.703,29,0 5,105,72,325,36.9,0.159,28,0 3,128,78,0,21.1,0.268,55,0 5,106,82,0,39.5,0.286,38,0 2,108,52,63,32.5,0.318,22,0 10,108,66,0,32.4,0.272,42,1 4,154,62,284,32.8,0.237,23,0 0,102,75,0,0,0.572,21,0 9,57,80,0,32.8,0.096,41,0 2,106,64,119,30.5,1.4,34,0 5,147,78,0,33.7,0.218,65,0 2,90,70,0,27.3,0.085,22,0 1,136,74,204,37.4,0.399,24,0 4,114,65,0,21.9,0.432,37,0 9,156,86,155,34.3,1.189,42,1 1,153,82,485,40.6,0.687,23,0 8,188,78,0,47.9,0.137,43,1 7,152,88,0,50,0.337,36,1 2,99,52,94,24.6,0.637,21,0 1,109,56,135,25.2,0.833,23,0 2,88,74,53,29,0.229,22,0 17,163,72,114,40.9,0.817,47,1 4,151,90,0,29.7,0.294,36,0 7,102,74,105,37.2,0.204,45,0 0,114,80,285,44.2,0.167,27,0 2,100,64,0,29.7,0.368,21,0 0,131,88,0,31.6,0.743,32,1 6,104,74,156,29.9,0.722,41,1 3,148,66,0,32.5,0.256,22,0 4,120,68,0,29.6,0.709,34,0 4,110,66,0,31.9,0.471,29,0 3,111,90,78,28.4,0.495,29,0 6,102,82,0,30.8,0.18,36,1 6,134,70,130,35.4,0.542,29,1 2,87,0,0,28.9,0.773,25,0 1,79,60,48,43.5,0.678,23,0 2,75,64,55,29.7,0.37,33,0 8,179,72,130,32.7,0.719,36,1 6,85,78,0,31.2,0.382,42,0 0,129,110,130,67.1,0.319,26,1 5,143,78,0,45,0.19,47,0 5,130,82,0,39.1,0.956,37,1 6,87,80,0,23.2,0.084,32,0 0,119,64,92,34.9,0.725,23,0 1,,74,23,27.7,0.299,21,0 5,73,60,0,26.8,0.268,27,0 4,141,74,0,27.6,0.244,40,0 7,,68,0,35.9,0.745,41,1 8,181,68,495,30.1,0.615,60,1 1,,98,58,32,1.321,33,1 8,109,76,114,27.9,0.64,31,1 5,139,80,160,31.6,0.361,25,1 3,111,62,0,22.6,0.142,21,0 9,123,70,94,33.1,0.374,40,0 7,159,66,0,30.4,0.383,36,1 11,135,0,0,52.3,0.578,40,1 8,85,55,0,24.4,0.136,42,0 5,158,84,210,39.4,0.395,29,1 1,105,58,0,24.3,0.187,21,0 3,107,62,48,22.9,0.678,23,1 4,109,64,99,34.8,0.905,26,1 4,148,60,318,30.9,0.15,29,1 0,113,80,0,31,0.874,21,0 1,138,82,0,40.1,0.236,28,0 0,108,68,0,27.3,0.787,32,0 2,99,70,44,20.4,0.235,27,0 6,103,72,190,37.7,0.324,55,0 5,111,72,0,23.9,0.407,27,0 8,196,76,280,37.5,0.605,57,1 5,162,104,0,37.7,0.151,52,1 1,96,64,87,33.2,0.289,21,0 7,184,84,0,35.5,0.355,41,1 2,81,60,0,27.7,0.29,25,0 0,147,85,0,42.8,0.375,24,0 7,179,95,0,34.2,0.164,60,0 0,140,65,130,42.6,0.431,24,1 9,112,82,175,34.2,0.26,36,1 12,151,70,271,41.8,0.742,38,1 5,109,62,129,35.8,0.514,25,1 6,125,68,120,30,0.464,32,0 5,85,74,0,29,1.224,32,1 5,112,66,0,37.8,0.261,41,1 0,177,60,478,34.6,1.072,21,1 2,158,90,0,31.6,0.805,66,1 7,119,0,0,25.2,0.209,37,0 7,142,60,190,28.8,0.687,61,0 1,100,66,56,23.6,0.666,26,0 1,87,78,32,34.6,0.101,22,0 0,101,76,0,35.7,0.198,26,0 3,162,52,0,37.2,0.652,24,1 4,197,70,744,36.7,2.329,31,0 0,117,80,53,45.2,0.089,24,0 4,142,86,0,44,0.645,22,1 6,134,80,370,46.2,0.238,46,1 1,79,80,37,25.4,0.583,22,0 4,122,68,0,35,0.394,29,0 3,74,68,45,29.7,0.293,23,0 4,171,72,0,43.6,0.479,26,1 7,181,84,192,35.9,0.586,51,1 0,179,90,0,44.1,0.686,23,1 9,164,84,0,30.8,0.831,32,1 0,104,76,0,18.4,0.582,27,0 1,91,64,0,29.2,0.192,21,0 4,91,70,88,33.1,0.446,22,0 3,139,54,0,25.6,0.402,22,1 6,119,50,176,27.1,1.318,33,1 2,146,76,194,38.2,0.329,29,0 9,184,85,0,30,1.213,49,1 10,122,68,0,31.2,0.258,41,0 0,165,90,680,52.3,0.427,23,0 9,124,70,402,35.4,0.282,34,0 1,111,86,0,30.1,0.143,23,0 9,106,52,0,31.2,0.38,42,0 2,129,84,0,28,0.284,27,0 2,90,80,55,24.4,0.249,24,0 0,86,68,0,35.8,0.238,25,0 12,92,62,258,27.6,0.926,44,1 1,113,64,0,33.6,0.543,21,1 3,111,56,0,30.1,0.557,30,0 2,114,68,0,28.7,0.092,25,0 1,193,50,375,25.9,0.655,24,0 11,155,76,150,33.3,1.353,51,1 3,191,68,130,30.9,0.299,34,0 3,141,0,0,30,0.761,27,1 4,95,70,0,32.1,0.612,24,0 3,142,80,0,32.4,0.2,63,0 4,123,62,0,32,0.226,35,1 5,96,74,67,33.6,0.997,43,0 0,138,0,0,36.3,0.933,25,1 2,128,64,0,40,1.101,24,0 0,102,52,0,25.1,0.078,21,0 2,146,0,0,27.5,0.24,28,1 10,101,86,0,45.6,1.136,38,1 2,108,62,56,25.2,0.128,21,0 3,122,78,0,23,0.254,40,0 1,71,78,45,33.2,0.422,21,0 13,106,70,0,34.2,0.251,52,0 2,100,70,57,40.5,0.677,25,0 7,106,60,0,26.5,0.296,29,1 0,104,64,116,27.8,0.454,23,0 5,114,74,0,24.9,0.744,57,0 2,108,62,278,25.3,0.881,22,0 0,146,70,0,37.9,0.334,28,1 10,129,76,122,35.9,0.28,39,0 7,133,88,155,32.4,0.262,37,0 7,161,86,0,30.4,0.165,47,1 2,108,80,0,27,0.259,52,1 7,136,74,135,26,0.647,51,0 5,155,84,545,38.7,0.619,34,0 1,119,86,220,45.6,0.808,29,1 4,96,56,49,20.8,0.34,,0 5,108,72,75,36.1,0.263,33,0 0,78,88,40,36.9,0.434,21,0 0,107,62,74,36.6,0.757,25,1 2,128,78,182,43.3,1.224,31,1 1,128,48,194,40.5,0.613,24,1 0,161,50,0,21.9,0.254,65,0 6,151,62,120,35.5,0.692,28,0 2,146,70,360,28,0.337,29,1 0,126,84,215,30.7,0.52,24,0 14,,78,184,36.6,0.412,46,1 8,112,72,0,23.6,0.84,,0 0,167,0,0,32.3,0.839,30,1 2,144,58,135,31.6,0.422,25,1 5,77,82,42,35.8,0.156,35,0 5,,98,0,52.9,0.209,,1 3,150,76,0,21,0.207,37,0 2,120,76,105,39.7,0.215,29,0 10,161,68,132,25.5,0.326,47,1 0,137,68,148,24.8,0.143,21,0 0,128,68,180,30.5,1.391,25,1 2,124,68,205,32.9,0.875,30,1 6,80,66,0,26.2,0.313,41,0 0,106,70,148,39.4,0.605,22,0 2,155,74,96,26.6,0.433,27,1 3,113,50,85,29.5,0.626,25,0 7,109,80,0,35.9,1.127,43,1 2,112,68,94,34.1,0.315,26,0 3,99,80,64,19.3,0.284,30,0 3,182,74,0,30.5,0.345,29,1 3,115,66,140,38.1,0.15,28,0 6,194,78,0,23.5,0.129,59,1 4,129,60,231,27.5,0.527,31,0 3,112,74,0,31.6,0.197,25,1 0,124,70,0,27.4,0.254,36,1 13,152,90,29,26.8,0.731,43,1 2,112,75,0,35.7,0.148,21,0 1,157,72,168,25.6,0.123,24,0 1,122,64,156,35.1,0.692,30,1 10,179,70,0,35.1,0.2,37,0 2,102,86,120,45.5,0.127,23,1 6,105,70,68,30.8,0.122,37,0 8,118,72,0,23.1,1.476,46,0 2,87,58,52,32.7,0.166,25,0 1,180,0,0,43.3,0.282,41,1 12,106,80,0,23.6,0.137,44,0 1,95,60,58,23.9,0.26,22,0 0,165,76,255,47.9,0.259,26,0 0,117,0,0,33.8,0.932,44,0 5,115,76,0,31.2,0.343,44,1 9,152,78,171,34.2,0.893,33,1 7,178,84,0,39.9,0.331,41,1 1,130,70,105,25.9,0.472,22,0 1,95,74,73,25.9,0.673,36,0 1,0,68,0,32,0.389,22,0 5,122,86,0,34.7,0.29,33,0 8,95,72,0,36.8,0.485,57,0 8,126,88,108,38.5,0.349,49,0 1,139,46,83,28.7,0.654,22,0 3,116,0,0,23.5,0.187,23,0 3,99,62,74,21.8,0.279,26,0 5,0,80,0,41,0.346,37,1 4,92,80,0,42.2,0.237,29,0 4,137,84,0,31.2,0.252,30,0 3,61,82,0,34.4,0.243,46,0 1,90,62,43,27.2,0.58,24,0 3,90,78,0,42.7,0.559,21,0 9,165,88,0,30.4,0.302,49,1 1,125,50,167,33.3,0.962,28,1 13,129,0,0,39.9,0.569,44,1 12,88,74,54,35.3,0.378,48,0 1,196,76,249,36.5,0.875,29,1 5,189,64,325,31.2,0.583,29,1 5,158,70,0,29.8,0.207,63,0 5,103,108,0,39.2,0.305,65,0 4,146,78,0,38.5,0.52,67,1 4,147,74,293,34.9,0.385,30,0 5,99,54,83,34,0.499,30,0 6,124,72,0,27.6,0.368,29,1 0,101,64,0,21,0.252,21,0 3,81,86,66,27.5,0.306,22,0 1,133,102,140,32.8,0.234,45,1 3,173,82,465,38.4,2.137,25,1 0,118,64,89,0,1.731,21,0 0,84,64,66,35.8,0.545,21,0 2,105,58,94,34.9,0.225,25,0 2,122,52,158,36.2,0.816,28,0 12,140,82,325,39.2,0.528,58,1 0,98,82,84,25.2,0.299,22,0 1,87,60,75,37.2,0.509,22,0 4,156,75,0,48.3,0.238,32,1 0,93,100,72,43.4,1.021,35,0 1,107,72,82,30.8,0.821,24,0 0,105,68,0,20,0.236,22,0 1,109,60,182,25.4,0.947,21,0 1,90,62,59,25.1,1.268,25,0 1,125,70,110,24.3,0.221,25,0 1,119,54,50,22.3,0.205,24,0 5,116,74,0,32.3,0.66,35,1 8,105,100,0,43.3,0.239,45,1 5,144,82,285,32,0.452,58,1 3,100,68,81,31.6,0.949,28,0 1,100,66,196,32,0.444,42,0 5,166,76,0,45.7,0.34,27,1 1,131,64,415,23.7,0.389,21,0 4,116,72,87,22.1,0.463,37,0 4,158,78,0,32.9,0.803,31,1 2,127,58,275,27.7,1.6,25,0 3,96,56,115,24.7,0.944,39,0 0,131,66,0,34.3,0.196,22,1 3,82,70,0,21.1,0.389,25,0 3,193,70,0,34.9,0.241,25,1 4,95,64,0,32,0.161,31,1 6,137,61,0,24.2,0.151,55,0 5,136,84,88,35,0.286,35,1 9,72,78,0,31.6,0.28,38,0 5,168,64,0,32.9,0.135,41,1 2,123,48,165,42.1,0.52,26,0 4,115,72,0,28.9,0.376,46,1 0,101,62,0,21.9,0.336,25,0 8,197,74,0,25.9,1.191,39,1 1,172,68,579,42.4,0.702,28,1 6,102,90,0,35.7,0.674,28,0 1,112,72,176,34.4,0.528,25,0 1,143,84,310,42.4,1.076,22,0 1,143,74,61,26.2,0.256,21,0 0,138,60,167,34.6,0.534,21,1 3,173,84,474,35.7,0.258,22,1 1,97,68,0,27.2,1.095,22,0 4,144,82,0,38.5,0.554,37,1 1,83,68,0,18.2,0.624,27,0 3,129,64,115,26.4,0.219,28,1 1,119,88,170,45.3,0.507,26,0 2,94,68,76,26,0.561,21,0 0,102,64,78,40.6,0.496,21,0 2,115,64,0,30.8,0.421,21,0 8,151,78,210,42.9,0.516,36,1 4,184,78,277,37,0.264,31,1 0,94,0,0,0,0.256,25,0 1,181,64,180,34.1,0.328,38,1 0,135,94,145,40.6,0.284,26,0 1,95,82,180,35,0.233,43,1 2,99,0,0,22.2,0.108,23,0 3,89,74,85,30.4,0.551,38,0 1,80,74,60,30,0.527,22,0 2,139,75,0,25.6,0.167,29,0 1,90,68,0,24.5,1.138,36,0 0,141,0,0,42.4,0.205,29,1 12,140,85,0,37.4,0.244,41,0 5,147,75,0,29.9,0.434,28,0 1,97,70,0,18.2,0.147,21,0 6,107,88,0,36.8,0.727,31,0 0,189,104,0,34.3,0.435,41,1 2,83,66,50,32.2,0.497,22,0 4,117,64,120,33.2,0.23,24,0 8,108,70,0,30.5,0.955,33,1 4,117,62,0,29.7,0.38,30,1 0,180,78,14,59.4,2.42,25,1 1,100,72,70,25.3,0.658,28,0 0,95,80,92,36.5,0.33,26,0 0,104,64,64,33.6,0.51,22,1 0,120,74,63,30.5,0.285,26,0 1,82,64,95,21.2,0.415,23,0 2,134,70,0,28.9,0.542,23,1 0,91,68,210,39.9,0.381,25,0 2,119,0,0,19.6,0.832,72,0 2,100,54,105,37.8,0.498,24,0 14,175,62,0,33.6,0.212,38,1 1,135,54,0,26.7,0.687,62,0 5,86,68,71,30.2,0.364,24,0 10,148,84,237,37.6,1.001,51,1 9,134,74,60,25.9,0.46,81,0 9,120,72,56,20.8,0.733,48,0 1,71,62,0,21.8,0.416,26,0 8,74,70,49,35.3,0.705,39,0 5,88,78,0,27.6,0.258,37,0 10,115,98,0,24,1.022,34,0 0,124,56,105,21.8,0.452,21,0 0,74,52,36,27.8,0.269,22,0 0,97,64,100,36.8,0.6,25,0 8,120,0,0,30,0.183,38,1 6,154,78,140,46.1,0.571,27,0 1,144,82,0,41.3,0.607,28,0 0,137,70,0,33.2,0.17,22,0 0,119,66,0,38.8,0.259,22,0 7,136,90,0,29.9,0.21,50,0 4,114,64,0,28.9,0.126,24,0 0,137,84,0,27.3,0.231,59,0 2,105,80,191,33.7,0.711,29,1 7,114,76,110,23.8,0.466,31,0 8,126,74,75,25.9,0.162,39,0 4,132,86,0,28,0.419,63,0 3,158,70,328,35.5,0.344,35,1 0,123,88,0,35.2,0.197,29,0 4,85,58,49,27.8,0.306,28,0 0,84,82,125,38.2,0.233,23,0 0,145,0,0,44.2,0.63,31,1 0,135,68,250,42.3,0.365,24,1 1,139,62,480,40.7,0.536,21,0 0,173,78,265,46.5,1.159,58,0 4,99,72,0,25.6,0.294,28,0 8,194,80,0,26.1,0.551,67,0 2,83,65,66,36.8,0.629,24,0 2,89,90,0,33.5,0.292,42,0 4,99,68,0,32.8,0.145,33,0 4,125,70,122,28.9,1.144,45,1 3,80,0,0,0,0.174,22,0 6,166,74,0,26.6,0.304,66,0 5,110,68,0,26,0.292,30,0 2,81,72,76,30.1,0.547,25,0 7,195,70,145,25.1,0.163,55,1 6,154,74,193,29.3,0.839,39,0 2,117,90,71,25.2,0.313,21,0 3,84,72,0,37.2,0.267,28,0 6,0,68,0,39,0.727,41,1 7,94,64,79,33.3,0.738,41,0 3,96,78,0,37.3,0.238,40,0 10,75,82,0,33.3,0.263,38,0 0,180,90,90,36.5,0.314,35,1 1,130,60,170,28.6,0.692,21,0 2,84,50,76,30.4,0.968,21,0 8,120,78,0,25,0.409,64,0 12,84,72,0,29.7,0.297,46,1 0,139,62,210,22.1,0.207,21,0 9,91,68,0,24.2,0.2,58,0 2,91,62,0,27.3,0.525,22,0 3,99,54,86,25.6,0.154,24,0 3,163,70,105,31.6,0.268,28,1 9,145,88,165,30.3,0.771,53,1 7,125,86,0,37.6,0.304,51,0 13,76,60,0,32.8,0.18,41,0 6,129,90,326,19.6,0.582,60,0 2,68,70,66,25,0.187,25,0 3,124,80,130,33.2,0.305,26,0 6,114,0,0,0,0.189,26,0 9,130,70,0,34.2,0.652,45,1 3,125,58,0,31.6,0.151,24,0 3,87,60,0,21.8,0.444,21,0 1,97,64,82,18.2,0.299,21,0 3,116,74,105,26.3,0.107,24,0 0,117,66,188,30.8,0.493,22,0 0,111,65,0,24.6,0.66,31,0 2,122,60,106,29.8,0.717,22,0 0,107,76,0,45.3,0.686,24,0 1,86,66,65,41.3,0.917,29,0 6,91,0,0,29.8,0.501,31,0 1,77,56,56,33.3,1.251,24,0 4,132,0,0,32.9,0.302,23,1 0,105,90,0,29.6,0.197,46,0 0,57,60,0,21.7,0.735,67,0 0,127,80,210,36.3,0.804,23,0 3,129,92,155,36.4,0.968,32,1 8,100,74,215,39.4,0.661,43,1 3,128,72,190,32.4,0.549,,1 10,90,85,0,34.9,0.825,56,1 4,84,90,56,39.5,0.159,25,0 1,88,78,76,32,0.365,29,0 8,186,90,225,34.5,0.423,37,1 5,187,76,207,43.6,1.034,53,1 4,131,68,166,33.1,0.16,,0 1,164,82,67,32.8,0.341,50,0 4,189,110,0,28.5,0.68,37,0 1,116,70,0,27.4,0.204,21,0 3,84,68,106,31.9,0.591,25,0 6,114,88,0,27.8,0.247,66,0 1,88,62,44,29.9,0.422,23,0 1,84,64,115,36.9,0.471,28,0 7,124,70,215,25.5,0.161,37,0 1,97,70,0,38.1,0.218,30,0 8,110,76,0,27.8,0.237,58,0 11,103,68,0,46.2,0.126,42,0 11,85,74,0,30.1,0.3,35,0 6,125,76,0,33.8,0.121,54,1 0,198,66,274,41.3,0.502,28,1 1,87,68,77,37.6,0.401,24,0 6,99,60,54,26.9,0.497,32,0 0,91,80,0,32.4,0.601,27,0 2,95,54,88,26.1,0.748,22,0 1,99,72,18,38.6,0.412,21,0 6,92,62,126,32,0.085,46,0 4,154,72,126,31.3,0.338,37,0 0,121,66,165,34.3,0.203,33,1 3,78,70,0,32.5,0.27,39,0 2,130,96,0,22.6,0.268,21,0 3,111,58,44,29.5,0.43,22,0 2,98,60,120,34.7,0.198,22,0 1,143,86,330,30.1,0.892,23,0 1,119,44,63,35.5,0.28,25,0 6,108,44,130,24,0.813,35,0 2,118,80,0,42.9,0.693,21,1 10,133,68,0,27,0.245,36,0 2,197,70,0,34.7,0.575,62,1 0,151,90,0,42.1,0.371,21,1 6,109,60,0,25,0.206,27,0 12,121,78,0,26.5,0.259,62,0 8,100,76,0,38.7,0.19,42,0 8,124,76,600,28.7,0.687,52,1 1,93,56,0,22.5,0.417,22,0 8,143,66,0,34.9,0.129,41,1 6,103,66,0,24.3,0.249,29,0 3,176,86,156,33.3,1.154,52,1 0,73,0,0,21.1,0.342,25,0 11,111,84,0,46.8,0.925,45,1 2,112,78,140,39.4,0.175,24,0 3,132,80,0,34.4,0.402,44,1 2,82,52,115,28.5,1.699,25,0 6,123,72,230,33.6,0.733,34,0 0,188,82,185,32,0.682,22,1 0,67,76,0,45.3,0.194,46,0 1,89,24,25,27.8,0.559,21,0 1,173,74,0,36.8,0.088,,1 1,109,38,120,23.1,0.407,26,0 1,108,88,0,27.1,0.4,24,0 6,96,0,0,23.7,0.19,28,0 1,124,74,0,27.8,0.1,30,0 7,150,78,126,35.2,0.692,54,1 4,183,0,0,28.4,0.212,36,1 1,124,60,0,35.8,0.514,21,0 1,181,78,293,40,1.258,,1 1,92,62,41,19.5,0.482,25,0 0,152,82,272,41.5,0.27,27,0 1,111,62,182,24,0.138,23,0 3,106,54,158,30.9,0.292,24,0 3,174,58,194,32.9,0.593,36,1 7,168,88,321,38.2,0.787,40,1 6,105,80,0,32.5,0.878,,0 11,138,74,144,36.1,0.557,50,1 3,106,72,0,25.8,0.207,27,0 6,117,96,0,28.7,0.157,,0 2,68,62,15,20.1,0.257,23,0 9,112,82,0,28.2,1.282,50,1 0,119,0,0,32.4,0.141,24,1 2,112,86,160,38.4,0.246,28,0 2,92,76,0,24.2,1.698,28,0 6,183,94,0,40.8,1.461,45,0 0,94,70,115,43.5,0.347,21,0 2,108,64,0,30.8,0.158,21,0 4,90,88,54,37.7,0.362,29,0 0,125,68,0,24.7,0.206,21,0 0,132,78,0,32.4,0.393,21,0 5,128,80,0,34.6,0.144,45,0 4,94,65,0,24.7,0.148,21,0 7,114,64,0,27.4,0.732,34,1 0,102,78,90,34.5,0.238,24,0 2,111,60,0,26.2,0.343,23,0 1,128,82,183,27.5,0.115,22,0 10,92,62,0,25.9,0.167,31,0 13,104,72,0,31.2,0.465,38,1 5,104,74,0,28.8,0.153,48,0 2,94,76,66,31.6,0.649,23,0 7,97,76,91,40.9,0.871,32,1 1,100,74,46,19.5,0.149,28,0 0,102,86,105,29.3,0.695,27,0 4,128,70,0,34.3,0.303,24,0 6,147,80,0,29.5,0.178,50,1 4,90,0,0,28,0.61,31,0 3,103,72,152,27.6,0.73,27,0 2,157,74,440,39.4,0.134,30,0 1,167,74,144,23.4,0.447,33,1 0,179,50,159,37.8,0.455,22,1 11,136,84,130,28.3,0.26,42,1 0,107,60,0,26.4,0.133,,0 1,91,54,100,25.2,0.234,23,0 1,117,60,106,33.8,0.466,27,0 5,123,74,77,34.1,0.269,,0 2,120,54,0,26.8,0.455,,0 1,106,70,135,34.2,0.142,,0 2,155,52,540,38.7,0.24,,1 2,101,58,90,21.8,0.155,22,0 1,120,80,200,38.9,1.162,41,0 11,127,106,0,39,0.19,51,0 3,80,82,70,34.2,1.292,27,1 10,162,84,0,27.7,0.182,54,0 1,199,76,0,42.9,1.394,22,1 8,167,106,231,37.6,0.165,43,1 9,145,80,130,37.9,0.637,40,1 6,115,60,0,33.7,0.245,40,1 1,112,80,132,34.8,0.217,24,0 4,145,82,0,32.5,0.235,70,1 10,111,70,0,27.5,0.141,40,1 6,98,58,190,34,0.43,43,0 9,154,78,100,30.9,0.164,45,0 6,165,68,168,33.6,0.631,49,0 1,99,58,0,25.4,0.551,21,0 10,68,106,49,35.5,0.285,47,0 3,123,100,240,57.3,0.88,22,0 8,91,82,0,35.6,0.587,68,0 6,195,70,0,30.9,0.328,31,1 9,156,86,0,24.8,0.23,53,1 0,93,60,0,35.3,0.263,25,0 3,121,52,0,36,0.127,25,1 2,101,58,265,24.2,0.614,23,0 2,56,56,45,24.2,0.332,,0 0,162,76,0,49.6,0.364,26,1 0,95,64,105,44.6,0.366,22,0 4,125,80,0,32.3,0.536,27,1 5,136,82,0,0,0.64,,0 2,129,74,205,33.2,0.591,25,0 3,,64,0,23.1,0.314,22,0 1,107,50,0,28.3,0.181,29,0 1,140,74,180,24.1,0.828,,0 1,144,82,180,46.1,0.335,46,1 8,,80,0,24.6,0.856,,0 13,158,114,0,42.3,0.257,44,1 2,121,70,95,39.1,0.886,23,0 7,129,68,125,38.5,0.439,43,1 2,90,60,0,23.5,0.191,25,0 7,142,90,480,30.4,0.128,,1 3,169,74,125,29.9,0.268,31,1 0,99,0,0,25,0.253,22,0 4,,88,155,34.5,0.598,28,0 4,118,70,0,44.5,0.904,26,0 2,,76,200,35.9,0.483,26,0 6,125,78,0,27.6,0.565,,1 1,168,88,0,35,0.905,52,1 2,129,0,0,38.5,0.304,41,0 4,110,76,100,28.4,0.118,27,0 6,80,80,0,39.8,0.177,28,0 10,115,0,0,0,0.261,30,1 2,127,46,335,34.4,0.176,,0 9,,78,0,32.8,0.148,45,1 2,93,64,160,38,0.674,23,1 3,158,64,387,31.2,0.295,24,0 5,,78,22,29.6,0.439,40,0 10,129,62,0,41.2,0.441,,1 0,,58,291,26.4,0.352,21,0 3,,74,0,29.5,0.121,32,0 7,,50,392,33.9,0.826,34,1 3,173,78,185,33.8,0.97,31,1 10,,72,0,23.1,0.595,56,0 1,108,60,178,35.5,0.415,24,0 5,,76,0,35.6,0.378,52,1 4,83,86,0,29.3,0.317,34,0 1,114,66,200,38.1,0.289,21,0 1,149,68,127,29.3,0.349,42,1 5,,86,105,39.1,0.251,42,0 1,111,94,0,32.8,0.265,45,0 4,,78,0,39.4,0.236,38,0 1,116,78,180,36.1,0.496,25,0 0,141,84,0,32.4,0.433,,0 2,,88,0,22.9,0.326,22,0 2,92,52,0,30.1,0.141,22,0 3,130,78,79,28.4,0.323,34,1 8,120,86,0,28.4,0.259,22,1 2,174,88,120,44.5,0.646,24,1 2,,56,165,29,0.426,,0 2,105,75,0,23.3,0.56,53,0 4,95,60,0,35.4,0.284,28,0 0,126,86,120,27.4,0.515,21,0 8,,72,0,32,0.6,42,0 2,99,60,160,36.6,0.453,,0 1,102,74,0,39.5,0.293,42,1 11,120,80,150,42.3,0.785,48,1 3,102,44,94,30.8,0.4,26,0 1,109,58,116,28.5,0.219,22,0 9,140,94,0,32.7,0.734,45,1 13,153,88,140,40.6,1.174,39,0 12,100,84,105,30,0.488,46,0 1,147,94,0,49.3,0.358,27,1 1,81,74,57,46.3,1.096,32,0 3,187,70,200,36.4,0.408,36,1 6,162,62,0,24.3,0.178,50,1 4,136,70,0,31.2,1.182,22,1 1,121,78,74,39,0.261,28,0 3,108,62,0,26,0.223,25,0 0,181,88,510,43.3,0.222,26,1 8,154,78,0,32.4,0.443,45,1 1,128,88,110,36.5,1.057,37,1 7,137,90,0,32,0.391,39,0 0,123,72,0,36.3,0.258,52,1 1,106,76,0,37.5,0.197,26,0 6,190,92,0,35.5,0.278,66,1 2,88,58,16,28.4,0.766,22,0 9,170,74,0,44,0.403,43,1 9,89,62,0,22.5,0.142,33,0 10,101,76,180,32.9,0.171,63,0 2,122,70,0,36.8,0.34,27,0 5,121,72,112,26.2,0.245,30,0 1,126,60,0,30.1,0.349,47,1 1,93,70,0,30.4,0.315,23,0 \ No newline at end of file diff --git a/hw2_regression_classification_webscraping.ipynb b/hw2_regression_classification_webscraping.ipynb new file mode 100644 index 0000000..6ad4a09 --- /dev/null +++ b/hw2_regression_classification_webscraping.ipynb @@ -0,0 +1,2744 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data-X Spring 2018: Homework 02\n", + "\n", + "### Regression, Classification, Webscraping\n", + "\n", + "**Authors:** Sana Iqbal (Part 1, 2, 3), Alexander Fred-Ojala (Extra Credit)\n", + "\n", + "\n", + "In this homework, you will do some exercises with prediction-classification, regression and web-scraping.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Data:\n", + "__Data Source__:\n", + "Data file is uploaded to bCourses and is named: __Energy.csv__\n", + "\n", + "The dataset was created by Angeliki Xifara ( Civil/Structural Engineer) and was processed by Athanasios Tsanas, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, UK).\n", + "\n", + "__Data Description__:\n", + "\n", + "The dataset contains eight attributes of a building (or features, denoted by X1...X8) and response being the heating load on the building, y1. \n", + "\n", + "* X1\tRelative Compactness \n", + "* X2\tSurface Area \n", + "* X3\tWall Area \n", + "* X4\tRoof Area \n", + "* X5\tOverall Height \n", + "* X6\tOrientation \n", + "* X7\tGlazing Area \n", + "* X8\tGlazing Area Distribution \n", + "* y1\tHeating Load \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Q1:Read the data file in python. Describe data features in terms of type, distribution range and mean values. Plot feature distributions.This step should give you clues about data sufficiency." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1 float64\n", + "X2 float64\n", + "X3 float64\n", + "X4 float64\n", + "X5 float64\n", + "X6 int64\n", + "X7 float64\n", + "X8 int64\n", + "Y1 float64\n", + "dtype: object\n", + " X1 X2 X3 X4 X5 X6 \\\n", + "count 768.000000 768.000000 768.000000 768.000000 768.00000 768.000000 \n", + "mean 0.764167 671.708333 318.500000 176.604167 5.25000 3.500000 \n", + "std 0.105777 88.086116 43.626481 45.165950 1.75114 1.118763 \n", + "min 0.620000 514.500000 245.000000 110.250000 3.50000 2.000000 \n", + "25% 0.682500 606.375000 294.000000 140.875000 3.50000 2.750000 \n", + "50% 0.750000 673.750000 318.500000 183.750000 5.25000 3.500000 \n", + "75% 0.830000 741.125000 343.000000 220.500000 7.00000 4.250000 \n", + "max 0.980000 808.500000 416.500000 220.500000 7.00000 5.000000 \n", + "\n", + " X7 X8 Y1 \n", + "count 768.000000 768.00000 768.000000 \n", + "mean 0.234375 2.81250 22.307201 \n", + "std 0.133221 1.55096 10.090196 \n", + "min 0.000000 0.00000 6.010000 \n", + "25% 0.100000 1.75000 12.992500 \n", + "50% 0.250000 3.00000 18.950000 \n", + "75% 0.400000 4.00000 31.667500 \n", + "max 0.400000 5.00000 43.100000 \n" + ] + }, + { + "data": { + "image/png": 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rsp5JDgb2Bv5yjeNbTD+x30tnTCJJnkOnSP76mkbZ25KxJ9mn66j3OcDFaxzj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\nkiRJJRbJkiRJUolFsiRJklTy/wFzrg2Vs4gCmwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data=pd.read_csv('Energy.csv')\n", + "energydata=pd.DataFrame(data)\n", + "print(energydata.dtypes)\n", + "print(energydata.describe())\n", + "energydata.hist(figsize=(12,10))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " __REGRESSION__:\n", + "LABELS ARE CONTINUOUS VALUES.\n", + "Here the model is trained to predict a continuous value for each instance.\n", + "On inputting a feature vector into the model, the trained model is able to predict a continuous value for that instance. \n", + "\n", + "__Q2.1: Train a linear regression model on 85 percent of the given dataset, what is the intercept value and coefficient values.__\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of total samples: 768\n", + "Number of samples in training data: 652\n", + "Number of samples in validation data: 116\n", + "\n", + "Coefficients:\n", + " [ -6.24938187e+01 1.14351200e+12 -1.14351200e+12 -2.28702401e+12\n", + " 4.32184148e+00 1.94553204e-02 2.00676946e+01 2.36007105e-01]\n", + "\n", + "Intercept: 77.891702454\n" + ] + } + ], + "source": [ + "from sklearn import datasets, linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "energy_x = energydata.iloc[:,:-1]\n", + "energy_y = energydata['Y1']\n", + "print('number of total samples: ',len(energy_x))\n", + "\n", + "xtrain, xtest, ytrain, ytest = train_test_split(energy_x, energy_y, test_size=0.15, random_state = 100)\n", + "print ('Number of samples in training data:',len(xtrain))\n", + "print ('Number of samples in validation data:',len(xtest))\n", + "\n", + "reg=linear_model.LinearRegression(fit_intercept=True)\n", + "reg.fit(xtrain, ytrain)\n", + "\n", + "ypred=reg.predict(xtest)\n", + "ypred1=reg.predict(xtrain)\n", + "\n", + "print('\\nCoefficients:\\n ',reg.coef_)\n", + "print('\\nIntercept: ',reg.intercept_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "#### Q.2.2: Report model performance using 'ROOT MEAN SQUARE' error metric on: \n", + "__1. Data that was used for training(Training error)__ \n", + "__2. On the 15 percent of unseen data (test error) __ \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. RMSE train data: 2.93551172582705\n", + "2. RMSE test data: 2.885331133638931\n", + "\n", + "linear regression naccuracy (%): 90.9952525941\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "from math import sqrt\n", + "\n", + "print('1. RMSE train data: ',sqrt(mean_squared_error(ytrain, ypred1)))\n", + "print('2. RMSE test data: ',sqrt(mean_squared_error(ytest, ypred)))\n", + "acc_reglin=reg.score(xtest,ytest)\n", + "print('\\nlinear regression naccuracy (%): ',acc_reglin*100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "__ Q2.3: Lets us see the effect of amount of data on the performance of prediction model.Use varying amounts of Training data (100,200,300,400,500,all) to train regression models and report training error and validation error in each case. Validation data/Test data is the same as above for all these cases.__ \n", + "\n", + "Plot error rates vs number of training examples.Comment on the relationshipyou observe in the plot, between the amount of data used to train the model and the validation accuracy of the model.\n", + "\n", + "__Hint:__ Use array indexing to choose varying data amounts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[100, 200, 300, 400, 500, 652]\n", + "Number of samples in train data: 100\n", + "Number of samples in test data: 116\n", + "Training RMSE for 100 observations in training set: 2.92760482324\n", + "Test RMSE for 100 observations in training set: 3.05435382389\n", + "Number of samples in train data: 200\n", + "Number of samples in test data: 116\n", + "Training RMSE for 200 observations in training set: 2.94940507493\n", + "Test RMSE for 200 observations in training set: 2.89522452089\n", + "Number of samples in train data: 300\n", + "Number of samples in test data: 116\n", + "Training RMSE for 300 observations in training set: 3.03196105191\n", + "Test RMSE for 300 observations in training set: 2.9246707638\n", + "Number of samples in train data: 400\n", + "Number of samples in test data: 116\n", + "Training RMSE for 400 observations in training set: 2.93409921214\n", + "Test RMSE for 400 observations in training set: 2.88615665111\n", + "Number of samples in train data: 500\n", + "Number of samples in test data: 116\n", + "Training RMSE for 500 observations in training set: 3.00664352621\n", + "Test RMSE for 500 observations in training set: 2.86352008296\n", + "Number of samples in train data: 652\n", + "Number of samples in test data: 116\n", + "Training RMSE for all observations in training set: 2.93551172583\n", + "Test RMSE for all observations in training set: 2.88533113364\n" + ] + }, + { + "data": { + "image/png": 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7KWU5ucqUlTu49t25nPrMVMb+tpZOjarzzjXd+O2f/Rl1WnNqVY4q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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Observations from the plot:\n", + "1. Training RMSE wavers with increasing training observations.\n", + "2. Test RMSE in general decreases with increasing observations in the training set.\n", + "3. The training error being higher than test error means that the linear regression model accurately captures the trend in \n", + " the data. This can be seen in that the predictions made by this model on the test set are quite accurate and have low \n", + " errors/deviations from their actual values.\n", + "4. The test and train RMSE values are very close to each other indicating a good model.\n" + ] + } + ], + "source": [ + "'''traindata=[100,200,300,400,500,652]\n", + "\n", + "n=[]\n", + "ytesterror=[]\n", + "ytrainerror=[]\n", + "\n", + "for i,x in enumerate(traindata):\n", + " xtrainpoints=xtrain[:x]\n", + " ytrainpoints=ytrain[:x]\n", + " n=np.append(n,x)\n", + " print('no of training points: ',int(n[i]))\n", + " print(len(xtrainpoints))\n", + " reg=linear_model.LinearRegression()\n", + " reg.fit(xtrainpoints, ytrainpoints)\n", + " \n", + " ypred=reg.predict(xtest)\n", + " ypred1=reg.predict(xtrainpoints)\n", + " \n", + " a=mean_squared_error(ytrainpoints, ypred1)\n", + " b=mean_squared_error(ytest, ypred)\n", + " \n", + " ytesterror=np.append(ytesterror,a)\n", + " ytrainerror=np.append(ytrainerror,b)\n", + " \n", + " print('\\n1.',x,'RMSE train data: ',a)\n", + " print('2.',x,'RMSE test data: ',b)\n", + "\n", + "plt.plot(n,ytesterror,n,ytrainerror)\n", + "plt.xlabel('no of training samples')\n", + "plt.ylabel('RMSE')\n", + "\n", + "plt.show()'''\n", + "\n", + "train_mse = list()\n", + "test_mse = list()\n", + "a = list([100,200,300,400,500,652])\n", + "print(a)\n", + "\n", + "# Assuming error rate being talked about is RMSE and not error rate = 1 - accuracy\n", + "\n", + "# 100 samples\n", + "# Splitting data \n", + "x_train_100, x_test_100, y_train_100, y_test_100 = train_test_split(energy_x, energy_y, train_size = 100, test_size=0.15, random_state=100)\n", + "print ('Number of samples in train data:',len(x_train_100))\n", + "print ('Number of samples in test data:',len(x_test_100))\n", + "\n", + "# Fitting the model\n", + "LinearRegressionModel_100= linear_model.LinearRegression()\n", + "LinearRegressionModel_100.fit(x_train_100, y_train_100)\n", + "\n", + "# RMSE for training data\n", + "y_pred_train_100 = LinearRegressionModel_100.predict(x_train_100)\n", + "train_mse_100 = np.sqrt(np.mean((y_train_100-y_pred_train_100)**2))\n", + "print(\"Training RMSE for 100 observations in training set:\",train_mse_100)\n", + "train_mse.append(train_mse_100)\n", + "\n", + "# RMSE for test data\n", + "y_pred_test_100 = LinearRegressionModel_100.predict(x_test_100)\n", + "test_mse_100 = np.sqrt(np.mean((y_test_100-y_pred_test_100)**2))\n", + "print(\"Test RMSE for 100 observations in training set:\", test_mse_100)\n", + "test_mse.append(test_mse_100)\n", + "\n", + "\n", + "# 200 samples\n", + "# Splitting data \n", + "x_train_200, x_test_200, y_train_200, y_test_200 = train_test_split(energy_x, energy_y, train_size = 200, test_size=0.15, random_state=100)\n", + "print ('Number of samples in train data:',len(x_train_200))\n", + "print ('Number of samples in test data:',len(x_test_200))\n", + "\n", + "# Fitting the model\n", + "LinearRegressionModel_200= linear_model.LinearRegression()\n", + "LinearRegressionModel_200.fit(x_train_200, y_train_200)\n", + "\n", + "# RMSE for training data\n", + "y_pred_train_200 = LinearRegressionModel_200.predict(x_train_200)\n", + "train_mse_200 = np.sqrt(np.mean((y_train_200-y_pred_train_200)**2))\n", + "print(\"Training RMSE for 200 observations in training set:\",train_mse_200)\n", + "train_mse.append(train_mse_200)\n", + "\n", + "# RMSE for test data\n", + "y_pred_test_200 = LinearRegressionModel_200.predict(x_test_200)\n", + "test_mse_200 = np.sqrt(np.mean((y_test_200-y_pred_test_200)**2))\n", + "print(\"Test RMSE for 200 observations in training set:\", test_mse_200)\n", + "test_mse.append(test_mse_200)\n", + "\n", + "\n", + "# 300 samples\n", + "# Splitting data \n", + "x_train_300, x_test_300, y_train_300, y_test_300 = train_test_split(energy_x, energy_y, train_size = 300, test_size=0.15, random_state=100)\n", + "print ('Number of samples in train data:',len(x_train_300))\n", + "print ('Number of samples in test data:',len(x_test_300))\n", + "\n", + "# Fitting the model\n", + "LinearRegressionModel_300= linear_model.LinearRegression()\n", + "LinearRegressionModel_300.fit(x_train_300, y_train_300)\n", + "\n", + "# RMSE for training data\n", + "y_pred_train_300 = LinearRegressionModel_300.predict(x_train_300)\n", + "train_mse_300 = np.sqrt(np.mean((y_train_300-y_pred_train_300)**2))\n", + "print(\"Training RMSE for 300 observations in training set:\",train_mse_300)\n", + "train_mse.append(train_mse_300)\n", + "\n", + "# RMSE for test data\n", + "y_pred_test_300 = LinearRegressionModel_300.predict(x_test_300)\n", + "test_mse_300 = np.sqrt(np.mean((y_test_300-y_pred_test_300)**2))\n", + "print(\"Test RMSE for 300 observations in training set:\", test_mse_300)\n", + "test_mse.append(test_mse_300)\n", + "\n", + "\n", + "# 400 samples\n", + "# Splitting data \n", + "x_train_400, x_test_400, y_train_400, y_test_400 = train_test_split(energy_x, energy_y, train_size = 400, test_size=0.15, random_state=100)\n", + "print ('Number of samples in train data:',len(x_train_400))\n", + "print ('Number of samples in test data:',len(x_test_400))\n", + "\n", + "# Fitting the model\n", + "LinearRegressionModel_400= linear_model.LinearRegression()\n", + "LinearRegressionModel_400.fit(x_train_400, y_train_400)\n", + "\n", + "# RMSE for training data\n", + "y_pred_train_400 = LinearRegressionModel_400.predict(x_train_400)\n", + "train_mse_400 = np.sqrt(np.mean((y_train_400-y_pred_train_400)**2))\n", + "print(\"Training RMSE for 400 observations in training set:\",train_mse_400)\n", + "train_mse.append(train_mse_400)\n", + "\n", + "# RMSE for test data\n", + "y_pred_test_400 = LinearRegressionModel_400.predict(x_test_400)\n", + "test_mse_400 = np.sqrt(np.mean((y_test_400-y_pred_test_400)**2))\n", + "print(\"Test RMSE for 400 observations in training set:\", test_mse_400)\n", + "test_mse.append(test_mse_400)\n", + "\n", + "\n", + "# 500 samples\n", + "# Splitting data \n", + "x_train_500, x_test_500, y_train_500, y_test_500 = train_test_split(energy_x, energy_y, train_size = 500, test_size=0.15, random_state=100)\n", + "print ('Number of samples in train data:',len(x_train_500))\n", + "print ('Number of samples in test data:',len(x_test_500))\n", + "\n", + "# Fitting the model\n", + "LinearRegressionModel_500= linear_model.LinearRegression()\n", + "LinearRegressionModel_500.fit(x_train_500, y_train_500)\n", + "\n", + "# RMSE for training data\n", + "y_pred_train_500 = LinearRegressionModel_500.predict(x_train_500)\n", + "train_mse_500 = np.sqrt(np.mean((y_train_500-y_pred_train_500)**2))\n", + "print(\"Training RMSE for 500 observations in training set:\",train_mse_500)\n", + "train_mse.append(train_mse_500)\n", + "\n", + "# RMSE for test data\n", + "y_pred_test_500 = LinearRegressionModel_500.predict(x_test_500)\n", + "test_mse_500 = np.sqrt(np.mean((y_test_500-y_pred_test_500)**2))\n", + "print(\"Test RMSE for 500 observations in training set:\", test_mse_500)\n", + "test_mse.append(test_mse_500)\n", + "\n", + "# All samples\n", + "# Splitting data \n", + "x_train_all, x_test_all, y_train_all, y_test_all = train_test_split(energy_x, energy_y, test_size=0.15, random_state=100)\n", + "print ('Number of samples in train data:',len(x_train_all))\n", + "print ('Number of samples in test data:',len(x_test_all))\n", + "\n", + "# Fitting the model\n", + "LinearRegressionModel_all= linear_model.LinearRegression()\n", + "LinearRegressionModel_all.fit(x_train_all, y_train_all)\n", + "\n", + "# RMSE for training data\n", + "y_pred_train_all = LinearRegressionModel_all.predict(x_train_all)\n", + "train_mse_all = np.sqrt(np.mean((y_train_all-y_pred_train_all)**2))\n", + "print(\"Training RMSE for all observations in training set:\",train_mse_all)\n", + "train_mse.append(train_mse_all)\n", + "\n", + "# RMSE for test data\n", + "y_pred_test_all = LinearRegressionModel_all.predict(x_test_all)\n", + "test_mse_all = np.sqrt(np.mean((y_test_all-y_pred_test_all)**2))\n", + "print(\"Test RMSE for all observations in training set:\", test_mse_all)\n", + "test_mse.append(test_mse_all)\n", + "\n", + "plt.plot(a,train_mse, label = 'train RMSE')\n", + "plt.plot(a,test_mse, label = \"test RMSE\");\n", + "#plt.legend(loc=3)\n", + "plt.xlabel(\"Number of samples in the test set\")\n", + "plt.ylabel(\"Training/Test RMSE\")\n", + "plt.show()\n", + "\n", + "print('''\\n\\nObservations from the plot:\n", + "1. Training RMSE wavers with increasing training observations.\n", + "2. Test RMSE in general decreases with increasing observations in the training set.\n", + "3. The training error being higher than test error means that the linear regression model accurately captures the trend in \n", + " the data. This can be seen in that the predictions made by this model on the test set are quite accurate and have low \n", + " errors/deviations from their actual values.\n", + "4. The test and train RMSE values are very close to each other indicating a good model.''')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"import numpy as np\\n#np.random.seed(100)\\n\\nxtrain1=xtrain[:100] # error becomes very large when I choose random samples - xtrain.sample(100). why??\\nxtrain2=xtrain[:200]\\nxtrain3=xtrain[:300]\\nxtrain4=xtrain[:400]\\nxtrain5=xtrain[:500]\\nytrain1=ytrain[:100]\\nytrain2=ytrain[:200]\\nytrain3=ytrain[:300]\\nytrain4=ytrain[:400]\\nytrain5=ytrain[:500]\\nprint(xtrain1.head())\\nprint(xtrain2.head())\\n\\nreg1=linear_model.LinearRegression()\\nreg1.fit(xtrain1, ytrain1)\\n\\nypred1=reg1.predict(xtest)\\nypred11=reg1.predict(xtrain1)\\n\\nprint('1. 100 RMSE train data: ',sqrt(mean_squared_error(ytrain1, ypred11)))\\nprint('2. 100 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred1)))\\n\\nreg2=linear_model.LinearRegression()\\nreg.fit(xtrain2, ytrain2)\\n\\nypred2=reg.predict(xtest)\\nypred12=reg.predict(xtrain2)\\n\\nprint('\\n1. 200 RMSE train data: ',sqrt(mean_squared_error(ytrain2, ypred12)))\\nprint('2. 200 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred2)))\\n\\nreg3=linear_model.LinearRegression()\\nreg.fit(xtrain3, ytrain3)\\n\\nypred3=reg.predict(xtest)\\nypred13=reg.predict(xtrain3)\\n\\nprint('\\n1. 300 RMSE train data: ',sqrt(mean_squared_error(ytrain3, ypred13))\\nprint('2. 300 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred3)))\\n\\nreg4=linear_model.LinearRegression()\\nreg.fit(xtrain4, ytrain4)\\n\\nypred4=reg.predict(xtest)\\nypred14=reg.predict(xtrain4)\\n\\nprint('\\n1. 400 RMSE train data: ',sqrt(mean_squared_error(ytrain4, ypred14)))\\nprint('2. 400 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred4)))\\n\\nreg5=linear_model.LinearRegression()\\nreg.fit(xtrain5, ytrain5)\\n\\nypred5=reg.predict(xtest)\\nypred15=reg.predict(xtrain5)\\n\\nprint('\\n1. 500 RMSE train data: ',sqrt(mean_squared_error(ytrain5, ypred15)))\\nprint('2. 500 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred5)))\\n\\nprint('\\nerror for all data is same as in previous part')\"" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## IGNORE\n", + "\n", + "'''import numpy as np\n", + "#np.random.seed(100)\n", + "\n", + "xtrain1=xtrain[:100] # error becomes very large when I choose random samples - xtrain.sample(100). why??\n", + "xtrain2=xtrain[:200]\n", + "xtrain3=xtrain[:300]\n", + "xtrain4=xtrain[:400]\n", + "xtrain5=xtrain[:500]\n", + "ytrain1=ytrain[:100]\n", + "ytrain2=ytrain[:200]\n", + "ytrain3=ytrain[:300]\n", + "ytrain4=ytrain[:400]\n", + "ytrain5=ytrain[:500]\n", + "print(xtrain1.head())\n", + "print(xtrain2.head())\n", + "\n", + "reg1=linear_model.LinearRegression()\n", + "reg1.fit(xtrain1, ytrain1)\n", + "\n", + "ypred1=reg1.predict(xtest)\n", + "ypred11=reg1.predict(xtrain1)\n", + "\n", + "print('1. 100 RMSE train data: ',sqrt(mean_squared_error(ytrain1, ypred11)))\n", + "print('2. 100 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred1)))\n", + "\n", + "reg2=linear_model.LinearRegression()\n", + "reg.fit(xtrain2, ytrain2)\n", + "\n", + "ypred2=reg.predict(xtest)\n", + "ypred12=reg.predict(xtrain2)\n", + "\n", + "print('\\n1. 200 RMSE train data: ',sqrt(mean_squared_error(ytrain2, ypred12)))\n", + "print('2. 200 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred2)))\n", + "\n", + "reg3=linear_model.LinearRegression()\n", + "reg.fit(xtrain3, ytrain3)\n", + "\n", + "ypred3=reg.predict(xtest)\n", + "ypred13=reg.predict(xtrain3)\n", + "\n", + "print('\\n1. 300 RMSE train data: ',sqrt(mean_squared_error(ytrain3, ypred13))\n", + "print('2. 300 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred3)))\n", + "\n", + "reg4=linear_model.LinearRegression()\n", + "reg.fit(xtrain4, ytrain4)\n", + "\n", + "ypred4=reg.predict(xtest)\n", + "ypred14=reg.predict(xtrain4)\n", + "\n", + "print('\\n1. 400 RMSE train data: ',sqrt(mean_squared_error(ytrain4, ypred14)))\n", + "print('2. 400 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred4)))\n", + "\n", + "reg5=linear_model.LinearRegression()\n", + "reg.fit(xtrain5, ytrain5)\n", + "\n", + "ypred5=reg.predict(xtest)\n", + "ypred15=reg.predict(xtrain5)\n", + "\n", + "print('\\n1. 500 RMSE train data: ',sqrt(mean_squared_error(ytrain5, ypred15)))\n", + "print('2. 500 RMSE test data: ',sqrt(mean_squared_error(ytest, ypred5)))\n", + "\n", + "print('\\nerror for all data is same as in previous part')'''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "__CLASSIFICATION__:\n", + "LABELS ARE DISCRETE VALUES.\n", + "Here the model is trained to classify each instance into a set of predefined discrete classes.\n", + "On inputting a feature vector into the model, the trained model is able to predict a class of that instance. You can also output the probabilities of an instance belnging to a class. \n", + "\n", + "__ Q 3.1: Bucket values of 'y1' i.e 'Heating Load' from the original dataset into 3 classes:__ \n", + "\n", + "0: 'Low' ( < 15), \n", + "1: 'Medium' (15-30), \n", + "2: 'High' (>30)\n", + "\n", + "This converts the given dataset into a classification problem, classes being, Heating load is: *low, medium or high*. Use this datset with transformed 'heating load' for creating a logistic regression classifiction model that predicts heating load type of a building. Use test-train split ratio of 0.15. \n", + "\n", + "*Report training and test accuracies and confusion matrices.*\n", + "\n", + "\n", + "**HINT:** Use pandas.cut" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " X1 X2 X3 X4 X5 X6 X7 X8 Heating Load\n", + "0 0.98 514.5 294.0 110.25 7.0 2 0.0 0 Medium\n", + "1 0.98 514.5 294.0 110.25 7.0 3 0.0 0 Medium\n", + "2 0.98 514.5 294.0 110.25 7.0 4 0.0 0 Medium\n", + "3 0.98 514.5 294.0 110.25 7.0 5 0.0 0 Medium\n", + "4 0.90 563.5 318.5 122.50 7.0 2 0.0 0 Medium" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energydata['Heating Load']=pd.cut(energydata['Y1'],bins=[0,15,30,45],labels=['Low','Medium','High'])\n", + "energydata=energydata.drop('Y1',axis=1)\n", + "energydata.head() #energydata['Heating Load'].unique to chech values of bands\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logistic Regression accuracy for train data: 0.773006134969 and for test data: 0.76724137931\n", + "Confusion matrix of train data is: \n", + " Predicted 0 Predicted 1 Predicted 2\n", + "Actual 0 244 1 0\n", + "Actual 1 71 120 41\n", + "Actual 2 0 35 140\n", + "Confusion matrix of test data is: \n", + " Predicted 0 Predicted 1 Predicted 2\n", + "Actual 0 40 0 0\n", + "Actual 1 12 29 9\n", + "Actual 2 0 6 20\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "energy1x=energydata.iloc[:,:-1]\n", + "energy1y=energydata['Heating Load'].map({'Low': 0, 'Medium': 1,'High' :2})\n", + "xtrain, xtest, ytrain, ytest = train_test_split(energy1x, energy1y, test_size=0.15, random_state = 100)\n", + "\n", + "logreg=LogisticRegression()\n", + "logreg.fit(xtrain,ytrain)\n", + "Ypred=logreg.predict(xtest)\n", + "acc_train=logreg.score(xtrain,ytrain)\n", + "acc_test=logreg.score(xtest,ytest)\n", + "print('Logistic Regression accuracy for train data: ',acc_train,'and for test data: ',acc_test)\n", + "\n", + "#acc_log = sum(Ypred == ytest)/len(ytest)*100\n", + "#print('Scikit Model score: ',acc_log)\n", + "\n", + "# CONFUSION MATRIX\n", + "\n", + "from sklearn.metrics import confusion_matrix\n", + "y_true_train=ytrain\n", + "y_pred_train=logreg.predict(xtrain)\n", + "ConfusionMatrix=pd.DataFrame(confusion_matrix(y_true_train, y_pred_train), \\\n", + " columns=['Predicted 0','Predicted 1','Predicted 2'],index=['Actual 0','Actual 1','Actual 2'])\n", + "print ('Confusion matrix of train data is: \\n',ConfusionMatrix)\n", + "\n", + "y_true_test=ytest\n", + "y_pred_test=logreg.predict(xtest)\n", + "ConfusionMatrix1=pd.DataFrame(confusion_matrix(y_true_test, y_pred_test), \\\n", + " columns=['Predicted 0','Predicted 1','Predicted 2'],index=['Actual 0','Actual 1','Actual 2'])\n", + "print ('Confusion matrix of test data is: \\n',ConfusionMatrix1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__ Q2.2: One of the preprocessing steps in Data science is Feature Scaling i.e getting all our data on the same scale by setting same Min-Max of feature values. This makes training less sensitive to the scale of features . Scaling is important in algorithms that use distance based classification, SVM or K means or involve gradient descent optimization.If we Scale features in the range [0,1] it is called unity based normalization.__\n", + "\n", + "__Perform unity based normalization on the above dataset and train the model again, compare model performance in training and validation with your previous model.__ \n", + "\n", + "refer:http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-scaler \n", + "more at: https://en.wikipedia.org/wiki/Feature_scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MinMaxScaler(copy=True, feature_range=(0, 1))\n", + "\n", + " Sample row from actual train values:\n", + " X1 X2 X3 X4 X5 X6 X7 X8\n", + "458 0.74 686.0 245.0 220.5 3.5 4 0.25 4\n", + "\n", + " Scaled values of the same row:\n", + " [[ 0.33333333 0.58333333 0. 1. 0. 0.66666667\n", + " 0.625 0.8 ]]\n", + "\n", + " Sample row from actual test values:\n", + " X1 X2 X3 X4 X5 X6 X7 X8\n", + "173 0.71 710.5 269.5 220.5 3.5 3 0.1 3\n", + "\n", + " Scaled values of the test values\n", + " [[ 0.25 0.66666667 0.14285714 1. 0. 0.33333333\n", + " 0.25 0.6 ]]\n", + "Training accuracy for scaled features: 0.799079754601\n", + "Accuracy of the model on test data: 0.784482758621\n", + "\n", + "model improves after scaling\n" + ] + } + ], + "source": [ + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "min_max_scaler = preprocessing.MinMaxScaler() \n", + "print(min_max_scaler)\n", + "\n", + "energy_x = energydata.iloc[:,:-1]\n", + "energy_y = energydata['Heating Load'].map({'Low': 0, 'Medium': 1,'High' :2})\n", + "xtrain, xtest, ytrain, ytest = train_test_split(energy_x, energy_y, test_size=0.15, random_state = 100)\n", + "\n", + "x_train_scaled = min_max_scaler.fit_transform(xtrain)\n", + "x_test_scaled = min_max_scaler.fit_transform(xtest)\n", + "\n", + "print(\"\\n Sample row from actual train values:\\n\", xtrain[0:1][:])\n", + "print(\"\\n Scaled values of the same row:\\n\", x_train_scaled[0:1][:])\n", + "print(\"\\n Sample row from actual test values:\\n\", xtest[0:1][:])\n", + "print(\"\\n Scaled values of the test values\\n\", x_test_scaled[0:1][:])\n", + "\n", + "Logreg_scaled = linear_model.LogisticRegression()\n", + "Logreg_scaled.fit(x_train_scaled, ytrain)\n", + "\n", + "training_accuracy_scaled=Logreg_scaled.score(x_train_scaled,ytrain)\n", + "print ('Training accuracy for scaled features:',training_accuracy_scaled)\n", + "\n", + "test_accuracy_scaled=Logreg_scaled.score(x_test_scaled,ytest)\n", + "print('Accuracy of the model on test data: ',test_accuracy_scaled)\n", + "print('\\nmodel improves after scaling')\n", + "#minmax=MinMaxScaler().fit(xtrain)\n", + "#scaled_xtrain=minmax.transform(xtrain)\n", + "#scaled_ytrain=minmax.fit_transform(ytrain.values.reshape(-1,1))\n", + "#scaled_xtest=minmax.transform(xtest)\n", + "#scaled_ytest=minmax.fit_transform(ytest.values.reshape(-1,1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Part 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "__ 1. Read __`diabetesdata.csv`__ file into a pandas dataframe. Analyze the data features, check for NaN values. \n", + "About the data: __\n", + "\n", + "1. __TimesPregnant__: Number of times pregnant \n", + "2. __glucoseLevel__: Plasma glucose concentration a 2 hours in an oral glucose tolerance test \n", + "3. __BP__: Diastolic blood pressure (mm Hg) \n", + "5. __insulin__: 2-Hour serum insulin (mu U/ml) \n", + "6. __BMI__: Body mass index (weight in kg/(height in m)^2) \n", + "7. __pedigree__: Diabetes pedigree function \n", + "8. __Age__: Age (years) \n", + "9. __IsDiabetic__: 0 if not diabetic or 1 if diabetic) \n", + "\n", + "__ 2. Preprocess data to replace NaN values in a feature(if any) using mean of the feature. \n", + "Train logistic regression, SVM, perceptron, kNN, xgboost and random forest models using this preprocessed data with 20% test split.Report training and test accuracies.__\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " TimesPregnant glucoseLevel BP insulin BMI Pedigree Age \\\n", + "0 6 148.000000 72 0 33.6 0.627 50.000000 \n", + "1 1 121.016349 66 0 26.6 0.351 31.000000 \n", + "2 8 183.000000 64 0 23.3 0.672 33.353741 \n", + "3 1 121.016349 66 94 28.1 0.167 21.000000 \n", + "4 0 137.000000 40 168 43.1 2.288 33.000000 \n", + "\n", + " IsDiabetic \n", + "0 1 \n", + "1 0 \n", + "2 1 \n", + "3 0 \n", + "4 1 \n" + ] + }, + { + "data": { + "image/png": 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VzzWSvinpOWCepG0kLZD0SPreXCZp16oYTpT0H2nZ/6uqnsX0esskXSHpeUkP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\ncA5wMMlTgGcB35182JK0cv5Gre5V1Vur6uyq2gR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data=pd.read_csv('diabetesdata.csv')\n", + "diabetes=pd.DataFrame(data)\n", + "diabetes1=diabetes.fillna(diabetes.mean())\n", + "print(diabetes1.head())\n", + "diabetes1.hist(figsize=(8,10))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import datasets, linear_model\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "diabetes_x=diabetes1.iloc[:,:-1]\n", + "diabetes_y=diabetes['IsDiabetic']\n", + "#diabetes_y=diabetes_y.to_frame('IsDiabetic')\n", + "xtrain,xtest,ytrain,ytest=train_test_split(diabetes_x,diabetes_y,test_size=0.2,random_state=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no. of correct predictions: 113 / 154\n", + "Logistic Regression Accuracy for test data: 73.0 % and fo rtrain data: 78.0\n" + ] + } + ], + "source": [ + "# LOGISTIC REGRESSION\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "logreg=LogisticRegression()\n", + "logreg.fit(xtrain,ytrain)\n", + "ypred=logreg.predict(xtest)\n", + "print('no. of correct predictions: ',sum(ypred==ytest),'/',len(xtest))\n", + "trainacc=logreg.score(xtrain,ytrain)\n", + "accuracy=logreg.score(xtest,ytest)\n", + "print('Logistic Regression Accuracy for test data: ',str(round(accuracy,2)*100),'% and fo rtrain data: ',str(round(trainacc,2)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no. of correct predictions: 101 / 154\n", + "Logistic Regression Accuracy for test data: 66.0 % and fo rtrain data: 100.0\n" + ] + } + ], + "source": [ + "# SVM\n", + "\n", + "from sklearn.svm import SVC, LinearSVC\n", + "svc=SVC()\n", + "svc.fit(xtrain,ytrain)\n", + "ypred=svc.predict(xtest)\n", + "print('no. of correct predictions: ',sum(ypred==ytest),'/',len(xtest))\n", + "trainacc=svc.score(xtrain,ytrain)\n", + "accuracy=svc.score(xtest,ytest)\n", + "print('Logistic Regression Accuracy for test data: ',str(round(accuracy,2)*100),'% and fo rtrain data: ',str(round(trainacc,2)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no. of correct predictions: 99 / 154\n", + "Logistic Regression Accuracy for test data: 64.0 % and fo rtrain data: 64.0\n" + ] + } + ], + "source": [ + "# PERCEPTRON\n", + "\n", + "from sklearn.linear_model import Perceptron\n", + "per=Perceptron()\n", + "per.fit(xtrain,ytrain)\n", + "ypred=per.predict(xtest)\n", + "print('no. of correct predictions: ',sum(ypred==ytest),'/',len(xtest))\n", + "trainacc=per.score(xtrain,ytrain)\n", + "accuracy=per.score(xtest,ytest)\n", + "print('Logistic Regression Accuracy for test data: ',str(round(accuracy,2)*100),'% and fo rtrain data: ',str(round(trainacc,2)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no. of correct predictions: 110 / 154\n", + "Logistic Regression Accuracy for test data: 71.0 % and fo rtrain data: 85.0\n" + ] + } + ], + "source": [ + "# KNN\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn=KNeighborsClassifier(n_neighbors=2)\n", + "knn.fit(xtrain,ytrain)\n", + "ypred=knn.predict(xtest)\n", + "print('no. of correct predictions: ',sum(ypred==ytest),'/',len(xtest))\n", + "trainacc=knn.score(xtrain,ytrain)\n", + "accuracy=knn.score(xtest,ytest)\n", + "print('Logistic Regression Accuracy for test data: ',str(round(accuracy,2)*100),'% and fo rtrain data: ',str(round(trainacc,2)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no. of correct predictions: 111 / 154\n", + "Logistic Regression Accuracy for test data: 72.0 % and fo rtrain data: 100.0\n" + ] + } + ], + "source": [ + "# XGBOOST\n", + "\n", + "import xgboost as xgb\n", + "gradboost=xgb.XGBClassifier(n_estimators=1000)\n", + "gradboost.fit(xtrain,ytrain)\n", + "ypred=gradboost.predict(xtest)\n", + "print('no. of correct predictions: ',sum(ypred==ytest),'/',len(xtest))\n", + "trainacc=gradboost.score(xtrain,ytrain)\n", + "accuracy=gradboost.score(xtest,ytest)\n", + "print('Logistic Regression Accuracy for test data: ',str(round(accuracy,2)*100),'% and fo rtrain data: ',str(round(trainacc,2)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no. of correct predictions: 108 / 154\n", + "Logistic Regression Accuracy for test data: 70.0 % and fo rtrain data: 100.0\n" + ] + } + ], + "source": [ + "# RANDOM FOREST\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rf=RandomForestClassifier(n_estimators=1000)\n", + "rf.fit(xtrain,ytrain)\n", + "ypred=rf.predict(xtest)\n", + "print('no. of correct predictions: ',sum(ypred==ytest),'/',len(xtest))\n", + "trainacc=rf.score(xtrain,ytrain)\n", + "accuracy=rf.score(xtest,ytest)\n", + "print('Logistic Regression Accuracy for test data: ',str(round(accuracy,2)*100),'% and fo rtrain data: ',str(round(trainacc,2)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "__3. What is the ratio of diabetic persons in 3 equirange bands of 'BMI' and 'Pedigree' in the provided dataset.__\n", + "\n", + " __Convert these features - 'BP','insulin','BMI' and 'Pedigree' into categorical values by mapping different bands of values of these features to integers 0,1,2.__ \n", + " \n", + "HINT: USE pd.cut with bin=3 to create 3 bins\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IsDiabetic 0 1\n", + "BMI_cat \n", + "0 49 2\n", + "1 437 244\n", + "2 14 22\n", + "IsDiabetic 0 1\n", + "Pedigree_cat \n", + "0 461 224\n", + "1 34 40\n", + "2 5 4\n", + "IsDiabetic 0 1 Ratio of diabetics by BMI\n", + "BMI_cat \n", + "0 49 2 0.007463\n", + "1 437 244 0.910448\n", + "2 14 22 0.082090\n", + "IsDiabetic 0 1 Ratio of diabetics by Pedigree\n", + "Pedigree_cat \n", + "0 461 224 0.835821\n", + "1 34 40 0.149254\n", + "2 5 4 0.014925\n" + ] + }, + { + "data": { + "text/html": [ + "
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Ratio of diabetics by BMIRatio of diabetics by Pedigree
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" + ], + "text/plain": [ + " Ratio of diabetics by BMI Ratio of diabetics by Pedigree\n", + "0 0.007463 0.835821\n", + "1 0.910448 0.149254\n", + "2 0.082090 0.014925" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_BP = pd.cut(diabetes[\"BP\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"]) \n", + "X_BP=X_BP.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes['BP_cat'] = X_BP\n", + "\n", + "X_insulin = pd.cut(diabetes[\"insulin\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"])\n", + "X_insulin=X_insulin.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes['insulin_cat'] = X_insulin\n", + "\n", + "X_BMI = pd.cut(diabetes[\"BMI\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"])\n", + "X_BMI=X_BMI.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes['BMI_cat'] = X_BMI\n", + "\n", + "X_Pedigree = pd.cut(diabetes[\"Pedigree\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"])\n", + "X_Pedigree=X_Pedigree.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes['Pedigree_cat'] = X_Pedigree\n", + "\n", + "tab1 = pd.crosstab(diabetes[\"BMI_cat\"], diabetes[\"IsDiabetic\"])\n", + "print(tab1)\n", + "tab2 = pd.crosstab(diabetes[\"Pedigree_cat\"],diabetes[\"IsDiabetic\"])\n", + "print(tab2)\n", + "ratio1 = pd.Series([2/268, 244/268, 22/268]) \n", + "tab1[\"Ratio of diabetics by BMI\"]=ratio1\n", + "print(tab1)\n", + "ratio2 = pd.Series([224/268, 40/268, 4/268])\n", + "tab2[\"Ratio of diabetics by Pedigree\"] = ratio2\n", + "print(tab2)\n", + "ratio_df = pd.concat([tab1[\"Ratio of diabetics by BMI\"],tab2[\"Ratio of diabetics by Pedigree\"]], axis = 1)\n", + "\n", + "ratio_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "__4. Now consider the original dataset again, instead of generalizing the NAN values with the mean of the feature we will try assigning values to NANs based on some hypothesis. For example for age we assume that the relation between BMI and BP of people is a reflection of the age group.We can have 9 types of BMI and BP relations and our aim is to find the median age of each of that group:__\n", + "\n", + "Your Age guess matrix will look like this: \n", + "\n", + "| BMI | 0 | 1 | 2 |\n", + "|-----|-------------|------------- |----- |\n", + "| BP | | | |\n", + "| 0 | a00 | a01 | a02 |\n", + "| 1 | a10 | a11 | a12 |\n", + "| 2 | a20 | a21 | a22 |\n", + "\n", + "\n", + "__Create a guess_matrix for NaN values of *'Age'* ( using 'BMI' and 'BP') and *'glucoseLevel'* (using 'BP' and 'Pedigree') for the given dataset and assign values accordingly to the NaNs in 'Age' or *'glucoseLevel'* .__\n", + "\n", + "\n", + "Refer to how we guessed age in the titanic notebook in the class.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " TimesPregnant glucoseLevel BP insulin BMI Pedigree Age IsDiabetic\n", + "0 6 148.0 72 0 33.6 0.627 50.0 1\n", + "1 1 NaN 66 0 26.6 0.351 31.0 0\n", + "2 8 183.0 64 0 23.3 0.672 NaN 1\n", + "3 1 NaN 66 94 28.1 0.167 21.0 0\n", + "4 0 137.0 40 168 43.1 2.288 33.0 1\n", + "\n", + " TimesPregnant 0\n", + "glucoseLevel 34\n", + "BP 0\n", + "insulin 0\n", + "BMI 0\n", + "Pedigree 0\n", + "Age 33\n", + "IsDiabetic 0\n", + "dtype: int64\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "Guess_Age table:\n", + " [[24 29 33]\n", + " [25 29 32]\n", + " [55 37 31]]\n", + "\n", + "Assigning age values to NAN age values in the dataset...\n", + "\n", + "\n", + " [[0 0 0]\n", + " [0 0 0]\n", + " [0 0 0]]\n", + "Guess_Glucose table:\n", + " [[115 112 133]\n", + " [127 115 129]\n", + " [137 149 159]]\n", + "\n", + "Assigning glucose values to NAN age values in the dataset...\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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TimesPregnantglucoseLevelBPinsulinBMIPedigreeAgeIsDiabeticBP_catBMI_catPedigree_catinsulin_cat
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" + ], + "text/plain": [ + " TimesPregnant glucoseLevel BP insulin BMI Pedigree Age IsDiabetic \\\n", + "0 6 148 72 0 33.6 0.627 50 1 \n", + "1 1 112 66 0 26.6 0.351 31 0 \n", + "2 8 183 64 0 23.3 0.672 29 1 \n", + "3 1 112 66 94 28.1 0.167 21 0 \n", + "4 0 137 40 168 43.1 2.288 33 1 \n", + "\n", + " BP_cat BMI_cat Pedigree_cat insulin_cat \n", + "0 1 1 0 0 \n", + "1 1 1 0 0 \n", + "2 1 1 0 0 \n", + "3 1 1 0 0 \n", + "4 0 1 2 0 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reading the data as a new file\n", + "diabetes2 = pd.read_csv(\"diabetesdata.csv\")\n", + "print(diabetes2.head())\n", + "print(\"\\n\",diabetes2.isnull().sum())\n", + "\n", + "# Converting features to categorical variables\n", + "X_BP = pd.cut(diabetes2[\"BP\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"]) \n", + "X_BP=X_BP.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes2['BP_cat'] = X_BP\n", + "\n", + "X_BMI = pd.cut(diabetes2[\"BMI\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"])\n", + "X_BMI=X_BMI.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes2['BMI_cat'] = X_BMI\n", + "\n", + "X_Pedigree = pd.cut(diabetes2[\"Pedigree\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"])\n", + "X_Pedigree=X_Pedigree.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes2['Pedigree_cat'] = X_Pedigree\n", + "\n", + "X_insulin = pd.cut(diabetes2[\"insulin\"],bins=3,labels = [\"Low\", \"Medium\", \"High\"])\n", + "X_insulin=X_insulin.map({'Low': 0, 'Medium': 1,'High' :2})\n", + "diabetes2['insulin_cat'] = X_insulin\n", + "\n", + "# Initiating our age guess table\n", + "guess_ages = np.zeros((3,3),dtype=int)\n", + "print(\"\\n\",guess_ages)\n", + "\n", + "# Forming the age guess table and modifying the diabetes2 dataframe to fill median values\n", + "for i in range(0, 3):\n", + " for j in range(0,3):\n", + " guess_df = diabetes2[(diabetes2['BP_cat'] == i) \\\n", + " &(diabetes2['BMI_cat'] == j)]['Age'].dropna()\n", + "\n", + " # Extract the median age for this group\n", + " # (less sensitive) to outliers\n", + " age_guess = guess_df.median()\n", + " \n", + " # Convert random age float to int\n", + " guess_ages[i,j] = int(age_guess)\n", + " \n", + " \n", + "print('Guess_Age table:\\n',guess_ages)\n", + "print ('\\nAssigning age values to NAN age values in the dataset...')\n", + " \n", + "for i in range(0, 3):\n", + " for j in range(0, 3):\n", + " diabetes2.loc[ (diabetes2.Age.isnull()) & (diabetes2.BP_cat == i) \\\n", + " & (diabetes.BMI_cat == j),'Age'] = guess_ages[i,j]\n", + " \n", + "\n", + "diabetes2['Age'] = diabetes2['Age'].astype(int)\n", + "print()\n", + "\n", + "# Initiating our glucose guess table\n", + "guess_glucose = np.zeros((3,3),dtype=int)\n", + "print(\"\\n\",guess_glucose)\n", + "\n", + "# Forming the glucose guess table and modifying the diabetes2 dataframe to fill median values\n", + "for i in range(0, 3):\n", + " for j in range(0,3):\n", + " guess_df2 = diabetes2[(diabetes2['Pedigree_cat'] == i) \\\n", + " &(diabetes2['BP_cat'] == j)]['glucoseLevel'].dropna()\n", + "\n", + " # Extract the median age for this group\n", + " # (less sensitive) to outliers\n", + " glucose_guess = guess_df2.median()\n", + " \n", + " # Convert random age float to int\n", + " guess_glucose[i,j] = int(glucose_guess)\n", + " \n", + " \n", + "print('Guess_Glucose table:\\n',guess_glucose)\n", + "print ('\\nAssigning glucose values to NAN age values in the dataset...')\n", + " \n", + "for i in range(0, 3):\n", + " for j in range(0, 3):\n", + " diabetes2.loc[ (diabetes2.glucoseLevel.isnull()) & (diabetes2.Pedigree_cat == i) \\\n", + " & (diabetes.BP_cat == j),'glucoseLevel'] = guess_glucose[i,j]\n", + " \n", + "\n", + "diabetes2['glucoseLevel'] = diabetes2['glucoseLevel'].astype(int)\n", + "print()\n", + "diabetes2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The guess matrix for age as a dataframe is shown below:\n", + "\n", + " BMI_cat \n", + " 0 1 2\n", + "BP_cat 0 24 29 33\n", + " 1 25 29 32\n", + " 2 55 37 31\n", + "The guess matrix for glucose as a dataframe is shown below:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " BP_cat \n", + " 0 1 2\n", + "Pedigree_cat 0 115 112 133\n", + " 1 127 115 129\n", + " 2 137 149 159" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_df = pd.DataFrame(guess_ages)\n", + "\n", + "columns = [(\"BMI_cat\",0),(\"BMI_cat\",1),(\"BMI_cat\",2)]\n", + "age_df.columns = pd.MultiIndex.from_tuples(columns)\n", + "\n", + "index = [(\"BP_cat\",0),(\"BP_cat\",1),(\"BP_cat\",2)]\n", + "age_df.index = pd.MultiIndex.from_tuples(index)\n", + "print(\"The guess matrix for age as a dataframe is shown below:\\n\")\n", + "print(age_df)\n", + "\n", + "\n", + "glucose_df = pd.DataFrame(guess_glucose)\n", + "\n", + "columns2 = [(\"BP_cat\",0),(\"BP_cat\",1),(\"BP_cat\",2)]\n", + "glucose_df.columns = pd.MultiIndex.from_tuples(columns2)\n", + "\n", + "index2 = [(\"Pedigree_cat\",0),(\"Pedigree_cat\",1),(\"Pedigree_cat\",2)]\n", + "glucose_df.index = pd.MultiIndex.from_tuples(index2)\n", + "print(\"The guess matrix for glucose as a dataframe is shown below:\\n\")\n", + "glucose_df\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "__5. Now, convert 'glucoseLevel' and 'Age' features also to categorical variables of 5 categories each.__\n", + "\n", + "__Use this dataset (with all features in categorical form) to train perceptron, logistic regression and random forest models using 20% test split. Report training and test accuracies.__\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TimesPregnantIsDiabeticBP_catBMI_catPedigree_catinsulin_catglucoseLevel_catAge_cat
061110032
110110020
281110040
310110020
401012030
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" + ], + "text/plain": [ + " TimesPregnant IsDiabetic BP_cat BMI_cat Pedigree_cat insulin_cat \\\n", + "0 6 1 1 1 0 0 \n", + "1 1 0 1 1 0 0 \n", + "2 8 1 1 1 0 0 \n", + "3 1 0 1 1 0 0 \n", + "4 0 1 0 1 2 0 \n", + "\n", + " glucoseLevel_cat Age_cat \n", + "0 3 2 \n", + "1 2 0 \n", + "2 4 0 \n", + "3 2 0 \n", + "4 3 0 " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# Converting glucose and age into categorical variables of 5 categories each\n", + "X_glucose = pd.cut(diabetes2[\"glucoseLevel\"],bins=5,labels = [\"Lowest\", \"Lower\", \"Medium\", \"Higher\", \"Highest\"])\n", + "X_glucose=X_glucose.map({'Lowest': 0, 'Lower': 1,'Medium' :2, 'Higher':3, 'Highest':4})\n", + "diabetes2['glucoseLevel_cat'] = X_glucose\n", + "\n", + "X_age = pd.cut(diabetes2[\"Age\"],bins=5,labels = [\"Lowest\", \"Lower\", \"Medium\", \"Higher\", \"Highest\"])\n", + "X_age=X_age.map({'Lowest': 0, 'Lower': 1,'Medium' :2, 'Higher':3, 'Highest':4})\n", + "diabetes2['Age_cat'] = X_age\n", + "\n", + "\n", + "diabetes2.drop(\"glucoseLevel\",axis=1, inplace=True)\n", + "diabetes2.drop(\"BP\",axis=1, inplace=True)\n", + "diabetes2.drop(\"insulin\",axis=1, inplace=True)\n", + "diabetes2.drop(\"BMI\",axis=1, inplace=True)\n", + "diabetes2.drop(\"Pedigree\",axis=1, inplace=True)\n", + "diabetes2.drop(\"Age\",axis=1, inplace=True)\n", + "\n", + "diabetes2.head()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " TimesPregnant BP_cat BMI_cat Pedigree_cat insulin_cat \\\n", + "0 6 1 1 0 0 \n", + "1 1 1 1 0 0 \n", + "2 8 1 1 0 0 \n", + "3 1 1 1 0 0 \n", + "4 0 0 1 2 0 \n", + "\n", + " glucoseLevel_cat Age_cat \n", + "0 3 2 \n", + "1 2 0 \n", + "2 4 0 \n", + "3 2 0 \n", + "4 3 0 \n", + "\n", + "\n", + " 0 1\n", + "1 0\n", + "2 1\n", + "3 0\n", + "4 1\n", + "Name: IsDiabetic, dtype: int64\n", + "Number of samples in training data: 614\n", + "Number of samples in validation data: 154\n", + "\n", + " Use this dataset (with all features in categorical form) to train perceptron,\n", + "logistic regression and random forest models using 20% test split. Report training and test accuracies..\n", + "\n", + " Training accuracy for logistic regression: 0.768729641694\n", + "Test accuracy for logistic regression: 0.714285714286\n", + "\n", + " Training accuracy for perceptron: 0.711726384365\n", + "Test accuracy for perceptron: 0.701298701299\n", + "\n", + " Training accuracy for Random Forest: 0.899022801303\n", + "Test accuracy for Random Forest: 0.655844155844\n" + ] + }, + { + "data": { + "text/html": [ + "
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TimesPregnantIsDiabeticBP_catBMI_catPedigree_catinsulin_catglucoseLevel_catAge_cat
061110032
110110020
281110040
310110020
401012030
\n", + "
" + ], + "text/plain": [ + " TimesPregnant IsDiabetic BP_cat BMI_cat Pedigree_cat insulin_cat \\\n", + "0 6 1 1 1 0 0 \n", + "1 1 0 1 1 0 0 \n", + "2 8 1 1 1 0 0 \n", + "3 1 0 1 1 0 0 \n", + "4 0 1 0 1 2 0 \n", + "\n", + " glucoseLevel_cat Age_cat \n", + "0 3 2 \n", + "1 2 0 \n", + "2 4 0 \n", + "3 2 0 \n", + "4 3 0 " + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Applying various ML methods\n", + "\n", + "# Getting features and response from the data as 2 separate dataframes\n", + "X=diabetes2.loc[:,[\"TimesPregnant\",\"BP_cat\",\"BMI_cat\", \"Pedigree_cat\",\"insulin_cat\",\"glucoseLevel_cat\",\"Age_cat\"]]\n", + "print(X.head())\n", + "\n", + "Y=diabetes2['IsDiabetic']\n", + "print(\"\\n\\n\",Y.head())\n", + "\n", + "# Splitting the data set\n", + "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=100)\n", + "print('Number of samples in training data:',len(x_train))\n", + "print('Number of samples in validation data:',len(x_test))\n", + "\n", + "print('''\\n Use this dataset (with all features in categorical form) to train perceptron,\n", + "logistic regression and random forest models using 20% test split. Report training and test accuracies..''')\n", + "\n", + "\n", + "\n", + "# Logistic regression\n", + "logreg = LogisticRegression() \n", + "logreg.fit(x_train, y_train) \n", + "\n", + "# Training accuracy\n", + "training_accuracy_logistic=logreg.score(x_train,y_train)\n", + "print ('\\n Training accuracy for logistic regression:',training_accuracy_logistic)\n", + "\n", + "# Test accuracy\n", + "test_accuracy_logistic=logreg.score(x_test,y_test)\n", + "print('Test accuracy for logistic regression: ', test_accuracy_logistic)\n", + "\n", + "\n", + "# Perceptron\n", + "perceptron = Perceptron()\n", + "perceptron.fit(x_train, y_train)\n", + "\n", + "# Training accuracy\n", + "training_accuracy_perceptron=perceptron.score(x_train,y_train)\n", + "print ('\\n Training accuracy for perceptron:',training_accuracy_perceptron)\n", + "\n", + "# Test accuracy\n", + "test_accuracy_perceptron=perceptron.score(x_test,y_test)\n", + "print('Test accuracy for perceptron: ', test_accuracy_perceptron)\n", + "\n", + "\n", + "\n", + "# Random Forest\n", + "random_forest = RandomForestClassifier(n_estimators=1000)\n", + "random_forest.fit(x_train, y_train)\n", + "\n", + "# Training accuracy\n", + "training_accuracy_rf=random_forest.score(x_train,y_train)\n", + "print ('\\n Training accuracy for Random Forest:',training_accuracy_rf)\n", + "\n", + "# Test accuracy\n", + "test_accuracy_rf=random_forest.score(x_test,y_test)\n", + "print('Test accuracy for Random Forest: ', test_accuracy_rf)\n", + "\n", + "diabetes2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 3\n", + "\n", + "1. __Derive the expression for the optimal parameters in the linear regression equation, i.e. solve the normal equation for Ordinary Least Squares for the case of Simple Linear Regression, when we only have one input and one output__\n", + "\n", + "Given a set of _n_ points $(X_i,Y_i)$ where $Yi$ is dependent on $Xi$ by a linear relation, find the best-fit line,$$Z_i = {aX_i + b}$$ that minimizes the __sum of squared errors in Y__,i.e: $$minimize \\sum_{i}{(Y_i- Z_i)^2}$$\n", + "__i. __ Show that $$ intercept \\quad b = \\overline{Y}- a.\\overline{X}\\quad and \\quad slope \\quad a= \\frac{\\sum_{i}(X_i- \\overline{X})\u0001(Y_i- \\overline{Y})^2}{ \\sum_{i}(X_i- \\overline{X})}$$\n", + "\n", + "\n", + " where $\\overline{X}$ and $\\overline{Y}$ are the averages of the X values and the Y values, respectively.\n", + " \n", + "__ ii. __Show that slope _a_ can be written as $ a = r.(S_y /S_x)$ where $S_y$ = the standard deviation of the Y values and $S_x$= the standard deviation of the X values and _r_ is the correlation coefficient.\n", + "\n", + "##### Please try to write a nice LateXed version of your answer, and do the derivations of the expressions as nicely as possible\n", + "\n", + "\n", + "_____" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "i. For least squares regression, the sum of the squares of the errors is minimized. Using calculus, we can find equations for the parameters a and b that minimize the sum of the squared errors, S.\n", + "\n", + "$\\min S = \\displaystyle\\min \\sum\\limits_{i=1}^n \\left(e_i \\right)^2= \\min \\sum \\left(Y_i - Z_i \\right)^2= \\min \\sum \\left(Y_i - b - aX_i\\right)^2$\n", + "\n", + "We want to find b and a that minimize the sum, S. We start by taking the partial derivative of S with respect to b and setting it to zero: \n", + "\n", + "$\\displaystyle\\frac{\\partial{S}}{\\partial{b}} = -2 \\sum \\left(Y_i - b - aX_i\\right) = 0$\n", + "\n", + "$=>\\displaystyle\\sum \\left(Y_i - b - aX_i\\right) = 0$\n", + "\n", + "$=>\\displaystyle\\sum b = \\sum Y_i -a \\sum X_i$\n", + "\n", + "$=>\\displaystyle nb = \\sum Y_i -a\\sum X_i$\n", + "\n", + "$=>\\displaystyle b = \\frac{1}{n}\\sum Y_i - a \\frac{1}{n}\\sum X_i $ \n", + "\n", + "Let's call the above as equation 1\n", + "\n", + "$=>\\displaystyle b = \\bar Y - a . \\bar X $\n", + "\n", + "where $\\bar Y$ and $\\bar X$ are the averages of the X values and Y values respectively. This completes the derivation of the\n", + "intercept.\n", + "\n", + "Before taking partial derivative of S with respect to a, substitute the previous result for b in the equation for S\n", + "\n", + "$\\displaystyle \\min S = \\min \\sum \\left[Y_i-\\left(\\bar Y-a\\bar X \\right)-aX_i\\right]^{2}=\\min \\sum \\left[\\left(Y_i-\\bar Y\\right)-a\\left(X_i-\\bar X\\right)\\right]^{2}$\n", + "\n", + "Now, taking the partial derivative of S with respect to a and setting it to zero:\n", + "\n", + "$\\displaystyle \\frac{\\partial{S}}{\\partial{b}} = -2\\sum \\left[\\left(Y_i-\\bar Y\\right)-a\\left(X_i-\\bar X\\right)\\right]\\left(X_i-{\\bar X}\\right)=0$\n", + "\n", + "$=>\\displaystyle \\sum \\left(Y_i-\\bar Y\\right)\\left(X_i-\\bar X\\right)-a\\sum \\left(X_i-\\bar X\\right)^{2}=0$\n", + "\n", + "$=>\\displaystyle a = \\frac {\\sum \\left(X_i-\\bar X\\right)\\left(Y_i-\\bar Y\\right)}{\\sum \\left(X_i-\\bar X\\right)^{2}}$\n", + "\n", + "This completes the derivation of the slope.\n", + "\n", + "ii. We can divide the numerator and the denominator in the above equation obtained for slope by n to get the following equation:\n", + "\n", + "$=>\\displaystyle a = \\frac {\\sum \\left(X_i-\\bar X\\right)\\left(Y_i-\\bar Y\\right)}{\\sum \\left(X_i-\\bar X\\right)^{2}}$\n", + "\n", + "$=>\\displaystyle a = \\frac {\\operatorname {Cov} (X,Y)}{\\operatorname {Var} (X)}$\n", + "\n", + "We know that the covariance can be written as $ r . S_x . S_y$ , where r is the correlation coefficient and $S_x$ and $S_y$ are the standard deviation of X and Y values respectively. We also know that Var(X) = $(S_x)^2$. Substituting this in the above equation for the slope a, we get:\n", + "\n", + "$=>\\displaystyle a = \\frac {\\operatorname {Cov} (X,Y)}{\\operatorname {Var} (X)}$\n", + "\n", + "$=>\\displaystyle a = \\frac {r . S_x . S_y}{(S_x)^2}$\n", + "\n", + "$=>\\displaystyle a = \\frac {r.S_y}{S_x}$\n", + "\n", + "$=>\\displaystyle a = r.(S_y/S_x)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two Extra Credit Points: Fun with Webscraping & Text manipulation\n", + "### (Mandatory for Grad students!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
`NOTE:` **If you are a Graduate Section student (enrolled in 290), the Extra Credit Questions are mandatory.**
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## 1. Statistics in Presidential Debates\n", + "\n", + "Your first task is to scrape Presidential Debates from the Commission of Presidential Debates website: http://www.debates.org/index.php?page=debate-transcripts.\n", + "\n", + "To do this, you are not allowed to manually look up the URLs that you need, instead you have to scrape them. The root url to be scraped is the one listed above, namely: http://www.presidency.ucsb.edu/debates.php\n", + "\n", + "\n", + "1. By using `requests` and `BeautifulSoup` find all the links / URLs on the website that links to transcriptions of **First Presidential Debates** from the years [2012, 2008, 2004, 2000, 1996, 1988, 1984, 1976, 1960]. In total you should find 9 links / URLs tat fulfill this criteria.\n", + "2. When you have a list of the URLs your task is to create a Data Frame with some statistics (see example of output below):\n", + " 1. Scrape the title of each link and use that as the column name in your Data Frame. \n", + " 2. Count how long the transcript of the debate is (as in the number of characters in transcription string). Feel free to include `\\` characters in your count, but remove any breakline characters, i.e. `\\n`. You will get credit if your count is +/- 10% from our result.\n", + " 3. Count how many times the word **war** was used in the different debates. Note that you have to convert the text in a smart way (to not count the word **warranty** for example, but counting **war.**, **war!**, **war,** or **War** etc.\n", + " 4. Also scrape the most common used word in the debate, and write how many times it was used. Note that you have to use the same strategy as in 3 in order to do this.\n", + " \n", + "**Tips:**\n", + "\n", + "___\n", + "\n", + "In order to solve question 3 and 4 above it can be useful to work with Regular Expressions and explore methods on strings like `.strip(), .replace(), .find(), .count(), .lower()` etc. Both are very powerful tools to do string processing in Python. To count common words for example I used a `Counter` object and a Regular expression pattern for only words, see example:\n", + "\n", + "```python\n", + " from collections import Counter\n", + " import re\n", + "\n", + " counts = Counter(re.findall(r\"[\\w']+\", text.lower()))\n", + "```\n", + "\n", + "Read more about Regular Expressions here: https://docs.python.org/3/howto/regex.html\n", + " \n", + " \n", + "**Example output of all of the answers to EC Question 1:**\n", + "\n", + "\n", + "![pres_stats](https://github.com/ikhlaqsidhu/data-x/raw/master/x-archive/misc/hw2_imgs_spring2018/president_stats.png)\n", + "\n", + "\n", + "\n", + "\n", + "----\n", + "\n", + ".\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests\n", + "import bs4 as bs\n", + "from collections import Counter\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "urls are:\n", + " ['http://www.debates.org/index.php?page=october-3-2012-debate-transcript', 'http://www.debates.org/index.php?page=2008-debate-transcript', 'http://www.debates.org/index.php?page=september-30-2004-debate-transcript', 'http://www.debates.org/index.php?page=october-3-2000-transcript', 'http://www.debates.org/index.php?page=october-6-1996-debate-transcript', 'http://www.debates.org/index.php?page=september-25-1988-debate-transcript', 'http://www.debates.org/index.php?page=october-7-1984-debate-transcript', 'http://www.debates.org/index.php?page=september-23-1976-debate-transcript', 'http://www.debates.org/index.php?page=september-26-1960-debate-transcript']\n", + "\n", + "titles: \n", + " ['October 3, 2012: The First Obama-Romney Presidential Debate', 'September 26, 2008: The First McCain-Obama Presidential Debate', 'September 30, 2004: The First Bush-Kerry Presidential Debate', 'October 3, 2000: The First Gore-Bush Presidential Debate', 'October 6, 1996: The First Clinton-Dole Presidential Debate', 'September 25, 1988: The First Bush-Dukakis Presidential Debate', 'October 7, 1984: The First Reagan-Mondale Presidential Debate', 'September 23, 1976: The First Carter-Ford Presidential Debate', 'September 26, 1960: The First Kennedy-Nixon Presidential Debate']\n" + ] + } + ], + "source": [ + "# PART A\n", + "\n", + "source = requests.get('http://www.debates.org/index.php?page=debate-transcripts').content\n", + "soup = bs.BeautifulSoup(source,features='html.parser')\n", + "#print(soup.prettify())\n", + "#print(soup.a)\n", + "soup.a.get('href')\n", + "\n", + "array = ['First', 'Presidential', 'Debate']\n", + "url=[]\n", + "title=[]\n", + "links = soup.find_all('a')\n", + "\n", + "for l in links:\n", + " if all(x in l.text for x in array):\n", + " url.append(l.get('href'))\n", + " title.append(l.text)\n", + "print('urls are:\\n',url)\n", + "print('\\ntitles: \\n',title)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[, , , , , , , , ]\n" + ] + } + ], + "source": [ + "# PART 2A\n", + "\n", + "source=[]\n", + "for i in range(len(url)):\n", + " source.append(requests.get(url[i]))\n", + "print(source) " + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[95108, 182428, 82726, 91071, 93095, 87736, 87000, 80837, 61013]\n", + "[3, 44, 64, 11, 14, 8, 2, 7, 3]\n", + "['the', 'the', 'the', 'the', 'the', 'the', 'the', 'the', 'the']\n", + "[757, 1470, 857, 919, 876, 804, 867, 857, 779]\n" + ] + }, + { + "data": { + "text/html": [ + "
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October 3, 2012: The First Obama-Romney Presidential DebateSeptember 26, 2008: The First McCain-Obama Presidential DebateSeptember 30, 2004: The First Bush-Kerry Presidential DebateOctober 3, 2000: The First Gore-Bush Presidential DebateOctober 6, 1996: The First Clinton-Dole Presidential DebateSeptember 25, 1988: The First Bush-Dukakis Presidential DebateOctober 7, 1984: The First Reagan-Mondale Presidential DebateSeptember 23, 1976: The First Carter-Ford Presidential DebateSeptember 26, 1960: The First Kennedy-Nixon Presidential Debate
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war_count3446411148273
most_common_wthethethethethethethethethe
most_common_w_count7571470857919876804867857779
\n", + "
" + ], + "text/plain": [ + " October 3, 2012: The First Obama-Romney Presidential Debate \\\n", + "Debate char length 95108 \n", + "war_count 3 \n", + "most_common_w the \n", + "most_common_w_count 757 \n", + "\n", + " September 26, 2008: The First McCain-Obama Presidential Debate \\\n", + "Debate char length 182428 \n", + "war_count 44 \n", + "most_common_w the \n", + "most_common_w_count 1470 \n", + "\n", + " September 30, 2004: The First Bush-Kerry Presidential Debate \\\n", + "Debate char length 82726 \n", + "war_count 64 \n", + "most_common_w the \n", + "most_common_w_count 857 \n", + "\n", + " October 3, 2000: The First Gore-Bush Presidential Debate \\\n", + "Debate char length 91071 \n", + "war_count 11 \n", + "most_common_w the \n", + "most_common_w_count 919 \n", + "\n", + " October 6, 1996: The First Clinton-Dole Presidential Debate \\\n", + "Debate char length 93095 \n", + "war_count 14 \n", + "most_common_w the \n", + "most_common_w_count 876 \n", + "\n", + " September 25, 1988: The First Bush-Dukakis Presidential Debate \\\n", + "Debate char length 87736 \n", + "war_count 8 \n", + "most_common_w the \n", + "most_common_w_count 804 \n", + "\n", + " October 7, 1984: The First Reagan-Mondale Presidential Debate \\\n", + "Debate char length 87000 \n", + "war_count 2 \n", + "most_common_w the \n", + "most_common_w_count 867 \n", + "\n", + " September 23, 1976: The First Carter-Ford Presidential Debate \\\n", + "Debate char length 80837 \n", + "war_count 7 \n", + "most_common_w the \n", + "most_common_w_count 857 \n", + "\n", + " September 26, 1960: The First Kennedy-Nixon Presidential Debate \n", + "Debate char length 61013 \n", + "war_count 3 \n", + "most_common_w the \n", + "most_common_w_count 779 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# PART 2B\n", + "\n", + "soups = []\n", + "content = []\n", + "character_length = []\n", + "war_count = []\n", + "counts = []\n", + "word = []\n", + "frequency = []\n", + "common_count = []\n", + "common_word = []\n", + "\n", + "for i in range(len(url)):\n", + " soups.append(bs.BeautifulSoup(source[i].content, features='html.parser')) \n", + " #print(\"The transcript of\", title[i], \"debate is as follows\")\n", + " content.append(soups[i].find(id='content-sm').text)\n", + " #print(content[i])\n", + " character_length.append(len(content[i]))\n", + " # Part 2C counting the word war in the text\n", + " war_count.append(sum(1 for _ in re.finditer(r'\\b%s\\b' % re.escape(\"war\"), content[i].lower())))\n", + " # Part 2D counting the most common word and its quantity\n", + " counts = Counter(re.findall(r\"[\\w']+\", content[i].lower()))\n", + " word,frequency = counts.most_common(1)[0]\n", + " common_word.append(word)\n", + " common_count.append(frequency)\n", + " \n", + "\n", + "print(character_length)\n", + "print(war_count)\n", + "print(common_word)\n", + "print(common_count)\n", + "\n", + "# making the final data frame\n", + "indices = [\"Debate char length\",\"war_count\",\"most_common_w\",\"most_common_w_count\"]\n", + "df_final = pd.DataFrame([character_length, war_count, common_word, common_count],columns=title,index=indices)\n", + "df_final\n", + "# number of years for reference\n", + "#[2012, 2008, 2004, 2000, 1996, 1988, 1984, 1976, 1960]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "## 2. Download and read in specific line from many data sets\n", + "\n", + "Scrape the first 27 data sets from this URL http://people.sc.fsu.edu/~jburkardt/datasets/regression/ (i.e.`x01.txt` - `x27.txt`). Then, save the 5th line in each data set, this should be the name of the data set author (get rid of the `#` symbol, the white spaces and the comma at the end). \n", + "\n", + "Count how many times (with a Python function) each author is the reference for one of the 27 data sets. Showcase your results, sorted, with the most common author name first and how many times he appeared in data sets. Use a Pandas DataFrame to show your results, see example.\n", + "\n", + "**Example output of the answer EC Question 2:**\n", + "\n", + "![author_stats](https://github.com/ikhlaqsidhu/data-x/raw/master/x-archive/misc/hw2_imgs_spring2018/data_authors.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "None\n", + "\n" + ] + } + ], + "source": [ + "source2 = requests.get(\" http://people.sc.fsu.edu/~jburkardt/datasets/regression/ \") \n", + "print(source2)\n", + "\n", + "soup2 = bs.BeautifulSoup(source2.content, features='html.parser') \n", + "print(type(soup2))\n", + "print(soup2.a)\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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# of occurence
names
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S Chatterjee3
D G Kleinbaum and L L Kupper2
R J Freund and P D Minton2
S C Narula2
K A Brownlee1
S Chatterjee and B Price1
\n", + "
" + ], + "text/plain": [ + " # of occurence\n", + "names \n", + " Helmut Spaeth 16\n", + " S Chatterjee 3\n", + " D G Kleinbaum and L L Kupper 2\n", + " R J Freund and P D Minton 2\n", + " S C Narula 2\n", + " K A Brownlee 1\n", + " S Chatterjee and B Price 1" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# KR code\n", + "source3 = requests.get(\"http://people.sc.fsu.edu/~jburkardt/datasets/regression/\",) \n", + "soup3 = bs.BeautifulSoup(source3.content, features='html.parser') \n", + "print(source3) \n", + "# If then the website has been downloaded succesfully\n", + "\n", + "links3 = soup3.find('table').find_all('a')\n", + "urls3 = ['http://people.sc.fsu.edu/~jburkardt/datasets/regression/'+l.get('href') for l in links3]\n", + "urls3 = urls3[6:33]\n", + "#urls3\n", + "\n", + "sourc = requests.get(urls3[15],) \n", + "soup3 = bs.BeautifulSoup(sourc.content, features='html.parser') \n", + "content3 = soup3.body\n", + "#content3 = soup3.find('p').text\n", + "chars3 = str(soup3)\n", + "#chars3\n", + "\n", + "cnt = 0\n", + "m=0\n", + "names=[]\n", + "while cnt<5: \n", + " if(chars3[m]=='\\n'):\n", + " cnt+=1\n", + " elif(cnt==4):\n", + " names.append(chars3[m])\n", + " m+=1\n", + "\n", + "#names\n", + "\n", + "names = ''.join(names)\n", + "#names\n", + "\n", + "finallist=[]\n", + "for i in range(len(urls3)):\n", + " lis=[]\n", + " sourc = requests.get(urls3[i],) \n", + " soup3 = bs.BeautifulSoup(sourc.content, features='html.parser') \n", + " content3 = soup3.body\n", + " chars3 = str(soup3)\n", + " cnt = 0\n", + " m=0\n", + " names=[]\n", + " while cnt<5: \n", + " if(chars3[m]=='\\n'):\n", + " cnt+=1\n", + " elif(cnt==4):\n", + " names.append(chars3[m])\n", + " m+=1\n", + " names = ''.join(names)\n", + " finallist.append(names) \n", + " #chars = chars.replace('\\n', '')\n", + " #lis.append(len(chars)) #Adding total word count to the list first\n", + " #warcts = Counter(re.findall(r\"[ ][w][a][r][!?.,]? \", content.lower()))\n", + " #lis.append(len(list(warcts.elements()))) # Adding 'war' word count to the list\n", + " #counts = Counter(re.findall(r\"[\\w']+\", content.lower()))\n", + " #most_common,num_most_common = counts.most_common(1)[0]\n", + " #lis.append(most_common) #Adding most common word\n", + " #lis.append(num_most_common) #Adding most common word count\n", + " #finaldf[title[i]] = lis\n", + "\n", + "# finallist\n", + "\n", + "namesdf = pd.DataFrame(finallist)\n", + "\n", + "namesdf = namesdf.rename(columns = {0:'names'})\n", + "\n", + "# namesdf\n", + "\n", + "namesdf['names']=namesdf.names.str.extract('([A-Za-z ]+)')\n", + "\n", + "ans= pd.DataFrame(namesdf.groupby('names')['names'].count())\n", + "\n", + "#ans.head()\n", + "\n", + "ans = ans.sort_values('names', ascending=0)\n", + "ans = ans.rename(columns = {'names':'# of occurence'})\n", + "\n", + "ans" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/web_scraping.ipynb b/web_scraping.ipynb new file mode 100644 index 0000000..98999ce --- /dev/null +++ b/web_scraping.ipynb @@ -0,0 +1,4522 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import bs4 as bs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rbt = requests.get('https://www.bloomberg.com/robots.txt')\n", + "soup=bs.BeautifulSoup(rbt.content,'lxml')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#print(soup.prettify())" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "source = requests.get('https://www.bloomberg.com/feeds/bpol/sitemap_news.xml').content\n", + "a=bs.BeautifulSoup(source,'xml')\n", + "#print(a.prettify())" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "news=a.find_all({'news'})" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "House Passes a Temporary Spending Bill\n" + ] + } + ], + "source": [ + "for news in print(news.title.text)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source = requests.post('https://www.caravanmagazine.in/')\n", + "source" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " PHP Security Audit Script: php_security.php\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + " \n", + " \"PHP\n", + " \n", + "

\n", + " \n", + " PHP\n", + " \n", + " Security Audit\n", + "

\n", + "
\n", + "
\n", + " \n", + "
\n", + "
\n", + "

\n", + " Script Name\n", + "

\n", + "
\n", + "
\n", + "

\n", + " Description\n", + "

\n", + "

\n", + " This web-accessible script will list security recommendations after scanning available PHP configuration variables and functions for common exploit vectors.\n", + "

\n", + "
\n", + "
\n", + "

\n", + " Download script\n", + "

\n", + "

\n", + " \n", + " Click here\n", + " \n", + " to download this script.\n", + "

\n", + "
\n", + "
\n", + "

\n", + " View Source\n", + "

\n", + "
\n", + " \n", + " \n", + " \n", + " <?php\n", + "
\n", + "
\n", + " \n", + " \n", + " /**\n", + "
\n", + " *  PHP Security Check Script\n", + "
\n", + " *  http://php-security-audit.com/\n", + "
\n", + " *\n", + "
\n", + " *  This security check script will evaluate the PHP runtime environment\n", + "
\n", + " *  for your configuration to determine whether any improvements could be\n", + "
\n", + " *  made to your configuration.\n", + "
\n", + " *\n", + "
\n", + " * * * * * * *\n", + "
\n", + " *\n", + "
\n", + " *  Revision History\n", + "
\n", + " *\n", + "
\n", + " *  2009-05-08 - 1.0 - Created\n", + "
\n", + " *\n", + "
\n", + " *  2009-06-10 - 1.1 - Classified security levels, added message constants\n", + "
\n", + " *\n", + "
\n", + " *  2009-11-23 - 1.3 - Removed \"PHP_VERSION constant set\" warning\n", + "
\n", + " *\n", + "
\n", + " *                     Note for splFileObject: (disable_classes config\n", + "
\n", + " *                     directive requires lowercase)\n", + "
\n", + " *\n", + "
\n", + " *  2011-07-17 - 1.4 - Contributed by kaotix: Added various tweaks to make\n", + "
\n", + " *                     script more compatible with Windows:\n", + "
\n", + " *\n", + "
\n", + " *                      - Backslashes\n", + "
\n", + " *                      - OS detection\n", + "
\n", + " *                      - User detection\n", + "
\n", + " *\n", + "
\n", + " * * * * * * *\n", + "
\n", + " *\n", + "
\n", + " *  Please freely distribute this script without modification - if any\n", + "
\n", + " *  improvements can be made, visit php-security-audit.com to submit\n", + "
\n", + " *  your recommendation.\n", + "
\n", + " *\n", + "
\n", + " */\n", + "
\n", + "
\n", + "
\n", + " // Application message declarations\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_TITLE'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'PHP Security Audit'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_DESCRIPTION'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'This audit script checks core PHP configuration, available functions, and available classes to determine potential vulnerabilities and offer configuration suggestions.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_INSTRUCTIONS'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'If errors are reported which you do not have access to correct (as in the case of a shared hosting account), you may want to contact your hosting provider or system administrator to ensure the security of your system.'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_HOSTNAME'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Server Hostname:'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_OS'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Operating System:'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_USER'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Username:'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_RUN_DATE'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Date Run:'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_COMMON'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Common Exploits'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_SHARED'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Shared Environment Protection'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_PARANOID'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Locked Down Configuration'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_ERROR_FEATURES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'PHP Configuration'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_DESCRIPTION_ERROR_FEATURES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Update your <a href=\"http://us2.php.net/configuration.changes\">PHP configuration</a> to correct the following issues:'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_ERROR_FUNCTIONS'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Insecure Functions'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_DESCRIPTION_ERROR_FUNCTIONS'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Add the following to the <strong class=\"ms\">disable_functions</strong> directive in your <a href=\"http://us2.php.net/configuration.changes\">PHP configuration</a> to reduce your risk exposure:'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_ERROR_CLASSES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Insecure Classes'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_DESCRIPTION_ERROR_CLASSES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Add the following to the <strong class=\"ms\">disable_classes</strong> directive in your <a href=\"http://us2.php.net/configuration.changes\">PHP configuration</a> to reduce your risk exposure:<br />Note: Configuration requires class names in lower case letters - i.e. \"splfileobject\"'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_LABEL_MESSAGES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Messages'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'APP_DESCRIPTION_MESSAGES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " // Message declarations\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'MSG_BASEDIR'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Your base directory is presently set to $$BASEDIR$$ - PHP scripts will not be able to access the file system outside of this directory.'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " // Error declarations\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_BASEDIR'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Your configuration should be changed to enforce a base directory (i.e. \"/var/www/\") to prevent PHP from accessing other directories on the underlying filesystem.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_BASEDIR_CHDIR'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'A base directory is presently set to $$BASEDIR$$ - you should remove this directory entry to ensure that PHP scripts will not be able to access other directories by changing the working directory.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_BASEDIR_SLASH'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'A base directory is presently set to $$BASEDIR$$ - you should add a trailing \"/\" to ensure that PHP scripts will not be able to access the file system outside of this directory.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_ERRORS'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Your configuration should be changed to avoid displaying error output when your scripts encounter errors as this information may be useful to a malicious user.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_EXECUTE'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Critical) Your configuration should be changed to restrict PHP from executing commands on the underlying system.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_INCLUDES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Critical) Your configuration presently allows PHP to include files from remote webservers - this functionality should be disabled and any applications which rely upon remote file includes should be replaced immediately as there exists an extremely high potential for abuse of this feature.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_GLOBALS'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Critical) You must disable register_globals in your PHP configuration to prevent malicious variable manipulation.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_PHPUSER'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'PHP is running under the underlying system\\'s \"root\" user account - this creates many possibilities for abuse. '\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_CONFIG_PROFILE'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Your configuration should be changed to reduce the amount of information a malicious user may gather regarding your PHP configuration.'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_FUNCTION_EXECUTE'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Critical) This function should be disabled to prevent PHP from executing commands on the underlying system.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_FUNCTION_DISRUPT'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'This function should be disabled to prevent PHP scripts from disrupting other processes.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_FUNCTION_LOGGING'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'You should disable this function to avoid the possibility of log file tampering on your system.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_FUNCTION_PROFILE'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'This function should be disabled to reduce the amount of information a malicious user may gather regarding your PHP configuration. While this function must be run to generate output, some applications may include this function as part of their feature set.'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " // Warning declarations\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'WARN_CONFIG_PHPUSER_WIN'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'PHP is running as $$USERNAME$$ - you should ensure that this user is not an administrator on the local system as this creates many possibilites for abuse.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'WARN_CONFIG_UPLOAD'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Warning) You may want to change your configuration to prevent PHP from accepting file uploads unless this feature is absolutely necessary.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'WARN_FUNCTION_FILES'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Warning) You may want to change your configuration to disable PHP access to the underlying filesystem unless this access is absolutely necessary.'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " \n", + " \n", + " // Awaiting implementation (need reliable mechanism for confirmation)\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'ERR_VERSION'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'Your version of PHP is outdated. Exploitable vulnerabilities may exist in your present PHP installation.'\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " define\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'WARN_VERSION'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " '(Warning) Your version of PHP could not be determined. While this greatly reduces the likelihood that your PHP information can be successfully profiled in mounting an attack on your system, it is recommended that you check php.net to ensure that your version of PHP is up to date.'\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + "
\n", + " function\n", + " \n", + " \n", + " build_output\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $section_title\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $section_class\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $section_description\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $section_array\n", + " \n", + " \n", + " ) {\n", + "
\n", + "
\n", + " global\n", + " \n", + " \n", + " $output_count_errors\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " \"\"\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " if (\n", + "
\n", + " (\n", + " \n", + " \n", + " is_array\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $section_array\n", + " \n", + " \n", + " ) ) &&\n", + "
\n", + " (\n", + " \n", + " \n", + " count\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $section_array\n", + " \n", + " \n", + " ) )\n", + "
\n", + " ) {\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '<div class=\"'\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " $section_class\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '\">'\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " ' <h2>'\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " $section_title\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '</h2>'\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " ' <p>'\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " $section_description\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '</p>'\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " ' <ul>'\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " foreach (\n", + " \n", + " \n", + " $section_array\n", + " \n", + " \n", + " as\n", + " \n", + " \n", + " $error_key\n", + " \n", + " \n", + " =>\n", + " \n", + " \n", + " $error_message\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\\t\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '<li><strong class=\"ms\">'\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " $error_key\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '</strong> - '\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " $error_message\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '</li>'\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $output_count_errors\n", + " \n", + " \n", + " ++;\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " ' </ul>'\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " \"\\r\\n\\t\"\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " '</div>'\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + "
\n", + " return\n", + " \n", + " \n", + " $build_output\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " = array();\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " = array();\n", + "
\n", + " \n", + " \n", + " $insecure_classes\n", + " \n", + " \n", + " = array();\n", + "
\n", + "
\n", + " \n", + " \n", + " $messages\n", + " \n", + " \n", + " = array();\n", + "
\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " = array();\n", + "
\n", + " \n", + " \n", + " $error_functions\n", + " \n", + " \n", + " = array();\n", + "
\n", + " \n", + " \n", + " $error_classes\n", + " \n", + " \n", + " = array();\n", + "
\n", + "
\n", + " \n", + " \n", + " $output\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $output_count_errors\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " 0\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " $flag_windows\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " false\n", + " \n", + " \n", + " ;\n", + "
\n", + " if (\n", + "
\n", + " \n", + " \n", + " 'win'\n", + " \n", + " \n", + " ==\n", + " \n", + " \n", + " substr\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " strtolower\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " PHP_OS\n", + " \n", + " \n", + " ),\n", + " \n", + " \n", + " 0\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 3\n", + " \n", + " \n", + " ) ||\n", + "
\n", + " \n", + " \n", + " 'cygwin'\n", + " \n", + " \n", + " ==\n", + " \n", + " \n", + " substr\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " strtolower\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " PHP_OS\n", + " \n", + " \n", + " ),\n", + " \n", + " \n", + " 0\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 6\n", + " \n", + " \n", + " )\n", + "
\n", + " ) {\n", + "
\n", + " \n", + " \n", + " $flag_windows\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " true\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " getenv\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'username'\n", + " \n", + " \n", + " );\n", + "
\n", + " } else {\n", + "
\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " posix_getpwuid\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " posix_geteuid\n", + " \n", + " \n", + " ());\n", + "
\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " = (\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " ) ?\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'name'\n", + " \n", + " \n", + " ] :\n", + " \n", + " \n", + " '[unknown]'\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " $app_audit_level\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " ;\n", + "
\n", + " if (\n", + "
\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " &&\n", + "
\n", + " \n", + " \n", + " array_key_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " )\n", + "
\n", + " ) {\n", + "
\n", + " \n", + " \n", + " $app_audit_level\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " trim\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ]);\n", + "
\n", + " }\n", + "
\n", + "
\n", + " switch (\n", + " \n", + " \n", + " $app_audit_level\n", + " \n", + " \n", + " ) {\n", + "
\n", + "
\n", + " case\n", + " \n", + " \n", + " 'paranoid'\n", + " \n", + " \n", + " :\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'expose_php'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_CONFIG_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'apache_child_terminate'\n", + " \n", + " \n", + " ]    =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'apache_get_modules'\n", + " \n", + " \n", + " ]        =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'apache_get_version'\n", + " \n", + " \n", + " ]        =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'apache_getenv'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'get_loaded_extensions'\n", + " \n", + " \n", + " ]    =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'phpinfo'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'phpversion'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_PROFILE\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'file_uploads'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " WARN_CONFIG_UPLOAD\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'chgrp'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'chmod'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'chown'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'copy'\n", + " \n", + " \n", + " ]                        =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'link'\n", + " \n", + " \n", + " ]                        =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'mkdir'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'rename'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'rmdir'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'symlink'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'touch'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'unlink'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " WARN_FUNCTION_FILES\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " case\n", + " \n", + " \n", + " 'shared'\n", + " \n", + " \n", + " :\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'openlog'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_LOGGING\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'proc_nice'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_DISRUPT\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'syslog'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_LOGGING\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " case\n", + " \n", + " \n", + " 'common'\n", + " \n", + " \n", + " :\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_classes\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'splFileObject'\n", + " \n", + " \n", + " ]                 =\n", + " \n", + " \n", + " ERR_CONFIG_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'register_globals'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_CONFIG_GLOBALS\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'allow_url_fopen'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_CONFIG_INCLUDES\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'display_errors'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_CONFIG_ERRORS\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'enable_dl'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_CONFIG_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'apache_note'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'apache_setenv'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'dl'\n", + " \n", + " \n", + " ]                        =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'exec'\n", + " \n", + " \n", + " ]                        =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'passthru'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'pcntl_exec'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'popen'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'proc_close'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'proc_open'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'proc_get_status'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'proc_terminate'\n", + " \n", + " \n", + " ]            =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'putenv'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'shell_exec'\n", + " \n", + " \n", + " ]                =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'system'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'virtual'\n", + " \n", + " \n", + " ]                    =\n", + " \n", + " \n", + " ERR_FUNCTION_EXECUTE\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " \n", + " \n", + " // Check PHP permissions\n", + "
\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " $flag_windows\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'PHP User Account'\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " str_replace\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " '$$USERNAME$$'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " WARN_CONFIG_PHPUSER_WIN\n", + " \n", + " \n", + " );\n", + "
\n", + " } else if (\n", + " \n", + " \n", + " 'root'\n", + " \n", + " \n", + " ==\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " ) {\n", + "
\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'PHP User Account'\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " ERR_CONFIG_PHPUSER\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " // Check open_basedir restriction\n", + "
\n", + " \n", + " \n", + " if ( !\n", + " \n", + " \n", + " ini_get\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'open_basedir'\n", + " \n", + " \n", + " ) )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'open_basedir'\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " ERR_CONFIG_BASEDIR\n", + " \n", + " \n", + " ;\n", + "
\n", + " } else {\n", + "
\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " \n", + " \n", + " $basedirs\n", + " \n", + " \n", + " = (\n", + " \n", + " \n", + " $flag_windows\n", + " \n", + " \n", + " ) ?\n", + " \n", + " \n", + " explode\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " ';'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ini_get\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'open_basedir'\n", + " \n", + " \n", + " )) :\n", + " \n", + " \n", + " explode\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " ':'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ini_get\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'open_basedir'\n", + " \n", + " \n", + " ));\n", + "
\n", + "
\n", + " foreach (\n", + " \n", + " \n", + " $basedirs\n", + " \n", + " \n", + " as\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " if (\n", + " \n", + " \n", + " '.'\n", + " \n", + " \n", + " ===\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " .= (\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " ) ?\n", + " \n", + " \n", + " '<br />'\n", + " \n", + " \n", + " :\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " str_replace\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " '$$BASEDIR$$'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ERR_CONFIG_BASEDIR_CHDIR\n", + " \n", + " \n", + " );\n", + "
\n", + " }\n", + "
\n", + "
\n", + " if (\n", + " \n", + " \n", + " $flag_windows\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " if (\n", + " \n", + " \n", + " '\\\\'\n", + " \n", + " \n", + " !=\n", + " \n", + " \n", + " substr\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " , -\n", + " \n", + " \n", + " 1\n", + " \n", + " \n", + " ) )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " .= (\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " ) ?\n", + " \n", + " \n", + " '<br />'\n", + " \n", + " \n", + " :\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " str_replace\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " '$$BASEDIR$$'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ERR_CONFIG_BASEDIR_SLASH\n", + " \n", + " \n", + " );\n", + "
\n", + " }\n", + "
\n", + " } else {\n", + "
\n", + " if (\n", + " \n", + " \n", + " '/'\n", + " \n", + " \n", + " !=\n", + " \n", + " \n", + " substr\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " , -\n", + " \n", + " \n", + " 1\n", + " \n", + " \n", + " ) )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " .= (\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " ) ?\n", + " \n", + " \n", + " '<br />'\n", + " \n", + " \n", + " :\n", + " \n", + " \n", + " ''\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " str_replace\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " '$$BASEDIR$$'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $basedir\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ERR_CONFIG_BASEDIR_SLASH\n", + " \n", + " \n", + " );\n", + "
\n", + " }\n", + "
\n", + " }\n", + "
\n", + " }\n", + "
\n", + "
\n", + " if (\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'open_basedir'\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " $error_basedir\n", + " \n", + " \n", + " ;\n", + "
\n", + " } else {\n", + "
\n", + " \n", + " \n", + " $messages\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'Base Directory'\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " str_replace\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " '$$BASEDIR$$'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " ini_get\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'open_basedir'\n", + " \n", + " \n", + " ),\n", + " \n", + " \n", + " MSG_BASEDIR\n", + " \n", + " \n", + " );\n", + "
\n", + " }\n", + "
\n", + " }\n", + "
\n", + "
\n", + "
\n", + " \n", + " \n", + " // PHP version checking\n", + "
\n", + " \n", + " \n", + " switch (\n", + " \n", + " \n", + " true\n", + " \n", + " \n", + " ) {\n", + "
\n", + "
\n", + " case (\n", + " \n", + " \n", + " defined\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'PHP_VERSION_ID'\n", + " \n", + " \n", + " ) ):\n", + "
\n", + "
\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " =\n", + " \n", + " \n", + " PHP_VERSION_ID\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " case (\n", + " \n", + " \n", + " function_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'phpversion'\n", + " \n", + " \n", + " ) ):\n", + "
\n", + "
\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " = (!\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " ) ?\n", + " \n", + " \n", + " substr\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " phpversion\n", + " \n", + " \n", + " (),\n", + " \n", + " \n", + " 0\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " strpos\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " phpversion\n", + " \n", + " \n", + " (),\n", + " \n", + " \n", + " '-'\n", + " \n", + " \n", + " )) :\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " case (\n", + " \n", + " \n", + " defined\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'PHP_VERSION'\n", + " \n", + " \n", + " ) ):\n", + "
\n", + "
\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " = (!\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " ) ? (\n", + " \n", + " \n", + " $version\n", + " \n", + " \n", + " {\n", + " \n", + " \n", + " 0\n", + " \n", + " \n", + " } *\n", + " \n", + " \n", + " 10000\n", + " \n", + " \n", + " +\n", + " \n", + " \n", + " $version\n", + " \n", + " \n", + " {\n", + " \n", + " \n", + " 2\n", + " \n", + " \n", + " } *\n", + " \n", + " \n", + " 100\n", + " \n", + " \n", + " +\n", + " \n", + " \n", + " $version\n", + " \n", + " \n", + " {\n", + " \n", + " \n", + " 4\n", + " \n", + " \n", + " }) :\n", + " \n", + " \n", + " $php_version_id\n", + " \n", + " \n", + " ;\n", + "
\n", + "
\n", + " break;\n", + "
\n", + "
\n", + " default:\n", + "
\n", + " \n", + " \n", + " $messages_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'PHP_VERSION'\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " WARN_VERSION\n", + " \n", + " \n", + " ;\n", + "
\n", + " break;\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " // Check features\n", + "
\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " is_array\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " ) )\n", + "
\n", + " {\n", + "
\n", + " foreach (\n", + " \n", + " \n", + " $insecure_features\n", + " \n", + " \n", + " as\n", + " \n", + " \n", + " $feature_key\n", + " \n", + " \n", + " =>\n", + " \n", + " \n", + " $feature_message\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " if ( (bool)\n", + " \n", + " \n", + " ini_get\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $feature_key\n", + " \n", + " \n", + " ) )\n", + "
\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " $feature_key\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " $feature_message\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " // Check classes\n", + "
\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " is_array\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $insecure_classes\n", + " \n", + " \n", + " ) )\n", + "
\n", + " {\n", + "
\n", + " foreach (\n", + " \n", + " \n", + " $insecure_classes\n", + " \n", + " \n", + " as\n", + " \n", + " \n", + " $class_key\n", + " \n", + " \n", + " =>\n", + " \n", + " \n", + " $class_message\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " if (\n", + " \n", + " \n", + " class_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $class_key\n", + " \n", + " \n", + " ) )\n", + "
\n", + " \n", + " \n", + " $error_classes\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " $class_key\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " $class_message\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " // Check functions\n", + "
\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " is_array\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " ) )\n", + "
\n", + " {\n", + "
\n", + " foreach (\n", + " \n", + " \n", + " $insecure_functions\n", + " \n", + " \n", + " as\n", + " \n", + " \n", + " $function_key\n", + " \n", + " \n", + " =>\n", + " \n", + " \n", + " $function_message\n", + " \n", + " \n", + " )\n", + "
\n", + " {\n", + "
\n", + " if (\n", + " \n", + " \n", + " function_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " $function_key\n", + " \n", + " \n", + " ) )\n", + "
\n", + " \n", + " \n", + " $error_functions\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " $function_key\n", + " \n", + " \n", + " ] =\n", + " \n", + " \n", + " $function_message\n", + " \n", + " \n", + " ;\n", + "
\n", + " }\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " // Build content for display\n", + "
\n", + " \n", + " \n", + " $output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " build_output\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " APP_LABEL_ERROR_FEATURES\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'error features'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " APP_DESCRIPTION_ERROR_FEATURES\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $error_features\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " $output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " build_output\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " APP_LABEL_ERROR_CLASSES\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'error classes'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " APP_DESCRIPTION_ERROR_CLASSES\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $error_classes\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " $output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " build_output\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " APP_LABEL_ERROR_FUNCTIONS\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'error functions'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " APP_DESCRIPTION_ERROR_FUNCTIONS\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $error_functions\n", + " \n", + " \n", + " );\n", + "
\n", + " \n", + " \n", + " $output\n", + " \n", + " \n", + " .=\n", + " \n", + " \n", + " build_output\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " APP_LABEL_MESSAGES\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " 'message'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " APP_DESCRIPTION_MESSAGES\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $messages\n", + " \n", + " \n", + " );\n", + "
\n", + "
\n", + " break;\n", + "
\n", + "
\n", + " default:\n", + "
\n", + "
\n", + " \n", + " \n", + " // Set default content for display\n", + "
\n", + " // ...\n", + "
\n", + "
\n", + " \n", + " \n", + " break;\n", + "
\n", + "
\n", + " }\n", + "
\n", + "
\n", + " \n", + " \n", + " ?>\n", + " \n", + " <!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Strict//EN\"\n", + "
\n", + " \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd\">\n", + "
\n", + " <html xmlns=\"http://www.w3.org/1999/xhtml\" xml:lang=\"en\" lang=\"en\">\n", + "
\n", + " <head>\n", + "
\n", + "
\n", + " <title>\n", + " \n", + " <?php\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " $output_count_errors\n", + " \n", + " \n", + " ) echo\n", + " \n", + " \n", + " $output_count_errors\n", + " \n", + " \n", + " .\n", + " \n", + " \n", + " ' Problems Identified - '\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?><?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_TITLE\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </title>\n", + "
\n", + "
\n", + " <style type=\"text/css\">\n", + "
\n", + " * {\n", + "
\n", + " margin:0;\n", + "
\n", + " padding:0;\n", + "
\n", + " }\n", + "
\n", + " body {\n", + "
\n", + " background-color:#EFEFEF;\n", + "
\n", + " font-family:Arial,sans-serif;\n", + "
\n", + " }\n", + "
\n", + " div {\n", + "
\n", + " margin:10px 20px;\n", + "
\n", + " padding:8px;\n", + "
\n", + " -moz-border-radius:8px;\n", + "
\n", + " border-radius:8px;\n", + "
\n", + " }\n", + "
\n", + " .application {\n", + "
\n", + " border:solid 1px #444444;\n", + "
\n", + " background-color:#F6F6F6;\n", + "
\n", + " }\n", + "
\n", + " .error {\n", + "
\n", + " color:#880000;\n", + "
\n", + " border:solid 1px #880000;\n", + "
\n", + " background-color:#EFCCCC;\n", + "
\n", + " }\n", + "
\n", + " .message {\n", + "
\n", + " color:#008800;\n", + "
\n", + " border:solid 1px #008800;\n", + "
\n", + " background-color:#CCEFCC;\n", + "
\n", + " }\n", + "
\n", + " ul {\n", + "
\n", + " list-style-type:square;\n", + "
\n", + " margin-left:20px;\n", + "
\n", + " }\n", + "
\n", + " li {\n", + "
\n", + " margin-bottom:0.4em;\n", + "
\n", + " }\n", + "
\n", + " p {\n", + "
\n", + " margin:0.4em 0.2em;\n", + "
\n", + " }\n", + "
\n", + " .ms {\n", + "
\n", + " font-family:monospace;\n", + "
\n", + " }\n", + "
\n", + " </style>\n", + "
\n", + "
\n", + " </head>\n", + "
\n", + " <body>\n", + "
\n", + " <div class=\"application\">\n", + "
\n", + " <h1>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_TITLE\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </h1>\n", + "
\n", + " <hr />\n", + "
\n", + " <p>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_DESCRIPTION\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </p>\n", + "
\n", + " <p></p>\n", + "
\n", + " <hr />\n", + "
\n", + " <p class=\"ms\"><strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_HOSTNAME\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " $_SERVER\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'HTTP_HOST'\n", + " \n", + " \n", + " ];\n", + " \n", + " \n", + " ?>\n", + " \n", + " </p>\n", + "
\n", + " <p class=\"ms\"><strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_OS\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " PHP_OS\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </p>\n", + "
\n", + " <p class=\"ms\"><strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_USER\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " $process_user\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </p>\n", + "
\n", + " <p class=\"ms\"><strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_RUN_DATE\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </strong>\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " date\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " \"Y-m-d H:i:s\"\n", + " \n", + " \n", + " );\n", + " \n", + " \n", + " ?>\n", + " \n", + " </p>\n", + "
\n", + " </div>\n", + "
\n", + "
\n", + " <div class=\"application\">\n", + "
\n", + " <form action=\"\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " $_SERVER\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'PHP_SELF'\n", + " \n", + " \n", + " ];\n", + " \n", + " \n", + " ?>\n", + " \n", + " \" method=\"post\">\n", + "
\n", + " <select name=\"app_audit_level\">\n", + "
\n", + " <option value=\"common\"\n", + " \n", + " <?php\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " &&\n", + " \n", + " \n", + " array_key_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " ) &&\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ] ==\n", + " \n", + " \n", + " 'common'\n", + " \n", + " \n", + " ) echo\n", + " \n", + " \n", + " ' selected=\"selected\"'\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " >\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_COMMON\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </option>\n", + "
\n", + " <option value=\"shared\"\n", + " \n", + " <?php\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " &&\n", + " \n", + " \n", + " array_key_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " ) &&\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ] ==\n", + " \n", + " \n", + " 'shared'\n", + " \n", + " \n", + " ) echo\n", + " \n", + " \n", + " ' selected=\"selected\"'\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " >\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_SHARED\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </option>\n", + "
\n", + " <option value=\"paranoid\"\n", + " \n", + " <?php\n", + " \n", + " \n", + " if (\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " &&\n", + " \n", + " \n", + " array_key_exists\n", + " \n", + " \n", + " (\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ,\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " ) &&\n", + " \n", + " \n", + " $_POST\n", + " \n", + " \n", + " [\n", + " \n", + " \n", + " 'app_audit_level'\n", + " \n", + " \n", + " ] ==\n", + " \n", + " \n", + " 'paranoid'\n", + " \n", + " \n", + " ) echo\n", + " \n", + " \n", + " ' selected=\"selected\"'\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " >\n", + " \n", + " <?php\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " APP_LABEL_PARANOID\n", + " \n", + " \n", + " ;\n", + " \n", + " \n", + " ?>\n", + " \n", + " </option>\n", + "
\n", + " </select>\n", + "
\n", + " <input type=\"submit\" value=\"Run Audit &gt;&gt;\" />\n", + "
\n", + " </form>\n", + "
\n", + " </div>\n", + "
\n", + "
\n", + " \n", + " <?php\n", + "
\n", + " \n", + " \n", + " echo\n", + " \n", + " \n", + " $output\n", + " \n", + " \n", + " ;\n", + "
\n", + " \n", + " \n", + " ?>\n", + "
\n", + " \n", + "
\n", + " </body>\n", + "
\n", + " </html>\n", + " \n", + " \n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "
\n", + "

\n", + " Copyright © 2009-2013 Hadean LLC\n", + "

\n", + "

\n", + " Retrieved 2013-02-23 12:51:40\n", + "

\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "\n" + ] + } + ], + "source": [ + "soup = bs.BeautifulSoup(source.content,'lxml')\n", + "print(soup.prettify())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "navigation=soup.find_all('ul')\n", + "print(navigation)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "ResultSet object has no attribute 'find_all'. You're probably treating a list of items like a single item. Did you call find_all() when you meant to call find()?", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0murl\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnavigation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mlink\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'href'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'caravanmagazine.in/'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlink\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# check link to a subpage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlink\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\bs4\\element.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1805\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1806\u001b[0m raise AttributeError(\n\u001b[1;32m-> 1807\u001b[1;33m \u001b[1;34m\"ResultSet object has no attribute '%s'. You're probably treating a list of items like a single item. Did you call find_all() when you meant to call find()?\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1808\u001b[0m )\n", + "\u001b[1;31mAttributeError\u001b[0m: ResultSet object has no attribute 'find_all'. You're probably treating a list of items like a single item. Did you call find_all() when you meant to call find()?" + ] + } + ], + "source": [ + "for url in navigation.find_all('a'):\n", + " link=url.get('href')\n", + " if 'caravanmagazine.in/' in link: # check link to a subpage\n", + " print(link) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}