{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib\n", "#matplotlib.use('Agg')\n", "path_data = '../../../../data/'\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import numpy as np\n", "import pandas as pd\n", "import math\n", "import scipy.stats as stats\n", "plt.style.use('fivethirtyeight')\n", "\n", "import warnings\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def standard_units(x):\n", " return (x - np.mean(x))/np.std(x)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def distance(point1, point2):\n", " \"\"\"The distance between two arrays of numbers.\"\"\"\n", " return np.sqrt(np.sum((point1 - point2)**2))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def all_distances(training, point):\n", " \"\"\"The distance between p (an array of numbers) and the numbers in row i of attribute_table.\"\"\"\n", " attributes = training.drop('Class')\n", " def distance_from_point(row):\n", " return distance(point, np.array(row))\n", " #return attributes.apply(distance_from_point)\n", " return attributes.map(distance_from_point) #--- check map function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def table_with_distances(training, point):\n", " \"\"\"A copy of the training table with the distance from each row to array p.\"\"\"\n", " #return training.with_column('Distance', all_distances(training, point))\n", " training['Distance'] = all_distances(training, point)\n", " return training" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def closest(training, point, k):\n", " \"\"\"A table containing the k closest rows in the training table to array p.\"\"\"\n", " with_dists = table_with_distances(training, point)\n", " sorted_by_distance = with_dists.sort_values(by=['Distance'])\n", " topk = sorted_by_distance.take(np.arange(k))\n", " return topk" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AgeBlood PressureSpecific GravityAlbuminSugarRed Blood CellsPus CellPus Cell clumpsBacteriaGlucose...Packed Cell VolumeWhite Blood Cell CountRed Blood Cell CountHypertensionDiabetes MellitusCoronary Artery DiseaseAppetitePedal EdemaAnemiaClass
048701.00540normalabnormalpresentnotpresent117...3267003.9yesnonopooryesyes1
153901.02020abnormalabnormalpresentnotpresent70...29121003.7yesyesnopoornoyes1
263701.01030abnormalabnormalpresentnotpresent380...3245003.8yesyesnopooryesno1
368801.01032normalabnormalpresentpresent157...16110002.6yesyesyespooryesno1
461801.01520abnormalabnormalnotpresentnotpresent173...2492003.2yesyesyespooryesyes1
..................................................................
15355801.02000normalnormalnotpresentnotpresent140...4767004.9nononogoodnono0
15442701.02500normalnormalnotpresentnotpresent75...5478006.2nononogoodnono0
15512801.02000normalnormalnotpresentnotpresent100...4966005.4nononogoodnono0
15617601.02500normalnormalnotpresentnotpresent114...5172005.9nononogoodnono0
15758801.02500normalnormalnotpresentnotpresent131...5368006.1nononogoodnono0
\n", "

158 rows × 25 columns

\n", "
" ], "text/plain": [ " Age Blood Pressure Specific Gravity Albumin Sugar Red Blood Cells \\\n", "0 48 70 1.005 4 0 normal \n", "1 53 90 1.020 2 0 abnormal \n", "2 63 70 1.010 3 0 abnormal \n", "3 68 80 1.010 3 2 normal \n", "4 61 80 1.015 2 0 abnormal \n", ".. ... ... ... ... ... ... \n", "153 55 80 1.020 0 0 normal \n", "154 42 70 1.025 0 0 normal \n", "155 12 80 1.020 0 0 normal \n", "156 17 60 1.025 0 0 normal \n", "157 58 80 1.025 0 0 normal \n", "\n", " Pus Cell Pus Cell clumps Bacteria Glucose ... Packed Cell Volume \\\n", "0 abnormal present notpresent 117 ... 32 \n", "1 abnormal present notpresent 70 ... 29 \n", "2 abnormal present notpresent 380 ... 32 \n", "3 abnormal present present 157 ... 16 \n", "4 abnormal notpresent notpresent 173 ... 24 \n", ".. ... ... ... ... ... ... \n", "153 normal notpresent notpresent 140 ... 47 \n", "154 normal notpresent notpresent 75 ... 54 \n", "155 normal notpresent notpresent 100 ... 49 \n", "156 normal notpresent notpresent 114 ... 51 \n", "157 normal notpresent notpresent 131 ... 53 \n", "\n", " White Blood Cell Count Red Blood Cell Count Hypertension \\\n", "0 6700 3.9 yes \n", "1 12100 3.7 yes \n", "2 4500 3.8 yes \n", "3 11000 2.6 yes \n", "4 9200 3.2 yes \n", ".. ... ... ... \n", "153 6700 4.9 no \n", "154 7800 6.2 no \n", "155 6600 5.4 no \n", "156 7200 5.9 no \n", "157 6800 6.1 no \n", "\n", " Diabetes Mellitus Coronary Artery Disease Appetite Pedal Edema Anemia \\\n", "0 no no poor yes yes \n", "1 yes no poor no yes \n", "2 yes no poor yes no \n", "3 yes yes poor yes no \n", "4 yes yes poor yes yes \n", ".. ... ... ... ... ... \n", "153 no no good no no \n", "154 no no good no no \n", "155 no no good no no \n", "156 no no good no no \n", "157 no no good no no \n", "\n", " Class \n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 \n", ".. ... \n", "153 0 \n", "154 0 \n", "155 0 \n", "156 0 \n", "157 0 \n", "\n", "[158 rows x 25 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ckd1 = pd.read_csv(path_data + 'ckd.csv')\n", "ckd1.rename(columns={'Blood Glucose Random':'Glucose'}, inplace=True)\n", "ckd1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
HemoglobinGlucoseWhite Blood Cell CountClass
0-0.865744-0.221549-0.5697681
1-1.457446-0.9475971.1626841
2-1.0049683.841231-1.2755821
3-2.8148790.3963640.8097771
4-2.0839540.6435290.2322931
...............
1530.7005260.133751-0.5697680
1540.978974-0.870358-0.2168610
1550.735332-0.484162-0.6018500
1560.178436-0.267893-0.4093560
1570.735332-0.005280-0.5376860
\n", "

158 rows × 4 columns

\n", "
" ], "text/plain": [ " Hemoglobin Glucose White Blood Cell Count Class\n", "0 -0.865744 -0.221549 -0.569768 1\n", "1 -1.457446 -0.947597 1.162684 1\n", "2 -1.004968 3.841231 -1.275582 1\n", "3 -2.814879 0.396364 0.809777 1\n", "4 -2.083954 0.643529 0.232293 1\n", ".. ... ... ... ...\n", "153 0.700526 0.133751 -0.569768 0\n", "154 0.978974 -0.870358 -0.216861 0\n", "155 0.735332 -0.484162 -0.601850 0\n", "156 0.178436 -0.267893 -0.409356 0\n", "157 0.735332 -0.005280 -0.537686 0\n", "\n", "[158 rows x 4 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ckd = pd.DataFrame({'Hemoglobin':standard_units(ckd1['Hemoglobin']), \n", " 'Glucose':standard_units(ckd1['Glucose']), \n", " 'White Blood Cell Count':standard_units(ckd1['White Blood Cell Count']), \n", " 'Class':ckd1['Class'].astype(str)})\n", "\n", "#type(ckd_su['Class'][0])\n", "\n", "ckd" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "color_table = pd.DataFrame(\n", " {'Class':np.array([1, 0]),\n", " 'Color':np.array(['darkblue', 'gold'])}, index=np.array([1,0]))\n", " \n", "color_table['Class'] = color_table['Class'].astype(str)\n", "\n", "ckd = pd.merge(ckd, color_table, on='Class')\n", "\n", "class_1 = ckd['Class']\n", "\n", "ckd.pop('Class')\n", "\n", "ckd.insert(0, 'Class', class_1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ClassHemoglobinGlucoseWhite Blood Cell CountColor
01-0.865744-0.221549-0.569768darkblue
11-1.457446-0.9475971.162684darkblue
21-1.0049683.841231-1.275582darkblue
31-2.8148790.3963640.809777darkblue
41-2.0839540.6435290.232293darkblue
..................
15300.7005260.133751-0.569768gold
15400.978974-0.870358-0.216861gold
15500.735332-0.484162-0.601850gold
15600.178436-0.267893-0.409356gold
15700.735332-0.005280-0.537686gold
\n", "

158 rows × 5 columns

\n", "
" ], "text/plain": [ " Class Hemoglobin Glucose White Blood Cell Count Color\n", "0 1 -0.865744 -0.221549 -0.569768 darkblue\n", "1 1 -1.457446 -0.947597 1.162684 darkblue\n", "2 1 -1.004968 3.841231 -1.275582 darkblue\n", "3 1 -2.814879 0.396364 0.809777 darkblue\n", "4 1 -2.083954 0.643529 0.232293 darkblue\n", ".. ... ... ... ... ...\n", "153 0 0.700526 0.133751 -0.569768 gold\n", "154 0 0.978974 -0.870358 -0.216861 gold\n", "155 0 0.735332 -0.484162 -0.601850 gold\n", "156 0 0.178436 -0.267893 -0.409356 gold\n", "157 0 0.735332 -0.005280 -0.537686 gold\n", "\n", "[158 rows x 5 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ckd" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glucose_color_darkblue = ckd[ckd['Color'] == 'darkblue']\n", "glucose_color_gold = ckd[ckd['Color'] == 'gold']\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(7,6))\n", "\n", "ax.scatter(glucose_color_darkblue['Hemoglobin'], \n", " glucose_color_darkblue['Glucose'], \n", " label='Color=darkblue', \n", " color='darkblue')\n", "\n", "ax.scatter(glucose_color_gold['Hemoglobin'], \n", " glucose_color_gold['Glucose'], \n", " label='Color=gold', \n", " color='gold')\n", "\n", "x_label = 'Hemoglobin'\n", "\n", "y_label = 'Glucose'\n", "\n", "y_vals = ax.get_yticks()\n", "\n", "plt.ylabel(y_label)\n", "\n", "ax.legend(loc='upper left')\n", "\n", "plt.xlabel(x_label)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "HemoG1 = ckd.drop(columns=['White Blood Cell Count'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ClassHemoglobinGlucoseColor
01-0.865744-0.221549darkblue
11-1.457446-0.947597darkblue
21-1.0049683.841231darkblue
31-2.8148790.396364darkblue
41-2.0839540.643529darkblue
...............
15300.7005260.133751gold
15400.978974-0.870358gold
15500.735332-0.484162gold
15600.178436-0.267893gold
15700.735332-0.005280gold
\n", "

158 rows × 4 columns

\n", "
" ], "text/plain": [ " Class Hemoglobin Glucose Color\n", "0 1 -0.865744 -0.221549 darkblue\n", "1 1 -1.457446 -0.947597 darkblue\n", "2 1 -1.004968 3.841231 darkblue\n", "3 1 -2.814879 0.396364 darkblue\n", "4 1 -2.083954 0.643529 darkblue\n", ".. ... ... ... ...\n", "153 0 0.700526 0.133751 gold\n", "154 0 0.978974 -0.870358 gold\n", "155 0 0.735332 -0.484162 gold\n", "156 0 0.178436 -0.267893 gold\n", "157 0 0.735332 -0.005280 gold\n", "\n", "[158 rows x 4 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HemoG1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### step by step" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'HemoG1' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHemoG1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mpoint1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpoint2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'HemoG1' is not defined" ] } ], "source": [ "training = HemoG1\n", "point1 = point[0]\n", "point2 = point[1]\n", "point = np.array([0, 1.5])\n", "\n", "def distance(point1, point2):\n", " \"\"\"The distance between two arrays of numbers.\"\"\"\n", " return np.sqrt(np.sum((point1 - point2)**2))\n", "\n", "np.sqrt(np.sum((point1 - point2)**2))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'HemoG1' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHemoG1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Class'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Color'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#training1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#train = np.array([training[:,1], training[:,2]])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'HemoG1' is not defined" ] } ], "source": [ "training1 = HemoG1.drop(columns=['Class', 'Color'])\n", "#training1\n", "\n", "#train = np.array([training[:,1], training[:,2]])\n", "\n", "#point1 = np.array([0, 1.5])\n", "\n", "#train = []\n", "\n", "#for i in range(len(training)):\n", " #j = training['Hemoglobin'].iloc[i]\n", " #k = training['Glucose'].iloc[i]\n", " #print(j,k)\n", " #train.append([j, k])\n", " #i += 1\n", "\n", "#train\n", "\n", "#point1 = np.array([0, 1.5])\n", "\n", "point2 = np.array(training)\n", "\n", "#point2 = np.array([training])\n", "\n", "#np.sqrt(np.sum((point1 - point2)**2))\n", "\n", "#len(point2)\n", "\n", "point2\n", "\n", "train = []\n", "n=0\n", "\n", "def all_distances(training, point):\n", " attributes = trainin-g\n", " def distance_from_point(row):\n", " return distance(point, np.array(row))\n", " \n", " return np.array(attributes.apply(distance_from_point, axis=1))\n", "\n", "#distance_from_point(point2)\n", "\n", "all_distances(training1, point)\n", "\n", "#df['add'] = df.apply(np.sum, axis = 1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#training" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'map'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance_from_point\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mall_distances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36mall_distances\u001b[0;34m(training, point)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m#return attributes.apply(distance_from_point)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance_from_point\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mall_distances\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5137\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5138\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'map'" ] } ], "source": [ "training = HemoG1\n", "point = np.array([0, 1.5])\n", "#point1 = point[0]\n", "#point2 = point[1]\n", "\n", "def all_distances(training, point):\n", " \"\"\"The distance between p (an array of numbers) and the numbers in row i of attribute_table.\"\"\"\n", " attributes = training.drop(columns=['Class', 'Color'])\n", "\n", " def distance_from_point(row=attributes):\n", " return distance(point, np.array(row))\n", "#return attributes.apply(distance_from_point)\n", "\n", " return attributes.map(distance_from_point)\n", "\n", "all_distances(training, point)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "training = HemoG1\n", "point = np.array([0, 1.5])\n", "\n", "#def table_with_distances(training, point):\n", "\"\"\"A copy of the training table with the distance from each row to array p.\"\"\"\n", "#return training.with_column('Distance', all_distances(training, point))\n", "training['Distance'] = all_distances(training, point)\n", "return training" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "training = HemoG1\n", "point = np.array([0, 1.5])\n", "k = 1\n", "\n", "#def closest(training, point, k):\n", "\"\"\"A table containing the k closest rows in the training table to array p.\"\"\"\n", "\n", "with_dists = table_with_distances(training, point)\n", "\n", "sorted_by_distance = with_dists.sort_values(by=['Distance'])\n", "topk = sorted_by_distance.take(np.arange(k))\n", "topk" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def show_closest(point):\n", " \"\"\"point = array([x,y]) \n", " gives the coordinates of a new point\n", " shown in red\"\"\"\n", " \n", " HemoG2 = ckd.copy()\n", " HemoG1 = HemoG2.drop(columns=['White Blood Cell Count'])\n", " \n", " t = closest(HemoGl, point, 1)\n", " x_closest = t.row(0).item(1)\n", " y_closest = t.row(0).item(2)\n", " \n", " ckd.scatter('Hemoglobin', 'Glucose', group='Color')\n", " plt.scatter(point.item(0), point.item(1), color='red', s=30)\n", " plt.plot(np.array([point.item(0), x_closest]), np.array([point.item(1), y_closest]), color='k', lw=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "alice = np.array([0, 1.5])\n", "show_closest(alice)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }