{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "remove_input" ] }, "outputs": [], "source": [ "path_data = '../../../../data/'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import math\n", "import scipy.stats as stats\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "plt.style.use('fivethirtyeight')\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "remove_input" ] }, "outputs": [], "source": [ "def standard_units(x):\n", " return (x - np.mean(x))/np.std(x)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [ "remove_input" ] }, "outputs": [], "source": [ "def distance(point1, point2):\n", " \"\"\"The distance between two arrays of numbers.\"\"\"\n", " return np.sqrt(np.sum((point1 - point2)**2))\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", "\n", " attributes = training.drop(columns=['Class'])\n", " #print(attributes)\n", " def distance_from_point(row):\n", " return distance(point, np.array(row))\n", " \n", " return attributes.apply(distance_from_point, axis=1)\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", " training1 = training.copy()\n", " training1['Distance'] = all_distances(training1, point)\n", "\n", " return training1\n", "\n", "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", "\n", " sorted_by_distance = with_dists.sort_values(by=['Distance'])\n", "\n", " topk = sorted_by_distance.take(np.arange(k))\n", "\n", " return topk\n", "\n", "def majority(topkclasses):\n", " \"\"\"1 if the majority of the \"Class\" column is 1s, and 0 otherwise.\"\"\"\n", " #print(topkclasses)\n", " \n", " ones = len(topkclasses[topkclasses['Class'] == 1])\n", " zeros = len(topkclasses[topkclasses['Class'] == 0])\n", "\n", " if ones > zeros:\n", " return 1\n", " else:\n", " return 0\n", "\n", "def classify(training, p, k):\n", " \"\"\"Classify an example with attributes p using k-nearest neighbor classification with the given training table.\"\"\"\n", " closestk = closest(training, p, k)\n", "\n", " topkclasses = closestk[['Class']]\n", "\n", " return majority(closestk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nearest Neighbors ###\n", "In this section we'll develop the *nearest neighbor* method of classification. Just focus on the ideas for now and don't worry if some of the code is mysterious. Later in the chapter we'll see how to organize our ideas into code that performs the classification." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chronic kidney disease\n", "\n", "Let's work through an example. We're going to work with a data set that was collected to help doctors diagnose chronic kidney disease (CKD). Each row in the data set represents a single patient who was treated in the past and whose diagnosis is known. For each patient, we have a bunch of measurements from a blood test. We'd like to find which measurements are most useful for diagnosing CKD, and develop a way to classify future patients as \"has CKD\" or \"doesn't have CKD\" based on their blood test results." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ckd = pd.read_csv(path_data + 'ckd.csv')\n", "ckd.rename(columns={'Blood Glucose Random':'Glucose'}, inplace=True)\n", "ckd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the variables are categorical (words like \"abnormal\"), and some quantitative. The quantitative variables all have different scales. We're going to want to make comparisons and estimate distances, often by eye, so let's select just a few of the variables and work in standard units. Then we won't have to worry about the scale of each of the different variables." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ckd_su = pd.DataFrame({'Hemoglobin':standard_units(ckd['Hemoglobin']), \n", " 'Glucose':standard_units(ckd['Glucose']), \n", " 'White Blood Cell Count':standard_units(ckd['White Blood Cell Count']), \n", " 'Class':ckd['Class'].astype(str)})\n", "ckd_su" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at two columns in particular: the hemoglobin level (in the patient's blood), and the blood glucose level (at a random time in the day; without fasting specially for the blood test). \n", "\n", "We'll draw a scatter plot to visualize the relation between the two variables. Blue dots are patients with CKD; gold dots are patients without CKD. What kind of medical test results seem to indicate CKD? \n", "\n", "Previously, to create a df containing the required columns we have used the `join` function, in this example we will use the `merge` function.\n", "\n", "[pandas.merge](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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HemoglobinGlucoseWhite Blood Cell CountClassColor
0-0.865744-0.221549-0.5697681darkblue
1-1.457446-0.9475971.1626841darkblue
2-1.0049683.841231-1.2755821darkblue
3-2.8148790.3963640.8097771darkblue
4-2.0839540.6435290.2322931darkblue
..................
1530.7005260.133751-0.5697680gold
1540.978974-0.870358-0.2168610gold
1550.735332-0.484162-0.6018500gold
1560.178436-0.267893-0.4093560gold
1570.735332-0.005280-0.5376860gold
\n", "

158 rows × 5 columns

\n", "
" ], "text/plain": [ " Hemoglobin Glucose White Blood Cell Count Class Color\n", "0 -0.865744 -0.221549 -0.569768 1 darkblue\n", "1 -1.457446 -0.947597 1.162684 1 darkblue\n", "2 -1.004968 3.841231 -1.275582 1 darkblue\n", "3 -2.814879 0.396364 0.809777 1 darkblue\n", "4 -2.083954 0.643529 0.232293 1 darkblue\n", ".. ... ... ... ... ...\n", "153 0.700526 0.133751 -0.569768 0 gold\n", "154 0.978974 -0.870358 -0.216861 0 gold\n", "155 0.735332 -0.484162 -0.601850 0 gold\n", "156 0.178436 -0.267893 -0.409356 0 gold\n", "157 0.735332 -0.005280 -0.537686 0 gold\n", "\n", "[158 rows x 5 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "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_combined = pd.merge(ckd_su, color_table, on='Class')\n", "\n", "ckd_combined" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glucose_color_darkblue = ckd_combined[ckd_combined['Color'] == 'darkblue']\n", "glucose_color_gold = ckd_combined[ckd_combined['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(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", "\n", "plt.xlabel(x_label)\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose Alice is a new patient who is not in the data set. If I tell you Alice's hemoglobin level and blood glucose level, could you predict whether she has CKD? It sure looks like it! You can see a very clear pattern here: points in the lower-right tend to represent people who don't have CKD, and the rest tend to be folks with CKD. To a human, the pattern is obvious. But how can we program a computer to automatically detect patterns such as this one?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A Nearest Neighbor Classifier ###\n", "\n", "There are lots of kinds of patterns one might look for, and lots of algorithms for classification. But I'm going to tell you about one that turns out to be surprisingly effective. It is called *nearest neighbor classification*. Here's the idea. If we have Alice's hemoglobin and glucose numbers, we can put her somewhere on this scatterplot; the hemoglobin is her x-coordinate, and the glucose is her y-coordinate. Now, to predict whether she has CKD or not, we find the nearest point in the scatterplot and check whether it is blue or gold; we predict that Alice should receive the same diagnosis as that patient.\n", "\n", "In other words, to classify Alice as CKD or not, we find the patient in the training set who is \"nearest\" to Alice, and then use that patient's diagnosis as our prediction for Alice. The intuition is that if two points are near each other in the scatterplot, then the corresponding measurements are pretty similar, so we might expect them to receive the same diagnosis (more likely than not). We don't know Alice's diagnosis, but we do know the diagnosis of all the patients in the training set, so we find the patient in the training set who is most similar to Alice, and use that patient's diagnosis to predict Alice's diagnosis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the graph below, the red dot represents Alice. It is joined with a black line to the point that is nearest to it – its *nearest neighbor* in the training set. The figure is drawn by a function called `show_closest`. It takes an array that represents the $x$ and $y$ coordinates of Alice's point. Vary those to see how the closest point changes! Note especially when the closest point is blue and when it is gold." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [ "remove_input" ] }, "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", " HemoG1 = ckd_combined.copy()\n", " HemoG1 = HemoG1.drop(columns=['White Blood Cell Count', 'Color'])\n", " \n", " t = closest(HemoG1, point, 1)\n", " x_closest = t.iloc[0,0]\n", " y_closest = t.iloc[0,1]\n", "\n", " fig, ax = plt.subplots(figsize=(7,6))\n", " ax.scatter(glucose_color_darkblue['Hemoglobin'], \n", " glucose_color_darkblue['Glucose'], \n", " label='Color=darkblue', \n", " color='darkblue')\n", " ax.scatter(glucose_color_gold['Hemoglobin'], \n", " glucose_color_gold['Glucose'], \n", " label='Color=gold', \n", " color='gold')\n", " x_label = 'Hemoglobin'\n", " y_label = 'Glucose'\n", " y_vals = ax.get_yticks()\n", " plt.ylabel(y_label)\n", " ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", " plt.xlabel(x_label)\n", " \n", " ax.scatter(point.item(0), point.item(1), color='red', s=30)\n", " ax.plot(np.array([point.item(0), x_closest]), np.array([point.item(1), y_closest]), color='k', lw=2)\n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# In this example, Alice's Hemoglobin attribute is 0 and her Glucose is 1.5.\n", "alice = np.array([0, 1.5])\n", "show_closest(alice)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus our *nearest neighbor classifier* works like this:\n", "- Find the point in the training set that is nearest to the new point.\n", "- If that nearest point is a \"CKD\" point, classify the new point as \"CKD\". If the nearest point is a \"not CKD\" point, classify the new point as \"not CKD\".\n", "\n", "The scatterplot suggests that this nearest neighbor classifier should be pretty accurate. Points in the lower-right will tend to receive a \"no CKD\" diagnosis, as their nearest neighbor will be a gold point. The rest of the points will tend to receive a \"CKD\" diagnosis, as their nearest neighbor will be a blue point. So the nearest neighbor strategy seems to capture our intuition pretty well, for this example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decision boundary ###\n", "\n", "Sometimes a helpful way to visualize a classifier is to map out the kinds of attributes where the classifier would predict 'CKD', and the kinds where it would predict 'not CKD'. We end up with some boundary between the two, where points on one side of the boundary will be classified 'CKD' and points on the other side will be classified 'not CKD'. This boundary is called the *decision boundary*. Each different classifier will have a different decision boundary; the decision boundary is just a way to visualize what criteria the classifier is using to classify points.\n", "\n", "For example, suppose the coordinates of Alice's point are (0, 1.5). Notice that the nearest neighbor is blue. Now try reducing the height (the $y$-coordinate) of the point. You'll see that at around $y = 0.95$ the nearest neighbor turns from blue to gold." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alice = np.array([0, 0.97])\n", "show_closest(alice)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are hundreds of new unclassified points, all in red." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [ "remove_input" ] }, "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", "
HemoglobinGlucose
0-2.0-2.0
1-2.0-1.9
2-2.0-1.8
3-2.0-1.7
4-2.0-1.6
.........
16762.01.6
16772.01.7
16782.01.8
16792.01.9
16802.02.0
\n", "

1681 rows × 2 columns

\n", "
" ], "text/plain": [ " Hemoglobin Glucose\n", "0 -2.0 -2.0\n", "1 -2.0 -1.9\n", "2 -2.0 -1.8\n", "3 -2.0 -1.7\n", "4 -2.0 -1.6\n", "... ... ...\n", "1676 2.0 1.6\n", "1677 2.0 1.7\n", "1678 2.0 1.8\n", "1679 2.0 1.9\n", "1680 2.0 2.0\n", "\n", "[1681 rows x 2 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_array = np.array([])\n", "y_array = np.array([])\n", "for x in np.arange(-2, 2.1, 0.1):\n", " for y in np.arange(-2, 2.1, 0.1):\n", " x_array = np.append(x_array, x)\n", " y_array = np.append(y_array, y)\n", " \n", "test_grid = pd.DataFrame(\n", " {'Hemoglobin':x_array,\n", " 'Glucose':y_array}\n", ")\n", "\n", "test_grid" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [ "remove_input" ] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_grid.plot.scatter('Hemoglobin', 'Glucose', color='red', figsize=(6,6), alpha=0.4, s=30)\n", "\n", "plt.scatter(ckd_combined['Hemoglobin'], ckd_combined['Glucose'], c=ckd_combined['Color'], edgecolor='k')\n", "\n", "plt.xlim(-2, 2)\n", "plt.ylim(-2, 2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each of the red points has a nearest neighbor in the training set (the same blue and gold points as before). For some red points you can easily tell whether the nearest neighbor is blue or gold. For others, it's a little more tricky to make the decision by eye. Those are the points near the decision boundary.\n", "\n", "But the computer can easily determine the nearest neighbor of each point. So let's get it to apply our nearest neighbor classifier to each of the red points: \n", "\n", "For each red point, it must find the closest point in the training set; it must then change the color of the red point to become the color of the nearest neighbor. \n", "\n", "The resulting graph shows which points will get classified as 'CKD' (all the blue ones), and which as 'not CKD' (all the gold ones)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [ "remove_input" ] }, "outputs": [], "source": [ "def classify_grid(training, test, k):\n", " #print(training, test, k)\n", " #c = np.array([])\n", " ckd_new1 = pd.DataFrame(columns=['Hemoglobin', 'Glucose', 'Class', 'Distance'])\n", " empty = np.array([])\n", " for i in range(len(test)):\n", " # Run the classifier on the ith patient in the test set\n", " \n", " ckd_new2 = closest(training, np.array([test.iloc[i]]), k)\n", " #topkclasses = ckd_new2['Class']\n", "\n", " ones = len(ckd_new2[ckd_new2['Class'] == '1'])\n", " zeros = len(ckd_new2[ckd_new2['Class'] == '0'])\n", " \n", " if ones > zeros:\n", " #return 1\n", " empty = np.append(empty, 1)\n", " else:\n", " #return 0\n", " empty = np.append(empty, 0)\n", " \n", " return empty\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [ "remove_input" ] }, "outputs": [ { "data": { "text/plain": [ "array([1., 1., 1., ..., 1., 1., 1.])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ckd_new = classify_grid(ckd_combined.drop(columns=['White Blood Cell Count', 'Color']), test_grid, 1)\n", "\n", "ckd_new" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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HemoglobinGlucoseClass
0-2.0-2.01
1-2.0-1.91
2-2.0-1.81
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4-2.0-1.61
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" ], "text/plain": [ " Hemoglobin Glucose Class\n", "0 -2.0 -2.0 1\n", "1 -2.0 -1.9 1\n", "2 -2.0 -1.8 1\n", "3 -2.0 -1.7 1\n", "4 -2.0 -1.6 1" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_grid['Class'] = ckd_new.astype(int)\n", "test_grid['Class'] = test_grid['Class'].astype(str)\n", "\n", "test_grid.head(5)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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HemoglobinGlucoseClassColor
0-2.0-2.01darkblue
1-2.0-1.91darkblue
2-2.0-1.81darkblue
3-2.0-1.71darkblue
4-2.0-1.61darkblue
...............
16762.00.60gold
16772.00.70gold
16782.00.80gold
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16802.01.00gold
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1681 rows × 4 columns

\n", "
" ], "text/plain": [ " Hemoglobin Glucose Class Color\n", "0 -2.0 -2.0 1 darkblue\n", "1 -2.0 -1.9 1 darkblue\n", "2 -2.0 -1.8 1 darkblue\n", "3 -2.0 -1.7 1 darkblue\n", "4 -2.0 -1.6 1 darkblue\n", "... ... ... ... ...\n", "1676 2.0 0.6 0 gold\n", "1677 2.0 0.7 0 gold\n", "1678 2.0 0.8 0 gold\n", "1679 2.0 0.9 0 gold\n", "1680 2.0 1.0 0 gold\n", "\n", "[1681 rows x 4 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_grid1 = pd.DataFrame({'Hemoglobin':test_grid['Hemoglobin'], \n", " 'Glucose':test_grid['Glucose'], \n", " 'Class':test_grid['Class']})\n", "\n", "\n", "test_grid2 = pd.merge(test_grid1, color_table, on='Class')\n", "\n", "test_grid2" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [ "remove_input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_grid3 = test_grid2[test_grid2['Color'] == 'darkblue']\n", "test_grid4 = test_grid2[test_grid2['Color'] == 'gold']\n", "\n", "plt.scatter(test_grid3['Hemoglobin'], test_grid3['Glucose'], color='darkblue', alpha=0.4, s=30)\n", "plt.scatter(test_grid4['Hemoglobin'], test_grid4['Glucose'], color='gold', alpha=0.4, s=30)\n", "\n", "plt.scatter(ckd_combined['Hemoglobin'], ckd_combined['Glucose'], c=ckd_combined['Color'], edgecolor='k')\n", "\n", "plt.xlim(-2, 2)\n", "plt.ylim(-2, 2);\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_grid5 = ckd_combined[ckd_combined['Color'] == 'darkblue']\n", "test_grid6 = ckd_combined[ckd_combined['Color'] == 'gold']\n", "\n", "fig, ax = plt.subplots(figsize=(7,6))\n", "\n", "ax.scatter(test_grid3['Hemoglobin'], \n", " test_grid3['Glucose'], \n", " color='darkblue', alpha=0.4, s=30)\n", "\n", "ax.scatter(test_grid4['Hemoglobin'], \n", " test_grid4['Glucose'], \n", " color='gold', alpha=0.4, s=30)\n", "\n", "ax.scatter(test_grid5['Hemoglobin'], \n", " test_grid5['Glucose'], \n", " color='darkblue', label='Color=darkblue', ec='darkblue', s=30)\n", "\n", "ax.scatter(test_grid6['Hemoglobin'], \n", " test_grid6['Glucose'], \n", " color='gold', label='Color=gold', ec='darkblue', s=30)\n", "\n", "x_label = 'Hemoglobin'\n", "\n", "y_label = 'Glucose'\n", "\n", "plt.xlabel(x_label)\n", "\n", "plt.ylabel(y_label)\n", "\n", "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", "\n", "plt.xlabel(x_label)\n", "\n", "plt.xlim(-2, 2)\n", "\n", "plt.ylim(-2, 2)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decision boundary is where the classifier switches from turning the red points blue to turning them gold." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### k-Nearest Neighbors ###\n", "\n", "However, the separation between the two classes won't always be quite so clean. For instance, suppose that instead of hemoglobin levels we were to look at white blood cell count. Look at what happens:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "glucose_color_darkblue = ckd_combined[ckd_combined['Color'] == 'darkblue']\n", "glucose_color_gold = ckd_combined[ckd_combined['Color'] == 'gold']\n", "\n", "fig, ax = plt.subplots(figsize=(7,6))\n", "\n", "ax.scatter(glucose_color_darkblue['White Blood Cell Count'], \n", " glucose_color_darkblue['Glucose'], \n", " label='Color=darkblue', \n", " color='darkblue')\n", "\n", "ax.scatter(glucose_color_gold['White Blood Cell Count'], \n", " glucose_color_gold['Glucose'], \n", " label='Color=gold', \n", " color='gold')\n", "\n", "x_label = 'White Blood Cell Count'\n", "\n", "y_label = 'Glucose'\n", "\n", "y_vals = ax.get_yticks()\n", "\n", "plt.ylabel(y_label)\n", "\n", "ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", "\n", "plt.xlabel(x_label)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, non-CKD individuals are all clustered in the lower-left. Most of the patients with CKD are above or to the right of that cluster... but not all. There are some patients with CKD who are in the lower left of the above figure (as indicated by the handful of blue dots scattered among the gold cluster). What this means is that you can't tell for certain whether someone has CKD from just these two blood test measurements.\n", "\n", "If we are given Alice's glucose level and white blood cell count, can we predict whether she has CKD? Yes, we can make a prediction, but we shouldn't expect it to be 100% accurate. Intuitively, it seems like there's a natural strategy for predicting: plot where Alice lands in the scatter plot; if she is in the lower-left, predict that she doesn't have CKD, otherwise predict she has CKD. \n", "\n", "This isn't perfect -- our predictions will sometimes be wrong. (Take a minute and think it through: for which patients will it make a mistake?) As the scatterplot above indicates, sometimes people with CKD have glucose and white blood cell levels that look identical to those of someone without CKD, so any classifier is inevitably going to make the wrong prediction for them.\n", "\n", "Can we automate this on a computer? Well, the nearest neighbor classifier would be a reasonable choice here too. Take a minute and think it through: how will its predictions compare to those from the intuitive strategy above? When will they differ?\n", "\n", "Its predictions will be pretty similar to our intuitive strategy, but occasionally it will make a different prediction. In particular, if Alice's blood test results happen to put her right near one of the blue dots in the lower-left, the intuitive strategy would predict 'not CKD', whereas the nearest neighbor classifier will predict 'CKD'.\n", "\n", "There is a simple generalization of the nearest neighbor classifier that fixes this anomaly. It is called the *k-nearest neighbor classifier*. To predict Alice's diagnosis, rather than looking at just the one neighbor closest to her, we can look at the 3 points that are closest to her, and use the diagnosis for each of those 3 points to predict Alice's diagnosis. In particular, we'll use the majority value among those 3 diagnoses as our prediction for Alice's diagnosis. Of course, there's nothing special about the number 3: we could use 4, or 5, or more. (It's often convenient to pick an odd number, so that we don't have to deal with ties.) In general, we pick a number $k$, and our predicted diagnosis for Alice is based on the $k$ patients in the training set who are closest to Alice. Intuitively, these are the $k$ patients whose blood test results were most similar to Alice, so it seems reasonable to use their diagnoses to predict Alice's diagnosis.\n", "\n", "The $k$-nearest neighbor classifier will now behave just like our intuitive strategy above." ] } ], "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": "3.6.12" } }, "nbformat": 4, "nbformat_minor": 1 }