{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. _iris_dataset:\n", "\n", "Iris plants dataset\n", "--------------------\n", "\n", "**Data Set Characteristics:**\n", "\n", " :Number of Instances: 150 (50 in each of three classes)\n", " :Number of Attributes: 4 numeric, predictive attributes and the class\n", " :Attribute Information:\n", " - sepal length in cm\n", " - sepal width in cm\n", " - petal length in cm\n", " - petal width in cm\n", " - class:\n", " - Iris-Setosa\n", " - Iris-Versicolour\n", " - Iris-Virginica\n", " \n", " :Summary Statistics:\n", "\n", " ============== ==== ==== ======= ===== ====================\n", " Min Max Mean SD Class Correlation\n", " ============== ==== ==== ======= ===== ====================\n", " sepal length: 4.3 7.9 5.84 0.83 0.7826\n", " sepal width: 2.0 4.4 3.05 0.43 -0.4194\n", " petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n", " petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n", " ============== ==== ==== ======= ===== ====================\n", "\n", " :Missing Attribute Values: None\n", " :Class Distribution: 33.3% for each of 3 classes.\n", " :Creator: R.A. Fisher\n", " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", " :Date: July, 1988\n", "\n", "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n", "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n", "Machine Learning Repository, which has two wrong data points.\n", "\n", "This is perhaps the best known database to be found in the\n", "pattern recognition literature. Fisher's paper is a classic in the field and\n", "is referenced frequently to this day. (See Duda & Hart, for example.) The\n", "data set contains 3 classes of 50 instances each, where each class refers to a\n", "type of iris plant. One class is linearly separable from the other 2; the\n", "latter are NOT linearly separable from each other.\n", "\n", ".. topic:: References\n", "\n", " - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n", " Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n", " Mathematical Statistics\" (John Wiley, NY, 1950).\n", " - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n", " (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n", " - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n", " Structure and Classification Rule for Recognition in Partially Exposed\n", " Environments\". IEEE Transactions on Pattern Analysis and Machine\n", " Intelligence, Vol. PAMI-2, No. 1, 67-71.\n", " - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n", " on Information Theory, May 1972, 431-433.\n", " - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n", " conceptual clustering system finds 3 classes in the data.\n", " - Many, many more ...\n", "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n", " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", "0 5.1 3.5 1.4 0.2 \n", "1 4.9 3.0 1.4 0.2 \n", "2 4.7 3.2 1.3 0.2 \n", "3 4.6 3.1 1.5 0.2 \n", "4 5.0 3.6 1.4 0.2 \n", ".. ... ... ... ... \n", "145 6.7 3.0 5.2 2.3 \n", "146 6.3 2.5 5.0 1.9 \n", "147 6.5 3.0 5.2 2.0 \n", "148 6.2 3.4 5.4 2.3 \n", "149 5.9 3.0 5.1 1.8 \n", "\n", " target \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", ".. ... \n", "145 2 \n", "146 2 \n", "147 2 \n", "148 2 \n", "149 2 \n", "\n", "[150 rows x 5 columns]\n", "\n", "RangeIndex: 150 entries, 0 to 149\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 sepal length (cm) 150 non-null float64\n", " 1 sepal width (cm) 150 non-null float64\n", " 2 petal length (cm) 150 non-null float64\n", " 3 petal width (cm) 150 non-null float64\n", " 4 target 150 non-null int32 \n", "dtypes: float64(4), int32(1)\n", "memory usage: 5.4 KB\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from sklearn import datasets\n", "from sklearn.preprocessing import scale\n", "iris=datasets.load_iris() #Fisher's famous dataset\n", "#iris is a sklearn \"bunch\" dataset - like a dictionary\n", "print(iris.DESCR) #uncomment this to see the description of the database\n", "\n", "print(iris.keys()) #print the keys. No target classification \n", "#labels included here, we'll add the labels as a column soon\n", "#first convert the keys to a pandas data frame\n", "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n", "#now add a target label column\n", "df['target'] = iris.target\n", "print(df)\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice the data has 150 samples and all have data in them. No need to fill with empty attribute values. Good.\n", "All are numeric - Scikit-Learn models do not accept categorical variables. If we ever get categorical variables we will need to transform these into numeric values.\n", "\n", "Now lets see if we can cluster these using k-means.\n", "First split the data into training and test data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5.1, 3.5, 1.4, 0.2],\n", " [4.9, 3. , 1.4, 0.2],\n", " [4.7, 3.2, 1.3, 0.2],\n", " [4.6, 3.1, 1.5, 0.2],\n", " [5. , 3.6, 1.4, 0.2],\n", " [5.4, 3.9, 1.7, 0.4],\n", " [4.6, 3.4, 1.4, 0.3],\n", " [5. , 3.4, 1.5, 0.2],\n", " [4.4, 2.9, 1.4, 0.2],\n", " [4.9, 3.1, 1.5, 0.1],\n", " [5.4, 3.7, 1.5, 0.2],\n", " [4.8, 3.4, 1.6, 0.2],\n", " [4.8, 3. , 1.4, 0.1],\n", " [4.3, 3. , 1.1, 0.1],\n", " [5.8, 4. , 1.2, 0.2],\n", " [5.7, 4.4, 1.5, 0.4],\n", " [5.4, 3.9, 1.3, 0.4],\n", " [5.1, 3.5, 1.4, 0.3],\n", " [5.7, 3.8, 1.7, 0.3],\n", " [5.1, 3.8, 1.5, 0.3],\n", " [5.4, 3.4, 1.7, 0.2],\n", " [5.1, 3.7, 1.5, 0.4],\n", " [4.6, 3.6, 1. , 0.2],\n", " [5.1, 3.3, 1.7, 0.5],\n", " [4.8, 3.4, 1.9, 0.2],\n", " [5. , 3. , 1.6, 0.2],\n", " [5. , 3.4, 1.6, 0.4],\n", " [5.2, 3.5, 1.5, 0.2],\n", " [5.2, 3.4, 1.4, 0.2],\n", " [4.7, 3.2, 1.6, 0.2],\n", " [4.8, 3.1, 1.6, 0.2],\n", " [5.4, 3.4, 1.5, 0.4],\n", " [5.2, 4.1, 1.5, 0.1],\n", " [5.5, 4.2, 1.4, 0.2],\n", " [4.9, 3.1, 1.5, 0.2],\n", " [5. , 3.2, 1.2, 0.2],\n", " [5.5, 3.5, 1.3, 0.2],\n", " [4.9, 3.6, 1.4, 0.1],\n", " [4.4, 3. , 1.3, 0.2],\n", " [5.1, 3.4, 1.5, 0.2],\n", " [5. , 3.5, 1.3, 0.3],\n", " [4.5, 2.3, 1.3, 0.3],\n", " [4.4, 3.2, 1.3, 0.2],\n", " [5. , 3.5, 1.6, 0.6],\n", " [5.1, 3.8, 1.9, 0.4],\n", " [4.8, 3. , 1.4, 0.3],\n", " [5.1, 3.8, 1.6, 0.2],\n", " [4.6, 3.2, 1.4, 0.2],\n", " [5.3, 3.7, 1.5, 0.2],\n", " [5. , 3.3, 1.4, 0.2],\n", " [7. , 3.2, 4.7, 1.4],\n", " [6.4, 3.2, 4.5, 1.5],\n", " [6.9, 3.1, 4.9, 1.5],\n", " [5.5, 2.3, 4. , 1.3],\n", " [6.5, 2.8, 4.6, 1.5],\n", " [5.7, 2.8, 4.5, 1.3],\n", " [6.3, 3.3, 4.7, 1.6],\n", " [4.9, 2.4, 3.3, 1. ],\n", " [6.6, 2.9, 4.6, 1.3],\n", " [5.2, 2.7, 3.9, 1.4],\n", " [5. , 2. , 3.5, 1. ],\n", " [5.9, 3. , 4.2, 1.5],\n", " [6. , 2.2, 4. , 1. ],\n", " [6.1, 2.9, 4.7, 1.4],\n", " [5.6, 2.9, 3.6, 1.3],\n", " [6.7, 3.1, 4.4, 1.4],\n", " [5.6, 3. , 4.5, 1.5],\n", " [5.8, 2.7, 4.1, 1. ],\n", " [6.2, 2.2, 4.5, 1.5],\n", " [5.6, 2.5, 3.9, 1.1],\n", " [5.9, 3.2, 4.8, 1.8],\n", " [6.1, 2.8, 4. , 1.3],\n", " [6.3, 2.5, 4.9, 1.5],\n", " [6.1, 2.8, 4.7, 1.2],\n", " [6.4, 2.9, 4.3, 1.3],\n", " [6.6, 3. , 4.4, 1.4],\n", " [6.8, 2.8, 4.8, 1.4],\n", " [6.7, 3. , 5. , 1.7],\n", " [6. , 2.9, 4.5, 1.5],\n", " [5.7, 2.6, 3.5, 1. ],\n", " [5.5, 2.4, 3.8, 1.1],\n", " [5.5, 2.4, 3.7, 1. ],\n", " [5.8, 2.7, 3.9, 1.2],\n", " [6. , 2.7, 5.1, 1.6],\n", " [5.4, 3. , 4.5, 1.5],\n", " [6. , 3.4, 4.5, 1.6],\n", " [6.7, 3.1, 4.7, 1.5],\n", " [6.3, 2.3, 4.4, 1.3],\n", " [5.6, 3. , 4.1, 1.3],\n", " [5.5, 2.5, 4. , 1.3],\n", " [5.5, 2.6, 4.4, 1.2],\n", " [6.1, 3. , 4.6, 1.4],\n", " [5.8, 2.6, 4. , 1.2],\n", " [5. , 2.3, 3.3, 1. ],\n", " [5.6, 2.7, 4.2, 1.3],\n", " [5.7, 3. , 4.2, 1.2],\n", " [5.7, 2.9, 4.2, 1.3],\n", " [6.2, 2.9, 4.3, 1.3],\n", " [5.1, 2.5, 3. , 1.1],\n", " [5.7, 2.8, 4.1, 1.3],\n", " [6.3, 3.3, 6. , 2.5],\n", " [5.8, 2.7, 5.1, 1.9],\n", " [7.1, 3. , 5.9, 2.1],\n", " [6.3, 2.9, 5.6, 1.8],\n", " [6.5, 3. , 5.8, 2.2],\n", " [7.6, 3. , 6.6, 2.1],\n", " [4.9, 2.5, 4.5, 1.7],\n", " [7.3, 2.9, 6.3, 1.8],\n", " [6.7, 2.5, 5.8, 1.8],\n", " [7.2, 3.6, 6.1, 2.5],\n", " [6.5, 3.2, 5.1, 2. ],\n", " [6.4, 2.7, 5.3, 1.9],\n", " [6.8, 3. , 5.5, 2.1],\n", " [5.7, 2.5, 5. , 2. ],\n", " [5.8, 2.8, 5.1, 2.4],\n", " [6.4, 3.2, 5.3, 2.3],\n", " [6.5, 3. , 5.5, 1.8],\n", " [7.7, 3.8, 6.7, 2.2],\n", " [7.7, 2.6, 6.9, 2.3],\n", " [6. , 2.2, 5. , 1.5],\n", " [6.9, 3.2, 5.7, 2.3],\n", " [5.6, 2.8, 4.9, 2. ],\n", " [7.7, 2.8, 6.7, 2. ],\n", " [6.3, 2.7, 4.9, 1.8],\n", " [6.7, 3.3, 5.7, 2.1],\n", " [7.2, 3.2, 6. , 1.8],\n", " [6.2, 2.8, 4.8, 1.8],\n", " [6.1, 3. , 4.9, 1.8],\n", " [6.4, 2.8, 5.6, 2.1],\n", " [7.2, 3. , 5.8, 1.6],\n", " [7.4, 2.8, 6.1, 1.9],\n", " [7.9, 3.8, 6.4, 2. ],\n", " [6.4, 2.8, 5.6, 2.2],\n", " [6.3, 2.8, 5.1, 1.5],\n", " [6.1, 2.6, 5.6, 1.4],\n", " [7.7, 3. , 6.1, 2.3],\n", " [6.3, 3.4, 5.6, 2.4],\n", " [6.4, 3.1, 5.5, 1.8],\n", " [6. , 3. , 4.8, 1.8],\n", " [6.9, 3.1, 5.4, 2.1],\n", " [6.7, 3.1, 5.6, 2.4],\n", " [6.9, 3.1, 5.1, 2.3],\n", " [5.8, 2.7, 5.1, 1.9],\n", " [6.8, 3.2, 5.9, 2.3],\n", " [6.7, 3.3, 5.7, 2.5],\n", " [6.7, 3. , 5.2, 2.3],\n", " [6.3, 2.5, 5. , 1.9],\n", " [6.5, 3. , 5.2, 2. ],\n", " [6.2, 3.4, 5.4, 2.3],\n", " [5.9, 3. , 5.1, 1.8]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Store the feature data\n", "X = iris.data\n", "# store the target data\n", "y = iris.target\n", "# split the data using Scikit-Learn's train_test_split\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", "X[:]\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialization complete\n", "Iteration 0, inertia 77.29000000000003\n", "Iteration 1, inertia 44.07713114094279\n", "Iteration 2, inertia 43.0457557904217\n", "Iteration 3, inertia 42.36210260582518\n", "Iteration 4, inertia 41.89654537129537\n", "Converged at iteration 4: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 77.49999999999997\n", "Iteration 1, inertia 48.204565585798846\n", "Iteration 2, inertia 45.503409983751794\n", "Iteration 3, inertia 43.71318267911788\n", "Iteration 4, inertia 43.5351827260459\n", "Converged at iteration 4: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 64.43000000000002\n", "Iteration 1, inertia 42.26275873735283\n", "Iteration 2, inertia 42.03176901529842\n", "Converged at iteration 2: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 58.57999999999999\n", "Iteration 1, inertia 42.296718093531794\n", "Iteration 2, inertia 42.08967632367631\n", "Converged at iteration 2: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 55.71999999999999\n", "Iteration 1, inertia 42.38718468387925\n", "Iteration 2, inertia 41.89654537129537\n", "Converged at iteration 2: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 51.04000000000003\n", "Iteration 1, inertia 42.223912003490895\n", "Iteration 2, inertia 42.11774275421547\n", "Iteration 3, inertia 42.06792154082477\n", "Converged at iteration 3: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 67.99999999999999\n", "Iteration 1, inertia 44.81063139167457\n", "Iteration 2, inertia 43.06293994904667\n", "Iteration 3, inertia 42.30939277382451\n", "Iteration 4, inertia 42.032529137529146\n", "Converged at iteration 4: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 65.17\n", "Iteration 1, inertia 42.61883946365644\n", "Iteration 2, inertia 42.19800618624208\n", "Iteration 3, inertia 42.032529137529146\n", "Converged at iteration 3: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 62.72999999999998\n", "Iteration 1, inertia 43.62814483618235\n", "Iteration 2, inertia 43.5351827260459\n", "Converged at iteration 2: center shift 0.0 within tolerance 0.00011467370854591837\n", "Initialization complete\n", "Iteration 0, inertia 59.64000000000002\n", "Iteration 1, inertia 47.15669837168892\n", "Iteration 2, inertia 42.33161518954765\n", "Iteration 3, inertia 42.08967632367631\n", "Converged at iteration 3: center shift 0.0 within tolerance 0.00011467370854591837\n" ] }, { "data": { "text/plain": [ "array([1, 1, 2, 0, 3, 0, 1, 1, 3, 1, 1, 2, 3, 0, 1, 1, 3, 2, 2, 2, 1, 1,\n", " 2, 1, 0, 3, 0, 2, 1, 2, 0, 3, 1, 1, 3, 2, 2, 2])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.cluster import KMeans\n", "clustering=KMeans(n_clusters=4,n_init=10, random_state=42,verbose=1)\n", "\n", "y_pred=clustering.fit(X_train)\n", "clustering.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KMeans(n_clusters=4, random_state=42, verbose=1)\n", "[[5.46363636 2.6 3.92727273 1.22727273]\n", " [6.26571429 2.84571429 4.94285714 1.76857143]\n", " [5.02307692 3.43589744 1.45384615 0.24102564]\n", " [7.10625 3.13125 6.0125 2.15 ]]\n", "[2 1 2 0 2 2 2 1 2 1 2 1 1 3 0 1 1 1 3 2 0 3 2 1 2 1 1 1 2 2 3 2 1 2 2 0 1\n", " 2 2 1 3 2 2 2 1 2 2 2 0 3 1 0 1 2 1 3 0 3 3 1 0 1 0 0 0 1 0 1 3 2 0 0 3 1\n", " 2 2 0 2 3 2 3 1 1 2 1 1 3 0 0 0 2 0 2 1 1 3 2 1 0 1 0 1 2 2 1 2 2 2 2 3 1\n", " 0]\n", "KMeans(n_clusters=4, random_state=42, verbose=1)\n" ] }, { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(y_pred)\n", "# Get the cluster centroids\n", "print(y_pred.cluster_centers_)\n", " \n", "# Get the cluster labels\n", "print(y_pred.labels_)\n", "print(y_pred)\n", "type(y_pred.labels_)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, '3 clusters')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "#plt.subplot(221)\n", "plt.scatter(X_train[:, 0], X_train[:, 2], c=y_pred.labels_)\n", "plt.title(\"3 clusters\")\n" ] }, { "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }