├── Chapter02 └── Ch 2 - Basic Terminology and our End-to-end Example.ipynb ├── Chapter03 └── Ch 3 - Collecting, Exploring, and Visualizing Data.ipynb ├── Chapter04 └── Ch 4 - Cleaning and Readying Data for Analysis.ipynb ├── Chapter05 └── Ch 5 - Grouping and Clustering Data.ipynb ├── Chapter06 └── Ch 6 - Prediction with Regression and Classification.ipynb ├── Chapter07 └── Ch 7 - Building a Data Processing Pipeline and Deploying.ipynb ├── LICENSE ├── README.md ├── data ├── boston.csv ├── boston.db ├── iris.csv ├── iris_missing_values.csv └── long_jump.csv ├── images ├── compare_clsuter_methods.jpg ├── compare_prediction_methods.jpg ├── hca_dendrogram.jpg ├── iris_scatter_raw_vs_lda.jpg ├── iris_scatter_raw_vs_pca.jpg ├── iris_violin_pca1-2.jpg ├── min_mix_scaler.png ├── missing_data_spreadsheet.png └── one_hot.png └── model_storage ├── dendrogram └── joblib │ └── sklearn │ └── cluster │ └── hierarchical │ └── ward_tree │ └── func_code.py └── model.pkl /Chapter07/Ch 7 - Building a Data Processing Pipeline and Deploying.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Python Data Mining Quick Start Guide\n", 8 | "## Ch 7 - Building a Data Processing Pipeline and Deploying\n", 9 | "### Copyright: Nathan Greeneltch, PhD 2019" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "#### These code examples and description are meant to accompany the book \"Python Data Mining Quick Start Guide\" by Nathan Greeneltch. For full background on the topics and introduction sections, please purchase the book. " 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "# initial imports\n", 26 | "import numpy as np\n", 27 | "import pandas as pd\n", 28 | "import seaborn as sns\n", 29 | "sns.set_context(\"paper\", font_scale=1.5)\n", 30 | "sns.set_style(\"white\")" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "# Pipelining Your Analysis\n", 38 | "\n", 39 | "A **pipelined** analysis is a series of steps stored as a single function or object. On top of providing a framework for your analysis, the most important reason for pipelining is apparent upon examining what is required to reproduce your workflow or apply it to new data. of Now that you've seen a nice collection of various data mining methods, it's a good time to acknowledge some facts:\n", 40 | "\n", 41 | "* Most analysis workflows have multiple steps (cleaning, scaling, transforming, clustering, etc..).\n", 42 | "* In order to reproduce the workflow, all the steps must be done in the exact right order.\n", 43 | "* Failure to reproduce the steps exactly can result in bad information, often failing silently.\n", 44 | "* Humans make mistakes so we need to guard against them.\n", 45 | "\n", 46 | "The perfect tool for guarding against mistakes is to build a pipeline, test it locally, and deploy the entire pipeline as a finished product. \n", 47 | "\n", 48 | "TIP: It is a good idea to build your pipeline while as you develop your analysis workflow. This will allow you to have confidence that the steps you applied are indeed captured correctly in the pipeline. \n", 49 | "\n", 50 | "## Scikit-learn's Pipeline Object\n", 51 | "\n", 52 | "Scikit-learn has a full service **Pipeline** object that is compatible with objects that use both the transformer and estimator APIs. It can also take a **GridSearchCV** as a step in the pipeline, so you can use the pipeline for tuning and the result will automatically be stored in the pipe. \n", 53 | "\n", 54 | "For our example, we will build a pipeline that transforms the data with PCA and then predicts labels with logistic regression. Let's start by loading the iris dataset, required modules, and splitting the data into a train/test set. We will use k-fold cross-validation in the grid search, so no need to make a separate validation set. Let's start with the following code:" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 2, 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "### Building a Pipeline ###\n", 64 | "# load iris and create X and y\n", 65 | "from sklearn.datasets import load_iris\n", 66 | "dataset = load_iris()\n", 67 | "X,y = dataset.data, dataset.target\n", 68 | "\n", 69 | "# import modules \n", 70 | "from sklearn.decomposition import PCA\n", 71 | "from sklearn.linear_model import LogisticRegression\n", 72 | "from sklearn.pipeline import Pipeline\n", 73 | "from sklearn.model_selection import GridSearchCV\n", 74 | "from sklearn.model_selection import train_test_split\n", 75 | "\n", 76 | "# create train and test sets\n", 77 | "X_train, X_test, y_train, y_test = \\\n", 78 | " train_test_split(X, y, test_size=.33)" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "We will first look at psuedocode for the use of the pipeline object. The process begins by importing transformer and estimator modules and instantiating the object exactly how we've done in the previous chapters. Then we create the pipeline and pass the steps as a list, in the order we'd like them to execute. This list can grow larger than two steps. You can give each step a helpful name that will help you remember its pupose. Most practitioners simply use the name of the method here, like \"PCA, or \"SVC\", or \"Random Forest\". Finally we can use the entire pipeline as any other method with the estimator api, with the **fit()** and **predict()** methods. Take a look at the following pseduo-code version that outlines the steps:" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 10, 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "ename": "ImportError", 95 | "evalue": "cannot import name 'transformer' from 'sklearn' (C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\__init__.py)", 96 | "output_type": "error", 97 | "traceback": [ 98 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 99 | "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", 100 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipeline\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPipeline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# instantiate the transformer and classifier objects\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 101 | "\u001b[1;31mImportError\u001b[0m: cannot import name 'transformer' from 'sklearn' (C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\__init__.py)" 102 | ] 103 | } 104 | ], 105 | "source": [ 106 | "### this is pseudocode. it will not execute ###\n", 107 | "# import modules\n", 108 | "from sklearn.pipeline import Pipeline\n", 109 | "from sklearn import transformer\n", 110 | "from sklearn import estimator\n", 111 | "\n", 112 | "# instantiate the transformer and classifier objects\n", 113 | "method1 = transformer(args)\n", 114 | "method2 = estimator(args)\n", 115 | "\n", 116 | "# instantiate a pipeline and add steps to the pipeline\n", 117 | "pipe = Pipeline([('helpful name 1', method1), ('helpful name 2', method2)])\n", 118 | "\n", 119 | "# print list of steps with names\n", 120 | "print(pipe.steps[0])\n", 121 | "\n", 122 | "# fit and predict\n", 123 | "pipe.fit(X_train,y_train)\n", 124 | "pip.predict(X_test)" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "Now we can instantiate the transformer and classifier objects and feed them into the pipeline (named **pipe**):" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 9, 137 | "metadata": {}, 138 | "outputs": [ 139 | { 140 | "name": "stdout", 141 | "output_type": "stream", 142 | "text": [ 143 | "('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n", 144 | " svd_solver='auto', tol=0.0, whiten=False))\n" 145 | ] 146 | } 147 | ], 148 | "source": [ 149 | "# instantiate the transformer and classifier objects\n", 150 | "pca = PCA()\n", 151 | "logistic = LogisticRegression(solver='liblinear', multi_class='ovr', C=1.5)\n", 152 | "\n", 153 | "# instantiate a pipeline and add steps to the pipeline\n", 154 | "pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n", 155 | "\n", 156 | "# print list of steps with names\n", 157 | "print(pipe.steps[0])" 158 | ] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "metadata": {}, 163 | "source": [ 164 | "Next we will create the parameter grid that the grid search will use and instantiate the grid search object. Here we will test a few values of **n_components** for PCA and **C** for logistic regression using 5-fold cross-validation. Finally we fit our model to the data and print out the best parameters:" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 4, 170 | "metadata": {}, 171 | "outputs": [ 172 | { 173 | "name": "stdout", 174 | "output_type": "stream", 175 | "text": [ 176 | "Best parameter (CV score=0.950):\n", 177 | "{'logistic__C': 10, 'pca__n_components': 2}\n" 178 | ] 179 | } 180 | ], 181 | "source": [ 182 | "# set the parameter grid to be passed to the grid search\n", 183 | "param_grid = {\n", 184 | " 'pca__n_components': [2, 3, 4],\n", 185 | " 'logistic__C': [0.5, 1, 5, 10],\n", 186 | "}\n", 187 | "\n", 188 | "# instantiate the grid search object and pass the pipe and param_grid\n", 189 | "model = GridSearchCV(pipe, param_grid, iid=False, cv=5,\n", 190 | " return_train_score=False)\n", 191 | "\n", 192 | "# fit entire pipeline using grid serach and 5-fold cross validation\n", 193 | "model.fit(X_train, y_train)\n", 194 | "print(\"Best parameter (CV score=%0.3f):\" % model.best_score_)\n", 195 | "print(model.best_params_)" 196 | ] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": {}, 201 | "source": [ 202 | "The full pipeline model can be used to predict on new data with the **.predict()** method:" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": 10, 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "# use the resulting pipeline to predict on new data\n", 212 | "y_pred = model.predict(X_test)" 213 | ] 214 | }, 215 | { 216 | "cell_type": "markdown", 217 | "metadata": {}, 218 | "source": [ 219 | "# Deployment of Model\n", 220 | "\n", 221 | "Often in a production environment, deployment is the step where you release your model into the wild and let it run on unforeseen data. However, data mining also produces many local analysis workflows that don't necessarily need to deploy but do need to be stored and re-loaded later in order to reproduce the analysis. Both of these use-cases require what is called **model persistence**. The persistence term means the model needs to be stored and loaded for later use. Python is an object-oriented language and appropriately Sci-kit learn uses objects for most of its analysis routines. Storing an object is not as simple as storing a basic text file full of strings. It instead requires a process called **serialization** to store in a reliable and error-free manner. One of the most popular serialization packages is a Python core library **pickle**. It's what we will use for our serialization examples.\n", 222 | "\n", 223 | "## Serialize Model and Store with Pickle Module\n", 224 | "\n", 225 | "**Pickle** is compatible with Scikit-learn's transformers and estimators. Conveniently (and more importantly) it is also compatible with Scikit-learn's grid search and pipeline objects. It is very easy to use as serialization and storage are accomplished with a single method called **.dump()**. The following example will use pickle to serialize our pipeline model and store it in a file named \"model.pkl\": " 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 11, 231 | "metadata": {}, 232 | "outputs": [], 233 | "source": [ 234 | "### Store Model for Later with Pickle ###\n", 235 | "# import module\n", 236 | "import pickle\n", 237 | "\n", 238 | "# save the pipeline model to disk\n", 239 | "pickle.dump(model, open('./model_storage/model.pkl', 'wb'))" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "## Load Serialized Model and Predict\n", 247 | "\n", 248 | "Now when we are ready to use the model either in production or locally, we simply load back up with pickle and store it in a new local object. We can name the new loaded model object **model_load** and after loading and **deserializing** with the **.load()** method. Then use model_load as if it were the original version of the model. See the following code example for demonstration:" 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": 12, 254 | "metadata": {}, 255 | "outputs": [], 256 | "source": [ 257 | "# load the pipeline model from disk and deserialize\n", 258 | "model_load = pickle.load(open('./model_storage/model.pkl', 'rb'))\n", 259 | "\n", 260 | "# use the loaded pipeline model to predict on new data\n", 261 | "y_pred = model_load.predict(X_test)" 262 | ] 263 | }, 264 | { 265 | "cell_type": "markdown", 266 | "metadata": {}, 267 | "source": [ 268 | "## Python-specific Deployment Concerns\n", 269 | "\n", 270 | "Python is not a compiled language. It is interpreted at the time of execution. It is important to remember that when you follow the steps in this chapter, you are not pickling an executable program. You are simply pickling an object. At load time, the environment must be compatible with the contents of the object. Often that means matching versions as libraries change over time. Also the default serialization protocol for pickle is not compatible with Python 2, so you will have to change the protocol if switching Python versions.\n", 271 | "\n", 272 | "Lastly the pickled object is similar to a zip file in that anyone can bundle up anything inside it and you will not know it until you unpickle/unzip it. **Security** should always be a concern with any file types that are not transparent\n", 273 | "\n", 274 | "NOTE: You should read the main pickle doc page for descriptions of compatibility and security before using. It is here: https://docs.python.org/3/library/pickle.html" 275 | ] 276 | } 277 | ], 278 | "metadata": { 279 | "kernelspec": { 280 | "display_name": "Python 3", 281 | "language": "python", 282 | "name": "python3" 283 | }, 284 | "language_info": { 285 | "codemirror_mode": { 286 | "name": "ipython", 287 | "version": 3 288 | }, 289 | "file_extension": ".py", 290 | "mimetype": "text/x-python", 291 | "name": "python", 292 | "nbconvert_exporter": "python", 293 | "pygments_lexer": "ipython3", 294 | "version": "3.7.1" 295 | } 296 | }, 297 | "nbformat": 4, 298 | "nbformat_minor": 2 299 | } 300 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # Python-Data-Mining-Quick-Start-Guide 5 | Python Data Mining Quick Start Guide, published by Packt 6 | 7 | Python Data Mining Quick Start Guide 8 | 9 | This is the code repository for [Python Data Mining Quick Start Guide](https://prod.packtpub.com/in/big-data-and-business-intelligence/python-data-mining-quick-start-guide?utm_source=github&utm_medium=repository&utm_campaign=9781789800265), published by Packt. 10 | 11 | **A beginner's guide to extracting valuable insights from your data** 12 | 13 | ## What is this book about? 14 | Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/or trend discovery phase in the pipeline and Python is a popular tool to perform these tasks as it offers a wide variety of tools for data mining. 15 | 16 | This book covers the following exciting features: 17 | * Grasp the basics of data loading, cleaning, analysis, and visualization 18 | * Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining 19 | * Your one-stop guide to build efficient data mining pipelines without going into too much theory 20 | 21 | If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1789800269) today! 22 | 23 | https://www.packtpub.com/ 25 | 26 | 27 | ## Instructions and Navigations 28 | All of the code is organized into folders. For example, Chapter02. 29 | 30 | The code will look like the following: 31 | ``` 32 | sns.pairplot(df,hue='species',diag_kind='hist', 33 | palette='bright',markers=['o','x','v'] 34 | ``` 35 | 36 | **Following is what you need for this book:** 37 | Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started. 38 | 39 | With the following software and hardware list you can run all code files present in the book (Chapter 1-7). 40 | 41 | ### Software and Hardware List 42 | 43 | | Chapter | Software required | OS required | 44 | | -------- | ------------------------------------| -----------------------------------| 45 | | 1-7 | Pandas 0.23.4 | Windows, Mac OS X, and Linux | 46 | | | seaborn 0.9.0 | | 47 | | | scikit-learn 0.20.2 | | 48 | | | sqlite 3.25.3 | | 49 | | | numpy 1.16.2 | | 50 | | | matplotlib 2.2.3 | | 51 | | | pickle 3.7 | | 52 | 53 | We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://www.packtpub.com/sites/default/files/downloads/9781789800265_ColorImages.pdf). 54 | 55 | 56 | ### Related products 57 | * Bayesian Analysis with Python - Second Edition [[Packt]](https://prod.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition?utm_source=github&utm_medium=repository&utm_campaign=9781789341652) [[Amazon]](https://www.amazon.com/dp/1789341655) 58 | 59 | * Big Data Analysis with Python [[Packt]](https://prod.packtpub.com/in/big-data-and-business-intelligence/big-data-analysis-python?utm_source=github&utm_medium=repository&utm_campaign=9781789955286) [[Amazon]](https://www.amazon.com/dp/1789955289) 60 | 61 | ## Get to Know the Author 62 | **Nathan Greeneltch** 63 | , PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. He’s worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida. 64 | 65 | ### Suggestions and Feedback 66 | [Click here](https://docs.google.com/forms/d/e/1FAIpQLSdy7dATC6QmEL81FIUuymZ0Wy9vH1jHkvpY57OiMeKGqib_Ow/viewform) if you have any feedback or suggestions. 67 | ### Download a free PDF 68 | 69 | If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF. 70 |

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124,0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8 127 | 125,0.16902,0,25.65,0,0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4 128 | 126,0.38735,0,25.65,0,0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26,15.7 129 | 127,0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2 130 | 128,0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18 131 | 129,0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3 132 | 130,0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2 133 | 131,1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6 134 | 132,0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23 135 | 133,0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4 136 | 134,0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6 137 | 135,0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1 138 | 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148,2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8 151 | 149,2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4 152 | 150,1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5 153 | 151,1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6 154 | 152,1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3 155 | 153,2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4 156 | 154,1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17 157 | 155,3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6 158 | 156,2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1 159 | 157,1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3 160 | 158,1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3 161 | 159,1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3 162 | 160,1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27 163 | 161,1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50 164 | 162,1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50 165 | 163,1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50 166 | 164,2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7 167 | 165,2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25 168 | 166,2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50 169 | 167,1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8 170 | 168,2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8 171 | 169,2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3 172 | 170,1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4 173 | 171,2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1 174 | 172,0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1 175 | 173,0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6 176 | 174,0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6 177 | 175,0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4 178 | 176,0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2 179 | 177,0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6 180 | 178,0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9 181 | 179,0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2 182 | 180,0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8 183 | 181,0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2 184 | 182,0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9 185 | 183,0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5 186 | 184,0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4 187 | 185,0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6 188 | 186,0.05602,0,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50 189 | 187,0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32 190 | 188,0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8 191 | 189,0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9 192 | 190,0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37 193 | 191,0.06911,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5 194 | 192,0.08664,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4 195 | 193,0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1 196 | 194,0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1 197 | 195,0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50 198 | 196,0.04011,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3 199 | 197,0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3 200 | 198,0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6 201 | 199,0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9 202 | 200,0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9 203 | 201,0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1 204 | 202,0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3 205 | 203,0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5 206 | 204,0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50 207 | 205,0.13642,0,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6 208 | 206,0.22969,0,10.59,0,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4 209 | 207,0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5 210 | 208,0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4 211 | 209,0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20 212 | 210,0.17446,0,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7 213 | 211,0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3 214 | 212,0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4 215 | 213,0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1 216 | 214,0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7 217 | 215,0.19802,0,10.59,0,0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25 218 | 216,0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3 219 | 217,0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7 220 | 218,0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5 221 | 219,0.11425,0,13.89,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23 222 | 220,0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7 223 | 221,0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7 224 | 222,0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5 225 | 223,0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1 226 | 224,0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8 227 | 225,0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50 228 | 226,0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6 229 | 227,0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6 230 | 228,0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7 231 | 229,0.44178,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5 232 | 230,0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3 233 | 231,0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7 234 | 232,0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7 235 | 233,0.33147,0,6.2,0,0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3 236 | 234,0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29 237 | 235,0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24 238 | 236,0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1 239 | 237,0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5 240 | 238,0.08244,30,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7 241 | 239,0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3 242 | 240,0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22 243 | 241,0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1 244 | 242,0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2 245 | 243,0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7 246 | 244,0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6 247 | 245,0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5 248 | 246,0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3 249 | 247,0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5 250 | 248,0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5 251 | 249,0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2 252 | 250,0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4 253 | 251,0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8 254 | 252,0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6 255 | 253,0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8 256 | 254,0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9 257 | 255,0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9 258 | 256,0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44 259 | 257,0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50 260 | 258,0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36 261 | 259,0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1 262 | 260,0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8 263 | 261,0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1 264 | 262,0.52014,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8 265 | 263,0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31 266 | 264,0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5 267 | 265,0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8 268 | 266,0.7857,20,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7 269 | 267,0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50 270 | 268,0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5 271 | 269,0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7 272 | 270,0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1 273 | 271,0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2 274 | 272,0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4 275 | 273,0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2 276 | 274,0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4 277 | 275,0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32 278 | 276,0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2 279 | 277,0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1 280 | 278,0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1 281 | 279,0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1 282 | 280,0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4 283 | 281,0.03705,20,3.33,0,0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4 284 | 282,0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46 285 | 283,0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50 286 | 284,0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2 287 | 285,0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22 288 | 286,0.01965,80,1.76,0,0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1 289 | 287,0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2 290 | 288,0.0459,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3 291 | 289,0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8 292 | 290,0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5 293 | 291,0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3 294 | 292,0.03615,80,4.95,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9 295 | 293,0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9 296 | 294,0.08199,0,13.92,0,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7 297 | 295,0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6 298 | 296,0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1 299 | 297,0.14103,0,13.92,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3 300 | 298,0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5 301 | 299,0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29 302 | 300,0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8 303 | 301,0.03537,34,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22 304 | 302,0.09266,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4 305 | 303,0.1,34,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1 306 | 304,0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1 307 | 305,0.05479,33,2.18,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4 308 | 306,0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4 309 | 307,0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2 310 | 308,0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8 311 | 309,0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3 312 | 310,2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1 313 | 311,0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1 314 | 312,0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4 315 | 313,0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6 316 | 314,0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8 317 | 315,0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2 318 | 316,0.31827,0,9.9,0,0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8 319 | 317,0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8 320 | 318,0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1 321 | 319,0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21 322 | 320,0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8 323 | 321,0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1 324 | 322,0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4 325 | 323,0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5 326 | 324,0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25 327 | 325,0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6 328 | 326,0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23 329 | 327,0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2 330 | 328,0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3 331 | 329,0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6 332 | 330,0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8 333 | 331,0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1 334 | 332,0.03466,35,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4 335 | 333,0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2 336 | 334,0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7 337 | 335,0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1 338 | 336,0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5 339 | 337,0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5 340 | 338,0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6 341 | 339,0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19 342 | 340,0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7 343 | 341,0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7 344 | 342,0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5 345 | 343,0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9 346 | 344,0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2 347 | 345,0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5 348 | 346,0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2 349 | 347,0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1 350 | 348,0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5 351 | 349,0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6 352 | 350,0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9 353 | 351,0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1 354 | 352,0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6 355 | 353,0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1 356 | 354,0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2 357 | 355,0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6 358 | 356,8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8 359 | 357,3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7 360 | 358,5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7 361 | 359,4.26131,0,18.1,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6 362 | 360,4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25 363 | 361,3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9 364 | 362,3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8 365 | 363,4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8 366 | 364,3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9 367 | 365,4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5 368 | 366,3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9 369 | 367,13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1 370 | 368,4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50 371 | 369,5.66998,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50 372 | 370,6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50 373 | 371,9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50 374 | 372,8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50 375 | 373,11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8 376 | 374,18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8 377 | 375,19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15 378 | 376,15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9 379 | 377,9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3 380 | 378,23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1 381 | 379,17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2 382 | 380,88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4 383 | 381,15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9 384 | 382,9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3 385 | 383,7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3 386 | 384,20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8 387 | 385,16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2 388 | 386,24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5 389 | 387,22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4 390 | 388,14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2 391 | 389,8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5 392 | 390,6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1 393 | 391,5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2 394 | 392,11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7 395 | 393,8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8 396 | 394,13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7 397 | 395,8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1 398 | 396,5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5 399 | 397,7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5 400 | 398,38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5 401 | 399,9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3 402 | 400,25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6 403 | 401,14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2 404 | 402,9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1 405 | 403,24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3 406 | 404,41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5 407 | 405,67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5 408 | 406,20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9 409 | 407,11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9 410 | 408,7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2 411 | 409,14.4383,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5 412 | 410,51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15 413 | 411,14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2 414 | 412,18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9 415 | 413,28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3 416 | 414,45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7 417 | 415,18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2 418 | 416,10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5 419 | 417,25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4 420 | 418,73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8 421 | 419,11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4 422 | 420,11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7 423 | 421,7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2 424 | 422,12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8 425 | 423,7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4 426 | 424,8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7 427 | 425,15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3 428 | 426,12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2 429 | 427,37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9 430 | 428,7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11 431 | 429,9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5 432 | 430,8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5 433 | 431,10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1 434 | 432,6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1 435 | 433,5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3 436 | 434,13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7 437 | 435,11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4 438 | 436,14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6 439 | 437,15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7 440 | 438,13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4 441 | 439,9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8 442 | 440,22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5 443 | 441,9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1 444 | 442,5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4 445 | 443,9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4 446 | 444,12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8 447 | 445,0.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8 448 | 446,6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9 449 | 447,9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6 450 | 448,9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1 451 | 449,7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13 452 | 450,6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4 453 | 451,5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2 454 | 452,5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1 455 | 453,8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8 456 | 454,9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9 457 | 455,4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1 458 | 456,4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7 459 | 457,8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5 460 | 458,7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9 461 | 459,6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20 462 | 460,4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4 463 | 461,3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7 464 | 462,6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5 465 | 463,5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2 466 | 464,7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4 467 | 465,3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9 468 | 466,3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19 469 | 467,4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1 470 | 468,15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1 471 | 469,13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1 472 | 470,4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9 473 | 471,4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6 474 | 472,3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2 475 | 473,4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8 476 | 474,8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8 477 | 475,6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3 478 | 476,4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7 479 | 477,15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12 480 | 478,10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6 481 | 479,14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4 482 | 480,5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23 483 | 481,5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7 484 | 482,5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25 485 | 483,2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8 486 | 484,2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6 487 | 485,3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2 488 | 486,5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1 489 | 487,4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6 490 | 488,0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2 491 | 489,0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7 492 | 490,0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1 493 | 491,0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6 494 | 492,0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1 495 | 493,0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8 496 | 494,0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5 497 | 495,0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1 498 | 496,0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7 499 | 497,0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3 500 | 498,0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2 501 | 499,0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5 502 | 500,0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8 503 | 501,0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4 504 | 502,0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6 505 | 503,0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9 506 | 504,0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22 507 | 505,0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9 508 | -------------------------------------------------------------------------------- /data/boston.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Data-Mining-Quick-Start-Guide/117d817cf7d2e1184ec5a40922c4b502ed3bf0af/data/boston.db -------------------------------------------------------------------------------- /data/iris.csv: -------------------------------------------------------------------------------- 1 | sepal length in cm,sepal width in cm,petal length in cm,petal width in cm,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3,1.4,0.1,setosa 15 | 4.3,3,1.1,0.1,setosa 16 | 5.8,4,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5,3,1.6,0.2,setosa 28 | 5,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.1,setosa 37 | 5,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.1,1.5,0.1,setosa 40 | 4.4,3,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5,3.3,1.4,0.2,setosa 52 | 7,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5,2,3.5,1,versicolor 63 | 5.9,3,4.2,1.5,versicolor 64 | 6,2.2,4,1,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3,5,1.7,versicolor 80 | 6,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6,2.7,5.1,1.6,versicolor 86 | 5.4,3,4.5,1.5,versicolor 87 | 6,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3,4.1,1.3,versicolor 91 | 5.5,2.5,4,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3,4.6,1.4,versicolor 94 | 5.8,2.6,4,1.2,versicolor 95 | 5,2.3,3.3,1,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3,5.8,2.2,virginica 107 | 7.6,3,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3,5.5,2.1,virginica 115 | 5.7,2.5,5,2,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6,2.2,5,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2,virginica 124 | 7.7,2.8,6.7,2,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6,3,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3,5.2,2.3,virginica 148 | 6.3,2.5,5,1.9,virginica 149 | 6.5,3,5.2,2,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /data/iris_missing_values.csv: -------------------------------------------------------------------------------- 1 | sepal length in cm,sepal width in cm,petal length in cm,petal width in cm,species 2 | ,3.5,1.4,0.2,setosa 3 | 4.9,3,1.4,0.2,setosa 4 | ,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5,3.6,1.4,0.2,setosa 7 | ,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | ,,,, 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3,1.4,0.1,setosa 15 | 4.3,3,1.1,0.1,setosa 16 | 5.8,4,1.2,0.2,setosa 17 | ,,,, 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | ,,,, 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5,3,1.6,0.2,setosa 28 | 5,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.1,setosa 37 | 5,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.1,1.5,0.1,setosa 40 | 4.4,3,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5,3.3,1.4,0.2,setosa 52 | 7,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5,2,3.5,1,versicolor 63 | 5.9,3,4.2,1.5,versicolor 64 | 6,2.2,4,1,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3,5,1.7,versicolor 80 | 6,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6,2.7,5.1,1.6,versicolor 86 | 5.4,3,4.5,1.5,versicolor 87 | 6,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3,4.1,1.3,versicolor 91 | 5.5,2.5,4,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3,4.6,1.4,versicolor 94 | 5.8,2.6,4,1.2,versicolor 95 | 5,2.3,3.3,1,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3,5.8,2.2,virginica 107 | 7.6,3,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3,5.5,2.1,virginica 115 | 5.7,2.5,5,2,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6,2.2,5,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2,virginica 124 | 7.7,2.8,6.7,2,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6,3,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3,5.2,2.3,virginica 148 | 6.3,2.5,5,1.9,virginica 149 | 6.5,3,5.2,2,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /data/long_jump.csv: -------------------------------------------------------------------------------- 1 | Person,Age,Height,Weight,Training Hours/week,Jersey Color,Jersey Size,Shoe Size,Long Jump 2 | Thomas,12,57.5,73.4,6.5,blue,small,7,19.2 3 | Jane,13,65.5,85.3,8.9,green,medium,10,25.1 4 | Vaughn,17,71.9,125.9,1.1,green,large,12,14.3 5 | Vera,14,65.3,100.5,7.9,red,medium,9,18.3 6 | Vincent,18,70.1,110.7,10.5,blue,large,12,21.1 7 | Lei-Ann,12,52.3,70.4,0.5,blue,small,7,10.6 8 | -------------------------------------------------------------------------------- /images/compare_clsuter_methods.jpg: -------------------------------------------------------------------------------- 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samples to be clustered 19 | 20 | connectivity : sparse matrix (optional). 21 | connectivity matrix. Defines for each sample the neighboring samples 22 | following a given structure of the data. The matrix is assumed to 23 | be symmetric and only the upper triangular half is used. 24 | Default is None, i.e, the Ward algorithm is unstructured. 25 | 26 | n_clusters : int (optional) 27 | Stop early the construction of the tree at n_clusters. This is 28 | useful to decrease computation time if the number of clusters is 29 | not small compared to the number of samples. In this case, the 30 | complete tree is not computed, thus the 'children' output is of 31 | limited use, and the 'parents' output should rather be used. 32 | This option is valid only when specifying a connectivity matrix. 33 | 34 | return_distance : bool (optional) 35 | If True, return the distance between the clusters. 36 | 37 | Returns 38 | ------- 39 | children : 2D array, shape (n_nodes-1, 2) 40 | The children of each non-leaf node. Values less than `n_samples` 41 | correspond to leaves of the tree which are the original samples. 42 | A node `i` greater than or equal to `n_samples` is a non-leaf 43 | node and has children `children_[i - n_samples]`. Alternatively 44 | at the i-th iteration, children[i][0] and children[i][1] 45 | are merged to form node `n_samples + i` 46 | 47 | n_components : int 48 | The number of connected components in the graph. 49 | 50 | n_leaves : int 51 | The number of leaves in the tree 52 | 53 | parents : 1D array, shape (n_nodes, ) or None 54 | The parent of each node. Only returned when a connectivity matrix 55 | is specified, elsewhere 'None' is returned. 56 | 57 | distances : 1D array, shape (n_nodes-1, ) 58 | Only returned if return_distance is set to True (for compatibility). 59 | The distances between the centers of the nodes. `distances[i]` 60 | corresponds to a weighted euclidean distance between 61 | the nodes `children[i, 1]` and `children[i, 2]`. If the nodes refer to 62 | leaves of the tree, then `distances[i]` is their unweighted euclidean 63 | distance. Distances are updated in the following way 64 | (from scipy.hierarchy.linkage): 65 | 66 | The new entry :math:`d(u,v)` is computed as follows, 67 | 68 | .. math:: 69 | 70 | d(u,v) = \\sqrt{\\frac{|v|+|s|} 71 | {T}d(v,s)^2 72 | + \\frac{|v|+|t|} 73 | {T}d(v,t)^2 74 | - \\frac{|v|} 75 | {T}d(s,t)^2} 76 | 77 | where :math:`u` is the newly joined cluster consisting of 78 | clusters :math:`s` and :math:`t`, :math:`v` is an unused 79 | cluster in the forest, :math:`T=|v|+|s|+|t|`, and 80 | :math:`|*|` is the cardinality of its argument. This is also 81 | known as the incremental algorithm. 82 | """ 83 | X = np.asarray(X) 84 | if X.ndim == 1: 85 | X = np.reshape(X, (-1, 1)) 86 | n_samples, n_features = X.shape 87 | 88 | if connectivity is None: 89 | from scipy.cluster import hierarchy # imports PIL 90 | 91 | if n_clusters is not None: 92 | warnings.warn('Partial build of the tree is implemented ' 93 | 'only for structured clustering (i.e. with ' 94 | 'explicit connectivity). The algorithm ' 95 | 'will build the full tree and only ' 96 | 'retain the lower branches required ' 97 | 'for the specified number of clusters', 98 | stacklevel=2) 99 | X = np.require(X, requirements="W") 100 | out = hierarchy.ward(X) 101 | children_ = out[:, :2].astype(np.intp) 102 | 103 | if return_distance: 104 | distances = out[:, 2] 105 | return children_, 1, n_samples, None, distances 106 | else: 107 | return children_, 1, n_samples, None 108 | 109 | connectivity, n_components = _fix_connectivity(X, connectivity, 110 | affinity='euclidean') 111 | if n_clusters is None: 112 | n_nodes = 2 * n_samples - 1 113 | else: 114 | if n_clusters > n_samples: 115 | raise ValueError('Cannot provide more clusters than samples. ' 116 | '%i n_clusters was asked, and there are %i samples.' 117 | % (n_clusters, n_samples)) 118 | n_nodes = 2 * n_samples - n_clusters 119 | 120 | # create inertia matrix 121 | coord_row = [] 122 | coord_col = [] 123 | A = [] 124 | for ind, row in enumerate(connectivity.rows): 125 | A.append(row) 126 | # We keep only the upper triangular for the moments 127 | # Generator expressions are faster than arrays on the following 128 | row = [i for i in row if i < ind] 129 | coord_row.extend(len(row) * [ind, ]) 130 | coord_col.extend(row) 131 | 132 | coord_row = np.array(coord_row, dtype=np.intp, order='C') 133 | coord_col = np.array(coord_col, dtype=np.intp, order='C') 134 | 135 | # build moments as a list 136 | moments_1 = np.zeros(n_nodes, order='C') 137 | moments_1[:n_samples] = 1 138 | moments_2 = np.zeros((n_nodes, n_features), order='C') 139 | moments_2[:n_samples] = X 140 | inertia = np.empty(len(coord_row), dtype=np.float64, order='C') 141 | _hierarchical.compute_ward_dist(moments_1, moments_2, coord_row, coord_col, 142 | inertia) 143 | inertia = list(six.moves.zip(inertia, coord_row, coord_col)) 144 | heapify(inertia) 145 | 146 | # prepare the main fields 147 | parent = np.arange(n_nodes, dtype=np.intp) 148 | used_node = np.ones(n_nodes, dtype=bool) 149 | children = [] 150 | if return_distance: 151 | distances = np.empty(n_nodes - n_samples) 152 | 153 | not_visited = np.empty(n_nodes, dtype=np.int8, order='C') 154 | 155 | # recursive merge loop 156 | for k in range(n_samples, n_nodes): 157 | # identify the merge 158 | while True: 159 | inert, i, j = heappop(inertia) 160 | if used_node[i] and used_node[j]: 161 | break 162 | parent[i], parent[j] = k, k 163 | children.append((i, j)) 164 | used_node[i] = used_node[j] = False 165 | if return_distance: # store inertia value 166 | distances[k - n_samples] = inert 167 | 168 | # update the moments 169 | moments_1[k] = moments_1[i] + moments_1[j] 170 | moments_2[k] = moments_2[i] + moments_2[j] 171 | 172 | # update the structure matrix A and the inertia matrix 173 | coord_col = [] 174 | not_visited.fill(1) 175 | not_visited[k] = 0 176 | _hierarchical._get_parents(A[i], coord_col, parent, not_visited) 177 | _hierarchical._get_parents(A[j], coord_col, parent, not_visited) 178 | # List comprehension is faster than a for loop 179 | [A[l].append(k) for l in coord_col] 180 | A.append(coord_col) 181 | coord_col = np.array(coord_col, dtype=np.intp, order='C') 182 | coord_row = np.empty(coord_col.shape, dtype=np.intp, order='C') 183 | coord_row.fill(k) 184 | n_additions = len(coord_row) 185 | ini = np.empty(n_additions, dtype=np.float64, order='C') 186 | 187 | _hierarchical.compute_ward_dist(moments_1, moments_2, 188 | coord_row, coord_col, ini) 189 | 190 | # List comprehension is faster than a for loop 191 | [heappush(inertia, (ini[idx], k, coord_col[idx])) 192 | for idx in range(n_additions)] 193 | 194 | # Separate leaves in children (empty lists up to now) 195 | n_leaves = n_samples 196 | # sort children to get consistent output with unstructured version 197 | children = [c[::-1] for c in children] 198 | children = np.array(children) # return numpy array for efficient caching 199 | 200 | if return_distance: 201 | # 2 is scaling factor to compare w/ unstructured version 202 | distances = np.sqrt(2. * distances) 203 | return children, n_components, n_leaves, parent, distances 204 | else: 205 | return children, n_components, n_leaves, parent 206 | -------------------------------------------------------------------------------- /model_storage/model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Data-Mining-Quick-Start-Guide/117d817cf7d2e1184ec5a40922c4b502ed3bf0af/model_storage/model.pkl --------------------------------------------------------------------------------