├── .gitignore ├── README.md ├── ch02_Toolbox.ipynb ├── ch03_Descriptive_Statistics.ipynb ├── ch04_Statistical_Inference.ipynb ├── ch05_Machine_Learning.ipynb ├── ch05_Preprocessing_Machine_Learning.ipynb ├── ch06_Regression_Analysis.ipynb ├── ch07_Unsupervised_Learning.ipynb ├── ch08_Network_Analysis.ipynb ├── ch09_Recommenders.ipynb ├── ch10_Sentiment Analysis.ipynb ├── ch11_Parallelization.ipynb ├── ch12_DeepLearning.ipynb ├── error_ms_1.png ├── files ├── ch02 │ └── educ_figdp_1_Data.csv ├── ch03 │ └── adult.data ├── ch04 │ ├── ACCIDENTS_GU_BCN_2010.csv │ └── ACCIDENTS_GU_BCN_2013.csv ├── ch05 │ └── dataset_small.pkl ├── ch06 │ ├── ExSimpleRegModels.png │ ├── IceExtentAllMonthsByYearlmplot.png │ ├── IceExtentCleanedByMonth.png │ ├── IceExtentLinearRegressionAllMonthsByYearPrediction.png │ ├── IceExtentNormalizedByMonth.png │ ├── LinearvsLR.png │ ├── LogisticRegression.png │ ├── SP1.csv │ ├── SeaIce.txt │ ├── corrplot.png │ ├── data-insurance.png │ ├── dataX1.npy │ ├── dataX2.npy │ ├── lmplotBostonLSTAT.png │ ├── lmplotBostonLSTAT_order2.png │ ├── lmplotBostonLSTATorder3.png │ ├── logregPrediction.png │ ├── price-insurance.png │ ├── relationTruePredictedPrices.png │ ├── scatterLossWin.png │ └── scatterplotBoston.png ├── ch07 │ └── educ_figdp_1_Data.csv └── ch08 │ ├── facebook │ ├── 0.circles │ ├── 0.edges │ ├── 0.egofeat │ ├── 0.feat │ ├── 0.featnames │ ├── 107.circles │ ├── 107.edges │ ├── 107.egofeat │ ├── 107.feat │ ├── 107.featnames │ ├── 1684.circles │ ├── 1684.edges │ ├── 1684.egofeat │ ├── 1684.feat │ ├── 1684.featnames │ ├── 1912.circles │ ├── 1912.edges │ ├── 1912.egofeat │ ├── 1912.feat │ ├── 1912.featnames │ ├── 3437.circles │ ├── 3437.edges │ ├── 3437.egofeat │ ├── 3437.feat │ ├── 3437.featnames │ ├── 348.circles │ ├── 348.edges │ ├── 348.egofeat │ ├── 348.feat │ ├── 348.featnames │ ├── 3980.circles │ ├── 3980.edges │ ├── 3980.egofeat │ ├── 3980.feat │ ├── 3980.featnames │ ├── 414.circles │ ├── 414.edges │ ├── 414.egofeat │ ├── 414.feat │ ├── 414.featnames │ ├── 686.circles │ ├── 686.edges │ ├── 686.egofeat │ ├── 686.feat │ ├── 686.featnames │ ├── 698.circles │ ├── 698.edges │ ├── 698.egofeat │ ├── 698.feat │ └── 698.featnames │ └── facebook_combined.txt ├── learning_curve_1.png ├── learning_curve_2.png ├── learning_curve_3.png ├── learning_curve_4.png ├── model_selection.png ├── pie.png ├── requirements.txt └── toy_problem.png /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Introduction to Data Science 2 | ## A Python Approach to Concepts, Techniques and Applications 3 | 4 | This repository is part of the book: "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications" 5 | 6 | http://www.springer.com/gp/book/9783319500164 7 | 8 | 9 | ## About the Textbook: 10 | This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. 11 | 12 | ## About the authors: 13 | Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution. The book was co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantí and Lluís Garrido. 14 | 15 | ## Subject Area of the Book 16 | In this era, where a huge amount of information from different fields is gathered and stored, its analysis and the extraction of value have become one of the most attractive tasks for companies and Society in general. The design of solutions for the new questions emerged from data have required multidisciplinary teams. Computer Scientists, Statisticians, Mathematicians, 17 | Biologists, Journalists and Sociologists, as well as many others are now working together in order to provide knowledge from data. This new interdisciplinary field is called Data Science. 18 | The pipeline of any data science goes through asking the right questions; gathering data; cleaning data; generating hypothesis; making inferences; visualizing data; assessing solutions; etc. 19 | 20 | ## Organization and Feature of the Book 21 | This book is an introduction to concepts, techniques and applications in Data Science. The book focuses on the analysis of data, covering concepts from statistics to machine learning; techniques for graph analysis and parallel programming; and applications such as recommender systems or sentiment analysis. 22 | All chapters introduce new concepts that are illustrated by practical cases using real data. Public databases such as Eurostat, different social networks and Movilens are used. Specific questions about the data are posed in each chapter. The solutions to these questions are implemented using Python programming language and presented in code boxes properly commented. This allows the reader to learn data science by solving problems which can generalize to other problems. 23 | The book is not intend to cover the whole set of data science methods neither to provide a complete collection of references. Currently, data science is an increasing and emerging field, so readers are encourage to look for specific methods and references using keywords in the net. 24 | 25 | ## Target audiences 26 | This book is addressed to upper-tier undergraduate and beginning graduate students from technical disciplines. Moreover, the book is also addressed to professional audiences following continuous education short courses and to researchers from diverse areas following self-study 27 | courses. 28 | Basic skills in computer science, mathematics and statistics are required. Code programming in Python is of benefit. However, even if the reader is new to Python this should not be a problem, since acquiring the Python basics is manageable in a short period of time. 29 | 30 | ## Previous Uses of the Materials 31 | Parts of the presented materials have been used in the Postgraduate course of Data Science and Big Data from University of Barcelona. All contributing authors are involved in this course. 32 | 33 | 34 | -------------------------------------------------------------------------------- /ch11_Parallelization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Chapter 11: Parallelization\n", 8 | "\n", 9 | "This notebook shows IPthon parallel capabilities. We will see the commands that allow\n", 10 | "us to parallelize tasks, see section Introduction. If properly combined, they can lead to \n", 11 | "interting parallel algorithms, see section A complete example. \n", 12 | "\n", 13 | "\n", 14 | "## Introduction\n", 15 | "\n", 16 | "In order to start IPython's parallel capabilities, the simplest way of proceeding \n", 17 | "it to click on the Clusters tab of the notebook dashboard, and press \n", 18 | "Start with the desired number of cores. \n", 19 | "This will automatically run the necessary commands to start the\n", 20 | "IPython cluster. We will now be able to send different tasks to\n", 21 | "the engines using the web interface.\n", 22 | "\n", 23 | "The next commands allows you to connect to the cluster" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": null, 29 | "metadata": { 30 | "collapsed": false 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "from IPython import parallel\n", 35 | "engines = parallel.Client()\n", 36 | "engines.block = True\n", 37 | "print engines.ids" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "These commands connect to the cluster and output the number of engines in it. \n", 45 | "If an error is shown when running the\n", 46 | "commands, the cluster has not been correctly created.\n", 47 | "\n", 48 | "### Direct view of engines\n", 49 | "\n", 50 | "The following commands,\n", 51 | "executed on the notebook (i.e., the IPython interpreter), send commands\n", 52 | "to the first engine:" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": null, 58 | "metadata": { 59 | "collapsed": false 60 | }, 61 | "outputs": [], 62 | "source": [ 63 | "engines[0].execute('a = 2') \n", 64 | "engines[0].execute('b = 10') \n", 65 | "engines[0].execute('c = a + b') \n", 66 | "engines[0].pull('c')" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "Observe that we do not have direct access to the command line of the first engine. Rather, we send commands to it through the client. The result is retrieved by means of the `pull` command.\n", 74 | "\n", 75 | "Since each engine is\n", 76 | "an independent process, the operating system may schedule each engine\n", 77 | "in a different core and thus execution may be performed in parallel. Take a look at the following example." 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": { 84 | "collapsed": false 85 | }, 86 | "outputs": [], 87 | "source": [ 88 | "engines[0].execute('a = 2') \n", 89 | "engines[0].execute('b = 10') \n", 90 | "engines[1].execute('a = 9') \n", 91 | "engines[1].execute('b = 7') \n", 92 | "engines[0:2].execute('c = a + b') \n", 93 | "engines[0:2].pull('c')" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "Observe that the operation `c = a + b` is executed on each egine. The latter calculation and does not show the power of parallization. For that purpose we are next perform more computational intenstive tasks. Let us now show that we are really doing computations in parallel. Let us try with something bigger!\n", 101 | "\n", 102 | "In order to simplify the code, let us define the following variable that references the first two engines (even if there are more active engines)." 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": null, 108 | "metadata": { 109 | "collapsed": true 110 | }, 111 | "outputs": [], 112 | "source": [ 113 | "dview2 = engines[0:2] " 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "metadata": {}, 119 | "source": [ 120 | "We are next going to focus on matrix multiplication, for instance. We begin by doing serialized computations on the notebook and compute the total processing time." 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "metadata": { 127 | "collapsed": false 128 | }, 129 | "outputs": [], 130 | "source": [ 131 | "import time\n", 132 | "import numpy as np\n", 133 | "\n", 134 | "# Create four 1000x1000 matrix\n", 135 | "A0 = np.random.rand(1000,1000)\n", 136 | "B0 = np.random.rand(1000,1000)\n", 137 | "A1 = np.random.rand(1000,1000)\n", 138 | "B1 = np.random.rand(1000,1000)\n", 139 | "\n", 140 | "t0 = time.time() \n", 141 | "\n", 142 | "C0 = np.dot(A0, B0)\n", 143 | "C1 = np.dot(A1, B1)\n", 144 | " \n", 145 | "print \"Time in seconds (Computations): \", time.time() - t0 " 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "And now we will do computations in parallel." 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": null, 158 | "metadata": { 159 | "collapsed": false 160 | }, 161 | "outputs": [], 162 | "source": [ 163 | "dview2.execute('import numpy as np') # We import numpy on both engines!\n", 164 | "\n", 165 | "t0 = time.time()\n", 166 | "engines[0].push(dict(A=A0, B=B0)) # We send A0 and B0 to engine 0 \n", 167 | "engines[1].push(dict(A=A1, B=B1)) # We send A1 and B1 to engine 1 \n", 168 | "\n", 169 | "t0_computations = time.time()\n", 170 | "\n", 171 | "dview2.execute('C = np.dot(A,B)')\n", 172 | " \n", 173 | "print \"Computations: \", time.time() - t0_computations\n", 174 | "\n", 175 | "[C0, C1] = dview2.pull('C')\n", 176 | "print \"Time in seconds: \", time.time() - t0" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": {}, 182 | "source": [ 183 | "The total computing time should decrease thanks to the divison of the computation in two tasks. Each task is then manually executed in two different engines and each engine is scheduled by the operating system on two different processors (if the computer has at least two processors).\n", 184 | "\n", 185 | "The previous commands show us how to execute commands on engines as if we were typing \n", 186 | "them directly on the command-line. Indeed, we\n", 187 | "have manually sent, executed and retrieved the results of computations.\n", 188 | "This procedure may be useful in some cases but in many cases there\n", 189 | "will be no need to do so. The `map` function may be used to that purpose" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": { 196 | "collapsed": true 197 | }, 198 | "outputs": [], 199 | "source": [ 200 | "def mul(A, B):\n", 201 | " import numpy as np\n", 202 | " C = np.dot(A, B)\n", 203 | " return C\n", 204 | "\n", 205 | "[C0, C1] = dview2.map(mul,[A0, A1],[B0, B1])" 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": {}, 211 | "source": [ 212 | "These commands, executed on the client, perform a remote call.\n", 213 | "The function `mul` is defined locally. There is no need to use the `push`\n", 214 | "and `pull` functions explicitly to send and retrieve the results; it is done\n", 215 | "implicitly. Note the `import numpy as np`\n", 216 | "inside the `mul` function. This is a common model, to ensure that the appropriate\n", 217 | "toolboxes are imported to where the task is run. \n", 218 | "\n", 219 | "The `map` call splits the tasks between the engines associated\n", 220 | "with `dview2`. In the previous example, the task `mul(A0,B0)`\n", 221 | "is executed on one engine and `mul(A1, B1)` is executed on\n", 222 | "the one. Which command is executed on each engine? What happens if\n", 223 | "the list of arguments to map includes three or more matrices? We may\n", 224 | "see this with the following example:" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": { 231 | "collapsed": false 232 | }, 233 | "outputs": [], 234 | "source": [ 235 | "engines[0].execute('my_id = \"engineA\"') \n", 236 | "engines[1].execute('my_id = \"engineB\"')\n", 237 | "\n", 238 | "def sleep_and_return_id(sec): \n", 239 | " import time \n", 240 | " time.sleep(sec) \n", 241 | " return my_id,sec\n", 242 | "\n", 243 | "dview2.map(sleep_and_return_id, [3,3,3,1,1,1])" 244 | ] 245 | }, 246 | { 247 | "cell_type": "markdown", 248 | "metadata": {}, 249 | "source": [ 250 | "Execute the previous code and observe the returned result that indicates us which engine executed the function. You may repeat this experment as many times as you wish, but the result will always be the same. The tasks are distributed in a uniform way among the\n", 251 | "engines before executing them no matter which is the delay we pass\n", 252 | "as argument to the function `sleep_and_return_id`. This is in fact a characteristic of the direct view interface: the tasks are distributed among the engines before executing them. \n", 253 | "\n", 254 | "This a good way to proceed if you expect each task to take\n", 255 | "the same amount of time. But if not, as is the case in the previous\n", 256 | "example, computation time is wasted and so we recommend to use the \n", 257 | "load-balanced view instead.\n", 258 | "\n", 259 | "### Load-balanced view of engines\n", 260 | "\n", 261 | "This interface is simpler and more powerful than the direct interface. We would like to point out, however, that with this interface the user has no direct access to individual engines. It is the IPython scheduler that assignes work to each engine. To create a load-balanced view we may use the following command: " 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": null, 267 | "metadata": { 268 | "collapsed": true 269 | }, 270 | "outputs": [], 271 | "source": [ 272 | "engines.block = True\n", 273 | "lview2 = engines.load_balanced_view(targets=[0,1])" 274 | ] 275 | }, 276 | { 277 | "cell_type": "markdown", 278 | "metadata": {}, 279 | "source": [ 280 | "We use the blocking mode since it simplifies the code. The `lview2` is a variable\n", 281 | "that references the first two engines.\n", 282 | "\n", 283 | "Our example here will be centered on the `sleep_and_return_id`\n", 284 | "function we have seen in the previous subsection:" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": null, 290 | "metadata": { 291 | "collapsed": false 292 | }, 293 | "outputs": [], 294 | "source": [ 295 | "lview2.map(sleep_and_return_id, [3,3,3,1,1,1])" 296 | ] 297 | }, 298 | { 299 | "cell_type": "markdown", 300 | "metadata": {}, 301 | "source": [ 302 | "Observe that rather than using the direct\n", 303 | "view interface (`dview2` variable) of the `map` function, we use the associated load-balanced view interface (`lview2` variable).\n", 304 | "\n", 305 | "In this case, the tasks are assigned to the engines in a dynamic way.\n", 306 | "The `map` function of the load-balanced view begins by assigning\n", 307 | "one task to each engine in the order given by the parameters of the\n", 308 | "`map` function. By default, the load-balanced view scheduler\n", 309 | "then assigns a new task to an engine when it becomes free" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "metadata": {}, 315 | "source": [ 316 | "## A complete example: the New York taxi trips database\n", 317 | "\n", 318 | "We next present a real application of the parallel capabilities\n", 319 | "of IPython and the discussion of several approaches to it. The dataset\n", 320 | "is a database of taxi trips in New York and it has been obtained through\n", 321 | "a Freedom of Information Law (FOIL) request from the New York City\n", 322 | "Taxi & Limousine Commission (NYCT&L) by University of Illinois at\n", 323 | "Urbana-Champaign (http://publish.illinois.edu/dbwork/open-data/).\n", 324 | "The dataset consists in $12\\times2$GBytes Comma Separated Files (CSV)\n", 325 | "files. Each file has approximately $14$ million entries (lines) and\n", 326 | "is already cleaned. Thus no special preprocessing is needed to be\n", 327 | "able to process it. For our purposes we are interested only in the\n", 328 | "following information from each entry: \n", 329 | "\n", 330 | "+ `pickup_datetime`: start time of the trip, mm-dd-yyyy hh24:mm:ss\n", 331 | "EDT. \n", 332 | "+ `pickup_longitude` and `pickup_latitude`: GPS coordinates\n", 333 | "at the start of the trip. \n", 334 | "\n", 335 | "Our objective is to perform an analysis of this data in order to answer\n", 336 | "the following questions: for each district, how many pickups are performed\n", 337 | "during week days and how many during weekends? And how many pickups\n", 338 | "are performed in the morning? For that issue the New York city is\n", 339 | "arbitrarily divided into nine districts: ChinaTown, WTC, Soho, Harlem,\n", 340 | "UpperTown, MidTown, DownTown, UpperEastSide, UpperWestSide and Financial. \n", 341 | "\n", 342 | "Implementing the previous classification is rather simple since it\n", 343 | "only requires checking, for each entry, the GPS coordinates of the\n", 344 | "start of the trip and the pickup datetime. Performing this task in\n", 345 | "a sequential may take a rather large amount of time since the number\n", 346 | "of entries, for a single CSV file, is rather large. In addition, special\n", 347 | "care has to be taken when reading the file since a 2GByte file may\n", 348 | "not fully fit into the computer's memory. \n", 349 | "\n", 350 | "We may take advantage of the parallelization capabilities in order\n", 351 | "to reduce the processing time. The idea is to divide the input data\n", 352 | "into chunks so that each engine takes care of classifying the entries\n", 353 | "of their corresponding chunks. We propose here an approach which \n", 354 | "is based on implementing a producer-consumer paradigm\n", 355 | "in order to distribute the tasks. The producer, associated to the\n", 356 | "client, reads the chunks from disc and distributes them among the\n", 357 | "engines using a round robin technique. No explicit `map` function\n", 358 | "is used in this case. Rather, we simulate the behavior of the `map`\n", 359 | "function in order to have fine control of the parallel problem. Recall\n", 360 | "that each engine is an independent process. Since we assign different\n", 361 | "tasks to each engine, the operating system will try to execute each\n", 362 | "engine on a different process.\n", 363 | "\n", 364 | "For further deails, please see corresponding chapter in the book.\n", 365 | "\n", 366 | "## The source code\n", 367 | "\n", 368 | "We begin by initializing the engines:" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": null, 374 | "metadata": { 375 | "collapsed": false 376 | }, 377 | "outputs": [], 378 | "source": [ 379 | "%reset -f\n", 380 | "\n", 381 | "from IPython import parallel\n", 382 | "from itertools import islice\n", 383 | "from itertools import cycle\n", 384 | "from collections import Counter\n", 385 | "import sys\n", 386 | "import time\n", 387 | "\n", 388 | "#Connect to the Ipython cluster \n", 389 | "engines = parallel.Client()\n", 390 | "\n", 391 | "#Create a DirectView to all engines\n", 392 | "dview = engines.direct_view()\n", 393 | "\n", 394 | "print \"The number of engines in the cluster is: \" + str(len(engines.ids))" 395 | ] 396 | }, 397 | { 398 | "cell_type": "markdown", 399 | "metadata": {}, 400 | "source": [ 401 | "We next declare the functions that will be executed on the engines. We do this thanks to the `%%px` parallel magic command." 402 | ] 403 | }, 404 | { 405 | "cell_type": "code", 406 | "execution_count": null, 407 | "metadata": { 408 | "collapsed": false 409 | }, 410 | "outputs": [], 411 | "source": [ 412 | "%%px\n", 413 | "\n", 414 | "# The %%px magic executes the code of this cell on each engine.\n", 415 | "\n", 416 | "from datetime import datetime\n", 417 | "from collections import Counter\n", 418 | "\n", 419 | "import pandas as pd\n", 420 | "import numpy as np\n", 421 | "\n", 422 | "# A Counter object to store engine's local result\n", 423 | "local_total = Counter();\n", 424 | "\n", 425 | "def dist(p0, p1):\n", 426 | " \"Returns the distance**2 between two points\"\n", 427 | " # We compute the squared distance. Since we only want to compare\n", 428 | " # distances there is no need to compute the square root (sqrt) \n", 429 | " return (p0[0] - p1[0])**2 + (p0[1] - p1[1])**2\n", 430 | "\n", 431 | "# Coordinates (latitude, longitude) of diferent points of the island\n", 432 | "district_dict = { \n", 433 | " 'Financial': [40.724863, -73.994718], \n", 434 | " 'Midtown': [40.755905, -73.984997],\n", 435 | " 'Chinatown': [40.716224, -73.995925],\n", 436 | " 'WTC': [40.711724, -74.012888],\n", 437 | " 'Harlem': [40.810469, -73.943318],\n", 438 | " 'Uppertown': [40.826381, -73.943964],\n", 439 | " 'Soho': [40.723783, -74.001237],\n", 440 | " 'UpperEastSide': [40.773861, -73.956329],\n", 441 | " 'UpperWestSide': [40.787347, -73.975267]\n", 442 | " }\n", 443 | "\n", 444 | "# Computes the distance to each district center and obtains the one that\n", 445 | "# gives minimum distance\n", 446 | "def get_district(coors):\n", 447 | " \"Given a coordinate inn latitude and longitude, returns the district in Manhatan\" \n", 448 | " #If dist^2 is bigger than 0.0005, the district is 'None'.\n", 449 | " dist_min = 0.0005\n", 450 | " district = None\n", 451 | " for key in district_dict.iterkeys():\n", 452 | " d = dist(coors, district_dict[key])\n", 453 | " if dist_min > d:\n", 454 | " dist_min = d\n", 455 | " district = key\n", 456 | " return district\n", 457 | "\n", 458 | "def is_morning(d):\n", 459 | " \"Given a datetime, returns if it was on morning or not\"\n", 460 | " h = datetime.strptime(d, \"%Y-%m-%d %H:%M:%S\").hour\n", 461 | " return 0 <= h and h < 12\n", 462 | "\n", 463 | "def is_weekend(d):\n", 464 | " \"Given a datetime, returns if it was on weekend or not\"\n", 465 | " wday = datetime.strptime(d, \"%Y-%m-%d %H:%M:%S\").weekday() #strptime transforms str to date\n", 466 | " return 4 < wday <= 6\n", 467 | "\n", 468 | "#Function that classifies a single data\n", 469 | "def classify(x):\n", 470 | " \"Given a tuple with a datetime, latitude and longitude, returns the group where it fits\"\n", 471 | " date, lat, lon = x\n", 472 | " latitude = float(lat)\n", 473 | " longitude = float(lon)\n", 474 | " return is_weekend(date), is_morning(date), get_district([latitude, longitude])\n", 475 | "\n", 476 | "# Function that given a dictionary (data), applies classify function on each element\n", 477 | "# and returns an histogram in a Counter object\n", 478 | "def process(b):\n", 479 | " #Recives a block (list of strings) and updates result in global var local_total()\n", 480 | " global local_total\n", 481 | " \n", 482 | " #Create an empty df. Preallocate the space we need by providing the index (number of rows)\n", 483 | " df = pd.DataFrame(index=np.arange(0,len(b)), columns=('datetime','latitude','longitude'))\n", 484 | " \n", 485 | " # Data is a list of lines, containing datetime at col 5 and latitude at row 11.\n", 486 | " # Allocate in the dataFrame the datetime and latitude and longitude dor each line in data\n", 487 | " count = 0\n", 488 | " for line in b:\n", 489 | " elements = line.split(\",\")\n", 490 | " df.loc[count] = elements[5], elements[11], elements[10]\n", 491 | " count += 1\n", 492 | " \n", 493 | " #Delete NaN values from de DF\n", 494 | " df.dropna(thresh=(len(df.columns) - 1), axis=0)\n", 495 | " \n", 496 | " #Apply classify function to the dataFrame\n", 497 | " cdf = df.apply(classify, axis=1)\n", 498 | " \n", 499 | " #Increment the global variable local_total\n", 500 | " local_total += Counter(cdf.value_counts().to_dict())\n", 501 | "\n", 502 | "# Initialization function\n", 503 | "def init():\n", 504 | " #Reset total var\n", 505 | " global local_total\n", 506 | " local_total = Counter()\n" 507 | ] 508 | }, 509 | { 510 | "cell_type": "markdown", 511 | "metadata": {}, 512 | "source": [ 513 | "Next we show the code executed by the client. The next code performs the next task\n", 514 | "\n", 515 | "+ It reads a chunk of `lines_per_block` lines form the file. The chunk is assigned to an engine which performs the classification. The result of the classification is updated on a local variable on each engine. This process is repeated until all chunks have been processed by the engines.\n", 516 | "+ Once finished, the client retrieves the local variable of each engine and computes the overall result.\n", 517 | "\n", 518 | "This is the principle of the **MapReduce** programming model: a MapReduce program is composed of a Map() procedure that performs filtering and sorting (such as counting the number of times each word appears in a file) and a Reduce() procedure that performs a summary operation (that is, taking each of the results and computing the overall result)." 519 | ] 520 | }, 521 | { 522 | "cell_type": "code", 523 | "execution_count": null, 524 | "metadata": { 525 | "collapsed": false, 526 | "scrolled": true 527 | }, 528 | "outputs": [], 529 | "source": [ 530 | "# This is the main code executed on the client\n", 531 | "t0 = time.time() \n", 532 | "\n", 533 | "#File to be processed\n", 534 | "filename = 'trip_data.csv'\n", 535 | "\n", 536 | "def get_chunk(f,N):\n", 537 | " \"\"\" Returns blocks of nl lines from the file descriptor fd\"\"\"\n", 538 | " #Deletes first line on first chunk (header line)\n", 539 | " first = 1\n", 540 | " while True:\n", 541 | " new_chunk = list(islice(f, first, N))\n", 542 | " if not new_chunk:\n", 543 | " break\n", 544 | " first = 0\n", 545 | " yield new_chunk\n", 546 | "\n", 547 | "# A simple counter to verify execution\n", 548 | "chunk_n = 0\n", 549 | "\n", 550 | "# Number of lines to be sent to each engine at a time. Use carefully!\n", 551 | "lines_per_block = 20\n", 552 | "\n", 553 | "# Create an emty list of async tasks. One element for each engine\n", 554 | "async_tasks = [None] * len(engines.ids)\n", 555 | "\n", 556 | "# Cycle Object to get an infinite iterator over the list of engines\n", 557 | "c_engines = cycle(engines.ids)\n", 558 | "\n", 559 | "# Initialize each engine. Observe that the execute is performed\n", 560 | "# in a non-blocking fashion.\n", 561 | "for i in engines.ids:\n", 562 | " async_tasks[i] = engines[i].execute('init()', block=False)\n", 563 | "\n", 564 | "# The variable to store results\n", 565 | "global_result = Counter()\n", 566 | "\n", 567 | "# Open the file in ReadOnly mode\n", 568 | "try:\n", 569 | " f = open(filename, 'r') #iterable\n", 570 | "except IOError:\n", 571 | " sys.exit(\"Could not open input file!\")\n", 572 | "\n", 573 | "# Used to show the progress\n", 574 | "print \"Beginning to send chunks\"\n", 575 | "sys.stdout.flush()\n", 576 | "\n", 577 | "# While the generator returns new chunk, sent them to the engines\n", 578 | "for new_chunk in get_chunk(f,lines_per_block):\n", 579 | " \n", 580 | " #After the first loop, first_chunk is False. \n", 581 | " first_chunk = False\n", 582 | " \n", 583 | " #Decide the engine to be used to classify the new chunk\n", 584 | " run_engine = c_engines.next()\n", 585 | " \n", 586 | " # Wait until the engine is ready\n", 587 | " while ( not async_tasks[run_engine].ready() ):\n", 588 | " time.sleep(1)\n", 589 | " \n", 590 | " #Send data to the assigned engine.\n", 591 | " mydict = dict(data = new_chunk)\n", 592 | " \n", 593 | " # The data is sent to the engine in blocking mode. The push function does not return\n", 594 | " # until the engine has received the data. \n", 595 | " engines[run_engine].push(mydict,block=True)\n", 596 | "\n", 597 | " # We execute the classification task on the engine. Observe that the task is executed\n", 598 | " # in non-blocking mode. Thus the execute function reurns immediately. \n", 599 | " async_tasks[run_engine] = engines[run_engine].execute('process(data)', block=False)\n", 600 | " \n", 601 | " # Increase the counter \n", 602 | " chunk_n += 1\n", 603 | "\n", 604 | " # Update the progress\n", 605 | " if chunk_n % 1000 == 0:\n", 606 | " print \"Chunks sent until this moment: \" + str(chunk_n)\n", 607 | " sys.stdout.flush()\n", 608 | "\n", 609 | "print \"All chunks have been sent\"\n", 610 | "sys.stdout.flush()\n", 611 | "# Get the results from each engine and accumulate in global_result\n", 612 | "for engine in engines.ids:\n", 613 | " # Be sure that all async tasks are finished\n", 614 | " while ( not async_tasks[engine].ready() ):\n", 615 | " time.sleep(1)\n", 616 | " global_result += engines[engine].pull('local_total', block=True)\n", 617 | "\n", 618 | "#Close the file\n", 619 | "f.close()\n", 620 | "\n", 621 | "print \"Total number of chunks processed: \" + str(chunk_n)\n", 622 | "print \"---------------------------------------------\"\n", 623 | "print \"Agregated dictionary\"\n", 624 | "print \"---------------------------------------------\"\n", 625 | "print dict(global_result)\n", 626 | "\n", 627 | "print \"Time in seconds: \", time.time() - t0\n", 628 | "sys.stdout.flush()" 629 | ] 630 | }, 631 | { 632 | "cell_type": "markdown", 633 | "metadata": {}, 634 | "source": [ 635 | "The results of the experiments performed with this code can be seen in the corresponding chapter of the book." 636 | ] 637 | } 638 | ], 639 | "metadata": { 640 | "kernelspec": { 641 | "display_name": "Python 2", 642 | "language": "python", 643 | "name": "python2" 644 | }, 645 | "language_info": { 646 | "codemirror_mode": { 647 | "name": "ipython", 648 | "version": 2 649 | }, 650 | "file_extension": ".py", 651 | "mimetype": "text/x-python", 652 | "name": "python", 653 | "nbconvert_exporter": "python", 654 | "pygments_lexer": "ipython2", 655 | "version": "2.7.12" 656 | } 657 | }, 658 | 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-------------------------------------------------------------------------------- 1 | year mo data_type region extent area 2 | 1979 1 Goddard N 15.54 12.33 3 | 1980 1 Goddard N 14.96 11.85 4 | 1981 1 Goddard N 15.03 11.82 5 | 1982 1 Goddard N 15.26 12.11 6 | 1983 1 Goddard N 15.10 11.92 7 | 1984 1 Goddard N 14.61 11.60 8 | 1985 1 Goddard N 14.86 11.60 9 | 1986 1 Goddard N 15.02 11.79 10 | 1987 1 Goddard N 15.20 11.81 11 | 1988 1 -9999 N -9999 -9999 12 | 1989 1 Goddard N 15.12 13.11 13 | 1990 1 Goddard N 14.95 12.72 14 | 1991 1 Goddard N 14.46 12.49 15 | 1992 1 Goddard N 14.72 12.54 16 | 1993 1 Goddard N 15.08 12.85 17 | 1994 1 Goddard N 14.82 12.80 18 | 1995 1 Goddard N 14.62 12.72 19 | 1996 1 Goddard N 14.21 12.07 20 | 1997 1 Goddard N 14.47 12.30 21 | 1998 1 Goddard N 14.81 12.73 22 | 1999 1 Goddard N 14.47 12.54 23 | 2000 1 Goddard N 14.41 12.22 24 | 2001 1 Goddard N 14.31 12.27 25 | 2002 1 Goddard N 14.45 12.19 26 | 2003 1 Goddard N 14.46 12.21 27 | 2004 1 Goddard N 14.03 12.19 28 | 2005 1 Goddard N 13.66 11.84 29 | 2006 1 Goddard N 13.60 11.57 30 | 2007 1 Goddard N 13.77 11.79 31 | 2008 1 Goddard N 14.05 11.95 32 | 2009 1 Goddard N 14.08 11.97 33 | 2010 1 Goddard N 13.80 11.68 34 | 2011 1 Goddard N 13.57 11.47 35 | 2012 1 Goddard N 13.77 11.87 36 | 2013 1 NRTSI-G N 13.78 11.57 37 | 2014 1 NRTSI-G N 13.73 11.66 38 | 1979 2 Goddard N 16.31 13.11 39 | 1980 2 Goddard N 15.98 12.82 40 | 1981 2 Goddard N 15.65 12.45 41 | 1982 2 Goddard N 16.06 12.79 42 | 1983 2 Goddard N 16.02 12.86 43 | 1984 2 Goddard N 15.32 12.17 44 | 1985 2 Goddard N 15.67 12.39 45 | 1986 2 Goddard N 15.89 12.53 46 | 1987 2 Goddard N 16.11 12.93 47 | 1988 2 Goddard N 15.61 13.49 48 | 1989 2 Goddard N 15.56 13.28 49 | 1990 2 Goddard N 15.56 13.33 50 | 1991 2 Goddard N 15.26 13.15 51 | 1992 2 Goddard N 15.50 13.51 52 | 1993 2 Goddard N 15.73 13.54 53 | 1994 2 Goddard N 15.61 13.46 54 | 1995 2 Goddard N 15.24 13.30 55 | 1996 2 Goddard N 15.17 12.90 56 | 1997 2 Goddard N 15.52 13.36 57 | 1998 2 Goddard N 15.77 13.70 58 | 1999 2 Goddard N 15.37 13.38 59 | 2000 2 Goddard N 15.18 13.02 60 | 2001 2 Goddard N 15.27 13.14 61 | 2002 2 Goddard N 15.36 13.36 62 | 2003 2 Goddard N 15.25 13.16 63 | 2004 2 Goddard N 14.93 12.87 64 | 2005 2 Goddard N 14.36 12.27 65 | 2006 2 Goddard N 14.42 12.28 66 | 2007 2 Goddard N 14.53 12.52 67 | 2008 2 Goddard N 15.01 12.87 68 | 2009 2 Goddard N 14.85 12.87 69 | 2010 2 Goddard N 14.59 12.60 70 | 2011 2 Goddard N 14.38 12.41 71 | 2012 2 Goddard N 14.59 12.37 72 | 2013 2 Goddard N 14.69 12.84 73 | 2014 2 NRTSI-G N 14.44 12.12 74 | 1979 3 Goddard N 16.45 13.13 75 | 1980 3 Goddard N 16.13 12.92 76 | 1981 3 Goddard N 15.61 12.62 77 | 1982 3 Goddard N 16.15 12.99 78 | 1983 3 Goddard N 16.10 12.84 79 | 1984 3 Goddard N 15.62 12.48 80 | 1985 3 Goddard N 16.06 12.66 81 | 1986 3 Goddard N 16.08 12.65 82 | 1987 3 Goddard N 15.95 12.75 83 | 1988 3 Goddard N 16.13 13.84 84 | 1989 3 Goddard N 15.52 13.14 85 | 1990 3 Goddard N 15.88 13.44 86 | 1991 3 Goddard N 15.50 13.35 87 | 1992 3 Goddard N 15.47 13.41 88 | 1993 3 Goddard N 15.88 13.71 89 | 1994 3 Goddard N 15.58 13.47 90 | 1995 3 Goddard N 15.32 13.28 91 | 1996 3 Goddard N 15.13 12.83 92 | 1997 3 Goddard N 15.58 13.24 93 | 1998 3 Goddard N 15.66 13.50 94 | 1999 3 Goddard N 15.40 13.47 95 | 2000 3 Goddard N 15.27 13.10 96 | 2001 3 Goddard N 15.61 13.57 97 | 2002 3 Goddard N 15.44 13.36 98 | 2003 3 Goddard N 15.49 13.36 99 | 2004 3 Goddard N 15.05 12.93 100 | 2005 3 Goddard N 14.74 12.67 101 | 2006 3 Goddard N 14.43 12.44 102 | 2007 3 Goddard N 14.65 12.49 103 | 2008 3 Goddard N 15.22 13.20 104 | 2009 3 Goddard N 15.14 13.08 105 | 2010 3 Goddard N 15.11 13.19 106 | 2011 3 Goddard N 14.58 12.48 107 | 2012 3 Goddard N 15.24 13.08 108 | 2013 3 NRTSI-G N 15.04 13.02 109 | 1979 4 Goddard N 15.46 12.43 110 | 1980 4 Goddard N 15.49 12.45 111 | 1981 4 Goddard N 15.12 12.16 112 | 1982 4 Goddard N 15.57 12.55 113 | 1983 4 Goddard N 15.30 12.12 114 | 1984 4 Goddard N 15.15 12.10 115 | 1985 4 Goddard N 15.34 12.46 116 | 1986 4 Goddard N 15.15 11.95 117 | 1987 4 Goddard N 15.33 12.28 118 | 1988 4 Goddard N 15.21 13.11 119 | 1989 4 Goddard N 14.44 12.26 120 | 1990 4 Goddard N 14.68 12.16 121 | 1991 4 Goddard N 14.93 12.79 122 | 1992 4 Goddard N 14.70 12.76 123 | 1993 4 Goddard N 15.18 12.95 124 | 1994 4 Goddard N 14.95 12.84 125 | 1995 4 Goddard N 14.59 12.32 126 | 1996 4 Goddard N 14.22 12.23 127 | 1997 4 Goddard N 14.59 12.48 128 | 1998 4 Goddard N 14.89 12.77 129 | 1999 4 Goddard N 15.13 13.08 130 | 2000 4 Goddard N 14.63 12.51 131 | 2001 4 Goddard N 14.86 12.99 132 | 2002 4 Goddard N 14.37 12.35 133 | 2003 4 Goddard N 14.57 12.38 134 | 2004 4 Goddard N 14.11 12.08 135 | 2005 4 Goddard N 14.07 12.16 136 | 2006 4 Goddard N 13.97 11.97 137 | 2007 4 Goddard N 13.87 11.75 138 | 2008 4 Goddard N 14.42 12.43 139 | 2009 4 Goddard N 14.57 12.58 140 | 2010 4 Goddard N 14.70 12.50 141 | 2011 4 Goddard N 14.16 12.11 142 | 2012 4 Goddard N 14.72 12.53 143 | 2013 4 NRTSI-G N 14.37 12.33 144 | 1979 5 Goddard N 14.06 11.04 145 | 1980 5 Goddard N 14.04 10.88 146 | 1981 5 Goddard N 13.90 10.93 147 | 1982 5 Goddard N 14.17 11.10 148 | 1983 5 Goddard N 13.54 10.80 149 | 1984 5 Goddard N 13.68 10.81 150 | 1985 5 Goddard N 14.23 11.19 151 | 1986 5 Goddard N 13.52 10.63 152 | 1987 5 Goddard N 13.81 10.91 153 | 1988 5 Goddard N 13.69 11.35 154 | 1989 5 Goddard N 12.98 11.30 155 | 1990 5 Goddard N 13.30 10.84 156 | 1991 5 Goddard N 13.51 11.44 157 | 1992 5 Goddard N 13.25 11.37 158 | 1993 5 Goddard N 13.54 11.35 159 | 1994 5 Goddard N 13.73 11.60 160 | 1995 5 Goddard N 13.04 10.76 161 | 1996 5 Goddard N 13.06 11.22 162 | 1997 5 Goddard N 13.32 11.17 163 | 1998 5 Goddard N 13.80 11.35 164 | 1999 5 Goddard N 13.86 11.61 165 | 2000 5 Goddard N 13.18 11.18 166 | 2001 5 Goddard N 13.72 11.32 167 | 2002 5 Goddard N 13.12 11.11 168 | 2003 5 Goddard N 13.00 10.82 169 | 2004 5 Goddard N 12.58 10.86 170 | 2005 5 Goddard N 12.99 10.83 171 | 2006 5 Goddard N 12.62 10.39 172 | 2007 5 Goddard N 12.89 10.71 173 | 2008 5 Goddard N 13.19 10.92 174 | 2009 5 Goddard N 13.40 11.15 175 | 2010 5 Goddard N 13.11 10.54 176 | 2011 5 Goddard N 12.81 10.41 177 | 2012 5 Goddard N 13.12 10.72 178 | 2013 5 NRTSI-G N 13.10 10.87 179 | 1979 6 Goddard N 12.59 9.28 180 | 1980 6 Goddard N 12.31 8.93 181 | 1981 6 Goddard N 12.57 8.96 182 | 1982 6 Goddard N 12.69 9.40 183 | 1983 6 Goddard N 12.36 9.20 184 | 1984 6 Goddard N 12.20 9.02 185 | 1985 6 Goddard N 12.40 8.88 186 | 1986 6 Goddard N 12.10 8.93 187 | 1987 6 Goddard N 12.57 9.32 188 | 1988 6 Goddard N 12.02 9.62 189 | 1989 6 Goddard N 12.31 9.90 190 | 1990 6 Goddard N 11.68 9.12 191 | 1991 6 Goddard N 12.23 9.60 192 | 1992 6 Goddard N 12.13 9.89 193 | 1993 6 Goddard N 11.99 9.20 194 | 1994 6 Goddard N 12.10 9.62 195 | 1995 6 Goddard N 11.55 8.86 196 | 1996 6 Goddard N 12.10 9.78 197 | 1997 6 Goddard N 11.91 9.14 198 | 1998 6 Goddard N 11.85 9.11 199 | 1999 6 Goddard N 12.10 9.18 200 | 2000 6 Goddard N 11.71 8.99 201 | 2001 6 Goddard N 11.69 9.01 202 | 2002 6 Goddard N 11.69 9.13 203 | 2003 6 Goddard N 11.77 9.05 204 | 2004 6 Goddard N 11.51 9.18 205 | 2005 6 Goddard N 11.29 8.74 206 | 2006 6 Goddard N 11.06 8.34 207 | 2007 6 Goddard N 11.49 8.15 208 | 2008 6 Goddard N 11.36 8.52 209 | 2009 6 Goddard N 11.46 8.92 210 | 2010 6 Goddard N 10.82 8.02 211 | 2011 6 Goddard N 10.99 8.20 212 | 2012 6 Goddard N 10.92 7.77 213 | 2013 6 NRTSI-G N 11.58 8.57 214 | 1979 7 Goddard N 10.47 6.59 215 | 1980 7 Goddard N 10.39 6.45 216 | 1981 7 Goddard N 10.62 6.23 217 | 1982 7 Goddard N 10.75 6.79 218 | 1983 7 Goddard N 10.91 6.79 219 | 1984 7 Goddard N 10.15 6.19 220 | 1985 7 Goddard N 10.09 5.89 221 | 1986 7 Goddard N 10.47 6.53 222 | 1987 7 Goddard N 9.98 6.84 223 | 1988 7 Goddard N 10.04 6.93 224 | 1989 7 Goddard N 10.38 7.24 225 | 1990 7 Goddard N 9.62 6.44 226 | 1991 7 Goddard N 9.68 6.66 227 | 1992 7 Goddard N 10.61 7.14 228 | 1993 7 Goddard N 9.66 6.17 229 | 1994 7 Goddard N 10.22 6.85 230 | 1995 7 Goddard N 9.15 6.05 231 | 1996 7 Goddard N 10.36 7.36 232 | 1997 7 Goddard N 9.59 6.41 233 | 1998 7 Goddard N 9.62 6.38 234 | 1999 7 Goddard N 9.59 6.49 235 | 2000 7 Goddard N 9.75 6.31 236 | 2001 7 Goddard N 9.22 6.22 237 | 2002 7 Goddard N 9.49 6.34 238 | 2003 7 Goddard N 9.46 6.06 239 | 2004 7 Goddard N 9.60 6.43 240 | 2005 7 Goddard N 8.93 5.81 241 | 2006 7 Goddard N 8.67 5.71 242 | 2007 7 Goddard N 8.13 5.03 243 | 2008 7 Goddard N 8.99 5.74 244 | 2009 7 Goddard N 8.80 5.77 245 | 2010 7 Goddard N 8.36 5.27 246 | 2011 7 Goddard N 7.91 5.04 247 | 2012 7 Goddard N 7.93 4.76 248 | 2013 7 NRTSI-G N 8.45 5.41 249 | 1979 8 Goddard N 8.15 4.96 250 | 1980 8 Goddard N 8.04 4.84 251 | 1981 8 Goddard N 7.86 4.38 252 | 1982 8 Goddard N 8.26 4.92 253 | 1983 8 Goddard N 8.36 4.88 254 | 1984 8 Goddard N 7.87 4.59 255 | 1985 8 Goddard N 7.46 4.51 256 | 1986 8 Goddard N 8.01 4.92 257 | 1987 8 Goddard N 7.69 5.35 258 | 1988 8 Goddard N 7.90 5.34 259 | 1989 8 Goddard N 7.92 5.48 260 | 1990 8 Goddard N 6.82 4.92 261 | 1991 8 Goddard N 7.40 5.14 262 | 1992 8 Goddard N 7.86 5.35 263 | 1993 8 Goddard N 7.29 4.65 264 | 1994 8 Goddard N 7.61 5.10 265 | 1995 8 Goddard N 6.68 4.61 266 | 1996 8 Goddard N 8.17 5.65 267 | 1997 8 Goddard N 7.30 5.03 268 | 1998 8 Goddard N 7.49 4.61 269 | 1999 8 Goddard N 7.38 4.67 270 | 2000 8 Goddard N 7.21 4.71 271 | 2001 8 Goddard N 7.47 4.87 272 | 2002 8 Goddard N 6.53 4.23 273 | 2003 8 Goddard N 6.85 4.44 274 | 2004 8 Goddard N 6.83 4.57 275 | 2005 8 Goddard N 6.30 4.14 276 | 2006 8 Goddard N 6.52 4.24 277 | 2007 8 Goddard N 5.36 3.11 278 | 2008 8 Goddard N 6.05 3.47 279 | 2009 8 Goddard N 6.28 3.84 280 | 2010 8 Goddard N 6.01 3.60 281 | 2011 8 Goddard N 5.55 3.06 282 | 2012 8 Goddard N 4.71 2.56 283 | 2013 8 NRTSI-G N 6.09 3.83 284 | 1979 9 Goddard N 7.20 4.53 285 | 1980 9 Goddard N 7.85 4.83 286 | 1981 9 Goddard N 7.25 4.38 287 | 1982 9 Goddard N 7.45 4.38 288 | 1983 9 Goddard N 7.52 4.63 289 | 1984 9 Goddard N 7.17 4.05 290 | 1985 9 Goddard N 6.93 4.17 291 | 1986 9 Goddard N 7.54 4.66 292 | 1987 9 Goddard N 7.48 5.60 293 | 1988 9 Goddard N 7.49 5.31 294 | 1989 9 Goddard N 7.04 4.81 295 | 1990 9 Goddard N 6.24 4.50 296 | 1991 9 Goddard N 6.55 4.46 297 | 1992 9 Goddard N 7.55 5.37 298 | 1993 9 Goddard N 6.50 4.52 299 | 1994 9 Goddard N 7.18 5.08 300 | 1995 9 Goddard N 6.13 4.38 301 | 1996 9 Goddard N 7.88 5.58 302 | 1997 9 Goddard N 6.74 4.84 303 | 1998 9 Goddard N 6.56 4.24 304 | 1999 9 Goddard N 6.24 4.22 305 | 2000 9 Goddard N 6.32 4.31 306 | 2001 9 Goddard N 6.75 4.55 307 | 2002 9 Goddard N 5.96 3.98 308 | 2003 9 Goddard N 6.15 4.01 309 | 2004 9 Goddard N 6.05 4.35 310 | 2005 9 Goddard N 5.57 4.03 311 | 2006 9 Goddard N 5.92 3.97 312 | 2007 9 Goddard N 4.30 2.78 313 | 2008 9 Goddard N 4.73 2.99 314 | 2009 9 Goddard N 5.39 3.47 315 | 2010 9 Goddard N 4.93 3.07 316 | 2011 9 Goddard N 4.63 2.94 317 | 2012 9 Goddard N 3.63 2.15 318 | 2013 9 NRTSI-G N 5.35 3.48 319 | 1979 10 Goddard N 9.39 6.11 320 | 1980 10 Goddard N 9.46 6.42 321 | 1981 10 Goddard N 9.19 6.19 322 | 1982 10 Goddard N 9.98 6.60 323 | 1983 10 Goddard N 9.64 6.70 324 | 1984 10 Goddard N 8.84 5.84 325 | 1985 10 Goddard N 8.88 6.03 326 | 1986 10 Goddard N 9.89 6.77 327 | 1987 10 Goddard N 9.29 7.44 328 | 1988 10 Goddard N 9.47 7.47 329 | 1989 10 Goddard N 9.52 6.90 330 | 1990 10 Goddard N 9.35 6.67 331 | 1991 10 Goddard N 9.16 6.70 332 | 1992 10 Goddard N 9.60 7.69 333 | 1993 10 Goddard N 9.18 6.98 334 | 1994 10 Goddard N 9.48 7.17 335 | 1995 10 Goddard N 8.94 5.91 336 | 1996 10 Goddard N 9.39 7.49 337 | 1997 10 Goddard N 8.76 6.39 338 | 1998 10 Goddard N 8.85 6.65 339 | 1999 10 Goddard N 9.10 6.77 340 | 2000 10 Goddard N 8.92 6.64 341 | 2001 10 Goddard N 8.59 6.59 342 | 2002 10 Goddard N 8.81 6.20 343 | 2003 10 Goddard N 8.65 5.93 344 | 2004 10 Goddard N 8.48 6.35 345 | 2005 10 Goddard N 8.45 5.71 346 | 2006 10 Goddard N 8.33 5.74 347 | 2007 10 Goddard N 6.77 4.21 348 | 2008 10 Goddard N 8.42 5.45 349 | 2009 10 Goddard N 7.52 4.92 350 | 2010 10 Goddard N 7.71 5.19 351 | 2011 10 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313 work;start_date;anonymized feature 166 315 | 314 work;start_date;anonymized feature 201 316 | 315 work;start_date;anonymized feature 168 317 | 316 work;start_date;anonymized feature 202 318 | 317 work;start_date;anonymized feature 169 319 | 318 work;start_date;anonymized feature 170 320 | -------------------------------------------------------------------------------- /files/ch08/facebook/1912.circles: -------------------------------------------------------------------------------- 1 | circle0 2540 2047 2543 2332 2183 2094 1941 2 | circle1 2014 2571 2009 2011 2657 1931 2639 2640 2645 1999 2000 1998 2284 2585 2001 2020 2192 2159 2024 2297 1921 1995 2004 2378 2006 2346 2116 2648 2647 2459 2337 2022 2658 2660 2659 2018 2268 2633 1976 2341 1968 1927 1954 2447 1928 2157 2472 1991 1950 2627 2626 1914 2016 2012 136 2364 2636 2494 2015 2097 2620 2583 1951 2538 1935 2143 2025 2027 2171 1919 3 | circle2 2456 4 | circle3 2108 2369 2046 2059 5 | circle4 2439 2531 2185 2254 2548 2461 2558 2375 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2503 2126 1960 2565 2448 2313 2406 2580 2628 2473 2612 2474 2182 2577 2168 2051 2498 2197 2117 2174 2114 2231 2130 2034 2041 2260 1926 2070 2480 2145 2603 2173 2091 2133 2610 2325 2146 2248 2405 2331 2211 1975 2296 1978 43 | circle42 2250 2384 2529 2531 2372 2400 2389 2355 1976 2203 2312 2319 2616 1913 44 | circle43 2598 2459 2470 2134 45 | circle44 2586 2073 2575 2229 1984 2244 2619 2433 46 | circle45 2453 2505 1974 2208 2651 47 | -------------------------------------------------------------------------------- /files/ch08/facebook/1912.egofeat: -------------------------------------------------------------------------------- 1 | 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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birthday;anonymized feature 2 7 | 6 birthday;anonymized feature 210 8 | 7 birthday;anonymized feature 4 9 | 8 birthday;anonymized feature 5 10 | 9 birthday;anonymized feature 6 11 | 10 birthday;anonymized feature 7 12 | 11 birthday;anonymized feature 377 13 | 12 birthday;anonymized feature 378 14 | 13 birthday;anonymized feature 733 15 | 14 birthday;anonymized feature 734 16 | 15 birthday;anonymized feature 735 17 | 16 birthday;anonymized feature 736 18 | 17 birthday;anonymized feature 737 19 | 18 birthday;anonymized feature 738 20 | 19 birthday;anonymized feature 739 21 | 20 birthday;anonymized feature 740 22 | 21 birthday;anonymized feature 741 23 | 22 education;classes;id;anonymized feature 742 24 | 23 education;classes;id;anonymized feature 743 25 | 24 education;classes;id;anonymized feature 744 26 | 25 education;classes;id;anonymized feature 745 27 | 26 education;classes;id;anonymized feature 746 28 | 27 education;classes;id;anonymized feature 747 29 | 28 education;classes;id;anonymized feature 748 30 | 29 education;classes;id;anonymized feature 749 31 | 30 education;classes;id;anonymized feature 750 32 | 31 education;classes;id;anonymized feature 751 33 | 32 education;classes;id;anonymized feature 752 34 | 33 education;classes;id;anonymized feature 753 35 | 34 education;classes;id;anonymized feature 754 36 | 35 education;classes;id;anonymized feature 755 37 | 36 education;classes;id;anonymized feature 756 38 | 37 education;classes;id;anonymized feature 757 39 | 38 education;classes;id;anonymized feature 11 40 | 39 education;classes;id;anonymized feature 758 41 | 40 education;classes;id;anonymized feature 759 42 | 41 education;concentration;id;anonymized feature 760 43 | 42 education;concentration;id;anonymized feature 13 44 | 43 education;concentration;id;anonymized feature 213 45 | 44 education;concentration;id;anonymized feature 386 46 | 45 education;concentration;id;anonymized feature 14 47 | 46 education;concentration;id;anonymized feature 761 48 | 47 education;concentration;id;anonymized feature 762 49 | 48 education;concentration;id;anonymized feature 214 50 | 49 education;concentration;id;anonymized feature 763 51 | 50 education;concentration;id;anonymized feature 15 52 | 51 education;concentration;id;anonymized feature 764 53 | 52 education;concentration;id;anonymized feature 338 54 | 53 education;concentration;id;anonymized feature 392 55 | 54 education;concentration;id;anonymized feature 765 56 | 55 education;concentration;id;anonymized feature 215 57 | 56 education;concentration;id;anonymized feature 394 58 | 57 education;concentration;id;anonymized feature 216 59 | 58 education;concentration;id;anonymized feature 766 60 | 59 education;concentration;id;anonymized feature 398 61 | 60 education;concentration;id;anonymized feature 400 62 | 61 education;concentration;id;anonymized feature 767 63 | 62 education;concentration;id;anonymized feature 768 64 | 63 education;concentration;id;anonymized feature 16 65 | 64 education;concentration;id;anonymized feature 769 66 | 65 education;concentration;id;anonymized feature 217 67 | 66 education;concentration;id;anonymized feature 18 68 | 67 education;concentration;id;anonymized feature 402 69 | 68 education;concentration;id;anonymized feature 416 70 | 69 education;concentration;id;anonymized feature 770 71 | 70 education;concentration;id;anonymized feature 771 72 | 71 education;concentration;id;anonymized feature 772 73 | 72 education;concentration;id;anonymized feature 423 74 | 73 education;concentration;id;anonymized feature 427 75 | 74 education;concentration;id;anonymized feature 773 76 | 75 education;degree;id;anonymized feature 774 77 | 76 education;degree;id;anonymized feature 20 78 | 77 education;degree;id;anonymized feature 21 79 | 78 education;degree;id;anonymized feature 775 80 | 79 education;degree;id;anonymized feature 22 81 | 80 education;degree;id;anonymized feature 435 82 | 81 education;degree;id;anonymized feature 776 83 | 82 education;degree;id;anonymized feature 223 84 | 83 education;degree;id;anonymized feature 224 85 | 84 education;degree;id;anonymized feature 437 86 | 85 education;school;id;anonymized feature 225 87 | 86 education;school;id;anonymized feature 777 88 | 87 education;school;id;anonymized feature 778 89 | 88 education;school;id;anonymized feature 779 90 | 89 education;school;id;anonymized feature 780 91 | 90 education;school;id;anonymized feature 781 92 | 91 education;school;id;anonymized feature 442 93 | 92 education;school;id;anonymized feature 782 94 | 93 education;school;id;anonymized feature 783 95 | 94 education;school;id;anonymized feature 444 96 | 95 education;school;id;anonymized feature 784 97 | 96 education;school;id;anonymized feature 449 98 | 97 education;school;id;anonymized feature 785 99 | 98 education;school;id;anonymized feature 786 100 | 99 education;school;id;anonymized feature 787 101 | 100 education;school;id;anonymized feature 788 102 | 101 education;school;id;anonymized feature 315 103 | 102 education;school;id;anonymized feature 455 104 | 103 education;school;id;anonymized feature 789 105 | 104 education;school;id;anonymized feature 790 106 | 105 education;school;id;anonymized feature 791 107 | 106 education;school;id;anonymized feature 792 108 | 107 education;school;id;anonymized feature 793 109 | 108 education;school;id;anonymized feature 794 110 | 109 education;school;id;anonymized feature 795 111 | 110 education;school;id;anonymized feature 796 112 | 111 education;school;id;anonymized feature 460 113 | 112 education;school;id;anonymized feature 797 114 | 113 education;school;id;anonymized feature 461 115 | 114 education;school;id;anonymized feature 798 116 | 115 education;school;id;anonymized feature 799 117 | 116 education;school;id;anonymized feature 800 118 | 117 education;school;id;anonymized feature 801 119 | 118 education;school;id;anonymized feature 802 120 | 119 education;school;id;anonymized feature 803 121 | 120 education;school;id;anonymized feature 804 122 | 121 education;school;id;anonymized feature 805 123 | 122 education;school;id;anonymized feature 469 124 | 123 education;school;id;anonymized feature 470 125 | 124 education;school;id;anonymized feature 806 126 | 125 education;school;id;anonymized feature 807 127 | 126 education;school;id;anonymized feature 808 128 | 127 education;school;id;anonymized feature 809 129 | 128 education;school;id;anonymized feature 810 130 | 129 education;school;id;anonymized feature 811 131 | 130 education;school;id;anonymized feature 812 132 | 131 education;school;id;anonymized feature 813 133 | 132 education;school;id;anonymized feature 814 134 | 133 education;school;id;anonymized feature 815 135 | 134 education;school;id;anonymized feature 816 136 | 135 education;school;id;anonymized feature 817 137 | 136 education;school;id;anonymized feature 481 138 | 137 education;school;id;anonymized feature 818 139 | 138 education;school;id;anonymized feature 819 140 | 139 education;school;id;anonymized feature 820 141 | 140 education;school;id;anonymized feature 236 142 | 141 education;school;id;anonymized feature 486 143 | 142 education;school;id;anonymized feature 821 144 | 143 education;school;id;anonymized feature 822 145 | 144 education;school;id;anonymized feature 823 146 | 145 education;school;id;anonymized feature 824 147 | 146 education;school;id;anonymized feature 825 148 | 147 education;school;id;anonymized feature 826 149 | 148 education;school;id;anonymized feature 827 150 | 149 education;school;id;anonymized feature 828 151 | 150 education;school;id;anonymized feature 829 152 | 151 education;school;id;anonymized feature 830 153 | 152 education;school;id;anonymized feature 831 154 | 153 education;school;id;anonymized feature 832 155 | 154 education;school;id;anonymized feature 833 156 | 155 education;school;id;anonymized feature 834 157 | 156 education;school;id;anonymized feature 835 158 | 157 education;school;id;anonymized feature 836 159 | 158 education;school;id;anonymized feature 837 160 | 159 education;school;id;anonymized feature 239 161 | 160 education;school;id;anonymized feature 838 162 | 161 education;school;id;anonymized feature 839 163 | 162 education;school;id;anonymized feature 840 164 | 163 education;school;id;anonymized feature 841 165 | 164 education;school;id;anonymized feature 842 166 | 165 education;school;id;anonymized feature 503 167 | 166 education;school;id;anonymized feature 843 168 | 167 education;school;id;anonymized feature 844 169 | 168 education;school;id;anonymized feature 504 170 | 169 education;school;id;anonymized feature 845 171 | 170 education;school;id;anonymized feature 846 172 | 171 education;school;id;anonymized feature 847 173 | 172 education;school;id;anonymized feature 848 174 | 173 education;school;id;anonymized feature 849 175 | 174 education;school;id;anonymized feature 850 176 | 175 education;school;id;anonymized feature 851 177 | 176 education;school;id;anonymized feature 852 178 | 177 education;school;id;anonymized feature 853 179 | 178 education;school;id;anonymized feature 854 180 | 179 education;school;id;anonymized feature 855 181 | 180 education;school;id;anonymized feature 856 182 | 181 education;school;id;anonymized feature 857 183 | 182 education;school;id;anonymized feature 858 184 | 183 education;school;id;anonymized feature 514 185 | 184 education;school;id;anonymized feature 859 186 | 185 education;school;id;anonymized feature 860 187 | 186 education;school;id;anonymized feature 861 188 | 187 education;school;id;anonymized feature 246 189 | 188 education;school;id;anonymized feature 862 190 | 189 education;school;id;anonymized feature 863 191 | 190 education;school;id;anonymized feature 864 192 | 191 education;school;id;anonymized feature 865 193 | 192 education;school;id;anonymized feature 866 194 | 193 education;school;id;anonymized feature 867 195 | 194 education;school;id;anonymized feature 868 196 | 195 education;school;id;anonymized feature 50 197 | 196 education;school;id;anonymized feature 523 198 | 197 education;school;id;anonymized feature 869 199 | 198 education;school;id;anonymized feature 249 200 | 199 education;school;id;anonymized feature 870 201 | 200 education;school;id;anonymized feature 871 202 | 201 education;school;id;anonymized feature 872 203 | 202 education;school;id;anonymized feature 533 204 | 203 education;school;id;anonymized feature 873 205 | 204 education;school;id;anonymized feature 874 206 | 205 education;school;id;anonymized feature 875 207 | 206 education;school;id;anonymized feature 535 208 | 207 education;school;id;anonymized feature 876 209 | 208 education;school;id;anonymized feature 877 210 | 209 education;school;id;anonymized feature 322 211 | 210 education;school;id;anonymized feature 878 212 | 211 education;school;id;anonymized feature 52 213 | 212 education;school;id;anonymized feature 879 214 | 213 education;school;id;anonymized feature 880 215 | 214 education;school;id;anonymized feature 537 216 | 215 education;school;id;anonymized feature 881 217 | 216 education;school;id;anonymized feature 538 218 | 217 education;school;id;anonymized feature 882 219 | 218 education;type;anonymized feature 53 220 | 219 education;type;anonymized feature 54 221 | 220 education;type;anonymized feature 55 222 | 221 education;with;id;anonymized feature 883 223 | 222 education;with;id;anonymized feature 884 224 | 223 education;with;id;anonymized feature 885 225 | 224 education;with;id;anonymized feature 886 226 | 225 education;with;id;anonymized feature 887 227 | 226 education;year;id;anonymized feature 543 228 | 227 education;year;id;anonymized feature 58 229 | 228 education;year;id;anonymized feature 888 230 | 229 education;year;id;anonymized feature 59 231 | 230 education;year;id;anonymized feature 544 232 | 231 education;year;id;anonymized feature 255 233 | 232 education;year;id;anonymized feature 889 234 | 233 education;year;id;anonymized feature 60 235 | 234 education;year;id;anonymized feature 890 236 | 235 education;year;id;anonymized feature 61 237 | 236 education;year;id;anonymized feature 62 238 | 237 education;year;id;anonymized feature 63 239 | 238 education;year;id;anonymized feature 64 240 | 239 education;year;id;anonymized feature 65 241 | 240 education;year;id;anonymized feature 66 242 | 241 education;year;id;anonymized feature 67 243 | 242 education;year;id;anonymized feature 68 244 | 243 education;year;id;anonymized feature 69 245 | 244 education;year;id;anonymized feature 258 246 | 245 education;year;id;anonymized feature 71 247 | 246 education;year;id;anonymized feature 259 248 | 247 education;year;id;anonymized feature 72 249 | 248 first_name;anonymized feature 891 250 | 249 first_name;anonymized feature 892 251 | 250 first_name;anonymized feature 893 252 | 251 first_name;anonymized feature 894 253 | 252 first_name;anonymized feature 895 254 | 253 first_name;anonymized feature 896 255 | 254 first_name;anonymized feature 897 256 | 255 first_name;anonymized feature 898 257 | 256 first_name;anonymized feature 899 258 | 257 first_name;anonymized feature 900 259 | 258 first_name;anonymized feature 901 260 | 259 gender;anonymized feature 77 261 | 260 gender;anonymized feature 78 262 | 261 hometown;id;anonymized feature 558 263 | 262 hometown;id;anonymized feature 902 264 | 263 hometown;id;anonymized feature 903 265 | 264 hometown;id;anonymized feature 82 266 | 265 hometown;id;anonymized feature 904 267 | 266 hometown;id;anonymized feature 905 268 | 267 hometown;id;anonymized feature 84 269 | 268 hometown;id;anonymized feature 134 270 | 269 hometown;id;anonymized feature 906 271 | 270 hometown;id;anonymized feature 907 272 | 271 hometown;id;anonymized feature 908 273 | 272 hometown;id;anonymized feature 86 274 | 273 hometown;id;anonymized feature 909 275 | 274 hometown;id;anonymized feature 910 276 | 275 hometown;id;anonymized feature 911 277 | 276 hometown;id;anonymized feature 618 278 | 277 languages;id;anonymized feature 90 279 | 278 languages;id;anonymized feature 912 280 | 279 languages;id;anonymized feature 91 281 | 280 languages;id;anonymized feature 92 282 | 281 languages;id;anonymized feature 93 283 | 282 languages;id;anonymized feature 913 284 | 283 languages;id;anonymized feature 94 285 | 284 languages;id;anonymized feature 572 286 | 285 languages;id;anonymized feature 265 287 | 286 languages;id;anonymized feature 101 288 | 287 languages;id;anonymized feature 914 289 | 288 languages;id;anonymized feature 915 290 | 289 last_name;anonymized feature 916 291 | 290 last_name;anonymized feature 917 292 | 291 last_name;anonymized feature 918 293 | 292 last_name;anonymized feature 894 294 | 293 last_name;anonymized feature 919 295 | 294 last_name;anonymized feature 110 296 | 295 last_name;anonymized feature 920 297 | 296 last_name;anonymized feature 273 298 | 297 last_name;anonymized feature 595 299 | 298 last_name;anonymized feature 921 300 | 299 last_name;anonymized feature 922 301 | 300 last_name;anonymized feature 923 302 | 301 last_name;anonymized feature 924 303 | 302 last_name;anonymized feature 925 304 | 303 last_name;anonymized feature 926 305 | 304 last_name;anonymized feature 927 306 | 305 last_name;anonymized feature 928 307 | 306 last_name;anonymized feature 602 308 | 307 last_name;anonymized feature 929 309 | 308 last_name;anonymized feature 930 310 | 309 last_name;anonymized feature 327 311 | 310 last_name;anonymized feature 931 312 | 311 last_name;anonymized feature 123 313 | 312 locale;anonymized feature 126 314 | 313 locale;anonymized feature 127 315 | 314 locale;anonymized feature 932 316 | 315 location;id;anonymized feature 558 317 | 316 location;id;anonymized feature 902 318 | 317 location;id;anonymized feature 128 319 | 318 location;id;anonymized feature 129 320 | 319 location;id;anonymized feature 280 321 | 320 location;id;anonymized feature 610 322 | 321 location;id;anonymized feature 611 323 | 322 location;id;anonymized feature 612 324 | 323 location;id;anonymized feature 933 325 | 324 location;id;anonymized feature 174 326 | 325 location;id;anonymized feature 934 327 | 326 location;id;anonymized feature 935 328 | 327 location;id;anonymized feature 132 329 | 328 location;id;anonymized feature 936 330 | 329 location;id;anonymized feature 281 331 | 330 location;id;anonymized feature 937 332 | 331 location;id;anonymized feature 176 333 | 332 location;id;anonymized feature 938 334 | 333 location;id;anonymized feature 134 335 | 334 location;id;anonymized feature 939 336 | 335 location;id;anonymized feature 282 337 | 336 location;id;anonymized feature 940 338 | 337 location;id;anonymized feature 907 339 | 338 location;id;anonymized feature 908 340 | 339 location;id;anonymized feature 283 341 | 340 location;id;anonymized feature 941 342 | 341 location;id;anonymized feature 942 343 | 342 location;id;anonymized feature 617 344 | 343 location;id;anonymized feature 284 345 | 344 location;id;anonymized feature 568 346 | 345 location;id;anonymized feature 619 347 | 346 middle_name;anonymized feature 943 348 | 347 work;employer;id;anonymized feature 944 349 | 348 work;employer;id;anonymized feature 945 350 | 349 work;employer;id;anonymized feature 946 351 | 350 work;employer;id;anonymized feature 442 352 | 351 work;employer;id;anonymized feature 140 353 | 352 work;employer;id;anonymized feature 947 354 | 353 work;employer;id;anonymized feature 948 355 | 354 work;employer;id;anonymized feature 288 356 | 355 work;employer;id;anonymized feature 949 357 | 356 work;employer;id;anonymized feature 950 358 | 357 work;employer;id;anonymized feature 951 359 | 358 work;employer;id;anonymized feature 952 360 | 359 work;employer;id;anonymized feature 953 361 | 360 work;employer;id;anonymized feature 954 362 | 361 work;employer;id;anonymized feature 955 363 | 362 work;employer;id;anonymized feature 956 364 | 363 work;employer;id;anonymized feature 957 365 | 364 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342 11 | 10 education;school;id;anonymized feature 370 12 | 11 education;school;id;anonymized feature 348 13 | 12 education;type;anonymized feature 53 14 | 13 education;type;anonymized feature 54 15 | 14 education;type;anonymized feature 55 16 | 15 education;with;id;anonymized feature 350 17 | 16 education;year;id;anonymized feature 59 18 | 17 education;year;id;anonymized feature 255 19 | 18 education;year;id;anonymized feature 60 20 | 19 education;year;id;anonymized feature 61 21 | 20 education;year;id;anonymized feature 64 22 | 21 education;year;id;anonymized feature 71 23 | 22 education;year;id;anonymized feature 259 24 | 23 education;year;id;anonymized feature 72 25 | 24 first_name;anonymized feature 355 26 | 25 first_name;anonymized feature 356 27 | 26 gender;anonymized feature 77 28 | 27 gender;anonymized feature 78 29 | 28 hometown;id;anonymized feature 359 30 | 29 hometown;id;anonymized feature 366 31 | 30 languages;id;anonymized feature 92 32 | 31 last_name;anonymized feature 371 33 | 32 last_name;anonymized feature 365 34 | 33 locale;anonymized feature 126 35 | 34 locale;anonymized feature 127 36 | 35 location;id;anonymized feature 359 37 | 36 location;id;anonymized feature 366 38 | 37 name;anonymized feature 372 39 | 38 work;employer;id;anonymized feature 373 40 | 39 work;employer;id;anonymized feature 140 41 | 40 work;employer;id;anonymized feature 154 42 | 41 work;end_date;anonymized feature 157 43 | 42 work;end_date;anonymized feature 161 44 | 43 work;location;id;anonymized feature 374 45 | 44 work;location;id;anonymized feature 366 46 | 45 work;start_date;anonymized feature 157 47 | 46 work;start_date;anonymized feature 375 48 | 47 work;start_date;anonymized feature 196 49 | -------------------------------------------------------------------------------- /learning_curve_1.png: -------------------------------------------------------------------------------- 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matplotlib==3.7.2 3 | numpy==1.24.2 4 | pandas==2.0 5 | -------------------------------------------------------------------------------- /toy_problem.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataScienceUB/introduction-datascience-python-book/15d474bf731560aab9d9125d1b3b1cd42b102f0e/toy_problem.png --------------------------------------------------------------------------------