Julia for Data Science

Introduction....9 CHAPTER 1: Introducing Julia....11 How Julia Improves Data Science....14 Data science workflow....14 Julia’s adoption by the data science community....17 Julia Extensions....18 Package quality....18 Finding new packages....18 About the Book....19 CHAPTER 2: Setting Up the Data Science Lab....22 Julia IDEs....23 Juno....24 IJulia....26 Additional IDEs....28 Julia Packages....29 Finding and selecting packages....29 Installing packages....31 Using packages....32 Hacking packages....32 IJulia Basics....32 Handling files....32 Creating a notebook....32 Saving a notebook....33 Renaming a notebook....34 Loading a notebook....35 Exporting a notebook....36 Organizing code in .jl files....37 Referencing code....38 Working directory....38 Datasets We Will Use....39 Dataset descriptions....39 Magic dataset....39 OnlineNewsPopularity dataset....40 Spam Assassin dataset....41 Downloading datasets....42 Loading datasets....43 CSV files....43 Text files....43 Coding and Testing a Simple Machine Learning Algorithm in Julia....44 Algorithm description....45 Algorithm implementation....48 Algorithm testing....51 Saving Your Workspace into a Data File....54 Saving data into delimited files....54 Saving data into native Julia format....55 Saving data into text files....57 Help!....58 Summary....58 Chapter Challenge....60 CHAPTER 3: Learning the Ropes of Julia....62 Data Types....63 Arrays....68 Array basics....68 Accessing multiple elements in an array....70 Multidimensional arrays....71 Dictionaries....71 Basic Commands and Functions....72 print(), println()....73 typemax(), typemin()....73 collect()....74 show()....75 linspace()....75 Mathematical Functions....76 round()....76 rand(), randn()....77 sum()....80 mean()....81 Array and Dictionary Functions....81 in....81 append!()....82 pop!()....82 push!()....83 splice!()....84 insert!()....85 sort(), sort!()....85 get()....86 Keys(), values()....87 length(), size()....87 Miscellaneous Functions....88 time()....88 Conditionals....89 if-else statements....89 string()....91 map()....91 VERSION()....92 Operators, Loops and Conditionals....92 Operators....92 Alphanumeric operators (<, >, ==, <=, >=, !=)....92 Logical operators (&&, ||)....93 Loops....94 for-loops....94 while-loops....95 break command....96 Summary....96 Chapter Challenge....97 CHAPTER 4: Going Beyond the Basics in Julia....98 String Manipulation....99 split()....100 join()....101 Regex functions....101 ismatch()....103 match()....103 matchall()....104 eachmatch()....105 Custom Functions....106 Function structure....106 Anonymous functions....107 Multiple dispatch....107 Function example....108 Implementing a Simple Algorithm....110 Creating a Complete Solution....112 Summary....118 Chapter Challenge....119 CHAPTER 5: Julia Goes All Data Science-y....121 Data Science Pipeline....122 Data Engineering....125 Data preparation....125 Data exploration....127 Data representation....129 Data Modeling....131 Data discovery....131 Data learning....132 Information Distillation....135 Data product creation....135 Insight, deliverance, and visualization....136 Keep an Open Mind....137 Applying the Data Science Pipeline to a Real-World Problem....138 Data preparation....138 Data exploration....139 Data representation....140 Data discovery....140 Data learning....141 Data product creation....141 Insight, deliverance, and visualization....142 Summary....143 Chapter Challenge....144 CHAPTER 6: Julia the Data Engineer....146 Data Frames....148 Creating and populating a data frame....148 Data frames basics....149 Variable names in a data frame....149 Accessing particular variables in a data frame....150 Exploring a data frame....151 Filtering sections of a data frame....152 Applying functions to a data frame’s variables....152 Working with data frames....153 Altering data frames....155 Sorting the contents of a data frame....155 Data frame tips....156 Importing and Exporting Data....157 Accessing .json data files....157 Storing data in .json files....158 Loading data files into data frames....158 Saving data frames into data files....159 Cleaning Up Data....159 Cleaning up numeric data....159 Cleaning up text data....160 Formatting and Transforming Data....161 Formatting numeric data....161 Formatting text data....162 Importance of data types....163 Applying Data Transformations to Numeric Data....163 Normalization....164 Discretization (binning) and binarization....165 Binary to continuous (binary classification only)....167 Applying data transformations to text data....167 Case normalization....167 Vectorization....168 Preliminary Evaluation of Features....170 Regression....170 Classification....171 Feature evaluation tips....172 Summary....172 Chapter Challenge....173 CHAPTER 7: Exploring Datasets....175 Listening to the Data....176 Packages used in this chapter....176 Computing Basic Statistics and Correlations....177 Variable summary....178 Correlations among variables....179 Comparability between two variables....179 Plots....180 Grammar of graphics....180 Preparing data for visualization....180 Box plots....181 Bar plots....181 Line plots....182 Scatter plots....183 Basic scatter plots....183 Scatter plots using the output of t-SNE algorithm....184 Histograms....186 Exporting a plot to a file....186 Hypothesis Testing....187 Testing basics....187 Types of errors....187 Sensitivity and specificity....188 Significance and power of a test....189 Kruskal-Wallis tests....189 T-tests....190 Chi-square tests....191 Other Tests....193 Statistical Testing Tips....193 Case Study: Exploring the OnlineNewsPopularity Dataset....193 Variable stats....193 Visualization....194 Hypotheses....195 T-SNE magic....196 Conclusions....197 Summary....197 Chapter Challenge....199 CHAPTER 8: Manipulating the Fabric of the Data Space....200 Principal Components Analysis (PCA)....201 Applying PCA in Julia....202 Independent Components Analysis (ICA): most popular alternative of PCA....204 Feature Evaluation and Selection....205 Overview of the methodology....205 Using Julia for feature evaluation and selection using cosine similarity....207 Using Julia for feature evaluation and selection using DID....208 Pros and cons of the feature evaluation and selection approach....210 Other Dimensionality Reduction Techniques....211 Overview of the alternative dimensionality reduction methods....211 Genetic algorithms....211 Discernibility-based approach....212 When to use a sophisticated dimensionality reduction method....212 Summary....213 Chapter Challenge....213 CHAPTER 9: Sampling Data and Evaluating Results....215 Sampling Techniques....216 Basic sampling....217 Stratified sampling....217 Performance Metrics for Classification....218 Confusion matrix....219 Accuracy metrics....219 Basic accuracy....219 Weighted accuracy....220 Precision and recall metrics....222 F1 metric....222 Misclassification cost....223 Defining the cost matrix....223 Calculating the total misclassification cost....224 Receiver Operating Characteristic (ROC) Curve and related metrics....224 ROC Curve....224 AUC Metric....227 Gini Coefficient....228 Performance Metrics for Regression....228 MSE Metric and its variant, RMSE....229 SSE Metric....230 Other metrics....230 K-fold Cross Validation (KFCV)....231 Applying KFCV in Julia....232 KFCV tips....233 Summary....233 Chapter Challenge....236 CHAPTER 10: Unsupervised Machine Learning....238 Unsupervised Learning Basics....239 Clustering types....240 Distance metrics....241 Grouping Data with K-means....243 K-means using Julia....244 K-means tips....246 Density and the DBSCAN Approach....246 DBSCAN algorithm....247 Applying DBSCAN in Julia....248 Hierarchical Clustering....249 Applying hierarchical clustering in Julia....250 When to use hierarchical clustering....252 Validation Metrics for Clustering....252 Silhouettes....252 Clustering validation metrics tips....253 Effective Clustering Tips....254 Dealing with high dimensionality....254 Normalization....254 Visualization tips....255 Summary....255 Chapter Challenge....257 CHAPTER 11: Supervised Machine Learning....259 Decision Trees....261 Implementing decision trees in Julia....262 Decision tree tips....266 Regression Trees....267 Implementing regression trees in Julia....267 Regression tree tips....268 Random Forests....268 Implementing random forests in Julia for classification....269 Implementing random forests in Julia for regression....271 Random forest tips....272 Basic Neural Networks....273 Implementing neural networks in Julia....274 Neural network tips....277 Extreme Learning Machines....278 Implementing ELMs in Julia....279 ELM tips....281 Statistical Models for Regression Analysis....282 Implementing statistical regression in Julia....283 Statistical regression tips....286 Other Supervised Learning Systems....287 Boosted trees....287 Support vector machines....287 Transductive systems....287 Deep learning systems....288 Bayesian networks....289 Summary....290 Chapter Challenge....292 CHAPTER 12: Graph Analysis....294 Importance of Graphs....296 Custom Dataset....299 Statistics of a Graph....301 Cycle Detection....303 Julia the cycle detective....304 Connected Components....306 Cliques....307 Shortest Path in a Graph....308 Minimum Spanning Trees....311 Julia the MST botanist....312 Saving and loading graphs from a file....313 Graph Analysis and Julia’s Role in it....314 Summary....315 Chapter Challenge....318 CHAPTER 13: Reaching the Next Level....319 Julia Community....320 Sites to interact with other Julians....320 Code repositories....321 Videos....322 News....322 Practice What You’ve Learned....322 Some features to get you started....324 Some thoughts on this project....325 Final Thoughts about Your Experience with Julia in Data Science....326 Refining your Julia programming skills....326 Contributing to the Julia project....326 Future of Julia in data science....328 APPENDIX A: Downloading and Installing Julia and IJulia....330 APPENDIX B: Useful Websites Related to Julia....333 APPENDIX C: Packages Used in This Book....339 APPENDIX D: Bridging Julia with Other Platforms....344 Bridging Julia with R....345 Running a Julia script in R....345 Running an R script in Julia....346 Bridging Julia with Python....346 Running a Julia script in Python....347 Running a Python script in Julia....347 APPENDIX E: Parallelization in Julia....349 APPENDIX F: Answers to Chapter Challenges....353 Chapter 2....354 Chapter 3....357 Chapter 4....357 Chapter 5....359 Chapter 6....361 Chapter 7....363 Chapter 8....364 Chapter 9....365 Chapter 10....365 Chapter 11....367 Chapter 12....368 Chapter 13....369 Index....370