[mlpack-git] (blog) master: Keon Final (59cb2ba)

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Repository : https://github.com/mlpack/blog
On branch  : master
Link       : https://github.com/mlpack/blog/compare/08a9aeeae2e2b3d4bec3fe1c661430eb33528b80...5ab87f052dff6bfb0610ca2bf1a41e49896f83b5

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commit 59cb2ba1803d9db52044e1b3a50e41b3533b554b
Author: Keon Kim <kwk236 at gmail.com>
Date:   Tue Aug 23 16:24:02 2016 +0900

    Keon Final


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+Title: Dataset and Experimentation Tools : Summary
+Date: 2016-08-23 14:00:00
+Tags: gsoc, dataset, data
+Author: Keon Kim
+
+In this blog post I'll try to describe my contributions I've made to mlpack this summer.
+
+### Summary
+
+Here is the link for all my pull requests [pull requests](https://github.com/mlpack/mlpack/pulls?q=is%3Apr+is%3Aclosed+author%3Akeonkim).
+Below is the list of the major pull requests with self-explanatory descriptions.
+
+ * Descriptive Statistics command-line program : [742]
+ * DatasetMapper & Imputer  [694]
+ * delete unused string_util : [672]
+ * fix default output problem and some styles : [680]
+ * Binarize Function + Test : [666]
+ * add cli executable for data_split : [650]
+
+### Descriptive Statistics
+
+I originally built a [class](https://github.com/keonkim/mlpack/commit/c2f5c5c2e6cbce084992629e192023519873e4cb) that calculates descriptive statistics. But after a few discussion, I ended up shrinking all of the functions down to minimum to provide maximum performance and maintainability.
+I also merged all commits to one to discard unnecessary commits.
+
+Sample output on "iris.csv" would be:
+```
+[INFO ] dim     var     mean    std     median  min     max     range   skew    kurt    SE      
+[INFO ] 0       0.6857  5.8433  0.8281  5.8000  4.3000  7.9000  3.6000  0.3149  -0.5521 0.0676  
+[INFO ] 1       0.1880  3.0540  0.4336  3.0000  2.0000  4.4000  2.4000  0.3341  0.2908  0.0354  
+[INFO ] 2       3.1132  3.7587  1.7644  4.3500  1.0000  6.9000  5.9000  -0.2745 -1.4019 0.1441  
+[INFO ] 3       0.5824  1.1987  0.7632  1.3000  0.1000  2.5000  2.4000  -0.1050 -1.3398 0.0623  
+```
+Users can control the width and precision using -w and -p flag.
+I tested the output using excel and they match perfectly.
+
+### DatasetMapper & Imputer
+
+I renamed DatasetInfo to DatasetMapper, which accepts template parameter of MapPolicy.
+( can be used to store different kinds of maps.)
+DatasetMapper, however, still provides backward compatibility with typedef:
+`using DatasetInfo = DatasetMapper<IncrementPolicy>`.
+The IncrementPolicy denotes the original mapping policy used,
+which increments numbers for different categories, starting from 0.
+
+Imputer class is also added in this pull request.
+Imputer also accepts template parameter called ImputationStrategy,
+so that different strategies can be applied.
+
+Lastly, a command line program called "mlpack_preprocess_imputer.cpp" was added to the mlpack.
+
+### Binarizer
+
+This is a simple implementation of binarize function which transforms
+values in matrix to 0 or 1 according to the threshold.
+You can use `umat A = (B > C)` but this function has a overload
+that applies binarize to only one dimension. Plus,
+it can produce any type of matrix, not umat.
+
+### Spliter
+
+I added TrainTestSplit() and renamed old ones to LabelTrainTestSplit() as discussed in #651 .
+This is just a naive implementation mostly copied from Tham's work.
+I believe LabelTrainTestSplit can just reuse the code in TrainTestSplit twice for both data and labels.
+
+I also implemented "mlpack_preprocess_split.cpp".
+
+### Other changes
+
+I also made minor contributions in debugging and fixing styles, especially related to data IO.
+
+### TODOs
+
+I wish to keep contributing to mlpack.
+I will try to polish the works a little bit more, and especially,
+I would LOVE to contribute to the deep learning modules.
+I've been personally reading papers about sequence-to-sequence models,
+which are used widely for natural language processing and timeseries data analytics.
+
+### Acknowledgement
+
+I thank the mlpack mentors and especially to Tham who gave me a lot of advises through code reviews.
+
+[742]: https://github.com/mlpack/mlpack/pull/742
+[694]: https://github.com/mlpack/mlpack/pull/694
+[672]: https://github.com/mlpack/mlpack/pull/672
+[680]: https://github.com/mlpack/mlpack/pull/680
+[666]: https://github.com/mlpack/mlpack/pull/666
+[650]: https://github.com/mlpack/mlpack/pull/650