Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Laurae2/LauraeDS

Laurae's Data Science R Package
https://github.com/Laurae2/LauraeDS

Last synced: 3 months ago
JSON representation

Laurae's Data Science R Package

Awesome Lists containing this project

README 

        
# LauraeDS: Laurae's Data Science Package



This package is the sequel to [Laurae2/Laurae](https://github.com/Laurae2/Laurae) R package.



It is meant to require less stuff and more robust.



## Installation



```r

devtools::install_github("Laurae2/LauraeDS", dep = FALSE)

```



Dependencies installation:



```r

install.packages(c("Matrix", "sparsio", "fst", "data.table", "pbapply", "parallel"))

devtools::install_github("fstpackage/fst@e060e62")

devtools::install_github("Laurae2/ez_xgb/R-package@2017-02-15-v1")

devtools::install_github("Microsoft/LightGBM/R-package@fc59fce") # Jul 14 2017, v2.0.4



```



---



## TO-DO



* [x] add fold generation

* [x] add sparse handling

* [x] add parallel fast csv/fst converter

* [x] add parallel handling (cluster)

* [ ] add parallel xgboost

* [ ] add parallel LightGBM

* [ ] add metrics

* [x] add metric optimizations

* [x] xgb.DMatrix generation

* [x] lgb.Dataset generation

* [x] xgboost trainer

* [ ] LightGBM trainer

* [ ] easy GLM (xgboost)

* [ ] easy Random Forest (xgboost)

* [ ] easy Random Forest (LightGBM)

* [ ] easy Gradient Boosted Trees (xgboost)

* [ ] easy Gradient Boosted Trees (LightGBM)

* [ ] grid learning ("grid search")

* [ ] Random Patches feature generation (Subsampling + Colsampling from feature groups)

* [ ] stacker

* [ ] add lot of stuff



---



## Available functions



---



### Parallel functions



Parallel functions are provided to make R fly on multi-core and multi-socket systems, provided enough RAM.



| Function | Packages | Description |

| :--- | :--- | :--- |

| parallel.csv | data.table, fst, parallel | Parallelizes and multithreads the reading of CSV files and writes to fst file format for fast reading. |

| parallel.threading | parallel | Sets processor affinity correctly on Windows machines. Provide a boost of up to 200% in memory bounded applications. |

| parallel.destroy | parallel | Stops a parallel cluster, or destroy any available clusters bound to the current R session. |



### I/O functions



I/O Functions allows to read files from sparse matrices quickly.



| Function | Packages | Description |

| :--- | :--- | :--- |

| sparse.read | sparsio, Matrix | Reads SVMLight file format (sparse matrices) |

| sparse.write | sparsio, Matrix | Writes SVMLight file format (sparse matrices) |



---



### Fold functions



Fold functions allow to generate folds for cross-validation very quickly.



| Function | Packages | Description |

| :--- | :--- | :--- |

| kfold | None | Generate cross-validated folds (stratified, treatment, pseudo-random, random) |

| nkfold | None | Generate Repeated cross-validated folds (stratified, treatment, pseudo-random, random) |



---



### Optimized Metrics



Optimized metrics might help get an edge when you can.



| Function | Packages | Description |

| :--- | :--- | :--- |

| metrics.acc.max | data.table | Maximum Binary Accuracy |

| metrics.f1.max | data.table | Maximum F1 Score (Precision with Sensitivity Harmonic Mean |

| metrics.fallout;max | data.table | Minimum Fall-Out (False Positive Rate) |

| metrics.kappa.max | data.table | Maximum Kappa Statistic |

| metrics.mcc.max | data.table | Maximum Matthews Correlation Coefficient |

| metrics.missrate.max | data.table | Minim Miss-rate (False Negative Rate) |

| metrics.precision.max | data.table | Maximum Precision (Positive Predictive Rate) |

| metrics.sensitivity.max | data.table | Maximum Sensitivity (True Positive Rate) |

| metrics.specifity.max | data.table | Maximum Specificity (True Negative Rate) |



## Metric Computation/Solving



Computing and/or solving metrics might help you understand what default values are the best for the metric.



| Function | Packages | Description |

| :--- | :--- | :--- |

| metrics.logloss | None | Logarithmic Loss (logloss) |

| metrics.logloss.unsafe | None | Logarithmic Loss (logloss) without bound checking |

| metrics.logloss.solve | stats | Logarithmic Loss Solver |



---



### Machine Learning, Binary Matrices



Generating binary matrices never got easier if you can throw lists and data.frames directly.



| Function | Packages | Description |

| :--- | :--- | :--- |

| Laurae.xgb.dmat | xgboost, Matrix | Wrapper for extensible xgb.DMatrix generation. |

| Laurae.lgb.dmat | lightgbm, Matrix | Wrapper for extensible lgb.Dataset generation. |



---



### Machine Learning, Supervised



Not remembering every existing hyperparameters? Now you can by pressing Tab to autocomplete hyperparameters.



| Function | Packages | Description |

| :--- | :--- | :--- |

| Laurae.xgb.train | xgboost, Matrix | Wrapper for xgboost Models |



---



### Machine Learning, Loss/Metrics Helpers



Creating loss/metrics can be a tedious task without templates. Use these as template wrappers: focus on loss/metrics, wrap them with a template quickly.



| Function | Packages | Description |

| :--- | :--- | :--- |

| xgb.wrap.loss | xgboost | Wrapper to make quick xgboost loss function. |

| xgb.wrap.metric | xgboost | Wrapper to make quick xgboost metric function. |

| lgb.wrap.loss | LightGBM | Wrapper to make quick LightGBM loss function. |

| lgb.wrap.metric | LightGBM | Wrapper to make quick LightGBM metric function. |



---



### Machine Learning, Loss/Metrics Functions



Need functions answering metrics quickly? Here are some.



| Function | Packages | Description |

| :--- | :--- | :--- |

| metrics.logloss | None | Computes the logarithmic loss. |

| metrics.logloss.unsafe | None | Computes the logarithmic loss faster by skipping out of bounds checks. | 

| metrics.logloss.solve | stats | Solves for a parameter involving the logartihmic loss (minimal loss, constant prediction value, ratio). |