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Best practices of orchestrating Python and R code in ML projects


 
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Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.



Feather API

Feather API is designed to improve meta data and data interchange between R and Python. It provides fast import/export of data frames among both environments and keeps meta data informations which is an improvement over data exchange via csv/txt file format. In our example Python job will read an input binary file that was produced in R with Feather api.

Let’s install Feather library in both environments.

For Python 3 on linux enviroment you can use cmd and pip3:

sudo pip3 install feather-format

For R it is necessary to install feather package:

install.packages(feather)

After successful installation we can use Feather for data exchange.

Below is an R syntax for data frame export with Feather (featurization.R):

library(feather)

write_feather(dtm_train_tfidf,args[3])
write_feather(dtm_test_tfidf,args[4])
print("Two data frame were created with Feather - 
        one for train and one for test data set")

Python syntax for reading feather input binary files (train_model_python.py):

import feather as ft

input = sys.argv[1]
df = ft.read_dataframe(input)

Dependency graph with R and Python combined

The next question what we are asking ourselves is why do we need DVC, why not just use shell scripting? DVC automatically derives the dependencies between the steps and builds the dependency graph (DAG) transparently to the user. Graph is used for reproducing parts/codes of your pipeline which were affected by recent changes and we don’t have to think all the time what we need to repeat (which steps) with the latest changes.

Firstly, with ‘dvc run’ command we will execute all jobs that are related to our model development. In that phase DVC creates dependencies that will be used in the reproducibility phase:

$ dvc import https://s3-us-west-2.amazonaws.com/dvc-share/so/25K/Posts.xml.tgz 
data/
$ dvc run tar zxf data/Posts.xml.tgz -C data/
$ dvc run Rscript code/parsingxml.R data/Posts.xml data/Posts.csv
$ dvc run Rscript code/train_test_spliting.R data/Posts.csv 0.33 20170426 data/
train_post.csv data/test_post.csv
$ dvc run Rscript code/featurization.R data/train_post.csv data/
test_post.csv data/matrix_train.feather data/matrix_test.feather
$ dvc run python3 code/train_model_python.py data/matrix_train.feather 20170426 
data/model.p
$ dvc run python3 code/evaluate_python_mdl.py data/model.p 
data/matrix_test.feather data/evaluation_python.txt

After this commands jobs are executed and included in DAG graph. Result (AUC metrics) is written in evaluation_python.txt file:

$ cat data/evaluation_python.txt
AUC: 0.741432

It is possible to improve our result with random forest algorithm.

We can increase number of trees in the random forest classifier — from 100 to 500:

clf = RandomForestClassifier(n_estimators=500, n_jobs=2, random_state=seed)
clf.fit(x, labels)

After commited changes (in train_model_python.py) with dvc repro commandall necessary jobs for evaluation_python.txt reproduction will be re-executed. We don’t need to worry which jobs to run and in which order.

$ git add .
$ git commit
[master a65f346] Random forest clasiffier — more trees added
 1 file changed, 1 insertion(+), 1 deletion(-)
$ dvc repro data/evaluation_python.txt
Reproducing run command for data item data/model.p. 
Args: python3 code/train_model_python.py data/matrix_train.txt 20170426 
data/model.p
Reproducing run command for data item data/evaluation_python.txt. 
Args: python3 code/evaluate_python_mdl.py data/model.p data/matrix_test.txt
data/evaluation_python.txt
Data item “data/evaluation_python.txt” was reproduced.

Beside code versioning, DVC also cares about data versioning. For example, if we change data sets train_post.csv and test_post.csv (use different spliting ratio) DVC will know that data sets are changed and dvc repro will re-execute all necessary jobs for evaluation_python.txt.

$ dvc run Rscript code/train_test_spliting.R data/Posts.csv 0.15 20170426 
data/train_post.csv data/test_post.csv

Re-executed jobs are marked with red color:

$ dvc run Rscript code/train_test_spliting.R data/Posts.csv 0.15 20170426 
data/train_post.csv data/test_post.csv
$ dvc repro data/evaluation_python.txt
Reproducing run command for data item data/matrix_train.txt. 
Args: Rscript — vanilla code/featurization.R data/train_post.csv 
data/test_post.csv data/matrix_train.txt data/matrix_test.txt
Reproducing run command for data item data/model.p. 
Args: python3 code/train_model_python.py data/matrix_train.txt 
20170426 data/model.p
Reproducing run command for data item data/evaluation_python.txt. 
Args: python3 code/evaluate_python_mdl.py data/model.p data/matrix_test.txt
data/evaluation_python.txt
Data item “data/evaluation_python.txt” was reproduced.
$ cat data/evaluation_python.txt
AUC: 0.793145

New AUC result is 0.793145 which shows an improvement compared to previous iteration.

Summary

In data science projects it is often used R/Python combined programming. Additional tools beside git and shell scripting are developed to facilitate the development of predictive model in a multi-language environments. Using data version control system for reproducibility and Feather for data interoperability helps you orchestrate R and Python code in a single environment.

Original. Reposted with permission.

Bio: Marija Ilic is Data scientist at Zagrebacka banka.

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