Reskit

Reskit (researcher’s kit) is a library for creating and curating reproducible pipelines for scientific and industrial machine learning. The natural extension of the scikit-learn Pipelines to general classes of pipelines, Reskit allows for the efficient and transparent optimization of each pipeline step. Main features include data caching, compatibility with most of the scikit-learn objects, optimization constraints (e.g. forbidden combinations), and table generation for quality metrics. Reskit also allows for the injection of custom metrics into the underlying scikit frameworks. Reskit is intended for use by researchers who need pipelines amenable to versioning and reproducibility, yet who also have a large volume of experiments to run.

Features

• Ability to combine pipelines with an equal number of steps in list of experiments, running them and returning results in a convenient format for human consumption (Pandas dataframe).

• Step caching. Standard SciKit-learn pipelines cannot cache temporary steps. Reskit includes the option to save fixed steps, so in next pipeline specified steps won’t be recalculated.

• Forbidden combination constraints. Not all possible combinations of pipelines are viable or meaningfully different. For example, in a classification task comparing the performance of logistic regression and decision trees the former requires feature scaling while the latter may not. In this case you can block the unnecessary pair. Reskit supports general tuple blocking as well.

• Full compatibility with scikit-learn objects. Reskit can use any scikit-learn data transforming object and/or predictive model, and assumably many other libraries that uses the scikit template.

• Evaluation of multiple performance metrics simultaneously. Evaluation is simply another step in the pipeline, so we can specify a number of possible evaluation metrics and Reskit will expand out the computations for each metric for each pipeline.

• The DataTransformer class, which is Reskit’s simplfied interface for specifying fit/transform methods in pipeline steps. A DataTransformer subclass need only specify one function.

• Tools for learning on graphs. Due to our original motivations Reskit includes a number of operations for network data. In particular, it allows a variety of normalization choices foradjacency matrices , as well as built in local graph metric calculations. These were implemented using DataTransformer and in some cases the BCTpy (the Brain Connectivity Toolbox python version)

Documentation

The documentation includes detailed tutorial, but for a quick view, we provide for you an example.

Example

Let's say we want to prepare data and try some scalers and classifiers for prediction in a classification problem. We will tune paramaters of classifiers by grid search technique.

Data preparing:

from sklearn.datasets import make_classification


X, y = make_classification()

Setting steps for our pipelines and parameters for grid search:

from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

scalers = [
    ('minmax', MinMaxScaler()),
    ('standard', StandardScaler())
]

classifiers = [
    ('LR', LogisticRegression()),
    ('SVC', SVC())
]

steps = [
    ('Scaler', scalers),
    ('Classifier', classifiers)
]

param_grid = {
    'LR' : {
        'penalty' : ['l1', 'l2']},
    'SVC' : {
        'kernel' : ['linear', 'poly', 'rbf', 'sigmoid']}}

Setting a cross-validation for grid searching of hyperparameters and for evaluation of models with obtained hyperparameters.

from sklearn.model_selection import StratifiedKFold


grid_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
eval_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)

Creating a plan of our research:

pipeliner = Pipeliner(steps=steps, grid_cv=grid_cv, eval_cv=eval_cv, param_grid=param_grid)
pipeliner.plan_table

To tune parameters of models and evaluate this models, run:

pipeliner.get_results(X, y, scoring='roc_auc')

Line: 1/4
Line: 2/4
Line: 3/4
Line: 4/4

Installation

Reskit currently requires Python 3.4 or later to run. Please install Python and pip via the package manager of your operating system if it is not included already.

Reskit depends on:

• numpy
• scikit-learn
• pandas
• scipy
• python-igraph
• networkx

If you don't want to use graph metrics, you should to comment scipy, python-igraph and networkx in requirements.txt.

To install dependencies run next command:

pip install -r https://raw.githubusercontent.com/neuro-ml/reskit/master/requirements.txt

Last stable version is 0.1.0. You can install it via:

pip3 install -U https://github.com/neuro-ml/reskit/archive/0.1.0.zip

To install the latest development version of Reskit, run the following commands:

pip install -U https://github.com/neuro-ml/reskit/archive/master.zip

Docker

If you just want to try Reskit or don’t want to install Python, you can build docker image and make all reskit’s stuff there. Also, in this case, you can provide the simple way to reproduce your experiment. To run Reskit in docker you can use next commands.

1. Clone repository:

   git clone https://github.com/neuro-ml/reskit.git
   cd reskit
   

2. Build docker image:

   docker build -t docker-reskit -f Dockerfile .
   

3. Run docker image.

• If you want to run bash in container:

  docker run -it docker-reskit bash
  

• If you want to run bash in container with shared directory:

  docker run -v $PWD/scripts:/reskit/scripts -it docker-reskit bash
  

• If you want to start Jupyter Notebook server at http://localhost:8809 in container:

  docker run -v $PWD/scripts:/reskit/scripts -it -p 8809:8809 docker-reskit jupyter notebook --no-browser --ip="*" --allow-root --port 8809
  

  You will see message:

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
      http://localhost:8809/?token=some_token