sklearn-clj

This library gives easy access to all estimators from sklearn in a Clojure friendly way, using internally libpython-clj.

It uses a tech.ml.dataset for input and output and converts to python data structures as needed.

As all estimators in sklearn uses the same interface, it should work for all estimators.

Usage

Setup libpython-clj including sklearn.

If you need to call py/initialize! as part of your libpython-clj setup, this needs to happen before require the other namesspaces. This can be done easely by adding a user.clj file which contains the call to py/initialize!

 
  (require
   '[libpython-clj.python :refer [py.-]]
   '[tech.v3.dataset :as ds]
   '[tech.v3.dataset.modelling :as ds-mod]
   '[scicloj.sklearn-clj :refer :all]
   )

;; example train and test dataset 

  (def train-ds
    (-> (ds/->dataset {:x1 [1 1 2 2]
                  :x2 [1 2 2 3]
                  :y  [6 8 9 11]})
     (ds-mod/set-inference-target :y)))

  (def test-ds
    (->
     (ds/->dataset {:x1 [3]
                  :x2 [5]
                  :y  [0]})
     (ds-mod/set-inference-target :y)))

;; fit a liner expression model from sklearn, class sklearn.linear_model.LinearRegression
 
  (def lin-reg
    (fit train-ds :linear-model :linear-regression {}))

;; Call predict with new data on the estimator
  (predict test-ds lin-reg {})
  ;; => _unnamed [1 3]:
  ;;    | :x1 | :x2 |   :y |
  ;;    |-----|-----|------|
  ;;    |   3 |   5 | 16.0 |

;; use an other estimator, this time: sklearn.preprocessing.StandardScaler
  (def data
    (ds/->dataset {:x1 [0 0 1 1]
                 :x2 [0 0 1 1]}))
                 
;; fit the scaler on data                 
  (def scaler
    (fit data :preprocessing :standard-scaler {}))

  (py.- scaler mean_)
  ;; => [0.5 0.5]
  ;;
  
;; apply the scaling on new data  
  (transform (ds/->dataset {:x1 [2] :x2 [2]})  scaler {})
  ;; => :_unnamed [1 2]:
  ;;    | :x1 | :x2 |
  ;;    |-----|-----|
  ;;    | 3.0 | 3.0 |

The library provides as well an adaptor to the scicloj metamorph library, in order to use the estimators inside a metamorph pipeline.

(:require [scicloj.sklearn-clj.metamorph :as sklearn-mm])

;; adding this as a pipeline operation somewhere in the pipeline
;; The estimate functon will do the right thing, depending on the :metamorph/mode   key being
;; :fit or :transform  , namely calling "fit" on :fit  and "predict" on :transform
(sklearn-mm/estimate :linear-model :linear-regression {})

License

Copyright © 2021 Carsten Behring

Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

sklearn-clj