mgl-logistic-regression.lisp

;;; -*- coding:utf-8; mode:lisp; -*-

(ql:quickload :mgl-user)
(in-package :mgl-user)

;;; データの読み込み
(ql:quickload :fare-csv)
(ql:quickload :parse-number)
(defparameter data-list
  (mapcar (lambda (line)
            (mapcar #'parse-number:parse-number line))
          (fare-csv:read-csv-file "/home/wiz/tmp/mgl-logistic-data.txt")))

(defparameter dataset
  (let ((dataset (make-array (length data-list))))
    (loop for i from 0 to (1- (length data-list))
          for line in data-list
          do
       (setf (aref dataset i)
             (make-datum
                  :id i :label (nth 2 line)
                  :array (make-mat 2 :initial-contents (subseq line 0 2)))))
    dataset))

;;; データの正規化
(defparameter dataset-normal (copy-dataset dataset))
(dataset-normalize! dataset-normal)

;;; データの可視化
(ql:quickload :clgplot)
(defun plot-dataset (dataset)
  (let ((positive-data (remove-if-not (lambda (datum) (= (datum-label datum) 1)) dataset))
        (negative-data (remove-if-not (lambda (datum) (= (datum-label datum) 0)) dataset)))

    (clgp:plot-lists
     (list (loop for datum across positive-data collect (mref (datum-array datum) 1))
           (loop for datum across negative-data collect (mref (datum-array datum) 1)))
     :x-lists (list (loop for datum across positive-data collect (mref (datum-array datum) 0))
                    (loop for datum across negative-data collect (mref (datum-array datum) 0)))
     :style 'points)))
  
;;; モデル定義
(defparameter fnn-sigmoid
  (build-fnn (:class 'fnn :max-n-stripes 100)
    (inputs (->input :size 2))
    (f1 (->sigmoid inputs))
    (prediction (->softmax-xe-loss (->activation f1 :name 'prediction :size 2) :name 'prediction))))

;;; 学習実行
(train-fnn-process-with-monitor fnn-sigmoid dataset-normal dataset-normal :n-epochs 100)

;; 2016-10-31 18:35:40: ---------------------------------------------------
;; 2016-10-31 18:35:40: training at n-instances: 10000
;; 2016-10-31 18:35:40: test at n-instances: 10000
;; 2016-10-31 18:35:40: pred. train bpn PREDICTION acc.: 91.00% (10000)
;; 2016-10-31 18:35:40: pred. train bpn PREDICTION xent: 3.456e-3 (10000)
;; 2016-10-31 18:35:40: pred. test  bpn PREDICTION acc.: 91.00% (100)
;; 2016-10-31 18:35:40: pred. test  bpn PREDICTION xent: 3.456e-3 (100)
;; 2016-10-31 18:35:40: Foreign memory usage:
;; foreign arrays: 0 (used bytes: 0)
;; CUDA memory usage:
;; device arrays: 114 (used bytes: 400,112, pooled bytes: 0)
;; host arrays: 0 (used bytes: 0)
;; host->device copies: 202, device->host copies: 20,602
;; 2016-10-31 18:35:40: ---------------------------------------------------

;;; 重みを見てみる
(let* ((f1-activation (aref (clumps fnn-sigmoid) 2))
       (bias (aref (clumps f1-activation) 0))
       (weight (aref (clumps f1-activation) 2)))
  (describe bias)
  (describe weight)
  (list bias weight))

;; #<->WEIGHT (:BIAS PREDICTION) :SIZE 2 1/1 :NORM 2.76652>
;;   [standard-object]

;; Slots with :INSTANCE allocation:
;;   NAME               = (:BIAS PREDICTION)
;;   SIZE               = 2
;;   NODES              = #<MAT 1x2 AF #2A((1.956225 -1.9562262))>
;;   DERIVATIVES        = #<MAT 1x2 A #2A((0.0 0.0))>
;;   DEFAULT-VALUE      = 0
;;   SHARED-WITH-CLUMP  = NIL
;;   DIMENSIONS         = (1 2)
;; #<->WEIGHT (F1 PREDICTION) :SIZE 4 1/1 :NORM 4.55551>
;;   [standard-object]

;; Slots with :INSTANCE allocation:
;;   NAME               = (F1 PREDICTION)
;;   SIZE               = 4
;;   NODES              = #<MAT 2x2 AF #2A((-2.4052894 2.4102905) (-2.1375985 2.1420684))>
;;   DERIVATIVES        = #<MAT 2x2 A #2A((0.0 0.0) (0.0 0.0))>
;;   DEFAULT-VALUE      = 0
;;   SHARED-WITH-CLUMP  = NIL
;;   DIMENSIONS         = (2 2)

;; (#<->WEIGHT (:BIAS PREDICTION) :SIZE 2 1/1 :NORM 2.76652>
;;  #<->WEIGHT (F1 PREDICTION) :SIZE 4 1/1 :NORM 4.55551>)

(defun plot-prediction (dataset fnn class)
  (let* ((min-x1 (loop for x across dataset minimize (mref (datum-array x) 0)))
         (max-x1 (loop for x across dataset maximize (mref (datum-array x) 0)))
         (min-x2 (loop for x across dataset minimize (mref (datum-array x) 1)))
         (max-x2 (loop for x across dataset maximize (mref (datum-array x) 1)))
         (x1-list (loop for x from min-x1 to max-x1 by 0.1 collect x))
         (x2-list (loop for x from min-x2 to max-x2 by 0.1 collect x)))
    (clgp:splot-list
     (lambda (x1 x2)
       (mref (predict-datum fnn
                            (make-datum :id 0 :label 0d0
                                        :array (make-mat 2 :initial-contents (list x1 x2))))
             class))
     x1-list x2-list :map t)))