Uploaded Feature Importance Detector (FID) implementations (Python + and...

Uploaded Feature Importance Detector (FID)  implementations (Python + and Matlab) related to arXiv:2303.09453

fid.m

+function [rX, rY, f2] = fid(X, Y, cutoff, plotToggle)
+    % Feature Importance Detector, published in the paper: 
+    % "Knowledge Discovery from Atomic Structures using Feature Importances" (arXiv:2303.09453). 
+    % Description: 
+    % Performs FID feature selection. 
+    %
+    % Inputs: 
+    %           X - Index vector. 
+    %           Y - Feature score vector. Assumed to be in descending
+    %           order. 
+    %           cutoff - The hyperparameter of FID. Range [0,1]. Determines the number
+    %           of features to be removed. 
+    %           plotToggle - Optional boolean input, default is false. 
+    %
+    % Ouputs:
+    %           rX - Feature selected X
+    %           rY - Feature selected Y
+    %           f2 - Boolean vector, used to from rX and rY out of X and Y.
+    %           
+
+    if exist('plotToggle','var')
+        plotT = plotToggle;
+    else
+        plotT = false;
+    end
+    
+    fun = @(a,b,c,d,x) -a./(b*x+c)+d;
+
+    x1 = X(1); y1 = Y(1);
+    Y1 = zeros(size(Y));
+    
+    if plotT
+        figure, plot(X,Y,'LineWidth',2);
+        title('FID: Input')
+    end
+    
+    Y1(2:end) = (Y(2:end,1)-y1)./(X(2:end,1)-x1);
+    Y1(1) = Y1(2);
+    
+    if plotT
+        figure, plot(X,Y1,'LineWidth',2);
+        title('FID: Rolling slope')
+    end
+    
+    fopt = fitoptions('Method','NonLinearLeastSquares','Lower',[0 -Inf -Inf -Inf],'Upper',[Inf Inf Inf Inf],'StartPoint',[1 10 10 0]);
+    if plotT
+        disp(fopt)
+    end
+    
+    fitobj = fit(X,Y1,fun,fopt);
+    
+    Y2 = fitobj(X);
+    
+    if plotT
+        figure, plot(X,Y2,'LineWidth',2);
+        title('FID: Curve fit')
+    end
+    
+    dY2 = Y2(end)-Y2(1);
+    f2 = Y2 < Y2(end) - dY2*cutoff;
+    
+    rX = X(f2);
+    rY = Y(f2);
+    
+    if plotT
+        nf2 = ~f2;
+        figure, plot(X,Y,'LineWidth',1);
+        hold on;
+        nrX = X(nf2);
+        nrY = Y(nf2);
+        plot(nrX,nrY,'LineWidth',2);
+        title('FID: Result')
+    end
+end
\ No newline at end of file

fid.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Feature Importance Detector presented in  	
+Knowledge Discovery from Atomic Structures using Feature Importances (arXiv:2303.09453)
+
+@author: Joakim Linja
+"""
+import numpy as np
+import scipy as sp
+
+def fid(X,Y,cutoff = 0.01):
+    """
+    1. Bound rolling slope
+    2. Curve fit a/(b*x+c)+d
+    3. Remove features based on cutoff-defined percentage of total variance
+    
+    Parameters
+    ----------
+
+    X: numpy.ndarray
+        Feature score index array in ascending numerical order.
+
+    Y: numpy.ndarray
+        Feature score array. Assumed to be sorted to descending order. 
+
+    cutoff: float, optional, [0,1]
+        Cutoff value to be used in the feature selection. 
+
+        Default: 0.01
+
+
+    Returns
+    -------
+
+    rX: numpy.matrix
+        Feature selected X.
+
+    rY: numpy.matrix
+        Feature selected Y.
+
+    f2: numpy.ndarray
+        Boolean indexing array, the feature selection result.
+        
+    """
+    try:
+        def f(x,a,b,c,d):
+            return -a/(b*x+c)+d
+        
+        x0 = X[0]; y0 = Y[0]
+        Y1 = np.zeros(len(Y))
+        for i in range(1,len(X)):
+            Y1[i] = (Y[i]-y0)/(X[i]-x0)
+        Y1[0] = Y1[1]
+        
+        
+        popt, pcov = sp.optimize.curve_fit(f,X,Y1,p0=[1,10,10,0],
+            bounds=([0,-np.inf,-np.inf,-np.inf],[np.inf,np.inf,np.inf,np.inf]))
+        Y2 = f(X,*popt)
+        
+        dY2 = Y2[-1]-Y2[0]
+        f2 = Y2 < Y2[-1] - dY2*cutoff
+        
+        rX = X[f2]
+        rY = Y[f2]
+        
+        return rX, rY, f2
+    except RuntimeError as e:
+        raise e