def plotFIAvg(data=dataAvg): plotFeatureImportance( data, "RandConv with original image (feature 0) and custom filters\n3x3 average pooling", None, lim=0.085, colorate=[("3x3 average", "b")], )
def plotFImax357(data=dataMax357): plotFeatureImportance( data, "RandConv with original image (feature 0) and custom filters\n3x3, 5x5, 7x7 maximum pooling", None, lim=0.025, colorate=[("3x3 maximum", "g"), ("5x5 maximum", "y"), ("7x7 maximum", "#F90FC6")], )
def plotFImMa(data=datanmaM): plotFeatureImportance( data, "RandConv with original image (feature 0) and custom filters\n3x3 minimum, average and maximum pooling", None, lim=0.025, colorate=[("no pooling", "m"), ("3x3 average", "b"), ("3x3 minimum", "r"), ("3x3 maximum", "g")], )
def plotFIavg357(data=dataAvg357): plotFeatureImportance( data, "RandConv with original image (feature 0) and custom filters\n3x3, 5x5, 7x7 average pooling", None, lim=0.025, colorate=[("3x3 average", "b"), ("5x5 average", "c"), ("7x7 average", "#F69E1B")], )
def plotFIMax(data=dataMax): plotFeatureImportance( data, "RandConv with original image (feature 0) and custom filters\n3x3 maximum pooling", None, lim=0.085, colorate=[("3x3 maximum", "g")], )
def plotFINo(data=dataNo): plotFeatureImportance( data, "RandConv with original image (feature 0) and custom filters\nNo pooling", None, lim=0.085, colorate=[("no pooling", "m")], )
def plotFI(data, nbTree): plotFeatureImportance(data, "RandConv with original image (feature 0) \n"+str(nbTree)+" trees", None)
def plotFI(data, tNum): plotFeatureImportance(data, "RandConv with original image (feature 0) \nTest number "+str(tNum), None)
def plotFIWholeImage(data): plotFeatureImportance(data, "RandConv with original image (feature 0) \nWhole image", None)
def plotFI(data, nbSW): plotFeatureImportance(data, "RandConv with original image (feature 0) \n"+str(nbSW)+" subwindow(s)", None)
def plotFI(data, k): plotFeatureImportance(data, "RandConv with original image (feature 0) \n"+str(k)+" inspected features for each node split", None)
def plotFIHist(data, nbTest): indices = np.argsort(data)[::-1] data = np.array(data)[indices] plotFeatureImportance(data, "RandConv with original image (feature 0) : feature importance histogram\nGlobal varibility test "+str(nbTest), None)
def plotFI(data, nbTest): plotFeatureImportance(data, "RandConv with original image (feature 0) : feature importances \nGlobal varibility test "+str(nbTest), None)
def plotFI(data, k): plotFeatureImportance(data, "RandConv with original image (feature 0) \nMinimum number of sample to split : "+str(k), None)