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)
