def compare(k):
N_INDCS = -1
map_test, map_train = map_overlay.basic_setup([100, 400], 50, label_name = "Jared")
clf = ObjectClassifier()
damage_labels = np.loadtxt('damagelabels50/Jared-3-3.csv', delimiter = ',').astype('int')
building_labels = np.loadtxt('damagelabels50/all_rooftops-3-3.csv', delimiter =',').astype('int')
segs = map_train.segmentations[50].ravel()
n_segs = int(np.max(segs))+1
all_labels = np.zeros(n_segs) #Nothing = Green
all_labels[damage_labels] = 1 #Damage = Red
all_labels[building_labels] = 2 #Building = Blue
all_data, y = clf._get_X_y(map_train, 'Jared')
clf.fit(map_train, label_name = 'Jared') #<-this one changesthe training for the importance order
names = clf.feat_names
print names
importances = clf.model.feature_importances_
indices = np.argsort(importances)[::-1]
print names[indices][:N_INDCS]
plt.figure()
std = np.std([tree.feature_importances_ for tree in clf.model.estimators_],
axis=0)
plt.title("Feature importances")
plt.bar(range(names.shape[0]), importances[indices],
color="r", yerr=std[indices], align="center")
plt.xticks(range(names.shape[0]), names[indices])
plt.xlim([-1, names.shape[0]])
all_histos(all_data[damage_labels], all_data[building_labels], all_data[np.where(all_labels == 0)[0]], names, indices[:N_INDCS])
#plt.show()
return names, indices
'''
def look_at_features(names, indices):
N_INDCS = -1
map_test, map_train = map_overlay.basic_setup([100, 400], 50, label_name = "Jared")
clf = ObjectClassifier()
damage_labels = np.loadtxt('damagelabels50/Jared-3-3.csv', delimiter = ',').astype('int')
building_labels = np.loadtxt('damagelabels50/all_rooftops-3-3.csv', delimiter =',').astype('int')
segs = map_train.segmentations[50]
n_segs = int(np.max(segs.ravel()))+1
all_labels = np.zeros(n_segs) #Nothing = Green
all_labels[damage_labels] = 1 #Damage = Red
all_labels[building_labels] = 2 #Building = Blue
all_data, y = clf._get_X_y(map_train, 'Jared')
'''
clf.fit(map_train, label_name = 'Jared') #<-this one changesthe training for the importance order
names = clf.feat_names
print names
importances = clf.model.feature_importances_
indices = np.argsort(importances)[::-1]'''
pbar = custom_progress()
j = names.shape[0]
for i in pbar(indices[::-1]):
j-=1
fig = plt.figure(names[i])
plt.imshow(all_data[:,i][segs.astype('int')], cmap = 'gray')
fig.savefig('/Users/jsfrank/Desktop/features/{}-{}'.format(j, names[i]), format='png')
plt.close(fig)
def transition():
N_INDCS = -1
map_test, map_train = map_overlay.basic_setup([100], 50, label_name = "Jared")
clf = ObjectClassifier()
damage_labels = np.loadtxt('damagelabels50/Jared-3-3.csv', delimiter = ',').astype('int')
building_labels = np.loadtxt('damagelabels50/all_buildings-3-3.csv', delimiter =',').astype('int')
segs = map_train.segmentations[50].ravel()
n_segs = int(np.max(segs))+1
all_labels = np.zeros(n_segs) #Nothing = Green
all_labels[damage_labels] = 1 #Damage = Red
all_labels[building_labels] = 2 #Building = Blue
all_data, y = clf._get_X_y(map_train, 'Jared')
clf.fit(map_train, label_name = 'Jared')
names = clf.feat_names
for i in range(50):
new_damage = np.concatenate((all_data[damage_labels], all_data[building_labels[:i*10]]), axis = 0)
print new_damage.shape
all_histos(new_damage, all_data[building_labels], all_data[np.where(all_labels == 0)[0]], names, [17], prefix = "{} ".format(i*10))
plt.show()
