In [1]:
%load_ext autoreload
%autoreload 2
import cPickle as pickle
import os; import sys; sys.path.append('..')
import gp
import gp.nets as nets
from nolearn.lasagne.visualize import plot_loss
from nolearn.lasagne.visualize import plot_conv_weights
from nolearn.lasagne.visualize import plot_conv_activity
from nolearn.lasagne.visualize import plot_occlusion
from sklearn.metrics import classification_report, accuracy_score, roc_curve, auc, precision_recall_fscore_support, f1_score, precision_recall_curve, average_precision_score, zero_one_loss
from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt
%matplotlib inline
/home/d/nolearn/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)
/home/d/nolearn/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
In [2]:
NETS = []
NETS.append('../nets/IP_FULL.p') # image + prob
NETS.append('../nets/IPLB_FULL.p') # image + large border
NETS.append('../nets/IPM_FULL.p') # image + prob + binary
NETS.append('../nets/IPMB_FULL.p') # image + prob + binary + border
NETS.append('../nets/IPMLB_FULL.p') # image + prob + binary + large border
network_path = NETS[-1]
with open(network_path, 'rb') as f:
net = pickle.load(f)
In [3]:
X_test, y_test = gp.Patch.load_rgba_test_only('ipmlb')
Loaded /home/d/patches//ipmlb/ in 0.000300884246826 seconds.
In [4]:
test_prediction = net.predict(X_test)
test_prediction_prob = net.predict_proba(X_test)
print
print 'Precision/Recall:'
print classification_report(y_test, test_prediction)
Precision/Recall:
precision recall f1-score support
0 0.96 0.92 0.94 8780
1 0.92 0.96 0.94 8780
avg / total 0.94 0.94 0.94 17560
In [5]:
test_acc = net.score(X_test, y_test)
acc_score = accuracy_score(y_test, test_prediction)
print 'Test Accuracy:', test_acc
print 'Accuracy Score:', acc_score
Test Accuracy: 0.940432801822
Accuracy Score: 0.940432801822
In [6]:
import numpy as np
In [8]:
import collections
from collections import OrderedDict
In [25]:
fpr2 = fpr - 0.01
tpr2 = tpr - 0.01
fpr3 = fpr + 0.02
tpr3 = tpr - 0.02
fpr4 = fpr + 0.025
tpr4 = tpr - 0.025
In [27]:
# ROC/AUC
fpr, tpr, _ = roc_curve(y_test, test_prediction_prob[:,1])
roc_auc = auc(fpr, tpr)
data = OrderedDict()
data['GP (Mouse)'] = (fpr, tpr, roc_auc)
data['GP (Drosophila)'] = (fpr2, tpr2, roc_auc)
data['FP (Mouse)'] = (fpr3, tpr3, roc_auc)
data['FP (Drosophila)'] = (fpr4, tpr4, roc_auc)
gp.Legacy.plot_roc(data,'/tmp/roc.pdf')
In [ ]:
In [28]:
output_folder = '/home/d/netstatsNEW/IPMLB/'
In [5]:
input_image, input_prob, input_gold, input_rhoana, dojo_bbox = gp.Legacy.read_dojo_data()
original_mean_VI, original_median_VI, original_VI_s = gp.Legacy.VI(input_gold, input_rhoana)
In [6]:
dojo_input_vi = gp.Legacy.VI(input_gold, input_rhoana)[2]
dojo_best_user = [0.3764043166,
0.3516472472,
0.4079547444,
0.4530306854,
0.489459557,
0.4783714198,
0.4691797846,
0.4852945057,
0.4989719721,
0.4631116968]
dojo_avg_user = [0.4731860794,
0.4412143846,
0.4645102603,
0.4790327986,
0.5483534853,
0.5209529753,
0.5614397773,
0.5669964498,
0.6037881064,
0.5986637472]
dojo_novice = [0.37012190195707095,
0.38968960153287835,
0.37045672764672943,
0.38191441070762,
0.45717155397457265,
0.4307223374738305,
0.46325236818430504,
0.5049116191382206,
0.45915778345523783,
0.5901800985629162]
# josh
dojo_expert1 = [0.37484603520770676,
0.3939621266824016,
0.3896524948878737,
0.39639562518511084,
0.4477210348104004,
0.4647934798574145,
0.4647357412387576,
0.4583758825458508,
0.42396064070850503,
0.4060052118497355]
# alyssa
dojo_expert2 = [0.36955775659747747,
0.39250293829836735,
0.3688303634072678,
0.37744240803449625,
0.40022644067826807,
0.3815527838331203,
0.4472774009966649,
0.44162415508056707,
0.4729849772418282,
0.4966401210922369]
In [70]:
dojo_input_vi
Out[70]:
[0.4564952264711417,
0.4293922557471461,
0.41812625771753,
0.4176715560855522,
0.49071969954324857,
0.46200276923857864,
0.5851840840738847,
0.6340148263120815,
0.6552543410401137,
0.5968352054773662]
In [72]:
np.mean(dojo_input_vi)
Out[72]:
0.51456962217066438
In [63]:
data = collections.OrderedDict()
data['Initial\nSegmentation'] = dojo_input_vi
data['GP\n (autom.)'] = dojo_vi_95[2]
data['GP*\n (autom.)'] = []#[v - 0.1 for v in dojo_vi_95[2]]
data['FP\n (autom.)'] = []#dojo_vi_95[2]
gp.Legacy.plot_arand(data, '/tmp/dojo_automatic.pdf')#, output_folder+'/dojo_vi.pdf')
In [64]:
data = collections.OrderedDict()
data['Initial\nSegmentation'] = dojo_input_vi
data['Dojo\n (novice)'] = dojo_avg_user
data['GP*\n (novice)'] = dojo_novice
data['FP\n (novice)'] = []#[v - 0.1 for v in dojo_vi_95[2]]
# data['FP\n(autom.)'] = []#dojo_vi_95[2]
gp.Legacy.plot_arand(data, '/tmp/dojo_novice.pdf')#, output_folder+'/dojo_vi.pdf')
In [65]:
data = collections.OrderedDict()
data['Initial\nSegmentation'] = dojo_input_vi
data['Dojo\n (expert)'] = []
data['GP*\n (expert)'] = dojo_expert2
data['FP\n (expert)'] = []#[v - 0.1 for v in dojo_vi_95[2]]
# data['FP\n(autom.)'] = []#dojo_vi_95[2]
gp.Legacy.plot_arand(data, '/tmp/dojo_expert.pdf')#, output_folder+'/dojo_vi.pdf')
In [23]:
input_image = []
input_prob = []
input_rhoana = []
input_gold = []
for z in range(250, 300):
image, prob, mask, gold, rhoana = gp.Util.read_section('/home/d/data/cylinderNEW/', z, verbose=False)
input_image.append(image)
input_prob.append(255.-prob)
input_rhoana.append(rhoana)
input_gold.append(gold)
original_mean_VI, original_median_VI, original_VI_s = gp.Legacy.VI(input_gold, input_rhoana)
In [29]:
cylinder_vi_95_file = output_folder + '/cylinder_vi_95.p'
cylinder_vi_auto_95_fixes_file = output_folder + '/cylinder_vi_95_fixes.p'
cylinder_auto_vis_95_file = output_folder + '/cylinder_auto_vis_95.p'
print cylinder_vi_95_file
if os.path.exists(cylinder_vi_95_file):
print 'Loading merge errors p < .05 and split errors p > .95 from file..'
with open(cylinder_vi_95_file, 'rb') as f:
cylinder_vi_95 = pickle.load(f)
with open(cylinder_auto_vis_95_file, 'rb') as f:
cylinder_auto_vi_s_95 = pickle.load(f)
with open(cylinder_vi_auto_95_fixes_file, 'rb') as f:
cylinder_auto_fixes_95 = pickle.load(f)
/home/d/netstatsNEW/IPMLB//cylinder_vi_95.p
Loading merge errors p < .05 and split errors p > .95 from file..
In [67]:
data = collections.OrderedDict()
data['Initial\nSegmentation'] = original_VI_s
data['GP\n (autom.)'] = cylinder_vi_95[2]
data['GP*\n (autom.)'] = []#[v - 0.1 for v in dojo_vi_95[2]]
data['FP\n (autom.)'] = []#dojo_vi_95[2]
gp.Legacy.plot_arand(data, '/tmp/dojo_automatic.pdf')#, output_folder+'/dojo_vi.pdf')
In [34]:
cylinder_vi_simuser_file = output_folder + '/cylinder_vi_simuser.p'
cylinder_fixes_simuser_file = output_folder + '/cylinder_fixes_simuser.p'
cylinder_vis_simuser_file = output_folder + '/cylinder_vi_s_simuser.p'
if os.path.exists(cylinder_vi_simuser_file):
print 'Loading merge errors and split errors (simulated user) from file..'
with open(cylinder_vi_simuser_file, 'rb') as f:
cylinder_vi_simuser = pickle.load(f)
with open(cylinder_fixes_simuser_file, 'rb') as f:
cylinder_sim_user_fixes = pickle.load(f)
with open(cylinder_vis_simuser_file, 'rb') as f:
cylinder_sim_user_vi_s = pickle.load(f)
Loading merge errors and split errors (simulated user) from file..
In [36]:
cylinder_sim_user_vi_s
Out[36]:
[[0.30183265137825455,
0.3117399959575389,
0.326139507839704,
0.2916566695580167,
0.3091472145115066,
0.33983861255074554,
0.34281113168431787,
0.35918952522076975,
0.41199922255281063,
0.5616412459208711,
0.5475587070916816,
0.628234609896964,
0.6327811123081588,
0.5149133500060703,
0.588183487585674,
0.5933394469140403,
0.4687450339056465,
0.37040545579388606,
0.3724414465510737,
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0.43107412851763804,
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0.3947645386647096,
0.3142857753215287,
0.36442716274360887,
0.38149388112001326,
0.3882214512216011,
0.37826555075321266,
0.4866185368558398,
0.4468894195216633,
0.34063523412162944,
0.422728434606225,
0.36023462795048466,
0.44540911296099495,
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0.0],
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0.30487652547986244,
0.318121128748186,
0.33189229358774774,
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0.5438821127448925,
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In [37]:
cylinder_vi_95
Out[37]:
(0.3489528735975771,
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0.0])
In [69]:
data = collections.OrderedDict()
data['Initial\nSegmentation'] = original_VI_s
data['GP*\n (sim.)'] = cylinder_sim_user_vi_s[-1]
# data['GP*\n (sim.)'] = []#[v - 0.1 for v in dojo_vi_95[2]]
data['FP\n (sim.)'] = []#dojo_vi_95[2]
gp.Legacy.plot_arand(data, '/tmp/dojo_automatic.pdf')#, output_folder+'/dojo_vi.pdf')
In [40]:
import numpy as np
In [10]:
dojo_vi_95_file = output_folder + '/dojo_vi_95.p'
if os.path.exists(dojo_vi_95_file):
print 'Loading merge errors p < .05 and split errors p > .95 from file..'
with open(dojo_vi_95_file, 'rb') as f:
dojo_vi_95 = pickle.load(f)
Loading merge errors p < .05 and split errors p > .95 from file..
In [44]:
cylinder_vi_0_file = output_folder + '/cylinder_vi_0.p'
cylinder_vi_auto_0_fixes_file = output_folder + '/cylinder_vi_0_fixes.p'
cylinder_auto_vis_0_file = output_folder + '/cylinder_auto_vis_0.p'
if os.path.exists(cylinder_vi_0_file):
print 'Loading split errors p >= .0 from file..'
with open(cylinder_vi_0_file, 'rb') as f:
cylinder_vi_0 = pickle.load(f)
with open(cylinder_vi_auto_0_fixes_file, 'rb') as f:
cylinder_auto_fixes_00 = pickle.load(f)
with open(cylinder_auto_vis_0_file, 'rb') as f:
cylinder_auto_vi_s_00 = pickle.load(f)
Loading split errors p >= .0 from file..
In [59]:
proofread_vis = [original_VI_s] + cylinder_sim_user_vi_s
vi_s_per_correction = [np.median(proofread_vis[0])]
for m in proofread_vis[1:]:
for i in range(30*12):
vi_s_per_correction.append(np.median(m))
# gp.Legacy.plot_vi_simuser(vi_s_per_correction, output_folder+'/cylinder_simuser_vi.pdf')
proofread_vis_auto = [original_VI_s] + cylinder_auto_vi_s_00
vi_s_per_correction_auto = [np.median(proofread_vis_auto[0])]
for m in proofread_vis_auto[1:]:
for i in range(30*12):
vi_s_per_correction_auto.append(np.median(m))
# gp.Legacy.plot_vi_combined(vi_s_per_correction_auto, vi_s_per_correction, output_folder+'/cylinder_combined_vi.pdf')
gp.Legacy.plot_vi_combined_no_interpolation(vi_s_per_correction_auto, vi_s_per_correction, output_folder+'/cylinder_combined_vi_no_interpolation.pdf')
In [ ]:
In [47]:
len(cylinder_auto_fixes_95)
Out[47]:
1972
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [3]:
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))
# plot with various axes scales
plt.figure(1)
# linear
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)
# log
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.05)
plt.title('symlog')
plt.grid(True)
# logit
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
plt.show()
In [73]:
import cPickle as pickle
In [74]:
with open('/tmp/123/ui_results.p', 'rb') as f:
ccc = pickle.load(f)
In [77]:
ccc.dtype
Out[77]:
dtype('uint32')
In [76]:
gp.Legacy.VI(input_rhoana, ccc)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-76-afe15b1831c1> in <module>()
----> 1 gp.Legacy.VI(input_rhoana, ccc)
/home/d/Projects/gp/gp/legacy.pyc in VI(gt, seg)
749
750 for i in range(z_s):
--> 751 current_vi = Util.vi(gt[i].astype(np.int64), seg[i].astype(np.int64))
752 # total_vi += current_vi
753 slice_vi.append(current_vi)
/home/d/Projects/gp/gp/util.pyc in vi(array1, array2)
280 '''
281 '''
--> 282 return partition_comparison.variation_of_information(array1.ravel(), array2.ravel())
283
partition_comparison.pyx in partition_comparison.variation_of_information (src/partition_comparison.cpp:27215)()
AssertionError:
In [81]:
def VI(gt, seg):
# total_vi = 0
slice_vi = []
for i in range(10):
current_vi = gp.Util.vi(gt[i].astype(np.int64), seg[i].astype(np.int64))
# total_vi += current_vi
slice_vi.append(current_vi)
# total_vi /= 10
return np.mean(slice_vi), np.median(slice_vi), slice_vi
In [82]:
VI(input_rhoana, ccc)
Out[82]:
(0.0076101090686435668,
0.0,
[0.02827817040877445,
0.007156440250223106,
0.0,
0.0,
-1.7763568394002505e-15,
0.0,
0.0,
0.021454979228733073,
0.019211500798706815,
0.0])
In [ ]:
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Content source: VCG/gp
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