In [1]:
%load_ext autoreload
%autoreload 2
from matplotlib.pyplot import imshow
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sys
sys.path.append('../')
import gp
/home/d/miniconda2/envs/NP/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/miniconda2/envs/NP/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 [5]:
e_p = []
p = []
In [3]:
import cPickle as pickle
with open('../nets/IPMLB_FULL.p', 'rb') as f:
cnn = pickle.load(f)
cnn.uuid = 'IPMLB'
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-3-af2ea7676244> in <module>()
1 import cPickle as pickle
2 with open('../nets/IPMLB_FULL.p', 'rb') as f:
----> 3 cnn = pickle.load(f)
4 cnn.uuid = 'IPMLB'
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/compile/function_module.pyc in _constructor_Function(maker, input_storage, inputs_data)
1021 if not theano.config.unpickle_function:
1022 return None
-> 1023 f = maker.create(input_storage, trustme=True)
1024 assert len(f.input_storage) == len(inputs_data)
1025 for container, x in zip(f.input_storage, inputs_data):
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/compile/function_module.pyc in create(self, input_storage, trustme, storage_map)
1639 theano.config.traceback.limit = 0
1640 _fn, _i, _o = self.linker.make_thunk(
-> 1641 input_storage=input_storage_lists, storage_map=storage_map)
1642 finally:
1643 theano.config.traceback.limit = limit_orig
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/link.pyc in make_thunk(self, input_storage, output_storage, storage_map)
688 return self.make_all(input_storage=input_storage,
689 output_storage=output_storage,
--> 690 storage_map=storage_map)[:3]
691
692 def make_all(self, input_storage, output_storage):
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/vm.pyc in make_all(self, profiler, input_storage, output_storage, storage_map)
1001 storage_map,
1002 compute_map,
-> 1003 no_recycling))
1004 if not hasattr(thunks[-1], 'lazy'):
1005 # We don't want all ops maker to think about lazy Ops.
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/sandbox/cuda/__init__.pyc in make_thunk(self, node, storage_map, compute_map, no_recycling)
254 enable_cuda=False)
255 return super(GpuOp, self).make_thunk(node, storage_map,
--> 256 compute_map, no_recycling)
257
258 theano.compile.debugmode.default_make_thunk.append(
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/op.pyc in make_thunk(self, node, storage_map, compute_map, no_recycling)
968 try:
969 return self.make_c_thunk(node, storage_map, compute_map,
--> 970 no_recycling)
971 except (NotImplementedError, utils.MethodNotDefined):
972 logger.debug('Falling back on perform')
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/op.pyc in make_c_thunk(self, node, storage_map, compute_map, no_recycling)
877 logger.debug('Trying CLinker.make_thunk')
878 outputs = cl.make_thunk(input_storage=node_input_storage,
--> 879 output_storage=node_output_storage)
880 fill_storage, node_input_filters, node_output_filters = outputs
881
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/cc.pyc in make_thunk(self, input_storage, output_storage, storage_map, keep_lock)
1198 cthunk, in_storage, out_storage, error_storage = self.__compile__(
1199 input_storage, output_storage, storage_map,
-> 1200 keep_lock=keep_lock)
1201
1202 res = _CThunk(cthunk, init_tasks, tasks, error_storage)
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/cc.pyc in __compile__(self, input_storage, output_storage, storage_map, keep_lock)
1141 output_storage,
1142 storage_map,
-> 1143 keep_lock=keep_lock)
1144 return (thunk,
1145 [link.Container(input, storage) for input, storage in
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/cc.pyc in cthunk_factory(self, error_storage, in_storage, out_storage, storage_map, keep_lock)
1593 else:
1594 module = get_module_cache().module_from_key(
-> 1595 key=key, lnk=self, keep_lock=keep_lock)
1596
1597 vars = self.inputs + self.outputs + self.orphans
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/cmodule.pyc in module_from_key(self, key, lnk, keep_lock)
1140 try:
1141 location = dlimport_workdir(self.dirname)
-> 1142 module = lnk.compile_cmodule(location)
1143 name = module.__file__
1144 assert name.startswith(location)
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/gof/cc.pyc in compile_cmodule(self, location)
1504 lib_dirs=self.lib_dirs(),
1505 libs=libs,
-> 1506 preargs=preargs)
1507 except Exception as e:
1508 e.args += (str(self.fgraph),)
/home/d/miniconda2/envs/NP/lib/python2.7/site-packages/theano/sandbox/cuda/nvcc_compiler.pyc in compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, rpaths, py_module, hide_symbols)
360 p = subprocess.Popen(
361 cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
--> 362 nvcc_stdout, nvcc_stderr = decode_iter(p.communicate()[:2])
363 finally:
364 os.chdir(orig_dir)
/home/d/miniconda2/envs/NP/lib/python2.7/subprocess.pyc in communicate(self, input)
798 return (stdout, stderr)
799
--> 800 return self._communicate(input)
801
802
/home/d/miniconda2/envs/NP/lib/python2.7/subprocess.pyc in _communicate(self, input)
1415
1416 if _has_poll:
-> 1417 stdout, stderr = self._communicate_with_poll(input)
1418 else:
1419 stdout, stderr = self._communicate_with_select(input)
/home/d/miniconda2/envs/NP/lib/python2.7/subprocess.pyc in _communicate_with_poll(self, input)
1469 while fd2file:
1470 try:
-> 1471 ready = poller.poll()
1472 except select.error, e:
1473 if e.args[0] == errno.EINTR:
KeyboardInterrupt: (KeyboardInterrupt(), <function _constructor_Function at 0x7f59b8e81848>, (<theano.compile.function_module.FunctionMaker object at 0x7f596e55f050>, [<None>, <None>, <CudaNdarray([[[[ 0.83101124 -0.45216268 -0.92241758]
[ 0.44926044 -0.79137021 -0.85471934]
[ 0.62316829 -0.22166604 -0.5351423 ]]
[[ 0.74415201 0.76112461 0.14936428]
[ 1.26601923 1.12129915 0.54457217]
[ 0.73289531 0.6029588 0.08996633]]
[[-0.20223477 -0.05420067 -0.5176177 ]
[-0.16366521 0.24429148 0.12103456]
[ 0.43135822 0.11404235 0.20076309]]
[[ 0.01085452 -0.14073524 0.35528407]
[ 0.1014398 -0.21538898 -0.13963255]
[-0.17260572 0.19859107 0.22175764]]]
[[[-0.42160168 -0.33796558 -0.10554801]
[-0.55112571 -0.89287311 -0.78223509]
[-0.5099296 -0.8778429 -1.06378591]]
[[-0.62700075 -0.55552375 -0.26756749]
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[ 0.03338762 0.01803556 0.15733203]]
[[-0.28123182 0.08279371 0.06133154]
[-0.26863402 -0.42603293 0.01725472]
[ 0.3666476 -0.98386133 -0.54569435]]
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[ 0.42529172 0.04707859 -0.48821592]
[ 0.33630458 0.67504847 0.016005 ]]]
[[[ 1.25536573 1.61147332 1.61617768]
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[-1.48116505 -1.34126687 -0.99922913]]
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...,
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[ 1.38801038 0.84957308 -0.27346233]]
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[ -1.17312081e-01 2.40765121e-02 2.72505522e-01]]]])>, <CudaNdarray([ 0.49784726 -2.71426821 -1.11221349 -1.1041913 -1.03609371 -3.04800701
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-0.02296904 -0.27035806 -0.42095849 -0.3561379 -0.19328526 -0.0071193
-0.53268999 -0.38141188]), CudaNdarray([[ 0.02255613 -0.02403588]
[-0.10956398 -0.02643624]
[ 0.01040248 0.05602583]
...,
[-0.09703961 0.09959773]
[ 0.0451781 -0.08670858]
[ 0.01042011 -0.07449926]]), CudaNdarray([-0.43991706 0.4402543 ])]))
In [34]:
# for z in range(0,1):
e_p = []
p = []
image, prob, gold, rhoana, bb = gp.Legacy.read_dojo_data()
for z in range(0,10):
error_patches, patches = gp.Patch.patchify_maxoverlap(image[z], prob[z], np.zeros((image[0].shape[0], image[0].shape[1])), rhoana[z], gold[z], sample_rate=1, clamp_result=False)
e_p.append(error_patches)
p.append(patches)
a
In [40]:
# cconvert to FP format with target
import neuroproof
import neuroproof.FocusedProofreading as fp
In [52]:
g_n = graph.find_close_bodies(5,0,0.)
zn = []
for n in g_n:
zn.append(n[1])
In [53]:
np.max(zn)
Out[53]:
0.71764704585075378
In [87]:
graphs = []
for z in range(10):
g = fp.Graph('/home/d/FP/dojoNEW/graph_'+str(z)+'.json')
graphs.append(g)
In [88]:
fp_y_test_proba = []
fp_y_test = []
gt_y_test = []
for z in range(10):
cur_e_p = e_p[z]
cur_p = p[z]
g = graphs[z]
for pa in cur_e_p:
# print pa['l'], pa['n']
label1 = pa['l']
label2 = pa['n']
graph_neighbors = sorted(g.find_close_bodies(label1,0,0.))
pred = [ne[1] for ne in graph_neighbors if ne[0] == label2]
fp_y_test.append(np.round(pred))
fp_y_test_proba.append(pred)
gt_y_test.append(1)
for pa in cur_p:
# print pa['l'], pa['n']
label1 = pa['l']
label2 = pa['n']
graph_neighbors = sorted(g.find_close_bodies(label1,0,0.))
pred = [ne[1] for ne in graph_neighbors if ne[0] == label2]
fp_y_test.append(np.round(pred))
fp_y_test_proba.append(pred)
gt_y_test.append(0)
In [30]:
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
In [90]:
print 'Precision/Recall:'
print classification_report(np.array(gt_y_test), np.array(fp_y_test))
Precision/Recall:
precision recall f1-score support
0 0.94 0.69 0.80 3488
1 0.14 0.51 0.21 332
avg / total 0.87 0.68 0.75 3820
In [91]:
acc_score = accuracy_score(np.array(gt_y_test), np.array(fp_y_test))
print 'Accuracy Score:', acc_score
Accuracy Score: 0.677486910995
In [ ]:
In [ ]:
In [31]:
Out[31]:
3820
In [92]:
gp_y_test_proba = []
gp_y_test = []
gt_y_test = []
for z in range(10):
cur_e_p = e_p[z]
cur_p = p[z]
for pa in cur_e_p:
# print pa['l'], pa['n']
label1 = pa['l']
label2 = pa['n']
pred = gp.Patch.test_and_unify([pa], cnn)
gp_y_test.append(np.round(pred))
gp_y_test_proba.append(pred)
gt_y_test.append(1)
for pa in cur_p:
# print pa['l'], pa['n']
label1 = pa['l']
label2 = pa['n']
pred = gp.Patch.test_and_unify([pa], cnn)
gp_y_test.append(np.round(pred))
gp_y_test_proba.append(pred)
gt_y_test.append(0)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-92-1fa747454865> in <module>()
13 label2 = pa['n']
14
---> 15 pred = gp.Patch.test_and_unify([pa], cnn)
16
17 gp_y_test.append(np.round(pred))
NameError: name 'cnn' is not defined
In [36]:
print classification_report(np.array(gt_y_test), np.array(gp_y_test))
precision recall f1-score support
0 1.00 0.92 0.96 3488
1 0.54 0.95 0.69 332
avg / total 0.96 0.92 0.93 3820
In [38]:
acc_score = accuracy_score(np.array(gt_y_test), np.array(gp_y_test))
print 'Accuracy Score:', acc_score
Accuracy Score: 0.924607329843
In [ ]:
In [ ]:
Content source: VCG/gp
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