In [5]:
cd ..
/afs/inf.ed.ac.uk/user/s13/s1320903/Neuroglycerin/neukrill-net-work
In [2]:
import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
import pickle
import neukrill_net.utils
import neukrill_net.encoding as enc
import neukrill_net.taxonomy as t
Using gpu device 1: Tesla K40c
:0: FutureWarning: IPython widgets are experimental and may change in the future.
Welcome to the HoloViews IPython extension! (http://ioam.github.io/holoviews/)
Available magics: %compositor, %opts, %params, %view, %%labels, %%opts, %%view
<matplotlib.figure.Figure at 0x7f3e37aeb7d0>
<matplotlib.figure.Figure at 0x7f3e37aebe90>
<matplotlib.figure.Figure at 0x7f3e37aebc90>
In [4]:
settings = neukrill_net.utils.Settings("settings.json")
run_settings = neukrill_net.utils.load_run_settings('run_settings/experiment_setting_colnorms_higher_aug.json',
settings, force=True)
model = pylearn2.utils.serial.load(run_settings['pickle abspath'])
# format the YAML
yaml_string = neukrill_net.utils.format_yaml(run_settings, settings)
# load proxied objects
proxied = pylearn2.config.yaml_parse.load(yaml_string, instantiate=False)
# pull out proxied dataset
proxdata = proxied.keywords['dataset']
# force loading of dataset and switch to test dataset
proxdata.keywords['force'] = True
proxdata.keywords['training_set_mode'] = 'test'
proxdata.keywords['verbose'] = False
# then instantiate the dataset
dataset = pylearn2.config.yaml_parse._instantiate(proxdata)
if hasattr(dataset.X, 'shape'):
N_examples = dataset.X.shape[0]
else:
N_examples = len(dataset.X)
batch_size = 500
while N_examples%batch_size != 0:
batch_size += 1
n_batches = int(N_examples/batch_size)
#n_classes = len(settings.classes)
model.set_batch_size(batch_size)
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X)
if type(X) == tuple:
f = theano.function(X,Y)
else:
f = theano.function([X],Y)
augment = 1
y = np.zeros((N_examples*augment,188))
# get the data specs from the cost function using the model
pcost = proxied.keywords['algorithm'].keywords['cost']
cost = pylearn2.config.yaml_parse._instantiate(pcost)
data_specs = cost.get_data_specs(model)
i = 0
for _ in range(augment):
# make sequential iterator
iterator = dataset.iterator(batch_size=batch_size,num_batches=n_batches,
mode='even_sequential', data_specs=data_specs)
for batch in iterator:
if type(X) == tuple:
y[i*batch_size:(i+1)*batch_size,:] = f(batch[0],batch[1])
else:
y[i*batch_size:(i+1)*batch_size,:] = f(batch[0])
i += 1
af = run_settings.get("augmentation_factor",1)
if af > 1:
y_collapsed = np.zeros((int(N_examples/af), 188))
for i,(low,high) in enumerate(zip(range(0,dataset.y.shape[0],af),
range(af,dataset.y.shape[0]+af,af))):
y_collapsed[i,:] = np.mean(y[low:high,:], axis=0)
y = y_collapsed
# and collapse labels
labels = dataset.y[range(0,dataset.y.shape[0],af)]
elif augment > 1:
y_collapsed = np.zeros((N_examples,188))
# different kind of augmentation, has to be collapsed differently
for row in range(N_examples):
y_collapsed[row,:] = np.mean(np.vstack([r for r in
y[[i for i in range(row,N_examples*augment,N_examples)],:]]),
axis=0)
y = y_collapsed
labels = dataset.y
else:
labels = dataset.y
In [5]:
y.shape
Out[5]:
(3089, 188)
In [6]:
predictions = np.zeros(y.shape)
np.copyto(predictions, y)
predictions.shape
Out[6]:
(3089, 188)
In [7]:
import neukrill_net.taxonomy as t
only_leaf_children = []
hier = enc.get_hierarchy(settings)
layer = t.TaxonomyLayer(1)
for s in hier[1]:
flag = True
for key, values in t.superclasses.items():
for i, v in enumerate(values):
if v == s and i != 1:
flag = False
if flag:
if s not in only_leaf_children:
only_leaf_children.append(s)
In [8]:
only_leaf_children
Out[8]:
['acantharia',
'appendicularians',
'calanoid',
'chaetognaths',
'cydippid',
'decapods_all',
'detritus',
'diatoms',
'euphausiids_all_ages',
'fish',
'no_class',
'oithona',
'other_hydromedusae',
'physonect',
'pluteus',
'pteropods',
'radiolarian',
'rocketship',
'seastar',
'sphaeronectes',
'sub_hydromedusae1',
'sub_hydromedusae2',
'sub_protists',
'trichodesmium',
'tunicate',
'unknown']
In [9]:
superclasses2 = np.zeros((y.shape[0], 16))
np.copyto(superclasses2, predictions[:,(121+38):(121+38+16)])
superclasses2.shape
Out[9]:
(3089, 16)
In [10]:
superclass2_children = {}
settings = neukrill_net.utils.Settings("settings.json")
hier = enc.get_hierarchy(settings)
for s in hier[2]:
children = []
for key, values in t.superclasses.items():
for i, v in enumerate(values):
if s == v:
if i > 0:
if values[i-1] not in children:
children.append(values[i-1])
superclass2_children[s] = children
In [11]:
superclass2_children
Out[11]:
{'calycophoran_siphonophores': ['rocketship',
'sphaeronectes',
'siphonophore_calycophoran_abylidae'],
'copepods': ['calanoid', 'cyclopoid_copepods'],
'crustaceans': ['shrimp_like',
'copepods',
'stomatopod',
'amphipods',
'crustacean_other'],
'ctenophores': ['ctenophore_cestid', 'ctenophore_lobate', 'cydippid'],
'cyclopoid_copepods': ['oithona', 'copepod_cyclopoid_copilia'],
'echinoderm': ['seastar',
'echinoderm_seacucumber_auricularia_larva',
'pluteus'],
'gastropods': ['pteropods', 'heteropod'],
'gelatinous zooplankton': ['ctenophores',
'pelagic_tunicates',
'hydromedusae',
'siphonophores',
'ephyra',
'jellies_tentacles'],
'hydromedusae': ['other_hydromedusae',
'sub_hydromedusae2',
'sub_hydromedusae1'],
'no_class': ['artifacts', 'artifacts_edge'],
'other_invert_larvae': ['echinoderm',
'tornaria_acorn_worm_larvae',
'trochophore_larvae',
'invertebrate_larvae_other_A',
'invertebrate_larvae_other_B'],
'pelagic_tunicates': ['appendicularians', 'tunicate'],
'plankton': ['other_invert_larvae',
'unknown',
'crustaceans',
'gastropods',
'gelatinous zooplankton',
'detritus',
'chaetognaths',
'protists',
'chordate_type1',
'trichodesmium',
'fish',
'diatoms',
'polychaete'],
'protists': ['acantharia',
'radiolarian',
'sub_protists',
'protist_noctiluca'],
'shrimp_like': ['decapods_all', 'shrimp-like_other', 'euphausiids_all_ages'],
'siphonophores': ['calycophoran_siphonophores',
'physonect',
'siphonophore_other_parts',
'siphonophore_partial']}
In [12]:
nr = np.zeros((y.shape[0], 121))
np.copyto(nr, predictions[:,:121])
weight = 0.01
nr.shape
Out[12]:
(3089, 121)
In [ ]:
layer = t.TaxonomyLayer(1)
weight = 0.01
for index, row in enumerate(nr):
for key, value in superclass2_children.items():
proportion = 0
leaf = []
for i,v in enumerate(value):
if v in only_leaf_children:
update = {}
for i, c in enumerate(settings.classes):
if v == layer[c]:
update[i] = settings.class_priors[i]
denom = sum(v for k,v in update.items())
proportion += denom
indUpdate = [k for k,v in update.items()]
row[indUpdate] = row[indUpdate] * (1-weight) + [v for k,v in update.items()/denom * weight]
else:
if v in settings.classes:
leaf.append(i)
for l in leaf:
col = int(np.where(np.array(hier[2]) == key)[0])
row[i] = (superclasses2[row, col] - proportion)
if index%1000 == 0:
print index
[ 9.98529964e-01 7.37711786e-04 7.30124076e-04 2.74924681e-15
1.12596497e-18 9.97450249e-20 2.95078822e-19 7.96512643e-18
4.07667508e-16 1.20062632e-15 2.16886575e-17 2.44781595e-15
1.95282553e-17 2.36403566e-18 1.77103644e-11 8.27115365e-17
1.51734975e-14 2.51452043e-15 7.82034011e-14 3.08219514e-15
2.42589264e-18 6.11954335e-13 2.34407491e-13 7.93886130e-17
2.06207656e-16 3.72602890e-15 2.87335599e-14 4.03988807e-18
7.24374826e-21 6.74299396e-18 2.11360335e-13 9.06943406e-18
5.24421716e-20 6.19541094e-13 7.04685659e-13 4.46554217e-11
2.50852828e-11 1.13929641e-14 5.84330895e-09 2.54163895e-11
1.86249270e-06 1.62784772e-10 1.75504075e-14 4.32162647e-15
6.12821410e-19 2.83428120e-13 4.67824535e-17 3.87268030e-17
5.28097952e-18 1.41001314e-14 8.51003745e-18 8.38094278e-19
1.24320531e-18 3.23111823e-18 1.05100928e-18 1.03741768e-19
2.77439190e-17 5.47781005e-12 3.50547499e-13 5.44162468e-15
4.90845005e-16 5.77522821e-18 1.81646798e-16 8.82921667e-17
5.93862253e-13 1.86787275e-09 1.58353667e-15 8.03158864e-16
2.64790275e-14 6.67390653e-16 1.45426426e-14 8.74568268e-17
6.44201218e-13 1.02942846e-16 1.13887061e-14 1.31545555e-14
5.43309432e-17 1.00868991e-16 3.94385113e-14 2.48614549e-11
9.56713701e-12 5.16686667e-15 1.14287857e-07 1.60793864e-13
5.01290745e-11 1.05631472e-07 9.50912474e-11 1.71689608e-13
1.96785557e-14 6.60664115e-20 9.80559817e-13 3.29670957e-09
3.47812697e-18 8.22173027e-18 3.55077634e-17 1.01852258e-16
6.65346241e-18 1.79206715e-18 1.43781485e-18 5.74610262e-21
1.90074483e-18 2.72397979e-18 2.39976082e-19 5.14993395e-19
1.17489045e-17 2.52653782e-16 1.31888458e-18 1.22164801e-14
5.91291424e-15 2.67769666e-13 2.33344055e-09 1.89987508e-14
3.50141427e-08 1.16019797e-16 1.92577842e-18 8.66662250e-21
2.59190494e-16 4.39109655e-19 5.54376206e-13 1.65371735e-16
3.49470248e-08]
[ 9.98529964e-01 7.37711786e-04 7.30124076e-04 2.74924681e-15
1.12596497e-18 9.97450249e-20 2.95078822e-19 7.96512643e-18
4.07667508e-16 1.20062632e-15 2.16886575e-17 2.44781595e-15
1.95282553e-17 2.36403566e-18 1.77103644e-11 8.27115365e-17
1.51734975e-14 2.51452043e-15 7.82034011e-14 3.08219514e-15
2.42589264e-18 6.11954335e-13 2.34407491e-13 7.93886130e-17
2.06207656e-16 3.72602890e-15 2.87335599e-14 4.03988807e-18
7.24374826e-21 6.74299396e-18 2.11360335e-13 9.06943406e-18
5.24421716e-20 6.19541094e-13 7.04685659e-13 4.46554217e-11
2.50852828e-11 1.13929641e-14 5.84330895e-09 2.54163895e-11
1.86249270e-06 1.62784772e-10 1.75504075e-14 4.32162647e-15
6.12821410e-19 2.83428120e-13 4.67824535e-17 3.87268030e-17
5.28097952e-18 1.41001314e-14 8.51003745e-18 8.38094278e-19
1.24320531e-18 3.23111823e-18 1.05100928e-18 1.03741768e-19
2.77439190e-17 5.47781005e-12 3.50547499e-13 5.44162468e-15
4.90845005e-16 5.77522821e-18 1.81646798e-16 8.82921667e-17
5.93862253e-13 1.86787275e-09 1.58353667e-15 8.03158864e-16
2.64790275e-14 6.67390653e-16 1.45426426e-14 8.74568268e-17
6.44201218e-13 1.02942846e-16 1.13887061e-14 1.31545555e-14
5.43309432e-17 1.00868991e-16 3.94385113e-14 2.48614549e-11
9.56713701e-12 5.16686667e-15 1.14287857e-07 1.60793864e-13
5.01290745e-11 1.05631472e-07 9.50912474e-11 1.71689608e-13
1.96785557e-14 6.60664115e-20 6.44943820e-03 3.55056506e-03
3.47812697e-18 8.22173027e-18 3.55077634e-17 1.01852258e-16
6.65346241e-18 1.79206715e-18 1.43781485e-18 5.74610262e-21
1.90074483e-18 2.72397979e-18 2.39976082e-19 5.14993395e-19
1.17489045e-17 2.52653782e-16 1.31888458e-18 1.22164801e-14
5.91291424e-15 2.67769666e-13 2.33344055e-09 1.89987508e-14
3.50141427e-08 1.16019797e-16 1.92577842e-18 8.66662250e-21
2.59190494e-16 4.39109655e-19 5.54376206e-13 1.65371735e-16
3.49470248e-08]
[ 9.98529964e-01 7.37711786e-04 7.30124076e-04 2.74924681e-15
1.12596497e-18 9.97450249e-20 2.95078822e-19 7.96512643e-18
4.07667508e-16 1.20062632e-15 2.16886575e-17 2.44781595e-15
1.95282553e-17 2.36403566e-18 1.77103644e-11 8.27115365e-17
1.51734975e-14 2.51452043e-15 7.82034011e-14 3.08219514e-15
2.42589264e-18 6.11954335e-13 2.34407491e-13 7.93886130e-17
2.06207656e-16 3.72602890e-15 2.87335599e-14 4.03988807e-18
7.24374826e-21 6.74299396e-18 2.11360335e-13 9.06943406e-18
5.24421716e-20 6.19541094e-13 7.04685659e-13 4.46554217e-11
2.50852828e-11 1.13929641e-14 5.84330895e-09 2.54163895e-11
1.86249270e-06 1.62784772e-10 1.75504075e-14 4.32162647e-15
6.12821410e-19 2.83428120e-13 4.67824535e-17 3.87268030e-17
5.28097952e-18 1.41001314e-14 8.51003745e-18 8.38094278e-19
1.24320531e-18 3.23111823e-18 1.05100928e-18 1.03741768e-19
2.77439190e-17 5.47781005e-12 3.50547499e-13 5.44162468e-15
4.90845005e-16 5.77522821e-18 1.81646798e-16 8.82921667e-17
5.93862253e-13 1.86787275e-09 1.58353667e-15 8.03158864e-16
2.64790275e-14 6.67390653e-16 1.45426426e-14 8.74568268e-17
6.44201218e-13 1.02942846e-16 1.13887061e-14 1.31545555e-14
5.43309432e-17 1.00868991e-16 3.94385113e-14 2.48614549e-11
9.56713701e-12 5.16686667e-15 6.12011162e-04 2.10764873e-03
5.01290745e-11 6.64033120e-03 6.40226723e-04 1.71689608e-13
1.96785557e-14 6.60664115e-20 6.44943820e-03 3.55056506e-03
3.47812697e-18 8.22173027e-18 3.55077634e-17 1.01852258e-16
6.65346241e-18 1.79206715e-18 1.43781485e-18 5.74610262e-21
1.90074483e-18 2.72397979e-18 2.39976082e-19 5.14993395e-19
1.17489045e-17 2.52653782e-16 1.31888458e-18 1.22164801e-14
5.91291424e-15 2.67769666e-13 2.33344055e-09 1.89987508e-14
3.50141427e-08 1.16019797e-16 1.92577842e-18 8.66662250e-21
2.59190494e-16 4.39109655e-19 5.54376206e-13 1.65371735e-16
3.49470248e-08]
[ 9.98529964e-01 7.37711786e-04 7.30124076e-04 2.74924681e-15
1.12596497e-18 9.97450249e-20 2.95078822e-19 7.96512643e-18
4.07667508e-16 1.20062632e-15 2.16886575e-17 2.44781595e-15
1.95282553e-17 2.36403566e-18 3.90705682e-03 9.92541595e-04
5.50774527e-04 1.02122777e-03 3.61445783e-04 1.64084911e-03
6.08146873e-04 2.81124499e-04 4.99139415e-04 7.93886130e-17
2.06207656e-16 3.72602890e-15 1.37693632e-04 4.03988807e-18
7.24374826e-21 6.74299396e-18 2.11360335e-13 9.06943406e-18
5.24421716e-20 6.19541094e-13 7.04685659e-13 4.46554217e-11
2.50852828e-11 1.13929641e-14 5.84330895e-09 2.54163895e-11
1.86249270e-06 1.62784772e-10 1.75504075e-14 4.32162647e-15
6.12821410e-19 2.83428120e-13 4.67824535e-17 3.87268030e-17
5.28097952e-18 1.41001314e-14 8.51003745e-18 8.38094278e-19
1.24320531e-18 3.23111823e-18 1.05100928e-18 1.03741768e-19
2.77439190e-17 5.47781005e-12 3.50547499e-13 5.44162468e-15
4.90845005e-16 5.77522821e-18 1.81646798e-16 8.82921667e-17
5.93862253e-13 1.86787275e-09 1.58353667e-15 8.03158864e-16
2.64790275e-14 6.67390653e-16 1.45426426e-14 8.74568268e-17
6.44201218e-13 1.02942846e-16 1.13887061e-14 1.31545555e-14
5.43309432e-17 1.00868991e-16 3.94385113e-14 2.48614549e-11
9.56713701e-12 5.16686667e-15 6.12011162e-04 2.10764873e-03
5.01290745e-11 6.64033120e-03 6.40226723e-04 1.71689608e-13
1.96785557e-14 6.60664115e-20 6.44943820e-03 3.55056506e-03
3.47812697e-18 8.22173027e-18 3.55077634e-17 1.01852258e-16
6.65346241e-18 1.79206715e-18 1.43781485e-18 5.74610262e-21
1.90074483e-18 2.72397979e-18 2.39976082e-19 5.14993395e-19
1.17489045e-17 2.52653782e-16 1.31888458e-18 1.22164801e-14
5.91291424e-15 2.67769666e-13 2.33344055e-09 1.89987508e-14
3.50141427e-08 1.16019797e-16 1.92577842e-18 8.66662250e-21
2.59190494e-16 4.39109655e-19 5.54376206e-13 1.65371735e-16
3.49470248e-08]
[ 9.98529964e-01 7.37711786e-04 7.30124076e-04 2.74924681e-15
1.12596497e-18 9.97450249e-20 2.95078822e-19 7.96512643e-18
4.07667508e-16 1.20062632e-15 2.16886575e-17 2.44781595e-15
1.95282553e-17 2.36403566e-18 3.90705682e-03 9.92541595e-04
5.50774527e-04 1.02122777e-03 3.61445783e-04 1.64084911e-03
6.08146873e-04 2.81124499e-04 4.99139415e-04 7.93886130e-17
2.06207656e-16 3.72602890e-15 1.37693632e-04 4.03988807e-18
7.24374826e-21 6.74299396e-18 2.11360335e-13 9.06943406e-18
5.24421716e-20 6.19541094e-13 7.04685659e-13 4.46554217e-11
2.50852828e-11 1.13929641e-14 5.84330895e-09 2.54163895e-11
1.86249270e-06 1.62784772e-10 1.75504075e-14 4.32162647e-15
6.12821410e-19 2.83428120e-13 4.67824535e-17 3.87268030e-17
5.28097952e-18 1.41001314e-14 8.51003745e-18 8.38094278e-19
1.24320531e-18 3.23111823e-18 1.05100928e-18 1.03741768e-19
2.77439190e-17 5.47781005e-12 5.36480687e-04 2.50357654e-04
4.90845005e-16 5.77522821e-18 1.81646798e-16 8.82921667e-17
5.93862253e-13 1.86787275e-09 8.58369099e-05 1.35908441e-03
2.94706724e-03 1.95994278e-03 1.07296137e-03 5.43633763e-04
6.44201218e-13 1.02942846e-16 3.07582260e-04 4.00572246e-04
1.00143062e-04 4.36337625e-04 3.94385113e-14 2.48614549e-11
9.56713701e-12 5.16686667e-15 6.12011162e-04 2.10764873e-03
5.01290745e-11 6.64033120e-03 6.40226723e-04 1.71689608e-13
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2.94706724e-03 1.95994278e-03 1.07296137e-03 5.43633763e-04
5.33788914e-03 9.33940774e-04 3.07582260e-04 4.00572246e-04
1.00143062e-04 4.36337625e-04 3.56732267e-16 2.14133329e-11
1.40404645e-16 5.58715570e-22 6.11898635e-04 2.10764873e-03
6.72311802e-14 6.64022824e-03 6.40226629e-04 5.80645161e-03
6.98924731e-04 3.49462366e-03 6.44943820e-03 3.55056180e-03
1.13689095e-03 1.40596622e-22 3.54988399e-03 4.03712297e-03
1.84453877e-20 2.18446602e-03 7.81553398e-03 3.70600414e-03
1.18012422e-03 5.11387164e-03 5.23337191e-22 9.16950716e-25
8.59060403e-03 1.40939597e-03 5.21853498e-24 9.87490881e-22
2.07078093e-03 1.57940918e-04 5.78824218e-03 1.98303598e-03
2.84221453e-12 2.89386948e-03 2.74884641e-03 2.32036915e-03
1.55570204e-03 4.81212920e-04 3.45692475e-03 1.90839695e-03
4.63468421e-03]
Content source: Neuroglycerin/neukrill-net-work
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