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Edge Featured Potts Model #99

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d83eb29
simple potts model
fgregg Oct 16, 2013
5f0a8e9
potts model
fgregg Oct 16, 2013
89227cf
remove commented out code
fgregg Oct 16, 2013
16cc62b
whitespace
fgregg Oct 16, 2013
483472c
pairwise loss augmentation
fgregg Oct 16, 2013
4c9609c
potts models
fgregg Dec 11, 2013
9d3603e
potts model in init
fgregg Dec 11, 2013
4e192ba
simple potts model
fgregg Oct 16, 2013
a1c167a
potts model
fgregg Oct 16, 2013
36bc9b3
remove commented out code
fgregg Oct 16, 2013
421c5c2
whitespace
fgregg Oct 16, 2013
e3a7dcb
pairwise loss augmentation
fgregg Oct 16, 2013
2ad1fa3
potts models
fgregg Dec 11, 2013
74064de
simplify potts model
fgregg Dec 20, 2013
977337c
pull in upstream
fgregg Dec 20, 2013
3812e94
think I fixed relaxed inference psi
fgregg Dec 31, 2013
4eedf4f
cleaning things up
fgregg Jan 1, 2014
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potts models
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@fgregg
fgregg committed Dec 11, 2013
commit 4c9609cb1dd5092764c68028796e8b54050f55ae
2 changes: 1 addition & 1 deletion pystruct/models/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@ def batch_inference(self, X, w, relaxed=None):
return [self.inference(x, w, relaxed=relaxed)
for x in X]

def loss(self, y, y_hat):
def loss(self, y, y_hat, x=None):
# hamming loss:
if isinstance(y_hat, tuple):
return self.continuous_loss(y, y_hat[0])
Expand Down
39 changes: 19 additions & 20 deletions pystruct/models/potts_edge_feature_graph_crf.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import numpy as np

from .edge_feature_graph_crf import EdgeFeatureGraphCRF
from .utils import loss_augment_unaries
from ..inference import inference_dispatch

class PottsEdgeFeatureGraphCRF(EdgeFeatureGraphCRF):
Expand Down Expand Up @@ -44,7 +45,10 @@ class PottsEdgeFeatureGraphCRF(EdgeFeatureGraphCRF):

"""
def __init__(self, n_states=None, n_features=None, n_edge_features=None,
inference_method=None, class_weight=None):
inference_method=None, class_weight=None,
markers=None):

self.markers = markers

EdgeFeatureGraphCRF.__init__(self, n_states,
n_features,
Expand All @@ -56,7 +60,7 @@ def _set_size_psi(self):
if not None in [self.n_states, self.n_features, self.n_edge_features]:
self.size_psi = (self.n_states * self.n_features
+ self.n_edge_features)


def _get_pairwise_potentials(self, x, w):
"""Computes pairwise potentials for x and w.
Expand Down Expand Up @@ -143,14 +147,14 @@ def psi(self, x, y):
psi_vector = np.hstack([unaries_acc.ravel(), pw.ravel()])
return psi_vector

def loss(self, y, y_hat, x):
edges = self._get_edges(x)
ground_truth = y[edges[:, 0]] != y[edges[:, 1]]
predicted = y_hat[edges[:, 0]] != y_hat[edges[:, 1]]
difference = (ground_truth != predicted).astype(np.int)
difference[ground_truth] *= 20
print 'loss', sum(difference)
return sum(difference)
# def loss(self, y, y_hat, x):
# edges = self._get_edges(x)
# ground_truth = y[edges[:, 0]] != y[edges[:, 1]]
# predicted = y_hat[edges[:, 0]] != y_hat[edges[:, 1]]
# difference = (ground_truth != predicted).astype(np.int)
# difference[ground_truth] *= 100
# print 'loss', sum(difference)
# return sum(difference)


def loss_augmented_inference(self, x, y, w, relaxed=False,
Expand Down Expand Up @@ -204,17 +208,12 @@ def loss_augmented_inference(self, x, y, w, relaxed=False,
unary_potentials = self._get_unary_potentials(x, w)
pairwise_potentials = self._get_pairwise_potentials(x, w)
edges = self._get_edges(x)
loss_augment_unaries(unary_potentials, np.asarray(y), self.class_weight)

ground_truth = y[edges[:, 0]] != y[edges[:, 1]]

for i in range(edges.shape[0]) :
if ground_truth[i] :
for j in xrange(self.n_states) :
pairwise_potentials[i, j, j] += 20
else :
pairwise_potentials[i, :, :] += 1
for j in xrange(self.n_states) :
pairwise_potentials[i, j, j] -= 1
for i, label in self.markers :
unary_potentials[i,:label] += 0.05
unary_potentials[i,label+1:] += 0.05



return inference_dispatch(unary_potentials, pairwise_potentials, edges,
Expand Down