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late_fusion.py
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late_fusion.py
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import cPickle
from ipdb import set_trace
import itertools
import multiprocessing as mp
import numpy as np
from sklearn.linear_model import Lasso
from sklearn.svm import SVC
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.grid_search import GridSearchCV
import sys
from per_video import data_to_kernels
from per_video import get_data
from per_video import remap_descriptors
from per_slice import binarize_labels
from fisher_vectors.evaluation.utils import average_precision
null_class_idx = 0
idx_to_class = {
0: 'null',
1: 'board_trick',
2: 'feeding_an_animal',
3: 'landing_a_fish',
4: 'wedding_ceremony',
5: 'woodworking_project',
6: 'birthday_party',
7: 'changing_vehicle_tire',
8: 'flash_mob_gathering',
9: 'unstuck_vehicle',
10: 'grooming_an_animal',
11: 'making_a_sandwich',
12: 'parade',
13: 'parkour',
14: 'repairing_an_appliance',
15: 'sewing_project',
21: 'attempting_a_bike_trick',
22: 'cleaning_an_appliance',
23: 'dog_show',
24: 'giving_directions_to_a_location',
25: 'marriage_proposal',
26: 'renovating_a_home',
27: 'rock_climbing',
28: 'town_hall_meeting',
29: 'winning_a_race_without_a_vehicle',
30: 'working_on_a_metal_crafts_project',
}
combinations = {
'sift_mu': ('sift2', {'dimensions' : (255, 255 + 32 * 256) }),
'sift_sigma': ('sift2', {'dimensions' : (255 + 32 * 256, 255 + 32 * 256 * 2) }),
'sift_mu_sigma': ('sift2', {'dimensions' : (255, 255 + 32 * 256 * 2) }),
'mbh': ('mbh', {}),
'audio': ('heng_audio', {'derivative': ''}),
'audio_D1': ('heng_audio', {'derivative': '_D1'}),
'audio_D2': ('heng_audio', {'derivative': '_D2'}),
'jochen_audio': ('jochen_audio',{'dimslice': 0})}
late_fusion_params = {
'score_type': 'scores'}
def weights_grid(dd, step = 0.02):
""" Generates weights on a regular grid. """
for ww in itertools.product(
*(np.arange(0, 1 + step, step) for ii in xrange(dd - 1))):
last_weight = 1 - sum(ww)
if last_weight < 0:
continue
yield ww + (last_weight, )
class LateFusion(object):
def __init__(self, score_type):
self.score_type = score_type
def fit(self, kernels, labels):
self.clf = []
kernels = list(kernels)
nr_kernels = len(kernels)
# Get a hold-out data for fitting the late fusion weights.
self.weight_scores = {}
ss = StratifiedShuffleSplit(labels, 3, test_size=0.25, random_state=0)
#tr_idxs, val_idxs = iter(ss).next()
# nr_samples = len(labels)
# tr_idxs, val_idxs = np.arange(nr_samples), np.arange(nr_samples)
for ii in xrange(nr_kernels):
self.clf.append(SVM())
for tr_idxs, val_idxs in ss:
k_tr_idxs = np.ix_(tr_idxs, tr_idxs)
k_val_idxs = np.ix_(val_idxs, tr_idxs)
scores = []
for ii, kernel in enumerate(kernels):
self.clf[ii].fit(kernel[k_tr_idxs], labels[tr_idxs])
scores.append(
self.predict_clf(self.clf[ii], kernel[k_val_idxs]))
scores = np.vstack(scores).T
self.fit_late_fusion(scores, labels[val_idxs])
self.weights = max(self.weight_scores,
key=lambda key:
np.mean(self.weight_scores[key]))
# Retrain on all the data.
for ii, kernel in enumerate(kernels):
self.clf[ii].fit(kernel, labels)
return self
def predict(self, te_kernels):
scores = []
for ii, kernel in enumerate(te_kernels):
scores.append(self.predict_clf(self.clf[ii], kernel))
scores = np.vstack(scores).T
fused_scores = self.predict_late_fusion(scores)
return fused_scores
def score(self, te_kernels, te_labels):
return average_precision(te_labels, self.predict(te_kernels))
def predict_clf(self, clf, kernel):
if self.score_type == 'probas':
return clf.predict_proba(kernel)
elif self.score_type == 'scores':
return clf.decision_function(kernel)
def get_weights_str(self):
return ' '.join(['%.2f' % ww for ww in self.weights])
def fit_late_fusion(self, scores, tr_labels):
# Equal weights.
#D = scores.shape[1]
#self.weights = np.array([1. / D] * D)
# Dumb crossvalidation.
best_ap = 0
D = scores.shape[1]
for self.weights in weights_grid(D):
ap = average_precision(
tr_labels, self.predict_late_fusion(scores))
if self.weights in self.weight_scores:
self.weight_scores[self.weights].append(ap)
else:
self.weight_scores[self.weights] = [ap]
# if ap > best_ap:
# best_ap = ap
# best_weights = self.weights
#self.weights = best_weights
# Fit small regressor, linear model.
#self.lm = Lasso()
#self.lm.fit(scores, tr_labels)
def predict_late_fusion(self, scores):
return np.sum(scores * self.weights, 1)
#return self.lm.predict(scores)
class MySVC(SVC):
def predict(self, X):
return self.decision_function(X)
class SVM(object):
def __init__(self, **kwargs):
self.nr_processes = kwargs.get('nr_processes', 1)
c_values = np.power(3.0, np.arange(-2, 8))
self.parameters = [{'C': c_values}]
def fit(self, tr_kernel, tr_labels):
splits = StratifiedShuffleSplit(
tr_labels, 3, test_size=0.25, random_state=0)
my_clf = MySVC(kernel='precomputed',probability=True,
class_weight='auto')
self.clf = (
GridSearchCV(my_clf, self.parameters,
score_func=average_precision,
cv=splits, n_jobs=self.nr_processes))
self.clf.fit(tr_kernel, tr_labels)
return self
def decision_function(self, te_kernel):
return np.squeeze(
self.clf.best_estimator_.decision_function(te_kernel))
def predict_proba(self, te_kernel):
return self.clf.predict_proba(te_kernel)[:, 1]
def score(self, te_kernel, te_labels):
return average_precision(te_labels, self.predict_proba(te_kernel))
def get_kernels_given_class(tr_kernels, te_kernels, tr_labels, te_labels,
class_idx):
tr_idxs = ((tr_labels == class_idx) |
(tr_labels == null_class_idx))
te_idxs = ((te_labels == class_idx) |
(te_labels == null_class_idx))
tr_kernel_idxs = np.ix_(tr_idxs, tr_idxs)
te_kernel_idxs = np.ix_(te_idxs, tr_idxs)
cls_tr_kernels, cls_te_kernels = [], []
for tr_kernel, te_kernel in itertools.izip(tr_kernels, te_kernels):
cls_tr_kernels.append(tr_kernel[tr_kernel_idxs])
cls_te_kernels.append(te_kernel[te_kernel_idxs])
return cls_tr_kernels, cls_te_kernels
def get_labels_given_class(tr_labels, te_labels, class_idx):
tr_idxs = ((tr_labels == class_idx) |
(tr_labels == null_class_idx))
te_idxs = ((te_labels == class_idx) |
(te_labels == null_class_idx))
cls_tr_labels = binarize_labels(tr_labels[tr_idxs], pos_label=class_idx,
neg_label=null_class_idx)
cls_te_labels = binarize_labels(te_labels[te_idxs], pos_label=class_idx,
neg_label=null_class_idx)
return cls_tr_labels, cls_te_labels
def get_kernels_and_labels():
selection = sys.argv[1:]
ref_tr_vidnames = None
ref_te_vidnames = None
tr_kernels, te_kernels = [], []
for cname in selection:
feature, params = combinations[cname]
print "load feature", cname
tr_data, tr_labels, tr_vidnames = get_data(feature, 'train', **params)
te_data, te_labels, te_vidnames = get_data(feature, 'test', **params)
print "compute kernels train %d*%d test %d*%d" % (
tr_data.shape + te_data.shape)
if ref_tr_vidnames != None:
print "remapping names"
# pdb.set_trace()
te_data = remap_descriptors(te_data, te_vidnames, ref_te_vidnames)
tr_data = remap_descriptors(tr_data, tr_vidnames, ref_tr_vidnames)
tr_labels_, te_labels_ = tr_labels, te_labels
else:
ref_tr_vidnames = tr_vidnames
ref_te_vidnames = te_vidnames
tr_kernel, te_kernel = data_to_kernels(tr_data, te_data)
tr_kernels.append(tr_kernel)
te_kernels.append(te_kernel)
return tr_kernels, te_kernels, tr_labels_, te_labels_
def per_class_worker(result_queue, tr_kernels, te_kernels, tr_labels,
te_labels, class_idx):
# Binarize labels + slice kernels.
cls_tr_kernels, cls_te_kernels = get_kernels_given_class(
tr_kernels, te_kernels, tr_labels, te_labels, class_idx)
cls_tr_labels, cls_te_labels = get_labels_given_class(
tr_labels, te_labels, class_idx)
# Train SVC for each channel, then late fuse.
late_fusion = LateFusion(**late_fusion_params)
late_fusion.fit(cls_tr_kernels, cls_tr_labels)
score = 100 * late_fusion.score(cls_te_kernels, cls_te_labels)
result_queue.put((class_idx, score, late_fusion.get_weights_str()))
def late_fusion_master():
processes, result_queue = [], mp.Queue()
# Load data.
tr_kernels, te_kernels, tr_labels, te_labels = get_kernels_and_labels()
for class_idx in xrange(1, 16):
processes.append(
mp.Process(
target=per_class_worker,
args=(result_queue, tr_kernels, te_kernels, tr_labels,
te_labels, class_idx)))
processes[-1].start()
for process in processes:
process.join()
results = sorted([result_queue.get() for ii in xrange(15)])
for (class_idx, score, weights) in results:
print '%s%2.3f %s' % (
'{0:34}'.format(idx_to_class[class_idx]), score, weights)
print '{0:34}'.format('MAP') + '%2.3f' % np.mean(
[score for _, score, _ in results])
def late_fusion_test():
qq = mp.Queue()
tr_kernels, te_kernels, tr_labels, te_labels = get_kernels_and_labels()
per_class_worker(qq, tr_kernels, te_kernels, tr_labels, te_labels, 1)
def main():
#late_fusion_test()
late_fusion_master()
if __name__ == '__main__':
main()