def test_adjective(classifier, adjective_report, store_values):
adjective = classifier['adjective']
phase = classifier['phase']
dynamic_features = utilities.load_adjective_phase('/media/data_storage/vchu/all_classifiers/icra2014/dynamic/adjective_phase_set/')
static_features = utilities.load_adjective_phase('/media/data_storage/vchu/all_classifiers/icra2014/static/adjective_phase_set/')
true_positives = 0.0
true_negatives = 0.0
false_positives = 0.0
false_negatives = 0.0
false_positive_list = []
false_negative_list = []
true_positive_list = []
true_negative_list = []
print '\n \nTesting Adjective: %s and phase %s' % (adjective, phase)
#Pull out test features/labels
dynamic_train = dynamic_features[adjective][phase]['train']['features']
dynamic_train_scaler = preprocessing.StandardScaler().fit(dynamic_train)
dynamic_train_scaled_X = dynamic_train_scaler.transform(dynamic_train)
dynamic_train_kernel = linear_kernel(dynamic_train_scaled_X, -2)
#dynamic_train_kernel = standardize(dynamic_train_kernel)
#Static Train
static_train = static_features[adjective][phase]['train']['features']
static_train_scaler = preprocessing.StandardScaler().fit(static_train)
static_train_scaled_X = static_train_scaler.transform(static_train)
static_train_kernel = linear_kernel(static_train_scaled_X, -2)
dynamic_test = dynamic_features[adjective][phase]['test']['features']
dynamic_test_scaled_X = classifier['dynamic_scaler'].transform(dynamic_test)
dynamic_kernel = linear_kernel_test(dynamic_test_scaled_X, dynamic_train_scaled_X, -2)
#dynamic_kernel = (dynamic_kernel - classifier['dynamic_kernel_mean']) / classifier['dynamic_kernel_std']
static_test = static_features[adjective][phase]['test']['features']
static_test_scaled_X = classifier['static_scaler'].transform(static_test)
static_kernel = linear_kernel_test(static_test_scaled_X, static_train_scaled_X, -2)
alpha = classifier['alpha']
test_X = (alpha)*static_kernel + (1-alpha)*dynamic_kernel
test_Y = dynamic_features[adjective][phase]['test']['labels']
object_ids = dynamic_features[adjective][phase]['test']['object_ids']
object_names = dynamic_features[adjective][phase]['test']['object_names']
# Pull out the classifier and merge features
clf = classifier['classifier']
# Predict the labels!
output = clf.predict(test_X)
# Determine if the true label and classifier prediction match
for val in xrange(len(test_Y)):
true_label = test_Y[val]
predict_label = output[val]
if true_label == 1:
if predict_label == 1:
true_positives += 1.0
true_positive_list.append(object_names[val])
else:
false_negatives += 1.0
false_negative_list.append(object_names[val])
else: # label is 0
if predict_label == 1:
false_positives += 1.0
false_positive_list.append(object_names[val])
else:
true_negatives += 1.0
true_negative_list.append(object_names[val])
# Compute statistics for the adjective
try:
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
except ZeroDivisionError: # The case when none are found
precision = 0
recall = 0
try:
f1 = 2.0 * precision*recall / (precision + recall)
except ZeroDivisionError:
f1 = 0
store_values[adjective+'_'+phase] = (f1,precision, recall)
print "%d False Positive Objects are: %s \n" % (false_positives, sorted(false_positive_list))
print "%d False Negative Objects are: %s \n" % (false_negatives, sorted(false_negative_list))
print "%d True Positive Objects are: %s\n" % (true_positives, sorted(true_positive_list))
print "%d True Negative Objects are: %s\n" % (true_negatives, sorted(true_negative_list))
return (precision, recall, f1)
def test_adjective(classifier, adjective_report ):
true_positives = 0.0
true_negatives = 0.0
false_positives = 0.0
false_negatives = 0.0
false_positive_list = []
false_negative_list = []
true_positive_list = []
true_negative_list = []
adjective = classifier['adjective']
dynamic_features = utilities.load_adjective_phase('/home/imcmahon/Desktop/mkl/dynamic/adjective_phase_set')
static_features = utilities.load_adjective_phase('/home/imcmahon/Desktop/mkl/static/adjective_phase_set')
#dynamic_features = utilities.load_adjective_phase(dynamic_path)
#static_features = utilities.load_adjective_phase(static_path)
#import pdb; pdb.set_trace()
#Dynamic Train
dynamic_train = utilities.get_all_train_test_features(adjective, dynamic_features, train=True)
dynamic_train_scaler = preprocessing.StandardScaler().fit(dynamic_train[0])
dynamic_train_scaled_X = dynamic_train_scaler.transform(dynamic_train[0])
dynamic_train_kernel = linear_kernel(dynamic_train_scaled_X, -2)
#dynamic_train_kernel = standardize(dynamic_train_kernel)
#Static Train
static_train = utilities.get_all_train_test_features(adjective, static_features, train=True)
static_train_scaler = preprocessing.StandardScaler().fit(static_train[0])
static_train_scaled_X = static_train_scaler.transform(static_train[0])
static_train_kernel = linear_kernel(static_train_scaled_X, -2)
#static_train_kernel = standardize(static_train_kernel)
#Recompute the GRAM matrix
#alpha = classifier['alpha'];
#train_X = (alpha)*static_train_kernel + (1-alpha)*dynamic_train_kernel
#import pdb; pdb.set_trace()
dynamic_test = utilities.get_all_train_test_features(adjective, dynamic_features, train=False)
dynamic_test_scaled_X = classifier['dynamic_scaler'].transform(dynamic_test[0])
dynamic_kernel = linear_kernel_test(dynamic_test_scaled_X, dynamic_train_scaled_X, -2)
#dynamic_kernel = (dynamic_kernel - classifier['dynamic_kernel_mean']) / classifier['dynamic_kernel_std']
static_test = utilities.get_all_train_test_features(adjective, static_features, train=False)
static_test_scaled_X = classifier['static_scaler'].transform(static_test[0])
static_kernel = linear_kernel_test(static_test_scaled_X, static_train_scaled_X, -2)
#static_kernel = (static_kernel - classifier['static_kernel_mean']) / classifier['static_kernel_std']
alpha = classifier['alpha'];
test_X = (alpha)*static_kernel + (1-alpha)*dynamic_kernel
print '\n \nTesting Adjective: %s' % classifier['adjective']
#Pull out test features/labels
#for phase in phases:
#import pdb; pdb.set_trace()
#test_set = classifier['test']
#test_X.append(test_set['features'])
#test_X.append(test_set['features'])
test_Y = dynamic_test[1]
object_ids = dynamic_test[2]
#object_names = test_set['object_names']
# Pull out the classifier and merge features
#test_X = np.concatenate(test_X, axis=1)
#import pdb; pdb.set_trace()
clf = classifier['classifier']
c_dict[classifier['adjective']] = clf.C
#import pdb; pdb.set_trace()
print clf
# Predict the labels!
#if 'scaler' in classifier:
# if type(classifier['scaler']) == preprocessing.Scaler:
# test_X = classifier['scaler'].transform(test_X)
#import pdb; pdb.set_trace()
output = clf.predict(test_X)
# Determine if the true label and classifier prediction match
for val in xrange(len(test_Y)):
true_label = test_Y[val]
predict_label = output[val]
if true_label == 1:
if predict_label == 1:
true_positives += 1.0
#true_positive_list.append(object_names[val])
else:
false_negatives += 1.0
#false_negative_list.append(object_names[val])
else: # label is 0
if predict_label == 1:
false_positives += 1.0
#false_positive_list.append(object_names[val])
else:
true_negatives += 1.0
#true_negative_list.append(object_names[val])
# Compute statistics for the adjective
try:
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
except ZeroDivisionError: # The case when none are found
precision = 0
recall = 0
try:
f1 = 2.0 * precision*recall / (precision + recall)
except ZeroDivisionError:
f1 = 0
print "Precision: %f, Recall: %f, F1: %f \n" % (precision, recall, f1)
print "Alpha = %1.1f" % alpha
adjective_report.write("%s, %1.1f, %f, %f, %f\n" % (classifier['adjective'], alpha, precision, recall, f1))
print "%d False Positive Objects\n" % false_positives
print "%d False Negative Objects\n" % false_negatives
print "%d True Positive Objects\n" % true_positives
print "%d True Negative Objects\n" % true_negatives
return (alpha, precision, recall, f1)
def test_adjective(classifier, adjective_report, store_values):
adjective = classifier['adjective']
phase = classifier['phase']
dynamic_features = utilities.load_adjective_phase(
'/media/data_storage/vchu/all_classifiers/icra2014/dynamic/adjective_phase_set/'
)
static_features = utilities.load_adjective_phase(
'/media/data_storage/vchu/all_classifiers/icra2014/static/adjective_phase_set/'
)
true_positives = 0.0
true_negatives = 0.0
false_positives = 0.0
false_negatives = 0.0
false_positive_list = []
false_negative_list = []
true_positive_list = []
true_negative_list = []
print '\n \nTesting Adjective: %s and phase %s' % (adjective, phase)
#Pull out test features/labels
dynamic_train = dynamic_features[adjective][phase]['train']['features']
dynamic_train_scaler = preprocessing.StandardScaler().fit(dynamic_train)
dynamic_train_scaled_X = dynamic_train_scaler.transform(dynamic_train)
dynamic_train_kernel = linear_kernel(dynamic_train_scaled_X, -2)
#dynamic_train_kernel = standardize(dynamic_train_kernel)
#Static Train
static_train = static_features[adjective][phase]['train']['features']
static_train_scaler = preprocessing.StandardScaler().fit(static_train)
static_train_scaled_X = static_train_scaler.transform(static_train)
static_train_kernel = linear_kernel(static_train_scaled_X, -2)
dynamic_test = dynamic_features[adjective][phase]['test']['features']
dynamic_test_scaled_X = classifier['dynamic_scaler'].transform(
dynamic_test)
dynamic_kernel = linear_kernel_test(dynamic_test_scaled_X,
dynamic_train_scaled_X, -2)
#dynamic_kernel = (dynamic_kernel - classifier['dynamic_kernel_mean']) / classifier['dynamic_kernel_std']
static_test = static_features[adjective][phase]['test']['features']
static_test_scaled_X = classifier['static_scaler'].transform(static_test)
static_kernel = linear_kernel_test(static_test_scaled_X,
static_train_scaled_X, -2)
alpha = classifier['alpha']
test_X = (alpha) * static_kernel + (1 - alpha) * dynamic_kernel
test_Y = dynamic_features[adjective][phase]['test']['labels']
object_ids = dynamic_features[adjective][phase]['test']['object_ids']
object_names = dynamic_features[adjective][phase]['test']['object_names']
# Pull out the classifier and merge features
clf = classifier['classifier']
# Predict the labels!
output = clf.predict(test_X)
# Determine if the true label and classifier prediction match
for val in xrange(len(test_Y)):
true_label = test_Y[val]
predict_label = output[val]
if true_label == 1:
if predict_label == 1:
true_positives += 1.0
true_positive_list.append(object_names[val])
else:
false_negatives += 1.0
false_negative_list.append(object_names[val])
else: # label is 0
if predict_label == 1:
false_positives += 1.0
false_positive_list.append(object_names[val])
else:
true_negatives += 1.0
true_negative_list.append(object_names[val])
# Compute statistics for the adjective
try:
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
except ZeroDivisionError: # The case when none are found
precision = 0
recall = 0
try:
f1 = 2.0 * precision * recall / (precision + recall)
except ZeroDivisionError:
f1 = 0
store_values[adjective + '_' + phase] = (f1, precision, recall)
print "%d False Positive Objects are: %s \n" % (
false_positives, sorted(false_positive_list))
print "%d False Negative Objects are: %s \n" % (
false_negatives, sorted(false_negative_list))
print "%d True Positive Objects are: %s\n" % (true_positives,
sorted(true_positive_list))
print "%d True Negative Objects are: %s\n" % (true_negatives,
sorted(true_negative_list))
return (precision, recall, f1)
def test_adjective(classifier, adjective_report):
true_positives = 0.0
true_negatives = 0.0
false_positives = 0.0
false_negatives = 0.0
false_positive_list = []
false_negative_list = []
true_positive_list = []
true_negative_list = []
adjective = classifier['adjective']
dynamic_features = utilities.load_adjective_phase(
'/home/imcmahon/Desktop/mkl/dynamic/adjective_phase_set')
static_features = utilities.load_adjective_phase(
'/home/imcmahon/Desktop/mkl/static/adjective_phase_set')
#dynamic_features = utilities.load_adjective_phase(dynamic_path)
#static_features = utilities.load_adjective_phase(static_path)
#import pdb; pdb.set_trace()
#Dynamic Train
dynamic_train = utilities.get_all_train_test_features(adjective,
dynamic_features,
train=True)
dynamic_train_scaler = preprocessing.StandardScaler().fit(dynamic_train[0])
dynamic_train_scaled_X = dynamic_train_scaler.transform(dynamic_train[0])
dynamic_train_kernel = linear_kernel(dynamic_train_scaled_X, -2)
#dynamic_train_kernel = standardize(dynamic_train_kernel)
#Static Train
static_train = utilities.get_all_train_test_features(adjective,
static_features,
train=True)
static_train_scaler = preprocessing.StandardScaler().fit(static_train[0])
static_train_scaled_X = static_train_scaler.transform(static_train[0])
static_train_kernel = linear_kernel(static_train_scaled_X, -2)
#static_train_kernel = standardize(static_train_kernel)
#Recompute the GRAM matrix
#alpha = classifier['alpha'];
#train_X = (alpha)*static_train_kernel + (1-alpha)*dynamic_train_kernel
#import pdb; pdb.set_trace()
dynamic_test = utilities.get_all_train_test_features(adjective,
dynamic_features,
train=False)
dynamic_test_scaled_X = classifier['dynamic_scaler'].transform(
dynamic_test[0])
dynamic_kernel = linear_kernel_test(dynamic_test_scaled_X,
dynamic_train_scaled_X, -2)
#dynamic_kernel = (dynamic_kernel - classifier['dynamic_kernel_mean']) / classifier['dynamic_kernel_std']
static_test = utilities.get_all_train_test_features(adjective,
static_features,
train=False)
static_test_scaled_X = classifier['static_scaler'].transform(
static_test[0])
static_kernel = linear_kernel_test(static_test_scaled_X,
static_train_scaled_X, -2)
#static_kernel = (static_kernel - classifier['static_kernel_mean']) / classifier['static_kernel_std']
alpha = classifier['alpha']
test_X = (alpha) * static_kernel + (1 - alpha) * dynamic_kernel
print '\n \nTesting Adjective: %s' % classifier['adjective']
#Pull out test features/labels
#for phase in phases:
#import pdb; pdb.set_trace()
#test_set = classifier['test']
#test_X.append(test_set['features'])
#test_X.append(test_set['features'])
test_Y = dynamic_test[1]
object_ids = dynamic_test[2]
#object_names = test_set['object_names']
# Pull out the classifier and merge features
#test_X = np.concatenate(test_X, axis=1)
#import pdb; pdb.set_trace()
clf = classifier['classifier']
c_dict[classifier['adjective']] = clf.C
#import pdb; pdb.set_trace()
print clf
# Predict the labels!
#if 'scaler' in classifier:
# if type(classifier['scaler']) == preprocessing.Scaler:
# test_X = classifier['scaler'].transform(test_X)
#import pdb; pdb.set_trace()
output = clf.predict(test_X)
# Determine if the true label and classifier prediction match
for val in xrange(len(test_Y)):
true_label = test_Y[val]
predict_label = output[val]
if true_label == 1:
if predict_label == 1:
true_positives += 1.0
#true_positive_list.append(object_names[val])
else:
false_negatives += 1.0
#false_negative_list.append(object_names[val])
else: # label is 0
if predict_label == 1:
false_positives += 1.0
#false_positive_list.append(object_names[val])
else:
true_negatives += 1.0
#true_negative_list.append(object_names[val])
# Compute statistics for the adjective
try:
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
except ZeroDivisionError: # The case when none are found
precision = 0
recall = 0
try:
f1 = 2.0 * precision * recall / (precision + recall)
except ZeroDivisionError:
f1 = 0
print "Precision: %f, Recall: %f, F1: %f \n" % (precision, recall, f1)
print "Alpha = %1.1f" % alpha
adjective_report.write(
"%s, %1.1f, %f, %f, %f\n" %
(classifier['adjective'], alpha, precision, recall, f1))
print "%d False Positive Objects\n" % false_positives
print "%d False Negative Objects\n" % false_negatives
print "%d True Positive Objects\n" % true_positives
print "%d True Negative Objects\n" % true_negatives
return (alpha, precision, recall, f1)
