def test_adj_motion_classifier(classifier_dict, adjective_name,
test_feature_objs, feature_list, scaler_dict,
classifier_text_report):
'''
Pass in the trained adjective classifiers that are motion specific and return the results
of the motion adjectives compared to the test set
'''
# For each adjective
object_run_probability = dict()
object_run_scores = dict()
object_run_prediction = dict()
# for each motion
for motion in test_feature_objs:
results = []
truth_vector = []
motion_test_obj_list = test_feature_objs[motion]
object_run_probability[motion] = []
for test_obj in motion_test_obj_list:
# import pdb; pdb.set_trace()
if test_obj.labels == None:
prediction, probability = utilities.compute_adjective_probability_score(
classifier_dict, test_obj, feature_list, adjective_name,
scaler_dict)
else:
prediction, probability, truth = utilities.compute_adjective_probability_score(
classifier_dict, test_obj, feature_list, adjective_name,
scaler_dict)
results.append(prediction)
truth_vector.append(truth)
# Store the probabilities
object_run_probability[motion].append(probability)
object_run_prediction[motion] = prediction
print "Motion is: %s" % motion
if len(results) > 0:
#print "f1 score is %s" %classification_report(truth_vector, results)
classifier_text_report.write("Adjective: " + adjective_name +
"\nMotion: " + motion + "\n")
classifier_text_report.write(
classification_report(truth_vector, results) + "\n")
# Store the scores
object_run_scores[motion] = (recall_score(truth_vector, results))
# import pdb; pdb.set_trace()
# Return the % of motions in a n x 5 vector (5 motions)
probability_feature_vector = []
for val in xrange(len(object_run_probability[motion])):
one_run_vector = []
for motion_name in object_run_probability:
one_run_vector.append(object_run_probability[motion_name][val])
probability_feature_vector.append(one_run_vector)
return (np.array(probability_feature_vector), object_run_scores,
object_run_prediction, truth_vector)
def test_adj_motion_classifier(classifier_dict, adjective_name, test_feature_objs, feature_list, scaler_dict, classifier_text_report):
'''
Pass in the trained adjective classifiers that are motion specific and return the results
of the motion adjectives compared to the test set
'''
# For each adjective
object_run_probability = dict()
object_run_scores = dict()
object_run_prediction = dict()
# for each motion
for motion in test_feature_objs:
results = []
truth_vector = []
motion_test_obj_list = test_feature_objs[motion]
object_run_probability[motion] = []
for test_obj in motion_test_obj_list:
# import pdb; pdb.set_trace()
if test_obj.labels == None:
prediction, probability = utilities.compute_adjective_probability_score(classifier_dict, test_obj, feature_list, adjective_name, scaler_dict)
else:
prediction, probability, truth = utilities.compute_adjective_probability_score(classifier_dict, test_obj, feature_list, adjective_name, scaler_dict)
results.append(prediction)
truth_vector.append(truth)
# Store the probabilities
object_run_probability[motion].append(probability)
object_run_prediction[motion] = prediction
print "Motion is: %s" % motion
if len(results) > 0:
#print "f1 score is %s" %classification_report(truth_vector, results)
classifier_text_report.write("Adjective: " + adjective_name+ "\nMotion: " + motion+"\n")
classifier_text_report.write(classification_report(truth_vector, results)+ "\n")
# Store the scores
object_run_scores[motion] = (recall_score(truth_vector, results))
# import pdb; pdb.set_trace()
# Return the % of motions in a n x 5 vector (5 motions)
probability_feature_vector = []
for val in xrange(len(object_run_probability[motion])):
one_run_vector = []
for motion_name in object_run_probability:
one_run_vector.append(object_run_probability[motion_name][val])
probability_feature_vector.append(one_run_vector)
return (np.array(probability_feature_vector), object_run_scores, object_run_prediction, truth_vector)
def compute_probability_vector(self, bolt_obj):
if bolt_obj.state == bolt_obj.TAP:
# Store results as they come in
self.adjective_vectors = dict()
self.all_motion_results = dict()
# Store dictionary of strings
self.state_string = {bolt_obj.DISABLED:'disabled',
bolt_obj.THERMAL_HOLD:'thermal_hold',
bolt_obj.SLIDE:'slide',
bolt_obj.SQUEEZE:'squeeze',
bolt_obj.TAP:'tap',
bolt_obj.DONE:'done',
bolt_obj.SLIDE_FAST:'slide_fast',
bolt_obj.CENTER_GRIPPER:'center_gripper'
}
current_motion = self.state_string[bolt_obj.state]
# Create feature vector
self.bolt_object = bolt_obj
utilities.normalize_data(self.bolt_object)
self.bolt_feature_object = extract_features.extract_features(self.bolt_object, self.pca_model[current_motion])
# Create a dictionary to store the results in
for adj in self.all_classifiers:
results, prob = utilities.compute_adjective_probability_score(self.all_classifiers[adj], self.bolt_feature_object, self.feature_list, adj, self.scaler_dict)
# Store off adjective probabilities for ensemble
if adj in self.adjective_vectors:
pass
else:
self.adjective_vectors[adj] = list()
self.adjective_vectors[adj].append(prob)
# Store classifier score based on best motion
best_motion = self.best_motion_dict[adj][1]
if current_motion == best_motion:
rospy.loginfo("Best Motion is: %s" % best_motion)
self.all_motion_results[adj] = results
print len(self.adjective_vectors[adj])
if len(self.adjective_vectors[adj]) == 5:
ensembled_results = dict()
print self.adjective_vectors
#for adj in self.adjective_vectors:
# ensembled_results[adj] = self.ensemble_classifiers[adj].predict(self.adjective_vectors[adj])
# Store off the adjectives that returned true
adjectives_found = []
for adj in self.all_motion_results:
if self.all_motion_results[adj] == 1:
adjectives_found.append(adj)
print "Results from max classification"
print self.all_motion_results
print str(adjectives_found)
self.adjectives_pub.publish(str(adjectives_found))
def compute_probability_vector(self, bolt_obj):
if bolt_obj.state == bolt_obj.TAP:
# Store results as they come in
self.adjective_vectors = dict()
self.all_motion_results = dict()
# Store dictionary of strings
self.state_string = {bolt_obj.DISABLED:'disabled',
bolt_obj.THERMAL_HOLD:'thermal_hold',
bolt_obj.SLIDE:'slide',
bolt_obj.SQUEEZE:'squeeze',
bolt_obj.TAP:'tap',
bolt_obj.DONE:'done',
bolt_obj.SLIDE_FAST:'slide_fast',
bolt_obj.CENTER_GRIPPER:'center_gripper'
}
# Get the current motion
current_motion = self.state_string[bolt_obj.state]
# Build the feature vector
self.bolt_object = bolt_obj
utilities.normalize_data(self.bolt_object)
if self.bolt_object.state == bolt_obj.DISABLED:
return
else:
self.bolt_feature_object = extract_features.extract_features(self.bolt_object, self.pca_model[current_motion])
# Create a dictionary to store the results in
for adj in self.all_classifiers:
results, prob = utilities.compute_adjective_probability_score(self.all_classifiers[adj], self.bolt_feature_object, self.feature_list, adj, self.scaler_dict)
# Store off adjective probabilities for ensemble
if adj in self.adjective_vectors:
pass
else:
self.adjective_vectors[adj] = list()
self.adjective_vectors[adj].append(prob)
# Store classifier score based on best motion
best_motion = self.best_motion_dict[adj][1]
if current_motion == best_motion:
rospy.loginfo("Best Motion is: %s"% best_motion)
self.all_motion_results[adj] = results
print len(self.adjective_vectors[adj])
if len(self.adjective_vectors[adj]) == 5:
ensembled_results = dict()
#print self.adjective_vectors
for adj in self.ensemble_classifiers:
ensembled_results[adj] = self.ensemble_classifiers[adj].predict(self.adjective_vectors[adj])[0]
# Store off the adjectives that returned true
adjectives_found = []
for adj in self.all_motion_results:
if self.all_motion_results[adj] == 1:
adjectives_found.append(adj)
# Store off the adjectives that returns true for ensemble
adjectives_ensemble = []
for adj in ensembled_results:
if ensembled_results[adj] == 1:
adjectives_ensemble.append(adj)
publish_string = AdjList()
publish_string = adjective_found
print "Results from max classification"
print self.all_motion_results
print str(adjectives_found)
self.adjectives_pub.publish(str(adjectives_found))
print "Results from ensemble"
print ensembled_results
print adjectives_ensemble
