def getTestPreds(train_obs_list, train_ne_list, test_obs_list, smooth, similarity):
emission_probs = getEmissionProbabilities(train_obs_list, train_ne_list)
state_init_probs, state_trans_probs = getBigramStateProbabilities(train_ne_list)
pred_ne_list = []
if smooth and not similarity:
smoothed_emission_probs = getSmoothEmissionProbs(emission_probs)
smoothed_state_trans_probs = getSmoothTransitionProbs(state_trans_probs)
for i in xrange(len(test_obs_list)):
predicted_states = SmoothViterbi(smoothed_emission_probs, state_init_probs, smoothed_state_trans_probs, test_obs_list[i])
pred_ne_list.append(predicted_states)
elif similarity and not smooth:
feature_type = 'text_features'
low_frequency_probabilities = json.load(open(dir_path + 'Training_Test_Data/{0}'.format(feature_type)))
reduced_similarity_probs = getReducedSimilarityProbs(low_frequency_probabilities, emission_probs)
for i in xrange(len(test_obs_list)):
predicted_states = SimilarityViterbi(emission_probs, state_init_probs, state_trans_probs, test_obs_list[i], reduced_similarity_probs, smooth=False)
pred_ne_list.append(predicted_states)
elif smooth and similarity:
feature_type = 'text_features'
low_frequency_probabilities = json.load(open(dir_path + 'Training_Test_Data/{0}'.format(feature_type)))
reduced_similarity_probs = getReducedSimilarityProbs(low_frequency_probabilities, emission_probs)
smoothed_state_trans_probs = getSmoothTransitionProbs(state_trans_probs)
for i in xrange(len(test_obs_list)):
predicted_states = SimilarityViterbi(emission_probs, state_init_probs, smoothed_state_trans_probs, test_obs_list[i], reduced_similarity_probs, smooth=True)
pred_ne_list.append(predicted_states)
else:
for i in xrange(len(test_obs_list)):
predicted_states = Viterbi(emission_probs, state_init_probs, state_trans_probs, test_obs_list[i])
pred_ne_list.append(predicted_states)
return pred_ne_list
def getTestPreds(train_obs_list, train_ne_list, test_obs_list, low_frequency_probabilities, smooth, similarity_based, test_pos_list):
emission_probs = getEmissionProbabilities(train_obs_list, train_ne_list, smooth)
state_init_probs, state_trans_probs = getStateProbabilities(train_ne_list)
pred_ne_list = []
for i in xrange(len(test_obs_list)):
predicted_states = Viterbi(emission_probs, state_init_probs, state_trans_probs, test_obs_list[i], low_frequency_probabilities, smooth, similarity_based, test_pos_list[i])
pred_ne_list.append(predicted_states)
return pred_ne_list
def getTestPreds(train_obs_list, train_ne_list, test_obs_list, smooth,
similarity):
emission_probs = getEmissionProbabilities(train_obs_list, train_ne_list)
state_init_probs, state_trans_probs = getBigramStateProbabilities(
train_ne_list)
pred_ne_list = []
if smooth and not similarity:
smoothed_emission_probs = getSmoothEmissionProbs(emission_probs)
smoothed_state_trans_probs = getSmoothTransitionProbs(
state_trans_probs)
for i in xrange(len(test_obs_list)):
predicted_states = SmoothViterbi(smoothed_emission_probs,
state_init_probs,
smoothed_state_trans_probs,
test_obs_list[i])
pred_ne_list.append(predicted_states)
elif similarity and not smooth:
feature_type = 'text_features'
low_frequency_probabilities = json.load(
open(dir_path + 'Training_Test_Data/{0}'.format(feature_type)))
reduced_similarity_probs = getReducedSimilarityProbs(
low_frequency_probabilities, emission_probs)
for i in xrange(len(test_obs_list)):
predicted_states = SimilarityViterbi(emission_probs,
state_init_probs,
state_trans_probs,
test_obs_list[i],
reduced_similarity_probs,
smooth=False)
pred_ne_list.append(predicted_states)
elif smooth and similarity:
feature_type = 'text_features'
low_frequency_probabilities = json.load(
open(dir_path + 'Training_Test_Data/{0}'.format(feature_type)))
reduced_similarity_probs = getReducedSimilarityProbs(
low_frequency_probabilities, emission_probs)
smoothed_state_trans_probs = getSmoothTransitionProbs(
state_trans_probs)
for i in xrange(len(test_obs_list)):
predicted_states = SimilarityViterbi(emission_probs,
state_init_probs,
smoothed_state_trans_probs,
test_obs_list[i],
reduced_similarity_probs,
smooth=True)
pred_ne_list.append(predicted_states)
else:
for i in xrange(len(test_obs_list)):
predicted_states = Viterbi(emission_probs, state_init_probs,
state_trans_probs, test_obs_list[i])
pred_ne_list.append(predicted_states)
return pred_ne_list
def getTestPreds(train_obs_list, train_ne_list, test_obs_list,
low_frequency_probabilities, smooth, similarity_based,
test_pos_list):
emission_probs = getEmissionProbabilities(train_obs_list, train_ne_list,
smooth)
state_init_probs, state_trans_probs = getStateProbabilities(train_ne_list)
pred_ne_list = []
for i in xrange(len(test_obs_list)):
predicted_states = Viterbi(emission_probs, state_init_probs,
state_trans_probs, test_obs_list[i],
low_frequency_probabilities, smooth,
similarity_based, test_pos_list[i])
pred_ne_list.append(predicted_states)
return pred_ne_list
