def SimilarityViterbi(emission_probs, state_init_probs, state_trans_probs, test_subseq, low_frequency_probabilities, smooth):
'''
For now we're ignoring the <UNK> tokens that were inserted
If we lookup an emission that doesn't exist, it will have probability 0, since we're using a Counter
'''
# Initialize paths and probabilities
path_dict = dict((state, [state[1]]) for state in state_init_probs.keys())
prev_probs = {}
for state in state_init_probs.keys():
prev_probs[state] = state_init_probs[state] * emission_probs[state[1]][test_subseq[0]]
# Iterate over the sentence
all_states = set(state2 for state1 in state_trans_probs for state2 in state_trans_probs[state1])
all_states.remove("<UNK>")
for emission in test_subseq[1:]:
new_path_dict = {}
curr_state_probs = {}
for curr_state in all_states:
temp_state_probs = {}
for prev_state in path_dict:
#Emission is unseen. Finding probability based on feature class
if emission not in emission_probs[curr_state]:
feature_class = findFeatureClass(emission)
# simple_curr_state = curr_state.split('-')[-1]
# emission_probability = low_frequency_probabilities[simple_curr_state][feature_class]
emission_probability = low_frequency_probabilities[curr_state][feature_class]
else:
emission_probability = emission_probs[curr_state][emission]
if smooth:
if prev_state not in state_trans_probs:
this_trans_prob = state_trans_probs["<UNK>"][curr_state]
elif curr_state not in state_trans_probs[prev_state]:
this_trans_prob = state_trans_probs[prev_state]["<UNK>"]
else:
this_trans_prob = state_trans_probs[prev_state][curr_state]
else:
this_trans_prob = state_trans_probs[prev_state][curr_state]
temp_state_probs[prev_state] = prev_probs[prev_state] * this_trans_prob * emission_probability
max_idx = np.argmax(temp_state_probs.values())
max_prob = temp_state_probs.values()[max_idx]
max_state = temp_state_probs.keys()[max_idx]
curr_bigram = (max_state[1], curr_state)
curr_state_probs[curr_bigram] = max_prob
new_path_dict[curr_bigram] = path_dict[max_state] + [curr_state]
prev_probs = curr_state_probs.copy()
path_dict = new_path_dict.copy()
# Identify overall most probable path
overall_max_idx = np.argmax(prev_probs.values())
overall_max_state = prev_probs.keys()[overall_max_idx]
return path_dict[overall_max_state]
def SimilarityViterbi(emission_probs, state_init_probs, state_trans_probs,
test_subseq, low_frequency_probabilities, smooth):
'''
For now we're ignoring the <UNK> tokens that were inserted
If we lookup an emission that doesn't exist, it will have probability 0, since we're using a Counter
'''
# Initialize paths and probabilities
path_dict = dict((state, [state[1]]) for state in state_init_probs.keys())
prev_probs = {}
for state in state_init_probs.keys():
prev_probs[state] = state_init_probs[state] * emission_probs[state[1]][
test_subseq[0]]
# Iterate over the sentence
all_states = set(state2 for state1 in state_trans_probs
for state2 in state_trans_probs[state1])
all_states.remove("<UNK>")
for emission in test_subseq[1:]:
new_path_dict = {}
curr_state_probs = {}
for curr_state in all_states:
temp_state_probs = {}
for prev_state in path_dict:
#Emission is unseen. Finding probability based on feature class
if emission not in emission_probs[curr_state]:
feature_class = findFeatureClass(emission)
# simple_curr_state = curr_state.split('-')[-1]
# emission_probability = low_frequency_probabilities[simple_curr_state][feature_class]
emission_probability = low_frequency_probabilities[
curr_state][feature_class]
else:
emission_probability = emission_probs[curr_state][emission]
if smooth:
if prev_state not in state_trans_probs:
this_trans_prob = state_trans_probs["<UNK>"][
curr_state]
elif curr_state not in state_trans_probs[prev_state]:
this_trans_prob = state_trans_probs[prev_state][
"<UNK>"]
else:
this_trans_prob = state_trans_probs[prev_state][
curr_state]
else:
this_trans_prob = state_trans_probs[prev_state][curr_state]
temp_state_probs[prev_state] = prev_probs[
prev_state] * this_trans_prob * emission_probability
max_idx = np.argmax(temp_state_probs.values())
max_prob = temp_state_probs.values()[max_idx]
max_state = temp_state_probs.keys()[max_idx]
curr_bigram = (max_state[1], curr_state)
curr_state_probs[curr_bigram] = max_prob
new_path_dict[curr_bigram] = path_dict[max_state] + [curr_state]
prev_probs = curr_state_probs.copy()
path_dict = new_path_dict.copy()
# Identify overall most probable path
overall_max_idx = np.argmax(prev_probs.values())
overall_max_state = prev_probs.keys()[overall_max_idx]
return path_dict[overall_max_state]
def Viterbi(emission_probs, state_init_probs, state_trans_probs, test_subseq, low_frequency_probabilities, smooth, similarity_based, pos_subseq):
'''
For now we're ignoring the <UNK> tokens that were inserted
If we lookup an emission that doesn't exist, it will have probability 0, since we're using a Counter
'''
# Initialize paths and probabilities
path_dict = dict((state, [state]) for state in state_init_probs.keys())
prev_probs = {}
for state in state_init_probs.keys():
prev_probs[state] = state_init_probs[state] * emission_probs[state][test_subseq[0]]
# Iterate over the sentence
all_states = set(state2 for state1 in state_trans_probs for state2 in state_trans_probs[state1])
# for emission in test_subseq[1:]:
for emission_idx in range(1, len(test_subseq)):
emission = test_subseq[emission_idx]
new_path_dict = {}
curr_state_probs = {}
for curr_state in all_states:
temp_state_probs = {}
for prev_state in path_dict:
if emission not in emission_probs[curr_state]:
#Using the smoothed values in case emission was something we had not seen before
if smooth == 'Laplacian' or smooth == 'Good-Turing':
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probs[curr_state]['<UNK>']
#Using feature classes from local context instead of smoothing
elif similarity_based:
feature_class = findFeatureClass(emission)
current_state = curr_state if '-' not in curr_state else curr_state.split('-')[1]
emission_probability = low_frequency_probabilities[feature_class][current_state]
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probability
else:
feature_class = pos_subseq[emission_idx]
current_state = curr_state if '-' not in curr_state else curr_state.split('-')[1]
emission_probability = low_frequency_probabilities[feature_class][current_state]
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probability
else:
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probs[curr_state][emission]
max_idx = np.argmax(temp_state_probs.values())
max_prob = temp_state_probs.values()[max_idx]
max_state = temp_state_probs.keys()[max_idx]
curr_state_probs[curr_state] = max_prob
new_path_dict[curr_state] = path_dict[max_state] + [curr_state]
prev_probs = curr_state_probs.copy()
path_dict = new_path_dict.copy()
# Identify overall most probable path
overall_max_idx = np.argmax(prev_probs.values())
overall_max_state = prev_probs.keys()[overall_max_idx]
return path_dict[overall_max_state]
def Viterbi(emission_probs, state_init_probs, state_trans_probs, test_subseq,
low_frequency_probabilities, smooth, similarity_based, pos_subseq):
'''
For now we're ignoring the <UNK> tokens that were inserted
If we lookup an emission that doesn't exist, it will have probability 0, since we're using a Counter
'''
# Initialize paths and probabilities
path_dict = dict((state, [state]) for state in state_init_probs.keys())
prev_probs = {}
for state in state_init_probs.keys():
prev_probs[state] = state_init_probs[state] * emission_probs[state][
test_subseq[0]]
# Iterate over the sentence
all_states = set(state2 for state1 in state_trans_probs
for state2 in state_trans_probs[state1])
# for emission in test_subseq[1:]:
for emission_idx in range(1, len(test_subseq)):
emission = test_subseq[emission_idx]
new_path_dict = {}
curr_state_probs = {}
for curr_state in all_states:
temp_state_probs = {}
for prev_state in path_dict:
if emission not in emission_probs[curr_state]:
#Using the smoothed values in case emission was something we had not seen before
if smooth == 'Laplacian' or smooth == 'Good-Turing':
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probs[curr_state]['<UNK>']
#Using feature classes from local context instead of smoothing
elif similarity_based:
feature_class = findFeatureClass(emission)
current_state = curr_state if '-' not in curr_state else curr_state.split(
'-')[1]
emission_probability = low_frequency_probabilities[
feature_class][current_state]
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probability
else:
feature_class = pos_subseq[emission_idx]
current_state = curr_state if '-' not in curr_state else curr_state.split(
'-')[1]
emission_probability = low_frequency_probabilities[
feature_class][current_state]
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probability
else:
temp_state_probs[prev_state] = prev_probs[prev_state] * state_trans_probs[prev_state][curr_state] * \
emission_probs[curr_state][emission]
max_idx = np.argmax(temp_state_probs.values())
max_prob = temp_state_probs.values()[max_idx]
max_state = temp_state_probs.keys()[max_idx]
curr_state_probs[curr_state] = max_prob
new_path_dict[curr_state] = path_dict[max_state] + [curr_state]
prev_probs = curr_state_probs.copy()
path_dict = new_path_dict.copy()
# Identify overall most probable path
overall_max_idx = np.argmax(prev_probs.values())
overall_max_state = prev_probs.keys()[overall_max_idx]
return path_dict[overall_max_state]
