class MutualBootStrapper:
def __init__(self, data, seeds, patterns=None, processing=1):
if processing == 0:
tokenized = self.tokenize(data)
self.pos_tagged_data = self.pos_tag(tokenized)
self.find_patterns = self.find_patterns_tagged
self.find_seeds = self.find_seeds_tagged
elif processing == 1:
self.chunked_data = data
self.find_patterns = self.find_patterns_chunked
self.find_seeds = self.find_seeds_chunked
self.permanent_lexicon = set(seeds)
self.temporary_lexicon = defaultdict(set)
for s in seeds:
self.temporary_lexicon[s] = set()
self.best_extraction_patterns = set()
self.pattern_alphabet = Alphabet()
if patterns is not None:
for p in patterns:
self.pattern_alphabet.add(p)
self.n_counter_sets = None # import for getting candidate seeds
self.f_counter_sets = None
self.n_pattern_array = None
self.f_pattern_array = None
self.first_pattern_words = set()
def tokenize(self, text):
print "tokenizing...",
all_entries = []
for entry in text:
tokenized_entry = self._nested_tokenize(entry)
all_entries.append(tokenized_entry)
print "[DONE]"
return all_entries
def _nested_tokenize(self, untokenized_sentences):
tokenized_sents = nltk.sent_tokenize(untokenized_sentences)
tokenized_words = [nltk.word_tokenize(sent) for sent in tokenized_sents]
self._postprocess_tokenized_text(tokenized_words)
return tokenized_words
def _postprocess_tokenized_text(self, tokenized):
for i,sent in enumerate(tokenized):
for j,word in enumerate(sent):
tokenized[i][j] = word.lower()
if "/" in word:
tokenized[i][j] = re.sub(r"/", r" / ", word)
#mutating the list
def pos_tag(self, tokenized_data):
print "POS tagging... ",
pos_tagged_data = []
for entry in tokenized_data:
new_entry = []
for sentence in entry:
tagged = [("<START>", "<START>")]
tagged.extend(nltk.pos_tag(sentence))
new_entry.append(tagged)
pos_tagged_data.append(new_entry)
print "[DONE]"
return pos_tagged_data
def build_patterns_tagged(self, sentence, index, size):
window_start = index-size
window_end = index+1
sentence_copy = list(sentence)
sentence_copy[index] = "<x>",
while window_start <= index: # this isn't quite right
try:
candidate = zip(*sentence_copy[window_start:window_end])[0]
except IndexError:
candidate = []
if len(candidate) > 1:
self.pattern_alphabet.add(tuple(candidate))
if candidate[0] != "<x>":
self.first_pattern_words.add(candidate[0])
else:
self.first_pattern_words.add(candidate[1])
window_start += 1
window_end += 1
def find_patterns_tagged(self):
for entry in self.pos_tagged_data:
for sentence in entry:
for i,(word,tag) in enumerate(sentence):
if word in self.temporary_lexicon:
self.build_patterns_tagged(sentence, i, 2)
self.build_patterns_tagged(sentence, i, 1)
def find_patterns_chunked(self):
for entry in self.chunked_data:
for sentence in entry:
for i,word in enumerate(sentence):
if isinstance(word, Chunk) and word.head in self.temporary_lexicon:
self.build_patterns_chunked(sentence, i, 2)
self.build_patterns_chunked(sentence, i, 1)
def build_patterns_chunked(self, sentence, index, size):
sentence_copy = list(sentence)
sentence_copy[index] = "<x>",
sentence_copy = self._flatten_chunks(sentence_copy)
index = sentence_copy.index("<x>")
window_start = index-size
window_end = index+1
while window_start <= index:
candidate = sentence_copy[window_start:window_end]
if len(candidate) > 1:
self.pattern_alphabet.add(tuple(candidate))
window_start += 1
window_end += 1
def _flatten_chunks(self, sentence):
flattened_sentence = []
for constituent in sentence:
if isinstance(constituent, Chunk):
flattened_sentence.extend(constituent.tokens)
else:
flattened_sentence.append(constituent[0])
return flattened_sentence
def set_counter_arrays(self):
tmp_lst = [[]] * self.pattern_alphabet.size() # must be careful about pointers here
self.n_counter_sets = map(set, tmp_lst)
self.f_counter_sets = map(set, tmp_lst)
def find_seeds_chunked(self):
for entry in self.chunked_data:
for sentence in entry:
for i in range(len(sentence)):
if isinstance(sentence[i], Chunk):
self.match_pattern_chunked(sentence, i, 2)
self.match_pattern_chunked(sentence, i, 1)
def match_pattern_chunked(self, sentence, index, size):
candidate_seed = sentence[index].head
sentence_copy = list(sentence)
sentence_copy[index] = "<x>",
sentence_copy = self._flatten_chunks(sentence_copy)
index = sentence_copy.index("<x>")
window_start = index-size
window_end = index+1
while window_start <= index:
window = sentence_copy[window_start:window_end]
pattern = tuple(window)
if len(pattern) > 1 and \
self.pattern_alphabet.has_label(pattern) and \
len(candidate_seed) > 2:
pattern_index = self.pattern_alphabet.get_index(pattern)
# increment our counters
self.n_counter_sets[pattern_index].add(candidate_seed)
if candidate_seed not in self.temporary_lexicon:
self.f_counter_sets[pattern_index].add(candidate_seed)
window_start += 1
window_end += 1
def find_seeds_tagged(self):
for entry in self.pos_tagged_data:
for sentence in entry:
for i in range(len(sentence)):
if sentence[i][0] in self.first_pattern_words:
self.match_pattern_tagged(sentence, i, 3)
self.match_pattern_tagged(sentence, i, 2)
def match_pattern_tagged(self, sentence, index, size):
window_start = index-1
window_end = index+size-1
window = sentence[window_start:window_end]
for seed_candidate_index in range(len(window)):
window_copy = list(window)
_,pos = window_copy[seed_candidate_index]
window_copy[seed_candidate_index] = ("<x>", pos)
pattern = tuple(zip(*window_copy)[0])
if len(pattern) > 1 and \
self.pattern_alphabet.has_label(pattern) and \
window[seed_candidate_index][1].startswith("NN") and \
len(window[seed_candidate_index][0]) > 2:
candidate_seed = window[seed_candidate_index][0]
pattern_index = self.pattern_alphabet.get_index(pattern)
# increment our counters
self.n_counter_sets[pattern_index].add(candidate_seed)
if candidate_seed not in self.temporary_lexicon:
self.f_counter_sets[pattern_index].add(candidate_seed)
def calculate_pattern_scores(self):
self.n_pattern_array = numpy.array(map(len, self.n_counter_sets), dtype=float) + 1.
self.f_pattern_array = numpy.array(map(len, self.f_counter_sets), dtype=float) + 1.
self.pattern_scores = numpy.nan_to_num((self.f_pattern_array/self.n_pattern_array)*numpy.log2(self.f_pattern_array))
def calculate_seed_scores(self):
self.candidate_seed_scores = {}
for candidate_seed,matched_patterns_set in self.temporary_lexicon.iteritems():
matched_patterns = list(matched_patterns_set)
score = numpy.sum((self.pattern_scores[matched_patterns] * 0.01) + 1)
#print score
self.candidate_seed_scores[candidate_seed] = score
def cull_candidates(self):
self.calculate_pattern_scores()
self.calculate_seed_scores()
sorted_candidates = sorted([(v,k) for k,v in self.candidate_seed_scores.iteritems()], reverse=True)
#print sorted_candidates
try:
return zip(*sorted_candidates)[1][:5]
except IndexError:
return []
def run_mutual_bootstrapping(self):
added_patterns = 0
best_score = 5
while added_patterns < 10 or best_score > 1.8:
self.find_patterns()
self.set_counter_arrays()
self.find_seeds()
self.calculate_pattern_scores()
best_pattern_index = numpy.nanargmax(self.pattern_scores)
while best_pattern_index in self.best_extraction_patterns:
self.pattern_scores[best_pattern_index] = -10000000.
best_pattern_index = numpy.nanargmax(self.pattern_scores)
if self.pattern_scores[best_pattern_index] < 0.7:
return
best_score = self.pattern_scores[best_pattern_index]
#print best_score, self.pattern_alphabet.get_label(best_pattern_index)
self.best_extraction_patterns.add(best_pattern_index)
for seed in self.n_counter_sets[best_pattern_index]:
self.temporary_lexicon[seed].add(best_pattern_index)
added_patterns += 1
def run_meta_bootstrapping(self):
best_five = self.cull_candidates()
self.permanent_lexicon.update(best_five)
self.temporary_lexicon = defaultdict(set)
for s in self.permanent_lexicon:
self.temporary_lexicon[s] = set()
def run(self, num_iterations=50):
for i in range(num_iterations):
print "Iteration: {:d}".format(i+1)
print "running mutual bootstrapping..."
self.run_mutual_bootstrapping()
print "[DONE]"
print "running meta bootstrapping...",
self.run_meta_bootstrapping()
print "[DONE]"
print "number of seed terms: {:d}".format(len(self.permanent_lexicon))
print "number of total patterns: {:d}".format(self.pattern_alphabet.size())
print "\n"
def save_seeds(self, outfile):
with open(outfile, "w") as f_out:
f_out.write("\n".join(s.encode("utf-8") for s in self.permanent_lexicon))
def save_patterns(self, outfile):
with open(outfile, "w") as f_out:
patterns = []
for pattern_index in self.best_extraction_patterns:
patterns.append(" ".join(self.pattern_alphabet.get_label(pattern_index)))
f_out.write("\n".join(s.encode("utf-8") for s in patterns))
class HMM(BaseClassifier):
def __init__(self):
self.label_alphabet = Alphabet()
self.feature_alphabet = Alphabet()
self.transition_matrix = None
self.emission_matrix = None
self.initial_probability = None
@property
def num_states(self):
return self.label_alphabet.size()
@property
def num_observations(self):
return self.feature_alphabet.size()
def _mutate_data(self, instance):
try:
_ = instance.old_data
except:
instance.old_data = instance.data
instance.data = self.feature_alphabet.get_indices(instance.data)
def _mutate_label(self, instance):
try:
_ = instance.old_label
except:
instance.old_label = instance.label
instance.label = self.label_alphabet.get_indices(instance.label)
def populate_alphabets(self, instance_list):
"""Populate alphabets
You guys have done this twice already. So I'm doing it for you this time.
But a few things to note
the labels get converted to label indices
the feature vectors get converted to sparse vector
each time step contains exactly one feature (observation)
Feel free to edit/modify/tear apart this function
"""
for instance in instance_list:
for label in instance.label:
self.label_alphabet.add(label)
for observation in instance.data:
self.feature_alphabet.add(observation)
self._mutate_data(instance)
self._mutate_label(instance)
#for test cases and unsupervised training
self.transition_matrix = numpy.zeros((self.num_states, self.num_states))
self.emission_matrix = numpy.zeros((self.num_states, self.num_observations))
self.initial_probability = numpy.zeros(self.num_states)
def collect_counts(self, instance_list):
"""Collect counts for fitting HMM parameters
Very similar to Naive Bayes, we have to collect counts for estimating parameters:
transition_counts[i,j] = the number of occurrences that state i comes before state j
observation_counts[i,j] = the number of occurrences that state i is aligned with observation j
initial_state_counts[i] = the number of occurrences that state i is at the beginning of the sequence
Add your implementation
"""
transition_counts = numpy.zeros((self.num_states, self.num_states))
initial_state_counts = numpy.zeros(self.num_states)
observation_counts = numpy.zeros((self.num_states, self.num_observations))
for instance in instance_list:
trans = zip(instance.label[:-1], instance.label[1:])
transition_counts[instance.label[:-1], instance.label[1:]] += \
map(trans.count, trans) #quirky workaround; commented code above doesn't work
obs = zip(instance.label, instance.data)
observation_counts[instance.label, instance.data] += \
map(obs.count, obs)
initial_state_counts[instance.label[0]] += 1 #increment initial state
return (transition_counts, initial_state_counts, observation_counts)
def train(self, instance_list):
"""Train the HMM
Collect counts and find the best parameters for
transition matrix, emission matrix, and initial probability
DO NOT smooth the counts
Add your implementation
"""
self.populate_alphabets(instance_list)
transition_counts, initial_state_counts, observation_counts = self.collect_counts(instance_list)
#fill in these matrices
#availing of columnar summation of numpy arrays
self.transition_matrix = transition_counts / numpy.sum(transition_counts, 1)
self.emission_matrix = (observation_counts.T / (numpy.sum(transition_counts, 0) + \
initial_state_counts)).T #p(Y1|X0) + p(Y1|X1) + ... = p(Y1)
self.initial_probability = initial_state_counts / sum(initial_state_counts) #p(X|Start)
def forward_algorithm(self, instance):
"""Run forward algorithm
Add your implementation
"""
sequence_length = len(instance.data)
alpha = numpy.zeros((self.num_states, sequence_length))
#initialization
alpha[:, 0] = self.initial_probability * self.emission_matrix[:,instance.data[0]]
#recursion
for t in range(1, sequence_length):
alpha[:, t] = numpy.sum(alpha[:, t-1] * self.transition_matrix.T * \
self.emission_matrix[:, instance.data[t]], 1)
return alpha
def backward_algorithm(self, instance):
"""Run backward algorithm
Add your implementation
"""
sequence_length = len(instance.data)
beta = numpy.zeros((self.num_states, sequence_length))
#initialization
beta[:, -1] += 1
#recursion
for t in reversed(xrange(sequence_length - 1)):
beta[:, t] = numpy.sum(self.transition_matrix * \
self.emission_matrix[:, instance.data[t + 1]] * \
beta[:, t + 1], 1)
return beta
def compute_likelihood(self, alpha):
"""Compute likelihood P(O1:T) given forward values
This function is necessary for computing expected counts.
It should assume that alpha (forward) values are computed correctly.
This function should be just one line
Add your implementation
"""
#return sum(alpha)[-1]
#return sum(alpha[:, -1])
return numpy.sum(alpha, 0)[-1]
def compute_expected_counts(self, instance):
"""E-step for EM Algorithm for learning HMM parameters
This function is fully implemented for you
"""
alpha = self.forward_algorithm(instance)
beta = self.backward_algorithm(instance)
sequence_length = len(instance.data)
likelihood = self.compute_likelihood(alpha)
gamma = alpha * beta / likelihood
expected_observation_counts = numpy.zeros((self.num_states, self.num_observations))
for t in xrange(sequence_length):
feature_index = instance.data[t]
expected_observation_counts[:, feature_index] += gamma[:, t]
expected_transition_counts = numpy.zeros((self.num_states, self.num_states))
for t in xrange(sequence_length-1):
feature_index = instance.data[t+1]
obs = self.emission_matrix[:, feature_index]
m1 = numpy.matrix(alpha[:, t])
m2 = numpy.matrix(beta[:, t+1] * obs)
xi = numpy.multiply(m1.transpose().dot(m2), self.transition_matrix) / likelihood
expected_transition_counts += xi
return (expected_transition_counts, expected_observation_counts, likelihood)
def _be_prepared_for_baum_welch(self, training_set, mode = 'uniform', inf = None):
"""Initialize transition_matrix, emission_matrix, and initial_probability for Baum-Welch.
@param training_set: the training data
@param mode: can be 'uniform', 'random', or 'sneaky'
@inf: used in sneaky mode; this is a file containing a dictionary
serialization of an HMM object
"""
self.populate_alphabets(training_set)
HVAL = 100 #added to high positions in sparse rows for weak training
LVAL = 1 #added to low positions in sparse rows for weak training
if mode == 'uniform': #all elements in a row are equal
self.transition_matrix += (1.0 / numpy.size(self.transition_matrix, 1))
self.emission_matrix += (1.0 / numpy.size(self.emission_matrix, 1))
self.initial_probability += (1.0 / numpy.size(self.initial_probability))
else: #elements will be unequal
#choose one element per row per matrix to be
#much higher than its dear siblings
if mode == 'random': #high element is selected randomly
random.seed()
trans = [random.choice(range(numpy.size(self.transition_matrix, 1))) \
for i in range(numpy.size(self.transition_matrix, 0))]
emits = [random.choice(range(numpy.size(self.emission_matrix, 1))) \
for i in range(numpy.size(self.emission_matrix, 0))]
init = random.choice(range(len(self.initial_probability)))
elif mode == 'sneaky': #use some information from the data, but don't tell anyone!
tempdict = HMM.from_dict(cPickle.load(inf))
tcounts, icounts, ocounts = [tempdict[i] for i in 'transition_matrix', \
'initial_probability', 'emission_matrix']
trans = numpy.argmax(tcounts, 1)
emits = numpy.argmax(ocounts, 1)
init = numpy.argmax(icounts)
#ensure that no element is zero and that the selected element is substantially higher
self.transition_matrix[range(numpy.size(self.transition_matrix, 0)), trans] += HVAL
self.transition_matrix += LVAL
self.emission_matrix[range(numpy.size(self.emission_matrix, 0)), emits] += HVAL
self.emission_matrix += LVAL
self.initial_probability[init] += HVAL
self.initial_probability += LVAL
#normalize
self.transition_matrix = (self.transition_matrix.T / numpy.sum(\
self.transition_matrix, 1)).T
self.emission_matrix = (self.emission_matrix.T / numpy.sum(\
self.emission_matrix, 1)).T
self.initial_probability /= sum(self.initial_probability)
def baum_welch_train(self, instance_list):
"""Baum-Welch unsupervised training
Before calling this function, you have to call
self.populate_alphabets(instance_list)
and then initialize transition matrix, observation matrix, and initial probability.
It's ok to fix initial probability to 1 / self.num_states (Uniform)
This function is not so optimized, so it can't turn the crank on too large a dataset.
"""
num_states = self.label_alphabet.size()
num_features = self.feature_alphabet.size()
old_total_loglikelihood = - numpy.Infinity
for i in xrange(30):
expected_observation_counts = numpy.zeros((num_states, num_features))
expected_transition_counts = numpy.zeros((num_states, num_states))
total_log_likelihood = 0
#E-Step
for instance in instance_list:
transition_counts, obs_counts, likelihood = self.compute_expected_counts(instance)
expected_observation_counts += obs_counts
expected_transition_counts += transition_counts
total_log_likelihood += numpy.log(likelihood)
#M-Step
self.transition_matrix = (expected_transition_counts.transpose() / numpy.sum(expected_transition_counts, 1)).transpose()
self.emission_matrix = (expected_observation_counts.transpose() / numpy.sum(expected_observation_counts, 1)).transpose()
print 'Iteration %s : %s ' % (i, total_log_likelihood)
if total_log_likelihood < old_total_loglikelihood:
break
old_total_loglikelihood = total_log_likelihood
self.initial_probability = numpy.zeros(num_states) + 1.0/num_states
def classify_instance(self, instance):
"""Viterbi decoding algorithm
Returns a list of label strings e.g. ['Hot', 'Cold', 'Cold']
Add your implementation
"""
self._mutate_data(instance) #just in case
#initialization
slength = len(instance.data)
v = numpy.zeros((self.num_states, slength))
backtrace = numpy.zeros((self.num_states, slength))
v[:, 0] = self.initial_probability * self.emission_matrix[:, \
instance.data[0]]
#recursion
for t in range(1, slength):
tempmat = v[:, t-1] * self.transition_matrix.T
maxis = numpy.argmax(tempmat, axis = 1)
backtrace[:, slength - t] = maxis #facilitates reversal later
v[:, t] = v[maxis, t-1] * self.transition_matrix[maxis, \
xrange(numpy.size(self.transition_matrix, 1))] * \
self.emission_matrix[:, instance.data[t]]
#termination
backtrace[:, 0] = v[:, -1]
return self._run_backtrace(backtrace)
def _run_backtrace(self, back_mat):
"""
Helper function for extracting
@param back_mat: a deque
"""
stack = [numpy.argmax(back_mat[:, 0])]
for ind in xrange(1, numpy.size(back_mat, 1)):
stack.append(back_mat[stack[-1], ind])
res = []
while stack:
res.append(self.label_alphabet.get_label(stack.pop()))
return res
def print_parameters(self):
"""Print the two parameter matrices
You should take advantage of this function in debugging
and inspecting the resulting parameters.
This function is implemented for you.
"""
state_header = map(str, [self.label_alphabet.get_label(i) \
for i in xrange(self.label_alphabet.size())])
obs_header = map(str, [self.feature_alphabet.get_label(i) \
for i in xrange(self.feature_alphabet.size())])
print matrix_to_string(self.emission_matrix, state_header, obs_header)
print matrix_to_string(self.transition_matrix, state_header, state_header)
def to_dict(self):
"""Convert HMM instance into a dictionary representation
The implementation of this should be in sync with from_dict function.
You should be able to use these two functions to convert the model into
either representation (object or dictionary)
We have enough of this. This is fully implemented for you.
"""
model_dict = {
'label_alphabet': self.label_alphabet.to_dict(),
'feature_alphabet': self.feature_alphabet.to_dict(),
'transition_matrix': self.transition_matrix.to_list(),
'emission_matrix': self.emission_matrix.to_list(),
'initial_probability': self.initial_probability.to_list()
}
return model_dict
@classmethod
def from_dict(model_dict):
"""Convert a dictionary into HMM instance
The implementation of this should be in sync with to_dict function.
This is fully implemented for you.
"""
hmm = HMM()
hmm.label_alphabet = Alphabet.from_dict(model_dict['label_alphabet'])
hmm.feature_alphabet = Alphabet.from_dict(model_dict['feature_alphabet'])
hmm.transition_matrix = numpy.array(model_dict['transition_matrix'])
hmm.emission_matrix = numpy.array(model_dict['emission_matrix'])
hmm.initial_probability = numpy.array(model_dict['initial_probability'])
return hmm
class Naive_Bayes(object):
""""""
def __init__(self, data, feature_function):
"""
Takes a dictionary mapping labels to lists of strings with that label, and a function which
produces a list of feature values from a string.
"""
# your code here!
self.data = data
self.feature_codebook = Alphabet()
# self.word_dict = Alphabet()
self.label_codebook = Alphabet()
self.feature_function = feature_function
# def _build_instance_list(self):
# """"""
# instance_list = {}
# for label, documents in self.data.items():
# instance_list[label] = []
# for doc in documents:
# vector = self.extract_feature(self.data, doc, s)
# instance_list[label].append(vector)
# self.instance_list = instance_list
#
# def _populate_codebook(self):
# """"""
# for label in self.instance_list:
# self.label_codebook.add(label)
# #here we use all the word set as features
# self.feature_codebook = copy.deepcopy(self.word_dict)
def extract_feature(self, string):
""""""
vector = np.zeros(self.feature_codebook.size())
tokens = set(nltk.regexp_tokenize(string, pattern="\w+"))
indice = 0
for word in tokens:
if self.feature_codebook.has_label(word):
indice = self.feature_codebook.get_index(word)
vector[indice] = 1.0
return vector
def _collect_counts(self):
""""""
self.count_table = np.zeros((self.feature_codebook.size(), self.label_codebook.size()))
self.count_y_table = np.zeros(self.label_codebook.size())
for label, docs in self.instance_list.items():
Y_index = self.label_codebook.get_index(label)
for vector in docs:
self.count_y_table[Y_index] += 1.0
self.count_table[:, Y_index] += vector
# for sparse vector we use different counting method
# for x in vector:
# self.count_table[x,Y_index] += 1.0
def train(self, theta):
""""""
self.instance_list = self.feature_function(self.data, self.label_codebook, self.feature_codebook, theta)
# self._populate_codebook_withSelectFeature()
# self.instance_list = self.feature_function(self.data, self.label_codebook, self.feature_codebook, select_feature)
self._collect_counts()
self.p_x_given_y_table = np.zeros((self.feature_codebook.size(), self.label_codebook.size()))
self.p_y_table = np.zeros(self.label_codebook.size())
self.p_x_given_y_table = (self.count_table + 0.2) / (self.count_y_table + self.feature_codebook.size() * 0.2)
self.p_y_table = self.count_y_table / self.count_y_table.sum()
def compute_log_unnormalized_score(self, feature_vector):
"""Compute log P(X|Y) + log P(Y) for all values of Y
Returns a vector of loglikelihood.
loglikelihood_vector[0] = log P(X|Y=0) + log P(Y=0)
"""
loglikelihood_vector = np.zeros(self.label_codebook.size())
for label in range(0, self.label_codebook.size()):
logpro = math.log(self.p_y_table[label])
for feature_index in range(0, self.feature_codebook.size()):
logpro += feature_vector[feature_index] * math.log(self.p_x_given_y_table[feature_index, label]) + (1 - feature_vector[feature_index]) * math.log(1 - self.p_x_given_y_table[feature_index, label])
loglikelihood_vector[label] = logpro
return loglikelihood_vector
def classify(self, string):
"""
Classifies a string according to the feature function and training data
provided at initialization.
Predict the label of the given instance
return the predict label for the input document
"""
# your code here!
feature_vector = self.extract_feature(string)
logvector = self.compute_log_unnormalized_score(feature_vector)
# print vector
pre_label_index = np.argmax(logvector)
return self.label_codebook.get_label(pre_label_index)
class MaxEnt(BaseClassifier):
def __init__(self, gaussian_prior_variance = 1):
"""Initialize the model
label_alphabet, feature_alphabet, parameters must be
consistent in order for the model to work.
parameters numpy.array assumes a specific shape. Look athe assignment sheet for detail
Add your implementation
"""
super(MaxEnt, self).__init__()
self.label_alphabet = Alphabet()
self.feature_alphabet = Alphabet()
self.gaussian_prior_variance = gaussian_prior_variance
self.parameters = numpy.array([])
self.feature_counts = None
def get_parameter_indices(self, feature_indices, label_index):
"""Get the indices on the parameter vector
Given a list of feature indices and the label index,
the function will give you a numpy array of the corresponding indices on self.parameters
This function is fully implemented for you.
"""
indices = numpy.array(feature_indices) + 1
intercept = numpy.array([0])
indices = numpy.concatenate((intercept, indices), 1)
indices = indices + (label_index * (self.feature_alphabet.size() + 1))
return indices
def compute_observed_counts(self, instance_list):
"""Compute observed feature counts
It should only be done once because it's parameter-independent.
The observed feature counts are then stored internally.
Note that we are fitting the model with the intercept terms
so the count of intercept term is the count of that class.
Additionally, we have to
1) populate alphabet
2) convert instance.data into a vector of feature indices aka sparse vectors
(use the alphabet)
Add your implementation
"""
#If it's already been counted, just return the value from the cache
if not self.feature_counts:
#populate alphabets here
for instance in instance_list:
self.label_alphabet.add(instance.label) #update label dictionary
for datum in instance.data:
self.feature_alphabet.add(datum) #update feature dictionary
self.feature_counts = numpy.zeros((self.feature_alphabet.size() \
+ 1) * self.label_alphabet.size()) #generate observed count vector
else:
return self.feature_counts
#compute the feature counts here
for instance in instance_list:
newinds = self.feature_alphabet.get_indices(instance.data)
sparse_vector = self.get_parameter_indices(newinds, \
self.label_alphabet.get_index(instance.label))
self.feature_counts[sparse_vector] += 1
#instance.data = newinds
if not instance.converted:
instance.data = numpy.array(sorted(set(newinds))) #remove duplicates
instance.converted = True #do not allow confusion
return self.feature_counts
def compute_label_unnormalized_loglikelihood_vector(self, sparse_feature_vector):
"""Compute unnormalized log score from log-linear model
log P(Y|X) is proportional to feature vector * parameter vector
But we use a sparse vector representation, so we need to use
index tricks that numpy allows us to do.
"""
loglikelihood_score_vector = numpy.zeros(self.label_alphabet.size())
for index, label in self.label_alphabet:
loglikelihood_score_vector[index] = sum(\
self.parameters[self.get_parameter_indices(\
sparse_feature_vector, index)])
#dot product of parameters and feature functions
#which yields sum of parameters at indices
return loglikelihood_score_vector
def compute_posterior_distribution(self, instance):
"""Compute P(Y|X)
Return a vector of the same size as the label_alphabet
Add your implementation
"""
posterior_distribution = numpy.zeros(self.label_alphabet.size()) #initialize
unnorm = self.compute_label_unnormalized_loglikelihood_vector(\
instance.data) #compute unnormalized log-likelihood
if DEBUG_2:
print unnorm
posterior_distribution = numpy.exp(unnorm)/ sum(numpy.exp(unnorm)) #normalize
return posterior_distribution
def _argmax(self, func, *args):
"""Not needed because numpy's is better"""
res = [func(arg) for arg in args]
m = max(res)
for arg in args:
if func(arg) == m:
return arg
def compute_expected_feature_counts(self, instance_list):
"""Compute expected feature counts
We take advantage of compute_posterior_distribution in this class to compute
expected feature counts, which is only needed for training.
Add your implementation
"""
expected_feature_counts = numpy.zeros((self.feature_alphabet.size() + 1) * self.label_alphabet.size())
for instance in instance_list:
#add posterior to expected_feature_counts at appropriate indices
post_dist = self.compute_posterior_distribution(instance) #posterior distribution
for jndex, label in self.label_alphabet:
indices = self.get_parameter_indices(\
instance.data, jndex)
expected_feature_counts[indices] += post_dist[jndex]
# increment expected counts at appropriate indices
return expected_feature_counts
def classify_instance(self, instance):
"""Applying the model to a new ins
tance
Convert instance.data into a sparse vector and then classify the instance.
Returns the predicted label.
Add your implementation
"""
if DEBUG_2:
print instance.data
if not instance.converted:
instance.data = self.feature_alphabet.get_indices(instance.data)
instance.converted = True
# get_indices eliminates any heretofore unseen features
if DEBUG_2:
print instance.data
print self.compute_posterior_distribution(instance)
return self.label_alphabet.get_label(numpy.argmax( \
self.compute_posterior_distribution(instance))) #return label corresponding to best index
def objective_function(self, parameters):
"""Compute negative (log P(Y|X,lambdas) + log P(lambdas))
The function that we want to optimize over.
You won't have to call this function yourself. fmin_l_bfgs_b will call it.
Add your implementation
"""
total_loglikelihood = 0.0
self.parameters = parameters
#add normalizing term
total_loglikelihood -= numpy.sum(parameters * parameters) / \
self.gaussian_prior_variance
# Compute the loglikelihood here
for instance in self.training_data:
#add posterior at correct label index
total_loglikelihood += self.compute_posterior_distribution(instance) \
[self.label_alphabet.get_index(instance.label)]
return - total_loglikelihood
def gradient_function(self, parameters):
"""Compute gradient of negative (log P(Y|X,lambdas) + log P(lambdas)) wrt lambdas
With some algebra, we have that
gradient wrt lambda i = observed_count of feature i - expected_count of feature i
The first term is computed before running the optimization function and is a constant.
The second term needs inference to get P(Y|X, lambdas) and is a bit expensive.
The third term is from taking the derivative of log gaussian prior
Returns:
a vector of gradient
Add your implementation
"""
gradient_vector = numpy.zeros(len(parameters))
# compute gradient here
gradient_vector += self.feature_counts - \
self.compute_expected_feature_counts(self.training_data) - \
2 * (parameters) / self.gaussian_prior_variance
if DEBUG_1:
print gradient_vector
return - gradient_vector
def train(self, instance_list):
"""Find the optimal parameters for maximum entropy classifier
We leave the actual number crunching and search to fmin_bfgs function.
There are a few tunable parameters for the optimization function but
the default is usually well-tuned and sufficient for most purposes.
Arg:
instance_list: each instance.data should be a string feature vector
This function is fully implemented. But you are allowed to make changes
"""
self.training_data = instance_list
self.compute_observed_counts(instance_list)
num_labels = self.label_alphabet.size()
num_features = self.feature_alphabet.size()
init_point = numpy.zeros(num_labels * (num_features + 1))
optimal_parameters, _, _ = fmin_l_bfgs_b(self.objective_function, init_point, fprime=self.gradient_function)
self.parameters = optimal_parameters
def to_dict(self):
"""Convert MaxEnt into a dictionary so that save() will work
Add your implementation
"""
res = {}
res['labalph'] = self.label_alphabet.to_dict()
res['feaalph'] = self.feature_alphabet.to_dict()
res['gpv'] = self.gaussian_prior_variance
res['param'] = self.parameters
return res
@classmethod
def from_dict(cls, model_dictionary):
"""Return an instance of MaxEnt based on the dictionary created by to_dict
Add your implementation
"""
res = MaxEnt()
res.label_alphabet = Alphabet.from_dict(model_dictionary['labalph'])
res.feature_alphabet = Alphabet.from_dict(model_dictionary['feaalph'])
res.gaussian_prior_variance = model_dictionary['gpv']
res.parameters = model_dictionary['param']
return res
class TranslationModel:
def __init__(self, aligned_sentences, max_iterations=-1, eta=None):
"""
:param aligned_sentences: a list of tuples of aligned sentences
:param max_iterations: the number of iterations to run EM
:param eta: the value that the delta of the EM probabilities must fall below to be considered converged
"""
self.aligned_sentences = aligned_sentences
self.e_alphabet = Alphabet()
self.f_alphabet = Alphabet()
if eta is None:
# very simple heuristic
self.eta = len(aligned_sentences)/100.
else:
self.eta = eta
self.max_iterations = max_iterations
if max_iterations == -1:
self.do_more = self.has_converged
else:
self.do_more = self.stop_iterations
def convert_to_vector(self, raw_data, lang="e", training=True):
"""
:param raw_data: a tokenized sentence
:param lang: whether it's source or target
:param training: whether this is during training or testing
:return: numpy array of the integers corresponding to words
"""
if lang == "e":
alphabet = self.e_alphabet
else:
alphabet = self.f_alphabet
if training:
return numpy.array(map(alphabet.get_index, raw_data), dtype=int)
else:
vector = numpy.zeros(len(raw_data), dtype=int)
for i,word in enumerate(raw_data):
try:
vector[i] = alphabet.get_index(word)
except KeyError:
continue #ignoring OOV words
return vector
def populate_alphabets(self):
"""
Populates the alphabets so that the tokens can have an integer
representation. Also converts the sentences into this format.
"""
for e_instance,f_instance in self.aligned_sentences:
for i,token in enumerate(e_instance.raw_data):
self.e_alphabet.add(token)
for i,token in enumerate(f_instance.raw_data):
self.f_alphabet.add(token)
e_instance.data = self.convert_to_vector(e_instance.raw_data, "e")
f_instance.data = self.convert_to_vector(f_instance.raw_data, "f")
def init_translation_table(self):
"""
Sets up the class field of the translation table and the cache of
the previous table in order to do the initial delta.
Initializes the probability of everything at 0.25
"""
self.t_table = numpy.zeros([self.e_alphabet.size(), self.f_alphabet.size()])
self.previous_t_table = numpy.zeros([self.e_alphabet.size(), self.f_alphabet.size()])
self.t_table.fill(.25)
def expectation_maximization(self):
"""
runs the EM algorithm for a specific number of iterations or until
it has converged.
"""
i = 0
while not self.do_more(i):
time1 = time.time()
print "iteration {:d}".format(i+1),
# initialize
self.total = numpy.zeros(self.f_alphabet.size())
self.counts = numpy.zeros([self.e_alphabet.size(), self.f_alphabet.size()])
for e_instance,f_instance in self.aligned_sentences:
self.s_total = numpy.zeros(self.e_alphabet.size())
# compute normalization
for e_word in e_instance.data:
self.s_total[e_word] += numpy.sum(self.t_table[e_word, f_instance.data])
# collect counts
for e in e_instance.data:
tmp = self.t_table[e,f_instance.data]/self.s_total[e]
self.counts[e,f_instance.data] += tmp
self.total[f_instance.data] += tmp
# estimate probabilities
self.t_table = self.counts/self.total
i += 1
print "\t{:.3f} seconds".format(time.time()-time1),
def has_converged(self, i):
"""
calculates the delta, sees if it is lower than eta
@param i: only used so this method can have the same signature as stop_iterations
@return: a boolean whether the EM iterations need to stop
"""
delta = numpy.sum(numpy.abs(self.t_table - self.previous_t_table))
self.previous_t_table = numpy.copy(self.t_table)
if i != 0:
print "\tdelta: {:.3f}".format(delta)
if delta < self.eta:
return True
return False
def stop_iterations(self, i):
"""
@param i: current iteration nubmer
@return: boolean whether EM need to stop iterating
"""
print
return i >= self.max_iterations
def train(self):
"""
does all tasks necessary to train our model
"""
self.populate_alphabets()
self.init_translation_table()
self.expectation_maximization()
self.build_language_model()
def evaluate(self, candidate_data):
"""
given candidate translations, this will select the best according to
our translation table and language model. prints the source sentence
and best candidate, which is argmax(p(t|s))
:param candidate_data: a list with a source sentence and translation candidates
"""
for (source, source_sent_tokenized), candidates in candidate_data:
candidate_scores = numpy.zeros(len(candidates))
for i,(c_sent, c) in enumerate(candidates):
candidate_scores[i] = self.translation_log_prob(c, source_sent_tokenized)
print u"source sentence: {:s}".format(source)
print u"best translation: {:s}".format(candidates[numpy.argmax(candidate_scores)][0])
print
def t_table_log_prob(self, e_sentence, f_sentence):
"""
gives the log(p(s|t))
:param e_sentence: tokenized target sentence
:param f_sentence: tokenized candidate sentence
:return: the log probability of a sentence translating to a candidate
"""
e_sentence = self.convert_to_vector(e_sentence, lang="e", training=False)
f_sentence = self.convert_to_vector(f_sentence, lang="f", training=False)
product = 1.
for e in e_sentence:
product *= numpy.sum(self.t_table[e,f_sentence])
return numpy.log(product/(len(f_sentence)**len(e_sentence)))
def build_language_model(self):
"""
creates the language model for our target language and saves it in a class
field
"""
all_data = []
for e_sentence,_ in self.aligned_sentences:
all_data.append(e_sentence.data)
self.language_model = BigramModel(all_data, self.e_alphabet)
self.language_model.train()
def translation_log_prob(self, e_sentence, f_sentence):
"""
:param e_sentence: tokenized target sentence
:param f_sentence: tokenized source sentence
:return: the log(p(s|t)*p())
"""
return self.t_table_log_prob(e_sentence, f_sentence) + self.language_model.log_prob(e_sentence)
def save(self):
raise NotImplementedError
def load(self, data):
raise NotImplementedError
class NaiveBayes(BaseClassifier):
def __init__(self):
"""Constructor
Utility classes and class variables should be initialized here.
Add your implementation.
"""
self.label_codebook = Alphabet()
self.feature_codebook = Alphabet()
def _collect_counts(self, instance_list):
"""Collect feature and label counts from the dataset
This function should first index all of labels and features
and update the two codebooks. Then go through the data again
and count all of labels and features in
self.count_x_y_table
self.count_y_table
For example,
self.count_x_y_table[12, 0] = Count of feature 12 co-occurring with label 0
self.count_y_table[1] = Count of label 1
If you want to know what feature 12 is, you should be able to look it up by
self.feature_codebook.get_label(12)
Add your implementation.
"""
for gen in set(map(lambda x: x.label, instance_list)):
self.label_codebook.add(gen)
for vector in map(lambda x: x.data, instance_list):
for feat in vector:
self.feature_codebook.add(feat)
self.count_x_y_table = numpy.zeros(map(len, [self.feature_codebook, self.label_codebook]))
self.count_y_table = numpy.zeros(len(self.label_codebook))
for i, instance in enumerate(instance_list):
print "Training on instance %d of %d." % (i, len(instance_list))
label = self.label_codebook.get_index(instance.label)
self.count_y_table[label] += 1
for index, feature in self.feature_codebook:
self.count_x_y_table[index,label] += int(feature in instance)
if DEBUG:
for index, label in self.label_codebook:
print label,
print ''
for i, (e1, e2) in enumerate(self.count_x_y_table):
print '%s: %d, %d' % (self.feature_codebook.get_label(i), e1, e2)
def train(self, instance_list, smoothmode = 'laplace'):
"""Fit model parameters based on the dataset
You should assume that self.label_codebook and self.feature_codebook are now
properly populated.
Populate p_x_given_y_table and p_y_table with their maximum likelihood estimates
For example :
self.p_x_given_y_table[10, 1] = P(X10 = 1|Y=1)
self.p_y_table[1] = P(Y=1)
You should also do some kind of smoothing.
Add your implementation
"""
self._collect_counts(instance_list)
if smoothmode == 'add-one':
self.smooth_table(smoothmode)
self.p_x_given_y_table = numpy.zeros((self.feature_codebook.size(), self.label_codebook.size()))
for row, counts in enumerate(self.count_x_y_table):
for col, count in enumerate(counts):
self.p_x_given_y_table[row,col] = float(count) / self.count_y_table[col]
self.p_y_table = numpy.zeros(self.label_codebook.size())
for col, count in enumerate(self.count_y_table):
self.p_y_table[col] = float(count) / sum(self.count_y_table)
if smoothmode == 'laplace':
self.smooth_table(smoothmode)
def smooth_table(self, mode):
"""
Implements smoothing algorithms for probability tables;
defaults to Laplace smoothing, distributing probability mass of
least-frequent elements to zero-probabiity elements.
"""
if mode == 'laplace':
#get lists of p(x|y) values for each y
newtabs = [self.p_x_given_y_table[0:,i] \
for i in range(self.p_x_given_y_table.size / \
len(self.p_x_given_y_table))]
#descry lowest-frequency nonzero elements in p(x|y) table
mincounts = map(lambda x: min([i for i in x if i]), newtabs)
#get indices of minimal and zero values
inds = [[j for j, elem in enumerate(li) \
if elem in [0, mincounts[i]]] \
for i, li in enumerate(newtabs)]
#average probability mass of minimal elements over zero elements
newvals = [float(mincounts[i])/len(inds[i]) \
for i in range(len(inds))]
#reassign minimal and zero elements
for i, li in enumerate(inds):
for ind in li:
self.p_x_given_y_table[ind,i] = newvals[i]
elif mode == 'add-one':
#add one to all counts in count tables
self.count_x_y_table += 1
self.count_y_table += 1
def compute_log_unnormalized_score(self, instance):
"""Compute log P(X|Y) + log P(Y) for all values of Y
Returns a numpy vector of loglikelihood.
The vector indices must be consistent with the codebook in the classifier
For example:
loglikelihood_vector[0] = log P(X|Y=0) + log P(Y=0)
Add your implementation
"""
loglikelihood_vector = numpy.zeros(self.label_codebook.size())
for col, loglike in enumerate(loglikelihood_vector):
for index, feature in self.feature_codebook:
loglikelihood_vector[col] += numpy.log(\
self.p_x_given_y_table[index, col]) if feature in instance \
else max(numpy.finfo(float).eps, \
numpy.log(1 - self.p_x_given_y_table[index, col]))
loglike += numpy.log( self.p_y_table[col] )
return loglikelihood_vector
def classify_instance(self, instance):
"""Predict the label of the given instance
Make a prediction given the features in the instance.
This function should be very short.
Add your implementation
"""
clus = self.compute_log_unnormalized_score(instance)
if DEBUG:
for index, label in self.label_codebook:
print label, clus[index]
for i, index in enumerate(clus):
if index == max(clus):
return self.label_codebook.get_label(i)
def to_dict(self):
"""Convert NaiveBayes instance into a dictionary representation
The implementation of this should be in sync with from_dict function.
You should be able to use these two functions to convert the model into
either representation (object or dictionary)
Add your implementation
"""
model_dict = {
'label_alphabet': self.label_codebook.to_dict(),
'feature_alphabet': self.feature_codebook.to_dict(),
'#x&y' : self.count_x_y_table,
'#y' : self.count_y_table,
'p_x|y_table' : self.p_x_given_y_table,
'p_y_table' : self.p_y_table,
}
@classmethod
def from_dict(cls, model_dict):
"""Convert a dictionary into NaiveBayes instance
The implementation of this should be in sync with to_dict function.
Add your implementation
"""
res = NaiveBayes()
res.label_codebook = Alphabet.from_dict(model_dict['label_alphabet'])
res.feature_codebook = Alphabet.from_dict(model_dict['feature_alphabet'])
res.count_x_y_table = model_dict['#x&y']
res.count_y_table = model_dict['#y']
res.p_x_given_y_table = model_dict['_x|y_table']
res.p_y_table = model_dict['p_y_table']
return res
class MaxEnt(BaseClassifier):
def __init__(self):
"""Initialize the model
label_codebook, feature_codebook, parameters must be
assigned properly in order for the model to work.
parameters and codebooks will be handled in the train function
"""
super(MaxEnt, self).__init__()
self.label_codebook = Alphabet()
self.feature_codebook = Alphabet()
#self.gaussian_prior_variance = 1
self.parameters = []
self.gaussian_prior_variance = 1.0
def compute_observed_counts(self, instance_list):
"""Compute observed feature counts
It should only be done once because it's parameter-independent.
The observed feature counts are then stored internally.
Note that we are fitting the model with the intercept terms
so the count of intercept term is the count of that class.
fill the feature_counts table with observed counts
"""
#the data and label in instance both use sparse vector
self.feature_counts = numpy.zeros((self.feature_codebook.size() + 1) * self.label_codebook.size())
for instance in instance_list:
Y_index = (self.feature_codebook.size()+1)*instance.label
self.feature_counts[Y_index] +=1
#instance.data is numpy array
indices = Y_index + instance.data +1
self.feature_counts[indices] +=1
#print self.feature_counts[:self.feature_codebook.size()+1]
#print self.feature_counts[self.feature_codebook.size()+1:]
def compute_expected_feature_counts(self,instance_list):
"""Compute expected feature counts
E(feature|X) = sum over i,y E(feature(Xi,yi)|Xi)
= sum over i,y feature(Xi,yi) P(Y=yi|Xi)
We take advantage of inference function in this class to compute
expected feature counts, which is only needed for training.
computing the expected feature counts by adding up all the expectation counts of all feature.
return expected feature counts table
"""
expected_feature_counts = numpy.zeros(len(self.parameters))
for instance in instance_list:
posterior = self.compute_label_unnormalized_loglikelihood_vector(instance.data)
posterior = numpy.exp(posterior-logsumexp(posterior))
for label in range(0,self.label_codebook.size()):
Y_index = label*(self.feature_codebook.size() + 1)
expected_feature_counts[Y_index] += posterior[label]
indices = Y_index + instance.data + 1
expected_feature_counts[indices] += posterior[label]
return expected_feature_counts
def classify_instance(self, instance):
"""Applying the model to a new instance
Returns:
label with the maximum probability
"""
vector = self.compute_posterior_distribution(instance)
#print vector
pre_label_index = numpy.argmax(vector)
return pre_label_index
def compute_posterior_distribution(self, instance):
"""Compute P(Y|X)
Return a vector of the same size as the label_codebook
the vector contains the unnormalized likelihood vector since we only use them for finding the most probable label, so we don't have
to normalized it.
"""
sparse_vector = numpy.array([self.feature_codebook.get_index(i) for i in instance.data if self.feature_codebook.has_label(i)])
posterior_distribution = numpy.zeros(self.label_codebook.size())
posterior_distribution = numpy.exp(self.compute_label_unnormalized_loglikelihood_vector(sparse_vector))
return posterior_distribution
def compute_label_unnormalized_loglikelihood_vector(self,sparse_feature_vector):
"""Compute unnormalized log score from log-linear model
log P(Y|X) is proportional to feature vector * parameter vector
But we use a sparse vector representation, so we need to use
index tricks that numpy allows us to do.
for each label compute the unnormalized loglikelihood (sum of lambdas) given the sparse_feature_vector
Returns:
a vector of scores according to different y(label)
"""
loglikelihood_score_vector = numpy.zeros(self.label_codebook.size())
for label in range(0,self.label_codebook.size()):
Y_index = label*(self.feature_codebook.size() + 1)
indices = Y_index + sparse_feature_vector + 1
if len(indices)!=0:
loglikelihood_score_vector[label] = self.parameters[Y_index] + sum(self.parameters[indices])
else:
loglikelihood_score_vector[label] = self.parameters[Y_index]
return loglikelihood_score_vector
def objective_function(self, parameters):
"""Compute negative (log P(Y|X,lambdas) + log P(lambdas))
The function that we want to optimize over. Here I use Gaussian distribution(mean=0.0 sigma=1.0) prior to model P(lambda)
Args:
parameters updated by the training procedure
Returns:
negtive total likelihood
"""
total_loglikelihood = 0.0
numerator = 0.0
denominator = 0.0
#prior = 0.0
#self.gaussian_prior_variance = 1.0
prior = sum([i**2/(2*self.gaussian_prior_variance**2) for i in parameters])
self.parameters=numpy.array(parameters)
# Compute the loglikelihood here
loglikelihood_score_vector = numpy.zeros(self.label_codebook.size())
for instance in self.training_data:
Y_index = instance.label*(self.feature_codebook.size() + 1)
indices = Y_index + instance.data + 1
numerator += (parameters[Y_index]+sum(parameters[indices]))
score_vector = self.compute_label_unnormalized_loglikelihood_vector(instance.data)
#print score_vector
denominator += logsumexp(score_vector)
#print numerator
#print denominator
total_loglikelihood = numerator - denominator - prior
print - total_loglikelihood
return - total_loglikelihood
def gradient_function(self, parameters):
"""Compute gradient of negative (log P(Y|X,lambdas) + log P(lambdas)) wrt lambdas
With some algebra, we have that
gradient wrt lambda i = observed_count of feature i - expected_count of feature i - lambda i / gaussian_prior_variance^2
The first term is computed before running the optimization function and is a constant.
The second term needs inference to get P(Y|X, lambdas) and is a bit expensive.
The third term is from taking the derivative of log gaussian prior
Returns:
a vector of gradient
"""
self.parameters = numpy.array(parameters)
#print self.parameters
#print parameters
gradient_vector = numpy.zeros(len(parameters))
observed_count_vector = self.feature_counts
expected_count_vector = self.compute_expected_feature_counts(self.training_data)
dprior = numpy.array([i/self.gaussian_prior_variance**2 for i in parameters])
# compute gradient here
gradient_vector = observed_count_vector - expected_count_vector - dprior
return - gradient_vector
def train(self, instance_list):
"""Find the optimal parameters for maximum entropy classifier
We setup an instance of MaxEnt to use as an inference engine
necessary for parameter fitting. MaxEnt instance and training set
are stored internally in the trainer just so we can avoid putting in
extra arguments into the optimization function.
We leave the actual number crunching and search to fmin_bfgs function.
There are a few tunable parameters for the optimization function but
the default is usually well-tuned and sufficient for most purposes.
Arg:
instance_list: each instance.data should be a string feature vectors
This function will create a sparse feature vector representation
based on the alphabet.
Returns:
Maximum entropy classifier with the parameters (MAP estimate from the data
and Gaussian prior)
"""
assert(len(instance_list) > 0)
######################################
# Do any further processing right here e.g populate codebook
# making sparse vectors, etc.
self.label_codebook.add('neg')
self.label_codebook.add('pos')
for index,instance in enumerate(instance_list):
sparse_vector = numpy.zeros(0,dtype=numpy.int)
for feature in instance.data:
if not self.feature_codebook.has_label(feature):
self.feature_codebook.add(feature)
sparse_vector = numpy.append(sparse_vector,self.feature_codebook.get_index(feature))
else:
sparse_vector = numpy.append(sparse_vector,self.feature_codebook.get_index(feature))
instance_list[index].data = sparse_vector
##################
self.parameters = numpy.zeros((self.feature_codebook.size() + 1) * self.label_codebook.size())
self.training_data = instance_list
self.compute_observed_counts(instance_list)
num_labels = self.label_codebook.size()
num_features = self.feature_codebook.size()
init_point = numpy.zeros(num_labels * (num_features + 1))
optimal_parameters, _, _ = fmin_l_bfgs_b(self.objective_function, init_point, fprime=self.gradient_function)
print optimal_parameters
self.parameters = optimal_parameters
def to_dict(self):
model_dict = {
'label_alphabet': self.label_codebook.to_dict(),
'feature_alphabet': self.feature_codebook.to_dict(),
'parameters': self.parameters.tolist(),
}
return model_dict
@classmethod
def from_dict(cls, model_dictionary):
model_instance = MaxEnt()
model_instance.label_codebook = Alphabet.from_dict(model_dict['label_alphabet'])
model_instance.feature_codebook = Alphabet.from_dict(model_dict['feature_alphabet'])
model_instance.p_x_given_y_table = numpy.array(model_dict['parameters'])
return model_instance
class HMM(BaseClassifier):
def __init__(self):
self.label_codebook = Alphabet()
self.feature_codebook = Alphabet()
# these two flags are for feature selection
self.filter_feature1 = True
self.filter_feature2 = False
def _collect_counts(self, instance_list):
"""Collect counts necessary for fitting parameters
This function should update self.transtion_count_table
and self.feature_count_table based on this new given instance
Add your docstring here explaining how you implement this function
Returns None
"""
#0B, 1I, 2O
#self.transition_count_table
#self.feature_count_table
for instance in instance_list:
#label[],data[]
# for transition_count_table, we read label[], m*m
# for feature_count_table, we read both label and data, to see how an observation is emitted from a certain state, p*m
for i in range(len(instance.label)):
self.feature_count_table[instance.data[i][0]][instance.label[i]] += 1
self.feature_count_table[instance.data[i][1]][instance.label[i]] += 1
if i == 0:
self.initial_state_count_table[instance.label[i]] += 1
elif i== len(instance.label)-1:
self.termination_state_count_table[instance.label[i]] += 1
else:
self.transition_count_table[instance.label[i]][instance.label[i-1]] += 1 # easy for matrix multiplication
def train(self, instance_list):
"""Fit parameters for hidden markov model
Update codebooks from the given data to be consistent with
the probability tables
Transition matrix and emission probability matrix
will then be populated with the maximum likelihood estimate
of the appropriate parameters
Add your docstring here explaining how you implement this function
Returns None
"""
# m states, q features
self.transition_matrix = numpy.zeros((1,1))
self.emission_matrix = numpy.zeros((1,1))
# m*m
self.transition_count_table = numpy.zeros((self.label_codebook.size(),self.label_codebook.size()))
# q*m
self.feature_count_table = numpy.zeros((self.feature_codebook.size(),self.label_codebook.size()))
#a table to store each state at the begining of a sequence.it is used for calculating the initial states
self.initial_state_count_table = numpy.zeros(self.label_codebook.size())
self.termination_state_count_table = numpy.zeros(self.label_codebook.size())
self._collect_counts(instance_list)
#TODO: estimate the parameters from the count tables
#Convert count tables into probability tables
#SMOOTHING here
self.initial_state_count_table=(self.initial_state_count_table+1)/(numpy.sum(self.initial_state_count_table)+3)
self.termination_state_count_table=(self.termination_state_count_table+1)/(numpy.sum(self.termination_state_count_table)+3)
# sum of each column, each column identifies the precious state, from previous state to current state
self.transition_matrix = (self.transition_count_table+1)/(numpy.sum((self.transition_count_table+1),0)+3)
# sum of each column, each column identifies the state, emit from state to observation
self.emission_matrix = (self.feature_count_table+1)/(numpy.sum((self.feature_count_table+1),0)+3)
def classify_instance(self, instance):
"""Viterbi decoding algorithm
Wrapper for running the Viterbi algorithm
We can then obtain the best sequence of labels from the backtrace pointers matrix
Add your docstring here explaining how you implement this function
Returns a list of label indices e.g. [0, 1, 0, 3, 4]
"""
instance_size = len(instance.label)
trellis, backtrace_pointers = self.dynamic_programming_on_trellis(instance, False)
best_sequence = numpy.zeros(instance_size)
best_sequence[-1] = numpy.argmax(trellis[:,-1])
for i in range(instance_size-2,0,-1):
best_sequence[i]=backtrace_pointers[best_sequence[i+1]][i+1]
return best_sequence
def compute_observation_loglikelihood(self, instance):
"""Compute and return log P(X|parameters) = loglikelihood of observations"""
trellis = self.dynamic_programming_on_trellis(instance, True)
loglikelihood = numpy.log10(numpy.sum(trellis[:,-1]))
return loglikelihood
def dynamic_programming_on_trellis(self, instance, run_forward_alg=True):
"""Run Forward algorithm or Viterbi algorithm
This function uses the trellis to implement dynamic
programming algorithm for obtaining the best sequence
of labels given the observations
Add your docstring here explaining how you implement this function
Returns trellis filled up with the forward probabilities
and backtrace pointers for finding the best sequence
"""
#TODO:Initialize trellis and backtrace pointers
# trellis, m*t, m states, t sequence length, trellis[j][0] is the first element in the sequence. the index is tricky
instance_size = len(instance.label)
label_size = self.label_codebook.size()
trellis = numpy.zeros((label_size,instance_size))# 3*t, if this instance's sequence is 10
backtrace_pointers = numpy.zeros((label_size,instance_size))#3*t
# Traversing the trellis from left to right
# Initialization, fill in the first column, t=1, index 0
if self.filter_feature1:
trellis[:,0] = self.initial_state_count_table*self.emission_matrix[instance.data[0][1]]
elif self.filter_feature2:
trellis[:,0] = self.initial_state_count_table*self.emission_matrix[instance.data[0][0]]
else:
trellis[:,0] = self.initial_state_count_table*self.emission_matrix[instance.data[0][0]]*self.emission_matrix[instance.data[0][1]]
# Recursion
for t in range(1, instance_size):
if run_forward_alg:
for i in range(label_size):
trellis[:,t] += trellis[i][t-1]*self.transition_matrix[:,i]
else:
for j in range(label_size):
candidate_pre_state = trellis[:,t-1]*self.transition_matrix[j]
trellis[j][t] = numpy.max(candidate_pre_state)
backtrace_pointers[j][t] = numpy.argmax(candidate_pre_state)
# times the observation, 2 features, using the emission matrix
trellis[:,t]=trellis[:,t]*self.emission_matrix[instance.data[t][0]]*self.emission_matrix[instance.data[t][1]]
# Termination ????
#alpha_F = numpy.argmax(self.termination_state_count_table)
#P_O_Lambda = trellis[i][-1]*self.transition_matrix[i][alpha_F]
return (trellis, backtrace_pointers)
def train_semisupervised(self, unlabeled_instance_list, labeled_instance_list=None):
"""Baum-Welch algorithm for fitting HMM from unlabeled data (EXTRA CREDIT)
The algorithm first initializes the model with the labeled data if given.
The model is initialized randomly otherwise. Then it runs
Baum-Welch algorithm to enhance the model with more data.
Add your docstring here explaining how you implement this function
Returns None
"""
if labeled_instance_list is not None:
self.train(labeled_instance_list)
else:
#TODO: initialize the model randomly
pass
while True:
#E-Step
self.expected_transition_counts = numpy.zeros((1,1))
self.expected_feature_counts = numpy.zeros((1,1))
for instance in instance_list:
(alpha_table, beta_table) = self._run_forward_backward(instance)
#TODO: update the expected count tables based on alphas and betas
#also combine the expected count with the observed counts from the labeled data
#M-Step
#TODO: reestimate the parameters
if self._has_converged(old_likelihood, likelihood):
break
def _has_converged(self, old_likelihood, likelihood):
"""Determine whether the parameters have converged or not (EXTRA CREDIT)
Returns True if the parameters have converged.
"""
return True
def _run_forward_backward(self, instance):
"""Forward-backward algorithm for HMM using trellis (EXTRA CREDIT)
Fill up the alpha and beta trellises (the same notation as
presented in the lecture and Martin and Jurafsky)
You can reuse your forward algorithm here
return a tuple of tables consisting of alpha and beta tables
"""
alpha_table = numpy.zeros((1,1))
beta_table = numpy.zeros((1,1))
#TODO: implement forward backward algorithm right here
return (alpha_table, beta_table)
def to_dict(self):
"""Convert HMM instance into a dictionary representation
The implementation of this should be in sync with from_dict function.
You should be able to use these two functions to convert the model into
either representation (object or dictionary)
"""
model_dict = {
'label_alphabet': label_codebook.to_dict(),
'feature_alphabet': feature_codebook.to_dict()
}
return model_dict
def test_hmm(self):
pp = PreProcessor()
pp.test_preprocess()
instance_list = pp.get_instance_list()
self.label_codebook=pp.get_label_codebook()
self.feature_codebook=pp.get_feature_codebook()
self.train(instance_list)
print "\ntransition_count_table--------------------"
print self.transition_count_table
print "\ntransition_matrix-------------------------"
print self.transition_matrix
print "\ninitial_state_count_table------------------"
print self.initial_state_count_table
print "\ntermination_state_count_table------------------"
print self.termination_state_count_table
print "\nemission matrix----------------------------"
print self.emission_matrix
for i in range(10):
self.test_classify_instance(instance_list[i])
def test_forward(self, instance):
print "run forward algorithm, print trellis----------"
print "instance used for test: "
print instance.label
trellis = self.dynamic_programming_on_trellis(instance,True)
print trellis
print "------forward done---------------------------"
def test_viterbi(self, instance):
print "run vertibi algorithm, print trellis----------"
print "instance used for test: "
print instance.label
trellis,backtrace_pointers =self.dynamic_programming_on_trellis(instance,False)
print trellis
print backtrace_pointers
print "------vertibi done---------------------------"
def test_classify_instance(self,instance):
print "test classify instance ----------------------"
print "instance used for test: "
print instance.label
best_sequence = self.classify_instance(instance)
print "best sequence:"
print best_sequence
counter = 0
for i in range(len(instance.label)-1):
if best_sequence[i]==instance.label[i]:
counter +=1
print "single run accurac: "+str(float(counter)/float(len(instance.label)-1))
print "classify instance done ----------------------------"
@classmethod
def from_dict(model_dict):
"""Convert a dictionary into HMM instance
The implementation of this should be in sync with to_dict function.
"""
return HMM()
class PreProcessor(object):
#np_chunking_wsj_15_18_train
def __init__(self, dataset = "C:\\Users\\DIAOSHUO\\Dropbox\\SNLP\\cs134assn2\\np_chunking_wsj_15_18_train"):
"""
Initialize the class variable here
"""
self.dataset_path = dataset;
self.label_codebook = Alphabet();
self.feature_codebook = Alphabet();
self.instance_list = []
def get_label_codebook(self):
"""Return the self.label_codebook"""
if self.label_codebook.size()==0:
self._make_label_codebook()
return self.label_codebook
def _make_label_codebook(self):
"""
populate the label_codebook according to the dataset.
For PA2, we populate it manually since there are only 3 labels.
For more dataset with more labels, we should populate label_codebook via reading all data points.
"""
self.label_codebook.add("B")
self.label_codebook.add("I")
self.label_codebook.add("O")
def _make_feature_codebook(self):
"""
Parse the training data set and build the feature_codebook
Dataset: a single file, with a HUGE number of lines, each line is a hidden state and an observation. The observation has two features
"""
instance_counter = 0
dataset = self.dataset_path
f = open(dataset,'r')
#reading file line by line in this way is memory efficient
for line in f:
if line == "\n":
instance_counter+=1
else:
parser = line.split()
#parser has 3 element: label, feature1, feature2
self.feature_codebook.add(parser[1])
self.feature_codebook.add(parser[2])
f.close()
print "total num of sentences: "+str(instance_counter)
def get_feature_codebook(self):
if self.feature_codebook.size()==0:
self._make_feature_codebook()
return self.feature_codebook
def _make_instance_list(self, dataset):
"""
Function:
This method converts a raw dataset file into a list of instances,
which is ready to be processed by the HMM classifier.
NOTE: We assume that the feature codebook and label codebook are built already.
Args:
dataset path. For now, we use a hardcoded one in self.dataset_path
Return:
a list of instance, wich the field of name, label[],data[[]],raw_data
"""
#dataset = self.dataset_path
#ensure feature_codebook and label_codebook already exist
self.get_feature_codebook()
self.get_label_codebook()
f = open(dataset,'r')
label = []
data = []
raw_data = ""
instance_counter = 0
for line in f:
if line == "\n":
#the end of a sentence, should add the instance to a instance list
instance_counter+=1
instance = Instance(instance_counter, label, data, raw_data)
self.instance_list.append(instance)
#clear the containers, ready for a new element
data =[]
label = []
raw_data=""
else:
#make part of the data point, we store index, consistent with the codebook
parser = line.split()
label.append(self.label_codebook.get_index(parser[0]))
observation = []
observation.append(self.feature_codebook.get_index(parser[1]))
observation.append(self.feature_codebook.get_index(parser[2]))
data.append(observation)
raw_data += line
f.close()
def get_instance_list(self, dataset = "C:\\Users\\DIAOSHUO\\Dropbox\\SNLP\\cs134assn2\\np_chunking_wsj_15_18_train"):
if len(self.instance_list) ==0:
self._make_instance_list(dataset)
return self.instance_list
def test_preprocess(self):
self.make_label_codebook()
print "label_codebook size: "+str(self.label_codebook.size())
self.make_feature_codebook()
print "feature_codebook size: "+str(self.feature_codebook.size())
self.make_instance_list()
print "instance_list size: "+str(len(self.instance_list))