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my_model_selectors.py
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my_model_selectors.py
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import math
import statistics
import warnings
import numpy as np
from hmmlearn.hmm import GaussianHMM
from sklearn.model_selection import KFold
from asl_utils import combine_sequences
class ModelSelector(object):
'''
base class for model selection (strategy design pattern)
'''
def __init__(self, all_word_sequences: dict, all_word_Xlengths: dict, this_word: str,
n_constant=3,
min_n_components=2, max_n_components=10,
random_state=14, verbose=False):
self.words = all_word_sequences
self.hwords = all_word_Xlengths
self.sequences = all_word_sequences[this_word]
self.X, self.lengths = all_word_Xlengths[this_word]
self.this_word = this_word
self.n_constant = n_constant
self.min_n_components = min_n_components
self.max_n_components = max_n_components
self.random_state = random_state
self.verbose = verbose
def select(self):
raise NotImplementedError
def base_model(self, num_states):
warnings.filterwarnings("ignore", category=DeprecationWarning)
# warnings.filterwarnings("ignore", category=RuntimeWarning)
try:
hmm_model = GaussianHMM(n_components=num_states, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(self.X, self.lengths)
if self.verbose:
print("model created for {} with {} states".format(self.this_word, num_states))
return hmm_model
except:
if self.verbose:
print("failure on {} with {} states".format(self.this_word, num_states))
return None
def base_selector(self, custom_scoring_method):
warnings.filterwarnings("ignore", category=DeprecationWarning)
split_method = KFold()
best_score_sum = -math.inf
best_hmm_model = None
for num_states in range(self.min_n_components, self.max_n_components):
hmm_model_score_sum = 0
if len(self.sequences) is 1:
return GaussianHMM(n_components=num_states, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(self.X, self.lengths)
elif len(self.sequences) is 2:
normalized_X, normalized_lengths = combine_sequences([0], self.sequences)
test_X, test_lengths = combine_sequences([1], self.sequences)
try:
hmm_model = GaussianHMM(n_components=num_states, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(normalized_X, normalized_lengths)
hmm_model_score_sum += self.custom_scoring_method(hmm_model, test_X, test_lengths, num_states)
if self.verbose:
print("model created for {} with {} states".format(self.this_word, num_states))
print("model score is {}".format(hmm_model_score_sum))
except:
if self.verbose:
print("failure on {} with {} states".format(self.this_word, num_states))
else:
for cv_train_idx, cv_test_idx in split_method.split(self.sequences):
normalized_X, normalized_lengths = combine_sequences(cv_train_idx, self.sequences)
test_X, test_lengths = combine_sequences(cv_test_idx, self.sequences)
try:
hmm_model = GaussianHMM(n_components=num_states, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(normalized_X, normalized_lengths)
hmm_model_score_sum += self.custom_scoring_method(hmm_model, test_X, test_lengths, num_states)
if self.verbose:
print("model created for {} with {} states".format(self.this_word, num_states))
print("model score is {}".format(hmm_model_score_sum))
except:
if self.verbose:
print("failure on {} with {} states".format(self.this_word, num_states))
if best_hmm_model is None:
best_hmm_model = hmm_model
if hmm_model_score_sum > best_score_sum:
best_score_sum = hmm_model_score_sum
best_n = num_states
best_hmm_model = hmm_model
self.print_best_score()
return best_hmm_model
def print_best_score(self):
if self.verbose:
print("best score is {}".format(best_score_sum))
print("best n is {}".format(best_n))
class SelectorConstant(ModelSelector):
""" select the model with value self.n_constant
"""
def select(self):
""" select based on n_constant value
:return: GaussianHMM object
"""
best_num_components = self.n_constant
return self.base_model(best_num_components)
class SelectorBIC(ModelSelector):
""" select the model with the lowest Bayesian Information Criterion(BIC) score
http://www2.imm.dtu.dk/courses/02433/doc/ch6_slides.pdf
Bayesian information criteria: BIC = -2 * logL + p * logN
"""
def custom_scoring_method(self, hmm_model, test_X, test_lengths, num_params):
return -2 * hmm_model.score(test_X, test_lengths) + len(test_X) * math.log10(num_params)
def select(self):
""" select the best model for self.this_word based on
BIC score for n between self.min_n_components and self.max_n_components
:return: GaussianHMM object
"""
warnings.filterwarnings("ignore", category=DeprecationWarning)
return self.base_selector(self.custom_scoring_method)
class SelectorDIC(ModelSelector):
''' select best model based on Discriminative Information Criterion
Biem, Alain. "A model selection criterion for classification: Application to hmm topology optimization."
Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on. IEEE, 2003.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.6208&rep=rep1&type=pdf
DIC = log(P(X(i)) - 1/(M-1)SUM(log(P(X(all but i))
'''
def custom_scoring_method(self, hmm_model, test_X, test_lengths, num_params):
antiLogL = 0
for word in self.hwords:
if word == self.this_word:
continue
X, lengths = self.hwords[word]
antiLogL += hmm_model.score(X, lengths)
return hmm_model.score(test_X, test_lengths) - 1 / ( len(self.words) * antiLogL )
def select(self):
warnings.filterwarnings("ignore", category=DeprecationWarning)
return self.base_selector(self.custom_scoring_method)
class SelectorCV(ModelSelector):
''' select best model based on average log Likelihood of cross-validation folds
'''
def custom_scoring_method(self, hmm_model, test_X, test_lengths, num_params):
return hmm_model.score(test_X, test_lengths)
def select(self):
return self.base_selector(self.custom_scoring_method)