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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):
# with warnings.catch_warnings():
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
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 Baysian Information Criterion(BIC) score
http://www2.imm.dtu.dk/courses/02433/doc/ch6_slides.pdf
Bayesian information criteria: BIC = -2 * logL + p * logN
"""
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)
best_model = None
best_score = 999999
for n in range(self.min_n_components, self.max_n_components + 1):
try:
hmm_model = GaussianHMM(n_components=n, covariance_type='diag', \
random_state=self.random_state, n_iter=1000, \
verbose=False).fit(self.X, self.lengths)
# BIC score is defined as -2*log(L) + p*log(N) where
# L: likelihood
# p: number of parameters
# n: number of states
# d: number of features
# N: number of data points (examples)
logL = hmm_model.score(self.X, self.lengths)
logN = np.log(len(self.sequences))
p = n**2 + 2*len(self.sequences[0])*n - 1
score = -2*logL + p*logN
if score < best_score:
best_score = score
best_model = hmm_model
except:
pass
return best_model
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 select(self):
warnings.filterwarnings("ignore", category=DeprecationWarning)
best_model = None
best_score = -99999999
words = self.words.keys()
for n in range(self.min_n_components, self.max_n_components + 1):
try:
hmm_model = GaussianHMM(n_components=n, covariance_type='diag', \
random_state=self.random_state, n_iter=1000, \
verbose=False).fit(self.X, self.lengths)
score_other_words = []
for word in words:
if word == self.this_word:
score_this_word = hmm_model.score(self.X, self.lengths)
else:
X, lengths = self.hwords[word]
try:
score_other_words.append(hmm_model.score(X, lengths))
except:
pass
# DIC = P(X|hmm_model) - average(P(Y|hmm_model) for all words Y except X)
# In words: the chance of getting the right word - the chance of getting the wrong word
DIC = score_this_word - sum(score_other_words) / len(score_other_words)
if DIC > best_score:
best_score = DIC
best_model = hmm_model
except:
pass
return best_model
class SelectorCV(ModelSelector):
''' select best model based on average log Likelihood of cross-validation folds
'''
def select(self):
warnings.filterwarnings("ignore", category=DeprecationWarning)
best_num_components = self.min_n_components
best_model = None
best_score = -999999
for n in range(self.min_n_components, self.max_n_components + 1):
score = []
if len(self.sequences) >= 3: n_splits = 3
elif len(self.sequences) == 2: n_splits = 2
else:
# print("For word {}: only {} sample available --> fail".format(self.this_word, len(self.sequences)))
next
try:
split_method = KFold(n_splits=n_splits)
for cv_train_idx, cv_test_idx in split_method.split(self.sequences):
try:
X, lengths = combine_sequences(cv_train_idx, self.sequences)
hmm_model = GaussianHMM(n_components=n, covariance_type='diag', \
n_iter=1000, random_state=self.random_state, \
verbose=False).fit(X, lengths)
X, lengths = combine_sequences(cv_test_idx, self.sequences)
score.append(hmm_model.score(X, lengths))
except:
pass
if len(score) > 0:
avg_score = sum(score)/float(len(score))
if avg_score > best_score:
best_score = avg_score
best_num_components = n
best_model = hmm_model
except:
pass
if self.verbose:
print("model created for {} with {} states".format(self.this_word, best_num_components))
return best_model