class IndividualTDE(BaseClassifier):
""" Single TDE classifier, based off the Bag of SFA Symbols (BOSS) model
"""
def __init__(self,
window_size=10,
word_length=8,
norm=False,
levels=1,
igb=False,
alphabet_size=4,
random_state=None):
self.window_size = window_size
self.word_length = word_length
self.norm = norm
self.levels = levels
self.igb = igb
self.alphabet_size = alphabet_size
self.random_state = random_state
binning_method = "information-gain" if igb else "equi-depth"
self.transformer = SFA(word_length=word_length,
alphabet_size=alphabet_size,
window_size=window_size,
norm=norm,
levels=levels,
binning_method=binning_method,
bigrams=True,
remove_repeat_words=True,
save_words=False)
self.transformed_data = []
self.accuracy = 0
self.class_vals = []
self.num_classes = 0
self.classes_ = []
self.class_dictionary = {}
super(IndividualTDE, self).__init__()
def fit(self, X, y):
X, y = check_X_y(X, y, enforce_univariate=True)
sfa = self.transformer.fit_transform(X, y)
self.transformed_data = [series.to_dict() for series in sfa.iloc[:, 0]]
self.class_vals = y
self.num_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
for index, classVal in enumerate(self.classes_):
self.class_dictionary[classVal] = index
self._is_fitted = True
return self
def predict(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
rng = check_random_state(self.random_state)
classes = []
test_bags = self.transformer.transform(X)
test_bags = [series.to_dict() for series in test_bags.iloc[:, 0]]
for i, test_bag in enumerate(test_bags):
best_sim = -1
nn = None
for n, bag in enumerate(self.transformed_data):
sim = histogram_intersection(test_bag, bag)
if sim > best_sim or (sim == best_sim and rng.random() < 0.5):
best_sim = sim
nn = self.class_vals[n]
classes.append(nn)
return np.array(classes)
def predict_proba(self, X):
preds = self.predict(X)
dists = np.zeros((X.shape[0], self.num_classes))
for i in range(0, X.shape[0]):
dists[i, self.class_dictionary.get(preds[i])] += 1
return dists
def _train_predict(self, train_num):
test_bag = self.transformed_data[train_num]
best_sim = -1
nn = None
for n, bag in enumerate(self.transformed_data):
if n == train_num:
continue
sim = histogram_intersection(test_bag, bag)
if sim > best_sim:
best_sim = sim
nn = self.class_vals[n]
return nn
class BOSSIndividual(BaseClassifier):
""" Single Bag of SFA Symbols (BOSS) classifier
Bag of SFA Symbols Ensemble: implementation of BOSS from Schaffer :
@article
"""
def __init__(self,
window_size=10,
word_length=8,
norm=False,
alphabet_size=4,
save_words=True,
random_state=None
):
self.window_size = window_size
self.word_length = word_length
self.norm = norm
self.alphabet_size = alphabet_size
self.save_words = save_words
self.random_state = random_state
self.transformer = SFA(word_length=word_length,
alphabet_size=alphabet_size,
window_size=window_size, norm=norm,
remove_repeat_words=True,
bigrams=False,
save_words=save_words)
self.transformed_data = []
self.accuracy = 0
self.class_vals = []
self.num_classes = 0
self.classes_ = []
self.class_dictionary = {}
super(BOSSIndividual, self).__init__()
def fit(self, X, y):
X, y = check_X_y(X, y, enforce_univariate=True)
sfa = self.transformer.fit_transform(X)
self.transformed_data = sfa.iloc[:, 0]
self.class_vals = y
self.num_classes = np.unique(y).shape[0]
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
for index, classVal in enumerate(self.classes_):
self.class_dictionary[classVal] = index
self._is_fitted = True
return self
def predict(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
rng = check_random_state(self.random_state)
classes = []
test_bags = self.transformer.transform(X)
test_bags = test_bags.iloc[:, 0]
for i, test_bag in enumerate(test_bags):
best_dist = sys.float_info.max
nn = None
for n, bag in enumerate(self.transformed_data):
dist = boss_distance(test_bag, bag, best_dist)
if dist < best_dist or (dist == best_dist and rng.random()
< 0.5):
best_dist = dist
nn = self.class_vals[n]
classes.append(nn)
return np.array(classes)
def predict_proba(self, X):
preds = self.predict(X)
dists = np.zeros((X.shape[0], self.num_classes))
for i in range(0, X.shape[0]):
dists[i, self.class_dictionary.get(preds[i])] += 1
return dists
def _train_predict(self, train_num):
test_bag = self.transformed_data[train_num]
best_dist = sys.float_info.max
nn = None
for n, bag in enumerate(self.transformed_data):
if n == train_num:
continue
dist = boss_distance(test_bag, bag, best_dist)
if dist < best_dist:
best_dist = dist
nn = self.class_vals[n]
return nn
def _shorten_bags(self, word_len):
new_boss = BOSSIndividual(self.window_size, word_len,
self.norm, self.alphabet_size,
save_words=self.save_words,
random_state=self.random_state)
new_boss.transformer = self.transformer
sfa = self.transformer._shorten_bags(word_len)
new_boss.transformed_data = sfa.iloc[:, 0]
new_boss.class_vals = self.class_vals
new_boss.num_classes = self.num_classes
new_boss.classes_ = self.classes_
new_boss.class_dictionary = self.class_dictionary
new_boss._is_fitted = True
return new_boss
def _clean(self):
self.transformer.words = None
self.transformer.save_words = False
def _set_word_len(self, word_len):
self.word_length = word_len
self.transformer.word_length = word_len