def fit(self, X, y):
"""Build an ensemble of BOSS classifiers from the training set (X,
y), through randomising over the para space to make a fixed size
ensemble of the best.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
y : array-like, shape = [n_instances] The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_numpy=True)
start_time = time.time()
self.time_limit = self.time_limit * 60
self.n_instances, _, self.series_length = X.shape
self.n_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.classifiers = []
self.weights = []
# Window length parameter space dependent on series length
max_window_searches = self.series_length / 4
max_window = int(self.series_length * self.max_win_len_prop)
win_inc = int((max_window - self.min_window) / max_window_searches)
if win_inc < 1:
win_inc = 1
possible_parameters = self._unique_parameters(max_window, win_inc)
num_classifiers = 0
train_time = 0
subsample_size = int(self.n_instances * 0.7)
lowest_acc = 1
lowest_acc_idx = 0
rng = check_random_state(self.random_state)
if self.time_limit > 0:
self.n_parameter_samples = 0
while (train_time < self.time_limit or num_classifiers <
self.n_parameter_samples) and len(possible_parameters) > 0:
parameters = possible_parameters.pop(
rng.randint(0, len(possible_parameters)))
subsample = rng.choice(self.n_instances,
size=subsample_size,
replace=False)
X_subsample = X[subsample] # .iloc[subsample, :]
y_subsample = y[subsample]
boss = IndividualBOSS(*parameters,
alphabet_size=self.alphabet_size,
save_words=False,
random_state=self.random_state)
boss.fit(X_subsample, y_subsample)
boss._clean()
boss.accuracy = self._individual_train_acc(boss, y_subsample,
subsample_size,
lowest_acc)
weight = math.pow(boss.accuracy, 4)
if num_classifiers < self.max_ensemble_size:
if boss.accuracy < lowest_acc:
lowest_acc = boss.accuracy
lowest_acc_idx = num_classifiers
self.weights.append(weight)
self.classifiers.append(boss)
elif boss.accuracy > lowest_acc:
self.weights[lowest_acc_idx] = weight
self.classifiers[lowest_acc_idx] = boss
lowest_acc, lowest_acc_idx = self._worst_ensemble_acc()
num_classifiers += 1
train_time = time.time() - start_time
self.n_estimators = len(self.classifiers)
self.weight_sum = np.sum(self.weights)
self._is_fitted = True
return self
def _fit(self, X, y):
"""Fit a c-boss ensemble on cases (X,y), where y is the target variable.
Build an ensemble of BOSS classifiers from the training set (X,
y), through randomising over the para space to make a fixed size
ensemble of the best.
Parameters
----------
X : nested pandas DataFrame of shape (n_instances, 1)
Nested dataframe with univariate time-series in cells.
y : array-like of shape (n_instances,)
The class labels.
Returns
-------
self : object
"""
time_limit = self.time_limit_in_minutes * 60
self.n_instances, _, self.series_length = X.shape
self.n_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.classifiers = []
self.weights = []
# Window length parameter space dependent on series length
max_window_searches = self.series_length / 4
max_window = int(self.series_length * self.max_win_len_prop)
win_inc = int((max_window - self.min_window) / max_window_searches)
if win_inc < 1:
win_inc = 1
if self.min_window > max_window + 1:
raise ValueError(
f"Error in ContractableBOSS, min_window ="
f"{self.min_window} is bigger"
f" than max_window ={max_window},"
f" series length is {self.series_length}"
f" try set min_window to be smaller than series length in "
f"the constructor, but the classifier may not work at "
f"all with very short series")
possible_parameters = self._unique_parameters(max_window, win_inc)
num_classifiers = 0
start_time = time.time()
train_time = 0
subsample_size = int(self.n_instances * 0.7)
lowest_acc = 1
lowest_acc_idx = 0
rng = check_random_state(self.random_state)
if time_limit > 0:
self.n_parameter_samples = 0
while (train_time < time_limit or num_classifiers <
self.n_parameter_samples) and len(possible_parameters) > 0:
parameters = possible_parameters.pop(
rng.randint(0, len(possible_parameters)))
subsample = rng.choice(self.n_instances,
size=subsample_size,
replace=False)
X_subsample = X[subsample]
y_subsample = y[subsample]
boss = IndividualBOSS(
*parameters,
alphabet_size=self.alphabet_size,
save_words=False,
random_state=self.random_state,
)
boss.fit(X_subsample, y_subsample)
boss._clean()
boss.subsample = subsample
boss.accuracy = self._individual_train_acc(
boss,
y_subsample,
subsample_size,
0 if num_classifiers < self.max_ensemble_size else lowest_acc,
)
if boss.accuracy > 0:
weight = math.pow(boss.accuracy, 4)
else:
weight = 0.000000001
if num_classifiers < self.max_ensemble_size:
if boss.accuracy < lowest_acc:
lowest_acc = boss.accuracy
lowest_acc_idx = num_classifiers
self.weights.append(weight)
self.classifiers.append(boss)
elif boss.accuracy > lowest_acc:
self.weights[lowest_acc_idx] = weight
self.classifiers[lowest_acc_idx] = boss
lowest_acc, lowest_acc_idx = self._worst_ensemble_acc()
num_classifiers += 1
train_time = time.time() - start_time
self.n_estimators = len(self.classifiers)
self.weight_sum = np.sum(self.weights)
return self