def _predict_proba_for_estimator(
self, X, X_p, X_d, classifier, intervals, dims, atts
):
c22 = Catch22(outlier_norm=True)
if isinstance(self._base_estimator, ContinuousIntervalTree):
return classifier._predict_proba_drcif(
X, X_p, X_d, c22, self._n_intervals, intervals, dims, atts
)
else:
T = [X, X_p, X_d]
transformed_x = np.empty(
shape=(self._att_subsample_size * self.total_intervals, X.shape[0]),
dtype=np.float32,
)
p = 0
j = 0
for r in range(0, len(T)):
for _ in range(0, self._n_intervals[r]):
for a in range(0, self._att_subsample_size):
transformed_x[p] = _drcif_feature(
T[r], intervals[j], dims[j], atts[a], c22
)
p += 1
j += 1
transformed_x = transformed_x.T
transformed_x.round(8)
np.nan_to_num(transformed_x, False, 0, 0, 0)
return classifier.predict_proba(transformed_x)
def _predict_proba_for_estimator(self, X, classifier, intervals, dims,
atts):
c22 = Catch22(outlier_norm=True)
if isinstance(self._base_estimator, ContinuousIntervalTree):
return classifier._predict_proba_cif(X, c22, intervals, dims, atts)
else:
transformed_x = np.empty(
shape=(self._att_subsample_size * self._n_intervals,
X.shape[0]),
dtype=np.float32,
)
for j in range(0, self._n_intervals):
for a in range(0, self._att_subsample_size):
transformed_x[self._att_subsample_size * j +
a] = _drcif_feature(X,
intervals[j],
dims[j],
atts[a],
c22,
case_id=j)
transformed_x = transformed_x.T
transformed_x.round(8)
np.nan_to_num(transformed_x, False, 0, 0, 0)
return classifier.predict_proba(transformed_x)
def _fit_estimator(self, X, X_p, X_d, y, idx):
c22 = Catch22(outlier_norm=True)
T = [X, X_p, X_d]
rs = 255 if self.random_state == 0 else self.random_state
rs = None if self.random_state is None else rs * 37 * (idx + 1)
rng = check_random_state(rs)
transformed_x = np.empty(
shape=(self._att_subsample_size * self.total_intervals, self.n_instances),
dtype=np.float32,
)
atts = rng.choice(29, self._att_subsample_size, replace=False)
dims = rng.choice(self.n_dims, self.total_intervals, replace=True)
intervals = np.zeros((self.total_intervals, 2), dtype=int)
p = 0
j = 0
for r in range(0, len(T)):
transform_length = T[r].shape[2]
# Find the random intervals for classifier i, transformation r
# and concatenate features
for _ in range(0, self._n_intervals[r]):
if rng.random() < 0.5:
intervals[j][0] = rng.randint(
0, transform_length - self._min_interval[r]
)
len_range = min(
transform_length - intervals[j][0],
self._max_interval[r],
)
length = (
rng.randint(0, len_range - self._min_interval[r])
+ self._min_interval[r]
)
intervals[j][1] = intervals[j][0] + length
else:
intervals[j][1] = (
rng.randint(0, transform_length - self._min_interval[r])
+ self._min_interval[r]
)
len_range = min(intervals[j][1], self._max_interval[r])
length = (
rng.randint(0, len_range - self._min_interval[r])
+ self._min_interval[r]
if len_range - self._min_interval[r] > 0
else self._min_interval[r]
)
intervals[j][0] = intervals[j][1] - length
for a in range(0, self._att_subsample_size):
transformed_x[p] = _drcif_feature(
T[r], intervals[j], dims[j], atts[a], c22
)
p += 1
j += 1
tree = _clone_estimator(self._base_estimator, random_state=rs)
transformed_x = transformed_x.T
transformed_x = transformed_x.round(8)
transformed_x = np.nan_to_num(transformed_x, False, 0, 0, 0)
tree.fit(transformed_x, y)
return [
tree,
intervals,
dims,
atts,
transformed_x if self.save_transformed_data else None,
]
def _fit_estimator(self, X, y, idx):
c22 = Catch22(outlier_norm=True)
rs = 255 if self.random_state == 0 else self.random_state
rs = (None if self.random_state is None else
(rs * 37 * (idx + 1)) % np.iinfo(np.int32).max)
rng = check_random_state(rs)
transformed_x = np.empty(
shape=(self._att_subsample_size * self._n_intervals,
self.n_instances_),
dtype=np.float32,
)
atts = rng.choice(25, self._att_subsample_size, replace=False)
dims = rng.choice(self.n_dims_, self._n_intervals, replace=True)
intervals = np.zeros((self._n_intervals, 2), dtype=int)
# Find the random intervals for classifier i and concatenate
# features
for j in range(0, self._n_intervals):
if rng.random() < 0.5:
intervals[j][0] = rng.randint(
0, self.series_length_ - self._min_interval)
len_range = min(
self.series_length_ - intervals[j][0],
self._max_interval,
)
length = (rng.randint(0, len_range - self._min_interval) +
self._min_interval if len_range -
self._min_interval > 0 else self._min_interval)
intervals[j][1] = intervals[j][0] + length
else:
intervals[j][1] = (
rng.randint(0, self.series_length_ - self._min_interval) +
self._min_interval)
len_range = min(intervals[j][1], self._max_interval)
length = (rng.randint(0, len_range - self._min_interval) +
self._min_interval if len_range -
self._min_interval > 0 else self._min_interval)
intervals[j][0] = intervals[j][1] - length
for a in range(0, self._att_subsample_size):
transformed_x[self._att_subsample_size * j +
a] = _drcif_feature(X,
intervals[j],
dims[j],
atts[a],
c22,
case_id=j)
tree = _clone_estimator(self._base_estimator, random_state=rs)
transformed_x = transformed_x.T
transformed_x = transformed_x.round(8)
if self.base_estimator == "CIT":
transformed_x = np.nan_to_num(transformed_x,
False,
posinf=np.nan,
neginf=np.nan)
else:
transformed_x = np.nan_to_num(transformed_x, False, 0, 0, 0)
tree.fit(transformed_x, y)
return [tree, intervals, dims, atts]