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(self, X, y):
"""Fit an estimator using transformed data from the Catch22 transformer.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, n_dims]
Nested dataframe with univariate time-series in cells.
y : array-like, shape = [n_instances] The class labels.
Returns
-------
self : object
"""
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
self.n_classes = np.unique(y).shape[0]
self._transformer = Catch22(outlier_norm=self.outlier_norm)
self._estimator = _clone_estimator(
RandomForestClassifier(n_estimators=200)
if self.estimator is None else self.estimator,
self.random_state,
)
m = getattr(self._estimator, "n_jobs", None)
if m is not None:
self._estimator.n_jobs = self.n_jobs
X_t = self._transformer.fit_transform(X, y)
X_t = np.nan_to_num(X_t, False, 0, 0, 0)
self._estimator.fit(X_t, y)
return self
def fit(self, X, y):
"""Fit a random catch22 feature forest classifier.
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 = check_X(X, enforce_univariate=False, coerce_to_numpy=True)
self.classes_ = class_distribution(np.asarray(y).reshape(-1, 1))[0][0]
c22 = Catch22(outlier_norm=self.outlier_norm)
c22_list = c22.fit_transform(X)
self.classifier = RandomForestClassifier(
n_jobs=self.n_jobs,
n_estimators=self.n_estimators,
random_state=self.random_state,
)
X_c22 = np.nan_to_num(np.array(c22_list, dtype=np.float32), False, 0,
0, 0)
self.classifier.fit(X_c22, y)
self._is_fitted = True
return self
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)
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
)
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] = _cif_feature(
X, intervals[j], dims[j], atts[a], c22
)
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]
def test_catch22_on_gunpoint():
# load gunpoint data
X_train, y_train = load_gunpoint(split="train", return_X_y=True)
indices = np.random.RandomState(0).permutation(10)
# fit catch22
c22 = Catch22()
c22.fit(X_train.iloc[indices], y_train[indices])
# assert transformed data is the same
data = c22.transform(X_train.iloc[indices])
testing.assert_array_almost_equal(data, catch22_gunpoint_data)
def test_catch22_on_unit_test():
"""Test of Catch22 on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
indices = np.random.RandomState(0).choice(len(y_train), 5, replace=False)
# fit catch22
c22 = Catch22(outlier_norm=True)
c22.fit(X_train.iloc[indices], y_train[indices])
# assert transformed data is the same
data = np.nan_to_num(c22.transform(X_train.iloc[indices]), False, 0, 0, 0)
testing.assert_array_almost_equal(data, catch22_unit_test_data)
def test_catch22_on_basic_motions():
"""Test of Catch22 on basic motions data."""
# load basic motions data
X_train, y_train = load_basic_motions(split="train")
indices = np.random.RandomState(4).choice(len(y_train), 5, replace=False)
# fit catch22
c22 = Catch22()
c22.fit(X_train.iloc[indices], y_train[indices])
# assert transformed data is the same
data = np.nan_to_num(c22.transform(X_train.iloc[indices]), False, 0, 0, 0)
testing.assert_array_almost_equal(data, catch22_basic_motions_data)
def test_catch22_single_feature_on_gunpoint():
# load gunpoint data
X_train, y_train = load_gunpoint(split="train", return_X_y=True)
indices = np.random.RandomState(0).permutation(10)
# fit catch22
c22 = Catch22()
c22.fit(X_train.iloc[indices], y_train[indices])
# assert transformed data is the same
data = []
for i in range(22):
data.append(c22._transform_single_feature(X_train.iloc[indices], i))
testing.assert_array_almost_equal(data, catch22_single_feature_gunpoint_data)
def test_catch22_single_feature_on_unit_test():
"""Test of Catch22 on unit test data."""
# load unit test data
X_train, y_train = load_unit_test(split="train")
indices = np.random.RandomState(0).choice(len(y_train), 2, replace=False)
# fit catch22
c22 = Catch22(outlier_norm=True)
c22.fit(X_train.iloc[indices], y_train[indices])
# assert transformed data is the same
results = catch22_unit_test_data.transpose()
for i in range(22):
data = np.nan_to_num(
c22.transform_single_feature(X_train.iloc[indices], i), False, 0, 0, 0
)
testing.assert_array_almost_equal(data, results[i][:2])
def _fit(self, X, y):
"""Fit a pipeline on cases (X,y), where y is the target variable.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
interval_transformers = (Catch22(outlier_norm=True, replace_nans=True)
if self.interval_transformers is None else
self.interval_transformers)
self._transformer = RandomIntervals(
n_intervals=self.n_intervals,
transformers=interval_transformers,
random_state=self.random_state,
n_jobs=self._threads_to_use,
)
self._estimator = _clone_estimator(
RotationForest() if self.estimator is None else self.estimator,
self.random_state,
)
m = getattr(self._estimator, "n_jobs", None)
if m is not None:
self._estimator.n_jobs = self._threads_to_use
X_t = self._transformer.fit_transform(X, y)
self._estimator.fit(X_t, y)
return self
def predict(self, X):
"""Make predictions for all cases in X.
Parameters
----------
X : The testing input samples of shape [n_instances,1].
Returns
-------
output : numpy array of shape = [n_instances]
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=False, coerce_to_numpy=True)
c22 = Catch22(outlier_norm=self.outlier_norm)
c22_list = c22.fit_transform(X)
X_c22 = np.nan_to_num(np.array(c22_list, dtype=np.float32), False, 0,
0, 0)
return self.classifier.predict(X_c22)
def predict_proba(self, X):
"""Make class probability estimates on each case in X.
Parameters
----------
X - pandas dataframe of testing data of shape [n_instances,1].
Returns
-------
output : numpy array of shape =
[n_instances, num_classes] of probabilities
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=False, coerce_to_numpy=True)
c22 = Catch22(outlier_norm=self.outlier_norm)
c22_list = c22.fit_transform(X)
X_c22 = np.nan_to_num(np.array(c22_list, dtype=np.float32), False, 0,
0, 0)
return self.classifier.predict_proba(X_c22)
def _fit(self, X, y):
"""Fit a pipeline on cases (X,y), where y is the target variable.
Parameters
----------
X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
The training data.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self :
Reference to self.
Notes
-----
Changes state by creating a fitted model that updates attributes
ending in "_" and sets is_fitted flag to True.
"""
self._transformer = Catch22(outlier_norm=self.outlier_norm)
self._estimator = _clone_estimator(
RandomForestClassifier(n_estimators=200)
if self.estimator is None else self.estimator,
self.random_state,
)
m = getattr(self._estimator, "n_jobs", None)
if m is not None:
self._estimator.n_jobs = self._threads_to_use
X_t = self._transformer.fit_transform(X, y)
X_t = np.nan_to_num(X_t, False, 0, 0, 0)
self._estimator.fit(X_t, y)
return self
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,
]
_print_array(
"ShapeletTransformClassifier - BasicMotions",
_reproduce_classification_basic_motions(
ShapeletTransformClassifier(
estimator=RotationForest(n_estimators=3),
max_shapelets=20,
n_shapelet_samples=500,
batch_size=100,
random_state=0,
)
),
)
_print_array(
"Catch22 - UnitTest",
_reproduce_transform_unit_test(Catch22(outlier_norm=True)),
)
_print_array(
"Catch22 - BasicMotions",
_reproduce_transform_basic_motions(Catch22()),
)
_print_array(
"RandomIntervals - UnitTest",
_reproduce_transform_unit_test(RandomIntervals(random_state=0, n_intervals=3)),
)
_print_array(
"RandomIntervals - BasicMotions",
_reproduce_transform_basic_motions(
RandomIntervals(random_state=0, n_intervals=3)
),
)