def transform(self, X, y=None):
"""
Takes as input a time series dataset and returns the matrix profile
for each single time series of the dataset.
Parameters
----------
X: pandas.DataFrame
Time series dataset.
Output
------
Xt: pandas.DataFrame
Dataframe with the same number of rows as the input.
The number of columns equals the number of subsequences
of the desired length in each time series.
"""
# Input checks
self.check_is_fitted()
check_X(X, enforce_univariate=True)
n_instances = X.shape[0]
# Convert into tabular format
tabulariser = Tabularizer()
X = tabulariser.fit_transform(X)
Xt = pd.DataFrame(
stomp_self(np.array([X.iloc[i]]), self.m)
for i in range(n_instances))
return Xt
def predict_proba(self, X, input_checks=True, **kwargs):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
input_checks: boolean
whether to check the X parameter
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
if input_checks:
check_X(X)
if isinstance(X, pd.DataFrame):
if X.shape[1] > 1 or not isinstance(X.iloc[0, 0], pd.Series):
raise TypeError(
"Input should either be a 2d numpy array, or a pandas "
"dataframe with a single column of Series objects "
"(networks cannot yet handle multivariate problems")
else:
X = np.asarray([a.values for a in X.iloc[:, 0]])
if len(X.shape) == 2:
# add a dimension to make it multivariate with one dimension
X = X.reshape((X.shape[0], X.shape[1], 1))
probs = np.zeros((X.shape[0], self.nb_classes))
for skdl_model in self.skdl_models:
if self.keep_in_memory:
keras_model = skdl_model.model
else:
keras_model = keras.models.load_model(
Path(self.model_save_directory) / (skdl_model + ".hdf5"))
# keras models' predict is same as what we/sklearn means by
# predict_proba, i.e. give prob distributions
probs = probs + keras_model.predict(X, **kwargs)
if not self.keep_in_memory:
del keras_model
gc.collect()
keras.backend.clear_session()
probs = probs / len(self.skdl_models)
# check if binary classification
if probs.shape[1] == 1:
# first column is probability of class 0 and second is of class 1
probs = np.hstack([1 - probs, probs])
return probs
def test_check_X_enforce_min_columns():
X, y = make_classification_problem(n_columns=2)
msg = r"columns"
with pytest.raises(ValueError, match=msg):
check_X(X, enforce_min_columns=3)
with pytest.raises(ValueError, match=msg):
check_X_y(X, y, enforce_min_columns=3)
def test_check_X_enforce_univariate():
X, y = make_classification_problem(n_columns=2)
msg = r"univariate"
with pytest.raises(ValueError, match=msg):
check_X(X, enforce_univariate=True)
with pytest.raises(ValueError, match=msg):
check_X_y(X, y, enforce_univariate=True)
def test_check_enforce_min_instances():
X, y = make_classification_problem(n_instances=3)
msg = r"instance"
with pytest.raises(ValueError, match=msg):
check_X(X, enforce_min_instances=4)
with pytest.raises(ValueError, match=msg):
check_X_y(X, y, enforce_min_instances=4)
with pytest.raises(ValueError, match=msg):
check_y(y, enforce_min_instances=4)
def _set_oob_score(self, X, y):
"""Compute out-of-bag score"""
check_X_y(X, y)
check_X(X, enforce_univariate=True)
n_classes_ = self.n_classes_
n_samples = y.shape[0]
oob_decision_function = []
oob_score = 0.0
predictions = [
np.zeros((n_samples, n_classes_[k]))
for k in range(self.n_outputs_)
]
n_samples_bootstrap = _get_n_samples_bootstrap(n_samples,
self.max_samples)
for estimator in self.estimators_:
final_estimator = estimator.steps[-1][1]
unsampled_indices = _generate_unsampled_indices(
final_estimator.random_state, n_samples, n_samples_bootstrap)
p_estimator = estimator.predict_proba(X.iloc[unsampled_indices, :])
if self.n_outputs_ == 1:
p_estimator = [p_estimator]
for k in range(self.n_outputs_):
predictions[k][unsampled_indices, :] += p_estimator[k]
for k in range(self.n_outputs_):
if (predictions[k].sum(axis=1) == 0).any():
warn("Some inputs do not have OOB scores. "
"This probably means too few trees were used "
"to compute any reliable oob estimates.")
decision = (predictions[k] /
predictions[k].sum(axis=1)[:, np.newaxis])
oob_decision_function.append(decision)
oob_score += np.mean(y[:, k] == np.argmax(predictions[k], axis=1),
axis=0)
if self.n_outputs_ == 1:
self.oob_decision_function_ = oob_decision_function[0]
else:
self.oob_decision_function_ = oob_decision_function
self.oob_score_ = oob_score / self.n_outputs_
def transform(self, X, y=None):
"""Transform X.
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, n_columns]
Nested dataframe with time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame
"""
# input checks
self.check_is_fitted()
X = check_X(X, enforce_univariate=True, coerce_to_pandas=True)
# get column name
column_name = X.columns[0]
self._starts = []
self._lengths = []
# find plateaus (segments of the same value)
for x in X.iloc[:, 0]:
x = np.asarray(x)
# find indices of transition
if np.isnan(self.value):
i = np.where(np.isnan(x), 1, 0)
elif np.isinf(self.value):
i = np.where(np.isinf(x), 1, 0)
else:
i = np.where(x == self.value, 1, 0)
# pad and find where segments transition
transitions = np.diff(np.hstack([0, i, 0]))
# compute starts, ends and lengths of the segments
starts = np.where(transitions == 1)[0]
ends = np.where(transitions == -1)[0]
lengths = ends - starts
# filter out single points
starts = starts[lengths >= self.min_length]
lengths = lengths[lengths >= self.min_length]
self._starts.append(starts)
self._lengths.append(lengths)
# put into dataframe
Xt = pd.DataFrame()
column_prefix = "%s_%s" % (
column_name,
"nan" if np.isnan(self.value) else str(self.value),
)
Xt["%s_starts" % column_prefix] = pd.Series(self._starts)
Xt["%s_lengths" % column_prefix] = pd.Series(self._lengths)
return Xt
def fit(self, X, y=None):
"""Calculate word breakpoints using _mcb
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The class labels.
Returns
-------
self : object
"""
if self.alphabet_size < 2 or self.alphabet_size > 4:
raise ValueError(
"Alphabet size must be an integer between 2 and 4")
if self.word_length < 1 or self.word_length > 16:
raise ValueError("Word length must be an integer between 1 and 16")
if self.igb and y is None:
raise ValueError(
"Class values must be provided for information gain binning")
X = check_X(X, enforce_univariate=True)
X = tabularize(X, return_array=True)
self.n_instances, self.series_length = X.shape
self.breakpoints = self._igb(X, y) if self.igb else self._mcb(X)
self._is_fitted = True
return self
def _transform_words(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=False, coerce_to_pandas=True)
if self.use_first_order_differences:
X = self.add_first_order_differences(X)
bag_all_words = [dict() for _ in range(len(X))]
# On each dimension, perform SFA
for ind, column in enumerate(self.col_names):
X_dim = X[[column]]
X_dim = from_nested_to_3d_numpy(X_dim)
for i, window_size in enumerate(self.window_sizes[ind]):
# SFA transform
sfa_words = self.SFA_transformers[ind][i].transform(X_dim)
bag = sfa_words[0] # .iloc[:, 0]
# merging bag-of-patterns of different window_sizes
# to single bag-of-patterns with prefix indicating
# the used window-length
highest = np.int32(self.highest_bits[ind])
for j in range(len(bag)):
for (key, value) in bag[j].items():
# append the prefices to the words to distinguish
# between window-sizes
word = MUSE.shift_left(key, highest, ind,
self.highest_dim_bit,
window_size)
bag_all_words[j][word] = value
return bag_all_words
def predict(self, X):
"""Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted values.
"""
self.check_is_fitted()
# Check data
X = check_X(X, enforce_univariate=True)
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# Parallel loop
y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
delayed(e.predict)(X, check_input=True) for e in self.estimators_)
return np.sum(y_hat, axis=0) / len(self.estimators_)
def transform(self, X, y=None):
"""
Transform X.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, n_columns]
Nested dataframe with time-series in cells.
Returns
-------
Xt : pandas DataFrame
"""
self.check_is_fitted()
X = check_X(X)
n_instances, n_dims = X.shape
arr = [X.iloc[i, :].values for i in range(n_instances)]
max_length = _get_max_length(arr)
if max_length > self.pad_length_:
raise ValueError("Error: max_length of series \
is greater than the one found when fit or set.")
pad = [
pd.Series([self._create_pad(series) for series in out])
for out in arr
]
return pd.DataFrame(pad)
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):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : The training input samples. array-like or sparse matrix of shape
= [n_test_instances, series_length]
If a Pandas data frame is passed (sktime format) a check is
performed that it only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : nd.array of shape = (n_instances, n_classes)
Predicted probabilities
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
X = X.squeeze(1)
_, series_length = X.shape
if series_length != self.series_length:
raise TypeError(
" ERROR number of attributes in the train does not match "
"that in the test data")
y_probas = Parallel(n_jobs=self.n_jobs)(
delayed(_predict_proba_for_estimator)(X, self.estimators_[i],
self.intervals_[i])
for i in range(self.n_estimators))
output = np.sum(y_probas,
axis=0) / (np.ones(self.n_classes) * self.n_estimators)
return output
def fit(self, X, y=None):
"""
Fit transformer, generating random interval indices.
Parameters
----------
X : pandas DataFrame of shape [n_samples, n_features]
Input data
y : pandas Series, shape (n_samples, ...), optional
Targets for supervised learning.
Returns
-------
self : an instance of self.
"""
X = check_X(X, enforce_univariate=True)
self.input_shape_ = X.shape
# Retrieve time-series indexes from each column.
self._time_index = get_time_index(X)
if isinstance(self.intervals, np.ndarray):
self.intervals_ = self.intervals
elif is_int(self.intervals):
self.intervals_ = np.array_split(self._time_index, self.intervals)
else:
raise ValueError(
f"Intervals must be either an integer, an array with "
f"start and end points, but found: {self.intervals}")
self._is_fitted = True
return self
def transform(self, X, y=None):
"""
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, n_dims]
Nested dataframe with multivariate time-series in cells.
Returns
-------
dims: Pandas data frame with first dimension in column zero,
second in column one etc.
"""
# Check the data
self.check_is_fitted()
X = check_X(X, enforce_univariate=False)
# Get information about the dataframe
num_atts = len(X.iloc[0, 0])
col_names = X.columns
# Check the parameters are appropriate
self._check_parameters(num_atts)
# On each dimension, perform PAA
dataFrames = []
for x in col_names:
dataFrames.append(self._perform_paa_along_dim(pd.DataFrame(X[x])))
# Combine the dimensions together
result = pd.concat(dataFrames, axis=1, sort=False)
result.columns = col_names
return result
def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as
the mean predicted class probabilities of the trees in the forest. The
class probability of a single tree is the fraction of samples of the
same
class in a leaf.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
# Check data
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
all_proba = Parallel(n_jobs=n_jobs,
verbose=self.verbose)(delayed(e.predict_proba)(X)
for e in self.estimators_)
return np.sum(all_proba, axis=0) / len(self.estimators_)
def transform(self, X, y=None):
"""Concatenate multivariate time series/panel data into long
univariate time series/panel
data by simply concatenating times series in time.
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, n_features]
Nested dataframe with time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame with same number of rows and single
column
"""
self.check_is_fitted()
X = check_X(X)
# We concatenate by tabularizing all columns and then detabularizing
# them into a single column
if isinstance(X, pd.DataFrame):
Xt = from_nested_to_2d_array(X)
else:
Xt = from_3d_numpy_to_2d_array(X)
return from_2d_array_to_nested(Xt)
def predict(self, X):
self.check_is_fitted()
if isinstance(X, pd.Series) or isinstance(X, pd.DataFrame):
X = check_X(X, enforce_univariate=True)
X = tabularize(X, return_array=True)
rng = check_random_state(self.random_state)
classes = []
test_bags = self.transformer.transform(X)
test_bags = test_bags[0] # .iloc[:, 0]
for test_bag in 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 transform(self, X, y=None):
"""
Transform X, transforms univariate time-series using sklearn's PCA
class
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, 1]
Nested dataframe with univariate time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame with the same number of rows and the
(potentially reduced) PCA transformed
column. Time indices of the original column are replaced with 0:(
n_components - 1).
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
# Transform X using the fitted PCA
Xtab = tabularize(X)
Xpca = pd.DataFrame(data=self.pca.transform(Xtab),
index=Xtab.index,
columns=Xtab.columns[:self.pca.n_components_])
# Back-transform into time series data format
Xt = detabularise(Xpca, index=X.index)
Xt.columns = X.columns
return Xt
def predict_proba(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
bag = self._transform_words(X)
bag_dict = self.vectorizer.transform(bag)
return self.clf.predict_proba(bag_dict)
def predict_proba(self, X):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = check_X(X, enforce_univariate=True)
X = dataset_properties.negative_dataframe_indices(X)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
distributions = parallel(
delayed(self._predict_proba_tree)(X, tree)
for tree in self.trees)
else:
distributions = [
self._predict_proba_tree(X, tree) for tree in self.trees
]
distributions = np.array(distributions)
distributions = np.sum(distributions, axis=0)
normalize(distributions, copy=False, norm='l1')
return distributions
def transform(self, X, y=None):
"""
Parameters
----------
X : pd.DataFrame
Univariate time series to transform.
y : pd.DataFrame, optional (default=False)
Exogenous variables
Returns
-------
y_hat : pd.DataFrame
Extracted parameters; columns are parameter values
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
param_names = self._check_param_names(self.param_names)
n_instances = X.shape[0]
def _fit_extract(forecaster, x, param_names):
forecaster.fit(x)
params = forecaster.get_fitted_params()
return np.hstack([params[name] for name in param_names])
# iterate over rows
extracted_params = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_extract)(clone(self.forecaster), X.iloc[i, 0],
param_names) for i in range(n_instances))
return pd.DataFrame(extracted_params,
index=X.index,
columns=param_names)
def predict(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
rng = check_random_state(self.random_state)
num_insts = X.shape[0]
classes = np.zeros(num_insts, dtype=np.int_)
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_dist = sys.float_info.max
nn = -1
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[i] = nn
return classes
def transform(self, X, y=None):
"""Takes series in each cell, train linear interpolation and samples n.
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, n_features]
Nested dataframe with time-series in cells.
Returns
-------
pandas DataFrame : Transformed pandas DataFrame with same number
of rows and columns
"""
self.check_is_fitted()
check_X(X)
return X.apply(self._resize_col)
def _transform_words(self, X):
self.check_is_fitted()
X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
bag_all_words = [dict() for _ in range(len(X))]
for i, window_size in enumerate(self.window_sizes):
# SFA transform
sfa_words = self.SFA_transformers[i].transform(X)
bag = sfa_words[0] # .iloc[:, 0]
# merging bag-of-patterns of different window_sizes
# to single bag-of-patterns with prefix indicating
# the used window-length
for j in range(len(bag)):
for (key, value) in bag[j].items():
# append the prefices to the words to distinguish
# between window-sizes
if isinstance(key, tuple):
word = (((key[0] << self.highest_bit) | key[1]) <<
3) | window_size
else:
# word = ((key << self.highest_bit) << 3) | window_size
word = WEASEL.shift_left(key, self.highest_bit,
window_size)
bag_all_words[j][word] = value
return bag_all_words
def fit(self, X, y=None):
"""
Fit transformer.
Parameters
----------
X : pandas DataFrame of shape [n_samples, n_features]
Input data
y : pandas Series, shape (n_samples, ...), optional
Targets for supervised learning.
Returns
-------
self : an instance of self.
"""
X = check_X(X, coerce_to_pandas=True)
if self.lower is None:
n_instances, _ = X.shape
arr = [X.iloc[i, :].values for i in range(n_instances)]
self.lower_ = self.get_min_length(arr)
else:
self.lower_ = self.lower
self._is_fitted = True
return self
def transform(self, X, y=None):
"""
Transform X, transforms univariate time-series using sklearn's PCA
class
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, 1]
Nested dataframe with univariate time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame with the same number of rows and the
(potentially reduced) PCA transformed
column. Time indices of the original column are replaced with 0:(
n_components - 1).
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
X = X.squeeze(1)
# Transform X using the fitted PCA
Xpca = pd.DataFrame(data=self.pca.transform(X))
# Back-transform into time series data format
Xt = from_2d_array_to_nested(Xpca)
return Xt
def predict_proba(self, X):
"""
Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = check_X(X, enforce_univariate=True)
X = dataset_properties.negative_dataframe_indices(X)
distances = self.distance_to_exemplars(X)
ones = np.ones(distances.shape)
distances = np.add(distances, ones)
distributions = np.divide(ones, distances)
normalize(distributions, copy=False, norm='l1')
return distributions
def fit(self, X, y=None):
"""Fit.
Parameters
----------
X : pd.DataFrame
nested pandas DataFrame of shape [n_samples, n_columns]
y : pd.Series or np.array
Target variable
Returns
-------
self : an instance of self
"""
check_X(X)
self.default_fc_parameters_ = self._get_extraction_params()
self._is_fitted = True
return self
def find_closest_exemplar_indices(self, X):
"""
find the closest exemplar index for each instance in a dataframe
:param X: the dataframe containing instances
:return: 1d numpy array of indices, one for each instance,
reflecting the index of the closest exemplar
"""
check_X(
X) # todo make checks optional and propogate from forest downwards
n_instances = X.shape[0]
distances = self.distance_to_exemplars(X)
indices = np.empty(X.shape[0], dtype=int)
for index in range(n_instances):
exemplar_distances = distances[index]
closest_exemplar_index = comparison.arg_min(
exemplar_distances, self.random_state)
indices[index] = closest_exemplar_index
return indices
