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_muse.py
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"""WEASEL+MUSE classifier.
multivariate dictionary based classifier based on SFA transform, dictionaries and
logistic regression.
"""
__author__ = ["patrickzib", "BINAYKUMAR943"]
__all__ = ["MUSE"]
import math
import numpy as np
from joblib import Parallel, delayed
from scipy.sparse import hstack
from sklearn.linear_model import LogisticRegression, RidgeClassifierCV
from sklearn.utils import check_random_state
from sktime.classification.base import BaseClassifier
from sktime.transformations.panel.dictionary_based import SFAFast
from sktime.utils.warnings import warn
class MUSE(BaseClassifier):
"""MUSE (MUltivariate Symbolic Extension).
Also known as WEASLE-MUSE: implementation of multivariate version of WEASEL,
referred to as just MUSE from [1].
Overview: Input n series length m
WEASEL+MUSE is a multivariate dictionary classifier that builds a
bag-of-patterns using SFA for different window lengths and learns a
logistic regression classifier on this bag.
There are these primary parameters:
alphabet_size: alphabet size
chi2-threshold: used for feature selection to select best words
anova: select best l/2 fourier coefficients other than first ones
bigrams: using bigrams of SFA words
binning_strategy: the binning strategy used to disctrtize into
SFA words.
Parameters
----------
anova: boolean, default=True
If True, the Fourier coefficient selection is done via a one-way
ANOVA test. If False, the first Fourier coefficients are selected.
Only applicable if labels are given
variance: boolean, default = False
If True, the Fourier coefficient selection is done via the largest
variance. If False, the first Fourier coefficients are selected.
Only applicable if labels are given
bigrams: boolean, default=True
whether to create bigrams of SFA words
window_inc: int, default=2
WEASEL create a BoP model for each window sizes. This is the
increment used to determine the next window size.
alphabet_size : default = 4
Number of possible letters (values) for each word.
p_threshold: int, default=0.05 (disabled by default)
Used when feature selection is applied based on the chi-squared test.
This is the p-value threshold to use for chi-squared test on bag-of-words
(lower means more strict). 1 indicates that the test
should not be performed.
use_first_order_differences: boolean, default=True
If set to True will add the first order differences of each dimension
to the data.
support_probabilities: bool, default: False
If set to False, a RidgeClassifierCV will be trained, which has higher accuracy
and is faster, yet does not support predict_proba.
If set to True, a LogisticRegression will be trained, which does support
predict_proba(), yet is slower and typically less accuracy. predict_proba() is
needed for example in Early-Classification like TEASER.
feature_selection: {"chi2", "none", "random"}, default: chi2
Sets the feature selections strategy to be used. Chi2 reduces the number
of words significantly and is thus much faster (preferred). Random also reduces
the number significantly. None applies not feature selectiona and yields large
bag of words, e.g. much memory may be needed.
n_jobs : int, default=1
The number of jobs to run in parallel for both ``fit`` and ``predict``.
``-1`` means using all processors.
random_state: int or None, default=None
Seed for random, integer
Attributes
----------
n_classes_ : int
The number of classes.
classes_ : list
The classes labels.
See Also
--------
WEASEL
References
----------
.. [1] Patrick Schäfer and Ulf Leser, "Multivariate time series classification
with WEASEL+MUSE", in proc 3rd ECML/PKDD Workshop on AALTD}, 2018
https://arxiv.org/abs/1711.11343
Notes
-----
For the Java version, see
- `Original Publication <https://github.com/patrickzib/SFA>`_.
- `MUSE
<https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/
classifiers/multivariate/WEASEL_MUSE.java>`_.
Examples
--------
>>> from sktime.classification.dictionary_based import MUSE
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
>>> clf = MUSE(window_inc=4, use_first_order_differences=False) # doctest: +SKIP
>>> clf.fit(X_train, y_train) # doctest: +SKIP
MUSE(...)
>>> y_pred = clf.predict(X_test) # doctest: +SKIP
"""
_tags = {
# packaging info
# --------------
"authors": ["patrickzib", "BINAYKUMAR943"],
"maintainers": "BINAYKUMAR943",
"python_dependencies": "numba",
# estimator type
# --------------
"capability:multivariate": True,
"capability:multithreading": True,
"capability:predict_proba": True,
"X_inner_mtype": "numpy3D", # which mtypes do _fit/_predict support for X?
"classifier_type": "dictionary",
}
def __init__(
self,
anova=True,
variance=False,
bigrams=True,
window_inc=2,
alphabet_size=4,
use_first_order_differences=True,
feature_selection="chi2",
p_threshold=0.05,
support_probabilities=False,
n_jobs=1,
random_state=None,
):
# currently other values than 4 are not supported.
self.alphabet_size = alphabet_size
# feature selection is applied based on the chi-squared test.
self.p_threshold = p_threshold
self.anova = anova
self.variance = variance
self.use_first_order_differences = use_first_order_differences
self.norm_options = [False]
self.word_lengths = [4, 6]
self.bigrams = bigrams
self.binning_strategies = ["equi-width", "equi-depth"]
self.random_state = random_state
self.min_window = 6
self.max_window = 100
self.window_inc = window_inc
self.window_sizes = []
self.SFA_transformers = []
self.clf = None
self.n_jobs = n_jobs
self.support_probabilities = support_probabilities
self.total_features_count = 0
self.feature_selection = feature_selection
super().__init__()
def _fit(self, X, y):
"""Build a WEASEL+MUSE classifiers from the training set (X, y).
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 :
Reference to self.
"""
y = np.asarray(y)
# add first order differences in each dimension to TS
if self.use_first_order_differences:
X = self._add_first_order_differences(X)
self.n_dims = X.shape[1]
self.highest_dim_bit = (math.ceil(math.log2(self.n_dims))) + 1
if self.n_dims == 1:
warn(
"MUSE Warning: Input series is univariate; MUSE is designed for"
+ " multivariate series. It is recommended WEASEL is used instead.",
obj=self,
stacklevel=2,
)
if self.variance and self.anova:
raise ValueError("MUSE Warning: Please set either variance or anova.")
parallel_res = Parallel(n_jobs=self.n_jobs, backend="threading")(
delayed(_parallel_fit)(
X,
y.copy(), # no clue why, but this copy is required.
ind,
self.min_window,
self.max_window,
self.window_inc,
self.word_lengths,
self.alphabet_size,
self.norm_options,
self.anova,
self.variance,
self.binning_strategies,
self.bigrams,
self.n_jobs,
self.p_threshold,
self.feature_selection,
self.random_state,
)
for ind in range(self.n_dims)
)
self.SFA_transformers = [[] for _ in range(X.shape[1])]
self.window_sizes = [[] for _ in range(X.shape[1])]
all_words = []
for (
ind,
sfa_words,
transformer,
window_sizes,
rel_features_count,
) in parallel_res:
self.SFA_transformers[ind].extend(transformer)
self.window_sizes[ind].extend(window_sizes)
all_words.extend(sfa_words)
self.total_features_count += rel_features_count
if type(all_words[0]) is np.ndarray:
all_words = np.concatenate(all_words, axis=1)
else:
all_words = hstack(all_words)
# Ridge Classifier does not give probabilities
if not self.support_probabilities:
self.clf = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
else:
self.clf = LogisticRegression(
max_iter=5000,
solver="liblinear",
dual=True,
# class_weight="balanced",
penalty="l2",
random_state=self.random_state,
n_jobs=self.n_jobs,
)
self.clf.fit(all_words, y)
self.total_features_count = all_words.shape[-1]
return self
def _predict(self, X) -> np.ndarray:
"""Predict class values of n instances in X.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
Returns
-------
y : array-like, shape = [n_instances]
Predicted class labels.
"""
bag = self._transform_words(X)
return self.clf.predict(bag)
def _predict_proba(self, X) -> np.ndarray:
"""Predict class probabilities for n instances in X.
Parameters
----------
X : nested pandas DataFrame of shape [n_instances, 1]
Nested dataframe with univariate time-series in cells.
Returns
-------
y : array-like, shape = [n_instances, n_classes_]
Predicted probabilities using the ordering in classes_.
"""
bag = self._transform_words(X)
if self.support_probabilities:
return self.clf.predict_proba(bag)
else:
raise ValueError(
"Error in MUSE, please set support_probabilities=True, to"
+ "allow for probabilities to be computed."
)
def _transform_words(self, X):
if self.use_first_order_differences:
X = self._add_first_order_differences(X)
parallel_res = Parallel(n_jobs=self._threads_to_use, backend="threading")(
delayed(_parallel_transform_words)(
X, self.window_sizes, self.SFA_transformers, ind
)
for ind in range(self.n_dims)
)
all_words = []
for sfa_words in parallel_res:
all_words.extend(sfa_words)
if type(all_words[0]) is np.ndarray:
all_words = np.concatenate(all_words, axis=1)
else:
all_words = hstack(all_words)
return all_words
def _add_first_order_differences(self, X):
X_new = np.zeros((X.shape[0], X.shape[1] * 2, X.shape[2]))
X_new[:, 0 : X.shape[1], :] = X
diff = np.diff(X, 1)
X_new[:, X.shape[1] :, : diff.shape[2]] = diff
return X_new
@classmethod
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return ``"default"`` set.
For classifiers, a "default" set of parameters should be provided for
general testing, and a "results_comparison" set for comparing against
previously recorded results if the general set does not produce suitable
probabilities to compare against.
Returns
-------
params : dict or list of dict, default={}
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
instance.
``create_test_instance`` uses the first (or only) dictionary in ``params``.
"""
return {
"window_inc": 4,
"alphabet_size": 2,
"use_first_order_differences": False,
"support_probabilities": True,
"feature_selection": "none",
"bigrams": False,
}
def _compute_window_inc(series_length, window_inc):
win_inc = window_inc
if series_length < 100:
win_inc = 1 # less than 100 is ok time-wise
return win_inc
def _parallel_transform_words(X, window_sizes, SFA_transformers, ind):
# On each dimension, perform SFA
X_dim = X[:, ind]
bag_all_words = []
for i in range(len(window_sizes[ind])):
words = SFA_transformers[ind][i].transform(X_dim)
bag_all_words.append(words)
return bag_all_words
def _parallel_fit(
X,
y,
ind,
min_window,
max_window,
window_inc,
word_lengths,
alphabet_size,
norm_options,
anova,
variance,
binning_strategies,
bigrams,
n_jobs,
p_threshold,
feature_selection,
random_state,
):
if random_state is not None:
rng = check_random_state(random_state + ind)
else:
rng = check_random_state(random_state)
all_words = []
# On each dimension, perform SFA
X_dim = X[:, ind]
series_length = X_dim.shape[-1]
# increment window size in steps of 'win_inc'
win_inc = _compute_window_inc(series_length, window_inc)
max_window = int(min(series_length, max_window))
if min_window > max_window:
raise ValueError(
f"Error in MUSE, min_window ="
f"{min_window} is bigger"
f" than max_window ={max_window}."
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"
)
SFA_transformers = []
window_sizes = np.arange(min_window, max_window, win_inc)
relevant_features_count = 0
for window_size in window_sizes:
transformer = SFAFast(
word_length=rng.choice(word_lengths),
alphabet_size=alphabet_size,
window_size=window_size,
norm=rng.choice(norm_options),
anova=anova,
variance=variance,
binning_method=rng.choice(binning_strategies),
bigrams=bigrams,
remove_repeat_words=False,
lower_bounding=False,
p_threshold=p_threshold,
feature_selection=feature_selection,
save_words=False,
n_jobs=n_jobs,
return_pandas_data_series=False,
return_sparse=True,
)
all_words.append(transformer.fit_transform(X_dim, y))
SFA_transformers.append(transformer)
relevant_features_count = transformer.feature_count
return ind, all_words, SFA_transformers, window_sizes, relevant_features_count