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[ENH] RDSTClassifier and transformer tests (#529)
* re-introducing tests failing on macOS * RDST Classifier, removing docs from RDST transformer that was not accurate * Adding RDSTClassifier to API docs * Change expected value from RDST test to correct one under manhattan distance instead of euclidean * updating docs * Possible fix for random state with numba ? * Adding checks for edge cases * Correcting std computation * Adding conformity checks at end of transform * Correcting indentation and test case
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Antoine Guillaume
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| # -*- coding: utf-8 -*- | ||
| """Shapelet based time series classifiers.""" | ||
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| __all__ = ["MrSQMClassifier", "ShapeletTransformClassifier"] | ||
| __all__ = ["MrSQMClassifier", "ShapeletTransformClassifier", "RDSTClassifier"] | ||
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| from aeon.classification.shapelet_based._mrsqm import MrSQMClassifier | ||
| from aeon.classification.shapelet_based._rdst import RDSTClassifier | ||
| from aeon.classification.shapelet_based._stc import ShapeletTransformClassifier |
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| # -*- coding: utf-8 -*- | ||
| """Random Dilated Shapelet Transform (RDST) Classifier. | ||
| A Random Dilated Shapelet Transform classifier pipeline that simply performs a random | ||
| shapelet dilated transform and build (by default) a ridge classifier on the output. | ||
| """ | ||
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| __author__ = ["baraline"] | ||
| __all__ = ["RDSTClassifier"] | ||
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| import numpy as np | ||
| from sklearn.linear_model import RidgeClassifierCV | ||
| from sklearn.pipeline import make_pipeline | ||
| from sklearn.preprocessing import StandardScaler | ||
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| from aeon.base._base import _clone_estimator | ||
| from aeon.classification.base import BaseClassifier | ||
| from aeon.transformations.collection import RandomDilatedShapeletTransform | ||
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| class RDSTClassifier(BaseClassifier): | ||
| """A random dilated shapelet transform (RDST) classifier. | ||
| Implementation of the random dilated shapelet transform classifier pipeline | ||
| along the lines of [1][2]. Transforms the data using the | ||
| `RandomDilatedShapeletTransform` and then builds a `RidgeClassifierCV` classifier | ||
| with standard scalling. | ||
| Parameters | ||
| ---------- | ||
| estimator : BaseEstimator or None, default=None | ||
| Base estimator for the ensemble, can be supplied a sklearn `BaseEstimator`. If | ||
| `None` a default `RidgeClassifierCV` classifier is used with standard scalling. | ||
| max_shapelets : int, default=10000 | ||
| The maximum number of shapelet to keep for the final transformation. | ||
| A lower number of shapelets can be kept if alpha similarity have discarded the | ||
| whole dataset. | ||
| shapelet_lengths : array, default=None | ||
| The set of possible length for shapelets. Each shapelet length is uniformly | ||
| drawn from this set. If None, the shapelets length will be equal to | ||
| min(max(2,series_length//2),11). | ||
| proba_normalization : float, default=0.8 | ||
| This probability (between 0 and 1) indicate the chance of each shapelet to be | ||
| initialized such as it will use a z-normalized distance, inducing either scale | ||
| sensitivity or invariance. A value of 1 would mean that all shapelets will use | ||
| a z-normalized distance. | ||
| threshold_percentiles : array, default=None | ||
| The two perceniles used to select the threshold used to compute the Shapelet | ||
| Occurrence feature. If None, the 5th and the 10th percentiles (i.e. [5,10]) | ||
| will be used. | ||
| alpha_similarity : float, default=0.5 | ||
| The strenght of the alpha similarity pruning. The higher the value, the lower | ||
| the allowed number of common indexes with previously sampled shapelets | ||
| when sampling a new candidate with the same dilation parameter. | ||
| It can cause the number of sampled shapelets to be lower than max_shapelets if | ||
| the whole search space has been covered. The default is 0.5, and the maximum is | ||
| 1. Value above it have no effect for now. | ||
| use_prime_dilations : bool, default=False | ||
| If True, restrict the value of the shapelet dilation parameter to be prime | ||
| values. This can greatly speed-up the algorithm for long time series and/or | ||
| short shapelet length, possibly at the cost of some accuracy. | ||
| 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, RandomState instance or None, default=None | ||
| If `int`, random_state is the seed used by the random number generator; | ||
| If `RandomState` instance, random_state is the random number generator; | ||
| If `None`, the random number generator is the `RandomState` instance used | ||
| by `np.random`. | ||
| Attributes | ||
| ---------- | ||
| classes_ : list | ||
| The unique class labels in the training set. | ||
| n_classes_ : int | ||
| The number of unique classes in the training set. | ||
| fit_time_ : int | ||
| The time (in milliseconds) for ``fit`` to run. | ||
| n_instances_ : int | ||
| The number of train cases in the training set. | ||
| n_dims_ : int | ||
| The number of dimensions per case in the training set. | ||
| series_length_ : int | ||
| The length of each series in the training set. | ||
| transformed_data_ : list of shape (n_estimators) of ndarray | ||
| The transformed training dataset for all classifiers. Only saved when | ||
| ``save_transformed_data`` is `True`. | ||
| See Also | ||
| -------- | ||
| RandomDilatedShapeletTransform : The randomly sampled shapelet transform. | ||
| RidgeClassifierCV : The default classifier used. | ||
| References | ||
| ---------- | ||
| .. [1] Antoine Guillaume et al. "Random Dilated Shapelet Transform: A New Approach | ||
| for Time Series Shapelets", Pattern Recognition and Artificial Intelligence. | ||
| ICPRAI 2022. | ||
| .. [2] Antoine Guillaume, "Time series classification with shapelets: Application | ||
| to predictive maintenance on event logs", PhD Thesis, University of Orléans, | ||
| 2023. | ||
| Examples | ||
| -------- | ||
| >>> from aeon.classification.shapelet_based import RDSTClassifier | ||
| >>> from aeon.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) | ||
| >>> clf = RDSTClassifier( | ||
| ... max_shapelets=10 | ||
| ... ) | ||
| >>> clf.fit(X_train, y_train) | ||
| RDSTClassifier(...) | ||
| >>> y_pred = clf.predict(X_test) | ||
| """ | ||
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| _tags = { | ||
| "capability:multivariate": True, | ||
| "capability:multithreading": True, | ||
| "algorithm_type": "shapelet", | ||
| } | ||
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| def __init__( | ||
| self, | ||
| max_shapelets=10000, | ||
| shapelet_lengths=None, | ||
| proba_normalization=0.8, | ||
| threshold_percentiles=None, | ||
| alpha_similarity=0.5, | ||
| use_prime_dilations=False, | ||
| estimator=None, | ||
| save_transformed_data=False, | ||
| n_jobs=1, | ||
| random_state=None, | ||
| ): | ||
| self.max_shapelets = max_shapelets | ||
| self.shapelet_lengths = shapelet_lengths | ||
| self.proba_normalization = proba_normalization | ||
| self.threshold_percentiles = threshold_percentiles | ||
| self.alpha_similarity = alpha_similarity | ||
| self.use_prime_dilations = use_prime_dilations | ||
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| self.estimator = estimator | ||
| self.save_transformed_data = save_transformed_data | ||
| self.random_state = random_state | ||
| self.n_jobs = n_jobs | ||
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| self.n_instances_ = 0 | ||
| self.n_dims_ = 0 | ||
| self.series_length_ = 0 | ||
| self.transformed_data_ = [] | ||
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| self._transformer = None | ||
| self._estimator = None | ||
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| super(RDSTClassifier, self).__init__() | ||
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| def _fit(self, X, y): | ||
| """Fit Classifier to training data. | ||
| Parameters | ||
| ---------- | ||
| X: np.ndarray shape (n_instances, n_channels, series_length) | ||
| The training input samples. | ||
| y: array-like or list | ||
| The class labels for samples in X. | ||
| Returns | ||
| ------- | ||
| self : | ||
| Reference to self. | ||
| Notes | ||
| ----- | ||
| Changes state by creating a fitted model that updates attributes | ||
| ending in "_". | ||
| """ | ||
| self.n_instances_, self.n_dims_, self.series_length_ = X.shape | ||
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| self._transformer = RandomDilatedShapeletTransform( | ||
| max_shapelets=self.max_shapelets, | ||
| shapelet_lengths=self.shapelet_lengths, | ||
| proba_normalization=self.proba_normalization, | ||
| threshold_percentiles=self.threshold_percentiles, | ||
| alpha_similarity=self.alpha_similarity, | ||
| use_prime_dilations=self.use_prime_dilations, | ||
| n_jobs=self.n_jobs, | ||
| random_state=self.random_state, | ||
| ) | ||
| if self.estimator is None: | ||
| self._estimator = make_pipeline( | ||
| StandardScaler(with_mean=True), | ||
| RidgeClassifierCV( | ||
| alphas=np.logspace(-4, 4, 20), | ||
| ), | ||
| ) | ||
| else: | ||
| self._estimator = _clone_estimator(self.estimator, self.random_state) | ||
| m = getattr(self._estimator, "n_jobs", None) | ||
| if m is not None: | ||
| self._estimator.n_jobs = self.n_jobs | ||
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| X_t = self._transformer.fit_transform(X, y) | ||
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| if self.save_transformed_data: | ||
| self.transformed_data_ = X_t | ||
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| self._estimator.fit(X_t, y) | ||
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| return self | ||
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| def _predict(self, X) -> np.ndarray: | ||
| """Predicts labels for sequences in X. | ||
| Parameters | ||
| ---------- | ||
| X: np.ndarray shape (n_instances, n_channels, series_length) | ||
| The data to make prediction for. | ||
| Returns | ||
| ------- | ||
| y : array-like, shape = [n_instances] | ||
| Predicted class labels. | ||
| """ | ||
| X_t = self._transformer.transform(X) | ||
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| return self._estimator.predict(X_t) | ||
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| def _predict_proba(self, X) -> np.ndarray: | ||
| """Predicts labels probabilities for sequences in X. | ||
| Parameters | ||
| ---------- | ||
| X: np.ndarray shape (n_instances, n_channels, series_length) | ||
| The data to make predict probabilities for. | ||
| Returns | ||
| ------- | ||
| y : array-like, shape = [n_instances, n_classes_] | ||
| Predicted probabilities using the ordering in classes_. | ||
| """ | ||
| X_t = self._transformer.transform(X) | ||
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| m = getattr(self._estimator, "predict_proba", None) | ||
| if callable(m): | ||
| return self._estimator.predict_proba(X_t) | ||
| else: | ||
| dists = np.zeros((X.shape[0], self.n_classes_)) | ||
| preds = self._estimator.predict(X_t) | ||
| for i in range(0, X.shape[0]): | ||
| dists[i, np.where(self.classes_ == preds[i])] = 1 | ||
| return dists | ||
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| @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 {"max_shapelets": 20} |
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