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[ENH] MrSEQLClassifier wrapper (#1756)
* mrseql * dependencies * params * Empty commit for CI * docs * Automatic `pre-commit` fixes * import --------- Co-authored-by: MatthewMiddlehurst <[email protected]> Co-authored-by: Tony Bagnall <[email protected]> Co-authored-by: TonyBagnall <[email protected]>
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| """Multiple Representations Sequence Learning (MrSEQL) Classifier.""" | ||
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| __maintainer__ = ["TonyBagnall", "MatthewMiddlehurst"] | ||
| __all__ = ["MrSEQLClassifier"] | ||
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| from typing import Union | ||
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| import numpy as np | ||
| import pandas as pd | ||
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| from aeon.classification import BaseClassifier | ||
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| def _from_numpy3d_to_nested_dataframe(X): | ||
| """Convert numpy3D collection to a pd.DataFrame where each cell is a series.""" | ||
| n_cases, n_channels, n_timepoints = X.shape | ||
| array_type = X.dtype | ||
| container = pd.Series | ||
| column_names = [f"channel_{i}" for i in range(n_channels)] | ||
| column_list = [] | ||
| for j, column in enumerate(column_names): | ||
| nested_column = ( | ||
| pd.DataFrame(X[:, j, :]) | ||
| .apply(lambda x: [container(x, dtype=array_type)], axis=1) | ||
| .str[0] | ||
| .rename(column) | ||
| ) | ||
| column_list.append(nested_column) | ||
| df = pd.concat(column_list, axis=1) | ||
| return df | ||
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| class MrSEQLClassifier(BaseClassifier): | ||
| """ | ||
| Multiple Representations Sequence Learning (MrSEQL) Classifier. | ||
| This is a wrapper for the MrSEQLClassifier algorithm from the `mrseql` package. | ||
| MrSEQL is not included in ``all_extras`` as it requires gcc and fftw | ||
| (http://www.fftw.org/index.html) to be installed for Windows and some Linux OS. | ||
| Overview: MrSEQL extends the symbolic sequence classifier (SEQL) to work with | ||
| multiple symbolic representations of time series, using features extracted from the | ||
| SAX and SFA transformations. | ||
| Parameters | ||
| ---------- | ||
| seql_mode : "clf" or "fs", default="fs". | ||
| If "fs", trains a logistic regression model with features extracted by SEQL. | ||
| IF "clf", builds an ensemble of SEQL models | ||
| symrep : "sax" or "sfa", or ["sax", "sfa"], default = "sax" | ||
| The symbolic features to extract from the time series. | ||
| custom_config : dict, default=None | ||
| Additional configuration for the symbolic transformations. See the original | ||
| package for details. ``symrep`` will be ignored if used. | ||
| References | ||
| ---------- | ||
| .. [1] Le Nguyen, Thach, et al. "Interpretable time series classification using | ||
| linear models and multi-resolution multi-domain symbolic representations." | ||
| Data mining and knowledge discovery 33 (2019): 1183-1222. | ||
| Examples | ||
| -------- | ||
| >>> from aeon.classification.dictionary_based import MrSEQLClassifier | ||
| >>> from aeon.testing.data_generation import make_example_3d_numpy | ||
| >>> X, y = make_example_3d_numpy(random_state=0) | ||
| >>> clf = MrSEQLClassifier(random_state=0) # doctest: +SKIP | ||
| >>> clf.fit(X, y) # doctest: +SKIP | ||
| MrSEQLClassifier(...) | ||
| >>> clf.predict(X) # doctest: +SKIP | ||
| """ | ||
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| _tags = { | ||
| "X_inner_type": "numpy3D", | ||
| "algorithm_type": "dictionary", | ||
| "python_dependencies": "mrseql", | ||
| } | ||
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| def __init__(self, seql_mode="fs", symrep=("sax"), custom_config=None) -> None: | ||
| self.seql_mode = seql_mode | ||
| self.symrep = symrep | ||
| self.custom_config = custom_config | ||
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| super().__init__() | ||
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| def _fit(self, X, y): | ||
| from mrseql import MrSEQLClassifier | ||
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| _X = _from_numpy3d_to_nested_dataframe(X) | ||
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| self.clf_ = MrSEQLClassifier( | ||
| seql_mode=self.seql_mode, | ||
| symrep=self.symrep, | ||
| custom_config=self.custom_config, | ||
| ) | ||
| self.clf_.fit(_X, y) | ||
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| return self | ||
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| def _predict(self, X) -> np.ndarray: | ||
| _X = _from_numpy3d_to_nested_dataframe(X) | ||
| return self.clf_.predict(_X) | ||
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| def _predict_proba(self, X) -> np.ndarray: | ||
| _X = _from_numpy3d_to_nested_dataframe(X) | ||
| return self.clf_.predict_proba(_X) | ||
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| @classmethod | ||
| def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]: | ||
| """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 {} |
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